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.

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.

Payload data transmitters in Earth Observation (EO) missions are moving from X-band to K-band (i.e. 25.5 to 27 GHz), where the downlink data rates can be increased due to the four times higher availability of bandwidth than in X-band. In addition, an interest is certainly growing in the field of small-size satellites and cube-sats. Therefore, there is a need to reduce the size/mass of the K-Band transmitter while increasing the DC-to-RF efficiency in order to relax the constraints of the thermal management systems at platform level.

Many use cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points – a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

In the ocean, transport and mixing processes tend to disperse matter in suspension over wide spatial (~100 km) and long temporal (~month) scales. Fronts appear at the boundary between water masses with different properties and are caused by diverse oceanic features and processes, including bottom topography. Frontal structures include tidal mixing fronts, shelf-break fronts, upwelling fronts, estuarine fronts, plume fronts, fronts generated by convergence or divergence of water masses, and frontal eddies (Acha et al. 2015; Largier 1993).

Currently, GNSS is the backbone of most Position, Navigation and Timing (PNT) solutions, in providing 24/7 continuous, all-weather, global coverage to an unlimited number of receiver-equipped users. There is an increasing dependency on the availability and reliability of GNSS. However, GNSS is widely known to be vulnerable to an evolving range of threats (natural and man-made, unintentional and deliberate).

GSTP project has increased OSS’ capabilities in the development and production of deployable helical antennas for MicroSats and NanoSats that are:
- Highly Stowage Efficient
- High Gain
- Low lead time
- Cost Effective
Indeed, manufacturing and test process and supply chain improvement have occurred throughout the lifecycle of the project.

The next generation of GNSS is expected to be more demanding in terms of RF power transmission in order to guarantee a proper availability and quality of the service, hence, enhanced interference immunity and indoor positioning could be achieved.The HPA currently under development for this application are targeting power levels in excess of 300 W.