Surface-informed active learning prediction of thermophysical properties for liquid refractory multicomponent alloy

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The challenge was clear: achieve a quantum leap in speed while preserving extreme flexibility, minimal storage, regional map support, and dynamic update capabilities. Standard Highway Hierarchies were a starting point, but we needed something more – a uniquely OsmAnd solution.

Yes, since hashes are calculated differently between ostree and rpm-ostree, they don’t match, so I have to retrieve the equivalent hash with ostree

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