esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Results and Discussion under Different Scenarios
#### 5.2.3 Case Study under Weekend Load Profile  
This scenario considers an analysis of the weekend load profile for domestic consumers (Figure 7). From the previous case study, it can be assumed that the proposed method is able to provide VUF, voltage in prescribed limits with the lowest total cost.  
Figure 19a,b shows the active and reactive power schedule obtained using different methods under this scenario, and it can be seen that the proposed method charges and discharges less under peak load hours. The plot of the bus voltage as given by Figure 20a shows that the bus voltage lies well below the prescribed limit with uncoordinated charging. However, the voltage profile is nearest to the prescribed limit with the proposed method for this case study as well. The VUF, as shown by Figure 20b, shows that the VUF lies within limits with the proposed method, for both weekdays and weekends.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**100\% Penetration** & **Weekday** & \begin{tabular}{c} **Uncontrolled** \\ **Charging** \\ \end{tabular} & **Method 1** & **Method 2** & **Method 3** & \
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline \multirow{4}{*}{Winter} & Max VUF (\%) & 2.1 & 2.2 & 4 & 2 & 1.98 \\  & Min Voltage (p.u) & 0.825 & 0.94 & 0.94 & 0.95 & 0.97 \\  & Max Voltage (p.u) & 1 & 1 & 1 & 0.98 & 1.01 \\  & Total Cost (EUR/day) & \(-3.00\) & \(-2.70\) & \(-2.47\) & \(-4.15\) & \(-1.723\) \\ \hline \multirow{4}{*}{Summer} & Max VUF (\%) & 2.1 & 2.2 & 4 & 2.1 & 1.98 \\  & Min Voltage (p.u) & 0.82 & 0.925 & 0.94 & 0.935 & 0.98 \\  & Max Voltage (p.u) & 1 & 1.01 & 1 & 0.99 & 1.02 \\  & Total Cost (EUR/day) & \(-3.015\) & \(-2.9\) & \(-2.4\) & \(-3.61\) & \(-1.99\) \\ \hline \multirow{4}{*}{Spring} & Max VUF (\%) & 2.1 & 2.5 & 5 & 2.1 & 1.95 \\  & Min Voltage (p.u) & 0.85 & 0.925 & 0.94 & 0.925 & 0.975 \\  & Max Voltage (p.u) & 1 & 1.01 & 1 & 0.97 & 1.01 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Total \\ Cost (EUR/day) \\ \end{tabular} } & Total & \(-3.006\) & \(-2.75\) & \(-2.169\) & \(-4.21\) & \(-1.94\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of case study under different price signals.  
Figure 19: Schedule of (**a**) active power for EVs using different methods for weekend electric load profiles and (**b**) reactive power for EVs under different methods for weekend electric load profiles.  
Figure 21a shows that the proposed method is able to provide the lowest total cost for weekdays/weekends for different residential power demands, which can also be inferred from the results in Table 6. Figure 21b shows that the final SOC lies within the prescribed limits for weekends with the application of the proposed method.
## 3 The Proposed Method
### ROI Extraction
Because the value of the PRDV of some interference water regions is far greater than that of other water regions, the pixels with the largest percentage of PRDV are removed from the sampling points first. The global threshold \(th_{PRDV}\) for the interference water extraction are then set by the CFAR method based on the fitted PDF. The area of the interference water in a port is large since the number of man-made buildings and ships is large. Therefore, the ROI of the port can be extracted by finding theinterference water with a large area. However, there are many isolated points in the CFAR detection result because the method only segments the water using a global threshold. To avoid the problem, global thresholding segmentation is carried out on multi-scale images. As shown in Figure 6, we obtain an image pyramid of the map of PRDV by \(m\times m\) downsampling first, where the downsampling is the same as the \(m\times m\) average pooling. Afterward, the suspicious interference water of the image in each scale is detected by global thresholding segmentation using the threshold \(th_{PRDV}\). The detection result of the \(k\)-th scale is the sum of the thresholding segmentation result of the \(k\)-th scale and the detection result of the \((k-1)\)-th scale in a process of \(m\times m\) upsampling, which is computed by the nearest neighbor interpolation method. Finally, the detection result of the image in the primary scale is taken as the result of the final suspicious interference water. The connected region and the corresponding minimum external quadrilateral are extracted from the binary result. If the area of a connected region is larger than \(A_{\text{ROI}}\), its external quadrilateral is identified as an ROI of ports.
## 5 Conclusions  
This study utilized ultrasonic vibrations in single-axis, dual-axis, and triple-axis configurations for ultrasonic-assisted milling experiments on HEAs with different elemental ratios using various process parameter combinations. By varying the process parameters and ultrasonic-assisted milling techniques, the cutting performance of HEAs in terms of surface roughness, cutting force, surface morphology, and cutting tool wear was examined and analyzed. The differences in effectiveness of different ultrasonic-assisted milling techniques were also validated. Based on the above analyses and discussions, the following conclusions were drawn:  
Figure 25: Surface morphology of HEAs with different elemental ratios using the process parameter combinations of the \(\mathrm{L_{9}}\) orthogonal array in the second phase experiment. (**a**) x = 0.1, Set 1. (**b**) x = 1.0, Set 1. (**c**) x = 0.1, Set 3. (**d**) x = 1.0, Set 3. (**e**) x = 0.1, Set 6. (**f**) x = 1.0, Set 6. (**g**) x = 0.1, Set 7. (**h**) x = 1.0, Set 7. (**i**) x = 0.1, Set 8. (**j**) x = 1.0, Set 8.  
The ultrasonic-assisted technique exhibits superior cutting performance in high Mn content alloys, resulting in cutting force reduction and lower cutting tool wear. The cutting force may be decreased by ultrasonic assistance in the HEAs with different Mn contents, but the effectiveness is limited in those with a low Mn content;
2. As compared to the non-assisted milling technique, the surface roughness may be reduced approximately 17.86% by single-axis ultrasonic assistance using the Set 1 process parameters in the first phase experiment, while using the Set 2 process parameters in the first phase experiment, surface roughness and cutting tool wear may be reduced up to 34.4% and 17.68%, respectively;
3. In comparison to low Mn content HEAs, those with a high Mn content have a more moderate variations in surface roughness, and the reductions of surface roughness, cutting force and cutting tool wear may be 22.03% to 314.27%, 3.64% to 54.45%, and 0.58% to 94.77%, respectively;
4. With the increase in the elemental ratio of Mn content, the integrity of the machined surface is notably improved when x = 1.0, resulting in a reduction in surface deformation defects and more consistent cutting marks left on the machined surface, indicating higher stability in machining quality.  
Conceptualization, supervision, writing, S.-Y.L.; methodology, analysis, B.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to a patent in review.  
The authors acknowledge the National Formosa University for their support in this research.  
The authors declare no conflicts of interest.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 4 Results
### Throughput Analysis
#### 4.2.1 Performance According to Frequency and Bandwidth  
When the HBF algorithm was used, Figure 7 shows the best throughput obtained in the 38 and 65 GHz frequency bands by applying the MCSs and BWs indicated in Tables 4 and 5, respectively. In the 38 GHz band, only MCS 19 and MCS 27 were shown, since with MCS 4 and MCS 10, a lower throughput was obtained at all Tx-Rx distances. Applying the MSC 27 scheme and a BW of 400, 200, or 100 MHz, the maximum throughputs achieved were  
Figure 6: Measured SNR at 38 GHz and 65 GHz (dB).  
4.30 Gbit/s up to 2.15 m, 2.15 Gbit/s up to 3.6 m, or 1.07 Gbit/s up to 4.1 m, respectively. Note that a higher BW implied a greater throughput, but shorter distances were achieved.  
The reason for the abovementioned was that the number of subcarriers were higher, resulting in better throughput, but the SNR was lower. For example, the SNR was 6 dB lower with a BW of 400 MHz than with a BW of 100 MHz; see Figure 6. If the MSC 19 scheme was used with a BW of 400 or 200 MHz, the throughput obtained at all distances was 2.97 or 1.48 Gbit/s, respectively. At 100 MHz, the graph associated with the MSC 19 scheme was not shown, since applying MSC 27 already reached all distances with a higher throughput.  
It was also observed that several aggregated bandwidths were better than a single larger one: for example, 4.30 Gbit/s was achieved up to a distance of 2.15 m when applying a BW of 400 MHz, while grouping two BWs of 200 MHz together would achieve the same throughput, but up to a distance of 3.6 m.  
As shown in Figure 7, the massive MIMO HBF architecture gave better results than SISO, increasing both throughputs by a factor of 2 (_Ns_), and the Tx-Rx distance was achieved. For example, with a BW of 100 MHz and an MCS 27 scheme, a distance of 4.1 m was achieved with a throughput of 1.07 Gbit/s, while in the SISO case, 0.53 Gbit/s was achieved up to a distance of 3.6 m.  
Finally, in the 65 GHz frequency band, much lower throughputs were achieved than those discussed for the 38 GHz band. This is because MCS schemes using higher order modulations (64 QAM and 256 QAM) cannot be used because the applied SNR was too low. Figure 7 shows that, up to a distance of 1.56 m, a throughput of 0.34 Gbit/s was achieved by applying a BW of 200 MHz with an MCS 4 scheme, while with a BW of 100 MHz and an MCS 4 scheme, a distance of 2.26 m was achieved with a throughput of 0.17 Gbit/s. This concludes that the SNR was a key parameter for the performance of the system.
## 2 Materials and Methods
### Methods
#### 2.2.3 Density Estimate as a Representation  
A desirable object representation should condense relevant information into a similar form whether the object is spatially small or large. Put more generally, objects should be  
Figure 3: Process diagram with data flow from left to right.  
commensurately represented for an arbitrary count of observations in the measurement space. All density estimates and normalized histograms formally fulfill this requirement and we can use them to represent the object, but as discussed next, practical methods differ in terms of comparability given different amounts of pixels.  
We consider a fixed-dimensional representation \(\mathbf{f}=[p(\mathbf{x}_{1}),\ldots,p(\mathbf{x}_{B^{D}})]\) suitable as an input for any classifier, where the \(\mathbf{x}_{g}\) are center points of elements (bins) in an equally spaced grid \(G\) overlaid on the density's support dimensions (pixel bands), so that \(G\) has \(B\) discretization intervals \(h_{d}\) in each of the \(D\) dimensions, with a total of \(B^{D}\) elements. \(p(\mathbf{x})\) is a probability density that we learn based on the object's pixel collection \(X\) and then evaluate the density at the points \(\mathbf{x}_{g}\) of \(G\) to form the representation. We consider only cases with \(D=2\), where the channels are two SAR polarisations or the two vegetation indices for MSI, so that we can directly model the joint density. For higher-dimensional cases, an alternative approach is to estimate a marginal density for each channel separately and evaluate it along a grid of \(B\) elements, resulting in a representation \(\mathbf{f}_{d}\in\mathbb{R}^{B}\) for each band separately. A combined representation can then be obtained by concatenating these as \(\mathbf{f}=[\mathbf{f}_{1},\ldots,\mathbf{f}_{D}]\).  
The representation can be computed for all density estimators, and next we discuss three practical alternatives and their properties.
## 6 Conclusions  
In this paper, the optimal data-prefetching scheme is derived using the TOPSIS algorithm based on the MADM theory: the DSA strategy. In order to determine the most suitable data prefetching strategy, we considered factors of different type which can influence user's viewing experience, such as playback delay and positioning satisfaction. The experiment demonstrates that the proposed DSA strategy in this paper improves the positioning satisfaction by approximately 60% relative to the API strategy. Moreover, the DSA strategy can ensure that the delay performance is relatively good. In addition, the delay performance increases the fastest with the increase in network bandwidth, compared with the other two strategies, and achieving this strategy requires less extra bandwidth. Therefore, the proposed DSA strategy in this paper has the best performance based on the comprehensive evaluation in these three strategies, and it can enable users to obtain a better viewing experience. Also, this paper has presented a model to determine the most suitable prefetching data strategy based on different factors. The model has good extensibility, and can be extended from two factors to multiple factors. This model can also be applied to the segment selection algorithm to determine the downloading order of different types of segments.  
Due to the limited conditions, the experiments were evaluated by simulations with Java. Hence, some future work is still needed. The performance of the DSA strategy needs to be validated in real experimental environments.
## 4 Methodology and Experimental Results
### Results for Clustering Algorithms
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Performance Results for Expectation Maximization**} \\ \hline
**Feature Set** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline ALL & 57.2625 & 0.691 & 0.559 & 0.618 \\ \hline TOP 5 (IG) & 59.6 & 0.625 & 0.591 & 0.607 \\ \hline Fresh-Knect & **no-55** & 0.784 & 0.545 & 0.648 \\ \hline EFLAW & 55.725 & 0.799 & 0.539 & 0.644 \\ \hline Kin. + EFLAW & 56.7 & 0.788 & 0.546 & 0.645 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results from WEKA in the form of metrics using the Expectation-Maximization algorithm, with class-to-cluster evaluation across different feature sets. In addition to the above tables, we visualized the clusters created (as seen in Figures 8 and 9) to obtain better insights regarding the situation at hand. \begin{table}
\begin{tabular}{l c c c c c} \hline \multicolumn{6}{c}{**Clustering Results for K-Means**} \\ \hline
**Feature Set** & **TP** & **TN** & **FN** & **FP** & **S.S.E.** \\ \hline ALL & 3164 & 1375 & 2625 & 836 & 7688.14 \\ \hline
**TOP 5(IG)** & **2580** & 2139 & **1861** & **1420** & **6463.61** \\ \hline Flesch–Kincaid & 2900 & 1721 & 2279 & 1100 & 43.87 \\ \hline
**HL - AW** & **3117** & **1156** & **2644** & **835** & **93.24** \\ \hline Kin. + EFLAW & 3159 & 1369 & 2631 & 841 & 126.38 \\ \hline \end{tabular}
\end{table} 
Table 12: Results from WEKA using the K-means algorithm, with class-to-cluster evaluation across different feature sets. Figure 8: Visualized clusters from the K-means algorithm. The Y-axis represents the Flesch–Kincaid Grade feature values. Crosses and squares represent complex and simple examples, while colors indicate the different clusters formed. In conclusion, it seems that the clustering approach is very much on par with the classification approach regarding performance. Looking at the results, we find that the increased number of false negatives compared to false positives indicates that the problem lies in the assignment of many complex sentences to the simple cluster. The opposite situation is not as common in our tests.
## 4 Proposed Scheme  
### Key Generation  
The signer generates a key pair \((K_{priv},K_{pub})\) using a standard cryptographic algorithm (e.g., RSA or ECC):  
* \(K_{priv}\): Private key, used to sign messages.
* \(K_{pub}\): Public key, used to verify signatures.  
The keys are generated such that \(K_{pub}\) is derivable from \(K_{priv}\). For RSA, for example:  
\[K_{pub}=(e,n),\ K_{priv}=(d,n) \tag{1}\]  
where \(n\) is the product of two large primes, \(e\) is the public exponent, and \(d\) is the private exponent, calculated as  
\[e\cdot d\equiv 1(mod\ \varphi(n)). \tag{2}\]
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 2 Related Works  
### Traditional Methods  
Traditional data-based methods enhance the visual effect by denoising and contrast adjusting, such as histogram-based [5] and color correction methods [6, 18]. [PERSON] et al. [5] proposed a Relative Global Histogram Stretching (RGHS) based on adaptive parameter acquisition to complete contrast and color correction. [PERSON] et al. [18] fused color-compensated and white-balanced versions of an underwater image with a multi-scale strategy to promote the edges and color contrast of the enhanced image. [PERSON] et al. [6] adjusted colors and details according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy, and then utilized the mean and variance of image patches to adjust the contrast adaptively. Although these methods can enhance the visual quality, they ignore imaging principles and usually cause color deviation, noise, oversaturation or undersaturation.  
Physical-based methods [19, 20, 21, 22] reconstruct images by modeling the degradation problem and using its inverse algorithm to recover images. Some researchers designed a series of underwater image enhancement methods based on the Jaffe-McGlamery imaging model [23, 24]. [PERSON] et al. [19] proposed a model-based restoration method that estimates the depth map of underwater scenes by blurriness and light absorption. [PERSON] et al. [20] estimated backward light and transmission maps by the scene depth map obtained by a linear model, and achieved underwater image restoration. Some researchers have applied dark channel prior (DCP) [25] in underwater image enhancement and obtained better performance [21, 22]. [PERSON] et al. [21] estimated backscatter using dark pixels and known range information for image enhancement. However, DCP is not suitable for red channels of an underwater image due to the severe absorption of red light in water and the color not being corrected well.
## 2 Modeling of PMSG
### Control Scheme of the Machine Side Converter  
The wind turbine generator based on a PMSG had a back-to-back converter, including a machine-side converter and a grid-side converter. Owing to the converter, a PMSG-type wind turbine generator can operate at variable wind speeds and perform the MPPT operation. The MPPT control operates at the tip speed ratio where the power coefficient can operate at its maximum. The power of the MPPT is expressed as  
\[P_{MPPT}=\frac{1}{2}\rho\tau R^{2}\bigg{(}\frac{R\omega}{\lambda_{opt}}\bigg{)} ^{3}c_{p,\ max}=K_{opt}\omega^{3} \tag{6}\]  
where \(c_{p,\ max}\) is the maximum power coefficient and \(\lambda_{opt}\) is the optimal tip speed ratio, which provides the maximum power coefficient. In this study, according to (2), \(c_{p,\ max}\) was 0.48, \(\lambda_{opt}\) was 10.3, and the constant \(K_{opt}\) was 0.6834. The torque of the MPPT is expressed as a quadratic function of \(\omega\).  
The electromagnetic torque of PMSG is given by  
\[\tau_{cmd}=-\frac{3}{2}p_{n}\psi_{f}i_{q} \tag{7}\]  
where \(p_{n}\), \(\psi_{f}\), and \(i_{q}\) are the pole pairs of the PMSG, the flux linkage of the permanent magnet, and the quadrature axis current, respectively. Using (7), the machine-side converter can control the MPPT operation. In this study, the stable operating range of \(\omega\) was from 0.55 pu to 1.0 pu and the stable operating range of \(\tau\) was 0 pu to 1 pu, as shown in Figure 3. When the maximum rotor speed is reached, there is no longer a rotor speed increase in the transition section.
## 3 Research Model and Methodology
### Methodology
#### 3.2.1 Site Selection and Study  
This section of the report aims to provide an overview of the selected signalized and non-signalized roundabouts in Qatar. These roundabouts have been chosen for in-depth analysis to gain perceptions of driver behavior within their respective contexts. The signalized roundabout is positioned in an urban area known as the Al-Asiri Interchange. This intersection serves as a crucial convergence point for Salwa Road and Al-Amiri Street,which is a part of the larger Al Shamal Road network. Figure 4a visually presents the layout of the roundabout, revealing the presence of an underpass in the direction of Al Shamal Road and an overpass leading toward Salwa Road. The roundabout comprises four distinct approaches and allows unrestricted right turns. In terms of its surroundings, the area surrounding the roundabout is primarily characterized by commercial establishments, including restaurants, banks, and a petrol station. The second roundabout chosen for examination is a non-signalized roundabout located in a suburban area at the intersection of Umm al Zubar and Bu Sillia Streets in Gharrafat Ar-Rayyan. Figure 4b provides a visual depiction of this roundabout, highlighting its four approaches. The roundabout is encompassed by a local hospital, a small stadium, and several small shops and residential homes. Both the signalized and non-signalized roundabouts are equipped with essential road markings and yield signs to guide drivers effectively. These markings and signs play a vital role in facilitating safe and efficient traffic flow within the roundabouts.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 2 Superimposed Training System Model and Detection Problem  
In the PU's network it is assumed that a low power training sequence is superimposed (i.e., added) to the data signal at the transmitter, which allows the PUs' receivers to improve the synchronization and channel estimation tasks. In the SU's network the ST information can be exploited to improve the spectrum sensing function. Moreover, the ST technique is suitable to overcome the synchronization requirement that comes with the use of the deterministic signal components of coherent processing, which has been scarcely investigated in the context of CR. For this, it is assumed that the PU shares its training information with the SUs' receivers according to the statutes of a previous cooperation agreement. Indeed, this can be done for profit or to benefit both users in the cognitive radio scenario, as it has been recently contemplated in [14] for different cooperation schemes. The ST PUs' transmitted signal and the spectrum sensing problem formulation at SU's receivers are explained in the following section.