Multi-dimensional clustering and network monitoring system for aeronautical ad hoc networks
Bibliographic record
Abstract
Network partition is an inevitable phenomena that prohibits existence of unified robust networks. Partition in static networks occur mainly due to uneven traffic. In mobile networks, a member of the network can be defined using n-dimensions. These dimensions account for the member profile and properties. A multi-dimensional profile can partition the network in multiple ways. Aeronautical ad hoc network (AANET) is a multi-dimensional network. It has a 3D topology spread across the airspace. The high ground speed of the airborne elements changes the network topology rapidly. In this paper, we present an `in-air' network monitoring system for the dynamic AANET environment. The monitoring system deals with the problem of network partitions in AANET by observing the network in n-dimensions. The partitions in the network are identified using multi-dimensional clustering. Multi-dimensional clustering spots the directional behavior in the mobility plane and congestion in the data plane of the network. The dynamic partition detection mitigates the effect of network disruption and accounts for isolated members in the network. The system uses a mixed network design and a quasi-stationary network of higher altitude platforms for achieving ubiquitous network monitoring in the AANET.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".