Modeling and Control of a Network of Cooperative Satellites Using Neural Networks
Bibliographic record
Abstract
An adaptable control system within and on-board a network of interacting satellites is an important module which is desired for unmanned space vehicles. However, such a control system is not easy to develop since it is the core of the network’s operation and all the earth-linked operational information is reviewed and analyzed through it. Due to multipurpose missions of satellites, several decisions are made simultaneously about necessary changes to the satellite’s operational parameters (i.e., orbit, inclination, etc.). In this paper, a neural network model-based control scheme is developed for a network of interacting satellites. The proposed neural control scheme refers to a methodology in which the controller is assumed as a neural network and the dynamical model of the system is identified through the training stages of the neural model. The simulation results show that the neural control method can be effectively applied in monitoring and controlling the satellite networks, without the necessity of determining the mathematical model of the system.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".