The Relativistic Electron-Proton Telescope (REPT) Investigation: Design, Operational Properties, and Science Highlights

The Relativistic Electron-Proton Telescope (REPT) instruments were designed to measure ∼2 to >18 MeV electrons and ∼18 to > 115 MeV protons as part of the science payloads onboard the dual Radiation Belt Storm Probes (RBSP) spacecraft. The REPT instruments were turned on and configured in their science acquisition modes about 2 days after the RBSP launch on 30 August 2012. The REPT-A and REPT-B instruments both operated flawlessly until mission cessation in 2019. This paper reviews briefly the REPT instrument designs, their operational performance, relevant mode changes and trending over the course of the mission, as well as pertinent background effects (and recommended corrections). A substantial part of this paper highlights discoveries and significant advancement of our understanding of physical-processes obtained using REPT data. We do this for energetic electrons primarily in the outer Van Allen belt and for energetic protons in the inner Van Allen zone. The review also describes several ways in which REPT data were employed for important space weather applications. The paper concludes with assessments of ways that REPT data might further be exploited to continue to advance radiation belt studies. The paper also discusses the pressing and critical need for the operational continuation of REPT-like measurements both for science and for space situational awareness.

Task Planning for Multiple-Satellite Space-Situational-Awareness Systems

Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.