A Backoff Algorithm Suitable for Burst Traffic and Its Application in Adhoc Network Simulation
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Bibliographic record
Abstract
Adhoc network traffic is generated abruptly and arrives in batches.It possesses a kind of statistical self-similar characteristic and has great impact on network performance.Analysis and evaluation method of the performance of Tactical Communication network based on Poisson model is no longer applicable.Based on the analysis of multiple ON/OFF sources generating self-similar traffic,a backoff algorithm,suitable for self-similar flow is proposed.This algorithm introduces appropriate competition coefficient and makes appropriate nodes assignments to channel accessing in accordance to the current bursting status.Simulation of tactical adhoc network is implemented using OPNET.Simulation results show that the improved algorithm has better performance in time delay and handling capability compared with the original BEB.The time delay reaches 5.92% and handling capability is increased by 6.17%.The research has great significance in protocol designing of adhoc network and optimization of the resources configuration.
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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 it