Adaptive Virtual Carrier Sense in Underwater Broadcasting
Bibliographic record
Abstract
The underwater acoustic communication medium introduces significant challenges that often limit communication to flooding protocols. This work presents the Network Allocation Technique in Flooding (NATFLOOD) protocol, intended for use with Underwater Acoustic Sensor Networks (UASNs). NATFLOOD applies a novel Adaptive Network Allocation Vector (ANAV) to reduce duplicates and collisions. The performance of NATFLOOD is tested in a comparative simulation against DFLOOD, a leading UASN flooding protocol. In simulation, NATFLOOD demonstrates an average reduction in duplicates and collisions of 24.29 ± 0.01% when compared to DFLOOD. This reduction is achieved by the ability of NATFLOOD to solve the broadcast problem with 19.96 ± 0.03% fewer packets than DFLOOD. This reduction is achieved with an increase to protocol completion time of 2.21 ± 0.02%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".