esa-sceva/satcom-chunk-collection huggingface dataset:
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 4 Application to Incompressible Navier-Stokes Equations
### Spatial Discretization
Advective form 1. components \(i\) and \(j\) are stagger-differentiated and iterpolated in the directions \(j\) and \(i\) respectively; 2. the product is performed; 3. the product is interpolated along direction \(j\). As regards both point 1 and 2 it is clear that \"pure\" products are performed at cell centers, whereas mixed products at cell corners (cell edges in 3D). The same holds for the advective form. From Figure 5 one sees that this procedure approximates the convective term on the locations of the variables \(u_{i}\). Please note that the interpolated quantities used to compute the divergence form (step 1) can be reused in the computation of the advective form (step 2). The divergence and gradient operators required in the pressure correction process (cf. Section 4.2) are built based on one-dimensional derivatives as well. Please note that for consistency, the Laplacian  
Figure 5: Fully staggered layout of variables. operator comes as a consequence of the discrete product \(\
abla^{2}=\
abla\cdot\
abla\). The main and essential difference with respect to the choice made for the convective and viscous terms is that in this paper the one-dimensional derivatives are explicit, instead of compact, with the same order of accuracy. The reason is that compact schemes would have led to a full matrix discretizing the Laplacian operator, which would have been impractical [37]. Please note that this approach, despite the stencil of explicit schemes being wider, only requires one non-symmetric boundary scheme (in the case of 4 th order of accuracy) for the application of both divergence and gradient operators.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Advanced Distribution Management  
The energy distribution network is the most advanced part of a smart grid as it is supported by information technology on issues such as security and optimization. These networks consist of high-voltage networks, which feed medium- and low-voltage subnetworks through power stations and substations, which subsequently supply electricity to the end consumers [25; 26]. The management system includes all the functions required for the distribution network to operate efficiently [27; 28] and its main responsibility is the monitoring of the energy demands as well as the control of the energy exchange in the distribution system.  
Given the wide coverage area of the distribution networks and the diversification of the employed telecom equipment, the communication specifications for these networks are extremely complex [7; 29]. As an example, the control mechanism requires standards that provide operational support to technologies and automation systems including remote control surveillance, geographic information system (GIS), forecasting demand, and fault management etc. [30; 31; 32; 33].  
These requirements are met by a set of available broadband services that are IEC 61968 compliant [34; 35], while this standard also provides compatibility in terms of network optimization, maintenance, and expansion. In addition, the harmonization of IEC 61968 with IEC 61850-7-420 and the adoption of an open architecture improves network scalability, whereas the integration of intelligent measurement models (advanced metering infrastructure, AMI), home networking (home area network, HAN), and their interfaces allows for demand forecasting and modeling of load behavior at the end-user level [36; 37].
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
###### Abstract  
In this work, the nonlocal strain gradient theory is applied to study the free vibration response of a Timoshenko beam made of triclinic material. The governing equations of the problem and the associated boundary conditions are obtained by means of the Hamiltonian principle, whereby the generalized differential quadrature (GDQ) method is implemented as numerical tool to solve the eigenvalue problem in a discrete form. Different combinations of boundary conditions are also considered, which include simply-supports, clamped supports and free edges. Starting with some pioneering works from the literature about isotropic nanobeams, a convergence analysis is first performed, and the accuracy of the proposed size-dependent anisotropic beam model is checked. A large parametric investigation studies the effect of the nonlocal, geometry, and strain gradient parameters, together with the boundary conditions, on the vibration response of the anisotropic nanobeams, as useful for practical engineering applications.  
anisotropic materials; differential quadrature method; free vibration; nonlocal strain gradient theory; variable thickness Article
## 4 Simulations and Results
### Simulation Results
#### 4.3.3 Performance Evaluation under Different Node Densities  
Figure 6 shows the performance of the proposed protocol at different node densities. Figure 6a shows that at higher node densities, the average residual energy of the network depleted more slowly, suggesting that the proposed protocol effectively managed the collective energy resource among a larger number of nodes. As shown in Figure 6b, the rate of the decrease in the number of surviving nodes was relatively slower in denser networks. However, the proposed protocol maintained a reasonable number of nodes across all node densities. This indicates that it adapted well to changes in the node density. As shown in Figure 6c, there was a noticeable increase in the number of packets received as the node density increased. This indicates that the proposed protocol scales efficiently with larger network sizes, resulting in improved data throughput. As shown in Figure 6d, the proposed protocol demonstrated the ability to delay the occurrence of the first node death as the node density increased. This implies that it effectively handles higher node densities, allowing for longer network operation.  
In summary, the proposed protocol exhibits robust performance over varying node densities. It effectively manages energy consumption, adapts to changes in node density, delays node failures, and scales well with increasing network sizes, ultimately improving data transmission efficiency.  
Figure 5: Performance of LEACH, ICA, PPWURC, and proposed protocol. (**a**) Average residual energy; (**b**) average number of surviving nodes; (**c**) the number of packets received by the base station; (**d**) average round when the first death node occurs.
## 2 System Model and Problem Formulation
### System Model
The instantaneous SINR of the received signal at CU \(C_{k}\) from the BS can be written as  
\[\gamma_{k}^{c}=\frac{P_{k}^{c}\ |\mathbf{g}_{i,c}^{H}\mathbf{\phi}\mathbf{g}_{b,j}+ \mathbf{g}_{b,c}^{H}|^{2}}{\sum\limits_{m=1}^{M}x_{k,m}P_{m}^{d}\ |\mathbf{g}_{b,j}^{H}\mathbf{\phi}\mathbf{h}_{t,i}+u_{t,c}^{H}|^{2}+ \sigma^{2}}, \tag{1}\]  
where \(P_{k}^{c}\) is the transmit power of CU \(k\), and \(P_{m}^{d}\) is the transmit power of D2D pair \(m\); \(x_{k,m}\) is a binary decision variable of resource reuse indicator, where \(k\) represents cellular communication link and \(m\) D2D communication link, \(x_{k,m}=1\) when \(m\) D2D link reuses the spectrum of CU \(k\), \(x_{k,m}=0\) otherwise, and \(\sigma^{2}\) is the additive white Gaussian noise (AWGN) power. The SINR of the received signal at the D2D receiver \(D_{m}^{r}\) from the D2D transmitter \(D_{m}^{t}\) can be written as  
\[\gamma_{m}^{d}=\frac{P_{m}^{d}\ |\mathbf{h}_{i,r}^{H}\mathbf{\Phi}\mathbf{h}_{t,i}+h_{t,r}^{ H}|^{2}}{\sum\limits_{k=1}^{K}x_{k,m}P_{k}^{c}\ |\mathbf{h}_{i,r}^{H}\mathbf{\phi}\mathbf{g}_{b,i}+v_{b,r}^{H}|^{2}+ \sigma^{2}}. \tag{2}\]  
Therefore, the network's SE (i.e., sum rate of both CU and D2D pairs) in \(\text{bps}/\text{Hz}\) is given as  
\[R(\mathbf{x},\mathbf{P},\mathbf{\Phi})=\sum\limits_{k=1}^{K}\log_{2}(1+ \gamma_{k}^{c})+\sum\limits_{m=1}^{M}\log_{2}(1+\gamma_{m}^{d}), \tag{3}\]  
where \(\mathbf{x}=[x_{1,1},\dots,x_{1M},x_{2,1},\dots,x_{K,M}]^{\top}\) is the resource reuse indicator vector, and \(\mathbf{P}=[P_{1}^{c},\dots,P_{K}^{c},P_{1}^{d},\dots,P_{M}^{d}]^{\top}\) is the power allocation vector.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Equations of motion for subsystem B:  
Similarly, for subsystem B, the generalized coordinates of interest are \({\rm{uc}_{1}}\) and \({\rm{uc}_{2}}\) at the connection points and \({\rm{u_{4}}}\) as an internal coordinate. The equations of motion are as follows:  
\[\begin{array}{c}\left[{\rm{M_{B}}}\right]\begin{B}{\rm{\{\{\ddot{u}_{4}}\atop \ddot{u}_{c1}}}}\\ {\rm{\{\dot{u}_{c2}}}}\end{B}+\left[{\rm{C_{B}}}\right]\begin{B}{\rm{\{\dot{u} _{4}}\atop\dot{u}_{c1}}}\\ {\rm{\dot{u}_{c1}}}\\ {\rm{\dot{u}_{c2}}}\end{B}+\left[{\rm{K_{B}}}\right]\begin{B}{\rm{\{u_{4}} \atop\mbox{uc}_{1}}}\\ {\rm{u_{c2}}}\end{B}={\rm{f}}({\rm{t}})\\ \mbox{where :}\\ \left[{\rm{M_{B}}}\right]=\begin{bmatrix}{m_{11}^{B}}&0&0\\ 0&{m_{22}^{B}}&0\\ 0&0&{m_{33}^{B}}\end{bmatrix},\left[{\rm{C_{B}}}\right]=\begin{bmatrix}{c_{11 }^{B}}&{c_{12}^{B}}&{c_{13}^{B}}\\ {c_{21}^{B}}&{c_{22}^{B}}&{c_{23}^{B}}\\ {c_{31}^{B}}&{c_{32}^{B}}&{c_{33}^{B}}\end{bmatrix},\left[{\rm{K_{B}}}\right] =\begin{bmatrix}{k_{11}^{B}}&{k_{12}^{B}}&{k_{13}^{B}}\\ {k_{21}^{B}}&{k_{22}^{B}}&{k_{23}^{B}}\\ {k_{31}^{B}}&{k_{32}^{B}}&{k_{33}^{B}}\end{bmatrix} \tag{37}\]  
The receptance matrix for subsystem B is then determined as follows:  
\[\begin{array}{c}\left[{\rm{H_{B}}}(\omega)\right]\begin{B}{\rm{F_{n1}^{B}}} \\ {\rm{F_{n0}^{B}}}\\ {\rm{F_{n0}^{B}}}\end{B}\end{array}=\left\{\begin{array}{c}\begin{array}{c} {\rm{u_{1}^{B}}}\\ {\rm{u_{1}^{B}}}\\ {\rm{u_{10}^{B}}}\\ {\rm{u_{n0}^{A}}}\end{
## 6 Discussion
### N-Type Crystalline Silicon Modules and Thin-Film Modules
One reason is that their operating efficiency could respond to the temperature change with various sensitivities, which is measured with a temperature coefficient with a unit of \(\%/^{\circ}\)C. For instance, cells made on \(p\)-type wafers (multi and mono) have TC around \(-0.43\%/^{\circ}\)C. \(N\)-type cells' TC is around \(-0.30\%/^{\circ}\)C. Assuming the average cell temperature in the field is 20 \({}^{\circ}\)C higher than the standard testing condition, \(n\)-type IBC PV modules have 2.6% more yield than \(p\)-type mono PERC products for the same nameplate power rating, and this gap cannot be captured by efficiency. Another important metric that affects actual yield is degradation rate, which measures how fast module efficiency declines over time. For example, Jinko Solar's mono PERC modules have a warranty for 3% degradation in the first year and 0.7% linear annual degradation in the following 24 years, suggesting the warranted module efficiency at the end of the 25 th year will be 80.2% that of the original nameplate efficiency. First Solar's CdTe thin film module power warranty includes a starting point of 2% degradation for the first year and a 0.5% annual drop afterwards until the 25 th year, indicating that the warranted module efficiency at the end of the 25 th year will be 86% of the original nameplate efficiency. In a simplified assumption, where the annual illumination is the same over the 25-year period, First Solar's modules could generate 4% more electricity than Jinko Solar's modules of the same nameplate capacity. Note that the purpose of deploying the discussion above is to illustrate the limitation of relying solely on module efficiency when evaluating actual performance and LCOE, rather than to set up a complete model to compare different types of modules.
# A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation
## 3 Part-in-Whole Point Cloud Registration
### The Patch of Point Cloud  
In this section, the random sphere cover sets (RSCS) method is adopted to improve the coverage of the whole point cloud [32]. The original RSCS method increases the matching accuracy of subsequent point cloud descriptors. However, the sphere radius associated with this method is fixed each time, which leads to the uncharacteristic patch obtained by a fixed radius. Therefore, the multi-scale RSCS is proposed in this study to increase the diversity of the radius scale of points to improve the matching probability between corresponding point clouds. The multi-scale RSCS division process of the point cloud is as follows:  
Input DMPC and LMPC.  
Output Multi-scale random sphere cover set and central points of DMPC and LMPC.  
Step 1 Down sampling DMPC and LMPC.  
Step 2 Randomly selecting a point as the centre of the sphere and dividing the point cloud via radius \(r_{i}\), all point clouds within the radius are identified as the point cloud of the first sphere \(s_{1}^{i}\).  
Step 3 Randomly select a point that does not belong to a sphere \(s_{k}^{i}\) as the next centre point and obtain the next sphere with \(r_{i}\).  
Step 4 Repeat step 3 until every point is covered sphere.  
Step 5 Resetting the radius and repeat step 4.  
According to the above steps, the multi-scale random sphere cover set of the DMPC \(p=\{s_{1}^{i},s_{2}^{i},\cdots,s_{k}^{i}\}\) and its corresponding set of central points \(p_{c}=\{c_{1},c_{2},\cdots,c_{k}\}\) can be obtained. Similarly, the set of LMPC \(q=\{s_{1}^{i},s_{2}^{i},\cdots,s_{K}^{i}\}\) and the corresponding set of central points \(q_{c}=\{c_{1},c_{2},\cdots,c_{K}\}\) can be obtained. In this study, three kinds of spheres with different radii were selected as multi-scale RSCS. After generating each sphere, it is also necessary to refer to some criteria for screening key points; the specific criteria can be found in [32]. Taking the rear floor beam of a car body as an example, the multi-scale RSCS of local measurement data on one side and the multi-scale RSCS of the digital model are shown in Figure 8. The different colours represent the point cloud covered by each divided sphere.
## 4 Numerical Simulation  
The proposed system is simulated on MatLAB (2020) for voice encryption in this section. The selected voice signal for this test has a sampling rate of 8 kHz. To study the efficiency of the increasing number of levels on the security of encryption, twelve different values of N are considered for comparison. Then, to demonstrate the efficiency of increasing the number of cascaded levels,from N = 1 to N = 20, on the security of encrypted signal/decrypted. In addition, the security analysis using different performance metrics is introduced for different values of N, i.e., the number of levels such as Signal-To-Noise Ratio (SNR), Peak Signal-To-Noise Ratio (PSNR), Percent Residual Deviation (PRD), Correlation Coefficient (CC) and encryption(te)/decryption time(td). In addition, we consider the utilization of FPGA resources and the clock frequency for each system to manage the cost of the systems.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**FPGA** & \multicolumn{10}{c}{**Number of Levels**} \\
**Resources** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **15** & **20** \\ \hline Logic Utilization (in ALMs) \% & 14 & 27 & 41 & 55 & 69 & 79 & 93 & 105 & 120 & 132 & 184 & 263 \\ \hline Maximum Frequency(MHz) & 31.08 & 30.19 & 30.97 & 31.29 & 30.42 & 30.58 & 29.69 & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 2: FPGA resource utilization and maximum operating frequency for the N-level chaotic-based secure communication system using different values of N.  
Figure 4: The basic blocks for FPGA-based 4D unified hyperchaotic systems.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
###### Abstract  
High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.  
unsupervised image classification; fuzzy clustering; Voronoi tessellation; spatial constraint +
Footnote †: journal: applied sciences
## 4 MIMO Performance Parameters
### Envelope Correlation Coefficient (ECC)  
Good MIMO performance is guaranteed by analyzing the antenna diversity presentation in terms of _ECC_. The envelope correlation coefficient (_ECC_) provides information on the degree of separation and correlation between various communication channels. The  
Figure 13: Absolute radiation efficiency of proposed design. (**a**) Circular disk-shaped MIMO antenna. (**b**) CSRR metamaterial-shaped MIMO antenna structure.  
ECC_ of a multi-antenna system may be calculated by observing how one antenna's emission pattern influences the others. The lower \(ECC\) is crucial for the robustness of the design. The \(ECC\) for a two-port MIMO antenna may be determined from the radiation pattern using Equation (1) and the transmittance and reflectance coefficient as per Equation (2).  
\[\rho e=\frac{\left|\iint_{4\pi}\left[\overset{\rightarrow}{F}_{1}(\theta,\phi) \times\overset{\rightarrow}{F}_{2}(\theta,\phi)d\Omega\right]\right|^{2}}{ \left|\iint_{4\pi}\left[\overset{\rightarrow}{F}_{1}(\theta,\phi)\right] \right|^{2}d\Omega\right|\left|\iint_{4\pi}\left[\overset{\rightarrow}{F}_{2} (\theta,\phi)\right]\right|^{2}d\Omega} \tag{1}\]  
\[\rho_{e}=\frac{\left|S_{11}S_{12}+S_{22}S_{21}\right|^{2}}{\left(1-\left|S_{11} ^{2}+S_{21}^{2}\right|\right)\left(1-\left|S_{22}^{2}+S_{12}^{2}\right|\right)} \tag{2}\]  
Using the far-field computation method, \(ECC\) is calculated. Here, \(\overset{\rightarrow}{F}_{1}(\theta,\phi)\) is the farfield pattern of the MIMO array during exciting the single port, and \(\Omega\) signifies the solid angle. Practically, the value of \(ECC\) must be lower than 0.5 [35]. Figure 14 represents the \(ECC\) value near zero over a broad spectrum. The lower correlation among both antennae is observed based on the graph.
# Antimicrobial Efficacy of Edible Mushroom Extracts: Assessment of Fungal Resistance
## 2 Materials and Methods
### Antibacterial Test: Disc Diffusion Assay  
_L. reuteri_ (ATCC 23272), _L. acidophilus_ (ATCC 43560), and _L. rhamn_ (ATCC 53103) were grown in Lactobacillus MRS (De Man, Rogosa and Sharpe) agar at 37 \({}^{\circ}\)C under anaerobic conditions. Strains grown aerobically at 37 \({}^{\circ}\)C were: _E. coli_ K-12 MG 1655 (USDA) in Luria Broth, _S. enterica_ pGFP (USDA) in Luria Broth, and _L. monocytogenes_ RM2194 (USDA) in Brain Heart Infusion, and _S. aureus_ (ATCC 6538) in Tryptic Soy. Empty BDL-sensi-discs (6 mm) (BD Life Sciences Franklin Lakes, NJ, USA) were saturated with either vehicle control (DMSO:water (50%:50%)) or mushroom extracts dissolved in the vehicle for 30 min at room temperature (see [40]). Discs containing vehicle control, mushroom extracts, or various antibiotic discs (levofloxacin (5 \(\upmu\)g), gentamicin (10 \(\upmu\)g), and gentamicin (120 \(\upmu\)g)) were placed onto the bacterial streaked (lawn) agar Petri plates (100 mm \(\times\) 15 mm; Corning Inc.-Life Sciences, Tewksbury, MA, USA) and incubated overnight at 37 \({}^{\circ}\)C (18-24 h). Sensitivity to antibiotics or mushroom extracts was determined via the measurement of zones of inhibition around each disc in mm.  
Risk Assessments: Resistance Testing of Mitogen-Activated Protein Kinase (MAPK) Mutants of A. fumigatus and P. expansum to Salicylaldehyde (SA)  
The tolerance of mitogen-activated protein kinase (MAPK) mutants of _A. fumigatus_ (_sakA_A, _mpkCA_) or _P. expansum_ (FR2) to SA was examined in PDA according to the modified method described previously [41]. SA (0.09 M for _A. fumigatus_; 0.12 M for _P. expansum_) was applied to membrane filters (1.5 cm diameter; GE Healthcare, Chicago, IL, USA), then either SA- or DMSO (control)-saturated membrane filters were placed onto the lower half of each PDA Petri plate (60 mm \(\times\) 15 mm; Corning Inc.-Life Sciences, Tewksbury, MA, USA) w/ or w/o FLU (50 \(\upmu\)M). SA was delivered to the target fungi as a fumigant. Fungal spores (1 \(\times\) 10\({}^{3}\) CFU in 20 \(\upmu\)L) were spotted onto the upper half of each PDA, and the inoculated plates were incubated at 35 \({}^{\circ}\)C or 28 \({}^{\circ}\)C for _A. fumigatus_ or _P. expansum_, respectively, to determine the development of fungal (MAPK mutants) tolerance to SA. SA was dissolved in DMSO (absolute DMSO amount: \(<\)2%, final concentration), and control plates contained DMSO only at levels equivalent to that of cohorts receiving SA, within the same set of experiments. Fungal growth (radial growth) was monitored for three to five days.
## 2 Materials and Methods
### Image Preprocessing and Segmentation  
All MR images were downloaded via the National Biomedical Imaging Archive (NBIA) Data Retriever (version 4.3, The Cancer Imaging Archive, Bethesda, MD, USA, [[https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+Images+Using+the+NBIA+Data+Retriever](https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+Images+Using+the+NBIA+Data+Retriever)]([https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+Images+Using+the+NBIA+Data+Retriever](https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+Images+Using+the+NBIA+Data+Retriever)), accessed on 20 March 2021) and were converted from DICOM to NifTI format using dcm2 niix (version 1.0, [PERSON], [[https://github.com/rordenlab/dcm2](https://github.com/rordenlab/dcm2) niix]([https://github.com/rordenlab/dcm2](https://github.com/rordenlab/dcm2) niix), accessed on 20 March 2021) [31]. To remove intensity nonuniformity with low-frequency, N4 bias field correction was applied [32]. Skull stripping was then applied using the Brain Extraction Tool (BET) from the Functional Magnetic Resonance Imaging of the Brain (FMRIB) software library (version 1.1, FMRIBGroup, Oxford, United Kingdom; [[http://www.fmrib.ox.ac.uk/fsl/](http://www.fmrib.ox.ac.uk/fsl/)]([http://www.fmrib.ox.ac.uk/fsl/](http://www.fmrib.ox.ac.uk/fsl/)), accessed on 21 March 2021) [33]. All imaging modalities (T1C, T1, T2, FLAIR) were co-registered using the FMRIB's Linear Image Registration Tool (FLIRT; [[http://www.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT](http://www.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT)]([http://www.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT](http://www.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT)), accessed on 20 March 2021) [34]. Registration with a linear affine transformation algorithm using 12 degrees of freedom, trilinear interpolation, and a mutual-information cost function was implemented. The reference volume was the postcontrast axial T1-weighted acquisition. After image registration, signal intensity normalization was performed using the WhiteStripe R package [35]. We obtained region-of-interest (ROI) masks drawn for the four MRI sequences (T1C, T1, T2, and FLAIR) for 81 patients from the previous study by [PERSON] et al. [36].