Model-based Method to Automate the Design of IMA Avionics System Based on Cosimulation
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In the aerospace industry, as the modern avionics systems became more and more complex, the Integrated Modular Avionics (IMA) architecture has been proposed as a replacement of the federated architecture, in order to offer better solutions on SWaP constraints (Size, Weigh and Power). However, the development process of IMA avionics systems is much more difficult. This paper aims to propose to the aerospace industry a set of time-effective and cost-effective solutions for the integration and functional validation of IMA systems.</div><div class="htmlview paragraph">Based on MBE methodology, which is considered as an interesting solution for the IMA systems development [<span class="xref">8</span>], this paper proposes a design flow, that integrates three steps of refinement, for the configuration and the validation of IMA platforms. In the first step of the design flow, the modeling language AADL is used to describe the IMA architecture. The AADL modeling environment OCARINA, a code generator initially designed for the real-time operating system POK, has been modified to generate software integration code and system configuration files for the IMA simulator named SIMA. This solution is a cost effective alternative to expensive commercial development environments to validate ARINC653 software applications. In the second step of the design flow, a cosimulation platform composed of two simulators is proposed: Simulink for the simulation of peripherals and SIMA for the simulation of IMA modules. In the third step, the validated avionics applications and system configuration can be ported with minimum effort from the cosimulation environment to an implementation platform.</div><div class="htmlview paragraph">A case study, which consists in integrating several avionics applications to SIMA and then porting them to PikeOS development environment, was brought in the purpose of demonstrating the proposed design flows and co-simulation platform. The research work realized in this paper is a part of collaboration between industrials and academics through the CRIAQ AVIO509 project.</div></div>
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".