esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Soot Deposits during Continuous Methane Pyrolysis in a Corundum Tube
## 2 Experimental Part
### Experimental Plant  
#### 2.2.1 Schematic Diagram of the Experimental Plant  
The experiments were carried out using a tube furnace. The diagram of the experimental plant is shown in Figure 1A.  
The experimental plant consists of a 6 kW XINYU SK2-6-17 TPB4 tube furnace. A corundum tube with internal diameter of 50 mm is installed within the furnace. The furnace heaters are made of molybdenum disilicide and the length of the heating zone is 440 mm. The furnace is equipped with a temperature regulator. Nitrogen, as an inert gas, is supplied to the reactor from a tank through a pressure reducer with the flow monitored via a rotameter. Methane is fed to the reactor from a tank connected to a methane ramp  
Figure 1: (**A**) Scheme of the experimental plant for methane pyrolysis in corundum tube: 1— tank with nitrogen, 2—tank with methane, 3—reducers, 4—rotameters, 5—flanges, 6—oven, 7—corundum tube, 8—control unit, 9—filter, 10—needle valves, 11—water trap (bubbler), 12—nanometer. (**B**) Scheme of the experimental filter for soot separation: 1—lid, 2—plastic bottle with holes, 3—bag, 4—inlet tubes, 5—bucket.  
equipped with a pressure reducer, the flow is also monitored with a rotameter. Changes in pressure in the tube are monitored with the help of the manometer installed at the inlet of the corundum tube.  
From the corundum tube (reactor), pyrolysis products enter the filter. The inner diameter of the pipeline between the corundum tube and the filter is 14 mm.  
Gas-tight rubber gaskets are used as seals in the flanges. After the furnace, the gas stream either enters the atmosphere under extraction or is filtered and sampled in a gas chamber (sampler) for analysis of the chemical composition.  
The experimental filter in Figure 1B is a plastic bucket with a tight-fitting lid. It has two openings for incoming tubes, through which the mixture of soot and gaseous product of methane pyrolysis enters the filter and the soot-free gas exits. In the bucket, the incoming gas passes through a fabric bag attached to a plastic bottle, which fixes the bag in place and prevents the filter from quickly becoming clogged with soot due to the holes in the bag. The soot is accumulated inside the bag.
## 6 Simulation Verification and Analysis
### Characteristic Analysis of the TVSG  
The simulation results for the TVSG in response to given power and grid frequency variations are presented in Figure 12. When \(D_{\omega}\) = 0, the efficient transient oscillations are observed under active power and grid frequency variations; in this scenario, the TVSG exhibits only steady-state deviations attributable to its PFR characteristics. With the increase of \(D_{\omega}\), the transient oscillations and frequency fluctuations of the TVSG system are effectively mitigated. However, when \(D_{\omega}\) values are set at 5, 10, and 20, the corresponding steady-state deviations are approximately 2.5 kW, 3.4 kW, and 5.5 kW. The simulation findings are in agreement with the theoretical analysis. These indicate that while the TVSG effectively mitigates transient oscillations in the system by introducing fixed damping, it simultaneously results in significant steady-state deviations, thereby failing to satisfy the power system's requirements for the TVSG active response characteristics.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 6 The ART\({}^{+}\) Structure
### Building the ART\({}^{+}\) Structure  
The backbone structure of ART\({}^{+}\) is similar to LRT (LRT: Level Range Tree), in which some interventions have been made to improve its performance and increase the robustness of the whole system. ART\({}^{+}\) is built by grouping cluster nodes with the same ancestor and recursively organizing them in a tree structure. A cluster node is defined as a bucket of ordered nodes. The innermost level of nesting (recursion) will be characterized by having a tree in which no more than \(b\) cluster nodes share the same direct ancestor, where \(b\) is a double-exponentially power of two (e.g., 2, 4, 16,...). Thus, multiple independent trees are imposed on the collection of cluster nodes. The height of ART\({}^{+}\) is \(O(\log\log_{b}N)\) in the worst case. The ART\({}^{+}\) structure remains WHP-number unchanged. Figure 4 illustrates a simple example, where \(b=2\).  
The degree of the cluster nodes at level \(i>0\) is \(d(i)=t(i)\), where \(t(i)\) indicates the number of cluster nodes at level \(i\). It holds that \(d(0)=b\) and \(t(0)=1\). At initialization step, the 1 st node, the \((\ln n+1)-th\) node, the \((2\cdot\ln n+1)-th\) node and so on are chosen as bucket representatives, according to the balls in bins combinatorial game presented in [20].  
Let \(n\) be \(w\)-bit keys, \(N\) be the total number of nodes and \(N^{\prime}\) be the total number of cluster nodes. Each node with label \(i\) (where \(1\leq i\leq N\)) of a random cluster, stores ordered keys that belong in the range \([(i-1)\ln n,i\ln n-1]\), where \(N=n/\ln n\). Each cluster node with label \(i^{\prime}\) (where \(1\leq i^{\prime}\leq N^{\prime}\)) stores ordered nodes with sorted keys belonging in the range \([(i^{\prime}-1)\ln^{2}n,i^{\prime}\ln^{2}n-1]\), where \(N^{\prime}=n/\ln^{2}n\) or \(N^{\prime}=N/\ln n\) is the number of cluster nodes.  
Figure 4: The ART\({}^{+}\) structure for \(b=2\).  
ART\({}^{+}\) stores cluster nodes only, each of which is structured as an independent decentralized architecture, which changes dynamically after node join/leave and element insert/delete operations inside it. In contrast to its predecessor, ART, whose inner level was structured as a BATON\({}^{*}\), each cluster node of ART\({}^{+}\) is structured as a \(D^{3}\)-Tree.  
Each cluster node is equipped with a routing table named Random Spine Index (RSI), which stores pointers (red arrows in Figure 4) to cluster nodes belonging to a random spine of the tree (instead of the LSI (LSI: Left Spine Index) of LRT, which stores pointers to the nodes of the left-most spine). Moreover, instead of using fat CI (CI: Collection Index) tables, which store pointers to the collections of nodes presented at the same level, the appropriate collection of cluster nodes is accessed by using a 2-level LRT structure. In ART\({}^{+}\), the overlay of cluster nodes remains unaffected in the expected WHP-case when nodes join or leave the network.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 5 Improved Harmony Search for Node Depth Adjustment
### Improvise a New Harmony  
To find the optimal solution vector, we need to improvise a new harmony during each iteration. The new harmony vector is also a combination of all activated sensors' moving distance. As shown in Equation (18), the new vector can be generated in two ways. To determine the way to generate new harmony, we need generate a random number within \([0,1]\), and compare it with the probability of \(HCMR\). If the number is less than \(HMCR\), the components of new harmony will get the value from the same column in HM. If the generated random number drop in the probability of \(1-HMCR\), the new components will be randomly generated within moving range in the same way as in Step 2. It should be noted that generation way of each component is determined respectively.  
\[d^{\prime}_{i}\leftarrow\left\{\begin{array}{cl}&d^{\prime}_{i}\in\{d^{1}_{ i},d^{2}_{i},...,d^{HMS}_{i}\}&\text{with probability HMCR},\\ &d^{\prime}_{i}\in[d_{min},d_{max}]&\text{with probability $1$-HMCR}.\end{array}\right. \tag{18}\]  
For those components selected from HM, they need to be examined to determine if it would be pitch adjusted. Similar to \(HMCR\), \(PAR\) is used to determine the pitch adjustment. If random number is less than \(PAR\), we will pitch adjust the component. The pitch adjustment decision process is as follows:  
\[\left\{\begin{array}{cl}&\text{\emph{Yes}}&\text{with probability PAR},\\ &No&\text{with probability $1$-PAR}.\end{array}\right. \tag{19}\]  
If a component needs to be pitch adjusted, the bandwidth \(BW\) and a random number \(\alpha\) between \([0,1]\) are used to adjust it as follows:  
\[d^{\prime}_{i}=d^{\prime}_{i}\pm\alpha\times\text{BW}. \tag{20}\]  
For the traditional HS algorithm, \(HCMR\), \(PAR\) and \(BW\) are static parameters in the iteration process. However, it would lead to local optimum easily. Therefore, it is important to dynamically update these parameters with increasing of iterations. The detail equations in generation \(gn\) are as follows:  
\[\begin{cases}\text{HCMR}(\text{gn})=\text{HCMR}_{min}\times\text{exp}(\frac{ \text{g}n\times\text{ln}(\frac{\text{HCMR}_{max}}{\text{HCMR}_{min}})}{\text {NI}}),\\ \text{PAR}(\text{gn})=\text{PAR}_{min}+\frac{\text{PAR}_{min}-\text{PAR}_{min }}{\text{NI}}\times\text{gn},\\ \text{BW}(\text{gn})=\text{BW}_{max}\times\text{exp}(\frac{\text{gn}\times \text{ln}(\frac{\text{BW}_{max}}{\text{NI}})}{\text{NI}}),\end{cases} \tag{21}\]  
where the subscript \(max\) and \(min\) denote maximum and minimum value of corresponding parameters.
# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
## 3 Algorithm for 3D Geometric Representation
### Data Processing
#### 3.3.2 Calculation of Tangent Points  
The tangent points are calculated among three planes grouped in the previous section. One group includes each one of the left and right walls (N1 in Figure 5), front and back walls (N2 in Figure 5), and the ceiling and floor (N3 in Figure 5). The tangent point is calculated by using Equation (1) based on a center point (\(x_{N},\ y_{N},\ z_{N}\) in Equation (1)) and the normal vector (\(\overrightarrow{nx_{N}},\overrightarrow{ny_{N}},\overrightarrow{nz_{N}}\) in Equation (1)) of each plane (N1, N2, N3), as shown in Figure 5. The number of tangent points is the same as the number of groups (\(2x^{2}\)), and those tangent points become the candidates for the vertex of the main structural member. These tangent points include the room data, group of planes, and 3D coordinate geometry data (\(x,y,z\) in Equation (1)).  
\[\begin{array}{l}D_{N1}=\overrightarrow{nx_{N1}}\times(x-x_{N1})+\overrightarrow {ny_{N1}}\times(y-y_{N1})+\overrightarrow{nz_{N1}}\times(z-z_{N1})\\ D_{N2}=\overrightarrow{nx_{N2}}\times(x-x_{N2})+\overrightarrow{ny_{N2}} \times(y-y_{N2})+\overrightarrow{nz_{N2}}\times(z-z_{N2})\\ D_{N3}=\overrightarrow{nx_{N3}}\times(x-x_{N3})+\overrightarrow{ny_{N3}} \times(y-y_{N3})+\overrightarrow{nz_{N3}}\times(z-z_{N3})\\ \left|\begin{array}{ccc}nx_{N1}&ny_{N1}&nz_{N1}\\ nx_{N2}&ny_{N2}&nz_{N2}\\ nx_{N3}&ny_{N3}&nz_{N3}\end{array}\right|\begin{array}{c}x\\ y\\ z\end{array}+\left|\begin{array}{c}D_{N1}\\ D_{N2}\\ D_{N3}\end{array}\right|\begin{array}{c}=\\ \end{array}0.\end{array} \tag{1}\]
## 3 Results and Discussion
### Study of Metasurface Mechanical Deformations  
The next experiment concerned the mechanical excitation caused deformations. The results of this numerical analysis are shown in Figure 11. As was mentioned in the previous paragraph, metasurfaces were subjected to external stress in \(x\)- and \(y\)-directions, separately. The application of stress in an \(x\)-direction caused an extension of all of the geometric parameters in an \(x\)-direction, and a decrease in the \(y\)- and \(z\)-direction. Similarly, the application of stress in the \(y\)-direction (see Figure 3) caused an extension of all of the geometric parameters in a \(y\)-direction, and a decrease in the \(x\)- and \(z\)-directions. Influence of mechanical excitation on the behavior of the evaluated metasurfaces was also analyzed using \(f_{\rm r}\) and \(|s_{11}(f_{\rm r})|\) values. In the case of the CR and the JCR structures, because of their symmetry in orthogonal directions, these were the same results in the case of \(x\)- and \(y\)-direction stress application. In the case of the SRR cells-based material, there were various responses for various directions of mechanical excitation. Generally, in the case of all of the evaluated structures, there was a linear dependence between strain and resulting resonant frequency \(f_{\rm r}\) (operation in the linear range of stress-strain curve). The slope of the linear function depends on the metasurface structure. The highest sensitivity to mechanical excitation was for the CR structure, while the lowest sensitivity had the SRR stressed in the \(y\)-direction. The high sensitivity of the CR structure, in this case, may seem to contradict the result of the previous experiment (regarding the change of parameters \(w\), \(d\), and \(t\)), where the CR structure was stable and relatively a little dependent on the changes in geometry. However, it should be noted that (as already mentioned), in the case of stretching, the geometry changed in a completely different way than in the first experiment. When stretching the CR structure in an \(x\)-direction, there was an elongation (increase of \(l\)) and narrowing (decrease of \(w\)) of the cross arm parallel to the \(x\)-axis, and at the same time a shortening (decrease of \(l\)) and extension (increase of \(w\)) of the cross arm perpendicular to this axis. This operation was significantly different from, for example, increasing the width of both arms (changing the parameter \(w\)) from the previous experiment. This allowed us to state that the analysis of the metasurfaces' susceptibility to geometry changes should be performed concerning specific applications and the resulting restrictions/parameters. Thanks to the consideration of the relative strain (in [%)), the influence of various dimensions of unit cells was minimized. Additionally (not shown in any Figure) the effect of the external mechanical excitation in the presented range had a negligible influence on the module of the reflection coefficient in the resonant state \(|s_{11}(f_{\rm r})|\).
# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 2 Materials and Methods
### Preprocessing  
The series of preprocessing steps applied to all the datasets are as follows:  
1. Head motion correction and eddy current correction were performed using the FSL tool.
2. Non-brain tissues were removed from human brain images using the FSL tool, while non-brain tissues were removed from macaque brain images using a deep learning method developed by our research group [37].
3. Paired high b-value and low b-value images were extracted from the dMRI images, where the high b-value images served as inputs to the model, and the low b-value images served as reference images. The task of extracting b-value images was accomplished using the FSL tool.
4. All high b-value images were scaled to the range of 0 to 1 using the min-max normalization method, and their dimensions were resampled to 256 \(\times\) 256 \(\times\) 256.
5. The data were divided into pre-training, training, and testing sets: the pre-training set included 96 pairs of human brain images and 467 pairs of UWM images. The remaining data from UWM, AMU, MountSinai-P, MountSinai-S, and UCDavis sites were divided into training and testing sets, with a ratio of 8:2.
# Effects of Blade Extension on Power Production and Ultimate Loads of Wind Turbines
## 3 Steady Power Curve and Operational Loads
### Steady Operational Loads
The variation percentage in the parenthesis is calculated based on the turbine with 37.5 m blades as benchmark. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **38.5 m** & **39 m/** & **39.5 m/** & & **37.5 m** & **38.5 m/** & **39 m/** & **39.5 m/** \\ \hline Wind rotor & \(T\) & 197.732 & 103.36\% & 104.56\% & 105.60\% & & **37.5 m** & **37.5 m** & **37.5 m** \\ \hline \multirow{2}{*}{Blade root} & \(M_{y}\) & 1790.12 & 103.89\% & 105.92\% & 107.13\% & & \(M_{y}\) & 12,249.6 & 102.43\% & 103.88\% & 104.42\% \\ \cline{2-8}  & \(M_{y}\) & 1790.12 & 103.89\% & 105.92\% & 107.13\% & & \(F_{x}\) & 209.888 & 102.18\% & 103.58\% & 103.99\% \\ \cline{2-8}  & \(M_{z}\) & 4.45414 & 126.66\% & 141.67\% & 153.75\% & & \(M_{y}\) & 322.444 & 102.64\% & 104.17\% & 104.79\% \\ \hline Hub & \(F_{x}\) & 198.929 & 102.72\% & 104.27\% & 104.93\% & & \(F_{x}\) & 199.598 & 102.65\% & 104.18\% & 104.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the steady loads of wind turbines with different blade lengths. absolute value of the pitch moment \(M_{z}\) at the blade root is slightly increased due to the blade extension, but the increase percentage is rather striking since the pitch moment is rather small compared to the other loads. The above analysis methods and variation rules for loads and AEP also can be used for the approximation for the blade extension of larger wind turbines. However, in the engineering applications, it is still necessary to carry out the detailed calculation and analysis for the wind turbine.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 1 Introduction
In addition, programmability in SDN enables flexible network configuration and fast new service provision based on the network operators' preference compared to the hardware/vendor-specific legacy networks. In other words, it is possible to provide QoS-based flow management in the forwarding plane based on the centralized intelligence and global network view of the control plane. Therefore, this paper proposes a QoS-based flexible handover scheme in SDN to provide differential handover procedures according to the QoS requirements of flows. The main contributions of our work are summarized as follows. Based on the interaction between the forwarding plane nodes and SDN controller, there are four types of handover procedure based on the flow classifications introduced in [14]. Since each handover procedure is designed only to guarantee the QoS requirement of each class compared that the conventional handover methods consider the general QoS requirements, efficient resource utilization can be provided through the proposed scheme. The paper is organized as follows. Section 2 describes the related works of mobility management and QoS in SDN. Then, the proposed QoS-based flexible handover scheme is explained in Section 3. Sections 4 and 5 present the performance evaluations and numerical results based on the analytic model for the proposed scheme. Finally, Section 6 concludes our work.
## 4 Results And Discussion
### Prediction Phase  
The performance of the trained classifiers is assessed on new unseen datasets generated at different AWGN powers ranging from \(-140\) to \(-125\) dBm, which is approximately equal to an SNR range from 5 to 9 dB. Table 2 shows the robustness of the trained CNNs in predicting new unseen data at different SNRs for RFI classification in the first classification level.  
Figure 9: AMC results in the presence of CI (AMC4) using (**a**) AlexNet (79.20%), (**b**) VGG16 (87.30%), (**c**) GoogleNet (78.10%), and (**d**) ResNet18 (81.90%).  
According to the results, VGG16 shows a more precise performance in detecting the type of unseen RFI at different noise levels.  
Tables 3-6 illustrate the prediction results for each AMC (SoI, SoI+CWI, SoI+MCWI, and SoI+CI ) using the trained classifiers for RFI recognition and AMC.
## 5 Simulation Analysis
### Influence of Reference Points  
Take \(\{U_{0}=0.1,U_{1}=0.1\}\), \(\{U_{0}=0.5,U_{1}=0.5\}\), \(\{U_{0}=1.0,U_{1}=1.0\}\), the strategy evolution process and results are shown in Figure 4.  
Figure 3: (**a**) Influence of quality improvement returns on system evolution under static reward and static penalty systems. (**b**) Influence of quality improvement returns on system evolution under dynamic reward and static penalty systems. (**c**) Influence of quality improvement returns on system evolution under static reward and dynamic penalty system. (**d**) Influence of quality improvement returns on system evolution under dynamic reward and dynamic penalty systems.  
Figure 4: _Cont._  
Figure 4a-d show that, under the static reward and static punishment mechanism, and the dynamic reward and dynamic punishment mechanism, when the reference point is decreased, the oscillation amplitude of the system development progressively decreases, and the system's stability gradually decreases. There is some improvement; under the static reward and dynamic punishment mechanism, as the reference point drops, the speed of system development and convergence accelerates, and the likelihood of supply side quality improvement increases.  
This corresponds to the conclusion of the 3.3 outcome analysis. The subjective perceptions of decision makers regarding objective objects, rather than the real items themselves, influence their behavior and decision-making. That is, when the influencing factors such as income and cost remain constant, the change in the decision-own maker's reference point will cause the decision-maker to have a different perception of the external influencing factors, which will then affect the decision-behavioral maker's decision-making.  
To promote the quality coordination of shared manufacturing, we can not only start with external regulations such as increasing the cost allocation ratio and establishing reward and punishment mechanisms, but we can also change the perceptions of costs and benefits of supply sides and other participating subjects by strengthening the overall quality view and establishing a long-term development concept.