esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
## 2 Materials and Methods
### Materials  
#### 2.2.1 Clay-Cement Slurry  
In this experiment, a laboratory-made graphene oxide-modified clay-cement slurry was utilized. Ordinary Portland cement (OPC) with a strength grade not lower than 42.5 and a fineness that meets the requirements of GB/T 1345 [29] was chosen for this purpose. The chemical compositions of the test cement are presented in Table 1. The fourth-order clay obtained from Henan Province, China, was mixed with water and allowed to soak for 2 to 3 days to ensure complete infiltration. Subsequently, the clay slurry was prepared by mixing it with a high-speed mixer and then sieving out the sand particles using a 0.05 mm mesh. The particle size distribution of the clay slurry was as follows: 6.9% for the 0.5-0.25 mm particle size, 17.8% for the 0.25-0.075 mm particle size, 34.3% for the 0.075-0.005 mm particle size, and 41.0% for the 0.005 mm particle size. The analysis of clay performance is provided in Table 1.  
The GO suspension used in this study, with a concentration of 2 mg/mL, is an industrial-grade single-layer graphene oxide dispersion produced by Suzhou Carbon Century Graphene Technology Co., Ltd. in Suzhou, China. This dispersion was prepared using an improved Hummer's method. The diameter distribution analysis revealed that particles ranging from 3 to 8 nm accounted for 95% of the suspension, while those with a thickness less than 3 nm constituted 90%. The purity of the GO suspension reached 99%.  
The specific gravity of the clay slurry was measured to be 1.15 g/cm\({}^{3}\). The total volume of mixing cement used in the experiment was 5%, 10%, and 15% of the clay slurry volume. Additionally, sodium silicate was added at a rate of 10 mL per liter. The GO suspension was prepared by incorporating graphene oxide into the solution at specific concentrations based on the solid mass. In particular, concentrations of 0 wt.%, 0.03 wt.%, and 0.05 wt.% of the solid mass of ordinary Portland cement (OPC) were used. To prepare the clay-graphene mixture, the clay slurry was combined with the GO solution and stirred for 5 min. Before that, the cement and mineral admixture were accurately weighed and thoroughly blended. The resulting mixture was then added to the clay-graphene suspension and stirred for an additional 5 min to ensure a uniform slurry. The ratio of clay-cement slurry is shown in Table 2.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
###### Abstract  
Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios.  
meta-learning; MSAD dataset; swin transformer; video anomaly detector +
Footnote †: journal: Applied sciences
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 2 Study Area and Data  
### Study Area  
As shown in Figure 1, the study area is Mt. Umyeon, located in Seocho-gu, Seoul. Seoul is the largest city in Korea (about 605 km\({}^{2}\)), located in the center of the Korean Peninsula, and is one of the most populous cities in the world. This city, with about 10 million citizens, has a complex transportation and social infrastructure system. This density allows natural disasters, including landslides, to cause more recurrent disasters and serious damage [46]. The study area, Mt. Umyeon, is also a shallow mountain, with a height of 321.6 m, located in the center of a complex in a large city.  
The slope near the summit is about 30 degrees, and most areas are less than 15 degrees. There is a greaisic rock outcrop in the area from weathering and geomorphological process. The biotite gneiss is weak to weathering, which could increase the susceptibility of debris flow in the area. The soil composition of this study area is mainly the brown forest soil developed on the parent rocks of biotite gneiss and granite gneiss. In some valley areas of the study area, brown forest soils with deep soil depth and high organic content are distributed, and the soil texture is silt and silt loam of sand, which drains well and easily causes severe weathering of the surface.  
In July 2011, Typhoons Muifa and Kompasu generated 1092 mm of rainfall in Seoul for a month, and this resulted in simultaneous debris flows in the Mt. Umyeon area of Seoul. Specifically, 588 mm of heavy rain, which is about 40% of the annual average rainfall of 1451 mm, was concentrated in Mt. Umyeon for 3 days from July 26 to 28; debris flows caused 16 casualties and dozens of houses were damaged [47]. Debris flows that occurred simultaneously around Mt. Umyeon poured into streets and residential areas in downtown Seoul, damaging to apartments, vehicles, and pedestrians under the mountain, causing damage to life and property [48; 49]. As shown in Figure 1, the debris flow of Mt. Umyeon occurred in the middle of the downtown area, different from the damage that occurred in the existing mountainous areas; the ripple effect was much greater than that in the past. This can be seen as a representative example, showing that even urban areas are not safe zones when a landslide, especially debris flow, occurs due to heavy rains [50; 51].
## 3 Materials and Methods  
### Vertical Electrical Sounding (VES) Basic Principle and Data Acquisition  
The geoelectrical method, using the vertical electrical sounding (VES) technique, consists of measuring the variations in apparent resistivity (\(\rho\)a) as a function of depth. In principle, the measurement protocol consists of injecting an electric current of intensity (I) through two current electrodes (A and B) and measuring the potential differences (\(\Delta\)V) created between the two receiving electrodes (M and N), called potential electrodes (Figure 2).  
According to [PERSON]'s law, the apparent resistivity is a function of \(\Delta\)V, I and the geometric coefficient (K). It is calculated by the following formula:  
\[\rho_{\text{a}}=\frac{\Delta\text{V}}{\text{I}}*\text{K}\text{ And K}=2\pi\left(\frac{1}{\frac{1}{\Delta\text{M}}+\frac{1}{\Delta\text{N}}+\frac{1}{ \Delta\text{N}}-\frac{1}{\Delta\text{N}}}\right)\]  
The curve \(\rho_{\text{a}}=\text{f}\left(\frac{\Delta\text{B}}{2}\right)\) is obtained by plotting the apparent resistivity values \(\rho_{\text{a}}\) against AB/2 (half spacing of the current electrodes, which can reach up to \(10^{1}\) km) in a bi-logarithmic scale.  
In the present study, the measurement of VES data at forty-seven stations was carried out and arranged according to seven profiles that were generally oriented NW-SE, perpendicular to the general direction of the flow of the volcanic lava. The coordinates of the measurement stations were taken by a Garmin MAP-64 s GPS. A Syscal Pro resistivity meter was used to acquire geoelectrical data. This automated instrument is powerful in DC electrical readings with the transmitter and receiver integrated in the same instrument. The measurements were made using the Schlumberger configuration. The distance between the current injection electrodes (AB) varied logarithmically from 6 to 1000 m for each measuring station. The position of the measuring stations was organized according to the profiles with a spacing of 3 to 5 km between them (Figure 3). The direction of the spread of the power cables was NE-SW, in the same direction as the regional fault system, to avoid polarity reversals created by the presence of faults or anomalous geological contacts. At the same  
Figure 2: (a) General scheme of a soil-resistivity measurement using the Schlumberger configuration with a four\(-\)electrode device (ABMN), (b) bi\(-\)logarithmic diagram for the representation of VES measurements.  
time, measurements of the piezometric level of the water table using a 200 m piezometric probe were acquired.
## 4 Experiment and Result Analysis
### Supervised Feature Selection
#### 4.2.2 Comparison Methods and Experimental Settings  
To verify the effectiveness of the proposed SFS-ID method with the synthetic data sets, we compared it with four competing SFS methods designed for use on incomplete data sets, including:  
* The SID method [13], a framework in which the objective function takes into account the uncertainty of instances due to the missing values and which solves the revised optimization problem using an EM algorithm;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Instance** & **Feature** & **Class** \\ \hline DLBCL & 141 & 661 & 3 \\ MNIST & 5000 & 780 & 10 \\ Splice & 1000 & 60 + 2000 & 2 \\ wpbc & 198 & 33 + 2000 & 2 \\ USPS & 9298 & 256 & 10 \\ Arcene & 200 & 10,000 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of data sets used.  
Figure 3: Convergence curve of the proposed UFS-ID approach.  
* KNN, an imputation feature selection framework which uses the KNN imputation method [12] on the IS and selects feature from the union of the IS and OS using RFS [29];
* Mean, an imputation feature selection framework, which uses the mean-value imputation method [15] on the IS, and which selects features from the union of the IS and OS using RFS;
* EM, an imputation feature selection framework, which uses the EM imputation method [16] on the IS and which selects feature from the union of the IS and OS using RFS;
* missForest [18], an iterative imputation framework based on a random forest on the IS, which selects features from the union of the IS and OS using RFS;
* DGM [20], a probabilistic framework based on the use of deep generative models for missing value imputation on the IS, and which selects feature from the union of the IS and OS using RFS; and
* MIWAE [21], an importance-weighted autoencoder framework, which maximizes a potentially tight lower bound of the log-likelihood on the IS and selects features from the union of the IS and OS using RFS.  
Unlike the synthetic data, in the experiments on real data sets, the optimal features are unknown as there might be some uncorrelated and weakly correlated features in incomplete data sets. We trained an SVM classifier on the features selected using different methods, and the classification error value was reported. For the SVM classifier, we adopted a Gaussian kernel and its width was set as the median distance between points in the instance. Ten cross-validations on data sets were are conducted in this experiment.  
We compared the results of our method with those of the following competing SFS methods on incomplete data sets: KNN + RFS, KNN + Simba, KNN + Relief, mean + RFS, mean + Simba, mean + Relief, EM + RFS, EM + Simba, EM + Relief, missForest + RFS, missForest + Simba, missForest + Relief, DGM + RFS, DGM + Simba, DGM + Relief, MIWAE + RFS, MIWAE + Simba, MIWAE + Relief, and SID.  
The parameters in the comparison schemes were consistent with the corresponding literature and the regularization parameters \(\gamma\) and \(\lambda\) in our proposed method were all tuned in the grid \(\{10^{-3},10^{-2},10^{-1},1,10,10^{2},10^{3}\}\).
## 5 Simulation
### Packet Interception Probability Evaluation  
We first fix the location of a source node that sends data to the sink. We first investigate the PIP for the source node under different numbers of compromised nodes. To each number of compromised nodes, we evaluate the average PIP for the source node. Figure 6(a) shows a plot of the PIP for the source node under different numbers of compromised nodes. It is obvious to see that the PIP increases when the number of compromised nodes becomes larger. When half of the sensor nodes are compromised nodes, most of the data packets are intercepted. We also compare the performance of the SPDC algorithm (including ADC and FSPC) with that of NRRP algorithm proposed in [3]. As can be seen in the figure, the performance of SPDC is better than NRRP with the same number of compromised nodes. When the number of compromised nodes is very small or large, the performance of the two algorithms is very close. However, SPDC behaves much better than NRRP with the number of compromised nodes falling into the extent (13, 18).  
Moreover, we also compare the performance of SPDC with H-SPREAD [5], SEEM [7] and INSENS [8]. We change the number of the sensor nodes to 100 and evaluate PIP under different numbers of compromised nodes (see Figure 7). Here H-SPREAD is implemented with T = 9, and M = 3. INSENS is implemented with two paths, and one base station. It can be seen from the figure that the over performance of SPDC is better than the other three approaches. SPDC behaves much better than the other approaches with the number of compromised nodes falling into the extent (20, 40).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Sensor Nodes & 50 \\ Threshold Value \(k\) & 5 \\ Drop Rate & 0.2 \\ Initial Counter Value \(\lambda\) & 3 \\ Number of Source Nodes in a Collection & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The major parameters for the simulation.  
Figure 6: The simulation results for the performance evaluation of SPDC.  
Figure 7: The performance of SPDC against H-SPREAD, SEEM and INSENS.
## 4 Results and Discussions
### Effect of Concrete Cover to Diameter (\(c/d_{b}\)) of Reinforcing Bar on Bond Strength  
Figure 8 shows the influence of concrete cover to diameter ratio (\(c/d_{b}\)) of reinforcement bar on the bond behavior of geopolymer concrete. The test data show two different trends when the \(c/d_{b}\) ratio is also raised from 3.25 to 5.75. Firstly, for GP2 and GP3, the bond strength increases with the increase of \(c/d_{b}\) ratio from 3.25 to 4.19. Then, the bond strength decreases with the rise of \(c/d_{b}\) ratio from 4.19 to 5.75. Secondly, for GP1, the trend is opposite to the other groups. The bond strength decreases when the \(c/d_{b}\) ratio increases from 3.25 to 4.19. Later, the bond strength slightly increases according to the increase of  
Figure 7: Relationship between compressive strength and bond strength with three types of reinforcing bars.  
\(c/d_{b}\) ratio. The behavior of GP2 and GP3 is consistent with the previous research [46]. In contrast, GP1 show the opposite behavior. It can be concluded that for specimens with the grade 20 MPa of compressive strength, the concrete cover to diameter ratio has an insignificant effect on the bond strength (with only a slight change observed).
## 3 Results and Discussion
### Net Energy Saving  
Figure 8 shows an energy reduction amount according to the four passive strategies proposed. Overall, in the four cities located in the coastal zone, the application of insulation on the walls and roof allows energy consumption to be reduced by 8%-15%; the introduction of PCM equipment reduced between 10%-18% of total energy per year, and finally, by combining insulation, shading, and PCM material, a reduction in the energy consumption is estimated by 15%-35%.These results also show that after 2050, passive strategies will be insufficient to improve the thermal performance of the building, due to the strong pressure of global warming. These results confirm the conclusion of [PERSON] and [PERSON] [39] in the case of San Diego city.  
Figure 7: Variation in electricity consumption in the four cities selected after applying passive strategies.  
The passive strategy allows savings of 30%-40% of energy consumption in warm tropical climates compared to the humid tropical climate. These findings show that the energy yield of the passive strategy technique is the most important in the warm tropical climate. The results found in this research confirm certain conclusions found by [PERSON] and [PERSON] [40], who found that PCM materials make it possible to improve the conditions of thermal comfort and to minimize the energy demand in buildings.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 3 Case Study
### Grutting Simulation Considering Fluid-Structure Interaction
#### 3.2.1 Analysis of Fluid Calculation
In summary, the filling of the secondary fractures is related to the fracture aperture and the intersection length between secondary fracture and primary fracture. For other fractures (3-1755, 3-699), the grout comes from secondary fractures and they are far from the grouting borehole, so the filling rate is low. In the actual project, the grout borehole spacing is 1.5 m, and thus the fractures that are not fully filled will be grouted by adjacent grouting boreholes. In addition, the real cement consumption is 244.74 kg, and the simulated cement consumption is 278.41 kg with an error of 13.76% compared to the real cement consumption. Therefore, considering the complexity of geological conditions, there is a good agreement between the simulated and actual values, and this approach can be used effectively to determine the grouting effect. The maximum radial diffusion length variation of the primary fractures is analyzed and compared in Figure 10. At the beginning of grouting, there is no significant difference in the variation of grout diffusion length with time in each fracture, after 10 s, the difference of diffusion rate in each fracture gradually appeared. The diffusion rate in fracture 1-952 is the lowest because its aperture is the smallest with 5.8 mm and the diffusion rate in fracture 1-1647 is the fastest because its aperture is the biggest with 12.9 mm. As for the other three fractures (1-1724, 1-872, 3-2881), they have similar fracture apertures, but their grout diffusion rate is different, this is due to their different dip angles, and the grout diffusion rate increases with the dip angle. In summary, fracture aperture and dip angle will  
Figure 9: Grouting diffusion process. affect grout diffusion rate and the grout diffusion rate is proportional to them. Besides, fracture 1-1647 with the biggest aperture but smaller dip angle, and the maximum grout diffusion rate indicates that the fracture aperture is the main effect factor.
## 4 Experiments and Results
### Analysis of Solutions Obtained with the Best Algorithm
This finding is significant, suggesting that two counterweights can be eliminated without compromising the optimization performance. A detailed justification about eliminating these counterweights can be found in the Appendix A. Furthermore, increasing the maximum allowable thickness (\(t_{n}\)) to 0.10 m enables smaller \(ShM\) and **ShF** values to be achieved. While a thickness of 0.10 m may not be practical for the current mechanism design, this observation provides valuable insight for future work, suggesting the exploration of alternative designs or materials. Figure 6: Boxplots of feature values for the best solutions obtained with SMS-EMOA. Figure 7: Feature values of the best solutions obtained with SMS-EMOA Based on \(ShM\) and **ShF**. Each line represents a solution. The bluer the line, the lower the value of **ShF**, while the redder the line, the higher the value of **ShF**. Conversely, the bluer the line, the higher the value of \(ShM\), and the redder the line, the lower the value of \(ShM\).
## 3 Results
### The Proposed Mapping Matrices  
Two mapping matrices were obtained using linear regression learning with SYNE-ANEW and NAWL-NAWL datasets. The procedure involved a 10-fold cross-validation with linear regression coefficients averaged over repetitions. Cross-validation is a validation technique for assessing how the results of a prediction model would generalize to an independent data set. It is a well-established alternative to the hand-made partitioning of a dataset to training and validation subsets for regression models [33]. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (training set), and validating the analysis on the other subset (validation set). Multiple rounds of partitioning are performed and the validation results are averaged over the rounds to evaluate the model. There are several methods of performing cross-validation [34]. In this procedure, a k-fold technique was used, with 10 folds. In 10-fold cross-validation, the original sample was partitioned randomly into 10 sub-samples of equal size. In a single fold, a single subsample was retained as the validation set, and the remaining nine sub-samples were used as training data. As a result, each of the 10 sub-samples was used exactly once as the validation data. The partitioning was randomized, and the cross-validation procedure was performed using Knime analytical tool.  
The cross-validation procedure was repeated 10 times. The matrix coefficients obtained via repetition were similar (with differences <0.03 for all matrix coefficients) and were averaged over the 10 resulting models.  
The two resulting matrices are provided as Equations (13) and (14) consecutively. The existing mapping matrix from literature was repeated with a column shuffle in Equation (15) for comparison.  
\[\begin{split}\text{PAD}[&\text{\emph{Happy, Sad, \quad Angry,\quad Scared,\emph{Disgusted,\emph{Surprised,\emph{1}}}}}]=\\ &=\left[\begin{array}{cccccc}0.46&-0.30&-0.29&-0.19&-0.14&0.24&0. 52\\ 0.07&-0.11&0.19&0.14&-0.08&0.15&0.53\\ 0.19&-0.18&-0.02&-0.10&-0.02&0.08&0.50\\ \end{array}\right]\end{split} \tag{13}\]  
\[\begin{split}\text{PA}[&\text{\emph{Happy, Sad, \quad Angry,\quad Scared,\emph{Disgusted,\emph{Surprised,\emph{1}}}}}]=\\ &=\left[\begin{array}{cccccc}0.54&-0.14&-0.21&-0.06&-0.16&0.00&0.46\\ 0.50&0.06&0.37&0.36&0.12&0.00&-0.01\\ \end{array}\right]\end{split} \tag{14}\]\[\text{PAD}[Happy,\,\,\text{Sad},\quad Angry,\,\,\,\text{S\ared},\,\,\text{Disgusted},\,\,Surprised,\,\,1]=\] \[=\left[\begin{array}{cccccccc}0.40&-0.40&-0.51&-0.64&-0.40&0.00&0. 00\\ 0.20&-0.20&0.59&0.60&0.20&0.00&0.00\\ 0.15&-0.50&0.25&-0.43&0.10&0.00&0.00\\ \end{array}\right] \tag{15}\]