This PDF file contains the front matter associated with SPIE Proceedings Volume 12814, including the Title Page, Copyright Information, Table of Contents and Conference Committee list.

The drone wireless communication network protocol is susceptible to the influence of URL (Unified Resource Locator) encoding conditions, resulting in a low vulnerability detection rate. Therefore, it is necessary to design a new protocol consistency vulnerability detection method for unmanned aerial vehicle wireless communication network based on improved genetic algorithm in power transmission and transformation engineering. A drone wireless communication network consistency vulnerability detection environment was configured, and an improved genetic algorithm was used to generate a protocol consistency vulnerability detection objective function for the wireless communication network. The objective function was solved and consistency vulnerability detection was completed. The experimental results show that this method has a high vulnerability detection rate, good detection effect, reliability, and certain application value in power transmission and transformation engineering.

A multifunctional, modular, and scalable digital radar electromagnetic environment simulation application system based on a local area network is designed and proposed to address the issues of high cost, long term, and difficulty in constructing a real radar electromagnetic environment. It can realistically simulate the electromagnetic environment faced by radar, reduce evaluation and verification costs, improve efficiency, and also have high security. By leveraging the VC development platform and adopting a client/server architecture pattern, distributed application processing of client program requests is achieved. Based on this system structure, computer processing power can be fully utilized, with fast response speed and low network communication volume, ensuring system flexibility while also reducing the demand for hardware resources.

In Software Defined Network (SDN), selecting a subset of switches for traffic monitoring is crucial for ensuring network security at minimum cost. However, identifying the smallest subset of switches with maximum coverage is NP-Hard. To address this challenge, this research proposes a fast measurement node selection algorithm based on the centrality metric in graph theory, which considers cases where flow path information is known and unknown. The importance of the nodes is ranked by considering the difference in the amount of information of the nodes, followed by the measurement node reduction method to select the measurement nodes with the coverage ratio in mind. Results show that our algorithm reduces the average unit time overhead by 74% while ensuring coverage in both scenarios compared to existing methods.

Addressing the issues of poor real-time data transmission, high network latency, and long response times in traditional Ethernet communication systems used in distributed energy systems within industrial parks, this research focuses on designing a distributed real-time communication system master station based on the EtherCAT transmission protocol. The system is developed using the Qt cross-platform development framework. The distributed real-time communication system consists of a master station that provides management and control functions and slave stations that receive response signals. The master station and slave stations are connected via standard Ethernet cables, and communication data is captured and analyzed using Wireshark. Test results demonstrate that the system exhibits good real-time performance and fast response speeds, effectively addressing issues such as network congestion, data frame collisions, and packet loss commonly encountered when using traditional Ethernet in distributed energy systems.

The carrier tracking loop face extreme difficulty in entering a locked-in state for high dynamic and low carrier-to-noise ratio (CNR) environment. To address this issue, this paper presents an improved Frequency-Locked Loop (FLL) serially assisted Phase-Locked Loop (PLL). The improved carrier tracking loop design consists of a high-dynamic frequency discriminator and a high-dynamic loop filter based on second-order FLL serially assisted third-order PLL. In particular, this proposed frequency discriminator executes an adaptive switching mechanism between the maximum likelihood estimation (MLE) discriminator and the conventional frequency discriminator. Once the Doppler frequency estimation error begins to accumulate, the MLE discriminator will be selected which can pull the Doppler frequency into the working range of the conventional frequency discriminator. Thus, a wider pull-in range can be obtained to provide good frequency discrimination capabilities under high dynamic conditions. The experimental results and comparative analysis demonstrate that the improved FLL serially assisted PLL can achieve an improved tracking performance under high dynamic and low CNR conditions.

A passive optical network (PON) fiber access system with two types of high spectral efficiency orthogonal frequency division multiple access (OFDMA) downlink signals is designed and demonstrated. In this scheme, 50Gbit/s 16/32 quadrature amplitude modulation (QAM) -OFDMA-PON downstream signals are transmitted over 20 km single-mode fiber (SMF) and detected using self mixing detection method, in which 256 subcarriers are divided into four sub-channels for multiple users access applications. The electrical frequency spectrums, optical spectrums, constellation diagrams, and bit error rate (BER) performance of the downstream signals are analyzed. The results show that under different received optical powers, we can select a certain sub-channel with the lowest BER, and the system also supports the dynamic selection of the appropriate signals type between 16QAM-OFDM and 32QAM-OFDM according to different users bandwidth requirements.

The automatic identification of the modulation mode of the communication signal is the main task of the intelligent receiver, which is now widely used in a variety of fields, including civil and military. In this paper, an XSRP-based analog modulated signal automatic identification system is designed, and the overall program block diagram is drawn using LabVIEW software. The design of the automatic identification system is divided into seven modules, focusing on the following two modules: the first is the extraction of each feature parameter when the modulated signals of AM, DSB, SSB and FM are subdivided layer by layer, and the second is the simulation of modulation mode prediction using the library function of MATLAB BP neural network algorithm. Simulations were carried out in conjunction with the XSRP software radio platform, with suitable parameters and sample sizes selected for validation, and improvements made to feature parameters to improve system performance. Computer simulations show that the XSRP-based analogue modulated signals are eventually identified correctly at no less than 95% and can be expected to be used for the detection and fast identification of signals in practical non-collaborative communication systems.

With the end of the COVID-19 epidemic in China, colleges and universities across the country have restored the original order and implemented offline teaching. However, the turnover rate of campus personnel is also gradually increasing, and the traditional campus supervision system has pain points such as old and complex system and low efficiency. In order to solve the above problems and better assist the monitoring and management of the campus, we propose and make a campus monitoring system based on computer vision technology. We use the Yolov5 model for object detection, the optimized deepsort algorithm for object tracking, and the SlowFast network model open sourced by the Facebook team for behavior prediction. Through verification and actual test, the model has a range accuracy of 0.5 meters to 30 meters, and has strong anti-physical interference. Therefore, we conclude that this model has better efficiency and a wide range of application scenarios.

Rotary-guided drilling technology is a cutting-edge automated and efficient drilling technology developed in the late twentieth century to meet the urgent needs of oil and gas resources exploration and development. In this paper, an in-depth study is made on the down-transmission communication technology in rotary-guided drilling, and an oil and gas well surface-down-hole fluid pulse communication device is designed. In this system, cable-free transmission is used to break the existing domestic and foreign rotary-guided drilling information transmission technology, and the drilling fluid, which is indispensable in drilling, is used as the transmission medium without changing the existing drilling process or breaking the rotary-guided drilling technology. Secondly, a unique coding method is used to code the commonly used guiding force and tool face angle according to the actual operation in the drilling process to form 36 guiding points, which greatly reduces the cost. This design completes the downhole decoder design and the experimental results meet the expectations. The design of the work is of great significance for the realization of an automatic closed-loop guided drilling control system.

With the development of the virtualization of 5G core (5GC) network, the network architecture is more complex. Once faults occur, the fault root cause location (RCL) is difficult. Traditional operation and maintenance (O&M) cannot satisfy the requirements of RCL based on manual experience. This paper proposes the k-nearest neighbor and Bagging (kNNBagging) scheme for locating the root cause of 5GC network faults. It first concludes the fault root causes from the historical fault orders, and extracts the fault features based on the related alarm databases. Then, it conducts the pre-processing of the alarm data in order to better understand and the input data for machine learning (ML) algorithms. At last, k-nearest neighbor (kNN), Naive Bayes (NB), Support Vector Machines (SVM), kNN-NB-SVM (KNS), SVM-Bagging and kNNBagging algorithms are adopted to establish the fault RCL models. Experimental results show that the average accuracy of fault RCL of kNN-Bagging algorithm is optimal value, which are 92.6% and 91.6% respectively in the offline and online environment of the real-world 5GC network. The proposed approach can effectively shorten the fault processing time and improve the O&M efficiency of the 5GC network.

The emergence of Software Defined Network (SDN) technology greatly simplifies network management, separating the control plane from the data plane, achieving the separation of network logic and data, making the deployment and management of network devices easier. However, the openness of SDN provides attackers with the opportunity to invade and disrupt the network, while traditional switches do not have access control capabilities. Therefore, in SDN environments, traditional switches will become a springboard for attackers to invade the network. Therefore, it is necessary to design a security mechanism to achieve secure communication between controllers and switches. This article first analyzes the security mechanism of traditional switches and points out their shortcomings and shortcomings; Next, the security issues brought about by separating network logic and data after separating SDN control plane and data plane were introduced; Then a solution was proposed.

Shared electricity consumption is a new type of power consumption mode, which has the advantages of saving resources and improving the utilization rate of power system assets. Compared with traditional power consumption, Shared electricity consumption has the characteristics of more metering parameters and high measurement frequency, so it needs better support of communication resource allocation method. At the same time, it also needs to meet different QoS rules applicable to different services. To solve this problem, a low complexity power system communication using transmission line as carrier signal transmission medium resource dispense algorithm is proposed in this paper. Based on the equal power allocation method, this algorithm fix on subcarrier every service’s requirement to fulfill its minimum speed demands, and then uses Lagrange multiplier method to optimize the energy dispensary of remaining sub bands to further improve the throughput of high priority services. The experiment results proclaim the algorithm has high data forwarding rate, it fulfill communication needs of plentiful customers and businesses.

In this article, we evaluate the outage performance for a wireless-powered relaying radio frequency-underwater wireless optical communication (RF-UWOC) network with an amplify-and-forward protocol. Specifically, a source transmits an RF signal to a power-constrained offshore relay, which first adopts a power-splitting protocol to harmonize the information encoding and energy collecting of the received signal and then converts it to an optical one and utilizes the harvested energy to broadcast it to the destination. Assuming the RF and UWOC links undergo Nakagami-m distribution and a mixture Exponential-Gamma distribution, the analytical expression for the outage probability is deduced. Moreover, the rightness of the analysis is validated by Monte-Carlo simulations.

In order to ensure the normal operation of the local area network (LAN), maintain the stability, accuracy and security of data transmission, it is necessary to monitor its lines in real time. Aiming at the current LAN network management without line interruption automatic monitoring function, a LAN line interruption monitoring module is designed. It realizes automatic monitoring of line interruption and timely alarm. The functional requirements of the LAN line interrupt monitoring module are analyzed in this paper. The design concept, implementation process, registration client, database, page, security policy and other design processes of the LAN line interrupt monitoring module are introduced in detail. The application and validation are given. It can be seen through the application that the module has good practical application value. It has important practical significance and guidance for network managers.

With the acceleration of information network construction, the service applications carried on the network are more and more rich, and the information volume is more and more large, which makes users have higher and higher requirements for network link bandwidth and reliability. People have been trying to find a way to solve the bandwidth bottleneck effectively and provide high network system reliability under the existing conditions. Link aggregation technology solves this network problem well. So the research and implementation of link aggregation technology on network equipment has very important practical significance.

Due to the harsh underwater communication environment, the design of unique routing protocols is essential for underwater sensor networks. This paper proposes a Hybrid Forwarding Strategy Pressure-Based Routing (HFS-PBR) protocol for the Underwater Wireless Sensor Network (UWSN). The protocol categorises forwarding nodes for every sending node into three different types, each of which employs a distinct routing forwarding strategy. Nodes in HFS-PBR can gather neighbour information by exchanging information regularly, which enables the protocol to mitigate the problem of void node and make more optimal routing decisions. Through extensive simulation experiments, our proposed routing protocol demonstrates lower end-to-end delay and improves packet delivery probability compared to various routing protocols. These advantages are particularly prominent in sparse network topologies.

Data gathering is an important research area in wireless sensor networks (WSNs), where network lifetime is an important factor affecting data gathering in WSNs. In recent years, a type of WSNs with a special structure that is fundamentally different from traditional WSNs has emerged in order to monitor infrastructure extending over hundreds of kilometers, because sensor nodes in such networks are deployed along narrow geographical areas, thus forming a network called chain-type WSNs. In order to maximize the lifetime of chain-type WSNs with multiple sink nodes, this paper proposes two routing algorithms: MMRE_EN, Chains_search. The case of data gathering without data aggregation is considered in the selection of sensor nodes, and this paper proposes an improved version of the existing MMRE algorithm: the MMRE_EN algorithm. Meanwhile, this paper proposes the Chains_search algorithm with linear computational complexity, whose core idea is to convert the problem of global route optimization into the problem of route independent optimization of multiple links. Extensive simulation results show that the two algorithms proposed in this paper can extend the lifetime of chain-type WSNs with multiple sink nodes by 18.5% and 12%, respectively, compared with the existing MMRE algorithm, while the Chains_search algorithm with linear computational complexity has only 1/200 of the running time of the MMRE algorithm.

Energy consumption of servers is highly concerned by each kind of user, it is important to select scientific and reasonable energy consumption test scheme. On the basis of present energy consumption evaluation, use SPECPower_ssj2008 as testing tool, and exact testing data from it. This test scheme includes modify parameters of SPECPower scripts on different conditions, then run SPECPower software to test server energy consumption. Result of such experiment shows setting different script parameters affect power performance of server. This scenario can be applied to scientifically and reasonably guide optimization server energy consumption, providing reference foundation for project evaluation, and better planning IT systems.

In order to solve the problem of data transmission in the Internet of Things system, based on the Socket.IO protocol and related software and hardware modules, this study carried out the modular design and implementation steps of the real-time two-way communication system of the Internet of Things gateway. The gateway is divided into functional modules to realize the interaction process between the gateway and the platform about the underlying device authentication, device data transmission and device heartbeat. After the design is completed, the function and performance of the system are tested. The test results show that the designed system has achieved the expected goal in terms of function and performance, and can effectively improve the efficiency and quality of data transmission of the system.

Reconnaissance communication jamming equipment can realize communication, reconnaissance, detection, jamming, anti-jamming and other functions in the same equipment. The system can adapt to different scenarios in different platforms and different environments, and adjust the working state in real time according to the task requirements to ensure the effective execution of various tasks. As a human-computer interaction interface, the control software of reconnaissance communication jamming equipment provides functions such as parameter setting, function selection, signal style setting, real-time device status monitoring, etc., which can meet the requirements of multi-function task scheduling and flexible setting of the system.

In order to effectively improve the efficiency and reliability of power grid communication and promote the intelligent development of the power grid, a multi agent 5G communication terminal resource allocation method considering randomness has been proposed for the power grid. Considering the randomness factor, the Monte Carlo method is used to complete the random power flow calculation of the multi-agent 5G communication terminal of the power grid. On this basis, a multi-agent 5G communication terminal resource allocation model for the power grid is constructed, and a hybrid particle swarm optimization algorithm is used to solve the multi-agent 5G communication terminal resource allocation model for the power grid, achieving multi-agent 5G communication terminal resource allocation. The experimental results show that the proposed method has good resource allocation performance for multi-agent 5G communication terminals in the power grid, and can effectively improve resource allocation efficiency.

This thesis presents an optimization technique for 3D positioning based on ultra-wideband (UWB), time-of-flight (TOF) and particle swarm optimization (PSO). The technique uses UWB technology to achieve wireless communication of the positioning system and TOF measurements to calculate the distance between the target and anchor nodes. The measurement data is then optimized using PSO algorithms to improve the accuracy and precision of positioning. Experimental results show that the technique is able to achieve highly accurate 3D positioning with positioning errors within the centimeter level. The technique has high positioning accuracy, real-time and stability, and is suitable for 3D positioning applications in both indoor and outdoor environments.

Command information system is the core support of information war. Different from most existing studies which based on complex networks, we evaluate command information system from the perspective of causality. Specifically, in this paper, we establish a causal network model of command information system, aiming to evaluate the causal relationship within the command information system. We abstract the command information system into three types of nodes: information obtaining, information processing and command decision. On this basis, we analyze the causal relationship between nodes, and conduct skeleton learning and causal orientation for command information system causal network. The result of simulation analysis on a synthetic travel command information system is consistent with the actual background, which verifies the validity of our research.

Satellite Internet is a new type of network, which can form a large-scale network by launching a certain number of satellites to provide broadband Internet access and other communication services to terminals on the ground and in the air, so as to radiate the whole world and build a large-scale satellite system with real-time information processing function. Satellite Internet integrates the advantages of satellite coverage, which can achieve global signal coverage and solve the problem of 5G base station signal coverage. In this paper, the convergence of satellite Internet and 5G are studied. Firstly, the current research status of satellite Internet is introduced, and the future development trend of satellite Internet is summarized. Three ways of convergence between satellite network and 5G are proposed. Finally, the possible application scenarios of satellite Internet are proposed.

In view of the problems of high data dimension, redundant features and low detection accuracy in current DDoS attack detection, This paper proposes a DDoS attack detection model CNN-Trans based on the combination of Convolutional Neural Network (CNN) and Transformer. The detection model firstly uses CNN integrated with CBAM attention for feature extraction. The model can better learn the depth characteristics of the input data, and then use Transformer to train the processed data to get classification results. Experimental results show that the accuracy and other aspects of the proposed model CNN-Trans have been improved

The current conventional transmission line communication relay selection strategy is mainly based on one-way relay system, which leads to large system capacity occupation due to the lack of calculation of relay transmission rate. In this regard, the study of transmission line communication relaying based on deep learning routing optimization algorithm is proposed. A two-way model of transmission line communication network is constructed by using double-half duplex mode to build orthogonal channels. The relay transmission rate is calculated, and the selection of relay nodes is completed by constructing a loss function and mapping the system operation. In the experiments, the performance of the proposed method is verified for relay selection. The experimental results show that the proposed method has a larger system capacity and a smaller space occupation when relay node selection is performed, and has a more desirable relay selection performance.

As the entrance to the organization's overall network, the richness of its display content directly affects the ability to promote the website system. To achieve the popularity and promotion of the website system, a Python-based website front-end system is designed. Using the soapy crawler framework, we improve the Python database operation mode, and define the execution criteria of the data mining process according to the total amount of web data samples stored, thus realizing the Python-based website data mining. On this basis, we determine the website design objectives, develop a complete security protection strategy according to the connection relationship between the main functional modules, and complete the overall design of the website front-end system. This paper analyzes the key design techniques of the front-end system of the website from three perspectives: redirection function, port closure function, and data detection function, so as to guarantee the richness of the website display content and promote the popularity and promotion of the website system.

Aiming at the low accuracy of the Fault Diagnosis (FuDg) methods used in the Intelligent Substation Secondary System Communication Network (ISSSCN), a FuDg method of the ISSSCN based on the improved Time Convolution Network (TCN) is proposed. Firstly, the overall structure of the ISSSCN and the mathematical description method of the fault signal are analyzed. Secondly, a basic model for f FuDg of the ISSSCN is built based on the TCN including the Extended Causal Convolution (ECC) and the ResNet. Finally, the TCN model is optimized by the improved Hybrid Attention Mechanism (H-AM) based on channel and spatial attention, which greatly improves the accuracy of ISSSCN FuDg. The experiment shows that the accuracy of the proposed method for four different types of FuDg reaches 95.72%, 96.83%, 95.32% and 95.28% respectively, which is higher than the other two comparison methods. Therefore, the proposed method has good FuDg ability

This article proposes a remote closed-loop simulation method for stability control systems based on 5G networks. Composed of a Closed-loop simulation system, stability testing server, 5G CPE module, stability testing terminal, and stability control device. The stability control testing server distributes the Closed-loop simulation system output simulation test data point-to-point through the 5G public network to the stability control testing terminals of each factory, and feeds back the stability control device action signals fed back by the stability control testing terminals of each factory to the stability control testing server through the 5G public network. We have achieved closed-loop testing of stability control devices distributed on site using Closed-loop simulation data from a 5G network carrying simulation laboratory. The communication implementation method is simple, the communication delay is small, and the current joint debugging method for stability control on site has been optimized and improved.

In recent years, pseudo-labeling methods can reduce the difficulty of building speech recognition systems, in end-to-end automatic speech recognition (ASR). Iterative pseudo-labeling (IPL) is a classical semi-supervised algorithm that can efficiently perform multiple pseudo-labeling iterations on unlabeled data as acoustic models evolve. We incorporate the language model to generate pseudo-labeling based on IPL using the language model for decoding and data augmentation, and make new attempts on the selection of pseudo-labeling. The effectiveness of the improved approach is demonstrated by simulating low resources and standard settings and obtaining a word error rate better than IPL on the LIBRISPEECH test.

Intelligent Vehicle Infrastructure Cooperative Systems is the key technology for vehicles to realize environmental awareness, information interaction and collaborative management. It can realize comprehensive network connection and intelligent information interaction between vehicles and people, vehicles, roads and service platforms, so as to further improve the perception, cognition and decision-making ability of vehicles and ensure a high degree of integration among people, vehicles and the environment. Among them, the vehicle-road collaborative data distribution technology is an important technology in Intelligent Vehicle Infrastructure Cooperative Systems. With the advent of the 5G era, large bandwidth and low latency will inject more vitality into V2X, and various new application scenarios and requirements will emerge as the times require. At the same time, facing the diverse V2X devices, it poses a great challenge to the timeliness and compatibility of V2X software framework. Based on this, this paper briefly analyzes the overall architecture of Intelligent Vehicle Infrastructure Cooperative Systems based on 5G technology, and introduces how to realize collaborative data distribution, improve the efficiency of vehicle-road collaborative data collection, and ensure the authenticity and real-time performance of data from the aspects of Android system architecture Socket communication module.

Based on the actual situation of the Internet of Things (IoT), this study focuses on key negotiation techniques. Guided by advanced and secure signature key protocols, it incorporates the semi group properties and chaotic properties of Chebyshev polynomials. A Signal key negotiation protocol is designed specifically for IoT environments. Through a comparison of security and performance, the proposed Signal key negotiation protocol demonstrates complete security attributes and lower communication costs. It provides data security transmission and privacy protection for IoT users, controls economic costs, and better safeguards the information security of IoT data during transmission. Additionally, this paper presents a research plan for a trusted key management technique based on the IoT, using narrowband IoT as an example. It offers new ideas for secure and trusted applications in the IoT domain.

When tracing data related to fresh food, it is often necessary to handle a substantial volume of real-time data, which grows rapidly and exhibits clear characteristics of Big Data. While traceability technology based on IoT terminals can process this data promptly, it still faces limitations in terms of real-time insights and decision-making capabilities. This paper introduces and implements an information collection and fusion scheme utilizing edge nodes. It also presents a complex event processing (CEP) system and method based on edge nodes, along with a hierarchical database model for simplifying complex events. In this approach, when real-time data of fresh food is collected in proximity, complex event processing occurs at edge nodes through IoT terminals. The perceived events from the IoT terminals are categorized into original events, simple events, and complex events, and are processed following predefined rules. This approach effectively enhances the information processing capabilities of edge nodes, ensuring the real-time and efficient collection of fresh food information.

Big Data Analytics-as-a-Service (BDAaaS) is an emerging cloud computing service designed to provide big data analytics. BDAaaS offers approximate query services to meet the requirements of fast response but without high accuracy by users. So, service response in Service Agreement Level (SLA) are more stringent and BDAaaS faces greater risk of penalty payout. In this paper, we proposed a task scheduling model that takes SLA into account for the problem of sequential dependencies of some tasks. And a greedy strategy was used to improve the genetic algorithm to solve the model efficiently. Simulation experimental results show that the method in this paper can obtain task scheduling schemes with lower cost and SLA violation rate under limited resources.

There are serious cyber security threats in wind farm SCADA systems drinking a large number of embedded terminals with scarce computing resources, so a lightweight key management scheme needs to be designed. Security threats to wind farm SCADA system keys and types of system communication are analyzed. Lightweight key management schemes based on CL-PKC and CRT are proposed. In this scheme, we have completed the generation and distribution of device public and private keys and unicast session keys by CL-PKC, and the generation and distribution of group session keys by CRT, and give the process of key update and key destruction. The verification results show that the key management scheme secures the keys and is resistant to forgery, replay, interruption and tampering attacks. In addition, the solution has good forward security and backward security.

The purpose of face attribute editing is to control the specific attributes in the face image according to the specified face attribute tags, and obtain a synthetic face image that meets the specific attributes. In recent years, face attribute editing has been widely used in the fields of beauty, entertainment and face recognition. Based on the problem of inaccurate attribute editing in the current model, this paper proposes a face attribute editing method based on self-attention mechanism and generative adversarial network. Taking selective transfer generative adversarial network (STGAN) as the basic model, a self-attention mechanism is added after the convolutional layer of the decoder. At the same time, the loss function is improved, and the latent space encoding consistency loss is added to the original objective function. Experiments were carried out on the CelebA face dataset, and it was found that the model has improved compared with StarGAN, AttGAN and STGAN in terms of image generation quality and face attribute editing accuracy.

Aiming at the problems existing in the practical application of zero trust scheme, this paper uses access record table, data classification and user dynamic risk measurement to realize data security risk control, and estimates data security level based on real-time data access situation. The dynamic risk measurement algorithm can realize real-time tracking of users ' unauthorized access risk, avoid the performance bottleneck of the access control policy engine, and realize fast and effective risk assessment.

eDRX has been introduced in 3GPP Release 17 for RedCap device as a key feature for optimizing UE power saving. This paper investigate the energy consumption factor and wake up delay of eDRX mechanism under different configuration parameters of RedCap device, a new type of low-cost device, which are applied in IOT, especially those scenarios with high energy efficiency. We study the theoretical calculation method of energy consumption saving factor and wake up delay when RedCap device is in RRC idle state, the traffic dada arrival rate follows the exponential distribution. We also verify the theoretical model by system simulation. Considering the tradeoff between energy consumption and wake up delay under actual traffic arrival rate, we give the recommended value of eDRX configuration parameters.

Online social networks serve as platforms facilitating communication, collaboration, and information exchange among hackers. Consequently, the detection and monitoring of hacker communities within these networks have emerged as significant research areas in the field of cybersecurity. Numerous methods have been employed to detect these communities, with some primarily utilizing network structural information and others incorporating user-generated content topics. However, most existing methods rely on probabilistic models such as TF-IDF or LDA to analyze textual similarity, resulting in limited accuracy. In this paper, we introduce a Siamese Attention-augmented Recurrent Convolutional Neural Network aimed at analyzing text similarity among users and evaluating the significance of different users by comparing their text similarity with seed users. Subsequently, a weighted network is constructed, and community detection is performed using a modularity maximization algorithm. To validate the proposed framework, we collected Twitter friendship data and user-generated content from a pre-existing regional hacker group. A comprehensive series of experiments were conducted on this dataset. The experimental results provide strong evidence of the effectiveness of our proposed framework, showcasing its remarkable performance across various indicators such as community quality and topic relevance. Consequently, our framework exhibits promising prospects for practical applications in the field of hacker community detection.

With the increasing number of power grid information systems and the deepening requirements for information intensification, the concept of cloud computing has entered the sight of power grid enterprises. This paper discusses the application of cloud computing in power grid enterprises from the aspects of the concept, service level and core technology of cloud computing. This paper holds that the introduction of cloud computing into power network and the construction of power network security immune system cloud can provide effective support for the development of power network. Based on the characteristics of power network and relying on a new storage and calculation mode of cloud computing technology, this paper expounds how cloud computing technology provides technical support for data storage and analysis of power network, and analyzes the benefits brought by cloud computing technology to power network. The possible security threats of power network cloud and the corresponding preventive measures are discussed.

In recent years, the frequency and complexity of IoT network attacks have significantly increased. NIDS, strategically located in IoT network nodes, is an essential tool for monitoring traffic and detecting and mitigating network-based attacks. However, with the significant increase in computer network attacks, many datasets used for training suffer from imbalanced data problems. Therefore, to address the traffic characteristics of IoT networks and the issue of imbalanced data, this paper proposes an intrusion detection method that combines graph neural networks(E-GraphSAGE) and generative adversarial networks. Based on experiments using datasets NF-BoT-IoT, we found that training ML classifiers on datasets balanced with synthetic samples generated by WGAN-gp increased their prediction accuracy to 93.7% .

Certificateless signature (CLess) scheme and paritial blind signature scheme with certificateless (CLess) are two important concepts in network security and cryptography. In a CLess scheme, the key escrow problem can be solved by the signature key being generated by the signer and PKG (Private Key Generator). In a CParBSS scheme, the abusing of the signature for illegal message can be solved by inserting a piece of common information negotiated by the signer and the requestor in advance. In this paper, we use a scheme as the CParBSS case to analyze its security by presenting the detail process of replacement attack of the public key and forgeability attack respectively. We also use a scheme as the CLess case to analyze its security by presenting the forgeability attack from anyone user. Based on the security analysis, we present an efficiency comparison and analysis between different schemes, and a design of the teaching method on CLess and CParBSS.

This paper focuses on optimizing integer data formatted output based on high-performance parsing software. We provide three methods for formatting integer fields and compare them with standard library functions sprintf in performance. These methods are experimentally proven to have reasonable practicality and usability.

In the information age, China's Internet technology has developed rapidly and is widely used in all areas of life. He also played an important role in the design and manufacture of industrial machinery, which advanced the development of China's manufacturing industry. With the increasing demand for the development of China's industrial sector, it is necessary to deepen the use of Internet of Things technology in production to accelerate the technological innovation and technological process of China's industrial sector. Starting with the specifics of Internet of Things technology, this article briefly describes its applications in engineering and manufacturing.

In order to solve the problem of pinning node selection in undirected weighted networks, a pinning control strategy based on the entropy of betweenness centrality and node strength is proposed in this paper. Firstly, a complex network model is given where each node is the same dynamical system, secondly, sufficient conditions for the synchronization of this network are given, and specific steps of the betweenness centrality and node strength entropy-based pinning control strategy are proposed and applied to different networks. Finally, by comparing the neighborhood information entropy-based pinning control strategy, it is found that in order to synchronize the network, the number of key nodes pinned by the proposed pinning control strategy is smaller than the neighborhood information entropy-based pinning control strategy under different coupling strengths.

The supply chain is a system that comprises multiple entities such as suppliers, manufacturers, carriers, customers, etc. Data sharing safely between these entities is vital for the success of a supply chain. This paper proposes a secure supply chain data sharing scheme based on blockchain and IPFS, including decentralized architecture, data management of capability-based access control mechanism and message acquisition mechanism based on the publish-subscribe model. Further more, a BIZi storage method is proposed to enhance the reliability of the above solution and streamline the backup of data by combining smart contract, the Bitswap protocol of IPFS and the Zigzag code. Finally, a prototype of a supply chain platform is implemented based on the Ethereum. The performance test shows that BIZi can save more storage resources at the same fault tolerant rate.

The demand for fall detection technology in elderly individuals is increasing due to the growing global aging population. Wi-Fi's Channel State Information (CSI) based fall detection is gaining popularity as it does not require the installation of expensive cameras or sensors, is not affected by lighting and obstructions, and protects individual privacy. However, previous studies using commercial Wi-Fi and mobile hosts to acquire CSI and recognize falls are expensive. To address this issue, this paper proposes a low-cost fall detection solution based on ESP32-S3 and LSTM model. The proposed solution collects data from 52 subcarriers in the L-LTF, applies Butterworth low-pass filter, and PCA to reduce data dimensionality. The LSTM model achieves a testing accuracy of 96.03%, with 89% classification accuracy for falls. This solution can significantly reduce the cost of fall detection technology and has potential deployment on ESP32-S3. Future research could focus on improving accuracy or exploring different application scenarios.

The integration of Internet of Things (IoT) technology in everyday life has significantly improved convenience, particularly through the provision of various services and the monitoring of patients' vital signs. However, as IoT devices collect sensitive user information and transmit it to remote servers, privacy and security concerns have become a major issue. Therefore, there is an urgent need for data access control and data security sharing in IoT environments. To address these challenges, this study proposes a CP-ABE access control scheme based on blockchain technology and Supporting Data Permissions Management. The scheme supports data access control and management, using plaintext partitioning and data access control policies to manage data access effectively. Access control processes are executed through smart contracts, and data units are hashed and uploaded to the blockchain for secure storage. Homomorphic encryption technology is also utilized for outsourcing encryption, while proxy private key technology is used for outsourcing decryption. In conclusion, this scheme effectively resolves data access control and tampering issues in IoT environments, and has high practical value and application prospects.

Cross-site scripting (XSS) is an important issue in the field of network security, and there have been many studies on XSS detection models. However, the emergence of XSS adversarial attack samples has affected the detection accuracy of these models. Therefore, this paper proposes a reinforcement learning-based XSS adversarial attack model, which aims to generate XSS adversarial attack samples and consists of a detection module and an adversarial attack module. In the detection stage, the detection module cleans the original XSS script data and vectorizes it using Word2Vec. Then, the processed data is input into the XSS detection model of the detection module. The detection module uses an ensemble learning method to construct an XSS detection model by combining LSTM, MLP, and SVM to form a higher accuracy model. Finally, the detection module obtains the classification result of the original XSS data as a preparation for the next escape stage. In the escape stage, the adversarial attack module is designed using the reinforcement learning algorithm TD3, and uses an adversarial generation module to generate legitimate adversarial samples that can bypass the XSS detection model. Experimental results show that the XSS adversarial attack samples generated based on TD3 have an evasion rate nearly 6% higher than those based on Soft-Q learning. This model provides a new idea and method for improving the accuracy of XSS detection models and providing more valuable XSS attack data samples.

DC carrier communication technology can use the existing DC to perceive the status of electrical equipment and information transmission in a timely manner, and can be used to support massive data transmission in zero-carbon buildings. However, the traditional DC carrier communication has problems such as slow networking speed, low optimization efficiency, and easy to fall into local optimum. This paper proposes a DC carrier routing optimization method based on heterogeneous overlapping clustering networking. Firstly, the heterogeneous overlapping clustering algorithm is used to form a network, and the network nodes are divided into multiple logical layers, and the nodes in the same logical layer are eliminated. The communication between them reduces the number of iterations to search for the optimal path and optimizes the network structure. Secondly, the ant colony algorithm is used for path optimization, and the optimal communication path between the gateway node and any node in the network is established. The simulation results show that, compared with the basic ant colony algorithm and the ant colony algorithm based on clustering spider webs, this method not only greatly improves the convergence speed of the ant colony algorithm, but also optimizes the routing optimization results, which has certain practical significance

In response to the problems of undetectable intrusions caused by the absence of authentication mechanism in the NTP protocol used in wind power monitoring systems, an intrusion detection algorithm for wind farm NTP messages based on message periodicity and autoregressive model was proposed. The periodic feature of the wind farm NTP messages under normal and intruded conditions was analyzed. An autoregressive model based on the current network conditions was built, the model parameters based on the Yule-Walker method were estimated and the detection thresholds were determined within 95% confidence intervals. The proposed algorithm was validated based on real capture messages from wind farms. The results show that the proposed intrusion detection algorithm can effectively identify denial-of-service attacks, man-in-the-middle attacks and clock offset attacks.

Aiming at the problem that the classification results of disaster recovery storage technology are inaccurate, resulting in poor load balance, in order to improve the stability of storage load and ensure the security and integrity of massive data, this paper studies the design of power marketing data cloud disaster recovery and consistent storage technology based on blockchain technology. The storage classification model of fuzzy comprehensive evaluation is established, the single factor evaluation standard is obtained, and the maximum unit cost of users is calculated; Design the consistency algorithm of power marketing data disaster recovery and storage, and get the consistency technology of data disaster recovery and storage. The parameters such as backup data volume, average bandwidth, multicast scale and number of nodes are changed respectively, and the comparative experimental results show that blockchain technology has higher load balance and better disaster recovery storage effect than other methods.

Intelligent agriculture is the product of the combination of technology and traditional agriculture in the computer era, mainly using IOT (Internet of Things technology), artificial intelligence and computer technology to reform and innovate the traditional agricultural production and management methods, and realize the informationization and intelligence of agricultural production management. In this context, how to use technology to improve crop yield and management efficiency has become an urgent social issue. This paper mainly discusses the application of IOT technology in greenhouse planting from two aspects, which is also the innovation point of this paper. Through the intelligent detection of soil and soil-less environment in greenhouses, it plays an important role in ensuring the normal and healthy growth of crops and increasing production and income.

In order to overcome the problems of low accuracy and poor stability of attack behavior detection in traditional active defense system, a new covert network active defense system based on attack behavior detection is proposed and designed in this paper. In terms of hardware, the overall architecture of the active defense system, network communication protocol and attack behavior data capture module are designed. In terms of software, information entropy combined with mutation detection is used to accurately detect the network attack behavior. The experimental results show that compared with the traditional defense system, the designed system can accurately detect the attack behavior in the network, and the detection accuracy is more than 98%; and the operation stability of the system has been effectively improved. Therefore, it shows that the designed system has strong practical application performance.

In recent years, the Internet of Things industry has developed rapidly, and various intelligent devices have been connected to the Internet of Things, gradually improving people's daily lives. With the continuous increase in the number of Internet of Things devices, the resulting user privacy data is also exploding, ensuring information security is very important. Decentralized blockchain technology involves disciplines such as computer programming and cryptography, and is a distributed database. Compared to traditional information technology, blockchain has significant technical advantages in information disclosure and transparency. This article builds a blockchain based IoT management platform based on the Hyperledger Fabric framework. The platform uses blockchain as the underlying technology, cooperates with storing source data to the IPFS network, uploads the returned hash value to the blockchain network, and applies blockchain technology to the field of information security. Combining the application characteristics of the technology, it provides a new solution for solving the information security issues of the Internet of Things.

In order to improve the accuracy of threat feature extraction and perception, a construction method of network security situation threat perception model based on cloud computing is proposed. Firstly, the basic network security situation data is collected through the mutual information function to obtain the sample data set. Secondly, the hierarchical mapping relationship model is used to compare the network users with their operation behavior data to extract the threat characteristics of network security situation. Finally, the fuzzy information entropy of network security situation threat distribution and the characteristic distribution of security situation information are calculated. According to the characteristic distribution, the characteristic decomposition model of network security situation threat information and network node deployment model are constructed. Through the interference suppression processing of the perception model through the filter function, the optimal analytical model of network security situation threat perception is obtained. The experimental results show that compared with the traditional perception model construction method, the threat feature extraction accuracy of this method is higher, and it can perceive the threat of network security situation more accurately.

In this paper, the management of scientific research problems faced in-depth analysis, on the basis of research-related big data processing technology, the framework of scientific research management platform based on big data analysis is proposed and the design of key modules is given, which promotes the development of scientific research management system from programmed management to decision assistant and intelligent management. It can help fine scientific research management, improve the quality of personalized scientific research service, and provide reference for the construction of scientific research management platform.

In this paper, a new strategy for pinning control node selection is proposed, namely: pinning control strategy based on ADRD (Adjacency Degree and Resistance Distance) algorithm. Firstly, the model of the undirected network and the sufficient conditions to achieve synchronization are given. Secondly, the steps of the pinning control strategy based on the ADRD algorithm are specifically given and applied to dolphins network, and by comparing the pinning control strategy based on the resistance distance, it is concluded that: the pinning control strategy based on the ADRD algorithm is better than the pinning control strategy based on the resistance distance. Finally, the correctness and effectiveness of the method are verified by numerical simulation.

Effective data collection is a prerequisite for energy saving, emission reduction and other sustainable operation of central air conditioning. However, current central air-conditioning data collection systems are costly to develop and difficult to integrate. To promote energy saving and emission reduction, this paper proposes an IoT-based data collection system for central air conditioning. Based on IoT and wireless sensor technology, this data acquisition system mainly consists of sub-systems such as host data acquisition sub-system, cooling tower data acquisition sub-system and pipeline pressure data acquisition sub-system to achieve multi-point acquisition of the central air-conditioning system. It also uses Kalman filtering technology to reject abnormal sensor data and optimise the data; it uses a combination of deep sleep and multiple power supplies to extend the service life of the cooling tower data acquisition subsystem; and it uses deep learning in the pipeline pressure data acquisition subsystem to greatly improve the accuracy and efficiency of meter reading. After experiments, the acquisition system can monitor and analyse real-time operational data of central air-conditioners for intelligent regulation and predictive maintenance. Finally, the proposed system was experimentally demonstrated to be feasible in a postal building in Zhengzhou.

This study explores a novel Bird’s Eye View (BEV) method for autonomous vehicles. By simplifying the converter, we have minimized computation in the process of converting 3D maps to BEV maps, leading to more effective solutions. Our understanding of the BEV model and its advantages over current solutions was informed by related works on BEV and Semantic Segmentation. Through in-depth research on similar BEV algorithms and experiments, we identified key operations crucial for the accuracy of BEV maps while eliminating unrelated operations. As a result, we developed a new algorithm model for pure electric vehicles based on a previous transformer, retaining only key operations for a more concise model. Our model may achieve higher accuracy than previous models by ensuring that critical operations are not disrupted.

The bus transit route network design problem (BTRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. In this paper, a road network model is established to describe actual road traffic conditions in detail. A model considering depot scale for minimizing the total length of bus network is formulated for the BTRNDP. Genetic algorithm with binary tournament selection and OX order crossover is used to generate iteratively new populations (set of bus networks). Each member of the population is evaluated by computing its total length and the best one is selected as the final solution. The effectiveness of the model and algorithm is proved by experiment carried out in Pinggu District. The bus network we get achieves a larger area coverage with shorter lines and more balanced depot use while all constraints satisfied.

With the development of new power grid under the development of a large number and variety of power terminals, terminals thus face different security risks. In recent years, a number of researchers have focused on researching lightweight authentication techniques applicable to power terminals. However, many existing schemes receive problems such as single scenario and limited effect enhancement, which make it difficult to meet the demand for widely applicable authentication of heterogeneous terminals. Therefore, we propose a lightweight adaptive authentication protocol that can achieve adaptive convergence of multiple lightweight authentication algorithms according to the characteristics and needs of terminals, which realizes low-power and low-arithmetic authentication. We design a pre-authentication protocol and an adaptive scheduling mechanism for matching appropriate authentication algorithms to achieve terminal authentication. The experimental results demonstrate the feasibility of our method in this paper.

Blockchain technology realizes information traceability by constructing chain network structure; Through distributed storage and consensus mechanism, each transaction can be verified and shared. Hyperledger Fabric has the characteristics of deep encryption, convenient expansion, flexible deployment and pluggability, and provides a modular distributed ledger platform for the needs of different applications. Combining the blockchain technology with the storage and sharing requirements of railway engineering data and drawing information, this paper first introduces the related concepts of blockchain technology, Hyperledger Fabric and critical algorithms and puts forward a Coverage Tag Validation (CTV) for the storage of railway engineering data and drawing information, which can improve the query efficiency and optimize the results, and provide some reference for future research.

With the continuous development of information technology, educational informatization has gradually become the mainstream of the future information technology industry. Building a complete, efficient, and flexible online teaching system based on distributed information systems is an inevitable trend. This article proposes a new idea based on the existing technology, that is, using the Internet of Things technology to create a complete and convenient network teaching resource library system to meet user needs. It focuses on students and teachers to establish an information platform and publish it to various regions, achieving issues such as interactivity and timeliness between educational data in this way. After that, this article also designed a test process for this access mechanism. The test results show that users have high requirements for the access mechanism of the Internet of Things in the education information center, with an average time between failures of 2356 hours to 2785 hours.

Due to the increasingly complex network environment, wind farm power monitoring systems are more and more likely to be exposed to vulnerabilities. Intrusion detection, as an important supplement to firewalls, can detect anomalies and effectively defend against network attacks. In this paper, a normal behavior feature model is constructed, and then rules are configured for Snort3. Finally, a Snort3-based wind farm behavioral characteristic inspector is designed based on the normal behavioral characteristic model and Snort3 rule configuration to detect unknown anomalous messages. The ability of the inspector to extract behavioral characteristics and detect anomalous messages was verified through experiments. The results show that the inspector can effectively detect data tampering attacks and man-in-the-middle attacks with reasonableness and effectiveness.

The Cellular automata was used to study the complex system of urban transportation network, and the BML model based automata model was modified to accommodate the basic traffic element of road segments in the model. We modeled and simulated traffic factors such as road section distribution and changes in road section length. The model proposed in this paper distinguishes road sections and intersections, and the study finds that low-density traffic flow and high-density traffic flow are distinguished significantly, which conforms to the characteristics of Second order phase transition and the real situation of urban traffic network.

Deep learning has significantly advanced object detection algorithms in the area of abnormal detection for substation equipment. However, there are still several challenges and problems that hinder its effective application in this domain. Firstly, the scale of data acquisition is limited, which may affect the performance of deep learning models. Secondly, there are various types and shapes of substation equipment with complex backgrounds, which will also affect the detection performance of deep learning models. This paper proposes an improved CenterNet-based method for target detection of substation equipment. Firstly, we embedded a Non-Local attention mechanism in the backbone network ResNet50. The integration of the attention mechanism can effectively improve the model's ability to prioritize significant targets and minimize the negative impact of intricate backgrounds on its overall performance. Then, the DIOU loss is used to replace the center point offset loss and width-height loss to accelerate network convergence and improve model performance. The proposed method exhibits superior performance in substation equipment anomaly detection, as evidenced by experimental results, with a detection accuracy increase of 10.8% over the original method and outperforming existing techniques like Faster R-CNN.

5GC migration to the cloud has become the common choice of operators and telecom equipment providers. Compared to the complete 5G core (5GC) of the large network, the lightweight 5GC is better suited for the public cloud. Low cost, easy operation and maintenance, resource flexibility, and business agility have become the main drivers of lightweight 5GC deploy on the public cloud. This paper systematically summarizes the deployment scheme of lightweight 5GC on the public cloud and selects the central cloud sharing mode for experimental verification. It designs two links: cloud process test and cloud function test, evaluates the complexity of lightweight 5GC on the cloud and to summarize the current problems on the cloud through experiments. The results show that lightweight 5GC on the public cloud is feasible.

Location-based map services are developing rapidly, and detailed scenic area information has important reference value for tourists. However, it is difficult to obtain scenic area information and the information obtained is not detailed and accurate, and the overexploitation of scenic areas has led to the lack of attention to the precious natural resources of scenic areas. This system collects the basic information resources and basic geographic information resources of scenic areas, selects the development scheme of the system from the perspective of users, and realizes the specific module development of scenic area management system with WebGIS technology OpenLayers, PostgreSQL and Vue framework. Design the database through E-R diagram and database table, and build the overall system architecture and various functional module design to realize the integration, management, query and analysis of scenic area information. Then, taking the Baili Cuckoo Scenic Area as the test and research area data, the system module was tested and improved, and the results showed that the system basically met the expectations and operated stably.

Dynamic circuits are an important part of circuit analysis. When we conducted dynamic circuit experiments, the error in collecting data such as the time constant of the circuit was relatively large ^[1-3]. If we use MATLAB software to complete the same experiment now, the accuracy will be higher. Moreover, the GUI platform can visually display the parameters, structure, and response process of a given circuit ^[4-7]. This GUI platform not only facilitates our understanding of the physical meaning of dynamic circuits, but also improves the efficiency of analyzing circuits.

At present, the main problem of urban rail transit is the long-term smooth traffic forecast based on the operation process. Because there is no reasonable distinction and prediction of congestion, it is easy to form a surge of passenger flow in some periods, resulting in congestion. Through the scientific management of commuting time, platform facilities, and entrance and exit allocation, rail transit can be better planned and operating costs can be reduced. In view of the difficulty of urban rail transit prediction due to a large amount of real-time data and frequent emergencies, this paper applies the matrix of vector data structure to improve the accuracy of the shallow prediction model, and extracts the feature expression of kernel density function from the database to realize the accurate analysis of the urban rail transit. Based on the data of part of the passenger flow of Guangzhou Metro, a simulation experimental platform is built to verify the prediction of the regional rail transit passenger flow. The test results present that the accuracy of the algorithm in this paper is 99.6%, which is better than the traditional prediction method. It can analyze and predict the commuting situation of different rail transit modes, realize intelligent guidance management, and has guiding significance for urban rail intelligent traffic management.

With the first successful establishment of 3GPP R18, the 5G Advanced has enforced the speed of 5G network construction, and the 6G technology enabling 5G evolution has gradually become the major research focus area. Reconfigurable Intelligent Surface (RIS), as an important technology that can improve network coverage and reconstruct imperfect signal propagation environment, which considered as one of the key technologies for 5G Advanced and 6G networks construction. This paper introduces the research progress of reconfigurable intelligent surface technology, analyzes the potential application scenarios and challenges, and proposes a possible landing plan for the 5G application of reconfigurable intelligent surface technology.

Traditional transformer fault diagnosis methods have low accuracy and cannot be accurately classified. In order to improve the accuracy of transformer fault diagnosis, a SMOTE-RF-IHPO-DBN transformer fault diagnosis model is established. Firstly, the synthetic minority oversampling technique is used to balance the data. Then the random forest is used to reduce the dimensionality of the data to extract important information in the data and improve efficiency. Next, IHPO is used to optimize DBN, and finally use DBN to diagnose and classify fault types. The simulation results show that the accuracy of the proposed method is improved by 13.4%, 7.08%, 4.14% compared with the DBN, HPO-DBN and IHPO-DBN, respectively, which proves the effectiveness of the model.

Attack detection is a crucial process that involves closely monitoring and identifying malicious attacks. To identify and locate such attacks with precision, there is a need for a thorough analysis and classification of malicious payloads. To this end, a deep learning-based method is proposed in this paper, which enables the efficient classification of payloads for attack detection. The method involves segmenting the payloads using regular expressions, which helps in preserving their syntactic structure. Also, an improved TF-IDF algorithm is introduced to construct a streamlined vocabulary that alleviates the slow training problem caused by a large vocabulary. By fusing the features of vectors extracted using CNN and BiLSTM-Attention, the payload content can be effectively represented, yielding more accurate recognition results and improving the problem of low detection accuracy associated with traditional methods. The experimental results reveal that the proposed method achieved an accuracy of 99.21% on the CSIC 2010 dataset, which is significantly higher than that of the traditional method, and has faster training speed. This suggests that the proposed method can detect more stealthy attacks and build a more effective Web attack detection system.

In the context of the new era, oilfield companies must transform the traditional oil industry through information technology to gain a foothold under the tide of the great era. Liao he Oilfield Special Oil Development Company is at the forefront of digital oilfield construction. It uses Internet of Things technology to realize remote monitoring of the production process, and uses smart equipment to intelligently control production dynamics, thereby effectively improving production and management efficiency. Therefore, this article will first briefly introduce the Internet of Things technology, then discuss the application of the Internet of Things technology in major domestic and foreign companies, and finally discuss the specific practice of the Internet of Things technology in the Liao he Oilfield Special Oil Development Company.

Garbage collection mechanism may cause system delay, Trim is a specific command for SSD to minimise the data volume of garbage collection, Trim function enhances SSD performance and extend SSD life. However Trim operation has impact on ordinary read-write IO delay, lead to business system delay and even system error. As to this issue, study on working schema of enterprise SSD trim, establish technical requirements for SSD testing workload model and QoS. Build up reliable testing plan on workload model, and adopt FIO tool to perform testing. The result shows such plan can conduct quantitative evaluation on SSD reliability scientifically and reasonably, assists the enterprises to choose credible SSD.

In ship transportation systems, various equipment requires periodic inspection to ensure the safe operation of the ship's engine room. Presently, professionals perform these inspections by checking off items on an inspection checklist and recording the status of each equipment item. However, this approach faces issues such as difficulty in the long-term preservation of paper records and an inability to effectively use them for fault data analysis, along with a lack of supervision, leading to missed inspections. To address these challenges, this paper presents a ZigBee technology-based data monitoring approach and fault intelligent diagnosis system that leverages the Internet of Things. This solution aims to enhance the efficiency and reliability of periodic inspections of ship engine rooms.

Autonomous System (AS) business relationship data serves as an important trust foundation for detecting route leaks. Some AS's reluctant to disclose all of their business relationship data for various reasons has led to a decrease in routing detection accuracy, requiring AS business relationship prediction. The existing AS business relationship prediction schemes have problems with data tampering and the accuracy needs to be improved. This article proposes a trusted AS business relationship prediction method based on neural networks. By collecting and storing AS business relationship data on the blockchain, a feature library of AS business relationship data is constructed. Finally, a deep neural network is used for AS business relationship prediction to achieve reliable and accurate AS business relationship prediction.

By summarizing and analyzing the main problems in the current development process of the Internet of Vessel (IoV), a universal architecture for software-defined Internet of Vessels is proposed and discussed in this paper. This architecture can meet the demands for heterogeneous interconnection, resource management, reliability and security, thus it should have a good application prospect and can satisfy multiple requirements of IoV users. Finally, taking marine rescue as an example, the advantages and prospects of software-defined IoV applications are clarified.

RDMA technology gradually being used in the network to solve the performance problems caused by frequent read and write memory during transmission. The industry has used a lossless network environment with flow control to ensure that the technology is rigorous. In order to improve the network efficiency, congestion control is used to provide the largest possible transmission bandwidth. At present, DCQCN, TIMELY, HPCC and other solutions are used in the mainstream. This thesis improves the congestion control scheme of DCQCN. By adding the delay as the auxiliary judgment reference, aiming at the conflict problem of a large number of burst flows encountered by the current DCQCN mechanism under severe congestion, the change of round-trip delay is used as the judgment standard to improve the bandwidth utilization of the network.

Ultraviolet has good inactivation effect on COVID-19 and other viruses, so it is widely recommended by the national health board. The disadvantages of mainstream UV disinfection lamps are high pressure and high mercury and no monitoring function, so a widely used UV virus inactivation system based on semiconductor ultraviolet light-emitting diode (LED) and avalanche photodetector (APD) was designed. The hardware of the system consists of LED drive circuit, APD drive circuit, signal conditioning circuit and control core myRIO. The function of ultraviolet radiation disinfection and dose monitoring can be realized by cooperating with the corresponding software. The experiment shows that the system has excellent linearity and stability, which is of great significance to the security of public living environment.

Low Power Lossy network (LLN) plays a significant role in the fields of battlefield target positioning, environmental investigation and monitoring, battlefield effect evaluation, and anti -terrorism equipment research. This article proposes a "active burden reduction" load balancing routing algorithm WB-RPL (Workload Balanced Routing Protocol In LLN). The new algorithm introduces the mechanism of "overload node avoidance", "selection of second -best father nodes", and "neighborhood entry update" mechanism, and realizes network load balancing. The algorithm simulation analysis was analyzed through the COOJA, and the results showed that the load balancing performance of the WB-RPL algorithm on the network under the same network scale increased the load balancing performance of the key node in the network by 57%compared to the RPL algorithm, which could make the relative load equilibrium between nodes at all levels of the network.

In order to improve the accuracy of the unmanned aerial vehicle (uav) when performing specific tasks path planning, based on information fusion is proposed in this paper, artificial potential field method, first of all, to install the radar and photoelectric detection device of the unmanned aerial vehicle (uav) as an example, the radar can obtain more accurate distance information, photoelectric detection device can obtain Angle information of target is of high precision, In this paper, the target state information is obtained by radar and photoelectric detection device respectively, and the more accurate target position information is obtained by distributed heterogeneous sensor fusion method. Secondly, the path planning of uav is carried out by improved artificial potential field method. Finally achieve the purpose of uav tracking target.

The industrial Internet can be seen as a combination of industrial software, sensors, networks and cloud platforms. Among them, industrial software is the key to data utilization while the cloud platform provides support for Industrial Internet. Through "visualization", "structuring", "modeling and simulation" and other means, industrial software can help solve the design and manufacturing problems of industrial products. But the difference between industrial Internet and traditional Internet lies in the different application scenarios and data characteristics, which also leads to the different emphasis of system architecture design. Considering the needs of high concurrency and huge data processing, the distributed system must provide high performance of communication and computing services. In addition, with the continuous expansion of the simulation model and the simulation scale, it becomes particularly significant to reduce the model development complexity and improve maintainability and flexibility. To make the distributed co-simulation engine more high-performance, we propose an asynchronous communication module as the underlying architecture of the distributed engine. Then combining the discrete event system specification and Actor model, a layered architecture has been proposed, which implements the modular programming of behavioral model by functional programming. Based on the architecture mentioned above, a distributed CAE cloud platform is proposed, which is hierarchical, modular, composable and reusable.

In the event of a disaster, mining social media tweets containing disaster information to analyze the dynamics of the disaster can help relevant authorities make quick emergency decisions and public opinion analysis. The data on social media jointly describe a thing to make it semantically related, but there are often structural and semantic imbalances between heterogeneous modalities. Most of the current research is devoted to formal complementation or semantic balancing, and multi-granularity resource balancing is beneficial to better obtain consistent information of heterogeneous modalities and remove redundancy. Based on the above problems, this paper proposes an end-to-end Multimodal Resource Balancing (MMRB) model, which designs cross-attention and two-way associative memory network modules while avoiding the loss of important information of unimodal modalities. The cross-attention module is used to capture the deep semantic correlations between modalities and weight the modal features to achieve semantic-level resource balancing. The associative memory module uses each modal feature to generate a complete feature representation of another modality on the pre-trained migration model to complement multimodal information at the feature level to improve the modal imbalance and modal missing problem of unstructured social media data. Finally, the fused features are weighted by the joint feature codec to reasonably focus on the contribution of different modal features in the joint features and the intrinsic association. Experiments are conducted on CrisisMMD, a public data of social media graphics used for disaster detection, and the accuracy of the model is stronger than the current multimodal resource balancing model, which verifies the effectiveness of our model.

Cloud computing has become an essential part of modern computing infrastructure, with many organizations adopting cloud-based architectures to improve scalability, reliability, and cost-effectiveness. In this context, service mesh has emerged as an essential technology for managing service-to-service communication and security in cloud computing. However, service mesh can pose significant challenges for resource allocation and management due to the dynamic nature of cloud-native applications and growing microservice instances. Cloud applications are subject to unpredictable traffic patterns, changing workloads, and shifting resource demands, which make it difficult to allocate resources appropriately. In this paper, we propose an adaptive resource scaling approach for service mesh that leverages practical method based on a feedback loop that continuously monitors the proxies’ CPU utilization and CPU throttling to predict its future resource requirements. Results show that our approach reduces the resource consumption by up to 40% compared to static resource allocation strategies, while maintaining the Service Level Object.

To adapt to the increasing demand for big data storage and analysis, and to solve the difficulties and pain points of Data Warehouse technology, it is necessary to build an efficient and high-quality data storage architecture. In this thesis, the evolution, technical background, architecture, and the current status of domestic and international research of the Data Lake concept were presented, Data Lake and Data Warehouse were compared and their advantages and disadvantages were summarized. The feasibility of Data Lakehouse and ‘Data Lake + Data Middle-end’ was discussed, and the problems that should be solved in the construction of ‘Data Lake + Data Middle-end’ were prospected. Finally, the challenges faced by the further development of Data Lake technology were analyzed, and some ideas to solve these problems were put forward.

The international railway logistics management system with a centralized system has problems such as information security, difficulty tracking logistics information, complex operating procedures, and information sharing and exchange. However, the decentralized, tamper-proof, and traceable features of blockchain technology can effectively enhance the data security and credibility of the system. Nonetheless, the development of smart contracts based on blockchain lacks a disciplined, standardized, and mature development process. On the other hand, adopting a Model-Driven Architecture (MDA) development approach can reduce human coding errors and improve development efficiency. This study proposed a modeling method based on UML class diagrams and state machine diagrams to analyze the static structure and dynamic behavior of the smart contract in the international railway logistics management system. By developing a conversion algorithm and combining the source and target metamodels, the platform-independent model (PIM) was converted into a platform-specific model (PSM), which was eventually transformed into code using a model-to-code transformation algorithm. The feasibility and correctness of this method were validated through experiments.

Forward Scatter Radar is a cutting-edge technical Radar to detect target across the baseline. In the first part, simply introduce some background of FSR application area. According to the calculation of the power budget, we can verify the data of the FSR system. Then it is the most crucial part, target signature, after simulation in Matlab we can get some figures regarding output, after low-pass filter and matched filter figures.