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In this investigation, the researchers have used a commercially available millimeter-wave (MMW) radar to collect data and assess the performance of deep learning algorithms in distinguishing different objects. The research looks at how varied ambiance factors, such as height, distance, and lighting, affect object recognition ability in both static and dynamic stages of the radar.
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A Latin square of order q is a q×q array with elements from {0,1,...,q-1} such that each value occurs exactly once in each row and column. Two Latin squares G and L are orthogonal if (G(x,y), L(x,y)) = (u,v) has exactly one solution for each (u,v) from {0,1,...,q-1}×{0,1,...,q-1}.
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Real sampled data of a moving vehicle is provided.
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MILP instances (in LP format) of the paper
Alberto Borghetti
A Mixed-Integer Linear Programming Approach for the Computation of the Minimum-Losses Radial Configuration of Electrical Distribution Networks
IEEE Transactions on Power Systems (Issue: 3, Aug. 2012, pp. 1264-1273)
https://doi.org/10.1109/TPWRS.2012.2184306
previously available at http://www.lisep.ing.unibo.it/optree.zip
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Service times for the scaling schemes compared in this paper with the administrative switching overheads averaged out
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SDTwittC consists of 200 authors evenly balanced by gender (100 for each). We identified the gender of the tweeters via their names and profile pictures. As potential copy-and-paste texts, both tweets and retweets are discarded in the first place. Only replies are compiled. The number of replies for each author varies from hundreds to thousands. Male authors produced 233926 replies whereas 219740 replies are generated by the female group
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This data set contains explementary data to demonstrate the utility of By-example data query in AutomationML
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The data is reduced Collatz dynamics that is denoted by occurred (3*x+1)/2 represented by "-'' and x/2 represented by "0", during the process from a starting integer to the first integer less than the starting integer. The format is: starting integer, binary representation, the count of (3*x+1)/2, the count of x/2, the reduced dynamics represented by "-'' and "0", the ratio - the count of x/2 over the count of (3*x+1)/2.
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This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.
The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.
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