esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Material Interpolation  
The governing equations for nonhomogeneous materials can be obtained by introducing a source term to the Navier-Stokes equation. The effect of the material distribution on the fluid motion is modeled with the penalty parameter [37]. Here, we use a porous medium model and introduce a dimensionless artificial Darcy friction force in the momentum equation to act as a source term, which is assumed to be proportional to the velocity defined as:  
\[\mathbf{f}=-\alpha\mathbf{u}\text{ in }\Omega_{s} \tag{5}\]  
where \(\alpha\) is the material impermeability which represents the ability to transport liquid. It can be expressed as:  
\[\alpha=\left\{\begin{array}{l}+\infty\text{ for }\gamma_{p}=0\\ 0\quad\text{ for }\gamma_{p}=1\end{array}\right. \tag{6}\]  
where \(\gamma_{p}\) is the material density obtained by successively implementing filtering and thresholding projection operations on the design variable \(\gamma\) taking consecutive values in [0, 1]. For avoiding problems such as gray scale, the design variable is regularized by Helmholtz filters in variable density topology optimization problems [41] as follows:  
\[\begin{array}{l}\
abla\cdot(-r_{f}^{2}\
abla\gamma_{f})+\gamma_{f}=\gamma \quad\text{in }\Omega_{s}\\ r_{f}^{2}\
abla\gamma_{f}\cdot\mathbf{n}=0\quad\quad\quad\quad\quad\text{on }\Gamma_{s}\end{array} \tag{7}\]  
where \(\gamma_{f}\) is the filtered design variable and \(r_{f}\) is the filter radius. The variational formulation of the PDE filter defined in \(\Omega_{s}\) can be derived as:  
\[\begin{array}{l}\text{Find }\gamma_{f}\in\mathcal{H}(\Omega)\text{ }f \sigma\gamma\in\mathcal{L}^{2}(\Omega)\\ \int_{\Omega}r_{f}^{2}\
abla\gamma_{f}\cdot\
abla\widetilde{\gamma}_{f}+ \gamma_{f}\widetilde{\gamma}_{f}-\gamma\widetilde{\gamma}_{f}d\Omega=0\\ \text{ for }\vec{\gamma}_{f}\in\mathcal{H}(\Omega)\end{array} \tag{8}\]  
where \(\widetilde{\gamma}_{f}\) is the test function of \(\gamma_{f}\). Filtering produces a significant gray scale, which can be reduced by imposing a smooth step function called projection in topology optimization. The threshold projection of the filtered design variable based on the hyperbolic tangent function is [42]:  
\[\gamma_{p}=\frac{\tanh(\beta\xi)+\tanh\Bigl{(}\beta\Bigl{(}\gamma_{f}-\xi \Bigr{)}\Bigr{)}}{\tanh(\beta\xi)+\tanh(\beta(1-\xi))} \tag{9}\]  
where \(\beta\) and \(\xi\) are the parameters of projection slope and projection point, respectively [43]. In order to distinguish the solid phase from the liquid phase, the material interpolation of impermeability can be implemented as:  
\[\alpha=\alpha_{\text{min}}+(\alpha_{\text{max}}-\alpha_{\text{min}})q\frac{1- \gamma_{p}}{q+\gamma_{p}} \tag{10}\]  
where \(q\) is the parameter for adjusting projection convexity and \(\alpha_{\text{max}}\) and \(\alpha_{\text{min}}\) are the impermeability of the solid and fluid phases, respectively. In theory, \(\alpha_{\text{max}}\) is the infinite value while \(\alpha_{\text{min}}\) is the infinitesimal value or zero, which is to ensure the stability of the numerical implementation and approximate the solid phase with enough accuracy [44].
## 5 Simulation Results
### Experiments with Different SNR  
In this section, we simulate a scenario to test the performance in different SNR. In this scenario, all the received signals have the same SNR, which changes from \(-10\) dB to \(10\) dB for different simulations. Additionally, the position errors are shown in Figure 17. In this figure, we can see that when the SNR is low, the position errors are slightly worse and the variance is big. When the SNR goes higher, the position errors converge to a constant. One thing worth noticing is that the DVB-T signals power is very high, above \(10\) dB, normally due to the error probability requirement of TV service. Additionally, it is normal for the DVB-T receiver to receive signals with an SNR higher than \(10\) dB.  
Figure 17: The position errors in different SNR.  
Figure 18: The position estimation errors with one transmitter unsynchronized.
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 3 Research Methods and Procedures  
Due to the complexity of construction sites, it was difficult for construction managers to forecast typhoon damages effectively. Therefore, a detailed analysis of construction sites in reference to relevant theoretical disaster science, including disaster-forming environment, disaster-causing factors, and disaster-affected bodies, was conducted to decompose the disaster chain in the construction site and establish an Analytic hierarchy process (AHP) model for the typhoon criteria. Various criteria of typhoon damage on construction sites were determined through qualitative and quantitative analysis. The effectiveness and feasibility of the developed SOP were verified through UAV experiments on an accurate construction site, the Xiamen Electronic City project.
## 3 Results and Discussion
### Simultaneous Quantification of the 10 Marker analytes in DSHT Samples Using HPLC  
The HPLC assay developed for the quality control of DSHT in this study was applied for the simultaneous quantitative analysis of DSHT samples. As shown in Table 4, the 10 selected marker compounds in the DSHT sample prepared by KIOM were detected at concentrations of 0.77-46.17 mg/g. Furthermore, the DSHT granules produced and distributed by a Korean pharmaceutical company were also analyzed using the established analytical method. In this case, the marker compounds were detected at levels up to 39.34 mg/g; however, poncirin, a major compound of _P. trifoliata_, was not detected in this sample. It is considered that this is due to the origin of the P. trifoliata, which is a component of DSHT [20]. Of the marker compounds assessed, baicalin, which is known to be the main compounds of _S. baicalensis_, was detected at 36.86-46.17 mg/g and was confirmed as the major component in the DSHT samples.
###### Abstract  
This paper presents a linear dynamic analysis on operable charge pressure and working frequency of free-piston Stirling engines (FPSE) along with experimental verifications. The equations of motion of the FPSE are formulated as a 2-degree-of-freedom (DOF) vibration system of the power piston (PP) and displacer piston (DP), based on the state equation of ideal gas and the isothermal Stirling cycle model. The dynamic models of FPSE we considered are the 1-DOF simple vibration model of each piston and the 2-DOF root locus model of coupled pistons. We developed a test FPSE for verification of the dynamic models and conducted a series of experiments to measure the dynamic behaviors of PP and DP under varying charge pressures for various masses and stiffnesses of the PP. As a result, both prediction models showed good agreements with experimental results. The 1-DOF vibration model was found to be simple and effective for predicting the operating frequency and charge pressure of FPSE. The root locus method showed reasonable predictions with an operation criterion of the PP-DP phase angle of 90\({}^{\circ}\). In addition, the FPSE was confirmed to operate in resonant oscillations when the DP-PP phase angle is 90\({}^{\circ}\), based on analysis of the force vector diagram of the two pistons.  
free-piston Stirling engine; linear dynamics; root locus analysis; simple vibration model; experimental verification +
Footnote †: journal: Applied Sci.  
**Keywords:** free-piston Stirling engine; linear dynamics; root locus analysis; simple vibration model; experimental verification
## 2 Materials and Methods
### Excitation Source Identification and Theoretical Analysis
#### 2.2.3 Excitation Analysis of the Threshing Cylinder and Vibrating Screen  
The threshing cylinder is the core component of the harvester responsible for crop threshing. According to the user manual for this harvester model, the rotational speed of the threshing cylinder is set to 580 r/min, and its radius is 310 mm. The threshing cylinder is equipped with multiple rows of threshing teeth, uniformly distributed on its surface; the unit under study has six rows of these teeth. As the threshing cylinder rotates, the interaction of each row of teeth with the crop and internal structures generates periodic impact excitation. Its excitation frequency,\(f_{t}\), can be calculated using Equation (3).  
\[f_{t}=\frac{kn_{t}}{60} \tag{3}\]  
In the above equation, \(n_{t}\) represents the rotational speed of the threshing cylinder (in r/min), and \(k\) is the number of rows of threshing teeth. Based on this calculation, with a cylinder speed of 580 r/min and six rows of threshing teeth, the theoretical excitation frequency of the threshing cylinder is calculated to be approximately 58 Hz.  
The vibrating screen is a core component used for grain cleaning. Its operating principle involves being driven by an eccentric bearing to produce reciprocating vibration, which separates the grain from impurities. For this study, its motion is simplified and analyzed primarily within the X-Z plane, and its vibration is generally approximated as simple harmonic motion. The excitation frequency of the vibrating screen can be calculated using Equation (4).  
\[f_{s}=\frac{n_{s}}{60} \tag{4}\]  
In the above equation,\(f_{s}\) represents the excitation frequency of the vibrating screen (in Hz) and \(n_{s}\) is the rotational speed of the vibrating screen's eccentric bearing (in r/min). Tak  
Figure 4: Kinematic model of the cutter mechanism.  
ing \(n_{s}\)= 960 r/min, the theoretical excitation frequency of the vibrating screen is calculated to be 16 Hz.  
The results of the above theoretical analysis are summarized in Table 3, which provides a theoretical reference for the excitation frequencies to be used in the subsequent vibration tests and data analysis.
## 1 Introduction
### Literature
In conclusion, the literature review indicates that there is a growing body of research on the development of MPPT techniques for TEG systems, particularly in the area of optimization algorithms, such as gradient-based algorithms (P&O, IC), evolutionary algorithms (GA, DE, SA), and nature-inspired metaheuristics algorithms (PSO, GWO). These optimization algorithms have been shown to be effective in improving the performance of TEG systems, by providing a more accurate and efficient method for tracking the maximum power point. However, each class comes with some limitations to it. Further research is needed to develop and validate these MPPT techniques for TEG systems, particularly in the context of heat-recovery applications. The main contributions of the presented work are summarized as:  
* A novel mud-ring-optimizer-based MPPT control technique is implemented and tested on dynamic non-uniform thermal gradients. * The proposed MRA-based MPPT technique can track the GMP under non-uniform temperature distribution with 99.96% efficiency within 220 ms.
* The comparison of the MRA-based technique is performed with WOA-, GWO-, and PSO-based state-of-the-art MPPT techniques. * The MRA-based technique has a strong capability to differentiate between the LMPP and GMPP. * The proposed technique is also tested using an experimental setup by implementing the proposed technique on a low-cost microcontroller. * During the experimental verification, the proposed technique achieved a higher efficiency and required less time to track the GMPP.
## 6 Implications and Future Directions
### Limitations and Research Directions  
Several methodological constraints must be acknowledged when interpreting these findings. The theoretical modeling necessarily incorporated simplifying assumptions, particularly regarding blockchain implementation and quantum-resistant cryptographic protocols. While these abstractions were grounded in established literature and realistic operational parameters, they may not fully capture the complexities encountered in production IoT environments.  
The simulation environment, though rigorously designed using Python libraries including NumPy, Pandas, and Matplotlib, remained a controlled computational abstraction. Real-world deployments will inevitably encounter variations in device capabilities, network conditions, and threat landscapes that could not be entirely anticipated in the simulated environment.  
These considerations necessitate that the current findings be interpreted as compelling preliminary evidence rather than conclusive empirical validation. Nevertheless, the convergence between theoretical and simulated results provides strong support for the framework's conceptual validity and practical feasibility.  
Several critical research priorities have been identified:  
**Empirical Validation:** Comprehensive field trials across diverse IoT ecosystems must be conducted to validate theoretical and simulated findings under real-world conditions.  
**Longitudinal Performance Studies:** Extended operational assessments are required to examine the framework's long-term impact on device reliability, battery longevity, and maintenance requirements.  
**Scalability and Resilience Testing:** Rigorous evaluation under high-stress conditions, including sophisticated attack scenarios and large-scale deployments, will be necessary to determine operational boundaries and failure modes.  
**Human Factors Investigation:** The effectiveness of security measures, user compliance patterns, and the impact of security awareness initiatives require systematic investigation in operational environments.  
Through the systematic pursuit of these research directions, the IASF-IoT framework can evolve from its current theoretical foundation to become a mature, empirically validated solution capable of addressing the dynamic security challenges facing IoT ecosystems.
# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 1 Introduction
VGG19, a CNN architecture, proved effective in achieving high classification accuracy by directly evaluating tumors from histopathological images [13]. Researchers utilized transfer learning to train a neural network on a small subset of retinal tomography optical coherence tomography (OCT) images, achieving 93.4% accuracy in classifying multiple eye conditions [13]. A weakly supervised multi-label image classification framework was developed to detect multiple pathologies in X-ray images using a DCNN model [13]. An interpretable deep learning model was developed for predicting microsatellite instability (MSI) status in cancer patients using H&E-stained WSIs [14]. A random forest model was also created, providing feature-level interpretability for MSI prediction. Both models highlighted the importance of color features and texture characteristics in classification [14]. PathCLIP, a model designed specifically for pathology image analysis, has shown impressive performance in zero-shot classification tasks [15]. The PathTree model introduced hierarchical pathological image classification, proposing a novel representation learning approach [16]. These studies demonstrate the significant progress in applying computer vision and machine learning techniques for pathological image classification, potentially improving diagnostic accuracy and efficiency in clinical settings. In addition, cerviLearnNet is a novel approach to cervical cancer diagnosis that combines reinforcement learning with convolutional neural networks [17]. This innovative model, also known as RL-CancerNet, integrates EfficientNetV2 and Vision Transformers (ViTs) within a reinforcement learning (RL) framework to enhance the accuracy of cervical cancer detection. Further, the novel teacher-student network model leverages staggered distillation methods to optimize both speed and model size [18]. This design enhances the student network's learning potential and offers a reshaped concise form upon convergence, leading to more effective knowledge transfer. In this review, we summarize the current advances, architectures, applications, and future directions of deep learning approaches for the diagnostic classification of various diseases.
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 3 Results and Discussion
### Adsorption Kinetics  
The effects of adsorption time on the removal efficiency of Pb\({}^{2+}\) at pH 5.0 are shown in Figure 7. The adsorption of Pb\({}^{2+}\) on the as-prepared GO increased significantly with an increasing reaction time of 100 min. Different adsorption kinetic models exist to describe adsorption mechanisms, including the pseudo-first-order kinetics model [38; 39] and the pseudo-second-order kinetics model [40], which can be represented as follows:  
\[\frac{q_{\mathrm{t}}}{q_{\mathrm{e}}}=1-e^{-k_{\mathrm{L}}t} \tag{2}\]  
\[\frac{t}{q_{\mathrm{t}}}=\frac{1}{k_{2}q_{\mathrm{e}}^{2}}+\frac{1}{q_{ \mathrm{e}}}t \tag{3}\]  
where \(q_{\mathrm{e}}\) is the equilibrium adsorption capacity in mg/g, \(q_{\mathrm{t}}\) is the adsorption capacity at time \(t\) in mg/g, and \(k_{\mathrm{1}}\) (min\({}^{-1}\)) and \(k_{\mathrm{2}}\) (g/(mg\(\cdot\)min)) are the rate constants of the pseudo-first-order and pseudo-second-order kinetics models, respectively.  
By nonlinear fitting, the fitting parameters of the pseudo-first-order model are \(k_{\mathrm{1}}\) = 0.5398 min\({}^{-1}\), \(q_{\mathrm{e}}\) is 358.40 mg/g and \(R^{2}\) is 0.5202, and the fitting parameters of the pseudo-second-order model are \(k_{\mathrm{2}}\) = 0.001438 g/(mg\(\cdot\)min), \(q_{\mathrm{e}}\) is 371.54 mg/g, and \(R^{2}\) is 0.8312. It can be observed that the pseudo-second-order model is a better fit to the adsorption results of Pb\({}^{2+}\) by the as-prepared GO, further suggesting that the adsorption rate depends primarily on chemisorption [4]. This result reveals that in addition to the formation of chemical bonds, the transfer and exchange of electrons occur in the adsorption reaction. In the initial 5 min, chemisorption was rapid, thus leading to a rapid increase in the adsorption reaction rate.
## 9 Conclusions  
In the three different power stages studied to drive the brushless direct current motor, it was measured that the performances of the cooling systems were ranked, from better to poor, as liquid cooling, fan-assisted air-cooled and, air-cooled systems. As per the configuration of the semiconductors, it is seen that the liquid-cooled system with at least 20 mm between them, using a single module, gives the best performance. The performance of fan-assisted air-cooled and fanless air-cooled systems decreases further. In power-semiconductor systems, if no cooling system is employed, the nominal operating temperatures are exceeded, especially during load operations. Therefore, using a cooling system is essential. According to the results of this study, the installation of power semiconductors and the choice of cooling system should be optimized according to the requirements of future studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surface Temperatures According to** & **No Cooling** & **Air-Cooled** & **Fan-Assisted Air-Cooled** & **Liquid-Cooled** \\ \hline Surface when IGBTs are combined & 96 \({}^{\circ}\)C & 78 \({}^{\circ}\)C & 75 \({}^{\circ}\)C & 38 \({}^{\circ}\)C \\ \hline IGBT 6 Module (\({}^{\circ}\)C) & 94 \({}^{\circ}\)C & 75 \({}^{\circ}\)C & 71 \({}^{\circ}\)C & 34 \({}^{\circ}\)C \\ \hline IGBT 20 mm apart (\({}^{\circ}\)C) & 90 \({}^{\circ}\)C & 72 \({}^{\circ}\)C & 69 \({}^{\circ}\)C & 30 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 8: Experimental application results of cooling system temperature comparisons according to the mounting method of IGBTs.  
Figure 26: BLDC motor phase currents (I\({}_{\text{f1}}\) Maroon; I\({}_{\text{Z}}\) Grey; I\({}_{\text{Z}}\) Orange), and BLDC motor interphase voltages (V\({}_{\text{23}}\) Blue; V\({}_{\text{12}}\) Green; V\({}_{\text{13}}\) Red).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surface Temperatures According to** & \multicolumn{4}{c}{**Efficiency Decline Rate in Percentage**} \\ \cline{2-5}
**Mounting Type** & **No Cooling** & **Air-Cooled** & **Fan-Assisted Air-Cooled** & **Liquid-Cooled** \\ \hline Surface when IGBTs are combined & 36 & 28 & 25 & 8 \\ \hline IGBT 6 Module & 34 & 25 & 21 & 4 \\ \hline IGBT 20 mm apart & 30 & 22 & 19 & 2 \\ \hline \end{tabular}
\end{table}
Table 9: Experimental application results of cooling system efficiency decline rate in percentage comparisons according to the mounting method of IGBTs.  
**Author Contributions:** A.B.: conceptualization, experimentation, data interpretation, and writing; O.A.: experimentation and data interpretation; E.B.: conceptualization, data interpretation, and writing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 3 Methods and Proposed Approach
### Feature Extraction  
Text can not be fed directly into the machine-learning models, and we need to transform the text into a numerical representation. For that, several feature extraction techniques are available, and this study uses three feature extraction techniques, such as BoW, TF-IDF, and Hashing.
## 2 Analysis of Inherent Residual Current of Low-Voltage Distribution Networks
### Generation of Residual Current from N-Phase Line Grounding  
In the TN configuration, the neutral line is typically grounded on the load side. Illustrated in Figure 2 where A, B and C represent three-phase power lines, N is a neutral line, PE represent protecting earthing line, grounding the PEN line results in an increased current \(i_{rRg}\) flow at the location, significantly elevating the inherent residual current at the upstream protection. Apart from the fault residual current, the neutral current returning to the power supply side through the repeated grounding point also contributes to an increase in the normal residual current. The measured normal residual current on the power supply side can reach several amperes, surpassing the predetermined threshold by a considerable margin.  
Moreover, \(i_{rRg}\) experiences substantial fluctuations during load switching, rendering it unfeasible to install a residual current protection device before the grounding point in such systems. The repeated earthing point in TN configurations is typically situated at the  
Figure 1: Schematic of inherent residual current of TT configuration.  
Figure 2: Schematic of inherent residual current of TN-C-S configuration.  
terminal pole or entry container, concentrating the inherent residual current loop at the end of the line (load side).