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An Ultracompact Hyperspectral imager in the Thermal Infrared

The activity followed the development of an innovative hyperspectral imaging system for remote sensing applications in the thermal infrared. The camera is compact, lightweight and robust. The activity covered the development of the three modules of the imager, namely the spectrometer, the telescope and the detector. The consortium designed and manufactured an innovative spectrometer based on Fourier transform spectroscopy, consisting on calomel birefringent crystals, with working range in the thermal infrared.

Italy

S 2021-11

CNR
BioCeMe: Cellular Agriculture in Space

Future long-duration space missions or planetary outposts, such as on the Moon or Mars, will need solutions for producing food in situ, with a view to:

UK

20-D-T-TEC-01

Kayser Space Ltd
Commercial cloud computing in space

The ESA Open Space Innovation Platform Campaign for Cognitive Cloud Computing in Space (3CS) called for project proposals for the use of edge and cloud computing technologies in space. D-Orbit created a consortium including UniBap and Trillium to respond to this opportunity with a proposal to engage the cloud computing community to identify and fly applications on the ION SCV-004 mission.

United Kingdom

EISI_I-2022-00373

D-orbit
Cognition - distributed data processing system for lunar activities

Lunar exploration has only started within the last few decades, and interest is continuing to grow. Space agencies and private investors are looking at the opportunities that it brings and see more commercial interest in it. According to a Euroconsult study, 51 missions to the Moon are planned in 2020–2029, compared to just seven in the previous decade, allowing us to better explore Earth's natural satellite and prepare for long-term lunar exploration and, in the future, deep space.

Poland

EISI_I-2022-00375

KP Labs
Cellular Agriculture

As humans travel further into the Solar System for longer periods of time, many challenges must be solved. One challenge is ensuring an adequate food supply for the crew. Currently all of the food that is consumed by astronauts on the International Space Station (ISS) is prepackaged on Earth and transported to the ISS. This approach requires a significant investment of mass and the long-term stability of nutrients in the stored food is unclear. Especially the impact of radiation outside of low-Earth orbit may have a negative impact on the stored food.

Germany

20-D-T-TEC-01

Yuri
ONBOARD MULTI-FRAME SUPER RESOLUTION IMAGE

CubeSats have the potential to revolutionise Earth observation from space by exploring different operating points in terms of spatial, temporal, and spectral resolution. However, physical limitations, mostly in terms of size, prevent them from obtaining high-quality images. OPS-SAT, while characteriaed by a small form factor, provides an impressively capable and flexible hardware/software platform for executing demanding machine learning algorithms (inference stage).

Greece

21-D-S-OPS-02-g

OHB Hellas
Data compression as a service

The Data Compression as a Service (DCaaS) study aimed to bring the benefits of data compression to the experimenters on ESA's OPS-SAT platform. It focused on using the POCKET+ algorithm for the compression of housekeeping telemetry data. The project contained a space component, which performed the compression of provided user data, and a ground component, for the decompression of transmitted user data. Three use cases were selected within the scope of the project: file-to-file, packet-to-packet, and stream-to-stream lossless data transfers.

Germany

21-D-S-OPS-02-d

VisionSpace Technologies
A Comparative Study Of High-Level And Low-Level Implementations Of Deep Learning Models For Spacecraft

In this activity, Mission Control Space Services deployed a low-level implementation of the OPS-SAT SmartCam model using a Field Programmable Gate Array (FPGA), comparing against a high-level CPU model using Tensorflow Lite. Experiments showed that the FPGA implementation reproduced the precision and accuracy of the high-level model, while running at a slower speed. Further optimisations of the FPGA are expected to close the gap in timing and unlock new methods for deploying deep learning on spacecraft.

Canada

21-D-S-OPS-02-e

Mission Control Space...
LEO-GEO for Greenhouse Gases LEO-GEO4GHG

The LEO-GEO4GHG project studied the feasibility of new frontiers of cloud computing in space and artificial intelligence (AI) combining multiple sources of data such as the SATLANTIS GEI-SAT constellation and other external meteorological and atmospheric data with the final aim of providing near real-time methane detection and quantification.

Spain

EISI_I-2022-00367

SATLANTIS MICROSATS SA
NEU4SST - Neuromorphic Processing for Space Surveillance and Tracking

In this project the authors looked at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate whether event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

United Kingdom

EISI_I-2022-00363

UNIVERSITY OF STRATHCLYDE

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