Neuromorphic architectures are of interest in space application due to their energy efficiency. However, space radiation has been shown to damage computational hardware. Research has been performed on the radiation robustness of (pre-trained) spiking neural networks, and the brain has shown to be able to recover from certain types of lesions. Other work has shown Spiking Neural Networks (SNNs) can obtain advantages for graph algorithms. Such (distributed) SNN graph algorithmsmay become relevant for space applications. For example, SNNs may form a neuromorphic implementation of the Minimum Dominating Set (MDS) algorithm that others have proposed for distributed satellite swarm coordination. This research combines the trend of SNN implementations of distributed graph algorithms with neuromorphic space applications and brain-adaptation induced robustness as inspiration. It shows that brain inspired adaptation mechanisms can increase the simulated radiation robustness of an SNN implementation of an unweighted, distributed Minimum Dominating Set Approximation (MDSA) algorithm. An SNN implementation of the MDSA algorithm by Alipour et al. is created and used for this experiment. That SNN is adapted with a population coding approach, and with a sparse redundancy approach. These three SNNs are exposed to simulated radiation effects in the formof synapticweight increaseswhich occurwith a probability of 0.001% to 20% per synapse per timestep. Separate simulations are performed with a simulated radiation induced permanent neuron death with a probability of 0.01 % to 25 % per neuron per timestep. The SNN performance is measured by comparing its output to the unradiated algorithm output. The sparse redundancy increases the robustness against simulated radiation induced neuron death for radiation probabilities from0.5 % to 25% per neuron per time step, for theMDSA SNN. Belowthese probabilities, the adaptation mechanism is contra productive. Similarly, population coding is contra productive below0.1% and increases radiation robustness for simulated synaptic weight increase probabilities of up to 5%.

The first launch of the Ariane 6 launch vehicle is planned for 2020, however in its current design no significant part of the launcher will be reusable. A current trend in the global space market is decreasing the costs of spacecraft launches through recovery, retrieval and refurbishment of parts of launchers. As a first step towards this market demand, it is to be investigated whether it is cost-effective to recover, refurbish and reuse the key components of the first stage of the Ariane 6, which are contained in the Vulcain Aft Bay (VuAB). This is where the engine, fuel lines, thrust frame and electronics are attached. The team has the task to develop a cost effective way of reusing the Vulcain Aft Bay. In the preceding report, multiple concepts were analysed and one concept was selected to complete the conceptual design. This concept consists of an Inflatable Heat Shield for re-entry, a Parafoil to control the flight at lower altitudes and a Mid-Air retrieval using a helicopter to perform a soft landing. A functional analysis was performed to define concept specific functions. This was done by means of a Functional Flow Diagram and Functional Breakdown Structure. In order to fulfil these functions, simulations were created of the most critical moments of the mission. One set of simulations analyse the trajectory of the system throughout the mission, predicting the location of landing. The other set of simulations is used to predict the critical load cases of the system. The aforementioned simulations were used to design the individual components of the system. By integrating these simulations and managing the iteration process, the overall system characteristics and configuration were established. The system was then analysed for sustainability, reliability, risk, maintainability and safety. The requirement compliance of the system was then updated, detailing which requirements have achieved full compliance, marginal compliance, no compliance and which have not been sufficiently investigated. Proposals to make all requirements fully compliant are included in a feasibility analysis. These include different design approaches for the team and design changes to the VuAB to accommodate the recovery system. From there strategies on future verification and validation activities was set forth together with operational, refurbishment and production plans. These plans and the design of the system were used to create an updated business model with return on investment figures, which predict a significant cost reduction on a per launch basis within five years. Finally a set of future recommendations and plan is proposed for the continuation of the project.