esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Baselines and Evaluation Metrics  
To highlight the performance of AGCHK, we choose the same benchmark methods as AGC [7].  
1. Methods that only exploit node features: classic spectral clustering methods such as \(k\)-means and spectral-f, which perform clustering on the similarity matrix constructed by node features directly.
2. Clustering methods that only handle graph structures: spectral-g clustering on the graph adjacency matrix simply, Deepwalk [2], and graph neural networks for graph representations (DNGR) [18].
3. Attributed graph clustering methods utilize graph structures and graph node features jointly: AGC [7], which does not need to train graph neural network parameters, Graph neural network methods based on autoencoder such as graph autoencoder (GAE) and graph variational autoencoder (VGAE) [4], marginalized graph autoencoder (MGAE) [5], adversarially regularized graph autoencoder (ARGE), and variational graph autoencoder (ARVGE) [6].  
To measure the ability of the model comprehensively, we adopt the following three cluster evaluation indexes [19]: graph clustering accuracy (Acc), normalized mutual information (NMI), and macro F1-score (F1).
## 6 The Significance of Frequent and Reliable FV Predictions While Drilling  
Accurately predicting FV variations over time is crucial for optimizing the filter cake thickness, the permeability, and the viscosity [10, 18, 23, 26, 30, 45]. By effectively managing the filter cake area, the differential pressure, and the solids content, drilling operators can minimize fluid incursion and optimize filtration rate, thereby improving the performance of drilling operations [46].  
Figure 16 illustrates the workflow for field data to predict the FV with the aid of HML models to enhance drilling performance efficiency. The HML model developed can assist the drilling crew by providing approximately three semi-real-time predictions of the FV per hour during the drilling operation based on just two easily measured parameters, the FD and the MFV, both routinely recorded in the well-site laboratory. The HML model proposed can computationally determine the FV in a much more frequent manner, enabling the crew to monitor this crucial DF property effectively. Such frequent computational monitoring enables the drilling crew to make informed decisions and take effective corrective actions to modify the DF with regard to the FV in case unexpected changes in the FV are detected. Such timely information helps to avoid severe drilling problems such as a stuck pipe. Semi-real-time FV determination offers benefits including managing formation damage, cleaning wellbores, minimizing blockages, and optimizing fluids. These improvements offer the potential to enhance reservoir productivity and wellbore stability once a well is completed.  
This study addresses knowledge gaps in the semi-real-time FV prediction during drilling using an extensive field database. It demonstrates the feasibility of forecasting short-term FV patterns from the DF density and viscosity inputs using the HML models. Potential applications include semi-automated fluid property monitoring and adjustments and integration into drilling data systems for more responsive drilling optimization. With further refinements, this approach could replace laboratory methods with semi-real-time measurement of FV, revolutionizing proactive DF management. The study establishes a framework for both scientific and practical advancements in drilling efficiency and well construction.  
Figure 15: Comparison of the measured FV values with those predicted by the RBFNN-GO model for the unseen data.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology  
This section introduces our entropy-regularized federated optimization (ERFO) approach. We first define the entropy-regularized client objective, deriving the entropy term \(H(p)\) and its gradient \(g_{H}\) and showing how this regularization modifies local gradients. We then present the overall ERFO procedure in Algorithm 1. Two variants of ERFO are described--one with a fixed regularization weight and another with a decaying schedule--corresponding to different experimental settings (PneumoniaMNIST and UNSW-NB15, respectively). Finally, we provide a formal convergence analysis for ERFO: first for the fixed-\(\lambda\) case under standard convexity assumptions, and then an extended theorem for the decaying-\(\lambda_{t}\) variant, discussing the convergence implications of the diminishing regularization.  
```
0: initial global model \(w^{0}\), total rounds \(T\), initial entropy weight \(\lambda_{0}\), local epochs \(E\), and client learning rate \(\eta\)
0: final global model \(w^{T}\)
1:for\(t=1\)to\(T\)do
2:Server: set \[\lambda_{t}=\begin{cases}\lambda_{0},&\text{(ERFO-Fixed)},\\ \lambda_{0}\big{(}1-\frac{t}{T}\big{)},&\text{(ERFO-Decayed)}.\end{cases}\]
3:server selects \(\mathcal{S}_{t}\) (e.g., all clients or a random subset)
4:for each client \(i\in\mathcal{S}_{t}\)in paralleldo
5:\(w_{i}\gets w^{t-1}\quad\text{// initialize local from global}\)
6:for\(e=1\)to\(E\)do
7: compute \(g_{H}=\
abla_{w}H(p(w_{i},\,w^{t-1}))\) using (5)
8:for each mini-batch \((X_{b},y_{b})\)do
9: compute \(\
abla_{w}F_{i}(w_{i};X_{b},y_{b})\)
10:\(w_{i}\gets w_{i}\;-\;\eta\Big{[}\
abla_{w}F_{i}(w_{i};X_{b},y_{b})\;+\; \lambda_{t}\,g_{H}\Big{]}\)
11:endfor
12:endfor
13: send model diff. \(w_{i}-w^{t-1}\) to server
14:endfor
15:Server: aggregate \[w^{t}\gets w^{t-1}\;+\;\frac{1}{\sum_{i\in\mathcal{S}_{t}}n_{i}}\sum_{i\in \mathcal{S}_{t}}n_{i}(w_{i}-w^{t-1}),\]

where \(n_{i}\) is client \(i\)'s sample count.
16:endfor
```  
**Algorithm 1** Entropy-regularized federated optimization (ERFO)
## 3 Materials and Methods
### Experimental Setup and Implementation
#### 3.3.7 Regression Models  
In our study, we adopted a linear regression model to express the relationship between the factors and the responses. The goal is to establish a relationship between each dependent variable (throughput, packet loss ratio, average end to end delay and routing overhead) and the independent variables (factors ND, NC, BKH and WMH). Models of this relationship or estimated regression equations are used to predict the value of the response when values for the independent variables are given, always within the defined ranges from each factor levels. These models will be very useful in the context of our study, where VANET simulations generally require high processing and execution times.  
This mathematical relationship between a response y and factors \(x_{1}\)... \(x_{k}\) is given in Equation (11).  
\[y=\beta_{0}+\sum_{i=1}^{k}\beta_{k}x_{k}+\varepsilon \tag{15}\]  
where \(y\) represents the response, \(x_{1}\)... \(x_{k}\) are the coded variables of the \(k\) factors, \(\beta_{0}\) is the constant of the model, \(\beta_{1}\), \(\beta_{2}\)... \(\beta_{k}\) are the linear model coefficients, and \(\varepsilon\) is the error term.  
To find out the coefficients, we used the linear least squares method [29]. This relationship can be expressed in a matrix form as follows:  
\[B=(X)^{-1}\Big{(}X^{T}Y\Big{)} \tag{16}\]  
\(B\) is the matrix of coefficients of order \(k+1\).  
\(X^{T}\) is the transposed matrix of \(X\) The order of this matrix is \(N\,x\,\mathrm{p}\) with \(\mathrm{p}=k+1\)  
\[X=\left(\begin{array}{ccccc}1&x_{11}&x_{12}&\cdots&x_{1k}\\ 1&x_{21}&x_{22}&\cdots&x_{2k}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&x_{n1}&x_{n2}&\ldots&x_{nk}\end{array}\right) \tag{17}\]  
where \(x_{ij}\) represents the level of the \(j_{\mathrm{th}}\) factor at the \(i_{\mathrm{th}}\) experiment, the \(Y\) column matrix of the \(n\) measured responses.  
\[Y=\left(\begin{array}{c}y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{array}\right)\quad B=\left(\begin{array}{c}\beta_{0}\\ \beta_{1}\\ \vdots\\ \vdots\\ \beta_{k}\end{array}\right) \tag{18}\]  
In order to assess the goodness of fit of these models, the adjusted R-squared coefficient is adopted. This coefficient is based on the R-squared coefficient that adjusts for predictors that are not significant in a regression model [30]. The values of adjusted R-squared are between 0 and 1. When their values are closer to 1, this suggests that the model has good predictive accuracy. The adjusted R-squared (Adj.R\({}^{2}\)) coefficient is measured by the following equations (Equations (19) and (20)):  
\[R^{2}=1-\frac{\mathrm{SS_{T}}}{\mathrm{SS_{E}}} \tag{19}\]  
\[\mathrm{Adj.R^{2}}=1-\frac{\left(1-R^{2}\right)\left(n-1\right)}{n-k-1} \tag{20}\]  
where \(\mathrm{SS_{T}}\) is the total sum of squares and represents the total variation, \(\mathrm{SSE}\) is the residual sum of squares, \(n\) is the total sample size, and \(k\) the number of predictors in the regression model.
## 3 In-Situ Monitoring  
### Monitoring Scheme  
The excavation for the foundation pit spanned 700 m in length and 110 m in width. The maximum excavation depth on the left side was 6.05 m, while on the right side it was 3.8 m. A diaphragm wall was formed using bored piles along the perimeter of the foundation pit. The left side of the pit was supported by horizontal cross bracing, while the right side was partially supported by corner bracing. A total of 80 monitoring points were set up around the foundation pit, with a spacing of approximately 20 m between each point.  
In order to ensure the safety of the foundation pit project, surrounding buildings, and underground pipelines, and to guide the construction, the project monitored various items such as the underground diaphragm walls, surrounding ground surface, buildings, and supporting axial force from the beginning to the completion of construction. The datum point is 50 m outside the foundation pit, and high-precision total station is used to measure the observation point. The project monitored the horizontal displacement of the enclosure wall, as well as the horizontal and vertical displacements of the top of the enclosure wall and the settlement of buildings around the foundation pit at each monitoring point.  
In this paper, the measured results of monitoring points W1, W2, W3, W4, and W5 around the foundation pit were selected for study, and the layout of the monitoring points is illustrated in Figure 3. The control values for monitoring parameters such as the horizontal  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Step** & **Construction Stage** \\ \hline
1 & Level the site, and the ground elevation is 3.6 m \\ \hline
2 & Bored piles are installed along the contour line of the pit to create an underground diaphragm wall \\ \hline
3 & Excavate from both ends of the foundation pit to the middle of the foundation pit at a depth of 0.7 m, and the top beam, horizontal support column, and horizontal support are applied \\ \hline
4 & Excavate from both ends of the foundation pit to the middle of the foundation pit at a depth of 2.2 m \\ \hline
5 & Excavate from both ends of the foundation pit to the middle of the foundation pit at a depth of 2.8 m \\ \hline
6 & Excavate from both ends of the foundation pit to the middle of the foundation pit at a depth of 3.8 m \\ \hline
7 & Stop the excavation on the right side of the foundation pit, and excavate the left side of the foundation pit at a depth of 5.4 m \\ \hline
8 & Area \⃝2 and area \⃝3 on the left side of the foundation pit are excavated at a depth of 6.05 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Foundation pit construction steps.  
Figure 2: Schematic diagram of foundation pits using zoned excavation.  
displacement of the enclosure wall, horizontal displacement of the top of the enclosure wall, vertical displacement of the top of the enclosure wall, and settlement of the surrounding buildings were set at 30 mm to ensure safety during construction.
## 3 Results and Discussion
### Analysis of Pollutant Sources
#### 3.5.2 Principal Component Analysis
Therefore, the fourth principal component, like the second principal component, indicates organic pollution sources. The sampling points with high scores on Factor 1 were WQH17, WQH18, and WQH19 (Table 8 and Figure 9), indicating that these three sampling points contributed more to the variance of Factor 1, and the pollutants were mainly TN, TP, and NH4\({}^{+}\)-N. These three sampling points were all located near the Tayang River, which may be related to the wastewater treatment plants distributed around the Tayang River. The sampling sites with high scores on Factor 2 were WQH21, WQH22, and WQH24, where the pollutant was mainly COD. All three sampling sites were located at the downstream mouth of the Wanquan River, and it is generally assumed that COD\({}_{\text{Mn}}\) and COD are associated with domestic sewage discharges due to urbanization. The lower reaches of the Wanquan River are located in the center of Bo'ao town, and in recent years, due to the rapid development of the construction of the Asia International Forum, the high rate of urbanization may have placed a burden on the sewage treatment system that has not yet been formed to support it, and it is presumed that the high COD at these points is mainly related to some untreated sewage. The sampling sites with high scores on factor 3 are WQH16 and WQH23, where the pollutants are mainly Cr\({}^{6+}\) and As. Both sampling sites are located near traffic arteries, and some studies have shown that elemental Cr is closely related to traffic. It is assumed that the high Cr\({}^{6+}\) values at these sites may be related to the migration of chromium from the plating of car wheels [34;35]. The sampling site with a high score on factor 4 is WQH01, located in the upper reaches of the Wenqu River, where the pollutant is mainly COD\({}_{\text{Mn}}\), the cause of which is similar to COD and presumably due to inadequate sewage treatment.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction
### Related Work
#### 1.1.3 Learning-Based Methods  
Learning-based methods heavily rely on data-driven techniques, such as deep learning. First, [PERSON] et al. [28] applied convolutional neural network (CNN) for NR-IQA. Namely, the authors trained a CNN of five layers from scratch on locally normalized \(32\times 32\) image patches. The overall quality estimation was obtained by averaging the image patches' quality scores. Similarly, [PERSON] et al. [29] trained a patch-based CNN, but a patch weighting scheme was also developed to reflect the importance of individual patches in quality estimation. [PERSON] and [PERSON] [30] assigned weights to image patches using image saliency.  
In [31], the predicted quality scores of image patches were directly weighted by local image saliency. In contrast, [PERSON] and [PERSON] [32] proposed a specific scanpath predictor, which mimics the movements of the human eye [33], for image patch selection. [PERSON] et al. [34] developed multi-expert CNNs for evaluating the perceptual quality of artificially distorted images. Namely, the authors trained a CNN for each possible distortion type (JPEG, JPEG2000, Gaussian, etc.) of an IQA benchmark database and a distortion identification network. The outputs of the previously mentioned networks were combined to obtain an input image's perceptual quality. Another line of works deals with deep feature extraction for NR-IQA [35; 36; 37; 38; 39; 40]. Namely, the first layers of a CNN usually capture elemental image features, such as blobs, edges, boundaries and color patterns, which are quality-aware features [41]. First, [PERSON] et al. [35] applied deep features extracted from pretrained CNNs for perceptual image quality estimation. Namely, random patches were sampled from an input image and deep features were derived from each patch. Subsequently, the extracted deep feature vectors were pooled together and mapped onto quality scores with a trained SVR. In [36], the inception modules [42] of a CNN and global average pooling layers were used to compile deep features which were mapped onto perceptual quality with an SVR. In contrast, [PERSON] et al. [39] combined viewing distance information with deep features to obtain a better performance. [PERSON] and [PERSON] [43] introduced weighted spatial pooling to aggregate arbitrarily sized spatial feature maps into a feature vector which is mapped onto quality scores with the help of a fully connected layer. In [40; 44], a multi-scale representation was constructed from deep features to get an effective image representation for NR-IQA. In contrast, [PERSON] et al. [45] utilized a recurrent neural network for spatial pooling of deep features. [PERSON] et al. [46] applied the pooling of regions' of interest features and the training across multiple IQA benchmark databases.
# FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle
## 5 Simulation Results and Discussion
### Performance Parameters  
Although the machines have been designed for operation as generators in the considered HEV, the performance parameters in this section are provided for both motoring and generating conditions to demonstrate the differences.  
The power, efficiency, and power factor maps of the designed IPMSM and IM are discussed in this section. The calculation procedure of these has been described in Ref. [60]. Figure 10 compares the output rated power of both machines. It is seen that the IPMSM offers a higher power (larger torque) capability at lower speeds. The maximum power of the IPMSM is 103 kW while the IM is limited to 75 kW. It should be highlighted that the induction machine offers 81 kW during generating operation at the rated speed which is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Outer diameter [mm] & 370 & Inner diameter [mm] & 260 \\ \hline Slot depth [mm] & 42 & Stator tooth width [mm] & 6.9 \\ \hline Rotor bar depth [mm] & 24.5 & Rotor tooth width [mm] & 5.3 \\ \hline Airgap length [mm] & 0.5 & Shaft diameter [mm] & 170 \\ \hline Winding end-winding length [mm] & 40 & Stator stack length [mm] & 65 \\ \hline Number of rotor bars & 78 & Number of poles & 12 \\ \hline Number of stator slots & 72 & Number of turns per slot & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The dimensions of the designed IM.  
Figure 9: (**a**) Cross-section of the designed induction machine, (**b**) flux density distribution in the full-load operating condition.  
6 kW greater than the power in the same motoring condition. The maximum output power of the IPMSM in motoring mode is 2 kW greater than its output generating power.  
The efficiency maps of the designed machines are compared in Figure 11. This shows the efficiency of the IPMSM is at least 2% greater than the IM in both motoring and generating modes. Note that at lower speeds this difference reaches 7%, which is significant. In addition, the high-efficiency region for the IPMSM covers a larger area than for the IM.  
Figure 11: Two-dimensional FEA efficiency map results: **(a)** IPMSM, **(b)** IM.  
Figure 10: Two-dimensional FEA output power results: **(a)** IPMSM, **(b)** IM.  
Table 5 compares the electromagnetic weight, power, and efficiency. The IM is approximately 10 kg heavier than the IPMSM because of the copper rotor cage. The power densities of the IPMSM and IM are about 2.1 kW/kg and 1.4 kW/kg, respectively.  
The comparison of the power factor of the two electric machines in Figure 12 demonstrates the larger power factor of the IPMSM at most of the operating points. At larger torques this difference is about 37%.
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
###### Abstract
The Madgwick algorithm [24] has been proposed to address some of the issues associated with traditional attitude estimation algorithms, and is widely used in these fields. It calculates attitude by fusing data from accelerometers, gyroscopes, and magnetometers, then converts sensor data (including from accelerometers and gyroscopes) into attitude information. It is derived by minimizing the error in attitude estimation based on the difference between the sensor measurements and the expected measurements, thereby avoiding the need for accurate model parameters and noise statistics. Furthermore, this algorithm is based on quaternions, which effectively addresses the problem of attitude estimation in nonlinear systems [25] with low computational demands and good real-time performance. Therefore, in this research we examined the impact of sensor orientation with sensors embedded in mobile devices such as smartphones during the execution of HAR tasks. The contributions of this study are outlined as follows. First, due to the lack of publicly available datasets specifically addressing the device angle change problem, we collected a dataset containing using accelerometer and gyroscope data, including various device angles. Second, the sensor data were fused, and superior results were achieved by concatenating the raw data with the fused data as input. The rest of this article is structured as follows. Section 2 provides a review of related works. Section 3 introduces the datasets used in our research and presents our proposed application of the Madgwick algorithm along with a brief overview of our baseline method. Section 4 details the experimental setup, results, and analysis, demonstrating our method's superiority. Finally, our findings are concluded in Section 5.
## 1 Introduction
For example, the color space used in previous work for skin recognition was YCbCr. Existing PAD algorithms used in finger photo authentication systems are based on texture descriptors, including Local Binary Patterns (LBP), Locally Uniform Comparison Image Descriptor (LUCID), etc., which are processed on RGB images. These approaches might limit the algorithm's performance since spoofs are extracted only in the RGB color model. Recently, we used appropriate color space liveness detection and found that choosing different color spaces can provide a more robust analysis. This research investigated finger photo patch-based training on a deep learning framework that integrates different color spaces via a late fusion mechanism. The color spaces considered in this work can be derived from RGB, thus the proposed investigation seeks to answer the research question \"Is it reasonable to fuse information provided from different color spaces?\" more specifically, \"Do different color spaces complement each other?\". Then, this study focuses on addressing the problem related to the question \"What is an effective fusion strategy to increase the robustness of the finger photo PAD systems?\". The contribution of this paper is two-fold: (i) carry out a correlation analysis among various color spaces to assess that they complement each other, and (ii) design and evaluate a deep learning framework that integrates different color spaces via late fusion. In the proposed approach, the input RGB finger photo is converted into various color spaces to train various CNNs by using minutia-centered patches. A single fusion layer is built with the input size equivalent to the sum of all the fully connected layers of each best net and the output size equivalent to binary classification (i.e., live or spoof). The last fully connected layers of the most accurate nets are combined through a fusion layer, which yields a global decision. The proposed framework is then compared to the state-of-the-art on a publicly available database. The rest of the paper is structured as follows: Section 2 reviews research conducted on liveness detection from fingerprints as well as finger photo PAD, Section 3 summarizes the approaches evaluated in this study, Section 4 presents the experimental procedure, Section 5 describes the experimental results, and Section 6 is the conclusions and discusses future work.