esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methods
### Numerical Modeling  
The numerical model was established to simulate the half-symmetric model based on the numerical modeling which was validated by the pre-existing model tests by [PERSON] et al. [27]. In this paper, a half- symmetric numerical model was established to study the influence of the subsidence width on the treatment effect of a subgrade collapse. Figure 3 shows the half-symmetric numerical model with the size of 20 m \(\times\) 5 m, and the interface strength reduction factor was set as 0.9. The model used 15-node triangular element for meshing, with a total of 4248 elements and 34,517 nodes. The Mohr-Coulomb constitutive model with the drainage condition was used. The soil element used in the numerical model had a unit weight of 17.0 kN/m\({}^{3}\), an elastic modulus of 30,000 kN/m\({}^{2}\), a Poisson's ratio of 0.3, a cohesion of 1.0 kPa, and an internal friction angle of 33\({}^{\circ}\), respectively. The tensile stiffness of the geotextile in the numerical model was 3000 kN/m. The process of the soil subsidence was simulated using the \"linear displacement\" in PLAXIS, and the \"geogrid\" simulated the geotextile, and the model boundary conditions were selected to be fixed in the normal direction.  
Table 4 lists the numerical models for the different conditions to study the influence of the subsidence width on the treatment effect of a subgrade collapse. The full-section numerical models denoted as P1-P3 were constructed with the subsidence widths of geotextile B of 1500 mm, 3000 mm, and 6000 mm, respectively. The half-symmetric model denoted as P4-P10 were constructed with the subsidence widths of geotextile \(B\) of 750 mm, 900 mm, 1050 mm, 1200 mm, 1350 mm, 1500 mm, and 3000 mm, respectively. The corresponded \(L/B\) for the ten models were 4.0, 2.0, 1.0, 4.0, 3.33, 2.86, 2.50, 2.22, 2.0, and 1.0, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variation**} & **Numerical** & **Subsidence** & **Anchorage Length of** & **Anchorage** & **Buried Depth of** & **Tensile Stiffness of Geotextile,** \\  & **Model** & **Width, \(B\) (mm)** & **Geotextile, \(L\) (mm)** & **Ratio, \(L/B\)** & **Geotextile, \(H\) (mm)** & **EA/(kN\(\cdot\)m\({}^{-1}\))** \\ \hline \hline \multirow{9}{*}{Subsidence} & P1 & 1500 & 6000 & 4.0 & 5000 & 3000 \\  & P2 & 3000 & 6000 & 2.0 & 5000 & 3000 \\ \cline{1-1}  & P3 & 6000 & 6000 & 1.0 & 5000 & 3000 \\ \cline{1-1}  & P4 & 750 & 3000 & 4.0 & 5
###### Abstract  
Diabetic retinopathy (DR) and glaucoma can both be incurable if they are not detected early enough. Therefore, ophthalmologists worldwide are striving to detect them by personally screening retinal fundus images. However, this procedure is not only tedious, subjective, and labor-intensive, but also error-prone. Worse yet, it may not even be attainable in some countries where ophthalmologists are in short supply. A practical solution to this complicated problem is a computer-aided diagnosis (CAD) system--the objective of this work. We propose an accurate system to detect at once any of the two diseases from retinal fundus images. The accuracy stems from two factors. First, we calculate a large set of hybrid features belonging to three groups: first-order statistics (FOS), higher-order statistics (HOS), and histogram of oriented gradient (HOG). Then, these features are skillfully reduced using a genetic algorithm scheme that selects only the most relevant and significant of them. Finally, the selected features are fed to a classifier to detect one of three classes: DR, glaucoma, or normal. Four classifiers are tested for this job: decision tree (DT), naive Bayes (NB), \(k\)-nearest neighbor (\(k\)NN), and linear discriminant analysis (LDA). The experimental work, conducted on three publicly available datasets, two of them merged into one, shows impressive performance in terms of four standard classification metrics, each computed using \(k\)-fold crossvalidation for added credibility. The highest accuracy has been provided by DT--96.67% for DR, 100% for glaucoma, and 96.67% for normal.  
retinal fundus imaging; diabetic retinopathy; glaucoma; genetic algorithm; feature extraction and selection; multi-classification  
[PERSON], [PERSON], [PERSON], H. Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm. _Appl. Sci._**2021**, _11_, 6178.  
[[https://doi.org/10.3390/app1136178](https://doi.org/10.3390/app1136178)]([https://doi.org/10.3390/app1136178](https://doi.org/10.3390/app1136178))
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
###### Abstract  
Hydrodeoxygenation (HDO) of bio-oil is a method of bio-oil upgrading. In this paper, x%CeO\({}_{2}\)-Ni-Cu/HZSM-5 (x = 5, 15, and 20) was synthesized as an HDO catalyst by the co-impregnation method. The HDO performances of x%CeO\({}_{2}\)-Ni-Cu/HZSM-5 (x = 5, 15, and 20) in the reaction process was evaluated and compared with Ni-Cu/HZSM-5 by the property and the yield of upgrading oil. The difference of the chemical composition between bio-oil and upgrading oil was evaluated by GC-MS. The results showed that the addition of CeO\({}_{2}\) decreased the water and oxygen contents of upgrading oil, increased the high heating value, reduced acid content, and increased hydrocarbon content. When the CeO\({}_{2}\) addition was 15%, the yield of upgrading reached the maximum, from 33.9 wt% (Ni-Cu/HZSM-5) to 47.6 wt% (15% CeO\({}_{2}\)-Ni-Cu/HZSM-5). The catalytic activities of x%CeO\({}_{2}\)-Ni-Cu/HZSM-5 (x = 5, 15, and 20) and Ni-Cu/HZSM-5 were characterized by XRD, N\({}_{2}\) adsorption-desorption, NH\({}_{3}\)-Temperature-Programmed Desorption, H\({}_{2}\)-Temperature-Programmed Reaction, TEM, and XPS. The results showed that the addition of CeO\({}_{2}\) increased the dispersion of active metal Ni, reduced the bond between the active metal and the catalyst support, increased the ratio of Bronsted acid to total acids, and decreased the reduction temperature of NiO. When the CeO\({}_{2}\) addition was 15%, the activity of catalyst reached the best. Finally, the carbon deposition resistance of deactivated catalysts was investigated by a Thermogravimetric (TG) analysis, and the results showed that the addition of CeO\({}_{2}\) could improve the carbon deposition resistance of catalysts. When the CeO\({}_{2}\) addition was 15%, the coke deposition decreased from 41 wt% (Ni-Cu/HZSM-5) to 14 wt% (15%CeO\({}_{2}\)-Ni-Cu/HZSM-5).  
Keywords:Ni-Cu/HZSM-5; CeO\({}_{2}\); bio-oil; hydrodeoxygenation +
Footnote †: journal: Applied Physics Letters
# Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
## 4 Feature Extraction and Model Construction
### Constructing Models for Multi-Label SER
Then, the comprehensive fusion model integrates ViT, 1D CNN-LSTM, and DNN, combining features from Log-mel spectrograms, MFCCs, and VQFs to analyze a diverse set of speech characteristics. To perform multi-label emotion classification with five emotions, the final output layer of the model is configured with five nodes, with a sigmoid function applied as the activation function to derive the probability values for each class. When the probability value for each class exceeded the threshold, it indicated the presence of that emotion class; when it fell below the threshold, it indicated the absence of that emotion class. Hence, since only five emotions are used; the model's output can represent a combination of up to five emotions or as few as zero emotions. Moreover, this study employed a binary focal loss as the loss function for model training. While focal loss was initially developed for object detection tasks, focal loss has been shown to exhibit advantageous characteristics for handling imbalanced datasets in classification problems as well [46]. Focal loss is a refinement of the cross-entropy loss that addresses class imbalance by assigning greater weight to difficult or easily misclassified cases [47]. Focal loss has demonstrated effectiveness in balancing loss by amplifying the loss in classes that are difficult to classify. As mentioned in Section 3.2, shift augmentation was performed to reduce the data variation for each label. However, the data imbalance of the original dataset was extreme, and augmentation could not entirely resolve this imbalance. Hence, the binary focal loss function is applied to enhance the performance by mitigating the imbalance during model training and giving higher loss to emotions that the model fails to predict well to improve its accuracy. The formula for binary focal loss is given by Equation (5). \[BFL\;(y,\hat{p})=-\alpha y(1-\hat{p})^{\gamma}\text{log}(\hat{p})-(1-y)\hat{p}^{ \gamma}\text{log}(1-\hat{p}) \tag{5}\]  
In Equation (5), \(\mathbf{y}\) represents the binary class label (0, 1), \(\hat{\mathbf{p}}\) represents the prediction probability, \(\mathbf{\alpha}\) is the weight to balance the classes, and \(\mathbf{\gamma}\) is the hyperparameter that determines how much weight to give the losses. Thus, adjusting the value of \(\gamma\) based on the model and the data is crucial, as the appropriate setting of \(\gamma\) values can significantly impact the model's performance.
# Global Buckling Investigation of the Flanged Cruciform H-shapes Columns (FCHCs)
## 3 Numerical Analysis Method and Verification  
### Finite Element Model  
The author's previous studies [20] successfully used ABAQUS software to perform finite element simulation analysis for steel components. The results show that our finite element modeling method and the selection of analysis units are reasonable and reliable. This research still follows the previous study and uses the same approach to construct the finite element analysis model. In the ABAQUS analysis, eight-node solid non-conforming elements C3D8I were used to reduce grid units and shorten the calculation time. Through trial calculations, it was found that when the mesh size was 20 and 15 mm, the error of the column's stability bearing capacity was less than 0.3%, but the calculation time was shortened by nearly one-third. That shows that when the mesh size is 20 mm, the calculation accuracy can meet the paper's research. Therefore, the maximum mesh size is twenty millimeters in the model (including H-shapes and welds). If the plate thickness of H-shapes and weld leg is less than twenty millimeters, the mesh size takes the actual plate thickness or weld leg size. To build the FE (Finite Element) model, select two kinds of steel recommended in GB 50017-2017 [16], Q345 and Q460. Coupon test results of Q345 steel and Q460 steel are adopted from references [20] and [21], respectively. The stress-strain three-fold line of steels used in the finite element analysis is shown in Figure 3, and the specific data are listed in Table 1. A three-fold line model could consider the strain hardening and descending. The plasticity model is based on the von Mises yielding criteria. Use the \"Tie\" command to bind the welds and H-shapes. Therefore, only consider the contact between the web of H-shape-1 and the flange of H-shape-2. Set the regular hard contact, and the tangential friction coefficient is set at 0.15. The 3D finite element model of specimens is shown in Figure 4.  
Considering this is the preliminary study, the column's boundary condition is simplified to the pin ended. Thus, the non-loading end of the model is set to pure pin-ended, which can only rotate freely around the \(X\)-axis and \(Y\)-axis, and the constraint is U\({}_{X}\) = U\({}_{Y}\) = U\({}_{Z}\) = 0. Compared with the non-loading end, the loading end can rotate around the \(X\)-axis and \(Y\)-axis and translate along the \(X\)-axis. That is, the constraint of the \(Z\)-axis direction needs to be released. The boundary condition is U\({}_{X}\) = U\({}_{Y}\) = 0.  
Figure 3: Stress-strain relationships: \(\sigma_{y}\) and \(\epsilon_{y}\) are the yield stress and yield strain, respectively; \(\sigma_{\rm u}\) and \(\epsilon_{\rm u}\) are the ultimate stress and ultimate strain, respectively; \(\sigma_{\rm st}\) and \(\epsilon_{\rm st}\) are the failure stress and failure strain, respectively.  
In order to realize the CGMNA analysis, non-linear static general analysis is selected in ABAQUS.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 1 Introduction
In recent years, machine learning techniques such as the support vector machine (SVM), k-nearest neighbors algorithm (KNN) and neural networks (NNs) have emerged as formidable contenders in traffic prediction tasks, offering the potential for improved prediction accuracy and more intricate data modeling capabilities. In contemporary traffic analysis, deep learning methodologies have gained widespread adoption and demonstrated significant success across many tasks. Notable advancements include the utilization of deep belief networks (DBNs) [9] and stacked autoencoders (SAEs) [9]. It is well known that densely connected networks encounter challenges in jointly extracting spatial and temporal features from input data, particularly in scenarios with limited or absent spatial attributes. To address these challenges, the present research proposes novel strategies for effectively modeling traffic flow temporal dynamics and spatial dependencies. Our solution, integrated spatio-temporal graph convolutional networks (ISTGCN), integrates graph convolutional layers and convolutional sequence learning layers within multiple spatiotemporal convolutional blocks. This makes it a pioneering effort to apply purely convolutional structures to extract spatiotemporal features concurrently from graph-structured time series data in the context of traffic analysis. Our contributions can be outlined as:  
1. Predictive graph modeling for road network traffic at particular time stamps. 2. Applying graph convolution operations to integrate the spatial information. 3. Integrated spatio-temporal graph convolutional network (ISTGCN) to capture the complex spatial and temporal dependencies inherent in the dataset. This paper is structured as follows: Section 2 reviews our research in comparison with other relevant studies. Section 3 categorizes the traffic forecasting problem and outlines the methodology, covering predictive modeling for road networks and graph-based convolutional operations, proposed network architecture, and the discussion on extracting spatial and temporal features. Section 4 explains the experimental setup and discusses results and analysis. Finally, Section 5 concludes by addressing challenges and outlining future directions.
## 2 Proposed WPT System with the MMs  
It is well known that the four-coil WPT system based on magnetic resonance coupling consists of the drive coil, the transmitting coil (\(Tx\)), the receiving coil (\(Rx\)), and the load coil. Normally, the distance between the drive coil (load coil) and the \(Tx\) (\(Rx\)) coil is relatively close, so the magnetic coupling is relatively strong. Nevertheless, the \(Tx\) coil is far from the \(Rx\) coil and the coupling coefficient is low conversely. Figure 1 presents a diagram of the four-coil WPT system with the MMs. The adjustable DC power source supplies the Class-E amplifier, and the signal generator can emit a high frequency Pulse Width Modulation (PWM) square wave. The Class-E amplifier is designed to amplify the power of a high frequency signal to meet the requirement of transmitting power. The MMs is placed between the \(Tx\) and \(Rx\) coil, the position of which is varied constantly and the optimal position of the MMs needs to be analyzed. The load resistance is the link to the load loop.
# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 2 Materials and Methods
### Motion Pattern Recognition Algorithm
#### 2.4.2 Finite State Machine (FSM)  
Finite state machine (FSM) is a mathematical model that represents a finite number of states and the transfer and action between these states [34]. The FSM can be expressed as the following five element function group:  
\[M=(Q,\Sigma,\delta,q_{0},F), \tag{2}\]  
where \(Q=\{q_{0},q_{1},\cdots,q_{n}\}\) represents a set of states of the finite state machine, including five common locomotion modes. \(\Sigma=\{\sigma_{1},\sigma_{2},\cdots,\sigma_{n}\}\) is a limited set of input information, including plantar pressure and joint angle. At any given moment, the finite state machine can only be in a certain state \(q_{i}\), the finite state machine can only receive one definite input \(\sigma_{j}\). \(\delta:Q\times\Sigma\to Q\) is the state transition function. In a certain state, the finite state machine will enter a new state determined by the state transition function after a given input. \(q_{0}\in Q\) is the initial state, and the finite state machine begins to receive input from this state from. \(F\subseteq Q\) is the final state set, and the finite state machine will no longer receive input after reaching the final state.
## 2 FEM-SPH Conversion Coupling Algorithm  
### FEM-SPH Coupling Algorithm  
The bank slope will gradually change from small deformation to large deformation from the initial stable state to the sliding destabilization of the soil after landslide formation. The Lagrange finite element method (FEM) can effectively deal with the small deformation case, but there is a mesh distortion problem for the large deformation case. The smooth particle hydrodynamics (SPH) method can avoid mesh distortion and better simulate the behavior of splash and damage, but its computational efficiency is low and boundary constraints are difficult [19; 20]. Additionally, the coupled FEM-SPH algorithm can combine the strengths of both and deal with large deformation problems efficiently and accurately.  
The general FEM-SPH coupling algorithm will start the analysis with larger deformations in regions that will likely be discretized as SPH particles, while other regions are still analyzed using FEM. Since this method requires some locations to be defined as SPH particles before the analysis, it was necessary to accurately predict the areas where large deformation is likely to occur. In this paper, it involves the whole process of the shore slope, from the initial stable state to the final deformation and destabilization. The range, shape, and location of the sliding zone are unknown, and it is difficult to determine which areas of the finite element mesh will be deformed. If the whole shore slope is discretized into SPH particles at the beginning, it will lead to low computational efficiency.  
In this paper, the FEM-SPH conversion coupling algorithm could effectively solve the above problem. In the initial stage of analysis, the whole bank slope was a finite element mesh, and the soil slides after the sliding surface is formed. When the sliding zone finite element mesh was excessively distorted, it was converted into SPH particles so as to realize the simulation of the whole process from deformation and sliding to destabilization damage of the bank slope.
## 3 Principle of Diagnosis Based on Optimized VMD-RF Algorithm
### The Introduction of RF  
The RF algorithm essentially belongs to the category of ensemble learning. It builds multiple individual decision trees based on randomly sampled information to make predictions, and finally selects the mode of the prediction target class as the final result by voting [22]. The randomness of RF is reflected in two aspects, one is the randomness of sample selection and the other is the randomness of the selection of feature attributes. Suppose that N decision trees are generated, there are M feature attributes of the sample. The RF model is based on the bootstrap sampling method to extract sample subsets from all training samples, and then randomly selects the specified number of feature attributes to establish each decision tree. Out-of-bag (OOB) data that is not in the sample subset can be used to evaluate the internal error of the decision tree classification accuracy. The most likely target category of OOB data can be comprehensively determined by voting. Through continuous iteration, the relationship between the OOB error and the number of decision trees is obtained, which provides a selection foundation for the number of decision trees [23]. The OOB error is expected to be lower, so the classification accuracy of RF is higher, and the generalization performance of the model is also better. The construction process for the RF classifier is shown in the Figure 5.  
Figure 4: The spectrum and mode obtained by VMD (Variational Mode Decomposition) in fault 4 condition: (**a**) VMD decomposition tree (**b**) spectrum (**c**) mode.  
In addition to excellent classification performance, RF can also calculate the importance score V for each feature. After calculating the OOB error (\(B_{1}\)) for each decision tree, the interference noise is randomly added to each feature variable of the out-of-pocket data. The values of other features are fixed and the OOB error (\(B_{2}\)) is calculated again. The sum of the differences of the two types of OOB errors for all decision trees is divided by the number of decision trees \(N\) to obtain \(V\). The importance score for any feature \(X\) can be expressed as:  
\[V(X)=\frac{1}{N}\sum\
olimits_{n=1}^{N}(B_{2}-B_{1}). \tag{6}\]  
According to the importance score of feature parameters, feature selection can be performed on multi-dimensional features. Extracting feature parameters with larger V-values to optimize feature vectors can greatly improve the efficiency of program operation.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.4 Packet Delivery Ratio  
Generally, the packet delivery ratio decreases with an increase in the number of nodes. The packet loss ratio is inversely proportional to PDR. When the nodes are increased in the network and randomly deployed with partial nodes in the interference range, it will take more time to communicate to the sink node. From Figure 7, it is understood that PDR decreases with the increasing number of nodes. In E-RPL, drastically, the PDR reduced from 60% to 40% when the number of nodes increased from 20 to 40, as nodes are in the interference range, and also, the farthest node would take more time to communicate to the border router, resulting in increased delay and packet loss.  
Figure 5: Average hop count vs. network size.  
Figure 6: Overhead vs. number of nodes.  
In the proposed CAA-ERPL, because of the aggregation, there is no drastic decrease in the PDR. The reason being that the formation of an aggregator for sensor nodes in the region is responsible for aggregating and compressing the data, thereby forming DODAG with a border router for communication. This ultimately increases the packet delivery ratio, as aggregators are involved in DODAG formation with the border router for data communication. Additionally, aggregators are formed for a small number of nodes in the region where there would be no packet loss. That is the reason the PDR of CAA-ERPL is 68% to 89% for varying levels of nodes in the network as compared to E-RPL.