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Stardust Reloaded

Periodic Reporting for period 1 - Stardust-R (Stardust Reloaded)

Reporting period: 2019-01-01 to 2020-12-31

Stardust-R addresses the growing need for a sustainable exploitation of space, the resilience of the space environment, the threat and opportunities coming from asteroids and comets.
Asteroids and space debris represent a significant hazard for both space and terrestrial assets, but they also represent an important commercial and scientific opportunity. From the exploitation of in-situ resources on asteroids, to on orbit servicing, from the exploration and characterisation of minor bodies to advanced concepts like the Phoenix programme of DARPA, both asteroids and space debris represent one of the most interesting challenges of space science and technology. Some scholars theorise that the Kessler syndrome (where the density of objects in orbit is high enough that collisions could set off a cascade) has already begun and is simply proceeding at a slow pace. Furthermore, current plans to inject increasingly larger constellations of small satellites into space and the emergence of the New Space trend, will critically increase the traffic. This requires a substantially new approach to collision avoidance and satellite operations, even before thinking of debris removal and satellite disposal. The impact on the space environment is going to be unprecedented, posing a serious question on its stability and resilience to any incident or anomalous event.
Although statistically less likely to occur, an asteroid impact would have devastating consequences for our plan-et. An impact with a large to medium (~10 km to ~300 m diameter) asteroid is unlikely, but not negligible and the long-term prediction of the probability of an impact is not a trivial matter. Furthermore, impacts with smaller size objects, between 10 m to 100 m diameter, are expected to occur more frequently and hence are, proportionally, equally dangerous for humans and assets on Earth and in space.

The aim of Stardust-R is to develop enabling technologies and effective solutions to critical and emerging problems in planetary defence, minor body exploration and the sustainable exploitation of space.
The key scientific objectives are: 1) to globally characterise the dynamics of objects around the Earth to define disposal solutions, 2) to correlate spatially and temporally distant events and families of debris to their parent object, 3) to quantify uncertainty in celestial mechanics to accurately predict the probability of impact and collision and quantify the resilience of space systems and environment, 4) to develop tools and methods for space traffic management, 5) to define a criticality index for small asteroids to identify the need for exploration/characterisation, the possibility for exploitation and the method of deflection, 6) to develop a new distribution model for small size asteroids(<100m), 7) to develop systems and algorithms to explore and land on minor bodies with small spacecraft.
Research, training and outreach activities started in December 2019 when recruitment of all 15 ESR was completed.
The Opening Training School (OTS) took place at the University of Strathclyde’s Insight Institute and Glasgow Science Centre on 2-7 December 2019. The OTS provided basic, but comprehensive training on all project Work Packages. http://www.stardust-network.eu/training/opening-training-school/
The Training School II (TS-II) “Exploration and Exploitation of Minor Bodies” took place at the Department of Aerospace Science and Technology of Politecnico di Milano on 10-13 February 2020. It was organised together with the UTOPIAE Local Training Workshop II. http://www.stardust-network.eu/training/local-training-school-i-2/
The Local Training Workshop I (LTW-I) organised by the Technical University of Madrid and Deimos Space took place 18-21 May 2020. LTW-I was dedicated to practical training on the topics covered in the OTS and the TS- II. Proposals for creating 4 project working groups (PWG) were presented by ESRs. http://www.stardust-network.eu/training/the-local-training-workshop-i/
The first Global Virtual Workshop (GVW-I) was organised by the University of Pisa (Italy) and the University of Belgrade (Serbia) in the period of 7-10 September 2020. GVW-I covered theme of “Exploration, Exploitation of Minor Bodies of the Solar System”. The event was held remotely using MS Teams videoconferencing software. http://www.stardust-network.eu/training/the-global-virtual-workshop-i/
During the 1st reporting period a number of outreach events hve been organised including 3 public lectures, several school visits, podcast, Instagram account, etc. http://www.stardust-network.eu/outreach/.
The project website (http://www.stardust-network.eu) and Twitter account (@Stardust_H2020) have been created at the start of the project and are maintain on a regular base.
Progress beyond the state of the art has been made in different directions.
Cutting edge computational intelligence techniques to multiple aspects of the resilience of the space environment. More concretely, novel neural network architectures have been employed in the task of forecasting space weather indices, and to build reduced order models of the atmospheric density. Deep symbolic regression was used to model epistemic uncertainty in the dynamic model of space objects.
Progress have been made on the characterization of the regular and chaotic dynamics, as well as the theoretical modelling and exploration of disposal strategies near lunisolar resonances for space debris at MEO, on the study of the orbital and rotational dynamics of a variety of space objects, on the use of machine learning in Celestial Mechanics, and on the study of the effect of lunisolar perturbations on highly eccentric orbits (HEO) and highly inclined orbits, and on the resonances connected to the effect of solar radiation pressure.
On the dynamics of small asteroids we identified the mechanisms that may affect especially the transport of small asteroids from the main belt, between Mars and Jupiter, to the vicinity of Earth.
Advanced machine learning techniques have been developed for Image segmentation, and for navigation about irregular bodies.
On the sustainability of the space environment good progress have been made on a problem often disregarded in the study of sustainable operations in a high traffic environment, due to its large scale. This is the all versus all problem, in which, instead of placing the focus on the potential high risk collisions of one single object versus the rest of the catalogue, the whole catalogue of objects is analysed. To reduce the computational complexity of this problem, the use of AI techniques is being investigated.
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