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These are the Matlab scripts to import EEG data and perform data preprocessing and dimension reduction. Script files are in MATLAB .m format. Also included are various support and information files for this process; these files are in various formats (.doc, .xls, .ced, .dat). There is a MS-Word .doc file that explains the various files and scripts.

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The authors show parts of the data obtained from the simulation model and the used MATLAB program. The detailed steps are shown as follows: Step 1: Import data into the MATLAB software.Step 2: Run the program named ‘fft_algorithm’.Step 3: Run the program named ‘Solve_XX’ based on the fault type of data.

These are the scripts used to import the empirical data and perform the fast fourier transform (FFT) analyses reported in Trujillo, Stanfield, & Vela (2017). The effect of electroencephalogram (EEG) reference choice on information-theoretic measures of the complexity and integration of EEG signals. Frontiers in Neuroscience, 11: 425. doi: 10.3389/fnins.2017.00425. Data is in Matlab M-files format. Also included are MS Word files explaining the scripts and the empirical data files, and MS Excel files containing the de-identified demographics of the study subjects and EEG trial information.

Function name "cdf2mat" Please use this function to open MS-based chromatographic data from NETCDF (*.CDF) files. Resampling included for non-integer acquisition rates. Outputs nominal mass. Script optimized to process data from comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GCxGC-MS). Updated to remove negative noise signal. INPUT file: Opens the netCDF like 'Sample01.CDF' rate_MS: Desired integer acquisition rate OUTPUT FullMS Full MS chromatogram (second order data tensor) axis_min Retention time axis in minutes axis_mz m/z axis in Daltons I/O: [TIC,FullMS,axis_min,axis_mz] = cdf2mat(file,rate_MS) Compiled with MATLAB R2021b (v.9.11.0.1809720). Requires the Signal Processing Toolbox (v.9.0). Based on netCDFload.m (Murphy, Wenig, Parcsi, Skov e Stuetz) e de iCDF_load (Skov e Bro 2008). K.R. Murphy, P. Wenig, G. Parcsi, T. Skov, R.M. Stuetz (in press) Characterizing odorous emissions using new software for identifying peaks in chemometric models of GC-MS datasets. Chem Intel Lab Sys. doi: 10.1016/j.chemolab.2012.07.006 Skov T and Bro R. (2008) Solving fundamental problems in chromatographic analysis, Analytical and Bioanalytical Chemistry, 390 (1): 281-285. doi: 10.1007/s00216-007-1618-z

Probe signals_ClassificationMachine learning was performed by MATLAB. More than 400 events of each analyte type were collected to form a dataset. The label for each event was assigned with the known identity of the analyte. The dataset was then split into a training set (80%) and a testing set (20%) for model training and model testing. I/I0 and SD of events were employed as event features. Model training was performed using the Classification Learner toolbox of MATLAB. Mainstream classifiers were estimated with default settings. According to results of five cross validation accuracy and the testing accuracy, the model demonstrating the best performance would be chosen for further use. ****************************************************************************************************************************************************1. System requirements Hardware Requirements: RAM: 16+ GB CPU: 4+ cores, 3.3+ GHz/core GPU: NVIDIA GTX 1080 Software Requirements: MATLAB R2022b2. Introduction Probe signals_Classification is meant to serve as a machine learning-based nanopore analysis platform using MATLAB for the identification of different PTMs peptides. The program contains two steps as follows: a. Import dataset. b. Open "Classification Learner" APP in MATLAB. c. Train Classifier splits the dataset into a training dataset(80%) and a testing dataset(20%), then outputs training accuracy, testing accuracy and confusion matrix. 3. Operating procedures: -Unzip the Probe signals_Classification.rar to local folder. -Open either Probe signals_Classification.m in MATLAB -Enter the file name of the training dataset. // example: dataset.xlsx -Run Probe signals_Classification.m4. Output The values of the variables 'validationAccuracy' and 'TestingAccuracy ' are output in the MATLAB.

This dataset contains four MATLAB scripts designed for the processing, analysis, and visualisation of acceleration data obtained from Smart Rock sensors. These scripts facilitate importing raw data from Excel files, processing it to extract meaningful insights such as frequency spectra, signal peaks, and orientation information. Below is a brief overview of each script:

Retreive_raw_data.m: The main script responsible for importing raw acceleration and quaternion data from user-selected Excel files. It initiates the data processing pipeline by calling functions to import, visualise, and analyse the data. The script plots the acceleration data along the X, Y, and Z axes and manages quaternion data for further processing, such as conversion to rotation matrices.

importfile.m: A supporting function specifically designed to import acceleration data from the specified Excel worksheet. It extracts time series data along with acceleration values on three axes (X, Y, Z) and prepares the data for visualisation and analysis in the main script.

frequenzspektrum.m: This function calculates the frequency spectrum of a given signal using Fast Fourier Transform (FFT). It returns the amplitude and phase spectra, enabling frequency-domain analysis of acceleration signals. This script is often called during the analysis phase for detailed signal processing.

Composite acceleration and signal smooth.m: This script processes the imported acceleration data by resampling it to equal time intervals, applying low-pass and high-pass filters, detecting peaks in the signal, and performing Fourier Transform to analyse the frequency spectrum. It provides a more detailed analysis of the composite acceleration derived from the X, Y, and Z components.

An open preclinical PET dataset. This dataset has been measured with the preclinical Siemens Inveon PET machine. The measured target is a (naive) rat with an injected dose of 21.4 MBq of FDG. The injection was done intravenously (IV) to the tail vein. No specific organ was investigated, but rather the glucose metabolism as a whole. The examination is a 60 minute dynamic acquisition. The measurement was conducted according to the ethical standards set by the University of Eastern Finland.

The dataset contains the original list-mode data, the (dynamic) sinogram created by the Siemens Inveon Acquisition Workplace (IAW) software (28 frames), the (dynamic) scatter sinogram created by the IAW software (28 frames), the attenuation sinogram created by the IAW software and the normalization coefficients created by the IAW software. Header files are included for all the different data files.

For documentation on reading the list-mode binary data, please ask Siemens.

This dataset can be used in the OMEGA software, including the list-mode data, to import the data to MATLAB/Octave, create sinograms from the list-mode data and reconstruct the imported data. For help on using the dataset with OMEGA, see the wiki.

Matlab scripts related to acquisition and import of EEG data in the dataset.

Instructions to read h5ad file in Matlab: A mat file of the complete lemur cell atlas dataset converted from the h5ad file is provided in the Figshare files. We also provide a Matlab script to import the h5ad file to mat file: please download the h5ad file of interest, Matlab script “LCA_h5ad2Mat.m” and Matlab function “read_csmatrix.m” to the same folder, and run “LCA_h5ad2Mat.m”. The mat file contains a single variable named “rawData”, a Matlab structure variable with the following fields:cells: a table of the sequenced cells with metadata for individual sequenced cells (features of the table includes above “/obs” and “/obsm” list for the h5ad file, e.g., cell_name, tissue, free_annotation_v1, and X_umap, but not the MHC counts which is included in tabMHC, see below).genes: gene tablename: NCBI gene symbol.highly_variable: whether the gene is highly variable (calculated for the entire dataset).mat_raw: a sparse matrix of the cell by gene transcript count (raw count).mat_X: a sparse matrix of the cell by gene transcript level after library size normalization and natural log transformation (i.e., smartseq2, ln(reads/N *1e4 +1); 10x, ln(UMI/N *1e4 +1), where N denotes the total number of reads or UMI of the cell).tabMHC: a table of the calculated raw counts for the major histocompatibility complex (MHC) genes (see the Tabula Microcebus manuscript for detail). Note the count is only available for cells sequenced by 10x method and count is NAN for cells sequenced by smartseq2 method. Both raw counts and normalized counts (labeled with prefix letter ‘n’) are provided.MHC_C_I, MHC_NC_I, MHC_all_II: sum of counts from classical Class I genes.nMHC_C_I, nMHC_NC_I, nMHC_all_II: sum of normalized counts from classical Class I genes.counts and normalized counts from individual classical Class I genes (Mimu_168, Mimu_W03, Mimu_W04, Mimu_249, nMimu_168, nMimu_W03, nMimu_W04, nMimu_249), non-classical Class I genes (Mimu_180ps, Mimu_191, Mimu_202, Mimu_208, Mimu_218, Mimu_229ps, Mimu_239ps, nMimu_180ps, nMimu_191, nMimu_202, nMimu_208, nMimu_218, nMimu_229ps, nMimu_239ps), and Class II genes (Mimu_DMA, Mimu_DMB, Mimu_DPA, Mimu_DPB, Mimu_DQA, Mimu_DQB, Mimu_DRA, Mimu_DRB, nMimu_DMA, nMimu_DMB, nMimu_DPA, nMimu_DPB, nMimu_DQA, nMimu_DQB, nMimu_DRA, nMimu_DRB). version: version of the data (name of the h5ad file converted from).

This is a preclinical positron emission tomography (PET) dataset containing the list-mode data of a NEMA image quality phantom measured with the preclinical Siemens Inveon PET scanner. Included are the list-mode datafile (.lst), sinogram file (.scn) created by the Siemens Inveon Acquisition workplace (IAW) software, MAP-OSEM3D reconstruction (.img) created by IAW, scatter correction sinogram (_sct.scn) created by IAW and the attenuation correction UMAP-file (.img) created by IAW. All the corresponding header files are included that contain all the relevant information, with the exception of reading the binary list-mode data. For documentation on reading the list-mode binary data, please ask Siemens.

No normalization data is included in this dataset. You can, however, use the normalization data from Preclinical PET data.

This dataset can be used in the OMEGA software, including the list-mode data, to import the data to MATLAB/Octave, create sinograms from the list-mode data and reconstruct the imported data. For help on using the dataset with OMEGA, see the wiki.

The CT data, that was used to create the UMAP-file, is available from https://zenodo.org/record/4646835.

The measurement data was collected by Jarmo Teuho.

A ground-based microwave scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and-temperature dynamics of the top soil layer and air-to-soil interface during the period August 2017 - July 2019. The radar return (amplitude and phase) of the ground surface was measured over 1 - 10 GHz in the four linear polarization combinations (vv, hh, hv, vh) at hourly or half-hourly intervals .This dataset contains Matlab scripts that can be used to import the scatterometer measurement data and process it into backscattering coefficients. Date Submitted: 2021-02-12

These are the Matlab scripts to import the integration and complexity analysis on the dimension reduced EEG data. Script files are in MATLAB .m format. is a MS-Word .doc file that explains the scripts.

This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).

Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements

The dataset includes a metadata file providing the following information for each campaign:

date of collection;

start time and end time of collection;

length;

type (walking/driving).

Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.

The dataset contains the following data for NB-IoT:

Raw data for each campaign, stored in two .csv files. For a generic campaign , the files are:

NB-IoT_coverage_C.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);

NB-IoT_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.

Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

Estimated positions of eNodeBs, stored in a csv file for each city;

A matlab script and a function to extract and generate processed data from the raw data for each city.

The dataset contains the following data for 5G:

Raw data for each campaign, stored in two .xslx files. For a generic campaign , the files are:

5G_coverage_C.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);

5G_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.

Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

A matlab script and a supporting function to extract and generate processed data from the raw data.

In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:

A campaign-by-campaign linear interpolation (both NB-IoT and 5G);

A bidimensional interpolation on all campaigns combined (NB-IoT only).

A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:

L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. DOI: 10.3390/s23094266.

The second interpolation approach is instead introduced and described in:

L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.

Positioning using the 5G data was furthermore in investigated in:

K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," IEEE Communications Magazine, Volume 62, Issue 5, pp. 44-49, May 2024. DOI: 10.1109/MCOM.011.2200707.

G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: 10.1109/OJCOMS.2024.3499370.

Please refer to the above publications when using and citing the dataset.

FT: Fourier Transform.*Textread: Time taken to import data from disk to a MATLAB variable.

This dataset contains all identification and validation data used in the conference paper "Modeling Load-, Velocity-, and Temperature-Dependent Transmission Errors of Cycloidal Drives for Industrial Robots Using Fourier Series". The experimental data was recorded using an experimental setup to identify the transmission error of cycloidal drives as described in the paper. The data is divided into the two folders, "identification" and "validation" containing one file per measurement. The file names contain the parameter combination of load torque tau_load in N m, input-side velocity theta_dot in rad/s and lubricant temperature T in °C. Each measurement file contains a table with the input-side position theta in rad in the first column and the transmission error theta_TE in rad in the second column. The MATLAB script "plot_figures.m" can be used to plot the position domain data from the figures 3 to 6 in the publication. The function "read_data.m" is applied to import all measurement data into two MATLAB structure arrays "identification" and "validation".

The recordings were started asynchronously, but they can be lined up based on the impulse from the first gunshot. The Matlab file, create_data.m, is provided to pre-process the data for import into Matlab.

The following firearems were discharged in order:

Firearm_ID Class Firearm Ammunition Nominal fps # of shots

Pistol  SigM11A1,    3.8" bbl                 124 gr 9mm NATO, STANAG 4090, MEN    1200    &n...

Matlab scripts to import and create the defect surfaces for the model and graphs for the journal article. Please read the ReadMe file as this contains information to run the scripts and view the data. All script are in Matlab .m and dat file, but the CT data is a CSV file so it can be read into any program.