esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Structure and Principle of the Antenna Sensor  
### The Formation of the Antenna Sensor  
In this study, the snowflake patch is selected as the radiation patch of the microstrip antenna. The snowflake patch, also known as Koch island-type patch, has a fractal structure consisting of three Koch curves. The Koch curve that resembles snowflakes is a type of classic fractal geometry structure and mathematical curve proposed by the Swedish mathematician [PERSON] in 1904 [18; 19]. The iterative process of the Koch curve is shown in Figure 2.  
In this iterative process, line segment \(n_{1}\) is initially divided into three equal segments. Then, the middle line segment is removed and two new line segments are constructed through the two breakpoints, with the angles between the new line segments and the original segment being \(\theta_{1}\) and \(\theta_{2}\), respectively. The two segments intersect with each other to form a new curve \(n_{2}\). The same process is applied to each segment of curve \(n_{2}\) to obtain curve \(n_{3}\). In this fashion the higher-order Koch curve can be obtained. Considering the fact that \(\theta_{1}\) and \(\theta_{2}\) in the figure can be any value, the curve will have different shapes under different values of \(\theta_{1}\) and \(\theta_{2}\). In this study, \(\theta_{1}\) and \(\theta_{2}\) are both set to \(60^{\circ}\) to form the Koch snowflake structure. The iterative process is shown in Figure 3.
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 3 Results
toughness values from Table 1 indicates that with POSS inclusion the resistance against the crack propagation increases up to 5 wt.% POSS inclusion. More than 5 wt.% POSS inclusion shows a decrease in the toughness value which may be caused by the deterioration of the material due to POSS agglomeration. The increase in the fracture toughness with the inclusion of POSS will be explained in terms of the presence of a stiffer silica cage that, well distributed, leads to the tortuous path; hence, more work is required to create more surface. Figure 7 illustrates the crack propagation at lower and higher loading of POSS molecules. At lower loading, the presence of a well-distributed POSS molecule creates a tortuous path for crack propagation, whereas at higher loading the presence of the compliant domain leads to crack blunting at the crack front, which leads to the stable crack growth. Figure 8 showed fractured surface of 1 wt% and 8 wt.% of POSS-epoxy nanocomposites under scanning electron microscopy. No phase separation was observed, indicating good dispersion of POSS. For 1 wt.% POSS inclusion, the toughening phenomena such as crack pinning and crack deflection was observed, indicating the brittle failure. However, extensive tearing and formation of shear lips at the fracture surface were observed in the case of 8 wt.% POSS inclusion suggests the shear yielding phenomena. This tearing of the fractured surface at higher POSS loading is due to the formation of soft domains that leads to plasticization in the nanocomposite. \begin{table}
\begin{tabular}{c c c} \hline \hline
**POSS (wt.\%)** & \(\mathbf{C_{2}}\) & \begin{tabular}{c} **Initiation Fracture** \\ **Toughness (\(\mathbf{J_{IC}}\))** \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} Neat resin \\ 0.5 wt.\% \\ 1 wt.\% \\ 3 wt.\% \\ 5 wt.\% \\ 8 wt.\% \\ \end{tabular} &
\begin{tabular}{c} 0.33 \(\pm\) 0.05 \\ 0.67 \(\pm\) 0.04 \\ 0.73 \(\pm\) 0.06 \\ 0.27 \(\pm\) 0.07 \\ 0.86 \(\pm\) 0.59 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fracture Properties of brittle and ductile POSS-epoxy nanocomposites. Figure 6: Fracture and initiation toughness of brittle POSS–epoxy nanocomposite compared to ductile POSS–epoxy nanocomposite. (**a**) Fracture toughness of neat resin and POSS–epoxy nanocomposites (0–1 wt.% POSS inclusion) in term of critical stress intensity factor. (**b**) Initiation fracture toughness of POSS–epoxy nanocomposites at higher loading of POSS.
## 2 Literature Review
### DL Applications in Lung Disease Diagnosis  
DL, a subfield of artificial intelligence, has emerged as a powerful tool for analyzing medical images and other complex data in lung disease diagnosis. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have shown promise in this field. [PERSON] et al. (2021) [17] provided a comprehensive overview of deep learning-enabled medical computer vision, highlighting its applications in various medical domains, including lung disease. The success of ML models hinges on thorough data assessment, proactive planning for model limitations, active community participation, and the cultivation of trust. [PERSON] et al. (2021) [8] reviewed DL concepts, CNN architectures, challenges, applications, and future directions. This review offers a valuable foundation for understanding the deep learning methodologies used in lung disease diagnosis.  
[PERSON] et al. (2021) [18] used a data mining approach with Artificial Neural Networks (ANNs) to classify lung cancer cases. Through the application of multiple data mining models within RapidMiner, they identified smoking as the principal risk factor for lung cancer. [PERSON] et al. (2024) [6] reported that the implementation of ML and DL in real-world medical settings is nascent and faces significant challenges. These technologies require further development, rigorous validation, and scalable implementation to be effectively utilized in healthcare. [PERSON] et al. (2023) [9] explored how medical image processing and biomedical models enhance the image quality, enabling doctors to better visualize lesion areas. Table 1 illustrates the significant role of ML and DL in improving the accuracy and efficiency of lung disease diagnosis.
# Fully Symmetric Convolutional Network for Effective Image Denoising
## 2 Related Works
Their architecture is a modified CNN structure with rectified linear units and local response normalization, and the sampling rate of all pooling layers was set to one. It is noted that many denoising methods are patch-based algorithms where images are split into patches and denoised patch by patch [13]. Here, the patch size is an important parameter that affects the performance. For example, BM3D [2], Weighted Nuclear Norm Minimization (WNNM) [11], MLP [13], Cascade Shrinkage Fields (CSF) [27], and Denoising Convolutional Neural Network (DnCNN) [28] are some examples of patch-based algorithms. Patch-wise training is common but lacks the efficiency of fully convolutional training. Among many learning-based methods, the DnCNN [28] method has achieved competitive denoising performance. It showed residual learning and batch normalization are particularly useful for the success of denoising. Although CSF [27] shows promising results towards bridging the gap between computational efficiency and denoising quality, its performance is inherently restricted to the specified forms of prior. The parameters are learned by stage-wise greedy training plus joint fine-tuning among all stages. Fully convolutional training is trained in an end-to-end fashion for pixel-wise prediction from supervised pretraining [24]. The fully connected layers are removed or replaced by FCNs so that the network becomes significantly smaller and easier to train compared to many other recent architectures [29]. Semantic segmentation [30] and image restoration [31] adopt fully convolutional inference. [PERSON] et al. [32] used FCN to learn an end-to-end part detector and spatial model for pose estimation. These methods have small models restricting capacity and receptive fields, and require postprocessing such as random field regularization or filtering while others require saturating _tanh_ nonlinearities [30; 31]. Most of the denoising methods proposed so far suffer from a few drawbacks despite the high denoising ability. The denoising process becomes time-consuming if a method involves complex optimization problems in the testing stage which would affect the computational efficiency. Although some methods find optimal parameters in a data-driven manner, they are limited to specific prior models [33]. Moreover, current CNN-based denoising networks are very deep, computationally very expensive to train, and time-consuming. Therefore, we propose a method comprising a deep convolutional architecture with supervised pretraining and fine-tuned fully symmetrical convolution to learn efficiently from the whole image inputs. The key objective is to build a \"fully convolutional\" network which takes in arbitrary-size images and produces corresponding-size images with efficient inference and learning. The proposed work uses a unique FCN architecture for easier training with less time consumption while achieving better performance.
# Dynamics and Stability on a Family of Optimal Fourth-Order Iterative Methods
## 3 The Scaling Theorem  
If the conjugation classes and the scaling theorem are verified for the method under study, it is possible to generalize a dynamical study on the polynomial of the second degree on which this study is performed. In this case, by applying this to a generic polynomial, such as \(p(z)=(z-a)(z-b)\), we can assume that the result obtained will be equivalent to the result that we will obtain with any quadratic polynomial, since there will always be an affine transformation that takes us from this generic polynomial to any quadratic polynomial (see, for example, [11]). We emphasize that this result is only fulfilled in methods that contain derivatives, which is the justification of this study.  
Let \(\hat{\mathbb{C}}\) be the Riemann sphere. Let \(R_{f}:\hat{\mathbb{C}}\to\hat{\mathbb{C}}\) be the fixed-point operator of iterative scheme (11) on a function \(f\), that is,  
\[R_{f}(z)=z-\bigg{(}\frac{\beta}{6}u^{3}+2u^{2}+u+1\bigg{)}\frac{f(z)}{f^{\prime }(z)}, \tag{12}\]  
where  
\[u=\frac{f(y)}{f(z)}\qquad\text{and}\qquad y=z-\frac{f(z)}{f^{\prime}(z)}.\]  
In order to generalize the dynamical analysis of family (11), we prove the following result.  
**Theorem 2** (Scaling Theorem).: _Let \(A(z)=\alpha z+\mu\), \(\alpha\
eq 0\) be an affine mapping. In addition, let \(f(z)\) be a holomorphic function and \(h(z)=\lambda(f\circ A)(z)\) with \(\lambda\
eq 0\); then, the fixed-point operator \(\overline{R}_{f}\) is an affine conjugate to \(\overline{R}_{h}\) by \(A\)  
\[\begin{split}\big{(}A\circ\overline{R_{h}}\big{)}(z)=& A\left(z-\frac{1}{\alpha}\bigg{[}\frac{\beta}{f(A(y))}\bigg{]}^{3}+2 \bigg{(}\frac{f(A(y))}{f(A(z))}\bigg{)}^{2}+\bigg{(}\frac{f(A(y))}{f(A(z))} \bigg{)}+1\bigg{]}\frac{f(A(z))}{f^{\prime}(A(z))}\right)\\ =& A(z)-A\left(\frac{1}{\alpha}\bigg{[}\frac{\beta}{ f(A(y))}\bigg{]}^{3}+2\bigg{(}\frac{f(A(y))}{f(A(z))}\bigg{)}^{2}+\bigg{(} \frac{f(A(y))}{f(A(z))}\bigg{)}+1\bigg{]}\frac{f(A(z))}{f^{\prime}(A(z))} \right)+\mu\\ =& A(z)-\Bigg{[}\frac{\beta}{6}\bigg{(}\frac{f(A(y)) }{f(A(z))}\bigg{)}^{3}+2\bigg{(}\frac{f(A(y))}{f(A(z))}\bigg{)}^{2}+\bigg{(} \frac{f(A(y))}{f(A(z))}\bigg{)}+1\bigg{]}\frac{f(A(z))}{f^{\prime}(A(z))}. \end{split} \tag{15}\]  
From the results obtained in (14) and (15),  
\[\Big{(}\overline{R_{f}}\circ A\Big{)}(z)=\big{(}A\circ\overline{R_{h}}\big{)} (z).\]  
Therefore, the method in (11) satisfies the scaling theorem.  
Now, we analyze the dynamical behavior of this class in terms of parameter \(\beta\) in order to determine the methods with good stability properties and avoid the members of the family whose behavior is unstable.
## 4 Experiments and Discussion
### Field Demonstration  
In this subsection, to verify the performance of presented crack detection model, field demonstration has been carried out. As shown in Figure 8a, the developed crack detector was utilized for finding the surface crack damage regions of a concrete beam, which was undergoing a fatigue experiment using a fatigue machine at Shijiazhuang Tiedao University, China. Obviously, it can be seen that there were many difficult background disturbances in the field demonstration. For example, Figures 8b,c show the two sides of concrete beam, which had iron chains, surface-mounted cable, reinforcement, illumination changes, painting markers, and so on. Figure 8d depicts the crack damages from the concrete bridge deck, and there was litter on the bridge deck. Figures 8e-i illustrate some stress-concentration areas of the concrete beam, and there were some cracks appearing in these areas. One can see that there were three-colored painting markers on the concrete surface (white, black, and red). From the experimental results, we can see that the proposed method addressed most of the background disturbances. It should be noted that there was one limitation for the advocated crack detection method. As shown in the dashed ellipses of Figure 8e, the black painted line was not correctly identified by our presented method. A possible reason is that, compared with other color painting markers, the black painted lines were more similar to the true cracks.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Results
#### 5.4.3 Third Experiment: Aggregation Rate  
Figure 16 shows the aggregation rate of the proposed framework. It indicates the aggregation rate for the walking speed of young adults and for elderly people. It also shows the aggregation rate for bike riding.  
According to previous research, the walking speed of young adults ranges from 1.0416 to 1.508 m/s [22], the walking speed of elderly is about 0.88 to 1.083 m/s [30], and the speed of riding a bike is about 4.166 to 6.944 m/s [31]. The vertical axis represents the aggregation rate in % and the horizontal axis represents the round. The total number of aggregated data is 6 million bytes for every round. The threshold of fog server was set to 0.4 m. This number indicates that the fog server continuously checks whether there is a distance change or not (i.e., if distance increases over the defined threshold \"0.4 m\"). Data will be synchronized only when the change is out of threshold to reduce the amount of data being transferred to the cloud. Results indicate that the proposed prototype is suitable for uploading and aggregating large amounts of data.  
Figure 16: Aggregation rate.
## 3 Results and Discussion
### Optimization Results Using PSO without and with DG integration
#### 3.2.2 Scenario 2: DG Integration at Bus 18 Using the CF Ranking Method  
In this scenario, a DG was installed at bus 18, which was a suitable location, as identified by the CF ranking method presented in Table 1. The voltage profile obtained from the load-flow simulation results using the forward/backward sweep method based on this scenario is presented in Table 4. For the sake of comparison, the results of the voltage profile for the base case scenario are also presented in Table 4. From the voltage profile results obtained, as presented in Table 4, it can be seen that installation of the DG at the identified suitable location (bus 18) significantly improved the voltage profile, and all the bus voltages were within acceptable limits. The minimum voltage magnitude was found to be 0.9843 pu at bus 30, while the maximum voltage magnitude was 1.0200 pu at bus 1. In this case, the power loss obtained stood at 0.3066 MW, which represents a substantial decrease of 70.1% compared to the base case power loss of 1.0268 MW.
## 3 Results and Discussion
### Quantitative Effect of Individual Alloying Element  
The Ms temperature is predominantly influenced by the chemical composition of steels [34; 35]. From the alloying elements, Carbon content is recognized as having the most substantial impact on the Ms temperature, as evidenced by the highest weight coefficients in the formulae presented in Table S1 of Supplementary Materials. C is known to significantly lower the Ms temperature by increasing the stability of austenite, while Mn addition is noted for its effectiveness in inhibiting martensitic transformation. The individual effects of alloying elements on Ms temperature have been acknowledged in previous studies; however, a detailed stepwise analysis of their influence remains unexplored, as well as the exact values of each alloying element [23; 42].  
The incremental quantitative effect of each alloying element on the Ms temperature is determined by utilizing the developed ANN model. We considered steel sample #290, with its chemical composition and experimental Ms temperature detailed in Table 1, i.e., Fe-0.5C-0.73 Mn-0.22 Si-0.83 Cr-0.04 Ni-0.41 Mo-0.15V-0.07 Al and 568 K, respectively. The baseline Ms temperature for pure iron, assuming zero content of all alloying elements, is predicted to be 703.0 K by the ANN model. Subsequently, the corresponding amount of each potential alloying element was incrementally introduced into the ANN model.  
This stepwise addition alters the Ms temperature progressively from the initial value for pure iron, as illustrated in Figure 5. Furthermore, through straightforward calculations, it is determined that the Ms temperature of medium-carbon steel sample #290 decreases by 86.5 degrees with the addition of 0.5 wt.% C. Thus, the quantitative effect of C on the Ms temperature of medium-carbon steel #290 is \(-\)173 K/wt.%, along with the values of other alloying elements, as shown in Figure 5, after a rough calculation. For this case, alloying element V shows a positive effect on the Ms temperature, whereas C, Mn, Ni, Cr, Mo, Si, and Al have a negative impact (in the order of their effect strength). The calculation process is independent of the sequence in which alloying elements are introduced. Moreover, the results vary from one case to another and could not be applied to a universal value across all situations or for all samples. In short, it can provide substantial advancements in the accuracy of materials design and manufacturing.  
Figure 4: Graphical representation of the experimental, ANN-predicted, and calculated Ms values using calculation formulas [8; 9; 10; 11; 13; 15; 18; 19; 22; 23; 25; 26; 29; 38; 39; 40; 41] selected in this study.
## 4 Conclusions  
We proposed a new method to improve the anti-dispersion ability of all-optical orthogonal frequency-division multiplexing (AO-OFDM) systems. By replacing the Sinc-shaped filter with a Gauss-shaped filter to generate a subcarrier, combined with the insertion of CP, the effect of dispersion on the system could be significantly reduced. Compared with the method mentioned in Ref. [7], the proposed scheme could minimize the overhead of the AO-OFDM signal time domain without reducing the duration of the signal in each time period, and at the same time, made the signal achieve better anti-dispersion ability. The simulation results show that, in an all-optical OFDM system with 32 subcarriers and a modulation format of QPSK, BER = 8.545 \(\times\) 10\({}^{-2}\) after 60 km transmission without a Sinc-shaped filter and no CP being inserted, and the Sinc-shaped filter was replaced with a Gauss-shaped filter and an optical CP with a size of 0.33 was inserted into the optical CP; that is, after increasing the subcarrier spacing by 1.5 times, BER = 1.596 \(\times\) 10\({}^{-3}\), and BER decreased by more than 50 times.  
Conceptualization, K.L. and C.Y.; methodology, C.Y.; software, L.F.; validation, X.S., K.L. and C.Y.; formal analysis, X.W.; investigation, X.W.; resources, C.Y.; data curation, A.Z.; writing--original draft preparation, H.L.; writing--review and editing, C.Y.; visualization, K.L.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Two all—optical OFDM systems based on different filters with 16 QAM subcarrier modulation format with the change in BER with transmission distance after inserting CP = 0 and CP with a size of 0.33. Two points (**a,b**) in the figure correspond to the two constellation diagrams in Figure 12.  
Figure 12: In Figure 11, the two points (**a,b**) correspond to these constellation diagrams.
## 3 Implementation of Constellation Dynamic Selection in ARAIM
### Feasibility of Constellation Dynamic Selection  
The feasibility of constellation dynamic selection can be talked about from three aspects. The first is the ARAIM algorithm feasibility. The baseline ARAIM algorithm is based on snapshot principle, which means the integrity evaluation only uses the data of present epoch. There is no correlation between neighbored epochs. It provides an opportunity to select observations from different constellations to assess the integrity. The receiver will process the multi-constellation signals as usual but only the constellations selected will be transited to ARAIM evaluation.  
The second aspect is the number of constellations. The satellite navigation has stepped into a new generation. All the four GNSS systems: GPS, GLONASS, Galileo, and BDS, have been providing global open service. The number of GNSS constellations supports the solution to select particular constellations to pursue high integrity while guaranteeing the positioning accuracy. In other words, it provides new ways to control the integrity risk rather than blindly pursuing the better positioning accuracy for safety-critical applications.  
The third aspect of feasibility is the fact that there is performance difference among above four GNSS constellations. This difference can come from space. GPS is the only constellations which has six orbits. The orbit inclination of GLONASS is far greater than those of the other constellations. BDS accepts the mixed constellation which includes GEO, IGSO, and MEO. The above differences will result in performance differences in different places. Even in the same place, the performance difference is dynamic with the period of satellites, the satellite outages or the number of backup satellites. This difference will be aggravated by the ground characteristics when GNSS is applied to ground applications, typically the GNSS application in urban canyon. Additionally, the satellite or constellation faults will affect the performance of GNSS apparently. So, it is feasible and necessary for a receiver to select better constellations based on observations at the present moment.
## 5 Conclusions  
The alpine environment is very complex and challenging to predict, with a very high spatial variability of the soil moisture and fluxes above the surface as well as the snowpack (on a typical 1-10 m scale). Remote observations of soil moisture and snow property determination at scales compatible with the environment's spatial variability would greatly enhance our ability to understand the physical processes in this type of environment. This would lead to new possibilities, e.g., hydrological modeling, parameterization of atmospheric models, data assimilation in real-time flood and avalanche forecasting systems, _etc_.  
Unfortunately, while different satellite products that would fulfill the large-scale requirements exist, there resolutions are intrinsically not compatible with the small scale and short temporal variations of the alpine environment. On the other hand, while the potential applications of GNSS-R techniques for non-destructive characterization of soil moisture and/or snowpack over large surfaces have been clearly identified, the research is still in its infancy. Indeed, few instruments have been conceived and the investigations were usually limited to simple terrain geometries using GPS signals (single frequency L1 or L2C). Nevertheless, it has been shown that the use of multiple constellation and new signals will have a high positive impact on the resolution and accuracy of GNSS-R measurements. Therefore, we strongly believe that, in the future and as a result of the various research activities that are on-going worldwide, GNSS-R techniques when combined with other techniques such as laser scanning and improved models for the soil parameter extraction will be a very good candidate for filling up the gap on the instrumentation level, thereby opening the use of this technology to applications over more difficult terrain such as the alpine environment.