esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study presents a novel approach to identify the progression of Alzheimer's disease (AD) by integrating feature extraction using the ImFCM-Ws technique and optimizing a hybrid CNN-LSTM architecture. The standard ADNI dataset was utilized to train and evaluate this model for classifying different stages of Alzheimer's disease. Upon analysis, the CNN-LSTM incorporating ImFCM-Ws features outperformed alternative methods on the ADNI dataset, achieving an impressive accuracy of 98.20%.  
This methodology can effectively identify brain regions associated with Alzheimer's disease and provide a valuable decision-supporting tool for physicians in assessing the severity of the illness based on the level of dementia. Furthermore, the robustness of the proposed method lies not only in its high accuracy but also in its potential for real-time clinical applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Image** & **Dataset** & **Classifier** & **Acc** & **Others** \\ \hline
[6] & 2019 & MRI-PET & ADNI &
\begin{tabular}{c} 3 DCNN-FSBi- \\ LSTM \\ \end{tabular} & 94.82 & Multiclass \\
[7] & 2019 & MRI & ADNI & CNN & 89.4 & AD vs. NC \\
[8] & 2019 & PET & ADNI & 3D-CNN & 88.76 & AD vs. NC \\
[9] & 2019 & MRI & OASIS & AlexNet & 92.85 & Multiclass \\
[10] & 2020 & MRI-PET & ADNI & Stacked & 92.62 & Multiclass \\
[11] & 2020 & MRI & ADNI & C3d-LSTM & 97 & AD vs. NC \\
[12] & 2021 & MRI & ADNI & 3D-CNN & 96.88 & Multiclass \\
[13] & 2021 & MRI & ADNI & 2D-CNN & 93 & Multiclass \\
[14] & 2022 & MRI & ADNI & 2D-CNN-LeNet-5 & 88.7 & Multiclass \\
**Proposed** & **2024** & **MRI** & **ADNI** & **CNN-LSTM** & **98.13** & **Multiclass** \\ \hline \hline \end{tabular}
\end{table}
Table 6: A summary of current studies implementing techniques using DL.  
By providing a precise categorization of Alzheimer's disease stages, healthcare professionals can make informed decisions regarding patient care and treatment strategies. Moreover, the integration of ImFCM-Ws features with CNN-LSTM architecture enhances the interpretability of the results, allowing clinicians to understand the underlying neurobiological processes contributing to Alzheimer's disease progression. Ultimately, this innovative approach holds promise for improving early diagnosis and intervention, potentially leading to better outcomes and quality of life for individuals affected by Alzheimer's disease.  
E.H.A., S.S., G.Z.E.N. and Z.F.M. all contributed to this work and agreed to publish this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This submission contains original results and figures that have not been previously published or are being considered by another publisher. Data are contained within the article.  
There are no conflicts of interest acknowledged by any of the authors.
## 5 Discussion
### Ppar  
FPAR is a crucial input of the light use efficiency model that can usually be obtained with the help of remote sensing data [69; 70]. Because many studies have found that several VIs and FPAR dominated linear or nearly linear relationships, a simpler and common method was adopted to estimate the parameter in this study. To evaluate our FPAR estimation method, FPAR products from Copernicus (a European system for monitoring the earth [71] were chosen as reference data because ground-measured data were not available. We selected three key periods (mid-March, mid-April and mid-May), corresponding to the early, middle and late wheat growing stages, respectively, to validate the relationship between the Copernicus FPAR and our estimated FPAR. The results (Figure 13) indicated that there was a good agreement between the two products (\(0.51\leq R^{2}\leq 0.75\), \(0.05\leq\text{RMSE}\leq 0.06\) and \(8.84\%\leq\text{RRMSE}\leq 16.64\%\)), which demonstrates the feasibility of this approach for FPAR estimation.  
Despite acquiring satisfactory results, several limitations also exist, including the varying correlations between the VIs and FPAR for different crop types and growing stages. Meanwhile, VIs based on red and near infrared wavelengths such as NDVI and SR tend to gradually saturate at moderate-to-high vegetation coverages because light absorption and scattering are very strong in the red and near infrared regions [42; 72]. Recently, many researchers have attempted to modify existing VIs or develop new VIs to reduce saturation and improve accuracy in FPAR estimations. The wide dynamic range vegetation index (WDRVI) and green chlorophyll index (CIgreen) have shown higher sensitivity to FPAR than NDVI over the entire growing season [73; 74]. VIs that incorporate red-edge bands such as red-edge NDVI and MERIS terrestrial chlorophyll index (MTCI) also have been proven to be more sensitive to FPAR at high LAI stages [54]. In future research, FPAR can be calculated based on VIs that are more sensitive, and additional field experiments should be carried out to validate the results. Moreover, estimations based on radiative transfer mechanism models are another potential solution [75].
## 4 Reliability, Availability and Fault Tree Analysis  
In this section, we provide a brief introduction to reliability, availability and fault tree analysis concepts that are closely related with the proposed methodology.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 2 Fracture Behaviors of MGs
### Typical Mode I Fracture of MGs
#### 2.1.2 Shear Band Delamination  
After the multiple shear bands formation and sliding, the material could reach the stage of shear band delamination. At present, the critical condition for shear band delamination is still complicated to be quantitatively predicted. In experiments, the shear bands in MGs will develop into fracture after sliding for a distance of a few tens of microns or less [6; 28; 29; 30; 26]. The physics behind the shear band delamination can be understood from different perspectives. As the model of [PERSON]'s fluid meniscus instability [31; 32] suggested, the very thin shear band could be considered as a viscous layer, which would delaminate when a critical stress condition (a critical suction stress gradient along the shear band) is met. On the other hand, the shear band will gradually become looser and softer during sliding, due to the shear dilatation effect [33; 34; 35]. Irreversible microstructure change (such as nano cavitation [36] and nanocrystallization [37]), micrometer-scale softening and cavitation [38] can also occur during this process and weaken the shear band. Finally, the shear band reaches the critical condition of delamination. When the delamination occurs, the crack tip will propagate rapidly along the shear band path, and the fractograph will reproduce the shape of the shear band, denominated as \"flat zone\" or \"planar zone\" in some reports [26; 28]. Figure 2 shows the fractograph of a Pd77.5 Cu6 Si16.5 metallic glass, which reveals that the \"flat zone\" on the fractograph is actually not straightforward but will deviate from the original notch tip direction [27]. The morphologies of the shear bands and the fractograph formed by shear band delamination depend on the local stress condition. Near the side surface of the sample, the local maximum shear stress points into the sample, and cone shape shear bands will form (Figure 2b,c). Near the notch-tip inside the sample, the local maximum shear stress deviates from the notch-tip direction, and curved shape shear bands will form (Figure 2c,d). The shear direction and sliding distance of the shear band before delamination can be determined by the smooth shear-offsets at the edge of the fractograph [26; 28; 39].  
Figure 1: (**a–f**) The side view of a Pd77.5 Cu6 Si16.5 metallic glass (MG) sample with a size of \(3\times 6\times 30\) mm\({}^{3}\) during 3-point bending test. (**g**) The load-displacement curve.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 6 Numerical Illustrations
### Example 1: Assumed \(c\)-\(\phi\) Soil Slope
#### 6.1.4 Effect of Correlation Coefficient on CCRF  
In order to further reveal the effect of cross-correlation on the CCRF, Figure 15 gives the variations of \(\theta\) and \(\rho_{c^{\prime},\phi^{\prime}}\) associated with different candidate copulas when the \(\rho_{c,\phi}\) is changed between \([-0.9,\,-0.1]\). It is apparent that as \(\rho_{c,\phi}\) increases, all four copula parameters \(\theta\) increase accordingly. There is an obvious positive correlation between them. In addition, the \(\rho_{c^{\prime},\phi^{\prime}}\) values of random variables recreated by different copulas are basically consistent with \(\rho_{c,\phi}\), indicating the accuracy of the copula-based CCRF method. It is noticeable that there are slight differences among values of \(\rho_{c^{\prime},\phi^{\prime}}\) corresponding to different copulas. This is mainly attributed to the fact that the joint distribution constructed by different copulas is different, although the same marginal distribution and correlation coefficient is employed. These results highlight the limitations in constructing cross-correlation random fields with cross-correlation coefficients. It is impossible to derive a unique cross-correlation structure with cross-correlation coefficients.  
Figure 14: Scatter plots of soil parameters associated with different CCRFs. (**a**) TN margins. (**b**) TG margins. (**c**) WB margins. (**d**) LN and TN margins.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Margins** & \(\rho\) & \(\tau\) & \(\mu_{c}\) & \(\sigma_{c}\) & \(\mathbf{COV_{c}}\) & \(\mu_{\phi}\) & \(\sigma_{\phi}\) & \(\mathbf{COV_{\phi}}\) \\ \hline TN & \(-0.5238\) & \(-0.3682\) & \(10.297\) & \(2.995\) & \(0.2909\) & \(28.918\) & \(6\) & \(0.2075\) \\ TG & \(-0.4468\) & \(-0.3682\) & \(10.293\) & \(3.132\) & \(0.3043\) & \(29.038\) & \(6.036\) & \(0.2079\) \\ WB & \(-0.5266\) & \(-0.3682\) & \(10.29\) & \(2.999\) & \(0.2915\) & \(28.893\) & \(6.003\) & \(0.2078\) \\ LN and TN & \(-0.5181\) & \(-0.3682\) & \(10.297\) & \(2.995\) & \(0.2909\) & \(28.963\) & \(6.024\) & \(0.2080\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of CCRF variables associated with candidate margins.
## 3 Results and Discussion
### Puncture Resistance  
In order to investigate puncture resistance of the WP and WPE knitted fabrics, the puncture tests were carried out. In this case, two characteristics are important--a high of deformation during puncture and a force at the moment of puncture. The obtained results are presented in Figure 5.  
The data presented in Figure 5 demonstrate that increase in the loop length causes the decrease in the puncture resistance. The increase of the loop length from 7.8 mm up to 8.8 mm (i.e., 11.4%) in the WP type knitted fabrics leads to the decrease in the puncture resistance by 33.3%. In addition, the 10.4% increase in the loop length of the WPE type knits leads to the 32.8% decrease in puncture  
Figure 4: Knitted specimens after the tearing test.  
Figure 5: Puncture resistance (**a**) and height of deformation (**b**).  
resistance. To conclude, the 1 mm decrease of a loop length in a knitted structure can determine approx. 33% higher resistance to puncture. The total puncture resistance of the plain structure WP type fabrics is higher than of the WPE fabrics with elastomeric yarn even when the loop length in both fabrics (WP3 and WPE1) is very similar. It can be explained by the different high of deformation during the puncture. During puncture of knitted fabrics, stitches in the knit usually take yarn from adjacent stitches and the height of deformation is relatively big. From the data presented in Figure 5b it is evident that plain structure fabric WP1 with higher stitch density is deformed in high level (even 9 mm) before the fabric is punctured. This high deformation is obtained due to the yarn movement from the adjacent stitches. However, when stitch density decreases (due to increased loop length) the high of deformation gradually decreases, too. The 11.4% (1 mm in total) increase in the loop length of the WP type specimens caused a 125% decrease of the maximum deformation at the moment of puncture. This can be explained by the fact that looser knitted structure may be not broken during the puncture; stitches in a knit move apart each other and the hole forms due to redistribution of the yarn in adjacent courses and wales. However, there is a different situation with the fleecy knitted structures WPE with the elastomeric yarn. By increasing the loop length (i.e., decreasing the stitch density in the fabric) the height of the deformation increases, too. The 10.4% increased loop length (1 mm in total) determined a 100% higher maximum deformation at the moment of puncture. The fleece pattern structure has higher tightness than the plain fabric because of the fleece yarn which forms tucks and floats in turn. In our case, the fleece yarn is elastomeric with high ability to be deformed before break. Thus, the looser structure of fleece pattern knit has higher ability for deformation at the point of puncture, while elastomeric fleece yarn prevents movement of adjacent stitches and hole formation.
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 4 Flutter Analysis Method  
### Frequency Domain Method  
#### 4.1.1 Dynamic Stiffness  
The describing function (DF) method is widely used in control engineering. [PERSON] and [PERSON] developed the DF method in the 1930s [51; 52]. They used the average method and introduced the concept of equivalent linearization. [PERSON] extended the DF method [53]. When using the DF method, the Fourier coefficients of the nonlinear term need to be calculated and divided into two terms, which are respectively interpreted as the equivalent stiffness and damping terms related to the excitation signal. In order to ensure the quasi-linearity of the actuator, the excitation signal amplitude needs to be small enough. The actuator dynamic stiffness is essentially the transfer function of the quasi-linear actuator model obtained by the DF method when the input amplitude is small. Dynamic stiffness performance reflects the robustness of the actuator to disturbance. It refers to the dynamic torque required to be applied to produce a unit angle at the output shaft of the actuator. Dynamic stiffness reflects the characteristic that the support stiffness provided by the actuator to the fin will change with the frequency of the external load. As shown in Figure 10, the dynamic stiffness of the actuator is given by:  
\[k_{\delta\delta}(\omega)=\frac{T_{\rm load}(\omega)}{\theta_{\rm out}(\omega) }=\overline{k_{\delta\delta}}e^{i\varphi} \tag{16}\]  
where \(T_{\rm load}(\omega)=\overline{T_{\rm load}}e^{i\omega t}\) is the external load on the actuator output shaft, and \(\theta_{\rm out}(\omega)=\overline{\theta_{\rm out}}e^{i(\omega t-\varphi)}\) is the rotation angle of the actuator output shaft. The dynamic stiffness can be expressed by the amplitude phase curve varying with the input frequency \(\omega\); \(\overline{k_{\delta\delta}}\) is the amplitude and \(\varphi\) is the phase.  
According to the relationship between the output and input power spectral densities (PSDs), the actuator dynamic stiffness can be calculated [6]. Taking the external load as the input signal \(x(t)\) and the deflection angle as the output signal \(y(t)\), the transfer function is:  
\[k_{\delta\delta}(\omega)=\frac{P_{xy}(\omega)}{P_{xx}(\omega)} \tag{17}\]  
where \(P_{xy}(\omega)\) is cross PSD of \(x(t)\) and \(y(t)\), and \(P_{xx}(\omega)\) is auto PSD of \(x(t)\).  
In numerical simulation, the excitation signal is a constant-amplitude sweep frequency signal, and the sweep frequency range should include the frequency range of interest. Another method to obtain dynamic stiffness is to combine a step sine sweep (SSS) signal  
Figure 9: Schematic diagram of the system model.  
with a least square (LS) frequency response function (FRF) estimation algorithm. The SSS signal contains considerable energy to excite out all the characteristics of the actuator, and the LS algorithm is an averaging process in a sense and essentially plays an effective filtering role, but this method is time-consuming.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 3 Experiments  
Simulations and experiments were conducted to evaluate the proposed method. The performance of the proposed method significantly depends on the measurement results of radiation, and it is substantially influenced by the distributed conditions of the radiation sources. Therefore, the evaluation of the proposed method was conducted under multiple conditions, in which the distribution of radiation sources in each condition was different.  
Simulation experiments were conducted under three conditions: (a) a single radiation source was positioned in the environment, (b) multiple radiation sources were concentrated in a specific area, and (c) multiple radiation sources were dispersed in the environment. In addition, experiments were conducted under the condition that a single radiation source was located in the environment.
# Innovative Vaporization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption
## 3 Results and Discussion
### Kinetics and Isotherms of Phenol Adsorption
The extended contact time of 48 h for BWP950 is attributable to the gradual diffusion within its predominant microporous structure, a common characteristic of physically activated biochars [47; 48]. Although this characteristic may limit rapid adsorption in batch processes, BWP950 remains applicable in continuous flow systems given adequate residence time [61]. Despite its higher SBET (866 m\({}^{2}\)/g), BWP950 shows lower adsorption capacity compared to chemically activated biochars because adsorption performance depends on not only surface area but also surface chemistry. Chemically activated biochars possess more oxygen-containing functional groups (e.g., carboxyl, hydroxyl, carbonyl) that facilitate phenol adsorption via hydrogen bonding and \(\pi\)-\(\pi\) interactions [20; 21]. The literature acknowledges the importance of these functional groups, as demonstrated by FTIR analyses in previous studies [20; 21; 25]. These findings confirm that thermal activation alone, when properly optimized, can produce biochars with effective adsorption properties while maintaining process sustainability and simplicity. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \cline{2-9}
**Biochar** & \(\mathbf{K_{f}}\) & \(\boldsymbol{n}\) & \(\mathbf{R^{2}}\) & \(\mathbf{Q_{max}}\) & \(\mathbf{RL}\) & \(\mathbf{R^{2}}\) & \(\mathbf{K_{1}}\)\(\mathbf{L}\)/mg & \(\mathbf{K_{2}}\) & \(\mathbf{R^{2}}\) \\ \hline BWP750 & 40.28 & 2.28 & 0.89 & 178.57 & 5.94 \(\times\) 10\({}^{-5}\) & 0.99 & 4.78 & 14.77 & 0.68 \\ \hline BWP850 & 33.83 & 0.03 & 0.88 & 144.93 & 8.21 \(\times\) 10\({}^{-5}\) & 0.99 & 16.89 & 46.52 & 0.98 \\ \hline BWP950 & 40.28 & 2.28 & 0.88 & 178.57 & 5.94 \(\times\) 10\({}^{-5}\) & 0.99 & 21.58 & 38.89 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of phenol adsorption isotherms on biochars.
## 3 The Problem Definition
### Internally Pressurized FGM Cylindrical Pipe Submitted to Cyclic Bending  
The axisymmetric, sufficiently long FGM pipe with the inner and outer radii \(a\) and \(b\), respectively, exposed to internal pressure \(P_{in}\) and cyclic bending moment \(M\), as illustrated in Figure 1a,b, is considered. The pattern of the employed cyclic bending moment \(M\) is presented in Figure 1b.  
According to Figure 1b, the bending moment in each loading step illustrated by triangle shape, linearly with respect to horizontal axis increases until reaches the maximum value and then in the second half cycle linearly decreases when the bending attains zero. It should be noted that since the rate-independent plasticity model was used in this work, the quasi-static bending load with so slowly changes was applied.  
Figure 1: (**a**) Geometrical model of functionally graded material (FGM) cylindrical shell under constant internal pressure and cyclic bending moment. (**b**) Cyclic bending moment applied to the FGM cylindrical shell.  
It is noteworthy that due to two open ends, the axial stress generated by the internal pressure is ignored. Based on these assumptions, a set of equations for this condition include strain compatibility Equation (13), equilibrium Equation (14), Hooke's law Equation (15) and flexural stress Equation (16) can be written as [29]:  
\[\frac{d\varepsilon_{\theta}}{dr}+(1/r)(\varepsilon_{\theta}-\varepsilon_{r})\ =\ 0, \tag{13}\]  
\[\frac{d\sigma_{r}}{dr}+(1/r)(\sigma_{r}-\sigma_{\theta})\ =\ 0, \tag{14}\]  
\[\varepsilon_{r}\ =\ \frac{1}{E}(\sigma_{r}-\upsilon(\sigma_{\theta}+\sigma_{z}) )+\varepsilon_{r}^{p}+\varepsilon_{r}^{\text{res}},\]  
\[\varepsilon_{\theta}\ =\ \frac{1}{E}(\sigma_{\theta}-\upsilon(\sigma_{r}+ \sigma_{z}))+\varepsilon_{\theta}^{p}+\varepsilon_{\theta}^{\text{res}}, \tag{15}\]  
\[\varepsilon_{z}\ =\ \frac{1}{E}(\sigma_{z}-\upsilon(\sigma_{\theta}+\sigma_{r}) )+\varepsilon_{z}^{\text{res}}+\varepsilon_{z}^{\text{res}},\]  
\[\sigma_{z}\ =\ M\text{rsin}(\theta)/I_{r} \tag{16}\]  
Subscripts \(r\), \(\theta\) and \(z\) determine the vector components of variables in radial, hoop and longitudinal directions of a cylinder, respectively, and \(\varepsilon_{r,\theta,z}^{\text{res}}\) are the residual strains; \(\upsilon\) is [PERSON]'s ratio and \(I_{r}\) represents the inertia moment of the cross-section with respect to the neutral \(r\)-axis. The mechanical boundary conditions are assumed to be:  
\[\sigma_{r}(a)\ =\ -P_{in},\ \ \ \ \ \ \ \ \sigma_{r}(b)\ =\ 0 \tag{17}\]