Cost-Effective Payload Operations Planning Software for Complex Small Spacecraft Mission Operations Presenter Information
Bibliographic record
Abstract
As small spacecraft become more capable, so does the complexity of their operations. Searching for potential concurrent observation or access opportunities and ensuring they are compatible with one another may become tedious and repetitive for operators to manually compute. Currently available commercial-off-the-shelf tools that automate this process are capable but expensive. To address this problem, a new payload operations planning tool has been developed by the Space Flight Laboratory to handle the deterministic aspects of mission planning, such as: detecting observation opportunities, validating observations in a schedule, and generating lists of commands to be sent to satellites. This lightweight tool is generalizable to any Earth-observing mission configuration and can support complicated observation geometries. Open-source libraries were used to reduce the overhead for development as they decrease the amount of code that must be newly created and maintained. Functionality has been compartmentalised through a containerized service-based architecture. In this way, new functionality can be added or replaced as needed. To enhance usability, a user may interact with the tool through a browser-based user interface. This paper outlines the features of the Payload Operations Planning Software, as well as details about its architecture and development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".