esa-sceva/satcom-chunk-collection huggingface dataset:
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 5 Conclusions  
An operations chain model for the description of the standard operations of a task is put forward for the first time. The Needleman-Wunsch sequence alignment algorithm is introduced to assess the operation procedure. Through example verification, the following conclusions are reached:  
1. The basic operations are combined to form an operation tree as operation nodes through the logical relationships of \"AND\", \"OR\", and \"MEX\", where each operation node represents a combination of multiple basic operations. These operation nodes are arranged in the required order to form an operation chain, which describes the various operation combinations for a training task. The multiple operation combinations provide multiple standard operation paths, enabling a comprehensive description of the standard operations for a specific problem. This approach effectively resolves the challenges of complex and difficult-to-describe structures for standard operations.
2. The Needleman-Wunsch algorithm is introduced to align multiple standard operation sequences with actual operation sequences, resulting in a similarity score between the standard and actual sequences. This score is considered the trainee's operation score, considering various scenarios such as erroneous operations, missed operations, additional operations, incorrect sequences, and critical operations. A unified approach is developed to handle these scenarios, deriving more objective operation procedure scores.
3. The overall assessment algorithm that combines the operations chain model with the sequence alignment algorithm demonstrates effectiveness in assessing the operation procedures. In future work, other alignment algorithms will be tested to find the best one. The method will be integrated into a comprehensive assessment algorithm and attempts will be made to integrate time distribution into the operations chain model, giving the method broader prospect for application and helping to improve equipment safety.  
Conceptualization, H.W. and B.H.; methodology, H.W.; software, B.H. and J.L.; validation, H.W., B.H. and J.L.; writing--original draft preparation, H.W. and B.H.; writing--review and editing, H.W.; visualization, J.L.; supervision, H.W.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 3 Simulation
### Application of the Kalman Filtering  
Simulations were performed to evaluate the performance in terms of noise rejection and response time for the EKF to estimate the reactor power when measurement data of the reactor power and the reactor period are assumed to be available. The simulation model composed of several function blocks is given in Figure 9.  
Figure 8: Comparison between true mean squared and averaged magnitude squared values.  
Figure 9: MATLAB Simulink model for the EKF process simulation.  
The process model implements the relationship between reactor period \((T_{e}(t))\) and logarithmic reactor power \((E(t))\) as defined in Equation (32). Measurement data of the reactor power is perturbed by process and measurement noises which are assumed as gaussian random numbers with zero means and variances of 0.005 [V\({}^{2}\)] and 0.057 [V\({}^{2}\)] respectively. But the measurement data of reactor power is assumed to be free of any external noise for simplicity. The EKF algorithm is implemented in the embedded MATLAB function block.  
Simulation results in Figure 9(a) show the estimates through EKF and discrete low-pass filter when the reactor power is steady at \(2\times 10^{-8}\) [%] and a cut-off frequency of 30 [Hz] for the low-pass filter. On the other hand, simulation results with increasing reactor power due to continuous step changes with a period of 2 [sec.] and duty cycle of 50% between 0.2 to 200.2 [sec] is in Figure 9(b).  
The comparative REMS for reactor power estimation when using extended Kalman filter and discrete low pass filter (with cut-off frequencies of 1, 10 & 30) is shown in Table 4. It showed that the error became lesser as cutoff frequency was zero for steady state but the RMSE jumped to 0.1755, which is much bigger than that of EKF, for increasing reactor power state with the same cutoff frequency. It can be seen that EKF performing steadily with an RMSE of 0.0457 for both steady state and increasing power. The response times of both filters satisfy the system requirements. Therefore,  
Figure 10: Simulated reactor power.  
it can be concluded that the application of EKF is beneficial for signal processing of the wide range monitoring instrumentation in order to efficiently reject both signals due to gamma ray interactions and electric noises from the logarithmic reactor power signal. This improved performance in noise rejection can reduce the possibility of unwanted reactor trips and boron dilution alarms.
## 3 Optimization Model for Load Balancing in Streets
### Vehicular Network Representation  
The vehicular network is modeled as a directed graph \(G=\textit{(N, L)}\) where \(N\) is the set of nodes representing the intersections, and \(L\) is the set of links representing roads connecting the nodes; \(i\), \(j\in N\) are nodes and \(\textit{(i,j)}\in L\) is an ordered pair corresponding to a street \(L\). For a vehicle \(\
u\), nodes \(e_{\
u},d_{\
u}\in N\) are defined, representing respectively, origin and destination node. A vehicle will move through the streets of the network until it reaches its destination, so its path corresponds to a set of streets \(P_{\
u}=\{(e_{\
u},j_{1}),...,(i_{n},d_{\
u})\) \(\}\) as shown in the Figure 8.  
The network has a set of states \(k\) corresponding to the way vehicles are located in the different network links, in certain moment of time. This states are illustrated in the Figure 9, where we have two vehicles moving through a vehicular network, and there are 4 total states: in the state 1, the **vehicle 1** is in the street (\(n1\), \(n2\)), and the **vehicle 2** is in street (\(n1\), \(n4\)); in the state 2, the **vehicle 1** is in (\(n2\), \(n5\)) and the **vehicle 2** is in (\(n4\), \(n6\)); in the state 3, **vehicle 1** is in (\(n5\), \(n7\)) and the **vehicle 2** is in (\(n6\), \(n5\)); in the state 4, **vehicle 1** is not in the network anymore, and **vehicle 2** is in (\(n5\), \(n3\)).  
A street \((i,j)\) has an use \(U_{(i,j)}(k)\) corresponding to the total number of vehicles on the street at a state \(k\), and a maximum capacity \(C_{(i,j)}\). A decision binary variable \(X_{\
u(i,j)d_{\
u}}(k)\) is defined such that \(X_{\
u(i,j)d_{\
u}}(k)=1\) if in a state \(k\) a vehicle \(\
u\) with destination \(d_{\
u}\) takes the street \((i,j)\), or 0 otherwise. Thus, the solution vector \(X\) is the set of assignments of each car to different streets of the network in each state.  
Figure 8: Illustration of a vehicle’s route in the vehicular network.
# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 2 Materials and Methods
### Operational Safety Situation of the Terminal Area Control System
#### 2.3.2 Assessment of the Operational Safety Situation of the Terminal Area Control System  
This study regards the number of aircrafts as a main element influencing the incidence of dangerous occurrences inside of the terminal area control system, given that the risk of aircraft collisions is a vital metric for assessing the operational safety. Furthermore, both of the probabilities of risk and the weights of the risk levels are considered to evaluate the operational safety situation value of the terminal area control system over a designated time frame.  
Let \(\mathrm{S_{A}(T)}\) denote the assessment value of the operational safety situation for the terminal area control system over time period T, which can be expressed as follows:  
\[S_{A}(T)=maxW_{f_{i}}+\frac{\sum_{i=1}^{k}P_{f_{i}}W_{f_{i}}C_{f_{i}}}{\sum_{i= 1}^{k}C_{f_{i}}\cdot W_{f_{i}}} \tag{12}\]  
In Equation (12), \(\mathrm{W_{f_{i}}}\) represents the weight corresponding to the risk level of the i-th unsafe event; \(\mathrm{P_{f_{i}}}\) denotes the probability of the i-th unsafe event occurring; \(\mathrm{C_{f_{i}}}\) indicates the impact range of the operational risk associated with the i-th unsafe event; \(\mathrm{f_{i}}\) refers to the i-th type of unsafe event, which constitutes the primary indicators of the operational safety situation of the terminal area control system; k represents the total number of unsafe events considered. In this study, three main types of unsafe events are selected, hence k = 3.  
The integer part of the safety situation value obtained from the formula indicates the most severe risk that may exist within the system, while the decimal part reflects the expected ratio of the number of aircrafts affected by unsafe events to the total number of aircrafts that could potentially be impacted within the system. A value closer to one signifies that more aircrafts are influenced by the associated risks.  
Furthermore, the \(\mathrm{S_{A}(T)}\) value represents the overall operational safety situation of the terminal area control system. When \(\mathrm{S_{A}(T)}=0\), it indicates that there is no risk present in the system. Conversely, when \(\mathrm{S_{A}(T)}=6\), it signifies that the risk probabilities for all terminal area operations are at 100%, indicating that the entire system is in an extremely hazardous condition.  
When \(\mathrm{S_{A}(T)}\) falls between 0 and 6, smaller values correspond to a better safety situation within the system. The interpretation of the safety situation values is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Risk Probability** & **Risk Level** & **Level Weight** \\ \hline
0 & No Risk & 0 \\ (0, 0.2] & Lower Risk & 1 \\ (0.2, 0.4] & Low Risk & 2 \\ (0.4, 0.6] & Medium Risk & 3 \\ (0.6, 0.8] & High Risk & 4 \\ (0.8, 1.0] & Higher Risk & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Risk level.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Anti-UAV in IoT Environments  
With the development of UAV technology, UAVs have become an important part of the modern IoT. Anonymous UAVs may hijack, steal, or disrupt data transmission within the Internet of Things. Therefore, Anti-UAV in IoT Environments face some new application scenarios, such as detecting only UAVs that attempt to invade the IoT, rather than all UAVs that invade areas. In the IoT, there are a great number of edge devices, and the detection and tracking of UAVs with the help of edge computing is also a research direction. This part reviews some research on counter-UAVs in the IoT environment.
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
###### Abstract  
High-precision navigation and positioning technology for indoor areas has become one of the research hotspots in the current navigation field. However, due to the complexity of the indoor environment, this technology direction is also one of the research difficulties. At present, our common indoor positioning methods are WIFI, Bluetooth, LED, ultrasound and pseudo satellite. However, due to the problem of inaccurate direct or indirect ranging, the positioning accuracy is usually affected, which makes the final application difficult to achieve. In order to avoid the ranging limitations of the existing methods, a new dual-frequency entanglement constraint (DFEC) ranging method based on homologous base station is proposed in this paper. The relationship between the homologous characteristics of dual-frequency signals and the phase relationship within the cycle is used to estimate the current carrier phase adjustment the true value of the cycle count is used to get rid of the constraints of the ranging conditions and improve the ranging accuracy. In order to verify the feasibility of this method, the wired environment test and the typical characteristic points of wireless environment are tested and analyzed respectively. The analysis results show that in the wired environment, the transmitting base station and the receiving terminal will introduce a ranging error of one wavelength; in the wireless environment, due to the influence of spatial noise and multipath, the error of the estimation of the whole cycles of the ranging value increases significantly. And this phenomenon is most obvious especially in the region where the signal is shaded, but the error estimate that satisfies \(\pm\) 1 wavelength still accounts for 90%. Based on this, we conduct multiple observation data collection at five typical feature points, and used existing MATLAB positioning algorithms to conduct positioning error tests. The analysis found that under this error condition, the positioning accuracy was about 0.6 m, and 93% of the points met the 1-m positioning accuracy.  
Keywords: indoor positioning; dual-frequency; homologous base station; ranging +
Footnote †: journal: Accepted: 10 April 2020; Published: 15 April 2020
## 3 Challenges and Future Directions  
The advancement of research in secure, sustainable smart cities and the IoT entails addressing multifaceted challenges while exploring promising avenues for future investigation. A primary hurdle involves fortifying cybersecurity measures to counter potential cyber threats and breaches within smart city infrastructures and IoT networks. Interoperability issues among diverse IoT devices and systems also present a significant challenge, necessitating standardization efforts and innovative solutions for seamless integration. Moreover, privacy concerns surrounding data collection, storage, and utilization in smart city environments underscore the need for developing privacy-preserving technologies and regulatory frameworks. Energy efficiency and sustainability emerge as critical imperatives, prompting the optimization of energy consumption and the integration of renewable energy sources within smart city architectures. Future research endeavors may delve into novel methodologies to enhance the resilience, scalability, and adaptability of smart city systems, leveraging emerging technologies such as edge computing, blockchain, and artificial intelligence. Interdisciplinary collaboration and stakeholder engagement will play pivotal roles in addressing these challenges and unlocking the full potential of secure, sustainable smart cities and the IoT.
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Green Ship Technology
#### 3.2.4 Integration of Energy-Saving Technologies  
To achieve the best match of the ship, engine, propeller, and electricity generator for inland ships, an evaluation of the heat, machinery, electricity, and fluid, focusing on integrated energy management, and using energy efficiency design index (EEDI) minimization as the objective, was conducted. This resulted in an integrated design for energy savings and environmental protection, an integrated power plant design and optimized configuration, a pollution-free stern tube and low-noise shafting design, and a smart power station design [39].  
First, an optimal arrangement of the exhaust system was designed by studying the characteristics of the exhaust noise of the main engine and comprehensively considering the exhaust volume and temperature. A new pollution-free stern tube and water lubrication system led to the design of a bilge water system that minimized waste oil discharge and achieved our environmental-protection goal. Next, an optimized propulsion system plan was designed based on comprehensive analysis of the characteristics of inland ship power plants, energy-saving and green design technical requirements, and LNG and lithium battery power characteristics [40]. An integrated design platform for inland ship power plants was built based upon power-integrated design methods and optimized configuration methods.  
In addition, the torsional vibrations, cyclotron vibrations, alignment of the propulsion shafting, and the effect of external and system disturbances on the dynamic characteristics of the key shafting components were analyzed. Further, parametric mathematical and physical simulations of the shafting alignment were conducted, a shafting vibration calculation method and model under complex factors were constructed, and a low-noise shafting design analysis process and method was established for the system [41]. Finally, smart power station management systems, broadband shafting generator output frequency control, and the energy-saving efficiency were analyzed to build smart inland ship power stations and provide support for energy savings and emission reduction in ships under complex working conditions.
## 2 Background
### SDN Security Issues
#### 2.3.3 Data Plane Security  
For data plane security issues, attention is paid to OpenFlow flows. The number of flow tables that can be installed in a switch are limited, depending on the switch's capacity. In addition, there are two different ways for installing a new flow rule in a switch: in a reactive way, where the rule is installed once the first packet comes from a new host, or in a proactive way, where rules are installed before a new host sends packets. Since the decision-making capacity is not a task of the switches, first, it is very important to differentiate the benign flows from the malicious flows. Second, a switch's capacity is limited by the number of flows that can be stored in the buffer flow. In particular, the buffer flow is a buffer that stores flow rules until the controller emits flow rules. The buffer flow limitation makes the data plane prone to saturation attacks due to storing malicious flows in the buffer.  
Security issues in the SDN control plane can directly impact the security of the data plane [41]. This is because if the security of the SDN controller is compromised, the behavior of the underlying network is consequently compromised. Indeed, the data plane might be unavailable, and consequently, the whole network goes offline if the switches do not receive flow instructions from the control plane due to security issues at the control plane [44]. In particular, several attacks, such as man-in-the-middle and black hole attacks, can exploit the control and data plane separation to maliciously manipulate flows and compromise the OpenFlow rules.  
Transport Layer Security (TLS) [45] and Datagram Transport Layer Security (DTLS) [46] are security protocols inserted into the OpenFlow protocol specification for ensuring secure controller-switch communication. However, the latest version of OpenFlow (version 1.4.0) defines the TLS protocol as optional due to its configuration complexity. These complex configuration steps include several aspects, such as the generation of controller and switch certificates, which makes switch vendors skip the TLS support in them.  
Consequently, the optional use of TLS in OpenFlow networks opens the way for several types of attacks that can compromise the security of the control channel. For instance, [PERSON] et al. [47] demonstrated that a man-in-the-middle attack can be more lethal in OpenFlow networks compared with traditional networks. This is because without TLS, there is no authentication mechanism in the plain-text OpenFlow TCP control channel,and therefore, a malicious user can take full control of any switches and execute fine-grained sniffing attacks.
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 1 Introduction
(3) Contactless: finger vein images can be acquired using contactless sensors, effectively preventing bacterial transmission caused by physical contact, making the method hygienic and convenient [9]. (4) High security: finger veins are internal human features that can only be captured using specialized devices, making the features difficult to leak. Moreover, because finger vein recognition relies on the flow of blood within the finger [10], it is highly resistant to forgery using inanimate molds, enhancing its anti-spoofing capabilities. Due to its superior security, low-cost acquisition devices, and potential for large-scale applications, finger vein recognition technology has become a research hotspot, with particularly rapid developments over the past decade. In recent years, numerous outstanding finger vein recognition algorithms have emerged. By incorporating deeper feature extraction networks and module stacking techniques, these algorithms have significantly improved the accuracy of finger vein recognition, thereby promoting its widespread application across various fields. However, the resulting increase in model complexity and lower computational efficiency have notably prolonged recognition time, making it challenging to simultaneously meet the demands of real-time performance, user experience, and security in practical applications [11]. In scenarios such as smartphone unlocking, access control systems, and financial payments, users expect identity verification to be completed almost instantaneously, as prolongedwaiting times can degrade user experience and even hinder the adoption of the technology [12]. In environments such as banks, subways, and train stations, where hundreds or even thousands of people may require identity verification per second, excessive system response times can lead to congestion and disrupt operations [13]. Simply reducing model depth and complexity can enhance computational efficiency, but it often results in decreased recognition accuracy, making it difficult to meet the core security and reliability requirements of biometric recognition technology. Therefore, achieving a balance between recognition accuracy and efficiency remains one of the key challenges in finger vein recognition technology. To address the aforementioned issues and enable finger vein recognition algorithms to achieve both recognition accuracy and efficiency, this paper proposes a deep learning network model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which ensures a high recognition accuracy while significantly improving recognition efficiency.