esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s GRASP&PR-Based Proposal  
In this paper [43], the authors focused on the point matching-based approach of the IR problem using 3D medical images. They contributed with improved hybridizations of two well-known metaheuristics, named greedy randomized adaptive search procedure (GRASP) and PathRelinking (PR) [44]. For instance, for the static GRASP and PR (noted by Stc-G&PR), the hybridization variant is an static one in which, first, GRASP is applied to build an elite set (ES) of solutions (see steps 1 to 14 in Figure 4[43]) and next, as a second step, PR is performed in order to generate solutions among all the pairs of solutions in ES. Thus, the optimization procedure aimed at finding the best matching of pairs of feature 3D points and the corresponding registration transformation is numerically derived by means of least squares methods. The best the matching, the more accurate the numerical estimation. Their hybrid proposals were based on specific designs focused on achieving a good trade-off between the intensification and the diversification components of the search space in order to obtain high quality IR solutions. Finally, the authors tested their new designs addressing real-world problems of medical imaging. In particular, they considered six medical images from two different image datasets for testing purposes. The first dataset consists of MR images obtained from the well-known database at McGill University, named BrainWeb [45], and the second dataset provides CT images from the Rhode Island Hospital [46]. Several IR methods of the SoTA were considered for computational comparison against their proposals. The reported results demonstrated the better trade-off between intensification and diversification offered by these new optimization designs when facing the point-matching-based IR problem.
# Conversion between Number Systems in Membrane Computing
## 2 Research Foundation
### Number System Conversion Method  
There are three concepts in the number system conversion: number system, cardinality, and bit weight. The number system, also known as the counting system, is a method of representing numerical values with a fixed set of symbols and uniform rules [20]. Cardinality is the number of different numeric symbols that can be used on each digit. For example, binary uses 0 and 1, with a cardinality of 2. Bit weight indicates the weight of the digit at different positions [21], i.e., the value of '1' in a certain place in the number system. For example, in binary, the 2 nd-bit weight is 2, and the 2 nd-bit weight is 4.  
The main methods used for conversion are the Positional expansion method and the Quotient division method.  
The positional expansion method is that we multiply the number on each bit with its corresponding bit weight and sum all the products. We assume that the conversion from r decimal to decimal. Then the desired decimal number is:  
\[\text{A}_{1}+\text{A}_{2}\text{*}\text{r}+\text{A}_{3}\text{*}\text{r}^{2}+ \text{A}_{4}\text{*}\text{r}^{3}\ldots+\text{A}_{n}\text{*}\text{r}^{n-1}\]  
\(\text{A}_{1}\), \(\text{A}_{2}\), \(\text{A}_{3}\), \(\text{A}_{4}\), \(\ldots\), \(\text{A}_{n}\) represents the number on each bit of the r decimal number, and \((3) Repeat (2) until the quotient is 0. The resulting remainder is arranged in reverse order to obtain the resulting binary number.  
In this paper, n-bit binary and n-bit m-binary are converted to decimals using the bitwise expansion method. The conversion of decimals to binary and m decimals is carried out by the division and remainder method.
## 2 Materials and Methods
\[F_{\beta}=~{}\left(1+~{}\beta^{2}\right)~{}*~{}\frac{precision* recall}{\left(\beta^{2}* precision\right)+recall} \tag{1}\]  
Choice of \(\beta\) signifies the weightage of precision and recall while \(0<\beta<1\) assigns more weightage to precision, \(1<\beta<+\infty\) assigns more weightage to recall. \(\beta=1\) assigns equal weightage to precision and recall and is known as the F1-score. ROC is a probability curve and the area under the ROC curve represents the degree of separability. It identifies how well the model can differentiate between classes as it balances between precision and recall. As the AUC gets higher, the model gives better prediction. This curve is plotted with true positive rate/recall against the false positive rate. However, the ROC plot can be misleading when applied to heavily imbalanced datasets as adding a lot of majority class observations can improve the AUC significantly without any improvement in precision and recall of minority class instances. For imbalanced class problems, the precision-recall curve offers better insight [26]. PRC shows the relationship between precision and recall,and its baseline moves with class distribution; e.g., for a dataset with imbalance ratio 1:10, the baseline moves to 0.09, i.e., \(1/(1+10)\). Stratified cross-validation was performed during oversampling to resolve the overoptimization problem [27]. Random forest was used as the classification algorithm on the resampled data. We created a combined estimator by using the pipeline to be applied on the data, which consisted of the oversampling method and the random forest classifier. In this study, we used a two-tailed \(t\)-test to show that the improved results obtained using our proposed oversampling method and the model using existing oversampling method were statistically significant. The statistical test performed on precision, recall and f1-score results. The \(p\)-value of 0.05 has been used as a threshold for the statistical significance.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 2 Quasi-Yagi Antenna
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline W\({}_{\text{sub}}\) & 60 & L\({}_{\text{sub}}\) & 120 & h\({}_{\text{S}}\) & 0.8 \\ W\({}_{\text{S}}\) & 4.5 & L\({}_{\text{g}}\) & 2.5 & W\({}_{\text{f}}\) & 0.5 \\ L\({}_{\text{f}}\) & 3.15 & L\({}_{\text{C}}\) & 3 & W & 1.95 \\ L & 0.1 & W\({}_{\text{a}}\) & 40 & L\({}_{\text{S}}\) = L\({}_{\text{a}}\) & 9 \\ W\({}_{1}\) & 1.8 & L\({}_{1}\) & 1.25 & r\({}_{1}\) & 0.5 \\ r\({}_{2}\) & 0.87 & r\({}_{3}\) & 0.15 & R & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values of the Quasi-Yagi antenna and its array design. Figure 2: S\({}_{11}\) function of the antenna. Figure 4: (**a**) Current densities and (**b**) 3D radiation pattern at 26 GHz. Figure 5: Simulated efficiencies. Figure 3: S\({}_{11}\) results of the Quasi-Yagi antenna for different values of (**a**) W (width of the antenna arm), (**b**) L\({}_{\rm g}\) (length of the ground plane), and (**c**) W\({}_{1}\) (length of the directors). In order to investigate the performance of the Quasi-Yagi antenna as a transmitter/receiver in a mobile terminal, the fidelity factor of the proposed design has been calculated. Two identical antennas as transmitter (T\({}_{\chi}\)) and receiver (R\({}_{\chi}\)) are placed with 100 mm shift of their center points. The antenna orientations are arranged in side by side and face to face configurations. Figure 7 illustrates the transmitted signal in free space and received pulses. The calculated fidelity factor of the antenna for the face to face and side by side configurations are equal to 0.90 and 0.98, respectively. The achieved fidelity results confirm that the designed Quasi-Yagi antenna imposes negligible effects on the transmitted pulses [23].
## 5 DT Workshop Planning Model-Based Decision Support Methodology
### Resource Demand Forecasting  
With the granularity refinement of the DT system, the resource demand forecast originates from the demand side of the operators, and the workshop resource plan and the enterprise resource plan are backed up in time with the progress of each workstation's assembly and welding operations. In contrast to the traditional resource supply mode,in which the top level conducts the whole resource plan and breaks down BOMs to the basic operation units, the real-time demand and buffer capacity of each workstation will be dynamically adjusted along with the manufacturing execution status to ensure the quantity and period of material supply, such as plate, steel, and welding material. The limitation of demand forecasting is the uncertainty of manufacturing progress and the uncoordinated procurement/storage plan with the workshop schedule.  
There are two stages of demand forecasting from the operation execution side: material demand clustering and quantity forecasting. Firstly, the material demand clustering is based on the steel, welding material and equipment necessary for each workstation in the process of job execution to establish the attribute class rules to complete the clustering analysis. Given that \(R=\{r_{1},r_{2},\cdots,r_{i},\cdots,r_{n}\}\) is the set of material resource demand, and \(k\) attributes exist under each material, the material attribute matrix is obtained as follows:  
\[(r_{i})_{n\times k}=\begin{bmatrix}r_{11}&r_{12}&\cdots&r_{1k}\\ r_{21}&r_{22}&\cdots&r_{2k}\\ \vdots&\vdots&\ddots&\vdots\\ r_{n1}&r_{n2}&\cdots&r_{nk}\end{bmatrix} \tag{39}\]  
\[c\prime=\frac{c-\min(c)}{\max(c)-\min(c)}\times 100\% \tag{40}\]  
\[S(c_{i},c_{j})=\text{dist}(c_{i},c_{j})=\sqrt{\sum_{l=1}^{k}{(c_{il}-c_{jl})^ {2}}} \tag{41}\]  
To reduce the impact brought by different quantiles, the material attribute matrix is normalized by Equation (40) and then solved for the similarity of demand at different workstations. As in Equation (41), the higher the similarity, the smaller the Euclidean distance value, representing the high demand for certain types of resources at certain workstations and the need to increase the advanced procurement and supply of this type of resource.  
The second step is quantity forecasting based on the work orders and BOMs of each workstation to count the demand of each type of resource combined with the real-time capacity of the material buffer monitored by the DT system. In addition, the material ratio must be outputted according to the clustering results of the dependence of each workstation on different materials, and the relevant information must be adjusted in MPS and Enterprise Resource Planning (ERP) in time.
## 4 Experimental Results and Discussion
### Maceral Composition Classification
The following reasons could contribute to the worse performance for these two components: semifusinite and inertodetrinite belong to inertinite group, and the difference in terms of gray level is too subtle to classify them properly [37]; semifusinite is intermediate between fusinite and vitrinite, and has a similar texture and general structure to fusinite; the origin of inertodetrinite is similar to fusinite and semifusinite [38]. The process of training a random forest involves the construction of multiple decision trees. In this study, we also evaluated the classification accuracy with the increase of the number of trees from 1 to 300. As shown in Figure 7, in general, more trees can provide the better performance, especially when the number of trees is smaller than 50. However, the improvement decreases as the number of trees increases from 50. Considering the tradeoff between the computational efficiency and the robustness of the developed model, in this study, we set the number of trees to be 200. Figure 6: Confusion matrix comparison of six classifiers, including the results of (**a**) Fine Tree; (**b**) Radial basis function kernel support vector machine; (**c**) Weighted K-Nearest Neighbors (KNN); (**d**) Linear Discriminant Analysis; (**e**) Subspace KNN and (**f**) Random Forest. The labels in each subfigure include 1: binder, 2: sporinite, 3: cutinite, 4: vitrinite, 5: fusinite, 6: semifusinite, 7: inertodetrinite, and 8: micrinite.
## 1 Introduction
It must to be noted that no previous paper tackles the implementation of hysteresis models for 3D structures using FAM. In other papers, [PERSON] et al. [13] presented a general framework for seismic damage analysis of RC plane frame structures using FAM. The inelastic behavior was modeled using two types of plastic hinges in order to account for damage behavior caused by concrete cracking and material nonlinearity. Moreover, a damage index is computed and discussed. [PERSON] et al. [14] used a modified force analogy method to simulate the nonlinear inelastic response of reinforced concrete plane structures. Beam-column elements with three different plastic mechanisms were utilized to simulate inelastic responses caused by moment and shear force. On the other hand, [PERSON] et al. [15] implemented an existing shear wall macroscopic model for use in the force analogy method. The shear wall model is composed of two vertical sliding hinges and one horizontal sliding hinge that are assigned to capture the relationship of lateral deflection or rotation versus the RC shear wall force. Moreover, shear hysteresis functions are implemented and discussed. Having said that, the main scope of this paper is to introduce a facile computational efficient algorithm for nonlinear dynamic analysis of reinforced concrete frames. The entire model is built in a non-commercial code developed in Matlab/Simulink. In order to accomplish the defined scope of this paper, the parameter inter-dependence when building such a code is also illustrated. The numerical efficiency and nonlinear insights are further evaluated through the concept of energy balance. Moreover, a damage assessment analysis is conducted using a model based on a well known damage index. At the same time, another addressed topic is the modeling of slab contribution. Consequently, a condensation technique is used to account for rigid diaphragm behavior. The authors' main contribution is the development of a FAM 3D-based algorithm that can be used to conduct, in a simple manner, nonlinear dynamic analysis for RC moment-resisting 3D frame structures. A related important contribution is that the paper is the first to present a detailed methodology for the implementation of polygonal hysteresis models in FAM 3D, which is validated against the results obtained from commercial software. Moreover, the entire algorithm is implemented in a finite element-based numerical routine developed in Matlab/Simulink.
## 2 Materials and Methods
### Characterization of (PAA-co-PAM)-DPNR
#### 2.3.3 Fourier-Transform Infrared Spectroscopy  
The Fourier-transform infrared spectroscopy (FTIR) was used to study the chemical structure of the (PAA-_co_-PAM)-_grafted_ DPNR using an attenuated total reflection (ATR) mode with a Tensor 27 FTIR spectrometer (Bruker, Billerica, MA, USA). The measurements were performed at a resolution of 4 cm\({}^{-1}\) for 64 scans. The background was run before sample measurement. The FTIR spectra of all samples were recorded in the range of 4000-400 cm\({}^{-1}\).
# Intelligent Target Design Based on Complex Target Simulation
## 4 Experimental Verification  
Figure 7 shows the RCS distribution characteristics of a certain type of a fighter aircraft.  
As Figure 7 shows, there are four peaks in the vicinity of \(-90^{\circ}\), \(0^{\circ}\), \(90^{\circ}\), and \(180^{\circ}\), out of which \(0^{\circ}\) corresponds to the position of the nose, \(-180^{\circ}\) corresponds to the position of the tail, and \(-90^{\circ}\) and \(90^{\circ}\) are directly opposite to the position of the wings. Therefore, four angle reflectors can be used to simulate the distribution characteristics of the corresponding sharp RCS. There are four stationary RCS distribution characteristics in the middle of the four peaks, whose fluctuation range is not more than 3 dB. The corresponding stationary RCS distribution characteristics can be simulated using four Luneburg balls. This means that four corner reflectors and four Luneburg ball reflectors can be used to form an array, where the included angle of the corner reflector near \(0^{\circ}\) is controlled at about \(20\)-\(30^{\circ}\). Figure 8 shows the test results.  
Figure 7: Radar cross-section distribution characteristics of a fighter aircraft.  
Figure 8 shows that a peak appears near 0\({}^{\circ}\), and its RCS value is roughly the same as that of Figure 7. The peaks corresponding to \(-90^{\circ}\) and 90\({}^{\circ}\) are also simulated accordingly. Therefore, the target design of the complex target simulation proposed in this paper meets the requirements. However, it can only correspond to the simulation of one target. Therefore, the simulated targets are added into a data set and classified using fuzzy clustering or deep learning algorithms. Figure 9 shows an example RCS of a target simulated in this paper.  
As Figure 9 shows, the RCS distribution characteristics of an F-22 fighter jet contain six spikes. Therefore, it can also be simulated with six angle reflectors and six Luneburg balls reflectors.
## 3 Materials and Methods
### Microfiltered Water and Cement Sludge  
The washing water, consisting of water and cement sludge, is classified as a liquid effluent, which can be destined for discharge or reuse. A possible alternative to the discharge is the reuse as mixing water to produce new concrete mixtures. In this context, the microfiltered water obtained downstream of the microfiltration process is characterized as new mixing water according to the requirements imposed by UNI EN 1008. In particular, the water must conform to the following:  
1. Preliminary assessment tests to exclude traces of oils, fats or detergents, which can cause a reduction in concrete mechanical strength, as well as organic pollutants or acid substances, which can slow down the cement hydration;
2. Chemical requirements (chlorides, sulphates, alkalis, sugars, phosphates, nitrates, lead, zinc);
3. Requirements for setting time expected for specimens manufactured with both demineralized and analyzed water;
4. Requirements for compressive strength at 7 days expected for concrete or mortar specimens prepared with both demineralized and analyzed water.  
The cement paste used to determine the initial and final setting time was obtained by mixing 500 g of Portland cement CEM II/A LL 42.5R with 125 g of water (demineralized and analyzed water). Subsequently, the two samples were placed in a truncated cone container with a height of 40 \(\pm\) 0.2 mm and subjected to Vicat needle penetration. Instead, the three normalized mortar samples (40 \(\times\) 40 \(\times\) 160 mm) used to determine the flexural and compressive strength were prepared using 1350 g of natural sand, 450 g of Portland cement and 270 g of water (demineralized and analyzed water).  
The compressive strength was subsequently evaluated at 7 days. In particular, the load was applied and increased at a variable speed between 50 N/s and 500 N/s to reach the specimen failure in a time between 30 s and 90 s. The average compressive strength (Rm,c) was then calculated as the ratio between the applied failure load (N) and the cross-sectional area of the specimen (mm\({}^{2}\)).  
Regarding the cement sludge, a monitoring plan was prepared to assess its moisture and dry matter content. In particular, the sludge was collected from two sampling points within the microfiltration system (Figure 4), as they were considered representative for the use of sludge in concrete production.  
Figure 4: Cement sludge sampling points.  
The isothermal conduction calorimetry test (UNI EN 196-11) was also performed on the cement sludge in order to evaluate the possibility of using this component as a partial substitute of Portland cement in concrete mixtures. In particular, the main aim was to identify any residual hydration capacity of the sludge.
## 2 Materials and Methods
### Mathematical Modeling  
The fitting of the apparent first-order rate constant and the TOC removal was performed using the response surface methodology (D-optimal custom design model, Version 13.0.5.0), which can be used for at least two input variables. The input variables were synthesis temperature (A) (40 \({}^{\circ}\)C, 70 \({}^{\circ}\)C or 100 \({}^{\circ}\)C) and calcination temperature (B) (400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, or 600 \({}^{\circ}\)C), while the TOC removal and the apparent DCF degradation rate constant were the output parameters. The statistical analysis parameters which were applied to prove whether the model equations strongly or weak fit the model are presented in Equations (4)-(8). For strong model fitting, the \(R^{2}\) and adjusted \(R^{2}\) must be greater than 0.9, and the adjusted \(R^{2}\) should always be less than \(R^{2}\). The discrepancy of the prediction \(R^{2}\) and adjusted \(R^{2}\) must not be greater than 0.2. The parameters were calculated as follows:  
\[R^{2}=\frac{\sum_{i}^{n}\left(\hat{Y}-\overline{Y}\right)^{2}}{\sum_{i}^{n} \left(Yi-\overline{Y}\right)^{2}} \tag{4}\]  
\[Adjusted\ R^{2}=1-\frac{\left(1-R^{2}\right)\left(n-1\right)}{n-p} \tag{5}\]  
\[Predicted\ R^{2}=1-\frac{\sum_{i}^{n}\left(Yi-\hat{Y}\right)^{2}}{\sum_{i}^{n} \left(Yi-\overline{Y}\right)^{2}} \tag{6}\]  
\[Adequate\ Precision=\frac{Max\left(\hat{Y}\right)-Min\left(\hat{Y}\right)}{ \sqrt{v_{y}}}>4 \tag{7}\]  
\[v_{y}=\frac{p\sigma^{2}}{n} \tag{8}\]  
where \(Yi\) is the actual response at each term, \(\overline{Y}\) is the average actual response, \(\hat{Y}\) is the predicted response at each term, \(n\) is the number of runs, \(p\) is the number of independent variables, and \(\sigma\) is the standard deviation.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
###### Abstract  
To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation's results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.  
interference conditions; clustered drone relay communication; Discrete Soft Actor-Critic algorithms; convex optimization