esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Power has been extracted from the wind over hundreds of years with historic designs, known as windmills, constructed from wood, cloth and stone for the purpose of pumping water or grinding corn. Historic designs, typically large, heavy and inefficient, were replaced in the 19 th century by fossil fuel engines and the implementation of a nationally distributed power network. A greater understanding of aerodynamics and advances in materials, particularly polymers, has led to the return of wind energy extraction in the latter half of the 20 th century. Wind power devices are now used to produce electricity, and commonly termed wind turbines.  
The orientation of the shaft and rotational axis determines the first classification of the wind turbine. A turbine with a shaft mounted horizontally parallel to the ground is known as a horizontal axis wind turbine or (HAWT). A vertical axis wind turbine (VAWT) has its shaft normal to the ground (Figure 1).  
The two configurations have instantly distinguishable rotor designs, each with its own favourable characteristics [1]. The discontinued mainstream development of the VAWT can be attributed to a low tip speed ratio and difficulty in controlling rotor speed. Difficulties in the starting of vertical turbines have also hampered development, believed until recently to be incapable of self-starting [2]. However, the VAWT requires no additional mechanism to face the wind and heavy generator equipment can be mounted on the ground, thus reducing tower loads. Therefore, the VAWT is not completely disregarded for future development. A novel V-shaped VAWT rotor design is currently under investigation which exploits these favourable attributes [3]. This design is currently unproven on a megawatt scale, requiring several years of development before it can be considered competitive. In addition to the problems associated with alternative designs, the popularity of the HAWT can be attributed to increased rotor control through pitch and yaw control. The HAWT has therefore emerged as the dominant design configuration, capitalised by all of today's leading large scale turbine manufacturers.
## 2 Modeling of IES
### HCNG-CCS-P2G IES Mathematical Model
#### 2.1.4 Energy Storage Devices  
Considering that the models for batteries, thermal storage tanks, and hydrogen storage tanks are similar, this paper uses batteries as an example for modeling:  
\[\begin{cases}S_{\text{e}}(t+1)=(1-\tau_{\text{es}})S_{\text{e}}(t)+\eta_{\text {es,chr}}P_{\text{es,chr}}(t)-P_{\text{es,dis}}(t)/\eta_{\text{es,dis}}\\ S_{\text{e}}(1)=S_{\text{e}}(T)\\ S_{\text{e,min}}\leq S_{\text{e}}(t)\leq S_{\text{e,max}}\\ U_{\text{es,chr}}(t)P_{\text{es,chr,min}}\leq P_{\text{es,chr}}(t)\leq U_{ \text{es,chr}}(t)P_{\text{es,chr,max}}\\ U_{\text{es,dis}}(t)P_{\text{es,dis,min}}\leq P_{\text{es,dis}}(t)\leq U_{ \text{es,dis}}(t)P_{\text{es,dis,max}}\\ U_{\text{es,chr}}(t)+U_{\text{es,dis}}(t)\leq 1\\ U_{\text{es,chr}}(t),U_{\text{es,dis}}(t)\in\{0,1\}\end{cases} \tag{12}\]  
where, \(S_{\text{e}}(t)\) is the energy stored by the energy storage device at time \(t\), \(\tau_{\text{es}}\) is the self-discharge rate of the energy storage device.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 2 Turbulence Model and CFD Methodology
### Meshing Requirement  
#### 2.5.1 Meshing Requirement near the Wall in the LES Portion  
Quite a number of suggestions on the meshing requirements of LES have been given in the related literature. [PERSON] et al. [30] gave the usual values of the grid spacing in three directions: \(\Delta z^{+}\) = 50-150, \(\Delta x^{+}\) = 15-40, and \(N_{y}\) = 60-80 beginning with \(\Delta y^{+}\)\(\leq\) 1, where \(x\), \(y\), and \(z\) are the spanwise direction, the wall normal, and the streamwise, \(\Delta i^{+}\) is the non-dimensional grid spacing defined as \(\Delta i^{+}=u_{\tau}\)\(\Delta i/v\) (\(i\) refers to \(x\), \(y\), and \(z\)), and \(N_{y}\) is the number of cells to cover the boundary layer in the wall-normal direction. This is a typical requirement for classic wall-resolved LES, and the values of the actual grid spacing (\(\Delta i\)) would be \(Re\)-dependent in practice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Average Velocity (m/s)**} & \multirow{2}{*}{**Momentum Ratio (\(M_{R}\))**} & \multirow{2}{*}{**Flow Pattern**} \\ \cline{2-2} \cline{4-4}  & **Main (\(V_{m}\))** & & & \\ \hline W-ref & 1.46 & & 8.1 & Wall jet \\ D-ref & 0.46 & 1 & 0.8 & Deflecting jet \\ I-ref & 0.23 & & 0.2 & Imping jet \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conditions of three typical cases.  
Figure 1: Schematic diagram of the computational domain.  
Meanwhile, in applications with a wall-modeled LES formulation, the requirement can be relaxed. In the wall-normal direction, although a coarser \(\Delta y^{+}\) value such as \(\Delta y^{+}\approx 20\)[31] can be tolerable, a near-wall resolution of \(\Delta y^{+}=1\) was taken in the current work and the number of cells across the boundary layer was set as \(N_{y}=60\) as recommended by ANSYS. A typical wall-normal grid spacing \(\Delta y\) of the main pipe was set as 0.02 mm to ensure the near-wall resolution in the current work had a \(Re\) number of approximately 3.9 \(\times\) 10\({}^{5}\). In the other two directions, the grid spacing \(\Delta x\approx\Delta z\approx\) (0.05-0.1)\(\delta\) was adopted in the current work as recommended by ANSYS, where \(\delta\) (mm) is the per boundary layer thickness. The \(\Delta x/\Delta z\) of the main pipe was fixed at 2.25 mm/3.75 mm in the current work, and (\(\Delta x^{+}\), \(\Delta z^{+}\)) \(\approx\) (113, 188) in wall units with a \(Re\) number of approximately 3.9 \(\times\) 10\({}^{5}\).
## 4 Slope Stability Analysis and Discussion
### Plastic Zone Impact  
The plastic zone can reflect the damage form and damage range in the slope body. The distribution of the plastic zone of the slope under different rainfall cycles is shown in Figure 6.  
As can be seen from Figure 6, the plastic zone of the slope shows mainly shear damage and only a small amount of tensile damage at the surface. The change in water storage in the slope rock body has a significant effect on the plastic zone of the slope rock body. In the process of the wetting-drying cycles, the plastic damage zone is gradually divided into two parts. One part is the plastic damage zone of the slope above the water table line, and this part of the shear damage zone is the main reason for the decrease in slope stability. The other part is the plastic damage zone below the water table line, where the damage zone has almost no effect on the slope stability. Since the plastic damage zone above the water table line has the greatest influence on slope stability, only the plastic zone within  
Figure 6: Slope state by average under wetting–drying cycles. (**a**). Slope state by average under 0 wetting–drying cycle. (**b**). Tension damage zone under 1 wetting–drying cycle. (**c**). Slope state by average under 1 wetting–drying cycle. (**d**). Slope state by average under 2 wetting–drying cycles. (**e**). Slope state by average under 3 wetting–drying cycles. (**f**). Slope state by average under 4 wetting–drying cycles. (**g**). Slope state by average under 5 wetting–drying cycles.  
this range is analyzed here. When there is no rainfall, the shear damage zone in the slope body has a large distribution range, and the sheared area is circular. At the beginning of rainfall, the slope rock body changes from the original state of the plastic zone to the state of tensile damage, but as the infiltration line decreases with the number of wetting-drying cycles, the tensile damage zone on the surface of the terrace then disappears. With the wetting-drying cycles, the range of shear damage zones in the body of the slope gradually decreases. Until the fourth rainfall cycle, the range of the plastic zone's basic change is not obvious; it is manifested in the surface of the shallow steps of the shear damage, which is mainly due to several wetting-drying cycles occurring under the surface of the rock body, caused by the reduction in the physical and mechanical properties.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method
### Deep Clustering Network
#### 3.2.2 Deep Clustering of User Multi-Source Behavior Sequences
The gradient update \(\frac{\partial O}{\partial z_{l}^{q}}\) for the feature vector \(z_{l}^{q}\) of the multi-source behavior sequences is backpropagated to the encoder, adjusting the embedding representation of the behavior event entities and the feature representation of the behavior event sequences from the recurrent neural network. On the other hand, the gradient update for the cluster centers \(u_{j}\) in the objective function \(O\) is given by:  
\[\frac{\partial O}{\partial u_{j}}=-\frac{\alpha+1}{\alpha}{\sum_{j}}\Bigg{(} 1+\frac{||z_{l}^{q}-u_{j}||^{2}}{\alpha}\Bigg{)}^{-1}\times||z_{l}^{q}-u_{j}|| ^{3}-2p_{jq}\Big{(}z_{l}^{q}-u_{j}\Big{)}-2{\sum_{j^{\prime}}}u_{j}-u_{j^{ \prime}} \tag{6}\]  
Based on the gradient \(\frac{\partial O}{\partial u_{j}}\), we iteratively refine the cluster centers. The gradient descent process stops when the cluster centers no longer change between two consecutive iterations. By simultaneously updating the cluster centers and the feature representations of the multi-source user behavior event sequences, we can obtain the optimal feature space for clustering and the clustering results. The process of deep clustering of multi-source user behavior sequences can be summarized as Algorithm 1. ```
Input: User multi-source behavior sequence training set \(S\), collection of user multi-source behavior sequences for detection \(S^{\prime}\), each behavior sequence contains \(I\) consecutive behavioral events. Output: Class labels and class assignment probabilities for sequences in the user multi-source behavior sequence collection \(S^{\prime}\). 1: for a sequence \(s=\{e_{i}\}_{i=1}^{s},\ s\in S\)do
2: To compute the feature vector \(z_{l}\) of a multi-source behavior sequence \(s\) using encoder based on Formula (1). 3: To predict the reference values of the polynomial generating function for the entity contained in the \((t+1)\)-th behavior event using the decoder based on Formula (2). 4: The true values of the polynomial fitting for the entity in the \((t+1)\)-th behavior event as the parameters of the generating function. 5: The deviation between the true values and predicted values of the parameters of the generating function as the loss function to adjust the encoder and decoder. 6: end for
7: To extract the feature vector \(z_{l}^{q}\) of a sequence from the user's multi-source behavior sequence collection \(S^{\prime}\) generated by the encoder. 8: Using k-means clustering to initialize the class centers \(u_{j}\) based on the collection \(S^{\prime}\). 9: for a sequence \(s^{\prime}=\{e^{\prime}i\}_{j=1}^{s^{\prime}},\ s^{\prime}\in S^{\prime}\)do
10: To update the feature vector \(z_{l}^{q}\) of a multi-source behavior sequence according to Formula (5). 11: To update the cluster centers \(u_{j}\) based on Formula (6). 12: end for
```  
**Algorithm 1** Algorithm for Deep Clustering of Multi-source User Behavior Sequences
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 4 Experiment and Discussion
### Dissolved Gas Analysis
The measured value and standard value of No.1 oil sample are both in the D+T area, and the transformer status is the electrical and thermal mixed fault. The results of No.2 and No.3 oil samples are in the D2 area, and arc discharges have occurred in transformers. Although the Duval triangle can classify the types of faults, it cannot determine whether the transformer fault involves the deterioration of the insulation material [34]. The insulation deterioration in the transformer will aggravate the electrical and thermal fault of the transformer and reduce the life of the transformer. Therefore, the ratio of CO\({}_{2}\) and CO is used to assist in judging the insulation aging of the transformer [35; 36]. When CO\({}_{2}\)/CO > 20 (such as No.1 oil sample), the solid insulation material will degrade at low temperature (<140 \({}^{\circ}\)C). When CO\({}_{2}\)/CO > 7, the solid insulation material deteriorates. When CO\({}_{2}\)/CO is more than 3 and less than 7 (such as No.2 oil sample), the insulation material is normally aging. When CO\({}_{2}\)/CO is less than 3 and a large amount of total hydrocarbons are generated (such as No.3 oil sample), the solid insulating material deteriorates. In this paper, through the measurement and analysis of the six kinds of dissolved gas in oil, the transformer state obtained is shown in Table 6.  
In summary, a small amount of gas is not comprehensive enough to obtain transformer status information, and the transformer status needs to be judged from multiple aspects. The transformer status information obtained by multicomponent gas online detection is more detailed than the small amount of gas. Consequently, the setup described in this paper is able to reveal the operating conditions of the transformer, relatively. Figure 16: The results of Duval triangle.
# Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images
## 2 Methods and Techniques
### Dataset
#### 2.1.3 Data Stain Normalization  
During the preparation and pre-processing stages, images undergo various distortions and inconsistencies. These are attributed to the H&E staining procedure used to magnify minute nuclei features, tissue structures, and image transformations resulting from data augmentation. Laboratory slide preparation, examination, analysis, and digitalization of scanning samples are other factors that lead to image variations [27]. These factors, negatively impact the training and testing of neural networks. Hence, a stain normalization technique is needed to remove color irregularities in medical images and improve the model's efficacy. In this proposed study, the [PERSON] et al. [28] stain normalization technique is used for BC images to separate nuclei features from tissue structures. Images in the dataset images are first converted from the BGR to RGB color space to enable smooth stain normalization.  
**Macenko stain normalization**: is used to prepare tissue slides. Image colors are converted to their optical density (OD) equivalent via a simple logarithmic transformation shown below,  
\[OD=-\log_{10}(I)\]  
with I as the RGB color vector and individual components normalized to [0, 1].  
A value \(\beta\), is used as a threshold value to remove data with higher OD intensity.  
Single value decomposition (SVD) is applied to optical density tuples to create a plane. The plane corresponds to the largest singular values. OD-transformed pixels are then projected onto the plane to determine the angle at each point related to the first SVD direction. The color space transformation is applied to the original BC histology image. An image histogram is stretched such that the range covers the lower \((100-\beta)\)% of the data.  
Minimum and maximum vectors are calculated and projected back to the OD space. The Hematoxylin stain corresponds to the minimum vector while the Eosin stain is the maximum vector. Stain concentrations are determined to form a matrix representing RGB channels and OD intensities respectively. This study has values of \(\alpha\) and \(\beta\) set at 1 and 0.15 respectively. Figure 2 shows original, augmented, normalized H&E, normalized H, and normalized E breast cancer histology images respectively. After this stain normalization process, our proposed approach focuses on the normalized H image (image with only nuclei objects), having isolated from the greater normalized H&E image set.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Experimental Design
#### 4.1.2 Experimental Setup  
For this study, 500 representative computer science courses were selected from the MOOCCubeX dataset based on criteria such as concept coverage, video quality, and student engagement. The video data underwent basic preprocessing of text, video, and audio features, with named entity recognition applied to extract concept entities from course descriptions and subtitles. This multimodal information was integrated to enhance the accuracy of the knowledge graph. Learner behavior data was filtered to retain records related only to computer science courses, and personalized knowledge subgraphs were generated based on historical behavior to support the recommendation model.  
The experimental hardware included an Intel Xeon Gold 6248R CPU, an RTX4090 24 GB GPU, and 64 GB of memory (Santa Clara, CA, USA), with the software environment comprising Ubuntu 20.04 LTS and Python 3.9. MOOCCubeX, as a publicly available dataset, was processed according to standard protocols, with a focus on multimodal information utilization and knowledge graph construction.  
For model implementation, we set the following hyperparameters based on preliminary experiments and model architecture requirements. In the multimodal feature preprocessing stage, we employed a pretrained base model with 768 hidden dimensions for text feature extraction. For video processing, we sampled frames at 2 frames per second and used a C3D network with 512-dimensional output features. The CRNN for audio processing was configured with 256 hidden units. In the multimodal fusion module, we implemented an 8-head attention mechanism with an attention dimension of 64 per head, and the gated fusion unit was set with a hidden dimension of 512. The graph attention network for knowledge graph processing consisted of 3 layers, each with 8 attention heads, and the differentiable pooling layer was configured to reduce the number of nodes to 1/4 of the original count. For model training, we utilized the Adam optimizer with an initial learning rate of 0.001, which was reduced by a factor of 0.1 every 20 epochs. The batch size was set to 256, and the model was trained for 100 epochs with early stopping  
Figure 5: Composition of MOOCCubeX dataset.  
monitored on validation loss with a patience of 10 epochs. The loss function combined recommendation loss and graph edit distance loss with weights of 1.0 and 0.3, respectively. We applied dropout with a rate of 0.3 and L2 regularization with a coefficient of \(1\times 10^{-5}\) to prevent overfitting.
## 4 Design Scheme of SIMP Method in the Abaqus-Matlab Topology Optimization Integration Framework
### Numerical Algorithm for Solving Topology Optimization  
When dealing with highly nonlinear problems such as topology optimization, the convex programming method has become an ideal solution tool. At present, the Moving Asymptote method (MMA), as a representative algorithm in this field [13], has developed to a mature stage. This method is based on Taylor series expansion. By ingeniously applying sensitivity data and function values, it decomposes complex nonlinear problems into multiple manageable sub-problems. This algorithm has excellent convergence characteristics and low requirements for initial values, making it particularly suitable for solving nonlinear problems. In this study, the Moving asymptote method (MMA) is adopted for optimization calculation. The key lies in using the moving asymptote technique to approximately transform the non-convex functions within the design region into convex functions, thereby gradually constructing sub-problems and iteratively solving them to  
Figure 6: SIMP interpolation model.  
ultimately meet the convergence conditions. The mathematical approximation formula of MMA can be described as follows:  
\[\widetilde{f}_{j}^{n}(x)=r_{j}^{n}+\sum_{i=1}^{l}\left(\frac{P_{ij}^{n}}{U_{i}^{n }-x_{i}}+\frac{q_{ij}^{n}}{x_{i}-L_{i}^{n}}\right) \tag{8}\]  
\[P_{ij}^{n}=\left\{\begin{array}{l}\left(G_{i}^{n}-x_{i}^{n}\right)^{2}\frac{ \partial f_{j}(x^{n})}{\partial x_{i}},\frac{\partial f_{j}(x_{i}^{n})}{ \partial x_{i}}<0\\ \\ 0,\frac{\partial f_{j}(x_{i}^{n})}{\partial x_{i}}\leq 0\end{array}\right. \tag{9}\]  
\[q_{ij}^{n}=\left\{\begin{array}{l}0\text{, }\frac{\partial f_{j}(x_{i}^{n})}{ \partial x_{i}}\geq 0\\ \\ -\left(x_{i}^{n}-L_{i}^{n}\right)^{2}\frac{\partial f_{j}(x^{n})}{\partial x_{ i}},\frac{\partial f_{j}(x_{i}^{n})}{\partial x_{i}}<0\end{array}\right. \tag{10}\]  
\[r_{j}^{n}=f_{j}(x^{n})-\sum_{i=1}^{l}\left(\frac{p_{ij}^{n}}{U_{i}^{n}-x_{i}^{ n}}+\frac{q_{ij}^{n}}{x_{i}^{n}-L_{i}^{n}}\right)(j=0,1\cdots,j+1;i=1,2\cdots n) \tag{11}\]  
Here, \(\widetilde{f}_{j}^{n}(x)\) represents an approximate function of the original function \(f(x)\), \(n\) is the number of iteration steps, and \(L_{i}^{n}\) and \(G_{i}^{n}\) are the asymptotes for left and right movement, mainly regulating convexity. \(P_{ij}^{n}\) and \(q_{ij}^{n}\) are the left and right asymptotes and the sensitivity of the design unit is determined, \(r_{j}^{n}\) is an undetermined parameter, and \(x^{n}\) is the current design unit.
## 6 Influence of Floods on GPS, GLONASS, and BDS CNR
### Influence of Floods on GPS with 1 s Data Set  
To further analyze the influence of floods on the GPS satellite, the flood that appeared in Xinxiang on DOY:203, 2021, is also used, and the sample rate of data set is 1 s. As introduced before, the ground track repeat period of the GPS satellite is a sidereal day. Thus, the data set collected on the day before the flood can be used to compare the characteristics of the CNR. However, DOY:200 (no flood) is used to compare with the DOY:203 (with flood) in this experiment instead of DOY:202. The reason for choosing DOY:200 for comparison is that there was localized flooding on DOY:201 and DOY:202, which could have affected the accuracy of the comparison results.  
Figure 13 presents the comparison results of DOY:200 and DOY:203. The blue dots denote the CNR collected on DOY:200 (no flood), and the red dots denote the CNR collected on DOY:203 (with flood). The green line is the satellite’s elevation, which can demonstrate the relationship between the time series of the CNR and the elevation. From Figure 13, it  
Figure 12: CNR of GLONASS satellite L1 frequency comparison between under the flood and the normal weather. The blue dots denote the CNR collected on DOY:193 (no flood), and the red dots denote the CNR collected on DOY:201 (with flood).  
can be seen that the CNR decreases on DOY:203 compared with DOY:200 at an elevation larger than 60 deg. When the elevation angle is close to 90 deg, the amplitude of the CNR dropped to approximately 5 dB-Hz. Moreover, the amplitude of the CNR on DOY:203 also decreases to between 1.5 \(\times\) 10\({}^{4}\) and 2.0 \(\times\) 10\({}^{4}\) epochs to a certain degree. Thus, on the basis of the above analysis, it can be concluded that the influence of floods on the CNR of the GPS satellite with a 1 s data set can be observed, especially when the elevation is high. These results are consistent with the data set where the sample rate is 30 s.  
In addition, it can be found that the CNR is seen as slightly larger than DOY:200 in some epochs, such as between 18,000 and 20,000 epochs. The amplitude of the CNR on DOY:203 fluctuates more seriously than that of on DOY:200. These phenomena could have occurred for two reasons; one reason is that the surrounding environment of the receiver is covered with corn, which may introduce more of a multipath effect. The other reason is that the receiver used in this experiment is a low-cost receiver, and the performance of signal tracking may perform more fluctuations under a multipath environment than in a normal environment.