Parametric Design Curves for Payload Power and Mass Capabilities of Non-Geo Smallsats Buses/Launchers
Bibliographic record
Abstract
There is a need to reduce cost and perform an optimization on smallsats and standardsats before options for a payload/bus/launch vehicle combination are selected. Bus manufacturers want naturally to protect their proprietary data; at the same time, a smallsat designer cannot decide on this combination without tradeoff analysis. Geosynchronous bus manufacturers realized, some time ago, that it was in their interests to release some of the payload trade-off curves to potential customers. This paper provides a methodology to reduce cost and optimize the selection of this combination, for Low & Medium earth orbits satellites. This generalized approach for elliptical orbits, is extended from a previous paper on circular non-GEO orbits. It provides equations for the net payload power and mass available to the system, for varying bus launcher sets, for elliptical (and circular) orbits, taking into account orbit eccentricity, mass to orbit, power generation/storage and fuel required for drag make-up. Some selected examples are provided for the payload power and mass for different launchers and the total payload equivalent mass at varying elliptical altitudes. This methodology is adequate for a first cut optimization, for elliptical LEO's and MEO' s, with direct injection launches. Further refinements require detailed knowledge of the power system as well as other data of a given bus, which are best evaluated with the bus manufacturers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".