3D Aerial Highway: The Key Enabler of the Retail Industry Transformation
Bibliographic record
Abstract
The retail industry is facing an inevitable transformation worldwide, which is accelerating with the current pandemic situation. Indeed, consumer habits are shifting from brick-and-mortar stores to online shopping. The bottleneck in the online shopping experience remains efficient and fast delivery to consumers. In this context, unmanned aerial vehicle (UAV) technology is seen as a potential solution to address cargo delivery issues. Hence, the number of cargo UAVs is expected to increase in the next few decades and the airspace to become crowded. To successfully deploy UAVs for mass cargo delivery, seamless and reliable cellular connectivity for cargo UAVs is required. Thus, organized and “connected” routes in the sky are needed. Like highways for vehicles, 3D routes in the airspace should be designed to fulfill cargo UAV operations safely and efficiently. We refer to these routes as “3D aerial highways.” In this article, we investigate the feasibility of the aerial highways paradigm. First, we discuss the related motivations and concerns. Then we present our aerial highways paradigm design. Finally, we present linked connectivity issues and potential solutions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".