esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Structural Characterization and Material Properties of the Nanofilers
As can be seen from Figure 3b, the spectra have been normalized for their G-band intensity at 1600 cm\({}^{-1}\). This fact implies that the modification by adding ODA on CNWs does not significantly change the graphitization of the material. On the other hand, the spectra are different in terms of the position of the frequency and width of the D-band. This band at CNWs-ox and CNWs-ODA spectra presents a maximum at 1355 cm\({}^{-1}\) and is narrower at the lower frequencies, whereas at the CNWs spectra the D-band has a peak at 1337 cm\({}^{-1}\) and appears to possess a shoulder at \(\sim\)1170 cm\({}^{-1}\). In Figure 3c, the CNWs and CNWs-ODA spectra are decomposed to Gaussian profiles and confirm that the differenced mentioned previously are attributed to the presence, besides the D and G-bands, of another band at \(\sim\)1195 cm\({}^{-1}\)[27]. This band can be assigned to the existence of nanocrystalline phases [28] or to phases rich in sp\({}^{3}\) hybridism [29; 30]. It is quite possible that the oxidation performed at the pristine CNWs resulted at the removal of these phases, while they were weakly bounded with the remaining part of the material, i.e., carbon nanowires. It is also possible to assess the graphitization degree of carbon nanowires from the intensity ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) and the full width at half maximum (FWHM) of the G-band. These parameters for all samples have been estimated and have similar values. The value of the ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) was calculated in the range 0.90-0.91. That proves that the graphitization of all samples remains unaffected through all stages of modification. These values are typical for that kind of structure, such as CMK-x [31], indicating a rather low ordering (bulk graphite has I\({}_{\text{D}}\)/I\({}_{\text{G}}\) = 0.1-0.3), due to the three-dimensional structure of the carbon nanowires. Figure 3: _Cont._  
As seen in the images atomic force microscopy (AFM) images in Figure 4, the formation of carbon nanowires with worm-like morphology is clear. Figure 4: Images from atomic force microscopy (AFM) of the CNWs sample (right) and its topographical profile in (**a**,**b**). Figure 3: (**a**) Raman spectra of CNWs, CNWs-ox and CNWs-ODA samples; (**b**) normalized spectra; and (**c**) spectra analyzed to Gaussian profiles. The images prove the successful synthesis of CNWs with diameters from \(\sim\)2 nm (Figure 4a) up to 5 nm (Figure 4b) and \(\sim\)2 \(\upmu\)m length.
## 4 Indoor Environment Modeling and Optimization of the Received Power
### Constraints  
The positions of the mirrors are limited by the dimensions of the indoor environment:  
\[0\leq x_{ij}\leq L_{x},\quad 0\leq y_{ij}\leq L_{y},\quad 0\leq z_{ij}\leq L_{ z},\quad\forall i,j \tag{26}\]  
Additionally, there may be a limitation on the number of flat and convex mirrors that can be used, respectively:  
\[\sum_{i=1}^{M}\sum_{j=1}^{N}\mathbf{1}_{flat}(x_{ij},y_{ij},z_{ij})\leq N_{flat}, \tag{27}\]  
\[\sum_{i=1}^{M}\sum_{j=1}^{N}\mathbf{1}_{convex}(x_{ij},y_{ij},z_{ij})\leq N_{ convex}. \tag{28}\]  
where \(\mathbf{1}_{flat}\) and \(\mathbf{1}_{convex}\) are indicator functions that define whether the position \((x_{ij},y_{ij},z_{ij})\) corresponds to a flat or convex mirror, respectively. Finally, optimization can be evaluated over a time interval \(t_{0}\leq t\leq t_{f}\), considering environmental variations during that period.
## 2 Design of Ferrite-Loaded AMC
### Ferrite-Loaded AMC Unit Cell Design
\(l\) is the length of the patch, \(g\) is the gap between each metal patch, and \(d\) is the diameter of the via pin. In Figure 3c, the total thickness of the AMC is \(h_{s}\), and the gap between the ground and dielectric substrate is \(h_{air}\). The values of the design parameter are \(l\) = 50.84 mm, \(g\) = 2 mm, \(d\) = 1.6 mm, \(h_{s}\) = 6.6 mm, and \(h_{air}\) = 3 mm. The thickness of the metal patch is 35 \(\upmu\)m. We loaded lumped capacitors of 0.43 nF between the patches for miniaturization. The design of the SML and IML is mainly focused on achieving the zero-reflection phase at the target frequency of 6.78 MHz. The dimension of the unit cell including the size of the FSS is fixed considering the physical volume of the capacitors used in the design. Therefore, the performance optimization is carried out mainly based on \(h_{air}\). This gap is tuned finely using parametric studies such that both array SML- and IML-based unit cells can operate at the target frequency. The simulated reflection phases of the two unit cells using SML and IML are plotted in Figure 4. In both cases, the lumped capacitance and other parameter values were kept the same. Figure 4 shows that the target frequency can be achieved in both cases owing to the additional inductance from the via pin and loaded ferrite cores.
## 3 Results and Discussion  
In this section, the results are displayed in four parts: (1) different flight plans to assess the influence of the angle on the variation of the error; (2) different flight plans to assess the influence of overlap and height on the variation of the error; (3) the selection of optimal parameters; and (4) the case study to validate the recommendations and results. The analysis of the results was made by the relative error presented by each pothole according to its actual characteristics.
###### Abstract  
For an ultra-shallow buried double-arch tunnel with a large cross-section, the arching effect is difficult to form in surrounding rock, and grouting method is often adopted to reinforce the surrounding rock. Hence, examining the grouting reinforcement parameters is of great significance for potential failure and collapse prevention. The land part of Haicang undersea tunnel was selected as a case study; laboratory experiments, theoretical analysis, and numerical simulation were performed to determine the grouting solid strength and grouting reinforcement parameters. The effects of different water-cement ratios on slurry fluidity, setting time, bleeding rate, and sample strength were studied by laboratory experiments. A method was proposed to determine the shear strength parameters of grouted surrounding rock through the grout water-cement ratio and the unconfined compressive strength of the rock mass. Numerical simulations were performed for grouting reinforcement layer thickness and the water-cement ratios. The deformation and stability law of tunnel surrounding rock and its influence on surrounding underground pipelines were obtained considering the spatial effect of tunnel excavation and grouting reinforcement. The reasonable selection range of grouting reinforcement parameters was proposed. The initial setting time and bleeding rate of cement slurry increased with the increasing water-cement ratio, while the viscosity of cement slurry and sample strength decreased with the increasing water-cement ratio. The shear strength parameters of grouted surrounding rock were determined by the water-cement ratio of grout and unconfined compressive strength of rock mass before grouting. When the thickness of grouting reinforcement layer \(h=1.5\) m and the water-cement ratio of grout was suggested \(\eta=0.85\), the surface settlement, the deformation of the vault, and the deformation of the nearby pipeline all met the design. Moreover, the construction requirements were more economical. Research results can provide a reference for the selection of grouting reinforcement parameters for similar projects.  
double-arch tunnel; grouting reinforcement; water cement ratio; reinforcement layer thickness; laboratory experiment; numerical simulation +
Footnote †: journal: Applied Sciences
# Performance of Concrete with Waste Granite Powder: The Effect of Superplasticizers
## 4 Results and Discussion
### Electrical Resistivity (ER)  
Changes in electrical properties of concrete are closely related with its evaporable water content, which varies with water/cement ratio, degree of saturation and hydration. The ER depends on the size of the specimen, while the resistivity is an essentially material-related property, so the latter is more frequently used to characterize concrete in electrical methods [27].  
The ER of the mixes was obtained after 56 and 180 days of water curing and the values are shown in Figures 14 and 15, respectively.  
Figure 14: ER at 56 days.  
Figure 13: Relative \(f_{\rm ctm}\) of the concrete mixes at 28 days.  
Figure 15: ER at 180 days.  
The ER of concrete is linearly proportional to its pores structure. The resistivity of the mixes of series W up to 10% replacement ratio was higher than that of the reference mix. Similarly to [45], the ER of concrete tends to decrease when the replacement ratio increases. In the other two series, the process is significantly different. In series PT, increasing the replacement ratio led to a decrease in ER. Also in this series, the ER is remarkably higher than that of their counterparts in series W due to lower water/binder ratio and the dispersion of cement particles caused by the SP.  
The ER of the mix with 40% WGP and 3 kg/m\({}^{3}\) (0.7% of binder weight) SP is 20% more than that of the reference concrete. Subsequently, using SP and WGP simultaneously not only eliminates the disadvantages of using waste as cement substitute but also improves the electrical properties of concrete mixes along with being eco-friendly.  
The ER of the PF mixes and a few WGP replacement ratios is lower than that of their counterparts in series PT due to reaching the detachment threshold resulting from using too much SP. But a subsequent increase in replacement ratio led to an improvement in the results. The results of series PF showed an upward trend. Finally, the simultaneous effect of reduced water/binder ratio and WGP incorporation can lead to good ER values.  
Figure 15 provides the results of ER at 180 days. The trends at 56 days did not change due to the poor pozzolanic activity of WGP but the ER is significantly higher at 180 days than at 56 days.  
The percentile changes of all mixes compared to the reference concrete at 56 and 180 days are shown in Figure 16. Mix PT0 experienced the greatest increase in ER.  
On the other hand, Figure 16 shows that mixes with SP and WGP have much greater ER than their counterparts in series W as well as the reference mix. Thus, it is clear that the use of SP can offset the disadvantages of WGP incorporation and enhance concrete's electrical properties.
# Genesis of the Baiyangping Cu-Co and Pb-Zn Mineralizations in Lamping Basin, SW China
###### Abstract  
The Oligocene to Miocene Baiyangping Cu-Co and Pb-Zn mineralization was deposited during the collision of the India and Asia continents. The mineralization is hosted in veins in faulted carbonate and elastic rocks, which act as fluid pathways. A detailed fluid-inclusion analysis and C-O-S isotopic study are reported, focusing on the origin of the mineralizing fluids and mineralizing processes. The microthermometry of fluid inclusions hosted in quartz and carbonates indicate that the fluid peak temperature for both Cu-Co and Pb-Zn mineralizations ranges from 160 to 200 \({}^{\circ}\)C and from 120 to 160 \({}^{\circ}\)C, respectively. During these metallogenic hydrothermal events, salinity evolved from 2.7 wt% NaCl equiv to 24.8 wt% NaCl equiv. The \(\delta^{34}\)S values measured on sphalerite and tenannite range from +5.2% to +9.5%, which indicates that sulfur originated from a reservoir characterized by thermochemical sulfate reduction (TSR). The C-O isotopic values obtained from the carbonate samples suggest that the fluid mainly originates from basinal brines. The combination of C and S isotopic values of the Cu-Co and Pb-Zn mineralization indicate the sedimentary source of the basin. Mineralization is controlled by faults, thermochemical sulfate reduction, and the physicochemical conditions of the fluid.  
Baiyangping; base metals; C-O-S isotopes; fluid source and evolution; basinal brines +
Footnote †: journal: Applied Sciences
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 3 Numerical Analysis
### Power Budget  
In the proposed work, PB can be evaluated as [48]:  
\[A(\text{dB})=\alpha L+\alpha_{s}n_{s}+\alpha_{c}n_{c} \tag{30}\]  
where \(\alpha\), \(\alpha_{s}\) and \(\alpha_{c}\) denote the fiber attenuation, splices' attenuation and connectors' attenuation, correspondingly. \(L\), \(n_{s}\) and \(n_{c}\) denote the length of the fiber, number of splices and number of connectors correspondingly.  
The total obtained attenuation, \(A(\text{dB})\) in the system with no splices loss is [48]:  
\[A(\text{dB})=(0.2)50+10=20\text{ dB} \tag{31}\]  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Transmission** & **DN** & & & & & & **UP** & & \\ \hline Distance (km) & 10 & 40 & 70 & 100 & 130 & 10 & 40 & 70 & 100 & 130 \\ Transmitted power (dBm) & 10 & 10 & 10 & 10 & 10 & 0 & 0 & 0 & 0 \\ Received power (dBm) & \(-20\) & \(-24\) & \(-28\) & \(-33\) & \(-38\) & \(-16\) & \(-20\) & \(-23\) & \(-26\) & \(-30\) \\ RS (dBm) & \(-36\) & \(-40\) & \(-44\) & \(-49\) & \(-54\) & \(-33\) & \(-37\) & \(-40\) & \(-44\) & \(-47\) \\ OSNR(dB) & 74 & 69 & 64 & 59 & 54 & 89 & 84 & 79 & 74 & 69 \\ log(BER) & \(-24\) & \(-16\) & \(-12\) & \(-4\) & 1 & \(-29\) & \(-24\) & \(-17\) & \(-11\) & \(-4\) \\ Q-Factor & 11.5 & 9.5 & 8.5 & 3.5 & 0 & 16.5 & 12.5 & 10.5 & 8.5 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Measured RS (dBm), log (BER), OSNR and Q-factor for DS and UP TWDM PON using 2D MNZCC code at 1596 nm wavelength channel operating for varied transmission distance.  
Also, PB is calculated below [48]:  
\[PB(\text{dB})=Transmitted\ Power-RS \tag{32}\]  
For the, DS and UP, the \(PB\) is given as below [48]:  
\[PB_{dn}(\text{dB})=10-(-36)=46\text{ dB} \tag{33}\]  
and  
\[PB_{up}(\text{dB})=0-(-33)=33\text{ dB} \tag{34}\]  
Insertion loss produced due to splitter because of \(n_{splitters}\), number of splitters is measured as [48]:  
\[PL_{splitter}=10\text{ log}\Big{(}1/n_{splitters}\Big{)} \tag{35}\]  
\[PL_{splitter}=10\text{ log}(1/128)=-21\text{ dB} \tag{36}\]  
Thus, splitter power budget (\(SPB\)) with Link Margin, \(L_{m}(dB)=3\) dB is given as [48]:  
\[SPB=PB-A_{total}-L_{m} \tag{37}\]  
For D/S and U/S, the \(SPB_{dn}\) and \(SPB_{up}\) are given in Equations (38) and (39), respectively, as [48]:  
\[SPB_{dn}=PB_{dn}-A_{total}-L_{m}=46-20-3=23\text{ dB} \tag{38}\]  
and  
\[SPB_{up}=PB_{up}-A_{total}-L_{m}=33-20-3=10\text{ dB} \tag{39}\]
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 5 Conclusions  
The manufacturing accuracy of the 90 GHz BPF using the cylindrical resonator designed by the multilayer chip platform has been proven. The machining accuracy in the thickness direction of the cavity can reach 4 \(\upmu\)m. The precision of multi-layer engraving can reach about 10 \(\upmu\)m. The angle of the cavity side-wall deviating from the vertical direction can be controlled at 1\({}^{\circ}\) on average. The measured \(S_{11}\) with press block I was better than 12.4 dB. By bringing the size of the actually fabricated filter into the simulation model and re-simulation, the re-simulation results show that the main factor affecting the filter performance is the device's steepness. This is further confirmed that the solution to the challenges encountered in the machining process proposed in this paper is feasible and has reference value for the improvement of the precision of the SU-8 micromachining process.  
Conceptualization, A.Z. and J.C.; methodology, C.G.; software, M.L.; validation, M.L. and C.G.; formal analysis, investigation, M.L. and C.G.; resources, A.Z. and J.C.; data curation, M.L.; original draft preparation, M.L.; writing--review and editing, M.L., C.G. and Q.Y.; visualization, M.L. and Q.Y.; supervision, M.L., C.G. and Q.Y.; project administration, A.Z., J.C. and C.G.; funding acquisition, A.Z., J.C. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the Natural Science Foundation of China (Grant No. 62001367) and the Shaanxi Provincial Key Laboratory of Intelligent Information Technology for Deep Space Exploration (Grant No. 2021 SYS-04). APC is funded by the Natural Science Foundation of China (Grant No. 62001367).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
# Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
## 3 Methodology
### System Methodology  
A layer-wise approach is used most of the time, as the defects involved in printed components go unnoticed in the sensor-wise approach for fault detection. Current FDM printers lack inbuilt sensors, which is why this study focuses on layer-wise monitoring of printing components for fault detection. Layer-wise monitoring involves capturing layer-wise images of printed components for training, processing, and classification.  
The experimental process flowchart is shown in Figure 1; the study starts with setup formation to capture layer-wise images. In the second stage, an image dataset is created by capturing multiple layer-wise images of the printing component. The prepared image dataset is processed for noise reduction, segmentation, and cropping in the next step. After successfully creating the dataset, a combination of a model algorithm is selected for training and testing. Upon identifying optimal combination, a model is implemented for real-time fault detection.  
Figure 1: Process flowchart.  
Pre-trained models are used only for feature extraction purposes. Training and validation are carried out with different algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, etc.  
Polylactic Acid (PLA) material is selected as the printing material; PLA is a polymer made from corn starch and other organic materials. PLA becomes slightly more liquid and harder during printing than ABS. As a consequence, the prints are typically more informative than Acrylonitrile Butadiene Styrene (ABS) prints. PLA and ABS are almost indistinguishable visually, with PLA being slightly shinier.
## 3 Results
### UV Scaling  
The results obtained from the PCA score plot (Figure 1A) indicated that there was not a clear separation trend between samples of patients with exacerbations and those without exacerbations. This is expected as PCA is an unsupervised method that does not consider the predefined categories or classes. It primarily shows the relationship and patterns among the observations themselves. However, the PLS-DA score plot (Figure 1B) demonstrated a clear separation between the patient samples based on exacerbation status. This is because PLS-DA is a supervised method that incorporates the class information into the analysis, allowing for better discrimination between the groups of interest. In this case, since the categories of exacerbations and non-exacerbations were defined before applying PLS-DA, it was able to effectively differentiate the two groups. To further validate the PLS-DA model, the Permutation Test for UV Scaling was performed (Figure 1C). The validity criteria were evaluated based on the Q2 and R2 values. The Q2 values (blue points, left side of the plot) are lower than the original points (right side), indicating that the model has predictive ability. The blue dashed line of Q2 points intersects the vertical axis (on the left) below zero, further confirming the validity of the model. Additionally, the green R2 values (left side) are lower than the starting point on the right, supporting the robustness of the supervised model. These results demonstrate that the PLS-DA model with UV scaling provides a valid and reliable separation between patients with exacerbations and those without exacerbations, indicating its potential for predicting and distinguishing the two groups based on the given data.
## 3 Results and Discussion
### Application of the Proposed Model to the G-Band Images Dataset
#### 3.7.1 Changes in the Number of Neurons in the Hidden Layer  
In order to improve the obtained accuracies in 3 wide classes for the GID, the number of neurons in the hidden layer of the ANN presented in Figure 12, was varied from 1 to the maximum number of neurons in the default configuration of Weka (70 neurons). The result of accuracy for the varying number of neurons is presented in Figure 14.  
For the phase 1 binary classification, the highest accuracies were 94.71 %, 89.56% and 92.7%, for the groups 1, 2 and 3, respectively. In phase 2, the obtained percentages were 91.71 %, 76.51% y 86.4%, for the same groups. Using this new configuration, the overall accuracy was 82.36% for the whole set. Table 6, summarizes the tested number of neurons in the hidden layer for each classifier, and their corresponding classification results.  
It is worth noting that the accuracy of classification do not show a radical change when using less neurons. For example, in the case of the group 1, when using binary classifiers and varying number of hidden-layer neurons form 1 to 70, the classification results were between 94% and 95%; and for the rest of the groups the observed behavior was similar. This fact is important because if the high classification results persist even using less neurons, the training and evaluation times are reduced, without affecting the functionality of the classifier. Figure 15 shows the mean time for training a classifier. This test was carried out on computer with an Intel Core Duo 2, 4 GB of RAM memory, running Linux Mint operating System. This plot shows only 35 form 70 iterations, because the rest of the tests were done over different computers. The training time increases as more neurons are used, although the classification accuracy results are preserved.  
Figure 13: G-band chromosomes 1 and 2.  
Figure 14: Accuracies obtained for three wide classes when the number of neurons in the hidden layer is varied. (**a**) Phase 1 classification. (**b**) Phase 2 classification.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 3 Imaging in BAV
### Echocardiography  
Echocardiography remains the cornerstone of BAV imaging, offering real-time, dynamic assessment of the valve and surrounding structures. Transthoracic echocardiography (TTE) is typically the first-line imaging tool due to its availability, low cost, and non-invasive nature. It is highly effective in identifying bicuspid morphology, particularly in young patients or when calcification is minimal [46]. TTE can also provide detailed information on valvular function, including stenosis severity (pressure gradients, valve area) and the presence of regurgitation [44].  
Despite its strengths, TTE has limitations in cases of extensive calcification, poor acoustic windows, or challenging patient anatomy. In such instances, transesophageal echocardiography (TEE) is used to provide higher-resolution images of the aortic valve and root. TEE is particularly useful for detecting leaflet fusion, mini-raphe, and eccentric jets of aortic regurgitation [34; 47]. Additionally, TEE has demonstrated utility in surgical planning by allowing precise measurements of the aortic root and proximal ascending aorta [46].  
Echocardiography can also evaluate the functional consequences of BAV, including left ventricular hypertrophy and systolic dysfunction, which may result from chronic pressure or volume overload [47]. Advances in 3D echocardiography and strain imaging have further enhanced its ability to quantify valve anatomy and function. However, for definitive preoperative evaluation or in cases with diagnostic uncertainty, complementary imaging modalities such as CMR or CTA may be required [45].