esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The term \"carbon footprint\" refers to the measurement of greenhouse gas emissions, expressed in carbon dioxide equivalent (CO2e), released into the atmosphere either directly or indirectly through various activities, processes, or products. These emissions originate from diverse sources, including energy consumption, manufacturing processes, transportation networks, and waste management strategies. Although monitoring and reducing carbon footprints have been extensively studied, there remain significant gaps in integrating modern technologies and strategies into industrial applications.  
One notable research gap involves the limited studies detailing the impact of Industry 4.0 technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence on carbon management and sustainability. Existing literature often addresses technological advancements or carbon footprint reduction strategies separately, highlighting the need for interdisciplinary approaches combining these fields [1]. This study aims to fill this gap by analyzing the potential of Industry 4.0 technologies to optimize energy efficiency, minimize environmental impacts, and transform traditional industrial systems into sustainable ecosystems [2, 3, 4].  
Additionally, current research underscores challenges in adopting Enterprise Resource Planning (ERP) systems as a critical tool for carbon management. Issues such as high initial costs, data integration complexities, and technological compatibility hinder the effective use of ERP systems in managing and reporting carbon data [5; 6]. These challenges reveal a lack of understanding of how technological innovations can be better integrated into sustainability efforts.  
This study addresses these gaps by comprehensively analyzing the integration of carbon footprint management, energy efficiency strategies, and Industry 4.0 technologies in sustainable manufacturing processes. It also seeks to close research gaps by providing new insights and strategic recommendations that contribute to the existing literature. In this context, the paper aims to enrich the limited body of work that explores the impact of Industry 4.0 technologies on carbon management and sustainability [2; 4; 5].  
In conclusion, this study examines how advanced technologies can serve as strategic tools for achieving environmental sustainability goals, offering practical pathways for manufacturing processes aligned with global sustainability objectives.
## 3 System Model
### Cluster Game Modelling  
The clustering game (CG) is a non-cooperative game that is used is used by the nodes to select the cluster head in the network. Every cluster in the network has a cluster head as a result of the clustering game. The cluster head collects data from the available nodes and sends it to the base station.  
In the scenario where the nodes differ in any attributes or have found any difference among each other, then synchronization cannot be achieved. This leads to a cluster size of one, where each node in the network declares itself as cluster head (CH). In such an equilibrium, the expected payoff of each node of being cluster head (CH) is equal to the expected payoff of not being cluster head (CH) [36].  
The non-cooperative clustering game (CG), is defined as \(CG=\{N,A,U\}\), where \"\(N\)\" number of nodes, \"\(A\)\" is action set and \"\(U\)\" is a utility function. The proposed game for cluster head selection can be modeled with a mixed strategy game with the following elements:  
* Player: \(N\) number of nodes
* Action: Cluster head (CH) and non-cluster head (NCH) are sets of actions for each player.
* Utility: Utilities of each player are denoted by the expected payoff function value, which is \"\(0\)\" which means no node declares as cluster head (CH).  
Regarding payoffs, if none of the players (i.e., node) in the network declares itself as cluster head CH, then the payoff is zero and the player will be unable to send the data to the base station. If at least one player declares itself as cluster head CH, then the player's payoff is \(z\) (i.e., the successful delivery of data to base station). Finally, if the player declares themselves as the cluster head CH, then the payoff \(z\) is reduced by subtracting the cost \(c\), being cluster head CH from the payoff \(z\) (i.e., \(z-c\)).  
For the analysis of possible equilibria for two nodes case, the expected payoffs of these two nodes (\(2\times 2\)) are presented in Table 2. The payoff shows that the game is symmetrical and the payoff only depends on the node's strategies. The strategies \((z-c_{j},z-c_{j})\) (i.e.,both the nodes declares itself as cluster head (CH)) is not a Nash equilibrium. This is because the node can get a better payoff if they change their strategies to Non-cluster head NCH (i.e., \(z>z-c\)). Similarly, the strategies (0, 0) do not follow a Nash equilibrium either because any node will prefer to deviate and declare itself as cluster head CH, leading to a positive payoff. The remaining conditions such as \((z-c_{j},z)\) and \((z,z-c_{j})\) are Nash equilibria (i.e., one of the nodes declares itself as cluster head CH and another declares itself as non-cluster head NCH such that none of the nodes have any incentive to change its strategy). The utility of function of this game is an optimal selection of energy-efficient cluster head (CH), which is given as \(U_{CG}\) for node \"\(i\)\" as follows:  
\[U_{CG}=\left\{\begin{array}{ccc}0,&if&S_{i,j}=NCH\\ z,&if&S_{j}=CH\\ z-c_{j},&if&S_{i}=CH&s.t,z>c>0\end{array}\right.\quad\exists\quad i\in N,\quad S _{i}=NCH \tag{8}\]
# A Review of Degradation and Life Prediction of Polyethylene
## 4 General Service Life of Polyethylene  
Understanding the degradation process is crucial for polyethylene applications. To gauge the degree of performance degradation of the product, or, in other words, to further gauge the robustness of polyethylene products in long-term use situations, it is important to first comprehend how long the process of degradation takes to become obvious [47]. As a result, when carrying out pertinent experimental tests, the material's aging must be sped up [77]. Accelerated aging techniques are useful for estimating the remaining useful life of polymeric materials like polyethylene, and they can be contrasted to choose the most appropriate technique [78; 79; 80; 81; 82; 83].  
According to a widely used standard protocol for accelerated aging tests, polyethylene materials are put through cyclic tests in one or more substances for a specified amount of time or a specified number of cycles. To alter the effect of the same substance, the content of the substance utilized as a variable in this test procedure should be significantly different from the level of the substance itself during usage [84; 85; 86]. Depending on the needs of the experiment, these studies are typically carried out in suitable climate chambers where polyethylene samples may be exposed to high temperatures or humidity [87; 88], UV radiation [89; 90], various acids, bases, salts, etc. [91]. The parameters, which are dependent on the particular test conditions, must be decided upon as the initial stage in constructing an accelerated aging technique [92]. The ability to manage whether environmental elements are increasing or decreasing has a significant impact on the test's dependability [93]. In a perfect scenario, the environmental elements that the experiment simulates would be as similar to those in the natural state as possible, and the experiment's duration would be kept to a minimum. The level of testing that is being conducted now, however, is still far below what is optimal for experiments.
## 4 Hydropneumatic Suspension Controller's Design and Parameter Optimization
### Fuzzy-Logic-Based Sliding-Mode Controller's Design  
The SMC is widely used in complex systems due to its robustness and disturbance rejection properties, but high-frequency switching can lead to chattering, which negatively affects the system's stability. To address this issue, a Fuzzy Control (FC) is combined with the SMC, forming a Fuzzy Sliding-Mode Controller (FSMC), which effectively reduces chattering while maintaining the robustness of the SMC [22].  
In designing the FC, input and output variables need to be determined first. In this section, the sliding surface (\(s\)) and \(\dot{s}\) are used as inputs, and the \(k\) values (required for the sliding mode's control) are used as outputs, and they are defined as follows:  
\[s=\{\text{NB},\text{NM},\text{NS},\text{ZO},\text{PS},\text{PM},\text{PB}\}, \dot{s}=\{\text{NB},\text{NM},\text{NS},\text{ZO},\text{PS},\text{PM},\text{ PB}\}\]  
\[k=\{\text{NB},\text{NM},\text{NS},\text{ZO},\text{PS},\text{PM},\text{PB}\}\]Let the fuzzy-logic-based domains for \(s\), \(\dot{s}\), and \(k\) be defined as follows:  
\[s=\left[-3,3\right],\dot{s}=\left[-3,3\right],k=\left[0,1\right]\]  
The input and output membership function curves are illustrated in Figure 7a-c, while Table 5 outlines the fuzzy-logic-based control rules. By applying fuzzy-logic-based inference, the system evaluates the input variables' membership degrees alongside the rule-based control logic to determine the appropriate output, and the fuzzy-logic-based control output is determined [23]. The defuzzification process is then carried out using the centroid method to obtain the precise control value. After this, the fuzzy-logic-based control rule's surface, shown in Figure 7d, is generated using Matlab's Fuzzy Logic Toolbox.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(k\)} & \multicolumn{8}{c}{\(\dot{s}\)} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline NB & NB & NB & NM & NM & NS & NS & ZO \\ NM & NB & NB & NM & NS & NS & NS & ZO \\ NS & NM & NM & NS & ZO & ZO & ZO & ZO \\ ZO & NM & NM & NS & ZO & ZO & PS & PS \\ PS & NS & NS & ZO & ZO & PS & PS & PM \\ PM & ZO & PS & PS & PS & PS & PM & PB \\ PB & PS & PS & PS & PM & PB & PB & PB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fuzzy-logic-based control rules.  
Figure 7: Membership function curves: (**a**) input \(s\) membership function curves; (**b**) input \(s\) membership function curves; (**c**) output \(k\) membership function curves; (**d**) fuzzy-logic-based control rule’s surface.  
Through multiple simulation experiments, the basic domain of the controller is \(s=[-0.03,0.03]\), \(s=[-0.03,0.03]\), and \(k=[0,0.1]\), with quantification factors and gains \(k_{s}=100\), \(k_{s}=100\), and \(k_{k}=0.1\) calculated.
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 3 Design of Experiments
### RSM Based Model for Surface Roughness  
According to the ISO 4287 norm, the average surface roughness (\(Ra\)) is the arithmetical average of the absolute values of the deviations in the roughness profile from the mean line along the measuring length, as is illustrated in Figure 5. The equation is given for calculating the \(Ra\) value as follows [21]:  
\[Ra=\frac{1}{I}\int\limits_{0}^{l}\left|y\left(x\right)\right|dx \tag{3}\]  
where \(l\) is the measuring length, and \(y\) is the ordinate of the roughness profile.  
In this research, the experimental model for predicting the average surface roughness was developed through RSM method. RSM is a statistical and mathematical technique that is often applied to build the empirical model and optimize the input factors (independent variables) for one or more output responses (dependent variables) in the machining process [22].  
The relationship between the surface roughness and cutting parameter variables can be expressed by the function as follows [23]:  
\[Ra=\varphi\left(V,\ f,\ a,H\right) \tag{4}\]  
where \(Ra\) is the average surface roughness (\(\upmu\)m), and \(V,f,a\), and \(H\) are the parameters of the cutting speed (m/min), feed (mm/tooth), axial depth of cut (mm) and material hardness (HRC), respectively.  
With \(k\) input factors, the surface roughness was expressed by the following second-order polynomial function:  
\[Ra=\beta_{0}+\sum\limits_{i=1}^{k}\beta_{i}x_{i}+\sum\limits_{i=1}^{k}\beta_{ ii}x_{i}^{2}+\sum\limits_{i,j}^{k}\beta_{ij}x_{i}x_{j}+\varepsilon \tag{5}\]  
where \(x_{i}\) is coded variables, \(\beta_{i}\) is the coefficient of first-order terms, \(\beta_{ii}\) is the coefficient of second-order terms, and \(\beta_{ij}\) is the coefficient of interactive terms of the equation.
## 2 Materials and Methods
### Methodology of FOA-LS-SVM Model
#### 2.2.4 Improved Fruit Fly Optimization Algorithm (IFOA)  
For the basic FOA, the fruit fly seeks the food by the pre-set step size. It means that the seeking step size of fruit fly is always fixed. Obviously, under such circumstance, if the seeking step size is larger, which can lead to the wider search space, the global search ability will be strengthened obviously, while its local search ability will be weakened remarkably. On the contrary, the smaller seeking step size will bring about the limited search space. In this case, the FOA easily traps into the local optimum, while its convergence speed is improved. Therefore, we need an appropriate step size, which can well balance the global search capability and the local search capability.  
In order to solve this issue, we introduced a novel adaptive step update factor to adjust the seeking distance of fruit fly in each iteration process. The main thought of the improved algorithm is summarized as follows. At the initial stage in the iteration process, assign the fruit fly a bigger step value, which is beneficial to rapidly seek the approximate position of food. Then, at the later phase, assign the fruit fly a smaller step size, which is conducive to accurately converge the target position of food. The following expressions are the step size update mechanism.  
\[w(i)=\left\{\begin{array}{ll}w_{\max}\text{exp}[-\text{ln}\frac{w_{\max}}{w_{ \min}}\times(\frac{g}{M})^{a}],&g\geq 1\\ 1,&g=0\end{array}\right. \tag{10}\]  
\[D_{\text{RandValue}}(i)=w(i)\times(2\cdot rand-1) \tag{11}\]  
\[X_{i}=X_{\text{axist}}+D_{\text{RandValue}}(i) \tag{12}\]  
\[Y_{i}=Y_{\text{axist}}+D_{\text{RandValue}}(i) \tag{13}\]  
\[Dist_{i}=\left(X_{i}^{2}+Y_{i}^{2}\right)^{1/2} \tag{14}\]  
\[S_{i}=\frac{1}{Dist_{i}} \tag{15}\]  
where \(w(i)\) is the step update factor; \(w_{\max}\) and \(w_{\min}\) represent the maximum and minimum of step factor, respectively; \(g\) represents the current iteration number and \(M\) is the corresponding maximum; \(a\) represents the parameter, which is used to regulate the fly location change; \(D_{\text{RandValue}}(i)\) represents the random iteration step; \((X_{i},Y_{i})\) and \((X_{\text{axist}},Y_{\text{axist}})\) denote the current position coordinate and the initial position coordinate at \(i\)th fruit fly, respectively; \(Dist_{i}\) is the distance of \(i\)th fruit fly to the original point; and \(S_{i}\) is the reciprocal of \(Dist_{i}\), which represents the smell concentration judgment value. The improved algorithm step variations at \(a=5\) is depicted in Figure 2.  
Figure 1: The iterative process for food seeking of fruit fly swarm.  
As can be seen, the step gradually decreases with the increasing of \(g/M\) from 0 to 1.0. That is to say, in the early stage in the iteration process, the improved FOA has a bigger step size that possesses a slowly reducing rate. In the later phase, the step reaches its minimal size with a rapidly reducing rate.
## 4 Result
### Result of ML Techniques
#### 4.2.1 Regression Analysis (Regression Model)
The standardized regression coefficient \(\beta_{i}^{*}\) expresses the extent to which the output variable \(Y\) changes with a unit change in the standard deviation of the independent variable \(X_{i}\) provided that the other input variables are constant. The higher the absolute value of the coefficient \(\beta_{i}^{*}\), the stronger the influence of the independent variable in question on the output variable. The values of the parameters of the obtained models show that the input variables have a positive effect on the value of the impact force. From the analysis of standardized Beta coefficients (column Beta, Table 2), we can obtain the relative importance of the variables. In all cases, the greatest influence is the impact height (it has the highest value of standardized coefficients), followed by the weight of the falling hammer. The type of impactor has by far the lowest influence. The accuracy of the model for prediction was verified using a test group consisting of 30% of all samples (112 samples). Figure 7 shows a graph of the actual and predicted (theoretical) values that were obtained from the test group. On the \(x\)-axis, the actual values of the impact force are shown, and on the \(y\)-axis, the predicted values of the impact force. The line is a reference line and represents an ideal prediction where the actual values are equal to the predicted values. The distance of each point from the line represents the prediction error (residual) for a given value. The farther the point is from the line, the greater the prediction error for this particular case. To evaluate the quality of the created regression model, the mean squared error (MSE) and the mean absolute error (MAE) were used. The results of MSE = 2.901 and MAE = 1.328  
Figure 7: Prediction vs. Reality (regression model). show that the created model is good and can explain a significant part of the variability. The value of R\({}^{2}\) is 0.936, which means that the resulting regression tree can explain 93.62% of the variability of the target variable F on the test data. This suggests that the regression model is able to capture the relationship between the input variables (H, W, I, S) and the output variable F very well.
## 2 Modelling
### Interferometry  
The interferometer is based on measuring the refractive index of the plasma by probing it through a laser beam. The phase of the electric field of the beam is [6]:  
\[\phi\ =\ \int n\frac{\omega}{c}dz \tag{8}\]  
and by comparing it with the phase of a reference beam, the phase shift can be directly linked with the plasma electron density [6]:  
\[\Delta\phi\ =\ \int(n-1)\frac{\omega}{c}dz\ =\ \frac{\omega}{c}\int\left( \sqrt{1-\frac{\omega_{p}^{2}}{\omega^{2}}}-1\right)dz\ =\ \frac{\omega}{c}\int\left( \sqrt{1-\frac{N_{e}}{N_{c}}}-1\right)dz \tag{9}\]  
where \(N_{c}\) is called the cut-off density, defined as \(N_{c}=\omega^{2}m_{e}\varepsilon_{0}/e^{2}\). When \(N_{e}\) is much smaller than \(N_{c}\), and after taking into account the series development of the binomial, the equation can be approximated as [6]:  
\[\Delta\phi\ =\ -\frac{\omega}{2 cN_{c}}\int N_{e}dz \tag{10}\]  
If the electron density is larger than the cut-off density, the probing beam is strongly absorbed by the plasma and the interferometer ceases to work (\(n^{2}\) is negative, thus \(n\) is imaginary).  
In a wave optics approach, the base equations of the model would automatically compute the phase shift, while in the ray optics model, the phase shift must be modelled. In our model, an auxiliary variable, called \(\phi\), which computes the phase of each ray hasbeen introduced. This variable is computed using Equation (8), where the refractive index of the plasma has been written as Equation (9). Thus, for any time instant t\({}_{i}\), the position of the ray is \(x^{\prime}\), y\({}^{\prime}\) and z\({}^{\prime}\) in the laboratory frame of the reference are known and the refractive index of the propagation medium (plasma, air, vacuum, glass, etc.) is calculated. The integration of Equation (8) is performed considering that \(dz\) is \(vdt\), where \(dt\) is the time step.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Multi-Person Pose Estimation
#### 2.2.1 Top-Down Approach of 2D Multi-Person Pose Estimation
[16] introduced TokenPose, a novel human pose estimation method based on token representations. By introducing keypoint tokens, TokenPose effectively unifies the modeling of visual appearance and structural constraints within a transformer-based framework. This approach offers a lightweight, efficient, and interpretable solution for pose estimation and opens new directions for applying transformer architectures to keypoint detection in computer vision. To balance accuracy and computational efficiency, [PERSON] et al. [18] introduced a high-resolution lightweight network specifically designed for human pose estimation. The proposed Lite-HRNet combines the multi-resolution and high-fidelity representation capabilities of HRNet with the lightweight design principles of ShuffleNet. This hybrid architecture maintains high-resolution feature representations while substantially reducing computational cost. Lite-HRNet contributes to the advancement of efficient human pose estimation models, particularly for deployment on resource-constrained devices. [PERSON] et al. [35] introduced ViTPose, a human pose estimation model built entirely on a pure Vision Transformer architecture. The model adopts a minimalist design that eliminates traditional convolutional components, demonstrating that top-down approaches based solely on transformer architectures can achieve high-precision keypoint detection. ViTPose establishes a new performance benchmark for transformer-based human pose estimation and offers valuable insights for future research in this domain.
###### Abstract  
Despite the use of transition metal dichalcogenides being widespread in various applications, the knowledge and applications of Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) compounds are relatively limited. In this study, we deposited a MoW alloy on a Si substrate using a sputter system. Consequently, we successfully utilized a furnace to sulfurize the MoW alloy from 800 to 950 \({}^{\circ}\)C, which transferred the alloy into a Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) ternary compound. The Raman spectra of the Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) samples indicated an additional hybridized Raman peak at 375 cm\({}^{-1}\) not present in typical MoS\({}_{\bf 2}\) and WS\({}_{\bf 2}\). With increasing sulfurization temperature, the scanning electron microscopy images revealed the surface morphology of the Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) gradually becoming a sheet-like structure. The X-ray diffraction results showed that the crystal structure of the Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) tended toward a preferable (002) crystal orientation. The I-V results showed that the resistance of Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) increased when the samples were sulfurized at a higher temperature due to the more porous structures generated within the thin film. Furthermore, a high-temperature coefficient of resistance for the Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) thin film sulfurized at 950 \({}^{\circ}\)C was about \(-\)1.633%/K\({}^{-1}\). This coefficient of resistance in a Mo\({}_{\bf x}\)W\({}_{\bf 1-x}\)S\({}_{\bf 2}\) thin film indicates its suitability for use in thermal sensors.  
20222017 May 20  
In this work, we sulfurized a MoW alloy at different temperatures from 800 to 950 \({}^{\circ}\)C to transform the metallic layer into a Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) compound semiconductor thin film. Raman spectrum, X-ray diffraction (XRD), and scanning electron microscopy (SEM) were implemented to observe the relevant microstructural characteristics of the samples and further understand the sulfurized mechanism. Furthermore, we utilized temperature-dependent I-V measurements to analyze the influence of the Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) microstructure on resistance and TCR. A series of experimental results indicated that the large-area and bulk Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) thin film has great potential as a thermal sensor.