Mermoz<sup>©</sup> simulator: use of a database to estimate the photovoltaic input on a UAV. <i>Application to a transatlantic voyage</i>
Bibliographic record
Abstract
This paper introduces the Mermoz simulator, developed to predict the photovoltaic energy contribution of solar cells mounted on a low-altitude unmanned aerial vehicle (UAV) over long-distance flights. This MATLAB-based tool implements a modular model of the UAV and its environment from an energy-focused perspective. Leveraging 20 years of data from the NASA Prediction of Worldwide Energy Resource Weather Database, the simulator provides an estimate of the confidence interval for solar and wind energy contributions expressed in Wh, or, to facilitate comparisons, in earned kilometers. Its modular design enables easy testing and comparison of various configurations and flight strategies (e.g., optimal departure times, autonomy gains, or cruise speeds). To facilitate understanding, the structure and operation of the simulator are illustrated using the Mermoz drone as a case study. The Mermoz UAV features a 4 m wingspan and is powered by an electric propulsion system. It combines a fuel cell with a liquid hydrogen tank, enabling fully electric, greenhouse gas-free flight. Simulation results indicate that wing-mounted solar cells can provide up to 5% additional electrical energy during a transatlantic flight.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".