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The links between SED-ML elements and KiSAO allow us to define restrictions on the SED-ML files we want to consider in a search result, e.g. to retrieve only models that can actually be simulated with a given simulation algorithm.
To simulate inter-link correlation among different MSs properly, the simulator simultaneously generates, in a given simulation run, the radio channel snapshots for all the MSs.
But the reality is that whether or not the right behavior emerges actually depends on how a given simulation is set up; there is nothing like a general theory of how to apply the theory to real-world data, nor a general means by which to make sure that the system's goals actually accord with those of a programmer.
This abstraction allows building steering clients independently of a given simulation.
The Petrov Galerkin finite element approach is used to allow for upstream weighting if required for a given simulation.
In this article, we present an approach to generate automatically a locally refined velocity grid adapted to a given simulation.
Additionally, the dependency graph is used to ensure that data requirements are consistent between all processes in a given simulation.
Large f values can be used to generate a rough contrast model for a given simulation cell in very short computation times.
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For a given recuperator design, model simulation was performed to provide insights and improve model performance.
Based on a given tracking controller, simulation results are discussed.
A well-selected design maximizes the amount of information that can be obtained for a given amount of simulation effort.
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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