Encoding and decoding tables for Enhanced Reduced Media-Independent Interface. Dataset contains 3 sets of lookup tables implementing encoder and decoder. Proposed encoder/decoder garantee that 2-bit TXD/RXD will change each data transmission cycle, making it possible for ERMII interface to work without REF_CLK, TX_EN and CRS_DV lines in difference to regular RMII.

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This is the dataset of mmWave massive MIMO beamspace channels, which is used for the experiment implementation of the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems". The source code of the experiment implementation is also open-access on the Github repository DeepBP-AE.

1. The "DeepMIMO_dataset.mat" is the spatial-domain channel dataset that can be reproduced by running "DeepMIMO_Dataset_Generator.m". For more details, refer to the public DeepMIMO dataset "DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications".

2. The "H_beam_sparsity_syn3.mat" is the beamspace-domain channel dataset that can be reproduced by running "deepMIMO_beamspace_channels.m".

3. For more detailed parameter settings, please refer to the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems" (currently under review).

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Reddit is one of the largest social media websites in the world and it contains valuable data about its users and their perspectives organized into virtual communities or subreddits, based on common areas of interest. Substance use issues are particularly salient within this online community due to the burgeoning substance use (opioid) crisis within the United States among other countries. A particularly important location for understanding user perceptions of opioids is the Philadelphia, Pennsylvania, USA region, due to the prevalence associated with overdose deaths. To collect user sen

Included is the dataset in a CSV file, data dictionary for all variables (column key) in a text file, keyword list used to query the Reddit API in a text file, and the targeted subreddit list in a text file. The dataset comprises entries (submissions, comments) that had keyword query results within targeted subreddits. The dataset includes designations for submissions and comments within the data dictionary; submission denotes the first order entry within a subreddit, comment denotes entries that are posted in response to submissions or other comments. Rows include all potential entries within the targeted subreddits from January 1, 2005 – May 14, 2020.

There are 56,979 rows of data in the CSV file.

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This dataset covers IP address assignment durations over 6 years, from 13/3/2014 to 26/6/2020. Around 600,000 assignment durations for IPv4 addresses and 190,000 durations for IPv6 addresses is covered. The dataset was processed from the archive of RIPE Atlas ftp.ripe.net/ripe/atlas/. IP addresses of the Atlas probes installed behind NAT are included.

Dataset files:

- dataset_IPv4.csv - IPv4 address assignment durations per end hosts

- dataset_IPv6.csv - IPv6 address assignment durations per end hosts

Fields:

- atlas_id - RIPE Atlas probe ID

- ip - active IP address

- duration - number of days of IP address assignment*

- dead - if TRUE then IP address change was observed; if FALSE then IP address change was not observed

*Changes were observed in day resolution. For example, change between the 1st and 2nd day is therefore indicated by an IP address assignment duration of 1.5 days.

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The Costas condition on a permutation matrix, expressed as row indices as elements of a vector c, can be expressed as A*c=b, where b is a vector of integers in which no element is zero. A particular formulation of the matrix A allows a singular value decomposition in which the eigenvalues are squared integers and the eigenvalues may be scaled to vectors with all integer elements. This is a database of the Costas constraint matrices A, the scaled eigenvectors, and the squared eigenvalues for orders 3 through 100.

Please refer to the file CC_SVD_Database_Readme.pdf for instructions on the format of the database, and its use. The database contains one file for each order. The files are CSV files in which each line ends with a comma, then a plain text remark that explains that line.

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The database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST http://www.ti.com/tool/IWR1843BOOST) deployed in different positions around a robotic manipulator.

The database that contains the raw range-azimuth measurements obtained from mmWave MIMO radars inside a Human-Robot (HR) workspace environment.

The database contains 5 data structures:

i) mmwave_data_test has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements of size 256 x 63: 256-point range samples corresponding to a max range of 11m (min range of 0.5m) and 63 angle bins, corresponding to DOA ranging from -75 to +75 degree. These data are used for testing (validation database). The corresponding labels are in label_test. Each label (from 0 to 5) corresponds to one of the 6 positions (from 1 to 6) of the operator as detailed in the image attached.

ii) mmwave_data_train has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements used for training. The corresponding labels are in label_train.

iii) LABELS AND CLASSES: label_test with dimension 900 x 1, contains the true labels for test data (mmwave_data_test). These are the (true) classes corresponding to integer labels from 0 to 5. Each class corresponds to a subject position in the surrounding of the robot, in particular:

CLASS (or LABEL) 0 identifies the human operator as working close-by the robot, at distance between 0.5 and 0.7 m and azimtuh 40-60 deg (positive).

CLASS 1 identifies the human operator as working close-by the robot, at distance between 0.3 and 0.5 m and azimtuh in the range -10 + 10 deg.

CLASS 2 identifies the human operator as working close-by the robot, at distance between 0.5 and 0.7 m and azimtuh 40-60 deg (negative).

CLASS 3 identifies the human operator as working at distance between 1 and 1.2 m from the robot and azimtuh 20-40 deg (negative).

CLASS 4 identifies the human operator as working close-by the robot, at distance between 0.9 and 1.1 m and azimtuh in the range -10 + 10 deg.

CLASS 5 identifies the human operator as working at distance between 1 and 1.2 m from the robot and azimtuh 20-40 deg (positive).

iv) label_train with dimension 900 x 1, contains the true labels for train data (mmwave_data_train), namely classes (true labels) correspond to integers from 0 to 5.

v) p (1 x 900) contains the chosen random permutation for data partition among nodes/device and federated learnig simulation (see python code).

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Supplementary material for the paper: 'Adaptive Block Compressive Imaging: towards a real-time and low complexity implementation'

All instructions are in the attached readme document.

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2D geometrically shaped constellations that are simultaneously robust to both residual phase noise and AWGN (GS-RPN) for 8, 16, 32 and 64-ary formats. The presented formats are optimised at the generalised mutual information (GMI) threshold of 0.96m bits/symbol, where m is the number of bits per symbol. Additionally, we added AWGN-only constellations (GS-AWGN) to serve as a reference.

Note: This is a supplementary dataset for a journal submission.

Each modulation order is placed in a separate folder, in which, every text file has the coordinates for the in-phase and quadrature components of each symbol in the first and the second column, respectively. The bit mapping for each symbol is natural mapping for the line number, i.e., 000 001 010 011 100 etc.

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This MATLAB dataset (.mat) contains the collected real measurement data from a total of 470 access points (APs) deployed in the Linnanmaa campus of the University of Oulu, Finland. The measurements include IDs, dates of data collection, number of users, received traffic data, transmitted traffic data and location names of each AP. Each observation of traffic data and number of users provide the data value at every 10-minute interval between December 18, 2018 and February 12, 2019. Please cite this as: S. P. Sone & Janne Lehtomäki & Zaheer Khan.

Major component description: There are 3 main major components: number of users connected at collected time (numb_users), received traffic data in bytes (rxbytes) and transmitted traffic data in bytes (txbytes) of each AP in this dataset. Dates and times of data collection (date) can be converted into the serial date number by using datenum() function in MATLAB.

Received and transmitted traffic data are in the cumulative time series format so that differencing every 2 consecutive observations is required to get the actual values at every 10-minute. It can be done by using diff() function in MATLAB, for example, "diff(ap184016.txbytes)".

Setup and running instructions: First, MATLAB must be installed in the computer correctly. Then, the downloaded dataset should be placed in the folder whose path is already specified in MATLAB (see https://in.mathworks.com/help/matlab/matlab_env/specify-file-names.html).

Once the dataset (APs_dataset.mat) is loaded correctly in MATLAB, total 470 structure arrays with the IDs of each AP will appear in MATLAB Workspace. Then, the desired time series can be called in MATLAB, for example, "Tx_data = diff(ap184016.txbytes);".

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Including the symbol under demodulation in data-aided reference/pilot recovery process, dominates the contribution of all the received sybmols. Therefore, excluding the contribution of the symbol under detection allows other symbols to equally contributeto the reference estimation.

Each folder is named by its corresponding figure.

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