esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Model
#### 2.3.1 Metamodel  
This study utilizes an artificial neural network (ANN) as the metamodel to predict outputs. A typical ANN works like a black box to predict outputs based on given inputs. The working concept of ANNs is inspired by the working process of a biological neuron system, where many neurons are interconnected to form a complex and nonlinear network. The adaptive nature of input changes allows the ANN method to produce the best output without altering the output criteria. A typical ANN architecture consists of three layers: (1) the input layer, (2) hidden layers, and (3) the output layer. Neurons at the input and output layers represent the number of input and output variables, respectively. Each pair of neurons is connected with the connection weight \(w\), and each neuron possesses a unique bias \(b\). Mathematically, the output of the \(l\)-th layer in an ANN algorithm can be expressed as follows:  
\[\mathbf{o}^{(l)}=f_{a}\Big{(}\mathbf{W}^{(l)}\mathbf{o}^{(l-1)}+\mathbf{b}^{i}\Big{)},\ l=1,2,...,k \tag{6}\]  
where \(k\) is the number of layers, \(f_{a}\) is the activation function, \(W^{(l)}\) is the weight matrix, and \(b^{(l)}\) is the bias vector. \(\mathbf{o}^{(0)}\) and \(\mathbf{o}^{(k)}\) represent the input and output layers, respectively. During the model training process, the weight and bias values undergo meticulous updates through tailored training algorithms and optimizers, aimed at improving prediction accuracy and minimizing the loss incurred during prediction. For regression problems, common loss functions used to evaluate model training include the maximum absolute error (MAX) and mean squared error (MSE). Additionally, R-squared (\(R^{2}\)) is employed to quantify how effectively the input variables explain the variability in the output variable. These metrics, MAX, MSE, and \(R^{2}\), are calculated as follows:  
\[\text{MAX}=\text{max}|y_{i}-\hat{y}_{i}| \tag{7}\] \[\text{MSE}=\frac{1}{n_{s}}\sum_{i=1}^{n_{s}}(y_{i}-\hat{y}_{i})^{2} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\rho\) & \(v_{D}\) & \(P_{CI}\) & \(A\) & \(B\) & \(R_{1}\) & \(R_{2}\) & \(\omega\) & \(E_{0}\) & \(V_{0}\) \\ (kg/m\({}^{3}\)) & (m/s) & (GPa) & (GPa) & (GPa) & (-) & (-) & (-) & (MJ/m\({}^{3}\)) & (-) \\ \hline
1630 & 6930 & 21 & 371.2 & 3.231 & 4.15 & 0.95 & 0.3 & 7000 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: High explosive burn material and Jones–Wilkins–Lee equation of state parameter of TNT [59].  
\[R^{2}=1-\frac{\sum_{i=1}^{n_{s}}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n_{s}}(y_{i}- \bar{y})^{2}} \tag{9}\]  
where \(n_{s}\) is the number of samples, \(y_{i}\) is the actual value, and \(\bar{y}\) is the mean value.
## 3 External Perturbations Affecting Geolocation
### Atmospheric Path Delay  
While the solid Earth perturbations amount to a change in the actual position of the reference targets, by far the largest effect on the achievable SAR geolocation accuracy is a slant range delay of the radar signal caused by the slightly slower speed of light in the ionosphere and troposphere. This can be described as a PD value, which lengthens the travel time of a radar signal, and is dependent on the frequency of the radar waves, as well as the local meteorological conditions. For S1A, the total PD was typically \(\sim\)3.5 m one-way for an SM product acquired over Switzerland using beam S4 (incident angle \(\sim\)37\({}^{\circ}\)). It was calculated that the ionosphere was responsible for \(\sim\)45 cm of the path length increase, with the troposphere accounting for the remaining \(\sim\)3 m. The mechanisms for the delay effect are different in the ionosphere and troposphere [11]. Estimates of the ionospheric component were based on measurements of the total electron content (TEC) along vertical profiles, provided bi-hourly by the University of Berne's Astronomical Institute [14], and adjusted for the nominal incident angle as described in Section 3.3 of [11].  
Tropospheric PD estimation is described in Section 3.2 of [11]. The height-dependent polynomial model requires only three meteorological measurements as inputs: the local temperature, pressure, and relative humidity. Using these measurements, the path delay can be estimated to better than \(\sim\)several cm. During the S1A in-orbit commissioning phase, we automated the retrieval of the meteorological measurements and subsequent PD calculation for a given site and product date/time. The meteorological measurements were obtained from the Swiss national weather service (meteoswiss), provided at 10-min intervals through their software interface CLIMAP. For the _Torny-le-Grand_ site, the nearest station was  
Figure 4: The three largest perturbations affecting the position of a surveyed reference target in a SAR image: (**a**) drift between local and global coordinate frames caused by plate tectonics; (**b**) solid Earth tide; and (**c**) atmospheric path delay.  
Payerne_, located \(\sim\)5 km north of the site. For the _Dubendorf_ site, the nearest suitable station was _Zurich Affoltern_, \(\sim\)10 km west of the site.  
The slant range delay caused by PD was incorporated into our geolocation processor in the form of a lengthened range prediction (echo travel time) for a given reference target.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 2 Slope Stability Analysis by the SRM  
For SRM, there may be a significant difference in the failure loads or FOS if different failure criteria are selected [49]. At present, there are several common failure criteria: (1) non-convergence of the numerical solution [10; 38; 50]; (2) a sudden increase in the deformation of a characteristic location [51]; (3) connectivity of the shear increment strain or shear-strain region [8]. Since many factors may lead to the non-convergence of this numerical calculation, non-convergence does not necessarily mean the failure of the structure, thus the first criterion is considered insufficient. For the second criterion, this method needs to constantly draw the displacement-reduction factor curve to judge the position of the turning point through observation, so this criterion is also considered not objective enough.  
For an elastic-perfectly plastic model with an associated flow rule, when a point within the slope is in the plastic state, [PERSON] et al. [52] proved that the elastoplastic matrix \(D_{\text{ep}}\) at that point must be a positive semidefinite matrix with a rank defect of one and found out the direction of the singularity of \(D_{\text{ep}}\). Based on this fact, [PERSON] suggested that the failure criterion of the slope should be considered the moment when the strip runs through from the toe to the top of the slope, and all the elements in the strip are in the plastic state. Therefore, the third criterion, i.e., plastic zone penetration, was chosen as the failure criterion in this study, and the assumptions are adopted too.  
In practice, we take the reduction factor \(F_{i}\) corresponding to the LES as the FOS. Once the failure criterion is selected, the FOS and the distribution of the equivalent plastic strain (EPS) in the LES can be easily obtained by RSM. The EPS is defined as follows:  
\[\overline{z}_{p}=\sum\Delta\overline{z}_{p},\Delta\overline{z}_{p}=\left[\left( \Delta\varepsilon_{p}\right)^{T}\left(\Delta\varepsilon_{p}\right)\right]^{ \frac{1}{2}} \tag{1}\]  
where \(\Delta\varepsilon_{p}\) is incremental plastic strain vector in a load; the sum is performed with all load steps.  
In this paper, the failure criterion based on plastic zone penetration is adopted. Once a slope arrives at the LES, a contour line of \(\overline{z}_{p}\) with a small value, it passes through the slope from the toe to the top. The larger \(\overline{z}_{p}\) of a contour line, the larger the plastic deformation of the closed area of the contour line, and the more serious the slope damage is [41]. Therefore, it can be known that the CSS goes through the points at which the equivalent plastic strain obtains the maximum in the vertical direction. Due to the interpolation characters of the isoparametric elements, the maximizers will generally not be on a smooth curve but take the form of a wave. Based on this observation, a new method was proposed by [PERSON] et al. for searching the CSS.
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### Experimental Results on Image Boundaries  
In order to evaluate the performance of our classification results on the boundary, we obtain the boundary results of our method (i.e., ice surface and ice bottom), which directly affect the accuracy of the ice thickness calculation. We use the widely used balanced _F-measure_ for evaluation. The F-measure equation is  
\[F\text{-}measure=\frac{2\star precision\star recall}{precision+recall} \tag{7}\]  
\[precision=\frac{TP}{TP+FP} \tag{8}\]  
Figure 8: Examples of (**a**) radargram, (**b**) corresponding classification maps generated with the presented network and (**c**) corresponding classification maps generated with the [10] method. In the classification maps, each color represents a different target class: free space (black); layers (blue); bedrock (red); noise and EFZ (yellow).  
\[\textit{recall}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{9}\]  
where _TP_ is a true positive, _FP_ is a false positive, and _FN_ is a false negative. The F-measure is the weighted harmonic mean of precision and recall.  
We calculate the precision, recall, and F-measure in the test set. Our method obtains 77% F-measure for the entire test set, which is same accuracy as the existing state-of-the-art boundary detection method [14]. Figure 9 visualizes the boundary result maps. Comparing Figure 9b, the result of the proposed network, with Figure 9a, the manually picked interfaces, it can be concluded that the proposed network gets the similar result as the manually picked interfaces; furthermore, our result appears to be even more accurate in some parts as shown in Figure 9c,d. In conclusion, our method not only gets high precision classification results, but also shows promising results with respect to manually picked data on the boundary.
# Percolation Distribution in Small-World Quantum Networks
## 3 Percolation Optimization
“WS” and “\(WS_{cn}\)” represent the degree distribution in the original WS network and in the network with the central node, respectively. “\(WS_{4 swap}\)” and “\(WS_{4 pswap}\)” represent the degree distribution in networks with the central node after 4-swap and 4-pswap, respectively. After the quantum preprocessing, the CEP operation is carried out. While changing the value of the edge conversion probability, the change in GCC in the network is observed (\(GCC=N_{G}/N\), where \(N_{G}\) is the number of nodes contained in the giant connected cluster). As shown in Figure 7, there is a significant decrease in the percolation threshold of the network due to the addition of the central node. So is the case after quantum preprocessing. The percolation threshold of the network after 4-pswap is lower compared to that after 4-swap. The clustering coefficient \(\langle C\rangle\) implies the \"cliquishness\" of a small-world quantum network and is expressed as follows [12]:  
\[\langle C\rangle=\frac{3(K-1)}{2(2K-1)}(1-P_{r})^{3},\quad\text{where }K=\frac{\bar{k}}{2}. \tag{1}\]  
The relationship between the clustering coefficients of the networks obtained by different swap operations and the of edge conversion is shown in Figure 8. It can be seen that the clustering coefficients \(\langle C\rangle\) of the networks after (3, 2)-swap are larger compared to one after 4-swap, and the networks are more clustered. A (3, 2)-swap can be seen in Figure 5c, in which swapping is performed on only three connected edges of the node with a degree of four, and the left one is swapped with peripheral nodes.
## 4 Results and Discussion  
### Qualitative Interpretation  
Complete Bouguer gravity anomaly (CBA), regional CBA anomaly, residual CBA anomaly and THD anomaly maps are shown in Figure 6a-d, respectively. The CBA map with terrain correction reflects the major geological and structural patterns. The regional anomaly map represents the long-wavelength gravity source which was produced from deep-seated bodies and was obtained by applying a low pass frequency filter of 30,000 m wavelength cut-off. The residual CBA map was produced by applying a high pass filter of 30,000 m wavelength cut-off that only allowed the short-wavelength gravity source to be retained. The THD map was produced from further filtration of the residual CBA map that marks the maximum ridge of the anomaly gradient that delineates geologic contacts or faults, which distinguish different densities of rock [40,41].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Gravity S.I.** \\ \hline Sphere & 2.0 \\ Horizontal & 1.0 \\ Fault (small step) & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural index for gravity.  
Figure 6: (**a**) complete Bouguer anomaly (CBA) map; (**b**) Regional CBA anomaly map; (**c**) Residual CBA anomaly map; (**d**) THD Map. All the maps were overlayed with the geological contacts between formations of Mahang (Mhg), Semanggol (Smgl), Kubang Pasu (KP), Jerai (J), and granitic (Gn) batholite.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### Genetic Algorithm Tests  
In this section, the focus is on the comprehensive exploration of GA tests and their impact on the modeling accuracy of PV systems under various conditions. The population size and number of generations, pivotal parameters in GAs, are investigated to optimize convergence and computational efficiency. Results from performance tests will present the influence of these parameters on the GA's execution time and convergence ability. Additionally, the critical selection of crossover and mutation probabilities is addressed through experimental tests, revealing their relationship and impact on the algorithm's performance. This analysis will lead to the identification of an optimal combination of hyperparameters for the GA crossover probability. Subsequently, the accuracy of the GA is thoroughly examined through tests where irradiance and temperature variations are introduced.
## 3 Results and Discussion
### Scenario Overview
Also, ionospheric disturbances associated with Tsunamis or earthquakes can potentially be detected using such a multi-point ionospheric measurement technique, which is applicable to natural hazard monitoring or prediction. The vertical boundaries of irregularities can be estimated by examining the TEC, \(Ne\), and S4, and the horizontal boundaries can be estimated by analyzing the TEC gradients and S4. To cater the observation needs for this scenario, a low inclination (\(\sim\)10\({}^{\circ}\)) orbit was selected to continuously monitor the equatorial region. A small-eccentricity (0.044) orbit is considered to have both spacecraft altitudes vary with time. The initial orbit states of the chief and deputy spacecraft are listed in Table 1. A constant in-track offset of 15 km and radial offset of 1 km was predetermined as the default desired relative orbit configuration during formation keeping, however, elliptic natural orbits should be implemented in the future for a better fuel budget. An HIL simulation of this ESF scenario was run using the VTFTFB 2.0. The history of relative orbit (radial and in-track) and thrust (radial, in-track, and cross-track) in the Hill's frame are presented in Figure 4. As shown in Figure 4a, it took 125 s to reach formation keeping in the radial direction (offset within \(\pm\)2 m); and it took 60 s for the in-track relative distance to reach less than \(\pm\)2 m offset in Figure 4b. As shown in Figure 4c, thrust in radial and in-track directions were \"on duty\" to perform formation acquisition (correct the relative orbit from the initial state into the desired state). The first available EKF-based estimation was available at 276 s, where the EKF initialization led to a transient orbit deviation and redundant thrust as well. The filter converged quickly after that, therefore, inconsequential thrust transients are observed for the rest of the simulation. When the satellite fleet entered the ionospheric irregularity (ESF) region around 2200 s, scintillation impacted the navigation performance and disturbed the relative orbit. Throughout this one hour HIL simulation with a maximum thrust limit (\(\Delta V_{max}\)) of 0.5 m/s\({}^{2}\), the total thrust used in radial, in-track, and cross-track directions are 22.4665 m/s, 7.6677 m/s, and 1.1758 m/s, respectively.
## 2 Dehghani Method (DM)
Optimization algorithms update the status of its population members according to its own process to achieve a quasi-optimal solution. Accordingly, with the improvement of the best member of the population, is expected that the population be updated more effectively and the performance of the algorithm in solving the optimization problem is improved. DM is designed with the idea of modifying the best population member with the aim of improving the performance of an optimization algorithm. In DM, just as the best member is influential in updating the population members, the population members even the worst member can influence to modification the best member. The measurement criterion for suggested solutions is the value of the objective function. However, a suggested solution that is not the best solution may provide appropriate values for some problem variables. The proposed DM modifies the best member considering this issue and using the values suggested by other of the population members. This concept is mathematically simulated in Equations (5) and (6). \[X_{DM}=X_{DM}^{i,d}=\ \Big{[}x_{best}^{1}\ \cdots\ x_{i}^{d}\ \cdots\ x_{ best}^{m}\Big{]}, \tag{5}\]\[X_{best}^{new}=\left\{ \begin{array}{ll}X_{DM},&OF(X_{DM}^{i,d})<F_{best}\\ X_{best},&else\end{array} \right., \tag{6}\]  
where, \(X_{DM}\) is the modified best member by DM, \(X_{DM}^{i,d}\) is the modified best member based on the suggested value for \(d^{\prime}\)th variable by \(i\)'th SS, \(X_{best}^{new}\) is the new status for best member based on DM, and \(OF(X_{DM}^{i,d})\) is the objective function value for modified best member by DM. The pseudo code of DM is presented in Algorithm 1. In addition, the different stages of the proposed method with the aim of improving the best member are shown as a flowchart in Figure 1.  
Figure 1: Flowchart of Dehghani method (DM).
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
###### Abstract
Accordingly, the current study addresses this issue by proposing a solution for enhancing the placement and visibility of AR advertising objects. We suggest a method that not only improves the accuracy of object localization in AR but also contributes to a more immersive and engaging user experience. This approach is not only a step forward in the realm of AR advertising but also sets the stage for broader AR technology applications. By addressing these localization challenges, we aim to unlock the full potential of AR and pave the way for more advanced practical applications in various fields. Studies have been conducted on the mapping and recognition of the location of objects and users. Methods utilizing markers for AR placement have been explored; however, they entail the inconvenience of physically installing markers, which are impractical for outdoor environments [22]. Additionally, indoor-based approaches lack adequate outdoor application [23]. Although outdoor AR solutions employing GPS have been proposed, the inaccuracies associated with GPS can significantly hinder the precise localization and recognition of objects [24]. This study aims to address these challenges by proposing a technique that not only accurately displays objects within the AR environment but also corrects their positioning, thus enhancing both the user experience and the accuracy of AR advertising objects. The remainder of this paper is organized as follows: Section 2 encompasses both the original system architecture and the exploration of 3D object display in AR environments. Section 3 details the experimental validation of our system, emphasizing the methods used for the AR image display and positional accuracy. Finally, Section 4 summarizes the study, presents our main conclusions, and points to avenues for future research in this field.
# Fabrication of Stretchable Transparent Electrode by Utilizing Self-Induced Vacuum Force
## 2 Fabrication Methods
### Preparation of Composite Slurry and Elastomer  
A porous-carbon particle and binder were mixed in a solvent for the preparation of the composite slurry to produce flexible and stretchable electrodes. First, two types of carbon nanoparticles, namely, a Denka Black granule (Denka Singapore Pte Ltd., Singapore) and Ketjen Black EC600 JD (Lion specialty chemicals Co., Ltd., Tokyo, Japan) were used for the preparation of the slurry. Ketjenblack EC is a carbon black that has excellent electrical properties compared with other carbon blacks, in general, due to its very good conductivity. This property has particular advantages in compounding to achieve conductivity. The superior property of Ketjenblack EC allows the preparation of relatively free compounding formulations to achieve the desired properties in this study. The Denka Black granule is known for its uniform particle size and good dispersion characteristic. Moreover, the Ketjen Black has very high absorption/desorption characteristics, as well as superior electric conductivity (Ketjen Black: 3.9 \(\Omega\)cm/conventional Acetylene Black: \(1\times 10^{8}\)\(\Omega\)cm). The performances of the stretchable electrodes, fabricated using each of the two types of carbon nanoparticles, will be discussed in Section 3.1. Polyvinylidene fluoride (PVdF), which is highly non-reactive thermoplastic fluoropolymer, is used as the binder. It was purchased from Sigma Aldrich. Subsequently, the carbon nanoparticles were dispersed into the solvent to establish a homogenous linkage between the binder and the carbon nanoparticles. Here, various solvents, such as acetone, tetrahydrofuran (THF), and ethanol, were used; the effect of each of these solvents will be discussed in Section 3.2. To ensure homogenous mixing and dispersion, the slurry was sonicated for 5 min. In this study, PDMS was used as the elastomer to fabricate the stretchable electrodes. Sylgard 184, which is a silicon elastomer kit, was purchased from Dow Corning, and the mixing ratio of the base to the curing agent was set to 10:1 for gelation and conversion to a solid elastomer.
## 2 Materials and Methods
### Compression Artifacts Removal Evaluation  
Two common metrics to evaluate the quality of reconstructed images after compression artifacts removal were calculated: Peak Signal-to-Noise Ratio (\(PSNR\)) and Structural Similarity Index Measure (\(SSIM\)) [34]. \(PSNR\) can be represented as follows:  
\[PSNR=10\cdot log_{10}\left(\frac{MAX_{1}^{2}}{MSE}\right) \tag{1}\]  
where \(MAX_{1}^{2}\) is the maximum possible pixel value for the given range, and the Mean Squared Error (\(MSE\)) is calculated by averaging the squared intensity differences betweentwo images. \(SSIM\) for given original image \(I\) and transformed image \(J\) is defined by the following formula:  
\[SSIM=\frac{(2\mu_{I}\mu_{I}+C_{1})(2\sigma_{IJ}+C_{2})}{(\mu_{I}{}^{2}+\mu_{I}{}^ {2}+C_{1})(\sigma_{I}{}^{2}+\sigma_{I}{}^{2}+C_{2})} \tag{2}\]  
where \(\mu\) is the average and \(\sigma\) is the variance of pixel intensity. \(SSIM\) is generally regarded as a closer metric to human perception than \(PSNR\).