Bibliographic record
Abstract
HexPak is a deployable satellite structure that offers several advantages to small satellite missions, including ease of scalability, a geometry naturally adapted for plug-and-play architectures and multiple missionspecific component layouts, and a large deployed aperture from an optimal stowed volume. It consists of hexagonal bays that stack when stowed to efficiently use payload fairing volume, but deploy to a planar structure with deck area many times the fairing cross-section. The large deployed area to fairing size ratio supports large aperture payloads, multiple payloads, heat rejection significantly beyond traditional designs, multiple manifest with minimal wasted support mass, and easy access on orbit for expansion and flexibility for reconfiguration on orbit. For missions that require a large number of platforms, the modular structure offers easy interchangeability of HexPak bays which makes it possible to maintain a consistent production flow even during periods of parts shortages. Standard physical interfaces also allows for commonality in tooling, fixturing, testing and ease of satellite integration. The hexagonal geometry is near optimum for taking advantage of available faring envelopes and the folded structure is self-supporting, which minimizes the need for additional structure to support launch. Mission and subsystem design trades, including launch vehicle accommodation, structural design, layout and configuration are described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".