esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 1 Introduction  
SPIDER (Source for the Production of Ions of Deuterium Extracted from Radio-frequency plasma) is the full-scale prototype of the ITER NBI (Neutral Beam Injector) ion source [1, 2, 3]. In SPIDER beam source, RF inductively coupled plasma drivers are used to produce hydrogen or deuterium plasma, from which negative ions are electrostatically accelerated by means of a series of grids. SPIDER electrical system provides power to the RF drivers for plasma generation and to the system of grids for the extraction and acceleration of the ion beam. Each RF circuit is designed to be isolated from the rest of the electrical system, but parasitic connections provide alternative circulation paths for RF currents, which caused destructive damage of a power supply (ISEG) output filter and introduced large noise in the measurements performed with the beam-source thermocouples. From a preliminary circuit analysis, the main parasitic element providing an alternative low-impedance path for the RF current was identified as a Capacitive Voltage Divider (CVD) installed at the output of each RF generator, used to obtain the output voltage reading. The proposed solution consisted in the replacement of the CVD at the primary side of the RF generator output transformer; this is based on the idea of increasing the impedance seen by the common mode RF currents [4]. The solution was implementedand tested in SPIDER with good results [5]. Even though the modification has proven to be effective, the circulation of RF stray currents in SPIDER electrical system pointed out a major issue that was not taken into account in the design phase. A deeper comprehension of the stray currents phenomenon and an adequate modeling tool would enable the possibility to assess this problem and to develop alternative solutions not only in the existing SPIDER and MITICA configurations, but also for alternative RF system layouts, since a major upgrade of the RF power supplies to solid-state amplifiers is programmed for SPIDER and MITICA [6]. This paper describes further thoughts and discussions on the problematic and the development of a circuital model for the analysis of RF stray currents in SPIDER electrical system. The section devoted to a simplified analysis of the problem starts from the original interpretation [4], further developing the understanding of the phenomenon. Then, the simulation results obtained from the model are compared to the measurements performed in SPIDER prior to and following the CVD modification and used to validate the hypothesis on RF stray currents generation.
## 2 Composition of LEO Satellite Network and APINT Model  
### Composition of LEO Satellite Network  
The composition of LEO satellite network is shown in Figure 1. A group of LEO satellites in the same layer and orbit constructs the satellite network through ISLs, and wireless communication is used for the transmission of inter-satellite services between nodes. When flying over the service area, satellites establish links with ground gateway stations or user terminals through SGLs, allowing for the backhaul of network awareness data.  
The LEO satellites in the network are represented as \(S_{i}\)= \(\{S_{1},S_{2},\ldots,S_{N}\}\) with \(N\) satellites. The set of ground gateway stations is represented as \(\text{Gat}=\{G_{1},G_{2},\ldots,G_{j}\}\). Considering the dynamic features of the network topology, a specific time range is divided into several time slots \(T=\{t_{1},t_{2},\ldots t_{k}\}\), each with a duration of \(\Delta t\). Within each time slot, the network topology remains stable, but it experiences dynamic changes between different time slots. At the same time, the task tuple \(\{Z,P_{Z},T_{Z}\}\) is used to represent each service flow, where \(Z\) is the set of service identifiers, \(P_{Z}\) is the set of service transmission paths, and \(T_{Z}\) represents the time from the initiation of service transmission to its reception at the gateway station. Table 1 lists the commonly used symbols in this article.  
Figure 1: LEO satellite network scenario.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 2 Controller Design
### Velocity Decision Controller  
The function of the velocity decision controller is to judge the three sets of control inputs. This controller is not required to stand out in terms of real-time performance, as there is no large-scale optimization solution. Instead, this controller is required to accurately predict the state of the articulated vehicle in a long time domain. According to these requirements, we believe that nonlinear MPC is well suited for velocity decision controllers.  
The kinematics model detailed in Equation (3) can be abbreviated as:  
\[\dot{\mathbf{x}}=f(\mathbf{x},\mathbf{u}) \tag{25}\]  
where:  
\[\left\{\begin{array}{ccc}\mathbf{x}=\left[\begin{array}{cc}x_{f}&y_{f}& \theta_{f}&\gamma\\ \mathbf{u}=\left[\begin{array}{cc}v_{f}&w_{\gamma}\end{array}\right]^{ \mathrm{T}}\end{array}\right]^{\mathrm{T}}\end{array}\right. \tag{26}\]  
After discretization, a nonlinear prediction model can be obtained:  
\[\mathbf{x}(k+i+1|k)=\mathbf{x}(k+i|k)+Tf(\mathbf{x}(k+i|k),\mathbf{u}(k+i|k)) \tag{27}\]  
Substituting the three sets of control inputs obtained in the first layer into Equation (27), the corresponding future state of the articulated vehicle can be obtained.  
The errors between the reference path and the future state of articulated vehicles are:  
\[\begin{split}\mathbf{e}(k+1|k)=\mathbf{x}(k+1|k)-\mathbf{x}_{ref}(k +1|k)\\ \vdots\\ \mathbf{e}\Big{(}k+N_{p}\big{|}k\Big{)}=\mathbf{x}\Big{(}k+N_{vp} \big{|}k\Big{)}-\mathbf{x}_{ref}\Big{(}k+N_{vp}\big{|}k\Big{)}\end{split} \tag{28}\]  
where \(N_{vp}\) is the prediction horizon in the velocity decision controller and \(\mathbf{x}_{ref}\) is the information on the reference path. \(\mathbf{x}_{ref}\) is obtained by the interpolation of the reference path, based on the reference velocity and control period. The arc length on the reference path between \(\mathbf{x}_{ref}(k+i|k)\) and \(\mathbf{x}_{ref}(k+i+1|k)\) is equal to the product of the reference velocity and the control period.  
Based on the above prediction model, the following decision function can be designed:  
\[J_{v}\Big{(}\mathbf{u}_{j}(k+i|k)\Big{)}=\sum\limits_{i=1}^{N_{vp}}\big{|} \mathbf{e}_{j}(k+i|k)\Big{|}^{2},\quad j=\text{A},\text{B},\text{C} \tag{29}\]  
In order to ensure safety, the path tracking controller with the slowest velocity is set at the highest priority. The judgment rules are as follows:  
\[\mathbf{u}(k+i|k)=\left\{\begin{array}{ll}\mathbf{u}_{\text{C}}(k+i|k),&J_{v }(\mathbf{u}_{\text{A}})>J_{v}(\mathbf{u}_{\text{C}})+\mu_{1}\\ \mathbf{u}_{\text{A}}(k+i|k),&J_{v}(\mathbf{u}_{\text{A}})<J_{v}(\mathbf{u}_{ \text{C}})+\mu_{1}\xi J_{v}(\mathbf{u}_{\text{B}})>J_{v}(\mathbf{u}_{\text{A }})+\mu_{2}\\ \mathbf{u}_{\text{B}}(k+i|k),&J_{v}(\mathbf{u}_{\text{A}})<J_{v}(\mathbf{u}_{ \text{C}})+\mu_{1}\xi J_{v}(\mathbf{u}_{\text{B}})<J_{v}(\mathbf{u}_{\text{A }})+\mu_{2}\end{array}\right. \tag{30}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are relaxation factors.
## 8 Results and Discussion
### Performance Analysis  
The performances of the developed HEM-AL + DCNN model were evaluated over extant models for disease diagnosis regarding \"positive measures such as accuracy, sensitivity, specificity, precision, neutral metrics such as F1-score, NPV, MCC and negative measures such as FNR, FDR, FPR\". Consequently, the results for positive metrics, negative metrics and neutral metrics were exposed in Figures 4-6, respectively. Upon analysis of all graphs, the presented HEM-AL + DCNN model obtained better outcomes than compared schemes. Minimal negative values guaranteed the enhanced detection rate of the model.  
As shown in Figure 4a, the presented HEM-AL + DCNN model has achieved better accuracy values (accurate diagnosis of disease), which show a 5.56%, 5.56%, 13.33% and 10% improvement over MFO + DCNN, EHO + DCNN, WOA + DCNN and EEPSO + DCNN models for test case 1.  
As shown in Figure 4b, the sensitivity measure has gained a higher value of 100 for the adopted scheme for test case 3, which is far superior to the values attained by traditional methods such as MFO + DCNN, EHO + DCNN, WOA + DCNN and EEPSO + DCNN. Thus, the high rates of positive measures and lower rates of negative measures have ensured the development of the suggested model.  
Altogether, the performance of the proposed HEM-AL + DCNN was proven superior over other schemes for accurate disease diagnosis.  
Figure 4: Performance of the developed scheme over extant schemes for “(**a**) Accuracy (**b**) Sensitivity (**c**) Specificity (**d**) Precision”.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 4 Optimization of Cable Distribution
### Analysis of Trajectory Tracking Control  
If the desired control of the wrench can be achieved, the robot's acceleration \(\hat{q}\), velocity \(\hat{q}\), and position \(q\) can satisfy the equation provided below due to the existence of the nonlinear compensated control wrench:  
\[\dot{q}+K_{d}\hat{q}+K_{p}(q-q_{d})+K_{i}\int_{0}^{\mathbf{t}}(q-q_{d})\text{ dt}=0 \tag{39}\]  
which indicates that \(q\) is well able to track the desired position \(q_{d}\). However, because of the existence of the tracking error of the desired wrench, Equation (39) becomes  
\[\ddot{q}^{\prime}+K_{d}\dot{q}^{\prime}+K_{p}(q^{\prime}-q_{d})+K_{i}\int_{0} ^{\mathbf{t}}(q^{\prime}-q_{d})\text{dt}=M^{-1}(A^{T}f-\tau_{d}) \tag{40}\]  
where \(q^{\prime}\) represents the novel system state.  
By comparing Equation (39) to (40), an auxiliary error system can be constructed as follows:  
\[\dot{q_{e}}+K_{d}q_{e}+K_{p}q_{e}+K_{i}\int_{0}^{\mathbf{t}}q_{e}\text{dt}=d_{e} \tag{41}\]  
where \(q_{e}:=q^{\prime}-q\).  
Here, during the procedure in which \(A^{T}f\) tracks \(\tau_{d}\), the term \(d_{e}=A^{T}f-\tau_{d}\) can be regarded as a disturbance that may cause a steady-state error in the robot's tracking control. In this study, the presence of the integral term \(K_{i}\int_{0}^{\mathbf{t}}(q-q_{d})\text{dt}\) in the nonlinear compensated PID controller can help to reduce the steady-state tracking error caused by this disturbance.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 1 Introduction
This paper argues that added noise traffic is needed only to mask data-volume-related features, which is non-discriminatory for devices with highly variable data rates (see Section 5.1.1). In fact, the data volume has been utilized for event detection of an already identified device, the step that our defense prevents in the first place. Thus, noise traffic is often unnecessary to hide device-level signatures, and hence, randomization can be achieved without noise, as proposed in this work. In summary, we present a new defense against packet-size leakage attacks with the following properties:  
* Data-efficient: Traditional countermeasures add noise traffic to hide packet-size-based signatures, resulting in a significant data overhead. Our defense thwarts such leakage without adding any noise; thus, it is much more efficient than noise-based solutions. * Adaptable: Effective techniques in the literature utilize a fixed dynamic for obfuscation (e.g., padding to the maximum transmission unit (MTU)), which poses a non-optimizable overhead. Our defense utilizes adjustable parameters within the application code, enabling greater flexibility and programmability in managing defense strength and overhead. The remainder of this paper is organized as follows: We examine the relevant literature and previous studies in Section 2. In Section 3, we present the threat model. Section 4 provides a detailed explanation of our approach for traffic obfuscation. We evaluate our technique and discuss our results in Sections 5 and 6, respectively. Finally, we conclude and discuss future work in Section 7.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 4 Results
### Lateral Loading  
Then, an external force is set at the top of the space elevator system in y-direction, whose value is 1000 N. The result is shown in Figures 8-10.  
In Figure 8, it can be observed that deformation of \(y\)-axis with three method is roughly the same. The deformation of ANCF is little bigger than the others. Focusing on the  
Figure 6: Stress results of three method.  
Figure 7: Deformation of \(x\) axis of three method.  
elements near the ground, as shown in Figure 9, it is expected that the contour line of the rope should resemble an S-shaped curve. The LM model cannot correctly calculate the contour of the rope as the rod element does not consider the curvature of the rod, while it can still be approximated when the number of rod elements is sufficient. The beam element of the FEM considers the curvature; however, because of the approximated curvature, the element stiffness is a constant matrix. Although the nonlinear equations can be linearized, the correlation between the element and the space coordinates is lost. The result of the ANCF method is a similar S-shaped curve. In other words, the rope angle is continuous and smooth. When the angle of the grounding point is 0, there will be no jump in the angle value.  
In Figure 10, as the deflection of the element is not considered in the LM model, it can be observed that the element rotation angle of the LM model is basically kept constant. When the number of the element reaches a certain amount, the flexibility of the rope can  
Figure 8: Deformation Y results of three method.  
Figure 9: Deformation Y close to the ground.  
be approximated, but will greatly increase the cost of calculation. The element rotation angle of the FEM has an oscillating process, and finally approaches a certain value. In contrast, the element rotation angle of the ANCF method changes smoothly, and the final convergence value is slightly larger.  
Similarly, when the \(Y\)-axis loading turns to the \(Z\)-axis, the resulting deformation curve is shown in Figure 11. Since the centrifugal force has no contribution to the coordinates in the \(Z\) direction, the deformation of the \(Z\) axis is much smaller than that of the \(Y\) axis, and the results calculated by each method are closer.  
Since a section of the rope is fixed, the outline of the rope should be an S-shaped curve. It can be seen from Figure 12 that the results calculated by the ANCF method fit the S-curve better. Therefore, when studying the contour of the rope close to the ground, the calculation result of ANCF is more reasonable.  
Figure 11: Deformation Z results of three method.  
Figure 10: Nodal angle \(\theta\) close to the ground.  
From Figure 13, the angle change law is basically the same as the \(Y\)-axis loading situation. The element rotation angle of the LM model is kept approximately constant. The element rotation angle of the FEM has an oscillating process, and finally approaches the same value. The element rotation angle of the ANCF method changes smoothly, and finally converges to a slightly larger value.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 4 Preliminaries and Background
### Performance Metrics  
To evaluate the classification performance, a confusion matrix [56] is used. The confusion matrix, also known as the error matrix, is a table that describes the performance of a classification model on a set of test data where the true values are known [57]. As shown in Table 1, it is a two-dimension matrix, one dimension represents the actual class of an object, and the other represents the class predicted by the classifier [58]. The confusion matrix represents four values, namely true positive (TP), false positive (FP), true negative (TN), and false negative (FN). TP values are correctly classified, FP values are wrongly classified into the relevant class, FN values belong to another class when they should be in the relevant class, and TN values are correctly classified values in the other class [59].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Confusion Matrix**} & \multicolumn{2}{c}{**Assigned Class**} \\  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Actual Class} & Positive & \(TP\) & \(FN\) \\  & Negative & \(FP\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification performance matrix: confusion matrix.  
Based on confusion matrix values, several metrics are defined to evaluate classification performance. The most commonly used performance metrics are accuracy (\(ACC\)), precision (\(P\)), sensitivity (\(S_{n}\)), specificity (\(S_{p}\)), and F-score values [56; 59]:  
* Accuracy indicates the proportion of correct predictions, reflecting the true positive rate: \[ACC=\frac{(TP+TN)}{TP+FP+TN+FN}.\] (7)
* Precision shows the positive predictive value: \[P=\frac{TP}{TP+FP}.\] (8)
* Sensitivity, or recall, shows the true positive rate, indicating the rate of correctly labeling objects of a certain class. For a good classifier, it should ideally be 1 (high) and is calculated as follows: \[S_{n}=\frac{TP}{TP+FN}.\] (9)
* Specificity, or the true negative rate, indicates the rate at which negative objects are correctly labeled. For a good classifier, it should ideally be 1 (high) and is calculated as follows: \[S_{p}=\frac{TN}{TN+FP}.\] (10)
* The \(F-score\) is a way to measure a classification model's accuracy and is the harmonic mean of recall and precision, as follows: \[F-score=2\times\frac{(P\times S_{n})}{P+S_{n}}.\] (11)  
In classification, the higher the \(F-score\), the more accurate the model is. The highest value of the \(F-score\) is 1.0, which indicates perfect precision and recall. The lowest possible value is 0, which occurs if either precision or recall is zero.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 2 Related Work
### Entropy and Mirror Descent  
#### 2.2.1 Entropy-Based Regularization Across Domains  
Entropy regularization is a workhorse in optimization because it simultaneously smooths objectives, convexifies otherwise intractable problems, and promotes principled exploration. In convex optimization, _entropic mirror descent_ treats the Kullback-Leibler divergence as its Bregman distance and updates the iterate via \(x_{t}=\arg\min_{x}\,\left\langle g_{t},x\right\rangle+\frac{1}{\eta}D_{\text{ KL}}(x\,\|\,x_{t-1})\), achieving \(O(\sqrt{T})\) regret under mild constraints [21]. In computational transport, the _Sinkhorn algorithm_ adds a negative Shannon-entropy term to the cost matrix, turning the earth-mover problem into a strictly convex program solvable by fast row-column normalization [22]. In reinforcement learning, _Soft Actor-Critic_ augments the reward with policy entropy so that the stochastic policy avoids premature collapse onto sub-optimal actions [23]; conversely, semi-supervised classifiers minimize output entropy to obtain confident pseudo-labels without ground-truth annotations [24]. Beyond Shannon entropy, Tsallis-entropy families introduce a temperature parameter \(q\) that yields heavy-tailed smoothing suited to power-law data [25]. At the parameter-optimization level, gradient-entropy penalties have been proposed to flatten sharp minima and thus improve generalization in deep networks [26].  
Across these settings the entropy coefficient is typically hand-tuned, and none of the methods supplies an information-theoretic bound that ties entropy to client-side distribution shift. ERFO bridges this gap by deriving a single-scalar entropy term from a pigeonhole-theoretic bound on KL divergence; the coefficient is data-dependent yet parameter-free and integrates into standard FedAvg updates without extra metadata or communication overhead.
# Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture
## 6 Conclusions  
This study presented a traffic mapping algorithm for a PON- and WLAN-based FiWi network to improve the Quality of Service (QoS). Both the DCF and EDCA modes were investigated and the comparative performance in terms of mean US delay and bandwidth utilization efficiency was also analyzed. OMNET++ was used to develop a FiWi testbed using XGPON for PON implementation as a front-haul and the fifth generation of WLAN at the access network end. The highest cost first (HCF) algorithm was presented to map the QoS mapping of traffic between the PON data frames, organized according to the HCF algorithm for different types of services of WLAN and XGPON. Our mapping scheme is very flexible and can be useful for any existing or any new integrated architecture of XGPON and WLAN without using any external node. To the best of our knowledge, previously, no such work has been reported which conducted a comprehensive study for the FiWi access network using different channel distributed schemes of 802.11 ac with XGPON. The simulation results prove that the HCF algorithm significantly improves the performance of the DBA scheme and leads to a reduction of up to 54.8% and 53.4% mean US delays compared to existing work. This happens due to better bandwidth utilization by the HCF-DBA process, which is evident from the UBR results which show 65% and 15% lower values for EDCA and DCF modes, respectively. We believe that our research will serve as a base for future studies on the low-latency FiWi access network. Our future plans focus on the adaptive guaranteed access category in EDCA mode for the FiWi access network.  
[PERSON]. Implemented the simulation model in Omnet++, did the formal analysis for the results analysis, wrote the paper and funding acquisition. [PERSON], [PERSON] did the supervision, validation and review of first and final version. [PERSON], [PERSON] and [PERSON] did writing--review and editing of the paper. All authors have read and agreed to the published version of the manuscript.  
State Key Laboratory of Information Photonics and Optical Communications (IPOC), Beijing University of Posts and Telecommunications (BUPT).  
Authors are thankful to the anonymous reviewers for their constructive and logical comments.  
The authors declare no conflict of interest.  
Figure 11: Un-allocated bandwidth versus offer load using DCF and EDCA in the FiWi network.
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model
#### 3.3.7 Adaptive Parameter Optimization Based on Multiple Seeds (Multi-Seed)  
This study proposes a multi-seed adaptive parameter optimization method to enhance the BIT* algorithm's performance. The method iteratively runs the algorithm multiple times with different random seeds, dynamically adjusting key parameters, such as the number of samples \(n_{samples}\) and the connection radius \(rBIT\), during each run. The optimal parameter combination is determined by comparing the convergence performance across these runs. The process initializes baseline values for \(n_{samples}\) and \(rBIT\) and sets a floating range. For each run, new parameter values are randomly generated within the defined range. The BIT* algorithm is then executed, and a log file records performance metrics, including path length, turning cost, energy consumption, and the final path cost.  
After each run, the best-performing set of parameters, based on convergence metrics, becomes the new baseline. The floating range is then reduced to refine the search in subsequent iterations. This iterative process continues until the floating range reaches a predefined threshold or a maximum number of iterations is completed.  
The final optimal parameter combination is then used to execute the BIT* algorithm, generating the final optimized path. This multi-seed adaptive optimization approach allows the algorithm to automatically identify optimal parameters, improving path planning performance by generating shorter paths with lower turning costs and minimal energy consumption, ultimately enhancing the algorithm's adaptability and robustness.