esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 3 Research Methodology
### Experimental Setup  
All the experiments were run in MATLAB with the image processing toolbox, computer vision toolbox, classification learner app, and neural network toolbox is used to implement this system. Intel Core-i5 (Intel, Santa Clara, CA, USA) 2.50 GHz CPU computer with 4 GB of RAM is used to run this program to recognize the traffic sign. Creating Bag-of-Features from 12 image sets, feature extraction selects the feature point locations using the detector method. Then extracting SURF features from the selected feature point locations, the detecting SURF features are used to detect key points for feature extraction. The balance of features across all image sets is done to increase the quality of clustering. Subsequently, the algorithm uses the most discriminative 3141 features from each image set. The \(k\)-means clustering is then used to create a 200-word visual vocabulary. The overall extracted features were 37,692, and the number of clusters (\(k\)) is 200. Clustering is completed by 29/100 iterations (\(\sim\)0.60 s/iteration) and converged in 29 iterations. Finally encoding 12 image sets are done by using Bag-of-Features. This feature vector is then fed as an input to the ANN, SVM, and Ensemble subspace  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Iterations**} & \multirow{2}{*}{**Training Time**} & \multirow{2}{*}{**Performance**} & \multirow{2}{*}{**Gradient**} & \multicolumn{2}{c}{**Results**} \\ \cline{5-6} \cline{7-7} \cline{6-7} \multicolumn{1}{c}{**Hidden Neurons**} & & & & & **Cross Entropy** & **Percentages of Error (\%E)** \\ \hline
10 & 152 & 0.00:01 & 0.0174 & 0.0237 & 0.4874 & 10.5833 \\
20 & 112 & 0.00:01 & 0.0028 & 0.0143 & 0.1905 & 4.7500 \\
30 & 123 & 0.00:01 & 0.00322 & 0.0126 & 0.1568 & 3.9167 \\
40 & 115 & 0.00:01 & 0.00239 & 0.00758 & 0.4729 & 2.8333 \\
50 & 115 & 0.00:01 & 0.00363 & 0.00931 & 0.1484 & 3.6667 \\
60 & 101 & 0.00:01 & 0.00627 & 0.0212 & 0.1912 & 3.6667 \\
70 & 98 & 0.00:01 & 0.00348 & 0.0177 & 0.1647 & 4.0000 \\
80 & 102 & 0.00:01 & 0.00304 & 0.0110 & 0.1388 & 3.7500 \\
90 & 136 & 0.00:01 & 0.00192 & 0.00739 & 0.3469 & 2.4167 \\
100 & 132 & 0.00:01 & 0.00234 & 0.00740 & 0.2021 & 1.0000 \\
110 & 115 & 0.00:02 & 0.00197 & 0.00492 & 0.1935 & 1.4167 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neural network performance with number of hidden neurons in hidden layer.  
kNN to recognize the class of detected road sign. To measure the performance of classification task, accuracy was used as the performance metric.
## 3 Results and Discussions
### Results of the Parametric Study
Figure 13 shows a typical displacement contour of a fully embedded helical pile under uplift load. It is clear that the piles having a spacing ratio of less than two, that is SHP-3 and SHP-4, seem to fail forming a cylindrical failure mechanism. The failure contours also seem to be the same as observed in previous research [8; 32; 43]. As seen from Table 3, there is a close covenant between the numerical model assumed here against theoretical results and experimental results analyzed here. It is also noticed from Figure 14 that the capacity ratios between experimentally obtained capacity and theoretically obtained capacity have a close agreement with the capacities obtained from the proposed numerical model. In the majority of all cases, the differences in calculated and experimental capacity values are found to be within 10% of the predicted values, which is suitable for design purposes. Figure 12: Load–displacement curve for Lateral loading—Experimental. Figure 13: A typical displacement contour of a fully embedded helical pile under uplift load is shown in cross section. **(a)** SHP-1-12; **(b)** SHP-2-8.75; **(c)** SHP-3-8.75; and **(d)** SHP-4-8.75.
# Wind Turbines Around Cut-In Speed: Startup Optimization and Behavior Analysis Reported to MPP
## 4 Operating the Turbine by Switching the Generator to Engine Mode
### Case Study 1
#### 4.1.1 Variant 1  
Only with the Wind Turbine and Without Power Absorption from the Grid  
In this instance, the equations governing power are as follows:  
\[136\frac{d\omega}{dt}\cdot\omega=5.692\cdot 10^{5}\left(\frac{3}{\omega}-1.7 488\cdot 10^{-2}\right)\cdot e^{-41.495\cdot\frac{3}{\omega}}\cdot 3^{3} \tag{41}\] \[\omega(0)=11\]At \(\omega(0)=11\) rad/s the optimal mechanical angular speed is reached after 58.5 s, \(\omega(58.5)=72.151\) rad/s.  
\[\begin{array}{c}136\frac{\omega}{dt}\cdot\omega=5.692\cdot 10^{5}\cdot\left( \frac{3}{\omega}-1.7488\cdot 10^{-2}\right)\cdot e^{-41.495\cdot\frac{3}{\omega}} \cdot 3^{3}\\ \omega(0)=8\end{array} \tag{42}\]  
At \(\omega(0)=8\) rad/s the optimal mechanical angular speed is reached after 829.85 s, \(\omega(829.85)=72.248\) rad/s. By solving them, the time variations in the _MAS_\(\omega\) are shown in Figure 2.  
The duration necessary for the wind turbine (_WT_) to achieve its maximum power point (_MPP_) is contingent upon the initial mechanical angular velocities, denoted as \(\omega(0)\). Specifically, when the initial mechanical angular velocities are low, below cut-in speed, the time required for the turbine to attain the optimal speed is significantly prolonged, often extending to several minutes.  
At a wind speed of 3 m/s, with \(\omega(0)=11\) rad/s, at the time moment t = 58.5 s, the values of the optimal mechanical angular speeds, \(\omega_{OPTIM}\), and real, \(\omega\), being equal, the system operates at the maximum power point of the turbine. For \(\omega(0)=8\) rad/s, the system gets to work in _WT_'s _MPP_ at t = 829.85 s, the values of the optimal mechanical angular speeds, \(\omega_{OPTIM}\), and real angular speeds, \(\omega\), are then equal.  
It is evident from Figure 2 that at the initial _MAS_, \(\omega(0)\), of low values, specifically below 8 rad/s, the wind system attains its maximum power point after 14 min. This time interval in which the turbine reaches the point of maximum power, becomes even higher, for example, at \(\omega(0)=7\) rad/s it reaches 70 min, and at \(\omega(0)=6\) rad/s it exceeds the order of hours: 11 h, as Figure 3.  
The turbine is brought to the MPP in the least amount of time, by converting the electric generator to operate in asynchronous motor mode and supplying it with power from the grid, utilizing variable frequency and regulated stator flow (see Figure 4).  
Figure 3: Time variations in mechanical angular speeds at \(\omega(0)=7\) rad/s and \(\omega(0)=6\) rad/s.  
Figure 2: Time variations in mechanical angular speeds at \(\omega(0)=11\) rad/s and \(\omega(0)=8\) rad/s.  
At the nominal voltage \(U_{N}\) = 690 V and \(f\) = 50 Hz, the stator flux has the value:  
\[\Psi_{S}=\frac{U_{N}}{\omega}=\frac{U_{N}}{2\cdot\pi\cdot f}=\frac{690}{2\cdot \pi\cdot 50}=2.1963\ [\mathrm{Wb}] \tag{43}\]  
At controlled stator flow, the \(U/f\) ratio is:  
\[\frac{U}{f}=2.1963\cdot 2\cdot\pi=13.8\ [\mathrm{Vs}] \tag{44}\]
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
###### Abstract
In particular, the contributions of our work can be outlined as follows:  
* First, we analyze the performance of the ZFBF combined with the co-phasing technique based on full channel state information (CSI) as the number of RIS elements increases towards infinity. * To make the analysis more practical, we propose a novel deterministic ZFBF and co-phasing technique that only requires partial CSI while eliminating IBI and achieving near-optimal performance. * Furthermore, we derive a closed-form expression for the asymptotic ergodic capacity of the proposed scheme. * We then analyze the outage probability and demonstrate that the outage probability decreases as the number of RIS elements increases, thereby confirming the RIS's ability to provide reliable SatCom services in scenarios with a large number of RIS elements. The simulation results validate the asymptotic optimality of the proposed scheme, showing that it closely approaches the ideal performance. Moreover, the outage probability exhibits a potential convergence to zero as the number of RIS elements increases, further supporting the reliability and efficiency of the proposed scheme. Notations: Throughout the paper, boldface upper- and lower-case symbols represent matrices and vectors, respectively, and \(\mathbf{I}_{N}\) and \(\mathbf{1}_{M\times N}\) are, respectively, a size-\(N\) identity matrix and an \(M\times N\) matrix of ones. The conjugate, transpose, inverse, and Hermitian transpose operators are \(\left(\cdot\right)^{*}\), \(\left(\cdot\right)^{\mathrm{T}}\), \(\left(\cdot\right)^{-1}\), and \(\left(\cdot\right)^{\mathrm{H}}\), respectively. The operator \(\mathrm{diag}\left(a_{1},\cdots,a_{n}\right)\) is a diagonal operator that constructs a diagonal matrix from a vector \([a_{1},\cdots,a_{n}]^{\mathrm{T}}\). The amplitude and phase of a complex number \(a\) are denoted by \(|a|\) and \(\angle a\), respectively. \(\mathrm{E}[\cdot]\) and \(\mathrm{Var}[\cdot]\) denote expectation and variance operators, respectively, and \(\mathcal{O}(\cdot)\) denotes the big O notation. \(\mathcal{L}^{2}\mathcal{N}\left(\eta,\sigma^{2}\right)\) denotes a double-log-normal distribution with mean \(\eta\)and variance \(\sigma^{2}\), and \(\otimes\) and \(\xrightarrow{\text{a.s.}}\), respectively, denote the Kronecker product and almost sure convergence.
## 3 MG Overview and Experimental Test Bench
### Lppt Control Strategies
#### 3.3.3 P&O with Variable Step Size Based on FL  
The variable step size based on the FL method is gaining popularity as a method for the MPPT technique. It was used by many authors [17] to extract the maximum power thanks to its fast convergence and handling the non-linearity of the system. However, few studies have been carried out using this method to investigate the WT's performances when limited power is required [34]. The FL method consists of mapping the input space and the output space through logical operations. This method's realization process is divided into three steps: fuzzification, fuzzy reasoning and defuzzification [8] (Figure 7). In order to calculate the variable step size, the inputs here are the slope (\(\Delta p_{WT}(k)/\Delta v_{WT}(k)\))and \(p_{diff}(k)\). They are converted, during the fuzzification process, into fuzzy linguistic sets using the Mamdani method [8] and the universe of discourse of the inputs and the output is normalized into [-1, 1]. Thus, \(u_{1}\) is the first normalized input corresponding to (\(\Delta p_{WT}(k)/\Delta v_{WT}(k)\)) and \(u_{2}\) is the second normalized input corresponding to \(p_{diff}(k)\). These fuzzy sets are then analyzed by the fuzzy reasoning surface where a fuzzy output \(s\) is obtained by using fuzzy rules. To simplify the study, forms of membership function were selected as triangular and trapezoidal. Furthermore, the defuzzification process converts this fuzzy output to the desired output value \(\Delta v\) and the operator used in this study is the center of gravity. The FL controller presented in this study allows for examining the first input and, if this value is greater than zero, the step size increases until the LPP is reached. If it is less than zero, the opposite occurs until the operating point is reached. The second input is then used to adjust the value of the step size and determine the region of the operating point.  
The fuzzy subsets corresponding to the first input (\(\Delta p_{WT}(k)/\Delta v_{WT}(k)\)) are divided into five values: negative big (NB), negative small (NS), zero (ZE), positive small (PS) and positive big (PB), while the second input \(p_{diff}(k)\) is divided into three values: negative (N), zero (Z) and positive (P). For example,  
Figure 7: Structure of fuzzy logic (FL) controller.  
if the value of \(p_{diff}(k)\) is zero, whatever the value of the slope is, the predefined value assigned to the output \(s\) is zero, which means that the LPP is reached. All rules are provided in Table 1. As in the previous method, the variable step sizes provided by this controller were limited to 2 V, 4 V and 6 V.
## 3 Performance Implication of NVM Based Storage  
We perform measurement experiments to investigate the effect of NVM based storage. We set the configuration of the CPU, memory, and storage to Intel Core i5-3570, 8 GB DDR3, and 1 TB SATA 3 Gb/s HDD, respectively. We install the Linux kernel 4.0.1 64 bits and Ext4 file system. We consider two types of NVM storage, PCM and STT-MRAM. For now, as it is difficult to perform an experiment with commercial PCM and STT-MRAM products, we make use of RAMDisk consisting of DRAM and set a certain time delays for emulating NVM performances. To do so, we implement an NVM device driver by making use of the RAMDisk device driver and the PCMsim that is publicly available [22].  
As the optimistic performance of STT-MRAM is expected to be similar to that of DRAM, we set no time delays for emulating STT-MRAM, which we call RAMDisk throughout this article. For the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DRAM** & **STT-MRAM** & **PCM** & **NAND Flash** \\ \hline Maturity & Product & Prototype & Product & Product \\ Read latency & 10 ns & 10 ns & 20–50 ns & 25 us \\ Write latency & 10 ns & 10 ns & 80–500 ns & 200 us \\ Erase latency & N/A & N/A & N/A & 200 ms \\ Energy per bit access (r/w) & 2 pJ & 0.02 pJ & 20 pJ/100 pJ & 10 nJ \\ Static power & Yes & No & No & No \\ Endurance (writes/bit) & 10\({}^{16}\) & 10\({}^{16}\) & 10\({}^{6}\)–10\({}^{8}\) & 10\({}^{5}\) \\ Cell size & 6–8 F\({}^{2}\) & \textgreater{}6 F\({}^{2}\) & 5–10 F\({}^{2}\) & 4–5 F\({}^{2}\) \\ MLC & N/A & 4 bits/cell & 4 bits/cell & 4 bits/cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of non-volatile memory (NVM) technologies in comparison with dynamic random access memory (DRAM) and NAND flash memory.  
PCM case, we set the latency of read and write operations to 4.4\(\times\) and 10\(\times\) that of DRAM, respectively, in our NVM device driver, which is the default setting of PCMsim [22].  
In our NVM experiments, we also install the Ext4 file system on the RAMDisk. For benchmarking, we use IOzone, which is the representative microbenchmark that measures the performance of storage systems by making a sequence of I/O operations [23]. In our experiment, sequential read, random read, sequential write, and random write of IOzone scenarios with the total footprint of 2 GB were performed. We also use Filebench benchmark for some additional experiments.
## 4 Results and Discussion
### Influence of Initial Conditions on Wind Parameter
#### 4.1.3 Wind Speed Profile  
Based on the previous section, surface roughness height of 0.12 m was set. Similarly, taking 102\({}^{\circ}\) oncoming flow as an example, the wind speed profile adopted followed the recommendations by [PERSON] et al. [35]. As above-mentioned, we used the outlet of the downstream river in the southwest as the elevation base point, set as 0 m, and the height difference between it and the peak in the southeast, 2075 m. The average height of the surrounding mountains was about 1550 m, and the gradient wind height was about 350 m, so the gradient wind elevation was 1900 m. Combined with the specification [34], the wind speed at the gradient wind height was calculated to be 47.87 m/s. Considering that the distance from the bridge deck to the valley bottom was about 600 m, according to Equation (11), the wind speeds at bridge deck height were 41.99, 40.10, 37.42, and 34.12 m/s, as shown in Figure 7.  
\[U_{z}=\left\{\begin{array}{l}U_{g}\Big{(}\frac{Z}{Z_{g}}\Big{)}^{\alpha},Z \leq Z_{g}\\ U_{g}\Big{(}\frac{Z}{Z_{g}}\Big{)}^{\alpha},Z>Z_{g}\end{array}\right. \tag{11}\]  
where \(Z\) is height (m); \(Z_{g}\) is the gradient wind height (m) and 1900 m was used in this paper; \(U_{g}\) is the wind speed at the gradient wind height (m/s), which is taken as 47.87 m/s; \(\alpha\) is the roughness index of ground surface type, taken as 0.12, 0.16, 0.22, and 0.30, named A, B, C, and D, respectively, with a group of uniform oncoming flow added for comparison, recorded as U; and the wind speed was taken as 47.87 m/s.  
Then, taking the condition with the wind speed profile of U as the reference condition, and according to Equations (7)-(9), the nondimensional factors are shown in Figure 5b,d,f.  
The influence of the wind speed profile on wind parameters is shown in Figure 8. As shown in Figure 8, compared with the uniform inflow condition, the influence of the wind speed profile on wind parameters was also obvious. Specifically, at the same height, the wind speed value was the largest under uniform oncoming flow, and the wind speed factor was smaller from class U to class D, indicating that the wind speed was increasingly affected by the wind speed profile, and the wind speed factor decreased with increased height. After reaching the gradient wind height, the wind speed factor tended to be consistent. At a 500 m height, the wind speed factor of class D was only 50% of that of class U. The setting of the wind speed profile for oncoming flow could increase the range of wind attack angle, which gradually increased from class A to class D; the increase range of class D was the largest, about 55%. The wind attack angle factorobvious, and the influence degree gradually increased from class A to class D; the wind azimuth angle factor was the largest in class D, about 6.5 times.
## Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011  
**[PERSON] \({}^{\dagger}\) and [PERSON] ***  
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering,  
Tsinghua University, Beijing 100084, China  
\({}^{\dagger}\) Current Address: Nicholas School of the Environment, Duke University, Durham, NC 27708, USA;  
E-Mail: [EMAIL_ADDRESS].  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-6278-3383; Fax: +86-10-6279-6971.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 23 March 2015 / Accepted: 23 July 2015 / Published: 11 August 2015_  
This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results show that on average, the growing season NDVI significantly increased by 0.0007 year\({}^{-1}\), with 76.5% of the research area showed increasing NDVI from 1982 to 2011. Seasonally, NDVI increased at high rates during the spring and autumn while changed slightly during the summer. At a national scale, the growing season NDVI was significantly and positively correlated to temperature and precipitation, with temperature being the dominant factor. At regional scales, the growing season NDVI was dominated by increasing temperature in most forest-covered areas in southern China and dominated by precipitation in most grassland in northern China. Within the past three decades, the increasing trend of national mean NDVI abruptly changed in 1994, slowing down from 0.0008 year\({}^{-1}\) to 0.0003 year\({}^{-1}\). To be regional specific, the growing season NDVI in forest covered southern China has accelerated together with temperature since mid 1990s, while parts of the grassland in northern China have undergone stalled NDVI trends corresponding to slowed temperature increment and dropped precipitation since around 2000. Typical region analysis suggested that apart from long term trends and abrupt change points of climatic drivers, the processes of NDVI variation were also affected by other external factors such as drought and afforestation. Further studies are needed to investigate the nonlinear responses of vegetation growth to climatic drivers and effects of non-climate factors on vegetation growth.  
**Keywords:** vegetation dynamics; trend; abrupt change; dominant climate driver; China
## 3 Results
### Ultrasonic Characteristics
#### 3.2.2 Wave Form  
The influence of various irradiation parameters on the wave form was also investigated and presented in Figure 7. The original rock-specimen case is shown in Figure 7a, from which a perfect sinusoid shape can be observed. It is indicated that the original rock specimen is homogenous without any cracks or defects inside the graphite specimen. Figure 7b,c illustrate the wave form of the irradiated specimen under the irradiation power outputs of 600 and 1000 W, respectively. It is seen that the wave form of the irradiated specimen is distorted and the voltage amplitude of the first wave considerably reduces from 15.63 to 2.38 mV when the laser power increases in the range of 600-1000 W. The effect of the beam diameter on the wave form is shown in Figure 7d,e, which demonstrates that the voltage amplitude of the first wave increases from 11.38 to 105.63 mV when the beam diameter increases in the range of 8-12 mm. The influence of the laser-beam moving speed on the wave form is shown in Figure 7f,g. It is seen that the voltage amplitude of the first wave increases from 47.50 to 110.25 mV when the moving speed increases in the range of 1-4 mm/s.  
Figure 6: The influence of (**a**) laser-power output, (**b**) beam diameter, (**c**) moving speed on wave velocity and first-wave amplitude.  
The wave form is significantly flattened by the increased laser-power output, decreased beam diameter and beam moving speed. Compared with the waveform of the original specimen presented in Figure 7a, the wave forms of irradiated specimens are distorted because the granite specimens are thermally damaged with cracks and defects generated by the cracks. The wave forms of irradiated specimens are distorted because the granite specimens are thermally damaged with cracks and defects generated by cracks.  
the laser beam. The reason for the variation in the wave form is attributed to the reflection and refraction of the ultrasonic wave caused by the fractures' interfaces, which leads to the formation of shear waves and phase superposition of the ultrasonic wave.