esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Vector Fitting Method  
Correspondingly, we have the rational function approximation used in Vector Fitting (VF) [11; 12]. With a set of initial resonances defined by the user, this methodology replaces these poles with an improved set of poles via a scaling procedure in the frequency domain. The problem to be linearly solved is defined in Equation (6),  
\[\begin{bmatrix}\sigma(s)f(s)\\ \sigma(s)\end{bmatrix}=\begin{bmatrix}\sum_{n=1}^{N}\frac{c_{n}}{s-a_{n}}+d+ sh\\ \sum_{n=1}^{N}\frac{c_{n}}{s-a_{n}}+1\end{bmatrix}, \tag{6}\]  
where \(f(s)\) is the frequency domain input signal, \(\sigma(s)\) is an unknown function having the same poles of \(f(s)\), \(N\) is the number of CNRs (different from Cauchy's Method \(N\)), \(h\) and \(d\) are real values, \(\bar{a}_{n}\) presents a set of starting complex conjugate poles, and \(c_{n}\) and \(\bar{c}_{n}\) are the complex conjugate residues. Then Equations (7) and (8) derived from Equation (6) are solved as an overdetermined linear problem.  
\[\sum_{n=1}^{N}\frac{c_{n}}{s-\bar{a}_{n}}+d+sh=\Bigg{(}\sum_{n=1}^{N}\frac{ \bar{c}_{n}}{s-\bar{a}_{n}}+1\Bigg{)}f(s) \tag{7}\]  
or  
\[(\sigma f)_{fit}(s)=\sigma_{fit}(s)f(s). \tag{8}\]  
Now, Equations (9) and (10) show how the poles of \(f(s)\) become equal to the zeros of \(\sigma_{fit}(s)\); so, calculating this will lead to a good approximation of the original function.  
\[(\sigma f)_{fit}(s)=h\frac{\prod_{n=1}^{N+1}(s-z_{n})}{\prod_{n=1}^{N}(s- \bar{a}_{n})},\quad\sigma_{fit}(s)=\frac{\prod_{n=1}^{N}(s-\bar{a}_{n})}{ \prod_{n=1}^{N}(s-\bar{a}_{n})}. \tag{9}\]  
Leading to  
\[f(s)=(\sigma f)_{fit}(s)\sigma_{fit}(s)=h\frac{\prod_{n=1}^{N+1}(s-z_{n})}{ \prod_{n=1}^{N}(s-\bar{z}_{n})}. \tag{10}\]  
Although this vector fitting approximation of the signal takes advantage of the same \(\bar{a}_{n}\) in both \((\sigma f)_{fit}(s)\) and \(\sigma_{fit}(s)\), which are positioned taking the original signal into account again, it does not include a system order calculation or the initial poles position. Therefore, it can not be used for system CNR extraction that leads to system identification from scratch.  
The algorithm proposed here, the Vector Fitting-Cauchy Method (VCM), can use both models to find CNR in a system and reduce the numerical noise for the final pole location in the complex plane. The formulation and validation for VCM is an extension to the preliminary results presented in [13].  
In Section 2, the VCM is presented, while in Section 3 the backscattering scenarios for the frequency domain signals processed are described, alongside examples of the reconstructions and CNRs extracted. Then, Section 4 contains the results for VCM that validate the reconstructions and CNRs calculated, as well as a comparison with the Cauchy Method.
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 3 Types of Chiral Metasurfaces  
Chiral metasurfaces are artificial structures made of various materials and configurations that are used to improve the chiroptical signal. Various chiral metasurfaces with excellent chiroptical properties have been developed. To explore the mechanisms of increasing the chiroptical response, we divided chiral metasurfaces into single-layered and multi-layered. In addition, we discuss tunable and deep-learning-enabled chiral metasurfaces.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 1 Introduction  
With the increasing functions and performance of satellite systems, the volume and weight of their microwave systems have also increased significantly, which has become a bottleneck restricting the development of payloads. Therefore, on the basis of ensuring high reliability, reductions in weight and improvements in the integration of microwave systems are urgently needed. The surface-mounted package used in the traditional space-borne microwave system is planar integration; the pin frequency is limited, the square shell shape is not conducive to cavity separation, and the matching restriction of thermal expansion coefficient limits the system integration of a large number of microwave dies [1, 2, 3, 4]. With the development of special substrate materials such as silicon-based and laminated fabrication technology, system-level encapsulated microwave components based on wafers, substrates with three-dimensional (3D) heterogeneous integration, have been developed [5, 6, 7, 8]. However, they are usually limited by their internal microwave interconnection structures, and their application frequencies cannot be further expanded. On the other hand, their interconnection structure is not reliable enough to meet the requirements of airtightness, severe changes of temperature, and changes of mechanical stress in space-borne applications. It is difficult to realize 3D integration and high reliability at the same time [9, 10].  
In this paper, a new method of space-borne microwave metallic system-in-package (SIP) based on highly reliable microwave interconnection is presented. This method uses metal airtight encapsulation and a novel gradient coaxial structure combined with microstrip impedance transformation and gold-ribbon interconnection to realize the horizontal and vertical interconnection of microwave signals. At the same time, it has thermaldeformation resistance and excellent microwave transmission characteristics. Complex microwave circuits are divided into 3D layouts, thus achieving the 3D distribution of the horizontal and vertical directions of the microwave circuits' cavity. By simulating and calculating the microwave characteristics of various highly reliable interconnections, a microwave transition structure with excellent microwave characteristics is proposed, and its design dimensions are derived. Its microwave transmission frequency can cover DC-40 GHz. The space-borne microwave SIP based on the novel interconnection can achieve airtight characteristics, temperature changes, and mechanical adaptability. Finally, a 3D SIP with high reliability that can adapt to the application of aerospace products is designed and manufactured, which has excellent microwave characteristics.
## 2 Materials and Methodology
### Evaluation of Leakage Management Efficiency and Determination of Formulation of Leakage
#### 2.3.2 Deep Neural Network  
Another technique applied in this study to evaluate the efficiency of leakage management is DNN [15] (Figure 2). DNN is a technique composed of two or more hidden layers of an Artificial Neural Network (ANN) [16], and DNN have the advantage of learning complex data with fewer neurons compared to ANN. In addition, it is suitable for predicting non-linear data, and the performance can be adjusted by adjusting the number of neurons and the number of hidden layers, which are parameters of the technique. However, DNNhas a high possibility of overfitting, and because this problem is directly related to the performance of the model, it is necessary to adjust parameters and select an activation function to prevent overfitting.  
In each hidden layer, a function is applied to the value that enters the neuron and is then transmitted. The function used at this time is called the activation function, and the Rectified Linear Unit (Relu) function is applied to each neuron as the activation function. Equation (2) is the expression defining the Relu function.  
\[f(x)=\max(0,\ x) \tag{2}\]  
Figure 3 is the graph form of the Relu function. If the input value is less than 0, it is output as 0, and if it is greater than 0, the input value is output as it is. The Relu function learns faster than other sigmoid and tanh functions and does not have a gradient loss problem. For the above reasons, the Relu function was applied in each layer when constructing the model.  
When training the model, an error function that calculates the error between the output value and the target value of the model is specified. The Mean Squared Error (MSE) function was used as the error function when constructing the learning model. The MSE function is the mean square error between the predicted value and the actual value. In addition, an optimization function is essential when training a model. The optimization function is a function that finds the parameter that minimizes the value of the loss function. The Adaptive Moment Estimation (Adam) function was applied to the per-rate model. Adam is an algorithm that combines the strengths of RMSProp and Momentum, which are used as optimization functions. Like Momentum, it stores the exponential average of the slope, and similarly to RMSProp, it stores the exponential average of the square of the device. In this study, when constructing a learning model, one input layer, five hidden  
Figure 3: Formulation of Relu function.  
Figure 2: Configuration of Deep Neural Network Model.  
layers, and one output layer were stacked, and the number of neurons in each hidden layer was composed of 64, 128, 256, and 512.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 7 Logistic Regression Model of Telecom Customer Churn Prediction
Moreover, the accuracy test for the logistic regression prediction model proved that it has good prediction performance, with an accuracy rate of 93.94%. Thus, it is possible to estimate whether or not a customer will stay with a telecommunications service provider based on information from the data. This investigation indicates that the logistic regression method could be used to predict customer churn with high accuracy.
## 3 Results and Discussion
### Dynamical Mechanical Analysis
#### 3.3.2 Multifrequency DMTA Analysis
(**a**) Master curves of storage modulus, (**b**) shift factor as a function of the temperature (at T\({}_{0}\) = 30 \({}^{\circ}\)C), (**c**) activation energy of shift factor below and above \(T_{g}\) as a function of the filler content, calculated from Equation (4). The dynamic fragility of PLA and relative fumed silica nanocomposites could be estimated following the thermal methods proposed by [PERSON] et al. for PLA and nanocomposites at 2.5 wt% of silica, montmorillonite, and multiwalled carbon nanotubes [53]. The fragility parameter \(m\) and the strength parameter (\(D\)) can be defined as reported in Equations (6) and (7), respectively:  
\[m=\frac{\Delta E_{Tg}}{\left(\ln 10\right)\ R\ T_{g}} \tag{6}\]  
\[D=\frac{\left(\ln 10\right)m_{min}^{2}}{m-m_{min}} \tag{7}\]  
where \(\Delta E_{Tg}\) is the activation energy at \(T_{g}\), \(R\) = 8.314 J/mol K, and \(m_{min}\) = 16 is the reference according to [PERSON] and [PERSON] [70]. The higher the \(m\) parameter, the higher the fragility, and the higher the \(D\) parameter, the higher the strength. In the present calculation, experimental data from DMTA analysis were used, such as the activation energy determined from multifrequency analysis (see Table 5) and \(T_{g}\) as the maximum of damping peak at 1 Hz, as reported in Table 4.  
The results evidenced a strength parameter \(D\) lower than 10 as an indicator of relative fragility, in accordance with the conclusions reported in the paper of [PERSON] et al., where they found that the addition of 2.5 wt% of different nanofillers (hydrophilic fumed silica, montmorillonite, or carbon nanotubes) determined an increase of the dynamic fragility at \(T_{g}\). On the contrary, in the present work, the addition of a hydrophobic fumed silica (i.e., Ar805) determined a reduction of the fragility parameter \(m\) in the range of about 9-18%, and a correspondent \(D\) increase of 12-28%, more pronounced for a filler concentration of 2-4 vol%.
# A Simulation Method for Layered Filling of Grain Piles Based on the Discrete Element Method
## 4 Conclusions  
The objective of this study was to minimize the formation time of grain piles by developing a layered filling model based on the Discrete Element Method. The kinetic energy of particles was utilized as the assessment element to verify the rationality of the model. Compared with the PFM, the LFM reduces the calculation cost of stabilized particles and reduces the calculation time. The main conclusions of this study are as follows:  
1. In comparison to the direct deposition method, the LFM generated grain piles with slightly higher porosity. However, as the particle kinetic energy decreased, the porosity gradually approached that of the direct deposition method, with average relative errors below 5.00%. Hence, the internal distribution of internal porosity in grain piles generated using the LFM model and the direct deposition method, respectively, exhibited similarities.
2. Different air entrance points resulted in distinct flow paths, and both the LFM and the PFM demonstrated a spiral-like upward trend in the simulated airflow paths. Under specific precision conditions, the errors in the tortuosity between the airflow paths obtained using the LFM and the direct deposition method, respectively, were observed to be small.
3. Compared to the PFM, the LFM significantly reduced the time required to formulate grain piles. For instance, under the D1 condition, the generation time for the grain pile was 80 min, while under the C1-C4 conditions, it was reduced to 70 min, 69 min, 68 min, and 25 min, respectively.  
Figure 10: Computation Time. (**a**) Computing time per 1.5 s; (**b**) total computing time.  
**Author Contributions:** Conceptualization, K.Y. and Y.W.; methodology, K.Y.; software, X.D. and X.L.; validation, K.Y. and X.D; formal analysis, X.D.; investigation, Y.M. and X.L.; writing--original draft preparation, X.D.; writing--review and editing, K.Y.; visualization, Y.M.; supervision, Y.W.; project administration, K.Y.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to thank the Shandong Provincial Natural Science Foundation (ZR2021 ME199), the Scientific and Technological Project of Suzhou City and the Plan of Introduction and Cultivation for Young Innovative Talents in Colleges and Universities of Shandong Province.  
**Data Availability Statement:** Data related to this paper can be requested.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare that this work is not submitted to another journal.
## 4 Results  
### Micro-Tensile Bond Strength (\(\upmu\)TBS) Test and Failure Mode Analysis  
Figure 2A shows the \(\upmu\)TBS values of all experimental groups. There were no significant differences in the bond strength among the restorative materials (\(p\) = 0.518). In groups R and A, there were no significant differences in bond strength between the immediate test and the pH 4-, pH 7-, and pH 10-buffered storage (\(p\) \(\geq\) 0.487). However, in group G,  
the immediately tested (\(p\) = 0.035) and the pH 7-stored specimens (\(p\) = 0.043) exhibited a significantly higher bond strength than the pH 4-stored specimens. Table 2 presents the mean and standard deviation of the uTBS data used in this study. At pH 4, group A showed significantly higher bond strength than group G (\(p\) = 0.042) (Table 2). Figure 2B exhibits the distribution of failure analysis of the debonded surface. There was a tendency to increase the proportion of adhesive failure in the sub-groups, which showed relatively lower bond strength. Cohesive failure in dentin was not observed in all experimental groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Group G** & **Group R** & **Group A** \\ \hline Immediate & 12.32 \(\pm\) 5.56 \({}^{\text{Aa}}\) & 8.86 \(\pm\) 4.85 \({}^{\text{Aa}}\) & 10.21 \(\pm\) 5.28 \({}^{\text{Aa}}\) \\ pH 4-storage & 7.44 \(\pm\) 4.58 \({}^{\text{Ba}}\) & 8.86 \(\pm\) 4.07 \({}^{\text{Aab}}\) & 9.35 \(\pm\) 4.02 \({}^{\text{Ab}}\) \\ pH 7-storage & 11.30 \(\pm\) 4.25 \({}^{\text{Aa}}\) & 7.64 \(\pm\) 3.43 \({}^{\text{Aa}}\) & 8.74 \(\pm\) 3.59 \({}^{\text{Aa}}\) \\ pH 10-storage & 8.69 \(\pm\) 4.62 \({}^{\text{Aba}}\) & 7.70 \(\pm\) 3.40 \({}^{\text{Aa}}\) & 7.66 \(\pm\) 3.47 \({}^{\text{Aa}}\) \\ \hline \hline \end{tabular} Values with same uppercase letters in same column and same lower letters in same row denote no significant differences, statistically (\(p\) > 0.05).  
\end{table}
Table 2: Micro-tensile bond strength (MPa).  
Figure 2: (**A**) Micro-tensile bond strength of the experimental groups. Same lower letters on top of the bar denote no statistical differences between groups (\(p\) > 0.05). Long bar means no significant differences among groups. (**B**) Distribution of failure modes.
## 3 Effect of Surfactants on Mechanico-Thermal Properties  
### Types of Surfactants  
Surfactants have frequently been categorized into four types, namely non-ionic, anionic, cationic, and amphoteric. The categories are based on the polarity of the surfactant head group, for example, non-ionic, anionic, cationic, and amphoteric or zwitterionic. There is no charge on a head group of non-ionic surfactants, while anionic and cationic surfactants have negative and positive charges on their head groups, respectively. On the other hand, there are both negative and positive charges for the amphoteric surfactants [33]. Table 5 displays the types of surfactants, types of nanofillers, and modification methods for the preparation of polymer nanocomposites. Non-ionic surfactants, such as Triton X-100 (chemical structure showed in Figure 1a), could modify inorganic nanofillers like CNTs via an ultrasonication method to serve as a bridge between CNTs and DGEBA [24]. The NCF organic nanofiller could be modified with the non-ionic surfactant Ultraric PE 105 by mechanical stirring to improve the dispersion of NCFs in the nanocomposite [19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Organic Nanofiller** & **Surfactant** & **Polymer Matrix** & **Mixing Process** & **Final Process** & **References** \\ \hline CNCs & Beycostat A B09 & PLA & Solution blending & Solvent casting & [15] \\ CNPs & Span 60 & PS & Solution blending & Solvent casting & [30] \\ NCFs & Niax L-595 & PEVA & Mel blending & Compression molding & [8] \\ CNWs & Pluronic & DGEBA & Solvent-free blending & Pour casting & [7] \\ NCFs & Ultraric PE 105 & PBAT & Solution blending & Solvent casting & [19] \\ CNWs & PEGMONO & PLA & Solution blending & Solvent casting & [16] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of organic nanofillers, surfactants, polymer matrices, and preparation processes of polymer nanocomposites.  
An anionic surfactant, for example, SDS (chemical structure showed in Figure 1b), is commonly employed in the modification of inorganic nanofillers, such as G NPs, through an ultrasonication method to assist G NP dispersion in a PVA matrix [21]. The CNC organic nanofiller could be modified with the anionic surfactant Beycostat A B09 by mechanical stirring to obtain stable dispersions of CNCs in nanocomposites [18]. On the other hand, cationic surfactants, for instance, DDAB (chemical structure showed in Figure 1c), could be utilized for the modification of inorganic nanofillers, such as MMT, via an agitation process [12]. CNCs could be modified with the cationic surfactant HTAB, also by mechanical stirring, for the preparation of such nanocomposites [31]. Table 5 also exhibits that the non-ionic, anionic, and cationic surfactants could modify both inorganic and organic nanofillers without any limitations. However, the modification of nanofillers by amphoteric surfactants is not only complicated [39], but also limited to inorganic nanofillers, such as MMT [40,41].
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map  
In this paper, a new one-dimensional compound chaotic system is constructed based on the 1D logistic map and 1D tent map. First, we briefly introduce the mathematical models of the logistic map and tent map and their basic characteristics (Lyapunov exponent and bifurcation diagram).
# Design of a Four-Axis Robot Arm System Based on Machine Vision
## 3 Vision-Guided Design of the Four-Axis Robotic Arm System
### Robotic Arm Control System Design
#### 3.1.2 Lower Computer Design
Ramps are powerful Arduino expansion boards with multiple functional interfaces such as a 12,864 screen interface, 5 stepper motor interfaces, temperature control, limit switch interfaces, etc. Its circuit schematic diagram is shown in Figure 11.  
A4988 motor driver is a common drive module in CNC technology and mechatronics. It has the characteristics of an affordable price, easy control, small size, and rich subdivision. The wiring diagram of the stepper motor driver A4988 is shown in Figure 12.  
Figure 11: Schematic diagram of ramps. Figure 12: A4988 pin definition map. The underlying code implementation mainly includes the communication program, the motion solver layer, and the motor motion control layer. The main way for the upper computer to control the work of the lower computer is to use serial communication to send different data. After the upper computer is connected to the lower computer, the corresponding baud rate and serial port number on the upper computer side need to be selected, and Open Serial Port is clicked to open the serial port. For example, to control the three-axis zeroing, the upper computer sends the \"H\" character when the serial communication is enabled and the communication is stable, and the lower computer will settle the character and match it to the corresponding action after receiving it. For the convenience of users, the specific communication method will be displayed in the upper computer receiving area after this connection, and users can follow the corresponding example to control the movement of the robotic arm. After the upper computer sends the corresponding 3-dimensional coordinate points to the lower computer, the motion solver layer program realizes the motor motion inverse solution using kinematic analysis to calculate the distance that the corresponding motor needs to move. The motor motion control layer is used to realize the specific motor motion control. To achieve program reuse and easy maintenance of the code, the underlying driver code is written and declared in the corresponding file using the C++ programming language and instantiated in the main file. For the motor control program, firstly, the motor class is defined, and its member variables and properties are declared, including motor pins, speed, etc. After that, functions are defined to control the movement distance of the motor by specifying the number of pulses.