esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of CNCs Properties
#### 3.1.5 Wide Angle X-ray Scattering (WAXS)  
Figure 3 reports the WAXS diffractograms for CNC\({}_{\text{COOH}}\), CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{SO3H}}\). All three samples showed similar diffraction patterns. In particular, the CNC\({}_{\text{SO3H}}\) diffractogram is characterized by three main peaks at 20 = 14.79\({}^{\circ}\), 16.58\({}^{\circ}\) and 22.83\({}^{\circ}\), respectively. These peaks were very similar to the ones detected for oxidized CNC\({}_{\text{COOH}}\) sample at 20 = 15.05\({}^{\circ}\), 16.61\({}^{\circ}\) and 22.75\({}^{\circ}\). As already reported [13], both CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{SO3H}}\) patterns are consistent with the IB phase crystalline pattern of cellulose. A spurious peak was also detected at 20 = 32\({}^{\circ}\) for CNC\({}_{\text{SO3H}}\)hand was attributed to a residue of Na\({}_{2}\)SO\({}_{4}\), likely coming from the synthesis. CNC\({}_{\text{COOH}}\) showed a similar crystalline pattern with respect to the other two CNCs samples. Peaks were shifted at 20 = 15.98\({}^{\circ}\); 17.55\({}^{\circ}\); 23.26\({}^{\circ}\), and an additional peak was detected at 20 = 35.26\({}^{\circ}\).  
In addition, the peaks appeared less sharp when compared to the other two analyzed samples. Such behavior can be explained by taking into account a more significant presence of amorphous phase, which partially hinders the detection of the crystalline phase.  
The preservation of the crystalline pattern demonstrated that the esterification reaction did not affect the crystalline structure of CNCs. Crystallinity of CNCs used for coating is mandatory when aiming at reliable barrier features.  
In particular, according to previous reports [36; 37] cellulose is characterized by a peak around 20 = 22\({}^{\circ}\)-23\({}^{\circ}\) which is attributable to the (200) crystallographic plane of cellulose I. The amorphous phase is usually detectable at 20 = 18\({}^{\circ}\). Therefore, the analysis of the diffractograms allowed to calculate the data using the Segal Method [38]. CNC\({}_{\text{COOR}}\), CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{SO3H}}\) showed a crystallinity index of 81%, 83% and 87%, respectively. The data are in good agreement with previous observations: CNC\({}_{\text{COOR}}\) show the lowest crystallinity among the tested samples, but, as shown by Raman data, all three types of CNCs appear to have very similar degrees of crystallinity.  
\begin{table}
\begin{tabular}{c c} \hline
**Types of Cellulose Nanocrystals** & **Total Charges Density (mmol/kg)** \\ \hline CNC\({}_{\text{SO3H}}\) & 0.30 \(\pm\) 0.04 \\ \hline CNC\({}_{\text{COOH}}\) & 0.52 \(\pm\) 0.07 \\ \hline CNC\({}_{\text{COOR}}\) & 0.88 \(\pm\) 0.06 \\ \hline \end{tabular}
\end{table}
Table 2: Total charges density of the three types of cellulose nanocrystals, mean \(\pm\) standard deviation (\(n=3\)).  
Figure 3: Wide Angle X-ray Scattering (WAXS) diffractograms for all three samples.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 3 Identity-Based Encryption Scheme
### Efficiency Analysis of IBE Scheme  
We compared the parameters of the proposed IBE scheme with the ABB-IBE scheme proposed by [PERSON] et al. [26] with the same security as this scheme. See Table 1 for comparison results.  
From the analysis in Table 1, it can be seen that the main efficiency parameters of the IBE scheme in this paper were significantly optimized. Compared with the ABB10-IBE trapdoor generation algorithm based on [23], this scheme uses the MP12 trapdoor generation algorithm to reduce the lattice security dimension from \(6n\log q\) to \(2n\log q\), and the size of the master secret keys is selected from a short vector in a reasonable Gaussian distribution, so the scale of the public parameters, key size, and ciphertext size of this scheme are reduced.
## 3 Project Overview and Data Preprocessing  
### Project Overview  
The Northern Xinjiang Water Supply Project has a total length of 516.2 km and currently holds the record for the longest water transfer tunnel under construction and in operation worldwide [34], with an average burial depth of 428 m and a maximum of 774 m. The Ka-Shuang Tunnel, which is a part of this project, extends over 283 km and was constructed using 11 open-type TBMs, covering a total excavation length of 226.58 km. This tunnel segment is notable for its TBM cluster excavation, deep burial, and extended single-heading excavation distances. The research section of this study focuses on a critical control segment within the Ka-Shuang Tunnel. Building on the traditional TBM construction experience, a \"single tunnel, dual machine\" construction mode was employed, where two TBMs were excavated in opposite directions until they connected with other segments. The individual excavation distances for TBMs 7 and 8 were 17.92 km and 19.67 km, respectively, totaling 37.59 km. Geological exploration data showed the proportion of different grades of surrounding rock in the tunnel section [35]; the surrounding rock of the KS Tunnel is primarily composed of rock mass types II and III, which account for 42.65% and 43.91%, respectively. With a circular excavation cross-section, this tunnel segment was constructed using open-type TBMs. The main technical parameters of the TBMs are illustrated in Table 1. The KS Tunnel data comprise the pile numbers KS 155 + 000 to KS 193 + 253; the geological cross-section at these pile numbers is illustrated in Figure 1a.
## 4 Performance of SERENA and TDMA/CA
### Simulations in an Ideal Environment for a Data Gathering Application
More energy saving can be achieved by allowing a node to sleep when the sender node has completed its transmissions before the end of the slot. Furthermore, a one-hop neighbor of the sender can quickly detect that it is not the destination of the message, if the sender orders its transmissions according to the increasing order of destination addresses. Figure 14: Percentage of time a node is awake in the active period. Figure 13: End-to-end delays obtained with a tree-based coloring.
## 6 Model Validation
### Model Parameter Estimation  
#### 6.2.1 Propeller Thrust and Torque Coefficient  
Figure 21 illustrates both the estimated propeller models and the experimental data based on the results of the additional static thrust test, showing that the models fit well with the data with determinant values of approximately 1. Table 5 summarized the modeled coefficient values for each propeller.  
Figure 20: Static thrust test results of sample set 3 (KDE10218 XF-105 with KDE-CF-305-DP) compared with datasheet: (**a**) Thrust, (**b**) torque, (**c**) rotational speed, (**d**) terminal voltage, (**e**) current consumption, and (**f**) power input.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Sample** & **Coefficient** & **k [m\({}^{b}\cdot\mathbf{s}^{-a}\)]** & \(a\) [ ] & \(b\) [ ] & \(c\) [ ] \\ \hline
**T-motor** & \(C_{T_{e}}\) & 0.075880 & 0.09323 & 0.1316 & 0.3516 \\
**G40x13.1** & \(C_{Q_{e}}\) & 0.006009 & 0.01175 & 0.4024 & 0.9575 \\ \hline
**KDE-CF-355-DP** & \(C_{T_{e}}\) & 0.076060 & 0.08517 & 0.1927 & 0.3854 \\  & \(C_{Q_{e}}\) & 0.007588 & 0.04714 & 0.4976 & 0.9844 \\ \hline
**KDE-CF-305-DP** & \(C_{T_{e}}\) & 0.080430 & 0.07712 & 0.1793 & 0.3577 \\  & \(C_{Q_{e}}\) & 0.007955 & 0.07292 & 0.4924 & 0.9867 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Static thrust and torque coefficient model for sample propellers (Equation (7)).
## 3 Deficiencies of Existing Membrane Materials Used in Fuel Cells  
Despite Nafion's high mechanical strength and chemical stability, it experiences some drawbacks, such as high swelling and loss of water under high temperature, which lowers the conductivity [46]. Different companies, such as Solvay and Dow Company, introduce membranes that contain high conductivity, low swelling, and high thermal stability as compared to Nafion membranes. On the other hand, fillers such as Titania, silica, and alumina, have been applied to modify the Nafion membrane, but the cost of production increases due to expensive Nafion material [47; 48; 49]. Moreover, anion exchange membranes (AEMs) used in AEMFCs face several critical deficiencies that hinder their performance and durability. These include low hydroxide ion conductivity, which results in higher ohmic losses, and poor chemical stability due to the degradation of cationic groups (e.g., quaternary ammonium) in high-pH environments [50]. AEMs are also highly sensitive to CO\({}_{2}\), leading to carbonation, which reduces ionic conductivity and forms precipitates that further degrade performance. Mechanical instability, caused by swelling and shrinkage during hydration cycles, and limited thermal stability at elevated temperatures exacerbate these issues. Additionally, water management is challenging, as AEMFCs require precise hydration to maintain conductivity without flooding [34; 51]. These deficiencies, combined with the limited commercial availability of high-performance AEMs, pose significant barriers to the widespread adoption of AEMFCs. Researchers are actively exploring solutions, such as developing chemically stable cationic groups, hybrid membranes, and advanced materials, to overcome these challenges and improve the viability of AEMFCs for clean energy applications [52].  
Several polymers with high glass transition temperatures, good ionic conductivity, and high resistance to fuel crossover and swelling are being used in research and commercial applications. These polymers are often combined with ceramics and nanomaterials to enhance the properties of polymer membranes for fuel cells. To further refine and tune the membrane structure, polymer chains are grafted [53; 54; 55; 56] or block copolymers are introduced [57; 58; 59]. Examples of these polymers used in fuel cell membranes (PEMFCs,DMFCs) include PBI [60; 61; 62], Polyether ether ketone (PEEK) [63; 64; 65], PSF [66; 67; 68], and PVDF [69; 70; 71]. On the other hand, in an AEMFC, the membrane materials are different from proton exchange membranes because it conducts hydroxide ions rather than protons [H\({}^{+}\)]. The common materials used in making AEMs are hydrocarbon-based materials: Polyphenylene Oxide (PPO) [72; 73; 74], Polyaryletherketones (PAEK) [75; 76; 77], Polybenzimidazole (PBI) [78; 79; 80], fluorinated materials such as modified PVDF [81; 82; 83], nanoparticles [84; 85; 86], ionic materials [87; 88; 89], and quaternary ammonium functional groups [90; 91; 92]. The key parameters are explained in the Supplementary Materials.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results  
In this section, we evaluate and analyze the performance of the proposed work. This approach requires different scenarios to extensively investigate its behavior. Therefore, to validate that COOF can quantitatively improve RPL performance in LLNs, we examine the proposed work through multiple simulation trials. In addition, to verify that COOF is suitable for heavy and dynamic traffic scenarios, we compare COOF against that of the existing related approach SCA-OF [34] and default RPL OFs [10; 11].
## 4 Determination of Indicator Weights Based on Combination Weighting Method
### Analytic Hierarchy Process  
Analytic hierarchy process (AHP) is often used to obtain the subjective weights of indicators. It is an evaluation method that combines quantitative and qualitative analysis with the advantage of simple calculation. In general, the overall hierarchy is divided into the target hierarchy, program hierarchy, and criterion hierarchy to make problems more rational when using AHP to make decisions [31; 32]. The process of determining subjective weights through AHP is shown, as follows:  
1. Divide the hierarchy of indicators In this paper, the specific hierarchy division of the indicator system is shown in Table 1.
2. Construct the pairwise judgment matrix between hierarchies The nine-level scale method is applied to compare the elements in pairs. The meaning of the scale \(a_{ij}\) of 1 to 9 is given in Table 2, as follows. The judgment matrix \(A\) is defined, as follows:  
\[A=\left[ \begin{array}{cccc}a_{11}&a_{12}&\cdots&a_{1m}\\ a_{21}&a_{21}&\cdots&a_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ a_{n1}&a_{n1}&\cdots&a_{nm}\end{array} \right]\]
3. Hierarchical single arrangement and consistency check Hierarchical single arrangement refers to calculating the maximum eigenvalue \(\lambda_{\max}\) of the single-layer judgment matrix and the corresponding eigenvector \(\xi\). The normalized \(\xi\) is used to get the ranking weights of each indicator at the same level when compared with the importance of a certain indicator at the previous level. The formula for determining \(\lambda_{\max}\) and \(\xi\) is given in Equation (4). \[A\xi=\lambda_{\max}\xi \tag{4}\]  
The procedure of consistency check for the constructed judgement matrix is shown, as follows:  
1. Calculate the consistency indicators \(CI\) \[CI=(\lambda_{\max}-r)/(r-1)\] (5)where \(r\) is the order of the judgment matrix. 2. Determine the average random consistency indicator \(RI\)  
After 1000 times repeated calculations through the order from 1 st to 15 th, the value of \(RI\) is measured in Table 3 [33]. 3. Calculate the consistency ratio \(CR\)  
\[CR=CI/RI \tag{6}\]  
A consistency check is the way to determine whether the consistency of judgement matrix is within a reasonable range by checking the values of \(CR\). When \(CR<0.1\), the judgment matrix is considered to have passed the consistency check, otherwise it needs to be properly corrected.
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
## 8 Local Search Improvement Procedures
### Local Search Improve3  
The third local search procedure starts by identifying periods where the capacity is not fully used. Given a capacity profile that has the form of a pulse, for each job, the pulse is lowered by one unit for the period during which it is active. This is iterated for all jobs, and, finally, it is possible to exist periods scattered across the horizon that have non-zero capacity remaining. So, jobs with finish times that fall inside these periods (gaps) can possibly be moved to the right, and the schedule should still be feasible. The main idea of this local search procedure is that it allows two jobs of marginally different durations to swap places. This occurs by first identifying two C-Paths with no common jobs that have jobs with finish times falling inside gaps as their rightmost jobs. Given two such C-Paths, each job of them can be swapped with a job of the other C-Path, provided that the slack that the gap provides is adequate for this move. This means that all jobs of a C-Path that  
Figure 5: Assuming that job \(j3\) has the same duration as the aggregated duration of jobs \(j1\) and \(j2\), two cases for swapping them become possible. The first one puts \(j1\) first and \(j2\) second, and the other one puts \(j2\) first and \(j1\) second.  
are to the right of the smaller of the two swapping jobs should shift to the right, and all jobs of the other C-Path that are to the right of the bigger job should shift to the left, giving the opportunity for further penalty gains. In principle, the number of jobs that might have finish times that fall inside gaps is \(\mathcal{O}(|\mathbb{J}|)\), but our experiments showed that, in practice, this number is a small fraction of \(|\mathbb{J}|\). Since two C-Paths are involved, and each job of a C-Path has to be checked with each job of the other C-Path, this contributes \(\mathcal{O}(|\mathbb{J}|^{2})\). Moreover, all possible pairs of jobs that fall in gaps are used as starting points in the construction of the corresponding C-Paths, resulting in another \(\mathcal{O}(|\mathbb{J}|^{2})\) term. So, the time complexity of the overall procedure is \(\mathcal{O}(|\mathbb{J}|^{4})\). It should be noted that shifts due to penalty reductions occur rarely, and their amortized contribution to the time complexity is neglected. In practice, the time needed for this move is comparable to the previous one due to the relatively small number of jobs that fall inside gaps. An example of this procedure is depicted in Figure 7.  
Figure 6: Given a C-Path, this local search procedure swaps two non-consecutive jobs (\(j1\) and \(j2\)) and appropriately shifts the in-between jobs (\(j3\)) so as to keep the C-Path property for all involved jobs.  
Figure 7: Jobs belonging to two C-Paths swap places to reduce the length or even remove gaps in the schedule.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
###### Abstract  
As a part of a damage tolerance assessment, the goal of this research is to estimate the two-dimensional crack propagation trajectory and its accompanying stress intensity factors (SIFs) using the adaptive finite element method. The adaptive finite element code was developed using the Visual Fortran language. The advancing-front method is used to construct an adaptive mesh structure, whereas the singularity is represented through construction of quarter-point single elements around the crack tip. To generate an optimal mesh, an adaptive mesh refinement procedure based on the posteriori norm stress error estimator is used. The splitting node strategy is used to model the fracture, and the trajectory follows the successive linear extensions for every crack increment. The stress intensity factors (SIFs) for each crack extension increment are calculated using the displacement extrapolation technique. The direction of crack propagation is determined using the theory of maximum circumferential stress. The present study is carried out for two geometries, namely a rectangular structure with two holes and one central crack, and a cracked plate with four holes. The results demonstrate that, depending on the position of the hole, the crack propagates in the direction of the hole due to the unequal stresses at the crack tip, which are caused by the hole's influence. The results are consistent with other numerical investigations for predicting crack propagation trajectories and SIFs.  
c pacs: **Keywords:** crack propagation; FEM; adaptive mesh; mesh refinement; SIFs; node splitting; DET
## 2 Topology Optimization Problem  
Topology optimization is one aspect among various optimization problems. Its core theory is to adjust and optimize the mass distribution variable, '\(x\)', which is the percentage of the mass distribution for each unit, leading to the optimal mass distribution of the overall structure and the minimum of the objective function value. Hence, in this work, topology optimization can be written as \(f(x)\), and the equation is shown as Equation (1)  
\[\min:f(x)=SE-g*MSE \tag{1}\]  
\[SE=U^{T}KU=\sum_{i=1}^{n}{(x_{e})^{p}u_{e}^{T}k_{uu}^{e}u_{e}} \tag{2}\]  
\[MSE=V^{T}KU=\sum_{i=1}^{n}{(x_{e})^{p}v_{e}^{T}k_{uu}^{e}u_{e}} \tag{3}\]  
The objective function consists of SE (strain energy) and MSE (mutual strain energy). SE refers to compliance, and the overall stiffness of the structure can be enhanced by minimizing SE. MSE refers to the compliance of a certain point, and the voltage output can be enlarged by maximizing MSE. It is expected that the piezoelectric transducer possesses a larger overall stiffness and a larger displacement of the top point, so as to achieve a larger electrical output. The step to solve MSE involves replacing the originally applied force with the unit force at the required point [26].  
Where the constraints of equality and inequality are presented as Equations (4)-(6).  
\[\frac{V(x)}{V_{0}}=f \tag{4}\]  
\[\left[\begin{array}{cc}k_{uu}&k_{u\phi}\\ k_{\phi u}&-k_{\phi\phi}\end{array}\right]\left[\begin{array}{c}U\\ \Phi\end{array}\right]=\left[\begin{array}{c}F\\ Q\end{array}\right] \tag{5}\]  
\[0<x_{\min}\leq x\leq 1 \tag{6}\]  
\(U,F,Q,\) and \(\Phi\) are global displacement, load, potential, and the global charge matrix, respectively. Correspondingly, \(k_{uu},k_{\phi\phi}\) and \(k_{u\phi}\) are overall stiffness matrix, piezoelectric matrix, and dielectric matrix, respectively. Meanwhile, \(k_{uu}^{e},k_{u\phi}^{e}\) and \(k_{\phi\phi}^{e}\) are that of a single element, respectively. \(n\) is the number of all elements. If we assume that \(nelx,\)\(nely,\) and \(nelz\) are the cuboid dimensions in three directions, then it is obvious that \(n=nelx*nely*nelz\). '\(x\)' is the variable in the topology optimization process, whose numerical value is between 0 and 1. If '\(x\)' of a single element is 0, we consider that the element does not exist. On the other hand, when an element's '\(x\)' is 1, then it exists completely. Furthermore, if the '\(x\)' is neither 0 nor 1, it is in a semi-existence state. Generally, a threshold is set artificially to judge the specific state of a certain unit. For example, when the threshold is set to be 0.5, and  
volume fraction. The value of 'f' is also between 0 and 1. Additionally, the variable '\(g\)' is the correlation coefficient which can adjust the relative values between MSE and SE, and can also adjust the iteration process and convergence speed.
# Qualitative Analysis of Sleeper Supporting Condition for Railway Ballasted Tracks Using Modal Test
## 4 Analysis and Discussion
### Analysis of Correlation Between Gravel Ballast Spring Stiffness and Natural Frequency  
This study analyzed the correlation between the spring stiffness of the gravel ballast and the natural frequency of the sleepers using modal testing, as shown in Figure 25.  
Figure 24: Damping ratio analysis before and during construction (down track). (**a**) Left side; (**b**) Right side.  
Figure 25: Correlation analysis results between gravel ballast spring stiffness and natural frequency.  
Based on the analysis of natural frequency under varying spring stiffnesses of the gravel ballast, a quadratic regression formula was proposed to estimate the natural frequency from the spring stiffness of the ballast. The spring stiffnesses of the ballast on the left and right sides of the concrete sleepers before and during construction are shown in Figures 26 and 27.  
The spring stiffness of the ballast under the left and right sides of the concrete sleepers on the up track before and during construction was evaluated using the spring stiffness prediction formula developed in this study. Sleepers #3, #10, and #11 on the left side, closest to the adjacent excavation site, showed spring stiffness values close to 0 \(\mathrm{kN/mm}\). On the right side, sleepers #3 and #11 also had spring stiffness values close to 0 \(\mathrm{kN/mm}\). The spring stiffness of the ballast approaching 0 \(\mathrm{kN/mm}\) suggests a high likelihood of floating sleepers.  
Figure 27 presents the spring stiffness analysis results for the ballast on the left and right sides of the concrete sleepers on the down track before and during construction. While most sleepers on the left side displayed spring stiffness within the normal range, sleepers #3 and #12 exhibited values close to 0 \(\mathrm{kN/mm}\). Similarly to the observations for the up track, the compromised conditions of sleepers #3 and #12 likely resulted from the impact of transverse cable installations at those specific locations.  
Figure 26: Spring stiffness analysis results before and during construction (up track). (**a**) Left side; (**b**) right side.  
This study investigated the spring stiffness of the ballast beneath concrete sleepers at various stages of adjacent excavation work. Most sleepers exhibited a decrease in both natural frequency and ballast spring stiffness before and during the excavation. These findings suggest that adjacent excavation activities influence both the vertical and lateral behavior of gravel-ballasted tracks. Consequently, the modal testing technique developed in this study is an effective quantitative method for assessing the condition of gravel ballast, applicable not only at sites near excavation activities but also on operational railway tracks.