esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
To verify our control method, we built a simplified model with the parameters in Table 1 in V-rep, a virtual robot experimentation platform. In the simulator, the model is controlled in position mode. In Figure 8, when the robot is going to take off, the jumping controller will not generate data any more, and the angles will be the same as the angles at the take-off time, and each joint will brake suddenly. Since the values of joint angular velocity and angular acceleration are large at the take-off time. If we don't smooth the joint trajectory after take-off, the motor will violate the torque constraint. Thus, we use the cubic interpolation in a joint trajectory to help the joint brake smoothly. The cubic interpolation is also used to smooth the joint trajectory at the beginning of the jump, and the initial state changes from \(X_{0}=(-57^{\circ},\,158^{\circ},\,-126^{\circ},\,0,\,0,\,0)\) to \(X_{0}=(-61.5^{\circ},\,155.2^{\circ},\,-123.8^{\circ},\,0,\,0,\,0)\). Figure 12 shows the joint trajectory during simulation. By using playback of this offline pattern, we get the velocity of COM as shown in Figure 13. At \(t=0.40\) (s), the translational velocity of COM in vertical direction reaches a maximum 3.42 (m/s). Without consideration of the influence of the added trajectory near the taking off time, the jump height is about 0.596 (m), which can be calculated by Equation (12). The added trajectory leads to the slight decrease in vertical velocity at the take-off time, which is a little delayed. From the simulation, we got the position of the foot and COM in vertical  
Figure 11: Assuming that \(X_{0}=(-57^{\circ},\,158^{\circ},\,-126^{\circ},\,0,\,0,\,0)\), the ZMP is limited by the ZMP constraint to maintain balance throughout the push-off phase.  
Figure 10: Assuming that \(X_{0}=(-57^{\circ},\,158^{\circ},\,-126^{\circ},\,0,\,0,\,0)\), **(a)** shows the torque of each joint; and **(b)** represents relationships between the motor torque and speed. At the beginning of the jump motion, the ankle joint torque is slightly reduced to satisfy the ZMP constraint. Similar effectiveness can also be observed when the robot is about to take off.  
direction and the torque of each joint as shown in Figures 14 and 15. If we take the distance between foot and ground as the jump height, then the maximal jump height is about 0.477 (m) and the COM is increased by 0.83 (m). During the entire motion, the joint torque profiles are within the profile of the motor torque speed curve. Finally, the jumping motion sequence is shown in Figure 16.  
Figure 14: The foot and COM (center of mass) position in vertical direction in the simulation.  
Figure 12: The trajectory of each joint during the simulation.  
Figure 13: The velocity of COM (center of mass) during the simulation.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 1 Introduction
A classification approach based on a novel multiple decision tree randomized (MDTR) algorithm was developed, utilizing the best performing observables along with static ancillary data (biomass density, digital elevation model) and classified inundation extent maps for high and low biomass forests derived from the Japanese Space Agency's (JAXA) PALSAR-2 data [10]. The PALSAR-2 wetland inundation maps were used in this study as reference datasets for training and validating the GNSS-R inundation classification obtained from the MDTR algorithm. In Section 2 we provide the theoretical background for GNSS-R and explain its sensitivity to inundation. In Section 3 we describe the GNSS-R measurements, the time averaging, and observables selected. In Section 4 we describe the datasets, provide details of the classification algorithm training, and analyze the performance of the classification and the main results of the MDTR algorithm applied to CYGNSS data from 2017 over the Pacaya-Samiria National Reserve, where accuracy was assessed through comparisons with the PALSAR-2 derived wetland inundation maps [10]. In Section 5 we provide discussion of the main sources of inaccuracy, and Section 6 summarizes our conclusions.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 1 Introduction
Multi-cavity steel-concrete composite floor is composed of a multi-cavity steel plate framework and self-compacting concrete. It has the following advantages:  
1. The bottom steel plate of multi-cavity steel plate framework can be used as the stressed steel bar in use phase, and the porous steel plates can be used as the distribution steel bar and shear steel bar in the floor, so as to enhance the bending resistance and the shear capacity of the floor, make full use of the material properties of the steel, and the process of binding the steel bars in the field is avoided;
2. The multi-cavity steel plate framework is produced by factory prefabrication, which can be used as the formwork in the construction stage of composite floor, and can bear the construction load such as concrete dumping load, simplifies the construction process on site and improves the construction efficiency;
3. The steel plate cavity inside the composite floor can effectively overcome the problems of concrete cracking and crack exposure. The steel plate cavity has a strong lateral restraint effect on the concrete and gives full play to the compressive performance of concrete;
4. In the composite floor, a steel plate cavity is used instead of the stressed steel bar and stirrup, which can avoid the excessive steel bar in the traditional floor due to the excessive bearing capacity of the floor, so that the internal concrete filling of the floor is not saturated during the concrete pouring, which affects the overall performance of the concrete components. Through the full-scale indoor static load test of multi-cavity steel plate concrete composite floor, the ultimate bearing capacity and failure mode of composite floor were studied, and compared with other forms of precast composite floor; numerical simulation was carried out by ANSYS software. The mechanical properties of composite floor in the elastic stage were studied and compared with the test results; the influences of different steel plate thickness, composite floor thickness, cavity size and span on the mechanical properties of composite floor were analyzed by numerical simulation [11; 12; 13; 14; 15].
# Net Ecosystem Exchange of CO\({}_{2}\) in Deciduous Pine Forest of Lower Western Himalaya, India
## 3 Results
### Seasonality in Carbon Use Efficiency (CUE)  
As per the 'constancy hypothesis' of CUE, it is assumed that CUE does not change but, it is ambiguously a variable factor depending on the plant functional types and the environmental conditions [31]. Our results showed that CUE varied from 0.3 to 0.87 across the seasons and the mean growing season CUE was \(0.74\pm 0.02\). A higher CUE of 0.74 in this subtropical region was expected due to the warm-moist regime. In winter 2011 (with an understory), mean CUE was \(0.63\pm 0.08\), while during winter 2010 (no understory), CUE was exceptionally low (\(0.29\pm 0.05\)). Generally, the forest management practices, such as understory removal favors the growth of the overstory (pine). However, in view of the C-balance, this practice has led to lower CUE and positive NEE flux. Irrespective of the seasons, both soil and air temperatures have a substantial control on CUE, wherein the \(r\)-value was estimated at 0.75 (\(p<0.001\)) at an annual-time scale covering the period January to December 2011. The relationship between CUE and temperature is shown in Figure 3. An observed decline in CUE during overcast days of the monsoon (i.e., July3d-Aug3d) could be because of a decline in canopy photosynthesis (\(Ac\)) due to a reduced PAR.  
Besides the anthropogenic interventions (clearing of understory), air temperature (AT) and EF emerged as the most important factors regulating the ecosystem carbon balance. CUE-EF relation was positively correlated (R\({}^{2}\) = 0.75, \(p<0.001\)) across the growing period (April-June) and suggestive of increasing CUE as wetness (EF) increases towards 0.75. Interestingly, a reverse (or negative) relationship was observed with R\({}^{2}\) = 0.75, when EF exceeded 0.75 during monsoon and post-monsoon period of growing season (Figure 4a). Importantly, whenever, EF dropped below 0.5, a decline in CUE was noted due to moisture deficit or dryness. VPD between 0.6 kPa and 2.0 kPa was optimal for higher CUE (R\({}^{2}\) = 0.63, \(p<0.01\); Figure 4b). During the monsoon and post-monsoon periods of the growing season with a VPD of more than 2 kPa, CUE remained almost constant. Therefore, in this moisture abundant environmental condition, CUE remained unaffected during the monsoon and post-monsoon period of growing season. The above pattern in the dynamics of CUE and its interaction with major environmental factors (AT, EF) was illustrated utilizing a wavelet coherence analysis. The phase difference between CUE-AT and CUE-EF are presented in Figure S3, which was hard to schematize with a linear regression analysis.  
Figure 3: Variations of carbon use efficiency (CUE; ratios of NPP\(-Ac\)) in relation to the day-time air temperature (AT) at a 10-day (Dekads) interval over the period November 2010 to December 2011. The period November 2010 to March 2011 has no understory, while the rest of the period was with an understory.
## 3 Results  
### Measurement Results (RGB Values)  
The underwater images that were captured by Video 2 clearly show the color changes resulting from the variations in SSC and turbidity (Figure 7).  
Figure 7: (**a**) Turbidity measurement results and (**b**) color changes in underwater images due to the changes in suspended sediment concentration and turbidity.  
The RGB and gray scale values obtained at the designated nine pixels that were obtained from the images captured by Video 2 are shown in Figure 8. The analysis results confirmed that the value of the red band increased as the SSC and turbidity increased. The other bands demonstrated the opposite tendency. Moreover, color changes occurred owing to the changes in the SSC and turbidity, even though slight differences were observed at the time of injecting the sediment, depending on the pixel coordinates.  
Considering the characteristics of the circulating channel where the flow was recirculated, the underwater images that were captured by Video 1 and Video 2 were compared; the comparison and analysis results of only the red band are presented in Figure 9.  
When the RGB values of the images that were captured by Videos 1 and 2 were analyzed, it was found that the results of the 1 st injection were captured immediately before the occurrence of the second injection (14:45:44), and the results of the second injection were captured at 14:54:18. In addition, it was found that changes in RGB values were directly affected by the suspended sediment injection results at the 10 m upstream position and RGB values severely fluctuated in the images that were captured during the injection of sediment, unlike the images of the recirculated sediment flow.  
Figure 8: Changes in RGB and gray scale values due to the turbidity change at (**a**) [1000, 250], (**b**) [1970, 250], (**c**) [3000, 250], (**d**) [1000, 1000], (**e**) [1970, 1000], (**f**) [3000, 1000], (**g**) [1000, 1700], (**h**) [1970, 1700], and (**i**) [3000, 1700].
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
## 3 Analysis of Fatigue Performance Results
### Hot-Spot Stress Distribution  
In order to improve the reliability of the test results, the stress results were taken to be the average value over multiple tests. Combining the modulus of elasticity obtained by the material property test, stresses can be obtained from the monitoring strain multiplied by the modulus of elasticity. The hot-spot stress at each key measurement point of the composite girder with CSW-CFST truss chords under peak load was calculated using the extrapolation method, as shown in Figure 9. The hot-spot stress distribution in the same chord of the composite girder with CSW-CFST truss chords was essentially symmetric with respect to the mid-span section, and the deviation was less than 4.2%. Furthermore, the hot-spot stress in the left- and right-hand-side chords essentially coincided, and the deviation was less than 7.6%, The reasons why the deviation was found include that the axis line of the composite girder was not located in the loading surface, and the left- and right-hand-side chords were not entirely parallel to each other, with some deviation existing between them. It was demonstrated that the designed test model and its boundary conditions were reasonable.  
In addition, according to Figure 9, the maximum hot-spot stress position was at point S, which was the next-nearest point to the mid-span section. This was mainly because the stress measurement points were arranged on the same plane of the corrugated steel web. The nearest and next-nearest point S to the mid-span section were on the molded side and the non-molded side, respectively, which was basically the same as that of composite girders with CSW-ST truss chords in [11], indicating that concrete in the chord does not change the hot-spot stress distribution in the welded zone.  
Figure 8: Comparison of hot-spot stress determination methods: (**a**) one-point representative method; (**b**) two-point extrapolation method; and (**c**) three-point extrapolation method.  
Figure 9 shows the ratios, denoted as \(\alpha\), of hot-spot stress in the composite girder with CSW-CFST truss chords to those in the composite girder with CSW-ST truss chords under the peak loads at the same measurement points. \(\alpha\) was in the range from 0.732 to 0.826. \(\alpha\) was 0.732 at the maximum hot-spot stress position, which shows that concrete in the chord reduced the hot-spot stress of the composite girder with CSW-ST truss chords by 26.8%.
## 3 Results and Discussions
### Effect of Curing Methods on the Compressive Strength of SRC  
Typically, SRC exhibits a much lower compressive strength than OPC-based concrete, and it is necessary to improve it using effective curing methods. Four commonly used curing methods (air-drying, water, atmospheric steam, and high-pressure and high-temperature curing methods) were compared to reveal their effect on the compressive strength of SRC (Figure 6) [25]. The results showed that the increasing sequence of compressive strength was water > high-pressure and high-temperature > air-drying > atmospheric steam, and all curing methods satisfied the disposal criterion. On the other hand, the air-drying, atmospheric steam, and high-pressure and high-temperature curing methods showed relatively lower compressive strengths, which was attributed to an insufficient hydration reaction due to the evaporation of water. Meanwhile, in terms of the water curing method, hydration products, such as CSH and ettrigite (Ca\({}_{6}\)Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)(OH)\({}_{12}\).26H\({}_{2}\)O), were continuously formed, increasing the compressive strength [25]. The experimental results showed that the best curing method was water curing, based on the disposal criterion of compressive strength. However, the air-drying curing method seems more relevant when considering the disposal standard, cost-effectiveness, and convenience [25].  
Figure 5: The effect of different types of alkaline reagents on the compressive strength of SRC.
## 2 Materials and Methods
### Effective Connectivity Analysis
#### 2.4.2 Parametric Empirical Bayes  
The posterior densities of the effective connectivity were then engaged for group-level analysis using Parametric Empirical Bayes (PEB). In PEB, a general linear model (GLM) is established on the basis of posterior density of model parameters at individual level to obtain group level results [28; 29; 30], which can be formulated as follows:  
\[\begin{array}{l}y_{i}=\Gamma(\theta_{i}^{(1)})+\varepsilon_{i}^{(1)}\\ \theta^{(1)}=X\theta^{(2)}+\varepsilon^{(2)}\end{array} \tag{4}\]  
where in the first layer, \(y_{i}\) is the BOLD time series of the \(i\)-th subject, \(\theta_{i}^{(1)}\) is the estimated DCM model parameters for this subject and \(\varepsilon_{i}^{(1)}\) is the observation noise. The second layer formula is the GLM established to estimate parameters from individual level to group level, where \(\theta^{(1)}\) are the estimated DCM model parameters for all subjects; \(X\) is the design matrix of GLM, where the row numbers represent the subject numbers, and each column is a regression variable (such as age, group, etc.) that may affect DCM parameters to be considered; \(\theta^{(2)}\) is the coefficient set corresponding to each regression variable, which quantifies the influence of regression variables on DCM parameters; \(\varepsilon^{(2)}\) is the model residual.  
In this study, the design matrix \(X\) was set with the first column of all \"1\" to uncover commonalities across all subjects, and the second column of \"1\" for drivers and \"\(-\)1\" for controls to uncover group differences between the drivers and the controls.
## 3 Design of the Neural Fault Classifier Based on the Kohonen Network
### Self-Organizing Kohonen Network
In order to find the neurons closest to the winner, a similarity function should be defined that uses the distance between the weight vector \(\mathbf{w}_{i}\) of the examined neuron and the input vector \(\mathbf{x}\), and on this basis the neuron for which this distance is the smallest is selected:  
\[\|\mathbf{x}-\mathbf{w}_{c}\|=\underset{i}{\text{min}}\big{\{}\|\mathbf{x}- \mathbf{w}_{i}\|\big{\}}. \tag{9}\]  
The most frequently used measure is the Euclidean distance:  
\[d\big{(}x_{j},w_{ij}\big{)}=\|\mathbf{x}-\mathbf{w}_{i}\|=\sqrt{\sum_{j=1}^{N }\big{(}x_{j}-w_{ij}\big{)}^{2}}. \tag{10}\]  
From the point of view of the neuron network learning process, the neighbourhood of the winning neuron is also important. At the beginning of learning, the neighbourhood includes a very large radius. Then it is gradually reduced until it covers only the winning neuron. This fact is taken into account in the formula for changing the weights in Equation (8), written now in matrix form:  
\[\mathbf{w}_{i}(k+1)=\left\{ \begin{array}{ll}\mathbf{w}_{i}(k)+\eta(k)f_{N_{ c}}(k)(\mathbf{x}(k)-\mathbf{w}_{i}(k)),&i\in N_{c}\\ \mathbf{w}_{i}(k)&\text{otherwise},\end{array} \right. \tag{11}\]  
where: \(f_{N_{c}}(k)\)--neighbourhood function. The simplest neighbourhood function proposed by [PERSON] is the rectangular neighbourhood function, defined as follows:  
\[f_{N_{c}}=\left\{ \begin{array}{ll}1&for\;d\leq R,\\ 0&\text{otherwise},\end{array} \right. \tag{12}\]  
or the Gauss function:  
\[f_{N_{c}}=\exp\biggl{(}-\frac{d^{2}(\mathbf{x},\mathbf{w})}{2R^{2}}\biggr{)}, \tag{13}\]  
where: \(R\)--neighbourhood radius, \(d\)--distance between the input vector \(\mathbf{x}\) and the weight vector \(\mathbf{w}\) of the neuron. During the learning process, both the neighbourhood and the learning rate are reduced, usually exponentially. In the basic algorithm of the self-organizing network, both the number of neurons and the network topology are determined from the beginning and do not change in the network learning process. On the other hand, the size of the neighbourhood decreases so that in the final stage the neighbourhood is of zero order and consists only of the considered neuron. Network learning is an iterative process associated with samples \(k\) = 1, 2,... The self-learning algorithms of neural networks lead to the sensible adaptation of weights during a self-organization process. Networks taught in this mode will react not only to the values of input signals, but also to the probability of their appearance. Learning will therefore require redundant input data. Rarely occurring input vectors will have less impact on the training process than vectors that appear frequently. The self-organizing Kohonen map has found many applications in practice. The basic function it performs is to map the topology of input data through a much smaller number of neuron weights that make up the network. Hence, it has found direct application in data compression and diagnostics and is also used in this work for fault classification of IM windings.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Harris Corner Detection  
The characteristics of the Harris corner point are used to segregate the background and foreground. Around a corner point, turning the window to any direction should make a high difference in intensity [22]. The Harris's point strength in the fingerprint foreground regions is extremely higher than that in the background regions [23]. The Harris algorithm first introduced by [PERSON] et al. [24] has appeared as a modification or development of the Moravec corner detection function, which utilizes the gray contrast among pixels to extract corner points. Given a grayscale image \(I\) and a window \(w(x,y)\) is going to be swept (with displacements \(u\) in the \(x\)-direction and \(v\) in the \(y\)-direction). Thus, the variation of intensity can be computed as follows,  
Figure 6: Fingerprint image pre-processing: (**a**) original, (**b**) enhanced, (**c**) binarized, and (**d**) thinned images.  
\[E(u,v)=\sum_{x,y}w(x,y)[I(x+u,y+v)-I(x,y)]^{2} \tag{8}\]  
where \(I(x,y)\) and \(I(x+u,y+v)\) are the intensities at the position \((x,y)\) and at the moving window, \((x+u,y+v)\), respectively. Since windows with corners are being looked for, windows with a large variation in intensity are being searched [25]. Consequently, the above equation should be maximized, particularly the following term:  
\[\sum_{x,y}[I(x+u,y+v)-I(x,y)]^{2} \tag{9}\]  
Using Taylor expansion, we get  
\[E(u,v)\approx\sum_{x,y}[I(x,y)+uI_{x}+vI_{y}-I(x,y)]^{2} \tag{10}\]  
Expanding the equation and canceling properly yield:  
\[E(u,v)\approx\sum_{x,y}u^{2}l_{x}^{2}+2 wI_{x}I_{y}+v^{2}l_{y}^{2} \tag{11}\]  
Equation (11) can be expressed in a matrix form as follows:  
\[E(u,v)\approx\begin{bmatrix}u&v\end{bmatrix}(\sum_{x,y}w(x,y)\begin{bmatrix}l_ {x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&l_{y}^{2}\end{bmatrix})\begin{bmatrix}u\\ v\end{bmatrix} \tag{12}\]  
Let us denote,  
\[M=\sum_{x,y}w(x,y)\begin{bmatrix}l_{x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&l_{y}^{2}\end{bmatrix}\]  
Thus, Equation (12) can now be rewritten as follows:  
\[E(u,v)\approx\begin{bmatrix}u&v\end{bmatrix}M\begin{bmatrix}u\\ v\end{bmatrix} \tag{13}\]  
Let \(\lambda_{1}\), \(\lambda_{2}\) be the eigenvalues of matrix \(M\) which form a rotationally invariant description. Here, there are three cases to be considered:  
* Once an eigenvalue (\(\lambda 1\) or \(\lambda 2\)) is large and considerably greater than the other, then an edge exists.
* If \(\lambda 1\approx\lambda 2\) and the values are small (near zero), then this pixel point is a flat region.
* If \(\lambda 1\approx\lambda 2\) and they have large positive values, then this pixel point is a corner.  
For each window, a score can be computed to determine whether it can probably contain a corner:  
\[R=det(M)-k(trace(M))^{2} \tag{14}\]  
where \(det(M)=\lambda 1\lambda 2\), \(trace(M)=\lambda 1+\lambda 2\) and \(k\) is a constant experimentally set between 0.04 and 0.06. A window with a score \(R\) larger than a certain threshold value is considered to have a \"corner\".