esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Interpolation  
Once the FE mesh is generated, the additional imaging data that may be available must be interpolated to this mesh. Most patient-specific models, especially tumour growth models, need as inputs not only the geometry, but also other spatial distributions of properties that describe the heterogeneous characteristics of tumours. In the case of PRIMAGE, this additional data was obtained from DWI and DCE sequences, respectively. These MRI sequences are commonly used in clinical practice to evaluate the cellularity and vascularization of the tumours. The cellularity values are derived from the Apparent Diffusion Coefficient maps generated from DWI sequences [32], while the vascularization is obtained from the analysis of DCE sequences [33]. Apart from these sequences, there are many other techniques to obtain different imaging data that might be included in the FE mesh. Therefore, our code includes a script to read any kind of data derived from imaging that is stored in NIfTI and DICOM formats and interpolate it to any FE mesh given. The interpolation process begins by transforming the imaging data to be interpolated from voxel coordinates to global coordinates using the affine matrix of each imaging sequence. Then, we use the Scipy [34] Python library to interpolate the spatial data to the elemental centroids of the mesh generated previously, exporting this interpolated data to an additional file that can be used as input for subsequent computational models.
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 5 Performance Analysis and Simulation
### Comparative Analysis of Simulation Results
Since the node moves at a relatively slow speed of 1 m/s, the ROEVA protocol that does not consider node mobility is the best about end-to-end delay among the changes in the number of 500-3000 nodes. As shown in Figure 19, with the increase of the number of nodes, the network lifetime of the VBF protocol, HHVBF protocol and ROEVA protocol without considering the residual energy of nodes are shorter than the DQELR protocol and the PDDQN-HHVBF protocol. The considering residual energy of nodes protocols will use candidate relay nodes in a load-balanced manner. The PDDQN-HHVBF protocol has a better network lifetime than the DQELR protocol when the number of nodes increases. This is since the protocol proposed in this paper is transmitted in a short distance, and thus the probability of collision of data packets transmitted in a short distance is small. Therefore, the energy consumption of nodes is less, thereby the network lifetime is longer. Otherwise, the PDDQN-HHVBF use  
Figure 17: The energy tax of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different number of network nodes. \"Store-Carry-Forward\" mechanism to let the AUV find the relay nodes which will have more opportunities to delivery data packets to next relay node. In this way, it will save more energy of AUV and relay nodes. Finally, we comprehensively reflect the PDDQN-HHVBF protocol results of the nodes movement speed of 0-3 m/s with 1000 nodes Table 2, and the nodes movement speed of 1 m/s with 500-3000 nodes in the following table in Table 3.  
Figure 19: The network lifetime of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different number of network nodes. Figure 15.0 The end-to-end delay of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different number of network nodes.
## 4 Deconvolution
### Maximum Correlated Kurtosis Deconvolution
#### 4.2.2 Application  
In recent years, researchers have pursued the development of efficient and robust methods for signal acquisition and processing in conjunction with maximum correlated kurtosis deconvolution (MCKD) technology, aiming to enhance the signal-to-noise ratio and the accuracy of feature extraction. A study presented an improved maximum correlated kurtosis deconvolution adjustment (IMCKDA) method for fault detection in wind turbine gears [142]. By introducing the concept of convolution adjustment and utilizing the correlated Gini index (CG) to guide the selection of filter length, the method improved both the signal-to-noise ratio and robustness.  
The combination of customized balanced multi-wavelets with adaptive maximum correlated kurtosis deconvolution (MCKD) has been demonstrated to be effective and feasible for the diagnosis of compound faults in rotating machinery [143]. Another approach, namely improved maximum correlated kurtosis deconvolution (IMCKD), enhances robustness and efficiency in rolling bearing fault diagnosis by automatically estimating the iteration period and selecting the filtered signal with the maximum kurtosis [144]. Furthermore, a maximum correlated kurtosis deconvolution based on deep networks (MCKD-DeNet) method has been proposed for bearing fault diagnosis, integrating deep learning to optimize the deconvolution filter coefficients. This method improves the accuracy and efficiency of fault feature extraction [145].
## 4 Stability Analysis of Crowd Flow Considering Internal Disturbances
### Stability Analysis of Crowd Flow with Internal Disturbance  
Based on the wavefront theory, the nonlinear dynamic model of crowd disturbance propagation, that is, Equations (6)-(8), are linearized, and then the stability of crowd flow is analyzed based on Lyapunov. It is assumed that internal disturbances occur in the equilibrium state \((\rho_{0},v_{h0},v_{l0})\). The initial solutions of Equations (6)-(8) are \(\rho_{0}=\rho_{e}\) and \(v_{h0}=V_{eh}(\rho_{e}),v_{l0}=V_{el}(\rho_{e})\). First, the solutions of the equations near the wavefront are expanded by the power series, as follows:  
\[\xi=(x,y)-(X,Y)(t) \tag{13}\]  
Here, \((X,Y)(t)\) is the location of the wavefront at time \(t\). Because the wavefront is the boundary of the disturbance influence range under the crowd equilibrium state, the eigenvalue method remains valid near the wavefront. Thus, the derivatives of \(X(t)\) and \(Y\left(t\right)\) can be obtained using the eigenvalues, as follows:  
\[\dot{X}(t)=v_{hc1,2}(\rho_{0},v_{h0})=v_{h0}+\lambda_{h1,2} \tag{14}\]  
\[\dot{Y}(t)=v_{lc1,2}(\rho_{0},v_{l0})=v_{l0}+\lambda_{l1,2} \tag{15}\]  
According to Equation (13), we can obtain the power series expansions of \(\rho(x,y,t)\), \(v_{h}(x,t)\), and \(v_{l}(y,t)\) as follows:  
\[\rho(x,y,t)=\rho_{0}+\xi\rho_{1}(t)+\frac{1}{2}\xi^{2}\rho_{2}(t)+\cdots \tag{16}\]  
\[v_{h}(x,t)={v_{h}}^{0}+\xi{v_{h}}^{1}(t)+\frac{1}{2}\xi^{2}{v_{h}}^{2}(t)+\cdots \tag{17}\]  
\[v_{l}(y,t)={v_{l}}^{0}+\xi{v_{l}}^{1}(t)+\frac{1}{2}\xi^{2}{v_{l}}^{2}(t)+\cdots \tag{18}\]  
where \(\rho_{i}(t)=\left.\frac{\partial\rho}{\partial x^{i}}\right|_{(X(t)^{-},Y(t)^ {-},t)^{\prime}}\), \({v_{h}}^{i}(t)=\left.\frac{\partial^{i}\rho_{0}}{\partial x^{i}}\right|_{(X(t)^ {-},t)^{\prime}}\), \({v_{l}}^{i}(t)=\left.\frac{\partial^{i}\rho_{0}}{\partial y^{i}}\right|_{(Y(t)^ {-},t)^{\prime}}\), \(i=1,2,3,\ldots\).
# Tunable Fano Resonances in an Ultra-Small Gap
###### Abstract  
A Fano resonance is experimentally observed in a single silver nanocube separated from a supporting silver film by a thin aluminum oxide film. The resonance spectrum is modulated by changing the size of the silver nanocube and its distance from the silver film. The system is fabricated by a bottom-up process with an accurately controlled nanogap at the sub-6-nm scale. The simulation result shows that the destructive interference between the dipole mode and the quadrupole mode in this \"nanocube on mirror\" (NCoM) structure is responsible for the resonance. The spectra red-shifted as the size of the silver nanocube increased and its distance from the silver film decreased. In addition, a refractive index sensitivity of the spectrum of 140 meV/RIU (refractive index unit), with a 2.4 figure of merit, is obtained by changing the dielectric environment around the silver nanocube. This work will enable the development of high-performance tunable optical nanodevices based on NCoM structures.  
Fano resonance; interference; nanocube; sensitivity; surface plasmon resonance +
Footnote †: journal: Applied Physics Letters
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Networking
#### 4.1.2 Wireless Sensor Networks (WSN)  
A Wireless Sensor Network (WSN), as illustrated in Figure 3, is a wireless network of multiple sensor nodes which have limited resources and are densely deployed in an environment or a restricted area. These nodes can sense data from the surroundings and deliver them, thanks to the wireless networking, to people who need this information. The main objective of WSN is to interconnect the IoT-based devices (e.g., of a patient) in order to deliver useful information wherever and whenever it is needed, such as in a medical center [33]. In addition, medical areas can be surveilled by measuring and evaluating the air pressure, sound, light, and vibration, if they belong to a healthy environment [30].  
A WSN consists of sensors that operate in close proximity to a person's body and transfer measurement values wirelessly via communication protocols. Sensor nodes have a processing unit with limited computational power and capacity. WSN are developed in specific environments, such as forests, mountains, and glaciers, in order to collect environmental quantitative metrics during long periods. These networks may interface and operate any kind of biomedical sensors such as electrocardiogram, blood oxygen level,  
Figure 2: Internet of Things.  
blood pressure, blood glucose, bodyweight, heart rate, oxygen saturation, and many other quantities so as to give minimal feedback for the maintenance of optimal health level.  
It is of great importance that WSN can offer ubiquitous real-time or close to real-time information about vital patient's state. Therefore, it may send alerts and notifications to the patient's IoT device, based on the sensor measurement values using specific thresholds or limits. This will lead to the production of large amounts of data. The benefits of a WSN are that it consists of autonomous devices with no need for wires, and it is energy efficient since the right algorithms and mechanisms are used. Transferring, managing, analyzing, and securing a large amount of data are the main challenges of WSN [34].
## 4 Verifications and Examples
### Influence of \(E_{1j}^{i}/E_{0j}^{i}\) on Consolidation  
Figure 7 depicts the changes in the EPAP, EPWP, and U\({}_{\text{S}}\) at different ratios of elastic modulus \(E_{1j}^{i}/E_{0j}^{i}\), where \(i\) denotes \(a\) (air) or \(w\) (water) and \(j\) denotes \(n\) (net normal tress) or s (suction); the subscripts 0 and 1 represent the same meaning as before. It can be found that the value of \(E_{1j}^{i}/E_{0j}^{i}\) has no effect on the prior dissipation of EPAP and EPWP but affects the U\({}_{\text{S}}\) in the prior period. The reason for this is that the degree of consolidation of viscoelastic unsaturated soils is not only determined by the dissipation of EPAP and EPWP, but also by the coefficient of volume change. Figure 7c indicates the variation curves of the average U\({}_{\text{S}}\) with time appear anti-S-typed when \(E_{1j}^{i}<E_{0j}^{i}\). When \(E_{1j}^{i}>E_{0j}^{i}\), the variation curves of the average U\({}_{\text{S}}\) along time appear double-S-typed. And the larger \(E_{1j}^{i}/E_{0j}^{i}\) is, the earlier and the faster the consolidation takes place. This is because the elasticity modulus is a key parameter that characterizes the ability of soil to resist elastic deformation. A higher modulus (with a higher coefficient of consolidation) results in smaller soil deformation under the same stress, which leads to a faster rate of consolidation.
# Simulation and Evaluation of the Radiation Dose Deposited in Human Tissues by Atmospheric Neutrons
## 4 Conclusions  
In this work, and with the help of Geant4 software, first, we calculated the RD that neutrons with energy between 1 and 1000 MeV deposit in blood, adipose, bone and brain tissue samples; second, we calculated the RD that these particles deposit in the tissue samples as a function of different altitudes and latitudes, in both cases, for a longitude of 0\({}^{\circ}\).  
When we considered neutrons with a flat spectra (5 \(\times\) 10\({}^{5}\) neutrons for each energy) and as a reference the RD deposited by them in the bone tissue sample, we found that in the range of energies from 1 to 100 MeV, the RD deposited in the blood, adipose and brain tissue samples is greater by 67.9%, 82.8% and 72.3% respectively. On the other hand, for the range of energies between 200 and 1000 MeV, the RD deposited in the blood, adipose and brain tissue samples is lower by 19.0%, 19.2% and 19.5% respectively.  
When we obtained the atmospheric neutron spectra from [11]; we calculated the RD deposited by these particles in different human tissues as a function of both altitude and latitude and for a longitude of 0\({}^{\circ}\). The results are shown in Figures 4 and 5. In general, we found that the RD deposited by atmospheric neutrons increases with the increase in altitude and latitude, e.g., for an altitude of 4 km (altitude of high mountain), the RD is increased \(\sim\)19 times; while, for an altitude of 10 km (altitude of commercial flights), the RD is increased \(\sim\)156 times, respectively; in both cases, regarding the RD at sea level.  
Conceptualization, E.O. and B.M.; Investigation, E.O.; Methodology, E.O.; Software, E.O.; Supervision, B.M. and C.G.; Writing--original draft, E.O.; Writing--review & editing, B.M., C.G., V.M.M., M.P. and R.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Posdoctoral Grant Programa de Becas Posdoctorales de la Universidad Nacional Autonoma de Mexico.  
Not applicable, for studies not involving humans or animals.  
Not applicable, for studies not involving humans or personal data.  
Data availability StatementData are available, contact the corresponding author.  
The authors thank The European Organization for Nuclear Research (CERN) for Geant4 software.  
The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.
## 2 Test Equipment and Specimens  
### End Surface Friction and Wear Testing Apparatus  
In this study, an HDM-2 end surface friction and wear testing apparatus was used, as shown in Figure 1. This apparatus was composed of a spindle, upper and lower specimen fixtures, upper and lower specimen boxes, loading devices, medium circulation devices, and heating and cooling circulation devices. It could operate at 0-3000 rpm and 20-3200 N. The temperature could be adjusted within the range of 0-200 \({}^{\circ}\)C. The upper specimen was mounted on the central screw hole of the spindle through the upper clamping plate and the fastening screw, and the lower specimen was mounted on the special lower specimen box and held with the lower clamping plate. To truly reflect the operating state of the friction pairs of the mechanical seals, the testing apparatus rotated the upper specimen and applied loads to the lower specimen so that the upper and lower specimens formed rotational friction. The structures, sizes, and materials of the upper and lower specimens could be designed independently. The lower specimen box was supported by spherical bearings and had good followability when the stationary ring was in contact with the rotational ring.  
The testing apparatus was equipped with rotational speed and pressure sensors at the transmission device and hydraulic loading device, respectively. The sensor converted physical signals into digital signals and transmitted them to external equipment for real-time adjustment of the rotational speed and load. During a test, the rise rates of the friction surface temperature of the lower specimen was measured by a temperature sensor inserted in the hole in the lower specimen fixture. However, the friction coefficient is dimensionless and cannot be directly measured with sensors. Therefore, a torque sensor was connected to the lower specimen. The measured friction torque was transmitted to the host computer and converted into the friction coefficient. The following equations were used to determine the friction torque and friction coefficient:  
\[M=F_{f}L=\int_{r}^{r}\pi 2\mu\sigma r^{2}\mathrm{d}r=\frac{2}{3}\pi\mu\sigma(R_{o} ^{3}-R_{i}^{3}) \tag{1}\]  
\[\sigma=F/\left[\pi(R_{o}^{2}-R_{i}^{2})\right] \tag{2}\]  
\[f=3F_{f}L/\left[2\pi\sigma(R_{o}^{3}-R_{i}^{3})\right]=3(R_{o}^{2}-R_{i}^{2})F _{f}L/\left[2(R_{o}^{3}-R_{i}^{3})F\right] \tag{3}\]  
where \(M\) is the friction torque; \(F_{f}\) is the friction force; \(L\) is the equivalent arm length, \(L=0.08\) m; \(\sigma\) is the contact pressure in a unit area; \(F\) is the load; \(R_{i}\) is the inner radius of the upper specimen; \(R_{o}\) is the outer radius of the upper specimen; and \(f\) is the friction coefficient.  
Figure 1: Friction and wear testing apparatus and specimen structure.
## 2 Theoretical Analysis of the Edge Effect  
### Generation of the Edge Effect  
The material removal in bonnet polishing could be controlled to be deterministic and its amount \(H(x,y)\), which is equal to the two-dimensional convolution between the material removal function per unit time \(R(x,y)\) and the dwell time function \(D(x,y)\), along with the tool path, can be expressed as [25]:  
\[H(x,y)=R(x,y)**D(x,y). \tag{1}\]Therefore, the surface residual error \(E(x,y)\) after the polishing process can be expressed as:  
\[E(x,y)=H_{0}(x,y)-H(x,y). \tag{2}\]  
where \(H_{0}(x,y)\) is the desired material removal.  
Uniform material removal simulations regarding the edge have been conducted based on Equations (1) and (2). Figure 1 shows the geometrical model when the bonnet tool overhangs the edge. The overhang ratio \(\tau\) is the ratio of overhang distance \(\omega\) to the diameter of the contact spot D. The assumption that the TIF over the whole surface is all the same including the edge zone is made in this simulation. The initial error surface is an 80 mm\(\times\)80 mm standard flat surface. Gaussian-like tool influence function with a diameter of 10 mm was adopted, as shown in Figure 2. Its peak removal rate is 3.0 \(\upmu\)m/min. The overhang ratio varies from 0 to 1, with an increment of 0.1. The distance in \(x\)- and \(y\)-directions between each dwell point is 1 mm. In addition, the dwell time at each dwell point is 1 s.  
Figure 2 shows the simulation results of this group of uniform removal based on Equations (1) and (2). When \(\tau=0\), the height and the width of the upstand edge are the largest, as shown in the sectional profile and 3D profile in Figure 2. The edge width is the diameter of TIF. When \(\tau=1\), which means that the bonnet tool is totally out of the workpiece, there is no edge upstand in the simulation result. This simulation demonstrates the removal results when there is no edge effect. However, in the practical manufacturing process, the shape of the tool influence function at the  
Figure 1: Modeling of the bonnet tool polishing overhanging the edge.  
Figure 2: Edge profile after uniform polishing with different overhang ratios.  
edge zone is irregular due to the change of the contact pressure when the bonnet tool compresses the edge [17]. In addition, the bonnet tool is impossible to totally overhang the edge thoroughly (\(\tau=1\)), which will damage the tool. This is the main reason for the generation of the edge effect.
## 3 Experimental Results  
The proposed MIMO antenna was fabricated using a Leiterplatten-Kopierfrasen, LPFK, machine, and was tested in anechoic chamber. Due to symmetricity of the design, only one side of the proposed antenna is tested. Figure 6 shows the proposed antenna structure's  
Figure 4: Single-Element Performance Parameters (**a**) Radiation Patterns (**b**) Gain and Efficiency.  
Figure 5: MIMO Antenna (**a**) Front (**b**) Back.  
copper layer with 2.4 mm end launch connectors. The antenna has been set with a scale to show the physical length of the proposed prototype.  
Figure 7 shows the isolation of the proposed antenna with and without the isolating structure. It can clearly be seen that with the insertion of a line resonator at the center, the resonator acts as a decoupling structure for MIMO antennas as the line resonator effectively cancels the constructive interference of currents among radiating elements, and thus the isolation levels are improved.  
Figure 6: Fabricated Prototype.  
Figure 7: Surface Current Distribution (**a**) Without Isolating Structure (**b**) With Isolating Structure.  
Furthermore, the simulated and measured S-parameters are show, in Figure 8. The reflection co-efficient in Figure 8a shows the resonance from 22 to 40 GHz, while the isolation value of antenna 1 and antenna 3 is found to be in between \(-15\) dB and \(-19\) dB without the presence of decoupling structure. The lowest isolation without decoupling structure is found to be \(-19\) dB at 29 GHz frequency. As with the insertion of decoupling structure, the isolation value improved from \(-22\) dB to \(-27\) dB. The isolation value with decoupling structure showed improvement up to 5 dB for antenna 1 and antenna 3. The isolation value between adjacent radiating elements, antenna 1 and antenna 2 was also improved. The measured results achieved after measurements are shown to be in excellent agreement with the simulations. A few minor alterations can be seen but can be attributed to errors during measurement set up.  
Figure 9 shows the antenna efficiency and simulated and measured gain of the proposed MIMO system. The efficiency of the MIMO antenna ranges in between 85% and 94%, while at 28 GHz, the efficiency is found to be almost 92 to 92.5% for all radiating elements. The range of the antenna gain is found to be 5.9 to 7 dB.  
Figure 8: Simulated and Measured S-Parameters. (**a**) Return loss (**b**) Ports Isolation.  
Figure 9: Efficiency and Gain of the MIMO Antenna.  
The radiation patterns both in the 2D and 3D plane are shown in Figure 10. The patterns of antenna 1 and antenna 2 are shown and discussed. The 28 GHz frequency patterns are shown at Phi = 90, Phi = 0 and Theta = 90 Plane. As seen from the figure, the proposed antenna system shows pattern and spatial diversity characteristics. The 3D patterns at 28 GHz are also shown in Figure 10. The gain ranges from 6.4 to 7.1 dB for all four radiating elements.