esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis of Switching Topology Formation
The figure below shows the construction and path changes of the topology graph of multiple agents when there is random damage in the time-varying formation topology. The arbitrarily initial positions of the seven agents can be obtained as \(q(0)=q^{*}+rand(2,7)\), where the desired positions of these agents are \(q^{*}= \begin{bmatrix}4&2&2&0&0&-2&-2\\ 0&2&-2&2&-2&2&-2\end{bmatrix} \). With these control parameters, the trajectory snapshots of the multi-agent system at different times are plotted in Figure 7. From the results, the formation changed three times in total by disabling one follower at a time in the simulation. At the initial moment t = 0, the agents are in random loose positions. With the formation control, the designed formation can be formed at t = 17 s. We note that the formation converges to the desired position from a random initial formation shape at the beginning using the given control law. At around the 40 s, the formation changes the scale and geometric pattern. As can be seen from the figure, the three leaders change from a triangle to a line formation at around 100 s. It can be observed that no matter how the formation changes, the parallel or collinearity lines are still preserved during the formation control. The pink line under the trajectory graph is the tracking error image, which can gradually converge to 0 with each change of formation. It can be clearly seen in Figure 8 that for the followers, the time-varying target formation would produce the corresponding tracking errors at 3 different time points (40 s, 70 s, 100 s). After adding the improved Hungarian algorithm to the target point position distribution, the tracking error is kept within a small range, and can converge to 0 as soon as possible. Figure 8 shows that the affine formation tracking error of followers converges to zeros within 16 s, 54 s, 83 s, and 109 s.  
Figure 7: Time-varying formation topology path diagram.
## 3 Analysis of BER for SISO System
### Scenario 1
#### 3.1.1 BER during Jamming Active
\begin{split} P_{e1a}(h_{i})&=\frac{\rho}{2}\bigg{[} 1-\exp\left(-\frac{\sigma_{20 aA}^{2}}{2\sigma_{aAaA}^{2}}\right)\frac{\alpha_{ j}\beta_{j}\zeta_{j}^{2}\sqrt{p_{f}/\rho}h_{i}}{A_{0_{f}}\Gamma(\alpha_{j}) \Gamma(\beta_{j})}\\ &\times G_{2A}^{4,0}\left(\frac{\alpha_{j}\beta_{j}h_{i}\sqrt{p_{f}/ \rho}}{A_{0_{f}}}\bigg{|}\begin{array}{c}\xi_{j}^{2},0\\ -1,\xi_{j}^{2}-1,\alpha_{j}-1,\beta_{j}-1\end{array}\right)\end{split}  \tag{19}\]  
According to [25], in the presence of jamming, we can derive the expression for ABER from (19) as follows  
\[ \begin{split} P_{e1a}\Big{(}\overline{\gamma}_{JD}\Big{)}=\int_{0 }^{\infty}P_{e1a}(h_{i})f_{h_{i}}(h_{i})dh_{i}\end{split}  \tag{20}\]  
By substituting (10) and (19) into (20) and integrating using 07.34.21.0011.01 [29], we can obtain \[ \begin{split}& P_{e1a}\Big{(}\overline{\gamma}_{JD}\Big{)}=\frac{\xi_{p} ^{2}}{\Gamma(x_{p})\Gamma(\beta_{p})}\frac{\xi_{q}^{2}}{\Gamma(x_{q})\Gamma( \beta_{q})}\frac{\beta}{\Xi}\bigg{[}1-\exp\left(-\frac{\phi_{pda}^{2}}{2\sigma_ {pda}^{2}}\right)\frac{\alpha_{p}\beta_{q}\xi_{q}^{2}}{A_{0}\Gamma(x_{q}) \Gamma(\beta_{q})}\\ &\times\frac{A_{0p}A_{0q}}{\pi_{p}\beta_{p}\pi_{q}\beta_{q}}\zeta_ {8,6}^{4.6}\left(\begin{array}{c}\alpha_{s}\beta_{f}\lambda_{Ap}A_{0q}\sqrt{ \rho\overline{\gamma}_{JD}}\\ \alpha_{p}\beta_{p}\alpha_{q}\beta_{q}\end{array}\right)^{2}\left[\begin{array}[ ]{c}-\xi_{p}^{2},-\alpha_{p},-\beta_{p},-\xi_{q}^{2},-\alpha_{p},-\beta_{q}, \xi_{p}^{2},0\\ -1,\xi_{p}^{2}-1,\alpha_{f}-1,\beta_{f}-1,-1-\xi_{p}^{2},-1-\xi_{q}^{2}\\ \end{array}\right)\end{split}  \tag{21}\]  
where \(\overline{\gamma}_{JD}=\frac{P_{p}}{P_{j}}\) is average signal jamming ratio (SJR).
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 3 3 GPP 5G Technology for TSN
### TSN Issues in Release 17  
In Release 17, 5 GS intends to support uplink synchronization for interworking with TSN [25]. In Release 16, only the architecture where the TSN GM is connected to the network side was supported. However, Release 17 intends to develop a standard technology in which 5 GS provides time synchronization in the architecture where multiple TSN GMs are connected to the UE side (Figure 4). In addition, Release 17 has proposed various methods to support UE-to-UE communication that inevitably occurs due to uplink synchronization [31]. As a result, the 5 GS uses a local switch, where traffic is forwarded locally by a single UPF, for UE-to-UE communication. The 3 GPP mobile communication system generally supports uplink/downlink traffic communication between UE networks. Nevertheless, for uplink synchronization, when a UE is connected to the TSN, the GM receiving a gPTP message must transmit the message to a network and other UEs belonging to the same TSN domain. Accordingly, UE-to-UE communication occurs when the uplink traffic and downlink traffic occur together.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 3 Rural Microgrid Modeling
### Energy-Storage Unit Modeling  
CES is a grid-based shared energy-storage model, as shown in Figure 2. The CES system consists of three main components: users, the CES operator and energy-storage facilities [25]. Users participating in a CES system do not need to purchase their own energy-storage devices with a fixed capacity. Instead, they get access to cloud batteries by subscribing to CES services, which not only meet their own storage needs but also allow them to get adjustable storage capacity. After subscribing to CES services, users can freely control the charging and discharging behavior of the cloud batteries through a software interface with the CES operator [26]. Moreover, the users can also change the storage capacity of their leased cloud batteries for a period of a day, a month or even a quarter [26]. There is two-way transmission of information and energy between CES and users, and this relies on the grid to realize two-way transmission of energy. On the other hand, the CES operator invests and manages large-scale energy-storage facilities, thus eliminating the trouble of installation and maintenance for users. The CES has lower energy-storage cost, higher energy-storage resource utilization and more variable energy-storage capacity than other energy-storage models.  
All the electricity generated from the rural microgrid can be stored in the CES, which will centralize the storage resources of all participants to help the microgrid achieve energy dispatching.  
There are battery charging/discharging behaviors and energy trading behaviors with the grid in the CES system. The energy-storage status of a CES system is as follows:  
\[E_{\text{\tiny{CES}}}(h)\ =\ E_{\text{\tiny{CES}}}(h-1)\ +\ (P_{\text{\tiny{CES}}}^{ ch}(h)A_{\text{\tiny{CES}}}^{ ch}-\ \frac{P_{\text{\tiny{CES}}}^{ ch}(h)}{\lambda_{\text{\tiny{CES}}}^{ ch}})\Delta h\ +\ E_{\text{\tiny{buy}}}^{grid}(h)\ -\ E_{\text{\tiny{self}}}^{grid}(h),\quad\forall h\in\Gamma, \tag{3}\]  
\[E_{\text{\tiny{CES}}}(0)=\ S_{b,\min}.E_{\text{\tiny{CES}}}^{ rent}, \tag{4}\]  
The CES also needs to meet the following capacity constraints and power constraints in the process of power dispatching:  
\[0\leq P_{\text{\tiny{CES}}}^{ch}(h)\leq P_{\text{\tiny{CES}}}^{ ch,\max},\forall h\in\Gamma, \tag{5}\]  
\[0\leq P_{\text{\tiny{CES}}}^{ch}(h)\leq P_{\text{\tiny{CES}}}^{ ch,\max},\forall h\in\Gamma, \tag{6}\]  
\[0\leq E_{\text{\tiny{CES}}}(h)\leq E_{\text{\tiny{CES}}}^{ total},\quad\forall h\in\Gamma, \tag{7}\]  
Figure 2: Model of CES system.
# A Performance Test and Internal Flow Field Simulation of a Vortex Pump
## 4 Numerical Calculation of Pump's Internal Flow Field
### 3D Modeling and Grid Division  
The 3D modeling software Pro/ENGINEER (Version 5.0, PTC, Needham, MA, USA, 2015) [16; 17; 18; 19] was used to perform solid modeling of the vortex chamber of the vortex pump and impeller region. The computational domain consists of the impeller region and volume region, in which the volute region is the stationary region. The impeller region is the rotating region and the transmission coupling flow parameter between the impeller and volute region is processed by the frozen rotor method. The calculation of the impeller flow field is carried out in the relative coordinate system, while the vortex chamber flow field calculation is carried out in the absolute coordinate system and the integral calculation of the dynamic and static components is realized through the coupling calculations between the two parts. Because the interface grid between the computational domains is not exactly the same, the general grid interfaces (GGIs) are used to connect the parts. The computational grids are divided by ANSYS(r) ICEM Tetra tools (Version 14.5, ANSYS, Canonsburg, PA, USA, 2012). The tetrahedral mesh is divided into the vortex chamber region and the impeller region, respectively, and the residual error is \(10^{-4}\). The number of mesh units is 616,369, and the number of nodes is 117,300, as shown in Figure 4.  
Figure 4: Simulation grids: (**a**) Volute; (**b**) Impeller.
## 2 Materials and Methods
### Classification Methods
#### 2.3.2 Discriminant Analysis  
Discriminant analysis (DA) is a classification method used to determine the discrimination boundaries separating two or more categories of objects [28]. DA assumes that different categories generate data based on different Gaussian distributions. DA has been shown to rank among the top classifiers, which is most probably due to the bias\(-\)variance trade-off where the data can only support simple decision boundaries such as linear or quadratic, and estimates obtained by Gaussian models are stable [29]. In this study, quadratic DA was applied.
## 3 Illustration of a Plate-Isolator System as a 2-Stage Vibration Isolation System
### Horizontal (X and Z)
Generally, the addition of a lateral elastic boundary to the structural plate can significantly enhance the vibration isolation efficiency of the system, particularly at higher frequencies. Lower lateral boundary stiffness values yield better overall vibration isolation efficiency. In the X and Z directions, if \(K_{\text{boundary-X}}\) = 563,210 N/m and \(K_{\text{boundary-Z}}\) = 610,108 N/m, the force transmissibility curves of the plate-isolator system exhibit a single peak at 13.5 Hz, which is in proximity to the fundamental frequency of the vibration isolation platform (13.354 Hz). For simplicity, a lateral boundary stiffness of \(K_{\text{boundary}}\) = 600,000 N/m is used for the structural plate in both X and Z directions in this study. Figure 9: Effect of the horizontal boundary elasticity on force transmissibility of the 3-stage plate-isolator system: (**a**) in the X direction (\(\text{C}_{\text{boundary}}\) = 10 Ns/m, \(K_{\text{isolator-X}}\) = 56,321 N/m, \(\text{C}_{\text{isolator}}\) = 600 Ns/m); (**b**) in the Z direction (\(\text{C}_{\text{boundary}}\) = 10 Ns/m, \(K_{\text{isolator-Z}}\) = 152,527 N/m, \(\text{C}_{\text{isolator}}\) = 600 Ns/m). The effect of lateral boundary damping on the force transmissibility of the plate-isolator system is analyzed in Figure 10. The results show that a large value of the lateral boundary damping leads to lower peak of the system response. However, to ensure the vibration isolation effect at high frequencies, a compromise value of lateral boundary damping coefficient \(C_{\text{boundary}}=2000\)Ns/m is selected. Based on the simplified 3-stage plate-isolator system, a group of boundary stiffness and damping coefficients are selected. In the vertical direction (Y), \(K_{\text{boundary}}=340\),000 N/m, \(C_{\text{boundary}}=2000\)Ns/m. In the horizontal direction (X and Z), \(K_{\text{boundary}}=600\),000 N/m, \(C_{\text{boundary}}=2000\)Ns/m. In the next section, finite element simulation will be used to further verify the influence of elastic plate boundaries on the kinetic response of the structural plate. Figure 10: Force transmissibility curve of the 3-stage plate-isolator system with different lateral damping of the structural plate boundary: (**a**) in the X direction (\(K_{\text{boundary}}=600\),000 N/s, \(C_{\text{isolator}}=600\)Ns/m, \(K_{\text{isolator-x}}=56321\)N/m); (**b**) in the Z direction (\(K_{\text{boundary}}=600\),000 N/s, \(C_{\text{isolator}}=600\)Ns/m, \(K_{\text{isolator-z}}=152\),527 N/m).
## 2 Comparison between Active and Passive Networks  
There is a massive energy shift from the traditionally centralized power systems in which power flows in one direction to unidirectional power flow, operating electrical networks with smart grids, communication flow, and information exchange [1]. The centralized electrical network has challenges, such as reliability, scalability, and slow responses from the central point [15]. Continuous technological development is pivotal in how electrical distribution networks are evolving. The solutions from these technological advancements provide benefits to entities operating in that space. Nevertheless, the implementation of such technologies in a slow-maturing distribution network undoubtedly results in challenges [16].  
In an ideal working operation, the distribution network works as a perfectly balanced three-phase system. However, the increased penetration of DG and power electronic interface devices are but some of the equipment that introduces disturbances that require mitigation for system stability. The electrical network's primary objective is to dispatch power between bulk power systems and the consumer and to regulate voltage to its nominal values and power quality [17]. The active distribution network is a system that has adequate control to manage a mix of different generators, loads, and energy storage systems [18]. Passive distribution networks are dependent on significant generating capacity to deal with disturbances in load variation to maintain continuous power flow and have a relatively simple control technique [19].  
Active distribution systems possess the ability to handle real-time monitoring and control of the electrical network and are capable of employing distributed generators to provide stability on low-voltage networks [20]. Controlling and monitoring of the active distribution network are carried out through centralized systems and distribution management systems. This is done at the primary substation and at medium voltage (MV) feeder to improve grid stability [21; 22].
# A Multimode Fusion-Based Aviation Communication System
## 3 Hardware Design
### Public Network Communication Module  
The fifth-generation mobile communication technology, also known as 5G, is a new and advanced broadband mobile communication technology that offers high speed, low latency, and large connectivity. The 5G communication facilities are the network infrastructure that enables the connectivity between humans, machines, and objects, as shown in Figure 5. The 5G public network communication link has become a crucial infrastructure for the digital, networked, and intelligent transformation of aviation communication. The communication functions of the public network communication link include:  
1. The optical data collected by the aircraft is transmitted to the ground control center via a public network communication link, where the images and videos are analyzed and displayed.
2. The aircraft control computer transmits flight status data to the ground control center via a public network communication link for analysis and display.
3. Ground control commands are transmitted to the aircraft by sending the command data to the aviation multimode fusion communication terminal via a public network communication link connected to the ground base station. The aircraft control computer processes this data.  
Figure 4: Data transmission networking module composition block diagram.  
Figure 3: Dedicated ad hoc link schematic diagram.  
In the coverage range of mobile base stations, the ground control center can communicate bidirectionally with thousands of aviation multimode fusion communication terminals.  
The circuit diagram of the public network communication module is shown in Figure 6.  
The development of the module is based on the 5G module, which is a 5G wireless communication module that supports a diversity reception function. Various network data connection standards are supported in this module, such as 5G NR SA/NSA, LTE-FDD, LTE-TDD, DC-HSDPA, HSPA+, HSDPA, HSUPA, WCDMA, etc. The power consumption of the module is about 5 W.
## 4 The Proposed Tracking Anchor Based Clustering Method  
In this section, a detailed illustration of a tracking-anchor-based clustering method (TACM) will be given. TACM contains four phases: tracking anchor selection, node activation, cluster formation, and state scheduling for CMs. First, the rough-fuzzy C-means (RFCM) algorithm is used to determine the location of the tracking anchor and the affiliation of surrounding nodes. Then the membership table is built, and the rules for node activation are proposed. After that, the activated nodes become the tracking node set, and the cluster head (CH) is selected by the delay broadcast mechanism. Finally, using linear 0-1 programming, CMs are scheduled to the appropriate state to reduce redundant transmissions.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.14 Dbscan  
Density-based spatial clustering of application with noise (DBSCAN) is described by [10] as an algorithm used in detecting anomaly events. An unsupervised clustering algorithm assigns data points to clusters based on their spatial density. The algorithm defines a neighbourhood around each data point and grouping points based on their proximity or density. The purpose of the algorithm is to detect distinct anomalies that belong to the same underlying event based on the anomaly level time-series value of the profile features. For example, it could cluster together data points representing normal system behaviour while leaving outlying data points as noise. The resulting clusters can predict future system behaviour and diagnose faults or failures. The self-healing functionality is achieved using each profiled feature or KPI (key performance indicators) to detect anomalies in timespan using DBSCAN. Observation for a time t is considered abnormal if the anomalous value density cap goes above a given threshold. DBSACN can also identify anomalies in network traffic or security data. By clustering together network traffic data, it could identify unusual patterns of activity that could indicate a potential security breach, which could trigger an automated remedial response.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 3 Example Analysis  
### Data Sources  
This paper is based on the ISCX VPN-NonVPN dataset [27], which is the time flow feature data. The dataset has 14 kinds of traffic data, including 7 kinds of conventional encrypted traffic and 7 kinds of VPN-encrypted traffic. This dataset has become a common dataset for current research. In this paper, the Scenario A2 folder data of the ISCX VPN-NonVPN dataset is identified and researched. It contains 7 kinds of VPN-encrypted traffic. After integrating the stream data at different times, the final ensemble data is 37,028. Each file contains 23 time-related feature columns. The traffic classes and corresponding application content in the dataset are shown in Table 1, and the time-related features and their descriptions are shown in Table 2.  
The dataset is generated by capturing traffic with Wireshark and tcpdump. This process uses an external VPN service provider and connects to the VPN using OpenVPN. In order to generate SFTP and FTPS traffic, an external service provider and Filezilla are used as clients. Filters are used to only capture data packets with source or destination IP, that is, the address of the local client. The capture path of VPN-encrypted traffic is shown in Figure 6.
## Materials and Methods
### Apparatus Description  
This study used an RDA to simulate external flow. The schematic representation of this setup is shown in Figure 1. This instrument mainly comprises a stainless steel cylindrical fluid container and 2 cm-thick stainless steel lid to seal the solution. The diameter and height of the stainless steel cylinder are 18 and 11 cm, respectively. The distance between the disk surface and fluid container lid is 10 mm, and such distant is constant for all disk types. At the center of the lid is a 22 mm diameter circle that enables the entry of a 1.9 cm diameter rotating shaft. This part is supported by two bearings to avoid vibration at high rotational speeds. An aluminum rotary disk with 0.6 cm thickness and 14 cm diameter is connected to one side of the shaft. On the other side of the shaft is an electric servo motor (XINJE, AC SERVO MOTOR, MS-80 STE-M02430B-20P7, Wuxi Xinje electric Co., Ltd., Wuxi, China.) that enables the disk to be rotated with different rotational speed values from 0 to 3000 rpm. The drive motor is supported by a cast iron frame, which ensures the accurate positioning of the motor. The volume of solution required to fill the container is approximately 4.2 L. RDA is connected to a computer display system for data collection, recording, and control of the rotational disk speed. The loaded torque on the disk is measured using a highly precise torque sensor (LONGLV-WTQ1050D, Shanghai Long Journey Electronics Technology Co., Ltd., Shanghai, China) Range: 3 Nm, Output: 1.397 mV/V). The torque values are transformed to readable form in the computer display system using the InduSoft Web Studio v7.1 software, by an Invensys company, London, UK, 2013. A temperature sensor (screw in thermocouple type-T) is used to measure the solution temperature during the operation process and is observed from the computer display system. An electric motor with a spiral thread shift is used to make the disk changing process easy by moving the disk holding shift up to change the disk and later move it down until the desired level of testing. For all experiments, the system temperature was maintained at 27 \(\pm\) 0.05 \({}^{\circ}\)C. When essential torques, that is, \(T_{\rm s}\) and \(T_{\rm c}\), rotate the disk in the pure solvent and the complex mixture solution, respectively, the percent DR can be determined from the following formula:  
\[\%DR=\frac{T_{\rm s}-T_{\rm c}}{T_{\rm s}}\times 100\% \tag{1}\]  
The rotational Reynolds number is calculated as follows:  
\[Re=\frac{\rho\omega r^{2}}{\mu} \tag{2}\]  
where \(\rho\) is the fluid density, \(\mu\) is the fluid viscosity, and \(r\) is the radius of the disk. The critical Reynolds number shows that the flow transfer from laminar to turbulent is 3 \(\times\) 10\({}^{5}\)[4; 19; 36; 37].