esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Seismostratigraphy and Tectonostratigraphy  
Seismostratigraphic interpretation of the chronostratigraphic image and Wheeler diagram is the most useful method to provide detailed recognition for interpretation of seismic data in the areas dominated by thin-bedded formations. Its most important idea is to transform the seismic image into a sequence of chronostratigraphic horizons that provide the necessary basis for further interpretation. Each chronostratigraphic horizon in the seismic image is associated with a specific position in the depositional sequence and is associated with a specific sedimentary event in the interpreted basin, associated with eusstatic sea-level changes. By possessing a set of chronostratigraphic horizons with the correct depositional succession, a Wheeler diagram can be constructed. The Wheeler domain is a plot that shows the continuity of the flatten horizon along the profile; this approach can also be adopted for seismic horizons and seismic profiles. As a result, the plot of chronological geological events is produced [50; 51]. Analyses of the relative position of the flatten horizons allow us to reason about transport direction and range of sedimentation within sedimentary episodes. Gaps that are present at the plot can be interpreted as hiatuses and/or erosional surfaces [50; 52]. The analyses aim to define sedimentary sequences by adopting its paradigms from the sequence stratigraphy method and depositional models developed for different sequence stratigraphy approaches and depositional models compiled for various types of sedimentary basins (vide [21; 53]). Falling stage systems tracts (FSSTs), lowstand systems tracts (LSTs), transgressive systems tracts (TSTs), and highstand systems tracts (HSTs) can be interpreted. Defining these systems tracts and their connections allows the interpretation of individual depositional sequences.  
This method is helpful in the interpretation of siliciclastic, carbonate, and mixed formations, characterized by small thicknesses of individual depositional sequences, high facies diversity, variability in the sedimentary material transport directions, and noticeable effects of tectonic processes. Following the continuity of seismostratigraphic horizons in the reconstruction can give insight into tectonic development and/or tectonic reconstructions of the analyzed interval [54; 55]. The integration of sequence stratigraphy methodology with tectonostratigraphic interpretation enables dividing the interpreted interval into higher-order structural units, characterized by a different tectonic style. These units usually correspond to megapsequences that are linked to the most crucial sedimentary stages [56]. Thus, tectonostratigraphic units most often correspond to regional events, representing particular phases (compressional and extensional) of basin development [55; 57].
## 3 Numerical Validations of Stiffened Ship Panel Strain Reconstruction
### Impact of Different Load Magnitudes on Strain Field Inversion Accuracy  
To investigate the influence of varying load magnitudes on the accuracy of strain field reconstruction in reinforced ship wall panels, three distinct loading cases were designed:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh Partitioning Scheme** & **RMSE [\(\mu\)e]** & **MRE [\%]** \\ \hline Scheme 1: 24 elements & 9.03 & 4.27 \\ \hline Scheme 2: 42 elements & 2.10 & 1.47 \\ \hline Scheme 3: 126 elements & 1.20 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strain field reconstruction accuracy for different mesh partitioning schemes.  
Figure 6: Various mesh partitioning schemes for stiffened ship panels: (a) mesh partitioning scheme \(1\); (b) mesh partitioning scheme \(2\); (c) mesh partitioning scheme \(3\).  
Figure 7: Strain fields reconstruction accuracy for three different mesh partitioning schemes.  
Case A: A load of 10 KN was applied at point A, while a load of 5 KN was applied at point B.  
Case B: A load of 10 KN was applied at point A, while a load of 7 KN was applied at point B.  
Case C: A load of 10 KN was applied at point A, while a load of 9 KN was applied at point B.  
Using Mesh Partitioning Scheme 2, as described in Section 3.3, strain data from the inner side of the elements corresponding to different loading conditions were individually extracted from the results of finite element simulations. Employing an inverse finite element method based on strain functions, the relative errors in strain reconstruction for each validation point were computed separately for the three distinct loading conditions, as illustrated in Figure 8.  
The reconstructed results illustrated in Figure 8 indicated that, as the load on point B increased, the proposed method in this study exhibited a decreasing trend in the average relative error of strain field reconstruction for ship reinforced wall panels. Furthermore, the strain field reconstruction outcomes for the three distinct loading conditions consistently fell within the permissible engineering range, as demonstrated in Table 3. These findings underscored the capability of the method to effectively reconstruct the strain field of reinforced ship wall panels under varying loading conditions. The results further affirmed the practical viability of the proposed approach in engineering applications.
# DEM Analysis of Track Ballast for Track Ballast-Wheel Interaction Simulation
## 2 Theoretical Background for Ballast Modeling and Analysis  
Several researchers have modeled ballast using the FEM with shell and solid elements for two- and three-dimensional analyses, respectively. In such studies, the equivalent soil properties, such as elastic modulus, Poisson's ratio, internal friction angle, dilation angle, and cohesion, should be defined using appropriate material models such as the Mohr-Coulomb model. Thus, the equivalent material properties should be evaluated for modeling track ballast filled with irregular pieces of gravel.  
Although the FEM has been widely used in numerical simulations of various structural and geotechnical engineering problems, it has significant limitations regarding the modeling and simulation of the behaviors of a track ballast filled with gravel, because of the fundamental assumptions of the method. Using the FEM approach, the interlocking and separation of the gravel pieces cannot be directly considered because the conventional FEM does not allow the separation of elements attached to each other. Specifically, the collision between the ballast and the wheel of a derailed train cannot be rationally simulated using conventional FE approaches because of this limitation. Thus, the ballast should be modeled using another approach.  
The DEM is one of the alternative solutions for modeling track ballast. Using the DEM approach, each piece of gravel can be modeled as an individual object that does not share nodes. Thus, all the pieces of gravel in contact can be detached and re-attached after their positions are updated. Therefore, the limitation of the FE approach can be overcome using the DEM. Based on the advantages of the DEM approach, an appropriate simulation strategy for modeling gravel ballast and simulating ballast-wheel collisions is suggested in this study.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 1 Introduction  
Gypsum rocks are mainly used in the manufacture of plasters, cement, sulfuric acid, and other industrial applications, due to their low cost and aesthetics [1, 2]. All modern homes in Morocco, as well as in other developed countries, use a great deal of wallboard for interior walls, while pure gypsum is used in glass making and other specialized industrial applications. In addition, plasterboards are among the building materials for thermal insulation and air purification in Moroccan buildings, but a high purity of natural gypsum products is desired in wallboard manufacturing, because the presence of impurities affects the gypsum properties [3]. However, the chemical composition of gypsum rock varies from one geographical site to another. Gypsum is primarily composed of CaSO\({}_{4}\), associated with a wide assortment of accessory minerals, mainly carbonates, silica, and metal oxides [4, 5]. The complete characterization of the raw material (as rock and powder) allows us to choose the best one to be used in the fabrication of plasters for building applications, with thermal and acoustic insulating purposes.
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 1 Introduction  
In recent years, geophysics has been widely used to monitor subsurface changes [1, 2, 3]. Compared with other geophysical methods, the multielectrode and multichannel system of the resistivity method allows for the effective collection of large-scale monitoring data, offering significant advantages. Therefore, time-lapse resistivity inversion has become a popular topic in geophysical inversion.  
The resistivity method has a long history in monitoring [4]. Initially, the international scholar [PERSON] [5] carried out experiments to confirm that the resistivity method could monitor the infiltration process of groundwater, but it involved only repeated measurements at different times, without considering the correlation among the data at those different times. Later, scholars at home and abroad reported that normalizing the observation dataset before inversion, filtering the false anomalies of the initial data, and then applying traditional inversion algorithms could highlight small changes in the physical structure underground [6]. In normalization research, scholars use the inversion results of background data as a priori model to participate in the inversion calculation of subsequent observation data. Thisapproach improves the convergence speed and reduces data fitting errors [7, 8, 9]. However, these methods do not account for the temporal continuity of physical property changes, making the accurate reflection of subsurface variations challenging. [PERSON] [10] realized time-lapse inversion on the basis of the constraint of time-lapse function. This method simultaneously inverts data from different time points while ensuring that the inversion results evolve continuously over time by constraining datasets at different times. However, time-lapse inversion requires the simultaneous inversion of monitoring data from multiple time points, demanding substantial memory resources and resulting in inefficiencies.  
In this study, we developed a three-dimensional inversion algorithm for the resistivity method, that incorporates time-lapse function constraints. We also integrated MPI parallelization into the program to increase computational efficiency, ensuring that the inversion results change continuously over time while significantly improving computational performance. The theoretical model was designed to synthesize data, which confirmed the anti-interference effect of the algorithm.
# Electrochemical Degradation of Crystal Violet Using Ti/Pt/SnO\({}_{2}\) Electrode
## 3 Results and Discussion
### Influence of Operating Parameters
#### 3.2.4 Influence of Initial CV Concentration  
The initial concentration of the organic pollutant is a very important parameter in the treatment of polluted water [40,48]. The influence of this parameter in this electrochemical oxidation process is studied for both concentrations 10 and 50 mg L\({}^{-1}\) by setting the pH of the solution and the applied current to 50 and 60 mA cm\({}^{-2}\), respectively. The results presented in Figure 8a, show that at 60 min the CV disappearance is complete for the CV solution of 10 mg L\({}^{-1}\) whereas a 120 min treatment is required for the 50 mg L\({}^{-1}\) solution to reach the same percentage, i.e., a processing time only doubled for a concentration five times higher.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Initial CV Concentration** & \
\begin{tabular}{c} **Current Density** \\ **mA cm -2** \\ \end{tabular} & **Removal of CV (\%)** &
\begin{tabular}{c} **Initial Rate Constant** \\ **(k \(\times\) 10\({}^{-3}\) min\({}^{-1}\))** \\ \end{tabular} & **Half-Lives (t\({}_{2}^{1}\) min)** & **R\({}^{2}\)** \\ \hline
20 & 23 & 3 & 245 & 0.9956 \\ \hline
30 & 98 & 28 & 25 & 0.9946 \\ \hline
40 & 99 & 53 & 13 & 0.9999 \\ \hline
50 & 100 & 56 & 12 & 0.9870 \\ \hline
60 & 100 & 60 & 12 & 1.0000 \\ \hline
50 mg L\({}^{-1}\) & 50 & 100 & 25 & 28 & 0.9896 \\ \hline \end{tabular}
\end{table}
Table 1: Kinetic parameters of the electrochemical degradation of the CV dye by means of a tin dioxide electrode.  
More specifically, Figure 8b highlights the CV abatement rate for the two initial concentrations as a function of the electrical charge in C/(mg L\({}^{-1}\)) for 50 and 60 mA cm\({}^{-2}\). This comparison of the data highlights a perfect superposition of the values for both currents and that an electrical charge of 7 C (mg/L) is sufficient to remove more than 90% of the dye.  
Therefore, it is necessary to adapt the applied electrical charge to render it as effective as possible.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Modeling the Problem
#### 3.1.3 The Signal Accessibility  
Contrary to the impact walls have on the physical accessibility to locations for, e.g., humans (which is boolean: either possible or not possible), walls have a more complicated effect on e.g., radio waves. Radio signals, while also affected by the physical barriers, can penetrate walls and other objects to a certain extent. In the absence of blocking obstacles, a radio signal can be expected to extend outwards from the source homogeneously, with its strength decreasing with distance to the signal source or beacon. Figure 3 visualizes this for a specific scenario and beacon as well as for specific properties of the walls in our scenario. The mathematical model for the signal strength and propagation is provided in Section 4.1.2 by Equation (5).
## 3 Results
### Analysis of Interviews
#### Dispute/Conflict Management Arrangements  
Two thirds of the interviewees consider that it is important to have some sort of arbitration in place to resolve disputes or conflicts between partners in the network and a number of modalities were suggested. Respective rules need to be set in advance.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies
### IEEE 300-Bus System
#### 5.3.1 Base Load Conditions  
Under the condition of N\(-\)1 contingencies in the IEEE 300-bus system, the values of security index \(PI_{c}\) are computed by using two methods, which are respectively calculation by NPLF and prediction via the MSA-Lasso algorithm. The time for contingency screening and ranking by adopting the proposed module is 4.1732 s, while the computation time is 76.8578 s by using NPLF. Obviously, the proposal is available for online application. Some 60 branches accounting for more than 15% of all of the lines are randomly selected, which can represent different operating states of the power system. The results of \(PI_{c}\) by adopting two ways, and the relative errors, are listed in Table 15 in the descending order of the NPLF method. From the above table, it can be seen that the values of \(PI_{c}\) obtained by adopting the two methods have similar ranking results, and the contingencies can also be screened. Thus, the conclusion can be drawn that the proposal is also effective for large-scale power systems. \begin{table}
\begin{tabular}{c c c} \hline
**Outage Line** & **Light Load Condition** & **Heavy Load Condition** \\ \hline L 133(85–86) & 44.3598 & 45.8894 \\ L 7(8–9) & 5.6771 & 5.6818 \\ L 97(64–65) & 3.2004 & 3.2143 \\ L 60(34–43) & 1.9922 & 2.9717 \\ L 121(77–78) & 1.7229 & 1.9869 \\ L 104(65–68) & 1.5353 & 1.7278 \\ L 128(77–82) & 1.3043 & 1.6184 \\ L 74(53–54) & 1.2932 & 1.5375 \\ L 40(29–31) & 1.1212 & 1.3955 \\ L 147(94–95) & 1.0863 & 1.2815 \\ L 155(94–100) & 0.9742 & 1.1406 \\ L 160(100–101) & 0.8713 & 0.9323 \\ L 156(95–96) & 0.8360 & 0.8709 \\ L 68(45–49) & 0.7522 & 0.0835 \\ L 103(66–67) & 0.5986 & 0.6553 \\ L 151(80–97) & 0.5215 & 0.5474 \\ L 169(105–106) & 0.4339 & 0.5734 \\ L 35(28–29) & 0.4283 & 0.4032 \\ L 131(83–85) & 0.4186 & 0.3385 \\ L 157(96–97) & 0.2857 & 0.3057 \\ L 135(85–88) & 0.2861 & 0.2875 \\ L 123(77–80) & 0.2069 & 0.2736 \\ L 106(49–69) & 0.2038 & 0.2687 \\ L 114(70–74) & 0.2022 & 0.2676 \\ L 108(69–70) & 0.2012 & 0.2682 \\ L 166(103–105) & 0.2004 & 0.2669 \\ L 164(100–104) & 0.1983 & 0.2656 \\ L 142(89–92) & 0.1928 & 0.2335 \\ L 139(89–90) & 0.1690 & 0.1997 \\ L 141(89–92) & 0.1385 & 0.1538 \\ \hline \end{tabular}
\end{table} 
Table 14: Security index results of light and heavy load conditions of the IEEE 118-bus system.
## 3 Results and Discussion
### WF Measurements  
The value of the potential difference between the Pt (vibrating electrode) and Al was found to be 1.0 \(\pm\) 0.1 V, and it was 1.7 \(\pm\) 0.2 V between Pt and Mg. Assuming the value of the WF of Pt to be equal to 5.3 eV [39], the WF of Al was found to be 4.3 \(\pm\) 0.1 eV, and the WF of Mg was 3.6 \(\pm\) 0.2 eV (Table 2). The obtained values were in good agreement with the data on the WF of Al and Mg presented in [39; 40; 41]. To confirm the obtained results, measurements of the Pt sample were carried out. The potential difference for Pt was found to be 0.1 eV, which supported the reliability of the performed measurements and results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**State**} & **Lattice Parameter,** & **Coherent Scattering Domain,** & \multirow{2}{*}{**Lattice Microstrains, \%**} \\  & **Å** & **nm** & \\ \hline As-deformed HPT Al-Mg & 4.05093(2) & 55 & 0.092 \\ HPT Al-Mg + annealing 150 \({}^{\circ}\)C & 4.050034(4) & 400 & 0.031 \\ HPT Al-Mg + annealing 275 \({}^{\circ}\)C & 4.049829(2) & 550 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lattice parameter, coherent scattering domain and lattice microstrains of the Al-Mg composite after HPT and subsequent annealing at 150 and 275 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Contact Potential Difference, V** &
\begin{tabular}{c} **WF, eV** \\ **(Relative to Pt = 5.3 eV)** \\ \end{tabular} \\ \hline Al & 1.0 & 4.3 \\ Al-Mg composite & 1.3 & 4.0 \\ Mg & 1.7 & 3.6 \\ Pt & 0.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact potential difference and WF of Al, Mg and Al-Mg composites measured in relation to the WF of Pt, considered to be equal to 5.3 eV [18], using the Kelvin–Zisman method (vibrating electrode method).  
As noted above, the surface of the Al-Mg nanostructured composite was characterized by an Al matrix with isolated Mg inclusions, as well as interlayers of intermetallic phases at the interphase boundaries. Since the sample surface was inhomogeneous in its chemical composition, measurements of the contact potential difference were carried out for the entire sample. For that, the surface of different sections, i.e., the center, the middle and the edge of the sample, were substituted under a vibrating electrode with an area of 3 \(\times\) 3 mm\({}^{2}\). The values of the contact potential differences over the surface of the entire Al-Mg sample ranged from 1.24 to 1.36 V, which, on average, resulted in a value of 1.3 V. Therefore, the value of the WF of the Al-Mg nanostructured composite was \(\approx\)4.0 eV, which was between the WF values of Al and Mg.
### Stiction
The methods used were histograms, the area ratio, curve fitting, and cross-correlation, with the inclusion of an index for each method. A study based on various distinctive regions between valve movement and controller output was reported in [44]. The final detection was carried out by observing the regions created by plotting the controller output with the valve position plot. In [45], an approach was used to quantify the deadband, stick band, and slip jump properties of valve stiction by using the special shape created on the Riemannian manifold. A method for detecting stiction by computing the Hurst exponent after the extraction of slow features from the processed controller output and controlled process variable data was reported in [46]. A technique that combines the moving window approach and K-means clustering to identify severe valve stiction or unexpected valve shuts was discussed in [47]. There are some issues in the process of stiction detection in industrial process systems, such as the presence of noise, disturbance, and low sampling, which affect the accuracy of detection [48]. In [49], stiction in control valves was detected based on the Poincare plot, which is mainly based on correlation in a given time series. These Poincare plots are given as input to a convolution neural network for training and detecting a stiction fault. A method for the detection of stiction was developed in [50] using a convolution neural network in which the network was trained using time series data. A method for stiction severity identification was reported in [51], where a convolutional neural network was used to detect a stiction fault. The severity is identified by the neural network based on the statistical process control chart generated by principal component analysis. Our work aims to develop a method for detecting stiction in control valves designed through pattern recognition using an ANN. The discussed method uses the process variable (PV) and controller output (OP) to determine if a control loop suffers from stiction. The data used to train the network are artificially generated using a data-driven model. Two preprocessing techniques, i.e., the D-value method, by calculating the distance between each data point from the centroid, and a feature reduction PCA method, are applied before training the network. The importance of features in stiction detection techniques is the primary objective of this paper. Validation and testing are performed on real industrial data provided by the International Stiction Database (ISDB) [51]. The data-driven model is implemented on SIMULINK, and the ANN is trained on MATLAB using the Pattern Recognition Toolbox.
## 3 Results and Discussion  
The results that were obtained through the experimentations were grouped into three categories for further analysis: (1) as input and response voltage waveforms, (2) as EEV TF plots and, thirdly, (3) as statistical parameter values estimated from the EEV TF data (two different cases considered at a time). They are referred as per the notations that are listed in the Table 1.
## 5 Discussion
### Accuracy of EUS Visualization of Ductal Anatomy in Pancreas Divisum  
EUS provides detailed visualization of the pancreatic ductal anatomy, correctly identifying the characteristic features of the pancreas divisum, such as the separate dorsal and ventral ductal systems (Figure 7) and the presence of the dominant dorsal duct entering the minor papilla. Specifically, EUS has proven to be highly accurate in visualizing the pancreatic ductal system and identifying key features of pancreas divisum, particularly in cases where non-invasive imaging modalities such as MRCP yield inconclusive results. The ability of EUS to detect the separation between the dorsal and ventral ductal systemswith real-time imaging provides a clear anatomical delineation of the pancreatic ductal arrangement. Furthermore, EUS allows dynamic evaluation of pancreatic secretions, which can be useful in assessing functional impairment related to pancreas divisum. The accuracy of EUS in identifying the dominant dorsal duct entering the minor papilla (a hallmark of pancreas divisum) is enhanced by the use of high-frequency probes and Doppler imaging, which help differentiate ducts from adjacent vascular structures. Additionally, secretin-stimulated EUS can provide functional insights by assessing ductal compliance and flow dynamics, further improving diagnostic confidence. As a result, EUS serves as a powerful tool not only in the detection of pancreas divisum but also in guiding patient management by identifying anatomical variations that may influence therapeutic decisions.