Model-Based Design Flow Driven by Integrated Modular Avionic Simulations
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
<div class="section abstract"><div class="htmlview paragraph">The Integrated Modular Avionics (IMA) architecture has been a crucial concern for the aerospace industry in developing more complex systems, while seeking to reduce space, weight and power (SWaP), as well as development, certification and production time. From a software perspective, that objective pushes developers to migrate toward safety critical space and time partitioning environment. However, mainstream commercial real-time operating systems (RTOS) offering such partitioning can be restrictive in early development due to very high licensing costs. That situation is even more striking when considering that low-cost alternatives could instead be used for system modeling and early simulation before acquisition of a target platform.</div><div class="htmlview paragraph">This paper reviews existing low-cost and open-source development environments to propose a novel design flow. The proposed methodology starts with model-based analysis in the AADL modeling language. Then, configuration files and software integration code are generated and executed using the Simulated IMA (SIMA) software from GMV. A case study experiment was created using a Multi-purpose Control and Display Unit (MCDU) communicating with an external Flight Management System (FMS) simulation provided by our industrial partner CMC Electronics. Results show reduction of time for system and partition configurations from hours to seconds, notably by reducing human error. It also proves useful in identifying design flaws in early development as well as facilitating software architectural exploration for integrated modular avionics.</div></div>
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it