esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This research demonstrates that the pulsed electric fields (PEF) technology has a positive effect on the infrared-convective (IR-CD) drying process of apples, but it may also be associated with the risk of deterioration of their quality. It was proven that the electric field-induced damage to the cell membrane resulted in a reduced drying time of apples by up to 20%, but at the same time, the physicochemical characteristic indicated more intensive browning of the samples and lower retention of antioxidants. The distance between the infrared lamps and the apple slices during drying was also significant; the shortest (10 cm) favored quick water removal, the highest (30 cm) limited color changes, but it was the medium (20 cm) that proved to be the most beneficial for the preservation of antioxidants.  
Figure 9: Values of (**a**) EC\({}_{50}\) ABTS and (**b**) EC\({}_{50}\) DPPH (antioxidant capacity) of dried apples depending on the various process parameters (distance between the infrared lamps and the apple slices during infrared–convective drying, IR-CD: 10, 20, and 30 cm; specific energy input during pulsed electric fields pretreatment, PEF: 0, 1, 3.5, and 6 kJ/kg); the uppercase letters (A–C) represent homogeneous groups in terms of distance, but the lowercase letters (a–d) indicate homogeneous groups in terms of specific energy input (\(\alpha\) = 0.05).  
**Author Contributions:** Conceptualization, A.S., M.N. and A.W.; methodology, A.S., M.N. and A.W.; software, A.S.; validation, A.S.; formal analysis, A.S.; investigation, A.S.; resources, A.S., M.N. and A.W.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S., E.G., M.N. and A.W.; visualization, A.S.; supervision, A.W.; project administration, M.N. and A.W.; funding acquisition, A.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 817683 (acronym FOX).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Design of Multimedia Independent Multipath Routing Algorithms
### Error Correction Calculation  
In most cases, the next hop node is closer than the virtual node. As the node forwards, this error accumulates. Therefore, a correction factor \(\rho\) is introduced to improve the actual transmission distance per hop and eliminate the error [19]. In Figure 6, \(v_{i+1}\) is the ideal next hop node of \(n_{i}\) node, \(n_{i}\) actually chooses the next hop node as \(n_{i+1}\) node. m is the center of gravity of the sector region with \(v_{i+1}\) as the center and r as the radius. From a statistical point of view, the actual selection of the next hop node is close to m point. In order to calculate the position of m, we need to calculate the center of gravity of the sector area. We need to determine the size of the sector area A and the center position \(v_{i+1}\). A is set here as 1/2 of the average area of the nodes. When A and \(R\) are given, then r and \(\alpha\) can be calculated according to the following formula:  
\[\left\{\begin{array}{c}\cos\frac{\alpha}{2}=\frac{\tau}{R}\\ A\approx\frac{\alpha}{2\pi}\cdot\pi R^{2}\end{array}\right. \tag{27}\]  
If \(D=R-M=R-\frac{4}{3}\cdot\frac{\tau}{\alpha}\cdot\sin^{2}\frac{\alpha}{2}\), the formula for calculating the correction factor is as follows:  
\[\rho= \frac{R}{D}=\frac{R}{R-\frac{4}{3}\cdot\frac{\tau}{\alpha}\cdot\sin^{2} \frac{\alpha}{2}} \tag{28}\]
# Integrated Digital-Twin-Based Decision Support System for
## 6 Case Study Implementation
### RMS Project Mapping  
The implementation of the DT framework for the RMS case study demonstrates a comprehensive platform designed to optimize the tracking, mapping, and logistical management of RMS projects. Figure 10 provides a detailed visualization of the system's capabilities, showcasing its utility in streamlining project oversight and supporting strategic planning. As depicted in Figure 10a, the RMS project map employs an intuitive interface that visualizes each project's geographic location with red dots on a dynamic 2D map. Each dot is interactive, displaying essential project details such as the project schedule, number of floors, module types, and hyperlinks to associated project documentation. The inclusion of a timeline bar at the bottom of the interface further enhances usability. This feature enables stakeholders to filter and analyze projects within specified time frames, providing a historical and temporal context. For instance, stakeholders can visualize trends in RMS deployments over the past decade, assess patterns of growth, and identify areas of focus for future initiatives.  
Figure 10b expands on this functionality by clustering RMS modules according to their availability within a given timeline. This clustering enables project managers to identify modules from nearing-completion projects that can be reallocated to new sites. By visualizing the proximity of RMS sites, the system supports optimized logistical strategies, such as reducing transportation costs and improving resource utilization by prioritizing the reuse of existing resources. By integrating these clusters into a timeline view, the platform provides a forward-looking perspective, enabling stakeholders to anticipate module availability and plan accordingly for future projects.  
Figure 8: Typical RMS project components.  
Figure 9: Typical RMS module types.  
Additionally, the DT platform also integrates BIM models for each project, accessible through an online interface. As shown in Figure 11, these BIM models are visualized as interactive 3D building scenes. Users can navigate through individual project points, zoom into specific details, or explore various projects using the slide bar. This integration facilitates a deeper understanding of project designs, allowing users to assess spatial layouts, structural configurations, and material specifications. This feature enriches the visual experience and provides a multifaceted understanding of each project, supporting a wide range of operational scenarios.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 4 Performance Evaluation and Results Discussion
### First Experiment Set with Different Traffic Loads  
In this experiment set, the simulator was run on a partial network as illustrated in Figure 4. Different loads of traffic (CBR and HTTP) generated at the edge routers 2 through 5, including both legitimate and malicious traffic, were considered. The data rate per link between routers was set to 100 Gbps. The simulator was run 15 times for four different traffic loads of 5000, 10,000, 20,000, and 40,000 packets generated from the edge routers. The number of observed periods \(n\) was set to 4, and the length of each period \(t\) was set to 20 s. The two thresholds \(\alpha\) and \(\beta\) are set to \(3\times\Delta f_{t}\) and \(2\times\mu f_{t}\) respectively. Following are the observations that were noted from the first experiment set.  
The results in Figure 5 show that the attack detection time is proportional to the amount of traffic. This is intuitive because, in order to achieve the condition that triggers the IP traceback algorithm at the edge router, a significant amount of malicious SYN packets needs to be injected. Therefore, the number of packets that must be forwarded before the detection of the attack will increase combined with the processing time at the edge routers. The tracing process requires more SYN packets destined to the victim before triggering the RAPT algorithm.  
It was noted that the attack tracing time is not influenced by the amount of traffic generated at the edge routers. The reason behind this is that the tracing process needs one tracing packet per attack path; as a result, the tracing time is only for one packet (TracePack). The RAPT algorithm does not depend on the amount of traffic as much as it depends on the distance between the attacker and the victim. Figure 6 depicts that it remains within certain boundaries ranging between 630 and 670 milliseconds on average. The obtained results are feasible to quickly identify malicious activity by the victim and take an action to drop requests coming from the source IP.  
Figure 4: Partial network used in the simulation experiments.  
In Figure 7, it can be noticed that the ratio of the average processing time taken by the RAPT algorithm to the total simulation processing time does not exceed 9% in its most extreme cases, which are acceptable rates, although not optimal. The results also reveal that, with the increase in the traffic, the overall processing time increases in a linear correlation. The reasoning for this is that the simulation time = processing time + packet routing time. The reason for the linear increase in simulation time can be attributed to the packet routing time, and this is normal because every packet needs routing at every router on the path; therefore, it can be concluded that the processing burden does not constitute a large proportion of the path simulation time.  
Figure 5: The relationship between the amount of traffic and the average detection time of the attack.  
Figure 6: The relationship between the amount of traffic and the tracing time.
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 2 Live-Line Maintenance and Robot System
### Live-Line Maintenance Operations  
The phased research goal of this paper is to achieve a drainage operation, and the main operation object is a swaying cable. The aim of the drainage operation of a 10 kV overhead line is to connect the distribution line and the drainage terminal through a drainage wire. The specific operation includes the following four parts:  
1. Wire Stripping: There is a 3-4 mm insulation layer on the surface of a 10 kV overhead cable, which needs to be removed, as shown in Figure 2a.
2. Voltage detection: This is an essential part of connecting lead-flow wires. Electrospors is used to detect whether the metal cable core is charged, as shown in Figure 2b.
3. Wire clamp installation: The function of a wire clamp is to fix the lead-flow wire and the distribution network cable together. A wire clip needs to be installed on the cable, and the bolt needs to be tightened, as shown in Figure 2c.
4. Lead-flow wire cut off: When the drainage power supply is not needed, the drainage line should be cut for safety reasons, as shown in Figure 2d.  
For the above tasks, special tools are designed, including strippers, electroscope, clamp installer, wire cutter, etc. During operation, the robot selects the appropriate tool, moves to the target position and operates the tool to perform the operation.  
Figure 1: Schematic diagram of live-line working scene. ((**a**) Insulated bucket vehicle, (**b**) robot system, and (**c**) stripping operations).  
Figure 2: Operations of live-line maintenance [27]. ((**a**) Wire stripping, (**b**) voltage detection, (**c**) wire clamp installation, and (**d**) lead-flow wire cut off).
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 3 Prototype Design
### Principles of Stratospheric
#### 3.1.2 Cells of Ethernet Frames  
In the stratosphere, the bridge port of ToR (a dedicated internal port connected to VCS modules) receives Ethernet frames and splits them into fixed-size cells, as shown in Figure 5. Cells of Ethernet frames are transmitted in the time slot corresponding to a virtual channel. The virtual channel ID is inserted into the cell header to identify the virtual channel. Figure 6 shows the transmitting of cells. Cells are matched with the forwarding table to select the output port and channel, and different cells of the same packet are transmitted through the same virtual channel. The input port in the next VCS passively follows the virtual channel of upper nodes and parses the virtual channel ID in the cell header embedded by source nodes to identify the current virtual channel.  
Figure 4: Data center network with virtual circuit switching.  
According to the rate limiting in the bridge port and the channel plan of the controller, no additional information to be notified to the network is required except for the channel ID, which is inserted in the header of the cell by the bridge port. Cells belonging to the same packet have the same channel ID, so they are forwarded on the same path. At the destination node, the cells are queued and then assembled when all of the cells have arrived. In the header of the cell, there is another flag to identify the first, middle, and last cells of the packet. After the packet is assembled completely, the CRC of the packet is computed and verified, and the packet can be dropped for CRC errors.
# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 5 Conclusions  
This paper presents a locomotion pattern recognition method based on SA-SVM. With the plantar pressure signals and joint angle signals, the mRMR algorithm was used to extract features from the combination locomotion time series signals. The experiments were performed on six healthy subjects in the laboratory and the results validated the proposed method. This paper proposed combination of the plantar pressure signals measured by force sensing resistors in the shoes and the posture information measured by inertial measurement units mounted on the mechanical structure of the thigh, shank and foot, which is used as the signal source for locomotion patterns recognition. The recognition accuracy of SVM optimized by SA is improved about 5.6% compared with SVM only. At the same time, accuracy improved nearly 3 % using FSM.  
Conceptualization, Z.Y. and J.Z.; methodology, Z.Y.; software, Z.Y.; validation, J.Z., L.H. and Y.G.; formal analysis, Z.Y. and H.P.; investigation, Z.Y.; resources, J.Z. and H.P.; data curation, Z.Y. and L.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, L.H.; visualization, Z.Y.; supervision, L.H.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key R&D Program of China \"The Study on Load-bearing and Moving Support Exoskeleton Robot Key Technology and Typical Application\" (2017 YFB1300502). The authors declare no conflict of interest.
## 3 Evaluation Experiments
### Evaluation Results and Discussion  
To confirm the effectiveness of the proposed methods, we conducted speech enhancement experiments using speech recorded with an LDV. We used wide-band perceptual evaluation of speech quality (PESQ), LSD, and short-time objective intelligibility (STOI) as evaluation criteria to compare sound quality, degree of deterioration, and intelligibility. The higher the PESQ and STOI, the higher the sound quality and intelligibility. The objects for comparison were clean speech, observed speech, the result of speech enhancement based on the conventional method [9], the result of the STFT-based method, and the result of the waveform-based method. Figure 15 shows the spectrogram, and Table 3 shows the evaluation results of each speech. From Figure 15, the noise was suppressed with both proposed methods. With the conventional method, band-pass filters were used to completely remove speech components that were lacking due to the frequency response, and the proposed methods restored these components. As shown in Table 3, it was confirmed that the PESQ  
Figure 14: LSD of enhanced speech with each method at each frequency band.  
Figure 13: Spectrogram of speech-enhancement results: (**a–d**) are clean speech, observed speech, speech obtained by only noise suppression, speech enhanced with proposed waveform-based methodscores of the proposed methods were almost the same as that of the conventional method; the PESQ was about 0.5 higher than the observed speech. The proposed methods had smaller LSDs than the conventional method. With the conventional method, the LSD was larger than the proposed methods because it is difficult to enhance speech in the 4-8 kHz frequency band of the observed speech where the noise is larger than the speech. For STOI, the proposed methods improved by about 0.08 compared with the recorded speech, and improved by about 0.06 compared with the conventional method.  
It can be concluded from the above experimental data that, compared with the conventional methods, the proposed methods significantly improve the sound quality of the observed speech. Moreover, compared with the single-stage DNN-based method, the two-stage processing can also better deal with the various deterioration in the observed speech.
# Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
## 1 Introduction
The air suspension system simulation model with PID controller was built, and the experimental results show that air suspension based on PID control strategy can reduce the body vertical acceleration and better increase the ride comfort of vehicles [10]. PID controller and LQR controller for an active suspension system are analyzed, evaluated, and compared. The results have shown that the PID controller supports the optimization of the body acceleration, and the LQR controller supports the optimization of the body displacement [11]. An advanced firefly algorithm was investigated to compute the PID controller for a semi-active suspension system. The study of the controllers has shown significant improvement as the proposed PID-AFA is capable of reducing the amplitude of the sprung acceleration and body acceleration responses up to 56.5% and 67.1% [12]. The PID controller is researched and applied to control the active suspension system of the cars under the different excitations of the road surface and the various car speeds. The research results show that the PID controller for the car suspension system has an obvious impact on reducing the vibration and controlling the car body shaking in comparison with the passive suspension system [13]. Using the Ziegler-Nichols method via the Control System Designer app to tune the PID controller to achieve the desired comfort traveling of passengers and reliable ride-holding of the car [14]. However, they did not provide a clear and effective method for selecting specific control parameters, and the use of PID structures introduced various uncertainties and nonlinearities, making it difficult to determine appropriate PID gains to enhance the robustness of the control system. Therefore, intelligent algorithms like fuzzy logic were employed to fine-tune PID controller gains, enhancing the robustness of the control system. Fuzzy PID control merges the features of PID control with fuzzy control characteristics, offering rapid convergence,robust performance, and a streamlined structure [15; 16; 17; 18]. However, due to the complexity of air suspension systems, the subjective selection of membership functions, fuzzy rules, and PID initial parameters based on experience hindered achieving optimal control effectiveness. Literature [19; 20; 21] proposed using genetic algorithms to optimize the initial PID parameters of fuzzy PID controllers. However, the fuzzy rules defined in the study still relied on expert experience, making it challenging to achieve optimal control. To address these issues, genetic algorithms were combined with fuzzy PID control to propose a genetic algorithm-optimized fuzzy PID control strategy. A genetic algorithm-optimized fuzzy PID control system tailored for air suspension systems was designed to conduct control research on air suspension systems.
## 3 Similarity Methodology
### Criteria for Distorted Stiffener Configuration (Distorted Model II)
When the requirements in Equations (21) and (22) are met by distorted model II, similar dynamic responses between distorted model II and distorted model I can be guaranteed. Hence, the primary criterion for altering the stiffener configuration, Criterion 1, emphasizes the maintenance of a sectional area (Equation (21)) and static moments (Equation (22)) of the integrated stiffener consistent with distorted model I. Consequently, the input load case, involving the impact mass and initial velocity, remain the same as that of distorted model I. It is worth noting that the dimensions resulting from Criterion 1 for distorted model II often do not align with the standard steel sheet thickness, which approximates to the designed dimensions. Therefore, the sectional area of the designed distorted model II and distorted model I will not be strictly similar, which necessitates a correction to the impact mass based on the dimensionless number \(\Pi_{5}\):  
\[\left(\Pi_{5}\right)_{d2}=\left(\Pi_{5}\right)_{d1}\rightarrow\left(\frac{M_{ I}}{M_{S}}\right)_{d2}=\left(\frac{M_{I}}{M_{S}}\right)_{d1}\rightarrow\eta_{M_{ 1}}=\eta_{M_{5}}, \tag{23}\]  
\[\left(M_{I}\right)_{d2}=\eta_{M_{I}}\left(M_{I}\right)_{d1}=\eta_{M_{5}}(M_{I} )_{d1}, \tag{24}\]  
Thereby, the similarity approach, which ensures the equality of these terms between distorted model II and distorted model I, is utilized to address the distortion in the stiffener configuration and supplement the distorted similarity laws. The correction procedure for designing a distorted model (based on the corresponding distorted model) is summarized as follows:  
1. First, calculate the dimensions of the distorted model II stiffeners configuration through the proposed criteria, according to the restrictions on the experimental and practical conditions: Criterion 1: To maintain a consistent sectional area Equation (21) and static moment Equation (22) with the corresponding distorted model I;
2. Establish distorted model II with the same attached plate and impact indenter dimensions and stiffener numbers as the corresponding distorted model I;
3. Calculate the corresponding scaling factor based on the detailed dimensions and corrected impact mass using Equation (24), if necessary;
4. Perform distorted model II with the new impact mass and the same initial velocity;
5. Reverse the simulated results to the corresponding distorted model I, then to the prototype.