How to Reconcile COTS Components and Tailored Future-Proof Data Acquisition System in Flight Test Instrumentation
Bibliographic record
Abstract
During decades, FTI (Flight Test Instrumentation) systems were based on home-made or build-to-spec designs to address the specificities of each test article and the habits of each instrumentation engineers. However, thanks to decades of standardization efforts, the flight test community progressively took benefits of the interoperability brought by the products and the instrumentation suppliers converted their offer into COTS (Commercial-Off-The-Shelf) product lines. Despite the obvious benefits of the COTS approach (lead time, maturity, cost, maintenance, etc.), the focus on the mainstream needs have marginalized some features that brought a lot of value to some instrumentation engineers who now usually must complete their instrumentation system with custom items to meet their former expectations. The aim of this paper is to present how FTI system based on COTS components can be tailored to embrace the uniqueness of each program and ensure future-proof capabilities, through modules that allow hardware and software customizations with a seamless integration into Safran Data Systems COTS solution ecosystem.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".