Information raining and optimal link-layer design for mobile hotspots
Bibliographic record
Abstract
In this paper, we propose a link layer design for mobile hotspots. We design a novel system architecture that enables high-speed Internet access in railway systems. The proposed design uses a number of repeaters placed along the track and multiple antennas installed on the roof of a vehicle. Each packet is decomposed into smaller fragments and relayed to the vehicle via adjacent repeaters. We also use erasure coding to add parity fragments to original data. This approach is called information raining since fragments are rained upon the vehicle from adjacent repeaters. We investigate two instances of information raining. In blind information raining, all repeaters awaken when they sense the presence of the vehicle. The fragments are then blindly transmitted via awakened repeaters. A vehicle station installed inside the train is responsible for aggregating a large enough number of fragments. In the throughput-optimized information raining, the vehicle station selects a bipartite matching between repeaters and roof-top antennas and activates only a subset of the repeaters. It also dictates the amount of transmission power of each activated repeater. Both the bipartite matching and power allocations are individually shown to be NP-complete. Matching heuristics based on the Hungarian algorithm and Gale-Shapley algorithm are proposed. A simplex-type algorithm is proposed as the power allocation heuristics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".