Bibliographic record
Abstract
This chapter addresses the optimization and implementation aspects of the onboard near-lossless data compression using SAMVQ and HSOCVQ in the image-acquisition and data-handling chain of a satellite system. The raw data acquired by a satellite sensor is often not perfect; there are anomalies in the raw data caused by detector and instrument defects. It is necessary to assess how these anomalies will affect the compression performance onboard. The outcome of the assessment will help determine whether the anomalies should be removed onboard before compression. Another implementation aspect related to optimization of the onboard near-data compression is preprocessing and radiometric normalization to convert raw sensor data to radiance. In otherwords, SAMVQorHSOCVQshould be applied to either the raw sensor data or the radiance data. The evaluation of the effect of onboard preprocessing and radiometric conversion needs to be performed in order to examine whether they should be carried out onboard before compression. Radiance data obtained after radiometric calibration often contains random noise and some artifacts induced during this process. How do the random noise and artifacts in radiance affect the compression performance if the onboard compression is applied to the radiance data? For hyperspectral imagery, there are two kinds of distortions: spatial distortion (often referred to as “keystone”) and spectral distortion (often referred to as “smile”). How do these two distortions compromise the SAMVQ or HSOCVQ performance onboard? Should these distortions be corrected onboard before compression? Finally, the resilience of the two compression techniques to bit errors caused by single-event upsets (SEUs) is evaluated. This feature helps add proper error-correction measures to enhance the robustness of the compressed files and to decide the appropriate system requirement that prevents error propagation and data loss.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".