Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Analysis of application range of simplified models for field to thermo-field to thermionic emission processes from the cathode

Electron emission plays a dominant role in plasma-cathode interactions and is a key factor in many plasma phenomena and industrial applications.It is necessary to illustrate the various electron emission mechanisms and the corresponding applicable description models to evaluate their impacts on discharge properties.In this study,detailed expressions of the simplified formulas valid for field emission to thermo-field emission to thermionic emission typically used in the numerical simulation are proposed,and the corresponding application ranges are determined in the framework of the Murphy-Good theory,which is commonly regarded as the general model and to be accurate in the full range of conditions of the validity of the theory.Dimensionless parameterization was used to evaluate the emission current density of the Murphy-Good formula,and a deviation factor was defined to obtain the application ranges for different work functions(2.5‒5 eV),cathode temperatures(300‒6000 K),and emitted electric fields(10^(5) to 10^(10) V·m^(-1)).The deviation factor was shown to be a nonmonotonic function of the three parameters.A comparative study of particle number densities in atmospheric gas discharge with a tungsten cathode was performed based on the one-dimensional implicit particle-in-cell(PIC)with the Monte Carlo collision(MCC)method according to the aforementioned application ranges.It was found that small differences in emission current density can lead to variations in the distributions of particle number density due to changes in the collisional environment.This study provides a theoretical basis for selecting emission models for subsequent numerical simulations.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Effects of friction stir processing and nano-hydroxyapatite on the microstructure,hardness,degradation rate and in-vitro bioactivity of WE43 alloy for biomedical applications

Nowadays,magnesium alloys are emerging in biomedical implants for their similar properties to natural bones.However,the rapid degradation of magnesium alloys in biological media hinders successful implantation.Refinement of microstructure,as well as reinforcement particles can significantly improve the degradation rate.In this work,multi-pass friction stir processing(FSP)was proposed to synthesize WE43/nano-hydroxyapatite(n HA)surface composite,the microstructure,reinforced particle distribution,micro-hardness,corrosion behavior and in-vitro bioactivity were studied.The subsequent FSP passes of WE43 alloy and WE43/n HA composite refined the grain size which was reduced by 94.29%and 95.92%(2.63 and 1.88μm,respectively)compared to base metal after three passes.This resulted in increasing the microhardness by 120%(90.86 HV0.1)and 135%(105.59 HV0.1)for the WE43 and WE43-n HA,respectively.It is found that increasing FSP passes improved the uniform distribution of n HA particles within the composite matrix which led to improved corrosion resistance and less degradation rate.The corrosion rate of the FSPed WE43/n HA composite after three passes was reduced by 38.2%(4.13 mm/year)and the degradation rate was reduced by 69.7%(2.87 mm/y).This is attributed to secondary phase(Mg24Y5and Mg41Nd5)particle fragmentation and redistribution,as well as a homogeneous distribution of n HA.Additionally,the growing Ca-P and Mg(OH)2layer formed on the surface represented a protective layer that reduced the degradation rate.The wettability test revealed a relatively hydrophilic surface with water contact angle of 49.1±2.2°compared to 71.2±2.1°for base metal.Also,biomineralization test showed that apatite layer grew after immersion 7d in simulated body fluid with atomic ratio of Ca/P 1.60 approaching the stoichiometric ratio(1.67)indicating superior bioactivity of FSPed WE43/n HA composite after three passes.These results raise that the grain refinement by FSP and introduction of n HA particles significantly improved the degradation rate and in-vitro bioactivity of WE43 alloy for biomedical applications.

Application of novel constrained friction processing method to produce fine grained biomedical Mg-Zn-Ca alloy

In order to obtain Mg alloys with fine microstructures and high mechanical performances,a novel friction-based processing method,name as“constrained friction processing(CFP)”,was investigated.Via CFP,defect-free Mg-Zn-Ca rods with greatly refined grains and high mechanical properties were produced.Compared to the previous as-cast microstructure,the grain size was reduced from more than 1 mm to around 4μm within 3 s by a single process cycle.The compressive yield strength was increased by 350%while the ultimate compressive strength by 53%.According to the established material flow behaviors by“tracer material”,the plastic material was transported by shear deformation.From the base material to the rod,the material experienced three stages,i.e.deformation by the tool,upward flow with additional tilt,followed by upward transportation.The microstructural evolution was revealed by“stop-action”technique.The microstructural development at regions adjacent to the rod is mainly controlled by twinning,dynamic recrystallization(DRX)as well as particle stimulated nucleation,while that within the rod is related to DRX combined with grain growth.

Application of Feature, Event, and Process Methods to Leakage Scenario Development for Offshore CO_(2) Geological Storage

Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substantial concern associated with this technology. This study introduces an innovative approach for establishing OCGS leakage scenarios, involving four pivotal stages, namely, interactive matrix establishment, risk matrix evaluation, cause–effect analysis, and scenario development, which has been implemented in the Pearl River Estuary Basin in China. The initial phase encompassed the establishment of an interaction matrix for OCGS systems based on features, events, and processes. Subsequent risk matrix evaluation and cause–effect analysis identified key system components, specifically CO_(2) injection and faults/features. Building upon this analysis, two leakage risk scenarios were successfully developed, accompanied by the corresponding mitigation measures. In addition, this study introduces the application of scenario development to risk assessment, including scenario numerical simulation and quantitative assessment. Overall, this research positively contributes to the sustainable development and safe operation of OCGS projects and holds potential for further refinement and broader application to diverse geographical environments and project requirements. This comprehensive study provides valuable insights into the establishment of OCGS leakage scenarios and demonstrates their practical application to risk assessment, laying the foundation for promoting the sustainable development and safe operation of ocean CO_(2) geological storage projects while proposing possibilities for future improvements and broader applications to different contexts.

Exploration and Application Practice of Energy Conservation Theory and Method in Steel Manufacturing Process System

This article briefly discusses the theoretical basis and overall goals of energy conservation in the steel manufacturing process system.It is proposed that in the process of implementing system energy conservation,it is necessary to fully recognize and utilize the characteristics and functional advantages of the steel manufacturing process,pay more attention to energy quality,firmly grasp the overall goal of system optimization,focus on the integrated optimization of gas,steam,and waste heat systems,and propose the idea of constructing a"steel chemi-cal gas electricity heating cooling multi generation system".Based on practice,the main principles,models,and effects of implementing systematic energy conservation in steel enterprises have been proposed.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Process model of IT impacts on firm competitiveness based on application capability of information technology

To investigate the process of information technology （IT） impacts on firm competitiveness, an integrated process model of IT impacts on firm competitiveness is brought forward based on the process-oriented view, the resource-based view and the complementary resource view, which is comprised of an IT conversion process, an information system （IS） adoption process, an IS use process and a competition process. The application capability of IT plays the critical role, which determines the efficiency and effectiveness of the aforementioned four processes. The process model of IT impacts on firm competitiveness can also be used to explain why, under what situations and how IT can generate positive organizational outcomes, as well as theoretical bases for further empirical study.

Assessment and Application of Beach Quality Based on Analytic Hierarchy Process in Yangkou Beach, Qingdao

The whole-beach quality assessment is the basis of building and preserving beautiful beaches.The beach quality assessment index system and assessment standard have been established based on the attributes of beaches(including the width,slope,landform,and types),sorting coefficient,and softness degree of surface sediment.The assessment weight of each index for quality evaluation was analyzed using the analytic hierarchy process,and comprehensive scores of selected beach profiles were calculated in accordance with the light assessment standard.A beach quality evaluation model based on index weight and scores was established in this paper.The factors of 12 profiles of Yangkou Beach in Qingdao City were surveyed to carry out a quality assessment,and the comprehensive scores of each profile were calculated in accordance with the evaluation model.The results showed that the quality of Yangkou Beach can be divided into four ratings:excellent,good,medium,and poor.The excellent-quality area includes a wide and flat dry beach zone and soft,flat,and clean intertidal and subtidal zones covered with well-sorted fine sand,and leisure sports,such as volleyball,running,and swimming,are suitable for tourists.The good-quality area features a slightly narrow and dry beach zone,moderately soft and uneven intertidal and subtidal zones covered with fine sand and a small tidal gully,and a small amount of foreign matter;leisure sports,such as walking and running,are suitable for tourists.This study recommends the building of fixed drainage ditches or underground culverts to reduce the tidal gully.The medium-quality area consisted of a narrow and dry beach zone,moderately soft and uneven intertidal and subtidal zones covered by poorly sorted medium sand,a tide ditch,and a small amount of foreign matter.In this area,walking is suitable for tourists.Sand should be supplemented in the intertidal zone.The poor-quality area contained a very narrow and dry beach zone covered with poor-sorted gravel,a very chaotic intertidal zone with a considerable amount of foreign matters,such as bricks and rocks,wide tidal ditches,and an uneven subtidal zone with some reefs;leisure sports are unsuitable here.Thus,foreign matter and reefs should be removed,and the dry beach zone should be supplemented with sand.Therefore,the beach quality assessment is a very useful tool for building beautiful beaches.

Effects of phosphorus fertilizer application rate on transformation processes of phosphorus fractions in the purple alluvial soil of a riparian zone

Effects of phosphorus(P)fertilizer application rate on soil transformation processes of P fraction are still unclear in the riparian zone.Purple alluvial soils in the riparian zone of the Three Gorges Reservoir were collected to conduct a 21-day incubation executed by two hydrological environments(drying and flooding)and ten application rates of P fertilizer.Transformation percentages of P fertilizer(TPPF)were calculated as content differences of soil P fractions between fertilizer addition and none fertilizer addition divided by soil total P increases caused by fertilizer addition.TPPF to inorganic P extracted by sodium hydroxide(NaOH-Pi)and hydrochloric acid(HCl-Pi)increase by 20.91%(9.71%)and 24.26%(40.72%)under the drying(flooding)environment.Instead,TPPF to the other fractions decrease.Phosphorus fertilizer input mainly has indirect positive and negative effects on organic P via precipitated P under the drying and flooding environments and finally has indirect positive effects on labile P(p<0.001).Percentage changes of water-soluble inorganic P(H2O-Pi)and HCl-Pi under the flooding environment are higher than that under the drying environment,and percentage changes of organic P extracted by sodium hydrogen carbonate(Na HCO3-Po)and NaOH-Pi show an opposite trend(p<0.01).?(differences in soil P fraction content between flooding and drying incubations)H2O-Pi is negatively correlated with?NaHCO3-Po,and?NaHCO3-Po is positively correlated with?NaOH-Pi(p<0.001).In conclusion,P fertilizer is transformed more into precipitated P than into other P fractions with an application rate increase.Phosphorus fertilizer input mainly increases organic P via precipitated P under the drying environment and decreases organic P via precipitated P under the flooding environment,and organic P is further transformed into labile P.With P fertilizer input,P release caused by flooding is derived from NaHCO3-Po release triggered by NaOH-Pi release.The results can be helpful for the understanding of P fertilizer migration processes from the riparian zone soil to the Three Gorges Reservoir under rain leaching and flooding.

A review of functional MRI application for brain research of Chinese language processing

As one of the most widely used languages in the world,Chinese language is distinct from most western languages in many properties,thus providing a unique opportunity for understanding the brain basis of human language and cognition.In recent years,non-invasive neuroimaging techniques such as magnetic resonance imaging(MRI)blaze a new trail to comprehensively study specific neural correlates of Chinese language processing and Chinese speakers.We reviewed the application of functional MRI(fMRI)in such studies and some essential findings on brain systems in processing Chinese.Specifically,for example,the application of task fMRI and resting-state fMRI in observing the process of reading and writing the logographic characters and producing or listening to the tonal speech.Elementary cognitive neuroscience and several potential research directions around brain and Chinese language were discussed,which may be informative for future research.

Photoelectrocatalytic principles for meaningfully studying photocatalyst properties and photocatalysis processes:From fundamental theory to environmental applications

Photocatalysis is critically important for environmental remediation and renewable energy technologies.The ability to objectively characterize photocatalyst properties and photocatalysis processes is paramount for meaningful performance evaluation and fundamental studies to guide the design and development of high-performance photocatalysts and photocatalysis systems.Photocatalysis is essentially an electron transfer process,and photoelectrocatalysis(PEC)principles can be used to directly quantify transferred electrons to determine the intrinsic properties of photocatalysts and photocatalysis processes in isolation,without interference from counter reactions due to physically separated oxidation and reduction half-reactions.In this review,we discuss emphatically the PEC-based principles for characterizing intrinsic properties of photocatalysts and important processes of photocatalysis,with a particular focus on their environmental applications in the degradation of pollutants,disinfection,and detection of chemical oxygen demand(COD).An outlook towards the potential applications of PEC technique is given.

Improved RF power performance of InAlN/GaN HEMT by optimizing rapid thermal annealing process for high-performance low-voltage terminal applications

Improved radio-frequency(RF)power performance of InAlN/GaN high electron mobility transistor(HEMT)is achieved by optimizing the rapid thermal annealing(RTA)process for high-performance low-voltage terminal applications.By optimizing the RTA temperature and time,the optimal annealing condition is found to enable low parasitic resistance and thus a high-performance device.Besides,compared with the non-optimized RTA HEMT,the optimized one demonstrates smoother ohmic metal surface morphology and better heterojunction quality including the less degraded heterojunction sheet resistance and clearer heterojunction interfaces as well as negligible material out-diffusion from the barrier to the channel and buffer.Benefiting from the lowered parasitic resistance,improved maximum output current density of 2279 mA·mm^(-1)and higher peak extrinsic transconductance of 526 mS·mm^(-1)are obtained for the optimized RTA HEMT.In addition,due to the superior heterojunction quality,the optimized HEMT shows reduced off-state leakage current of 7×10^(-3)mA·mm^(-1)and suppressed current collapse of only 4%,compared with those of 1×10^(-1)mA·mm^(-1)and 15%for the non-optimized one.At 8 GHz and V_(DS)of 6 V,a significantly improved power-added efficiency of 62%and output power density of 0.71 W·mm^(-1)are achieved for the optimized HEMT,as the result of the improvement in output current,knee voltage,off-state leakage current,and current collapse,which reveals the tremendous advantage of the optimized RTA HEMT in high-performance low-voltage terminal applications.

Application and evaluation of layering shear method in LADCP data processing

The current velocity observation of LADCP(Lowered Acoustic Doppler Current Profiler)has the advantages of a large vertical range of observation and high operability compared with traditional current measurement methods,and is being widely used in the field of ocean observation.Shear and inverse methods are now commonly used by the international marine community to process LADCP data and calculate ocean current profiles.The two methods have their advantages and shortcomings.The shear method calculates the value of current shear more accurately,while the accuracy in an absolute value of the current is lower.The inverse method calculates the absolute value of the current velocity more accurately,but the current shear is less accurate.Based on the shear method,this paper proposes a layering shear method to calculate the current velocity profile by“layering averaging”,and proposes corresponding current calculation methods according to the different types of problems in several field observation data from the western Pacific,forming an independent LADCP data processing system.The comparison results have shown that the layering shear method can achieve the same effect as the inverse method in the calculation of the absolute value of current velocity,while retaining the advantages of the shear method in the calculation of a value of the current shear.

Application of GIS based analytical hierarchy process and multicriteria decision analysis methods to identify groundwater potential zones in Jedeb Watershed, Ethiopia

The hydrogeological situation of the study area requires the identification of groundwater potential.Remote sensing and satellite data have proven to be reliable tools for understanding various factors that affect groundwater occurrence and movement.This study employed weighted overlay analysis based on satellite imagery and secondary data to create a thematic map for characterizing groundwater potentials in the study area located within Abbay Basin,Ethiopia.Remote sensing(RS)and GIS-based Fuzzy-Analytical Hierarchy Process methods were utilized to classify groundwater potential(GWP)zones into five categories:Very good,good,moderate,poor,and very poor.The central and eastern parts of the study area were identified as having high(33.186%)and very high(2.351%)groundwater potentials,while the western part exhibited poor and very poor potential areas.The groundwater potential map delineated higher and moderate potentials,suitable for installing shallow and production bores.This research demonstrates the effectiveness of RS and GIS techniques for delineating groundwater potential zones,which can aid in the planning and management of groundwater resources.The research findings have the potential to contribute to the formulation of improved groundwater management programs in the study area.

Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

With the rapid development of sports,the number of sports images has increased dramatically.Intelligent and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry.In this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various environments.At the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put forward.The mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.

Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

A Review of the Application of Process Evaluation in Junior High School English Teaching under “Double Reduction”

The “Double Reduction” policy is not only to reduce the excessive learning burden of students, but also to improve the quality of students’ learning and to promote their overall development. In order to achieve the goal of the “Double Reduction” policy, it is necessary to focus on the implementation of measures to strengthen process evaluation as proposed in the “General Plan for Deepening Education Evaluation Reform in a New Era”. Therefore, the article will analyze the current situation of the research from three aspects: the connotation of “Double Reduction” and process evaluation, process evaluation under “Double Reduction” and process evaluation in English teaching, and look forward to the future development trend, with the aim of implementing the “Double Reduction” policy and giving full play to process evaluation. The aim is to implement the policy of “Double Reduction” and give full play to the role of process evaluation, so as to effectively guide the practice of English teaching.

Nitrogen application levels based on critical nitrogen absorption regulate processing tomatoes productivity, nitrogen uptake, nitrate distributions, and root growth in Xinjiang, China

The unreasonable nitrogen(N)supply and low productivity are the main factors restricting the sustainable development of processing tomatoes.In addition,the mechanism by which the N application strategy affects root growth and nitrate distributions in processing tomatoes remains unclear.In this study,we applied four N application levels to a field(including 0(N0),200(N200),300(N300),and 400(N400)kg/hm^(2))based on the critical N absorption ratio at each growth stage(planting stage to flowering stage:22%;fruit setting stage:24%;red ripening stage:45%;and maturity stage:9%).The results indicated that N300 treatment significantly improved the aboveground dry matter(DM),yield,N uptake,and nitrogen use efficiency(NUE),while N400 treatment increased nitrate nitrogen(NO_(3)^(-)-N)residue in the 20–60 cm soil layer.Temporal variations of total root dry weight(TRDW)and total root length(TRL)showed a single-peak curve.Overall,N300 treatment improved the secondary root parameter of TRDW,while N400 treatment improved the secondary root parameter of TRL.The grey correlation coefficients indicated that root dry weight density(RDWD)in the surface soil(0–20 cm)had the strongest relationship with yield,whereas root length density(RLD)in the middle soil(20–40 cm)had a strong relationship with yield.The path model indicated that N uptake is a crucial factor affecting aboveground DM,TRDW,and yield.The above results indicate that N application levels based on critical N absorption improve the production of processing tomatoes by regulating N uptake and root distribution.Furthermore,the results of this study provide a theoretical basis for precise N management.