Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

Challenges,Opportunities,and Future Research in the Integration of 5G/6G Networks,LEO Satellites,and IoT for Environmental Protection and Sustainable Development at ECSTAR

The rapid proliferation of connected IoT(Internet of Things)devices,along with the increasing demand for 5G mobile networks and ubiquitous high-speed connectivity,poses significant challenges in the telecommunications sector.To address these challenges,a comprehensive understanding of the integration of 5G/6G networks and LEO(Low Earth Orbit)satellite networks is required,forming the concept of“integrated networks”.Integration offers valuable advantages,including service continuity,wide-area coverage,and support for critical communications and emerging applications.This paper provides a high-level overview of the convergence of 5G/6G,LEO satellites,and IoT devices,shedding light on the technological challenges and standardization issues associated with the transition from 5G to 6G networks using NTNs(Non-Terrestrial Networks)based on LEO satellites.Furthermore,this research delves into the emerging social issues,potential possibilities,and the paradigm shift from the IoT to the IoI(Internet of Intelligence),which is poised to revolutionize the landscape of 6G wireless networks.By highlighting the interconnectedness of 5G/6G networks,LEO satellite systems,and IoT devices,it underscores the importance of leveraging these converging technologies to address environmental protection and achieve the United Nations SDGs(Sustainable Development Goals).In addition to providing valuable insights for readers seeking to comprehend the convergence of 5G/6G networks,LEO satellite systems,and IoT devices,this paper represents the outcomes of a comprehensive analysis conducted at the ECSTAR(Excellence Center of Space Technology and Research).Through an examination of technological challenges and advancements,it identifies future research directions and potential avenues for exploration at ECSTAR,thereby contributing to a broader understanding of integrated networks and their profound impact on future telecommunications systems.This research serves as a significant resource for advancing the knowledge and discourse surrounding the linkages between the convergence of these technologies,environmental protection,and the pursuit of the SDGs.

Analysis and Optimization on Partition-Based Caching and Delivery in Satellite-Terrestrial Edge Computing Networks

As a viable component of 6G wireless communication architecture,satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks.In the paper,we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load.Focusing on the interference effect from varied nodes in different geographical distances,we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry.Considering the constraint of nodes cache space and file sets parameters,we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user.The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization(PSO)method and greedy based multiple knapsack choice problem(MKCP)optimization method.Numerical results show that compared with the terrestrial only cache strategy,Ground Popular Strategy,Satellite Popular Strategy,and Independent and identically distributed popularity strategy,the performance of the proposed scheme improve by 30.5%,9.3%,12.5%and 13.7%.

System Integration of Terrestrial Mobile Communication and Satellite Communication——The Trends, Challenges and Key Technologies in B5G and 6G

Mobile communication standards have been developed into a new era of B5G and 6G.In recent years,low earth orbit(LEO)satellites and space Internet have become hot topics.The integrated satellite and terrestrial systems have been widely discussed by industries and academics,and even are expected to be applied in those huge constellations in construction.This paper points out the trends of two stages towards system integration of the terrestrial mobile communication and the satellite communications:to be compatible with 5G,and to be integrated within 6G.Based on analysis of the challenges of both stages,key technologies are thereafter analyzed in detail,covering both air interface currently discussed in 3GPP for B5G and also novel network architecture and related transmission technologies toward future 6G.

Performance Analysis of the Packet-Based PNT Service in NGSO Broadband Satellite Communication Systems

Providing alternative PNT service to GNSS-challenged users will be an important function of next-generation NGSO broadband satellite communication systems.Herein,a packet-based PNT service architecture in NGSO broadband systems is proposed in which a primary satellite and selected assistant satellites work together to provide PNT service to requesting users.Its positioning performance bounds are mathematically formulated by rigorously analyzing the bounds constrained by different waveforms.Simulations are conducted on different configurations of Walker Delta MEO constellations and Walker Star LEO constellations for corroboration,revealing the following:(1)Both MEO and LEO constellations achieve sub-meter-level positioning precision given enough satellites.(2)Compared to the GNSS Doppler-based velocity estimation method,the position advance based velocity estimation algorithm is more precise and applicable to the PNT service in NGSO broadband systems.(3)To provide PNT service to users in GNSS-challenged environments,the primary and each assistant satellite need only∼0.1‰of the time of one downlink beam.

基于GF-6/WFV卫星遥感的大田冬小麦叶片氮素含量估测

为对大田冬小麦叶片氮素含量(LNC)进行快速、准确及无损监测,通过在江苏省泰州泰兴市、盐城大丰区和南通如皋市布设冬小麦遥感监测大田试验,在获取试验样点冬小麦冠层红光波段反射率(REDref)、近红外波段反射率(NIRref)和计算的十个光谱指数(RVI、NDVI、DVI、SAVI、OSAVI、MSR、RDVI、EVI2、NLI和SVI)基础上,将12个遥感光谱指标与冬小麦LNC进行相关分析,选出与LNC相关性较好的作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型,并利用GF-6/WFV卫星遥感影像对县域冬小麦LNC的空间分布开展监测。结果表明,12个遥感光谱指标与冬小麦LNC之间存在不同程度的相关性,其中NDVI、RVI、MSR、OSAVI和NLI与冬小麦LNC的相关性较好(相关系数不低于0.65)。将优选的5个遥感光谱指标作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型(LNC-BPEM),模型的估测精度r^(2)=0.866,RMSE=0.246%,ARE=12.9%。将冬小麦LNC-BPEM估测模型和GF-6/WFV影像结合对县域冬小麦LNC的空间信息监测,获得了如皋县域冬小麦LNC的空间分布特征,该区域冬小麦LNC范围在0.9%~2.0%(长势正常)的种植面积为29 693.3 hm^(2),占冬小麦总种植面积的74%。这说明利用GF-6/WFV卫星的多个遥感光谱指标与神经网络结合建模可有效估测县域大田冬小麦叶片氮素含量。

6G Service Coverage with Mega Satellite Constellations

The rapid development and continuous updating of the mega satellite constellation(MSC)have brought new visions for the future 6G coverage extension, where the global seamless signal coverage can realize ubiquitous services for user terminals. However, global traffic demands present nonuniform characteristics. Therefore, how to ensure the on-demand service coverage for the specific traffic demand, i.e., the ratio of traffic density to service requirement per unit area, is the core issue of 6G wireless coverage extension exploiting the MSC. To this regard, this paper first discusses the open challenges to reveal the future direction of 6G wireless coverage extension from the perspective of key factors affecting service coverage performance, i.e., the network access capacity, space segment capacity and their matchingrelationship. Furthermore, we elaborate on the key factors affecting effective matchings of the aforementioned aspects, thereby improving service coverage capability.

Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget.In this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6)against recent satellite retrievals.The CMIP6 models capture some aspects of the observed snow depth climatology and variability.The observed variability lies in the middle of the models’simulations.All the models show negative trends of snow depth during 1993-2014.However,substantial spatiotemporal discrepancies are identified.Compared to the observation,most models have late seasonal maximum snow depth(by two months),remarkably thinner snow for the seasonal minimum,an incorrect transition from the growth to decay period,and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice.Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic.Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099.Under the SSP5-8.5 scenario,the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January.Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution,the inclusion of a hightop atmospheric model,and biogeochemistry processes are important factors for snow depth simulation.

Geophysical Modeling with Satellite Gravity Data: Eigen-6C4 vs. GGM Plus

Satellite data sets are an asset in global gravity collections;their characteristics vary in coverage and resolution. New collections appear often, and the user must adapt fast to their characteristics. Their use in geophysical modeling is rapidly increasing;with this in mind we compare two of the most densely populated sets: EIGEN-6C4 and GGMplus. We characterize them in terms of their frequency histograms, Free Air anomalies, power spectrum, and simple Bouguer anomalies. The nature of the digital elevation models used for data reduction is discussed. We conclude that the GGMplus data set offers a better spatial resolution. To evaluate their effect in geophysical modelling, we chose an inland region with a prominent volcanic structure in which we perform 3D inversions of the respective Bouguer anomalies, obtaining density variations that in principle can be associated with the geologic materials and the structure of the volcanic edifice. Model results are analyzed along sections of the inverted data;we conclude that the GGMplus data set offers higher resolution in the cases analyzed.

Double-Edge Intelligent Integrated Satellite Terrestrial Networks

The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.

Evaluation of high-resolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China

Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing technique precipitation product （CMORPH）, were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation： 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 （with 10%, 50%, and -5% biases for 3B42V6, 3B42RT, and CMORPH, respectively）. Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.

Glacier mapping based on Chinese high-resolution remote sensing GF-1 satellite and topographic data

The precise glacier boundary is a fundamental requirement for glacier inventory,the assessment of climate change and water management in remote mountain areas.However,some glaciers in mountain areas are covered by debris.The high spatial resolution images bring opportunities in mapping debris-covered glaciers.To discuss the capability of Chinese GaoFen-1 satellite lacking the short wave infrared band and thermal infrared band in mapping glaciers,this study distinguished supraglacial terrain from surrounding debris by combining GaoFen-1(GF-1)wide-field-view(WFV)images,the ratio of the thermal infrared imagery and morphometric parameters(DEM and slope)with 30 m resolution.The overall accuracy of 90.94%indicated that this method was effective for mapping supraglacial terrain in mountain areas.Comparing this result with the combination of GF-1 WFV and low-resolution morphometric parameters shows that a high-quality DEM and the thermal infrared band enhanced the accuracy of glacier mapping especially debris-covered ice in steep terrain.The user's and producer's accuracies of glacier area were also improved from 89.67%and 85.95%to 92.83%and 90.34%,respectively.GF data is recommended for mapping heavily debris-covered glaciers and will be combined with SAR data for future studies.

m^(6)A甲基化酶相关基因在牛骨骼肌生成中的表达

【目的】近年来,RNA m^(6)A甲基化修饰在肌肉发育中的作用不断被发现,通过探究m^(6)A甲基化酶相关基因,包括METTL3,METTL14,WTAP,FTO和ALKBH5,在牛肌肉组织以及骨骼肌卫星细胞(skeletal muscle satellite cells, SMSCs)增殖和分化过程中的表达,同时分析体外成肌分化过程中m^(6)A甲基化水平的变化,为阐明m^(6)A修饰在骨骼肌发育中的作用及机制提供参考。【方法】使用实时荧光定量PCR(RT-qPCR)技术检测m^(6)A甲基化酶相关基因在新生和成年牛不同部位骨骼肌中的表达。随后在秦川牛肉用新品系背最长肌中分离SMSCs,通过生长曲线、免疫荧光和RT-qPCR技术验证SMSCs的增殖和成肌分化功能,使用RT-qPCR检测m^(6)A甲基化酶相关基因在SMSCs增殖期24、36、48、60、72 h和分化第0、2、4、6、8天中的时序表达谱。最后利用LC-MS/MS和Dot blot技术检测SMSCs分化过程中m^(6)A甲基化水平的变化。【结果】METTL3、METTL14和WTAP等m^(6)A甲基化转移酶基因在成年牛背最长肌、前腿肌和后腿肌中的表达量均显著低于新生牛(P<0.01)。FTO和ALKBH5等m^(6)A去甲基化酶基因在成年牛后腿肌中的表达更高(P<0.01),ALKBH5在成年牛背最长肌中的表达也较高(P<0.01)。分离的SMSCs具有良好的生长状态且可正常成肌分化。在SMSCs增殖期,METTL3表达逐渐下降,但在增殖后期时明显提高。METTL14在增殖期的表达变化差异不明显,WTAP则在增殖48 h后表达逐渐降低。而FTO和ALKBH5在增殖期的时序表达类似,60 h之前变化不显著,但在72 h时显著提高。在SMSCs成肌分化过程中,METTL3、METTL14和WTAP的表达模式基本一致,分化前期上升,随后下降,在分化末期表达增加。而FTO的表达随分化进行逐渐增加,ALKBH5则在分化前4天表达上升,随后不断下降。此外,在SMSCs分化过程中,mRNA的整体m^(6)A水平下降(P<0.01)。【结论】m^(6)A甲基转移酶和去甲基化酶在新生牛和成年牛骨骼肌中的表达变化存在较为明显的差异,表明m^(6)A修饰可能对秦川牛骨骼肌的发育具有重要作用。同时,这些m^(6)A相关甲基化酶可能参与调控牛骨骼肌卫星细胞的增殖和分化。这一发现为研究m^(6)A甲基化修饰调控骨骼肌生成的作用及机制提供理论依据。

白细胞介素-6在小鼠肌肉损伤修复中的机制

目的观察白细胞介素-6基因敲除小鼠与野生型小鼠肌肉损伤后的恢复进程,探究白细胞介素-6在肌肉损伤修复中的机制。方法以8周龄野生型和白细胞介素-6基因敲除小鼠为材料,采用注射氯化钡的方式构建肌肉损伤及修复模型,在损伤过后的不同时间点检测血浆和组织中的脂质含量,并观察肌纤维的形态。结果在野生型和白细胞介素-6基因敲除小鼠中,肌肉损伤修复过程中标志性事件的发生发展无差异。结论白细胞介素-6在调节肌肉稳态和肌卫星细胞增殖中发挥了重要作用,但是本研究表明它在肌肉损伤修复中并不是必需的。

m^(6)A去甲基化酶FTO对猪肌卫星细胞分化的影响

【目的】探究N^(6)-甲基腺苷(m^(6)A)去甲基化酶FTO表达水平对猪肌卫星细胞分化的影响,并比较不同表型猪FTO表达和m^(6)A甲基化修饰水平。【方法】收集分化第0、2和4天的猪肌卫星细胞,用Western blotting和实时荧光定量PCR分别检测FTO和肌球蛋白重链(MyHC)蛋白表达、肌分化因子(MyoD)和肌细胞生成素(MyoG)的mRNA表达水平;利用免疫荧光法检测肌卫星细胞分化标志基因MyHC表达情况;采用Dot blotting检测m^(6)A甲基化修饰水平。将过表达载体(OE-FTO)、空白对照(NC)和FTO基因干扰载体(siRNA-FTO)、阴性对照(siRNA-NC)分别转染猪肌卫星细胞并诱导分化,检测FTO、肌细胞分化相关基因表达情况以及m^(6)A甲基化修饰水平,利用免疫荧光法检测MyHC表达以及肌管形成情况;利用Western blotting和Dot blotting分别检测大白猪、宁乡猪不同组织中FTO蛋白表达情况以及m^(6)A甲基化修饰水平。【结果】在猪肌卫星细胞诱导分化过程中,与诱导分化第0天相比,第2和4天时FTO、MyHC蛋白表达水平极显著升高(P<0.01),FTO、MyoD和MyoG基因mRNA表达水平显著或极显著升高(P<0.05;P<0.01);第4天时m^(6)A甲基化修饰水平显著下降(P<0.05)。与NC组相比,OE-FTO组FTO蛋白表达量极显著升高(P<0.01),在诱导分化的第0、1和2天,OE-FTO组FTO和MyHC基因mRNA表达水平均极显著升高(P<0.01),MyoD基因mRNA表达水平极显著下降(P<0.01);在诱导分化的第1、2、3和4天,OE-FTO组m^(6)A甲基化修饰水平显著或极显著下降(P<0.05;P<0.01)。与siRNA-NC组相比,siRNA-FTO组FTO蛋白表达水平显著降低(P<0.05),在诱导分化第0、1、2和3天,siRNA-FTO组FTO、MyHC基因mRNA表达水平极显著或显著降低(P<0.01;P<0.05),m^(6)A甲基化修饰水平显著或极显著升高(P<0.05;P<0.01);在诱导分化第0、2和3天,siRNA-FTO组MyoG基因mRNA表达水平显著降低(P<0.05)。大白猪背最长肌中FTO蛋白表达水平显著高于宁乡猪(P<0.05),m^(6)A甲基化修饰水平极显著低于宁乡猪(P<0.01)。【结论】FTO表达对猪肌卫星细胞分化过程有显著影响,m^(6)A甲基化修饰水平与猪肌卫星细胞分化程度呈负相关。干扰FTO会抑制猪肌卫星细胞分化,提高m^(6)A甲基化修饰水平;过表达FTO可以促进猪肌卫星细胞分化,降低m^(6)A甲基化修饰水平。本研究结果为进一步探究FTO在肌肉分化中调控机制提供参考。

Fitting of magnetic measurements of Swarm satellites based on 3D surface spline model

This study utilizes the 62208000 Swarm satellite data to establish a high-precision main magnetic field at the height of the satellites in China and its adjacent regions.The CHAOS-6 model is used to remove the crustal and external fields and obtain 2788 main field grid data.We use the main field grid data to build a three-dimensional(3D)surface spline(3DSS)model of the satellite altitude in China.Other regional models(namely the 3D,two-dimensional(2D)Taylor,and 2D surface spline models)and the CHAOS-6 model are employed to model and analyze the same region.The results show that the 3DSS model can represent a good fi tting for the northward(X)and eastward(Y)components and the total intensity(F).This model demonstrates the most stable results for the 20 points that did not take part in the modeling.Compared with the other three regional models,the root–mean–square error values and the average residuals of the new model are approximately 65%and 69%lower for each component,respectively.This study does not rely on ground station data to derive a more accurate regional main fi eld model.The results further show that less data height difference and high-density data distributions greatly improve the regional model accuracy.The new model has a certain application value to related space geophysics,such as in spatial positioning and navigation,and to the study of regional magnetic anomalies.

基于GF-6卫星的棉花种植面积提取研究——以山东省夏津县为例

国产高分卫星6号(简称GF-6)具有高分辨率、宽覆盖、高质量和高效成像等特点,可为农业资源遥感监测提供可靠的数据支撑。本研究以GF-6卫星影像为主要数据源,综合利用遥感技术理论和方法开展对夏津县棉花种植面积的提取研究,利用同时相的哨兵2号(简称Sentinel-2)影像解译结果进行对比分析。研究结果表明:基于GF-6卫星的夏津县棉花种植面积提取方案高效、准确,提取精度明显优于Sentinel-2卫星,进一步证明了新增了红边波段的GF-6卫星能够显著增强作物的识别能力,其在大规模作物种植信息提取方面具有广阔的应用潜力。

改进DeepLab v3+模型下的梯田遥感提取研究

[目的与意义]梯田作为农业生产的关键要素之一,其面积估算对于农业政策制定、土地规划和资源管理至关重要。为解决复杂的地形条件、种植环境导致传统遥感数据和监测方法难以开展梯田自动化提取问题,探索一种利用深度学习技术在高分辨率遥感影像中精准提取梯田面积的方法。[方法]以休耕期梯田高分六号影像构建语义分割数据集,同时提出一种改进的DeepLab v3+模型。该模型使用轻量级网络MobileNet v2作为骨干网络,为了同时兼顾局部细节和全局语境,使用多尺度特征融合(Multi-scale Feature Fusion module,MSFF)模块代替空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块,利用扩张率依次增大的空洞卷积级联模式改善信息丢失的问题。此外,对浅层特征和深层特征使用坐标注意力机制以加强网络对于目标的学习。[结果与讨论]利用红、绿和近红外波段组合方式在梯田提取的精度和效果上表现最佳。相比于原始DeepLab v3+网络,精确率、召回率、F_(1)评分和交并比指标分别提升4.62%、2.61%、3.81%和2.81%。此外,与UNet和原始DeepLab v3+相比,改进的DeepLab v3+在参数量上和浮点运算数有着更为优越的性能,其参数量仅为UNet的28.6%和原始DeepLab v3+的19.5%,同时浮点运算数仅为UNet和DeepLab v3+的1/5。这不仅提高了计算效率,也使得改进后的模型更适用于资源有限或计算能力较低的环境中。[结论]深度学习在高分辨率遥感影像梯田识别中具有较高的精度,有利于为梯田精细化监测和管理提供参考依据。

基于高分六号卫星观测数据监测太湖蓝藻水华研究

该文基于2018年和2019年的高分六号卫星数据,利用归一化差值植被指数实现太湖蓝藻水华的提取,验证红边波段对蓝藻水华的监测效果。结果表明,蓝藻水华在太湖的梅梁湾、湖心区北部和西太湖北部发生频次最多。但存在有代表性的最小整体和季节阈值范围,且整体阈值与夏季阈值相关性高;红边I波段(0.71波段)阈值范围的稳定性优于红边II波段(0.75波段)和近红外波段(0.83波段),且最小阈值范围呈现为正态分布。因此,高分卫星具有监测太湖蓝藻水华的潜力,在业务上运用高分卫星能够对太湖蓝藻水华的暴发作出快速监测。