esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow
### Step 2: Select Proper Seismic Attributes for Seismic Lithology
A better approach is the RGB color blending of high-, moderate-, and low-frequency spectral components or seismic travel time traces, also called frequency fusion [22; 47]. Notably, when using 90\({}^{\circ}\) data, frequency fusion allows for integrating both thick and thin beds in the vertical view. As a result, the typically constant width of seismic events in conventional stacked and migrated color display (Figure 5a) aligns more accurately with the true thickness of lithofacies' bodies (in this case, sand bodies a through g, Figure 5b) without substantial interference. In the horizontal view, the broken images of channels in the conventional display (#1-#8, Figure 5c) appear more continuous, revealing more realistic, thickness-adjusted channel geometry (Figure 5d). Additionally, frequency fusion proves advantageous for reconstructing a formation's sequence-stratigraphic and sedimentological profile in the vertical sectional view. It creates a display illustrating lithofacies and stratigraphic cyclicity, resembling outcrop photos (Figure 6a). In contrast, MPD-processed frequency decomposition in the frequency domain does not achieve the same effect (Figure 6b). Figure 4: The result of the 90\({}^{\circ}\)-phase shift in the synthetic model in Figure 2 indicates lithofacies. The central trough is a reasonable estimate of the low-impedance wedge sand lithofacies. Except for the bed which is thinner than \(\uplambda/8\), the top and base of the wedge are tied to the zero-crossings of seismic traces. Figure 5: Frequent fusion can further improve the interpretability of 90\({}^{\circ}\)-phase data. (**a**) On a vertical section (labeled SEC) of stacked and migrated data, seismic events (a–g) do not fit the geometry of channel sand bodies. (**b**) In the same section, using frequency-fused data, RGB events (a–g) roughly fit the geometry of channel sand bodies. (**c**) On a statal slice (labeled SS) imaged with stacked and migrated data, channel images (1–8) are mostly blurred. (**d**) On the same statal slice with frequency-fused data, channel images (1–8) are more continuous with sharper boundaries and valid thickness definitions. Figure 6: High-resolution stratigraphic imaging and analysis are achieved by fusing three frequencies from a 90\({}^{\circ}\) seismic volume. (**a**) System tracts and sequence stratigraphy influenced sand thickness (ranging from 15 to 50 m in the gamma ray log at the well) and depositional cycles [10]. The left deflection of the log indicates sand. (**b**) An RGB blending of 14, 30, and 50 Hz iso-frequencies created with MPD is not optimal for stratigraphic imaging. Modified from Zeng [13].
## 2 Region, Data, Method
### Stations Identification
#### 2.2.3 Coastal and Continental Stations  
Stations located near seacoasts were allocated to the group of coastal stations. Other stations were allocated to the group of continental stations (Figure 1, Table 1). It should be noted that the diurnal and semidiurnal tidal harmonics are more pronounced in the spectra of hourly time series of noise from coastal stations, compared to stations located at a distance from seacoasts. In records from continental stations, mainly diurnal maxima are distinguished. Whereas diurnal and semidiurnal maxima, as well as maxima on the periods of powerful lunar waves \(M_{2}\) (period 12.42 h) and \(O_{1}\) (period 25.82 h), are clearly distinguished in the spectra of records from coastal stations [35; 36]. Such features of seismic noise records showed a high degree of signal noisiness at coastal stations due to sea tidal waves.  
The distributions of stations by groups, taking into account the number of stations affected by sea waves (coastal stations, 11 or 52% in total) and volcanic activity (volcanic stations, 6 or 29% in total), are presented in Table 2.  
Figure 2: Location map of seismic stations and active volcanoes: 1—non-volcanic station, 2—volcanic station, 3—active volcano near which a seismic station is located, 4—active volcano at rest or near which there is no seismic station, 2011–2021.
# Specific Mutations in Aph1 Cause \(\gamma\)-Secretase Activation
## 1 Introduction
Upon recruitment of substrates, the extracellular domain of NCT [22; 23] and the recognition sites in PS1 interact with the substrates [24]. A cross-linkage study between \(\beta\)-CTF and PS1 revealed that \(\beta\)-CTF first interacts with PS1 NTF; it is then transferred to PS1 CTF for proteolysis [25]. Recent updates of \(\gamma\)-secretase structures in complex with the substrate APP or Notch have revealed the structural basis of \(\gamma\)-secretase recognition [26; 27]. PS1 forms a hybrid \(\beta\)-sheet with substrates in the catalytic pore. The hybrid \(\beta\)-sheet pulls the substrate helix and unwinds the cleavage sites of the substrates. In addition, \(\gamma\)-secretase structures in complexes with chemicals have revealed the binding sites of \(\gamma\)-secretase inhibitors (GSI) and GSM [28]: GSI resides in the catalytic pore of PS1 and GSM binds the TMD1 of PS1. Previously, we established a \(\gamma\)-secretase assay system in yeast, which does not possess homologs of \(\gamma\)-secretase or APP [29]. We detected A\(\beta\) production using the Gal4 reporter system and analyzed A\(\beta\) species using an in vitro assay with yeast microsomes [29; 30]. We identified PS1 mutations that restored the function of PS1 FAD mutants [31]. Suppressor mutations modulated \(\gamma\)-secretase activity and decreased the ratio of A\(\beta\)42/43 [31]. Yeast cells are useful to screen for mutations that regulate \(\gamma\)-secretase activity. In the present study, we identified Aph1 mutants that possess protease activity in the absence of NCT. We found that these mutants enhanced \(\gamma\)-secretase endoproteolysis activity with full subunits. We propose that the underlying mutations cause conformational changes in PS1 to enhance protease activity.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation
### Execution Time (Logarithmic Scale) vs. Number of Worker Nodes  
Figure 6b, which employs a logarithmic scale to represent execution time as a function of the number of worker nodes, is particularly insightful in illustrating the efficiency gains achieved through parallel processing. The use of a logarithmic scale is significant because it allows for a clearer visualization of the relative changes in execution time across different scales, especially when dealing with varying dataset sizes. These results reveal a substantial reduction in execution time as the number of worker nodes increases, a trend that is particularly pronounced for larger datasets. For instance, with the dataset sized at 10,854.4 MB, the execution time decreases sharply as the number of worker nodes increases, indicating that the algorithm is highly effective at parallelizing tasks across multiple nodes. This capability is critical in distributed computing environments, where the ability to efficiently distribute workloads can lead to significant reductions in processing time, thereby enhancing overall system performance.  
The impressive reduction in execution time when handling larger datasets also suggests that the algorithm is well suited for handling substantial data numbers, a common requirement in massive data applications, such as deployments of complex networks in smart cities. However, the results also reveal that for smaller datasets, such as those around 108.1 MB, the reduction in execution time is less significant, and the curve flattens more quickly. This indicates that the overhead associated with managing multiple worker nodes may begin to outweigh the benefits of parallel processing when the dataset size is relatively small. This observation is crucial for understanding the trade-offs involved in using Apache Spark for smaller workloads and highlights the importance of optimizing node allocation to avoid unnecessary computational overhead.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 5 A Selective MEC Service Model Based on a Multitier Architecture
### End-to-End Latency Model
Otherwise, the network latency between an AP and \(n_{e}\) should be considered for all other cases. The first and second items denote the network latency of a service request for the access MEC node in zone\(-1\). If \(n_{r}\) is selected by the service MEC node, \(L_{E2E}^{n,j}\) is equal to the first item. In case of selecting the access MEC node in zone\(-1\), except for \(n_{r}\) for the service request, \(L_{E2E}^{n,j}\) is the summation of the first and second items because the traffic between different APs is transferred via \(g_{e}\). The third and fourth items are related to the network latency for a service request for the edge MEC node in zone\(-2\) and the core MEC node in zone\(-3\), respectively. In other words, if an edge MEC node in zone\(-2\) is selected by the service MEC node, \(L_{E2E}^{n,j}\) is calculated by the first and third items according to the path between the \(r_{s}\) and the selected MEC node. In case of selecting a core MEC node in zone\(-3\), in the same manner, \(L_{E2E}^{n,i}\) is calculated by the first and fourth items. A MEC node can consist of a finite set of physical and virtual machines. However, we assume that each MEC node is one entity including the resources of all physical and virtual machines. When a service request is generated, a suitable MEC node is selected and then the computing resource of the MEC node is assigned to process the service request. Because each service has various requirements according to the corresponding service characteristics, we define the average processing bits and the required processing cycles per bit in a MEC node per service type separately. Furthermore, if there are previous service requests that need to be processed, the current service request should wait for them to be completed. In this regard, the computing latency in MEC node \(n\) for a service request \(i\) is expressed as  
\[L_{C}^{n,\ i}=\frac{\mu_{n}\cdot\lambda_{n}^{i}\lambda_{n}^{i}\cdot\eta_{n}^{ i}}{P_{n}}. \tag{3}\]
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 4 Construction of Selection Criterion
### Method Comparison Study
#### 4.1.1 Unchanged Number of DOFs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Example No.** & **Modification Type** & **Indicator** & **Cholesky** & **Epsilon** & **PCG** \\ \hline \multirow{6}{*}{\(1\)} & & \(s\) or \(k\) & - & 3 & 2 \\  & (1) & Computation time & 1.5196 s & 0.9577 s & 1.0967 s \\  & & Proportion & 100\% & 63.0\% & 72.2\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 13 & 8 \\  & (2) & Computation time & 1.5786 s & 5.6932 s & 4.6567 s \\  & & Proportion & 100\% & 360.6\% & 295.0\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 5 & 3 \\  & (3) & Computation time & 1.4979 s & 2.0093 s & 1.7246 s \\  & & Proportion & 100\% & 134.1\% & 115.1\% \\ \hline \multirow{6}{*}{\(2\)} & & \(s\) or \(k\) & - & 3 & 2 \\  & (1) & Computation time & 39.3519 s & 6.7370 s & 7.6099 s \\  & & Proportion & 100\% & 17.1\% & 19.3\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 13 & 8 \\  & (2) & Computation time & 39.5669 s & 42.5712 s & 30.0116 s \\  & & Proportion & 100\% & 107.6\% & 75.9\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 13 & 8 \\  & (3) & Computation time & 40.1709 s & 13.5988 s & 11.3561 s \\  & & Proportion & 100\% & 33.9\% & 28.3\% \\ \hline \multirow{6}{*}{\(3\)} & & \(s\) or \(k\) & - & 3 & 2 \\  & (1) & Computation time & 153.3645 s & 2.0374 s & 2.3564 s \\  & & Proportion & 100\% & 1.3\% & 1.5\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 9 & 6 \\  & (2) & Computation time & 150.3360 s & 6.8872 s & 6.6025 s \\  & & Proportion & 100\% & 4.6\% & 4.4\% \\ \cline{2-6}  & & \(s\) or \(k\) & - & 5 & 3 \\  & (3) & Computation time & 149.7456 s & 3.6079 s & 3.4680 s \\  & & Proportion & 100\% & 2.4\% & 2.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of structural examples with unchanged DOFs. Based on this analysis, the selection criterion for the reanalysis method under the condition of unchanged DOFs can be summarized as follows: The Epsilon method can be used for size modifications, while the PCG method can be used for shape and topology modifications. However, when the DOFs of the structure are below 50, the complete analysis should be used for shape modifications.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.4.1 Machine Learning Techniques  
Traditional machine learning (ML) pipelines typically rely on engineered features extracted from raw audio, such as prosodic attributes, spectral coefficients, or lexical indicators. These features are then fed into algorithms such as random forests, support vector machines (SVMs), decision trees, or logistic regression to perform classification, regression, or clustering tasks. A core strength of these approaches lies in their relative transparency compared to deep learning approaches: many models allow for the direct inspection of feature importance, aiding both explainability and pedagogical interpretation.  
Numerous studies in this category focus on classifying aspects of classroom discourse and instructional style. For instance, [PERSON] et al. [86] introduces a PCA-based feature selection strategy to manage high-dimensional audio in noisy classrooms, subsequently comparing multiple classifiers. Random forests are especially popular: [PERSON] et al. [87] uses them to infer teaching methodologies (e.g., lecture vs. group work) by identifying the most salient acoustic features, while [PERSON] et al.[47] applies a similar strategy to detect instructional formats, discussing the most predictive feature sets for each activity type.  
Some researchers go further by combining audio with other data streams. [PERSON] et al. [88], for example, fuses audio with wearable sensor data to segment teacher-student interactions using naive Bayes classifiers. In contexts with limited technological resources, simpler classifiers such as KNN or naive Bayes have been tested for low-cost classroom monitoring and analysis [89, 90]. Similarly, [PERSON] and [PERSON] [91] proposes a multimodal summarization pipeline combining KNN, speech-to-text models, and textual clustering to automatically generate lecture summaries.  
Feature engineering remains central to these ML-based studies, and authors frequently report which features (e.g., pitch variation and lexical tokens) contribute most to the model's predictions [32]. This emphasis on transparency distinguishes ML from deep learning approaches. However, more advanced explainability techniques, such as SHAP [92] and LIME [93], are rarely used in practice. One notable exception is [29], which leverages SHAP to visualize the contribution of emotional features to model decisions.  
Overall, while ML pipelines strike a useful balance between performance and interpretability, the integration of explicit xAI practices remains inconsistent. There is considerable room for improvement in using these tools to strengthen transparency and trust, especially when outputs are intended to guide pedagogical decision-making.
## 3 Results
### Overall Opinion: Global Satisfaction, BIM, 3D Tool and Acquired Skills  
Taking the Q20 variable as a dependent variable and factors DBR1, DBR2, DBR3, DO1, D02, and D03 as independent variables, a multiple regression analysis was performed.  
Figure 6: Agglomerative hierarchical clustering (AHC): Students’ opinions regarding the use of BIM and the skills acquired during the second implementation phase. (**a**) Evolution of variance; (**b**) dendrogram and (**c**) profile plot.  
The resulting model was (1):  
\[\text{Q20}=3.340+0.376\times\text{DBR1}+0.289\times\text{DBR2}+0.220\times\text{ DBR3} \tag{1}\]  
The \(p\)-values for the t-test suggest that DBR1 (\(p<0.001\)), DBR2 (\(p<0.001\)), and DBR3 (\(p=0.012\)) were significantly different from zero. The standardized coefficients showed that the variable of greatest influence was DBR1, followed by DBR2 and DBR3. The value of R2 was 0.43, indicating that approximately 43% of the variability of the satisfaction variable was explained by factors DBR1, DBR2, and DBR3. The mean square error (MSE) was 0.327. The \(p\)-value for the analysis of the variance F-test (\(p<0.001\), significance level of 5%) suggests that the model containing DBR1, DBR2, and DBR3 is more useful for predicting Q20 than not taking the three predictors into account. The VIF coefficient shows the absence of multicollinearity problems in the model.  
It was possible to obtain a more complex nonlinear model with all the variables. The model was (2):  
\[\begin{array}{l}\text{Q20}=3.368+0.115\times\text{DBR10.043}\times\text{DBR2 0.003}\times\text{DBR3}+0.273\times\\ \text{DO1}+0.408\times\text{DO2}+0.313\times\text{D03}+0.048\times\text{DBR1} \times\text{DR10.070}\times\\ \text{DBR2}\times\text{DR2}+0.045\times\text{DBR3}\times\text{DBR3}+0.030\times \text{DO1}\times\text{DO1}+0.072\times\\ \text{DO2}\times\text{DO2}-0.115\times\text{D03}\times\text{DO3}.\end{array} \tag{2}\]  
The value of R2 was 0.602, indicating that approximately 60% of the variability in the satisfaction variable was explained using all the factors posed by the model. The MSE was 0.291.
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
## 2 Materials and Methods
### Participants
#### 2.2.1 Children with RD and Delayed EEG Maturation  
The teacher reported whether the child in question had reading problems. Reading skills were measured with the reading scale of the ENI-2, which includes three subscales: reading accuracy, reading comprehension, and reading speed; children included in the RD group had percentile scores <9 in at least one of these subscales.  
All the participants in the RD group met the diagnostic criteria for RD according to the Diagnostic and Statistical Manual of Mental Disorders 5 th Edition. These criteria were as follows: (a) the child showed persistent difficulties in acquiring and/or using academic skills related to reading (accuracy, comprehension, and/or speed); (b) reading difficulties began in the first years of schooling and not because the participants had intellectual disabilities (the IQ score obtained by each child was greater than or equal to 80); and (c) they did not have any neuropsychiatric disorder other than RD.  
Although children had some attentional disabilities, they did not satisfy the criteria to be diagnosed with attention deficit hyperactivity disorder (ADHD) according to the DSM-5 [7].  
All participants had an abnormally high EEG theta/alpha ratio compared to a normative database [77,78] in at least one lead. This is an essential requirement to be a candidate for NFB treatment. Additionally, if epileptiform activity is present, it should not be in the alpha frequency range.  
For the second stage of this study, 20 out of the 25 children with RD were randomly assigned to an NFB group using nonreplacement random sampling, and the remaining 5 were assigned to a placebo control group (the Sham group).
## 3 Results and Discussion
### Kinetic Model for Sulfamethoxazole Oxidation
Experimental conditions: [SMX]\({}_{0}\) = 50.0 mg/L; [H\({}_{2}\)O\({}_{2}\)]\({}_{0}\) = 2.0 mM; [UV] = 150 W; [pH]\({}_{0}\) = 3.0; [T] = 25 \({}^{\circ}\)C.  
Mass balance for the degradation of SMX:  
\[-\frac{\mathrm{d}[\mathrm{SMX}]}{\mathrm{dt}}=\mathrm{k_{SMX}t} \tag{5}\]  
being,  
[SMX]0: initial concentration of SMX contained in the water (=50.0 mg/L)  
[SMX]: concentration of SMX contained in the water (mg/L)  
k\({}_{\mathrm{SMX}}\): first-order kinetic constant for the degradation of SMX (1/min)  
t: time (min)  
First-order kinetic equation for degradation of SMX:  
\[[\mathrm{SMX}]= [\mathrm{SMX}]_{0}\exp(-\mathrm{k_{SMX}t}) \tag{6}\]  
From the adjustments of the proposed kinetic model to the experimental results obtained, the value of the first-order kinetic constant for the oxidation of SMX has been estimated as a function of the concentration of ferrous ion added in the tests, obtaining the relationship shown in Equation (7). The values of the estimated kinetic parameters are shown in Table 1. The results, shown in Table 1, show a progressive increase in the SMX degradation rate, reaching a maximum value of 1.3079 min\({}^{-1}\) at 5.0 mg/L Fe(II). This constant greatly exceeds that observed in other advanced systems [15], demonstrating the high reactivity of the photo-Fenton system under acidic conditions (pH 3.0) and UV light. However, the use of Fe(II) requires controlling possible side effects such as the accumulation of residual iron or the stabilization of partially oxidized aromatic intermediates. \[\mathrm{Fe^{2+}+H_{2}O_{2}\to Fe^{3+}+HO^{\bullet}+HO^{-}} \tag{1}\]0
## 2 Insights into Material Behaviours  
[PERSON] et al. [5] presented the role of soil stabilisation in halting volumetric deformation and associated reduction in shear strength derived from the wetting processes (e.g., rainfall periods), which was examined for an expansive soil. Two stabilizers commonly used in road construction are examined, i.e., hydrated lime and Portland cement. An additional non-traditional stabiliser composed of a blend of ground granulated blast furnace slag and hydrated lime was also considered. A series of one-dimensional swelling and direct shear box tests were conducted, adopting vertical stresses relevant for pavements and simulate wetting process that can take place after a period of rainfall. Results indicated that while all stabilisers contribute to a reduction in swelling and smaller losses in shear strength upon wetting, the blend of blast furnace slag and hydrated lime is the most favourable in terms of carbon footprint.  
[PERSON] et al. [6] discovered new findings for rotating machinery in cold and humid regions that are prone to icing. A cylinder rotating around the vertical axis was selected as the research object in this study. Three cylinders with different diameters were selected, and icing tests were carried out under different tip speed ratios (TSR) in a self-built icing wind tunnel. The icing characteristics were quantitatively assessed using characteristic parameters, including the icing area, the dimensionless icing area, the stagnation point thickness and the dimensionless stagnation point thickness. The research findings in this study have laid the theoretical and experimental foundations for further exploring VAWTicing in depth.  
[PERSON] and [PERSON] [7] highlighted a novel meta-functional auxetic unit (MFAU) cell designed to improve performance and weight ratio for structural bridge bearing applications. Robust numerical simulations were conducted using three-dimensional finite element models validated by full-scale experimental results. The validated models were later exposed to compression and buckling actions to identify structural failure modes, with special emphasis on the global behaviours of the meta-functional auxetic (MFA)composite bridge bearing. Numerical predictions of the elastic local critical buckling loads of the MFAU cell were in excellent agreement with both the analytical and experimental results, with an observed discrepancy of less than 1%. These results demonstrated that local buckling failures of MFAU cells can potentially be incurred prior to yielding under compression due to their slenderness ratios. Surprisingly, the designed sandwich core used in the MFA composite bridge bearing model can mimic an auxetic structure with significant crashworthiness, implying that this novel core composite structure can be tailored for structural bridge-bearing applications.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 4 Discussion
In conclusion, if the real-time IGS ionospheric products are used, single-frequency receiver users will be able to achieve 1-meter-level or higher positioning accuracy in the NEP.