This dataset contains video-clips of five volunteers developing daily life activities. Each video-clip is recorded with a Far InfraRed (FIR) camera and includes an associated file which contains the three-dimensional and two-dimensional coordinates of the main body joints in each frame of the clip. This way, it is possible to train human pose estimation networks using FIR imagery.


The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.

General Metrics

§  Node ID: The unique identifier for each node.

§  Timestamp: The time at which data or a packet is sent or received.

§  IP Address: Internet Protocol address of the node.


Dataset description:

This contains ten categories of gas data, each category contains 5 concentrations, 10, 20, 30, 40, 50ppm.

There are 160 groups of 10, 20, 30, 40, each group contains 6000 sampled voltage signals, and the sampling frequency is 10HZ.

There are only 80 groups for 50ppm concentration, and each group also contains 6000 sampled voltage signals.

The label corresponding to each gas includes category and concentration, which can be split by gas category and concentration.


This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.


For gesture recognition, radar sensors provide a unique alternative to other input devices, such as cameras or motion sensors. They combine a low sensitivity to lighting conditions, an ability to see through surfaces, and user privacy preservation, with a small form factor and low power usage. However, radar signals can be noisy, complex to analyze, and do not transpose from one radar to another.


The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner.


The dataset contains fitted three-pole Debye dielectric model parameters of 567 soil spectra. Three soils of loamy sand, sandy loam, and silt loam textures were tested. Of each soil, 20 samples of various water contents were prepared with the use of distilled water and potassium chloride solutions, 5 samples for each liquid. Air-dry samples were also prepared. Dielectric spectra were obtained with the use of a six-channel coaxial-transmission-line cell system at 9 controlled temperature steps from 0.5 to 40°C in the 0.02 – 3 GHz frequency range.


1.The spectrum of the dataset is obtained by applying force to the tactile sensor based on Chirped Bragg gratings.

2.The applied force ranges from 0N to 10N on the sensing pad of 4cm×4cm.

3.The folder name (x, y) represents the specific coordinates of the point at which the force is applied, and the xN name of the subfolder represents the xN force applied at that point.

4.A total of 120 spectral data were collected in each applied force state.

5.The first column of each spectrum is wavelength and the second column is intensity.


The significance of having sustainable water quality data cannot be overstated. It plays a crucial role in comprehending the historical variations and patterns in river conditions and also helps in understanding how industrial waste impacts the well-being of aquatic ecosystems. To achieve sustainable water management practices, it is imperative to rely on dependable and extensive data. Therefore, accurate monitoring and assessment of various water quality parameters become essential.


This dataset contains data collected from multiple paths, such as Unequally space path, Curved path, ESPLB, data collected from actual paths, and concentration prediction data. This experiment adopts a new concentration data collection path ESBLP method efficiently divides the study area into three parts and measures concentration data along the boundaries to calculate gradients.