Abstract: In accordance with one aspect, a system for facilitating carbon-equivalent offsets from contrails includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the system at least to: identify a flight designated for a contrail reduction procedure; determine, after the flight is completed, whether the flight executed the contrail reduction procedure and achieved contrail reduction; and based on determining that the flight executed the contrail reduction procedure and achieved contrail reduction, include, in a carbon offset exchange service, a carbon-equivalent offset listing corresponding to the flight.

Abstract: Methods and system for improved aircraft safety are described herein. A machine learning model may be trained using historical flight data for a number of previously flown flights where an automation function autonomously disengaged. The trained machine learning model may be used to generate probabilistic alert rules. The probabilistic alert rules may be used by a computing device onboard an aircraft to provide contextual information to flight crew relating to engagement status for one or more automation functions of the aircraft.

Abstract: Methods and system for improved aircraft safety are described herein. A machine learning model may be trained using historical flight data for a number of previously flown flights where an automation function autonomously disengaged. The trained machine learning model may be used to generate probabilistic alert rules. The probabilistic alert rules may be used by a computing device onboard an aircraft to provide contextual information to flight crew relating to engagement status for one or more automation functions of the aircraft.

Abstract: Traditional approaches to predicting aircraft trajectories using kinematic models cannot work due to the complexity of the approach maneuver including flaps/slats, landing gear, ATC vectors, winds, and other traffic. The methods and systems disclosed can utilize Big Data Analytics (e.g., massive amounts of data of flights on each approach) to nowcast the approach stability given the state of the flight prior to the 1000?/500? check points.

Abstract: Traditional approaches to predicting aircraft trajectories using kinematic models cannot work due to the complexity of the approach maneuver including flaps/slats, landing gear, ATC vectors, winds, and other traffic. The methods and systems disclosed can utilize Big Data Analytics (e.g., massive amounts of data of flights on each approach) to nowcast the approach stability given the state of the flight prior to the 1000?/500? check points.

Abstract: A controller communicates a series of commands to a subject within a control area. A control area sensor generates movement data by monitoring the movement of the subject executing the command. Rebound surface sensor(s) generate contact data by monitoring contacts with rebound surface(s) resulting from the subject executing the commands. Performance of the subject is analyzed using the movement data and the contact data.

Abstract: The embodiment of the invention relates generally to the methods and systems to determine the reliability of human-computer interactions for achieving a mission task in an automated system using a Task Reliability Analysis Tool (TRAT). In some examples, the TRAT can capture the details of human computer interactions while performing operator actions, allocating time-on-action distribution and conducting task evaluations. The allocated time-on-action distribution to the operator actions and to each task can be used subsequently to predict time to complete a task, and likelihood of failure for an infrequently performed task.

Abstract: A taxi-out time predictor includes an airport simulation processing module, a state vector creation processing module, an actual taxi-out value input processing module and a learning processing module. The airport simulation processing module models airport taxi-out dynamics for a predetermined time period. The actual taxi-out value input processing module collects actual taxi-out measurements from departure aircrafts. The learning processing module includes a reinforcement learning estimation processing module, an update utility processing module and a reward processing module. The reinforcement learning estimation processing module generates a predicted taxi-out time value using the variables in the state vector and an output utility value. The aircraft taxi-out time predictor operates iteratively to predict the taxi-out time.

Abstract: In an aircraft, there is included a Flight Management System (FMS), Autopilot and Autothrottle for controlling the aircraft. The apparatus has a plurality of outputs for definition of the real-time targets, controlled by the Autopilot and Autothrottle, to guide the vertical position of the aircraft to a desired vertical position along the desired vertical flightplan (or profile) according to a set of operational procedures. The FMS includes an apparatus that comprises an element which provides information denoting actual vertical position of the aircraft, and an element which generates information specifying the desired vertical position of the aircraft along the predetermined desired flightplan.