esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Evolution of Polarimetric Observables
#### 3.1.2 Polarimetric Decompositions
Anisotropy of all crops was around 0.2 along the season, indicating that in addition to the main scattering mechanism at each stage, the difference between the second and third scattering mechanisms was relatively small. * Neumann Decomposition  
The Neumann decomposition outputs three parameters, namely the degree of orientation randomness (\(\tau\)), the magnitude of particle scattering anisotropy \(|\delta|\) and the phase of the particle scattering anisotropy \(\phi_{\delta}\)[48]. In addition, the parameter \(|\delta|\) can represent the scattering mechanism type if a mixed scattering mechanism is assumed [48]. Figure 6g-i shows the temporal variation of the three parameters. According to Figure 6g, the \(\tau\) value of most crops was large (>0.8) and maintained a stable state throughout the growing season, indicating that the scattering randomness of crops during the whole growing season was large. The variation of winter wheat was the most obvious. From Figure 6h, it is clear to the seasonal patterns of \(|\delta|\) in all crops showed large variations and were very similar to the \(\alpha\) angle from the Cloude-Pottier decomposition, which proves its usability in scattering mechanism interpretation. In Figure 6i, the variations of \(\phi_{\delta}\) among different crops are quite different, providing helpful information for further distinguishing crop types.
# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
## 5 Example Application and Analysis of Results
### Comparison of Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & (0.01–0.1] & (0.1–0.2] & (0.2–0.3] & (0.3–0.4] & (0.4–0.5] \\ \hline PSNR(GLCIC) & 31.726 & 30.215 & 26.836 & 24.537 & 21.368 \\ PSNR(WGAN) & 33.537 & 31.634 & 28.472 & 26.759 & 24.957 \\ PSNR(PDGAN) & 35.735 & 33.292 & 31.583 & 29.834 & 28.573 \\ PSNR(URURS) & 37.654 & 34.826 & 33.685 & 31.253 & 28.247 \\ MSSIM(GLCIC) & 0.875 & 0.835 & 0.774 & 0.746 & 0.762 \\ MSSIM(WGAN) & 0.915 & 0.873 & 0.805 & 0.784 & 0.723 \\ MSSIM(PDGAN) & 0.948 & 0.926 & 0.839 & 0.805 & 0.764 \\ MSSIM(OURS) & 0.963 & 0.937 & 0.905 & 0.862 & 0.827 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparison of imaging log image restoration with improved GAN (PSNR(dB)/MSSIM). Figure 10: Qualitative comparison of imaging logging image restoration models with improved GAN. (**a**) Original. (**b**) Input. (**c**) GLCIC. (**d**) WGAN. (**e**) PDGAN. (**f**) OURS model. deep separable convolutional residual block on the underlying FCN architecture; (II) model(Inception): adding the Inception module to the generative network; (III) model(FEM): adding the multi-scale feature extraction module to the generative network; (IV) model(CAR): the channel attention residual block is added to the generative network; and (V) model(Full): the full model of the imaging logging image restoration method using improved GAN. The ablation experiments conducted used the same set of datasets with the same ratio of missing imaging log images above all, and the designed modules were gradually added for training separately.
## 4 Discussion  
### Improvements of MTL-LUE, MTG, and BTL in Simulating GPP over Mountainous Areas  
LUE models, VI-based models, and process-based models are the three main approaches for spatial-continuous GPP estimation, such as the MOD17, TL-LUE, TG, and BEPS models. However, most of these three GPP models overlook the effect of topography on the vegetation photosynthesis process, and treat the terrestrial surface as flat areas. Over the last decade, [PERSON] and [PERSON] [22], [PERSON] and [PERSON] [23], and [PERSON] et al. (2009) [18] have proposed a mountain LUE model (i.e., MTL-LUE), a mountain VI-based model (i.e., MTG), and a mountain process-based model (i.e., BTL) to improve the GPP estimation over mountain areas. The GPP estimates from these three mountain GPP models have been proved to better match tower-based GPP than those from the original models (i.e., TL-LUE, TG, and BEPS) at the site scale.  
In this study, three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BTL) were adopted to obtain GPP estimates for a mountainous watershed (i.e., Wanglang Reserve). Results showed that multiple annual GPP estimates from different models presented a similar spatial distribution over Wanglang Reserve. The annual GPP estimates from mountain GPP models were found to have a higher spatial variation than those from the original models, highlighting that incorporating topographic information into GPP models might improve the understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 2 Basic of the Hyperbolic Technique
### Selected Localization Algorithms for Hyperbolic Solution
Then, in each subsequent iterative step, a so-called correction vector is determined \([\Delta x_{UTi},\Delta y_{UTi}]\), which is added to the estimate of the position determined in the previous step, which can be recorded as follows [20]:  
\[\left[ \begin{array}{c}x_{UT}\\ y_{UT}\end{array} \right]=\left[ \begin{array}{c}x^{\prime}_{UT}\\ y^{\prime}_{UT}\end{array} \right]+\left[ \begin{array}{c}\Delta x_{UTi}\\ \Delta y_{UTi}\end{array} \right]+\ldots,for\ i\in<1,2,\ldots i_{\max}. \tag{8}\]  
The correction vector \([\Delta x_{UTi},\Delta y_{UTi}]\) should be determined using the following formula:  
\[\left[ \begin{array}{c}x_{UT}\\ y_{UT}\end{array} \right]=\left([\Delta]^{T}\cdot[\Delta]\right)^{-1}\cdot[ \Delta]^{T}\cdot[\varepsilon], \tag{9a}\]  
whereby  
\[[\Delta]=\left[ \begin{array}{c}\left(\frac{\Delta x_{G}}{d_{G}}-\frac{\Delta x _{P1}}{dp_{1}}\right)\left(\frac{\Delta y_{G}}{d_{G}}-\frac{\Delta y_{P1}}{dp_ {1}}\right)\\ \left(\frac{\Delta y_{G}}{d_{G}}-\frac{\Delta x_{P2}}{dp_{2}}\right)\left( \frac{\Delta y_{G}}{d_{G}}-\frac{\Delta y_{P2}}{dp_{2}}\right)\end{array} \right], \tag{9b}\]  
and  
\[[\varepsilon]=\left[ \begin{array}{c}\varepsilon_{1}\\ \varepsilon_{2}\end{array} \right]=\left[ \begin{array}{c}\Delta d_{1(P)}- \Delta d_{1(e)}\\ \Delta d_{2(P)}-\Delta d_{2(e)}\end{array} \right], \tag{9c}\]  
whereby  
\[ \begin{array}{l}\Delta x_{G}=x_{G}-x_{UT},\\ \Delta x_{P1}=x_{P1}-x_{UT},\\ \Delta x_{P2}=x_{P2}-x_{UT},\end{array}  \tag{9d}\]  
and  
\[ \begin{array}{l}\Delta y_{G}=y_{G}-y_{UT},\\ \Delta y_{P1}=y_{P1}-y_{UT},\\ \Delta y_{P2}=y_{P2}-y_{UT},\end{array}  \tag{9e}\]  
However, \(\Delta d_{i(P)}\) and \(\Delta d_{i(e)}\) mean values are determined on the basis of respectively measured differences \(\Delta\varphi(\Delta t)\) and estimated in subsequent iteration steps, in which the error vector \([\varepsilon]\) is sought to minimize, which can be expressed as follows:  
\[\varepsilon_{1}=\Delta d_{1(P)}-\Delta d_{1(e)}\Rightarrow 0\cap\varepsilon_{2}= \Delta d_{2(P)}-\Delta d_{2(e)}\Rightarrow 0. \tag{9f}\]As usual, the accuracy of the solution depends on the number of iteration steps. In the present case, practice shows that satisfactory accuracy is obtained after only five iteration steps [20]. The last of the listed algorithms, [PERSON]'s algorithm, is based on a non-iterative approach. However, its significant limitation is the rigid geometry of the spacing of reference stations, which should be placed at strictly defined points of the local rectangular system, which are expressed by the following relationships [21]:  
\[ \begin{array}{l}x_{G}\cap y_{G}=0,\\ x_{P1}\
eq 0\cap y_{P1}=0,\\ x_{P2}\
eq 0\cap y_{P2}\
eq 0.\end{array}
# Weighted Optimal Quadrature Formulas in Sobolev Space and Their Applications
## 3 An Approach to the Construction of Weighted Optimal Quadrature Formulas with Derivatives  
In this section, we consider the following quadrature formula  
\[\int_{0}^{1}p(x)\varphi(x)dx\cong\sum_{\beta=0}^{N}\sum_{\
u=0}^{m-1}C_{\beta }^{(\
u)}\varphi^{(\
u)}(x_{\beta}), \tag{26}\]  
i.e., the case \(t=m-1\) in Formula (3). The error of this formula is  
\[(\ell_{N}^{(m-1)},\varphi)=\int_{0}^{1}p(x)\varphi(x)dx-\sum_{\beta=0}^{N} \sum_{\
u=0}^{m-1}C_{\beta}^{(\
u)}\varphi^{(\
u)}(x_{\beta})\]  
\[=\int_{-\infty}^{+\infty}\ell_{N}^{(m-1)}(x)\varphi(x)dx,\]  
where  
\[\ell_{N}^{(m-1)}(x)=\mathcal{E}_{[0,1]}(x)p(x)-\sum_{\
u=0}^{m-1}\sum_{\beta= 0}^{N}(-1)^{\
u}C_{\beta}^{(\
u)}\delta^{(\
u)}(x-x_{\beta}). \tag{27}\]The quadrature Formula (26) with error functional (27), considered in the space \(L_{2}^{(m)}(0,1)\), can be characterized in two ways. On the one hand, it is defined by the coefficients \(C_{\beta}^{(v)}(v=\overline{0,m-1},\beta=\overline{0,N})\) subject to the conditions:  
\[(\ell_{N}^{(m-1)}(x),x^{\alpha})=0,\;\alpha=\overline{0,m-1}, \tag{28}\]  
and, on the other hand, the extreme function \(\psi_{\ell}(x)\) of the quadrature formula, which is obtained as a solution of Equation  
\[\frac{d^{2m}U(x)}{dx^{2m}}=(-1)^{m}\ell_{N}^{(m-1)}(x)\]  
and can be written in the form  
\[\psi_{\ell}(x)=(-1)^{m}\ell_{N}^{(m-1)}(x)*G_{m}(x)+P_{m-1}(x), \tag{29}\]  
where  
\[G_{m}(x)=\frac{x^{2m-1}sign(x)}{2(2m-1)!},\]  
\(P_{m-1}(x)\) is some polynomial of degree \(m-1\).  
In this case, the square of the norm of the error functional \(\ell_{N}^{(m-1)}(x)\) is calculated by the formula  
\[\|\ell_{N}^{(m-1)}\|^{2}=(\ell_{N}^{(m-1)},\psi_{\ell}). \tag{30}\]  
As previously established, the function \(\psi_{\ell}(x)\) is given by formula (29). Therefore, by computing the square of the norm of the error functional with the help of Equation (30), we arrive at  
\[\|\ell_{N}^{(m-1)}\|^{2}=(-1)^{m}[\sum_{\beta=0}^{N}\sum_{\beta^{\prime}=0}^{ N}\sum_{\
u=0}^{m-1}\sum_{\
u^{\prime}=0}^{m-1}(-1)^{\
u+\
u^{\prime}}C_{\beta}^{ (\
u)}C_{\beta^{\prime}}^{\
u^{\prime}}G_{m}^{(\
u+\
u^{\prime})}(x_{\beta}-x _{\beta^{\prime}})\]  
\[-2\sum_{\
u=0}^{m-1}\sum_{\beta=0}^{N}(-1)^{\
u}C_{\beta}^{(\
u)}\int_{0}^{1 }p(x)G_{m}^{(\
u)}(x-x_{\beta})dx\]  
\[+\int_{0}^{1}\int_{0}^{1}p(x)p(y)G_{m}(x-y)dxdy, \tag{31}\]  
where  
\[G_{m}^{(k)}(x)=\frac{x^{2m-1-k}}{2(2m-1-k)!},\;k\leq 2m-1.\]
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 5 Experimental Results  
A novel scheduling technique was implemented to address performance deterioration in the LTE network. This problem was addressed using the ANNA technique for radio resources. The researchers presented the results for the three LTE downlink scheduling processes' models. Detailed simulations were conducted with a network simulator, LTE Sim [24], that helped to assess the proposed algorithms' efficiency (PF, MLDWF, EXP-PF, and FLS). Table 1 presents the parameters used for the simulations. After considering seven cells with a fixed eNodeB, the simulator was run on an interference scenario for a single cell. Each cell had a radius of 1.5 km, and the number of users was randomly distributed in every cell, starting with three users. The interval between users was three, while the maximal number of users was 30.  
The results will be analyzed and discussed based on implementing different LTE-DL scheduling algorithms, including PF, MLWDF, EXP-PF, and FLS. In addition, comparing three ANN algorithms titled RBFNN, Exact RBFNN, and GRNN, which were implemented and analyzed, the ANN-predicted LTE fairness index models were more accurate in the current research.
## 2 Generation of Motion Trajectory Library  
The process of constructing the motion trajectory library in this paper is illustrated in the yellow part of Figure 1. The motion trajectory library is formed by merging multiple standardized reference trajectory datasets \(D_{s}^{i}\), each assigned a distinct weight \(w^{i}\), where \(D_{s}^{i}\) is derived from reference trajectory datasets \(D^{i}\) through standardization. Different weights \(w^{i}\) are designed to encourage the robot to learn and utilize diverse motion data from the library, rather than focusing solely on a specific action.  
Figure 1: Overview of BCAMP framework.  
Optimal control is used to generate \(D^{i}\in\mathbb{R}^{(n+6+12+12)\times t},i\in\{1,2,\ldots,k\}\). \(k\) represents the number of reference trajectory datasets, and \(n\) is the total number of controlled joints on the robot. Each trajectory includes trunk state; hip, thigh, and calf joint angles; trunk velocity; joint velocities; foot positions and velocities; and \(t\in N^{+}\) represents a time series.  
\[D^{i}=\big{[}S(t),\theta(t),\dot{S}(t),\dot{\theta}(t),P(t),\dot{P}(t)\big{]} \tag{1}\]  
where \(S(t)=[x(t),y(t),z(t),\beta(t),\varphi(t),\dot{\delta}(t)]\in\mathbb{R}^{6 \times t}\) represents the trunk state of the robot in the world coordinate system; \(\beta(t)\) (roll about \(x\)-axes), \(\varphi(t)\) (pitch about \(y\)-axes), and \(\dot{\delta}(t)\) (yaw about \(z\)-axes) represent the trunk orientation angles; \(\dot{S}(t)\) is the trunk velocity of the robot, which includes linear velocity and angular velocity; \(\theta(t)=[\theta_{1}(t),\theta_{2}(t),\ldots,\theta_{n}(t)]\in\mathbb{R}^{n \times t}\) is the joint positions; \(\dot{\theta}(t)\) is the joint velocities; and \(P(t)\in\mathbb{R}^{12\times t}\) is the positions of the robot four feet in the trunk coordinate system.  
The quadruped robot model is illustrated in Figure 2. To generate stable quadruped robot motion trajectories, the optimization problem is defined as follows:  
\[\begin{array}{ll}\min&L_{1}+L_{2}\\ s.t.&\text{All constraints}\end{array} \tag{2}\]  
where \(\tau\) represents the joint torque, and it indicates the sum of all joint torques throughout the entire motion process; \(\vec{q}\) and \(\vec{q}\) are the joint acceleration and jerk, respectively; and \(L_{1}\) and \(L_{2}\) represent the cost functions, specifically shown in Equations (4) and (5). All constraints represent all the constraints involved in this optimization, as shown in Table 1.
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 3 Background Knowledge
### SDN Controllers
#### 3.2.1 ONOS Controller  
The ONOS controller is an open-source controller that is often applied in a variety of research and testing settings. \"Open Network Operating System\" (ONOS) is a distributed network controller that uses the Replicated Agreement for a Fault-Tolerant (RAFT) scheme for switching and mapping to its key controller. This scheme is stored in ONOS's central database. In order to ensure the consistency throughout its cluster, the leader node is the one that starts the process of checking for updates and delivering those updates to the nodes that are closer to it. In order to keep the scalability of ONOS intact during data transmission, the RAFT algorithm is executed [7; 31; 32].  
A web interface for the ONOS controller is available online. Using a web-based graphical user interface, the user can quickly manipulate the architecture of the network. It stores information on the network architecture, roles (slave or master), switches, links, hosts, flows, and pathways in a network that was built using the ONOS controller. The representation of the network topology displays information about the hosts, as well as linkages between the switches, data flow channels, controllers, and the capacity of the links. The web-based graphical user interface is helpful for performing analysis on SDN networks [10; 33; 34].
## 3 Results and Analysis
### Evolutionary Characteristics of Dynamic Strain
#### 3.2.1 Fatigue Evolution Model  
The S-shaped curve of fatigue strain can be divided into three stages: the first stage is a disproportionate increase in deformation; the second stage is a linear increase in deformations; and the third stage is a destruction stage, caused by the unstable increase in the crack. Considering the initial strain, instability rate, and fatigue life, the structural strain evolution model can reflect the three-stage strain characteristics of the structure under cyclic loading, and describe the evolution of material fatigue strain quantitatively [31]. The specific strain evolution equation is expressed as follows:  
\[\frac{\varepsilon_{\text{n}}}{\varepsilon_{0}}=\frac{\varepsilon_{1}}{ \varepsilon_{0}}+\alpha(\frac{\beta}{\beta-N/N_{f}})^{\frac{1}{\rho}} \tag{4}\]  
where \(\varepsilon_{0}\), \(\varepsilon_{1}\), and \(\varepsilon_{n}\) are the initial, first, and cumulative strains, respectively; \(N\) and \(N_{f}\) are the number of cycles and fatigue life, respectively; the ratio \(N\)/\(N_{f}\) is the cycle ratio; and \(\alpha\), \(\beta\), and \(\rho\) are the evolution equation parameters: \(\alpha\) refers to the instability scale factor, \(\beta\) refers to the instability factor, and \(\rho\) refers to the instability velocity factor.  
The instability velocity factor \(\rho\), related to the convergence speed of the strain evolution curve, is generally recommended to be in the range of 2-8. The instability scale factor \(\alpha\), which characterises the ratio of the failure stage in the structural fatigue life curve, is generally recommended to be in the range of 0-(\(1-\varepsilon_{1}/\varepsilon_{0}\)). The relationship of the instability factor \(\beta\) is related to \(\alpha\) and \(\rho\) when \(N=N_{f}\) is presented as follows:  
\[\beta=\left(\frac{1-\varepsilon_{1}/\varepsilon_{0}}{\alpha}\right)^{-\rho}+1 \tag{5}\]
## 4 ADRC-Based ABS Slip Ratio Tracking Control
### Design of ADRC  
The active disturbance rejection control (ADRC) system comprises three main components: the tracking differentiator (TD), the extended state observer (ESO), and the nonlinear state error feedback (NLESF) [30].
# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 3 Elderly Stroke Monitoring System Based on Machine Learning and EEG
### Real-Time EEG Data Collection  
This section describes the process used to measure and collect different types of biometric signal data, including EEG, ECG, EMG, pulse wave (PPG), and motion (motion), to validate the performance of the AI-based elderly stroke disease prediction and analysis system. Brain waves are the most basic electrical signals used to determine brain abnormalities, and they are measured through electrodes attached to the surface of the head. In this work, scalp EEG is used to measure and collect brain waves from the general elderly and stroke elderly patients. We hypothesize that these EEG vital-signal data can represent medical and kinematic parameters if the balance of the body collapses or if a walking disorder occurs during walking. The measurement and collection of the brainwave vital-signal data tested in this paper were conducted by the Emergency Medical Center and Rehabilitation Department of Chungnam National University Hospital for the elderly from 2017 to 2018. The subjects were aged 65 or older and had received rehabilitation treatment for stroke, since our focus is on elderly health monitoring. Various biometric signals, including EEG, ECG, EMG, voice, pulse, and motion, were collected using wearable sensors. To separate the general patients from the stroke patients, patients who had been diagnosed as a confirmed stroke case within one month were classified as stroke patients. The EEG data were collected at a sampling rate of 1000 Hz at a total of six channels (Fz, Oz, T7, T8, C1, and C2), with the location of each channel highlighted in Figure 2.
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
## 3 Proposed Methodology
### Emergency Help  
The fifth layer of the RTSHPMP is emergency help (ERH). ERH includes authorities such as police, first aid, hospital, and fire brigade, which are responsible for providing legal and medical help to the passenger so that passenger life and assets could be saved in case of an accident.  
The detailed working of RTSHPMP is shown in our Algorithm 1 and flow chart in Figure 3.  
Figure 3: RTSHPMP workflow.
# Corrosion and Scaling in Geothermal Heat Exchangers
## 4 Corrosion in Geothermal Environments
### Hydrogen and Hydrogen Related Stress Corrosion Cracking  
Hydrogen cracking is a phenomenon characterized by the diffusion of hydrogen atoms into the crystalline lattice structure of a metal, causing subsequent damage. Hydrogen can originate from various sources, including manufacturing processes or as by-products of corrosion reactions (such as cathodic reactions involving reduction in water) [39; 40]. Thesehydrogen atoms when inside the metal can recombine to form molecular hydrogen (H\({}_{2}\)), which occupies a significantly larger volume than the atoms. This localized accumulation of hydrogen can create areas of elevated stress within the metal, potentially initiating and propagating cracks. Under the influence of tensile stress, these cracks have the potential to propagate, ultimately leading to the brittle failure of the material. In the presence of certain substances, such as hydrogen sulphide (H\({}_{2}\)S) or arsenic compounds, the hydrogen cracking process accelerates. These substances act as recombination poison and delay the formation of molecular hydrogen (H\({}_{2}\)), extending the residence time of atomic hydrogen on the metal surface thus increasing the probability of atomic hydrogen ingress in the metal. When this phenomenon occurs in an environment saturated with H\({}_{2}\)S, followed by cracking of the metal in the presence of stress, it is commonly referred to as Sulphide Stress Cracking (SSC).  
SCC shares similarities with hydrogen cracking with both qualifying as environmentally assisted cracking (EAC). The key difference is that corrosion serves as the catalyst for crack initiation and propagation in SCC. SCC requires both a corrosive environment and sustained surface tensile stress on a susceptible material [41; 42]. Crack initiation can result from various factors, including the formation of a pit or crevice, microstructural defects, or high local stress. Once the crack initiates, the combined effects of the corrosive environment and stress accelerate crack propagation. Hydrogen cracking and SCC can occur simultaneously, further enhancing crack propagation.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 4 Experimental Results and Discussion
### Forecast Result Analysis
Figure 12 shows that the error curve was obviously too large. The calculated RMSE error is shown in Table 5, and the average RMSE error of the transmission loss data under each array element was 17.594 dB. As shown in Figure 11d, it can be seen that there was a large error between the actual sea test data and the data predicted by the Scooter model. It is obvious that this model does not fit under the current marine environmental conditions, and the calculated average RMSE error was 55.334 dB. Comparing Figure 11c,d, it is obvious that these two models are not suitable for the current marine environmental conditions. If this model is selected incorrectly for calculation, it will cause the calculation result to deviate too much from the actual result. The results verified by actual data showed that the new method had higher matching for the model selection of transmission loss prediction, and the prediction of transmission loss had higher accuracy.