ACPM: An associative connectivity prediction model for AANET
Bibliographic record
Abstract
Aeronautical ad hoc networks (AANETs) can have a hybrid topology of intermittently connected clusters and mesh network. The hybrid network topology compounded with highly dynamic nature of AANET leads to variable connectivity in the network. The connectivity in the network for direct air-to-air communication between aircrafts is primarily a function of velocity of air vehicles, position of air vehicles, direction of flight, range of communication and congestion. In this paper, we present a connectivity prediction model for the AANET. The proposed model does a space time analysis of connectivity in the regions of AANET. The model can identify areas of low connectivity in a region by grading connectivity in the AANET. The connectivity prediction complements the change in state of an aircraft as it joins the AANET in a cluster or mesh. The model directs idle or migrating members of the network to the higher regions of connectivity. Connectivity gradation reduces network setup time in the events of network disruptions, strengthening the network's self-healing capability. FCM clustering is used for translating 3D topology of the network in conjunction with network traffic to multiple domains, that represent an aircraft.
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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 | 0.001 |
| 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".