The Design of a Lunar Farside Gravity Mapping Nanosatellite for the European Student Moon Orbiter Mission
Bibliographic record
Abstract
The construction of a high-resolution map of the lunar gravity field would be very useful for studies of the lunar interior, and would be invaluable for accurately planning future lunar orbiter missions. Previous gravity-mapping missions have tracked the gravitational perturbations of lunar satellite orbits from Earth to construct nearside gravity maps, but have only been able to provide extrapolated measurements of the far side gravity field due to the lack of tracking data while the satellite's orbit is occluded by the Moon. Gravity-mapping payloads utilizing satellite-tosatellite range-rate tracking between a pair of lunar orbiters have been proposed on previous lunar missions, but have not yet flown. The University of Toronto Space Flight Laboratory, using expertise and design heritage from the CanX nanosatellite program, is in the process of developing a payload for the European Student Moon Orbiter (ESMO) called “Lunette,” a gravity-mapping nanosatellite that will separate from a parent spacecraft and fly along track in a 100 km altitude circular polar lunar orbit. The Lunette nanosatellite is based on SFL’s Generic Nanosatellite Bus and includes a coherent S-band radio transponder, three-axis attitude determination and control, and a 100 m/s propulsion system, allowing it to maintain an along-track orbital formation and measure the rangerate between itself and the parent spacecraft using Doppler tracking. These range-rate measurements will be used to construct a full-sphere lunar gravity map with an accuracy of 20 mGal or better, comparable to the current bestaccuracy nearside gravity map from the Lunar Prospector mission data. Lunette has been selected as a payload for the ESMO project under the Student Space Exploration and Technology Initiative (SSETI) program of the European Space Agency. ESMO is currently in Phase A study, and is targeting a launch in 2011.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".