An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals Article Swipe
Wenxuan Zhang
,
Peng Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/taes.2024.3496857
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/taes.2024.3496857
The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/taes.2024.3496857
- OA Status
- green
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404307148
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https://openalex.org/W4404307148Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/taes.2024.3496857Digital Object Identifier
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An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground TerminalsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-13Full publication date if available
- Authors
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Wenxuan Zhang, Peng HuList of authors in order
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https://doi.org/10.1109/taes.2024.3496857Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2511.00211Direct OA link when available
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Computer science, Transfer of learning, Weather forecasting, Remote sensing, Meteorology, Environmental science, Artificial intelligence, Physics, GeologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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18Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.components | 89 |
| abstract_inverted_index.conditions | 65, 156 |
| abstract_inverted_index.disrupting | 61 |
| abstract_inverted_index.increasing | 1 |
| abstract_inverted_index.mitigation | 103 |
| abstract_inverted_index.operations | 48 |
| abstract_inverted_index.real-world | 124 |
| abstract_inverted_index.satellites | 9, 68 |
| abstract_inverted_index.scenarios. | 195 |
| abstract_inverted_index.ubiquitous | 13 |
| abstract_inverted_index.components, | 55 |
| abstract_inverted_index.conditions. | 93, 146 |
| abstract_inverted_index.diagnostics | 101 |
| abstract_inverted_index.performance | 29, 46, 167 |
| abstract_inverted_index.reliability | 31 |
| abstract_inverted_index.significant | 25 |
| abstract_inverted_index.connectivity | 14 |
| abstract_inverted_index.deployments. | 125 |
| abstract_inverted_index.fine-grained | 83, 91 |
| abstract_inverted_index.region-based | 78 |
| abstract_inverted_index.space-ground | 63 |
| abstract_inverted_index.effectiveness | 117 |
| abstract_inverted_index.generalizable | 192 |
| abstract_inverted_index.significantly | 60 |
| abstract_inverted_index.generalization | 119 |
| abstract_inverted_index.representative | 144 |
| abstract_inverted_index.low-Earth-orbit | 7 |
| abstract_inverted_index.weather-related | 145 |
| abstract_inverted_index.mega-constellations | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.28702579 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |