Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy(LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve(CC) method and the partial least squares regression(PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient(R^(2)), average relative error(ARE), root mean square error of calibration(RMSEC), and root mean square error of prediction(RMSEP). The results demonstrate that the PLSR method significantly improved both R^(2) for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.

Big data-driven water research towards metaverse

Although big data is publicly available on water quality parameters,virtual simulation has not yet been adequately adapted in environmental chemistry research.Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic(e.g.,climate impact and water-related environmental catastrophe)or difficult to design and monitor in a real time(e.g.,pollutant and nutrient cycles in estuaries,soils,and sediments).Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios,including drinking water contamination.

Characteristics and Preliminary Causes of Tropical Cyclone Remote Precipitation over China

In this study,the characteristics and preliminary causes of tropical cyclone remote precipitation(TRP)over China during the period from 1979 to 2020 are investigated.Results indicated that approximately 72.42%of tropical cyclones(TCs)in the Western Pacific produce TRP over China.The peak months for TRP are July and August.The four key regions of TRP are the adjacent areas between the Sichuan and Shaanxi Provinces,the northern coast of the Bohai Sea,the coast of the Yellow Sea,and the southern coast area.The typical distance between the station with TRP and the TC center ranges from 1500 to 2500 km.Most of these stations are situated north to 60°west of north of the TC.The south–west water vapor transportation on the west side of the TC is crucial to TRP.TRP has a decreasing trend because of the decrease in the number of TCs that generate TRP.From the perspective of large-scale environmental conditions,a decrease in the integrated horizontal water vapor transport in China' Mainland,the weakening of upward motion at approximately 25°–35°N,which is inconducive to convection,and an increase in low-level vertical wind shear,which is unfavorable for the development of TC in areas with high frequencies of TRP-related TCs,are the factors that result in the decreasing trend of TRP.

Bioinspired Multifunctional Self-Sensing Actuated Gradient Hydrogel for Soft-Hard Robot Remote Interaction

The development of bioinspired gradient hydrogels with self-sensing actuated capabilities for remote interaction with soft-hard robots remains a challenging endeavor. Here, we propose a novel multifunctional self-sensing actuated gradient hydrogel that combines ultrafast actuation and high sensitivity for remote interaction with robotic hand. The gradient network structure, achieved through a wettability difference method involving the rapid precipitation of MoO_(2) nanosheets, introduces hydrophilic disparities between two sides within hydrogel. This distinctive approach bestows the hydrogel with ultrafast thermo-responsive actuation(21° s^(-1)) and enhanced photothermal efficiency(increase by 3.7 ℃ s^(-1) under 808 nm near-infrared). Moreover, the local cross-linking of sodium alginate with Ca^(2+) endows the hydrogel with programmable deformability and information display capabilities. Additionally, the hydrogel exhibits high sensitivity(gauge factor 3.94 within a wide strain range of 600%), fast response times(140 ms) and good cycling stability. Leveraging these exceptional properties, we incorporate the hydrogel into various soft actuators, including soft gripper, artificial iris, and bioinspired jellyfish, as well as wearable electronics capable of precise human motion and physiological signal detection. Furthermore, through the synergistic combination of remarkable actuation and sensitivity, we realize a self-sensing touch bioinspired tongue. Notably, by employing quantitative analysis of actuation-sensing, we realize remote interaction between soft-hard robot via the Internet of Things. The multifunctional self-sensing actuated gradient hydrogel presented in this study provides a new insight for advanced somatosensory materials, self-feedback intelligent soft robots and human–machine interactions.

Occurrence of microplastics in natural and farmland soil in the Qilian Mountains of the Northern Tibetan Plateau

Microplastics(MPs)become ubiquitous in soil and are an environmental and public health concern worldwide.However,the status of MPs in natural and farmland soils in remote areas remains poorly understood.In this study,we investigated the characteristics of MPs in natural and farmland soils along two transects in the Qilian Mountains of the northern Tibetan Plateau.The average abundance of MPs in natural and farmland soils was 29,778 and 56,123 items kg^(-1),respectively,with a detection size range of 10-1000μm.MPs in the size range of 10-100μm accounted for 84.1%of particles detected.Among the 21 polymers detected,polyethylene dominated in both farmland and natural soils.The shape of MPs was dominated by fragments(95.8%),followed by fibers(3.8%)and beads(0.4%).The abundance of MPs was positively correlated with increasing altitude in natural soils.There was no significant correlation between the abundance of MPs and soil physicochemical properties due to the narrow range of values of soil physicochemical properties.With the growing concern regarding MPs pollution,research on the status of MPs in high altitude and remote areas is critical to understanding their global cycle.

Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Comprehensive analysis of glacier recession(2000-2020)in the Nun-Kun Group of Glaciers,Northwestern Himalaya

Himalayan glaciers are shrinking rapidly,especially after 2000.Glacier shrinkage,however,shows a differential pattern in space and time,emphasizing the need to monitor and assess glacier changes at a larger scale.In this study,changes of 48 glaciers situated around the twin peaks of the Nun and Kun mountains in the northwestern Himalaya,hereafter referred to as Nun-Kun Group of Glaciers(NKGG),were investigated using Landsat satellite data during 2000-2020.Changes in glacier area,snout position,Equilibrium Line Altitude(ELA),surface thickness and glacier velocity were assessed using remote sensing data supplemented by field observations.The study revealed that the NKGG glaciers have experienced a recession of 4.5%±3.4%and their snouts have retreated at the rate of 6.4±1.6 m·a^(-1).Additionally,there was a 41%increase observed in the debris cover area during the observation period.Using the geodetic approach,an average glacier elevation change of-1.4±0.4 m·a^(-1)was observed between 2000 and 2012.The observed mass loss of the NKGG has resulted in the deceleration of glacier velocity from 27.0±3.7 m·a^(-1)in 2000 to 21.2±2.2 m·a^(-1)in 2020.The ELA has shifted upwards by 83.0±22 m during the period.Glacier morphological and topographic factors showed a strong influence on glacier recession.Furthermore,a higher recession of 12.9%±3.2%was observed in small glaciers,compared to 2.7%±3.1%in larger glaciers.The debris-covered glaciers showed lower shrinkage(2.8%±1.1%)compared to the clean glaciers(9.3%±5%).The glacier depletion recorded in the NKGG during the last two decades,if continued,would severely diminish glacial volume and capacity to store water,thus jeopardizing the sustainability of water resources in the basin.

Aging Mongolian pine plantations face high risks of drought-induced growth decline:evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Landsat Image-Based Spatiotemporal Variation Analysis of Erosion and Deposition off the Qingshuigou of the Yellow River Delta from 1984 to 2021

Owing to climate change and human activity,the Qingshuigou of the Yellow River Delta(YRD)has undergone dynamic changes in erosion and deposition.Therefore,studying these changes is important to ensure ecological protection and sustainable development.In this study,the trend of erosion-deposition evolution in the Qingshuigou was investigated based on 38 coastline phases extracted from Landsat series images of the YRD at one-year intervals from 1984 to 2021.The periodicity of the scouring and deposition evolution was also analyzed using wavelet analysis.Results showed that the total area of the Qingshuigou was affected by deposition and erosion and that the fluctuation first increased and then decreased.The total area reached a maximum in 1993.The depositional area first increased and then decreased,whereas the overall erosion area decreased.Deposition and erosion areas showed periodic changes to some extent;however,the periodic signal intensity decreased.Furthermore,factors including channel morphological evolution and variations in water and sediment discharge affect the spatiotemporal dynamics of erosion and deposition processes.The application of nonconsistency tests finally revealed that deposition area and flushing magnitude exhibited non-stationarities,which are potentially attributed to impacts from climatic change drivers.

Automatic recognition of landslides based on YOLOv7 and attention mechanism

Landslide disasters comprise the majority of geological incidents on slopes,posing severe threats to the safety of human lives and property while exerting a significant impact on the geological environment.The rapid identification of landslides is important for disaster prevention and control;however,currently,landslide identification relies mainly on the manual interpretation of remote sensing images.Manual interpretation and feature recognition methods are time-consuming,labor-intensive,and challenging when confronted with complex scenarios.Consequently,automatic landslide recognition has emerged as a pivotal avenue for future development.In this study,a dataset comprising 2000 landslide images was constructed using open-source remote sensing images and datasets.The YOLOv7 model was enhanced using data augmentation algorithms and attention mechanisms.Three optimization models were formulated to realize automatic landslide recognition.The findings demonstrate the commendable performance of the optimized model in automatic landslide recognition,achieving a peak accuracy of 95.92%.Subsequently,the optimized model was applied to regional landslide identification,co-seismic landslide identification,and landslide recognition at various scales,all of which showed robust recognition capabilities.Nevertheless,the model exhibits limitations in detecting small targets,indicating areas for refining the deep-learning algorithms.The results of this research offer valuable technical support for the swift identification,prevention,and mitigation of landslide disasters.

Mapping lineaments using Landsat 8 OLI and SRTM data;a case study of the eastern part of the Ouarzazate Basin,Morocco

The integration of remote sensing and geographic information system(GIS)was employed in this study to delineate the structural lineaments within the eastern section of the Ouarzazate Basin,situated between the southern front of the Central High Atlas and the northern slopes of the Eastern Anti-Atlas(also known as the Saghro Massif).To achieve this objective,Landsat 8 Operational Land Imager(OLI)and Shuttle Radar Topography Mission(SRTM)data were used.Principal Component Analysis(PCA)was computed and a directional filter was applied to the first PCA and the panchromatic band(B8).Additionally,shading was applied to the SRTM data in four directions;N0°,N45°,N90°,N135°.After removing of the non-geological linear structures,the results obtained,using the automatic extraction method,allowed us to produce a synthetic map that included 1251 lineaments with an average length of 1331 m and was dominated by NE-SW,ENE-WSW and E-W directions,respectively.However,the high lineament density is clearly noted in the Anti-Atlas(Saghro Massif)and at the level of the northern part,extending from the Ait Ibrirne to Arg-Ali Oubourk villages.High lineament density can always be found around the major faults affecting this area.The data collected during the field investigations and from geological maps show that the major direction of the faults and structural accidents range mostly between N45°,N70°and N75°.The correlation of remote sensing results with those collected in the field shows a similarity and coincidence with each other.From these results,it is possible to consider the automatic extraction method as a supplementary kind that can serve classical geology by quickly enriching it with additional data.As shown in this work,this method provides more information when applied in arid areas where the fields are well outcropped.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

Enhancing the Trustworthiness of 6G Based on Trusted Multi-Cloud Infrastructure:A Practice of Cryptography Approach

Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.

Untethered Micro/Nanorobots for Remote Sensing:Toward Intelligent Platform

Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities.Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems,enabling in situ detection of substances that traditional sensing methods struggle to achieve.Over the past decade of development,significant research progress has been made in designing sensing strategies based on micro/nanorobots,employing various coordinated control and sensing approaches.This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots,robot behavior,microrobotic manipulation,and robot-environment interactions.Providing recent studies and relevant applications in remote sensing,we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments,translating lab research achievements into widespread real applications.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

Mapping and understanding degradation of alpine wetlands in the northern Maloti-Drakensberg, southern Africa

The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods,yet they face escalating degradation from various factors including overgrazing and climate change.This study employs advanced Digital Soil Mapping(DSM)techniques coupled with remote sensing to map and assess wetland coverage and degradation in the northern Maloti-Drakensberg.The model achieved high accuracies of 96%and 92%for training and validation data,respectively,with Kappa statistics of 0.91 and 0.83,marking a pioneering automated attempt at wetland mapping in this region.Terrain attributes such as terrain wetness index(TWI)and valley depth(VD)exhibit significant positive correlations with wetland coverage and erosion gully density,Channel Network Depth and slope were negative correlated.Gully density analysis revealed terrain attributes as dominant factors driving degradation,highlighting the need to consider catchment-specific susceptibility to erosion.This challenge traditional assumptions which mainly attribute wetland degradation to external forces such as livestock overgrazing,ice rate activity and climate change.The sensitivity map produced could serve as a basis for Integrated Catchment Management(ICM)projects,facilitating tailored conservation strategies.Future research should expand on this work to include other highland areas,explore additional covariates,and categorize wetlands based on hydroperiod and sensitivity to degradation.This comprehensive study underscores the potential of DSM and remote sensing in accurately assessing and managing wetland ecosystems,crucial for sustainable resource management in alpine regions.

Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.