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A critical feature of this dataset is the implementation of a wireless local synchronization system across all nodes, ensuring a common timestamp reference for precise spatiotemporal analysis of the acoustic data.</p>\n<h3>Description of the Files</h3>\n<ul>\n<li><strong>Trial 1: 2026.03.04</strong>\n<ul>\n<li><em>2026_03_04_datasets.zip</em>: dataset</li>\n<li><em>2026_03_04_drone_dji_top_view.mp4:</em> video from the drone camera at the top of the experiment</li>\n</ul>\n</li>\n<li><strong>Trial 2: 2026.04.20</strong>\n<ul>\n<li><em>2026_04_20_datasets.zip</em><strong>:</strong> dataset</li>\n<li><em>2026_04_20_top_view.zip</em>: video from the drone camera at the top of the experiment</li>\n</ul>\n</li>\n</ul>\n<h3><strong>Experimental Setup &amp; Hardware</strong></h3>\n<p>The experimental architecture consists of four primary nodes&mdash;three stationary sensory buoys and one mobile ground-truth vessel.</p>\n<ul>\n<li><strong>Sensory Nodes (x3 USV Buoys):</strong>\n<ul>\n<li><strong>Acoustic Sensors:</strong> Each buoy is equipped with an array of three H1a Hydrophones (Aquarian Audio).</li>\n<li><strong>Acquisition &amp; Compute:</strong> Acoustic signals are processed via a HiFiBerry DAC+ ADC HAT connected to a Raspberry Pi.</li>\n<li><strong>Synchronization:</strong> A SmartMesh (LTC5800) wireless synchronization system is deployed across all nodes, providing a common time-span reference updating at a frequency of 2Hz.</li>\n</ul>\n</li>\n</ul>\n<div>\n<ul>\n<li><strong>Ground-Truth Node (x1 Vessel):</strong>\n<ul>\n<li><strong>Vessel Specifications:</strong> A 4-meter boat powered by a petrol engine, acting as the primary acoustic source for validation and testing.</li>\n<li><strong>Spatial Tracking:</strong> Equipped with a RTK (Real-Time Kinematic) GPS module for high-precision localization.</li>\n<li><strong>Synchronization:</strong> Integrated with the same SmartMesh (LTC5800) system to perfectly align the boat's spatial data and engine noise events with the acoustic data captured by the sensory buoys.</li>\n</ul>\n</li>\n</ul>\n</div>\n<h3><strong>Data Structure &amp; Formats</strong></h3>\n<p>The dataset provides raw and processed data streams across multiple standard formats to facilitate robotic framework integration and standalone signal processing.</p>\n<ul>\n<li><strong>ROS 2 Bags (</strong><code><strong>.db3</strong></code><strong>):</strong> Contains the complete serialized message streams for all sensor topics, allowing for full playback of the experimental trials within a Robot Operating System 2 (ROS 2) environment.</li>\n<li><strong>CSV Files (</strong><code><strong>.csv</strong></code><strong>):</strong> Extracted tabular data for straightforward data analysis. These files contain:\n<ul>\n<li>High-precision RTK GPS positioning (Ground Truth).</li>\n<li>SmartMesh synchronization timestamps (2Hz).</li>\n<li>Corresponding ROS time (rostime).</li>\n<li>Sensor telemetry and metadata.</li>\n</ul>\n</li>\n<li><strong>Audio Files (</strong><code><strong>.wav</strong></code><strong>):</strong> Uncompressed, raw multi-channel acoustic data captured by the H1a hydrophone arrays, synced to the timeline established by the SmartMesh system.</li>\n</ul>\n<h3>Topics</h3>\n<pre class=\"language-python\"><code>/mavros/gpsstatus/gps1/raw\n   mavros_msgs/GPSRAW\n/rms_stream/data\n   msg/StreamData.msg</code></pre>\n<ul>\n<li>mavros_msgs/GPSRAW</li>\n</ul>\n<pre class=\"language-python\"><code># FCU GPS RAW message for the gps_status plugin\n# A merge of mavlink GPS_RAW_INT and \n# mavlink GPS2_RAW messages.\n\nstd_msgs/Header header\n## GPS_FIX_TYPE enum\nuint8 GPS_FIX_TYPE_NO_GPS     = 0    # No GPS connected\nuint8 GPS_FIX_TYPE_NO_FIX     = 1    # No position information, GPS is connected\nuint8 GPS_FIX_TYPE_2D_FIX     = 2    # 2D position\nuint8 GPS_FIX_TYPE_3D_FIX     = 3    # 3D position\nuint8 GPS_FIX_TYPE_DGPS       = 4    # DGPS/SBAS aided 3D position\nuint8 GPS_FIX_TYPE_RTK_FLOATR = 5    # TK float, 3D position\nuint8 GPS_FIX_TYPE_RTK_FIXEDR = 6    # TK Fixed, 3D position\nuint8 GPS_FIX_TYPE_STATIC     = 7    # Static fixed, typically used for base stations\nuint8 GPS_FIX_TYPE_PPP        = 8    # PPP, 3D position\nuint8 fix_type      # [GPS_FIX_TYPE] GPS fix type\n\nint32 lat           # [degE7] Latitude (WGS84, EGM96 ellipsoid)\nint32 lon           # [degE7] Longitude (WGS84, EGM96 ellipsoid)\nint32 alt           # [mm] Altitude (MSL). Positive for up. Note that virtually all GPS modules provide the MSL altitude in addition to the WGS84 altitude.\nuint16 eph          # GPS HDOP horizontal dilution of position (unitless). If unknown, set to: UINT16_MAX\nuint16 epv          # GPS VDOP vertical dilution of position (unitless). If unknown, set to: UINT16_MAX\nuint16 vel          # [cm/s] GPS ground speed. If unknown, set to: UINT16_MAX\nuint16 cog          # [cdeg] Course over ground (NOT heading, but direction of movement), 0.0..359.99 degrees. If unknown, set to: UINT16_MAX\nuint8 satellites_visible # Number of satellites visible. If unknown, set to 255\n\n# -*- only available with MAVLink v2.0 and GPS_RAW_INT messages -*-\nint32 alt_ellipsoid # [mm] Altitude (above WGS84, EGM96 ellipsoid). Positive for up.\nuint32 h_acc        # [mm] Position uncertainty. Positive for up.\nuint32 v_acc        # [mm] Altitude uncertainty. Positive for up.\nuint32 vel_acc      # [mm] Speed uncertainty. Positive for up.\nint32  hdg_acc      # [degE5] Heading / track uncertainty\nuint16 yaw          # [cdeg] Yaw in earth frame from north.\n\n# -*- only available with MAVLink v2.0 and GPS2_RAW messages -*-\nuint8 dgps_numch    # Number of DGPS satellites\nuint32 dgps_age     # [ms] Age of DGPS info</code></pre>\n<ul>\n<li>msg/StreamData.msg</li>\n</ul>\n<pre class=\"language-python\"><code>string timestamp\nsensor_msgs/NavSatFix localization\nstd_msgs/Float64MultiArray rms_data\nstring node_id\nstring utc_time\nstd_msgs/Float64MultiArray raw_data</code></pre>",
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