Explore the vast range of titles available.

The development of satellite communications has received considerable attention in recent years. Early satellite communications were dominated by voice and low-speed data services, but now they must support high-speed multimedia services. Low Earth Orbit (LEO) satellites, because of their lower altitude orbits, have much smaller transmission loss and delay than Geostationary Earth Orbit (GEO) satellites, and they are an important part of the future realization of high-bandwidth and low-latency multimedia services. Among them, the on-demand streaming service has a large number of users in terrestrial communication and is also an important service component that will be in satellite communication environments in the future. However, LEO satellites face many challenges in handover and accessing due to their fast moving speed. Although many handover and access schemes for LEO satellites have been proposed and evaluated in existing studies, most of them stay at the level of quality of service (QoS), and few of them have been studied at the level of quality of experience (QoE). These studies also rarely consider the performance of multimedia services, including streaming services, in satellite communication environments, and there is no relevant simulation system to evaluate and examine them. Therefore, this paper builds a simulation system for streaming services in LEO satellite communication environments in order to simulate the initial buffering, rebuffering, and idle state of the users during service. Then, access and handover schemes for the QoE level of streaming service are proposed. Finally, our proposed scheme is evaluated based on this simulation system. From the simulation results, the simulation system proposed in this paper can successfully realize the various functions of users in on-demand streaming services and record the initial buffering and rebuffering events of users. And the streaming QoE-based access and handover scheme proposed in this paper can perform well in satellites, which operate within a resource-constrained environment.

In the integration of terrestrial and non‐terrestrial networks, massive access to wireless signals poses a significant threat to communication systems. However, existing signal recognition models cannot accurately identify signals with low signal‐to‐noise ratios (SNRs). To alleviate this issue, this paper proposes a high‐efficient information extraction mechanism based on complex convolution. Specifically, complex convolution and complex max‐pooling operations have been integrated into a real‐value network to efficiently acquire anti‐noise information. The extracted features contain both in‐phase and quadrature (IQ) structure information and complex relationship information. Experiments on the public RML2016.04C dataset indicated that the model exhibits rapid convergence and superior noise‐robustness under low SNRs. When SNR =0 dB, the model outperforms the suboptimal model by 4.05%. Furthermore, our model demonstrates excellent generalization performance at high SNRs. Considering most signal recognition models cannot accurately identify under low SNRs, this manuscript presents a high‐efficient information extraction mechanism based on complex convolution. Specifically, it innovatively integrates complex convolution and complex max‐pooling into a real network, combining the extraction of IQ structure information and complex relationship information. Experiments demonstrate that the model exhibits rapid convergence, superior generalization performance, and noise robustness.

Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID) data, making it challenging to balance data heterogeneity and privacy protection. To address this, we propose a privacy-preserving federated learning method based on cloud–edge–end collaboration. Our method fully considers the three-tier architecture of cloud–edge–end systems and the non-IID nature of terminal node data. It enhances model accuracy while protecting the privacy of terminal node data. The proposed method groups terminal nodes based on the similarity of their data distributions and constructs edge subnetworks for training in collaboration with edge nodes, thereby mitigating the negative impact of non-IID data. Furthermore, we enhance WGAN-GP with attention mechanism to generate balanced synthetic data while preserving key patterns from original datasets, reducing the adverse effects of non-IID data on global model accuracy while preserving data privacy. In addition, we introduce data resampling and loss function weighting strategies to mitigate model bias caused by imbalanced data distribution. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms existing approaches in terms of model accuracy, F1-score, and other metrics.

The Space-Air-Ground Integrated Network (SAGIN) has emerged as a core architecture for future intelligent communication due to its wide-area coverage and dynamic heterogeneous characteristics. However, its high latency, dynamic topology, and privacy–security challenges severely constrain the application of Federated Learning (FL). This paper proposes a Privacy-Preserving Federated Learning framework for SAGIN (PPFL-SAGIN), which for the first time integrates differential privacy, adaptive transfer learning, and bi-level reinforcement learning to systematically address data heterogeneity, device dynamics, and privacy leakage in SAGINs. Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global–local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. Finally, experimental evaluations validate the proposed method. Results demonstrate that PPFL-SAGIN significantly outperforms baseline solutions such as FedAvg, FedAsync, and FedAsyncISL in terms of model accuracy, convergence speed, and privacy protection strength, verifying its effectiveness in addressing privacy preservation, device selection, and global aggregation in SAGINs.

For mega satellite constellations, it has been a great challenge to achieve global routing and guarantee the performance of inter‐satellite transmission. To address the problem, a robust routing strategy based on deep reinforcement learning is proposed in this letter. The proposed method is applicable to degraded transmission performance, which exhibits better conformity to real‐world scenarios. Moreover, the age of information (AoI) of packets are utilized as one of the multi‐optimization targets to ensure the effectiveness of message transmission throughout the network. Numerical simulations show the outstanding average AoI performance of the proposed method and its greater robustness to jamming compared to existing methods. Meanwhile, it is also more effective for utilizing resources. In this letter, a robust routing strategy based on deep reinforcement learning (RRS‐DRL) is proposed for mega satellite constellations. The Age of Information of packets are regarded as an optimization target, and ensures the effectiveness of data collection and transmission throughout the network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on–off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on–off design.

The surface uplift history of the Tibetan Plateau and Himalaya is among the most interesting topics in geosciences because of its effect on regional and global climate during Cenozoic time, its influence on monsoon intensity, and its reflection of the dynamics of continental plateaus. Models of plateau growth vary in time, from pre-India-Asia collision (e.g., [almost equal to]100 Ma ago) to gradual uplift after the India-Asia collision (e.g., [almost equal to]55 Ma ago) and to more recent abrupt uplift (<7 Ma ago), and vary in space, from northward stepwise growth of topography to simultaneous surface uplift across the plateau. Here, we improve that understanding by presenting geologic and geophysical data from north-central Tibet, including magnetostratigraphy, sedimentology, paleocurrent measurements, and ⁴⁰Ar/³⁹Ar and fission-track studies, to show that the central plateau was elevated by 40 Ma ago. Regions south and north of the central plateau gained elevation significantly later. During Eocene time, the northern boundary of the protoplateau was in the region of the Tanggula Shan. Elevation gain started in pre-Eocene time in the Lhasa and Qiangtang terranes and expanded throughout the Neogene toward its present southern and northern margins in the Himalaya and Qilian Shan.

In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR) is low.

The subduction model of the Neo-Tethys during the Early Cretaceous has always been a controversial topic, and the scarcity of Early Cretaceous magmatic rocks in the southern part of the Gangdese batholith is the main cause of this debate. To address this issue, this article presents new zircon U–Pb chronology, zircon Hf isotope, whole-rock geochemistry and Sr–Nd isotope data for the Early Cretaceous quartz diorite dykes with adakite affinity in Liuqiong, Gongga. Zircon U–Pb dating of three samples yielded ages of c. 141–137 Ma, indicating that the Liuqiong quartz diorite was emplaced in the Early Cretaceous. The whole-rock geochemical analysis shows that the Liuqiong quartz diorite is enriched in large-ion lithophile elements (LILEs) and light rare-earth elements (LREEs) and is depleted in high-field-strength elements (HFSEs), which are related to slab subduction. Additionally, the Liuqiong quartz diorite has high SiO2, Al2O3 and Sr contents, high Sr/Y ratios and low heavy rare-earth element (HREE) and Y contents, which are compatible with typical adakite signatures. The initial 87Sr/86Sr values of the Liuqiong adakite range from 0.705617 to 0.705853, and the whole-rock ϵNd(t) values vary between +5.78 and +6.24. The zircon ϵ Hf(t) values vary from +11.5 to +16.4. Our results show that the Liuqiong adakite magma was derived from partial melting of the Neo-Tethyan oceanic plate (mid-ocean ridge basalt (MORB) + sediment + fluid), with some degree of subsequent peridotite interaction within the overlying mantle wedge. Combining regional data, we favour the interpretation that the Neo-Tethyan oceanic crust was subducted at a low angle beneath the Gangdese during the Early Cretaceous.

Climate evolution and hydrological conditions have been crucial factors for cultural development on the east coast of China. The Hangjiahu Plain, given its unique geographic and bioclimatic location, and plentiful Neolithic sites, is an ideal area in which to examine the relationship between environmental change and cultural development along the Lower Yangtze River. We report high-resolution pollen, algae, spore, charcoal, total organic carbon and grain size data from core BHQ2, collected from the Beihu Wetland on the Hangjiahu Plain. The record spans the period from 6300 to 400 cal yr BP. We infer that the climate shifted from warm and wet during the middle Holocene to dry in the late Holocene. Warm and stable hydrological conditions in the Beihu Wetland area during the intervals 6300–6000 and 5300–4250 cal yr BP were optimal for the development of the Majiabang and Liangzhu Cultures. Turbulent, brackish-water conditions in the Beihu Wetland area from 6000 to 5300 cal yr BP, however, led people of the Songze Culture to abandon the area. Collapse of the Neolithic culture and abandonment of the site at Liangzhu City could have been linked to climate drying in the interval 4250–3500 cal yr BP. Dry climate and the turbulent, saltwater environment that prevailed from 3800 to 3600 cal yr BP made conditions difficult for the Maqiao Culture. After 3600 cal yr BP, brackish water gradually receded and local rice agriculture expanded. Persistent warfare during the Warring States Period and the Qin-Han Dynasties may have been a response to colder climate from 2300 to 2000 cal yr BP. The decrease in monsoon precipitation in the Hangjiahu Plain region was influenced by summer solar radiation during the middle to late Holocene.