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Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers. [ABSTRACT FROM AUTHOR]

LIDAL (Light Ion Detector for ALTEA, Anomalous Long-Term Effects on Astronauts) is a radiation detector designed to measure the flux, the energy spectra and, for the first time, the time-of-flight of ions in a space habitat. It features a combination of striped silicon sensors for the measurement of deposited energy (using the ALTEA device, which operated from 2006 to 2012 in the International Space Station) and fast scintillators for the time-of-flight measurement. LIDAL was tested and calibrated using the proton beam line at TIFPA (Trento Institute for Fundamental Physics Application) and the carbon beam line at CNAO (National Center for Oncology Hadron-therapy) in 2019. The performance of the time-of-flight system featured a time resolution (sigma) less than 100 ps. Here, we describe the detector and the results of these tests, providing ground calibration curves along with the methodology established for processing the detector's data. LIDAL was uploaded in the International Space Station in November 2019 and it has been operative in the Columbus module since January 2020. [ABSTRACT FROM AUTHOR]