Considerations of data handling system of a spaceborne imaging spectrometer with onboard data compression
Bibliographic record
Abstract
This paper outlines the data handling system (DHS) of a spaceborne hyperspectral imager with an onboard data compressor. The data compression techniques to be used are successive approximation multistage vector quantization and hierarchical self-organizing cluster vector quantization. Considerations and implementation aspects of the DHS related to the onboard data compression are addressed. The impact of anomalies (spikes, saturation, etc.) in the raw data on compression performance is evaluated for the purpose of determining whether or not onboard data scrubbing is required before compression. The evaluation results show that anomalies in raw data have no significant effect on compression. This paper evaluates the impact of preprocessing and the conversion of raw data to radiance units on data compression using remote sensing applications to examine whether or not they should be applied on board before compression. The evaluation results show that preprocessing and radiometric conversion applied either before or after compression have no impact on an application using leaf area index but have impact on a target detection application using spectral unmixing. This paper also examines the combination of the two compression techniques to see if there is a performance improvement over a single technique. The experimental results show that the combined compression system does not perform better than either technique alone. Lastly, the resilience of the two compression techniques against bit errors caused by single event upsets is examined. The experimental results show that there is no loss of data fidelity when the error rate is ≤10–6.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".