Bibliographic record
Abstract
Quite often, space vehicle development programs experience significant schedule delays and cost overruns, in part, due to issues associated with flight software development and integration. Trends indicate that hardware has become more sophisticated and user requirements more demanding while flight software development practices have not kept pace. Historically, flight software design has focused on completing multiple small on-board tasks within static time constraints. The increase in hardware capabilities and user demands has breached this form, thereby creating significant new demands on flight software. While many factors contribute, a primary step toward resolving these problems is to develop tools that allow for a more complete understanding of proposed flight software behavior much earlier in the development lifecycle. By applying a simple Monte Carlo algorithm to spacecraft timing requirements, it is possible to create a dynamic model that parallels flight software behavior in mission environments. Constructing this model before software development begins is facilitated by hardware design. Hardware communication is a critical responsibility of flight software and its requirements are typically established before software development is approached. When a basic Monte Carlo algorithm is used as a representation of time and applied to these requirements, the result is a model that mimics the behavior of a particular flight software implementation. Understanding the behavior of flight software as it is constrained by defined boundaries gives developers the ability to foresee and resolve issues that could otherwise surface in a later stage of development. The advantages of avoiding these obstacles in an earlier stage of development include lower project cost, less time debugging, better resource allocation and improved schedule compliance.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".