esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Implementation Analysis
### Experimental Setup and the RIS Configuration Analysis  
We describe the experimental parameters in the following before evaluating the proposed method. As depicted in Figure 4, the experimental setup consists of two universal serial radio peripherals (USRPs) version Ettus X300 and a 1-bit RIS with 64 \(\times\) 64 elements. One USRP serves as the transmitter, positioned 3 m from the RIS, while the other USRP is equipped with two channels with horn antennas and serves as two separate receivers, representing Bob and Eve, positioned 5 m from the RIS and situated at 45\({}^{\circ}\) degrees on either side of the line connecting the RIS and the first USRP. In this experiment, a single antenna is installed on all terminals. The carrier frequency is set to 3.75 GHz, and the sampling rate is configured at 200 KHz for an OFDM system with 256 subcarriers.  
We calculated \(\overline{\text{SNR}}_{i}^{Bob}\) and \(\overline{\text{SNR}}_{i}^{Eve}\) for each configuration matrix \(H_{i}\in HD\), where \(HD\) is the Hadamard codebook of order \(|HD|=64\times 64=4096\) configurations. Figure 5a illustrates the relationship between \(\overline{\text{SNR}}_{i}^{Bob}\) and \(H_{i}\), while Figure 5b presents the relationship between \(\overline{\text{SNR}}_{i}^{Eve}\) and \(H_{i}\), for \(i=1,\ldots,4096\). It can be observed from Figure 5b that some configurations enhance the transmitted signals' received power, while others result in a reduction ranging from \(-3.5\) dB to \(6.5\) dB. We applied Algorithm 1 to the estimated measurements to compute \(C_{i}=\frac{\overline{\text{SNR}}_{i}^{Bob}}{\overline{\text{SNR}}_{i}^{Bob}}\) for each configuration, as shown in Figure 5c. This figure shows that the configurations associated with the top three peaks are good candidates for \(H_{opt}\). Therefore, we maximize the value of \(C_{i}\) to determine \(H_{opt}\).  
In Figure 6, we display the impact of the RIS on the received OFDM symbols at the sides of [PERSON] and [PERSON]. When the RIS is activated using the optimized configuration \(H_{opt}\), it is evident that the received power at [PERSON]'s side is boosted by approximately \(2\) dB compared to the scenario when the RIS is turned off. Additionally, the figure highlights the effectiveness of the RIS in reducing the received power at [PERSON]'s side. This reduced received power at [PERSON]'s side effectively reduces the impact of DoS attacks carried out by [PERSON].  
Figure 4: Experiment setup for the secret key generation scheme.  
Figure 5: The average SNR values for different configurations and their optimized value.
## 3 Results and Discussion
### Spatial Variability of Fresh Phytomass  
The relationship between ground-based measurements of fresh phytomass and EVI calculated from calibrated MCA imagery was used in a first step to produce a map of fresh phytomass of lucerne for the eight plots of the CarboZALF experimental area (Figure 17). The high resolution enables a clear spatial differentiation of areas with extremely low (<250 g\(\cdot\) m\({}^{-2}\)) and high amounts of fresh phytomass (>3000 g\(\cdot\) m\({}^{-2}\)). The spatial patterns across and within the individual plots will be discussed in the context of total C export by harvest. The eye-catching area in the southwest corner of plot 9 with extremely low amounts of fresh phytomass relates to an inundated spot (two months) as a result of high rainfall in spring. This part will be excluded in the further evaluation.  
Figure 16: Relationships obtained between (**a**) NDVI; (**b**) TSAVI; (**c**) TBVI\({}_{\rm b899/b953}\); and (**d**) EVI constructed from VIS bands in combination with NIR band b\({}_{899}\) (except TBVI\({}_{\rm b899/b953}\)) and fresh phytomass of lucerne at the 22 permanent observation sites.
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 3 Proposed Method
### Proposed Reflectance Function  
The reflectance function of the proposed Retinex model aims to find the best basis that guarantees more detailed structures or features. Therefore, the proposed reflectance function should generate the most appropriate basis dictionary from the example image, and there should be a negligible redundancy between each dictionary patch. As mentioned in Section 2.3, CSC is based on an image decomposition into spatially-invariant convolutional features. Compared to the atoms of a dictionary, the learned filters of our CSC scheme (Figure 1c) show a much richer variance (e.g., they span a larger range of orientations), which leads to better reconstructions.  
The components of the reflectance function of the proposed Retinex model are as follows.  
\[\begin{split}\frac{1}{2}||r-\sum_{k=1}^{K}d_{k}*z_{k}||_{2}^{2}+ \beta\sum_{k=1}^{K}||z_{k}||_{1}\\ \text{subject to }\ ||d_{k}||_{2}^{2}\leq 1\quad\forall k\in\{1, \dots,K\}\end{split} \tag{8}\]  
In this paper, CSC was used as given in Equation (8) to construct the reflectance function. Therefore, we learn dictionaries and coefficient maps through CSC. The reflectance is generated through the convolution of the dictionary and coefficient map. When a component of the reflectance function is generated through convolution, various nonlinear shapes of an image can be expressed, including both local and global features. For this reason, the reflectance can be expressed even in a more complex area than the conventional sparse coding method, following which the illumination can be accurately decomposed in the objective function. Learning in CSC takes longer than the previous method, but if we learn the dictionary, we can quickly proceed with the algorithm through the stored dictionary when creating the reflectance function component in the test stage.  
We can reconstruct the reflectance component by using CSC to learn a general dictionary with a large dataset. In sparse coding, each image patch can be sparsely expressed using a linear combination of atoms from a specially selected dictionary. However, in such a linear combination, there is a limit to the number that can be expressed, and since the dictionary patch of sparse coding has high redundancy, this number is further reduced. In CSC, each image patch can be sparse expressed using a convolution of atoms from a specially selected dictionary. The convolution can express more cases than a linear combination; moreover, the redundancy of the CSC dictionary is also lower than the sparse coding, thereby leading to the expression of more cases (Figure 1). Therefore, if we generate a general dictionary sufficiently learned from a large dataset through CSC, an appropriate reflectance component can be reconstructed even in an untrained test image. This is advantageous as there is no need to generate a dictionary every time, when compared to the SCR model that has to generate a dictionary for each image. To do this, we construct a basis dictionary using SVD.
## 3 Methodology
### Definition of Hazardous Road Sections  
The AVD were used to define hazardous sections of real-world roads. Hazardous road sections were determined by whether the autonomous emergency braking (AEB) system, which detects unexpected events based on longitudinal deceleration, was activated. The driving risk index (DRI) was defined as the proportion of events exceeding 0.5 g (4.9 m/s\({}^{2}\)), the maximum deceleration of the AEB function proposed in the international standard ISO-22179, among all events in which the AEB system was activated [19]. A total of 219 sections were divided into sections based on the intersection influence area of 30 m. However, school zones, within which the AD mode is not allowed under current Korean law, were excluded from the analysis; thus, a total of 186 sections were utilized in the analysis. The DRI was computed for each section, and a section was defined as hazardous if its DRI was greater than the average for the entire section; otherwise, the section was considered as normal. The DRI-based hazardous sections and normal sections were the output variables of the DT analysis. The \(DRI\) computation is presented in Equation (1).  
\[DRI^{i}_{AEB}=\frac{\sum HDE^{i}_{AEB}}{n^{i}_{AD}} \tag{1}\]  
where \(DRI^{i}_{AEB}\) is the autonomous driving risk index based on hazardous events in segment \(i\), \(HDE^{i}_{AEB}\) is the number of hazardous events occurring in segment \(i\), and \(n^{i}_{AD}\) is the number of driving events in AD mode for segment \(i\) (every second).
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 9 Conclusions and Future Perspectives in SPME  
An important turning point in analytical chemistry was reached with the use of nanoparticles as fiber coatings in solid-phase microextraction (SPME). The sensitivity, selectivity, and reliability of extraction methods have been significantly enhanced by these cutting-edge materials, which include silica-based composites, metal-organic frameworks (MOFs), nanoporous carbons, and covalent organic frameworks (COFs). In environmental applications, the SPME method is frequently recommended for the extraction of several analytes. Due to their efficiency, NMs are a perfect substitute for traditional materials when it comes to the SPME of several analytes in environmental samples. Environmental sample SPME has made extensive use of nanomaterials (NMs), including polymer-based NMs, nanoparticles, nanofibers, nanoflakes, nanocomposites, nanorods, nanotubes, nanohorns, nanosheets, nanocubes, and nanospheres. Among these NMs' many benefits are their large specific surface area, strong catalytic activity, ease of synthesis, and favorable mechanical, electrical, optical, and physical characteristics [9]. As demonstrated by their capacity to endure numerous extraction cycles without suffering appreciable efficiency loss, nanomaterial-based fibers have exceptional stability, durability, and reusability. The use of novel functionalization methods has increased
## 3 Results and Discussion
The coefficient k values (Table 2) are based on the average of the data collected from different CT scanners. Therefore, the E\({}_{\text{DLP}}\) based on ICRP 60 underestimates the E\({}_{60}\) based on CT-Expo2.6 simulation by 1.5 mSv and the E\({}_{60}\) based on organ dose by 1.87 mSv for the retrospective data studied in the head region. This underestimation was noticed as a result of the uncertainties in estimating the E\({}_{\text{d}}\) based on organ doses. Generally speaking, the uncertainty of the E\({}_{\text{d}}\) values modeled by Monte Carlo calculations can be improved only if the statistical fluctuation of the data or phantom simulated is highly significant; however, uncertainties or errors will exist due to simulated data variations. The relative uncertainty of using organ doses to estimate the effective dose was reported by [PERSON] for a reference patient as \(\pm\)40% [27]. The International Cooperation of Radiological Protection has defined the effective dose as a single parameter to throw back an all-inclusive risk averaged for the overall age and gender for a reference patient [28]. Hence, the CT-Expo2.6 or the DLP-based coefficients are not suitable to estimate the effective dose of a single patient [28]. The results of this study reveal that determining an effective dose using DLP underestimated the effective dose calculated based on organ doses. Different tube voltages were chosen because the effective doses based on DLP were independent of the tube potential and would be the same for all of them. However, \(E_{DLP}\) underestimated the effective dose relative to the \(E_{60}\) calculated using CT-Expo, \(E_{60}\), and \(E_{103}\) by 45-54%, 55-59%, and 77-79%, respectively (Table 3). In the future study, the limitations presented in this work will need to be considered. The retrospective data were collected from different scanners, which showed variations in estimating the effective doses for the most common radiological investigations using coefficient k values and DLP compared to the estimated effective dose based on organ doses. Although the Monte Carlo simulation CT-Expo v2.6 was updated for all new scanners, using the organ dose coefficient, there are still variations in the scanner matching. In conclusion, all estimates of individual organ doses and calculations of the effective dose are subject to the biases inflicted by the assumptions of the phantom model, scanner geometry, beam quality, and exam input variables; therefore, no significant agreement should be expected.
# Flexibility Based Day-Ahead Generation-Reserve Bilevel Decision Model
## 4 Case Studies
### Flexibility Based Generation-Reserve Bilevel Decision Model
When the penetration rate reached 10% or more, the UC scheme of first iteration failed to meet the reserve requirement, and the reserve model in the lower level corrected the flexibility constraint and then re-solved the entire model. To facilitate analysis, Figure 9 only displays the reserve at 2 a.m. under different wind power penetration rates. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Power** & \multirow{2}{*}{**Solution Time**} & **Number of** & **Objective** \\
**Penetration Rates** & & **Iterations** & **Function Value** \\ \hline
0\% & 51\({}^{\prime}\)31\({}^{\prime\prime}\) & 1 & \(\$2.00\times 10^{6}\) \\
5\% & 52\({}^{\prime}\)08\({}^{\prime\prime}\) & 1 & \(\$1.96\times 10^{6}\) \\
10\% & 93\({}^{\prime}\)32\({}^{\prime\prime}\) & 2 & \(\$1.92\times 10^{6}\) \\
15\% & 91\({}^{\prime}\)17\({}^{\prime\prime}\) & 2 & \(\$1.92\times 10^{6}\) \\
20\% & 94\({}^{\prime}\)43\({}^{\prime\prime}\) & 2 & \(\$1.95\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solution information under different wind power penetration rates. Figure 9: Comparison of reserve under different wind power penetration rates. Figure 9 shows that wind power had a significant influence on system flexibility. The flexibility based reserve was positively correlated with the wind power penetration rate. The growth rate of the flexibility based reserve also shows a positively correlation with the wind power penetration rate.
## 4 Results
### Mean Analysis Result by the Factor of SW Competency and Learning Orientation  
The results for each SW competency and learning orientation factor by gender are shown in Table 6.  
The result of a mean analysis by gender in elementary schools showed that the difference in the score of modeling related to algorithm design was the largest in the SW competency area. For male students, the high-achieving group was 88.80 (7.83), and the low-achieving group was 11.00 (7.91), with a difference of 77.80. As for female students, the high-achieving group scored 88.44 (7.69), while the low-achieving group scored 11.61 (7.68), with a difference of 76.83. In short, modeling, as a step to establish a problem-solving procedure before implementing a program based on the analysis, showed the biggest learning gap, regardless of gender.  
In learning orientation, the average of the high-achieving group was higher in all items. For male students, the learning type showing the most difference was data collection  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Male Students**} & \multicolumn{2}{c}{**Female Students**} \\ \cline{3-6}  & & \begin{tabular}{c} **High-** \\ **Achieving** \\ **Group** \\ \end{tabular} & \begin{tabular}{c} **Low-** \\ **Achieving** \\ **Group** \\ \end{tabular} & \begin{tabular}{c} **High-** \\ **Achieving** \\ **Group** \\ \end{tabular} & \begin{tabular}{c} **Low-** \\ **Achieving** \\ **Group** \\ \end{tabular} \\ \cline{3-6}  & & \begin{tabular}{c} **Mean (Std.)** \\ \end{tabular} & \begin{tabular}{c} **Mean (Std.)** \\ \end{tabular} & \begin{tabular}{c} **Mean (Std.)** \\ \end{tabular} \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} SW \\ \end{tabular} } & Analysis & 73.31 (18.98) & 41.60 (20.17) & 72.14 (18.60) & 42.02 (21.07) \\ \cline{2-6}  & Modeling & 88.80 (7.83) & 11.00 (7.91) & 88.44 (7.69) & 11.61 (7.68) \\ \cline{2-6}  & Implementation & 43.96 (27.75) & 17.08 (20.53) & 37.89 (27.88) & 15.49 (19.87) \\ \cline{2-6}  & Generalization & 72.23 (44.83) & 35.91 (48.07) & 69.38 (46.14) & 28.63 (45.30) \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Learning \\ Type \\ \end{tabular} } & 1 & 3.88 (1.02) & 3.5 (1.08) & 4.06 (0.88) & 3.67 (1.09) \\ \cline{2-6}  & 2 & 3.38 (1.11) & 3.05 (1.03) & 3.47 (1.04) & 3.21 (1.09) \\ \cline{2-6}  &  
(LearningType3) at 0.43, followed by dictionary and internet-based surveys (LearningType1) and book-based surveys (LearningType1). In contrast, the difference was the smallest for evaluation (LearningType13) at 0.02.  
Among female students, the difference between the high-achieving group and the low-achieving group was the biggest for thought organization (LearningType10) and data collection (LearningType3) at 0.53 each, followed by devising and proposing a solution (LearningType11) and post-learning evaluation (LearningType12). In contrast, restructuring into figures or tables (LearningType4) showed the smallest difference of 0.15.
## 5 Vibration Experimental Verification of Converter Transformer  
The converter-transformer vibration-measurement-test platform was built as shown in Figure 10. The acceleration sensor was arranged on the surface of the converter-transformer box, and the vibration signal of the converter-transformer box was converted into the voltage signal. The voltage signal was collected by the data-acquisition device, and the data was transmitted to the user through the USB cable.  
In this experiment, a 1A314E piezoelectric acceleration sensor with a sampling frequency of 50 kHz was used. The axial sensitivity was installed on the wall of the converter-transformer box. The data acquisition instrument adopted the AY5922D signal test and analysis system. Its analysis device includes signal conversion, signal amplification, electromagnetic interference and power supply functions. It can store the vibration signal collected by the experiment on the user side, and post-process the signal through the analysis software. The specific process is shown in Figure 11.  
The 1-6 measuring points of the vibration sensor were symmetrically distributed along the center line of the converter-transformer box, as shown in Figure 12. The vibration intensity of the converter-transformer box under the load current of 1470 A was measured, that is, the peak value of vibration acceleration at different measuring points.  
In the Figure 12, 1-6 refers to the position of different measuring points. The vibration acceleration at the same measuring point was obtained by introducing a 1470 A current into the grid-side winding of the simulation test. The experimental and simulation results are shown in Table 5.  
Figure 10: Construction of converter-transformer vibration-test platform.  
The vibration-acceleration deviation between the simulation test and the actual converter transformer-box vibration value is less than 7.6%. Since the vibration under the inrush current cannot be accurately measured in the actual working conditions, this fi  
Figure 11: Converter-transformer vibration-test process.  
Figure 12: Vibration-measurement-point distribution of converter transformer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Point** & **Measured Vibration** & **Model Vibration** & **Deviation** \\ \hline
1 & 1.31 m\(\cdot\)s\({}^{-2}\) & 1.41 m\(\cdot\)s\({}^{-2}\) & 7.6\% \\
2 & 1.12 m\(\cdot\)s\({}^{-2}\) & 1.18 m\(\cdot\)s\({}^{-2}\) & 5.4\% \\
3 & 4.80 m\(\cdot\)s\({}^{-2}\) & 4.92 m\(\cdot\)s\({}^{-2}\) & 2.5\% \\
4 & 2.67 m\(\cdot\)s\({}^{-2}\) & 2.48 m\(\cdot\)s\({}^{-2}\) & 7.1\% \\
5 & 2.00 m\(\cdot\)s\({}^{-2}\) & 1.95 m\(\cdot\)s\({}^{-2}\) & 2.5\% \\
6 & 2.41 m\(\cdot\)s\({}^{-2}\) & 2.23 m\(\cdot\)s\({}^{-2}\) & 7.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vibration-experiment and simulation results.  
nite element simulation model can be used as the main basis for studying the vibration characteristics of the converter transformer under the inrush current.
# Experimental Demonstration of Terahertz-Wave Signal Generation for 6G Communication Systems
## 1 Introduction
### Paper's Contributions  
This paper introduces a novel approach to generating photonic vector THz-wave signals in the THz frequency range. By integrating a Mach-Zehnder modulator (MZM) with a semiconductor optical amplifier (SOA) to create an optical frequency comb, and employing an IQ modulator for signal modulation, we demonstrate the effective generation of QPSK vector THz-wave signals. Our methodology includes simulations and laboratory experiments that showcase the transmission of these signals over long distances.  
In our experimental setup, a 4 Gbps QPSK-modulated baseband signal is applied to an IQ modulator, while a 12.5 GHz radio frequency source drives a Mach-Zehnder modulator (MZM) to generate an optical frequency comb. The optical signals are then mixed in a UTC-PD through heterodyne detection, producing a 0.1 THz radio frequency signal. This THz wave is transmitted over a 35 km length of single-mode fiber. The resulting 0.1 THz electrical vector signal, encoded with QPSK modulation, achieves a bit error rate (BER) below the hard-decision forward error correction (HD-FEC) limit of \(3.8\times 10^{-3}\).  
UTC-PDs, known for their high-speed performance, play a crucial role in our THz signal generation experiments. Their efficiency in signal modulation enhances the overall performance of our THz systems, directly impacting the quality and feasibility of our methods.  
Our innovative approach offers several advantages over existing systems, particularly those using intensity, phase, and polarization modulators. These advantages include:  
**Wider OFC Line Generation:** The nonlinear effects, such as four-wave mixing (FWM) enabled by the SOA, result in a greater number of OFC lines, thereby extending the range of the generated THz signal.  
**Enhanced Signal Quality:** Our system benefits from the optical frequency comb, providing improved clarity and coherence, which enhances overall performance.  
**Flexibility in Modulation**: The IQ modulator allows for superior manipulation of both phase and amplitude, enabling more complex signal encoding compared to traditional modulators.  
**Improved Efficiency**: The combination of the MZM and SOA optimizes power consumption and bandwidth efficiency, which is essential for high-frequency THz applications.  
In summary, our method stands out due to these enhancements, leading to better performance in terms of signal fidelity and operational versatility. This contribution is significant, opening new avenues for sensing and communication applications within the THz frequency range.  
Table 1 offers a concise overview of THz generation techniques, outlining the methods used and their respective frequencies, along with brief remarks on each.
## 4 Results
### Results of FFT Application to Phase Image
#### 4.2.3 Sine Wave Phase Thermal Equalization Trend Analysis  
Figure 16 shows the Line 2 profile graph with the FFT algorithm applied to the phase image shown in Figure 15. In Figure 15, the boundary line of the defective area is converted to a high-frequency form, allowing relatively more thinning defects to be detected, which can be confirmed in the box outlined by the red dash in the graph. As the Gaussian weight increases, the thermal equalization is better applied, but more noise occurs around the defective area, making it difficult to clearly identify the boundary of the defective area. For the Line A thinning defects, it is difficult to identify the boundaries of the defective area due to the noise, regardless of the Gaussian weight. As a result, the number of defects detected in the phase image increased, but a lot of noise was generated, so the effectiveness of the FFT algorithm with the Gaussian filtering was judged to be reduced.