This dataset is used for network anomaly detection and is based on the UGR16 dataset network traffic flows. We used June week 2 to 4 tensors generated from raw flow data to train the models. The dataset includes a set of tensors generated from the whole UGR’16 network traffic (general tensor data) and several sets of port tensors (for specific port numbers). It also includes the trained models for each type of tensor. The tensors extracted from network traffic in the period from July week 5 to the end of August can be used for evaluation. The naming convention is as follows:


A synthetic dataset designed to evaluate transfer learning performance for RF domain adaptation in the publication Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation. The dataset contains a total of 13.8 million examples, with 600k examples each of 22 modulation schemes (given below) and AWGN noise (200k each for training, validation, and testing); 512 raw IQ samples per example.


The dataset comprises of several files that contain smart grid communication, namely protocols IEC 60870-104 (IEC 104) and IEC 61850 (MMS) in form of CSV traces. The traces were generated from PCAP files using IPFIX flow probe or an extraction script. CSV traces include the timestamp, IP addresses and ports of communicating devices, and selected IEC 104 and MMS headers that are interesting for security monitoring and anomaly detection. Datasets were by obtained partly by monitoring communication of real ICS devices and partly by monitoring communication of virtual ICS applications.


The UOWC system employing 450 nm blue laser with 1.25 Gbps in the 6-meter distance was measured under the various environmental parameters. The transmission channel was improved using the slight reflected angle. This condition can compensate for the attenuation caused by the scattering and absorption in the transmission channel. The UOWC transmission system experiment was carried out under several environmental parameters, including depth of surface turbulence, temperature, and turbidity.


Testing Australian standard consumers’ understanding of the language used to describe wine



The complete set of TikTok (douyin) videos related to sustainable poverty alleviation in Yunnan and Guizhou provinces of China. The video publication date was amongest August, 2018 (the earliest date available as per this topical series), and September, 2021.


Abstract—Network slicing (NwS) is one of the main technologies

in the €…h-generation of mobile communication and

beyond (5G+). One of the important challenges in the NwS

is information uncertainty which mainly involves demand

and channel state information (CSI). Demand uncertainty is

divided into three types: number of users requests, amount

of bandwidth, and requested virtual network functions workloads.

Moreover, the CSI uncertainty is modeled by three

methods: worst-case, probabilistic, and hybrid. In this paper,


# 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.


We have designed a ZYNQ SDR-based platform that utilizes real on-air 5G new radio (NR) signals to develop and test the performance of channel estimation for wireless channel estimators. On-air samples are obtained via the SDR platform to determine the unknown values of the channel response using known values at the pilot locations. We have collected extensive channel estimation data under a variety of scenarios: 1) line-of-sight (LOS), 2) LOS multipath and 3) non-LOS multipath. We have considered 2m,4m,6m test cases to simulate meter-level indoor positioning for indoor scenarios.