Blockchain sharding storage scheme based on concatenated coding
Bibliographic record
Abstract
Traditional blockchain faces the challenge of storage scalability. Existing research has reduced the storage overhead of blockchain based on erasure coding theory, but it brings high computational and communication spending during the decoding and recovery of blocks. To solve these problems, a blockchain sharding storage scheme based on concatenated coding was proposed. By adding a pre-coding layer to improve the existing rateless erasure code, a decoding complexity of Οn <?fx-imagestate width="6.94266701" height="3.21733332"?> <?fx-imagestate width="6.94266701" height="3.21733332"?> was achieved. Considering the communication delay skew between nodes during the decoding process, a delay-sensitive sharding algorithm based on Metis was proposed, which cut down the communication expenditure in the decoding process by delaying weights to determine the shard ownership of nodes. Simulation results show that the proposed scheme not only ensures the reliability of blockchain data, but also has lower computational and communication cost compared to traditional schemes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".