esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FTIR and XPS Results  
Figure 5 showed the FTIR spectra of pure ZnO, pure TOCgel and TOCgel/ZnO composite films. FTIR spectrum of the pure ZnO showed a high intensity broad band around 480 cm\({}^{-1}\) due to the stretching mode of the zinc and oxygen bond [54]. The similar band was also observed in the case of TOCgel/ZnO confirming the presence of ZnO species in the final composite. In addition, FTIR spectrum performed onto the composite indicating any significant change in the characteristic FTIR peak of the TOCgel (C-O-C ether bonds at 1030 cm\({}^{-1}\)) which is clearly similar to those of the pure TOCgel (Figure 5). Thus, the TOCgel backbone was maintained after the hydrothermal synthesis, suggesting that there was some degradation of the TOCgel.  
Moreover, the FTIR spectrum of TOCgel exhibits a peak at 1610 cm\({}^{-1}\) corresponding to the carboxylate groups. The position of this peak appears to be slightly shifted in the former composite film (\(\sim\)1625 cm\({}^{-1}\)), which could be related to the adsorption of carboxylate onto ZnO structures [41,55].  
To further prove the presence of ZnO superstructures incorporated in the TOCgel/ZnO composite film, X-ray photoelectron spectroscopic study has been used. As can be seen in XPS spectra (Figure 6A), XPS survey spectra of the composite shows three main peaks, one peak at 532 eV corresponding to O 1s, one peak at 285 eV corresponding to C 1s, and two peaks at 1020-1045 eV corresponding to Zn 2p. Of course, the presence of Zn element is due to ZnO structures present in the final composite.  
Moreover, high resolution scans of the XPS spectra of Zn 2p (Figure 6B) can be decomposed into two main components centered at 1020.2 and 1045.3 eV which correspond to Zn 2p3/2 and Zn 2p1/2, respectively. According to the literature, these subpeaks of the Zn 2p were found to be related to Zn-O bonding in ZnO [56]. In addition, the peak corresponding to the nitrogen element appears to be very weak (0.17%) which is possibly due to the fact that the obtained composite film may be possibly free of residual HMT and nitrate ions. In addition, the increase in O/C ratio from 0.58 to 0.97 for the TOCgel/ZnO composite can be attributed to the incorporation of ZnO structures (rich in oxygen atoms)  
Figure 4: SEM images of TOCgel/ZnO composite film indicating the alignment of flower-like ZnO superstructures.  
within the fibrous structure of the cellulose. We also observed that the O/C ratio (0.58) is similar to that of the pure TOCgel [57], which confirms that the TOCgel structure did not significantly alter during the hydrothermal synthesis.
## 4 Experimental Demonstration
### Result
#### 4.4.2 Experiment II: To Verify the Performance of the Model on Different Bearings  
The accuracy curves of our models running on the HUST dataset are shown in Figure 11. In these models, the ACN_BM_2 model is a version of the ACN_BM model with the improved ReLU function removed, also known as an ablated model. The ACN_BM_1 model, on the other hand, is an ablated model of ACN_BM that has removed dilated convolutions, dilation rates, and the improved ReLU function. The 1 DCNN_BM model is a variant of the ACN_BM model, where a regular CNN convolution module has replaced the ACN module. The 1 DCNN_BiGRU model has removed the multi-head self-attention mechanism module from the 1 DCNN_BM model.  
The figure shows that the ACN_BM (represented by a red line) converged very quickly, stabilizing by the sixth iteration, and its accuracy had already exceeded 99.8%. When comparing the ACN_BM, ACN_BM_1, and ACN_BM_2 models, we find that the ACN_BM_2 model (represented by a light green dotted line) does not have as high prediction accuracy as the ACN_BM model in the first two iterations of the process, indicating that the improved ReLU function learned more features during training. However, compared to the ACN_BM_1 model (represented by a blue dotted line), the ACN_BM_2 model performed better in the first two iterations, indicating that dilated convolution helped the model capture more short-term features at the beginning of the training.  
Figure 10: Box plot of CWRU test.  
When comparing the ACN_BM model with the 1 DCNN_BM model, we can see that the ACN convolution module we designed had a much higher prediction accuracy in the first two iterations of training than the regular CNN convolution, indicating that the can convolution module is more efficient at learning features. [PERSON] et al.'s [20] 1 DCNN+GRU model also performed well under the influence of a large 64 \(\times\) 1 convolution kernel, but it still lagged slightly behind our model.  
These models all performed well in terms of prediction accuracy on the HUST dataset, and there is not much difference compared with the training results on the CWRU dataset. This also effectively demonstrates that these models maintain a high degree of accuracy in diagnosing different types of bearings.
## 4 Experimental Evaluation
### Evaluating the Proposed Approach
#### 4.4.1 Compression Performance  
Intuitively, different probes should reflect different information about the underlying block data. It should then be possible to use this information to, for example, select the best compression technique for a specific use case. Figure 7 shows the relation between the RLE-, HOMO-, and LZ77-compressed block sizes and the importance calculated by the used probes. The Distinct probe provided an almost linear relationship between the importance and the compressed data size, as seen in the figure. Similarly, the AVGSEQ and Entropy probes had clear connections between their calculated importance and the compressed block sizes.  
Figure 6: The average importance calculation times using up to eight Mean probes on (**a**) cluster A and (**b**) cluster B for the MF data set. The error bars display the standard deviation of the computation times.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \textless{}\(\mathbf{<1\times 10^{-3}}\) & \textless{}0.005 & \textless{}0.01 & \textless{}0.05 & \textless{}0.1 \\ \hline Mean & 71.9 & 98.5 & 100 & 100 & 100 \\ Range & 72.9 & 75.0 & 76.4 & 86.4 & 95.1 \\ SD & 71.9 & 99.4 & 100 & 100 & 100 \\ Distinct & 51.2 & 60.1 & 63.9 & 82.2 & 98.3 \\ Entropy & 50.2 & 56.4 & 61.3 & 85.4 & 99.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the sampled probes. Percentage of all sampled block importance values which absolute error is less than 0.001, 0.005, 0.01, 0.05, and 0.1. Tests were run on cluster A, using the MF data set and time step 80. The AVGSEQ probe is not included, as it is only used on non-sampled data.  
The compressed data size achieved by using RLE compression was generally smaller than when using LZ77. This trend was caused by the entropy of the used data set, which does not contain many repeating sequences of values. Similarly, the effectiveness of the RLE compression is linked to the many homogeneous regions in the investigated data set. However, as seen in Figures 8 and 9, the performance of the various compression methods vary throughout the simulation. The initial data set, at time step 0, contains many homogeneous regions. Consequently, the initial compressed data size is minimal. As the simulation progresses, the homogeneous regions become smaller and disappear, which increases the compressed data size. The total compressed data size remains lower than that of the uncompressed data throughout the simulation. However, for some blocks, the compression time overhead becomes higher than the speedup of data transfers, as seen for the RLE method in Figure 8. This further reinforces our notion of selectively choosing which compression method to use on a per-block basis for each time step.  
Figure 7: The compressed block sizes compared to the calculated importance for the MF data set using the Mean, Range, SD, AVGSEQ, Distinct, and Entropy probes. Tests were conducted on cluster A at time step 80.
## 3 Results
### Multivariate Linear Regression Analysis of Factors Influencing MR Severity Grades  
The multivariate linear regression model was statistically significant in predicting MR [F (17,23) = 3.414, \(p\)-value = 0.003]. The overall model explained a substantial proportion of the variance in MR (R\({}^{2}\) = 0.716, Adjusted R\({}^{2}\) = 0.506). A positive coefficient [Beta, B = 3.253, 95% CI (1.204-5.301), \(p\)-value = 0.003] in systolic RMS B-A indicated a potential predictor of increased MR severity. Similarly, a positive coefficient [B = 5.413, 95% CI (0.227-10.6), \(p\)-value = 0.029] in the diastolic RMS S-L direction suggested its potential as an indicator for increased MR severity. The visual representation of the predictive result is presented in Figure 8.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Spearman’s Rho** & \(p\)**-Value** \\ \hline HDF (mN/mL) & & \\ \hline RMS Systole B-A & 0.621 & \textless{}0.001 ** \\ RMS Systole Ratio & \(-0.543\) & \textless{}0.001 ** \\ RMS Diastole Ratio & \(-0.350\) & 0.025 * \\ AVG HDF B-A & 0.457 & 0.003 ** \\ AVG SystoleS-L & 0.318 & 0.043 * \\ AVG Systole B-A & 0.555 & \textless{}0.001 ** \\ AVG Diastole B-A & 0.317 & 0.043 * \\ \hline LV function & & \\ \hline LVEDV (mL) & 0.554 & 0.003 ** \\ LVEDVI (mL/m\({}^{2}\)) & 0.538 & 0.005 ** \\ LVM (g) & 0.547 & 0.004 ** \\ LVMI (g/m\({}^{2}\)) & 0.602 & 0.001 ** \\ \hline \end{tabular} Notes: Correlation is presented as [PERSON]’s rank order coefficient, no (\(p\)-value). The correlation was significant at 0.05, 0.01 and 0.001 level: \(p\)-value < 0.05 *, \(p\)-value < 0.01 **, \(p\)-value < 0.001 **, respectively (2-tailed). Abbreviations: HDF = hemodynamic force; LV = left ventricle; AVG = average; RMS = root mean square; S-L = septal–lateral; I-A = inferior–antieror; B-A = base-apex; LVEDV = left ventricle end-diastolic volume; LVEDVI = LVEDV indexed; LVM = left ventricle mass; LVMI = LVM indexed.  
\end{table}
Table 6: [PERSON]’s rank order correlation between mitral severity and HDF.  
Our analysis revealed significant correlations between advanced HDF parameters and LV function and MR severity, which had not been previously reported to the best of our knowledge. These findings suggest that this enhanced approach provides a more nuanced understanding of the hemodynamic alterations, which may help in planning surgical time specifically in asymptomatic MR patients. To conclude, HDF may play a vital role in a comprehensive assessment of MR and clinical decision-making.
# Experimental Demonstration of Terahertz-Wave Signal Generation for 6G Communication Systems
## 1 Introduction
### Optical Frequency Comb Generator (OFCG)  
The optical frequency comb (OFC) generation technique has garnered significant interest among researchers due to its various advantages, including the ability to transmit high data rates at a low cost. In optical networks, OFC is commonly used as a multi-wavelength source at the optical line termination (OLT) side, facilitating optical communication systems that utilize optical time division multiplexing (TDM), dense wavelength division multiplexing (DWDM), and quadrature amplitude modulation (QAM) [32; 33; 34; 35; 36; 37]. Additionally, OFC is a compelling area of study because of its relevance in optical wave generation, optical communications, and optical frequency meteorology [38; 39; 40]. For effective use, the generated OFC should feature variable frequency spacing, a high tone-to-noise ratio (TNR), excellent flatness, and the potential to reduce OLT costs by replacing laser sources and other system components [41].  
In recent years, numerous techniques and methods for OFC generation have been proposed [38; 42; 43; 44; 45; 46; 47]. The most commonly employed methods include recirculating frequency shifting (RFS) loops, mode-locked lasers (MLL), and nonlinear effects in highly nonlinear media. The RFS loop technique is noted for its ability to generate a larger number of comb lines; however, its complexity requires additional components, making it less manageable. For example, the structure of the proposed dual RFS loops in [48] is intricate, leading to instability in the generated comb lines and difficulties in maintenance. This technique also exhibits a maximum power deviation exceeding 10 dB between the resulting comb lines in the generated spectrum. The MLL approach allows for high bandwidth in the produced OFCs, but controlling the OFC generator presents challenges. Lastly, the method leveraging nonlinear effects in highly nonlinear media often results in issues with comb line flatness and lacks continuous tunability for the generated spectrum.  
A widely studied and intriguing method for generating OFCs involves optical modulation. This method can be divided into two main components: the first component includes intensity modulators (IMs) [49; 50; 51], parallel or cascaded IMs [41; 52], phase modulators(PMs) [46; 53], and polarization modulators [54; 55]. The second component involves hybrid modulators, such as cascaded combinations of IMs and PMs [56]. This technique is favored for its simplicity and cost-effectiveness, making it a popular choice for generating OFC lines. However, it may sometimes require high driving RF voltages.  
In conclusion, among the various techniques discussed for OFC generation, the most commonly utilized method is based on IMs due to their high quality and flexibility. IMs are typically driven by radio frequency (RF) sources alongside light sources. The RF driving frequency is tunable and can be adjusted to achieve an ultra-flat OFC spectrum.
## 1 Introduction
To improve the solution efficiency and accuracy of the integer ambiguity in fast positioning, a search strategy based on LAMBDA-GWO is proposed in this paper, and the correctness test method of integer ambiguity with prior coordinate accuracy information and TEC as feedback index is introduced into the integer ambiguity solving process. Consequently, the problem of search efficiency of traditional methods and the low accuracy caused by the lack of checking and correction is solved. Finally, experimental verification and comparative analysis are then carried out.
## 3 Results
### Site Assessment
#### 3.3.2 Evaluation of GNSS CORS Sites  
The requirements needed for a GNSS permanent station to fulfil, in order to participate in applications such as crustal motion monitoring, are strict, and the attributes that any agency sets are pretty similar (e.g., visibility, stability, instrumentation) [1]. Amongst others, a reference station is considered stable if coordinate changes do not exceed 10 mm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **CR ID** & **Average RCS (dBm2)** & **Difference 1 (dBm2)** \\ \hline \multirow{2}{*}{SOUN} & 1 & 35.61 & 2.77 \\  & 2 & 35.69 & 2.69 \\ \hline \multirow{2}{*}{MATS} & 1 & 34.24 & 4.14 \\  & 2 & 35.27 & 3.11 \\ \hline \multirow{2}{*}{TROU} & 1 & 34.25 & 4.13 \\  & 2 & 35.17 & 3.21 \\ \hline \multirow{2}{*}{ALEV} & 1 & 34.22 & 4.16 \\  & 2 & 34.19 & 4.19 \\ \hline \multirow{2}{*}{AKMS} & 1 & 34.43 & 3.95 \\  & 2 & 35.33 & 3.05 \\ \hline \multirow{2}{*}{ASGA} & 1 & 34.81 & 3.57 \\  & 2 & 34.72 & 3.66 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The average normalized RCS values derived from each CR in each location.  
Figure 21: The sigma 0 of the CR for the descending pass in the SOUN site. The RCS was calculated at 36.79 dBm2. (**a**) The backscatter coefficient before the CR installation and (**b**) after the CR installation.  
horizontally and 15 mm vertically in 24 h [54]. Moreover, the receiver should track at least ten satellites above the elevation cut off angle of 0 degrees [60].  
An example of satellite availability and horizontal stability is given in Figures 22 and 23, respectively, representing the ALEV GNSS permanent station. At first glance, the evaluation indicates that the GNSS site fulfils the criteria, hence, the goal of the suitability analysis was achieved.
## 4 Analysis of Test Variation Parameters
### Sea Sand Replacement Ratio  
Figure 10 shows the effects of different sea sand replacement rates (0%, 50%, 100%) on the initial rigidity, cracking and peak load, peak deflection, energy dissipation index and ductility index of reinforced sea sand concrete beam specimens.  
As seen in Figure 10A, the initial rigidity of the specimens was 29.56 kN\(\cdot\)mm\({}^{-1}\), 44.41 kN\(\cdot\)mm\({}^{-1}\) and 53.35 kN\(\cdot\)mm\({}^{-1}\) in order with the increase in the sea sand substitution rate, with an overall increasing trend of 50.2% and 20.6%.  
As seen in Figure 10B, the cracking loads of the specimens were 35.6 kN, 20.6 kN and 30.4 kN in order with the increase in the sea sand substitution rate, which decreased by 42.5% and then increased by 47.6%. The peak loads of the specimens were 184.3 kN, 156.6 kN and 161.3 kN in order, which decreased by 15% and then increased by 3% with the increase in sea sand replacement rate, with an overall decreasing trend. The reason for this is that the surface of sea sand is smoother and may contain a certain amount of shell-like fragments compared with river sand. This will have a certain weakening effect on the bond between cement gel and aggregate, which will reduce the ultimate bearing capacity of the specimen.  
As seen from Figure 10C, the peak deflections of the specimens are 12.93 mm, 12.76 mm and 14.67 mm, respectively. The peak deflection of the specimens with 0% and 50% sea sand replacement is similar, with only a 1.3% difference, and the specimens with 100% sea sand replacement have a 14.9% improvement on this basis, with little overall change. As shown in Figure 10D,E, the energy consumption index and ductility index of the specimens decreased and then increased with the increase in the sea sand replacement rate, and the variation ranges were 11.8-21.7% and 6.3-16.2%, respectively.  
In summary, the increase in sea sand replacement rate can improve the initial rigidity of the specimen, but it will reduce the peak load of the specimen. The peak deflection, energy dissipation index and ductility index of the specimens showed a trend of decreasing and then increasing with the increase in the sea sand replacement rate.
# A 28-nm CMOS Low-Power/Low-Voltage 60-GHz LNA for High-Speed Communication
## 3 Integration Technology and Passive Component Design  
The proposed LNA has been designed in a TSMC 28-nm bulk CMOS technology that features a 9-metal back-end-of-line (BEOL) with a top 3.5-\(\upmu\)m ultrathick metal (M9) plus a thick alucap layer (AP) of 2.8 \(\upmu\)m. The process provides low-voltage-threshold (LVT) 0.9-V CMOS transistors that are suited for low-power/high-frequency operation. Moreover, the design kit (DK) also includes accurate models of CMOS transistors based on measurements up to 110 GHz with metal connections up to metal 3. Unfortunately, no inductive components are available, while standard metal-oxide-metal (MOM) interdigitated capacitors have a very poor quality factor (\(Q\)-factor) at mm-wave frequencies. The lack of passive components suited for low-loss LC resonant networks is a big challenge and requires the customized design of both integrated transformers and capacitors.
## 3 Results
### Results of Spectrophotometric Measurements  
Measurements of enzymatic activity of HRP using the reaction with ABTS substrate were carried out for all the samples (control and working), as described in Materials and Methods. Typical time dependencies of light absorbance at 405 nm are shown in Figure 4. From this figure, it is clearly seen that virtually no change in the enzymatic activity of HRP is observed for all the solutions tested.  
Figure 4: Spectrophotometric measurements of enzymatic activity of HRP. Characteristic time dependencies of change in solution absorbance at 405 nm obtained for control HRP sample and for HRP samples incubated in the experimental setup. The measuring cell (containing HRP solution) was placed either far away at a 10-m distance from the polymeric pipe coil (control experiment; black line) or over the coil (red line). Experimental conditions: HRP:ABTS:H\({}_{2}\)O\({}_{2}\) = 10\({}^{-9}\) M:3 mM:2.5 mM. T = 23 \({}^{\circ}\)C.
## 3 Design Approach
### Open Source Network Stack
#### 3.2.3 Contiki OS  
Finally, the board supports Contiki-NG [26], an open-source operating system for next generation IoT devices. The operating system is designed for networked, low power IoT devices. To this end, it supports IPv6, along with standardized IETF low-power wireless standards: 6 LoWPAN, RPL, CoAP, and OMA LWM2M. All of these protocols can be run on top of all support radio platforms, allowing the design of heterogeneous mesh protocols that utilize UWB, sub-GHz, and 2.4 GHz communications in parallel.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 3 Approximated BER without FEC
Using Equation (10), CHF definition, Euler's formula, the contour integrals of sine and cosine function [36], the definition of the gamma function, and Fox's H function given by Equation (1.2) in [37], we obtained the real part of \(\phi_{\alpha_{i}}(w)\) as  
\[ \begin{array}{l}\text{Re}\Big{\{}\phi_{\alpha_{i}}(w)\Big{\}}=\frac{\sqrt{ \pi}}{\Gamma(m_{i})}\frac{1}{2\pi\mathfrak{j}}\int_{\theta-\mathfrak{j}\circ \mathfrak{j}}^{\phi+\mathfrak{j}\circ\mathfrak{j}}\Bigg{(}\Big{(}\frac{w}{2} \Big{)}^{2}\Big{(}\frac{\Omega_{i}}{m_{i}}\Big{)}^{\frac{1}{\gamma_{i}}} \Bigg{)}^{\frac{1}{\gamma_{i}}\Gamma(-s)\Gamma\Big{(}m_{i}+\frac{s}{m_{i}} \Big{)}}ds\\ =\frac{\sqrt{\pi}}{\Gamma(m_{i})}H_{1,2}^{1,1}\Bigg{[}\bigg{(}\Big{(}\frac{w}{2 }\Big{)}^{2}\Big{(}\frac{\Omega_{i}}{m_{i}}\Big{)}^{\frac{1}{\gamma_{i}}} \Bigg{)}\begin{array}{c}(1-m_{i},1/\
u_{i})\\ (0,1),(1/2,1)\end{array}\Bigg{]}\end{array}  \tag{24}\]  
where \(\mathfrak{j}\) is the imaginary unit, \(\varrho\) is a constant chosen in the region of convergence on the complex plane, and \(H_{p,d}^{e,f}\).\(\mathfrak{j}\) is [PERSON]'s H function. Similarly, the imaginary part of \(\phi_{\alpha_{i}}(w)\) is obtained as  
\[\text{Im}\Big{\{}\phi_{\alpha_{i}}(w)\Big{\}}=\frac{\sqrt{\pi}}{\Gamma(m_{i})} H_{1,2}^{1,1}\Bigg{[}\bigg{(}\Big{(}\frac{w}{2}\Big{)}^{2}\bigg{(}\frac{ \Omega_{i}}{m_{i}}\bigg{)}^{\frac{1}{\gamma_{i}}}\Bigg{)} \begin{array}{c}(1- m_{i},1/\
u_{i})\\ (1/2,1),(0,1)\end{array} \Bigg{]} \tag{25}\]  
Substituting Equations (21), (24) and (25) in Equation (20), we obtained a closed-form expression in the form of [PERSON]'s H function for the approximated BER of \(M\)-QAM with EGC diversity over GG fading channels. The BER of CAP was efficiently calculated on the basis of MATHEMATICA implementation for [PERSON]'s H function presented in [38].