esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Anisotropic Maritime Atmospheric Spectrum
### Longitudinal Component of the WSF  
First, consider the longitudinal component of the WSF. By substituting Equation (5) into Equation (9), under geometrical optics approximation \(\exp(-\frac{\Lambda Lx^{2}\xi^{2}}{k})\cong 1\), and expanding \(J_{0}(\cdot)\) as Maclaurin series representation, the longitudinal component of the WSF can be expressed, as follows:  
\[\begin{split} d(\rho,L)=& 8\pi^{2}k^{2}LA(\alpha)\tilde{C}_{n}^{2} \xi^{2-\alpha}\sum_{n=1}^{\infty}\frac{(-1)^{n-1}}{n!(1)_{n}}(\frac{\rho}{2})^ {2n}\int_{0}^{1}(1-\Theta\xi)^{2n}d\xi\\ &\times\int_{0}^{\infty}\kappa^{2n+1}(\kappa^{2}+\xi^{2})^{-\frac {\alpha}{2}}[1+a_{1}\frac{\kappa}{\kappa_{H}}+a_{2}(\frac{\kappa}{\kappa_{H} })^{3-\frac{\alpha}{2}}]\exp(-\frac{\kappa^{2}}{\tilde{\kappa}_{H}^{2}})d \kappa,\end{split} \tag{11}\]  
One can use the confluent hypergeometric function of the second kind [28], which is defined, as follows:  
\[U(a;c;z)=\frac{1}{\Gamma(a)}\int_{0}^{\infty}e^{-zt}t^{a-1}(1+t)^{c-a-1}dt, \quad a>0,\quad\text{Re}(z)>0 \tag{12}\]  
\[U(a;c;z)\sim\left\{\begin{array}{ll}\frac{\Gamma(1-c)}{\Gamma(1+a-c)}+\frac {\Gamma(c-1)}{\Gamma(a)}z^{1-c},\quad|z|\ll 1\\ z^{-a},\quad\text{Re}(z)\gg 1\end{array}\right. \tag{13}\]  
The longitudinal component of the WSF for Gaussian beam wave can be expressed, as follows:  
\[\begin{split} d(\rho,L)=& 4\pi^{2}k^{2}LA(\alpha)\tilde{C}_{n}^{2}\xi^{2- \alpha}\left\{\begin{array}{ll}\frac{\rho^{2}\tilde{\kappa}_{0}^{4-\alpha} }{12\Gamma(\frac{\alpha}{2})}(1+\Theta+\Theta^{2})\\ &\times[\Gamma(\frac{\alpha}{2}-2)+b_{1}\Gamma(\frac{5}{2})\Gamma(\frac{ \alpha-5}{2})+b_{2}\Gamma(\frac{14-\alpha}{4})\Gamma(\frac{3\alpha-14}{4})]\\ &+\xi_{H}^{2-\alpha}\Gamma(1-\frac{\alpha}{2})[1-\frac{J_{1}(\alpha)}{1- \[PERSON]}+\frac{\[PERSON] J_{2}(\alpha)}{1-\[PERSON]}]\\ &+a_{1}\tilde{\kappa}_{H}^{2-\alpha}\Gamma(\frac{3}{2}-\frac{\alpha}{2})[ 1-\frac{J_{3}(\alpha)}{1-\[PERSON]}+\frac{\[PERSON] J_{4}(\alpha)}{1-\[PERSON]}]\\ &+a_{2}\tilde{\kappa}_{H}^{2-\alpha}\Gamma(\frac{5}{2}-\frac{3\alpha}{4})[ 1-\frac{J_{5}(\alpha)}{1-\[PERSON]}+\frac{\[PERSON] J_{6}(\alpha)}{1-\[PERSON]}]\ \right\},\end{split} \tag{14}\]  
where \(Q_{H}=\rho^{2}\tilde{\kappa}_{H}^{2}/4\), \(b_{1}=a_{1}\tilde{\kappa}_{0}/\tilde{\kappa}_{H}\), \(b_{2}=a_{2}\tilde{\kappa}_{0}^{2-\alpha/2}/\tilde{\kappa}_{H}^{3-\alpha/2}\). \(J_{1}(\alpha)\), \(J_{2}(\alpha)\), \(J_{3}(\alpha)\), \(J_{4}(\alpha)\), \(J_{5}(\alpha)\), \(J_{6}(\alpha)\)[28].  
\[\begin{split} J_{1}(\alpha)=& _{2}F_{2}(\frac{1}{2},1-\frac{\alpha}{2};\frac{3}{2},1;-Q_{H}),\\ J_{2}(\alpha)=&_{2}F_{2}(\frac{1}{2},1-\frac{\alpha}{2 };\frac{3}{2},1;-Q_{H}\Theta^{2}),\\ J_{3}(\alpha)=&_{2}F_{2}(\frac{1}{2},\frac{3}{2}- \frac{\alpha}{2};\frac{3}{2},1;-Q_{H}),\\ J_{4}(\alpha)=&_{2}F_{2}(\frac{1}{2},\frac{3}{2}- \frac{\alpha}{2};\frac{3}{2},1;-Q_{H}\[PERSON]^{2}),\\ J_{5}(\alpha)=&_{2}F_{2}(\frac{1}{2},\frac{5}{2}- \frac{3\alpha}{4};\frac{3}{2},1;-Q_{H}),\\ J_{6}(\alpha)=&_{2}F_{2}(\frac{1}{2},\frac{5}{2}- \frac{3\alpha}{4};\frac{3}{2},1;-Q_{H}\[PERSON]^{2}),\end{split} \tag{15}\]
## 2 Materials and Methods
### Method
#### 2.3.3 Importance Determination of Different Variables  
The feature importance (FI) analysis was employed to identify the importance of different variables in crop yield prediction. The 'feature' here refers to model predictors, including SIF, EVI, and climate variables. The FI technique randomly permutates the values of one feature while keeping the values of remaining features unchanged and then evaluates the relative importance of the aimed feature by measuring the reduction of model performance [81]. The rationale behind this algorithm is that by shuffling the feature values randomly, meaningful information of the feature is replaced with random noise. As a result, the association between feature and response variables is broken, leading to the decrease of the variance explained by the model (R\({}^{2}\)) [82; 83]. In the current study, we calculate FI scores by averaging the results from the 100 repeated experiments, and the FI score for a feature is defined as follows:  
\[FI_{feature}=R_{original}^{2}-\frac{1}{M}\sum_{i=1}^{M}R_{shuffled}^{2} \tag{6}\]  
where \(R_{original}^{2}\) is the R\({}^{2}\) of the original model without permutation and \(R_{shuffled}^{2}\) is the value of the model after permutating the feature variable; M is the number of times the feature is shuffled, which aims to increase the robustness of the method [81].
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 1 Introduction
Considering _SM_ can better reflect the _ET_ rate at a large regional scale [33; 34], _ET_ often dominates the _WUE_ variability in arid and semi-arid regions [7]. Therefore, whether from _NPP_ or _ET_ pathways, _SM_ should be the dominant factor affecting _WUE_. However, the influencing mechanism by which _SM_ variability affects _WUE_ is still unclear. Soil water variation has a dual effect on the _WUE_ by affecting both _NPP_ and _ET_. However, current research on the quantitative analysis of this complex process is insufficient. Thus, this study focuses on three questions: (1) What were the spatiotemporal variation characteristics of _WUE_ in the terrestrial ecosystems of China over the past 30 years (1985-2014)? (2) What is the cause and effect mechanism by which _SM_ influences _WUE_ and how can it be quantitatively evaluated? (3) To what extent does the variation of _SM_ affect _WUE_?
## 5 Conclusions and Future Works  
In this paper, exact mathematical expressions towards accurate speed-time characteristics IMs modeling during no-load direct startups were derived. The derived expressions were based on the Kloss equation and electromagnetic torque expression derived from the Thevenin single-cage equivalent circuit, and unlike the literature, both of them include the bearing losses. Both expressions are novel and are not presented before in the literature.  
The speed-time characteristics during direct startup for different IMs (in respect to power) obtained using the proposed methods are compared with the characteristics obtained using the corresponding expressions presented in the literature. The simulation and experimental results observed validate the figured-out conclusions about the accuracy of derived expressions.  
The small differences in the calculated acceleration times are due to the fact that the investigated machine sizes are small. However, for higher machines power, the differences will be higher. Although the differences are small, the mathematical relation proposed in this work is accurate as they include bearing losses, simple and straightforward. In addition, the proposed expressions can generally be applicable for all IMs, for old and new machines, regardless of the machine power or voltage level.  
Figure 6: Measured and simulated speed-time characteristics of IM#2.  
The derived speed-time dependence can be used for different engineering applications--protective device settings in power networks with an IM, settings phase current protection relay actions of IMs', testings of the impact of IM starting on sensitive loads, and others.  
In future works, much attention will be paid to calculating the loaded IM's speed-time curve. In such a case, starting the loaded IM with ventilation characteristics will be investigated. In addition, a speed response characteristic with a switched-off motor at high speed will be addressed to accurately determine the friction torque. Besides, the nonlinear saturation of the magnetic circuit and current displacement in the rotor bars will be investigated and solved as these phenomena significantly impact torque-time and current-time curves, which may change the engine startup time.  
[PERSON] designed the problem under study; [PERSON], [PERSON], M.R., N.K., performed the simulations and obtained the results; [PERSON], [PERSON] and [PERSON] analyzed the obtained results; [PERSON] wrote the paper, which was further reviewed by [PERSON], [PERSON], M.R., N.K. and B.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/278), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their large size.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Handover Latency Analysis  
Figure 15 shows the change in handover latency of the mobility protocol based on changes in the wireless link delay. The handover delay can be as large as the number of the control packets during the handover between the WSN BS and CoAP node increases. As Figure 15 shows, the handover latency of the proposed CoMP is similar to the results of the HMIPv6. For HMIPv6, a BU message is exchanged between the CoAP node and MAP. In contrast, for CoMP, a PUT BU request message and an ACK BU response message are exchanged between the CoAP node and WMMS.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Carrier Aggregation
[68], single and multi-flow relay-assisted CA systems were analysed, and it was observed that the relay can be used as a capacity booster for low-frequency CCs. It will improve the capacity and fairness of the CA system. LTE and 5G-NR carriers are aggregated to enhance the coverage, channel capacity, and data speed [69]. CA has several advantages, including an unutilized spectrum that can be effectively used to improve spectral efficiency, to increase the UL and DL data rates with higher throughput, and improve the system performance. As has been summarised in Table 6, different techniques have been used in recent research to resolve the coverage and capacity issues in 5G networks with CA. Figure 7: Types of carrier aggregations. CA is facing DL, UL, and implementation challenges that need to be addressed in the future:  
* DL Sensitivity: Instead of designing a duplexer for each carrier component, the interference between DL and UL at the receiver is used to design it. A separate multiplexer or duplexer is required if there is a large frequency separation between the UL and DL frequency bands. Therefore, designing a multiplexer for reducing RF front-end design at large frequency variations is a future challenge;
* Harmonic Generations: The harmonics are generated by the use of non-linear components in duplexers, power amplifiers, and transceivers. These harmonics are generated to reduce system complexity. This is one of the open challenges for researchers to design the best harmonic generator;
* Filter Design: In California, proper filter design is required to decode the carriers at the receiver without any interference;
* Power Amplifier: The intra-band UL CA signals use a higher bandwidth and a high peak-to-average power ratio (PARP) to reduce the maximum power. The maximum power can be reduced by adjusting the resource block configurations. Therefore, to achieve reduced power with high bandwidth and high PAPR, tuned power amplifiers are required. So, the open challenge is the design of tuned power amplifiers for UL CA;
## 3 Results and Discussion
In the case of the inner hydrophobic treatment, EPP-MOC and EPP-MOC-IH exhibited very similar water vapor transmission parameters which pointed out the fact that the inner hydrophobisation predominantly affected the transport of liquid water and not the water vapor properties. This finding is crucial for practical use of the developed lightweight composites as the permeability for water vapor eliminates the possible condensation problems and health risks of the indoor environment, such as respiratory diseases, allergies, etc. The phase composition of prepared composites was measured using XRD (see Figure 6). Presence of MOC phase (ICDD 00-007-0420) was confirmed in all three samples. The reaction between MgCl\({}_{2}\) and MgO followed the equation below:  
\[5\;\mathrm{MgO}+\mathrm{MgCl}_{2}\cdot 6\mathrm{H}_{2}\mathrm{O}+3\;\mathrm{H}_{2} \mathrm{O}\to 2\;[\mathrm{Mg}_{3}(\mathrm{OH})_{5}\mathrm{Cl}\cdot 4\mathrm{H}_{2} \mathrm{O}] \tag{3}\]  
All samples contained over-stoichiometry of MgO (ICDD 01-075-1525), hence MgO was present in all samples after the complete depletion of magnesium chloride in the reaction mixture. This was expected as MgO was used partially as filler. Sample R-MOC contained mainly quartz (ICDD 01-083-2471) and MOC phase and some residual MgO. Sample R-MOC contained mainly quartz and MOC phase. Also some residues of MgO were present. Sample EPP-MOC-IH contained two major phases: MOC and MgO. The last sample EPP-MOC contained mainly MOC phase and only very low amount of MgO. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & \(\mathbf{D}\) **(m\({}^{2}\cdot\)s\({}^{-1}\))** & \(\mathbf{\mu}\) **(–)** \\ \hline R-MOC & \(5.46\times 10^{-7}\) & 46.0 \\ R-MOC-LO & \(3.14\times 10^{-7}\) & 78.9 \\ EPP-MOC & \(6.47\times 10^{-7}\) & 38.3 \\ EPP-MOC-LO & \(5.72\times 10^{-7}\) & 43.3 \\ EPP-MOC-IH & \(6.40\times 10^{-7}\) & 38.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Water vapor transport properties of MOC composites. The microstructure of prepared composites was measured using SEM and the results are shown in Figure 7. In all samples, the needle-like structures typical for MOC phase were detected. The boundaries between the matrix and EPP (samples EPP-MOC-IH and EPP-MOC) are highly compact without cracks or other defects. Selected areas of composites were also analyzed using EDS. While the chemical composition varies significantly from region to region, the elemental composition values are not shown here. However, according to elemental maps the grain boundaries between EPP and MOC matrix are clearly visible. Figure 6: Diffractograms of R-MOC **(a)**; EPP-MOC-IH **(b)**; EPP-MOC **(c)**. Figure 7: SEM micrographs and elemental maps of R-MOC **(a)**; EPP-MOC-IH **(b)**; EPP-MOC **(c)**.
## 1 Introduction
### State of the Art
#### 1.1.3 Deep Learning Approach  
Currently, deep learning algorithms, in particular convolutional neural networks, are used when attempting handwriting recognition. Their operation is based on the representation of human senses. When using this algorithm, the user is relieved to create feature vectors. Convolutional neural network by way of convolution, or splicing operation. Successive layers of the neural network are designed to generate a set of features without user intervention. Subsequent layers of the convolutional network create some generalisation of the information from the previous layer. In the case of the publication created by [PERSON], [PERSON], and [PERSON] [9], the previously mentioned convolutional network was applied to Chinese handwriting recognition. An additional aspect considered was the acquisition of a Direction Decomposed Feature Map. The authors report an accuracy of 97.37%. The presented solution works on images in shades of gray, while the algorithm presented by the authors can work on data also containing information in all colours.
## 4 Experiment
### Evaluation of Feature Matching Accuracy
#### 4.2.2 Metrics  
In this experiment, the mean matching accuracy (MMA) across all image pairs is adopted as the evaluation metric. A predicted correspondence is considered correct if its reprojection error is below a specified threshold, ranging from 1 to 10 pixels; otherwise, it is regarded as incorrect.
## 4 System Analysis and Experiment
### System Setup and Dual-Receiving Method
If both chirp signals' RSS values exceed the retention threshold, then only the time corresponding to the _first_ chirp acoustic signal and the received signal shall be taken as the standard. 2. If only one of them has enough RSS intensity that exceeds the retention threshold, then only the time corresponding to the stronger one and the received signal shall be taken as the standard. 3. If both RSS values fail to exceed the retention threshold, the \"invalid reception\" command is supposed to return to the main anchor, and then the system terminates this localization loop in advance. Therefore, the execution process of the main anchor can be shown in Figure 7 below:In order to cooperate with the feature extraction algorithm implemented in Android smartphones, the system may need to consider the Chan algorithm [42] or Taylor [43] algorithm to assist in the NLOS identification process after the audio segmentation is finished. When the system contains four different effective acoustic signal transmission paths, these two algorithms can also be used to calculate the appearance probability on NLOS acoustic signal. The maximum likelihood estimation is expected to be used according to prior information by statistics and necessary mathematical operations to assist the system in completing necessary judgments. Finally, the system will give the final result by displaying the coordinates on the Android APP appropriately. To guarantee that, even after the compulsory processing in the system, the next loop will still not be affected, the time interval required for the necessary system termination is set as 5 s. If NLOS scenarios happen too frequently in the system, the normal function may be affected passively. However, on the one hand, to make the final results in complex and flexible NLOS scenarios more accurate, we still consider them as a necessary sacrifice, although the protection intervals reduce the efficiency of system utilization. On the other hand, the deployment scheme also needs to be adjusted according to the indoor sites, which will further reduce the possibility of NLOS scenarios. The CTGSA-AFSA method is used to optimize DT for NLOS acoustic signal identification method, and the subsequent NLOS situation coping mechanism is adopted when necessary. Meanwhile, the working conditions and the Android APP permissions needed should also be considered cautiously. In total, the system working time axis in an indoor localization loop is as follows in Figure 8.  
Figure 7: Main anchor working time axis in one localization loop. The NLOS solutions are adopted from [22].
## 2 Geological Setting  
The NQ is situated within the Qinling-Qilian-Kunlun orogenic belt, a part of the Central Orogenic Belt of China in the northern Tibetan Plateau. It delineates the boundary between the Qilianshan block to the north and the Qaidam block to the south, stretching approximately six hundred kilometers from Tanjianshan, through Xitieshan and Luliangshan, to Dulan in a southeast-northwest direction (Figures 1 and 2).  
The cover sequence consists of Neoproterozoic metamorphic sedimentary rocks interspersed with marbles and eclogite blocks of varying size [30]. Additionally, it includes volcano-sedimentary deposits overlain by the Tanjianshan Formation and Ordovician to Silurian clastic rocks.  
The NQ area in the Qinghai Province is a significant nonferrous metallogenic belt in China, characterized by the occurrence of Cu-Au and Cu-Co deposits primarily within the Cambrian-Ondovician meta-volcanic rock group and fault tectonic fracture zone. These deposits are controlled by intrusions and ductile and/or brittle faults [31], which not only provide energy but also serve as material sources for magmatic- hydrothermal mineralization. The structures, depicted in Figure 2 and predominantly oriented in a southeast-northwest direction, act as the primary ore-controlling faults for Cu, Ag, Zn, and Pb deposits. A regional geological survey of Qinghai revealed numerous basic and ultrabasic rocks in the NQ [30]. Notably, the discovery of the Niubiliang ultrabasic Cu-Ni sulfide deposit represented the first of its kind in the NQ. Subsequently, the Beishan and Xiarihamu Cu-Ni deposits were also identified. In recent years, 13 Cu ore bodies have been discovered in the Luliangshan Cu deposit. However, the relatively limited exploration of resources in Luliangshan, compared to other areas, suggests great potential and the need for further exploration.  
Figure 1: Satellite image of China (**a**), Qinghai Province (**b**) and the research area (**c**) in the NQ. Data from Google Earth.  
Figure 2: Sketch map of geological structures in the NQ (**a**) and the research area (**b**).
## 5 Comparative Results and Analysis
### Test case 1
#### Test case 2
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Min. Cost (S)** & **Methods** & **Min. Cost (S)** \\ \hline CSO [10] & 115,967.7205 & QQNLP [29] & 116,993.2 \\ \hline EMA [11] & 115,611.84 & IMPAO [30] & 116,640.6 \\ \hline IGA\_NCM [13] & 115,685.2 & CLWSMA [31] & 116,389.588 \\ \hline HBJSA [18] & 116,140.34 & TVAC-PSO [36] & 115,610.465 \\ \hline OSO [19] & 116,678.2 & ETLBOIPSO [37] & 115,126.32 \\ \hline MGSO [24] & 115,606.5482 & COA [42] & 116,789.91535 \\ \hline TVAC-GSA-PSO [25] & 116,393.4034 & TLBO [44] & 116,739.3640 \\ \hline KKO [28] & 115,422 & OTLBO [44] & 116,579.2390 \\ \hline  & & MPSO [51] & 116,919 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Costs obtained by different heuristic optimization techniques for the 48-unit system (power and heat demands of 4700 MW and 2500 MWth). reported a minimum generation cost of $288,157.4297, which was a minimum compared to all the other techniques, and it took 76.65 s to compute, which was also the least amount of computation time, compared to all the other listed techniques in Table 6.  
Figure 6 shows the total costs obtained by the different optimization techniques for a load and heat demand of 12,700 MW and 5000 MWth, respectively. In this case, CLWSMA [31] gave the best results, whereas CODED GENETIC ALGORITHM [49] gave the worst results.  
_Test Case 6_  
In this case, again, a larger test data set of a 96-unit system was available, with 52 traditional power units, 24 cogeneration units, and 20 heat-only units [12; 17; 30; 31]. All the algorithms' comparative results are listed in Table 7 for the load demand of 9400 MW and the heat demand of 5000 MWth. The CLWSMA [31] method reported a minimum generation cost of $235,083.367, which was the least, compared to the other listed techniques in Table 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Minimum Cost (S)** & **CPU Time (s)** \\ \hline TVAC-PSO [9] & 295,680.9138 & 90.21 \\ \hline WOA [12] & 290,123.97424 & 158.18 \\ \hline HBOA [17] & 289,822.39 & 114.5 \\ \hline IMPAO [30] & 289,903.8 & 134.4 \\ \hline CLWSMA [31] & 288,698.9636 & 124.2 \\ \hline SMA [31] & 288,978.8 & 89.5 \\ \hline CODED GENETIC & & \\ ALGORITHM [49] & 298,417.18704 & 140.91 \\ \hline MPHS [50] & 288,157.4297 & 76.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test results for the test data of 84-unit system. Figure 6: Total costs for power and heat demands of 12,700 MW and 5000 MWth, respectively. Figure 7 shows the total costs obtained by different optimization techniques for a load and heat demand of 9400 MW and 5000 MWth, respectively. In this case, HBOA [17] gave the best results, whereas RCGA-IMM [49] gave the worst results.