esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Filtering Model of Graph Neural Network Based on Random Walk
## 6 Conclusions  
The existence of implicit connectivity information in the spectral domain is important for establishing connections between users and items in recommender systems. In this study, a graph convolutional collaborative filtering recommendation algorithm based on a random walk and matrix decomposition is proposed to optimize the adjacency matrix by random walk, exploit the higher order connectivity of the graph, explore the user association items, achieve to alleviate the matrix sparsity problem, and then construct a neural network by finite order polynomials, which can reduce the computation and optimize the model performance, and allow the method to learn the potential factors of users and items directly from the spectral domain. The experimental results show that this method outperforms other advanced algorithms. The performance of the model can be further improved in the future by changing the spectral convolution method, such as spectral graph attention network (GAT), graph wavelet neural network (GWNN), and simple spectral graph convolution (S2 GC), or adding heterogeneous information and item content of the graph structure.  
Conceptualization, H.M. and J.W.; methodology, H.M.; software, J.W. and K.L.; validation, J.W. and K.L.; formal analysis, X.Z. and X.C.; investigation, H.M.; resources, H.M.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M.; visualization, J.W.; supervision, H.M.; project administration, H.M.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Innovation Project of Postgraduate Education Reform of Liaoning University of Technology (YJG2021020), Liaoning Natural Science Foundation Mentoring Program Project (2019-ZD-0700), Liaoning Education Department Scientific Research Project (JZL202015404, IJKZ0625) and Liaoning Higher Education Innovation Talent Support Project (LR2019034).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 7: Effects of hyper-parameter epoch in terms of Recall@20 and Precision@20 in the dataset of MovieLens-1M: (**a**) effects of hyper-parameter epoch in terms of Recall@20; (**b**) effects of hyper-parameter epoch in terms of Precision @20.
# Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment
## 4 MRI-Controlled SI and RDD--Performance and Compliance
### Application Example  
Let us consider a simple example illustrating the mentioned methodology for checking the compliance of the materials inserted into the birdcage coil positioned in the tunnel of an MRI. The corresponding case geometry involves a 30 cm diameter and 30 cm length birdcage coil-antenna located in a 60 cm diameter tunnel. The tested matters have a form of a cube with 5 cm side (125 cm\({}^{3}\)) embedded in the center of the birdcage coil. A RF field in the birdcage coil at 63.87 MHz corresponds to the frequency f\({}_{\text{L}}\) (for a value of B\({}_{0}\) of 1.5 T) tuned to the value in the center of the considered geometry. The corresponding RF field distribution in the tunnel can be computed for the reference case (without tested matter) and for the different cases, involving tested matters in the studied scheme. Computations are based on 3D discretization of the field **E** using edge finite elements with appropriate boundary conditions. Figure 8 illustrates the RF field distribution in the reference case.  
We will illustrate now the case of piezoelectric actuation involving piezo material coated on two opposite faces by very thin electrodes. The characteristics of the piezo are \(\mu_{\text{r}}=1.0\), \(\varepsilon_{\text{r}}=[450^{\circ}990^{\circ}990]\), \(\sigma=0\) S/m. Note that the relative permittivity is given by an anisotropic vector where the value in polarization direction is less than in the other two directions. The electrodes conductors are characterized as \(\mu_{\text{r}}=1.0\), \(\varepsilon_{\text{r}}=1.0\), \(\sigma=3.77\times 10^{7}\) S/m.  
Since the currents induced by a field develop in the section of the conductor perpendicular to the direction of the field, we can consider the following two opposite cases. Field distributions were calculated for both situations of electrodes perpendicular and parallel to the direction of the field. Figure 9 displays the field distributions for both cases. The results confirmed that the orientation of the electrodes and hence the actuator (evoked in Section 4.3) plays an important role. The influence of the conducting electrode can be considerably reduced if it is parallel to the field. Note that Figure 9c shows almost the same field distributions as Figure 8.  
Figure 8: RF magnetic field (vertically directed) distribution in the axial cross section of the birdcage inside the tunnel. Reference case: no material.  
Notice that the field distributions of Figures 8 and 9 are reached for equal input conditions; consequently, they can offer a behavioral evocation. At that point, the present EMC analysis in MRI light up a procedure for checking any disturbs through image-guided maneuvers.
## 1 Introduction  
### Background and Motivation  
Seismic events pose a substantial threat to global building infrastructure, leading engineers and researchers to develop novel approaches aimed at improving structural resilience. Over the past few decades, the field of earthquake engineering has made remarkable strides in understanding and mitigating the effects of seismic forces on structures. A significant development in this area is the incorporation of additional damping devices, which have been shown to effectively enhance the seismic performance of both new and existing buildings [1, 2, 3].  
The concept of supplementary damping is fundamentally rooted in the dissipation of energy. In the context of seismic events, ground motions impart kinetic energy to structures, which must be absorbed or dissipated to prevent damage or structural failure [4, 5, 6]. Historically, seismic design approaches have largely relied on the inherent damping properties of construction materials and the ductile behavior of structural elements to manage this energy. However, these traditional methods often result in significant structural damage during severe seismic events, leading to substantial economic losses and potential losses of life [7].  
Supplementary damping devices offer a practical solution by introducing additional energy dissipation mechanisms within the structure. These devices, which include viscous dampers, viscoelastic dampers, friction dampers, and tuned mass dampers, can significantly reduce the seismic demands on primary structural elements. By effectively absorbing and dissipating seismic energy, these systems can minimize structural deformations, decrease internal forces, and enhance overall structural performance during earthquakes [8; 9; 10].  
The motivation for this review stems from the growing body of research and practical applications of supplementary damping systems in the realm of seismic-resistant design. As urban populations continue to rise and the demand for infrastructure intensifies, particularly in areas susceptible to seismic events, the importance of effective and resilient seismic protection strategies becomes increasingly paramount. Supplementary damping technologies possess the capability to not only improve structural safety but also to reduce construction costs, minimize the need for post-earthquake repairs, and enhance the overall resilience of urban environments [11; 12; 13; 14; 15]. Additionally, recent advancements in materials science, control theory, and computational technology have enabled the development of more sophisticated damping systems [16; 17]. These innovations promise to enhance the effectiveness of seismic hazard mitigation while simultaneously introducing new challenges in terms of analysis, design, and implementation. Therefore, a comprehensive review of the current understanding of the seismic performance of structures fitted with supplementary dampers is both timely and necessary.
## 4 Results
### Comparison of Typhoon Intensity Simulations  
A comparison is made between the extreme wind speeds from SMAP data, WRF simulations driven by FNL and ERA5, and the empirical parameterized typhoon models, Holland and Jelesnianski, as shown in Figures 10 and 11. Subfigure (a) of Figure 10 presents the comparison of extreme wind speeds for Typhoon Surigae. The SMAP extreme wind speeds are generally higher than those recorded by the CMA. The empirical parameterized typhoon models, Holland and Jelesnianski, match the CMA data more closely, whereas the extreme wind speed simulations from WRF driven by FNL and ERA5 deviate significantly from CMA data. In the simulation of typhoon extreme wind speed, the results from the empirical parameterized typhoon models Holland and Jelesnianski are nearly identical. Specifically, from 17 April to 19 April 2021, the FNL-driven WRF simulation provides a better match to the extreme wind speeds compared to the ERA5-driven simulation, although neither simulation reaches the typhoon intensity recorded by the CMA during the rapid intensification phase.  
Figure 10: Comparison of Typhoon Surigae Intensity Simulations. (**a**) shows the comparison of extreme wind speeds, (**b**) shows the minimum surface pressure. The black line represents the WRF simulation driven by FNL, the blue line represents the WRF simulation driven by ERA5, the green line corresponds to the empirical parameterized typhoon model Holland, the yellow line corresponds to the empirical parameterized typhoon model Jelesnianski, the purple dots represent the maximum wind speed data recorded by CMA, and the red triangles represent the maximum wind speed data from SMAP.  
Subfigure (b) of Figure 10 compares the minimum surface pressure. Similarly, the empirical parameterized typhoon models, Holland and Jelesnianski, provide simulations that are closer to the CMA data than the WRF simulations driven by FNL and ERA5. However, during the rapid intensification phase of the typhoon, the minimum surface pressures simulated by both empirical parameterized models are slightly higher than those recorded by the CMA.  
Figure 11 shows the intensity simulation for Typhoon Nepartak. Unlike Typhoon Surigae, the extreme wind speed simulated by the empirical parameterized typhoon model Jelesnianski is generally slightly higher than the CMA recorded data. For surface pressure, both the empirical parameterized typhoon models, Holland and Jelesnianski, align well with the CMA data. In contrast, the simulations of WRF driven by FNL and ERA5 deviate significantly from the CMA data.
## 2 Study Area and Data  
### Study Area  
As experimental sites representative of inland and coastal waters in China, WeiFang City in Shandong Province and Ji'An City in Jiangxi Province were selected, respectively (Figure 1). WeiFang is in the north-central part of Shandong Province and has a continental climate, being in a warm temperate monsoon zone. Floods are common from June to August, and many reservoirs and small lakes exist in the area. Ji'An is in the middle of Jiangxi Province and has a humid subtropical monsoon climate. This site is to be found in the Ganjiang River Basin and is shown as a linear river on images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Category** & **Pros and Cons** & **Example** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Image \\ Thresholding \\ \end{tabular} } & \multirow{2}{*}{Otsu Binarization} & an exhaustive algorithm for searching the global optimal & [6; 27; 48; 49] \\  & & threshold; easy to be severely corrupted by noise & [6; 27; 48; 49] \\ \cline{2-4}  & Entropy Threshold & features are easy to select and replace; a large amount of & \\  & & computation & [34; 35; 40; 50] \\ \cline{2-4}  & Bimodal Histogram & effectiveness and efficiency; strict requirements are necessary for & \\  & & the shape of the histogram; low applicability & [50; 51; 52] \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Support Vector Machine \\ (SVM) \\ \end{tabular} } & achieves accurate extraction results and improves the influence of & \\  & & noise; the parameters greatly affect the classification results & [44; 53; 45] \\ \cline{2-4}  & K-Nearest Neighbor & decrease the overfitting and variance problems in the training & \\  & (KNN) & dataset; a large amount of calculation and debugging is required & [43; 54] \\ \cline{2-4}  & Random Forest (RF) & the most accurate method for the classification of water bodies at & \\ \cline{1-1} \cline{2-4}  & Random Forest (RF) & present; a training dataset is difficult to obtain, and the classifier & [45; 47; 55; 56; 57] \\ \cline{1-1} \cline{2-4}  & & training is relatively time-consuming & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of water bodies extraction method from radar data.  
Figure 1: Map of the study sites: locations of (**a**) the two provinces in China, (**b**) WeiFang in Shandong Province, (**c**) Jiangxi Province, and (**d**) Ji’An City.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 2 Experimental Test Bed  
The reliability of the damage diagnosis approach presented in this paper is verified using different types of damage in an experimental test bed modeling a jacket-type WT as in [19]. For a very detailed description of the function generator, the amplifier and inertial shaker, the sensor network, the data acquisition system, how the vibration signals are acquired and how the time domain waveforms are processed, readers are referred to [19; 20].  
A brief characterization of the experimental setup of the small scale wind turbine is described below. First, a function generator (model GW INSTEK AF-2005) is used to produce a white noise signal with four different amplitudes (\(0.5,1,2,\) and \(3\)) that account for different wind speed regions. This signal is then amplified and used as input to a modal shaker (GW-IV47 from Data Physics) that induces vibration in the structure. The overall description of the test bench is displayed in Figure 1a.  
Figure 1: (**a**) The test bench detailing the location of the damaged bar (red circle); and (**b**) Location of the sensors.  
The structure is 2.7 m high and consists of three parts:  
(i) the top beam;  
(ii) the tower; and  
(iii) the jacket.  
The top beam is 1 meter wide and 0.6 meters high and the inertial shaker is attached to one of the ends of the beam. Three tubular sections united with bolts form the tower. Finally, the jacket is a pyramidal structure composed of steel bars of different lengths as well as steel sheets.  
The vibration of the structure is measured by means of the data acquisition system cDAQ-9188 (National Instruments) and through 8 triaxial accelerometers (model 356A17, PCB Piezotronic) optimally placed following the work by [PERSON] (2014) [17], as can be seen in Figure 1b.  
In this work we have considered the same 4 different structural states as in the work by [PERSON] et al. [20]. All of the structural states refer to the jacket bar illustrated in Figure 1a. These states are:  
(i) the healthy structure with the original healthy steel bar;  
(ii) the healthy structure where the original bar is replaced by a replica;  
(iii) the structure with a 5 mm crack damaged bar; and  
(iv) the structure with an unlocked bolt in the jacket.
## 5 Performance Analysis
### Receiver Sensitivity  
As discussed earlier in Section 3, the LoRa sensitivity is based on the bandwidth, noise figures, and spreading factor parameters for its calculation. Equation (2) is utilized to calculate the receiver sensitivity. SNR is fixed for a particular spreading factor. The SNR value is \(-7.5\) dB, \(-10\) dB, \(-12.5\) dB, \(-15\) dB, \(-17\) dB and \(-20\) dB for SF 7, SF 8, SF 9, SF10, SF 11, and SF 12, respectively.  
The above SNR value is chosen based on the SF for calculating receiver sensitivity. The bandwidth values considered here are 41.7 kHz, 62.5 kHz, 125 kHz, 250 kHz, and 500 kHz.  
Figure 14: (a) LoRa Gateway; (b) Customized Hardware of LoRa Gateway.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications** \\ \hline Microcontroller: & ATMega328P \\ LoRa Module: & SX1278 (433 MHz) \\ Wi-Fi Module: & ESP8266 \\ Voltage converter: & +5 V \\ Display: & \(20\times 4\) Alphanumeric LCD \\ Power jack: & +12 V External power supply \\ Programming pins: & FTDI and ICSP \\ \hline \hline \end{tabular}
\end{table}
Table 8: Technical Specifications of LoRa Gateway.  
In general, the receiver sensitivity is a negative value; for example, \(-132\) dBm, if the value increases further then it means poor sensitivity. Figure 15 shows the receiver sensitivity for the spreading factors 7 to 12; the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz. LoRa sensitivity values are presented in Table 9.
# An Efficient Network Resource Management in SDN for Cloud Services
## 2 Related Works
In [14], the authors modified the OSPF routing protocol to dynamically change the metric calculation based on the network requirements. They give the privilege to multimedia traffic flows based on network requirements to improve the quality of multimedia services. Their protocol increases the network bandwidth utilization and reduces the loss rate and the delay of multimedia traffic flows. However, the goal of their protocol differs from that of our mechanism. Their protocol focuses on the service differentiation and provides more resources to multimedia traffic flows. Meanwhile, our mechanism focuses on the overall network resource utilization and provides more efficient network resources to the flows that can use them more. In addition, their protocol requires the flows to be explicitly classified into multimedia and non-multimedia types when the traffic enters the network. In [14], the authors used an artificial intelligence module, proposed in [15], for flow classification. However, that module is just optimized for separating small sensor data from multimedia data and does not consider diverse traffic data. In addition, their protocol requires the installation of a new message exchanging protocol on both the controller and the switches. In [16], the authors proposed a way to reduce the delay for the significant traffic in SDN due to the separation of the control and data planes. In [17], the authors proposed a hierarchical resource allocation scheme to decrease the interference between the macro and the micro cells in SDN-based cellular networks. Our study does not focus on the situation where there are so many network nodes that the distributed protocols cannot operate on them. We also do not focus on the performance comparison of routing protocols with the legacy network and the SDN either in symmetry. The performance of routing protocols in the legacy network and the SDN was symmetrically compared and analyzed well in [12; 13]. In this study, we focus on efficient network resource management with SDN and propose a new network resource allocation mechanism that can be used in SDN. As mentioned above, many studies have been conducted on the load balancing and routing in SDN. However, there has been no approach to increase the overall network resource utilization by giving more efficient network resources to the flows that can use them more like ours.
## 3 Results and Discussion
### Mass Loss (ML) Test  
Figure 6 shows the comparison of ML of the five mixes obtained through Equation (2). According to the results, all ML values of the five mixes were well below the 15% standard limit set by PWD [63]. Although there were certain differences between the mixes, statistical analysis showed that there were no significant differences between them. The R50 mix obtained the highest ML value (3.5%), which was well below the acceptable limit. Therefore, it can be confirmed that all mixes produced promising results, which indicated that they were highly resistant against ravelling. The ML test provided an indirect indication of the possible impact of such improvements on mix disintegration resistance, which was linked to mix durability and abrasion resistance [94]. Hence, the presence of CRM and WEO reduced the ML considerably, and all mixes were highly durable [82; 83].
## 4 Results and Analysis
### Affected Area of the Transition Area
The distance distributions required for the driving workload to decrease to normal levels under different lane closure forms are shown in Figure 12. Overall, the data had a log-normal distribution (K-S test, R\({}^{2}\) > 0.9). When there were more lanes, the transition area had a greater impact on the driving workload, and the distance that must be traveled to reduce the driving workload to normal levels was larger. Based on the distances traveled by most participants, the 85 th-percentile distance was selected as the downstream affected area of the transition area. The 85 th-percentile distances traveled by participants for four lanes, three lanes, and two lanes were 433.5 m, 385.7 m, and 354.2 m, and the downstream affected areas of the transition area were 450 m, 400 m, and 350 m.