esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Structure and Design  
The PSC design in this study is simulated to obtain the best performance in terms of the lattice constant and radius of nanodisk variation. Typical structures of PSC include the FTO, ETL, perovskite, HTL, and metal electrode [36]. The simulated structure in this work is presented in Figure 1. The photogenerated charge carriers are generated in the absorber layer and go to the transport layers by the drift process. HTL and ETL block the electrons and holes, respectively, and then collect at the electrodes to create a potential difference. The best material for the absorber layer is methylammonium lead iodide, also known as MAPbI3, as mentioned in [PERSON] et al. [5], with efficiency reaching 29%. TiO\({}_{2}\) was commonly used as an ETL because of its proper electronic band levels and ease of fabrication [37]. The other common materials used in PSC was FTO, which is used as a transparent conductive layer, Spiro-OmeTAD was used as HTL, and gold (Au) was used as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Material** & **Size** \\ \hline Transparent Electrode & FTO (fluorine doped tin oxide) & 80 nm thick \\ Electron Transport (ETL) & TiO\({}_{2}\) & 100 nm thick \\  & & 100 nm thick, 37.5–225 nm \\ Photonic Crystals & TiO\({}_{2}\) & radius, 450–1125 nm lattice \\  & & constant \\ Perovskite & MAPbI3 & 500 nm \\ Hole Transport (HTL) & Spiro-OMeTAD & 300 nm \\ Metal Electrode & Au & 80 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials and size parameters for each layer of solar cells used in the simulation. Variations were made on the radius and lattice constant of the PC of TiO\({}_{2}\) nanodisk.  
a metal electrode layer. The PC structure was introduced to the interface ETL/absorber to enhance the absorption of incident light and increase the interaction between carrier charges with a slow photon effect to reduce the recombination [38]. As with ETL, TiO\({}_{2}\) was used as a PC material. The radius of the nanodisk PC is varied between 37.5 and 225 nm, and the lattice constant is varied between 450 and 1125 nm, while the thickness is fixed at 100 nm. This range is quite representative of finding the optimum size of the photonic crystal and providing a better search for the optimum photonic bandgap, based on the work of [PERSON] et al. [29]. The PC was introduced at ETL/absorber because the energy band alignment at the interface is an essential factor for high-efficiency solar cells [5]. The flat-structured PSC is also simulated to study the enhancement of PSC performance.
## 4 Simulation Results and Discussion
### Simulation Settings  
The BSN deployment area is covered by the WLAN, commonly based on an IEEE 802.11a/b/g/n network. We assume that APs of WLAN follow traditional hexagonal layout, and the coverage radius of each AP is 50 meters. BSNs move inside the BSN deployment area following WPAN model. The pedestrian characteristics of each BSN user are set according to [26]. The sensor radio of a BSN has a 1024 Kbps data rate and outage SNR of \(-10\) dB. To remove the effect of differing initial conditions on the performance, we run the simulation fifty times with different initial BSN positions and then calculate the average results. In the simulation, the position of a BSN is predicted through the experimental results from WLAN RSSI-based Horus system [21] and inertial sensor-based kinematic tracking [27].  
The performance of the proposed handover is compared with the basic scheme and the hysteresis-based handover. The basic scheme [9] performs handover once the BSN crosses the AP boundary, while the hysteresis-based handover scheme performs handover until the hysteresis requirement is met.
## 4 Optimal Exercise Time of American Option
### Least Square Monte Carlo  
The widely used approach in the finance industry to price the American Option is the Least Square Monte Carlo (LSM) method (also known as the Longstaff-Schwartz pricing algorithm [7]). The LSM method for pricing an American Option is based on the following features:  
* Monte Carlo Simulation (MCS). LSM uses MCS, based on a stochastic process model, to generate a sufficient number (more than 10,000) of price paths from the current time \(t=0\) to the terminal time \(t=T\) (expiry date). The value of exercising an option at each time step of each price path is calculated.
* Least Square Regression. The continuation value--the conditional expected value of continuing holding the option--given a possible underlying asset price at a time step is approximated using a least squares regression function for in-the-money states. Various regression functions can be used. The simplest one is a linear function. With this specification, a functional form of conditional expected value \(F(\omega;t_{K-1})\) can be represented with \[F(\omega;t_{K-1})=\sum_{j=0}^{\infty}a_{j}L_{j}(X)\] (15) In Equation (15), \(\omega\) represents a sample path, \(a_{j}\) the coefficients to be estimated through the least square regression, and \(L_{j}(X)\) is the basis function. In their work, [7] explored various types of basis functions, including Hermite, Legendre, Chebyshev, Gegenbauer, and Jacobi polynomials. The numerical results demonstrate that the Least Squares Monte Carlo (LSM) algorithm exhibits robustness to the selection of basis functions. Among the possible choices, a set of Laguerre polynomials was used in their paper for numerical convenience [7]. In Equation (15), \(F(\omega;t_{K-1})\) has been represented as a linear combination of basis functions, where coefficients \(a_{j}\) are constant.
* Option Valuation. After finding the coefficients of the regression function at each time step, a new set of paths for the underlying asset's price, 'Test Paths', is generated. The regression function for the continuation value at each time step is then applied to these test paths to identify the paths where 'early exercise' is optimal. The value of the option is determined by computing the average of the discounted payoffs derived from these test paths.
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.9 Accelerometer  
An accelerometer is used to measure the acceleration of an object. In patient monitoring systems, it is attached with the patient to get location information in case of mobility. Considering sampling rate 25 Hz and the sampling resolution 12 bits we have SIT = 40 ms; MPDS = 37.5 bytes/s; DNS after 40 ms = 1.5 byte; DNS after 40 ms including minimum header size = 132 bits; Total data rate required by the accelerometer packet = 3.3 Kbps. Table 3 presents the summary of traffic patterns for a patient monitoring system.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 3 System Calibration
### Single Camera Calibration  
For the calibration of a single camera with a telecentric lens, we have proposed a flexible calibration approach in [20]. According to the description, the distortion-free telecentric camera model is expressed as  
\[\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=\begin{bmatrix}M/d_{u}&0&0&u_{0}\\ 0&M/d_{v}&0&v_{0}\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}X_{c}\\ y_{c}\\ z_{c}\\ 1\end{bmatrix}=\begin{bmatrix}M/d_{u}&0&0&u_{0}\\ 0&M/d_{v}&0&v_{0}\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}R&t\\ 0_{1x3}&1\end{bmatrix}\begin{bmatrix}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{bmatrix}, \tag{4}\]  
where \((x_{w},y_{w},z_{w})\) and \((x_{c},y_{c},z_{c})\) are the 3D coordinates of the object point \(P\) in the world coordinate system and camera coordinate system, respectively. \((u,v)\) is the image coordinate of \(P\) in pixels. \(M\) is the effective magnification of the telecentric lens, and \((u_{0},v_{0})\) is the coordinate of the image plane center. \(d_{u}\) and \(d_{v}\) denote the cell size in \(u\) and \(v\) directions for the camera, respectively. Generally, it is defined that \(m=M/d_{u}=M/d_{v}\) when \(d_{u}\) and \(d_{v}\) are the same for a sensor. Equation (4) can be rewritten as follows, in which \(R_{s}\) is the upper left \(2\times 2\) sub-matrix of \(R\) while \(t_{s}\) is the first two truncated translations of \(t\).  
\[\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}m&0&u_{0}\\ 0&m&v_{0}\\ 0&0&1\end{array}\right]\left[\begin{array}{c}x_{c}\\ y_{c}\\ 1\end{array}\right]=K\left[\begin{array}{cc}R_{s}&t_{s}\\ 0_{1\times 2}&1\end{array}\right]\left[\begin{array}{c}x_{w}\\ y_{w}\\ 1\end{array}\right]. \tag{5}\]  
The calibration method mentioned is a two-step procedure with a coded calibration board. In the first step, camera parameters with a distortion-free telecentric camera model are achieved by a closed-form solution, which is solved from a homographic matrix. In the second step, a non-linear optimization is performed to refine the coordinates of control points with distortion-free camera parameters, and follows another non-linear optimization for all the camera parameters including the distortion coefficients and distortion center.  
In this way, the camera calibration parameters are achieved. The calibration results contain effective magnification \(M\), and extrinsic parameters for each calibration image \((R_{i},t_{si})\). Note that the rotation matrix \(R_{i}\) is completed while \(t_{si}=\left(t_{xi},t_{yi}\right)^{T}\) is the truncated translation.
## 2 Material and Methods
### Full Factorial Design  
A factorial design methodology is used in this study. It aims to analyse the existing relationships between the studied quantities (responses) and their sources of variation (factors or parameters), through a Taylor-Mac Laurin series limited development (Equation (1)). The analyses have been conducted for two responses (Y). The two chosen responses are detailed in the following Section 2.3. The overall average of a response is represented by the parameter \(b_{0}\). \(X_{i}\) represents the investigated parameters, \(b_{i}\) corresponds to the main effect and \(b_{i>}\) corresponds to the first-order interaction between two parameters. The purpose of the full factorial design is to determine the most influencing parameters. The developed numerical strategy presented in Section 2.1 allows variation of seven parameters. The diameter, the length, the taper, and the Young's modulus of the implant and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Young’s Modulus (GPa)** & **Poisson Ratio** \\ \hline
**Ti-6 Al-4V alloy** & 110 & 0.3 \\ \hline
**Cortical bone** & 15 & 0.3 \\ \hline
**Cancellous bone** & 1.5 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the materials: Young’s modulus and Poisson ratio.  
Figure 1: (**a**) Finite element model (unrepresented mesh) with boundary conditions and loading, (**b**) implant design: conical on the left and cylindrical on the right, (**c**) mandibular section: presentation of the four cortical thicknesses (1.0, 1.5, 2.0 and 2.5 mm) and (**d**) configuration of the implant in the bone (cutting in the vestibulo-lingual plane).  
cancellous bone are 2-level parameters, and the implant angle and the cortical thickness are 4-level parameters. The levels of the studied parameters are summarized in Table 3. A full factorial design is implemented with all possible 512 configurations (\(2^{5}\times 4^{2}\)). All 512 FE models are generated using Abaqus, and the calculations are performed on a high-performance computer with 8 cores.  
The full factorial design allows for calculation of the effect of different parameters on the responses. For each response, the main effect of the parameters is determined by the difference between the partial average over the models by setting a studied parameter and the overall average \(b_{0}\). The first-order interactions between the parameters two by two are also calculated by the differences between the partial average performed on the models by setting two studied parameters and the overall average. Quantification of the main effects and first-order interactions makes it possible to describe the response Taylor-Mac Laurin series limited development, where the original variables are translated into reduced centred variables (between \(-1\) and \(1\)).  
\[Y=b_{0}+\ \sum_{i}b_{i}\ X_{i}+\ \sum_{i,\ j}b_{i-j}\ X_{i}\ X_{j} \tag{1}\]
## 5 Conclusions and Outlook  
One possible improvement to the existing approach would be to reduce the spatial resolution; this, however, would require a revision of the digital twin. The simulationduration achieved in this work could be further reduced, but the resulting effects on the quality of the measurement data must be carefully examined.  
In summary, this work offers a promising approach to improving object detection in the railway sector through the use of a specially adapted simulation environment (gprMax). Despite the identified limitations, there is great potential for further applications and development. This work opens up the possibility of detecting a wide range of buried objects and differentiating between different types of objects while eliminating the need for extensive manual data preparation in the future. By enhancing the simulation environment's capabilities to model scenarios like ballast contamination or water anomalies, significant potential exists for further refining the ability to simulate various subsoil conditions. This opens up opportunities for developing and testing algorithms aimed at assessing the railway bed or verifying assumptions about its condition, all while minimizing the need for extensive labeling tasks. Consequently, this work provides a solid foundation for further developing methods of all kinds based on the use of ground-penetrating radar systems in the railway sector.  
Conceptualization, L.L. and D.G.; methodology, L.L.; software, L.L.; investigation, L.L. and D.G.; resources, M.B. and G.Z.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and G.Z.; visualization, L.L.; supervision, G.Z.; project administration, D.G. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Plasser & Theurer, Export von Bahnbaumaschinen, Gesellschaft m.b.H.  
Not applicable.  
The datasets presented in this article are not readily available because confidentiality reasons. Requests to access the datasets should be directed to [PERSON].  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Plasser & Theurer. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 3 Results and Discussion  
### Fringe-Resolved Autocorrelation  
The spectral width measured for the fundamental beam of the Ti:sapphire laser was 27 nm (420 cm\({}^{-1}\)). The Fourier-transform-limited (FTL) pulse width calculated by assuming a Gaussian temporal profile was 35 fs, identical to the value provided by the manufacturer of the laser. An FRAC trace is shown in Figure 3A. Suppression of the signal to the background level by destructive interference, in addition to a full modulation of the signal shown in the expanded view (Figure 3B), suggests the pump beams were superimposed in generating the THG beam. The ratio of the signal and the background was _ca._ 25, slightly smaller than the predicted value of 32 for an FTL pulse using the third-order nonlinear effect such as THG [10]. Figure 3C shows the FRAC trace calculated for FTL pulse. The observed trace is slightly wider than this trace, suggesting the chirp of the pulse. Figure 3D shows the FRAC trace calculated under the assumption that the FTL pulse (35 fs) is chirped to 41 fs with a group delay dispersion (GDD) of 500 fs\({}^{2}\), which is nearly identical to the value of GDD (_ca._ 400 fs\({}^{2}\)) roughly estimated from the thickness of the optical components in the beam path. The pulse width calculated from the IAC trace, which can be obtained by low-pass filtering the data shown in Figure 3A, was 41 fs, which is in good agreement with the above value. Another possible explanation for the discrepancy between the results of (A) and (C) would be the error in the measurement of the spectrum since a wider band width would be observed due to a finite resolution of the spectrometer, which provides a shorter transform-limited pulse width; see a lack of small wings at round \(\pm\)50 fs in the observed data (A), which is in contrast to the calculated data (D), suggesting that the effect of chirp is minimal.  
Figure 3: Autocorrelation trace for (**A**) observed data (**B**) expanded view (**C**) theoretically predicted trace for a Fourier-transform-limited (FTL) pulse (**D**) theoretically predicted trace for a chirped pulse (GDD = 500 fs\({}^{2}\)). A random noise was calculated using a computer and was added to the calculated data for better visual comparison. In order to check the baseline level of the observed trace (A), the laser beam was interrupted during a period of 110–130 fs in the experiment, suggesting that the signal was suppressed to zero at the bottom of the trace.
## 1 Introduction
### An Equation for the Vertical Distribution of an Optical Property  
The mass concentration of a population of particle type \(i\) is \(C_{i}=N_{i}V_{i}\rho_{i}\), where \(\rho_{i}\) is individual particle density, \(N_{i}\) is the number concentration of particles of type \(i\), and \(V_{i}\) is individual particle volume. By the Beer-Lambert law the bulk optical response (e.g., backscattering or beam attenuation) of a sub-population \(b_{x,i}\), is the simple sum of the individual contributions so that:  
\[b_{x,i}=\alpha_{v,i}N_{i}V_{i} \tag{5}\]  
and the bulk response to the combined sub-populations is:  
\[b_{x}=\sum_{i}b_{x,i} \tag{6}\]  
where \(\alpha_{v,i}\), is a volume-specific optical property. Volume- or mass-specific optical properties typically are calculated by using Mie theory (which assumes homogeneous spherical particles) for solid particles, or by using other models for flocs (e.g., [31]). The calculations of \(\alpha_{v,i}\) require as input particle diameter, index of refraction (a function of composition), wavelength of light, and, for flocs, the fractal dimension that relates volume concentration to mass concentration. The results are resonance-like functions of size (Figure 1). Although the application of Mie solutions to backscattering has been challenged based on observations (e.g., [42]), it is reasonable to relate constant values of \(\alpha_{v,i}\) to specific particle types. Both Equations (5) and (6) rely on the Beer-Lambert law, with underlying assumptions that the light is monochromatic with parallel rays and, most importantly, that the particles do not scatter the light multiple times. This is clearly not the case as particle concentrations rise but, at low concentrations, we may be able to assume that Equations (5) and (6) are valid. Substituting optical response for concentration in Equation (3) yields an equation for the optical-response profile for a homogenous sub-population \(i\) with identical \(\alpha_{v,i}\), \(V_{i}\), and \(w_{i}\):  
\[b_{x,i}(z)=b_{x,i}(z_{a})\bigg{(}\frac{z}{z_{a}}\bigg{)}^{-\frac{w_{i}}{z_{a}}} \tag{7}\]  
Equations (6) and (7) can be combined to yield an equation for the bulk response of a heterogeneous population:  
\[b_{x}(z)=\sum_{i}b_{x,i}(z_{a})\bigg{(}\frac{z}{z_{a}}\bigg{)}^{-\frac{w_{i}}{ z_{a}}}=\sum_{i}\frac{\alpha_{v,i}C_{i}(z_{a})}{\rho_{i}}\bigg{(}\frac{z}{z_{a}} \bigg{)}^{-\frac{w_{i}}{z_{a}+}} \tag{8}\]  
As with particle concentrations, we find that the information provided by a profile of optical properties will depend on how heterogeneous the suspension is. Unlike the concentration profile, the averaging done by the optical properties depends on how the optics respond to different particles (Equation (8)). Hence, the more biased an optical property is towards a specific particle type, the better it can provide information on its specific settling speed.
# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 1 Introduction
These include the dynamic nature of the wireless environment, inherent uncertainty, temporal dynamics, and the robustness and efficiency of various algorithms. Such challenges indicate room for considerable improvements and innovative approaches in the future. In response to these limitations, this work presents a proof-of-concept (PoC) that sets the foundation for the application of advanced image processing algorithms in mobile network management. A novel framework that utilizes DL image classification algorithms in zero-touch network tasks is introduced. This framework presents the concept of Network Synthetic Images (NSIs), an innovative approach to synthesizing information about network operations in a specific area. In the NSI application and generation processes, aspects of image classifiers such as convolutional neural networks, transfer learning, and data augmentation techniques are carefully considered. These techniques, which have proven effective across a wide range of applications, are expected to impact this context [12] significantly. The key contributions of this work are:  
* The proposition of a novel Network Synthetic Images (NSI) framework transforms the process of synthesizing and interpreting information in network operations. This innovative framework leverages the capabilities of image classifiers to detect hidden patterns and uncover unforeseen effects often beyond the reach of human perception and traditional REMs. A comprehensive discussion of the essential aspects required to effectively apply image classifiers to NSIs for network management tasks, including an in-depth exploration of CNNs. * An evaluation of the proposed NSI framework using a generic CNN and comparing it against state-of-the-art REM image classifier methods. The results highlight the superior performance of the proposed framework, with improved F1 and accuracy scores achieved, even with coarser time and spatial resolution, effectively reducing the training time. * An outline of practical implementation aspects and challenges of integrating this methodology into future 6G network technologies. This exploration provides insight into potential obstacles, laying the groundwork for future research to address these challenges. In this way, the present work is organized as follows. Section 2 presents previous works related to REMs and their usage in cellular network automation. Subsequently, the proposed Network state Synthetic image creation framework for using image classification algorithms is presented in Section 3, describing the details of the proposed NSIs approach and the rest of the system's functionalities. The proposed system is evaluated in its application to supervised Fault diagnosis in Section 4. Finally, Section 5 summarizes the findings and identifies future research lines.