esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Post-Processing on Properties
#### 3.2.2 Vacuum Pressure Infiltration of Silica Sol Solution and High-Temperature Sintering
The porous structure, especially the closed pores, plays a vital role in ensuring the low thermal conductivity of the sample. Although the porosity of the sample is gradually reduced by several rounds of vacuum pressure infiltration in silica sol solution, the effect of subsequent infiltration is weakened for the decrease of average size of pores and the increase of closed porosity caused by drying after infiltration. The porosity of the sintered sample with three rounds of infiltration in silica sol solution decreased by less than 1%--in other words, it was almost unchanged--and the density, compressive strength, and thermal conductivity increased by 1.80%, 6.37%, and 8.16%, respectively. Compared with the previous two infiltrations, the change of samples in structure, strength, and thermal properties is not significant. Moreover, the compressive strength of 3D graphite/ceramic composite parts after sintering shows an order of magnitude improvement over the 3D graphite parts directly fabricated by SLS (from 1.25 to 13.87 MPa), while the thermal conductivity and density have relatively small change (from 1.40 to 2.12 W-m\({}^{-1}\)K\({}^{-1}\) and 0.53 to 1.13 g-cm\({}^{-3}\), respectively). The fabrication scheme developed in this paper allows the manufacture of 3D graphite/ceramic composite parts with low thermal conductivity but high mechanical properties.
## 3 Results and Discussion
### Thermodynamic Models  
Thermodynamic investigations are also important approaches to describe the spontaneous processes of the Cu\({}^{2+}\) adsorption from aqueous solutions by the AgNPs. Based on the results obtained from studying the effect of the initial Cu\({}^{2+}\) concentration on the Cu\({}^{2+}\) adsorption by the AgNPs at 298 and 323 K, the Gibbs free energy (\(\Delta G^{\circ}\)), the enthalpy (\(\Delta H^{\circ}\)), and the entropy (\(\Delta S^{\circ}\)), were calculated using the following equations [33; 34]:  
\[\Delta G^{\circ}=-RTln(K_{L}\ M_{\mathit{adsorbate}}\times 10^{3}\times 55.5), \tag{10}\]  
\[ln\left(\frac{K_{1}}{K_{2}}\right)=\Delta H^{\circ}{}_{\mathit{ads}}\left( \frac{1}{T_{2}}-\frac{1}{T_{1}}\ \right), \tag{11}\]  
\[\Delta G^{\circ}{}_{\mathit{ads}}=\Delta H^{\circ}{}_{\mathit{ads}}-T\Delta S ^{\circ}{}_{\mathit{ads}}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{4}{c}{**Langmuir Model**} & \multicolumn{4}{c}{**Freundlich Model**} \\ \cline{2-9}  & \(\boldsymbol{q_{\max}}\) **(mg/g)** & \(K_{L}\) **(I/mg)** & \(R_{L}\) & \(R^{2}\) & \(K_{F}\) **(mg/g)** **(I/mg)**\({}^{1/\text{n}}\)** & \(\boldsymbol{n}\) & \(\boldsymbol{R^{2}}\) \\ \hline
298 K & 12.092 & 0.243 & 0.254 & 0.998 & 2.585 & 1.883 & 0.980 \\ \hline
323 K & 9.980 & 0.135 & 0.426 & 0.996 & 1.517 & 1.807 & 0.959 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adsorption isotherm parameters.  
Figure 8: The adsorption isotherm model plots (Langmuir and [PERSON]) for the adsorption of Cu\({}^{2+}\) on AgNPs (adsorbent dosage, 0.2 g; concentration of Cu\({}^{2+}\), 10 and 50 mg/L; pH 7; contact time 3 h, at 298 K).  
where \(M\) is the molecular weight of the adsorbate, \(K\) (L/mg) is the equilibrium constant of the experimental, 55.5 (mol/L) is the concentration of water, \(R\) is the constant of the universal gas (8.314 mol/K), and \(T\) (K) is the solution temperature absolute.  
The values of \(\Delta G^{\circ}\), \(\Delta H^{\circ}\), and \(\Delta S^{\circ}\) are listed in Table 3. The negative values of \(\Delta G^{\circ}\) indicate that the process of Cu\({}^{2+}\) ions adsorption by the AgNPs is natural and spontaneous [35], and because these values ranged from \(-40\) to \(-20\) kJ mol\({}^{-1}\), it suggests that the adsorption is a mixed type (physicochemical adsorption). Generally, the values of \(\Delta G^{\circ}\) of less than \(-20\) kJ mol\({}^{-1}\), more than \(-40\) kJ mol\({}^{-1}\), and between \(-40\) and \(-20\) kJ mol\({}^{-1}\) indicate that the adsorption is physical adsorption, chemical adsorption, and physicochemical adsorption, respectively.  
In addition, the negative values of \(\Delta H^{\circ}\) indicate that the adsorption is exothermic, and the positive values of \(\Delta S^{\circ}\) indicate an increase in the randomness because of the competition of the Cu\({}^{2+}\) ions on the active AgNPs' surface sites [35].
###### Abstract  
The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval's theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through [PERSON]'s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.  
Keywords:discrete wavelet transform; extension neural network; power quality disturbance; power system; Parseval's theorem; renewable energy +
Footnote †: journal: Applied Sciences
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.6 Network Scalability  
The scalability of the RAN architecture is a critical factor in addressing the growing demand for capacity and coverage.  
The virtualization and centralized management capabilities of c-RAN and v-RAN can provide better scalability, allowing the network to adapt more easily to changing demands and the introduction of new technologies.  
The modular and open nature of Open RAN may also enable greater scalability, as the network can evolve with the addition of new components and the integration of emerging technologies.  
In contrast, the d-RAN architecture may face challenges in scalability as the deployment of dedicated hardware at each cell site can limit the network's ability to adapt to changing requirements.  
v-RAN, which builds on c-RAN architecture, further enhances the flexibility and scalability of the network by virtualizing RAN functions and leveraging cloud computing technologies.
## 4 Discussion
It is also worth noting that scaling changes the position of details on the surface of the model, while offset alone only modifies the size by shifting the outline in the perpendicular direction by a given value. Therefore, compensation by offset alone is not a universal method. For this reason, it is recommended to first carefully select the scale and only then set the offset value. In more advanced slicers it is possible to set the scale and offset. In general, the operation is such that first the object is scaled. This is undertaken at the 3D model level. Then, in post processing and after generating layers, pixels are subtracted from the inner and outer contours depending on the offset value. Often, slicers allow you to define the offset for the outer and inner contours separately. However, a difficulty arises in classifying them unambiguously. Internal offsets usually refer only to closed internal contours, which can lead to unexpected results. Hence, the study adopted a single offset value, regardless of the type of geometry. Taking into account the above considerations and the experimental results, the proposed geometry in the form of a square plate with a square hole in the center, equipped with markers in the form of small holes and a cross detail with a small indentation, was found to be suitable for scale calibration and offset. Errors due to shrinkage and compensated by the scale factor decreased from a value of 0.044% to 0.003%. The offset was selected to primarily reduce the negative error of the inner dimension. However, the selection of the offset depends largely on the relevance of specific dimensions, such as in terms of obtaining a proper fit. Previous research has shown that light scattering in the XY plane is not uniform and depends not only on exposure time but also on layer thickness, optical properties of the resin, and irradiation intensity [32]. In the conducted studies, it was also found that contour shifts relative to the nominal position resulted from the combined interaction of shrinkage and light scattering, which varied depending on the model geometry. These findings are consistent with earlier works, including [31], who emphasized that accurate dimensional compensation requires consideration of the spatial distribution of curing energy. Developing predictive models linking geometry with light distribution, shrinkage, and degree of polymerization remains a challenging but crucial direction for future research.
## 4 Performance Evaluation
### Effect on Elephant Flow
#### 4.3.2 Results Analysis  
Figure 12 shows the average ping completion time of ping requests (mice flows) in msec. With RAD, our hybrid proactive-reactive flow rule management performs better, even with the elephant flow. However, the network with the normal SDN controller is severely affected by the elephant flow (e.g., 83.84 times at the 5 th second). With a simple simulation, we conclude that mice flows can be easily influenced by elephant flows and perform better when the elephant flows are reactively routed by the controller.
## 3 Results and Discussion
### Transmission Electron Microscopy (TEM) and X-ray Diffraction Spectroscopy (XRD)  
Figure 6 shows the results of transmission electron microscopy (TEM) for the morphological characterization of the as-synthesized CDs-EDA and CDs-LPh. The average particle size of CDs-EDA and CDs-LPh was estimated to be 2.10 \(\pm\) 0.06 nm for CDs-EDA and 2.00 \(\pm\) 0.06 nm for CDs-LPh, respectively. The shapes are all approximately spherical  
Figure 5: Fluorescence intensity plot of quinine sulfate (QS), CDs-EDA, and CDs-LPh against absorbance. Inset is the value of parameters of equation for the straight-line plot.  
and are monodispersed with both CDs having visible lattice fringes with a lattice space of 0.23 nm according to the TEM results, which indicates crystallinity in the CDs [83]. The similar physical properties observed for the two types of CDs may be due to the precursors' subjection to the same time and temperature of carbonization. The nanomaterials synthesized here in this research have similar shapes, particle size, and excitation/emission wavelengths at 360 nm/430 nm despite utilizing different dopants, and the different dopants may be responsible for the different levels of crystallinity and peak intensities of the CDs [1,65,75,83,84].  
X-ray diffraction (XRD) technique is a fundamental analytical process that helps in studying, determining, and identifying powdered heteroatoms dopped CDs structure, purity, and crystallinity [85,86]. The XRD was based on diffraction angles of 0\({}^{\circ}\)-100\({}^{\circ}\) at a scan rate of 2.00 (\({}^{\circ}\)/min) at room temperature. The XRD data in Figure 7a,b were obtained to ascertain the CDs' crystallinity, nature, and purity. CDs-EDA shows very sharp absorption peaks at 32.4\({}^{\circ}\) and 46.12\({}^{\circ}\). In comparison, CDs-L-Ph peaks are at 32.62\({}^{\circ}\), 46.26\({}^{\circ}\), and 66.78\({}^{\circ}\) at two theta angle measurements for diffraction angles between 32\({}^{\circ}\) and 47\({}^{\circ}\) for CDs-EDA and 30\({}^{\circ}\) to 70\({}^{\circ}\) for CDs-L-Ph, which is an indication of high crystallinity of the CDs samples [86]. Though the two CDs have similar optical properties and size, this difference may be due to the different dopants used in their fabrication.  
Figure 6: TEM images for (**a**) CDs-EDA and (**b**) CDs-L-Ph. Histogram showing particle size distribution for (**c**) CDs- and (**d**) CDs-L-Ph.
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 5 Experimental Results and Discussions
### Performance and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Algorithms** & \multicolumn{3}{c}{**ERLE (dB)**} & \multicolumn{3}{c}{**PESQ**} \\ \hline SER(dB) & **-** & **0** & **5** & **10** & **15** & **0** & **5** & **10** & **15** \\ \hline \multirow{4}{*}{RT\({}_{60}\) = 0.3 s} & Unprocessed & - & - & - & - & 1.85 & 2.12 & 2.46 & 2.70 \\  & PBFDLMS & 13.74 & 13.89 & 15.00 & 14.01 & 2.36 & 2.69 & 2.82 & 3.00 \\  & Singlechn Model & 59.54 & 59.53 & 56.92 & 52.43 & 2.69 & 3.03 & 3.25 & 3.50 \\  & D-format Model & 53.16 & 55.80 & 55.75 & 52.57 & 2.72 & 3.05 & 3.31 & 3.56 \\  & B-format Model & **63.67 Figure 5: Spectrograms processed by different methods with standard loudspeakers layout. (**a**) Microphone signal, \(\text{PESQ}=2.42\), (**b**) clean near-end speech, (**c**) PBFDLMS algorithm, \(\text{ERLE}=15.04\) dB, \(\text{PESQ}=2.74\), (**d**) D-format model-based algorithm, \(\text{ERLE}=41.92\) dB, \(\text{PESQ}=3.01\), (**e**) Singlechn model-based algorithm, \(\text{ERLE}=56.21\) dB, \(\text{PESQ}=2.84\), (**f**) B-format model-based algorithm, \(\text{ERLE}=58.38\) dB, \(\text{PESQ}=2.98\). Figure 6: Spectrograms processed by different methods with nonstandard loudspeakers layout. (**a**) Microphone signal, \(\text{PESQ}=2.45\), (**b**) clean near-end speech, (**c**) PBFDLMS algorithm, \(\text{ERLE}=14.07\) dB, \(\text{PESQ}=2.59\), (**d**) D-format model-based algorithm, \(\text{ERLE}=17.71\) dB, \(\text{PESQ}=2.76\), (**e**) Singlechn model-based algorithm, \(\text{ERLE}=52.56\) dB, \(\text{PESQ}=2.85\), (**f**) B-format model-based algorithm, \(\text{ERLE}=54.73\) dB, \(\text{PESQ}=2.97\).
## 4 Results
With regard to the time requirement per cell, steady solutions had similar time requirements for one iteration. Since the meshing numbers were different, the time requirement was calculated for one iteration per element, which is shown in Figure 19.  
During the first 500 iterations, the average time requirement for one iteration per cell was 3.11816 \(\times\) 10\({}^{-5}\) s for the mixing plane solution, 1.40947 \(\times\) 10\({}^{-5}\) s for the frozen rotor, and 1.18686 \(\times\) 10\({}^{-9}\) s for the sliding mesh method.
## 4 Results
### Seismic Facies Analysis
#### 4.2.1 Facies A: Fill-Type Seismic Facies  
_Description._ Facies A is characterized by low-to-moderate amplitude and low-to-high-continuity reflections, with a fill-type external form (Figure 5A,B). It normally contains a concave-upward base indicated by a V- or U-shaped high-amplitude reflector, against which underlying reflections are truncated. According to the differences in filling characteristics, this facies can be further subdivided into two subfacies: divergent fill-type and complex fill-type. The divergent fill-type subfacies consists of high-continuity reflections that gradually converge and thin from the axis to the sides, resulting in a thickness that is thicker in the middle and thinner on the sides (Figure 5A,B). The complex fill-type subfacies may contain both chaotic to discontinuous reflections and high-continuity divergent reflections (Figure 5C,D).  
_Interpretation._ Facies A is interpreted as topographic depression or channel fills [33,38,39]. The V- or U-shaped concave-upward reflectors are interpreted as channel incisions. Variations in fill patterns may result from the lateral or vertical migration and evolution of the channels [33]. The divergent fills reflect their lateral variations in thickness related to the lateral changes in depositional rate. The vertically successive development of the divergent fills may reflect that the channels are dominated by vertical aggradation with little lateral migration. The complex fills are linked to frequent lateral migration or multiple cut-and-fill processes [33,39,40,41]. The chaotic to discontinuous reflections in the complex fills are interpreted as mass transport deposits, including landslides and clumps [21,42,43], which could be triggered by nearby tectonic uplift and/or the oversteepening of the channel walls caused by turbidity current erosion, while the moderate-to-high-amplitude reflections are linked to the interbedding of coarse-grained turbidites with fine-grained turbidites or the interbedding of turbidites with hemipelagites [39,42,43,44].  
Figure 6: Isochron maps showing the variations in the thickness of individual seismic sequences. (**A**) Sequence S1, (**B**) Sequence S2, (**C**) Sequence S3, (**D**) Sequence S4, (**E**) Sequence S5, and (**F**) Sequence S6.
## 2 Materials and Methods
### Feature Extraction  
The main goal of this work is to compare a set of commonly used kinematic features with newly proposed FD-based features in terms of quantitative PD dysgraphia analysis. All of the handwriting features were computed using both on-surface as well as in-air movements. The two movements were quantified separately using _velocity_ (rate at which the position of the pen changes with time [mm/s]), _acceleration_ (rate at which the velocity of the pen changes with time [mm/s\({}^{2}\)]), _jerk_ (rate at which the acceleration of the pen changes with time [mm/s\({}^{3}\)]), and their horizontal and vertical variants [8; 44; 45]. FD-based features were extracted for different values of \(\alpha\). In the frame of this work, \(\alpha\) ranging from 0.1  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**N** & **Task** & **Czech (Original)** & **English (Translation)** \\ \hline
1 & Archimedean spiral & - & - \\
2 & repetitive loops & - & - \\
3 & letter & 1 & 1 \\
4 & syllable & le & le \\
5 & word & les & forest \\
6 & word & lektorka & lecturer \\
7 & word & porovnat & compare \\
8 & word & nepopadnout & not grasped \\
9 & sentence & Tramvaj dnes uz nepojede. & The tram will no longer go today. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the PaHaW handwriting tasks.  
Figure 2: Filled template of the PaHaW database.  
to 1.0 with a step of 0.1 was used. Subsequently, the statistical properties of the computed handwriting features were described using the mean, median, standard deviation (std), and maximum (max). Finally, all of the extracted features were divided into nine different feature sets according to the type of the movement (on-surface, in-air, and combined) and the calculation approach, i.e., the type of feature (FD-based, conventional, and combined). For more information, see Table 4.
## 1 Introduction
The sensing decision will be sent to each sensing node once the final decision is made at the FC. Distributed CSS can effectively improve the sensing accuracy of the CR system. However, it is at the expense of network burden and system overhead. In consequence, centralized CSS is mainly considered in this paper. Classical centralized CSS contains the data fusion-based schemes and the decision fusion-based schemes. The data fusion-based schemes mainly contain equal gain combining (EGC) [24], selection combining (SC) [25], maximal ratio combining (MRC) [26], etc. The decision fusion-based schemes mainly contain the AND rule [27], the OR rule [28], the N-out-of-K rule [29], etc. The reason why the decision fusion-based schemes are considered in this paper is that the computational complexity of the decision fusion-based scheme is lower than that of the data fusion-based scheme. For the decision fusion-based CSS schemes, under HCRN situations, the N-out-of-K rule is widely accepted due to its superb performance. However, the closed-form expression of its corresponding optimal sensing threshold is unavailable at the FC since the objective function of the sensing performance is non-convex on the sensing threshold [24]. To obtain the closed-form expression of the optimal sensing threshold for the N-out-of-K rule-based CSS scheme under the HCRN situation, this paper is devoted to an approximation evaluation of the false alarm probability and detection probability for N-out-of-K rule-based CSS schemes for the HCRNs within the error range allowed by the system. The proposed approximation scheme balances the sensing accuracy and the sensing complexity, which helps to obtain the closed-form expression of the optimal sensing threshold for the N-out-of-K rule-based CSS scheme under the HCRN situation at the FC. The main contribution of this paper is summarized as follows:  
1. We provide an effective approximation of the N-out-of-K rule for heterogeneous cognitive radio networks and give the closed-form solution of the detection probability and its corresponding false alarm probability. On this basis, the closed-form expression of the optimal sensing threshold for the N-out-of-K rule-based CSS scheme is obtained under the HCRN situation. 2. Theoretical analysis is conducted on the approximation error and it is demonstrated that the approximation error is within the tolerance of the system. 3. We conduct simulation experiments in the Rayleigh fading channel to validate our conclusions proposed in this paper. The rest of this paper is organized as follows. Section 2 reviews the related work of SS. The main contribution of this paper is shown in Section 3, which contains the whole theoretical derivations. Simulation experiments and result analysis are accomplished in Section 4. Finally, Section 5 concludes this paper.
## 4 Simulation Results and Discussion
### Simulation Method and Parameter Design
From a practical perspective, the time taken for the entire handover procedure can be divided into the selection and handover phases: (1) The time taken to complete the selection process: the time taken for the central processing unit (CPU) to calculate and determine a suitable SBS for a handover, and (2) the time taken to complete the handover process: the time taken to complete the handover procedure with the selected SBS. In our simulations, it takes a train about 4.1362 s to pass through an MSBS on average (the trains moved at a speed of about 301.5 km/h, and the path length through each MSBS was about 346.41 m). According to the simulations, the CPU took about 500 ms to complete the selection process; and as highlighted in an aforementioned study [2], the time taken to complete a handover is negligible if the fixed track pre-handover authentication scheme proposed in that study is implemented. In other words, the entire process can be completed in approximately 500 ms, making it a feasible solution when considering that a train stays in the range of an MSBS for about 4.1362 s.
## 4 Results and Discussion
### Simulation Results
Observing the positions of the direct path (\(P_{d}\)), the first surface reflection path (\(P_{s}\)), and the first bottom bounce path (\(P_{b}\)) on the delay axis, in the results generated, we can say that our proposed emulator platform reliably represents the UWAC scenarios. Figure 6: Generated channel samples using our formulated channel emulator platform with the possibility of wideband (20 MHz) and narrowband (10 KHz) acoustic signals travelling through water with high (100 MHz) and low (1 MHz) centre frequencies.