Development of a simulation environment to test space missions COTS technologies
Bibliographic record
Abstract
The Canadian Space Agency's (CSA) Software and Ground Segment Section (SGS) has the mandate to develop innovative emerging software and on-board satellite and ground segment computer technologies. To that end, there is an ongoing development of a simulation environment to test COTS (Commercial-Of-The-Shelf) technologies. There are severe cost constraints in all aspects of many space missions due to the limited return on investment and scarce commercialization opportunities that come with many science missions. There is an opportunity to explore the innovative implementation of COTS technologies to reduce the mission cost and maximize performance available from COTS components. However, using COTS technologies in the space environment has ist constraints and therefore designing a spacecraft mission has to involve some new techniques that allow implementation of these components and minimize the risk of failure. The goal of our project is to develop a simulation environment, itself using COTS components, and then to allow the seamless integration of various components to test spacecraft mission concepts. For example, one of the aspects of using COTS processors in space is to protect them from the radiation environment. The current state of the simulation tests an innovative software EDAC (Error Detection and Correction) package and a redundant processor configuration to investigate protection against the effects of radiation and other failures on a generic mission. It also includes the capability to test formation-flying concepts that have the potential to revolutionize cost reduction efforts for space missions and to enable new space applications. This paper describes the simulation environment in detail and illustrates some of the technologies being tested for possible future space missions. The paper concludes with a look at the future development of the simulation environment and possible benefits of its use as well as the lessons learned to date.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".