esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Optimization Algorithms
#### 5.4.2 Evolutionary Optimization Algorithms  
In contrast to the gradient-based methods, evolutionary algorithms that belong to the class of stochastic search methods have become very popular in the last two decades. The evolutionary algorithms do not require any gradient information and can employ a set of design points to find the optimum. This type of method is typically inspired by natural phenomena and is extremely robust. Two most popular evolutionary algorithms are the more established genetic algorithm (GA) [181] inspired by [PERSON]'s principle of survival of the fittest, and particle swarm optimization (PSO) [182] based on a simplified social model. Other algorithms include evolutionary programming [183], genetic programming [184], differential evolution [185], simulated annealing [186], tabu search [187], ant colony optimization [188], harmony search [189], etc. [PERSON] et al. [190] presented a comprehensive review on the nature-inspired metaheuristic algorithms. The most important process of evolutionary algorithms is the mutation (random change) of single genes of a parental design to produce a descendant. Genetic algorithms are thereby especially useful for a relatively wide-ranged search in the design space. In general, evolutionary algorithms perform much better in finding the global or near global optimum in the presence of multiple local extrema than gradient-based methods and are well suited for discrete optimization problems [175]. The main drawback is the high numerical effort since a large number of problem solutions are required. Evolutionary algorithms should be applied to optimization problems where discrete or binary variables dominate the response and problems with a very high number of variables and/or constraints.
## 4 Discussion
Lewis (Le) glycans are the determinants of blood group antigens in glycolipids and glycoproteins of normal tissues such as erythrocytes and epithelial cells [39]. There are two types of Le glycans: type I Le glycans include Le\({}^{\text{a}}\) [Gal$1-$3(Fuc$\alpha$1-$4)GlcNAc] and Le\({}^{\text{b}}\) [Fuc$\alpha$1-$2 Gal$1-$3(Fuc$\alpha$1-$4)GlcNAc] glycans, while type II Le glycans are comprised of Le\({}^{\text{x}}\) [Gal$\beta$1-$4(Fuc$\alpha$1-$1-$0\alpha$1-$12 Gal$1-$21-$1-$3\alpha$1-$43)GlcNAc] glycans. Aside from their basal expression in normal colon epithelium cells, Le glycans reportedly emerge during the course of oncogenesis, and are then referred to as tumor-associated Le glycans. We previously identified a unique Le glycan complex expressed in the colon cancer cell line SW1116 through affinity chromatography of mannan-binding protein (MBP), which includes a C-type lectin with specificity to type I, but not type II, Le glycans [40; 41]. The glycan structure was found to be tetraantennary, containing N-glycans with $1-6 branching that harbor an unusual Le\({}^{\text{a}}\) tandem repeat and end with Le\({}^{\text{b}}\) at the nonreducing terminus. Our findings strongly suggest the presence of tumor-associated Le glycans in human colon tumors. Indeed, we observed strong expression of MBP high-affinity ligands in 38.5% of human colorectal carcinoma tissues, but not in adjacent nonmalignant tissues where blood-type Le glycans were expressed [42]. Thus, it is possible that MSPL expressed in colon cancer cells harbors complicated tumor-associated Le glycans. Because antibodies generally recognize a few oligosaccharides at most, lectins such as MBP and DC-SIGN that form large multimers can be developed into new diagnostic systems for detecting complex glycan structures in colorectal cancer.
## 4 Results and Discussion  
### Behavior of Normal Waves in the Pipeline  
For working condition 1, the transmitting voltage level response of the transducer is shown in Figure 6. The measurement results from the hydrophones at different positions along the axial direction in the pipeline are shown in Figure 7. It can be seen that the acoustic signal in the pipeline exhibits a significant cutoff phenomenon, with a cutoff frequency of 4.7 kHz, which is close to the theoretically determined cutoff frequency of 4.6 kHz.  
Figure 5: Deployment and labeling of hydrophones and vibration sensors.  
Figure 6: Transmitting voltage level response of transducer.  
The sound energy of the far field can be divided into four intervals from the spectrum: (1) Below 4.7 kHz; (2) 4.7-10.6 kHz; (3) 10.6-16 kHz; (4) 16 kHz and above. The boundary points of these intervals are the frequencies of the normal wave in the pipeline, which are basically consistent with the calculated frequencies of the corresponding orders, as shown in Table 4.  
As mentioned before, the experimental pipeline is slotted. PE is not a very rigid material and, as a result of the slotting process, the tube is deformed radially. This is the main reason for the error between the actual measured frequency and the theoretically calculated frequency. In addition, the parameters in Table 1 are the material elastic parameters of standard high-density PE, which are not necessarily exactly the same as those of the wall of the experimental pipeline, which also leads to an error between the theoretical and experimental results.  
In interval (1), the curve of the power spectrum is very close to the background noise. Close to 4.7 kHz, however, the curve suddenly rises, exhibiting a cutoff phenomenon. Then, in interval (2), the curve changes relatively gently, which is basically consistent with the corresponding frequency band of the transmitting transducer frequency response curve. As the distance between the measurement  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **1** & **2** & **3** \\ \hline Calculated frequency (kHz) & 4.6 & 10.4 & 16.3 \\ \hline Experimental frequency (kHz) & 4.7 & 10.6 & 16 \\ \hline Relative error (\%) & 2.17 & 1.92 & 1.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of calculated and experimental normal frequencies.  
Figure 7: Measurement results from hydrophones at different positions along the axial direction under white noise excitation: (**a**) 0.05, 0.1, and 0.15 m from the source; (**b**) 1.1 m from the source; (**c**) 5.95 m from the source; (**d**) 0.1, 5, 6, and 7 m from the source.
## 2 Materials and Methods
### Cell Viability  
Cell viability was determined using the trypan blue assay, an efficient and economical method for counting and testing cell viability. Normal human dermal fibroblast cells were seeded in 24-well plates at 1 \(\times\) 10\({}^{5}\) cells/well and incubated at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) for 24 h. The following treatment was applied in each separate plate: AALipo15%, AALipo 20%, AALipo 25%, and blank liposomes. After 24 h, the cells were trypsinized (Trypsin/EDTA solution (0.25 mg/mL), LONZA), neutralized (TNS--Trypsin Neutralization Solution, LONZA), and centrifuged (1000 rpm/5 min). The resultant pellets were suspended in culture medium and cell viability was determined using the EVE Automatic devices (NanoEnTek Inc., Seoul, Republic of Korea) [26; 30].  
The percentage of cell viability was calculated according to the Formula (1) and the results were expressed as a percentage of the cell viability of the treated cells compared to the control (untreated cells-CTRL). Measurements were performed in triplicate and data were represented as mean \(\pm\) standard deviation (SD).  
\[\text{Cell viability }(\%)=\frac{\text{number of live cells}}{\text{number of total cells}}\ \times\ 100 \tag{1}\]
## 2 Parametric Study Based on CFD Calculations
### Numerical Setup
Figure 4 shows the results of the CFD computations against the Xfoil results for all angles of attacks of the airfoil. A mesh independency study was carried out to verify enough grid resolution with three mesh sizes using a refinement ratio of 2. The coarse mesh contains 72,500 cells. For the medium and fine mesh the number of cells is 145,000 and 290,000, respectively. Drag and lift results obtained for the finer mesh are compared with the results of a regular and a coarse mesh. Less than 4% mesh dependency has been found for both drag and lift. The simulations were converged until a satisfactory residual convergence was achieved on the velocities, pressure and turbulence quantities. The CFD results follow reasonable good the trend of DOWEC results. Lift and drag coefficients were calculated according to the Equations (1) and (2), respectively:  
Figure 3: Mesh distribution on the airfoil the trailing edge (TE). \[C_{L} =\frac{L}{\frac{1}{2}\rho U_{\infty}^{2}c} \tag{1}\] \[C_{D} =\frac{D}{\frac{1}{2}\rho U_{\infty}^{2}c} \tag{2}\]  
The air density was defined by \(\rho\) = 1.204 kg/m\({}^{3}\) and the free stream velocity, far ahead of the airfoil, corresponds to \(U_{\infty}\) = 10.66 m/s. \(L\) and \(D\) represent the lift and drag forces per unit of area, since the simulations are in 2D.
## 5 Challenges and Future Research Directions
### Future Research Directions
#### 5.2.1 Special Scenario Applications  
In Section 4, we summarized the current application fields of UAV-assisted SAGINs. In the future, the networks will have even more widespread application scenarios, particularly in terms of optimization and application in special scenarios.  
1. _Near-zero-infrastructure Self-organizing Networks_: Zero-infrastructure self-organizing networks refer to dynamic network structures that do not require pre-deployed communication infrastructure. They rely on the self-organizing capabilities of nodes for neighbor discovery, routing maintenance, and resource scheduling, making them suitable for scenarios such as disaster recovery, battlefield communication, and remote area coverage. Their advantages include rapid deployment and flexible topology, but they face technical challenges like high energy consumption and poor routing stability.
2. _Multi-domain Collaboration and Cross-network Integration_: Due to environmental and resource constraints, HAPs often take on more computational tasks but higher latency than LAPs [165]. Different communication protocols and differentiated resource scheduling methods need to be designed for different networks to improve the communication efficiency of integrated networks.
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 1 Introduction
In particular, the zero Hough-transformed energy percentage analysis result shows that the interference detection performance obtained with the smoothed pseudo-Wigner-Hough transform method is at least double that of the Wigner-Hough transform approach; in contrast to the pseudo-Wigner-Hough transform-based technique, the zero Hough-transformed energy percentage obtained with the smoothed pseudo-Wigner-Hough transform-based GNSS interference detection method is improved at least 20% in comparison to the pseudo-Wigner-Hough transform-based technique. Therefore, the proposed novel smoothed pseudo-Wigner-Hough transform method is recommended in interference detection for GNSS receivers, particularly in challenging interfering environments.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 2 AC/DC Distribution Network Model
### AC Distribution Network Power Flow Model  
\[P_{jk}=P_{ij}-\frac{r_{ij}[(P_{ij})^{2}+(Q_{ij})^{2}]}{{(V_{i})}^{2}}-P_{j} \tag{1}\]  
\[Q_{jk}=Q_{ij}-\frac{x_{ij}[(P_{ij})^{2}+(Q_{ij})^{2}]}{{(V_{i})}^{2}}-Q_{j} \tag{2}\]  
\[(V_{j})^{2}=(V_{i})^{2}-2(r_{ij}P_{ij}+x_{ij}Q_{ij})+\frac{[(r_{ij})^{2}+(x_{ ij})^{2}][(P_{ij})^{2}+(Q_{ij})^{2}]}{{(V_{i})}^{2}} \tag{3}\]  
\[P_{j}=P_{j,L}-P_{j,\text{DG}} \tag{4}\]  
\[Q_{j}=Q_{j,L}-Q_{j,\text{DG}} \tag{5}\]  
\[Q_{j,\text{DG}}=P_{j,\text{DG}}\tan\varphi \tag{6}\]  
where \(V_{i}\) and \(V_{j}\) are the voltage amplitudes of node \(i\) and node \(j\) respectively; \(r_{ij}\) and \(x_{ij}\) are the branch resistance and reactance between node \(i\) and node \(j\), respectively; \(P_{ij}\) and \(Q_{ij}\) are the active and reactive power of branch \(ij\), respectively; \(P_{j,L}\), \(P_{j,\text{DG}}\), \(Q_{j,L}\), and \(Q_{j,\text{DG}}\) are the load active power, DG active power, load reactive power, and DG reactive power at node \(j\), respectively. \(\varphi\) is the power factor angle.
# On Ghost Imaging Studies for Information Optical Imaging
## 4 Outlook
### Further Utilizing High-Dimensional Light-Field Information Capacity  
Currently, the imaging equipment based on GI LiDAR has demonstrated the ability to acquire additional vibration information during imaging by combining with the coherent detection method and the micro-Doppler effect [73], making it possible to additionally \"see the sound\" via an imaging system. Though this scheme is naturally suitable to detect and image scenes with vibration modes with discrete frequency spectra, it is still a challenging problem for it to perform imaging on scenes in transparent media that have vibration  
Figure 4: Schematic diagram of the diffraction GI. In the reference path, the distance from the source to the detector array is \(z\). In the object path, the light passes through the imaging object which is \(z_{1}\) away from the source and then propagates \(z_{2}\) to be recorded by a point-like detector located at \(r_{t}\) (the point-like detection is represented by a painted grid here that denotes only the intensity of one grid is recorded). The Fourier-transform diffraction spectral of the sample can be obtained via the second-order correlation when \(z=z_{1}+z_{2}\).  
modes with 3-D vibrating directions or scenes with continuous spatial-temporal frequency spectra since these kinds of vibration mode and wave propagation essentially have a much more complicated structure [119, 120].  
For the GI camera, our short-term research focus will be theoretical and application studies on its ability for 2-D spatial super-resolution imaging achieved by exploiting the discrepancies of the imaging object in the high-dimensional light-field domain. Particularly, in the theoretical aspect, the first step is to further improve the quantitative description of the super-resolution ability on imaging objects which are apparently sparse in the high-dimensional light-field domain under the noisy case. To achieve this, one of potential approaches would be to incorporate the concept of statistical resolution [17, 18] into the high-dimensional domain, while combining with the object's prior information [121, 122, 123]. This statistical-based description is expected to be rather meaningful for the application of a GI camera in related scenarios, such as fluorescence microscopy [124] and astronomy observing [125, 126]. Further, for spatial-domain continuous, non-sparse scenes commonly existing in the remote-sensing scenario, to quantitatively describe the super-resolution ability in the 2-D spatial domain resulting from discrepancies in the high-dimensional light-field domain is a very important problem that remains unresolved.
## 5 Conclusions  
In this study, the effects of drawbacks on sheet metal forming were analyzed. Based on this, a digital twin (DT) model was proposed to predict the quality indicators in real time. This approach has resulted in reduced time and costs compared to traditional methods that utilize experiments or analyses for predicting forming quality, and a user-friendly GUI has been developed that can be utilized without specialized knowledge. Detailed research findings are presented below.  
The effects of drawbacks based on their holding force and position were analyzed through finite element analysis (FEA). As drawback forces increased, wrinkles decreased; however, cracks showed a tendency to increase in the straight line part. In the curved part, similar patterns to the straight line part were observed, and the influence was relatively small.  
When the models for classifying the occurrence or non-occurrence of wrinkles and cracks were trained using artificial intelligence (AI), the models showed a high accuracy of 98%, with no significant difference in accuracy. This confirmed that the occurrence or non-occurrence of wrinkles and cracks can be classified with high accuracy.  
In the case of the regression models for predicting wrinkles and cracks, the Gradient Boosting Machine (GBM) model showed a mean square error (MSE) of 0.063 for wrinkles and an MSE of 0.042 for cracks. It appears that the ensemble technique showed high prediction accuracy because wrinkles and cracks were composed of nonlinear data.  
Through the classification and regression models, a GUI capable of predicting quality indicators in real time according to the input of the force and position of drawbacks was constructed. Based on this, it was confirmed that wrinkles and cracks can be predicted in real time after forming.  
This study presented the possibility of predicting quality indicators according to drawbacks in real time. In future research, it is expected that a DT model capable of predicting quality in the production stage as well as the initial design stage can be developed by constructing prediction models that consider design parameters as well as the holding force of drawbacks and reflect process data.  
S.Y.: Model design, Methodology, Software, Writing--original draft. D.H.: Writing--modification draft, Writing--review and editing. S.H.: Supervision, Writing--modification draft, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Prediction model GUI for wrinkles and cracks based on drawbacks.  
**Funding:** This work was partly supported by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) [(2021 RIS-004)].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declared that there are no conflicts of interest.
## 2 Materials and Methods
### Scalability and Computational Complexity Considerations  
The computational complexity of the EA is determined by the population size (P), the number of generations (G), and the dimensions of the layout matrix. Everyone represents a different configuration of workstations within a 4 \(\times\) 8 matrix, and its evaluation is based on the cumulative calculation of Euclidean distances according to operator paths. The per-generation complexity is approximately O(P \(\times\) E), where E represents the time required to evaluate an individual. The implementation in C programming language allowed for acceptable execution times, even under large configurations.  
In exploratory tests using larger matrices (e.g., 6 \(\times\) 10 or 8 \(\times\) 12), execution time increased linearly with population size, while the search space expanded combinatorially. Although the model remains feasible for medium-sized problems, future scalability can be improved through adaptive control of population size, parallel evaluation of individuals, or the integration of hybrid local search strategies. These enhancements would enable the algorithm to be applied to more complex and less variable manufacturing environments while maintaining computational efficiency.
# On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy
###### Abstract  
The paper describes the strategy adopted to assess the structural condition of the tallest historic tower in Mantua (Italy) after the Italian seismic sequence of May-June 2012 and exemplifies the application of health monitoring using (automated) operational modal analysis. The post-earthquake survey (including extensive visual inspection, historic and documentary research, non-destructive (ND) material testing, and ambient vibration tests) highlighted the poor state of preservation of the upper part of the tower; subsequently, a dynamic monitoring system (consisting of a few accelerometers and one temperature sensor) was installed in the building to address the preservation of the historic structure, and automated modal identification was continuously performed. Despite the low levels of vibration that existed in operational conditions, the analysis of data collected over a period of about 15 months allowed to assess and model the effects of changing temperature on modal frequencies and to detect the occurrence of abnormal behavior and damage under the changing environment. The monitoring results demonstrate the potential key role of vibration-based structural health monitoring, implemented through low-cost hardware solutions and appropriate software tools, in the preventive conservation and the condition-based maintenance of historic towers.  
automated modal identification; continuous dynamic monitoring; earthquake; environmental effects; masonry towers +
Footnote †: journal: Applied Sciences
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 4 Lubrication of the Concave/Convex Textured Bearing  
The numerical simulation in this study is initiated with the following parameters: (i) the radius of the journal bearing \(R\) is 0.035 m; (ii) the width of the journal bearing \(L\) is 0.0656 m; (iii) the radial clearance \(c\) is \(5\times 10^{-5}\) m; (iv) the eccentric ratio \(e\) is 0.65; (v) the linear sliding velocity of the shaft relative to the bearing \(U\) is 0.5 m/s; (vi) the dynamic viscosity of the lubricant \(\eta\) is 0.028 Pa\(\cdot\)s; and (vii) a convergence study concerning the mesh size is investigated, the suitable numbers of grids \(m\) and \(n\) are 310 and 100, respectively. The texture is uniformly arranged on the inner wall of the bearing, and there are 31 spherical segments along the \(X\) direction and 10 spherical segments along the \(Z\) direction.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## Appendix A
### Singular Spectrum Analysis (SSA)  
Singular Spectrum Analysis is a nonparametric time series analysis technique that decomposes a time series into a sum of components representing the trend, season, and remainder. This process forms a trajectory matrix by embedding the time series into a high-dimensional space and then decomposes it using Singular Value Decomposition (SVD). Its computational steps are as follows:  
1. Embedding: Time series \(\mathrm{x}=[\mathrm{x}_{1},\mathrm{x}_{2},\dots,\mathrm{x}_{\mathrm{N}}]\) is embedded into a Hankel matrix \(\mathrm{X}\) of size \(\mathrm{K}\times\mathrm{L}\) by sliding a window of length \(\mathrm{K}\) over the series. The embedding process can be expressed as follows: \(\mathrm{X}=\begin{bmatrix}\mathrm{x}_{1}&\cdots&\mathrm{x}_{\mathrm{L}}\\ \vdots&\ddots&\vdots\\ \mathrm{x}_{\mathrm{K}}&\cdots&\mathrm{x}_{\mathrm{K}+\mathrm{L}-1}\end{bmatrix}\)  
2. Singular Value Decomposition: The trajectory matrix \(\mathrm{X}\) is decomposed by SVD into  
\[\mathrm{X}\ =\ U\Sigma\mathrm{V}^{\mathrm{T}}\]  
3. Grouping and reconstructing components: Singular vectors are grouped to form components. Each group corresponds to a specific temporal pattern. The components are reconstructed by  
\[\mathrm{X}_{\mathrm{r}}=\mathrm{U}_{\mathrm{r}}\Sigma_{\mathrm{r}}\mathrm{V} _{\mathrm{r}}^{\mathrm{T}}\]  
where \(\mathrm{U}_{\mathrm{r}},\Sigma_{\mathrm{r}},\mathrm{andV}_{\mathrm{r}}^{ \mathrm{T}}\) are the selected singular vectors and values used for reconstruction.  
4. Component Summation: The final reconstructed time series is obtained by summing the reconstructed components:  
\[\hat{\mathrm{x}}=\sum_{\mathrm{r}}\mathrm{X}_{\mathrm{r}}\]