Integration and Performances Analysis of a Data Distribution Service Middleware in Avionics
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The amount of functionalities in modern aircrafts is increasing to satisfy performance, safety and economic benefits. Therefore, the communication needs of avionic systems are growing. Furthermore, the portability and reusability of applications are current challenges of the aerospace industry. The use of the Data Distribution Service (DDS) middleware technology would reduce the complexity of communications and ease the portability and reusability of applications with its standardised interface. Few previous works used a DDS middleware within the aerospace industry and those didn't take into account the impact of this technology on the applications performances. Therefore, this paper presents an impact evaluation of using a DDS middleware on the performances of avionic applications. To do so, a design methodology was proposed to design an automatic flight control system (AFCS) from a high abstraction level representation of a control loop to a low-level implementation on a development board. The AFCS was modeled with Simulink® to control a Boeing 747-400 simulated within the X-Plane flight simulator. The AFCS code was then ported on a Freescale 8572 embedded platform running VxWorks operating system to allow hardware-in-the-loop (HIL) testing. The performances of the AFCS were evaluated through the stabilisation of the aircraft's altitude, speed and roll angle. To measure the impact of using a DDS middleware, the performances of the AFCS with and without a DDS middleware were compared. The results shows that using a DDS middleware allows the aircraft to stabilise at the desired altitude, speed and roll angle without having any significant impact on the performances of the AFCS. However, due to the limitations of this paper's works, there is still much to do before using a DDS middleware in an actual aircraft becomes a common practice.</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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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 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".