Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

A Novel Hydrogen-bonded Three-dimensional Network Complex Containing Nickel

A novel complex, (H 3O) 2[Ni(2,6-pydc) 2]·2H 2O was synthesized in an aqueous solution and characterized by means of single-crystal X-ray diffraction, elemental analyses and IR spectra. The X-ray structural analysis revealed that the novel compound forms three-dimensional(3D) networks by both π-π stacking and hydrogen-bonding interactions. The crystal data for the complex are a=13.853(3) nm, b=9.6892(19) nm, c=13.732(3) nm, α=90.00°, β=115.52(3)°, γ=90.00°, Z=3, R 1=0.0786, wR 2=0.1522.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

A New Three-dimensional Network Constructed by Heptamolybdate, Sodium Ions and Hexamethylene Tetramine Cations via Hydrogen Bonds

The crystal structure of the title compound [Na2(OH2)5]2+[C6H12N4H2]2-2+ [Mo7O24]6 ?4H2O, prepared from an aqueous solution of Na2MoO4 ?2H2O in the presence of MoCl3 and hexamethylene tetramine, has been determined by single-crystal X-ray diffraction. The crystal is of orthorhombic, space group Pnma with a = 14.6113(2), b = 18.6833(1), c = 15.3712(2), V = 4196.14(8)3, Z = 4, Mr = 1548.13, F(000) = 3016, = 2.157 mm-1 and Dc = 2.451 g/cm3. The final R factor is 0.0526 for 3818 unique observed reflections (I > 2(I)). The structural analysis reveals that heptamolybdate anions in the title compound consist of seven edge-sharing MoO6 octahedra, and are linked into a three-dimensional framework by sodium ions and hydrogen bonds.

A New Eight-connected Three-dimensional Network Based on a Tetranuclear Zinc Cluster Building Block

One interesting coordination polymer, [Zn2（1,2,4-BTC）（OH）（H2O）2]2·2H2O 1, has been synthesized from 1,2,4-BTC （1,2,4-BTC = 1,2,4-bentricarboxylate） under hydrothermal conditions and characterized by elemental analyses, IR, TG and single-crystal X-ray diffraction. Complex I crystallizes in triclinic, space group P^-1, with a = 6.5200（13）, b = 9,0600（18）, c = 10.968（2） A^°, α = 111.55（3）, β = 92.07（3）,γ= 95.03（3）°, C9H10O10Zn2, Mr = 408.91, V= 598.7（2） A^°^3, Dc = 2.268 g/cm^3, F（000） = 408 and Z = 2. X-ray diffraction analysis reveals that complex 1 is a three-dimensional network built from tetranuclear Zn（Ⅱ） building unit. In this complex, the Zn4 unit is an eight-connected knot, while 1,2,4-BTC a four-connected knot. This results in a CaF2 topology. To the best of our knowledge, such Zn4 unit is the first 8-connected building block built from asymmetry ligand.

Polynomials of Degree-Based Indices for Three-Dimensional Mesh Network

In order to study the behavior and interconnection of network devices,graphs structures are used to formulate the properties in terms of mathematical models.Mesh network(meshnet)is a LAN topology in which devices are connected either directly or through some intermediate devices.These terminating and intermediate devices are considered as vertices of graph whereas wired or wireless connections among these devices are shown as edges of graph.Topological indices are used to reflect structural property of graphs in form of one real number.This structural invariant has revolutionized the field of chemistry to identify molecular descriptors of chemical compounds.These indices are extensively used for establishing relationships between the structure of nanotubes and their physico-chemical properties.In this paper a representation of sodium chloride(NaCl)is studied,because structure of NaCl is same as the Cartesian product of three paths of length exactly like a mesh network.In this way the general formula obtained in this paper can be used in chemistry as well as for any degree-based topological polynomials of three-dimensional mesh networks.

A Three-dimensional IP-based Telecom Metropolitan Area Network Model

The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves the existing problems but also brings new ideas for the development of next-generation MAN.

Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models

The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.

Three-dimensionally interconnected Co9S8/MWCNTs composite cathode host for lithium–sulfur batteries

Several challenging issues,such as the poor conductivity of sulfur,shuttle effects,large volume change of cathode,and the dendritic lithium in anode,have led to the low utilization of sulfur and hampered the commercialization of lithium–sulfur batteries.In this study,a novel three-dimensionally interconnected network structure comprising Co9 S8 and multiwalled carbon nanotubes(MWCNTs)was synthesized by a solvothermal route and used as the sulfur host.The assembled batteries delivered a specific capacity of1154 m Ah g-1 at 0.1 C,and the retention was 64%after 400 cycles at 0.5 C.The polar and catalytic Co9 S8 nanoparticles have a strong adsorbent effect for polysulfide,which can effectively reduce the shuttling effect.Meanwhile,the three-dimensionally interconnected CNT networks improve the overall conductivity and increase the contact with the electrolyte,thus enhancing the transport of electrons and Li ions.Polysulfide adsorption is greatly increased with the synergistic effect of polar Co9 S8 and MWCNTs in the three-dimensionally interconnected composites,which contributes to their promising performance for the lithium–sulfur batteries.

Synthesis and Crystal Structure of a Three-dimensional (3D) Complex Mn(H_2O)_2(HNic)_2 (HNic=2-Hydroxynicotinic Acid)

The title complex Mn（H2O）2（HNic）2 （C22H12MnN2O8, Mr = 367.18） crystallizes in monoclinic, space group P21/c with a = 7.5735（8）, b = 12.5295（13）, c = 7.6466（8）A.β = 101.2790（10）°, Z = 2, V= 711.59（13） A^3, D, = 1.714 g/cm^3,μ（MoKa） = 0.974 mm^-1, F（000） = 374, R1 （1255 observed reflections （Ⅰ 〉 2σ（Ⅰ）） = 0.0250） and wR2 = 0.0662 （all data）. In this paper, we report the complexation of Mn（Ⅱ） by the bidentate ligand 2-hydroxynicotinic acid （HNic）. In the crystal the Mn（Ⅱ） ion exhibits a deformed octahedron structure. The title complex Mn（H2O）2（HNic）2 has a three-dimensional （3D） network structure extended by hydrogen bonds, which are formed by two typical eight-membered hydrogen-bonded rings.

Hydrothermal Synthesis and Crystal Structure of a Three-dimensional Compound {Mn(H_2O)_4(VO)_2(PO_4)_2}_n

The title compound, {Mn(H2O)4(VO)2(PO4)2}n 1, was synthesized by the hydro- thermal reaction of Mn(OAc)2, Na2VO3 and H3PO4 in aqueous solution and its crystal structure was determined by X-ray single-crystal analysis. Crystallographic data for 1: H4MnO14P2V2, tetragonal system, space group I4/mmm, a = 6.251(3), c = 13.410(9) ?, Mr = 446.79, V = 524.0(5) ?3, Z = 2, F(000) = 434, μ = 3.320 mm-1, Dc = 2.832 g/cm3, the final R = 0.0577 for 163 observed reflections (I > 2σ(I)). X-ray crystal structure analysis shows that the vanadium phosphorous oxide layers are further connected by MnII(H2O)4 cations to form a three-dimensional network.

Three-dimensional histology:new visual approaches to morphological changes during neural regeneration

Three-dimensional（3D） histology utilizes tissue clearing techniques to turn intact tissues transparent,allowing rapid interrogation of tissue architecture in three dimensions.In this article,we summarized the available tissue clearing methods and classified them according to their physicochemical principles of operation,which provided a framework for one to choose the best techniques for various research settings.Recent attempts in addressing various questions regarding the degenerating and regenerating nervous system have been promising with the use of 3D histological techniques.

Constructal design of a rectangular parallel phase change microchannel in a three-dimensional electronic device

Based on constructal theory,a rectangular parallel phase change microchannel model in a three-dimensional electronic device(TDED)is established with R134a as the cooling fluid.Based on the minimization of a complex function(CF)composed of linear weighting sum of maximum temperature difference and pumping power consumption,constructal design of the TDED is conducted first;and then,maximum temperature difference and pumping power consumption are minimized by non-dominated sorting genetic algorithm-II methods.The results reveal that there exist an optimal mass flow rate(0.0012 kg/s)and a quadratic optimal aspect ratio(AR)(0.39)of the microchannel which lead to quadratic minimum CF(0.817).Compared with the original value,the CF after optimization is reduced by 18.34%.Reducing the inlet temperature of cooling fluid and microchannel number appropriately can help to enhance the overall performance of TDED.By using the artificial neural network and genetic algorithms in the toolboxes of Matlab software,the optimal AR gained in the Pareto solution set is located between 0.2–0.45.The smallest deviation index among three discussed strategies is 0.346,and the corresponding optimal AR is 0.413,which is selected as the optimal design strategy of the microchannel in the TDED under multiple requirements.The findings in this study can serve as theoretical guides for thermal designs of electronic devices.

Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks

The hybrid electric propulsion system(HEPS)holds clear potential to support the goal of sustainability in the automobile and aviation industry.As an important part of the three-dimensional transportation network,vehicles and aircraft using HEPSs have the advantages of high fuel economy,low emission,and low noise.To fulfill these advantages,the design of their energy management strategies(EMSs)is essential.This paper presents an in-depth review of EMSs for hybrid electric vehicles(HEVs)and hybrid electric aircraft.First,in view of the main challenges of current EMSs of HEVs,the referenced research is reviewed according to the solutions facing real-time implementation problems,variable driving conditions adaptability problems,and multi-objective optimization problems,respectively.Second,the existing research on the EMSs for hybrid electric aircraft is summarized according to the hybrid electric propulsion architectures.In addition,with the advance in propulsion technology and mechanical manufacturing in recent years,flying cars have gradually become a reality,further enriching the composition of the three-dimensional transportation network.And EMSs also play an essential role in the efficient operation of flying cars driven by HEPSs.Therefore,in the last part of this paper,the development status of flying cars and their future prospects are elaborated.By comprehensively summarizing the EMSs of HEPS for vehicles and aircraft,this review aims to provide guidance for the research on the EMSs for flying cars driven by HEPS and serve as the basis for knowledge transfer of relevant researchers.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

Behavior recognition algorithm based on the improved R3D and LSTM network fusion

Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.

An improved micro-expression recognition algorithm of 3D convolutional neural network

The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.

Construction of three-dimensional hierarchical porous nitrogen-doped re duce d graphene oxide/hollow cobalt ferrite composite aerogels toward highly efficient electromagnetic wave absorption

The development of graphene-based composites with low density,robust absorption,wide bandwidth and thin thickness remained a great challenge in the field of electromagnetic(EM)absorption.In this work,nitrogen-doped reduced graphene oxide/hollow cobalt ferrite(NRGO/hollow CoFe_(2)O_(4))composite aerogels were constructed by a solvothermal and hydrothermal two-step route.Results demonstrated that the as-fabricated composite aerogels had the ultralow density and a unique three-dimensional(3D)network structure,and lots of hollow CoFe_(2)O_(4)microspheres were almost homogeneously distributed on the wrinkled surfaces of lamellar NRGO.Moreover,superior EM absorbing capacity could be achieved by modulating the ferrite structure,addition amounts of hollow CoFe_(2)O_(4)and thicknesses.It was noteworthy that the NRGO/hollow CoFe_(2)O_(4)composite aerogel with the addition amount of ferrite of 15.0 mg pos-sessed the minimum reflection loss of-44.7 dB and maximum absorption bandwidth of 5.2 GHz(from 12.6 to 17.8 GHz)at a very thin thickness of 1.8 mm and filling ratio of 15.0 wt.%.Furthermore,the possible EM attenuation mechanism had been proposed.The results of this work would be helpful for developing RGO-based 3D composites as lightweight,thin and highly efficient EM wave absorbers.

3DMKDR:3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

Depression has become a major health threat around the world,especially for older people,so the effective detection method for depression is a great public health challenge.Electroencephalogram(EEG)can be used as a biomarker to effectively explore depression recognition.Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel,this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition(3DMKDR),which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals.A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix.By the major depressive disorder(MDD)and the multi-modal open dataset for mental-disorder analysis(MODMA)datasets,the experiment shows that the accuracies of depression recognition are up to99.86%and 98.01%in the subject-dependent experiment,and 95.80%and 82.27%in the subjectindependent experiment,which are higher than alternative competitive methods.The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.

Three-dimensional boron nitride network/polyvinyl alcohol composite hydrogel with solid-liquid interpenetrating heat conduction network for thermal management

Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power devices is limited.To solve this problem,melamine sponge(MS)was used as the skeleton to wrap boron nitride nanosheets(BNNS)through repeated layering assembly,successfully preparing a three-dimensional(3D)boron nitride network(BNNS@MS),and PVA hydrogels were formed in the pores of the network.Due to the existence of the continuous phonon conduction network,the BNNS@MS/PVA exhibited an improvedκ.When the content of BNNS is about 6 wt.%,κof the hydrogel was increased to 1.12 W m^(-1)K^(-1),about two times higher than that of pure hydrogel.The solid heat conduction network and liquid convection network cooperate to achieve good thermal management ability.Combined with its high specific heat capacity,the composites have an important application prospect in the field of wearable flexible electronic thermal management.