Offline-Online Multiple Agile Satellite Scheduling using Learning and Evolutionary Optimization
Why this work is in the frame
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Bibliographic record
Abstract
The recent generation of Agile Earth Observation Satellite (AEOS) has emerged to be highly effective due to its increased attitude maneuvering capabilities. However, due to these increased degrees of freedom in maneuverability, the scheduling problem has become increasingly difficult than its non-agile predecessors. The AEOS scheduling problem consists of finding an optimal assignment of user-requested imaging tasks to the respective AEOSs in their orbits by satisfying the operational resource constraints in a specified time frame. Some of these tasks might require imaging the same area of interest (AOI) multiple times, while in some tasks, the AOIs are too large for the AEOS to image in a single attempt. Some tasks might even arise while the AEOSs are preoccupied with existing tasks. This thesis focuses on formulating the AEOS scheduling models where onboard energy and memory constraints while operating and the task specifications are diverse. A mixed-integer non-linear scheduling problem with a reward factor has been considered in order to handle multiple scan requirements for a task. Although initially, it is assumed that the AOIs are small, this work is extended to a three-stage optimization framework to handle the segmentation of large AOIs into smaller regions that can be imaged in a single scan. The uncertainty regarding scan failure is handled through a Markov Decision Process (MDP). These two proposed methods have significant benefits when tasks are available to schedule prior to the mission. However, they lack the flexibility to accommodate newly arrived tasks during the mission. When multiple new tasks arrive during the mission, predictive scheduling based on learning historical data of task arrivals is proposed, which can schedule tasks in an online manner faster than complete rescheduling and minimize disruption from the original schedule. Evolutionary optimization-based solution methodologies are proposed to solve these models and are validated with simulations.
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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 | 0.000 | 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) | 0.023 | 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