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 ## **Navigation:** [nimCSO](nimcso.html) (core) | [Changelog](_._/docs/changelog.html) | [nimcso/bitArrayAutoconfigured](nimcso/bitArrayAutoconfigured.html) |
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-## |GitHub| |License| |ArticleDraft|
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-## |TestMac| |TestLin| |TestWin|
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-## .. |GitHub| image:: https://img.shields.io/badge/GitHub-Repository-Badge?logo=github&color=black
+## .. figure:: https://img.shields.io/badge/GitHub-Repository-Badge?logo=github&color=black
 ##   :alt: GitHub
 ##   :target: https://github.com/amkrajewski/nimCSO
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-## .. |License| image:: https://img.shields.io/badge/License-MIT-yellow.svg
+## .. figure:: https://img.shields.io/badge/License-MIT-yellow.svg
 ##   :alt: MIT License
 ##   :target: https://opensource.org/licenses/MIT
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-## .. |ArticleDraft| image:: https://img.shields.io/badge/JOSS%20Article-Draft-Badge?color=orange
+## .. figure:: https://img.shields.io/badge/JOSS%20Article-Draft-Badge?color=orange
 ##   :alt: JOSS Article Draft
 ##   :target: https://github.com/amkrajewski/nimCSO/blob/1588ef66cbce11b4aa4a7243a41b274d324789eb/paper/paper.pdf
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-## .. |TestMac| image:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Apple.yaml/badge.svg
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+## .. figure:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Apple.yaml/badge.svg
 ##   :alt: macOS Testing
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-## .. |TestLin| image:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Linux.yaml/badge.svg
+## .. figure:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Linux.yaml/badge.svg
 ##   :alt: Linux Testing
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-## .. |TestWin| image:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Windows.yaml/badge.svg
+## .. figure:: https://github.com/amkrajewski/nimCSO/actions/workflows/testingOnPush_Windows.yaml/badge.svg
 ##   :alt: Windows Testing
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 ## **nim** **C**omposition **S**pace **O**ptimization is a high-performance tool implementing several methods for selecting components (data dimensions) in compositional datasets, which 
 ## optimize the data availability and density for applications such as machine learning (ML) given a constraint on the number of components to be selected, so that they can be designed in 
 ## a way balancing their accuracy and domain of applicability. Making said choice is a **combinatorically hard