SkyNaute by Safran – How the HRG technological breakthrough benefits to a disruptive IRS (Inertial Reference System) for commercial aircraft
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
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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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it