Modeling towards incremental early analyzability of networked avionics systems using virtual integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it