Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)fram...Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach.展开更多
Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state esti...Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state estimation(RSE)is an indispensable functional module of CPSs.Recently,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance degradation.This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against RSE.Firstly,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of RSE.Secondly,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from adversaries.Thirdly,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'perspectives.Finally,several challenges and open problems are presented to inspire further exploration and future research in this field.展开更多
In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is studied.In the communication channels ...In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is studied.In the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is proposed.Firstly,the minimum number of channels and the channel set to be attacked are given.Secondly,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,respectively.Finally,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is proposed.The effectiveness of the proposed scheme is verified by simulation examples.展开更多
This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the meas...This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.展开更多
This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,th...This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.展开更多
Suspended particulate matter(SPM)in lakes exerts strong impact on light propagation,aquatic ecosystem productivity,which co-varies with nutrients,heavy metal and micro-pollutant in waters.In lakes,SPM exerts strong ab...Suspended particulate matter(SPM)in lakes exerts strong impact on light propagation,aquatic ecosystem productivity,which co-varies with nutrients,heavy metal and micro-pollutant in waters.In lakes,SPM exerts strong absorption and backscattering,ultimately affects water leaving signals that can be detected by satellite sensors.Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy.There are still rooms for model accuracy improvements,and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters.We assembled more than 16,400 in situ measured SPM in lakes from six continents(excluding the Antarctica continent),of which 9640 samples were matched with Landsat overpasses within±7 days.Seven machine learning algorithms and two simple regression methods(linear and partial least squares models)were used to estimate SPM in lakes and the performance were compared.To overcome the problem of imbalance datasets in regression,a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise(SMOGN)was adopted in this study.Through comparison,we found that gradient boosting decision tree(GBDT),random forest(RF),and extreme gradient boosting(XGBoost)models demonstrated good spatiotemporal transferability with SMOGN processed dataset,and has potential to map SPM at different year with good quality of Landsat land surface reflectance images.In all the tested modeling approaches,the GBDT model has accurate calibration(n=6428,R^(2)=0.95,MAPE=29.8%)from SPM collected in 2235 lakes across the world,and the validation(n=3214,R^(2)=0.84,MAPE=38.8%)also exhibited stable performance.Further,the good performances were also exhibited by RF model with calibration(R^(2)=0.93)and validation(R^(2)=0.86,MAPE=24.2%)datasets.We applied GBDT and RF models to map SPM of typical lakes,and satisfactory result was obtained.In addition,the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors(L5-TM,L7-ETM+,and L8-OLI),thus the model has the potential to map SPM of lakes for monitoring temporal variations,and tracks lake water SPM dynamics in approximately the past four decades(1984-2021)since Landsat-5/TM was launched in 1984.展开更多
Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD f...Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model（DEM） data are employed to reflect topography, and moderate-resolution imaging spectroradiometer（MODIS） cloud products（Aqua MYD06-L2 and Terra MOD06-L2） are used to estimate sunshine percentage. Based on the terrain（e.g.,slope, aspect, and terrain shadowing degree） and the atmospheric conditions（e.g., air molecules, aerosols,moisture, cloud cover, and cloud types）, observation data from weather stations are also incorporated into the model. Verification results indicate that the model simulations match reasonably with the observations,with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD differs significantly between the south-facing and north-facing slopes,and its seasonal variation is also large throughout the year.展开更多
Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimati...Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.展开更多
In this paper,the authors consider how to design defensive countermeasures against DoS attacks for remote state estimation of multiprocess systems.For each system,a sensor will measure its state and transmits the data...In this paper,the authors consider how to design defensive countermeasures against DoS attacks for remote state estimation of multiprocess systems.For each system,a sensor will measure its state and transmits the data packets through an unreliable channel which is vulnerable to be jammed by an attacker.Under limited communication bandwidth,only a subset of sensors are allowed for data transmission,and how to select the optimal one to maximize the accuracy of remote state estimation is the focus of the proposed work.The authors first formulate this problem as a Markov decision process and investigate the existence of optimal policy.Moreover,the authors demonstrate the piecewise monotonicity structure of optimal policy.Given the difficulty of obtaining an optimal policy of large-scale problems,the authors develop a suboptimal heuristic policy based on the aforementioned policy structure and Whittle’s index.Moreover,a closed form of the indices is derived in order to reduce implementation complexity of proposed scheduling policy and numerical examples are provided to illustrate the proposed developed results.展开更多
The crust of the Moon records the complete history of collisions by different-sized projectiles from various sources since its early solidification.Planetary bodies in the inner Solar System experienced similar source...The crust of the Moon records the complete history of collisions by different-sized projectiles from various sources since its early solidification.Planetary bodies in the inner Solar System experienced similar sources of impactors,and the Moon is an ideal witness plate for the impact history.Impact flux on the Moon connects planetary endogenic evolution with orbital dynamics of celestial bodies,and the resulting crater chronology enables remote age estimation for geological units on extraterrestrial bodies.Therefore,defining the lunar impact history has long been a core pursuit in planetary sciences.Ubiquitous impact structures on the Moon and their widespread impact melt deposits are the major agents used to untangle lunar crater chronology.Anchored by 10 successful sample return missions from the Moon,cumulative crater densities were derived for 15 geological units based on their interpreted exposure ages(~3.92 Ga to 25 Ma)and superposed crater densities.Afterword,crater production rates in the entire history of the Moon were constructed on the basis of hypothesized change patterns of impact flux.Following this commonly adapted strategy,it has been a consensus that impact flux in the first billion years of the lunar history was orders of magnitude larger than that afterward,and the latter was not only more or less stable but also punctuated by discrete spikes.However,different versions of lunar crater chronology exist because of insufficient constraints by available anchor points and widespread disagreements on both sample ages and crater densities of existing anchor points.Endeavors from various disciplines(e.g.,sample analyses,remote observation,and modeling crater formation and accumulation)are making promising progresses,and future sample return missions with both optimized sampling strategy and analyzing techniques are appealed to fundamentally improve the understanding of lunar impact flux.展开更多
基金supported in part by the Frontier Technology R&D Plan of Jiangsu Province(BF2024065)the Shenzhen Science and Technology Program(JCYJ20230807114609019)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX22_0236).
文摘Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach.
基金the Natural Sciences and Engineering Research Council(NSERC)of Canada。
文摘Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state estimation(RSE)is an indispensable functional module of CPSs.Recently,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance degradation.This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against RSE.Firstly,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of RSE.Secondly,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from adversaries.Thirdly,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'perspectives.Finally,several challenges and open problems are presented to inspire further exploration and future research in this field.
基金supported by the National Natural ScienceFoundation(NNSF)of China(61973082)Six Talent Peaks Project inJiangsu Province(XYDXX-005)。
文摘In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is studied.In the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is proposed.Firstly,the minimum number of channels and the channel set to be attacked are given.Secondly,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,respectively.Finally,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is proposed.The effectiveness of the proposed scheme is verified by simulation examples.
基金supported by the National Natural Science Foundation of China(61925303,62173034,62088101,U20B2073,62173002)the National Key Research and Development Program of China(2021YFB1714800)Beijing Natural Science Foundation(4222045)。
文摘This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.
文摘This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.
基金The research was jointly supported by the National Key Research and Development Project of China(2021YFB3901101)the National Natural Science Foundation of China(42171374,42071336,42001311,42101366)+3 种基金the Natural Science Foundation of Jilin Province,China(20220203024SF)Youth Innovation Promotion Association of Chinese Academy of Sciences,China(2020234)Young Scientist Group Project of Northeast Institute of Geography and Agroecology,China(2023QNXZ01)Chinese Academy of Sciences and Postdoctoral Fellowship of Jilin Province of China to Yingxin Shang.
文摘Suspended particulate matter(SPM)in lakes exerts strong impact on light propagation,aquatic ecosystem productivity,which co-varies with nutrients,heavy metal and micro-pollutant in waters.In lakes,SPM exerts strong absorption and backscattering,ultimately affects water leaving signals that can be detected by satellite sensors.Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy.There are still rooms for model accuracy improvements,and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters.We assembled more than 16,400 in situ measured SPM in lakes from six continents(excluding the Antarctica continent),of which 9640 samples were matched with Landsat overpasses within±7 days.Seven machine learning algorithms and two simple regression methods(linear and partial least squares models)were used to estimate SPM in lakes and the performance were compared.To overcome the problem of imbalance datasets in regression,a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise(SMOGN)was adopted in this study.Through comparison,we found that gradient boosting decision tree(GBDT),random forest(RF),and extreme gradient boosting(XGBoost)models demonstrated good spatiotemporal transferability with SMOGN processed dataset,and has potential to map SPM at different year with good quality of Landsat land surface reflectance images.In all the tested modeling approaches,the GBDT model has accurate calibration(n=6428,R^(2)=0.95,MAPE=29.8%)from SPM collected in 2235 lakes across the world,and the validation(n=3214,R^(2)=0.84,MAPE=38.8%)also exhibited stable performance.Further,the good performances were also exhibited by RF model with calibration(R^(2)=0.93)and validation(R^(2)=0.86,MAPE=24.2%)datasets.We applied GBDT and RF models to map SPM of typical lakes,and satisfactory result was obtained.In addition,the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors(L5-TM,L7-ETM+,and L8-OLI),thus the model has the potential to map SPM of lakes for monitoring temporal variations,and tracks lake water SPM dynamics in approximately the past four decades(1984-2021)since Landsat-5/TM was launched in 1984.
基金Supported by the National Natural Science Foundation of China(41175077)Jiangsu Innovation Program for Graduate Education(CXZZ12-0506)
文摘Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model（DEM） data are employed to reflect topography, and moderate-resolution imaging spectroradiometer（MODIS） cloud products（Aqua MYD06-L2 and Terra MOD06-L2） are used to estimate sunshine percentage. Based on the terrain（e.g.,slope, aspect, and terrain shadowing degree） and the atmospheric conditions（e.g., air molecules, aerosols,moisture, cloud cover, and cloud types）, observation data from weather stations are also incorporated into the model. Verification results indicate that the model simulations match reasonably with the observations,with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD differs significantly between the south-facing and north-facing slopes,and its seasonal variation is also large throughout the year.
基金supported by the project of the National Technology Extension Fund of Forestry,‘Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm’([2019]06)the National Natural Science Foundation of China,‘Study on Crown Models for Larix olgensis Based on Tree Growth’(31870620).
文摘Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.
基金supported by the National Natural Science Foundation of China under Grant No.20231120102304001,STIC under Grant Nos.62303212 and ZDSYS20220330161800001.
文摘In this paper,the authors consider how to design defensive countermeasures against DoS attacks for remote state estimation of multiprocess systems.For each system,a sensor will measure its state and transmits the data packets through an unreliable channel which is vulnerable to be jammed by an attacker.Under limited communication bandwidth,only a subset of sensors are allowed for data transmission,and how to select the optimal one to maximize the accuracy of remote state estimation is the focus of the proposed work.The authors first formulate this problem as a Markov decision process and investigate the existence of optimal policy.Moreover,the authors demonstrate the piecewise monotonicity structure of optimal policy.Given the difficulty of obtaining an optimal policy of large-scale problems,the authors develop a suboptimal heuristic policy based on the aforementioned policy structure and Whittle’s index.Moreover,a closed form of the indices is derived in order to reduce implementation complexity of proposed scheduling policy and numerical examples are provided to illustrate the proposed developed results.
基金supported by the National Natural Science Foundation(42241108,42273040,12203036,and L2224032)B-type Strategic Priority Program of the Chinese Academy of Sciences(XDB41000000)+1 种基金the National Key Research and Development Program of China(grant no.2022YFF0503100)Chinese Academy of Sciences(XK2022DXC004).
文摘The crust of the Moon records the complete history of collisions by different-sized projectiles from various sources since its early solidification.Planetary bodies in the inner Solar System experienced similar sources of impactors,and the Moon is an ideal witness plate for the impact history.Impact flux on the Moon connects planetary endogenic evolution with orbital dynamics of celestial bodies,and the resulting crater chronology enables remote age estimation for geological units on extraterrestrial bodies.Therefore,defining the lunar impact history has long been a core pursuit in planetary sciences.Ubiquitous impact structures on the Moon and their widespread impact melt deposits are the major agents used to untangle lunar crater chronology.Anchored by 10 successful sample return missions from the Moon,cumulative crater densities were derived for 15 geological units based on their interpreted exposure ages(~3.92 Ga to 25 Ma)and superposed crater densities.Afterword,crater production rates in the entire history of the Moon were constructed on the basis of hypothesized change patterns of impact flux.Following this commonly adapted strategy,it has been a consensus that impact flux in the first billion years of the lunar history was orders of magnitude larger than that afterward,and the latter was not only more or less stable but also punctuated by discrete spikes.However,different versions of lunar crater chronology exist because of insufficient constraints by available anchor points and widespread disagreements on both sample ages and crater densities of existing anchor points.Endeavors from various disciplines(e.g.,sample analyses,remote observation,and modeling crater formation and accumulation)are making promising progresses,and future sample return missions with both optimized sampling strategy and analyzing techniques are appealed to fundamentally improve the understanding of lunar impact flux.
