esa-sceva/satcom-chunk-collection huggingface dataset:
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 2 Materials and Methods
### Methods
#### 2.2.1 Machine Learning Methods  
In our methods, we employed the XGBoost algorithm [13] to model PM\({}_{2.5}\) as a nowcasting task. XGBoost (Extreme Gradient Boosting) is a tree-based machine learning algorithm that implements gradient boosting to produce highly accurate predictions. It constructs trees using a histogram-based method, which groups continuous feature values into discrete bins to efficiently identify optimal split points. The general principle is that each new tree is trained to correct the residual errors made by the previous ensemble of trees, typically by splitting on feature thresholds. The model predicts the target variable using the following formulation:  
\[\hat{y_{i}}=\phi(x_{i})=\sum_{k=1}^{K}f_{k}(x_{i}),f_{k}\in\mathcal{F} \tag{1}\]  
where \(\hat{y_{i}}\) is the predicted PM\({}_{2.5}\) value for input \(x_{i}\), and \(f_{k}\) is \(k\)-th tree in the model. The initial prediction starts from a base score (mean value), which is then adjusted by the series of trees. The objective function that is optimized during training includes both a loss term and a regularization term to prevent overfitting:  
\[\mathcal{L}(\theta)=\sum_{j=1}^{n}l(y_{i},\hat{y_{i}})+\sum_{k=1}^{n}\Omega(f_ {k}) \tag{2}\]where \(l(y_{i},\dot{y}_{i})=(y_{i}-\dot{y}_{i})^{2}\) represents the error loss between the true value \(y_{i}\) and the observed value \(\dot{y}_{i}\). The term \(\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\sum w_{j}^{2}\) is a regularization parameter that controls the tree complexity. Here, \(T\) is the number of leaves in the tree, \(w_{j}\) is the score assigned to leaf \(j\), and \(\gamma\) is a parameter that penalizes the addition of new leaves to control overfitting. Lastly, \(\lambda\) is the L2 regularization parameter that reduces large leaf values to avoid overfitting cases.  
XGBoost performs a form of internal feature selection by prioritizing features that improve the model's objective during the tree-splitting process. Features with little predictive power are rarely chosen for splits, resulting in low importance scores. However, this is not a strict feature elimination process; unimportant features remain in the dataset but have minimal influence on the final model. In cases where features are highly correlated, the model may favor one feature while ignoring the other, even if both hold predictive value. XGBoost feature-importance metrics, such as gain or cover, can indicate which features contribute least to the model's performance. In theory, removing these low-importance features can simplify the model with minimal impact on accuracy, though it is advisable to validate their effect by iteratively assessing performance metrics.
# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 3 Materials and Methods
### Proposed Lower-Body Detection Framework Using Anthropometric Data
#### 3.3.1 Anthropometric Data  
In this section, anthropometric data [48] representing human body information are introduced. A survey of anthropometric data is generally related to the size, motion, and mass of the human body. Such survey data can be applied to design suitable clothing, ergonomic devices, or workspaces. In this study, human body size data from the NASA Anthropometry and Biomechanics Facility [21] are selected to provide information on the height and width of various body parts in a standing posture from the frontal view. This information was collected from healthy adults with an average age of approximately 40 years and from a wide range of ethnic and racial backgrounds. The example of anthropometric dimensional data is shown in Figure 10. There are three dominant parts of the human body considering in this experiment:  
1. Full-body: the width of the full-body is similar to the width of the upper-body, and the height of the full-body is measured from the foot to the top of the head.
2. Upper-body: the width of the upper-body is recorded from the edge of the left hand to the edge of the right hand with the hands resting on the body, and the height of the upper-body is measured from the waist to the top of the head.
3. Head: the width of the head is measured from the left ear to the right ear, and the height of the head is measured from the chin to the top of the head.  
To address occlusion problems affecting the lower-body, this research aims to detect the lower-body indirectly by using anthropometric data. To apply anthropometric data for lower-body detection, a suitable human ROI ratio can be used to transform an ROI corresponding to any other part of the body into the lower-body ROI. In this study, three main ROIs are considered for transformation to the lower-body:  
* Full-body: the HOG-SVM or CNNs algorithm is used to detect the full-body of the target, as shown in Figure 11.
* Upper-body: the VJ algorithm for upper-body detection (\(VJ_{Upper}\)) is applied to detect the upper-body of the target, as illustrated in Figure 12.
* Head: the head of the target is detected by using the VJ algorithm for face detection (\(VJ_{Face}\)), under the assumption that the head ROI is close to the face ROI, as demonstrated in Figure 13.  
Figure 11: The process for lower-body detection using full-body ratio.  
Figure 12: The process for lower-body detection using upper-body ratio.  
Figure 10: The example of the anthropometric dimensional data.  
The anthropometric data ratios are constructed from the median scaled sizes of the head, upper-body, lower-body, and full-body in a standing posture, as shown in Tables 1 and 2. However, the anthropometric data collected from female subjects are not sufficiently comprehensive; therefore, in this study, only male anthropometric data are selected for lower-body detection.
## Appendix A Experimental Basis for Configuration Selection
### Aggregation Strategy  
In the inference phase, multiple patch-level predictions must be aggregated to produce a single parcel-level classification result. We compared three aggregation methods: majority voting, class-wise summation, and class-wise product of softmax probabilities. Among the three, the product method consistently yielded the most stable and accurate results. This appears to be particularly effective in our setting, where parcels often have uniform internal textures, leading to consistent patch-level predictions that can be reinforced through multiplicative aggregation. To ensure a fair and consistent comparison across all experiments, we adopted product-based aggregation as the unified strategy for final prediction. To summarize the effects of all configuration settings discussed above, we provide a comparative overview in the following table. The experimental results discussed above are summarized in Table 11, which provides a consolidated comparison of performance across all configuration combinations--including patch overlap settings, valid-area thresholds, and aggregation methods--based on accuracy, macro F1, and the Kappa coefficient. Table 12 presents the classification performance under domain-shift conditions across various configurations. The results indicate that our proposed method consistently maintained higher and more stable performance compared to the baseline, demonstrating its robustness to spatio-temporal variability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Minimum Overlap**} & \multirow{2}{*}{**Valid-Area Threshold**} & \multirow{2}{*}{**Aggregation**} & \multicolumn{3}{c}{**NCB + FPE (Baseline)**} & \multicolumn{3}{c}{**CBS + RPE (Ours)**} \\ \cline{5-8}  & & & **Accuracy** & **Macro F1** & **Kappa** & **Accuracy** & **Macro F1** & **Kappa** \\ \hline \multirow{8}{*}{0\%} & \multirow{8}{*}{5\%} & vote & 0.8763 & 0.7896 & 0.8593 & 0.9093 & 0.8510 & 0.8970 \\  & & sum & 0.8770 & 0.7925 & 0.8601 & 0.9132 & 0.8591 & 0.9014 \\  & & product & 0.8762 & 0.7909 & 0.8591 & 0.9123 & 0.8587 & 0.9004 \\ \cline{2-8}  & \multirow{8}{*}{10\%} & vote & 0.9643 & 0.9146 & 0.9595 & 0.9910 & 0.9815 & 0.9898 \\  & & sum & 0.9643 & 0.9120 & 0.9595 & 0.9909 & 0.9814 & 0.9897 \\  & & product & 0.9645 & 0.9130 & 0.9598 & 0.9910 & 0.9815 & 0.9898 \\ \cline{2-8}  & \multirow{8}{*}{15\%} & vote & 0.9019 & 0.8482 & 0.8885 & 0.9330 & 0.8587 & 0.9241 \\ \cline{2-8}  & & sum & 0.9039 & 0.8508 & 0.8908 & 0.9346 & 0.8639 & 0.9259 \\ \cline{2-8}  & & product & 0.9037 & 0.8512 & 0.8905 & 0.9353 & 0.8632 & 0.9268 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Parcel-level classification performance (accuracy, macro F1, and Kappa) on the validation set, comparing the baseline (NCB + FPE) and our method (CBS + RPE) under different combinations of minimum overlap, valid-area thresholds, and aggregation strategies.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Constraints on Network Reconfiguration of Ship Power Systems
#### 2.3.3 Branch Current and Node Voltage Constraints  
Limitations are placed on system branch currents to ensure that cable currents are within specified operating ranges therein:  
\[li\leq Ii_{\rm max}\,, \tag{17}\]while \(Ii\) is the current flowing in line \(i\) and \(Ii_{\_}{max}\) is the maximum value for normal operation of this line.
## 3 Results and Discussion
### Immobilization of Lacase onto Different Types of Nanoparticles
The immobilized increase retained 95%, 97%, and 100% of its initial activity at pH 5, 6, and 7, respectively, after incubation at room temperature for 24 h.  
The reusability of the nanobiocatalyst was assessed in consecutive cycles of 24 h. It was observed that phenol transformation was higher than 60% and was maintained constant after four cycles (Figure 2). In other reports, phenol was almost entirely biotransformed in consecutive cycles with lacase immobilized on magnetic mesoporous silica nanoparticles with a dose of enzyme 18 times higher [45]. The immobilized lacase retained 97% of its initial activity after the consecutive batch treatments of phenol with magnetic separation. Figure 1: Phenol transformation with free enzyme (\(\blacksquare\)), \(\mathrm{Tv}\) immobilized onto fsNPs (\(\blacktriangle\)), and onto FeO-2206W (\(\bullet\)) over 24 h at pH 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**pH** & **Phenol Biotransformation (\%)** & **Biotransformation Rate (mg L\({}^{-1}\) h\({}^{-1}\))** \\ \hline
5 & 67.9 & 0.383 \\
6 & 63.9 & 0.326 \\
7 & 48.1 & 0.189 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Phenol biotransformation at variable pH levels by lacase immobilized onto silica-coated magnetic nanoparticles (FeO-2206W) for 24 h.
## 3 Results and Discussion
### Effects of AM Constraints and Objective Functions  
In this section, the impact of different objective functions on the structural topology optimization for AM is analyzed. To provide comprehensive insights, three different cases are considered: the cantilever beam, bridge shape, and L-shaped beam. For each case, the results are presented for topology optimization in different scenarios, with or without AM  
Figure 8: L-shaped beam in the \(-Y\) build direction with (**a**) 50% and (**b**) 80% volume fraction.  
Figure 9: (**a**) New loading condition of the L-beam; (**b**) Optimal result along the \(-X\) build direction; (**c**) Optimal result along the \(-Y\) build direction.  
constraints. Based on the results shown in Table 2, \(-X\) is selected as the build orientation for the cantilever beam, and \(+Y\) is preferred for the bridge shape and the L-beam.
## 2 Related Work
### Knowledge Distillation  
KD [32] transfers valuable insights from a complex teacher model to a more compact and efficient student model. Contemporary KD techniques, predominantly employed in image classification tasks, can be categorized into probability-based, feature-based, and relation-based approaches. The probability-based method [32] relies on the teacher model's class probabilities as soft labels to train the student model. Feature-based KD [33; 34] facilitates knowledge transfer by leveraging intermediate feature maps or their refined versions from the teacher network. Meanwhile, relation-based KD [35] aligns the structural relationships among multiple instances within both the teacher and student networks.  
Despite extensive research on KD techniques for image classification, their direct application to dense pixel tasks often yields suboptimal outcomes due to the inflexible alignment of low-resolution feature representations between the teacher and student networks, which can impose constraints that hinder the effective transfer of knowledge. Consequently, these methods may fail to capture the structured relationships among pixels. Unlike image-level recognition, depth completion tasks necessitate dense pixel-wise predictions, making it a more intricate challenge. Therefore, utilizing KD methods to enhance the student model by leveraging the teacher model within our proposed few-shot learning framework for depth completion remains both beneficial and demanding.
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 6 Experiment
### Experimental Protocol  
First, the camera estimated the position of the marker attached to the coil. The robot controller read the TMS location information from the camera and calculated the matrix \({}^{b}T_{c}\). The navigation system used the location information of both ears, the nose, and the middle of the forehead to estimate the location of the center of the head and the target point of the TMS coil. In this study, we set the left dorsolateral prefrontal cortex as the target point.  
After preparing the robotized TMS task, the robot manipulator was instructed to locate the TMS coil at the target point. The force/torque control is applied by using the built-in force control function of the URScript programming language [30]. First, it is shown that the position controller of the hybrid position/force controller compensates for the error between the current TMS coil position and the target TMS coil point by comparing two controllers with the same forces: the hybrid position/force controller, that we suggested in (9), and the force controller without the position controller, which is \(\mathbf{F}=F\mathbf{u_{1}}\). Second, we applied the suggested force controller with the desired forces of 5, 10, 20, 30, and 40 N without torque control. This was to explore the relationship between the magnitude of the desired force and the error of the TMS coil to the desired point and decide how to set the desired force. After selecting, the force controller was applied with a proportional torque controller with gains \(k_{p}=0.0,\ 1.0,\ 2.0,\ 4.0\), and 4.5 to optimize. Until parameter \(F\) and \(k_{p}\) were chosen, the dummy was fixed during the experiment. After determining all control parameters for the next experiment, we moved the dummy 7 mm to the \(x\) and \(y\) axes, as shown in Figure 11, after the coil arrived at the target point. We estimated a new target point from the navigation system and made the robot-manipulated coil track that. From all the experiments, we obtained the error data between the current and estimated target positions of the TMS coil, the torque applied to the center of the coil, and the force applied to the coil.
## 4 Numerical Study
### Grouting Diffusion Simulation under Isotropic Rough Fractures  
#### 4.2.1 Modeling  
The roughness is very complex and irregular in actual joint fractures. Therefore, there is a certain difference between the theoretical rock mass fracture and the actual rock mass fracture. In order to study the mesoscopic flow laws of fluid under different roughness, many researchers have described roughness units in simplified geometrical structures based on the basic structure of a rough fracture [23; 24; 25; 26]. The more widely used roughness units include sinusoidal, triangular and rectangular units. The generalized fracture roughness unit which is shown in Figure 25, whether triangular units or sinusoidal units, consists of three geometric parameters: the undulation height \(\Delta v\), unit spacing \(c\), and average fracture openness \(b\). The average fracture openness is independent of the roughness and properties of the fracture surface. The roughness parameter for a single fracture surface can be expressed in terms of two dimensionless shape parameters.  
\[\xi_{1}^{\prime}=\frac{\Delta v}{b},\ \xi_{2}^{\prime}=arctan\bigg{(}\frac{2 \Delta v}{c}\bigg{)} \tag{17}\]  
In Equation (15), \(\xi_{1}^{\prime}\) denotes the relative height of the crack roughness unit, and \(\xi_{2}^{\prime}\) denotes the angle of inclination of the crack roughness unit.  
By observing Equation (15), it can be found that \(\xi_{1}^{\prime}\) and \(\xi_{2}^{\prime}\) are two dimensionless parameters independent of average openness \(b\) and independent of the absolute size of fracture roughness element. Under the condition that the average openness is determined, the roughness element can be completely described by these two parameters. The two dimensionless shape parameters have good consistency at different scales, so the fracture roughness element constructed by this method can well describe the cross-scale effect of fracture roughness.  
A fixed pressure of 2 kPa is applied at the inlet of the fracture characterized by serrated roughness units, and the outlet is a free boundary. The flow is driven by the pressure difference. The upper and lower walls of the fracture are slip-free boundaries. The numerical model is shown in Figure 26. Two-phase flow is used for the simulation, and the slurry and air parameters are the same as the values in Section 4.1. The grouting diffusion characteristics of different fractures characterized by roughness units can be obtained.  
Figure 26: Numerical model of the grouting diffusion process in isotropic rough fractures.  
Figure 25: Generalized model of the fracture roughness element.  
The geometric parameters of the roughness unit are taken as follows. The average fracture openness is 5 mm. The relative heights are 0, 0.2, 0.4, 0.6, and 0.8. The relative inclination angles are 0\({}^{\circ}\), 20\({}^{\circ}\), 40\({}^{\circ}\), 60\({}^{\circ}\), and 80\({}^{\circ}\). There is a total of 17 simulative conditions in this set of simulations.
## 2 Related Work
### Embedding a Watermark to Enable Integrity-Checking Functionality  
Figure 1 is an example of encapsulating integrity-checking functionality using a watermark-based method of JPEG Privacy and Security. This example aims to verify whether an image stream and/or a specific portion of metadata has been modified. The procedure utilizes the owner's key for encapsulation. When performing integrity checks, if the owner's key does not match, the result will always indicate that the checked image has been modified [4].  
Figure 1 also shows the watermark-generating process, which takes the image stream, selected metadata from a JPEG image, and the owner's key as input. It generates a watermark, which is then embedded in the image stream. This process produces a watermarked  
image stream. Watermark strength and detection fineness are adjustable parameters that determine the amount of watermark embedded and the level of detail during the integrity-checking process using the watermarked image stream, respectively. The selected part of the metadata, watermark strength, and detection fineness for watermarking are stored as an XML content type JUMBF (JPEG Universal Metadata Box Format) box [30]. Optionally, this JUMBF box can be protected to increase security.  
The JUMBF box generated through this encapsulation procedure follows the same type as shown in Figure 2. Table 1 provides a sample XML document showcasing the index of the selected part of the metadata, watermark strength, and detection fineness.  
\begin{table}
\begin{tabular}{l} \hline \hline \textless{}?xml version=“1.0” encoding=“UTF-8”?\textgreater{} \\ \textless{}!- \\ \textless{}!- \\ \textless{}!- \\ \textless{} strength: amount of watermark embedded in the image stream \\ - fineness: controlling the detection fineness \\ \textless{} This sample is an XML document that indexes one JUMBF box and two segments of 2048 bytes and 4096 in length at 0 xFF and 0 xFF00 from the beginning of the file, respectively. \\ Embedding strength is set to 90, and the detection fineness is set to 100. \\ \textless{}
## 2 Materials and Methods
### Characterisation  
The crystalline phase of the ZnO-NPs was analyzed using Bruker D8 Advanced X-ray diffraction (XRD) instrument (Karlsruhe, Germany)), with a nickel filtered CuK\(\alpha\) radiation (k = 1.5418 A) at room temperature, at a scanning rate of 0.0018\({}^{\circ}\) min\({}^{-1}\). The external shape and structure of the NPs was studied using scanning electron microscopy (SEM)on a JEOL 6400F field-emission SEM, (Zeiss, Oberkochen Germany). While the internal shape and sizes of the NPs were studied using a TECNAI G2 (ACI) Transmission electron microscopy (TEM) (FEI, Bellaterra Spain). Elemental composition was determined using energy-dispersive X-ray (EDX). For optical examination, the UV-visible analysis of the samples was conducted using the Perkin Elmer Lambda 20 UV-vis spectrophotometer.
## 1 Introduction
First, because codebook generation and retraining are usually performed repeatedly, it takes a lot of time to construct the final quantized model [8]. This problem is a limitation when we want to deploy the quantized model quickly. The existing approaches usually use non-uniform quantization rather than uniform quantization in order to minimize the loss of performance inherent in quantization, even though it involves retrain processes [8; 9; 10]. In general, non-uniform quantization methods including k-means are often applied to the weights quantization of neural networks [8; 9; 10; 11; 12; 13]. However, the simple k-means  
Figure 1: Process of weights quantization in convolutional neural networks. have a problem because it takes a lot of time to reconstruct a codebook. Because the range of weights is not wide and weights have similar values, to perform distance calculations for all the weights is not efficient. In this paper, we utilize kernel density estimation to obtain the probability density function of pre-trained weights of CNN. Then we perform sampling through the previously obtained probability density function, and non-uniform quantization is applied on those sampled data. The contribution of this paper is summarized as follows. * The existing weights quantization methodology is inefficient in terms of computational costs. We propose a method that performs quantization more efficiently by using kernel density estimation and sampled data from the probability density function. * A number of recent studies on weights quantization have focused on the recovery of lost performance through retrain phase. The proposed methodology can perform fast quantization suitable for this retrain phase and fast deployments. * We compare the performance of CNN weights quantization using quantization methodologies such as uniform quantization, Lloyd-Max quantizer, and k-means clustering. * Experiments on various CNN architectures such as AlexNet, VGGNet, and ResNet show that the proposed methodology can be widely applied to various CNN architectures.