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极端环境（山地/太空）数字孪生研究数据

The University of Adelaide
Shu Liang (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25909/27116761.v1&rft.title=极端环境（山地/太空）数字孪生研究数据&rft.identifier=https://doi.org/10.25909/27116761.v1&rft.publisher=The University of Adelaide&rft.description=The study involved growing lettuce plants under extreme conditions, using a custom digital twin approach that meets the conditions expected in space. The interaction between a “physical twin sender” (“space”) and a “physical twin receiver” (“Earth”) was used in this study with the help of a machine learning (ML) model.&rft.creator=Shu Liang&rft.date=2024&rft_rights=CC-BY-4.0&rft_subject=digital twin models&rft_subject=Machine learning not elsewhere classified&rft.type=dataset&rft.language=English Access the data
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The study involved growing lettuce plants under extreme conditions, using a custom digital twin approach that meets the conditions expected in space. The interaction between a “physical twin sender” (“space”) and a “physical twin receiver” (“Earth”) was used in this study with the help of a machine learning (ML) model.

Issued: 2024-09-27

Created: 2024-09-27

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Machine learning not elsewhere classified | digital twin models |

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