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Privacy-aware community infrastructureHaichuan Du, Shaojuan Ma, Changlin Xu, Xinyi Xu, Shuo Ma
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10.1016/j.jcp.2018.10.045 · doi-reference
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10.1016/j.energy.2021.121712 · doi-reference