Miracle-dz/UniFAR-FTUs huggingface dataset:
FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.
Biotite supports long-range diffusive transport in dissolution–precipitation creep in halite through small porosity fluctuations

Abstract. Phyllosilicates are generally regarded to have a reinforcing effect on chemical compaction by dissolution–precipitation creep (DPC) and thereby influence the evolution of hydraulic rock properties relevant to groundwater resources and geological repositories as well as fossil fuel reservoirs. We conducted oedometric compaction experiments on layered NaCl–biotite samples to test this assumption. In particular, we aim to analyse slow chemical compaction processes in the presence of biotite on the grain scale and determine the effects of chemical and mechanical feedbacks. We used time-resolved (4-D) microtomographic data to capture the dynamic evolution of the porosity in layered NaCl–NaCl/biotite samples over 1619 and 1932 h of compaction. Percolation analysis in combination with advanced digital volume correlation techniques showed that biotite grains influence the dynamic evolution of porosity in the sample by promoting a reduction of porosity in their vicinity. However, the lack of preferential strain localisation around phyllosilicates and a homogeneous distribution of axial shortening across the sample suggests that the porosity reduction is not achieved by pore collapse but by the precipitation of NaCl sourced from outside the NaCl–biotite layer. Our observations invite a renewed discussion of the effect of phyllosilicates on DPC, with a particular emphasis on the length scales of the processes involved. We propose that, in our experiments, the diffusive transport processes invoked in classical theoretical models of DPC are complemented by chemo-mechanical feedbacks that arise on longer length scales. These feedbacks drive NaCl diffusion from the marginal pure NaCl layers into the central NaCl–biotite mixture over distances of several hundred micrometres and several grain diameters. Such a mechanism was first postulated by Merino et al. (1983).
Documenting at‐risk status of terrestrial ecosystems in temperate and tropical North America

The International Union for Conservation of Nature (IUCN) Red List of Ecosystems (RLE) is an emerging global standard for ecosystem risk assessment that integrates data and knowledge to document the relative risk status of ecosystem types. Here, we summarize initial findings from applying four IUCN RLE criteria to 655 terrestrial ecosystems in temperate and tropical North America, or 8.5% of the global land surface. A series of indicators are measured for each criterion to address trends in ecosystem extent (A), the relative restricted nature of its distribution (B), and the extent and relative severity of environmental degradation (C), and the extent and relative severity of disruption of biotic processes (D); all to gauge the probability of range wide “collapse. ” Ecosystems are listed as collapsed, critically endangered, endangered, vulnerable, near threatened, least concern, data deficient, or not evaluated. Taking uncertainty into account, 219 (33%) of terrestrial ecosystem types were listed as threatened (i.e., either critically endangered, [7%], endangered [14%], or vulnerable [13%]). Examples include tallgrass prairies, oak savannas, longleaf pine woodlands, floodplain forests, mesic hardwood forests, and dry tropical forests. Historically, these threatened ecosystems occurred across about 45% of the continental study area, and today account for about 30%. The RLE provides one important focus for prioritizing conservation effort.
Study on the internal model control tactic of PMSG for direct-driven wind power system

Based on the analysis of PMSG control tactic,the work principle of internal model control(IMC) is explained and used to design the speed regulator and the current regulator of the generator-side PWM converter controller,with this design,the controller parameters are directly associated with generator parameters in order to control the PMSG.The simulation results show that IMC has good steady-state and dynamic performances,and has fast response to the changes of the input mechanical torque,the given speed and has good adaptability to the generator parameters' errors.It is verified by the simulation results that IMC application in the PMSG control in direct-driven wind power system,can improve the system performance effectively due to easily obtaining the optimal controller parameters.
Vasodilatation Measurement Using Finger Vascular Images by Near-Infrared Light and Comparison with RH-PAT

Examination of vascular endothelial function can help infer atherosclerosis progression. This study investigated whether vascular visualization by near-infrared (NIR) light can detect vasodilatation after cuff pressure release of the upper arm and what the correlation is between the brightness decrease ratio (R1) corresponding to vasodilation and the reactive hyperemia index (RHI). We obtained finger vascular images of 53 male subjects by photographing NIR light (wavelength 850 nm) transmitted through the middle phalanx of the middle finger with a charge-coupled device camera. The upper arm was compressed for 10 min using a cuff (200 mmHg), and vascular images before and after cuff compression release were obtained. We analyzed the finger vascular images by NIR light and digital pulse volume using endothelial peripheral arterial tonometry (Endo-PAT). We also calculated the average brightness of each vascular image. Using only the data of the ischemic finger, R1 was defined using the average brightness just before cuff release and the minimum average brightness after cuff release. The brightness of vascular images of the ischemic finger decreased after cuff release probably because of vasodilation. We found significant correlation between R1 and the RHI (r = 0.52; P < 0.001). R1 in the lowest RHI quartile was significantly smaller compared to the highest and second-highest RHI quartiles (P < 0.05). Vascular visualization by NIR light can detect vasodilation after cuff release. This is significantly correlated with the RHI on Endo-PAT.
A study on application of mathematical morphology to seismic signal processing

Mathematical morphology is a new developing science.It is based on strict mathematical theory,and provides a systematic approach to analyze the geometric characteristics of signals or images.And it has been applied widely to many fields such as image processing,medical signal processing,speech signal processing and so on. Seismic signal filtering and fracture detection are important to geologists and oil reservoir engineers.Thus,this paper attempts to introduce mathematical morphology in the seismic exploration field,and mainly discusses applications of mathematical morphology to seismic signal processing.Then according to the characteristics of he seismic signal,we construct and design a morphological filter,which is applied to the filtering of synthetical seismic sections,yielding a satisfied result.On this basis,the paper studies and constructs an operator of morphological edge detection,which is applied to the seismic fracture detection with a good result.The result of research indicates it is feasible to process seismic signals based on mathematical morphology.And it has a prospect of popularizing and applying.
Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.
Economies of diversification and stochastic dominance analysis in French mixed sheep farms

Abstract Farm diversification is mainly driven by risk mitigation effects and economic gains related to complementarities between production activities. By combining these two aspects, we investigate diversification economies in a sample of French mixed sheep farming systems and rank these systems using stochastic dominance criteria. Partially diversified systems (Sheep-Grass, Sheep-Crop, Sheep-Landless) and fully diversified systems (Sheep-Grass-Crop-Landless) were evaluated. We find a high degree of diversification diseconomies in the sheep farming systems considered. The results also indicate that the fully diversified system is driven by its risk-reducing effects (including downside risk exposure) and that Sheep-Crop is the dominant system in terms of risk-adjusted returns.
Visualising the coupled mechanics of CO2 breakthrough

Successful geological CO2 storage relies on the availability of an impermeable caprock formation that overlies the injection reservoir and prevents CO2 migration to the surface through a combination of trapping mechanisms that are activated at different scales. Shales have been widely studied as potential caprock formations because of their favourable properties such as very low permeability and high capillarity. Shales are highly heterogeneous and anisotropic materials whose response is governed by strong Thermo-Hydro-ChemoMechanical (THMC) couplings that conventional constitutive models often fail to represent. In the context of CO2 storage, characterisation of caprock integrity and sealing capacity is usually evaluated in terms capillary entry pressure [1, 2]. In addition to the hydromechanical response, CO2 introduces a chemical component (mineral dissolution and/or precipitation) the impact of which on shales remains a challenging topic due to the extreme mass transfer limitations. In this study, the impact of CO2 injection in a Swiss shale, the Opalinus Clay, is for the first time assessed with live x-ray tomography. This approach aims to identify and decouple the different THMC mechanisms that take place, through quantitative 3D image analysis.
Solar Integrated Wireless Drone Charging System for Smart City Applications

Drone has taken over the market by storm, with sales of $14.1b in 2018 to over $22.5b in 2021 and is expected to grow to $42.8b by 2025. Photography and Nature documentary have drastically changed with the integration of drone technology. Smart cities are equipping themselves with surveillance drones for safety and monitoring applications. However, a drone is a power-hungry device that exhausts its battery within a few minutes. Therefore, they have to be recharged frequently or replace the exhausted battery with a charged one. The battery swapping process requires human intervention and thus limits the autonomous drone application. To make a drone fully autonomous, Wireless Power Transfer (WPT) using the Inductive Power Transfer (IPT) technique was adopted in this study. IPT allows power transfer to a few centimetres efficiently with few losses. The proposed WPT for the drone system was practically implemented with the incorporation of solar cells. The system was tested under different solar lighting conditions to charge the drone's battery efficiently. At 0 cm lateral misalignment and 7 mm vertical alignment, the average power transferred was approximately 13W at 88.5% efficiency. The drone battery was fully charged in 22 minutes at a fast-balance charging condition. Comparison results provide insight into the deployability of the autonomous WPT drone charging in remote areas.
First detection of Lake Sinai virus in honeybees (Apis mellifera) and wild arthropods in Japan

Lake Sinai virus (LSV), an RNA virus, is suspected to be associated with poor health in honeybees (Apis mellifera). We examined LSV in 26 specimens of healthy honeybees and 44 specimens of wild arthropods in the Gifu Prefecture, Japan. LSV was found more frequently in honeybee specimens (11/26, 42.3%) than in wild arthropod specimens (1/44, 2.3%) (P<0.01). Phylogenetic and nucleotide sequence analysis revealed two lineages: LSV3 in honeybees, and LSV4 in both honeybees and wild arthropods. To our knowledge, this is the first report of LSV prevalence in honeybees and wild arthropods in Japan.
Assessing an effective collaboration in higher education: A study of students’ experiences and challenges on group collaboration

Studies emphasize that collaboration remains an essential part of learning and working in the 21st century. This study seeks to report on the group collaboration of higher education students. Using Explanatory sequential mixed method design, this study tried to discuss the students’ experiences and challenges on group collaboration environment in the online learning cases. Adopting Razmerita and Kirchner's (2014) questionnaire, 287 students involved in this study. The result indicated that overall students performed positive perception on group collaboration. In addition, to have deep analysis, 15 students were interviewed. This study contributes to an understanding of how students perceived collaboration and how they explain the obstacles recognized in their classes.DOI: 10.26905/enjourme.v6i2. 6971
Analyzing the Effects of Parameters for Tremor Modulation via Phase-Locked Electrical Stimulation on a Peripheral Nerve

(1) Background: Non-invasive neuromodulation is a promising alternative to medication or deep-brain stimulation treatment for Parkinson’s Disease or essential tremor. In previous work, we developed and tested a wearable system that modulates tremor via the non-invasive, electrical stimulation of peripheral nerves. In this article, we examine the proper range and the effects of various stimulation parameters for phase-locked stimulation. (2) Methods: We recruited nine participants with essential tremor. The subjects performed a bean-transfer task that mimics an eating activity to elicit kinetic tremor while using the wearable stimulation system. We examined the effects of stimulation with a fixed duty cycle, at different stimulation amplitudes and frequencies. The epochs of stimulation were locked to one of four phase positions of ongoing tremor, as measured with an accelerometer. We analyzed stimulation-evoked changes of the frequency and amplitude of tremor. (3) Results: We found that the higher tremor amplitude group experienced a higher rate of tremor power reduction (up to 65%) with a higher amplitude of stimulation when the stimulation was applied at the ±peak of tremor phase. (4) Conclusions: The stimulation parameter can be adjusted to optimize tremor reduction, and this study lays the foundation for future large-scale parameter optimization experiments for personalized peripheral nerve stimulation.
Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot

In this study, we propose a smart hopper system that automatically unblocks obstructions caused by rocks dropped into hoppers at mining sites. The proposed system captures RGB (red green blue) and D (depth) images of the upper surfaces of hopper models using an RGB-D camera and transmits them to a computer. Then, a virtual hopper system is used to identify rocks via machine vision-based image processing techniques, and an appropriate motion is simulated in a robot arm. Based on the simulation, the robot arm moves to the location of the rock in the real world and removes it from the actual hopper. The recognition accuracy of the proposed model is evaluated in terms of the quantity and location of rocks. The results confirm that rocks are accurately recognized at all positions in the hopper by the proposed system.
Research on Internal Solitary Wave Detection and Analysis Based on Interferometric Imaging Radar Altimeter Onboard the Tiangong-2 Space Laboratory

The Tiangong-2 space laboratory was launched by China on 15 September 2016, carrying the Interferometric Imaging Radar Altimeter (InIRA), the first of the latest generation of imaging altimeters that can perform imaging and acquire elevation information simultaneously. This paper analyzes the feasibility of using InIRA images to obtain two-dimensional characteristics of oceanic internal solitary waves (ISWs) and information about vertical sea surface fluctuations caused by the propagation of ISWs. The results show that InIRA demonstrates a relatively reliable ability to observe ISWs with high resolution and can identify the fine-scale features of ISWs of different forms. Furthermore, InIRA can observe centimeter-level changes in the Sea Surface Height Anomaly (SSHA) caused by ISWs. The geometric relationship between the sensor’s flight direction and the propagation direction of ISWs does not affect its detection effect. However, the swath width of InIRA is too narrow to fully capture ISW information, and the height accuracy of InIRA height product images is not insufficient to detect the height information of small-scale ISWs. These shortcomings need to be considered in the future development of imaging altimeters to increase their potential for detecting mesoscale phenomena in the ocean.
A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.