http://hdl.handle.net/10174/37666 2026-05-08T00:00:48Z 2026-05-08T00:00:48Z Mesbahi, Oumaima Afonso, Daruez Janeiro, Fernando M Grilo, Frederico Tlemçani, Mouhaydine http://hdl.handle.net/10174/41963 2026-05-06T13:49:43Z 2025-10-21T23:00:00Z

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.

2025-10-21T23:00:00Z Mesbahi, Oumaima Chabane, Souhila Pereira Santos, Nuno Del Pino Lino, Adriano Tlemçani, Mouhaydine Lourenço Da Saúde, José Manuel http://hdl.handle.net/10174/41962 2026-05-06T13:46:58Z 2025-10-21T23:00:00Z

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.

2025-10-21T23:00:00Z Chabane, Souhila Mesbahi, Oumaima Pereira Santos, Nuno Tlemcani, Mouhydine Lourenco de Saude, Jose http://hdl.handle.net/10174/41951 2026-05-05T14:05:33Z 2025-10-21T23:00:00Z

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.

2025-10-21T23:00:00Z Carvalho, J. Cabral, J. Borges, J. Dias, R. http://hdl.handle.net/10174/40476 2026-01-16T00:16:17Z 2024-01-01T00:00:00Z

Title: Reappraisal of active tectonics of the Porto Alto buried fault zone (Portugal) considering new geophysical shallow studies Authors: Carvalho, J.; Cabral, J.; Borges, J.; Dias, R. Editors: LNEG - Comunicações Geológicas Abstract: The Lower Tagus Valley area (LTV), where Lisbon is located, has been affected by several destructive, M 6+ earthquakes whose sources remain to be determined. The identification of expectable surface or near surface ruptures in the area is a challenging task that requires a multidisciplinary analysis that includes geophysical techniques, as the source faults are mainly buried despite likely to deform Upper Pleistocene to Holocene alluvial cover of the Tagus River. This paper focuses on the characterization of the Porto Alto fault zone for seismic hazard mitigation purposes. The Porto Alto fault zone was recognized in oil-industry P-wave 1980’s seismic reflection data as an important, Miocene reactivated, deep structure in the LTV. High-resolution P-wave seismic reflection data were later acquired in the early 2000’s to investigate related Holocene fault activity, leading to the identification of a shallow fault zone near the surface. However, the vertical resolution of the acquired P-wave seismic reflection data was considered insufficient to corroborate any presumably small vertical offset related to fault rupture in the ca. 50 m thick alluvium cover. Trenching for the recognition and characterization of surface faulting was previously tested in the study region but it proved to be a challenging and poorly efficient methodology due to the very shallow water table (at ~1 m) and low cohesion of the sediments. Due to these constraints, we revisited the former fault study site to acquire higher resolution S-wave seismic and ground penetrating radar (GPR) data. The new seismic profiles show interruption of the reflectors in the stacked sections. Diffracted energy, changes in amplitude/shape of the reflection hyperbolae in the shot gathers and spatially coincident low velocity anomalies, also indicate the presence of several shallow fault strands deeper than 10 m. The GPR profile, overlapping and extending the seismic profiles in 30 m reaches a maximum investigation depth of about 15 m and shows the presence of deformation at three locations, one of which matches with one of the fault strands detected in the high-resolution S-wave seismic data. In this profile, sediment disruption was detected extending upwards to a depth as shallow as ca. 3.5 m, corresponding to alluvium with a poorly constrained age of ca. 2,300 yrs. Slip rate, maximum earthquake magnitude and recurrence, and other parameters are also estimated for the Porto Alto fault zone. These recently acquired seismic and GPR datasets indicate that there were at most three to five maximum earthquakes generated by the fault in the last 13,100 years, with an average recurrence of approximately 4,400 to 2,600 years respectively. However, the data show a grouping of these earthquakes in time, the first two in the period 13,100-12,300 years, separated by about 800 years, and the third or the last grouped three having occurred in the past 2,300 years with a similar average recurrence time of ca. 800 years. However, the regional historical and instrumental seismicity does not show an obvious link of any known major earthquake with the Porto Alto fault zone.

2024-01-01T00:00:00Z