LSTM
This paper investigated how to increase the number of connections among users in hierarchical non-terrestrial networks (HNTNs) assisted disaster relief service (DRS). We aim to maximize the number of satisfactory connections (NSCs) by optimizing the unmanned aerial vehicles (UAV) radio resources, computing resources, and trajectory at each time slot. In particular, the UAVs are exploited as aerial base stations (ABSs) to provide a link for the reduced capability (RedCap) user equipment (UE) based on power domain non-orthogonal multiple access (PD-NOMA).
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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.
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The data-set used in the paper titled "Short-Term Load Forecasting Using an LSTM Neural Network."
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The dataset consists of tracking data of over 23,000 vehicles travelling though five different roundabouts in Sydney, Australia. This data was collected by a vehicle outfitted with a ibeo.HAD Feature Fusion detection and tracking system. This system uses 6 ibeo LUX 4 beam, 25 Hz Lidar scanners to identify road users at a range of up to 200m, and has an on-board computer for classification and tracking, in real time.
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The dataset provides data for the article " LSTM-based Argument Recommendation for Non-API Methods"
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