esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
### Study Selection  
The PubMed search strategy identified 1684 articles for \"filler AND complications\", 7602 articles for \"HA AND complications\", 138 articles for \"tear trough AND complication\", 112 articles for \"tear trough AND filler\", 89 articles for tear \"trough AND hyaluronic acid\", 26 articles for \"tear trough AND HA\", 44 articles for \"tear trough AND (swelling OR bruising OR dyschromia), 13 articles for \"tear trough AND volumization\", and 4 articles for \"tear trough AND non-surgical\". Clinical trials and randomized clinical trials were selected. The Cochrane Library search strategy identified 89 articles using \"filler AND complications\" as keywords, 1199 when \"HA AND complications\" was searched, 5 articles for \"tear trough and complication\", 7 articles for \"tear trough and filler\", 4 articles for \"tear trough AND rejuvenation\", 4 articles for \"tear trough AND volumization\", and 4 articles for \"infraorbital hollowing AND volumization\". Trials were filtred. The Ovid search reported no results. The total amount of articles included in the review was 11017. Trials were filtered for each database, and case reports and reviews were excluded; 4238 articles were excluded from the research because they were duplicated, and 5338 were excluded for other reasons. A total of 1441 records were screened, and 507 were excluded because they were out of topic. Nine hundred and thirty-four articles were sought for retrieval, and of these, 905 articles were screened by title, and 17 were screened for abstract. Five of them were excluded because they did not correspond to the inclusion criteria. Seven articles were considered eligible to be included in the review, and among the references of these, two studies were evaluated and defined as eligible for the study. A total of nine studies were selected as eligible at the end (Figure 1).
## 3 Numerical Simulations  
### Parameter Definitions  
The average convective heat transfer coefficient \(h\) can be expressed as:  
\[h=\frac{q}{T_{w}-T_{in}} \tag{10}\]  
where \(q\) is heat flux applied to the bottom surface of heat sink, \(T_{in}\) is inlet air temperature and \(T_{w}\) is average temperature of heat transfer wall.  
The average Nusselt number of the heat sink:  
\[Nu=\frac{hD}{k_{f}} \tag{11}\]  
where \(D\) denotes the equivalent diameter of heat sink, \(k_{f}\) denotes the thermal conductivity of the cooling medium, the equivalent diameter can be expressed as  
\[D=\frac{2 HW}{H+W} \tag{12}\]  
where \(H\) and \(W\) represent height and width of the channel respectively.  
The temperature control coefficient \(\alpha\) and temperature uniformity coefficient \(\beta\) are defined to assess the comprehensive thermal performance as follows:  
\[\alpha=1-\frac{T_{av}-T_{in}}{T_{acm}-T_{in}} \tag{13}\]  
\[\beta=1-\frac{T_{max}-T_{min}}{\Delta T_{ac}} \tag{14}\]  
where \(T_{av}\), \(T_{max}\), \(T_{min}\) and \(T_{in}\) are the average temperature, maximum temperature, minimum temperature and initial temperature of cooling air, respectively; \(T_{acm}\) and \(\Delta T_{ac}\) are the acceptable maximum temperature and acceptable temperature difference, which are defined as 40 \({}^{\circ}\)C and 5 \({}^{\circ}\)C, respectively; \(T_{in}\) represents the initial temperature of the cooling medium, which is 26.85 \({}^{\circ}\)C. The closer the \(\alpha\) is to 1, the closer the maximum temperature of the battery is to \(T_{in}\). Similarly, the closer the \(\beta\) is to 1, the smaller the temperature difference of the battery is achieved, and negative \(\beta\) indicates that the temperature difference is greater than 5 \({}^{\circ}\)C.  
Considering both thermal performance and flow performance, the effectiveness of improving the convective performance of the porous distribution design is further compared according to the figure of merit (FOM) [38]. FOM represents the improvement based on average heat transfer coefficients against the accompanied pumping power consumed, which means the higher the FOM, the better the comprehensive heat transfer performance of the heat sink.  
\[FOM=\frac{\left(h_{f}/h_{non}\right)}{\left(\Omega_{f}/\Omega_{non}\right)^{ \frac{1}{3}}} \tag{15}\]  
where \(h_{f}\) and \(h_{non}\) indicate the convective heat transfer coefficient with and without aluminium foam, respectively; \(\Omega_{f}\) and \(\Omega_{non}\) indicate the required pumping power with and without aluminium foam, respectively.  
\[\Omega=\frac{\dot{m}}{\rho}(p_{in}-p_{out}) \tag{16}\]  
where \(m\) is the mass flow rate of cooling air, \(p_{in}\) and \(p_{out}\) are the average pressures at the inlet and outlet, respectively.
## 3 Proposed Methodology
### Deep Neural Network (DNN)  
DNN belongs to the family of supervised learning algorithms to train the model using multiple layers. The DNN adopted in this study is based on the idea of the feed-forward artificial neural network with multiple hidden layers to enhance the abstraction features for increased capability [66]. DNN structure consists of input layers, multiple hidden layers, and an output layer as shown in Figure 1. Let \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) is the input vector with \(n=86\) features. Similarly, \(Y=\{y_{1},y_{2}\}\) is the output vector containing the probability values in the range of \([0,1]\) for classifying anomaly and benign traffic. The output calculation of each hidden layer \(H_{i}\) is mathematically given as:  
\[H_{i}(x)=A\Big{(}w_{i}^{T}x+b_{i}\Big{)} \tag{1}\]  
where, \(A(.)\) represents the nonlinear activation function, \(w_{i}\) and \(b_{i}\) represents the weight and bias of the hidden layer \(i\). The activation functions used in this study are '\(ReLU\)' for hidden layers and '\(sigmoid\)' for the output layer that is calculated using the mathematical formulas given as,  
\[ReLU(x)=\max(0,x) \tag{2}\]  
\[sigmoid(x)=\frac{1}{1+e^{-x}} \tag{3}\]  
The DNN structure used in this study consists of an input layer with \(80,\ 32,\ 16\), and \(8\) neurons representing the numerical feature set. Then, we used four dense layers with \(2^{10},2^{9},\ 2^{8}\) and \(2^{7}\) neurons followed by a sigmoid classification layer considering two outputs to represent the anomaly and benign traffic classification. Similarly, for the experiment considering both the numerical and categorical features, the input layer is furnished with only five neurons, followed by two dense layers with \(2^{8}\) and \(2^{7}\) neurons and the output layer with a sigmoid activation function to classify the network traffic into benign and anomaly traffic.  
Figure 1: A general Deep Neural Network architecture.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Impacts of Single Satellite on the Accuracy and Integrity of Subset  
In the minimal size satellite subset which we pursue, each satellite should hold an important place in the accuracy or integrity of the subset, otherwise, it could be removed without great effect on the performance of the subset. In another word, the satellite which greatly improves the accuracy or HPL of the subset when it is introduced is quite likely to be a member of the minimal size satellite subset. This assumption has also been employed by [25; 32]. However, different from abounding less impact satellites from the all-in-view solution, this study tries to iteratively adding important satellite to the current subset so that a performance-requirement-satisfied subset can be efficiently constructed. In any event, evaluations on the accuracy and HPL effects of a single satellite are necessary for the satellite selection strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Calculation Process** & \(\boldsymbol{G^{T}WG}\) & \(\left(\boldsymbol{G^{T}WG}\right)^{-1}\) \\ \hline Multiplication & \(pn^{2}+p^{2}n\) & \(\mathsf{g}(p)\) \\ Addition & \(pn^{2}+\left(p^{2}-p\right)n-p^{2}\) & \(\mathsf{g}(p)\) \\ \hline
**Calculation Process** & \(\boldsymbol{K}\) & \(\boldsymbol{S}\) & \(\boldsymbol{\Delta\hat{x}}\) & \(\boldsymbol{HPL}\) \\ \hline Multiplication & \(p^{2}n\) & \(pn^{2}\) & \(pn\) & \(4n+1\) \\ Addition & \((p^{2}-p)n\) & \(pn^{2}\) & \(pn-p\) & \(2n-1\) \\ \hline  & \(\boldsymbol{\mathsf{Sum}}\) & \multicolumn{2}{c}{**Time Complexity**} \\ \hline Multiplication & \(2 pn^{2}+(2p^{2}+p+4)n+\mathsf{g}(p)+1\) & \(\mathsf{O}\!\left(n^{2}\right)\) \\ Addition & \(2 pn^{2}+(2p^{2}-p+2)n+\mathsf{g}(p)-p^{2}-p-1\) & \(\mathsf{O}\!\left(n^{2}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amount of calculation for the weighted least-squares RAIM process.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 1 Introduction
Section 3 discusses the applications of the laser feedback interferometer in displacement sensing, vibration sensing, particle sensing, liquid evaporation rate measurement, refractive index measurement, and thermal expansion coefficient measurement. In Section 4, the microstructure imaging and measurement, profilometry, lens thickness measurement, and imaging, combined with other technologies, are presented based on the laser confocal feedback tomography. A summary and conclusion are given in Section 5.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Robustness
#### 4.3.2 Different Data Distribution  
Similarly, Four different data distributions named as uniform, center, lower bound and upper bound distribution (Section 4.1) were considered with the same variation of the network field sizes where the value of \(\alpha\) was 9. Figure 15a presents the average energy consumption by each node in different distribution. It is evident that energy consumption in uniform data distribution grows radically. This is because, the data are distributed uniformly through every sector and hence each possible sector (in this case, an average nine neighboring sectors) needed to be scanned to find the target query. The other three distributions had data concentrated in a particular portion of the network. Hence, EDDS required no transmission for the null sector, as they did not store any data. Thus, energy consumption was relatively lower than the uniform distribution. However, center and lower bound distribution have almost similar energy consumption while upper bound distribution falls in the middle. The distribution presented in Figure 12 gives the rational of this behavior. Figure 15b shows the latency performance of EDDS in different distribution. Based on the aforementioned reasoning, it is obvious that the latency in uniform distribution is higher compared to other distribution. Average latency of other three distributions is almost same. Figure 15c shows the accuracy of EDDS in different distributions. It is observed that, accuracy of EDDS drops sharply with the increase of the size of the network. The accuracy of EDDS varies in between 70% and 96% at uniform distribution, 85%-99% at center and lower bound distribution and 80%-99% at upper bound distribution.  
Figure 14: (a) Energy consumption; (b) latency; and (c) accuracy.
## 2 Methodology
### Determination of Weights
#### 2.3.2 Time Weights  
The entropy weight method is an excellent objective weighting method among all objective weighting approaches. It calculates the entropy value of each indicator based on the variation in the values of the indicators and their overall impact and then determines the weights. This method can objectively reflect the differences and importance of different indicators. Therefore, it is very reasonable to use the entropy weight method to determine the time weights.  
First, the decision results for \(K\) electricity sales packages are arranged in sequence according to \(h\) time stages, forming a \(K\times h\) matrix E, i.e.,  
\[E\ =\ \begin{bmatrix}x_{01}(1)&x_{02}(1)&\cdots&x_{0h}(1)\\ x_{01}(2)&x_{02}(2)&\cdots&x_{0h}(2)\\ \vdots&\vdots&\ddots&\vdots\\ x_{01}(K)&x_{02}(K)&\cdots&x_{0h}(K)\end{bmatrix}. \tag{38}\]  
Next, calculate the entropy value for each stage.  
\[e_{t}\ =\ -\frac{1}{\ln K}\cdot\sum\limits_{k\ =\ 1}^{K}\left[\frac{x_{0t}(k)}{ \sum\limits_{k\ =\ 1}^{K}x_{0t}(k)}\cdot\ln\frac{x_{0t}(k)}{\sum\limits_{k\ =\ 1}^{K}x_{0t}(k)}\right]. \tag{39}\]  
Finally, the weight for each stage can be calculated using the entropy values.  
\[\lambda_{t}\ =\ \frac{1-e_{t}}{h-\sum\limits_{t\ =\ 1}^{h}e_{t}}. \tag{40}\]
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 2 Methodology and Resources  
To create effective forecasting algorithms for photovoltaic generation, comprehensive datasets are needed, containing both climate information and power records. Recently, several datasets have been released and are frequently used in related research, presented in Table 1. In this section, the main datasets and their characteristics are highlighted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Location** & **Period** & **Frequency** & **Included Data** & **Observations** \\ \hline \hline PVOD [14] & Hebei, China & 1 July 2018 13 June 2019 & 15 min & \begin{tabular}{c} NWP, Local \\ Measurement Data \\ (LMD) from \\ photovoltaic power \\ stations, includes \\ features such as \\ irradiance, \\ temperature, wind \\ speed, etc. \\ \end{tabular} & \begin{tabular}{c} NWP, Local \\ Measurement Data \\ (LMD) from \\ Data from \\ 10 photovoltaic power \\ systems with metadata \\ dataset \\ \end{tabular} \\ \hline SOLETE [15] & Roskilde, Denmark & 1 June 2018 9 January 2019 & 1 s and 1:05:60 h & \begin{tabular}{c} Data such as \\ pressure, wind speed, \\ global irradiance, \\ active power from a \\ wind turbine and a PV \\ inverter. \\ \end{tabular} & Without NWP forecasts \\ \hline Solar and wind power & \begin{tabular}{c} China \\ data from the Chinese \\ State Grid [16] \\ \end{tabular} & \begin{tabular}{c} 1 January 2019 \\ 31 December 2020 \\ \end{tabular} & 15 min & \begin{tabular}{c} Solar power \\ generation, irradiance, \\ temperature, humidity \\ \end{tabular} & Without NWP forecasts \\ \hline Comprehensive dataset & \begin{tabular}{c} California, \\ forecasting [17] \\ \end{tabular} & \
\begin{tabular}{c} 2 January 2014 \\ USA \\ \end{tabular} & 1 min &
\begin{tabular}{c} Irradiance, sky images, \\ forecasts, weather data \\ \end{tabular} & Complete and diverse \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of datasets related to solar power generation and climatic parameters.  
Figure 1: General overview of this research.
## 4 Conclusions  
A highly porous PolyHIPE membrane prepared with 20 wt % organic phase (30 wt % St, 60 wt % EHA, and 10 wt % DVB) and 80 wt % aqueous phase has structural characteristics as the membrane support for supported liquid membrane. The PolyHIPE membrane has a bimodal pore size distribution with two distinct pore diameter regions (10-30 nm and 1000-9000 nm). The relative pore volume of the two distinct pore size ranges is 11.6% and 88.4%, respectively. The tortuosity of PolyHIPE is within the range of 1.08-1.21. Due to the high porosity (0.77) and low tortuosity, the Aliquat 336-PolyHIPE membrane shows high flux for Cr(VI) separation. The effective diffusion coefficient of Cr(VI) is as high as 1.75 \(\times\) 10\({}^{-11}\) m\({}^{2}\) s\({}^{-1}\). Based on the characteristic parameters of pore structure, transport study shows that the diffusion of Cr(VI) through Aliquat 336/PolyHIPE membrane can be attributed to the jumping transport mechanism. The hydraulic stability study shows that the membrane is quite stable with recovery rate remain 95% even after 10 consecutive cycles. The separation study demonstrates the potential of practical application of this new type of membrane for Cr(VI) recovery.  
**Acknowledgments:** The authors gratefully acknowledge the financial support of the Ministry of Science and Technology of Taiwan, R.O.C through its grants NSC 100-2221-E-026-041.  
**Author Contributions:** Study conception and design was conducted by [PERSON], [PERSON] and [PERSON]. Acquisition of data was conducted by [PERSON], [PERSON] and [PERSON]. Analysis and interpretation of data was conducted by [PERSON] and [PERSON]. Drafting of manuscript was accomplished by [PERSON] and [PERSON]. Critical revision was accomplished by [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ion Matter** & **Recovery (\%)** & **Separation Factor (a)** \\ \hline Cr(VI) & 95.09 & 1 \\ \hline \multicolumn{3}{c}{Cation} \\ \hline Ni(II) & 1.23 & 77.07 \\ Mg(II) & 0.78 & 120.60 \\ Cu(II) & 1.61 & 86.75 \\ \hline \multicolumn{3}{c}{Anion} \\ \hline SO\({}_{4}\)2\({}^{-}\) & 3.49 & 27.19 \\ F\({}^{-}\) & 8.51 & 11.17 \\ \hline \end{tabular}
\end{table}
Table 4: Separation of Cr(VI) from mixture solution by Aliquat 336/PolyHIPE membrane.
## 2 Study Area and Data  
### Study Area and Reference Area  
Huanghuaihai Plain (Figure 1) (32\({}^{\circ}\)-40\({}^{\circ}\) N, 114\({}^{\circ}\)-121\({}^{\circ}\) E), which includes Beijing, Tianjin, Hebei, Henan, and Shandong, is the main planting area of summer maize in China. The region belongs to the monsoon climate of medium latitudes with four clearly distinct seasons [38]. The annual precipitation is 500-900 mm. The precipitation is mostly concentrated in summer, accounting for about 70% of the annual precipitation. The mean annual temperature is 8-15 \({}^{\circ}\)C [39]. Summer maize phenology is significantly different due to the difference in climate among regions over Huanghuaihai Plain.  
Ruzhou City (Figure 1), with a relatively single planting structure, was selected as the reference area, which is located in the middle of Henan Provence (33\({}^{\circ}\)56\({}^{\prime}\)-34\({}^{\circ}\)20\({}^{\prime}\) N, 112\({}^{\circ}\)31\({}^{\prime}\)-113\({}^{\circ}\)07\({}^{\prime}\) E). Summer maize is the main summer crop in the region, accounting for about 70% of the summer crop planting area. Therefore, choosing Ruzhou City as the reference area can reduce the event of mixed classification.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 4 Simulation and Analysis
### Simulation of Path Optimization
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Environment** & **Algorithm** & **IPL (m)** & **Std** & **IT (s)** & **FPL (m)** & **Std** & **FT (s)** & **Nodes (n)** \\ \hline \multirow{3}{*}{Map1} & RRT & 919.42 & 63.83 & 0.21 & 919.42 & 63.83 & 0.21 & 141.72 \\  & RRT* & 843.29 & 30.51 & 0.28 & 750.91 & 8.62 & 9.68 & 1411.96 \\  & APSD-RRT & 832.89 & 33.5 & 0.09 & 754.24 & 14.37 & 0.13 & 40.5 \\ \hline \multirow{3}{*}{Map2} & RRT & 906.23 & 96.95 & 0.23 & 906.23 & 96.95 & 0.23 & 176.9 \\  & RRT* & 830.46 & 62.36 & 0.31 & 680.49 & 8.95 & 8.28 & 1295.06 \\  & APSD-RRT & 848.91 & 72.7 & 0.1 & 734.83 & 47.09 & 0.14 & 50.64 \\ \hline \multirow{3}{*}{Map3} & RRT & 847.07 & 50.34 & 0.72 & 847.07 & 50.34 & 0.72 & 466.23 \\  & RRT* & 742.39 & 41.51 & 1.33 & 666.48 & 12.69 & 11.87 & 1661.47 \\  & APSD-RRT & 779.44 & 56.94 & 0.13 & 671.78 & 9.46 & 0.17 & 80.98 \\ \hline \multirow{3}{*}{Map4} & RRT & 825.07 & 56.41 & 0.34 & 825.07 & 56.41 & 0.34 & 143.34 \\  & RRT* & 733.8 & 29.99 & 0.78 & 693.19 & 15.32 & 5.51 & 960.5 \\ \cline{1-1}  & APSD-RRT & 797.78 & 61.31 & 0.17 & 674.35 & 8.44 & 0.22 & 76.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical data for the simulation of path optimization. of final path in Map2, the APSD-RRT method has significant advantages in terms of the balance between the computing cost and performance.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 4 Experimental Results and Discussions  
In our experiment, the external disturbance acting on the sensing optical fiber is simulated by a PZT phase modulator whose acting length is 1.5 m and driving voltage is 10 Vpp. The PZT phase modulator acts, at a position of 1000 m, as the single-point vibration source in sinusoidal form with a frequency of 100 Hz, while the total length of the optical fiber is 2020 m. Thus, the number of effective sampling points is reduced to 1010 for one RBS curve, and the data volume of each effective RBS curve is 1.01 kB. With the repetition frequency of optical pulses of 8 kHz, the total number of RBS curves is 8000 in 1 s, and the effective data volume is 8.08 MB during an acquisition time of 1 s. The data is then sent to the USB port of the upper computer via the USB 3.0 transmission bus at a speed of 100 MB/s, so the minimum data transmission time is about 80.8 ms. Moreover, in the interior of the upper computer, the USB port is connected with a south bridge USB I/O interface chip through the I/O bus, and the USB I/O interface chip is linked with a north bridge chip  
Figure 5: The processing architecture of compute unified device architecture (CUDA).  
Component Interconnect (PCI) bus. The north bridge chipset is responsible for communication with the Dynamic Random Access Memory (DRAM), Cache and CPU through the memory bus and the front side bus. As a result, the data transmission time in the interior of the upper computer is hard to calculate accurately because of its complexity, but the data transmission time between the upper computer and lower computer could be considered as a fixed value if the experimental parameters do not change.  
Figure 6a shows the superposition of the first 100 consecutive RBS traces and the enlarged plot at the single vibration position of 1000 m. It can be clearly seen that the RBS curves evidently fluctuate in the range from 1000 to 1020 m, which corresponds to the spatial resolution of 20 m. However, the RBS curves remain stable within the other range. Figure 6b shows a two-dimensional time-distance RBS figure, with the \(X\)-axis being time and the \(Y\)-axis being distance. The evolution of the time verifies that the RBS curves remain stable outside the vibration area. This result verifies the feasibility of vibration detection and location by the \(\Phi\)-OTDR sensing system.
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
###### Abstract  
Based on the characteristics of aero surfaces and reaction control system (RCS), a control allocation scheme is needed to specify aero surface deflections and RCS thruster's firing commands, during the re-entry phase of reusable launch vehicle (RLV). To analyze the control performance differences introduced by different allocation algorithms, this paper presents a proportional allocation scheme based on dynamic pressure, a daisy-chaining allocation scheme based on the control error, and a multi-object-based optimal allocation scheme, to deal with the allocation problem of RCS and aero surfaces. The first allocation scheme is the simplest, and the second one is more complicated, when considering the actuators' physical constraints in the allocation problem. The third scheme not only takes the physical constraints into account, but also the rate constraints, and other constraints of RCS and aero surfaces, while for the objective optimization function, not like the single-object function of the other two schemes, it combines the allocation error minimization, fuel consumption minimization, and other functions, with the adaptive weight values. After the stability analysis, the feasibility of the aforementioned allocation schemes are verified by the simulation, and the contrastive analysis of their control performances are followed.  
reusable launch vehicle; control allocation; reaction control system; re-entry phase +
Footnote †: journal: Applied Sciences  
_Article_  
RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase