Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with ...Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.展开更多
On January 7,2025,an Ms6.8 earthquake struck Dingri County,XigazêCity,in the Xizang Autonomous Region.The epicenter,located near the Shenzha-Dingjie fault zone at the boundary between the Qinghai-Xizang Plateau a...On January 7,2025,an Ms6.8 earthquake struck Dingri County,XigazêCity,in the Xizang Autonomous Region.The epicenter,located near the Shenzha-Dingjie fault zone at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,marked the largest earthquake in the region in recent years.The Shenzha-Dingjie fault zone,situated at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,is a key tectonic feature in the India-Eurasia collision process,exhibiting both thrust and strike-slip faulting.This study analyzed the disaster characteristics induced by the earthquake using Differential Synthetic Aperture Radar Interferometry(DIn SAR)to process Sentinel-1 satellite data and derive pre-and post-earthquake surface deformation information.Additionally,high-resolution optical remote sensing data,UAV(unmanned aerial vehicle)imagery,and airborne Li DAR(light detection and ranging)data were employed to analyze the spatial distribution of the surface rupture zone,with field investigations validating the findings.Key results include:(1)Field verification confirmed that potential landslide hazard points identified via optical image interpretation did not exhibit secondary landslide activity;(2)D-In SAR revealed the co-seismic surface deformation pattern,providing detailed deformation information for the Dingri region;(3)Integration of Li DAR and optical imagery further refined and validated surface rupture characteristics identified by optical-In SAR,indicating a predominantly north-south rupture zone.Additionally,surface fracture features extending in a near east-west direction were observed on the southeast side of the epicenter,accompanied by some infrastructure damage;(4)Surface fracture was most severe in high-intensity seismic areas near the epicenter,with the maximum surface displacement approximately 28 km from the epicenter.The earthquake-induced surface deformation zone spanned approximately 6 km by 46 km,with deformation concentrated primarily on the western side of the Dingmucuo Fault,where maximum subsidence of 0.65 m was detected.On the eastern side,uplift was dominant,reaching a maximum of 0.75 m.This earthquake poses significant threats to local communities and infrastructure,underscoring the urgent need for continued monitoring in affected areas.The findings highlight the effectiveness of multi-source data fusion(space-air-ground based observation)in seismic disaster assessment,offering a methodological framework for rapid post-earthquake disaster response.providing a valuable scientific foundation for mitigating secondary disasters in the region.展开更多
The Macao satellites differ from their predecessors in their orbits:MSS-1(Macao Science Satellite-1)is in low inclination and the planned MSS-2 will be in highly elliptical orbits.This paper reviews the fundamental ad...The Macao satellites differ from their predecessors in their orbits:MSS-1(Macao Science Satellite-1)is in low inclination and the planned MSS-2 will be in highly elliptical orbits.This paper reviews the fundamental advantages and disadvantages of the different possible magnetic measurements:the component(declination,intensity,etc.)and location(satellite,ground,etc.).When planning a survey the choice of component is the"What?"question;the choice of location the"Where?"question.Results from potential theory inform the choice of measurement and data analysis.For example,knowing the vertical component of magnetic field provides a solution for the full magnetic field everywhere in the potential region.This is the familiar Neumann problem.In reality this ideal dataset is never available.In the past we were restricted to declination data only,then direction only,then total intensity only.There have also been large swathes of Earth's surface with no measurements at all(MSS-1 is restricted to latitudes below).These incomplete datasets throw up new questions for potential theory,questions that have some intriguing answers.When only declination is known uniqueness is provided by horizontal intensity measurements on a single line joining the dip-poles.When only directions are involved uniqueness is provided by a single intensity measurement,at least in principle.Paleomagnetic intensities can help.When only total intensity is known,as was largely the case in the early satellite era,uniqueness is provided by a precise location of the magnetic equator.Holes in the data distribution is a familiar problem in geophysical studies.All magnetic measurements sample,to a greater or lesser extent,the potential field everywhere.There is a trade-off between measurements close to the source,good for small targets and high resolution,and the broader sample of a distant measurement.The sampling of a measurement is given by the appropriate Green's function of the Laplacian,which determines both the resolution and scope of the measurement.For example,radial and horizontal measurements near the Earth's surface give a weighted average of the radial component over a patch of the core surface beneath the measurement site about in radius.The patch is smaller for shallower surfaces,for example from satellite to ground.Holes in the data distribution do not correspond to similar holes at the source surface;the price paid is in resolution of the source.I argue that,in the past,we have been too reluctant to take advantage of incomplete and apparently hopeless datasets.展开更多
To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electron...Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021.By performing weighted processing on two-dimensional point-of-interest(POI)data,clustering hotspots of high-dimensional restaurant data were identified.A hierarchical network of restaurant hotspots was constructed following the Central Place Theory(CPT)framework,while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery.Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry.At a finer spatial scale,the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern,with a developmental trend radiating outward along the city’s ring roads.This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces,leading to the reconfiguration of urban spaces,expansion of restaurant-dedicated land use,and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions.展开更多
This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event wit...This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.展开更多
Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from...Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.展开更多
The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under...The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.展开更多
As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique ...As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.展开更多
The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in dete...The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in determining Earth Rotation Parameters(ERPs).In this study,we determine the ERPs based on the observations of BDS-2,BDS-3 and BDS-2+BDS-3,with the time spanning from August18,2022,to August 18,2023.The IERS EOP 20C04 series is used as a reference to evaluate the accuracy of the ERP estimates.We analyze the impact of different numbers of reference stations,polyhedron volumes,observation arc lengths,satellite types,and satellite systems on solving ERPs using BDS-2 and BDS-3 observation data provided by the International GNSS Service(IGS)stations.When selecting a specific satellite type,it is necessary to choose an appropriate observation arc length based on different numbers of reference stations while maximizing the volume of the formed polyhedron to achieve optimal efficiency and accuracy in parameter estimation.When both the number of reference stations and observation arc length are fixed,higher precision of the ERPs can be achieved using observations from MEO than MEO+IGSO and MEO+IGSO+GEO.Moreover,when considering only IGSO and MEO satellites as options for analysis purposes,BDS-3 provides higher accuracy compared to BDS-2.In summary,when using BDS for ERP estimation and MEO satellite observations with the same observation arc length,selecting stations from reference stations with larger polyhedral volumes can significantly improve the efficiency and accuracy of parameter estimation.展开更多
The infrared band contains rich opportunities for astronomical research,but due to the limitations of infrared technology,the development of infrared astronomy in China has been far from satisfactory for a long time,e...The infrared band contains rich opportunities for astronomical research,but due to the limitations of infrared technology,the development of infrared astronomy in China has been far from satisfactory for a long time,especially for solar observation.“Accurate Infrared Magnetic field Measurements of the Sun”project(AIMS)is a National Major Scientific Research Instrument Development Project(recommended by the Ministries)supported by the National Natural Science Foundation of China.It is aimed at improving the accuracy of magnetic field measurement by an order of magnitude,by measuring the“Zeeman splitting”directly.In addition,as AIMS is also the first equipment specifically designed for mid-to far-infrared solar observation in the world,we also hope to utilize AIMS to explore potential new scientific research opportunities in the vast infrared region.This article will briefly introduce the scientific objectives,the telescope,the scientific post-focus instruments,and finally summarize the commissioning observations of AIMS.展开更多
Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distanc...Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distances,helping to break parameter degeneracies generated by traditional cosmological observations.This advancement can lead to much tighter constraints on sterile neutrino parameters.This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future shortγ-ray burst observations from a THESEUS-like telescope,an approach not previously explored in the literature.Both massless and massive sterile neutrinos are considered within theΛCDM cosmology.We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos,reaching 3σlevel.For massive sterile neutrinos,GW data can also greatly assist in improving the parameter constraints,but it seems that effective detection is still not feasible.展开更多
The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typic...The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.展开更多
Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelengt...Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelength coverage.In this study,we report the development of a push-broom airborne multimodular imaging spectrometer(AMMIS)that spans ultraviolet(UV),visible near-infrared(VNIR),shortwave infrared(SWIR),and thermal infrared(TIR)wavelengths.As an integral part of China's HighResolution Earth Observation Program,AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads.It has been mounted on aircraft platforms such as Y-5,Y-12,and XZ-60.Since 2016,AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data.This study describes the system design,calibration techniques,performance tests,flight campaigns,and applications of the AMMIS.The system integrates UV,VNIR,SWIR,and TIR modules,which can be operated in combination or individually based on the application requirements.Each module includes three spectrometers,utilizing field-of-view(FOV)stitching technology to achieve a 40°FOV,thereby enhancing operational efficiency.We designed advanced optical systems for all modules,particularly for the TIR module,and employed cryogenic optical technology to maintain optical system stability at 100 K.Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data.The AMMIS features more than 1400 spectral bands,with spectral sampling intervals of 0.1 nm for UV,2.4 nm for VNIR,3 nm for SWIR,and 32 nm for TIR.In addition,the instantaneous fields of view(IFoVs)for the four modules were 0.5,0.25,0.5,and 1 mrad,respectively,with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode.This study reports on land-cover surveys,pollution gas detection,mineral exploration,coastal water detection,and plant investigations conducted using AMMIS,highlighting its excellent performance.Furthermore,we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms.This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.展开更多
基金Meteorological Research in the Public Interest,No.GYHY201106014Beijing Nova Program,No.2010B037China Special Fund for the National High Technology Research and Development Program of China(863 Program),No.412230
文摘Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.
基金supported by the National Natural Science Foundation of China(No.42477170)the Major Project of the National Natural Science Foundation of China(No.42090054)+1 种基金the Research Fund Program of Hubei Key Laboratory of Resources and Eco-Environment Geology(No.HBREGKFJJ-202411)Innovative Group Project of Natural Science Foundation of Hubei Province(No.2024AFA015)。
文摘On January 7,2025,an Ms6.8 earthquake struck Dingri County,XigazêCity,in the Xizang Autonomous Region.The epicenter,located near the Shenzha-Dingjie fault zone at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,marked the largest earthquake in the region in recent years.The Shenzha-Dingjie fault zone,situated at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,is a key tectonic feature in the India-Eurasia collision process,exhibiting both thrust and strike-slip faulting.This study analyzed the disaster characteristics induced by the earthquake using Differential Synthetic Aperture Radar Interferometry(DIn SAR)to process Sentinel-1 satellite data and derive pre-and post-earthquake surface deformation information.Additionally,high-resolution optical remote sensing data,UAV(unmanned aerial vehicle)imagery,and airborne Li DAR(light detection and ranging)data were employed to analyze the spatial distribution of the surface rupture zone,with field investigations validating the findings.Key results include:(1)Field verification confirmed that potential landslide hazard points identified via optical image interpretation did not exhibit secondary landslide activity;(2)D-In SAR revealed the co-seismic surface deformation pattern,providing detailed deformation information for the Dingri region;(3)Integration of Li DAR and optical imagery further refined and validated surface rupture characteristics identified by optical-In SAR,indicating a predominantly north-south rupture zone.Additionally,surface fracture features extending in a near east-west direction were observed on the southeast side of the epicenter,accompanied by some infrastructure damage;(4)Surface fracture was most severe in high-intensity seismic areas near the epicenter,with the maximum surface displacement approximately 28 km from the epicenter.The earthquake-induced surface deformation zone spanned approximately 6 km by 46 km,with deformation concentrated primarily on the western side of the Dingmucuo Fault,where maximum subsidence of 0.65 m was detected.On the eastern side,uplift was dominant,reaching a maximum of 0.75 m.This earthquake poses significant threats to local communities and infrastructure,underscoring the urgent need for continued monitoring in affected areas.The findings highlight the effectiveness of multi-source data fusion(space-air-ground based observation)in seismic disaster assessment,offering a methodological framework for rapid post-earthquake disaster response.providing a valuable scientific foundation for mitigating secondary disasters in the region.
文摘The Macao satellites differ from their predecessors in their orbits:MSS-1(Macao Science Satellite-1)is in low inclination and the planned MSS-2 will be in highly elliptical orbits.This paper reviews the fundamental advantages and disadvantages of the different possible magnetic measurements:the component(declination,intensity,etc.)and location(satellite,ground,etc.).When planning a survey the choice of component is the"What?"question;the choice of location the"Where?"question.Results from potential theory inform the choice of measurement and data analysis.For example,knowing the vertical component of magnetic field provides a solution for the full magnetic field everywhere in the potential region.This is the familiar Neumann problem.In reality this ideal dataset is never available.In the past we were restricted to declination data only,then direction only,then total intensity only.There have also been large swathes of Earth's surface with no measurements at all(MSS-1 is restricted to latitudes below).These incomplete datasets throw up new questions for potential theory,questions that have some intriguing answers.When only declination is known uniqueness is provided by horizontal intensity measurements on a single line joining the dip-poles.When only directions are involved uniqueness is provided by a single intensity measurement,at least in principle.Paleomagnetic intensities can help.When only total intensity is known,as was largely the case in the early satellite era,uniqueness is provided by a precise location of the magnetic equator.Holes in the data distribution is a familiar problem in geophysical studies.All magnetic measurements sample,to a greater or lesser extent,the potential field everywhere.There is a trade-off between measurements close to the source,good for small targets and high resolution,and the broader sample of a distant measurement.The sampling of a measurement is given by the appropriate Green's function of the Laplacian,which determines both the resolution and scope of the measurement.For example,radial and horizontal measurements near the Earth's surface give a weighted average of the radial component over a patch of the core surface beneath the measurement site about in radius.The patch is smaller for shallower surfaces,for example from satellite to ground.Holes in the data distribution do not correspond to similar holes at the source surface;the price paid is in resolution of the source.I argue that,in the past,we have been too reluctant to take advantage of incomplete and apparently hopeless datasets.
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金Under the auspices of the Key Program of National Natural Science Foundation of China(No.42030409)。
文摘Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021.By performing weighted processing on two-dimensional point-of-interest(POI)data,clustering hotspots of high-dimensional restaurant data were identified.A hierarchical network of restaurant hotspots was constructed following the Central Place Theory(CPT)framework,while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery.Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry.At a finer spatial scale,the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern,with a developmental trend radiating outward along the city’s ring roads.This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces,leading to the reconfiguration of urban spaces,expansion of restaurant-dedicated land use,and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions.
基金supported by the National Natural Science Foundation of China(Grant No.41705081)the Shandong Natural Science Foundation Project(Grant No.ZR2019ZD12)the Laoshan Laboratory(Grant No.LSKJ202202203).
文摘This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.
基金Sponsored by Beijing Youth Innovation Talent Support Program for Urban Greening and Landscaping——The 2024 Special Project for Promoting High-Quality Development of Beijing’s Landscaping through Scientific and Technological Innovation(KJCXQT202410).
文摘Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.
基金supported by the National Natural Science Foundation of China(Grant Nos.42475100 and 42405091)supported by the CMA Key Innovation Team(Grant No.CMA2022ZD10)+1 种基金the CMA Weather Modification Centre Innovation Team(Grant No.WMC2023IT02)the National Key R&D Program of China(Grant No.2019YFC1510305).
文摘The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20598 and 52104107)the"Qinglan Project"of Jiangsu Colleges and Universities,Young Elite Scientists Sponsorship Program of Jiangsu Province(Grant No.TJ-2023-086).
文摘As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.
基金received financial support from the National Natural Science Foundation of China(Grant No.42030105,No.42204006,No.42274011,No.42304095)Funded by State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM(Grant No.2024-01-01)+2 种基金Open Fund of Hubei Luojia Laboratory(Grant No.230100020,230100019)the China Postdoctoral Science Foundation(Certificate Number:2023M743580)the Key Project of Natural Science Research in Universities of Anhui Province(No.2023AH051634)。
文摘The Bei Dou satellite system(BDS)has progressed with the full operationalization of the secondgeneration regional system(BDS-2)and the third-generation global system(BDS-3).This technology plays a crucial role in determining Earth Rotation Parameters(ERPs).In this study,we determine the ERPs based on the observations of BDS-2,BDS-3 and BDS-2+BDS-3,with the time spanning from August18,2022,to August 18,2023.The IERS EOP 20C04 series is used as a reference to evaluate the accuracy of the ERP estimates.We analyze the impact of different numbers of reference stations,polyhedron volumes,observation arc lengths,satellite types,and satellite systems on solving ERPs using BDS-2 and BDS-3 observation data provided by the International GNSS Service(IGS)stations.When selecting a specific satellite type,it is necessary to choose an appropriate observation arc length based on different numbers of reference stations while maximizing the volume of the formed polyhedron to achieve optimal efficiency and accuracy in parameter estimation.When both the number of reference stations and observation arc length are fixed,higher precision of the ERPs can be achieved using observations from MEO than MEO+IGSO and MEO+IGSO+GEO.Moreover,when considering only IGSO and MEO satellites as options for analysis purposes,BDS-3 provides higher accuracy compared to BDS-2.In summary,when using BDS for ERP estimation and MEO satellite observations with the same observation arc length,selecting stations from reference stations with larger polyhedral volumes can significantly improve the efficiency and accuracy of parameter estimation.
文摘The infrared band contains rich opportunities for astronomical research,but due to the limitations of infrared technology,the development of infrared astronomy in China has been far from satisfactory for a long time,especially for solar observation.“Accurate Infrared Magnetic field Measurements of the Sun”project(AIMS)is a National Major Scientific Research Instrument Development Project(recommended by the Ministries)supported by the National Natural Science Foundation of China.It is aimed at improving the accuracy of magnetic field measurement by an order of magnitude,by measuring the“Zeeman splitting”directly.In addition,as AIMS is also the first equipment specifically designed for mid-to far-infrared solar observation in the world,we also hope to utilize AIMS to explore potential new scientific research opportunities in the vast infrared region.This article will briefly introduce the scientific objectives,the telescope,the scientific post-focus instruments,and finally summarize the commissioning observations of AIMS.
基金supported by the National Natural Science Foundation of China under Grant Nos.12305069,11947022,12473001,11975072,11875102,and 11835009the National SKA Program of China under Grants Nos.2022SKA0110200 and 2022SKA0110203+1 种基金the Program of the Education Department of Liaoning Province under Grant No.JYTMS20231695the National 111 Project under Grant No.B16009。
文摘Sterile neutrinos can influence the evolution of the Universe,and thus cosmological observations can be used to detect them.Future gravitational-wave(GW)observations can precisely measure absolute cosmological distances,helping to break parameter degeneracies generated by traditional cosmological observations.This advancement can lead to much tighter constraints on sterile neutrino parameters.This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future shortγ-ray burst observations from a THESEUS-like telescope,an approach not previously explored in the literature.Both massless and massive sterile neutrinos are considered within theΛCDM cosmology.We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos,reaching 3σlevel.For massive sterile neutrinos,GW data can also greatly assist in improving the parameter constraints,but it seems that effective detection is still not feasible.
基金supported by the National Natural Science Foundation of China (Grant No. 42288101, 42375062, 42476192, 42275158)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)the GHfund C (202407036001)
文摘The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.
基金supported by the Shanghai Industrial Collaborative Innovation Fund(HCXBCY-2021-001)the Academy of Finland(349229)。
文摘Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades.Despite the high sensitivity of push-broom hyperspectral imagers,they experience limited swath and wavelength coverage.In this study,we report the development of a push-broom airborne multimodular imaging spectrometer(AMMIS)that spans ultraviolet(UV),visible near-infrared(VNIR),shortwave infrared(SWIR),and thermal infrared(TIR)wavelengths.As an integral part of China's HighResolution Earth Observation Program,AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads.It has been mounted on aircraft platforms such as Y-5,Y-12,and XZ-60.Since 2016,AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data.This study describes the system design,calibration techniques,performance tests,flight campaigns,and applications of the AMMIS.The system integrates UV,VNIR,SWIR,and TIR modules,which can be operated in combination or individually based on the application requirements.Each module includes three spectrometers,utilizing field-of-view(FOV)stitching technology to achieve a 40°FOV,thereby enhancing operational efficiency.We designed advanced optical systems for all modules,particularly for the TIR module,and employed cryogenic optical technology to maintain optical system stability at 100 K.Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data.The AMMIS features more than 1400 spectral bands,with spectral sampling intervals of 0.1 nm for UV,2.4 nm for VNIR,3 nm for SWIR,and 32 nm for TIR.In addition,the instantaneous fields of view(IFoVs)for the four modules were 0.5,0.25,0.5,and 1 mrad,respectively,with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode.This study reports on land-cover surveys,pollution gas detection,mineral exploration,coastal water detection,and plant investigations conducted using AMMIS,highlighting its excellent performance.Furthermore,we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms.This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.
