esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis of Theoretical and Experimental Results
### Seismic-Response Analysis under Single and Bidirectional Seismic Motion
#### 4.3.3 Comparison of the Shear Response between Floors  
Figure 28 shows the average interlayer shear force comparison between the rigid and soft soil interlayer isolation structures under four types of ground motions with 0.2 g input in the X-direction and both directions.  
By analyzing Figure 28, it can be concluded that (1) under long-term seismic action, the interlayer shear response of the interlayer isolation structure is significantly greater than that under ordinary seismic action. When the PGA is 0.2 g, the interlayer shear force of the soft soil interlayer isolation system is similar to that of the rigid interlayer isolation system. With the increase in PGA, the SSI effect weakens its impact on the enhancement of interlayer shear force of the interlayer isolation structure. Therefore, in the design of seismic isolation structures, it is necessary to consider the enhanced effect of long-term ground motions on interlayer shear forces. At the same time, for areas with low seismic fortification intensity, it is also necessary to consider the enhanced effect of SSI on interlayer shear forces in the structure. (2) Under bidirectional seismic motion, there is still a certain gap between the interlayer shear force values of rigid interlayer isolation and soft soil interlayer isolation systems and the unidirectional input situation. The interlayer shear force of the bidirectional input ordinary seismic motion, rigid interlayer isolation system, and soft soil interlayer isolation system is slightly greater than that of the unidirectional input case while the interlayer shear force value of the bidirectional input long period  
Figure 27: Comparison of floor-displacement response under single and bidirectional seismic excitations (PGA = 0.2 g). (**a**) Near-field ordinary ground motion; (**b**) Far-field ordinary ground motion; (**c**) Near-field pulse ground motion; (**d**) Far-field harmonic ground motion.  
seismic motion is closer to that of the unidirectional input case, indicating that different input seismic motion modes have a smaller impact on the shear response of the structure under long period seismic action than ordinary seismic motion.
# Continuous Rotor Dynamics of Multi-Disc and Multi-Span
###### Abstract  
Continuous rotor dynamics remains stagnant. In this paper, aim at multi-span and multi-disc rotor-bearing system, the continuous rotor dynamic analysis method (CRDAM) is proposed. The force acting on the shaft by the rotating eccentric disc is simulated as a point force. The counterforce of bearing is also considered as a point force. The shaft is considered free-ended. A continuous rotor dynamic model is obtained and an analytical solution is proposed to express the unbalance response as function of the position, unbalance, support stiffness and damping. The proposed method is validated by numerical experiments in which unbalance responses obtained by it are compared with that obtained by the two classical methods the finite element method (FEM) and Ricatti method. The results indicate that the proposed method is applicable to calculating unbalance response of multi-disc and multi-span rotor. Moreover, it is closer to FEM than Ricatti and can be applied to actual high speed rotors. Among the three methods, the calculating speed of Ricatti is the fastest, CRDAM is the second fastest and FEM is the slowest. The proposed method, which solves the forward problems of the continuous rotor dynamics for the multi-disc and multi-span rotors, can provide theoretical basis for further studies on inverse problems such as identification of rotor unbalance and bearing stiffness and damping coefficients without test runs and external excitations.  
continuous rotor dynamics; multi-span and multi-disc rotor-bearing system; continuous rotor dynamic analysis method; continuous rotor dynamic model; analytical solution
## 2 Materials and Methods  
The analysis conducted employs the ascendant SAR images acquired by the COSMO SkyMed (CSK) satellite constellation in the period from March 2011 to March 2019, the same data employed in the DPC-ReLUIS 2019-2021 project [18]. CSK is an earth observation mission conceived for civil and military purposes, developed by the Italian Space Agency (ASI) [19] in cooperation with the Ministry of Defense. It is based on a constellation of low earth orbit satellites equipped with SAR working in X-band. The differential interferometric SAR information is processed by adopting the Small Baseline Subset algorithm (SBAS-DinSAR) [20; 21]; thus, it is possible to obtain the displacements with the accuracy of centimeters and millimeters.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 5 Applications, Use Cases, and Cost Analysis
### Application and Use Cases
The second phase was executed on 13 nodes with 28 CPUs and 80 GB of RAM for 144 h, and the third one required significant resources, with 11 nodes, 44 CPUs, and 148 GB of RAM, making it the most resource-intensive experiment, lasting for 240 h.
# Design Approach and Mechanism Analysis for Cavitation-Tolerant Torque Converter Blades
## 3 Application of Suppression Slot in Stator Cascade
### Analysis of the Flow Field Mechanism with Different Slot Designs
For Plan 2, the slot outlet pressure is slightly lower than the inlet pressure, and the fluid is transported from the upstream of the blade through the slot to the downstream. The fluid flows at the inlet and outlet of the slot shear each other inside the slot to form a vortex flow inside the slot. For Plan 3, due to the increased slot width, the fluid inside the slot has a more obvious convection phenomenon, which will form a relatively large slot vortex structure. The fluid at the slot inlet flows to the downstream slot outlet, which is what causes the velocity vortex structure to sag near the leading edge of the blade. For Plans 2 and 3, the slot design will cause most of the vortex flow on the trailing edge of the blade and disturb the flow velocity in the mainstream area, thereby reducing the flow velocity and reducing the risk of cavitation. For Plan 4, the downstream of the blade suction side belongs to high-pressure fluid, and the upstream of the blade suction side belongs to low-pressure fluid due to the action of the high-speed fluid. The downstream fluid flows upstream through the slot, impinging on the high-velocity fluid in the mainstream area, causing the fluid to slow down and reducing the risk of cavitation. The fluid at the outlet of the slot will significantly reduce the fluid velocity in the mainstream area, causing more separation vortices in the downstream fluid, which is consistent with the velocity vortex structure distribution in Figure 10.  
Figure 10: Iso-surfaces of the \(\lambda_{2}\) criterion near the stator blade with different suppression slots (\(t=0.12\) s, \(SR=0\)).
## 3 Numerical Validation
### FSI with Forced Motion
#### 3.1.2 uCRM-13.5 Wing in Harmonic Motion  
The ALE-LBFS is further applied to the uCRM-13.5 wing in harmonic motion. A schematic planform of the uCRM-13.5 [52] is plotted in Figure 10. The semi-span of interest \(b\) is 35.996 m and the length of the root chord \(c_{root}\) is 12.893 m. The Young's modulus of structural material is \(6.9\times 10^{10}\) Pa, with Poisson's ratio of 0.3 and density of 2700 kg/m\({}^{3}\).  
In order to analyze the calculated pressure coefficient distribution accurately, the harmonic motion is forced on each mode of the uCRM-13.5 wing, respectively. Thus, ahead of harmonic simulations, the first four modes are extracted from the wing structure by a modal approach. Here, we specify that the harmonic motion obeys the following dynamic function  
\[f=\eta\sin(\omega t) \tag{53}\]  
where \(\eta\) is the factor of amplitude and \(\omega\) is the natural frequency calculated from reduced frequency \(k_{c}=\omega c_{root}/V\). In the harmonic simulations, the reduced frequency is selected as \(k_{c}=0.1\) and \(\eta\) is equal to 6.  
There exist two complete cycles of harmonic motion in each mode. Measuring the steady-state \(C_{p}\) at the specified spanwise slices, namely, \(y/b=0.35\) and \(y/b=0.89\), their results are presented in Figure 11. Figures 12 and 13 show how \(C_{p}\) changes in the unsteady harmonic motion. In each figure, four plots are shown, which correspond to the first four respective modes. Three curves are drawn for the maximum, middle, and minimum points in the second cycle of the harmonic motion with their phase angles \(\Phi\) being 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\).  
Figure 10: Schematic diagram of the uCRM-13.5 wing.  
Normalized by the maximum amplitude, the discrepancy \(\Delta C_{p}\) in each curve between the time-dependent \(C_{p}\) and the steady-state \(C_{p}\) is displayed. Similar to the results by [PERSON] et al. (2019), it can be seen that \(C_{p}\) at the phase angle of \(180^{\circ}\) is close to the steady state compared with the other curves. For \(\Phi=90^{\circ}\) and \(\Phi=180^{\circ}\), the two plots exhibit a nearly symmetrical relationship with each other. This could be accounted for by the nonsymmetrical airfoil of uCRM-13.5. Although the pressure difference between \(\Phi=90^{\circ}\) and \(\Phi=180^{\circ}\) is not equal to zero, their magnitude values are nearly close. This concurs with the idea that pressure is linearly dependent on the displacement.  
Figure 11: Distribution on the wing surface in a steady state. (**a**) Pressure coefficient contour. (**b**) Pressure coefficient distribution along \(y/b=0.35\). (**c**) Pressure coefficient distribution along \(y/b=0.89\).  
Figure 12: _Cont._
## 3 Approach and Architecture
### Offline Processing Level  
At this level, most of the basic processing over the data takes place. The offline, fully-featured versions of the entity, topics, opinions and events (ETOE) analysis and the analysis of the social contents operate over the cleansed data from the crawl, which are stored in the ARCOMEM storage. These processing tools perform linguistic, machine learning and natural language processing (NLP) methods in order to provide a rich set of metadata annotations that are interlinked with the original data. The respective annotations are stored back in the ARCOMEM storage and are available for further processing, text and data mining. After all of the relevant processing has taken place, the Web pages to be archived and preserved are selected in a semi-automatic way. For the selection, the user defines a lower threshold of the relevance score for the documents to be preserved. Finally, the selected original pages are transferred to the Web archive in the form of Web Archive (WARC) files (see Section 3.5). WARC [7] is an ISO standardized format for storing and archiving Internet content that aggregates crawled content into a single file. Besides the main content, WARC files also accommodate various meta-information (e.g., crawler information, crawl date and time, MIME type).
## 2 Materials and Methods
### Grid Independence Test  
When designing a grid, the smaller the size of the grid, the higher the accuracy, but this also leads to a significant increase in computation time and the possibility of errors due to unnecessary details. To evaluate grid independence, CFD simulations are run multiple times. The numerical analysis is compared with reference analyses or field data, allowing for the determination of an error metric that represents the difference between the two values. The error metric can be measured by methods such as RMS (root mean square) and maximum error and can also be determined by quantitatively measuring the physical quantities of the object [24].  
Grid independence evaluations can be performed using various methods, including the Richardson extrapolation method, grid convergence index, and grid refinement study. Out of these, the grid refinement study method was utilized to evaluate grid independence. To reduce the computation time while improving the accuracy of the calculations, an analysis of skewness based on grid size and an evaluation of independence for sectional average temperatures were conducted. The grids for analysis were set to sizes of 1, 3, 5, 7, and 10 mm.  
The analysis of the number of grid cells according to grid size showed that there were 840,403 grid cells at 1 mm, 96,221 at 3 mm, 34,125 at 5 mm, 17,765 at 7 mm, and 8742 at 10 mm. The skewness values were 0.61 at 1 mm, 0.56 at 3 mm, 0.46 at 5 mm, 0.49 at 7 mm, and 0.49 at 10 mm. The lowest skewness value of 0.46 at 5 mm indicated the highest model quality, with a lower number of grid cells than at 1 mm and 3 mm, serving as an indicator for efficient simulations (Figure 3).  
The grid independence evaluation conducted for temperatures across different sections revealed that in the B layer, the temperature was 43.8 \({}^{\circ}\)C at the 1 mm grid size, 44.0 \({}^{\circ}\)C at 3 mm, 44.1 \({}^{\circ}\)C at 5 mm, 44.3 \({}^{\circ}\)C at 7 mm, and 44.3 \({}^{\circ}\)C at 10 mm. In the MB layer, the temperature was 61.1 \({}^{\circ}\)C at the 1 mm grid size, 61.1 \({}^{\circ}\)C at 3 mm, 61.0 \({}^{\circ}\)C at 5 mm, 61.0 \({}^{\circ}\)C at 7 mm, and 61.4 \({}^{\circ}\)C at 10 mm. In the MT layer, the temperature was 64.2 \({}^{\circ}\)C at the 1 mm grid size, 64.1 \({}^{\circ}\)C at 3 mm, 64.0 \({}^{\circ}\)C at 5 mm, 63.9 \({}^{\circ}\)C at 7 mm, and 63.2 \({}^{\circ}\)C at 10 mm. In the T  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Value** \\ \hline Viscosity coefficient & 435,560,000 m\({}^{-2}\) \\ Coefficient of inertia resistance & 18,150 m\({}^{-1}\) \\ Porosity coefficient & 0.3 \\ Inlet velocity & 0.088 m\(\cdot\)s\({}^{-1}\) \\ Wall density & 8000 kg\(\cdot\)m\({}^{-3}\) \\ Wall specific heat & 515 J/kg-k \\ Wall thermal conductivity & 15.5 w/m-k \\ Inlet temperature & 25 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions of numerical analysis model.  
layer, the temperature was 46.5 \({}^{\circ}\)C at the 1 mm grid size, 46.6 \({}^{\circ}\)C at 3 mm, 46.6 \({}^{\circ}\)C at 5 mm, 46.7 \({}^{\circ}\)C at 7 mm, and 47.9 \({}^{\circ}\)C at 10 mm (Figure 4).
## 3 Results and Discussion  
### Numerical Simulation of the Corona  
Applying 13 kV voltage to the anode, and the initial number density of positive ions and electrons as 10\({}^{15}\) m\({}^{-3}\), the calculated result of the corona current is shown in Figure 8. It can be seen that within a simulation time of 400 ns, the current shows a slow growth at the initial stage and then rises to the peak value of 0.81 A rapidly. At this time, the channel is broken down, and then, the current decreases to 0.52 A at a short time. Afterwards, the current gradually decreases to zero.  
In order to clarify the development process of charged particles, five key time nodes were selected for analysis as shown in Figure 8, and the 2D distribution of electron number density is shown in Figure 9. During the discharge process, electrons are mainly gathered around the discharge channel. This is because the electric field intensity near the channel is larger, and most reactions of ions occur around there. The electrons are evenly distributed inside the channel during the development process of plasma, and when the plasma reaches the cathode, the electrons are concentrated in front of it. The main reason is that a large number of secondary electrons gathered in front of the cathode when the plasma approaches the cathode, and the number of electrons around the cathode increases dramatically.  
Figure 8: Waveform of corona current.  
Figure 9: Electron number density distribution at different moments of positive corona (ions densities are in m\({}^{-3}\) and the axes are in mm): (**a**) \(t_{1}\); (**b**) \(t_{2}\); (**c**) \(t_{3}\); (**d**) \(t_{4}\); (**e**) \(t_{5}\).  
Figure 10 shows the electric field distribution at four different times during the formation of the discharge channel. It can be seen that the electric field inside the plasma channel remains at a very low level, while the electric field ahead of the plasma is the highest. This is mainly because a large number of positive and negative ions are gathered inside the channel, and the space charges are counteracting each other thus, the overall performance is neutral, and the electric field inside the channel is very small. The continuous ionization in front of the plasma causes the accumulation of positive ions, and the electric field formed by the positive ions and the cathode strengthens the original spatial electric field. At the same time, due to the effect of electron diffusion, the radius of the plasma channel is increased, which makes the electric field at the plasma head decrease slowly.
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
###### Abstract
There are various kinds of AI approaches for time series analysis such as recurrent neural network (RNN), artificial neural network (ANN), and long short-term memory (LSTM). In contrast to other AI approaches, the bidirectional long short-term memory (Bi-LSTM) is an extension of LSTM, which is more suitable for the time series data because it can preserve the future and the past information. Thus, Bi-LSTM may have better accuracy in prediction as compared to RNN and LSTM. More technical details for how Bi-LSTM works are discussed in Section 4. The major contributions of this paper are as follows:  
1. First, a bidirectional long short-term memory (Bi-LSTM)-based [20] proactive autoscaling system architecture designed for Kubernetes is proposed, and it is shown that the proposed autoscaling system achieves a higher accuracy in resource estimation than the default horizontal pod autoscaler of Kubernetes. 2. Second, Bi-LSTM prediction model for time-series analysis is implemented, showing that it can outperform existing proactive autoscaling time-series prediction models. 3. Third, a resource removal strategy (RRS) is proposed to remove part of the resource when the workload is decreased so it can better handle a burst of workload occurring in the near future. The results of the experiments conducted using different real trace workload datasets indicate that Bi-LSTM achieves better accuracy with not only the LSTM model but also a well-known state-of-the-art statistical method, ARIMA model, in terms of short- and long-term forecasting. In addition, the prediction speed is 530 to 600 times faster than that of the ARIMA model and almost equal compared to the LSTM model when examined with different workloads. Furthermore, compared to the LSTM model, the Bi-LSTM model performs better in terms of autoscaler metrics such as provision accuracy and elastic speedup. The remainder of this paper is organized as follows. In Section 2, we review the recent autoscaling studies conducted using time-series analysis. In Sections 3 and 4, the system design and concept of the proactive custom autoscaler are introduced. The prediction model is discussed in Section 5. Section 6 presents the experiment conducted for evaluating the proposed prediction model followed by the discussion in Section 7. We conclude the paper in Section 8.
## 5 Conclusions  
In summary, we propose a differential phase shift quantum secret sharing protocol over asymmetric channels and give the security proof of our protocol against individual attacks. Moreover, we extend the asymptotic key rate of TF-DPSQSS [29] to finite-key region. Through implementing free parameter optimization on the numerical simulations, we demonstrate that our asymmetric protocol can dramatically improve the key generation rate and the transmission distance compared with the original TF-DPSQSS [29] protocol. As we have shown before, when the difference in length between [PERSON]'s channel and [PERSON]'s is fixed at 14 km, the key rate of the asymmetric protocol is two orders of magnitude higher than the original TF-DPSQSS [29] protocol. Furthermore, our protocol obtains a high key rate when the difference is large. In addition, it is convenient and efficient to implement by allowing [PERSON] and [PERSON] to set asymmetric intensities independently, especially in a network setting. Due to the remarkable performance of our asymmetric protocol, it can be applied directly to the QSS experiments over asymmetric channels and represents a further step towards practical application of QSS.  
[PERSON] and [PERSON] contributed equally to this work. All the authors contributed to the refinement and generalization of the concept and its presentation of the present paper. [PERSON] and [PERSON] supervised the whole project. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (61801420); Key-Area Research and Development Program of Guangdong Province (2020B0303040001); Fundamental Research Funds for the Central Universities (020414380182, 020414380141).  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time, but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 4 Zeroize Method Performance Analysis  
This section analyzes the proposed Zeroize method. Although intended for 5G networks, the Zeroize method applies to many other wireless technologies (e.g., IEEE 802.11). The proposed method is analyzed on the basis of the layer three header and above because the layer two header changes depending on the used technology. The Zeroize method is tested and analyzed versus the standard RTP (S-RTP method) method of transferring the VoIP packets, the SVF method proposed in [14], and the ZSP method proposed in [15]. The SVF and ZSP methods were selected because they are similar to the proposed Zeroize method. The SVF and ZSP methods share the same primary objective as the Zeroize method, conserving the network bandwidth when using VoIP applications. The SVF and ZSP methods are used to evaluate the performance of the Zeroize method.  
ZSP methods use the same main concept to save bandwidth: carrying the voice data in the superfluous fields of the packet header. However, the SVF and ZSP methods work over IPv4 networks and are intended only for point-to-point calls. The proposed Zeroize method was tested versus the other methods in terms of channel capacity and apportioned bandwidth reduction. The LPC, G.726, and G.723.1 codecs were assumed for the comparison.  
Network Simulation 3 (NS3) was used to simulate the proposed Zeroize method. After simulating the Zeroize method, a network topology using NS3 was designed to evaluate the Zeroize method and compared with the SVF and ZSP methods. The simulation topology included two components of the Zeroize method, which were situated at the sender and receiver sides, as discussed in Section 3. Each component utilized a buffer with a maximum size of five. The connection between the two components was mimicked as a first-in-first-out buffer. A constant bit rate traffic generator was attached to each end.