esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Controller Design
### Design of Fuzzy Sliding Mode Control (FSMC) for Path-Positioning Controller
\begin{array}{c}\textit{sig}(w)=\left\{\begin{array}{ccc}-1&\textit{if}&z<-1 \\ \frac{-(21w^{2}+27w+10)}{2(9w^{2}+12w+5)}&\textit{if}&-1\leq z<-\frac{2}{3}\\ \frac{-(15w^{2}+9w+2)}{2(9w^{2}+6w+2)}&\textit{if}&-\frac{2}{3}\leq z<-\frac{1}{ 3}\\ \frac{-(9w^{2}-1)}{2(9w^{2}+1)}&\textit{if}&-\frac{1}{3}\leq z<0\\ \frac{(9w^{2}-w)}{2(9w^{2}+1)}&\textit{if}&0\leq z<\frac{1}{3}\\ \frac{(15w^{2}-9w+2)}{2(9w^{2}+6w+2)}&\textit{if}&\frac{1}{3}\leq z<\frac{2}{ 3}\\ \frac{(12w^{2}-27w+10)}{2(9w^{2}-12w+15)}&\textit{if}&\frac{2}{3}\leq z<1\\ 1&\textit{if}&z\geq 1\end{array}\right.\end{array}  \tag{3}\]  
Figure 10: The membership function of the output variable \(u\) of the fuzzy sliding surface. \begin{table}
\begin{tabular}{c c c} \hline
**Rules** & \(\sigma\) & \(u\) \\ \hline R\({}_{1}\) & If \(\sigma\) is PB & Then \(u\) is PB \\ R\({}_{2}\) & If \(\sigma\) is PM & Then \(u\) is PM \\ R\({}_{3}\) & If \(\sigma\) is PS & Then \(u\) is PS \\ R\({}_{4}\) & If \(\sigma\) is ZR & Then \(u\) is ZR \\ R\({}_{5}\) & If \(\sigma\) is NS & Then \(u\) is NS \\ R\({}_{6}\) & If \(\sigma\) is NM & Then \(u\) is NM \\ R\({}_{7}\) & If \(\sigma\) is NB & Then \(u\) is NB \\ \hline \end{tabular}
\end{table} 
Table 3: Fuzzy rules. Figure 9: The membership function of the input variable \(\sigma\) of the fuzzy sliding surface. This study uses the Mamdani method as the inference principle of the system. Since the output of the controller requires a crisp value, it is necessary to defuzzify the fuzzy set obtained by the inference. In the method of defuzzification, the centroid method is used as the defuzzification rule. Table 4 shows the rule base of the fuzzy sliding surface, where the fuzzy sliding surface requires only seven rules, thus greatly simplifying the traditional rule base. Therefore, the use of fuzzy sliding mode controller can greatly simplify the design difficulty of the fuzzy rule base. Figure 11 shows the control block diagram for the 1-DOF robotic lower limb system driven by the novel single-PMA with a torsion spring. The parameters of the FSMC designed in this study are shown in Table 5.  
The experimental results show that the steady angular tracking error without additional loading is about 0.085 degrees, and with a 2 kg loading mass is 0.0338 degrees by using the PID controller, respectively. By FSMC the steady angular tracking error can achieve 0.003 degrees without additional loading and 0.00338 degrees with a 2 kg loading mass, respectively. The performance of the step response, including transient response and steady state response, is summarized in Table 6.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
###### Abstract  
This article reports on the development of nano-MgO/epoxy resin composites with various mass ratios via a solution blending method. The influence of MgO nanofillers on the thermal properties and the effect of environmental temperature on the insulating properties of the composite material were analyzed. The results show that the thermal conductivity of the composites increased with an increasing MgO nanofiller content. Compared with the pure epoxy resin, the thermal conductivity increased by 75% when the content of MgO nanoparticles was 7%. The volume resistivity first increased and then decreased with an increasing doping concentration. The volume resistivity increased by 26.8% in comparison with the pure epoxy resin when the content of MgO nanoparticles was 1%, while its dielectric constant and dielectric loss increased with temperature. In addition, the dielectric constant increased and the dielectric loss first decreased and then increased with an increasing MgO nanoparticle content. Moreover, the MgO composites changed from a glassy to a rubbery state, and the breakdown strength was significantly reduced with an increased temperature. When the temperature was higher than the glass transition temperature, the breakdown strength decreased by 51.3% compared with the maximum breakdown strength at 20 \({}^{\circ}\)C. As the content of MgO nanoparticles increased, the breakdown strength of the composite first increased and then decreased. The highest breakdown strength was achieved when the content of MgO nanoparticles was 1%, which was 11.1% higher than that of the pure epoxy resin. It was concluded that the MgO nanofillers can significantly improve the thermal properties of epoxy composites and their insulation performance at high temperatures.  
e 20202010 10 7018 10.3390/app10207018
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Botnet
This model uses likelihood models to filter out the noise of darknet traffic and leverages temporal analysis as well as context triggered piecewise hashing to report probing activities. Experimenting with 50 GB of darknet data, the authors disclosed more than 9000 hidden malicious CPS events coming from stealthy adversaries.
## 3 Methods
### The Proposed Algorithm
An improved ICP algorithm is employed, selecting the minimum distance as the similarity value (\(sim\)) between the two models. The rotation matrix and translation vector output from RANSAC are used as the initial values, and the Tukey kernel function is integrated to suppress outlier interference. The formula is as follows:  
\[e_{i}=\left\|p_{i}-(Rq_{i}+t)\right\|^{2} \tag{16}\]  
\[\rho\Big{(}e_{i}^{2}\Big{)}=\left\{ \begin{aligned} &\frac{e_{i}^{2}}{2}-\frac{e_{i}^{4}}{6c^{2}}+ \frac{e_{i}^{6}}{6c^{4}},if|e_{i}|\leq 0.1\\ &\frac{c^{2}}{6},if|e_{i}|>0.1\end{aligned} \right. \tag{17}\]  
Finally, the formula for the model similarity value is as follows:  
\[sim=\min\frac{1}{m}\sum_{i=1}^{m}\rho\Big{(}\|p_{i}-(Rq_{i}+t)\|^{2}\Big{)} \tag{18}\]  
Step 5: Model Node Weight Assignment  
The Analytic Hierarchy Process (AHP) is used to assign weights to attributes at each hierarchical level. A judgment matrix\(A=(a_{ij})_{n\times n}\) is constructed based on the hierarchical factors, and its characteristic equation \(AW=\lambda_{max}W\) is solved to obtain the maximum eigenvalue \(\lambda_{max}\) and the corresponding eigenvector W. After normalizing W, the weight vector \(W=[w_{1},w_{2},\,\ldots,\,w_{n}]^{T}\) is determined. Finally, the consistency ratio (CR) of A is tested using formula (19). If the consistency condition is not satisfied, A must be adjusted until a valid weight distribution scheme for the attributes at each level is obtained. \[CR=\frac{\lambda_{max}-n}{RI(n-1)}<0.1 \tag{19}\]  
Finally, the similarity evaluation of each node attribute in the case model is performed to determine the case solution with the highest similarity to the refrigerated van assembly model. The formula is as follows:  
\[S=W_{k}*sim_{k} \tag{20}\]  
where \(W_{k}\) is the weight corresponding to each node, and \(sim_{k}\) represents the similarity measure between any two nodes. The value of \(k\) is determined by the number of nodes at each level of the model. Step 6: Screening of Key Components  
Based on the similarity \(S\) between the historical case model and the refrigerated van assembly model, the case models are ranked. The historical case reference model with the highest similarity is selected. Using the GetSystemValue2 function, key dimensioning information related to the components of a similar reference model is identified. It is then determined whether these components are key parts of the target model, the \"van body as sembly\". This information is used as a basis for reference reuse and modification, ultimately identifying and filtering the key components of the refrigerated van assembly model.
## 5 Difference Analysis  
### Difference Analysis Considering the Effect of Pulse Characteristic  
Difference analyses were conducted between the responses of the PL and NP motion and the positive and negative incident directions. The definition of the seismic response ratio (RatioEDP) for exploring the differences between different types of NF motion can be expressed as follows:  
\[\text{Ratio}_{\text{EDP}}=\frac{\text{EDP}_{\text{pulse-like}}}{\text{EDP}_{ \text{non-pulse}}},\ \ \ \text{EDP}=\text{DVR}\ \ \ \ \ \text{or}\ \ \ \text{U}_{\text{C}}, \tag{15}\]  
where \(\text{EDP}_{\text{pulse-like}}\) and \(\text{EDP}_{\text{non-pulse}}\) denote the engineering demand parameter (EDP) under PL and NP motion, respectively. The EDP represents the seismic response of the dam, such as DVR or U\({}_{\text{C}}\) response, based on the numerical results presented in Section 4.  
The damage or displacement response ratio of each ground motion can be determined using Equation (15), and the average value of \(\text{Ratio}_{\text{EDP}}\) for each incident angle can be calculated. Figure 11 shows the variation of the average \(\text{Ratio}_{\text{EDP}}\) value with the incident angle ranging from \(-75^{\circ}\) to \(75^{\circ}\). The results show that the \(\text{Ratio}_{\text{EDP}}\) value exceeds one, except for the DVR response under NF motion, suggesting that the dynamic response under PL motion is larger than that under NP motion. From a seismic amplitude perspective, it is evident that the response ratio at the 0.6 g peak is greater than that at 0.3 g, demonstrating that strong earthquakes can further stimulate the effect of the pulse characteristics. The maximum response ratio reaches 1.5 and 1.33 at angles of \(-45^{\circ}\) and \(-60^{\circ}\) for the DVR and  
Figure 10: Mean value of displacement response under NF motion with oblique P waves: (**a**) 0.3 g; (**b**) 0.6 g.  
displacement indices, respectively, indicating that owing to the upstream and downstream asymmetry of the dam body as well as pulse effect, the maximum response difference more likely occurs during negative oblique incidence. Although the average response ratio reaches its peak at certain negative incidence angles, this primarily indicates the relative amplification between the dynamic responses under pulse-like and non-pulse motions, rather than representing the absolute response or damage level. In contrast, positive incidence angles tend to induce larger absolute structural responses and more severe cumulative damage, as demonstrated in Figures 8-10. The results demonstrate the impact of negative oblique incidence for PL ground motion, which results in destructive damage to or vibration of the gravity dam.
## 4 Performance of KF Used for Parameter Estimation in RTHS
### Convergence of KF  
It can be seen in the state equation and observed equation of the parameter estimation problem, namely Equations (5) and (6), that the process noise is absent. Hence, the stability of the KF cannot be proven based on the uniform complete controllability and observability of the line system [40]. Let \(\mathbf{e}_{k}\) denote the difference between the estimated parameter and actual value, namely  
\[\mathbf{e}_{k}=\overset{\Lambda}{\mathbf{x}}_{k}-\mathbf{x}, \tag{18}\]  
Substituting Equations (6) and (18) into Equation (8), one can obtain the dynamics of the mathematical expectation of the estimation error, i.e.,  
\[\mathbf{e}_{k+1}=(\mathbf{I}-\mathbf{G}_{k+1}\mathbf{H}_{k+1})\mathbf{e}_{k}. \tag{19}\]  
Here, defining the Lyapunov function for system Equation (19) as follows,  
\[V_{k}=\mathbf{e}_{k}^{\mathrm{T}}\mathbf{\Gamma}_{k}^{-1}\mathbf{e}_{k}, \tag{20}\]  
then substituting Equations (9), (10), and (19) into Equation (20), the following equation can be obtained.  
\[V_{k}=V_{k-1}-[\mathbf{H}_{k}\mathbf{e}_{k-1}]^{\mathrm{T}}\Big{(}\mathbf{H} _{k}\mathbf{P}_{k-1}\mathbf{H}_{k}^{\mathrm{T}}+\mathbf{R}\Big{)}^{-1}[ \mathbf{H}_{k}\mathbf{e}_{k-1}]. \tag{21}\]  
In RTHS, the elements in the observation matrix \(\mathbf{H}_{k}\) are measured from the test, and they are varying and linearly independent considering the measurement noise. If \(\mathbf{H}_{k}\mathbf{e}_{k-1}=0\) holds for all values of \(k\), one will obtain \(\mathbf{e}_{k-1}=\mathbf{0}\), namely \(\overset{\Lambda}{\mathbf{x}}_{k}=\mathbf{x}\). If \(\mathbf{H}_{k}\mathbf{e}_{k+1}\
eq 0\), then the Lyapunov function is decreased,and the system of Equation (19) is stable. Furthermore, the spectral radius of the matrix \(\mathbf{I}\)-\(\mathbf{G}_{k}\mathbf{H}_{k}\) is not greater than 1, and is equals to 1 does not hold. Thus, \(\mathbf{e}_{k-1}\)\(\rightarrow\)0 as \(\mathbf{k}\)\(\rightarrow\)\(\infty\), namely the estimated parameters by KF can converge to the true value. Hence, the KF is convergent in parameter estimation problems in Section 3.2.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 4 Results and Discussion
### Convergence Time and Positioning Accuracy  
Positioning accuracy is evaluated by comparing the coordinates with external reference values, which are from the final IGS weekly solution. As for the statistics of convergence time, we use the statistical method of 68% and 95% quantiles mentioned above. In the 68% quantile case, we make the convergence time as the first epoch time whose error is less than 0.1 m and the errors in the following epochs are all less than 0.1 m. As for the 95% quantile, the criterion is less than 0.2 m.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### Lookup Table Method  
According to the above method, the FlexRay message \(M\) must be retransmitted to improve the communication reliability when \(GS\) fails to meet \(SR\). The multiplication result of \(GS_{mi}\) is defined as Equation (6), where \(k\) denotes the number of scheduled messages. We assume that \(k\) is 5 and \(SR\) is 0.99, which means \(GS_{mi}^{5}\geq 0.99\), and \(GM_{mi}\) for each scheduled message must be greater than or equal to 0.998. Then, by the square root formula, we can now convert Equation (6) into Equation (7), where \(New\_SR_{mi}\) is used to obtain a new \(GS_{mi}\) value:  
\[GS_{mi}^{k}\ \geq\ \text{System Reliability Goal }(SR) \tag{6}\]  
\[New\_SR_{mi}\ \geq\ \sqrt[k]{SR} \tag{7}\]  
The communication requirements for automotive systems are the means of data stability and speed of processing. Many studies have been published on the method of calculating new \(GS_{mi}\) values [8; 10]. However, these methods have a high computational cost due to their iterative algorithm, and it is difficult to estimate the total running time of the procedure. In addition, the reliability of the global message may fail to meet the system reliability goal in the case of a greater number of messages. The proposed FRSA utilizes a pre-computed lookup table as a database to construct \(New\_SR_{mi}\) very quickly, where the \(New\_SR_{mi}\) is of a number of scheduled messages from 10 to 60. In general, the time complexity of the lookup table method and iterative algorithm are O(1) and O(n\({}^{2}\)), respectively, where n is related to the number of messages. This makes sure that the \(New\_SR_{mi}\) meets SR at only one step in time (O(1)) in this process. The proposed method improves the execution time over the previous algorithms, even in the case of a greater number of messages. This will be shown in the experimental results:
# PrismParser: A Framework for Implementing Efficient P4-Programmable Packet Parsers on FPGA
## 2 Protocol-Agnostic Packet Parsing: Background  
### Introduction to Packet Parsing in P4  
Data are sent in packets encapsulated using various protocol layers in digital communication. When the data is processed, the layers must be appropriately decoded, as parsing involves methodically examining incoming packets to identify and separate their headers. Given the layered nature of network protocols, headers are nested within each other, each signifying a specific protocol layer. For example, an Ethernet header can contain an IP header, which might enclose a TCP header.  
In P4, a packet parser is implemented using a state machine to navigate through the packet's hierarchical structure. This state machine comprises states specifically configured to recognize and extract different headers based on predefined keys and their exact positions within the packet. The configuration of these keys, their respective locations, and the dimensions of the headers are all defined within the P4 code. Upon encountering a key at a specific location, the parser determines which header comes next, extracts it, and transitions to the appropriate state. This systematic process ensures the orderly extraction of headers, aligning with the structured layers of network protocols. This parsing procedure is often visualized using a parser graph, typically represented as a directed acyclic graph (DAG). Figure 1 showcases a classic parse graph for enterprise networks, similar to what is explained in [21]. In this graphical representation, nodes show individual protocols or headers, and edges outline the potential transitions between them.
# How to Optimize Training Design? A Narrative Review of Load Modulators in Basketball Drills
## 3 Discussion of the Present Evidence
### Drill Structure: Duration, Regime (Continuous vs. Intermittent), and Work-to-Rest Ratios  
This pertains to how exercises are structured, including the arrangement of sets, the duration of each set, and the timing of participation and rest intervals. This approach enables the assessment of recovery capacity between sets and facilitates the management of workload for each drill [25].  
This aspect holds significant importance when the exercise objective is conditioning, as varying rest durations can impact the intensity of subsequent sets or actions. BDs, SSGs, and SGs have demonstrated efficiency, based on the training regimen, in improving the fitness levels of basketball players, similarly to high-intensity interval training (HIIT) activities involving running [27]. Furthermore, due to the specificity of SSGs, they contribute to enhancing defensive movements, shooting proficiency, and upper limb strength [25]. Therefore, they emerge as a viable option for conditioning improvement. Drills can be structured in a continuous or intermittent manner [30; 43] aiming to target either aerobic or anaerobic aspects more prominently. [PERSON] et al. (2019) [29] proposed modifying the workload of SGs by implementing tasks with and without breaks. Tasks without breaks involved continuous play without interruptions such as free throws or timeouts, while tasks with breaks allowed for these interruptions. They found that tasks without breaks resulted in higher intensity and load values in various performance metrics, including PL, decelerations/min, jumps/min, high-intensity jumps/min, changes of direction (CODs) /min, and high-intensity COD/min, compared to tasks with breaks.  
In this regard, when structuring the exercise, two types of pauses or rests should be considered: (a) intra-set pauses; and (b) inter-set pauses [44]. Intra-set pauses refer to the time that the player stops during the exercise, either due to rotation or exercise circumstances, while inter-set pauses refer to the time and type of rest that the coach plans between different sets. In full-court drills, it has been observed that intra-set rest is greater [44], which could result in a lower average relative workload of the exercise, i.e., a lower work-to-rest ratio, despite the intensity of the actions potentially being higher than in other drills. For this reason, when planning the workload of different tasks, it is important to consider not only the number of players or the playing space, but also the duration ofthe exercise, how players are organized, their participation and rotation, any pauses that may arise during the exercise, and the timing and type of rest planned between sets [44].
## 3 Method and Model
### Dataset  
In this study, audio data from the IEMOCAP dataset, synthetic audio, and students' spoken English audio are used as the primary data sources. These audio recordings undergo noise addition, speed variation, pitch alteration, and other preprocessing steps to create a comprehensive dataset consisting of 54,464 audio samples. Specifically, this dataset comprises three main components.
## 4 Discussion
### Scientometric Analysis Insight: Role of Co-Citation Reference and Keyword Co-Occurrence Analysis
#### 4.2.3 Climate Change Impacts on Forest WUE Along Altitudinal Gradients  
Climate change is intensely affecting forest ecosystems globally, and forests along altitudinal gradients are no exception. One of the most critical effects of these climate changes is the significant impact on forest WUE. WUE is generally influenced by various factors which are being altered by climate change, such as temperature, precipitation rate, and atmospheric CO\({}_{2}\) levels. Climate change impacts on forest WUE along altitudinal gradients are complex and multifaceted. That is because altitudinal gradients form distinct ecological zones due to environmental factors. With the increase in elevation, temperature normally drops, and precipitation patterns may also change [110]. These gradients provide a unique insight for studying how climate change impacts forest ecosystems.  
Temperature is a crucial factor that significantly influences WUE. Global temperatures are rising and the effect of these changes exhibits different trends. Meanwhile, global warming may prolong the growing season at different altitudes [111]. At lower elevations, rising temperatures remain WUE stable [112] by increasing photosynthetic activity. On the other hand, at higher elevations, where temperatures are usually cooler, evapotranspiration rates lead to reduction in soil moisture and potentially lessen WUE [60; 113].  
Precipitation is another important factor which can be impacted by the climate changes along with elevation and consequently has profound impacts on WUE [112]. For instance, in tropical rain forests, ample soil water availability from excessive rainfall reduces transpiration and decreases water consumption and whole-day photosynthesis activity increases, ultimately enhances WUE [114]. On the other hand, variable precipitation patterns and rates can lead to drought or excessive moisture in forest soil, both of which have the potential to stress an ecosystems [115; 116]. Drought can reduce WUE by restraining water availability to trees, whereas excessive moisture can lead to waterlogging, which negatively impacts WUE [117; 118].  
The magnitude of elevated atmospheric CO\({}_{2}\) levels may vary with elevation and other environmental parameters. For example, one study found that rising CO\({}_{2}\) generally increased WUE; however the extent of this effect varied with altitudinal gradient and other climatic factors [119]. A 60-year study was conducted in Central Europe on naturally growing oak trees along altitudinal gradients, and revealed that intrinsic water-use efficiency (iWUE) has increased at lower and upper altitudes due to rising CO\({}_{2}\) and warmer conditions. The evaporative water-use efficiency (eWUE) has remained stable due to the increase in vapor pressure, which offsets the benefits of higher CO\({}_{2}\)[120].