esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 2 Materials and Methods
### LR-EMD: Image Registration Algorithm Based on EMD Levels  
Taking advantage of the EMD's capacity in removing the bias field noise, we propose the following hierarchical multi-resolution registration algorithm. First, we find the IMFs for both floating and reference images. These IMFs represent the feature maps in various resolutions. Starting from the coarsest resolution, we estimate the transformation from floating image IMF to the reference image IMF via free form deformation (FFD) algorithm [44]. The estimated transformation provides an initial mapping for the free form deformation in the next level with a higher resolution. The process continues until the registration of the finest level IMFs. We call this algorithm _Level-based Registration using EMD or LR-EMD_. Note that other transformation techniques could be used instead of the free form deformation. Our algorithm is also agnostic to the type of similarity measure. In this paper, we have used SSD, CC, RC and MI as similarity measures. Algorithm 1 shows the details of LR-EMD.  
LR-EMD takes advantage of IMFs in different levels (scales) and performs registration in a hierarchical order. Therefore, it benefits from the information stored in each level of IMFs during the process of registration. The registration at each resolution level of IMFs provides an initial transformation for the registration in the next level. In principle, the hierarchical order increases the robustness of LR-EMD against the local minimum in the optimization process.  
Figure 2: EMD separates bias field noise from the intrinsic components. The set of IMFs and residuals for three levels of EMD are shown for both floating and reference images. The Gaussian function is explained in the residuals and IMFs are noise free.  
```
Input:\(I_{f\_l}\) floating image, \(I_{ref}\), reference image, \(n\) number of IMFs or EMD levels, \(\mathbb{SIM}\) Similarity measure Extract \(n\) IMFs of both \(I_{f\_l}\) and \(I_{ref}\), Initialize registration with unity transform \(f(x)=x\) for\(i=1\)to\(n\)do\(\triangleright\) where \(i=1\) is the coarse-grained level and \(i=n\) is the fine-grained level  Register the \(i\)th IMF of \(I_{f\_l}\) to the \(i\)th IMF of \(I_{ref}\) based on \(\mathbb{SIM}\) Similarity measure and find transform \(f(x)\)  Initialize transform for next level of IMF with \(f(x)\) endfor
```  
**Algorithm 1** LR-EMD: Image Registration using EMD levels.
## 4 Refined Numerical Simulation of Shield Tunneling
### Simulation of Synchronous Grouting  
In synchronous grouting, the grouting pressure gradually decreases with slurry penetration and eventually stabilizes near the pore water pressure [31]. In the current study, the synchronous grouting process is simulated by applying a flux boundary to the equivalent zone element based on seepage-stress coupling theory, and the slurry hardening process is simulated by altering the elastic modulus of the equivalent zone element and the permeability coefficient. The pore water pressure and volume increment of the equivalent zone element indicate the synchronous grouting pressure and quantity, respectively. Variations in pore water pressure will alter volume strains in the fluid-structure interaction numerical simulation. Therefore, the constitutive equation for porous media in incremental form is as follows:  
\[\Delta\sigma^{\prime}_{ij}+\alpha\Delta p\delta_{ij}=H_{ij}(\sigma_{ij}, \Delta\epsilon_{ij}) \tag{3}\]  
where \(\Delta\sigma^{\prime}_{ij}\) is the effective stress increment; \(\alpha\) is the Biot coefficient; \(\Delta p\) is the pore water pressure increment; \(\delta_{ij}\) is the Kronecker delta; \(H_{ij}\) is the function related to the mechanical behavior of the medium; \(\sigma_{ij}\) is the total stress; \(\Delta\epsilon_{ij}\) is the total strain increment.  
The pore water pressure of the equivalent zone and vertical displacement of monitoring point B are illustrated in Figure 10 for one excavation step of the shield passing through measuring point B. According to findings, in case the segments fall from the shield tail, the elastic modulus of the equivalent zone was taken as a minimum due to the shield-end structural spacing, and therefore the settlement of point B increased. The injection pressure in the shield tail gap gradually increased as the synchronous grouting progressed, and the elevation of point B increased accordingly. After completing synchronous grouting, the grouting pressure in the shield tail gap gradually decreased with a slight settlement at point B.  
Figure 10: Grouting pressure and settlement at point B.  
Figure 9: Displacement vector diagram in the longitudinal section.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.12 Italy  
C-ROADS Italy project is focused on the following use-cases: anticipated disengagement of vehicle L3 highway pilot, truck platooning, passenger cars highway chauffeur, and combined scenarios of trucks and passenger cars.  
The coordinator is the Italian Ministry of Infrastructure and Transport, which, at the beginning of 2018, set up a monitoring body exclusively dedicated to cooperative driving tasks. Among the implementing bodies there are different organisations, including: motorway operators (i.e., Brenner Motorway SPA, Autovie Venete Motorway SPA, and CAV-Concessioni Autostradali Venete Motorway SPA), carmakers (Iveco, PSA Group, Renault, Volvo, and Scania), research bodies (the Polytechnic University of Milan and FCA Research Centre), communication and logistic industries (North Italy Communications SRL, Telecom Italia SPA, Azcom Technology SRL, and Codognotto SNC). According to the planned timeline, by the end of 2019, 50 km of the Brenner Motorway, 10 km of the Autovie Venete Motorway, and 10 km of the CAV Motorway will be operational. Moreover, cross-border tests with Austria are planned for 2020. Specifically, ITS-G5 and cellular communication technologies will be tested.
# Hybrid Power System for the Range Extension of Security Robots: Prototyping Phase
## 2 Unit Testing  
### Acquisition and Installation  
The components for the HPS were acquired based on the developed specifications and installed to construct a prototype. Fuel cells in a stack with 1 kW nominal power were selected (Horizon Fuel Cell Technologies, H-1000W). The fuel cell stack is a series connection of 48 proton exchange membrane cells. From an open-circuit voltage of 45.5 V, the voltage range reaches 28.8 V, from which the peak power comes out with 35 A. Lithium-ion battery (LIB) cells with 2.5 Ah nominal discharge capacity and 3.6 V nominal voltage were selected (Samsung SDI, 18650-25R). To match the rated voltage of the load (24 V), seven cells were connected in series, producing a 7S configuration. To withstand the load demand higher than the power supply from the fuel cell, the series-connected cells were reconnected in parallel, producing a battery pack based on a 7S8P configuration. In response to the fuel cell power, a DC/DC converter with 960 W rated power was selected (Mean Well, SD-1000L-24). The converter features an input voltage range of 19 V to 72 V and an output voltage of 24 V that is manually adjustable from 23 V to 30 V. Despite manual adjustments with a control knob, fine-tunable output voltage of the converter is useful in controlling the power flow because this allows the conditions under which the fuel cell starts sharing the power demand with the battery to be adjusted. The procured components are listed in Table 1 with their brief specifications.  
The battery and DC/DC converter required no installation because their parts are completely enclosed in a single unit. Contrary to them, the fuel cell needed to be installed because most of its parts, which include a controller, switches, connectors, valves, and tubes, were provided as external parts. Only a blower was built into the air-cooled fuel cell. To enclose these separate parts within a single unit, an all-in-one-box was developed in-house. In addition, the fuel cell enclosure helps withstand temperature extremes by integrating an air conditioner that consists of chillers and heaters.
# Facial Expression Recognition Based on Vision Transformer with Hybrid Local Attention
## 4 Experimental Results
### Comparison with State-of-the-Art Methods  
Results on RAF-DB: Table 2 evaluates HLA-ViT with the state-of-the-art methods on the RAF-DB dataset. We compare our method with the lately published approaches including SCN [27], PSR [28], RAN [29], DMUE [30], FDRL [31], FER-VT [32], MetaFace2 Exp [33], SPWFA-SE [34], and RCL-Net [35]. The experimental results demonstrate that the method proposed in this paper achieved a recognition rate of 90.45%, which is 0.98% better than the second-best method (FDRL [29]). Table 3 shows the class-wise accuracy on RAF-DB. As shown in Table 3, our method achieves the best results in most  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\lambda_{1}\) & \(\lambda_{2}\) & **Acc. (\%)** \\ \hline
0.1 & 0.9 & 87.83 \\
0.2 & 0.8 & 89.93 \\
0.3 & 0.7 & 90.21 \\
0.4 & 0.6 & 90.45 \\
0.5 & 0.5 & 89.03 \\
0.6 & 0.4 & 88.71 \\
0.7 & 0.3 & 88.53 \\
0.8 & 0.2 & 88.84 \\
0.9 & 0.1 & 88.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of \(\lambda_{1}\) and \(\lambda_{2}\) on RAF-DB.  
Figure 3: Evaluation of different numbers of transformer encoder on RAF-DB.  
categories including Angry, Disgust, Fear, Happy, and Neutral expressions. The RCL-Net [33] achieves higher accuracies in Sad (86%) and Surprise (88%), but its performances in Disgust (57%) and Fear (57%) are far from satisfactory.  
Results on FERPlus: Table 2 displays the outcomes of a comparison of this paper's approach using the FERPLUS dataset with other state-of-the-art techniques. The FERPlus dataset is an enhanced version of the FER2013 dataset. Our HLA-ViT achieves the best accuracy of 90.13% on FERPlus. In Table 3, the proposed model showed a relatively good performance for Angry, Disgust, Fear, Sad, and Neutral expressions on the FERPlus dataset. The best performance was achieved in recognizing Happy expressions with a 96% accuracy, followed by Neutral, Surprised, Angry, and Sad with a 91%, 89%, 86%, and 81% accuracy, respectively, as well as Fear and Disgust with a 56% and 62% accuracy, respectively.  
Results on AffectNet: The model presented in this paper is compared to several state-of-the-art methods in Table 2 for the AffectNet dataset. The images in AffectNet come from different backgrounds, races, and genders and include various poses, expressions, and lighting conditions. It focuses on real-life scenarios and captures human facial expression changes in different environments. Our method achieves the best accuracy of 65.07%, which is 0.84% higher than the second-best method (Meta-Face2 Exp [33]). Table 3 illustrates the detailed comparison results on AffectNet. It can be seen that our proposed method achieves a superior performance in terms of the mean class accuracy. This indicates that our model has a better average recognition effect on all facial expression categories. Additionally, our method also achieves the highest accuracies for the three facial expressions (Angry, Disgust, and Surprise) among these methods, which are 57%, 61%, and 61%, respectively.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 4 Case Study
### Data Augmentation with FEM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Thermal Conductivity** & **Specific Heat at Constant Pressure** & **Density** & **Emissivity** \\ \hline Symbol & \(k\) (J/m\(\cdot\)K) & \(c_{p}\) (J/kg\(\cdot\)K) & \(\rho\) (Kg/m\({}^{3}\)) & \(\epsilon\) \\ \hline Aluminum & 220 & 2710 & 1890 & 0.9 \\ \hline Carbon Steel & 54 & 456 & 7830 & - \\ \hline Insulation & 0.086 & 387 & 208 & - \\ \hline Water & 0.6 & 4186 & 1000 & - \\ \hline Mobil Therm 603 & 0.127 & 2330 & 784 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermophysical properties considered in the computational simulation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Pipe ID** & **Size of Defects (mm)** \\ \hline
1, 2 & 25.4, 50.8, 76.2, \(\ldots\), 101.6 \\ \hline
3, 4, 5, 6 & 25.4, 50.8, 76.2, \(\ldots\), 127 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Range of simulated defects for each pipe ID. heat transfer coefficient, \(T_{ext}\) is the external temperature, and \(T\) is the surface temperature. Inside the pipe, forced internal convection is set to replicate the experimental conditions, with the fluid being Mobil Therm 603. The convection heat transfer is described by \(q=h\left(T_{fluid}-T_{solid}\right)\) where \(q\) represents the heat transfer rate per unit area, \(h\) is the convective heat transfer coefficient, \(T_{fluid}\) is the fluid temperature, and \(T_{solid}\) is the solid surface temperature. The outer surface of the aluminum cladding experiences both natural convection with the ambient environment and radiation heat transfer, with a surface emissivity set at 0.9. The radiation heat transfer is governed by \(q=\varepsilon\sigma\left(T_{amb}^{4}-T^{4}\right)\), where \(\varepsilon\) is the emissivity and \(T_{amb}\) is the ambient temperature. Mesh size was set to automatic, allowing for finer mesh in areas with defects and around thin structures, while coarser mesh was applied elsewhere. This strategy optimized computational resource utilization. The simulations were conducted under steady-state conditions to mimic the experimental setup, focusing on achieving a uniform thermal regime. Figure 5 showcases the simulation results for Pipe ID 1, featuring a defect characterized by 70% wetness and a size of 2.54 cm. These results highlight the thermal patterns and anomalies induced by the wet insulation defect.
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 1 Introduction
[31] examined multi-wire EDM for slicing silicon ingots to improve productivity so that it could replace conventional multi-wire saws. It was reported that a constant kerf width was required on each of the processing wire electrodes to cut the uniform thickness of the silicon wafer. [PERSON] et al. [32] evaluated the electric forces on multi-wire EDM for machining silicon wafers. It was found that single-wire EDM was able to slice thinner wafers with less kerf loss compared to conventional machining, but it lagged behind multi-wire saws in terms of productivity when slicing silicon ingots. To overcome this problem, multi-wire EDM was introduced. However, in the case of multi-wire EDM, the vibration was higher than in single-wire EDM. Hydrodynamic, electrostatic, and electromagnetic forces were the main sources of this wire vibration or deflection. They analyzed the electromagnetic forces during the slicing of silicon ingots and predicted the vibration of wire in terms of electromagnetic force. [PERSON] et al. [33] tried to improve the EDM efficiency by using ohmic contact in slicing silicon ingots. To improve the MRR, the contact resistance between silicon and metal was reduced via ohmic contact, which was a process of changing the rectifying contact resistance to non-rectifying contact resistance. Ohmic contact was carried out by doping another metal in a silicon workpiece. For this study, p-type silicon and n-type silicon were doped with aluminum and gold antimony, respectively. It was found that for both cases of doped silicon (p-type and n-type), the slicing speed was higher than that of silicon without doping. Extensive research has been conducted on the effectiveness of the electrode material, shape, and design in improving the EDM performance of various functional materials, with a limited focus on silicon. The electrode design methodologies for the EDM of silicon have not been investigated in detail. In addition, although it is reported that flushing mechanisms influence the EDM performance, limited studies have focused on investigating various flushing mechanisms for the EDM of silicon. Furthermore, not many studies have focused on increasing the material removal rate or machining speed during the EDM of silicon using innovative electrode design strategies, although the majority of the studies have reported comparatively slower material removal rates in the EDM of silicon. Therefore, in this study, rather than focusing on the effects of electrode shape, we will look into two electrode design methodologies that have not yet been explored on any type of workpiece, let alone silicon. The impacts of electrode arrays designed with different electrode leg heights and variations in the wall thickness of hollow electrodes on the EDM performance of silicon in terms of the MRR, TWR, and quality of the machined parts have been examined.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Confidentiality  
The system's confidentiality is guaranteed by the access control of the patient's mobile device. A possible user must insert a login and password to access his/her information in the system. Consequently, sensitive information is available only to authorized users. An authentication procedure is performed between the cloud and an entity in each phase for the generation of a session key that will encrypt the patient's data before it is exchanged on a public channel.
# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 5 Conclusions and Recommendations
### Applications and Development Recommendations
#### 5.2.1 Building a Big Data-Based AI E-Commerce Decision Support System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Training Loss** & **Training Accuracy** & **Test Loss** & **Test Accuracy** \\  & **(MSE)** & **(MAE)** & **(MSE)** & **(MAE)** \\ \hline Market Positioning Modality with Adam Optimization & 2.6202 & 1.2045 & 3.9180 & 1.4277 \\ Market Positioning Modality with RMSProp Optimization & 2.7196 & 1.2271 & 4.1295 & 1.4984 \\ Product Attribute Modality with Adam Optimization & 1.4559 & 0.8144 & 4.1140 & 1.4259 \\ Product Attribute Modality with RMSProp Optimization & 1.8072 & 0.9188 & 4.2477 & 1.5463 \\ Multimodal Adam Optimization & 0.5282 & 0.4083 & 3.3166 & 1.0464 \\ Multimodal RMSProp Optimization & 0.5500 & 0.4818 & 3.3702 & 1.0887 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Learning performance of various modalities. visual data, extracting key information for prediction models. Utilize GIS technology to analyze consumer distribution and market characteristics, integrating geolocation as a core dimension in model data. Develop and train hybrid neural network models to identify regional differences in product preferences. Use machine learning algorithms to process structured (product specifications, user geolocation) and unstructured data (user reviews, social media content, images). For instance, text analysis can interpret review sentiments, while image recognition assesses the impact of product images on decisions. This approach builds comprehensive user profiles and purchasing behavior models, allowing for tailored product recommendations and customized assistance. Regularly review and adjust model parameters to maintain prediction accuracy and relevance, particularly when entering new markets or responding to emerging consumer trends. Establish a real-time feedback mechanism to translate predictions into actionable strategies, enabling swift responses from marketing and sales teams. Implement automated business-intelligence dashboards to monitor key performance indicators (KPIs) and provide instant data viewing for rapid decision-making. Adopt a continuous learning strategy using new data to constantly train and improve models, ensuring alignment with market dynamics. Ensure the system's scalability to expand functionalities and data processing as the business grows. These strategies will provide deep insights into consumer behavior and market trends, enhancing the business's responsiveness and accuracy in adapting to market changes, thus maintaining a competitive edge.
## 2 Bearing Fabrication and Characterization Tests
### Measurement of Cleanance in Test Bearing  
In general, the normal clearance of gas bearings is not clearly identified in most applications [15]. The bearing clearance of the GFP and GFPB is defined as the air gap between the journal and the foil. Bearing clearance is the most important characteristic for determining the performance of bearings. Therefore, it is important to keep the clearance constant during the test. A common way to measure the bearing clearance in GFBs and GFPBs is to use the static load-deflection test proposed by [PERSON] et al. [18].  
Figure 3 shows a static load-deflection test rig using a lathe. Both sides of the stationary shaft are fixed by connecting it to the main stock and tail stock. The test gas bearing mounted on the stationary shaft is connected to the load cell and tool stock and transferred in the Y-direction. If the transfer range exceeds the bearing clearance, mechanical contact between the bearing substructure and the stationary shaft occurs. At this point, the reaction force is measured in the load cell. The displacement sensor measures the transfer distance of the bearings.  
Figure 4 shows the measured static load and bearing transfer distance for the GFPB and GFP. The load-displacement curve of the structure can be analyzed as a hysteresis curve. The slope of the hysteresis curve represents the ratio of the static load to the displacement, i.e., the coefficient of stiffness. The area of the curve represents the energy dissipated by damping [19]. Therefore, the experimental results indicate that GFPB has relatively low structural stiffness and high damping compared with GFP. Note that the results of the dynamic loading tests for the same bearing structures in [9] showed that the GFPB structure has higher damping properties of 200-400% than the GFP structure, mainly due to significant hysteresis damping of the polymer layer in the GFPB.  
Meanwhile, a large nonlinear behavior occurs near \(\pm\)200 \(\upmu\)m. This is judged to be nonlinear by increasing the contact area of the shaft and bearing structure as the bearing transfer distance exceeds the bearing clearance. Therefore, it can be assumed that the region before the load increases nonlinearly, i.e., the displacement region close to null stiffness region, is equal to twice the bearing clearance (=2 \(\times\) C\({}_{\rm{r}}\)). As a result, the clearances of the two bearings were measured as approximately 200 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c} \hline
**Part** & **Parameters** & **Values** \\ \hline \multirow{3}{*}{Test bearings} & Length & 35.00 mm \\  & Geometric clearance (designed) & 0.20 mm \\  & Geometric clearance (adjusted for tests) & 0.15 mm \\ \hline \multirow{3}{*}{Bump foil} & Thickness & 0.13 mm \\  & Bump height & 0.50 mm \\ \cline{1-1}  & Bump pitch & 4.57 mm \\ \cline{1-1}  & Bump half length & 1.81 mm \\ \cline{1-1}  & Material & Inconel X-750 \\ \hline \multirow{3}{*}{Polymer layer} & Thickness & 2.00 mm \\  & Width & 35.00 mm \\ \cline{1-1}  & Material & Nitrile Butadiene rubber \\ \hline \end{tabular}
\end{table}
Table 1: Test bearing design parameters.