esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling  
As precised above, the aim in this study is to investigate a situation featuring a cone and a disk holding a non-compressible hybrid nanoliquid subjected to a magnetic field. It is assumed that the cone and disk are either moving or stationary, with their rotational velocity specified in cylindrical coordinates \((r,\phi,z)\). The symbols \(\Omega\) and \(\omega\) represent the rotational speeds of the cone and disk, respectively. \(B_{0}\) signifies the strength of the magnetic field applied along the \(z\)-axis, while any induced magnetic field is ignored. Figure 1 illustrates the geometry of the studied apparatus. The modeling of heat transfer includes the consideration of viscous dissipation, which is effectively incorporated on the surface of the disk. The surface temperature varies radially and is represented as \(T  
\[\left[u\frac{\partial v}{\partial r}+\frac{uv}{r}+w\frac{\partial v}{ \partial z}\right]\rho_{lmf}=\left[\frac{\partial^{2}v}{\partial z^{2}}+\frac{1 }{r}\frac{\partial v}{\partial r}+\frac{\partial^{2}v}{\partial r^{2}}-\frac{v }{r^{2}}\right]\mu_{lmf}-B_{0}^{2}v\sigma_{lnf}, \tag{3}\] \[\left[w\frac{\partial w}{\partial z}+u\frac{\partial w}{\partial r }\right]\rho_{lmf}=\left[\frac{\partial^{2}w}{\partial r^{2}}+\frac{\partial^ {2}w}{\partial z^{2}}+\frac{1}{r}\frac{\partial w}{\partial r}\right]\mu_{lmf} -\frac{\partial p}{\partial z},\] (4) \[\left[w\frac{\partial T}{\partial z}+u\frac{\partial T}{\partial r }\right](\rho C_{p})_{lmf}=B_{0}^{2}(v^{2}+u^{2})\sigma_{lnf}+\frac{\partial^ {2}T}{\partial z^{2}}k_{hnf}, \tag{5}\]  
In the given equations, \((u,v,w)\) denote the velocity components in the \((r,\phi,z)\) directions, while \(B_{0}\) stands for the magnetic field intensity. Moreover, \(k_{lmf}\), \(v_{lmf}\), \(\rho_{lmf}\), \(\sigma_{lnf}\), and \((\rho C_{p})_{lmf}\) signify the thermal conductivity, viscosity, density, electrical conductivity, and heat capacity of the hybrid nanoliquid, respectively. The mandatory constraints are as follows:  
\[ \begin{gathered} u=0,\ \ T=T_{w},\ \ v=r\omega,\ \ w=0\ \ \text{at}\ \ z=0\\ u=0,\ \ T=T_{\infty},\ \ v=\Omega r,\ \ w=0\ \ \text{at}\ \ z= rat\gamma\end{gathered}  \tag{6}\]  
Here, \(\gamma\) represents the angular separation between the disk and cone. To nondimensionalize, we utilize the following similarity transformation [40]:  
\[ \begin{gathered} u=\frac{F(\zeta)v_{f}}{r}=U_{w}F(\zeta),\ \ v=\frac{G(\zeta)v_{f}}{r}=U_{w}G(\zeta),\ \ u=\frac{H(\zeta)v_{f}}{r}=U_{w}H(\zeta),\\ p=\frac{v_{f}^{2}\rho P}{r^{2}}U_{w}^{2}P\rho,\ \ \zeta=\frac{z}{r},\ \ M= \frac{B_{0}^{2}v_{f}\sigma_{f}}{u_{w}^{2}\rho_{f}},\ \ \theta=\frac{T-T_{\infty}}{T_{w}-T_{\infty}},\ \ Pr=\frac{C_{p}\mu_{f}}{k_{f}}.\end{gathered}  \tag{7}\]  
\(U_{w}\) denotes the surface velocity, \(Pr\) stands for the Prandtl number, and \(M\) represents the magnetic field. Applying these transformations as described in Equation (7), formulated  
Figure 1: Rotating disk and cone. Equations (2)-(5) and their respective boundary conditions undergo the following modifications:  
\[\zeta F^{\prime}-H^{\prime}=0 \tag{8}\]  
\[
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
In the research process of this paper, we further discover a new idea and way to improve MOEA by using EFA method, which helps to establish the relationship between different methods in DA and MOO modules, and promotes the model to form an organic whole. Therefore, EFA is adopted as the PFIA method in this paper. As the last significant method in the complete PFDMOEAM, the MADM method in the OSS module provides guidance for model performance optimization. To the best of the authors' knowledge, there are very few studies using the MADM method to construct a complete PFDMOEAM. Only [PERSON] et al. [10] and [PERSON] et al. [12] used the fuzzy analytic hierarchy process (AHP) method and fuzzy integration method to accomplish this task, respectively. However, the accuracy and operability of PFDMOEAMs established in the two studies are not high due to the poor objectivity and insufficient convenience of the MADM methods mentioned above. Compared with fuzzy AHP, fuzzy integral and other subjective evaluation methods, the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) method is more objective and accurate, because its multi-objective evaluation in the OSS module comes entirely from the mining of the inherent laws of historical data, without involving subjective reasoning [15]. In addition, it is simple in principle,operable and can evaluate multiple objectives [16]. These characteristics are well in line with the objective of OSS module. Therefore, this paper employs entropy-TOPSIS as the MADM method in the OSS module. The main contributions of this paper are summarized as follows:  
(1) To eliminate the major performance obstacle of PFDMOEAM, a novel ISPEA2 algorithm is first proposed based on the SPEA2 algorithm selected from renowned second-generation classical MOEAs. By improving the original operators in SPEA2 and introducing a new operator, ISPEA2 has better convergence and diversity than SPEA2, which lays a solid foundation for improving the performance of PFDMOEAM. (2) Taking the ISPEA2 algorithm as the core and combining the EFA and entropy-TOPSIS method, we construct a novel PFDMOEAM. The model makes full use of ISPEA2's algorithm advantages, and further combines the EFA method's high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method's good objectivity and operability, so it has excellent comprehensive performance in the multi-objective evolutionary design of product form. (3) We applied the proposed PFDMOEAM to the car form design problem to demonstrate the innovation and application value of the model. The result proves that it can be used as an effective Kansei engineering design expert system and provides a useful tool for designers. The rest of this paper is organized as follows. Section 2 presents a background overview of the related theory. Section 3 details the implementation procedures of the proposed model, and Section 4 demonstrates the discussion of the results. Finally, Section 5 provides some brief conclusions.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Experimental Scenarios and Classification Measures
#### 4.3.2 Classification Measures  
The classification measures used in our experiments include PPV (Positive predictive value), FPR (False positive rate), TPR (True positive rate), ACC (Accuracy) and F1 (F1 measure). These measures are computed using the following formulas:  
\[\text{PPV}=\frac{TP}{TP+FP}\times 100\% \tag{11}\]  
\[\text{FPR}=\frac{FP}{FP+TN}\times 100\% \tag{12}\]  
\[\text{TPR}=\frac{TP}{TP+FN}\times 100\% \tag{13}\]  
\[\text{ACC}=\frac{TP+TN}{TP+FP+TN+FN}\times 100\% \tag{14}\]  
\[\text{F1}=\frac{2 TP}{2 TP+FP+FN}\times 100\% \tag{15}\]  
where, \(TP\) (True Positives) is the number of records labeled \"bad\" that are classified correctly, \(TN\) (True Negatives) is the number of records labeled \"good\" that are classified correctly, \(FP\) (False Positives) is the number of records labeled \"good\" that are misclassified to \"bad\" and \(FN\) (False Negatives) is the number of records labeled \"bad\" that are wrongly classified as \"good\". Table 2 provides the confusion matrix of \(TP\), \(TN\), \(FP\), and \(FN\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Datasets for Training and Testing**} & \multicolumn{2}{c}{**The Number of Records From**} \\ \cline{2-3}  & **Dataset Labeled “Good”** & **Dataset Labeled “Bad”** \\ \hline T1 training dataset & 10,000 & 10,000 \\ T2 training dataset & 5000 & 15,000 \\ T3 training dataset & 15,000 & 5000 \\ TEST testing dataset & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The training datasets and the testing dataset.  
For the kNN machine learning algorithm, the model is tested with \(k=\{\text{from 1 to 20}\}\) with two cases of non-weighted and weighted neighbors. In particular, the weight is assigned based on the principle: the smaller the distance the greater the weight. Based on the test results, we determine the coefficient \(k\) that gives the highest classification accuracy.  
For the random forest algorithm, the general prediction result is based on the predictions of the decision trees. Therefore, the number of decision trees that may affect the accuracy of the algorithm. The model was tested with the number of decision trees of {from 5 to 50} to determine the number of trees that produce the highest classification accuracy.
## 3 Aircraft Low Altitude Operation Scene
### Low-Altitude Operation Scenario Supporting Intelligent Network Construction  
If low-altitude aircraft can operate smoothly in low-altitude airspace, it is necessary to build a low-altitude intelligent network on the ground. The construction of a low-altitude intelligent network is the basic condition for achieving smooth operation of low-altitude aircraft. In addition to the corresponding intelligent network and digital service network on the ground, the corresponding technical configuration is also needed on low-altitude aircraft. Wireless transmission, aircraft network security, improved flight automatic control, and ground matching functions should be added to the low-altitude aircraft avionics system.  
Design Requirements for Aircraft Low-Altitude Operational Avionics System Development of Research Questions  
Aircraft in the operation process should follow the operation regulations formulated by the 'Civil Aviation Administration'. However, when the aircraft operates at low altitude, it has its own characteristics. According to the characteristics of low-altitude operation scenarios, the adaptability analysis of low-altitude aircraft design should be carried out, and the design work should be improved. Starting with the avionics system, which is closely related to the achievement of low-altitude operation functions, the low-altitude design requirements and low-altitude airworthiness requirements of aircraft avionics systems are analyzed.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters
### Basic Factors  
#### 4.1.1 System Parameters  
The system parameters mainly include: the rated DC current \(I_{\mathrm{dN}}\), the rated DC voltage \(U_{\mathrm{dN}}\), the rated DC resistance \(R_{\mathrm{dN}}\), the rated firing angle of the rectifier side \(\alpha_{\mathrm{N}}\), the rated apparent extinction angle at the inverter side \(\gamma_{\mathrm{appN}}\), the rated real extinction angle at the inverter side \(\gamma_{\mathrm{realN}}\), the relative inductive voltage drop \(d_{\mathrm{xR}}\) and \(d_{\mathrm{xI}}\), and the relative resistive drop \(d_{\mathrm{rR}}\) and \(d_{\mathrm{rI}}\).
## 5 Proposed Optimization Problem
### Operational Constraints of BESS  
The BESS is an essential element of the HEMS, offering flexibility and resilience by storing excess energy and supplying power during high demand periods. The operational constraints for BESS are designed to ensure that the battery operates within safe and efficient limits. These constraints define the acceptable SOC range, preventing the battery from overcharging or deep discharging. By maintaining the SOC within 20% to 80%, the system can maximize the battery's lifespan and efficiency. Additionally, constraints on the energy storage capacity and the charging and discharging rates ensure that the battery's operation is optimized for both daily cycling and long-term durability. These measures help maintain a reliable and sustainable energy supply, support the integration of RESs, and enhance the overall performance of the HEMS. It can be expressed as follows:  
\[20\%\ \leq\text{SOC}\ \leq\ 80\% \tag{36}\]  
This range ensures that the battery has enough capacity to handle daily charge and discharge cycles while preserving its structural integrity and performance over time. Batteries naturally lose charge over time even when not in use, a phenomenon known as self-discharge. The SOC at the next time step takes this into account, ensuring the system correctly anticipates the battery's available charge.  
\[SOC(t+1)=SOC(t)(1-\delta) \tag{37}\]  
The energy that a battery can store is bound by its maximum and minimum capacities. These constraints ensure that the battery operates within its designed limits, which are determined by the battery's physical characteristics and safety requirements [58; 59]:  
\[E_{batt-min}\leq E_{batt}(t)\leq E_{batt-max} \tag{38}\]  
where  
\[E_{batt,max}=SOC_{max}\times C_{B}\times V_{Batt.} \tag{39}\]  
\[E_{batt,min}=(1-DOD_{max})\times C_{B}\times V_{Batt.} \tag{40}\]  
The rate at which a battery charges or discharges is also regulated to prevent overloading and to maintain system stability. These constraints ensure that the charging and discharging processes occur within safe limits, promoting battery longevity and reliable performance:  
\[0\leq P_{dis}(t)\leq N_{Batt}\times P_{dis.max} \tag{41}\]  
\[0\leq P_{ch}(t)\leq N_{Batt}\times P_{ch.max} \tag{42}\]
# Kriging-Based Variable Screening Method for Aircraft Optimization Problems with Expensive Functions
## 3 Proposed KRG-VSM
### Airfoil Aerodynamic Optimization Example
#### 3.3.4 Comparisons of the Optimized Results  
To validate the effectiveness of the variable screening process, the comparison results between the performance of direct numerical optimization with ten inputs and four inputs  
Figure 3: Comparison of the CFD result and experiment result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Selected** & \(\theta_{\text{common}}\) &
\begin{tabular}{c} **Log-** \\ **Likelihood** \\ \end{tabular} & \(\theta_{\text{end}}\) & **Flag** \\ \hline \(x_{1}\) & 0.0013 & 74.05 & 0.0030 & 0 \\ \(x_{2}\) & 0.0010 & 78.50 & 0.0030 & 0 \\ \(x_{4}\) & 7.4 \(\times\) 10\({}^{-  
after variable screening are summarized in Table 10. The values of other inactive inputs are fixed as medium values in the domain. The optimization results in Table 10 demonstrate that reducing the input variables from ten to four (a 60% reduction) achieves an excellent balance between computational efficiency and aerodynamic performance. While the simplified four-input model shows only a marginal 2.4% decrease in lift coefficient, it maintains over 90% of the original lift performance. The trade-off appears in the 12.8% increase in drag coefficient and 13.5% reduction in lift-to-drag ratio. Most remarkably, this variable screening method decreases the number of function evaluations (Nfe) from 22,540 to 1120, which means that the computational costs are reduced by 95%, highlighting the method's effectiveness in preserving key aerodynamic characteristics while dramatically improving optimization efficiency. The method's ability to maintain aerodynamic performance while drastically reducing computational burden demonstrates its strong potential for airfoil optimization problems where both accuracy and efficiency are critical.  
Figure 4 shows the results of the optimal airfoil. The lift-drag coefficient after direct numerical optimization with four inputs increases by 88.38% compared with the baseline. The maximum thickness and lift coefficient all meet the constraints. Figure 4 shows the optimal airfoil profile, and the pressure coefficient distribution of the optimal airfoil is presented in Figure 5.
## 2 Materials and Methods
### Image Preprocessing Techniques
#### 2.2.2 Data Augmentation  
To enhance the performance and generalization ability of the model, data augmentation is a critical technique, especially when dealing with an imbalanced dataset as the one used in this research, with some classes having far fewer images than others, as shown in Figure 3.  
This imbalance can cause the model to become biased towards the majority class, leading to poor generalization and accuracy for the minority classes [25]. Consequently, the model may struggle to properly classify rare but important cases, such as identifying diseased leaves in a dataset where healthy leaves are predominant. Additionally, this imbalance can lead to overfitting to the majority class, reducing the model's ability to make accurate predictions on new, unseen data.  
This method can artificially increase the size of the minority classes by applying transformations such as rotation, scaling, and flipping to existing instances. This introduces diversity into the dataset and mitigates the effects of class imbalance. In the research, data augmentation techniques--specifically rotation and adjustments to brightness and contrast--were used to enhance model performance and robustness in real-life scenarios. These techniques help the model learn from varied image conditions, improving its ability to generalize and perform accurately on new data. Rotation augmentation involves rotating images by a certain angle, introducing variations in orientation. By randomly rotating images during training, different viewpoints and perspectives are simulated, thereby enhancing the model's ability to recognize objects from various angles. This augmentation technique is particularly useful in scenarios where objects may appear in different orientations in real-world images [26]. For instance, in the case of detecting tomato leaf diseases, rotating images allow the model to learn to identify diseased leaves regardless of their orientation, mimicking the variability encountered in natural environments.  
The adjustment of brightness and contrast involves modifying the intensity and distribution of pixel values in an image. Increasing or decreasing the brightness simulates changes in lighting conditions while adjusting the contrast alters the difference in intensity between pixels. This augmentation technique helps the model become invariant to changes in illumination levels, enhancing its ability to generalize across diverse lighting conditions [27]. In the context of detecting tomato leaf diseases, adjusting brightness and contrast ensures robust performance in real-world settings where lighting may vary significantly.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol  
This section proposes our protocol (OPRP) enabling V2V communication betwist nodes traveling in the real-time bi-directional highway environment. The timely propagation of WMs is crucial in VANET environments, necessitating an effective routing mechanism. In the following subsection, we propose the system model for bi-directional highway VANETs. CH selection, WM distribution, and dynamic cluster formation are all included in the proposed approach.
## 2 Materials and Methods
### Data  
#### 2.2.1 Global Orbiting Carbon Observatory-2 SIF (GOSIF) Products  
GOSIF data represent a SIF dataset developed based on OCO-2 data, which have better spatial and temporal resolution than other SIF datasets. Moreover, these data have great potential for applications to assess terrestrial photosynthesis and ecosystem function and benchmark terrestrial biosphere and Earth system models [40]. We used the GOSIF dataset from 2010 to 2020 with a temporal resolution of 8 days and a spatial resolution of 0.05\({}^{\circ}\).
# Fused Gromov-Wasserstein Distance for Structured Objects
## 2 Structured Objects as Distributions and Fused Gromov-Wasserstein Distance
### Comparing Structured Objects
**Definition 7** (Strong isomorphism of structured objects). : _Two structured objects are said to be strongly isomorphic if there exists an isomorphism 1 between the structures such that \(f=(I,id)\) is bijective between \(supp(\mu)\) and \(supp(v)\) and measure preserving. More precisely, \(f\) satisfies the following properties:_  
1. \(f\#\mu=\
u\)_._
2. _The function_ \(f\) _satisfies:_ \[\forall(x,a)\in supp(\mu),f(x,a)=(I(x),a).\] (4)
3. _The function_ \(I:supp(\mu_{X})\to supp(\
u_{Y})\) _is surjective, satisfies_ \(I\#\mu_{X}=\
u_{Y}\) _and:_ \[\forall x,x^{\prime}\in supp(\mu_{X})^{2},d_{X}(x,x^{\prime})=d_{Y}(I(x),I(x^{ \prime})).\] (5)  
It is easy to check that the strong isomorphism defines an equivalence relation over \(\mathbb{S}_{p}(\Omega)\). **Remark 1**. : _The function \(f\) described in this definition can be seen as a feature, structure, and measure preserving function. Indeed, from **P.1**\(f\) is measure preserving. Moreover, \((X,d_{X},\mu_{X})\) and \((Y,d_{Y},\
u_{Y})\) are isomorphic through 1. Finally using **P.1** and **P.2** we have that \(\mu_{A}=\
u_{B}\), so that the feature information is also preserved._  
**Example 3**. : _To illustrate this definition, we consider a simple example of two discrete structured objects:_  
\[\underbrace{ \begin{pmatrix}(x_{1},a_{1})\\ (x_{2},a_{2})\\ (x_{3},a_{3})\\ (x_{4},a_{4})\end{pmatrix} }_{x_{i},\mu_{i}}\underbrace{ \begin{pmatrix}0&1&1&1 \\ 1&0&1&2\\ 1&1&0&2\\ 1&2&2&0\\ \end{pmatrix} }_{d_{X}(x_{i},x_{j})}\underbrace{ \begin{pmatrix}1/4\\ 1/4\\ 1/4\\ 1/4\end{pmatrix} }_{h_{i}}\quad\text{and}\quad\underbrace{ \begin{pmatrix}(y_{1}, b_{1})\\ (y_{2},b_{2})\\ (y_{3},b_{3})\\ (y_{4},b_{4})\end{pmatrix} }_{y_{i},\mu_{i}}\underbrace{ \begin{pmatrix}0&1&1&1 \\ 1&0&2&2\\ 1&2&0&1\\ 1&2&1&0\\ \end{pmatrix} }_{d_{Y}(y_{i},y_{j})}\underbrace{ \begin{pmatrix}1/4\\ 1/4\\ 1/4\end{pmatrix} }_{h_{i}^{\prime}}\]  
_with for \(i\), \(a_{i}=b_{i}\) and for \(i\
eq j\), \(a_{i}\
eq a_{j}\) (see Figure 4). The two structured objects have isometric structures and same features individually, but they are not strongly isomorphic. One possible map \(f=(f_{1},f_{2})\), such that \(f_{1}\) leads to an isometry is \(f(x_{1},a_{1})=(y_{1},b_{1})\), \(f(x_{2},a_{2})=(y_{3},b_{3})\), \(f(x_{3},a_{3})=(y_{4},b_{4})\), \(f(x_{4},a_{4})=(y_{2},b_{2})\). Yet, this map does not satisfy \(f_{2}(x,. )=I_{d}\) for any \(x\), since \(f(x_{2},a_{2})=(y_{3},b_{3})\) and \(a_{2}\
eq b_{3}\). The other possible functions, such that \(f_{1}\) leads to an isometry are simply permutations of this example, yet it is easy to check that none of them verifies **P.2** (for example, with \(f(x_{2},a_{2})=(y_{4},b_{4})\)).