esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Studies
### Analysis of Simulation Results
#### 4.2.2 Effects of P2G on Flexibility of Integrated Power-Natural Gas-Heating Energy Systems  
The total gas redundancy and flexibility metric of integrated energy systems without P2G and with P2G are given in Table 5. Moreover, the hourly flexibility metric of integrated energy systems without P2G and with P2G are shown in Table 6.  
Owing to the gas injection from P2G, the gas redundancy of natural gas network is increased from 10.267 Mm\({}^{3}\) to 11.749 Mm\({}^{3}\) (increased by 14.43%) as well as flexibility metric is raised from 0.244 to 0.419 (raised by 71.72%). It is noted that the peak load of power, gas and heating almost appears at 20:00 and at this time there is no wind curtailment. The integrated power-natural gas-heating energy systems has great challenge in reliable operation and flexibility. If gas flow of gas turbines is not declined to decrease gas load of natural gas network, the simulated pressure of node 6 is below zero which means the natural gas network cannot operate normally. After the adjustment of gas flow of gas turbines, pressure of node 6 is still below its lowest value (10 bar) which can be found from Figure 8. Even though there is no wind curtailment at the time of peak load, this problem still can be solved through gas injection from P2G which can be stored, transported in natural gas network and supply gas and heating load when peak demand arrives. That is exactly why flexibility of the integrated power-natural gas-heating energy systems can be raised significantly by P2G.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Time/h** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Without & 0.0137 & 0.0063 & 0.0051 & 0.0052 & 0.0066 & 0.0081 & 0.0086 & 0.0062 & 0.0172 & 0.0835 & 0.0048 & 0.0057 \\ PM2G & 0.0131 & 0.0091 & 0.0070 & 0.0068 & 0.0068 & 0.0054 & 0.0052 & 0.0183 & 0.0177 & 0.0078 & 0.0052 & 0.0046 \\ \hline
**Time/h** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline Without & 0.0062 & 0.0055 & 0.0081 & 0.0063 & 0.0132 & 0.0040 & 0.0047 & 0.0044 & 0.0043 & 0.0061 & 0.0050 & 0.0057 \\ PM2G & 0.0059 & 0.1460 & 0.0962 & 0.0107 & 0.0044 & 0.0047 & 0.0047 & 0.0057 & 0.0056 & 0.0062 & 0.0095 & 0.0125 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hourly flexibility metric of integrated energy systems without P2G and with P2G.  
Figure 11: Total volume of gas storage without P2G and with P2G.  
The increase of flexibility metric from 0.244 to 0.419 greatly benefits the integrated power- natural gas-heating energy systems. More specifically, the ability to deal with high ramp caused by gas demand/heating demand is better, the linepack of pipelines is more adequate to maintain gas pressure of gas nodes and gas supplying for gas turbines in power systems is more abundant.
# Research on a Service Load Prediction Method Based on VMD-GLRT
## 3 Result and Discussion  
### Data Acquisition and Processing  
To test the prediction performance of the model proposed in this paper, Alibaba's open-source service load data Cluster-trace-v2018 is selected as the data set [21], which contains the machine load data of more than 4000 machines for eight days. In this paper, the resource usage of an apparatus for eight days is selected, and a total of 3300 load data are used as the data set. The combination of CPU and memory resource utilization is used to load data [27]. At the same time, the data of multiple dimensions, such as network usage and disk IO, are also included to form a data set. The cross-validation method is used for verification, and the last 300 records are used as the verification set to verify the model's generalization ability.  
First of all, because different software services have other performance characteristics in different application scenarios, even on various modules of the same software, there will be many differences in their load conditions. Therefore, in this paper, before using the data, firstly, for the unstable sequence, the VMD mode decomposition is used to divide the multidimensional features into multiple relatively stationary IMF components so that they tend to be relatively stationary. This paper's decomposition effect of the prediction divided into five modes and feature combination columns is shown in Figure 4 below. The first layer is the original sequence, and the following five layers are the data after modal decomposition.  
Referring to the method in the study in [28], the IMF is set to 5 components. As shown in Figure 4, IMF1-IMF5 are data from different center frequency bands. Since it is not possible to completely decompose each time, there is always some error in the result. Assuming that IMF\({}_{\text{res}}\) is the residual sequence after the decomposition of the original sequence, the difference between the original sequence and the decomposed sequence is the value of the residual sequence IMF\({}_{\text{res}}\). This paper considers that IMF\({}_{\text{res}}\) is also an additional component of the modal decomposition, except that the current component is not derived directly from the algorithm, but from the difference between the original sequence and the already decomposed variables.  
Figure 4: Before and after mode decomposition.
# The Use of Asymmetric Polynomial Profiles for Planning a Smooth Trajectory
## 3 Results
### Concatenation of Polynomials  
The chosen polynomials were used for generation of smooth trajectory with two intermediate points. The planned trajectory was formed combining three different polynomials. The polynomial 2-1-3 was taken in the time interval t\(\in\)(0,t1) the polynomial of 3-1-3 in t\(\in\)(t1,t2) and the polynomial 2-1-2 in t\(\in\)(t2,tend). The polynomial coefficients, time of motion along individual sections as well as asymmetry coefficients were calculated based on: the distance between the assumed points, the velocities which must be reached in each point and the constraints of velocity, acceleration and jerk quantities. There were assumed the following data: s\({}_{\text{begin}}=0\) m, s\({}_{1}\) = 0.5 m, s\({}_{2}\) = 1.5 m, s\({}_{\text{end}}\) = 2 m, v\({}_{\text{begin}}=0\) m\(\cdot\)s\({}^{-1}\), v\({}_{1}\) = 0.2 m\(\cdot\)s\({}^{-1}\), v\({}_{2}\) = 0.5 m\(\cdot\)s\({}^{-1}\), v\({}_{\text{end}}\) = 0 m\(\cdot\)s\({}^{-1}\) as well as the constraints v\({}_{\text{max}}\) = 1 m\(\cdot\)s\({}^{-1}\), \(\mid\)a\({}_{\text{max}}\) \(\mid\) = 2 m\(\cdot\)s\({}^{-2}\), \(\mid\)jmax\(\mid\) = 10 m\(\cdot\)s\({}^{-3}\). Figure 5 presents the displacement, velocity, acceleration, jerk and snap profiles for the planned trajectory.
# Acoustical Properties of Fiberglass Blankets Impregnated with Silica Aerogel
## 4 Results and Discussion
### Acoustical Properties
The inverted value of the median pore size (Table 1) decreases progressively from 99.4 to 20.5 \(\upmu\)m as the filling ratio increases from 0 to 75%. This makes physical sense, as the SEM images in Figures 3-7 illustrate this. This range of pore sizes is also consistent with that measured non-acoustically for similar materials [6]. When the filling ratio increases, the inter-fiber pores are progressively replaced with much smaller inter-grain pores. The transport (inner) pores in the grains of aerogel do not seem to contribute significantly to the measured acoustical properties. This is reflected in a consistently underpredicted porosity value, \(\phi^{(i)}\). The progressive change in the inverted porosity value make sense for the filling ratios between 0 and 50%, dropping from 99.4 to 92.9%, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filling Ratio,** & \begin{tabular}{c} **Layer** \\ **Thickness, \(d\),** \\ **mm** \\ \end{tabular} & \begin{tabular}{c} **Pore Size, \(\mathbf{\bar{g}^{(i)}}\),** \\ **\(\upmu\)m** \\ \end{tabular} & \
\begin{tabular}{c} **Porosity, \(\boldsymbol{\phi^{(i)}}\)** \\ **Pore Size, \(\sigma_{5}^{(i)}\)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation in** \\ **Porosity, \(\boldsymbol{\phi}\)** \\ \end{tabular} & **RMS Error, \%** \\ \hline
0 & 8.12 \(\pm\) 0.77 & 99.4 \(\pm\) 4.15 & 0.994 \(\pm\) 0.0098 & 0 & 0.965 \(\pm\) 0.0041 & 1.4 \\ \hline
25 & 9.33 \(\pm\) 1.60 & 48.0 \(\pm\) 20.2 & 0.938 \(\pm\) 0.018 & 0.160 \(\pm\) 0.213 & 0.960 \(\pm\) 0.0044 & 1.7 \\ \hline
50 & 9.26 \(\pm\) 0.47 & 32.8 \(\pm\) 2.00 & 0.929 \(\pm\) 0.011 & 0 & 0.952 \(\pm\) 0.0026 & 1.8 \\ \hline
75 & 10.35 \(\pm\) 0.85 & 20.5 \(\pm\) 1.35 & 0.959 \(\pm\) 0.032 & 0 & 0.951 \(\pm\) 0.0036 & 3.6 \\ \hline
100 & 9.34 \(\pm\) 0.84 & 83.0 \(\pm\) 2.04 & 0.505 \(\pm\) 0.091 & 0.55 \(\pm\) 0.015 & 0.94 \(\pm\) 0.0067 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of the non-acoustical parameters inverted from fitting the model [28] to the measured complex reflection coefficient data for the five types of fiberglass blankets. respectively. These values match the measured porosity values within 3%. When the filling ratio increases to 100%, the inverted porosity of \(\phi^{(i)}=50.5\%\) is significantly below the measured porosity of \(\phi=93.6\%\). Additionally, the median pore size inverted for this type of blanket is not realistic. This suggests that the physical behavior of the blanket layer with 100% filling ratio is no longer captured accurately by the model. As the proportion of aerogel powder in the material approaches 100%, the sorption and thermal diffusion effects are likely to become much more important [31]. These effects are not captured by the adopted model [28], which only accounts for the classical visco-thermal and inertia effects.
###### Abstract  
Poly(phenylene methylene) (PPM) is a thermally stable, hydrophobic, fluorescent hydrocarbon polymer. Recently, blended PPM has been proposed as a valuable anti-corrosion coating material, and, in particular, rheological additives such as external plasticizers resulted crucial to prevent crack formation. Accordingly, to avoid common problems related to the use of external plasticizers, the development of PPM-related copolymer-based coatings containing \(n\)-octyloxy side chains and their anti-corrosion behavior were explored in this study. The aluminum alloy AA2024, widely employed for corrosion studies, was selected as a substrate, covered with a thin layer of a polybenzylsiloxane in order to improve adhesion between the underlying hydrophilic substrate and the top hydrophobic coating. Gratifyingly, coatings with those copolymers were free of bubbles and cracks. The \(n\)-octyloxy side-chains may be regarded to adopt the role of a bound plasticizer, as the glass transition temperature of the copolymers decreases with increasing content of alkoxy side-chains. Electrochemical corrosion tests on PPM-substituted coatings exhibited good corrosion protection of the metal surface towards a naturally aerated near-neutrally 3.5% wt.% NaCl neutral solution, providing comparable results to blended PPM formulations, previously reported. Hence, the application of rheological additives can be avoided by use of proper design copolymers.  
poly(phenylene methylene) coatings; PPM-related copolymer; rheological additive-free polymer formulation; AA2024; corrosion protection; electrochemistry +
Footnote †: journal: Applied Physics Letters
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Target Recovery
#### 3.3.1 Detector Activation  
When the tracking failure occurs, it is very robust for the tracking result as the detector can be stimulated in time to re-detect. In this paper, we adopt the peak-to-side lobe ratio (termed \(PSR\)) referred by [5] to be our tracking quality evaluation. The tracking quality is evaluated according to the strength of the response map peak. The higher the \(PSR\) score, the more excellent the tracking quality. The \(PSR\) is defined as follows:  
\[PSR=\frac{R_{\text{max}}-\mu_{s1}}{\sigma_{s1}} \tag{12}\]  
where \(R_{max}\) is the peak value of the response map \(R_{t}\). The subscript \(s1\) is the peak side lobe region around the peak, which is \(15\%\) of the response map area in this paper, \(\mu_{s1}\) and \(\sigma_{s1}\) are the mean value and standard deviation of the side lobe area.  
Figure 2 illustrates the distribution of the response map generated by DCFs and its corresponding \(PSR\) score. It is clear that the tracking perform well when the \(PSR\) scores are much larger than the  
response peak score (\(R_{max}\) = 7.126) as shown in Figure 2 (point A and point D), and the tracking results in these two frames are regarded to be highly reliable. Therefore, there is no need for activating detector in this case. However, when the target object underwent significant appearance changes, such as occlusion and deformation, the \(PSR\) scores are lower than the response peak score (from point B to point C). As you can see in Figure 3, the target is experiencing occlusion from #70 to #79 in Jogging-1. In order to ensure accurate tracking of the target in subsequent frames, we activate the detector timely to re-detect the target object and avoid tracking drift under the circumstance. In this work, we consider the response map peak to be a dynamic threshold in comparison to its corresponding \(PSR\) score. Only when the \(PSR\) score is lower than its corresponding response map peak score \(R_{max}\) and the tracking failure occurred at this point, is the detector activated online for re-detecting. Otherwise, the detector is not activated at this time.  
In MOSSE [5], when the \(PSR\) drops below 10, the occlusion appears or the tracking fails. Therefore, we first set the fixed threshold to 10 and the other parameters were consistent. We select multiple video sequences in the OTB dataset for testing. The results are shown in Table 1. We consider the response map peak of each sequence to be a dynamic threshold, which is better than using a fixed threshold in most video sequences.
## 2 Angular Momentum Compensation
### Upper-Body Control Method Based on Angular Momentum
\[\mathbf{L}_{upperbody\_target}=\mathbf{L}_{all}-\mathbf{L}_{legs}-\mathbf{L}_{ waist\_target} \tag{3}\]  
The angular momentum reference of the upper body is divided into torso and arms components for compensation in the yaw direction. \(\mathbf{L}_{trunk\_target}\) and \(\mathbf{L}_{arms\_target}\) are the references of angular momentums generated by the movements of the torso and arms, respectively. The arm movement contribution is represented with gain \(\mathbf{K}\) as shown in Equations (4) and (5). \[\mathbf{L}_{arms\_target}=\mathbf{K}\cdot\mathbf{L}_{upperbody\_target} \tag{4}\]  
\[\mathbf{L}_{trunk\_target}=(\mathbf{E}-\mathbf{K})\cdot\mathbf{L}_{upperbody \_target} \tag{5}\]  
The gain should be determined by the desired motion and the capacity of generating the angular momentum by each part of the robot. For stable running, both the trunk and arms should be used for generating large angular momentum. When the robot has very light arms and cannot generate angular momentum with arms, the gain should be lower. Finally, the movements of the joints are determined. To generate \(\mathbf{L}_{trunk\_target}\) and \(\mathbf{L}_{arms\_target}\), the robot controls its trunk and shoulder joints. The joint angular velocity reference of each joint is calculated using Equations (6)-(8). \[\boldsymbol{\omega}_{right\_shoulder\_ref}=\mathbf{R}_{right\_arm}\mathbf{T} \mathbf{T}_{right\_arm}^{-1}\mathbf{R}_{right\_arm}(\frac{\mathbf{L}_{arms\_ target}}{2}-\mathbf{c}_{right\_arm}\times\mathbf{P}_{right\_arm}) \tag{6}\]  
\[\boldsymbol{\omega}_{left\_shoulder\_ref}=-\boldsymbol{\omega}_{right\_shoulder\_ ref} \tag{7}\]  
\[\boldsymbol{\omega}_{trunk\_ref}=\mathbf{R}_{upperbody}\mathbf{T}\mathbf{T}_{ upperbody}^{-1}\mathbf{R}_{upperbody}(\mathbf{L}_{trunk\_target}-\mathbf{c}_{upperbody} \times\mathbf{P}_{upperbody}) \tag{8}\]  
where, \(\mathbf{c}_{right\_arm}\) and \(\mathbf{c}_{upperbody}\) are the COM positions of the right arm and the upper body, respectively. \(\mathbf{P}_{right\_arm}\) and \(\mathbf{P}_{upperbody}\) are the linear momentum of the right arm and the upper body, respectively. \(\mathbf{R}_{right\_arm}\) and \(\mathbf{R}_{upperbody}\) are the rotation matrices of the right arm and the upper body, respectively. \(\mathbf{T}_{right\_arm}\) and \(\mathbf{T}_{upperbody}\) are the inertial matrices of the right arm and the upper body with respect to the COM position of each part, respectively. The left and right shoulder joint motions are symmetrical with respect to the center. Using this method, the joints of the torso and arms are controlled so that the angular momentum of the waist in the yaw direction is kept close to zero. The method will be used for various humanoid robots by changing the arm movement gain according to the capacity of generating the angular momentum by each part of the robot.
## 6 Discussion and Future Works  
We have observed in the dynamic results of the system presented in the past section that even in the event of having a steady state regarding the communication range (expressed as no changes in the transmission power), the number of neighbors of each node is not the always the same. For instance, we can observe in Table 3 and in Figure 10c that Experiment e05 has the least number of changes in the transmission power of the deployed nodes. Additionally, if we look at Figure 11c, we will see that the number of neighbors registered in each node is not the same for all of them and also changes frequently. We can guess that this is due to the possible uncontrollable changes in the environment. Therefore, we can make the following proposition:  
**Proposition 1**.: _In an arbitrarily-deployed wireless sensor network, the node degree that guarantees the network connectivity is unbalanced._  
We also have obtained results that show how there are configurations and conditions where the use of the proposed self-adaptive system improves both the connectivity and the energy consumption from the point of view of the whole network.  
During the analysis of the system, we have observed other interesting areas where the proposed system can be improved and that we consider worth further exploration.
## 2 Methodology
### Yearly Plan Development  
After the development and verification of the calibration model, deterministic and stochastic models were carried out for the 2023 yearly plan.  
Step 1. Creation of the yearly model.  
After verification of results produced by the calibration model in Arena, each of the 12 months from the 2023 yearly plan was modeled under the deterministic and stochastic approach. On the one hand, the probability distribution functions from the calibration model were maintained for the stochastic models, whereas mean values were utilized for the deterministic approach. On the other hand, target tonnages were modified according to the requirements of the yearly plan for each of the months to be simulated. Furthermore, variable and fixed times were calculated based on the new haul roads. It is worth noting that an additional front-end loader (FEL) was introduced around April of the next year, so a new cycle had to be modeled in Arena. Finally, the number of hours to simulate for each month was based on the planification of the yearly plan.  
Step 2. Analysis of results.  
After the creation and running of stochastic and deterministic models, the results produced were transformed into five indicators in order to explain the behavior of those developed models.  
Truck productivity: Indicator measured in tons per hour and obtained as follows:  
\[\text{Productivity}\;(\text{t/h})=\frac{\text{Tonnages\;moved}\;(\text{t})}{( \text{Total\;Cycle\;Time\;(min)}\;/60)} \tag{1}\]  
Number of hours worked by truck fleet: Indicator calculated through the following expression.  
\[\text{Worked\;hours}=\sum\frac{\text{Tonnages\;moved}}{\text{Productivity}} \tag{2}\]  
Overall cycle time: Calculated through the following expression.  
\[\text{Overall\;Cycle\;Time}=\frac{\sum\text{Tonnages\;moved}\times\text{Total \;Cycle\;Time}}{\sum\text{Tonnages\;moved}} \tag{3}\]  
Overall haul distance: Indicator measured in kilometers and obtained with the following formula.  
\[\text{Overall\;Haul\;Distance}=\frac{\sum\text{Tonnages\;moved}\times\text{Traveled\;Distance}}{\sum\text{Tonnages\;moved}} \tag{4}\]  
Number of trucks: Obtained as the number of available trucks in the Arena model and divided by the mechanical availability planned in the month, and calculated as follows:  
\[\text{Required\;Trucks}=\frac{\text{Available\;Trucks}}{\text{ Mechanical\;Availability}\;(\%)} \tag{5}\]
## 4 Evaluation of Nonlinear Performance Curve Subjected to In-Plane Rocking Behavior
### Evaluation of Nonlinear Performance Curve after Mortar-Joint Cracking
The curvature \(\phi_{tc,r}\) is calculated by dividing the strain by the length of the compressive side, as shown in Equation (12), and the ultimate deformation \(\Delta_{tc,r}\) is finally obtained by integrating the curvature twice, as shown in Equation (13). \[k=\frac{1}{\frac{k^{3}}{a^{2}\cdot E_{m}\cdot T_{gm}}+\frac{h}{\mathcal{C}_{m }\cdot\Delta_{m}}} \tag{11}\]  
Figure 16: Modeling of URM walls subjected to rocking behavior: (**a**) flexural and shear hinge model of URM walls and (**b**) nonlinear performance curve. Figure 15: The relationship between rocking strength, axial force, and stress distribution of a perforated masonry wall after mortar-joint cracking: (**a**) rocking strength and (**b**) effective wall height. \[P_{r2}\cdot H=(N_{D}+W)(0.5l+0.5a) \tag{7}\]0  
\[P_{r2}\cdot H=(N_{D}+W)(0.5l+0.5a) \tag{7}\]1  
where  
\(k\): Elastic stiffness (N\(\cdot\)mm);  
\(a\): 3 for a cantilever wall and 12 for a fixed-fixed wall (=3, herein);  
\(E_{m}\): Masonry elastic modulus (=1810 N/mm\({}^{2}\) from the results measured using displacement transducers between first and third layers during the 3-layered prism tests, herein);  
\(I_{gm}\): Moment of inertia for the gross section of the wall;  
\(G_{m}\): Masonry shear modulus (=0.4\(E_{m}\), herein);  
\(A_{m}\): Cross-sectional area of the wall;  
\(\phi_{tc,r}\): Curvature at toe crushing failure;  
\(\varepsilon_{mn}\): Strain at toe crushing failure (=0.0035, herein);  
\(c\): Depth to neutral axis;  
\(a\): Length of uniformly distributed stress block in Figure 14;  
\(\Delta_{tc,r}\): Ultimate deformation;  
\(\beta\): Coefficient depending on the dominant behavior mode, 1/3 for single curvature and 1/4 for double curvature (1/3 for Specim M-N, 1/4 for Specimens M-W and M-D, herein). The estimated nonlinear performance values subjected to the rocking behavior are shown in Table 4, and the nonlinear performance curves are shown in Figure 17, respectively, compared to the test results. For all specimens, not only the estimated rocking strengths, but also the overall nonlinear performance curves based on the proposed procedure, were in good agreement with the experimental results, regardless of the presence or absence of an opening. Using the proposed procedure in this study, it is possible to estimate the nonlinear performance curve of the rocking behavior, including the ultimate deformation level, as well as the mortar-joint tensile crack strength of solid and perforated rocking URM walls.