esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Feature Selection  
Feature selection is the most important part of the ML process. The main goal of this process is to select the most valuable features and remove the worthless features in a dataset. In this research, it was observed that all the extracted features were not equally valuable for liver cancer classification. The acquired dataset had a large FVS of 5,486,400 (254 \(\times\) 21,600), making it very difficult to deal with. This problem was solved by maximizing the size of the functionality, as indicated in [31], so that the faithful representation of all data was consistent and resulted in an adequate classification with a minimum error rate. The principal component analysis (PCA) technique provided excellent results on linearly separated data, since PCA facilitated the linear transformation of the input data [32]. In addition, it was also used in the selection of features. Using PCA, the most important feature set was obtained, which had less functionality than the original FVS. Regrettably, this optimized feature set did not provide an accurate picture of the entire data set because the PCA was unable to retain many discrete data. Additionally, the PCA method was an unsupervised approach [33], but the liver cancer dataset was labeled, and the PCA results were not as promising on the labeled data. To solve this problem, ML-based supervised feature selection techniques, namely, probability of error (POE) and average correlation (AC), were used to select the optimized features from this large-scale and high-dimensional liver cancer acquired fused dataset. This approach was better compared to PCA and was able to obtain the sub-dataset with the optimal characteristics for this large dataset. The proposed approach selected 10 optimized features out of 254 features using MATLAB software [34]. Mathematically, (POE + AC) is defined as:  
\[PA(f_{w})=\frac{\textit{MissclassifiedSamples}}{\textit{TotalSamples}} \tag{26}\]  
\[f_{2}=f_{w}\;:\;\textit{Min}_{w}\Big{[}\;PA(f_{w})+\big{|}\textit{Correlate}\;(f _{1},\;f_{w})\big{|}\Big{]} \tag{27}\]  
\[f_{k}=f_{w}\;:\;\textit{Min}_{w}\Big{[}\;PA(f_{w})+\frac{1}{N-1}\;\big{|} \textit{Correlate}\;(f_{1},\;f_{w})\big{|}\Big{]} \tag{28}\]  
When (POE + AC) was deployed on the acquired dataset, it selected 10 optimized features for further processing. The optimized features are described in Table 1.  
Finally, 5,486,400 (254 \(\times\) 21,600) fused hybrid-feature vector spaces were reduced to the 216,000 (10 \(\times\) 21,600) shown in Figure 4, a (POE + AC)-based optimized dataset for each type of liver cancer, and this optimized fused hybrid-feature dataset was deployed to four machine-learning classifiers.  
Figure 4: Liver cancer dataset visualization in 3D vector space.
# Promoting Urban Farming for Creating Sustainable Cities in Nepal
## 3 Materials and Methods
### Urban Agricultural in Nepal: Study Area
Thus, the urban-farming technique not only can contribute to atmospheric amelioration but also to human health and food security. In Nepal, urban areas are developing in different stages, and urban farming is emerging in different forms (Table 3). In the dense and well-developed urban centers, urban agriculture happens in smaller hardscapes on and around the buildings. In suburban areas, urban agriculture can be seen on the front, back, and side yards of residential lots. In emerging urban areas, urban farming can be found on the several vacant lots and open spaces spread across the cities and towns. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage of Urbanization** & **Typical Characteristics and Examples** & **Typical Locations and Types for Urban Agriculture** \\ \hline High-density urban core & High-rise, mid-rise, high-density areas & Rooftops, balconies, indoor plants, hydroponics, and plants in vaes \\ \hline Suburban & Single family and low-density multifamily & Front, back, and side yards; porches; balconies; and rooftops \\  & Outer ring road settlements in the & \\  & Kathmandu Valley & \\ \hline Peri-urban & Agricultural areas being converted into & \\  & urban ones & Front, back, and side yards; and community parks and gardens \\  & Immediate boundaries of cities and towns & \\ \hline Newly urbanizing & Newly defined as urban, transitioning from & \\  & agriculture to urban & \\  & Itahari, Tokha, and Lekhanath & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A schematic depiction of the stages of urbanization and urban agriculture potential in Nepal. Conceptualized and drawn by the authors.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 4 Security and Performance Analysis  
### Security Analysis  
Table 2 shows the comparative analysis result on the security between the other group sensor management studies and the proposed scheme. As shown in the table, the proposed scheme has an outstanding level of security compared to the existing studies. In addition, it was confirmed that the proposed dynamic group management scheme would not only support for dynamic group allocation but also be secure to all the security vulnerabilities in a wireless sensor network environment by utilizing only the four fundamental arithmetic operations, logical operation and substitution operation.
## 2 Integrated Modeling
### Structural Dynamic Model  
The general equation that governs the structural dynamics of the satellite can be written as:  
\[M\ddot{q}+C\dot{q}+Kq=F, \tag{3}\]  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{C}\) is the damping matrix, \(\mathbf{K}\) is the stiff matrix, \(\mathbf{F}\) is the disturbance force or torque, and \(\mathbf{q}\) is the displacement response. Use the mode shape matrix to convert the physical coordinates to modal coordinates as follows:  
\[\mathbf{q}=\mathbf{\Phi}\mathbf{\eta}, \tag{4}\]  
where \(\mathbf{\Phi}\) is the mode shape matrix and \(\mathbf{\eta}\) is the modal coordinates.  
Substituting Equation (4) and the second time derivative of Equation (4) into Equation (3), equates to:  
\[M\mathbf{\Phi}\ddot{\mathbf{\eta}}+\mathbf{C}\mathbf{\Phi}\dot{\mathbf{\eta}}+\mathbf{K}\mathbf{\Phi}\mathbf{ \eta}=F. \tag{5}\]  
The equations forming Equation (5) are coupled. To uncouple the equations, pre-multiply Equation (5) by the transpose of the mode shape matrix \(\mathbf{\Phi}^{\rm T}\).  
\[\mathbf{\Phi}^{\rm T}M\mathbf{\Phi}\ddot{\mathbf{\eta}}+\mathbf{\Phi}^{\rm T}C\mathbf{\Phi}\dot{ \mathbf{\eta}}+\mathbf{\Phi}^{\rm T}K\mathbf{\Phi}\mathbf{\eta}=\mathbf{\Phi}^{\rm T}F. \tag{6}\]  
Figure 4: Test site for the RWA disturbance.  
By doing so, the mass, stiffness, and damping matrices are diagonalized. The modal (generalized) mass matrix and the modal (generalized) stiffness matrix are simplified to:  
\[\begin{array}{c}-\\ \textbf{M}\textbf{=}\begin{bmatrix}1&&\\ &\ddots&\\ &&1\end{bmatrix},\\ \\ \textbf{K}=\begin{bmatrix}\omega_{1}^{2}&&\\ &\ddots&\\ &&\omega_{n}^{2}\end{bmatrix}.\end{array} \tag{7}\]  
Figure 5: The disturbance force and tongue of RWA. (**a**) \(F_{x}\); (**b**) \(F_{y}\); (**c**) \(F_{z}\); (**d**) \(M_{\rm x}\); (**e**) \(M_{y}\); (**f**) \(M_{\rm z}\).  
While the uncoupled form of the dynamic equations can be written as:  
\[\ddot{\eta}+2\zeta\Omega\dot{\eta}+\Omega^{2}\eta=\Phi^{\mathrm{T}}F, \tag{9}\]  
where the modal damping matrix is provided as:  
\[\zeta=\begin{bmatrix}\zeta_{1}&&&&\\ &\ddots&&\\ &&\zeta_{i}&&\\ &&&\ddots&\\ &&&&\zeta_{n}\end{bmatrix}. \tag{10}\]  
Generally, the modal damping coefficients are different for every mode and typically vary between 0.1% and 3% for lightly damped space structures [16].  
The normal frequency matrix is denoted as:  
\[\Omega^{2}=\begin{bmatrix}\omega_{1}^{2}&&\\ &\ddots&\\ &&\omega_{n}^{2}\end{bmatrix}. \tag{11}\]  
Conversion of Equation (9) to the state space form is as follows:  
\[\begin{bmatrix}\dot{\eta}\\ \dot{\eta}\end{bmatrix}=\begin{bmatrix}\mathbf{0}&\mathbf{I}\\ -\Omega^{2}&-2\zeta\Omega\end{bmatrix}\begin{bmatrix}\eta\\ \dot{\eta}\end{bmatrix}+\begin{bmatrix}\mathbf{0}\\ \mathbf{\Phi^{\mathrm{T}}}\end{bmatrix}F=A_{\mathrm{s}}\begin{bmatrix}\eta\\ \dot{\eta}\end{bmatrix}+B_{\mathrm{s}}F. \tag{12}\]
## 3 Results and Discussion
### Magnetic Behavior Analysis of Fe\({}_{3}\)O\({}_{4}\)/Ag Decorated Nanoparticles  
The magnetic behavior of Fe\({}_{3}\)O\({}_{4}\)/Ag nanoalloys was comprehensively investigated through hysteresis curve analysis conducted at 300 K (Figure 4a). The obtained hysteresis loops exhibit distinctive characteristics typical of superparamagnetic materials, demonstrating nearly zero magnetic coercivity. Notably, the magnetization value recorded in this plot was determined to be 52 emu/g. This saturation magnetization value can be attributed to several factors, including the presence of non-magnetic surface layers, spin canting phenomena at the surface, or even a surface spin-glass transition [40; 41]. In our specific case, the observed phenomenon is plausibly explicable in terms of the chemical bonding of organic molecules, such as polyvinylpyrrolidone (PVP), incorporated into the Fe\({}_{3}\)O\({}_{4}\) nanostructures during the synthesis process, leading to a reduction in the magnetization of the surface layers [42; 43; 44].  
Furthermore, the magnetization curves exhibit the characteristic blocking process inherent in assemblies of superparamagnetic nanostructures, showcasing a distribution  
Figure 4: Magnetic Characterization of Fe\({}_{3}\)O\({}_{4}\)/Ag Nanoalloys. (**a**) Hysteresis curve analysis at 300 K confirms superparamagnetic behavior in Fe\({}_{3}\)O\({}_{4}\)/Ag nanoalloys, with negligible magnetic coercivity and a magnetization value of 47 emu/g. (**b**) Zero-field-cooled (ZFC) curve depicts superparamagnetic behavior across temperatures, with a broad peak indicating the average blocking temperature.  
Figure 3: Experimental X-ray Diffraction (XRD) Pattern of the Fe\({}_{3}\)O\({}_{4}\)-Ag Decorated Nanoparticles.  
of blocking temperatures. This behavior is influenced by several factors, including inter-particle distance, size, and shape distribution of particles [45; 46]. Such observations are consistent with the behavior typically observed in hybrid materials based on magnetic constituents [47].  
The zero-field-cooled (ZFC) curve depicted in Figure 4b displays a broad peak, the maximum of which (TP) is influenced by various factors such as inter-particle distance, size, and shape distribution of particles [42]. Despite the multitude of factors affecting TP, this temperature serves as a rough approximation of the average blocking temperature of the material. This ZFC and field-cooled (FC) behavior aligns with observations seen in other magnetite nanoparticle-based hybrid materials. The broad and flat plateau observed in the ZFC curve can be interpreted within the context of particles exhibiting variations in size and inter-particle distance, further elucidating the complex magnetic behavior of the nanoalloy system [41; 48].
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
###### Abstract  
In this research, the authors set out to propose a new method of predicting and adjusting the main cutting parameters of chip machining, namely spraying on the substrate. In the experiments, a total of 28 turning cuts on a Stellite 6 coating layer were realized. The base materials of the Stellite 6 layers were EN S235 JRG1 and carbide WC-Co. This work aimed to find out and optimize the influence of different combinations of cutting parameters. Due to the coating solution, the work is focused on the evaluation of the roughness \(Ra\) (\(\upmu\)m) of the turned substrate, the grain size \(D_{gr}\) (\(\upmu\)m) of the spraying, the normal stress \(\sigma_{rz}\) (MPa), the residual stress \(\sigma_{rz}\) (MPa), the specific cutting force \(F_{c}\) (N), the quality of adhesion to the substrate \(A_{dump}\) (MPa) and other parameters. For the sake of a comprehensive solution to the problem, several new predictive equations and subsequent suggestions for solutions have been derived from this research topic based on the newly obtained experimental results. The proposed models and procedures make it possible to get new results and insights into coating technologies.  
Stellite 6; coating; mechanical parameters; prediction; spraying technology Article
## 2 Finite Element Model of FSW
### Finite Element Formulation  
In the finite element simulation of plastic forming, according to the nonlinear constitutive of materials, the finite element method is divided into three categories: elastic-plastic, rigid plastic, and viscoplastic. These methods use the energy equation to establish a functional analysis and perform variational solving. In friction stir welding, the elastic deformation of the component material is negligible compared to the plastic deformation. Considering that friction stir welding is formed at high temperatures, the material exhibits a viscous property, so the component is set as an incompressible rigid viscoplastic model, and a rigid viscoplastic finite element method is used for finite element analysis. A rigid viscoplastic model with von [PERSON] yield criteria is used [28; 29; 30]. The stress-strain rate relationship is used in the deformation zone, and the formulas are as shown in Equations (1)-(3):  
\[\dot{\epsilon}_{ij}=\frac{3}{2}\frac{\dot{\tilde{\epsilon}}}{\overline{\varphi} }\sigma^{\prime}_{ij} \tag{1}\]  
\[\overline{\sigma}=\sqrt{\frac{3}{2}}\sigma^{\prime}_{ij} \tag{2}\]  
\[\dot{\tilde{\epsilon}}=\sqrt{\frac{3}{2}\dot{\epsilon}_{ij}\dot{\epsilon}_{ij}} \tag{3}\]  
where \(\overline{\sigma}\), \(\dot{\tilde{\epsilon}}\), \(\dot{\epsilon}_{ij}\), \(\sigma^{\prime}_{ij}\) are effective stress, effective strain rate, strain rate component, and deviatoric stress component, respectively. The finite element formula of rigid viscoplastic materials is based on the variational principle, in which the allowable speed should meet the conditions of compatibility and incompressibility, as shown in Equation (4).  
\[\pi=\int\limits_{V}E(\epsilon_{ij})dV-\int\limits_{S_{F}}F_{i}u_{i}dS \tag{4}\]  
where \(F_{i}\), \(V\), \(S_{F}\) and \(E(\dot{\epsilon}_{ij})\) are functions of surface traction, workpiece volume, stress surface, and plastic deformation power, respectively. The penalty function of incompressibility is added to eliminate the incompressibility constraint on the allowable velocity field. The actual velocity field can be determined by the stable value of the change equation [31], which is expressed as:  
\[\delta\pi=\int\limits_{V}\overline{\sigma}\delta\overline{\epsilon}dV+\lambda \int\limits_{V}\epsilon_{V}\delta\epsilon_{V}dV-\int\limits_{S_{F}}F_{i}\delta u _{i}dS \tag{5}\]  
Equation (5) is the basic equation of the Lagrange finite element method, where \(\dot{\epsilon}_{V}\) is the volume strain rate, \(\delta\tilde{\epsilon}\) and \(\delta\tilde{\epsilon}_{V}\) are the strain rate changes derived from \(\delta u_{i}\), and \(\lambda\) is the penalty factor for the volume incompressible condition. To ensure an incompressible condition, the \(\lambda\) value is an infinite positive number.
## 3 Analysis of the Final Equilibrium State
### Validity of the Model  
In the above analysis, we considered that the concentration of ionized toxins follows the Boltzmann distribution in the final equilibrium state, i.e.,  
\[C_{-}=C_{M}e^{\frac{+\frac{\pi}{RT}\varphi}{RT}\varphi} \tag{17}\]  
This assumes that the electrochemical potential obeys the relationship:  
\[\widetilde{\mu}=\mu_{0}+RT\;lnC_{-}+ze\varphi \tag{18}\]which is true if the solution is dilute and the ions are considered as points (ideal solution). So, in our case, this equation is valid as long as the concentration is low and the toxin ions, although large in size, are still extremely far from each other. Of course, the approximation becomes the better the lower the concentration is.  
Furthermore, the limitations posed by the fact that protein toxin ions have very large dimensions and are not point-based will be discussed. Let's consider, on a case-by-case basis, that the toxin ions have the diameters mentioned in the Table 3. The maximum concentration is reached where the Stern layer (compact layer) ends and the diffuse layer begins. This cannot be greater than [31]:  
\[C_{max}=\frac{1}{N_{A}a^{3}} \tag{19}\]  
The relationship that connects \(C_{max}\) with \(\varphi_{s}^{max}\) prevailing at this point is  
\[C_{max}=C_{M}e^{+\frac{rE}{kT}\varphi_{s}^{max}}\]  
So,  
\[\varphi_{s}^{max}=\frac{RT}{zF}\ln\left(\frac{C_{max}}{C_{M}}\right) \tag{20}\]  
while the corresponding \(\varphi_{max}(0)\) is calculated from Equation (10) ( \(\varphi_{max}(0)\)) is the maximum potential for which the above model is valid). All the above values are calculated on a case-by-case basis in Table 3. We cannot claim that the above values are more than indicative because of the approximations with which they were exported. However, again, the closer we get to \(\varphi_{max}(0)\), the more the concentration of ions in OHP (Outer Helmholtz Plane) increases and approaches \(C_{max}\), and the less the dilute solution approximation (Equation (18)) necessary for the Boltzmann distribution can be applied. Thus, referring to the previous analysis for potentials above 0.24 V for the first two toxins, 0.4 V for the third toxin, and 0.082 V for the fourth toxin, the results can be considered only qualitatively. However, given the extremely large reduction achieved in the concentration of toxins even with voltages of 0.24 V, 0.4 V, and 0.082 V, as we already mentioned, the further increase in voltage, although it cannot be supported by the model theoretically, in practice could result in a more efficient output (the blue lines of Figures 7-10).
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 4 Discussion
It could, for example, be done more efficiently with the right instrumentation, e.g., thermal imagers, on manned or unmanned aircrafts. The Copernicus Land Monitoring Service (CLMS) [21] in cooperation with the Copernicus Marine Environment Monitoring Service (CMEMS) is working on a comprehensive coastal zone monitoring solution that can address the complex and dynamic situations found in coastal environments. The first delivery of a coastal zone product for Europe including Norway was released at the end of 2020. It shows, however, that the ITZ is still poorly resolved [52]. The method presented here could contribute to better map and further describe the ITZ in these kinds of land-cover products. From this study and confirmed by discussions with the Norwegian Mapping Authority [53], we also realized that the MSL water line, which should, by definition, be the border between land and water in all digital maps, is poorly mapped, and that the land area in maps generally starts above the MHW tidal reference height. The water line detected in median S1 backscatter time series would, therefore, improve the official land limit in Norway.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 3 Experiment and Results
### Performance Metrics  
According to the research carried out in Ref. [47], the typical metrics chosen to evaluate the algorithm performance involve two aspects of the disaggregation problem: the classification of the switching activity or state transition of all appliances, and the energy disaggregation, i.e., appliances' contribution in energy consumption.  
The representative metrics to evaluate classification performance include precision (\(P_{c}\)), recall (\(R_{c}\)), \(F\)-Measure (\(F_{c}\)), and Accuracy (\(Acc_{c}\)) [47]. Specifically, precision is the proportion of samples recognized as class \(c\) that are correct, recall is the proportion of samples belonging to class \(c\) that are recognized correctly, \(F\)-Measure is taken to be the weighted harmonic mean of precision and recall, and accuracy is the classification ability for all samples. These metrics are defined as follows,  
\[P_{c}=TP_{c}/(TP_{c}+FP_{c}) \tag{14}\]  
\[R_{c}=TP_{c}/(TP_{c}+FN_{c}) \tag{15}\]  
\[F_{c}=2\times(P_{c}\times R_{c})/(P_{c}+R_{c}) \tag{16}\]  
\[Acc_{c}=(TP_{c}+TN_{c})/(TP_{c}+TN_{c}+FP_{c}+FN_{c}) \tag{17}\]  
where \(c\in\Omega\), \(\Omega\) denotes the set of electric appliances. \(TP_{c}\) indicates the samples correctly labeled as ON state of class \(c\); \(TN_{c}\) refers to the samples correctly labeled as OFF state of class \(c\); \(FP_{c}\) corresponds to OFF state samples wrongly labeled as ON state of class \(c\); \(FN_{c}\) denotes ON state samples incorrectly labeled as OFF state of class \(c\).  
Figure 1: Flow chart of the proposed GSQF-APD algorithm.  
Finally, in order to present the total performance of the disaggregation system, the average classification accuracy is computed,  
\[\overline{A}cc=\big{(}1/N_{p}\big{)}\sum_{c=1}^{N_{p}}Acc_{c} \tag{18}\]  
where \(Acc_{c}\) is the \(c\)th appliance classification accuracy.  
From the perspective of energy disaggregation, the effectiveness of the algorithm is evaluated by comparing the estimated power with the ground truth power consumption.  
The related metrics are defined as follows:  
* Disaggregation accuracy (\(DA\)): \[DA=1-\frac{\sum_{i=n+1}^{N}\sum_{k=1}^{N_{p}}\left|\hat{p}_{k}(i)-p_{k}(i) \right|}{2\sum_{i=n+1}^{N}P(i)}\] (19) where \(\hat{p}_{k}(i)\) is the disaggregated power consumption of appliance \(k\) at time \(t_{i}\); \(p_{k}(i)\) refers to the actual power of appliance \(k\) at time \(t_{i}\), and \(P(i)\) is the measured aggregate power consumption at time \(t_{i}\).
* Disaggregation error (\(DE\)): \[DE=0.5\sum_{i=n+1}^{N}\sum_{k=1}^{N_{p}}\left|\hat{p}_{k}(i)-p_{k}(i)\right|^{2}\] (20) \(DE\) provides a global comparison between the ground truth and estimated signal.
* Percentage of contribution in energy consumption (\(PCEC\)): \[PCEC^{k}=\sum_{i=n+1}^{N}\hat{p}_{k}(i)/\sum_{i=n+1}^{N}P(i)\] (21) where \(PCEC^{k}\) represents the contribution of appliance \(k\) in energy consumption.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 5 Experiments  
In this section, the proposed method is analyzed to evaluate its effectiveness over relevant images. The methodology contains two experiments. The first is designed to determine if the proposed approach is superior to related techniques over a set of eleven generally used images in the field of thresholding. After we concluded that the MCE-EO is competent for general image thresholding, we moved towards a more in-depth analysis of the performance of MCE-EO on the specific domain of prostate image segmentation with six magnetic resonance transaxial-cut prostate images. In both cases and for comparative purposes, the experiments consider six metaheuristic algorithms: the sunflower optimization (SFO) algorithm [40], the sine cosine algorithm (SCA) [41], particle swarm optimization (PSO), differential evolution (DE) [42], the genetic algorithm (GA) [43], and the Hirschberg--Sinclair algorithm (HS) [44].  
In this study, all of the metaheuristic (stochastic) algorithms used the minimum cross-entropy criterion as an objective function with the same solution representation. It is important to note that the proposed thresholding-based segmentation method does not rely on ground truth data, as is often required for metrics like pixel accuracy (_PA_) or intersection over union (_IoU_). Instead, we evaluate segmentation effectiveness using no-reference image quality metrics such as peak signal-to-noise ratio (_PSNR_) [14], structural similarity index measure (_SSIM_) [46], and feature similarity index measure (_FSIM_) [47]. These metrics provide a robust analysis of image fidelity, structure preservation, and overall segmentation quality, which are crucial for medical imaging applications where precise anatomical boundaries are essential for diagnosis.  
All experiments were performed with MATLAB R2016a using a device equipped with an 2.4 GHz Intel Core i5 CPU and 12 GB of RAM. This approach allowed us to assess the performance of the segmentation process without the need for manually annotated reference images.