## 1 Introduction  
The use of FRP (fiber-reinforced polymer) composite components in modern civil and military aircraft is steadily increasing. To give an example, currently, more than half of the components of the Boeing 787 and Airbus A350 XWB are composite parts [1]. They are employed due to their lightweight and high-strength properties, which in turn contributes to a lighter overall aircraft, improving fuel efficiency and reducing emissions. The advantages of composite components however come at the cost of higher complexity when compared to conventional metal components, requiring an increased focus on maintenance and inspection [1, 2, 3]. On the other hand, from an operational point of view, there is the need to reduce aircraft maintenance and down-times as much as possible. Structural health monitoring (SHM), in the sense of permanently installed sensor networks which enable in-service monitoring of aircraft structures [4], presents a promising approach for ensuring the safety, reliability, and longevity of such structures. It further enables the efficient operation of components, ultimately saving costs and resources, e.g., through optimized maintenance strategies. SHM systems can be divided into four main levels regarding their capability: Level 1--Detection of the existence of damage; Level 2--Localization of the position of damage; Level 3--quantification of the extent of damage; Level 4--classification of the damage type; and Level 5--assessment of the structures integrity [5]. When focusing on the various methods currently used in SHM applications, [PERSON] et al. [6] summarizes them as vibration methods [7], guided waves [8], acoustic emission [9] and strain-based methods [1]. Out of these, strain-based methods are particularly interesting, where cost efficient and robust strain gauges are applied to or embedded into the surface of a structure to measure the deformation experienced under various loads [10]. A popular example is the structural health and usage monitoring system of the Eurofighter Typhoon aircraft, which utilizes the data of strain gauges [11]. More recent strain-based SHM systems make use of fiber optical sensors (FOSs), particularly those based on fiber Bragg gratings (FBG) [1]. They are particularly interesting for the monitoring of composite parts as they can be embedded into the structure. By providing multi-point measurements along the fiber, FBG sensors allow the monitoring of a large area [3]. Parallel to this, rapidly advancing machine learning (ML) techniques are increasingly applied, promising to further enhance the capabilities and application of SHM methods [4]. ML techniques, particularly those designed for pattern recognition and anomaly detection, are well-suited for interpreting the large and often noisy datasets generated in SHM. These methods can automate and optimize the detection of structural damages by learning complex relationships within the data. [PERSON] et al. [12; 13] propose a machine learning-based health monitoring approach that utilizes discrete strain measurements as health indicators. Their work introduces a novel data-driven methodology for detecting debonding using an artificial neural network (ANN). The approach analyzes the strain distribution within the structure and employs the ANN to predict the location and size of the disbond, independent of the magnitude and direction of the applied load. [PERSON] et al. [14] developed an artificial neural network-based SHM system to detect and quantify delaminations in composite airfoils using simulated strain data. Their approach demonstrated an accurate prediction of damage size and location, highlighting the potential of ANN models for real-time structural assessment under manufacturing uncertainties. [PERSON] et al. [15] demonstrated the use of convolutional neural networks (CNNs) trained on simulated strain data from a digital twin of an aircraft composite wing to detect and localize damage with high accuracy, even under noisy conditions. [PERSON] et al.