Certification challenges for next-generation avionics and air traffic management systems
Bibliographic record
Abstract
Air traffic is doubling every 15 years, and aviation systems must modernize to address sustainability challenges. The need to balance capacity, efficiency, safety, and environmental requirements is reflected by the several air traffic management (ATM) and avionics modernization initiatives under way. The major collaborative research programs today are the European Union's Single European Sky ATM Research (SESAR) project and the United States' Next-Generation Air Transportation System (NextGen) led by the Federal Aviation Administration (FAA). Other modernization initiatives include the Collaborative Action for Renovation of Air Traffic Systems in Japan, SIRIUS in Brazil, OneSky in Australia, and similar programs in Canada, China, India, and Russia [1]. The International Civil Aviation Organization (ICAO) has authorized a globally coordinated plan, published as the Global Air Navigation Plan (GANP) [1], to guide the harmonized implementation of communication, navigation, surveillance, and avionics (CNS+A) enhancements across regions and states. In the CNS+A context, aircraft safety is a shared responsibility between airborne and ground-based resources [1]. Hence, this is a safety challenge requiring changes to the current regulatory framework to properly capture the nature of this shared responsibility and the concept of integrated CNS+A systems. Certification of aircraft and ground equipment (hardware and software) and organizational approvals are essential elements to ensure continued and enhanced safety.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".