NCCMF:Non-Collaborative Continuous Monitoring Framework for Container-Based Cloud Runtime Status

The security performance of cloud services is a key factor influencing users’selection of Cloud Service Providers(CSPs).Continuous monitoring of the security status of cloud services is critical.However,existing research lacks a practical framework for such ongoing monitoring.To address this gap,this paper proposes the first NonCollaborative Container-Based Cloud Service Operation State Continuous Monitoring Framework(NCCMF),based on relevant standards.NCCMF operates without the CSP’s collaboration by:1)establishing a scalable supervisory index system through the identification of security responsibilities for each role,and 2)designing a Continuous Metrics Supervision Protocol(CMA)to automate the negotiation of supervisory metrics.The framework also outlines the supervision process for cloud services across different deployment models.Experimental results demonstrate that NCCMF effectively monitors the operational state of two real-world IoT(Internet of Things)cloud services,with an average supervision error of less than 15%.

Large Eddy Simulation of Vertical Structure and Size Density of Deep Layer Clouds

In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of-1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection,but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.

The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

基于CloudSim的电力系统计算云原生模型与仿真方法

分析基于云计算的电力系统计算过程是实现资源高效利用的基础。因此,提出一种基于CloudSim的电力系统计算的云原生建模与仿真方法。基于云原生技术阐述了电力系统计算架构的关键要素,提出基于CloudSim的电力系统计算架构的云原生模型,分析电力云任务调配时的资源供需平衡问题。建立基于微服务的电力云任务模型,分析微服务的处理时序逻辑及其层级关系并生成微服务队列。据此提出微服务的动态调配模型,采用微服务-容器映射策略与实体动态整合策略,实现微服务在实体间的动态映射。利用CloudSim进行仿真分析,结果证明所提方法可行且有效,可为电力云计算的资源配置、任务调度等提供基本的仿真工具与方法。

Calculating the Excitation Temperature for H_2CO Absorption Lines in Molecular Clouds

The excitation temperature T_(ex)for molecular emission and absorption lines is an essential parameter for interpreting the molecular environment.This temperature can be obtained by observing multiple molecular transitions or hyperfine structures of a single transition,but it remains unknown for a single transition without hyperfine structure lines.Earlier H_(2)CO absorption experiments for a single transition without hyperfine structures adopted a constant value of T_(ex),which is not correct for molecular regions with active star formation and H II regions.For H_(2)CO,two equations with two unknowns may be used to determine the excitation temperature T_(ex)and the optical depthτ,if other parameters can be determined from measurements.Published observational data of the4.83 GHz(λ=6 cm)H_(2)CO(1_(10)-1_(11))absorption line for three star formation regions,W40,M17 and DR17,have been used to verify this method.The distributions of T_(ex)in these sources are in good agreement with the contours of the H110αemission of the H II regions in M17 and DR17 and with the H_(2)CO(1_(10)-1_(11))absorption in W40.The distributions of T_(ex)in the three sources indicate that there can be significant variation in the excitation temperature across star formation and H II regions and that the use of a fixed(low)value results in misinterpretation.

Research on Trust Model in Container-Based Cloud Service

Container virtual technology aims to provide program independence and resource sharing.The container enables flexible cloud service.Compared with traditional virtualization,traditional virtual machines have difficulty in resource and expense requirements.The container technology has the advantages of smaller size,faster migration,lower resource overhead,and higher utilization.Within container-based cloud environment,services can adopt multi-target nodes.This paper reports research results to improve the traditional trust model with consideration of cooperation effects.Cooperation trust means that in a container-based cloud environment,services can be divided into multiple containers for different container nodes.When multiple target nodes work for one service at the same time,these nodes are in a cooperation state.When multi-target nodes cooperate to complete the service,the target nodes evaluate each other.The calculation of cooperation trust evaluation is used to update the degree of comprehensive trust.Experimental simulation results show that the cooperation trust evaluation can help solving the trust problem in the container-based cloud environment and can improve the success rate of following cooperation.

Optical Extinctions of Inter-Arm Molecular Clouds in M31:A Pilot Study for the Upcoming CSST Observations

Recent submillimeter dust thermal emission observations have unveiled a significant number of inter-arm massive molecular clouds in M31.However,the effectiveness of this technique is limited to its sensitivity,making it challenging to study more distant galaxies.This study introduces an alternative approach,utilizing optical extinctions derived from space-based telescopes,with a focus on the forthcoming China Space Station Telescope(CSST).We first demonstrate the capability of this method by constructing dust extinction maps for 17 inter-arm massive molecular clouds in M31 using the Panchromatic Hubble Andromeda Treasury data.Our analysis reveals that inter-arm massive molecular clouds with an optical extinction(A_(V)) greater than 1.6 mag exhibit a notable A_(V) excess,facilitating their identification.The majority of these inter-arm massive molecular clouds show an A_(V) around 1 mag,aligning with measurements from our JCMT data.Further validation using a mock CSST RGB star catalog confirms the method's effectiveness.We show that the derived A_(V)values using CSST z and y photometries align more closely with the input values.Molecular clouds with A_(V)> 1.6 mag can also be identified using the CSST mock data.We thus claim that future CSST observation clouds provide an effective way for the detection of inter-arm massive molecular clouds with significant optical extinction in nearby galaxies.

Estimating wood quality attributes from dense airborne LiDAR point clouds

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.

What is the Role of Gravity,Turbulence and Magnetic Fields in High-mass Star Formation Clouds?

To explore the potential role of gravity,turbulence and magnetic fields in high-mass star formation in molecular clouds,this study revisits the velocity dispersion–size(σ–L)and density–size(ρ–L)scalings and the associated turbulent energy spectrum using an extensive data sample.The sample includes various hierarchical density structures in high-mass star formation clouds,across scales of 0.01–100 pc.We observeσ∝L^(0.26)andρ∝L^(-1.54)scalings,converging toward a virial equilibrium state.A nearly flat virial parameter–mass(α_(vir)-M)distribution is seen across all density scales,withα_(vir)values centered around unity,suggesting a global equilibrium maintained by the interplay between gravity and turbulence across multiple scales.Our turbulent energy spectrum(E(k))analysis,based on theσ–L andρ–L scalings,yields a characteristic E(k)∝k^(-1.52).These findings indicate the potential significance of gravity,turbulence,and possibly magnetic fields in regulating dynamics of molecular clouds and high-mass star formation therein.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

On Electron Clouds and Light

The wave equation of the electron, recently improved, allows physics to obtain all the quantum numbers and other results explaining the hydrogen spectrum. The Pauli exclusion principle then gives the description of electron clouds used in chemistry. The relativistic wave equation is associated with a Lagrangian density, thus also with an energy-momentum tensorial density. The wave of an electron cloud adds these energy-momentum densities, while photons in light are precisely those differences between such energy-momentum densities.

A review of road 3D modeling based on light detection and ranging point clouds

Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of road scenes is crucial for reference in asset management,construction,and maintenance.Light detection and ranging(Li DAR)technology is increasingly employed to generate high-quality point clouds for road inventory.In this paper,we specifically investigate the use of Li DAR data for road 3D modeling.The purpose of this review is to provide references about the existing work on the road 3D modeling based on Li DAR point clouds,critically discuss them,and provide challenges for further study.Besides,we introduce modeling standards for roads and discuss the components,types,and distinctions of various Li DAR measurement systems.Then,we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction.Furthermore,we systematically introduce point cloud-based 3D modeling methods,namely,parametric modeling and surface reconstruction.Parameters and rules are used to define model components based on geometric and non-geometric information,whereas surface modeling is conducted through individual faces within its geometry.Finally,we discuss and summarize future research directions in this field.This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on Li DAR point clouds.

美国CLOUD法案数据跨境执法中的安全风险与中国的应对

数据是数字经济时代重要的国家战略资源,数据跨境流动亦是其中的重要一环。囿于犯罪活动呈现数字化和跨境化态势,数据跨境执法成为打击网络犯罪的重要手段,但基于执法目的的数据出境对数据存储国的影响重大。美国凭借“数据自由”话语体系通过CLOUD法案推出以“数据控制者标准”为核心的数据管辖模式,进而依托网络服务提供者实施“长臂管辖”,使得我国数据被动出境和被调取而引发的国家数据安全风险大幅提升。我国应将数据主权作为数据跨境流动的法理基础,探索控制数据安全风险的制度工具,包括基于国家主权调适“数据控制者”标准模式,完善数据跨境调取安全规则和审查机制,以阻断法限制单边数据跨境执法,对美国的相关“长臂管辖”进行有效制衡。

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

基于CloudSat-CALIPSO数据的黄土高原地区云特征分析

云是地气系统的重要组成部分,为深入分析黄土高原地区云特征,利用2007-2016年搭载首部云探测雷达云卫星(CloudSat)与云-气溶胶激光雷达和红外探测者观测卫星(The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO)资料,选取黄土高原半湿润、半干旱、干旱和寒旱四个区域,对云的宏、微观物理特征进行了分析。结果表明:(1)黄土高原各区域云出现频率年均值达到了55%以上,其中,春、夏季云出现频率最高,秋冬两季相对较低;半湿润区云出现频率高于其他区域,但其他三个区域云出现频率最高的月份均早于半湿润区。(2)各区域中单层云出现频率最高,占到总云量的60%以上,多层云中主要是双层云,约占总云量的25%。云层高度在不同区域表现为春、夏季节大于秋、冬季节,半湿润区的云层高度在四季均大于其他区域。各区域云几何厚度季节变化不显著,均在1~4 km之间,主要以薄云为主,且78.13%的云几何厚度不超过2 km。(3)各区域的云液态水含量年均值均达到了220.5 mg·m^(-3),约为冰水含量年均值的6.5倍,主要分布在8.5 km以下的高度层。随着高度的减小,液态水含量逐渐增多,其中半湿润区云液态水含量大于其他区域。各区域全年冰水含量占比较小,主要分布在16.5 km以下的高度层。(4)液滴有效半径在各区域的值主要集中在12~16μm,在半干旱区的春季出现了最大值,约为24μm;冰粒子有效半径最大值出现在半湿润区的夏季。液滴数浓度在各区域的值集中在60~80 cm^(-3),均小于冰粒子数浓度平均值,其峰值出现在各区域的夏季,冰粒子数浓度的峰值出现在半湿润和半干旱区的春季。该研究结果有助于深入认识黄土高原云的特征,为区域气候模式对黄土高原地区云特征的模拟提供一定的参考依据。

利用CloudSat资料分析青藏高原、高原南坡及南亚季风区云高度的统计特征量

云与辐射的相互作用对全球的天气和气候变化过程有着重要的影响,不同高度的云有着不同的辐射强迫,获得云体高度及其在时空上的变化对研究全球气候的变化有着重要意义。本文利用云卫星上的云廓线雷达(CloudSat/CPR)2006年6月—2007年12月期间的资料,对比分析了青藏高原、高原南坡和南亚季风区域不同云类的云顶、云底高度和云厚统计量。结果表明,在所研究区域单位面积上的云顶和云底高度变化具有一定的时空连续性,不同云类的云顶和云底高度存在不同的变化范围,且随着季节的改变均有明显的变化;同时各区域不同云类的云体厚度在夏季较大,冬季较小;各区域不同云类所占的比例(云量)也具有一定的季节变化规律。

东亚地区云垂直结构的CloudSat卫星观测研究

本文利用卫星CloudSat同时结合了与其同轨道的卫星CALIPSO(Cloud-AerosolLidarandInfraredPathfinderSatelliteObservations)2007至2009年3年的观测资料,将东亚地区划分为六个研究区域,着重研究了东亚地区云垂直分布的统计特征。结果表明:东亚地区不同高度的云量之和具有明显的季节变化趋势,夏季最大,春秋次之,冬季最小。海洋上空的单层云量最大值出现在冬季,而在陆地上空则出现在夏季。从云出现概率来看,东亚地区单层云出现的概率在春、夏、秋、冬季节依次为52.2%,48.1%,49.2%和51.9%,而多层(2层和2层以上)云出现的概率在春、夏、秋、冬季节分别为24.2%,31.0%,19.7%,15.8%。云出现的总概率和多层云出现的概率,在六个区域都呈现出夏季最大,冬季最小;对4个季节都呈现出东亚南部比东亚北部大,海洋上空比陆地上空大的特点,表明云出现的总概率的季节变化主要由多层云出现的概率的变化决定。东亚地区云系统中最高层云云顶的高度,在夏季最高,为15.9km,在冬季最低,为8.2km;在东亚南部和海洋上空较高,平均为15.1km;在东亚北部较低,平均为12.1km,且呈现东亚南北部之间差异较大的特点。东亚地区云系统的云层厚度基本位于1km到3km之间,且夏季大,冬季小;对同一季节,不同区域的云层厚度差别较小;当多层云系统中的云层数目增加时,云层的平均厚度减少,且较高层的云层平均厚度大于较低层的。云层间距的概率分布基本呈单峰分布,出现峰值范围的云层间距在1到3km之间,各区域之间没有明显差别,季节变化也不大。本文的研究为在气候模式中精确描述云的垂直结构提供了有用的参数化依据。

东亚地区云微物理量分布特征的CloudSat卫星观测研究

本文利用2007～2010年整四年最新可利用的CloudSat卫星资料,对东亚地区（15°～60°N,70°～150°E）云的微物理量包括冰/液态水含量、冰/液态水路径、云滴数浓度和有效半径等的分布特征和季节变化进行了分析.本文将整个东亚地区划分为北方、南方、西北、青藏高原地区和东部海域五个子区域进行研究,结果显示：东亚地区冰水路径值的范围基本在700 g m-2以下,高值区分布在北纬40度以南区域,在南方地区夏季的平均值最大,为394.3 g m-2,而在西北地区冬季的平均值最小,为78.5 g m-2;而液态水路径的范围基本在600 g m-2以下,冬季在东部海域的值最大,达到300.8 g m-2,夏季最大值为281.5 g m-2,分布在南方地区上空.冰水含量的最高值为170 mg m-3,发生在8km附近,南方地区夏季的值达到最大,青藏高原地区的季节差异最大;而液态水含量在东亚地区的范围小于360 mg m-3,垂直廓线从10km向下基本呈现逐渐增大的趋势,峰值位于1～2 km高度上.冰云云滴数浓度在东亚地区的范围在150 L-1以下,水云云滴数浓度的值小于80 cm-3,垂直廓线的峰值均在夏季最大.冰云有效半径在东亚地区的最大值为90 μm,发生在5km左右;水云有效半径在东亚地区的值分布在10km以下,最大值为10～12 μm,基本位于1～2 km高度上.从概率分布函数来看,东亚地区冰/水云云滴数浓度的分布呈现明显的双峰型,其他量基本为单峰型.本文的结果可以为全球和区域气候模式在东亚地区对以上云微物理量的模拟提供一定的观测参考依据.

基于CloudSat卫星资料分析青藏高原东部夏季云的垂直结构

本文利用CloudSat卫星资料,对青藏高原东部2006-2010年6-8月云垂直结构的空间分布进行分析,结果表明：（1）夏季青藏高原东部云发展可达到平流层,且高原东部云在5km以下以水云存在,5-10km以液相和固相共存的混态存在,在垂直高度10km以上以冰云存在。由于CloudSat卫星资料云相的反演问题,可能会造成水云和混态云的发展上限偏低,冰云的发展下限抬升。（2）研究区整层水汽输送和云水平均路径空间分布存在一定的差异性,云水含量纬向分布表现为在26.5°-30.5°N附近存在一个明显的峰值区,经向分布表现为95°E以西云水含量低于以东。（3）研究区以单云层为主,尤其在青藏高原主体。单云层平均云层厚度4182 m,云顶高度、云厚限于水汽的输送,表现为由南向北波动下降。多层云发生频率在27°N以北明显减少,说明强烈的对流运动更容易激发多层云的产生。