diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index ca44eb0..0b27592 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -28,17 +28,17 @@ the small problem in each subsection. And you will find a link to the Jupyter notebook containing the solution for the problem. -The Jupyter notebooks are structured similarly as follows. -The first blocks are used to prepare and visualize the mock measurements +Each Jupyter notebook is structured similarly as follows. +All notebooks begins with preparing and visualizing the mock measurements that we will be processing. -In reality, this is not required as data will be collected by other means. +In reality, the data will be measured instead of generated. With the measurement data obtained, we will proceed to demonstrate -the use of the ``Kontrol`` package to solve the problem according to the data. +the use of the ``Kontrol`` package to solve the underlying problems. At last, we will export the results in some way for further usages, e.g. for further processes or implementation. This way, the notebooks can be thought as a process in a data pipeline. -The content in this tutorial is inspired from the actual commissioning +The style of this tutorial is inspired from the actual commissioning of a KAGRA suspension. As in, these are all the necessary steps that we need to go through when setting up the active isolation systems. @@ -82,6 +82,10 @@ Content #. :ref:`H-infinity sensor correction` + #. :ref:`Seismic noise spectrum modification and modeling` + + #. :ref:`Sensor correction filter synthesis` + #. :ref:`General Utilities` Kontrol has more than what's covered in the tutorials. @@ -104,12 +108,6 @@ Check the reference out if you wish to understand what the functions/methods are actually doing in the background. -.. [1] - Terrence Tak Lun Tsang. Optimizing Active Vibration Isolation Systems in - Ground-Based Interferometric Gravitational-Wave Detectors. - https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/DocDB/ShowDocument?docid=14296 - - Sensors and actuators ^^^^^^^^^^^^^^^^^^^^^ The first step is to set up the sensors and actuators for the @@ -124,6 +122,7 @@ To achieve this, we need to 2. Align the sensors (and actuators, using control matrices) so we get a readout in the control basis. + Linear sensor calibration ************************* We were told to calibrate a relative displacement sensor for the @@ -293,6 +292,7 @@ the system that we'd like to control. The goal is to obtain a model of the system so we can eventually design a controller for it. + Transfer function modeling ************************** Using the actuation in the :math:`x_1` direction, we excite @@ -416,7 +416,7 @@ to achieve all the above. And we eventually obtained a controller. The open-loop transfer function has a unit gain frequency of 0.128 Hz and phase margin of 45 degrees. -The gain at 10 Hz is 2 orders of magnitude than the one we obtained in +The gain at 10 Hz is 2 orders of magnitude lower than the one we obtained in :ref:`Damping control`. It follows that the noise injected is also that much lower. From simulation, the system is able to trace a unit step input without @@ -456,23 +456,6 @@ sensor fusion, sensor correction, and feedback control, and propose an H-infinity method to optimize these subsystems in a seismic isolation system. -We will firstly use the H-infinity method to solve a sensor fusion problem. -However, we must note that the problem presented here is not realistic -since we will be ignoring the seismic noise coupling in relative sensors -for the sake of simplicity. -In reality, we should use a sensor correction scheme to reduce that -coupling before solving the sensor fusion problem. -But, because all filter design problems presented can be generalized -to a sensor fusion problem, it is perhaps beneficial to show how a -sensor fusion problem is solved before going into other problems. -And, this is why we begin by solving a senor fusion problem. - - -.. [1] - Terrence Tak Lun Tsang. Optimizing Active Vibration Isolation Systems in - Ground-Based Interferometric Gravitational-Wave Detectors. - https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/DocDB/ShowDocument?docid=14296 - H-infinity sensor fusion ^^^^^^^^^^^^^^^^^^^^^^^^ @@ -498,6 +481,42 @@ The first two subsections show how we can obtain the necessary materials mentioned above and the last subsection shows how we can obtain the optimal complementary filters given those materials. +We also assume that the relative sensor and the inertial sensor are +aligned and inter-calibrated so they read the same signal. +**This is extremely important**. + + +*Caveat* + +This section solves the sensor fusion problem using the H-infinity method. +The case presented is a hypothetical scenario as we only take the intrinsic +sensor noises of the relative and inertial sensors into account, whereas +there're more noises involved in reality. +Doing so allows the demonstration to be simplified without much complications. +So, please keep in mind that this section only serves the purpose of laying +down the necessary procedures to use the method. +Therefore, if you wish to use this method, do take into account all necessary +noise sources in the sensors. + +In some systems, the relative sensors are "corrected" via a control scheme +called "sensor correction" and it is the corrected relative sensor that +is combined with the inertial sensor. +The corrected sensor has a noise profile depending on the sensor correction +filter that we will be optimizing in the next section +:ref:`H-infinity sensor correction`. +But because the sensor correction and other problems can be treated +as a sensor fusion problem, it is beneficial to show how a +sensor fusion problem is solved before going into other problems. +This is why we begin with a hypotheical sensor fusion scenario. + +The way to go is to + +#. Firstly optimize a sensor correction filter. + +#. Evalute the effective noise presence in the corrected relative readout. + +#. Model and use that to optimize the complementary filters instead. + Estimating inertial sensor noise ******************************** @@ -543,7 +562,7 @@ magnitude responses of transfer functions. In the tutorial, we have fitted empirical models to the noise data as an optional intermediate step. With the empirical models obtained, we can rescale the frequency axis to -logspace with fewer data, which helped speeding up the final modeling. +logspace with fewer data points, which helped speeding up the final modeling. It also allows us to flatten the spectrums at both ends, which is necessary for the H-infinity method. In the end, we've obtained 2 ``kontrol.TransferFunction`` objects @@ -602,13 +621,125 @@ Tips do no have the necessary features to properly roll-off the noises beyond the frequency band of interest. We can add prefilters to fix the problem but this may require some tweaking. - - +- Sometimes the synthesis method can produce filters that make no sense. + Interchanging the relative and inertial sensor noises (and weights) may + solve the issue. H-infinity sensor correction ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Relative sensors are seismic noise-coupled so they inject seismic noise +to the isolation platform under feedback control. +We have a seismometer on the ground that measures the ground motion +close to the isolation platform and we can use that to reduce the +seismic noise coupling in the relative sensors. +This is simply done by subtracting the seismometer readout from the +relative readout, thereby "correcting" the relative sensors. +However, doing so injects seismometer noise to the relative readout. +Therefore, we have to design a "sensor correction filter" to filter +excessive noise while retaining seismic signal for sensor correction. + +In :ref:`H-infinity sensor fusion`, we have used +the ``kontrol.ComplementaryFilter`` class to optimize complementary filters +and we've successfully obtain a pair of complementary filters that can be +implemented. +It turns out that the sensor correction problem can be solved using the +very same class and methods. +Instead of complementary low-pass and high-pass filters, we'd be obtaining +a seismic transmissivity and a sensor correction filter, which are also +complementary. + +The required procedure to optimize a sensor correction filter is very similar +to that already presented in section :ref:`H-infinity sensor fusion` so we +will not repeat what's already demonstrated. +Instead, we assume that we have already obtained + +#. The transfer function model with magnitude response matching + the noise spectrum of the relative sensor. + +#. The same for the seismometer. + +We will also need the seismic noise model but it turned out to be more +involved so we will demonstrate what needs to be done. +There's another notible difference between the sensor correction and the +sensor fusion problem. +The noise performance of the super sensor in a sensor fusion problem is +completely dependent on the filtered sensor noises. +In sensor correction, the corrected relative sensor is dependent on the +filtered seismometer noise and filtered the seismic noise but is also +dependent on the unfiltered intrinsic relative sensor noise. +The intrinsic relative sensor noise acts as an ambient noise and we shall +see how this can be utilized in the optimization. + + +Seismic noise spectrum modification and modeling +************************************************ +We read Chapter 8.3.3 in Ref. [1]_ and realized we need to modify the seismic +noise spectrum before modeling it. +Otherwise the sensor correction filter might actually amplifies the +seismometer noise and the seismic noise, instead of attenuating them. +Click the link below to see how we can obtain a proper seismic noise model +for the optimization purpose. + +.. toctree:: + :maxdepth: 1 + + ./tutorials/sensor_correction/seismic_noise_spectrum_modification_and_modeling + +And we've obtained a seismic noise model that has a flat spectrum above +the secondary microseism. +The main reasons we had to do this are because + +#. Make the sensor correction filter a high-pass filter. +#. So the sensor correction filter won't amplify any seismometer and seismic + noise below the relative sensor noise level. + +Again, this model is not a representation for the seismic noise. +It is created for the sole purpose of H-infinity optimization. + + +Sensor correction filter synthesis +********************************** +With all the necessary components obtained, we can use +``kontrol.ComplementaryFilter`` again to optimize, not complementary filters, +but the sensor correction filter. +Instead of specifying two sensor noise models to the ``ComplementaryFilter`` +instance, we specify the seismic noise model and the seismometer noise model. +The prescense of the relative sensor noise changes how we specify the +target attenuation (weights) as the lower boundary of the +corrected sensor noise depends on it. +Click the link below to see how we cope with that and optimize +a sensor correction filter. + +.. toctree:: + :maxdepth: 1 + + ./tutorials/sensor_correction/sensor_correction_filter_synthesis + +Using the ``kontrol.ComplementaryFilter.hinfsynthesis()`` method, +we were able to obtain a sensor correction filter. +We have also computed the expected noise spectrum of the corrected relative +sensor. +It has suppressed seismic coupling, particularly around the secondary +microseism. +However, seismometer noise is injected at lower frequencies, which is expected. +To justify the tradeoff, we compared the noise RMS of the seismic noise +(uncorrected relative sensor) and the corrected relative sensor. +And, we found that we've indeed reduced the noise RMS of the relative sensor +with the sensor correction scheme and the H-infinity optimal filter. + +For here, to finally complete the job, the next step would be to model the +noise of the corrected relative sensor and use that to optimize a +set of complementary filter for combining the corrected relative sensor +and the inertial sensor in a sensor fusion configuration. +But we'll just leave it here since this would be a repetition of +the :ref:``H-infinity sensor fusion`` section. + General Utilities ----------------- +.. [1] + Terrence Tak Lun Tsang. Optimizing Active Vibration Isolation Systems in + Ground-Based Interferometric Gravitational-Wave Detectors. + https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/DocDB/ShowDocument?docid=14296 diff --git a/docs/source/tutorials/sensor_correction/noise_spectrum_frequency.pkl b/docs/source/tutorials/sensor_correction/noise_spectrum_frequency.pkl new file mode 100644 index 0000000..10364e0 Binary files /dev/null and b/docs/source/tutorials/sensor_correction/noise_spectrum_frequency.pkl differ diff --git a/docs/source/tutorials/sensor_correction/noise_spectrum_relative.pkl b/docs/source/tutorials/sensor_correction/noise_spectrum_relative.pkl new file mode 100644 index 0000000..550b809 Binary files /dev/null and b/docs/source/tutorials/sensor_correction/noise_spectrum_relative.pkl differ diff --git a/docs/source/tutorials/sensor_correction/noise_spectrum_seismic.pkl b/docs/source/tutorials/sensor_correction/noise_spectrum_seismic.pkl new file mode 100644 index 0000000..4e8a48c Binary files /dev/null and b/docs/source/tutorials/sensor_correction/noise_spectrum_seismic.pkl differ diff --git a/docs/source/tutorials/sensor_correction/noise_spectrum_seismometer.pkl b/docs/source/tutorials/sensor_correction/noise_spectrum_seismometer.pkl new file mode 100644 index 0000000..c6fd26f Binary files /dev/null and b/docs/source/tutorials/sensor_correction/noise_spectrum_seismometer.pkl differ diff --git a/docs/source/tutorials/sensor_correction/seismic_noise_spectrum_modification_and_modeling.ipynb b/docs/source/tutorials/sensor_correction/seismic_noise_spectrum_modification_and_modeling.ipynb new file mode 100644 index 0000000..3f33468 --- /dev/null +++ b/docs/source/tutorials/sensor_correction/seismic_noise_spectrum_modification_and_modeling.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7f731f98", + "metadata": {}, + "source": [ + "# Seismic Noise Spectrum Modification and Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ef8d64ab", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "import control\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "\n", + "import kontrol\n", + "\n", + "\n", + "f = np.linspace(1e-3, 1e2, 1024*512)\n", + "def noise_model(f, na, nb, a, b):\n", + " return np.sqrt((na/f**a)**2 + (nb/f**b)**2)\n", + "n_geophone = noise_model(f, na=1*10**-5.46, nb=1*10**-5.23, a=3.5, b=1)\n", + "\n", + "# Let's assume seismometer is 2 times less noisy than geophone.\n", + "n_seismometer = n_geophone / 2\n", + "\n", + "# Generate time series measurements\n", + "np.random.seed(1)\n", + "t, seismometer_noise = kontrol.spectral.asd2ts(n_seismometer, f=f)\n", + "\n", + "# Ground motion\n", + "s = control.tf(\"s\")\n", + "wn = 0.15*2*np.pi # Secondary microseism\n", + "q = 10\n", + "ground_motion_tf = 0.1 * wn**2 / (s**2+wn/q*s+wn**2)\n", + "\n", + "# Generate time series measurements\n", + "fs = 1/(t[1]-t[0])\n", + "u = np.random.normal(loc=0, scale=np.sqrt(fs/2), size=len(t))\n", + "_, ground_motion = control.forced_response(ground_motion_tf, U=u, T=t)\n", + "\n", + "# Obtain the measured spectral densities.\n", + "f_, p_seismic = scipy.signal.welch(ground_motion, fs=fs, nperseg=int(len(t)/5))\n", + "_, p_seismometer = scipy.signal.welch(seismometer_noise, fs=fs, nperseg=int(len(t)/5))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "019864ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Frequency (Hz)')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seismic_modified = kontrol.spectral.pad_above_maxima(p_seismic**0.5, order=7500)\n", + "# order is passed to scipy.signal.argrelmax() which is used to determine the local maxima. Keep increasing until it works.\n", + "\n", + "plt.loglog(f_, p_seismic**0.5, label=\"Seismic noise\")\n", + "plt.loglog(f_, seismic_modified, label=\"Seismic noise (modified)\")\n", + "plt.loglog(f_, p_seismometer**0.5, label=\"Seismometer noise\")\n", + "plt.legend(loc=0)\n", + "plt.grid(which=\"both\")\n", + "plt.ylabel(\"Amplitude spectral density (some unit)/$\\sqrt{\\mathrm{Hz}}$\")\n", + "plt.xlabel(\"Frequency (Hz)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "053b6ad6", + "metadata": {}, + "outputs": [], + "source": [ + "# We don't need to fit the whole spectrum because the modified\n", + "# spectrum is flat all the way above the microseism frequency.\n", + "# Let's get rid of the data above 1 Hz.\n", + "# Data to be modelled:\n", + "ydata = seismic_modified[(f_>0) & (f_<1)]\n", + "xdata = f_[(f_>0) & (f_<1)]\n", + "order = 3\n", + "tf_seismic = kontrol.curvefit.spectrum_fit(xdata, ydata, nzero=order, npole=order)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2b34a055", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Frequency (Hz)')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(f_, p_seismic**0.5, label=\"Seismic noise\")\n", + "plt.loglog(f_, seismic_modified, label=\"Seismic noise (modified)\")\n", + "plt.loglog(f_, abs(tf_seismic(1j*2*np.pi*f_)), \"--\", label=\"Seismic noise (model)\")\n", + "plt.loglog(f_, p_seismometer**0.5, label=\"Seismometer noise\")\n", + "plt.legend(loc=0)\n", + "plt.grid(which=\"both\")\n", + "plt.ylabel(\"Amplitude spectral density (some unit)/$\\sqrt{\\mathrm{Hz}}$\")\n", + "plt.xlabel(\"Frequency (Hz)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68d98dba", + "metadata": {}, + "outputs": [], + "source": [ + "# Export the transfer function model and noise spectrum for future usages.\n", + "tf_seismic.save(\"tf_seismic.pkl\")\n", + "\n", + "with open(\"noise_spectrum_seismic.pkl\", \"wb\") as fh:\n", + " pickle.dump(p_seismic**0.5, fh)\n", + "with open(\"noise_spectrum_seismometer.pkl\", \"wb\") as fh:\n", + " pickle.dump(p_seismometer**0.5, fh)\n", + "with open(\"noise_spectrum_frequency.pkl\", \"wb\") as fh:\n", + " pickle.dump(f_, fh)" + ] + } + ], + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/sensor_correction/sensor_correction_filter_synthesis.ipynb b/docs/source/tutorials/sensor_correction/sensor_correction_filter_synthesis.ipynb new file mode 100644 index 0000000..a7a336e --- /dev/null +++ b/docs/source/tutorials/sensor_correction/sensor_correction_filter_synthesis.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2e103c8f", + "metadata": {}, + "source": [ + "# Sensor Correction Filter Synthesis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c1eb051e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Frequency (Hz)')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "\n", + "import control\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import kontrol\n", + "\n", + "\n", + "# Load the transfer function models \n", + "tf_seismic = kontrol.load_transfer_function(\"tf_seismic.pkl\")\n", + "tf_relative = kontrol.load_transfer_function(\"tf_relative.pkl\")\n", + "tf_inertial = kontrol.load_transfer_function(\"tf_inertial.pkl\")\n", + "\n", + "# We've assumed the seismometer noise to be 2 times lower than that of the inertial sensors.\n", + "tf_seismometer = tf_inertial / 2\n", + "\n", + "# Let's visualize before moving on\n", + "f = np.logspace(-3, 1, 1024)\n", + "\n", + "plt.loglog(f, abs(tf_seismic(1j*2*np.pi*f)), label=\"Seismic noise (model)\")\n", + "plt.loglog(f, abs(tf_seismometer(1j*2*np.pi*f)), label=\"Seismometer noise (model)\")\n", + "plt.loglog(f, abs(tf_relative(1j*2*np.pi*f)), label=\"Relative sensor noise (model)\")\n", + "plt.legend(loc=0)\n", + "plt.grid(which=\"both\")\n", + "plt.ylabel(\"Amplitude spectral density (some unit)/$\\sqrt{\\mathrm{Hz}}$\")\n", + "plt.xlabel(\"Frequency (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "id": "9686e256", + "metadata": {}, + "source": [ + "Now, let's take a moment to think about how the sensor correction is going\n", + "to affect the noise of the relative sensor.\n", + "\n", + "Without sensor correction, the effective noise of the relative sensor is\n", + "approximately the upper bound of the relative sensor (green curve) and the seismic noise (blue curve).\n", + "The blue curve domainates at high frequencies and that's what we want to get rid of\n", + "by means of sensor correction.\n", + "\n", + "With sensor correction, we have the ability to suppress the blue curve.\n", + "However, this introduces seismometer noise (orange curve) to the relative sensor\n", + "and we want to suppress this as well.\n", + "\n", + "The simultaneous suppression of seismic noise and seismometer noise constitute\n", + "a sensor fusion problem and we already have the tools to solve it as discussed in last section\n", + "of the advanced control methods tutorials.\n", + "However, if we do it that way, the seismic noise (blue) is going to be suppressed to a level\n", + "close to the seismometer noise (orange) at high frequencies.\n", + "While the traditional wisdom says we want to suppress seismic as much as possible, this is\n", + "not necessarily optimal.\n", + "This is because the relative sensor noise (green) is still in play and that is not being suppressed\n", + "by anything.\n", + "Any noise suppressed below that level is not useful as the corrected relative sensor noise is\n", + "going to be dominated by the relative sensor noise itself anyway.\n", + "Instead, we should indentify what is true lower boundary of the corrected relative sensor noise\n", + "and use that as the target attenuation.\n", + "\n", + "In this case, the lower boundary is roughly the relative sensor noise except between\n", + "~$10^{-2}$ Hz and ~$5\\times 10^{-2}$ Hz, where the seismic noise and seismometer noise dominates.\n", + "To do this propoerly, we should model that lower bound using a transfer function like how\n", + "we modelled other noise spectrums.\n", + "However, let's just assume that there's not much difference so we can use the relative sensor noise\n", + "as the lower boundary for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0faaca86", + "metadata": {}, + "outputs": [], + "source": [ + "# Synthesis\n", + "comp = kontrol.ComplementaryFilter()\n", + "comp.noise1 = tf_seismic\n", + "comp.noise2 = tf_seismometer\n", + "comp.weight1 = 1 / tf_relative # Using the relative sensor noise as the lower boundary.\n", + "comp.weight2 = 1 / tf_relative\n", + "h1, h2 = comp.hinfsynthesis()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "192690e0", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's not forget about the prefilter.\n", + "s = control.tf(\"s\")\n", + "wc = 2*np.pi*2e-3 # cut-off frequency\n", + "prefilter = (s / (s+wc))**3\n", + "\n", + "# Redefine the sensor correction filter and seismic transmissivity\n", + "h2_prefilt = h2 * prefilter\n", + "h1_prefilt = 1 - h2_prefilt # Complementary condition." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e207a39c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Frequency (Hz)')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Inspect\n", + "plt.loglog(f, abs(h1(1j*2*np.pi*f)), label=\"Seismic transmissivity\")\n", + "plt.loglog(f, abs(h2(1j*2*np.pi*f)), label=\"Sensor correction filter\")\n", + "plt.loglog(f, abs(h1_prefilt(1j*2*np.pi*f)), \"--\", label=\"Seismic transmissivity (with prefilter)\")\n", + "plt.loglog(f, abs(h2_prefilt(1j*2*np.pi*f)), \"--\", label=\"Sensor correction filter (with prefilter)\")\n", + "plt.legend(loc=0)\n", + "plt.grid(which=\"both\")\n", + "plt.ylabel(\"Magnitude\")\n", + "plt.xlabel(\"Frequency (Hz)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68d2752c", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's forecast the noise of the corrected relative sensor.\n", + "\n", + "# Load noise data\n", + "with open(\"noise_spectrum_relative.pkl\", \"rb\") as fh:\n", + " noise_relative = pickle.load(fh)\n", + "with open(\"noise_spectrum_seismometer.pkl\", \"rb\") as fh:\n", + " noise_seismometer = pickle.load(fh)\n", + "with open(\"noise_spectrum_seismic.pkl\", \"rb\") as fh:\n", + " noise_seismic = pickle.load(fh)\n", + "with open(\"noise_spectrum_frequency.pkl\", \"rb\") as fh:\n", + " f_ = pickle.load(fh)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3cb78321", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Frequency (Hz)')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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D+eyzz7h3716m5U2YMIGNGzdy8+ZNLl26xObNm/H19TUqr0ePHkbljRgxIsvyXrZ582bmz5/P2bNnuXPnDr///jtarZZy5cphZWXF4MGDGTNmDNu3b+fy5ct8/PHHJCYm0q9fv1yVnxeffvope/bsYerUqVy/fp3ly5fz448/Mnr06EzzL1u2jN9++42LFy9y+/Zt/vjjDywsLPDy8qJs2bL06NGDDz/8kPXr1xMSEsKpU6f49ttv2bJli8nbLmRPBEpvGWcrZz6s+CH/a/M/tnTcwrCqwyhlWwq1Vs2/d/9lzL4xNF7dmM/2f8a+u/tQa/K/orIgCAUnl8vp378/M2fOJCEhgalTpzJ+/Hi+++47ateuTatWrdiyZUuWt7fGjRuHn58fLVu2pEmTJri4uNC6dWujPKNHj0Ymk1GhQgWcnJyyfKRfIpHw3nvvce7cOXr06GG0z9LSkv379+Pp6UmnTp3w9fVlwIAB2Y6HUiqVfPHFF1SpUoVGjRohk8n4888/DeUFBwdTokQJunTpgq+vL/369SM5OTnXt5fs7OxYv34977zzDr6+vvzyyy8sXrzYcDXt22+/pXPnzvTs2ZMaNWpw+/Zttm3blu2TbflVrVo1/vrrL/78808qVarEhAkTmDJlSqYDudPb/uuvv1K/fn2qVKnC7t27+eeffwxjkJYuXcqHH37Ip59+iq+vLx988AEnTpzA09PT5G0Xsiduvb3FPFWefOT3EQOrDORG9A22hWxjW8g27sffZ2vIVraGbEWlVNHMqxldy3alrG3ZnAsVBCHfli5dmumtk5EjRzJx4kTDvo8//pjhw4dnerulSZMmPH361BBM2Nvbs3LlSkO+zG6HlS1bliNHjhjV6e3tnenYqu+++87wFN7LXFxcjK5s5XTr7auvvuKrr77KqjtwcXFh4cKFWd5SWrZsWYa0OXPmGPI2aNAgwxxUsbGxhm1zc3Pmz5/P/PnzM21rkyZN0Gg0RsdkxtvbO0M+Ozs7o9cBoHPnznTu3DnLcl6ckqFDhw506NAhy7wKhYLJkyczefJkceutiIlA6T9AIpFQtlhZyhYrywj/EVyIvMC2kG1sD91OZFIk626sY8PNDfzY9MeibqogCIIgvFZEaPofI5FIqOJUhc9qfcbuLrv5rflvNHBvgEan4bODnxGpiSzqJgqCIAjCa0MESv9hMqmMWq61mNt0LlWcqhCbGssfCX8QlxpX1E0TBEEQhNeCCJQEzGRmzGs6DxdLFyK1kXx28DPStGlF3SxBEARBKHIiUBIAcLRwZE7jOShQcPThUWaemFnUTRIEQRCEIicCJcGgXLFydLXsCsCqq6v469pfRdwiQRAEQShaIlASjFRQVmCo31AAph+bzvHw40XcIkEQBEEoOnkKlLKahl14u/St0JfWJVuTpktjZPBIwmIzn5xOEARBEN52eQqU8rLw45siOjqaGjVqULVqVSpVqsSvv/5a1E0qchKJhMn1JlPFUf8k3LB/hxGbmv2EbIIgvBqTJ0+mYcOGhVqHRCJhw4YNhVpHTry9vZk7d26RtkEQII+BUvpqyW8TGxsb9u/fz9mzZzl27BjffPMNT548KepmFTkzmRnz3pmHs6UzITEhjN03VjwJJwgF9PjxYwYPHoynpydmZma4uLgQFBTE0aNHc13Gp59+ysaNGwuxlRAeHk7Lli0LtY6cnDhxgoEDBxZpGwQBxBglZDIZlpaWAKSkpKDT6d7KK2f54WjhyA/v/ICF3IJDDw7x/cnvi7pJgvBG69q1K2fOnGH58uVcv36dTZs20aRJE6KionJdhrW1Nfb29oXYSv3SImZmZoVaR06cnJwMn82CUJRMEii98847TJ48OUP606dPeeedd0xRRZb2799P27ZtcXNzy/Jy8U8//YS3tzfm5ubUrl2b48eNByhHR0fj5+dHiRIlGDNmDI6OjoXa5jeJr4MvXzf4GoA/rvzB2utri7hFgvBmiomJ4cCBA3z33Xc0bdoULy8vatWqxeeff06rVq0M+aKjo+nfvz/Ozs54enoSGBjIuXPnDPtfvvUWHBxMQEAANjY22NnZ0bBhQ8Oit5MmTaJq1aosWbIET09PrK2tGTJkCBqNhhkzZuDi4kLx4sX5+uuvjdr68mfpvXv3eO+997C3t8fKyooaNWpw7NixTM8zNDQUiUTC+vXradq0KZaWlvj5+WVYa27dunVUrlwZZ2dnSpYsyfffG/8j9uKtN51Ox+TJk6lUqRIWFha4ubkxYsQIQ96UlBRGjx6Nh4cH7u7u1K1b12gNOEEoCJMESsHBwfz444906NCBhIQEQ3pqair79u0zRRVZSkhIwM/Pj59++inT/atXr2bUqFFMnDiR06dP4+fnR4sWLYiIiDDksbOz49y5c4SEhLBq1SoePXpUqG1+0zTzasbQqvon4b4++jUnHp4o4hYJQiZ0OkhNMP5SJ2ZMy+1Xbo/N5RVoKysrrK2t2bBhAykpKVnm69q1KxEREWzZsoW9e/fi7+9PQEBApled0tLS6NSpE/Xq1ePs2bMcOXKE/v37Gw2TuHXrFtu2bWP79u3873//47fffqN169bcu3ePffv28d133/HVV19lGfjEx8fTuHFj7t+/z6ZNmzh37hxjx45Fq9Vme77jxo1j9OjRnD17lrJly/Lee++Rlqa/fX/q1Cm6detG9+7dOXToEBMmTGD8+PGZLoIL+qBq7ty5zJkzh2vXrrFhwwYqV65s2D9s2DCOHDnCqlWrOHjwIF26dCEoKIgbN25k20ZByA2TLYq7e/duPvroI+rUqcM///yDt7e3qYrOVsuWLbO9lz579mwGDBhAnz59AFi0aBFbtmxhyZIlfP7550Z5nZ2d8fPz48CBA3Tp0iVDWSkpKUYfcOkrSavVatRqteHnF7+nyyw9N2lZlVcYsqurr29fbj69yY47Oxi5dyS/t/gdDxuPXB+fl7pyk+dt7WdTH59T3vzuz0+fmrKP1Wo1Op0OrVb7/A92agLSb0sY8kgBu3yWn5djtZ/fQ6fQ3yJKb9PLdDodcrmcJUuW8NFHH7Fo0SKqVatGo0aN6NatGz4+Puh0Ovbv38/x48d5+PAhSqWSuLg4ZsyYwcaNG/nrr78YOHCgYWiATqcjOjqamJgYgoKCKFmypH4B7LJliYuLMwwj0Gq1LF68GBsbG8qXL0+TJk24du0amzdvRiqVUqZMGb777jv+/fdfatas+fy8nvXtH3/8wePHjzl27Jjhll/JkiUNbXj5vNO/jxo1yvDZPHHiRCpXrsz169cpX74833//Pe+88w7jxo0jLi4Of39/rly5wsyZM/nwww+N+k2r1XLnzh1cXFxo0qQJ9vb2eHp6UqNGDbRaLWFhYSxdupTQ0FBcXV2Ji4tj1KhR7NixgyVLlhiulmXW1qzSc5OWVXmFoaB15eX43OTNLs/r1s/pZajVaqMn9/PyOWSyQMnV1ZV9+/bRp08fatasyZo1a/D19TVV8fmSmprKqVOn+OKLLwxpUqmUwMBAw2XgR48eYWlpiY2NDTExMezfv5/BgwdnWt706dMzvcW4c+fODPfSd+3alWkZmaXnJi2r8gpDVnXV0dXhouwi91Pv039Lfz6y+QhziXmuj89LXbnN8zb2c2Ecn1Pe/O7PT5+aoo/lcjkuLi7Ex8eTmpqqT1Qn5jswKojYuDhQaACIi8t+ncRmzZpx+fJljhw5wsmTJ9m1axczZ85k/vz5vP/++xw7doz4+HicnJyMjktKSuLKlSvExsYazjcuLg65XM77779P586dadKkCU2aNKFDhw64uLgQFxdHSkoKnp6e6HQ6wz929vb2lClThvj4eEP5Dg4O3Lt3z5Anvc7Y2FhOnDhB5cqVkcvlRvtf9OJ5p5dbqlQpQ35ra2sAQkJCcHNz49KlS7Rq1cpwXHqwNG/ePJ4+fYpMJkOr1ZKcnExsbCwtWrRgzpw5VK1alcDAQJo1a0ZQUBByuZxjx46h0WgoX768UZtSUlJQqVQZ2pzVa5RZem7ScnrNTamgdeXl+NzkzS7P69LPqampJCUlsX//fsMVTYDExMRcl2GSQCn9Mq+ZmRmrVq1i2rRpBAUF8dlnn5mi+HyLjIxEo9Hg7OxslO7s7MzVq1cBuHPnjuG/NJ1Ox/Dhw40u6b7oiy++YNSoUYbt2NhYPDw8aN68OSqVCtBHqbt27aJZs2YoFApD3szSc5OWVXmFITd11UusR88dPYlIimCv5V7mNp6LTCrL9fF5qSu7PG97P5vq+Jzy5nd/fvrUlH2cnJzM3bt3sba2xtz8WbCus0H7+T1DHp1OR1x8PDbW1nl+Yjcvx6oUlujQf5Db2Nhkml+n0xn2q1Qq2rdvT/v27Zk6dSoDBgxg+vTpfPTRR2g0GlxdXfn333/R6XQkJCRgZWWFRCLBzs4OlUqFUqkEMNT1+++/89FHH3HgwAE2bdrE119/zfr163nnnXcwMzPDzMzM8PkEoFQqsbCwMEpTKBTI5XKjtPQ8tra2GfZldl7p550eFKW3F55fZUovUyaTYWZmho2NjeF4CwsLfX8+2y+VSjE3N0elUlGhQgWuXr3KP//8w+HDhxkzZgwLFixg7969aLVaZDIZJ06cQCqVGvWZtbW1oQ2ZtTWr9NykZVVeYShoXXk5Pjd5s8vzuvVzcnIyFhYWNGrU6PlnBWQZ9GfGJIHSy0+JffXVV/j6+tKrVy9TFF+oatWqxdmzZ3OVN/1D52UKhSLDB39maXnJ+3JaVuUVhuzqcrN1Y37AfHpv682h8EP8cP4HxtQck+vj81JXbvK8rf1s6uNzypvf/fnpU1P0sUajQSKRIJVKkUpfGGopszH8qNVqIUWLxMzaOE8u5PXY9EAgvU152V+hQgU2bNiARCKhevXqhttunp6exMbGolKpjI5J/2ORXpZWq6VKlSo0aNCAcePGUbduXdauXUtAQIAh78vHZ9aOl9PS+9bPz4/ffvuN6OjoDE/bZXZeL37PKs3X15fDhw8bncuRI0coW7as0XvjxXItLS1p2bIl3bt3Z9iwYZQvX55Lly5RvXp1NBoNkZGR1K9fP9M+y+41yCw9N2k5veamVNC68nJ8bvJml+d162epVIpEIsn0cyjXZeS51kyEhIRkeFKsc+fOHD16lCVLlpiiinxxdHREJpNlGJz96NEjXFxciqhVb76KDhWZ1mAaAL9f/p31N9YXcYsE4fUXFRVFYGAgf/zxB+fPnyckJIQ1a9Ywc+ZMw1NvgYGB1K1blw4dOrBz507CwsI4fPgw48aN4+TJkxnKDAkJ4csvv+T48ePcuXOHnTt3cuPGDcqWLWuydr/33nu4uLjQoUMHDh06xO3bt1m3bl2Gp9jy4tNPP2XPnj1MmzaNmzdvsnz5cn788UdGjx6daf5ly5bx22+/cfnyZW7fvs0ff/yBhYUFXl5elC1blh49evDhhx+yfv167ty5w/Hjx5k+fTpbtmzJdxsFIV2BriilX7oqVqyY0f3udJ6ennh6ehakigJRKpVUr16dPXv20KFDB0Afre7Zs4dhw4YVWbveBi28W3A7+jYLzi1g6tGpeNp44ufgV9TNEoTXlpWVFbVq1WLOnDncunULtVqNh4cH/fv3Z+hQ/VOlEomErVu3Mm7cOPr168fjx49xcXGhUaNGGYYQgP4qy9WrV1m+fDlRUVG4uroyZMgQw8MrpqBUKtm5cyeffvoprVq1Ii0tjQoVKmT5pHFuVKtWjb/++osJEyYwbdo0XF1dmTJlCr179840v52dHd9++y2XL19Gq9VSuXJl/vnnHxwcHABYunQp06ZNY8yYMdy/fx9HR0fq1KlDmzZt8t1GQUhXoEDJzs4u23uGOp0OiUSCRqMpSDXZio+P5+bNm4btkJAQzp49a3gyYtSoUfTq1YsaNWpQq1Yt5s6dS0JCgkk/SP6rBvkN4lbMLXaE7mDIniF8Veurom6SILy2zMzM+Oabb/j222+N0rVardF4CRsbG+bPn8/cuXMzvY00ceJERo4cCejHW65fv94o34vlTZo0iUmTJhnVl9kj+C/POfTycAovLy/Wrs04h1pmTyF5e3tnON7Ozi5DWufOnenYsWOWt8pCQ0MNP3fo0IF27dplmVehUDB58mQmTpyYZR5ByK8CBUp79+41/KzT6WjVqhWLFy/G3d29wA3LrZMnT9K0aVPDdvpg6169erFs2TK6d+/O48ePmTBhAg8fPqRq1aps37490//OhLyRSCRMrT+V2JRYjoQfYdzhcdRW1iZQE/jKxvkIgiAIQmEqUKDUuHFjo22ZTEadOnUMc2y8Ck2aNMlxyZFhw4aJW22FxEJuwcLAhSw4t4Bfzv/CsdRj9NvVj9lNZ+Nm7VbUzRMEQRCEAhHXJoUCk0llDPcfzg9NfsBCYsGlqEt0/acr++/tL+qmCYIgCEKBiEBJMJn6bvUZYjOECvYViE2NZeieofxw5gc02sIboyYIgiAIhcnkgVJhT7wlvN6KSYuxpNkSupfrDsAv539h0O5BRCXnfnV0QRAEQXhdFGiMUqdOnYy2k5OTGTRoEFZWVkbp69eLeXb+S5QyJV/V+Qr/4v5MPjKZo+FH6fpPV75v/D1Vi1ct6uYJgiAIQq4VKFCytbU12v7ggw8K1Bjh7dK6ZGvKFSvHyOCRhMaG0md7H0bVGMUHvuJ9IgiCILwZChQoLV261FTtEN5SpYuV5s82fzLp8CS2h25nxokZnH98nsl1Mi4uLAiCIAivG5Os9SYI2bFSWDGj0Qz8i/sz8+RMtodux0JmQTVdtaJumiAIgiBkK0+DuXOar0gQsiKRSHjf933mNpmLVCLl71t/sz9FTB8gCHk1efJkGjZsWNTN+M8KDQ1FIpHkejF14c2Xp0BpxYoVLFiwgD179hTqsiTC26uxR2O+qPUFALuSd7E9dHsRt0gQXp3Hjx8zePBgPD09MTMzw8XFhaCgII4ePZrrMj799FM2btxYiK0sOpMmTaJq1apF3YxseXh4EB4eTqVKlYq6KcIrkqdbbz169ADg5s2bLFmyBLVajaenJ82aNcPMzKxQGii8fd4t/y53Yu7wx9U/mHh0Iu4qd6o5i9twwtuva9eupKamsnz5ckqWLMmjR4/YvXs3UVG5nz7D2to60zXWhOdSU1MxNzcvlLJlMhkuLi7iNfgPydc8SqVLl2bAgAEMGTIEf39/Vq1axYIFC1i7di3x8fGmbqPwFvrE/xMqKCqg1qoZsXcEoTGhRd0kQShUMTExHDhwgO+++46mTZvi5eVFrVq1+Pzzz2nVqpUhX3R0NP3798fZ2RlPT08CAwM5d+6cYf/Lt96Cg4MJCAjAxsYGOzs7GjZsSFhYGPD8Cs2SJUvw9PTE2tqaIUOGoNFomDFjBi4uLhQvXpyvv/7aqK1hYWG0b98ea2trVCoV3bp149GjR4b9L5ZbqVIlVCpVrspNPzcnJydUKhWBgYFcuHAB0C/WO3nyZM6dO4dEIkEikRgW8I2OjmbAgAGULl0aOzs73nnnHaM+SW/P4sWL8fPzw9LSMtPXYNWqVdjb27Njxw58fX2xtramZcuWPHz40JBHq9UydepUKlasiIWFhWF90HQv33p7+vQpPXr0wMnJCQsLC8qUKWP0oNPdu3fp1q0bdnZ22Nvb0759e6MFf4XXX4EHc7u7u9OnTx8AIiMj2bBhA7Gxsdja2hIUFISDg0OBGym8faQSKV0su7Bevp6LTy4yZM8Q/mj1B/bm9kXdNOENpdPpSEpLMmxrtVqS0pKQq+V5Xkk+L8dayC1yVaaVlRXW1tZs2LCBOnXqZHkVvmvXrlhYWLBlyxZkMhmrVq0iICCA69evY29v/PuRlpZGp06d6NmzJ3/++SdpaWkcPXrUaOLfW7dusW3bNrZv386tW7fo0qULt2/fpmzZsuzbt4/Dhw/Tt29fAgMDqV27Nlqt1hAk7du3j7S0NIYOHUr37t0JDg42Knf79u2sXbuWR48e0a1bt2zLffHctm3bhq2tLYsWLaJDhw5cu3aN7t27c/HiRbZv387u3buB51PQdO3aFXNzc9asWYObmxu//vprhj65efMm69evZ8WKFahUqixfh8TERGbNmsWKFSuQSqV88MEHjB8/ntWrVwMwb948Zs+ezezZs6lXrx7Lli2jXbt2XLhwIdPF1CdMmMDly5fZtm0bjo6O3Lx5k6Qk/ftQrVbTokUL6taty4EDB5DL5UybNo2goCDOnz+PUqnM9j0jvB5M+tSbo6MjlStXZt26dfz+++8cOnSIH374AZlMZspqhLeEUqJkTqM59N7Vm7txd/n4349Z3GIxZjJxG1fIu6S0JGqvqv3K6z32/jHMZTnf5pHL5SxZsoSPPvqIRYsWUa1aNRo3bky3bt3w9vYG4ODBgxw/fpyIiAgUCgWxsbHMnDmTjRs3snbtWgYOHGhUZmxsLDExMQQFBVGqVCmkUinlypUjNjbWkEer1bJkyRJsbGyoUKECTZs25dq1a2zdutWQ/7vvvmPv3r3Url2bPXv2cOHCBUJCQvDw8ADg999/p2LFipw4cYKaNWsayv3tt9/Q6XTUqlUrx3JfPLf0IHHmzJn8/fffrF27lkGDBmFtbY1cLsfFxcXQ/vTjHj58SEpKCiqVilmzZrFhwwajPkm/pWlmZpZtoKRWq1m0aBGlSpUCYOjQoUyZMsWwf9asWYwdO5bOnTujUqkM5zBv3jy++eabDOWFhYXh7+9PjRo1AAyvJcDq1avRarUsXrzYELwuXboUOzs7goODad68eTbvGOF1YZIlTG7cuMFnn31GkyZN+O233wgICODw4cMsWLBABElCthwsHFgQsAAbpQ1nH59l3MFxaHXi3r/wdurcuTMPHjxg06ZNBAUFERwcTI0aNVi1ahUA586dIz4+HgcHB1QqFSVKlEClUhESEsKtW7cylGdvb0+vXr3o3Lkz7dq1Y968eYSHhxvl8fb2xsbGxrDt7OxMhQoVjK6UOTs7ExERAcCVK1fw8PAwBEkAFSpUwM7OjitXruS73BfPzdra2nBb786dO9y+fTvLPks/zsnJydAf1tbWGfrEy8sLJyenLMtJZ2lpaQiSAFxcXHj8+DGgDzwfPHhAvXr1jI6pX78+V69ezbS8QYMG8eeff1K1alXGjh3L4cOHjdp+8+ZNbGxsDOdsb29PcnJypq+n8HoyyRUlrVaLvb290WVZQcitknYlmdd0HgN3DWRH6A7crd0ZWX1kUTdLeMNYyC049v4xw7ZWqyUuLg4bG5t83XrL7bEWcos8TZ1ibm5Os2bNaNasGePHj6dfv35Mnz6dQYMGER8fj6urK8HBwWi1WuLj47G2tkYqlWJnZ5dpeUuWLKFv374cPHiQ1atX89VXX7F+/XoCAgIAUCgURvklEkmmaXkdnJzXcl88t3Tp51iiRIks60k/7t9//zXqD8CoT15eOisv7S7I1DctW7bkzp07bN26lV27dhEQEMDQoUOZNWsW8fHxVK9enZUrV2Y4LjdBnfB6MEmgVK5cOUaNGmWKooT/qJouNZlSbwpfHvySJReX4GzpTNfSXYu6WcIbRCKRYKl4PohXq9WSJk/DUmGZr0ApL8cW5A9thQoV2LBhAwDVqlXj4cOHyOVyPD09iY2NRaVS5diGKlWq0KBBA7788kvq1q3L2rVrDYFSXvn6+nL37l3u3r1ruKp0+fJloqOjqVChQr7KBONzS789pdVqDecIoFQqM0w98+JxJUuWzFV/5JdKpcLNzY3Dhw/j7+9vSD906JDhlmNmnJyc6NWrF7169aJhw4aMGTOGWbNmUa1aNVavXk3x4sWzvR0ovN4K9G6bMGECp06dAjJG6YKQV21LtWVI1SEATD8+ne9PfY9GJ+brEt4OUVFRBAYG8scff3D+/HlCQkJYs2YNM2fONDz1FhgYSN26denQoQM7d+4kLCyMw4cPM27cOE6ePJmhzJCQEL788kuOHz/OnTt32LlzJzdu3KBs2bL5bmdgYCCVK1emR48enD59muPHj/Phhx/SuHFjwzic/Jb74rmFhoZy+PBhpk6dajg3b29vQkJCOHv2LJGRkaSkpBiO69SpE//++6/huKz6pKDGjBnDjBkzWL9+PdeuXePzzz/n7NmzjBgxItP8EydOZOPGjdy8eZNLly6xefNmfH19Af2UOo6OjrRv354DBw4QEhJCcHAwI0aM4N69eyZvu1A4CnRF6d69e7Rs2RKlUknbtm1p164dAQEBYiS/kG+DqgwCYMHZBay8tpJS8lI0Tm2Mg0I8PSm82aysrKhVqxZz5szh1q1bqNVqPDw86N+/P0OHDgX0V8W2bt3KuHHj6NevH48fP8bFxYVGjRpl+sSVpaUlV69eZfny5URFReHq6sqQIUMMTyLnh0QiYePGjQwfPpxGjRohlUoJCgrihx9+yHeZ6eWmn1ufPn0M51anTh3DuXXu3Jn169fTtGlToqOjWbp0Kb1792br1q18+eWXDBs2jMjIyGz7pKBGjBhBdHQ048eP5/Hjx1SoUIFNmzZRpkwZo0Hy6ZRKJV988QWhoaFYWFjQsGFD/vzzT0D/+uzfv5/PPvuMTp06ERcXh7u7OwEBAeIK05tEV0AajUa3f/9+3ZgxY3Rly5bV2djY6Dp16qRbvny57smTJwUt/rUWExOjA3QxMTGGtNTUVN2GDRt0qampRnkzS89NWlblFYaC1pWX43PKuyt0l67Gihq6Sssq6Vqubam79fRWro4X/Zy3vPndn58+NWUfJyUl6S5fvqxLSkrKMo9Go9E9ffpUp9Fo8lx+Xo/NKX92+7Pal9v0nLYLU0Hrysvxuclrqn7OTZro5zejn7P6rMjs73dWCnyjVyqV0rBhQ2bMmMG1a9c4duwYtWvX5ueff8bNzY1GjRoxa9Ys7t+/X/CoTvjPCPQKZFnzZdhJ7Lgbf5ceW3uw/55YG04QBEF4tUw+Is7X15exY8dy6NAhwsLC6NWrFwcOHOB///ufqasS3nJli5VlsM1gqhWvRrw6nmF7hrH04lKxOLMgCILwypgsUFKr1dy9e5dr164Z1i0qXrw4/fr1Y+PGjYwePdpUVQn/IVZSKxY2XUiXsl3QoWP2qdlMOTpFBEuCIAjCK1GgQCkuLo6FCxfSuHFjVCoV3t7e+Pr64uTkhJeXFwMGDODEiROmaqvwH6WQKZhQZwLjao9DJpGx9vpadtzZUdTNEgRBEP4D8h0ozZ49G29vb5YuXUpgYCAbNmzg7NmzXL9+nSNHjjBx4kTS0tJo3rw5QUFB3Lhxw5TtFv5jJBIJ75Z/l4+qfATAtye/JU4bV8StEgRBEN52+Z4e4MSJE+zfv5+KFStmur9WrVr07duXRYsWsXTpUg4cOECZMmXy3VBBAOhfpT977+7lStQVNmo30k3XraibJAiCILzF8n1F6X//+1+WQdKLzMzMGDRoEH379s1vVYJgoJAq+LrB1yikCq6mXWVzyOaibpIgCILwFjPJYO6wsLBMB9fqdDrCwsJMUYUgGJQpVoZBlfUTU848NZOHCQ+LuEWCIAjC28okgZKPj49h9eUXRUVF4ePjY4oqBMFIT9+elJCVIF4dz8TDE8VTcIIgCEKhMEmgpNPpkEgkGdLj4+MxNzc3RRWCYEQuldPZsjNmMjMOPzjM2htri7pJgiDkYNmyZdjZ2Zm83NDQUCQSCWfPnn0tyhFyr02bNowcObKom5GtAq31NmrUKED/RNL48eOxtHy+crdGo+HYsWNUrVq1QA0UhKw4yZwYWmUos8/MZtaJWdRzq0dxs+JF3SxByNbDhw/5+uuv2bJlC/fv36d48eJUrFiRTz/9lGbNmhV18zJYtmwZn3zyCdHR0UXdFJPq3bs30dHRrF+/3pDm4eFBeHg49vb2JCYmFmHr/jtWrFiBvb19UTcjWwUKlM6cOQPoryhduHDBaDFcpVKJn5+fmGhSKFTvlXuP4PvBnI44zRcHvuDHJj8WdZMEIUuhoaE0bNgQOzs7Zs6cSeXKlUlJSWHTpk0MHz6cq1ev5qvc1NTUTNPVajUKhaIgTS5yr/IcZDIZLi4uaLXaV1Lfm6IwX4NixYphY2NTKGWbSoFuve3du5e9e/fSq1cvtm/fbtjeu3cvO3bs4OeffxZTAgiFSiaVMbX+VCzllpyJOMPgfweTpE0q6mYJQqaGDh2KRCLh+PHjdO7cmbJly1KxYkWGDh3K4cOHDfnCwsLo0KEDJUqUwM7Ojm7duvHo0SPD/m+//ZZq1aqxePFiSpUqhYuLC6C/ur9w4ULee+89bGxs+PrrrwHYuHEj1apVw9zcnJIlSzJ58mTS0tIM5UVHR/PRRx/h7OyMubk5lSpVYvPmzQQHB9OnTx9iYmKQSCRIJBImTZoEQEpKCmPGjKFChQrY2NhQu3ZtgoODjc532bJleHp6YmlpSceOHXny5Em2/ZN+62v16tU0btwYc3NzVq5cCcDixYupXbs2lpaWlC9fngULFmRZjkajoV+/fvj4+GBhYUG5cuWYP3++Yf+kSZNYvnw5GzduRCaTUaxYMYKDg41uvWm1Wjw9PVm4cKFR2WfOnEEqlXLnzh1D3/Xv3x8nJydUKhXvvPMO586dy7JtqampDBs2DFdXV8zNzfHy8mL69OlGr0V25U2aNImqVauyYsUKvL29sbW15d133yUu7vm8cmvXrqVy5cpYWFjg4OBA8+bNSUhIAECr1TJlyhRKlCiBmZkZVatWZfv27UavQbFixTJ9DV4mk8lYvHgxHTt2xNLSkjJlyrBp0yajPPv27aNWrVpYWFhQvnx5vvjiC6P33su33hYuXEj16tWxtLTE2dmZrl27GvZptVqmT59ueF39/PxYu7bwh13k+4rSqFGjmDp1KlZWVtjZ2TFx4sQs886ePTu/1QhCjjxVnvzS/BcG7x7M+cjzREgjaJzUGFeFa1E3TXiFdDoduqTnQbJWq0WblIRWLgdp3v4nzMuxEguLXJX59OlTduzYwddff42VlVWG/eljd7RaLe3bt8fa2prNmzdjZmbG8OHD6d69u1EgcvPmTdatW8fatWtJeuG8p0yZwoQJE/jhhx9QKpUcOHCADz/8kPnz59OwYUNu3brFwIEDAZg4cSJarZaWLVsSFxfHH3/8QalSpbh8+TIymYx69eoxd+5cJkyYwLVr1wCwtrYGYNiwYVy+fJnFixdTpkwZNm7cSFBQEBcuXKBMmTIcO3aMfv36MX36dDp06MD27duz/Tvxos8//5zvv/8ef39/wx/qSZMm8d1331GvXj3OnTvHgAEDsLKyolevXhmO12q1lChRgjVr1uDg4MDhw4cZOHAgtra29OrVi9GjR3PlyhViY2P57bffiIuLw8vLi4cPnz9BK5VKeffdd1m1ahWDBw82pK9atYr69evj5eVFbGws3bp1w9LSkm3btmFra8vPP/9MQEAA169fz/SW0vz589m0aRN//fUXnp6e3L17l7t37xr2d+3aFQsLC6PymjVrxokTJ1CpVADcunWLDRs2sHnzZp4+fUq3bt349ttv+frrrwkPD+e9995jxowZdOzYkbi4OPbv32944GXevHl8//33/Pzzz/j7+7NkyRLatWvHpUuXjC5sfPnll0avQVYmT57MjBkzmDlzJj/88AM9e/bk/PnzqFQq7t+/T6tWrejduzfLli3jzJkzfPLJJ1hYWBgC7hedPHmSjz/+mEWLFhEQEEB0dDT79z9fDH369On88ccfLFq0iDJlyrB//34++OADnJycaNy4cZZtLKh8B0pnzpxBrVYDZDvwLbNB3oJgan5OfiwLWsZHOz/iYfJD+u3qx+IWi3GzdivqpgmviC4piWvVqmdIf5RJ3tzKzbHlTp+CXDy0cvv2bXQ6HeXLl8823549e7hw4QK3bt3C1tYWlUrF77//TsWKFTlx4gTVq+vPMTU1ld9//x0HBwdiY2MNx7/33nv06NEDlUqFVCqlb9++fP7554aAomTJkkydOpWxY8cyceJEdu/ezfHjx7ly5Qply5Y15Elna2uLRCIxXLUC/RWvpUuXEhoairW1NSqVitGjR7N9+3aWLl3KN998w7x58wgKCmLs2LEAlC1blsOHDxtdvcjKJ598QqdOnQzbEydOZObMmbRu3RqVSmUI5n7++edMAyWFQsHkyZMN2z4+Phw+fJgNGzbQq1cvrK2tsbCwICUlBRcXFywtLY2GjqR7//33mT17NmFhYZQoUQKtVsvq1av56quvADhy5AgnTpwgIiICMzMzAGbNmsWGDRtYu3atISB9UVhYGGXKlKFBgwZIJBK8vLwM+w4ePMjx48czLW/jxo2MGDEC0AeCy5YtM9yy6tmzJ3v27DEESmlpaXTq1MlQdsWKFQ3vkVmzZvHZZ5/x7rvvAvDdd9+xd+9e5s6dy08//WRoy8cff2z0GmSld+/evPfeewB88803zJ8/n1OnTuHl5cWCBQvw8PDgxx9/RKfT4ebmRnR0NJ9//jkTJkzItG+srKxo0aIF7u7u+Pj44OfnR2xsLCkpKXzzzTfs3r2bunXrAvr36cGDB/n5559fz0Bp7969mf4sCEWlbLGyLG62mN5benM3/i49t/Xk12a/4mHlUdRNE4RcT2Fx5coVPDw88PDwMPxxq1ChAnZ2dly5csUQKHl5eeHk5JRhPE36/nTnzp3j0KFDhttwoL81lZycTGJiImfPnqVEiRKGICk3Lly4gEajyRD0paSk4ODgYDiPjh07Gu2vW7durgKlGjVqGH5OSEjg1q1bDBgwAOkLV/fS0tKwtbXNsoyffvqJJUuWEBYWRlJSEqmpqVSuXDlX55euatWq+Pr6smrVKsaOHcuhQ4eIiIgw3A66ePEi8fHxhnNOl5SUxK1btzIts3fv3jRr1oxy5coRFBREmzZtaN68OaB/rbIqLyQkxLDt7e1tNK7H1dWViIgIAPz8/AgICKBy5cq0aNGC5s2b06lTJ2QyGbGxsTx48ID69esblV+/fv0Mtwtffh9lpUqVKoafraysUKlUREZGAvr3QN26dZFIJIb3f7169YiPj+fevXuUKFHCqKxmzZrh5eWFv78/QUFBtGzZkvbt2wP6K6iJiYkZHnhITU3F398/V23NrwIN5haE142njScDrQeyhjWExIbQe3tvFr6zMOcDhTeexMJCf3XnGa1WS2xcHCobG6M/sLmRl2MlFha5CoJKlSqFRCLJ94Dtl2V2+y6z9Pj4eCZPnpzp1QFzc3Mscnnr8OUyZTIZJ06cICkpCWtra0M/pd+aK4gXzyE+Ph6An3/+mYoVKxrVJZPJMj3+zz//ZPTo0Xz//ffUrVsXGxsbZsyYwZEjR/Lclh49ehgCpbVr19KiRQscHBzQarUkJCTg6uqaYWwWkOU0CNWqVSMkJIRt27axe/duunXrRmBgIGvXriU+Pj7T8rRardG5vjywWiKRGAJmmUzGrl27OHz4MDt37uSHH35g3Lhx7Nq1y+jqVU6yen+9LLu25JWNjQ0nT55k69atHDp0iAkTJjBp0iR2795teB9s2bIFd3d3o+PSr74VFpMFSnv27GHPnj1ERERk6KQlS5aYqhpByJFKqmLxO4sZvm84l59cZtC/g/hA+UFRN0soZBKJBMkLU5Sg1SJNS0NqaZnnQCmvx+YmUCpWrBjNmzfnp59+YsSIERn+EEVHR2Nvb4+vr69h3Er6FZPLly8THR1NhQoV8nYe6P8wX7t2jdKlS2e6v0qVKty7d4/r169nelVJqVSi0WiM0vz9/dFoNERERODn52e4zfciX19fjh07ZpR29OjRPLff2dkZNzc3QkJCaNu2baZ1vezw4cPUq1ePIUOGGNJu376d43ll5v333+err77i1KlTbNy40Whwt5+fHw8fPkQul+Pt7Z3rc1KpVHTv3p3u3bvTpUsXgoKCiIqKolq1apmWp9VqjW6v5kQikVC/fn3q16/PhAkT8PLyYvPmzXzxxRe4ublx6NAho1tVhw4dolatWrkuP7d8fX1Zt26d0e/H4cOHsbGxyXA1KZ1cLqdJkya0a9eOSZMmYWdnx/79+2nXrh1mZmaEhYUV6m22TNtkikImT57MlClTqFGjBq6urmJcklDkipkX49fmvzJw50AuPbnEktQlNHjagIrFc16fUBAKy48//kjDhg2pVasWU6ZMoUqVKqSmprJ582aWLVvGlStXCAwMpHLlyvTs2ZOpU6diZmbGsGHDaNy4MTVq1Mjzf+sTJkygTZs2eHp60qVLF6RSKefOnePixYtMmzaNxo0b06hRIzp37szs2bMpXbo0V69eRSKREBQUhLe3N/Hx8ezZswc/Pz8sLS0pW7YsPXr0oHfv3kyZMoV69erx5MkT9uzZQ5UqVWjdujUjRoygfv36zJo1i/bt27Njx45c3XbLzOTJkxkxYgRmZma0b98etVrNyZMnefr0qWE+vxeVKVOGFStWsGPHDnx8fFixYgUnTpzA09PTkMfb25sdO3Zw7do1lEplllfWvL29qVevHgMGDECr1dKuXTvDviZNmlC3bl06dOjAjBkzKFu2LA8ePGDLli107NjR6BZiutmzZ+Pq6oq/vz9SqZQ1a9bg4uKCnZ0dgYGBmZa3efNmmjVrlqsA4dixY+zZs4fmzZtTvHhxjh07xuPHjw1B8JgxY5g4cSKlSpWiatWqLF26lLNnz2b5ZFtBDBkyhLlz5zJ8+HCGDBnC2bNnmTRpEqNGjUIqlWZ4L2/evJlbt25RrVo1SpQowfbt29FqtZQuXRobGxtGjx7NyJEj0Wq1NGjQgJiYGA4dOoRKpcp0rJqpmGRm7kWLFrFs2TKOHTvGhg0b+Pvvv42+BKEoqJQqfmn+CxXsK5CoS2TQv4O4FnWtqJsl/IeVLFmS06dP07RpUz799FMqVapEixYt2Ldvn2EgrUQiYePGjdjZ2dG6dWuaN29OyZIlWb16db7qbNGiBZs3b2bnzp3UrFmTOnXqMGfOHKPbMOvWraNmzZq89957VKhQgbFjxxquttSrV49BgwbRvXt3nJycmDFjBgBLly6lZ8+efPXVV/j6+tKhQwejYKROnTr8+uuvzJs3Dz8/P3bu3GkYBJ1X/fv355dffmHlypX4+fnRuHFjli1bluUSWQMHDqRTp050796d2rVr8+TJE6Mn1wAGDBhAuXLlqFWrFqVLl+bQoUNZ1t+jRw/OnTtH69atjQIqiUTC5s2badSoEX369KFs2bK8++673LlzB2dn50zLSr8NWKNGDWrWrEloaChbt25FKpUikUjYunVrpuU5OTnlqq9UKhX79++nVatWlC1blq+++opZs2YZxvaMGDGCUaNG8emnn1K5cmW2b9/Opk2bCmUqH3d3d7Zu3crx48fx9/dn1KhR9O3bN8v3gZ2dHX///Tft2rWjYsWKLFq0iJUrV+Lr6wvA1KlTGT9+PNOnT8fX15egoCC2bNlS+Eul6UzA3t5ed/PmTVMU9UaJiYnRAbqYmBhDWmpqqm7Dhg261NRUo7yZpecmLavyCkNB68rL8bnJm12evPTzk/gnuhYrWugqLauka/C/BrrLkZdFPxdwf37eu6bs46SkJN3ly5d1SUlJWebRaDS6p0+f6jQaTZ7Lz+uxOeXPbn9W+3KbntN2YSpoXXk5Pjd5TdXPuUkT/fxm9HNWnxWZ/f3OikmuKPXv359Vq1aZoihBMDkbpQ29rXpTwb4C0SnR9Njag8UXF6PR5Tw+QRAEQfhvM8kYpeTkZH755Rd2795NlSpVMoyCFxNOCkXNQmrBwncWMvHoRILvBbPg/ALcZG6Ue1pOjFsSBEEQsmSSQOn8+fOGxW8vXrxotE8M7BZeFzZKG+a/M5/Ntzfz7fFveZD6gA92fMBnNT+jc6nORd08QRAE4TVkkkBJTDgpvCkkEgltS7WlhlMNPt78MVfUV/juxHfUca5T1E0TBEEQXkMmGaP0puvYsSPFihWjS5cuRd0U4RVxtHDkfcv3qe1SmzRtGr9c+KWomyQIgiC8hkxyRWnKlCnZ7s9sTZfXyccff0zfvn1Zvnx5UTdFeIUkEgnD/IZx7OExtoRuwce6kB8xFUwuvzMAC4Lw32CKzwiTBEovz5WkVqsJCQlBLpdTqlSp1z5QatKkSaZT0Atvv4oOFQnwDGBP2B52J++mN72LuklCLiiVSqRSKQ8ePMDJyQmlUplhPKRWqyU1NZXk5OR8LWGSl2Nzyp/d/qz25TY9p+3CVNC68nJ8bvKaqp9zkyb6+fXuZ51OR2pqKo8fP0YqlWa66HFumSRQOnPmTIa02NhYevfunWFRRFPbv38/M2fO5NSpU4SHh/P333/ToUMHozw//fQTM2fO5OHDh/j5+fHDDz8UynTtwptpuP9w9t7dy2X1ZS49uURVl6pF3SQhB1KpFB8fH8LDw3nw4EGmeXQ6HUlJSVhYWOT5oZK8HptT/uz2Z7Uvt+k5bRemgtaVl+Nzk9dU/ZybNNHPb0Y/W1pa4unpWaBgttAWxVWpVEyePJm2bdvSs2fPwqqGhIQE/Pz86Nu3b6aLPq5evZpRo0axaNEiateuzdy5c2nRogXXrl2jePHihdYu4c1Ryq4Urb1b80/IP/x07id+dfm1qJsk5IJSqcTT05O0tLRM1+xSq9Xs37+fRo0aZZiyJCd5PTan/Nntz2pfbtNz2i5MBa0rL8fnJq+p+jk3aaKfX/9+lslkyOXyAgeyhRYoAcTExBATE1OYVdCyZUtatmyZ5f7Zs2czYMAA+vTpA+iXW9myZQtLlizh888/z1NdKSkppKSkGLbTFylUq9Wo1WrDzy9+T5dZem7SsiqvMBS0rrwcn5u82eUxdT/39e3L1pCtHH14lL2he2ng3iDHc8iv16mf87s/P+/dwnwvZ7aKvFarJS0tDZlMluUq81nJ67E55c9uf1b7cpue03ZhKmhdeTk+N3lN1c+5SRP9/Gb0c1paWqbpefkckuh0uVj2Ogfz58832tbpdISHh7NixQoaN278ymbtlkgkRrfeUlNTsbS0ZO3atUa343r16kV0dDQbN240pAUHB/Pjjz+ydu3aLMufNGkSkydPzpC+atUqLF9ctVx4I21O3MzR1KPIkBFkEUQdZR0xD5ggCMJbKDExkffff5+YmBhUKlW2eU1yRWnOnDlG21KpFCcnJ3r16sUXX3xhiiryJTIyEo1Gk2FxQmdnZ65evWrYDgwM5Ny5cyQkJFCiRAnWrFlD3bp1M5T3xRdfGK1UHRsbi4eHB82bNzd0tFqtZteuXTRr1izDZceX03OTllV5haGgdeXl+NzkzS5PYfRz6s5UrIpbsefeHrYkbUHtpGZ87fFYKazy3BcFPXdTHZ9T3vzuz89791W+l3NzbqY8tiD9nJc+zixdfGbkLo/4bBb9/KL0O0K5YZJAKSQkxBTFFJndu3fnKp+ZmRlmZmYZ0hUKRYYXMLO0vOR9OS2r8gpDQevKy/G5yZtdHlP2s1KiZEbDGfx18y++P/k9O8N2EhoXyu8tfzd5sJRd2wvj+Jzy5nd/ft67r/K9XND68npsQfo5L32cWbr4zMhdHvHZLPo5vbzceqsnnHR0dEQmk/Ho0SOj9EePHuHi4lJErRJeZxKJhA8qfMDSoKU4mDtw/el1vjjwBVqdmK9HEAThv+itDpSUSiXVq1dnz549hjStVsuePXsyvbUmCOmqFq/K/Hfmo5Qq2Xt3Lz+d/amomyQIgiAUgUJ96u1ViI+P5+bNm4btkJAQzp49i729PZ6enowaNYpevXpRo0YNatWqxdy5c0lISDA8BScIWaniVIWJ9SYy7uA4fjn/CyqlCgcLB6KTo/F18KW6c/WibqIgCIJQyN74QOnkyZM0bdrUsJ0+2LpXr14sW7aM7t278/jxYyZMmMDDhw+pWrUq27dvzzDAWxAy065UO65FXeP3y78z6+QsQ7pcKmdNmzWULla6CFsnCIIgFLY3PlBq0qQJOc1wMGzYMIYNG/aKWiS8bUZWH0lcahwXIi/gYO5AZFIkt2JuMfHwRH5v+TsyaeHOoSIIgiAUHZONUTpw4AAffPABdevW5f79+wCsWLGCgwcPmqoKQSgScqmcKfWn8Hf7v1ncYjE/N/sZa4U15yPPs/LKyqJuniAIglCITBIorVu3jhYtWmBhYcGZM2cMs1fHxMTwzTffmKIKQXhtOFs582mNTwH44cwP3I29W8QtEgRBEAqLSQKladOmsWjRIn799VejuQnq16/P6dOnTVGFILxWOpfpTC2XWiRrkumzow8nH54s6iYJgiAIhcAkgdK1a9do1KhRhnRbW1uio6NNUYUgvFYkEglT60/FS+XFo8RH9NvZjwVnF+Q4Xk4QBEF4s5gkUHJxcTF6RD/dwYMHKVmypCmqEITXjpu1G3+1+Yv2pdqj1WlZeG4hKy6vKOpmCYIgCCZkkkBpwIABfPzxxxw7dgyJRMKDBw9YuXIlo0ePZvDgwaaoQhBeS5YKS6Y1mMaYGmMAmHNqDmcizhRxqwRBEARTMcn0AJ9//jlarZaAgAASExNp1KgRZmZmjB49muHDh5uiCkF4rfWs0JOLTy6yLWQbo4NHs6r1KpytxFxdgiAIbzqTXFGSSCSMGzeOqKgoLl68yNGjR3n8+DFTp041RfGC8NqTSCRMqjuJkrYliUiKoO2Gtiw4u4BEdWJRN00QBEEoAJNNOJmcnMz58+eJiIhAq9Xy8OFDw7527dqZqhpBeG1ZKiyZ/858Pt//ORefXGThuYWcenSKxc0XI5FIirp5giAIQj6YJFDavn07PXv25MmTJxn2SSQSNBqNKaoRhNeel8qLVa1XsfPOTsYfGs/xh8fZdWcXzb2bF3XTBEEQhHwwya234cOH061bN8LDw9FqtUZfIkgS/mskEgktvFvQu2JvAGafmk2qJrVoGyUIgiDki0kCpUePHjFq1Cix0KwgvKB3xd4UtyjO/fj7LLm4RMyxJAiC8AYySaDUpUsXgoODTVGUILw1LBWWjKg2AoCfzv5E7+29ufzkchG3ShAEQcgLk4xR+vHHH+natSsHDhygcuXKRsuYAIwYMcIU1QjCG6dtqbbcib3D75d/53TEaQbuGsimtpuKulmCIAhCLpkkUPrf//7Hzp07MTc3Jzg42OgJH4lEIgIl4T9LKpEyotoIupfrzoBdAwiJCeH3K7/jg09RN00QBEHIBZPcehs3bhyTJ08mJiaG0NBQQkJCDF+3b982RRWC8EZztnLmY/+PAVh1bRXx2vgibpEgCIKQGyYJlFJTU+nevTtSqUmKE4S30jue71DZsTJJaUkEJwcXdXMEQRCEXDBJZNOrVy9Wr15tiqIE4a0lkUgY7q9f0ud06mni1fFEJkWyM3SneCJOEAThNWWSMUoajYYZM2awY8cOqlSpkmEw9+zZs01RjSC88eq41sFb5U1obChbQraw4dYGrj29xsxGMwnyCSrq5gmCIAgvMUmgdOHCBfz9/QG4ePGi0T6xdIMgPCeRSOhUuhOzT89m3pl5JGuSAfg37F8RKAmCILyGTBIo7d271xTFCMJ/QhufNsw/Pd8QJAEcenAIjVaDTCorwpa9xiKuwqX10GAkKCyKujWCIPyHiNHXgvCK2ZnZUVFREQB3a3dUShWxqbFciLxQxC17jS2oDfu+g69dirolgiD8x5jkihJAdHQ0v/32G1euXAGgQoUK9OvXD1tbW1NVIQhvjQDzAIo5F6Nv5b6surKK7aHbmXFiBvfj7zO6xmjalmpb1E18fU2yhY8OgGuVom6JIAj/ASa5onTy5ElKlSrFnDlziIqKIioqijlz5lCqVClOnz5tiioE4a1iL7NndqPZ+Bf3p2GJhgBciLxAVHIUv174VTwFl06ng03DM6ZnliYIglAITBIojRw5knbt2hEaGsr69etZv349ISEhtGnThk8++cQUVQjCW6u+W30UUgUSJMilckJiQrgYeTHnA/8L7p2A079nTDcXV6oFQXg1THZF6bPPPkMuf34nTy6XM3bsWE6ePGmKKgQBAF1a2lt3tcXBwoElLZawstVKmns1B2DWyVkM2j2Ic4/PFXHritjT0MzTQ/bBxqGQmvBKmyMIwn+PScYoqVQqwsLCKF++vFH63bt3sbGxMUUVwn+MTqdDHR6O1ZUrRN2/T9rNmyRfu05qaCgKNzeK9e8HL83X9SarWrwqAO1T27M1ZCunI/S3rFPSUlgatLQIW1bE4h4+/9mxHMjN4OF5/faZP8DWE5p8VjRtEwThP8EkgVL37t3p168fs2bNol69egAcOnSIMWPG8N5775miCuENp9PpSDh4iCe//ELy9etIZFJKqtMInTsPiVyORCYDuQyJTA5SKep799DGxeEORL1UlvruXSImTsLb3p7YNA32nToikZvsuYQiVdu1NmWKlSEkOoQ0XRqnHp0iMikSlVKFUqYs6ua9Wte2wa7xz7e7rwBrZ/jO63laVlecBEEQTMQkf11mzZqFRCLhww8/JC0tDQCFQsHgwYP59ttvTVGF8IbS6XQk7N/P4wULSD533mifHEiLi8v6YLmcFEdHHKpXx9K3PGblyqH08SFu126e/PoryqgoIiZM4OnixTgOHoyqVUt0eZzg9HW7jSeTyljRcgUanYaPdn7ExScXafpXU+QSOb82/5UaLjWKuomvhiYN/vfu8+2ACeBUDrQa43yp2bx/BEEQTMAkgZJSqWTevHlMnz6dW7duAVCqVCksLS1NUbzwBtLpdMTvDSZywQKSn83WLjE3p1j37th27ECaVsvBffuoX7cucp6NPUrToNOkgUaD3NkFqUcJtu3aRcVWrYyWxXHo0xvrTh05NnkKLkePoA4LI/yLLwj/4guQSCgtk3F72tdIzM2QWdsgc3bGOS2NqDt3ULq4kBbxmOTr1/E6e5Zb477CqlYtnOe8PsvsWCmsAAj0CuTiE33fpenS+O3ib/+dQCkl1njb0lH//eUJOVPiX017BEH4zzJJoJSUlIROp8PS0pLKlStz584dfvnlFypUqEDz5s1NUYXwptBqid/zL09/+ZmUy/o5tSQWFhR77z0c+vZB7qj/g6dWq0m5cQPzihUzrA2YTq1WZ1mN1NKSp40bUXviBOLXrOHJkqVooqJAp0OaloY2Lg7i4tA8joSQEGyBqJceLDB79j3h8GEeDBmKpH273J1iQgLI5UjNzHLOXADNvJox9/Rcw/ah+4e4G3sXF4v/wKSLydHG2+VbZ54vRVxREgShcJkkUGrfvj2dOnVi0KBBREdHU7t2bRQKBZGRkcyePZvBgwebohrhNabTaonftQuv+fN5GK4fgCuxtMS+x/vY9+6N3MGhUOqVWlri0L8/9n36oE1MJDU+gX93bKdJvXrIdDo0T6NJvn+Py/v2UdJGheZxBHJ7B+QlfTj/9Ck1atbk0djPSD51CveoJ2hbtIBsJkmNP3CQ+59+ikQqpfiYMdh26ohEIkETF4cuLQ15sWImOzdPlSfT6k9Dh45tIds4/OAwa66vYbjfWzqHkDoZLm+Eci1h73TjfVaOz38u2xKub9P/fP8k7JoAjcaCmfWra6sgCP8ZJgmUTp8+zZw5cwBYu3Ytzs7OnDlzhnXr1jFhwgQRKL2GtImJpIaGknI7hNTbt9HExKCVgOOdO0RevYpMrgCZFIlUpv8uk4FUhkQmzfBdp1YTvWYtKTduYAZIrKyw/+AD7Hv3MmngkB2JTIbMxga5uTlpxYqh9PExXKlSqqsRJZdT54VbeGq1moStW7Fq2BDPJb8R1q8/liGhPBg8mGJduqJLSUabkoLcwQELPz8UHh5ELVtOxMyZoNUCED5uHNF//QVSKUnnz4NWi01gAHbduqOJfkrK9RtY+FfFumnTfC8O3b50ewBslbYcfnCY9TfX82H5D4nRxpig114jaSlwYjHsHJdz3ndXQdgRWNZKv31oHti4Qh3xOSMIgumZJFBKTEw0TAOwc+dOOnXqhFQqpU6dOty5c8cUVQj5oNPpSIt4TGrIbVJu3yY1JJTU27dJCblN2oPwTI+xB6IPHMxXfVJrax7XrkWNyZMxd3TM+YDXhEWVKrj98jN3+vQl+fQZwk+fyZBHamOjv50H2HbpjJmPD49//Imkc8bzHMXt2k3crt1GaVaNGmJZvQbJly5h4eeHTc8P8tzGRiUa4WrlSnhCOE3XNUWGjOpPq1OxeMU8l/Xa0eng58bw+Eru8kulYF3cOG3751C+Ddh5mL59giD8p5kkUCpdujQbNmygY8eO7Nixg5EjRwIQERGBSqUyRRVCNnSpqaSGhemDodshzwIj/ZUibULWE/LJ7O1RlvTBzKckMgd7tGlp3Lpxk5LeXkgBnUYLWo3xd40GnTbjd4tKFbHu2o2rhw4iewPX9zOvVIl7AwdQ4dJlSE1Fam6GRGmG+t49ki9f1gdJUinOn39OsZ4fIJFIULVsSfTffyMvXhzrevXQJiURtXw5cXuDUbq7o/DwIG7HDhL2HyBh/wEA4nbuJCU8HCrpA5wnixfz9K81lPhhPublyqFNTkZqbp6hfTKpjK5luzL/zHwANGhYeXUl3xT/5tV1UmFJict9kJROmclttpO/QeAkkzRJEAQhnUkCpQkTJvD+++8zcuRIAgICqFu3LqC/uuTv72+KKgRAEx2tD4DSrxA9C4ZS790DjSbzg6RSlB4eKEuW1AdFJUui9CmJ0sc7w20xtVrNsa1bqfXSU2a5ld3g6zdBirs7rgMGZDh3bWoqKVeuIHNwRFnC3ZCucHPDaehQo7yuU6fi+mKZQ4fwZNHP6NSpyIrZ83TlSmL++APHhg1Jq12Hxz/8iC4lhYcTJqJq345H077G6ZNPcBw4IEP7OpbpyJKLS4hX65/02n5nOx8nfIyzlbPpOqEopGbx5Fq9EXB4PnjWzbgvs/FIN3aLQEkQBJMzSaDUpUsXGjRoQHh4OH5+fob0gIAAOnbsaIoqXmsx27ahezYVgiZNg825s8Rpdcjkzx9lziw92zSNFl1MDMk3b1Li9ClCvvsOTdTTLNsgtbJCWbIkZiV99IHQs6BI4emJVPkfm6jQxKRKJRYvvK/zwszHB7fvns8lZl7Bl/BxX2F/4AAPP/0UXUoKAEnnzhlu4z3+4QcAYrdswWn4MHRqNVEr/sCuSxfWt11H2qkL/P73VP6qGs27W97lPe9OdPf7EFuz1/9Knpk6Btmf70LdIVA6QJ+4rn/GjK5+8M5X4FEbvBtkUlAmM/4/ugBr+0KXJaZttCAI/2kmm87YxcUFFxfjx5Zr1aplquJfaw+/Gk+87HlQ5Ao8+nN1hnyZpecmzRJIv14kd3XFzMcnwxUieXGnfA8YFl4du86dSbx6jZgVK0g+rV+mxKphQxIO6G/NSS0t0SYm8ni2fl6n8PET0KnVaOPjSTp9GrMVvqRcv04njQbb1GLcl0RQN3gBwRVW0WZlMNrISHRqNQovL8P7QfH4MbrU1CyXfLG6dImoBw+w79ABhatrpnlMpfSjLUgf74Zbu+GzO7CyK9w7njGjSxX9ciW+bbIurHJXuLDGOO3iOhEoCYJgUvkOlMLCwvD09Mx1/vv37+Pu7p5zxjeQZe3aWD27aqPV6YiMjMTR0RHpC4FLZuk5pcltbZF7e3E5JoZaHTtiWbo0UjGJ5xvP8ZOPebR7N+bh4ZiVK0eJn37k/qhRSOQKnEaMIKRTJ3TJyUgsLdE81V9FlLu5oo2JJeXK87E8AXufX2EsezGay73fQ3E1FF1SEtbvvEOJH3/gyfz5+Py6mPvB+3D5/HO0CQlY1qpJWng4UpUtSRcv4P77CqKAlLNncZ81C1mhrs/4wkzoLy5F8iKv+lBnSM5FFfM2SYsEQRCyk+9AqWbNmnTo0IH+/ftTs2bNTPPExMTw119/MW/ePAYOHMiIESPy3dDXmceCnwyD1tVqNee2bsXvpXE+maXnJk2tVhO3dStmvr5I36JFYP/LJEol4T0/oMrlKzj0+hCpUonHjz8a9nuvWknakyfoUlO5N3QYoB/7ZObjQ8TsOUhsbAi7fh3bU6eQqlRcr+VK6d3XkJ+5YghD4v/9l6sVnj8Rl3zyJKFdugBgWbMmiadOYVmtGrwwcDxh335ut2lLqe3bkFpYPG+wRkPK9evIK1RAGx9PWP/+mFXxA1/9Itg6jYYnvy5GVqKEflur5dG334GlBXgZB0MStZrHF61ReSZhpspkXF3gZGjwSe46sv7HcPgHSEs2TteoQSZ+VwRBMI18B0qXL1/m66+/plmzZpibm1O9enXc3NwwNzfn6dOnXL58mUuXLlGtWjVmzJhBq1atTNluQXijqR0ccJ7+TaaD5s0rVAD00zs4jhiORKHAun59ANxnzUStVnNi82aqfD0NCw8PrJMecrRjIBXu6o+XOzuT9ujRsw05qXZ2KCMjDeUnnjih//7STOUAaY8ekXjiBNaNGqFTq4nfswfXVau4e/ESAPa9epF87jzJ585TFkh0dEJ99SqP584FqRTJ5EkkHjlC1LJlAFh8NJCkkyeRp9+GPxlO5EUVkRdV+L77wLjypuNyHySBfpxSsymwbaxx+oOz4JH5P2+CIAh5Jc3vgQ4ODsyePZvw8HB+/PFHypQpQ2RkJDdu3ACgR48enDp1iiNHjoggSRDyQSKR4DRkCI4DMj4Bh1SKwt0diUyGu7U7ofV8DLuOTGxHUtUyWDVsiMeqlYSO/hT3Fb9T5uABlD4+GYpKcXVB8UJ6/EH9PFoPp0zh4ScjsXkWJAFELV9udOyDgQMN46nQarG8eZPEA/sN+z1+/oX7ffry6NO+EB+BecRDwz7dixeUui6Hxi8FPC/QxMeTcvNmxh2ZPTH3W2CW5QiCIORVgQdzW1hY0KVLF7o8u6z/X/TrgVtYWOnHdWg1Wq7el3B3fwhS2fM4NLP0nNLMlXIsFRJuPJFgczOSYlbm2JgrUJnLsTFXYK6QigHcAgCeXT9g17VphBWXsOPuUiQtJZz8YC1H7x8lSvsUi6qtkSsUeK1aSVpEBPHB+3j8bDb9+PK+lPnkEyKnTSPxxAliN27CrnNnoteszXM7rK5dI00dmyH96fbjFK/SH5lSa0h7fNGG4n5xYF8SjUtdiI1Flsm8azq1mrBevUm+dAnX6dOx69jh+c5nCwhnkBQN8iz2CYIg5IHJnnr7L5u3+yZSsxcHWcvYHHYjk5yZpecmTcbS66czlCaXSrA2l2NjLsfGTKH//iyQMqSbK4y/m72YJsdKKUcqFcHWmy6obFu6d/uDu3H6+286dIzeN5q9d/fiLnPnA/SzgcuLFUNerBgK9xJokxJJvXefW/5VUZb0wXPpEkI6diTlxk1C2ncAwKxKZS527UpQmzbc8q+WYztszpwl4dmUBy/TXjvK05vPn4x9csUGpznroER1bjVugubJE0osWIBV/XqGBYe1CQncbt8B9b17AIR/8YVxoFTtQwg9AFc3G1f2nReSnpuQ6NJy032CIAhZEoES0LFjR4KDgwkICGDt2rz/F93R3w0zS/0EeFqtjrt37+Lh4WEUgGSWnlOaWqsjJjGVu+GPUVjaEJeiIS5ZTXxKGlodpGl1RCeqiU5UA0n5OneJBKzN5NhbKXG2MUMTL+XSzuuUsLfCRWWOq60FLrbmOFgpRUCVHa0GNKmgsMg5byGwUdrwV5u/WHF5BQvOLQBg7929ANzX3EetVaPg+XgombUVxT/5BLVajWbrVgAkcjmu078lrG9ftLH6q0KWteugUyqRyOVYBwQQv2dPhrolMgnFSseR9ERJUmSG3QY3NrpkSLsa1BszX180T54AcG/IEGxatKDEvLkkX7tO7ObNhiApU0pLeHcl/NkjQ7AkX9GOdoC6VUTWxwuCIORABErAxx9/TN++fVn+0viL3JraxA6VSn/rTZ2Wxt69ETRtWhGF/Hn3ZpaeY5qZJWqpOVt3hdOqdT3DwF+dTkdC6rOgKTmN2OQ04pLVxCWnPfvS/xyfkkasIT3j/jStDp0OQ/qdJ4mAlFMHQjOco1ImxdnWDFeVPnBytTPHVWWOi60FrrbmFFeZYaPM95C3N1tqIvzeDh5dhjZzwK+78f6Y+/qZpM0Ld0JIa6U1g6vqF4ZND5bSnXt8jppuNZFLX/qV16jxC/sN2aol0PQLLCrVwX3ObO4NGYrCxQWbDu2Rnj+D5N4J3KZN5ZHuKdw+SEyo/gqq14DSWMbpxySlJUu5seF5MGTtnkT8/ZwDxxenPACI27EDnUZDSIcO+nXgXnKlvC9lDuxH7uT0PLHjzxCyD/4eDCnGCwZLg7+B5pNzbIcgCEJmRKAENGnShODg4PwXsLAumD2b3A9oDnDJOEtm6blJUwBtkSK5qgJzFZipkJipsDazwdpcpX/yx+zZd3NbMLcB2/Rt1bN9tvpt+fMZunU6HSlpWkMg9SQ+lXtR8QQfO4utmw8RcSk8jEkmPCaZx/EppGq03I1K4m5U9leuLGQyZl87iKONGQ5WShys07/rf3a0UmJpJketVhMSB6fuPEUqk6PV6dDq9IGbVqfDQiHDUiEhJhWSUjXI5fLXczyWTgf/fAz39E+S8fdAeHAGmk3WT5h4+nf45xNQWELdofovWeFedaromHGh3IF7BuKt8mZBwAI8VB7wNBQsiiG5GYz3k33wBAgJhk6Lsa7flbLHjyFRKklLS6PitrHIz+kX+nVzlhGbqjAESuZRB0i/UCU31yKz0KFJ0r9OzvWBg8nEP8i4dl1OUu+EZRokpQufNBmPn55PqYCZNZRvrX+fvxwoXd4gAiVBEPLNJIFSr1696NevH40aNTJFcUb279/PzJkzOXXqFOHh4fz999906NDBKM9PP/3EzJkzefjwIX5+fvzwww+vdlZwmTnI9VdTdIBGo0Emk/Hin/XM0nNM06SATosULSRH678KQm5uCKwk5irMzWwwN1NR3ExFKUt7qtu4UcLqIf7VXFHYe4OVI0gkqDVaHsUmGwKnhzHJPIhJMmyHxyTxJD6VNK2OJI2EO1GJ3IlKzE2D4OKJHPNMOLUHhUyCylyBykJBeRcb2lR2Jk2bw6HxjyHxCShtQZdT5jxIiQetWh+EnlgMF/4CiQyqdIdzq+DYQji/GjzrwDX9bS1S42Dft3BxLfTcnH35OdWdnMXaaBo1rO1LDRtXPG08KWFTAvvYx2yO1493C40N5eej3zCtylD4tSl41kFq6Whcxvr+4FELaTEveHQJ+bqBlIy8+Hy/ToNE8vxjQyJ/HszolFbYeiYQdc0auYUGab+/sLHqR/zSvI8Tup3Dk7I6dWrmOzKZP0kSHaoPDO289PeaBUEQ8sAkgVJMTAyBgYF4eXnRp08fevXqZbJZuBMSEvDz86Nv37506tQpw/7Vq1czatQoFi1aRO3atZk7dy4tWrTg2rVrFC9eHICqVauSlpbxw3rnzp24ubkVvJFjb8Kzp3XS1Gq2bt1Kq5cmnMwsPcc0uRx1YjT/btvIO/VrotAkQXIMpMTqV1xPfvY9JVb/ZbT9wn51wrNGJOu/Eh5nehoyoBZAiH6FeuTmoHJHYetOCVsPSqjcwbYEuJUA3xKgKmdYnFSn0/EkLon1W3dRqXpdYpI1PElI5Ul8Kk8SUp79nEJkfCpJqRokEkhOTMTa2gqpVIJUIkEqwTBDeZJaQ2ySmpjEVLRIUGt0+jISUgmJTGDbxYdYyGQc11yiUzUPanpYI310EWnYUaqHbkL+0wSIDgX0FzzaIYHr9mDpCOYqZApLakXFIdu4CSq0gzI5TGFxahmcWQlPQzLvv+ZT9VeLyreCbZ9B7P3nQVKjMeBcCXZ8CU9uIvurBzKnQcbHH10ET26CUzl9wGX+0tNfOh0cmgfB36JIS6KhZSloXBPiH8C6flCyKVTsAFc2YQls6b4SXfnWzPijqVExR+4Gozu8Sh+Yh+xHIs1kYsabu6BGP9jyKZKIixl2W7mkoFSpMS+mfh53NBhFWtWeFE/yR26hwcY9GZ2bP/vKT+OdKbdIDt6JouMnhA8fCYDr9Ok8/mE+aQ/Cs+/3Z0os+Am5oyOh3fS3NdMHe2cglWWePs8PmnwBTT7PVX2CIAjpTBIobdiwgcePH7NixQqWL1/OxIkTCQwMpF+/frRv3z5fK9Gna9myJS1btsxy/+zZsxkwYAB9+vQBYNGiRWzZsoUlS5bw+ef6D8WzZ8/mu/4XpaSkkPLCEz2xzwa8qtVq1Gq14ecXv6fLLD03aWqJGcmKYqhtfbJcqytH2jT9lYhnAZQkPbAy/BwHCY/Rxdwj7u5l7CTxSBIi9EFV1C39VxZ05nagKoHOrSrWZVrhYp5GVXfrHF9ztVrNrl27aNasdpZ51Wo1O3fuon6TpiSlSYhLVhOVoGbfjUj+ORfOo7gUVp+8h/r0/yij/B/2xCADShi1zxZJcgwSdPqrS4n6QcNS9GvqEXNaf5Wn9jDQ1cjwugFI7h1H/s/HWXdvpa5oqg8AtRpKB8HQZkhu/4v0yka0Po3RVeqqz2hfBvnyVkgfnKJOzCzSIv3AsSSSO4eQb//seXln/kDzwUZQPn+8XXLmd+S7Jxq27RNvoVndA138QyRxD+DUUv1X+nlvHIIm9XtaxsbxhzXUT0zilLkZEXI5c4rZUUqtpn18AhKtGo1EjqbhWJT7v9HXf3kTGu+mKMKOAHDGsx/lOo7Fcl45fd/JoGTLx0gkoHGvibbLMrB21g8MN7PDoXw0AInP+lLWZhB2HYeT9MKgbKs2rbFq05rwUZ+SsGtXln2bzqxBAyQSCc4zZ/BozFjidu3mwdff4PjJx0heWPRZrk4iy2tGwdNJ82qErkTOk1Fm9Xuc3/zZ7c/LZ0Zm6Tl9L0wFrSsvx+cmr6n6OVefzaKf3+h+zkt5Ep0um4EA+XT69GmWLl3K4sWLsba25oMPPmDIkCGUKVOmQOVKJBKjW2+pqalYWlqydu1ao9txvXr1Ijo6mo0bN+a67ODgYH788cdsn3qbNGkSkydnHOuwatUqLN+yNdikWjXm6qdYpEZhoX6CZeoTzFOfYKl+ok9LfYJCm3G8UqrMioe21XhgV5PHNhXRZnbFwgS0OoiKfEjt8GVU0lwG4KnOmtPaMpzVluICpYm0KImZuSU6TRoW2ngsNXHY6OLwNE+inHUSLooU7JLD8InUP8n1wLY6p70GoZG9cLVCp6PhjanYJ9zkvl0tbji3IcGsOBqJEoUmEZkulSSFPUhyN5DdPv4adW/NQq5NQS0156xnf7wj9+AUf4UnVmWxTn6AmSaeh6qqHCv5CUikSHQaAi6PxSr1Mddc2vNIVZWG16fog79cuCuX4aTRMrK4Iwctn4+POnjnLrZaHdEWnuwrPw3LlAgCL49Bgo47Do3xerKPaAtv9pWfAkCpR1up9OBPo7J3VvieJLPng6p9Hu+iyr0VPLKpwtHSozO0xezePbQWFqgdHIzSleEPsbl4AYfd+tcixdUFs/Dnk1Ne/+5bAGSxsZT6+htD+qP27YmpV9ewXTzmHHVvf59tf5zwHsqDYrWzzSMIwtstMTGR999/n5iYGMMSZFkxeaAUHh7O77//ztKlS7l37x6dO3fm/v377Nu3jxkzZjBy5Mh8l/1yoPTgwQPc3d05fPgwdes+/7AcO3Ys+/bt49ixY7kqNzAwkHPnzpGQkIC9vT1r1qwxKi9dZleUPDw8iIyMNFrrTX+lpFmGtd5eTs9NWlblFYY815UcC7H3kUSHIrn1L9Jrm5G8cFtKZ2aDrkwQ2vJt0ZV6R38rLw91ZZknLRn2f4/06A/IdGno5BZcLTeIdcoOnHuQwPm7T0nR5jwWxd5MR6caXtRP2kvDK1OQ69SEKkqzvux3lPAqQ0U3G7wf7cbqnwHoFJakDT4ONhkfcc+rtIjrJK7qhUPC87mydFIFaUNOIIkLR7ayI5K0ZNLa/oSuSnckF9ci3zgInaUDacPOoEbBveUDKfvoH7QlA9AEfYt89ftInujLS+u8DEnoAWSnftP3o9QCSclGbHUrzbi7/xjqHBAdw0lzM4aleFKp/z8oFApkGwcjvbjGkEft15Ot0mb610AuR3L/BDy+inzrKAASP76Gwlof9KjVanbt3EGLUnJkXrVRy63z/F6O3bSJiImTcJ0/j4ivxqOJigKg9IXz+n5KTeVW9RqG/DYdO+I85aV/XqJuIT35G2nutTHb0C/z16D3TnTuz+aF0mkzBLp5/V3IKX92+/PymZFZ+hv1mVGA4wv0mZHNvrfys7kAx/9X+jk2NhZHR8dcBUomufWmVqvZtGkTS5cuZefOnVSpUoVPPvmE999/39CAv//+m759+xYoUCosu3fvzlU+MzMzzDIZG6FQKDK8gJml5SXvy2lZlVcYcl2XwgFsHMC9ClRshzrlW46tmUdduwhkVzcjiX+I5OIa/R9epTWUbQEV2oN7DUiKwS7xNsr7R5FrkvVLUaTEPfseDxIJUomcUo9uY3b2ATIzC5CZ6QdRH5wDUbcB0JYKRNrme3yLefMV+vfi5i1b8a3VmCuP4omMS0Uh1XHtyiVqVquKTCZj79UIdl5+RFSKhsWHwlhMKapLvuRn5Ry81Tfpe/FDPj8zgHHaauxSfoWVFH5MbsmaxTepUzKKuqUcaFjGCUfrzMfJpKRpMJNnMVYGoHhZDpYZRxvlEWTHfwZA4v8BCkcfcPTRj6PZPQl58DQoGwjBX+vz1B6MwtIW1GquuHXF58MfUVjZ6dchKvUOPAuU5F61oVIH8GmAJvIW+x9Z0ajTAJpLNIxb+TxQ+tVOP13BXKWa5emvedu5+jFtz8ZXSbzrQdgL7wmf+pAc9fwtYFXM+L0ikSIrH6RPe3ZpOy/vZYfOnbHv2BGJVEq0568kPQuUDPlfPi4hIWNZzuWh9Uy0ajX39v1KiadHM9QjX9Ycev0Dj6/Bninw4QZwr54hX15/73LKn93+vHxmZJb+RnxmmOD43OQ1VT+/sZ/NJjj+be/nvJRlkkDJ1dUVrVbLe++9x/Hjx6latWqGPE2bNsXOzs4U1Rk4Ojoik8l4lL4A6DOPHj3CxaXg//kLeSSV8cSmPNoWo5C1mgn3jsPljfqv2PtwcZ3+C/0A68YA17IuTgZUAnjwvwz7dNYunHDsgn/3iUhfGKMCIJVAKScryrvZAfrgaeuTi7Sq4opCoaB9VXdiE5KZvXonkWbuaAEHK09+uKtimHoJTrGXWKScy0VdSbwlj4jQ2bFQ3ZrEqETCohL56+Q9FDIJQZVc+aC2J9W8iqGQSbkZEc/MHVfZefkRHau6M6l9RVTmWfwySqRom32NzLUK3N6nXxA2XZ0h+sHjT0Php9r6px3tvKDOYOMyXhjDhG9beBZ0YeOqf7qrUie0ajXxzyaUNJebsyhwEYN2Gw8kvySN5mrUVco7lUdhZg1dlsLxX8DGBV25dhC2w7he50pG52FqEqm+TLeZM3g07WscBvTPMq82PosnAJ855T0E545fo1gSkHHn8rbPf944HIYczld7BUF4u5kkUJozZw5du3bF3Dzr+VLs7OwICQkxRXUGSqWS6tWrs2fPHsPtOK1Wy549exg2bJhJ6xLySCrVPx7vWQeafw0PTsPlDXB5E8TcRae0Jlkrx9zWEYmZjf6KU/r3ZwGAVp3M/bAQ3J0dkWrV+pmvNangXo20up8QvucA/vl83NtCKcPfQUerVlUMl3e3br2NXYtdcHAmHJxLJYn+qpVViwnsqtCKG4/iOHL7CQeuR3I5PJZ/zj3gn3MPkEjAydqMyPgUtM9uZK8/c59jIVGMDSpHswrOWCrlpGm0HA+J4t+rj5BHS2gF4P8B+H9AapoWuVann/1cbgZt5sL/3ns+JUT7nwxPGGbKpyF0X6l/KjGbPqnvXp+PqnzEz+d/NqRp0fH+9vf5qMpHDPMfBgpzqD9CvzOzAY/2PqT12kbwiYv6YLeQKD088Ph5UbZ5NAnZB0oAuPqZqEWCIPwXmSRQaty4caa3pHQ6/XIcnp6e+S47Pj6emy+sGh4SEsLZs2ext7fH09OTUaNG0atXL2rUqEGtWrWYO3cuCQkJhqfghNeAVAolaui/mk8DnY60tDR2ZjKNwos0ajWnt27FpVUrpC/nKawnTWRKCJykv5W1eRTYlsCqdi+sZHLc7SxoUq44X7SEi/djWHnsDpvOPiAhVUNEnH7sWvMKzrSr6saM7dcIi0rk4z/PYq6QYm+pJDIhldRnkz9JkFL8aBhOKgu2nA9n77UIrMzkDGlSig/reqMs1RRGXdZfgbN21gdCOfFtk6tTLG5Z3PBzgEcAe+7qB1D/fP5nKjlWoolHkxzL0JWoScL5zKeZKGzea9bw9M//EbNuPcnnzqOJj0dmnU0QCdD5N/00ClkphCtjgiC8HUwSKPn4+BAeHm6YtyhdVFQUPj4+aDSafJd98uRJmjZ9PhfMqFH6QaS9evVi2bJldO/encePHzNhwgQePnxI1apV2b59O87OzvmuUyhkb8Kkfz6NYPhJ/fxFmbS3krst0ztV4esOlXmSkMrDmGQslDJKF9f/wW5arjg/77/NxrP3ufMkkQcxyQAUs1RQysmKk3eimbLlqlGZKWmpTNtyhf03Ivm6QyW2XXxKdGJD3LQWBMWnZDkmKq+8Vd6Gn7+o+YUhUAIY/u9w9nbbi6OFYyZHvh4sKldCIutBzLr1AFyvUZOS27Zi5uOT9UGVu+i/do6Hw/Mz7peKQEkQhMyZJFDS6XSZLi8RHx+f7e243GjSpAk5PZg3bNgwcatNKBw5BHVSqQQnGzOcbIyDGCszOaOalWVkYBmuPYojRa3F3kqJq605Wk0aw3/ZwY57UjzsLWhTxY3WlV25eD+Gyf9cZv/1xzScsdeovImbLlG/tCPjW/vibW9OaBxM3nyFwU3L4G6XtyVRarrUZHK9yZSxK4O9uT0jbUYyJ26OYf/1qOs4ur++gRKA9KUrSE9++w23adNyPtDv3cwDpfBz+lnWa/R7MwJ5QRBemQIFSulXdyQSCePHjzeaS0ij0XDs2LFMB3YLwn+FRCKhvIvxo6dqrYRWHlq++TAAexsLwz8Zldxt8bC3pM+yE6SmafHzsMPfw44zYU85dy+G/dcfMywmmfndq7DoiowkzV0O345iSa+auNqZo5RJeRyXgkSiD94O3ojE0Srjr7hEIqFTGf0s92q1Glup8WK9iy8upqZrTRSFNAeWKbwcKMWsXYdNQAA2L1x9zpRDGXCrph8z97Itn+q/uv+hnzhUEASBAgZKZ86cAfRXlC5cuIDyhaePlEolfn5+jB6dcdI5QRBAZaHIcCW2fmlHNg2rz+O4FBqUdjTsvxkRT5sfDnDtURwt5h+CZ/NP336cQJNZwQBYKmUkphrf5ra3UvBZRbgTlciETVe49jCORT2rU8OrmGHNWbnE+GPgxMMT9NvRj85lOhNYItD0J24CmY1JerL4t5wDJbkSBu6FSbZZ51n9AXx2v4AtFAThbVGgQGnvXv3tgT59+jBv3rwcJ20SBCFn5V1UlH9pdovSxa1p7+fO6pN3AVApdCz8sCY/BYdw9PYTtDoyBEkAUQlq/rotZeGt04Q+0S9U3OPXYyhkEswVMoY2KcmJuxKwMT7uTMQZzkSc4ZDXIerp6hXKeRaERKnEzNeXlCtXDGlJp04Ru2MnqhbNC1y+fFkQFo7igRBBEEw0Rmnp0qU5ZxIEoUA+qONlCJR6lNZSy9ue+gOcSVZrSEnTEhmfgoOVkicJqXyx7gLn7kWTkqblVKQUSEQulZCm1ZGq0ZKqgYRUzbMB5TKUDkEo7A/gkjyQMj6hHHr8NwDb7mzDytKK1rQmKiGVYpYZr4IVFds2bYh4IVACuP/xx6iuXsniiBf03wOLM5lb6RnJows0fzQK3dUvYHxEQZsqCMIbLN+B0qhRo5g6dSpWVlaGsUpZmT17dn6rEQThmcolbJnZpQoKKUjuPh9jY66QYa6QYWuhH1NkZ6nkr0F10el0/Lr/Fov+vUrxYiomtKnA9UdxTPrnslG5VnIdxTRB3LvRmFtIuPOgGC0bweUnV4hQX2Vt4locjtdi1j9ampZz4of3qyHVaVl6XcrOuPNMbF+R4jbmaLUmXzYyW8V6vM+TZUvRPI40StdptYZJK7NUogZ8fB6ub4dtY7PMJtGkwNWt+sk8m08Dl8qmaLogCG+QfAdKZ86cMay+mz5WKTOvy3+fgvA26FrDQz855t2c80okEvrU88I5+hKtWtVFoVBQxcOORfv0E2l+1rIcfx4P4x3VY3p2bMCyo3eZsf0aaRol/+ytjdS8BFY++ikMfr45FksfN/ZeG8Y7s4KRSyU8iJFy9slDtlzUL14rl0pwtZDh4RdDNW9HwqISuRItoUxEPMWszXn4NBGtjhyfYs0tqbk59h9+yOPvjf8Ri9mwEbtOHXMuoJgX1P4IvBvCwoxrOxr8+Z7++7LWMPQE2IipRwThvyTfgVL6+KSXfxYE4fVlbSZnx8hGANhaKGhTyZmtW7eikEkZ0qQ0rSu70nRWMFodaJONAwKZ+QNkFmFExHlnWnaaVsfdBAmdFh2jfwMf/nc8jIRUGYuuvLg0iJy51w9hZ6mgeUUXhjYtXbAT0mgzJIV/+SWq1q1yH5A5V8hdvuQY+L6s/kpUMa88NFIQhDeZSWZZS0pKIjEx0bB9584d5s6dy86dO01RvCAIJmRroTDcpnuZl4MVP75fjYGNSrJ71Ds4y42X/7D0XoREHp1jHYsPhpCQyeBy0D+Bd+5eDDN3XKPLwsP8c+4Bq0+EUWHCdgb8fpJbj3OxLMkzdt26onBzy5B+za8qt6r647Tpn0yOykSxZ5NVvvdnznl/b5/r9gmC8OYzyWDu9u3b06lTJwYNGkR0dDS1atVCqVQSGRnJ7NmzGTx4cM6FCILwWmhV2ZVWlV0BWNhsJr1+24bC4x+iNPqlhCy9F1IsqTufenhSvcE7hMem8lPwLT5rXpqFmw6yOUyW67pO3nnKyTtPDdu7Lj9i1+VH+HnYUdldxR9HwwDoVM2dmV380Ol0JKRoUMglKGRSFPb2lNqzm3uDBhO/b1+G8osdOkTakycocloke8QZSE3Qr6fXZi5s/iTrvE+frVkZfh7iH0GZZrk+X0EQ3jwmCZROnz7NnDn6mX3Xrl2Li4sLZ86cYd26dUyYMEEESoLwhvIu5shnFRxYqtMRFaNPkypiiFH8QpLuC1xtzfF0tKF2SQfUajXN3HXM6teM9WcfYq2U8Ojaab4+q/+YMVdI8S+WxpGInC9kn7sbzbm70Ybt9afvs/50xrmNBjYqSf8GPkSM+ALPoJZEfvF5hjyhTZpi160bckdHLKpVw7pB/YwVSiTPFx2u3ps0c3vkaz/MuoEvzsNUcwAETs3xnARBeDOZJFBKTEzExkY/EcvOnTvp1KkTUqmUOnXqcOfOHVNUIQhCERpdfTSD/zX+h+e8+jxd6Zohr0wq4f3anvpB52FwYUIAvx68Q8uKxTl9ZD8Ozm60qOTKvadJfLf9aobj8+KX/bf5Zf/tZ1tyJjn7UvtRxukBov/6y2i7xMIFWU9OKZGgK9eKKMtS2CfeyrkRJ35FfucwuH+Wx9YLgvAmMMkYpdKlS7Nhwwbu3r3Ljh07aN5cP+FbRESEmIRSEN4CtV1qsyBggVHalqQtjD4wmm7/dONqVNYBj7lCxqjm5Shd3BprBczpVoW2fm4MblKKYS8M5v60WVmCKhrfIitRzAKZNPdPzs6u9i5/lGtGtNIq23z3Bg/hZtt2XGnQiIj583n05xo08QlGec549c91vZKISyjSEnLOKAjCG8ckV5QmTJjA+++/z8iRIwkICKBuXf2jtjt37sTf398UVQiCUMQalmjI8qDlbA/dzv+u/g+Af+/+C0DXf7oyq+GsPJc5oFFJLj2IoWUlV7rV9CAlTcPBG5EcuBFJ8LUINgytj7WZnN8OhlDdqxjbLz5k8cGQLMuLNbNipW8LVvq2wCXhCfOD52KjTso0r/rGDQCeLFgIQNSkCYZ9cc3bkuhWHACtBpKeKLF0SEWSzfCrVhcGo3GLhVr9QPZssHxKPKiTwMwuD70iCMLrxCSBUpcuXWjQoAHh4eH4+T1/SiYgIICOHXMxn4kgCG+Eas7V8C/uj1wiZ8WVFUb7Rh8YzRTbKXkqz9ZCwdI+tQzbZnIZAb7OBPg6AxUN6R81LgVADW970rQ6Dtx4zPrB9Rm66jQHb0a+XCwAD60c6NZ6Kj/9+z0lY8Pz1C6bnf9gA1zB+Im64qPbotuxEvNiaqzdUjIcJ932GWhTwb4k/DsVHj+70jbyep7qFwTh9WGSQAnAxcUFl5eeLKlVq1YWuQVBeFNJJBJG+o8kOjSaf5KMH7+P1cUWev2T2j0PoJb3rUVUQipXH8bS87fjAOz5tDFWSjkztl9h/ZkHjGk4FNeEJ4SoXHBLeEKUuQ3rtozPV90Rs/4BjIcTyMw1OJSPR6bUEn68GLINP+H5TiTqBBlyMwUWjmokjy4CoNVquXf5Fm7lfZDmNHu4IAivBZMFSnv27GHPnj1ERESg1RpPArdkyRJTVSMIwmvCVmqbIe1oylE873vS1DuLgdImJpNKcLIxw8nGidBvWxvtG9u8DBdu3eNGrDm37Nx5t6YHZ+/a0amiC4OSPmXRv98DcNCtMg0eXMh3GzTJMiLOPu8LTaqUkO3FjTP9OYyywO3P9E/lXQMiPvmKxoN65LteQRBeDZMESpMnT2bKlCnUqFEDV1dXsWyJIPwHWEosM6QdTDnIwX0HWWK+hKoOVV99o17gYG3GsIpaGgUE8jBOja/r8ytBHaq6MSPqDpEWtpx09qX7tT30vrLtlbav+NxpXJk7jUhrB+K8ynLTsQSuZbw5/OMypObm1OjVGU1q2ittkyAIGZkkUFq0aBHLli2jZ8+epihOEIQ3gJPUKct9fXf05fuG37/C1mTN2kyOr7WFUVpJJ2vGLfiCR7HJHL71hA8nNGP59veoNWkwdqkJrCobiLU6iXYhhwq9fY7xT3C8dAT6HSEceDZHOI9//xFf4Ob4rwCIsrTD+ttZOJYqgfm389m2+ySt50wq9PYJwn+dSQKl1NRU6tWrZ4qiBEF4Q1hILdjQdgNppHEm4gzHHhxj993dhv2fHvg0z4O7XyUPe0s87C2p4W0PwMedanKq2g5W7b/CsHb+7Nm9m4N7PWiw50+OO5dHU8IL81q18V+oP6cEhTnW2kR0Gv1YI2u3ZOIfmBdae+0To2FEf2IBT4Bda7leaS3XAzvh9e8mbn72OaElyqNIiieyaSBb/zlAxcF9KF29YvYFC4KQLZMESv3792fVqlWMH5+/AZKCILyZPG08USgUlLMvR4o6xShQAliZsJLWutZZHP36qe7tQHXvBqjVaqwUEPTtGPbubEKrwJo42epvNSb0a8/eJeuo1j4Qt6XljY5/fMGGyEs2hm1bn0TU8TISH5sVWpvL7l5v+Nn7nv4pO/e1ywBQH9xCxuk39UK6D8SjUR28q1Xk0e0wSlevVGhtFIQ3mUkCpeTkZH755Rd2795NlSpVUCiMF9ycPXu2KaoRBOE11rVMVxadWUS87vmittfSrjEseBi/NP+lCFuWfzbmCjp3bmyUZmVtSZsRz4YZKG0gNQ6Ax9a+OFW+gkSmI/GxEo8GUYZ5l678+XyaAXP7VJKjlK+k/dnxWf0LrP6Fe8+2XwyoQt3LgkSCupgjsvhYPMd8im/D6uyZvRgLJ0ca9e1SFE0WhCJhkkDp/PnzVK1aFYCLFy8a7RMDuwXhv0EhUzBWNZYJMROM0o+EH+FhwkMclA5F1LJCNOYGrO4JN3dxw7ktTvFXcKwQnyFbmQ4PCQt2ICVaQYkGUSgsjZ8MTomVEbK9ODrt6/F56X3/2bxP967pvw/pzQ2e3fIDrsx4fvfArJg7249coXS75tyY8xOuH7xH6dr+nNu0i/q9OqEwK/qgUBAKwiSB0t69e01RjCAIbzipRMrGthtp/097o/SVV1bSx7dPEbWqECksoMca1DHhPN53IstscnMtPi0eo9NIkMp1GfabqTSU7fSQa2tdDWlhTt7EfNSLYjFqnH/8DoVOUyinUFBeT+/DhuWwYTllAD47zEPAGbg5e3Kmx9x3KIH7k3uE9RtJnKWMOxdv4FbaC0ub7JeeEYSiYLJ5lA4cOMDPP//M7du3WbNmDe7u/2/vzuNjut4Hjn9mJpNEFhFCYgkRIUVWQSyxxt5aaykqtmqrqlRp6aK2H1+UqtpatRfVUktrr9r3IJbWUvtOSIVYYpLM74/UEMlkmzszWZ7365WXzLnnnvPM6XQ87j33nJIsWrSIsmXLEhYWplQ3QogcztPZM1XZ/L/mE/M4hurkwUVoVSpwTH4CMKHFZGzWDYLX58Cl3RA5N0U1VRpJ0jMvJlBX3Txp+Oca1q1bR/1OrxPftTXH1u8geu16KkQmbxtzvlMfvJfNNtObMq+Sd5Nv+JWe8zWlAd23kNH26ede70mMox1rl76PTekyuNesRuXGtYn8eR2oIKRdM/45cJSncU/MHr/IXxRJlFasWEG3bt3o2rUrhw8fJj4+eWn/2NhYxo4dy7p165ToRgiRS4yoOYKpR6byOP4xj/XJe62tOb+GNazh6MGjDK81PIMWcid9cAQEdQZbB/BvD/WGQmI8TPHPUjsVi12AW8+nMTgVKkitzq+x48kT+C9RKlG7OvyXKO15vQfuhw5R8X+jKF25HKuWr8Rv5JfKvbEcoNyKeZR79iIKWLOIS8Pg2SIVl8d/gR3gB5wdPSLNNioAu6Pjcf/q+dj882oX7uiecijRHv3TBMqGBnLnyg3+PXOFaL8blChXOs22RP6hSKI0ZswYZs2aRUREBD/99JOhvHbt2owZM0aJLoQQucjrFV6npVdLFv2+iDNFzrDu4vN/LP3yzy90fKUjrxR+JZ0WcjHbFxbidHaHh3ez3IQ26Qma3wdA8Y/g6UOIvgjFAwnr3o7NMfe4pUnEP+n5FSi36q/QdMQgtFotOp0OWwc7vI9GcT4wCICzvtVwbdeWIuM+BeBSifLYPXqAx72bprzTXOnFJAmg/NolybcMNy0H4F9AA4QCsXOmE/tC3QrA2f9WV0/rNUC8Rotdoi5Vv+d8gqFiZcr99iMA58v6U+l/o7lz/jI29nZc/m09xeqG8Ur9GuydtZCkA/sI/W4KeyfOxL6MJ9GXL/O4Tj1wdODqqQskPNVRvpofSUlJJOoSuHb6Il4BFZLfw827uBRzzfI2OS/vqpGexKcJJCYkpnp4Ky9SJFE6ffo0devWTVXu4uLCvXv3lOhCCJHLqFQq3DRuRNSKILREKF/uef4XVIffOrClwxaKORRLp4U8Qpu9tZXUN49CcbBZ0AJu/wUdF6Ku1JqGH3Rn3bp1FPUpzYP0zlerKbZlOyfWbaXJm62xK2DPuh8XUvbKKXy/nkjZQF/+nLaQ4tPGZe99iTSllSQBlDt7BM4eMbz2vnCcJ53a4PTf6woAf67iJs8XHb3TuGFyEgd4Atc2Lk/R5stLP7z4+uUUOK2kzpjM1K0IXPhvMdTMnp9WeWbKnpauDC1aZBy4mSiyK6OHhwdnz55NVb5r1y68vb2V6EIIkYu1K98uVVn4L+Es/GuhFaKxMJsCGdcxovWRCFS3/0p+8XMEPHl+fcO9TAkeTZ6F7jvjY1ikZDHq9emEXYHkZK3p+uUU37mbsoG+ANR7tyvnWnUjdtQkCq3dSMVTJym0diMP7BxJQsWl4j6Gtu4VKMi1vh/j+/dfVDx1Etd1mwDYW6Nptt+fEJnhd/kvq/avSKLUp08fBgwYwP79+1GpVFy/fp3FixczePBg+vbtq0QXQog8aGLkRBKTcubTXIoxdvsj3Pg8LTuXtK9K8L/SqCPnoEl8gnrLCEICnKhYKyjToWhsNBQqWjjF69cmfEqNji0o/t9cnOLlSlP9aCSVT/1Ns62/8Y9/8q4LNsPH0GhAT8PtHA9vT3yOH6NI2wbETZgGwNnXuvBP5RrEOBTinHdApuMSIidT5Nbb0KFDSUpKIjw8nEePHlG3bl3s7OwYPHgw/fv3V6ILIUQu19K7Jb+d/w2NSkPiC4+6By0K4lXvV/lfnf9ZMTrzSvJrz/1zkTg1/wKbHRPg9R+geABsSbnFi1eTaB7fscXF67HRtjQbP6GGoy+ah6dh3zT47I5ZY39t2Wz+vXGHiiWN3yYNal4XdYsTVLTRkJSURFJiEus6v51m3bufjiVm30EqvNkRu4IOnOnTl9L/XjccL7xhC4m6BAp5FOHpk3hu1Kmt+HsSIisUSZRUKhWfffYZQ4YM4ezZs8TFxVGpUiWcnJwyPlkIkS98HPIx2pta+jXrR/iv4SmOrT2/lncD3sXLxcs6wZlZYutZbF+3jhYVW0CA8VWtCxTWUaCwkatJL3B7eNrwu3rPN2CYyaI8tVpNkXSSpGc0NhpDfbVaDWqN4VjBNeu5efo8j/+NJSyiLUS0BUCn03Fs6AfcuB5H8W+SH/xx93q+irmDsyM7y1fB55/DSr4lIbJEkVtvz9ja2lKpUiWqV68uSZIQIgVHrSNVbKvgau/Kjy1+pLdf7xTHW65qydrza60UnZX0+ZOEltM4X7RxtpvQbB2tYEDKKdmtMwCXivtQsoIXIS0bJidJaShTK8RoO+FLUm5/E/PlxOQnyP5zJqgOAHHjppoashBpyvYVpUGDBmW6ruz1JoR4UWDRQAKLBjLnxJwU5UN3DqWZVzM0L1yNyNNKhqAvFsDJixq8ozdnu5l6p4ZD3argWlLB4EwT0rIhVyuso2GZEhnWLV2pHJcW/4JLsdTb3Dg4O3KmZlMq7N0IQPX2TdG3bczhGnWI19rTcsksw7ypk8NSnnvZvSylb10wvD49bhwVS3pTLrgS//hnbW0rkX9lO1E6cuRIiteHDx8mISEBX9/kpynOnDmDRqMhJMT4vxSEEPnbgCoD+ObwNynKJkZOpI9/HwrYFMBB62DkzLwlwcYR3eALaL8qm3HlNBR6fBG+qQQtp4JfR2WDM0Ep38y/H58QP6PH6oz9jF19Y4iuXAkfwNbejir7dqJSqVKsFXTjw+EU/zp53teFN96mxYgP+XtnJLcHf0RSz7dRqVV4B1fERmvDuTYRJJ0/j96jOHfi46m1fU2KPs8W9cIn+mKKsoMBYbiWK0fjLwawZcJ3RDtqad2zMzs+Hk2FPRsM9S4V9yG+oh+lWzbH/sN3DOVnQhpQ4ZBs+ZXbZDtRenF/t8mTJ+Ps7MyCBQtwdXUF4N9//6Vnz57UqVPH9CiFEHlS98rdUyVKi08uZvHJxdiqbTnU7ZCVIrMCO2f46AzcvwaFvWF8may38dsHaH/7AILmKx6eNRUuXpTmv8xJscuDrb1dqnp1erVnlZMaT1sXmrRpBEClOlWptH87Op0uxfmv/S/58tOzcrfhg9g74FNwK0qjr77g+OrfeTRuHDdLlSfwmwk8uHsPl+jLNGvRAq1WS5PP+rFu3TqcXJxpPfdrNo73RXfnLklnz1B90hg8vJO38vlzpAfF790kcdYCWtdP3sInKSmJ6Ms3cfcqwYWjpzn+f19R/tguYhwKobOxxf3+bUOcZ4LqUuKvg5zq8y5VZ3yd7TE871Ya7zuXs31+fqbIZO5JkyaxadMmQ5IE4OrqypgxY2jSpAkfffSREt0IIfIYrVrL8pbLmR41na1XUv5L+2nSU5L0SahVik6lzNmc3ZN/gCSvuqgv7shWM62jeqCrug2ci4FLzrkdZwm2TvaEtAjHRpu1v94KFStMq+XJt4J1Oh12Tvb47tlFsJMDarUanU7HmXXGE42mn7ybZnmd7RuJjf4Xt1LuhjK1Wm2YtF420JeyP8/m0YOHVPxvU+CkpCR+6/Y+Gk9PWv9vGDqdjuvr1nH3y/EUGfkJAOfKBVLu3FFDmxVOnECv13Mh6hTlQipxLvIECRGdALjSZxAJPsUgnUUkn21UbEzJPfu4VquG0eN5mSLfQPfv3yc6OjpVeXR0NA8epLd2rBAiv/Mt7MvUhlPZ3ml7qmOBCwO5FnfNClFZX1JgFwD0hbyydb72h/rwdSX4oTHk9bWqzMSugF2WtwF5mdbONkWSZIzDf0kSJCdSrRfPMFz1eia0fXMqnDiB6/rNvLb2+XZh57wD0dhosNHaUL6aH2q1mvLVA6h46iQVT52kwQc90uzzyTff80/TDpSJPESj3Zvx3LefUvsPGI7HOBTihmsJLvcYQMHCLiTOWmA4tqdhG3Qz5xl9P0+mpJyEf7pWM3Y3aof30Shu9P+UM7WaZTgmhjjsC2a6rjkokii1bduWnj178uuvv3L16lWuXr3KihUr6N27N+3apV6RVwghXlbYvjD9g1Ovu9Z9fXcrRGN9+kpt2ev9EQk9N5nW0NUDMKowjHCBy/uUCU5YjcZGg0fZUskv5i7mTK1mVJuRuQem3Hfs4vbgESSo1Jxr+SbBTevQ6ptRODglzwV0KlQQewd7djdsyy2XYpRatIiGe7fQdGjy1TK/+tXhhx8536kPruHVqBgWgm7aHP6pFJqin31Vw/ELr8E/zToZyl79bgJFG1dHrVbTsF83Ws/9GtdNf7CnQWtcN/1BxVMn8T5yhDKRkVx3Tt7q+EzYq8T+39fcGfmpqcNmEkVuvc2aNYvBgwfTpUsXdLrkNUBsbGzo3bs3EydOVKILIUQ+8Jb/W3x75NsUZbce3WLlPytZfHIxbcu3pWvFrlaKzsLUGm67BIJD4eS5S5MqmN7m3KYwIjbjeiJXqFirChVrVcl0fWfXgtR7qxO6bm3xt7M1Wq9o01BqfzMyzQ1vK4aF4BMaYJjvFdCoFgGNanHpr7McX/AzNT7sTeEDyQl5i4mfc6RBGL51qqXZT5HixXBrVpMixZPX6bIrYI9Op+P+sA+p3qAhFV1dUs0tswZFEiUHBwdmzJjBxIkTOXfuHADlypXD0dExgzOFEOI5Y/ORhu9J3u7jfwf+l38SpRc5P791o7dzRhVvwpSGmycgagnUHZychIl8R5tOkpRdZSr7UGbCp4aLJQA2WhuqtU6eVP9ieUbUGjX2TjnniVdFZ0k6OjoSEBBAQECAJElCiGwJ9QhN97guKfNfuHlRUthgzrs1yn4Ds2rDvukwoSys+UC5wITIo/LR4yRCiNzgy5pfElg0kMn1JxP5ZmSq41UWVeHOY/Pub5aT6YtW5LhnBAnNJ0EVE+dvHV6QfHUpKUmZ4ITIgyRREkLkKJ4FPfmxxY80LtMYO40dAUVT70LfcmVLK0RmZX22Qqtp6L0bAKCv0h1aTSXhzVVEO1XMfrur+sKRRQoFKUTeI4mSECJHm95weqqyOF0cXx38ygrRWFHJKlClG6hUKYr1ZcI4WcLE1bh/+wCWvSlXloRIQ75OlO7du0fVqlUJCgrCz8+P2bNnWzskIcRLCtkXSrN8wd8L+OH4DyTKGkHKOPkbjHKF6TXg9klJmoT4T77eFNfZ2ZkdO3bg4ODAw4cP8fPzo127dhQpknpjRiFEzvPN4W8o4ViCZmWb5a8VvF/ywM5DucaiT8KM/1ZgHnYN7JyUa1uIXEixTXGNUb10mTgn0Wg0ODgkP4IYHx+PXq9Hr9dbOSohxMtqFq/J3ht70zz2yc5PGLF3BPu67Mu3yVKCjSO6/sfRPrkDsxso1/CyrhCxWrn2hMiFFNkU11x27NjBxIkTOXToEDdu3GDlypW0adMmRZ3p06czceJEbt68SWBgIN9++y3Vq1fPdB/37t2jXr16/PPPP0ycOBE3NzeF34UQwlRTGkzh+2PfcyrmFLuv7051/HHCY3Zd28UrhV+hmEMxK0SYAxQsDkVKk/DmKnZE/k3disWw2fQp6B7D02yuu3R+G0yuBA0+g+B8uH6VEOTwOUoPHz4kMDCQ6dNTT+YEWLZsGYMGDeLLL7/k8OHDBAYG0rRpU27ffr7z8rP5Ry//XL9+HYBChQpx9OhRLly4wJIlS7h165ZF3psQIvMctA4MDBnIzEYzqVSkUpp1+m3pR/gv4fzvwP8sHF3Ooi8TxoMCpdBXagND/oFPr8Ln0fB+6qUWMuX+NVj9Hqx+H54+gi2j4Xrm7igIkRcosjL3M3///TeXL1/m6dOnKcpbtWqVrfaaN29O8+bNjR6fPHkyffr0oWfPnkDyVipr165l7ty5DB2avEtyVFRUpvpyd3cnMDCQnTt30r59+zTrxMfHEx8fb3h9//59IHnF0Werjr785zNplWemzFh75mBqX1k5PzN106sj46zMOGf3eHbGVKkxnhA2gVZrWpGkT3uy8eKTi6latCq13Gtlu7+sxmrKOGdljNMqz9w4q8DFi9QbUmTBkUXPlxHY+RW6z+7kqM9yRnXkO0PGOa14MkOlV2BSzvnz52nbti3Hjx9HpVIZ5vk8m5+UmGj6UykqlSrFrbenT5/i4ODA8uXLU9yO6969O/fu3WP16ozvq9+6dQsHBwecnZ2JjY2ldu3aLF26FH9//zTrjxgxgpEjR6YqX7JkiWGukxDCcjY83sCu+F1pHuvs0JnKtpUtHFHOVipmN+Vub8Am8TFOT29nfEI6NvhN5amNE3qVov/eFsIiHj16RJcuXYiNjaVgwYLp1lXkEz5gwADKli3Lli1bKFu2LAcOHODu3bt89NFHfPWVedY6uXPnDomJibi7u6cod3d359SpU5lq49KlS7z99tuGSdz9+/c3miQBDBs2LMXTfvfv38fT05MmTZoYBlqn07F582YaN26cYkPBtMozU2asPXMwta+snJ+ZuunVkXFWZpyzezw7Y2qOMW6a1JRqP6W94ebSR0t5ndf5+NWPs9xfVmM1ZZyzMsZplWdtnFsA/4dmRS84tSZLY/KyZieStz/Z6jua0NZvWf2znFEd+c6QcX7RsztCmaFIorR3717+/PNP3NzcUKvVqNVqwsLCGDduHB988EGmn5CztOrVq2f61hyAnZ0ddnZ2qcq1Wm2q/4BplWWl7stlxtozB1P7ysr5mambXh0ZZ2XGObvHszOmSo6xFi17O++l5tKaaR5f8WgFn2k/y3Z/WY3VlHHOyhinVZ6lcXZ2T7s8Gxqc/oLE7TfQVOsFbuWz1YZ8Z2ROTvrOyKhOTh/nrLSlyGTuxMREnJ2dAXBzczNMlC5TpgynT59WootU3Nzc0Gg0qSZf37p1Cw8PBdcUEULkaE62ThyLOGb0uLG5TPlag0/BpzHU6q9Ic5r9M2BaVUXaEiKnUSRR8vPz4+jRowCEhoYyYcIEdu/ezahRo/D29laii1RsbW0JCQlhy5YthrKkpCS2bNlCzZpp/+tSCJE3qVQq6paqm+axobuGEhsfK2ukvcihMLy5HJqMgaFXIOANZdq9cxZknEUeo0ii9Pnnnxu+hEaNGsWFCxeoU6cO69atY+rUqdluNy4ujqioKMPtsQsXLhAVFcXly5eB5NXBZ8+ezYIFCzh58iR9+/bl4cOHhqfghBD5x7SG09Is/+PKH4T9FEbAwtSb6wrAviC0+06ZtqaFwPpP4O452QJF5Bkmz1HS6XRMmDCBWbNmAeDj48OpU6eIiYnB1dXVpJW5IyMjadDg+SqzzyZSd+/enfnz59OpUyeio6MZPnw4N2/eJCgoiA0bNqSa4C2EyPtUKhW9/Xqz/ep2zt47m2ad2PhYXOxcLBxZLlGkPNz9x/R2DnyX/FO2LoS+C7++A62/hcptTW9bCCsw+YqSVqvl2LHU8wMKFy5s8vYl9evXNzyR9uLP/PnzDXXef/99Ll26RHx8PPv37yc0NNSkPoUQudfAkIGsbL3S6PGwn8IsGE0u03N98p91BsOIWNPbu7ADfuqSvCr4Lz1Mb08IK1Hk1tubb77JnDlzlGhKCCHMqs+mPiQmmb62W57jVDQ5QQr/Ivn1G0uVbX+ES/KPELmMIssDJCQkMHfuXP744w9CQkJwdHRMcXzy5MlKdCOEEJkypOoQtl3ZRvMnzRkVOyrFsX039hG0KIijEUfz7Sa6mVKhGdR4D1Rq2Jv2/K9s+WczeIWBtoBybQphRookSidOnKBKlSoAnDlzJsUxU2+/CSFEVkVUjqBzhc6sW7cOHxcfzsamnrMUuDCQvZ334mTrZIUIcwG1GpqNS/696f/BtGpw50z652TG4v+2iBoeI0/IiVxBkURp69atSjQjhBCKCysZlmaiBFBzaU2Odz9u4Yhyqb574OYxmN1QmfZGFUYLOFaaqEx7QpiJItedL1++bHSNkmeP8gshhDWUcS6T7vGPd3zMncd3LBRNLqbRgluF569LBCvSbKO/h6DeMQHmNIGrhxRpUwglKXJFqWzZsty4cYNixYqlKL979y5ly5ZVZFNcIYTIjtfKvkb0k2iqelQlUZ9In019Uhxff2E96y+slytLmWHnDO/tA7UWCpUm4e812Pza2+RmNTsnJP8yvwV8fiv9ykJYmCJXlPR6fZpzkeLi4rC3t1eiCyGEyBaNWkPfoL5U86hGqEcoBW3T3il8yPYhnI4xz5ZLeUqxiuDmAza26Cu25oJbuHJtJzyBs1vg79XKtSmEiUy6ovRsAUiVSsUXX3yBg4OD4VhiYiL79+8nKCjIpACFEEIpKpUK/6L+7L62O9WxDRc3sOHiBj6p9gkdfTpaIbrc6ZhndzybfYDNjvFweY/pDf7YLvnPD45AYfNsgSVEVpiUKB05cgRIvqJ0/PhxbG1tDcdsbW0JDAxk8ODBpkUohBAKUmdwIX38wfFsv7Kd13jNQhHlfvoytaHXemXXSZoaDD03wKVdUL4puFVUrm0hssCkROnZ0249e/Zk6tSpODs7KxKUEEKYy+CqgzkafZSefj2p5lGNN9e9marOvpv7aOzS2ArR5XJqG0hKUK69ec2S//xzDHxyTbl2hcgCReYolS9fnl9++SVV+dy5cxk/frwSXQghhCK8C3mz842dvOX/FoFFA9nXZV+a9UbHjmb/zf2yindWdFhgtqa140tSOE7mkAnLUyRR+v7773nllVdSlVeuXNmwWa4QQuQUL67I7ah1NFqv7599abxcrixlWsXX4NMb8PoLW1oFdlas+Tr//B+qy3sh5oJibQqREUUSpZs3b1K8ePFU5UWLFuXGjRtKdCGEEGYzqtYoo8eiH0fjv8DfgtHkcrYO4N8eBp+F4f9C21nw2hTFmrdZ1BKmBsH2ibKyt7AIRRIlT09Pdu9O/RTJ7t27KVGihBJdCCGE2bQt35ZjEcfSrWNsUV1hhFPR5G1QAEJ6wFtblG1/6xjYPwvk1qgwM0USpT59+jBw4EDmzZvHpUuXuHTpEnPnzuXDDz+kT58+GTcghBBWltG+lN03dLdQJHmQSgWlqsKI2OSrTErZMBRGFYbIecq1KcRLFEmUhgwZQu/evXnvvffw9vbG29ub/v3788EHHzBs2DAluhBCCLNb2Wql0WNHbh/ho20foUvSWTCiPEithoZfgE0BABJVWtPb/H0g/D7I9HaESIMiiZJKpWL8+PFER0ezb98+jh49SkxMDMOHD1eieSGEsAgfV590j2+6tIkqi6qw69ouC0WUR9UdDJ9eR/fZHX4PnK1Mm5Fz0P6fGzaJj5VpT4j/KJIoPePk5ES1atXw8/PDzs5OyaaFEMIixtcZT/1S9WlboK3ROn3/6Ctzlkz1bP6SSo3u02jFmn312DuoLmxXrD0hFEuUdu7cyZtvvkmtWrW4di15YbBFixaxa5f8y0sIkXu08G7B5LqTqWJbhdLOpY3WC1gYwPFo2UhXERnMD8sqmyWvw6U9cPMEnFqraNsi/1EkUVqxYgVNmzalQIECHD58mPj4eABiY2MZO3asEl0IIYRFqVQqVrVchb+b8aUBuqzrQtd1XS0Ylci0ec1hVm34qQtcjbR2NCIXUyRRGjNmDLNmzWL27Nlotc8n5tWuXZvDhw8r0YUQQlhFRKWIdI8fi05/WQGRSX3+hG4rk3+U9kM4mh9bgz5J+bZFnqdIonT69Gnq1q2bqtzFxYV79+4p0YUQQlhF4zKN6VChA+Vdyxut031jd5mzZKqSIVCuYfLPl/egvbKP/Ksv7Sb48pyMKwrxEkUSJQ8PD86ePZuqfNeuXXh7eyvRhRBCWIVGrWF4zeEMrDLQaJ3jd49z+KlcPVeMSgV+7WB4jKLNlo7ZieriDtj/HTxWcD0nkacptuDkgAED2L9/PyqViuvXr7N48WIGDx5M3759lehCCCGsqk7JOgyvOZwlLZakeXzl45XM+WsOCUkJFo4sD1Nr4JXXFG3SZnE7WP8xjPeCh3cUbVvkTYokSkOHDqVLly6Eh4cTFxdH3bp1eeutt3jnnXfo37+/El0IIYRVqVQqOlTogH9Rf76q91WadaYfnU7womBW/mOGeTb51etzoOd6dMNucqtggLJtTywn+8WJDCm24ORnn31GTEwMJ06cYN++fURHRzN69GglmhdCiBylqVfTdI8P3zOci7EXLRNMXqe1hzK1QG1DEhrlmx9bFP8rCxVvV+Qdii44aWtrS8WKFalWrRpOTk5KNi2EEDnS/4X9X5rlLVe15PL9yxaOJm+75lojdWG/Aya3633nD7T/5wZzmsoVJpGKYonSnDlz8PPzw97eHnt7e/z8/Pjhhx+Ual4IIXKUr+t/zYAqA2hVrpXROq+ufJW159fKE3EKueZag4Tu66DfQajzEQw8AUV9levgyj5Y3gvVX7+ikrlm4j82SjQyfPhwJk+eTP/+/alZsyYAe/fu5cMPP+Ty5cuMGjVKiW6EECLHaFSmUabqDd05lH+r/oszzmaOKB9QqdCXqg5aLYSbaS/Rv37F5q9faQXows+Ctqh5+hG5hiKJ0syZM5k9ezadO3c2lLVq1YqAgAD69+8viZIQIk9r5NmIP678YfT4+MjxDCs4zIIR5TNv/gpXD5JQzB+bn7so1qx2ss/z9sukXitQ5A+K3HrT6XRUrVo1VXlISAgJCXL5UgiRt4V7hmdYZ9z9ccQnxlsgmnzIJxzqD0Vfvglbfcco3/6P7VD9LU8y5leKJErdunVj5syZqcq///57unaVfZCEEHlbkzJN6O3Um8l1J6dbr+aymsyImmGhqPKn+w6l0b29W/F2bVb2oVTMbpC5S/mO4pO533rrLd566y38/f2ZPXs2arWaQYMGGX6EECKvUalUlLUpS/1S9fmhyQ/Ya+yN1p15dCYXYi9YMLp8qKgv1B6oeLMhl75DO84DEiVZyk8UmaN04sQJqlSpAsC5c+cAcHNzw83NjRMnThjqqVQqJboTQogcK7R4KHu77KX9mvaciz2XZp1Wq1pxuNthtGptmseFAhqPhEYj4N4l+CZQ2bZHF0FTsTWOqjBl2xU5kiKJ0tatW5VoRggh8gQbtQ2r2qxi34199NnUJ806VRZVYU2bNZRyKGXh6PIRlQpcvczStPrkahqxGv3dheDhB21nmaUfYX2K3Hp7/Pgxjx49Mry+dOkSU6ZMYdOmTUo0L4QQuVJwseB0j7da1YpJhyZZKJp8rPMy9MX82FX+U5KC3lS0adWt43B0Kez+Bh7elQUr8yBFEqXWrVuzcGHyEvD37t2jevXqTJo0idatW6c5yVsIIfKDzNxaW3x6MWd0ZywQTT7m24yEPtu46/QKiTXNtP/o5uEw0RvmvwZJSebpQ1iFIonS4cOHqVOnDgDLly/Hw8ODS5cusXDhQqZOnapEF0IIkeuoVWr+7PAna1uvTbfewoey15jFFC4Hhb3N1/6lXbD2Q/O1LyxOkUTp0aNHODsnrzq7adMm2rVrh1qtpkaNGly6dEmJLoQQIlcq6lCU4o7FM6xXZUkVxh8Yb4GIBL4tzNv+ofmQlGjePoTFKJIo+fj4sGrVKq5cucLGjRtp0qQJALdv36ZgwYJKdCGEELmai8olwzo/nvyR8/fOWyCafK7+MKg9ABp8br4+RhWGlX0h4an5+hAWoUiiNHz4cAYPHoyXlxehoaGG/d42bdpEcHD6kxmFECI/6ObUjaCiQcxtOhevgl5G6629kP5tOqEAOydoPArqDUH36W3+qDSRJJ/GyvdzdAksbJ38+5NYeBSjfB/C7BRZHqB9+/aEhYVx48YNAgOfr1cRHh5O27ZtlehCCCFyNQ+NB3Mbz0Wr1TKh7gQ6/t4xzXrfH/ueIvZFaFW2lYUjzKdUah7auZPYdinqtQMh6kdl27+8B0a8cDXx0xtg66BsH8KsFFuZ28PDg+DgYNTq501Wr16dV155RakuhBAiT6hYpCKRb0YaPT7uwDhqLqvJr49+RS+Pm1tO6VDDr2sDZqEv5KV8H2OLw4UdyrcrzEaxREkIIUTm2Wns6OTbKd06h58eZuzBsRaKSBD0Jrz2Nbo+O0nQOJDw3sHkW3RKW9ASHtxUvl1hFvk+UfLy8iIgIICgoCAaNGhg7XCEEPnI5zU+58AbBwizM74VxoqzKzhw44AFo8rH1Gqo2guKVUx+rVIlT/o2h0m+EBcNW8cm/ylyrHyfKAHs2bOHqKgo2YpFCGFxNmqbdBMlgN6bevPBnx/IbThreeU187T7lQ9sH5/85/7v4P4N8/QjTCKJkhBCWJmNKuPnarZe2UrAwgAO3jpogYhECvWHmb+P9R/DvGbm70dkmWKJ0s6dO3nzzTepWbMm165dA2DRokXs2rUr223u2LGDli1bUqJECVQqFatWrUpVZ/r06Xh5eWFvb09oaCgHDmTtErVKpaJevXpUq1aNxYsXZztWIYTILnuVPR8Gf0iHCh3oF9Qv3brvbHmH6ES5VWNRHn7oPjjBmqC5JHRcYr5+/r0Id87CnX9kwcocRJHlAVasWEG3bt3o2rUrR44cIT4+HoDY2FjGjh3LunXrstXuw4cPCQwMpFevXrRr1y7V8WXLljFo0CBmzZpFaGgoU6ZMoWnTppw+fZpixYoBEBQUREJCQqpzN23aRIkSJdi1axclS5bkxo0bNGrUCH9/fwICArIVrxBCZFe3it3QapP3hns74G0CFwYarfvNg2+o+W9NKhataKnwhLMHepUN+vJNzNvPtJDnv4+INW9fIlMUSZTGjBnDrFmziIiI4KeffjKU165dmzFjxmS73ebNm9O8eXOjxydPnkyfPn3o2bMnALNmzWLt2rXMnTuXoUOHAhAVFZVuHyVLlgSgePHitGjRgsOHDxtNlOLj4w1JIMD9+/cB0Ol06HQ6w+8v/vlMWuWZKTPWnjmY2ldWzs9M3fTqyDgrM87ZPZ6dMbXkGJvaX1bPNWWcjR2rXLgyf8X8ZbTPN9a/QRnnMvzc7Oc028lvn+WM6ij5naHquBj1n6NQ3zmdYdwmGeFCYt1P0FV7D/T6fDfOWY09K7LSnkqvwOxABwcH/v77b7y8vHB2dubo0aN4e3tz/vx5KlWqxJMnT0ztApVKxcqVK2nTpg0AT58+xcHBgeXLlxvKALp37869e/dYvXp1hm0+fPiQpKQknJ2diYuLo169esyaNYtq1aqlWX/EiBGMHDkyVfmSJUtwcJAFxIQQyln8cDEndSczrBegDaCjY9qLVwrzc4iPpvHfH5m9n8uFwzhS+i20iY/Q2TiZvb+87tGjR3Tp0oXY2NgMt1pT5IqSh4cHZ8+excvLK0X5rl278PY2zy7Nd+7cITExEXd39xTl7u7unDp1KlNt3Lp1y7ByeGJiIn369DGaJAEMGzaMQYMGGV7fv38fT09PmjRpYhhonU7H5s2bady4seEyurHyzJQZa88cTO0rK+dnpm56dWSclRnn7B7Pzphacowz896UPNeUcTZ2rNiNYry99W0alGzA1mvGn8g9pjvG3/f+ZneH3fn6s5xRHXN9Z9R+rQuJxePQbPkyy+8/K0rH7KJkQTWaizvQvbUN3P0yfW5eGGelP8/P7ghlhiKJUp8+fRgwYABz585FpVJx/fp19u7dy+DBg/niiy+U6MIsvL29OXr0aKbr29nZYWdnl6pcq9Wm+g+YVllW6r5cZqw9czC1r6ycn5m66dWRcVZmnLN7PDtjaskxNrW/rJ5ryji/fKxq8ap8WvBTXq/7OiFLQ9I855kEEvj90u+0822HFm22YjdFTvosZ1THHN8ZmjoDQRcHOyZm6j1kl+Zi8ore2qM/wquTsnx+bh9nJT/PWWlLkafehg4dSpcuXQgPDycuLo66devy1ltv8c4779C/f38lukjFzc0NjUbDrVu3UpTfunULDw8Ps/QphBCW5KB2QKVSsb7d+gzrjj4wmuBFwSw6uYgEfeoHWISZVXsLtBaagnHwB9g30zJ9CWUSJZVKxWeffUZMTAwnTpxg3759REdHM3r0aCWaT5OtrS0hISFs2bLFUJaUlMSWLVuoWbOm2foVQghLK+VciqhuUZRwLJFh3a+PfM2I2BH8++RfC0QmDJw9YOhl6PfCOlfmWqgSYMNQOLrMfO0LA0UXnLS1taVSpUpUr14dJyfTJ5vFxcURFRVleHLtwoULREVFcfnyZQAGDRrE7NmzWbBgASdPnqRv3748fPjQ8BScEELkFRq1ho3tN7KwycJM1Q//NdzMEYlUNFooWuH566KvwLCr5utv5dsQfRqO/QJJSebrJ5/L9hylFyc1Z2Ty5MnZ6iMyMjLF/mvP+uzevTvz58+nU6dOREdHM3z4cG7evElQUBAbNmxINcFbCCHyinKFymW6rv8Cf/Z23ouTrTwlZRX6JLBzhk8uweEFsHm48n1Mr57859MHyfvU3T0Hl3ZDYBfQKDINOd/L9igeOXIkxevDhw+TkJCAr68vAGfOnEGj0RASkv4kxPTUr18/w72N3n//fd5///1s9yGEELlJAZsCNLJvhLePN9+f+D7D+jWX1uR49+MWiEwYqLWQpAOf/67qFSiUvLlu1d7wax84nb1FmNP1+4cQ8AZ8WyX5te4JhL6tfD/5ULZvvW3dutXw07JlS+rVq8fVq1c5fPgwhw8f5sqVKzRo0IBXX31VyXiFECLfq29fn3cD3uVYxLFM1fdf4E+/Lf04eTfjdZmEAj46DW9tAa+XNju2c4I3lkCbWebpd2zx579f2W+ePvIhReYoTZo0iXHjxuHq6mooc3V1ZcyYMUyalPVHGIUQQmRMpVKxvs16atvVxtHGMd26O67uoOPvHVly0ox7lYlkjkWgVNW0j6lUENTZ/DGcWG7+PvIJRRKl+/fvEx2depPG6OhoHjx4oEQXQggh0uDu4E7zAs3Z2XFnpuqPOzAO/wX+jD8w3syRiXTZuZi/jxEuoHts/n7yOEUSpbZt29KzZ09+/fVXrl69ytWrV1mxYgW9e/dOczNbIYQQynu9/OuZrvvjyR8Zsn0If935i9h42XzV4t7ZBvWGwscXoNdGKJn9+bzp+j8PtP/nRpG4zO1YIVJTJFGaNWsWzZs3p0uXLpQpU4YyZcrQpUsXmjVrxowZM5ToQgghRAZG1BqRpfobLm7gjbVvEPZTWMaVhbIKe0ODYeBQGErXgD5/wpu/mq27sH/GwpOXEmK9PvlHpEuRRMnBwYEZM2Zw9+5djhw5wpEjR4iJiWHGjBk4OqZ/31wIIYRyptabmq3z/Bf4czrmtMLRiCzxCYfO5ltEUjupXPLtuBEucOxnmN0AllpgvlQup+iCk46OjgQEBBAQECAJkhBCWEFYyTAGFxzMLy1+4YPgD7J0bvvf2tNtXTfuPbmX4dIswkx8m0Gnxebv59c+cP0InMl4e5z8TpHVqEaNGpXu8eHDzbDIlhBCiDQVUheiXKFy+BTxYeqRrF1hioqOos6yOhSwKcCBrgfMFKFIV8XX4PNoGFPU2pEIFEqUVq5cmeK1TqfjwoUL2NjYUK5cOUmUhBDCCmzUNhx68xDvbH6HyFuRWTr3ccJj/Bf4A3A04ihqlaI3IERGbGwt19e1w3BiBQR3A9fMr/yeXyiSKL28SjckLxnQo0cP2rZtq0QXQgghssFWY8u8ZvMADIlPVgUuDMRGZUPjMo0ZW2csNmrZGsMi+u6FqMXowgajnehlvn5m/7dV2N5p8Nkd8/WTS5ntnwgFCxZk5MiRfPHFF+bqQgghRBa42rlmXMmIBH0C6y+uJ3hRMAdvHuRJwhMFIxNpcq8ETf8PbJ1YHbwQ3SdXoUD2/xtmhmZ1X7O2nxuZ9VpqbGwssbGyPocQQuQETb2aKtJOr429qLa4GlWWVOFR0iNOxpyUyd+WYGMPn1w062KV6hO/yJIBL1Hk+unUqSknC+r1em7cuMGiRYto3ry5El0IIYQw0UdVPyKoWBC1StTC1d6VLZe3MHDrQJPaHHt/LGyAr+p9pVgiJjLw7g44NB9K10q+wjSnkaLNt47qDlHACLnQAQolSl9//XWK12q1mqJFi9K9e3eGDRumRBdCCCFMZG9jz6vezzcqDy8dzv4u+/l89+dsvrTZpLYHbx9MiHsIReyLoFKpTA1VpMfVCxqNeP46tC/sn6l8P8eXg3975dvNZRRJlLZt24anpydqdco7eXq9nitXruDs7KxEN0IIIRTmoHXgq3pfcebfM3T4rYNJbTX4uYHh97Y+bRlVO/2lY4RCmow2T6K0ojccWQSBnaFyW7CxU76PXECROUre3t7cuZN6pnxMTAxly5ZVogshhBBmolapeaXwK6xuvVqxNleeXYn/An/8F/jTa2MvxdoVadBoIUK5/3YpnN8GK9+BMcXy7dwlRRIlY5P44uLisLe3V6ILIYQQZuZdyNss7R68eRD/Bf4sPbVUnpYzF+/65ttY95lxpeDSXtA9Mm8/OYxJt94GDRoEgEqlYvjw4Tg4OBiOJSYmsn//foKCgkwKUAghhOVteH0DO6/upKp7VdquSV4PL7R4KPtv7M92m2P3j2Xs/rGG10Xsi1CNarSghcnxCqDdbFjTP3mT3Z2TlG//aRzMa4YWKFeyM+ST/24mJUrPFprU6/UcP34cW9vnK4na2toSGBjI4MGDTYtQCCGExezpvIdHuke4O7rzxitvAHCw60FsNbaG1bmzu3Dly+4+ucsGNvDu/Xcp7FgYV3vzrhGU5xUpBz3XJf9ujkTpBX7XlpJwsSOUb2jWfnICkxKlrVu3AtCzZ0+++eYbChYsqEhQQgghrMPZ1hln25QP4NjbpJxC8WurX2m3ph0eDh6UTSjL3qd7Teqzze9tDL+3Ltea3v69Kesi81tN0n4uLO8NjUeSUMANmzXvKd6FzeK2yXOjytROnieVRyny1Nu8efOUaEYIIUQuUN61PMe7H0en07F27VrKli3LktNLFGl79bnVrD63mtG1R2NvY0+4ZzjaPPyXsNn4vQ4VW4PGBr1Ox4lDO/G7tlT5fha2Tv5z8D/gVEz59nOAbCdKgwYNYvTo0Tg6OhrmKhkzefLk7HYjhBAiB1OpVAwOGcywGsM4Hn2c+MR4em7saXK7X+xOuf1Vh/IdqKyvbHK7+Yrm+V/x54o1N0+i9MxX5eGLvLlPXLYTpSNHjqDT6Qy/GyMLjwkhRP7gXzR57tLWjlsZvXc0lx9c5uy9s4q0/cs/v/ALv7Dhzw183eBrEvWJuJhxK4+8SO9YDNXD21C4HMScU76D0W7w9m7l27WybCdKz+Ynvfy7EEKI/M2tgBvfNPwGgEe6R4QuCVWs7X0391FzaU3D68FVB9PGp40kTZmQ8PYutDFnwCsMfuoCp9cp3of2+9rUdvIFfd7Zvsysm+IKIYTI3xy0z5eNCSwayNsBb6NVa7FRKzJFlq8ivyLspzD8F/jz7pZ3SdQnKtJunuRQGMrWAZUqeSmBENNvkabFLe40mvnNzNK2NZg0RymzZI6SEEIIvV5P/+D+9A/uD8D12Os0XaXcRroHbh3gAAf4csmXFLYvTESlCHr791as/TzFzglaToGwDyH2CiQ+hUVtFWteff0QjHCB4f+COndfkzFpjlJmyBwlIYQQAI5axxSvizoUZbTLaGqF1yL813BF+4p5EsOUw1OYcngKADs77FS0/TzDtUzyj7m2JxnlChVboSrfDHAyTx9mpsgcpRc9285EEiQhhBAAU+pPYd5f8/ii5hepjqlUKossNFnnlzoAfL7kcza334yHo4fZ+8xVVCp4PxKmVVW+7ZNrsDm5BoIXKt+2BSh2PWzOnDn4+flhb2+Pvb09fn5+/PDDD0o1L4QQIpcKLxPOjy1+xNPZ02idKfWnULdUXbZ32s6OTjvMGk/j5Y3xX+DPaytf43zsee4m3jVrf7mGW3kSm04wW/NF4k6ZrW1zUmQ23fDhw5k8eTL9+/enZs3kpxH27t3Lhx9+yOXLlxk1apQS3QghhMijwsuEE17m+e03T2dPrjy4YtY+L92/RPu17QH4esnXrGq9iiL2RXDUOGZwZt6VVLUXW67aEO52B832sRmfkAVh/4wl8c84cC0NlduB1jnjk3IARRKlmTNnMnv2bDp37mwoa9WqFQEBAfTv318SJSGEEFkSWDTQkCgFFA2gT+U+9N+WPAl8UfNFXIu7xtCdQxXts83qNobfg7XB+Xaz3se2biSFRaDxbQrf11O0bc3eqcm/rBuMjWNR3N27kdM311UkUdLpdFStmvq+ZkhICAkJCUp0IYQQIh8ZFjqMkk4laeHdAm8Xb3Q6He86vUur8FZ4FvIkqFgQLcq2oMHPDbj7RPlbZ0d0R6iypAqQnJhVds2Hq4KXCIIRsclPr5mB6mE0Nc5PRqf7ELQ5d5saReYodevWjZkzZ6Yq//777+natasSXQghhMhHCtoW5P3g9/F28TaUlbIplWIStkqlYnOHzWaPpdv6blRZUoXP733OoduHzN5fjtN6hlmb107whOuZe5LeGpRZ8YvkydybNm2iRo0aAOzfv5/Lly8TERGRYs0lWVNJCCGEUrTqtK9EFCtQjNuPbzOk6hAmRk5UrL8+f/Qx/F7Vtmr+uD0X3BVeeRUex8DUYPP08X19KFUNOiw2T/smUCRROnHiBFWqJF+iPHcuef8YNzc33NzcOHHihKGeLBkghBDCEja8voH4xHicbJ2IqByBTqcz3EpTSuTTyBRtTmkwhYaeDfPm33UFCiX/VGoDf68yTx9XD6L9ugJlPHug3nUKQvug4PWcbFMkAtnrTQghhLV81+g7Vp1dRR+/PgzbMIy3a76NVqNFq0l5telzl8+ZmzCX6w+vmyWOgVsHAuBTyIdJ9SdRtmDZvJc0vfY1iYXKoNnzjdm6CLoyH64A28eiBUp79sKaE76tn6oJIYQQJqhVsha1StZCp9PRzakbDTwbpFnPXmXP761/R6vV4r/AH4Cq7lVxv+/O2sdrFYvn7L2ztF7V2vB6aoOpPNA9oKV3y9yfODkUJqnBF6yPq8xrx962SJfBV+aiw3zrO2VEsUTpyZMnHDt2jNu3b5OUlJTiWKtWrZTqRgghhFBM2YJlCYwPxLuCN98e/dYsfXyw9QMAxu0fx5jaYwgsFohbATez9GUpiRp7kvzaoz6x3NqhmJ0iidKGDRuIiIjgzp07qY6pVCoSE2U3ZyGEEDlPQlLyEjY9K/ckwi+Caourma2vOF0cA7cNNLx+y/8tBlQZYLb+zC7phb/b31wBP75uvVjMSJHlAfr370+HDh24ceMGSUlJKX4kSRJCCJFT6Xm+Gay9jT3HIo7xccjH9HDswbha43gv6D2z9f3D8R/wX+CP/wJ/Bvw5wLBXaq6hdXj+u08jGHrZerGYkSKJ0q1btxg0aBDu7u5KNCeEEEJYRKI+5T/mVSoVb/i+gY/Wh6ZeTWnn084icfx55U8CFgbgv8CfRr80IjY+1iL9miKx/mdQogq0/G+1bXsXdEMuWjUmc1AkUWrfvj3btm1ToikhhBDCYiq6Vkz3uJ3GLlXZi4tgmsOtR7cI+ykM/wX+BC8MNttTeiZzKgZvb4WQ7s/LbJ045dHWejGZgSJzlKZNm0aHDh3YuXMn/v7+aF9aivyDDz5QohshhBBCEStareDgzYO09W7L5nPGV/cuZF+IQSGDQA+FzxemcdPGONg7EPMwhjG/j+GS/SXO3DsDQFOvpmy8uFHROBP0Cby2+jUAduzawaQGkxRt3xxOF2+LT0h9bNbm4vlXL1AkUVq6dCmbNm3C3t6ebdu2pXj8UaVSSaIkhBAiR6ngWoEKrhXQ6XQZ1u3p1xOdTse68+sMazM52zpTz74ezZs3Jy4pjsL2hQH4osYXhP0UZpaYN13eZFjWAGBv57042TqZpS9T6YO6QuVWMKGstUMxmSK33j777DNGjhxJbGwsFy9e5MKFC4af8+fPK9GFWZw+fZqgoCDDT4ECBVi1apW1wxJCCJFLqFQqQ5IE4GLnQr1S9Szy+H/NpTUNk8F3Xt2Z8yaDOxSGjy9Aux/QFzbv7UpzUuSK0tOnT+nUqRNqtSJ5l8X4+voSFRUFQFxcHF5eXjRu3Ni6QQkhhMjVvm34LXr0NP6lMbcf38ZeY8+TxCdm7fO9Lc+fzpvbdC7VPMy3zEGWOBSGgA4kVGyD9v9y59pRimQ23bt3Z9myZUo0ZTVr1qwhPDwcR0dHa4cihBAiF1OpVKhVamY3mU146XAWtVhk0f57bexluNJ08ObBHHOlaW3AdyQ2GG7tMLJMkStKiYmJTJgwgY0bNxIQEJBqMvfkyZOz1e6OHTuYOHEihw4d4saNG6xcuZI2bdqkqDN9+nQmTpzIzZs3CQwM5Ntvv6V69epZ7uvnn38mIiIiW3EKIYQQL/Mu5M2UBlPSPNbLrxfvB71PlR+V3ag3VT8bexl+v37iOr0DemOrsTVrn8YkaAqQVKMfmq2jrNJ/dimSKB0/fpzg4GAATpw4keKYKfvaPHz4kMDAQHr16kW7dqnXsli2bBmDBg1i1qxZhIaGMmXKFJo2bcrp06cpVqwYAEFBQSQkJKQ6d9OmTZQoUQKA+/fvs2fPHn766adsxyqEEEIYMyN8Bgv+WsCIWiOwUdvg4egBwBu+b/DTacv83TPj2AxmHJsBwKLmi6hYpGKayx+YlVoDI2Lh4V1092+i/a6WZfvPBkUSpa1btxo99nLilBXNmzenefPmRo9PnjyZPn360LNnTwBmzZrF2rVrmTt3LkOHDgUwzEFKz+rVq2nSpAn29vbp1ouPjyc+Pt7w+v79+wDodDrDkxMv//lMWuWZKTPWnjmY2ldWzs9M3fTqyDgrM87ZPZ6dMbXkGJvaX1bPNWWcszLGaZXLZzlzdaz9nVHDvQY13Gukaq9XpV7su7EPVztXjkQfAWD5q8v5eNfHnI8138NQ3dZ3M/xe3LE4v7f6HZVKZblxti2IzqUAapUWjT7z//2VkpX2VHoz3Lx88OABS5cuZc6cOURGRiqyjYlKpUpx6+3p06c4ODiwfPnyFLfjunfvzr1791i9enWm227ZsiVvv/02LVu2TLfeiBEjGDlyZKryJUuW4ODgkMYZQgghRObcS7qHo8oRrUrLU/1T1jxaQ5QuyuJxDCs4DEe1Zebr2unu0exExksIrQ5eqGi/jx49okuXLsTGxlKwYMF06ypyRemZHTt2MGfOHFasWEGJEiVo164d06ZNU7ILgzt37pCYmJhq2xR3d3dOnTqV6XZiY2M5cOAAK1asyLDusGHDGDRokOH1/fv38fT0pEmTJoaB1ul0bN68mcaNG6eYq5VWeWbKjLVnDqb2lZXzM1M3vToyzsqMc3aPZ2dMLTnGmXlvSp5ryjhnZYzTKpfPcubq5MbvjDa0ISo6il6bn88zauHVgpJOJZl9YrZZ+gQYd38cACUdSzKk6hBCPUJT3KJTepwffXwDrY0N2rFF04wn1t5T8XF+dkcoM0xOlG7evMn8+fOZM2cO9+/fp2PHjsTHx7Nq1SoqVapkavNm5+Liwq1btzJV187ODju71PdztVptqv+AaZVlpe7LZcbaMwdT+8rK+Zmpm14dGWdlxjm7x7MzppYcY1P7y+q5poxzVsY4rXL5LGeuTm77zqhWohqzGs5iwJ8DGBU2ihblWnD70W2zJkrPXHt4jYHbBxpeb+24NcX6UIqPc6XW8Hfy3SC9a1lU/14AYPsro2iu8DhnpS2Tlgdo2bIlvr6+HDt2jClTpnD9+nW+/fZbU5rMNDc3NzQaTaok59atW3h4eFgkBiGEEMLcqntU54tCX9C4dPI6f8UcilHEvojF42jwcwP8F/hTZUkVzujOKL9xb+sZ6AuX47xbIxLe3Qdvb0P36W30Ko2y/WSRSYnS+vXr6d27NyNHjuTVV19Fo7Hcm7G1tSUkJIQtW7YYypKSktiyZQs1a9a0WBxCCCGEpW3rtI19XfZZrf+FDxfSYEVy4nQq5pQyazXZOZHw7j6Oe0YkPx1XIhhU1l/I2qQIdu3axYMHDwgJCSE0NJRp06Zx584dpWIjLi6OqKgow5NrFy5cICoqisuXLwMwaNAgZs+ezYIFCzh58iR9+/bl4cOHhqfghBBCiLzKUetIv6B+Kco6+XZiZK2RHO52mOBiwRaJo8NvHQhYGID/An/23thLot6EB7hMWFLIXExKlGrUqMHs2bO5ceMG77zzDj/99BMlSpQgKSmJzZs38+DBA5OCi4yMJDg42LBG06BBgwgODmb48OSVPTt16sRXX33F8OHDCQoKIioqig0bNqSa4C2EEELkRe8Gvkvkm5HYqGxQoWJAlQG0K98OrVrLwubKPimWGf229uPL2C+psqQKM6Nm8jjhscVjUJoiT705OjrSq1cvevXqxenTp5kzZw7/+9//GDp0KI0bN2bNmjXZard+/foZXs57//33ef/997PVvhBCCJHb2WnsOBJxBL1en2qR53Iu5TgXe46CqoJs7LgRJ3sn/Bf4WySuGUdnMONo8gKXvq6+vKF/I8dsp5IVit/88/X1ZcKECVy9epWlS5cq3bwQQggh0pDWThjTwqfRoXwHejv1Njzi/1ub3ywdGqf/Pc3I2JGELA3Bf4F/rkqYzDZLSqPR0KZNm2xfTRJCCCGEaUo5l2JYtWEU0Tx/Ss7LxYumXk0B+L7x9ylu0U2qM8kicQUsDKDKkip8fu9zzt07Z5E+s0vRBSeFEEIIkfNNrDuRUbVG4aBN3lXiWMQx4hPj0egt/yh+h3UdDL/PbDjT4v1nxPrP3QkhhBDColQqlSFJevba3iblfqeF7QtbOiz6/tmXz+99TpUlVVh1dpXF+0+LJEpCCCGEMGhk3wj/Iv6sb7eemY2sd4Xni91fUGVJFZY/XG61GEASJSGEEEK8oL59fRY0XYCD1oGwkmGG8qHVh1olHmtsDPwiSZSEEEIIYdTo2qOJqBRBl1e6WDsUq5BESQghhBBGtfFpw5BqQ1CpVBzoeoDpDaZT364+AP2D+xvq9fLrBYC72p1ZDWdZI1SzkERJCCGEEJlSwKYANYvXpFGBRuzttJdulboBUMS+CB+GfMiODjvo59yP6h7VaV2utZWjVYYkSkIIIYTIMjuNHQVsCnCg6wE2d9gMgJPWCfV/G9mOCRvDT6/+RO0Sta0ZpskkURJCCCFEthWwKYBWrU3zWGW3ysxqPIst7bYYygKLBloqNEXIgpNCCCGEMCtXe1dGuoyk5ast0Wq1RN2O4u3Nb+eKTXPlipIQQgghzE6jer7qd1CxIOY1nmfFaDJPEiUhhBBCWJye1Bvjjqo5io8LfszBNw5aIaK0SaIkhBBCCIsrVqBYit9d7VxpVqYZBdUF0agtv+ecMTJHSQghhBAW52rvyjtO7xBeNxyfIj7JhYnPjzcq3Yg/Lv9BTdua1gnwP5IoCSGEEMIqPG088SnkY3hqTpeoMxwbV2ccr994nVuHblkrPEBuvQkhhBAiB7K3sSfUIxQblXWv6UiiJIQQQghhhCRKQgghhBBGSKIkhBBCCGGEJEpCCCGEEEZIoiSEEEIIYYQkSkIIIYQQRkiiJIQQQghhhCRKQgghhBBGSKIkhBBCCGGEJEpCCCGEEEZIoiSEEEIIYYQkSkIIIYQQRkiiJIQQQghhhHW35M3l9Ho9APfv3zeU6XQ6Hj16xP3799FqtemWZ6bMWHvmYGpfWTk/M3XTqyPjrMw4Z/d4dsbUkmOcmfem5LmmjHNWxjitcvksZ66OfGfIOL/o2d/bz/4eT48kSiZ48OABAJ6enlaORAghhBBZ9eDBA1xcXNKto9JnJp0SaUpKSuL69es4OzujUqkM5dWqVePgwYOp6qdVnlHZ/fv38fT05MqVKxQsWNAM7yLjGM11fmbqpldHxlmZcc7u8cyMszXH2FiM5jrXlHHOyhinVS6f5czVke8MGedn9Ho9Dx48oESJEqjV6c9CkitKJlCr1ZQqVSpVuUajSfM/aFrlmS0rWLCgRf5nNBa7Oc7PTN306sg4KzPO2T2emTG15hgb699c55oyzlkZ47TK5bOcuTrynSHj/KKMriQ9I5O5zaBfv36ZLs9smaWY2ndWzs9M3fTqyDgrUze7xzMzptYcY1P7z+q5poxzVsY4rXL5LGeujnxnKFc3r45zWuTWWw53//59XFxciI2Ntdi/wvMjGWfzkzG2DBlny5BxtoycMM5yRSmHs7Oz48svv8TOzs7aoeRpMs7mJ2NsGTLOliHjbBk5YZzlipIQQgghhBFyRUkIIYQQwghJlIQQQgghjJBESQghhBDCCEmUhBBCCCGMkERJCCGEEMIISZTyiHv37lG1alWCgoLw8/Nj9uzZ1g4pT7py5Qr169enUqVKBAQE8Msvv1g7pDyrbdu2uLq60r59e2uHkqf8/vvv+Pr6Ur58eX744Qdrh5NnyefXvCz5XSzLA+QRiYmJxMfH4+DgwMOHD/Hz8yMyMpIiRYpYO7Q85caNG9y6dYugoCBu3rxJSEgIZ86cwdHR0dqh5Tnbtm3jwYMHLFiwgOXLl1s7nDwhISGBSpUqsXXrVlxcXAgJCWHPnj3yPWEG8vk1L0t+F8sVpTxCo9Hg4OAAQHx8PHq9HsmBlVe8eHGCgoIA8PDwwM3NjZiYGOsGlUfVr18fZ2dna4eRpxw4cIDKlStTsmRJnJycaN68OZs2bbJ2WHmSfH7Ny5LfxZIoWciOHTto2bIlJUqUQKVSsWrVqlR1pk+fjpeXF/b29oSGhnLgwIEs9XHv3j0CAwMpVaoUQ4YMwc3NTaHocw9LjPMzhw4dIjExEU9PTxOjzn0sOc7iOVPH/fr165QsWdLwumTJkly7ds0Soecq8vk2PyXH2NzfxZIoWcjDhw8JDAxk+vTpaR5ftmwZgwYN4ssvv+Tw4cMEBgbStGlTbt++bajzbP7Ryz/Xr18HoFChQhw9epQLFy6wZMkSbt26ZZH3lpNYYpwBYmJiiIiI4Pvvvzf7e8qJLDXOIiUlxl1kTMbZ/JQaY4t8F+uFxQH6lStXpiirXr26vl+/fobXiYmJ+hIlSujHjRuXrT769u2r/+WXX0wJM9cz1zg/efJEX6dOHf3ChQuVCjVXM+fneevWrfrXX39diTDznOyM++7du/Vt2rQxHB8wYIB+8eLFFok3tzLl8y2f38zJ7hhb6rtYrijlAE+fPuXQoUM0atTIUKZWq2nUqBF79+7NVBu3bt3iwYMHAMTGxrJjxw58fX3NEm9upcQ46/V6evToQcOGDenWrZu5Qs3VlBhnkXWZGffq1atz4sQJrl27RlxcHOvXr6dp06bWCjlXks+3+WVmjC35XSyJUg5w584dEhMTcXd3T1Hu7u7OzZs3M9XGpUuXqFOnDoGBgdSpU4f+/fvj7+9vjnBzLSXGeffu3SxbtoxVq1YRFBREUFAQx48fN0e4uZYS4wzQqFEjOnTowLp16yhVqpT8JZSBzIy7jY0NkyZNokGDBgQFBfHRRx/JE29ZlNnPt3x+sy8zY2zJ72Ibs7QqLK569epERUVZO4w8LywsjKSkJGuHkS/88ccf1g4hT2rVqhWtWrWydhh5nnx+zcuS38VyRSkHcHNzQ6PRpJp8fevWLTw8PKwUVd4j42wZMs7WIeNuGTLO5pfTxlgSpRzA1taWkJAQtmzZYihLSkpiy5Yt1KxZ04qR5S0yzpYh42wdMu6WIeNsfjltjOXWm4XExcVx9uxZw+sLFy4QFRVF4cKFKV26NIMGDaJ79+5UrVqV6tWrM2XKFB4+fEjPnj2tGHXuI+NsGTLO1iHjbhkyzuaXq8bYrM/UCYOtW7fqgVQ/3bt3N9T59ttv9aVLl9bb2trqq1evrt+3b5/1As6lZJwtQ8bZOmTcLUPG2fxy0xjLXm9CCCGEEEbIHCUhhBBCCCMkURJCCCGEMEISJSGEEEIIIyRREkIIIYQwQhIlIYQQQggjJFESQgghhDBCEiUhhBBCCCMkURJCCCGEMEISJSGEsLKnT5/i4+PDnj17FG13w4YNBAUFWWyXdSHyIkmUhBCK6tGjByqVKtXPi/s6iZRmzZpF2bJlqVWrlqFMpVKxatWqVHV79OhBmzZtMtVus2bN0Gq1LF68WKFIhch/JFESQiiuWbNm3LhxI8VP2bJlU9V7+vSpFaLLWfR6PdOmTaN3795mab9Hjx5MnTrVLG0LkR9IoiSEUJydnR0eHh4pfjQaDfXr1+f9999n4MCBuLm50bRpUwBOnDhB8+bNcXJywt3dnW7dunHnzh1Dew8fPiQiIgInJyeKFy/OpEmTqF+/PgMHDjTUSesKTKFChZg/f77h9ZUrV+jYsSOFChWicOHCtG7dmosXLxqOP7ta89VXX1G8eHGKFClCv3790Ol0hjrx8fF88skneHp6Ymdnh4+PD3PmzEGv1+Pj48NXX32VIoaoqKh0r6gdOnSIc+fO8eqrr2ZxlOHixYtpXr2rX7++oU7Lli2JjIzk3LlzWW5fCCGJkhDCwhYsWICtrS27d+9m1qxZ3Lt3j4YNGxIcHExkZCQbNmzg1q1bdOzY0XDOkCFD2L59O6tXr2bTpk1s27aNw4cPZ6lfnU5H06ZNcXZ2ZufOnezevRsnJyeaNWuW4srW1q1bOXfuHFu3bmXBggXMnz8/RbIVERHB0qVLmTp1KidPnuS7777DyckJlUpFr169mDdvXop+582bR926dfHx8Ukzrp07d1KhQgWcnZ2z9H4APD09U1y1O3LkCEWKFKFu3bqGOqVLl8bd3Z2dO3dmuX0hBKAXQggFde/eXa/RaPSOjo6Gn/bt2+v1er2+Xr16+uDg4BT1R48erW/SpEmKsitXrugB/enTp/UPHjzQ29ra6n/++WfD8bt37+oLFCigHzBggKEM0K9cuTJFOy4uLvp58+bp9Xq9ftGiRXpfX199UlKS4Xh8fLy+QIEC+o0bNxpiL1OmjD4hIcFQp0OHDvpOnTrp9Xq9/vTp03pAv3nz5jTf+7Vr1/QajUa/f/9+vV6v1z99+lTv5uamnz9/vtHxGjBggL5hw4apygG9vb19inF0dHTU29jY6Fu3bp2q/uPHj/WhoaH61157TZ+YmJjiWHBwsH7EiBFGYxBCGGdj3TRNCJEXNWjQgJkzZxpeOzo6Gn4PCQlJUffo0aNs3boVJyenVO2cO3eOx48f8/TpU0JDQw3lhQsXxtfXN0sxHT16lLNnz6a6cvPkyZMUt6UqV66MRqMxvC5evDjHjx8Hkm+jaTQa6tWrl2YfJUqU4NVXX2Xu3LlUr16d3377jfj4eDp06GA0rsePH2Nvb5/msa+//ppGjRqlKPvkk09ITExMVbdXr148ePCAzZs3o1anvFlQoEABHj16ZDQGIYRxkigJIRTn6Oho9FbTi0kTQFxcHC1btmT8+PGp6hYvXjzTT8upVCr0en2KshfnFsXFxRESEpLmE2BFixY1/K7ValO1++zx+gIFCmQYx1tvvUW3bt34+uuvmTdvHp06dcLBwcFofTc3N0Mi9jIPD49U4+js7My9e/dSlI0ZM4aNGzdy4MCBNG/hxcTEpHiPQojMk0RJCGFVVapUYcWKFXh5eWFjk/orqVy5cmi1Wvbv30/p0qUB+Pfffzlz5kyKKztFixblxo0bhtf//PNPiqsoVapUYdmyZRQrVoyCBQtmK1Z/f3+SkpLYvn17qis9z7Ro0QJHR0dmzpzJhg0b2LFjR7ptBgcHM3PmTPR6PSqVKssxrVixglGjRrF+/XrKlSuX6vizK2bBwcFZblsIIZO5hRBW1q9fP2JiYujcuTMHDx7k3LlzbNy4kZ49e5KYmIiTkxO9e/dmyJAh/Pnnn5w4cYIePXqkur3UsGFDpk2bxpEjR4iMjOTdd99NcXWoa9euuLm50bp1a3bu3MmFCxfYtm0bH3zwAVevXs1UrF5eXnTv3p1evXqxatUqQxs///yzoY5Go6FHjx4MGzaM8uXLU7NmzXTbbNCgAXFxcfz1119ZGLVkJ06cICIigk8++YTKlStz8+ZNbt68SUxMjKHOvn37sLOzyzAOIUTaJFESQlhViRIl2L17N4mJiTRp0gR/f38GDhxIoUKFDMnQxIkTqVOnDi1btqRRo0aEhYWlmus0adIkPD09qVOnDl26dGHw4MEpbnk5ODiwY8cOSpcuTbt27ahYsSK9e/fmyZMnWbrCNHPmTNq3b897773HK6+8Qp8+fXj48GGKOr179+bp06f07Nkzw/aKFClC27Zts7UoZGRkJI8ePWLMmDEUL17c8NOuXTtDnaVLl9K1a9d0b/8JIYxT6V++qS+EELlA/fr1CQoKYsqUKdYOJZWdO3cSHh7OlStXcHd3z7D+sWPHaNy4MefOnUtzUnt23blzB19fXyIjI9Nc8FMIkTG5oiSEEAqJj4/n6tWrjBgxgg4dOmQqSQIICAhg/PjxXLhwQdF4Ll68yIwZMyRJEsIEMplbCCEUsnTpUnr37k1QUBALFy7M0rk9evRQPJ6qVatStWpVxdsVIj+RW29CCCGEEEbIrTchhBBCCCMkURJCCCGEMEISJSGEEEIIIyRREkIIIYQwQhIlIYQQQggjJFESQgghhDBCEiUhhBBCCCMkURJCCCGEMEISJSGEEEIII/4f704ewxNMwqUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The corrected relative sensor noise is composed of two components,\n", + "# the intrinsic relative sensor noise and the part related to sensor correction\n", + "\n", + "# The part related to sensor correction:\n", + "noise_sensor_correction = comp.noise_super(f_, noise1=noise_seismic, noise2=noise_seismometer, filter1=h1_prefilt, filter2=h2_prefilt)\n", + "\n", + "# The corrected relative sensor noise:\n", + "noise_corrected = kontrol.core.math.quad_sum(noise_relative, noise_sensor_correction)\n", + "# ^Remember, uncorrelated noises sum in quadrature (power).\n", + "\n", + "# Visusalize\n", + "plt.loglog(f_, noise_relative, label=\"Relative sensor noise\")\n", + "plt.loglog(f_, noise_seismic, label=\"Seismic noise\")\n", + "plt.loglog(f_, noise_seismometer, label=\"Seismometer noise\")\n", + "plt.loglog(f_, noise_corrected, label=\"Corrected relative sensor noise\")\n", + "plt.legend(loc=0)\n", + "plt.grid(which=\"both\")\n", + "plt.ylabel(\"Amplitude spectral density (some unit)/$\\sqrt{\\mathrm{Hz}}$\")\n", + "plt.xlabel(\"Frequency (Hz)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7a79b682", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seismic noise RMS: 0.1434520357166474\n", + "Corrected relative sensor RMS: 0.10451631749875732\n" + ] + } + ], + "source": [ + "# Now we're seeing some reduction especially around the secondary microseim.\n", + "# However, we're also injecting noise at lower frequency.\n", + "# To justify this tradeoff, let's compare the RMS of the seismic noise and the corrected relative sensor\n", + "print(\"Seismic noise RMS:\", kontrol.spectral.asd2rms(noise_seismic, f=f_, return_series=False))\n", + "print(\"Corrected relative sensor RMS:\", kontrol.spectral.asd2rms(noise_corrected, f=f_, return_series=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a1a00a29", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sensor correction filter:\n", + " zpk([-0;-0;-0;0.000634057;0.000567+i*0.002048;0.000567+i*-0.002048;0.0155221;0.0332424;0.102979;0.048242+i*0.106069;0.048242+i*-0.106069;0.814654;2.06538],[0.00199992;0.002000+i*0.000000;0.002000+i*-0.000000;0.00376468;0.022393+i*0.030733;0.022393+i*-0.030733;0.050882+i*0.005582;0.050882+i*-0.005582;0.0563568;0.033088+i*0.096811;0.033088+i*-0.096811;0.837395;2.06538],29011.4,\"n\")\n" + ] + } + ], + "source": [ + "# As you can see, with the sensor correction scheme, the noise RMS is lowered,\n", + "# given that the RMS of an uncorrected relative sensor is mostly contributed from the seismic noise.\n", + "# (we aren't even minimizing the RMS with H2 optimization)\n", + "# Of course, this is not always true in reality and this is only an example with mock data.\n", + "\n", + "# And, the corrected relative sensor noise is the one that we need to model for sensor fusion!\n", + "\n", + "# Export the sensor correction filter.\n", + "print(\"Sensor correction filter:\\n\", kontrol.TransferFunction(h2_prefilt).foton(root_location=\"n\"))" + ] + } + ], + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/sensor_correction/tf_inertial.pkl b/docs/source/tutorials/sensor_correction/tf_inertial.pkl new file mode 100644 index 0000000..178d65d Binary files /dev/null and b/docs/source/tutorials/sensor_correction/tf_inertial.pkl differ diff --git a/docs/source/tutorials/sensor_correction/tf_relative.pkl b/docs/source/tutorials/sensor_correction/tf_relative.pkl new file mode 100644 index 0000000..f972be5 Binary files /dev/null and b/docs/source/tutorials/sensor_correction/tf_relative.pkl differ diff --git a/docs/source/tutorials/sensor_correction/tf_seismic.pkl b/docs/source/tutorials/sensor_correction/tf_seismic.pkl new file mode 100644 index 0000000..f21ab7a Binary files /dev/null and b/docs/source/tutorials/sensor_correction/tf_seismic.pkl differ diff --git a/docs/source/tutorials/sensor_fusion/complementary_filter_synthesis.ipynb b/docs/source/tutorials/sensor_fusion/complementary_filter_synthesis.ipynb index effd5d5..6e79582 100644 --- a/docs/source/tutorials/sensor_fusion/complementary_filter_synthesis.ipynb +++ b/docs/source/tutorials/sensor_fusion/complementary_filter_synthesis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5311a205", + "id": "d7f430da", "metadata": {}, "source": [ "# Complementary Filter Synthesis" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "96334f46", + "id": "05adff31", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ "comp.weight1=1/tf_relative\n", "\n", "# Synthesis.\n", - "# Note. Sometimes this produces filters that makes no sense, or kind of suboptimal.\n", + "# Note. Sometimes this produces filters that make no sense, or kind of suboptimal.\n", "# If that happens, try interchanging the number 1 and 2 from the above noise and weight specifications.\n", "# (This is one of the cases so we happened to start with 2, instead of 1.)\n", "h2, h1 = comp.hinfsynthesis()" @@ -49,7 +49,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "fa52e4f5", + "id": "7faac593", "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "3de1d929", + "id": "d647eddd", "metadata": {}, "outputs": [ { @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "2b8eb3b9", + "id": "ae140bc0", "metadata": {}, "outputs": [ { @@ -146,7 +146,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "b714fad8", + "id": "96354dc1", "metadata": {}, "outputs": [ { @@ -208,7 +208,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "c688b8cd", + "id": "55fbc7d2", "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "0b9c4ebc", + "id": "eb44956b", "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "b4b9a4d5", + "id": "5c1a508a", "metadata": {}, "outputs": [ {