Bibliographic record
Abstract
In recent years, UASs have become more and more popular. There are many reasons for this; one of the most popular is the enhancement of drones' functionalities and improvement in battery life, stabilization, navigation, sensor technology, and much more. The growth is driven by many benefits. However, as the number of drones is expanding and levels of their technological functionalities evolving, the use of drones brings a lot of concerns as well as challenges that should not be underestimated. This refers to issues in the area of cybersecurity, privacy, and public safety. UASs, under the international, regional, and national regime of aviation law, are considered aircraft. Since there is no existing cybersecurity framework for UASs, the civil aviation cybersecurity framework should apply to their operations. This paper will focus on the potential cyber threats against UASs, providing some examples of cyberattacks from the past. Further, the overview of the aviation cybersecurity framework will follow in order to determine the current status of maturity at the international and regional (European Union) levels. The conclusion of the paper will identify the necessary steps to be taken and potential solutions in terms of applying the aviation cybersecurity framework into the operation of UASs.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".