The objective of the Space Weather impact on Arctic Navigation (SWAN) project has been to
develop and demonstrate new techniques, which are expected to improve on the current
state-of-the-art space weather forecasting techniques and products related to space weather
effects for Positioning, Navigation and Timing (PNT) systems in the Arctic, with particular
attention to the Greenland area.

The main objectives of this project were to develop a thruster prototype based on magnetic powder electrostatic propulsion and to test it in vacuum with direct thrust measurements. The aim was to check the robustness of the concept and to measure and calculate important performance parameters such as thrust and specific impulse, to allow a comparison with other thruster technologies. This project demonstrated for the first time the feasibility of electrostatic propulsion based on powder emission.

Permanence in microgravity conditions is well known to have detrimental effects on biological systems and living organisms, limiting in particular human space operations and exploration both over short and long term periods, as it promotes/accelerates some degenerative processes associated to aging and often to pathology onset on Earth.

Detecting, identifying, quantifying and tracking strategies for leakage plastic litter in terrestrial as well as aquatic environments using remote sensing technologies is expected to support future continuous monitoring of waste pollution at wide covering geo-spatial scales. The methodologies to detect plastic waste from airborne or satellite observations should be based on fundamental and well-characterised optical features.

Model based approaches have proved to be efficient in supporting engineering activities, models replacing traditional document-based approaches. Nevertheless even in most advanced deployments, a lot of engineering artifacts are textual either because the return on investment of introducing models is too low on this particular case or because the information is more efficiently expressed in natural language even if consistency and correctness issues appears.

Marine Litter Aggregation Forecast presents a new estimation of long-term global distribution of marine litter proceeding from land-based and from sea-based sources at global scale, taking advantage of numerical reanalysis databases of historical met-ocean conditions. Different machine learning techniques are applied to generate long-term climate-based series of those environmental variables that may affect the drift of marine litter over the sea surface such as currents, wind and wave-induced Stokes drift.

This work has primarily focused on establishing scenarios where inflatable devices could be the most useful. Given that these missions rely on aerodynamic drag that results in (potentially significant) heating, the authors have sought to find solutions to minimize heat fluxes acting on the device surface, and especially keeping it within the limits of flexible/foldable TPS materials for the aerocapture scenario.