docs/notebooks/use_user_defined_graphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 3: Use user defined processing graphs to pre-process a time series of Sentinel-1 images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Requirements\n",
    "\n",
    "- Installation of SenSARP\n",
    "- Installation of ESA's SNAP Toolbox version >8.0.3\n",
    "    - Currently only SNAP version 8.0 can be downloaded from the ESA website (https://step.esa.int/main/download/snap-download/). To update SNAP to a version >8.0.3 please start the SNAP software. You will be asked if you want to search for update. After the updates are installed you need to restart SNAP to initialize the installed updates.\n",
    "    - SNAP Toolbox need libgfortran for specific operations but currently libgfortran is not installed during the installation process of SNAP therefore you might use ```sudo apt-get install gfortran```\n",
    "\n",
    "- Sentinel-1 SLC data\n",
    "    - Instruction how to download Sentinel 1 data are given in Section 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download sample data from Sentinel Data Hub\n",
    "### Option 1: Download data from Sentinel Data Hub manually or via python package sentinelsat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <span style=\"color:red\">Create Account (https://scihub.copernicus.eu/dhus/#/self-registration)</span>. (**<span style=\"color:red\">Attention: Problem by using Copernicus Open Access Hub might be that older data is offline and need to be triggered first</span>)**. More information can be found at https://scihub.copernicus.eu/userguide/DataRestoration. Instruction to manually download data from Copernicus Open Access Hub can be found at https://blogs.fu-berlin.de/reseda/esa-scihub/. You can also try to download the data via python package sentinelsat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect to the API\n",
    "from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt\n",
    "from datetime import date\n",
    "user = 'user'\n",
    "password = 'password'\n",
    "# initialize settings\n",
    "api = SentinelAPI(user, password)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search for available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sentinelsat.SentinelAPI:Found 2 products\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Following products will be downloaded\n",
      "['S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B'\n",
      " 'S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E']\n",
      "These 2 product need 3.28 Gb of disk space\n"
     ]
    }
   ],
   "source": [
    "# search by polygon (MNI test site coordinates), time, and SciHub query keywords\n",
    "footprint = geojson_to_wkt(read_geojson('coordinates_mni.geojson'))\n",
    "products = api.query(footprint,\n",
    "                     date=('20210704', '20210708'),\n",
    "                     platformname='Sentinel-1',\n",
    "                     producttype='GRD')\n",
    "print('Following products will be downloaded')\n",
    "print(api.to_dataframe(products).title.values)\n",
    "\n",
    "print('These {} product need {} Gb of disk space'.format(len(products), api.get_products_size(products)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start download process (**<span style=\"color:red\">Attention: might take a while and data will requries some free disk space</span>)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sentinelsat.SentinelAPI:Will download 2 products using 4 workers\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading products:   0%|          | 0/2 [00:00<?, ?product/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      },
      "text/plain": [
       "Downloading S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E.zip:   0%|          | 0.00/986…"
      ]
     },
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       "model_id": "24314bb911fb4ba5aa82851454e7a8cd",
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      },
      "text/plain": [
       "Downloading S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B.zip:   0%|          | 0.00/983…"
      ]
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     "output_type": "display_data"
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       "MD5 checksumming:   0%|          | 0.00/986M [00:00<?, ?B/s]"
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     "data": {
      "text/plain": [
       "ResultTuple(downloaded={'ce796d52-4f70-48f0-87bf-d698ec372e9f': {'id': 'ce796d52-4f70-48f0-87bf-d698ec372e9f', 'title': 'S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E', 'size': 986204913, 'md5': '56c6d22b25610fb30b01212d0b35101b', 'date': datetime.datetime(2021, 7, 6, 5, 25, 51, 753000), 'footprint': 'POLYGON((12.682894 47.832607,9.216668 48.236717,9.567789 49.733212,13.138339 49.327972,12.682894 47.832607))', 'url': \"https://apihub.copernicus.eu/apihub/odata/v1/Products('ce796d52-4f70-48f0-87bf-d698ec372e9f')/$value\", 'Online': True, 'Creation Date': datetime.datetime(2021, 7, 6, 7, 26, 2, 888000), 'Ingestion Date': datetime.datetime(2021, 7, 6, 7, 24, 14, 101000), 'quicklook_url': \"https://apihub.copernicus.eu/apihub/odata/v1/Products('ce796d52-4f70-48f0-87bf-d698ec372e9f')/Products('Quicklook')/$value\", 'path': '/home/test/Desktop/data4/S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E.zip', 'downloaded_bytes': 986204913}, 'caf75225-cb85-4410-81cd-ff6ef52256ed': {'id': 'caf75225-cb85-4410-81cd-ff6ef52256ed', 'title': 'S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B', 'size': 983271059, 'md5': '4ba73f80273607ea8106cce310ba4d93', 'date': datetime.datetime(2021, 7, 7, 5, 18, 22, 410000), 'footprint': 'POLYGON((14.717325 47.687527,11.218726 48.096413,11.575086 49.592331,15.178372 49.182144,14.717325 47.687527))', 'url': \"https://apihub.copernicus.eu/apihub/odata/v1/Products('caf75225-cb85-4410-81cd-ff6ef52256ed')/$value\", 'Online': True, 'Creation Date': datetime.datetime(2021, 7, 7, 10, 32, 2, 46000), 'Ingestion Date': datetime.datetime(2021, 7, 7, 10, 30, 11, 282000), 'quicklook_url': \"https://apihub.copernicus.eu/apihub/odata/v1/Products('caf75225-cb85-4410-81cd-ff6ef52256ed')/Products('Quicklook')/$value\", 'path': '/home/test/Desktop/data4/S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B.zip', 'downloaded_bytes': 983271059}}, retrieval_triggered={}, failed={})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download all results from the search\n",
    "# files will be downloaded to specified path\n",
    "import os\n",
    "path = os.path.expanduser('~/Desktop/data4')\n",
    "try:\n",
    "    os.makedirs(path)\n",
    "except: FileExistsError\n",
    "api.download_all(products, path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: Download data from NASA Earth Data Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can search for Sentinel-1 data at https://search.asf.alaska.edu/. A NASA EOSDIS Earthdata Login account is required for downloading data and tools from ASF. Registering for an Earthdata Login account is free (https://urs.earthdata.nasa.gov/home). Instructions how to download data from ASF can be found at https://asf.alaska.edu/wp-content/uploads/2019/02/asf_datarecipe_bulk_download_from_vertex_python_script_v1.pdf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Use user defined xml graph to process Sentinel-1 data (test case with Sentinel-1 GRD product)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set paths for\n",
    "\n",
    "- input_folder (path to stored Sentinel-1 SLC data (zip files) e.g. \"~/Downloads\")\n",
    "- output_folder (path where processed data will be stored e.g. \"~/output\")\n",
    "- gpt_loction (gpt is located in the bin folder of your SNAP installation)' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = path\n",
    "output_folder = path\n",
    "gpt_location = os.path.expanduser('~/snap/bin/gpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create config file with information about input, output and gpt location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "\n",
    "with open('sample_config_file.yaml') as stream:\n",
    "   data = yaml.safe_load(stream)\n",
    "\n",
    "data['input_folder'] = input_folder\n",
    "data['output_folder'] = output_folder\n",
    "data['gpt'] = gpt_location\n",
    "\n",
    "with open('test_config_file.yaml', 'wb') as stream:\n",
    "   yaml.safe_dump(data, stream, default_flow_style=False, \n",
    "                  explicit_start=True, allow_unicode=True, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expert user might create their own processing chain and use the functionally of SenSARP to automate the processing on scale \n",
    "\n",
    "1. Create a user defined xml graph via SNAP’s Graph Builder\n",
    "    - information how to use SNAP’s Graph Builder can be found at the step forum (https://forum.step.esa.int/t/graph-builder/5403)\n",
    "2. Modify user defined xml graph to be used by SenSARP\n",
    "    - Needed modification\n",
    "        - Placeholder for input image within xml graph need to be specified\n",
    "        - Placeholder for output image within xml graph need to be specified\n",
    "        - If subset operator is used within user defined xml graph a placeholder for subset extent need to be specified\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional functionality of SenSARP that can be used with user defined xml graph\n",
    "\n",
    "- Filter option (year, area of interest)\n",
    "- default pre_process_step2 (co-registration of time series images)\n",
    "- creation of netCDF4 stack containing all processed images and information about orbit direction, relative orbit, satellite name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find and show xml parts that need to be changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Read\">\n",
      "    <operator>Read</operator>\n",
      "    <sources />\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\" />\n",
      "  </node>\n",
      "  \n",
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Write\">\n",
      "    <operator>Write</operator>\n",
      "    <sources>\n",
      "      <sourceProduct refid=\"Subset\" />\n",
      "    </sources>\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\">\n",
      "      <file>/home/wodan/target.dim</file>\n",
      "      <formatName>BEAM-DIMAP</formatName>\n",
      "    </parameters>\n",
      "  </node>\n",
      "  \n",
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Subset\">\n",
      "    <operator>Subset</operator>\n",
      "    <sources>\n",
      "      <sourceProduct refid=\"Terrain-Correction\" />\n",
      "    </sources>\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\">\n",
      "      <sourceBands />\n",
      "      <region>0,0,0,0</region>\n",
      "      <referenceBand />\n",
      "      <geoRegion />\n",
      "      <subSamplingX>1</subSamplingX>\n",
      "      <subSamplingY>1</subSamplingY>\n",
      "      <fullSwath>false</fullSwath>\n",
      "      <tiePointGridNames />\n",
      "      <copyMetadata>true</copyMetadata>\n",
      "    </parameters>\n",
      "  </node>\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Load xml graph\n",
    "import xml.etree.ElementTree as ETree\n",
    "user_xml_graph = '/home/test/Desktop/sar-pre-processing/sar_pre_processing/user_defined_graphs/example_of_expert_user.xml'\n",
    "tree = ETree.parse(user_xml_graph)\n",
    "root = tree.getroot()\n",
    "\n",
    "# find Operators Read, Write and Subset\n",
    "read = root.find(\"./node/[operator='Read']\")\n",
    "read2 = read.find(\"./parameters[@class='com.bc.ceres.binding.dom.XppDomElement']\")\n",
    "write = root.find(\"./node/[operator='Write']\")\n",
    "subset = root.find(\"./node/[operator='Subset']\")\n",
    "\n",
    "# Old Version of user defined xml graph\n",
    "print(ETree.tostring(read, encoding='utf8').decode('utf8'))\n",
    "print(ETree.tostring(write, encoding='utf8').decode('utf8'))\n",
    "print(ETree.tostring(subset, encoding='utf8').decode('utf8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify xml parts so the xml-graph can be used by SenSARP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Read\">\n",
      "    <operator>Read</operator>\n",
      "    <sources />\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\"><file>$input</file></parameters>\n",
      "  </node>\n",
      "  \n",
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Write\">\n",
      "    <operator>Write</operator>\n",
      "    <sources>\n",
      "      <sourceProduct refid=\"Subset\" />\n",
      "    </sources>\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\">\n",
      "      <file>$output</file>\n",
      "      <formatName>BEAM-DIMAP</formatName>\n",
      "    </parameters>\n",
      "  </node>\n",
      "  \n",
      "<?xml version='1.0' encoding='utf8'?>\n",
      "<node id=\"Subset\">\n",
      "    <operator>Subset</operator>\n",
      "    <sources>\n",
      "      <sourceProduct refid=\"Terrain-Correction\" />\n",
      "    </sources>\n",
      "    <parameters class=\"com.bc.ceres.binding.dom.XppDomElement\">\n",
      "      <sourceBands />\n",
      "      <region>0,0,0,0</region>\n",
      "      <referenceBand />\n",
      "      <geoRegion>$area</geoRegion>\n",
      "      <subSamplingX>1</subSamplingX>\n",
      "      <subSamplingY>1</subSamplingY>\n",
      "      <fullSwath>false</fullSwath>\n",
      "      <tiePointGridNames />\n",
      "      <copyMetadata>true</copyMetadata>\n",
      "    </parameters>\n",
      "  </node>\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Apply changes to user defined xml graph\n",
    "ETree.SubElement(read2, \"file\").text = \"$input\"\n",
    "for output in write.iter('file'):\n",
    "    output_new = '$output'\n",
    "    output.text = output_new\n",
    "for area in subset.iter('geoRegion'):\n",
    "    area_new = '$area'\n",
    "    area.text = area_new\n",
    "\n",
    "tree.write('/home/test/Desktop/sar-pre-processing/sar_pre_processing/user_defined_graphs/example_of_expert_user_modi.xml')\n",
    "\n",
    "# print modified version of operators Read, Write and Subset\n",
    "print(ETree.tostring(read, encoding='utf8').decode('utf8'))\n",
    "print(ETree.tostring(write, encoding='utf8').decode('utf8'))\n",
    "print(ETree.tostring(subset, encoding='utf8').decode('utf8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set additional config options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test_config_file.yaml') as stream:\n",
    "   data = yaml.safe_load(stream)\n",
    "\n",
    "# Filter option\n",
    "## Filter via year of interest\n",
    "data['year'] = '2021'\n",
    "\n",
    "## Define region of interest\n",
    "data['region']['lr']['lat'] = 48.2 # lower right latitude\n",
    "data['region']['lr']['lon'] = 11.9 # lower right longitude\n",
    "data['region']['ul']['lat'] = 48.4 # upper left latitude\n",
    "data['region']['ul']['lon'] = 11.6 # upper left longitude\n",
    "data['region']['subset'] = 'yes'\n",
    "\n",
    "## Set options to use user defined xml graph\n",
    "data['use_user_defined_graphs'] = 'yes'\n",
    "data['xml_graph_path'] = '/home/test/Desktop/sar-pre-processing/sar_pre_processing/user_defined_graphs'\n",
    "data['pre_process_step1'] = 'example_of_expert_user_modi.xml'\n",
    "\n",
    "with open('test_config_file.yaml', 'wb') as stream:\n",
    "   yaml.safe_dump(data, stream, default_flow_style=False,\n",
    "                  explicit_start=True, allow_unicode=True, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start pre-processing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Found files within input folder: 2\n",
      "INFO:root:Number of found files for year 2021: 2\n",
      "INFO:root:area of interest not specified\n",
      "INFO:root:Number of found files that were double processed: 0.0\n",
      "INFO:root:Number of found files with border issues: 0\n",
      "INFO:root:area of interest specified\n",
      "INFO:root:normalisation angle not specified, default value of 35 is used for processing\n",
      "INFO:ComponentProgress:0\n",
      "INFO:ComponentProgress:0\n",
      "INFO:root:Process S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B.zip with SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start step 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.esa.snap.core.gpf.operators.tooladapter.ToolAdapterIO: Initializing external tool adapters\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Incompatible GDAL 3.3.1 found on system. Internal GDAL 3.0.0 from distribution will be used.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n",
      "INFO: org.esa.snap.core.util.EngineVersionCheckActivator: Please check regularly for new updates for the best SNAP experience.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing processing graph\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.hsqldb.persist.Logger: dataFileCache open start\n",
      "WARNING: org.esa.s1tbx.sar.gpf.orbits.ApplyOrbitFileOp: No valid orbit file found for 07-JUL-2021 05:17:13.000000\n",
      "Orbit files may be downloaded from https://scihub.copernicus.eu/gnss/odata/v1/\n",
      "and placed in /home/test/.snap/auxdata/Orbits/Sentinel-1/POEORB/S1A/2021/07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:A AND producttype:AUX_RESORB AND beginposition:[2021-07-01T00:00:000Z TO 2021-07-31T24:00:000Z]\n",
      "OpenSearch: 35 total results on 1 pages.\n",
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:A AND producttype:AUX_RESORB AND beginposition:[2021-07-01T00:00:000Z TO 2021-07-31T24:00:000Z]\n",
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:A AND producttype:AUX_RESORB AND beginposition:[2021-06-01T00:00:000Z TO 2021-06-31T24:00:000Z]\n",
      "OpenSearch: 0 total results on 1 pages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: org.esa.s1tbx.sar.gpf.orbits.ApplyOrbitFileOp: ApplyOrbit ignoring error and continuing: java.io.IOException: No valid orbit file found for 07-JUL-2021 05:17:13.000000\n",
      "Orbit files may be downloaded from https://scihub.copernicus.eu/gnss/odata/v1/\n",
      "and placed in /home/test/.snap/auxdata/Orbits/Sentinel-1/POEORB/S1A/2021/07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version = 3.31\n",
      "...10%....21%....33%....44%...54%....65%....77%....88%. done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:0\n",
      "INFO:ComponentProgress:50\n",
      "INFO:ComponentProgress:50\n",
      "INFO:root:Process S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E.zip with SNAP.\n",
      "INFO: org.esa.snap.core.gpf.operators.tooladapter.ToolAdapterIO: Initializing external tool adapters\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Incompatible GDAL 3.3.1 found on system. Internal GDAL 3.0.0 from distribution will be used.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n",
      "INFO: org.esa.snap.core.util.EngineVersionCheckActivator: Please check regularly for new updates for the best SNAP experience.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing processing graph\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.hsqldb.persist.Logger: dataFileCache open start\n",
      "WARNING: org.esa.s1tbx.sar.gpf.orbits.ApplyOrbitFileOp: No valid orbit file found for 06-JUL-2021 05:24:42.000000\n",
      "Orbit files may be downloaded from https://scihub.copernicus.eu/gnss/odata/v1/\n",
      "and placed in /home/test/.snap/auxdata/Orbits/Sentinel-1/POEORB/S1B/2021/07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:B AND producttype:AUX_RESORB AND beginposition:[2021-07-01T00:00:000Z TO 2021-07-31T24:00:000Z]\n",
      "OpenSearch: 36 total results on 1 pages.\n",
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:B AND producttype:AUX_RESORB AND beginposition:[2021-07-01T00:00:000Z TO 2021-07-31T24:00:000Z]\n",
      "OpenSearch: https://scihub.copernicus.eu/gnss/search?q=platformname:Sentinel-1 AND platformnumber:B AND producttype:AUX_RESORB AND beginposition:[2021-06-01T00:00:000Z TO 2021-06-31T24:00:000Z]\n",
      "OpenSearch: 0 total results on 1 pages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: org.esa.s1tbx.sar.gpf.orbits.ApplyOrbitFileOp: ApplyOrbit ignoring error and continuing: java.io.IOException: No valid orbit file found for 06-JUL-2021 05:24:42.000000\n",
      "Orbit files may be downloaded from https://scihub.copernicus.eu/gnss/odata/v1/\n",
      "and placed in /home/test/.snap/auxdata/Orbits/Sentinel-1/POEORB/S1B/2021/07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version = 3.31\n",
      "....11%....23%....34%....46%....57%....69%....80%.... done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:0\n",
      "INFO:ComponentProgress:0\n",
      "INFO:ComponentProgress:0\n",
      "INFO:root:Scene 1 of 2\n",
      "INFO:root:Process S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B_GC_RC_No_Su.dim with SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start step 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.esa.snap.core.gpf.operators.tooladapter.ToolAdapterIO: Initializing external tool adapters\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Incompatible GDAL 3.3.1 found on system. Internal GDAL 3.0.0 from distribution will be used.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n",
      "INFO: org.esa.snap.core.util.EngineVersionCheckActivator: Please check regularly for new updates for the best SNAP experience.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing processing graph\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.hsqldb.persist.Logger: dataFileCache open start\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14638 [main] INFO serverStartup - Nc4Iosp: NetCDF-4 C library loaded (jna_path='/home/test/.snap/auxdata/netcdf_natives/8.0.5/amd64', libname='netcdf').\n",
      "14676 [main] INFO serverStartup - NetcdfLoader: set log level: old=0 new=0\n",
      "14676 [main] INFO serverStartup - Nc4Iosp: set log level: old=0 new=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11%....22%...32%....43%...53%....64%....75%...85%.."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-- org.jblas INFO Deleting /tmp/jblas4819000381521070295/libjblas.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4819000381521070295/libgfortran-4.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4819000381521070295/libjblas_arch_flavor.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4819000381521070295/libquadmath-0.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4819000381521070295\n",
      "INFO:root:0\n",
      "INFO:root:2021-08-29 20:28:51.715338\n",
      "INFO:ComponentProgress:50\n",
      "INFO:ComponentProgress:50\n",
      "INFO:root:Scene 2 of 2\n",
      "INFO:root:Process S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E_GC_RC_No_Su.dim with SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.esa.snap.core.gpf.operators.tooladapter.ToolAdapterIO: Initializing external tool adapters\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Incompatible GDAL 3.3.1 found on system. Internal GDAL 3.0.0 from distribution will be used.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n",
      "INFO: org.esa.snap.core.util.EngineVersionCheckActivator: Please check regularly for new updates for the best SNAP experience.\n",
      "INFO: org.esa.s2tbx.dataio.gdal.GDALVersion: Internal GDAL 3.0.0 set to be used by SNAP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing processing graph\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: org.hsqldb.persist.Logger: dataFileCache open start\n",
      "INFO: org.esa.snap.core.datamodel.Product: raster width 3341 not equal to 3340\n",
      "INFO: org.esa.snap.core.datamodel.Product: raster width 3341 not equal to 3340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14858 [main] INFO serverStartup - Nc4Iosp: NetCDF-4 C library loaded (jna_path='/home/test/.snap/auxdata/netcdf_natives/8.0.5/amd64', libname='netcdf').\n",
      "14895 [main] INFO serverStartup - NetcdfLoader: set log level: old=0 new=0\n",
      "14895 [main] INFO serverStartup - Nc4Iosp: set log level: old=0 new=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11%....22%...32%....43%...53%....64%....75%...85%.. done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-- org.jblas INFO Deleting /tmp/jblas4294428937644417987/libjblas.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4294428937644417987/libgfortran-4.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4294428937644417987/libjblas_arch_flavor.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4294428937644417987/libquadmath-0.so\n",
      "-- org.jblas INFO Deleting /tmp/jblas4294428937644417987\n",
      "INFO:root:0\n",
      "INFO:root:2021-08-29 20:29:42.545034\n",
      "INFO:root:combination of default multi temporal speckle filter and user defined graphs is not supported yet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start step 3\n",
      "start add netcdf information\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Number of scenes found for processing: 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start create netcdf stack\n",
      "\n",
      "Scene 1 of 2\n",
      "/home/test/Desktop/data4/step2/S1B_IW_GRDH_1SDV_20210706T052551_20210706T052616_027669_034D60_888E_GC_RC_No_Su_Co.nc\n",
      "\n",
      "Scene 2 of 2\n",
      "/home/test/Desktop/data4/step2/S1A_IW_GRDH_1SDV_20210707T051822_20210707T051847_038667_04901E_1B6B_GC_RC_No_Su_Co.nc\n"
     ]
    }
   ],
   "source": [
    "from sar_pre_processing.sar_pre_processor import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "processing = SARPreProcessor(config='test_config_file.yaml')\n",
    "processing.create_processing_file_list()\n",
    "print('start step 1')\n",
    "processing.pre_process_step1()\n",
    "print('start step 2')\n",
    "processing.pre_process_step2()\n",
    "print('start step 3')\n",
    "processing.pre_process_step3()\n",
    "print('start add netcdf information')\n",
    "processing.add_netcdf_information()\n",
    "print('start create netcdf stack')\n",
    "processing.create_netcdf_stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. View processed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load netcdf file with processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/test/Desktop/sar-pre-processing/docs/notebooks\n",
      "/home/test/Desktop/data4\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.getcwd())\n",
    "print(output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "\n",
    "my_example_nc_file = os.path.join(output_folder, 'data4.nc')\n",
    "data = Dataset(my_example_nc_file, mode='r') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View information about dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    dimensions(sizes): lat(2227), lon(3341), time(2)\n",
       "    variables(dimensions): float32 time(time), float32 orbitdirection(time), float32 relorbit(time), float32 satellite(time), float32 lat(lat), float32 lon(lon), float32 Sigma0_VH(time, lat, lon), float32 Sigma0_VV(time, lat, lon)\n",
       "    groups: "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data from netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['time', 'orbitdirection', 'relorbit', 'satellite', 'lat', 'lon', 'Sigma0_VH', 'Sigma0_VV'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.variables['orbitdirection'][:]\n",
    "data.variables['time'][:]\n",
    "lons = data.variables['lon'][:]\n",
    "lats = data.variables['lat'][:]\n",
    "data.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vv = data.variables['Sigma0_VV'][:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot vv polorized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa5712a611614067a28769e7d42d2284",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='x', max=1), Output()), _dom_classes=('widget-interact',)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nHt7EdgrfSRbzIW8MZwyyluUmZztNePR4D5NZvu7Gm2y6jIN0y5N6zM1syfVkyY89eh+/en6d66M1xjiS6xt224zh9S2FaVn1G8BYNxxPN9Q23s4vDU8xwvFjyw9xo1gxbwse7qa0XlM7wz83e4N71T5dUKTKIpXDKcXZcki3TegKyySvSbSjXGe0mWZ0e02Rtqx2eZydSEGWxQ378W7M2WLEaFCznRcIHbjxD0FXjnvfDTduX9BajfqhjMmrO175oznhjYzkA1vaWpPlLdODFW3fbmkqw/atMaFw7N2+YG+2oJloHpztkRcNbauxjwtUZuOG20jMQY2zKm7oXqBPNLoG+1bOo4c53/fGdyGHHWnWIRvZVzUBPITck6SWtjKEMt6bZtL0zyzs6oT6PKfpN7Z0VlHXhm6ZIWtJU2jUWiE8tHshjknWCcs0ZzyoyYcN1aMheiORNlbAoVLsqhFBBkQnOLo15/nJObnuON0O2ZUpqbYEQPX3uhBxhtC08TpvNxlhnRByx+xw/e6sUfDbBuL5xcSXnAj2G/bI9IIksWjpL7Pz82qAC4LTlw/xk47ZwYZEO7TwvHZygNrGCzid7BilLZ2XDEzDeT3AOclkVLPpUoZ5Q562LJcJTDrSoiMEOJmPkdL3vaJA8JL5akDwgmLQgBOorcIPJIO0xQVB5xTT2Za9omLXJsxXA9443aerNR969hEhCLZ1Gh9SCUnaUTuB2/YPTr94VdZwZ7zgtBwhsoZlnbPYFHgnUf1QzDlBXSVo4/oBpmOxLmLm5kXstRYWKf1lL3mnU6bDilFRc34+AidItI0bURoIhSVLO8rWQCdp5jkNOVnS8XAzwQeY5jUnpxNCpS/PcTtQkDn+61dfQEi4cbCk0C1vzfewbdw8i3FNeV7QPR6z6sCNAo9rwwt3Trk7nHNWD9l1KTfzJa3TfGp+h3FyxCwvkSKw6xImec04rXl5m8XjBm4OlszMjlRabg2Xl8P57xp/hjoY/tzjb+FDo8eMdI0koKVjmng+XrzBx4s3WLqCnw3PkyQOKRu09nSAbRT37h0iMwuVYtcMOLy9IATBZFBRdZrOKdpGYxLL2XwMj1OWI8ONH1e0Q8Hoh34RMRqhvu15LtYDbKcpjiU2HyKHNY32sE1IBy1SBjZ1SmYs1SZFCECBOTc8TKe879YJKZbZdIvzgrbVMXEBEAEzaehqTeiBDwRopx6f9cNbEWdIwcbEJOiAPqgwxlE9HKIHHUXW0mxiFi06QVcazKDFe0m9zOKcqW/lNMsMRGxrEQAdCNcaulqhcofbxQTEdhopAnuDinKQYyXotcRsBC6HUDiQsb32odljXihOOalGlHWC6JOczss49LcK6yVNowleEhKLbxT0Scj80eQ3syy9I66GwO+t0LEgs17imoQQBEmPmpGNwLcSKSLi5d7FHq7W3P7ICfNdgbWSujVkSYcN6rKM3EvjwlJbzXwzAGA4qdhtM/zaoHYKp/syO/UEHcAEZGYxyvGh9z1gUed0TnG2GtJuEmTmKAYNb53OLhELQoBcGj7/8BpJ2tG2mvffOOG8HNBaRbXOcLlH1pK6NmRZd1lx5LpjVxZYp+gajU4sqbFo5bk1WmKD4s3FHusno3ieUgeVQngRF4LUkWUdO6sIG0PjMk4rQz5sMJnFO8ni3h6qkqgGxCqlepIgOzAJ2OsNSd5RmI57ixF+nrJqBGQBjMcN43GjAirxvHD9lNdP97n/aMauNVRNQpJZZqMdQ9PySAa2qkCmjvfdOmFoGqQI3N9NyZTlo3sPL6/NWT1kYuK0Y9ulEY0TBK88PiJJLeOsQUvP5y6u8TCryXXH0DRMTcWTasR//OA7WDQFiXR8TlzjO/d/lYfVFOsVuer44eVHeVKPuZ0v+IXz2zgnee7wgrcWe5hBByJgGx3bFolH6MB6l5GnHcejDTr3VNZwTkS5uFLDfoc6Txi9uqa6NUCMRrA3BsB2Grc2bD7cxgVzG4egAN3K0AwcZthSlil4gbowuCwaE/pKc2++d9l66TqFswqdWlynsK1BKA9bg95KfBpQrYj31bDDbwykHp06pPIkiSUcVgzyhqbTJNdKrk03LMqcwV5FnnRcnI9QiSNNLdtlftm2RHvoJGqjcEOHmDUgIvBHKU867Wgqg9yqCHRYJKzTjDuzBTJxBO3pEkW3T3wNJxCJIx80+CBpvKGyhjTtsP2Cb6THB0Hdmh5FBuGtnMblPP9jDfe+w+DHAjN/d5ayqyHweyyMdnRWUS1y9KAj0Q6Uo2oNsoXkXDOvZmxWEv/SDlO0bOqU65M1b53vkWiHkoHKGlqryNOWXZfiEVysBzSrDHVYo0SIcMpxhxPEB/RGTZG3eC9oahMzMyL0bpLWbNuUs3mGKiVeR6geD3J0JXBpwKchon1KTW0FQgWmScUHxk8AmNyt+Muf+TpE36d3TtJ0mtcX++wPSryX7MoUqSKEcvFwwuB4hx0qdl0S20CFJVgJXURosExAB/YmO16YnvPp5jbmuGV3VhAaRVkNEFZgFpJ0J54WObTjHh2loZs6Dva37OqEe6czxFs5aRWhhWIJXivaA4c4qtkbVqTGMk0rusogNpqFGMW+dCd4tE0QKvDM9Qu+4vgRZ9WQTHXcyFfYHs6ppefVzRG10wxNw8emD8hkxw+ef4RdnSBlYH9Q4jaGyY2SUdKwaVPOH004Z8LxrQWzvR1PqtHlXGQvLbmWb3hzO+OnVi+y7VK0dNwtLpAi8IQx96s9Hp5OGY5qznZDynWGShy21mTDFqU8VZngW0VzkdPIjLJOyNKOPOn4S1/xV/hTb/5LPNAT6toQfMLj3zGhuhbgG17ADjxqv8Z7gd4obOYQW41sZByuGg+JZ7hf0rYa6SHbq5D7OzaPRvhbNWliqbYpodJ0mUMoj0ktSnlcp+LwtTLIJiLcfOIBGTNrLxC5I3QS288axoMa7yXrV/ZQTZz1PHw+zjKUdijpCa3EBdh1Eb0VCgetjEPc1OEmAdFJTGrZG5WEIBilDamybNuUe3YfGhUTKC8ou3gNnROYYYttFepEI1uNHUhu3zrhOF1zK5mjpcd7SbXIeTjPQQbMuWHwQLB+3uPHlts/6zj9So1sPQdfcYqRnvPEvmtrzlNo9JdDvOc3ACkCWkeIoy01K5lRGkNdJfhrFmE8YmlQDdggKPI4tCo7w639JYsyJwTB49UY1beRtl3Cqsxpz4pYRheC1aogeTPFJxDyABLEw4zKpHFoayOkrjSWe34v4pCthNTjBNBJdhcF0gTsxKKGliSxPLM/Z9umfGz/ITfSJf/NxfN8ZPSQjct4Nj1jOKp5fnbOa/MDlPQsHo9Jpg23JqvLG9FbgTIBOewoNymfb49ia6eHHaICethFCGbimV5fE4Lg5+89A48yQifIGhH7rV7QDeLA0SfE7N8JZCcIIRAUqK3k/PEENehwpUZmAeffhs4GBbjIdXhu74JNm/FgM0UuDdmdDdNBxel8jF0nqNThak3SL76105xXw7iJmppH2wl3J3OyPosf6IatS/mFxW2cF1TblOnejlWVke9XZMbyZDtiVyeISiH2YzUwNRVTU7GxGY3TXCvWfPr8NtcHa76wPojQwbrgpBmzcwnrNuONs/2I+ug02zcmSA/uMCBXhrqT6GEXYbkqnntfadqLjFZmbDLH/1J+DxebAV0b+/jhhTXDr6jwVYaWnrJO4rxnbcjOBeUNTxhbXIhtMaU8Rjk6p5gMK2Z5ydlugHUqQnwLSdOmEX7pBWJpEA58l9ElAdkJdAtmK2ingW4WF0GfeeSgw28NZhmzcXetYTrdMUwbpnnF6gMtTadZnwxhY0BAOuvYKyqOnt/yxsk+4TS2ekLmEDbCkiFu7MJBWyaEYRU5GATWTYZRjtFeyWZREBKB7uGroz5R2Msqtm3K4yfXIkqpcCzrnB944yN4/1ES7TDKoS907FzVguwMNs8GBvcl248Gnnytgrs7XntO8r+68ykAnknOgH//N73eBARdeM8vi+9avOc/6dN+nEksbhMHT2q/YjIu0Xs+3sTNEFu8g7ATBJsqYyM95S7D1QozaOm2OcmoJUs72lbFDExAmrWR1KVSzFqgSoHLA3bkI34aIjTSCbpGx2FZozCjlmTQMjmuuHh5P2ZUAnwuYlsA+MKTQ/w85eEbB5GMoz2vn+1ze3/JchwHzpOkprOK9aaIRKFdwmfvXyfth4wAadZxfbJmXuZ4L1muUggilufG45QiKVrksGP15pRkKVEy4LJIdpMNlHcteqXRpbj8vh2EuGBUgmQlcWlAtwJ/5JDS42SEmwYVh4bWEHkCXtC1cTGflzmbJyOkgLpKOKkTtLGYgzKSp1LB482In+he5OOHb/HGcsbH9pd8sHjE65t9Xj4/4iNHjyhtQq46PjO/Gecojydcuz0nBMH5YsTeZEdhOh5uC6rzAn1YYxLLH37mZ/n24hV+srrLT67ex3cc/BI/PP8Io6ThKIsQ3Vx12KTmTj7np8+fY9slAEjtaRsNB02El64MPo2QxCxv2a4TRCXxSSQqyVbiswiTXVUZzx5c8Pn719itMrI3U5bdhPbDJbbWpPcTsi6e43YUECcp4nqNKzXWKrpW02kf+/JBsBTx2q4vBpB7skGsPotPD8kWnpOvC/gs4IxH1ApnAkGLmO2rHmaZ9zDLnekX7IgwkqcJi53GXo/otqbrH/0gUDtJ9uyGUV5TW83peoifp+hK4JPYfsPHjeX2s2eM05plD7vMTUeuOySB42LNSTlmmtdsLgYgwFrJqsqoG8N6mxOm8fnUz22ZDCpGacPDxYTutRHuuEWMY8Xkb9Xs721xP3jA8KGj/OYa8WLHELh++4RUW37nwef5mvx1/sH2g/yZN343/9Lzv/n15moI/B4LQSAzlu2yQBSW0bjiYLjj9fuH6LMkQu1ST7fnYZGyXSc0E0NXGoQKhFpBgOm1ioWXtKsUuRcHu6KJULhqm8LWkHYClwVs0fMFZEBUipB5rh8tqVqD85K209SdRGlPW2vOz0eEkUPfbLCPC2Qt8ZO4eLt1EkmSxiOMRxlPs8h47eQGb+5u4YYee1fRthqVOp6/c4JH8IW3jqiaDDNokamnbRXzMme1HjAaVqhRh9uayDpNPYhAe1KQnSrMNmbrNhPoMp5HnwA6IDuwA39JyQsqDv2CApf1CA5F3OzKBJ1Z7DhuNGoXe8HeBGQr6BYpF1mE1epxy//k636eH7j3EdabgmaZIQvLZFxyuL9g1yW4IJjoit916xV+YX6bV9ZH7Gc7Bqblrc2Mo2LD1JQ8WY7Jko5iPx78yaMp124uUCLExUM7rj1zwflixN39OW/WB5yk98lkx61swS+Xt1m2BSeb0SWZ604+55PVM7y6O0L0cM3j6YZtk7At4+C13esiesoLgpXUVRJbbP1jIpyIg1XjUalDiMAb5/uwMbz0V0rUvfuIJOGVP3YbkXvMjkhy7RGKyVLSdTkyDXSNiotqK3Ajh953nJ6P4xDfC1QpqVcpw/2S3TdtMaOKF7OKRZ1jnWT1hT1UK+gOO3ypCCognjKAtUc0ERrqsoCfWFRmoVHs3pzQLWQkRApIZCRD5knHMGmpOkOetoRjQT1IwEpkHslfwQnmu4Jda+icwto4rzoY7ei8pHaaR+sxUgSGs7j5Oycpy5ThoKasE7Z1ynP7F9wYril0i0fwZruPv1lz52iBEIHT9RBtHIXpWP2eBRdO4d4YoV4V7L51xy5LePXXbvJrs+v8xK33cVYNePTGAfyu3/x6ExBXLaD3UlgvWW5yEIHBqEYrx4OLaUT+XHMsNgVtbTCppd3EtsC12Zr8qOPxZsRmPkCUirPTMXJhYGJxTuB6xEwsqSWqlHTDONxEhJittzKW4l1E8KTGRkkBY+lyjXt1GIeiChjaCHkcWvJpjbWS4CVqFJFFoZOkRYdzPQU+89jUIzKH8wLXKD707CMSaXltfkA6aGl2Cd06RaSO0CoWFzl4WJa9ZIOKyAxRKvTORLZzB9VxQDYC2XK50HsTMKcG2T39Riyvn2aP7cTHuUUSMGuJujAx+9toVCuQtlfS0LES8AqQsD0bILq4oPzVz34C17cTEBGu2lp9SdKpO81b1YzH5ZiyM/zu669xbFb8xde/AecF33z8Ki9vj/FesKsSsqzjyb19XnrxEUp6tm1cSQ+HO/bSkpvDFftpZPn+dPkibzUzXt8e8Cuv3bpk5S7nA25cX/A3v/Axqk1GMa4ZZg2Jdpfckjzr2G4zTGZJs9jfT1SUbsh0x4PllLbRhCCwfWU3Ge8i+qrVqJ1EzbewP4W65ejTgZOv6SsGG1s0dhATC9US220mZtV6J6gGHmsVx4crzhcj/Lzv6aeOukoYj0qOBlsWdc7FYogvNcIEuqEjGzfUXY5wcdNwPa9eWEFIAkEHnrl1TtkZFqsBdixohI73fR6ZtyJzzPK42aY63uOjomZ/tGNdp4QgaBqNR9K2Ef3kXBzIZnncNJT0bNr0csMt0igbYpRjW6fsypTpuOSg2HFWxjbXLC9JdeStHM8itLfzCq085XnBIs1p2njeDz8Ne794zupixvz9Q+58qqMbJpz7u8w/rOBO+66tOVdD4PdQhFZhTwqEh21PbEIHFpWJpKjUIU8THAmMHQfPzRmnNR+dPORHyvdDJ2DcIecJPvNk4wbbKUKjUB24keP4xpLJs3XsoXvFwLS8MZ+xPyjZtQkXb+6xeTCmOqjIsu5Su8IOfUTdpB5RKuS5QQJ1reLgjEjjN6mltQlKedo6iZ9BBrJxlBVYzofQSk53Q/ayitvTJa+dHSBKjTmoSNMuSgWY2IZSueVguuX09X3MUiK7p0xj2N21MTv+lSN0JQgSunGgm8bWgDrRyObtDEfYuAGoRsTPlfdkpI2kOXAw7vAbjZcg2rjBeBUzTVUqxM2KJI2sWKk8vk0vz4dLHK1UpEaQG8tqm/PZi2scDbbcHK44acd84/BltlWK7RR/+42PkicdWdoxG5ScrEbs3VihpKfzinFaY71klpZ8YXnA0WDLjTS+zt9/+P44M2gihFAMOzhN8TJQtYYi7WibODsSInBtvOH1RweRiAT4TYTLhj3Bi/tnlDZh06Z8cHTByWYEadQb2tYaWsliMcSkFuck3KpYfOIYaeMGuXpeYu5uaJshqhZxzuLjkLYd9KiyXgeoOwqYYcveqKTpNLbSSAlBw2RSYr2kahL8MLZOlIotIySYc023HsDMgvL4SSBJLPZelPvw0qM2insPDkgGLdNxiRsKlnqAdyJukiJybV55/Tpyo5A2Xls7cWT7Fc0uQSwMwkN6d8P+sGSc1iTSUjvDos55cn8GMpCMWoIXOCtZ+QJlPK6T5MOG2WTHxXIYW7ZnQ1RhKRsTJSTWCXK6wQfBtk2YDUpmz5U8vJjQrVJEI3nyrY7Vs4fUxw69DejaIQKkFzXCF5xq8+6sN4ErGOh7KWTiCKmHNkLykAGxNsiDBr9KkWfm7cHksCPTll976zqfdTciMsZKwk4QJh03ri+4OVxxWo6op5rz5RDpBYtNwaZKmQ4qyiZhIXLqKqHJGvaLHedqip60TEcVhel4shwRTlJUJ7D7HcmopWs01sQNyowaulUKJj7kRdbSVobdeQFeoJcKO3Y0pwUMO4TyhKA4ezRlN0si2/GtARholyltiDhsWVhG05qD4Y43HhwiK4kuRczKe+x49lhzvj5C1dANA6oRmLUAr3BFwCUB4YnVQk/KaSc+ZqUCivsK2UE3IFZISxORQraXnoBL1BNEPsMob9CDimWZ02Seg+sr9osdF+WAxiqqJsEoz/F0Q2FaDrMtjdO8tj7gz5bfHslLpaaSTwXRIr+hbTWzYUnnFUp4Ghdv10/eewa7Srj1oSX/+uyT/OXlx/iJ+gWqRY4adOiso6sM8rCBVrGrUgZ5g9IuQij7LPVwf8O6zOL7Fyr2/51g1eZ857XP0nnNp5Z3SI2lagxF1jKa7diuc+RJSjtR6GGHm6ecfaVAOiKxSgXseY7smb3CCXzPrE1TS71OEX1rMuiI8JpmFQDbMqXzAp8Kyjoh6TWFKmvYNQldHSsOvMAOPcm1ksNRSd0aRllD5yUnySAmICLgh5bD/Q27OuF4GBfZYc+NKZuEzTpHG4c9y0jWkckLYPc99TaBRhHGFlN0vYhhIFOxvVnoli5RyJ7LoqY1k3FFqhyn6yF52lL2FYTzEv8ko7oeNa2mw3jM5bxA5HF4vW1TTH9tEumwtaF409AceGgVPg38/m/6OX70L3495qLECIGwnmo2xI7eHSPfOAS+koJ4z4RvFGor8QZEpdBriaoFtTGYnUDvBD4Bl3qMcTy+mKAfpHRHHdPjDWWd0C4y5MLwqDlgeZgjZWQVul54zW5TBi8sWO5yymUeVQ61Z5Nl1K1hfH3Ti52JqF/jJL6I2bjcaNpWIgqHyC06tRGv3EmCC/hGsnetYrUuEFuF7OKAWfheoMtKKNVlJVGeDKI66UFH8AK10BFznTpm0x3P753zcy8/h6jiTVofeWQjosaPCYgQF2uXxfYCPiJ9pBXUGrqZQy8VSvZKqDL+nGzjALsbxd6w7zeKpxsDgJtaRCthEBcA70Qk4ElP1WkSbRndmpNpy7LOWW0zbG0YTCJSxAfB/cWUbZte6io92Y6o1lFZMniJEI5ZUfHoYsLeOLYIGqsZJg2P18MohLcyiIHltBzxL/7KH2FTpbSNoZiVHI527NqEJuu4u7fg84+Osa1iGyIJzySW7S677PMKEZgOKkIQdMYjlWddZ3x+e53KGQ6zLX/4+s/w73/2X+iRX4qsaKmPQc4TrIDsTCFsbPd4E5OR7ELT7MW+vPBxBhM3IIla6jh3MYFgBS4kPC4iZ6BbZnHIHiIBcpxF5u98V7B5MkKPW+ygX6C0p9klPFnFBGGd5BEiWgvcxBPqOBswylGeDLgoapyXdFaxqxK6bYIwHmfjbKc+tvG4koDQAZ11pNOqJzcacmPjkLgcUdvIcG86jZ9aZOIwOvbtOxdh2812eDmI9s9s8QOHX6WEcdxlUmNBBCbTkifzMSEI7hzN2c92PNmNufF3NZOfep3zb38W+/sjm/rnz59h+bGW9QvTy5an8AKfv4sw0KsW0HsrVCVws5gpu0nACUjuZXRjjx1EJkowAbdJMU8MPg2YM8PSjUBGWKMuBa5V1MMEX6uIaw4CMWkJjWScNWxEoDIpcqkJRx2piWqgVWtorabaJREWaAXCi4iRTmJLKjgBThKepKADysb2iz9uaKzG1wqhYwYeCofYKQaziuPxhjcf7+NLTXIWafjd1CMHlsPZmno/EtnKJmG1yfm5s2ejiNewgz2P6PvS1kU0R1BRYkI4QZDxOJv9gNnGhTw9VZhNHPo+FcVTXV8N5H3VUEfIn7RxM3na90d7gofjoxVla9icDllvclarWNkU4yifsKoi4c4YxyBvWa9ztk+GyGHHdLrj1mjJssmprYkqrK2ExJOkHamxPDyfcvNgyTBpqKzhZDlieq3COhmFyKaB8aikbA3bMuVoumV2cMF+uuNxOebZ8QWffHSHX3n1Vsy0BXTGk0wa0sRS2jh0D0HgneSkMbhGcXC44eZoxZPdiF88vcmf/uAP8EFzzv/94psicbA11JsUaRxJ1uGvWfwyw5tASOPoqJtEJm7jxWUW3m01PvOMso4i6TizEacvRLxvjHGUZYp3AjnoLlVAAdZ1SlsbmnWKyG3cRHo+AGcp2bmMm7aEoDXNwCCuNQgZkDsDO8nJxRFSwpNHe8iNJoi4WTG18b2WCWSOG89coESIXIBe4TXXcbNX0jMwLesm4+HZFN+qCE8tHOkwyoxstxnr80isFFVsTaoa2kmIPIOtIj2X1B/sqFsTpR3yKL19bbam85IHF1PqiWZdZfCsohk/y/a2oOkVc1dVxo1bc1Qvzb5rk8jveZfWmoD4sjKEee9vAP21yN5K6EYen0XCVhBgVrEy8Gn8OVFqkOAGnqAkZtKQJI6dzBHeIJtI89eF5eDmhqbTMRt0grcez0CATiw2MUwnJa2NlP9yl+E2BjXqIhkqREx9dlwyzBvOT8YRcdTLN3sNMrytCX+6HIITZDd2vHR4xt3hBT/42Y+Smo6z7YDbx1FeeX09pek0qfJ87PghtTN89uQa25f3Yo+/A5XFhcYKEKYlyVpGext2TcL2Sez9ykb2i3hcvPXNkmaTRJhgKylvxj603srYHgoxQxUWZAcu93HBTzzmJLbY7O0GBQQds76mMeAEg2HNZj5AJo620ZyssijfMWw5nGw5mY/xddS/96Xmmz74Ov/q/k/yP/3sH2aS1QzHFaXKSLNIvNpsc/YmO6yX3F9Oo9SxF3zu4TUAbN+Db7KOP/LSz/KjJx/k337m7/F6e8QbzSGfXxzx+ul+1Kmp4nBatgJKjVsrNnlAtAI7cKhhFGgLKlAc7ki1ZWgadk0k4v2/zz/ODynLzz25w/JkFIEBIuBLTbtIEE6gm1iBBhlx+em5wmYBO3aYcx03BxXbQNt7E3Z9hRaJYAEydykNIkSg2qaMJhXDrKG1mrIxPHP9gtpqrFOsd1nM7FEwdJTDp325EDkx/WOjdITwBhWPK+hwKbwuXJT/0Lm9lIbACepOM0g6pIifs7Ga68Wag3RL5Qy56lAjT201dRehrN6LSzRQ8AJzZvrWFvFvCboS2JMCDlrqo8BwVDNIo5hjXjSsT4Z0+4pB1tLOMx6f56i9BvtCx/YDgcG04iCN4ofnixGbXhdIyBCrcS9iYvYuxVUF8F4KD9JFRItZSeR5FJySfcWnGi5VFPXNkn/+hV/lp06e4/RsTLdOCXONUsSbX0DyxCCbhPNBjvDx+9pDWyiu3Y7ti8G1MzqnuL+YUs1zkkmDzyRZ3rKrFEF40mlNUxnqRYa50NhxRA/JGxV51lGVKW6rUSJq71+7M4+IJa/4zPwmJrVRR36dUhZR2z4EQZZ0fMONN5i3BZ/8/LPIjUb5OHCWs4ZwkuF1YHC0Q0pPqh2bKqWpI65dbyK80BY+ZvGtoF1k5Acl1TLD1X27qJd76A6jnoswHv0ojUNBHcgOKoyx1KdTXAauVohaoWc1PohLnZ/NfBD73k7QbdK4EHUShlC2JmLpV0lEJR02fPvkV/m+J7/7UtZXS8/x/oqL9QDvJQd7G5QInK2itPbBcMdGZ3Fw/5S2rAJtq/iLv/b13D2Y8+ObD/Eryxt84eSAbp7FhTrx6L2adpvgBkS9glrFOdIgkBYtSWLZWolYRImRVFtar3hx/4xXLw55eP4CttKInUKXMiLEiDMQO4w6NqISuCSgK0Gy7Ifuw0ByEdt6AN3Iku7VGOMuCWKhi6qsodTRYCdvqcqEfNhwNIqmOj4ImqZgvitItGOc1Ty/d87rw32MctSdRsmYse9lFesm42Q+xlYaM2iwSRR7E51AEhGuQQYEsXp1VhI6GUFbTrAts8ia7+JA1Qd4ZXHIm2pG2UOgtXIY5bkzXfZD2xSjHKmyNE5z/8FNVCMQO3B5QFWx2vQmIBOHczK2/0zHrcGSTz66g8gd9TaNA/Ukwmy9E6ih5aUbJ1F22ksap/EuSmYDCO0jZLeO9/y7EQHwV0Pg91D0+PSn5ihPiUg+uVzTowZKEjeBF/MT9m6V/Lh5H2++dRjZhjJCHqUTtBOPv94hE4d/hyaLSB11G2/8SVLjpSA1lmanaH3ssVZvjhA6kFwr+c7nP8cPv/pBeJBG5n0tcFOHkAFrVXTxmjZ0lSF4OFuMopKh06zrlK4yqMQxOtixfWtMuy8Yj0q+8/bn+AePX2L1s0fkDto9jy08er/GNhGSaTMfmdBOUKqAnyfRTCNzsFboUiC8pJs5fAYEoimLCrFiyjwh8ZjEkYwayvsjZCsu/5i1xG6H2P78exMQO0VIov77etFnw0+zSgGEaDaitKfexIe5cypyOELs0w6Lhr/w8Jt5+fSILOnYVQlpalmdFwgZeP74nKo3rnn28IJtmzIyDcO8YdkVuFYitY8IWBX4xjuv8zXjN/gH8/fz8oNjfKmRtUSEOLtox/2C4kXUsBnHdoaUURPnxnhNN9yx3MvIjWU/20WGclKTmShAuLIF6fWGrkeOiVLjdUBvFXboIroq8/jOYIuedGcFrogQXZ95slmNlD4KB3rJIG9wqWSzjvBNYRzDrEH2bl/3L6ZRP6hWIAMff+4+b+32+MDkCU/qyGgvm6R3wvKstjnWKRIdkwhRKUpboLcSXcU5kMvBEc+LT/oqukcBiTbCoaUMZNpeupEl0tNazckvH0cQQO+g5/LAyXgazV1KHX0LQpSjTgIRIKAjwMClIXpgDKKrXZ62SOmjOJ+I/JbQxM8ZAqjcsjcuuZgPIUTplNImNCFW47eOF+xaw3I1YDyqOBpu+cLjQ8IqeZcWnN8+do9fTLz3NwAZ6PYsotNIJ2iuWZK9OqIhnqT4vsQVVtDOM/7i699Aoi2bOsUUHS51+I1BbBXdIOBTz2BaoaTH5i3VJiM0MQuSMtpKVjbC26yXEfkiA8V+SSlzsHEQ+EsXt6Kr1kbQTQIuj+Sq4AVCeabjkr2sYtVknDzYY3BQIkRgXWcMko7RjQsAZtmOX3prjHeCb7zxOktbcP65A1IbB7zquMIt44ZBF+Uo1FYRGomu+xvVBJwRvHDnlNfUIXanMQuFWahI+lIgFwY3toSRRWwVeqlxO0U5a6NnwMjjE4nsQO/k5YDNDiOnACEIDYRSxplHEtVSZeIujWSS1BJCRG5JEairBGsVYmgJ05a6NdxfT6jnGbVJGc127A9KHn5uwrWvfhJ7wtIz1B2Z6ii7hFWbUTYGuzMI47l+uEKKwIuTMxqv+FuPvopXXruO6OLCH0wkqjkVkFtF+khjiyjtYRsZEWXjFmsVp9shz+1dsChzFrucW6MlWng+c3qDujVU6wxRKmorUQsN015qofCELuLuhRewUYjQE+lk//fAQhd5JE0V+91PqzSlHbZViPMkunIL6FzEzCsZqBuDnyeYrcRlgV9bHHNY7HhlfcTD1YSqSi7nBL7SiFJxnmRx7pM5hBOYjY7D5560B1wCBy7XNxmz6MgEl4QAyyoj1S66i7UmciKeX8fsHEjMU/vIyLr/wDNvsWgKGqu5f3+f9rhnrz+dY/SzKdEJ2m1C6wXmaMOj9ZhXXr3xth+gEHTbhOnhNs5G2ti+e7Cd0nmJ6gUBz1ZDJsOKP/XxH+JBO2PjMs52Axbtu4PcCXCFAnpPhYjwx24akQrZfkWetoiiZqkCfqejqmA/mHVeMEoajosNmbJo6Xhzvc/9ZB+50eitonw8BA/Z9R3ZsEFNPF0X4YreSx493oNGRbSMi5nT3qCiXOWYhaLzCbNbO9Z5yuqwIFlJulngxo05880gqpB2Gp8Jbo2WtNcUi9MRqoiQvjrTDLOGYdJS2gR9WGMvMv7Opz9GMm0I4qk4W8CeZ7EFtlKXWH/hBbaImVZQ0VwDFXjt4SE6sRwdLXlcRR6AcAKfeHzuMfPYk9Y7SXOrRZ+bSGxKPKKVEZV0YMFY2toQtjGjRr4DNppE9ApdNCfRp1lkHY8dZs9FspxVFFnM9J76ADsnkTKwq1JkYdmfbSlMF8lVg8BRseG8Gl56Lp9XQ1JteePJAVK5S+7EsswJAc7Wz9KUBmV6D9leVx5FbAfomHn6JNDNHKTRxQorSRJLvU2pfMYvLAaIRYIfWe4Ve2TakmhH02myUUOjDALwWUCudd9Tl5ExLUHVRCcvHWU3IL43Vkb9oFQx29vi/dttheX5ML5WEmDcMRrWVE3kiUgRaLdJdBOz8bw/eTJlXgzQ2lE9GaLXcWPwuSd7oiO0dx03oeYgRB7GRkV0XOEvvYdRAacEmHi/mMwyKBrq3FCvI1wzMxbnI7prVlRs24Qs6f4x5q8PgvcdnPIf3vkBXjIDfrQ0zN2Qv5Z9Lcs6p+oM1kU/Y+skbWOQjzKC04ijmtaq+L1aEvbaSIys4rA+S7roNW0lhNiOmuYVjdUsdtFf4Ps+8Df4TPUM182SX15/iG2ZUkyqd2W5iVIyVy2g904EETPMScdwVJPoCLN0XjKb7sgPO+a7Iqo2bg2L0xHrTcHB3oaDYhez+TKqCvrUo2qFLCXCQ30yQG8knQQ7cshhR562EQaaOty1qG0uaslil/eldEA2kl97ci0ukhNLKzRqo3j8ZI/9gw0Ay3XBvdqg9SQSdwS4RqGK6DUbguDJekRTG7pFitlEXKa6b6CXo5BNNNAIkp6hDFhBKCwicWSDNg4ONxnpoI3OX4njdDGieHZNXSUkny+QZ5J2EjAbQTeMWSGNZPar4BLF6tsqhPTYRvfGG6CNo0tjjxgZIs46xKEiqp/Ce+jGnjBwFJMKKT2rXY5zkuV8gMk76OcF0nh0bxQ+ne6YZDWpiobe+rhk02aoHgP+VP7ZeomrFE4qTBEXimqXkBUtedrSNhq7NZetqJD0wna1xOcuQmufPstWorMWlXdU5wWiFZBESRBUnKoPk5Yn6xHeC/aHJcsqwxqFkJ5Jr45ab1N8G2G+EUbVn8+nbzOx0dM5t9H3eFxjnaKqYstGyiipHHRcqJUKcbH0gmpZxNeUAf3MNg5oveRgHM2CZkXFAy+p8xSZONiat9nrqUMlHi0i4sb2G4hsIhos2jBa5E4hdhJpBf5OROAUWcNstOPkYkKmLYVp2bRplB7ZFAQnIuqnh0ebQccv/PLz/A8u/mc8tzfn+eEZW5fSukjWW1YZzkWfilHekI123CsPQMJ0EMl8ro0SK8eH63jPpBIhA87LKNo4q3CvD7lIhyRHFiUC/8YHfoK/dv8T/Jn738Gyycl0x7LO8U4hEveuLTlfCiKYEOI/AP5V4Kz/1r/be6V8SeO9vwH0w53gBLt1hp6W+BAzFOcF918/jIqHQx+1wr3E14qTZspJPUNV0eaQ/Y7x9Q3rEKGh6axCC6jzNLKFTUBpzzhrqPdLsqTj5njN/eWU1VuTyCDVgbAX2Y7taUEYWGZHa3ajFHt/gDpJmC9mvPDRB3xg/wk/99ZdqmWGGXSx/A7QycCq7rXmG4WZK4pNLJdlR2wJIHpYXw/TrAXJStFOAu5aQzFsaBpD10ZDEnq3tDxvsU4yGlbkxlLu0jgsD2CPWlyuI4krCei14uwTnuS4xFWG/f0SVwiUDLRWoVLPJgisDHHxNo6u1tG7toq4dzdxJOOGYVFfesk2pWE4qTg82pEqy7wqOF+McOuEeiC4drhiaFpGSU3rNY3TdNuEu89d8PLymMZLct2xn5d8x+Fn+X9sv4nt2QB7kSPaaIN4eLzDB8GiGyJSx96st+rc5thWEbxhOK1wo4au1UwHNZtdFoeevYAeEJvaaUBmjmeOL7g1WHK6GdK05tJi0LUKoQWLdcGN/RXp3orXPn8DWUcCXnsQ20Kib1nQe0cASONpKkOn+uHlIkHuJKaH37a5w60N7f0MJGRl39ZRgW6g8UWUCjl/PAEnWDQzgvGIwiFl4Prdc4yKC18io5TzvCo4PRtjNuJS9kNENCrlMFaDZOASH4ulINhsczYi4C8S7q8O41ygr6qDCai9hnyvwjlJvY1V8p0XTzjIt0xMzVjXnLURgfb6+T71owHCCaq9OGszxmEGHc5KrJdM8jpuiMDp2Tiy8odRgmO9y1DKM8gbtm4YCYKt4a9+9Pv5UJJzzfx9/s1/9D3IzHHtYMV8XZAXDfuD8l1ZbqIfwJdsBvB/CSF835fqzf5J8d7fACAyZXvEggsikmOcIk8sy9xhvUCMo4cqtUKUMpqjdCK6XQ0dom8VoAJqaLm9v0QJzyvVMQHJwdEa5wW7NmF/WJLrjrGp0coThjYiDQSxb6oibA4bFTfTxNJqMFUcsr3+qdu8cadkNKxYVAb3OEceNxzsbS5JU/P7U4C+hcMlZI6ehJVdCLpBT8jqWbh6J5BvpVQT01cMUI89qpJ0MqW7WTEa1OSmh0iFSOySrcDkHR3gJoHgJL6XGL6+t+ZeecByXaCNwxhL12mU8th19I4VnYjyw21E4QwexvcurUDPoh582Wq6TqETR9MY7tdTpAxMh31pLgP7sy3HxYZdl3JRD+icYl2nyLXm9c0BXd8mqazhYlfwfS9/J6KWUDjUXhM3OyJ+Pu1lhzNjL9nZdp5FIpMODLOGVMVFsbaaujXkw0hAWj/OcEUcznatZjCoed/klBvpkk/6O4S3Cja3IzZ9EwpYxYX75I08ig/mgfy5NdtFAU6gBx1Ku+ioZUWckTQqVgGNwkuY7m/ZaY9zOcmy34AkoAN26hCNxB62FOOa+qJA9OddDDyhk5hFTCBUq5Ef2XFjukbyduXROI0K0TwlyTu8SdGbvnpMiSTB1PHi86esmoyuH9JDhIzW8wzpBSG3UeiuP89dHSU+jscb1nVGfZ4z2Cs5yLecV0P+i+d+hL+1PeCzm+ucbodYKxGzFm1clFXoFK3TCAm+iyzs1incMrmUdUFEPa5tk8eZQR4rh8nHzimbhF2V8l8sP8GfOPgktd+DTpLPKg7zHasy59p403sEvyurzZUUxHsq+vJzdrSmrFOaxhCyhkxbnixGhFYixy3jcRXx6cIQKoXeRLkCn/fWjhvD7ixBBYEctzHzafJImMlszHplRFSc15GQ9UYyI8s68lFDNc8RiScf1ZSrHNUK6BRnbg+ROSZ3l8wGJfdOZ/hWEZYp82UkhQkZJadvDFfYoHjQTlA7hdkK0kVkj3bDiGySbcTuN3sBvRME3WP0+2fdbAWyU9S3u8vzE9o4C5D3c9ZpRnt7wyBrOTpYc3aSomoREUQXBuEE9qDD7DU4J7n3eJ9s2DDIWnwAJQNdp/FecHBryWJdRK2WOj4Uqh88t1PwN6O5yOJi2GMMI7yQNKKh8mHD2XxEsJIbdy4oTMemy7D9Qh+AIukoj6OZCEBjNY3V5ElH+djQjaK9pRCBpopCc8meY5JWSAKLOqdxirYykTGeRqOVJw9mQIS3Bh83riJr8F7idRRLa04LQup43zOnLNucz1zcwFqJeX7D8WRDCJHcVoYc1+vhy1bg9juMcmSjBmclk1GFVo4yiS0t2bOet6cDRC/PvFoMopzHUUM9iRBQdRHZ7F4Huonn7q1ztPTY8Rbn5SVb+fHJlO4gfha70/BkSDmo+h67wnnBdpfFCq1S5EclzczSTQSoQD6ryLQjBc7Lgs0ui1LiMtDuej9sCexH6Qx/kV4OcaWDbqNZFpHk99Uffp1nBxcY4fgXD3+JE1ex87Fa2pYpwUu0cZds+3ihxeU5aq1iW6XIHpHlu4hEyodNbBfp6C09ShuWVU5dG/7YV/wE1/SK1y1c06v4mZKOZZMzG5SUnaFq3yUtIPiNEMEOhBCfesfXfz6E8Od/A2/3x4QQfxj4FPBvhRAWv4HffVfivb8B9Neicyr6sbaGs8UowigvDCL1FIcRQjcu6sjmDQI3iw5dxjhsF3VehOsHmV4y3xVcH6+5++ycR9sJZ+ueRCUD2aCla6PAzjhreHIxQdSSdK9mXNQIATsrogZKK6AIlzeNlB7XGpJZzfW9NbXVnC9GlMuce8ket8crdlWKrmLLx+a9/LKEZBWzNW/ig2eHfW9bBqQV2GGIbN09x/BgR1MbnJN4myCswE4tIo3zBeskZ4+mpKWg3fPoxCFulxFq5yXOSqSKePjZoGSYNHzhySHPHl1Ez+CVIX+25dteeJlNl/HWZo/TxYh2k9BeDyTDFr9LqHdRkvqpbaAet5gkLtjWKoKVmDxqNHVOsWt7JIz0cRM/n7C/F1s4RjqslKx7f1yXB8JBzNpHwypq5W9yznYDJmnFvCpwXlz6NduZjRUDxAzaCYLs+925Z70pkMrHDF0F1Lglz1s+8/BmbDtkLW1tkCpw7+EB2bDBaIfKLK6RdClkvSlNa3UEI2SBpk8e9oqKZ8cXNE7zufPjCEFdRqtGO+iRQbmL/XvAOYE/itWlLCz3z/aimmqIM4TQSUSjkHVkcSdLQ9CBdhrhmeePJ9DFmQcCdBNF4qo8JZ1GXX0pA+OiRkvPfFuwORtizvQlTFVICGmUJPEbcylS99StTOYuDp+bhLIWNJ3mwWaKlp7XRwf81PpFHlcTHm/GzMYlrVWXPtUQFWAHactBESG2r1/sc3tveSkPXreGptOXPh6Ni2qgnVNUreFwb8PHsrf4ltwDGT9bx43j/NGE5mjLNK/Z1ilt9+4sZb9BLaDzEMLH/3/9pxDix4Br/4T/+veA/wz408Q9508D/zHwR39jR/ubj/f+BgCIraYukh6hILGNhrrXNM98/0AqyjrFOUGSRU9Rv9MEHan5Tx+Sdj8iVZ6SWFZNRtUauk5FToFxZElHlnSsVgUP3zwg2atxOqCUZ5i0XCyHkYBi4vuL0NPgz4aRVRbiRvLgbA/XxNJdVIqDouQzr9/CPE5QjcA+1QRSIUpVpHEjSBdxaOey2KZ2aaA+cuQ3ttRlglgnsYdqZVQHnTWxpBaQDlq0dlgvUWsV0SqzljTrSPpFuNxl+Fozu75imDbMdwUPHs8IlWK3lyDmCaoSlHXKzz9+hmlR8dzknK86uM8vz2/yeDGO+kp1XPTVuEUMe1tLK7l7vOLJekS7TRjOSg6G8eEfpDWrKmOQtgyThgfLKVnecme84PFuTGE6EunIjKVsEuQzO+7sL5nvCrL+mvlhnFOs2wi5WS4HEUfuAROiIYqKcMyn6qmBiC+P0EWY3FrF87DO2O4GlzOgKuTgBa73V65XQ6okbhgid+wfbC7PYWcVrdWXdqGlyBiPSnZFghaRLY3tK51ebdWOABFQMmDPMnQtsYlHjzrcIs6ihIqcF3rV2KjbL1CtoJ16nq5NF/f2IsNXRclnJLgkRMharWiqPA6qdeBknWKGLUoFhgc7wn7U6c/Tlr2iItcdb1zMcJ8d416oGPaDWikCRRrhu7sqoVtkNO2AMLRk44bcdCzqAo+4zPbXmwLfSUKpUKVk/NKCUdrQuejJPcprRknNtkuiuF2nL9uOZZOQ6Oh77YOg3KT88Q/+fb4l93TBce4q/qvVNzI83EWbTelZVRnr0yH57N1BAcG7JwcdQviiHAqEEH8B+Lvvypv+BuM9vwHoPA45u51hdNhwa7RkM8k43QwJEGUavKDraelA7MX2Wv7O+Jj9P0WwAM06pUs1u12GnyfIWYtUsWyt1ym1iLCy4CJCRMr+AXNxiOW9BOPRky5WFxsTUSepYzSuWM8H1MsMM2qQRTRnv7u3wAeBeZwgLTT70d8UH8v0rlQEE8XD9PptHL43YG81DEY1u3WGPE+iwYfyFEVD1+koR7EzyI2ilinmID5QeIE3Ud5i+2QIAtS4xbXxc13Mh6zTLKo8Gg+JY2Ba/LQj7MEzszgn+cKTQ1ZVxv6g5NE8MqxjO6O+hHgKIaL8gJPMy5ztyZC9GytuTVYs62jLWXYGrTy7JuHx2QQh4flrZ1ivOC42SBFYtTnOS+rWsDcqqW3MIHPTXbaOWqvYtCm56QhOxPlOLw0QgiDNo+9CmtrIyG4lk1H0WM6SmEHW2ygnLpoIEvBpPzfpxcuQcfPF9hvIwHH+cILsh/kIMOuYmWsiG32xMvzCpmA0rKhWkUOgmjiTEJ1ArjWuEzgJ5B6rI8HO7SIihiAJiY9eAX081fW3Ji70T4ezOOJxyF5qOhBhvCJCf5/6RYhOgNXYStHpwOGdBbO8jFar0jM2NbNkxzSp+OnHw3j/qUhae8r+tV7S7RJUGeGv1Ao/FEySitNyRGFajIpzg8EgDnjtTjN535zro6hA2nnFnfECKQKfOzsm0Y5pXlMbza5OmA7jMUGsGloXkWMvJk8AiRGK63rIz5w+29vExirB95pc46J+V9abKAf9Wz8EFkJcDyE87r/8buBXf8vf9J8Q7/kNwLaq1/lRvHU642i2pnOx5M6TDkHsIz9Fr+x2WRR661mOiEDIHDYVkEQnpxCiPIMQASZRgyXr5Rtk4kjzKH176/ocgFWV0Y06msqwK5KIS88i8Wk6LlnoAbJHLjgvL8XNCIL33zjhA+MnfOriDm98/jrGQXNswQRM0Ub1RxXJVn7SRS0ilyO8vIQw6ocp25nCjFu4UbE3itnOMG1462TG+ToOH33hUUvNthkjxi3yZhXbAD5i0hHgSo1IPKGShFbTrUysZgrHC8894Xcdf44/8/zf4NP1bT5b3WJjM1yQPFmPsF6SphY5DbR1JDeNhhWdi9VTAFLtWO8y0ln0gN20KdOsYtclrKqMXRlV02Z7u6jfbw02RMy48/JSdTJLOmZ5SWWj+9RTUTItPUuX8+jxHsfHK9JBJHX5Nhqkj6YluzLF7Qy20ZjMkhdNRP+IwHw9iBsWvZQAcUHX5+ZSvC/U0U3Lj+ylQxf3czSxbdeNIhTWFvH6+DQK/OEEbmVYbkzPSYi+ywQukUGi73kHGRBBXEpC0y/aoo1GMrqU/fA/CqpBlD1JNtGMp94X1Nfc24t9GhBtbHs9Zd4S+g3EeGSfEG2rlPfPTqLcRj3EB8Gbu30erCZk13bR5vN0HI+lVjC0UXiujfMVBODic9l6jVEOF2RkEHtJaixKeo6O4zyjcZHBmyrLm6sZ611GkbU8v3fOus3YtYbpIMqsN1ZfylBoGau3/+j+d/H92ZZVl/FwO2FTp4yyhrrTZMqTaou+7jk5mbxra86XSAzu/yyE+Bjx7ngT+Ne+FG/63473/AYgrIjwv6HDbgyLpGBc1NwYr/mzz/4N/uzZt/IP779IXSWkWcftoznJNcerD44Iy4Sw02SHUY74afZX1gnOKsajkuPhlmlS8fL8kPJRFmWTrwvSosMHwdcdvUEXFD/08oeRCjZlxtF0e6l/oqXn2nDDG/NZZI9e5FFELe843NvQOsXf+KmvRdYCU0UvXkIkdnU9g1mOLX6nIuY+iUiibhiHmaZv46i1Jiw0PglcnMf2x4WISqdCB9x+hz5LkE00cwnnebS2TD3Z7Q3b00HEgCvAtJhxeymm5bo4D7herJipHf/Z2bfw8vqIk82QUdYwNC3XxpHf4L2gLRNk4uh2hi6PC7DtVISKdpq9Uck0ixLLuy7hjfnsUl3ymaM59h0zmKdZ39kuav+s1gPkZMft6TLqzXvFqsp4eP82opZMn1le3hvrMsN2OrYcQjyvbafZG5e0hbp8kIUIl+2artaxt17H+Y1sozx36FsvoYp6QaJUiEbHlosI2ImLCKM2LqgAQfdS2YFYOSQerEJWEp97ZB1F+YKIgmiq4m0ynwNdR+lt1UTIbxDg0v6YPXSj2BJ8OiMCSB/E36n3RZwtFAEZYuYaEt/7QoS4uQVx6dsgN3GzqU8GfIo7hCBoa00+iDMWayV5Gp8Pu0owq9hr6pQiaIleq8iVqXvFWSt5vBljnYxzEukx0nP2eILKHYeDHY3TbJqU1io6V1DuUvzG0BaG7XjN0DR0hWLTpOylJRdhQOsUqfRMs4p5XvDGYsZDPWG1yaMvuJNsiIStqkoYDeJwmuqL7tv/uhHVQH/rUUAhhD/0W/4mX0S85zcAAoSDlizv0Dp6l1atYSFz/ub6K/nc6hpNn41WZcLDZhqz/kqjm6iLUoucwfEuZqoitnSadcJ8nbAaF4wGUavFH0aM/2hSoUSgbA0/8MpH6LYJw/0e7rhJcUPBajvg49fv87Xj1/nR8w8yyWuMcty++RY/+dmXSLOOxbbg7NVjdADVvv0wEqIdn2gkjC1JYqlMVOu0yhCmvYxA1TNOe4VRESCZR7mGp4uC65UnsZL8pSV1lVAvUvRGIV3MZrYnccCtrlfRqKZViCy2PLRxFEXTE5Ukv1be4K1yjyfrEduzAfXY4MYliXIY5Qi9/EOSWGQW5bI3naarDF1lKCYVzks2bcrQtDw+mzAY1ewVFVIElPBsupSj0ZZExVmF8xIfYLUacvtojhSBXZcgCaTKRmOUU43sBNuDjDxvoVE0pnfxaiNZDS+oNyl1D8cUjYwsXUlk3fbZsHBxkQ1JgC5+zysBPmbR9MbvspLxXjLhEg4rLeitRjVc+i2rOiBdvFbJJtCOBN5odBXQVaA6lOgqRGnksaA+eArvjdIewkc2sa4jA9z3sjbC0/s5iF5TR2AzgewCo7cC3QC2d2MFkJ5L7EDS3W5iIrE1Ua5D9/wYHe0+Q+LjYp9Fq1Il/aXTmO0z+EpFSXCvI1/kKdntKS8ACfmo4XCw5bwcUHc6enbPDflCUh855pOC/WLHyVszZClRN8pIJks8e7ModrduM3wQjNKGVZNT27gcreuUi82AttbUIYmtulLj9xqCEzireP7aGZs8ZVOnyABi+O74AUQpiCsY6HsqgotsRSUD613MfqUI/NVXP0FdG/x5GhEfSaDLo1a6yBx6PypaZsSSMtERhlZbgyz7AeYbOatRyq33nXLz7iPOygF1a9hsc9w60tXJPVWZ0is8Y/veYyItP7d+jueH59wZLKic4cODRzy8O+WNhwfIswTR95KDiPBOZIiOTn2V6TeaZmEwVRz0dZXAzWwcrI4hLyJ0cTePfXRXiqibMow9YT+y6MLiTzO2zTj2iQuHOqiwnY4aKosUtZV0MkEOOoQK0b94G6V769STj2s+d3HEg3QKwD934x5vjmc8XEw4Xw5Jkjh3qBY5etDhnCQ1kXg2zKNgW9UaDobRRxbg1fvHCOW5MV6TKUvtNGe7qPo5y0suqoJnx3OWbc5iHmcU6zqNXrubIjKStYu2hLcb9va25MZyvh6g1wrxVOsoiYt/nPKCLKPxjuziOb+UEL9shUT3rqAit8JsdPzZpDfPaUC1TxFYkmQbemGzXu1zEPX0ZQvp2lPvSaqj+Ls2F5dw3vJY4PKozKqap8cXb6J0Hq0im2msQKJekUBVgskX4vvV+5FsBjGByOaB6ecjYiooyeIDBYP7AjsA24v+hVbFecg4Jk1SxkGu2xeM0pZFmcfRgYjV324XHdECkKYdjYstwvgm8TO1U8/gduSw+CDQysXrUA4YpQ1SBLabSFxrjxtCpbi4t8fFKIIi8rsRsYYVjI+2jNLoKwAwTms6r6isoTAdVWfIjCXVjlJ6youCbK9mdLSO50H6OCOyhnWVsXs0Qu41b0NOf9NxJQXxnopgAtPZjsxYjPSMZg27NonORk5FjPpTRI6Kg0Bp4oqbJXFw2HWKTsAwjQ9PkjiqowacoFMGHDy6mHB/ewBWMLi246O3H/DK+RG7iwK1Vvg2iVpE72gn/fzjZ3Dv0E95cXbOV+T3+M+3X488SyL8UAd8Ct3EQeqjIcc0ltpC+ciCnJuYcfXa8bQSZyMkb1PrtweTXuCfqenWJlpjWolca6yV6FZgzmN57nJFlxj8XofJLLISuNwjd3HjkoMuCuR1fYY86pgNS16cnrFqM7T0SOH59+7+EH+y/W5OdhOqJoU6tjdcqbDGM7xdc3u8onaaexczhAgMk6Y36Yhs52duzmMryCYRmSQD07xk3WQkyvFgO+VkNeLgYMNRn1GWTUJbx9lE3caB+51r82gw3kabxFL0piZBIGtxSQATLqpxRkE4Lt3MZC0gSJKlYPoFT7awNBPF8sVeY+np5l4AWWzDiR6Jtb3bn34d5ThkI9CloJkFtkpACMgWko0gWwSaqaAdxzZP8QhAUB/GDUD0a2s3gHQeJTVqJdBthP4SoDqKWb1LiQcWov2jSwT1/oigwOYBnwX8KM520mET+Q7rlMF+ZO1mScd2l1HtIkJs3o4RypPkHYO8uYTWHg3jc+ERnG6GVFbgsviZ4kYqLmco1kYElBRgnaSUhovlMAIsEo9bGZCxHSXPE0LhaRsdZygC1moQq4zWkGjLYrdH9WiI3G/IsmjCNCuq6P2cl+xde8zP/9KLPPvBC2ZpybwpLi1Cn59dcF9bjPLvGg/g6Xn4con3/AYAsFoVrEJBknfsj6Oj1L3VjHKdMTjaMSmqS2105yN8sT7PWVmB6rXFfat43MUWkNKeNIsInk5ozFYR6hzVX/cQYNXmGOUYH27ZJDn5sEH2KJPVNqctDWIXCWMbGZDDjk88+yk+VT7HbpdF+WkrsHsBREBPW1wn4+IvAya1dI2Or0Fs5SCIUs6buHK5NCDu7kgSR7lJCZ3Euyi7bBJL+4VxhJCu1KWXq95BdiGwhWA7lNgyQ6hoEvPU+ASiqVg4TeMi10k+fvgWe7pkm6Q8ridcS9f80Oor2DUJJosqnx0x0Ra5BSfJjeVbDl7mc7vrPF6PL9sJ46zmZD1CpdGxiaRlvisYZc2l2NvAtOy6hIvNICKaepTPtk4ptynyNIkZehMXv/PtgERbVusCvzGkVURICd8PUeHS4N6LCAMVvm+V9S0e0buebW9KqoMEXQa6cbhUzFSNQDY9HyOLZDzg0kMZBNkZmF1geztWD0FFQbigoB2B2YJqAulC0OxBfRh7/6FnkQfVSyQH2D4jItGtemoqE/9fWC5x+mHgEKWCcYdfGYKWuMwjDxryrOP6ZE1jNUfFhm2X8qo9iglPrak3KSp1vHjzlNdPDvDvwLePs4ZR0lyauyfSYt/x/y6NInqysAgZSHqRvNZq9oc7XBC0NmWxKbAbEwfaWwXTLkp2n2Vw1HDjYEVrNe37Woq0ZZw0HBdrXlkcRej2ySAK3wFto3sGt+Uwq5k3Ba/ODzAHsX34uYuj+Fqt5sXjM24VS+Z1Qa67d21w+6VCAb1X4r8XG4A2LjI4naRqDRuTMUha1NGKjx/ep3KG1zf71FbjfFzkny6SUgbaVtPNU2wjkaMukoEQ7E+3XIhAJ7NI6Op1/btOYaTj+nhNZQ1Nq6kejDCbqFzpxxax1Zf9YQRcO1zxsJnyDx68FDXS01j6h9xFbPkijf3nRhJMoPOCUEabSO/B94Yy6NjOeEpoGqSWRFsYcfkQOh/Pgys86UUcErs8LmSqiW0Dm0XESXIRma8+idVFeTLoz0GA4xptHM/NFry12+Pz9pjaGrIecbPtUlId4X2jrOFCDajbyCIdzXbcGK740dMPsqhzRllzCdPctQnlJmU4iQ+u85IijTvU0zI/Nx3n60HU6ukdwC7KAXWVxEEsb2fLDC1tq9itMqgUso3Dz/DUqtLHv30S3h5SZm+n9aKORvdBQ3YuSFaB4sxhdhabZ9RHolf3DAQl0FXADuKGgABR0XM0As2ewA4Ftujfr+2VWdMQdXaIg3rfSzyoUccHbz3m3mIP5yXP7V+waVPONnEuU58OCDIOi5863QlJRCYdVigVqEUSUVY6RHTPwBJOM3YDw71Go43j7jjKi/tW0fnIJ0B49iY7Xhid8/rJAbOjdQ+djJpI6ya7rKqsU5cuX9mkQc2qy4x/Oiwx0tN5SV0b7q9m/YEGaFSE0j5VWjGOLG8Jd1oOhrtIxkxahDAMkzZWGdWI3HSM0oaj929jC6lNqTrD6Rf2mR8V+J6HgAhMhjVaeAZJrA4mScXD7YQXRmf80Wd+ir9y/+siefFdiqsW0HsodOL4+J23uKgHHOZb3ljP8AiGScOz4wtS2XHeDiKF3kVyTtt7vHalIRm23JiteCwCzS7Bt4qmUYjEc9aOcY2KRtvryKYNMuqXPF6PaTtN22j0GxliEOj2fMRay3CJsVa1xF1r+PqjN/jhNz5IucyRu2hwIZyIuvtpQB7WuEpjjiqKLC6GK1kQdhoS4msaH4lAMhCMJCnaS02b3ESjjhAE2yqNTmRbiVdPLQd77+QM2mlEioTeAEbvBHIdzcnLayDKFLtnMUVH12peuXct9ntzS563ZMZyUQ74nTdf4Q9c/1n+m/VLfPLsDgDpXk2aWCZ5zUU9iDhxp0A5tnXKIImfLXSSqkzZXAy4dmNx6Y0gCf0Dn9BsUm7fusAHwZP5OGL1nYhY+6ddLx214Qd7LcttgmwlvnCIzMXedaMi4coDucPLnvRnPOP9HQfDHa/fO0KeGkQTX9NUgWYi2V1PaWaxneKyeE2DDiCixLIuBdU1H3X1B45sr6apdVQzNZFYluk4fDzMt5Q24c3XnsM8iZtX+89vOB5tGZuaF/fP+NjkAf/o7EUe/ewNkh6VwwtdnF0V0WEMJ3AqoArL0XTLo9NpVA9tBOiAHETrzNYJbt6cc7GJMtF/7NqP8x+8+fsu/XkTE5OF33H9C9wrZzgr2WzjHCkAp35IUxuUjsmVUm+rtc5GO4amRUl/2asHKJsEt41yHDK3+E7GeZYX8f4NEXSge1XXbZPiA9wcr3l+fE4XJMu2QIsIHe2covTRb3u9yxgParIbOyaDiieP95BFS551WCeRwnN3fMGdfMEnL55BicA/vP8ib+7tMzAtX//i6+/KenPlCfweC0HgV8+usV0WvJHuYzvFwf6GTFsWdU5rr1/Sz4u0ZZC2ceA0T0kWEpcZ5h/p4g1+muCKHh+91bg+Sxzu1zCoqVtDCJAmFtszJetFFnHbs45iHIlPu3X/PROwheNbXnqVX5jfppwXET7YCdzIRSOMvrVglwnkju48Z6kyRGHJBw02tdgu9lSFCLhW4RvFYL/kcLSldRHOuGsSdlVUYjyerXm0TSIySILZ9I5ONvawvQKzE3SyN3h3sa0QyXDErHan6HpNf9kIwn6LbxXbsmArQS00/+WDT9B8jWbeDkiVY5g3zJcD2sowzBpwimWV8Ttvv8o/uP8idW145Ca43vTcruPQ+WwxYv9GyX5e8ngz5nCw5cFqwrUbC4xybJoUrR3zRxOu3Zlz8fAoZvG9UBjGM8lrOIRVNeGpd7N3inTcRCirlUynuygtUCd85NZj/sj1n+aXq9tsmpRyL+HubE5lDSPT8OLolG8Zfw4XJD+6/AgD3fCPHr/A6dmYZl+SLGSU4thrGU9KRlnD3/nQX+OPP/xdvL7ZRwuPDZLTzZDfc/fX+D8d/xIAz9/+n7P3q4LBE0fy1ye89o1j3qgkZiv49J27fO37X+dBr9LZDYmLfuirlMM6srutxLWSh49mUEtUJXG5x/QmPsWzS9ptwsN7+3E+MG751375D5Eayzff/QK/eHaL1Tans4pfXt6k7AzTaWz9Pb3PtlVKmnV4LxgWFUoGysYwKmqOiw2TpKZyBi09jzdjBklL3cS5kRi3FIPIrdj1THf6wXGiohSJlJ5ER6G+Qrc8rsZU1rBtUmZ5yarOmC+H5EVDuU1RxvHs9IK31nusdjnj2Q6tHMfDLVp6amewQXF/u4cUMYlItOP1RdR8YvrurDcBsFcVwBcXQog3gQ2Rl2hDCB8XQsyA/xdwl0hw+B8+FTkSQvxJ4F/pf/5/EUL4kf9/7xGIQ9ZNpehKhRjEAZKSHiWiDkvWk09aq9g2Kd02QRiobsfsrLs3QbYCn4VLc+xAzPhwgiJtSZRjf3bOs4MLztshi6bgpIySD904Coo9hZsq49FH5SXb9Hq64ucf3okww8xFLLf2uAH4LGZ6atLhe5avHXiCVIRCXOoFPfVbXWwKmouc3TxHiMA4r6MFXm9KboqWqjXokwQ7dZB47D4QRBxWZwFZRWSIWmmeqoy6FLpJ5BaorULUAmd7sbyZjRuah+F+GfXkH455/m/U/ID+GMc3lqx28XiClbDTVHuGYdpSlSm/cH6bUdbw3N6c82rAo1cOEdMOmVncOgEP59OIGJnmFWe7IXUdh3YXqwF5Fmc7m8SyLjO64w5aidpEdrTJokb9Nx+/yt+0H0PKwPsOTrm3mvFvv/Qj/O2zr+Kkii2AnfJ8853X+N9d+3H+zPnX8aCe8l23fo3OKw7MlmOz4pvyezxxKZ9IDT+4K0hlx1cW9/jh5oNI40lPJHuvOpZ/YEsqPXVrGGUNc+f4vbPP8ObwgF/Z3EJLx8f2HlA5wyvdjpfMIBqckBI0lIeSv/Bdf46f2b3I9//It5I8Mbx6dEDzXIPLOj584zGffXydtMffl2VKKHWUE+kUyVzF6mRiETpgBxadOLa7DLHVUddqGolu1ktS4DDZ8gfufpK//OrXEYBcR4IV0NtG9pj9/m8hApmJ8hZFfxw+SHY2SloMdcOuThhnNe0iw6wlYZMRXmqZFBWjZ6LfQfw9MCpuBM5LXpqe0XnFg+30coaz2eaUjaFIO/KiYbvMo/aRhC/MD6iahLbRDKYtrY3ooKfIsHHSsJ/t+NzpcWwtaccgbTk5mbJ7l+Sgn37+L5d4NyqAbw0hnL/j6z8B/HgI4f8ohPgT/df/jhDig8D3AB8CbgA/JoR4KYTw6zo5JDJmEbNbSzbbnG6RUp1OqES4NOneyIAadqRpdJiSW4VwMWufTnfMH00QlY4EHhUYHu54fnaBDZK3llOq1rD1KQPT8rCaMm8KXJAcF1vOxiNc3Q9qO4lOIhZeKU+9yTHG8QOvf4TqyRBy17NLiSJo0xrbasJOE3xk9AYV8ecijZpD0Uxbcf5owvR4g1Kea3cv2NYp3gtWZU65zpBbFdEfMrB2KnaMlgo7ipZ7LveRkao9wmm6fYtaK5AC0cUZgT1syYYttcqQpYrIjcwRSgWZR2iPtQqtHd04IDpH+jDhJOyBBzmwJG8l+AQ2n5uxmDjMtKa2kRG66dJonCPj3EbIQBh2URK5F/vqnOL8ZMx4f8f6bIjcKjYqsFHE3nYrL5Uon27W3ku+au8+f3D68/zc3l3GSc0HRk+4lm34fz7+OvbTHWVnWJdZzAo3+4xuJCxtwa+c3uD19CC+Xn9P/SVteWF8xpO9z+CJveb//Wd/D12nKIqGzXOG0VuKujZ803Ov8fHxmzxup9zQKSt/wfe99rsZJg2Zsvy5F3+EH68Uf3P1Vfy7By/zPR/9FD/86W8kSEGyCSzdgN81/Cyrb8v5of/q66h+/gD5/opnDyPL3DvJC7Nzzqshm/MBqEAyamlLQ3fTEWycQ+g0DmOztGNzP+r9d1NPNm5oW42zir2i4pMXz/An7/4w/9b7t/yXT76GXZeQ9iYvtX1q9h6Tnl2TUJUpCyuj3LkMyDQO6Wd5ybwqaDpNvUt4s9oHz9vkQhEusTK56WIC5t4eIgsReFyOI/dDRjnp118/JjnTlEeG4vqKQdqybYaYScPNgyW57qjyKD9xvhlQPxmwTofoQcf1/RUA59WQr7z+kMflmHUd21PT2ZaBaX8DS9avE+GqBfSbjd8HfEv/778C/ATw7/Tf/+shhAZ4QwjxBeATwM/8ei9mg8L2feaXrp9yPhlwvhzizzKSM0Uw0I0d3kpUERewpwbywQuqJkLghCeyGSeO+uUJvzwcsX93wXN7czZdyuPlmMebEV2h8AiWVU6XKA6mW1qrWK0KfK3pdhpVSXi2ZTCtoll4ALnXMJvsKJuEqkxA+1iJ1Ap0RB51x10UA0s8Sjs+fPiYVxZHzNcFethRt4Z6mVHOC+QmwhP9YctoWqJmUQGyKhP8PMGPPUEGzF6N6wqSecR/t5MoEIaN5t/d0KNUVBJVF4ZmbWBg8YOoXSRVVDIVW0VIJE2Aukshczz+HROaax35m3HhOPpFGPziG4RhwfKrj1j9/h1H4+0lm3dgWm5NV4i9JZmy3F9P2GxzvFDcGK5pveL++ZTBXsWuTBF1ZMyqnSQ4CE4zurNmt83wrcKnUV5BBvg7b36Yryzu0TjNaTli3WYcZDsO0y1TU/L1R2/wd179MNYqtmnK//b0a/j//PKHUbnlJ7/6+7lvPT+y+yB/9qe/HQIsr2e8vjngyXoUjU52CaNJxVdde8BPLF5CtRKtHW9t97hoBgx1w3+q38f/evY633b9ZSSBrxu8yk9Ukv96+36umyXA5bnwWqDrwH9y71v5tuOX+Y+u/SJ/V34d2QWkwyoy1UuNGnf86sMbjIYVQgeSIprYiK0GCclBRbvIogAivXyJ7J3Y+jmI1p6P3XzILClZdxk/tPoKHlZTtm2KEIE/dOtnGcmaMzvik+tn+ck3n4tOaECWt2jp2egstp+C4OStGSfMLvWE1KjD2945rQM57JAisK5Ttuv8kuiXKIeWnklSsWpzCt0yrwf4IHhxcsbp4ZAyTzmcRSZ91Rr0uGU22XGYb1m3Ges65btuf44f6T6AuB7oOsUgj1pDlTXsWsNrbp/VLqdaZojEI7W/VB/9zUbgCgb6G4kA/KiILIw/12thHz8VOQohPBZCHPU/exP42Xf87oP+e/+dEEJ8L/C9AGp/yvl6AMCvvXUdAmSDFntcYmcKv4tkpqcyBO02QT5lMp4nNPOEdCMu2Z8+6wlgpeT8wZT6WEeTisZcislty6itvkpcL3AWbSlF6hB5wIeE3SJnvL8jLbp+YY4a5kp6ZoOSBy8fIYIgub7D9Ebag6KJG4kV+GXOz7jnMIllNi7jwK3KLhmtfmoRlUIsEsqTFDvryKc1fmPQlcSlgeGdDbtNBrOWZiQxp+YSsmjWGl1DNwx0w35RMsQNaKVxI0eodOQlqahlIzNH8AK51fjEM/nOx6zfOiA/Cxz/wxPCoxNc26HShPOPCZSX1FaTaUuqLBdVwX5ekkjLk92Ysk6jmci1JSflkLo1TIY1uemQw8BZOqBtDDaJXIfDm0v+6LM/zV947RuQArSK3rydU6yfjPjjT74HgNG1DUYl3B4s+T/c+FHuWUMXFH9XfJiu1jxxY/7W/GMIFXDLhK/4iX/9EsKLi5yB+cMp82Qc8bC9ZtSGnOrQ8K989U/xd3/8W1C/OGJ1OmQdIsFL/sEAs9f5HcPP822549zt+On6EICPZPcByffOfoYfst+E8AEQnK6H/OH3fQoXCpIVJKvAcFDSfHKGtLB7wXP7mch+7qYqirA1CetKI3PLdzz/OT67vM69k31cK6MNadb7GzeSrtWkg5p/4eAz/MHRBW90WzIB/9eLb+R/c+PvUQfNp+u73DXnHOo1f+/sw2RZbPWk2mGdZLvLYpUrwmX1pcctRdFEzwGraKyM4AYBvlFslmMYdUgdLmU+tIwCba/NY8V1TsT8Oy/5pbMbbOcF/1/2/jTa1u0+6wN/c863f9e7ut2f/tx7z22kK+lasozlDoMVDLbBGIoALggpCJBhQlOMSiVFpZKQgROqKsOQwgngkaLAGZiycZBtbANuZctqLFm97tVtzz3tbtde3ds38531Ya6zJcCOhblmXArNL2eftdde/ZrN//88v8cJO2azhL3dFVtxwcBvaLTic8cHdK1CSMOr+Q555VkII5DlAU2nGMclrupZZhF14SJ9jVl6yO2K7Sj/DU1gv9r48gngSx9fa4w53EzyPy2EePF/47q/2qv6q9r3NgvJ9wH4N6+YSVIw8BqWZchiFVMdxraMEnYb0FZPNw/oemyAemulkHrcIdyexnWRtbANYLe3MsvG5v5WpUd/EuAUguZqzfYwx4Q12rfqg7Zx7O5r7VizmTTgW6b7+myAaCSytJr/188PSK6teXw6Qz5td0h1a3N2Oy2txLFSFwHsQdjQ98LG4mmJP6iJJzZbtyx8Oi2Qkxbd2EZb/WCA0pv84qRlENhGXFV66M6+wv5c4qabEBn9KEBm42AdGPrI0I8aKB3LnC8c1KBle5zZSMfPjLnxvjXZ4wMWByGDl12iMw2eC1ojxyPu/OErtDsN7SJg1ioOtlbMm4jlOuJkPrQh6r3AD1r2RtlFdmsS1FwarDjOh/iq4617x6zqkKz1ODyygewfWj3OTpyzqgNL9nQtHjjbGKKEFqSnA4JJxcdPrvDtiz/Kugyoawe5CaGpCg/l9Binx/gC5WiePjjl1bNthpOURRphNo9vf5jy1vERP33nKYpZxHkV8wIHGCEYv6o5/CbD6HmH+Fjz2dMDuAEvVFf4Hx48Q9r45I3HX3nqR/gH5+/hay//MtecAcVlg5tL/LSnPB7wofIq+84KoSGcddy+u4s40CSvKOJXPI7vXya5axhmPXUiMd++JBhXGAM//sLbMJnD9OqSsvYoZ5GVHwMy6kg2iWuH7Zgfy0uGUvKwm/CewavcdHv+wfoa+84KKXruNNsMvZLYb5gtEhpHM0kK4k05KPBsCbXVkmQj280bj3pjfFzPPLyFpEqst4LcwdstaBqb7Nb3kvU63PSTBNGgZnk+gB6CUW2Be8rBbLW4sid2GkKn5fMP9+nWHrg973n6Nd4xfMBxPuThbHyhzmum4CY5TefYBnPY0hSexWM0ioer0W9wCvuXJ6QvLwBf4jDGHG7+PRVCvA9b0jl5hDoVQhwAp5urPwCuftGfXwEOf907EYZ1EdjySOPaBCEAabi0t6TuHJbryJplN78T2pZBki3rIM5jj65TXBqnPDwb49yxRq1qR+BcapDXbO1dtg4n8yGDuOLmZG6bwBH2uLlxwfax5e44SYO5H+A8maJfTHAKQbvVsz3Ieelsl8BrLzT76TJCeZrxKKcKXYqzGDY886wMMJ1ELR3atUM96AhGNYO4Yq3FBXPHGNBxg+4UcdCSzyL0xCqfpnHBogjJqgHuyrHgMAPVttWpi34DExNmg8fQdEGH42iK0sLUWm2JnDyd8dKfjvFPJfqlEf1Bj5Mrhp+uIfDpr+9RPlMh1pY1o1tJ20vWecAoKfEdK4/0lKZsXXojOM0GZIVviautbaSf5xEr39Zwm05Zr4QRfOrkMl2n7PshDGXpIZVl5m+NM9LSR6kedyONzWvPBpB7tqnZdsru/DeqoNhr+a4b70cj+WfeW/ma0Wv81OwtTLySHsGN8JwewddcucMHzU1WVWB18REM7zS4iwD925c0PzYiXYcsdMHbgvv8o+Y5HNkzCUoq4/Lndn8OsCfVLrSkTyNg98OC7777hwEYP9TItufKjzvIzuDkNcIYukhRTh0Wt6xfoUgD6+rF1v5bTzKfJSiv/0JfqVL0lcNwr2bg1XxydY2fqZ7hqdEJ+96av7T9En9j8TQfW1/nL1/6SV5pJ/z88hk+f77PfBnTV4qu9aj8lmlc8PDhFHqBO2gYJSVHiyFaS3TmIgLNczfu88pNSXF3aP0yfoc4se9F11pvQdW49Lm9vhc1FKlPOKyoK9ea0q5UhFHN/jDlSrwkbX0+8fo1TKkINtiWtyRHRLLhymBpN1C+zfaIgoaidalah50kZxrkvDbfpkskkd8yOx7+xiaxf2EYxIWf5d+F8RteAIQQMSCNMenm598B/DfAjwF/DPirm39/dPMnPwb8gBDie7BN4FvAR7+U+ypzHzGoUarHNBJVS/pNv+k/fOzDnLZDPr64xr3lmHQR0boK3J6q9GxjrZW445q0sh/YZrezcrbaytXAKjDkIyaK2zFwa77r1s/x373+LQSjFG+y5ChNuDRccxCt+cBrT1jzVqtoLze0a5fBds48tyaWVRrZPsVBjhO0tGuf3PHpOhsErwPDqh0hRg20Ej3UuIMGaQTVMqBqQ0QvKEsHZ2DNa72WeL6NI3SThrQISKKKRiuGYUV8o+F0MESeehclLzezPJzyQCO3GtrUGuKM39MF9gQlFh5LGVtYlxZ4k4ro0zF7H1ry0l8M2frHYNIcMZ1w+A1D6OsLhPHuzppVHqJUzzCoqDeKplwr4o0DeL2IiEcVgdtdcOgL41E0rj0tGMH23hq1+d3eJOPhYkTdScbDghujOZV2+eyrV+ybvsFDOFGHejVElYLyrSVPXT7h869cBsfgBB1pHlDWHn/73jdwI5lzLVzwfxjd4T8eP2Smcz5Rj3mnvyQSLpH04OoH+dMP3sNPffJZthtL+2ynHX/xqV/gb46+HXXoozFcd9bM84hOS3aHGX/hQ3+Im5dnVJ3DB9/+j2wyWgVO1VOP7ILcuzB7VtGMbX9KbrXo2i5WRhuCJCOJKquH920YEb2gq1zbB8lsua4NN6fYTSnr7u1dxgdr7vYTwKp+Hjl6v2v8Ompyl+cbxe+IWv6rxS6ze2Or409ajGMYRyXHq8S+pkC78DlvFEZL5NLBaQTdjmFRR4ReS5ZYZZ0xAhMY+l6wM0kvQIt6LOh7Sb0MiLa+oMwRpaIvFSZskBge5GPunlp8yLWbZwy8mrJz+cj8Ji8+3LeZFfWGa+T37G7P6YxkEtgAm0vhmkNvxOnZ0PZMqjeGBgpf7gF8qWMPeJ8Q4tHt/IAx5p8KIT4G/JAQ4k8A94A/AGCMeV4I8UPAC0AH/JlfTwEEWFZ94ZBnjlXZVJLe7xGt4OG9LX5QfCWR2zAvN6lRI0vlrBqX6jwkvO/Q+waxbVOabu6ek1yp+MTr15CpT3U/offtl0orQzIp0L1k6Fa8P32Gx5JzzuoB16KFBVY5DR947QnE/QAz0uhO4kUtTa3IziN2DlaUtcsoKVg0inoR2PBxA/UqQNSS+ESgQ0F+s4XeTrhdo2hTDxFoZNBhasseEpWV3XWbRnbwVEHodkhp9daB0+EqzbqyddI4qejjmq5TNIuA0tnkCgw761DuBKqQyJVEew6OxiIKJHgDS0PVWpJfgezxIabuuf9NDsnjT1CPBdlba8TKRTUCcdnm0jaNw/XdObHbkLg1L5/t0PeC5TpCa8lwUjCNbeDHsgwuZLyPxiQqUdLiHqQwPFyMqHKLoHhycsZ/e+XH+Z6zb+SF5Q3L6fcMvYCucOh2rSlsMKjIGp/f/c5P8TXJq3xNeJ+fKx7jV9KbfOr8MqV2+aXTx/lP6iFPxsfc8k/4odN389ebkJNswJ9+4pd4V3CHX7r/GDiG7KpgcKTY+ZDD/yP4Zsa1YfoC/HD6JCsdUdUujqM5Xg7xo5bErbl3OuWJ9/+HIGD1mKR3pA3+CTQE1hFOJ5CFQtwP8BvoHq9Q91zqiaImwptLllcbvAceUguaUY9qLDiuTTZmwUpZ9HNgm+TrNCSKa0ZhxUkx4MbAuoKVkLzWZvyPZzaY6vjBFGEEIml44uCM2LXOOG+saYbWczJbDWgWgS0POoYu6nFCm9O7N8g49wYICcOkIHVCAq/dZDlYk2LfW6yDijua2mV7knJlvGIxCTm7P6EsPB6YEdenCzy/o9QeVecw9Cu2gi/U8YU0hDu55Xlt0sQexYg6UjNvIhK/Jt4svMfH4b/GVPZFw3y5BPQlDWPMbeAdv8rl58A3/Rp/893Ad/+r3RFEOznl0QDSjQLH9tcQWvHgxT0mj81tYHzhotyevAptvF4nKK+3yKjD6QW9VhynCQ/7Eaxdi0sGRG9Z7nFS4W+IoQD3yik3onPu5RN+5ewq+3FKoDpcr6PeBG70nYR7AWEpaBPDWTNBDlsWpYtwbIi8Uj2VY1CBxqQ+xSWDuFbgYkNljME2rwuFXDp0Q41IWtSpj2psELtRoJ/KkQLSyqdubI5qWXvUpUs0sA3oqvTQZwHG71FJS7hnjTYmd/FmCiOhG/V0G3gXWuANa1xX47stfW+bgqPnZuRvkyS9pBj4ZE9q6sy31v8edKK5urWyX/7SNmpzPIrWtU7sUuFE9rUyQNU5TIISuTHAJoGNCTRGsCoDW+uXPWkWXpAdv+LKA/b8Nd/x6T+OkobJU3N8p+Pbr3yad4ev833Hv5WP3r2OvBeSlopUDvi9lz/Nbw3v87F6l7MuYewWXBqseGW+zfKVKeuXD/hs+jZ6V9AFYJQgPO/53ku/l+LAMH3LjN1rGbNpzKE/wkhwH/gsn+2RheS//9S/Z0N1akWzyULGMXx6eQ1RSZxc4laCwYOe9KrEW0pkK1GVJY4mDzThaYURUG97LJehNfAl9mTQhSCXLl1sAOvT6EOQk4p+aSmrRhrUWqEn1ignpGF/mOJKu5+6FZ7w/lJSGZf3zd/DL9x5gqZ0bd+rEyhX89rxDtNxxm6ckbceQ7/iZJVwbXuBu6t5+WiXXtgM5K61XhuA6SSnbFyKymMyym0TufLJzmJEI3FX1pXdXq9RxwHnsufpG6ccrofga8zCo8hcfssTn7Tgv/sJ840n4V3b9/m5e0/CYYAZdbzryft0vWJeR9w+2UY5PYOwxpXWSVx3Dr7TWQ7Y/hsTCfnlHsCbbSgbORgeZBQn8YWT13gWq2zizqo7AFMruk7ixg2O0yOlYSfJSLyaunPoNvZzbQRcgmIQ4Bx7OOeS0uu5deOM1xdb+G7HX9j9Wf5fp7+d3zX8NFMn5+dmT7Hl57yy2iGJKuSlnrZx8PzOuiGlIdnNEEDdONTnIVtXl7xz9z7vGb7GVGX8t698CyepaxedVpEMLTtfa4kKNL0yljiZubgPfLrQ0Gz1Np0p7tgeFtYTEXaUncuqDHCUlQJO4+IClRt9MqQeO6TP6QtlEwa62LKCVG7Z9XoA8V5O5De0m0zlR8EuRW2h9FL2xFHN49MZry+2WK9Dwt2GvWHKxC/IWp/tvTXtJs1LSUMQNnSuxQtU9xNqv8e9smRRhYxDe2qY+AVH+ZDZasBoYBOhVp2yR//WSg6/YnifPXeFc6XnT0w/yJOurbE/6DJ+NHuGrxjd4zOffoboxFqc24Hgf4x+K3/L+XqCj8U4hbkIWEFCLDd0z5HATaEbQJMYVk+C8ToGtx1m3hZnfo8IO4JK4BSW2pneULgZmFlkm5zClte6eAN6E7ZRb5Q9qXlZj7e2CqN2YDlEvQDZGop9nzaSdCHUU7vYyUqgIzvhX4TTbBj8ohM054Et6/k2WN5UEpErDm6dsbWhqxaty2w14HsevPciGrPvBV27CRvyNd52w629M/LWY55HLKrQTuiNi9aSt46P+OziEhhxEb6zLOzrXnYuRe0SeC3FOmF2Flkl0KlHuJQIDe3Qokn81wJLFJWGXT+139FWIsYNAvgHL72LOvfA72lTnzKqeTZ+yE/kz2LCHm9UE6oW5TQ8f7aPuRtRhwa9K+k2pNNJUDJwrQnuIEnfsCnnywvAm2loe7TcSXLul55l2LsWy+tulVyfrJj4Ba8vp1R9iAw7fL9jGFY8OT6j1g6H+Yi2l0RuS+i0LKqQJ7dPWQ1DHoRjypWP8DQP0jGB19J0iv/znd+HIzXfP/tahk7Fuyd3qXuH8yBmVsa0jqZtHPLTGNEIxKQlz2xD11QKWUmq1uF2us2iieh6ydk8ueAOKaenKHyiqKbRjp34SkW0X5GnLu2wR04bnA0eIhxY7roQhtCxz0MKw+l6QOC1tL3kPIuoa5fiG613IOgU5fHAopCHGrFbozeSWdMLKz1sFcva5tjujVLWlX9Rz20ah9DXfP3l1/jM/DJ16zCd5Az8+iLFaehVrOqAVW6P4PGGc9SUrk3ecjchK4ASxjpWVcdpkVhuzGb3N/ZL1pWlV/bKMBoV3K22aI2i1C6/8+f/HGFSUSxDgvseot1QOjuLvqi2BW4Gk/cHlvUvrSeiGfEFXpKxOb4IKPcswM3JBc6xwDiS5F6PvxS0sYMOHLyVRSKHc007cOxErixyw1uDtza0iUUna89e7uQ2a2DxpEM9sYA+N5MXbKb501aeqypDtWWJr4+GbK3KyYa/gGi/qIHPxhjXSpKdjLRMwMDZIiFyW4rWJa882tLFjxvq0mU8KPCVpuoc5qsY1+vYHdowlrPUurGncQGevZ0gbPjg0WMWrSwMvttZxVThcLYcsDPOvtBwFeCuJCbz0cFGaixsFKVRBqRC3ch45+UH5J2P77aYrYK6tp6SrnVQ/iZHe1ByabimNbaEWSjDdJhTapdQtbb3cLnCtIogbGi1QnZWdpq1FiKXePUbMt0YbDTpvyvjTb8AOJ7m2vaCunMYDQsWpXWRlqWVrVWdw2hgufTzkyGmFza2sLMT/8i3bPHIbYmdxtZIR3Ma7dAbwXSY00SWA1S3DklQMw4r5AZv+MmzKyR+zY3BnA8/uMF2ktsA7E0IuUpapNJ4nr3skfOx1QrP6ZhlMYfLIXXt0q9dgr0cMe4xvWRrnJHXHghDmNQ0nku+DG29WIDOHNvsizse3z7nrIjJan+ToCWoO4dhVFlLPza0XgpDPC7YTTKOVkNGV1akeYAre1ufFaA7hS6sztwPWjTyQmX1yHizWsQgDHFYcy+fMg1y2g2F9M79HfxBDUP43PEBxghC334xl+uIKKpx/A4ZGpKoJit9ms4h8Ruqzvou8sbdcGksUE2Knv0kJfJatBE8PjrnF+8/zpPbp7y+2EI4PWXmM/ysxUT3rt2Z966guLSB+AWWi+Rmhnpq8c+qBGejyLGyWIObQ3Eg6CvxCLd/gZKWLRco6S4S6AmkN62z/FEKmFFQbdtFpxn39nZTiarsYzAaVAPeStArgZvb27X5BRYFrX1B79rLdGjBgjrsbWYwG5qpMshS2Z83mnjha3YGOdPHS9LaY7WOOV4nXB0vSYuAMKm4NF5zmlrfRSMVqzRCrz2cbW3r/FlMvgq4cWVG6LTcPdyybuO4t6DBlU+yndNphZQGNWrYGtn6/OOTGQO/5nA+ogHkysHJLbIb7ML7SIXXVC7nVcy8tv25q8MVnurIWp8Hq9GFnBfg/nLMP2zedQGmK2qPlxe7KNkz8ivC/ZZWK8ZBSaBaVk3I6/MpXacYhDWOeGOMYPDlJvCbaghhqDuHvHGJvJZ6WlAUPt3aY5l5rEpJecNlP0ktguDIp3Y8uJkSew5F59FoRew2vDLbZhyXnFcxodOSeDWX4xVF59ryRhoyCGpCp2Vexawrn2FgzTC/8Not+k5wf7mxn+9kDCL74X3EQhlsrptVPq7SLFcx7mshvWfYee6Uk3ZEVdrSSt8o1kXAs/tHZK2PJzWvL6as8wE4VmanN7GQfSB59WzbhpkLw0qHKKenyjyENCQjG8Due92FI/L++RghLGP98b0ZiVfR9Ypr8Zy0C1jUES+e7FLOQ4SvMb5dGPrMpXZ6BuOCa+MlTyUnPL86oOqsBK/rbY336taSl2a7jOLyYvcfuDaztTeC7jTExJqtYU4SmQtqJmB3cMKG2tednZCOFkN8r7toDt9LJ+THMZ88uYnoBGqrZmuacvKUw7WfAFVpZGdoxg7N0KEP7KStA1uacXLL/tebpKxHqfW9Z08FwthJuou4iN1cPmHjNp0KSxWtwS8MorcLyOCB3em7pUF7gmYocEqJbGwGgA7sxCGMPZU8gu+Ve/Yk0PsWo2B9KAJZW0GDcQz4m16UY70m1l9iMd70IDqJcXuSYcn92ZjRoCLxG7ypptOKs3xA2yowDkerIVVlm9RP7M5wVc9JOqFtHNLKpyzsSWuWWYquH7VoLVguNvUsLchWIZXf0a59cAyrPKSpbYmpWfqopOX61RnuDc1pOmA9jy+MZCrs0HMf1i4vF5cQUYfj2UCnm8NzpDA8MW2RW2f8kf2P8F8+/7tJjxOyYoSqLHI9uG77S1kRcni+deEYetgLUIZkJ2N/mJLWPicPJ8xOhvB1//rzjflyE/jNNfSmtnxzPKfoPBzZ86AeWxlg2NHhslpFKGnw4obOdzCeoViGnBrBkR6iVM98OUA30qoPpLbmpDrkrBvYLN/xEjlecJwnPP/aZdCC7UsrlOy5fWcXtXAJ1vaDUV7u6HpJsQ5RrmZrlDPyK6rOJW88PKezzc6lR3AOq6e1pWmObFD6JCp5cDSlKjzOygHrymd+PLKN69Y2FbW2vPXe76GRVMcxohUWhdxKTC7xNuWPwu2RsWESldRacXI2wnSSeFyiO8nrp1u4m8Uh2IRnlJ3L9jCnDGvK2sNxNL5TMWuTC4v9oex55WSHSVJQNlbTX9cOwajmJB0QetbQM4wqPKUZeDVFPbHGolGL63c0ncMqC3Ccnjho8JyOS/GKzPe5fb5FvgwRhULWglZuyhxJx1Ja5IHQwk6Y90JOMhcnVayvCZqRg9SgXft+yEbaHX8tsKh9g2wFzhf1BoXe7MRbLhYENzO4maELNhcAXbw5PTSG3rEKnCaB9LpFavQO9n2Rtkwjmw3LX/UYx+b7/osEVj2yih06YSd6QA/0Jv8B+xzrTQpZaCxUcFPywesxncA9d1g7MfHEqqZcpVkUIXke4PktUVTbCFAjcF3NeFDw3PgBP3z6HPSCIGyIPKurF8JQvD7EuVxwZWvJLIuZbq3QveTBycSebpUh3M3otKSpXcKothTYYX5Rdup6aRv6Q8UwqlgXAV2rYNCBNERxTeC1KGmRI584vsLvvP55AB4PTvmp5bP0vcSbVJiRoKsdwqTi6nhJ1vikRWCNk9Iwuby6wKEcJCm3hmd8Zn4J4WsGwzemCQxc9BT/XRhv+gVACIO7MQX1RrCufLbHGWd9Yl2Qg5LF+YDlOgIeBYGAE3a2WVV5VIWHF7SEkWa6aVx++vAyrtvx9PYp68bu6j21ISQ6PUYritql07aR10c9lWfRzkILysJnZyvlxmjO7eXWJtDCQQpI/Ia88Ygvp6Rjn71pysCrGfoVgWp5fbnF7u7q4jlKAbQC5xHETgqMssqfPrEkSLG0sZGysbLWRxOYUaBTFx3VFK2L7gWjUWGRE72grWz3sS1cSqfn82bPmqeVZhjYuqkteRlcqXGUZrZI6AqH0Gt5bHLOUT4kywKU6mlLl8F2yjCwqqPQaclbK+WblxFK9daWvwNF4zI7HDF4xaWeGlab5mn6lD1ZVaWHM3OtrDMwGGFsclnqYDYTiIGLgHJRSXrPUG8JgjODU1rHs+wcnNJO8I92ik5lE7XqsUA29nXqIqzKZpP/+yhW0ShLSRWt2ASpW5aSjZvcZA1sTglG2dAZVcjNiWOzY99M6rKxpRzMZjELepSvUY/CaQAhe4KgvfBABG7HOg9ozoONus0y+1Wq0OOOeFxSrAOLQRnW1vimNIlbkXq2JKikLaEBNBuTzOV4xVE1YjwoWWwW7624oHY7xlHJ+lbLTpxTa4dRWJHVvkU6eHbnrx1N3gS2RBk2dJ1ilW8yBeKSxK/ZCTLupRPKechkUHAwXl9A/3ynYyfMCJXNGgZo+ymvZds8nZwQy5pZHZPPQ+KpncD9DaZiVliG0OM7M8qJa0urwtb9+4kFtr2y3sFVGlKXrH+jJu0vw+DeVEMISxs8LRLaXrJaRxgtMLVi66DgqeEpP1M9aSWKrUCEmt3dFUIYWwMtXYsQEFBXLp84tGai8cDq0k+LBCV7Jn5B0zvsRSnPPnPE7XSbB/Mxy/PQ6q6FAd+gDkqkEUySgicnpwychjtiahtSRWAVPeMe3+kY+DXzjZM58WuU6NkLUjptnbOe19EbQdU6eFsV/qUWtVno6sahznzEBq/Ldo3GSv6EEQhl+wSyF0ySkrpTzJcDpOwZxBVqQyV1fI3japTq2UkyHp6PaAvPoiUmJb77iAvTUbQeShj2pmuakSKtfBbZPuWZXVxNZCejdRoxiayC6byM/rk0qSvTJaf54AJ9gbby2PCZJQBZFpAeJeRJgNGCC/uOBtnbjOLeAIVjJ9vS+hiMa38WvUVadLHN5hUbQmWl7GQuK1tbR5mLerR41FwddtbtulB2l73VWApmJ6xEEWWBZ5syA5uXXhis+qy3k7OtyVvVSx/bgCC1dGxEpbGLBK5h7/KCwOk4zyMc2du+irT1oa2tFVntW+Rx4xIFDWLb9lxWaYQ5taqfYFTzxNYMud3zymjnQiJ7nkVIYdiPU6LROes2oOslgbLpaffPJuSNh6s007BgulEKXYpXTIOcpnfYCS3I7346xpU9q8xC56KkZufaOYNNbOfDB1OyymFvf3nRbwrdlmUZcudki25pg3qa7gvTSeLVeErz8vkOi5OhdaFHHZNRTuQ0rLuAHz17jk/cvYaXNHSd7U8NwhpHaVzZc55FvHB2CaF6tqYZ21HOTpCxqCPmVbT5zLoYZRiMvnwC+I2MN/0C0G8MJu5GzWDmHqqQdNstr71wiTtbtrwB2PqpwR6PZU+Fi+Np4qhGG4EQiirz2dtbErnthQElbz2y1qfWDm+bHOJL27xVypZfnFSir1S2Pr4KNioMn0/WV6hKq4fujcD3W4rC8n92x0vbfPYbFk1k6+K9Q955jCMbtzfYNEU9x2rgtRE4qqfTNv6SVkDmYmLNaJwhN43ceMNp6TY7v7TyrQkutJd7jmZrnDFfR0jV47kdTetwtBjS1Q7TnTWJ37CufNZpxLxyGG/Z2286h7ZVhL5djLKHA3sCGTX4gcX+en7LsgyoW5edJKM0glFYkdceh8vhptSjGSUl69cinMIqKw5GayK/YSYTurWHu7S+BNGB8a1KxjhfOMXh9fTKIDyN0ZLe2LQv426iOCPACBu72dlJvvfNF3bxBuRy48SNeh4lxHej3l6ntZ4SEWnCpKJMA7sgAHbLb0s9ohdEOznFKsQ0EmdlT2rCQL9toXo67gm2S3aGGW0vaTplsRWxPb22WhENKxtrCTw8H9FrhVTaSpbdnv1xevH+HZcO7qWat+4fAXB3NbWbi81p2BjBg9mY9cDniemMsWfltVOvoOm3CALL9TmdD0kjn+f2HtoTdBvYHb9X4oieStu+wFZcIOVG2LARMczaiMRvCMcV5SK0t+9XjL2SzkjO8wi9KVH1Es7nA8JNUMzWTkHeeixmiRUyNJLet/Li+9mEnWnGnZV1AsdhTV76uG5Hb7goL7Wtsu9H49AObehT2vp4qrtIlTs5HSEam9XxRgxjQL9hp4k3/3jzLwDaTnKx06BEz0nQoxXcuH7Gg7MJ3/rU5/ip159GLh27Uxto0sonncf2yz9tyQF9HNIPNLSCk5MxftwwTXIcobkcL7mfTUi8mlfTHV769DW7E3UN0mAVDlow3E9pWod0FiMz+9IZAd2gRG0SkYaDkklUbtyRVn101jrcOdri2v6cX7l3Da0lQdjQdA6XR7YU9NrJNn0nqVyNVD2u1yHGBt3ZbmLXS3Rr77M3grZTF6C0dBZTDRra0kU6PU2j6LWi14IgakiCmrPavaArplnITpyzPck4D2NmWUzkN6SVT1V6hFHN8miILCTOQXmRyxp7DavSTmCL+YCDveWFImld+WQPhqAFRaDZv7bh3T9WUsx8ZOWShR7rIkDPfdSkoaul9SVsymo2FN1c4LzRtiFpKmXDYRxsX2RoSanG7y8Sw3CtD8RZKjrXmqjoBf12a0tJm5xh2/G1t0P7qLxjUGpT+vNs78WLWpq1b+/fsQY7Gom7tP2K3jN0oeHm5RmtVjRa4Smrsmk6izB3VM88jfHcjlFUIgDfbcmKwMp5g4pBUNsToKM5nA8RAtrawY0brm8tuBSueDXdIXRbstKnrq3qrdPWcbtaRdxRU3ZjK+98dbnN2enQTuSjnu1Jiu4lLy922Y5y9oKUh8WIyGl4Oj7hh+88R+B2NFpxMFlvGvSGRivWZUDstUwGBXXpkhYBUhheO922FFkjcDwNnrZ5D40NnEk2pcW09nGCDgLQCx95FJBvWXz6K94uWenTZS6L1EWtHfKgJwt65nKICjTX986RO4ZlGVK1DvcXY1xH87bdI26Nzkg7n6L2yKShLd+YBQC+rAJ6c41NTXfolXjaZbibsV5E3D3c4g+9/Vf48Owm00HB6mZPNothQ4t0zx2cTFAGik4YnMpqvR+RCkdxSasVlXbpjeRyvOL5s31Wd0fIzu5K3cpOTO1TBbp0aFqHKvOQa9toxoAJNUXlXXxxlLQh6F0vOctj6o9scesnl3QDj9P/U0S79mww/KUWpaxiZ1UHFtMAtI1EBhoVNjaeMmiIA8tDf1RH7o0FpylhOJkPwYiLsk6fuvTKQfg27rIuXVbK1u/7XiCkNdadpAlFYEsEw7BinlqzzyP9P3KTngaMBlZqWmzgbrvDjIeNLRscLxN6LelqB2dtoyD11O7QYq8h3Gppxoq6dYjcljUBJC2P75/xcn6AO3NtTd6F8mYDtbQlm1ZCu2ncdpu6ugAjBOGwoiRAFMpO4JVEVZIusWUxEdgdvdYSvWl4doDrdyjV25JWZ41R1ArTSdrWyoy34uICBvbQjOhmNrHKNB5i2NL6GjfoUE7PKKyJ3YYXTg6Q9wLarQ4RaJuaBvzJr/wAP3X8DHnjMQlKys7FVfoimUv3tlbuqp6s8jG9xPVboqCxEyqCV9MdpDAsitCiTbYLVmnIIK6Ig01ubxHw8noXIa3xyg06dCdtr0f2LNKISVLw1dPX+aroNb734TdxWiaMXbtZ8VXHug5ssI/syWqP1TpiMKg4Oh9ZDlWjqHKXar5pykaanf0VWelTLgPk2tn4TKy/5CgdomRPFNXUjcPkxpyycRmFFbHb8Pp8SrkIkZlCNoJuqLn02IzIbel6yb2TKbdf3beN5K2CcVySVj6+23FWDux3ppcMgprdJKNs36ATAF8uAb3pxt2TLe6YLXa31hwM14CtJf/Ia2/nse1zXjzbY2eSwjY2Yk4LuqRHhwLZSKi9jdZbYDyDXDushwFP7MzY8TMOghWfW10ifXWMn1rWfrvd4SwdnBz61rqL28YhTGpKIxCldX7K1MGZluwmmcUhNO6FH6DViuHrPbxyF297SrraQQ06+tqjqV3ioKHH8nwGWwWe01HWHpNBQdU6+G7H7iAja3yKxsV3u4vjcb2RZHa5i8wVZtziBC19oCwu17e1YIygbRXDuOLsaIQTdfbxbRKcFkVom7Gups49vKihrS2AzploHMc2hoGLnWvXS4TsGXg1BxPD0WKIWLp0SW9VOwKOTsfESXUROTgKKxK3poodnn/P3wfgW+S38FJ+DW9h5ZciU3gLdbEgGGmQnaALDSawNfjooKDMfdxjF1VbvbnZsIxEKzBXKsZJYfNq2w2grJMX5rdWO4i4QypjYWxhx8Hekr0oxVPagsqyMQCDqGZpQovVGLTsT9cXPCO9OZkeZwlS6S8ogR6dNLTgQ/PH2I1SXil3OMkSysa1TnXX4kaa1iH0GxzVE/u2ybqT5JznEdlpTN04hH7LKKzoOsXetTneRvmzWkc4rr4gdobTEikNdeUShI3lRLkdh+cj+rnPeS/4Jf9x3pe/ndU6Zme65k6+RW8EX7f1Gu+M7vBXb/8uqs5hcWrjNcOp7Q8p2dtT68RSacuHA2glWenjOpo26hCDhr6XG6+JYSvKLxLI6sbhfD4gGtSsK5/zNKY6D60UthUbNZW8MArGboOUhl4ZnNg+hlURYgwsq5B1Hth+0/aS33XwPO+JX+ET5Y03aLb5chP4zTWkoV/Yuv/JymO5W6A7iet1lOuA9z7zeV58uM8ijZDSsLe3Yr6OuHx9xcPZGMAqYdzeNo9bCbFhd5jhyY4H+Ziff/lJWLuYUYceCmTQEccNzVDRHEaY3MFLaorUoawCm/3r9ciwIwgb/M2kLB9Ndp41ktW1Q/mV4JZvpQskmBbldNAKdO7gTC0yOa18ytKjcxVtqxBYJVHRumgjLwilACfrhKpycV3rRH7UrLSxjjZxzBgLdHuU+tR1m9NJZL0Fjm+zWdvKQaQOcrvGGDCNpG4CcAxBVFKcxMT3HM7eVjJMCtsTARZ5iLkTM7j6gLz12B1lLD2L5CgyH7PyMG5P3odEQ6sw8lVHjyCtLM9Gm57vf+If0j5u+E8ffBsf/MyT9j1KbSqV9fXYCb7bafEGdmH6pusvU/cOP9W+lVaAdLUtQxjwlcFzO4ZBzaoMcF1NXXkIZeh7ia43k3Mt0X5vm79yc5IqEqrWsaapR27z2r4XMuy4tLXiqfEpaeeTNy6+owmcjrM0ZpyULILYlqOkQdQ2brPsXIZuRZYHdKkLrkG6Gt0pupXl3w/2bbPUVZracwicFkf2FgooDaPQnr52hhlV56Bkz9Xxkjva9gT6zkZolouQ4U7G1jjjSrLkKB9ycr5h5A86+1poh7L20LUtU9WdQ9m6/PCd5/gR9XbU5rRAI3GnlUV2RCVjvySPC9LaJowVcYgb2Yk5zwOC0CLJXaVJa5915SOFT9XafpJSPQf7czypyVuP4zzAHVdc31lwuBxSnMWIqCNvXOrWZak2CBOn59bBKYFqub+eMA5Lbh9t01a2WX+0GPIB7wk+cP4EgWr5i2/QlGPMr3+d/38Zb/oFwHFsw67vrIa6WgYITxMOOsZbGe+fPYVUmnoeEu3kVo8e1eyEGc1EcbYc4Aad3eFWLo7XcnVnwY3BnI8eXaN+aYTTW/14vQ0m0niBJWy2woLPZCOpbicobW3/JulQCwc1rJnEJWXroDa7/kYr8s6jbF3isCHdV9RJwOnXd0RJZaF2rqU66l5SdYosDWDm07UCPdTILUO9MZelG+ev7m1PoW0VOnXpOx8TdUhXY7Z6hBE8fXDKaT5A9xLfsWUYKYz9Anceqe9TaQ/dKcubNwJnt2RrlFO3DrXZHO+1RUSMP+dw6X23Of/tN1h/R0voNyR+w/0X9umnHUXnsSoDboznPLf1gNM64ZePn0BVwqp4HENeKuqRS+r7XJ8u+NNP/hIAH6lhR0EgoDOS937F83zvlffzfzz8ev7JZ59FZOpCWomAKGi4dfkhnVG8ut7h6pVzmg2ywpU9qzK4AHkVrUvsN0Q+HJ7GF+ohv9g4gg2Aovegv2rNfLqXrNYxOtughTcNYuPaXsJ5FvFZfYCzobAu05DZeoyMW3y3w1yp8N2OrWF+kePcG8G8jvCDFiEMo6RE95Y3XxhhG8CbvoE2Nqf3wXJMElbIaYaretpeMvZLzrqYunXwlMaTmse2z/n8/X0wsHN1QexZQcEqD0l96/yejrPNcxMoaZjnEdNBgUwstvzVwx0GScWtrTMWdcTD+QjTC8Sgo6tczgqPJ66fkHcerrSnlsX5wPKNKsvBuro752SVXHCp7GNWyM1moTmNoIdz1TONSgZejZQ9l6ZrJIZndk8oph61dihal/mp5fonWzk7g5x3Tu7zwnqfd+/do9Qur64PcFdWPNCGDq/JbZrKwSw8+No3Zs75cgnoTTT6XnDp2jmH97ZsXVgLTKMoZhFmq+SstO7Fyzdm5LUNwD5dDTivYq4mSwYbVPQzW8fcWW/x8HTM8Srh4XyE4/S0Oy0q7GgLB5k6iLVDpQVMLFqhDw1yUuP7dsdTnEfIpYOsxReiHj37u7Ty0UYw8isOjycEr/qItxS4ZY/MFd1IYSKNyGwAytnxCLV0kPpRKcOArzle2CO44/Q4jqbrlDXXCEMQtDjbX7gsjBrqykWqntdmW+wkOb7quDU843OLAy7FK+6lE64kS37oq36QP3PvWzkpExZFaOV5gK9scE2yl6GEIc1CnOcHHPzgi/SXdzl7Fwxda247WSX0Xs+Nm3axAZh4JVtuzrzZTLat5eFogVV/nARUBNwT8AefeBGI+bOf+8NUjcvXX3uN79z9CLsq5R+kl/m/7f0M7xrc4ZdWt/jM2QFpFiKN4GC45u3Dh6Q6wJVWEdZqiRSWMWSA6ab5/szkmM8v9q2hqbUlKSMtDM+ojT5/I+kcxBV6496WsrdwQb/Hjxu8TX5B7DcXi+mj8HPDRhLaKqrGxfU6tpKc/XhN4tXc6yfcP5uAsYlkUvWMw5Ks8dC1x2iY46r+QsMfOB3VRkYZuTYfuu4lNzc+jIHXEDgd4cbId5oPeNu1Q3osEmRVB1xJloz8iqL1aHt7klrntlzlOJrdUcZ2mDGvYrSWjEYFQhg+df+KPR2VCn+7xJXGAuQaxbIM0b24CPTZ3km5Pprz8Vev0zTKoj0cTVW7tGXEaJJbaWvtMh6UcDmlLKwoI/YbFoV1jae1hwgMR/mQxLO9FN1Lblw7o2wtnK7uHH7x5AkeHE0xrcQfVfgzRb1rS5LRTm7ZRIsAp3hj+D1WBfRlFtCbZvSdxc1ibE14fGCNJgZwZM/pfEiXuzysJqAFeeEThg3Hq4TL8ZLfuvMK//ToLXz+fJ/z8wHhwDoT52dDamFsiWnuIwT0oUX+ogVaC1sj7kHnLl5cUbc2PtFkNhs1X4QcjNa4UtP2ChX13D+d4k8WTLZSits+8vWQ06+E4FSgLxsu4DObUBM96aAHFXVEUY3vWKbQIymrNoKi9hjFJQOvuWjESmFYFSFlbsFyXWsXhGi64L07L/LO8A49X8V/vf+zfMfzf5Tr0ZxPNAm+6mzGbhba4PqVz8N+jFQ9ulNMRjlXd+fcP7zEwz/yNOtnW/YuzxDCcDobIpXh6uNnLIuQvcRSHs/rmFJb7ksf9Qgj0WoTwRn0yLmD0IJ8HvKj2eMkquQt2yf88p0bfOjhTf72lQ/z/tLlm6PbvNQOeS64x0/P38KffPyD/E8vfwMCKFqPXzi7RdG6lI3LZKOqcWTP0LdBNFXnMA5K7qRbjIOSB+muzVSYNChXEwZ2tz4KKu6eTukaxSCoKWoPKXv8oLVO50GFANpOEQfNBX+m6+3prmod68Pz9UVT/cb2nLz1+OTdq/S1wgk7PK+jaxWe1xG4dvKuO9vbaTuFcju2g5JlZc1VnVaMopKidXEdTew1vL7YIi98zCgnLX12hxlZ7dNpiSM1lXbJWg8le47yIQfxmrM8Ji99+l4wiGpujOdETkulHV4+3yFbh/hhyzt2D3FEzwfKxzBaIMbWL3JlvGLd+KRlQKclq2WEdAwHyZwbyTkDVfOZ4DLdacj56zG9Y09KJtRcHq3IGp9FEXJyNLYnKWmQueIk37YnKrdnvvZIhw3tykdlEh31DA4yHNlTNS5a25wJIWBrOyUt7PNRz2SI3ANpaGqXMg1wlg7y5huXCfzlEtCbaAint02ZDb/+6njJQbji84t9Qqe1NU1fIpXBcTvqWch4WAAOnzi6yunY7qbzyuPK/gKwTa1+K6PpFNX9BCcVqEZQXu7oE7t1bTMPFWrMuMFkrr1u7tkQ7GlLl1uHrcQw9Cp7rO88orji/nLM9iBn+WSJuhfQ7rZ0+wYKD+VrvMs1rqPxHM1BYo/Cj9ybrx7v0M0CG9ISasZbmVXmdIp5F6I2Uk6lNJ7T0XoK37cc/6Z2uLeY8An/Gl8TvcLb4/sANJ1D3vn8vdOv5agYkW7c1K2WzI9H9CcBXdgz3E9xlCarfS69/Rj3KzQjrchqq+cWqufawbmdaKOSyGk4KRLmZYSjNFthwaUbM5rOoe4UVeValoyw5M3r12Z8trjCc/E9pOh5x9UHPDk45e+nW3xb/IDaCO61Uz5bXOVzJwd8/O41C5ZTViLZNQ6jkQ2X2QtTmxDlWQPfi4d76E6yCkL2Rikjv2R0ZWVfZ6UvYionfsGDdGw/XEawzEM8RxN5LYlfkzVWqtr3krp06VrF1eGCyGlYNhFla5lUkdeS+v5FuWBRhdYZO005O0/QjaIrHfykth4NA2dFbPszvcB1NnLlyif2G5ZZjJI907Cg7FyUMOSNhzaC8bAg8WtC1xJgt6Kcszym6DzWdWDVbK2D1taM5TkaFVfklUfdOl94vkDktTx2fc5XTe7wn269QGs037La4XA+RClDmQa8fGYVYWrYsLebEngtWeUzKyISryKVAVd3FtzRW3TC29A/AS24M59ybbKgCyTOrqbVyuZbP5pUpU0HY9Dh+R37N9c0WjFfxRS5z2hYEPoNg82pq2hdAqfDG3Z4yt7ew4Xtw7m7BcOkwNvTfMP+a2/YnPPlEtCbaFhipCGaWDv8Cw/3OZvEFoC1HtD3gr2dFScnY+rMuifXecBb9o+5lZzx2eUlztYDGwWpla3LLgcYAzvTlGbXRbch7Wjj7HF6VKzRtULnjo0XHDUUq5Dpzpo0Cxlt5TQjh+xkwHGa2H6BVtTaumHTWcTSG1g20dUKUtcmm3k9flLhOXqjE9csq5DIbShaK8n0/I7Ws5LHcFhZiWltg+XZ0DNdr8NzO8qN+aUofDCCXlv0w6vBNs9ea/lQ4fN/P/163r13j2fiQ/7aJ99LMihZLWKE0zMe5UTTAqZQZj7NRo8uhLkoR8zziCLz2T9YoHtpscO1h+9oEq8mq+xu1HcFD5sRkd8w8Gs8R7GXZKjdnpM0oW4dBl7NL59e53SY8I7hA94Tv8Lb3IL/z+pZ/p+zS9z0z/hoepM76RbP7h1xPZrz2eUljtOEMGhx4srukr2SdRvYoPlxzWE+4tnLh6RtQNm6TIP8onRTtg4oGPkWW/3ybNdiFVYuooeilfRjS4PNKp/It7JbKQzlZqd+ZzkFIC99Ar/lYLhGG1u6qTb4D90L8sazMtntNZ1WZKXPKC5pOsWloVUQpY2PI23N/5EkdF1YVctoVHKWxxdmwGFgTVV161A7NvzEVx2zIkZJG5/5qCz1KGBHCrORmVoUSt9L+7naLOyO0hxmQ36yfCsv5nt0ve2jjAaVdWADYmBoGwfH1czziLq14UNSGG4vtoi8livJkofeiH7HknBDz/oU8tOYY6djEpU4sqfWyj62kWI2Swg2arqutAuWNoLEr5kTY3pBmtkNnLspS7pKc//BFvSC6f4Kz9FsX1oxOxwR+g07cc5BtOb3jD7xhsw3BvHlBeDNNDzVsTPIWVcBbahI4grdS2YPxoiow/U767zdtSCrrPS5Ml2yqCN+8vwtZPPIFqOlQWxZVcVklBN7jd1VVg6DJ1bkh/akEI8qIr9hlVnZmZTGunIr+4Uch9Wm/myVCOt5zCu1S1u6VobZS0QjUEsXOhdhYYro2EoJxWZBc2RP0ym0tF+CR2ainSRjFJVUrcPiPMEUClXYXoRxrfLIczsiv8FRmsDtWGQR5cKmoOH3FI3Ld93/HfRG8m1bn+Yt/hHLPuArb9zlzmqK9DR9K1m8PsF4BndUMxiVDAMr02sah+2hdTfnpzFP3Dpiltldoedr/E154jhLyAur6vEczWoVkToBUvboVqFcjVKGaZJzMFyzrEKOH044vjflI8FN/tft53h6egLAi/M9riT7SGH4D658mMfdU0ay5rPxAT9y9k4eZGNCp2UnzBi7Jc8v93Fkz3kd82A5oliG3Lhms2X3w5RSu5StwzqN+MonXuKjR9cpKo+mcKG06p5+oC9wDYfzIVUWkJuQ4dSWE5TsKRqf6jBG9BYB4e+3zMuISWBDbASgN6a/+SpGqp5hXNn3VltJYV76rH2rsw8cG68YPQKk9cY6twcFu1HKg35M6LabuM+WgVdfOHYD1fEwHbFKQ4Q0NglLGBzVU9QunqPZS9KLzcRelNL0DmXn2lq90swzG9PpeR2vsU1Re7iO5t1791i3AUfF8OKzWDQuaR4g7oe2FPrUmlFom+afObpEFDRcGa1whCZtA3vqUzazQveSWitcaRvZi1XMlf0F2SZoaBBXpHnA4f0tVNwySgrq1qWqXGbHQxZhTBRXXBsv8TZ9gfkq/oLhSxq+8dKrfPz8Gr9ydJUXF3+AX77xxsw5/w5VgN78C8Cj6LeqdexkEpUo0TO6VXGSDkgCG/YutP0iOY7m9eNtEAbX1YSjiqqwH7r8QUIWRjx245SRVxLsttw+2bbSRX9jItrIKJ3Nl3KWxkAPruE8j4i8llUeUhc2NMMNW7SW+HHDILR44zR00UbQBZrppRXjzZdmUYQXYSvWKwCdltStY2Fsjm1u6l5Sty5i7uIW1ozWeBp32jySsGyah1YBE/oNletjtA11z9Yh96IpXS/5/vo9F/TTkWubpNvjjLp1WLYW8xCGdsebVraksTuyjJi7d3cItwtcqZGyt7hnIy54NLtxRtm4lKVHuQn60JWD8bSVXmpFmzocZh4nXo8u1T/n7j17OOb8lS16r0dEVgo5cBu2VManqmv8wP2vYhoUDL0SV2luDc/osQapvShl6hXM6pibW3O8nY5VE1K0HjteynkzsNAyI3h1vcM6DS+c0GJTUhSuhYtdS+bcP50gl65N5MI2luvWIQgb3BuWMyOEYX44Ii8V852aZ68ckrgVRefR9orMtaaos8y3ElPHcJZbg14euTYzVwkir6XVklUaMRla/4fa7OYvDVasmhBXWTxIoFqa3n5N886j05LL20vK1sVTmlujM0rtsmxCVnVga+idYyfw1mY8lLWLEHBluqRuHbLCyo4focmDsOGDD2/SacnWoKDRilZLIq+lKj2Lxu6geDhg8LiVrQ7CmshtSRufrPYZBhVt6yB8zUGScpQmzM+GqMD2QpSjCZ2WyG1IGysRNY+It50kdDt24tzeXuQjhSEvPW7PthiENYHTcXP3HCV7zvIB5+cDfvSltxME9vs3++wufPMbMOEYaxT9zR5CiD8A/NfAM8BXGWN+5Yt+938B/gSggT9njPlnv1mP402/AABcSxY8X+0zHliXZuJXNL3DNLJfzKZxEB4Mg5rGUcjY7sx1rRiMS0wjYQObcs9cbstdblw9I3YbtsaZPVHMEli5dEPFsvTolh6Rb+V7/UYVkB4mFKMWvXa5dHNmL6t8lDCslhGOo2lbhTtoaFu7M56fDOm2rWpEa8lTO6cM3JpKu2z7OffyCS/cO8ALWmsG6iVpEbA3ShFvy7jz2h7bV5bsDVJ2AivrO69jjrOEtAhY6hDdKbZ31lwarJlXtk59lsYEXkvsSjphWDcBz5/uU5UeUvW89eCIS5de53a2xdF6SOC1pGWA53REbsPd8ykow8F4Tdb4hG5H3TnEXkO+KaVljY+z4ff32urRv/gL5ActZeYgl67F8ASWuMmwtQtEK+mTDZJh7XBYbeMMG358+BwPijHuplxRBnZxeWm9i8Twruk9JIa6d7gaLvjRF99BMijJSw/X1fz4vbdSVL51w/Zw+9V93HHFIKoxsYApOKqnbFxC1zZ3wYoARGgzDYKgIc1C+lZiNilcb9894jVHc3o+5Oa+ff8r7dqshE04z3RQ8NzWQ3oEnzm/hDaCZRaxXFqfgBrn7MQZqzogdzS+07HcNPPNnrgIR6+0S+Q0eLKDDvLWlpdGYWUlr8YaCF9Y7OEri2KINj2CaVhQa4dlEZK+OkYPNN64ZlmGdL1ESkNbKgtfGxc2SEgZkrDmqfEpHz+5wuLByPoaHIMIbWNftILz5YCtsd0ggM16fmR8/LbHP8c/vfsMh+shqw2dV2y+n1d3FvhOR+Q06E3EWTSoKZXHKCnQRuApbVVMtUfXS0aDirpTrPOA1m+5tXeGrzoLIWSAzl2M31KtfZLjN27S/jdUAvoc8PuAv/3FFwoh3gL8IeCtwCXgZ4QQTxpj3rjEmy8ab/oFIFAth/kIuTlWbgU5I7di0YT0xh6rJ0nBwGvIGg8pIPRamqSmLlyy44FF+gLs1OjGUkMfno+IQzvhTqKSZFTSDWpr0Xc7ci0u+CvtMkDGLd64I/QbFtmQtPK5MVkwWw3YGWWsVUjXWYjaeh0yfNkBA9W2wdvXF7r8VRNSdB4Dt2bq5lzfsnpuKa2H4Hwx4GB7xbOTIz7w8DFQht5Y8Nvr6y1mWUzbKsup6cQFNG2xipHC4CnNzfE5L5zuszhPKGvvAhlwfbIg2mlotMP/++aP4guHv7V8mh9p3oE2gtBr6bTk9ukWUhq2dy0bZlGEFpEgewKnxVcdTa8oW5d1Gl4YxMyGrWO0QsbWqKdShR5q28TvhOXln3v0icYdWOSBCKHLXegE3crjJz7zNoTTs7WVkZU+lwZrXjjaQx9G7D5zRqZ9Su1S9w7vSu7w9OVjXj7eoc08Wi0osPJT0Qn6pEPGLaaXLOcDq3ZxepTbI2TPq6/t81qzYQIlHc/dvM9JkaCEYTTMOUhSDtdDnpjOWDYhkdvyZ577Bf7ZyVv4/PEek6RAb+BvSVDbko20E3LotpaH42iq1keUCj0saHtbGhkPSurOoa5c+kZxtBgy2KtZ1SF567GW1nn9WHIOwHkVkzcei5Mh3rC2JrGNya7plY0JxVgVTh7iux1PvvMeWeNfuFutG1nZDVHYMY1KpkHOUT5kXQQsmpBLwzXeDb3hPEGaB7TLANwez9UX2dPbYXYRy9j1koflmGd2Tlg3AYuHI3B7xsPCoq61upANOxvZq/YbhDAsl7b+nw19rk8WPD6dMSsHHC2G9FriB9YNnWuPXHv0WAZRMCrpe8lgWpC/842bH/9NqICMMZ8HEOJfWmy+Hfj/GmNq4HUhxKvAVwEf/s14HG/6BUAbyZXBkrTepe0UTe/QGslpkeA7HasyIEsDTnpBEDeU85DxXorrdlRVgLNSlhDpGny/w3usYnuQ02rFqgwI3O7CiYuwZaRhUF/QOatzK5cM44ZRVBI4HWkSkp4OKJMM32/Ja4+D7RVDv7poPs4m4UXcX9m4XB0urEMYw0mVcDM+p9RW0nl1b0HbS9ZlgJB2B/fz956gupcgt2s6rQhUZ80yDwc4ucRhkxa1W+O4mukw52w+RJeKxVZIfhqD29tGoIF1Gl3o5s9nCd+c/TFGQcWVeEnby4tITGMEO+PMulyzmHFYEnrthhApSRv/om68HeVg+6OkpU/nKUzpwCOE9SPY26lDF22wDkNtk9vmDjpTOJcKPK9jsrXi4dkYs/AwRmI6wex4iIo6Xj7boU19nE6wE+Xse2s+kt+k7Fx+pHqOWjt4nqaVQCdQSYs59Rm+Ilk95SAWrj2ZJBp3UtE1Dl1mG/NCC2QpbSlCwKwccL6OkdJQziJWSYzjdvzKqzcYTXLW65D/efk1lKcRTqY4DUKbGOYa5JUVDx9OiW41PD6YWax3a2mvk+3UqnUal9cWOwhpLthEXW19IW3psq4DpoFVAnW9vMho3g5zRp7tf7lJTeC3F3nQsdtwOV7hSs1rq20eHto3pQAW5wMLtpsHqFFjo0AzB2nAn5Tsx2tOioQ/eO3jLLqY14ststZnN844LyPSymc0qFgLUKpnOiguNjMvn+/QNA5R0PCuvQfs+Wt+W/ICf+PBe5leWZJmIYtVbJU6SnOah9bpvpnzRoOSG5MFD9WQ1TqmLHzygd39W8zImtON0KPWimUd0vaK03SA3pSoHGlLdsNNmfVfd/wrsoC2hRC/8kX//z5jzPf9az6Ey8BHvuj/DzaX/aaMN/0C0PWSsVtyeWjlYrfPt8jnV6CTuOMKIbCThmsotUQNWrLcEitVqnCfSNlOch6ejUkiS7T0leXwbw9ydC+5e3sXnB7pa3I81msbuSiEQQ0bdGUt+J7S1J1DFNWkC5/752MuT1e8friNM+2Z6ZhJYNUPk686YV0EyMahqlyuxQu23Yyb/hn/5Pxt1L3Dvdx+UQdezVGasD3IuZf7vHS8C4DZatia5DhqExd5NgDHbBRLIBphJarKYWvvDKawzCKGYUU9dmhzj+V8gFA9XtARbybyWTXm/KUtZlsND+MRvtuxWsQc7C82+byG03RA01h3JsBZFtP3kvksQZ276ETDFS5KAV1nJ3/RCWgUvdvjjBrktrX197lnG9pJixxrusbBtJJmEdD4lvC4KEKyzLVlorhDbXaQs2NLGX3qq+5wJVpyWI95/v4Bw6S8yDF+8uCUB7InTwOSQUm8teJkb4hZedBKdGAQoUa3ytaee2FxC56mTySmcBDS8OBoSjIuiPyGpnbRa5fh5YIkqskrWzMv56ENrnkUEhNo/IEFoalA89LZLofZiKKxTc3psCB0Wxte7tfMXBt28ihhTWwApaa3kL+z0n6O8taj7yVl63DcJyR+Tdm6mF6SrkKkY9gbWTls0ysqbX0Q56OSb7r+Mv/40++AVuJPWtSOhQHG2zXhJWtcfGx0ztgrWTcBde8iMVTaZVmFeEoTuTY2tWg9wrH9m3keUaQ+jt+xNcrpe8lqFfH2xx/wqfQq72u/ki0/59rBnJ+/f4u6cskKi5sexyXn65imdElGJZcGa6SwiHPdSKgV98UEx9F4Xsc4rBjFJa7sOVkmFL5nT1lBbT0BsqfelKAS/40JhWdDOf0Sx8wY85W/1i+FED8D7P8qv/q/GmN+9Nf6s1/jUf2mjDf9AuCrjsfCM3Lt8bnZAc3LQ5Rj81W7xmF3e03hb5pVhyF6DNKzx8F+3FHOQ4bb57j75zYAezGhvD1k+5kZsdcw9CvYoJz73MV0NsnrkeU/imqCUU7bqQvKp+doSFqawuO+nmAKh/P1BBNq1KX+QgJYzQPQApm0vLja4xu2U35u8QxZ6yNFz7V4zp18i/MyYm+QcTla8bbJIbezbY7ShFtXZqybYJOGZCgT18r6XG2ppKlHT4+Ytrx1dMTvuPpZ/uqdb+HhYsQ4KZnszHnt2IaI3No9Q2KInIY7Ypd+0jKZWATE8iRhemBVVI8m+jL3CKIGV/ZcTxa8vp5Stw55I9FxjyzUBX7g+nDBbDmwLB8jEUagTnx6z1DFGiduGU5zupG0iAQpMHpjECok3qHDwdtXJJcr3t/cwnE0b9s/4uXzHYZBRbVld9GPDWbM6gGvr6f0mctq4SG0YPepM37X7vP8iH4HgduxykLq1mEyymljy+IJvA7X0axPBzaURdnsCL3yLjIB3nnzHl1vd5qR23DaDxGtRTc0nbTO8NwuiEKD1EAp0UGP79mFczS0CqKysddzXc214QJH9NxLJ7x6bxe5dHEv5xxM1uhIXqSqFY17gY/WvU39AuvjKGtJsbnNvena4sF7aXXxD6ecTEpGUckkKLk0XvPSao/hVk5R2FJl2likiBSGtrfgtaLzeHW5bV3IvS2p9UawHeW8errN/jhFKjv3+I7dNPluy3ivRG9ON1pLHE/zEyfP8srDXUajgqe3Tlk0IdkiItoEtRSt9TYcTNbksV1IH/Wr0izEjxv8ib2PbBHRh5I+sM3fvPEYDawy7uHtbbvJMILSGXDp8TOmoW1cv1HjjSoBGWPe+xv4swfA1S/6/xXg8I15RP/yeNMvAFXn8ref/zqa3LMyx60O4fYYY1EJZ/OEyztLcqWZDz1E5tAHljCo4ha99pgVsW30djF5GuAWdvcwy2LmIgID3qAh3rauydcXW6zujFGloNttEdOcQVAzS2MOxmsc0aO2e06Ox7QrH1HLTQasQ71h9o8HBeedpDsL6Atr1f/Z06esE9TpeHZ0yIG3ZFYP2JumTD07cXxqfoV149NpxefP9phEJYFjm3tKGYSwJ5NoWFG5PaaVjJKSA2/J1wUVv/fgU/xg95VcGSz5itE9fv+lT/BSYeWVd4sp8zri8vVz8toj9FrWacRwNyPZ2PSzZYQpFMmldCNV7Fm3Fr3rOZsUsloidusN/6XnfjoGI3Cjls5RKLenKx0b0lJJdOmzDl2Er1G+xnU1Wgva3AMJzaTnH336nRfy1IPLa54ZHBOqlpMy4bdeeY1ZE1P3Ds8mh0y9guXonKJz+eQr17k5nDPrBtw92UJK64qORgWR26L8GiEM6yzEdTTO0sGIR6EztrHJTs1brx6xH6TMmhjqkHuLCX3hQKxJTwc2f8DtkZVEdptN2sbUTS1JH1qshnF6RGgzagO3Q3qGV853AAv0G4xL2tjSLqvOYejVm36KLb81tUPXKpsIl1vjYBg2lIsQ78TBf3ZJ0biEXkvsNZysbeCKMbAT5vQIJn5BpV2e2Tmh6lyGnnUy31lNOXs4tqefVnDYiItIzB9rXL75yotcClfczrZ5YndGoOyuXwjDw9WIvhcEXsvQryg7l+O7W5ucBsNL5wGqkKzOfT58NATXXOCpPecLJq7j2RghDc3SBwky6DC9oBMKubm+P6jxvY6hb4F0Kx3YhnwvINSYSuEuJEYKGxFpBE8OT9+gGUf8G1EB/W+MHwN+QAjxPdgm8C3go79Zd/amXwCaTtHOQstuNwLhahuTOPfpfIXQgmM1JI5qhK8xrU0fOrh1RuS2zJOQsnGpG8eGfghDs6U5Op5YdK/bE4xqHt+ZIYVhVg5YHg1xc7uIyKVD7geojdpF91YxEbqtNXp5il469kPj2kSt7UHOn73xc3zP7fdyuPZwkpbLG3lf7FoE9J1ii3oj7zuvI0LV0BnFJCjojLyQZba9pKpCmk5xbbogdFoGbo0vO+reYd0GSAynzZDvuv/b2PPXvHP7Pq+mO/zs6dP8ySsf4KnomLHKaY3iueEDPjR/jEYrEq/mMHPwx5ZDMwhq1n2MM24ujEV151B3dnI6eTDGO3XoEkMyKMkKn76XXN1ZMB1nFLWHH7Q2YCSyO7JOS4pVaN+zXqAzFy1cZNgRjUua0EU3NpaxbyWDccmt0RmH9YiTMmHg1sROzdApWXchn0kvc5wPed8zP8CP5jd45XyHF893+fxsj9EwJ3A7qtZhO7Jmoq6X7MQ5vtuxSCN01NvQmVAjHIvNvjRZcWtwyufX+7x498CmUBmQSctoWNBqRdcpXLejbZ2LZLamdgijhnFUWnOc2xG6ti5ddw56o9RJS58y8xHSMB7neE5H3bqcLwec9UM8v6WpXas4KpVt+vs9btzgOL2dQDd9lJFrJ9PtMGfVBGwPcrrOqstm5cAawVxpTYm95LXTbZqVD9Kwvb8mmFQXz0NrQT8LcYYN47Diw7ObNnM7KggdO/k/SMc0nSJfBxgtCXZa9sKU8zrGO1eoQlBe1YhA08cdppUWkV4Imi1N4fhcOljTI8hqnyBsLKl22NDmLmZtybG6UnRDY4OSigDdKXRijWBl7dnJH0gmBV2nqCIPjCCtrToqD/w3btL5N9AEFkJ8B/A3gB3gJ4QQnzLGfLMx5nkhxA8BLwAd8Gd+sxRA8G/BAgAQHWRWq99KSB2bwxpp1NKBSxXtwmdZuihfE19KWS8iqs1OvNWKPLWyOde3yGLRC4Tq2b2+4nw5oLkfEx0c4kn7Ot/3Nb1va7yyFfSnPqtTnz7smUnDtcnCxuMNc5ZZRCsNo6Tgaw9ep+4dnp8f8Fdf/p30BvavzS9yfid+wSvnO2gjOF4nyP2eULVknc+yDXn38C5zL+bO6i2cPJhY+unURSnLtAe4NFzT9IplExIoCwY7zhM6Y8tTD/IxZefiOx03BnPGKudus82dahuA9917B1XrXDTN1MC+Jt7miC9dmyEsgElQcn8xpr43wL+WgWvoYkN4Lb2ItWy1suaxziFfhAhlyKXBaEE8sqElw8g2x7/r5i/wPS9/E2keoJTd7SWD0kLL1iFxUvGWnROb9NR5PJGc8Z3Tj/Cwm/BTy7cSqpb76ZjjF3b53f0fsWWI2sGLOxYPRnhbFbVrERmH6yGx37AbZ8zLyKpWhjl1VLPObUBOuQqoSgc9SnlYjXm4GmEKu6kwrkFtcB2hZ19nV/ao2J4ctbb6eXdD82y1zWEom4hRaJ+vNoJ1FSClsRuEtUu3cYILYVBOj6esqc8EDXWnaGOLUJ6OckZBReJWrJqQlW8DepbriL/yrh/hsJ3wg/fexdft3aaceJzWA7SxuO1HTVQAx9E0m37HO3fv87CwzzOvHfa3VxzVDl2tWG6S3m5NZxxmIxKvImt9Yq8h9myKnPA7Er9h6uY4oudlDW4BVSswjsBLWgigaSWtlMhC0iuH4zSxpNTIBtBntY8TVTQDxXIVYwoHXDv5J0FNaiL0WcDLxT7+sKZrHZ66fEJvBPfmE1vzH5UYYDsqeO/e57nqzt+Yycb8m5GBGmPeB7zv1/jddwPf/Zv+IPi3YQHYBJqY3D5UWUl0pFFxC0sHnbq4a0Ub2Kza9ekA4W8YJLVnd/3KMBoUNuhCwPiqZcQkfo23pTl9NeRjzz/Gu97yOgOn5tLBgmLqkZce5l6McQyysWqRIvU5cQc0nUPT2J1/3yjSPOCzi0s8Mz7hsdGMo2JE6LSs6+CC8V5pC/mSvWBvkBGqlt5IAtUydktO2iHzNra1XrdHRr2VNm7KDIvMI8sDXK/j8W1LiSxqj2lsdd8n6QBH9lwZrVhWIWO34EcX7+S3jz6PRvBjL7+NUVTibHTj2ki2JymnsyF7OyurAuoFUVDbnVfnMvmhAcOX17z0xxPCSzm169L3gnsnU6Q0hFFNWXuUqWX+OL4FoGEE+SogL62LFgP/xZ3fb0Pahw1hYI/3j1yjUtkshcSxE9e6Cdj1Un58/RwAJ+WQk2JAVvm4V3JityFrPTxPs04j/DMHZjHFyFBu1fSpSx51/K7LL/C8OuBjzz+GSlrb0/EtFK6cRXhnigerfe6rfcxWgzNuUI628kPfLorXBquLXkzZubx19xhH9MyqmFVt0cudliS+lds+gvU9OjVWrWOzBTrb+M1Ku1t13Y66dqmlY+miQBTUhIntJ9Sdg68cZllsFyWv53//lo8RiJZMB+S1xyvpLqsmwJPW+Zu6AfMiZLaO6TpFELRcvXlG1Tl8frHPwKsZRyWrByNOjndxK0F3o0IKiL2Grxje52O3r3PsJjhOz+4wI2+8jXkRZlnMJ8RV8saj3utoJhITauJpied0LA5HiEaiKkE30siwI88D+qgmGHRoI8kr7yLf2PM7tOrpGkV6NiDzQuJRRRu1NCufrlPEccW7p3e55p2zdTXjh07fzefO9i1Az0g+cH6LO4sJ33nrjZpz3qDb+bdgvPkXgE4wjCtmKx9ZKJxCIDuXLnIgsl+wdqdFSGMdsZ6PcnvSw4TgxCF494yy9FguYugkl2/M7OS2oWr6Tod8SwqziLSxgdkH8ZojhghhWIx8ZGn54/2wQwgoa4+uVbSVgxt0SE8jhDXFeLLj+69/hI/WLWPZ8F/c/z002mHkWmDcH7/5IQA+tr7JSTlkL1yTODW9kZTaZc9bM41KsmVEXyvLptdiE3hvcFzN/ihl6G528MLgiJ7EqygCl1YrrkULJIZ1F6CE4YPpLe4WU/Jzq+CIhzaLtu4sX95xbbhJ5Las1hF163JSe2wnOfm+ZPwrBU//zZa737EDbykvoHh9b1Onci0RaxcxrfH8jkFcWeiZEThbPWXt0dQOZu2Chj5zWRcO7rBhNKnww4oqqLmaLBm7Be+K7/Ah9QR3yy2mXo4rNMs65PHROZ/vdvGcjjuzKW1llUSiVrZZacC4vTVLB5or+wu+bvASvzy/gTt3YO5QiZDBW+ZMw4JZa1VYqhQY1563ey1QDnSNQjk9QVRyNVzw8/NbqA1+4Wt3bhPJhn+avsVGXUb2FHNlsOTx+IzX8h2k6Gl6h19+8TGkr9m/fk61iURsNw1c3csLhPWj0JzEq3h9OWWxGCCEIR/ndFpa+azbc6+c8lqxzSePryCE4ayM8ZQm8Sp2g4y89Ym9lk4rmtKl9wTthqmjNzvbRiuM10Ol6CK783aU5vue+vv89dNvAkAfR7Sx5u7aSpMP9hcXmReJV+OrjvixBk9q5qU1fS035i/GDTpzEWHHu27cI3FqXlrucn89sidxR2OMoMo93KBDSMPe7opOKxyledf2A46rhOflAVL2HAzXHFZj/vLO8xR9w4ejBb+c3aBvbT4GQHcvfgMnnS+zgN48w9g6stVsQzPRNnbQMUyuLXBVz+nZEFPaNCfpafTcRwD1zZorYckqtUxz0QrO0xgpe65PF0z9gko7FAOX8iwi27gtz6vYMuaNINnLyB4OMcoQDGt0J6kKDz9s6YQ9Ln71jTv8+zsf5ZPFDf6rnRcAKHqfd3hwKVzxgcPHOM6tjO/9i6dwhG2s3ojPCVXLw3JMrj22vZzXigFPjU94cDZBVy4m1NBI1LC1jB0tWVcBodPy/KuXoZWEOzYCUame5/YfAnApWvENw5f46cVbOdyEgD/KUhiF1UUN+VGtt9rU+R1XczCyBrCqcxh/2yEvXd9ncFdS3GyRgFAGXSqiSXlxvzxSi7gtnqMvUNbrMqCpHfrSHvNN0KOCDgEop+c/ufnzVMblU/k1tl3rdP5MYUUQe/6aO8UWRedStC4vzPboermpX0uk29v+gbGsfwyo3LqURdJyeD7iT53+B5i1B3GPKmwwTJYHKGlPdQhAQBfZhrrRghaQjmEUW7XL/XKC73Z0WhF6LYmqmLUDrgyWvN5PcURvJ6pgRSQbQtVS9wpH9IhKMd1bc3mwImt9ssYSQNPzGBVoxvvWDDbLYrpOkkQ1deviBS1dp6hbK3N1JxUYwceOr7IzyCkLH10r0m7A9GDFY8m5NZgpTdZu+hE7azylGQf2Pg7XQ5urUUtUI9AD29QvTmMKP+TPen+QWRExHue0SUVVerSFi2mkRbFIwyoLOTRDLk3XXIpXdEaSeBWfPbyEbiXhdoGUhgJQbs9njy7R94Jm7SODjr5VVswBRNvFJqtafgFg53TMm4jjfEi19vEGDe+c3OdbR59i1df8rcXbmTcxty6d8vK9fdrawaw8xBs5k/Vv4G29ycebfgFwI+sCLKcuTRsht2p04eAlDU3nMD+JrDJl0NK3ElYushXouCceVhxEK5ZJyKyysLfu9gAdGG5ryfhyyZaf0/WK862Yw6MJb715SL7hqwthuDJc81IawNKjmgc4SYtyLH8nCmreufuQPX/ND519FZ2RfGb0Cd7uBfyd06/jf9Iuv/LadTCCgw2KOmt9G0W5GtH1kieHpwzdiqmb88vnNxh5Ja7oefryMTtBxsvLHY5Oxxe8+lFYcS1Z8J7xbZ5//RJ0UBUe21spVeswq2KOiiG6l/zi/cfJzyKEb1VTspSYacPx+YgisVjjqrPSP7Nxrha+jbccuDZ74N7Le+AaVm+xkZTm3Ee2oABvxyZaVa1DdCmjbW1N1/U6hnHFMg3pTkNbPlNglLE9Fcd+7LafPONrwvv8pQffxrIJ+anZ0/hux1fu3+dhMeLGwNZ1XznfoSo9utLBGzR0jaLfNI4fPTeERE5s+ejatjW3ubLnwdmEPupQXg9lgNBgXo9YESGxC8fOUzN244y7iwlV7TKIKnbinL904yf4cH6LV4td/titn+DvHX8tL812+V9e+Sr+0BMf50iMUJtST1H5vJTuMfNtLX7slnx2cQkGLc9uH/EgH/Pbdl7mp0+eJj2PQQv6TvBgPqZKfUTqIHqYbzt4gT1puq5FiwRBS57ZnXhVuSxVYEt3r2/hnUsWwYAPZI/T5i6T3fRCIRRuSo+3z7fIzyKQ4M4cm9gGcFBtGs8Owuk5ShPSWYwMNEHYEIQN42FhQ3A69c+VbmZZfHGCBos5efLqCZejFS8udxHCsD3IabRithow3MkuVEmrJuBoMURrSRw0lI3L+SzBGLh8sKDStoEeT0p+y6W7fOvoU7zW7PKpKiSSDTfDGaFqePptJxxXQ5qNUfINGf9qPoB/68evuwAIIf4O8G3AqTHm2c1lU+AHgRvAHeDfN8YsNr/7VUFGQoh3AX8XCIGfBP68MV+a4lYIg+d16K2ax/fPuDefWEZ54V8odfpQIgctRoHYrtifpAROx6wa8MzWMR/JArTb07dWEVHPQ24PtriSLPFUx+XpijMvJm89HNkz3yQXzcuI0ahg0SqE0/PM5WOeSk7Itc+8iZCi5yfuvhUpe2Kv5b+5/218zeQ237X383x1oPjq9f+Ok3tTssrnXdv3qXuHDz54DM/pWNUBr6Y7PJWcMG9jlmVIbwTvHN0nVC2zJqZqHYJN6lddudRux2dOLvHhzz+OqBXEHfSC5TqyxqLYNtke8fiRNqhctJvUsdKhbwSZsgz8ehNu8shI4ypN0zkIr0bJHn+voD6MCY4U9ba2Wb0C+sAQ+TalKqt8jLGxjasypuldZoVnG5+RRnsSf1oSBY3twzQS/B4lDH/pwbddYApCv8FzNIsm5Ou2XuMx/5RfWj/J/jBltF1ylA/JKp9GGjplmIxyvvPGx7hd7vDTrz9NFFjX9KIIeWr7lBdO93G9jv2tnLJ1aJ+qLthFy6UtGZhSWbhf69iQFtVzebhmXQf8F698B+/cvs+f3ftZLinNj3kFeeGjVM/9asInjy4zjkueSM54uBqRNnZx743gznqLoV9x7WDOy8sdJkHJ59JL7EUpoycqG9FZ+5yeDS0+w7eOVlKXeu1hhMHdLGjpLMYdNDy5f8ZJlrDKAjxP426XNH2Ic+jTxT1CwOJ4yCrquLY358HKllyq0vsC/O7J8iKLIq88ZGDwNn6QqvRwog4DFCv7+c+kQageoyVuaDdZpre9m4dnYw62V5wuEjgMeHl2hVcmu/aEuPas5Nrv6B5GrMOeq9de5GE5ptIOT+zOOC8jmk5xebSiiEoWRci68gndlq/ZfZ26d7nkL/lrD38HjuxJnJqvH73Enrtk2035mfNnuJ+OUcKq8t6o8eVAmH9+/F3ge4Hv/6LL/nPgZ40xf1UI8Z9v/v+f/Togo78J/Cmszfkngd8J/JNf784fMYCKwkfcC7nvj7k8WfFwMSKJK5adwmz1JKPSShenCk9pisZl5Fe8bXzIw3LM9Z0F53lEHTaURwNEoGm15Di3+u1Hub5V57AT5YCdFHQvGYUVwysz3r19l203Y+LkvFbt0vaKk3LIU9unHGYjnhqf8jsnn+W90QkvtQ6g+Eu3fpI/f/idrI8Snh8ccGMw5+17h5xXMbeGZ3x+tcfr+RbzKmYaFmStx0+fPH0RaO96HdemC6QwzMuIwOmoO9sXkJVAK2WfS+Eifc1BtIYIXl1uIyJoPbszktIan8rMx/iCIGgvTG3DpKDunE3socUgr2qrCrm5fc7LjUOfBTb1adCBFgSj+kLb3TQ2FrBuHeSZh95uuHywYLWBy7mq58bI7ubj3Yd89Ogaj0/P+aMHH+Z2s8NpM+Qdw4ztvZS5jpmqnLQPGKuCdecz8kpm5QBX9kyiEn+DxeiN4BfPb3FnOUWpnuUqZjComMYFn7x3Fd0o3NCmcD0SBACs1hFCGC7vLlmWAW3r4DqasracnEegvb4XvO3aA97uBbzc5nzk8Aa6VnzN07e55K+oa5c+qvi5e09S5h4/8Nzf4f3Fk/zDB+/ieJHgbmv+0JWP8T88/9vxlOZ88/5djlfknUfsNURJTei1FyEv87MhYuXgXS54YndG5DSclQPun0558eE+UmmS2DZtn9074u5wQtMpVmu7oE1GOVXrcJ5HFHlA3wmipKbCw3E7BqFd2M9XMW3q4w1rrk0W3J1PcFyN73Y4qseZaPLa7viFgKZ2iMMaLynojd1wmE4Sui3Xd+fcZYpee4gzH9EJXA2tMqioofUN0T2HHw7eyXuevA3AwK2tN2Ux4XA95E/e+hAfXj7Gi+e7HEQrPrO8zMgr+YbRi0TKLoSJrPiBw9/CX7j2M/zi6mkqbc1lZevw+6586kuYyr7E8eUF4AvDGPOLQogb/8LF3w584+bnvwe8H/jP+DVARkKIO8DQGPNhACHE9wO/ly9hARACztYDyzDxDMUsoh+llPOQZuDgeh1SGgQwcBviqLYM9NZhWYV8enGZtre7wtU64h3XHvDJdYBU/YXkbVVahUfXWzRz3VnS6IPK5z96+pf46dkzHKVD/vz2B7jiDHi9zbhXb/GJ08t827Xnedw/4X/Ovp5QNXw8v8F/+dnfzX/39vfxF+Zv4eX1LsPtnElUshNm1L1DqFpit2bZhjwzOuFOPuVbL32WD54/wVkeMxmWfP3N11i1AdejOcs24kP3b1It7aSMFngzRZsY2w9JHZxcwrWOs2pwodv3HCtrfRSs0Rs7GewNU+rOuUAHp3lAsgFz7Q1Sssa/AH7NihhdKYSCwV5moVy1x+4gw1Wah6sRrqsZRSXHZyPbPqts4EngdjSdsgoTZREcn5xdJk8DvuKx+/z+wZq/vpiSax9XaD5w/gQ7QcYf2/kgz9eX+TvHX8driy2uDleMfVuuOyqHlJ3Lw9kYcxTwwN3F+D3euCaMGtYnA/JBgE6tkqpJPRadsmHvoW1496mLTFoWRcgwrIiSzCLHvY6uU5wdj3Aju6P8w8k9jrqGv376XrLXR4RXbW7yx5fXmI5y3r1zD192fH69zyvtDkfNmCuDJYsi5M7RFn/t/JtoS5d71RTH01ya2oxmd4MVGUUl6zLg/mLMJC6ZbKcs9NBKVrXDogppOutq9h1LZO0NeE7HSWHLmgdJynZU0PTWrT4OSySGvb2HLJuI03yAqyzSezvKcaR1q682/Zt1HVyE0KyWtpFrSgd8zfXL54z9kh6bftcjaLSi05LUhKS1z7XhguMg4erOgqPlkOokBgP7V+bsRDn99jmv7mzjbU56TyQzTusB3SYP+dmdI/7s5C7/8fg2fzl+jmUb8d7LH+afnr+NWNbcr6Z8/Pwaae0RuB1/9+RrLb5C28/w77/+afbc1a83lXzp48sloF937BljjgCMMUdCiN3N5b8WyKjd/PwvXv6rDiHEn8KeFlBbY+rDGEJNP2kRa5fXH+zgDu2uIA5r0iykqV0WbofvdByENn3pP7r0i/zw7Cs5LRO24oIsD5iVA+JRRX4e8cLZHuONZlsIQ+i1eErz1duv86HZY+yNUt4d3qbdUryfJ/mF8jqJLPlg9hxPBsc8tzPCFZpfyW7yR679Mr+4eJJJXLA/SvkrL38rkdvy7u27PDY4Z95EHOYjhn7FsgqZBgXLJiRrfS5HS17O95Gi55uuvMxJnfB4dMbdaspJPbSSzU4iKolxDGoTgG3cHlnLix1LrwUPFyMABqFNkxqFFfM82mjve6KguXAjgzXaSWlYlQF7SfoFRk3r4srelogKRTfSxH5DWvm8c/8BaetzdzW1LJ7GmqPEqS2viUaS15bboqRl7n/q7JJ9jL0kTiref3qL95/esk1k2fPU6JQXD/d40exzVAzZDVM+d3xAuQ64NZ3hiJ4XFnvsRDbt6/L2knvNFtLT+H5LsQ5oNqeWfrkpeZQKWQlYOTgHFcnAgu2YpKxLy84/OR+hK4UbtYRhc/EZ1J1kZyvl56sh+2rNT738DH2smQwKTsqEWRHz1Xt3eDZ+iCc6novv8YH1k7yS7bKubZauUIYm9xhMCoZhRdlYbPQj8F63QZQP44qdOLOve+ewd3lB2TqklaV49r3AUT3rPGA0KAkcTdm4LNuQOLDI6EC1VNpl7JWclYNNM9hnWYXsxyl74Zq0DeiMxBE9N8fnMIZVHfLKw10bup7UxMOKyaYcE256RLnycKUmchrSJuDaYMFpNmCYFGxHOesmIPRavvPyR/lf1Tt5cZO/AZA2Pr7qeGLX4rPXm8fQaIe2VxgjOCpG/L5X/z3uLKcIYXjr9jE/dPxuDtdD/vvim5llNlDeczTv2DrkYyfX8Bxr+Ht8ck5rFJedxZcwbX1pQ3z5BPAbHr8WyOhfCXC0Iep9H4D/2GXz6B0ZTgqYwPp0gHYUfe7Qhw2jxHJCXKW5EZ/z0mqPP3z5o/yeuGCpX+DHuufY8nOU2JQAZIDwNHkW4Cp77NW9pO0Uf/zmh3CFxt3RHNZj/vbpN/IdWx/nbx19Hd9bfCN/4Oon+fTiMr/UPMZ33/oRElnxoeIWr5R7fOT1m3xo8RTOdonraptg1Ev+wNVP8I/mX8GytPdXdQ5S9AydhnvphLzzyBqPSVAyb2LurLf4+OFVdocZntTU2gaTcLWhbS0iwL3t42QK2WDTmgIwK4+ycLh83eKDtbG7NSV7krii3RiWDBbqlQQ1XS/xPbtTfzTK1mUYVJxlMdnDIU4u8Z5ck9ceTePwudmBJbFmAaNBxflJRLb0UFogOlClrVsnV2qaTjGKqwt2TFm71KnNEchLD9/vaFvF4WJEELS0reL+Yszt0y2EgKduHPG91/4xf+H+t3JyMiYd2slmlQXcunpC1noUtUft9eha4S6UVfXEPbK2Kh896oj8Fkf17EUps3JgIWWlC61tJDuupqysG5dW0m8iF/+Xk68ha33MqU90zfaV5mVEVvpsuTkfT6+z4/3/2PvzqN2y/L4L++y9z3ye8Z3fO9Wtqq6qbrW61ZJloSFIsiVbsg0GO4HEy0QYTMCEELJCSIhDlkkcVgLYYJYJLAzEXnaIDR6RwcJGbku2rAlJ3ZJ6qLnq3rrDOz/jmc/eO3/8zn2qZSR3yyqbaqy91l33vc99h+c9z3n28Pt9v5/vlky3VC7aeRvGacM4bbhejuh7syOoFo30mKpNjA4dxjj2s4JZVNF7zU2VMUskGrK3wh+Ko5406tjLBT1ddeGO6182Ee/avZ35bFGnmGFDAwFPrmZ0exqHYt0kUgZtAtJEoi1bZ0jzhvI8x+VirFsgaPPeWKbpkE/Qh5R9xJO1lEzL+v2S2qaNSYKev7b4KJG2fM29pyzrlEA7bucrruqcZZ3uktP20pLzzXgnf33r8SE6ECXVyXzDuhUc9nzI+zgab/n2wzf5gcdfw3dNv8Dz6SUX7YSrdkRjA96t9jmNTvj1v4yJ6pccXkCBf7+Mv9MF4FwpdTrs/k+BZyCOXwpk9Gj4+G99/Csao3trvvZQ2DBPqwnbjfB10v2KLG530KyqDblqRnzzwTuMdc2f2sz5zfkD/r03fj2/6SOf49eMH/C54jZP1xOU8cRJR2cNz00XzKOKwkb8yPIl1l3C4/WEcSxOzx8MPs633X+H82rM1ybvMTqtedrO+NHiJf7K2cd48O6hYIgLgzKe/jpldv8G5zTbOubPvPcNxIGw1y82I+ZZxetXRwLWSsXteZxuAHg+u+K8HnN2M9kFfzyTfzZNKHXWXuEN6Ba8Ria8vY58r0IPyVKzpGLdJJRdOBitpATwrAkKskBMkgYPXK9zrsucUSwh7DfFUApQHhfIhFJVEeO8JjCWRZHS1yHXFynBVmNTYcroRjT1DNJBpTzvXg2msbhFKRjvFxyMit2EFg6vX9MHVAN9NMgk//d2tmLlPLOwYjQTvMPVkynhdcDrRcRkv5D7oZCENuVA9YqoFSd3N3eEo5amFrJmm4vCpKtCaKU5HUc9m5W4mIPI0tUGHTjujRcE2vL5t16A2DHPRbPf9rJz/a/e+1qsU9yarLmf31DZkOsqI/wSbEiWNWyWGfUm5vhkSRwIT388l99FK7gscp6uJ+zlgk32XjFLaxZesa4znFPEYc84avj5N77kbeQVs6MN86yi7MJdROXji5mopLRnMi8lYL6L6JymeG+M6hWb/ZbTyZrrIqPayMnpucMFm0agcTXCLmqt4cF6zqaSqM++N9xUGX1v6NqAz79+h9FhwSsHF7y73uPeeEHZR8ySasf/t156Bc9KSMs6pWrEUOidZjSpKIuEo9mWbz9+k6f1lE0X704hz40XvFvtE2jH56o7LPqMLy5PuDdacDu/4qbLeTk6+0qnky8/fvUE8GXH9wP/JPD/Gv7+L7/k8f8eyMh7b5VSG6XUNwM/AXwfwsH4skNrz/F4yziscV4YJ6NxTZvI5OK8EldoE3NvuuR2umTVpyxtRu1DfqrZIwosr1anfNfkC0zGFU/2pzxNJpxdTXGR4q2bA8ZJw/c99+P8fx/+AxykWw6yklWT8ENf+xdYuYr/w+Pv5oXRNX/ovd9AOchE33l8gG+F5a56wQe41BJOJFM4TxsC41gUKeOh+RaHPYshAaoNRef9W17+HJ03WK/50ZsXJPVsVPHS3hU/8/AuYdRTVxHmvQQN2NjTHPVSbkkt40nF4RCNGRhL0YZkoYTdgDR1tXYEih3yIQwk8OMZTKtZJoyzhjToaK0hDqVEVE47bCIa7S4Qjb11eqcSCbca3Sn0oDJCg+pkEr65nIiCpDYkexK8/gx29sRq2iYkijuMcTu1ilLvQ8S8h0fFjL9Z3ee19REKMeGpWqN6oNO7yV/XelA6yaJlGiW0zqXGr3O0hvqoIzrumcY1N+Nmp1xaPJqiOo2LHaOTDYfzDc+NF9zPrnl9e0RwHaLulpL1ELY7YcCqSlguc17evyQ1wnja1jFRIMiEug8YJz3jk4ZlmWK0w2jHto6ZpTVPrmZMxuXuWj94so8vAvSkY28mmRV2FRIe9nxi/ymPyymqESgdgUdHvSAWkoJltY8Z+liTiaSj4RUeMX7lYUsWdozurel7eX3LQfEW5y2NDjlON/y6w9f5qxevULVjbtY5p/M1SdCx2GSEoRc5ctAzn5Qc5lse3MypypizYsI3HLzHT1/dZVWmHI63RMby4+/ex13F6P2Wg/lGTkRlAJ0m2qsZj0u2pchav+HgPbY25tXlkQTyDNf4jcUh//bH/gzvzg44CVb8B49/HU/WE4ouYu+44KIe8Qfe+x5+/fN/h7PZ3zp+dQF4fyil/iTS8D1QSj0Cfh8y8f8XSqnfDTwE/jGALwMy+ud5Xwb6A3wFDWCQBaCzhveKOW9dCM/meLbhrB5j+4A2MjzdjEnCHofivBnzcn7BR+Mn/Mnrb+Gzm3tkYcfnV6c8rmbEpmca1myjmFXWUpUR3iu+6fQhf+HsU8ySit4b/oV7n+Yv3nw9AD/VjPiu2Rd4uznis9e3xYxkNemokYlQebxRJPOK2/MVVReyKNIdQTOLO6o2ZC8v2Q513TjtqLcxxnietlMKG/PW+oCqD9lLxNj1hctjnBPjmduERI2iyyXcJj8upO4ONK2olU6na27KFOc0l0VOaBx1J42ySdLsdvVVG3I82cjpYACW4dQO+fss4co6jd2EjI637GUVs7Sm7gMubiaCUK4NplEoB10+5O066KcODOhVgBv3BONut9tz6xCXyi3hvaJrA6rOEES9BKMAUdaRxi23JmuyoMUoz3G6YVmltF2AeZbwZjyuDlBZj9Nmp63HKbrDXvoAW4PPLYcnK7JQ6uRlF3FrvtqVH4gcPnbgYbtNGCUNn3lyh/o44Ml2Sj/rOZltJdM2aNmLC2ZhxU/bu/STim+avstVN2LdJUO+r2cWV5z1Y4omYpYJqrnuAooBA/FouYd3ivmx7N69VxwfrahbeU2W6wzbaZE5a8dlM+LpUH5RieVgf0NvNVUb8u5qjyTsKduQoorp6oAo7Yijju02QY1q3loesjeTxn3fG1arjOX5GBVbZvOCV44uSE3H5za3iE3Pdz/3Go/KGU+LCcYZTmYbVlVC8eqcx4cpJ7cXPFpNqTaJwPP24awWmW75eMTFreEN7BVqLniN80dzVOSg1wTTljC0HI+2zNKaj8+eEuueB+XeLpOi6kKMlkS8f/W1304S9Nwb39D0AWnUUbYhP3bxPJO45jjZfCXTyVc2fnUBeH9473/HL/Ff3/VLfP4vCjIaQo+/9pf17ACFSDPXdQxv5rS3WrrJwGlxir2s2qV7Xa9z7h/c0LqAL2xOeVpO0Mpzf3TDo2LG3WxB0cdcNCNmcUUzDrC55mwx5q+/+yIH0y3ffPguf/D0Z/i955/ki4tj/pXw6/m5xW0e3swFM5w1Q3SiTFb5rNr1EYx2LKtUJt2hPvosFKSvQ6pawG4gsjrv5e+/8OrXyZsdCQW5Goukr6lCmeBChykMNpYyi6m0aLbDnqYQBLY+Lfjk7DGf3r5M7zSuN7RDCPoobrkuMqzV4mRNa0JtOc0ryl4WQDNpCYby0ThuqHTI+Y1o1A9GBbHp6azUsm2vUaGDwmBTj3tm8uoVLvDiErNgaoVLNUrJbr5vAkgt46EsoZSXCdFLc1gpUIHDWeHcHyZbfuzhfd7bzNhPSzELdRodePqpJdsr6TpDHPc0UUC3jvHao7Oe44M1L06veX0hGvxVk/D85JpFk3HeRLIwh50wphJ5TtZKXkHbB9TrmHfjPYz2EMquHaD3mtv5kp9f3GJdJXz9ySNu+pzzZsLPPLmDMU7KVJW8htYrOmuYxjVtP6JZx5isx3sI04406JhGFbUNuSpz2cGnNXFgub4ZoXpFVcS82h4LfO6gIo471kVCs5V402DcsT/bopRnOqrwuWK1SdmsU3wRMDpYsb4cUaYRcdhxf/+Gp+GE5LAjCXrSoOOqyjkvR5zmaz41e8R3TT7Pf1p/O5GxvDK94Kway/29Lz6Os6dz0kmN7xW61lwsR1xvcuqbhLDQ9F+Y0B5Ybj9/xUm+5qoa8djO5BpaxWxSMoobqj4kDTp+8uI5zp/OMIsAO7Ec3pYA+6qK2JsWO53/u+t9/qXn/yp/+vIbeXe1xySumYQ1b6wOf7lTyy8+ftUI9uEaXRtw/mjOc/cvWexbVBlQNhFH4+0uEGMvKRhFDUUX7foB47DmdG/FPCx5qzzkssj5b4tXOBptyYJWMMOhKCg2dcxqkdONDH/w9GcA+ObRm/zIxYt8+vFL9NZgrWASiiKRXZX23NpfkYUtqyZhWwu4CoTA2PWGeiuThm81KOi6mC5wEgeY9BDaQWctZQ8Q+3zXBvSF8PNVbInTji6yOECDJGxVRlKUeo2PHU0d8uc++w2SEzygqXVscUXIVuXE85osaWl7wzyruC5zNoEE2j++nLE/l91h2UU01pCFHX0dQOTYTwrKPhKTnHEEUY9zEtvoQaIvQ8CB8gpdyqnAawgXBnWd0d1t0KHj1sGSNOio+pCqCyXnoVfYTqMCuQ6fuPOYB6s5n7s6pe+EmX9V5kRZS98F+LTn2154h0fbGZ2TXbBOPLY3uG2I6/UgP3XkkZRmkqAnNR2XXpM+K291oUDWYIe/1trT9AZVygnIaAuNoakFyeBHFQ/dHg/ePSSe1ZyXE5atmPDqJznRSblryLpnUlhr6JxhllZcMcGfJQSdotvTvHUpp1rnFH07NLB7IxuERk5Y/jrCetikorIy+27XgNV5j7OKsg1RQGchizq0cUTG0XSap2dz8NC2hq4zHI+27OWCud42MdsmpqglO1orT+ki/uOz7+CF7IqHmzl7YcHDYg7A7XvXXK5G2PcymtWIsFH0Y0e3iemUh9DTHchkHWRynWsrst0w6kmijmU74upsgj3aYLQnDTruTSSNrj4NGMcNeSCv2zJNKdqQq8UetgwI8o7reyOmYcWd8ZJAO/6tu3+RjdPAv/WBzDlfTSogpdRzQOG9vxpK7P8T4K2BNvplx4d+AcAqgmXAk5sJB3eWuwksNj06EBDas97A1Tbn7smCWViRmo6ij3mrPOT15SHTtJabvY0l8rAcc5RtmEcVV0lDkURcXU74fy/vctWN+czyLtsmEiZ+HaL0M2UFxEm345MDXC7GuF6TjRr6XlNuY9K8JR3XlDcZemsEgRB71GGPrQ3dRQoeklsFSnmS1O2w0b3V9I0RLPPwc7UWp2cSd7va6DOEA4hb2meKug7xTuGtwpUButa4UU8QOOpWSg3ei6zQKM/1NsPfRCSH/a5nAHCxGaE2Afc+dkagHZG2fNfpa/yl9z6Od1oWOS2pWDb2BFuFqRQ28bgQfOglOWwkNX5ag92ELLIUlz5L2TLCXVGgEwtXMZ6QzzT30IHjcH/DN95/wHk5pu2FyY/yRJHltZsjtlVM2wTESYfWAsrr6xinPcejLaFyjCPZZWZhy8NizrJOWZYpcSi5BUw6KEVymcftDiH9APim04e8vdnnSnvStJVGbCwgtOM7C5ou4GI7QmtHGvaovZb5uCQYwoaMdkSBJY9aNo04XGeHW5btBGWlZGXfGonwJPSYWhbO+rBHDW5pt9/BNhAgoAICTxz2aO3wE0Xfaw5nW5ZFSjCcPoomolklqFajRh2+NgTjjjxtCYxjEtWs24R3n+7jrRZMSOjIk5br+llcpebTT19mXSb81w8+jvWKLOpoe0MYWtrMEe7VdGUkr2E/qBEi4UKpIqC3iieLQy4XGjv29JOOJggJ4h4baLZFwtFcSje1DXl+cs1pvKJxATddzqLJMINnwQ5QRBNY3qiOuW5yXhxd8ROX9/kPrr+VrY359+99QHPOV8kCoJT6vwK/C/BKqT8FfDfiyfotSqnv8N7/777c9/jwLwChw9+ucZ1h8eoe3KoloNxpRmHLoksZRw0vTSTy8NnED/CkmrBqJd+0aCPa3lB3YhI7zMUQ8z17P89nL79HuCtFxB/+3HdyMtswiWuM9nRWShdR1hIEljRuJVNgmKzLLmI+KQnM+5kNbfosDMWgIouPNPp2SaC9lGwqI1C07H1+Sd8b6l7vpJBR1u1ol3glCpdI+ESX3Yi2DtDG704P01FFHrV0mZiwtnUME2jbgFEmv8tykxKGEpJin7F/LnOiY1G3ZKbnYisUyr43+FFPFrY82Yp/4Y2tTLpqULn4wGMjRXytCSqwsfA/FJJX7MJhIdKK8bRi71ZJ0UYSWt5J4A7Gg/e4xsjpJpETUjZq+O5br/E3L18AYJI0rFZCSPWxYtEGYu7qNVVtUIHHt5r0zpbjqZjZ/rvVXZKwZ7nOOJpvSMOOy9WIdpHgDwu0guPDFZeLMdt1ShXEzCYlh/GWYM/ysfwpD7dzwrHQMvOoxXnF7WzJ/fyGz1zfFiFBYzi5tSAf1WShZAccjrd4r7hYj0jDjjTseO9yznxSkh6VQ/qVo55GuF7jlxG+U9jME05akaXeSKOdSYe6jkgG0NryegStgNeM8ZxfT9FGiLRVHUrS2gBcCyJLvw1JUlH9VH3IFy+PicMebzXBWYRXYE8b4qDnYj3iqZ1If6aSyfrZ/fAMDof26EbTn2cQOwjkflBWwVYSwnAQTlrStGXbTWVb7RWjcS1lyLgh1JbOyemosQEaT9FHBMrROsOmi3nvco67jlFO4RPJCu68YT8u+C3TzzI2NdZr7sXXH9iU81V0AvgdwMeADOnFnnjvS6VUAHz2K/kGH/oFIAotn7r3Hps24W1zQBDaHW2y95p5UvFkPeE0W/PNB+9Q2oibNudJNSXQjk/Mn1DZkE0nUre72QKDI9Y9P3r1An/s8bcB0DYhNJr+ZsS5AjMXtcNinYH2dE1AHEmwuqht3M6VGRrHNK4534xZPp6QHpXkiYRbxGHPMk5ptxE0GtVLUyxJO5KoY3E9gtqIueu05iN7V1xVI6ou5LoZoROL1hJO0jUBWfK+WSkIrLg6B0fls4D2pgt2qVN9a7hZTVGxFYljZyRgxirOm5BgbZjeFb31Mzf01xyecxRv+JEnL/B0Pdm5Qz95/zGjtME6OUUcvXTBndGSn3z3PmURoBoNk05cpMpDIAx52xi2LmGzzFBGJnivh5OL9kRZi+0NetTK6cUrRknDssuItN2dvEbjmkKLHBHlsTrAKy+U0yEwJ09avmHvPd7YHvHofE4wK0D5XbygUkINrcsIrT1HSU06pFRV6wQ19RQ2QivPyqacbcY4p2m6ED2E6Ly+OiINOkLtSNIWF0vG7n4uSVqdNbSD+1wpTxz0LKt0t1FI45abp1NZ0I3DhJbk7pok7OmtZpI0NFZKiGoZ8qz2V12n0lS1CixUm5ggthzurYmNpXOay3YkO1gNx89dcX45Rc9atjcZD4ZTaxJ1LNcZ8dsx7dwR3SlIAsuylFPE4VhOpashJGYUtzvjYB60vHWzz7YyEHrSeYVz0sz3N2IAU53GR4792ZZQOzZ7Kfo8xoayaZrGNUr5nffhshrhveLtxd4unGgylOQ+euucL7S38I2GyNF1Aasu5SDaEiGbrjeKI97giP/1BzXpfPX0AGrvfQu0Sqm3vPclgPe+V0q1X+Zrga+CBaDrDY82M9GLRz1p3JJHLes6oe0Fz3trsmYvLHhSz/jp8zvcmy45TLb0XvOkmtJa+TW/ff8NOm943Mz4wvoUh2I1sMzvH1/zNJlQXOS4pxmXoej2o6jHGk1bRJRlLMqLTUKQdCRJJ5b+LuCt5SHOKUypqZYJ09sVZSuGnSiydAuNHnekeUvfa9o2kNLSOkQ5cJHHAIsmo3Oa3kpJKQ478kgygRdlytFoS92Ltn+zTomSjjSWZp4ddvbPuPV1FeGtJtuXBakcovWCwNLWiUC9Ak8S9DvfwGaR8SiZ8Zv3f467L9zwpx98A7eOl2jlOSvGxIH0TI7GW4xybPuYr7nzVBrPQ8jMs53de9czuiYAI81tHUnSmF0GaCshMaoPaKNQVDhaFg2T9DRdwOcWp2Rhy7aNmcRSVzeBJQgkE+EqyZlnFY+vZthOE6UdVxcTPm1fJo/l/rdOY4ync4ayC0njjux2uwtId16RRh1Z2LGJW05GGzSebRfzp9/4eqp1ArWmPot5O8+ZnGx2xNCDrORwXOzYRIF2NDbAKEc7sO0DYyUQXTs6q1luM9o6wKwC7NjiY0BBHNTspSWbNt4puY6OVrR7ZpdI1ls5wWrlWZ+NMdchfWbYpjEmrVmVKd1NAqkVORagA4etA4K0p1wnJKOWPOrI9zraby6o2pD1xUhKS6OGNOvYNBF51DEaAm6eeUf0UBs5mWx4p4yxq5DqKiOYCDm0tsIBeoaZ9l5R9wH7e1vqcc3JZEMetDzZThjHDde1wBcvFmOO5hvCgS21WIy46Sco7bkZZcR5i0sVv+kjX+Brsid8rrjNF9cn/HD4UWoXchBvd6f+X/HwfNWUgICZUuq3I4fu6fAxz/79lXyDD/0CoLXnaiksIBT4iSIfwsqdD9hUMQdZwf3kmj/x7jcxiltWbUKgLa2TgJZ7+Q2bPuFWuOD7rz5F7zV3siXLLuWVyQV/7eFHeLBNCaOecNrgHmXUX5ix+YTsvsFhYkvfGOxNjI8c7jJnk1ui/VoULlVANqsopwHReciZk7CReFYzH5eEtyyrRU5xLT0Blw6GrNRyfPcGrTyhdrtM1lEkE9gzSJsbkAmRsdzOVjw08919utkOO82BvbOXVVSBBIcb7ZhEDY0NsE4UKXFgaYd4TN/GWC81eaU8OnCcff6IP1D/Bl7ev+TWaA0gO9paShLP0qr2kpKrKh8gcn6Hkdi4WJhK1ym60hB6cArvQJ9UTO9J+lgSyW5ZDxgOOzR0y23MepsOEkCZRLbNhLoN0dqTRJ1A74A06DjeW+8mj000lCycJgitnMJGoqmHodk7TP5mCGEhkjyEWVoLb6ic7Mp8qjSY7dAbGbIp+tawcekuAUwpT9FGREFP2we7SbPtgx3TXykJR+86QzZq6J7rORjJyWtTxUySmsT0XLQjNjc5B0dr8qhlHHvu5EvGQc2mT3i4nVO0EevI4Y0m2q9Jwl4UONepJK4FguU4s3vSB/BgRg3xz2aUdwK6UclkiIwcjwXKVtYRs5GUAg/SiuNsLY7cPuJ8M2KWSun1YinmLm0sNjKElyHmLKM6tPjEDfU/j857btYZadLhkSb3e9cz2qc54VpxtecI9iu6ZYIZS4mstSIfG08q1tc5ahlSlYZbL1zxtXtP2XQJr5UnpEZAhp++eEV8K87sTr8fyPjqWQB+GCE1/60fq+HfX3Z86BcA22lsLU9TGUmhKtqQcGh4WaV5sJjzp5tv4NZITCv/6MFn+P6rT/F4O+UwK3htdUwc9PzRR9/Gtov42r2n5EGDVo4fevQRysscBnVOFPd09wqpE3uF91A/yfGZHerVECyE3e4nHd4p+iZAbwLKLgfjMaVi/GqID6C4bbgcgsS9U6hKE90pOJ5ueHw1o9+EnOZrAu1Yt+LcLTopQWRhS9GGgmdWnrKM+fw6Y29W7PT9vdPYxsBYdrun4w338xselnPevt6n6wyXdiyYhKTnaL7hYjkiDC0vHlzzuer2ANJrWDUJk0nFah0RB5arakTnNNO4pnOiDGr6QCbaOubeeMGqlTJBbHrqQdnzrGQTTFv6RIxyo71SEsQiCYxJoo79VJQobiCFVF1I2YRo47FFyEonXNdjvFMESc9kXBIO6pfXbo5oe8M4ETVXaCyTrGadSHN828aS2xB2hNpSOilPtFbMbGYomWzbmHBw38amZ9Fkv8At7RNLn1hMKhTOqpSdpjaOsguZJlKDbnoJcx/HLZ3TlG3I9jqT5uhQ/5YTj0Lfbbh/cLM7lbRNyMVmRJfJIhWPGiaJnHjGYc07632Osg2tDXjvaoY9yzCdws562jLkah2jSgOjnvGJgAcfuj3UMiI6LSQ6sQuIG4lU9V5R9SGbOibMLW1vaK8TtqFllDQSjFTKBvJ8M2JzMcIfKkJjWW9SXCnlPt1L07rPPGrSoo0f+mSiAorDnpfnF/zke8/RnmcEG03cKpzxmELTZSFmq0mOJHvi6npMNmoYJQ39rGJ2u+IfvfOzfCJ5xI9sX2ZrRcDxW6af5eeXtzjbjqiaiCjsuTtbfmBzjvrqCYT53Jd8/KXIna94CfvQLwBov0vo8a2mNwYz8kRmcLKantBYLtdSN/+207d5KTrn6yaPaJ00i4o+Ztmm7CcFadBx3YhG+7IeifLjuWuOsy2fe3SL/nGG35fdt/eKW7M1j5ym3sTQapENGk9yrSnyiOjOlq4I0RbCGyMKmED+4CF/rOmHZp5R0M4cz+3fcF3mmMDitwk/9+g2YWgZpQ1VG7KxmnEmSh/rRFXkrRJ2TGC5Op8gEhwpmRwcrfm+53+CUPWEyvIjq5d48/JAcnqHBjKVwYzEHTzKGqzTrJsEtQyxx4qyj6jakHHSUOwJ7mFVJfROc5xtdnrtdS2Y5CByLNsU7xWbJiINpflcte/vdg/mm4H/E/HxozPeWhzQ9oayjrG9pmyiIUzlfXiYc4q92ZZNlJDGLaX2jLOGKJAUsdBYUYApz7vXe1xvcqpE0qoetBIYbq0mMI4saXBecbYZEw39EudlIguNRX9J7+QZl2YUNpS94Cjuzpf4mVAmlfKcb8b02xDVadqRYqs92yoWV20Zo7VjUyQohfgBRi1RJAgO5zRNHe4qDNdlvjvt+F6zbUTlNEobJnnNNKooupjeG9Z1zF4iCW5KIRjwsePgdCXO7ybCKM+mSFgvMrbbBN9rgsOak5kA/m6KDP+dNXuDXwXkNOS8EhSE8bvmbNWFbNuIUdSyn5dwBNtHE4KtJiqHZL6Zx847fB3iQo9fRbjMks6l9xUay7pO+Buf/agktbUKF4KLHXZsufvcFWeLMd3+cG2vp3LC6jVJ0FNouR//7MOv52dnd3l9cShmudGGp/V30jnDfl4Sjjd89+GrfH57iw9sfPWcAEbD368AvxYhMijgHwb++lfyDT70C0DwLG6w06jIce/khm/cf0jnDa+vj7gsRqyrhPomoTYx7kTx6eJjxLrjueyGf3j2WV5tToXf7w2rLt1JR+dRxapNuSkyonHPfFpwfRNJE9PBpkhE+pfV2F7TuRB12OCsom1i0qeGIpLAblMpvIZoKUqOcurJnmiCQpg9fSZ6eWcUr711Sww8sxY766Ex2Mbw/ME12zDmfDne4ZgBYaWUMd4pujJCxxaTtzgrsshvP32T75u+ysZZNk5z1Y/5oauPEawMNnP4yKE6TX2d0jYBQWhpFwnpvQ6vJfqyscFOKrs/23K5GA/BH3CT58SmJ9LSdDsYF9KkHvj2ZR2zXOUSAOMhGzfcXI1RSsI10nHDthNMRdUKA8YEjs0yI0w78UIAaoDWfevJO1w1Ix5tZ4zill978IC7yQ0/dPWKSDoD0asb4ygeTmhUJo1RREVE5EgmMrldbfOdSS8NO6xXlE1EN+TSPmtuhsYyCWtqKyW3aVjL5Nsku8WhKGPC6+Ets4pp0gg3sthJQxhKb6LvjUQgtgHPHUkGQtmF9NYwyyo8EhLf9CIv9q2Y6rzV1MsEaxWzcSUZwUHPJKw5GWtGoeRGd1PD5fMQBZYoEKyFUV7Y/RtZuEzSE457oqjncpNTLlOwitmx1Nm3teC+s6ijbiNUIE31qydTrvoZyiqS04JRJNf5dLzhrb0YtUylV1ULZsP2AwNq2Heq0lCYZHfSW1yPBc+RWVGHeYWPLeODgroXIF0wKIhQHm8NtpcFr20DXKBYLnMuLqaMZiV9b1gE6e6abuqYrgv4E+tvIok+mEAY5b96VEDe+/8bgFLqrwDf4L3fDP/+14E//ZV8jw/9AqC1Z+9gQxRY9tOSSViTmZaRqRnNG36Gu6ybhGocYZcRf/HnP0kQW77l+beZhRV7puRrksd8+uajLJoMjReeUNTwdDOmbkPqMuJhOGe1SeWIXmvsrMddxVRZQxZLzkAX91LDLyQkvnyhI7wM6HNPnw9vjB70WhEtFT6APgcbiV4eD0Gp6LcGl1mRUyowscWuo8F45NDaM41FcXJ5M+FwT+rdzmn0yFHXIeFArzSR5bXNMX80+Rj/s/HnOLcjOm8wG0O4UXitsaHHj3tUYfCLiA6Ij0vR4Q9v3jToWNYp0dDkdVcxyiqchoc3xxy8cEMbCVl0VSUcjArKLmRdxzinUOcxYaFojqS/sHewwXtFUcUkUcf5dryLFJTX1ZGO693k7wEzLEYAB/GWbRdT9BGViwiVRSth6SSmp+wjKd3EwwTSKPyoR0eW2awgCftdyepZ7OWmidnWMdOsYhS2NDagsWZX769twDyq6LymsBFNH5AMsYrOK9K0pYhSVKcIGuEOOaTBbUIrzeS8lO819G6qLpQeR9jtsOPTuCbSlrf8PjbqadsAQkcU9aIeixveXuyL6W4sJ5eLStj/o6hh76Sk6kM0nsR0VCbEecmvfrYAOavpe0MQWLJZRVNHbN6csT5qOD1c4YGDtGDZpOTHN1ysR5R1hp50BKGU6J6uJtyeroiDHm8VNga0px95SYaLHJPTNcsnE8GSO1BXEcG0kmChyKKOZGK2A3Z8Pi2wThbUOOx5br4gMR0///QWtjEo7dgWiUigU6Qc2AuzKsukZ/R4NaWsIyZ5TdsGxGFP/CUy7F/x+OpRAT0b94AvVf20SFrjlx0f/gVAefayinDAIq9JeFLPAHhzfSB5oEHP4XyDnylO8g2tM/zNt15Ea89PXjzHnfGSVy+PdwqYug6xQ/xf3wRyczvN80fXvL4+RZfyzlbzlvVNTnzcEwZWzFOHW9b9GLwmGHaD6ZmmvN9h6gAXAFqQyLoDFwyTP3IKsIlsL3RlGJ021IG4OlUmTuaL5Yi+CViNxF3srMjmXppd7q7J569O0INj9PnDa/bigh+6eoVPJQ/5ifJFfvzmeVwkUksfeIgtk3nJNkrQ2jMeVUSBZblNmT235M54KcwXJYogG2k2qYNK40dDKL0WZYt1mmxQ2GRhR9lI+Sa+v6FcJ2AVXRFxcHhDMYSTF01EUYkk1TUG1RjcvGGU1xSD6zmZ1Ty3L3XxR6W8vpsupuxC3t7ss+4Sii7ehaT0A7Du4M4S56GsY5oiAgU3T6ag4fbda/bSkqebCW1vMNozTms5bTxrrCtPP/y96RJGYbO7zvOkZFFnrJuEzmnisKd7bkvbhNStBqsIRy1J0hEHko3svdr1AjRCZX1WPtMDlfOmyrg3WRAa2cUfjCXspR+c7RebEduLHNVqbvZzDmZb9lKB0SWm28l1qz7kZj0fAmLk5/ed4e7hgieLKU0VonOR4irlsaEneJRwdhHjFYRfe0YyPK9ZXrE/KtnUUsoy2rOfS3P97et99FPJU+4zCI4qOUW2AbOsYpnkqK1BWYkKdYOUUxuH7Y1c68bgjZff2UgCXxRYLooRi01GuxiyHGIpmX3Ly2/zyuic5+Ir7odX/FsPvxetPJ9/S2JE9CZgdawYD4307ktMjL/i8VVyAviS8SeAn1RK/Xnk2f82JKjry44P/QJgnSQRlV1E3QcQw6IV1nigHUUrPPiyjkjijiIWJrsJLN0i4byeUbXhIKWDbSk3mtKergpJxo3sYJXnssiZvBoyeWCp9gK2v7GmKUJuVrmgcNsAXwSEC4O9J2Uh76GfBIJf8NJA8iCKJQXeQDvx9Hs9wajj9sGSWVzROkOkLeflCDOoaLZNRHeToDrN5E5DEvQ8v3dDFrR85uwOgXaEgcUNTcY47vi6+WO+uD7hTrbkJ8oX+enVPd44PyS+VeBOFG4bQWNYX46gU8RHJZOkGTTqsJ+XXNc586RiUaeMTE+c9pzbPWl8WwVaFEqd0wTG7lRAedgyTd/X18/2CooqkgWsSUQG2xvxWCjZ3Zusx0WONGuomhATOLqBFXSUbij7iLKPiLTlhfE17273iIxl2aY0NuDx1Qx3IfGUet7wyTuPOS/lJOdLMYlFVwHtUY/1aijhyAKRRB0EkETtLoimbqUPcZRtKLqY2oacFRMmcS3SVhRnizH2acbo+RUvH17SOsNlMWK5ztifFgDUA4rBaEc79EHmSUnvNInpiLTlsh6xaaSJXNuQo5EQM6temsCblbB71IDySE4LJlnNOBYUxlG+levTRbs+iBoazEUd0b0tp4RiWpAlDW0TUG5jzNNnjWtPn3l83mNSu8tidl5Rd9JH0UN/IAl6YtMTKMfBqODBofQf9g827GeSVvbg8QFVF3L7dMF5POFgyOGOTS/I9eF1tUWITnvun14Tastlke9KhvdObsiSln4k+R62CrCd5p3pHntRyZ3ohrVL+Fef+0v8rr/xTxPmLXuTEnXL70x3df/BTmNfLSWgZ8N7/28opX4A+AeHh/4p7/1nvpKv/dAvAN3AH6/akOrBmIvIk50Ky6RoQ5arnCQVA9H6bMz6bMzxvRtO5hueOs1kXJJHHR+bn/G0mvIYeGnvitR0NDbgJ96+D1cxyxPF5Acz9t6uUZ0jWhmutzEqsfA0Qd0puXO8wHrFEw5QNxH6oGE6Llms90iuDO3EoRyYRslJQA10zHnPx156jHWaeVJykkg60zis0crxaDMjMhajPfFBRdcGvLeYYYzjilwkklazXaYEsWV/tmW9SUmijp9d3GYWV8yCkky3fHz8lOv9nMc3U7wX9n6xTfBWoYqIvjNcrEf0neFkKC3lYUvRRYIyNpZIW8x4OLpvA3RpuN5morIpE5KoE0gakcQDDru8PJfdWAGsipRgUPt4L4lmttPYMth9b2CnGLK9oXXBbmGXwJxqpz2vetHSh1FPrb04jTstE+KQXUvkUIGjmznmx0PZzCtC44jCfieTtU5TWEOgBUM9iRo2XcKySrmpMpbrjPeqfcl4GPU8d3zNx158jdS0/I2zFwm04+5kwdcePOWnnt7dLczeK9ZlglKemyKjtYZx1NA5Q6jtLpCobCIe2emu3LW8yaE2u4nfGwe9nFbLNuTyZoxfRFwmM/ZOVuSRsJRGUUOYWrat4KUXH5XruqkkuD7LG8mRuO13IoI4tIyzelfKe4Y7CY0lCWRCvViOaLpwN7Ea7Tg+XgnOeni9ijYizls6q7k1WmG9omgi9tOSQDmyuCWNOimHjWVBfHixJ030Vu/MajeTlFlaM8sqLtYj2jcn6B4ut4f80AsJnxud0ljD9XIEqxAOrfQ9tKPqQ+ZxSZB+gLId/1WlAtoN7/3PAD/zy/26D/0CYIyjakPCT085/mJDvR/SZRMun5vS7Dl83uMTAbWpudTNn9XPnVWM45ajbMN+WNA7w02YMQ0rTuI1obL8d+Y5bOjprxO6iSJ6tASt2XzNPjpscdsQ1SmOZxtenF5xXo1ZHhWUF7KDCY3DTzrcNsJlju6gxz+JhevSSeM3ugg4Ox1za7Jm2aS7LNdIW67KTIJjqlhiKeOOg0kh8k6ncX5oGlYhehnijxxtL5ygzkp6TFrztgAA0ApJREFU1nkw5gv+WFAxxpJHYg5zXhEYS5aLiqYehcLc156TvTVZ2GK9puiiHbraOk0L5HlNbzXRqGKzTWmakMs6Is9rxnFLM0wagXbUYcBmkcmEH/aMc7n+2zKhaESRks8q+s7ge4WtDIVPOD1Ycb3JiSeNZNMOklLnFY0PeFDu0XtN30XkYSuLgHH4yIMVWfCmFRXOOGnQsw1FG8GgLLHDNUyCns5I3rNSfqdWigIxahVdRNUFlHXMOKu5c7hgXceycBjLabZmP9ry2vZYAmGs4QtnJ0ItLUJBWng4GBYdazVdZ+h6g8vVDhR4MlvjBxXSsxCXswf7qEbjEysGrl6hjEfFlrKMd41dcstkT3obbuD8d85glEiBc+V2CVoCeYtkAgbCqSzqVS+9gk0thsa6kvKd7zXjWUnVheylJbNxJSeZ3pAOMLvIWOEBDVTOl+eXMJfXat0ljKKWdZlwUYz4TXe+wA83L0nze8ge2NYx/TraNetVL3+Pk4aDdMuiySivM4yWU7OyiqYO2YQRZR3TLWNMJ3iK0FhmUcWb1QHjyOCspnXvJ9r9isdX2QngVzI+9AuAUtB1BnsA3ijSixYzCwAjDc4mpFKg1wHZ/TUHo2JHmnRFKLtdJzvFxkrZouhjLtWYSPdEcUezB94q1p8wRMtjfADVwdAIMh6XeJ78/DHvTQ8Ed6Ag3KuJk46iiZjvb9mmMbo3JGlLeazoiwBTamwjR8rlezOKAzmKT0cVWdixbmPaPmC7yCSaMLH0Sc9eXnISl5R9xNP1RHaqTknDs9Usb0b4VtP2CmLHKy89IDKWso+4LPMdqkIpcQQ/QwwExuGt4vlb13TOUA8TQuf0gI+QssC2StisU1iHVFZ+bjgV0F3bBXI9m0gcxHHF5SYfPBpisEqinqoboHSDyqUqI0aTijYJ6LuA6bDI7Y0LTvM1iemHOr/kz67beAdxC7Tjew4/z8Nmn7/ZvcDk/pCv24bStxgYPc/MWMmQa1AM5R2jHeO4ZVVJnGTVyqQqC4Skbd2drNikkkIlLmEIhrLXeTWm9xqNZy8tebKe0F5kBAcVs8MtVRPu6ux9Z4SX02n6rGeaV9ydLan6kG0T7/wbIBuV5tYK6/TOWX2xHVH/xD7HP92B95x9S0j0qQUHo2KXnrasxPgXxjVlFzGNa67KTJ6zscTGMkkakqDjrbND0qzhowcXHKbijv/ZzW28RzZNypNPG5zTFLXwsqLAcn45FR/FcEr0CGIkNqLzT03HeT3mveUMEGT4OG3orFynZ9GT05G8VsUqAQ9eiwdA9Yp+ZimbiKd+wtnjOWYtHKE+Ah85qANuSslA0I1GNwqnPR+fPqXoY16YXlP2EffyG27Hyw9u0vnVBeDDM/pew5s5e19wFMch15/yhBst0src4UKRn3EoBpJtExMHPdsqRpea5uGIx1nGzUHJ8XTD1+095t3tPo+LKfXg1HymQtGh5epbFB958YxuMyJsA3xksbkheCtB9wEu8LhU4iRBvAKTpOHedMn3Hn6Of//V70QHDhd47LFMmqqW3Ul7k6A6xdY4slBYMtZqDo7W9FYTDhjptx8eobZGEAqhyGD1KkA7cJ0mv7ememuCHVtGeyUvjq7ovOHzy1MB0CkvpwRjdw1PpTzFNuXOyYK6DynaiKY3HI+3FKXo+e1g+krDjtU6Q81bbGUwWU8Y9QQDvvpqnZMlLWfLCY/7Gc4rjg9XlE3EukzIk5beag7nG7qJqGzqTpy2XawJteMg3dK6gPPtmFWb4sJaGpzDu68eShJH6YZtFzPWFVNTiSHKBWRhxzXZLmf5GXXTOs2mEXPXs92qNF8laMUNVMvLlZjhoqAfGsASXr64HrE9j/CAHctEfb3SPHllzdedPCHQjs1NDqklHCZQ7xVZIolv03FFPN9QdcHOKbxtY+o+4PpqTJh2qLij7EKSQO1OXnkgjfV5VvEkgIuvDymf6xkdLxknDY9vpvQPc/Z+XlEfKvJff8GqSeisGRRHEAc9TR/QdIHo90EyBIwlUI7aBmy7mK+/9Yh1l9DYgHlc8s5yX4xttfCQRvMSbxV9L4v9NKklcjLqmMQ1gbK8u93jzYdHqHWITyzNfsAkq2m6kD/z9qco1gm+CnB5TRa3bMMEVxmCtZESi4LkacB2NWcdekgcdtajAtFh6usIs5TGsnIipvABxHnLnWjBU6ZUNmQvKqlsxEU7/sDmnK+2HsCvZHzoFwBdaUytCErL9jQgfaoIt57FNwyRiIOGWmvP+eVU0omGY2bQS0i5y2SiToOOUFkCLY3MookE0gZirDIeM+o434yYJA0n4w2NDST67zQgOgvwsZAui2dJYI2hWCU8ii0/++ZdIX3mPSoVfbkKB1qiQqBl3ojsD3G+3p0vOUy3xLqn85qn5ZRZVnFTZNRNSNcE+DLAzXoJEEk6bk9XPHjOcDIuOMo2fHZxR3bcsGPFaOVpBgaNdYptkZDlNaOo4bIYsSlj8lQiIGepYhwKNvliO2IvL7l9uNwpU56pf5ouoHIhXRuwagL0Y2nGuthzxfs7ysU6wzaGpR8x2SsoSjGyea+YZpXwhLqE2PRMkppNE++yaJ1HykhRIzTNakxkLK/Xp5w3E5wX5pDGExiJ2JxEDes2xnvFQbrlvBxTNhF6wDFPjPgArNW7GnYYWrwX6akddP56MMx5JX2bf/fX/Un+zTe/h4svHjKOOkmc245RxjGdSZRj0wUkcUfVRKw3GbaS+0/FjoXy5JN6Z07zrebOnSXrOuHs6RxakU4SO2nitoFkMNxtmR1uGQ8pX2fXU9LPpLz4p9/DXV6jJ2O+eP8+oztr7kxXrJtEBBGDIiuJOmnEbsZ83fETbpqMB5s546hhLy55OuyqD9Mt6zbhd9z/KX7g/OOsi4TbtyRxa9tpCDx51DKNKt5+ekAY9UxiuYecb2WjEzrieU0ayylMP1sga0kzW28ysqyRUmqvcIkX78m4xwbSgwkexXijUa3s8n3gsSOLG8l7RlmF1x5iRxJY/tr1y3zh7ATvFc8fXvO7bv8oD9qDv0cz0v+4xod+AQg3jtO/2dDOAoLKs/g1jQSsaIQNn3ZM8ppoUKcUbcjm8/uEK9Fpo8BWmuYi44vrmDfzA7oyYjQvaduAMJegcmu1lCycZNMejgq2XcQobDkeb8jilquLY6F5etBpx3xccv7eHFUGwiW0SuSjCnxrZPLH462WwBerUF7hes3lJqcu5ci97SLmSTXwgCy/+86PcBis+UJ9hx9bvsDnL09ou4BPnD5hHpWMTMNHxle8kp3xwzcvcbXNCQO7U4Vo5anbkCxpSMKeqhUJ5vHRls6ZXfD7JGpoBxbPsnmfJ2QHsFtnDY019NbQWz0gFBwusPRNIMqmmwDtYTyq2M9LLjYjcSAPC+r6YoSqBWldFCHq+H35ZdMHxIOZqepDzKCV18Ou+plS6l62kMD1JsOhiLTFaUfXS1Rh0cl1HEUtm052xcGz3pFxFJ2Uq7JYGDJJ0GOtJhxKY2nYMQ7lHprdXrNep2RZy6dXH+PsbAaTnudnNzivuL4Z4XvN8mKMaiSH2JwKptk7CUExgSVPG9o+2F3/rjMEo44niyn1NpIjpwNTapxTVHpwmrcan0t/Z/lkwsFPGm793Jrlx2Ie/yN3OfmxGZvb4kW4P1/whZ98nskrN0SB3fGOusoM1xF++vFdorBnnlXSpO4EcdI7zUmy5jcf/Dz3wyv+xPab0NpTtiG3Jmuie+IHeWF8zXWTcfdowYO3jngcTdEzz4PFHLcJRf+PLKRXV8MuvFeoTLTPk3HJXlaJO3kp2QHRScnXnEiI+9NiwmUwJjSOtohI3ozwRtEM8QLeePyol8XSKrYXOT+7jfGrCCYdj1dTuA3v1Xsf3KTzqyeAD89wocYmmu2JoZuAHzC0OrR4p8iSlnuTBYsm2wWam1pcuaYGFOTvaUwNLjSgIlIFm48YDu7fcJRvxbDTh1wXGUZ7bpY5N0VGYBxbRO/e9gF96ncKn64KOTguyF+UsI+jfMv5dsz123PYBqiJKCBcJ54CHTus1zCWx6uzEcFezShpdk3rSPc8WBzyh5tfx//+hR/kbnTNw3SP4NhyVY94vJ3icsVvPf0Mf+7qG/lr1y/z2XfuEiY9SSwwNe9FEx0EViSvWS1GJ+0xynFd5ozihroNd+YZcaqKzn2WyETxdCGZtdYKVC1LWtpObpcostjOyPtEg4s9VRPxtBfmUTapKa8zWQCNkjIWYCJH3xu2Q/iKdXpnmAq0Qw/9iHiIKew7+fiqzYcSRkigHas2kdNcYNH4HQhsO0iCp2lNHPREQc8obNl2sjPezwoe3sypAsveuMAoT+f0gIrWXNeyeERJz8ePz0hNhyoCoiMpp2y7WLKLCYiSjmhIRru/d0OgRJUC4ip2XnFV5qy2Kc5qXji+4mwzFvJmrzDTDp11dDqGWFj+B3eWHOXbwfWreO2HX+HwbzzFzXKaiaa47XntnxbgW357zec/c5+7n+7ZfFTQKLOs2p0CFLCXluxnBd0AS6v6kKebMVUZ0zeGZZlydjThO/Zel+jKzhCGmotiRNcbik3CZ4PbuzLZreeveH5yw89fnFI8nBCWcsLu38u5OgjZ399ydTYZwmvk9GO05zDZok89F+MRq0WOMY5Vm+5AgHHcsT8qYQZnZycipY6Eu6XnLWkmaiZj3sd2bHtZEOoq4vd/7jfzO1766Q9mwvkqVQH9nY4P/QLQjeHRrzfoTiznwTKQDNnbFt8aljcjfnabkqQtTRPQX6ckFuoj976OPXKoTUD+SBYC3XvSR4ar8ZjlJuXuwZJQD0avgfG+ejglOKw4mUt4+vXNiLBS6E4RFND5iPWpTETTpOYw2XIvX/DfvDPHbDV9aFB5jw4FjauUx0Rux/X3xuMfZjxZxdz+2rd49eqIOLD0VvP1+495Mbzkp+vneG1zzPccfp4fti9T9SG1DfkzV78W6xVX1QhfG9rBj4BXuzdKN+QXw2B66jU3VSZESmuYpjWhsewnBY82MzalNEi9VzxeTWnLUNjzxmG1YKafqXzKRmIXo2lDeFAOGclCE62GCSgYddjWoCP7/vOA4YShuFkLhvvZcz7aX7MX15xtxzy+nBHFPbbX5FmD9QoFfNvtd3hYzHn34SEv3z+j6w2jsCHQEnLjvYK4pepkInZecbEdEQ4mqTTsCQJxUGvlKaoIY4QKKqofu6N7rtuEn2nuojrFbFRxJ1vy2es77E1KyqGJnIQ9B1nBe8sZxcAC2puIE7jpAto+IE8bjkZbft3h6/wn59+Gii3hVMqDXR1AIAvm3flyh5M+345RypN97zlf/Lp96XFNapKs5XQsPKs46HmtDVi8PCILhG2Uh8LtL1pxSfdec5hsAUE5t84wSyo+vzmFXtP3mkhbXitP2FyOUK1ms47YZj1R0hEOYTBGeZZVwnaVsthmVMsEZcT97mMpbypgscoJbkLwYLOePG5RyvPOWnbngXEcHy/xXkqOl30uC7SJuSkymjrETh2MO44O1qyKdMeVmoyaXfN820ZUsTj/+9pw9Pya5+P3jZK/4vGrJ4APzwiTjvwjK9Y3uTRCm8G8UoTovMOvI/TjiL7LCFtJpLMRMG/Zm29xTtM7TZOG2MuccCMOWeXFtt6biHfaAF8bdNbzyu1zPjq/4IeLl+jKiMmJUBnLacT6OiLcAFpKPasqgVR23G90h6Rhh08tvpWgbqUdQeBI45beGrbbweJeBhB4XOhBexZNxvbxhHpfFpyfOH+Ozy//cbZDjOBfvvw4mzbm47MzLpoRL2aXlDbi81cnu5+zPxWVSG81bR/sgurdQDRVA589C7sd9sF5xXUtOOfZqKTtA1ZOS3kk6RmPKpJQdn92aORuq5iuM6TjRkJAAkvZSIlDEqQsdw4X3BQZZKJKCY1jU8XUVcS6CjB5x3RcymX0IltsukAUQEYQ3GHY0zYJy4ux7Moix7vTPTnyWykP9U4Pf0upyCESS41n3cYUTUQetzskxCyppL59MWEZSkO+yy2bsGe7SvFWQ6fQWc8mjQm1Y/rigvOnM/5q8zKBFlNU0wUExgkJdCiXjYdmZ9mI+3lbxnin+cjtK0Zhw+e3pxzvrYmDnnceH6AvYog8DKeiVZMwTyq2g5LJeSXadwfRUUmWtESB5WqT03eGo/mGj58+JfnHe6nvhw23shXrLuHN9oCijcjCThr+vSirnpkpXWtQjaZrAn7u4pS9rCKdV1TLBJ1Ybh8uKdqQySCquCky2tagQ1kc05kgGOxGNP0+dgSRhXcz6bnFwCZknSdMMimtZWHLTZXRDzkJjzdCG32wzknjjs4p+lWEbhR67thLy5301CjPk5sptjd86wtvcaVHbJKEvRdXrKqEtg/4z89+Lf/Ll37l881As/77ZnyA/um/O8Mjjs2T0wXBrZJ+3mNHjtv3r3jlzjnHL1zR3mpxZsiiVdCPPGkuTcTOChslCC3t3OEihU1A9ZA91ZhGkbyRMHo9RD9OeG854zRe8fytK1RhuChGHCcbDkYFbtxjE2jnDjt2rC8El+y9YlmmbNsI3JD9+yzDN+pwTnMwKnZB3oSOeFqjjhswnjffOMWHjjjuKdqQm0XOk5vpDgncO9mp/dzNLQLleK+e89M397h5PBNOSiC5qeO4IQqkrFM1IYtNxuXFhKKO8G6w5yNH6DjoKbtwlzNgB5dvZCx9ZzicbyiqmLvjJUY7lkXK6XhDGPbEsWTwRnE35NN6yeQdMhMensmOryxjNtuU7SBLNcahGo3dSG3+2eIQB7KDv9rmrCsxUlVljGuNhNwrUX+UXUTXy2OPr6ekUUdtQzZdTNFFrOoEoxwORd2GzNKa+TDph8YyChu+584X+Q3f/HNSXw48yaSR+rTVRHk7nHo8l8vRLldgfrjhE0dPmWUVv+n488Rhz+3xijv5kserKSeTDS/tXzJPKvbykk0VszcpmU8L7mRLtHK8enOMB662ufSlZr3wdGLHrT3JRzjfjuitYT+VPIgkbdk7XvPHvvGP8c2nD8ijlmoT05UCQvvc41v82OsvsCxSeq95VM54c3nAxcWU1Tblpkx5sJzzdDURaGIdc7PKQYsbOE5FJWW0I4k6gqxnOimp+4DQOKoupGrD4VQpiO5ykVLdpPLaRE50/drLSe1uTXunJX15ydEL16RxyzSuycKWZZ2ilWeS1EyiZsj61WRJO/hfWjlJeOiLkLcuDnaS3M5p2psEdxXzN998kafrCVHQ86n9R3zjyXs4DxfF6BeZPf6OJ52v7M//CMaH/gQAkgdbtBHzccns8JpVk3CQFrTOEGpHOm5QZUw79fTHlvigIos7RnHDdZHRDQag/MUVVTkjXihM8z6mvbrTkb8dEpSK7U3G8k7GOGzwkWO5SZmdlqzqWzI53C1xlaANaAxXizH3j68pGqk/q07WVO80SdbQdMFu4u87g68CdN5xNN3uAlWqLsA50elHQc8n7j3h99z+Ib43a/hDi/v8lYuvYdUkXC1HPLmakQ1Gq3DaEMU95Sam3sYEx3ZHELW9wfYaZTzzUUm1iSVVymuaVq5HPGQld1az2mSM85reOOxVzNOrQ3Sr+Mmzl4R2elLRWJFypoG4qDdNxDhuqfuALtDiAxgLR2a5zuiXkexwOwOrkKDQhM8X3NlfCo66jt8PhukC9sYFZ5dTXC00Vp31xJOaPGlxnh3GWHmFfZLRvdhyWQra23vFtor5xP5TLpvRDlFR9SL/zIcy0Xkz4bIeEUxa+kKMZcY48r1aTHKJxV3FOAOPAWcNJ/sr3lwe0FvNH/nCt5GnDd84f0CmW/6ZT/0wzwUlP1rf5luTx/z5zcf53OQWqy6VnoG2pEa8B4WLqKuIg/0NRR1RmhRazbsPDkUqPPCnVpsUbRxZ0nJ3suI/uvhOXl8eUrUhB4cbbpY5202Caw1mGVBaxSKXhTYOe77jo6/z+vKQsomG0picxD55cM7nb05gKpr+ppOF8abI+I47b7LpEt5aH8h7JRRVT2QsdR9QdwFt0uNaQzRtBIHSBux95IZNGXM02pLHLR52oD3nQ1pnBBkS1+wnBbUNKHu556xXOyaXUR6V9fiRR1klJUiQspDVZEcFrxxesGjEYd32Ae8W+7TW8M995Ed4t/6AVEB/j2igSql/DPjXkUzfb/Le/9Tw+H3gi8Brw6f+uPf+9/zdeh4f+gVA49k8A44NmupJ1FDbgIvtSHaBj0fknTRvnqkSQCaFJBTaotbSLOzmjuTG0I1EV5xeKPSTEBsNsYzLgB/4zCfQW4OKPb/xxdf4t08+w2/dHHO93afzER9/+RFf/Mxzu1SvUNsh0Uqh5w22CnaQKj/Ur4s2Is0bGiO8lW0TiRZ+iMCL047DyZZRJDXt7198PX/wwSFNH2C9AOHakZRK9rKKk3zNFy6PiQOLmcpu+jAruK4yVpuUvgghcJycLMX41BhpelpD5zRFE1HX4c685a5i1hcJ0e0CP+4Zz0vqOsQoiKKe/bxkWaWUg+lJfjephkVBLwlYg2O0GqBkaKDX+F6jNHQzSwSs64S6C6iqCLuOKAGVWBbK4xcR2gpz3jWaRkHXBtjW4PfEtFXnPZ6A8aC9D7Sjs4bCOK6bXNAhQw3+WYN4sckIQ7tz4UZxJ0a+62yQ5ypsa5jOC6osxFnNdCSeiLoPWG1T7h/ckE6XBNryenFEbUOmpuTSbvnRzUu8Xp/y2dUdEtMzDhq+cHnM59++Db1Cpz0nhyvqqGOeVGzKhHjU0KxjgqsQm4vDeTSuySIxGHbWcFaMeW8t5RLrNG0/9HiqUDDOJ43U6IuUWV5RNhFPiimbOsYoT9sG3DtYkActndd8fO9M5KzVmEhbsqDlpsn49HsvkQ2pbGnYcZhsqWy4a2QHgUUbj68NNtVSRjKOTRnTrBLeeHoHtdcwGtdCXB0SxIo2oupCxnFD7zW1Ddm2MYd5QaAdrz4+wdkhq9kpnFWCfu80/TJEHdeYQHwen3tySp62JEPPqXea/+Qj/zl3ghH/0PnXfXCTzt+bJvDngN8O/Ee/yP+95b3/1N+LJ/GhXwA8kIYCvmqHMkfrBiplJHz39M6GbZwTzmoM7BKJmmfyRadIY0sYWKKjknoxQluFC74kqckhppNOoQtDUCk6A5UN+b3nn+Tt632SC830HXh9fISb9rxy/ymRsTLpblPhr2QN+4cLXhhf8+ryiK4SJMDpZM2t0YqnxYTrlexaL8ox3ivycT0Yl0QZ83A956yYcGu0wkWKx9upSEWzitj0XJU5P/PeHZzTbMqA4DrEnjbsZTIBRLGYtsJAXKFNFzA7XeOBSVxTdNEOSTCOW54uJuAVLpI7P4gtWdRhBjnpPKvoh5PF7jTTD+lVQU/VBRSV6PC7OiCIrDQQB6VMEvastintMqa9Tri6SVC9woeevdtLJonIUW82OcFRhXeaOOnoh/KWUp5wZLk/u+EbZw/5r+OPs6ljLtcjnFMcTLeDoarnssp58nQOjdS51X7DycGKqorYXqckB9WAgBDEs2q1NOQfp/hZT2Ack7xmtU1ZrjParKEZfBvPkNHaGZ7Lblj3KX/0wbfukBhVF+4ol+Gz00pl8LFcj1CLhPbpRpq8QeBwo5bkoGAvqxhFzfvE0FVOt40oE3GPHx2safuArjNo45jtbxknDRerEc1Nit17/2c+Xk2Jw47vvvM6LyYXfGf2Bp8uXgbg54q7cpKLKrZ9zEG8JQ9a3j6XHfR+XrKqEt5YHnK9yek6A17k0ZNxyU0RYtcR4awmTTrWlyP01qBbhV1FtElPNLxfs7Bj08Tcmyx47epoZxyb3l+yl5Y4r3Z4aKO9hAO1IV0XiFTWBsSRkHizuGUvL0mDjlHYsG4TOmf4967+QT6Rvcf59qvLCOa9/yKAerab+h9ofOgXgGcs9ZsyJY/++/S/2FiOJlv8eODneAnPBtjW8uaZjSsJc2+Fo7+93ZE8iAg3cvH7EdT3WlAeE1vSpKMqI3wZ8NryiB/b3mfy50fEqx7lPDzICIG3ErHZP2tcGuNompB02rEXFbtM2DDsGYUNm1aC7G1vmGfVrpTyXjmjqmTH90zFE6WWh+s5y20q5ZxFjDdeTGWATixH+2vasWE7Tui3EVfbnLKO6JoAHTgmWU3dSyLXOJOyUWMl0rGzmiQUcmMU9bSTDpwiDHvcoNJJk07CcLwiGSb6tg0G34TgTtd1zHad4nsBfL3w3MVOEhkOMtO6F7OUHWuiSPAI0p/RpGHPskwp60iyHyYld8ZLVq30VPTA9FkVKe8s9omM5WZohNrLBD/piPYshTUkocg+6bUgO7RA0MomwjmNSi31dTro8JUotPIerMJOPOFlwHUzF/TFdUB2rlh/LITQMdsrOEo3rNuUs2LMFzkBBBdxtc1pmoBR1mC0GAyPx1u+9vCMN4fJsGpD3ruY4xoDrSY5qDgab0nnHYmRDUtiep6WE4ndLEJUaQSl0SnOyj3ieS2hQK3kLS+NJxy3zE7XBMbx8GwPXwaoXlHvNfzgo5f5seh5/tlPPOEseczvff23y+nMGv6Jj/wkF+2EmzZn08d89Nb57gR3tc158oYEw7jEoUcdXR1ws5yJD2beyvtok0CvSO5tJNfBapoiYnpUERpL1YVYp/i5J7ewvYFeYyrFZptyEwviY5ZWdNZwvhoPdF3FK7fOCY7cLm7Ue0USdGRBy1U14sl6wtccnAPwu/Z+lH/mi//ELtjnAxlf+QJwoJT6qS/59x/x3v+RD+AZPK+U+gywBv417/3f+AC+5y86PvQLQD+oeGZpTWx6Vk0iWalRw3UpGONVk+wcnqNI8kXLRtQUq3VO2wYEgZVab9wS7Du2V3Oi1Zfs/iuDTwcz2XmO2RpU6rhcjZjkNev7mvrldohYbIRMeBOzbQ1x3hIMkYMq7uic4bwZi6s16mi6gN6ZnU4cZFLcNJLM9MLhNQCTqOa9zYzRkHF7UYx26h09E1SA1o4o7okHlEQYObSSOyUMLJ+49YTrOpcYwbTk4WLOKBW0dBa2bAb+UBJKgDlhy63JmqdDuUABWdJStyFVFVFVkThAs1o+H+llOCeLp7UabTzOeUwk1691krt7thrTNPI7H++tORlvSIOOTRcPkDYpjTmviKKeqoq4GkLHp3HNflqybhLObsa4IqBJQl7jiKYM0ZcRYaXoXUh1LCWGUFvxWnQKAo9qFK4MWHYj9DYgubPF54rDyZZllVC+McMnDvMs3CUEU2gs4EJPnygx7yWeLG6ZhRXvrvfZ1uL7mEUlay0JVSst8ZmB6fFecb4ZcSeXne6mldc5intaQKc9YdizKFMe11P6LuBwb00WSuDJskwFiTBrBQx3FqN6TbONUZsAHzvMpCNJW4qzHJe2InMdSox2Ixymxc2Imyrg49vfyeG44MnFTBRoseVPvfNrKOuYKOw5GW/ovWYel+KniTqKaYdfh6is5/RgxeNHe5hC41KP24SUlTjj+1wc2Vp7CC1dGdJZTdmFhNoxS2tWmwy7jkB5+oMO1iHXUU6WDKfWLsT2mv4yAQ3FYcTtfEUSdJR9xLqRnIZPTJ7wFxafxHrFm8sDpnHNv3P2G1huM5FBfxDjl9fgvfLef+Mv9Z9KqR+EYafwC8f/xXv/X/4SX/YUuOe9v1ZK/RrgLyilPu69X3/Fz+qXMT70C8Cz+m7TiwTROcWtvRVlF2Gd2k2qWr2fwmQGs0g1EDa7JiCOB6RyLcHS7rSmbhJ8IJwR1Siwhj5wpI8DXAjN2NMWEVvtqe/LDnk8L7FWU3mFuglxHto6IBwLkbK1RpKn2sOd47IuI95Z7O1SmyfjCgXkkUzq59sRs1R26OXQTN7LS6aJlIau11Iy6toApT1NHVKVEdsBZ9APJYq1Svj5IRt1kte8+vCEo8M1kbEUg3a97QMCI+lqN1XGxXZEEnXM0hqdeZKgIzKWs+2Yy/Mp9AqXa7baUxYxcdIRZQIPq2vpB8RJi8kFtfB0NcFazTSvCIyjHhaKm01Olxm2Rib8TZUIIMyJ6sZbhVkGkp8wqqhMSGOlSbk/33K5nsMyYhs6fGMwlUI5aeavy4S9tMQ6+fmqV/jcoiYWqoAw7wjejWj7Ed7Ak2ODv0gIK3aJZP1BiysNyitxslpFP/IQeIJQjF1vbg4p2pC6Dnn9/JAsaTnMC/6Bw3f5WPqEH1u/yGU9ouhiVo2UUco2ZLNN6atAMpwbA7Vmu6eZz7eMswajK+o25Goxpl9FUpYKHSqzmEcxLvToWqGKEN1AWASUtxRFbtDjjnHSkA5S1y6rWUYZWSIqOD9TWC+5zfP5ljKNBVhnDfU2ou4SyjLmdH/Fg5Wot2ZphZ0rNmGK7TWP3z2QMPkAfGrJZ5XEeh44sgGFXdQRVRGjNwGLcj5EvIGeS1nLhoK4ptWYwtBNDX1odv6NNG3ZKAHGLUvJ725dgMYPRj3F1sYDcC9g1QUkQc+ryyNmo5K6fX9z9SsdH1QJyHv/3X8HX9MAzfDxTyul3gJeBn7qb/uFf4fjQ78AhNqyqWOqOqRbJqhW8eAyxWtPdlxQhwFGSdJQX8dUPqQZ6tPGiA5f8LwBYdjvoGdx2lHd0btasakUDkFI97k4fnFAq6mv0oFGGLDxinxWoYxD9wpXG3wud4zRjvEgr7ROjFGjpJEdVR3RNCFBYPnkrSckpsN5TW0DTrM1v+v4R3gpXPA/vf7dlHVEOLYs65SyDQkCR1OHuzJTMsgv6y6gKmOU9rhOIgCzrGHzdMx1FbJ/sCGPWtHAp/VOIZQM0DDrFVncEmpHbPod+0fjOcgK5s/JUT7SPV84O+H24ZLRwLfvrGFlEuo2xFq1+926JsA1hnoTE2YtUdyTxu2AKtBopXfehL2DDXsDwvidswPsXPJsJ0lDY83OqauVJz8pCI1lmtY8YkbwTkhQgwsUjR/zxYuMYNaSZQ3hcUV3mcIqkDhpF2MGx7KpNP1GFi4XQbQedv/XIUGhaA4tZmD0OCMsp1v7K2ZxxXk5QiuwdYA+T1kcdszvV/y+w5/kJ5uER8ke72726f2gBAOWyxxfBGA8KnDoSUu4b5mNSozynN1McJ2oteKkw40Uad5KNOc2pZtYVK+YvGlYfazn7tdd0jnNYdAzj0veujlgVSWMooZR1IgXJDUD7rpnmtbcGS1ZtimLOmVxPUaHjiTsqdOOXktGdGgsk7jmONnwhcUxWsHLpxd8cvqYv/DWJ6UR32nysUSkei+y4roLuF6MCKMeHTjstINOE89qDiYSlrMoUrqVZGtEWYudGdKsIR7Kf7enK0n7i0agYLNN+VxzSluF6MATJy3jtOH1qyOqIsLfxPjY8cQq9iYlt0YrFkH2wU06/wNKPJVSh8CN994qpV4AXgLe/rv18z70C0DrAmGfLxLiS4OyCt0KM7xMUrK4o3GK603ONK8EL1BF4rJsNarXqFYcvL0Ddb+gP8+IlppYiXcAL8d/b8DchEIfNB5TGMy9AvduLg1iK9jpKopwjcE4aRrjFFUZs1SeJHqGZFBEgWWeVLwwkhJP4wIWrRiw9gamT6gs/8jkM3wqjoER3/+p/5Q7wX9f0/x9D76dp+WUPGi5ky35N05/iN/8ud8p5S0FxBCFPXUTEs4aDucbYbXXCUUdcTzdANLI7KyhtcLOcV7R9AEQSM6s8uio4R/Ye5fjcEXpYkoXUXSy+1o3Mtn0w0RurWI0OH3TQeGybpIdc/8wL3YT4qaJd72Hk+mG02y9k0p+5PSCxgakQUc5sJGs0zQ2wDrN7elqYPlr0rSlDzP0GsKtJ16AsoZ2klF9i+OTt5/wOXUqEZGNwRSafuSJrww29USXAd3dhpOPLnjv7UOSswCbO1yo0I2WbOMDK1LU0An8bAhPbnqD2gTYxBONBQPy1+oJf/jhd/Hgeo97e4sd2mJdx4RxTwcEUU9XSQPVJZY66igq8WccH62YxjWBdjzdjNlspayUpC1dYOm7gOQjG26lpURilikbnxBpMfuti4Q3zw7JswYPkjuhU9Zlwq3JmhfzS17zx5xvxxwfL/neW1/krzz9KKOsYVFE1OuYd+t99ve2JKYjCXqum4i3Lg94uJxJwzqUICKlvATwOOmpec9g3BM6qs68cKYSuSe8V8zzir43jLKafBBumIHblUQdV2XOOG44vLPk8slMSlgjiDPpQRV1xOX1WMphxqM6sLEgSbZ1zFM14TArPrA55+8FCkIp9duAPwwcAv+1Uuqz3vvvAb4d+L8rpXrAAr/He3/zd+t5fPgXgCpk+3hCuNYyiWfi5NUtu6zawPidSsV5sP37O3vdg6nUAJaCJOkoO8nr9UZh+sEPUEsIhU08uheWkO68LCS5I740uMgTbBVtEhJMWrp9haq0SOMAPXaEA5UxH9y/Z5sxizrFDM3sPGhZtQmLJqPpAz6+93SY/GX8Z6uv5/+0/8YvuAZP+y1//Lm//rdcmZT/58t/jlebW1x1Y/761Ud47dXbpEcltw9WgDB+lPJMMumfWK9JBt4OyIT8rHTQD8Hnz2STie6ofcjGJly2Y1on6VZp2HGxHRGH0nTT2pNGkg18fjPhnImooZKWwDhuqvd3ZmZw0j5eTanNQC3Fk4eOUdiwbWNpKpeJhN6XEaNRzd3Zkm0b7553Hrec3+6wSUBQKYJSFoKw8KxvEh5NZrRnGaZVwp0/btGrkHpiUWlPNm7Ig55llaCcnPjCpcEFHlMp+kSY9SjoNyHvLWc0reQYoARO5geA4NXbe/yL7/yT+MwSjxoWtUzedRdQ1yF52lI4RRz3eKexRq5vYBzPHS4wyrGsU96+2Mc7TRR3zCbigm16kcomqZil3lvOqMpYiKPAW3WIP39fUVUewZ3DBR8ZX3EWjllEGYs65a+dvUwWtuRRyySu+Ymb+zivGMUt26zDP8iwacA27Ujmggp5Jl4IhhSwWbal7oPd5A/QNpKBPJ8WRMYK5bWJd/2YzhnKAU/dFhFt1JNH0rtyXpFHHdsmkvukTDnISqK7llkijeGql1SyOOypfEyatMyyiqdWkabd7ud7L2XAD2T8PTJ5ee//PPDnf5HH/yzwZ//uPwMZX3YBUEr9f4B/CLjw3n/t8Ni/DvyvgGcAjt/rvf9Lw//9n4Hfjaxe/1vv/V8eHv81wB8DUuAvAf+S919B68Z4GHd0JkB3RiZu7bEZ6FEnJRLtaNuALOpoupB8XGMmUlpY3uREb8d4DTb1dF0wBKYjDeCBN86QRBQvFX0CLvWyaBQB8VGJW47QjejTg6VooSeHWzYPpuhK4WLRKls/KIsGJVLvNG2V0HVGFDBpzUm+Zhw2vLE85CT+hb2d3zr+WeAXHmdPf5ETwb95/RI/u75D6wxvXB9ineaFl8+kDmwNRjtGYUsfaPJQykDbNiI2YhaLjKXsAlZFSj9IC82AV7ZO82cffkqu38Ds+Y4X3uTHnzxHsZF4Sbzi1q0bjsbCmrn8zDHaQz91xCdbMZ1ZzbZ4lsHsSJKO6+URfWNIRi16YCQ9WU92hh+AMOopLqTvsW4NRV7u6KTPdtfj4y3qWHg/m0XG3o8Lg8YUhvN394g2mnbPEq4EW7F3usAD1+ucg1FBFracb0ekJ9tdNGPfa9pqYCBVhujKYFNFGnWDdFT6DHomPaauN6wuE0ylsB6aPuFyJcoY1Sv0fsNeXlI1IduzEcoqgq2opRbG89Jc3j4X2xGjrOEwLzDasW4SigFnPRlVgugoUuoh9H58UFBXkWx0Dhq8gjRricOOaVRTWamH52Er5bOgpXVmF/komcKWWVyRHbe8trkFXnpMX7g6BmCaV6zLhLqKMIGjCCzjuNmF6ITG4eKWbRWzXGekacuT9QwdOJY6pW8DtPGMRxXLqxF6HTC5Ja7gZ0FEgZb7TStPZERS3DpDYjqOkw0/c36HJOq4NVpT1hHjVHwZd48XFG3EbC6y6CxoKfvoy04lX8lQw5+/X8ZXcgL4Y8C/D/zxv+Xxf9d7/we+9AGl1NcA/wvg48At4AeVUi977y3wHwL/LPDjyALwvcAPfNmf7kBfRtjcYT6+Zpw2u/r6MwDZapPhLxLOkxTVyuT0rJETls8i6CDYKJr3ckwrzHFl5fNcKNGNXgl8Di+LAciE0o0D/NwRXckCFNSKvgoYHTcUh7XswrYBV4sxadYIFnk9GnYnAlF7Fj5SNBEP+j3SsON/fven+RfnD3a/6r998yKfSB7xsaj5216Sf/npN/DG9khq8+cnWKt56fiSQInx5iRfU9uQootEatoHbIc3fWyGHoVXrDcZ0eCZ6OqAtjHUiWU0qXBOU5URrjaEo5ZJUNEM2QQAh3cX3B6t2HYxl8WI7qTl4HCzC41/9iYS046h7WVRsq1BGU82lAjWRUIQSNbvUV5RttI/YU+kolp7tk3MapOyP9vuchwARokgl/2sYvFJQ7DW2LFIT70BtKcfO6aJqKrefeMYYoearznfjijrmDRuyaKOUdSIwmz2fpTkcp79As6+6mVnbK3QVp3TqL2GfjtgkRs9bCoULnYEgRVCbRViCo3X0E0tJJZAeT779DbVUhrh0bRhWwiqWRvHaJDtLpe5XHMjFlUVOTZPxphSw3FDkrU7J23bB1xVOVo5Nl1C0wdcbzPmeUWgHY+uZthGdP3ppCaci1QzntW0ZUScdOznJZ2V3Oj6LEfX4gG4PIg5eP4JtQmo2gTnpE4Shz1J1LF8MCN9KlkKNvMwdcSn4g7Ob99QHcl9s6xTImPxwF5S8GQ73RkJN0Omw9uLfbxXFGXMapXRzMTIuamSAZnuGMUNTS9NYgJ2AfcfyPgfCebhKxlfdgHw3v/1wZ78lYx/BPhTQyf7HaXUm8A3KaXeBSbe+x8DUEr9ceAf5StYAFSvmL6mUNZw8x1SKonDHqORZmlo8ecJulOY2khsXCivoLJgWrXDOKMgKBUulBoi3mO0wtTssgOc8TCcFqIbjQo97umgR9YeFytsLKjasolwvahIvPE4J67fVZVQXmfowuBGloPTFUUdYbSnrKVZap3iN48+D4z4zzb7/PFH34LRjn/llbe+7EW+6XI2bYxWEdV1ysk9KREWg8X+GWq37gNiM5AnrTRus1DgaL3TBGFPU4fszQquHonu2zvF1omsLh033Lt1wUFSEOtedO4jyQpuuoDXro7QynN7uiIdIHPH2Zp1K2ya3mmerEUVFA2h6cfHS3prWG3lms7GFXnUDqUGtytBPSlm2G0AGpqnGT70XHnFx26f4bxiuc1YDRuBOOzwp1vcscYA9SamPxHzVZyIQ3tVJcQHFcYI9ygJe9KwZ1UlBLrh8WqKtZp7ewv8gJAwsy2X12OK63SHJgawq5Bu1IszdhmhFJIBbRXhx9bMsopJXNMNwDodeGzmJAdXe2g1dh1iAy/N4V7RXaSM766ZpjWtNRRNJAyrqKdrNSrwKOMI456mNthZP5ygxLyVBiI/3rYR7yz2WT2c4gOPHnestCcJe4G3NRofeOoy56kW0qazmiDpqOuQbiRO8TCwNO0zyzfQK1aNgNcC7X4B4uT2dMXklYb+Jc3NNiMJLAlwMt6Iea4POcjKXfZ0GLVk2vJoM9t5Rcou3Em5V6tMTt+9Jh3Xck36gOI6Y5pVJEGH9SIOuKkyHt7M5TT0AY2/n2Bwv5IewP9GKfV9iDzpX/beL4DbyA7/2Xg0PNYNH/+tj/+iQyn1zyKnBaJ8Tn5h6TLRm1fbmMonQu+sNZ2DYMA029jvJn8z3Lw28u8j77zgH7wGbd//eX3m8eb9XX8/tahOyj0agbv1IwsTQRaEacc47lhvUnyn8akTxZBnSOIKGB0W2D1JKjPakSctTRdge029SEB7Xu0OeDGsOe+mPLiec3tv9Quuw2ebhk/FMaVrybTsQq13FL04eR9c7BFOWvYHV2XvNEZ7VnVCFkruwLJOGccNedSyHVK35BSQ0tykmCGfQI+63W5/lMkpq+sNl0XO77n7w7zRHHNvuqT3mnWT8OjVY8KN1M+Tj0qQi0NxXk52QS6SNlaLW9Ur0rhlEknyWJu8H+KtlafqwsE92nJd5qRZg85ryjKmdyE66/HAukm4Xo7othGHt5aClj6bCkcntZweLXmyjknH9Q6NoJRncTNif38rpsIiI4tbNmWC1oOByiqoDa8tUtJ5xd35UrIlAkcXOnTWk2YNk7SmGodsyxj7JMPnloPbK67fnhOUmsOx4D0erwRWV7YDN0oJ4z6ctDinsaFHrwN0Jf0mlzraLqAKRMXWNAG2MygtJZ/QWKmfG0szEYyCUp7z1ZhVlRDl0jOwgxkyvSWlrTjsd5Gg2UFJeSmltWCtKdcJelbSrSOO7y44P5/y6GrG3UNpZLt0WLS8R2c9CqQHVI6II3k9krDnvcWMjx2d86npI370+oXdIv54JQiLPG7ZNBl7WSXlSBtwvh6jtSShRYFFa0fdhuKijntOZhv2koJR2HDT5DxuAtJ5xTyphjJWIN6KoGdv3HOzyX+p6eSXP351Afiy4z8Efj9yqX4/8AeBf5pfvHzm/zaP/6JjcNP9EYD8pVO/+afWtL3hYDA09U6zKFLqh2OCcoiMG4p3LvS7ifxZN1/18rhyoDpwuYNaC28mHvoBvP+5ZquxI0d1r0MllnTUoHuDd4rbB0tenl7w7mafd2qptaplIG/iyFDWEe0mQseWl25fkAUtqzZlUabSSGwNKpaMgD958c38lvs/xNelD/jeF/f4Q6fvS32vbMGFnfH9RcjHoy3Wdrwc5hilua5z9pKCR2rGbFISKMdNk7GqEsmlTWuqLiQJepk4gpabOpOM4C/Z3mSHBV1nuHw4R7UaJh1JIguCdZr6LKcyGT9w/An2Q1FZXFcZ80T6K33m4bDhZp3TrmPwkA/5AMt1Rt8Ybp8uaOsQbxX39284GPj0ZRZxUY6ZxJIF/KidERnLe4sZSsE0E69EYCzjAzH3ea+4WI/oVjEYT1FHZHFHOG7oCiGxhlr49EnUMY1rNnVMsU2Js47b45WEyUQd47jZlZP8NpB7KHYE1wH9WHO2GRMFlum44morqq8uMpyXU+xGUORoIJQFnllHlxoeX0/pL1N84Cmn7XBDK/S4YzKuiMOezspCvR4l2N4Qx5LD0LUBpQ6pK3FFP3vX3JqIlwOkhHKzzXbN9zyRn1F04poW70i4a9ReL0eCABlJSU31Cp9YuhlEqSzQG0asipTxvGRzNsbuy8lKSqGygZpNSq43OfU2QocD2VUJVG6cNmy7mL/y9GO73OW6DamacPg8ee0e3Qi9VnpOHm0s05FEZkbG7oCIs5FM8vOoonEBF8WI6bgiMJJ6No7qnWChHprMz+1/QEIZ/6uBMF92eO/Pn32slPqPgf9q+Ocj4O6XfOod4Mnw+J1f5PEvO5wXUFQ+YGMfX83o17IbNo7ByOXRHe8vNUrq+lLflxtZd++7fk2h5fO1nAZULz0AF8jnudiTHRY7F2vThNiVkC0v1iPOV2Phoa/lMa1kBzc5KKSJmtU8N13w6/ZfJ9YdK5vyYzcv8PrVEWYkBp26jPji9RHbezVn/W3O6skv+L3/+OoTfGv2BpG3vNHt843xDStX8UdXH+NiM6JOxbU7jhtWrcguR0kzaOwVedTuyj7PaqRZKEfnpg92p5LL5RRdaXzkUdqzfTwZjFBA7AlnDZsu4SRe0zrDcbbl2/ff4OalbEeUrM8EMWymgtAolik6tJjYsqoSbh0tJXHKGWJtuZUsyXTLq+EJuWm5aTM6q7m4mIL2kjXrFE0VkmTtLvB928RUVxn5UcE0q1iVKW1vSBPpYwSBIwtb6BXjIXHMPeP1JwIQXJapOFeVTNyTtGZrcryG0WFBMw45nG158mSPMO3Yn205uXMjpzcnJzobSBpdlzhUabi8HuOtAN9sb2DakY8aMUsZ2al3XUDVhKyWGcp4Tg9WZEm7YygZ4+iVp2sDXKdJpzVp1OE8bFsxL54vx9heNPSBdqyLhHYdo2qNTy2zwy0rL6W1KLAsvrgvJ10FRZ9iE4+f95ycLrhejmhvEp70Bl0Yapty+OIlGzPicj2iXiZENwYfQJ85lg9mhCuNzmVhqvdBaYjijsBYLoucuhVc9Gi/ZJLWxGHPxdWEWkfko1pUO05x53jB5XpEFPakoZxqOivu8dU24Wi25aXpJcfxmv/mvY+xuBijIsfRwZpFnRIay15c8s56jyToKZuI1987/kqmk69s/OoJ4G8/lFKn3vunwz9/G0K2A/h+4P+nlPp3kCbwS8BPDqaGjVLqm4GfAL4P0cB+2eG9Yr1NcZ0miKWebQpp9tmxxW/FG/DsNbNjC06hvcYFHp9ZwqxDvZVJ3R/hwHun0D1SulEMjWO/q/+VVxloODhdsfjiPvd/sKM4Dbn8dt4PnZ+0zKcFyzTDbSPhyo/EvRsZy1U/Euml7vi66WP+3Ef+293v9f+4+ij/2sGrXFnLoVkT6Z7PNg2FD3ncz/lzjz7FZ6d3MMrzfzz5y8x1yrWr+PTVR3fKG2fVTnaXhR1X25y9vGRdJ4ziZtcYq63UbUNt2bTvS043ZYIKHf6gxQQOd54Q32hcMPRAntswTht+7vwWtQ12eOU/9+hTXLyzj+qkmR4fl2RJwyytCZTjzaeHcCEnguoUpmm9c7ruJwUvJ2dYr3hg9nm32BPZoTWoZYiuRbbZAia0jNOGSSQkyZtFDspTnOX4Y+kFVU1EFMrEG4aWmypDxZZ1HbPZzujLgGTaoBVcl7k8lz6g7EJuBu9IflhSLFP285JrMozyQ5KbwPDisCeLOi6WI7oyknxa4zHjDmcl43k8LzkcFeRhS6R7Vm3KxUbUW1p5JuOCspUd8SyveHo1xS8i0ltbxmlD2YaMBsx3Ou9Qw9dt6pizm4n827jd5F9UEf1lKhLm0KNjS1HKa6sH0J0/qem9+FT6VkOvmO1viY1llNesH6foRSI73mqo/Y8bmouMaKHRrcI7j5+IYMJFsoFyiQer8d5TrRPcSDHJhxjQypCeSoTqpozxrUZnPfVwCmQZcTPKdnnVmzqWftSQSTEd1fRO863TN/mrNx9j8XSCWQe4xHHez8DDJVPicUPfG8Z5jUc8Ax/U+NUewJcMpdSfBL4TAR89An4f8J1KqU8ha+W7wD8H4L3/vFLqvwC+APTAvzAogAD+ed6Xgf4AX4kCCJEJ/oMvvMWiyfi99/4r/vnP/U5WwCyvxUgz0pjASpmhNnzdKw/JBtlbpCUWb9WkXI0qlqscfxETLzQu9LihHyAvuJjC+tThEkc4bdDaM4obok+c8+7BnP0fQdC9Ry2sA1yiuDdZYLRjm8Q0TcBmkYFXvK09Z8UEM6AszhZjfuLmPoF2TMKab5y+C8DGeX7fG7+Vf/eV/2LnB/itb3wLWnkOoy2/ff5T/IGz30hlQ7Z9zE2VMRlMQyZwXC9GZEcdaMc4kbJGHgmjfxS1rOrk/RzV/z97/x1t25bX94GfOefKa+109gn33PhyqFfhFTkVqYRAQglhIVnqVuyBcbesZGMhuYc11G3GkCyZttvqoRayLIk22EIBgYwCIEBQUBRUQaX3Xr1487kn7rzymnP2H3Pd/UAqEBIPxvPQm//cc/fZ55yd1gy/3/f7+fYcoHHs6J6jtKT0O5q2p0w2roFuJehLNUnYMl8nNOuAl/QlulbR1coF3BuQtcBqiMOG3aRg2dfnTaUQOy3xsKJpFPfv74CFYOCMZ2fdgGv+jE3nePUPjWIm67BSIXoEdVW4sHcTCmZ5gsl9R2utBeX9jHq3Jux3oEHoXN5N55KrqsanqxW0kmrlUswu7axY1wGdVqwuUoRvGOws2E1ybtodaq1oW8XpMkOchJhD53oFOF+ldI3HZHdN2DvKRb9AjJOSJ4bn5F3ARZVur6rdLGcv3rBqIuZVzKafoI/PRiAgOChcCa4cODyIsChpeXA8QSx8mNZI5Yi1Fmhrj2YTQCuRaUt2bUVVutOwtYJ2FTiS7YVk/6vuYbLcLa5GMghrij4qMwscwLB7QrK+SFELj2vvfcAX7N7m+xbvc4twZvFXUO9pkv2cYhmjBx3CM06VVEtEpLl0OEcJ6zZgApCWtlNUjU9zmjh0eKxp5xH+uMZ0kM9jBpcrt9BKA33TuWp8Ls4HICw/Nn6Gm6sd1KDFlgpVSOzUGRylsJwvM6wWrmFsXcP4LRvvLABvDmvtf/xZbv5bv8L9vx349s9y+0eBd/87PTocIO2/uPRD/FT5OH/t+IMuSarX20tpscKgO8V0Z8M0yXn1fJdiljh1hTJup++5yfLa/pzlIKL45MQFxwuLqlwj8+GbLi5VsAyIopadpCSQTjd/5XDO4utj9Dp00YGDDrTkF25fQ/WSQHsSoTRgBXrPEQzvzscYI/miG7dYNAl3F2NW65gP20f5np0Ns0WGaSXfpr6Rd02O8YRmVUecLjN+uHyaWZvymfk+5/MBz115QOq7CanRbkK7e2eX23d2Obi82L5mid9Qa8UoKLdpYg9dtg8/28eLIdOBq+sL4dKeokKgY3ePdFChjSCJGp47fMCj6QXf//J7oXRSP2JNF2tE4Ba41092HUguqWkDl8GQnyWIVpIcbphmTqmyaUM+NHuC+5sRTad4cueck2LAk9MzkoP73FpNndRPaR7IIYGnKVqfxczt/pEO3eDlEtNGFDuKZK8l7vNnrXU7chUbJtMNdetRzGPiuHHKmv5UJEONKT0u8oS9NN8mqelOEUYt9UgTh+2Wy9Q2Hjb3WHmxqzJK97kzuUe7p5z+vnP8olp7ZEFN3ga8OtulqMK+HGTpWtdYjuKG0NM0nWK99NjkEYOspNMCW0tsZAgCjdYS0wls5eSbQgtHMIUtQycOWvbTDfeWI5qfmhJduNr4sogxRnBtsiD1aySWTRtwXqR40nAw2OArjX/oUNY/cfw4WVqxmLqIVFMq8CxXx0uWscteWJURm42PrFxp7SFxt658vLRFZE4EYS0waLl8sHDBPzcjGhGiLtWoByHdgZMGu7An18+p8wDbCZ549JSfP7qKMYLJsKBOXObBIKqJPdejGuw55dZmESMeymTfovHOCeBtNDqr+BvnX85r6z02TchuknN1sGBeJw5FYCR3709592MPuBIteO3BPt6Fh4ksVjpXr3AnVu7MItRG4jd9+acSrjFsgb4XYLQgnJaEnqtfL5cJQlqyzNVk42nLut/JdXdSZCfwnljjey25ftON+N69BzwSX3C0eh/f8syH+M8mt7nTbfhUs8tPrJ/hn99+lvkyxXQCP25R0vD6apdplHO8GCClpShCfvzFp6EVBOOaV872uL4zJ/EaNm3odvm7OcXNIc2+63ynQcNulFO0AfM6cTrvyDWDl3XkIv6MZDrI3dHZ7+j6LNyHza8uMzw2WnJvMUZKw2uzXT517wq68CAwhEN3Ogp9Z2y7yBOu7i6otWJThVQPUry1RAoXAnN1vGTTBjRGsSoSp4KxgqoMOIkGhKpj3URcVCmh17HpCaFJ2HA+H2wlqUoZ8mWM1R7tyDC8vHbQvKBi2UTcvxjRbgIuXZlTNT6XhytWtculHYUVL988RC49TKrJ9nMqadnkEfPjISLSdIOKLvcZDgrqtHUsKWW4ez4mihuG0xXH9yeoWDMZlARKI/fcZHtzvoOShr00p9YedxZj6r4J2rUKfR7ibSTdbke84xrceeXyg22j0BsfMSgYRjWrLMGchzSbAC/ukGcBJrZ40xLf1zS175K3opqmUwhhWTeha75f11ilGCjNuM9xeP1kF9NJbN9bqCv3uKK4ceBEabk6WPB54zv847vvZXdvxfnZEKEVtC43QhtJ0fjkyxikxYTuwqkr12wzRpKkFWnYsJfk3FuOnArJ67g8rHll0CuC0orV0ONilrE7XTvfg7AEnqYJNFevzHnv+D6vvXyIsILzKNzCAvMkJEsr14/R0p0wAAyEe+VbM+FYfqMCYd4W422/AFTa47zO+O4n/gETlfADecJPrp8mUh2zOmE32vDBSy/zF/ZeBOCNfJfiasDvOvgFchPyTHjE3zr+cl6d77nw8peHiM6VflTb/1s4j0A7cAqSKztL7p5NXMj5mXOYrrMAm3aITf+SWSCwmNDyTU98gvvVmI/27kaAeZ3gCUMStLRWcapzfq66zO9I53x98nEeVEM+cucRVM96eePuHiowmEvC6bK9jqu7iz4AJmGdR2RxzdFqyLivY7edcuHv1zfMj0ZE05JLgzUn5YDWSLKg5t7aqWse6q2lsOzEBcfrwba3kg4rbjxxyvJKhOgUpgq4KFJHPS0CPF9zfX+29RGcrAdsXprQKFhGBiRsgtQ1Iz0LkaGzTvZI6E4fmd/QWYmvDMsqxOt33HdOdsiyikcnM1aFC2I/ORsRxq2bOCtFMi65sTNn07gyW9sJVKw5HK6oOp9XzvbwPU1X+ciVx0kw4vL+gs5I5y6tQ4eJ7jXwGME0LaijmuNbU1AW20rq2sO/8Oj23GJaVz75KsKWHmbYcF77TPbXJEG7JVQu64h1ETlYWq22BqbRoERruc1MzkMHzROVU9g8lEoWVQAGrG+I/Y5AavZ2VpwxxA86qkWEiCzxpc3WBFmcJwRjtyuOfCfPvH00xbYSYZzHJW8cVRZwpjVh8ZKGphc26K5vkOMEAkebEZ9Wl7k6WNAYR97lmZqx33FRJg5t8coIEVgILDbU0Drm1ZXpksRvKDufQGoklr00d4bIdUYSNkwfn2Es+Mqw+9gJ2kjWddA/Tp+28egKj9RveHWzj2hdmI+nDO06QG4UJtIsV4nzcvQlHystQrhqwFsxBO+cAN5WQxvBz92+wTdVv5er6YJl49jgiyYmUu2/wcj5yp2Xud9M+MPD0+1tH3z0x6gfafn/zJ/mH2fv42QxoD2L8ZfSyeJ6yZ2JLdNxznsmRzw3fsAnZ1e4bXf7iDqByD2s74w7snY4Yh0bfvD2c1wbL7gxeXN3LoVhL1izqR7hH99/H5Xx+fO7L/NdqwP+1eIZPnFyxU0E0qJrhQo1w0HBpgmI44bNJuKodtGSvjJMRzkHyWbL0g/6RnPR+IzSkioPqAufe8sRw8hNDpsmZNRL6hx/vyMNGpZ1RF6Grv7qCSYPmex9eSRJXfkni2tKZbZZvMPQlTlGccXqSuXCTSQI6RRE44PVtj6+2MQY7cBtxgoMAq8/YgySisjrWAhL8dqI8qqm7Hz2EhfqozyDfch0aiR1FXDzfIoxgjRu2N+bUWuPRRVzejrC1hKxW7iMBoClTznx+L8/8YP8pTd+C1F/qhC2x0T7hqL1WfbQNX/Q0FYu6MYaaFoPYwSm8RzC2Dfo0kMUitLXLGYZxY7T2detR3czc6TR3ZqicoiG2SIlSR3e4XSTQR+ugnReFjGoKdehw0OHmnRUMQwrau0CW4KwpcoDRC3x+oxrR1OFeOpwzOerFCmdckieBzzxv26QRcPN37PrsOLrENtJVOQa2koZrAFTeviDhqZRhGHHJCn53OkdXt/skXgNr81cLnAUtBxkGwKpUcJSyhGyFmjlms5GWbrS4/a9XdJxSRy07KUbZlXCbuxc2/e7EbM6JY0d/TPyHPpBKlfzL3NHs3UMHsE3X/lXfNsnfrdT8NUKmwcEByXZYYW1gvnJEJm2CGUxiQYtiHdy2uYtnMreWQDePsOThv2dFcYKPm94i1fLA06rAas64mq2AGCuCybKMWv++dlzZP4vRSn84zzjkloy61J+15VPsHtjxQvlVb5q8BL/zWtfz/3bU9RaYfumL8BpPeB0laHiDplYt5uLnF7bGBdm3dUKUSi0FRxEa/xf5C472oy4u5qwm+V8xcGrtFbx/11c4ZP5Nf7Q/of4ivEuz4VHfO/iC/jnt5/datNTr2E4LvkFe7VPzXJ10ivpksYo1pvhduIP/Y4sdJiDx6+e8frdfZrGIx5sGPgVD/omdNl621hNYQVfdelV/EPNz81v0BlJaxxjRwpLqyVN520TukTf4HvIGGp70unTV08YBhVnpVO6+FITqY7Ia5lXiSuPCOvq92XE9fHCabj75/jQzGYCSxS4163RitYoLOD7mq5TmE5gzkN06cprq6tqu1AVeYQtFfS6dBsYZOHor1Xj8/+++0HyxuVG7Gcb/CdOaY2kanzWRYS1ML68YictONukbBYJDAymVZiNv01fQ1po3Y6zaxX+/YDFcoLcrZHSOEhgLmh9n71HVyhhmW0S12i10i20fdwmVjAYla5ZP6qQ0rhTwzLmpp6SxTXnp0MX/BIYt/i0iouLzJUiBxVZXG9zIqqjlP2fFTQDQbUfAzH1ruaZyZzX212ktD22wp1OhXRmxlHvwJ4XMVXn8bGL6865fXoFL+iIopbI77Y9k87IbQ9G7dYMBwXLdQL33SJaKMPzT97npBzgK815mZE3/tankAYNJ8sBvt/Rtl6f8+wWeGsFeIZ4UvJPZu93ZaH9yjV2R4bJoHAspAuHetGh3PpZ/Kxhb5BvQYFvxRBvWbrM23+87RcAgKTHDLyQX+GLBq/xCa6zaiNSz324jrRgKA1/fzMlb0PWbcS3Hr+fv3LpFwD4qviM71o+wxdnr7Kn1rRW0VqP15t9np2c8NzOAz49O2RVRjyYD/nLT/wDPl7dcIqVMiNvgu0u+Pj+BBlq4rRBB5Kqibk+XnA5WpCpim/deZ2P1Q3/3dHXYhB8YPIqX528zPet3s+n86u8sZ7S7ij+4PAcCLifvcLPpjfwpNuVPpLNuLOZsLlI8JLOyfWqEBhtaYylcDkDdethPU2jFZ402M5drK1WVPLNHfWD1ZCu18P7SvMv7j3LH3j0Z3lu9IC8C/nZ0+ss1nE/4bNF/GZxTdtnK5zOB3SzyKl/GsHiWs616WILntNWUnY+Xq+8Ol+nlOeJk4qGhm64QvS14kUd0xlXcpGNQPWu1odmJ9NJ0rBhc5EgtUA2YgvuM2ufdaOQgca0EjVqMK1ks0gQxnk4rj91skVgBF7HIo9dCaj/LIVKbyd9ISx3zyZ4vkZ4BpNqx8sJtUNJr1Vf73amqG7jI29URFGLMYIw6PAed4EuNB6h0nRGksU1Zycj5t2Q3ctLnnj0hLL16XqMMsDBcE3deayqEC9qsdYZ3+JhRdnGfV61wJSeC4mxEExyOu2AbGlcI2YDph+6x/JzD0lurzBJwFPPzfi2G/+Un5g+wz87ehfL3vtgrMAPOrpWUbUenXZu70lSshtv+JzRXT4UPs7t2YSiCF2Wcx2xEZbzRYaqHEZlMspd3m8euROF78Lir8czKu3xsRcfxb9w5kj12AYh3POqZhGVFuBbRKARyiKzjiBsKc8TjBHc2rhQmsPpknUVOtdvj432L3Xkt0YIz+D7mir3yXYr1n3k51syLO+cAN5OQ0mLEsYpKlZ7HAQr9oM1d70JUhh+IE9o7ZDnghW+0Dy/c48XlodcCefbRWAkY94V3WMgKy6rmttdwlgVfCB+gMJwyV/yN65+mG89fj+PROd8QejziPcqH0xe4b89+Roa4/HHD/4l/+Xr/xH+mY+VPvmOuyjTww1//OqP8nXJm6eOf5U/w7sHRwxUxTePbvEds/dRmIChV/J1By9w1g35yxe7fGT+CEUXOMVGHlOXPg+OJyAtXtIxyEqysKFsHRZXG8koKVnksdOWR07a13aKdacQviEInPU/kJqmj6H0ejPSY6MZF1XKM+NTXsoPWbcRoeo4uztBrRVcy4mjlqwHtSlhifqs1dUqRpb9ZCygu5tys3Mnh7byUIEr2wRh5zJ3a7dw2MRd6BdF6hYKI2k6RVX7TkWTuNvWTbgNqpnubNzfNcIFtivodjvn3jYC2wmMUSQTF/Ceho52el6OwXfPNW8DfOV48VHwpkbcl5pplFNpnxMzoMPVwLO47pU+ChsaZNwRDmrCQxcU33QKbRx+3AJt6zHOSvdc+glqb5Bvnam+58ouaNeHiD0X91g0PqvjAXvX5i68xUiUsExHOYtNwvl8wGSU0ww8zDyEHtURTV0Ii7FQNT5x6Ix+3VMFL37bJWTWcD8fIhrJN4xeYG0ifvLsCdZViK80X3z5Fv/i5WcxhYdKO+raI8oqPufgFusu5EYy4+eX17h1sePq6VaQVwFp1KCUdgtRAF2mqVrXfzJGunq5ADzLJ5dX3EnBt3SpxfoGaQVKaVZFhBq0jAYFdeuks1ngEOAAx7VPGrvTodaCsvVZ59HWJa38zqW3tQKrBaNxSRS0eMpxiVYn/yYx9993vNMDeBsNYwSR6nhmdMKuv+GTqytcilbcXY+ZBwk/qt5F3oUcd3f45OYqJ6Vz1N6vJxgrmOuCv7d+kh86fxf/3Y3vY2kUZ3rIk8Epx1px2Z/zUnUF0lf5lulPol1YIxMZ8fN1whPJKXerHdYmwpeaa59/n+fGDwhlxw/deYaf+Lz/mVGfCwvwExV88/hFjrTmslJ8X37Aa8U+TySnnLfuQ/ozm8d5YXHIpgk4vruDaCTetGJnkrNYuZ2QBVbrhLwM8X1NHMAwcqleSegwB4nXcKedAFBuXL3XV5pB4NzBk7Bg04RMkxwpLG8spvhKc78YEakOKQyfudgHA5NnXWjNpgxpPEcuNVaQBTW19gjjluYQ2sJzpZFaoe7HeAuB12++2tTSXqvwow4barrCQ2xchm3dKZLAsVvO747x1g6UR6zJLxLy8wS0QyYAnK/H2wAg41mCG/2uuJIQOipm23jsDXKGYcWnb112mbWR4NarB8hGYiLD7tUF7959wM3VlGX5Zsi4QTiInBUkifNPBGFLkbl+jOjvF/odg7BmVsRb/IDuGfTLPCaJam7szKm1K5GJ3nMhhWUy3bDauMW67HzO1inFPMZbKdZFBAl0Wm7d29NBzroKqVrPZST7PsKCCjSTtHQBM8pQCUsatCxL5wrOjSBOGgot2L264GY+5b+89Y0U85jx3oa/9/zfIhGWV1d7nK4zVg8GMLIUfUN41UR8aP0YdevxhVdv8/PHV0FYimVMMY+RkZPMysAZwjZ3hjDs2N1bcTb1nTGylbx6ssfuaMO1KxdUnbeF/AHcPpsg7sSsb7jks2XpaKWB1+FLw+OXznh2fMxHTm/Qlj7rvnTVNq4P5npWFu/Ghrb2WJchVRFgWsX7Hr/Lq/otLAG9owJ6+4yH3f2RV/Ll2Wf4oQfPclGldL3B5efOrpP5DbVRLJqEP3n1h7ms1vxk+QRfl77CXzr/Mv7g5MM8f/UO/2D9Pp4OH/BqfcA1b0ZlPZ4LTpnpjKUp+bnqGo8Fp3zn8jIbHfHT88f4/PFt9oM1p3rATljwvuE9vmXySf7Mva9hf7DhL599IY9Hp3xl8hqJgLPuMj+qE467MS8Xl4hVSyg7TpsB96sxH59f5dbJlG4VOE130kFguLSz6uV2bxqMrBV0raJYhxSExJdawr6Ru6ojzouUsnHsGBYB6bU1adBSdj55E2xZKXnjUzU+deUY8zMvdcllVlAUIf5OxboIXYIW0LaKxw/OaYxCW+lKFJ7G9zQmcciJh3kH6yLEvuzKA14pqJa+M0pbGFxeEx06CepDftBFmeCPa+TUoHA0S3AET9FI/KBjnJWUmc+mdGgIExm8xkOWyilmGhf408aSYVjxqRev460UVrnsXH/tvu5axfnZgOvXXuDWeof17RGrOENlLcPMGa/ioN2WsfaHG9q03CZWPTgfMdMJReRjjGTQO5q1loyzklFYMSudtwMc9KxuPdarmI2MHKM/bKk6j8R3ATl+2mLPfcQnBgSfSVCB4Ozra1bnKXQSmbWEUYvvabz9DUUeMR6UrKsQgWPvdJ3ifJ1uERJeH54zGDlp6s35lKb2UbFmN8v583d+J51RnKwzmsbDn1RgBcVZyo8dPcf4xoLAcwqb5TiiLAPCsKW2Ar10Bks8ixWu/GemLcIzXMwyJ5k0OGzELOUkSDD7DY9fOaPt87GVNLSbgKCF7jim3JccTJc0ncc0Lri3HOEpn12/z2YoFXUZM7m6ZDHLWOgUqwVh0hIGHfUmpL45wO627B0sybtg22t4S8Y7J4C3z+iMJO8CXs/3OK6/eNusfHZ6zPV4zhv5Lu8b3uPPTl/lTxx9Pj+0eg/Pxkfseyuuexl/cPJh7ndDfnPS8vnhq/xgkTFSJYno+NwwYGO8XjEU8xXxXX7HJ/8o5/fGIC3+oKHSPmXn8/c372d1PODD3uP87eEX0RQB6ajk+xfvYZoVPPfkfV5v91nrCF9oljrmT+/9BP9g/W5+2/DjfN/ycznajMgb32Ww7hYo5XbEdU/CXFUOVJb5DSK11NpjliesFgOnHml9Qq+jNXIbjlKWAXrjk15bczhaoYTZclmsFXhKs8qddG6QldStR7mK0IlgPCgRCc4VWypnpvEsMnLBHA8XkTRoWVfSNVL75m7iNXjCcNPb4TTIthA+VUoX29nCOkppB2+Wk2LPxWVGcbO1/jeth5SGx66f4veGtUlYcG89ZrNfI3qCsp6F21KD6EPb6Tw+fesy0bEHFqrLGjyDbNzOWWjwjwO++4XPJwg67KBDrD1MqVieRljPupPXKHeN794oVtU+o6wkihvKdcQ6D0gnJb40dMqwn21YNSGdlcS+y3tuGo/NPCHMamzhYaUlLzyG+5s+fCdgNU9IRxX5tIPrLUf7Icl919AUgWsUh1FL10nK0wSVK0xssCNBWbkcY9/XSGkpZw5RjXS5xcmw2qKkOyPRrcRqSey1W9Od1pI0dot32ylazxCc+szjAXuHS77w0Vt4UiOVYW+Qs/Q0Oq0oixCpDE0ewNxDeIbhoGRxPHB9itDQ7Dl5LQIoFbdPd4jihsjvGPgtyaSkO/HxCgGe5pnxKZ7U5F3I5x6suV+MyFS1be4KK/CU62slw4ayCFDKsDrLEKV0f3fjkVx2J0Zt3qI8APtOCehtNXypmYQFszrhznpC2bqw69NyAMBZlTFLUv7Ug8/jw8ePUjY+P6SeQQjL9+8fcWcz4Y9e+ylqe0wofH5HWgAFD1O3MhlR25ZQ+Bx6GY+OL1jlEUHgat93FmOqMsDeTYgKQZta6lY6umNc8ehwRurV/C8XX0KsGj56cZ0r6ZJv2P15/tb8C7hoU54Mj3k8OuX+YMynTg+dKgQYxhVFHRAHLbVW5FXAsoso+3Qo3Tdu472C0O/YiQs27ZuRfKdnQ2zusXNtQex3LMqYLHTcG8fgd/F+w9RJ6KZJzrKO6FrFtd0Fo6Dk429cR1QKb90zgMYdfuACOi5nKzypOS0GpGHDJCq4FK0IZcfdYkIonQb9IWnVxBbRujAUConwDJcnS2Kv3UZ2dlqSb9zuGHBsnKTkSrpg0zow2r31GCksl/poy6OjHYKlxCpoR05KaY1AVhK79ulii87cBCpK5Zzd0nGghGfw+is6zGq8cUl+nmD7CSMIXc/EU5qiN24ZLZktMqK44eBgwfHxmPws4Zm9E4ouoOx8Oq0wXkfiNzTNCI4ipAIddY4U2p+CANZVyCCqCdMG/fERhy8b2sQjOdOcPQ9XdhfsJ2u+eucz/MMHn8MbR7sQOSCev+comGHYUeQh1oJS1vkJtDPvGV+ixoZN7TInLg9XHAGbRcxnjg7YGeVOcrmOCHc6ysp3Df/cc5nYyuEbPKl5NLng48EVHsyHTAYOM24iSVX5CGUw0xbPc41uAgNGEA1d/0RX7lQYDpxRMPI72k5xkSc0tY9JnNxadIrHkzMeC095qbpMoQPumAmnzZDHRxccRxOsFpyfuXJufpaQ7BaUReA2KeOWIHHO76PZcOvaf8vGOwvA22eYHi/7VXsv84NH73FAMOCBFbzCAdIz3Dmf8OTBGc/v3Sf1aj6zPHDaayBUHf/tS7+ZxVMf4pnwiA/G+pfw9QFC4Wqh/9f7X8RpMWBvtEFbhxY+X6fo45hgI1zS0ZWSQDlp2uOjc37rzqf42uQ+E5Xwnz/4HL7u0os8HT3gO974Gu6/cIB/NeePf+GP8/VJRSQaxn7Jh49vMIxq9uINR4zwlUtmEsLSziPmm4BNGnFjf8aT0zPOy2wrxzNWcHo+RJ4GpI+v2L2csywjpzwJayZhQeo35JV7fvupA8fNq5iq8/GlYTQoOdukFKHP4aU54Pg0pvC2LPzWKDorqVqHkDAI5lWCxHIYLSm6AE9qytbb0lcxwLQmCDvS2MHhDpMVH7n1yFY5IoRlPMr5wkt3+JHXngagCRV31jvMi5jHd86ZlQl1p0hDF2kopOM06dighi3WgFAWtWNoCp/OcxOP2Chn9AotKMt4d8ModnCx0/mA7iKisbhUsLhDKEtZBFRlgOllntPdNVGfcXspXRFI3TdfRe++du9T6HVbpdFoUDDfdcqkg76UZ/t8hrzPvPWVZpyVnA8yVo9KmqFl/jUNcbxhVsQcfeyQT5inEE9uSAcVbeuR7dd0WrIqIrzeuVucpbQGsmtr6tpz5rcLn9VZxsGVOWcXA4raNf6zcYmvNCd3dhwwsc/OTuOGKGipwpbFgyFh2uB7mvMq4/XlLuO44rxT1O2b08MwKymqAM8zjhslnGJMLjxM1hCELa2wdIsAb9ITUDtF6Hesy5CuVkjAhAbRSn569hj/tHqOz9m9i7GuUf5GsUujlUtWE2C1Qm0k1oPyOMMKy+Tq0uE9FjF0ApV1HAzmnG/emjyAd4xgb8MxrxOWXcI4LDmNM6S0W1ywXvuYWPPCq1d5MdCMxgUHgzVP7JyjreCVowPMechfvfha4lHFH3r6I/yx8cdJ+KUZouc6Z9YkiJ5D41lBqDqGScXskqJKA+dONALpW57dOeFvX/9J7nQbJirjpaZgP1jzZ6ev8oNFxKYOkJcqxlnJG+0OT/k1vzu7x0xnfPv7f5y/vXyWn7h4kquDhSsN1QHDpKLKAkzZG8CkZt32jUvcJH5+a4dwv2D0rhWR1zkJqDK9fv/NJucoLbcqEykso97E1XQeQlimaUEgNQbBpglQvsZ4iqoIqEce0yjntBhsG8Gddi7Xsyrry3CGnaBgL82ZxWNELbeJVhebhMUiJS8drsLzNWcnI9csVJZoUPPjt59wKhJhWRcRZ8sRwrO8rPddaImwLJTlkRtniLkrHchGousQqQXdQCMnNZcP5yzymOIk7XeXTnaa7BREQUvRl9eiuGEzUNhGIQJNlDZkcb0NjIn8jk0Zcn4yhEbyue9+g0lQcmczIfI6ztYp82VKFDfcmMyRgQvbGYYV6zyCtYcxPg856dPxhsUmpikCWPqoJ8/xpMEc1OQDh9SQxxHydsLq3S0qsGS3JMtpTBN3jMYFQlhWqxguQsygw08bvGGzLaFt8sCRbq+2jJKKVkvsMsBEHVWnMFpSC+tw38oiG0l+c4Q4cIti4HV4w4YkaijqgJmwlI1PEjZcmywc0uJoCtKSF2kfHuQWPSmce1rHBr0OnDrOA09DlQ8xiWF0dekCiPyOcLphFcakPbr7aDVkfjrg/s1dF6cJPPGee0yiApm1JFmPvH5jjLpU0K5DRKgdUM8K4lFFOY/RS5/wSsdze8dv2XwjzH84K8DbfgEQwF684f3Jbf6b/U/x3ZemPB/e43uXn8fH5teptcfr9/fwzz3iE5/l4z6LcNAnODXojYfwLNSS8izhO8sv4+/GX8gkKwiU5iBZ8/7hXTLlTE2tVsR+ixJmG149HW8o4oBOS8pNSOB3HOUjvvX4/Q7Z20WMvYLroVPS7Kk1X3b5DU7rAV+z8yIXOgNqMhnxpya3gJg9b81LJ5fYG7odehbVVI2PVJYrN857E5WbnGLPsYJeeuMyO9fnPDqeMa8Tqs5lAhR9gHjku9JN2bqd/qZ2ZqvWSorWd/I9rRy2WZqtVFL2+nyhHIu/0YqiC8gbn7r1iX2X+CWloTOSO+sJWVDzwuLQESb73sF6FbNexbB0sLAmNhzXHlZL/LgFYUnjhmvjBas64u7JBJ0HdIAsJKoWtFGLiDuyUcm7947ZCQrucEgXW7q9liBtyJLK4Z0vMo7u7+DFHWrcYLXAaslwXABuIXxoSFv1i6NKOnTjJqXlJqZZuomlDhzQT648bGB56fSAZ/dP+IbDX+CkHfEh9Tiv3dmnMCGvNnukcY2SrsmeJTWbPVefHyYV615t1JQ+rD2sbzhfZEyGBUHU0gBB1CKHFdm7Cro8pvQj9LELurHGqY/AMfftbk0YdEwGhUNlLAbkpQ+tpC18VNyxKdzfFCMno7RGkqUVO2nBeug+66fzAfZOgj6L0Fc64rjD9Ajq/CyhGviEYetCl6yTvF6+NOfPPfFP+X++8tvQRnJtOKcxHnkbsKxGyEYgJzV25mN9V+JRtUCndiuPDf2OUVhxdbREYjkpMk7ORg7bXkpkJ+hGHUoaPjB5lRcHB86Ulzt3dncaExyUdK1yOO5K0iWa4MTDevDC61f4Le99SKT/NY53fABvrxGqjpFf8ncffAnvuvGP0HaX54KYDw5e4OuHH+ePv/j78YIObxOhQ0eJVOcKoX20F+NFlm7aEY0rZ9wJW8oi5PgzTv54Kz7gF3aucjBes6mDLX2yaj3q1u9j5zRZVPPgeMJ0d03VejxYD9i0jzKN3WTTRoqRV3Kz3fBD68/bIpg/tnkEbQWfLEp2/TXLLuFWMeVn71ynXUTcXYYgIRmVVEXA01dPiFRL3oaUnU/V+mysYLmOCQc114ZL5nXCvIgZxe7i9ZUmClryOqBqMwa9jj/wNOdFwiBsEDhPRezXLjSlH06X77k6cetRVT4nsyGL0ElbQ99hFLKg4Xg1IF9H2Np5DoRnENIy2NvgSUPTOax0K31MYrCRhlmIVwqCpyq+8PJtauNxUg5YVaGrw/vGZRK0Pl1gEccJjFs2y5ifK244JEOiMbHAizrXCFwnKE+7fIgQpg9r3LVPW3vUjUccukUz9DrONild5/IkhIAwciWPtvYQoTsNjNKSvA7YeAFo6HoD3P9++l4y31EoL11aUNTOFJgELWXr0XUeSdiQjJ2qar5OCIKO8/nAQdz2m23ofdn4JFHDsEdtSAFN55FGDUnYkn9hTaZM/1q68snOeEMaNJyt3ckr9lqu7865yBNW6wTdSPTGR3cCkXXEab1NFKsanzObUtc+XaMwrYKR3k5w2gq0FWwWCf7MQz3wkRu4fW1Aen3lTo5xxf9454PbCMq8Dd2/TQCjFnnfNYhbz/V/rLJ0sYXAQfBy7dzsgdJODWQU6zIijFts1NKEPtY37AwKzvKM+/WE9+4/4KfvPY0se95PaGnPXclHGhfaZHNFOzSoyrmJn8/uvGVzzjsy0LfRUMJwOVzywuyQ3/vxP4YSlr/mad4zfcCDcsjZ8YjggU87ssjapYO1lztQFv/Mx6sE0cs+1b6H3m/oGoUXaLqsg06CgSypOJ4N6WYRtg/6iJN626w1FjZViJj7nHdDhGfxwo58E7FMXELROgmpjSIdf5xvnX6KP3LrNzPyKxaN04GP/RJfaF7Z7HNnNcFoRbhTMkwrFquEYhFzcOhq7615My93N8mZlQldq7g+XXK0GZL4LUng3NEPE9OkcKlTvt/RdB4XNyf4K4lsBcVza8ZZsQWQPTRdna9SmlPXDPemFdf25tzaTJH3IoodH39Yc3m84nyT8t7pEZOw4KY/xVgwRpKXLhC9aTzCpO5xzPDo0w+oOo9NFbKqB9um9U6QU+qARRMTBy3z0nNY4cg4ykAl0VPXJ7ClR9f39YQWWN+gG7Xl8HueoVhF+HHL2WyI6QRiHmBCQ2MEXafY5JELbKmdwmky3Wzr+Yu1W+CkbxgmbiGN/A5zZUN+mmK0ZF4nWw3/NMqpW4/Q75gvU9pYuXzgh3jp/j14GNIeepqLTrA7XXN+PiAI7dYI9/D3aAtlHTDOCqwVDJNqe5/5OmG5TDCdZJ01GCNY5DHB0KVnhX5HklasmwQRGLxhS3eS0ByFmD0XVHP9sRmzPHFxnYBKW7RwGQnGCBflOYuQpcRfu4ayVSBbl2UQey2bJuTBasj+YMNetOEzF/skQUvVekRJQ7knEZWP2KuRvibqcSm7We58FKpjXsVbJ33deRSrCC9qMVphO4lZBMwWAWrU8spon0BqyDooA8dPAof5GHbYVYD2LDbroFbYxhLsVPz9o8/lW55+iyadd04Ab58xUgVfmL7O2XTATx09St165GXAx81lt7PIGsxjHWHkdnxZ5IiJi00Mw5q69Al+oa+NlworJU2tQIA/rogjB7yqGt+Ff+cKU4fkKiC7uiLud9abk8xlyxuBrQWt9knGJbtZzr2zCYuzjNvBlC+/+y3EYUtZ+3ieQWuJ52nupWPeu3Ofl8/3scBomBP7TiN/VowYTnN2k5xNE5I3AePY2fMj1fHayS7jcb7l3edNQNgbtaSwTJKSk9WA+iil0bAeaMSgo4kFIjAkypDXAZHfbSmW68YZzGrfuBJOf2KJ4oZix0MmHcZIjlcDyjLgE+dXXIxk0JA3AZ0VbvIvfOTS50KkeIVAPbHhaD7CGOdhEIlGjFzs308eP06gNInvTiQI24P4LDayTkXoOWUJsC0FFUWIPY0gV+hBh4o15e0BwVpQXxEIz6B8Q/bowkVUFn0DfGdF2fhb7fzD5KmqCsC6aMKLjYujjPwOpTQ2aLF7OVLabbLaYbLitcUu84sMP27pKo9aWCrPuYTB7aar1kMpw/FiSDWL8Oce54sAm2jKPMQWHtG0JItrqh5pHIcupyC/NcJkHZP99TbEnYsQCUwP58R+y+3THe6eTZwrtxOkw2p7nXSNh401Yr8hkJYscelsV0bLbTTj7niD6QF261UM8wC5UxPsdhSDiORmD9M7rFkUMTZ270MW1VxP57yxnlK3HpOk5HDQsGmd/r5qvW3vLPBcyW0Q1HRGMg5KDIJlFbmJHRDK8Y0eObzgfJOyqgcQuVNSZxSdUYx3NlSZezye52i2vjKc6hHWiO1pbH1niJ5H3BXjt2zOeacJ/DYarfX4cP4En54fugkqaFk8GFO8mCEsNNdb9i4vUNKlQG2qkKp2So1hWrG0gvLAYq+WUHkEaYPRTietevnbQ3etmVRUNnJh31qwPs3Ik44krRGNwAwcnmDv6sKxVDrFvbMJZtHL0wIXEqKDDs8zNLWHkE6rf/si4fZr+8j6zTD6Wdbhxy3j3Q1PTs8cFkA6rT+AsXK7a7dW9A1fTeS5Wr8nDd906ef4scWzPD065YeqZzAXIWhBNKmp8oDBsCSLahZ5jB9pvmz6OvMu4aXVJfbinNdxbmshcO7M2gffOBBXn80rpWFVROxkBUoavJ7ZU5ZuImVaIyVYYakLHy/QtKXv0A1ph9Fu0mk6hVKuR7HcRIhIIzMnO20bDy08JuOcvAwRSYMQ8N79B/zUZ55AaTCBU/fotU9QuGAYWomKHYE0CRsenVywbqMtuC7sT0RZv8vfFBFGO8NZ2TrZZ1GFKOkmMCUN77n0gEB23N1M8KTh66af4sfUs6yHc37+9RtgcF6OvlyjrSDqF+TY77h/e0pw7mEfStMN2I0HscZaZ56rlxHBsHZlnNKloIlKMT8Zku3m1OsQrxF005bUd5OtMZKre3N2opxP3btCkYeIlY+q3f0ee+xkm60LLig+pmW2TLGV4uSOS9gCkCchSEvwSkwXW2RsKR5rGO9tCPuNxkMF3rt3HjBvEhqtCDzN0XzEdJBzvsxoazeF2E7iRQ5foqTleDOgaj2WUcTR7SlYQfRoR+h1XDuYs6pCVlXft7B9OJMyLBuXYLe4M8YmPU6jRz/LqEP6BnMRcDYfsDPKWY9bdnc2b92EY3EwrP9Axtt+AVBoPnT2OHkTMIhqzlcpwUy5UHcALRy+oMfBDjNXN2gepJyrBKusq3tWHirUdI3CGoEXaEeQtE5dE3gdTZ1uf6foBDYymMJjUylIXbZwFDdczlYYBKd5xsII2OvoOhfq0VkFSe0yUI8jVO0m16hy2GlZC6wPprToKmDwzJrP2b/PG+spi9JNXFEflt0YRWNc1u0gqlmW7uJQfbnhK3ZfIZUN78nuc+Av+cNf/JOc6QEKyw/M38+nZ4eOGRRWW0/Bi5tDfKnJ24CL3BErk7gl9p1WP/Q76tZjGNWcLAcUZykql3RXCorG52KdOvSucCEdYu1hccYnVUrYd/0FIS3Ws0jPlYW6VuEHzjdwfjpELj3kfk2avrmL9fyOogpoSx/bCeTG42e7G2STglxFSN+gVw6RbHzQoSWalmgt6OYRR9UO6qol8RsmScGmDTkvUqR0k3zbKsKwRQbdlqvTNB5N4VDaLH1sYBk96QxJTW+2+7v3vsTlQyxC17Q0UNUp3Y7iyu4CtKLuPJSwFI3P8GBDOQxoK4/BpHD+NaWJ/I5V/x62UefiHXszF6pHIitHnn1IHxXKsqwjWi3ZnawBWLcR0/GGk5OROxUKp8U3VrAoI/J1xHBYuowEIx1uWVnkRiEXEaoQxKcuLEkHEHcAguJSQD12PQlPdWgrSPyWS+GKxjjBQRo2JGHD6SKDmylKgo4sQlk6YVmaBD/oeOrgjKFf8fHjK4hWukyNKkSHgpPZEDMPHcvfCOgEovAo1hm3RUZyZYOsBJS+KwFZkLXE7BnCV0IufaTh7PmEs2cVYuUjppZV/mYY0691vNMDeBuN+/WEpIhR0jFYPM9Q3KjxjgKnPfcNze0M2Qq44WqpUdjShMY5UrMW0/P2w77Ztlw7rouShkcnsy2d0nbSTf4PXYW2p1DWivG1BVXj88TOOQ/yoXPU1gFB0DGOK7KgZllHnM6G5OsIU3kw6vCOfVQj0JFDBgPIGvQI/Ec27CQl875PEHjaNWyFZVbExH7HeZG6nfDOEZ+eHzq8cV/68YXmG7MVH6/P+M7zr+CT3jX+8sHHAfiLr15xkLUiohtI0qDh7tmE4zs7Ti4Zat79yBGe0BxtRhwthtuQdb/HAPt+R+UZdAy69Fk0Hgd7SxK/pe48tBUclzuolcIqQZdpkkHlmC2+QxDX8wiMwATOiWytIMxqmp710mlJ0OOKtZbOsVspEBZvI2gGPoOspI1b2nsp/mXXdBfC5UIDFLVPF2tsIzm6GPHEpTMa7ZF4DZNIcqZToj6DoNGKVREhhGW5TtibrFHDnJPZELvrmECnmwxPGYraZ5I6aNneZM0y7MhP094A5fogiyImCV3mwcMG8DiuKMuAKHOxjwBl43AS1gpWs8RNep1kemPOfJmiDahYozzn9MUzdEMQuYPLrVYxZu1DZLjUl4SuHM5p9hWzZUoWu8Z/kUcoXxP6HeeLjDSpmQwKTlZObNAONfGJ8260qcCE0MWC6kbD5cszBkHNRZGyLiOkNJSNz8fUdb5692UeFENePdrHLgOstMRPrl1y3b2szwlQhIOanaxg3YSc5hlaS+SkQXma1TpmtYqxRmAjZ9KzxnGERC8FlU2PWx93+Gc+Outcz0I4BIh8/5KT5yHwNnB/CMJydm+MaN4aFtA7PoC32+gbZdZCU7ldWnihwIKODGhXCtDSInrSZOh3xLsFcdgwOxsicg+tLJUNtrtXU3uQwP31iNh3jJ3Hr506WNZFfxKQFjn3sbtuV1vOYz5+/3HETo2pFcGg2TLTa+0xW6d0peeO1sOaNK6Z1yP8pes5VHsGG7rthRq0TLMCJYwLr+8bpQ93nsZILHB2d8ITTz7gvdldjsohl9MlsWr5M5d+mAvjGpk/mj/Lq6s9vmz3dQB+/82vcjLQKqSaRZy0Cs/XtKvAoXiVQfpOBlp3niNcWkHZa+IHUc1+sib2WqrKp12FiNzDW0tOGGH73enuwYrxwZpl6BrJUlrnSegVNEoZ1MBJSNOkJvI7ZuuUJ/bPOS9STt+Yoi7lyF5holcBVeuYNypXdJlFeK60V29CSDUKuLa72Or7W61cWErjoZVDBBRtwLoJyfyGeRXTarVtRC/WMW0eOJx05aFHkvfv3+VH+3AVz9PkpcuEltKyKGLqzsNYiIOW8SPnhF7HunauZdW/zllUbx3XyzKiXQXoUnExDhiMXfh7voiRgcZPG3xf9zRLjVQakVjipKbrFGUeEPQhNfIscHnIfSM7SF2M47KM0EbiKe0WMWE5SNYsB24nPFum6NKjDTrWd4d4hUQf1kRxS16kCCMIn1gyiN3pcKo0+8kaY901VOYBQoIuFXkZEqmWSHV87iN3+Pnb19G1om084qQmueoa5yqX1Pcyjvc94tiZ+DxPU58kGOtw2ukll0M9Skoir8OXmqPVkOJ1FxlpQku5itg7XHIRZexNnArq7ukOYdSQXzhooN4pEa3EBmZ7wnhr5hv7Tgno7TayuKbulMMHa4FseuepAG/huZp6YNFLn2LlU8aaMG3otEJFHXbloU4DV05MDJNH5+zszTFWbOuQz40fcCVc4B9ofiB5L3deuoT13WSdDlwOaTSu0JmkXYaotaKtFOcnsZPDeRZdKbzYMfyvjReAmzROZkN0rbh2eYa2AgFkQU2kOsrOxyDYizecFgO3Q+115CezIfiG03XGj1w8y9VkQd6FpF7NTxZP8BPzp1jsf4SnoyN+3HuKf/DG8/zL46e3voF8HYEVeP1kI6Z2y+QHtp6HWR8e3jYepvBcylQPexukFfPKxxvWsOts/LZxSWrzlcuuVaVERy44Ro0KsqxmXbmynO0VOatOsrQJGMGrZg9rBGLk5JjLVYpUuj/qC6wHOjUwaomShuI0RbQCOW0IQzdpXMkKii6g1t425tIFsqttrvB5kTCMatLAIbVny9ShqyNNnNRscp/zmzv8i1mGF2iMgaoM6GqPWrgFrS58bOZkoWHoEq9Oepf5Q1OdrzQnsyF7kzWJ3zIKSvLBmqPVkM1Jxvr2yJV4UtfvACfRjX2Hv94Zut5KqxWrxkM+DI/3DXq3xY9aiFvaTUCTBxzrIYPUsX98pR2/qPN46ezAhekozXhYsCBxTKHayTOZB9QWgus5XevR1B6n+RBTeIhWct/bdYqrfncOFrnx6DYeH1s9yhOPHdNZyeOXzhxquwm5/ca+4zIFBj3UeDMPcxLRXDZc3lmyqUPyYYuYBXhrSe4nqNTVbx+sImwjiSYVetyRjF3y2boISYOG/avHZH7NfrhmFFQcbYYUMiY88mnaBBsasoONy4mu3lTO/VrHOyeAt9HQvVtUAGHU0h4azBXrMk1LH1NJtwOQFjlosdp93b2R0SqHEFCVC4bfNuVwcrT9ZM0oKIlU1zdcDa1V3DvvG1CtYPSECxgH+H1PfQyAv/OxL0Hv6l4R4sPGRwtLMK0co0Ya1k3oFDpRySKMuXbpzD0fKzldZy7A5eEOsg3pjKTqPCKvY3+wIW+c8mh8uGKclDw7OGbeJgz8iuNqyH+y+xqfE9/ikqq5ohJ+dnyXizJxu89QEirN4f7CafxjN8Gc5RnGwuzcQbzuLkNnkpMWFj6yFUgJ7DrqpKc0O0nJOK7I+6D2xSrB+AZiiz2OCJeuqa0qgQ4t8/sjlpmTAkpl8YOOxviYynP8mk7QbjyssnhLRVPF6IMOHSqHcw4dVMx6liStKeYxwblCRyClYRRXFG3ArfMdmiJgMl0zCBuOLkYMspLYd/wjawW+MmzqkHURbjcPqk/m8pVGJB3iIsD25Se9CggmFZcvzZHCcrFJYOVTzQNMYNAjxa2+kV3XHnHkZLiR3+GPN33ObUjdeW6CPMlIb3roGMcrsh5qWDFJS8rWw5OGMHJlrOPlAE8ZBklN4HVoI2m1ZL2J6RqH426NgFYQDB2/aFFGVJXPztAxe0K/20LRzs+GyAsf1UctmsASLCTiIqJ+zBH29nY2jqo7lCyOhq5ZHbnITIzAFIqedoFaKZZVtKW61totfgfXZ+4ErToCpXn1dI9qHbI32hCqjkp52FaiKuECYpYKrSytMshAQ+A2JwxdWa/uFPUq5FgM+fyrt1k1MbkK8XoFkYq0g/jlEh1Ydx1Vir1r87du0nlnAXj7DKsFqzyinsUul7fts1WVdXU/4SYfE4C8H6FTg40M3aiXnLXOlahySbfjVDzGSO6fjTmSI4KgIw4bfGX4KNdIg4YoaskLD2GkC3XXiv3BhqvBjLWJ+Nwnb3FRpaR+Q6097veyxxvTOavGZfYuy4jFLOPGlXOe3jul6AJaoxgFJQxA4kJLtBWESpM3ATtxwf3lCCUN18YLrj63YBK4HkGhA55Ojvmi+HX+9vkHuOEJ/srF84xUyZ16B4Xht1x+kdNmwJ18h6PNcEsDnZXJ1uK/lxSOfJmHmFohz95EYsgO2oOWL7/2OveKMY1W3DrfQXduQrUGTO6jhg2mc/r9aqBdbm6kUaEmCVvGabk1/8zzGDELHDQu0TBwE7AQFltIOgGikS6QBkfwFBqMFKRRQ6ET/FzQDQwISxbUVJ0jnYqZz2K+w9K4k0P7rsbtrJXeUiUbrRAiRHcS2SdJNbXH+qUdSFzSl38vpJlospsewmScjVP04yW74w3VJMCsffxJRRBotJZcGy+4txzRackwqbYngbZTW1no7ZcuceOfGU6+AC79TMv9L/ewmWs+l62Hrwx5EzBbpK4mnnvIrOXS3pK9OGfdhsyLGIRzZ2stnDS3D54HCD1NaULOLgYEUccoLemsZH6RoWY+eqfFOw3wSkFnoUus2whVDqtdjz0XCdp6pAf5Nh9hsUhBWgYHG4Y3ai7WKUoZ8iqg7RQ7aYEnnGM88lyQfdEGVJ11PhTf4+hkzMG+6xcFWYOJO6QydI0i6AUYO8N869aOgpaLZcp40JCvPapGcrIzZBSWnNUZQ79isY7RtULvdYhSbU/o8e6bHpe3YrxzAng7DWmdYzPuCKetkxmehC4pSoIqBbJzzSPZCqwn0L7DBYtGYAOLt5IES0E3EkS7ZQ/C8tGdxPc0s546KANNmTkzjts6wf0HE6Ksoeo8/l8vfZDdLOfJ0RlvzKcsipi8R+U+sjvbGrjSoGGWJyTDytXR9Zv16sZ4W/2+J11iUmsk6yJklUe0pc+7HjniA9PX+NjyOhd1QmcVr292+X2Tj/CZ5pC/duUjzLXhvfFdBrLkQTPiueQ+ax1zZMcs6pjZIiOMGsZpx1dfeYX3Jnf5m7c/4HJ8vZRKBqi0oSsUwVzSXG9IxwXP757y6dkhw7Byk/9NV+7QQ01w7noZXSYQ0rJ7Y07it9y+u4tc+KB9ionHpdGasvHZlCFdqwiuOA+D7KWWDyfnlWfpNh5I6PpMHREYRjsbiipglUcuI+CLZwyCfnKqYpZ5TFt72NggS3dq2HvXGUXtHMvX0znL1qltOiOZRCUv3j5E104BZlY+QSnQMTCt6XZBnjuz1OFPbcivxBzthCx9TfRiTJtZdOZ2rlIaVnVEErTsJjmZX3NSDEj9hkUVO6GCNAyur7j92weItGJ9L6Tdc5Lfch4jdizG0yRhw3SyYZVHeFlNsYo4ejBhMwnZSYttuU5XHv6gJooqvKEh9DvyOmCablDSvBlUYyTz+yOiEw8dWVfa6Sd+E1j8yzlNETCe5OxmOUeLIV3nQmzy05SykNjAOXoB1rXi8LE13sgtVnWnWK1iVuvYgeCUxvQhOJ6vqUuHOkeAuAgoRgGh0uhOwVFEM2nx0/YXOaFdvyvXDm9ujeTs/tiB/kZ99oXXcGu5w0wlXJqsKTMfY6Go3GdrnJQEUm/hj7/mYQH9H84K8PZfAPrdzniSuyaltJjDCtMq/GMf2R9Rg7Vwd904pgwW/I1AdNCMLeUlg4g1V3cWnG9SAr+j7AIsDsMgBOTzmFWbQCcRVa9KWPg0c5/NYx0HwzXaSO4XI6wVrDcxOvfwspabZ1PaynM196h14d9ZuW0Qh6pjP1mzbGK0kch+gdmJCl6fTQmDjjho2T3I8YThU+sr3F2P2Uty3j++y1kzIBEdP7V6kivenH80/wrWXURrFFfiBZFoibyW1+UeAHs7K4o6YBoXvLrewxeayGvxhGE3KfCVoWh8ukODORBMk5p1EXKcD9FWcH85cj2B3RbhGybjnObA45m9E14+3ydfxuzEBZlf4z+it2TMWnuUre9yifMA6VlMI/H8jqifxJV0aVRB2GKDbhtEg7Ao3/GGur7UYpUlL0JWvXPXtNLlFleKaLdESkNT+8zXiVMYjTtK7Up2xgouxyvOm5T9vVWv3PLRY0slnMMY4NLukqN6h80zcOvzoC07vKilzgOGawBBZ5wqyu8zf40VXJQJtfac8WsdQuPKj/7QKXJE5l6TzddueG7/nE0TcrebOBdv0LpoyU7hec6wle6eclIMWJURp6uM0O/4okdu8YmTy1wZLbk7H5NvIrKsQgnLunaLVtspJ589iQmWzt3ejjX+uKL1QkSsSQcVk6TkWA+dAqqK2B9uuMgTF7WoBV4uUHNXWhEWdOvx+skuVjvOv9n4BJOKMHSoDr2KkLUTK9S++zy3sYRWQNIvVE3gMNGZJh5XPLZ7wQuvXkXmilWHWyy0cKqqwMKwQ0wawqjh9nzCex+5z61gh0lY8NLJJZq7KSYxDC+ticOGpi/3/eLYz1/reOcE8DYaQlqHx9Wqb4w5u3nTeNR7Am/uuaDy0EUSdrst/rlPsBCunGBBxxZ2azxlWFYRRRWgtcQ0igpnsupq5SSglUuTGt9YkJchzdw1ZHfSguvZnI+fXOF4MUAIHH4g1GSpc9dqLdG5T1tF7FxZMAjdrnAclbTGAdZmRYzfB3THXsvrsylp2KCNZBDWXEkWvLHe5axMCZUmUi2+cOWsv3j/t7Eb5Pz3974GT2rurCb8T8/9/3hvEHGuc0605KuTe/yJ6rdzUaU8OpzxZHbKSJX89Pwx7i3GnHpuYkmDhotFRrcIEIlmI+BwsmJVhaw3MTf2Zzy9e8qrs1320pzEa1i3EZX2uTJa8kbls25CNk2IJw2p13CcDxiFFRanFxcjRx29fTRlMKoZx65+nHoNN7sdsqgm8Vu6sVts8yYg6vNz66Sk1oomqTBGUjXOtFVXPrqVyLTF9zsnuW0VXRMgOsELy6u85+m7eEJTaZ9Pzi6798ZINp+ZEM4EesciL1fbHkXkdQyn+Rb2h7S0swg1bqg+sGaQ1ESWrY4/8Gpk/7lZ16Erz6w8vNwp0szSw0QGm3Xs7Ttq64P1ANXLY60VHM+GfQxlR7EJeeX0MpPLS0ZxxTQtyJuAw8GK6/GMRx694MeOn0RridUusMZa2M82zKuY5SKBRYAw0Ew1/qQm60Pru0phJaS7TpVjrWB+3yWtnWkBscYLNWQtTaCg7U/P/SnALAOnjfQNItKMstItWoGmW/qEZxJhoB1ZjAIdaoYHmz5IqOF8k+KduwW5VDEvnF8jOvYQLVT77jV6qL0UypJmNb5yklFxEvLj0ZPEfsuyiZ2z/FLFzqAg7mF5FmdgzMtfSvf9NY3fABWQEOKvAL8daIDXgT9irV303/tzwB8DNPAnrLX/4tfrcbztFwApnfFpcZohGul472PYG28405KuUPhrQXVJY5MOsXFPqR08TI5yiGDdSmchz9Y8NTnjMFryoZPHOL49RUS6n/wlardGt67xnEQN/r4DwVWdx83VdCsRPBhsMBPByWrAcpmgPOOkg0mNAIZRvQ1Cyfx6S1AcRvW2hjsrE+rKJw0bHh1f8PL5Pu8d3+e3HLzAT88fY9nEVNrnvM34yPF1dhJ3ongku+Be4UJTvnfx+Xyf0DwdPaCxiq9MbrETFFxUKbM64SV7iU0bEkjN73/io/zNj34A4RkXvB43yKTeTtiPDGZ8oryMkG53+Tk7d1nUMUXrqKISh4s43WRIZThbZIwHTiefeA2XsyXGSqZRzoPC7TQv8gTpWTott94BYwVN66GDFomblLKg7hu3mnePjwD4mbNH6LQiDlqe3j0lVi2xarmTT9g0IcsycqqlpMO0Aps4ON3N2Q5aS5ra357QlKfphhoTStRBibAC3bjyxc2jXUYjh48OJ65OLyeGNGhJ/IZ1EzJbp8wvXDJb5Hdusah9jJa08xAxabCXHGxOr0KEbxhPcrRxk73zVbjyV2ckpnNu9LbyXMlj0LJaJ8zPB/hxy5c++gYAz6e3+Vj+KO+anPBjF08h5gGt9GHU8sodl4chpMXErjl6pVeauRwCy+CGC9VptQQ8dOPiHdHCpYmtfLpUIEONVRb/3EM8lnM4WXHnMwcc3JhhrLtu6s7bps1pLbGxQSe9CKAUEFve9eS9rQtYG0nbKvRhjZgFBKder/Ry17aqBFoqwguF8S3NpZZRUpL4bje/qBWzTYLnaTbLGBYBybXeDFe5MlkgNcsipl2Gb9mc8xt0Avhh4M9ZazshxF8G/hzwZ4UQ7wJ+H/AccBn4ESHEU9Za/evxIN72CwBYfE8z2N/QdYqm9sEKLlYp7SZATmuaoSS6GWJ8STsxtNPONc/6XZnQ4J0FeI9u+P2XPsKXxHf5rR/9T/ijT32Y7+4+j4NswzCo+MTRFbQW+JHZZt4aIygbH09pms4laR0mK4wVFF3A3IvJdURX+uhZiBg1XNlfMAoqIq8lUi2bNmRZR2RBQ+y9mUF772yC8jTXh3Ma7TGIaq6EC/7I6NO8L77N988/h9fWe3z84ipSwDce/jz//OzdfGp+GSks++mGN/JdUq/hajDje+5/AT+SvovzKmNRxsR9U89XmnUb8tHFdeebMJJhVLETG5Z1xGyZMohqtBW8a/eEs9LVU3/k3lMslilm43Ol17+frjIW94ckBzld53Z2F0VK0Te0lbCsVchFnhB6muvjBWYkOMszfKUZei2rOnJZDrELxdm07uLdiXKWTczHLq5Ta8XZbIiuFGvPMAorToziZDmgXEYuwtICiSaIOhrhcAS2lhSbkCevnHJ3PqaVLmbRWkE7aPF2NE0eOHmwEc5w5ms8ZdiUoXMN9wH1+W7JIHESZIB4UDlkxjpGP4jd6TLTEBoGo5L1MsaWHngGFbhS0TSu+LyDu3RG8eL8gFXhnLpSWazUrmYOGC0R0vD4jTOupgtK7ROrlr9++ytptGK+SZDSMHh8xqaISOOaLGw4WQxojxPUXsWN/Rlv3Non3SkZJyWzTUIcNltJr7GCdFSyOUuJ77reS7VrQOJIocIiHsuJwpYsqGHc0nSKYVRTdx5l5zuYXeGTDGpUVhFedhjn2SqlXYTcXw2dyqxWrGWGLBRMGvafPmNThdSfGTmEy9DSjTtU1lGLAIzjQAlcDOworkiuOM+DsYIkaDnF9eoCT1M1Prdu70Hjylf++i0Khf8NwkFba3/oF/33Z4D/qP/6dwL/m7W2Bm4KIV4DvgD48K/H43jbLwCuueWUO8YIzHkILWgJUoJ/5GNCSzvooWK+iwwMsgY5qlG9KqMqA25MZ/yjs8/hz7/xDdhK8Z3dl1KvQ/IyxGjJM5dPeLAeUtR+T3a0WI/t0XkY1fzeKx8llTV/8/YHXNh5UhL3dMT1Jkb1PH0pDIs6xlrBeZEQ+912B+0rTSA7dOHxrqce8ER6xq6/4enoiK9PKiDhg7FG8THycYCP5ic2z/DTy8dZt46h8vjknMcSlxuwaBN+bP4MizICxoyCkjRoaI2k7CdpKayjW0YdO5OcVismiWs0tlpS1AGvzPfxpOsNJEFL5DsOUu1pGq3wlWYnLVh4A8pNiO0k592Ay/sLrmYLOit5fniPjQ75kfppVnnExfnAnagbyTxzQS++r8EILi4ybnmd8wy03hYgJ6Vlf2flNPPCEkUtD9YuAlRruVWA+KOaNG6oGp8krR0OJHYGtFeP9gEwjaLNfdTSQxxW/L5nPsbH5te5ebFDbYVjFgHnxdBhqUuF6CmxTeXThT07p6dZNrXn1CzThiBp8SzUs9hl1foGmbaEccv+cEPVedw7m3D77i5+0jIeFi70pwzQSx8kNDj2EgJoBa8sLvOKf8mhIDzDe568x5+8+sN8z9kX8x1Xf5j/dfUE//vpe3l+fI8fvP0c7VmMVwi685A3VpeQpURMLXkd4HuapnPQufXc9bZk1EHn0u2a/c5hvXEuWxA8sjuj1g53/jDY/fb9KX7cksYNRR/IkoQNQc+EyoKayX5JPfUoWp/BpKAsQrrKwwQGP9CMwoqDZMOd5zR16xEISJVrhB9cW9NZxQt3D5nnMfvDDeOwpOxcHnfZJ5Olw4rNSUZOiog7d4rxLHLc0A3fQifwr74JvCuE+Ogv+v93Wmu/89/jz/5R4O/1X1/BLQgPx73+tl+X8bZfANpOMbs3xls7/k9Yu6Nrvaudq3Yp8ZfO0m6Fq8Faz9IQMJjmrC9SV/KYunDuj75xw/HsW+kwBb4r9VwfLVi3IbN5iugbtFK6RaBqfA5GjtL4N1//MtKgYVlGVI1PHDZMEhcYvlyl+LJzzVWryHx31p1XMRbcDqrnqxythkTjiveP7/IX9174rM/9p/Mn+U8nv0BuDevkNkoYzquMcVzygcmr3KmnrFqnkZbCMIxqHiyGnHkpXecm0nFS0hjFqgoxRpIOKmK/3ZZtJkHJOCxZ1K7JWrQuV/bBuaMuSmWxBpabGDmwjKOSYFQ7Xo1vUMpwtsw4mQ3xg26r7NhNcpQ0XGgJwtLiYzqBF2qqVYia+djDyjH1tUQpQxa3TJOciyJlGhdbJy843T64OvzCQls62mpeBr1MFZp1gCgUppIo7fQDwrcutWqnJQlb/rfPfC7NKkQtFeGFxM+d7FS2Fq8E1bpadrknqaaSxWVJPKqoCidAiJOG0G8J+tzkvAxdFrFnXCBN7tMqS9H6XM5WPDE+p9LeNsOhbj2Gg5IVTveuS4UMNabw8BYe5nLF4d6SYVixrCOGfsXT/pKvmbzAXzj5AP/ijWepLmJekFcRtSuJZs/N8JTDNhS3nfkM6+S1XunEEb5xvTDbuJq9fqTiSx+9yYNiyMlqQD6PyXYK8jZgP1mTBfUWqIcV7PUk0bwIsY1klUf4vnapXVWI1pLyKINBxzM3HmCGTuUTqTebs5X2GcUVpedyNlY9++rheOzwnNtnEw6TFZ2VvHK2t5Vor8uQYVKxUSmiloRJixo45ZSuvLd01y5+9T2Ac2vt5/2yv0eIHwEufZZv/VfW2u/v7/NfAR3w3Q9/7LPc/9ftTPK2XwBkKQlPPVQFOnIXKxZULlEz6T7U/WdIaIFXCGcSiSTlxRgGGhlpDgcrXjvbhZXvGl/Tkq72tpN9pT0eLIZYLTG1QEQu9D1QmvNNyrxwDHshLOvaNZyqeUSlY9ajmDip2dtZkQXuyN30gDBwi9gkKhHCEijNRenCPL7h2Y//spP/z9Ytrxd7/Pf28zn0F1wLLrhT7rCsI65kS35+dYPr8YydKOe1Yp9NG7o84N2GVR25+rgVHF+MMJ1gMCqZJO4xrKrQxTwaxQvnl5idDxDS8vjVs21YzMOySeB3rC5SmrXPQlgGYU0YttS135veLNU8QuaKMtHcSnYAXBxi5RLOpNKowDAcFASe5qxTCOuDACncjr3II8Z9sLen9NZMFSiNxeUxSGFZ5xG69PDOfKqVj7BgAkPteYjaGc2sdK4/rxQED4Q7KhqPai+gOWx59O9bwtMVoukQRYUNA8wgwvgKb1VBp7GRj+gMehBS7SbkB4rNDSgeEYQTN6ltipBnDk+3jf7PHB3ASQCLmPNLig9cep1Fm/DybJ+ulzomYcNOXLCb5Xz+zm1+9MFTLnt3AOXYxxPOpNj6zhH+U/ef4AOf/tNOlty4SFJ/6fj9AMV1h6RW0pFbw6sbylmMf+45Vc0jOVenzpELsNxE2FspWgR8Kj3EAvl5gspavuraq/zMySM8yIcUdbClvtIK9uKcZRMRhB1do6jzgLqV+MOadhHhzxVBI0gfmZN4DS+f7zNJSqLYNXBD1fHG2bQPZerYnKV4mbvGHuRDOq22J/1X53tczDJs4dGNFHuTNaHfUdQBftawd82hL+aF2xyIQmGDt4jg9haWgKy1v+lX+r4Q4g8Bvw34oLXbVececO0X3e0qcPTWPKJ/c/xbFwAhxDXgu3ArmcEdc/4HIcQO7tjyCHAL+CZr7bz/mc/axRZCfC7wd4AY+KfAn/xFT/yzDiudPMwrBOHC3SZb19gFtyB0qd02lowH7dAiWvCXAi/36JaKF+whJndZrPSBJlZLbC2oIp+j1ZCm9vudnCLwuu3uJA7aLUhsuYnZHW2YrVNU5uSe42FB2COafekC3rWV21jJQVSjpKFoAx6shlS1z598/kf5zya3AXjQbUikIhEBvlD8YBFx3F7i0eScz2wucThe8LcffBmRcg7ReZ1wI5nxtYNPsaMq1NDy5+7+TjxhmIaFo3p6HTePd7m8u0Bbwdl8wGYdOfVGo1hvYuLYQcyGk4I8j7hzPsH39ZYN1NQu4SsZl3SdIugRyuu5q5HTSrpOQKoxow4hLSf3JojAmbZsozC+QdcKL3Kkz/k8QnQSHVpspVj7IaoPPL9Yp5y0Q3QrOW6dyY+ub1bWb/JevNLtbP1c4K8gXArClcEKCOctqurjFI0FY6n2Y4S2eJ/Q3P66EON1WCUQncYmEVZKRNO5n2s7UBK52GB9D+lJkjsN4Txi+ilNOw5ZPjKlehS6KzX3liMuD1c8WA8ZDUryJzTlecLOdM3dcsKyjtHGyRR/9N3/M//F/d/Mso24qFJuFVPOZgNsnxngJ43DT3SKTRMwTQvSsOHk3gQVd0wOCufkPh6hY6fuEbF7vw7TFYtNwjgrKNcO/BbMJc1lyYP5kGvTBaOgZB4lvJEHDqPdh8+ku64UeK8Ysy4itBYY7XoCSVIj046b8x0nW20VVkvHUrKCdh4RH7kN2uZdNftByxvzKZHfkTc+UqQYK3iwGLqIzB7oqFYKs1EUacVqFTMclgyi2p0QWh/WLmq0u4g4lxajJYOe9DtbpzS154J+pO2PN59t4/zvM35jWEBCiK8D/izwFdba4hd96weA7xFCfAeuCfwk8LO/Xo/jV3MC6ID/3Fr780KIAfAxIcQPA38Y+JfW2r8khPg24Nv4t3ex/zrwzbga1z8Fvg74Z7/SHxcG+hxqrAAdArFzrcoWRI4zecVuIdBp7zTtHFNGaHf0jeOGye6SL96/yYdPH+V0kSGThnYV0tQ+5SoiGZW9U1MyfzAEZbl0ZU4aNISqI/QkeR0QKM3ecMMsT9gbOMt7rZ3qoex8x8QRhvvLkSuJtB7DvYrWSLKo5spoyaxLt8/x0MtorcYXbsH5+qSitnf5X1bwoj3kp5ePE6mOk3LA5+zd4xMXlyl1wM+Uj/Oe6C5TWfDabBchLF995VXu2AmdkURxg7Zu91a1HpHvcMyzTUJd+q5p2Shk7AJ13nVwTKQ6Fk3M0WroMMuVT5H7oAWNCFmrFNGXPFTWYPu/U5WBk5S2LqLP9WIgymqqkxRx7GOkK+Hp0KGimfm0a4+mv3ZlCzozqLVCVe599SoI54ZorglnNbJsEWWDaFqs7z6+QhtoWuwwxcQ+OvYRxmKVRKcKYS1+0eHNS3ZecDvhei/Bj3zUukKuS2wUIMp+F1E3oBSiKbFRiI19jC+R1hKe5OyUEdNPGYS1FJfHnMcT9EBQHgjqqUZqwez+mNnZkMnumm958ic5bwf8o81jjH13rXvScHs9IcsqdNJQVT5KWZ679ICLKuUwWVFpj9NiAL5BFx46c83QvcsLNmVIuYwYT3IOEie7bCqP4/WE4MgnWDnUs859tKdYZyGp50QI8aiirvx+o6AZRjmh6li3jgBabSKEsni+i77UsWR1PEANWnTp5NLBoMNLXQpcXo1AwN7+iuP5gO4iRq3d6XwWa7ysdSfKwkMVkkpYgus5SeTkz1HS4ClD1Xmcnw3x4xY5qTGdJBuWlIVzci9uTpAd2NbFaRJaTNbhDVr0/P9wKqC/BoTADwshAH7GWvst1toXhBDfC7yIm3v/b79eCiD4VSwA1toHwIP+67UQ4iVcU+J3Al/Z3+3vAj+OW9E+axdbCHELGFprPwwghPgu4Hfxb1kAoJeLRaBqtyB0qYXKqXtk7YKz/Q1YJWiHLVJZzDwAek2ztkzSkv/T9Y/wW7OXuVtOOJ0PHG0x6ZBKYwNXk21qDz/q6FYxNjRb9PGmDsmrgIPR2uXZWsE4KZmEBcsmpmxdCE0W1C44Q2mKPCRJax7dvSDzHa/9SrLkS0ev8oeHp7/kOT6c/B+OUPj8sdExShheKK5wM58y8CsGXsWjwxn/w+UP8x3zJ/nrR1/FvE5YLFKEsHwycclduvN5YnrOG/Mdov40MzsagW9JxyXK13SnMX7h5IHNNTgvMy6lK+ZVTNX4jAclDEoWq4QsdciDqvIdk7+QaNzFkoch8eGGTrpgHDlpQFh07pNGDd1ygAkteqiRa4VVuB38UhCsBfGZIbpo8fIOb1ki5ivMcrV9LUSaIgIfO3LqJNF26OkAuSrdm2YtNo3pxjEmVBhf4q8arBLIziByd0XrNMSrLSYQJPcKlk9leGVI8iBArSqs7yHWOXaQghCYQYRoteslWIv1Jd3ATTQ68QiPN2SvNAit0YMIE3qookEnrkQo646L9+zwV7/mN/Ho3ozGKPbiDasm4s5ssqWBVrXPjf0ZizLitBjwZfuv85uHn+Z/OvlyVlXo+EUPQtazHZa+xYxcczy8F1BkAY9kF7y23kN5xjGDznxUBfXE5QAEScumCrnZ+BRFSBQ3mNIjJ8LrOULzKuZsPiBJanZ210gBqzzifJG53Ie4wxqcSbJ1yGYpLDcmc14sQgZpxW6SOyjf1OPWnT23K7fQFZ7jMG2Ui53UknoW0+jEbbg9SyFdHkJw6tFOPJLLG7SWHAw2vL5y4gozrZ0TvXEZB2HSupOAcYEyb9n4DTgBWGuf+BW+9+3At/+6Pwj+HXsAQohHgPcDHwEO+sUBa+0DIcR+f7dfrovd9l//67d/tr/zzbiTAt5ogqrBGLebVw0II1CVWwxMiOsJVBavFLD20b7B30hkC+3AIC9VPD+9z+/MXua/fvC1/PbdT/CnDn+YgWz41pvfyGsnu4yGBWUdoDxNU/mupugZitbHl4ZVHjEZFJStT9O5WLydqOC8zLbZAonf4klDoDS35xMOd5eumWYUF1WKLzV/89pP/bKv76nO+X8cfzVfPHyNPzC4QFvDv1o8zVeMX+bl9QHTMOd6eMEHBy+wsTW3y10+9uojqKjj+qUZRxcjXr23T5zVLlA8KmhbjzfeOABp8RaOA59b2NlfkV+y1HmA9AzjYc7ZOuUid94EfRxTBSnxpQ2qRzJXG5eLTKaxQa8l1w4g9mfe9S/5THnIv3rwBLNFhi491MJjcTElPRZEM4tXSqKLBn9eOru950ot5vQc4XnYpkFXFWo8QsR9wIdUrlEQ+FhfIS9Wbrdea8wocb9Hgk4CTCDpEkWwaulSH2/dgJGYxMMIgV9rgpWm2PPIFjnpUUAz9lg9mtDFKcmZJvt4gxnGqHmO0Jb6Uoa3aVFFR7UfYaUgWLXIWmM9iShqbBzQjkMwluDVc6S1MBqAkpT7AwZpxX/96A/wXedfyi+cXWVdREwHOU+PT5k3MQ+8Ies6ZDdx0L6PXDzCp1eXWTchO0nJ4WDNaifk+OYUf6FQtY9o3Sm4fpBwenXA3cWYIOycGTEzLJ93JSXPSPZGG7eJKd1OOotqNirBzgNWxyHryxWTUU6auDjVxgrioCVLnAEzX0fY3iCJxZXmXkkpQrj5pECvfZazkEWcIUPNaFgw3nU4j65RcBFicw8z7raIFdEKrO/KN8I32LzPKNgxiNaVH7O04rXbB071NW6cbFdaRO5hPUvVSTAC78LjLWOB2n8nFdD/4cevegEQQmTAPwT+lLV21R9bPutdP8tt9le4/d+80UmpvhMgPrxmhQGpXWC10G7Hb3y3+zQeeDl0iSsDiUag1h7BXNBmFvZqnr58Qm08ZkaReu6Y7wvNc0HMl0zf4P5yRBK0bPLIGbqShrLwnLUfOJ4PCMNuO9F7PX/loXLGV3rL+umMJPUb7lcjDocrFxmoJaHq+KZLP/dZX7C/s9rnG7LbHHUeTyfH/IHBBQDfvd7n9eUujydnPJmdcr8a848fPI8ShlfuHiAuAvxa0I4kZiocIE05Jv9qnfCpVYLJPfxhw95kzenswC2ikUMRL5YpT984RlvZx1G6t2MYVyzDjqaP+1PKuISzyjHpRaCJdwo8ZVivYqK4ZaBKRl7J2emQ7KXQIbsDiE8syWlLdFwg1wUEPiIv0btDRKsxwwQp9qCqwfdQcYwYZphRilzmoBR2nUOnQVts0i8MSmClO921owjZaKwS+JuOLlLoWCI7D9EahLGYUNGlPsYXVLsCM0oQ2pLcKzC+mz78kyU2ixGtRk8HWAFGCVdKkgJZG3cCjRRepRGdAWsxkU9wUaLmOQiBUAoLrJ/doXik5Qv2HnBZFbw7PWLdRlxEqcspsJKiC5hEJa+d7HJ2PGJnf8U4rpBYDpMVF1VK7LUMgormmmImx/gLRTsxdOOGMGrZDTcOVpgu+PEXn4ZIEw9dME9dya2kOQpapDREXrdNbKMRDAcFkef0/A+d2OdFit9LqK0VyLRzvTMBMtK0IiA8VeT3B4hhy+UbF6yrkPUyZjHLCNOGMGxd/KrvFo6HEmQhLOd9Tkc4rXnP4RH3NyOq1mP10pRuryGNG7KwITx0vKmT1YBiHWLnAbJyyjKz1xHELU2ZoIq3yAcA79BA//UhhPBxk/93W2v/UX/ziRDisN/9HwIPaxq/XBf7Xv/1v377rzycvJ8uAKPcIhAucO1o4RaCZgTNxNJl2rl+E0sRC2xoCALNlWTBXrDhe5efR2cU33Xvi/nagxf5ntmY7//M+wijN5Uvm3mCilygjJSWk5MxVgtHgjQCvweCWdiGs8c9qrYy3rbc4vuOwhl5jh9zfznin/vvYep9mFfrS/xfRp/hbmf4Z5t384PH70ZeNRQm5LzLKExDYVv+6mf+Y66OnJNTI9kJCt5YuGDup66dsNyPaDpF0ytmzGlEfH3NTlqwvjvcctrbdUCZ+nTTFj9teP7KEUUXwC6c5SmrF6d0I83B9Rll47PKI/ZHG4Y7FSebAfNVgt74iLjDn3Q0i5ASVwqxlaIRlj/7od/D8FMBB6cGYQz5ZYlXwOTlHO/+DL0/gjBAbApsGCA37lhn4772JiUiccEyZpAiihqkxCoJ4wE2cLp5M+yVH63GKkkzCTGBRPoC2Vq6xJEuVWVBW3TkYQKJqjRe3iIbDys92nHkyg8qwNs0iKYDYxDrAvICcXmf6soAVRt0KN1u3zgAoVXCnTwapwby7l+g9ydYJbHzBSJLaZ844PT9kmefusu7siN+uHiKl4tLxKrFV5qz+2PmyxQhDXvjzbbcttzEzC8GfON7f55SB1xUKY1RVNpzlE7P4D1REHmag8GGL9t9nRvhOb9p9AK/Iy348+GSH7j5HooHGVZZRKQ5XWboTiGVIQpbFwCzuyavAqS0xL4TOOzEBZsm5PXTXdrzmBtPHSOEx+H+gtPZ0PV4OoF336dLLNWV1vWDhOX+/R2EbwjilnoRYWLBNC0og5ZN1NJ1ju90McuQnkNBW8/w5P4ZRRegjSQvQ+SNnJ2kcoDGHZimBb7U7A023JrFLmLVgLhR8MwlFzv5UXONdv1W9gD+w1kBfjUqIAH8LeAla+13/KJv/QDwh4C/1P/7/b/o9n+ji22t1UKItRDii3AlpD8I/I//tr9vJVS7dot8NoGlUv2HQNMbwKAb9cfL1GAb1UOABFlSMfQqrocXXPFn/JWTr6PqPP7Oa1/o6tmFR9kJmtqnW/vIQqErybWnz3l2fMwnZle4dXOffB7j72rn19EPqZ8ueEUIS2uUc/gKS64DinnM8IqjgaZ+TdrLK//G/a/kokz4nlufz8U8w6x8hBb8hdu/Cxm6OMDXH9ljVic0jccj2YxPrq5wLZ7TGmfGKn5yj1f3R/hXc7pOcXm6ZJYnLnYxrrjIE0bX3cIR+h3zdUJnJAeHC5rOlaOksKR+Q14H6IOGvT3nbp4kJSvpLqay82k6hV4FxLsF5SKiaZzu3WoJnUCtPNKXfAZ3NVJ3GCVIHlRk9ySy7lCzDTYOURdrbBpjRimiakFrRNthhjFmECM8heg0epRiIg8ZeRhfIbTZ1tZN4KETD+MJVGMQrSFYNph+csb2kYKhi6ispwGysXSJREcCf1HR7kV0CehAuixeA0L7KMB6CjyFnQ5pd2Kqieckxha82rpewEO14cBHlTHqfIWta+r9GC8LaJ+Ycva8T3GtY3xlzuunu7x874Akq7k8XHE5XXIQrfmKL/lR/s7LX0RdBRzd2+HSlTnHDyaoqEOe+/zDT73fhQwVHrQCb+w+P089esxBsiJWLd9++CN8/+Zxbte7zFQG6Rs8qEc0tctoFnFHmLR4nsb3NdfGC47XLnQorwKUMkzTgmUfQDQXjrSaJRXzzKPRiqZ3QVvrgHB20GGKAK8QyM5zmO5hg1x6mMTQ9qoca4T7fDUB5a3BFuA26XEmxgo+ePgyL60vMQkcVPC2mHB8f8L8PEa2gjUp+a0RO0/M0EbgzZxpb/99JwyCmpPNgBMGtJuAcPRmtvSvebyzAPyS8aXA/xn4lBDi4/1tfx438X+vEOKPAXeA3wPwb+li/6e8KQP9Z/wqGsAPC0dWQjMyiIMaKw1+4PTPTRHS1Qo/6lyYuJaotO1jBuHKcMVhsOSbR+6w8adnzk6uO8Wl3SVnQFd7dKWHTF0erTdueH7nHp2RztR0eUnVeixOB+RZQxB2DJKSxHeArUUVs6pCBpErL3nSIEONEpbYa2mMx2fuX0JvPIKxu08UtgwHJYWvtxiAdhMglh4/ffE01ncX8I/efJIkanj02gV3NhMABl9xwoHfcn82Qq99qpHHOCmp9z3evXPMpy4OHdIaWJURw7RiXYTblzNUzrB1vB7QasXu3gprBWnQMC9isj4S8qJKKcsANWx4Zv+EB+nQAeQuIlQuHXDPQnZk3lROCNhcj/FzQ7ACTxv0JKPaj4hvrxGtm/gBJ7O8cwrjAaLTmEGM3JTISmFS93hl3bndd6sh8PAXFerIlcgI/O3pQY9ShDFUhxk6dOWhh5JQ2fpYT2KF4P5XenSDjmpHEeQGVVnqHZ9iN6JLh1RTpxrrdlvCzC2wOveRG4fClo1LpAOJbEdYNcQq12tCKFcjGVcMRiVp2Di0hHRGqVUTYhgD8PGzy462qgUq1px+Zo+9py4YRRUXWeLq563DSsSTepuudn854unRCf/kF54nlB218fjJO4/z3sMjvmnxKJ88uow+jvErQZd0jsXTSdrC5+WbQ8yk5Xe/9xf4J6++G9Fr6eN+IwNQLiOa2IW4HN2ZotaK6FHH3/HWCtbKRWX/ooKuXQagQMQdh7tLjk7G6E5ysnHubav60qx0PTVtBIeDNUfVmKeyU15cXaIxHmfzAaJSyD48JpsUFEHoTGN5gj6oOdhf8uhwxksX+yxe24HdmmRSOkTMWzEsrrrwH8j41aiAPsRnr98DfPCX+ZnP2sW21n4UePe/ywN0MlAnHVS1oK3V9oKQ0iKkIcoci1z2pEVrBH6ouTRacxgvuRpcsDQlv/2F34/udcxi4XPU7KBC7RpQkdu12P9/e2ceLFmW1/XPOXe/N9f3Mt9aa3dX90xvtMNszAi0iNosIQqBDhoyGhqKAaFE+AeIGqgRGoYLGIaCAYpiiCwGq4gDAyKDOMNsNAw9vVVXV3VVvTX3vPt2/OPcelMaTNMzXd31mMpvxI2XeTPfy1+ed+/5nfNbvl+r5sxwysBa8ly4TcvKefGox87GDHtLE4VN9ztsdpYnk/8s9ihLSSwUa0HM4aKNaVc4ppZ8LJWkWuqkRXHoofyKfOpidHKqha0vuoagq7aaQKtVIaQim3h0zqa8uByS11qAIy1NLFmx3ok4LExUIwl4YX3CJ492Gw1lgWlWeA0FM4BvF5xpz5BCcTPs4lilbsyatFFTm+VmjNnw7rw8W2O+9Ol3YmyzZJr5+FbBcS3xrxusf7qkcnSVhzMtqW1J5QiWZ3UIZv2ZAnOeoWYLDCHwkgI5W6KyHCUF5AXCdVBlicgLqr1DZLeNcF1UGCIu7GKNl1CUiNgB20KWNUXPJd06S7Rpknc13XcZ6AWCM1Vk/abzNYbiUQMr1NeRVobrUvkVVi/j6J2ervyqJaKbE7SWmLJm20sZeiFxaTOKAw4Puwi7om4rhF1TFlI7d7PSVVCCk2TpchIgpMJyS5ZHLcSG5t+xrZLFcYv0MOBAKHBqNrdndJsKK23gZ9hFp6O2Tnz6JcJQmuJCCVRiEqUGv1w/BIXgF55/lH43oshNPn28yXKvrXVy3RplCx48e4hrlMxzV1NWC/DaGRv2kmEvZLwIyDKLKNY6zFUhcdoZtl2Cn7E8aFMPc8pSX7+GaEqvm3xc6SmUU/HYpRv4Zs7v7O8wCX1kI9QyD126rZSdh45Ypg5FZXChN+HZwy1eCD0uGwMe39nDNiq6dsoLYoPKrfR1JRRdT0urThIf2y5xnaYfpzaYHHSRSo9ZfBToXoA7AIFahYBOE5TU1Q5K3wNIuyJa6KqEwjWRVk2Z6bIww6y1gEYucbsFf3b349zM+/zi5DG+/9qTXN9ba5oJBFiK/mCptX47EWHsIqWisipsWeGKkivLdT2R2iWT0Kffihl6EY5VsjftohT0Wgm+k6NsQVUL2lbGK5d3se7TDsIyKi5fH+ryyEhXUpw/N+LGcZ9uO2auBPXYRiQSmQvd2dxMEAB5V5eUHsVtOk7KNPUY+pFWugJ2BjMGXshe2D1Z0SslaHkZZaUTY5stTWMhhMKWOmFtyZq9SYdy1CitDTOK3KQsFcdRi2XkIoDxuMVgsGQa+nrFOnII9hWiUtiLmmjTAmHiHebIXNK5JvD2U6zrI1QnQHRaECeIZcToqx4gbwu2fmOGiFLynS5Z38JIatL37lJZgsr5THLfmXVIBpJ4R9+QZqhDf96Rons1Z/KQw/C3IoRSjB8N6D+fEm871CasfeSAgz+2jX9UowzIW5LBxybMH+7y+JmbPK3OUEUW2Fqn17cL2k6GJSsWuUulJKas9cQb69W/MhSd9Yit9pKw0BKZeWlgmxV1Lemth2y0tEjLqBWwiFzNFppYmiMfQIDhlWwE2jN13JQb4x7FID+hIRGJoatkasH29hTbqDT1iGNhmRVZbmL2csrEJLQd+t2IrDARQYm572COtCDMPHN5YHCDvF7n0sYx9VBrGPyXl96ud3ndkL1Rj2phIVOts+D3IwypiFJb6wOHJnlDmicrLdpe9UvIJNbcoKpNJonPZn9JN0iIM5t+S/c6FJXBVmuJKbRmcpkZTIKAdO5ALRjuzjhOWtiywgvmurS1kzHoROyPuowWAVPDI164kBlaA8AseH60AWaNfzHSDWe+TdDk8e4I6ntnC3DqHQDolYYsNPFTHVqa/z/XzI+VrRC5oA4qDKuiSg2kXfHkzmU6MuHHj9/O1f11XD+HzACnIlhPqGvBe7avcpS1CMycw6TNg50jnpltk5QWvzp+kMDKSUqtuXqL0/8WwVvLy5hMAw5nfexudsLOeHk8oHJrttsRgZnz7MHmCd957dTsXBzRsjOkoYm6giDF6YWMRm2qpYWSiuHZKVWtOzWjxCErTIbtCCkUTkOTIITCs7Qm7Y1lD9uomEQBRWTRHkRkpUGaar6cspLsdBZa5zYNToTMH9gcMe+5HIy6iEN9U1adkrXNmO32guf2N/BaBdN5QDV18K8b9F+oEFVNEUjMVNG6maMkmIsUcTDGlZJ62NPVMMsYlWYsvuw+uh+9iX9YEH5dwvPvdJGjDrWrkKnAiA1NaBYoNj5ZEw8leVeX+iabiu4LkPcEW7+xpHZMxo+4yLLGm9QoW2JdOcZ8wNfJWcDIFBQltSloPzch2+5Q+DYiSnAO15lf8thcW5C2TdLcIs8sRrMW9LSaW1qatKxcK3utRQggSTQXkCFr9hYd4tDRO8amFFHkeidxaf2Yvh1jyprxpEVdavI6Sn39GpGkEjYvOesIAUIo8lBzGDlHmu8q7yjKMxlr/RCBJvIzpCYkDJcutlvi+RnKy7FNvStVaDZUlF40AZSVwYuLIa9M+idEe/XCQhSSpVSawnpqY+TiJKQTRi5CKvKlDWaN9DVff6sXwyZkmcWwEwFwcNBDhCb7n97AebzkYmeiP1dJjuK2XkAkPofjrhbyMRRPrN/gz539KJ+Od7i8HHIhmLCXdPjY3jnKpgdn76CPKiRZaiBivdBBCZYzn5FVUpQGTpAzaEU6x2bbJyGs141VCOiUQejtplI6CWfODF2+pgRSCUq7on9xyteee4a4srme9GmbGRfcMYvaYzeY8cBDxxynLZ4Oz+IGOXUteGA4YlE6mKLm4dYe7x++zI+O38Uyc0hyiw2/iXuKmp6bUCl5wu1jyJrAzhFriuk8IJ+6OE5BUWsq4vX7pnhmwYujAXmkqW4pJf5GxFftfJq+GfHWzoD/fXgfcWazjF1Nr9tN6HopsqHAlUKdhBeyyiAK2xhS8760zYyotFmkLlWt46vnNyZcfnkTQygcS1NTLF7sk/haTN0zNaumKesTh3C2PQNgL11DOhUP7hyT1wZJ05kazz2kXbH2W5KNXzsA26JY86kciRmVWHv6pqeswLapNntUrkm11SLeMFlckCT35YwfPQcCUv1xGLFg42MQ7kr6L5QEH3mZ/W+8RPvXr+A9sMP8ks/6zzxD/U2P4CxrvKli/FiLjQ8dYt7vcvh2j9KH0RMu1uIc8cWCybFDGSiEEizOnyO8WJGuD3W4wlek/TNkW7qxz7d0OCHoLMgrg6I2qGrJwVyL/RznLerKoMolJAa79404mrWYHrd1iK4pbBZN17PyS1RusBd2yT2TWerRauvEZOJZ2HalCesyPQHHIx8ZGchS4MaCylZk6zXWXCenhdRykMvUYa5c7ShSUyvhtbUimNXoEphmSa2AQnPzKwmVq+h5CZ5Z6I7usQelwF7oYoqyo/M2spn8634BmUEdW3TWI1p+SmDrBcatsmfLqKAFaWlSVAbtfkzZNnDtgqEX0rMTksrimYMzJwuYspJIo8K0SrZ6SwxqPnD8CFHh8NbeAY/5N3h6/B6imQeFoFAC5ha0S4JeQuLaqFpgWhVba/p/5doFtql1tG+RMprmnWuWXYWAThGEW6HOpBQzPZHKVBNiyQI9sdo1l9ZG/Jnux/FFxYtFny1zyQ+P38Nh1uGr1j/Fh+YP8lMPfJD3GV/B7+zv8MT2Tf7i5v/m09ku396/yncdPs6TazXt4a/xl/beT5rYhIVWuooKG8/6DKOhAs1NH7bwrJKNtQX2sGJv0iUKXbwgI7D1zuFWYkqkBt2zc77m/DOcs0d8YPwYl2cDrYZlViSJTbbvo2yFsaMlEXc6C2yjOqGZALBkTVFLDhdtjkSrYfzUkoiZUzBZBHqSzS0cqySMHezzIZZZcbRo4TsF53ozXT0RtlnzYiSKTX8JZ3S8epL4VLVgetzG8Eo2t2ZEmU3pe8zetsHyrKRsgXsMRm6jHvPJe4K8o6mzS0/hjPUStHYUogTnuo211HF474rN5icK5heh98ljep8yufEn1gmea+uS3kfO6rJLCfWD51ASJg8Zmr54u2D89gHCz1hfD3mwO+HF8ZDFONC7F1/hnVlqao/Mw96MeejxI5LSIqtMrvWGUMNoGbDbn5OWJtv+Asup2Iu6ICEJHQyrPhEQMmz9+B8/+FN81wtfz0HdOYmXowRWJ9O6x45ObC9Th2mkNXPLUrOc9toJAz86uZ6i3GYRu6SOQ1kL6pmJchTt3QXhlS5GLqgzg3kZIGbWZ+rSNS0SYdzRYbsaqkBTUJt2hTeIEUNFURhQGVw77uO5hWZLlQqBHqOqU+H1ExyrZCnBcQoMoyaceQhDaRbPRv4yLTXFs93sPBepg2vpXYcAAjfHswpmmYcpama5p4XjWzGT0Cdwc2pba2C07IxJEXAUt7l5ZcBD7zzElQX5LUbQSqBUs2FemsSmgzRr6kJieHXThKl7LLLExDYrkswim3iaE+hOYeUATg8cs2RnMGMeuCSJrSmHpSKJHUyrpG2XzDKPf3Tzqxk4IWfdCVutZ/jyznP805ee4keyd7HtLfixZZ9xGnBubcq2O+e/jt/JU/1P8Rtpzbtbl/k3s7O8mGxS15Jhf8k8c9kONB2BIXR37zJzcE1NyOZZJVvBgrBwmCQ+ZW7gtzKipcvSLghjlzK0oBS0ziz4hotP8yXBi/zS4jF+8+ULrPdDBl5IWvYocwOZSapmFRM4OWlpnYjML1KtWXBmfUZammx2lk01haSsJHYjMN51UqYdHaIqKkkR2hSlJDFrrFZOZZXcnHdZLDxNl6EgyWySkY9MZCOeI/CemGi6Zsvk6NgFU6HeUjJ/GIRbwNIkWwdrIXFHAu9Q4Yxh+JtTogc62LMce3/B4ZcP2fyf+5TDDvmajf/CmJtfs4V3bYY197nxtZusPV+wfKjgufuHmOshN96ryOYu0kuZPSVxnDllphP+j2yMABjFAWteTFpZ1EroTtLYRPVzLe+ZmFg11FdaPAcUmcn6WsgD9x0wjnTSVdIozeVNX4FQSHQCtyy0eLxoQnftjZC/8tFv1rtOqXtLhKdIGipq06qwmh2XEArbrBgda1EUs5Njm7onpKikDh86OQ8OjzluBcxjj6jwMduF1v29KIj22kinQhw6yFxQBjUqqHTtfCURTYNWVUooJWphU0eSSkDt1rjbEQ9sH3Fz0WF6ra81AmyF6hRUwGC4pOclBGbOxM04mLZJI5vBcIln6Ql5mTqkhokhtbRj0jiBwMlZJi51Q3BXK+300tLkbDDDciuuyjUqJTi3NuWVSR8pFb6jqb33Fh1mkxbWWsq72lf4n9O3sohcpFVRV7qRMTi3II4cTKvCtkui0j2Zk+PYoQwtpFfi9yMCLyMz3D9wZHCnBafeAYAWTs8rA8cq+eO7z7GfdgF4T/cylihZN0Oi2qEjU24Wff7GC+9jEnv0vJRF5rK/6PDrV+/DdQv+9eM/yg8cPElgZrzH3eM/L76IaRHwFm8PgxrLqGhZuS5ZK3WjV8vS1Mm3bvCosFnzYl4YD+m4GWHi0GqneHZxQllcZqaODSvBcr/Nz9uP8uvOA4xiH8PQtBG+qamBx1aLyqu1lqujCdyEUGSlqSUlnZw4tZlEvlbdalgci9LgYn+Ma5SklUlUOOy25sSlTVaZLFsuVWlgmBWqllo4PDcx7YoisbTaV2Zw4WcU1iIh2XRpPzNi/3iTi59KOHrCwxvXZF3tiOylItw1GT5dMLtk0bpZEVyPMY8XjP7wDuKVPdrLiOm7d3FejKlNzbgp05LpgwH+iwJ7rjh4ckBtCcLHM8wnQwZSkRYmO50F88wlsiq6Xnoy3man5sakx/VZ70TP2JQ1EkXg5CSxQ+2XBO2UONTlo7UJ9TDHUgI1cTjODIqhQdvNNNskuoN7f9lmNg8wrYoz6zPWOtHJDmoeeaQjjxgtCON6+nPzUrOkSqF07L3WJG2eVTCOdP+GNGucthYkKpp6eiGg6ydUSpBWJl1Hh4isrQrZ/E+T2NEr/mOHqlNRlZpDx5hLEBZlS1F3SkRiYPYzesMl41Eb2TRpVUpimhUShRQgM0G1pgng3rJxyFHc1noVtcRsGhhVLdkYau0CAClq9uMuEn2tKyVoOxnb/pyyNtgzuiwzm7w02e3OORvMWJYO88JlnnkM2yFftnGZ356doeVlSKF1JExZM15oEkTPLfjBa19KWUuq0qBOTERi4K3FmobCzWjZOctM825lqcV43MK0K3bOjZksA+ZLD4TCWBj/T1nq64JCN/ndIzj1DkAKpWPSucXZnpbKiyqbs96UCsmWGTI0llyyxmwakr83e5xXnt9EeRXvePQ6P3D2Q/y9oyf4+WuPEM49vuP5b+B8Z0pSWXzP6Et5drHFfa0RP3X4xZRKstEKmSQ+HTclK03d0i9c8sYBVLVkf9Rl2XI0U6ISBG6OaArhq1riOCWWVRGnui5clJKjl9YZZfoqrYc5LTvjMO5oAY61BXVf4BgVy8zW/CuhSx1auPsmooSqrZgNC5CKyHQ1FS7wMpz0HwDUCIpKC8AYhsJ1U6pKkmWSLHQgl5Sxg2pXmG6BELDcddn4+BUW73oIM+43dd4CM1WYiaI2FZUDnZdTwMW/NifeWKf1Ssz0LS0GxwsqF+ZPPUzhCSaPwuzSBdLNinh3V1Ny9FKefWgdzILeMMQ2K8ylx3S/g7EwETW8fN5ECLi0eUxZS4rawGw6r9p+ymzhE77chQfH7AZzZPOaa5ZMY4+qlnoiySXybMT5wYyhF3LQ73D9sI8QikoJyqYmf92LmeFSRyaFI4lym3OdKa8s+iS5RVXpHVE9djA2ErLMwglSen7CMnVQwLAVkZUmnlWwN+uQhA62V+AFOZ5dMA9dytxksL7UK2gr5/7OiFEWNFTRmoZDVQY351q7QZkKM5TIwuTc225y5eqGVtZySlRqQmKizJoyMZmqAKTOjylHd8ObTUJ0GIQEj+UczNokU498YLLhL1laLpPYIy1NosxGNDoPx0mLtp3iGpoOwhSVFp1PXPp+wmHcwTFLOk7asONqehRLaoW7SRY0K/6aX6ke0jKSuUVd6wXNph+y3omo2zEH+32W17o6Z+Eo/R3cinjkE9cBRqegs3VMVmhn67gFfjcnyS1G8xZ5ZCNt3ThZW5oP6k5hlQM4RTCEnnSlUDx3c4vL1oAndm+S1Sa/G+2y25tQK0mqDK6V8PT4DHQL3nX/VR5p7XG1jPmG3se5Gq/z4dH9ZIXJXtilqCWfyne42B/zzGybw0Wbrzz/PHtJl3nmEhfWCRNoUloUtSTNLWaHbZxeqpNQmY1lVGy0Ql0hlLh4dkFZSWohdP5Cgt3JyGNNy4sA06rZX7axzQrHqJjGHo5VsijME/Kt9m+59C6XLM7C5ofnHH5JlzSxsUJIhor+SxBvCRZhj7mhNLGW3bTYV0J3kYYmJ/2RpjqJk9oLSR0JCqtGKRi/owLxIMuLFYuHJFg5yYZOqC7PS2pbUVuQrnukg5p4c0B4qWD0Dh/RSzl+xxbKL/D7CeutmGEtyQqTdBaQW41aU2xihAbKkMxKTVOhDIWoBFVQgQDXULS8DFPUuE2FE+hFgGcW+FbBvlRMZi3CllaNunw0wLFLfCenrAxit8JdS5BScTBvY8mKlp3R6STkTfVUrYSmHihsznbmpLlFvHB1XD7QalVFaVDXEmuYUOYGgacVsuahy1Z/SZabZDOXNV+vbAMzZ6e3YOk5J1rMi9ShiGzIJCPVxm9nTJXHzbnewcahg4pNRCVwNyPKUmq+JaumGGrdiqNli/Yg0vH43KKyJGoscc8viQ8D5KFFHSiMSyF1LRgEKRd7Yxa5S89JNGPnesnE97k+6/HO7VeY5x6Tww4I2Nye8fDwEFuWXA/7vDxbY7LXRTg1Kpe4/RQpa5apQ6ujGW2LysAzC/pOTFaZXA3XuDbpk0QOXpBxiybMaHiwhFDkpUGpJH03YZbqlbuooA4qragmld6ZLmxN8VIJoqbU1rZL4pc7xINcR2hyg2A9pusn7B/2EJWge2F65yadlQM4PdDkahnv3B5zPerreHsteX6+yf2dEQ9bI1yhOfUBBl7IxsUlYeHwbLSNIwve33mRK/N1pFOx1V4ySXxmoUe/HVMqg7Q0eXRzn0veITfinhahNsuTjtmi0tv9xUJXUgw6ETutOS/P1hFC4Zs5N5Y96loyOW6Boeivh5zfGTOJfJYLT8conRpSSTV1mMQmfl8LXAihwxFKCTw7pSwN+s8ZlL7UK3DfIlsHZ6KlC/1KsPkLVxn/kXPwMvR/Z84rX7OGqCG6r9Csnwq8SJAOK51f6JWYxxayguxCpjnXmySy1UmJnxKopQOZgQhNinMZ7a7WO14mDvFhQPxAidPKCAMXYddaeasWmhm0SXg6ZkldWNhmpeUHK6GbnNyaql9oOmFXx7Pr3KC3FtL1dKWTKWvmicuV6RpnunPiwiYtTaZLn6o0qJYWwUbEsBsySX1Gy4Bs5JEZCjZ1/X2vF51QdVhGzXEUsNkKeWhwRFzafOryGURoEg0yts7sY8qK82tTjh19/RzHAZasyRILRg6FrRBdvcO7FeJLCgvPKciEy9GihWsXTGJPq6QpaPkZtlnp3ovGOUtToRRsdEJsWVEqycIumMiAOjfIUgvLLjHaBf1OTNo0+FWVZLu3YH/W0UIvgFkIktBpnLruk1lrRxwcd8kcg5thl729tUYzt+ah8wd4VsF04fPLv/2w1tFdmNStisObfY7HbbrdmI6rnZyMDUgMVKvEskqyzGKjE3I2mFI3NaalkifFCVIo4omPyCRxZoCC2NWhLJUaDHbnnO9O+ORL5096KpAKuZUiAWnUepKPHGSgier6nZg419rcAMZO01uw0GMQHQZE0kekBmJDs9/eESjQJVX3Bk69A3CNkoc7BzzgHnLWXeNass7VcI23rV3nnDPmuHZ4p6Njui8XIReDMetWxFHR5uv7H+fD0SX+5eQJJvOAc1uTk5K2wNMxxlnqUVQGx0mL/54/Rpg7J/w+Ye5gGRWWoW/mOrJwh8lJJ+186aGu+6iHdVhBylpPjLGuL7dNLaJe1ZJ44WL7OaWrlbUG7YjrN9ehhvYg0snAuUtvLeLSxjHPffMmRWpiHtksL/jkgwpRGpipwB0pqq0+eUuw9aExYhkjyzXO/twRN75mg/D+CmsmKT2FtZTkG6XeYp9NUbLGd0qd9HQKlgsP06roBgnJzMVcGFpdbeEQBjbZmYg8trHHBvlQU2hgajUvmUhqT4cbWpshZ7pzfDNHomkmuFWXbiiMQFeaOE7BWiP9WDcdzLfq2K+9MtAOwit5bu6hSonh6uSgNGvsfopo4u6WUdHyMhJX163XSpAmDmsdzTYpBToRW0p2H1xwdb7GZOEjIkPrS8xs9rsd1jw9sXTcVGve5pqYrNuNKdspvl2QlwaepRcDSokTavDN3SlZYTKbBhBaGJGkbFVUbsF06ZBHNkEvoaokRWFQ5CZXbwwQhsJySr74zHXK3pibYfek2SvJbOLM0k4pajGdByxSl+3eAnpwuGiTHdmIqY1y6hPFPAEE7RSlBIeTjq6QkwoKyfVpTzseuySxDCia/1upc1R1abE0vBNSQbGRYdklbT/VDKENWdy1cI2BG/Fl/RewRcm/u/qHuXljDZEZiIbXX6RaL0DlEuXr1X1VC14YD5Eji6pVn/B01ZVuditnNjngb0bUtSRdOIiuQimpk8ehz0Yv1Mp2lUE9bvQWCkntKHYGM61dfEewSgKfKuS1yWHW4dvXP8J/KNtsOgsuLwa4suCsNT6Z/AEKBGHlMCt8Bk7IP7/+FOf8KYvSwfNy7CbptdVacm3aZ566lJXEsUoOF21ss6TjZjiGrjFe83RDjylqSiXBqdjsLjleBmRXOmx8QrE8I4jus6kqyVonImin1IEgnvgkY4/H3nKdJ+9/kR/59Dv4zsd/kR+69l5tay1xWhlKCVyrPGFJFELx/MEGCIXbysmkojYUrlvAliLJTVKpOP5jDioryfoDQIvkLB5bJ96pEZ2cQumbxB7r7bLv5ISJw25/Tlaa3DzuaZWmQpKGDg8MR0w7PnlmUHlghhJ7IUlnLkZQUrYU1FBGuhHP6OQ4w4Kqkth2ybu2X6FUWq7yQnvMtXFfy0KaYHZy3nH+GlIoRmlAURtMY4+ulxLYunJnGvp0BhFrgR7zo2XrhDgsr3SNflqY5LnJrPbo+LpfQtoVtqNLFt1AlydKAXFmYR3YlK2al0brmi5AAI7WzqWCMHFY8+KT0lfTqJmHHmVhsDucARDlutoKdEXa3riLvOpxtGPx8IU9tr0FNztdnr2yQykURrdgOfNhYWKmknRiUjsKc5hgWdWJ3JRh1Lyy7J/klSqh6PsJwyAiLPT/7mxnelKV88rRGuVCJ3rxar3rMhSVW+uGLvTXi2OHKjQRbsXGcMFWsMSUFS9NBkip6K1pB5kVJoZRYwhdoZMWJmVlsFh6J3PgrS7eWkmuL7ocjLq8UAsGj4SUTYUapcQexjh2yfJGByQ6HFlDZy1io607nl+6vgEbORQSMok9NShTCYMce2KQ7xT0g4RF6mCvlycU1klu6Z2QEsSZTV1IVFBhBbkOr0nF4ax9Z+fslQM4PaiUoFSSb7n6daSVxSPdffLK4JnFNu/rfoyfDLd4zNnnF8JH2DLn3Ix7uEbB/f4x333u5/jF5WN8YvQob9vSWjTTzOePDp7jay88w/9Jz/Mje+8iLmy6Pc2Rk5SWpnM2KtJSq3ypW00wlWCZ2ajf6mK4ioMvAdXOEbHNzsaMR9b2ubpcZxT75EFBrxPzpesv8oGDR7Dsku976cspSgMhFB0344GNEZvukhrBVXuNqzcGLENPJ82koh3E9FoxeWkghRZLt1o1VRMbb9sZ6XmLuNA3SfJFsGWVulJoqDietyj7Br0mSSyl4nDZoqok9dIirQUUEtU4Rtsu6VyYsObFvHhzA264iEziDnKqMyWuXbBYeIhmsZVnFlVu0GvpBqDjpIUha64sB/hujrOpuYby0iAubea5e8JCOlE+14/7OsEaS8p+yfZ9h6y7EXll0nYz9qcdzWkf2fTXlwROzlZnSVaajEOfeOqBofC7GW0np6ilboJzMmyzZK8XINsFjlWSd3OqsYPya+SG3kmstSPW3YiDZRuz0XQIvIxlpRXeel5CWpqUFSf5nk4rYeG4iKlFtGszNTy2vQXxWZu4sMgKk8WBbiarvBrVKnnsvpu8tXMAwMvROi9N1xvpUZPALk6u87o08dyEtwRH7CcdSqXLMaWsqXIdWhG5bgZQsm7i6ELLStaSvNBUHWYqqFqSvK/j7kM7JIxcOu2YnpeyP+swaEdUSnB/d0RYOFyb9xm0ItLMIh+7ZKXkpdE69w/G9OwY22yBAtfPeWGxwYOdI94+fIWkMD/jcIcJ+cJBJLpX51YO58akh6oFO1tT9g97eL2EpGyh3BrPz6kulThCcfPqAOyas7tjilqyHsQcLVu616U0WAt0hVCYOjpxHaRMJy2UanamdwIKqO6dVmDx+2iy33UIIZbA83fbjlfBABjdbSNeBSv7Xh9W9r0+vNn2nVdKDT/fX+46m+o9O3/+Nb33A1e/9xNKqbd/vp91GnDqdwDA86d5kIUQH1/Z9/ljZd/rw8q+NwCnfFF8J/EHwQGssMIKK7w5WFUBrbDCCivcw1jtAE4VfuBuG/D7YGXf68PKvteHlX13GveQAzj1SeAVVlhhhTcLXWtDvWfwja/pvR84+L5VEniFFVZY4QsK99CieOUAVlhhhRVuxz3kAO4gh96dhRDiKSHE80KIy0KI77yLdlwVQnxKCPG0EOLjzbk1IcQHhRAvNj/7t73/bzc2Py+E+BNvgD0/JIQ4EkL87m3nPmd7hBBf3Hyvy0KIfyXELQqvN8S+vy+EuNmM4dNCiK++i/adFUL8qhDiWSHEM0KIv9mcPxVj+Cr2nYoxFEK4QoiPCiF+u7HvHzTnT8X4vX4oXQX0Wo4vAJxKByCEMIB/A3wV8DDwTUKIh++iSX9EKfXEbfG+7wR+RSl1CfiV5jmNje8DHgGeAr6v+S53Ev+x+du34/Ox5/uBvwpcao7//2/eSfsAvrcZwyeUUr9wF+0rgb+llHor8G7gWxs7TssYfjb74HSMYQZ8hVLqi4AngKeEEO/m9Izf64MCperXdHwh4FQ6AOCdwGWl1BWlVA78GPB1d9mm2/F1wA83j38Y+FO3nf8xpVSmlHoZuIz+LncMSqkPAZPXY48QYhvoKKU+rHQVwH+67XfeCPs+G+6GfftKqU82j5fAs8Aup2QMX8W+z4Y32z6llAqbp1ZzKE7J+N0RVPVrO74AcFodwC5w/bbnN3j1m+CNhAJ+SQjxCSHEX23ObSql9kHfsMBGc/5u2f252rPbPH4z7fw2IcTvNCGiW+GBu2qfEOIC8IeA3+QUjuH/Zx+ckjEUQhhCiKeBI+CDSqlTOX6fF5SCun5txxcATqsD+L1igXcr6PZepdTb0OGobxVCfNmrvPc02Q2f3Z43287vB+5Hhwz2gX/RnL9r9gkhWsBPAt+ulFq82ls/iy1vqI2/h32nZgyVUpVS6gngDHo1/+irvP20XIOvHUq9tuMLAKfVAdwAzt72/AywdzcMUUrtNT+PgJ9Gh3QOmy0szc+j5u13y+7P1Z4bzeM3xU6l1GEzadTAD/KZsNhdsU8IYaEn1x9RSv1Uc/rUjOHvZd9pG8PGphnwv9Cx+1Mzfq8Xqq5f0/GFgNPqAD4GXBJCXBRC2Ogk0s+92UYIIQIhRPvWY+CPA7/b2PL+5m3vB362efxzwPuEEI4Q4iI6sfXRN8HUz8meZou+FEK8u6m8+ObbfueO49bE0OBPo8fwrtjX/L1/DzyrlPqe2146FWP42ew7LWMohBgKIXrNYw/4SuA5Tsn4vX68xtX/F8gO4FT2ASilSiHEtwG/CBjADymlnrkLpmwCP91Up5nAf1FKfUAI8THgJ4QQfxl4BfjGxu5nhBA/AXwaXc3xrUqp6k4aJIT4UeBJYCCEuAF8N/BPPg97/jq6YscD/kdzvFH2PSmEeAK9xb8K/LW7ZR/wXuAvAJ9q4tgA38XpGcPPZt83nZIx3AZ+uKnkkcBPKKV+XgjxYU7H+L0+3GNkcCsqiBVWWGGFBh25rt5tvrb2nQ8WP/oHngritIaAVlhhhRXefCgFqn5tx+uAEOKfCSGea6q6fvq2sNoFIUQiPtPw92/vxNf6bFg5gBVWWGGF26Bq9ZqO14kPAo8qpR4HXgD+9m2vvXRbw9+3vN4PejWsHMAKK6ywwu14E3YASqlfUkqVzdOP8P9WRL1pWOUAVlhhhRUaCCE+gNYxfi1wgfS25z+glPqc9Q+EEP8N+HGl1H9umv+eQe8KFsDfVUr9+uf6N1/zZ68cwAorrLDCnYcQ4peBrd/jpb+jlPrZ5j1/B3g78PVKKSWEcICWUmoshPhi4GeAR36fZsXPG6eyDHSFFVZY4Q86lFJf+WqvCyHeD3wt8EcbPiSUUhmacA+l1CeEEC8BDwIffyNsXOUAVlhhhRXeZAghngK+A/iTSqn4tvPDW2ypQoj70I1zV94wO1YhoBVWWGGFNxdCiMuAA4ybUx9RSn2LEOIbgH+IbpqrgO9WSv23N8yOlQNYYYUVVrg3sQoBrbDCCivco1g5gBVWWGGFexQrB7DCCiuscI9i5QBWWGGFFe5RrBzACiussMI9ipUDWGGFFVa4R7FyACussMIK9yj+LwLDk5QY2cWXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from ipywidgets import interactive\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def f(x):\n",
    "    # Problem: border pixel might be zero or negative\n",
    "    # pixel eqal or smaller than zero are set to nan\n",
    "    array = np.copy(vv[x])\n",
    "    array[array <= 0] = np.nan\n",
    "    # plot backscatter data in dB scale\n",
    "    plt.imshow(10*np.log10(array))\n",
    "    cbar = plt.colorbar()\n",
    "    cbar.set_label('dB')\n",
    "    plt.clim(-25, 0)\n",
    "\n",
    "interactive_plot = interactive(f, x=(0,len(vv)-1))\n",
    "interactive_plot"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}