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This study introduces a novel soil texture dataset designed to overcome geographic constraints and improve the generalization of classification models. Using the USDA soil classification triangle as a framework, the dataset is systematically generated by combining pure sand, silt, and clay in varying proportions to create diverse soil texture classes. The soil mixtures are captured using a multispectral sensor with seven bands, ensuring a rich representation of spectral information.
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This study identifies representative sensors for monitoring fan performance by analyzing vibration data collected from piezoelectric sensors during various operational modes. The dataset, which includes measurements at a rate of 300 samples/sec from 10 sensors, covers six modes of operation: Maximum Speed, Maximum Speed with Oscillation, Minimum Speed, Minimum Speed with Oscillation, Minimum to Maximum Speed, and a comprehensive dataset combining all modes.
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Object tracking systems within closed environments employ light detection and ranging (LiDAR) to address privacy and confidentiality. Data collection occurred in two distinct scenarios. The goal of scenario one is to detect the locations of multiple objects from various locations on a flat surface in a closed environment. The second scenario describes the effectiveness of the technique in detecting multiple objects by using LiDAR data obtained from a single, fixed location.
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