esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A three-dimensional (3D) finite element (FE) dynamic frictional rolling contact model is presented for the study of short pitch corrugation that considers direct and instantaneous coupling between the contact mechanics and the structural dynamics in a vehicle-track system. In this study, we examine the system responses in terms of vibration modes, contact forces and the resulting wear with smooth rail and corrugated rail with progressively increasing amplitude to infer the conditions for consistent corrugation initiation and growth. Wear is assumed to be the damage mechanism, and short pitch corrugation is modeled using wavelengths from field observations of a Dutch railway. The contribution of this paper is a global perspective of the consistency conditions that govern the evolution of short pitch corrugation. The main insights are as follows: (1) the longitudinal vibration modes are probably dominant for short pitch corrugation initiation; (2) during short pitch corrugation evolution, the interaction and consistency between longitudinal and vertical modes should determine the development of short pitch corrugation, and once a certain severity is reached, vertical modes become dominant; and (3) in the case simulated in this paper, corrugation does not grow probably due to not only the different resulting main frequencies of the vertical and longitudinal contact forces, but also the inconsistency between the frequencies of the vertical and longitudinal vibration modes and the resulting wear. It is inferred that in the continuous process of initiation and growth of the corrugation, there should be a consistency between them, and this could be done by the control of certain track parameters.  
short pitch corrugation; development mechanism; 3D-FE dynamic vehicle-track modeling; vertical and longitudinal track vibrations; frictional rolling contact +
Footnote †: journal: Applied Sciences
## 3 Results
### Thermal Conduction in Depth  
Temperature sensors S2, S3 and S4 were the ones used for the estimation of the heat conduction in depth and the thermal conductivity of the filling material. The selection of these sensors, located in the copper tubes of the experimental setup at the depth of 0.5 m, was made with the aim of avoiding thermal influences from the heat source and covering the central area of the sandy material (dismissing the registers in the middle and the opposite end of the setup). The results of the measurements of the sensors can be observed in Figure 9.  
Figure 8: Absolute errors obtained when comparing temperatures calculated by the low-cost and the manufacturer calibration with respect to the reference temperatures measured with thermocouples.  
Figure 9: Temperature registers of sensors S2, S3 and S4 during all the setup operation.  
From all the data presented in Figure 8, only the registers belonging to the stationary regime are valid to be included in further calculations, since it is in this period when the equalities presented in Equations (1)-(4) can be used. The absolute error of the calibration of the thermographic camera in this temperature range (28-40 \({}^{\circ}\)C) is below the nominal precision of the camera (2 \({}^{\circ}\)C), in such a way that the error in the thermal conductivity introduced by the temperature measurement is minimized.  
After applying the corresponding correction factors to each sensor, the stationary regime was selected considering the moment in which temperature values remain constant over time.  
Once the stationary part of the test is identified and the temperature values are corrected, the heat transferred by conduction was calculated by applying Equation (3). With that aim, the mass flow rate was firstly defined by multiplying the flow rate of the pump (registered by one of the setup sensors) and the density of the working fluid (water). Table 1 presents the calculated average mass flow rate and the average inlet and outlet fluid temperatures required in the estimation of the global heat transfer.  
The next step consists of the calculation of the ground thermal conductivity from the expression presented in Equation (4). After defining the thermal resistance between each couple of sensors from the quotient of the temperature difference and the heat transfer rate, the thermal conductivity of the sandy material was calculated for the portion of material between sensors S2-S3, S3-S4 and S2-S4. The results of these calculations are included in Table 2, which also presents the average temperature values of sensors 2-4 used for the estimation of the thermal conductivity parameter.
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 3 Methodology  
In the context of AD detection, the proposed deep learning framework comprises the following steps. Initially, the sMRIs are acquired from two benchmark datasets (ADNI and AIBL) and a real-time dataset and further, the interested regions are segmented by the proposed fuzzy-based superpixel-clustering algorithm. Next, hybrid feature extraction is carried out by integrating the ResNet-50, VGG-16, and PLTP models, and then, the dimensions of the extracted features are decreased by introducing the MGTO algorithm. Finally, the obtained informative features are fed as input into the CapsNet model for classifying the types of AD classes. The processes that are involved in the proposed deep learning framework are pictorially specified in Figure 1.  
Figure 1: Processes involved in the proposed deep learning framework.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
###### Abstract
Due to their operational altitude being closer to the Earth, LEO satellites circle the Earth more frequently than satellites in higher orbits. LEO satellites have the advantage of proximity to Earth, enabling rapid data transmission. \\ \cline{2-3}  & & The altitude of LEO satellites is up to 2000 km above the Earth’s surface [16]. Due to their operational altitude being closer to the Earth, LEO satellites circle the Earth more frequently than satellites in higher orbits. HAPSs are also known as high-altitude pseudo-satellites and operate in the stratospheric layer at altitudes of 17-22 km [19]. This operation altitude is above the maximum altitude for commercial flights and above the clouds. HAPS can remain airborne through aerodynamic means (resembling airplanes) or aerostatic means, such as balloons or airships [17]. Unlike HAPSs, UAVs operate at lower operational altitudes (i.e., the troposphere) in the range of a few dozen meters to several km [20]. UAVs can take the form of a fixed-wing or rotary-wing types. Rotary-wing UAVs are typically designed to operate at altitudes up to a few km. They have different configurations, such as quadcopters, hexacopters, etc., depending on the number of rotors employed for lift and control. These UAVs utilize vertically oriented propellers to generate upward thrust and maintain flight. By adjusting the speed and angles of their rotors, UAVs can alter their altitude, direction, and orientation. Multirotor UAVs have the ability to take off vertically, hover in a fixed/semi-fixed position, and have the ability to maneuver. Fixed-wing UAVs are typically designed to serve at higher altitudes within the troposphere. For example, the military-type MQ-9 Reaper [21] can fly at an altitude up to around 15 km. ScanEagle [22] is an example of a civilian fixed-wing UAV, with an operation altitude of around 6 km.
## 3 Results
### Results Regarding Energy and Distance covered by VIPV
#### 3.1.3 Yearly Distance Covered by VIPV  
Figure 6 presents the simulation results of the model described in Figure 1 in terms of yearly distance covered by the VIPV system on a passenger car for the three-studied energy transfer architectures.  
Figure 5: Yearly PV energy in Malaga and Paris, passenger car, 0.312 kWp solar roof, 50 kWh battery, direct transfer in the main battery, effect of frequency of recharge with the grid.  
Figure 6: Yearly distance covered by VIPV in Malaga and Paris, passenger car, 0.312 kWp solar roof and 157 Wh/km electrical consumption.  
Figure 6 shows that the VIPV can generate up to 1444 km per year at midlife in Malaga with 30% shading share and with transfer of energy through a 48 V battery. This distance represents 12% of the driven mileage (12,250 km). The range extension is down to 293 km per year in Paris with 49% shading share, direct charge of HV battery and with losses due to a full battery. It represents about 2% of the driven mileage. With the normalization of the results with the peak power of the solar roof, the annual mileage covered by the VIPV is up to 4628 km/kWp and down to 939 km/kWp.  
Figure 7 presents the simulation results regarding the influence of the frequency of recharge for the case of directly charging an HV battery on the yearly distance covered by the VIPV. It shows that charging the battery with the maximal charging frequency (every 5 nights) obtains between 36 km and 151 km more than charging every night.
## 6 Numerical Examples
### Parametric Studies and Discussions
Figure 2: Temperature variation effect on the dynamic instability region (DIR) of the graphene sheet without elastomeric foundation. than the elastomeric Winkler (\(\mathrm{K_{W}}\
eq 0\) and \(\mathrm{K_{P}}=0\)) one, on the DIR of the SLGS. The elastomeric Winkler foundation is able to describe only the normal loading condition of the elastomeric foundation, whereas the Pasternak model considers normal stresses as well as transverse shear loading conditions. From these figures, it is also worth observing that the foundation effects become even more significant for increased temperatures. Figure 5a,b illustrates the aspect ratio \(a/b\) effect on the unstable region of graphene sheets embedded in the elastomeric foundation for \(T=300\) K and \(T=700\) K, respectively. In this example, two different values of aspect ratio (i.e., \(a/b\simeq 1\) (zigzag sheet I) and \(a/b\simeq 2\) (zigzag sheet II)) are considered [39]. This figure indicates that the excitation frequency reduces and the dynamic instability moves to the left side as \(a/b\) increases. This trend is almost predictable, since the nanoplate becomes softer for increasing aspect ratios, with a global reduction of the structural stiffness. Moreover, the width of the instability region increases with a decrease in the slenderness ratio. The NP effect on the DIR of embedded SLGS is displayed in Figure 6a,b, for \(T=300\) K and \(T=700\) K, respectively. The dimensionless pulsation frequency seems to decrease as the NP becomes higher, since the DIR of graphene sheet forms at lower excitation frequencies. Such phenomenon is owing to the fact that NP indicates the softening effect on stiffness which leads to lower pulsation frequency. Furthermore, comparing these figures exhibits that an increase in temperature change increases the effect of NP on the unstable region of the nanoplate. Figure 4: Effect of the elastomeric foundation on the DIR of the SLGS. Figure 5: Aspect ratio effect on the DIR of the SLGS embedded on an elastomeric foundation. Finally, Figure 7a,b demonstrates the static load factor effect on the unstable region of the SLGS resting on the elastomeric foundation for \(T=300\) K and \(T=500\) K, respectively. As can be concluded, with an increase in the static load factor the excitation frequency decreases and therefore, dynamic instability shifted to the left side due to the reduction in the stiffness of the nanoplate. Furthermore, the results indicate that the DIR becomes wider for a higher static load factor. Any static force component is present, in the particular case when \(\alpha=0\).
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Model Evaluation  
The evaluation step plays a crucial role in assessing the model's capabilities using data that have not been previously utilized for learning. By subjecting the model to this independent dataset, we gain valuable insights into its real-world potential and how it would perform in practical scenarios.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Policy-Based Network Security  
As has been seen in the past years, the complexity of network structures and environments has become increasingly complex, and this is a pattern that will only increase as the technologies evolve [53; 54]. Network security policies are, therefore, one of the methods used to streamline security in such complex environments since, unlike network environments, which are becoming increasingly flexible and malleable in terms of structure and functionalities, network policies and even more so in network security policies are immutable. They are a set of invariable specifications or instructions to achieve a desired objective, which in the context of our study is the security of deployed network slices.
## 4 Experimental Results
### Classification Results  
#### 4.3.1 Training and Validation Performance of the LSTM-Attention-BiTCN Model  
The proposed LSTM-Attention-BiTCN model underwent an optimization process spanning 30 epochs with a set learning rate of 0.001. The accuracy trends depicted in Figure 8 illustrate that the model's performance improved steadily over the course of 30 epochs. The graph indicates a positive trajectory in both training and validation accuracies, with the training accuracy initiating at approximately 0.91 and ascending to just over 0.95, while the validation accuracy began near 0.89 and rose to about 0.93. This convergence of training and validation lines suggests that the model generalizes well and is not overfitting.  
Figure 9 portrays the trajectory of the training and validation losses throughout the model's optimization phase. Both metrics exhibit a downward trend, with the training loss initiating at higher levels and concluding just above 0.1 after 30 epochs. Conversely, the validation loss starts slightly lower and converges toward a nearly similar value by the end of the training period. This parallel reduction suggests that the LSTM-Attention-BiTCN model, assessed via binary cross-entropy, is learning effectively without overfitting.  
Figure 8: Training and validation accuracy.  
Figure 9: Training and validation loss.
## 2 Existing Expected Threat (xT) Models Theory
### Lack of Realism in Expected Threat (xT) Models
#### 2.3.1 Oversimplified Decision Modeling  
In traditional xT models, the probability of shooting s\({}_{ij}\) is calculated by dividing the number of shots taken from the grid cell \((i,j)\) by the total number of shots and moves executed within that grid cell. This frequentist approach, based solely on the ball's location, offers a simplified representation of the decision-making process, reducing the decision to shoot to the ball's position alone. However, real-world scenarios deviate from this simplification.  
A player's decision to shoot from a given position is heavily influenced by their perceived likelihood of scoring. If they estimate a high probability of success, they are more inclined to take the shot; otherwise, they may opt to pass to a better-positioned teammate. For example, a player might attempt a shot at an unguarded goal, even if such attempts have been historically rare from that distance.
## 2 Theory and Model
The x-PC and y-PC emitted by the M-VCSEL are injected into the PPLN as the initial input of the o-light and the e-light, respectively, so we have  
\[U_{\text{o},\varepsilon}(0,t)=\sqrt{\frac{\hbar\omega_{0}V}{S_{A}T_{L}v_{ \text{o}}n_{1}n_{2}}}E_{Mx,My}(t) \tag{5}\]  
where \(U_{\text{o}}\) and \(U_{\text{e}}\) are the amplitudes of the o-light and the e light, respectively; \(\hbar\) is the Planck constant; \(S_{\text{A}}\) is the effective area of the light spot; \(V\) is the volume of the active layer of the VCSEL; \(v_{\text{c}}\) is the light velocity in a vacuum; \(T_{L}=2n_{g}v_{c}/L_{\text{v}}\) refers to the round trip time in the laser cavity; \(L_{\text{v}}\) is the length of the laser cavity; \(n_{g}\) is the effective refractive index of the laser active layer; \(\omega_{0}\) is the central frequency of the M-VCSEL; \(n_{1}\) and \(n_{2}\) are the undisturbed refractive indices of the o-light and the e-light, respectively. With the phase mismatch and the weak second-order nonlinear effect, the analytical solutions of the wave coupling equations of the linear EO effect for the two PCs in the PPLN are written as follows:  
\[U_{\text{o},\varepsilon}(L,t)=\rho_{x,y}(L,t)\exp(i\beta_{0}L)\exp[i\phi_{x,y} (L,t)]. \tag{6}\]  
where \(L\) is the length of the PPLN; \(\rho_{x,y}\), \(\beta_{0}\) and \(\phi_{x,y}\) are presented in Ref. [30]. The amplitudes of x\({}_{\text{P}}\)-PC and y\({}_{\text{P}}\)-PC after undergoing electro-optic modulation in PPLN can be represented as follows:  
\[E_{Px,Py}(t-\tau)=\sqrt{\frac{S_{A}T_{L}v_{\text{c}}n_{1,2}}{\hbar\omega_{0}V }}U_{\text{o},\varepsilon}(L,t-\tau) \tag{7}\]  
The rate equations for the S-VCSEL subjected to strong optical injection with a delay of \(\tau\) can be represented as follows:  
\[ \begin{array}{ll}\frac{d}{dt}\binom{E_{Sx}(t)}{E_{Sy}(t)}=&k(1+ia)[N_{S}(t)- 1]\binom{E_{Sx}(t)}{E_{Sy}(t)}\pm ik(1+ia)n_{S}(t)\binom{E_{Sy}(t)}{E_{Sx}(t)} \mp(\gamma_{a}+i\gamma_{p})\binom{E_{Sx}(t)}{E_{Sy}(t)}\\ &+\sqrt{\beta_{sp}\gamma_{e}N_{S}}\binom{\xi_{x}}{\xi_{y}}+\binom{k_{inj}}{k_ {inj}}\binom{E_{Px}(t-\tau)}{E_{Py}(t-\tau)}\exp(-i\omega_{0}\tau+i\Delta \omega_{S}t)\\ &\frac{dN_{S}(t)}{dt}=&-\gamma_{e}\Big{\{}N_{S}(t)-\mu_{S}+N_{S}(t)(|E_{Sx}(t) |^{2}+\big{|}E_{Sy}(t)\big{|}^{2})\\ &+in_{S}(t)[E_{Sy}(t)E_{Sx}^{*}(t)-E_{Sx}(t)E_{Sy}^{*}(t)]\Big{\}}\end{array}  \tag{9}\]\[ \begin{array}{ll}\frac{dn_{\rm g}(t)}{dt}=&-\gamma_{s}n_{S}(t)-\gamma_{e}\Big{\{}n _{S}(t)(|E_{Sx}(t)|^{2}+\big{|}E_{Sy}(t)\big{|}^{2})\\ &+iN_{S}(t)\big{[}E_{Sy}(t)E_{Sx}^{*}(t)-E_{Sx}(t)E_{Sy}^{*}(t)\big{]}\Big{\}} \end{array}  \tag{10}\]  
where the subscript \(S\) refers to the S-VCSEL; \(\Delta\omega_{S}\) is the center frequency detuning between the M-VCSEL and the S-VCSEL; \(k_{injx}\) and \(k_{injy}\) are the injection strength of the \(\rm x_{P}\)-PC and \(\rm y_{P}\)-PC, respectively; \(\rm\mu_{S}\) is the normalized bias current of the S-VCSEL.
# A Novel ICT Framework for Sustainable Development Goals
## 2 Background of the Problem and Our Contribution
### A Changing-Discourse Perspective on Sustainability
Means of implementations have been outlined in [1] stressing that \"quality, accessible, timely and reliable disaggregated data\" are needed \"to help with the measurement of progress and to ensure that no one is left behind\" [1]. The 2030 Agenda indicators are essential for \"the measurement of progress\" and \"key to decision-making\" [1]. In point of fact, indicators are central to many initiatives for both generating knowledge and communicating about complex phenomena. Similarly, experts are placed in a better position to measure an otherwise immeasurable entity by properly selecting and estimating a predetermined group of quantified indicators that approximate the crucial components of a certain concept [45]. The use of indicators in this way could be found in sustainability research [46] where they can operate as an analytical structure that mediates between the complex (and difficult to study) concept of sustainability, and the analytical tools appropriate for exploring the complex systems. \"While indicators remove contextual information and obscure the process through which this happens, they also force a clarity and rigour that exposes political priorities and beliefs\" [47]. This is clearly visible in SDGs, which often refer to such wide concepts that are widely held by the global society and consistent with the national sustainability initiatives. Nevertheless, the SDG indicators disclose very specific perspectives on these issues, some of which are in direct conflict with national perspectives. For example, the Goal 8 intends to promote such a worthy purpose that few would disagree with, i.e., \"decent work\". Nevertheless, the SDG indicators disclose very specific angles on these issues, some of which are in direct conflict with national prospects [48]. Some authors use the concepts of job satisfaction or fulfillment [49], yet there are no indicators available to reflect these ideas of \"decent work\". In short, for some indicators one needs \"to operationalize the Sustainable Development Goals' targets and evaluate the indicators' relevance, the characteristic of utmost importance among the indicators' quality traits\" [50]. Other indicators are able to achieve their goal of helping to make the SDGs relevant and useful. For example, in a recent study [51], researchers have created an overall health-related SDG index by considering the performance of a given country across all of the 37 health-related SDG indicators. Finally, the SDG indicators may also permit a quantified form of analysis that can improve our understanding of how the broader targets may be achieved (e.g., in the use of models) [47]. The SDGs work in highly complex, non-linear systems which are difficult to examine without models [52]. Indicators are critical because they provide a mechanism for comparing the model, theory and reality.
## 3 Research Results
### Questionnaire Survey Results
#### 3.2.5 An Example of Regression Analyses with Y1 as the Dependent Variable
* Case 2 -- with control variables C1-C3 and independent variables X3 and X4 as input:  
The result of the Step 1 analysis for this case is the same as that of Case 1 above. In Step 2, after controlling C1, C2, and C3, the inclusion of X3 and X4 can increase the variance of Y1 by 10%, and this effect is significant with F(2, 64) = 4.93, \(p\) < 0.05. Specifically, X3 (with \(\beta\) = \(-\)0.02, p = 0.89) has no significance in explaining the variance of Y1, while X4 (with \(\beta\) = 0.44, \(p\) < 0.05) is _significant_ in this aspect, and the higher the value \(\beta\) of X4, the higher its significance to explain the variance of Y1. In short, the user's feeling \"X4: by using the system, I can remember the puppet roles' characteristics quickly\", together with the thoughts of C1 through C3, significantly demonstrates the resulting user's experience \"Y1: by experiencing the role play, I can learn a lot of the puppeteer's skills\". * Case 3 -- with control variables C1-C3 and independent variables X5 and X6 as input:  
The result of the Step 1 analysis for this case is again the same as that of Case 1 above. In Step 2, after controlling C1, C2, and C3, the inclusion of X5 and X6 can increase the variance of Y1 by 11%, and this effect is significant with F(2, 64) = 5.12, \(p\) < 0.01. Specifically, X5 (with \(\beta\) = \(-\)0.00, \(p\) = 0.98) has no significance in explaining the variance of Y1, while X6 (with \(\beta\) = 0.38, \(p\) < 0.01) is _significant_ in this aspect, and the higher the value \(\beta\) of X6, the higher its significance to explain the variance of Y1. In short, the user's feeling \"X6: by using the system, I can feel the sense of being a professional puppeteer\", together with the thoughts of C1 through C3, significantly demonstrates the resulting user's experience \"Y1: by experiencing the role play, I can learn a lot of the puppeteer's skills\". As an integral consideration of the three cases above, Table 13, which shows the value of the coefficient of determination R\({}^{2}\) computed from the sum of the data \(\Delta\)R\({}^{2}\) of the two steps (Step 1 and Step 2) in each hierarchical regression process, shows that the R\({}^{2}\) values for the three pairs (X1 and X2), (X3 and X4), and (X5 and X6) are 0.38 ***, 0.30 **, and 0.31 **, respectively, which mean that the three pairs of independent variables, together with the three control variables C1 through C3 very significantly or just significantly imply the dependent variable Y1. The contributions to this conclusion mainly, as indicated by the summaries drawn in the above three cases, come from X2, X4, and X6, respectively.