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    Enhancing the Understanding of Perceptual-Motor Skills through Video Notational Analysis : A Video Notational Analysis of Visual Exploration, Team Communication, and Creativity in Elite Soccer
    (Netherlands Aerospace Centre NLR, 2025) Caso, S.
    This doctoral thesis investigates the perceptual-motor skills essential for elite soccer performance, with a focus on creativity, variability in actions, visual exploratory activity (VEA), and intra-team communication. Using video notational analysis, a method that bridges the gap between controlled laboratory studies and the complex and dynamic conditions of competitive sport, this thesis examines how these skills manifest and influence performance. The studies are grounded in the ecological psychology framework, which emphasizes the continuous interaction between perception and action within the sporting environment. The thesis comprises five studies. The first study explores how small-sided games encourage creative actions, proposing that increased action variability fosters creativity. The second and third studies examine the VEA rates (considering the number of VEA in the time frame prior to ball possession) of elite soccer players, comparing different field positions and distinguishing between super-elite (award-winning) players and their elite teammates. The findings highlight how VEA, defined as purposeful body or head movements to gather environmental information, correlates with successful passing outcomes. The fourth and fifth studies focus on intra-team communication. A case study assesses the effectiveness of traditional video notational analysis compared to methods incorporating audio recordings, allowing for a more comprehensive examination of verbal and nonverbal communication. The final study challenges static, cognitive models of communication in sport, instead proposing an ecological perspective in which communication dynamically facilitates collective attention and shared affordances among teammates. This thesis contributes to a deeper understanding of how perceptual-motor skills can be analyzed, trained, and applied to enhance elite soccer performance. It advocates for methodologies that preserve ecological validity, ensuring that research findings remain relevant to real-world sporting contexts.
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    Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning
    (MDPI, 2025) Hartmann, D.E. ; Gravey, M. ; Price, T.D. ; Nijland, W. ; De Jong, S.M.
    Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensing, in combination with machine learning techniques, are gaining interest. Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutional neural networks. The U-Net, trained on 49 Sentinel-2 images, and the 2D-3D CNN, trained on PRISMA imagery, had a Mean Absolute Error (MAE) of approximately 1 m for depths up to 20 m and were superior to band ratio models by ~40%. Problems with underprediction remain for turbid waters. Sentinel-2 showed higher performance than PRISMA up to 20 m (~18% lower MAE), attributed to training with a larger number of images and employing an ensemble prediction, while PRISMA outperformed Sentinel-2 for depths between 25 m and 30 m (~19% lower MAE). Sentinel-2 imagery is recommended over PRISMA imagery for estimating shallow bathymetry given its similar performance, much higher image availability and easier handling. Future studies are recommended to train neural networks with images from various regions to increase generalization and method portability. Models are preferably trained by area-segregated splits to ensure independence between the training and testing set. Using a random train test split for bathymetry is not recommended due to spatial autocorrelation of sea depth, resulting in data leakage. This study demonstrates the high potential of machine learning models for assessing the bathymetry of optically shallow waters using optical satellite imagery.
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    Test Results for a Novel 20 kW Two-Phase Pumped Cooling System for Aerospace Applications
    (MDPI, 2025) Gerner, H.J. van ; Luten, T. ; Scholten, S.B. ; Mühlthaler, G. ; Buntz, M.B.
    In the EU-funded BRAVA project, technologies for a fuel cell-based power generation system for aviation are being developed. In this paper, the test results for a demonstrator of a novel two-phase pumped cooling system with 20 kW cooling capacity are presented. This system uses the evaporation of a liquid to remove waste heat from the heat sources. Several concepts have been tested with this demonstrator, including the ‘no accumulator’ concept, which offers a large mass reduction compared to conventional cooling systems. Additionally, the system can be rotated, and the influence of the orientation has been tested.
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    Ultrasonic Frequency Analysis of Adhesively Bonded Joints
    (MDPI, 2025) Jansen, H.P. ; Elburg, B. van ; Veen, E.S. van ; Platenkamp, D.J.
    Bonded joints are commonly used for aircraft construction. The non-destructive testing of these components for debonds can be performed using traditional NDI. However, in the case of weak bonds and “kissing bonds”, inspection becomes more difficult. In this work, we have investigated weak bonds that have been produced by contaminating the bondline interface with different release agents. By carrying out an analysis of ultrasonic data from the frequency domain, the bondline thickness can be determined. Additionally, indications of the presence of contaminants/weak bonds can be detected at specific frequencies, while the effects of a non-uniform bondline thickness can be suppressed by averaging the power around the selected frequency. Mechanical testing at different locations showed that indications in the frequency domain were able to find one weak bond, while the other indications showed no decrease in lap shear strength
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    Non-Contact Non-Destructive Testing Methods for Large-Scale Carbon Fiber-Reinforced Polymer Aircraft Parts
    (MDPI, 2025) Deutz, D.B. ; Bosch, A.F. ; Baptista, D.E. ; Veen, E.S. van ; Platenkamp, D.J. ; Jansen, H.P.
    Non-contact NDT methods that can provide fast, automated, in-line quality assurance information on the manufacturing and maintenance of large-scale, thin-walled aircraft parts are necessary for the implementation of thermoplastic CFRP in the next generation of aircraft. Infrared thermography (IRT) is a promising method to fill this gap. Here, the detection of flat bottom holes, inclusions, and interlaminar delaminations in fuselage skin is studied for two types of IRT and compared with ultrasound inspection. Unique to this work are three demonstrations of the potential of IRT to deliver a time-effective, automated inspection approach for large-scale, thin-walled thermoplastic CFRP aircraft parts.