esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Docking Analysis
#### 3.2.2 Molecular Interactions between CYP1A2 (2 HI4 PDB ID) and AFB1, B2, G1, and G2
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ligand**} & **DFT** & **Binding** & \multirow{2}{*}{**Active Site**} & **Hydrogen** & \(\pi\)**-\(\pi\)** \\  & **Functional/** & **Energy** & & **Bonds** & **Interactions** \\  & **6-311G(d,p)** & **(kcal/mol)** & & & & \\ \hline \multirow{4}{*}{AFB1} & \multirow{4}{*}{B3 LYP} & \multirow{4}{*}{\(-6.66\)} & Thr124, Phe125, Phe226, & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Asp313, Gly316, Ala317 & & & \\  & & & Asp320, Thr321, Leu382, & & & \\  & & & Ile386, Thr498, HEM900 & & & \\ \cline{2-6}  & \multirow{4}{*}{M06-2X} & \multirow{4}{*}{\(-6.96\)} & Thr124, Phe125, Phe226, & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Asp3313, Gly316, Ala317, & & & \\  & & & Asp320, Thr321, Leu382, & & & \\  & & & Ile386, HEM 900 & & & \\ \hline \multirow{4}{*}{AFB2} & \multirow{4}{*}{B3 LYP} & \multirow{4}{*}{\(-8.41\)} & Ile117, Thr118, Thr124, & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Phe125, Phe226, Val227, & \multirow{4}{*}{Nn312, Asp313, Ala317,} & \multirow{4}{*}{Thr321, HEM900} & & \multirow{4}{*}{0} \\  & & & Asn312, Asp313, Ala317, & & & \\  & & & Thr124, Phe125, Phe226, & \multirow{4}{*}{Asp320, Thr321, Leu382,} & \multirow{4}{*}{Asp320 \(-\)O4} & \multirow{4}{*}{0} \\  & & & Asp313, Gly316, Ala317, & & & \\  & & & Asp320, Thr321, Leu382, & & & \\  & & & Ile386, Thr498, HEM900 & & & \\ \hline \multirow{4}{*}{AFG1} & \multirow{4}{*}{B3 LYP} & \multirow{4}{*}{\(-4.92\)} & Thr124, Phe125, Phe226, & \multirow{4}{*}{Gly316-O4} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Asp313, Gly316, Ala317, & & & \\  & & & Asp320, Thr321, Leu382, & & & \\  & & & Ile386, Thr498, HEM900 & & & \\ \hline \multirow{4}{*}{AFG2} & \multirow{4}{*}{M06-2X} & \multirow{4}{*}{\(-5.52\)} & Thr124, Phe125, Asp313, & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Gly316, Ala317, & & & \\  & & & Thr321, Leu382, Ile386, Leu497, Thr498, & & & \\  & & & & Hern900 & & \\ \hline \multirow{4}{*}{AFG2} & \multirow{4}{*}{B3 LYP} & \multirow{4}{*}{\(-3.51\)} & Thr124, Phe125, Phe226, & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\  & & & Asp313, Gly316, Ala317, & & & \\  & & & Asp320, Thr321, Leu382, & & & \\  & & & Ile386, Leu497, Thr498, & & & \\  & & & & Hern900 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of the molecular interactions between CYP1A2 (2 HI4 PDB ID) and aflat provided by molecular docking simulations.
## 3 Test Results
### Comparison Test Results with Analytical Results  
In order to verify the wave force pattern from the test for sea levels and wave heights under regular wave conditions, a numerical analysis was performed on three test models using an in-house hydrodynamic analysis tool [25; 27] developed to reflect the diffraction effects of a large-sized circular member. A comparison of test and analytical results for wave forces is presented in Figure 20[25].  
As a result of the comparison of the wave force, the ratios of the test wave force to the analytical wave force in the HSWL and MSL range from 1.03 to 1.21 for the monopile, 0.82 to 0.98 for the GBS, and 0.65 to 0.74 for the hybrid, respectively. The variation of the ratios between the test and analytical results due to wave height changes is not large in all three types in the HSWL and MSL. This means that both the test and analytical results are consistent. The overall tendency in HSWL and MSL indicates that the ratios of the test wave force to the analytical wave fore tend to decrease slightly as the wave height increases in the three test models. This means that as the wave height increases, the test results are slightly smaller than the analytical results in the HSWL and MSL.  
On the other hand, the test wave forces indicate a considerable difference from the analytical wave forces in LSWL. The ratios of the test wave force to the analytical wave force in the LSWL range from 1.14 to 1.60 for the monopile, 0.98 to 1.15 for the GBS, and 0.67 to 0.87 for the hybrid, respectively. The ratios of the test wave force to the analytical wave force increase significantly with the increase of wave height in LWL, as presented in Figure 20. This means that as the wave height increases, the test results become much larger than the analytical results. It seems to be due to the wave nonlinearity such as shoaling or breaking, which cannot be considered in the analysis tools of this study, occurring in the shallow water. In addition, GBS results in a slower rate of increase in wave force at higher wave heights, which is similar to the trend in the test. It seems to be due to the fact that the diffraction effect generated in the large-sized circular member compensates the nonlinear effect of the wave force due to the shoaling, as in the test results presented in Figure 11.  
The analytical wave forces for the hybrid model has resulted in an over-estimation compared to the test results, which seems to be caused by the complicated shape of the hybrid model that combines a large diameter member at the bottom with slender members at the upper part. In order to obtain accurate wave force in shallow water analytically, it needs a more sophisticated hydrodynamic model to account for wave nonlinearity in the shallow water as well as complicated structural shapes.  
Figure 19: Wave force patterns under irregular waves: (**a**) monopile; (**b**) GBS; (**c**) hybrid.
## 3 Results and Discussion
### Porosity and Surface Area Measurement  
Surface area and porosity are the main properties that influence the adsorption behavior of porous materials. Indeed, materials having the same weight and volume, but different specific surface area, may differ in terms of surface activity and adsorption volume. For this reason, bulk porosity and BET analyses for 3% _w/w_ agarose aerogels and cryogels were performed and the obtained values are reported in Table 4.  
Agarose-based samples produced by supercritical drying showed higher values of bulk porosity and much larger values of specific surface area with respect to the samples produced by freeze-drying technique. Especially, surface area was more than ten times larger in the case of aerogels: 154 m\({}^{2}\)/g for aerogel against 11 m\({}^{2}\)/g for cryogel. These results agree with FE-SEM images showing that the aerogels structure was entirely characterized by small pores, responsible for the larger surface area. Therefore, a larger number of active sites available for dye adsorption can characterize aerogels.
# Applying the DEMATEL - ANP Fuzzy Comprehensive Model to Evaluate Public Opinion Events
## 3 Research Method
### Calculate Indicator Weights Using the DEMATEL-ANP Model
As there are \(n\) number of criteria, the direct relation matrix is marked as \[A=\big{(}a_{ij}\big{)}_{n\times n}.\]
2. Calculate the normalized matrix. Matrix \(N\) is obtained by normalizing the direct relation matrix \(A\). \[N=A/(\max_{1\leq i\leq n}\sum_{j=1}^{n}a_{ij})\] (1)
3. Calculate the total relation matrix. The total relation matrix \(T\) is calculated with the help of the MATLAB software tool, where \(N\) is the normalized matrix, \(I\) is the unit matrix, and \((I-N)^{-1}\) is the inverse matrix of \((I-N)\). \[T=N+N^{2}+\cdots N^{\text{K}}=\sum_{K=1}^{\infty}N^{\text{K}}=N(I-N)^{-1} \tag{2}\]  
This study applied the 5-level scale to label the relationship between factors (0: none; 1: very weak; 2: normal; 3: strong; 4: very strong) by means of expert scoring to construct the direct relation matrix \(A\). With the help of MATLAB software, the direct relation matrix was normalized Formula (1), from which the total relation matrix \(T\) was calculated using Formula (2), as displayed in Table 3.  
(4)  
ANP network relationship diagram. Based on the correlation analysis of the indicators in the DEMATEL model, the ANP network structure was drawn, as shown in Figure 2. The ANP divided the factors in the system into two parts: the control layer formed by the target layer and criterion layer, and the network layer constituted by the second level indicators, which are interdependent. (5) Generate the weighted matrix. (i)  
Let \(Tm=Um\), where \(Um\) represents the unweighted matrix and \(Tm\) denotes the total relation matrix. (ii)  
\(Wm=NTm\), passing \(Tm\) as the argument, \(NTm\) represents the normalized total relation matrix. (6) Generate the limit hyper matrix. \(LW_{m}\) (limit hyper matrix) = \(W_{m}^{m}\), where n denotes the \(W_{m}\)th power. The total relation matrix (similar to the ANP unweighted matrix) in DEMATEL was used as the input to the ANP model, which is normalized by Formula (3) to obtain the weighted hyper matrix \(\omega\). In the ANP, to reflect the stable influence relationship among the indicator factors, the weighted hyper matrix needed to be stabilized, which was solved for the limit relative ranking vector by using the power method (Table 4). The nth power of the weighted hyper matrix was solved for the vectors of the columns of the matrix, resulting in the  
\begin{table}
\begin{tabular}{c c c c c c c  
comprehensive weights of each indicator, from which the weights of the network layer indicators could be obtained, as shown in Table 5.  
The normalization formula:  
\[\frac{x}{\sum_{i=1}^{n}x_{i}} \tag{3}\]
## 3 Y-Branch Coupler
### Parameter Optimization  
Matching degree between the mode in the SMF and mode in the input part (\(\eta_{y1}\)) can be calculated using the follow overlap integral:  
\[\eta_{y1}=\frac{\left|\iint E_{\text{f}}(x,y)E_{\text{w}}^{\ast}(x,y)dxdy \right|^{2}}{\iint\left|E_{\text{f}}(x,y)\right|^{2}dxdy\iint\left|E_{\text{w}} (x,y)\right|^{2}dxdy} \tag{2}\]  
where \(E_{\text{f}}(x,y)\) denotes the mode in the SMF, \(E_{\text{w}}(x,y)\) denotes the mode in the input part, \(\ast\) denotes the complex conjugate.  
Influence of the width \(W\) and gap \(G\) on the mode matching degree \(\eta_{y1}\) is shown in Figure 4a,b. Firstly, compared with TM mode, mode matching degree \(\eta_{y1}\) is greater when TE mode is in the SMF, indicating that mode matching degree \(\eta_{y1}\) is polarization sensitive. Therefore, mode in the SMF must always be TE mode to ensure the maximum mode matching degree. Secondly, mode matching degree \(\eta_{y1}\) increases with the decrease of the width \(W\), because area of the mode in the input part is inversely proportional to the width \(W\). Thirdly, mode matching degree \(\eta_{y1}\) first increases and then decreases, when gap \(G\) increases from small to large. Because area of the mode in the input part also increases first and then decreases, during the growth of the gap \(G\). According to above results, width \(W\) should be as small as possible to improve the mode matching degree \(\eta_{y1}\). However, limited by the current waveguide fabrication technology, minimum width \(W\) is only 0.10 \(\upmu\)m. Therefore, maximum mode matching degree \(\eta_{y1}\) can reach 0.24, when TE mode is in the SMF and width \(W\) and gap \(G\) are 0.10 \(\upmu\)m and 0.86 \(\upmu\)m, respectively.  
Figure 3: Structure of the Y-branch coupler. (**a**) Three-dimensional view; (**b**) Top view.  
Transmission efficiency of the branch part (\(\eta_{y2}\)) is closely related to length of the branch part (\(L_{b}\)), and it can be calculated as follows:  
\[\eta_{y2}=\frac{P_{o}}{P_{i}} \tag{3}\]  
where \(P_{i}\) denotes the power on the right of the branch part as shown in Figure 3b, \(P_{o}\) denotes the power on the left of the branch part. Figure 4c shows the relationship between the transmission efficiency \(\eta_{y2}\) and length \(L_{b}\). When length \(L_{b}\) is small, transmission efficiency \(\eta_{y2}\) increases sharply as the length \(L_{b}\) increases. With the further increase of the length \(L_{b}\), increase tendency of the transmission efficiency slows down. After length \(L_{b}\) is greater than 10 \(\upmu\)m, transmission efficiency \(\eta_{y2}\) is nearly equal to 1, no matter for TE mode or TM mode in the SMF.  
In summary, when TE mode is in the SMF, and width \(W\), gap \(G\), and length \(L_{b}\) are 0.10 \(\upmu\)m, 0.86 \(\upmu\)m, and 10 \(\upmu\)m respectively, maximum mode matching degree \(\eta_{y1}\) can reach 0.24, and transmission efficiency \(\eta_{y2}\) is approximately equal to 1. Therefore, the maximum coupling efficiency of the Y-branch coupler (\(\eta_{y}\)) is 0.24.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.6 Inception-RegNet Model  
In this study, it introduces the Inception-RegNet model, a novel fusion aimed at enhancing the extraction of spectral-spatial features from high-resolution wetland images [39]. This hybrid model harnesses the multi-level feature extraction capabilities of Inception and the scalable architecture of RegNet, facilitating a sophisticated interpretation of intricate wetland attributes [40]. The adoption of this cohesive strategy is informed by its capacity to amalgamate the virtues of both foundational models synergistically, enhancing the precision and intricacy of wetland image segmentation.  
The model architecture starts with an initial convolutional layer for feature extraction, followed by a series of Inception-RegNet blocks incorporating Inception Attention modules. These modules apply channel-wise attention to prioritize key features. Each block contains Conv2D layers with escalating filter numbers and a MaxPooling2D layer for spatial reduction. The features extracted are then flattened and processed through fully connected layers, culminating in an output layer that classifies the wetland into three distinct categories. The model's debut in wetland segmentation brings together the best of both Inception and RegNet, offering enhanced precision for analyzing complex wetland landscapes. The model was trained over 130 epochs, a parameter determined through cross-validation to assess model complexity, learning rate, and the propensity for overfitting or underfitting. To mitigate overfitting, data augmentation strategies, including rotation and flipping, were implemented. Figure 8 delineates the architecture of the Inception-RegNet model.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
###### Abstract
A necessity has emerged to develop an innovative, portable BMS framework for collecting voltage, current, and temperature data in a versatile range of applications like high C-rates, encompassing both stationary battery loads and dynamic mobile systems. A modular in-operando system that incorporates a BMS with DL that integrates with modern cloud data can ensure scalability and flexibility to adapt to the evolving technological needs of systems based on LIBs. In this work, we present a comprehensive framework for a BMS capable of meeting electric propulsion-specific requirements of in-operando monitoring while integrating a capacity degradation network (CD-Net) DL model developed previously, which uses only four historical data points to predict the upcoming cycle capacity with higher accuracy and lower computational power [24] than support vector regression, Bayesian regression, and GPR methods. The next section outlines the general design and key components of such a system, followed by a detailed explanation of the specific components leading to a proof-of-concept hardware system demonstration with 42 standard charge-discharge cycles and 20 in-operando charge-discharge cycles [4; 30]. Subsequently, an experimental setup is described for deploying a DL SOH estimation model that can be used for both static battery packs and electric propulsion systems. Outcomes demonstrate high-quality data capture and successful integration of DL models with the collected data. Finally, comparisons between BMS data and secondary DAQ source-based data are presented to validate the efficiency of the DL-based BMS solution. This approach contributes to BMS development as well as facilitates accurate integration with edge and cloud platforms, incorporating DL or ML models.
# Vibrational Analysis of Building Structures with Irregularities
## 2 Definitions of Random Irregularities  
In structural vibration analysis, random irregularities are commonly modeled within the framework of statistical energy analysis (SEA). However, definitions of randomness often vary significantly and can appear somewhat arbitrary depending on the structure. [PERSON] and collaborators [24; 25; 26] provided detailed definitions of irregularities across components and junctions, often within hybrid SEA approaches. These hybrid methods assume weak coupling and diffuse fields, assumptions which tend to fail in lightweight structures due to their strong coupling and directional energy propagation; see [PERSON] [28].  
SEA's effectiveness hinges on accurately calculating the coupling loss factor (CLF), representing the energy dissipation between structural elements. Several methods exist for determining the CLF (see [PERSON] and [PERSON] [29]). [PERSON] and [PERSON] [25; 30] illustrated how SEA can handle structures with both deterministic and random boundaries, assuming structural randomness correlates directly with excitation randomness, yielding diffuse fields within SEA sub-systems.  
In this paper, we explicitly define randomness in structural components and junctions as random fluctuations characterized by an empirically derived power spectral density (PSD). This approach builds on the extensive studies of multiple scattering by inhomogeneities [18; 19; 31], which have concluded that PSD effectively captures the influence of such irregularities. However, these analyses often depend on restrictive assumptions about the relative scales of wavelength and inhomogeneities.  
Our deterministic model [15; 27], outlined later in Section 3.1, can similarly incorporate the variations in component properties and junction conditions in terms of the auto-correlation or PSD for individual irregularities. When specific junction conditions are unknown or unmeasurable, we approximate their influence as an equivalent energy contribution in the overall variational formulation; see Section 3. Unlike SEA, which relies on assumptions that preclude specifying irregularity details, our approach tailors the model to observed or estimated irregularities based on the structure's physical properties.
## 3 Results
### Eigenmodes and Eigenvalues  
The natural period of the structure was obtained by eigenvalue analysis. To perform this, the analysis model was copied and modified by removing the blast loading and maintaining the structural and piping masses. The dynamic analysis step was replaced by linear perturbation frequency analysis type. Ten values of the eigenvalues were requested for the analysis output. The mode shape which is parallel with the direction of blast loading was selected as the representation of the dynamic characteristics of the structure. Therefore, the structural natural period associated with this mode shape was used in the step duration of self-weight dynamic analysis. By relating the structural natural period to the dynamic loading duration, the structural dynamic response was classified into impulsive t\({}_{\text{d}}\)/T\({}_{\text{n}}\) \(<\) 0.3, dynamic 0.3 \(<\) t\({}_{\text{d}}\)/T\({}_{\text{n}}\)\(<\) 3, or quasi-static 3 \(<\) t\({}_{\text{d}}\)/T\({}_{\text{n}}\) categories [50]. Where t\({}_{\text{d}}\) is the dynamic load duration and T\({}_{\text{n}}\) is the structural natural period. The structural dynamic characteristic was identified by undertaking the eigenvalue analysis. Four mode shapes considered to to be the most important shapes are presented in Figure 19. The first mode as the highest eigenvalue is usually considered as the critical case. However, mode shapes 1 and 2 in this study were considered not to be the critical cases because the columns, bracings, and lateral beams provide lateral restrains in the longitudinal direction of the actual structure. The mode shape-3 that deflects in the same direction as the blast excitation loading was selected to represent the dynamic characteristic for the structure. Subsequently, the eigenvalue based on mode shape-3 was used in all calculations that are relevant with the structural dynamic characteristics.  
By considering the dynamic load duration of t\({}_{\text{d}}\) = 0.376 s and the structural natural period of 0.717 s, the ratio of the dynamic loading duration to the natural period is determined as t\({}_{\text{d}}\)/T\({}_{\text{n}}\) = 0.52. The ratio is greater than 0.3 and less than 3, hence falls into the dynamic category. Therefore, dynamic analysis was solved using numerical integration of the dynamic equations of equilibrium that is already implemented in the ABAQUS numerical computational technique.  
Figure 18: Yielding and buckling in the validated model.
## 5 Reliability Optimization Design Method Oriented to the Stability of Configuration Transformation  
Reliability optimization design is an important part of reliability design, which means that under certain constraints, the best design scheme is given in accordance with the corresponding objective requirements. The general approach is to establish a mathematical model of design variables, objective functions and constraints for engineering problems and then use intelligent optimization algorithms to calculate the optimal results. The general mathematical optimization model can be described as follows [49; 50]:  
\[\left\{\begin{array}{ll}\min&f(d,X,Y)\\ s.t.&\Pr\{S_{i}(d,X,Y)\geq 0\}\geq[R_{i}]\\ &d^{l}<d<d^{u}\\ &\mu_{X}^{l}<\mu_{X}<\mu_{X}^{u}\end{array}\right.\quad i=1,2,\cdots n_{g} \tag{39}\]  
\(f(d,X,Y)\) is the objective function; \(d\) is the deterministic design variable; \(X\) is the random design variable vector, which is controlled by the designer during the design process; \(Y\) is the uncertainty design parameter, which is not controlled by the designer during the design process; \(\Pr\{\bullet\}\) is the probability of no failure; \(g_{i}(d,X,Y)\) is the performance function related to the failure mode; \(n_{g}\) is the number of performance functions; \(d^{l}\) and \(d^{u}\) are the lower and upper limits of \(d\), \(\mu_{X}\) is the mean value of random design variable X, \(\mu_{X}^{l}\) and \(\mu_{X}^{u}\) are the lower and upper limits of \(\mu_{X}\).  
Compared with the traditional mechanical structure, metamorphic structure has the function of configuration transformation, and the failure of configuration transformation is a common failure form of metamorphic structure. Compared with the reliability optimization results of motion accuracy, the reliability optimization results oriented to configuration transformation ability can meet the needs of practical work of metamorphic structures.
# Smart Grid Security: A PUF-Based Authentication and Key Agreement Protocol
## 4 Proposed Protocol (PPSG)
Once the legitimacy of both \(SM_{i}\) and \(NAN_{j}\) has been verified and they have been successfully authenticated, the mutual authentication and key agreement process concludes, and the shared key will be \(SK=H(I_{NAN}\|I_{SM}\|r_{NAN}.r_{SM}.d_{SM}.d_{NAN}.P\|\)