esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 3 Proposed PLSR-SVR Hybrid Predictor
### SVR for the Prediction of the Residual Components
Assume the DVL works well at time \(T\), the sample set for SVR contains the SINS calculating velocities (i.e., \(\left(V_{eSINS(T)}^{n}\,V_{nSINS(T)}^{n}\,V_{uSINS(T)}^{n}\right)\)) as inputs and the residual components of the PLSR prediction (i.e., \(\delta V_{eDVL(T)}^{n}\), \(\delta V_{nDVL(T)}^{n}\) and \(\delta V_{uDVL(T)}^{n}\)) as desired outputs. The SVR predictor is trained with the sample set. As stated above, there are two parameters to be determined in the training process, i.e., the Lagrange multipliers \(\alpha_{i}\) and \(\alpha^{*}\). The two parameters can be obtained by solving the problem listed in Equations (28) and (29). After obtaining the Lagrange multipliers, the SVR prediction model can be formed by Equation (31). When the DVL fails, the SVR predictor is employed to predict the residual of PLSR. Assume the DVL malfunctions at time \(T_{0}\), the input for the SVR predictor is described as \(\left(V_{eSINS(T_{0})}^{n}\,V_{nSINS(T_{0})}^{n}\,V_{uSINS(T_{0})}^{n}\right)\). Then the SVR predictor outputs the prediction of the residual components, symbolized by \(\delta\hat{V}_{eDVL\_S(T_{0})}^{n}\,\delta\hat{V}_{nDVL\_S(T_{0})}^{n}\) and \(\delta\hat{V}_{uDVL\_S(T_{0})}^{n}\).
# High Performance Graph Data Imputation on Multiple GPUs
## 4 Efficient Convolutional Imputation Algorithm on GPU
### Design and Implementation of the Baseline GPU Convolutional Imputation Algorithm
#### 4.1.3 Parallelization of the Algorithm
To perform SVD of each frontal slice of \(\widetilde{\mathcal{G}}^{\text{impute}}\), we utilize the cusolverDnSgesvdj(.) routine in the cuBLAS library [24], which is implemented via the Jacobi method and is faster than the standard method. Besides, we arrange the computations in Algorithm 2 into batched computation to achieve better parallelism and performance. Because \(\mathcal{S}(k)\) only stores non-zero diagonal elements, we design a GPU kernel to batch the execution of \(N\) matrices (line 3), where each thread is responsible for an element of tensor \(\mathcal{W}\) (i.e., \(\mathcal{W}(i,j,k)\) is paired with thread \((i-1,j-1,k-1)\)). Considering that tensor \(\mathcal{U}\) of size \(m\times m\times N\), tensor \(\mathcal{S}\) of size \(m\times n\times N\) and tensor \(\mathcal{V}^{T}\) of size \(n\times n\times N\) in SVD results are stored in 1D arrays of size \(m*m*N\), \(\min(m,n)*N\) and \(n*n*N\), respectively, the value of \(\mathcal{W}(i,j,k)\) with the index \((i-1)+m*(j-1)+(m*n)*(k-1)\) in the 1D array is \(\max(\mathcal{S}[(i-1)+\min(m,n)*(k-1)]-\lambda^{i}_{k},0)\mathcal{V}^{T}[(i-1 )+n*(j-1)+(n*n)*(k-1))]\). Using this kernel is more efficient than calling the cuBLAS library API for each frontal slice. Further, we convert the diagonal matrix to the left of the operator (Figure 4a) rather than the right (Figure 4b) to allow for the coalesced memory access to the device memory. As shown in Figure 4a, threads access contiguous memory blocks, and so they are benefiting from coalesced memory access instructions for better efficiency. In line 4, we utilize a routine in the cuBLAS library to perform the batched matrix-matrix multiplication on \(N\) matrices in parallel.
## 3 Results  
The complete information on concentrations of each element determined at each sampling site is available in Supplementary Materials Table S1; synoptic data are shown in Table 1. Respecting the fact that the arithmetic mean should not be directly applied to compositional data because it relies on Euclidean geometry and depends on all of the data, including extreme values, and thus not representative of the centre for skewed data [89], the median values are taken for further discussion, and subsequently, the clr-transformed data are used for analyses.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Min** & **Mean** & **Median** & **Max** & **MAD** & **Skewness** & **Kurtosis** \\ \hline
**June Al** & 0.05 & 0.36 & 0.18 & 1.20 & 0.097 & 1.5 & 0.4 \\
**October Al** & 0.07 & 0.21 & 0.17 & 0.70 & 0.069 & 2.3 & 6.2 \\
**June V** & 1.4 & 7.7 & 4.7 & 24.1 & 2.6 & 1.3 & 0.3 \\
**October V** & 2.1 & 6.2 & 5.0 & 19.3 & 2.5 & 1.7 & 2.7 \\
**June Cr** & 2.9 & 15.6 & 14.3 & 39.4 & 6.2 & 1.0 & 0.9 \\
**October Cr** & 4.1 & 18.9 & 14.8 & 87.4 & 8.9 & 2.8 & 8.9 \\
**June Mn** & 122.0 & 334.7 & 327.5 & 718.0 & 108.0 & 1.1 & 1.7 \\
**October Mn** & 93.3 & 320.6 & 259.0 & 843.0 & 58.0 & 1.9 & 4.9 \\
**June Fe** & 0.12 & 0.70 & 0.70 & 1.20 & 0.423 & \(-\)0.1 & \(-\)1.8 \\
**October Fe** & 0.2 & 1.0 & 0.6 & 2.5 & 0.393 & 0.8 & \(-\)0.8 \\
**June Ni** & 2.4 & 6.0 & 4.8 & 13.1 & 1.9 & 1.0 & \(-\)0.03 \\
**October Ni** & 1.9 & 3.5 & 3.3 & 6.5 & 0.9 & 0.9 & \(-\)0.01 \\
**June Cu** & 5.1 & 15.3 & 9.1 & 99.7 & 1.7 & 3.7 & 13.7 \\
**October Cu** & 4.7 & 7.9 & 7.1 & 16.9 & 1.8 & 1.9 & 5.1 \\
**June Zn** & 73.4 & 116.7 & 112.5 & 159.0 & 17.5 & 0.1 & \(-\)0.5 \\
**October Zn** & 69.9 & 108.2 & 102.8 & 156.0 & 26.2 & 0.4 & \(-\)1.2 \\
**June As** & \textless{1.0} & 2.7 & 0.5 & 14.6 & 0.5 & 1.8 & 2.4 \\
**October As** & \textless{1.0} & 1.0 & & 5.1 & 1.9 & 2.4 \\
**June Cd** & 1.0 & 1.5 & 1.4 & 3.0 & 0.33 & 1.7 & 3.0 \\
**October Cd** & 0.6 & 1.7 & 1.5 & 4.5 & 0.35 & 1.9 & 4.2 \\
**June Sb** & \textless{1.0} & 1.0 & 1.2 & 2.7 & 0.97 & 0.3 & \(-\)1.1 \\
**June Hg** & 0.041 & 0.075 & 0.074 & 0.108 & 0.016 & 0.01 & \(-\)1.2 \\
**October Hg** & 0.067 & 0.102 & 0.087 & 0.168 & 0.014 & 1.0 & \(-\)0.2 \\
**June Pb** & 4.62 & 191.1 & 12.4 & 70.1 & 4.75 & 2.1 & 4.4 \\
**October Pb** & 10.8 & 33.2 & 24.85 & 79.8 & 13.05 & 0.9 & \(-\)0.5 \\
**June N** & 1.6 & 2.1 & 2.1 & 2.7 & 0.159 & 0.4 & 1.3 \\
**October N** & 1.8 & 2.5 & 2.5 & 3.0 & 0.357 & \(-\)0.3 & v1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synoptic table of minimum (Min), maximum (Max) value, mean, median, median absolute deviation (MAD), skewness and kurtosis of elements content (\(\text{ug}\cdot\text{g}^{-1}\)) and (\(\text{wt}\).%) for Al, Fe and N.
## 3 Review Results
### Note on the Methodologies Applied in the Studies
#### 3.9.2 Data Collection Methods  
**Survey-Based and Online Questionnaire Data Collection:**  
The primary method for data collection in these studies has been survey-based, using structured questionnaires to gather data from participants. These surveys are often distributed online and include a range of closed-ended questions measured on Likert scales, providing quantifiable data for analysis.  
Online questionnaire surveys are a common tool, enabling researchers to reach a broad audience and collect data efficiently. Typically, these surveys include items on a seven-point Likert scale to capture respondents' attitudes and intentions regarding ChatGPT adoption [4; 5; 27].  
**In-Depth Interviews:**Qualitative studies and mixed-methods approaches often involve in-depth interviews. These semi-structured interviews provide rich, detailed insights into individual experiences, perceptions, and the contextual factors influencing the adoption of ChatGPT [15; 21].
## 2 Materials and Methods
### Thermomechanical Fatigue Experiments  
Out-of-phase (oop) thermomechanical fatigue (TMF) experiments were conducted in total strain control in a temperature range of 50 to 650 \({}^{\circ}\)C. The specimens were inductively heated. Type R sling thermocouples were installed at the gauge length of the specimens to monitor gauge temperature. Strain measurement was accomplished by high temperature extensometers directly attached to the gauge proportions. The specimens were both heated and cooled with a rate of dT/dt = 10 Ks\({}^{-1}\). Below 200 \({}^{\circ}\)C the cooling rate was limited and accordingly the cooling half cycle duration was approximately 85 s. Furthermore, no hold times were implemented at the hot ends of the cycles to reduce the influence of creep to a minimum.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Process Parameter**} & **Value (3 mm Beam Diameter)** & **Value (0.66 mm Beam Diameter)** \\ \hline Laser power & 1375 W & 360 W \\ Feed speed & 400 mm/min & 1500 mm/min \\ Powder mass flow rate & 11.3 g/min & 2.4 g/min \\ Track offset & 2.3 mm & 0.35 mm \\ Height offset & 1.4 mm & 0.3 mm \\ Beam diameter & 3 mm & 0.66 mm \\ Deposition strategy & Unidirectional & Unidirectional \\ Nozzle type & Three-jet nozzle & Three-jet nozzle \\ Shielding gas & Argon & Argon \\ \hline \hline \end{tabular}
\end{table}
Table 4: HiperFer\({}^{\rm AM}\) laser metal deposition (LMD) process parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Crofer22 H**} & **C** & **S** & **N** & **Cr** & **Ni** & **Mn** & **Si** & **Mo** & **Ti** \\  & **0.009** & **0.002** & **0.02** & **22.8** & **0.27** & **0.45** & **0.31** & **0.01** & **0.06** \\  & **Nb** & **W** & **Cu** & **Fe** & **P** & **Al** & **Mg** & **Co** & **La** \\  & **0.52** & **2.11** & **0.02** & **Balance** & **0.017** & **0.015** & **<0.01** & **0.01** & **0.05** \\ \hline \multirow{3}{*}{HiperFer 17 Cr2 *} & **C** & **S** & **N** & **Cr** & **Ni** & **Mn** & **Si** & **Mo** & **Ti** \\  & \textless{}0.003 & \textless{}0.001 & \textless{}0.003 & 17–18 & - & 0.2–0.5 & 0.2–0.3 & - & - \\  & **Nb** & **W** & **Cu** & **Fe** & **P** & **Al** & **Mg** & **Co** & **La** \\  & 0.5–0.6 & 2.4–2.6 & - & Balance & - & - & - & - & - \\ \hline \multirow{3}{*}{HiperFer\({}^{\rm AM}\)} & **C** & **S** & **N** & **Cr** & **Ni** & **Mn** & **Si** & **Mo** & **Ti** \\  & \textless{}0.003 & \textless{}0.001 & \textless{}0.003 & 22 & - & 0.2–0.5 & 1 & - & - \\ \cline{1-1}  & **Nb** & **W** & **Cu** & **Fe** & **P** & **Al** & **Mg** & **Co** & **La** \\ \cline{1-1}  & 1.5 & 2 & - & Balance & - & - & - & - & - \\ \hline \hline \end{tabular} \({}^{*}\)[25].  
\end{table}
Table 3: Chemical compositions of the ferritic steels (wt.-%).
## 2 Theoretical Analysis of the Edge Effect
### Discussion on the Edge Error Profile  
The sharp increase of the contact pressure at the edge area would lead to the dramatic increase of material removal there. It makes the material removal become unpredictable at the edge zone. As shown in Figure 4, there are three cases of the error profile at the edge zone after polishing using the bonnet tool according to the reported results by [PERSON] et al. [16] and [PERSON] et al. [19], which are the profile with only the upturned (case 1), the profile with upturned and downturned (case 2), and the profile with only the downturned (case 3), respectively. When polishing the segmented mirrors, the downturned edge profile is unacceptable. However, in the case of polishing LAE optics, a downturned edge is acceptable if the downturned zone is outside EA. Hence, a hypothesis can be made that there exists a perfect \(\tau\) (case 4, as shown in Figure 4), which makes the edge profile have the smallest PV value and edge width, without damaging the bonnet tool in the meantime. It would be perfect for the pre-polishing of LAE optics. In case 4, no complicated control of the tool-lift will be needed anymore. Generally, the gradient of the upstand edge is too high to be measured using the interferometer after a few pre-polishing steps. Many corrective polishing iterations on the edge zone are needed to reduce its  
Figure 3: Finite element modeling of the contact pressure in the edge zone with different overhang ratio. (**a**) finite element model and corresponding displacement deformation result when \(\tau=0.5\); (**b**) simulation results of the contact pressure distribution shape with different \(\tau\); (**c**) sectional profile of the contact pressure along the white line plotted in (**b**).  
gradient. However, this hypothesis gives the hope for pre-polishing LAE optics to the accuracy level, which can be measured by the interferometer within just in a few iterations. If large size TIF is used to enhance the pre-polishing efficiency firstly, a part of the upstand edge zone may be in EA as shown in Figure 4. Nevertheless, its height and width are much smaller compared to cases 1 and 2, which dramatically reduces the processing time in the following step. If relatively small size TIF is used, the upstand edge zone may be outside EA. Then, there will be no need for corrective polishing the edge in this case. Hence, if this hypothesis can be implemented, it will dramatically reduce the edge processing time in the pre-polishing stage, and largely shorten the manufacturing cycle of LAE optics.
## Numerical Simulation Method
### Boundary Condition  
The numerical simulation solver was ANSYS 15.0, based on the Reynolds-averaged Navier-Stokes (RANS) equations. The boundary conditions of the cascade in this paper were obtained from the corresponding element in a transonic tandem rotor tip. The inlet Mach number was 1.2 and the Reynolds number based on the chord length was \(1.54\times 10^{6}\), using the k-epsilon turbulence model. Since the state of \"unique incidence\" [21] exists in supersonic inflow, the specified method of the boundary conditions should be different in the following two states:  
* Unique Incidence  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Case1** & **Case2** & **Case3** & **Case4** & **Case5** \\ \hline Number of grids (\(\times 10^{3}\)) & 330 & 405 & 470 & 530 & 590 \\ \(\omega_{\text{Total}}\) & 0.2068 & 0.2122 & 0.2148 & 0.2151 & 0.2150 \\ Incidence range (\({}^{\circ}\)) & 1.9490 & 1.9408 & 1.9366 & 1.9362 & 1.9363 \\ \hline \end{tabular}
\end{table}
Table 4: The aerodynamic parameters simulated with different number of grids.  
\begin{table}
\begin{tabular}{c c c} \hline  & **FB** & **AB** & **Tandem** \\ \hline S (mm) & 72.6 & 72.6 & 78.1 \\ C (mm) & 97.4 & 61.4 & 186.6 \\ Inlet Metal Angle (\({}^{\circ}\)) & 66.2 & 57.1 & - \\ Outlet Metal Angle (\({}^{\circ}\)) & 62.4 & 35.4 & - \\ Installation Angle (\({}^{\circ}\)) & 62.1 & 51.3 & 45.8 \\ Solidity & 1.34 & 0.87 & 2.39 \\ AO & - & - & \(-0.15\) \\ PP & - & - & 0.38 \\ \hline \end{tabular}
\end{table}
Table 3: Tandem blade geometric parameters.  
Figure 2: Tandem blade CFD (computational fluid dynamics) mesh.  
With a lower outlet static pressure, the bow shocks are attached to the leading edge (LE), and the type of flow is known as \"unique incidence\". The inlet boundary conditions remain constant with different outlet pressures in this condition. The unique incidence control loop method present by [PERSON] et al. [22] was used to search for the appropriate inflow angle with the corresponding inflow Mach number of 1.2 in \"unique incidence\" operating condition.  
Inlet boundary conditions: specify the total temperature, total pressure, three velocity values, and directions (radial velocity is zero).  
Outlet boundary condition: specify the average static pressure.  
Positive Incidence  
With the increase of outlet static pressure, the passage shocks move out of the channel and then the bow shocks are detached from the LE. The airflow upstream of the bow shock can be influenced by the outlet condition. With different inflow angles, the inlet velocity and outlet mass flow rate should change correspondingly to keep the inlet Mach number constant.  
Inlet boundary conditions: specify the total temperature, total pressure, three velocity values, and directions (radial velocity is zero).  
Outlet boundary condition: specify the mass flow rate.
# 6G Use Cases and Scenarios: A Comparison Analysis Between ITU and Other Initiatives
## 2 The ITU Activities on 6G
Examples can be found in assisted automated driving, autonomous intelligent devices for the eHealth sector, data offloading, support of Digital Twins and assisted collaborative robots (Cobots). To realize the 6G scenarios, ITU has identified a specific list of system capabilities for 6G that enhance those described in IMT-2020 in Recommendation ITU-R M.2083 [6]. The system capability can be specified for one usage scenario in a different way in terms of performance requirements, frequency range, bandwidth, deployment scenario, etc. Table 1 summarizes the main 6G capabilities that have been identified. \begin{table}
\begin{tabular}{c c} \hline
**6G Capabilities** & **Details** \\ \hline Peak data rate & Maximum data rate the user terminal can achieve. Possible values are 50, 100, 200 Gbit/s. \\ \hline User-experienced data rate & Data rate the user terminal can achieve in a ubiquitous modality. Possible values are 300 Mbit/s and 500 Mbit/s. \\ \hline Spectrum efficiency & Measure of the average data throughput per single unit of spectrum resource and per cell. Possible values are 1.5 and 3 times greater than 5G cases. \\ \hline Area traffic capacity & Total traffic throughput served per geographic area. Possible values are 30 Mbit/s/m\({}^{2}\) and 50 Mbit/s/m\({}^{2}\). \\ \hline Connection Density & Total number of devices deployed per unit area. The target value could be \(10^{6}\) or \(10^{8}\) devices/km\({}^{2}\). \\ \hline Mobility & Maximum speed able to guarantee the target QoS levels and a seamless data transfer. The target value could be 500 and up to 1000 km/h. \\ \hline Latency & Measure of the time needed by the destination to receive a specific packet over the radio interface. Possible values are 0.1 and up to 1 ms. \\ \hline Reliability & Capability to transmit data successfully within a specific time interval over the air interface. Target values could be 1, 5, and 7 and up to 10. \\ \hline Coverage & User access to communication network in a specific area. It is measured by the link budget analysis (cell edge distance). \\ \hline \end{tabular}
\end{table} 
Table 1: 6G’s capabilities [3]. In the following sections, we describe in more detail the 6G proposals from other initiatives.
# Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
## 3 Results and Discussion
### Generalized Workflow  
This work presents a simple and robust approach based on a frequency domain algorithm along with a user-friendly interface. Usually, in a stack data, the reflection events have higher and stronger amplitudes. Removing those events exposes other comprehensible information, often in the form of seismic diffraction. After using a Fourier transform to convert our data from the time domain into the frequency domain, the F-K (frequency-wavenumber) spectrum shows the amplitude distribution with the wavenumber. Through minimizing the forecast for outstanding work, many iteration tasks were performed whilst developing the local slope filter for separation of specular reflection from the full-wave seismic behavior, which is based on equivalent impression and prediction error filters [5; 29]. The separation of reflection and diffraction energy is accurate, as this method accommodates wave responses from small subsurface discontinuities, such as faults, fractures, karst sections, and unconformities, which appear as diffraction in the data. The uniqueness of our methodology is the integration of essential approaches for diffraction modeling, separation, and imaging. A generalized workflow and steps taken to perform diffraction separation for data are given below.  
* Generate a velocity model, i.e., the fractured or Marmousi model;
* Use finite difference wave propagation to record the seismic behavior in the provided geological model;
* In the case of raw data from the field, initial processing is needed to improve the data quality (including sorting, CDP ordering, NMO correction, and stacking, if the data are prestacked);
* Apply a Fourier transform to observe the spectrum of amplitude distribution in the frequency wavenumber;
* Design the filter based on the dip component of the data in the dt/dx plane;
* Convolute the proposed dip frequency filter (DFF) with seismic data to separate the diffraction from reflection;
* Apply an inverse Fourier transform to convert the frequency-wavenumber data into the time-distance domain.  
Figure 7: (**a**) Migrated seismic section, The point diffractor shows the location of the discontinuity and the fault indicator. (**b**) Full-wave seismic migrated section; all components of the fault are missing. (**c**) Amplitude spectrum (frequency bandwidth) of the migrated data, showing that the diffraction amplitude is biased.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 6 Results and Discussion
### Results Summary  
After evaluating the different algorithms using various criteria, the results indicate that the RF model has superiority over the other models. This finding was expected (and consistent with other studies that evaluated ML models) when considering the advantages and disadvantages of each of the evaluated ML algorithms. Specifically, the LR and LSVM algorithms are more suitable for binary classification, while intrusion detection is a multi-class classification problem. Additionally, based on theory, the GNB algorithm does not perform well in real-world scenarios, which was confirmed by this research's results.  
Our findings demonstrate that RF, in combination with the Synthetic Minority Oversampling Technique (SMOTE) and grouping techniques, is a good solution when dealing with the imbalance problem and subsequently predicting attacks. The precision, recall, F1, and accuracy scores of all algorithms can be seen in the summarized results shown in Figure 6. The figure shows that there is a large difference in all four categories between the RF model and the other three models. This figure also shows that the LSVM model has the worst results. It is also worth highlighting that the accuracy of all four models is rather high, which is consistent with the results of the related studies that we surveyed in Section 3. However, the high accuracy of some models does not guarantee their overall good performance. Interestingly, our RF model performs very well in terms of all used metrics over all classes.  
Figure 5: Models’ performance as precision–recall curves (PRC). The classes are denoted as follows: Benign \(\to\) 1, DoS \(\to\) 2, Port Scan \(\to\) 3, Bot \(\to\) 4, Infiltration \(\to\) 5, Web Attack \(\to\) 6, Brute Force \(\to\) 7.
## 3 Results and Discussion
### Calcite Percentage Results
For example, in the S1 soil, calcite contents ranging from 1.39% to 5.12% were obtained through this method. This wide range indicates that both low and high levels of mineralization are achievable, demonstrating the high potential of SA + MICP combinations applied via mixing for calcite precipitation. However, increasing the pellet or SA content did not always result in higher precipitation; in some mixtures, a decline in calcite content was observed. The relevant literature highlights that in MICP applications using the mixing method, the distribution of additives and the duration of precipitation directly influence the amount of mineralization [38]. Figure 11: The effect of SA and SA + MICP applications on calcite precipitation under different SA ratios in the injection method.