Bibliographic record
Abstract
Traditional tactical networks are well known for having error-prone and limited capacity links. A common technique for improving throughput and increasing spectrum efficiency over such low bandwidth links is the use of Header Compression (HC). Removing static protocol entries and synchronizing state information between a sender and receiver reduces per-packet overhead for long lived communication flows. Previous work in header compression such as the IETF's Robust Header Compression (ROHC) standard focused on single-hop unicast traffic. This is not closely aligned with tactical networks, such as NATO's Narrowband Wave Form (NBWF), which have a more challenging multi-hop multi-cast environment and a routing layer that is already highly optimized. In this work, we investigate performance gains from header compression in tactical networks. A novel multi-hop multi-cast header compression (MMHC) scheme is evaluated in a simulated tactical scenario. The use of MMHC increased the packet delivery success rate and decreased packet latency. The simulations also confirmed the significant impact to performance caused by fragmentation due to the small frame sizes in tactical 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".