## 1 Introduction  
In recent years, many research works have been devoted to the placement of sensors inside composites for structural health monitoring (SHM) of the offshore wind sector (e.g., offshore wind turbine blades and high-voltage power cables) [1; 2; 3; 4; 5] because it is fast-growing and more promising than its onshore counterpart in helping wind energy stay on course for the \(1.5\,^{\circ}\mathrm{C}\) and net zero pathway set out by International Renewable Energy Agency [5; 6; 7; 8]. Current offshore wind turbine blades manufactured by companies General Electric, Vestas, MingYang, and Siemens exceed 100 m to realize exceptional energy-generation performances and reduce the leveraged cost of energy [10; 11; 12]. This increase in size entails the switch from glass to carbon-fiber-reinforced polymers (CFRP) for lighter, stronger, and stiffer blades [11; 13], along with appropriate SHM strategies that can ensure reliable service over 20 years, at least. Areas of weakness in a CFRP blade have been identified as the root sections and trailing edge bonds, and corresponding reinforcement strategies have been suggested to ensure reliability [1; 2; 3; 4; 5; 13; 14]. Meanwhile, a continuous, automatic, and in situ SHM system is highly desirable. Because, compared to conventional inspection programs, it is promising in remotely alerting operators to any dangerous event (e.g., overloads or damage initiation)in a timely manner, thereby approaching maintenance-free operation and avoiding sudden catastrophic failures of deteriorated CFRP structures due to uncertainties [15; 16; 17]. The SHM concept can be more easily understood by comparison with the way a human body works [16]. First of all, an \"unhealthy\" condition must be detected by the nervous system, which is similar to the sensing subsystem in SHM where sensors react to environments and provide measurements of parameters (e.g., mechanical strain) [1; 2]. To gather maximum information concerning health conditions of structures under various load conditions, ideally, nerves/sensors provide full coverage of not only structural surfaces but also structural volume. This requires sensors be bonded/embedded into structures at representative locations, in addition to being installed on surfaces, which can also enhance protection of sensors from external harsh environments [1; 2; 18; 19; 20; 21; 1]. Fiber-optic sensors (FOSs), as shown in Figure 1, have proven strain-measuring capabilities and suitability for embedment into CFRP composites [15; 22; 23; 24; 25; 26]. Their immunity to electromagnetic interference and noise and their intrinsic link to optical transit cables allow them to keep the signal quality with low loss, such that their sensing functions can be extended for long-distance applications [1; 23; 24]. Furthermore, rather than monitoring a measurand only at a limited number of pre-determined points (discrete locations), distributed FOSs can determine the continuous spatial distribution of one or more measurands along long fibers of many kilometers [23]. This is a unique property of distributed FOSs, which is unparalleled by any other techniques, especially when the area to be monitored is extended and/or when the required density of sensing points is high [27]. Nevertheless, the main drawbacks of SHM through embedded distributed FOSs are (1) one single-mode FOS is only capable of sensing accurately a local area around 10 mm away from it, (2) the FOS's interrogator system is expensive (e.g., several hundred thousand euros), and (3) FOSs are \"foreign\" materials whose diameters are much larger than those of carbon fibers, thereby inevitably resulting in perturbed strain states around them. Therefore, it is preferable to achieve the largest monitoring coverage with a minimum number of FOSs used. In light of this, linear placement of FOSs is not an optimal solution, as pointed out in previous studies [1; 2; 3]. Thus, in a preliminary FOS embedment study, [PERSON] et al.