esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aim, Materials, and Methods
### Multi-Enhancement Command and Scheduling Model for Communication Network  
The multi-dimensional enhancement of command and dispatch mode encompasses crucial components such as monitoring and early warning, disaster research and assessment, emergency command, and subsequent post-disaster reconstruction and experience analysis. Firstly, in terms of monitoring and early warning, advanced communication technologies including the Internet of Things, big data, and artificial intelligence are utilized to real-time collect diverse disaster information, providing accurate and timely risk notifications for government departments, as well as the general public. Secondly, regarding disaster research and assessment, various types of disasters along with their impact scope and severity are swiftly identified by integrating all forms of monitoring data alongside on-site information. This is accomplished through the comprehensive utilization of technical means such as communication networks, geographic information systems (GIS), expert systems, etc., thereby offering a scientific foundation for subsequent emergency command.  
Subsequently, in terms of emergency command procedures, the command and dispatch system is employed to transmit on-site information promptly while ensuring seamless communication between emergency management departments at all levels including rescue teams as well as affected individuals. Additionally, rational rescue strategies along with resource allocation plans are formulated based on distinct characteristics exhibited by different disasters coupled with on-site conditions so as to enhance the overall rescue efficiency, as depicted in Figure 3.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 3 Results  
### Data Collection Results  
The buoy operated for seven days. As shown in Figure 3, the buoy moved randomly within a relatively small area. The collected data included crucial indicators for fishery prediction, such as water temperature, current speed, and location. In addition, among the 82 sets of sonar data collected, six sets contained strong echo signals (as depicted in Figure 3), which suggests the presence of Antarctic krill swarms at those locations. We divided these six strong echo signals into three levels based on signal strength; in the future, we will correlate the divided strength levels with krill density ranges. The collection of sonar data plays a positive role in assessing the biomass of Antarctic krill species and in guiding the establishment of fishing quotas [13; 16].
## 4 Experiments and Results
### Analysis Results for the Urban Area
#### 4.3.2 Weekday Urban Analysis  
Figure 12 shows the clustering validity indices curves derived from the four explored metrics using the STP data analysis from the urban environment on the weekday. As for the suburban analysis, the \(Q=2\) clusters solution was obtained for all the methods despite the Gap index, which attained the best suboptimum solution for \(Q=3\) clusters.  
Figure 13 shows the dendrogram of the agglomerative hierarchical clustering for the urban environment on the weekday, where the solutions for \(Q=2\) and \(Q=3\) are highlighted in color. Moreover, Figure 14 shows the similarity matrix **S**. As can be observed,  
Figure 11: Examples of original (left-side) and interpolated (right-side) STPs for the urban environment from (**a**) sensor hb117 and (**b**) sensor hb145, respectively.  
the solution is coherent with the clustering solution \(Q=2\) (shown in red rectangles on the left-side) as well as \(Q=3\) (shown with white dashed lines across the image and also as green rhomboids at the bottom of the figure). It can be seen that, as in the suburban case, the expert-based analysis stage yields a sensor ordering where the similarity matrix presents lower Euclidean distances near its diagonal, with higher values for positions far off the diagonal, including hb133 and hb144 sensor locations with higher distances from hb109 and hb108 sensor locations. From the analysis of the similarity matrix, it can also be concluded that the sensor locations belonging to clusters C2 and C3 present more different behaviour (see higher values in \(D_{23}\) region) than if they are individually compared to cluster C1 for the \(Q=3\) solution.  
Figure 12: Clustering validity indices obtained for the acoustic urban environment on the weekday, resulting from the agglomerative hierarchical clustering algorithm for the considered number of clusters sweep \(2\leq Q\leq 23\). The suboptimal number of clusters are marked as red asterisks, being the local maxima in all clustering indices despite the Davies–Bouldin index, which is a local minimum.  
Figure 13: Dendrogram of the agglomerative hierarchical clustering for the urban environment on the weekday. Clustering solutions for \(Q=2\) (in red) and \(Q=3\) (in green) are also highlighted.
## 1 Introduction
In detail, the proposed Topic-HGNN framework involves: (1) adopting a hypergraph to model the multivariate relationship among users, query topics, items, and item features and applying the dual-embedding mechanism to handle complex and high-order correlations; (2) applying hyperedge corruption to generate a user-query hypergraph and an item-feature hypergraph and utilizing the hyperedge convolution layer to obtain user embedding and item embedding; (3) developing an Aggregated Latent Dirichlet Allocation model to jointly extract users' content preference topics from queries and webpages and applying the Latent Dirichlet Allocation model to extract product feature topics from online reviews; (4) integrating topic distribution and convolutional embedding to represent each user and item; and (5) using multilayer perceptron to calculate the soft match score between query entities and item entities. We summarize the main contributions in the paper as follows:  
* Despite the importance of inferring user preferences from online query sessions, very little research has focused on this area. In this paper, we propose a sustainable recommender system architecture based on inferring users' preferences from online query sessions, which can more accurately predict user purchase intentions;
* We develop an Aggregated Latent Dirichlet Allocation (ALDA) model, a novel topic model that can simultaneously learn user query topics and topics of corresponding clicked webpages. The ALDA model treats the joint topic distribution of queries and webpages as the topic distribution of user preferences. The data sparsity of online query data is avoided by aggregating corresponding webpages to assist in learning users' content preferences;
* To handle the complex multivariate relationship among users, query topics, items, and item features, we design a topic augmented hypergraph neural network (Topic-HGNN) framework to more accurately represent each user and item by integrating the convolution information and the topic information. The Topic-HGNN framework can significantly improve the accuracy and the novelty of recommended items;
* Extensive tests verify that our approach can better capture consumers' multi-dimensional preferences for product attributes and can better predict consumers' purchase intentions. We organize the rest of this paper as follows. Section 2 summarizes the related works. Section 3 describes the proposed recommender system in details. Section 4 presents the extensive experiments designed to evaluate the effectiveness and the efficiency of the proposed framework. Section 5 summarizes the paper.
# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Computation Time  
We perform the feed-forward inference average computation time comparison for different methods. All these methods are evaluated with a machine of an Intel Core Xeon E5-2640 v3 CPU and a TITAN X (Pascal) GPU by processing 1000 256 \(\times\) 256 images with \(128\times 128\) holes. The results are listed in Table 5. DFNet achieves the shortest computation time because it only contains a smaller network than other methods. GLCIC has the longest computation time because its generation network contains much more feature map channels than other methods. Our PIC-EC method has to obtain the edge and color priors before the image completion; therefore, it takes a little more computation time. Still, PIC-EC takes less computation time than GLCIC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & GLCIC & DeepFill & GMCNN & DFNet & EdgeConnect & PIC-EC (ours) \\ \hline
**Time (ms)**\({}^{\ddagger}\) & 215.15 & 75.14 & 93.45 & **43.50** & 96.62 & 190.82 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The feed-forward inference average computation time comparison for methods: GLCIC, DeepFill, GMCNN, DFNet, EdgeConnect and PIC-EC. Boldface indicates the best performance. \({}^{\dagger}\) Lower is better.  
Figure 9: Effect of different masks over Paris StreetView. (**a**) results of \(\ell_{1}\) metric; (**b**) results of PSNR metric; (**c**) results of SSIM metric; (**d**) results of FID metric.
## 2 Data Collection  
Generally, the tail-growing process in the Czochralski silicon single-crystal growing process includes basic processing parameters and control processing parameters. Basic processing parameters include the crystal rotation, crucible rotation, argon flow, magnetic field strength, furnace pressure, liquid level position, and other process parameters. The control processing parameters include the temperature and pulling speed, which are used to adjust the crystal diameter in the tail-growing process.  
Due to the lack of technology to measure the tail diameter at present, we have selected four measurable and directly related variables--temperature, pulling speed, tail length, and mass increment--as data sources for predicting the crystal tail diameter.  
The data in this article were collected from crystal-growing equipment on-site. The model of the single-crystal furnace was TDR-120 CZ, as shown in Figure 3. This equipment can produce CZ silicon single crystals of 100-310 mm diameter. The diameter measurement range is 4-350 mm. The equipment has a maximum power of 180 kW. The crucible rotation speed can be adjusted within the range of 0-15 rpm, while the crystal rotation speed can be adjusted within the range of 0-20 rpm. The crucible lifting speed can be adjusted within the range of 0-0.5 mm/min, and the crystal lifting speed is within the range of 0-6 mm/min. The ultimate vacuum degree is 0.4 Pa, and the inlet air flow can be adjusted within the range of 0-150 L/min, with a liquid level adjustment range of 0-100 mm. The crystal lifting stroke is 2.8 m, and the crucible lifting stroke is 600 mm. In terms of on-site process parameters, the target set value for the diameter is 308 mm, and the casting speed can be adjusted within the range of 0.2-1.1 mm/min. The temperature adjustment is determined based on the input of the silicon material quantity. The general rule is to first cool and then raise the temperature, with the adjustment range typically being 2100-2500 (dimensionless).  
The crystal diameter signal used for the data fusion was acquired using a Microvision MV-300 UC Camera. The temperature signal in the growing process of the crystal was obtained through a RAYTEK Marathon FR Infrared Thermometer sensor. The pulling speed and tail length of the crystal were acquired using Schneider XCC Series Absolute Encoders. The mass increment of the crystal was obtained using an S-Type Stainless Steel Load Cell. The data of the temperature, pulling speed, tail length, and mass increment were sampled every 2 s. All signal-acquisition devices are shown in Figure 3.
## 6 Results and Discussion
### Performance Comparison of Different Features  
The performance of the proposed Blind Image Quality Assessment (BIQA) methodology on three frequently used benchmark datasets is shown in Table 1. The table displays the results produced by employing various image attributes in the suggested technique. The proposed model employed four types of image features in this study: Haar wavelet coefficients, Prewitt and Sobel edge detection, Log filters, and Prewitt, Sobel, and Gaussian filter combinations.  
Table 1 also includes the results of using the Information Gain feature selection algorithm with the WEKA tool on the Prewitt and Gaussian (WEKA PG) and Prewitt, Sobel, and Gaussian (WEKA PSG) features, which achieved a performance improvement over the non-selected features.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**TID2013**} & \multicolumn{6}{c}{**CISO**} & \multicolumn{6}{c}{**LIVE**} \\ \cline{2-13}  & & **SSR** & **SCN** & **HFN** & **IN** & **ID** & **GBLUR** & **JPEG** & **Avgn** & **JPEG** & **Jp2K** & **Fnoise** & **Jp2K** & **Jp2G** & **Wn** & **Gblur** & **FF** \\ \hline \multirow{4}{*}{**WT**} & **sroc** & 0.9054 & **0.9553** & 0.9320 & 0.9069 & 0.9014 & 0.9181 & **0.9308** & 0.9365 & 0.9012 & 0.9217 & 0.8928 & 0.9245 & **0.9204** & 0.9724 & 0.9586 & 0.9216 \\  & **lcc** & 0.9111 & **0.9684** & 0.9064 & 0.9078 & 0.9201 & 0.9219 & **0.9779** & 0.9517 & 0.9348 & 0.9356 & 0.9011 & 0.9376 & **0.9082** & 0.9819 & 0.9634 & 0.9253 \\  & **krocroc** & 0.7467 & **0.8333** & 0.7043 & 0.7446 & 0.7323 & 0.7667 & **0.7933** & 0.7931 & 0.7537 & 0.7655 & 0.7149 & 0.7277 & **0.7751** & 0.8966 & 0.8244 & 0.7924 \\  & **mrose** & 0.8177 & **0.2038** & 0.2792 & 0.2820 & 0.6634 & **0.4972** & **0.3323** & 0.0547 & 0.1117 & **0.1140** & **0.1066** & **5.76** & **5.1151** & 2.7912 & 0.8431 & 3.6078 \\ \hline \multirow{4}{*}{**PG**} & **sroc** & 0.9169 & 0.9292 & 0.9363 & 0.9120 & 0.9201 & 0.9331 & 0.9199 & 0.9429 & 0.8987 & 0.9253 & 0.8921 & 0.9240 & 0.9167 & 0.9773 & 0.9655 & 0.9231 \\  & **krocroc** & 0.9346 & 0.9345 & 0.9710 & 0.9131 & 0.9440 & 0.9404 & 0.9610 & 0.9539 & 0.9310 & 0.9295 & 0.8999 & 0.9340 & 0.9389 & 0.9698 & 0.9267 \\  & **krocroc** & 0.7533 & 0.7867 & 0.8001 & 0.7600 & 0.7713 & 0.7990 & 0.7036 & 0.7448 & 0.7571 & 0.7611 & 0.7627 & 0.7648 & 0.8916 & 0.8621 & 0.7635 \\  & **krocroc** & 0.7210 & 0.2865 & 0.2850 & 0.2409 & 0.5465 & 0.4460 & 0.3936 & 0.0548 & 0.1053 & 0.1197 & 0.1051 & 0.6187 & 5.4605 & 2.5000 & 3.9771 & 0.8169 \\ \hline \multirow{4}{*}{**LG**} & **srocroc** & **0.9311** & 0.9310 & 0.9313 & 0.9021 & 0.9032 & 0.9042 & 0.9042 & 0.9123 & 0.9369 & 0.8891 & 0.9150 & 0.9034 & 0.9174 & 0.9124 & **0.9482** & 0.9542 & 0.9097 \\  & **kroc** & **0.9482** & 0.9172 & 0.9709 & 0.
###### Abstract  
(1) Background: Our previous studies indicated that low-intensity extremely low-frequency electric field (ELF-EF) treatment alters autonomic activities, as revealed through heart rate variability (HRV) analysis. However, the high-frequency (HF) component of HRV that reflects parasympathetic activity showed no changes either during or after the end of the treatment, suggesting the involvement of sympathetic nerves. (2) Methods: To examine this issue in the present study, the effect of ELF-EF on skin conductance (SC), which is controlled solely by sympathetic nerves, was analyzed. Twelve healthy subjects underwent a 20 min ELF-EF treatment (applied voltage: 9 kV, induced current density: below 6 mA/m\({}^{2}\)) and a sham treatment in a random order with an interval of more than 2 weeks. SC and HRV were recorded under the eyes-open condition during a 2 min period both before and after the treatment. (3) Results: The number of spontaneous fluctuations in skin conductance (SC-SFs) significantly decreased after the ELF-EF treatment, suggesting psychological changes, including relaxation. The skin conductance level, heart rate, and HRV indices did not change after the ELF-EF treatment. (4) Conclusion: The results support the idea that low-intensity ELF-EF affects autonomic nerves by reducing sympathetic activity, as reflected by SC-SFs.  
extremely low frequency electric field; skin conductance spontaneous fluctuation; skin conductance level; heart rate; heart rate variability; autonomic activity; sympathetic nerve
## 2 System and Channel Models
### Statistical Models  
Statistical models play a crucial role in understanding and predicting the behavior of transmitted signals under various environmental conditions. This section delves into three significant aspects of these models: deterministic attenuations, turbulence models, and pointing errors. Each subsection provides a comprehensive analysis of the factors affecting the signal integrity and the mathematical frameworks used to quantify these effects.
## 2 FSCMR Wall Panel Structure
### Functions of CMR Wall Panel Structure with Energy-Efficient Blocks and Its Manufacture and Assembly  
With respect to materials, CMR wall panel structures with energy-efficient blocks are composed of brittle materials with a relatively high stiffness (or in other words, blocks), and of reinforced concrete with fair ductility: the CMR frame and cast-in-place edge frame. Therefore, the overall structure has moderate stiffness, with its force bearing performance falling between that of a frame structure and that of a shear wall structure. Under lateral loading, the blocks with a low modulus of elasticity crack first. However, because of the restraint of the grid, which is formed by the ribbed columns and ribbed beams, blocks consume this energy in friction, which can be considered to be the first seismic line of defence. The ribbed beams and ribbed columns enhance the connectivity of the concealed ribbed frame. Furthermore, with the support of the blocks, the CMR frame offers even greater stiffness in  
Figure 1: Diagram for CMR wall panel structure with energy-efficient blocks.  
Figure 2: Residence pilot projects using CMR wall panel structure with energy-efficient blocks. (a) Xing-Guang Hua District in Yin Chuan, the Ningxia Hui Autonomous Region; (b) Geng-xin-street District in Xi’an, Shanxi Province; (c) Zhong-zhou-yu-fu project, in Lankao, Henan Province.  
comparison with the cast-in-place edge frame, meaning that the failure of the former occurs before that of the latter. Therefore, the CMR frame can be seen as the second seismic line of defence. At later stages of force bearing, the CMR frame and cast-in-place edge frame cooperate to prevent structure collapse during a major earthquake, which is the final seismic line of defence. The overall CMR wall panel structure is a layered energy consumption system with the following failure pattern: blocks, then CMR frame, then cast-in-place edge frame. In other words, multiple seismic lines of defence are present.  
See Figure 3 for the arrangement and configuration of multiple seismic lines of defence for the CMR wall panel structure with energy-efficient blocks, while Figure 4 shows the manufacture and assembly of a CMR wall panel structure with energy-efficient blocks.
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 2 Methods
### Multi-Location Cross-Attention
As shown in Equation (2), a pixel-level dot product interaction is performed between the slices. To ensure the integrity of the attention of the feature maps, we perform a linear transformation with learnable weights on \(su\) and \(sv\), resulting in \(su^{\prime}\) and \(sv^{\prime}\). A dot product is then performed between \(su\) and \(sv^{\prime}\), as well as \(sv\) and \(su^{\prime}\), to guide the backpropagation and update the learnable weight coefficients. It is worth noting that in order to ensure multi-perspective learning of attention, the local branch performs the slice interaction along the \(w\) dimension. We believe that a pixel-level multi-perspective dot product can better cope with the challenges brought by salt-and-pepper noise in SAR imaging. Such high-granularity attention calculation can accurately focus on the pixel values of the target object from the features, thereby ignoring small salt-and-pepper noise. \[\text{Att}_{\text{spatial}}=\text{GELU}(\text{Down}(\text{ concat}((su\odot sv^{\prime}\pm esp),((sv\odot su^{\prime})\pm esp)))) \tag{2}\]  
Figure 2: The framework of the Multi-Location Cross-Attention module consists of a dual-branch structure, with local and global branches. We sum up the obtained \(\text{Att}_{\text{channel}}\) and \(\text{Att}_{\text{spatial}}\) to obtain the Multi-Location Attention, as shown in Equation (3), adding the global and local attention linearly to obtain the final MLCA. \[\text{MLCA}=\text{Local}(\text{Att}_{\text{channel}}+\text{Att}_{\text{ Spatial}})+\text{Global}(\text{Att}_{\text{Channel}}+\text{Att}_{\text{ Spatial}}) \tag{3}\]
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
###### Abstract  
Faulting refers to the common and significant distress in Jointed Plain Concrete Pavement (JPCP), which has an adverse impact on the pavement roughness. Nevertheless, the existing fault prediction models continue to heavily rely on conventional linear regression techniques or basic machine learning approaches, which leaves room for improvement in training efficiency and interpretability. To enhance training efficiency and accuracy, this study developed five novel faulting prediction models. These models are based on five basic machine learning algorithms: Random Forest (RF), Additive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Categorical Boost (CatBoost), combined with the tree-structured Parzen estimator (TPE). The five models are TPE-RF, TPE-AdaBoost, TPE-GBDT, TPE-LightGBM, and TPE-CatBoost. In addition to selecting the best-performing model, this study incorporated the Shapley Additive Explanation (SHAP) technique and developed TPE-SHAP-CatBoost to improve the interpretability of the model's predictions. The process involved extracting historical data on pavement performance, including 17 variables, from the Long-Term Pavement Performance (LTPP) database for 160 instances of observation. Firstly, the Boruta method was used to identify the final set of input variables. Secondly, the TPE technique, which is a Bayesian optimization method, was applied to automatically select the optimal hyperparameters for the base models. Finally, SHAP was used to provide both global and local explanations of the model's outputs. The results indicate that the TPE-CatBoost model achieves the highest accuracy with an R\({}^{2}\) value of 0.906. Furthermore, the TPE-SHAP-CatBoost model identified the primary factors influencing faulting by incorporating SHAP and provided explanations of the model's results at both the global and local levels. These research findings highlight the ability of the proposed model to accurately predict faulting, providing precise and interpretable guidance for pavement maintenance while reducing workload for pavement engineers in data collection and management.  
LTPP; rigid pavement; faulting prediction; CatBoost regression +
Footnote †: journal: Applied Sciences  
Article  
Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model  
Wei Xiao  
School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huanan 232001, China; [EMAIL_ADDRESS] (W.X.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (J.W.)  
* Correspondence: [EMAIL_ADDRESS]