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In this project the authors looked at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate whether event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

Tables and graphs are knowingly used to organise data within a company with different levels of depth and complexity. Knowledge graphs (KGs) are particularly useful because they can cope with data diversity (high-quality complete data and sparse and incomplete data), they have a high degree of scalability and flexibility (the semantic data model can be inter-operational, large, wide and as deep as needed) and, last but not least, they provide reasoning and inference capabilities.

This study addresses the problem of generating analytic approximations for spacecraft orbits in highly inhomogeneous gravitational fields. The project aims to develop an analytical tool that can be implemented algorithmically in standard symbolic software tools, such as Mathematica, or specialised algebraic tools, such as Piranha. The aim is to develop a general perturbative theory implementable in technical software that can rapidly generate orbits in highly inhomogeneous gravitational fields, for example, frozen orbits around asteroids for mission analysis.