The dataset was gathered from a virtual learning environment course at Constantine the Philosopher University in Nitra. It includes online activity logs of 152 university students enrolled in a blended Computer Science course during the winter semester from September 25, 2023, to December 21, 2023. This course combined traditional lectures and lab sessions with online interactions and digital access to course materials via the Learning Management System Moodle platform. 


The (CSV) table DataSet contains the aggregate results of the 16000 following simulation cases, defined by the first ten columns:

A) ID: # case identifier

B) n=10,20,40,80,160 # number of robots

C) S2M=0,0.5 # Stall-to-Maximum, 0 for differential-drive and quadrotor-like robots, 0.5 for fUAV-like robots

D) PF=0.25,0.5,0.75,1 # Prudence Factor, max. velocity is PF*r/s (+/- 10%), where r is the communication factor, and s is one second

E) OF=1,2 # Overpopulation Factor

F) T=0.25,0.5,0.75,1 # high-level control period (+/- 10%)


Seismocardiography (SCG) Signal Processing Dataset is a comprehensive collection of data samples to simulate the real-world application of the advanced technique in cardiac health monitoring. The dataset has been collected in different medical conditions of the patient in a real-time medical environment at varying timestamps. This dataset contains 1,000 samples collected over a period from 10 November 2023 to 10 January 2024, providing a robust timeframe in various conditions.


This dataset includes channel delay data for 5G and TSN networks.The 5G and TSN channel delay dataset includes a training set and a test set, with 600 sets of data in the training set and 200 sets of data in the test set, which are used for channel model prediction. The data in these datasets are real, collected in real-time from the running 5G-TSN system using network testers and data packet capture tools.


The dataset includes Pakistan most popular YouTube videos for each category from year 2021- 2023. There are two kinds of data files, one includes video statistics and other one related to comments on those videos. They are linked by the unique video_id field. Both datasets are merged in final videos file which contains all videos statistics and sentiment extracted from comments. Here’s a breakdown of each column:


The massive damage caused by COVID-19 worldwide over the past two years has highlighted the importance of predicting the spread of infectious diseases. Therefore, with advances in deep learning, numerous and diverse methods have been considered for predicting the spread of infectious diseases. However, these studies have shown that the long-term prediction abilities of deep learning models are insufficient to predict the course and propagation of COVID-19 outbreaks.


Phase noise is a common hardware impairment, resulting from the frequency instability of voltage-controlled oscillators (VCO). To improve the phase noise performance of a VCO, they are typically connected to a control circuit. This control circuit is known as phase locked loop (PLL). It is commonly used as a frequency synthesizing circuit for the carrier frequency in mobile communication transceivers. Universal Software Radio Peripherals (USRP) are widely used in mobile communication research.


In the realm of global agriculture, the imperative of sustaining an ever-expanding population is met with challenges in optimizing crop production and judicious resource management. SmartzAgri heralds a groundbreaking approach to modern agriculture. This innovative system represents a convergence of machine learning algorithms and Internet of Things (IoT) technology, aimed at reshaping traditional paradigms of crop recommendation.


Modern automotive embedded systems include a large number of electronic control units (ECU) responsible for managing sophisticated systems such as engine control, ABS brake systems, traction control, and power steering systems. To ensure the reliability and effectiveness of these functions, it is essential to apply rigorous test approaches and standards. The integration of diagnostic functions in automotive embedded systems demands consistent tests and a detailed analysis of data.


The Human Activity Recognition (HAR) dataset comprises comprehensive data collected from various human activities including walking, running, sitting, standing, and jumping. The dataset is designed to facilitate research in the field of activity recognition using machine learning and deep learning techniques. Each activity is captured through multiple sensors providing detailed temporal and spatial data points, enabling robust analysis and model training.