RESUMO
The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500-3700 m with slope angles of 10-30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.
RESUMO
The ability to realize the self-removal of condensed droplets from a surface is of critical importance for science and applications such as water harvesting and thermal engineering. Despite the enormous interest in micro/nanotextured superhydrophobic materials for high-efficiency condensation, a clear picture of the wetting state transition of condensed droplets is missing, particularly, on a single-droplet level of the order of micrometers. Herein, by varying a substantial parameter space of the contact angle and the geometry of the pillared textures, we have quantified the wetting transition of individual droplets during condensation. We found that a droplet is finally either spontaneously removed from the textures due to a Laplace pressure difference or wets the textures; four different wetting state transition modes have been identified numerically and they are classified in a phase diagram. Simple theories have been constructed to correlate the critical conditions of the wetting state transition to the wettability and geometry of the textures, and they were verified experimentally. We found that the self-removal of condensed droplets benefits from the contact angle and the height of the pillars. These findings not only enhance our fundamental understanding of the wetting state transition of condensed droplets but also allow the rational design of micro/nanotextured water-repellent materials for anti-fogging and anti-wetting.
RESUMO
On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.
RESUMO
A falling liquid drop, after impact on a rigid substrate, deforms and spreads, owing to the normal reaction force. Subsequently, if the substrate is nonwetting, the drop retracts and then jumps off. As we show here, not only is the impact itself associated with a distinct peak in the temporal evolution of the normal force, but also the jump-off, which was hitherto unknown. We characterize both peaks and elucidate how they relate to the different stages of the drop impact process. The time at which the second peak appears coincides with the formation of a Worthington jet, emerging through flow focusing. Even low-velocity impacts can lead to a surprisingly high second peak in the normal force, even larger than the first one, namely when the Worthington jet becomes singular due to the collapse of an air cavity in the drop.
RESUMO
As a species of insects living on water, water striders jump from the water surface to avoid predation and then steadily land without piercing the surface. This spectacular property has attracted extensive interests since it provides bio-inspirations for designing functional microrobots moving on water. In this work, we investigate the landing dynamics of water striders by using artificial striders with different masses and leg lengths. It is found that once a water strider has landed, it oscillates on the water surface and the amplitude decays gradually, triggering a sequence of surface waves. Through scaling analysis, we relate the depth of the dimple that the strider leg displaces to its landing velocity, as well as its leg length and body mass. The subsequent time evolution of the interface where the strider lands is modeled as a damped oscillator, and its energy is exhausted by the surface waves. Moreover, we discuss the maximum depth of the dimple excited by the landing and find that the dynamic process can store more energy than the statically deforming process. Finally, we put forward a criterion of piercing the water surface from the energy point of view. These findings should be of great importance for understanding the locomotion of insects on water and for designing robust water-walking bionic robots.