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Remote sensing of the light-obscuring smoke properties in real-scale fires using a photometric measurement method - MaRDI portal

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Remote sensing of the light-obscuring smoke properties in real-scale fires using a photometric measurement method (Q6725577)

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Dataset published at Zenodo repository.
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English
Remote sensing of the light-obscuring smoke properties in real-scale fires using a photometric measurement method
Dataset published at Zenodo repository.

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    This data set complements the article https://doi.org/10.1007/s10694-023-01470-z. The data set contains the raw image data of three representative experiments and the corresponding input configuration data files for the LEDSA simulations. The data set contains: - The processed LEDSA simulation data of all experiments, mainly consisting of the measured and normalized intensities of all LEDs and the computed extinction coefficients according to the applied layer discretization (ledsa_simulations.zip). - The recorded measurement data of all experiments from three MIREX devices (MIREX.zip). - An exemplary data set consisting of the LEDSA raw data as RAW image files from three representative experiments in shortened form, containing every third image (image_data_{...}.zip). - LEDSA Input and Config files for analysis and simulation of the provided exemplary image data (ledsa_simulations_short.zip). - Python scripts for reading and post-processing the LESDA simulation data (postprocessing_tools.zip) - Juptyter Lab Python scripts that have been used to create the plots in the associated paper (data_analysis.zip). - The figures from the associated paper (figures.zip). Notes In order to run the Python scripts in Juptyer Labs properly, the given directory structure must be maintained, resulting when all archives are unpacked on the same hierarchical level. The authors gratefully acknowledge the financial support of the German Federal Ministry of Education and Research. The extensive LEDSA computations were performed largely on the high-performance computer system funded as part of the CoBra project with the grant number 13N15497.
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    23 August 2022
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