Ahmed Abdullahi Hassan

Jost Wittmann, Ahmed Abdullahi Hassan, Nils Kömpe et al.

Inspection of offshore wind turbine rotor blades is critical for predictive maintenance to maximise efficiency and extend operational lifetime. However, it remains a challenging task due to remote locations, large structural dimensions, and the limitations of current UAV-compatible sensor systems. While existing approaches can detect certain types of surface anomalies, reliable classification of defect types often remains a manual and error-prone process. This paper presents the design of a UAV-mounted multimodal sensor network combining an industrial RGB camera, a passive thermal infrared camera, and an in-house developed 3D scanner. All sensors are co-calibrated into a common coordinate frame, enabling spatial superimposition of geometric, colour, and thermal data. The system is designed to operate at close range, addressing three fundamental sensing challenges: platform motion, large field of view, and millimetre-level measurement accuracy. Preliminary laboratory results demonstrate synchronised multi-sensor acquisition and initial point cloud reconstructions, forming the basis for future airborne inspection trials.

Adekunle Ajibode, Oussama Ben Sghaier, Keheliya Gallaba et al.

Pre-trained language models (PTLMs) hosted on platforms such as Hugging Face form complex lineage structures similar to software dependency graphs. However, unlike traditional software ecosystems, PTLM repositories often lack reliable provenance due to missing metadata, such as licenses, reuse methods, pipeline tags, model types, and training libraries. To address this gap, we introduce Semantic Fingerprinting (SemFin), a lightweight approach that combines Hugging Face (HF) configuration files with model repository tags to automatically impute missing model metadata fields and reconstruct model lineage chains. We evaluate SemFin on a large-scale dataset of 317,133 PTLMs. Our results show that configuration files typically encode the technical requirements necessary to instantiate and reuse models, enabling them to serve as a structural blueprint for model reuse, particularly for transformer-based architectures. By combining these configuration files with model repository tags, SemFin significantly outperforms the existing propagation-based imputation approaches, improving prediction accuracy by up to 31.4% and 26.6% compared to Graph Avg and Hub Avg baselines. Importantly, SemFin also imputes metadata for 16.6% of isolated models where propagation-based methods fail. Applying SemFin to impute missing reuse-method and license metadata for 167,089 unlabeled models reveals that traceable reuse method chains expand by 75.9% and license lineage chains by 53.6%, uncovering 86 previously invisible reuse method patterns, while the proportion of incompatible license patterns only increases from 34.8% to 36.8%. These findings demonstrate how automatically derived structural signals can support the automated construction of AI Bills of Materials (AIBOMs), helping transform metadata from an error-prone manual declaration into information inferred directly from model artifacts.