Certification challenges for next-generation avionics and air traffic management systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it