Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses.In this study,we propose an automatic workflow based on machine learning(ML)to monitor seismicity in the southern Sichuan Basin of China.This workflow includes coherent event detection,phase picking,and earthquake location using three-component data from a seismic network.By combining Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local seismicity.The approach allows us to use synthetic samples covering the entire study area to train Phase Loc,addressing the problems of insufficient data samples,imbalanced data distribution,and unreliable labels when training with observed data.We apply the trained model to observed data recorded in the southern Sichuan Basin,China,between September 2018 and March 2019.The results show that the average differences in latitude,longitude,and depth are 5.7 km,6.1 km,and 2 km,respectively,compared to the reference catalog.Phase Loc combines all available phase information to make fast and reliable predictions,even if only a few phases are detected and picked.The proposed workflow may help real-time seismic monitoring in other regions as well.

Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall

Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The Climate Hazards Group Infra Red Precipitation with Station Data(CHIRPS), ERA5-Land total precipitation and temperature data are used from 1981–2022 to predict spatial rainfall by applying an artificial neural network(ANN). The recurrent neural network(RNN) is a nonlinear autoregressive network with exogenous input(NARX), which includes feed-forward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from the European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system(ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change(CMCC) to the specific locations of rainfall stations in Ethiopia for the period 1980–2020. Across the stations, the results of NARX exhibit strong associations and reduced errors. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature, as well as the combination of these two variables, show promising results.

Physics package based on intracavity laser cooling ^(87)Rb atoms for space cold atom microwave clock

This article proposes a new physics package to enhance the frequency stability of the space cold atom clock with the advantages of a microgravity environment. Clock working processes, including atom cooling, atomic state preparation,microwave interrogation, and transition probability detection, are integrated into the cylindrical microwave cavity to achieve a high-performance and compact physics package for the space cold atom clock. We present the detailed design and ground-test results of the cold atom clock physics package in this article, which demonstrates a frequency stability of 1.2×10^(-12) τ^(-1/2) with a Ramsey linewidth of 12.5 Hz, and a better performance is predicted with a 1 Hz or a narrower Ramsey linewidth in microgravity environment. The miniaturized cold atom clock based on intracavity cooling has great potential for achieving space high-precision time-frequency reference in the future.

The Continental Scale USMTArray:Lessons Learned and Synergies with SinoProbe-Ⅱ

The USMTArray was completed on June 27,2024,comprising a network of 1779 transportable long-period magnetotelluric(MT)stations(Fig.1)with nominal 70-km grid spacing spanning the conterminous United States,an area of 8.1×10^(6)km^(2).Each station operated for weeksto-months,as required to meet data quality standards over the period band of 10–10000 s.The USMTArray shares similarities with the planned SinoProbe-II MT Array,with its 1-degree station spacing(~111 km in the latitudinal direction)spanning an area of 9.6×10^(6)km^(2).

Extreme Antarctic Cold of Late Winter 2023

Extreme cold temperatures were observed in July and August 2023,coinciding with the WINFLY(winter fly-in)period of mid to late August into September 2023,meaning aircraft operations into McMurdo Station and Phoenix Airfield were adversely impacted.Specifically,with temperatures below−50℃,safe flight operation was not possible because of the risk of failing hydraulics and fuel turning to gel onboard the aircraft.The cold temperatures were measured across a broad area of the Antarctic,from East Antarctica toward the Ross Ice Shelf,and stretching across West Antarctica to the Antarctic Peninsula.A review of automatic weather station measurements and staffed station observations revealed a series of sites recording new record low temperatures.Four separate cold phases were identified,each a few days in duration and occurring from mid-July to the end of August 2023.A brief analysis of 500-hPa geopotential height anomalies shows how the mid-tropospheric atmospheric environment evolves in relation to these extreme cold temperatures.The monthly 500-hPa geopotential height anomalies show strong negative anomalies in August.Examination of composite geopotential height anomalies during each of the four cold phases suggests various factors leading to cold temperatures,including both southerly off-content flow and calm atmospheric conditions.Understanding the atmospheric environment that leads to such extreme cold temperatures can improve prediction of such events and benefit Antarctic operations and the study of Antarctic meteorology and climatology.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

Toward Establishing a Low-cost UAV Coordinated Carbon Observation Network(LUCCN):First Integrated Campaign in China

In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ measurement.We use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based instruments.The first integrated measurement campaign took place in Shenzhen,China,4 May 2023.During the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in Shenzhen.The emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross section.The LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.

Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

The sudden ocean warming and its potential influences on earlyfrozen landfast ice in the Prydz Bay, East Antarctica

The ocean conditions beneath the ice cover play a key role in understanding the sea ice mass balance in the polar regions.An integrated high-frequency ice-ocean observation system,including Acoustic Doppler Velocimeter,Conductivity-Temperature-Depth Sensor,and Sea Ice Mass Balance Array(SIMBA),was deployed in the landfast ice region close to the Chinese Zhongshan Station in Antarctica.A sudden ocean warming of 0.14℃(p<0.01)was observed beneath early-frozen landfast ice,from(−1.60±0.03)℃during April 16-19 to(−1.46±0.07)℃during April 20-23,2021,which is the only significant warming event in the nearly 8-month records.The sudden ocean warming brought a double rise in oceanic heat flux,from(21.7±11.1)W/m^(2) during April 16-19 to(44.8±21.3)W/m^(2) during April 20-23,2021,which shifted the original growth phase at the ice bottom,leading to a 2 cm melting,as shown from SIMBA and borehole observations.Simultaneously,the slowdown of ice bottom freezing decreased salt rejection,and the daily trend of observed ocean salinity changed from+0.02 d^(-1) during April 16-19,2021 to+0.003 d^(-1) during April 20-23,2021.The potential reasons are increased air temperature due to the transit cyclones and the weakened vertical ocean mixing due to the tide phase transformation from semi-diurnal to diurnal.The high-frequency observations within the ice-ocean boundary layer enhance the comprehensive investigation of the ocean’s influence on ice evolution at a daily scale.

Research on Site Planning of Mobile Communication Network

In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is proposed.Firstly,based on practical needs,mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives,coverage objectives,and quality objectives.Then,a multi-objective optimization model was established by combining threshold and traffic volume constraints.In order to reduce the time complexity of optimization,a non-dominated sorting genetic algorithm(NSGA)is used to solve the multi-objective optimization problem of site planning.Finally,a strategy for clustering and optimizing weak coverage areas was proposed.In order to avoid redundant neighborhood retrieval during cluster expansion,the Fast Density-Based Spatial Clustering of Applications with Noise(FDBSCAN)clustering method was adopted.With different sub-objectives as the main objectives,this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations,as well as relevant site planning maps,and provided three planning schemes for different main objectives.The simulation results show that the traffic coverage of the three station planning schemes is above 90%.The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.

A guidance strategy for rendezvous and docking to the space station in the Earth-Moon NRHO orbit

With the development of space technology,it is possible to build a space station in Earth-Moon space as a transit for Earth-Moon round-trip and entering in the deep space.Rendezvous and docking is one of the key technologies for building an Earth-Moon space station.A guidance strategy for rendezvous and docking from the Earth orbit to the space station in the Earth-Moon NRHO orbit is proposed in this paper,which is suitable for engineering applications.Firstly,the rendezvous and docking process is divided into three sections,i.e.,the large-range orbit transfer section,far-range guidance section,and close-range approaching section.The suitable terminal of large-range orbit transfer is selected according to the eigenvalue of NRHO orbit state transition matrix.The two-impulse guidance method based on the relative motion equation in the three-body problem is adopted for the far-range guidance section.The impulse time and amplitude are solved with the optimization algorithm.The linear constant three-body relative motion equation is proposed for the close-range approaching section,and the rendezvous and docking is completed by a two-stage linear approximation.Finally,a simulation analysis is carried out,and the simulation results show that the adopted dynamics equations and the designed guidance law are effective,and the three flight phases are naturally connected to accomplish the rendezvous and docking mission from the Earth orbit to the space station on the Earth-Moon NRHO.

Simulation of Underground Reservoir Stability of Pumped Storage Power Station Based on Fluid-Structure Coupling

Based on global initiatives such as the clean energy transition and the development of renewable energy,the pumped storage power station has become a new and significant way of energy storage and regulation,and its construction environment is more complex than that of a traditional reservoir.In particular,the stability of the rock strata in the underground reservoirs is affected by the seepage pressure and rock stress,which presents some challenges in achieving engineering safety and stability.Using the advantages of the numerical simulation method in dealing deal with nonlinear problems in engineering stability,in this study,the stability of the underground reservoir of the Shidangshan(SDS)pumped storage power station was numerically calculated and quantitatively analyzed based on fluid-structure coupling theory,providing an important reference for the safe operation and management of the underground reservoir.First,using the COMSOL software,a suitablemechanicalmodel was created in accordance with the geological structure and project characteristics of the underground reservoir.Next,the characteristics of the stress field,displacement field,and seepage field after excavation of the underground reservoir were simulated in light of the seepage effect of groundwater on the nearby rock of the underground reservoir.Finally,based on the construction specifications and Molar-Coulomb criterion,a thorough evaluation of the stability of the underground reservoir was performed through simulation of the filling and discharge conditions and anti-seepage strengthening measures.The findings demonstrate that the numerical simulation results have a certain level of reliability and are in accordance with the stress measured in the project area.The underground reservoir excavation resulted in a maximum displacement value of the rock mass around the caverns of 3.56 mm in a typical section,and the safety coefficient of the parts,as determined using the Molar-Coulomb criterion,was higher than 1,indicating that the project as a whole is in a stable state.

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Recent Progress in Space Science and Applications on Chinese Space Station in 2022–2024

Chinese Space Station(CSS)has been fully deployed by the end of 2022,and the facility has entered into the application and development phase.It has conducted scientific research projects in various fields,such as space life science and biotechnology,space materials science,microgravity fundamental physics,fluid physics,combustion science,space new technologies,and applications.In this review,we introduce the progress of CSS development and provide an overview of the research conducted in Chinese Space Station and the recent scientific findings in several typical research fields.Such compelling findings mainly concern the rapid solidification of ultra-high temperature alloy melts,dynamics of fluid transport in space,gravity scaling law of boiling heat transfer,vibration fluidization phenomenon of particulate matter,cold atom interferometer technology under high microgravity and related equivalence principle testing,the full life cycle of rice under microgravity and so forth.Furthermore,the planned scientific research and corresponding prospects of Chinese space station in the next few years are presented.

An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.

EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.

Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.