Fire Localisation And Mitigation Emergencies Satellites:A Constellation of CubeSats in LEO for Monitoring Wildfires in Near Real-Time
Bibliographic record
Abstract
The effects of global warming are already taking a toll on our planet. According to the US congressional research service an average of > 7 million acres are being burnt every year in the USA alone. The European Commission's “European Forest Fire report” has shown that the deforestation rates are increasing at an alarming rate and with hotter years and drier climates the amount of burnt land is only expected to increase. The paper outlines the Fire Localisation And Mitigation Emergencies Satellites (FLAMES), a constellation of 16 CubeSats (6U) in Low Earth Orbit (LEO) designed to monitor wildfires in near real-time with the ability of having worldwide coverage 24/7. The constellation consists of 8 High Field Of View (FOV)-Low Resolution satellites and 8 Low FOV-High Resolution satellites observing in the thermal infrared spectrum. The mission is designed to be launched by a single Vega-C rocket making use of the Small Spacecraft Mission Service (SSMS) platform and placed into a single plane Sun-synchronous orbit (SSO). FLAMES is a Phase 0 mission designed by university students during ESA Academy's CubeSat Summer School 2022 in the Training and Learning Facility at ESEC-Galaxia, Belgium. This mission study was a deliverable of the Concurrent Engineering Workshop guided by ESA System Engineers. The students were divided into several disciplines, each group dedicated to a different subsystem and worked together, making use of the Concurrent Model-based Engineering Tool (COMET) and supervised by ESA System Engineers. As a result, each different group showed a detailed analysis of their respective subsystem and reported on the lessons learnt during the Concurrent Engineering process. This paper provides an overview of the preliminary design of FLAMES and the Concurrent Engineering process applied during ESA Academy's CubeSat Summer School 2022 to develop this mission.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".