esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Experiment  
### The Setup  
The communication experiment was conducted on 7 September 2011, in Trondheim harbor (Norway), where the water depth varies from 10 m to 400 m. The transmitter was carried by the NTNU research vessel R/V Gunnerus, and it used a hemispherical acoustic transducer deployed at a depth of 20 m. The dynamic positioning system of R/V Gunnerus was activated during the trial to reduce drifting.  
A cross receiving array of 12 hydrophones was deployed from a pier, where the water depth was about 10 m. The array consisted of a vertical array of eight hydrophones (hydrophones No. 1-8) with 1 m element spacing and a horizontal array with four hydrophones (hydrophones No. 9-12) with 1.5 m element spacing. Hydrophone No. 1 was located 0.5 m below the sea surface, and the depth of the horizontal array was 4.5 m. The range between the source and the receiving array was 7.4 km.  
Digital modulations of phase shift keying (BPSK), quadrature phase shift keying (QPSK), and eight quadrature amplitude modulation (8 QAM) were used. The carrier frequency of the transmitted signal was 12 kHz. A 0.1 s LFM chirp with a Hanning window was used for coarse time synchronization in each data packet, and its effective bandwidth was 2.2 kHz. When the LFM was used as the channel probe signal, it was also used as a shaping pulse. As a shaping pulse, the roll-off coefficient of RCC was 1.  
Figure 1 shows the signals which were repeatedly transmitted every 202.044 s for 15 periods. The signals of the same modulations, but using different shaping pulses were transmitted continuously with a time gap of 2.2 s. The symbol rates were 1 kilo-symbols/s and 2 kilo-symbols/s, and the respective bandwidths were 2 and 4 kHz when the RRC was used. The received waveforms were recorded at a sampling frequency of 96 kHz for offline processing in the laboratory.
## 3 Results and Discussion
### Case 2: Experimental Analysis of QoS Guarantee
#### 3.3.3 Fairness  
To further determine the tradeoff between the guarantee of a desired user data rate and system throughput, we developed the graph in Figure 9 to indicate that the Jain fairness based on PLS and FTPA is worse than in our proposed NOMA FL model. Furthermore, the max-min fairness based on NOMA FL is greater than PLS and OFDMA FL. Due to the power law ratio, PLS exacerbates imbalances among the power allocation of users, whereas FTPA alleviates inhomogeneous power allocation. In Figures 4 and 9, NOMA FL is shown to exchange a certain degree of enhancement of system throughput for the fairness of the QoS guarantee. It is clear that NOMA FL outperforms the other methods in terms of fairness without much decrease in system throughput. Particularly under UDN conditions, NOMA FL was shown to reach the performance level of PLS and FTPA if data rate maximization is regarded as the static target. For dynamic user priority, NOMA FL surpassed OFDMA FL in both system throughput and fairness.  
Figure 8: 3D plot with varied QoS, number of users, and AUDR.  
Figure 9: Comparison of fairness with a QoS expectation of 30 Mbps.
## 5 Ndn-Bda
### Data Authentication Process  
The NDN-BDA employs a data authentication process that requires each consumer vehicle \(CV\) to send an _Interest_ packet to request the associated blockchain of the network before it can request and receive the _Data_. To initiate this process, the \(CV\) sends an _Interest_\(i\) with the name \"/NDN-BDA/MS/BC/request\" towards the \(MS\). Upon receiving the _Interest_, the \(MS\) responds with _Data_ containing the requested blockchain. The \(CV\) will then use the received blockchain as an authentication tool for all the _Data_ it receives from producer  
Figure 7: The steps involved in the producer vehicle registration process.  
vehicles. In order to do so, the consumer vehicle needs to check the blockchain to find the certificate associated with the received \(Data\). It checks if it can find the name included in the \(Data\)'s KeyLocator. If the certificate is found, the \(Data\) are validated and accepted. If not, the \(Data\) are discarded. This process ensures the authenticity of the \(Data\) being transmitted within the network and provides a secure means of authentication for the \(CV\).
## 2 Related Work  
### Performance Comparison and Analysis  
Recent years have seen the introduction of secure container runtimes, leading to numerous studies assessing their performance across various metrics, like startup time, CPU, memory, and I/O. Previous research [9; 18] introduces gVisor and Firecracker, comparing their performance with existing container runtimes. Similarly, [PERSON] and [PERSON] [24] and [PERSON] and [PERSON] [11] delve into Kata containers' performance via qualitative and quantitative analyses. [PERSON] and [PERSON] [20] explore the runc, gVisor, Kata containers, and Firecracker performance alongside other runtimes. [PERSON] et al. [19] conduct an architecture analysis comparing native Linux, gVisor, and Firecracker, finding differences in the kernel code execution and network latency. [PERSON] et al. [25] demonstrate deployment and execution speed differences between gVisor and Kata containers, highlighting their security strengths but with significant performance trade-offs.  
Additionally, studies by the authors of [26; 27] investigate gVisor's and Firecracker's performance with other serverless computing platforms during concurrent execution, emphasizing the runtime architecture and scheduling approaches as key performance determinants. Another study [26] shows Firecracker's superiority over gVisor in concurrent execution.  
While prior research offers quantitative performance measures, it often lacks an in-depth analysis explaining runtime performance disparities. This paper fills this gap, presenting a sophisticated performance analysis focusing on file I/O performance and CPU usage. Moreover, it provides a detailed, symbol-level analysis of the file I/O stack, elucidating the root causes of performance differences and CPU usage among representative secure container runtimes.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### RAN Slicing
#### 3.2.1 gNB Configuration  
A gNB can be configured to support multiple slices. This configuration, done via the NSSMF, is based on the following principles:  
1. Network slices are defined within a PLMN. In RAN sharing scenarios, where multiple PLMNs share the same cell, each operator needs to link S-NSSAIs with the PLMN ID.
2. The gNB serves a cell. The cell belongs to a tracking area, which is identified with two artifacts: Tracking Area Code (TAC), i.e., local identifier, and Tracking Area Identifier (TAI), i.e., universal identifier. The TAI is a [PLMN ID, TAC] tuple, and it is relevant in RAN sharing scenarios. To indicate the tracking area to which the cell belongs to, the gNB broadcasts one or more TAIs, i.e. one TAI per hosted PLMN ID [30].  
A network slice is linked to a tracking area. This is because S-NSSAIs are managed per tracking area [31].  
Based on the above principles, it can be noticed that all cells belonging to the same tracking areas must serve the same set of network slices. Once the gNB is set with supported slices (per TAI), its mission is to map traffic from individual PDU sessions into appropriate NG-RAN resources. This is done by associating the tuple [S-NSSAI, PLMN ID] with one Dedicated Radio Bearer (DRB). The profile of the DRB is configured with RRM parameters which are tailored to the service requirements of the slice.
## Nomenclature  
### Abbreviation  
IGV  
Inlet guide vanes  
MLP  
MAPE  
Mean absolute percentage error  
R\({}^{2}\)  
Coefficient of determination/R-squared  
LR  
Linear regression  
SVR  
Support vector regression  
MAE  
Mean absolute error  
GAM  
Generalized additive model  
GPR  
Gaussian process regression  
DT  
Decision tree  
BBT  
Bootstrap-aggregated tree  
CFD  
Computational fluid dynamics  
SID  
Subspace identification  
MaxAPE  
Maximum absolute percentage error  
APE  
Absolute percentage error  
GE  
General Electric  
\begin{tabular}{l l} TCS & Turbine control system \\ MW & Megawatt, equals to 1000 Watt. \\ Pa & Pascal \\ K & degree Kelvin \\ _Symbols_ \\ \(T_{1}\) & compressor inlet temperature \\ \(p_{1}\) & compressor inlet pressure \\ \(T_{d}\) & inlet dew point temperature \\ \(T_{f}\) & fuel temperature \\ \(p_{f}\) & fuel pressure \\ \(m_{f}\) & fuel flow \\ \(n\) & rotate speed \\ \(p_{4}\) & turbine exhaust pressure \\ \(P_{w}\) & power \\ \(cov(\cdot)\) & covariance calculation function \\ \(\sigma\) & standard deviation \\ \(X\), \(Y\) & different features of data \\ \(P\) & Pearson correlation coefficient \\ \(M_{basic}\) & basic model \\ \(\hat{P}_{w}\) & prediction of power \\ _para_ & model parameters learned by the machine learning algorithm \\ \(\mathcal{L}(\cdot)\) & loss function \\ \(\mathcal{D}(\cdot)\) & dataset \\ \(x_{i}\) & input value \\ \(y_{i}\) & target value \\ \(n\), \(m\) & number of points \\ \(d\) & dimension of input variables \\ \(C\) & regulation parameter \\ \(e\) & tolerance parameter \\ \(w\) & regression coefficient \\ \(\xi_{i}^{-}\), \(\xi_{i}^{+}\) & deviation \\ \(b\) & bias \\ \(\Phi(\cdot)\) & kernel function \\ \(k\) & number of clusters \\ \(t\) & \(t\)th iteration \\ \(distance_{i,j}^{t}\) & Distance between point \(i\) and centroid of \(j\)th cluster in \(t\)th iteration \\ \(\mu_{j}^{t}\) & center point of \(j\)th cluster in \(t\)th iteration \\ \(C_{j}\) & \(j\)th cluster \\ \(M_{i}\) & the \(i\)th modification model \\ \(\overline{y}\) & initial predicted value \\ \(\Delta y\) & deviation prediction value \\ \(\hat{g}\) & prediction value \\ \(P_{t}\) & point at time \(t\), here \(t\) means a time moment \\ \(\mu\) & mean value \\ \end{tabular}
# Control of Porosity in Parts Produced by a Direct Laser Melting Process
## 4 Conclusions  
The effects of powder morphologies and processing parameters on porosity formation were investigated and the following conclusions were obtained:  
1. Powder morphologies and laser processing parameters are critical to porosity formation during DLM. This means that porosity during the DLM process can be actively controlled by powder morphologies and laser processing parameters.
2. In the case of a mixed powder composed of spherical and non-spherical powders, the packing density decreases as the non-spherical powder size increases. The porosity of the laser-melted layer increases with the degree of size misfit between the non-spherical and spherical powders. Decreased laser absorption and enlargement of the PD zone as a result of decreased packing density increases the porosity during DLM.
3. Densification of a laser-melted layer is strongly dependent on the heat concentration. Increased layering thickness and scan rate reduce the heat concentration and increase the possibility of porosity. When the powder layering thickness is large, a small overlapped area may create porosity as a result of insufficient melting of the deposited powder.
4. The successful fabrication of functionally graded parts with variable porosity densities confirms the possibility of active control during the DLM process.  
All the authors have contributed equally to the conception and idea of the paper, implementing and analyzing the processing methods, evaluating and discussing the experimental results, and writing this manuscript.  
This work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant, funded by the Korean government (MSIP) (no. R75201600010003003, multi-material convergence 3D printing solution development).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Analysis of Factors Affecting the Spread of HBNG Leakage under Normal Ventilation Conditions
#### 3.2.5 Different Leak Locations  
An analysis was conducted to compare the leakage pattern of HBNG at various leakage locations under normal ventilation settings in cases 4 and 10-15. A graph illustrating the changes in gas concentration of HBNG leakage at various positions of the leakage holes was obtained (Figure 13). When the leakage occurs at x = \(-\)97.5 m or x = \(-\)99.5 m, the sluggish air movement in the upwind region of the leak makes it challenging for the HBNG to be propelled toward the exhaust vent. The gas that has been released gathers in this region and nears the threshold at which it can explode. The downwind area of the leaking hole was filled with HBNG, and the air-driven HBNG posed a combustion and explosion hazard in much of the natural gas compartment. The distribution pattern of the leaking holes remains consistent when they are positioned at x = \(-\)50 m and x = 50 m, as it is at x = 0 m. As a result of ventilation, the upwind area of the leakage holes is nearly allowed for gas components, while the downstream area experiences rapid accumulation of HBNG. When the leakage location is x = 97.5 m and x = 99.5 m, the leakage location is close to the air vent, so the HBNG can be rapidly discharged out of the integrated pipe corridor under the effect of ventilation. However, due to the limitations of the vent size, it was challenging to vent the HBNG, resulting in the area from the edge of the vent to the firewall being filled with leaking gas. We therefore suggest installing diversion devices and combustible gas alarms in the blind spots of the air inlet and outlet for ventilation.  
Figure 12: The components hydrogen and methane in gas leaks with varying HBR at different monitoring points, (**a**) HBR = 5%, (**b**) HBR = 10%, (**c**) HBR = 15%, (**d**) HBR = 20%.
## 2 Integrated Modeling
### Structural Subsystem
To achieve mode coordinates \(q\), a finite element model, as shown in Figure 3, is built in finite element software (Nastran). The mode shape is shown in Figure 4. The satellite model has a total of 234,570 units and 330,118 nodes. In this model, flywheels are approximated as three mass nodes. By adjusting mode selection matrix \(\alpha\), micro-vibration disturbance input Td and control input Tc are applied to the mass nodes of flywheels. Take the natural mode within 0-500 Hz (a total of 232 states). Adjusting mode selection matrix \(\beta\) can locate the output position at concerned nodes, including the primary mirror, secondary mirror, focal plane, and star sensor (as detailed in Sections 2.4 and 2.5). In the finite element model, most of the structures are solid elements with types of penta6 and hex8, and the thin-walled structures such as solar array use shell elements with types of tria3 and quad4. This meshing method meets the requirements of the aerospace field for finite element analysis. No loading is applied, and the constraint is a free boundary condition. Figure 3: Finite element model. In the finite element model of the structural subsystem, the simulation results in the frequency domain often show many responses that are not present in the test or have a very small amplitude. The reason is that the uniform mode damping is adopted in the modeling, which causes the response amplitude to be inconsistent with the reality, or the finite element model is different from the actual structure of the satellite. Therefore, we conducted a mode test on the satellite by using the hammer excitation method, compared the frequency of the main modes of the satellite, modified and checked the mode damping of some important natural modes, and modified the mode damping in the state-space equation. Finally, the model after modified mode damping is used for simulation analysis. Table 3 shows the modified mode damping of some typical modes.
## 3 Meshless Approach Overview  
One of the most common techniques among meshfree methods is the Radial Basis Functions (rbf). The formal definition of a radial function is given below.  
Definition 1: A function \(\Phi:R^{d}\times R^{d}\to R\) is called radial provided there exists a univariate function \(\psi:[0,\infty]\to R\) such that \(\Phi(\mathbf{x}-\mathbf{y})=\psi(r)\), where \(r=||\mathbf{x}-\mathbf{y}||\), where we choose \(||\cdot||\) to be the Euclidean norm on \(R^{d}\).  
Figure 1: Coordinate system and geometry of Longitudinal Ray transform with the node set.  
The proposed approach is similar to the three-layer feed-forward rbf neural network [29; 30]. The first layer is the network inputs, the second a hidden layer consisting of a collection of nonlinear rbf activation units, and the third corresponds to the final network output. In Radial Basis Function Networks (rbfn), activation functions are usually implemented as Gaussian functions. An overview of rbfn structure is shown in Figure 2. To illustrate how the rbfn works, assume that we have a set of \(D\) data with \(N\) patterns of \((x_{p},y_{p})\) in which \(x_{p}\) is the input of the set of data and \(y_{p}\) is the actual output. The \(i\)th activation function in the hidden network layer can be determined from the gap between input pattern \(x\) and centers \(i\) based on Equation (2)  
\[\phi_{i}(||\mathbf{x}-\mathbf{c}_{i}||)=\exp\left(-\frac{||\mathbf{x}- \mathbf{c}_{i}||^{2}}{r_{i}^{2}}\right), \tag{2}\]  
Here, \(||\cdot||\) is the Euclidean norm, \(c_{i}\) and \(r_{i}\) are the center and width of the hidden neuron \(i\), respectively. Then the contribution of the \(k\) node from the network output layer can be computed with Equation (3)  
\[y_{k}=\sum_{i=1}^{n}w_{ik}\,\phi_{i}(\mathbf{x}). \tag{3}\]
## 4 Analysis of the Experimental Results
### Inclined-Sumping Running Positioning Experiments
#### 4.2.2 Analysis of Positioning Results
Methods to improve the accuracy of the system include the use of high-precision instruments and better data processing methods, including high-efficiency filtering techniques and map-matching algorithms;
3. The positioning accuracy of the inertial navigation instrument is related to the system running time. Periodic correction of the positioning data by the odometer can reduce the influence of time on the system. Initial calibration of the instrument can be performed when the shearer starts running, and the high-precision inertial navigation instrument can achieve the above function. Moreover, the odometer accumulates little error over time; therefore, the navigation data can be corrected by the odometer to achieve higher precision shearer positioning.
# Effects of New Tunnelling on a Buried Pipeline with Joints and Its Detachment
## 3 Verification from Centrifuge Tests  
In this section, two centrifuge tests conducted by [PERSON] et al. [43] are used to verify our proposed method. A pipeline modeled in the centrifuge tests had nine joints, and the middle joint was located directly above a new tunnel. The jointed pipeline was made of aluminum alloy with 1.22 mm thickness and 15.85 mm outer diameter, and was subjected to tunnelling-induced ground movement. The model tunnel, with 60 mm diameter, was modeled and filled with water to induce a given volume loss by extracting water. The volume loss ratio was set at 0.3% and 2.0% in Test 1 and Test 2, respectively, performed under 75 \(g\) acceleration. Table 1 shows the calculation parameters of the prototype scenario. The stiffness reduction factor is considered to be zero. Figures 9 and 10 show the experimental data of centrifuge tests, the calculated deflections, and the comparison with the analytical results calculated by other methods.  
Figure 8: Nonlinear solution procedure based on Newton–Raphson method.  
Figures 9 and 10 indicate that the calculated results proposed by our method agree with the experimental data. The profiles of pipeline deflection obtained by [PERSON] et al. [29], [PERSON] et al. [28], and our proposed method are similar. [PERSON] et al. [28] provided an analytical  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pipeline** & \(D\) **(m)** & \(L\) **(m)** & \(E\) **(GPa)** & \(I\) **(m\({}^{4}\))** & \(EI\) **(10\({}^{9}\) Nm\({}^{2.3}\))** & \(z_{p}\) **(m)** & \multicolumn{2}{c}{\(V_{I}\) **(\%)**} \\ \hline  & 1.19 & 48.06 & 70 & 0.0474 & 3.3149 & 4.165 & 0.3 & 2 \\ \hline
**Tunnel** & \(R_{0}\) **(m)** & \(Z_{0}\) **(m)** & & **Soil** & \(E_{s}\) **(MPa)** & \(\mu\) & \(K\) & \(G_{c}\) \\ \hline  & 2.25 & 11.25 & & & 10 & 0.3 & 0.7 & 10D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical and physical parameters of centrifuge tests and analytical methods ([PERSON], [43]; [PERSON] et al. [29]).  
Figure 10: Verification and comparison for Test 2.  
Figure 9: Verification and comparison for Test 1.  
method without considering the variable stiffness reduction in the joints. In contrast, [PERSON] et al. [29] consider both the rotation (bending moment) and dislocation (shearing force) with variable stiffness reduction factors, but without considering the separation between the existing tunnels and underlying soil. For Test 1, the curve's width by [PERSON] et al. [28] is slightly wider than those of the others. For Test 2, the curve's width results are close in all three methods. The maximum deflections by our method are slightly larger than those of the others for Test 1 and Test 2.  
The proposed method can calculate the deflection of the existing pipelines with joints and predict the potential detachment of the buried pipeline. As shown in Figures 9 and 10, our method is applicable for estimating the deflection of the existing pipelines with joints. Thus, our proposed method can be used to study the effects of tunnelling-induced ground movement on pipelines with joints and detachment.
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 2 Physical and Mathematical Model
### Cavitation Model and Turbulence Model  
In view of the research results [30; 31], for the commonly used Smagorinsky model of the LES method and the standard SST model based on the RANS method, the cavitation model of the viscosity-modified SST model can accurately predict the cavitation shedding frequency and simulates the return jet at the wake of the hydrofoil (which is the main cause of cavitation shedding). For the turbulence model, it is necessary to calculate the dense boundary layer with high Reynolds number, and the k-\(\epsilon\) turbulence model is suitable for this kind of working condition. Therefore, the SST cavitation model and the k-\(\epsilon\) turbulence model are used for numerical simulation. The vaporization rate and liquefaction rate derived from the Rayleigh-Plesset equation in the Schnerr-Sauer model are shown in Formulas (1) and (2), respectively, and the volume fraction of gas phase (\(\alpha_{v}\)) is shown in Formula (3).  
\[\dot{m}^{-}=\frac{\rho_{1}\rho_{v}}{\rho_{m}}\frac{3\alpha_{v}(1-\alpha_{v})} {R_{B}}\sqrt{\frac{2}{3}\frac{(p_{v}-p)}{\rho_{m}}} \tag{1}\]  
\[\dot{m}^{+}=-\frac{\rho_{1}\rho_{v}}{\rho_{m}}\frac{3\alpha_{v}(1-\alpha_{v})} {R_{B}}\sqrt{\frac{2}{3}\frac{(p-p_{v})}{\rho_{m}}} \tag{2}\]  
\[\alpha_{v}=n_{0}\frac{4}{3}\pi R^{3}/\left(n_{0}\frac{4}{3}\pi R^{3}+1\right) \tag{3}\]  
Furthermore, we modify the turbulent viscosity coefficient in the SST k-\(\epsilon\) model, and its expression is shown in Formulas (4)-(6).  
\[u_{t}=f(p)C_{\omega}\frac{k}{\omega} \tag{4}\]  
\[C_{v}=\frac{1}{max\left[\frac{1}{a^{*}},\frac{SF}{a_{1}v}\right]} \tag{5}\]  
\[f(\rho)=\rho_{v}+\frac{\left(\rho_{m}-\rho_{v}\right)^{n}}{\left(\rho_{l}-\rho _{m}\right)^{n-1}} \tag{6}\]  
In the above formula, \(R_{\text{B}}\) is the bubble radius; \(n_{0}\) is the density of gas nuclei; \(p,p_{v}\) are local pressure and saturated vapor pressure, respectively; \(\rho_{m}\), \(\rho_{1}\), \(\rho_{v}\) are mixed phase density, liquid phase density and gas phase density, respectively; \(u_{t}\) is turbulent viscosity; and \(k\) and \(\omega\) are turbulent kinetic energy and dissipation rate.