Despite beliefs to the contrary, what happens in the stance phase is not associated with running speed. So, if we want to run faster, hip extensor and flexor power, and knee extensor and flexor power need to increase.

For these reasons, autonomous detection of substations is an important goal for future HV mapping work. We did map substations as part of this project, but our ML model was not built to explicitly detect them. We relied on the Data Team to trace HV lines to their end points, and they generally terminate at a substation. Building an ML model explicitly capable of detecting substations would accomplish two major goals. First, it would facilitate the mapping process as these substations represent important hubs within the grid's network. Knowing their location is especially useful in crowded urban environments or where HV lines run underground near residential areas. Second, substation detection would provide a much needed tool for integrating renewable energy projects into the larger electricity network. Now that our team has mapped about a thousand substations across three full countries, we expect that we can use this data to train a ML model capable of automatically detecting substations.


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