Search Results (3)

The accurate prediction of the surfacing position of underwater gliders (UGs) is critical for mission success and cost-effective retrieval. However, current state-of-the-art (SOTA) methods often rely on complex multi-model integrations or large volumes of ocean current data, thereby increasing operational costs and system complexity. In this study, we systematically introduce—for the first time—a coordinate-transformation-based prediction framework, originally applied in other navigation contexts, into the UG surfacing-position-prediction task. By projecting both the glider’s entry and surfacing positions into a Universal Transverse Mercator (UTM) planar coordinate system and treating the resulting displacement as the prediction target, we avoid dependence on heavily parameterized current models, simplify the training process, and maintain robust predictive accuracy. Our approach combines common machine learning predictors (e.g., AdaBoost, LGBM, gradient boosting, random forest, decision trees) instead of advanced deep learning architectures, thus reducing computational overhead. Experiments on two real-world sea trial datasets (containing 2159 and 1456 profiles, respectively) show that, compared with direct regression approaches, this method improves positioning accuracy by up to 50% within a 500-meter range, yet requires minimal multi-source data. Overall, this study integrates the concept of coordinate transformation into the task of predicting the surfacing position of underwater gliders, effectively streamlining the method without sacrificing accuracy. The result is a highly flexible and cost-effective approach, providing theoretical support for future optimizations of underwater glider navigation systems. Full article

To enable climate-neutral aviation, improving the energy efficiency of aircraft is essential. The research project Synergies of Highly Integrated Transport Aircraft investigates cross-disciplinary synergies in aircraft and propulsion technologies to achieve energy savings. This study examines a fuel cell electric powered configuration with distributed electric propulsion. For this, a reverse-engineered ATR 72-500 serves as a reference model for calibrating the methods and ensuring accurate performance modeling. A baseline configuration featuring a state-of-the-art turboprop engine with the same entry-into-service is also introduced for a meaningful performance comparison. The analysis uses an enhanced version of the Stanford University Aerospace Vehicle Environment (SUAVE), a Python-based aircraft design environment that allows for novel energy network architectures. This paper details the preliminary aircraft design process, including calibration, presents the resulting aircraft configurations, and examines the integration of a fuel cell-electric energy network. The results provide a foundation for higher fidelity studies and performance comparisons, offering insights into the trade-offs associated with hydrogen-based propulsion systems. All fundamental equations and methodologies are explicitly presented, ensuring transparency, clarity, and reproducibility. This comprehensive disclosure allows the broader scientific community to utilize and refine these findings, facilitating further progress in hydrogen-powered aviation technologies. Full article

The profile of students applying to BA Fine Art undergraduate programs has shifted in the United Kingdom (UK). Until recently the usual academic pathway was to proceed after A-level to a one-year Art Foundation program; this route is increasingly challenged by a sense of urgency to enter university earlier. Students more frequently enter straight from school. To accommodate the recruitment of younger applicants there are significant implications for Higher Education Fine Art pedagogy. This article reports on some of the approaches implemented at Northumbria University to support positive transition and learning within the BA Fine Art program. Using the Year 1–Level 4 Fine Art as a case study this reflects on how one university fine arts team has responded to the challenge of induction. Full article