diff --git a/.coveragerc b/.coveragerc
index 9ecf53f..0a59480 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -6,6 +6,8 @@ omit =
     */__init__.py
     */src/data/accel/accelerometer.py
     */src/data/accel/aligner.py
+    */src/functions/natural_freq.py
+    */src/methods/pyoma/*
     */src/examples/*
 
 [report]
diff --git a/.pylintrc b/.pylintrc
index e545389..0e10c48 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -24,7 +24,7 @@ ignore=CVS
 
 # Add files or directories matching the regex patterns to the ignore-list. The
 # regex matches against paths and can be in Posix or Windows format.
-ignore-paths=
+ignore-paths=src/methods/pyoma
 
 # Files or directories matching the regex patterns are skipped. The regex
 # matches against base names, not paths. The default value ignores emacs file
diff --git a/config/production.json.template b/config/production.json.template
index 24f96f8..5ed33c7 100644
--- a/config/production.json.template
+++ b/config/production.json.template
@@ -7,12 +7,21 @@
         "ClientID": "test_client_id",
         "QoS": 1,
         "TopicsToSubscribe": [
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/1/acc/raw/data",
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/1/acc/raw/metadata",  
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/2/acc/raw/data",
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/2/acc/raw/metadata",
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/3/acc/raw/data",
-            "cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/3/acc/raw/metadata"
+        "cpsens/d8-3a-dd-37-d2-7e/3160-A-042_sn_999998/1/acc/raw/data",
+        "cpsens/d8-3a-dd-37-d2-7e/3160-A-042_sn_999998/1/acc/raw/metadata",
+        "cpsens/d8-3a-dd-37-d2-7e/3160-A-042_sn_999998/2/acc/raw/data",
+        "cpsens/+/+/1/acc/raw/data",
+        "cpsens/+/+/2/acc/raw/data"
         ]
+    },
+
+    "sysID": {
+      "host": "",
+      "port": 1883,
+      "userId": "",
+      "password": "",
+      "ClientID": "sub.232.sds.213s",
+      "QoS": 1,
+      "TopicsToSubscribe": ["cpsens/d8-3a-dd-f5-92-48/cpsns_Simulator/1_2/oma_results"]
     }
 }
diff --git a/poetry.lock b/poetry.lock
index 0eef372..97c077b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,17 @@
 # This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
 [[package]]
 name = "astroid"
 version = "3.3.9"
@@ -40,6 +52,83 @@ files = [
 ]
 markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\""}
 
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
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+    {file = "contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5"},
+    {file = "contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54"},
+]
+
+[package.dependencies]
+numpy = ">=1.23"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.15.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+
 [[package]]
 name = "coverage"
 version = "7.8.0"
@@ -116,22 +205,112 @@ files = [
 [package.extras]
 toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 
+[[package]]
+name = "cycler"
+version = "0.12.1"
+description = "Composable style cycles"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
+    {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
+]
+
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
 name = "dill"
-version = "0.3.9"
+version = "0.4.0"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "dill-0.3.9-py3-none-any.whl", hash = "sha256:468dff3b89520b474c0397703366b7b95eebe6303f108adf9b19da1f702be87a"},
-    {file = "dill-0.3.9.tar.gz", hash = "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"},
+    {file = "dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049"},
+    {file = "dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0"},
 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
 profile = ["gprof2dot (>=2022.7.29)"]
 
+[[package]]
+name = "fonttools"
+version = "4.57.0"
+description = "Tools to manipulate font files"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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 testing = ["pytest", "pytest-benchmark"]
 
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 testutils = ["gitpython (>3)"]
 
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 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
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+    {file = "scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db"},
+    {file = "scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.5"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+]
+
 [[package]]
 name = "tomlkit"
 version = "0.13.2"
@@ -410,7 +1196,68 @@ files = [
     {file = "tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
+    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.13.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
+    {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
+]
+
+[[package]]
+name = "typing-inspection"
+version = "0.4.0"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f"},
+    {file = "typing_inspection-0.4.0.tar.gz", hash = "sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+files = [
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
+]
+
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.12, <3.13"
-content-hash = "a842a5ec02fa3e8e411a5caae5436cbfa15e5cc742429a2a26a2521661c268ea"
+content-hash = "9ee50f778ede318f27ded544713802078b2590c6f7ad21f4f584437d8c66a5a1"
diff --git a/pyproject.toml b/pyproject.toml
index 4fb2e0c..916f5dc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,8 +13,10 @@ packages = [{include = "*", from="src"}]
 [tool.poetry.dependencies]
 python = ">=3.12, <3.13"
 paho-mqtt = "^2.1.0"
-numpy = "^2.2.4"
+numpy = "^2.2.5"
 click ="^8.1.8"
+pyOMA-2 = "1.0.0"
+
 
 [tool.poetry.group.dev.dependencies]
 pylint = "^3.3.6"
diff --git a/src/data/accel/constants.py b/src/data/accel/constants.py
index dba40e2..906259f 100644
--- a/src/data/accel/constants.py
+++ b/src/data/accel/constants.py
@@ -1,7 +1,9 @@
-MAX_MAP_SIZE = 52200  # The maximum number of samples saved in FIFO
+MAX_MAP_SIZE = 160100  # The maximum number of samples saved in FIFO
 
 TIMEOUT = 2  # Max wait time until enough samples are collected for the test_Accelerometer
 
 INTERVAL = 0.001  # Check every 0.001s to see if samples are collected
 
 MIN_SAMPLES_NEEDED = 500  # Minimum samples needed before running it to sysid
+
+WAIT_METADATA = 11 # Wait max 11 seconds for getting metadata message
diff --git a/src/data/accel/hbk/accelerometer.py b/src/data/accel/hbk/accelerometer.py
index 1df62ae..9cbb2d9 100644
--- a/src/data/accel/hbk/accelerometer.py
+++ b/src/data/accel/hbk/accelerometer.py
@@ -84,7 +84,7 @@ def process_message(self, msg: mqtt.MQTTMessage) -> None:
                     if not oldest_deque:  # Remove the key/deque from the map if it's empty
                         del self.data_map[oldest_key]
                     total_samples = sum(len(dq) for dq in self.data_map.values())
-            print(f" Channel: {self.topic}  Key: {samples_from_daq_start}, Samples: {num_samples}")
+            #print(f" Channel: {self.topic}  Key: {samples_from_daq_start}, Samples: {num_samples}")
 
         except Exception as e:
             print(f"Error processing message: {e}")
diff --git a/src/data/accel/hbk/aligner.py b/src/data/accel/hbk/aligner.py
index d0baf68..335e4ef 100644
--- a/src/data/accel/hbk/aligner.py
+++ b/src/data/accel/hbk/aligner.py
@@ -111,9 +111,15 @@ def _extract_aligned_block(self, group: List[int], batch_size: int,
             for i in range(batch_size):
                 if samples_collected >= requested_samples:
                     break
+                # Iterate over each channel index and
+                # its corresponding data entries for the current key
                 for ch_idx, channel_data in enumerate(entries):
-                    if channel_data is not None:
-                        aligned_data[ch_idx].append(channel_data[i])
+                    # Get the i-th sample from the channel, if it exists
+                    sample = channel_data[i] if channel_data and i < len(channel_data) else None
+                    # If a valid sample was retrieved,
+                    # append it to the aligned data for that channel
+                    if sample is not None:
+                        aligned_data[ch_idx].append(sample)
                     else:
                         print(f"Missing data for channel index {ch_idx} skipping")
                 samples_collected += 1
@@ -131,7 +137,7 @@ def _extract_aligned_block(self, group: List[int], batch_size: int,
     def extract(self, requested_samples: int) -> Tuple[np.ndarray, Optional[datetime]]:
         with self._lock:
             batch_size, key_groups = self.find_continuous_key_groups()
-            print("Keys", key_groups)
+            #print("Keys", key_groups)
 
             if batch_size is None or key_groups is None:
                 # No data or groups to align, returun empty
diff --git a/src/data/accel/metadata.py b/src/data/accel/metadata.py
new file mode 100644
index 0000000..80e68bd
--- /dev/null
+++ b/src/data/accel/metadata.py
@@ -0,0 +1,37 @@
+import json
+import time
+from typing import Any, Dict
+from paho.mqtt.client import Client as MQTTClient
+from data.accel.constants import WAIT_METADATA
+from data.comm.mqtt import setup_mqtt_client
+
+def extract_fs_from_metadata(mqtt_config: Dict[str, Any]) -> int:
+    fs_result = {"fs": None}
+
+    def _on_metadata(client: MQTTClient, userdata, message) -> None:
+        try:
+            payload = json.loads(message.payload.decode("utf-8"))
+            fs_candidate = payload["Analysis chain"][0]["Sampling"]
+            if fs_candidate:
+                fs_result["fs"] = fs_candidate
+                print(f"Extracted Fs from metadata: {fs_candidate}")
+                client.unsubscribe(userdata["metadata_topic"])
+        except Exception as e:
+            print(f"Failed to extract Fs: {e}")
+
+    metadata_topic = mqtt_config["TopicsToSubscribe"][1]
+    client, _ = setup_mqtt_client(mqtt_config, topic_index=1)
+    client.user_data_set({"metadata_topic": metadata_topic})
+    client.message_callback_add(metadata_topic, _on_metadata)
+    client.connect(mqtt_config["host"], mqtt_config["port"], 60)
+    client.subscribe(metadata_topic)
+    client.loop_start()
+
+    start_time = time.time()
+    while fs_result["fs"] is None and (time.time() - start_time) < WAIT_METADATA:
+        time.sleep(0.1)
+
+    client.loop_stop()
+    if fs_result["fs"] is None:
+        raise TimeoutError("Sampling frequency not received within timeout")
+    return fs_result["fs"]
diff --git a/src/examples/README.md b/src/examples/README.md
index 9c38d68..bb43e4d 100644
--- a/src/examples/README.md
+++ b/src/examples/README.md
@@ -42,11 +42,21 @@ There are two examples.
 * **example_2** demonstrates the use of `Aligner` class to collect and
   align accelerometer measurements from multiple MQTT data streams.
 
+* **example_3** demonstrates the use of `sys_id` with 3 cases: 
+    1. run_experiment_3_plot: plots natural frequencies.
+    2. run_experiment_3_print: prints OMA results to console.
+    3. run_experiment_3_publish: publishes OMA results via MQTT to the config given under [sysid] config.
+
 To run the examples with the default config, use:
 
 ```bash
 python .\src\examples\example.py experiment-1
 python .\src\examples\example.py experiment-2
+python .\src\examples\example.py experiment-3-print
+python .\src\examples\example.py experiment-3-plot
+python .\src\examples\example.py experiment-3-publish
+
+
 ```
 
 To run the examples with specified config, use
diff --git a/src/examples/example.py b/src/examples/example.py
index f3547e7..e215b2a 100644
--- a/src/examples/example.py
+++ b/src/examples/example.py
@@ -2,9 +2,15 @@
 import click
 from examples.experiment_1 import run_experiment_1
 from examples.experiment_2 import run_experiment_2
+from examples.experiment_3 import (
+    run_experiment_3_plot,
+    run_experiment_3_publish,
+    run_experiment_3_print,
+)
+
 
 @click.group()
-@click.option('--config', default="config/mockpt.json", help="Path to config file")
+@click.option('--config', default="config/production.json", help="Path to config file")
 @click.pass_context
 def cli(ctx, config):
     ctx.ensure_object(dict)
@@ -20,5 +26,21 @@ def experiment_1(ctx):
 def experiment_2(ctx):
     run_experiment_2(ctx.obj["CONFIG"])
 
+
+@cli.command()
+@click.pass_context
+def experiment_3_publish(ctx):
+    run_experiment_3_publish(ctx.obj["CONFIG"])
+
+@cli.command()
+@click.pass_context
+def experiment_3_plot(ctx):
+    run_experiment_3_plot(ctx.obj["CONFIG"])
+
+@cli.command()
+@click.pass_context
+def experiment_3_print(ctx):
+    run_experiment_3_print(ctx.obj["CONFIG"])
+
 if __name__ == "__main__":
     cli(obj={})
diff --git a/src/examples/experiment_2.py b/src/examples/experiment_2.py
index d05f5f5..04cf511 100644
--- a/src/examples/experiment_2.py
+++ b/src/examples/experiment_2.py
@@ -7,7 +7,7 @@
 def run_experiment_2(config_path):
     config = load_config(config_path)
     mqtt_config = config["MQTT"]
-    topic_indexes = [0,1]
+    topic_indexes = [0,2]
 
     all_topics = mqtt_config["TopicsToSubscribe"]
     selected_topics = [all_topics[i] for i in topic_indexes]
diff --git a/src/examples/experiment_3.py b/src/examples/experiment_3.py
new file mode 100644
index 0000000..6373a09
--- /dev/null
+++ b/src/examples/experiment_3.py
@@ -0,0 +1,83 @@
+import sys
+import matplotlib.pyplot as plt
+from methods import sys_id as sysID
+from data.comm.mqtt import load_config
+from data.accel.hbk.aligner import Aligner
+from functions.natural_freq import plot_natural_frequencies
+
+
+def run_experiment_3_plot(config_path):
+    number_of_minutes = 0.2
+    config = load_config(config_path)
+    mqtt_config = config["MQTT"]
+
+    # Setting up the client and extracting Fs
+    data_client, fs = sysID.setup_client(mqtt_config)
+
+    # Setting up the aligner
+    data_topic_indexes = [0, 2]
+    selected_topics = [mqtt_config["TopicsToSubscribe"][i] for i in data_topic_indexes]
+    aligner = Aligner(data_client, topics=selected_topics)
+
+    fig_ax = None
+    aligner_time = None
+    while aligner_time is None:
+        results, aligner_time = sysID.get_oma_results(number_of_minutes, aligner, fs)
+    data_client.disconnect()
+    fig_ax = plot_natural_frequencies(results['Fn_poles'], freqlim=(0, 75), fig_ax=fig_ax)
+    plt.show(block=True)
+    sys.stdout.flush()
+
+
+def run_experiment_3_print(config_path):
+    number_of_minutes = 0.2
+    config = load_config(config_path)
+    mqtt_config = config["MQTT"]
+
+    # Setting up the client and extracting Fs
+    data_client, fs = sysID.setup_client(mqtt_config)
+
+    # Setting up the aligner
+    data_topic_indexes = [0, 2]
+    selected_topics = [mqtt_config["TopicsToSubscribe"][i] for i in data_topic_indexes]
+    aligner = Aligner(data_client, topics=selected_topics)
+
+    aligner_time = None
+    while aligner_time is None:
+        results, aligner_time = sysID.get_oma_results(number_of_minutes, aligner, fs)
+    data_client.disconnect()
+    sys.stdout.flush()
+
+    print(f"\n System Frequencies \n {results['Fn_poles']}")
+    print(f"\n Cov \n{results['Fn_poles_cov']}")
+    print(f"\n damping_ratios  \n{results['Xi_poles']}")
+    print(f"\n cov_damping \n{results['Xi_poles_cov']}")
+
+
+def run_experiment_3_publish(config_path):
+    number_of_minutes = 0.02
+    config = load_config(config_path)
+    mqtt_config = config["MQTT"]
+    publish_config = config["sysID"]
+
+    # Setting up the client for getting accelerometer data
+    data_client, fs = sysID.setup_client(mqtt_config)
+
+    # Setting up the aligner
+    data_topic_indexes = [0, 2]
+    selected_topics = [mqtt_config["TopicsToSubscribe"][i] for i in data_topic_indexes]
+    aligner = Aligner(data_client, topics=selected_topics)
+
+    # Setting up the client for publishing OMA results
+    publish_client, _ = sysID.setup_client(publish_config)  # fs not needed here
+
+    sysID.publish_oma_results(
+        number_of_minutes,
+        aligner,
+        publish_client,
+        publish_config["TopicsToSubscribe"][0],
+        fs
+    )
+
+    print(f"Publishing to topic: {publish_config['TopicsToSubscribe'][0]}")
+    sys.stdout.flush()
diff --git a/src/functions/__init__.py b/src/functions/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/functions/natural_freq.py b/src/functions/natural_freq.py
new file mode 100644
index 0000000..ae70e1e
--- /dev/null
+++ b/src/functions/natural_freq.py
@@ -0,0 +1,59 @@
+from typing import Optional, Tuple
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import ticker
+
+
+def plot_natural_frequencies(
+    fn_poles: np.ndarray,
+    freqlim: Optional[Tuple[float, float]] = None,
+    fig_ax: Optional[Tuple] = None
+) -> Tuple:
+    """
+    Plots a stabilization diagram with optional frequency range filtering.
+
+    Args:
+        Fn_poles (np.ndarray): 2D array [model_order x mode] of natural frequencies.
+        freqlim (tuple, optional): (f_min, f_max) frequency range to plot.
+        fig_ax (tuple, optional): Reuse (fig, ax) for interactive updates.
+
+    Returns:
+        (fig, ax): The matplotlib figure and axes used for plotting.
+    """
+    if fig_ax is None:
+        plt.ion()
+        fig, ax = plt.subplots(figsize=(8, 5))
+    else:
+        fig, ax = fig_ax
+        ax.clear()
+
+    num_orders, num_modes = fn_poles.shape
+    freqs, orders = [], []
+    for i in range(num_orders):
+        for j in range(num_modes):
+            freq = fn_poles[i, j]
+            if not np.isnan(freq):
+                model_order = num_orders - i
+                if freqlim is None or (freqlim[0] <= freq <= freqlim[1]):
+                    freqs.append(freq)
+                    orders.append(model_order)
+
+    ax.scatter(freqs, orders, color='red', s=10)
+
+    ax.set_title("Stabilization Diagram")
+    ax.set_xlabel("Frequency (Hz)")
+    ax.set_ylabel("Model order")
+    ax.set_ylim(0, num_orders + 1)
+    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    ax.grid(True)
+
+    if freqlim:
+        ax.set_xlim(freqlim)
+    else:
+        ax.set_xlim(0, max(freqs) * 1.05)
+
+    fig.tight_layout()
+    fig.canvas.draw()
+    fig.canvas.flush_events()
+
+    return fig, ax
diff --git a/src/functions/util.py b/src/functions/util.py
new file mode 100644
index 0000000..d69fa61
--- /dev/null
+++ b/src/functions/util.py
@@ -0,0 +1,33 @@
+from typing import Any
+import numpy as np
+
+# pylint: disable=R0911
+def convert_numpy_to_list(obj: Any) -> Any:
+    """
+    Recursively convert NumPy arrays and complex numbers to JSON-safe types.
+
+    Args:
+        obj: Any data type.
+
+    Returns:
+        A fully JSON-serializable version of the input.
+    """
+    if isinstance(obj, dict):
+        return {k: convert_numpy_to_list(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [convert_numpy_to_list(item) for item in obj]
+    if isinstance(obj, tuple):
+        return tuple(convert_numpy_to_list(item) for item in obj)
+    if isinstance(obj, np.ndarray):
+        return convert_numpy_to_list(obj.tolist())
+    if isinstance(obj, complex):
+        return {"real": obj.real, "imag": obj.imag}
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    try:
+        # fallback: try converting if it's a NumPy scalar or unknown object
+        if hasattr(obj, 'item'):
+            return obj.item()
+    except Exception:
+        pass
+    return obj
diff --git a/src/methods/__init__.py b/src/methods/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/methods/constants.py b/src/methods/constants.py
new file mode 100644
index 0000000..81ae96d
--- /dev/null
+++ b/src/methods/constants.py
@@ -0,0 +1,7 @@
+DEFAULT_FS = 250 # In case the Fs from metadata doesn't arrive
+
+MIN_SAMPLES_NEEDED = 540  # Minimum samples for running sysid
+
+BLOCK_SHIFT = 30 # Used in sysID algorithm
+
+MODEL_ORDER = 20 # Used in sysID algorithm
diff --git a/src/methods/pyoma/__init__.py b/src/methods/pyoma/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/methods/pyoma/genWrapper.py b/src/methods/pyoma/genWrapper.py
new file mode 100644
index 0000000..656ee8b
--- /dev/null
+++ b/src/methods/pyoma/genWrapper.py
@@ -0,0 +1,229 @@
+import logging
+import typing
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+
+def applymask(list_arr, mask, len_phi) -> typing.List[np.ndarray]:
+    """
+    Apply a mask to a list of arrays, filtering their values based on the mask.
+
+    Parameters
+    ----------
+    list_arr : list of np.ndarray
+        List of arrays to be filtered. Arrays can be 2D or 3D.
+    mask : np.ndarray
+        2D boolean array indicating which values to keep (True) or set to NaN (False).
+    len_phi : int
+        The length of the mode shape dimension for expanding the mask to 3D.
+
+    Returns
+    -------
+    list of np.ndarray
+        List of filtered arrays with the same shapes as the input arrays.
+
+    Notes
+    -----
+    - If an array in `list_arr` is 3D, the mask is expanded to 3D and applied.
+    - If an array in `list_arr` is 2D, the original mask is applied directly.
+    - Values not matching the mask are set to NaN.
+    """
+    # Expand the mask to 3D by adding a new axis (for mode shape)
+    expandedmask1 = np.expand_dims(mask, axis=-1)
+    # Repeat the mask along the new dimension
+    expandedmask1 = np.repeat(expandedmask1, len_phi, axis=-1)
+    list_filt_arr = []
+    for arr in list_arr:
+        if arr is None:
+            list_filt_arr.append(None)
+        elif arr.ndim == 3:
+            list_filt_arr.append(np.where(expandedmask1, arr, np.nan))
+        elif arr.ndim == 2:
+            list_filt_arr.append(np.where(mask, arr, np.nan))
+    return list_filt_arr
+
+
+
+
+def HC_realEigen(Lambds) -> typing.Tuple[np.ndarray, np.ndarray]:
+    """
+    Apply Hard validation Criteria (HC), retaining only those elements which are negetive real part in eingenvalues.
+
+    Parameters
+    ----------
+    Lambds : np.ndarray
+        Array of eigenvalues.
+
+    Returns
+    -------
+    filt_lambd : np.ndarray
+        Array of the same shape as `Lambds` with elements that do not satisfy the condition set to NaN.
+    mask : np.ndarray
+        Boolean array of the same shape as `Lambds`, where True indicates that the element is with negetive real part of eigenvalues.
+
+    """
+    mask = (np.real(Lambds) < 0).astype(int)
+    filt_lambd = Lambds * mask
+    filt_lambd[filt_lambd == 0] = np.nan
+    # should be the same as
+    # filt_damp = np.where(Lambds, np.logical_and(np.real(Lambds) < 0), Lambds, np.nan)
+    return filt_lambd, mask
+
+
+
+def MAC(phi_X: np.ndarray, phi_A: np.ndarray) -> np.ndarray:
+    """
+    Calculates the Modal Assurance Criterion (MAC) between two sets of mode shapes.
+
+    Parameters
+    ----------
+    phi_X : ndarray
+        Mode shape matrix X, shape: (n_locations, n_modes) or n_locations.
+    phi_A : ndarray
+        Mode shape matrix A, shape: (n_locations, n_modes) or n_locations.
+
+    Returns
+    -------
+    ndarray
+        MAC matrix. Returns a single MAC value if both `phi_X` and `phi_A` are
+        one-dimensional arrays.
+
+    Raises
+    ------
+    Exception
+        If mode shape matrices have more than 2 dimensions or if their first dimensions do not match.
+    """
+    if phi_X.ndim == 1:
+        phi_X = phi_X[:, np.newaxis]
+
+    if phi_A.ndim == 1:
+        phi_A = phi_A[:, np.newaxis]
+
+    if phi_X.ndim > 2 or phi_A.ndim > 2:
+        raise Exception(
+            f"Mode shape matrices must have 1 or 2 dimensions (phi_X: {phi_X.ndim}, phi_A: {phi_A.ndim})"
+        )
+
+    if phi_X.shape[0] != phi_A.shape[0]:
+        raise Exception(
+            f"Mode shapes must have the same first dimension (phi_X: {phi_X.shape[0]}, "
+            f"phi_A: {phi_A.shape[0]})"
+        )
+
+    # mine
+    # MAC = np.abs(np.dot(phi_X.conj().T, phi_A)) ** 2 / (
+    #     (np.dot(phi_X.conj().T, phi_X)) * (np.dot(phi_A.conj().T, phi_A))
+    # )
+    # original
+    MAC = np.abs(np.conj(phi_X).T @ phi_A) ** 2
+    MAC = MAC.astype(complex)
+    for i in range(phi_X.shape[1]):
+        for j in range(phi_A.shape[1]):
+            MAC[i, j] = MAC[i, j] / (
+                np.conj(phi_X[:, i]) @ phi_X[:, i] * np.conj(phi_A[:, j]) @ phi_A[:, j]
+            )
+
+    if MAC.shape == (1, 1):
+        MAC = MAC[0, 0]
+
+    return MAC.real
+
+
+# -----------------------------------------------------------------------------
+
+
+
+
+def HC_removeZeroImg(Lambds) -> typing.Tuple[np.ndarray, np.ndarray]:
+    """
+    Apply Hard validation Criteria (HC), retaining only those elements which have non zero imaginary part in eingenvalues.
+
+    Parameters
+    ----------
+    Lambds : np.ndarray
+        Array of eigenvalues.
+
+    Returns
+    -------
+    filt_lambd : np.ndarray
+        Array of the same shape as `Lambds` with elements that do not satisfy the condition set to NaN.
+    mask : np.ndarray
+        Boolean array of the same shape as `Lambds`, where True indicates that the element is with negetive real part of eigenvalues.
+
+    """
+    # Create a mask where the imaginary part is not zero
+    mask = ~np.isclose(np.imag(Lambds), 0).astype(bool)
+    
+    # Retain only the values where the imaginary part is not zero
+    filt_lambd = np.where(mask, Lambds, np.nan)
+    return filt_lambd, mask
+
+
+def SC_apply(Fn, Xi, Phi, ordmin, ordmax, step, err_fn, err_xi, err_phi) -> np.ndarray:
+    """
+    Apply Soft validation Criteria (SC) to determine the stability of modal parameters between consecutive orders.
+
+    Parameters
+    ----------
+    Fn : np.ndarray
+        Array of natural frequencies.
+    Xi : np.ndarray
+        Array of damping ratios.
+    Phi : np.ndarray
+        Array of mode shapes.
+    ordmin : int
+        Minimum model order.
+    ordmax : int
+        Maximum model order.
+    step : int
+        Step size for increasing model order.
+    err_fn : float
+        Tolerance for the natural frequency error.
+    err_xi : float
+        Tolerance for the damping ratio error.
+    err_phi : float
+        Tolerance for the mode shape error.
+
+    Returns
+    -------
+    Lab : np.ndarray
+        Array of labels indicating stability (1 for stable, 0 for unstable).
+    """
+    # inirialise labels
+    Lab = np.zeros(Fn.shape, dtype="int")
+
+    # SOFT CONDITIONS
+    # STABILITY BETWEEN CONSECUTIVE ORDERS
+    for oo in range(ordmin, ordmax + 1, step):
+        o = int(oo / step)
+
+        f_n = Fn[:, o].reshape(-1, 1)
+        xi_n = Xi[:, o].reshape(-1, 1)
+        phi_n = Phi[:, o, :]
+
+        f_n1 = Fn[:, o - 1].reshape(-1, 1)
+        xi_n1 = Xi[:, o - 1].reshape(-1, 1)
+        phi_n1 = Phi[:, o - 1, :]
+
+        # Skip the first order as it has no previous order to compare with
+        if o == 0:
+            continue
+
+        for i in range(len(f_n)):
+            try:
+                idx = np.nanargmin(np.abs(f_n1 - f_n[i]))
+
+                cond1 = np.abs(f_n[i] - f_n1[idx]) / f_n[i]
+                cond2 = np.abs(xi_n[i] - xi_n1[idx]) / xi_n[i]
+                cond3 = 1 - MAC(phi_n[i, :], phi_n1[idx, :])
+                if cond1 < err_fn and cond2 < err_xi and cond3 < err_phi:
+                    Lab[i, o] = 1  # Stable
+                else:
+                    Lab[i, o] = 0  # Nuovo polo o polo instabile
+            except Exception as e:
+                # If f_n[i] is nan, do nothin, n.b. the lab stays 0
+                logger.debug(e)
+    return Lab
diff --git a/src/methods/pyoma/ssiWrapper.py b/src/methods/pyoma/ssiWrapper.py
new file mode 100644
index 0000000..44803c1
--- /dev/null
+++ b/src/methods/pyoma/ssiWrapper.py
@@ -0,0 +1,409 @@
+import typing
+import logging
+
+from pyoma2.algorithms.data.result import SSIResult
+from pyoma2.algorithms.data.run_params import SSIRunParams
+from pyoma2.algorithms.base import BaseAlgorithm
+from pyoma2.functions import plot, ssi
+from pyoma2.support.sel_from_plot import SelFromPlot
+from methods.pyoma import genWrapper as gen
+
+class SSIdat(BaseAlgorithm[SSIRunParams, SSIResult, typing.Iterable[float]]):
+    """
+    Data-Driven Stochastic Subspace Identification (SSI) algorithm for single setup
+    analysis.
+
+    This class processes measurement data from a single setup experiment to identify
+    and extract modal parameters using the SSIdat-ref method.
+
+    Attributes
+    ----------
+    RunParamCls : Type[SSIRunParams]
+        The class of parameters specific to this algorithm's run.
+    ResultCls : Type[SSIResult]
+        The class of results produced by this algorithm.
+    method : str
+        The method used in this SSI algorithm, set to 'dat' by default.
+    """
+
+    RunParamCls = SSIRunParams
+    ResultCls = SSIResult
+    method: typing.Literal["dat"] = "dat"
+
+    def run(self) -> SSIResult:
+        """
+        Executes the SSIdat algorithm and returns the results.
+
+        Processes the input data using the Data-Driven Stochastic Subspace Identification method.
+        Computes state space matrices, modal parameters, and other relevant results.
+
+        Returns
+        -------
+        SSIResult
+            An object containing the computed matrices and modal parameters.
+        """
+        Y = self.data.T
+        br = self.run_params.br
+        method_hank = self.run_params.method or self.method
+        ordmin = self.run_params.ordmin
+        ordmax = self.run_params.ordmax
+        step = self.run_params.step
+        sc = self.run_params.sc
+        hc = self.run_params.hc
+        calc_unc = self.run_params.calc_unc
+        nb = self.run_params.nb
+
+        if self.run_params.ref_ind is not None:
+            ref_ind = self.run_params.ref_ind
+            Yref = Y[ref_ind, :]
+        else:
+            Yref = Y
+
+        # Build Hankel matrix
+        H, T = ssi.build_hank(
+            Y=Y, Yref=Yref, br=br, method=method_hank, calc_unc=calc_unc, nb=nb
+        )
+        # Get state matrix and output matrix
+        Obs, A, C, Q1, Q2, Q3, Q4 = ssi.SSI_fast(
+            H, br, ordmax, step=step, calc_unc=calc_unc, T=T, nb=nb
+        )
+
+        # Get frequency poles (and damping and mode shapes)
+        Fns, Xis, Phis, Lambds, Fn_cov, Xi_cov, Phi_cov = ssi.SSI_poles(
+            Obs,
+            A,
+            C,
+            ordmax,
+            self.dt,
+            step=step,
+            calc_unc=calc_unc,
+            Q1=Q1,
+            Q2=Q2,
+            Q3=Q3,
+            Q4=Q4,
+        )
+
+        hc_conj = hc["conj"]
+        hc_xi_max = hc["xi_max"]
+        # print(f'highest limit for damping: {hc_xi_max}')
+        hc_mpc_lim = hc["mpc_lim"]
+        hc_mpd_lim = hc["mpd_lim"]
+        hc_cov_max = hc["cov_max"]
+        
+        
+        # Criteria regarding eigenvalue stability
+        # HC - remove eigevalues with positive real part
+        Lambds, mask6 = gen.HC_realEigen(Lambds)
+        lista = [Fns, Xis, Phis, Fn_cov, Xi_cov, Phi_cov]
+        Fns, Xis, Phis, Fn_cov, Xi_cov, Phi_cov = gen.applymask(
+            lista, mask6, Phis.shape[2]
+            )
+        
+        # Criteria regarding zero imaginary part in eigenvalues
+        # HC - remove eigenvalues with zero imaginary part of eigenvalues
+        Lambds, mask7 = gen.HC_removeZeroImg(Lambds)
+        lista = [Fns, Xis, Phis, Fn_cov, Xi_cov, Phi_cov]
+        Fns, Xis, Phis, Fn_cov, Xi_cov, Phi_cov = gen.applymask(
+            lista, mask7, Phis.shape[2]
+            )
+       
+      
+        # Get the labels of the poles
+        Lab = gen.SC_apply(
+            Fns,
+            Xis,
+            Phis,
+            ordmin,
+            ordmax,
+            step,
+            sc["err_fn"],
+            sc["err_xi"],
+            sc["err_phi"],
+        )
+
+        return SSIResult(
+            Obs=Obs,
+            A=A,
+            C=C,
+            H=H,
+            Lambds=Lambds,
+            Fn_poles=Fns,
+            Xi_poles=Xis,
+            Phi_poles=Phis,
+            Lab=Lab,
+            Fn_poles_cov=Fn_cov,
+            Xi_poles_cov=Xi_cov,
+            Phi_poles_cov=Phi_cov,
+        )
+
+    def mpe(
+        self,
+        sel_freq: typing.List[float],
+        order: typing.Union[int, str] = "find_min",
+        rtol: float = 5e-2,
+    ) -> typing.Any:
+        """
+        Extracts the modal parameters at the selected frequencies.
+
+        Parameters
+        ----------
+        sel_freq : list of float
+            Selected frequencies for modal parameter extraction.
+        order : int or str, optional
+            Model order for extraction, or 'find_min' to auto-determine the minimum stable order.
+            Default is 'find_min'.
+        rtol : float, optional
+            Relative tolerance for comparing frequencies. Default is 5e-2.
+
+        Returns
+        -------
+        typing.Any
+            The extracted modal parameters. The format and content depend on the algorithm's implementation.
+        """
+        super().mpe(sel_freq=sel_freq, order=order, rtol=rtol)
+
+        # Save run parameters
+        self.run_params.sel_freq = sel_freq
+        self.run_params.order_in = order
+        self.run_params.rtol = rtol
+
+        # Get poles
+        Fn_pol = self.result.Fn_poles
+        Xi_pol = self.result.Xi_poles
+        Phi_pol = self.result.Phi_poles
+        Lab = self.result.Lab
+        # Get cov
+        Fn_pol_cov = self.result.Fn_poles_cov
+        Xi_pol_cov = self.result.Xi_poles_cov
+        Phi_pol_cov = self.result.Phi_poles_cov
+        # Extract modal results
+        Fn, Xi, Phi, order_out, Fn_cov, Xi_cov, Phi_cov = ssi.SSI_mpe(
+            sel_freq,
+            Fn_pol,
+            Xi_pol,
+            Phi_pol,
+            order,
+            Lab=Lab,
+            rtol=rtol,
+            Fn_cov=Fn_pol_cov,
+            Xi_cov=Xi_pol_cov,
+            Phi_cov=Phi_pol_cov,
+        )
+
+        # Save results
+        self.result.order_out = order_out
+        self.result.Fn = Fn
+        self.result.Xi = Xi
+        self.result.Phi = Phi
+        self.result.Fn_cov = Fn_cov
+        self.result.Xi_cov = Xi_cov
+        self.result.Phi_cov = Phi_cov
+
+    def mpe_from_plot(
+        self,
+        freqlim: typing.Optional[tuple[float, float]] = None,
+        rtol: float = 1e-2,
+    ) -> typing.Any:
+        """
+        Interactive method for extracting modal parameters by selecting frequencies from a plot.
+
+        Parameters
+        ----------
+        freqlim : tuple of float, optional
+            Frequency limits for the plot. If None, limits are determined automatically. Default is None.
+        rtol : float, optional
+            Relative tolerance for comparing frequencies. Default is 1e-2.
+
+        Returns
+        -------
+        typing.Any
+            The extracted modal parameters after interactive selection. Format depends on algorithm's
+            implementation.
+        """
+        super().mpe_from_plot(freqlim=freqlim, rtol=rtol)
+
+        # Save run parameters
+        self.run_params.rtol = rtol
+
+        # Get poles
+        Fn_pol = self.result.Fn_poles
+        Xi_pol = self.result.Xi_poles
+        Phi_pol = self.result.Phi_poles
+        # Get cov
+        Fn_pol_cov = self.result.Fn_poles_cov
+        Xi_pol_cov = self.result.Xi_poles_cov
+        Phi_pol_cov = self.result.Phi_poles_cov
+
+        # call interactive plot
+        SFP = SelFromPlot(algo=self, freqlim=freqlim, plot="SSI")
+        sel_freq = SFP.result[0]
+        order = SFP.result[1]
+
+        # and then extract results
+        Fn, Xi, Phi, order_out, Fn_cov, Xi_cov, Phi_cov = ssi.SSI_mpe(
+            sel_freq,
+            Fn_pol,
+            Xi_pol,
+            Phi_pol,
+            order,
+            Lab=None,
+            rtol=rtol,
+            Fn_cov=Fn_pol_cov,
+            Xi_cov=Xi_pol_cov,
+            Phi_cov=Phi_pol_cov,
+        )
+
+        # Save results
+        self.result.order_out = order_out
+        self.result.Fn = Fn
+        self.result.Xi = Xi
+        self.result.Phi = Phi
+        self.result.Fn_cov = Fn_cov
+        self.result.Xi_cov = Xi_cov
+        self.result.Phi_cov = Phi_cov
+
+    def plot_stab(
+        self,
+        freqlim: typing.Optional[tuple[float, float]] = None,
+        hide_poles: typing.Optional[bool] = True,
+    ) -> typing.Any:
+        """
+        Plot the Stability Diagram for the SSI algorithms.
+
+        The Stability Diagram helps visualize the stability of identified poles across different
+        model orders, making it easier to separate physical poles from spurious ones.
+
+        Parameters
+        ----------
+        freqlim : tuple of float, optional
+            Frequency limits for the plot. If None, limits are determined automatically. Default is None.
+        hide_poles : bool, optional
+            Option to hide poles in the plot for clarity. Default is True.
+
+        Returns
+        -------
+        typing.Any
+            A tuple containing the matplotlib figure and axes of the Stability Diagram plot.
+        """
+        if not self.result:
+            raise ValueError("Run algorithm first")
+
+        fig, ax = plot.stab_plot(
+            Fn=self.result.Fn_poles,
+            Lab=self.result.Lab,
+            step=self.run_params.step,
+            ordmax=self.run_params.ordmax,
+            ordmin=self.run_params.ordmin,
+            freqlim=freqlim,
+            hide_poles=hide_poles,
+            fig=None,
+            ax=None,
+            Fn_cov=self.result.Fn_poles_cov,
+        )
+        return fig, ax
+
+    def plot_cluster(
+        self,
+        freqlim: typing.Optional[tuple[float, float]] = None,
+        hide_poles: typing.Optional[bool] = True,
+    ) -> typing.Any:
+        """
+        Plot the frequency-damping cluster diagram for the identified modal parameters.
+
+        The cluster diagram visualizes the relationship between frequencies and damping
+        ratios for the identified poles, helping to identify clusters of physical modes.
+
+        Parameters
+        ----------
+        freqlim : tuple of float, optional
+            Frequency limits for the plot. If None, limits are determined automatically. Default is None.
+        hide_poles : bool, optional
+            Option to hide poles in the plot for clarity. Default is True.
+
+        Returns
+        -------
+        typing.Any
+            A tuple containing the matplotlib figure and axes of the cluster diagram plot.
+        """
+        if not self.result:
+            raise ValueError("Run algorithm first")
+
+        fig, ax = plot.cluster_plot(
+            Fn=self.result.Fn_poles,
+            Xi=self.result.Xi_poles,
+            Lab=self.result.Lab,
+            ordmin=self.run_params.ordmin,
+            freqlim=freqlim,
+            hide_poles=hide_poles,
+        )
+        return fig, ax
+
+    def plot_svalH(
+        self,
+        iter_n: typing.Optional[int] = None,
+    ) -> typing.Any:
+        """
+        Plot the singular values of the Hankel matrix for the SSI algorithm.
+
+        This plot is useful for checking the influence of the number of block-rows, br,
+        on the Singular Values of the Hankel matrix.
+
+        Parameters
+        ----------
+        iter_n : int, optional
+            The iteration number for which to plot the singular values. If None, the last
+            iteration is used. Default is None.
+
+        Returns
+        -------
+        typing.Any
+            A tuple containing the matplotlib figure and axes of the singular value plot.
+
+        Raises
+        ------
+        ValueError
+            If the algorithm has not been run before plotting.
+        """
+        if not self.result:
+            raise ValueError("Run algorithm first")
+
+        fig, ax = plot.svalH_plot(H=self.result.H, br=self.run_params.br, iter_n=iter_n)
+        return fig, ax
+
+
+
+
+
+
+
+
+
+
+class SSIcov(SSIdat):
+    """
+    Implements the Covariance-driven Stochastic Subspace Identification (SSI) algorithm
+    for single setup experiments.
+
+    This class is an extension of the SSIdat class, adapted for covariance-driven analysis.
+    It processes measurement data from a single setup to identify system dynamics and extract
+    modal parameters using the SSIcov-ref method.
+
+    Inherits all attributes and methods from SSIdat.
+
+    Attributes
+    ----------
+    method : str
+        The method used in this SSI algorithm, overridden to 'cov_bias', 'cov_mm', or 'cov_unb' for
+        covariance-based analysis.
+
+    Methods
+    -------
+    Inherits all methods from SSIdat with covariance-specific implementations.
+    """
+
+    method: typing.Literal["cov_R", "cov_mm"] = "cov_mm"
+
+
+
+
+
diff --git a/src/methods/sys_id.py b/src/methods/sys_id.py
new file mode 100644
index 0000000..4ec7402
--- /dev/null
+++ b/src/methods/sys_id.py
@@ -0,0 +1,162 @@
+import time
+import json
+from datetime import datetime
+from typing import Any, Dict, Optional, Tuple
+from paho.mqtt.client import Client as MQTTClient
+from pyoma2.setup.single import SingleSetup
+from functions.util import convert_numpy_to_list
+from data.accel.metadata import extract_fs_from_metadata
+from data.comm.mqtt import setup_mqtt_client
+from data.accel.hbk.aligner import Aligner
+from methods.pyoma.ssiWrapper import SSIcov
+from methods.constants import MODEL_ORDER, BLOCK_SHIFT, DEFAULT_FS
+
+
+
+def sysid(data, params):
+    """
+    Perform system identification using the Covariance-based
+            Stochastic Subspace Identification (SSI-COV) method.
+
+    Args:
+        data (numpy.ndarray): Input time-series data, where rows represent time steps and
+                              columns represent different sensor channels.
+        params (dict): Dictionary containing parameters for the system identification process:
+            - 'Fs' (float): Sampling frequency of the input data.
+            - 'block_shift' (int): Block shift parameter for the SSI algorithm.
+            - 'model_order' (int): Maximum model order for the system identification.
+
+    Returns:
+        tuple: Contains identified model parameters (frequencies, cov_freq, damping_ratios,
+               cov_damping, mode_shapes, poles_label).
+    """
+    if data.shape[0]<data.shape[1]:
+        data = data.T                           # transpose it if data has more column than rows
+    print(f"Data dimensions: {data.shape}")
+    print(f"OMA parameters: {params}")
+
+    my_setup = SingleSetup(data, fs=params['Fs'])
+    ssi_mode_track = SSIcov(
+        name="SSIcovmm_mt",
+        method='cov_mm',
+        br=params['block_shift'],
+        ordmax=params['model_order'],
+        calc_unc=True
+    )
+
+    my_setup.add_algorithms(ssi_mode_track)
+    my_setup.run_by_name("SSIcovmm_mt")
+
+    output = ssi_mode_track.result.model_dump()
+    return {
+        'Fn_poles': output['Fn_poles'],
+        'Fn_poles_cov': output['Fn_poles_cov'],
+        'Xi_poles': output['Xi_poles'],
+        'Xi_poles_cov': output['Xi_poles_cov'],
+        'Phi_poles': output['Phi_poles'],
+        'Lab': output['Lab']
+    }
+
+
+def setup_client(mqtt_config: Dict[str, Any]) -> Tuple[MQTTClient, float]:
+    """
+    Sets up and starts the MQTT client for subscribing to sensor data.
+    Also extracts sampling frequency from metadata if available.
+
+    Args:
+        mqtt_config: Configuration dictionary for the MQTT client.
+
+    Returns:
+        A tuple of the connected MQTTClient instance and the extracted sampling frequency.
+    """
+    if len(mqtt_config.get("topics", [])) > 1:
+        fs = extract_fs_from_metadata(mqtt_config)
+    else:
+        fs = DEFAULT_FS
+
+    data_client, _ = setup_mqtt_client(mqtt_config, topic_index=0)
+    data_client.connect(mqtt_config["host"], mqtt_config["port"], 60)
+    data_client.loop_start()
+    return data_client, fs
+
+
+def get_oma_results(
+        sampling_period: int, aligner: Aligner, fs: float
+        ) -> Optional[Tuple[Dict[str, Any], datetime]]:
+    """
+    Extracts aligned sensor data and runs system identification (sysID).
+
+    Args:
+        sampling_period: How many minutes of data to pass to sysid.
+        aligner: An initialized Aligner object.
+        fs: Sampling frequency to use in the OMA algorithm.
+
+    Returns:
+        A tuple (OMA_output, timestamp) if successful, or None if data is not ready.
+    """
+    oma_params = {
+        "Fs": fs,
+        "block_shift": BLOCK_SHIFT, 
+        "model_order": MODEL_ORDER  
+    }
+
+    number_of_samples = int(sampling_period * 60 * fs)
+    data, timestamp = aligner.extract(number_of_samples)
+
+    if data.size < number_of_samples:
+        return None, None
+
+    try:
+        oma_output = sysid(data, oma_params)
+        return oma_output, timestamp
+    except Exception as e:
+        print(f"sysID failed: {e}")
+        return None, None
+
+
+def publish_oma_results(sampling_period: int, aligner: Aligner,
+                        publish_client: MQTTClient, publish_topic: str,
+                        fs: float) -> None:
+    """
+    Repeatedly tries to get aligned data and publish OMA results once.
+
+    Args:
+        sampling_period: Duration (in minutes) of data to extract.
+        aligner: Aligner object that provides synchronized sensor data.
+        publish_client: MQTT client used for publishing results.
+        publish_topic: The MQTT topic to publish results to.
+        fs: Sampling frequency.
+    """
+    while True:
+        try:
+            time.sleep(0.5)
+            oma_output, timestamp = get_oma_results(sampling_period, aligner, fs)
+            print(f"OMA result: {oma_output}")
+            print(f"Timestamp: {timestamp}")
+
+            if oma_output:
+                payload = {
+                    "timestamp": timestamp.isoformat(),
+                    "OMA_output": convert_numpy_to_list(oma_output)
+                }
+                try:
+                    message = json.dumps(payload)
+
+                    if not publish_client.is_connected():
+                        print("Publisher disconnected. Reconnecting...")
+                        publish_client.reconnect()
+
+                    publish_client.publish(publish_topic, message, qos=1)
+                    print(f"[{timestamp.isoformat()}] Published OMA result to {publish_topic}")
+                    break
+
+                except Exception as e:
+                    print(f"Failed to publish OMA result: {e}")
+        except KeyboardInterrupt:
+            print("Shutting down gracefully")
+            aligner.client.loop_stop()
+            aligner.client.disconnect()
+            publish_client.disconnect()
+            break
+        except Exception as e:
+            print(f"Unexpected error: {e}")
diff --git a/tests/integration/input_data/Acc_4DOF.txt b/tests/integration/input_data/Acc_4DOF.txt
new file mode 100644
index 0000000..f6d5f51
--- /dev/null
+++ b/tests/integration/input_data/Acc_4DOF.txt
@@ -0,0 +1,3 @@
+  -2.0617137e+01  -1.3348853e+02  -1.1142522e+02  -2.4942656e+02  -2.1535569e+02  -3.4123537e+02  -3.2953196e+02  -3.6546598e+02  -4.9860487e+02  -4.8357942e+02  -5.3621571e+02  -5.2121651e+02  -4.8415762e+02  -4.2998413e+02  -5.4837938e+02  -4.9937988e+02  -4.3526913e+02  -3.3053113e+02  -2.9028077e+02  -3.8307399e+02  -3.5020377e+02  -3.7891202e+02  -3.1225635e+02  -2.3503565e+02  -2.1131191e+02  -3.6025573e+02  -1.7659176e+02  -4.5977025e+01  -1.1313811e+02  -1.9520100e+02  -1.4637022e+02  -2.7478330e+02  -1.8951365e+02  -6.8729513e+01  -9.0088482e+01  -7.1800261e+01   9.4706232e+01   4.2547498e+01   1.0696451e+02   5.7306220e+01   4.1919757e+02   4.4587066e+02   5.4130056e+02   5.4088838e+02   2.9002861e+02   4.7071384e+02   4.2818368e+02   2.6937567e+02   2.4894928e+02   3.1989378e+02   3.0692715e+02   2.6506189e+02   2.4730689e+02   1.1749052e+02   3.4360325e+01   9.2281785e+01   1.3459127e+02   9.7296308e+01   5.5997079e+00   1.2155888e+01   3.2911973e+00   5.6594770e-01  -6.4898418e+01   2.6163514e+01   9.4943039e+00   1.0123482e+02  -1.5728419e+01  -2.2529713e+02  -1.9712590e+02  -2.4531853e+02  -4.1497367e+02  -3.1409857e+02  -3.7555563e+02  -4.9285196e+02  -4.1193765e+02  -6.3269256e+02  -4.8906147e+02  -4.5337794e+02  -3.8028741e+02  -4.1397792e+02  -6.3093998e+02  -5.7658246e+02  -6.0153087e+02  -7.0706649e+02  -7.4675577e+02  -8.6272187e+02  -5.5532123e+02  -5.7116131e+02  -5.3271335e+02  -4.1557155e+02  -4.5370081e+02  -3.6431442e+02  -3.6764722e+02  -2.6891343e+02  -2.2565361e+02  -2.8726644e+02  -3.2387543e+02  -2.2121929e+02  -2.2600609e+02  -2.3845899e+02   4.6504008e+01   2.2972158e+02   4.4238749e+02   4.9181601e+02   5.6151017e+02   6.1410058e+02   7.3357567e+02   6.2911066e+02   6.6211943e+02   7.4590313e+02   9.2123822e+02   9.3412779e+02   8.5059674e+02   8.8723392e+02   8.8266766e+02   8.9362039e+02   9.4278055e+02   8.9763546e+02   8.7644057e+02   8.3807149e+02   9.8573352e+02   8.8820458e+02   8.9274830e+02   1.0065229e+03   9.2787642e+02   1.0493807e+03   1.0242318e+03   1.1326782e+03   1.0332724e+03   1.0645770e+03   1.1012127e+03   1.0665079e+03   8.6380023e+02   8.7018687e+02   8.7011555e+02   9.1648667e+02   1.2170150e+03   1.2180654e+03   1.2363745e+03   1.1717774e+03   1.0707835e+03   1.1142798e+03   1.0913355e+03   9.7636087e+02   9.9433225e+02   9.2178043e+02   9.0859134e+02   7.4452589e+02   4.5973145e+02   5.1963929e+02   3.1876960e+02   2.4744246e+02   2.8959621e+02   1.6243552e+02   4.7403828e+01   3.0029647e+02   3.0223944e+02   4.5433971e+02   6.9381274e+02   8.4315351e+02   9.4459804e+02   8.8219837e+02   7.1447755e+02   7.2533193e+02   5.9881745e+02   6.2360430e+02   3.6037563e+02   2.8831672e+02   1.5864395e+02   2.8638426e+02   1.5349355e+02   2.0912589e+02   1.6634980e+02   2.0063266e+02   7.7733199e+01  -2.8354922e+01  -1.0667969e+02  -3.1181088e+02  -3.6446918e+02  -6.0819573e+02  -5.5481282e+02  -3.7899935e+02  -4.2607161e+02  -4.8271082e+02  -6.0401290e+02  -5.1935874e+02  -5.3926461e+02  -7.2059980e+02  -6.4868527e+02  -7.1684023e+02  -8.8571047e+02  -9.2760229e+02  -9.8018780e+02  -9.6444308e+02  -9.8963090e+02  -1.0894842e+03  -1.0899665e+03  -1.0327688e+03  -1.0831046e+03  -1.2834247e+03  -1.4534175e+03  -1.4477711e+03  -1.4006080e+03  -1.4681404e+03  -1.5325245e+03  -1.4739315e+03  -1.5820429e+03  -1.5519367e+03  -1.4694714e+03  -1.4608150e+03  -1.2977100e+03  -1.5329757e+03  -1.4283966e+03  -1.3465149e+03  -1.3159817e+03  -1.2544294e+03  -1.4071032e+03  -1.4536547e+03  -1.4389553e+03  -1.4275052e+03  -1.5933073e+03  -1.5488148e+03  -1.6789382e+03  -1.6557030e+03  -1.7983628e+03  -1.6788162e+03  -1.8353355e+03  -1.7060874e+03  -1.7395993e+03  -1.8457191e+03  -1.9035514e+03  -1.9093628e+03  -1.9995208e+03  -2.0128280e+03  -2.0129962e+03  -1.9851914e+03  -1.9556594e+03  -2.0377450e+03  -2.0438393e+03  -1.9934879e+03  -2.0129017e+03  -1.7739035e+03  -1.5956100e+03  -1.5329987e+03  -1.5649304e+03  -1.5294611e+03  -1.5217677e+03  -1.2275153e+03  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7.2441774e+02   8.7512340e+02   1.1500224e+03   1.2697800e+03   1.2442708e+03   1.4317125e+03   1.5221416e+03   1.4765947e+03   1.4466548e+03   1.4046788e+03   1.3174550e+03   1.1570524e+03   1.2577755e+03   1.1106665e+03   9.2412822e+02   8.9444825e+02   7.8339464e+02   6.9497184e+02   6.5378313e+02   6.2136679e+02   5.4356001e+02   4.8200755e+02   6.3004130e+02   7.5530329e+02   1.0465682e+03   1.0525129e+03   1.3349428e+03   1.4675765e+03   1.7005006e+03   1.5453464e+03   1.5549760e+03   1.5346382e+03   1.3611054e+03   1.1934959e+03   1.1383923e+03   9.3895631e+02   9.4749456e+02   8.2783116e+02   7.9213935e+02   8.0905973e+02   7.4873083e+02   5.6946898e+02   2.6747427e+02   1.3456037e+02   2.5104767e+02   3.4193118e+02   5.1415766e+02   2.0942014e+02   6.5259930e+01   1.4060009e+02   1.1126460e+02   2.5956068e+02   2.4966455e+02   6.0796647e+01  -8.6465564e+01  -5.2578919e+01  -5.5853358e+01   6.4383718e+00  -3.0353936e+02  -2.9352933e+02  -3.1362148e+02  -5.4655172e+02  -5.3338581e+02  -5.3525181e+02  -5.6573817e+02  -4.7393847e+02  -5.9962851e+02  -7.8409680e+02  -7.7753250e+02  -7.2781290e+02  -8.8168404e+02  -6.3320723e+02  -6.8852701e+02  -5.1608717e+02  -7.3844931e+02  -6.5187605e+02  -8.3222544e+02  -8.6465747e+02  -8.9873459e+02  -8.8067286e+02  -8.3184038e+02  -8.0150360e+02  -8.3436751e+02  -7.3604805e+02  -7.4754960e+02  -6.1655897e+02  -4.6919789e+02  -4.0132408e+02  -4.5964688e+02  -4.5733366e+02
diff --git a/tests/integration/input_data/expected_sysid_output.npz b/tests/integration/input_data/expected_sysid_output.npz
new file mode 100644
index 0000000..641df3f
Binary files /dev/null and b/tests/integration/input_data/expected_sysid_output.npz differ
diff --git a/tests/integration/input_data/test_data.md b/tests/integration/input_data/test_data.md
new file mode 100644
index 0000000..eaa0055
--- /dev/null
+++ b/tests/integration/input_data/test_data.md
@@ -0,0 +1,21 @@
+## Data for Testing the System Identification (sysID)
+
+Acc_4DOF.txt:
+    This file contains acceleration time-series data for a 4-degree-of-freedom (4-DOF) system.
+    Format:
+        - Each row represents a separate sensor channel.
+        - Each column represents a time sample.
+        
+expected_sysid_output.npz:
+    This file stores the expected output from running the `sysid()` function on the Acc_4DOF data.
+    It contains the following
+        - frequencies       : Identified natural frequencies (Fn_poles)
+        - cov_freq          : Covariance of frequencies (Fn_poles_cov)
+        - damping_ratios    : Identified damping ratios (Xi_poles)
+        - cov_damping       : Covariance of damping ratios (Xi_poles_cov)
+        - mode_shapes       : Identified mode shapes (Phi_poles)
+        - poles_label       : Labels or indices of modal poles (Lab)
+
+The `Acc_4DOF` data is fed into the system identification algorithm from an external file.
+
+The output is captured and saved in `expected_sysid_output`. Since the `sysid()` function is deterministic, it should always produce the same output. This output is used to verify that `sysid()` in this project behaves as expected.
diff --git a/tests/integration/methods/test_sys_id.py b/tests/integration/methods/test_sys_id.py
new file mode 100644
index 0000000..c047e0b
--- /dev/null
+++ b/tests/integration/methods/test_sys_id.py
@@ -0,0 +1,111 @@
+import pytest
+import numpy as np
+from datetime import datetime
+from unittest.mock import MagicMock
+
+from methods import sys_id
+
+def test_sysid():
+    # Define OMA parameters
+    oma_params = {
+        "Fs": 100,  # Sampling frequency in Hz
+        "block_shift": 30,  # Block shift parameter
+        "model_order": 20  # Model order
+    }
+    
+    # Load test data
+    data = np.loadtxt('tests/integration/input_data/Acc_4DOF.txt').T
+
+    # Perform system identification
+    sysid_output = sys_id.sysid(data, oma_params)
+    
+    # Extract results using dictionary keys
+    frequencies = sysid_output['Fn_poles']
+    cov_freq = sysid_output['Fn_poles_cov']
+    damping_ratios = sysid_output['Xi_poles']
+    cov_damping = sysid_output['Xi_poles_cov']
+    mode_shapes = sysid_output['Phi_poles']
+    poles_label = sysid_output['Lab']
+
+    # Load stored reference results
+    stored_data = np.load('tests/integration/input_data/expected_sysid_output.npz')
+    stored_frequencies = stored_data['frequencies']
+    stored_cov_freq = stored_data['cov_freq']
+    stored_damping_ratios = stored_data['damping_ratios']
+    stored_cov_damping = stored_data['cov_damping']
+    stored_mode_shapes = stored_data['mode_shapes']
+    stored_poles_label = stored_data['poles_label']
+
+
+    tolerance = 0.4
+    assert np.allclose(frequencies, stored_frequencies, atol=tolerance, equal_nan=True), "Frequencies do not match!"
+    assert np.allclose(cov_freq, stored_cov_freq, atol=tolerance, equal_nan=True), "Covariance frequencies do not match!"
+    assert np.allclose(damping_ratios, stored_damping_ratios, atol=tolerance, equal_nan=True), "Damping ratios do not match!"
+    assert np.allclose(cov_damping, stored_cov_damping, atol=tolerance, equal_nan=True), "Covariance damping ratios do not match!"
+    assert np.allclose(mode_shapes, stored_mode_shapes, atol=tolerance, equal_nan=True), "Mode shapes do not match!"
+    assert np.array_equal(poles_label, stored_poles_label), "Pole labels do not match!"
+
+
+def test_sysid_full_flow_success():
+    """
+    Simulates full OMA flow: aligned data → sysid → conversion to JSON-safe format.
+    """
+    # Simulate 600 samples, 3 channels (e.g., 1 min * 10 Hz)
+    data = np.random.randn(3, 600)
+
+    oma_params = {
+        "Fs": 100,
+        "block_shift": 30,
+        "model_order": 20
+    }
+
+    oma_result = sys_id.sysid(data, oma_params)
+
+    # Check output structure
+    assert isinstance(oma_result, dict)
+    for key in ["Fn_poles", "Xi_poles", "Phi_poles"]:
+        assert key in oma_result
+        assert isinstance(oma_result[key], list) or isinstance(oma_result[key], np.ndarray)
+
+    # Convert to JSON-safe structure
+    converted = sys_id.convert_numpy_to_list(oma_result)
+    assert isinstance(converted, dict)
+    assert isinstance(converted["Fn_poles"], list)
+
+
+def test_get_oma_results_integration(mocker):
+    from datetime import datetime
+    import numpy as np
+    from methods import sys_id
+
+    fs = 100  # sampling frequency
+    mock_aligner = MagicMock()
+
+    number_of_minutes = 0.1
+    samples = int(fs * 60 * number_of_minutes)  # 600 samples
+    mock_data = np.random.randn(samples, 3)
+    mock_timestamp = datetime.now()
+
+    mock_aligner.extract.return_value = (mock_data, mock_timestamp)
+
+    oma_output, timestamp = sys_id.get_oma_results(number_of_minutes, mock_aligner, fs)
+
+    assert isinstance(oma_output, dict)
+    assert "Fn_poles" in oma_output
+    assert timestamp == mock_timestamp
+
+
+def test_sysid_raises_on_empty_data():
+    """
+    SSI should raise an error if data is empty (simulating a low-data scenario).
+    """
+    data = np.empty((0, 3))  # No samples
+
+    oma_params = {
+        "Fs": 10.0,
+        "block_shift": 5,
+        "model_order": 6
+    }
+
+    with pytest.raises(Exception):
+        sys_id.sysid(data, oma_params)
diff --git a/tests/unit/methods/test_sys_id_unit.py b/tests/unit/methods/test_sys_id_unit.py
new file mode 100644
index 0000000..22efc4a
--- /dev/null
+++ b/tests/unit/methods/test_sys_id_unit.py
@@ -0,0 +1,146 @@
+import pytest
+import numpy as np
+from unittest.mock import MagicMock, patch
+from datetime import datetime
+import json
+from methods.sys_id import (
+    sysid,
+    get_oma_results,
+    publish_oma_results,
+    setup_client,
+)
+from paho.mqtt.client import Client as MQTTClient
+
+
+@pytest.fixture
+def sample_data():
+    return np.random.randn(600, 3)
+
+
+@pytest.fixture
+def oma_params():
+    return {
+        "Fs": 100.0,
+        "block_shift": 5,
+        "model_order": 6
+    }
+
+
+def test_sysid_returns_expected_keys(sample_data, oma_params):
+    result = sysid(sample_data, oma_params)
+    assert isinstance(result, dict)
+    expected_keys = {'Fn_poles', 'Fn_poles_cov', 'Xi_poles', 'Xi_poles_cov', 'Phi_poles', 'Lab'}
+    assert expected_keys.issubset(result.keys())
+
+
+def test_sysid_transposes_data_if_needed(oma_params):
+    data = np.random.randn(3, 600)  # More columns than rows
+    result = sysid(data, oma_params)
+    assert isinstance(result, dict)
+    assert "Fn_poles" in result
+
+def test_get_oma_results_success(mocker):
+    fs = 100
+    samples_needed = int(fs * 60 * 0.1)  # 600
+    mock_data = np.random.randn(samples_needed, 3)
+    mock_aligner = MagicMock()
+    mock_aligner.extract.return_value = (mock_data, datetime.now())
+
+    result, ts = get_oma_results(0.1, mock_aligner, fs)
+
+    assert result is not None
+    assert "Fn_poles" in result
+
+def test_get_oma_results_no_data(mocker):
+    fs = 100
+    mock_aligner = MagicMock()
+    mock_aligner.extract.return_value = (np.empty((0, 3)), datetime.now())
+
+    result, ts = get_oma_results(1, mock_aligner, fs)
+
+    assert result is None
+    assert ts is None
+
+
+def test_get_oma_results_not_enough_samples(mocker):
+    fs = 100
+    mock_aligner = MagicMock()
+    data = np.random.randn(100, 3)
+    mock_aligner.extract.return_value = (data, datetime.now())
+
+    result, ts = get_oma_results(10, mock_aligner, fs)  # ask for too many samples
+
+    assert result is None
+    assert ts is None
+
+
+def test_get_oma_results_sysid_failure(mocker):
+    fs = 100
+    mock_aligner = MagicMock()
+    mock_aligner.extract.return_value = (np.random.randn(600, 3), datetime.now())
+
+    mocker.patch("methods.sys_id.sysid", side_effect=Exception("fail"))
+
+    result, ts = get_oma_results(1, mock_aligner, fs)
+
+    assert result is None
+    assert ts is None
+
+
+def test_publish_oma_results_retries_and_publishes_once(mocker):
+    fs = 100
+    dummy_result = {
+        'Fn_poles': np.array([[3.2, 3.3], [3.4, 3.5]]),
+        'Fn_poles_cov': np.array([[0.1, 0.1], [0.1, 0.1]]),
+        'Xi_poles': np.array([[0.02, 0.03], [0.04, 0.05]]),
+        'Xi_poles_cov': np.array([[0.001, 0.001], [0.001, 0.001]]),
+        'Phi_poles': np.array([[1.0, 0.0], [0.0, 1.0]]),
+        'Lab': ['mode1', 'mode2']
+    }
+
+    mocker.patch("methods.sys_id.time.sleep", return_value=None)
+
+    mocker.patch(
+        "methods.sys_id.get_oma_results",
+        side_effect=[
+            (None, None),
+            (dummy_result, datetime(2024, 1, 1))
+        ]
+    )
+
+    mocker.patch(
+        "methods.sys_id.convert_numpy_to_list",
+        return_value={k: v.tolist() if hasattr(v, "tolist") else v for k, v in dummy_result.items()}
+    )
+
+    mock_client = MagicMock(spec=MQTTClient)
+    mock_client.is_connected.return_value = True
+    aligner = MagicMock()
+    aligner.client = MagicMock()
+
+    publish_oma_results(0.1, aligner, mock_client, "test/topic", fs)
+
+    assert mock_client.publish.called
+    assert mock_client.publish.call_count == 1
+    assert mock_client.publish.call_args[0][0] == "test/topic"
+
+
+def test_setup_client_with_multiple_topics(mocker):
+    mqtt_config = {
+        "host": "localhost",
+        "port": 1883,
+        "topics": ["topic1", "topic2"]
+    }
+
+    extract_mock = mocker.patch("methods.sys_id.extract_fs_from_metadata", return_value=123.0)
+
+    mock_mqtt_client = MagicMock()
+    mocker.patch("methods.sys_id.setup_mqtt_client", return_value=(mock_mqtt_client, None))
+
+    client, fs = setup_client(mqtt_config)
+
+    extract_mock.assert_called_once_with(mqtt_config)
+    client.connect.assert_called_once_with("localhost", 1883, 60)
+    client.loop_start.assert_called_once()
+    assert client == mock_mqtt_client
+    assert fs == 123.0