esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Analysis
### Convergence Performance
Compared with MSA, step size of MSWA decreases more reasonably (i.e., decreases slower as iteration continues). The choice of \(d\) reveals the weight of the later auxiliary solution. Bigger \(d\) means assigning a larger weight to the later auxiliary solution and a different \(d\) can influence convergence performance [20]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Step Size**} & \multicolumn{2}{c}{**Number of Iterations**} \\ \cline{3-4}  & & **Sioux Falls** & **Anaheim** \\ \hline predetermined & \(\eta^{(n)}=0.0005/\left\lfloor n/1000+1\right\rfloor\) & 9533 & 909 \\ MSA & \(\eta^{(n-1)}=1/n\) & 19,802 & 9104 \\ MSWA(d=1) & \(\eta^{(n-1)}=2/(n+1)\) & 199 & 304 \\ MSWA(d=2) & \(\eta^{(n-1)}=6n/(n+1)(2n+1)\) & 39 & 135 \\ SRA & \(\eta^{(n)}=\begin{cases}\left(\xi^{(n-1)}+1.5\right)^{-1}\!\!,when\!\left\|f_ {k}^{s(n)}-g_{k}^{s(n)}\right\|-\left\|f_{k}^{s(n-1)}-g_{k}^{s(n-1)}\right\| \geq 0\\ \left(\xi^{(n-1)}+0.2\right)^{-1}\!\!,when\!\left\|f_{k}^{s(n)}-g_{k}^{s(n)} \right\|-\left\|f_{k}^{s(n-1)}-g_{k}^{s(n-1)}\right\|<0\end{cases}\) & 32 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Step size calculating methods and number of iterations convergence. Figure 8: Anaheim network used to test the effectiveness of solution algorithm.
###### Abstract
Continuous structural health monitoring of concrete pavements and deciding on appropriate actions for maintenance at the right time and place due to budget constraints is one of the main goals in the field of sustainable development of any country. Estimating the life of concrete pavement helps to forecast its remaining life and is a key step to defining a proper maintenance plan that leads to pavement efficiency. In general, the methods for estimating remaining pavement life can be divided into two categories: functional and structural [32]. These methods have been developed for use at different levels of a project or network [33]. Generally, each of these methods uses specific pavement data such as PCI [34], PSR, severity, and extent of a particular type of distresses such as edge faulting, fatigue cracking, roughness levels [35], pavement deflection [36], layer thickness, layer modulus, and so forth. Many different methods of analysis have been found in the literature accordingly, including empirical studies based on field data [37], probabilistic methods [38, 39], mechanistic-empirical methods [35], or methods based on soft computing analysis using pavement history data [40]. In all these methods, pavement data have been collected in different time steps and have been the basis for presenting different models. Collecting structural or functional pavement data in different periods reduces the model precision levels due to differences in the data-gathering process, which come from variations in the instrumentation, location, and pavement conditions. To tackle this issue, one of the main novelty aspects of this research is that the authors present here a novel continuous pavement data-gathering technique by using self-sensing concrete sensors that have the potential to provide a more accurate estimate of pavement conditions. This paper proposes a novel concept in multifunctional smart concrete that can be used as a new NDT technique to monitor damage propagation in concrete media. In this regard, a relationship between the slope of the electrical response of the concrete sensor as a function of the number of cycles, denoted as G-value, and the damage level was experimentally observed, which constitutes the main original finding of this research. Subsequently, the study proposes to model the damage level and remaining life based on the classical hypothesis of linear cumulative damage by [PERSON] [41].
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 7 Conclusions  
The proposed method for estimating PSF from an optical system is novel in a direct estimate of optical aberrations of the optical system. In our work, we have used findings reported in previous papers devoted to a complex description of optical systems and their aberrations. It is a difficult task to describe a system of this type, and UWFOV systems further greatly complicate this situation, since they are heavily aberrated. This paper has summarized a complex mathematical approach, and provides an algorithm for modeling the PSF of space-variant optical systems. The proposed algorithm has been verified with simulated data, and has been applied to real image data, showing error of the model around 5%. A comparison of images with different SNR provide MAXDIFF results  
Figure 15: Results of the fitting. The PSF model is shown with different orders of polynomials. The object was placed at 20\({}^{\circ}\) with respect to the optical axis (\(H\) = 0.33). In Table 4 this PSF is marked with the blue column. The first row relates to the 4 th order results, the second row relates to the 6 th order results and the third row relates to the 8 th order results.  
around 3% for images with SNR greater than 20 dB. The algorithm has been compared with other space-variant modeling methods. It has been shown to be competitive since our results are better than or equal to the results provided by other modeling methods. Other space-variant models provide accuracy around 8.5%. Our results demonstrate that the approach described here is also suitable for UWFOV systems. The results compare models of imaging systems of 4 th, 6 th, and 8 th orders of Zernike polynomials and show some benefits of using different orders. The contribution of this paper to the description of aberration is via a method for obtaining aberration coefficients in an unknown optical system and for using them in the model of space-variant PSF. The algorithm was used to model the WILLIAM system [51], and the model provided by the approach described here was used in a comparison [50] of deconvolution methods. Further research should address pixel profile sensitivity and sampling methods aimed at improving the way in which the model describes sensor PSF.  
This work has been supported by Grant No. GA14-2525 IS \"Nonlinear imaging systems with the spatially variant point spread function\" of the Grant Agency of the Czech Republic and by the Grant Agency of the Czech Technical University in Prague, Grant No. SGS16/165/OHK3/2T/13 \"Algorithms for Advanced Modeling and Analysis of Optical Systems with Variable Impulse Response\".  
[PERSON] conceived and designed the algorithm and the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] contributed in field dependency analysis; and [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## Materials and Methods
### Cell Culture and Treatments  
HaCaT cells (a human keratinocyte cell line) were obtained from KIOM (Daegu, Korea). Raw264.7 cells (a mouse macrophage cell line), EL4 cells (a mouse T lymphoblast cell line), and HMC-1 cells (a human mast cell line) were obtained from the Korean Cell Line Bank (Seoul, Korea). The HaCaT, Raw264.7, and EL4 cells were cultured in DMEM supplemented with 1% penicillin-streptomycin and 10% FBS in a humidified environment of 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. The HMC-1 cells were cultured in IMDM supplemented with 10% FBS, 1% penicillin-streptomycin, 1.2 mM 1-thioglycerol, in a 5% CO\({}_{2}\) humidified incubator at 37 \({}^{\circ}\)C. All cell types were pre-treated with selected concentrations of GMP for 1 h before stimulation. HaCaT, Raw264.7, and EL4 cells were stimulated with TI (TNF-\(\alpha\) and IFN-\(\gamma\), 10 ng/mL each), LPS (1 \(\upmu\)g/mL), and PI (PMA 10 ng/mL; ionomycin 100 ng/mL), respectively, for 24 h. The HMC-1 cells were stimulated with SP (10 \(\upmu\)M) for 48 h.
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 3 Results
### Residual Element  
The residual element was evaluated by electron probe microanalysis to analyze elements on the surface of the material before and after performing the fine-particle shot peening process. This analysis was important, as it helped us identify whether there were any residual elements from the process left on the CoCr surface.  
Using X-ray mapping and characteristics, results from the electron probe microanalysis showed that there were some residual elements from the particle that was used in the peening process. In a conventional workpiece (Figure 7) that did not experience the fine-particle shot peening process, we found that there were only elements of cobalt and chromium. By comparison, we detected elements of each particle type, i.e., ceramic and silica, on the workpieces used for those respective peening processes. The workpiece that used ceramic beads contained elements of ceramic, including alumina, zirconia, oxygen, and silica, and the workpiece that used silica beads contained silica and oxygen (Figures 8 and 9). Therefore, we concluded that the fine-particle shot peening process leaves residual elements on the surface of the material. Residual elements must be taken into consideration, especially for medical device applications, in which specific elements could create reactions in the human body.  
Figure 6: Surface hardness compared between pre- and post-peening process in various types and sizes of particle.  
Figure 8: Residual elements of sample peened with ceramic particles showing the elements of alumina, zirconia, silica, and oxygen on the surface.  
Figure 7: Residual elements of un-peened sample showing the elements of cobalt and chromium on the surface.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### RNN, LSTM and GRU Model Training Comparison  
#### 3.3.1 Comparison of Normalization Methods  
This section compares different normalization methods, including No-Normalization, Min-Max Normalization, RP-Normalization, and linear interpolation plus RP-Normalization methods. In this study, we set hidden neurons to 64, learning rate 0.01, and observation dataset training and verification curves.  
Figure 15 shows the RNN training and verification loss. As shown in Figure 15a, the maximum and minimum convergence speed is the fastest as it converges at Epochs 350 compared to others. The convergence speed refers to the time to reach the minimum error. Figure 15b shows the verification loss. Although the maximum and minimum convergences faster than other methods, there are many large fluctuations between Epochs 100 and 300. The reason for this is that the dataset is divided into missing values and non-missing values, which causes the data distribution change, thus increasing the uncertainty in training.  
For RNN model, because its structure is simple, it can convergence fast for the dataset in a small value interval, and the method of normalization, relative position and linear interpolation plus relative position is not used. The dataset is a picture where the size is interval, so the convergence speed is slow. The improvement method can be adjusted from the structure and parameters.  
Figure 16 shows the training and verification loss for LSTM. Figure 16a is the training loss. The structure of LSTM improves the long-term memory compared to RNN, so it has better performance. The relative position normalization method converges at Epochs 50, while for linear interpolation and the relative position method, convergence is at Epochs 250, and the loss error is reduced to less than 0.25. For the maximum and minimum, the training loss jumps up at Epochs 420. The reason for this is also that the training data distribution is damaged. Figure 16b is the verification loss. It shows that the proposed method using linear interpolation suffers from over-fitting because the structure or learning parameters are inappropriate.  
Figure 17 shows the training and verification losses for GRU. Figure 17a is the training loss. Except for the maximum and minimum methods, the other three methods have lower error loss. The relative position normalization is the fastest to converge at Epochs 50. Figure 17b is the verification stage. The relative position normalization is the best, and the verification error loss is the smallest and the most stable. After adding linear interpolation, there is an over-fitting problem, so this article considers the structure, or the training parameters are inappropriate.  
Figure 16: LSTM model training and verification loss. (**a**) Training loss; (**b**) Verification loss.  
Figure 17: GRU model training and verification loss. (**a**) Training loss; (**b**) Verification loss.  
Figure 15: RNN model training and verification loss. (**a**) Training loss; (**b**) Verification loss.
## 4 Results, Comparison, and Limitations
### Analysis of Experiments Results  
In this study, we investigated the effectiveness of using Yolo v7 for face and muzzle detection, SIFT for feature extraction, and FLANN for feature matching in the identification of horses as an example of a method for identifying livestock without the use of Hot/Freeze Branding, RFID tagging and microchipping. We found that this method achieved a matching accuracy of 100%, as shown in Table 5. All the horses in our sample were correctly identified by the system. This indicates that the proposed method is a reliable and accurate way to identify horses without the need for physical contact or the use of RFID tagging or microchipping. A few examples from horses that were successfully identified by the system during the experiment are shown in the following Figure 13.  
We have analyzed the performance of the proposed model against similar research studies and found the outcome more accurate than any other methodologies previously applied. These examples demonstrate the effectiveness of the proposed identification method in correctly recognizing and matching the keypoints on the horses' noses with those in the database. The high accuracy of the system in identifying these horses highlights its potential usefulness in the livestock industry for identifying and tracking animals. The proposed method offers several advantages for livestock identification. First, it allows for the identification of animals from a distance, which can be particularly useful in  
Figure 13: Examples of Successfully Identified Horses. situations where it is not possible or practical to physically handle the animals. Second, it does not require the use of physical devices, such as RFID tags or microchips, which can be uncomfortable or even painful for the animals. This means that the proposed method can be used to identify and track animals without causing any discomfort or stress to the animals.
# Effect of Bond-Slip on Dynamic Response of FRP-Confined RC Columns with Non-Linear Damping
## 4 Effect of Bond-Slip on the Dynamic Response of the FRP-C RC Columns
### Seismic Simulation Results and Discussion
So the structure designed may be unsafe on this basis;
4. finally, under biaxial harmonic load, the strength and stiffness degradation of FRP-C RC columns in two bending directions affect each other, which aggravates the decline of their ductility capacity and the seismic capacity will weaken significantly. In summary, the nonlinear damping model considering the bond slip of reinforcement is adopted for the structure designed will be safer. Some studies could be carried out in the following aspects: Firstly, the zero-length element model of FRP-confined concrete is improved by conducting bond slip tests of reinforced concrete members in various FRP wrapping forms and establishing corresponding bond slip models; secondly, the cyclic loading and unloading tests of reinforced concrete members with various FRP wrapping forms considering bond-slip effect are carried out to establish the corresponding calculation model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cross** & **External Load** & \(D_{1}\)**/mm** & \(D_{2}\)**/mm** & \(D_{3}\)**/mm** & \((D_{1}-D_{2})/D_{2}\) & \((D_{1}-D_{3})/D_{3}\) \\ \hline \multirow{2}{*}{Circular column} & Uniaxial harmonic load & 13.1 & 12.5 & 11.2 & 4.80\% & 16.96\% \\  & Biaxial harmonic load & 12.9 & 12.3 & 11.6 & 4.87\% & 11.21\% \\ \hline \multirow{2}{*}{Square column} & Uniaxial harmonic load & 15.9 & 14.8 & 13.0 & 7.43\% & 22.31\% \\  & Biaxial harmonic load & 15.6 & 14.7 & 12.8 & 6.12\% & 21.88\% \\ \hline \hline \end{tabular} Note: \(D_{1}\) is the maximum of column top displacement by the proposed model; \(D_{2}\) is the maximum of column top displacement by [PERSON]’s model; \(D_{3}\) is the maximum of column top displacement by the constant damping model.  
\end{table} 
Table 3: The comparison results of the maximum of column top displacement in the above three models.
# Modeling and Simulation of Sky Survey
## 4 Design of the Model Solving Algorithm
### Overview of Stage Design Algorithm  
Figure 2 illustrates the flow chart of the Staged Design Algorithm proposed in this paper. Specifically, first, the initialization parameters, the start time, the end time, the number of orbital roots, and the statistics of inefficiency periods (_Low_) and difficult observing sky areas (\(DifSky\)) are prearranged in advance according to Algorithms 1 and 2. The simulation time is initialized, and the orbit constraint is checked. Under the condition that the orbit constraint is satisfied, it is checked whether it belongs to the prearranged low efficiency time period. If it belongs to this, then the prearranged scheme of low efficiency for observation directly follows. Otherwise, it judges whether the observation constraint  
is satisfied. After it does not satisfy, the time of simulation is increased and returned. If it is satisfied, then it check whether it belongs to the difficult observation sky area. If yes, then it executes the prearrangement. If it does not satisfy, it finds the sky zone with the largest weight factor for observation. Finally, the simulation time is judged to be over. If it is met, the observation result is output. Otherwise the time is increased, and the observation continues.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Issue**} & \multirow{2}{*}{**Learning Method**} & \multirow{2}{*}{**Proposed Method**} & \multirow{2}{*}{**Projection Map**} & \multicolumn{3}{c}{**Data Set: OCTA-500**} \\ \cline{5-6}  & & & & & **Accuracy (Acc)** & **Precision** & **Recall (Rec)** \\ \hline
1 & RV and FAZ & Unsupervised & K-Means clustering & 0.89 & 0.67 & 0.79 \\
2 & segmentation & Supervised & ASDC & UTA_6M & **0.96** & **0.91** & **0.83** \\
3 & RV segmentation & Supervised & ASDC & **0.97** & **0.92** & **0.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of the proposed methods on OCTA-500 data. In Figure 9, we compared the latest advanced algorithm for segmenting RV biomarkers. Our study extracts RV from the OCTA scan and segments the FAZ area, which is crucial for detecting retinal diseases such as DR. We have included additional results in Figure 10 to further highlight the effectiveness of our proposed method.
# Design and Development of a Wideband Planar Yagi Antenna Using Tightly Coupled Directive Element
The antenna operates at 2.31 GHz center frequency with an excellent fractional bandwidth of 63.5%. As only two directive elements are used, the gain performance of the antenna is compromised to 5.8 dBi. In Table 1, we have compared the performance of our proposed antenna with those published in [28; 29; 30; 31]. It can be inferred that in printed Yagi antenna designs, simultaneous improvement in gain, bandwidth and size is quiet uncommon. In this work, a moderate-size modified printed Yagi antenna is presented. This antenna is designed to exhibit high gain and wide bandwidth, simultaneously. The conventional printed Yagi antenna comprises a driven element followed by multiple directive elements of a typical rectangular shape. As the electromagnetic energy is coupled from the driven element to the directive element, the inter-element spacing must be in the range 0.15x-0.3x. Moreover, the typical rectangular shape can affect matching over a wide bandwidth due to rapid changes in the input impedance. Therefore, in the proposed printed Yagi antenna design, an elliptical shape for the dipole antenna and directive elements was selected. Additionally, contrary to the typical spacing (0.15x to 0.3x) used for the directive elements, the input impedance can be matched over a wider bandwidth by placing the first directive element in a closer proximity (i.e., between 0.019x to 0.0299x). Thus, the proposed antenna exhibits a \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Center Freq. (GHz)** & **BW (\%)** & **Gain (Max)** & **Dimensions (L \(\times\) W)** \\ \hline
[28] & 2.72 & 42 & 4 & \(0.39\lambda_{0}\times 0.47\lambda_{0}\) \\
[29] & 8.74 & 3 & 8.34 & \(1.15\lambda_{0}\times 1.15\lambda_{0}\) \\
[30] & 2.44 & 3.2 & 5 & \(0.84\lambda_{0}\times 0.58\lambda_{0}\) \\
[31] & 2.31 & 63.54 & 5.8 & \(0.41\lambda_{0}\times 0.41\lambda_{0}\) \\ Proposed & 3.6 & 49.3 & 8.7 & \(1.68\lambda_{0}\times 0.72\lambda_{0}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of wideband printed Yagi Antenna with proposed work. wide bandwidth (49.3% fractional bandwidth) covering the S-band from 2.6 GHz to 4.3 GHz. In the proposed design, the directive elements are printed on both the top and bottom sides of the substrate. The bottom-plane directive elements are placed by some offset with respect to the top-plane directive elements. This enhances the effective length toward the direction of propagation, resulting in enhanced gain without increasing the overall dimensions of the antenna.
## 3 Results and Discussion
### Frontier Molecular Orbitals (FMO) Studies  
The chemical reactivity and the most likely reactive sites of the FLP were studied using FMO analysis (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Beta-Selinene** & **Sulcatone** & **Beta-Caryophyllene** & **Alpha-Ylangene** \\ \hline Surface Area (Approx) (Å\({}^{2}\)) & 377.12 & 854.51 & 316.37 & 244.90 \\ Surface Area (Grid) (Å\({}^{2}\)) & 436.07 & 789.53 & 383.68 & 386.42 \\ Volume (Å\({}^{3}\)) & 746.45 & 1357 & 626.27 & 612.24 \\ Hydration Energy (Kcal/mole) & 1.02 & \(-\)38.48 & \(-\)0.04 & \(-\)0.10 \\ Log P & 5.20 & 6.45 & 0.77 & 0.47 \\ Refractivity (Å\({}^{3}\)) & 41.81 & 73.79 & 28.31 & 19.75 \\ Polarizability (Å\({}^{3}\)) & 27.82 & 48.93 & 14.17 & 14.31 \\ Mass (amu) & 214.35 & 542.37 & 180.17 & 180.17 \\ Total Energy (kcal/mol) & 18.4378 & 15.5186 & 53.0244 & 96.0766 \\ Dipole Moment (Debye) & 0 & 0.5526 & 0 & 0 \\ RMS Gradient (kcal/ Å mol) & 0.08942 & 0.09325 & 0.0975 & 0.09742 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physico-chemical properties of the FLP computed from their QSAR parameters.  
Figure 4: Optimized structures of the four lead phytocompounds from the docking studies using FMOs.  
The calculated energies, EHOMO and ELUMO, of compounds help to explain the global reactivity descriptors such as chemical potential, chemical hardness, and electrophilicity of the FLP were elucidated from the computed EHOMO and ELUMO, while the negative values obtained for the EHOMO and ELUMO were confirmatory of the stability of the FLP. It was observed that \(\beta\)-caryophyllene had the least band energy gap (EHOMO-ELUMO) compared to other phytocompounds. Therefore, \(\beta\)-caryophyllene demonstrated the highest chemical reactivity. The parameters that measure the chemical reactivity of the FLP, such as chemical potential (\(\mu\)), electronegativity (\(\chi\)), global hardness (\(\eta\)), global electrophilicity index (\(\omega\)), and global softness (S), were calculated (Table S2). Sulcatone had the least chemical softness (S) hence, predicted to have higher stability than the other FLP. The electrophilicity (\(\chi\)) is another important parameter that measures the ability of a compound to accept electron(s) from its surrounding. B-caryophyllene showed less electrophilicity than other compounds.