The overall goal of the TRACE project was to build a remote sensing based fully automated system for the detection and tracking of large marine litter and accumulation patches of smaller litter items in order to obtain precise and reliable data on floating macro-litter regarding their quantity, position, accumulation zones, material properties, floating depth, and sources. The tracking mechanism has been implemented by coupling the daily satellite-based detections with an oceanographic forecasting system and by a two-step object matching approach.

Remote detection and mapping of floating marine plastic litter (FMPL) from space needs to operate in synergy with clean up actions. This implicates that an observation system is needed combining high revisit and high spatial resolution. Currently there is no single remote sensing technology with sufficient revisit capabilities and spatial/spectral resolution relevant to FMPL dynamics. Therefore a combination of small drones, satellites and High Altitude Pseudo-Satellite (HAPS) is anticipated to provide the long term sustainable technical solution.

The goal of the project was to develop a service as a product to simplify fitting DNN (Deep Learning Neural Networks) in on board HW to make better use of AI on space missions The role of the service is to support data processing engineers in reducing HW resources requirement of powerful DNN for image analysis to be executed onboard. 

Space missions benefit greatly by the capability of the on-board GNC system to adapt rapidly to unknown environment. Autonomous vision-based navigation is a particular technology under implementation in several ESA missions. One of the most interesting applications in that field is the proximity operations around a small asteroid, like those in HERA.
The goal of this activity was to develop a navigation algorithm with the capability to fly over an unknown terrain and achieve better navigation performances than current vision-based techniques based on unknown feature tracking.

“Remote Sensing for Marine Litter - RESMALI” has identified the ideal parameters to study marine litter (ML) from space, including spectral bands with affordable signal-to-noise ratio (SNR) at top-of-atmosphere (TOA) level, in a range of wavelengths from visible to SWIR. To continue this path towards a dedicated mission, performance assessment against simulated real-case scenarios of ML is key. We want to build a simulator to estimate ML signal at TOA from a set of scenarios representing the various applications already targeted by the community (e.g.

With the increased frequency of shipping activities, such as tourism and freight’s transport, navigation safety has become a major concern, especially when human casualties, environmental issues and economic losses are considered. Even if new technologies have already supplied aids to pilots for the navigation risk reduction, the International Maritime Organization (IMO) reports that the majority of accidents could have been avoided by providing suitable input to the navigation decision-making process.

Quantum algorithms process information stored in qubits, the basic memory unit of quantum processors, and quantum operations (called gates) are the building blocks of quantum algorithms. In order to be run on quantum computing hardware, quantum algorithms must be compiled into a set of elementary machine instructions (i.e., quantum gates).

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.

SMARTIE (Smart Resource Management based on Internet of Things to support Off-Earth Manufacturing of Lunar Infrastructures) is an ESA study to assess the requirements, architecture, hardware and software needs and costs for implementation of an Internet of Manufacturing Things (IoMT) for a lunar human base and associated infrastructure.