esa-sceva/satcom-chunk-collection huggingface dataset:
# Diffusion-Weighted Imaging of the Macaque Brain Using Diffusion-Prepared Turbo Spin Echo in MRI
## 2 Materials and Methods
### MR Data Acquisition and Analysis  
We performed full-brain macaque diffusion-weighted imaging with the proposed DP-TSE sequence and vendor's product DW-rsEPI sequence to evaluate dMRI quality and time efficiency. The dMRI series comparisons were acquired using the following imaging parameters, and the total scan time was recorded. DP-TSE/DW-rsEPI: TE = 11/80 ms, TR = 4000/4500 ms, FOV = 128 \(\times\) 128/160 \(\times\) 160 mm\({}^{2}\), matrix = 128 \(\times\) 128/160 \(\times\) 160  
Figure 1: Sequence diagram of monopolar diffusion-prepared TSE. The green trapezoids represent diffusion gradients, which are changed by diffusion directions; the gray trapezoids are applied in three directions.  
Figure 2: The diagram of macaque placement in a sphinx position with a custom-built 16-channel receive-only helmet-like RF coil, which was constructed on a 3D-printed former made from polycarbonate [13].  
in-plane resolution = 1 \(\times\) 1 mm\({}^{2}\), slice thickness = 2 mm, 24 slices, diffusion weighting \(b\) = 0, 500, 1000, 1500, 2000 s/mm\({}^{2}\), TSE turbo factor = 11, rsEPI segment = 7, no repetition or average scan. The diffusion weighting was applied along three orthogonal axes, defined as SS, RO and PE. During the experiment, the TR could not be identical for the 24-slice series dMRI scan due to restrictions of the DW-rsEPI protocol; meanwhile, as the \(b\)-value increased to 1500 s/mm\({}^{2}\), DW-rsEPI suffered severe signal loss and was unable to be used for comparison. Therefore, we acquired an extra slice for ADC curve fitting with a set of \(b\) = 0, 100, 300, 600, and 900 s/mm\({}^{2}\), TR = 1500 ms for ADC accuracy check. In addition, another DP-TSE slice only in the RO direction with the same measurement parameters and \(b\) = 500 s/mm\({}^{2}\) were acquired with parallel-imaging acceleration (GRAPPA, iPAT 2) for comparing the efficiency of SNR.  
To compare the dMRI image quality visually, the diffusion-weighted images from both sequences were not edited by filtering or distortion-correction after acquisition. The ADC was calculated using an implicit mono-exponential model as follows:  
\[\text{S}(b)/\text{S}_{0}=\text{ exp}(-b\times\text{ ADC}) \tag{1}\]  
where S(\(b\)) is the signal magnitude with diffusion weighting \(b\), and S\({}_{0}\) is the signal magnitude with no diffusion weighting (\(b\) = 0 s/mm\({}^{2}\)). Post-processing, including the co-registration and reconstruction of ADC maps, was performed in MATLAB.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 3 Mutual Induction Model: The Dynamic Aspects
### Induced Electro-Motive Force  
Finally, in order to provide a complete description of the whole mathematical formulation that is introduced in the co-simulation numerical model, it is necessary to define how the dynamic inductive coupling phenomena has been modeled.  
The auto and mutual electro-motive force (e.m.f.) induced among the coupled \(n\)-coils, can be defined on the basis of Equations (3) and (4); thus, according to the Faraday-Neumann-Lenz law, the induced e.m.f. \(V_{ii}\) on the \(i\)-coil is equal to minus the derivative of the flux linkage (23).  
\[V_{ii}=-\left(\frac{d\big{(}\lambda_{ij}\big{)}}{dt}\right) \tag{23}\]  
Matching all the possible combinations of \(i\) and \(j\), where \(i\), \(j\) are cycled between \(1\) and \(n\)-coils, the general formulation of (23) results in Equation (24). For the sake of completeness, the resultant formulation in the case of \(n=2\) coupled coils is reported in Equations (25) and (26).  
\[V_{ii}=-\left(\sum_{n}^{i,j=1}L_{ii}\frac{di_{ii}}{dt}+i_{ii}\frac{dL_{ii}}{dt} +L_{ij}\frac{dj_{ij}}{dt}+i_{jj}\frac{dL_{ij}}{dt}\right) \tag{24}\]  
\[V_{11}=-\left(L_{11}\frac{di_{11}}{dt}+i_{11}\frac{dL_{11}}{dt}+L_{12}\frac{ di_{22}}{dt}+i_{22}\frac{dL_{12}}{dt}\right) \tag{25}\]  
\[V_{22}=-\left(L_{22}\frac{di_{22}}{dt}+i_{22}\frac{dL_{22}}{dt}+L_{21}\frac{ di_{11}}{dt}+i_{11}\frac{dL_{21}}{dt}\right) \tag{26}\]  
Due to the complexity of the phenomenon, the behavior depicted in (25) and (26) cannot be directly implemented through a classic Spice-based formulation of mutual inductor, since Spice does not take into account the inductance variation in the induced voltage formulation; thus, it is necessary to integrate the inductance variation phenomenon through a co-simulation approach. Further clarification on the complete co-simulation multi-domain model can be found in Section 4.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Rockbolt Modulus of Elasticity  
In underground mining engineering, two types of rockbolts are widely used. One is steel rockbolts, which have a modulus of elasticity around 220 GPa [1]. The other one is fibre-reinforced polymer (FRP) rockbolts, which have a modulus of elasticity around 50 GPa [31]. FRB rockbolts have better flexibility. Therefore, FRB rockbolts can be used to reinforce coal ribs in the working face. Moreover, FRB rockbolts can be used to reinforce roadway sides along the working face side.  
To analyse the impact of rockbolt modulus of elasticity on the axial performance of rockbolts, this article used five rockbolt moduli of elasticity: 50 GPa, 90 GPa, 130 GPa, 170 GPa, and 220 GPa. Figure 10 compares the tensile performance of rockbolts.  
Figure 10: Axial performance of rockbolts when the rockbolt modulus of elasticity varied: (**a**) force-deformation curves; (**b**) peak force variation trend.  
Figure 9: Deformation at the peak force when the rockbolt length varied.  
Apparently, the rockbolt modulus of elasticity significantly influenced the axial performance of rockbolts. With the rockbolt modulus of elasticity increasing, the peak force of rockbolts increased slightly. The increasing law of peak force followed Equation (10).  
\[F_{bp}=0.036E_{b}+205.5 \tag{10}\]  
The impact of rockbolt modulus of elasticity on the axial performance of rockbolts is more reflected by deformation at the peak force. Figure 11 shows that when the rockbolt modulus of elasticity was 50 GPa, the deformation at the peak force was 21.9 mm. When the rockbolt modulus of elasticity increased to 220 GPa, the deformation at the peak force non-linearly decreased to 5.8 mm.  
For the deformation at the peak force, the decreasing trend can be calculated with Equation (11).  
\[u_{bp}=731.5E_{b}^{-0.899} \tag{11}\]  
[PERSON] et al. [32] compared the axial performance of rockbolts with two moduli of elasticity. One was glass FRP rockbolts, which had a modulus of elasticity of 51 GPa. The other one was steel rockbolts, which had a modulus of elasticity of 200 GPa. They found that when glass FRP rockbolts were used, the force-deformation curve was apparently composed of two phases: the elastic phase and the elastic-plastic phase. Moreover, the turning point between the elastic phase and the elastic-plastic phase was quite apparent. However, when steel rockbolts were used, the turning point between the elastic phase and the elastic-plastic phase was not apparent. For this article, this difference was not considered. For rockbolts with different moduli of elasticity, the full force-deformation curve was consistently composed of three phases. And the turning point between the elastic phase and the ED phase was apparent. This was different from the experiment results obtained by [PERSON] et al. [32]. Further work should be continued to modify the current ED model to indicate the mechanical difference between FRB rockbolts and steel rockbolts.
## 2 Background
[1] are the creators of the USTC-TFC2016 dataset used by several teams, including this one. They also generate.PNG image files by using the SplitCap tool to separate the dataset.pcap files into unidirectional flows or bidirectional sessions, and include either entire.pcap files or only application layer data. The first 784 bytes of the selected data are encoded into integers used to create pixel values. These images are then input into several types of 2D-CNN. [PERSON] et al. show that the entire.pcap file containing flow data is best for analysis. [PERSON] et al. [6] encode packet data into eight-bit integers in a zig-zag format in the corner of an image and then run a discrete cosine transform on the image. They then conduct analysis with a hybrid 2D-CNN and Deep Random Forest model based on the GoogLeNet model. All of these techniques have produced near-100% detection accuracy, but they all rely on either extensive preprocessing methods, intricate multi-layer models with thousands of nodes, or both. Deep neural networks such as CNN are capable of running as standalone analysis tools without doing extraneous work to give them the answers, and even a small network can produce sufficiently high accuracy without as much computational effort. Furthermore, several of these positive results rely heavily on the USTC-TFC2016 dataset, which does not have representative benign TCP flows. The benign traffic categories within this dataset contain virtually no samples of the traditional TCP connection sequence; the captured traffic in these categories is almost exclusively taken from connections that were already in progress at the start of the captures. Therefore, any published results that depend heavily on USTC dataset will need to be verified with more rigorous datasets. We present a viable solution to real-time front-line threat detection based on the concept of minimal requirements. By focusing specifically on transport layer data and altering that data as little as possible except to add the file header necessary to label it an image, our model provides a promising starting point from which to develop an accurate, efficient detection tool.
## 1 Introduction
In his study, the loads were applied on the conical surface, which was 4 mm away from the tip. In order to obtain the good accuracy of the stress and strain solutions, these references used the hexahedral elements (H8), instead of the four-node tetrahedral element (T4), to mesh the problem domain. However, it is well known that H8 mesh cannot be automatically constructed and consumes more time for pre-process procedure. Besides, when the mesh is distorted, the result will become worse, and even in the standard FEM program it will break down because of the poor quality of the Jacobian matrix. In consideration of these problems, smoothed finite element methods (S-FEMs) [19] have been developed using the strain smoothing technique, which has been developed based on G space theory [20; 21] and weakened weak form (W2 form). It can overcome some disadvantages of FEM to obtain more convergent, stable, and accurate solutions in displacement and strain using T4 meshes. Besides, it was found that S-FEMs possess the excellent and distinctive properties, like softening effect, upper bounds, and ultra-accuracy [22; 23; 24]. When solving 3D problems, S-FEM can be classified as cell-based S-FEM [25; 26], face-based S-FEM (FS-FEM) [27], node-based S-FEM (NS-FEM) [28], and edge-based S-FEM (ES-FEM) [29], according to the types of smoothing domains [30; 31; 32]. These different models have been employed to analyze mechanics problems, contact problems, heat transfer problems, and so on. In these previous works, it was found that ES-FEM using T4 meshes has several excellent properties, comparing with other S-FEMs and FEM, such as the nearly quadratic accuracy [33; 34], which is very critical to solve the practical engineering problems. Because the structure and the working condition of conical picks are more complex, it is unrealistic to obtain higher accuracy relying on higher older elements. Nevertheless, ES-FEM can solve the problems easily, which can guarantee the nearly quadratic accuracy only using linear elements. Thus, in this paper, for the analysis of the coal mining equipment, the ES-FEM is presented to reveal the stress and strain distributions of the conical pick, which can better master its working condition and effectively improve its wear resistance for engineers. T4 elements are employed as the background mesh and the edge-based smoothing domains are created based on it. Meanwhile, an algorithm is given for establishing connection between nodes, edges, faces, and elements. Then the smoothed strain-displacement matrices are constructed following the S-FEM theory for the smoothed stiffness matrices. Next the discrete linear algebraic system of equations is established like the procedure in FEM. What is more, the stress and strain distributions for conical picks are calculated, and the values of nodal displacement are calculated. Finally, the ES-FEM results for the conical pick with experimental measured forces were given and some comparisons with other S-FEM models and FEM were made.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 3 Traffic-Assisted BOF Vulnerability Mining and Exploitation Method in ADASs
### Locate Risk Points with Communication Traffic  
BOF does not occur in all functions but typically happens in those that involve input/output interactions or data passing. In this subsection, we use reverse engineering with communication traffic to locate potential BOF vulnerabilities or risk points.  
In the previous subsection, we acquired some fields/features of the communication traffic, port listening and process information, and corresponding ADAS manufacturer applications.  
In this subsection, we decompile the ADAS manufacturer program, use the field-s/features to search for binary program data fields, and attempt to locate data structures within the program. In addition, we determine the receiving and passing processes of data by searching for library functions, such as socket, listen, and recv, based on the port listening information and system-background process names.  
As the definition of BOF indicates, failure to truncate user input appropriately is a necessary condition for the vulnerability. Therefore, risk point identification focuses on two aspects: (1) external input/output interaction and (2) whether the length is appropriately controlled during parameter copying and passing. By locating data structures through characteristic fields and checking for cross-references in the program while paying attention to data copying or passing processes, such as gets, memcpy, and strcpy, we efficiently detect BOF risk points or vulnerabilities.
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 4 Modified Two-Zone Temperature Field Model
### Correction of the Model
\begin{table}
\begin{tabular}{c c c c c} \hline
**Condition** & **CASE1** & **CASE2** & **CASE3** & **CASE4** \\ \hline
**Smoke layer height (m)** & 6.16 & 7.11 & 8.5 & 9.56 \\ \hline
**Mass flow rate at the opening (kg/s)** & 165.97 & 175.99 & 194.7 & 209.98 \\ \hline
**Calculate the temperature of flue gas layer (\({}^{\circ}\)C)** & 62.17 & 59.78 & 55.95 & 53.33 \\ \hline
**Simulate the temperature of flue gas layer (\({}^{\circ}\)C)** & 64.62 & 62.78 & 56.52 & 53.58 \\ \hline
**Error** & 0.04 & 0.05 & 0.01 & 0.005 \\ \hline \end{tabular}
\end{table} 
Table 8: Calculation results of flue gas layer temperature. It can be seen from the calculated temperature change curve with height (Figure 26) that the temperature change rate in the flue gas layer is in good agreement with the calculated slope, so the temperature near the ceiling can be obtained more accurately. The calculated temperature near the ceiling is shown in the table. The calculated temperature at a higher height is in good agreement with the simulation results. However, the calculated temperature of the air layer is quite different from the simulated value. It is because the air layer temperature is assumed to maintain the initial temperature of 20 \({}^{\circ}\)C, but in fact the air temperature also changes with height, and the boundary between the smoke layer and the air layer is not obvious. Therefore, it is not accurate to use this model to describe the relationship between the temperature of the large space and the height. The temperature model of the double zone should be modified. The temperature-height curve is highly fitted with the logarithmic curve [55]. The logarithmic curve model is used to modify the temperature distribution at the height. The modified temperature model is shown in Figure 32, and the formula is Equation (47). Because the maximum temperature of the flue gas layer calculated by the linear distribution relationship is in good agreement with the simulated value, it can be applied to the calculation of the logarithmic curve. Therefore, the unknown parameters \(\alpha\), \(\beta\), and \(\lambda\) in the formula can be obtained by calculating the curve through three points--initial temperature \((0,~{}T_{0})\), average temperature of the smoke layer \(((H-h),~{}T_{s})\), and maximum temperature of the smoke layer \((H,~{}T_{\max})\). \[H=log_{\beta}(T(z)+\beta)+\lambda=\frac{\ln(T+\beta)}{\alpha}+\lambda \tag{48}\]
## 5 Results and Analysis  
### Autoencoder Performance  
Reconstruction Error  
To evaluate autoencoder performance, reconstruction error, measured as the RMSE between the real engine data set and the reconstructed data set, was calculated.  
Reconstruction error of 5.20 \(\times\)\(10^{-2}\) was achieved, while the loss function converged to \(6.3\times 10^{-2}\). These values indicate that the autoencoder can reconstruct the high dimensional sensor inputs with an average RMSE of 0.052, and that the training loss (0.063) converged to a similar level. The small difference between reconstruction error and loss demonstrates stable training with minimal overfitting, confirming that the latent representation retains the essential structure of the original data.
# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Documents
#### 3.3.3 Power Control Technique Occurrences  
Figure 4 shows the number of occurrences of power control techniques from the summary of the included articles in Table 12. The figure shows the methods and their corresponding occurrences (also referred to as count in the figure). The results show that dynamic power control (DPC) has the highest count. In bibliometric analysis, a high occurrence of an element under study, such as the author's keywords and number of citations, is typically recognized as significant. Thus, this analysis concludes that the research hotspot is in dynamic power control. Likewise, we recommend further research in this area of study.
## 2 Materials and Methods
### CFD Simulation
#### 2.4.3 Assumptions for CFD Analysis  
Transient analysis with a pressure-based solver and coupled algorithm was defined ([PERSON] et al., 2017). The residual convergence criterion was used when conducting CFD calculations. The default Fluent absolute criterion for scaled residuals, which requires residuals lower then \(10^{-3}\), was adopted for all of the equations. The angular velocities of the rotor and the blade were calculated according to Equation (17).  
\[\omega_{R}\ =\ \lambda W/R,\ \omega_{B}\ =\ -\omega_{R}/2 \tag{17}\]  
where \(R=D/2\) is the rotor radius. The angular increment of the rotor angle \(d\alpha=0.5^{\circ}\) was adopted. According to a study ([PERSON] et al., 2017) by [PERSON] et al., it is the highest value that can be accepted when analyzing vertical-axis wind turbines. The time step size (TSS) for transient simulation was calculated using Equation (18).  
\[TSS\ =\ 2\pi\theta_{\beta}/360\omega_{R} \tag{18}\]  
The analysis parameters are summarized in Table 1.  
The minimum number of rotor revolutions required to obtain convergence of the torque coefficient \(C_{m}\) depends on the tip speed ratio \(\lambda\). The higher the \(\lambda\), the higher the number of revolutions needed. According to studies [22; 23], for a low \(\lambda\) of 0.2, two revolutions are sufficient to obtain convergence of \(C_{m}\). Figure 3 shows that the number of rotor revolutions required in this study, considering \(\lambda\) of 0.27, is also two.
# A Low-Phase-Noise 8 GHz Linear-Band Sub-Millimeter-Wave Phase-Locked Loop in 22 nm FD-SOI CMOS
## 4 Conclusions  
An 8 GHz linear tuning sub-mm-wave PLL applied in a transmitter front-end with antenna was designed and measured in this work. The proposed wideband sub-mm-wave I/Q VCO is based on a current tuning through differential voltage application, as it is more practical in the mm-wave frequencies and beyond. The PMOS-injected divider is based on a CPWG resonator and exhibits wider frequency locking range compared to the inductor-based NMOS frequency divider, covering the VCO bandwidth. The designed PLL was applied in a transmitter front-end along with an on-chip wideband bowtie antenna for performance testing purposes. The implemented PLL produces a phase noise of \(-128\) dBc/Hz @ 100 KHz for the worst-case scenario, with outstanding performances in terms of power and area consumption compared to other recent relevant works (as represented by Table 1). To the knowledge of the authors, this is the lowest phase noise produced by a sub-mm-wave PLL to date.  
Conceptualization: M.K. and M.S.; methodology: M.K. and M.S.; formal analysis: M.K.; investigation: M.K.; writing--original draft preparation: M.K.; writing--review and editing: M.K. and M.S.; supervision: M.S.; funding acquisition: M.S. All authors have read and agreed to the published version of the manuscript.  
This publication is based upon work supported by the Khalifa University of Science and Technology under award [RC2-2018-020].  
The authors confirm that the data used in this study is either experimentally extracted and provided throughout the article or referenced below.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & **[4]** & **[9]** & **[8]** & **[5]** \\ \hline Technology & 22 nm CMOS & 65 nm CMOS & \begin{tabular}{c} 0.13 \(\mu\)m SiGe \\ BiCMOS \\ \end{tabular} & \begin{tabular}{c} 0.25 \(\mu\)m SiGe \\ BiCMOS \\ \end{tabular} & 0.13 \(\mu\)m CMOS \\ \hline Frequency (GHz) & 157.5–167.5 & 198–274 & \begin{tabular}{c} 81–82 86–92 \\ 162–164 \\ \end{tabular} & 37.2–40 & 53–58 \\ \hline PN (dBc/Hz) & \(-128\) @ 100 kHz & \(-78.2\) @ 100 kHz & \begin{tabular}{c} \(-89\) @ 100 kHz, \\ 79.4 GHz \\ \end{tabular} & \begin{tabular}{c} \(-92.5\) @ 100 kHz, \\ 40 GHz \\ \end{tabular} & \
\begin{tabular}{c} \(-92.5\) @ 100 kHz, \\ \(-85.2\) @ 1 MHz \\ \end{tabular} \\ \hline Output power (dBm) & 2 & \(-11\) & \(-25\) @ 163 GHz & - &
\begin{tabular}{c} \(-37.85\) @ \\ 58 GHz \\ \end{tabular} \\ \hline Power consumption (mW) & 120.75 & 49.5 & 1150–1250 & 323–380 & 35.7 \\ \hline Chip area (mm\({}^{2}\)) & 1.25 \(\times\) 0.9 & 0.58 & 1.1 \(\times\) 1.7 & 0.9 \(\times\) 0.5 & 0.96 \(\times\) 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark and comparison with other relevant works.