esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Failure Recovery in SFC and Challenges
### SFC Creation and Operation  
A new service request triggers the creation of SFC at the service overlay layer, which is later embedded into the underlay network layer through an embedding function. The overlay layer creates a directed graph of logical connections among SFs in a specific order to logically represent SFC for the requested service. This graph representation of SFC is called the Virtual Network Function Forwarding Graph (VNF-FG), and we take each hop in the VNF-FG, from one SF to the next, as a segment in the overlay layer. The underlay network is defined as the topology of physical links connecting different network elements. The embedding function maps each segment in the VNF-FG to one or more hops in the underlay network. Hence, SFC is a logical VNF-FG in the overlay layer that is then embedded into the physically interconnected underlay network [12].  
The SFC creation process is defined in the NFV management and orchestration (MANO) reference architecture by ETSI [12]. It consists of the NFV Orchestrator (NFVO), VNF Manager (VNFM), and Virtualized Infrastructure Manager (VIM). Service requests are received by the NFVO, and it creates a representative SFC model for the service to define the VNF-FG. Based on the available capacity of the physical servers and current usage of the network resources, the NFVO determines the optimal sites in physical servers for SFs' deployment using VMs/containers [13] or selects the best-suited SF from already deployed instances [14]. SFs' deployment and traffic routing information is then passed onto the VNFM and VIM for resource allocation, placement, and path selection in the physical infrastructure and underlay network. After deployment completion, the SDN controller installs the entries for traffic routing through SFC based on the received paths and policies.  
The protection of SFC from failure requires the NFVO to create a backup VNF-FG with backup SFs. The VNFM and VIM take care of the placement and deployment of the backup SFs in the physical servers, and the SDN controller handles the setup of backup paths and traffic re-routing in the case of failure. The focus of this paper was to reduce the resource footprint of backup paths and expedite the traffic re-routing functionalities of the SDN controller while taking the locations of backup SFs as the input.
# Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations
## 4 Results and Discussion
### Estimation of Entropy Due to Detection Unbalancing
Since the total impinging optical power does not increase, it is trivial to attribute this to excess LO noise, and we conclude that a larger asymmetry increases the impact of the excess LO noise on the measured entropy. Figure 4: Voltage variance as a function of the LO optical power at the BS input port. Each variance was evaluated over \(15.625\times 10^{7}\) noise samples. Figure 5: (**a**) Quantum entropy contribution in function of the detection asymmetry. Dark blue shaded area shows the corrected \(H_{\text{quant}}\), when \(H_{\text{unbal}}\) is considered. (**b**) Fraction of entropy from classical effects attributed to the electronic and excess LO noise. Red lines present the entropy values for the theoretically expected variances. To quantify \(H_{\text{unbal}}\), we start by calibrating the measurement scheme and consider that the most balanced state achievable (asymmetry of 50.75%) has a negligible excess LO noise contribution. Assuming that \(H_{\text{quant}}\) remains relatively constant, all posterior increases are thus the result of introducing excess LO noise. As seen in Figure 5b, this contribution increases linearly with the asymmetry. By contrast, as a consequence of a higher signal-to-noise ratio, the contribution from the electronic noise decreases in the same proportion that \(H_{\text{unbal}}\). We thus find that given a constant total entropy and impinging optical power, the fraction of entropy from classical effects is in reality constant and independent of the asymmetry. If the excess LO noise is unaccounted for, this results in more bits accessible to an eavesdropper. Naturally, our assumptions result in an overestimation of \(H_{\text{quant}}\), since our measurement scheme is always slightly unbalanced. To obtain an acceptable estimation, we performed a linearisation with \(r^{2}=0.9523\) of the \(H_{\text{unbal}}\) values, which yields \(H_{\text{unbal}}=0.0741\)\(\Delta\)P. Then, knowing the maximum possible asymmetry, we estimate that \(H_{\text{unbal}}=0.0554\) bits for our balanced implementation. We thus find a corrected quantum contribution of 0.0282 bit, 66.3% lower than originally measured. Unfortunately, these assumptions hold true only when no additional attenuation is introduced. Here, the decrease in \(P_{\text{total}}\) leads to a reduction in the quantum noise contributions with an increasing attenuation, and thus this value should be adjusted accordingly. Nonetheless, the value obtained is an acceptable estimation for asymmetries close to a balanced detection. Although the electronic noise is still the major contribution to a balanced system, this method allows us to increase the QRNG security by discarding these insecure bits.
## 1 Introduction
Track membranes are made by irradiating a thin polymer film with high-energy ions, followed by physical and chemical treatment. As a result, a system of cylindrical pores of identical size is obtained in the polymer. Pore diameters can vary from 20 nm to several micrometers. Pore surface density is set at the initial stage of irradiation of the polymer film and can reach the value of \(10^{9}\) cm\({}^{-2}\)[25]. Variation of the pore diameter and its surface density makes TMs a very promising basis for template synthesis [26; 27; 28; 29; 30; 31]. The electrochemical deposition method is then used to obtain the substrates with NWs array. This method allows producing SERS substrates with parameters that repeat the pore structure of the track membrane. Due to the features of the template synthesis technology, it is possible to vary not only the diameter and surface density of NWs but also their length. Of particular note is the fact that template synthesis technology allows one to control the geometry of the tips [32]. This makes it possible to adjust the position of the plasmon resonance to the experimental wavelength range, and, as a result, to increase the contribution of the resonance to the amplification of the obtained SERS-signal. Such substrates with the leaning neighboring silver nanowires (Ag-NWs) amplify the signal due to the presence of a gap hot spots. A similar effect of the gap hot spot formation due to the Ag-NWs leaning with the following bundle formation had also been noted for the substrates with Ag-nanorod arrays fabricated by the electrodeposition using porous anodic aluminium oxide (AAO)membrane as a template. Ag-nanorods were left in distilled water before the analyte was added, to avoid preliminary leaning of the tips of Ag-nanorods. After the analyte adding and substrate drying, analyte molecules were trapped in the gap hot spot region [33]. However, dozens of Ag-nanorods were leaning together due to the high surface pore density of the AAO membrane, thus the point of contact between the tips of individual Ag-nanorods was impossible. Control over NWs leaning can be achieved by the dissolving of the template. The template layer is decreased by etching, and the NWs length can be controlled by etching time. If the analyte is added during membrane dissolving then many analyte molecules will be trapped in the spacing between neighboring NW tips, i.e., in the hot spots region [34]. As well as the porous aluminium oxide, TMs have a unique intrinsic structure, which produces various metal structures of nanometer dimensions. However, porous aluminium oxide has a high surface density, which affects the NWs leaning and does not allow controlling it. TMs do not have this drawback, since the surface pore density of TMs and, consequently, the NWs surface density can be varied within wide limits (typical values are from \(10^{5}\) to \(10^{9}\) cm\({}^{-2}\)) regardless of the diameter. That makes possible the optimization of NWs density on the surface of the substrate for the gap hot spots formation.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 2 Background
### Problem Definition  
For data collection from multiple LiDAR sensors, the sequential data processing method has a limitation in processing time. The fusion procedure for multiple LiDAR sensors involves the collection, parsing, transformation, and formatting of LiDAR data. Processing time in the fusion system is determined by system requirements. Data processing should be completed within 33 ms in 16-channel 30 Hz LiDAR sensors. However, in the data processing method based on a single process, the processing time increases linearly according to the number of connected LiDAR sensors. Therefore, the single processing method is difficult to apply in a highly complex sensor network. Figure 1 shows the processing time of a client and a server using sequential processing. We can observe that it is impossible when the number of connected LiDAR sensors is more than two. To use \(n\) multiple LiDAR sensors where each sensor has the \(T_{sensor}\) processing time, total data processing time (\(T_{process}\)) for multiple LiDAR sensors in the fusion system should be as low as within the threshold, \(T_{threshold}\) (i.e., 33 ms), as the service requirement.  
Problem:  
\[T_{process}\leq T_{threshold},\]  
\[T_{process}=\sum\limits_{j=1}^{n}T_{sensor}^{i}\]  
Figure 1: Response time of a fusion system using sequential processing.
# Low-Profile Dual-Band Reflector Antenna for High-Frequency Applications
## 1 Introduction
To achieve dual-band radiation capability, the elements of the SRA must function well in both of the bands. However, due to limitations on the operating frequency bandwidth of the elements, the application of the SRA is limited, especially when the dual-band reflector antennas operate with a large frequency ratio. In this paper, a high-gain low-profile reflector antenna with dual-band radiation capability is proposed. A composite dual-band feed with an \(f_{h}/f_{l}\) of around 5 is utilized as the feed for the reflector antenna, where \(f_{l}\) and \(f_{h}\) are 30.9 GHz and 158 GHz, respectively. Two SRs are designed separately, one is the SRA, employed for lower-band operation, and the other is the metal SR, employed for higher-band operation. Compared to conventional dual-band reflector antennas, the proposed dual-band reflector antenna features a lower profile. Furthermore, the proposed reflector antenna is fabricated, and the measured results verify the simulated dual-band performance in the two bands.
## 8 Numerical Results and Discussion  
The following section presents a comprehensive analysis of the numerical results obtained from the proposed indoor VLC system. The evaluation focuses on the performance of ADRs, including pyramidal and hemispherical configurations, combined with two signal processing schemes, MRC and SBC. The optimization of the IRS placement using an RNN is also explored to assess its impact on the system's performance.  
The study was carried out within an indoor environment with dimensions \(5\,\rm m\times 5\,\rm m\times 3\,\rm m\), with a central transmitter and an IRS mounted on one wall. The metrics of interest include (\(P_{r}\)) and the SNR, evaluated as functions of various parameters, such as the number of optimization epochs and ADR configurations. Special emphasis is placed on comparing system performance before and after applying the RNN-based IRS optimization, highlighting its role in enhancing both maximum and minimum power levels across the scenario.  
This section also discusses the implications of using different ADR configurations and combining schemes. The numerical results provide insight into how IRS and ADR design choices influence system performance. They demonstrate their combined potential to mitigate signal degradation caused by NLoS conditions and optimize the overall efficiency of the VLC system.  
The results of graphical and numerical analyses were obtained through computer simulations in MATLAB R2023a. The most important parameters of the scenario, the VLC system and the RNN algorithm, are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**VLC and Scenario Parameters** & **Value** \\ \hline Room Dimensions (Length \(\times\) Width \(\times\) Height) & \(5\times 5\times 3\,\rm m\) \\ LED position (x \(\times\) y \(\times\) z) & \(2.5\times 2.5\times 3\,\rm m\) \\ Transmitter Power (\(P_{\rm tx}\)) & 10 W \\ Receiver Height (\(z_{\rm receiver}\)) & 1 m \\ Bandwidth (\(B\)) & 1 MHz \\ Noise spectral density (\(N_{0}\)) & \(1\times 10^{-21}\,\rm W/Hz\) \\ Number of Evaluation Points & \(50\times 50\) grid \\ \hline
**IRS Parameters** & **Value** \\ \hline Number of mirrors (\(M\times N\)) & \(10\times 10\) \\ Initial position of the IRS prior to optimization (x-coordinates) & [4.5 m, 4.9 m] (horizontal spread across the wall) \\ Initial position of the IRS prior to optimization (y-coordinates) & [1.5 m, 3.5 m] (vertical spread) \\ Initial position of the IRS prior to optimization (z-coordinates) & Constant at 1.5 m \\ Mirror Dimensions & \(0.05\times 0.05\,\rm m\) \\ Initial Height of IRS Mirrors (\(z\)) & 1.5 m \\ Coverage area & Entire room wall \\ Adjustment of Positions & Dynamic (RNN-based) \\ \hline \end{tabular}
\end{table}
Table 1: Scenario parameters.
# Propagation of Elastic Waves in a One-Dimensional High Aspect Ratio Nanoparticle Phononic Crystal
## 2 Model and Method  
The sample is made of a periodic grating of epoxy (POSS) nanoridges grafted on the surface of a glass substrate. Figure 1 shows a three-dimensional (3D) representation of the structure, together with the elementary unit cell used for the calculations, in which \(a\) = 630 nm is the lattice parameter, and the height and the width of the nanoridges are \(h\) = 2.7 \(\upmu\)m and \(w\) = 250 nm, respectively. With these geometrical parameters, the nanoridges present a high aspect ratio (\(AR\)), larger than ten (\(AR\) = \(h\)/\(w\) = 10.2) that has not yet been considered in the literature. The density, transverse velocity, and longitudinal velocity of the involved materials, i.e., epoxy and glass, are given in Table 1.  
The dispersion curves (frequency versus wave vector) of the nanoridge structure are calculated by solving the elasticity equations of motion with the help of the finite element method (FEM) performed with the commercial code COMSOL multiphysics. Due to the periodicity of the structure along the \(y\) direction (Figure 1a), the problem is solved in a one-unit cell by assuming periodic boundary conditions at the limits of the unit cell. Free stress boundary conditions are used at the surfaces of the nanoridge that are in contact with vacuum. To simulate the semi-infinite nature of the substrate, we have considered a finite height of the glass substrate equal to \"\(4\times h\)\", under which we add an artificial medium of height \"\(h\)\" with transverse and longitudinal velocities that are two times higher than those of the glass substrate. Thus, the surface mode at the bottom will not appear in the non-radiative area of the dispersion curves. The dispersion curves are plotted in terms of frequency as a function of the wave vector \(q\) in the \(x\) (resp. \(y\)) direction parallel (resp. perpendicular) to the nanoridges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constant** & **Epoxy (POSS)** & **Glass** & **Cargille** \\ \hline \(\rho\) (kg/m\({}^{3}\)) & 1190 & 2275 & 1000 \\ \(c_{t}\) (m/s) & 1350 & 3400 & / \\ \(c_{l}\) (m/s) & 2620 & 5670 & 1670 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density (\(\rho\)), transverse (\(c_{l}\)), and longitudinal (\(c_{l}\)) velocities of sound used in the calculation.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 5 Conclusions  
In the domain of liver lesion (trauma or tumor) segmentation, several models stand out due to their ability to handle the complexity and variability of liver anatomy and lesion characteristics. Among these, U-Net++ and RA-UNet demonstrate exceptional performance, particularly in detecting small and irregular lesions. U-Net++ excels with its nested and dense skip connections, which enhance feature extraction and multiscale representation, making it well suited for challenging lesion segmentation tasks. Similarly, RA-UNet integrates residual and attention mechanisms, providing superior accuracy in distinguishing lesions with weak boundaries and irregular shapes.  
For advanced segmentation tasks requiring global context understanding and fine detail extraction, Swin-Unet leverages Transformer-based architectures to capture long-range dependencies and small lesion details effectively. Additionally, Dense V-Net is highly effective for 3D volumetric segmentation, preserving fine details, and handling complex lesion morphologies, though it demands substantial computational resources.  
Emerging architectures like MSLUNet offer a balanced approach, combining efficiency and accuracy, making them suitable for real-time applications, although they may not match the precision of more computationally intensive models. Furthermore, conditional GANs (cGANs), including Pix2 Pix, excel in capturing intricate details and complex lesion structures, particularly in noise-robust environments, although their dependency on paired training data and high resource requirements can be limiting.  
Overall, the choice of model should align with the specific clinical requirements, computational resources, and dataset characteristics. For scenarios prioritizing segmentation accuracy and detail, U-Net++, RA-UNet, and Swin-Unet are leading contenders, while MSLUNet and Dense V-Net provide practical alternatives for resource-constrained or large-scale applications. These models collectively set a strong foundation for advancing liver lesion diagnosis and treatment planning.  
Conceptualization, A.N.; methodology, N.T.; validation, A.N. and N.T.; formal analysis, N.T.; investigation, A.N.; resources, N.T.; writing--original draft preparation, A.N. and N.T.; writing--review and editing, A.N. and N.T.; visualization, A.N. and N.T.; supervision, C.Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the use of Grammarly (www.grammarly.com) for proofreading and grammar checking throughout the manuscript preparation.  
The authors declare no conflicts of interest.
# An Improved Monte Carlo Reliability Analysis Method Based on BP Neural Network
## 3 Calculation Theory of Improved BP-MCS Method  
The prediction accuracy of the BP neural network for sample points near the failure surface largely determines the calculation accuracy of failure probability. In order to improve the prediction accuracy of the BP model near the failure surface, this paper proposes a sampling method of sampling points encryption near the failure surface. For low-dimensional problems (the number of variables \(<4\)), the key points generated by setting \"*\" shape and random integer method cover the whole design space evenly; For high-dimensional problems (the number of variables \(\geq 4\)), the whole design space is evenly covered by setting the key points generated by random integer method and random sampling of variables. Through finite element calculation, the failure of key points is determined, and the region containing the failure surface is identified. In this region, a set of additional sample points is generated by reducing the standard deviation, thereby constructing a BP neural network with high accuracy. Finally, the sample points near the failure surface are predicted by the BP model. Taking the distance from the sample point to the failure surface as the screening criterion, the sample point with a closer distance is substituted into the FEM for testing and added to the training set. Iterative training is repeated to obtain a BP neural network prediction model with high prediction accuracy for the sample points near the failure surface.  
To solve the problem that the calculation time of implicit structural performance function constructed by the BP neural network is long in MCS reliability analysis, an improved calculation method of BP-MCS is proposed in this paper. In order to reduce the number of BP model calls when calculating MCS failure probability, the product of random variable probability density function is defined as the weight of sample point \(\omega_{i}\). Considering that the probability of failure point of engineering structure is very low, that is, its weight is very small, there must be a weight-critical value \(\omega_{0}\). When the weight of the sample point extracted by MCS is \(\omega_{i}>\omega_{0}\), the sample point is in the reliable region, and there is no need to call the BP model to calculate the structural response value at this time. However, when the weight of sample points is \(\omega_{i}<\omega_{0}\), it needs to be substituted into the BP model to judge whether it is failed. This method filters out most of the sample points located in the reliable region from the probability of occurrence, which greatly reduces the number of calls of the BP model, thus shortening the calculation time.