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.


This dataset corresponds to the paper Calibration of a Hail-Impact Energy Electroacoustic Sensor, submitted to IEEE Transactions in Instrumentation and Measurement by Florencia Blasina, Andrés Echarri, and Nicolás Pérez. 

The dataset corresponds to the voltage signals acquired regarding several steel-ball impacts on the proposed hail-sensor plate to calibrate it. 


Experimental measurement data was obtained utilizing RCbenchmark 1780 with full-range PWM signals. Measurements were made for two series of setups.

First series is related to low-voltage setups using the following T-MOTOR components: - motors: MN4014 400Kv, MN5212 340Kv, MN501-S 360Kv, U7 280Kv, MN6007 320Kv, P60 340Kv, MN701-S 280Kv; - ESC: Air 40A, Flame 40A, Flame 70A, Alpha 60A, Flame 100A; - propellers: P17×5.8, P18×6.1, P20×6, P22×6.6, P24×7.2, G26×8.5; - battery: 6-cell (6S) Lithium polymer (LiPo).


This report presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets in the Indian context by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to capture the dynamic movement patterns associated with handwriting, enabling more accurate recognition of alphabets. The Indian context introduces various challenges due to the heterogeneity in writing styles across different regions and languages.


This is an indoor environment data set collected from our research team's laboratory, and the data is collected from the Intel RealSense D435i camera. There are a total of 12 datasets, each in the format of a `.bag` file in ROS packet format. Each file contains RGB images and IMU data.