esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Effects of cq-9 on Vascular Barrier Disruption under Septic Death Model  
The cq-9 was artificially synthesized by confirming the structure and subsequently used for the sepsis experiment [33]. LPS (serotype: 0111:B4, L5293), Evans blue and crystal violet were purchased from Sigma (Cat. 0111:B4, L5293, Saint Louis, MO, USA). Human recombinant HMGB1 was purchased from Abnova (Cat. H00003146-AP41, Neihu District, Taipei, Taiwan). Fetal bovine serum and Vybrant DiD were purchased from Invitrogen (Cat. V22887, Waltham, MA, USA). Primary HUVECs were obtained from Cambrex Bio Science (Cat. 10 HU-012, East Rutherford, NJ, USA) and maintained, as previously described [34; 35]. HUVECs at passages 3-5 were used. Male C57 BL/6 mice (6-7 weeks old, 27 g) were purchased from Orient Bio Co. (Cat. C57 BL/6, Jungwongu, Seongnam, Korea) and acclimatized for 12 days before starting the experiment. Animals (five per polycarbonate cage) were housed under controlled temperature (20-25 \({}^{\circ}\)C) and relative humidity (40-45%), with a 12:12 h lightdark cycle. Animals received a normal rodent pellet diet and water ad libitum during acclimatization. All animals were treated in accordance with the Guidelines for the Care and Use of Laboratory Animals issued by Kyungpook National University and the study's design was approved by the Animal Care Committee of the University (IRB No. KNU 2016-54). To induce sepsis, male mice were anesthetized with Zoletil(r) (tiletamine and zolazepam, 1:1 mixture, 30 mg/kg) and Rompun(r) (xylazine, 10 mg/kg). The CLP-induced sepsis model was prepared as previously described [36]. Briefly, a 2 cm midline incision was made to expose the cecum and adjoining intestine. The cecum was then tightly ligated with a 3.0 silk suture 5.0 mm from the caecal tip and punctured once using a 22 gauge needle to induce high-grade sepsis [37]. The cecum was then gently squeezed to extrude a small amount of faces from the perforation site and returned to the peritoneal cavity. The laparotomy site was then sutured with 4.0 silk. In sham control animals, the caecum was exposed, but not ligated or punctured, and then returned to the abdominal cavity. Briefly, HUVECs were plated (5 \(\times\) 104 cells/well) in Transwell plates with a pore size of 3 \(\upmu\)m and a diameter of 12 mm and cultured for 3 days. Confluent monolayers of HUVECs were treated with LPS (100 ng/mL) for 4 h or HMGB1 (1 g/mL) for 16 h, followed by treatment with SFN. Transwell inserts were then washed with PBS (pH 7.4), and growth medium containing 0.5 mL Evans blue (0.67 mg/mL) and 4% BSA was added. Fresh growth medium was then added to the lower chamber, and the medium in the upper chamber was replaced with Evans blue/BSA. Ten minutes later, the optical density in the lower chamber was measured at 650 nm. For the spectrophotometric quantification of endothelial cell permeability in response to increasing concentrations of each compound, the flux of Evans blue-bound albumin across functional cell monolayers was measured using a modified two-compartment chamber model, as previously described [38; 39].
## 5 Case Study
### Results  
#### 5.3.1 Usability Results  
Table 3 shows the mean (\(\mu\)), median (M), and standard deviation (\(\sigma\)) measures of the four categories of the IBM CSUQ [63]. The results show that the overall satisfaction of the participants has a positive trend, with a mean result of \(\mu=5.92\) out of a maximum of 7 and a standard deviation of \(\sigma=1.19\). Table 3 shows the question with the lowest score is question 9 (\(\mu=5.3\); M = 6; \(\sigma=1.37\)). This indicates that consideration should be given to improving how the system displays the Mensa scores when an error has been made in the handling of the interface. On the other hand, the best-rated question is number 15 (\(\mu=6.1\); M = 6; \(\sigma=0.93\)), which indicates that the application provides ease of use because the information provided is clear and displayed in an orderly manner on the screen. In addition, questions 6 (\(\mu=6.04\); M = 6; \(\sigma=1.21\)) and 7 (\(\mu=6.04\); M = 6; \(\sigma=1.09\)) are the third best rated, indicating that the students felt comfortable with the use of the application and in a short time were able to learn to use it correctly. It can also be observed that SYSUSE and INTER-QUIAL have the highest scores, which means that the overall satisfaction is related to the quality of the interface design and usability of the system. On the other hand, Figure 6 shows the usability values of the system (SYSUSE), which has a positive trend with a mean value (\(\mu=5.99\)) higher than the other analysis factors, which shows that usability is satisfactory for users. Information quality (INFOQUAL) presents the lowest results, with a mean value (\(\mu=5.81\)); this means that the application should improve the way it presents information to users. Finally, the interface quality (INTERQUAL) shows a mean value (\(\mu=5.97\)), which indicates that users perceive that the web application has adequate quality in its graphical interface. The participants also chose a word that defines the web application from a group of words presented in task number five. Figure 7 shows that the students consider the web application as attractive, useful, organized, sophisticated, efficient, and that its use allows them to save time in the work to be completed. This result shows only the most relevant words chosen by the students. Figure 5: Simulation two. Figure 6: IBM CSUQ question graph.
## 3 Experiments and Analysis
### Experimental Analysis
#### 3.3.1 Experiment A: The Advantage of the Feature Extraction Method  
This experiment is designed to verify that the three-dimensional features used in the ResNet18 model could yield better results compared with other feature extraction methods. As mentioned in [17], MFCC features have been experimentally proved to be the best hand-crafted features for the recognition, we use MFCC features and its deltas(\(\Delta\)) and deltas-deltas(\(\Delta^{2}\)) for comparison. Besides, considering that LM is a simple but effective feature, we also analyze its performance. The results of different feature extraction methods are shown in Table 3.  
From the results of MFCC and MFCC + \(\Delta+\Delta^{2}\), we can see that deltas or deltas-deltas do not contribute in improving the average accuracy, even if the number of parameters increases due to the extra two dimensions. Using the LM feature extracted from the raw signal, we can achieve an average accuracy of 0.906. Therefore, we can see that such a feature is distinguishable with different classes similar to MFCC features. Compared with MFCC or LM, the recognition accuracy of LM + MFCC improves at 0.027 and 0.005, respectively. An important remark is that LM + MFCC + CTZZ achieves the highest recognition accuracy of 0.943, which surpasses 0.032 of the average accuracy of LM + MFCC, although the extraction of such a three-dimension feature increases the computation time.  
Figure 6: The confusion matrix of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MFCC** & **MFCC + \(\Delta+\Delta^{2}\)** & **LM** & **LM + MFCC** & **LM + MFCC + CTZZ** \\ \hline Class A & 0.955 & 0.982 & 0.966 & 0.967 & 0.964 \\ Class B & 0.821 & 0.844 & 0.914 & 0.899 & 0.947 \\ Class C & 0.792 & 0.821 & 0.851 & 0.840 & 0.915 \\ Class D & 0.924 & 0.919 & 0.913 & 0.934 & 0.950 \\ Class E & 0.898 & 0.845 & 0.900 & 0.907 & 0.938 \\ Average & 0.884 & 0.882 & 0.906 & 0.911 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of different feature extraction methods with ResNet18.
# Numerical Simulations of Novel Conning Designs for Future Super-Large Wind Turbines
## 2 Modeling and Methods
### Mesh Structure and CFD Method  
The baseline rotor has been studied elsewhere [26; 27; 28]; past studies provided references for the mesh configuration applied here. The mesh employed a commonly used O-O configuration with the surface mesh on one blade containing 256 points in the airfoil circumferential direction and 128 points in the span-wise direction. The volume mesh was expanded from the surface mesh to the far-field boundary (approximately 17R away) with 128 cells along the normal direction. To meet the computational requirement of Y + <2, the first cell height was \(2\times 10^{-6}\) m. Finally, the grid was constructed with 432 blocks which contained 14.16 million structural cells in total. A similar mesh configuration is accurate enough to simulate the aerodynamic performance of the DTU 10 MW RWT rotor [26; 28] which can be found on the DTU 10 MW RWT project website [27]. Such mesh settings were used for all the coned configurations in the present paper. The blade surface mesh of C4S1 is shown in Figure 4a, and the mesh distributions on two cross-sections are illustrated in Figure 4b,c.  
The flow state is treated as incompressible, and the turbulence flow is fully developed. The flow-field was solved by the Reynolds-Averaged Navier-Stokes (RANS) equations with the \(k-\omega\) SST turbulence model [29]. The SIMPLE algorithm was utilized to couple the pressure and velocity equations. EllipSys3D, developed by the Technical University of Denmark and widely validated over the past 20 years, was used as the CFD solver. Detailed descriptions of the solver can be found in [26; 30]. Additionally, detailed boundary condition descriptions and baseline rotor validation can be found in a previous publication [25], where the same numerical methods were adopted. At a wind speed of 12 m/s, few force differences appeared between steady and unsteady simulations in the root region (\(r<R/3\)) where flow separations and 3D rotational augmentations were expected [28]. The forces along the outer part of blade remained identical. In this paper, steady CFD simulations were performed to investigate the influence of coning at a wind speed of 9 m/s, i.e., at which the unsteady effects were negligible. The operational parameters of the baseline rotor, listed in Table 1, were applied for all the coned configurations.  
Figure 4: Mesh around the blades: (**a**) blade surface mesh; (**b**) mesh on a section away from the near-blade blocks; (**c**) mesh on an airfoil cross-section in the near-blade region.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 5 Conclusions  
In this work, we proposed FDCA_Net, a 3D vehicle detector based on multi-sensory data fusion for autonomous driving scenarios. First, a seven-channel BEV map was generated to enhance its representational ability for vehicles by aggregating local and global features. Second, we proposed feature deep continuous aggregation structure, and the generalization ability of the network was improved by continuous features and deep fusion at different stages. Furthermore, a light fully convolutional network was adopted to reduce the computation load and memory requirements in the 3D proposal generator. Finally, three refined loss weight masks were added to the loss function to improve the regression accuracy of the 3D bounding boxes. The comparable experiments using the KITTI validation set show that FDCA_Net achieved a good detection accuracy with real-time  
Figure 14: The PR curve of the baseline method and ours. The base network—the blue curve, our FDCA with no loss weight mask—the green curve, our FDCA—the red curve.  
Figure 13: The AP curves of the base network and ours. The baseline network—the blue curve with a maximum AP of 74.19%, our FDCA with no loss weight mask—the green curve with a maximum AP of 75.40%, our FDCA—the red curve with a maximum AP of 76.82%.  
performance. The ablation experiments showed that the point cloud representation, feature deep continuous aggregation structure, and refined loss weight mask proposed in this paper had a positive impact on improving the detection performance of 3D vehicles. At the same time, the convergence speed and stability were greatly improved. This method is applicable to the urban scene (maximum speed 60 km/h) and detects vehicles within 70 m in front of the vehicle. The limitation of this method is that it cannot work normally in some extreme scenarios; for example, it cannot collect high-quality images in low light, and there are many noise points in the point cloud data under rainy or snowy weather. Thus, further work will involve improving the robustness of the detection system, especially when a sensor fails under extreme scenes, which will involve distinguishing and managing it for the detection system to get reliable detection results.  
[PERSON] and [PERSON] were the leaders of the research team, [PERSON] conceived and designed the research and drafted the article, [PERSON] completed the English check.  
This research was funded by the National Key Research and Development Program of China (grant no. 2018 YFE0192900, 2018 YFC0810500, 2018 YFC0604403), the National High Technology Research and Development Program of China (863 program) (grant no. 2011 AA060408).  
There are no known conflicts of interest.
## 3 Results and Discussion
### Proposed Mechanism of Inhibition  
The following description provides the ways through which SM inhibits corrosion. Anodic and cathodic reactions take place in acidic solution conditions (pH < 7) as follows:  
\[Fe\to Fe^{2+}+2e^{-}\ (Anode)\]  
\[2H^{+}+2e^{-}\to H_{2}\ (Cathode)\]  
The following equation explains how surface iron atoms can react with hydrogen ions forming corrosion:  
\[Fe+2H^{+}\to Fe^{2+}+H_{2}\]Fe\({}^{2+}\) is unstable and can form a relatively stable \(FeOOH\) film after reacting with O\({}_{2}\). Moreover, the \(FeOOH\) changes into the stable iron oxidation Fe\({}_{2}\)O\({}_{3}\)/Fe\({}_{3}\)O\({}_{4}\). Because the generated corrosion layer separates the solution from the steel surface, the corrosion rate gradually decreases as the corrosion production builds up [63].  
The negatively charged chloride ions (Cl\({}^{-}\)) can interact with positively charged iron ions (Fe\({}^{2+}\)/Fe\({}^{3+}\)) in the solution. It can aggressively erode the corrosion product layer, enhance the capacity of oxygen species to penetrate, and eventually speed up the corrosion of iron:  
\[Fe^{2+}+2 Cl^{-}\to FeCl_{2}\]  
\[Fe^{3+}+3 Cl^{-}\to FeCl_{3}\]  
When molybdate is added to a 1 M HCl solution, the chloride ions that were previously trapped between the host layers are exchanged for molybdate ions. In a 1 M HCl solution, the components of the passive film [FeMoO\({}_{4}\) and Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\)] are generated after the molybdate ions from the double-layered hydroxides are released into the solution and inhibit the corrosion in steel. The following reactions arise [64]:  
\[Fe^{2+}+MoO_{4}^{2-}\to FeMoO_{4}\]  
\[2 Fe^{3+}+3 MoO_{4}^{2-}\to Fe_{2}(MoO_{4})_{3}\]  
The inhibition mechanism of the sodium molybdate solution in HCl to metal weldment relies on the interaction of molybdate ions with the metal surface through adsorption and passivation. Sodium molybdate, acting as a corrosion inhibitor, binds to the iron oxide/molybdate film formed on the metal weldment surface in acidic environments. This film acts as a barrier, hindering aggressive anions like Cl\({}^{-}\) from reaching the metal surface and preventing metal cations from leaching into the solution [65]. Furthermore, the adsorption of molybdate ions diminishes the corrosion potential and bolsters the corrosion resistance of metal [66]. Schematic of the inhibition mechanism of Sodium Molybdate can be seen in Figure 14. The effectiveness of the sodium molybdate solution in HCl as an inhibitor hinges on multiple factors, including the concentration, inhibitor type and ratio, and prevailing environmental conditions [66; 67; 68].  
Figure 14: The corrosion mechanism is shown schematically (**a**) during the corrosion process in 1 M HCl solution without sodium molybdate and (**b**) the process when molybdate inhibits the corrosion process of the metal.
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
## 1 Introduction
Because the proposed Vivaldi antenna has the same gain in the two polarizations, the different performances that will be evidenced in the following can be attributed to the effect of the polarization of the radiated wave and to its interaction with the multilayer medium. As it will be shown, by selecting the TM mode in input to the transmitting antenna, the overall performance of the point-to-point link can be significantly improved. This opens new possibilities for the design of on-chip optical wireless interconnection networks. In Section 2, the antenna characteristics are investigated by considering either the TE or the TM polarizations of the optical mode launched in input to the SiN strip waveguide. In Section 3, we analyze the performances of transmission links in an on-chip multilayer structure for both the TE and the TM polarizations. Conclusions are finally drawn in Section 4.
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 4 Experiments  
### The Experimental Setup  
Our HarmoDehazeDiffusion was implemented in PyTorch 1.7.0. In our experiments, we trained our network on an NVIDIA GeForce GTX 3090 Ti. The training epoch was set as 1000. We used the Adam optimizer, with the momentum as (0.9, 0.999). The initial learning rate was \(3\times 10^{-5}\). Following [34], we used the Kaiming initialization technique [40] to initialize the weights of the proposed model and used a 0.9999 Exponential Moving Average (EMA) for all of our experiments. We used 1000 diffusion steps T and a noise schedule \(\beta_{t}\) linearly increasing from 0.0001 to 0.02 for training and 25 steps for inference.  
**Benchmark datasets.** Experiments were conducted on the RESIDE [41], Haze-4K [42], and Dense Haze [43] datasets to validate our method. RESIDE, with its diverse data sources and image content, is subdivided into ITS (an Indoor Training Set), OTS (an Outdoor Training Set), and SOTS (a Synthetic Objective Testing Set). The Haze4K dataset, a synthetic dataset, includes 4000 paired images from both indoor and outdoor scenes. Dense Haze is a high-quality, real-world dataset that includes 55 pairs of images with and without haze.  
**Metrics.** To verify the effectiveness of our method, we evaluated the image dehazing performance of each method using two metrics, which were the PSNR \(\uparrow\) and SSIM \(\uparrow\). Here, \(\uparrow\) indicates that a higher value is better.
###### Abstract  
The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.  
Keywords:grounding grid; electrical impedance tomography; generative adversarial network; corrosion detection
## 5 Experimental Verification
### Interference Analysis of the Data Transfer Channel to Power Transfer  
As is indicated before, the interference from data transfer channel to power transfer can be monitored by the output voltage \(U_{L}\). Figure 12 shows the experiment results with and without data transfer, where channel 1 indicates the output voltage, channel 4 indicates the voltage of \(C_{p}\), and channel 2 indicates the output data.  
Figure 12a shows that the mean value of \(U_{L}\) with data transfer is 16.4 V, while Figure 12b shows that the mean value of \(U_{L}\) without data transfer is 16.2 V. Such a small difference indicates the interference of data transfer to power transfer can be ignored. This verifies the Bode plots shown in Figure 8. Furthermore, the system efficiency of Figure 12a is 71%, and the data transfer rate is 560 kbps.
## 2 Original Model and Improved Design
### Improved Design  
There was a serious problem with the original Invelox system, where the Venturi tube was horizontally positioned so that the system's exit could be facing the incoming wind, which would cause a significant reduction in the aerodynamic performance of the system. In order to improve the performance of the Invelox system, we provide a new design idea, which is to remove the bending part of the system and vertically arrange the Venturi tube of the wind turbine under the funnel, as shown in Figure 3. For the convenience of description, the leftmost model in Figure 3, namely the original  
Figure 2: Dimensions of the Invelox wind turbine system.  
configuration, is denoted as Model 0, the middle model is denoted as Model 1, and the last model is denoted as Model 2.  
Model 1 removes the red curve from Model 0, so that there is no system exit facing the incoming flow. However, under any wind direction, the outlet plane of Model 1 is parallel to the incoming flow, which is like the encounter of Model 0 at the wind direction angle of 90\({}^{\circ}\), so the maximum speed ratio may be affected. Therefore, based on Model 1, we added a windshield around the exit. The windshield can avoid the interference of the ambient airflow to the system outlet airflow, and then improve the performance of the system. The windshield is a cylinder, coaxial with the Venturi tube, and arranged symmetrically along the system exit. The radius and height of the windshield are both 3.05 m.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result
### Simulation Data, Simulation Results, and Analysis
#### 3.1.2 Simulation Analysis of the Algorithm's Performance with Impure Reference Signal
From the Figure 15, when the SNR of the reference channel is lower to \(-5\) dB, the algorithm in this paper is greatly affected, and its estimation performance is lower than that of the traditional R-D algorithm. When the SNR of the reference channel is relatively lower, it may be necessary to use reference channel purification methods (such as reference reconstruction) to improve the SNR of the reference channel to ensure the algorithm performance. In the above simulation, the output SNR did not reach the theoretical maximum, which was caused by the fact that the effective bandwidth of the signal could not be obtained accurately and the sampling rate was not equal to the effective bandwidth.