Image-level weakly-supervised land-cover classification (WSLC) relies on image labels to free land-cover classification networks from expensive pixel-level annotations. Existing weakly-supervised methods commonly generate class activation maps (CAMs) from a trained classification network to produce pixel-level pseudo labels for training land-cover classification networks. However, CAMs struggle to completely and accurately capture land-covers in remote sensing images due to the lack of precise localization information in weak supervision. To alleviate semantic uncertainty in CAMs, this paper proposes an Expanding-Separating (ES) scheme to generate high-quality CAMs of land-covers. The ES scheme expands activation areas in CAMs by lifting activation values in inactivated regions and suppresses CAM noises by decreasing values in confusing regions. Experimental results on the DeepGlobe dataset demonstrate that the proposed ES scheme significantly improves the quality of CAMs and benefits the training of land-cover classification, achieving state-of-the-art performance.

In recent years, additive manufacturing has gained significant attention in the production of high-temperature and high-pressure mini-channel heat exchangers. Among the various techniques, selective laser melting (SLM) is widely utilized in metal additive manufacturing. This study experimentally investigates the flow and heat transfer performance of rectangular and circular mini-channel heat exchangers produced by SLM. The experiments were conducted under pressure conditions ranging from 8 to 10 MPa and temperatures between 200 and 310°C. Heat transfer correlations for supercritical carbon dioxide (SCO2) in both rectangular and circular mini-channels were refined. Additionally, flow characteristics without heat transfer were analyzed, revealing that the Darcy friction factor in both types of mini-channels initially increases with the Reynolds number (Re), followed by a decrease, and stabilizes at higher Re values.

The inference of topological principles is a key problem in structured reconstruction. We observe that wrongly predicted topological relationships are often incurred by the lack of holistic geometry clues in low-level features. Inspired by the fact that massive signals can be compactly described with frequency analysis, we experimentally explore the efficiency and tendency of learning structure geometry in the frequency domain. Accordingly, we propose a frequency-domain feature learning strategy (F-Learn) to fuse scattered geometric fragments holistically for topology-intact structure reasoning. Benefiting from the parsimonious design, the F-Learn strategy can be easily deployed into a deep reconstructor with a lightweight model modification. Experiments demonstrate that the F-Learn strategy can effectively introduce structure awareness into geometric primitive detection and topology inference, bringing significant performance improvement to final structured reconstruction. Code and pre-trained models are available at https://github.com/Geo-Tell/F-Learn.