In-flight performance of a multi-mode software defined radio architecture for universal avionic radios
Bibliographic record
Abstract
Recently, a lot of effort has gone into responding to the increasing demands of a new generation of RF avionics, which must not only meet the Size, Weight, Power, and Cost (SWaP-C) constraints but also be compatible with the current and future standards. Among the solutions in studied, the implementation of Software Defined Radio (SDR) into avionics has been proven as one of the most promising. Previously presented as the Multi-Mode Software Defined Avionics Radio (MM-SDAR) architecture, the SDR avionics prototype of the AVIO-505 project at LASSENA has shown its potentials in laboratory tests using certified equipment. Results obtained in controlled environments experimentation show that the MM-SDAR can meet the Minimum Operational Performance Standards (MOPS) for the Signal-Of-Interest (SOI), naming just a few, Automatic Dependent Surveillance-Broadcast (ADS-B In/Out), Distance Measuring Equipment (DME) and Transponder Mode S (TMS). Over the past three years, flight tests have been completed in order to evaluate the potential and performances of the MM-SDAR, with promising results. This article aims mainly to examine the details of selected flight tests (scenarios, installation, configuration, etc.), and most importantly, the associated performance analysis. On the one hand, the results described herein confirm the operation of the MM-SDAR in flight condition, which is crucial for avionics architecture. On the other hand, they illustrate the benefits as compared to the corresponding avionics system, and the current limits of the MM-SDAR, which will become valuable data for further future development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".