Training Dataset supporting the publication: Single Shot Line-of-Sight Atmospheric Turbulence Profiling with STORM for Laser Satellite Communications

This is a training dataset for Speckle-based Turbulence Observation and Reconstruction via Machine Learning (STORM), a technique which aims at estimating atmospheric turbulence profiles from the measurement of a single star or laser beam speckle pattern on the ground. Used as to train the first STORM model through surrogate learning, this dataset was generated using a MATLAB wave propagation simulator to generate single star speckle patterns with the parameters described in the associated paper.It contains 50 speckles per 5064 turbulence profiles, thus a total of 253200 sample speckle files (sample_{profile#}-{speckle#}.h5) and 5064 tag files (sample_{profile#}_tag.h5).The github codes for the machine learning model, speckcn2, are documented on the following site:https://males-project.github.io/SpeckleCn2Profiler/.<br><br>