esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Control System
### Design of ANFI
#### 3.2.1 Architecture of ANFIS  
Assuming that ANFIS had two inputs \(\Delta I\) and \(\Delta\varphi\), and one output \(q\). As shown in Figure 4, ANFIS covered the input variables with 8 fuzzy subsets. The rules of the rule base are:If \(\Delta l\) is \(A_{i}\) and \(\Delta\varphi\) is \(B_{i}\), then \(f_{i}=p_{i}\Delta l+q_{i}\Delta\varphi+s_{i}\), (\(i=1,2\cdots,8\)).  
Where \(A_{i}(B_{i})\) is the fuzzy subset of \(\Delta l(\Delta\varphi)\), \(p_{i},q_{i},s_{i}\) called the consequence parameters are the linear parameters of the function \(f_{i}\).  
The structure of each layer of ANFIS was as follows.  
Layer 1  
This layer is called the \"fuzzification layer\". The nodes are adaptive.  
\[O_{i}^{1}=\mu_{A_{i}}(\Delta l)\text{ or }O_{i}^{1}=\mu_{B_{i}}(\Delta\varphi), \ i=1,2\cdots,8 \tag{24}\]  
where \(\Delta l\) or \(\Delta\varphi\) is the input of the node \(i\). \(A_{i}\) or \(B_{i}\) is the language tags associated with this node function, \(O_{i}^{1}\) is the membership function of \(A_{i}\) or \(B_{i}\), which specifies how much given \(\Delta l(\Delta\varphi)\) satisfies \(A_{i}\) (\(B_{i}\)). The superscript 1 of \(O_{i}^{1}\) indicates the first layer, and the subscript \(i\) indicates the \(i\)th node.  
Layer 2  
This layer is called the \"rule layer\". The nodes were fixed and labeled as \(\Pi_{i}\), \((i=1,2\cdots,64)\). The nodes realized the process of fuzzy inference. The output, which represented the firing strength of a rule, was the product of all the incoming signals.  
\[O_{i}^{2}=\omega_{i}=\mu_{A_{i}}(\Delta l)\bullet u_{B_{i}}(\Delta\varphi),\ i=1,2 \cdots,8 \tag{25}\]  
Layer 3  
This layer is called the \"normalization layer\". The nodes were fixed and labeled as \(N_{i}\) (\(i=1,2\cdots,64\)). Every node in this layer received all outputs from layer 2 and the normalized firing strength of the \(i\)th rule.  
\[O_{i}^{3}=\overline{\omega_{i}}=\frac{\omega_{i}}{\sum\limits_{i=1}^{64} \omega_{i}},\ i=1,2\cdots,64 \tag{26}\]  
Layer 4  
This layer is called the \"defuzzification layer\". The nodes were adaptive.  
\[O_{i}^{4}=\overline{\omega_{i}}f_{i}=\overline{\omega_{i}}(p_{i}\Delta l+q_{i }\Delta\varphi+s_{i}),\ i=1,2\cdots,64 \tag{27}\]  
where \(\overline{\omega_{i}}\) is a normalized firing strength value received from layer 3, \(\Delta l\) and \(\Delta\varphi\) are the input of the node \(i\), and \(p_{i},q_{i},s_{i}\) are the linear parameters of the function \(f_{i}\).  
Layer 5  
Figure 4: The architecture diagram of an adaptive neural-fuzzy inference system (ANFIS).  
This layer is called the \"summation layer\". The single node was fixed, and labeled as \(\Sigma\). This layer calculated the sum of all outputs from layer 4 and provided an output of ANFIS.  
\[O_{1}^{5}=\sum_{i=1}^{64}\vec{\omega_{i}}f_{i}=\frac{\sum\limits_{i=1}^{64}\omega _{i}f_{i}}{\sum\limits_{i=1}^{64}\omega_{i}} \tag{28}\]
## 5 Conclusions  
In the context of global climate change, enhancing our understanding of the effects of droughts on vegetation is of great significance for maintaining the balance of the carbon cycle in the ecosystem. In this study, we used SPEI and VPD to analyze spatial-temporal patterns of drought in China from 2001 to 2020. The vegetation parameter GPP was used to evaluate vegetation productivity and analyze the annual and seasonal trends of drought and GPP under different vegetation types. The response of vegetation GPP to water deficit/drought under different vegetation types was assessed using Pearson correlation analysis. The conclusions of this study are as follows:  
In terms of annual trends, China's SPEI and GPP showed an upward trend in most regions, whereas VPD showed an overall downward trend. Among the different seasons, both SPEI and VPD showed the most areas with an increasing trend of drought in spring and the least in autumn. GPP shows an upward trend, with the largest share of areas showing this trend in autumn.  
About 70.7% of the pixels of the SPEI and GPP were positively correlated, which was greater than that of the negative correlation. About 66.2% of the total area showed that GPP and VPD were negatively correlated. The relationships of SPEI, VPD, and GPP indicated that the negative effects of water deficit on vegetation were more pronounced in summer and autumn. In spring, savannas were the most sensitive to water deficit. Grasslands were most sensitive to water deficit in both summer and winter. By contrast, croplands were the most sensitive to water deficit in autumn. During drought periods, vegetation growth was inhibited, especially in northern China, southwest China, and the Qinghai-Tibet Plateau.  
Conceptualization, X.W., R.Z. and Q.W.; data curation, R.Z.; formal analysis, X.W. and R.Z.; funding acquisition, Q.W.; investigation, X.W., R.Z., V.A.B., S.L., J.Q. and J.Z.; methodology, X.W. and Q.W.; project administration, Q.W.; software, X.W.; supervision, Q.W.; validation, V.A.B., S.L., J.Q., J.Z. and Q.W.; visualization, X.W.; writing--original draft, X.W.; writing--review and editing, R.Z., V.A.B., S.L., J.Q., J.Z. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian Province (2021J01627) and the National Natural Science Foundation of China (41601562).  
The SPEI and VPD data in this study are available from the corresponding author upon reasonable request.  
Thanks to the Natural Science Foundation of Fujian Province (2021J01627) and the National Natural Science Foundation of China (No. 41601562) for funding this work. Special thanks to [PERSON]'s team for providing the interpolated meteorological data.  
The authors declare no conflict of interest.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 2 Proposed Method Framework
### Ship Tracking
#### 2.3.2 MHT Tracker  
After ship detection, we can get a preliminary detection result for each frame, which contains not only the true targets, but also the false detections such as broken clouds, waves, and other interference. Therefore, a stable tracker is needed for effective data association. In this paper, an MHT tracker with amplitude information is used for ship tracking. MHT algorithm which is regarded as theoretically the best data association algorithm is widely used in modern tracking system. The key idea of MHT is to use multi-frame measurements to resolve the uncertainty of data association hypotheses which are propagated into the future to accumulate the temporal information. The log likelihood ratio (LLR) in MHT is often used to evaluate alternative track formation hypotheses and track score for track \(j\)th at frame \(k\) can be obtained by the recursion given below:  
\[L^{ij}(k)=L^{ij}(k-1)+\Delta L^{ij}(k) \tag{20}\]  
where \(\Delta L^{ij}(k)\) is the increments of the score, which is calculated as  
\[\Delta L^{ij}(k)=\left\{\begin{array}{c}\ln(1-P_{D}),no\,update\\ \Delta L^{ij}_{k}(k)=\ln\biggl{(}\frac{p_{D}}{2\pi\lambda_{j}/\sqrt{|\mathbf{S }|}}\biggr{)}-\frac{d_{ij}^{2}}{2},trackupdate\end{array}\right. \tag{21}\]  
where \(P_{D}\) is the probability of detection, \(\lambda_{f}\) is false alarm density, \(\mathbf{S}\) is residual covariance matrix, \(d_{ij}\) is the Mahalanobis distance between measurement \(i\) and track \(j\). To increase data association, the amplitude information is incorporated into the traditional MHT framework in this paper. As target amplitude is independent from its kinematics, the joint likelihood of a target is the product of the kinematic likelihood and the amplitude likelihood, and the original LLR increment can be represented as  
\[\Delta L^{ij}(k)=\Delta L^{ij}_{K}(k)+\Delta L^{ij}_{A}(k) \tag{22}\]  
where \(\Delta L^{ij}_{K}(k)\) and \(\Delta L^{ij}_{A}(k)\) are respectively the kinematics and amplitude increment, both of which contribute sufficient information to the track score and data association. In this paper, \(\Delta L^{ij}_{A}(k)\) takes the form as  
\[\Delta L^{ij}_{A}(k)=\ln\{\exp[-(a^{i}-amp^{j})^{2}/\sigma_{a}^{2}]/c_{1}\} \tag{23}\]  
where \(a^{i}\) is the amplitude of measurement \(i\), and \(amp^{j}\) is the estimated amplitude of track \(j\). \(c_{1}\) is the constant probability for the posterior of the background (null) hypothesis.  
As some false targets are also contained in the tracking, the gating test with a distance threshold and the limit of target speed (maximum and minimum) is used. We also use well-known \(M/N\) logic method for track management. A tentative track is confirmed if there are at least \(M\) detections falling in the subsequent gates over \(N\) consecutive scans, otherwise the track is discarded. A confirmed track is terminated if no detections in the past \(L\) consecutive frames have been validated or the speed of a target is unfeasible.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Hormones
#### 4.6.2 DHEA (Dehydroepiandrosterone)  
Dehydroepiandrosterone (Figure 5) is a metabolic intermediate in the synthesis of testosterone, estrone, and estradiol. It is a steroid secreted by the adrenal glands and brain. In both men and women, DHEA levels decrease by about 2% per year. Several clinical studies have assessed the effect of dehydroepiandrosterone treatment on oxidative damage and renal dysfunction. DHEA has been shown to protect many tissues from oxidative damage and to have beneficial effects on vascular function, atherosclerosis, and diabetic complications. DHEA interferes with early prooxidant responses and increases in TNF (tumor necrosis factor) caused by infrared radiation [158].  
Research has demonstrated that DHEA treatment can increase the rate of sebum production, a positive effect particularly for menopausal individuals who often experience a decline in sebum levels due to hormonal changes. Sebum is essential for maintaining skin hydration and preventing dryness, which is common during menopause. Topical DHEA has been found to enhance the brightness of the skin, which can become dull and uneven due to hormonal aging. It helps combat the papery texture and thinning of the skin (epidermal atrophy) that often accompanies hormone-related skin aging. While there is evidence suggesting that topical DHEA could influence the skin's aging process, especially in terms of wrinkle formation, this aspect still requires further research to confirm its effectiveness in wrinkle reduction [159].  
The production of DHEA for cosmetic use involves both chemical and biotechnological methods, each offering distinct advantages. A common chemical synthesis route for DHEA starts with 16-dehydropropgenolone acetate. This compound undergoes oximation, followed by a Beckmann rearrangement, hydrolysis, and refinement to yield DHEA. Advancements in biotechnology have led to the development of methods to produce DHEA using microbial biotransformation. For instance, certain Mycobacterium species can convert phytosterols into DHEA in a vegetable oil/aqueous two-phase system, offering an efficient production method [160; 161].
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Traditional Method  
This paper focuses on the critical role of specific technology or methods in enhancing a multiple-input multiple-output (MIMO) system in Figure 2. By leveraging this technology, we aim to augment the system's capacity, primarily by improving performance metrics such as the bit error rate. Our study involves a comprehensive comparison between conventional signal detection techniques, including ZF and MMSE, and advanced methods based on the ELMx algorithm, namely ELM, RELM, and ORELM. Moreover, we introduce the convolutional neural network-based signal detection CNN-SD algorithm, proposing it as a novel approach for further performance enhancement in MIMO systems.  
Figure 2: A block diagram of how to estimate signal detection.
###### Abstract  
In order to improve the flight efficiency of a flying-walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots.  
flying-walking power transmission line inspection robot; energy consumption model; flight missions' path; multiobjective optimization; improved NSGA-II +
Footnote †: journal: Applied Sciences
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Varying Area Size  
In the previous experiment, we have increased the number of APs while keeping the area size fixed. In the third experiment, we vary the area size while keeping the number of APs and stations fixed. It is true that both changing the number of APs and changing the area size affect the cell size. The difference is that with fixed number of stations, increasing the number of APs lead to smaller number of stations per cell, and vice versa. On the other hand, increasing area size while keeping the number of APs and number of user stations fixed will only increase the average distance between user stations and their associated APs, while keeping the number of user stations per cell the same.  
In this experiment, the area size is varied from 25 m \(\times\) 25 m to 250 m \(\times\) 250 m. As we increase the area size, the size of each cell increases, meaning that the average distance from a station to an AP will be longer. The result is shown in Figure 11. In the figure, the X-axis is the edge length, which is the length of an edge in the square-shaped area.  
Figure 11: _Cont._  
When the area is very small, the spatial reuse opportunity is rare, and therefore the throughput is low. As the area size increases to some extent, the network throughput increases because more parallel transmission can take place in the area. However, if the area becomes larger while the number of APs is fixed, the average distance between a station and its associated AP increases. Because of that, the acceptable interference level at the receivers decreases, and the throughput starts to decrease as shown in Figure 11a.  
The pattern is different for OBSS/PD. The total throughput continues to increase, while the fairness measures start to drop rapidly, as shown in Figure 11b-e. This is because OBSS/PD does not consider SNR at the receiver, but only uses signal strength measured at the sender to determine whether a node should start a concurrent transmission. When the distance between nodes increases, nodes tend to transmit more aggressively in OBSS/PD. As a result, nodes at the edge fail to have their packets received, while the nodes closer to their APs enjoy successful communication.  
For PSC-UL, similar observations can be made in this experiment. It achieves higher total throughput than compared methods except for PSR, which shows a higher throughput than PSC-UL. However, PSC-UL achieves significantly higher measures in terms of bottom  
Figure 11: Performance of methods varying area size. (**a**) Total throughput; (**b**) Bottom 50% throughput; (**c**) Bottom 25% throughput; (**d**) Fairness; (**e**) Non-starvation Ratio; (**f**) Delivery Ratio.  
50% and 25% throughput, [PERSON]'s fairness index, and non-starvation ratio. The performance of PSC-UL is steady with increased area size, unlike OBSS/PD and PSR.
# Design Method Using Response Surface Model for CFRP Corrugated Structure under Quasistatic Crushing
## 4 Analysis and Discussion
### Mathematical Model of Energy Absorption Efficiency
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Partial Regression**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**95\% Confidence Interval**} \\  & **Coefficient** & & & & **Lower** & **Upper** \\  & (\(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\)) & & & & **Lower** & **Upper** \\ \hline Intercept & 42.6 *** & \(p<0.001\) & 5.14 & 31.0 & 54.2 \\ Number of stacks & 1.29 ** & 0.00564 & 0.356 & 0.481 & 2.09 \\ Pitch & \(-0.332\) & 0.145 & 0.208 & \(-0.803\) & 0.139 \\ \hline Multiple & & & & & \\ R-squared (R\({}^{2}\)) & & & & & \\ Adjusted & & & & & \\ R-squared (R\({}^{2}\)) & & & & & \\ \hline \hline \end{tabular}  
* Coefficient estimated with statistical significance of 0.001; ** coefficient estimated with statistical significance of 0.01.  
\end{table} 
Table 4: Multiple regression analysis results using all results (EAE). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Explanatory Variable**} & \multicolumn{2}{c}{**Response Variable**} \\ \hline \multicolumn{2}{c}{**Number of Stacks (ply)**} & \multicolumn{1}{c}{**Pitch (mm)**} & \multicolumn{1}{c}{**EAE (J/g)**} \\ \hline  & 5 & 52.9 \\ \cline{2-3}  & 10 & 64.9 \\ \cline{2-3}
16 & 15 & 58.5 \\ \cline{2-3}  & 20 & 55.6 \\ \hline \hline \end{tabular}  
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Partial Regression**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**95\% Confidence Interval**} \\  & & **Coefficient** & & & **Lower** & **Upper** \\  & (\(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\)) & & & & **Lower** & **Upper** \\ \hline Intercept & 42.6 *** & \(p<0.001\) & 5.14 & 31.0 & 54.2 \\ Number of stacks & 1.29 ** & 0.00564 & 0.356 & 0.481 & 2.09 \\ Pitch & \(-0.332\) & 0.145 & 0.208 & \(-0.803\) & 0.139 \\ \hline Multiple & & & & & \\ R-squared (R\({}^{2}\)) & & & & & \\ Adjusted & & & & & \\ R-squared (R\({}^{2}\)) & & & & & \\ \hline \hline \end{tabular}  
* Coefficient estimated with statistical significance of 0.001; ** coefficient estimated with statistical significance of 0.01.  
\end{table} 
Table 5: Multiple regression analysis results using progressive crushing mode results (EAE). This model indicates good values with determination coefficient R\({}^{2}\) being 0.925, and the adjusted coefficient of determination R\({}^{2}\) being 0.906. In addition, since the \(p\) values of this model were smaller than 0.05, and 0 was not included in the 95% confidence interval, the response variable was explained by the explanatory variables. This model, therefore, represents the response surface to be used to obtain EAE. Figure 17 shows the response surface obtained using the estimation equation and the test results. The red asterisk shown in the figure represents the excluded results of 16 ply-P5.
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion
#### 5.3.4 Average End-to-End Delay  
Figure 9 describes the ability of the BOLSR-PSO algorithm to deal with the frequent changes in the network topology, which performs better in dynamic mobility than the OLSR-Gomez, OLSR RFC-3626,  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **BOLSR-PSO** & **OLSR-RFC** & **OLSR-RAND** & **OLSR-Gomez** & **OLSR-PSO** \\ \hline Mean & 220.1 & 388.5 & 325.4 & 416.5 & 36.1 \\ Median & 190.0 & 366.1 & 276.2 & 424.4 & 32.3 \\ Mode & 8.1 & 4.1 & 6.7 & 4.1 & 4.1 \\ Best & 0.06928 & 0.0992 & 1.9821 & 0.0972 & 0.0872 \\ Worst & 596.7082 & 1028.6217 & 940.3557 & 1058.9915 & 106.7772 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical Result of NRL Performance Analysis.  
Figure 8: Normalized Routing Load vs. Number of Nodes.  
and OLSR-RAND routing protocols. The optimal parameter values give routing functionality the time needed to send packets successfully, process, and select the path. Moreover, the optimal configuration enables the OLSR functionality to make the best use of the available bandwidth, which results in a moderate delay under different urban scenarios. Table 8 shows the statistical results of average end-to-end delay in which BLOSR-PSO was good pertaining dynamic mobility. The mean is 0.28, median 0.23, mode 0.35, best 0.009 and worst 0.95. As expected, the percentage of End-to-End delay for all the selected protocols increases with an increase in the node density. The worst results were obtained by OLSR-RFC and OLSR-Gomez protocols, which required addition time to deliver the packets. This drawback limited the routing functionality to support the most cooperative vehicular application. The reason for this problem is the lack
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
## 3 Evaluation Result and Discussion
It shows that the actual supersonic BL can also have an impact on the QKD performance, and the effect is more obvious. In addition, due to the higher speed, the effective communication time between the vehicle and the ground station will be shorter, and the total key rates will be smaller. Further, although the impact of the deflection angle and the drifted offset could be ignored, some potential factors will also affect the performance of QKD which from ATP system. Therefore, it is feasible to implement QKD by supersonic aircraft, but the results would be unsatisfactory. Table 1.1 The photons distribution probability with different azimuth when the photon via the boundary layer or not. (**a–c**) show the photons distribution probability of the Gaussian beam without BL. (**d–f**) show the photons distribution probability of the Gaussian beam that passes through the BL. (**a**,**d**) show the situation when the azimuth angle is 10\({}^{\circ}\). (**b**,**e**) show the situation that the azimuth angle is 30\({}^{\circ}\). (**c**,**f**) show the situation the azimuth angle is 50\({}^{\circ}\).