esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 5 Overall Algorithm
### Convergence and Complexity Analysis  
The target value after iteration using Algorithm 1 is always increasing or not decreasing, and then converges back to a bounded value [31]. Assuming that the average achievable rate based on \(Q\), \(\Phi_{1,2}\) and \(P_{1,2}\) is denoted by \(f\Big{(}Q^{i},\Phi_{1,2}^{i},P_{1,2}^{i}\Big{)}\), since the optimized phase shift does not change the transmit power and trajectory, \(f\Big{(}Q^{i},\Phi_{1,2}^{i},P_{1,2}^{i}\Big{)}\leq f\Big{(}Q^{i},\Phi_{1,2}^{ i+1},P_{1,2}^{i}\Big{)}\) holds when the phase is optimal. Since \(Q^{i+1}\) is the optimal solution obtained by using \(\Phi_{1,2}^{i+1}\) and \(P_{1,2}^{i}\) to solve the problem \(\mathrm{P}(3)\), \(f\Big{(}Q^{i},\[PERSON]_{1,2}^{i+1},P_{1,2}^{i}\Big{)}\leq f\Big{(}Q^{i+1},\[PERSON]_{1,2}^{i+1},P_{1,2}^{i}\Big{)}\) holds. Since \(P_{1,2}^{i+1}\) is the optimal solution obtained by solving the problem \(\mathrm{P}(5)\) with the help of \(Q^{i+1}\) and \(\[PERSON]_{1,2}^{i+1}\), it can be determined that the inequality \(f\Big{(}Q^{i+1},\Phi_{1,2}^{i+1},P_{1,2}^{i}\Big{)}\leq f\Big{(}Q^{i+1},\Phi_{ 1,2}^{i+1},P_{1,2}^{i+1}\Big{)}\) holds.  
Algorithm 1 is monotonic, meaning that its output does not decrease in each iteration. In other words, the output either remains unchanged or increases in each iteration, ensuring that the algorithm does not diverge in an unfavorable direction. Specifically, monotonicity guarantees that the algorithm consistently progresses in a predictable direction without exhibiting erratic or unstable fluctuations. The monotonicity of the algorithm is directly linked to its convergence. By analyzing the update rules and constraints, we observe that after sufficient iterations, the output of Algorithm 1 will approach a stable value, ultimately converging to a finite value. Therefore, Algorithm 1 is convergent.  
The complexity of the proposed algorithm is described as follows. According to [32], the complexity of RIS phase optimization is \(\mathcal{O}(iM)\), the complexity of UAV trajectory optimization is \(\mathcal{O}(iJ^{3.5})\), and the complexity of UAV power allocation optimization is \(\mathcal{O}(iY^{3.5})\). \(M\) denotes the number of array elements, \(M\)] represents the total flight cycle, and \(Y\) indicates the quantity of users, while \(i\) is the number of iterations. Therefore, the algorithm has an overall complexity of \(\mathcal{O}\big{(}i(M+J^{3.5}+Y^{3.5})\big{)}\).
## 3 Results
### Performance Analysis  
#### 3.3.1 Initial Model Results (Accuracy, F1 Score, and Recall)  
Accuracy (training and test), F1 score, and recall were calculated to analyze the performance of the different models. Table 6 shows the performance values obtained for the matrix created by calculating the mode per year for each variable. Table 7 shows the performance values for the matrix formed by calculating the mode every three years for each variable.  
The results shown in Tables 6 and 7 reveal that the random forest model has the best performance in predicting mortality, followed by SVM and logistic regression. Furthermore, a comparison of the results shown in Tables 6 and 7 shows that using the feature matrix created from the mode every three years improves the performance. However, there may be some overfitting in the results simultaneously, which is especially evident in the random forest results. Cross-validation was used to check for overfitting.
# Methods for Grating Lobe Suppression in Ultrasound Plane Wave Imaging
## Materials and Methods  
### GL Conditions in PWI  
Let us consider \(N\) PWs with different steering angles, \(\theta_{n}\) (\(n=1,\ 2,\ \ldots,\ N\)), in a range of [\(\theta_{\min}\), \(\theta_{\max}\)], which are selected in terms of \(\alpha_{n}(=\sin(\theta_{n}))\) for the convenience of analysis such that \(\alpha_{n}=\alpha_{\min}+(n-1)d_{\alpha}\), \(n=1,\ 2,\ \ldots,\ N\), where \(\alpha_{\min}=-(N-1)d_{\alpha}/2\) and \(d_{\alpha}\) is a constant \(\alpha\)-interval between successive \(\alpha_{n}\). Assuming that the monochromatic (i.e., CW) plane waves are emitted from an infinite-length transducer, PWI provides a synthetic Tx beam pattern given by:  
\[\psi\big{(}x^{\prime}\big{)}=\sum_{n=1}^{N}\exp\big{\{}-jkx^{\prime}(\alpha_{ \min}+(n-1)d_{\alpha})\big{\}}=c_{0}\frac{\sin(\pi x^{\prime}d_{\alpha}N/ \lambda)}{\sin(\pi x^{\prime}d_{\alpha}/\lambda)}, \tag{1}\]  
where \(c_{0}=\exp\{-jkx^{\prime}(\alpha_{\min}+(N-1)d_{\alpha}/2)\}\), \(x^{\prime}=x-x_{\rm f}\), \(x_{\rm f}\) is the lateral position of a focus, \(\lambda\) is the wavelength, and \(k\) is the wave number (\(k=2\pi/\lambda\)) [3]. Note that almost the same beam pattern can be obtained from Equation (1) when using PW angles spaced by a constant \(\theta\)-interval, as in [1,2], if the angles are sufficiently small such that \(\sin\theta_{n}\approx\theta_{n}\) and \(d_{\alpha}\approx d_{\theta}\). In the beam pattern, the main lobe is at  
Figure 1: Schematic diagrams of a linear transducer array, transmitted plane waves (PWs) with uniformly distributed steering angles, and the synthetic transmit (Tx) beam pattern when (**a**) five PWs with a larger steering angle interval and (**b**) eleven PWs with a smaller angle interval are compounded. As the number of compounded PWs increases when the total range of the steering angle is fixed, the side lobe level decreases and grating lobes (GLs) occur less frequently.  
the focus, \(x^{\prime}=0\) (\(x=x_{\rm f}\)), and its amplitude is \(N\). GLs must be observed for which both the numerator and denominator of Equation (1) have zeros except \(x^{\prime}=0\), that occur at:  
\[x^{\prime}=\pm m\lambda/d_{\alpha},m=1,\;2,\;3,\ldots. \tag{2}\]  
By substituting Equation (2) into Equation (1), the amplitude of the \(m\)-th GL can be expressed as:  
\[\psi\big{(}x^{\prime}=\pm m\lambda/d_{\alpha}\big{)}=\left\{\begin{array}{cc }(-1)^{m}N&\text{for even }N\\ N&\text{for odd }N\end{array}\right.. \tag{3}\]  
It should be noted that the GLs predicted by Equations (2) and (3) arise when (1) all of the \(N\) PWs pass through the GL positions, preserving their linear wave-front (LWF) (LWF condition), and (2) PWs with a constant \(\alpha\)-interval are employed (uniform \(d_{\alpha}\) condition).
## 2 Theoretical Analysis
### Parallel Model  
As shown in Figure 2, the standard equation of the model is:  
\[M\ddot{X}+C\dot{X}+KX=F \tag{1}\]  
where \(M\) represents the overall mass of the vibration absorber structure, \(C\) represents the overall damping of the structure, and \(K\) refers to the overall stiffness of the structure.  
The structure stiffness in the above equation changes with the air pressure in the vibration absorber air chamber. The change in air pressure is caused by the relative motion of the liquid column in the air chamber. The damping has two components. Some damping is due to fluid vortex shedding at the bottom of the vibration absorber and the friction between the outer wall and water and this  
Figure 2: Parallel model.  
damping is related to the motion of the vibration absorber structure. Damping is also caused by the effect of the air in the air chamber and the orifice as well as the friction between inner wall and water, and this damping is related to the motion of the liquid column in the vibration absorber to the vibration absorber.  
For harmonic excitation \(F=F_{0}e^{i\omega t}\), the structural response \(X\) is also a harmonic motion which can be represented as \(X=X_{0}e^{i(\omega t-\varphi)}\), substituted into Equation (1) and using Euler's Formula, \(e^{i\varphi}=\cos\varphi+i\sin\varphi\), the stiffness and damping of the vibration system can be written as:  
\[K=\frac{F_{0}}{X_{0}}\cos\varphi+\omega^{2}M \tag{2}\]  
\[C=\frac{1}{\omega}\frac{F_{0}}{X_{0}}\sin\varphi \tag{3}\]  
The mass matrix can be \(M=[m_{1},m_{2},m_{3},m_{4},m_{5},m_{6},m_{7}]^{T}\). In the experiment, the mass of each different air chamber length structure is shown as follows:  
\(m_{1}=5.769,m_{2}=5.967,m_{3}=6.178,m_{4}=6.386,m_{5}=6.593,m_{6}=6.773,m_{7}= 7.001\), in kg.  
The periodical energy consumption of damping is:  
\[Q=\int C\dot{X}dX=\int\limits_{0}^{T}\left(C\dot{X}\right)\dot{X}dt=\int \limits_{0}^{T}C\dot{X}^{2}dt=C\int\limits_{0}^{T}\dot{X}^{2}dt=\frac{1}{ \omega}\frac{F_{0}}{X_{0}}\sin\varphi\sum\limits_{i=1}^{i=f_{s}\cdot T}\dot{X} _{i}^{2}t_{i} \tag{4}\]  
where \(f_{s}\) refers to the sampling frequency of the experiment, \(f_{s}=1024\) Hz. Due to the equal-interval sampling of the experiment data, \(t_{i}=\frac{1}{1024}\).
## 5 Case Study and Discussion  
This section considers the public transit operation of a bidirectional line, the Shenzhen No.10 bus line, as the scenario. There is one depot and two different routes in this case. We perform a series of experiments that simulate breakdown disruptions to assess the effectiveness of our proposed model in ensuring the smooth operation and service regularity of this transit system.  
The Shenzhen No.10 bus line boasts comprehensive operational data, allowing us to establish operation-related parameters based on actual data. This particular line covers a total distance of 13.25 km, with 22 stops in the upward direction (departing from stop \(s=1\)) and 21 stops in the downward direction (departing from stop \(s=2\)), as illustrated in Figure 3. The data reveal that the line operates approximately 285 trips per day from 06:00 to 23:00, with a maximum departure interval of about 20 min and an average interval of about 8 min. The average travel times for trips in the upward and downward directions are roughly 50 min and 46 min, respectively. In conjunction with navigation information, we established the travel times for buses to deadhead in the upward and downward directions at 35 min and 30 min, respectively. Finally, the experiments are conducted based on operational data for the No.10 bus line over the course of a single day in September 2019.
## 4 Results and Discussion
### Binder Testing Results and Discussion
#### 4.1.4 Fluorescence Micrographs  
The fluorescence microscopy test was performed to investigate the compatibility and stability among EMAA, SBR, and virgin binder and also provide an explanation of self-healing ductility and rheological results. The fluorescence micrographs of different binders are presented in Figure 7. As illustrated in Figure 7a, there is no pattern observed for virgin binder. After adding 4% EMAA, the morphology of virgin binder is converted to a biphasic structure where the EMAA particles are swollen by absorbing light components and dispersed in the continuous asphalt phase, as shown in Figure 7b. The EMAA particles also present the strip type features. In this situation, the expanded EMAA particles mainly have a significant influence on the viscosity of virgin binder [36]. In addition, there is an obvious interface between EMAA and asphalt, which indicates poor compatibility. However, the SBR particles are distributed like spherical features after the stirring and shearing in virgin binder, as shown in Figure 7c. Compared with EMAA-modified asphalt, the asphalt/SBR system produces a more homogenous material. As can be seen in Figure 7d, the EMAA/SBR/asphal system has more  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Properties** & **Virgin** & **4\% EMAA** & **3\% SBR** & **4\%EMAA/3\%SBR** \\ \hline Average \(L_{\text{Original}}\)/(cm) & 15.2 & 12.8 & 16.2 & 20.3 \\ Average \(L_{\text{Healed}}\)/(cm) & 19.2 & 18.9 & 21.9 & 31.2 \\ D/(\%) & 26.3 & 47.7 & 35.2 & 53.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ductility test results of various binders.  
Figure 6: Glover–Rowe parameter analysis in black space for different binders.  
uniform dispersion and smaller particle size of additives, which presents a better compatibility between binder and additives. Therefore, EMAA are beneficial for the dispersion of SBR modifier, resulting in better mechanical and rheological properties.
## 3 Methodology
### Planning for the Dimensional Inspection
#### 3.1.1 Mesh Pre-Registration  
To compare the scanned mesh \(M\) with the reference CAD model \(\mathcal{C}\), it is crucial that both of these surface representations share the same coordinate system \(W=\{\vec{w}_{x},\vec{w}_{y},\vec{w}_{z};\vec{p_{w}}\}\). If \(W\) and \(W_{M}\) are the coordinate systems of \(\mathcal{C}\) and \(M\), respectively, the objective is to compute a rigid transformation \(T_{0}\in SE(3)\) such that \(T_{0}(W_{M})\approx W\).  
To compute \(T_{0}\), the developed system uses an alignment-of-correspondences algorithm [28]. Let \(\{p_{0},p_{1},p_{2}\}\subset\mathcal{C}\) and \(\{q_{0},q_{1},q_{2}\}\subset M\) be three non-collinear points sampled from the reference CAD and the workpiece mesh, respectively. The alignment-of-correspondences algorithm computes the rigid transformation \(T_{0}\) that minimizes the distance between the two sets of points:  
\[\begin{split} T_{0}=\arg\min&\sum_{i=0}^{2}\|p_{i} -T_{0}(q_{i})\|\\ s.t.& T_{0}\in SE(3)\end{split} \tag{1}\]  
where \(SE(3)=SO(3)\times\mathbb{R}^{3}\) is the special Euclidean group (group of all rigid transformations in \(\mathbb{R}^{3}\)).  
In Equation (1), \(p_{i},q_{i}\) are corresponding points in the CAD and the mesh, respectively. These points are interactively selected by the metrologist as illustrated in Figure 2. This pre-registration is performed only once per CAD reference \(\mathcal{C}\).  
Figure 1: Workflow of the reverse engineering system for dimensional inspection. The system provides the dimensional inspection results to the operator directly in the production line.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sensor Calibration  
In this study, in order to ensure the accuracy and reliability of water quality monitoring, the pH, TDS, temperature (DS18B20), and turbidity sensors used in this research system may be affected by a variety of factors in practical applications, such as environmental conditions, temporal degradation, and external disturbances, etc. To address these challenges, we have developed an exhaustive calibration and maintenance program for the sensors to ensure their long-term stability and measurement accuracy.
# Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant
## 1 Introduction
This makes DROP model an interacting two-input-two-output (TITO) system. First, a decoupler is designed using feed-forward method [24] of decoupling to make the system non-interacting in nature. Thus obtained model has two non-interacting loops, one for flux and other for conductivity. Then, two PID controllers are obtained for two non-interacting loops using SLSSO algorithm. The integral-of-squared-error (ISE) is taken as tuning criterion. For the ease of simulation, the ISE is formulated in terms of alpha and beta parameters. The key features of proposed method are: (a) it is utilizing SLSSO for tuning of PID controllers, (b) a decoupler is designed for interacting Doha RO system, (c) the ISE is formulated in terms of alpha and beta parameters, and (d) the results of SLSSO are also compared with other algorithms to prove its supremacy. This study also obtained the PID controllers using artificial bee colony (ABC) algorithm, LJ algorithm, Nelder-Mead simplex (NMS) algorithm, sine cosine algorithm (SCA) and salp swarm optimization (SSO) algorithm for fair comparisons of performance of SLSSO tuned controllers. Additionally, time-domain specifications are tabulated for comparisons. Further, the statistical analysis of ISE obtained for controllers is presented. Moreover, the closed-loop responses (step, impulse and variable step responses) are also shown for different controllers. The structure of the paper is as follows: Section 2 presents the description of reverse osmosis system in consideration. The interacting TITO system is given in the same section. In addition, the decoupler design is described in this section. The controller structure is presented in Section 3. Section 4 is presenting the SSO algorithm. The description of SLSSO algorithm is also given in the same section. Section 5 presents results and discussion where two case studies are discussed in detail. Finally, the findings of paper are concluded in Section 6.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 3 Results
### Neural Network Training
#### 3.3.2 Experimental Dataset  
The neural network based on the Xception [58; 59; 60] architecture was trained using a dataset consisting of experimentally obtained interferograms corresponding to the superposition of two aberrations. A fragment of the dataset is presented in Tables 4 and 7.  
In several numerical experiments, the recognition errors of the superposition of aberrations were obtained from experimental cylindrical interferograms with a reference beam tilt angle in the range from \(2\pi\) to \(6\pi\) (Figure 11). The results are presented in Table 8.
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 3 Materials and Methods
### Multiple-Instance Classification
Once the mean, \(M\), is computed, it serves as a representative instance for the entire bag, enabling the use of traditional single-instance classifiers, \(f\), for classification. The predicted class label, \(\hat{c}\), for the bag can then be obtained through Equation (2), as follows:  
\[\hat{c}=f(M) \tag{2}\]  
This two-step process--calculating the mean and then applying a classification function--enhances the speed and simplicity of multi-instance learning while ensuring that essential data characteristics are preserved. To further enhance classification performance, we integrated the SimpleMI classifier with the partial decision tree (PART) as the classification function \(f\) within an ensemble framework using bootstrap aggregating (Bagging). This combination aimed to improve the overall effectiveness of our proposed multi-instance partial decision tree (MIPART) method, leveraging the strengths of both SimpleMI and PART approaches. The PART algorithm [38] is a rule-based classifier that constructs decision rules by building partial decision trees and converting them into rules. Unlike conventional decision tree algorithms, which generate a complete tree, PART constructs only a part of the tree, focusing on the most informative nodes at each step. This approach yields a single, best rule for each iteration, simplifying the model while maintaining classification accuracy. The process of rule generation in PART is expressed in Equation (3), as follows:  
\[R=argmax_{i}(G(z_{i})) \tag{3}\]  
where \(R\) is the rule selected from each partial decision tree, and \(G(z_{i})\) represents the information gain of splitting on instance \(z_{i}\). The notation \(argmax_{i}\) indicates the index \(i\) that maximizes the gain function, identifying which instance yields the highest information gain for the split. This rule-building strategy allows PART to handle multi-instance data efficiently, making it well-suited for ensemble learning frameworks like Bagging. By using PART within SimpleMI, our MIPART method utilizes the interpretability and flexibility of rule-based classification while benefiting from the robustness of an ensemble approach.
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 5 The User Perspective: Observation Requirements and Current Obstacles
### Current Practices Versus Current Satellite Capabilities
#### 5.2.2 Irrigation Strategies  
When asked about the number of irrigation applications, the answers ranged from 2 to 30 irrigation applications per month. We also asked about a typical crop they refer to, but no answer was provided. This probably highlights the difficulty in the control of irrigation on single fields or areas where specific crops are grown. Another complicating factor is crop rotation (i.e., crop types might change from year to year), which might also depend on the very uncertain forecast of water resources for that year [140]. Farmers may also change the size of the irrigated surface, the crop, the number of harvests per season, and so on. Accordingly, the irrigation strategy may change for each field and each year. The amount of irrigation applications is central to understanding the importance of the revisit time of the sensors used to retrieve information about irrigation. With 30 irrigation applications per month (i.e., once per day), it is very likely that products characterized by a temporal resolution of 2-3 days will miss irrigation events. On the other hand, both techniques based on soil moisture and evapotranspiration provide an indirect detection and quantification of irrigation exploiting the memory of the soil moisture and evapotranspiration signals. That is, if water is applied in the morning, the process of transpiration does not interrupt instantaneously and might continue as long as the water is available and the plant is able to transpire it. As a secondary effect, this determines a soil moisture depletion which is also visible after the satellite passes.
## 2 Theoretical Approach
### Theoretical Analysis
#### 2.3.1 Linear and Nonlinear Interfacial Stiffness  
The stiffness of an interface can be modelled as a series of springs created by the individual asperity contacts [20; 24]. The series of the springs is then equivalent to a single distributed spring with an equivalent stiffness \(K\) (expressed per unit area) [2]:  
\[K=-\frac{dp_{0}}{dh} \tag{25}\]  
Figure 3: A theoretical approach for Contact Acoustic Nonlinearity (CAN) mechanics at incident wave amplitude 20 nm: (**a**) comparison between incident wave and gap separation h variation with time; (**b**) comparison between reflected wave from solid-air contact (reference signal) and solid-solid contact at 2 MPa; (**c**) effect of nominal contact pressure on reflected wave from solid-solid contact at 2 MPa and 3 MPa; (**d**) frequency spectrum comparison between incident wave, solid-solid contact at 2 MPa and 3 MPa.  
For practical rough surface contacts, the spring is nonlinear since the relation between the applied pressure and surface separation is not directly proportional. As the contact pressure is increased, more asperity contact occurs and the interface becomes stiffer. If the deflection is small (and less than the elastic limit of the spring material [42]) the nonlinearity can be neglected. For large deflections, the pressure-separation relationship and the resulting nonlinear stiffness must be considered. One of the approaches to deal with the nonlinear contact pressure-relative surface approach of the interface (boundary or contact nonlinearity) is to express it as a series of polynomial terms in a Taylor series around \(h=h_{0}\)[39]:  
\[p(h)=p(h_{0}+Y)=p_{0}-K_{1}Y+K_{2}Y^{2} \tag{26}\]  
where \(K_{1}\) and \(K_{2}\) are the linear and second order nonlinear interfacial stiffness and \(p_{0}\) is the nominal contact pressure at \(h_{0}\). The linear \(K_{1}\) and nonlinear \(K_{2}\) terms of the interfacial stiffness are then defined by:  
\[K_{1}=-\frac{dp}{dY}\,\ h=h_{0} \tag{27}\]  
\[K_{2}=\frac{1}{2}\frac{d^{2}p}{dY^{2}}\,\ h=h_{0} \tag{28}\]