In the approach phase of a flight, aircraft transitions from the en-route phase to the final approach. The transit time between these phases is highly uncertain and frequently subject to delays. Efficient and reliable prediction of this time is essential for airline fuel planning and flight scheduling, yet current practice still relies largely on fixed deterministic buffers. Most existing work on arrival delay prediction focuses on deterministic models and aggregate indicators (e.g the difference between planned and actual arrival times), often at forecast horizons during the airborne phase. This paper develops and validates an explainable probabilistic forecasting model for flight duration within the Amsterdam Schiphol (AMS) Flight Information Region (FIR), with a forecast moment in the pre-departure phase. The primary objective is to forecast the duration within the AMS FIR using information available at planning while providing interpretable contributors of delay that can be clearly communicated to flight dispatchers and pilots. The study uses a historical operational dataset of roughly 280,000 inbound flights, combining airline planning data, AMS traffic data, and METAR/TAF weather reports. On the held-out test set, the model achieves a reduced MAE of 33% and a reduced RMSE of 26% relative to the current operational baseline, while capturing about one third of the variance in FIR duration (R2= 0.33). Error analysis shows that typical delay contributors are captured logically and have intuitive effects on the predictions. It is found that the largest under-predictions are mainly driven by weather forecast errors and unexplained tactical ATC interventions. The results indicate that quantile-based transit time forecasts can provide airlines with a more risk-aware basis for fuel and schedule planning than fixed deterministic buffers. However, the relatively low R2 shows that a substantial share of the variation in FIR duration remains unexplained, largely associated with tactical ATC interventions that occur under otherwise acceptable weather and capacity conditions. ... Read more

This report aims to detail the design process and final design for a Search and Rescue (SAR) drone that will search for victims under collapsed buildings. Firstly, the objectives and requirements for the drone system are given by the overall mission objectives. Then a detailed description of the design for each subsystem is presented. This is followed by an overview of the combined and integrated system. Subsequently, a performance analysis and a use case example of the system are given to indicate how the drone will perform during a mission.
Furthermore, the Reliability, Availability, Maintainability and Safety (RAMS) characteristics are displayed together with an overview of how sustainability has been integrated into the design. The plan for how further development and eventual production will be tackled is proposed. The
operational aspect is also discussed. The report finishes with an overall financial evaluation of the product and the main conclusions and recommendations found during the design process. The report is a continuation and overview of the work done and detailed in the Project Plan,
Baseline Report and Midterm Report by Bergmans et al. [2–4]... ... Read more