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This observation justifies our choice of material categories.
This observation verifies our choice of material categories, in addition to the reason that they occur most frequently on runways or taxiways.
In contrast to [14] where the dataset is used to study recognition of material categories we will use images from all four material samples as examples of the same class when training the classifier.
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This dataset has 1000 images of 10 material categories.
Most researchers focus on a single core/shell system and optimize the morphology of nanomaterials to enhance the electrochemical performance, but limited reports studying the effect of the material category in the same morphology on the performance of the core/shell electrode.
The pros and cons of each material category are discussed.
Images of each material category were taken from 9 a.m. to 5 p.m. on separate days to ensure different illumination circumstances.
The distribution of the other two main material categories, i.e., fossil fuels and metal ores, shows a different pattern.
The first analysis of category 3 particles (hereafter, first examination) was performed in parallel with the preliminary examination of silicate materials (categories 1 and 2).
The initial material properties are defined using tabulated values available in literature and knowledge of the construction material category.
The abiotic raw material category of the material footprint considers all mineral resources.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com