The Baseline set described in the submitted IEEE article  as Baseline_set  contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects;  the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.

The data set in .csv format contains the columns timestamp, user_id, session_id, acc_var, hr, rmssd, sdnn, st, eda, eda_freq, bm, location, and concentration. The time step is 5 seconds.

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Four independently built Twitter JSON files containing between 40,000 - 50,000 tweets with a common Hashtag to be used with network discovery algorithms.

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Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results. 

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This dataset is a collection of images and their respective labels containing multiple Indian coins of different denominations and their variations. The dataset only contains images of one side of each coin (Tail side) which contains the denomination value.

The samples were collected with the help of a mobile phone while the coins were placed on top of a white sheet of A4-sized paper.

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 The crime rate is increasing at a high rate in India. Terrorist attacks like Mumbai 26/11, Pulwama attack, Pune German Beckary attacks have created terrific fear amongst Indian Society. Video analytics plays a significant role in detecting and predicting such suspicious human activities using deep learning models It will help in reducing the increasing crime rate by preventing treacherous actions. Video analytics analyzes the video content and adds brains to eyes i.e. analytics to the camera. It extracts contents from the video by monitoring the video in real-time.

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The University of Turin (UniTO) released the open-access dataset Stoke collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP).

Instructions: 

Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.

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599 Views

The DroneDetect dataset consists of 7 different models of popular Unmanned Aerial Systems (UAS) including the new DJI Mavic 2 Air S, DJI Mavic Pro, DJI Mavic Pro 2, DJI Inspire 2, DJI Mavic Mini, DJI Phantom 4 and the Parrot Disco. Recordings were collected using a Nuand BladeRF SDR and using open source software GNURadio. There are 4 subsets of data included in this dataset, the UAS signals in the presence of Bluetooth interference, in the presence of Wi-Fi signals, in the presence of both and with no interference.

Instructions: 

Sample rate: 60Mbits/s

Bandwidth: 28MHz

Centre Freq: 2.4375GHz

Each recording consists of 1.2 x 10^8 complex samples equating to 2 seconds recording time. Data is saved into ‘.dat’ files  and the complex data is saved as interleaved floats. ‘load_data.py’ is included for the data to be loaded into python and further split into smaller samples 20ms in length.

Files are categorised by interference, then by flight mode –

Switched on = ON

Hovering = HO

Flying = FY

Each file name uses an interference identifier, 00 for a clean signal, 01 for Bluetooth only, 10 for Wi-Fi only and 11 for Bluetooth and Wi-Fi interference concurrently. An example file name for Mavic Mini switched on in the presence of Bluetooth and Wi-Fi interference would be:

MIN + 11 + 00 + 00 = MIN_1100_00.dat

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This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a water distribution hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.

Instructions: 

This dataset has related to the paper "A hardware-in-the-loop Water Distribution Testbed (WDT) dataset for cyber-physical security testing".
We provide four different acquisitions:
1) A normal acquisition without attacks ("normal.csv" for network traffic and "dataset_norm.csv" for physical measures)
2) Three acquisitions where different types of attacks and physical faults are reproduced ("attack_1.csv", "attack_2.csv" and "attack_3.csv" for network traffic and "dataset_att_1.csv", "dataset_att_2.csv" and "dataset_att_3.csv" for physical measures)
In addition to .csv files we provide four .pcap files ("attack_1.pcap", "attack_2.pcap", "attack_3.pcap" and "normal.pcap") which refer to network acquisitions for the four previous scenarios.
A README.xlsx file summarizes the key features of the entire dataset.

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955 Views

This dataset contains (1) the Simulink model of a three-phase photovoltaic power system with passive anti-islanding protections like over/under current (OUC), over/under voltage (OUV), over/under frequency (OUF), rate of change of frequency (ROCOF), and dc-link voltage and (2) the results in the voltage source converter and the point of common coupling of the photovoltaic system during islanding operation mode and detection times of analyzed anti-islanding methods.

Instructions: 

The anti-islanding protection relays are included in the "Relay Protection Bus B20 (20 kV)" subsystem.

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784 Views

SDU-Haier-ND (Shandong University-Haier-Noise Detection) is a sound dataset jointly constructed by Shandong University and Haier, which contains the operating sound of the internal air conditioner collected during the product quality inspection. We collected and marked a batch of quality inspection sounds of air conditioners in real production environments to form this data set, including normal sound samples and abnormal sound samples.

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305 Views

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