ESA Swarm mission after 10 years in Space: new opportunities through enhanced processors and data quality
Bibliographic record
Abstract
On 22nd November 2023 Swarm ESA’s Earth Explorer mission celebrated 10 years in Space, characterizing Earth’s geomagnetic, ionospheric and electric fields, for a better understanding of our planet’s interior and its environment. After a decade in orbit, the mission is still in excellent shape and continues to contribute to a wide range of scientific studies, from the core of our planet, via the mantle and the lithosphere, to the ionosphere and interactions with Solar wind, opening the door for many innovating applications largely beyond its original scope.Moreover, the processing algorithms have been continuously improved since the beginning of the mission, to cope with the evolving needs of the scientific community, to keep providing excellent quality data and to maintain good instruments performances.In April 2023 a “Fast” processing chain has been transferred to operations, providing Swarm L1B products with a minimum delay respect to the acquisition. This Fast data production adds significant value to Swarm mission’s scientific purposes and applications, making it eligible for monitoring Space Weather phenomena, modelling and nowcasting the evolution of several geomagnetic and ionospheric events.This work provides an overview of the Swarm enhanced data processing chain, instruments performances, Fast chain applications and upcoming evolutions, together with other innovative Swarm-based data products and services.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".