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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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We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”.

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(a) Refractive index and (b) extinction coefficient spectra of Ge2Sb2Te3S2 (GSTS) measured by spectroscopic ellipsometry in the wavelength range from 200 nm to 2500 nm.

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The dataset is generated from the ice-cream factory simulation environmen that is composed of six modules (Mixer, Pasteurizer, Homogenizer, Aeging Cooling, Dynamic Freezer, and Hardening). The values of analog sensors for level and temperature are modified using three anomaly injection options: freezing value, step change and ramp change. The dataset is composed of 1000 runs, out of which 258 were executed without anomalies.

Link to github: https://github.com/vujicictijana/MIDAS

 

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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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