Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Unmanned aerial vehicles (UAVs) are an emerging technology with a large variety of commercial and military applications. In-flight icing occurs during flight in supercooled clouds or freezing precipitation and is a potential hazard to all aircraft. In-flight icing on UAVs imposes a major limitation on the operational envelope. This report describes the unsettled topics related to UAV icing. First, typical UAV applications and the general hazards of icing are described. Second, an overview of the special technical characteristics of icing on autonomous and unmanned aircraft is given. Third, the operational challenges for flight in icing conditions are discussed. Fourth, technologies for ice protection that mitigate the icing hazard are introduced. Fifth, the tools and methods required to understand UAV icing and to develop aircraft with cold-weather capabilities are presented. Finally, an assessment of the current and future regulations regarding icing on UAVs is provided.</div><div class="htmlview paragraph">Icing is a key challenge that the UAV industry needs to address in order to unlock the full potential of this emerging technology. UAVs must be capable of safe and reliable operation in a wide range of weather conditions. This report outlines the most important challenges and gives short- and long-term recommendations on how to solve UAV icing issues.</div><div class="htmlview paragraph">NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".