esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Technologies for Geosite Visualization and Valorization: A Review
## 3 Examples of Geoheritage Visualization and Valorization through Innovative Technologies
Such a methodology has been used for models with diverse ranges of aerial extents and resolutions, from 50 to 1000 m (the longest), and from 0.8 to 4 cm/pixel. Figure 2: 3D model of the volcanic crater formed on Nea Kameni volcano in 1570–1573 AD within Santorini Caldera, Greece. Annnotations are as follows: 1. Cone flank. 2. Eruptive fissure. 3. Cone crater. Modified after [PERSON] et al. [108]. During the nine events, [PERSON] et al. [118] quantitatively evaluated the perception of the virtual experience by a total of 459 respondents. The subsequent evaluation of the questionnaires, articulated in nine items, allowed the authors to gain major insights into the usefulness of this approach for educational aims. Most respondents showed major interest in the immersive activity. Especially appreciated was the chance to hover above the geosites in \"drone mode\". By analyzing the results of the survey, the authors were also able to appreciate the participants' satisfaction with the quality of the environments, reproduced through innovative, UAV-based photogrammetry methodologies. As pertains to the educational potential of this method, the majority of respondents gave high marks to their experience (totaling 94%); the percentage peaked to 96% among geoscience academics. Thus, the paper by [PERSON] et al. [118] confirms that students and teachers perceive VR as a major strategy for geoscience-related education. Finally, a paramount work by [PERSON] et al. [121] was devoted to illustrating twelve geosites in the geopark called \"Arribes del Duero\" (Spain). The Authors created a 3D virtual geotrail based on Google Earth for educational purposes, integrated with georeferenced cartographic products, thematic maps, itineraries, didactic and interpretive panels. They also made a field guide and an app available to users. Users can access the field guide (in PDF format), through a QR code. The field guide and the geoapp helped make the georoute more entertaining and helpful for the users.
# Lithium Concentrations in Saline Brines of the Shu-Sarysu Depression
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & \begin{tabular}{c} **Minerali-** \\ **zation, g/L** \\ \end{tabular} & **Na, g/L** & **K, g/L** & \begin{tabular}{c} **Ca,** \\ **g/L** \\ \end{tabular} & **Mg, g/L** & \begin{tabular}{c} **Chlorides,** \\ **g/L** \\ \end{tabular} & \begin{tabular}{c} **Sulfates,** \\ **g/L** \\ \end{tabular} & \
\begin{tabular}{c} **Bromides,** \\ **mg/L** \\ \end{tabular} &
\begin{tabular}{c} **Average Li** \\ **Content, mg/L** \\ \end{tabular} \\ \hline Karakoin & 372 & 108.4 & 22.5 & 0.8016 & 16.5 & 145.373 & 115.998 & & 5.13 \\ \hline Tampaly & 322 & 118.4 & 0.64 & 0.2004 & 14.2 & 166.648 & 50.215 & & 1.7 \\ \hline Telikol & 356 & 111.1 & 1.8 & 0.802 & 21.4 & 148.919 & 105.205 & & 3.39 \\ \hline Lower Sarysu sors & 360 & 98.5 & 6.5 & 1.002 & 28.5 & 163.202 & 98.043 & 256 & 15 \\ \hline Ashysor & 362 & 119.7 & 1.3 & 0.2004 & 14.47 & 184.376 & 76.146 & & 2.75 \\ \hline Lower Shu sors & 306 & 104.1 & 1.96 & 0.6012 & 17.2 & 170.193 & 43.629 & & 2.57 \\ \hline Atacama (Chile) & \textless{}400 & 113.4 & 32.1 & 0.5 & 11.4 & 210.6 & & & 0.5-2 \\ \hline De Yuni (Bolivia) & 400 & 105.2 & 24.2 & 0.5 & 16.6 & 190.5 & & & 0.8-2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of chemical analysis of brine in the studied areas of the Shu–Sarysu depression. Figure 7: Diagram of statistical data processing of analytical research results on lithium in liquid samples of field seasons 2022–2023. The correlation of elements within mineralized waters of salt flats is depicted in the diagram (Figure 10). Analysis of the results from analytical studies indicates that there is no direct correlation between lithium content and the content of other elements. However, a careful analysis of the data in Table 3 reveals a weak correlation between the concentrations of Li and Mg, which nonetheless can be used as a relative exploration criterion [18; 26; 27]. In the Sarysu salt flats area, the authors propose drilling several structural wells throughout the expansion zone to extract highly mineralized brines from deep layers and evaluate the lithium and other valuable components.
## 3 Doping of Perovskites with Zinc(II)
When the spectrum was locally enlarged, the peak with the diffraction angle (\(\theta\)) of 110 (see Figure 6b), a shift towards a higher degree was observed upon increase of Zn content. This is attributed to the Zn atoms with smaller atomic radii occupying larger Pb-sites. This finding is a second strong indication that Zn dopants are incorporated in the perovskite lattice. Figure 5: Field-emission scanning electron microscopy image of the perovskite doped with (from left to right) 0%, 5%, 10%, 15% Zn annealed at 80 \({}^{\circ}\)C (**a**), 100 \({}^{\circ}\)C (**b**) and 120 \({}^{\circ}\)C (**c**). MA(Pb:Zn)I\({}_{3}\). Figure reprinted with permission from, _Mater. Today Energy_**2017**, \(5\), 205–213, Reference [15]. [PERSON] et al. came forward with a thorough work [14] on the doping of Pb perovskite with 3% of the transition metals Zn\({}^{2+}\), Mn\({}^{2+}\) and Ni\({}^{2+}\). The resulting materials were mixed dimensional perovskite films (MA(Pb:M)I\({}_{3}\)) with a higher stability and in the case of the Zn\({}^{2+}\) (from ZnI\({}_{2}\)), a superior photovoltaic performance of 18.35% (PCE, power conversion efficiency) (a 30% increase relative to undoped devices). Especially interesting was the moisture resistance and enhanced thermal resistance of the devices. The device operated for 800 h at 50% relative humidity and for 100 h at 60 \({}^{\circ}\)C. The results regarding blue shift, lattice contraction and XRD peak-shifts were similar to other papers discussed in this review. However, a deep reasoning behind the improved stability was provided by the authors. Three reasons were provided to unravel the mechanism behind the improved stability. At first, the doped perovskite features a much higher film quality with less internal defects. This suppresses degradation, which is taking place on those defect surfaces: the edges of pinholes. Secondly, the mixed dimensional perovskite now features a 2D layer that forms an insulating 'wall' around the 3D material and consequently guards this system from decomposition. A third reason is that the tolerance factor of the doped mixed dimensional perovskite structures yields tolerance factors much closer to 1 than pristine perovskite, thus, displaying improved stability against humidity and heat. It appears that no chloride or bromide components were used in this work.
## 2 Related Work
### Vision Transformer
#### 2.2.3 Transformer Blocks  
The Transformer blocks process the sequence of embeddings using two main components:  
**1. Multi-Head Attention**  
Each vector in the sequence interacts with all the others using the attention mechanism. This allows the model to learn the global relationships between patches.  
For each input vector, the matrices Q (Query), K (Key), and V (Value) are calculated:  
\[Q=XW_{Q},K=XW_{K},V=XW_{V} \tag{3}\]  
where WQ, WK, and WV are matrices that can be learned.  
Attention is calculated as follows:  
\[Attention(Q,K,V)=Softmax\bigg{(}\frac{QK^{T}}{\sqrt{D}}\bigg{)}V \tag{4}\]  
In multi-head attention, multiple attentions are calculated in parallel and combined:  
\[MultiHead(Q,K,V)=Concat(head_{1},head_{2},\cdots)W_{O} \tag{5}\]  
**2. Feedforward Network**  
Each vector in the sequence passes through a fully connected neural network with two layers:  
\[FFN(x)=ReLu(xW_{1}+b_{1})W_{2}+b_{2} \tag{6}\]  
**3. Normalization and Residual**  
Residual connections and layer-wise normalization are used to stabilize training and improve convergence.  
A Transformer block contains the following: 1. a multi-head attention layer; 2. a feedforward network; and 3. normalization and residual connections.  
ViT applies multiple Transformer blocks sequentially to refine the patch representations.  
ViT architecture is shown in Figure 2.
# Modeling the Directional Clumping Index of Crop and Forest
## 3 Results and Evaluation
### Forest Directional Clumping Index Validation  
Figure 7 shows the comparison result in the old Black Spruce forest. Since the simulated and measured \(\Omega(\theta)\) were both calculated from the gap fraction, we directly compared the gap fraction for validation. There is high consistency between the measured and simulated gap fraction, validating the proposed simulation method. At zenith angles of \(>\)50\({}^{\circ}\), the simulated gap fraction tends to be slightly lower than the measured value due to multiple photon scattering impacting on optical measurements [8,18].  
We calculated the gap fraction using Equation (12), with the intra-crown gap pre-processed through simulation as a Look Up Table. The other parameters, including tree density, crown depth, and radius, were set as for previous simulation scenarios. The algebraic method includes the ideal assumption that the trees were Neyman distributed. However, the simulated Poisson distribution may demonstrate higher clustering than ideal situations [49], due to restrictions of the pseudo random number generator in the simulation. One subset of our simulated tree distribution image is demonstrated in Figure 8. Therefore, the calculated gap fraction from the simulation is a little lower than the expected value.  
Figure 8: Simulated distribution pattern of black spruce (Neyman distribution, \(m_{2}\) = 4, 1000 trees distributed in 4 ha). The white circles represent the nadir projection of the crowns to mark the tree positions, while the black represents the soil background.  
Figure 7: Measured and simulated gap fraction from an old black spruce forest, near Candle Lake, Saskatchewan. Parameters are referred to Column 1 in Table 2.  
Figure 9 shows the comparison result using the measurements of Larch stand. It shows high consistency between the simulated and measured gap fractions, with small fluctuations at small angles due to random errors and sampling restrictions. When zenith angle of \(>\)70\({}^{\circ}\), discrepancies mainly arise due to the hemispherical image being influenced by topography at large angles. Overlapping between trees in the simulation and approximation of the projection area to accumulated discrete square units determine that the average gap fraction in the simulation method is slightly larger than the model in nadir direction, although the number of trees is equal. This difference weakens with increased zenith angle and disappears between 40\({}^{\circ}\)-60\({}^{\circ}\), then increases to 0.02 at 80\({}^{\circ}\).  
\(\Omega(\theta)\) was indirectly validated through the measured, simulated, and modeled gap fraction, and demonstrated that the model is able to predict the main variation characteristics of \(\Omega(\theta)\).
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 2 Related Work
On the other hand, in [25], predictions of demand for natural gas were made for the purpose of realizing energy cost savings. The methods used in all the approaches are based on artificial neural networks [23; 25] and the hybrid ARIMA-ANFIS model [24]. The resulting prediction models are or can be applied to support decision-making. Liquefied petroleum gas was considered from the logistics perspective in [26], where the planning and scheduling of LPG deliveries to petrol stations were analyzed. The work by [27] presented the analysis of gas usage for different types of buildings. Gas consumption depends on the building destiny; hence, three classes were identified in this work: housing, housing with high thermal capacity, and industry. Depending on the building class and its characteristics, different models should be applied in order to predict gas consumption. The models utilized in the research were based on auto-correlations of gas usage time-series and different cross-correlations between gas consumption and temperature time-series. Finally, the work by [28] presented a meta-learning-based approach to LPG consumption prediction. This solution uses stacking for the automation of predictive model selection without the definition of the customer classes. The above-presented review shows that there are works presenting decision support systems of various types and subsystems offering functionality crucial for decision-making. Other works undertake analyses of gas consumption prediction. However, according to the authors' best knowledge, there is no single study of a decision support system in the field of LPG supply that covers the overall workflow, functionality, and application supported by use cases.
## 6 Conclusions  
This study investigated the use of a character-level model to solve the ADI problem applied to a short Arabic sentence. This study focused on the Saudi dialects problem and particularly tested two-, three-, and four-way identification tasks. The main adaptive approach consisted of five phases: dialect data collection, data preprocessing and labelling, character-based feature extraction, classical machine learning/deep learning character-based models, and model evaluation performance. In the first phase, 3768 short dialect texts were collected from the Internet in four main Saudi dialects, including Hijazi, Najdi, Janobi, and Hasawi. The MultinomialNB, BernoulliNB, LogisticRegression, SGDClassifier, SVC, LinearSVC, NuSVC, and CNN approaches were then used in the learning phase, and their performance was evaluated and compared. The results showed that the best-performing algorithm was LR and NuSVC, reaching 40.9% accuracy. In the three-way task, the CNN approach (Model 6), in which the tashkeeel was removed and no drop layers were added, outperformed the other models, reaching a 47.0% accuracy. Moreover, using TF-IDF with a combination of character n-grams ranging from unigrams to four-grams achieved the best performance for the considered classical machine learning algorithms.  
In future research endeavours in ADI, the plan is to improve the CNN approach further, especially for the multiway problem. Different character feature construction techniques could also be considered, such as by representing a short part of the words, and not just characters.  
This research received no external funding.  
The data presented in this study are available upon request from the author. The data are not publicly available, as this research is ongoing.  
The authors declare no conflict of interest.
## 3 Results
### Experimental Milling Result  
The most important goal of the EAOVP method is to accurately predict the best clamping conditions for a workpiece, based on the observation of its behavior during  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tightening Torque [Nm]**} & \multicolumn{4}{c}{**Time Interval for RMS Calculation [s]**} \\  & **20–40** & **45–65** & **95–115** & **145–165** & **170–190** \\ \cline{2-6}  & \multicolumn{4}{c}{**RMS of Displacements [\(\mu\)m]**} \\ \hline
50 & **0.156** & **0.185** & **0.361** & **0.253** & **0.206** \\
70 & 0.193 & 0.253 & 0.490 & 0.325 & 0.239 \\
90 (nominal) & 0.171 & 0.246 & * 0.528 & 0.346 & 0.228 \\
110 & 0.186 & 0.240 & 0.469 & 0.309 & 0.228 \\
130 & 0.183 & 0.231 & 0.454 & 0.307 & 0.232 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RMS values of relative vibration displacements between the tool and the workpiece for point S (Figure 2) for time intervals equivalent to the surroundings of individual accelerometers during full milling of the surface for different clamping conditions. The best results are shown in bold, and the worst results are underlined; * marks the case used as a criterion of compliance of the computational model.  
Figure 6: Simulated relative displacements between the tool and the workpiece for point S (Figure 2) during full milling for selected screw settings.  
Figure 7: The simulated RMS values of relative vibration displacements between the tool and the workpiece for point S (Figure 2).  
standard clamping (Figure 1). Nevertheless, to further demonstrate the advantages of the proposed method, the experimental results for other clamping cases are also presented.  
For the workpiece described earlier in Section 3.1, the full face milling of the selected surface was performed, and vibration acceleration was measured in five points (Figure 4b). The acquired signals were later converted to displacements by its double integration. A high-pass ideal filter (cut-off frequency set to 25 Hz) was applied to remove bias and low-frequency trends from the signal. The experimental results for three selected clamping conditions are presented in Figure 8. However, Table 3 and Figure 9 present the RMS values of vibration displacements in the surroundings of individual accelerometers, for all variants of clamping the workpiece.  
It must be underlined that the RMS values for simulations and experiments are not direct equivalents. During simulations, vibration displacements are calculated for point S, along the \(x_{3}\) direction (Figure 2). This point moves during milling with the tool, so that data regarding the actual position of the tool center are obtained at each time step. Meanwhile, during the real milling experiments, vibrations are measured by sensors placed at the workpiece in fixed positions. The tool passes over these positions. In order to make the results of the simulation and experiments comparable, only 20 s fragments of the signals
# Serial Decoders-Based Auto-Encoders for Image Reconstruction
## 4 Experiments
### Experimental Results  
The experimental results of the proposed and classical algorithms on the MNIST data set, EMNIST data set, FMNIST data set, and MMNIST data set are respectively shown in Tables 5-8. SSIM is the average structure similarity between reconstruction images and original images. \(\Delta\)SIMM is the average SSIM difference between the proposed approaches  
Figure 14: Composition of the discriminator. Figure 13: Composition of the decoder. and the conventional AE approach. The experimental results are also displayed in Figure 15, where the horizontal coordinate is data sets and the vertical coordinate is \(\Delta\)SIMM. It can be found in Tables 5-8 and Figure 15 that the proposed methods, except for ACDAE and ACDAAE, are superior to the classical AE and AAE methods in the performance of image reconstruction. Therefore, it proves the correctness and effectiveness of the proposed cascade decoders-based auto-encoders for image reconstruction. It can also be discovered in Tables 5-8 and Figure 15 that the proposed RCDAE and RACDAAE algorithms post the best recovery performance across nearly all four data sets. Hence, residual learning is very suitable for image recovery. This is owing to the fact that the residual has a smaller average and variance than the  
It can further be observed in Tables 5-8 and Figure 15 that the proposed ACDAE and ACDAAE algorithms yield some minus \(\Delta\)SIMM across the four data sets. Thus, in line with the predictions of this paper, pure adversarial learning is unsuitable for image re-establishment. However, a combination of residual learning and adversarial learning, such as the aforementioned RACDAAE, can obtain high re-establishment performance. It can additionally be found in Tables 5-8 and Figure 15 that the AAE algorithm possesses some minus \(\Delta\)SIMM across the four data sets. Therefore, consistent with the circumstances of this article, pure AAE cannot outperform AE in image reconstitution. Nevertheless, a combination of residual learning and adversarial learning, such as aforementioned RACDAAE, can produce high reconstitution performance. Finally, it can be found in Tables 5-8 and Figure 15 that SSIM differences for MMNIST-axial and MMNIST-sagittal are substantially higher than for other data sets. The reason for this may be that these training and testing samples are more similar than other data sets. In order to clearly compare the reconstruction performance between the proposed algorithms and the classical algorithms, the reconstruction images are illustrated in Figures 16-25. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **SSIM** & \(\Delta\)**SSIM** \\ \hline AE & 0.96335 & 0.00000 \\ GCDAE & 0.96463 & 0.00128 \\ RCDAE & 0.96620 & 0.00285 \\ ACDAE & 0.96329 & \(-\)0.00006 \\ RACDAE & 0.96625 & 0.00290 \\ AAE & 0.96366 & 0.00031 \\ GCDAAE & 0.96458 & 0.00123 \\ RCDAAE & 0.96630 & 0.00295 \\ ACDAAE & 0.96353 & 0.00018 \\ RACDAAE & **0.96637** & **0.00302** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results on the FMNIST data set.
## 2 Numerical Theories
### Quasi-Static and Multi-Segment Theory  
Quasi-static theory [40; 41] is used to calculate the tension of mooring lines. For a single line between two fixed points, when no portion of the line rests on the seabed, the analytical equation is as follows:  
\[x_{F}(F_{H},F_{V})=\frac{F_{H}}{\omega}\Bigg{\{}\{ln\left[\frac{F_{V}}{F_{H}}+ \sqrt{1+\left(\frac{F_{V}}{F_{H}}\right)^{2}}\right]-ln\left[\frac{F_{V}- \omega L}{F_{H}}+\sqrt{1+\left(\frac{F_{V}-\omega L}{F_{H}}\right)^{2}} \right]\right\}+\frac{F_{H}L}{EA}, \tag{7}\]  
\[z_{F}(F_{H},F_{V})=\frac{F_{H}}{\omega}\left[\sqrt{1+\left(\frac{F_{V}}{F_{H} }\right)^{2}}-\sqrt{1+\left(\frac{F_{V}-\omega L}{F_{H}}\right)^{2}}\right]+ \frac{1}{EA}\left(F_{V}L-\frac{\omega L^{2}}{2}\right), \tag{8}\]  
where \(EA\) is the cross section axial stiffness, \(\omega=\mathrm{gA}(\rho_{c}-\rho)\) is the weight per unit length in the submerged fluid, \(\rho_{c}\) is the cable density, \(\rho\) is the fluid density, and \(g\) is the acceleration due to gravity; \(F_{H}\) and \(F_{V}\) are the applied horizontal and vertical force at the fairlead, respectively; \(L\) is the upstretched line length; \(x_{F}\) and \(z_{F}\) are the cable profile in the horizontal and vertical planes at distance \(s\) along the line, respectively.  
By using the multi-segmented theory, a mooring line can be divided into two segments. The first segment is from the vessel node to the location of clump masses, while the second segment is from the location of clump masses to the fixed node. Each of the segments is analyzed by the quasi-static theory and solved using the Newton-Raphson method iteratively.
# Assessment of Fatigue Life and Failure Criteria in Ultrasonic Testing Through Thermal Analyses
## 4 Results
### Fracture Surfaces  
In this section, selected specimens are used to demonstrate and compare the effect of temperature. As observed in the S-N curve, materials subjected to high stress levels were not significantly affected by temperature. However, specimens tested under pulse-pause conditions of 300-200 ms, 200-200 ms, and 200-300 ms, which failed before 10\({}^{7}\) cycles, experienced significant temperature effects that reduced their fatigue life. This effect is evident on the fracture surfaces, which display different burn colors. Figure 18 shows the fracture surfaces obtained through stereoscopy, revealing the burned areas.  
The fracture surface in Figure 18a failed at 1.20 \(\times\) 10\({}^{6}\) cycles, with 200-200 ms of pulse-pause and 360 MPa for \(\sigma_{\mathrm{a}}\). The specimen in Figure 18b was performed with 200-300 ms of pulse-pause under 405 MPa and failed at 1.66 \(\times\) 10\({}^{5}\) cycles. These specimens achieved temperatures above 350\({}^{\circ}\)C. It is important to highlight that 360 MPa under 300-500 ms of pulse-pause reached 2 \(\times\) 10\({}^{9}\) cycles without failure.