In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress.


This dataset contains LTE access signals (PRACH) emitted by 7 mobile phones and 1 USRP B205 software defined radio platform equipment. Files are stored in MAT form that can be read by MATLAB. The dataset of the mobile phone includes data from 3 different locations (\dif_loc), 3 different test times (\dif_date), and 3 different power-on times (\dif_working). The software defined radio platform equipment dataset consists of 3 datasets at different locations and 3 different test times.


The e-commerce market heavily relies on e-coupons, and their digital nature presents challenges in establishing a secure e-coupon infrastructure, which incurs maintenance costs. To address this, we explore using public blockchains for the e-coupon system, providing a highly reliable decentralized infrastructure with no maintenance costs. Storing coupon information on a blockchain ensures tamper resistance and protection against double redemption. However, using public blockchains shifts gas cost responsibility to users, potentially impacting user experience if not managed carefully.


These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings.  Odors of different 12 samples are taken from these six sensors


1- Natural Air


2- Fresh Onion


3- Fresh Garlic


4- Black Lemon


5- Tomato


6- Petrol


7- Gasoline


8- Coffee 


9- Orange


10- Colonia Perfume



The dataset explores the linguistic characteristics of Ukrainian online community members on "Lviv. Forum Ridne City" ( based on gender (female/male). It includes vectors of male and female profiles, along with 36 control vectors for 18 women's profiles and 18 men's profiles. The dataset includes 48 linguistic characteristics of gender in online communication. The linguistic features analyzed encompass a wide range, including apology, modal designs, emotions, profanity, sports and politics references, and more.


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.


This data collection focuses on capturing user-generated content from the popular social network Reddit during the year 2023. This dataset comprises 29 user-friendly CSV files collected from Reddit, containing textual data associated with various emotions and related concepts.


Data related to figures 3 and 7 in T. Fordell, K. Hanhijärvi, A.E. Wallin, J. Myyry, T. Lindvall ”Out-of-Band Fibre-Optic Time and Frequency Transfer Using Asymmetric and Symmetric Opto-Electronic Repeaters”, IEEE
IEEE Trans. Ultrason. Ferroelectr. Freq. Control (2023).


AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.