## 3 Experiment Setup  
The schematic design of a novel fiber-optic coil-based AE sensing and monitoring system is shown in Figure 1. The setup features a grid-marked aluminum plate with a fiber-optic AE sensor, developed in [37], comprising a fiber coil with two identical FBGs that form a low-finesse FPI. The sensor has an 8 mm outer diameter and a 6 mm inner diameter, and is flexibly mounted on the sample surface. The reflection features fine fringes with a free-spectral range of 2 pm within the reflection bandwidth determined by the FBGs. Ultrasound waves impinging on the coiled fiber create refractive index variations, modifying the FPI cavity length and causing spectral fringe shifts. The sensor was interrogated using a modified phase-generated carrier method that allows for the extraction of the AE signal with good linearity and high sensitivity, regardless of the laser wavelength with respect to the sensor fringes [38]. As illustrated in Figure 1, a laser source generates light that passes through a phase modulator driven by a \(sin(wt)\) signal. The modulated light travels through a circulator and optical fiber to the FBG-FPI sensor. Reflected light returns through the circulator to a photodetector (PD). The output of the PD is processed through two parallel paths. In the upper path, the signal is mixed with \(sin(wt+\varphi_{1})\), low-pass filtered at 500 kHz, band-pass filtered from 50-500 kHz, and amplified. The lower path involves mixing with \(sin(2 wt+\varphi_{2})\), followed by similar filtering and amplification steps. Both paths include additional low pass filtering at 25 kHz. The processed signals are then combined to produce the final output \(\theta(t)\), which represents the AE signal. This system facilitates high-quality AE signal detection with an omnidirectional response and high ultrasound sensitivity while adapting to environmental perturbations. The AE signals obtained from the fiber-optic AE sensor were used to train and test the proposed hybrid deep learning models for source localization. The Hsu-Nielsen pencil lead break (PLB) test, a widely accepted method for AE signal generation [39], was used in this study. We conducted the test in a sequential manner across 35 distinct locations on the 1/10-inch-thick aluminum plate. The plate was partitioned into 35 distinct locations, as shown in Figure 2. Each location underwent ten PLB tests using a 2H mechanical pencil with a 0.5 mm diameter lead. Figure 2a presents a photograph of the experimental setup, illustrating the aluminum plate divided into a grid for AE testing. This plate is marked with blue numbers indicating specific test points. The numbering is reorganized for simplicity in Figure 2b. The fiber-optic sensor, glued onto an aluminum carrier disk, is affixed to the plate with a liquid couplant to facilitate ultrasonic coupling. Figure 2b provides a schematic representation of the plate, detailing the grid layout and test points. The 35 distinct locations are marked by red dots, corresponding to the PLB test points. This layout ensures the reliability and reproducibility  
Figure 1: Schematic of the fiber-optic coil-based acoustic emission sensing system. Inset: Close-up image of the sensor, showing the flexible mounting and dimensions (8 mm outer, 6 mm inner diameter). of the collected AE data, crucial for training and testing deep learning models for source localization. The clear separation of test points and consistent methodology across the grid contribute to the robustness of the experimental design. Figure 3 illustrates the time series augmentation technique employed in this study to balance and expand the dataset, thereby enhancing deep learning model performance for acoustic emission source localization. The image depicts data distribution across 35 different locations, representing distinct labels in our acoustic emission source localization task. The figure highlights the variability in the original dataset, where some locations (e.g., Location 2) have more raw data than others (e.g., Location 1).