Bibliographic record
Abstract
This chapter describes the hardware implementation of SAMVQ and HSOCVQ algorithms for near-lossless data compression onboard satellites. Three top-level topologies were considered to meet the initial design objective, including a digital signal processor (DSP) engine-based approach, a high-performance, general-purpose CPU-based approach, and an application-specific integrated circuit (ASIC) or field programmable gate array (FPGA) approach. The resulting onboard data compression engines were evaluated for various configurations. After studying the topologies, the hardware and software architectural options, and candidate components, an architectural preference was placed on a hardware compressor with modularity and scalability. The performance trade-off studies for these architectures showed that the best performance and scalability could be achieved using dedicated compression engines (CEs) based on an ASIC/FPGA topology. The advantages of the ASIC/FPGA approach include the ability to • Apply parallel processing to increase throughput, • Provide for successive upgrades of compression algorithms and electronic components over a long term, • Support high-speed direct memory access (DMA) transfers for data read and write operations, • Optimize the scale of the design to mission requirements, and • Provide data integrity features throughout the data handling process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".