Feature article: A UAV software flight management system using arinc communication protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most large commercial aircraft use a flight management system (FMS) to provide aircraft state estimation, trajectory prediction, lateral and vertical guidance, flight plans, and target setpoints (e.g., optimal cruise speed), among other functionality [1]. With unmanned aerial vehicles (UAVs) gaining popularity in commercial and military applications [2], the integration of UAVs in shared airspace with current aircraft has become an important problem to address. There has been consideration for the use of unmanned aircraft for package delivery with the use of small quadrotor UAVs for local demand [3] and large unmanned commercial aircraft for shipping goods internationally [4]. With the UAV industry poised to grow [2], one must consider how to integrate UAVs with the current systems for manned aircraft. Due to the importance of FMS to aircraft, a logical step would be to consider an FMS for UAVs that can adhere to current aircraft regulations and protocols. Flight management systems for UAVs can involve certification, collision detection, mission planning, and many other topics. One can find a number of simulated FMS for consumer flight simulators (i.e., Microsoft Flight Simulator and X-Plane [6]) that try to replicate the interface and functionality of currently available flight management systems. The purpose of this article is to take this idea further by building an FMS in software that can interface with real aircraft and UAVs using industry standard communication protocols.
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.
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 it