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Shear mode‐guided ultrasonic waves are highly regarded for submerged or subterranean structural health monitoring (SHM), owing to their non‐dispersive feature and minimized acoustic energy loss when in contact with liquid or solid. High‐performance shear mode ceramic ultrasonic transducers with robustness and cost‐effectiveness are highly demanded for underwater or underground SHM applications, especially in harsh environments. However, the implementation of discrete shear mode piezoelectric ceramic ultrasonic transducers is hindered by the inconsistency with manual installation, lack of conformability on curved surfaces, and unreliable acoustic coupling between the transducers and the structure. Here, direct‐write conformable shear mode ultrasonic transducers made from piezoelectric lead‐free ceramic coatings, which are in situ produced on steel structures by a scalable thermal spray process, are proposed. The obtained lead‐free lithium‐doped potassium sodium niobate (KNN‐LN) ceramic coatings exhibit a high effective shear piezoelectric strain coefficient (d24, f) above 60 pm V−1 in a broad frequency range from 100 Hz to 200 kHz. The resulting conformable shear mode KNN‐LN ceramic coating transducers successfully showcase the functions of exciting and detecting stable shear mode ultrasonic wave signals with operation temperature exceeding 200 °C and demonstrate reliable capability in defect detection in both air and liquid environments. [ABSTRACT FROM AUTHOR]

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data. [ABSTRACT FROM AUTHOR]