More than 85% of traffic utilization via mobile phones are consumed in the urban area, and most of the traffic is used for downloading. Improving the throughput in LTE for 1 user equipment (UE) in cities is an urgent problem. The collected data is intended to study a dependence of the KPI mobile base station and neighboring from installation extra technology. This study will support the development of methods for comparing traffic utilization of urban area and carry out recommendations for the Channel Quality Indicator (CQI) increases.


Transistor models are crucial for circuit simulation. Reliable design of high-performance circuits requires that transistor characteristics are adequately represented, which makes accurate and fast models indispensable. Scattering (or S-)parameters are perhaps the most widely used RF characteristics, employed in the design and analysis of linear devices and circuits for calculation of the input and output impedance, isolation, gain, as well as stability, all being important performance figures for small-signal or low-noise amplifiers.


Good knowledge about a radio environment, especially about the radio channel, is a prerequisite to design and operate ultra-reliable communications systems. Radio Environment Maps (REMs) are therefore a helpful tool to gain channel awareness. Based on a user’s location, the channel conditions can be estimated in the surrounding of the user by extracting the information from the radio map. This data set contains two measured high-resolution REMs of an indoor environment.


This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.


 Millimeter-wave (mmWave) spectrum with wide bandwidth provides a promising solution to enable high throughput in next-generation wireless agricultural networks, characterized by swarms of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels due to the dynamic crop growth behavior.


This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.


This dataset has been used to evaluate different consistent hashing algorithms for non-peer-to-peer contexts. Further information can be found at



The repository contains the data collected during User Behavior Experiment (UBE) in [1].



[1] D. Wei and S. Han, "An experimental study of recommendation for wireless edge caching," in Proc. IEEE ICCC, 2022.


This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.