esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Shear Strength  
Figure 3 demonstrates peak shear strength and residual shear strength with different variables. As shown in Figure 3a, with the increase of \(b\) from 0.5 mm to 2 mm, \(\tau_{\rm p}\) decreases by 5.32% and 10.65%, respectively, and \(\tau_{\rm c}\) does not change obviously. Figure 3b illustrates that as \(k\) increases from 20% to 40%, \(\tau_{\rm p}\) diminishes by 12.30% and 19.72%, while \(\tau_{\rm c}\) decreases by 3.13% and 10.10%. Figure 3c shows that as \(\sigma_{\rm n}\) escalates from 1 MPa to 5 MPa, \(\tau_{\rm p}\) grows by 22.4%, 51.18%, and 96.34%, while \(\tau_{\rm c}\) climbs by 54.84%, 116.13%, and 200.81%. The test results indicate that peak shear strength is inversely connected with joint aperture and joint persistency, while being favorably correlated with normal stress. Residual shear strength exhibits a negative correlation with joint persistency and a positive correlation with normal stress.  
The reasons for the influence of each factor on the peak shear strength may be as follows: An augmentation in joint aperture diminishes contact and extrusion between joint surfaces, thereby decreasing friction. An elevation in joint persistency results in a reduction of the rock bridge ratio, facilitating specimen failure. An increase in normal stress causes the particles inside the sample to become more closely packed, which improves shear strength.  
Figure 3: Histograms of peak shear strength and residual shear strength: (**a**) joint aperture; (**b**) joint persistency; (**c**) normal stress.  
Considering that the residual strength is predominantly derived from the friction at the shear surfaces, the impact of joint persistency and normal stress on residual shear strength can be examined from a friction aspect. The shear surfaces can be categorized into the joint section and the rock bridge section. In comparison to the joint portion, the rock bridge section is rougher and offers better frictional resistance. An improvement in joint persistency results in a reduction of the rock bridge proportion on the shear surfaces, hence diminishing the roughness of the shear surfaces and subsequently decreasing friction. Nevertheless, as normal stress escalates, the shear surfaces are compressed more firmly, which augments the frictional force.
## 3 Results  
### Problem Statement  
In real-world applications measurement conditions will likely vary between tests and there will be incomplete object information, i.e., its position (XYZ)/ surface geometry, curvature, and orientation. Although we focus on a 'local' measurement, i.e., inductive measurement performed at the single point (XY) over the object of interest, measurement output depends on all these factors.  
A change in lift-off, i.e., object's position along Z axis, affects the measurement in two distinct ways. Firstly, the strength of the primary field is related to the distance from the coil as described by the Biot-Savart law. The secondary field amplitude is directly proportional to the primary field. Hence, without prior knowledge, a change in lift-off during the measurement may obstruct the proper interpretation of the test outcome. Secondly, magnetic fields diverge away from their source. Divergence length scales are related to the coil geometry, with the field from smaller coils diverging quicker over short distances relative to larger coils. The consequence is the effective integration area of the measurement (resolution) changes with lift-off. The object's geometry will also influence induction with the primary field. The curvature of the object's surface and its orientation relative to the primary field influence the distribution of the primary field across its surface. To adequately perform material identification, the influence these parameters have on the measured secondary field must be understood. This section discusses the problems created by variation in lift-off, in the context of composition identification measurements. We present results for objects that are square plates. Discussions will be extended to different geometries where relevant.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique  
In this section, the proposed dead-time compensation (DTC) method is investigated. First, the DTC for the PWM inverter is performed. Then, the modeling of the proposed DTC technique is introduced.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 5 Experimental Results
### Performance as a Function of the LSG  
Figures 2-5 illustrate the behavior of the four performance metrics as a function of the value of the LSG. In the figures, each data point corresponds to a network topology found by SGTC during the annealing process, and the solid lines represent curves fitting the data points computed using logarithmic regression. The data points that belong to the Pareto front of the solutions that minimize the network diameter and maximize the network robustness and entropy are highlighted in black. For these results, we set the value of the normalized radio ranges \(R_{M}\) and \(R_{S}\) to 0.5 and 0.2, respectively. For the sake of clearness, the figures only present a representative sample of all the results; however, the tendencies shown in the figures hold across all our experiments.  
From Figure 2, we can observe that for all the values of the network size and the two types of MBS placement, the value of the network entropy \(S_{G}\) increases as the value of the LSG increases. This result indicates that networks with larger LSG distribute the shortest paths connecting the stations evenly among all the data links. This is important because this type of topology will promote the even distribution of the data traffic among the whole network, thus offloading traffic from the MBSs to the SCs and reducing the probability of contention hot spots.  
Figure 3: Network diameter \(D_{G}\) with increasing Laplacian Spectral Gap (LSG, \(\lambda_{2}\)).  
Figure 2: Network entropy \(S_{G}\) with increasing Laplacian Spectral Gap (LSG, \(\lambda_{2}\)).  
From Figure 3, we can observe that, in general, the value of the network diameter \(D_{G}\) decreases as the value of the LSG increases, which indicates that the network gets better connected in terms of the length of the network's largest shortest path. This positively impacts the upper bounds of the end-to-end delay and the energy needed to support an end-to-end data flow. Similarly, Figure 4 shows that the network robustness \(R_{G}\), measured as the cardinality of the network's global minimum cut-set, increases as the LSG increases. This result indicates that networks with a larger value of LSG tend to be more robust to link breaks.  
Lastly, Figure 4 shows that the network cost \(C_{G}\) also increases as the LSG increases; however, for the case of networks of size 100 and 150, the rate at which the cost increases is reduced as the value of the LSG becomes larger. We can observe a similar trend for the networks of size 200 but with some exceptions for values of LSG larger than four. It is important to point out that SGTC avoids selecting these high-cost networks by selecting the Pareto efficient solution of minimum cost.
## 4 Results and Discussion
### Rise Time and Overshoot Analysis  
Figures 9-11 illustrate the performance analysis for three conditions on the basis of rise time and overshoot. It depicts the average altitude holding capability of pilots at different altitude instances. To determine the effect of force feedback on operator's performance in terms of achieving the set point  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Degrees of Freedom \(v\)** & **\(F\) Statistic** & \(p\)**-Value** \\ \hline Conditions & 2 & 39.9186 & \(1.1102\times 10^{-16}\) \\ Error & 28,062 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA results.  
Figure 8: Mission analysis.  
(i.e., altitude) we calculated rise time for increasing altitudes from time intervals 35 to 70 s and 70 to 100 s, respectively, as presented in Table 4. We can not use the rise time as a performance parameter from 0 to 30 s because the simulation starts at 1000 ft, and the operator only has to hold that altitude for 30 s.  
Analyzing the rise time from 35 to 70 s and 70 to 100 s intervals, as shown in Figures 10 and 11 and Table 4, we see that the rise time increases as the force getS stronger, this is because an operator has to put in more effort to counter the forces acting on the control surfaces of the UAV by pulling the control stick (having control surface forces), which significantly increased '\(t_{r}\)'. It is actually a good performance sign OF our application because by increasing the rise time we are controlling our system's overshoot.  
Figure 11: Altitude analysis from 1200 to 2000 ft.  
Figure 9: Altitude analysis at 1000 ft.  
If we look at the average of first 30 s time of all the 15 users, it can be seen that users experiencing exaggerated force feedback have a better performance in terms of achieving a close to steady state condition and with the smaller % overshoot, as presented in Table 5. Similarly, from time intervals 35 to 70 s and 70 to 100 s in Figures 10 and 11, we can see that the overshoot is controlled using force feedback conditions. The numerical values presented in Table 5 support this analysis. Hence, we concluded that in the altitude hold task, where users have to increase the altitude and hold it to the specified value (i.e., 1000 ft, 1200 ft and 2000 ft) for 30 s, force feedback conditions help in controlling the overshoot, whether the altitude difference is small or large.
## 4 Results and Evaluation
### Quality Evaluation Experiments
However, given the inherent complexity and variability of terrain, developing a specialized diffusion model that performs well might be challenging. This further highlights the limitations of using diffusion models alone for three-dimensional spatial perception and generation, and the superiority of our method. As shown in Table 4, in the digit dataset, our method's ability to accurately process numerical descriptions of spatial features led to its overwhelming superiority, highlighting the effectiveness of the chain-of-thought behavior tree in handling direct numerical inputs for precise 3D terrain generation. Diffusion models typically fail to process features described numerically, especially when numbers are associated with spatial concepts. In contrast, our method benefits from the complexity of the chain-of-thought behavior tree and the limitations of large language models in processing thought. Direct numerical inputs provided a more reliable standard for the large language models, enabling the generation of 3D terrain models with higher accuracy and fidelity to the textual descriptions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Regular Score** & **Adjusted Score** \\ \hline DreamFusion & 1.67 & 1.72 \\ PureClipNeRF & 2.01 & 1.95 \\ DreamGaussian & 1.68 & 1.74 \\ Ours (without modify) & 4.74 & 4.79 \\ Ours (with 1 step modify) & 4.83 & 4.77 \\ Ours (with 5 steps modify) & 4.81 & 4.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality assessment experiment results on the digit dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Regular Score** & **Adjusted Score** \\ \hline DreamFusion & 2.89 & 2.91 \\ PureClipNeRF & 2.96 & 2.93 \\ DreamGaussian & 2.84 & 2.87 \\ Ours (without modify) & 3.87 & 3.89 \\ Ours (with 1 step modify) & 3.98 & 4.01 \\ Ours (with 5 steps modify) & 4.32 & 4.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality assessment experiment results on the medium dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Regular Score** & **Adjusted Score** \\ \hline DreamFusion & 2.03 & 2.12 \\ PureClipNeRF & 2.59 & 2.43 \\ DreamGaussian & 2.54 & 2.61 \\ Ours (without modify) & 3.53 & 3.57 \\ Ours (with 1 step modify) & 3.75 & 3.82 \\ Ours (with 5 steps modify) & 4.05 & 3.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quality assessment experiment results on the hard dataset. The data presented above further demonstrate the efficacy and utility of our terrain editing approach based on the multiagent update strategy. This method allows for extensive updating of the terrain even when the initial generation results are not optimal, leading to significantly improved experimental outcomes. The adaptability of our approach is such that, without imposing limits on the number of adjustments, our method can generate infinitely refined 3D terrain content under any circumstances.
## 4 Finding Corresponding Points Using the Keypoint Detector and Descriptor  
The keypoint-matching process is shown in Figure 4. As mentioned previously, the cGAN training model was used to obtain \(G\), which generated images that were similar to the original optical images. Afterwards, keypoint matching was performed between the optical and generated optical images.  
Finding corresponding points consists of three major steps: keypoint detection, keypoint description, and keypoint matching. A typical process using SIFT [24], which is a major algorithm in keypoint matching, is outlined below. In the feature point detection step, the DoG (difference of Gaussian) image is used to detect tentative keypoints and the range (scale) is used for feature description. In the subsequent localization step, the sub-pixel positions are estimated by deleting the sub-pixels from the detected tentative keypoints that are not suitable for keypoints. In the feature description step, the direction of each feature point is determined from the bright gradient to obtain rotation-invariant features,  
Figure 4: The keypoint-matching process. The trained \(G\) obtained in Section 2 predicted optical images (generated optical images) from the input of SAR images.  
Figure 3: Structure of the training generator and discriminator model with an edge enhancement filter of the proposed method.  
and 128-dimensional feature vectors are described for each feature point according to the direction and scale. In the final keypoint-matching step, two points in two images with similar feature vectors are extracted as corresponding points.  
During the process of finding the corresponding points, false correspondences were also obtained. These false correspondences might decrease the registration accuracy; therefore, a process was required to remove them. In the case of matching between map-projected images, the difference between the two images could be regarded as scale, rotational, and translational transformation. Hence, the false corresponding points were removed based on the scale value and the gradient direction information of the correspondences [36]. Figure 5 shows the results of the corresponding points after eliminating the false correspondences.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Conclusion 1: The LoE of the Available Studies Comparing SPN vs. IPN Is Very Low (LoE1- and LoE 2-)
#### 4.2.1 IPN with Pharmaceutical Individualization  
The concept that IPN with pharmaceutical individualized is superior to SPN organized by neonatologists is supported by the above-mentioned randomized trial [20] and by one of the observational trials [24]. In a cohort of 140 VLBW infants born between 2000 and 2007, significantly higher daily glucose, AA, lipid intakes and achievement of complete enteral intake in a shorter time were found with IPN together with pharmaceutical individualization [24].
## 3 Results and Discussion
### Interpretation of FTIR Spectra  
The attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy of flower extract samples revealed 20 different bands (Figure 1, Table 3), which were located between the wavenumbers from 3300 to 550 cm\({}^{-1}\). All spectra exhibited a strong double peak from 3300 to 3500 cm\({}^{-1}\), due to N-H stretching, indicating the presence of amines and amides, as well as a broad absorption band between 3200 and 3300 cm\({}^{-1}\), which is assigned to O-H stretching vibrations of alcohols or phenols [37]. The moderate peak at 2920-2940 cm\({}^{-1}\) of C-H stretching exhibited the presence of methyl and methylene groups in carbohydrates or carboxylic acids [38]. The band from 1740 to 1700 cm\({}^{-1}\) is assigned to C=O stretching vibration of carbonyl compounds, such as aldehydes, ketones, esters, carboxylic acids, or carbohydrates and polyphenols [37]. The band from 1640 to 1680 cm\({}^{-1}\) is defined either as the olefin compound absorption band due to carbon double bonds or as the amide band due to C=O stretching, whereas the double peak at 1490-1610 cm\({}^{-1}\) is related to the skeletal vibrations of phenolic aromatic rings [11; 39]. The peak at 1430-1470 cm\({}^{-1}\) is associated with CH\({}_{3}\) and CH\({}_{2}\) bending vibrations and deformation of aliphatic compounds, whereas the peak at 1390-1410 cm\({}^{-1}\) is ascribed to C-C stretching vibration of phenyl groups [11]. The band at 1350-1380 cm\({}^{-1}\) is related to O-H bending vibrations of organic acids, while the existence of a double peak at 1500-1610 and at 1330-1350 cm\({}^{-1}\) is attributed to N=O symmetrical and asymmetric stretching vibration [37]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.
## 4 Simulation Results and Discussions
### Impact of Observation Site Density  
In the case of a coarse-scale grid of 32 \(\times\) 32 (1024 observations), we gradually and randomly reduce the number of observations to obtain nine cases of \(N\) = 1024, 974, 424, 124, 74, 24, 14, 9, 4. Figure 9 depicts the plots of the simulated values of the \(K_{\rm s}\) field against the reference values for these cases with the boundary condition denoted Model A. It can be observed that the values of NRMSE are stable around a small value for all the cases where the \(N\) value of the total number of actual observation points is above 74. That is, when the number of observations is greater than or equal to 7% of the total grid, a very satisfactory agreement between the estimated and true values is obtained, and when the number of observations is less than 7% of the total grid, the gap between the simulated and real values gradually becomes larger. We also conduct the investigation with other two boundary conditions denoted Model B and C, and the corresponding results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Spatial Distribution of \(K_{\rm s}\) Field (NRMSE)**} \\ \cline{2-9}  & \(N\) = 974 & \(N\) = 424 & \(N\) = 124 & \(N\) = 74 & \(N\) = 24 & \(N\) = 14 & \(N\) = 9 & \(N\) = 4 \\ \hline A & 0.0009 & 0.0003 & 0.0007 & 0.0008 & 0.0029 & 0.0094 & 0.0157 & 0.0179 \\ B & 0.0058 & 0.0045 & 0.0029 & 0.0031 & 0.0017 & 0.0037 & 0.0026 & 0.0313 \\ C & 0.0169 & 0.0244 & 0.0445 & 0.0882 & 0.0763 & 0.0105 & 0.0079 & 0.0178 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variation in the NRMSE with the spatial distribution for the simulated \(K_{\rm s}\) and the reference \(K_{\rm s}\).
## 2 Materials and Methods
### Model Validation  
#### 2.4.1 Correlation Analysis  
We used many statistical methods to quantitatively evaluate the applicability of our models, including the relative error (RE), the correlation coefficient r, the root mean square error (RMSE), and the coefficient of determination R\({}^{2}\) between the simulated and measured values. Detailed descriptions of these statistical tests and formulas are found in [PERSON] et al. (1982) [48].
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 4 Experiments and Discussion
### Results and Analysis  
Figure 9 shows the raw detected signal at 300 m and the corresponding signal enhancements by the different methods. Figure 9a is the raw detected signal. Figure 9b shows the noise localization and interpolated signal when using AR. Figure 9c is the results for decorrelation of the signal using KR. Figure 9d shows the results obtained by the proposed method. Figure 9e is the clean signal. It can be seen that the missed detection rate for the KR and AR methods is very high. As the noise length increases, the probability of missed detection becomes higher. On the contrary, the proposed method is able to identify the full range of noise with different amplitudes and lengths. Its noise positioning accuracy is also greater.  
Table 1 shows the SNR and PESQ results for the signals enhanced by the different methods. It can be seen from Table 1 that the signal enhanced by the proposed method has significantly higher scores than the AR and KR methods. When the detection distance is 100 m, the SNR of the speech signal enhanced by the proposed method is 2.064 dB. This is 0.54 dB and 0.42 dB higher than the AR and KR methods, respectively. When the distance is 200 m and 300 m, the SNR of the speech signal enhanced by the proposed method is 0.77 dB and 0.88 dB higher, respectively. This is a more significant improvement than that achieved by the other methods. The PESQ score of the proposed method at 100 m (2.064) is 0.39 points and 0.43 points higher than the score of the AR method (1.768) and KR method (1.726), respectively. This is even more pronounced when the distance is 300 m, with the PESQ for our method being 1.560 points, which was 0.78 points and 0.73 points higher than the AR and KR results, respectively. As speckle noise usually contaminates a relatively  
Figure 9: Laser captured speech signal and signal enhanced by different methods. (**a**) Raw signal; (**b**) AR results; (**c**) KR results; (**d**) proposed method results; (**e**) clean signal.  
small fraction of each overall sample, these experimental results do not seem surprising and are perfectly satisfactory for the removal of speckle noise.