esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 3 Our Approach
### Classifier
#### 3.1.2 Multi-View Image Encoder  
Each medical study consists of \(m\) chest X-ray images \(\{X_{i}\}_{i=1}^{m}\), as DenseNet can achieve better performance than ResNet [34] with fewer parameters and computational cost. We extract its corresponding latent features \(\{\mathcal{X}_{i}\}_{i=1}^{m}\in\mathbb{R}^{c}\) via a Densenet-121 [35] image encoder with shared weights, where \(c\) is the number of features. Then, we obtain multi-view latent features \(\mathcal{X}\in\mathbb{R}^{c}\) by combining \(m\) latent feature sets \(\{\mathcal{X}_{i}\}_{i=1}^{m}\), referring to the method proposed by [PERSON] et al. [36]. When \(m=1\), the multi-view encoder reduces to a single-image encoder.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 2 Related Work
### Traditional Methods  
Traditional image registration approaches can be broadly divided into two categories: intensity-based methods and feature-based methods.  
Intensity-based methods formulate the registration task as an optimal transport problem, where transformation parameters are iteratively optimized using metrics such as mutual information [10], entropy, cross-correlation [9], among others. Through iterative optimization, the final deformation parameters are derived to align the images. However, intensity-based methods often struggle with repetitive textures and low-contrast regions, where structural information is insufficient.  
Feature-based methods estimate deformation parameters by establishing correspondences between matched feature point pairs. Descriptors encode local geometric and photometric information around feature points, ensuring robustness to geometric distortions and noise. The Scale-Invariant Feature Transform (SIFT) [17], proposed in 2004, laid the foundation for feature-based registration by introducing scale-invariant keypoints and gradient-based descriptors. Subsequent advancements, such as RootSIFT [18] (improving descriptor discriminability) and ASIFT [19] (enhancing affine invariance), further refined feature matching robustness. The Speeded-Up Robust Features (SURF) [20] algorithm accelerated computation through integral images and Haar wavelets, while KAZE [21] leveraged nonlinear diffusion filtering for stable keypoint detection in noisy images. Recent work includes POS-GIFT [8], which leverages phase congruency maps to handle intensity variations, and RIFT2 [7], a rotation-invariant descriptor tailored for multimodal registration.  
Traditional methods face limitations when applied to historical map images, which frequently exhibit noise artifacts, stylistic variations across eras, and degraded structural continuity. Our method addresses these challenges through a dedicated preprocessing module for noise suppression and a graph attention network to model contextual relationships among feature points.
## 6 Experimental Evaluation
### Experiment I: Service Classifier
#### 6.1.3 Service Classifier Results  
The results from the Gaussian naive Bayes model used for classifying Ubunye services are summarized as follows. The model achieved an accuracy of 99.59%, indicating a strong ability to distinguish between the low-latency and high-bandwidth classes. The precision achieved 94.82%, which shows that the model effectively reduces false positives, although there is still potential for improvement. A recall of 99.99% indicates that the model rarely misses a confirmed positive case, which is crucial for applications where overlooking a positive instance can be costly. The F1-score achieved 97.33%, indicating that the model successfully balances the reduction in false positives while maximizing true positives. The average accuracy from cross-validation was 99.77%, accompanied by a very low standard deviation of \(\pm\)0.0001. This suggests that the model performs consistently well across different data subsets. The stable performance indicates that the model is robust and less susceptible to overfitting.  
The confusion matrix (Figure 7) demonstrates the performance of the Gaussian naive Bayes model in classifying network traffic. The model correctly classified 441,596 samples as low latency and 35,706 as high bandwidth. However, 1951 low-latency samples were incorrectly classified as high bandwidth, while only 5 high-bandwidth samples were misclassified as low latency. These results highlight the high accuracy of the model, particularly in identifying low-latency traffic, with a minimum number of errors in both categories. Overall, the results emphasize the robustness of the classifier in capturing the key characteristics of network traffic for both classes.  
Each feature created plays a crucial role in capturing different aspects of network traffic, providing the model with detailed information that helps differentiate between low-latency and high-bandwidth classes. The results obtained show good accuracy in classifying low-latency and high-bandwidth traffic profiles.
## 3 Results
### Surface Roughness  
The measured average surface roughness Ra of the as-received substrates and selective plated copper coatings deposited on them using either Cu acid or Cu alkaline electrolyte solutions is illustrated in Figure 4.  
For the stainless steel substrates, the initially measured average surface roughness was 1.463 \(\pm\) 0.450 \(\upmu\)m. After the deposition of copper coatings using the acidic electrolyte solution, the roughness decreased to 1.173 \(\pm\) 0.430 \(\upmu\)m. Similarly, when the alkaline electrolyte solution was used, the roughness further decreased to 0.857 \(\pm\) 0.190 \(\upmu\)m. These results indicate a reduction in surface roughness after the selective copper-plating process on stainless steel substrates, regardless of the electrolyte solution employed.  
In the case of nickel alloy substrates, the initially measured average surface roughness was 4.178 \(\pm\) 0.490 \(\upmu\)m. Following the deposition of copper coatings using the acidic electrolyte solution, the roughness decreased to 3.406 \(\pm\) 0.330 \(\upmu\)m. When the alkaline electrolyte solution was used, the roughness further decreased to 2.601 \(\pm\) 0.680 \(\upmu\)m. These findings demonstrate a considerable reduction in surface roughness after the selective copper-plating process on nickel alloy substrates, regardless of the electrolyte solution utilized.  
The obtained results indicate that the selective plated copper coatings effectively reduce the surface roughness of both stainless steel and nickel alloy substrates. This reduction in roughness can have implications for improving the surface properties of the coated materials, such as reducing the potential for bacterial adhesion.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Intensity [\%]** & **d-Spacing [Å]** & **2 Theta [\({}^{\circ}\)]** & **HKL** & **FWHM** \\ \hline
1 & 100.00 & 2.08554 & 50.797 & 1 1 1 & 0.483 \\
2 & 46.00 & 1.80620 & 59.371 & 2 0 0 & 0.839 \\
3 & 20.00 & 1.27711 & 88.921 & 2 2 0 & 1.211 \\
4 & 17.00 & 1.08936 & 110.396 & 3 1 1 & 1.623 \\
5 & 5.00 & 1.04297 & 118.108 & 2 2 2 & 1.041 \\ \hline \end{tabular}
\end{table}
Table 2: Peak parameters in terms of intensity, distance between the crystallographic planes (d-Spacing), angle between the transmitted and reflective beam (2 Theta), Miller indices of the crystallographic plane (HKL), and full width at half maximum of the peak (FWHM) derived from the diffractogram obtained from the selective plated copper coatings using Cu alkaline solution.  
Figure 4: Surface roughness Ra of the as-received medical-grade stainless steel (SS 1.4021) and nickel alloy (Ni200) substrates (initial), and of the selective plated copper coatings deposited on them using either Cu acid or Cu alkaline electrolyte solutions.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 2 Project Overview
### Numerical Models and Simulation Scheme  
#### 3.2.1 Hoek-Brown (H-B) Criterion  
The H-B criterion, which perfectly reflects the non-linear failure characteristics of rocks and rock masses, was proposed by [PERSON] [PERSON] in 1980. After years of continuous development, it has become one of the most accepted criteria in the research on rock mass stability analysis and strength prediction [31]. The expression of the criterion is as follows:  
\[\sigma_{1}=\sigma_{3}+\sigma_{ci}\bigg{(}m_{b}\frac{\sigma_{3}}{\sigma_{ci}}+s \bigg{)}^{a} \tag{1}\]  
where \(\sigma_{1}\) and \(\sigma_{3}\) are the maximum and minimum principal stress of the rock mass, respectively; \(\sigma_{ci}\) is the uniaxial compressive strength of the rock specimen; \(m_{b}\), \(s\), and \(a\) are the material parameters of the rock mass related to lithology and the structural plane, which can be determined by the correlation:  
\[\left\{\begin{array}{c}m_{b}=m_{i}e^{(\frac{\text{CSI}-100}{28-142})}\\ s=e^{(\frac{\text{CSI}-100}{9-380})}\\ a=\frac{1}{2}+\frac{e^{(-\text{CSI}/15)}-e^{(-20/3)}}{6}\end{array}\right. \tag{2}\]  
where \(m_{i}\) is the material constant reflecting the softness and hardness of the rock mass. \(D\) is the parameter reflecting the disturbance of rock mass, and its value ranges from 0 to 1.
## 4 Analysis: ISM Processes and Results
### Developing the Reachability Matrix and Check the Transitivity  
A matrix was derived from the SSIM by replacing the symbols (V, A, X, O) with binary digits (0, 1). This transformation helped to calculate and reflect the direct and indirect impacts between the enablers [46]. The points below show the transition criteria with examples:  
* For V, (i, j) is substituted by 1 while (j, i) is substituted by 0. For example, in SSIM, the cell of (1, 2) has V, so (1, 2) is assigned \"1\" while (2, 1) is assigned \"0\".
* For A, (i, j) is substituted by 0 while (j, i) is substituted by 1. For example, in SSIM, the cell of (2, 4) has A, so, (2, 4) is assigned \"0\" while (4, 2) is assigned \"1\".
* For X, (i, j) and (j, i) are substituted by 1. For example, in SSIM, the cell of (2, 3) has X; both (2, 3) and (3, 2) are assigned \"1\".
* For O, both (i, j) and (j, i) are substituted by 0. For example, in SSIM, the cell of (2, 5) has O; both (2, 5) and (5, 2) are assigned \"0\".  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Code** & **Enablers** & **E12** & **E11** & **E10** & **E9** & **E8** & **E7** & **E6** & **E5** & **E4** & **E3** & **E2** & **E1** \\ \hline E1 & Establishing clear law and legislation & V & V & V & V & V & V & V & O & V & V & V & V \\ \hline E2 & Raising awareness of CE & O & O & O & O & V & O & O & O & A & X & \\ \hline E3 & Education and training & O & O & O & O & V & X & O & V & A & & \\ \hline E4 & CE initiatives & V & V & V & V & V & V & V & V & & & \\ \hline E5 & Advanced technology related to CE & X & V & O & A & O & A & X & & & & \\ \hline E6 & Developing a mature market and supply chain & X & V & A & A & O & A & & & & & \\ \hline E7 & Existing examples of case studies that adopt CE principles & O & V & O & X & X & & & & & & \\ \hline E8 & Promoting a culture of acceptance of reclaimed materials & O & V & O & A & & & & & & & \\ \hline E9 & Providing incentives to adopt CE & V & O & O & & & & & & & \\ \hline E10 & Certification of reclaimed materials & O & O & O & & & & & & & & \\ \hline E11 & Cost reductions for materials and construction & A & & & & & & & & & & \\ \hline E12 & Ensuring data availability & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural Self-Interaction Matrix of the enablers of CE.  
Once the initial reachability matrix shown in Table 3 was complete, a transitivity rule was incorporated to define the final reachability matrix. Transitivity is an assumption that attempts to capture the indirect relationships between variables [46]. For instance, if element A influences or leads element B, and element B leads element C, then element A also leads element C. Applying transitivity was essential to identifying the direct and indirect influences. Table 4 shows the final reachability matrix that resulted from this characterisation; note that the star symbol (*) indicates assumed transitivity.
## 2 Mathematical Model
### Potential Distribution  
The net charge density \(\rho_{e}\) follows the Boltzmann distribution [12], given as  
\[\rho_{e}\ =\ -z_{e}\varrho\Big{(}\overline{n}^{-}-\overline{n}^{+}\Big{)}, \tag{12}\]  
here the anions (\(\overline{n}^{-}\)) and cat-ions (\(\overline{n}^{+}\)) are defined through \(\rho_{e}\) of the Boltzmann Equation:  
\[\overline{n}^{\pm}\ =\ \overline{n}_{0}e^{(\pm\frac{\rho_{0}}{T_{a}\kappa_{B}} \overline{\Phi})}, \tag{13}\]  
where \(n_{0}\) represents bulk concentration, \(z_{o}\) the charge balance, \(K_{B}\) the Boltzmann constant, \(e\) the electronic charge and \(T_{a\upsilon}\) is the average temperature.  
Applying Debye-H\(\ddot{u}\)ckel linearization approxiamtion [12], Equation (11) becomes  
\[\frac{d^{2}\Phi}{dy^{2}}\ =\ m_{e}^{2}\Phi. \tag{14}\]  
Here \(m_{e}\) is the electroosmotic parameter. The analytical solution of above Equation (14) subject to boundary conditions \(\frac{\partial\Phi}{\partial y}\ =\ 0\), at \(y\ =\ 0\) and \(\Phi\ =\ 1\), at \(y\ =\ h(x)\) is obtained as;  
\[\Phi(y)\ =\ \frac{\cosh(m_{e}y)}{\cosh(m_{e}h)}. \tag{15}\]
# Energy and Distance-Aware Hopping Sensor
## 4 Conclusions  
Sensors are randomly distributed and the desired data is collected by the sensors in order to characterize areas that are difficult to access. However, due to the energy limitations of the sensor nodes, sensing holes could be generated, and then a network disconnection is also caused. In order to recover the sensing hole, the rearrangement of the hopping sensor node is considered in this paper. Most of previous relocation protocols have been based on the shortest path between a cluster header which can move hopping sensors and a sensing hole header. However, it is practically impossible that the cluster header of the sensing hole already knows the location of all the cluster headers of a given network.  
In this paper, we overcome the limit of requirements of the network topology information and propose new relocation protocol in a distributed environment which is very suitable for actual cases. Considering the characteristics of the collected data, designing the relocation protocol of the sensors to be able to consider both the distance to the sensing hole and the energy which enables the hopping sensors to move is the major achievement of the proposed methods. As far as we know, this study is the first simulation using OMNeT++ which reflects actual environments. Future research on the relocation protocol considering various geographical environments will further improve the proposed protocol.  
[PERSON], [PERSON], and [PERSON] designed the protocol and the simulation process. [PERSON], [PERSON], and [PERSON] coordinated the grant funding, conducted the study, analyzed the data, and drafted the first version of the manuscript. The simulation was conducted by [PERSON] and [PERSON], and [PERSON] supervised the software development. All authors read and approved the final manuscript.  
This work was supported by the Incheon National University Research Grant in 2018.  
The authors declare no conflict of interest.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Comparison
#### 5.3.1 Comparison on Virtex 4 Platform
Similarly, the proposed 2-stage pipeline architecture surpasses the performance of the [38] architecture by utilizing 89.7% fewer hardware resources. This optimization leads to an impressive 5.19 times higher throughput/area ratio, further enhancing the computational efficiency of our design.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection
#### 3.4.3 Scaling the Images  
Scaling alters the size of the image or its elements, either enlarging or reducing them while maintaining the same aspect ratio. In medical imaging, scaling is critical as it compensates for the variation in the physical size of anatomical structures among different patients. For example, a nodule in a CT scan might appear larger in a smaller patient compared to a larger patient. By incorporating images scaled at different ratios, the model learns to identify structures of interest across a range of sizes, which is essential for robust diagnostic applications. This technique not only broadens the diversity of the dataset but also mimics potential real-world scenarios where equipment settings or patient physiology result in size variations of anatomical features. Scaling ensures that the model's performance is consistent regardless of such differences.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 3 Proposed Fusion Methods  
### Loosely Coupled Approach  
The loosely coupled approach, referred to also as the decentralized one, is supposed to integrate the estimated positions, obtained from multiple sensors, to determine the final position. The ultrasonic measurements, coming from the three ULPSs, are used to obtain individual position estimates using the GN algorithm by applying Equations (1) and (2). These position estimates are consequently used as input position measurements in the sensor fusion algorithms (LKF and AKF involved hereinafter), then merged to obtain the final position estimate output of the integrated system with less noise, as well as more accuracy, than the individual estimated positions, as shown in Figure 6. Three identical branches are applied for the three ultrasonic receivers (\(R_{A}\), \(R_{B}\) and \(R_{C}\)). It is worth noting that the availability of nine position estimates at the input of the fusion module is optimistic, since some of them are often not available.
## 2 Related Works
### Optimization Algorithms for Controller Deployment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Objectives** & **Number of Controllers** & **Approach** \\ \hline Reference [17] & the maximum propagation delay between the switches the load of controllers & \(\times\) & hierarchical K-means algorithm \\ \hline Reference (CCPP) [18] & the maximum propagation delay between the switches the capacity of controllers & \(\times\) & the capacitated K-center problem \\ \hline Reference (LBCPP) [19] & the load of controllers the average delay between switches and controllers & \(\times\) & based on topological potential and minimum cost flow \\ \hline Reference [20] & the delay between switches and controllers & \(\surd\) & bargaining game \\  & the communication overhead inter-controller the load of controllers & & \\ \hline Reference (RCP-DCP and RCP-DCR) [21] & single-link and node failures & \(\times\) & Mixed Integer Linear Programming (MILP) \\ \hline Reference (GreCo) [22] & the path delays and load of controllers the energy consumption & \(\times\) & Binary Integer Program (BIP) \\ \hline Reference [23] & multiple-link failures & \(\times\) & Betweenness Centrality Principle (BCP), Statistical Model (SM), Minimizing Maximum Regret (MMR), Hurwicz Criterion (HC) \\ \hline Reference [24] & single-link and multi-link failures failures & \(\times\) & the heuristic algorithm and the greedy algorithm based on the Monte Carlo Simulation \\ \hline Reference [26] & the delay between switches and controllers & \(\surd\) & hierarchical clustering \\  & the load of controllers & & \\ \hline Reference (CPCNS and CPSLF) [27] & node failures and link failures & \(\times\) & \begin{tabular}{c} a network \\ states-traversal-based \\ algorithm and greedy-based \\ algorithm \\ \end{tabular} \\ \hline Reference [28] & \
\begin{tabular}{c} inter-controller delay, load \\ distribution \\ \end{tabular} & \(\surd\) & genetic algorithm \\ \hline Reference (CNCP) [29] &
\begin{tabular}{c} worst-case latency \\ multiple controller failures \\ \end{tabular} & \(\times\) & simulated annealing heuristic \\ \hline Reference (DDCP) & the delay between switches and controllers & & \\ \([30]\)—Dynamic & the Control Load (CL) & \(\surd\) & Deep Q-Network \\  & the Intra-Cluster Delay (ICD) & & \\  & the Intra-Cluster Throughput & & \\  & (ICT) & & \\ \hline Reference (D4 CPP) & delay and load in the network & \(\times\) & Deep Q-Network \\ \hline Reference [31]—Dynamic & controllers-switches delay & & \\ \(\text{inter-controllers delay}\) & \(\times\) & a quadratic program controllers load & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant studies.
## 4 Soil Parameter Updating
### Response-Surface Method  
The response-surface method (RSM) is an approach with many mathematical processes that dig into the relationship between input factors and output responses. RSM is advantageous not only for cost-efficient and time-saving processes of numerical model updating through optimization [37], but also for the modeling, analysis, and construction of technical models [38]. The relationship between the responses and the independent input variables can be described as follows.  
\[S=f(t_{1},\ t_{2},\ t_{3}\ldots\ldots)+z \tag{4}\]  
where \(S\) is the optimal response that is a function of multiple input variables, \(t\) is the input variables, and \(z\) is the residual or offsets. The main theme for the response-surface method was developed as a second-order polynomial equation that obtains the optimal response using a series of designed experiments [39]; to solve the curvature system in updating a numerical model, a higher-order polynomial equation should be considered [25]. Second-order polynomial equations are used in this analysis to construct the response surface.  
Figure 5: Flowchart of model updating with the response-surface method.  
Equation (5) shows the second-order polynomial equation to obtain the optimal response in terms of strain (S).  
\[S=u_{0}+\sum_{m=1}^{p}u_{m}t_{m}+\sum_{m=1}^{p}u_{mm}t_{m}^{2}+\sum_{m=1}^{p} \sum_{m<n}^{p}u_{mm}t_{n}+z \tag{5}\]  
where \(P\) is the number of input variables, \(u_{0}\) is the constant coefficient, \(u_{m}\) is the first-order coefficient, \(u_{mm}\) is the pure second-order coefficient, \(u_{mm}\) is the intersectional second-order coefficient, and \(t_{m}\) and \(t_{n}\) are the values of input variables. To update multiple variables, the central composite design (CCD) determines how many experiments are conducted [40]. The number of runs for the experiment is determined with Equation (6).  
\[N=2^{p}+2p+n_{c} \tag{6}\]  
where \(2^{p}\) is the cubical points number, \(2p\) is the axial points number, \(n_{c}\) is the number of central points, and \(p\) represents the number of input variables. The gap between an axial point and the center point is alpha \((\alpha)\). For the \(p\) number of input variables in the fully circumscribed central composite design method, \(\alpha\) is calculated with Equation (7) [41].  
\[\alpha=[2^{p}]^{1/4} \tag{7}\]  
The three-factor orientation of axial and cubical points in a fully circumscribed central composite design is shown in Figure 6. CCD provides six axial points, eight cubical points, and two central points for three input factors.