http://hdl.handle.net/10174/37666 Thu, 30 Jul 2026 20:45:31 GMT 2026-07-30T20:45:31Z http://hdl.handle.net/10174/41963 Title: Impact of Measurement Noise and Fitting Window Placement on Single-Diode PV Parameter Extraction Authors: Mesbahi, Oumaima; Afonso, Daruez; Janeiro, Fernando M; Grilo, Frederico; Tlemçani, Mouhaydine Abstract: The problem of photovoltaic (PV) cell degradation can affect the shape of the I-V curve, which can lead to variations in the five parameters of the PV cell. This is the motivation behind the importance of knowing and extracting these parameters. The process starts by the measuring the output current and voltage (I-V curve) then applying a best fit to obtain the parameters. Both the noise of the instruments used for measurement and the size of the measured window can affect the accuracy of the obtained parameters. This paper presents a study about the effects of both the noise of instruments and the interval size. Varying the RMS of the noise of both current and voltage from 1 to 10%, the parameters are extracted from two case studies, first one starting the interval from the short circuit coordinates and the second one from the open circuit voltage, the size of the intervals are increased till reaching the whole curve. Results demonstrated that to obtain optimized parameters a 40−60% segment of the I-V curve should be measured staring from Voc region. Tue, 21 Oct 2025 23:00:00 GMT http://hdl.handle.net/10174/41963 2025-10-21T23:00:00Z http://hdl.handle.net/10174/41962 Title: Automated Detection of Aircraft Surface Defects Using Deep Learning with Integrated Human Validation Authors: Mesbahi, Oumaima; Chabane, Souhila; Pereira Santos, Nuno; Del Pino Lino, Adriano; Tlemçani, Mouhaydine; Lourenço Da Saúde, José Manuel Abstract: Visual inspection of aircraft surface is one of the many steps in the maintenance routines. Usually performed by operators, this procedure might last days to be accomplished. The use of automated process can help reduce time and results in accurate detection of surface defects on aircraft, as they are vital to maintain structural soundness and flight safety. This paper proposes a deep learning framework for automated defect detection based on Faster R-CNN with ResNet-50 Feature Pyramid Network (FPN) as the backbone model. This model was trained and validated on a sizable, labeled aircraft images with a maximum F1-score of 0.555 achieved in the test set. This is the result of preliminary study, where the authors aimed to detect all types of defects without classification. To further enhance reliability and allow for human input, a custom annotation validation user interface was implemented via Python, which allowed aircraft inspectors to view, edit, add, and acknowledge predictions made by the model in an attempt to hold onto precise level of annotation. This system also facilitated the management of annotations, visualization on irregular aircraft zones, and the creation of reports thus allowing for inspection workflows. The results show that combining state-of-the-art object detection with domain expertise in validation as route to reliable semi-automatic, standards-compliant aircraft defect detection is plausible. Future work will involve expanding the dataset, tuning for accuracy, and incorporating human feedback for enhancement of model utility over time. Tue, 21 Oct 2025 23:00:00 GMT http://hdl.handle.net/10174/41962 2025-10-21T23:00:00Z http://hdl.handle.net/10174/41951 Title: Detection and Classification of Aircraft Structural Defects for Database Creation and Findings Identification Authors: Chabane, Souhila; Mesbahi, Oumaima; Pereira Santos, Nuno; Tlemcani, Mouhydine; Lourenco de Saude, Jose Abstract: In aviation, maintaining structural integrity [1] It is crucial to maintain aviation safety and operational security. Surface wear, corrosion, and cracks are [2], [3], [4] typical structural defects that can seriously compromise components for aircraft. Employing innovative image processing techniques [5]This study provides a comprehensive approach to support the creation of systems that enable the automatic recognition and classification of these findings. The primary objective is to develop a verified image-based database that improves maintenance processes and inspection performance. The process basis is a structured finding catalogue that was created after an extensive examination of scientific and industrial sources. This catalogue standardises terminology and makes it easier to manually annotate and classify defects consistently. A rigorous pipeline that includes image collection from various sources, data augmentation to improve generalisation, manual annotation based on the catalogue, and expert validation to guarantee accuracy and consistency is used to build the dataset. A crucial component of this initiative is the Aircraft Inspection [6], [7], [8] Repository. By acting as a centralised platform that improves data accessibility, expedites maintenance workflows, and guarantees regulatory compliance, it is intended to address the challenges of gathering, monitoring, and analysing inspection data. The repository greatly improves maintenance planning and decision-making by arranging inspection records across various aircraft models, providing dynamic data analysis tools, and enabling collaborative access to findings. Tue, 21 Oct 2025 23:00:00 GMT http://hdl.handle.net/10174/41951 2025-10-21T23:00:00Z http://hdl.handle.net/10174/41767 Title: Heat flow and thermal gradients in Portugal Authors: Duque, Maria Rosa; Mendes-Victor, Luís Alberto Abstract: In this paper we present the available heat flow data in Portugal.The number and location of the boreholes don't allow contouring of heat flow; so, we show the heat flow values and thermal gradients, making some considerations about the anomalous values. Fri, 01 Jan 1988 00:00:00 GMT http://hdl.handle.net/10174/41767 1988-01-01T00:00:00Z