Flavors of the Next Generation of Unmanned Aerial Vehicles Networks
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs)–also known as drones or Unmanned Aircraft–have found diverse applications in various fields owing to their significant advantages, including fast mobility and rapid deployment. UAVs are crucial in aerial networks, providing increased coverage and on-demand connectivity as mobile nodes. In recent years, UAVs have made room to leverage the Internet of Things (IoT) to the sky, enhancing air-to-ground communication and pointing toward the next generation of UAV networks. As this expansion is still in its early stages, there are several aerial network terminologies, each with similarities and differences, depending on the deployment domain and the services they offer. However, studies have yet to discuss these different terminologies consistently. Key aspects have yet to be thoroughly explored, such as the anticipated requirements for deploying these networks and how they relate to the various terminologies. This work systematically analyzes the existing terminologies of UAV networks, considering their requirements and applications, shedding light on their intersections and differences. Furthermore, we present the demands for the next generation of UAV networks and discuss how they impact the design of UAV-related applications, aiding in the design of new protocols, tools, and technologies for both industry and academia. Lastly, we highlight the emerging trends and challenges associated with deploying and integrating these networks.
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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 | 0.000 |
| 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 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".