·       9/11 hijackers network dataset [20]: The 9/11 hijackers network incorporates 61 nodes (each node is a terrorist involved in 9/11 bombing at World Trade Centers in 2011). Dataset was prepared based on some news report, and ties range from ‘at school with’ to ‘on the same plane’. The Data consists of a mode matrix with 19*19 terrorist by terrorist having trusted prior contacts with 1 mode matrix of 61 edges of other involved associates.

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wagyu

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xxx

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Twitter is one of the most popular social networks for sentiment analysis. This data set of tweets are related to the stock market. We collected 943,672 tweets between April 9 and July 16, 2020, using the S&P 500 tag (#SPX500), the references to the top 25 companies in the S&P 500 index, and the Bloomberg tag (#stocks). 1,300 out of the 943,672 tweets were manually annotated in positive, neutral, or negative classes. A second independent annotator reviewed the manually annotated tweets.

Instructions: 

Twitter RAW data was downloaded using the Twitter REST API search, namely the "Tweepy (version 3.8.0)" Python package, which was created to make the interaction between the REST API and the developers easier. The Twitter REST API only retrieves data from the past seven days and allows to filter tweets by language. The tweets retrieved were filtered out for the English (en) language. Data collection was performed from April 9 to July 16, 2020, using the following Twitter tags as search parameter: #SPX500, #SP500, SPX500, SP500, $SPX, #stocks, $MSFT, $AAPL, $AMZN, $FB, $BBRK.B, $GOOG, $JNJ, $JPM, $V, $PG, $MA, $INTC $UNH, $BAC, $T, $HD, $XOM, $DIS, $VZ, $KO, $MRK, $CMCSA, $CVX, $PEP, $PFE. Due to the large number of data retrieved in the RAW files, it was necessary to store only each tweet's content and creation date.

 

The file tweets_labelled_09042020_16072020.csv consists of 5,000 tweets selected using random sampling out of the 943,672 sampled. Out of those 5,000 tweets, 1,300 were manually annotated and reviewed by a second independent annotator. The file tweets_remaining_09042020_16072020.csv contains the remaining 938,672 tweets.

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Laminated Object Manufacturing (LOM) is an efficient rapid prototyping technology (RPT), a suitable technique with many applications. However, its application for wood product prototyping (WPP), especially in micro-thin wood objects, namely different types of wood veneers, the thermal heat effect emanating from LOM heat sources, challenge the quality of the final 3D wood products.

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This dataset consists of realistic simulated inverse synthetic aperture radar (ISAR) images of five commonly found automotive targets- a full-size car, a mid-size car, a bicycle, an auto-rickshaw, and a four-wheel truck.

Instructions: 

Due to the large size of the dataset, we have divided it into five .zip files, corresponding to each target, namely: Autorikshwa.zip; Bicycle.zip; Fullsize_car.zip; Midsize_car.zip; and Truck.zip.  Each *.zip file consists of five different signal to noise ratio (SNR) cases (+10, +5, 0, -5, -10 dB), and four range-Doppler based clutter cases (wind velocity +2.5, 5, 7.5 and 10 m/s). There are 16- different trajectories (four left turns, four right turns, four u-turns, and four straight trajectories) for each of these cases. We have approximately 45-49 images (.png and .mat format) corresponding to each case. 

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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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This code is provided here for research purpose(s) only. You are allowed to use this code/data provided that you cite the following papers:

 

R. Pilipović and P. Bulić, "On the Design of Logarithmic Multiplier Using Radix-4 Booth Encoding," in IEEE Access, vol. 8, pp. 64578-64590, 2020, doi: 10.1109/ACCESS.2020.2985345.

  

For questions and suggestions, please email Ratko Pilipović (ratko.pilipovic@fri.uni-lj.si).

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This dataset supports the manuscript entitled "Beam Diffraction by the Tip of a Cone and Creeping Beam Waves: Phenomenology Analysis Via the Generalized Complex-Source Method", accepted for publication in the IEEE Transactions on Antennas and Publication. The dataset consists of movies in both formats .avi and .mp4, which show time-domain representations of figures presented in the publication. They are intented to give a further insight into the wave physics and particularly to give evidence that the results are causal.

Instructions: 

All animations are provided in standard .avi-format in an all-in-one zip-file, and as individual files in .mp4-format, which each may be viewed by any suitable standard viewer. The meaning of the animations and the parameters are found in the above mentioned manuscript, where the name of the animation corresponds to the number of the figure in the manuscript.

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