In this paper, an algorithm to plan a continuous wind-optimal path is proposed, and simulations are made for aircraft trajectories. We consider a mobile which can move in a two dimensional space. The mobile is controlled only by the heading direction, the speed of the mobile is assumed to be constant. The objective is to plan the optimal path avoiding obstacles and taking into account wind currents. The algorithm is based on Ordered Upwind Method which gives an optimality proof for the solution. The algorithm is then extended to spherical coordinates in order to be able to handle long paths.

This paper employs the Bidirectional Encoder Representations from Transformers (BERT), a language model, finetuned on the question answering task, on the Aviation Safety Reporting System (ASRS) dataset's free text reports, that describe incident occurrences in an International aviation safety context. A four-step method is used to evaluate the produced results. This paper outlines what are the limitations of this approach, as well as its usefulness in trying to extract information from thirty randomly selected free text reports when asking the following question: "When did the incident happen?". We aim to try to integrate one of the algorithms resulting of the recent advances in Natural Language Processing (NLP) to leverage information in natural language narratives, as opposed to working directly with the structured part of the ASRS dataset. We find that our approach yields interesting results, with roughly seventy percent correct answers, including answers that have information that is not overlapping with the ASRS dataset's metadata.

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