Flight Test Results of an Adaptive RF Front-End for Multi-Band SDR in Avionics
Bibliographic record
Abstract
This paper presents an in-depth evaluation of the reconfigurable and agile RF front-end (RFFE) architecture, previously demonstrated in laboratory settings through real-world flight tests. The study aims to validate the practical performance, reliability, and robustness of the RFFE architecture, developed in conjunction with software-defined radios (SDRs) by the LASSENA lab, in dynamic aviation environments. By transitioning from controlled lab conditions to actual flight scenarios, we assess the ability of the architecture to adapt to varying signal requirements, frequencies, and protocols. Preparation for flight situations includes temperature resilience, vibration tolerance, and mobility tests. An initial airport ground test will be conducted to ensure the system’s readiness before actual flight deployment. Key performance metrics such as spectrum utilization, signal integrity, receiver performance, and transmitter linearity are examined to ensure compliance with stringent aviation safety and performance standards. The results provide critical insights into the operational benefits and potential enhancements of the RFFE architecture, supporting its adoption in aviation and potentially other fields requiring high communication and system efficiency levels.
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.
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 | 0.000 | 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".