In my datasets, the "training data" is the data for the model training,the "static" is the data where peristaltic pump speed at 10pm. And others are the validaiton data of variable rates level descent and three constant rates level descent respectively as their title says
. For every set of data, there are 3 columns. The first is time, the second is capacitance and the third is liquid level.
The data are the original data of UAV sound source localization by ODAS system consisting of 12-channel spherical microphone array and circular MEMS microphone array of dual system. The data contains four different types of UAV sound sources, and the UAV sound sources in different frequency bands are used to locate the sound sources of multiple UAVs.
The Smart Home Device Dataset consists of 5000 samples collected at an hourly interval starting from January 2022, representing consumer electronics and IoT-enabled devices in a home automation environment. Each entry is associated with a unique device ID, ensuring identification of distinct devices. The dataset captures real-time sensor readings, including temperature variations (18°C to 30°C), power consumption levels (10W to 500W), and user activity states (Active, Idle, or Sleep), which provide contextual insights into device operation.
The Drone Sensor Fusion Dataset features high-quality telemetry data from real and attack-modeled UAV flights, leveraging the PX4 flight log dataset. This includes normal flight data prepared for machine learning model training and simulated attack data generated using the 'Coordinated Sensor Manipulation Attack' (CSMA) model. CSMA simulates advanced threats by subtly altering GPS and IMU data to induce undetectable navigation drift.
This dataset contains partial raw data based on Coastal Acoustic Tomography (CAT) technology, covering observations from multiple measurement sites. The data primarily include received acoustic signals, station positioning information, and relevant environmental parameters recorded during the experiment. It can be utilized for research on ocean flow field inversion, environmental noise analysis, and acoustic propagation characteristics. The data were collected in a specific sea area and have undergone basic quality checks but have not been further processed.
This paper presents an enhanced methodology for network anomaly detection in Industrial IoT (IIoT) systems using advanced data aggregation and Mutual Information (MI)-based feature selection. The focus is on transforming raw network traffic into meaningful, aggregated forms that capture crucial temporal and statistical patterns. A refined set of 150 features including unique IP counts, TCP acknowledgment patterns, and ICMP sequence ratios was identified using MI to enhance detection accuracy.
This dataset contains deep-sea measured sound velocity profile (SVP) data, which was used in a hybrid experiment that integrates both real-world measurements and simulations for ultra-short baseline (USBL) acoustic positioning. The dataset supports research on underwater acoustic propagation, sound ray tracing, and positioning accuracy improvements. By utilizing actual deep-sea SVP data, the hybrid experimental approach enhances the realism and reliability of USBL performance evaluation.
The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed.
A collection of Python pickles objects containing a Pandas DataFrame. Each Dataframe corresponds to the postprocessed firing rate (fr) in Hz and mean amplitude of the spikes (AMP) in microV/s of the vagus nerve recordings obtained from 12 adult female Sprague-Dawley rats. Additionally, the blood-glucose level in mg/dL is included. The fr and AMP signals have 0.1 miliseconds of resolution, whereas the glucose level was measured approximately every 5 minutes. Temporal variations are due to experimental factors. The number of available glucose samples changes across recordings.
