A Radio Frequency Payload Testing Platform for Small Satellite Missions using Synthetic Spectrum Generation Techniques
Bibliographic record
Abstract
As spacecraft technology improves and smaller companies are able to fund their own space missions, desire for smaller spacecraft is constantly increasing. Radio Frequency Earth monitoring is a common payload type for these spacecraft, but these can be difficult to test before operations. Collecting representative testing data using an on-orbit pathfinder mission is expensive and time-consuming. Collecting testing data terrestrially requires the user to alter the collected data such that it represents on-orbit RF data, which is technically challenging. Testing RF payloads designed to monitor a specific terrestrial network can be accomplished using Network simulators, but these are usually designed to accurately reflect correct network behaviour. If the payload under test is designed to detect anomalous behaviour in these networks, network simulators are not capable of providing representative testing data. In this thesis a cost-effective, fast, and easy to use RF payload testing system is presented, including software and hardware components. Software Defined Radio technology is a core piece of this system, both as the subject of testing and as part of the test equipment. Due to the prevalence of complex architectures involved in Earth observation missions, especially multi-spacecraft missions, this testing system is designed to be simple and flexible. A module-based software architecture is used for the generation of simulated RF signals to ensure scaling is possible, allowing users to set up a wide range of testing scenarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".