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Dataset results
32 results for “sensor location”
Year 2013, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2013 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2014, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2014 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2015, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2015 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2016, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2016 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2017, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2017 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Temperature sensor data for locations in Smart-Santander testbed of Fed4FIRE+
<p>This is a database dump with temperature sensor-values (called "phenomenons" in testbed API), collected during the Fed4FIRE+ project SECTOR (Algorithm to determine a cost-effective, optimal SpatiaL-dEployment for smart-City environmenTal sensOr netwoRks).</p> <p> </p>
Sensor location affects skeletal muscle contractility parameters measured by tensiomyography
<p><span>Tensiomyography (TMG) is a non-invasive method for measuring contractile properties of skeletal muscle that is increasingly being used in research and practice. However, the lack of standardization in measurement protocols mitigates the systematic use in sports medical settings. Therefore, this study aimed to investigate the effects of lower leg fixation and sensor location on TMG-derived parameters. Twenty-two male participants underwent TMG measurements on the m. biceps femoris (BF) in randomized order with and without lower leg fixation (fixed vs. non-fixed). Measurements were conducted at 50% of the muscle's length (BF-mid) and 10 cm distal to this (BF-distal). The sensor location affected the contractile properties significantly, both with and without fixation. Delay time (T<sub>d</sub>) was greater at BF-mid compared to BF-distal (fixed: 23.2 ± 3.2 ms vs. 21.2 ± 2.7 ms, <em>p</em> = 0.002; non-fixed: 24.03 ± 4.2 ms vs. 21.8 ± 2.7 ms, <em>p</em> = 0.008), as were maximum displacement (D<sub>m</sub>) (fixed: 5.3 ± 2.7 mm vs. 3.5 ± 1.7 mm, <em>p</em> = 0.005; non-fixed: 5.4 ± 2.5 mm vs. 4.0 ± 2.0 mm, <em>p</em> = 0.03), and contraction velocity (V<sub>c</sub>) (fixed: 76.7 ± 25.1 mm/s vs. 57.2 ± 24.3 mm/s, <em>p</em> = 0.02). No significant differences were revealed for lower leg fixation (all <em>p</em> > 0.05). In summary, sensor location affects the TMG-derived parameters on the BF. Our findings help researchers to create tailored measurement procedures in compliance with the individual goals of the TMG measurements and allow adequate interpretation of TMG parameters.</span></p>
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
Sensor location affects skeletal muscle contractility parameters measured by tensiomyography
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Microclimate sensor data from 3 locations
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Data from: Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model
In diabetes research, the development of the artificial pancreas has been a major topic since continuous glucose monitoring became available in the early 2000's. A prerequisite for an artificial pancreas is fast and reliable glucose sensing. However, subcutaneous continuous glucose monitoring carries the disadvantage of slow dynamics. As an alternative, we explored continuous glucose sensing in the peritoneal space, and investigated potential spatial differences in glucose dynamics within the peritoneal cavity. As a secondary outcome, we compared the glucose dynamics in the peritoneal space to the subcutaneous tissue. Eight-hour experiments were conducted on 12 anesthetised non-diabetic pigs. Four commercially available amperometric glucose sensors (FreeStyle Libre, Abbott Diabetes Care Ltd., Witney, UK) were inserted in four different locations of the peritoneal cavity and two sensors were inserted in the subcutaneous tissue. Meals were simulated by intravenous infusions of glucose, and frequent arterial blood and intraperitoneal fluid samples were collected for glucose reference. No significant differences were discovered in glucose dynamics between the four quadrants of the peritoneal cavity. The intraperitoneal sensors responded faster to the glucose excursions than the subcutaneous sensors, and the time delay was significantly smaller for the intraperitoneal sensors, but we did not find significant results when comparing the other dynamic parameters.
Data from: Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model
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International Brain Laboratory public data
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OpenNeuro
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