This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.


Video is a sequence of pictures, which are taken by camera at a short interval of time. A picture in video is called as frame, and the number of frames per second is defined as the frame rate, which denotes the temporal resolution of video. With the higher frame rate, the video contains the more details, such as to the improvement of visual quality for human interpretation or the fine representation for automatic machine perception. A high frame rate relies on the hardware configuration of camera, the higher the frame rate, the more expensive hardware devices.


Please cite the following paper when using this dataset:

N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049



This file contains digital elevation information for 28 troposcatter links from SRTM3 with 90 m resolution. Each folder contains five files, including the subfolder xxxx_DEM_DATA, which contains digital elevation information for the block in which the link is located and can be opened using Global Mapper software via the file xxxxDEM.gmw. The xxxx_PATH.csv stores the elevation information on the tropospheric scattering link. xxxxmap.png gives the link's location on the planar map. xxxx_profile.bmp is the elevation profile of the link.


This dataset contains raw captured packet headers from six commercial drones.


Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) has gained popularity in recent years and is expected to be part of 5G networks and beyond. However, the GA links are susceptible to frequent blockages at millimeter wave (mmWave) frequencies. During a link blockage, the channel information cannot be obtained reliably. In this work, we provide a novel method of channel prediction during the GA link blockage at 28 GHz.


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


# RSS data from smartwatch for Contact Tracing


This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.

We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.

The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.



Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.