In this appendix, the tested implementation in Matlab of our 2D-TDOA localization algorithm is given for the easier repetition of the obtained results and the future hardware implementation, due to the complexity of the formulas (25)-(31).


This dataset was used to quantify the effects of environmental change on SSTDR measurements from solar panels. We collect illuminance (Lux), temperature (deg F), and humidity (%) alongside SSTDR waveforms on a fault free string. Data is collected once per minute in January 2020, and twice per minute in August-September 2020. 


This is a dataset is an example of a distribution of 20 correlated Bernoulli random variables.


Q_joint ... is 5 cells each consists of the joint distributions of 4,8,12,16,20 bits, respectively. The dimension of each cell is 2^n X 1, .e., a vertical column and n=4,8,12,16,20.

Q_conditional... is 5 cells each consists of the conditional distributions of 4 bits given 0, 4, 8,12,16 bits, respectively. In other words, 1:4 bits, 5:8 bits given 1:4 bits, 9:12 bits given 1:8 bits, 13:16 bits given 1:12 bits, 17:20 given 1:16 bits. The dimension of each cell is 2^4=16 X 2^n, i.e., a vertical column and n=4,8,12,16.

Q_ marginal... is 5 cells each consists of the marginal distributions of each 4 consecutive bits, i.e., 1:4 - 5:8 - 9:12 - 13:16 - 17:20, respectively.  The dimension of each cell is 16 X 1, i.e., q vertical column.

Also, a MATLAB code is uploaded to extract conditional and marginal distributions from any given discrete distribution.


Three raw (i.e., In-Phase and Quadrature data with a software radio, and observation files) GNSS dataset were recorded using a LabSat Version 3 inside of the West Virginia University  greenhouse and two outside recordings were also made to provide a quality reference and comparison. The outdoor location had to be an ideal location for satellite signal reception  and  the  indoor  location  was  a  greenhouse  room  where satellite visibility was limited, susceptible to attenuation, occlusion and multipath.


GNSS-SDR: These recordings were also played back into a software defined receiver(GNSS-SDR), using the L1_E1.conf file (Also attached) which allows Galileo signals in thesolution and obtained the following rinex files:Note: These rinex files are in a 3.02 format.

• GSDR302c05.20O

• GSDR315b49.20O

• GSDR307x52.20O

• GSDR309n41.20O

• GSDR310p27.20O

Other useful outputted files by GNSS-SDR are also included in the Run folder.RTKLIB: The rinex output files from NovAtel and GNSS-SDR were then post processed usingRTKLIB (rtkpost) ver.2.4.2. For RTKLIB the following settings were used.

• Position Mode/Solution Type: PPP Kinematic

• Combined filter type

• Iono-Free LC Correction/Broadcast

• Saastamoinen Tropospheric Correction

• Broadcast Satellite Ephemeris/Clocks (Used GPS and Galileo Broadcast ephemeris files and considered sp3 and clk from CDDIS/IGS)

The station and orbit files downloaded from CDDIS used to process the data areincluded. Rtkpost outputs its solutions in a .pos and .stat file format. For post processingof GNSS-SDR solutions, the output files are named GSDR#Month_#day and forNovAtel are NV#month_#day. In this folder, .mat files for each day for NovAtel andGNSS-SDR are also attached. These files contain the extracted data from the .pos and.stat files so they can be analyzed in MATLAB. Output figures from Rtkpost are alsoincluded.


A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.


fGn series used for simulations in the article "Sobre la Generación de Tráfico Autosimilar con Dependencia de Largo Alcance Empleando Mapas Caóticos Unidimensionales Afines por Tramos (Versión Extendida)", "On the Generation of Self-similar with Long-range Dependent Traffic Using Piecewise Affine Chaotic One-dimensional Maps (Extended Version)". Available at:

They should be used in MATLAB R2009a.


This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.


fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series".  Available at:

They should be used in Selfis01b.


Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)


"Object classification from randomized EEG trials"


If you use this code or data, please cite the above paper.


See the paper "Object classification from randomized EEG trials" on IEEE Xplore.


Code for analyzing the dataset is included in the online supplementary materials for the paper.


The code from the online supplementary materials is also included here.


If you use this code or data, please cite the above paper.


We provide a public available database for arcing event detection. We design a platform for arcing fault simulation. The arc simulation is carried out in our local lab under room temperature. A general procedure to collect the arcing and normal current and voltage wave, is designed, which consists of turning on the load, generating arc, stoping arc, turning off the load. The data is collected by a 16bit, 10KHz high resolution recorder and a 12bit, 64000Hz low resolution sensor.


This file is collected from a tool holder with four stain gauges for detecting machining variation in a machine tool. The associated collected signal data and description will be provided soon. 


The dataset is composed of digital signals obtained from a capacitive sensor electrodes that are immersed in water or in oil. Each signal, stored in one row, is composed of 10 consecutive intensity values and a label in the last column. The label is +1 for a water-immersed sensor electrode and -1 for an oil-immersed sensor electrode. This dataset should be used to train a classifier to infer the type of material in which an electrode is immersed in (water or oil), given a sample signal composed of 10 consecutive values.


The dataset is acquired from a capacitive sensor array composed of a set of sensor electrodes immersed in three different phases: air, oil, and water. It is composed of digital signals obtained from one electrode while it was immersed in the oil and water phases at different times. 

## Experimental setup

The experimental setup is composed of a capacitive sensor array that holds a set of sensing cells (electrodes) distributed vertically along the sensor body (PCB). The electrodes are excited sequentially and the voltage (digital) of each electrode is measured and recorded. The voltages of each electrode are converted to intensity values by the following equation:

intensity = ( |Measured Voltage - Base Voltage| / Base Voltage ) x 100

Where the Base Voltage is the voltage of the electrode recorded while the electrode is immersed in air. The intensity values are stored in the dataset instead of the raw voltage values.

## Experimental procedure 

The aim of the experiments is to get fixed-size intensity signals from one electrode (target electrode) when being immersed in water and oil; labeled as +1 (water) or -1 (oil). For this purpose, the following procedure was applied:

- The linear actuator was programmed to move the sensor up and down at a constant speed (20 mm / second).

- The actuator stops when reaching the upper and bottom positions for a fixed duration of time (60 seconds).

- At the upper position, the target electrode is immersed in oil; intensity signals are labeled -1 and sent to the PC.

- At the bottom position, the target electrode is immersed in water; intensity signals are labeled +1 and sent to the PC.

- The sampling rate is 100 msec; since each intensity signal contains 10 values, it takes 1 second to record one intensity signal 

## Environmental conditions

The experiments were perfomed under indoors laboratory conditions with room temperature of around 23 degree Celsius. 

## Dataset structure 

The signals included in the dataset are composed of intensity signals each with 10 consecutive values and a label in the last column. The label is +1 for a water-immersed electrode and -1 for an oil-immersed electrode.

## Application

The dataset should be used to train a classifier to differentiate between electrodes immersed in water and oil phases given a sample signal.