In this paper we present the enhanced X-ray Timing and Polarimetry mission. eXTP is a space science mission designed to study fundamental physics under extreme conditions of density, gravity and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring effects of QED, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, eXTP will be a very powerful observatory for astrophysics that will provide observations of unprecedented quality on a variety of galactic and extragalactic objects. In particular, its wide field monitoring capabilities will be highly instrumental to detect the electro-magnetic counterparts of gravitational wave sources. The paper provides a detailed description of: 1) The technological and technical aspects, and the expected performance of the instruments of the scientific payload; 2) The elements and functions of the mission, from the spacecraft to the ground segment.X-ray instrumentation, X-ray Polarimetry, X-ray Timing, Space mission: eXTP PACS number(s): 95.55. Ka, 95.85.Nv, 95.75.Hi, 97.60.Jd, 97.60.Lf

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As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment. Index Terms-Arbitrary geometry, direction-of-arrival (DOA) estimation, Internet of Vehicles (IoV), mutual coupling, sensor array, vehicle positioning. I. INTRODUCTION R ECENT decades have witnessed explosive growth in the demands on the Internet of Vehicles (IoV) [1]-[8]. Generally speaking, the IoV refers to the infrastructure that connects vehicles to intervehicle networks [9]-[12], intravehicle networks, and the vehicular mobile Internet. The IoV is a complex system that integrates vehicle technology with Manuscript

Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this paper, we first form an aviation big data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long shortterm memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.