TOOLKIT SPECTRUM — AN INNOVATIVE SOLUTION FOR CHAPTER 10 DATA ANALYSIS

RF data analysis in Chapter 10 files traditionally requires replaying them on recorders, even for ba- sic tasks such as assessing the file label or performing frequency spectrum analysis. However, input files can be poorly labeled or may require significant transfer times to the recorder. The Chapter 10 Toolkit addresses these challenges by providing a portable software-based solution for recorded data representation. The Toolkit offers a fast and comprehensive solution to streamlining Chapter 10 file analysis via waterfall spectrum display, packet data and header visualization, channel ex- traction tools, and specific packet search functions. Additionally, the Toolkit integrates a dynamic sampled representation of the recorded signal and cutting-edge AI-based prediction tool to analyze input data, predict signal waveforms and other characteristics. By leveraging advance algorithms, this predictive tool aids in the visualization, evaluation, and interpretation of test telemetry data.

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Publication Details

Journal
UA Campus Repository (The University of Arizona)
Published
2025-10-01
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
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article

TOOLKIT SPECTRUM — AN INNOVATIVE SOLUTION FOR CHAPTER 10 DATA ANALYSIS

Alexis Chatail–Rigolleau
UA Campus Repository (The University of Arizona)
Computational Physics and Python Applications
article

TOOLKIT SPECTRUM — AN INNOVATIVE SOLUTION FOR CHAPTER 10 DATA ANALYSIS

Alexis Chatail–Rigolleau
article en

Abstract

RF data analysis in Chapter 10 files traditionally requires replaying them on recorders, even for ba- sic tasks such as assessing the file label or performing frequency spectrum analysis. However, input files can be poorly labeled or may require significant transfer times to the recorder. The Chapter 10 Toolkit addresses these challenges by providing a portable software-based solution for recorded data representation. The Toolkit offers a fast and comprehensive solution to streamlining Chapter 10 file analysis via waterfall spectrum display, packet data and header visualization, channel ex- traction tools, and specific packet search functions. Additionally, the Toolkit integrates a dynamic sampled representation of the recorded signal and cutting-edge AI-based prediction tool to analyze input data, predict signal waveforms and other characteristics. By leveraging advance algorithms, this predictive tool aids in the visualization, evaluation, and interpretation of test telemetry data.

UA Campus Repository (The University of Arizona)
Openalex Percentile: Top 14%
Computational Physics and Python Applications
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