"Leveraging breakthroughs in remote sensing capabilities, including quantum-based methods and onboard intelligence, will provide highly accurate and timely environmental data critical for understanding and mitigating ecological changes."

ESA Strategy 2040

Summary Description

With this SysNova challenge ESA aims to analyse novel mission concepts powered by disruptive computing paradigms (e.g., neuromorphic computing, quantum computing, photonic computing, event-driven processing, etc.) possibly coupled with corresponding sensing technologies (e.g., quantum sensing or neuromorphic sensing) that could enable novel applications or significantly improve missions trade-offs.

The SysNova builds on numerous past studies that had addressed AI algorithms on-board as well as edge and neuromorphic computing on-board. Examples are Ф-sat-1, OPS-SAT, Ф-sat-2, a study on space-based datacenters, and the Discovery Campaign on Cognitive Cloud Computing in Space.

By integrating novel computing paradigms and accelerating their maturation for space applications, ESA aims to prepare the future of space computing and their emerging capabilities, ensuring that next-generation missions can enable novel applications, handle increasing complexity, and data demands with greater autonomy, efficiency, and intelligence. 

Background

The proposed mission study aligns with the ESA Technology Vision 2040 as well as ESA’s Strategy 2040 objective of advancing technologies, missions, applications, and services to address climate change, environmental degradation, and increasing pressures on natural resources [1, 2]. By developing cutting-edge computing technologies, ESA seeks to enhance Europe's leadership in sustainability and remote sensing while driving innovation in space-based solutions. 

Indeed, the SysNova challenge builds upon past and running ongoing initiatives, which include particularly those involving application-specific AI models deployed onboard spacecraft (edge computing). These efforts have led to numerous concept studies, low TRL prototypes, and IOD missions using  Artificial Intelligence (AI) on board Earth Observation (EO) satellites (e.g., Ф-sat-1, OPS-SAT, Ф-sat-2) and on board the ISS (e.g.,  ORBIT-STAR demonstrator) or for other applications (e.g., EdgeSAT, Hera).   Numerous edge computing activities were also conducted by the ESA Φ-lab.

In addition, a plethora of previous activities have investigated the use of neuromorphic computing technologies on board spacecraft - also coupled with AI [3] - for computer vision, Earth Observation [4],  satcom applications [5], and others [6]. In addition to neuromorphic computing, other previous works have investigated the usability of neuromorphic cameras for various space applications [6, 7, 8], also in combination with neuromorphic computing [9].  The THOR-DAVIS project [7,8], for instance, aimed at using event-based cameras on board the ISS to augment thunderstorm monitoring by leveraging the high-speed acquisition capabilities of these camera and their event-based nature. 

Moreover, through previous OSIP campaign on Cognitive Cloud Computing (3CS),  ESA investigated the  potential benefits of combining AI with  other computing paradigms (edge computing, cloud computing in space, neuromorphic computing) and other technologies (inter-satellite links, blockchain) on responsiveness, autonomy, data efficiency of space infrastructure. 

Additionally, ESA has been investigating quantum technologies through a set of activities covering quantum sensing (e.g., [10]) Quantum Key Distribution [11], Quantum Computing (e.g., [12]) and Atomic Clock Networks (e.g., [13]).

Capitalizing on these previous activities, the SysNova Challenge aims to investigate the impact of disruptive computing paradigms (e.g., neuromorphic computing, photonic computing, etc) in enabling novel mission paradigms by funding mission concepts studies.

What Ideas are we looking for?

We are seeking high-level mission concepts with disruptive potential to impact the space sector. Each idea should include a comprehensive mission outline, emphasizing the integration of novel computing and sensing paradigms to enable novel applications or significantly improve mission trade-offs.

The proposed mission concepts must clearly demonstrate improvements over traditional systems, either through novel applications or by addressing existing system-level bottlenecks. 

We are looking for ideas that:

• Focus on one (1) or more target applications and clearly elaborate the mission objectives. 

• Propose a complete mission concept (not a subsystem), driven by one (1) or more disruptive computing paradigms, possibly combined with sensing technologies and other relevant technologies (e.g., low-latency tasking systems).

• Clearly explain how the mission concept would deliver transformative benefits for the targeted application(s) and/or significantly improve mission trade-offs compared to traditional approaches with respect to the target application-level or mission-level Key Performance Indicators (KPIs) (see below). Incremental improvements are out of scope.

• Rely on enabling technologies that are either already available (in COTS form) or whose development is straightforward and unlikely to delay the mission timeline (i.e., technologies that can be developed in parallel with future activities).

• Include a preliminary mission development plan, covering launch, commissioning, operations, and decommissioning.

Mission KPIs

Each idea shall significantly improve at least three (3) of the following application-level and/or system-level KPIs (e.g., 1 application-level KPI and two system-level KPIs) without impacting other KPIs (overall mission/application trade-offs improved). We encourage to address at least one (1) application-level KPI.

Application-level KPIs

Please notice that number between brackets are not target KPIs improvements but are cited for sake of examples.

• Reduction in end-to-end latency from anomalies acquisition to end-users notification (e.g., from hours to minutes).
• Improvement in EO events detection/classification/segmentation performance (e.g., using accuracy, IoUs, mAP, or other application-specific metrics).
• Improvement in fault prediction accuracy (e.g., 40% increase in accuracy).
• Autonomous navigation accuracy improvement: Target improvement from meters down to centimeters.
• Reduction in communication latency (e.g., achieve latency reductions of at least 50%).
• Increased mission duty-cycles (e.g., 30% increment in mission duty cycle).
• Enhanced spatial resolution. 
• Other application-specific KPIs (to be clearly explained in the submission form - including justification why this KPI is relevant to optimize)

System-level KPIs

Please notice that number between brackets are not target KPIs improvements but are cited for sake of examples.

• Average power-consumption per-orbit reduced (e.g., 50% power consumption reduction),
• Enhance mission reliability and reduced mission [un]availability,
• Increase onboard autonomy via adaptive learning, real-time decision-making, and anomaly response,
• Enhanced processing data-rate with the  same power budget  (e.g., 30% increment of processing data-rate with the same power budget),
• Reduced satellite mass (e.g., with other KPIs to be constant),
• Reduced operational costs,
• Reduced downlink data-rates required a parity of information insights delivered to the users,
• Reduced data-processing latency (e.g., enabling online data processing) with the same power budget,
• Reduced Cost-of-acquisition-per-pixel.
• Other system-level KPIs, (to be explained clearly in the submission form - including justification why this KPI is relevant to optimize)

Mission Requirements

The following  mission requirements are applicable:

• The total reference mission cost (including development, operations and decommissioning  but excluding launch) shall be lower or equal to 50 M€,
• The mission category shall be gamma or delta (Figure 1),
• Satellite operational lifetime: Target satellite lifetime of min 1 year,
• The reference acceptance review of the mission and its sub-elements shall be not later than 30th June 2028.

Figure 1: ESA Mission classification. 

Target applications

The application focus for the SysNova Challenge is set on Earth Observation (EO), where novel computing paradigms can be combined with recent advancements of Artificial Intelligence (AI) to enhance data processing capabilities addressing the ever-increasing volumes of data where traditional computing architectures face growing constraints in efficiency, responsiveness and energy consumption.  Examples of target EO applications include:

• Early-latency systems for civil security applications and immediate environmental monitoring (e.g., early detection for disaster response, maritime security, etc),
• Novel commercial EO systems enabled by onboard intelligence (e.g., space-based datacentres, satellite as services missions, autonomous traffic monitoring, etc),
• Novel autonomous Observation services (e.g., cognitive SAR systems, heterogeneous satellite constellations with autonomous coordinated tasked acquisitions, autonomous anomaly detection systems in oceanic and desertic areas, etc),
• Others.

In addition to EO applications, the SysNova Challenge will also consider ideas for which disruptive computing paradigms are expected to be an enabling technology or bring significant and transformative benefits, such as:

•  Secure satellite communications and enhanced connectivity, such as:
  • Communications: Adaptive resource allocation and interference management,
• Mission Concepts aiming to perform in-orbit demonstration of autonomous mission critical operations, such as:
  • Health Monitoring: Predictive maintenance and proactive fault handling,
  • Autonomous operations: Enhanced satellite autonomy and adaptive AOCS, pointing, docking,
  • Autonomous onboard domain adaptation (e.g., unsupervised continual learning from domain changing data, etc),
  • Others.

In which mission segment shall I apply disruptive computing paradigms?

The challenge considers exclusively upstream applications of the disruptive computing paradigms. More specifically, the challenge focuses into the mission space segment (i.e., on board spacecraft, both at platform and payload level). However, applications of such computing paradigms at ground segment level to enable mission operations are also considering when significant advantages for the mission concepts is proven. Downstream applications (e.g., on ground processing for specific downstream applications) are considered out of scope for this challenge.

Expected study outputs

Submissions selected for the analysis phase (evaluated positively at idea stage and proposal stage) are expected to propose a mission concept enabled by disruptive computing paradigms, clearly highlighting the expected benefits for the target application(s) and/or mission trade/offs. It will also identify the critical technologies required to ensure mission feasibility. The expected maturity level at study closing is expected to be equivalent to a pre-phase A study at mission-level.

To these aims, the expected outputs from each study will include:

• Mission and requirements definition: refine in detail the purpose and mission objectives deriving consistently user and mission requirements and  preliminary system-level requirements (at mission-level). 
• Conceptual design: based on the mission definition and system requirements, a conceptual design of the mission will be developed. This includes estimations of power management, size, mass, deployment, scalability, and thermal management. 
• Preliminary cost and schedule: provide an initial cost and timeline estimate based on the conceptual design and preliminary system-level requirements. 
• Risk assessment: identify, evaluate, and assess potential risks associated with adopting novel computational methods onboard. 
• Feasibility analysis: assess the technical, financial, and schedule feasibility of integrating advanced computational techniques into the mission. 
• Benchmarking and preliminary trade-off analysis: clearly assessing advantages of the proposed novel computing paradigms (and related sensing, if used) for matching the mission requirements with respect to standard computing paradigms. Benchmarking shall clearly showcase how the introduction of disruptive computing paradigms overall leads to improved application/mission trade-offs (with significant improvements of the target application and mission KPIs). This can be done,  for instance, by comparing to previous applications in the available literature. Furthermore, you should investigate market opportunities for the mission concept and the adoption of advanced computational methods in the broader space industry. 

Process

Step 1) Idea Phase 

This campaign serves as the first step of the SysNova process. It will remain open for ~10 weeks (please refer to the campaign timeline for up to date & accurate information). Companies or consortia are asked to submit their system concepts ideas, following the requirements and points stated above (see ‘What are we looking for’). Ideas that are deemed eligible (see special conditions below) to this campaign will be evaluated in a batch at the end of the campaign, based on the evaluation criteria defined at the bottom of this page. It is anticipated that following the evaluation, a group of up to eight (8) ideas will be selected to enter into the step 2 (proposal phase). At the end of idea Phase, before being evaluated, ideas will be forwarded to Community Discussion. During this period, authors will have the possibility to improve their ideas based on ESA's inputs.  

Step 2) Proposal Phase

Following the idea evaluation in this campaign, the selected teams will be invited to submit a full proposal to ESA via a Call for Proposal without Procurement on esa-star Publication. This full proposal will include in addition to a Technical Proposal also a small cover Letter. Example template of the technical proposal for this second step can be reviewed in the attachments of the campaign (the template is only for information and is subject to change). Details on the process for the second step will be communicated to idea authors in case of a successful 1st step evaluation. 

During this second step, teams will typically have four weeks to submit the full proposals to esa-star, including all elements.

Following the proposal evaluation, ESA will confirm up to five (5) winners and place a Cooperative Agreement (see below attached draft cooperative agreement for information) with them. The invite teams will following this start the analysis phase.  

Step 3) Analysis Phase (SysNova Study Activity)

The selected proposals will perform the pre-phase A analysis of a mission leveraging innovative computational paradigms. This analysis phase will last no more than six months and will cover the concept development up pre-phase A maturity. It will result in a final report (analysis report mentioning all the points mentioned in the section "Expected study outputs").

A final review will be held one month after the submission of the first version of the analysis final report. This session will involve both the study teams and ESA experts.

Any comments agreed upon during the final review will be implemented in the final challenge analysis report.

The implementation path foreseen for this campaign are small system study, addressing overall mission architecture design. These have maximum budget from ESA of €100 000 per activity and a maximum duration of six months. Activities are funded within ESA’s Preparation Element.

Step 3) SysNova Challenge Winner

Following the review of the analysis phase ESA intends to select the winning concept to be invited for a joint CDF study with ESA to further elaborate the mission concept.

It should be noted, that a potential follow up after the joint CDF study will as a baseline be implemented in Open Competition and will require separate approval by ESA's boards and member states.

References

1. ESA Strategy 2040. (link)
2. ESA Technology Vision 2040. (link)
3. Neuromorphic AI Onboard. (link)
4. Lunghi, Paolo, et al. "Energy efficiency analysis of Spiking Neural Networks for space applications." arXiv preprint arXiv:2505.11418 (2025). (link)
5. NeuroSat - The Application of Neuromorphic Processors to Satcom Applications. (link)
6. Dario Izzo et al., "Neuromorphic Computing and Sensing in Space",  Artificial Intelligence for Space: AI4SPACE. CRC Press, 2023. 107-159. (link)
7. Chanrion, Olivier, et al. "Observations of thunderstorms with a neuromorphic camera: First results of the THOR-DAVIS experiment on the International Space Station." EGU General Assembly Conference Abstracts. 2024. (link)
8. New images of rare thunder. (link)
9. NEU4SST - Neuromorphic Processing for Space Surveillance and Tracking. (link)
10. Quantum-based MAGSCA aboard Juice. (link)
11. The Eagle-1 mission, Europe’s first satellite-based Quantum Key Distribution system. (link)
12. QUANTUM COMPUTING FOR EARTH OBSERVATION STUDY (QC4EO STUDY). (link) (link)
13. The Atomic Clock Ensemble in Space (ACES) onboard ISS. (link)
14. H. Al-Hraishawi, J. u. Rehman and S. Chatzinotas, "Quantum Optimization Algorithm for LEO Satellite Communications based on Cell-Free Massive MIMO," 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023, pp. 1759-1764, doi: 10.1109/ICCWorkshops57953.2023.10283753. (link)
15. A. Guillaume et al., "Deep Space Network Scheduling Using Quantum Annealing," in IEEE Transactions on Quantum Engineering, vol. 3, pp. 1-13, 2022, Art no. 3102413, doi: 10.1109/TQE.2022.3199267. (link)