esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
###### Abstract  
As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method's adaptability across various regions.  
lindslides; susceptibility assessment; information value model; geographical similarity; geographical-optimal-similarity model +
Footnote †: journal: Applied sciences  
Article
## 1 Introduction
After extracting features, the approach uses a Bayesian network to learn the feature property and classify the faults. Classification research, which is learning the patterns of the extracted features through image processing, is actively carried out to diagnose skin cancer not only in manufacturing but also in dermatology [14]. It is indispensable to extract meaningful features that can explain the properties of shape and color in order to effectively detect a melanoma [15]. [PERSON] suggested a method to find meaningful features by combining three characteristics of shape, texture, and color [16]. Based on the concept of a learning property, we propose a new non-referential method for fault detection by extracting features based on an image-processing method and learning the fault information using random forests. The proposed method utilizes features obtained through speeded-up robust features (SURF) to describe the fault information. Therefore, the method can detect a fault quickly and accurately without a reference image and without being affected by environmental changes, such as the size, rotation, and location of the PCB. The proposed method first extracts robust features using an image-processing technique and learns the fault pattern using an efficient classification technique utilizing high-dimensional data. We then calculate the probability and draw a weighted kernel density estimation (WKDE) map weighted by the probability and identify whether a fault has occurred. The remainder of this paper is organized as follows: In Section 2, the background of the proposed method is briefly reviewed. Section 3 describes the procedure of the proposed inspection algorithm. Section 4 presents the experimental results through a comparison of the receiver operating characteristic (ROC) curves. Finally, some concluding remarks are given in Section 5.
## 3 Various Damage Distribution Functions and Solution of Critical Parameters
### Various Damage Distribution Functions
#### 3.1.1 Weibull Distribution  
The Weibull distribution function has been widely used to describe the damage variable of rock [24; 29]. The Weibull-PDF is defined as follows:  
\[P(u)=\frac{m}{u_{0}}(\frac{u-u_{y}}{u_{0}})^{m-1}\exp[-(\frac{u-u_{y}}{u_{0}} )^{m}],(u\geq u_{y}) \tag{7}\]  
where \(m\) and \(u_{0}\) are the parameters of Weibull distribution function, \(u_{y}\) is the displacement of the yield point, and the Weibull-PDF curve is shown in Figure 5 when \(u_{y}\) is 0.  
Figure 5: Microelement strength distributions of the soil material are obtained when both parameters of the statistical functions are 1. The black curve represents microelement strength distribution; the blue shaded area represents the damage variable.  
The damage variable based on Weibull-PDF can be obtained by integral of Equation (7) as follows:  
\[D=\int_{0}^{u}\!P(u)du=1-\exp[-(\frac{u-u_{y}}{u_{0}})^{m}],(u\geq u_{y}) \tag{8}\]
## 4 Identification and Summary of Main Contributions
This paper has many advantages, such as being a reference in terms of predicting complexity in airspace using machine learning tools, but it has the main disadvantage of being based on the human component for the labeling of variables, which can influence the quality of the machine learning models. Many of the models that have been developed so far are based on human subjectivity [10], although artificial intelligence tools have changed this trend in recent years. After analysing the five selected references, several commonalities can be drawn. Table 6 shows the advantages and limitations that have been obtained as common points after reading and analysing the main references. This provides recommendations for the creation of future indicators, as well as the conclusion of the bibliometric analysis. Once the contributions have been analysed and conclusions such as the general advantages and limitations that can be extrapolated from them have been drawn, the main objective of the paper is fulfilled, and the questions can be answered:  
What are the main solutions proposed by academia to the complexity problem? What are the weaknesses and strengths of the main complexity indicators? \begin{table}
\begin{tabular}{c c} \hline \hline
**Benefits** & **Limitations** \\ \hline Complexity models are developed based on & Trajectory-based models do not take into account other aspects, \\ aircraft trajectories & such as regulations \\ Models classifying complexity according to the & This classification is usually based on expert opinion, although artificial \\ scenario as low, medium, or high & intelligence is increasingly being taken into account \\ Newer indicators are beginning to try to make & The most commonly used indicators do not take into account the uncertainty \\ predictions of complexity & of aircraft trajectory, although new ones are starting to take it into account \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Benefits and limitations of the above models. The answer is as follows: through the above analyses, five main contributions have been identified, as shown in Table 5. After an analysis of these, the main strengths and weaknesses of the main contributions are summarised in Table 6.
## 3 FRA Applications on Induction Motors  
### Measurement Setup  
The FRA measurement is conducted on a three-phase 5.5 HP at 4 kW, 415 V, and 50 Hz IM as shown in the laboratory setup in Figure 5. Various SC faults are emulated on the stator winding including turn-to-turn SC, coil-to-coil SC, and phase-to-phase [29]. On the other hand, there is a ground fault which can be developed at phase, coil, turn and neutral points. In this study, the ground short circuit fault is artificially developed at four different points. A wire is connected to the ground from one turn, one full coil, or one full phase or the neutral point as shown in Figure 5 right inset. Figure 6 shows the mode of these faults along with the OC fault. Figure 7 shows the FRA measurement connection for star and delta stator windings. It is to be noted that in the case of star connection, the FRA analyzer can be connected between the two phases (U-V) or between one phase and the neutral point (U-N).  
Figure 5: Laboratory setup for IM FRA measurement and faults development (**a**) at fault development (**b**) during the FRA measurement.  
Figure 6: Schematic diagram for the emulated SC and OC faults on the stator windings.  
Like power transformers, the mechanisms of short circuit faults are due to insulation degradation. The number of operating turns reduces and winding conductor resistance increases.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 3 Methodology
### Model Architecture  
The core of the proposed architecture is based on ResNet-50, a variant of Residual Network (ResNet) consisting of multiple residual blocks interconnected by shortcut (skip) connections [20]. Each residual block contains convolutional layers followed by batch normalization and ReLU activation functions. Skip connections enable efficient gradient flow, allowing deeper networks to learn complex hierarchical image representations while addressing the vanishing gradient problem prevalent in traditional CNN architectures [16].  
To further enhance the discriminative capability of ResNet-50 in identifying subtle features inherent to synthetic images, an SE attention mechanism is integrated into each residual block. This integration produces a residual block with channel-wise attention, known as the SE-ResNet block.
# Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph
## 3 Experimental Results
### Results on a Simple Network  
Figure 4 shows a simple railway network with 10 blocks (denoted as 1, 2,..., 10) and three trains (denoted as A, B, and C). Train A enters from Block 1 and exits from Block 10, passing through Blocks 2, 3, 5, 6, and 8. Train B travels from Block 9 to Block 1, passing through Blocks 8, 6, 5, 4, and 2. Similarly, Train C enters from Block 7 and exits to Block 1, passing through Blocks 6, 5, 4, and 2. We set the travel time of each block as 300 s, except Blocks 3 and 4, which were platforms in the station that each required 420 s. The safety headway between two consecutive trains on each block was set to 120 s, including switching and signaling time. Therefore, the weight of all alternative arcs was 120 s.  
All trains were scheduled to arrive at their entry blocks at 07:00:00. To train a Q-network, the entry delay time of each train was randomly generated from a uniform distribution [0, \(U\)] in seconds and added to the planned times for each episode. This enables the trained Q-network to accommodate the various train rescheduling problems in the training phase. In Figure 5, we present the convergence performance for up to 48,000 episodes with \(U=600\) in the training phase. In the plots shown in Figure 5, the x and y axes indicate the cumulative number of episodes and the si  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline The number of hidden layers & 4 \\ The numbers of nodes in each hidden layer & 1024, 512, 256, 128 \\ Decay factor of \(\epsilon\) per episode & 0.9999 \\ Replay buffer size & 20,000,000 \\ Minibatch size & 256 \\ Discount factor & 0.9 \\ Target \(Q\)-network update frequency & 16 \\ \(M\) & 99,999 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for the DQN agent.  
Figure 4: Simple network.  
the previous 100 reward values, respectively. As shown in Figure 5, fluctuations caused by the \(e\)-greedy policy were observed.  
We performed testing on the generated instances with respect to the maximum entry delay time (\(U=\{300,600,900,1200\}\)). We generated 100 instances for each \(U\), and thus, a total 400 instances were tested. Table 3 summarizes the performance of the DQN with respect to three dimensions: the number of instances finding an optimal solution, the average CPU runtime (in seconds), and the average GAP value obtained by Equation (10). Column 1 indicates the category of instances; Columns 2-3 contain the performance of the exact method; Columns 4-6 report the performance of the DQN. The exact method finds the optimal solutions for all instances in 0.005 s. Tested on instances with \(U=300\), the DQN also finds the optimal solutions for all instances, although it takes more time to compute the solutions. The number of times that DQN finds the optimal solution decreases to 93, 80, and 71 when \(U=600\), \(U=900\), and \(U=1200\), respectively. However, the GAP values are less than 0.25% on average, and the runtime of the DQN does not vary significantly.
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 3 System Overview
### Device Installation Process  
When a new smart device enters the environment, the DIP automatically integrates it into the orchestrator following a well-defined sequence (see Figure 2). This process spans from the initial device announcement to the activation of its service within the OSGi framework, ensuring minimal manual intervention. The high-level steps include:  
1. Device Announcement: Upon installation, the device broadcasts a registration message (for instance, via MQTT) indicating its identity and capabilities.
2. Registration Processing: The orchestrator records the device's information in the system's databases, mapping device attributes, capabilities, and configuration details for use in future automation scenarios.
3. Service Activation: After registration, the orchestrator triggers the installation of the device's service bundle into the OSGi framework. Once activated, the device's functionality is exposed as a service, ready for orchestration within the smart home.  
Figure 1: System architecture of the orchestrator; representing the integration of MQTT, databases, AI-driven code generation, and OSGi-based deployment for dynamic smart home automation.  
This sequence demonstrates how the system leverages MQTT for event-driven integration alongside OSGi for dynamic bundle deployment. Errors during installation, such as malformed device announcements, are logged, and the orchestrator gracefully handles partial failures by isolating problematic bundles, preserving the stability of the overall environment.
## 3 Results
### Reconstruction of Time Series for the Whole Period of SD Measurements  
The linear relationships obtained between CS manual measurements and automatic instruments (GI) at Larnaca and Pafos during the period of parallel measurements (2009-2012) are shown in Figure 9. The regression equations can be used to estimate daily CS sunshine duration and extend therefore the time series from the starting year until recently. In this way we could examine the trend of sunshine recorded at these sites. The trend lines  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **Period** & **Regression Variables** & **a** & **b** & **R\({}^{2}\)** \\ \hline Larnaca & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)CS & 0.245 & 0.524 & 0.901 \\ Larnaca & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)GI & 0.267 & 0.495 & 0.906 \\ Larnaca & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)CSD3\(\_\)Men & 0.298 & 0.468 & 0.810 \\ Larnaca & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)CSD3\(\_\)Pent & 0.244 & 0.508 & 0.728 \\ \hline Pafos & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)CS & 0.227 & 0.533 & 0.895 \\ Pafos & 2009–2012 & K\({}_{\rm T}\) vs. \(\sigma\)GI & 0.258 & 0.518 & 0.907 \\ \hline Athalassa & 2012–2015 & K\({}_{\rm T}\) vs. \(\sigma\)CS & 0.242 & 0.504 & 0.877 \\ Athalassa & 2012–2015 & K\({}_{\rm T}\) vs. \(\sigma\)Pr & 0.266 & 0.496 & 0.895 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Annual empirical linear regression coefficients for the selected stations and different periods. K\({}_{\rm T}\) is the clearness index and \(\sigma\) is the relative sunshine at the given location.  
Figure 12: Linear regression between the daily clearness index (K\({}_{\rm T}\)) and daily relative sunshine (\(\sigma\)CS) for Larnaca during the period 2009–2012.  
Figure 13: Quadratic relationship between the daily direct horizontal irradiation (B\({}_{\rm d}\), MJ/m\({}^{2}\)) and the daily SD obtained from CS and pyrheliometer (Pr) in h/d at Athalassa during the period 2012–2015.  
of the selected sites are presented in Figure 14. In the case of Larnaca the slope is positive, and it is statistically significantly different from zero at the 5% significant level. In contrast, the slopes at Pafos and Athalassa are negative, but they are not statistically significant from zero at the 5% significant level.  
Figure 14: Trend lines of the annual sunshine duration totals during the whole period of measurements at the three synoptic stations. The reference line of 2011 indicates the end of CS measurements at Larnaca and Pafos, while at Athalassathe CS was recorded until 2015.
## 1 Introduction  
The integration of distributed generation requires the development of new concepts for active grid operation, where microgrids are the most promising one [1]. Microgrids can operate in grid connected as well as isolated modes [2; 3]. In isolated mode, the active power balance to maintain the grid frequency has become one of the main challenges. The integration of large amount of photovoltaic (PV) generation can further stress the power balance due to the lack of inertia and the fast power variations of the resource. One possible solution to avoid frequency deviations produced by PV power generation is its curtailment [4]. Frequency deviations can also be limited by increasing the grid inertia, which can be achieved by connecting rotating machines [5]. The main drawback is that these solutions have and adverse effect on the operation cost.  
To solve the power balance problems while minimizing the operation cost, a hierarchical control architecture is commonly used [6; 7; 8; 9]. The primary control layer stabilizes the voltage and frequency deviations due to power imbalances by adjusting the active and reactive power references in a time frame of milliseconds. Then, the secondary control is responsible for recovering the voltage and frequency to their reference values. Commonly, it is done by using PI based closed loop controllers in a slower time scale than the primary control response time. Finally, The tertiary control determines the power references to perform the optimal operation of the microgrid.
## 6 The Multilayer Plate Concept in the Numerical Calculus by FEM of FGPs
### Calculus of the Free Vibration Frequencies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(k\)} & \multirow{2}{*}{**Freq. No.**} & \multicolumn{6}{c}{**Multilayer Plate Concept**} \\ \cline{3-8}  & & **Analytical Solution** & **FGP Model I** & **Er. [\%]** & **FGP Model II** & **Er. [\%]** \\ \hline \multirow{4}{*}{0.05} & 1 & 7574.12 & 7588.80 & 0.19 & 7468.72 & \(-\)1.39 \\  & 2 & 15,730.93 & 15,501.50 & \(-\)1.46 & 15,335.30 & \(-\)2.51 \\  & 3 & 25,827.78 & 25,014.80 & \(-\)3.15 & 24,780.60 & \(-\)4.05 \\ \hline \multirow{4}{*}{0.50} & 1 & 6580.30 & 6613.50 & 0.50 & 6491.30 & \(-\)1.35 \\  & 2 & 13,666.84 & 13,523.00 & \(-\)1.05 & 13,332.00 & \(-\)2.45 \\  & 3 & 22,438.86 & 21,854.00 & \(-\)2.61 & 21,549.00 & \(-\)3.97 \\ \hline \multirow{4}{*}{1.00} & 1 & 5940.63 & 5980.30 & 0.67 & 5858.50 & \(-\)1.38 \\  & 2 & 12,338.27 & 12,230.00 & \(-\)0.88 & 12,024.00 & \(-\)2.55 \\  & 3 & 20,257.55 & 19,772.00 & \(-\)2.40 & 19,422.00 & \(-\)4.12 \\ \hline \multirow{4}{*}{2.00} & 1 & 5414.63 & 5445.40 & 0.57 & 5354.50 & \(-\)1.11 \\  & 2 & 11,250.51 & 11,123.00 & \(-\)1.13 & 10,931.00 & \(-\)2.84 \\  & 3 & 18,471.62 & 17,960.00 & \(-\)2.77 & 17,626.00 & \(-\)4.58 \\ \hline \multirow{4}{*}{4.00} & 1 & 5188.45 & 5186.70 & \(-\)0.03 & 5101.60 & \(-\)1.67 \\  & 2 & 10,780.55 & 10,564.00 & \(-\)2.01 & 10,436.00 & \(-\)3.20 \\  & 3 & 17,700.01 & 16,998.00 & \(-\)3.97 & 16,796.00 & \(-\)5.11 \\ \hline \multirow{4}{*}{6.00} & 1 & 5117.49 & 5098.00 & \(-\)0.38 & 5028.20 & \(-\)1.74 \\  & 2 & 10,633.12 & 10,368.00 & \(-\)2.49 & 10,281.00 & \(-\)3.31 \\  & 3 & 17,457.95 & 16,651.00 & \(-\)4.62 & 16,537.00 & \(-\)5.28 \\ \hline \multirow{4}{*}{8.00} & 1 & 5053.26 & 5026.10 & \(-\)0.54 & 4964.40 & \(-\)1.76 \\  & 2 & 10,499.65 & 10,217.00 & \(-\)2.69 & 10,150.00 & \(-\)3.33 \\ \cline{1-1}  & 3 & 17,238.82 & 16,395.00 & \(-\)4.89 & 16,327.00 & \(-\)5.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Some results regarding natural frequencies [Hz], by FEM calculated. Figure 23: Compared free vibration frequencies of analyzed FGP. Figure 26: The shape of the third (**a**) frequency (\(n=2\); \(s=0\)), Model_I (**a**) and Model_II (**b**). Figure 24: The shape of the first frequency (\(n=0\); \(s=0\)), Model_I (**a**) and Model_II (**b**) frequency. Figure 25: The shape of the second frequency (\(n=1\); \(s=0\)), Model_I (**a**) and Model_II (**b**). The errors, shown in Table 12, are graphically presented in Figure 27a for the first frequency and in Figure 27b for the second frequency. It can be immediately seen that Model_I lead to results closer to those calculated analytically.
## 3 Results and Discussion  
### XRD Studies  
Figure 1 shows the XRD patterns of ZnO-NPs, which were biosynthesized using _C.__chinese_ fruit extract. The diffractions identified at 20 values of 31.70\({}^{\circ}\), 34.37\({}^{\circ}\), 36.23\({}^{\circ}\), 47.50\({}^{\circ}\), 56.60\({}^{\circ}\), 62.98\({}^{\circ}\), 66.26\({}^{\circ}\), 67.93\({}^{\circ}\), 69.10\({}^{\circ}\), 72.62\({}^{\circ}\), and 76.98\({}^{\circ}\) could be indexed to the (100), (002), (101), (102), (110), (103), (200), (112), (201), (204), and (202) planes, respectively, of the hexagonal wurtzite structure (JCP2 card no. 36-1451), which has lattice constants of a = b = 3.242 A and c = 5.205 A [49]. The narrow width and strong intensity of the diffraction peaks of the ZnO shows that they were highly crystalline in nature [50]. Furthermore, the absence of any impurity peaks approves the formation of pure phase ZnO. The Debye-Scherrer equation (Equation (2)) was employed to estimate the average crystallite size of the ZnO-NPs [51].  
\[\text{D}=\text{K}\lambda/\beta\text{cos}\beta \tag{2}\]  
where D = particles size (nm), K = Scherrer's constant (0.90), \(\lambda\) = X-ray wavelength (1.5406 A), \(\beta\) = full width at half the maximum (FWHM), and \(\theta\) = Braggs's angle of re flection. The average crystallite size of the synthesized ZnO-NPs was found to be 19.27.