The ECAPS Experiment for Solar Cell Characterization in the Stratosphere
Bibliographic record
Abstract
The ECAPS project (Experimental Characterization of Advanced Photovoltaics in the Stratosphere) aims at the characterization of performance of a number of different solar cells in the stratospheric environment. ECAPS has been selected to fly as a zero-pressure balloon payload in the frame of the HEMERA H2020 project. Flight is scheduled for August 2022 from CNES’ base in Timmins, Canada. \n\tTesting solar cells in the stratosphere is of great interest for the development of High-Altitude Pseudo Satellite (HAPS) platforms, which will be equipped with high efficiency, flexible solar cells capable to operate at 20-30 km altitude for weeks or months, as well as to perform high-quality calibration of spacecraft solar cells in a near-air mass zero environment. The experiment includes a panel with up to 4 solar cells of different kinds (multi-junction GaAs, CIGS, perovskite, etc.), a dedicated I/V curve recording circuit, temperature and irradiance sensors, and an inertial measurement unit to monitor the instantaneous attitude of the gondola. \n\tDuring the ascent part of the flight, the I/V characteristic curves of the cells will be continuously recorded so to allow for comparison of performance of the different photovoltaic technologies in identical, real stratospheric flight conditions, as well as to detect performance changes with external temperature, irradiance and altitude. Upon recovery of the experiment, post-flight inspection will also yield useful information on the solar cell compatibility with the high altitude environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".