esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Sonar Target Classification Method Based on [PERSON]'s Dictionary Learning
### Active Sonar Target Classification Method Based on Fisher's Dictionary Learning  
The flowchart of the classification method is shown in Figure 1, and the specific steps are as follows.  
(1) We input the active sonar target echo signal under the reverberation background and use the iterative two-dimensional peak search method to determine the optimal order \((P_{1},\ P_{2},\ \cdots,\ P_{i})\) (different types of signals have different orders, \(P\), and there are small differences in the orders obtained from different signals in the same signal).  
(2) To obtain the U-domain value of each signal, we use the optimal order \(P_{i}(i=1,2,\cdots,n)\) to perform an FFFT on the measured signal under the reverberation background.  
(3) We divide the U-domain value of each signal into a training sample set and a test sample set.  
(4) For a comparison with the final classification results, we calculate the accuracy of the classification model by attaching its own initial category label to each type of test sample set.  
(5) We import the test sample sets serially, and calculate different sparse coefficients according to the dictionary of each category and the sparse coding algorithm (each testing signal generates four sparse coefficients).  
(6) The four sparse coefficients generated by each testing signal are reconstructed according to the corresponding category dictionary to reconstruct the reconstructed data (each testing signal generates four reconstructed signals, which are based on the sparse reconstruction of the testing signal based on the four types of sparse dictionaries).  
(7) We calculate the matching degree between each testing signal and the four reconstructed signals it generates, and mark them as \(m_{1}\), \(m_{2}\), \(m_{3}\), and \(m_{4}\).  
(8) We find the reconstructed signal with the highest matching degree by using the testing signal (the maximum of the four matching degrees), and determine that the test signal category is the same as the dictionary category corresponding to the maximum matching degree (\(m_{1}\) corresponds to category 1, \(m_{2}\) corresponds to category 2, \(m_{3}\) corresponds to category 3, and \(m_{4}\) corresponds to category 4).  
(9) After storing the judgment category, we return to Step 3 to enter the loop (the number of cycles is determined by the number of selected order \(P\)), and finally, all of the categories determined under the order \(P\) are obtained.  
(10) We find the category with the largest number of occurrences among all of the results of the judgment category; that is, the final judgment is the category of the current test signal.  
(11) We determine whether the initial category labels of all of the test signals are the same as those of the classification results. If they are the same, it means that the classification is correct, and the correct number is counted to obtain the accuracy rate.
# Antenna on Chip (AoC) Design Using Metasurface and SIW Technologies for THz Wireless Applications
## 3 Comparison with State-of-the-Art AoC  
Table 2 compares the salient features of the proposed on-chip antenna with AoC reported in the literature. The maximum gain of the proposed antenna of 7.4 dBi is lower than [17; 18] but it is comparable to references [11; 19; 20]. Additionally, it has a maximum  
Figure 4: Radiation efficiency as a function of the frequency of the proposed AoC (i) without metamaterial slots, (ii) without metallic vias, and (iii) with metamaterial slots and metallic vias.  
Figure 5: Normalized radiation pattern of the proposed antenna on chip at 0.45 THz in the \(xy\)-plane and \(xz\)-plane.  
Figure 3: Radiation gain as a function of the frequency of the proposed AoC (i) without metamaterial slots, (ii) without metallic vias, and (iii) with metamaterial slots and metallic vias.  
efficiency of 70%, which is lower than [17; 19; 20] but it is comparable to [11]. However, compared with the cited work in Table 2 the proposed antenna operates over a much higher frequency (between 450 and 500 GHz). Moreover, other than the antenna in [21] that operates over a much lower frequency range (50-70 GHz), the proposed antenna is much less thick with respect to the operating wavelength than other on-chip antennas reported to date. This is attributed to combining metasurface and SIW technologies in the implementation of the on-chip antenna. The thinner antenna structure is important to prevent the generation of surface or substrate modes that can adversely affect the antenna's performance, especially at THz frequencies [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Antenna Design**} & \multicolumn{2}{c}{**Fractional Bandwidth**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Eff. (\%)**} & \multicolumn{2}{c}{**Dimensions**} \\  & & **(\%)** & **(Feq. Range (GHz))** & & & **(Physical and Electrical)** \\ \hline
[11
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 4 The Application of CT in Loess Microstructure
### Relationship between Microstructure and Macroscopic Mechanical Behavior of Loess
Significant advancements have been achieved in the field of loess microstructure research through the application of Computerized Tomography (CT) technology, which greatly facilitates the quantitative analysis of loess particle and pore characteristics. Utilizing high-precision \(\mu\)-CT technology combined with 3D reconstruction software, researchers can delve into the microstructural features of the loess microstructure, such as particle size, shape, contact relationships, arrangement, pore morphology, distribution, and connectivity. Such attributes substantially dictate the macroscopic mechanical response of loess, including collapsibility, strength, deformation characteristics, and permeability. Additionally, the combination of CT technology with other micro-analysis methods, such as SEM, mercury intrusion porosimetry, and serial sectioning techniques, provides new perspectives for multi-scale studies of loess microstructure. Future research should further optimize CT image processing techniques, establish more precise quantitative models that link the microstructure to macroscopic mechanical behavior, and consider the anisotropy and heterogeneity of loess, as well as the impact of environmental factors on loess microstructure and macroscopic mechanical behavior. Concurrently, conducting long-term monitoring studies on loess microstructure, in conjunction with numerical simulation techniques to predict the long-term mechanical behavior of loess under various conditions, can provide a more solid scientific foundation for geotechnical engineering and geological disaster prevention in loess regions.
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 2 The Principle and Realization of the Adaptive Equalizer
### Theoretical Analysis and Implementation of Adaptive Equilibrium
#### 2.2.2 Digital Equalization Process  
After pre-equalization, the data path was made to roughly satisfy the DB processing. At this time, the parameters of CTLE are fixed, and the DFE equalization value is used as the initial value of the next stage. In order to avoid error transmission during data transmission, precoding is required for DB-PAM4 data [7]: \(b_{k}=(a_{k}-b_{k-1})\text{mod}4\), bk precoded signal; ak is the original PAM4 signal. The precoded signal still belongs to the PAM4 type signal, and its corresponding z-domain expression is: \(H(z)=\frac{1}{1+z^{-1}}\)  
At this time, although the data path has undergone pre-equalization processing, the quality of the DB-PAM4 signal received at the receiving end is very poor, and a digital equalizer needs to be used for further data recovery. The digital equalization process is shown in the DSP part of Figure 1. The 64-channel parallel data sampled by the ADC is digitally processed by the 16-tap FFE and then enters the 1-tap pre-decision DFE for further equalization. The whole digital equalization part adopts the LMS algorithm for self-adaptive adjustment. The clock frequency of the digital part is 875 M. The specific design of digital equalization is explained in detail in the next chapter.
## 2 Materials and Methods
### Reagents and Antibodies  
The compounds PD098059, LY294002, PP2, lovastatin, simvastatin, and actinomycin D were from Sigma Aldrich (Milan, Italy). MK2206 was from Aurogene (Rome, Italy). All compounds were dissolved in dimethyl sulfoxide (DMSO) vehicle. Unless otherwise stated, PD098059, LY294002, MK2206, and lovastatin were used at 10 \(\upmu\)M concentration. PP2 was used at 20 \(\upmu\)M, simvastatin at 5 \(\upmu\)M, and actinomycin D at 0.5 nM concentration. The following primary antibodies were used: total and phosphorylated Akt1 (Ser473) (MAB-9432 and MAB-94111, Immunological Sciences, Rome, Italy), total and phosphorylated Src (Tyr416) (AB-84358, Immunological Sciences, Rome, Italy; #2101, Cell Signaling Technology, Danvers, MA, USA), total and phosphorylated Cav1 (Tyr14) (AB-81830, Immunological Sciences, Rome, Italy; 611338, BD, Milan, Italy), total and phosphorylated Erk1/2 (Thr202/Tyr204) (sc-135900 and sc-7383, Santa Cruz Biotechnology, Dallas, TX, USA), catalase (sc-271803, Santa Cruz Biotechnology, Dallas, TX, USA), phosphorylated histone-2 AX (H2 AX) (Ser139) (#9718, Cell Signaling Technology, Danvers, MA, USA), and \(\upalpha\)-tubulin (T5168, Sigma Aldrich, Milan, Italy). Secondary anti-mouse (IS20400) and anti-rabbit (IS20402) antibodies were from Immunological Sciences (Rome, Italy).
## 2 Results  
In this work, for the first time, emissions of VOCs from CRDF was studied qualitatively and quantitatively. Qualitative analysis consisted of identifying compounds based on MS spectral database and available literature (Kovats Retention C7-40 Index). Table 1 shows the VOCs emitted from the analyzed CRDF with the GC column retention time and the coefficient both in the literature and with the GC software presented in the database (Kovats Retention C7-40 Index). Also included was the internal standard (2-undecanone) added during analyzes (compound #80). The 84 VOCs (without internal standard) have been identified. These compounds belong to various groups such as alcohols (e.g., pentanol), aldehydes, (e.g., nonanal, octanal, heptanal, hexanal, furfural), ketones (e.g., heptanone), aromatic compounds (including toluene and benzene derivatives), polycyclic aromatic hydrocarbons (PAHs; including naphthalene derivatives considered toxic), acids (e.g., acetic, benzoic), alkenes (e.g., styrene), phenols and a large group of heterocyclic compounds (including pyridine and pyrazine with derivatives). The largest (by number) group were derivatives of benzene and naphthalene (e.g., tetralin). The highest density of peak elution of VOCs from the chromatographic column occurred between 7 to 12 min (Table 1). Most of the identified compounds had boiling points between 100 and 240 \({}^{\circ}\)C; i.e., the typical range of VOCs [16]. One compound was classified as very volatile (VVOCs) and one as a semi-VOC (Table 2). Among the identified compounds, many have been known to have a negative impact on human health and the natural environment, including mutagenic and carcinogenic aromatic compounds, e.g., toluene, benzene, ethylbenzene or cumene, and PAH, e.g., naphthalene. The total mass of VOCs emitted from CRDF was 16.4 mg/kg (Table 2) based on 7 days of accumulation in the headspace of a sealed storage vessel. The top 10 compounds with the highest emissions were as follows: nonanal, octanal, heptanal, butylbenzene, hexanal, 1-methyl-4-prop-1-en-2-ylcyclohexene, benzaldehyde, decanal, toluene, and hexylbenzene. Among the analyzed compounds, the highest emission (as a group) from the CRDF was determined for aldehydes: nonanal, followed by octanal, and heptanal (Table 2). The top 10 of the most emitted VOCs consisted almost 65% of total emissions.
## 3 Application of Uncertainty Quantification Methods and Results
### Application Outline
#### 3.1.4 Post-Processing  
Post-processing precipitation bias correction methods, i.e., the gauge-to-radar (G/R) ratio method of [34] and the local gauge correction (LGC) method of [35], were performed to correct the bias in radar precipitation estimated by the QPE model. The fundamental concept of the G/R method is that the bias correction (G/R ratio) factor, also known as the mean-field bias, is computed as the ratio of the spatial average (mean) between the estimated radar precipitation and observed precipitation in a corresponding area (or point, pixel). Then, the radar-based precipitation estimate multiplied by the G/R ratio factor was calculated to produce corrected precipitation estimates. In this study, radar precipitation estimates tended to be underestimated and all G/R ratio factors were larger than one. The LGC method, which assigns weights to the bias between precipitations observed by the AWSs and the radar-based precipitation estimates, is a modified version of the inverse distance weighting method. The LGC method can correct local bias cases that affect precipitation estimates by modifying the radar precipitation estimates in each pixel. However, the time required to compute the bias in each pixel varies according to the radius of the radar measurement scan.
## 3 Results and Discussion
### Model Parameters Estimation
This finding is consistent with the observation by [PERSON] et al. [12] that catalyst degradation contributes significantly (84.3%) to the overall voltage degradation in PEMFC. Substituting the estimated \(E_{a1}\) value into Equation (3) enables the calculation of the acceleration factors. The degradation rate is accelerated by 1.516 times at 65 \({}^{\circ}\)C and 4.923 times at 80 \({}^{\circ}\)C, relative to the normal operating temperature of 60 \({}^{\circ}\)C.  
Figure 6 illustrates the excellent agreement between the fitted model (8) (blue lines) and experimental relative voltage data (red dots) for each cell at 1.19 A/cm\({}^{2}\) over 1000 h under accelerated conditions of 65 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. The histogram of residuals shown in Figure 7 further supports the model's accuracy, using CELL8 as a representative example. The symmetric distribution of residuals around zero, observed for all cells at both temperatures, confirms the model's ability to capture the degradation trends in the experimental data. The generally larger deviations observed at 80 \({}^{\circ}\)C compared to those at 65 \({}^{\circ}\)C can be attributed to the more pronounced voltage drop at the higher temperature. The accelerated degradation at 80 \({}^{\circ}\)C leads to a wider range of voltage values, resulting in larger residuals. By contrast, the less severe degradation at 65 \({}^{\circ}\)C results in smaller deviations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Estimate**} & \multicolumn{2}{c}{**95\% Confidence Intervals**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**DF**} & \multirow{2}{*}{**t-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\  & & & & & & & \\ \hline \(\phi_{1}\) & 95.314 & 95.123 & 95.505 & \(9.794\times 10^{-2}\) & 395 & 973.238 & \(0.000\times 10^{0}\) \\ \(\gamma_{1}\) & \(1.474\times 10^{-5}\) & \(1.224\times 10^{-5}\) & \(1.724\times 10^{-5}\) & \(1.282\times 10^{-6}\) & 395 & 11.499 & \(1.361\times 10^{-26}\) \\ \(\phi_{2}\) & 4.802 & 4.609 & 4.995 & \(9.891\times 10^{-2}\) & 395 & 48.552 & \(1.331\times 10^{-168}\) \\ \(\gamma_{2}\) & \(1.529\times 10^{-2}\) & \(1.237\times 10^{-2}\) & \(1.822\times 10^{-2}\) & \(1.497\times 10^{-3}\) & 395 & 10.213 & \(6.899\times 10^{-22}\) \\ \(E_{a1}\) & 0.808 & 0.706 & 0.909 & \(5.188\times 10^{-2}\) & 395 & 15.570 & \(5.813\times 10^{-43}\) \\ \(E_{a2}\) & 0.528 & 0.356 & 0.701 & \(8.816\times 10^{-2}\) & 395 & 5.994 & \(4.616\times 10^{-9}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Fixed-effect coefficients estimated from the accelerated degradation test.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 3 UWB Notch Antenna Design Approach
### Parametric Analysis of UWB Notch Antenna
#### 3.2.2 Effect of Lower Resonator-Width 'x'  
The variation in width 'x' is simulated and studied to investigate the effects of width 'x' on the S\({}_{11}\) features. Figure 8 illustrates the S\({}_{11}\) features. The result is best when the width is 0.5 mm.  
Figure 7: S\({}_{11}\) for variation of width ‘t’.
# Stationary Target Identification in a Traffic Monitoring Radar System
## 3 Signal Measurement and Target Detection Using Traffic Monitoring Radar  
### Measurement Environment  
We conducted signal measurements with the radar system described in Section 2. As shown in Figure 1, we installed our radar on an overpass, with the radar facing the highway. The specifications of our radar are given in Table 1.  
The radar we designed can detect up to 300 m for up to four lanes, as given in Table 1. Considering the range resolution of 80 cm, vehicles such as trailers will be detected as more points than sedans. In addition, looking at Figure 1, we can expect that not only the vehicles driving on the road, but also structures such as guardrails will be detected because they are within the FOV of the radar.
## 3 Results and Discussion
### Tool Wear  
After cutting the TC11 using the ball end milling cutter and the revolving cycloid milling cutter under the same milling parameters, the variation laws of the flank with increasing cutting length are presented in Figures 7 and 8, respectively.  
Figure 5: Comparison of helix angles under different machining inclination angles.  
Figure 6: Comparison of working rake angles under different helix angles.  
The worm topography of the tool of the ball end milling cutter at different milling lengths under consistent milling parameters is presented in Figure 7. In the figure, when the milling length is 75 m, obvious scrap marks can be seen in the rake face, and a wear belt of a certain width is formed in the flank face. The wear of the tool speeds up with the cutting progress. When the milling length is 150 m, chipping of the tool materials and notches generated under the effect of impact load are observed in the rake face of the ball end milling cutter. Meanwhile, the width of the wear belt in the flank face is extended greatly along the direction away from the cutting edge. When the milling length is 225 m, an increasing number of dense notches can be found in the rake face of the ball end milling cutter, while a spoon-shaped wear belt is formed in the flank face.  
The tool wear form of the revolving cycloid milling cutter under comparable cutting conditions is shown in Figure 8. When the milling length is 75 m, slight tool tipping can be observed in the revolving cycloid milling cutter. However, as the cutting process advances, no obvious tool material chipping is observed in the rake face of the revolving cycloid milling cutter, with only a handful of notches when the milling length is 150 m. As the tool wears, there is not such a great number of dense notches as that shown in the ball end milling cutter when the milling length is 225 m. The length of the wear zone on the flank face of the revolving cycloid milling cutter is significantly longer than that of the ball end milling cutter. The wear band changes uniformly with a thin and long morphology.  
Figure 8: The evolution of tool wear in the revolving cycloid milling cutter.  
Figure 7: The evolution of tool wear in the ball end milling cutter.  
Considering the cutting method used in the experiment, it can be seen from the figures that the maximum wear zones of the two tools are at the position where the cutting edge of the tool first cuts into the workpiece, rather than at the position of maximum cutting depth. The cutting edge of the revolving cycloid milling cutter is longer and the working rake angle is larger, which reduces the mechanical load during cutting.
# A Simplified Free Vortex Wake Model of Wind Turbines for Axial Steady Conditions
## 2 Simplified Free Vortex Wake
### Near Wake Model  
The wake that extends downstream from the blade is divided into two parts: the near wake and the far wake (Figure 1b). The near wake is modeled by the vortex sheets, which consist of the trailing and shed vortex filaments. The circulation of each blade element is assumed to be constant. However, the adjacent segments may have unequal loadings and, therefore, have different circulation strengths. The circulation strength of each trailing vortex is equal to the difference between the bound vortex strengths of the two adjacent segments. The circulation strength of each shed vortex is equal to 0 in the axial steady condition. The vortex filaments are allowed to freely distort under the influence of the local velocity field. The convection of these vortex filaments can be described by the Helmholtz equation. For a blade with fixed coordinates, the governing equation of the vortex filaments can be written as the partial differential form:  
\[\frac{\partial r_{0}(\psi,\zeta)}{\partial\psi}+\frac{\partial r_{0}(\psi, \zeta)}{\partial\zeta}=\frac{1}{\Omega}[V_{0}+V_{ind}(\psi,\zeta)] \tag{3}\]  
where \(r\) is the position vector of the vortex collocation point; \(V_{ind}\) equals the mean value of the induced velocities at the surrounding four grid points [9].  
To solve the convection equation of the vortex filaments, numerical solutions have been investigated over the last decades, including relaxation methods [21] and time-marching methods [9; 11]. The time-marching methods can potentially provide the best level of approximation to the rotor wake problem with the fewest application restrictions. However, these methods have been found to be rather susceptible to numerical instabilities [22]. On the other hand, the relaxation methods improve the ability to control the non-physical wake instabilities by explicit enforcement of the wake periodicity. The relaxation methods can only be used for steady-state problems.  
In this study, the axial steady conditions are predicted. It is sufficient to apply a steady relaxation scheme to solve the convection equation of the vortex filaments in the near wake. A five-point central difference approximation is used for both the temporal and spatial derivatives. The detailed information can be found in Reference [21]. A pseudo-implicit technique [23; 24] has been introduced to improve the stability of the free-vortex iteration. The effective range of the azimuthal discretization is usually between \(\Delta\psi=5^{\circ}\) and \(\Delta\psi=20^{\circ}\)[22; 25] and \(\Delta\psi=10^{\circ}\) was selected for use in this study. The spatial step \(\Delta\zeta\) uses the same value as the azimuthal step.  
The velocity in the plane of rotation of the blade induced by the near wake is remarkable, so the length of the near wake is significant for the accuracy of the proposed model. The determination of the length of the near wake will be discussed further in Section 3.