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Transportation

This document is a supplementary file to the our article entitled “A Wireless Charging System Based on a DR-IPT to Power a UAV from Distribution Poles” which is published in IEEE Transactions on Industry Applications journal. This documents presents the result dataset regarding the optimizations performed to determine optimum parameters for domino resonant inductive power transfer system under several case studies.

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With the rapid development of high-speed trains (HSTs), safety and reliability in HSTs operation have attracted plenty of attention. The wheel, as one of the most crucial components, requires absolutely high reliability. The non-roundness wear is a typical failure mode of the HST wheel, which can have a detrimental influence on track and train components, leading to increased risks of rail breaks, sleeper cracking, high-cycle fatigue of wheels and axles, and bearing damage.

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This data provides an overview of the simulation of Hybrid Electric Vehicles (HEVs) using MATLAB. HEVs have emerged as a promising solution for reducing emissions and improving fuel efficiency. MATLAB offers a flexible platform to accurately model and analyze the complex dynamics of hybrid powertrains, including various components and control strategies. The simulation capabilities enable the evaluation of HEV performance under different conditions, optimization of designs, and exploration of factors impacting energy consumption.

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The provided dataset encompasses information essential for analyzing and modeling the research findings presented in the paper, "Driver-pedestrian interactions at unsignalized crossings are not in line with the Nash equilibrium." This dataset includes an array of data related to road user kinematics, demographic characteristics, and personality traits such as social value orientation.

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Since the longitudes and latitudes of the drivers in the Gaia dataset are mainly in city of Chengdu, it is not in the same area as the longitudes and latitudes in the EUA-dataset from Australia, we translate the latitudes and longitudes of drivers to Melbourne, Australia.The drivers will be located around the users and the base stations of the Melbourne subset of EUA-dataset. 

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The high-precision and long-distance extraction and construction of vehicle trajectory data and microscopic traffic flow characteristics are critical for traffic safety studies. Current research typically relies on a limited number of datasets which suffer from vehicle detection inaccuracy and limitation of the coverage area. Therefore, we establish a high-precision and long-distance vehicle trajectory dataset of urban scenarios.

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The extraction and construction of high-precision and long-distance vehicle trajectory data and microscopic traffic flow characteristics are critical for traffic safety studies. Current research typically relies on a limited number of datasets which suffer from vehicle detection inaccuracy and limitation of the coverage area. Therefore, we establish a high-precision and long-distance vehicle trajectory dataset of urban scenarios, which is also named as WUT-NGSIM.

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