esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Materials Properties  
Material properties were based on [PERSON] et al. [31]. The tibial and femoral properties were a rigid body material (Table 1) [31]. The reconstructed ACLr graft was modeled as isotropic hyperelastic (no linear) material with Veronda-Westmann coefficients (Table 1). The mechanical properties of the LCL and liliotibial band LET were modeled as hyperelastic isotropic materials of Mooney-Rivlin (Table 1). The septum was considered a rigid material as a proximal attachment based on the previous anatomical description [30] and on the experience of the knee orthopedic senior surgeon (RY).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Knee Element** & **Material** & **Parameters** \\ \hline \multirow{2}{*}{Bones} & \multirow{2}{*}{Rigid} & Young modulus = 0.4 GPa \\  & & Poisson coefficient = 0.33 \\ \hline \multirow{2}{*}{ACLr graft} & Isotropic hyperelastic & Veronda–Westmann coefficients of \\  & (no linear) & \(\alpha\) = 0.3 MPa \\  & & \(\beta\) = 12.20 \\ \hline \multirow{2}{*}{Lateral collateral ligament} & \multirow{2}{*}{Hyperelastic isotropic} & Mooney–Rivlin with \\  & & \(c_{1}\) = 30.1 MPa \\  & & \(c_{2}\) = -27.1 MPa \\ \hline \multirow{2}{*}{Iliotibial band LET} & \multirow{2}{*}{Hyperelastic isotropic} & Mooney–Rivlin with \\  & & \(c_{1}\) = 30.1 MPa \\  & & \(c_{2}\) = -27.1 MPa \\ \hline \multirow{2}{*}{Septum} & \multirow{2}{*}{Rigid} & Young modulus = 0.4 GPa \\  & & Poisson coefficient = 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the study.  
Figure 2: **Anteromedial (AM) portal, transtibial (TT), and hybrid transtibial (HTT) tunnel and liliotibial band–intermuscular septum lateral extra-articular tenodesis.**
## 5 Experimental Results and Analysis  
### Experimental System Platform  
To further verify the effectiveness of the proposed fusion algorithm in the real environment, a practical scenario was built to realize the path planning task. ROS was used as an experimental platform (Melodic). The operating system was 64-bit Ubuntu 18.04, and the RAM was 4 GB. Table 6 displays the hardware setup. The robot gathered information about the external world using the LiDAR sensor. The Gmapping method, AMCL, and AMCL mapping modules were then used to finish the positioning and two-dimensional graph creation. Lastly, global and local route planning were implemented using the global and local planners in the move_base module, respectively. Figure 30 shows the overall process, and Figure 31a-c shows the Gmapping process.  
Figure 30: Mini Robot Planning flow chart.  
Figure 29: Algorithm training comparison graph.  
Given that the real mobile robot measures 281 \(\times\) 306 \(\times\) 641 mm\({}^{3}\), a test scene with 12 squares on the long side and 10 squares on the short side was constructed, with a 25 cm square as the unit in the experiment. The mapping of the environment was finished using the AMCL and Gmapping modules, as shown in Figure 32a. As seen in Figure 32b, the theoretically generated map was a 10 \(\times\) 10 grid map since the robot generated a 25 \(\times\) 25 cm grid. The impediment is represented by black, and its growth area is represented by the blue region around it. The obstacle's inflating radius was chosen at 25 cm, considering the real mobile robot's size.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Parameter** \\ \hline \multirow{3}{*}{Lidar} & Angle of scan: 0–360\({}^{\circ}\) \\ \cline{2-3}  & Scan range: 0.1–25 m \\ \cline{2-3}  & Sampling frequency: 16,000 \\ \hline \multirow{3}{*}{Camera} & FPS: 15 \\ \cline{2-3}  & Scope of detection: 0.8–6.0 m \\ \hline IMU & MPU9250 \\ \hline Controller & Control period: 0.01S \\ \hline \multirow{3}{*}{Kalman filter} & Kalman filter gain: 0.9 \\ \cline{2-3}  & Kalman filtering process noise covariance: 0.01 \\ \cline{2-3}  & Kalman filter observed noise covariance: 1 \\ \hline Master control & Stm32 \\ \hline \end{tabular}
\end{table}
Table 6: Robot hardware parameters.  
Figure 31: Robot mapping process (**a**) Initial stage of drawing (**b**) Middle period of drawing (**c**) Middle and later stages of drawing construction.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## Appendix A Comment of Maximum SAR  
Section 2.2.5 introduces the effective exposure volume, which is a volume larger than \(SAR_{max}/e\), as an assessment of the extent of the SAR pattern. One of the disadvantages of using voxelized anatomical human models is that the computed electromagnetic absorbed power suffers from computational artifacts, especially around the model surface. This error is notable at low frequencies [61], but may still occur in computation at high frequencies. We defined the voxel \(SAR_{n}\) as the \(n^{\text{th}}\) element of the list of SAR values sorted in ascending order. The gradient \(\Delta\) was computed for each \(SAR_{n}\) by the following equation:  
\[\Delta_{n}=\frac{SAR_{n+1}-SAR_{n}}{(SAR_{n}+SAR_{n+1})/2}, \tag{1}\]  
The first significantly different value of the gradient is defined as the detection point of the outlier [62]. Figure 11 shows the voxel values corresponding to an SAR with the highest value of 0.2%, and the gradient in the TARO model. As shown in Figure 11, the gradient significantly increased above 99.99% owing to the effect of calculation using a voxelized model. The gradient of the SAR value in the other head models also significantly increased above 99.97-99.99%. \(SAR_{max}\) was the value obtained by removing the highest value of 0.01%. The removed volumes were 0.4-0.5 cm\({}^{3}\) and 0.02 cm\({}^{3}\), which corresponded to 0.5-0.8 g and 0.04 g in human and rat models, respectively. The removed volumes are generally located in complex shapes, such as around pinna. Note that an extensive analysis was conducted for low-frequency exposure where in situ electric fields were evaluated in voxelized models [62]. Similarly, the metric for evaluating SAR should be averaged over 10 g of tissue, and thus is not discussed extensively for RF exposures. The main purpose of this metric is to evaluate the SAR volume appropriately rather than for defining the physical meaning of the temperature.
## 3 Results and Discussion
### Analysis of Small-Angle X-ray Scattering Data  
The majority of the observed SAXS images exhibited concentric rings suggesting spherically shaped clusters. Additionally, we observed a few images indicating nonspherical clusters under the present conditions [41], as reported in the preceding works [20; 21; 23]. For the concentric ring SAXS images, we deduced both the radii of clusters and XFEL fluences by assuming a single uniformly dense sphere. It is noted that the absorption efficiency of 5.5 keV photons by a Xe atom is very low and the scattering from electrons bound to Xe atoms can be expressed by Thomson scattering [42]. In the regime of infrared to soft X-ray, the scattering is well described by Mie scattering [43], where the absorption efficiency of a Xe atom is much higher [20]. Figure 3a shows the typical radial profiles derived from diffraction images and the corresponding fitting curves. The fits, assuming a single uniformly dense sphere and Thomson scattering, reproduced the experimental results well. Figure 3b shows the histogram of radii of the observed Xe clusters and a curve (black dotted line) describing a log-normal distribution of the cluster size. The cluster radii peaked around 70 nm, but also much larger clusters were observed. The large cluster's radii go well beyond the well-known scaling law [44; 45] (25-44 nm) as a result of our specific experimental conditions, i.e., timing the jet to an after pulse to generate extremely large clusters [23].  
Figure 2: Characteristic results of the multispectroscopic measurements. Each diffraction image, fluorescence spectrum, and ion TOF spectrum shown in **(a–c)**, **(d–f)**, and **(g–i)** are observed from Xe clusters with the radii of cluster and the fluence (\(r_{0}\). \(J_{0}\)) of (130 nm, 9.2 \(\upmu\)J/\(\upmu\)m\({}^{2}\)), (68 nm, 6.7 \(\upmu\)J/\(\upmu\)m\({}^{2}\)), and (72 nm, 2.1 \(\upmu\)J/\(\upmu\)m\({}^{2}\)), respectively. Scattered intensity was observed in the upper region of the diffraction images (**a**,**d**,**g**), which came from the parasitic scattering of the XFEL pulses due to the configuration of the upstream optical system.
## 4 Simulation Results
### Capacity Performance
We adopt the Ground-to-Ground(G2G) channel model, and the channel gain is: \(h\)\(=(\frac{c}{4\pi f_{c}})^{2}\cdot d^{-k}\), where \(c\) is the light speed, \(f_{c}\) is the carrier frequency, \(d\) is the distance between user and base station, and \(k\) is a constant determined by link. The throughput of network is studied at different values of \(k\). Figure 8 compares the influence of ground base station and UAV base station on network throughput when \(k\) is 3.5 and 4. As shown in the figure, when the ground mobile base station is used to provide services for users, the network throughput is lower than that of the UAV base station. Figure 6: Network capacity in three different optimization methods (\(N\ =\ 20\)). Figure 7: Throughput \(C_{sum}\) in three different optimization methods (\(N\ =\ 20\)).
## 4 Case Study: The Northeast Part of the Slovenian Transmission System
### Case 2--Faults in Radial Topology with Weak IIDGs  
The second case assumes faults on the Radenci-Sobota line, while the Sobota-Ljutomer line was out of service, leading to a radial topology between Maribor and Mackovci, as shown in Figure 10. Consequently, a typical situation was obtained, where a strong network (SS 400/110 kV Maribor) operates against a weak network (PVs Sobota and Mackovci) during the faults. Furthermore, when the faulted line was disconnected, the SSs Sobota and Mackovci operated as an island. Figures 13 and 14 show the power generation of the PV and BES units and line voltage magnitudes at the 110-kV level for a Ph-Ph fault. Since the loop was opened between SSs Sobota and Ljutomer, the PVs Lendava, Ljutomer, Ormoz, Ptuj, Breg, and BES Kidricevo showed minor changes in generated power. Consequently,  
Figure 11: Generated power for case 1 during normal operation, Ph-Ph fault, and after the faulted line was disconnected.  
Figure 12: Line voltage magnitudes for case 1 during normal operation (State 1), Ph-Ph fault L2-L3 (State 2), and after the faulted line was disconnected (State 3).  
the voltages during the fault at these locations practically did not change. PVs Lenart, Sl. Vrh and BES Pekre showed minor responses in the generated power, since the fault was supplied mainly from the SS 400/110 kV Maribor. The largest repose in generated power was shown by PVs Radenci, Sobota, and Mackovci, which were very close to the fault location. Voltages at those PVs were reduced by approximately 50% during the fault; however, the reduction in voltages at PVs closer to the SS 400/110 kV Maribor (PVs Sl. Vrh and Lenart) was smaller. Furthermore, it can be observed that the voltages at Mackovci and Sobota increased slightly after the disconnection of the faulted Radenci-Sobota line, since these SSs operate as an island. The distance relay at the side of the strong network (SS Radenci) operated correctly for all fault types and admissible locations. On the other hand, the distance relay from the weak side (SS Sobota) failed to operate in all cases, due to the limited PV currents in Sobota and Mackovci.
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
###### Abstract
3. We evaluate the performance of MCNN with a broad set of WV data and deep learning methods. Our experimental results demonstrate that MCNN achieves perfect classification accuracy and significantly improves the data quality, outperforming two state-of-the-art methods by 23.7% and 18.3%, respectively. The rest of the paper is organized as follows: Section 2 illustrates the Sentinel-1 wave mode and TenGeoP-SARwv datasets and Section 3 details the design of the MCNN framework. Section 4 provides an analysis of ocean SAR scene classification accuracy and presents the experimental results, followed by Section 5, which concludes this paper.
## 3 Materials and Methods  
Experiments were carried out on a die sink type electrical discharge machine, of Electronica Machine Tool Limited (Pune, India), model C400x250. Commercial grade EDM oil was used as dielectric fluid with side-wise flushing and 0.5 kg/cm\({}^{2}\) flushing pressure. NiTi alloy and NiCu alloy of \(\mathcal{O}\)20 mm and length of 20 mm were used whereas BeCu of size 20 \(\times\) 20 \(\times\) 30 mm\({}^{3}\) were used. The tool material selected for experimentation was electrolytic copper due to its high electrical conductivity of 6 mm diameter and 90 mm length. Prior to conducting the experiments, both the workpiece and tool were cryogenically treated. Experimental layout was design as per Taguchi L\({}_{18}\) orthogonal array for conduction of experiments. Table 1 depicts the thermal and electrical conductivity of workpiece and tool electrode before and after cryogenic treatment. Table 2 shows the Taguchi L\({}_{18}\) orthogonal array and obtained experimental results. Material removal rate (MRR) is computed using Equation (1). Weighing is done before and after machining of the workpiece. This is done by utilizing a digital weighing balance of model- GR-300 with an accuracy of 0.0001 g.  
\[MRR=\frac{\Delta W}{\rho_{w}\,\times\,t_{m}} \tag{1}\]  
where \(\Delta W\) is change in weight of work piece in grams, \(\rho_{w}\) is work piece density, gm/cm\({}^{3}\), \(t_{m}\) is machining time in mins. For this study, sensitivity for anti-arc and servo motors was set at 50% and 25%, respectively. Depending on the favourable machining conditions, the workpiece and the tool were used as the negative and positive electrodes, respectively. Therefore, reverse polarity is considered in all experiments. The formulation of operational ranges for process parameters is based on literature review, favourable machining conditions, and sparks existence. In the study, instead of keeping the machining time as constant, each experiment was performed till 5 mm depth was achieved on the work piece. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**NiTi Alloy**} & \multicolumn{2}{c}{**NiCu Alloy**} & \multicolumn{2}{c}{**BeCu Alloy**} & \multicolumn{2}{c}{**Copper Electrode**} \\ \cline{2-9}  & **Untreated** & **Treated** & **Untreated** & **Treated** & **Untreated** & **Treated** & **Untreated** & **Treated** \\ \hline Thermal conductivity (k), W/mk Electrical conductivity (\(\sigma\)), S/mm & 10 & 12.9 & 21.8 & 22.2 & 130 & 135.9 & 391.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal and electrical conductivity of workpiece and tool.
# A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
## 2 Narrow Slots in FDTD
### Subgridding Approximation Model  
In general terms, SG methods involve progressively subdividing cells into finer cells. This approach enables an increase in resolution only where it is necessary to resolve fine geometric details, such as apertures. Most SG methods employ recursive division, successively reducing the cell size according to a specific aspect ratio (refinement ratio), from coarsest to finest. This results in a set of regions where the conventional Yee method can be used, except at the boundaries between two regions of different sizes, which require a specific procedure. The complexity of these algorithms lies in the transitions between different levels of subgrid, and each SG algorithm offers various solution approaches based on its particular algorithm.  
The SG method used in this work [13; 14] is based on dividing each FDTD cell using a 1:2 refinement ratio, which is applied recursively, resulting in multiple subgrid levels. Each subgrid level consists of cells with half cell size of the adjacent level (see Figure 4). To handle narrow apertures, the aim of this method is to position the aperture within the finest grid (with higher resolution). With a \(N_{\text{sg}}\) number of subgrid divisions, the aperture is resolved with a cell size \(2^{-N_{\text{sg}}}\) times finer than the coarsest one.  
This technique allows us to reduce the cell size within and around the aperture, where the field variations are significant. All of the field components are computed to naturally consider every polarization and propagation direction. Though more costly computationally than DMMA and CA, this method is more accurate and allows us to handle arbitrarily-sized apertures. Furthermore, the algorithm is globally much more efficient than the usual FDTD when combined with causal local time stepping technique, only requiring reductions of the CFL by a factor of 67% [14].  
It should be noted that, as a disadvantage compared to CA and DMMA, the SG method requires adjusting the size of the slot aperture to fit an integer multiple of the cell size. This adjustment may result in an error equal to or less than half of the cell size in the finest grid. However, this error is mitigated when using a high number of subgrid levels.  
Figure 3: Cross-section of a conformal cell used for aperture treatment using the CA procedure. Detail of the electric (E) and magnetic (H) fields involved in the method.  
Figure 2: FDTD cells with a narrow slot centered on the primary grid (electric edges). Furthermore, details of the fields that are involved in the CA model.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 4 Our Proposed Solution
### Routing Protocol  
We created a multi-hop routing protocol to deliver messages across the network. Nodes periodically exchange network information. At first, each node broadcasts what it knows about the network topology (i.e., who its neighbors are). However, over time, the information exchanged grows with the knowledge of each node, and, at a certain point, each node will have complete knowledge of the network's topology.  
With this knowledge, when a node needs to send a message to a node out of its transmission range, it will calculate all the possible paths and choose the shortest one (i.e., the one that requires the least amount of hops). Once the node chooses the path, it forwards the message and the previously calculated path to the next hop.  
Our algorithm is a hybrid one. We chose not to use a reactive protocol since that would mean flooding the whole network when searching for a route, thus requiring large amounts of time and reducing the responsiveness of the network. Similarly, we did not choose a proactive protocol since it would require all nodes to calculate and store every possible route, wasting time computing routes that would likely never be used, especially in larger networks.  
To keep the network representation that the nodes have consistent with the real one, it removes inactive (i.e., nodes that ran out of battery) nodes from path calculations. If the dead node is powered up again, it will act as a new node that wants to join a mesh network.  
The path is calculated with a modified Dijkstra algorithm with a depth-first search. Since there are no negative weight cycles and the graph is unweighted, the algorithm has complexity \(O(V+E)\), where \(V\) represents the number of nodes in the network and \(E\) represents the number of connections between these nodes. The algorithm returns all possible paths between the nodes and chooses the one with the least number of hops. To improve its responsiveness, our algorithm can also stop and return the first path found. This version of the routing algorithm may be used in larger networks where the high number of nodes and connections would mean that finding the best path could be consume too much time and energy.
## 2 Materials and Methods
### Breast Tumor Segmentation  
ROIs containing breast masses were segmented using two different segmentation methods:  
* _Automated delineation_. This is a computer-assisted method based on the spatial fuzzy c-means (sFCM) clustering algorithm [19; 20]. The sFCM algorithm, compared to the traditional FCM, takes into account the spatial relationship among neighboring pixels, making it less sensitive to noise and other imaging artifacts. This approach has been previously implemented and validated in [21; 22];
* _Manual free-hand delineation_. In order to quantify the inter-operator dependence of the robustness of the features, two delineations were performed by two radiologists with more than 5 years of experience with breast MRI, in consensus with a consultant breast radiologist (with more than 30 years of experience with breast imaging).  
Segmentations--in terms of original MR slices containing the breast mass and the corresponding masks--were converted from DICOM to the Neuroimaging Informatics Technology Initiative (NIfTI) format [7], to be used successively as input to PyRadiomics [18] for feature extraction. Both automated and manual free-hand delineations were performed by using MATLAB-coded custom segmentation tools. The size of the tumor is included among the exclusion criteria [21]: the masses with maximum diameters lower than 5 mm were excluded. Considering that the images have a pixel spacing varying between 0.6875 and 0.7422 mm, this means that in the limiting case, the ROI has at least 7-8 voxels (along one dimension), which is sufficient to calculate the features. Figure 1 shows two segmentation results obtained through two different segmentation methods.  
\begin{table}
\begin{tabular}{c c} \hline
**Protocol Characteristic** & **Value** \\ \hline series & Ax VIBRANT mphase \\ MR scanner & GE Signa HDxt \\ magnetic field & 1.5 Tesla \\ repetition time & (37.72–56.92) ms \\ echo time & (17.64–26.80) ms \\ flip angle & \(10^{\circ}\) \\ matrix size & \(512\times 512\) pixels \\ slice thickness & (2–3) mm \\ spacing between slices & (1–1.5) mm \\ pixel spacing & (0.6875–0.7422) mm \\ \hline \end{tabular}
\end{table}
Table 1: Some relevant characteristics concerning the DCE-MRI imaging protocol.
## 2 Materials and Methods
### Long Short-Term Memory (LSTM) Network
#### 2.3.1 Experimental Setup  
The experiment utilizes a 10-fold cross-validation approach, ensuring a comprehensive evaluation of the LSTM model's performance across different subsets of the data. This method involves dividing the dataset into 10 parts, using nine for training and one for testing in each fold. This strategy enhances the reliability of the performance metrics, providing a holistic view of the model's generalizability.