The approximate analytical expressions of the apertured broadband beams in the far field with Gaussian and Laguerre-Gaussian spatial modes are presented. For the radially polarized Laguerre-Gaussian beam, the result r...The approximate analytical expressions of the apertured broadband beams in the far field with Gaussian and Laguerre-Gaussian spatial modes are presented. For the radially polarized Laguerre-Gaussian beam, the result reveals that the electromagnetic field in the far field is transverse magnetic. The influences of bandwidth （Г） and truncation parameter （Co） on the transverse intensity distribution of the Gaussian beam and on the energy flux distribution of radially polarized Laguerre-Gaussian beam are analysed.展开更多
Based on the generalized truncated second-order moments,an approximate analytical formula of the beam propagation factor M^2 of high-power laser beams passing through the optical system with multiple hard-edged apertu...Based on the generalized truncated second-order moments,an approximate analytical formula of the beam propagation factor M^2 of high-power laser beams passing through the optical system with multiple hard-edged apertures is deduced.Numerical examples of the beams passing through an aperture-spatial filter are enclosed,and the influences of amplitude modulations(AMs)and phase fluctuations(PFs)on the beam propagation quality of high-power laser beams passing through the multi-apertured ABCD optical system are considered and discussed.It is shown that PFs are able to degrade the beam propagation quality of laser beams more than AMs when the high-power laser beams passing through the aperture-spatial filter,furthermore,one or two aperture-lens optical systems configured appropriate aperture parameters are both able to upgrade the beam propagation quality of high-power laser beams.The M2 factor of Gaussian beam passing through the multi-aperture optical system is a special case in this paper.展开更多
Based on the vectorial Ftayleigh-Sommerfeld integral formula and the complex Gaussian expansion of the hardedge aperture function, an analytical propagation expression for a nonparaxial vectorial off-axis Lorentz beam...Based on the vectorial Ftayleigh-Sommerfeld integral formula and the complex Gaussian expansion of the hardedge aperture function, an analytical propagation expression for a nonparaxial vectorial off-axis Lorentz beam passing through a rectangular aperture is derived. The unapertured case, the far field expression and the scalar paraxial result are also presented as special cases of the general formulae, respectively. Some numerical examples are also given to show the propagation characteristics of a nonparaxial vectorial off-axis Lorentz beam through a rectangular aperture. It is indicated that the f parameter, the off-axis displacement and the truncation parameter all play an important role in determining nonparaxial propagation behaviour.展开更多
The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality....The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike sc...Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.展开更多
Dome A,in Antarctica,offers an exceptional site for ground-based infrared astronomy,with its extremely low atmospheric infrared background noise and excellent seeing conditions.However,deploying near-infrared telescop...Dome A,in Antarctica,offers an exceptional site for ground-based infrared astronomy,with its extremely low atmospheric infrared background noise and excellent seeing conditions.However,deploying near-infrared telescopes in the harsh environment of Antarctica faces the critical challenge of frost accumulation on optical mirrors.While indium tin oxide heating films effectively defrost visible-band Antarctic astronomical telescopes,their thermal radiation at infrared wavelengths introduces significant stray light,severely degrading the signal-to-noise ratio for infrared observations.To address this limitation,we have designed a mechanical snow-removal system capable of efficiently clearing frost from sealing window surfaces at temperatures as low as–80°C.Aperture photometry of target sources,Canopus and HD 2151,revealed that after six days without intervention,floating snow extinction reduced target brightness by up to 3 magnitudes.Following mechanical defrosting,the source flux recovered to stable levels,with measured magnitudes showing rapid initial improvement followed by stabilization.Data analysis indicates that a frost removal strategy operating every 48 h,with each operation consisting of 4–6 cycles,enables efficient removal of frost and snow without introducing additional thermal noise.Future work will focus on optimizing the adaptive control algorithm and exploring novel low-temperature defrosting materials to extend the periods during which Antarctic infrared telescopes can operate unattended.展开更多
In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accurac...In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.展开更多
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.展开更多
The next generation of synchrotron radiation light sources features extremely low emittance,enabling the generation of synchrotron radiation with significantly higher brilliance,which facilitates the exploration of ma...The next generation of synchrotron radiation light sources features extremely low emittance,enabling the generation of synchrotron radiation with significantly higher brilliance,which facilitates the exploration of matter at smaller scales.However,the extremely low emittance results in stronger sextupole magnet strengths,leading to high natural chromaticity.This necessitates the use of sextupole magnets to correct the natural chromaticity.For the Shanghai Synchrotron Radiation Facility Upgrade(SSRF-U),a lattice was designed for the storage ring that can achieve an ultra-low natural emittance of 72.2 pm·rad at the beam energy of 3.5 GeV.However,the significant detuning effects,driven by high second-order resonant driving terms due to strong sextupoles,will degrade the performance of the facility.To resolve this issue,installation of octupoles in the SSRF-U storage ring has been planned.This paper presents the study results on configuration selection and optimization method for the octupoles.An optimal solution for the SSRF-U storage ring was obtained to effectively mitigate the amplitude-dependent tune shift and the second-order chromaticity,consequently leading to an increased dynamic aperture(DA),momentum acceptance(MA),and reduced sensitivity to magnetic field errors.展开更多
In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal...In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.展开更多
To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths ...To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.展开更多
For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchma...For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.展开更多
As the dominant seepage channel in rock masses,it is of great significance to study the influence of fracture roughness distribution on seepage and heat transfer in rock masses.In this paper,the fracture roughness dis...As the dominant seepage channel in rock masses,it is of great significance to study the influence of fracture roughness distribution on seepage and heat transfer in rock masses.In this paper,the fracture roughness distribution functions of the Bakhtiary dam site and Oskarshamn/Forsmark mountain were fitted using statistical methods.The COMSOL Multiphysics finite element software was utilized to analyze the effects of fracture roughness distribution types and empirical formulas for fracture hydraulic aperture on the seepage field and temperature field of rock masses.The results show that:(1)The fracture roughness at the Bakhtiary dam site and Oskarshamn/Forsmark mountain follows lognormal and normal distributions,respectively;(2)For rock masses with the same expected value and standard deviation of fracture roughness,the outflow from rock masses with lognormal distribution of fracture roughness is significantly larger than that of rock masses with normal distribution of fracture roughness;(3)The fracture hydraulic aperture,outflow,and cold front distance of the Li and Jiang model are significantly larger than those of the Barton model;(4)The outflow,hydraulic pressure distribution,and temperature distribution of the Barton model are more sensitive to the fracture roughness distribution type than those of the Li and Jiang model.展开更多
0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit freque...0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit frequency(Zhou et al.,2024;Dai et al.,2021).However,in densely vegetated or humid mountainous regions such as the Three Gorges Reservoir(TGR),C-band signals suffer from temporal decorrelation,limiting their effectiveness for landslide monitoring.展开更多
During gas extraction from deep coal,the rock endures high effective stress,with both the time-dependent deformation and anisotropic structure of the rock controlling the permeability evolution.To reveal this phenomen...During gas extraction from deep coal,the rock endures high effective stress,with both the time-dependent deformation and anisotropic structure of the rock controlling the permeability evolution.To reveal this phenomenon,a numerical simulation framework of the finite volume method and transient embedded discrete fracture model is proposed to establish a new constitutive model that links poroelastoplastic deformation,adsorption-induced swelling,and aperture compression.From this model,anisotropic permeability tensors were derived to further achieve the simulation of coevolution.Meanwhile,our permeability model was verified against the measured permeability data,and the history match of the numerical model showed better results where the mismatch was less than 5%.The results indicate that(1)the long-term permeability evolution clearly showed the competitive effects of multiple deformation mechanisms,which involves three stages:compaction-dominated decline,adsorption-dominated rebound,and creep-controlled loss.(2)The increased number of compressible cleats/fractures accelerated the initial permeability decline,while the increased desorption-induced strain promoted faster rebound and enhancement and higher viscosity coefficients enhanced the creep effect,which led to significant long-term permeability loss.(3)Massive hydraulic fracturing created a larger drainage area,accelerating methane desorption and causing sharp permeability rebound with reduced residual gas,which shows that the permeability remained higher than the initial values even after the extensive extraction via the fractured horizontal wells.The permeability evolution mechanisms displayed varying properties,such as coal rank and burial depth,and distinct characteristics.A precise understanding of multiple competitive stress effects is crucial for optimizing coalbed methane extraction techniques and improving recovery efficiency.展开更多
Spaceborne microwave instruments possess the capability of day-and-night and all-weather measurements that can penetrate clouds and fog,and directly measure tropical cyclone(TC)ocean surface winds.In this study,we est...Spaceborne microwave instruments possess the capability of day-and-night and all-weather measurements that can penetrate clouds and fog,and directly measure tropical cyclone(TC)ocean surface winds.In this study,we establish an effective methodology to estimate TC dynamic characteristic parameters(DCP),including the storm center location,intensity,radius of maximum wind(RMW)and wind structure,purely from TC ocean winds measured by multi-platform spaceborne microwave instruments.Combining measurements from active and passive sensors can provide long time series data for monitoring changes in storm DCP.Here,the evolution of the DCP for TC Freddy(2023),from its genesis to its landfall,is evaluated using data from synthetic aperture radars(SARs),as well as radiometer(RAD)and scatterometer(SCA)observations.Comparing the results to the best-track datasets for the longitudes and latitudes of the storm centers,we show that the root-mean-square errors(RMSEs)are 0.22°and 0.31°,respectively,both with a correlation of 0.99.For the detected intensity,the RMSEs are 6.8 m s^(−1) for SARs and 7.3 m s^(−1) for RADs.However,TC intensities measured by C-band SCAs are significantly underestimated,especially for wind speeds less than 50 m s^(−1).In terms of RMW and wind radii,the SARs,RADs and SCAs demonstrate good accuracy and applicability.Our investigation emphasizes the crucial role played by spaceborne microwave instruments in the study of TCs.This is helpful in monitoring,and in the future,will help improve the forecasting of TC intensities and their characteristic structures.展开更多
Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of IS...Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of ISAR systems.Also,data scarcity poses a greater challenge to the accurate recognition of components.To address the issues of component recognition in complex ISAR targets,this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features.The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure.Then,the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes,thereby achieving significantly higher structural adaptability.Meanwhile,the scattering trait enhancement module and the statistical quantification module are designed.The edge texture is enhanced based on the scatter quantization property,which alleviates the segmentation challenge of edge blurring under low SNR conditions.The coupling of query/support samples is enhanced through four-dimensional convolution.Additionally,to overcome fusion challenges caused by information differences,multimodal feature fusion is guided by equilibrium comprehension loss.In this way,the performance potential of the fusion framework is fully unleashed,and the decision risk is effectively reduced.Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion,and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.展开更多
基金supported by the Doctorial Start-up Fund of Hengyang Normal University,China (Grant No. 09B06)the Natural Science Foundation of Hunan Province,China (Grant No. 08jj3001)
文摘The approximate analytical expressions of the apertured broadband beams in the far field with Gaussian and Laguerre-Gaussian spatial modes are presented. For the radially polarized Laguerre-Gaussian beam, the result reveals that the electromagnetic field in the far field is transverse magnetic. The influences of bandwidth （Г） and truncation parameter （Co） on the transverse intensity distribution of the Gaussian beam and on the energy flux distribution of radially polarized Laguerre-Gaussian beam are analysed.
基金Science Fund from the Shaanxi Provincial Education Department,China(Grant No.18JK0723).
文摘Based on the generalized truncated second-order moments,an approximate analytical formula of the beam propagation factor M^2 of high-power laser beams passing through the optical system with multiple hard-edged apertures is deduced.Numerical examples of the beams passing through an aperture-spatial filter are enclosed,and the influences of amplitude modulations(AMs)and phase fluctuations(PFs)on the beam propagation quality of high-power laser beams passing through the multi-apertured ABCD optical system are considered and discussed.It is shown that PFs are able to degrade the beam propagation quality of laser beams more than AMs when the high-power laser beams passing through the aperture-spatial filter,furthermore,one or two aperture-lens optical systems configured appropriate aperture parameters are both able to upgrade the beam propagation quality of high-power laser beams.The M2 factor of Gaussian beam passing through the multi-aperture optical system is a special case in this paper.
基金supported by the Scientific Research Fund of Zhejiang Provincial Education Department,China(Grant No 20060677)
文摘Based on the vectorial Ftayleigh-Sommerfeld integral formula and the complex Gaussian expansion of the hardedge aperture function, an analytical propagation expression for a nonparaxial vectorial off-axis Lorentz beam passing through a rectangular aperture is derived. The unapertured case, the far field expression and the scalar paraxial result are also presented as special cases of the general formulae, respectively. Some numerical examples are also given to show the propagation characteristics of a nonparaxial vectorial off-axis Lorentz beam through a rectangular aperture. It is indicated that the f parameter, the off-axis displacement and the truncation parameter all play an important role in determining nonparaxial propagation behaviour.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1603601,2021YFF0601203,and 2021YFA1600703)。
文摘The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
基金supported by the National Nat-ural Science Foundation of China(No.42376174)the Natural Science Foundation of Shanghai(No.23ZR 1426900).
文摘Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.
基金supported by the Space Debris Research Project,China(KJSP2020010102)the National Key R&D Program of China(2022YFC2807300)the National Natural Science Foundation of China(12573081)。
文摘Dome A,in Antarctica,offers an exceptional site for ground-based infrared astronomy,with its extremely low atmospheric infrared background noise and excellent seeing conditions.However,deploying near-infrared telescopes in the harsh environment of Antarctica faces the critical challenge of frost accumulation on optical mirrors.While indium tin oxide heating films effectively defrost visible-band Antarctic astronomical telescopes,their thermal radiation at infrared wavelengths introduces significant stray light,severely degrading the signal-to-noise ratio for infrared observations.To address this limitation,we have designed a mechanical snow-removal system capable of efficiently clearing frost from sealing window surfaces at temperatures as low as–80°C.Aperture photometry of target sources,Canopus and HD 2151,revealed that after six days without intervention,floating snow extinction reduced target brightness by up to 3 magnitudes.Following mechanical defrosting,the source flux recovered to stable levels,with measured magnitudes showing rapid initial improvement followed by stabilization.Data analysis indicates that a frost removal strategy operating every 48 h,with each operation consisting of 4–6 cycles,enables efficient removal of frost and snow without introducing additional thermal noise.Future work will focus on optimizing the adaptive control algorithm and exploring novel low-temperature defrosting materials to extend the periods during which Antarctic infrared telescopes can operate unattended.
基金supported in part by the National Natural Science Foundation(Nos.62271248,62401256)in part by the Natural Science Foundation of Ji-angsu Province(Nos.BK20230090,BK20241384)in part by the Key Laboratory of Land Satellite Remote Sens-ing Application,Ministry of Natural Resources of China(No.KLSMNR-K202303)。
文摘In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
文摘The next generation of synchrotron radiation light sources features extremely low emittance,enabling the generation of synchrotron radiation with significantly higher brilliance,which facilitates the exploration of matter at smaller scales.However,the extremely low emittance results in stronger sextupole magnet strengths,leading to high natural chromaticity.This necessitates the use of sextupole magnets to correct the natural chromaticity.For the Shanghai Synchrotron Radiation Facility Upgrade(SSRF-U),a lattice was designed for the storage ring that can achieve an ultra-low natural emittance of 72.2 pm·rad at the beam energy of 3.5 GeV.However,the significant detuning effects,driven by high second-order resonant driving terms due to strong sextupoles,will degrade the performance of the facility.To resolve this issue,installation of octupoles in the SSRF-U storage ring has been planned.This paper presents the study results on configuration selection and optimization method for the octupoles.An optimal solution for the SSRF-U storage ring was obtained to effectively mitigate the amplitude-dependent tune shift and the second-order chromaticity,consequently leading to an increased dynamic aperture(DA),momentum acceptance(MA),and reduced sensitivity to magnetic field errors.
文摘In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.
文摘To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.
文摘For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.
基金College Students Innovation and Entrepreneurship Project of Guangzhou Railway Polytechnic(2025CXCY015)。
文摘As the dominant seepage channel in rock masses,it is of great significance to study the influence of fracture roughness distribution on seepage and heat transfer in rock masses.In this paper,the fracture roughness distribution functions of the Bakhtiary dam site and Oskarshamn/Forsmark mountain were fitted using statistical methods.The COMSOL Multiphysics finite element software was utilized to analyze the effects of fracture roughness distribution types and empirical formulas for fracture hydraulic aperture on the seepage field and temperature field of rock masses.The results show that:(1)The fracture roughness at the Bakhtiary dam site and Oskarshamn/Forsmark mountain follows lognormal and normal distributions,respectively;(2)For rock masses with the same expected value and standard deviation of fracture roughness,the outflow from rock masses with lognormal distribution of fracture roughness is significantly larger than that of rock masses with normal distribution of fracture roughness;(3)The fracture hydraulic aperture,outflow,and cold front distance of the Li and Jiang model are significantly larger than those of the Barton model;(4)The outflow,hydraulic pressure distribution,and temperature distribution of the Barton model are more sensitive to the fracture roughness distribution type than those of the Li and Jiang model.
基金supported by the National Natural Science Foundation of China(Nos.42371094,41907253)the Fundamental Research Funds for the Central Universities(No.B250201054).
文摘0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit frequency(Zhou et al.,2024;Dai et al.,2021).However,in densely vegetated or humid mountainous regions such as the Three Gorges Reservoir(TGR),C-band signals suffer from temporal decorrelation,limiting their effectiveness for landslide monitoring.
基金support of the National Natural Science Foundation of China(U23B6004 and 52404045)the CAST Young Talent Support Program,Doctoral Student Special Project.
文摘During gas extraction from deep coal,the rock endures high effective stress,with both the time-dependent deformation and anisotropic structure of the rock controlling the permeability evolution.To reveal this phenomenon,a numerical simulation framework of the finite volume method and transient embedded discrete fracture model is proposed to establish a new constitutive model that links poroelastoplastic deformation,adsorption-induced swelling,and aperture compression.From this model,anisotropic permeability tensors were derived to further achieve the simulation of coevolution.Meanwhile,our permeability model was verified against the measured permeability data,and the history match of the numerical model showed better results where the mismatch was less than 5%.The results indicate that(1)the long-term permeability evolution clearly showed the competitive effects of multiple deformation mechanisms,which involves three stages:compaction-dominated decline,adsorption-dominated rebound,and creep-controlled loss.(2)The increased number of compressible cleats/fractures accelerated the initial permeability decline,while the increased desorption-induced strain promoted faster rebound and enhancement and higher viscosity coefficients enhanced the creep effect,which led to significant long-term permeability loss.(3)Massive hydraulic fracturing created a larger drainage area,accelerating methane desorption and causing sharp permeability rebound with reduced residual gas,which shows that the permeability remained higher than the initial values even after the extensive extraction via the fractured horizontal wells.The permeability evolution mechanisms displayed varying properties,such as coal rank and burial depth,and distinct characteristics.A precise understanding of multiple competitive stress effects is crucial for optimizing coalbed methane extraction techniques and improving recovery efficiency.
基金supported by the Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LZJMZ25D050008 and LQ21D060001)the National Natural Science Foundation of China (Grant No. 42305153)+4 种基金the East China Meteorological Science and Technology Collaborative Innovation Foundation Cooperation Project (Grant No. QYHZ202307)the Zhejiang Meteorological Science and Technology Plan Project (Grant Nos. 2021YB07, 2022ZD06 and 2023YB06)the Youth Innovation Team Fund of the China Meteorological Administration (Grant No.CMA2023QN12)support of the Canadian program “Transforming Climate Action” led by Dalhousie University in Canadathe Canadian Space Agency (CSA) projects “Ocean surface features related to aggregation of North Atlantic Right Whales (NARWs)” and “Fine resolution classification of sea ice from the RADARSAT Constellation Mission (RCM)”
文摘Spaceborne microwave instruments possess the capability of day-and-night and all-weather measurements that can penetrate clouds and fog,and directly measure tropical cyclone(TC)ocean surface winds.In this study,we establish an effective methodology to estimate TC dynamic characteristic parameters(DCP),including the storm center location,intensity,radius of maximum wind(RMW)and wind structure,purely from TC ocean winds measured by multi-platform spaceborne microwave instruments.Combining measurements from active and passive sensors can provide long time series data for monitoring changes in storm DCP.Here,the evolution of the DCP for TC Freddy(2023),from its genesis to its landfall,is evaluated using data from synthetic aperture radars(SARs),as well as radiometer(RAD)and scatterometer(SCA)observations.Comparing the results to the best-track datasets for the longitudes and latitudes of the storm centers,we show that the root-mean-square errors(RMSEs)are 0.22°and 0.31°,respectively,both with a correlation of 0.99.For the detected intensity,the RMSEs are 6.8 m s^(−1) for SARs and 7.3 m s^(−1) for RADs.However,TC intensities measured by C-band SCAs are significantly underestimated,especially for wind speeds less than 50 m s^(−1).In terms of RMW and wind radii,the SARs,RADs and SCAs demonstrate good accuracy and applicability.Our investigation emphasizes the crucial role played by spaceborne microwave instruments in the study of TCs.This is helpful in monitoring,and in the future,will help improve the forecasting of TC intensities and their characteristic structures.
文摘Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of ISAR systems.Also,data scarcity poses a greater challenge to the accurate recognition of components.To address the issues of component recognition in complex ISAR targets,this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features.The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure.Then,the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes,thereby achieving significantly higher structural adaptability.Meanwhile,the scattering trait enhancement module and the statistical quantification module are designed.The edge texture is enhanced based on the scatter quantization property,which alleviates the segmentation challenge of edge blurring under low SNR conditions.The coupling of query/support samples is enhanced through four-dimensional convolution.Additionally,to overcome fusion challenges caused by information differences,multimodal feature fusion is guided by equilibrium comprehension loss.In this way,the performance potential of the fusion framework is fully unleashed,and the decision risk is effectively reduced.Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion,and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.
