Enhanced Emergency Communication Services for Post–Disaster Rescue: Multi-IRS Assisted Air-Ground Integrated Data Collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cellular networks are difficult to meet emergency rescue due to the destruction of base stations and infrastructure caused by natural disasters. Unmanned Ground Vehicles (UGVs) and other mobile communication devices encounter significant challenges when operating in disaster areas due to limited coverage and resources. To tackle this problem, this paper integrates Unmanned Aerial Vehicles (UAVs) into the emergency communication network and constructs an air-ground integration network architecture with UAV-UGV collaboration. Specifically, multi-UGV collaborate to collect disaster information, and multi-aerial intelligent reflecting surfaces with high maneuverability can effectively assist UGVs in transmitting the collected data to the remote control center. However, there is also a serious challenge to optimize the collaboration strategy between UGVs and UAVs. To address the concern, the collaboration between UAVs and UGVs is modeled as bipartite graph, where UAVs and UGVs belong to different sets of nodes, respectively. The problem is transformed into a matching game based on the bipartite graph. A stable Bidirectional Matching Game (BMG) algorithm is proposed, where matching players maximize the utility by adjusting the selection strategy. Extensive experimental results show that the proposed BMG algorithm outperforms other benchmark algorithms in terms of utility for both UAVs and UGVs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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