Autonomous Airborne Transportation: Field Trials in Urban Water Landscapes
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) operating on unlicensed spectra have short operational range. To perform complex beyond visual line-of-sight UAV operations, licensed-spectrum cellular systems can be leveraged for intelligent transportation applications, including parcel delivery via UAVs. This work investigates the long-term evolution (LTE) network-enabled UAV performance at different altitudes under real propagation conditions over a water body in an urban area. In this work, connectivity characterization relies on real data from commercial cellular networks. The performance metrics in this investigation include reference signal received power (RSRP), reference signal received quality, and signal-to-interference-and-noise ratio (SINR). This research discusses the impact of UAV altitude, location of base stations (BSs), and cellular radio frequency on UAV connectivity. Our preliminary data show that handover to higher frequency bands limits the RSRP at UAV terminals, although higher frequency bands have increased bandwidth for better data throughput. The RSRP and SINR are poor when the serving BSs are comparatively distant and obstructed by nearby BSs, respectively. This work also compares the over-the-water propagation environment to other published propagation environments to identify cellular coverage challenges over water surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".