esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detailed Operators
### Subsystem Weight Considering the Failure Diagnosis Based on FRPN
#### 3.2.5 Fuzzy Reasoning Algorithm of FRPN  
Three operators are introduced to describe the fuzzy reasoning algorithm of FRPN.  
Addition operator \(\oplus\): If \(g=[g_{1},g_{2},\ldots,g_{n}]\) and \(b=[h_{1},h_{2},\ldots,h_{n}]\) are two vectors,  
\[q=g\oplus h\Rightarrow q_{i}=\max(g_{i},b_{i}),i=1,2,\cdots,n. \tag{4}\]  
Minimum multiplication operator \(\times\): If \(G\), \(H\) and \(Q\) are \(m\times p\), \(p\times n\) and \(m\times n\) dimensional matrices, respectively,  
\[Q_{ij}=G\oplus H\Rightarrow q_{ij}=\min_{1\leq k\leq p}\Big{(}g_{ik}\times h _{kj}\Big{)},i=1,2,\cdots,n. \tag{5}\]  
Maximum multiplication operator \(\otimes\): If \(G\), \(H\) and \(Q\) are \(m\times p\), \(p\times n\) and \(m\times n\) dimensional matrices, respectively,  
\[q_{ij}=G\otimes H\Rightarrow q_{ij}=\max_{1\leq k\leq p}\Big{(}g_{ik}\times h _{kj}\Big{)},i=1,2,\cdots,n \tag{6}\]  
Based on the definition of FRPN, the fuzzy reasoning algorithm is  
\[u^{k+1}=u^{k}\oplus\Big{[}(O\times C)\otimes\Big{(}I^{T}\times u^{k}\Big{)} \Big{]} \tag{7}\]  
The reasoning process has three steps. Firstly, \(k=0\). Secondly, obtain \(u^{k+1}\) with \(u^{k}\). Thirdly, \(k=k+1\) and repeat the second step if \(u^{k+1}\
eq u^{k}\) The reasoning process is finished until \(u^{k+1}=u^{k}\). The truth degree of the subsystem divided based on failure decoupling can be obtained. The product has \(n\) subsystems which are denoted as \(S_{1}\), \(S_{2}\),..., \(S_{n}\). The truth degree of the subsystem \(S_{i}\) is denoted as \(u_{i}\). The weight of the subsystem \(S_{i}\) considering product failures can be denoted as \(w_{i}^{f}\) (\(i=1,2,\ldots,n\)). The subsystem weight vector considering product failures is \(w_{i}^{f}=\Big{[}w_{1}^{f},w_{2}^{f},\cdots,w_{n}^{f}\Big{]}\).  
\[\left\{\begin{array}{c}w_{1}^{f}:w_{2}^{f}:\ldots:w_{n}^{f}=u_{1}:u_{2}: \cdots:u_{n}\\ w_{1}^{f}+w_{2}^{f}+\cdots+w_{n}^{f}=1\end{array}\right. \tag{8}\]
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 3 Results and Discussion
### Usability Challenges  
Regarding usability, several studies indicate that VR interfaces are often complex and unintuitive, which makes them challenging to use, particularly for individuals with no prior experience. This issue poses a significant barrier not only for students but also for educators, who require specialized training to effectively incorporate these tools into their teaching practices [44; 52]. Furthermore, some studies suggest that the cognitive load associated with interacting with these technologies, in addition to physiological discomfort  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Study** & **Implied Categories** \\ \hline [PERSON] (2016) [43] & Ethical considerations \\  & Psychological and social impact \\ \hline [PERSON] (2004) [44] & Usability challenges \\  & Pedagogical aspects \\ \hline [PERSON] et al. (2024) [38] & Technical issues \\  & Usability challenges \\  & Pedagogical aspects \\ \hline [PERSON] (2023) [45] & Technical issues \\  & Economic aspects \\ \hline [PERSON] et al. (2024) [7] & Psychological and social impact \\  & Social and cultural acceptance \\ \hline [PERSON] (2024) [30] & Technical issues \\ \hline [PERSON] and [PERSON] (2023) [46] & Usability challenges \\ \hline [PERSON] and [PERSON] (2021) [47] & Psychological and social impact \\ \hline [PERSON] et al. (2021) [48] & Pedagogical aspects \\  & Pedagogical aspects \\ \hline [PERSON]. (2019) [49] & Social interaction in virtual environments \\  & Pedagogical aspects \\ \hline [PERSON] (2017) [50] & Technical issues \\ \hline [PERSON] et al. (2016) [51] & Technical issues \\ \hline [PERSON] (2008) [52] & Psychological and social impact \\  & Pedagogical aspects \\ \hline [PERSON] (2007) [53] & Economic aspects \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the studies included in the review.  
(such as dizziness and disorientation), may reduce the effectiveness of immersive learning experiences [38].  
It was found that VR applications were difficult to navigate due to non-intuitive and poorly designed interfaces, complex controls, and ergonomic issues, such as discomfort caused by the weight of the devices. The user experience can be negatively impacted by a lack of familiarity with these technologies. Additionally, motion sickness emerged as one of the most frequently reported difficulties, particularly during extended usage sessions, which can affect both the comfort and the continuity of learning within VR environments. This limitation may reduce the effectiveness of immersive experiences and necessitate adjustments in the design of educational activities to mitigate its adverse effects.
## 7 Performance Evaluation and Simulation Results
#### 7.3.1 High Social Media Interaction. Node's Density = 100 Nodes/km\({}^{2}\)  
Figure 12 shows a percentage of packet losses in the range 22% (when \(w_{TS}=0\), i.e., only QoS parameters are considered to select the forwarding path, see Equation (9)) to 26% (when \(w_{TS}=1\), i.e., only TS parameter are considered). Between those \(w_{TS}=\) values packet losses fluctuates around 23%. The \(\overline{TS}\) measured in the forwarding paths established during simulations varies around 3, see blue line in Figure 12, slightly increasing as \(w_{TS}=\) grows since we are giving more importance to the social confidence in front of the QoS offered by the forwarding path. The average end-to-end packet delay ranges between 0.7 and 1.2 s increasing while \(w_{TS}\) grows and reaching 1.6 s for \(w_{TS}=1\). To sum up, a proper value for \(w_{TS}=\) could be 0.8, that well balances a trade-off with low packet losses (23%), low packet delay (0.94 s) and high \(\overline{TS}=2.6\).
## 4 Discussion  
The experimental studies performed, including measurements with dial indicators (Section 3.3), as well as with the use of DIC (Section 3.4), allowed for a detailed analysis of the magnet holder behavior. It is revealed that to provide high accuracy of the magnet positioning, it is necessary to pay special attention to the precise tension of the screws. It has been proven that tightening of the clamp bolts leads to changing of the position of the magnet by pulling it out of the holder. Moreover, uneven tightening of bolts or tightening with too large of torque \(M_{c}\) increases results in an angular positioning of the magnet. To ensure the optimum position of the magnet for further calibration, the clamp-mounting screws should be tightened with small torque increments to a constant value on all screws. Further calibration and positioning of the magnet is possible by adjusting the adjustment bolts. Both performed studies (with dial indicators and DIC) proved that the range of the positioning does not depend on the clamp bolt tension. Moreover, the relationship between the magnet displacement and the tensioning torque \(M_{r}\) is almost linear. The maximal displacement of the magnet was equal to 0.45-0.55 mm for the maximal torques 10 Nm (Figures 18 and 24). Such calibration is possible by both tightening and loosening the adjustment screws and is predictable and repeatable. Because of this, the initial pretension of the adjustment bolts should be set as \(M_{r}\) = 5 Nm and further calibration by \(\pm\)0.15 mm can be made by tightening (pushing in the magnet into the holder) or loosening (pulling out the magnet of the holder) the adjustment bolts.  
The magnets arranged in the form of the Halbach array are subjected to forces acting in opposite directions. The direction of the magnetic force does not have an impact on the calibration process. The results presented for the calibration of the \"pushed-in\" magnet  
Figure 31: Vertical deformation of magnet holder for \(M_{c}\) = 4 Nm, \(M_{r}\) = 7 Nm, and \(F\) = 2.5 kN during the following: (**a**) stage 4—tightening of adjustment screws; (**b**) stage 5—loosening of adjustment screws.  
Figure 30: Vertical deformation of magnet holder for \(M_{c}\) = 4 Nm, \(M_{r}\) = 10 Nm, and \(F\) = 2.5 kN.  
will be similar to the pull-out magnet. However, it should be noted, that the direction of the magnetic force slightly affects the initial position of the magnet under magnetic load, but this can be pre-corrected by using brass shims of different thicknesses. It is obvious that the magnet holder changes its deformation under a load change, but it should be noted that the magnetic forces acting on the magnets are almost constant and almost independent of the gap between undulator jaws. Besides that, no other unfavorable behavior of the system under load was observed.
## 4 Deconvolution
### Minimum Entropy Deconvolution
#### 4.1.1 Definition  
Minimum entropy deconvolution (MED) is a blind deconvolution method based on signal sparsity. The basic idea is that when mechanical failure occurs, the resulting shock vibration signal usually has sparsity, that is, the energy is mainly concentrated in a few shock pulses. Therefore, MED estimates the deconvolution filter by maximizing the negative entropy of the signal, thereby enhancing the sparsity of the output signal and better highlighting the impact features. [PERSON] et al. [130] first applied MED to the field of fault detection successfully. The core formulas are as follows:  
1. Signal Model Assume that the observed signal \(y[n]\) is the result of the original impulsive signal \(x[n]\) convolved with the system response \(h[n]\) and superimposed with noise \(e[n]\): \[y[n]=x[n]*h[n]+e[n],\] (16) where \(*\) denotes the convolution operation.
2. Deconvolution Objective Design a deconvolution filter \(w[n]\) such that the output signal \(s[n]\) approximates the original signal \(x[n]\) while minimizing the entropy: \[s[n]=w[n]*y[n].\] (17)
3. Definition of Entropy In MED, \"entropy\" typically refers to a measure of signal sparsity, commonly in the following two forms: 1. Kurtosis Maximization Kurtosis measures the impulsiveness of the signal and is defined as \[\text{Kurtosis}(s)=\frac{E[s^{4}]}{(E[s^{2}])^{2}}-3,\] (18) where \(E[\cdot]\) denotes the expectation. MED enhances impulsive components by maximizing kurtosis. \[w_{\text{opt}}=\text{arg}\max_{w}\frac{E[s^{4}]}{(E[s^{2}])^{2}}.\] (19) 2. Shannon Entropy Minimization Normalize the signal energy to a probability distribution \(p_{n}=\frac{|s[n]|^{2}}{\|s\|^{2}}\) and minimize its Shannon entropy: \[H(s)=-\sum_{n}p_{n}\log p_{n},\] (20) where the optimization problem is \[w_{\text{opt}}=\text{arg}\min_{w}H(s).\] (21)
4. Optimization Update the filter coefficients \(w\) using gradient descent or iterative methods. For example, with kurtosis maximization, the objective function is \[J(w)=\frac{\frac{1}{N}\sum_{n=1}^{N}s[n]^{4}}{\left(\frac{1}{N}\sum_{n=1}^{N} s[n]^{2}\right)^{2}},\] (22)where the derivative is computed, and \(w\) is iteratively updated until convergence.
* Summary of Formulas The core formula of MED can be summarized as \[w_{\text{opt}}=\arg\max_{w}\frac{E[s^{4}]}{(E[s^{2}])^{2}},\] (23) where \(s=w*y\). Key Points
* Adaptivity: No prior system model is required; the method is entirely data-driven.
* Sparse Enhancement: Impulsive components are highlighted by minimizing entropy (or maximizing kurtosis).
## Appendix B Estimating \((\sigma^{2},\rho)\)  
Below, we describe the Metroplolis-Hastings steps for updating \(\sigma^{2}\) and \(\rho\) within the MCMC estimation scheme when the residual covariance matrix is structured by the AR(1) model.  
**Updating \(\sigma^{2}:\)** In each MCMC cycle, a candidate value of \(\sigma^{2}\) denoted by \(\sigma^{2*}\) is generated from its proposal distribution, which can be specified as:  
\[q(\sigma^{2^{*}}\,|\,\sigma^{2})=IG(\frac{1}{\sigma^{2}}+1,1).\]  
This proposal will be accepted with probability:  
\[\min(\alpha_{\sigma^{2}},1),\]  
where:  
\[\alpha_{\sigma^{2}}=\frac{\pi(\sigma^{2^{*}}|\,\mathbf{y},\mathbf{\lambda},\mathbf{Q},\mathbf{\Theta},\rho)\cdot q(\sigma^{2^{*}}|\sigma^{2})}{\pi(\sigma^{2}|\mathbf{y}, \mathbf{\lambda},\mathbf{Q},\mathbf{\Theta},\rho)\cdot q(\sigma^{2}|\sigma^{2^{*}})}. \tag{11}\]  
**Updating \(\rho:\)** The proposal distribution of \(\rho\) can be specified as a uniform with a moderate range around the current value of \(\rho\). In other words, \(q(\rho^{*}\,|\,\rho)\,=\,Uniform(\,max(-1,\rho-\delta_{\rho}),\,min(\rho+\delta _{\rho},1))\). A new value of \(\rho\), \(\rho^{*}\), is generated from this proposal distribution and accepted with probability:  
\[\min(\alpha_{\rho},1),\]  
with:  
\[\alpha_{\rho}=\frac{\pi(\rho^{*}\,|\,\mathbf{y},\mathbf{\lambda},\mathbf{Q},\mathbf{ \Theta},\sigma^{2})\cdot q(\rho^{*}\,|\,\rho)}{\pi(\rho\,|\,\mathbf{y},\mathbf{\lambda },\mathbf{Q},\mathbf{\Theta},\sigma^{2})\cdot q(\rho\,|\,\rho^{*})}. \tag{12}\]
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 2 Hybrid Enhanced Polarization Image Clarity
### Variational Contrast and Saturation Enhancement Algorithm  
The improved GrayWorld method can play a corresponding role in the process of image color restoration, aiming at the problems of image turbidity and loss of edge details caused by underwater strong scattering environment. To better restore the visibility of water degradation images, a variational model algorithm with single data item and double regular item is proposed. It is used to enhance the contrast and saturation of water degradation image.  
First, by comparing the difference between the clarified image \(D\) and the improved GrayWorld method image \(W\), the color distortion of the output result image is prevented. Secondly, the contrast and saturation parameters in the image are enhanced by using the biregular term. The method of image contrast enhancement is as follows: by calculating the weighted difference between all pixels in the image and the whole image, the method of image saturation enhancement is to calculate the difference between the intensity of a single channel and other channels in the R, G, B channels of the image, respectively.  
\[\begin{array}{ll}R(D_{m})=&\frac{1}{2}\underset{x}{\sum}(D_{m}(x)-W_{m}(x))^{2} \\ &-\frac{\eta}{2}\underset{x,y}{\sum}u(x,y)S(D_{m}(x)-D_{m}(y))\\ &-\frac{\eta}{2}\underset{x,y}{\sum}\Big{(}\big{(}D_{m}(x)-D_{m+1}(x)\big{)}^{2 }+\big{(}D_{m}(x)-D_{m+2}(x)\big{)}^{2}\Big{)}\end{array} \tag{7}\]  
In Equation (7), \(m\in R_{3}\), represents an integer field with 3 as a cycle, which corresponds to the RGB three channels color of the image (\(D_{1}=D_{R}\), \(D_{2}=D_{G}\), \(D_{3}=D_{B}\)). \(D\) is the clear output image. \(W\) is the guide image obtained by improving the GrayWorld method. \(\mu\) and \(\eta\) are regularization parameters. \(u(x,y)\) is the Euclidean distance weight, which weights the image's local-to-global clarity. The filtering result of the image edge pixel can be improved by introducing weight function \(u(x,y)\). The halo around the image boundary caused by underwater scattering is avoided. It can be expressed by Equations (8) and (9):  
\[u(x,y)=\frac{E(x)}{\|x-y\|},x\
eq y \tag{8}\]  
\[E(x)=\left(\underset{y\in\Omega}{\sum}\frac{1}{\|x-y\|}\right)^{-1} \tag{9}\]
## 2 Study Area and Data Processing  
### Study Area  
Zayu County is located in the southeastern part of the Tibet Autonomous Region, on the southeast edge of the Qinghai-Tibet Plateau, at approximately 97\({}^{\circ}\)27\({}^{\prime}\) E and 28\({}^{\circ}\)24\({}^{\prime}\) N. The county is characterized by steep terrain, overlapping mountains, and deep valleys. The average elevation is 2300 m, with a general tilting trend from northwest to southeast, resulting in a higher elevation in the northwest and a lower elevation in the southeast, with a relative height difference of 3600 m. The significant vertical height difference forms a typical high mountain canyon and mountain-valley landform. The valley's southern edge is only 1400 m above sea level, while there are more than ten peaks above 5000 m, with the highest peak being the 6740 m Meili Snow Mountain. The area is traversed by dozens of major and medium rivers, including the Zayu River and the Nu River, featuring large drops, both tributaries of the Yarlung Tsangpo River. Due to differences in terrain elevation and precipitation triggers during the rainy season, severe geological disasters, such as landslides and debris flows, occur frequently, making it one of the critical areas for research and development of geological disaster prevention. The schematic diagram of the study area is shown in Figure 1.  
Figure 1: Study area: (**a**) satellite image; (**b**) overview map of Zayu county.
# X-Band Photonic-Based Pulsed Radar Architecture with a High Range Resolution
## 1 Introduction
The proposed photonic-based pulsed radar could overcome the narrow-band disadvantages of an MLL-based pulsed radar by adding a Mach-Zehnder modulator (MZM) operating as an optical switch to the topology with a CW laser source. Therefore, the proposed architecture can be applied to wideband surveillance radars as it can clearly identify the shape of the target due to its excellent range resolution. The organization of this paper is as follows--Section 2 represents the operation principles of the proposed photonic-based pulsed radar architecture and its theoretical analysis. In Section 3, the implementation of the prototype applied the proposed photonic-based pulsed radar architecture and the results of indoor experiments are described. Finally, Section 4 shows the concluding remarks.
## 3 Results and Discussion
### Influence of Projectile Material
#### 3.1.1 Energy Absorption  
Figure 5 shows the energy absorbed by 2D triaxial braided composites as a function of impact velocity, and Figure 6 shows the energy absorbed by the composite target when impacted by projectiles made of various materials. The composite target absorbs the most energy (>612.5 J) when impacted by the gelatin projectile. Because the composite target was not perforated by this projectile at any of the velocities tested, still more energy can be absorbed by this material. When the composite target was impacted with CFRP and titanium-alloy blade-like projectiles, the energy absorption of the composite target first increases and then decreases with increasing impact velocity. When the triaxial braided composite was perforated by a CFRP projectile, the average energy absorbed is 267.1 J, which is 77.59% greater than the maximum average energy absorbed of 150.4 J for a titanium-alloy projectile. According to the study by [PERSON] et al. [50], the ballistic limit velocity decreases significantly with the increase in projectile hardness after the projectile hardness exceeds that of the target. In the current study, the composite target and CFRP projectile has moderate hardness while the titanium projectile has obviously greater hardness than the target panel. Therefore, the ballistic limit velocity as well as energy absorption decreases when used titanium alloy projectile. It is worth noting that the previous research on the hardness of the projectile was generally in the field of metal materials. The projectile studied in the current research has a larger material span, ranging from soft gelatin to resin-based composite materials to metal alloy materials.  
Figure 5: Energy absorption of two-dimensional (2D) triaxial braided composites with incident velocity of projectile.  
For both titanium-alloy and CFRP projectiles, first, the energy absorption increases with the incident velocity, and then, it decreases when the incident velocity exceeds a critical value. This phenomenon is widely observed in ballistic impact, whether it is a metal target plate or a composite material target plate.
## 4 System Design and Goals
### Protocol Parameters and Constants  
When designing and implementing wireless network protocols, particularly for D2D communication networks, specific parameters and constants are usually defined to control the behavior and performance of the protocol. These parameters and constants can vary depending on the type of protocol and specific goals of the protocol design. These parameters and constants equally played an important role in determining the behavior and performance of wireless network protocols in simulations, and testbed and real deployments. When simulating a D2D wireless network protocol, it is vital to carefully choose and tune these parameters to reflect realistic operating conditions and to ensure accurate evaluation of the protocol's performance. This paper proposes new protocol parameters with their constants and also proposes the modifications to existing parameters and constants defined in [46; 47] for efficient energy consumption and link management. These parameters and constants are of primary importance for tuning the behavior and performance of routing protocols in various network environments. The specific values chosen for these parameters can significantly impact the efficiency, reliability, and scalability of our proposed routing protocol.  
Figure 10: HI packet format; note that the message type is HI, but the format is similar to the HELLO packet [46].
# Attosecond Streaking Time Delays: Finite-Range Interpretation and Applications
## 1 Introduction
While we focus here on interpretation and applications related to streaking time delays in one-photon ionization, we may note that recently the extension of the streaking concept to two-photon ionization within the present approach [11; 12] as well as in other context [13; 14; 15; 16] has been considered. Before proceeding, we may briefly mention that results of ab initio numerical calculations as well as other theoretical work showed a significant discrepancy with the data of the original streaking experiment [5]. Only recently, new insights into the origin of this disagreement has been achieved via an alternative measurement [17] based on the RABBITT (Reconstruction of attosecond beating  
Figure 1: Conceptual visualization of the different streaking time delays in photoionization, namely the experimentally observed delay \(\Delta t_{s,expt}^{(nl,nl^{\prime}l^{\prime})}\) between streaking traces for emission from initial states \(|n,l>\) and \(|n^{\prime},l^{\prime}>\) and the theoretically considered delays \(\Delta t_{s}^{(nl)}\) and \(\Delta t_{s}^{(nrl^{\prime}l^{\prime})}\) with respect to the streaking trace of a free electron. Panel (**b**) is an enlargement of the full traces shown in panel (**a**). by interfering two-photon transitions, [18]) technique. Due to the better spectral resolution in the RABBITT experiment signals from the direct photoionization and a shake-up process, in which a second electron is excited along with the photoionization of the first (streaked) electron, could be disentangled, which overlapped in the original streaking experiment. The time delay measurement for the photoionization signal in the RABBITT experiment is in much better agreement with the theoretical predictions. The article is organized as follows: in Section 2, we briefly discuss the finite range interpretation, which arises from a classical analysis of the streaked electron dynamics and comparison with results of quantum simulations. Furthermore, we show that the finite-range feature allows for developing an analytical formula, which provides accurate estimates of the time delay. Beyond this capability, applications of the formula provide insights in different aspects of the streaking time delay, which we will discuss in Section 3. The latter part includes, besides a review, discussion of the relevance of the first half-cycle of the streaking field (Section 3.2) and the resolution of dynamics (Section 3.4), which have not been analyzed before. We end with a brief summary.