SkyNaute by Safran – How the HRG technological breakthrough benefits to a disruptive IRS (Inertial Reference System) for commercial aircraft
Bibliographic record
Abstract
Safran is a world leader in inertial equipment for commercial avionics; APIRS, its FOG AHRS is a best seller in helicopter and turboprop aircrafts avionics, most of Electronics Stand-by Instruments make use of Safran inertial sensors, not to speak about its sensors used in critical fly by wire systems. Such products are, of course, certified by EASA at the highest critical level (DO178B level A for software and DO-254 level A for electronic hardware).Safran is also the European leader in military high grade Inertial Navigation Systems and supply its products to Air, Land, Space and Naval applications. Even if some of these products, when used on military transport aircrafts, are also certified by EASA according to civilian standard for use in non-segregated airspace, Safran is not yet a supplier of IRS for commercial aircraft.Thanks to its technical and industrial expertise in navigation, and thanks to its foot print in aerospace business and especially the civil market, Safran knew that challenging the quasi-monopolistic position of the leader could not be successfully achieved without a disruptive approach.This paper explains how HRG, a technical breakthrough compared to legacy Sagnac Effect based gyros (RLG and FOG), enables the design of SkyNaute, the smallest, lightest, and lowest power consumption IRS in the industry.Finally, the best SWAP and the most cost-effective IRS in the industry would be of poor help if such IRS would not be able to also offer a demonstrated maturity at Entry Into Service. This paper explains on the basis of simple examples the Safran methodology to achieve the suitable maturity level of its SkyNaute.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".