Convolutional Neural Networks (CNNs) are more and more being used to solve very different kind of problems on ground and are also suitable to solve Space problems, e.g. visual based tasks for rovers. Embedding a CNN into an operational on-board software (OBSW) or hardware is challenging as the CNNs are composed by hundreds of operations and they request an extensive amount of memory that need to be used efficiently.

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This research integrates a complete diving mask with a custom-made VR headset to replicate a space-like setting. Alongside designing and building a waterproof VR headset, a pivotal aspect involves implementing a tracking system to ascertain spatial positioning and orientation. Employing an external tracking system, comprising four cameras housed underwater and affixed to aluminum rails this is achieved using fiducial markers.

The ASIL2ECSS ESA-funded activity aims to reduce the space qualification cost by reusing hardware and software developed for the automotive domain, which complies with the ISO 26262 functional safety standard. In order to do so, ASIL2ECSS created a mapping between the various ECSS standards and ISO 26262 and related automotive standards in a structured way.

The outer space is commonly perceived as one of the most hazardous environments for human beings. Nowadays, the training of astronauts relies on a wide range of courses and facilities to prepare them for any situation they may encounter in space. The combination of these training technologies has successfully prepared multiple generations of astronauts. However, these assets lack flexibility to customise the training process, and are limited in scalability to accommodate an increasing number of trainees.

The space mission development process tends to show several commonalities across projects. Indeed, systems often use the same generic set of technologies, with similar assumptions and requirements. The use of a Digital Assistant would be able to recognise such commonalities and propose suggestions during the early phase of system development based on previous projects. This would speed the definition stages, while also allowing to re-use potential lessons learned and hopefully to avoid simple mistakes/omissions.

The main objective of the activity was to evaluate the usage of Rust programming language in space applications, by providing an RTOS targeting ARM Cortex-M7 SAMV71 microcontroller, a BSP (Board Support Package) and a Demonstration Application. RTOS is implemented in the form of an executor instead of a classic scheduler. The scope of this project doesn’t include preemption. This executor runs tasklets, which are fine-grained units of computation, that execute a processing step in a finite amount of time.

The main objective of this activity was to design and develop an innovative virtual lab for Mars experiments, where AI methods are used to improve orbital imagery resolution, as well to obtain terrain features based on local images from rovers. Two different methods were used to recreate the virtual environment. The first one was dedicated to process low resolution images from orbital information. The main contribution in this sense was the use of AI to increase the resolution of these images.

In recent years, advances in Internet-of-Things (IoT) makes it easy to apply sensors at distributed applications like smart farming. However, scaling to large areas is possible but cumbersome. Another possibility is to acquire earth observation (EO) data from satellites which scales easily for large areas and even global wide monitoring. However, satellite data is not available continuously for a certain place and specific data is not available in case of clouds.

Documents are currently the preferred information exchange and distribution mechanism However maintenance, change traceability and cross referencing in documents is almost impossible to achieve. Thus, increasing not only the costs during the analysis stages but also the possibility of errors and use of outdated information.