esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Emission Sequence and Velocity Estimation  
[PERSON] et al. [23] steered the beam emissions by the rows and columns. A scheme of this sequence is presented below. Denoting the \(m\)th column emission in sequence repetition \(i\)\(C_{m}^{(i)}\), and the \(n\)th row emission \(R_{n}^{(i)}\), the emission sequence is written as  
\[\begin{array}{l}C_{1}^{(1)}\to C_{2}^{(1)}\to\ldots\to C_{M}^{(1)}\to R_{1}^{(1)} \to R_{2}^{(1)}\to\ldots\to R_{N}^{(1)}\to\\ C_{1}^{(2)}\to C_{2}^{(2)}\to\ldots\to C_{M}^{(2)}\to R_{1}^{(2)}\to R_{2}^{( 2)}\to\ldots\to R_{N}^{(2)}\to\ldots\end{array} \tag{2}\]  
This sequence has the issue described above that \(T_{prf,eff}=(M+N)T_{prf}\). Such a sequence is also simulated to provide a comparison with the interleaved sequence.  
Figure 2: Two 1D arrays (\(1\times N\) and \(N\times 1\)) are mounted orthogonally on top of each other to form 2D RC-addressed \(N+N\) array.  
Adapting the interleaved sequences for SA imaging [30] to plane-by-plane RC TVI, the emission sequence becomes  
\[\begin{split} R_{1}^{(1)}\to R_{1}^{(2)}\to R_{2}^{(1)}\to R_{2}^{(2)} \rightarrow\ldots\to R_{N}^{(1)}\to R_{N}^{(2)}\rightarrow\\ C_{1}^{(1)}\to C_{1}^{(2)}\to C_{2}^{(1)}\to C_{2}^{(2)} \rightarrow\ldots\to C_{M}^{(1)}\to C_{M}^{(2)}\rightarrow\ldots\end{split} \tag{3}\]  
reducing the time between two emissions being correlated to \(T_{prf}\).  
The directional transverse oscillation velocity was used [29]. To perform the velocity estimation, two sets of lines are beamformed for each emission: one set in the respective lateral direction (\(x\) for row and \(y\) for column emissions, respectively) and one line in the \(z\)-direction [29]. To estimate the velocity in a single point, the lines beamformed in the lateral direction are centered on the estimation point and span a pulse length in the \(z\)-direction and have a two-peak apodization on the receiving aperture to induce a TO. A line length of \(10*\lambda\) at \(f_{prf}\) = 5 kHz corresponds to a velocity of 25 m/s. The line beamformed in the \(z\)-direction is also centered on the estimation point, spans a pulse length and has a symmetric Hann apodization on the receiving aperture. The lateral sets of lines are pre-processed to remove the axial oscillation [29]. The two sets of lines are correlated between emissions \(C_{m}^{(i)}\) and \(C_{m}^{(i+1)}\) for \(i\) odd resulting in the correlation function \(\hat{R}_{m}^{(i)}\), and the \(K\) correlation functions \(\hat{R}_{m}^{(i)}\), \(\hat{R}_{m}^{(i+1)},\ldots,\hat{R}_{m}^{(i+K-1)}\) are averaged. The location of the peak is found and interpolated to find the estimated velocity component. This is illustrated in Figure 3.  
In this work, \(M=N=11\), and the different row and column emissions are performed by sliding a window of 32 elements in transmit with a 9 elements step. Stationary echo cancelling was conducted by averaging 16 emissions for each direction and subtracting this mean value to remove the tissue signal.
## 2 Materials and Methods
### Physical-Layer Security Analysis: Colluding Eavesdroppers
#### 2.1.4 Probability of Non-Zero Secrecy Capacity  
The probability of non-zero secrecy capacity \((P_{nsc})\) is recognized as the probability that the main channel's capacity is larger than the wiretap channel's capacity. This implies that the main channel is more reliable than the wiretap channel. Mathematically, \(P_{nsc}\) is expressed as  
\[P_{nsc}=P_{r}(C_{s}>0)\] \[=1-P_{r}(\gamma_{B}\leq\gamma_{E})\] \[=1-\int_{0}^{\infty}F_{\gamma_{B}}(y)f_{\gamma_{E}}(y), \tag{20}\]Substituting (8) and (9) into (20) and with the help of ([34] Equation (2.24.1.1)), \(P_{nsc}\) is solved as  
\[P_{nsc} =1-\sum_{r_{1}=0}^{\infty}\sum_{r_{2}=0}^{\infty}\cdots\sum_{r_{n_{ \epsilon}}=0}^{\infty}\sum_{r_{1}=0}^{\infty}\sum_{r_{2}=0}^{\infty}\cdots\sum_ {r_{N}=0}^{\infty}\frac{c_{x,\epsilon}a_{1}}{4}\Bigg{(}\frac{\prod_{l=1}^{N}E \left[X_{i}^{2}\right]}{\gamma_{B}}\Bigg{)}^{n_{1}+v_{1}}\left(\frac{\prod_{j= 1}^{n_{\epsilon}}E\left[X_{j}^{2}\right]}{\gamma_{E}K}\right)^{n_{\epsilon 1}K+r_{1}}\] \[\times\Bigg{(}\frac{\prod_{l=1}^{N}E\left[X_{i}^{2}\right]\mu_{i} (1+\kappa_{i})}{\gamma_{B}}\Bigg{)}^{-\mu_{1}-\
u_{1}-\mu_{\epsilon 1}K-r_{1}}G \frac{n_{\epsilon}+1}{N+1}\begin{array}{c}N\\ \psi^{\prime}\end{array}\Bigg{|}\frac{\gamma_{B}\prod_{j=1}^{n_{\epsilon}}E \left[X_{i}^{2}\right]\mu_{i}K_{j}(1+\kappa_{j})}{\gamma_{E}K\prod_{i=1}^{N} E\left[X_{i}^{2}\right]\mu_{i}(1+\kappa_{i})}\Bigg{)}, \tag{21}\]  
where \(\psi=1-\mu_{\epsilon 1}K-r_{1}-\mu_{2}-v_{2},\cdots,1-\mu_{\epsilon 1}K-r_{1}- \mu_{N}-v_{N},1-\mu_{1}-v_{1}-\mu_{\epsilon 1}K-r_{1},1-\mu_{\epsilon 1}K-r_{1}\) and \(\psi^{\prime}=-\mu_{\epsilon 1}K+\mu_{\epsilon 2}K-r_{1}+r_{2},\cdots,-\mu_{ \epsilon 1}K+\mu_{\epsilon n_{\epsilon}}K-r_{1}+r_{n_{\epsilon}},0\), \(-\mu_{\epsilon 1}K-r_{1}\).
# Prediction of Mechanical Performance of Acetylated MDF at Different Humid Conditions
###### Abstract  
Change of relative humidity (RH) in surrounding environment can greatly affect the physical and mechanical properties of wood-based panels. Commercially produced acetylated medium density fiberboard (MDF), Medite Tricoya(r), was used in this study to predict strength and stiffness under varying humid conditions by separating samples in parallel (/) and perpendicular (\(\perp\)) to the sanding directions. Thickness swelling, static moduli of elasticity (MOE\({}_{\text{stat}}\)) and rupture (MOR\({}_{\text{stat}}\)), and internal bond (IB) strength were measured at three different humid conditions, i.e., dry (35% RH), standard (65% RH) and wet (85% RH). Internal bond (IB) strength was also measured after accelerated aging test. A resonance method was used to determine dynamic modulus of elasticity (MOE\({}_{\text{dyn}}\)) at the aforementioned humid conditions. Linear regression and finite element (FE) analyses were used to predict the MDF's static bending behavior. Results showed that dimensional stability, MOE\({}_{\text{stat}}\), MOR\({}_{\text{stat}}\) and IB strength decreased significantly with an increase in RH. No reduction of IB strength was observed after 426 h of accelerated aging test. A multiple regression model was established using MOE\({}_{\text{dyn}}\) and RH values to predict MOE\({}_{\text{stat}}\) and MOR\({}_{\text{stat}}\). In both directions (// and \(\perp\)), highly significant relationships were observed. The predicted and the measured values of MOE\({}_{\text{stat}}\) and MOR\({}_{\text{stat}}\) were satisfactorily related to each other, which indicated that the developed model can be effectively used for evaluating the strength and stiffness of Medite Tricoya(r) MDF samples at any humid condition. Percent errors of two different simulation techniques (standard and extended FE method) showed highly efficient way of simulating the MDF structures with low fidelity.  
acetylation; wood fiber; strength; stiffness; internal bonding strength; thickness swelling; regression; finite element analysis +
Footnote †: journal: Applied Soft. 2020, 10, 8712; doi:10.3390/app10238712
## 3 Seismic Input, Load Scenarios and Analyses
### Nonlinear-Dynamic Analysis (NLDA)--Time History Analysis  
Nonlinear dynamic analysis consists in computing the seismic response of a structure following a step-wise integration of the equation of motion. This was performed following the application of ground-motion time-histories to a nonlinear model of the structure as will be defined in the present section. To carry out this type of analysis, it is necessary to use nonlinear models that are able to reproduce the post-elastic behaviour of the structural elements in order to accurately represent their dissipative capacity due to hysteresis. In the present case, the nonlinear elements introduced were identified in the plastic hinges modelled at the base of the piers to simulate the post-elastic behaviour of the bridge. For investigating the behaviour of the viaduct, a simplified kinematic-kinetic rule was adopted to define the plastic hinge action. The latter would enable the identification of both the energy dissipation through the anticipated hysteresis of the materials and the exclusion of complete collapse of individual piers upon reaching maximum resistance and ductility. Figure 5: Graphic results of SSRS calculation according to the procedure described through seven steps. In the framework of NLDA, two main input seismic motions were further defined (two more configurations to add to scenarios n. A and B defined above in Section 3): n. 1 is based on a spectrum-compatible accelerograms approach, and n. 2 is based on a site-specific and spectrum-compatible accelerograms experimental approach. Starting from configuration n. 1, for the selection of accelerograms for local seismic response studies, reference is made to the work of [PERSON] et al. [48] and the Rexel software [48] where the geographical position and the site parameters serve as inputs (Long. 10.011, Lat. 44.627, soil type A, \(T_{2}\) topography, Class IV, SLV), and disaggregation analysis was subsequently carried out within the software itself. Following the procedure suggested in [49], we arrive at the graph in Figure 6 which illustrates the results of the disaggregation analysis in terms of a three-dimensional graph representing the contribution of the seismogenic sources placed at a distance R and capable of generating an earthquake of magnitude M to be the source of an earthquake that dominates the site scenario [50]. It is evident from Figure 6 that the most significant contribution to hazard was due to seismogenic sources within a radius of 20 km with magnitudes between 4.0 and 6.0. Therefore, the accelerogram selection process was carried out using Rexel and assuming the European Strong Motion catalogue as the starting database (Table 4).
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 2 Related Work
### Gradient-Based Methods for Explaining Medical Disease Diagnostic and Predictive Models  
The literature extensively demonstrates the use of heatmaps for explaining deep learning models, particularly in medical imaging [18]. Explanation algorithms such as Vanilla Gradient, Guided Backpropagation, Integrated Gradients, Guided Integrated Gradients, SmoothGrad, and Grad-CAM play a vital role in enhancing the interpretability of complex models used for image-based medical disease detection and prediction. By addressing the \"black box\" nature of deep learning models, these methods offer visual explanations of features captured by Convolutional Neural Networks, aiding in the understanding, verification, and justification of model predictions. This is particularly critical for fostering clinical acceptance and trust in AI systems deployed in high-stakes fields such as medical diagnostics [19; 20; 21].  
Grad-CAM, for example, highlights the importance of various regions in an image by leveraging activation maps. However, its initial implementation often underestimated the significance of certain features, a limitation subsequently addressed by Integrated Grad-CAM. The latter employs a path integral of gradient-based terms to enhance object localization and improve model interpretation [13]. Similarly, Geometrically Guided Integrated Gradients refine traditional integrated gradient methods by incorporating the local geometry of the model parameter space, leading to more accurate attributions and explanations of predictions [22].  
Despite these advances, gradient-based methods often produce explanations with excessive high-frequency noise that can obscure meaningful insights in medical imaging analysis. This noise arises from operations in convolutional neural networks and can mislead interpretations of model decisions [23]. Additionally, the transformation of gradient outputs into interpretable images frequently results in considerable noise, complicating the extraction of clinically useful information [24]. These limitations are particularly problematic in medical contexts where clear and reliable interpretations are essential for clinical decision making and building trust in AI-assisted diagnosis.  
These advancements not only bolster the interpretability of AI models but also enhance their overall accuracy by ensuring that models focus on clinically relevant features. Consequently, these methods significantly improve the reliability and utility of medical image classification systems, paving the way for more robust integration of AI in healthcare settings.
# The Role of Decision-Making Styles in Shaping Cybersecurity Compliance Behavior
## 1 Introduction  
While cybersecurity expenditures continue to take a larger share of organizations' budgets yearly, cyberattacks remain a challenge for organizations. Despite the increasingly large cybersecurity expenditures, human factor remains the most critical source of data leaks [1]. Although technological solutions help reduce cyber incidents caused by human factors, it is impossible to prevent them entirely.  
Many studies have been conducted to explain individuals' cybersecurity compliance behaviors, based on different theories, such as rational choice theory (e.g., [2; 3]), theory of planned behavior (e.g., [4; 5]), general deterrence theory (e.g., [6; 7]), neutralization theory (e.g., [8; 9]), and protection motivation theory (e.g., [10; 11; 12; 13]).  
However, most of these studies have been carried out by focusing on perceived factors, such as perceived cost [14], perceived effectiveness [15], and perceived susceptibility [16], or external factors, such as subjective norms [4; 15; 17; 18; 19], social influence [20], and rewards [21]. Despite the importance given to perceived and external factors [22], the internal factors of individuals, such as decision-making styles, have not attracted the necessary attention [23; 24; 25]. This study examines individuals' decision-making styles' impacts on cybersecurity compliance behaviors. For this purpose, we determined our main research question: \"To what extent do employees with different decision-making styles differ in their cybersecurity compliance behavior?\".  
In light of the abovementioned gap, we proposed a conceptual model based on the protection motivation theory (PMT) [26; 27] and empirically tested it. This study extended current research by examining the role of decision-making styles identified through the General Decision-Making Style Inventory (GDMS) within the framework of the PMT approach.  
Study findings also contributed to literature by presenting new evidence in explaining individuals' cybersecurity compliance behaviors. This study analyzed individuals' actual cybersecurity protective behaviors rather than behavioral intentions. By doing this, we aimed to shed light on how cybersecurity could be improved by offering more realistic, practical applications based on individual decision-making.
## 3 Experiments and Analyses
### Ablation Experiments
It converges faster and the loss value between the ground truth box and the predicted box is smaller because it takes into account the vector angle between the ground truth box and the predicted box. Consequently, it solves the directional matching problem of direction matching between the ground truth box and the predicted box. Moreover, the SIoU loss function optimizes the convergence performance of the network model and improves the detection and regression accuracy of the improved model. In order to verify the effect of introducing the SIoU loss function on the detection accuracy of the network model, the mean average precision curves of the network model with the SIoU and CIoU loss functions are compared and analyzed after 100 iterations. As shown in Figure 9, the [EMAIL_ADDRESS] values of the network model with the introduction of the SIoU loss function represented by the gray curve are higher than those in the network model with the introduction of the CIoU loss function represented by the brown color from the beginning to the end of training. The comparison shows that the introduction of the SIoU loss function has improved the regression accuracy of the model in the prediction process.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Non-Metals
#### 2.3.2 Graphene  
Graphene is a two-dimensional (2D) monolayer [84] ultra-thin carbon film [85] with a hexagonal structure [86] that has become the center of attraction for researchers due to its exceptional electrical conductivity, as well as its thermal, mechanical, optical, and electrochemical properties [87]; moreover, some extra mentionable intrinsic properties added extra advantages when embedded into microstructures, which is crucial for promoting its potentiality in practical uses [88; 89; 90]. As a consequence, the development of graphene-based 3D aerogels (GBAs), 2D membranes (GBMs), and 1D fibers (GBFs) have progressed. Three-dimensional GBAs have achieved the current world record as the lightest material, having a density of 0.16 mg cm\({}^{-3}\)[91]. Two-dimensional GBMs can be produced by CVD or infiltration processes for energy storage and conversion. However, one-dimensional GBFs are more applicable than GBFs and GBMs for their electrical and mechanical properties. GBFs are flexible and fabricable; therefore, they can be used in any form and in multifractional applications. Graphene can be produced in monolayer and multi-layer forms based on the application. The rapid acceptance of graphene as a substance of interest lies in its actual availability in the preparation technique and perhaps mostly because monolayer and multi-layer graphene and graphene oxide (GO) have several versatile properties.  
Graphene can be chemically modified to obtain inexpensive materials such as graphene oxide (GO) [92], reduced graphene oxide (rGO) [93], and their derivatives (fGO, frGO and mG) on a large scale. This chemical modification is done to get fibers, films, porous frameworks, or modified or hybridized forms to diversify their function. Graphene-based materials such as GO and rGO have great prospects for use as a material for eco-friendly wearable e-textiles [94; 95; 96; 97; 41; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107;  
finishing processes such as a deposition [134]; coatings such as dip coating, spray coating, rod coating, or roller coating on the textile substrate (fiber, yarns, fabrics) [135]; and printing textiles with conductive materials [136]. The choice of the process depends on the process parameters and properties such as flexibility, stability, and degree of conductivity of the final products [59]. The common approaches are described in the following subsections.
## 3 Analysis of Horizontal Bearing Characteristics  
### Analysis of Horizontal Bearing Performance under Different Compactness  
The experiment conducted horizontal monotonic loading tests on pile-bucket composite foundations with three different diameter ratios and analyzed the effect of different sand compactness levels on the horizontal load-bearing performance of such foundations.  
As depicted in Figure 3, the variation trend of the composite foundation curve under three distinct sand compactness conditions which were loose sand, medium-density sand, and dense sand. The curves can be segmented into two stages: elastic deformation and elastoplastic deformation. In the initial stage of load application, the displacement at the loading point exhibited linear growth with relatively minor increments, indicative of the soil being in a state of linear elastic deformation. With increasing load, the displacement exhibited nonlinear growth and the increment in displacement gradually escalated, indicating the translation of the soil into an elastoplastic deformation stage.  
Figure 4 illustrates the relationship between horizontal load and pile head rotation. The pile head rotation of 0.5\({}^{\circ}\) corresponds to the horizontal ultimate bearing capacity Fu (1995), with specific values detailed in Table 2. Compared to the state of medium-density sand, the horizontal ultimate bearing capacity of the composite foundation decreased by approximately 40-50% in the loose sand state. Conversely, in the dense sand state, the horizontal ultimate bearing capacity of the composite foundation registered an increase of 10-30%.  
Figure 3: Load–displacement relationship curves under different compactness. (**a**) Pile-bucket diameter ratio d/D = 0.5; (**b**) Pile-bucket diameter ratio d/D = 0.417; (**c**) Pile-bucket diameter ratio d/D = 0.357.
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 4 Evaluation
### Comparisons of TB-PBFT with Other BFT-Type Consensus Algorithms  
We compare the performance of the TB-PBFT algorithm with other BFT-type algorithms, as shown in Table 1. In terms of Byzantine fault tolerance, the BFT-type algorithms in the chart all have Byzantine fault tolerances of \((n-1)/3\). Our algorithm also has \((m-1)/3\) Byzantine fault tolerances in each group. However, whether each primary node is a Byzantine node will greatly affect the fault tolerance of the TB-PBFT algorithm. In the worst case, when more than one third of the primary nodes are Byzantine nodes, our algorithm has low fault tolerance. However, the primary node is selected based on the comprehensive evaluation model. Therefore, as shown in the previous results, the maximum fault tolerance of the TB-PBFT algorithm is \((5 mn-2n-2m^{2}-m)/9m\). The communication complexity of the PBFT algorithm is \(O(n^{2})\), and the asynchronous HoneyBadgerBFT [27] protocol has significant performance improvement in other aspects, but its communication complexity of \(O(n^{2}+n^{3}\text{log}^{n})\) is higher. Other algorithms such as Tendermint, Zyzzyva, and HotStuff use different mechanisms or models to significantly reduce the communication complexity. Our algorithm has lower communication complexity than the PBFT algorithm and HoneyBadgerBFT algorithm, but slightly higher complexity than other BFT-type algorithms. Our algorithm, like the Tendermint and HotStuff algorithms, has excellent node scalability and can support a large number of nodes. Compared with other BFT-type algorithms, the TB-PBFT algorithm has a higher degree of decentralization, and the consensus approach of multi-primary nodes solves the problem of the heavy communication burden of a single primary node due to large transactions. At the same time, our algorithm also reduces the probability that the primary node is the Byzantine node, so the probability of view change is low, and additional communication consumption is further reduced. Therefore, the TB-PBFT algorithm is suitable for most scenarios of consortium blockchain.  
Figure 8: The influence of comprehensive evaluation model on TB-PBFT algorithm, (**a**) m = 4, n = 16, (**b**) m = 10, n = 40.