The dataset is divided into two parts. The measurement dataset and simulation dataset. The measurement dataset contains received power measurements at 28 GHz in an indoor corridor and outdoor open area. The received power and other channel statistics, e.g., root mean square delay spread, power and time of arrival of multipath components, and path loss were obtained using the PXI channel sounder system. Two different gain antennas 17 dBi and 23 dBi were used. The transmitter was fixed, whereas the receiver was moved in a straight line aligned to the boresight of the transmitter antenna.


The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to evaluate the feasibility of in-band full-duplex acoustic communications in the underwater environment. The experiment was conducted in Lake Tuscaloosa in June 2021. Two boats, each equipped with an instrument line, were deployed for the experiment. For the local instrument line, the transducer was mounted 6 meters below the water surface. The receiving array was placed 7 meters below the transducer.


The dataset is oriented on encrypted traffic classification problems. The dataset contains three classes of flows: web flows, YouTube flows, and Netflixflows. These classes are chosen because web and video traffic account for 90% of global traffic, while YouTube and Netflix are the largest video services. The structure of the dataset is as follows. It includes 100 download traces of the most popular web pages according to, 100 the most popular YouTube videos, and 50 Netflix series and movies.


This dataset includes the measured Downlink (DL) signal-to-noise ratios (SNRs) at the User Equipments (UEs), adopting one of the beams of the beamforming codebook employed at the Base Stations (BSs). First, we configured a system-level simulator that implements the most recent Third Generation Partnership Project (3GPP) 3D Indoor channel models and the geometric blockage Model-B to simulate an indoor network deployment of BSs and UEs adopting Uniform Planar Arrays (UPAs) and a codebook based transmission.


The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.


This code and related data is related to research work on quantified benchmarking of Reconfigurable Intelligent Surface (RIS). Related research article has been submitted to VTM titled "Reconfigurable Intelligent Surfaces: Tradeoff between Unit-Cell- and Surface-Level Design under Quantifiable Benchmarks". 'ReadMe.text' file in '' explains how to use the code to generate RIS configurations for RIS of arbitrary size and unit cell design, which can accomodate restricting to certain size grouped control.


This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R18 standards complied



The corresponding paper is published here (

5G NR is normally considered to as a new paradigm change in integrated sensing and communication (ISAC).


The OFMC back-end in AVISPA is used to carry out security verification experiments in our scheme for the login and authentication phase,  Case1 in the password and biometric renewal phase, and Case2 in the password and biometric renewal phase, respectively.

Here's the experimental result for the login and authentication phase as a show.


Simulation code for the paper:  "AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks" 

we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output.