These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.
The data made available are the simulations of a time-resolved Monte Carlo model for use in quantitative as well as qualitative analysis of different types of particle atmospheres.
1. Set the geometry
2. Define the atmosphere
2.1 Define the scattering profile of each type of particle in the atmosphere.
2.2 Define the relative amount of each type of particle.
2.2 Define the mean free path.
3. Define other test variables
3.2 Refraction index (complex or real)
4. Run the simulations
5. With the data obtained, perform data analysis.
BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019).
There are two folders under each user; session I and sessions II. Each session folder contains four different folders; one for each activity performed by the user. Each activity folder contains .csv files; (1) EEG Data (brainwave.csv), (2) Handmovement Accelerometer Data (accelerometer.csv), and (3) Handmovement Gyroscope Data (gyroscope.csv).
A more deatailed description of the data is given in BS-HMS-Dataset-Documentation.pdf file.
Acknowledgement: This data collection was supported in part by the National Science Foundation (NSF) under grant SaTC-1527795.
Please cite:  Diksha Shukla, Sicong Chen, Yao Lu, Partha Pratim Kundu, Ravichandra Malapati, Sujit Poudel, Zhanpeng Jin, Vir Phoha, "Brain Signals and the Corresponding Hand Movement Signals Dataset (BS-HMS-Dataset)", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/my1k-dd23. Accessed: Dec. 05, 2019.
A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.
Database Folder name "RSSI_6AP_Experiments" which includes:
+ Nexus 4 database
+ Nexus 5 database
Training & Testing data are seperated & collected in different time with different locations.
* The general format for csv data file is:
-- X & Y & The list of RSSI
-- X | Y | RSSI Vector
This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.
WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.
The dataset contains measurement results of Radar Cross Section of different Unmanned Aerial Vehicles at 26-40 GHz. The measurements have been performed fro quasi-monostatic case (when the transmitter and receiver are spatially co-located) in the anechoic chamber. The data shows how radio waves are scattered by different UAVs at the specified frequency range.
Some of DJI, Walkera, Parrot and Kyosho drones were measured.
The data is in ".csv" format. Each file contains the following information: frequency, theta, phi, and RCS.
The RCS signatures of the following drone models are available:
-DJI Phantom 4 Pro
-DJI Mavic Pro
-DJI Matrice M100
-Walkera Voyager 4
-Custom built hexacopter
-Tricopter HMF600, frame only
Polarization is mentioned in the file name:
HH - horizontal polarisation of the transmitter and the receiver
HV/VH - horizontal and vertical or vice versa
VV - vertical polarization of the transmitter and the receiver
In addition, 6S LiPo battery RCS is available.
Published article can be found at: https://ieeexplore.ieee.org/document/9032332
- Drift types (A): gradual, incremental, recurring and sudden
- Drift perspectives (B): time and trace
- Noise percentage (C): 0, 5, 10, 15, 20
- Number of cases in the stream (D): 100, 500, 1000
- Change patterns (E): baseline, cb, cd, cf, cp, IOR, IRO, lp, OIR, pl, pm, re, RIO, ROI, rp, sw
The file name follows the pattern [A]_[B]_noise[C]_[D]_[E]
An identical version of this dataset in the MXML format is available at: http://www.uel.br/grupo-pesquisa/remid/?page_id=145
We introduce a dataset concerning electric-power consumption-related features registed in seven main municipalities of Nariño, Colombia, from 2010 to 2016. The dataset consists of 4423 socio-demographic characteristics, and 6 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDNEAR) according to the client consumption records.
Power consumption is obtained by manually recording the amount of
Kw/h consumed each month using the electric energy meter of the
clients. Socio-demographic data were collected from the records
available in databases of CEDENAR. Here in, to set the stratum
(categorization of socioeconomic level in Colombia) of the clients,
Colombian Law 732 of 2002  was adopted. It has six level are
individually incorporated as follows: 0 = Low-low, 1 = Low, 2 = Medium-
Low, 3 = Medium, 4 = Medium-High, 5 = high.