esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Secondary Earthquake Environmental Effects
### Hydrological Anomalies  
Hydrological anomalies caused by the 2020 earthquake included changes in the flow rate in water springs and the reappearance of springs with increased water flow (Figure 18). Such an anomaly was reported north of Ampelos, in north Samos (Figure 18). Namely, increased water discharge at the Moutsara (or Karagatsa) spring was reported after the earthquake. This spring is located north of Ampelos village, at a distance of \(\sim\)13 km southeast of the earthquake's epicenter and a few meters north of the Ampelos surface ruptures described earlier (Figure 18).  
Reappearance of springs was observed at the Kiourka site of the Marathokampos area and at a site over Pythagoras Cave in the eastern part of Mt. Kerketeas (Figures 18 and 26). Both sites are located in the western part of the island. These water springs used to yield water after the first autumn rains. However, the springs remained dry during the autumn of 2020, but returned with increased water flow the same day of the earthquake. Based on eyewitness and footage documentation, the increased water discharge resulted in a temporary disruption of a local pedestrian crossing (Figure 26). Moreover, empty concrete water reservoirs in the Kiourka area were found to be full of water a few hours after the earthquake (Figure 26). A hydrological anomaly took place in the area of Kazania of Mytilinii. Water reappeared in springs and the discharge was increased for days after the mainshock.  
Figure 25: Ground cracks in the (**a**) Malagari and (**b**) Petalides coastal areas. They were observed on artificial deposits used during works for the widening of the coastal roads. Red arrows indicate the direction of slide towards the sea located in the left part of the images.
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
###### Abstract  
Recently, Alzheimer's disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer's Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback's quasi-refraction position for further improving the optimal position's quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.  
Alzheimer's disease; capsule network; gorilla troops optimizer; hybrid feature extraction; superpixel-clustering algorithm +
Footnote †: journal: Applied  
0000-0001-998-2270][PERSON]  
1 Department of Electronics and Communication Engineering, St. Peter's Institute of Higher Education and Research, Chennai 600077, India; [EMAIL_ADDRESS]  
2 Department of Electronics and Communication Engineering, Dayananda Sagar University, Bengaluru 560068, India  
3 Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (A), Visakhapatnam 530048, India; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]
## 2 Basic Mechanisms in Plasmon-Mediated Heterostructure
### Process of Photocatalysis
#### 2.2.2 Carriers Separation  
As described in Section 2.1, interfacial Schottky barrier is built for the efficient HCs separation, to reduce the probability of are combination of HCs. The behaviors of HCs that reach the interface are as follows [42]: (1) blocked by Schottky barriers, (2) overcoming the Schottky barrier and (3) the tunneling effects. In most works, the third approach, electron tunneling effects, is often neglected for the extreme low possibility of tunneling resulting from a broad charge depletion layer at the interface. The interfacial doping of metal ions is considered an effective way for enhancing the electron tunneling possibility [46; 47]. For example, improving the concentrations of doped Ti\({}^{4+}\) at the interface of Au/TiO\({}_{2}\) can create a narrower depletion layer due to the neutralization reaction with negative charges of Au [47]. In such cases, the electron tunneling effects are remarkably enhanced, and the electrons with a lower energy can pass through without considering the influence of the Schottky barrier.  
In addition, the life of HCs is extremely short (hundreds of femtoseconds) and is finally dissipated into heat over a certain period of time (the scale of a picosecond) [27; 31]. Their fast injection is of equal important for an efficient utilization. Up to now, there have been two types of mechanisms for HCs separations: one is the classical mechanism that is used for most photocatalysis, a plasmon induced hot-electron transfer (PHET) (Figure 2c) [48; 49; 50]. In such a system, the behaviors of HCs experience a complex process, including the decay of SPs to generate HCs, transportation, interfacial separation and injection. Within this process, in order to efficiently utilize the HCs before they release their energy to the surroundings, it is crucial to elaborately engineer both the metal and semiconductor. It has been reported that the utilization efficiency of HCs (\(QY\)) can be calculated by following [PERSON]'s equation [51; 52]:  
\[QY(\omega)=\left(\hbar\omega-\varphi_{B}\right)^{2}/\left(4E_{F}\hbar\omega\right) \tag{2}\]  
where \(\hbar\omega\) is the energy of the excitation photons. From this, the improvement of \(QY\) can be carried out in two aspects: (1) the maximum absorption of solar energy and (2) the reduction of the interfacial barrier. In other words, the \(QY\) is related to the efficiency of both the generation and extraction of HCs.  
Moreover, an alternative direct electron transfer mechanism was proposed by [PERSON] and colleagues [53], named the plasmon induced metal-to-semiconductor interfacial charge transfer transition (PICTT) (Figure 2d). In this process, the electrons are excited directly to the conduction of the semiconductor with a hole left in the metal, led by strong interdomain coupling effects of the electronic levels. Compared to PHET, the transfer process of HCs is shortened and simplified, which is thought to be much more effective for the utilization of HCs, but the internal physical mechanisms still need to be revealed with deep insight.
## 2 Materials and Methods
### Geoaccumulation Index  
The geoaccumulation index, \(I_{geo}\), estimates the degree of soil contamination by heavy metals, and is calculated on the basis of the concentrations of the metal present in the soil, the geochemical background value of this metal, and a coefficient of 1.5 to take possible deviations into account [33;34]:  
\begin{table}
\begin{tabular}{c c c c} \hline \(E_{r}\) & **Levels** & \(R_{I}\) & **Levels** \\ \hline Er \(<\) 40 & Low & RI \(<\) 150 & Low \\
40 \(\leq\) Er \(<\) 80 & Moderate & 150 \(\leq\) RI \(<\) 300 & Moderate \\
80 \(\leq\) Er \(<\) 160 & Considerable & 300 \(\leq\) RI \(<\) 600 & Considerable \\
160 \(\leq\) Er \(<\) 320 & High & RI \(\geq\) 600 & High \\ Er \(\geq\) 320 & Very high & & \\ \hline \end{tabular} \(R_{I}\) is the comprehensive potential ecological risk index; \(E_{r}\) is the individual potential ecological risk index of heavy metal.  
\end{table}
Table 1: Interpretation of the value of the potential environmental risk indicator.  
\[I_{g\alpha}=log_{2}(C_{n}/1.5\times Bn) \tag{4}\]  
where \(I_{g\alpha}\) is the geoaccumulation index of the HM, \(C_{n}\) is the concentration of the HM in the soil, \(B_{n}\) is the geochemical background value of the HM, and 1.5 is the deviation coefficient. The level of pollution is divided into 7 classes, ranging from no pollution to extremely high pollution:  
\(I_{g\alpha}\leq 0\), uncontaminated (Class 0);  
\(0<I_{g\alpha}\leq 1\), uncontaminated to moderately contaminated (Class 1);  
\(1<I_{g\alpha}\leq 2\), moderately contaminated (Class 2);  
\(2<I_{g\alpha}\leq 3\), moderately to heavily contaminated (Class 3);  
\(3<I_{g\alpha}\leq 4\), heavily contaminated (Class 4);  
\(4<I_{g\alpha}\leq 5\), heavily to extremely contaminated (Class 5);  
\(I_{g\alpha}>5\), extremely contaminated (Class 6).  
The bioconcentration factor was used to determine the phytoremediation properties of plants. According to this, if the index value of the factor is below 1, the plant is tolerant of HMs, and if it is above 1, it is a hyperaccumulator. The bioconcentration factor denotes the ability of the parts of the plant to elementally accumulate pollutants from the environment [35]:  
\[BCF=C_{plant}/C_{soil} \tag{5}\]  
where \(BCF\) is the bioconcentration factor, \(C_{plant}\) is the concentration of heavy metals present in the sweet potato, mg\(\cdot\)kg\({}^{-1}\), and \(C_{soil}\) is the concentration of heavy metals in the soil, mg\(\cdot\)kg\({}^{-1}\).  
The translocation factor (TF) is the value of the metal concentration in the aerial part of the plant in relation to the concentration in the root of the plant. This value is used to estimate the ability of plants to transfer HMs to their aerial parts [35]:  
\[TF=C_{ aerial}/C_{roots} \tag{6}\]
## 5 Conclusions and Future Work
### Future Work  
The results presented in this study clearly demonstrate the superiority of the IGTO-VMD-PE-IGTO-LSTM model in rolling bearing fault diagnosis. The model outperforms other benchmark models in both accuracy and stability. The relatively low standard deviation and narrow confidence intervals further confirm the robustness of the proposed model, indicating its potential for real-world applications where stability and reliability are crucial.  
However, the model's performance has been primarily validated in the CWRU and Jiangnan University datasets, which may introduce dataset dependency. To enhance generalizability, further validation of diverse datasets from different domains is necessary. Second, the scalability of the model to real-time systems has not been fully explored. Future work should focus on optimizing the model for real-time applications, ensuring its feasibility in systems with strict time constraints. Lastly, the model's sensitivity to parameter initialization could impact performance, particularly during the optimization process. While predefined settings based on prior research were used, further research could explore adaptive initialization techniques or robust hyperparameter tuning methods to improve stability and reliability.  
In conclusion, the IGTO-VMD-PE-IGTO-LSTM model represents a significant advancement in rolling bearing fault diagnosis, offering high accuracy, stability, and adaptability. With further optimization in terms of computational efficiency and real-time implementation, the model could become a valuable tool for industrial applications in predictive maintenance and fault detection.  
Conceptualization, F.Y. and J.Q.; methodology, X.L.; software, X.L.; validation, B.A. and N.D.; formal analysis, F.Y.; investigation, F.Y.; resources, J.Q.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, X.L.; supervision, F.Y.; project administration, J.Q.; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Program of National Natural Science Foundation of (NSFC) under Grant 52269020 and Yunnan Province Unveils Marsh Science and Technology Projects under Grant 2202204 BW050001.
## 4 Results  
### Sea Fog Identification  
#### 4.1.1 Identification Precision  
The MLP sea fog identification method was applied using Python programming, and the algorithm was verified using the CALIPSO-VFM classification points; the results it produced were then compared with those obtained with the NDSI threshold method proposed by [11]. The accuracy evaluation indices used in this paper are based on the work of [7], including the probability of detection (POD), probability of false detection (PFD), probability of missing detection (PMD), and equitable threat score (ETS). The calculation formulas of the four indicators are respectively shown in Equations (21)-(24):  
\[\mathrm{POD}=\frac{H}{H+M}, \tag{21}\]  
\[\mathrm{PFD}=\frac{F}{F+C}, \tag{22}\]  
\[\mathrm{PMD}=\frac{M}{H+M}, \tag{23}\]  
\[\mathrm{ETS}=\frac{H-(H+F)(H+M)/N}{H+M+F-(H+F)(H+M)/N} \tag{24}\]  
where \(H\) represents the number of verification data points related to sea fog, \(C\) represents the number of verification data points that are all non-sea fog related, \(F\) represents the number of non-sea-fog data points that the algorithm incorrectly identifies as sea fog,and \(M\) represents that the verification data that are sea fog, but the algorithm incorrectly identifies as non-sea-fog. \(N\) represents the total number of points.  
The confusion matrix of the results from the two algorithms (i.e., the MLP sea fog identification algorithm and NDSI threshold method) is shown in Table 5, and the precision comparison results are shown in Table 6.  
It can be seen from the results that the POD of both MLP sea fog identification algorithm and NDSI threshold method are more than 80%; indeed, the NDSI threshold algorithm identifies eight more sea fog points relative to the MLP method. However, the NDSI threshold algorithm identifies more non-sea-fog points as sea fog, resulting in lower accuracy indices tested by the algorithm. These non-sea-fog points are mainly low clouds, indicating that the NDSI threshold algorithm is not able to effectively distinguish between sea fog and low clouds. The MLP sea fog classification method is relatively stable for the various accuracy indices. Although the POD value of the MLP method is smaller than that of the NDSI threshold algorithm, the former algorithm is more able to distinguish between sea fog and non-sea fog points than the NDSI threshold algorithm, with an ETS of 59.22%. Therefore, the MLP sea fog classification method constructed in this paper performs better than the NDSI threshold algorithm on the whole.
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
###### Abstract  
The subgrade slope, when exposed to the natural environment for a long time, is easily affected by rainfall scouring, which leads to a large loss of filling materials and soil sliding, affecting the stability of the subgrade slope. In this paper, the model test of a high-speed railway subgrade slope under rainfall scouring was conducted to quantitatively study the occurrence and development process of subgrade slope erosion. Compared with the model test results and the theoretical results, the incipient flow velocity formula of coarse-grained soil was verified. Then, the curve of rainfall intensity varied with the incipient particle size under different rainfall intensities, slope gradients and soil particle grading conditions was analyzed. Results show that during rainfall scouring, the smaller the particle size, the earlier the scouring erosion occurs. In addition, the soil particles on the slope bottom were scoured more severely than those on the slope upper. With the increase in rainfall intensity, slope gradient, and the change in soil particle gradation (removing the minimum particle size), the incipient flow velocity of soil particles on the slope will be reduced. The curve of the rainfall intensity varied with the incipient particle size, which plays an early warning role in the analysis of slope erosion stability and reflects the particle size range of the scouring erosion incipient on the slope surface under different rainfall intensities, providing the basis for the analysis of slope erosion stability and the slope protection design of the high-speed railway subgrade slope.  
subgrade slope; scouring erosion; model test; incipient velocity; incipient particle size Article
## 1 Introduction  
In recent years, numerical methods have been used extensively to simulate the movement of contaminants to groundwater [1, 2]. Applications of groundwater pollution problems as well as inverse problems require accurate input data and parameters for solving the convection-diffusion equation [3]. However, the parameters are sometimes difficult to measure directly from the physical point of view. Meanwhile, measurement error and measurement uncertainty are often encountered. The inverse problems of the convection-diffusion equation involve the findings of the missing geometric shape, unknown physical parameters, and other conditions from historical observations [4, 5].  
Several numerical approaches have been successfully applied in the inverse problems for groundwater relative problems. The methods such as the model reduction technique, the differential evolution algorithm, and the collocation method have been used [6, 7, 8]. Identifying sources requires the solution of an inverse problem since this information has to be generally inferred from sparsely available measurements [9].  
Inverse problems are often ill-posed, and the measurement uncertainty may enlarge numerical error exponentially in the solution [10, 11]. To solve the ill-posed inverse problems, the meshless method, such as the Trefftz method and the radial basis function (RBF) method, may be a better alternative due to its meshless and high accuracy [12, 13, 14, 15]. In order to model the backward inverse heat conduction problem, a meshless method based on the Trefftz formulation was presented [16]. Recently, the RBF method has become a promising numerical approach for solving direct problems [17; 18; 19; 20]. Compared to the conventional RBF collocation method, the RBF method with the polyharmonic polynomials can obtain highly accurate solutions [21; 22]. Accordingly, the attempt of using the collocation meshless method for solving inverse problems with cavities was made in this study.  
The polyharmonic polynomials (PPs) are a series of polyharmonic RBFs, including even radial terms and natural logarithms [23]. Several inverse problems of convection-diffusion equation for the multiply-connected domains containing a finite number of cavities were investigated. Numerical solutions are approximated using polyharmonic RBFs instead of using the certain term of the polyharmonic RBF in the conventional polyharmonic spline at each source point. Additionally, highly accurate solutions can be obtained with the increase in the terms of polyharmonic polynomials. Additionally, examples in simply and multiply connected domains were carried out. A brief outline of the article is as follows: the description of the governing equation and boundary conditions are defined in Section 2; the mathematical formulation of the proposed method is introduced in Section 3; the convergence analysis is described in Section 4; several numerical examples are provided in Section 5; the disucssion is described in Section 6; and finally, conclusions are drawn in Section 7.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 3 Modeling of Wireless Sensor Networks in Farms
### Anycast Transmission Cost  
If the probability of successful transmission between node \(i\) and node \(j\) is \(pe_{ij}\), the probability that the data sent by node \(i\) is received by at least one node in the candidate set \(C_{i}\) is as follows:  
\[pc_{i}=1-\prod\limits_{j\in C_{i}}(1-pe_{ij}) \tag{4}\]  
According to the importance model, we define the transmission cost as shown below. The more important a node is to maintain the network transmission performance, the higher the transmission cost is:  
\[cost_{i}=\frac{\lambda_{i}+1}{\lambda_{\text{max}}} \tag{5}\]  
where \(\lambda_{i}\) represents the algebraic connectivity of node \(i\). Additionally, \(\lambda_{\text{max}}\) represents the maximum algebraic connectivity in the current network.  
One anycast transmission from node \(i\) to the candidate set \(C_{i}\) requires \(T_{i}\) broadcasts. \(T_{i}\) is the expected transmission count. The transmission cost of node \(i\) broadcasting \(T_{i}\) times is as follows:  
\[Ct_{iC_{i}}=\frac{1}{T_{i}}\times cost_{i}=\frac{1}{(1-\prod\limits_{j\in C_{ i}}(1-pe_{ij}))}\times\frac{(\lambda_{i}+1)}{\lambda_{\text{max}}} \tag{6}\]
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Summary of the Statistical Model Performance
#### 6.5.3 Performance on Malicious Traffic Datasets  
For malicious datasets, the model exhibited strong attack detection capabilities, although variation in classification rates was observed:  
* MiTM attacks: 63.54% of malicious packets were detected, with 30.21% accurately linked to MiTM signatures.
* Brute force attacks: 90.42% of malicious packets were detected, with 9.56% specifically associated with brute force signatures.
* False negatives: A small portion of malicious packets in each scenario was misclassified as normal traffic, suggesting scope for refinement in edge cases.