Heavy-haul railways are critical for transporting freight. However, prolonged wheel–rail interactions cause frequent rail defects, particularly in small-radius curve sections. Ultrasonic A-scan signals are essential for the non-destructive evaluation of internal rail defects. In real heavy-haul environments, these signals suffer from strong non-Gaussian coupled noise. Such noise includes structural noise, low-frequency irrelevant components, and high-frequency electrical noise. Noise aliasing obscures defect echoes and increases the risk of missed detections. Conventional denoising methods are limited by poor noise–signal separability, mode mixing, and inadequate adaptability to complex non-Gaussian signals. To address these challenges, an A-scan signal model under noise-coupled conditions is constructed by analyzing the statistical and time–frequency characteristics of different noise components.

The replication package contains the questionnaire, datasets and analysis process used for the study.

The basic code and date  of DLE

STPredictor is an explainable ship trajectory prediction framework powered by large language models. It reformulates trajectory prediction as a language modeling task using natural-language prompts and integrates Chain-of-Thought reasoning for more transparent and reliable predictions. Experiments on two large-scale AIS datasets show strong performance and improved interpretability over existing baselines.

This replication package provides the complete source code and configuration files used in the study “CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy.”

The package enables full reproduction of the proposed cognition-driven multimodal trajectory prediction and safety-aware planning framework, including model architecture implementation, training procedures, evaluation scripts, and planning modules.

The provided code supports both open-loop prediction evaluation and closed-loop planning simulations as reported in the manuscript. All scripts are organized to facilitate reproducibility of the main experimental results on public autonomous driving benchmarks.