An Efficient Approach to Subsystem Design: Autonomous GNC - A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Budgets are shrinking, therefore the space community is focusing on small satellites to satisfy a wide range of applications. New and emerging technologies are helping to make small satellite development less costly. However, matching improvements in the design and development process are also required. In this regard, simulation is playing an increasingly important role. The paper describes the design and development of a typical small satellite's autonomous Guidance, Navigation and Control (GNC) subsystem supported by a graphical simulation tool. The GNC is one of the critical technology elements and could be a significant cost driver on future small satellite programs due to the high costs of the ground segment. The paper will focus on different aspects of GNC design including the on-board software development through concurrent simulation and hardware-in-the-Ioop integration and testing. In introduction, the paper explains the concurrent engineering design approach using a graphical simulation tool. The focus is then shifted onto the Canadian Smart Satellite Mini Platform program. This is followed by a general discussion on autonomous GNC subsystem. The need for establishing a framework for a complete spacecraft simulation architecture is presented followed by a brief description of individual simulation models. In addition, the advantages of using concurrent simulation for on-board software development and GNC design are discussed. In conclusion, the paper describes the advantages of using simulation as a concurrent design tool and the potential benefits gained from autonomous GNC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it