Sensors

Well logs are interpreted/processed to estimate the in-situ reservoir properties (petrophysical, geomechanical, and geochemical), which is essential for reservoir modeling, reserve estimation, and production forecasting. The modeling is often based on multi-mineral physics or empirical formulae. When sufficient amount of training data is available, machine learning solution provides an alternative approach to estimate those reservoir properties based on well log data and is usually with less turn-around time and human involvements.
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This dataset comprises a range of features, including time slots, device IDs, geographic coordinates (x, y), energy consumption, uplink history, emergency status, QoS pool identifiers, data flags, resource IDs, and data sizes. The device locations are modeled using a Poisson distribution with a spread of \(100\) meters within a \(500 \times 500\) meter area. The uplink history, QoS pool assignments, and data flags are derived from the probabilities of data availability and priority values.
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This video demonstrates the real-time data acquisition and noise reduction capabilities of a CMOS capacitive sensor array (CSA) implemented on an FPGA. The system captures the evaporation process of a deionized water droplet placed on the sensor array, using multiple sampling (MS) and pixel-wise accumulation (PWA) techniques to enhance signal quality and reduce random noise. The system efficiently processes and transmits the data, showcasing the gradual reduction in the droplet's size as it evaporates.
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The TiHAN-V2X Dataset was collected in Hyderabad, India, across various Vehicle-to-Everything (V2X) communication types, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Vehicle-to-Cloud (V2C). The dataset offers comprehensive data for evaluating communication performance under different environmental and road conditions, including urban, rural, and highway scenarios.
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A modern Wi-Fi-enabled device (e.g., a smartphone) can spontaneously emit unencrypted and anonymized signals to the environment in search of an access point. This signal is called a probe request. Since it is freely available in the open air, one can build a sensor from a Wi-Fi adapter to capture the signal. Once captured, its signal strength can be measured in the form of a Received Signal Strength Indicator (RSSI).
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Radar signals can penetrate non-conducting materials, such as glass, wood, and plastic, which could enable the recognition of user gestures in environments with poor visibility, occlusion, limited accessibility, and privacy sensitivity. This dataset contains nine gestures from 32 participants recorded with a radar through 3 different materials (wood, glass, and PVC) to explore the feasibility of sensing gestures through materials.
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This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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This work presents a specialized dataset designed to advance autonomous navigation in hiking trail and off-road natural environments. The dataset comprises over 1,250 images (640x360 pixels) captured using a camera mounted on a tele-operated robot on hiking trails. Images are manually labeled into eight terrain classes: grass, rock, trail, root, structure, tree trunk, vegetation, and rough trail. The dataset is provided in its original form without augmentations or resizing, allowing end-users flexibility in preprocessing.
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This data was collected using a Bosch Parking Lot Sensor (TPS110 EU) placed in a time-limited public parking space over a period of two months. Each time the sensor detected a change in the parking status, it transmitted the new state via The Things Stack LoRaWAN network to the server.
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