Communications

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.
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Emulating a RT task and measuring the response latency of its thread by means of the high-resolution testing tool Cyclictest. The thread was clocked at 10ms, and a FIFO scheduling policy was used, with the thread being assigned the highest priority. Measurements were performed in distinct testing environments, some of which had best effort concurrent threads competing for the machine resources. For this purpose, the workload generator tool stress was used.
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The following folder contains the data simulated using the metholodgy described in the paper titled "Federated Learning based Base Station Selection using LiDAR Data"
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Network topologies with link parameters; some statistical data.
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This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.
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This dataset was created using Wireshark. The dataset contains a total of 30 encrypted communication records, 3 records (.pcap) were created for each application. The records were obtained from a mobile device that was connected to the laptop using wifi technology. The laptop was connected to the Internet and contained a running instance of Wireshark to create a record. The telephone had been restarted before each record was created. After connecting to the network, the device was left without user interaction for 5 minutes.
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Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and
communities. In this context, detection and localization of UAVs will be critical for maintaining safe and secure airspace in the
future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring
the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various
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The script "numericalExample.m" obtains per-source average AoI and per-source age violation probabilities for First Source First Serve (FSFS), Earliest Served First Serve (ESFS) and Single Buffer with Replacement (SBR) policies for N sources using analytical models based on Markov Fluid Queues (MFQ). It is also possible to obtain the exact per-source CDF using this script. To do so, only the input parameters of used functions, namely "FSFS.m", "ESFS.m" and "SBR.m", in this script should be modified accordingly.
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