The rapid evolution of communication networks and the ever-increasing demand for efficient data transfer have led to the development of cognitive networking, which aims to enhance network performance through intelligent and adaptive protocols. To facilitate research and development in this domain, we present a comprehensive dataset detailing the parameters of a Network Protocol Stack which can be used to develop a Cognitive Network Protocol Stack designed for efficient networking.


The dataset contains 5G positioning measurements simulated using a MATLAB raytracer tool in realistic environments (outdoors and indoors).
Outdoor scenarios include static and dynamic users in the urban area of Città Studi, Milan, Italy, near the Politecnico di Milano - Campus Leonardo.
The indoor context is reconstructed using a LiDAR acquisition in the MADE Competence Center I4.0 located in Politecnico di Milano - Campus Durandò, Bovisa, Milan, Italy.
The datasets include:


The datasets consists of the traffic-delay pairs collected in a real network.  For each link connecting two switches, we measured the link delay associated with 1000 evenly-divided traffic test points. To obtain the traffic-delay pairs at a traffic test point of a link, the traffic generation machine, connected to the starting switch of the link, firstly sent data packets to the traffic generation machine, connected to the ending switch of the link, at the corresponding rate (i.e., the traffic value of the  traffic test point).


The "RF Jamming Dataset for Vehicular Wireless Networks" presents a comprehensive collection of data used in the research paper titled "RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks." This dataset comprises diverse scenarios of RF jamming attacks and interference in Vehicular Ad-hoc Networks (VANETs), along with corresponding ground truth labels. The dataset is designed to support the evaluation and development of detection algorithms for RF jamming attacks in VANETs.


This dataset contains the simulation results for the microfluidic molecular simulation using the OpenFOAM MPPIC solver. In addition to the simulation results, a template for easy entry into the simulation environment of OpenFOAM and a Python script for data analysis are included.


P. Hofmann, P. Zhou, C. Lee, M. Reisslein, Frank H.P. Fitzek, and C.-B. Chae, “A Computational Study of Microfluidic Molecular Communication Using OpenFOAM,” submitted, June 2024.

Brief Explanation of the OpenFOAM Structure:


JVNV is a Japanese emotional speech corpus with verbal content and nonverbal vocalizations whose scripts are generated by a large-scale language model.

Existing emotional speech corpora lack not only proper emotional scripts but also nonverbal vocalizations (NVs) that are essential expressions in spoken language to express emotions.

We propose an automatic script generation method to produce emotional scripts by providing seed words with sentiment polarity and phrases of nonverbal vocalizations to ChatGPT using prompt engineering.


Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends is focused on understanding the climate science, trends, and public awareness of climate change. The use of dataset for analytics of climate change trends greatly helps in researching and comprehending global climate change trends.


The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.


Modern, industrial use cases for wireless communications are related to mobile applications such as moving robotics in industrial environments. For the design of communication systems, the behavior of the radio channel, especially over time, is of great importance. Most of the existing data sets for industrial radio channels originate from static measurement procedures, containing an arbitrary subset of the environment.