esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method  
### Envelope Demodulation-Based Empirical Wavelet Transform  
The analytical signal \(g(t)\) of the original signal \(x(t)\) is obtained by Equations (7) and (8):  
\[\overset{\wedge}{x}(t)=x(t)\ast\frac{1}{\pi t}=\frac{1}{\pi}\int_{-\infty}^{ +\infty}\frac{x(\tau)}{t-\tau}d\tau \tag{7}\]  
\[g(t)=x(t)+\overset{\wedge}{j}\overset{\wedge}{x}(t) \tag{8}\]  
The amplitude of \(g(t)\):  
\[A(t)=\sqrt{x^{2}(t)+x^{2}(t)} \tag{9}\]  
\(A(t)\) is the envelope of the original real signal \(x(t)\).  
A Fourier transform is then performed on Equation (7):  
\[\overset{\wedge}{X}(f)=X(f)\cdot F[1/(\pi t)]=X(f)\cdot[-j\text{sgn}(f)] \tag{10}\]where \(F\) represents a Fourier transform of the function, and \(\text{sgn}()\) is a symbolic function.  
Equation (10) shows that \(\overset{\wedge}{X}(f)\) is obtained by phase shifting in the frequency domain by \(X(f)\), delaying \(\pi/2\) in the positive frequency domain and leading \(\pi/2\) in the negative frequency domain. The obtained demodulated signal is taken as the input. Based on the empirical scaling function and the empirical wavelet expressions presented in Section 2.1, the approximation coefficients \(w_{f}^{e}(0,t)\) and the detail coefficients \(w_{f}^{e}(n,t)\) are obtained, and are respectively represented by Equations (11) and (12).  
\[w_{f}^{e}(0,t)=\left\langle A,\phi_{1}\right\rangle=\int A(\tau)\overline{\phi _{1}(\tau-t)}d\tau=\left(\overset{\wedge}{A}(\omega)\overset{\wedge}{\phi_{1} (\omega)}\right) \tag{11}\]  
\[w_{f}^{e}(n,t)=\left\langle A,\psi_{n}\right\rangle=\int A(\tau)\overline{\psi _{n}(\tau-t)}d\tau=\left(\overset{\wedge}{A}(\omega)\overset{\wedge}{\psi_{n} (\omega)}\right)^{\vee} \tag{12}\]  
where \(\overset{\wedge}{\phi_{1}}(\omega)\) and \(\overset{\wedge}{\psi}(\omega)\) are defined by Equations (1) and (2), respectively.  
The schematic diagram of the proposed method is illustrated in Figure 2. We are assuming that the Fourier support \([0,\pi]\) is segmented into contiguous \(N\) segments. The red dashed lines in the figure represent the boundary detected by EWT, and the solid blue lines represent the spectrum contour. Due to the existence of high-frequency modulation, most of the boundaries detected by traditional EWT are located in the bearing resonance frequency band, which leads to modal aliasing in EWT. The proposed method demodulates the compound fault feature information through resonance demodulation. This boundary detection mechanism solves the abovementioned problems well.  
Figure 2: Comparison of boundary detection mechanism between the proposed method and traditional method.
# High-Precision Manufacture and Alignment of Image Slicer Based on Thin Glass Bonding
## 4 Error Analysis
### Eccentricity Error
#### 4.1.2. Fixture Error  
The accuracy of the fixture for the installation of the glass slices affects the stack accuracy, which determines the eccentricity of the focus. The precision of the angles between surfaces \(S_{1}\), \(S_{2}\), and \(S_{3}\), including the surfaces morphology, determine the precision of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slice Parameter** & **Error** & **Influence Quantity** \\ \hline \(L\) & \(\Delta L_{1}\) & \(\Delta L_{1}\) \\ \(A_{1}\) & \(\Delta A_{1}\) & \(W\times\text{tan}\Delta A_{1}\) \\ \(A_{2}\) & \(\Delta A_{2}\) & \(W\times\text{tan}\Delta A_{2}\) \\ \(A_{3}\) & \(\Delta A_{3}\) & \(H\times\text{tan}\Delta A_{3}\) \\ \(A_{4}\) & \(\Delta A_{4}\) & \(H\times\text{tan}\Delta A_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of length errors for \(X\) eccentricity influence.  
Figure 8: Error decomposition diagram: (**a**) ideal long thin glass slicer; (**b**) different parameters on the thin glass slice; (**c**) \(\Delta A_{1}\), \(\Delta A_{2}\) error schematic diagram in top view of the thin glass; (**d**) \(\Delta A_{3}\) error schematic diagram in side view of the thin glass.  
stack. The \(Pv\) of surfaces \(S_{1}\), \(S_{2}\), and \(S_{3}\) need to be controlled under 0.5 \(\upmu\)m. The errors \(\Delta A_{5}\), \(\Delta A_{6}\), and \(\Delta A_{7}\) in the angles \(A_{5}\), \(A_{6}\), and \(A_{7}\), shown in Figure 9b, result in the tilting of all slices, which leads to the eccentricity of the X- and Y-axes. In the ideal condition, \(A_{6}\) is 90\({}^{\circ}\) and \(\Delta A_{6}\) is 0\({}^{\circ}\), i.e., the right side of the slices is in contact with surface \(S_{2}\). When \(\Delta A_{6}\) is non-zero, the slice is firstly in contact with \(S_{1}\) and moves to \(S_{2}\) along the boundary of \(S_{1}\) to make a line contact with \(S_{2}\). Due to the geometrical limitation of the \(S_{1}\)-\(S_{2}\) boundary, the slices can be aligned within the same XZ position; hence, the tilt effect caused by the error \(\Delta A_{6}\) can be ignored. The relationship between the error factors and influence quantities is listed in Table 5. The error diagram is given in Figure 9 with the different error angles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slice Parameter** & **Error** & **Influence** \\ \hline \(S_{1}\) & \(Pv_{1}\) & Z eccentricity \\ \(S_{2}\) & \(Pv_{2}\) & X eccentricity \\ \(S_{3}\) & \(Pv_{3}\) & Z eccentricity \\ \(A_{5}\) & \(\Delta A_{5}\) & X eccentricity \\ \(A_{7}\) & \(\Delta A_{7}\) & Z eccentricity \\ \hline \hline \end{tabular}
\end{table}
Table 5: A summary of fixture error.  
Figure 9: Error decomposition diagram: (**a**) ideal frock fixture; (**b**) different parameters on frock fixture; (**c**) \(\Delta A_{5}\) error schematic diagram in XY view of thin glass; (**d**) \(\Delta A_{7}\) error schematic diagram in YZ view of thin glass.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 4 Results  
### Results of Urban Areas  
To distinguish the boundary between urban areas and rural areas, the perimeter of the region extracted by each DN value was calculated and shown in Figure 6. The curve describes the changing trend of the perimeter with the increase in the DN values in Wuhan. A decreasing trend is shown with DN values ranging between 151 to 190, reaching the extreme point (190,0.925). The perimeter then shows an increasing trend from 190-238 and turns to keep decreasing when the DN values are more than 238. According to the extraction model, the extreme point (190,0.925) is thought to be the threshold value of the boundary between urban areas and rural areas. The extraction results when adopting 190 as the threshold are shown in Figure 7.
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 3 Circuit Design
### The FFE Adaptive Circuit  
The FFE circuit is the core of the digital equalizer, providing about 16 dB of equalization strength for the entire equalization system. Figure 6 is a block diagram of the 16-tap parallel FFE structure, 64 parallel 8-bit input data are combined into a 78 \(\times\) 8 array, in which D0-D2 is the data of the previous clock cycle, and D66-D78 is the data of the next clock cycle, which is used to match the tap coefficient setting of the FFE. Due to the relative symbol characteristics of DB-PAM4 signals, a controllable forward intersymbol interference is used for signal generation; therefore, in the FFE circuit design of this paper, there are only 3 forward taps, 1 main tap and 12 post-item taps. The 78 \(\times\) 8 data array uses shift multiplication in addition to obtaining 20-bit data. Finally, the upper 12 bits of the reserved data are used in subsequent DFE circuits. The LMS-based adaptive FFE tap coefficient update can be expressed in vector form as [10]:  
\[W_{i}(n+1)=W_{i}(n)+2\mu\sum_{i=1}^{N}e(n)d_{n}(n-i) \tag{4}\]  
In the formula, \(W_{i}(n)\) is the tap coefficient of the \(i\)-th tap; \(e(n)\) is the error signal output by DFE; and \(d(n)\) is the ideal value after judgment. For 16-tap, the 64-way parallel data formula can be expressed as:  
Figure 5: (a) CTLE structure, (b) its frequency response.  
\[\left\{\begin{array}{l}W(0)=W^{-1}(0)+2\mu*[ek(3)Y(0)+ek(4)Y(1)...+ek(51)Y(48)]\\ W(1)=W^{-1}(1)+2\mu*[ek(3)Y(1)+ek(4)Y(2)...+ek(51)Y(49)]\\ \cdots\\ W(15)=W^{-1}(15)+2\mu*[ek(3)Y(0)+ek(4)Y(1)...+ek(51)Y(63)]\end{array}\right. \tag{5}\]
## 6 Discussion
Moreover, its simplicity and ease of use align with the research objectives of enhancing the traditional FMEA method while maintaining its accessibility, a key factor in its longstanding adoption across industries. Moreover, the current study focuses on a specific industrial case study, which may limit its generalizability to other industries with fundamentally different risk profiles. As the model is based on predefined weightings for FMEA variables, it needs to be carefully adapted and validated for new applications, particularly in industries where the relative importance of severity, occurrence, and detectability can vary widely. To further refine and extend the applicability of the FMEA-ROC-CoCoSo model, future research should focus on several areas. First, extending the application of the model to a wider range of industries will help to verify its generalizability and adaptability to different operational contexts. Comparative studies comparing the model's performance with other advanced risk assessment methods such as neural networks or machine learning-based models could provide valuable insights into its competitive advantages and limitations. Secondly, research could investigate dynamic or real-time adjustments to the weighting system that allow for continuous adaptation to changing operating conditions. This could improve the model's responsiveness in environments with rapidly fluctuating risks. Finally, integrating the model into automated systems or software tools that assist in decision making could further simplify its application and improve its accessibility and usability for practitioners.
# Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems
## 1 Introduction
The literature [26] shows that in a massive MIMO system, the sub-channels between different transmitting and receiving antenna pairs have the same sparse support set. In [27], an adaptive and structured subspace pursuit algorithm (ASSP) is proposed for massive MIMO channel estimation. Because of its step-by-step approach, achieving sparseness adaptation and the deficiencies have been underestimated, and the computational complexity is high. This paper proposes a sparsity-adaptive channel estimation algorithm based on the joint sparsity feature of a massive MIMO channel. When the channel sparsity is unknown, a block sparse adaptive channel estimation algorithm is proposed, which is block sparsity adaptive matching pursuit (BSAMP). By setting the threshold and finding the maximum backward difference position, the support set element is preliminarily selected. The element is secondarily selected by regularization to improve the accuracy of the selected element. The proposed solution can be applied to any scenario of the massive MIMO channel in which the sparse attributes are utilized for effective channel estimation. The key factor is to set the threshold value and determine the position of the non-zero elements in the support set. The simulation results show that this method can obtain better channel estimation performance with lower complexity under the condition of unknown sparsity. _Notations:_ Lowercase and upper-case boldface letters denote vectors and matrices, respectively; \((\cdot)^{T}\), \((\cdot)^{H}\), and \((\cdot)^{-1}\) denote the transpose, conjugate transpose, and inverse of a matrix, respectively.
## 3 Results and Discussion
### Analysis of Electrochemical Stability  
Electrochemical stability is an important performance parameter for lithium-ion battery diaphraggrams, which must maintain the stability of the electrolyte and electrode in terms of electrochemical properties to avoid degradation during the charge and discharge process. A high electrochemical stability window facilitates the long-term stable operation of Li-ion batteries at a high voltage. To evaluate the electrochemical stability of the diaphragm, the potential range was set to 2.5 V-6.0 V to perform LSV tests on the Celgard 2400 and PU/PAN fiber diaphraggrams. Figure 9 shows that the electrochemical stability windows of the PU, PU/PAN = 82, and PU/PAN = 7:3 diaphragms were 4.8 V, 5.1 V, and 5.2 V, respectively, which were much larger than the 4.3 V obtained for the commercial Celgard 2400 diaphragm, as shown by the voltage corresponding to the sudden increase in current. This was attributed to the strong chemical stability of the PU/PAN fiber diaphraggrams, and the increased PAN content also improved the electrochemical stability window. [PERSON] et al. [11] prepared PAN/polyimide (PI) composite films by electrostatic spinning to improve the electrochemical performance of a single PAN diaphragm with an electrochemical stability window of about 4.0 V. The centrifugal spinning method used to prepare the PU, PU/PAN = 82, and PU/PAN = 7:3 diaphragms resulted in a wide electrochemical stability window. Lithium-ion batteries with a wide electrochemical stability window will charge and discharge without degradation. The PU/PAN fiber diaphraggrams showed a good electrolyte affinity, and the excellent electro  
Figure 8: Thermal dimensional stability test results of the commercial Celgard 2400 and PU/PAN fiber diaphragms.  
chemical stability of PU/PAN composite diaphragm allows it to have better compatibility with the cathode material in lithium-ion batteries, which can be applied to work in adverse environments, such as high voltage.
## 2 Experimental Procedure and Data Reduction
### Data Reduction
#### 2.2.3 Thermal Efficiency Coefficient  
The mean cooling temperature drop of cooling water does not fully reflect cooling tower performance. The thermal efficiency coefficient [30] of the cooling tower is adopted to characterize the difference in cooling performance of the indirect air-cooling tower under crosswind conditions or windless conditions. The thermal efficiency coefficient \(\varepsilon_{\mathrm{th}}\) is defined as the ratio of heat transfer rate at a certain wind velocity to that with no wind [30]:  
\[\varepsilon_{\mathrm{th}}=\frac{Q_{\mathrm{v}}}{Q_{0}}=\frac{\left(T_{ \mathrm{i}}-T_{\mathrm{o}}\right)_{\mathrm{v}}}{\left(T_{\mathrm{i}}-T_{ \mathrm{o}}\right)_{0}}=\frac{\Delta T_{\mathrm{v}}}{\Delta T_{0}} \tag{3}\]  
where the subscript \"v\" stands for crosswind with a certain velocity, and the subscript \"0\" indicates no crosswind.  
The thermal efficiency coefficient of the cooling tower is a dimensionless parameter, denoting cooling performance between the experimental model and numerical model. The thermal efficiency coefficient from the experiment is calculated according to the temperature decline of water flowing through the cooling tower radiators. The coefficient from the numerical simulation can be obtained according to the ratio of the heat transfer rate through FLUENT software. Then, results from the experiment can be compared and analyzed against those from the numerical model based on the thermal efficiency coefficient.
# Application of RFID to Soil-Erosion Research
## 4 Analysis and Discussion
### Experiment 2
#### 4.2.1 Survey of In-Situ Particles
A second possible explanation is that it is affected by the orientation of the tag within the particle. A regression of the difference between the left and right detection range of particles along the fourth line of data on the number of observations is just significant (R\({}^{2}\) = 0.19, \(p\) = 0.05), so this may be an explanation. The sampling frequency affects the accuracy with which the RFID locations are detected. By removing alternate rows and columns in the grid used to site the reader (giving a sampling interval of 50 mm) the average error in locating the tagged particles is increased to 21.3 mm. However, the same weak (but statistically significant) positive relationship between the number of observations and the error in determining RFID locations is maintained. Figure 9: Regression of error in locating particles on the number of times it was detected showing error in (i) the \(x\) coordinate, (ii) the \(y\) coordinate, and (iii) total error. The analysis demonstrates that with even a small detection range there is a significant error in the location of the tagged particles. In a 16-year study of travel distance of particles in southern Arizona, Reference [13] estimated that soil particles had travelled, on average, of the order of 0.18 m. The error associated with the present remote detection system is of the order of 10% of this value. As such, it is indicative of the likely temporal application of the approach. In other environments, where rates of soil erosion may be higher, the approach may be useful over shorter timescales.
## 2 ITS Architecture and Applications
### ITS Applications
#### 2.3.3 Infotainment and Comfort Applications  
Infotainment and comfort applications aim at enhancing the driving experience by providing the drivers with various added-value services. These services are generally offered by trusted service providers, where the corresponding applications and services are downloaded and installed on the vehicles application units (AUs). AUs communicate with the remote SPs data centers through their OBUs, using different V2I communication technologies (e.g., 4G/LTE, 5G). A typical example of such an application consists in the remote vehicle diagnostic and maintenance application in which the SPs collect information from the in-vehicles sensors and send notifications to the drivers regarding detected safety defects and/or to remind them about planned car maintenance.  
Another application consists of providing global Internet access to the vehicle's passengers to enable a wide range of comfort services, including online gaming, video streaming, weather information and many others [28, 29, 30]. As shown in Table 1, these applications rely mainly on V2I communications (vehicle-to-infrastructure/back office), whose latency should typically not exceed five hundred milliseconds.
## 2 Materials and Methods
### R-G-B Digital Imaging  
The image components of potato tubers were extracted after processing the images captured by a digital R-G-B camera, Nikon D3300 DSLR (Nikon Inc., Melville, NY, USA) that consisted of a 24.2 megapixel image sensor with 12-bit resolution. This type of cameras created images by combining red (R), green (G), and blue (B) channels. All images were taken in a dark room, after the tubers were placed 22 cm below the camera lens and were illuminated by 4 halogen lamps (70 W, 2800 K, 1200 lumen) placed at an angle of 45\({}^{\circ}\) above them. The captured images were processed with suitable software (Mutispec v4.4, 2015) and the R, G, and B values of a selected section around the center of each tuber were extracted. Two sections at two opposite areas around the equatorial diameter of each potato tuber covering \(>\)75% of the surface area were selected and an average for each tuber was calculated. The R-G-B images were calibrated using black and white references following the equation R = (I - Bl)/(W - Bl), where I is the raw R-G-B image and Bl and W are the signals for black and white references, respectively.  
Figure 1: Images from the skin and the flesh of representative tubers from three commercial potato cultivars: Spunta (**A**), Banba (**B**), and Sylvana (**C**).
## 3 Architectural Model
### The Dashboard for Performance Analysis  
The data collected through the architectural model proposed in Section 3.1 are processed and made viewable in a monitoring dashboard. The dashboard has two objectives: to offer the possibility of monitoring the status of the different production units in real-time and to analyze the performance of the plant. The real-time monitoring takes place through a special screen that allows the status of the single production unit to be viewed using different colors according to the status shown in Section 2.1. For performance analysis, however, the dashboard allows you to calculate the OEE indicator for each production unit in its sub-indicators--availability, performance, and quality [12; 13]--as shown in Figure 7. For the sub-indicators, the dashboard also presents the trend over time, as shown in detail in Figure 8. Furthermore, the dashboard shows the Shutdown Time by Reason Type (STRT) and the Stop Time by Operator (STO), as shown in Figure 9. For the analysis of performance, the dashboard allows for the analysis of the trend over time for each piece of information shown; it is also possible to apply filters to analyze the information by type of production unit, time interval, and type of operator.  
Figure 8: Dashboard—zoom on performance over time panel.  
Figure 7: Dashboard—analysis panel of the OEE and its sub-indicators.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 2 High-Frequency Phenomena in Electrical Machines
### High-Frequency Phenomena in Magnetic Cores  
Regarding the magnetic core of electrical machines, in this region also demonstrates some high-frequency phenomena that might have a significant influence on the motor behavior. Generally, the stator and the rotor magnetic cores are laminated and made of electrical steel sheets to limit the Eddy currents induced by alternating magnetic fields, reducing the magnetic losses. However, above a certain frequency, the effect of eddy current is significant despite the insulation given by the lamination, because the skin depth becomes thinner than the thickness of the sheets. The induced Eddy currents creates a shielding effect inside the electrical sheets that push the magnetic flux out of the iron core, leading to a decrease in the relative permeability of the magnetic material, and so in the value of the inductance [52; 58; 59]. The threshold frequency of this transition depends on the thickness of each steel sheet and its resistivity [24; 55; 58].  
[PERSON] et al. created a one-slot 2-D model to simulate a bulk iron core and a 3-D model to represent a laminated iron core. In the case of the laminated iron core, the inductance decreases less than in the bulk core since the magnetic flux is not completely pushed out of the laminated core. It also happens at a higher frequency. On the other hand, the resistance increases more in the laminated core, as the surface area where Eddy current flows is larger and the proximity effect between sheets increases the eddy currents in the core. It should be noted that the non-linearity of the magnetic saturation of iron core is neglected for those simulations, as well as the displacement current term, which is negligible at least below 1 GHz [55].  
Figure 3: Proximity effect in a middle slot of a machine. (**a**) 1 kHz (**b**) 10 kHz.  
Figure 2: Skin effect in a conductor of 2 mm of diameter. (**a**) 1 kHz. (**b**) 100 kHz. (**c**) 1 MHz.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## Materials and Methods
### Characterization and Testing
#### 2.3.4 Dielectric Performance  
A high-frequency LCP digital bridge (Tonghui Electronics Co., Ltd., TH2826, Guangzhou, China) was used to measure the dielectric performance of the composites with different mass fractions at different temperatures. A three-electrode connection method was used to measure the capacitance and dielectric loss of the sample, and the dielectric constant of the sample was obtained according to Equation (1):  
\[\varepsilon_{\mathrm{r}}=\frac{4*C*h}{\pi(\mathrm{d}_{1}+\mathrm{g})^{2} \varepsilon_{0}} \tag{1}\]  
where \(C\) is the capacitance of the sample, \(h\) is the thickness, \(\mathrm{d}_{1}\) is the diameter of the protected electrode, \(\mathrm{g}\) is the electrode gap, and \(\varepsilon_{0}\) is the dielectric constant of the vacuum.