Peer Review #1 of "TCP adaptation with network coding and opportunistic data forwarding in multi-hop wireless networks (v0.1)"
Bibliographic record
Abstract
Opportunistic data forwarding significantly increases the throughput in multi-hop wireless mesh networks by utilizing the broadcast nature of wireless transmissions and the fluctuation of link qualities.Network coding strengthens the robustness of data transmissions over unreliable wireless links.However, opportunistic data forwarding and network coding are rarely incorporated with TCP because the frequent occurrences of outof-order packets in opportunistic data forwarding and long decoding delay in network coding overthrow TCP's congestion control.In this paper, we propose a solution dubbed TCPFender, which supports opportunistic data forwarding and network coding in TCP.Our solution adds an adaptation layer to mask the packet loss caused by wireless link errors and provides early positive feedbacks to trigger a larger congestion window for TCP.This adaptation layer functions over the network layer and reduces the delay of ACKs for each coded packet.The simulation results show that TCPFender significantly outperforms TCP/IP in terms of the network throughput in different topologies of wireless networks.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.072 |
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".