Abstract: A method for assessing technical and non-technical skills of an operator, includes a step of collecting endogenous data relating to physical manifestations of the operator, and exogenous data relating to the context of a session; steps, implemented by data processing modules, of: correlating the collected data in order to link endogenous data to exogenous data; detecting, using the correlated data, observable behavior data comprising at least one trigger event parameter and one action parameter; analyzing the observable behavior data in predefined analysis sequences, each predefined analysis sequence being specific to a skill to be assessed, and comprising a trigger event parameter and an action parameter characterizing an expected observable behavior according to a predefined situation, the analysis generating a measurement indicator; assessing the behaviors of the operator, by comparing an observed behavior with an expected reference behavior; assessing each skill of the operator on the basis of the results

Abstract: A method of generating a peripheral image of an aircraft having pixels obtained from a plurality of cameras equipping the aircraft. The method is implemented by an electronic generation device. The method comprises a step of receiving a position of a peripheral point of view from which the peripheral image is to be generated. The method comprises steps of obtaining an orientation of the aircraft and of acquisition of a respective image from each camera. The method comprises a step of calculating a respective transfer function for each camera, each transfer function being intended for being applied to a pixel of the peripheral image so as to identify a possible corresponding pixel of the acquired image associated with the transfer function. The method comprises a step of generating the peripheral image from the images acquired from each camera and from each calculated transfer function.

Abstract: A computer-implemented method for generating synthetic training data for an artificial-intelligence machine, the method includes at least steps of: defining parameters for at least one approach scenario of an aircraft approaching a runway; using the parameters of the at least one scenario in a flight simulator to generate simulated flight data, the flight simulator being configured to simulate the aircraft in the phase of approach toward the runway and to simulate an associated automatic pilot; using the simulated flight data to generate a plurality of ground-truth images, the ground-truth images corresponding to various visibility conditions; and generating, from each ground-truth image, a plurality of simulated sensor images.

Abstract: A computer-implemented method for generating labeled training data for an artificial intelligence machine is provided.

Abstract: A computer-implemented method for generating synthetic training data for an artificial-intelligence machine, the method includes at least steps of: defining parameters for at least one approach scenario of an aircraft approaching a runway; using the parameters of the at least one scenario in a flight simulator to generate simulated flight data, the flight simulator being configured to simulate the aircraft in the phase of approach toward the runway and to simulate an associated automatic pilot; using the simulated flight data to generate a plurality of ground-truth images, the ground-truth images corresponding to various visibility conditions; and generating, from each ground-truth image, a plurality of simulated sensor images.

Abstract: Systems and methods implemented by computer for the sharing of weather data are provided, a method notably includes the steps of collecting data during aircraft flights, receiving data relating to the trajectory of a client aircraft; based on said trajectory, selecting relevant weather data in a shared weather database comprising the collected weather data; in response to a request from the client aircraft, communicating the weather data selected according to an exchange control mechanism. Developments describe a weather data exchange control mechanism using smart contracts, the use of confidence scores, computations of correlation between meteorology declared and measured in flight, the use of drones as probes, the use of open sources, of machine learning, of management of time-based and/or space-based validity of the weather data.

Abstract: Computer-implemented methods and systems for managing the lifecycle of aeronautical data stored in a blockchain, include steps of receiving or sending aeronautical data, and encrypting and/or decrypting these data using a smart contract. The use of a plurality of blockchains, and the facts and rules of management of the lifecycle of the data (e.g. programmed obsolescence, time-dependent quality indicator, etc.) are described. Transactional aspects; the use of oracle services; asymmetric, homomorphic and post-quantum encryption methods; the use of chameleon hash functions, so as to manipulate at least partially redactable blockchains; and machine-learning techniques, are in particular described with respect to a number of embodiments. Software and system aspects are described.

Abstract: Systems and computer-implemented methods for sharing aeronautical data, include steps of: maintaining a private blockchain, the blockchain involving a plurality of predefined parties; conditionally communicating aeronautical data, in response to a request by one party, via a mechanism for controlling the exchanges, the data being collected beforehand from aeronautical computers, e.g. on-board flight management systems (FMS) of aircraft. Extensions in particular describe the use: of mechanisms for providing compensation or remuneration for and managing access rights and/or licenses to use; smart contracts; mechanisms for auctioning or trading datasets; management of avionic and non-avionic data; learning techniques applied to the shared and consolidated data; management of side chains; post-quantum encryption. Software aspects are described.