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Summary: Variation in leaf venation network architecture may reflect trade‐offs among multiple functions including efficiency, resilience, support, cost, and resistance to drought and herbivory. However, our knowledge about architecture‐function trade‐offs is mostly based on studies examining a small number of functional axes, so we still lack a more integrative picture of multidimensional trade‐offs.Here, we measured architecture and functional traits on 122 ferns and angiosperms species to describe how trade‐offs vary across phylogenetic groups and vein spatial scales (small, medium, and large vein width) and determine whether architecture traits at each scale have independent or integrated effects on each function.We found that generalized architecture‐function trade‐offs are weak. Architecture strongly predicts leaf support and damage resistance axes but weakly predicts efficiency and resilience axes. Architecture traits at different spatial scales contribute to different functional axes, allowing plants to independently modulate different functions by varying network properties at each scale.This independence of vein architecture traits within and across spatial scales may enable evolution of multiple alternative leaf network designs with similar functioning. [ABSTRACT FROM AUTHOR]

For many vision tasks, utilizing pre-trained features results in improved performance and consistently benefits from the rapid advancement of pre-training technologies. However, in the field of stereo matching, the use of pre-trained features has not been extensively researched. In this paper, we present the first systematical exploration into the utilization of pre-trained features for stereo matching. To provide flexible employment for any combination of pre-trained backbones and stereo matching networks, we develop the deformable neck (DN) that decouples the network architectures of these two components. The core idea of DN is to utilize the deformable attention mechanism to iteratively fuse pre-trained features from shallow to deep layers. Empirically, our exploration reveals the crucial factors that influence using pre-trained features for stereo matching. We further investigate the role of instance-level information of pre-trained features, demonstrating it benefits stereo matching while can be suppressed during convolution-based feature fusion. Built on the attention mechanism, the proposed DN module effectively utilizes the instance-level information in pre-trained features. Besides, we provide an understanding of the efficiency-accuracy tradeoff, concluding that using pre-trained features can also be a good alternative with efficiency consideration. [ABSTRACT FROM AUTHOR]

Channel pruning can reduce the number of neural network parameters and computational cost by eliminating redundant channels, its main purpose is to adapt to resource constrained devices. Evaluation-based global pruning and network search-based pruning are two common methods of channel pruning. However, the network architecture pruned by the global mask is often not optimal, while the method that directly searches for the optimal architecture will introduce a large number of hyperparameters, which greatly increases the training cost. In this paper, we propose a novel Two-dimensional information Entropy based Channel Pruning method (TECP). The pruning process consists of two steps. First, a global mask pruning scheme is employed to obtained a pre-pruning model. Then, the two-dimensional information entropy is calculated by using feature maps of dense network to adjust the pre-pruning model adaptively to get a compact network. Moreover, the entropy values are used to determine the minimum number of reserved channels per layer based on to avoid the imbalance of network architecture and the layer collapse caused by global pruning. Extensive experiments with a variety of networks on several datasets clearly demonstrate the effectiveness of our proposed TECP method. For example, results show that on CIFAR-10, the compressed model achieves comparable accuracy to the original model, but with a significantly lower number of parameters (44.29% for ResNet-20 and 46.79% for VGG-16). This is beneficial for industrial deployment. And experimental results also show that TECP method obtain the better performance compared with state-of-the-art method. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled edge computing combines mobile edge computing technology with UAV platform. It can provide timely and effective computing services for wireless devices by taking advantages of the flexible deployment and mobility of UAV. Starting with the network architecture of UAV-enabled edge computing, the technical architecture based on network function virtualization and software- defined networks is proposed. For the key technologies of UAV-enabled edge computing, different multiple access schemes in UAV-enabled edge computing network are compared. Furthermore, according to different optimization objectives, the computation offloading strategies based on classical non-convex optimization, game theory, and artificial intelligence are summarized and analyzed. Finally, the future research direction is discussed and prospected. [ABSTRACT FROM AUTHOR]

The paper initially provided a brief comparison of the direct-to-satellite solution of three LEO satellite communication companies: Starlink, AST SpaceMobile and Lynk Global.While Starlink has successfully conducted with SMS, Voice and Data service test, AST SpaceMobile and Lynk Global have not made significant progress.Although all are in early stages, their technological paths demonstrate the future trends of direct-to-satellite communication.Focused on Starlink's success, the paper analyzed its business model and explored the primary technological pathways for future integration of space, air and ground telecommunication systems, which was a hot topic in the industry.Based on publicly available information about Starlink, the paper utilized the business model canvas theory to delved into its direct-to-satellite service, detailing aspects like core value, key resources, customer segments, cost structure, customer relations, key partnership, and revenue streams.It then discussed Starlink's network architecture strategies, compared and analyzed the self-built core network and shared core network approaches, ultimately inferring the most viable network technology solution for Starlink.

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN. [ABSTRACT FROM AUTHOR]

As the global deployment of Fifth Generation (5G) is being well consolidated, the exploration of Sixth Generation (6G) wireless networks has intensified, focusing on novel Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) that extend beyond traditional metrics like throughput and latency. As 5G begins transitioning to vertical-oriented applications, 6G aims go beyond, providing a ubiquitous communication experience by integrating diverse Radio Access Networks (RANs) and fixed-access networks to form a hyper-converged edge. This unified platform will enable seamless network federation, thus realizing the so-called network of networks vision. Emphasizing energy efficiency, the present paper discusses the importance of reducing telecommunications' environmental impact, aligning with global sustainability goals. Central to this vision is the proposal of a novel user plane network protocol architecture, called 6G Recursive User Plane Architecture (6G-RUPA), designed to be scalable, flexible, and energy-efficient. Briefly, 6G-RUPA offers superior flexibility in network adaptation, federation, scalability, and mobility management, aiming to enhance overall network performance and sustainability. This study provides a comprehensive analysis of 6G's potential, from its conceptual framework to the high-level design of 6G-RUPA, addressing current challenges and proposing actionable solutions for next-generation mobile networks. [ABSTRACT FROM AUTHOR]

This research presents a model for designing the architecture and structure of participation of scientific elites in science and technology policy-making. To achieve this model, an interpretive approach using content analysis method has been employed. Based on the analyses conducted and the scientific elite's context in our country, it is concluded that this network should be layered and segmented. These layers and segments should be connected at various points. Formality and flexibility differ across different regions of the network, and communication tools and mechanisms are based on this formality and required functions. Based on another characteristic of the scientific elite context, depth, breadth, and quality of participation in the network should be controlled, and ultimately, achieving the network's goals requires discourse-building and understanding among its various components. [ABSTRACT FROM AUTHOR]

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To respond to the development requirements of Fuzhou Metropolitan Area, it is necessary to construct an efficient city (suburban) railway network that seamlessly integrates with other rail transit modes. The goal is to significantly enhance the spatial effect between the central urban area of Fuzhou and the surrounding clusters as well as neighboring suburban counties, aiming to reduce travel time within Fuzhou Metropolitan Area and meet the transportation needs for 1-hour commuting circle in the main layer of Fuzhou Metropolitan Area. Method Based on an exposition of the socio-economic conditions and the overview of rail transit in Fuzhou Metropolitan Area, and referring to the development experiences of metropolitan areas globally, the scale within Fuzhou Metropolitan Area is identified, which is divided into one main layer (including a 40 km commuting circle and a 70 km radiation circle) and four secondary layers. Employing a technical approach based on the ′functional hierarchy method′ and supported by the ′node anchoring method′ and ′point-line-surface method′, a planning scheme for city railway network in the main layer of Fuzhou Metropolitan Area is proposed. The effectiveness of this scheme is also evaluated. Result & Conclusion The proposed scheme holds significant supporting meaning for promoting new urbanization, expanding the development space of the central city, enlarging the radiation radius of urban rail transit, and enhancing the coordinated development level of the 1-hour commuting circle in Fuzhou Metropolitan Area.

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

The emerging concept of Intent-Based Networking has gained prominence as an innovative solution for autonomous network management. Representing a paradigm shift in network planning and management, the novelty of this approach has resulted in the absence of a network architecture that ideally guarantees the life cycle of intent. While important manufacturers and institutions have proposed architectures to facilitate this life cycle, challenges still persist to fully achieve this objective, largely due to the lack of understanding of the functionalities of Intent-Based Networks and the great difficulty present when implementing these functionalities based on the advances of current neural networks. This work proposes an architecture that resolves the deficiencies and limitations identified in previous architectures, ensuring the affective realization of the life cycle of the intention, integrating artificial intelligence tools and both physical and virtual network devices. This solution represents a significant step towards the establishment of Intent-Based Networks, as it facilitates the automation of management. [ABSTRACT FROM AUTHOR]

This essay makes a bibliographic review in the field of the seminal research of Organizational Networks, through a conceptual and instrumental analogy using an integration analysis of MaccInnis. In its development, seven different components of Organizational Networks and phenomena linked to Computer Networks were identified. By delimiting the specific configurations of each network, it is possible to propose a link between the elements of computer architecture and the field of study of Organizational Networks to support the understanding of the latter and thus contribute both to the strengthening of academic research and to the evaluation of their managerial implications in current environments of growing complexity and competitiveness. [ABSTRACT FROM AUTHOR]

Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. However, the best choice is unknown a priori as it is case specific. Here, we investigated network shapes, sizes, and types for unsupervised physics-informed deep learning of the two-dimensional Reynolds-averaged flow around cylinders. We trained mixed-variable networks and compared them to traditional models. Several network architectures with different shape factors and sizes were evaluated. The models were trained to solve the Reynolds-averaged Navier-Stokes equations incorporating Prandtl's mixing length turbulence model. No training data were deployed to train the models. The superiority of the mixed-variable approach was confirmed for the investigated high Reynolds number flow. The mixed-variable models were sensitive to the network shape. For the two cylinders, differently deep networks showed superior performance. The best fitting models were able to capture important flow phenomena such as stagnation regions, boundary layers, flow separation, and recirculation. We also encountered difficulties when predicting high Reynolds number flows without training data. [ABSTRACT FROM AUTHOR]

Satellite communications systems provide a means to connect people and devices in hard-to-reach locations. Traditional geostationary orbit (GEO) satellite systems and low Earth orbit (LEO) constellations, having their own strengths and weaknesses, have been used as separate systems serving different markets and customers. In this article, we analyze how satellite systems in different orbits could be integrated together and used as a multi-layer satellite system (MLSS) to improve communication services. The optimization concerns combining the strengths of different layers that include a larger coverage area as one moves up by each layer of altitude and a shorter delay as one moves down by each layer of altitude. We review the current literature and market estimates and use the information to provide a thorough assessment of the economic, regulatory, and technological enablers of the MLSS. We define the MLSS concept and the architecture and describe our testbed and the simulation tools used as a comprehensive engineering proof-of-concept. The validation results confirm that the MLSS approach can intelligently exploit the smaller jitter of GEO and shorter delay of LEO connections, and it can increase the availability and resilience of communication services. As a main conclusion, we can say that multi-layered networks and the integration of satellite and terrestrial segments seem very promising candidates for future 6G systems. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time‐consuming process. In recent years, deep convolutional neural networks (DCNN) have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long‐range dependency is still limited, and this can result in sub‐optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long‐range information in several semantic segmentation tasks performed on medical images. Purpose: Despite the impressive representation capacity of vision transformer models, current vision transformer‐based segmentation models still suffer from inconsistent and incorrect dense predictions when fed with multi‐modal input data. We suspect that the power of their self‐attention mechanism may be limited in extracting the complementary information that exists in multi‐modal data. To this end, we propose a novel segmentation model, debuted, Cross‐modal Swin Transformer (SwinCross), with cross‐modal attention (CMA) module to incorporate cross‐modal feature extraction at multiple resolutions. Methods: We propose a novel architecture for cross‐modal 3D semantic segmentation with two main components: (1) a cross‐modal 3D Swin Transformer for integrating information from multiple modalities (PET and CT), and (2) a cross‐modal shifted window attention block for learning complementary information from the modalities. To evaluate the efficacy of our approach, we conducted experiments and ablation studies on the HECKTOR 2021 challenge dataset. We compared our method against nnU‐Net (the backbone of the top‐5 methods in HECKTOR 2021) and other state‐of‐the‐art transformer‐based models, including UNETR and Swin UNETR. The experiments employed a five‐fold cross‐validation setup using PET and CT images. Results: Empirical evidence demonstrates that our proposed method consistently outperforms the comparative techniques. This success can be attributed to the CMA module's capacity to enhance inter‐modality feature representations between PET and CT during head‐and‐neck tumor segmentation. Notably, SwinCross consistently surpasses Swin UNETR across all five folds, showcasing its proficiency in learning multi‐modal feature representations at varying resolutions through the cross‐modal attention modules. Conclusions: We introduced a cross‐modal Swin Transformer for automating the delineation of head and neck tumors in PET and CT images. Our model incorporates a cross‐modality attention module, enabling the exchange of features between modalities at multiple resolutions. The experimental results establish the superiority of our method in capturing improved inter‐modality correlations between PET and CT for head‐and‐neck tumor segmentation. Furthermore, the proposed methodology holds applicability to other semantic segmentation tasks involving different imaging modalities like SPECT/CT or PET/MRI. Code:https://github.com/yli192/SwinCross_CrossModalSwinTransformer_for_Medical_Image_Segmentation [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The upcoming sixth-generation (6G) networks are expected to be more heterogeneous, scalable, reliable, secure and energy-efficient. 6G services and applications will benefit from the fast data transmission rates, global coverage, precise positioning, and widespread intelligence capabilities provided by 6G technology. In this complex scenario, new attack surfaces and vectors will emerge, due to the distributed, disaggregated, dynamic, programmable and open nature of the entire end-to-end 6G infrastructure along with the fragmentation of data, as well as the need for supporting cross-platforms interoperability, making the application of security and trust fundamentally challenging. To securely manage services in the future 6G mobile networks along with the set of functions ruling its management, conceptual work is needed to defining functional blocks supporting a secure end-to-end 6G systems management. This is especially critical in handling specific functionalities, such as network disaggregation, risk and threats business impact, energy-efficiency, self-configuration or dynamic discovery. This paper positions an innovative 6G security services management architecture, which builds on a set of innovative building blocks, leveraging key technologies, such as AI-assisted models and Digital Twin, while enabling a human-centric approach toward an end-to-end security solution. Two canonical workflows along with two illustrative application scenarios are proposed, Light Rail Transit and Extended Reality, to conceptually highlight the proposed architecture impact and its expected benefits when high bandwidth, ultra-low latency, and secure communications are required.

Future metro-access optical networks will be large-capacity, low-latency, high-reliability, large-scale, cost-efficient platforms that support fiber-wireless convergence. However, previously proposed schemes cannot meet the requirements of such a networking scenario. In this paper, we propose a scalable wheel-and-star architecture based on wavelength division multiplexing (WDM) technology and design a protection mechanism based on integrated lightpath protection and wireless protection methods that is optimized to improve the delay performance. In addition, a wavelength allocation scheme based on the wavelength routing properties of arrayed waveguide gratings (AWGs) and the spectral structure of optical carrier suppression (OCS) modulation is designed to improve spectrum utilization and eliminate Rayleigh backscattering (RB) noise and latency asymmetry from signal transmission. Finally, the feasibility of the proposed scheme and its advantages over existing works are verified through simulation. The future research prospects and practical application value of this method for mobile optical transport, long-reach access, and smooth network upgrades are discussed in detail.

As the fifth generation (5G) mobile communication systems are commercially deployed, they bring new services, enhance user experiences, and offer various opportunities to different industries. Despite its advancements, 5G encounters several challenges. To tackle these issues, global industrial, academic, and standards organizations are actively researching on sixth generation (6G) wireless communication systems. 6G networks are envisioned as a transformative shift in the interactions between the physical, digital, and human realms, paving the way for a pervasive human-centered cyber-physical world. 6G is positioned to be a platform that offers communication and beyond communication services considering both performance and value centric technological development approaches. In this paper, we present the way forward towards the design of 6G endto-end (E2E) system as a consolidated view of leading industry stakeholders and academia in the European level 6G flagship project Hexa-X-II. We discuss the key challenges with 5G and how 6G is expected to tackle those by introducing new technological innovations and supporting novel use case requirements. We provide a comprehensive methodology for the design of a 6G E2E system including ten principles, a blueprint, and a structured design process. The architecture design principles prioritize environmental sustainability, digital inclusiveness, and trustworthiness, considering their impact on the 6G E2E system. The blueprint is described corresponding to the infrastructure, network centric application, and application layers, as well as the pervasive functionalities and the relevant technological innovations. Following the design principles and the system blueprint, the design process is demonstrated as two-way approaches (i.e., 1) key performance and value indicators based design process. 2) top-down versus bottom-up alignment process) through the lens of a collaborative robot use case. Through this use case, a special attention is given to the technological enablers that cover management and orchestration functionalities and their 6G enhancement to go beyond the limitations characterizing the previous network generation. In addition, virtual modelling aspects related to digital twining and simulations for 6G E2E system design are also discussed.

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.

The wide application of network technologies has meanwhile brought about increasingly complex challenges regarding network environment for the key infrastructure of the rail transit industry. Therefore, deepening the research on cyberspace security in the rail transit industry becomes increasingly important. This study expounds on the basic concept and major characteristics of cyberspace security in the rail transit industry and summarizes its macro situation from the perspectives of strategies in China and abroad as well as security system and standards. It sorts out the development status of cyberspace security protection systems in the rail transit industry from three aspects: basic, enhanced, and collaborative protection. Moreover, the study analyzes the corresponding security risks and causes, involving practical cases of traditional network threats and data attack threats, and concludes that internal and external risk factors become the double threats to the industry’s dedicated network and the traditional network architecture restricts network security and reliability in the rail transit industry. Furthermore, we propose the following suggestions: (1) strengthening the original basic research and systematic innovation of networks, (2) building an independent and controllable industrial chain of network security, (3) improving the operation and management of network security, and (4) enhancing talent cultivation for network security in the rail transit industry.

Recent works have experimentally proven that metal matrix composites (MMCs) with network architecture present improved strength–ductility match. It is envisaged that the performance of architecturally designed composites is particularly sensitive to reinforcement strength. Here, reinforcing particles with various fracture strengths were introduced in numerical models of composites with network particle distribution. The results revealed that a low particle strength (1 GPa) led to early-stage failure and brittle fracture. Nevertheless, a high particle strength (5 GPa) delayed the failure behavior and led to ductile fracture at the SiC/Al–Al macro-interface areas. Therefore, the ultimate tensile strengths (UTS) of the network SiC/Al composites increased from 290 to 385 MPa, with rising particle strength from 1 to 5 GPa. Based on the composite property, different particle fracture threshold strengths existed for homogeneous (~2.7 GPa) and network (~3.7 GPa) composites. The higher threshold strength in network composites was related to the increased stress concentration induced by network architecture. Unfortunately, the real fracture strength of the commercial SiC particle is 1–2 GPa, implying that it is possible to select a high-strength particle necessary for efficient network architecture design. [ABSTRACT FROM AUTHOR]

The scientific article examines the problems associated with the risks of cybersecurity violations and information protection from cyber attacks. Given the dramatically increased amounts of information transmitted over networks, the number of cyber attacks is simultaneously increasing, with the aim of stealing various kinds of secret and confidential information. At the same time, the role of specialists involved in the development and implementation of information system security technologies has increased. The article analyzes vulnerabilities in corporate systems and suggests methods for improving encryption protocols for users' personal data. Special attention is paid to the technologies for implementing protection mechanisms at the software development stage. [ABSTRACT FROM AUTHOR]

The digital environment is constantly evolving with a growing diversity of network access technologies, such as ADSL, WiFi, 5G, LiFi, Zigbee, and the deployment of innovative services such as mobility, location, and telemetric services, as well as new applications such as smart parking, smart cities, machine-to-machine communication, and pervasive gaming. A few years ago, the services offered were dependent on the type of network, such as voice for telecommunications networks, data for computer networks, and audio/video for broadcast networks. However, service providers now need to adapt and anticipate changing consumption patterns, such as user-centric services, in their offerings. The challenge lies in how to quickly and efficiently deploy new services in this rapidly evolving technological landscape. The primary aim of this paper is to examine the impact of virtualization on the network deployment process in this new landscape. We concentrate on the integration of virtualization into the Network Deployment Process (called Virtual Network Virtual Deployment-VNVD). VNVD considers the properties of flexibility, adaptability, and dynamicity, which are crucial for Network-as-a-Service. In this context, Software-Defined Networking and Network Function Virtualization play a significant role in the design of new network architectures. [ABSTRACT FROM AUTHOR]

Background: Computer‐aided diagnosis is of great significance to improve the diagnostic accuracy of pancreatic cancer that has an insidious course and does not have obvious symptoms at first. However, segmentation of pancreatic cancer is challenging because the tumors vary in size with the smallest tumor having a size of about 0.5 cm$cm$ in diameter, and most of them have irregular shapes and unclear boundaries. Purpose: In this study, we developed a deep learning architecture Multi‐Scale Channel Attention Unet (MSCA‐Unet) for pancreatic tumor segmentation and collected CT images of 419 patients from The Affiliated Hospital of Qingdao University and a public dataset. We embedded the multi‐scale network into the encoder to extract semantic information at different scales and the decoder to provide supplemental information to overcome the loss of information in the upsampling and the drift of the localized tumor due to the upsampling and skip connections. Methods: We adopted the channel attention unit after the multi‐scale convolution to emphasize the informative channels, which was observed to have the effects of accelerating the positioning process, reducing false positives, and improving the accuracy of outlining very small, irregular pancreatic tumors. Results: Our results show that our network outperformed the other current mainstream segmentation networks and achieved a Dice index of 68.03%, a Jaccard of 59.31%, and an FPR of 1.36% on the private dataset Task‐01 without data pre‐processing. Compared with the other pancreatic tumor segmentation networks on the public dataset Task‐02, our network produced the best Dice index, 80.12%, with the assistance of the data pre‐processing scheme. Conclusions: This study strategically utilizes the multi‐scale convolution and channel attention mechanism of the architecture to provide a dedicated network for segmentation of small and irregular pancreatic tumors. [ABSTRACT FROM AUTHOR]

Model filter pruning has shown efficiency in compressing deep convolutional neural networks by removing unimportant filters without sacrificing the performance. However, most existing criteria are empirical, and overlook the relationship between channel saliencies and the non-linear activation functions within the networks. To address these problems, we propose a novel channel pruning method coined gradient flow based saliency (GFBS). Instead of relying on the magnitudes of the entire feature maps, GFBS evaluates the channel saliencies from the gradient flow perspective and only requires the information in normalization and activation layers. Concretely, we first integrate the effects of normalization and ReLU activation layers into convolutional layers based on Taylor expansion. Then, through backpropagation, the derived channel saliency of each layer is indicated by of the first-order Taylor polynomial of the scaling parameter and the signed shifting parameter in the normalization layers. To validate the efficiency and generalization ability of GFBS, we conduct extensive experiments on various tasks, including image classification (CIFAR, ImageNet), image denoising, object detection, and 3D object classification. GFBS could feasibly cooperate with the baseline networks and compress them with only negligible performance drop. Moreover, we extended our method to pruning scratch networks and GFBS is capable to identify subnetworks with comparable performance with the baseline model at an early training stage. Our code has been released at https://github.com/CUHK-AIM-Group/GFBS. [ABSTRACT FROM AUTHOR]

The finite difference forward modeling has been widely used in geophysics exploration and petroleum fields. Because of its high efficiency and easy application for graphical processing units, it has been widely concerned by industry and academia. However, owing to many factors, the problem of numerical dispersion has been an important factor hindering this method. To overcome the numerical dispersion, this paper proposes a method for removing numerical dispersion using deep learning. Unlike the conventional optimized algorithms target to optimize the finite difference coefficients, our strategy is based on big data training to eliminate the dispersion data after seismic data modeling. We design a neural network architecture based on cycle-consistent generative adversarial networks (Cycle-GANs) and residual learning for elastic wave propagation. Under the premise of not significantly increasing the calculation time, we can obtain higher calculation accuracy. Compared with the high-order finite difference algorithm, the calculation time is the advantage of our proposed deep learning method. Tests prove the efficiency and stability of our proposed algorithm. [ABSTRACT FROM AUTHOR]

As the Internet evolves from a consumer network to a productive network, emerging industry applications necessitate differentiated and deterministic service quality assurance and communication capabilities that span all dimensions of time and space.How to realize the heterogeneous compatibility of ubiquitous multi-domain networks and support deterministic transmission between nodes in space-air-ground integrated network (SAGIN) has become a significant research problem.Firstly, the emerging full-domain 6G scenarios along with their requirements for deterministic services were analyzed.Then, the hierarchical space-air-ground integrated deterministic networking architecture, including global cooperative network management and control layer, multi-domain dynamic deterministic fusion layer, and ubiquitous heterogeneous deterministic networking layer, was proposed, and key technologies for fixed-mobile-satellite fusion networks in relevant scenarios were studied.Finally, the challenges and development trend of the space-air-ground integrated deterministic networking were presented.

Abstract Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localisation, and sustainability requirements of next-generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardised interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signalling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflect array measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth of influence and the RIS area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future wireless networks.

With the public land mobile communication network transitioning from the 5G era where information flows freely to the 6G era where everything is interconnected, satellite networks have become a hot development topic all around the world.At the same time, with the improvement of satellite on-board computing capabilities and the integration of computing and satellite networks, cloud network integration has become a new development trend.On the basis of a brief introduction to the current development status of satellite networks and satellite computing, the development roadmap of satellite network development architecture was summarized.Then, a satellite network architecture based on cloud-network integration (i.e.satellite cloud network) was proposed, and the advantages of satellite cloud network architecture from five aspects: computing driven communication enhancement, computing driven network enhancement were analyzed, computing driven service enhancement, computing driven control enhancement, and computing driven management enhancement.

By analyzed the scenario characteristics of 6G integrated satellite and terrestrial network, the current state of research on edge computing capabilities on satellite, and the demand of cloud-edge collaboration, a network architecture that combines computing power network technology to achieve cloud-edge collaboration for integrated satellite and terrestrial networks was proposed, and global resource awareness and collaborative policy scheduling for integrated satellite and terrestrial networks by introducing a network function cloud-edge collaboration function was achieved .Based on this architecture, a unified resource management mechanism of the integrated satellite and terrestrial network was proposed, included a global resource awareness technology that could managed the computing and network resources of the integrated satellite and terrestrial network, and a policy generation technology that could collaborate with the computational capabilities of the cloud-edge and the integrated satellite and terrestrial network.Finally, the rationality of the cloud-edge collaborative architecture proposed in this article, as well as the correctness of resource awareness technology and policy scheduling technology, were verified through a self-developed mobile communication network platform.

The integrated satellite-terrestrial communication system is considered one of the development trends of future communication systems for its ability to provide seamless coverage and service.However, the differences between satellite and terrestrial communication networks, e.g., large propagation delays, large Doppler shifts, and large cell radius, pose challenges to fulfilling integrated satellite-terrestrial communication systems.The network architectures and key enabling technologies were discussed and analyzed and a prototype system for the terrestrial verification of integrated satellite-terrestrial communication systems was proposed.

Satellite internet is an important means to meet the ubiquitous network access in the future.It has a wide range of application scenarios in disaster prevention, marine operation and scientific research broadband on account of its wide coverage, large communication capacity and global seamless connection.Started from the vision of the deterministic transmission of satellite internet, combined with the existing deterministic network technology, the satellite deterministic network architecture was put forward, and the existing challenges and future development in deterministic access, multi-service differentiation guarantee, dynamic resource allocation and path backup of satellite deterministic network were pointed out.

As the global deployment of Fifth Generation (5G) is being well consolidated, the exploration of Sixth Generation (6G) wireless networks has intensified, focusing on novel Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) that extend beyond traditional metrics like throughput and latency. As 5G begins transitioning to vertical-oriented applications, 6G aims go beyond, providing a ubiquitous communication experience by integrating diverse Radio Access Networks (RANs) and fixed-access networks to form a hyper-converged edge. This unified platform will enable seamless network federation, thus realizing the so-called network of networks vision. Emphasizing energy efficiency, the present paper discusses the importance of reducing telecommunications’ environmental impact, aligning with global sustainability goals. Central to this vision is the proposal of a novel user plane network protocol architecture, called 6G Recursive User Plane Architecture (6G-RUPA), designed to be scalable, flexible, and energy-efficient. Briefly, 6G-RUPA offers superior flexibility in network adaptation, federation, scalability, and mobility management, aiming to enhance overall network performance and sustainability. This study provides a comprehensive analysis of 6G’s potential, from its conceptual framework to the high-level design of 6G-RUPA, addressing current challenges and proposing actionable solutions for next-generation mobile networks.