Recent US Census Data the American Community Survey,


This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion.


The dataset contains Multivariate Time Series (MTS) of the miniature vehicle’s in and out movement in the agricultural field. The miniature vehicle collects the sensory data of the Inertial Measurement Unit (IMU) sensor (MPU-9250) deployed on it. MPU-9250 is a 9-axis sensor used for recording the linear and angular motion of the vehicle in the jerking condition due to the uneven surface of the farmland. MPU-9250 comprises a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. These sensors are connected to a NodeMCU with an attached SD card, which stores the data. The sensory data is collected from sixteen different agricultural fields at a sampling rate of 5 Hz for 5 minutes each. Therefore, each field produces 1500 instances of tri-axial sensors (accelerometer, gyroscope, and magnetometer). Hence, the total instances we have collected is 1500 X 16 =24000.


This dataset was prepared to estimate the winding temperature of a BLDC motor for a variable load and speed profile. It contains two files. The first one is the measurement results for the motor without cooling, while the second one is the measurement results after installing an additional cooling fan on the shaft. The data included in the files are time stamp, winding temperature, casing temperature, speed, current, power loss, mean and standard deviation of the measured quantities for 14400 data records.


Here is the paper abstract:


Medium-Voltage Arc-Flash Data Analysis

Tom Short,

This code base is using the Julia Language (

To (locally) reproduce this project, do the following:

  1. Download this code.
  2. Open a Julia console and do:julia> using Pkg julia> Pkg.activate("path/to/this/project") julia> Pkg.instantiate()

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box.

The main code is in:

  • paper-analysis.jl

The data is in three CSV files. These files contain measurements and predictions of incident energies. Each row of the file is one test. Key variables in each file are shown.

  1. epri.csv -- EPRI tests of MV equipment with predictions based on IEEE 1584-2018
  • V -- system voltage, kV (L-L)
  • Ib -- bolted fault current, kA
  • gap_mm -- electrode gap, mm
  • D_mm -- working distance, mm
  • t -- duration, msec
  • Ia -- predicted arcing current, kA
  • IE -- predicted incident energy, cal/cm²
  • config -- test configuration (EPRI HCB, EPRI Transformer, EPRI VCB, ...)
  • Iameas -- measured average arcing current, kA
  • IEmeas -- measured incident energy, cal/cm²
  • joules -- arc energy, J
  1. ieeeall.csv -- Test data from IEEE/NFPA used to develop IEEE 1584-2018
  • Lab -- test site
  • config -- VCB, HCB, ...
  • Voc -- system voltage, kV (L-L)
  • Ibf -- bolted fault current, kA
  • gap_mm -- electrode gap, mm
  • D_mm -- working distance, mm
  • t -- duration, msec
  • width_in -- box width, in
  • height_in -- box height, in
  • depth_in -- box depth, in
  • Iameas -- measured average arcing current, kA
  • IEmeas -- measured incident energy, cal/cm²
  1. hcb.csv -- Combined EPRI and IEEE/NFPA dataset for HCB configurations
  • Ib -- bolted fault current, kA
  • gap_mm -- electrode gap, mm
  • d_mm -- working distance, mm
  • t -- duration, msec
  • height_in -- box height, in
  • config -- test configuration (EPRI HCB or EPRI VCB)
  • iameas -- measured arcing current, kA
  • iemeas -- incident energy measured, cal/cm²
  • joules -- arc energy, J
  • energyratio -- ratio of incident energy to arc energy, cal/cm²/MJ

The electrodes are sensors capable of reading EMG signals or ocular myoelectric activity during eye movements [1]. For this purpose, two vertical electrodes and two horizontal electrodes were used, with a reference electrode on the forehead (See the figure). 10 subjects performed 10 pseudo-random repetitions of each of the following eye movements during the experiment: Up, Down, Right, Left, no movement (fixation in the center) and blinking.


The activities carried out by each of the 10 subjects were: Up, Down, Right, Left, no movement (fixation in the center) and blinking.

The tasks were separated by folders as detailed below:

  • CN - Normal Behavior
  • MD - Downward Movement
  • ML - Movement to the left
  • MP - Blink
  • MR - Right movement
  • MU - Upward Movement

Each folder contains 100 .CSV files, corresponding to the 10 tasks performed by each of the 10 subjects.

These files were numbered randomly in each of the folders.

Each file contains two columns corresponding to horizontal and vertical movement. In addition, each file contains 250 endpoints corresponding to a sampling of 120 data per second during the approximately 2 seconds of task completion.



Three well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, and Slammer, occurred in May 2017, May 2005, and January 2003, respectively.
The Route Views BGP update messages are publicly available from the University of Oregon Route Views Project and contain:
WannaCrypt, Moscow blackout, and Slammer:


Raw data from the "route collector route-views2" are organized in folders labeled by the year and month of the collection date.
Complete datasets for WannaCrypt, Moscow blackout, and Slammer are available from the Route Views route collector route-views2 site:
University of Oregon Route Views Project:
Route Views Collector Map:
University of Oregon Route Views Archive Project:
MRT format RIBs and UPDATEs (quagga bgpd, from
The date of last modification and the size of the datasets are also included.

BGP update messages are originally collected in multi-threaded routing toolkit (MRT) format.
"Zebra-dump-parser" written in Perl is used to extract to ASCII the BGP updated messages.
The 37 BGP features were extracted using a C# tool to generate uploaded datasets (csv files).
Labels have been added based on the periods when data were collected.



The data include:

  • Demographic data of the participants including: gender, group of participation and number of years in the company.
  • Results of the use of Ethool including: expended time and subjective evaluation of if using a Likert of 5 points. Two different files are available corresponding to each iteration (prototype 1 and prototype 2).
  • Results of the SUS questionnaire for both iterations (prototype 1 and prototype 2).

Design and fabrication outsourcing has made integrated circuits vulnerable to malicious modifications by third parties known as hardware Trojan (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the chip has been extensively studied. However, the suggested approaches mostly suffer from two major limitations: reliance on trusted identical chip (e.i. golden chip); untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase.


See the attached document.


Assessing students self-awareness on career choice is an important element in career guidance. This dataset is assessed during pre-treatment of career guidance programme involcing three Holland's constructs, i.e. occupational knowledge, realism, and attitude.


This dataset contains the measurement data of a channel sounding campaign in the hull of a bulk carrier vessel at mmWave frequency 60.48 GHz. The directive radio channel for Line-of-Sight (LOS) communication is measured using the Terragraph channel sounder. An antenna beam width dependent PL model is created. At mmWave frequencies, LOS PL in the vessel is close to PL in a free space environment, but angular spread values are lower compared to other indoor scenarios.

The processing results of these measurements are presented in the following two papers.


We performed Line-of-Sight measurements in the hull of a bulk carrier vessel using the Terragraph channel sounder. The measurements are performed in the engine room and steering control room of the Premium Do Brasil, a 200 m long juice carrier. We selected 6 locations and measured at different distances in order to have a measurement every 0.25 m and 2 measurements (at 2 different locations) for every 0.5 m. The measurement data can be found in the ZIP archive, which also contains some pictures in the **Setup** directory.

The **MeasurementData** directory of the ZIP archive contains a single folder for every measurement, with the following naming structure: X_locY_Z in which:
X: Date of measurement
Y: Location of measurement (see **Setup** folder)
Z: Distance between the two nodes

Every folder contains the log file with the configuration settings, the results and normalized results files and a plot of the beam sweep path loss info. No GPS info is recorded. All the path loss data is combined and fitted to a one slope model.