Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Evaluation of frictional pressure drop correlations for air-water and air-oil two-phase flow in pipeline-riser system

Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to pipeline-riser flow needs evaluation since the flow condition in pipeline-riser is quite different from the original data where they were derived from. In the present study, a comprehensive evaluation of 24prevailing correlation in predicting frictional pressure drop is carried out based on experimentally measured data of air-water and air-oil two-phase flows in pipeline-riser. Experiments are performed in a system having different configuration of pipeline-riser with the inclination of the downcomer varied from-2°to-5°to investigated the effect of the elbow on the frictional pressure drop in the riser. The inlet gas velocity ranges from 0.03 to 6.2 m/s, and liquid velocity varies from 0.02 to 1.3 m/s. A total of885 experimental data points including 782 on air-water flows and 103 on air-oil flows are obtained and used to access the prediction ability of the correlations. Comparison of the predicted results with the measured data indicate that a majority of the investigated correlations under-predict the pressure drop on severe slugging. The result of this study highlights the requirement of new method considering the effect of pipe layout on the frictional pressure drop.

Dynamics of Non-Markovianity, Quantum Correlations and Information Scrambling of Three Qubits Systems Interacting via Rashba Interaction

The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate between their upper and lower bounds, where the number of oscillations increases as the Rashba interaction strength increases. The exchanging rate of these three quantities depends on the Rashba strength, and whether the entangled state is generated via direct/indirect interaction. Moreover, the coherence parameter can be used as a control parameter to maximize or minimize the three physical quantities.

Correlation Study of Operational Data and System Performance of District Cooling System with Ice Storage

The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R2) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .

Correlation between preoperative systemic immune inflammation index, nutritional risk index, and prognosis of radical resection of liver cancer

BACKGROUND Radical surgery is the most commonly used treatment for hepatocellular carcinoma(HCC).However,the surgical effect remains not ideal,and prognostic evaluation is insufficient.Furthermore,clinical intervention is rife with uncertainty and not conducive to prolonging patient survival.AIM To explore correlations between the systemic immune inflammatory index(SII)and geriatric nutritional risk index(GNRI)and HCC operation prognosis.METHODS This retrospective study included and collected follow up data from 100 HCC.Kaplan–Meier survival curves were used to analyze the correlation between SII and GNRI scores and survival.SII and GNRI were calculated as follows:SII=neutrophil count×platelet count/lymphocyte count;GNRI=[1.489×albumin(g/L)+41.7×actual weight/ideal weight].We analyzed the predictive efficacy of the SII and GNRI in HCC patients using receiver operating characteristic(ROC)curves,and the relationships between the SII,GNRI,and survival rate using Kaplan–Meier survival curves.Cox regression analysis was utilized to analyze independent risk factors influencing prognosis.RESULTS After 1 year of follow-up,24 patients died and 76 survived.The area under the curve(AUC),sensitivity,specificity,and the optimal cutoff value of SII were 0.728(95%confidence interval:0.600-0.856),79.2%,63.2%,and 309.14,respectively.According to ROC curve analysis results for predicting postoperative death in HCC patients,the AUC of SII and GNRI combination was higher than that of SII or GNRI alone,and SII was higher than that of GNRI(P<0.05).The proportion of advanced differentiated tumors,tumor maximum diameter(5–10 cm,>10 cm),lymph node metastasis,and TNM stage III-IV in patients with SII>309.14 was higher than that in patients with SII≤309.14(P<0.05).The proportion of patients aged>70 years was higher in patients with GNRI≤98 than that in patients with GNRI>98(P<0.05).The 1-year survival rate of the SII>309.14 group(compared with the SII≤309.14 group)and GNRI≤98 group(compared with the GNRI>98 group)was lower(P<0.05).CONCLUSION The prognosis after radical resection of HCC is related to the SII and GNRI and poor in high SII or low GNRI patients.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Over-limit risk assessment method of integrated energy system considering source-load correlation

In an integrated energy system,source-load multiple uncertainties and correlations lead to an over-limit risk in operating state,including voltage,temperature,and pressure over-limit.Therefore,efficient probabilistic energy flow calculation methods and risk assessment theories applicable to integrated energy systems are crucial.This study proposed a probabilistic energy flow calculation method based on polynomial chaos expansion for an electric-heat-gas integrated energy system.The method accurately and efficiently calculated the over-limit probability of the system state variables,considering the coupling conditions of electricity,heat,and gas,as well as uncertainties and correlations in renewable energy unit outputs and multiple types of loads.To further evaluate and quantify the impact of uncertainty factors on the over-limit risk,a global sensitivity analysis method for the integrated energy system based on the analysis of covariance theory is proposed.This method considered the source-load correlation and aimed to identify the key uncertainty factors that influence stable operation.Simulation results demonstrated that the proposed method achieved accuracy to that of the Monte Carlo method while significantly reducing calculation time.It effectively quantified the over-limit risk under the presence of multiple source-load uncertainties.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Variability and Correlation in Biomarkers of Exposure from Two Randomized Controlled Studies of JUUL Electronic Nicotine Delivery Systems

The data included in this analysis were from two clinical studies (Study A and Study B), which evaluated JUUL electronic nicotine delivery systems (ENDS) against combustible cigarettes. In both studies, biomarkers of exposure including nicotine equivalents, NNAL, 3-HPMA, MHBMA, S-PMA and COHb were measured. Coefficients of variation (CV) of the biomarkers were calculated and compared. Pearson correlation analysis was used to examine the correlation between the biomarkers. Seven out of the nine biomarkers of exposure in Study A were highly variable (CV > 30%). Higher variability was observed in NNAL, MHBMA and S-PMA than in other biomarkers. After adult cigarette smokers switched from combustible cigarettes to JUUL ENDS, the correlation between nicotine equivalents and other biomarkers became weaker. A similar trend was observed between NNAL and other biomarkers. In Study B, the participants in the 5% ENDS group had higher nicotine equivalent levels than those in the 3% ENDS group. The higher nicotine levels did not result in a substantial increase in the levels of other biomarkers (except 1-OHP). The correlations between nicotine equivalents and 3-HPMA, MHBMA, S-PMA, COHb, HMPMA, and 1-OHP were weak in both the 5% and 3% ENDS groups.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

Repeatability,interocular correlation and agreement of optic nerve head vessel density in healthy eyes:a sweptsource optical coherence tomographic angiography study

AIM:To assess the repeatability,interocular correlation,and agreement of quantitative swept-source optical coherence tomography angiography(OCTA)optic nerve head(ONH)parameters in healthy subjects.METHODS:Thir ty-three healthy subjects were enrolled.The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm×3 mm scanning protocol.Images of the radial peripapillary capillary were analyzed by a customized Matlab program,and the vessel density,fractal dimension,and vessel diameter index were measured.The repeatability of the four scans was determined by the intraclass correlation coefficient(ICC).The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient,ICC and Bland-Altman plots.RESULTS:All swept-source-OCTA ONH parameters exhibited certain repeatability,with ICC>0.760 and coefficient of variation(CoV)≤7.301%.The obvious interocular correlation was observed for papillary vessel density(ICC=0.857),vessel diameter index(ICC=0.857)and fractal dimension(ICC=0.906),while circumpapillary vessel density exhibited moderate interocular correlation(ICC=0.687).Bland-Altman plots revealed an agreement range of-5.26%to 6.21%for circumpapillary vessel density.CONCLUSION:OCTA ONH parameters demonstrate good repeatability in healthy subjects.The interocular correlations of papillary vessel density,fractal dimension and vessel diameter index are high,but the correlation for circumpapillary vessel density is moderate.

Intensity correlation properties of x-ray beams split with Laue diffraction

Beam splitting is one of the main approaches to achieving x-ray ghost imaging, and the intensity correlation between diffraction beam and transmission beam will directly affect the imaging quality. In this paper, we investigate the intensity correlation between the split x-ray beams by Laue diffraction of stress-free crystal. The analysis based on the dynamical theory of x-ray diffraction indicates that the spatial resolution of diffraction image and transmission image are reduced due to the position shift of the exit beam. In the experimental setup, a stress-free crystal with a thickness of hundredmicrometers-level is used for beam splitting. The crystal is in a non-dispersive configuration equipped with a double-crystal monochromator to ensure that the dimension of the diffraction beam and transmission beam are consistent. A correlation coefficient of 0.92 is achieved experimentally and the high signal-to-noise ratio of the x-ray ghost imaging is anticipated.Results of this paper demonstrate that the developed beam splitter of Laue crystal has the potential in the efficient data acquisition of x-ray ghost imaging.

An intelligent automatic correlation method of oilbearing strata based on pattern constraints:An example of accretionary stratigraphy of Shishen 100 block in Shinan Oilfield of Bohai Bay Basin,East China

Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Quantum correlations and entanglement in coupled optomechanical resonators with photon hopping via Gaussian interferometric power analysis

Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to differentiate between quantum entanglement and quantum correlation.Nonetheless,this indistinguishability is no longer holds for mixed states.To contribute to a better understanding of this differentiation,we have explored a simple model for both generating and measuring these quantum correlations.Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities,coupled through the photon hopping process.this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes.The key ingredient in analyzing quantum correlation in this system is the global covariance matrix.It forms the basis for computing two essential metrics:the logarithmic negativity(E_(N)^(m))and the Gaussian interferometric power(P_(G)^(m)).These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations,respectively.Our study reveals that the Gaussian interferometric power(P_(G)^(m))proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system,particularly in scenarios featuring resilient photon hopping.

Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area

The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.