esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calculation Results and Analysis
### Comparison of Liao's Benchmarks
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Problem** & **ISP-MCTS** & **IDABC** & **HVNS** & **PSO** & **GA\({}_{\text{R}}\)** & **ISA** & **ABC\({}_{\text{P}}\)** \\ \hline \(20\times 4\) & 0.02 & 0.01 & 0.27 & 0.62 & 0.88 & 0.57 & 0.29 \\ \(20\times 6\) & 0.04 & 0.06 & 0.16 & 0.68 & 1.03 & 0.66 & 0.57 \\ \(20\times 8\) & 0.21 & 0.23 & 0.47 & 0.89 & 0.96 & 1.01 & 0.44 \\ \(40\times 4\) & 0.02 & 0.03 & 0.15 & 0.24 & 0.41 & 0.29 & 0.36 \\ \(40\times 6\) & 0.01 & 0.02 & 0.11 & 0.15 & 0.56 & 0.17 & 0.51 \\ \(40\times 8\) & 0.10 & 0.13 & 0.53 & 0.73 & 1.15 & 1.01 & 0.99 \\ \(60\times 4\) & 0.05 & 0.07 & 0.09 & 0.21 & 0.47 & 0.31 & 0.42 \\ \(60\times 6\) & 0.06 & 0.10 & 0.17 & 0.33 & 0.48 & 0.36 & 0.51 \\ \(60\times 8\) & 0.12 & 0.13 & 0.31 & 0.42 & 0.92 & 0.62 & 0.93 \\ \(80\times 4\) & 0.02 & 0.02 & 0.08 & 0.11 & 0.29 & 0.18 & 0.31 \\ \(80\times 6\) & 0.09 & 0.13 & 0.17 & 0.23 & 0.65 & 0.46 & 0.48 \\ \(80\times 8\) & 0.05 & 0.09 & 0.25 & 0.33 & 0.47 & 0.28 & 0.42 \\ \(100\times 4\) & 0.00 & 0.00 & 0.02 & 0.03 & 0.13 & 0.03 & 0.09 \\ \(100\times 6\) & 0.02 & 0.02 & 0.07 & 0.11 & 0.28 & 0.16 & 0.31 \\ \(100\times 8\) & 0.06 & 0.06 & 0.21 & 0.39 & 0.46 & 0.31 & 0.48 \\ Ave. & 0.058 & 0.073 & 0.204 & 0.365 & 0.609 & 0.428 & 0.474 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Computational results of the algorithms (\(\rho\) = 20). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Problem** & **ISP-MCTS** & **IDABC** & **HVNS** & **PSO** & **GA\({}_{\text{R}}\)** & **ISA** & **ABC\({}_{\text{P}}\)** \\ \hline \(20\times 4\) & 0.03 & 0.03 & 0.31 & 0.71 & 1.01 & 0.68 & 0.38 \\ \(20\times 6\) & 0.12 & 0.11 & 0.24 & 0.77 & 1.27 & 0.78 & 0.69 \\ \(20\times 8\) & 0.33 & 0.34 & 0.61 & 1.03 & 1.2  
As shown in Tables 9-11, the ISP-MCTS solution quality is better than that of the other six algorithms. When \(\rho=10\), the average deviation of ISP-MCTS in the seven algorithms is the smallest, and the value is 0.115. The average deviation of the ISP-MCTS algorithm is less than IDABC (0.121), HVNS (0.263), PSO (0.495), GA\({}_{\text{R}}\) (0.743), ISA (0.509), and ABC\({}_{\text{P}}\) (0.591). Therefore, the solution quality of the ISP-MCTS algorithm is better than the other six algorithms. When \(\rho=20\) and \(\rho=30\), the average deviation of all algorithms is improved through comparing to \(\rho=10\). At the same time, the ISP-MCTS algorithm still has the smallest deviation among all algorithms, so the validity of the ISP-MCTS algorithm can be verified. Here, a multi-factor analysis of variance was used to further compare the seven algorithms, and the algorithm type and \(\rho\) were taken as factors. From the mean graph and the two-factor Tukey HSD test (95% confidence interval) as shown in Figure 3, the ISP-MCTS algorithm is significantly better than the HVNS, PSO, GA\({}_{\text{R}}\), ISA, and ABC\({}_{\text{P}}\) algorithms at different CPU times. According to the above average variance value, the superiority of the ISP-MCTS algorithm in solving the HFSSP problem was verified.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 3 Preliminaries
### k-Nn  
\(k\)-NN is an instance-based learning method that classifies instances within a dataset by assigning the label of the closest neighbour to each new pattern during the testing phases. If the instances are tagged with a classification label, then the majority class of the closest \(k\) neighbours is assigned to the unclassified instance. Although the power of \(k\)-NN has been proven in a number of real domains, they have large storage requirements and their performance is sensitive to the choice of the \(k\).
## 6 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**GCP**} & \multirow{2}{*}{**Parameter Liberation**} & \multirow{2}{*}{**\(\sigma\) [mm]**} & \multicolumn{3}{c}{**RMS [cm]**} & \multicolumn{3}{c}{**Lever-Arm [cm]**} & \multicolumn{3}{c}{**Correlation**} \\ \cline{4-13}  & & & \(xy\) & \(z\) & 3D & \(xy\) & \(z\) & 3D & \(\delta_{L_{\alpha},f}\) & \(\delta_{C_{\alpha},L_{\alpha}}\) & \(\delta_{C_{\alpha},f}\) \\ \hline \multicolumn{13}{l}{**Calibration**} \\ \hline s2\(\cdot\)n50 + & \multirow{2}{*}{0} & camera model, lever-arm & 5.2 & 1.9 & 8.3 & 8.5 & na: & 51.4 & 34.8 & 62.1 & \multirow{2}{*}{0.18} & \multirow{2}{*}{0.99 \(\pm\) 0.00} & \multirow{2}{*}{0.18 \(\pm\) 0.00} \\ s2\(\cdot\)n50 & & & & & & & & & & & \\ \cline{3-13} s2\(\cdot\)n50 + & \multirow{2}{*}{1} & \multirow{2}{*}{camera model, lever-arm} & \multirow{2}{*}{4.9} & \multirow{2}{*}{0.9} & \multirow{2}{*}{0.6} & \multirow{2}{*}{1.1} & na: & 51.0 & 41.8 & 65.9 & \multirow{2}{*}{0.84} & \multirow{2}{*}{0.80 \(\pm\) 0.06} & \multirow{2}{*}{0.68 \(\pm\) 0.05} \\  & & & & & & & & & & & & \\ \hline s2\(\cdot\)n50 + & \multirow{2}{*}{0} & \multirow{2}{*}{camera model, lever-arm} & \multirow{2}{*}{5.0} & \multirow{2}{*}{1.9} & \multirow{2}{*}{5.9} & \multirow{2}{*}{6.2} & na: & 51.9 & 36.8 & 63.7 & \multirow{2}{*}{0.99 \(\pm\) 0.00} & \multirow{2}{*}{0.07 \(\pm\) 0.00} \\ s2\(\cdot\)n50 + & & & & & & & & & & & & \\ \cline{3-13} s2\(\cdot\)n3070 & & & & & & & & & & & & \\ \cline{3-13}  & & & & & & & & & & & & \\ \cline{3-13}  & & & & & & & & & & & & & \\ \hline \multicolumn{13}{l}{**Acquisition**} \\ \hline  & 0 & camera model, lever-arm & 4.8 & 3.3 & 15.0 & 15.3 & na: & 53.8 & 47.7 & 71.9 & 0.28 & 0.99 \(\pm\) 0.00 & 0.28 \(\pm\) 0.00 \\ s1\(\cdot\)n50 & & & & & & & & & & & & \\ \cline{3-13} s2\(\cdot\)n50 & & & & & & & & & & & & \\ \cline{3-13}  & 0 & & & & & & & & & & & \\ \hline \multicolumn{13}{l}{\(\sigma\) represents the sigma naught in bundle adjustment; \(\delta_{L_{\alpha},f}\) represents the correlation coefficient between the radar flight lever-arm on z-axis and the focal length \(\delta_{C_{\alpha},L_{\alpha}}\) represents the correlation coefficient between the camera height and the radar flight lever-arm on z-axis \(\delta_{C_{\alpha},f}\) represents the correlation coefficient between the camera height and the local length; and N/A means the camera model and the lever-arm are not estimated but given as a priori informations} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of photogrammetric data processing. Given the little elevation difference along the dike, a strong correlation exists between the camera focal length, the height of the camera and the \(z\) component of the lever-arm. The correlation coefficients of these parameters are given for each configuration. In the following, the results are discussed in more details focusing on the influence of the adopted acquisition geometry on the final accuracy in object space.
## 2 Materials and Methods  
The overall system architecture (engineering software platform) of the proposed methodology is presented in Figure 1. Starting from this general picture, the main modules are described, namely, (i) the knowledge-based system and (ii) the CAD feature recognition system. According to the framework presented in Figure 1, design data (i.e., product features, feature parameters, and product manufacturing information) stored within the 3D CAD model (CAD file) is read by the CAD feature recognition system, which analyzes the model through the use of dedicated algorithms for feature extraction based on the given scheme reported in the following section (Section 2.2). Once the features and parameters are analyzed, these parameters are compared with the features and parameters stored in the DFW rules DB which are the core of the knowledge-based (KB) system. DFW rules database (DB) are the repository collecting all the welding guidelines according to the rule-based method described in the following section (Section 2.1). When a design parameter of a specific feature (i.e., the length of a triangular weld bead) is not compliant with the rule stored within the DFW rules DB (i.e., the type of the feature is not allowed for a given geometry or the value of the parameter overcomes the threshold) the feature is highlighted in the 3D CAD model and a warning (non-validated DFW rule) is provided to the designer through the DFW graphic user interface (GUI) for corrective action. The DFW GUI is a dedicated window developed to be integrated within the CAD environment (CAD plug-in), which allows displaying both results, validated and non-validated rules. The types of features to be recognized, the parameters to be checked and related thresholds for each parameter are reported in a structured repository (DfW rules DB). This database is the core element of the KB system, which can translate tacit implicit knowledge into explicit knowledge.  
For clarity, the materials and methods used to develop the two systems of the engineering software platform in the following sections are presented in the KB system (Section 2.1) and the CAD feature recognition system (Section 2.2).
## 3 Materials and Methods
### Data Preprocessing
#### 3.1.3 Unwanted Points Removal  
Following ellipse fitting, the cross-sectional point cloud data is refined by evaluating the geometry consistency of individual points with the fitted ellipse. Specifically, points are classified based on their distances to the ellipse: those within two standard deviations of mean distance are retained as part of the tunnel lining, while outliers typically corresponding to noise, track points, bolt holes, or auxiliary facilities are flagged for removal. This statistical thresholding utilizes the normal distribution of lining points to distinguish structural components from non-lining features. This refinement process is iterative: after removing outliers, the ellipse is refitted to the remaining points, and the distance-based filtering is reapplied until the point set stabilizes (i.e., no further significant changes occur in the fitted ellipse parameters). This iterative approach ensures that the final point cloud accurately represents the geometric integrity of tunnel lining, effectively suppressing interference from ancillary structures. By progressively isolating the lining points, this step lays a robust foundation for subsequent leakage detection by minimizing false positives from irrelevant features.
## 2 System Model
### Computation Model
#### 2.4.1 Local Execution  
Based on the decision \((s_{k}^{local}=1)\) of the agent \(l\), the SUE \(k\) performs the computation locally, where the computation time depends on the implanted resources of the SUE. As sume for the moment that \(f_{k}^{local}\) represents the SUE's computing power, which is evaluated regarding the count of CPU cycles executed per second [1; 2]. \(0\leq\delta_{k}\leq t_{k}^{max}\) is the maximum allowable computation time. Therefore, the local computation delay can be expressed as follows:  
\[D_{k}^{local}=\frac{C_{k}}{f_{k}^{local}}, \tag{7}\]  
where \(D_{k}^{local}\) is the processing delay for accomplishing the computing tasks \(\phi_{k}\) and \(C_{k}\) represents the total required CPU cycle.  
\[E_{k}^{local}=\zeta_{k}C_{k} \tag{8}\]  
The coefficient \(\zeta_{k}\) represents the energy consumption per CPU cycle and depends on the UE's chip architecture. In this study, we set \(\zeta_{k}\) equal to \(10^{-27}\) times the square of the local frequency, based on the research presented in [30].
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 4 Results
### Testing Results per Dataset  
The radial plot presented in Figure 10 illustrates a comprehensive comparison between the performance of using a pure virtual dataset and a hybrid dataset. The radial plot was constructed by first calculating the average of each metric (precision, recall, accuracy and F1-score) for both datasets across the two validation environments.  
The area under each line was filled to visually enhance the comparison. This visualisation helps in determining whether the incorporation of a small number of real images into the predominantly virtual dataset provides a significant improvement in model performance. The graph clearly shows the following:  
Figure 10: Radial comparison of results for the virtual dataset and the hybrid dataset.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Min** & **Max** & **Avg** \\ \hline Pre-processing time (ms) & 0.00 & 5.00 & 1.93 \\ Inference time (ms) & 11.5 & 72.58 & 30.45 \\ Post-processing time (ms) & 0.00 & 32.59 & 1.63 \\ CPU usage (\%) & 0.30 & 2.20 & 0.77 \\ RAM usage (\%) & 13.80 & 14.40 & 14.24 \\ GPU usage (\%) & 0.00 & 21.00 & 6.57 \\ GPU memory usage (MB) & 2079.00 & 2585.00 & 2546.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real-time performance metrics for YOLOv9-based posture detection.  
1. The hybrid dataset shows a more balanced and generally higher performance across most metrics compared to the virtual dataset. This suggests that adding real images to the virtual dataset can enhance the model's ability to generalise better, particularly in a real-world validation environment.
2. While the virtual dataset performs exceptionally well in a controlled virtual environment, its performance drops significantly when validated in a real-world setting. The hybrid dataset, while slightly lower in virtual settings, maintains more consistent performance across different environments.
# On the Subrange and Its Application to the R-Chart
## 2 The Distribution of the Subrange
### The Breakdown Points  
Another way of choosing \(k\) is considering the finite-sample breakdown point, denoted by \(\varepsilon_{n}\). This is the maximum proportion of arbitrarily extreme observations where an estimator results in a reasonable value. For more detail see [6,10]. This finite-sample breakdown point is generally a function of sample size \(n\). The proposed estimator, based on the subrange, has additional merit because of a positive breakdown point. It is clear that \(\widehat{\sigma}_{\left[k\right]}\) has a positive breakdown point of \(k/n\) for \(k>0\),\(\partial_{\left[0\right]}\) has a zero one. Thus, we can choose \(k\) based on the value \(k/n\).  
It should be noted that the maximum attainable value of the asymptotic breakdown point is given as follows. Since the maximum value of \(k\) is \(\left\lfloor n/2\right\rfloor-1\), the maximum finite-sample breakdown point available is given by  
\[\varepsilon_{n}\ =\ \frac{\left\lfloor n/2\right\rfloor-1}{n}.\]  
Since \(\left\lfloor n/2\right\rfloor\) can be expressed by \(\left\lfloor n/2\right\rfloor\ =\ n/2-\delta\) with \(0\leq\delta<1\), we have  
\[\varepsilon_{n}\ =\ \frac{1}{2}-\frac{\delta+1}{n}.\]  
Thus, taking the limit of \(\varepsilon_{n}\) as \(n\to\infty\), we have the asymptotic breakdown point  
\[\varepsilon\ =\ \lim_{n\to\infty}\varepsilon_{n}\ =\ \frac{1}{2},\]  
which is the maximum attainable asymptotic breakdown point.  
It is likely that the RE value tends to decrease as the finite-sample breakdown point increases. Thus, when we choose \(k\), we need to consider the RE value and the finite-sample breakdown point as well.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 2 Magnetic Field Induction Using Coils
To obtain the experimental measurements a magnetic field meter (model ELT-400 manufacturer Narda imported from Germany) was used, making a sweep in the \(X\) axis in the center of the transmitter coil, taking samples every 2.5 cm of separation. An error of less than 5% was obtained. Due to the propagation restrictions of the magnetic field, it was necessary for both coils to be close enough to each other to achieve transmission. There are applications in which this property results in an advantage, such as when transmitting geolocation data in a precise area [24], when transmitting bank information between devices, the transfer of information between mobile devices, access control [25], biomedical applications (e.g., health care and disease monitoring) [26], and classification of vehicles or containers [27]. Figure 3: Induced voltage at a height of 0.2 m.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 3 Proposed Method  
### Improved SPF Function  
The main strategy of the ACM based on region information is to construct a driving force, which is based on the information of the image region [43]. The region function modulates the sign of the pressure forces using region information such that the contour shrinks when it is outside the object of interest and expands when it is inside the object. For this reason, these external forces are sometimes called SPF [43]. [PERSON] et al. [22] proposed the SBGFRLS model, which utilizes the statistical information inside and outside the contour to construct a region-based SPF function [37]. However, an SPF function is simply based on image information. Thus, the corresponding model cannot segment intensity-inhomogeneous images or images with weak boundaries [36; 41].  
In this study, the global information of image \(I\) is used to divide the image into two parts, \(inC\) and \(outC\), and the level set function is then introduced into the new SPF function.  
Using global region information and combining \(c_{1}\) and \(c_{2}\), a global fitted image formulation is defined as  
\[f=H_{\epsilon}(\varphi).*(I-c_{1})+(1-H_{\epsilon}(\varphi)).*(I-c_{2}), \tag{18}\]  
where \(H_{\epsilon}(\phi)\) defined in Equation (9) is the regularized Heaviside function and \(c_{1}\) and \(c_{2}\) are calculated by Equations (7) and (8), respectively, and \(.*\) describes matrix multiplication.  
By employing the above-defined global fitted image, a new SPF function is defined as  
\[spf(I(x))=\frac{I(x)-f(x)}{\max(|f(x)|)}. \tag{19}\]  
According to the construction approach of the SPF function, a new partial differential equation is defined as  
\[\frac{\partial\varphi}{\partial t}=spf(I(x))\cdot\alpha, \tag{20}\]  
where \(\alpha\) is the balloon force that controls the shrinkage or the expansion of the contour. In this paper, according to the concept of a balloon force established previously [44], a balloon force is reconstructed to change the evolution rate of the level set function adaptively, which is defined as  
\[\alpha_{new}=c_{1}+c_{2}. \tag{21}\]  
The new SPF is more efficient than the traditional ACM models because this function avoids the expensive cost of the reinitialization step. Moreover, the SPF is less sensitive to the initialization location. However, the SPF function is constructed with only global image information. Therefore, it appears difficult to handle images with intensity inhomogeneity using this approach.
# Monotonic and Fatigue Properties of Steel Material Manufactured by Wire Arc Additive Manufacturing
## 3 Results  
### Microstructure and Local Distribution of Hardness  
To gain a first impression of the influence of the interlayer temperature on the material properties, the microstructure was examined. The corresponding micrographs are shown in Figure 5. For both interlayer temperatures, it can be seen in region A that the appearance of the microstructure changes within the last three to five top layers between typical textures known from welds for the material group of steel and a rather fine grain structure. The finer grain size in the lower layers can be explained by the heat treatment through the sequel layers leading to a fine-grained microstructure due to the transition from ferrite to austenite and back in accordance to the formation mechanisms regarding heat-affected zones in joint welding. There, the temperature was kept at a higher level for a longer time than in the top layers for which sequel layers are missing.  
For the lower layers, which were the focus of the following investigations, the microstructure is largely homogeneously distributed for both interlayer temperatures. Representative micrographs for this region are given in Figure 5 (see section C). On closer examination, it is found that the microstructure consists mostly of globular ferrite and pearlite for both interlayer temperatures. Due to the very long holding time at an elevated temperature, the first signs of dissolution may be seen in the pearlitic microstructural regions. Bainitic shares cannot be excluded and are not easy to distinguish from the pearlitic components. Optical determination of the phase fractions (digital threshold value correlation) showed an increase in the pearlite fraction by a factor of 1.6 from 15 to 24% when the interlayer temperature doubled. The increased share of perlite will lead to a larger distance between the Fe\({}_{3}\)C lamellas due to the very low carbon content of 0.07 mass-% in the material and is not expected to have a noticeable influence on the hardness. In order to verify this, the microhardness was measured over the centerlines of the examined cross-sections (see path B in Figure 5). The results for the upper parts of the cross-sections and the two interlayer temperatures are shown in Figure 5. In the non-normalized weld layers, the hardness values are slightly above 200 HV 0.2. In the normalized area, the hardness decreases to approximately 175 HV 0.2 on average. The latter value is also found in the lower (normalized) parts of the examined material (not shown here). The hardness seems to show largely homogeneously distributed values at about the same level for both interlayer temperatures. A distinguishable difference in hardness between layer and layer boundaries cannot be observed.  
Figure 4: Locally forming hysteresis loops under cyclic deformation and measured strain in tension by digital image correlation (DIC; schematic).