Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

Creep constitutive model considering nonlinear creep degradation of fractured rock

Stability analysis of underground constructions requires a model study of rock masses’ long-term performance. Creep tests under different stress conditions was conducted on intact granite and granite samples fractured at 30° and 45° angles. The experimental results indicate that the steady creep strain rates of intact and fractured rock present an exponential increase trend with the increase of stress level. A nonlinear creep model is developed based on the experimental results, in which the initial damage caused by fracture together with the damage caused by constant load have been taken into consideration. The fitting analysis results indicated that the model proposed is more accurate at identifying the full creep regions in fractured granite, especially the accelerated stage of creep deformation. The least-square fit error of the proposed creep model is significantly lower than that of Nishihara model by almost an order of magnitude. An analysis of the effects of elastic modulus, viscosity coefficient, and damage factors on fractured rock strain rate and creep strain is conducted. If no consideration is given to the effects of the damage, the proposed nonlinear creep model can degenerate into to the classical Nishihara model.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

Unmanned Aerial Vehicles General Aerial Person-Vehicle Recognition Based on Improved YOLOv8s Algorithm

Considering the variations in imaging sizes of the unmanned aerial vehicles(UAV)at different aerial photography heights,as well as the influence of factors such as light and weather,which can result in missed detection and false detection of the model,this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s(YOLOv8s)algorithm used in natural light and infrared scenes(L_YOLO).The algorithm proposes a special feature pyramid network(SFPN)structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module(SDCN).Moreover,the model undergoes pruning to eliminate redundant channels.Finally,the non-maximum suppression algorithm of intersection-union ratio based on minimum point distance(MPDIOU_NMS)algorithm has been integrated to eliminate redundant detection boxes,and a comprehensive validation has been conducted using the infrared aerial dataset and the Visdrone2019 dataset.The comprehensive experimental results demonstrate that when the number of parameters and floating-point operations is reduced by 30%and 20%,respectively,there is a 1.2%increase in mean average precision at a threshold of 0.5(mAP(0.5))and a 4.8%increase in mAP(0.5:0.95)on the infrared dataset.Finally,the mAP on the Visdrone2019 dataset has experienced an average increase of 12.4%.The accuracy and recall rates have seen respective increases of 9.2%and 3.6%.

Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin,China

Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".

The effects of population aging on industrial structure upgrading:Empirical analysis of provincial and threshold characteristics in China

Global population aging trends are intensifying,presenting multifaceted economic and social challenges for countries worldwide.As the world’s largest developing country,China has entered a phase of extreme demographic aging,posing significant questions about its impact on the ongoing upgrading of industrial structures.How does this demographic shift influence the upgrading of industrial structures,and does technological innovation mitigate or exacerbate this impact?The empirical results indicate that population aging impedes upgrading the industrial structure,while technological innovation positively affects the relationship between the two.Moreover,using technological innovation as a threshold variable,the impact of population aging on industrial structure upgrading evolves in a“gradient”manner from“impediment”to“insignificant”to“promotion”as the technological innovation levels increase.These findings offer practical guidance for tailoring industrial policies to different stages of technological advancement.

Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

An Investigation into the Issues Concerning the Copyright of Content Generated by Text-to-Video AI

Text-to-video artificial intelligence(AI)is a new product that has arisen from the continuous development of digital technology over recent years.The emergence of various text-to-video AI models,including Sora,is driving the proliferation of content generated through concrete imagery.However,the content generated by text-to-video AI raises significant issues such as unclear work identification,ambiguous copyright ownership,and widespread copyright infringement.These issues can hinder the development of text-to-video AI in the creative fields and impede the prosperity of China’s social and cultural arts.Therefore,this paper proposes three recommendations within a legal framework:(a)categorizing the content generated by text-to-video AI as audiovisual works;(b)clarifying the copyright ownership model for text-to-video AI works;(c)reasonably delineating the responsibilities of the parties who are involved in the text-to-video AI works.The aim is to mitigate the copyright risks associated with content generated by text-to-video AI and to promote the healthy development of text-to-video AI in the creative fields.

Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe

The diagnostic of poloidal magnetic field(B_(p))in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its highβ,is a huge challenge because B_(p)is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP)has been proven to diagnose the B_(p)profile in FRCs recently,whereas the existing iterative reconstruction approach cannot handle the measurement errors well.In this work,the machine learning approach,a fast-growing and powerful technology in automation and control,is applied to B_(p)reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach.The machine learning approach achieves a more accurate reconstruction of B_(p)profile when 20%detector errors are considered,15%B_(p)fluctuation is introduced and the size of the detector is remarkably reduced.Therefore,machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.

Types,composition and diagenetic evolution of authigenic clay minerals in argillaceous limestone of sepiolite-bearing strata:A case study of Mao-1 Member of Middle Permian Maokou Formation,eastern Sichuan Basin,SW China

The types,occurrence and composition of authigenic clay minerals in argillaceous limestone of sepiolite-bearing strata of the first member of the Middle Permian Maokou Formation(Mao-1 Member)in eastern Sichuan Basin were investigated through outcrop section measurement,core observation,thin section identification,argon ion polishing,X-ray diffraction,scanning electron microscope,energy spectrum analysis and laser ablation-inductively coupled plasma-mass spectrometry.The diagenetic evolution sequence of clay minerals was clarified,and the sedimentary-diagenetic evolution model of clay minerals was established.The results show that authigenic sepiolite minerals were precipitated in the Si4+and Mg2+-rich cool aragonite sea and sepiolite-bearing strata were formed in the Mao-1 Member.During burial diagenesis,authigenic clay minerals undergo two possible evolution sequences.First,from the early diagenetic stage A to the middle diagenetic stage A1,the sepiolite kept stable in the shallow-buried environment lack of Al3+.It began to transform into stevensite in the middle diagenetic stage A2,and then evolved into disordered talc in the middle diagenetic stage B1and finally into talc in the period from the middle diagenetic stage B2to the late diagenetic stage.Thus,the primary diagenetic evolution sequence of authigenic clay minerals,i.e.sepiolite-stevensite-disordered talc-talc,was formed in the Mao-1 Member.Second,in the early diagenetic stage A,as Al3+carried by the storm and upwelling currents was involved in the diagenetic process,trace of sepiolite started to evolve into smectite,and a part of smectite turned into chlorite.From the early diagenetic stage B to the middle diagenesis stage A1,a part of smectite evolved to illite/smectite mixed layer(I/S).The I/S evolved initially into illite from the middle diagenesis stage A2to the middle diagenesis stage B2,and then totally into illite in the late diagenesis stage.Thus,the secondary diagenetic evolution sequence of authigenic clay minerals,i.e.sepiolite-smectite-chlorite/illite,was formed in the Mao-1 Member.The types and evolution of authigenic clay minerals in argillaceous limestone of sepiolite-bearing strata are significant for petroleum geology in two aspects.First,sepiolite can adsorb and accumulate a large amount of organic matters,thereby effectively improving the quality and hydrocarbon generation potential of the source rocks of the Mao-1 Member.Second,the evolution from sepiolite to talc is accompanied by the formation of numerous organic matter pores and clay shrinkage pores/fractures,as well as the releasing of the Mg2+-rich diagenetic fluid,which allows for the dolomitization of limestone within or around the sag.As a result,the new assemblages of self-generation and self-accumulation,and lower/side source and upper/lateral reservoir,are created in the Middle Permian,enhancing the hydrocarbon accumulation efficiency.

Mechanical response and dilatancy characteristics of deep marble under different stress paths:A sight from energy dissipation

Dilatancy is a fundamental volumetric growth behavior observed during loading and serves as a key index to comprehending the intricate nonlinear behavior and constitutive equation structure of rock.This study focuses on Jinping marble obtained from the Jinping Underground Laboratory in China at a depth of 2400 m.Various uniaxial and triaxial tests at different strain rates,along with constant confining pressure tests and reduced confining pressure tests under different confining pressures were conducted to analyze the mechanical response and dilatancy characteristics of the marble under four stress paths.Subsequently,a new empirical dilatancy coefficient is proposed based on the energy dissipation method.The results show that brittle failure characteristics of marble under uniaxial compression are more obvious with the strain rate increasing,and plastic failure characteristics of marble under triaxial compression are gradually strengthened.Furthermore,compared to the constant confining pressure,the volume expansion is relatively lower under unloading condition.The energy dissipation is closely linked to the process of dilatancy,with a rapid increase of dissipated energy coinciding with the beginning of dilatancy.A new empirical dilatancy coefficient is defined according to the change trend of energy dissipation rate curve,of which change trend is consistent with the actual dilatancy response in marble under different stress paths.The existing empirical and theoretical dilatancy models are analyzed,which shows that the empirical dilatancy coefficient based on the energy background is more universal.

Depositional model of the Member Deng-2 marginal microbial mound-bank complex of the Dengying Formation in the southwestern Sichuan Basin, SW China: Implications for the Ediacaran microbial mound construction and hydrocarbon exploration

Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China,where the depositional process is regarded confusing. The microfacies, construction types, and depositional model of the Member Deng-2 marginal microbial mound-bank complex have been investigated using unmanned aerial vehicle photography, outcrop section investigation, thin section identification,and seismic reflections in the southwestern Sichuan Basin. The microbialite lithologic textures in this region include thrombolite, dendrolite, stromatolite, fenestral stromatolite, spongiostromata stone,oncolite, aggregated grainstone, and botryoidal grapestone. Based on the comprehensive analysis of“depositional fabrics-lithology-microfacies”, an association between a fore mound, mound framework,and back mound subfacies has been proposed based on water depth, current direction, energy level and lithologic assemblages. The microfacies of the mound base, mound core, mound flank, mound cap, and mound flat could be recognized among the mound framework subfacies. Two construction types of marginal microbial mound-bank complex have been determined based on deposition location, mound scale, migration direction, and sedimentary facies association. Type Jinkouhe microbial mound constructions(TJMMCs) develop along the windward margin owing to their proximity to the seaward subfacies fore mound, with a northeastwardly migrated microbial mound on top of the mud mound,exhibiting the characteristics of large-sized mounds and small-sized banks in the surrounding area. Type E'bian microbial mound constructions(TEMMCs) primarily occur on the leeward margin, resulting from the presence of onshore back mound subfacies, with the smaller southwestward migrated microbial mounds existing on a thicker microbial flat. The platform margin microbial mound depositional model can be correlated with certain lateral comparison profile and seismic reflection structures in the 2D seismic section, which can provide references for future worldwide exploration. Microbial mounds with larger buildups and thicker vertical reservoirs are typically targeted on the windward margin, while small-sized microbial mounds and flats with better lateral connections are typically focused on the leeward margin.

A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.

The Innovative Expression of Traditional Chinese Cultural Archetypes in Animated Films: Establishing a Reliable, Admirable, and Respectable Image of China

China’s domestic animation industry is deeply rooted in its rich traditional cultural heritage.While continuously exploring and showcasing the unique charm of such cultural heritage through storytelling,imagery establishment,and spirit creation,Chinese animation also seamlessly integrates modern aesthetic characteristics and cultural values into its development within the context of the new era.By adopting a contemporary perspective,it innovatively expresses the essence of traditional culture while striving to shape and present a reliable,admirable and respectable image of China.Chinese animation aims to create credible contemporary animation images by drawing inspiration from traditional Chinese cultural archetypes.It also seeks to revitalize admirable traditional cultural imagery using vibrant and prevailing ACGN visuals,while shaping credible characters with richer cultural connotations and crafting stories with more enchanting plots to convey the spiritual essence of the Chinese nation.By establishing reliable,admirable and respectable animation images,the Chinese animation industry strives to enhance its capability to“tell China’s stories well and make the voice of China heard,”to better promote the image of China in the new era from a global perspective.

Effect of Types and Orders of Electromagnetic Field Finite Element Meshes on Power Communication Harmonic Parameters Calculation Results of Tubular Hydrogenerators

In generator design field,waveform total harmonic distortion(THD)and telephone harmonic factor(THF)are parameters commonly used to measure the impact of generator no-load voltage harmonics on the power communication quality.Tubular hydrogenerators are considered the optimal generator for exploiting low-head,high-flow hydro resources,and they have seen increasingly widespread application in China's power systems recent years.However,owing to the compact and constrained internal space of such generators,their internal magnetic-field harmonics are pronounced.Therefore,accurate calculation of their THD and THF is crucial during the analysis and design stages to ensure the quality of power communication.Especially in the electromagnetic field finite element modeling analysis of such generators,the type and order of the finite element meshes may have a significant impact on the THD and THF calculation results,which warrants in-depth research.To address this,this study takes a real 34 MW large tubular hydrogenerator as an example,and establishes its electromagnetic field finite element model under no-load conditions.Two types of meshes,five mesh densities,and two mesh orders are analyzed to reveal the effect of electromagnetic field finite element mesh types and orders on the calculation results of THD and THF for such generators.

Thermal transport in composition graded silicene/germanene heterostructures

Through equilibrium and non-equilibrium molecular dynamics simulations,we have demonstrated the inhibitory effect of composition graded interface on thermal transport behavior in lateral heterostructures.Specifically,we investigated the influence of composition gradient length and heterogeneous particles at the silicene/germanene(SIL/GER)heterostructure interface on heat conduction.Our results indicate that composition graded interface at the interface diminishes the thermal conductivity of the heterostructure,with a further reduction observed as the length increases,while the effect of the heterogeneous particles can be considered negligible.To unveil the influence of composition graded interface on thermal transport,we conducted phonon analysis and identified the presence of phonon localization within the interface composition graded region.Through these analyses,we have determined that the decrease in thermal conductivity is correlated with phonon localization within the heterostructure,where a stronger degree of phonon localization signifies poorer thermal conductivity in the material.Our research findings not only contribute to understanding the impact of interface gradient-induced phonon localization on thermal transport but also offer insights into the modulation of thermal conductivity in heterostructures.

MBTI Assessment as Social Currency:A New Digital Cultural Phenomenon in the Meta-media Era

The MBTI assessment has become a hot topic online and a common method for netizens to identify themselves across China’s social platforms.These netizens generally consider the results of their MBTI assessments as personal emblems suitable for public display,leading to a remarkably widespread cultural phenomenon.Through structured interviews,this study has found that Internet users are experiencing a psychological crisis of identity due to the dual influences of technological embodiment and the meta-media ecology revolution.Moreover,their behavior on social platforms can be regarded as an invisible form of affective labor.To date,researchers have not paid adequate attention to the immense popularity of MBTI assessment in China,and they have not fully investigated whether MBTI assessment is a symbolic representation of the current identity crisis in the context of meta-media and digital labor.This study has two purposes:First,this study aims to explore whether the dynamic interactions of the MBTI assessment are a form of social currency.Second,it aims to construct an MBTI topic production chain to help understand MBTI’s underlying mechanisms regarding the interactions,the reasons behind its popularity,and the reflection on broader social phenomena.

Hydraulic fracturing behaviors of shale under coupled stress and temperature conditions simulating different burial depths

Fracture propagation in shale under in situ conditions is a critical but poorly understood mechanical process in hydraulic fracturing for deep shale gas reservoirs. To address this, hydraulic fracturing experiments were conducted on hollow double-wing crack specimens of the Longmaxi shale under conditions simulating the ground surface(confining pressure σ_(cp)=0, room temperature(Tr)) and at depths of 1600 m(σ_(cp)=40 MPa, Ti=70 ℃) and 3300 m(σ_(cp)=80 MPa, high temperature Ti=110 ℃) in the study area.High in situ stress was found to significantly increase fracture toughness through constrained microcracking and particle frictional bridging mechanisms. Increasing the temperature enhances rather than weakens the fracture resistance because it increases the grain debonding length, which dissipates more plastic energy and enlarges grains to close microdefects and generate compressive stress to inhibit microcracking. Interestingly, the fracture toughness anisotropy in the shale was found to be nearly constant across burial depths, despite reported variations with increasing confining pressure. Heated water was not found to be as important as the in situ environment in influencing shale fracture. These findings emphasize the need to test the fracture toughness of deep shales under coupled in situ stress and temperature conditions rather than focusing on either in situ stress or temperature alone.

Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

Mechanical behaviors of mylonitic granite and granitic protomylonite in a deep ductile shear zone

For projects near the tectonic belt,mylonite of varying metamorphic degrees may be present.The matrix proportion of rock reflects its internal microscopic characteristics,thus it is beneficial for engineering geology to study the effect of the matrix proportion on the mechanical properties and rupture behaviors of rock.Samples of mylonitic granite and granitic protomylonite with varying matrix proportions were obtained from a ductile shear zone for a series of uniaxial compression and acoustic emission(AE)tests.The results showed that with the increase in matrix proportion,the average strength and elastic modulus of the samples increased,and the rock sample with the largest matrix proportion exhibited the maximum peak stress of 244.42 MPa,which was 45.86%greater than the average peak stress of the rock samples with the smallest matrix proportions.For the rock samples with larger matrix proportion,their mechanical parameters exhibited greater dispersion and the large-scale appearance of AE events occurred earlier,showing a relatively gradual failure process.These samples had larger accumulated AE parameter values and greater degree of failure.In contrast,for samples with smaller matrix proportions,the large-scale appearance of AE events occurred close to the peak stress,indicating that the occurrence of damage and fractures was centralized and instantaneous.These samples had lower accumulated AE parameter values and fewer cracks after failure.Additionally,for the rock samples with more matrix proportion,the average variance of the b-value was 1.1,which was lower than that of rock samples with the smallest matrix proportion(the average variance of the b-value was 3.7).Furthermore,it can be predicted that under certain stress,the failure depth around a tunnel is generally smaller when the strength of rock samples with larger matrix proportion is greater.