Modeling towards incremental early analyzability of networked avionics systems using virtual integration
Bibliographic record
Abstract
With the advance of hardware technology, more features are incrementally added to already existing networked systems. Avionics has a stronger tendency to use preexisting applications due to its complexity and scale. As resource sharing becomes intense among the network and the computing modules, it has become a difficult task for the system designer to make confident architectural decisions even for incremental changes. Providing a tailored environment to model and analyze incremental changes requires a combination of software tools and hardware support. We have built a virtual integration tool called ASIIST which can provide a worst-case end-to-end latency of data that is sent through a network and the internal bus architecture of the end-systems. Also, we have devised a new real-time switching algorithm which guarantees the worst-case network delay of preexisting network traffic under feasible conditions. With the real-time switch support, ASIIST can provide an early modularized analysis of the end-to-end latency to make architectural design choices and incremental changes easier for the user.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".