esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Model
### Influence of Water-Cement Ratio
#### 4.5.4 Pipeline Deformation Analysis  
Figure 23 shows the deformation of the pipeline. With the increasing distance from the middle partition wall, the pipeline deformation first increased and then decreased, and the pipeline deformation reached the maximum near the central axis of the left tunnel. When the distance from the partition wall was greater than 21 m, the deformation of the pipeline was small and the deformation was basically stable. The slurry water-cement ratio had a great influence on the deformation of the pipeline. The deformation of the pipeline increased with the increase of the slurry water-cement ratio, and the increasing range also gradually increased.  
Figure 24 shows the relationship between the maximum displacement of pipeline and water-cement ratio \(\eta\). When the slurry water-cement ratio \(\eta\) = 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00, the maximum displacement of the pipeline was 13.0 mm, 14.0 mm, 16.6 mm, 24.5 mm, 59.4 mm, and 128.2 mm, respectively. Compared with the results of the previous slurry water-cement ratio, the variation amplitude of the maximum displacement of the pipeline was 1 mm, 2.6 mm, 7.9 mm, 34.9 mm, and 68.8 mm. With the increase of water-cement ratio, the maximum displacement of the pipeline increased gradually, and the increasing amplitude also increased. When \(\eta\) = 0.85 and \(\eta\) = 0.90, the maximum displacement of the pipeline was small and the variation amplitude was large, indicating that the effect of grouting reinforcement was good.  
According to the analysis, when the slurry water-cement ratio \(\eta\) = 0.85 in actual engineering, the tunnel surrounding rock reinforcement effect was b  
Figure 23: Pipeline deformation.
## 4 Finite Element Modeling Approach
### Model Calibration  
Calibration was necessary to tune the initially assumed material properties of the model to better represent those obtained experimentally from dynamic identification. An eigenvalue analysis of the FE model was used to identify the frequencies and mode shapes of the structure in the model for direct comparison with those obtained experimentally. The frequencies determined in this method are directly impacted by both the mass and the stiffness of the structure; therefore, mass was fixed, while stiffness was adjusted for calibration. The relevant frequencies from DIANA are obtained from an eigenvalue analysis, in which the solutions to the free vibration equation (Equation (4)) are represented by the eigenvalue \(\lambda\) (Equation (5)) [32]. K represents the stiffness matrix, M the mass matrix, \(\phi\) the corresponding mode shape vector, \(\omega\) the angular frequency, and f the frequency:  
\[\mathrm{K}\phi=\omega^{2}\ \mathrm{M}\phi \tag{4}\]  
\[\lambda\equiv\omega^{2}=4\pi^{2}\ \ell^{2} \tag{5}\]  
For the calibration, the initial elastic modulus was reduced from 2.0 GPa to 1.5 GPa, which reduced the frequency error for similar mode shapes to under 2.0% for the first two frequencies, and under 9% for the third. The average absolute error was therefore 3.8% with a maximum error of 9.0%. Modes were further validated from the eigenvector analysis by selecting the first three that moved above 20% of the mass of the wall each, from an initial analysis that considered up to 70% of the mass moved from 20 eigenfrequencies computed. A visual comparison between the numerical and experimental modes demonstrates their similarity (Figure 11).
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 4 Experimental Results
### Comparisons with State-of-the-Arts
#### 4.3.2 Comparison of Experimental Results on the SPMCS-11 Test Set
As we have seen, our model can recover better visual effects on both regular and irregular patterns, demonstrating that this new method of gated motion compensation is effective and that it can make better use of inter-frame information to recover better detail than other methods.
## 2 Improvement of DPIV Algorithm
### Improved WIDIM Algorithm
#### 2.2.2 MLS Algorithm
Equation (11) is written as  
\[I=\left(\mathbf{\alpha}\mathbf{M}-\mathbf{Z}\right)\Omega\left(\mathbf{\alpha}\mathbf{M}-\mathbf{Z} \right)^{\mathrm{T}}, \tag{13}\]  
where, \(\mathbf{Z}=[z_{22},\,z_{32},\,z_{42},\,\ldots,\,z_{ij}]\), \(\mathbf{M}=[\mu^{\mathrm{T}}\left(x_{22},\,y_{22}\right)\), \(\mu^{\mathrm{T}}\left(x_{32},\,y_{32}\right)\), \(\ldots,\,\mu^{\mathrm{T}}\left(x_{ij},\,y_{ij}\right)]\), and \(\mathbf{\Omega}=\mathrm{diag}\) (\(\omega\)), \(i,j=2,\,\ldots,\,5\). Consider the extremum of \(I\), that is,  
\[\frac{\partial l}{\partial\alpha}=\mathbf{\Gamma}(x,y)\mathbf{\alpha}^{\mathrm{T}}(x,y )-\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\where, \(\mathbf{U}\) and \(\mathbf{V}\) are horizontal and vertical displacement matrices, respectively. T\({}_{ij}\) is the translation vector of each sub-image calculated by ICP.  
Step 2: Reduce the interrogation window size, perform MLS fitting for the initial displacement field. Boundary fitting value of the displacement field is obtained according to Equations (9)-(12).  
Step 3: Construct pixel displacement field \(\mathbf{U}^{\prime}\), \(\mathbf{V}^{\prime}\) using bicubic uniform B-spline surface interpolation. Calculate the bicubic uniform B-spline surface slice as follows  
\[\mathbf{\Theta}(\xi,\zeta)=\mathbf{AB}_{q}\mathbf{CB}_{w}^{\mathrm{T}}\mathbf{\gamma}^{\mathrm{ T}}, \tag{19}\]  
where, \(\mathbf{\Lambda}=[\xi^{3},\xi^{2},\xi,1]\), \(\mathbf{\gamma}=[\zeta^{3},\zeta^{2},\zeta,1]\) and \(\mathbf{B}_{q}\), \(\mathbf{B}_{w}\) represent the quad node B-spline basis function that corresponds to control vertex \(\mathbf{C}\). When \(\xi\), \(\zeta\) traverse between [0, 1], the displacement value at any point on surface slice \(\mathbf{\Theta}\) can be obtained. Step 4: Deform the image according to pixel displacement field \(\mathbf{U}^{\prime}\), \(\mathbf{V}^{\prime}\):  
\[\mathbf{G}^{\prime}{}_{2}(\mathbf{X},\mathbf{Y})=\mathbf{G}_{2}(\mathbf{X}+\mathbf{U}^{\prime},\mathbf{Y}+ \mathbf{V}^{\prime}); \tag{20}\]  
Step 5: Perform ICP on \(\mathbf{G}_{1}\), \(\mathbf{G}^{\prime}{}_{2}\) with a small interrogation window to obtain the new displacement field \(\mathbf{U}^{e}\), \(\mathbf{V}^{e}\):  
Step 6: The displacement field is updated:  
\[\mathbf{U}^{\prime}=\mathbf{U}^{\prime}+\mathbf{U}^{e},\mathbf{V}^{\prime}=\mathbf{V}^{\prime}+\bm {V}^{e}; \tag{21}\]  
Step 7: Iteratively perform Steps 1-6 until the interrogation window is reduced to the specified size, and the final flow velocity field is calculated as  
\[\mathbf{L}=\frac{\sqrt{\mathbf{U}^{\prime 2}+\mathbf{V}^{\prime 2}}}{\Delta t}, \tag{22}\]  
where, \(\Delta t\) is the time interval. For the convenience of illustration, the DPIV algorithm based on WIDIM algorithm is called the WIDIM-DPIV algorithm, and the improved WIDIM-DPIV algorithm based on ICP and MLS is called the IM-WIDIM-DPIV algorithm.
## 6 Directional Scattering Characteristics: Forward and Backscattering Conditions
### Directive Scattering Conditions for Dielectric Resonances
**Right**: Similarly, the backscattering radiation pattern, i.e.,: **(a)** 3D far-field pattern; **(b)** the surface electric field; and **(c)** the tangential magnetic field. Note the differences in direction of the radiation patterns and the differences in the orientation of the magnetic field. At this point, a couple of general conclusions can be drawn. First, Equations (35) and (36) exhibit an inverse-square dependence with respect to the size parameter. This is can be intuitively explained since a similar dependence has been found either for the magnetic multipoles (Table 1) or for the electric multipole terms. For a given material dispersion, e.g., Drude-like materials, the obtained conditions can be used for predicting the optimum size for desired directive scattering. This fact can lead to inverse material engineering philosophies, where the material dispersion will be adjusted following the presented forward and backscattering conditions. Alternatively, Equations (35) and (36) provide a very simple and intuitive rule for the design of direction selective scattering sources, recently denoted as Huygens sources [4; 104; 105; 106; 107; 108; 109]. A potential generalization of the above formulas can be done if we assume an arbitrary-shaped dielectric scatterer, exhibiting its first electric and magnetic resonances. The scattered field can be decomposed in a set of spherical multipole harmonics [49], and hence the above formulas could be used for a first, rough estimation of the positions of the forward and backscattering maxima.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 5 Discussion  
### Data Distribution  
As shown in Figure 11, a statistical analysis of the Jenks classification results for all models was conducted. This analysis serves two purposes: firstly, to understand the data distribution and subsequently infer the overall occurrence probability of landslide disasters in the study area; secondly, to compare the Jenks classification results among different models, which facilitates the cross-validation between them.  
Throughout the various stages of this study, the Jenks classification results of the prediction outcomes from the two models did not seem to align perfectly. Referring to the results of model performance evaluation, during the 2000-2004 period, SVM's predictions had a higher proportion of low and moderate susceptibility areas, while RF's predictions tended to have a higher proportion of the three middle categories. Given that RF outperformed SVM in overall performance during this period, combined with the analysis of the actual situation in Figures 9 and 10, it is believed that the data distribution of RF's prediction results is closer to the true values. For the intermediate ten years from 2005 to 2014, the distribution of prediction results from the two models was relatively similar. However, it is worth noting that in the 2015-2019 period, there was a significant difference in data distribution between SVM's prediction results and RF's. Based on the findings in Section 4.3, SVM's predictions for this period cannot be considered reliable for assessing landslide susceptibility in the area.
## 3 Analysis and Design
### Semi-Physical Simulation Test for the Establishment of Inter-Satellite Laser Links
If the resolution of 100 nrad can be achieved in the time domain, then the motion capability in the low millihertz frequency band will be less than 100 nrad/\(\surd\)Hz; thus, the specification requirements for the customized piezo-driven hexapod are as listed in Table 4. The 3D model of the customized piezo-driven hexapod designed by PI (Physik Instrumente) [45] is depicted in Figure 5. By taking the parallel kinematics design using piezo actuators and parallel metrology capacitive sensors, motion in 6 DoF (degrees of freedom) can be achieved. The technical solution for the semi-physical simulation test for the establishment of inter-satellite laser links is shown in Figure 6. The SCI is applied to achieve high-precision angle measurements using DWS technology. The piezo-driven hexapod is used to simulate high-resolution attitude adjustments for the spacecrafts. With the combination of DWS technology and piezo-driven hexapods, not only can the residual error of 1 \(\upmu\)rad for the acquisition procedure based on the CCD be verified, but the pointing jitter of 100 nrad/\(\surd\)Hz for the establishment of the inter-satellite laser link can also be tested. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Technical Specifications** \\ \hline Resolution in \(\upalpha\beta\gamma\) & 100 nrad \\ Travel range \(\upalpha\beta\gamma\) & bigger than \(\pm\)23.9 \(\upmu\)rad \\ Load & 50 kg \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Specification requirements for the customized piezo-driven hexapod. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Technical Specifications** \\ \hline Working wavelength & 1064 nm \\ Diameter of photosensitive surface & 1.2 mm \\ Interval between quadrant & 40 \(\upmu\)m \\
3 dB bandwidth & 20 MHz \\ NEP @20 MHz & \(4.5\times 10^{-12}\) W/\(\surd\)Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main parameters of the GD4542-20M QPD.
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion
### Comparison with Four Popular Methods
#### 6.2.4 K-Means  
K-means is one of the simplest and most well-established unsupervised learning algorithms that solves the clustering problem. It follows a basic and straightforward method for classifying a given dataset into a defined number of clusters (assuming k clusters). Therefore, it requires providing how many clusters are needed from the dataset. The central concept is to identify k centroids, one for each cluster. These centroids are positioned ingeniously as their placements affect the outcome. With this algorithm, we achieve a total of 8 clusters for dataset 1 and 50 clusters for dataset 2. Here, we set the values of K to 8 and 50, respectively. Since it provides the required clusters, prior knowledge of the dataset is required. Therefore, finding the optimal number of clusters from a new dataset is also very challenging. As each iteration produces different results, the random initial center clustering outcomes are the average of 100 iterations. Furthermore, the evaluation of performance is conducted by utilizing the silhouette coefficient. When applying K-means, dataset 1 demonstrates a relatively low coefficient of 0.226, whereas dataset 2 achieves a significantly higher coefficient of 0.832. Although the maximum coefficient value is achieved with dataset 2, the parameter of K-means (K value = 50) is set manually.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM
### Mapping and 3D Reconstruction for Underwater SLAM  
Accurate mapping of underwater environments is crucial for various applications, such as navigation and exploration. This section explores recent advances in underwater mapping using SLAM. Wang et al. [142] survey this rapidly growing field, dividing the work into four key areas: 3D reconstruction from binocular cameras, reconstruction from multiple images, object-focused reconstruction with relaxed calibration requirements, and SLAM-based techniques.  
For 3D maps, DL-based underwater monocular depth estimation methods have emerged recently for accurate mapping without the need to utilize stereo cameras or other sensors [143; 144]. For instance, some methods utilize domain knowledge to utilize image formation characteristics for synthetic underwater depth map creation [145; 146]. Marques et al. [118] introduce a DL-based SLAM method for estimating 3D underwater environments from single video frames. This self-supervised approach leverages a novel depth map prior based on GANs to enhance depth prediction. Beyond monocular cameras, sonars have also proven to be a cheap alternative for 2D mapping tasks [147].  
Semantic mapping has recently gained significant traction in underwater SLAM. For instance, Li et al. [148] propose a system combining a spatiotemporal deep neural network for semantic segmentation with a SLAM algorithm to create 3D point cloud maps annotated with semantic labels. Similarly, Abdullah et al. [149] introduce CaveSeg, a four-stage SwinTransformer network for semantic segmentation of underwater cave environments, as illustrated in Figure 14. This method, when paired with visual-inertial odometry (VIO), demonstrated accurate map reconstruction with minimal inference time. These advancements in DL-based mapping highlight innovative methods for generating accurate and detailed representations of underwater environments using SLAM techniques and multi-sensor solutions.  
Recent progress in deep learning emphasizes the development of adaptive models tailored for underwater applications. Transfer learning and domain adaptation techniques minimize the need for large, labeled underwater datasets by leveraging pre-trained CNNs that can be fine-tuned on limited underwater imagery. This significantly enhances feature extraction accuracy in turbid conditions. Additionally, GANs have been employed for underwater image enhancement, reducing noise and correcting color distortions to produce cleaner inputs for SLAM pipelines.  
Figure 13: Siamese CNN for loop closure. (Adapted from Burguera et al. [119]).
## 5 Cased Study
### Random Noise Reduction by EMD Decomposition  
The GNSS signal after the multipath error eliminated through wavelet de-nosing is decomposed by EMD, and the autocorrelation function is calculated. Generally speaking, that the signal power is concentrated in low-frequency ranges, and the noise is concentrated in high-frequency ranges. Figure 13 shows the intrinsic mode functions and its autocorrelation functions obtained through EMD decomposition. As shown in the autocorrelation functions, the intrinsic mode functions from the first order to the seventh order are all dominant noise, which can be regarded as the noise signal, while the eighth order is the measured signal, which is required to be maintained. Figure 14 shows the GNSS signal denoised by SWT and EMD. Compared with the GNSS signal treated only by wavelet de-nosing, the EMD method greatly reduces the noise power of the GNSS signal and has the same trend with the elevation of the measuring point obtained by precise leveling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SNR** & **RMSE** \\ \hline sym1 & 13.1 & 5.4 \\ sym2 & 16.9 & 3.5 \\ sym3 & 14.7 & 4.5 \\ sym5 & 15.5 & 4.1 \\ sym8 & 14.4 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The SNR and RMSE of different wavelet basic functions.  
Figure 12: Elimination of the errors from GNSS multipath by stationary wavelet transform.
## 4 Case Study
### Deployment of the CVROSS
#### 4.2.1 Noise Reduction  
Firstly, the set-up of the parameters for real-life implementation was required, especially the size of vehicles and traffic cones and their dimensions in pixels, according to Section 3.2. Then, data collection commenced via vision acquisition and noise reduction. As shown in Figure 12, the original image from vision acquisition included the traffic-lane lines, which are unrelated to vehicle and object recognition and matching. These lines are likely to negatively influence the results of recognition and matching. It was found that, after noise reduction, only the shapes of related vehicles remained. This could facilitate the subsequent processing of the image. As a result, better and more accurate processing could be achieved, to enhance the effectiveness of the designed system. However, different roads with their own features and characteristics may require different techniques for noise reduction, to remove unrelated signals from an image. This may represent a time-consuming modification when the system is applied to different roads in Hong Kong.  
Figure 11: System deployment environment of the CVROSS.  
Figure 12: Image differences before and after noise reduction.
## 3 Applications of Game Theory in WSN
### Game Theory in Applications of WSN
#### 3.4.2 Data Collection  
The collection of sensor data from a site is one of the most important issues in WSN. A good packet forwarding strategy has to be developed for balancing and selecting transmission packets. In data collection, it is important to ensure that all sources have equal access to the network bandwidth so that the sink receives a set of data with complete coverage in the site. [PERSON] and [PERSON] [29] developed a Packet Forwarding algorithm based on the Dynamic Bayesian Game named PFDBG for WSN. The adjacent nodes in WSN use the Bayesian amendment method to reasonably calculate and predict the opponent's energy level based on an evaluation of its historical actions to maximize the expected utility function. [PERSON] and [PERSON] [127] introduced a Prisoner's Dilemma-based model of packet forwarding and next using an evolutionary game-theoretical approach. They demonstrated that cooperation is very likely to be developed on the basis of conditionally cooperative strategies similar to the TIT-FOR-TAT strategy. [PERSON] _et al_. [183] proposed a bargain-based mechanism to encourage cooperative message trading among selfish nodes to maximize their rewards. [PERSON] _et al_. [184] proposed an approach for MAC design with prioritized event reporting in WSNs based on a game theoretic framework. The MAC priority classes are adaptively assigned based on accuracy of location information for individual sensor nodes, and on data correlation structure. The MAC solution makes event reporting more accurate and event localizing faster. [PERSON] _et al_. [185] proposed a differential game model to stimulate forwarding based on the payoff value of the differential game called forwarding contribution by which selfish avoidance clustering in WSNs was considered. [PERSON] [186] used game theory to analyze cooperative mechanisms and derive incentive strategies enforcing cooperation in forwarding.
###### Abstract  
Herein, we present a facile synthesis route for the mesoporous silica nanoflakes on two types of templates and evaluate their potential as potential drug delivery systems. Silica materials are attractive due to their biocompatibility, low cytotoxicity, high surface area, and tunable pores. In addition, they can be multifunctional. These properties were used to create multifunctional drug delivery systems combining folic acid as a target molecule and methotrexate (MTX) as an anticancer drug. The silica nanoflakes were formed using graphene oxide and double-layered hydroxide as templates, respectively. After the removal of matrices, the silica flakes were functionalized by folic acid and loaded with methotrexate. The differences in drug release performance and structural stability were analyzed with respect to the detailed physicochemical characterization of the produced silica nanoflakes.  
Keywords:silica flakes; anticancer drugs; targeted therapy; drug carrier; multifunctional drugs; folic acid/methotrexate +
Footnote †: journal: Applied Sciences
## 3 Communication-Efficient Secure Batch Division
Please see a full discussion based on the experimental results on the tradeoffs of this method as compared to the standard SMPC method in Section 6. The full secure division algorithm based on MHE is presented below in Algorithm 2. ```
0: Encryptions \(\hat{a},\hat{b}\), Range of denominator \(b_{\ell},b_{u}\), Number of iterations \(T\)
0: Decode(ColDecrypt(\(\ell\))) \(\approx\frac{\ell}{\theta}\) \(\hat{w}_{0}=\text{HAdd}(\text{HMul}(\frac{-8}{b_{\ell}^{2}+6b_{\ell}b_{u}+b_{u}^ {2}},\hat{b}),\frac{8(b_{\ell}+b_{u})}{b_{\ell}^{2}+6b_{\ell}b_{u}+b_{u}^{2}})\) \(\hat{a}_{0}\leftarrow\hat{a}\) \(\hat{b}_{0}\leftarrow\hat{w}_{0}\) \(i\gets 0\) while\(i<T\)do \(\hat{a}_{i+1}\leftarrow\text{HMul}(\hat{a}_{i},\hat{w}_{i})\) \(\hat{b}_{i+1}\leftarrow\text{HMul}(\hat{b}_{i},\hat{w}_{i})\) \(\hat{w}_{i+1}\leftarrow\text{HSub}(2,\hat{b}_{i+1})\) if\(\hat{w}_{i+1}\) will exceed mult. depth then \(\text{ColBootstrap}(\hat{a}_{i+1})\) \(\text{ColBootstrap}(\hat{b}_{i+1})\) \(\text{ColBootstrap}(\hat{w}_{i+1})\) endif \(i\gets i+1\) endwhile return\(\hat{c}=\hat{a}_{T}\)
```  
**Algorithm 2** Homomorphic division (HDiv)
## 4 Hierarchical Cooperative MIMO Clustering
### Hierarchy Creation  
Once the clustering process at level zero of hierarchy is completed, next step is selection of Routing Clusterheads (RCH). The neighboring clusterheads exchange a ROUTE message. In this message, clusterheads announce the cluster ID of the cluster whose clusterhead possesses the highest residual energy. Such a cluster becomes a routing cluster. The routing clusterheads announce their presence to all neighbor clusterheads in the network. The clusterheads update their routing tables. A RCH gets added in the routing table of a clusterhead if it takes less hops than the clusterhead itself to reach the CCS.