esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 3 Methods  
### Sources of Water Quality Data  
Water quality data were collected from six key locations across Peninsular Malaysia--MRANTI Lake, Klang River, Semenyih River, Labu River, Malacca River, and Muar River by the Department of Environment (DOE), Malaysia, and RedTone company. As shown in Figure 1, these sites are geographically distributed across four different Malaysian states: Selangor, Negeri Sembilan, Malacca, and Johor, offering a spatially diverse dataset for this study. The selection of these sites is due to a wide range of hydrological settings, climatic variations, and anthropogenic stressors. These include rapid urbanization, agricultural activities, industrial discharges, and tourism-related pressures factors known to influence surface water quality in complex and interrelated ways.  
Klang and MRANTI Lake are situated within nodensely developed areas of Selangor, where urban runoff, domestic sewage, and industrial waste contribute significantly to pollutant loading. Semenyih and Labu Rivers, while located in less densely populated zones, represent peri-urban and agricultural catchments, respectively. These areas are influenced by mixed land use, including farmland, residential expansion, and light industrial zones, making them valuable for studying the impacts of transitional land-use patterns. In contrast, the Malacca and Muar Rivers, located in coastal and heritage-rich areas, are affected by tourism activities, municipal waste discharge, and point-source pol  
Figure 1: Map of Peninsular Malaysia showing the six selected study locations: MRANTI Lake, Klang River, Semenyih River (Selangor), Labu River (Negeri Sembilan), Malacca River (Melaka), and Muar River (Johor).  
lution from nearby industries. This selection allows for a multi-contextual assessment of water quality under various environmental and socioeconomic settings. It also enables the investigation of spatial trends and localized pollution patterns, enhancing the robustness and generalizability of the water quality prediction models developed in this study. By encompassing both high-pressure urban basins and relatively moderate-impact zones, the study can provide more holistic insights for policy formulation, watershed management, and future monitoring strategies.
## 4 Results
### \(l_{c}\) Sensitivity Analysis
#### 4.1.2 Cohesive Phase-Field Fracture  
In this study, a sensitivity analysis of the length scale parameter \(l_{c}\) is performed using a CPFM approach on the 2D benchmark model. All parameters are held constant except \(l_{c}\), which is systematically varied between 0.1 mm and 0.2 mm to assess its effect on the fracture paths and the reaction force-displacement relationship.  
Figure 7 shows different fracture paths corresponding to different \(l_{c}\) values. As indicated by [10] and reflected in Figure 8, two key observations emerge. First, the CPFM approach shows minimal sensitivity to \(l_{c}\), with changes in the parameter having a negligible effect on the reaction force-displacement response. This suggests that the proposed modifications have effectively converted \(l_{c}\) from a material parameter to a numerical one. Secondly, the reaction force-displacement curves exhibit cohesive behaviour, with a gradual decrease in reaction force after the softening zone, rather than a sudden decrease. This behaviour is particularly suitable for simulating semi-brittle materials, such as concrete.  
These results suggest that at smaller length scales, such as mesoscale or microscale, the \(l_{c}\) parameter no longer restricts the simulations. The CPFM approach therefore offers a more robust and flexible option for fracture simulations in multiphase materials at smaller scales.  
Figure 6: Sensitivity analysis of the parameter \(l_{c}\) conducted using the AT2 standard phase-field fracture model.
###### Abstract
However, the model must include all suspension elements, such as the leaf springs, shock absorbers, and anti-roll bars, to reproduce the real system best. This can be configured in the ADAMS software under \"Suspension Assembly\" and by using the \"Parallel Travel Wheel\" (PWT) test. After validating the suspension systems, the multi-body truck model can be created in ADAMS software. This multi-body model provides a good understanding of truck dynamics in different situations [34]. However, before this, verifying and validating the truck model experimentally is essential to ensure its dynamic accuracy. The validation process begins with selecting appropriate scenarios that meet the engineering team's objectives, allowing the analysis of the truck's maximum parameters. Then, a comparison between the simulation and experimental results is conducted to verify the accuracy of the truck model. The validated model can be used to improve truck performance without costly experimental tests. Some examples of studies aimed at validating the multi-body model of a vehicle, where different scenarios are used to compare the simulation results with experimental tests, are given in [5; 35; 36]. The study carried out in [5] aims to model the off-road vehicle Defender 110 in the ADAMS software and validate it using a bump test and a double lane change maneuver, where the results showed good agreement. Another study [35] involves modeling the heavy vehicle ISUZU FSR two-axle Single-Unit Truck in the IPG TruckMaker software. The truck's lateral acceleration and velocity are the parameters used to verify the truck's dynamic. The simulation results closely match the experimental data, confirming the validity of the multi-body model. The methodology for validating the model has been discussed in [36], and modeling of the 2017 International 4300 SBA 4 \(\times\) 2 truck has been conducted using dSPACE ASM software. Three scenarios have been chosen to compare the simulation results with experiment data and thus validate the developed model, leading to the conclusion that the truck model is valid within a limited range. This paper aims to model a full truck (4 \(\times\) 4) and validate it experimentally, focusing on its lateral dynamics. The organization of this paper is as follows: Firstly, experimental determination of truck characteristics such as the mass, center of gravity coordinates, and tire vertical stiffness is achieved. These parameters are essential inputs for the full truck model in ADAMS software. Secondly, the truck's front and rear suspension systems are modeled, including leaf springs in each suspension. Due to their modeling complexity, validation is achieved through a static experimental test. Finally, a full truck is modeled and validated through the dynamic experimental test, with a focus on lateral dynamics. The results show a good correlation between the simulated model and the real truck, concluding that the truck model is validated in terms of lateral dynamics.
## 2 Materials and Methods
### Methods
#### 2.2.2 Riemannian Geometry Classifier  
The introduction of Riemannian Geometry classifier in the BCI field challenges the status of some traditional and classic classification methods. The idea of the Riemannian Geometry classifier (RGC) is to directly map data on the Riemannian manifold with measurement metrics. In this way, we can directly manipulate these data, such as averaging, stretching, and even direct classification.  
The Riemannian manifold [25; 26] is a non-Euclidean space, and the neighborhood of each point on the manifold is homeomorphic to Euclidean space. To simplify, a Riemannian manifold can be seen as a space that locally looks flat. The surface of the Earth can be seen as a Riemannian manifold. The reason we want to map data directly into the Riemannian manifold space is based on the assumption that, under the P300-speller task, our mental state, as well as the power and spatial distribution of the EEG signals we generate, can be considered to have a certain degree invariance, which can be encoded by the covariance matrix.  
In the BCI field, when dealing with the covariance matrix we get from the EEG signal, the most commonly used matrix manifold is a symmetric positive definite (SPD) matrix manifold. Suppose we have two SPD covariance matrices C1 and C2. Both can be represented as a point on the Riemannianmanifold (Figure 3). The distance between them is called the Riemannian distance. The square of the Riemann distance can be expressed by the following equation:  
\[\delta^{2}\left(\mathbf{C}_{1},\mathbf{C}_{2}\right)=\sum_{n}\text{log}^{2}\lambda_{n} \left(\mathbf{C}_{1}^{-1}\mathbf{C}_{2}\right) \tag{3}\]  
where \(\lambda_{\text{n}}(\text{M})\) represents the \(n\)th eigenvalue of the matrix M.  
Using the Riemannian distance in Equation (3) the centroid G of a set of K SPD matrices C1,..., CK (Figure 3), also known as the Riemannian Geometry mean (RGM), can obtain the optimized solution as follows:  
\[\mathbf{G}=\underset{\mathbf{G}}{\text{argmin}}\sum_{\mathbf{K}}\delta^{2}\left(\mathbf{C}_{ \mathbf{k}},\mathbf{G}\right) \tag{4}\]  
During training, we map all the K-type SPD matrices on the Riemannian manifold and calculate the Riemannian Geometry mean of the K-type (G1, G2, G3,... Gk) (Figure 4). When new test data come in, we calculate the Riemannian distance between the point and the rest of the RGM point, and classify it as the one with the smallest Riemannian distance to the mean (RMDM).
## 3 Proposed Iterative Signal Detection Scheme
The mean square error can be computed as  
\[E\left[e_{j,k}^{2}\right]=\mathbf{w}^{T}\mathbf{Q}\mathbf{w}-2\mathbf{w}^{T} \mathbf{C}\mathbf{g}+\mathbf{g}^{T}\mathbf{A}\mathbf{g}-2\lambda\left(\mathbf{ J}^{T}\mathbf{g}-1\right) \tag{7}\]  
where \(\mathbf{Q}=E\left[\mathbf{q}_{j,k}\mathbf{q}_{j,k}^{T}\right]\) is the auto-correlation matrix of size \((2M_{w}+1)(2N_{w}+1)\) by \((2M_{w}+1)(2N_{w}+1)\), \(\mathbf{C}=E\left[\mathbf{q}_{j,k}\mathbf{a}_{j,k}^{T}\right]\) is the cross-correlation matrix of size \((2M_{w}+1)(2N_{w}+1)\) by \((2N_{g}+1)\), \(\mathbf{A}=E\left[\mathbf{a}_{j,k}\mathbf{a}_{j,k}^{T}\right]\) is the auto-correlation matrix of size \((2N_{g}+1)\) by \((2N_{g}+1)\), \(\lambda\) is the Lagrange multiplier, and \(\mathbf{J}\) is the vector of length \((2N_{g}+1)\) such that the second element is \(1\) and the remaining elements are \(0\). By taking the derivatives of the righthand side of Equation (7) with respect to \(\lambda\), \(\mathbf{g}\), and \(\mathbf{w}\), respectively, and setting the resulting expressions to zero, the optimized target and equalizer coefficients can be obtained as follows:  
\[\lambda=\frac{1}{\mathbf{J}^{T}\left(\mathbf{A}-\mathbf{C}^{T}\mathbf{Q}^{-1} \mathbf{C}\right)^{-1}\mathbf{J}} \tag{8}\]  
\[\mathbf{g}=\lambda\left(\mathbf{A}-\mathbf{C}^{T}\mathbf{Q}^{-1}\mathbf{C} \right)^{-1}\mathbf{J} \tag{9}\]  
\[\mathbf{w}=\mathbf{Q}^{-1}\mathbf{C}\mathbf{g} \tag{10}\]  
(Step 4) To consider the effect of ITI from the adjacent track, the branch metric in the Viterbi detector after equalization is calculated as follows:  
\[\beta_{j,k}(t_{i},t_{i+1})=\left\{s_{j,k}-(g_{N_{g}}a_{j,k+N_{g}}(t_{i+1})+ \cdots+g_{-N_{g}}a_{j,k-N_{g}}(t_{i})-\left(p_{j-1,k}+p_{j+1,k}\right)\right\} ^{2} \tag{11}\]  
where \(t_{i}\) is the state; \(a(t_{i})\) is the decision at a state; and \(p_{j-1,k}\) and \(p_{j+1,k}\) are extrinsic information from the upper and lower tracks, respectively. (Step 5) To deliver appropriate information as a priori information for the MLP, the Viterbi output \(\hat{a}_{j,k}\) is modified to the following:  
\[\widetilde{a}_{j,k}=\alpha\cdot\hat{a}_{j,k}. \tag{12}\]In addition, the combined value of the modified output of the Viterbi detector \(\widetilde{a}_{j,k}\) and the readback signal is input to the MLP. (Step 6) During the iterative signal detection, the MLP delivers the output only to the Viterbi detector, and the Viterbi detector delivers the output only to the MLP. In addition, the Viterbi detector outputs the estimated data. When the number of iterations is zero, Steps 1 to 5 are performed.
## 4 Detrimental Effects of SSI
### Case Studies
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**Time History \(\widetilde{u}_{\textbf{b}}\)(\(\textbf{t}\))**} & \multicolumn{2}{c}{**RS(\(\widetilde{u}_{\textbf{b}}\))**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**Maximum (g)**} & \multicolumn{2}{c}{**Mean (g) \(\times\)10\({}^{-2}\)**} & \multicolumn{2}{c}{**Maximum (g)**} \\ \cline{3-8}  & & **SSI System** & **Fixed-Base** & **SSI System** & **Fixed-Base** & **SSI System** & **Fixed-Base** \\ \hline \multicolumn{8}{c}{Hollywood Storage Building (NS)} \\ \hline Kern County & 1952 & 0.43 & 0.28 & 7.72 & 5.25 & 1.67 & 1.40 \\ \hline San Fernando & 1971 & 0.37 & 0.15 & 5.52 & 3.12 & 1.17 & 0.85 \\ \hline Whititer Narrow & 1987 & 0.32 & 0.15 & 4.92 & 4.34 & 0.97 & 0.90 \\ \hline Northridge & 1994 & 0.33 & 0.21 & 4.33 & 2.92 & 0.96 & 0.80 \\ \hline Chino Hills & 2008 & 0.41 & 0.18 & 7.24 & 2.75 & 1.73 & 0.68 \\ \hline Encino & 2014 & 0.27 & 0.11 & 1.49 & 0.61 & 0.57 & 0.23 \\ \hline \multicolumn{8}{c}{Millikan Library (NS)} \\ \hline Lyle Creek & 1970 & 0.41 & 0.33 & 11.40 & 9.83 & 3.44 & 2.56 \\ \hline San Fernando & 1971 & 0.13 & 0.21 & 3.41 & 5.61 & 0.57 & 1.10 \\ \hline Whititer Narrow & 1987 & 0.16 & 0.37 & 3.12 & 7.10 & 0.90 & 2.66 \\ \hline Yorba Linda & 2002 & 0.13 & 0.26 & 2.91 & 5.76 & 0.69 & 1.96 \\ \hline San Simeon & 2003 & 0.12 & 0.13 & 3.45 & 3.49 & 0.87 & 1.03 \\ \hline \multicolumn{8}{c}{Sherman Oaks Building (NS)} \\ \hline Lander & 1992 & 0.21 & 0.28 & 5.64 & 6.55 & 1.30 & 1.66 \\ \hline Whititer Narrow & 1987 & 0.13 & 0.15 & 1.38 & 1.52 & 0.34 & 0.33 \\ \hline Northridge & 1994 & 0.20 & 0.22 & 2.43 & 3.70 & 0.78 & 1.51 \\ \hline Chatsworth & 2007 & 0.095 & 0.102 & 1.00 & 1.04 & 0.33 & 0.47 \\ \hline Chino Hills & 2008 & 0.20 & 0.16 & 2.44 & 2.57 & 0.73 & 1.01 \\ \hline Encino & 2014 & 0.061 & 0.12 & 0.053 & 0.06 & 0.18 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dynamic responses of structures in the time domain of target buildings in the XOZ-plane. Figure 6: (**Top**) The acceleration record obtained at the ground floor of the Hollywood Storage Building in a NS direction during the Northridge earthquake in 1994; (**a**) the structural response of an SSI system for time history (**left**) and for response spectrum (**right**); (**b**) the structural response of a fixed-base oscillator for time history (**left**) and for response spectrum (**right**).
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### Model Performance Evaluation Metrics
#### 2.4.2 Mean Pixel Accuracy (MPA)  
The _MPA_ is first calculated by separately calculating the ratio of the number of pixels in each class that are predicted to be correctly categorized to the total number of pixels in that class in the predicted labels and then averaged cumulatively. The calculation formula is shown in Equation (4).  
\[MPA=\frac{1}{c}\times\sum_{i=1}^{c}\frac{n_{ii}}{\sum_{j=1}^{c}n_{ji}} \tag{4}\]  
where \(n_{ij}\) is the number of pixels in which the observed category \(j\) is predicted into category \(i\). \(c\), \(n_{ii}\) are defined as above.
## 3 Test on Simulated Data  
The simulated data are generated in order to emulate as closely as possible the most suitable observation and geometry characteristics. The bridge is designed with a longitudinal axis perfectly perpendicular to the range direction, a bridge width of about 30 pixels and a maximum variation of double-bounce scattering of about 1.5 pixels. The radar is designed with the same features as the COSMO-SkyMed sensor.  
In this specific case, simulated data consist of a sub-ROI reflectivity range profile for which the energy is shown in Figure 4a. From inspection of the figure, it is possible to notice the beginning and the end of the bridge edges. These scattering events, denoted by the numbers 1 and 2, are generated by the direct reflection events and they are directly projected onto the slant-range line. The energy peak generated by the double-bounce scattering mechanism is the one indicated by the number 3 and, thanks to the layover effect, is located behind the main scatterers. The position in time of peaks 1 and 2 remain stable over time because the bridge does not move while peak 3 changes its position due to the variation of the height level of the river's water surface. According to the DFT property (6), a time-variation position of the peak number 3 is corresponding to a frequency variation of its DFT. The variation of the phasor oscillation frequency will increase its absolute phase value (unwrapped phase) [21]. This distance, appropriately scaled and then calibrated at the first observation performed by the ground station (terrain gauge), is being used as an indication of the river's height. The input signal is represented by the sinusoidal sub-pixel variation indicated by arrow number 3. In Figure 4b), the unwrapped phases related to each interferometric pair are reported (the lines with different colors are corresponding to the different observations). The reconstructed level variation is depicted in Figure 4c. From Figure 4b we see that the output response of the function \(Unwrapp(.)\) is linear so the parameter \(A_{c}\) of Equation (5) represents the angular coefficient of the linear phases. The ripple in Figure 4c is present due to an oversampling factor of 10 has been used. The higher the oversampling factor, the less visible this noise will be. In the experimental data using the satellite sensor, a very high oversampling factor of 64 will be chosen.
## 4 Results and Discussion
### Results and Discussion of the Numerical Simulation
This significant reduction clearly demonstrates that grouting into overburden separation is a highly effective method for mitigating surface subsidence, thereby enhancing the stability and safety of surface structures. The influence of ground surface tilting deformation on the stability of surface structures is more pronounced than subsidence [36], especially for tall structures with small base areas and significant heights, such as high-voltage transmission towers, chimneys, and water towers, which are highly sensitive to tilting deformation. Even a slight tilt in the ground surface can result in significant overturning moments for these structures. Therefore, determining the influence of different water injection-extraction ratios on the variation law of surface tilt is particularly important. The curves depicting surface tilt under different injection-extraction ratios are presented in Figure 14, where the deformation value \(i\) represents the corresponding slope of the subsidence curve. According to \"The Specification of Design for Pillars of Buildings, Water Bodies, Railway, Main Shafts, and Drifts [37]\", surface tilt is categorized into four damage grades: Damage Grade I denotes slight damage requiring minimal repair for tilts less than 3 mm/m; Damage Grade II indicates mild damage for tilts less than 6 mm/m; Damage Grade III represents moderate damage for tilts less than 10 mm/m; and Damage Grade IV requiring significant repairs or potential demolition and applies to tilts exceeding 10 mm/m. Figure 14 demonstrates the consequences of varying injection-extraction ratios on surface tilt at a working face: below 10% results in severe damage; below 20% leads to moderate damage; below 30% causes mild damage, and at 40%, the tilt remains below 3 mm/m, indicating only minor damage that does not necessitate repairs. Therefore, maintaining an injection-extraction ratio above 30% is advisable to minimize damage to surface structures during grouting into overburden separation.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Performance Comparison with Baselines
For example, DualGNN uses LightGCN to aggregate node information in the interaction graph between users and items and the user co-occurrence graph in each modality to obtain user and item representations, which have better recommendation performance than LightGCN. Moreover, most graph-based models have better performance than general models (e.g., BRP). However, as shown in Table 2, the MMGCN based on LightGCN has not achieved better performance than LightGCN. We believe the reason for this is that MMGCN directly fuses the user and item representations in different modalities, which summarizes all modalities' features but ignores the important contribution of a single modality to improving performance. Meanwhile, multimodal information usually has similarities, so how to distinguish these similarities to highlight the single-modality features is crucial. Therefore, we designed a two-step enhanced fusion method to distinguish the similarities among multimodal features and fully utilize their complementarity. As a result, the model we proposed, UPGCN, has attained best recommendation performance. User modal preferences exert a substantial influence on recommendation performance. Building upon MMGCN, DualGNN constructs a user co-occurrence graph to extract user preferences across various modalities, thereby significantly enhancing model's performance. However, some models overlook the critical role of user preference information in a specific modality for mining more accurate user representations. LATTICE constructs item-item graphs in different modalities and a latent item-item graph to augment item representations in each modality. FREEDOM introduces improvements to LATTICE by freezing the item-item graph and denoising the user-item interaction graph. The aforementioned models primarily emphasize methodological enhancements to improve item representations; however, it inadequately accounts for user preferences in different modalities, resulting in the model's failure to accurately capture user representations. Consequently, we developed UPEM in response to this issue, aiming to extract users' preference information across various modalities to enhance their representations. As shown in Table 2, our model exhibits competitive recommendation performance, thereby validating the effectiveness of the proposed method. However, if the extraction of multimodal features from the items is insufficient, the recommendation performance of our proposed UPGCN may be weaker than that of other state-of-the-art approaches. This is because we need to use the modal features to obtain user modal representations across different modalities, thereby capturing users' preferences for various modalities. If the initially extracted multimodal features are not accurate enough, it may affect the subsequent embedding learning. In contrast, other methods primarily use modal features as auxiliary information, which makes them less susceptible to this issue.