esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Linear and Dynamical Coupling  
To investigate the relationship between the VTEC and DFS, two approaches, linear and nonlinear, were considered. The linear approach assumes a linear relationship between the two variables. However, it fails if other forms of relationship exist, such as a quadratic relationship. To determine if there are relationships beyond linear between the two variables, a nonlinear metric called the transfer entropy (TE) was considered. The Pearson correlation (Equation (4)) was used to investigate the linear relationship. Granger causality is the most common indicator for investigating causality between two time series. However, in the presence of nonlinearities and in the context of a nonlinear system, it is not sufficient. Transfer entropy is an information theory-based measure of time-directed information transfer between jointly dependent processes that detects causality in the presence of nonlinearities.  
Transfer entropy is a model-free approach to determining causality in time series. It is defined as:  
\[TE=\sum P(Y_{i+1},Y^{\prime},X^{\prime})\log\frac{P(Y_{i+1}|Y^{\prime},X_{i})} {P(Y_{i+1}|Y^{\prime})} \tag{10}\]  
where \(Y^{\prime}=(Y_{1},\cdots,Y_{i})\) and \(p(\cdot)\) is the joint probability. TE assumes the stationarity of data; hence, the data were detrended using the first difference, \(x_{i+1}-x_{i}\), to achieve stationarity. It is obtained from the difference between two successive values. This approach also removes the temporal correlation and reduces the impact of extreme values on the data without a significant impact on the dynamical structure of the system. A non-zero value of TE implies that VTEC influences DFS in some way, while a zero value means there is no influence at all. The TE values obtained in this study were implemented using the algorithm by [37].  
We have presented the main experimental setup in Figure 1, and the Doppler shift has been described appropriately as the time derivative of the phase path of the radio signal with the relevant equations. The estimated DFS from the energy spectra of the received signal, which provide critical information about the conditions of the bottomside ionosphere, were compared with GNSS data. However, due to the significant number of missing values from the observation data, the GNSS TEC data from NASA for Lagos and Abuja were considered after data validation. To determine the level of complexity in both the ionosphere and signal quality, the dynamic measures (Lyapunov exponents) and metric (correlation dimension) were used. Meanwhile, transfer entropy (TE) and the Pearson correlation were used to investigate the nonlinear and linear relationships between VTEC and DFS, respectively.
## 3 Methodologies
### Discrete Wavelet Transformation  
Discrete Wavelet transform consists of processing the image in the frequency domain where the decomposition will happen in two subspaces. By using digital filters, the information will be separated into low-frequency sub-band and high-frequency sub-band. With this transformation, the image is decomposed into small Wavelets with varying frequencies and limited duration. The Wavelet transform coefficients contain the positions of the information. The original signal can be completely reconstructed by performing a reverse Wavelet transformation on the same coefficients [31].  
Based on [29], If we consider a low pass filter equation \(H\left(\omega\right)\) and a high pass filter as \(G(\omega)\):  
\[H(\omega)=\sum_{k}h_{k}e^{-j\omega},\qquad G(\omega)=\sum_{k}g_{k}e^{-jk\omega} \tag{1}\]  
then a signal \(x[n]\) can be decomposed recursively as:  
\[c_{j-1,k}=\sum_{n}h_{n-2k}c_{j,n}\qquad\text{and }dj-1,k=\sum_{n}g_{n-2k}c_{j,n} \tag{2}\]  
for \(j=J+1,J,\ldots J_{0}\) where \(c_{J+1,k}=x\,|\,k\,|\,\,\varepsilon Z,\,J+1\) is the high resolution level index and \(J_{0}\) is the low resolution level index. The coefficients c and d are called the DWT of signal \(x[n]\), where \(c_{j_{0},k}\) is the lowest resolution part of \(x[n]\) and \(d_{j,k}\) are the details of \(x[n]\) at various bands of frequencies.  
The signal \(x[n]\) can be reconstructed from its DWT coefficients recursively:  
\[c_{j,n}=\sum_{k}h_{n-2k}c_{j-1,k}+\sum_{k}g_{n-2k}d_{j-1,k} \tag{3}\]  
The classic DWT is implemented through the combination of two filters (low and high) to create four zones of interests LL, LH, HL and HH as per Figure 1.  
The low-frequency part includes the main information of the signal while the high-frequency part includes information related to the edge components. From these sub-bands, the LL is the most sensitive part since it contains most of the energy and serves as the base of the image. The other three sub-bands are of interest for watermarking due to the imperfection of the human visual system. Using several layers of decomposition makes the watermarking more robust and difficult to manipulate [32].  
Figure 1: One-layer, two-layer and three-layer decomposition of DWT.  
Alpha Blending Technique is a popular technique used in image processing and for watermarking. According to the alpha blending technique, the watermark image is obtained by:  
\[WMI=k\times(LL1)+q\times(WM1) \tag{4}\]  
where:  
* _WMI_ = watermarked image;
* _LL1_ = low frequency approximation of the original image;
* _WM1_ = watermark;
* \(k\), \(q\) = scaling factors for the original image and watermark, respectively. The alpha blending formula used for watermark extraction is given by:  
\[RW=(WMI-k\times LL1) \tag{5}\]  
where:  
* _RW_ = recovered watermark;
* _LL1_ = low frequency approximation of the original image;
* _WMI_ = watermarked image.
## 4 Discussion
### Ecological Engineering's Impact on CUE  
It is imperative to recognize the pivotal role of ecological engineering in bolstering vegetation health and, by extension, enhancing CUE. The primary objective of ecological engineering is to enhance surface vegetation coverage while reducing the necessity for human intervention, with the ultimate objective of improving vegetation vitality. A review of historical data from the Three Rivers Source region reveals evidence of vegetation degradation occurring during the late 20 th century. This decline was attributed to the combined effects of climatic changes and human activities, which have exerted a significant impact on the region's ecosystems. During this phase, CUE exhibited a notable decline, with a rate of change of \(-0.0022\) per decade. However, a paradigm shift was discernible post-2005, marked by the systematic implementation of diverse ecological restoration interventions within the Three Rivers Source region. This strategic endeavor has resulted in a noteworthy resurgence in CUE, with a positive trend emerging at a rate of \(0.0027\) per decade.  
Ecological engineering has been demonstrated to be an effective technique for enhancing vegetation growth and increasing carbon sequestration capabilities, as evidenced by the findings of [PERSON] et al. [66]. It follows that the revitalization of vegetation inevitably results in enhanced productivity, with CUE dynamics defined by a complex interplay of multiple factors. The research insights from the colder regions of the Qinghai-Tibet Plateau indicated that the metabolic demands of the vegetation were relatively subdued. Consequently, the NPP growth trajectory was observed to exceed that of the GPP. This dynamic ultimately results in an increase in CUE, as demonstrated by [PERSON] et al. [67]. In contrast, in warmer terrains, the autotrophic respiration growth trajectory is the dominant process, outstripping that of vegetation productivity. This results in a contrasting CUE paradigm [68]. Further evidence from [PERSON] [58] corroborates the hypothesis that autotrophic respiration is subject to modulation compared to biomass. This emphasizes the adaptive modulation of respiration in ecosystems where resources are limited. Our empirical findings are in accordance with the existing literature, indicating that CUE is typically higher in frigid regions compared to their warmer counterparts. Following the implementation of ecological engineering interventions, a notable increase in regional vegetation biomass is observed, accompanied by a reduction in autotrophic respiration. It is of particular significance to note that this transformation was underpinned by a considerable increase in CUE. This lends support to the hypothesis that ecological engineering played a pivotal role in fostering favorable CUE dynamics within the Three Rivers Source region.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms

Table 8: _Cont._Based on the sensing, hardware, and communication protocol employed, the various physical implementations of the smart meter are reviewed as follows. The authors in [175] use a commercial TED meter [176] and the ARM Linux board to implement a smart meter to detect user activity patterns from consumption data. Ref. [177] uses a simple microcontroller to implement a smart meter that uses GSM as a communication protocol. A smart meter-based complete home energy management system is proposed using power line communication in [178]. An energy meter using a PIC microcontroller, an ADE7756 energy meter, and a Bluetooth module is implemented in [179]. A smart meter for remote PV installation is implemented in [180]; it allows flexibility in the power measurement and communication setup. In [181], a smart multi-power tap is added to a smart meter for service management in a smart home. Modal analysis for power measurement by a smart meter is reported in [182]. The Portable Power System Monitor (PPSM) is LabVIEW-controlled hardware that performs basic measurements which are then processed by MATLAB for oscillation analysis in power systems [183]. A smart meter using a PIC microcontroller and an ADE 7757 with a current shunt is designed for the power consumption-based control of home appliances in [184]. A low-cost system to measure electrical power from PV panels is designed in [185]; it uses a single-board computer and a split-core transformer for current sensing and a simple parallel circuit for voltage readout. A high reliability and a long-life energy measurement IC interfaced to a DSP chip for smart metering function is reported in [186]. A smart meter for low-voltage micro-grids is reported in [187]; it uses op-amps for signal conditioning, PIC for signal processing, and ZigBee for communication. A wireless metering for a smart solar grid is implemented in [188]; it uses Hall effect sensors for measurements of current, voltage, and power and ZigBee for communication systems. In [189], a STM32F4 DISCOVERY board is used for measurement and signal processing, and Bluetooth is used for data communication with computers where MATLAB/Simulink is used for data analysis. A phase identification method for smart metering is given in [190]. Another technique for a phase identification
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 2 Modeling of the Communication Channel of UWOC  
### Analysis of Communication Range in UWOC  
The early development of UWOC technology was quite slow due to the strong attenuation of visible light by water. Light travels through water in the UWOC system, starting from the emitting terminal where it is emitted. It then passes through the optical device at the light source terminal and the transmission channels in the water, eventually reaching the optical device at the receiving terminal. Throughout this process, light is subject to interference from various constituents, including the water itself, inorganic salts, yellow substances, phytoplankton (chlorophyll), and suspended particulates. These constituents cause absorption or scattering of the light, leading to signal degradation. Additionally, the emission angle of the optical communication device and geometric losses affect the received optical power at the receiving end, thereby limiting the communication range.
## 3 Results and Discussion
### Optimization with Multiple Response Performance Index (MRPI)
In order to display the estimates on the same graph, the following adjustments were made: the MRPI values were multiplied by 24, the penetration values after aging were multiplied by 2.5, the unit of RTFOT mass loss was considered as one ten-thousandth, the elastic recovery values were multiplied by 2, and the Marshall Stability values were divided by 12. The numbers displayed around the radar graph represent the levels of the experimental parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \begin{tabular}{c} **Penetration** \\ **(Before RTFOT)** \\ \end{tabular} & \begin{tabular}{c} **Penetration** \\ **(After RTFOT)** \\ \end{tabular} & \begin{tabular}{c} **Softening Point** \\ **(Before RTFOT)** \\ \end{tabular} & \
\begin{tabular}{c} **Softening Point** \\ **(After RTFOT)** \\ \end{tabular} &
\begin{tabular}{c} **Marshall** \\ **Stability** \\ \end{tabular} \\ \hline Expected MRPI at & 37.268 & S/N & 32.943 & S/N & \(-\)34.066 & S/N & \(-\)35.637 & S/N & 60.970 & S/N \\ optimum levels & 73.010 & 0.1 mm & 44.378 & 0.1 mm & 50.502 & \({}^{\circ}\)C & 60.511 & \({}^{\circ}\)C & 1118.19 & kg \\ \hline Verification & 37.013 & S/N & 32.690 & S/N & \(-\)34.134 & S/N & \(-\)35.577 & S/N & 61.331 & S/N \\ experiment results & 70.90 & 0.1 mm & 43.10 & 0.1 mm & 50.90 & \({}^{\circ}\)C & 60.10 & \({}^{\circ}\)C & 1165.58 & kg \\ \hline S/N confidence interval (\(\alpha\) = 95\%) & 36.855 & 37.680 & 32.516 & 33.371 & \(-\)34.139 & \(-\)33.993 & \(-\)35.720 & \(-\)35.554 & 60.430 & 61.511 \\ \hline \end{tabular}
\end{table} 
Table 18: MRPI performance statistics and validation experiment results for A3, B4, C4, and D1 optimum levels.
# Lightweight Design and Evaluation of Square Reflector
## 3 Analysis and Evaluation of Simulation Results
### Firmness Tests and Analysis  
Modal analysis is an important parameter for the initial evaluation of the design rationality, and its analysis results can objectively reflect the mechanical characteristics of the structure and characterize the vibration response of the structure under mechanical loads.  
Modal analysis theory is a technique used to determine the vibration characteristics of structures, through which mass frequency, vibration, and mode shape participation coefficients can be determined [17; 18]. Modal analysis at the beginning of the design can prevent the structure from resonating or vibrating at a specific frequency, allowing designers to discover how the structure responds to different types of dynamic loads in the early stage and helping to estimate and solve control parameters in other dynamic analyses.  
The fundamental frequency of the structure is known before the parts are processed, and then the design is optimized to ensure that it avoids forced vibration during launch and transportation to avoid resonance phenomena that could damage the structure.  
Through simulation, the first four order modes of the reflector assembly as a whole are shown below, and the analysis results are shown in Table 6. From the simulation results, it can be concluded that the first-order mode of the mirror assembly is 173.3 Hz, which is a high stiffness compared with the mirror assembly of the same size. Shown in Table 7.  
Figure 5: Schematic diagram of mirror assembly simulation.  
Figure 6: Finite element model of mirror assembly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\begin{tabular}{c} **Density** \\ **g/cm3** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Coefficient of** \\ **Linear** \\ **Expansion** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Modulus of** \\ **Elasticity** \\ **Gpa** \\ \end{tabular} } & \multirow{2}{*}{**Poisson**} \\  & & \(\alpha\times\)**10**–6**/K** & & \\ \hline ULE & 2.21 & 0.03 & 67.6 & 0.17 \\ TC4R & 4.45 & 9.1 & 110 & 0.34 \\ InvarSteel & 8.18 & 0.05 & 145 & 0.25 \\ AlSiC & 2.9 & 9.8 & 145 & 0.32 \\ Optical cement & 1.7 & 110 & 600 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mirror material parameters.
## 5 Effects of ABH Parameters and Damping Layer  
### Effect of the ABH Diameter  
To investigate the influence of the ABH diameter on the transmission loss of the circular plate, the TLs of a series of circular plates embedded with different ABHs with diameters of 10 mm\(\sim\)-50 mm in the center were calculated, respectively, and they were compared with the TL of an ABH circular plate embedded with a diameter of 60 mm ABH, which is shown in Figure 9. It can be seen from  
Figure 8: Experimental results of TL measurement for ABH circular plate and uniform plate: (**a**) TL of plates with aluminum material; (**b**) TL of plates with steel material.  
Figure 7: Comparison between the results of simulation and experimental measurement.  
Figure 9 that in the frequency range below 1 kHz, the sound transmission loss values of ABH circular plates ABH with different ABH diameters are substantially the same, and the TLs of a circular plate with an ABH diameter of 10 mm is 10.01 dB and 11.64 dB higher than those of a circular plate with ABH diameters of 60 mm and 20 mm at 4 kHz. In addition, in the range of 4 kHz-10 kHz, the sound transmission loss of the circular plate with ABH diameter of 10 mm is still higher than that of the circular plate with the ABH diameter of 60 mm. It can also be observed that, at almost all frequencies in the range of 1 kHz-10 kHz, the sound insulation performance of the circular plate with an ABH diameter of 30 mm is better than those of other ABH circular plates. Among them, the TL of a circular plate with an ABH diameter of 30 mm is 12.32 dB and 11.79 dB higher than those of a circular plate with an ABH diameter of 60 mm at 4 kHz and 5 kHz.  
To summarize, for the circular plate studied above, there is an obvious promotion of the sound insulation performance when the diameter of the ABH is 30 mm. Furthermore, it can be concluded that there is a fixed value or interval of ABH diameter for a circular plate with a clamped boundary condition to obtain the best sound insulation performance. Whether the diameter of the ABH is below or above this specific interval, the sound insulation effect of the corresponding ABH circular plate will decrease. This may be because when the diameter of the ABH is 40-60 mm, the aggregation effect of the single acoustic black hole region on the flexural wave is no longer increased. On the contrary, due to the enlargement of the acoustic black hole region, a thin region with a large area and the thickness attenuated by power is formed, so that the areal density is sharply reduced at this region, which will significantly affect the sound transmission loss of the plate.
## 3 FMCW LiDAR Research Progress  
The cutting-edge trends in FMCW LiDAR technology are focused on enhancing detection precision and range, improving signal processing, optimizing frequency modulation linearity, and employing narrow-linewidth techniques. Through these technological advances, researchers are able to enhance the performance of LiDAR systems significantly, better adapting them to complex and dynamic application environments. Additionally, improvements to laser light sources can optimize overall system performance and data quality. Collectively, these efforts are poised to advance the development of FMCW LiDAR technology greatly, propelling its applications in critical areas such as autonomous driving and safety monitoring.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification** & **Specific Factors** & **Description of Performance Degradation** \\ \hline \multirow{7}{*}{Time Change} & \multirow{3}{*}{Aging of electronic products} & \multicolumn{2}{c}{Deterioration in the performance of electronic components, resulting in resistance changes, leakage currents, and other effects} \\ \cline{2-3}  & & Lens degradation & Attenuation and refraction due to lens wear and aging \\ \cline{2-3}  & \multicolumn{2}{c}{Mounting vibration} & \multicolumn{2}{c}{Loose mounting due to long-term effects of vehicle vibration} \\ \cline{2-3}  & \multicolumn{2}{c}{Pollutant entry} & \multicolumn{2}{c}{Entry of particles such as dust, water, condensation, etc.} \\ \cline{2-3}  & \multicolumn{2}{c}{Pixel degradation} & \multicolumn{2}{c}{Exposure to electromagnetic waves leads to degradation of silicon doping and reduced pixel performance} \\ \hline \multirow{7}{*}{Employ} & \multirow{3}{*}{Sensor misalignment} & Change in sensor position, resulting in a change in the sensor coordinate system relative to the original \\ \cline{2-3}  & \multicolumn{2}{c}{Vehicle collision} & Lens misalignment of the sensor \\ \cline{2-3}  & \multicolumn{2}{c}{Hindrance} & Light refracted by materials or particles \\ \cline{2-3}  & \multicolumn{2}{c}{Vehicle dynamic settings} & Changing the sensor coordinates by adjusting the vehicle height \\ \hline \multirow{7}{*}{Environment} & Extreme temperatures & The sensor operates at conditions other than the appropriate temperature \\ \cline{2-3}  & \multicolumn{2}{c}{Severe weather} & Rain, snow, sleet, frost, fog, etc. \\ \cline{2-3}  & \multicolumn{2}{c}{Visual impairment} & Copper sheets or windscreen obstructions that block or refract light \\ \cline{2-3}  & \multicolumn{2}{c}{Solar} & Solar-induced local saturation, lens vignetting, IR-detected color channels \\ \hline \hline \end{tabular}
\end{table}
Table 4: Source factors of potential performance degradation [36].
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 3 Damage Detection in Wind Turbine Blades Based on the CBNLM-BLS
### The Acquisition of Wind Turbine Blade Images  
To develop a database containing cracked, normal wind turbine blades, multiple images were truncated using the region of interest (ROI) method on the images captured by the DJI UAV Royal Mavic 2. The database was then expanded by rotating, panning, adding noise, changing brightness, contrast, etc. The final 741 images of the wind turbine blades were generated. All images collected were canonically processed and were 256 \(\times\) 256 in size. The image dataset was divided into a training set and a test set on a scale of 3:1. The details of the dataset's categories are shown in Table 1. Two collected image samples of wind turbine blades are shown in Figure 2.
## 4 Optimal Operation Model of the RIES System  
### Objective Function  
The RIES optimal operation model in account of DR and EV under the CTM aims to achieve the best economy of the whole system under the precondition of the following constraints. The objective function of RIES can be indicated as:  
\[\text{min}F=C_{\rm buy}+C_{\rm c}+C_{\rm op}+C_{\rm PC}-C_{\rm ev}-C_{\rm sell}, \tag{21}\]where \(F\) is the overall cost of the RIES optimization model; \(C_{\text{buy}}\) and \(C_{\text{sell}}\) are the interaction costs of the RIES system with the outside world; \(C_{\text{c}}\) is carbon transaction cost; \(C_{\text{op}}\) is the total running and maintaining cost.