Jianli Jin

Phase-sensitive optical time domain reflectometry ( φ -OTDR) systems frequently encounter a bottleneck where high-accuracy event recognition compromises real-time edge processing capabilities due to the computational demands of complex deep learning models. This paper presents an end-to-end classification framework that resolves this trade-off by integrating discrete first-order difference preprocessing with a lightweight 1D convolutional neural network (1D-CNN). By applying a differential operator to suppress non-stationary baseline drift and Z-score standardization for distributional stability, the proposed method extracts high-frequency transient features from raw time-series data. The subsequent 1D-CNN architecture, characterized by a pyramidal channel expansion and global adaptive average pooling, captures temporal dependencies with minimal structural redundancy. Experimental validation on a public benchmark dataset demonstrates that the model achieves an average accuracy of 99.84%, surpassing state-of-the-art vision transformer (ViT) and MobileNetV2 architectures. Furthermore, the model maintains a parameter footprint of only 42k and achieves an end-to-end inference latency of 0.517 ms on GPU. These results demonstrate that the proposed architecture offers an effective balance between precision and efficiency, enabling robust real-time monitoring on resource-constrained edge devices.

The US Federal Aviation Administration (FAA) has recently mandated that small unmanned aerial vehicles (UAVs) be equipped with a transmitter that broadcasts the UAV's serial number, location, and altitude. The inclusion of a unique identifier in the form of a UAV serial number has stoked fears that the identifier will be used to track UAV operators and has even led some UAV operators to file a lawsuit against the FAA. In this paper, we propose CAPSID, an implementation of the FAA session ID concept that provides message authentication-something current Remote ID implementations lack-and strong operator anonymity. The FAA (or its equivalent in other jurisdictions) retains the ability to de-anonymize operators, but only in circumstances prescribed by law. To make this possible, CAPSID introduces a partially trusted third party, the Custodian, that serves as the bridge between anonymous identifiers and true operator identity. The Custodian ensures that the legal requirements for de-anonymization are met, preventing unnecessary mass collection of personally identifying information by a government agency. We formally verify the message authentication property of CAPSID using a cryptographic protocol checker and provide a formal proof of identifier non-linkability, even in the presence of corrupt (but non-colluding) authorities.