Raw Data Processing is the initial stage of data handling in which unprocessed information is cleaned, transformed, and structured for analysis. It involves removing errors, normalizing formats, and converting data into usable representations. Effective processing ensures data quality and consistency across analytical systems. Techniques include parsing, filtering, and feature extraction. This step is foundational for all machine learning, scientific computing, and business intelligence applications.

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End-to-End AI for Distributed Fiber Optics Sensing: Eliminating Intermediate Processing via Raw Data Learning

For the first time, we present an end-to-end AI framework for data analysis in distributed fiber optic sensing. The proposed model eliminates the need for optical phase computation and outperforms traditional data processing pipelines, achieving over 96% recognition accuracy on a diverse acoustic dataset.