esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Kinematic Model of Continuum Robotics  
### Center Axis Curve Parameters  
Due to the arrangement of the tendons (discs and springs), the robotic arm is driven in line, and these continua exhibit a telescopic movement or bend into a circular shape. Therefore, the continuum arm's central axis can be described in space precisely as a circular arc with a variable radius of curvature and length. As shown in Figure 2a, the diagram on the left shows the state of the continuum arm of the section when it is not driven, i.e., \(t=0\). The central axis of the disc is a straight line with four drive lines of length \(L_{ij}^{0}(j=1,\ 2,\ 3,\ 4)\). After the continuous manipulator is driven for time t, the state is shown in Figure 2b. The line length becomes \(L_{ij}^{t}(j=1,\ 2,\ 3,\ 4)\). Let the change in rope length between the driven state and the undriven state be \(l_{ij}\left(t\right)\), as shown in Equation (6).  
\[l_{ij}\left(t\right)=L_{ij}^{0}-L_{ij}^{t} \tag{6}\]  
When the continuum arm is driven, the overall curve is assumed to be circular based on continuum curvature [24]. The radius of the curvature is described by \(\rho_{i}\in\left(0,\infty\right)\), and the bending angle is described by \(\varphi_{i}\in\left(0,\frac{\pi}{2}\right)\), which is on a plane that forms an angle \(\delta_{i}\in\left(-\pi,\pi\right)\) with the \(x\)-axis as a whole in space. The curve parameters in joint space variables are given by Equations (7)-(9). A comprehensive derivation of these variables is provided in Appendix B.  
\[\varphi_{i}=\frac{1}{2R_{i}}\sqrt{\left(l_{i4}-l_{2}\right)^{2}+\left(l_{i3}-l _{i1}\right)^{2}} \tag{7}\]  
\[\delta_{i}=\arctan\left(\frac{2(l_{i1}-l_{2})}{\left(l_{i1}-l_{i3}\right)}-1\right) \tag{8}\]  
\[\rho_{i}=\frac{2\sqrt{\left(l_{i4}-l_{2}\right)^{2}+\left(l_{i3}-l_{i1}\right) ^{2}}}{R_{i}\sum l_{ij}} \tag{9}\]  
Figure 1: Structure of a continuum tendon-driven robot. (**a**) Schematic diagram of the robotic arm model in the natural state; (**b**) Schematic diagram of the robotic arm model in the driven state; (**c**) Cross-sectional schematic diagram of the robotic arm model. Among them, \(L_{ij}\) represents the number of the driving lines, \(i\) represents the \(i\)th robotic arm, and \(j\) represents the \(j\)th driving lines.
## 3 Experimental Results
### Characterization of LNAPL Migration Rates
#### 3.1.2 The Low-Permeability Lens Test Group  
In the early stage of LNAPL leakage, the LNAPL diffused uniformly in the form of an ellipsoid within the homogeneous fine sand. Upon reaching the weak lens body, there was an obvious \"flow-around\" effect [32]. The LNAPL oil front generally diffused uniformly from the center outwards, with similar horizontal migration distances on both sides at any given time. By 300 min, LNAPL had infiltrated to a depth of 33 cm, marking a transition in both vertical and horizontal migration rates (Figure 4). The infiltration process was twofold:  
In the first stage (0-300 min), initially, the LNAPL migrated like in homogeneous sand, with a horizontal rate of 0.16 cm/min exceeding the vertical rate of 0.12 cm/min. Upon encountering the clay lens, the oil front was disrupted due to reduced permeability, impeding vertical migration. Despite this, some LNAPL infiltrated the lower part of the lens due to higher leakage rates at the center. Most of the LNAPL bypassed the lens, converging at its lower part with a vertical rate surpassing the horizontal rate of 0.16 cm/min. In the second stage (300-690 min), the LNAPL accumulated at the lens's lower part from 300 to 510 min, with horizontal migration rates rebounding and vertical rates continuing to decline as the distance exceeded 20 cm. From 510 to 690 min, as more LNAPL accumulated, the vertical migration rate slowly increased. Overall, horizontal diffusion was more pronounced than vertical.  
Figure 3: Variation in the LNAPL migration velocity (No lens-shaped aquifer). **(a)** Migration process. **(b)** Lateral migration velocity at different heights. **(c)** Vertical migration velocity.  
Figure 4: Variation in the LNAPL migration velocity (low-permeability lens). (**a**) Migration process. (**b**) Lateral migration velocity at different heights. (**c**) Migration process.
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
## 4 Experimental Verification
The simulation results (for \(R=20\)\(\Omega\), \(C=0.25\) pF; \(R=80\)\(\Omega\), \(C=0.15\) pF; and \(R=150\)\(\Omega\), \(C=0.1\) pF) align well with the measured data. At lower bias voltages, the reflection coefficients remain above \(-3\) dB across the 7-13 GHz range, indicating a reflective state, whereas higher voltages lead to absorptive behavior in specific frequency bands. Minor variations in the reflection peaks around 8 GHz at different voltages suggest practical adjustments due to manufacturing conditions. These results confirm the device's tunability and its ability to switch between reflective and absorptive states effectively.
## 3 Results and Discussion
### Flood Mapping with 2D Hydraulic Model  
To support the field activities of the Civil Protection in the immediate post-event, we used the TELEMAC-2D hydraulic model to map the flooded areas of the Catanian floodplain (red dot in Figure 2). The TELEMAC-2D model was coupled with the Continuum model receiving the hydrographs simulated by the run on observation to represent the dynamic evolution, in space and time, of the floods in floodplains. Figure 8 shows the results of TELEMAC-2D for the Catanian floodplain (Sicily) in terms of maximum water depth (left panel) and maximum water velocity (right panel) for the event that hit the area between the 24 th and 25 th of October 2021.
## 3 Solution Methodology
### First Order System \(\Big{(}W_{e}{}^{2}\Big{)}^{1}\)  
\[\frac{\partial^{4}\Psi_{1}}{\partial y^{4}}+3\Bigg{(}\frac{n_{i}-1}{2}\Bigg{)} \Bigg{(}\frac{\partial^{2}\Psi_{0}}{\partial y^{2}}\Bigg{)}^{2}\frac{\partial ^{4}\Psi_{0}}{\partial y^{4}}+3(n_{i}-1)\Bigg{(}\frac{\partial^{3}\Psi_{0}}{ \partial y^{3}}\Bigg{)}^{2}\frac{\partial^{2}\Psi_{0}}{\partial y^{2}}=0, \tag{42}\]  
\[\frac{\partial^{2}\Theta_{1}}{\partial y^{2}}+B_{\tau}\Bigg{[}2\Bigg{(}\frac{ \partial^{2}\Psi_{0}}{\partial y^{2}}\Bigg{)}\Bigg{(}\frac{\partial^{2}\Psi_ {1}}{\partial y^{2}}\Bigg{)}+\Bigg{(}\frac{n_{i}-1}{2}\Bigg{)}\Bigg{(}\frac{ \partial^{2}\Psi_{0}}{\partial y^{2}}\Bigg{)}^{2}\Bigg{]}=0, \tag{43}\]  
\[\frac{\partial p_{1}}{\partial x}=\frac{\partial^{3}\Psi_{1}}{\partial y^{3}} +3\Bigg{(}\frac{n_{i}-1}{2}\Bigg{)}\Bigg{(}\frac{\partial^{2}\Psi_{0}}{ \partial y^{2}}\Bigg{)}^{2}\frac{\partial^{3}\Psi_{0}}{\partial y^{3}}, \tag{44}\]  
subject to  
\[\frac{\partial^{2}\Psi_{1}}{\partial y^{2}}=0,\Psi_{1}=0,\Theta_{0}=0\;at\;y =0,\;\;\frac{\partial\Psi_{1}}{\partial y}=0,\Psi_{1}=f_{1},\Theta_{0}=0\;at \;y=\mathrm{h}. \tag{45}\]  
The solutions for zero and first order system is defined in Appendix A. By putting the zero and first order solution of stream function \(\Psi\), temperature \(\Theta\) and pressure gradient \(dp/dx\) in the Equations (34)-(36) we get the final first order solution for \(\Psi,\;\Theta\) and \(dp/dx\).  
The dimensionless pressure rise per wavelength \(\Delta P_{\lambda}\) is as follows:  
\[\Delta P_{\lambda}=\int_{0}^{1}\frac{\partial p}{\partial x}dx, \tag{46}\]  
the above expression is also solved by using final first order solution of \(dp/dx\).  
The rate of heat transfer (Nusselt number \(N_{u}\)) is defined as:  
\[N_{u}=\left.\frac{\partial h}{\partial x}\frac{\partial\Theta}{\partial y} \right|_{y=h(x)}. \tag{47}\]
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 8 Performance Analysis  
This section assesses the performance of our protocol in terms of the computation cost, communication cost, and security properties. We also compared the proposed protocol with other related protocols [6,8,15,16]. We demonstrated that the proposed scheme provides better security properties and efficiency as compared to other related schemes.
# Advancing in RGB-D Salient Object Detection: A Survey
## 3 Attention Mechanisms in RGB-D SOD  
Attention mechanisms have significantly impacted natural language processing [31] and computer vision [32] by focusing model resources on relevant features and suppressing less useful information. Thus far, the attention mechanism has been a transforming force in various areas of machine learning, especially in computer vision tasks such as RGB-D salient target detection. It helps to enhance feature discrimination and improve the robustness of models against noisy or irrelevant depth cues [33]. At its core, the attention mechanism allows the model to dynamically focus on the most informative parts of the input data, thereby enhancing the ability of the model to discriminate relevant features while suppressing irrelevant information. In the context of RGB-D SOD, utilizing the attention mechanism to accurately control the fusion of RGB and depth data provides a fine-grained modulation to understand the specific impact of each modal feature on the final detection result [34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Strategy** & **Advantages** & **Disadvantages** \\ \hline Early Fusion & Maximizes data interaction & Less flexible \\ \hline \multirow{4}{*}{Middle Fusion} & \multirow{4}{*}{Balances independent processing} & Designing and training \\  & & multiple feature extractors \\  & & and the fusion of features at \\  & & intermediate layers increase \\  & & model complexity and \\  & & training difficulty \\ \hline \multirow{2}{*}{Late Fusion} & Ensures robustness to & Limited in capturing \\  & modality-specific noise & beneficial cross-modal \\ \hline Hybrid Fusion & Integrates advantages of & Complex in implementation \\  & & all stages & and optimization \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of fusion strategies for multimodal data integration.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Vvictims 1** & **Nonvictims** & & \\  & **(\(n\) = 184)** & **(\(n\) = 139)** & **\(F\)** & **\(p\)** \\  & **\(M\) (SD)** & **\(M\) (SD)** & & \\ \hline Workplace satisfaction & & & & \\ (Range: 6–30) & 19.40 (3.94) & 21.01 (3.95) & 13.24 & \textless{}0.001 \\ \hline \hline \end{tabular} Note: mean (\(M\)); standard deviation (\(SD\)); Fisher’s ratio (\(F\)); p value (\(p\)); 1 The number of victims is lower than 192 (the total number of victims of WV) because subjects who did not answer all questions on the workplace violence scale were excluded from the analysis.  
\end{table} 
Table 1: Workplace satisfaction: comparison between victims and nonvictims (one-way ANOVA). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Vvictims 2** & **Nonvictims** & & \\  & **\(M\) (SD)** & **\(M\) (SD)** & **\(F\)** & **\(p\)** \\ \hline
**Burnout** & (\(n\) = 185) & (\(n\) = 130) & & \\ (Range: 16–64) & 35.21 (8.13) & 32.98 (7.95) & 5.84 & 0.016 \\ \hline
**Exhaustion** & (n = 190) & (n = 133) & & \\ (Range: 8–32) & 18.78 (4.77) & 17.74 (4.42) & 3.96 & 0.04 \\ \hline
**Disengagement** & (n = 187) & (n = 133) & & \\ (Range: 8–32) & 16.41 (4.49) & 15.18 (4.48) & 5.85 & 0.016 \\ \hline \hline \end{tabular} Note: mean (\(M\)); standard deviation (\(SD\)); Fisher’s ratio (\(F\)); p value (\(p\)); 2 The number of victims is lower than 192 (the total number of victims of WV) because subjects who did not answer all questions on the OLBI were excluded from the analysis.  
\end{table} 
Table 2: Burnout (exhaustion and disengagement): comparison between victims and nonvictims (one-way ANOVA). The results reported in Table 3 show that, compared with nonvictims, victims of workplace violence reported a significantly lower level of regulatory emotional self-efficacy (M = 39.76 vs. 41.68; s.d. = 7.50 vs. 7.72). Considering the two subscales, no significant difference was found in the case of positive emotions, while a lower level of regulatory emotional self-efficacy in coping with negative emotions was found in victims (M = 18.02 vs. 19.21; s.d. = 4.49 vs. 4.57).
## 3 Methodology
### Data Pre-Processing
#### 3.2.3 Data Normalization  
Min-max normalization plays a vital role in data pre-processing to ensure that features are scaled within a consistent range, typically [0, 1]. Min-max normalization can be represented by the following formula:  
\[X_{normalized}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{2}\]  
where \(\mathbf{X_{normalized}}\) indicates the value after normalization, and \(\mathbf{X}\) represents the value before normalization, while \(\mathbf{X_{min}}\) and \(\mathbf{X_{max}}\) correspond to the minimum and maximum values of the feature, respectively.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Selected Plant Species with Antioxidant Activity
#### 4.11.10. Acai Berries from _Euterpe oleracea_  
The acai berry, the fruit of the palm tree _Euterpe oleracea_ (family Arecaceae), originates from the Amazon rainforest in Brazil. It has garnered significant scientific attention due to its remarkable antioxidant capacity, which is primarily attributed to its rich polyphenolic profile, with anthocyanins comprising over 90% of these compounds. Additionally, the acai fruit is characterized by the presence of high levels of procyanidin trimers and dimers, several flavonoid acids (e.g., ferulic acid, vanillic acid, syringe acid), flavonoids (e.g., catechhin and quercetin), lignans, and carotenoids (lutein, alpha-carotene, 13-cis-beta-carotene, 9-cis-beta-carotene). Both the pulp and seeds are rich in polyphenols, the majority of which are cyanidin 3-glucoside and cyanidin 3-rutinoside [236; 237; 238].  
_Euterpe oleracea_, commonly known as acai, is renowned for its potent antioxidant properties, primarily attributed to its high content of polyphenols, anthocyanins, and flavonoids. These compounds contribute to the fruit's ability to neutralize free radicals, thereby reducing oxidative stress and potentially offering anti-inflammatory benefits. Studies have demonstrated that acai exhibits significant antioxidant activity, which may play a role in its reported health benefits [239; 240; 241].  
Euterpe Oleracea pulp powder is used at concentrations of up to 3% in leave-on products, such as face and neck treatments, and up to 0.6% in rinse-off formulations like paste masks. Additionally, both Euterpe Oleracea fruit extract and Euterpe Oleracea pulp powder may be incidentally ingested, with maximum concentrations of 0.025% and 0.3% in lipsticks, respectively [32].  
Euterpe Oleracea juice is produced by cold pressing the thin pulp of the fruit, followed by filtration, freeze-drying, and quality control before packaging and microbiological analysis. Euterpe Oleracea pulp powder is made by freezing the fruit pulp, then spray-drying it on an industrial scale with anionic maltodextrin DE10 as a carrier agent. The final juice is reconstituted with water for use in products [32].
## 2 Materials and Methods  
### Subjects  
All subjects provided written informed consent prior to data collection. This study protocol was approved by the local ethics committee (approval number: 2019-164). Sixteen ambulatory LOPD patients with a genetically confirmed diagnosis were included (9 women; mean age: \(54.2\pm 15.3\) years, range: 19-82 years, Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Subject ID (No.)** & **Sex** & \multicolumn{2}{c}{**GAA-Genotype**} & **Age** & **Disease Duration** & **Duration ERT** & \multicolumn{1}{c}{**NIV**} & **Use of Ambulatory** \\  & & & & (y) & (y) & (m) & & **Aids** \\ \hline
1 & M & IVS1 (\(-\)13T \(>\) G) & p.C103G & 58 & 14 & 135 & Yes & None \\
2 & F & IVS1 (\(-\)13T \(>\) G) & c.925G \(>\) A & 62 & 2 & 9 & No & None \\
3 & M & IVS1 (\(-\)13T \(>\) G) & p.C309R & 82 & 6 & 37 & No & Rolling walker \\
4 & F & IVS1 (\(-\)13T \(>\) G) & p.1552P & 53 & 17 & 99 & Yes & E-wheelchair \\
5 & M & IVS1 (\(-\)13T \(>\) G) & IVS1 (\(-\)13T \(>\) G) & 62 & 13 & 29 & Yes & Rolling walker \\
6 & M & IVS1 (\(-\)13T \(>\) G) & p.P493L & 51 & 15 & 63 & No & Walking sticks \\
7 & F & IVS1 (\(-\)13T \(>\) G) & p.P493L & 61 & 21 & 63 & No & None \\
8 & F & IVS1 (\(-\)13T \(>\) G) & IVS9 \(<\) C & 26 & 15 & 67 & No & None \\
9 & F & IVS1 (\(-\)13T \(>\) G) & c\(\times\)07 T \(>\) G & 50 & 23 & 119 & Yes & Rolling walker \\
10 & M & IVS1 (\(-\)13T \(>\) G) & c.8326L & 46 & 6 & 17 & Yes & None \\
11 & F & IVS1 (\(-\)13T \(>\) G) & c.2481 \(+\)102,2646 \(+\) 3 ldel & 54 & 19 & 48 & No & None \\
12 & F & IVS1 (\(-\)13T \(>\) G) & c.2481 \(+\)102,2646 \(+\) 3 ldel & 57 & 18 & 44 & No & Rolling walker \\
13 & M & IVS1 (\(-\)13T \(>\) G) & c.52564T & 19 & 19 & 132 & No & None \\
14 & F & IVS1 (\(-\)13T \(>\) G) & c.52564T & 66 & 7 & 33 & Yes & Rolling walker \\
15 & M & IVS1 (\(-\)13T \(>\) G) & c.2136.74 elGT & 50 & 40 & 116 & Yes & None \\
16 & F & IVS1 (\(-\)13T \(>\) G) & c.1019T \(>\) C & 70 & 20 & 0 & No & Rolling walker \\ \hline \hline \end{tabular}  
M—Male: F—Female; ERT—Inzyme replacement therapy: NV—Non-invasive ventilation; y—years; mo—months.  
\end{table}
Table 1: Characteristics of late-onset Pompe disease (LOPD) patients. IVS1—first intervening sequence (of glucosidase alpha acid (GAA) gene).  
Fifteen of sixteen patients (94%) were on ERT at the standard dose of 20 mg/kg biweekly (range: 9-135 months; mean: 67.4 months). The duration of the disease ranged from 2 years to 40 years (mean: 15.9 years). One female was tested before ERT was started. A total of 44% of the patients required temporarily non-invasive ventilation and 50% of the patients used walking aids. All LOPD patients were individually matched with healthy controls based on the relevant selection criteria sex, age, and body height to guarantee a valid comparison with asymptomatic subjects and to avoid the recruitment of an asymptomatic control group [12; 13]. The reference data were obtained from 1860 subjects for mobile gait analysis [14], from 141 subjects for treadmill analysis [15] and from 1724 subjects for the posturographic parameters [16].
## 4 Results and Comparison
### RUL Estimation for Test Instance Bearings
#### 4.1.1. RUL Estimation for Test Instance Bearing1-4  
Using Bearing1-2 as the learning instance, the pair of the 1 st PC sequences with similar trends is compared to estimate the RUL for the test instance Bearing1-4. As illustrated in Figure 11, Bearing1-4 has passed its degradation stage and entered its close-to-failure stage at time cycle 1039 and records are given up to cycle 1137. Therefore, the usage of Bearing1-4 can be estimated pessimistically, and the RUL is estimated as 0 at time cycle 1137. On the other hand, the RUL of Bearing1-4 can also be optimistically estimated as the duration of close-to-failure stage of Bearing1-2, which is known as 45-time cycles from the turning point. Averaging the pessimistic and optimistic estimates, we conclude the final RUL estimate as \(RUL_{e}=23\) (time cycles). As the actual RUL of Bearing1-4 is known as \(RUL_{a}=34\) (time cycles), the estimation has the relative error \(E_{r}=32.4\%\).
## 3 Proposed S&S Analysis Methodology
This can be facilitated by leveraging on a graphical tool called 8-Matrix Diagram (8 MD) that enables the analyst team to holistically view the essences of analysis artefacts. As illustrated in Figure 3, 8 MD is formed by 8 matrices (described in Table 1) built of artefacts obtained throughout the analysis process. In each matrix, as described in Table 1, particular symbols may be placed at intersections of row headers and column headers to denote their relationships. An example of 8 MD is given in Section 4 to demonstrate the application of the proposed methodology for a realistic CPS in power domain. The analysis could just terminate at the end of _Step 7_, if the derived set of S&S requirements is considered reasonably satisfied. Or, with all the artefacts (including 8 MD) established thus far, it can be iterated from _Step 1_. Such iteration would be necessary, if there are changes in the system architecture because of addition/removal/modification of particular system elements. The changes also need to be analysed to make sure they do not introduce unacceptable risks. At each iteration the analysis artefacts are revised accordingly. It is worth noting that the scales, metrics, criteria, or benchmarks used for judging if the derived set of S&S requirements is reasonably satisfied are determined on a case-by-case basis, which typically includes stakeholder expectations, applicable national/international standards, local rules and regulations. \begin{table}
\begin{tabular}{l l l l} \hline
**Matrices** & **Rows** & **Columns** & **Possible Symbols and Their Meanings** \\ \hline HH & Hazards & Hazards & X—The (row) hazard arises due to the (column) hazard. \\ \hline CH & Components & Hazards & X—The component is the final trigger of the hazard; \\ \hline CC & Components & Components & \(<\)—The (row) component receives signal from the (column) component; \\  & & \(>\)—The (row) component provides signal to the (column) component. \\ \hline SC & Signals & Components & P—The component is the legitimate provider of the signal; \\  & & & R—The component is the intended receiver of the signal; \\  & & & T—The component is the transmission medium of the signal. \\ \hline RC & Requirements & Components & X—The requirement may be violated if the component malfunctions. \\ \hline RR & Requirements & Requirements & X—The (row) requirement conflicts the (column) requirement. \\ \hline MC & Measures & Components & X—The component is required for implementing the measure. \\ \hline MR & Measures & Requirements & O—The measure contributes to satisfying the requirement; \\  & & & X—The measure may violate the requirement. \\ \hline \end{tabular}
\end{table} 
Table 1: Description of eight matrices that form 8-Matrix Diagram (8 MD).