esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion  
In reference to the theoretical analysis described in the Section 2.1, it is clear that the biomimetic structure exhibited excellent antiadhesion and resistance reduction properties within a suitable range of parameters. Here, \(h_{0}\) corresponds to the height \(h\) of the bionic structure, and \(S_{0}\) corresponds to the width \(B\) of the bionic structure plus the transversal spacing \(S_{1}\) (or the longitudinal spacing \(S_{2}\)). As Equation (3) suggests, for the unsmooth surface, the larger the ratio of the wave amplitude to the wave period is \(h_{0}/s_{0}\), the easier the formation of the composite interface between the soil and the unsmooth surface and the better the reduction soil adhesion. That finding indicates that the larger the value of \(h_{0}/s_{0}\) is, the easier the formation of the composite interface. These results are in agreement with the research of [PERSON] and [PERSON] [2; 3]. Thus, for the bionic structure, the size of the bionic structure is given, i.e., the height and width are given. For an S-U structure, its width B can be calculated from the side length \(l\), and its calculation formula is \(B=l^{\prime}+2l\cdot\cos 53^{\circ}=6\cdot 1.175+2l\cdot\cos 53^{\circ}\). The value of \(h_{0}/s_{0}\) depends on the value of the arrangement spacing \(S_{i}\). The smaller the spacing \(S_{i}\), the larger the \(h_{0}/s_{0}\) is, which indicates that the more favorable the formation of the composite interface is, the more favorable the antiadhesion and resistance reduction effects are. This conclusion is consistent with the simulated results. From the simulated results, the correctness of the theoretical analysis was verified. In this paper, the drag reduction performance of the bionic shovels was when the arranging spacings were \(S_{1}=S_{2}=1\) mm; the resistance was the smallest here. However, for engineering applications, the arrangement spacing of bionic structures should be combined with the economics and manufacturing processability. Spacing that is too small can lead to processing difficulties and significantly increase costs, which is not conducive to engineering practice. Thus, the minimum spacing determined in this paper was \(1\) mm.  
Furthermore, the size of the bionic structure also required a suitable range. Here, it did not mean that the larger the height \(h\) of the bionic structure was, the better the drag reduction performance. Through the simulation analysis, it is clear that when the height \(h\) of the bionic structure exceeds a certain critical value, the resistance does not decrease but instead increases significantly. This was because, when \(h\) exceeded a critical value, the composite interface could not be created, and a barrier formed, hindering the continuous movement of the soil. Therefore, an accumulation of soil at the contact was formed, resulting in increasing resistance.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 6 Experimental Results
### Experiments with Randomly Placed Objects  
This section focuses on experiments with tasks placed randomly, as can be seen in Figure 1a. All the experiments have been performed with 500 different environments, with different number of tasks (\(m=50,100\)) and different values of the power \(n\) in the expression of the indistinguishability operators (11) and (12). The threshold \(\theta\) values under consideration are obtained from (13), setting \(nTH=2,4,8\).  
In [12], it was shown that OPRF and EPRF in these randomly generated environments needed the same number of steps to converge. This number of steps does not depend on \(\theta\) nor the power value \(n\). This is because the minimum between two transition possibilities is the same regardless of the indistinguishability operator (OPRF or EPRF) or parameters are used. Therefore, the result of the composition of two matrices with a max-min algebra does not depend on the operator. Informally, this fact can be seen in the following way. Recall that, as was introduced in Section 5.1, the composition of a possibilistic matrix with itself, \(M^{2}=M\circ M\), is calculated as follows.  
\[p_{ij}^{2}=\bigvee_{k=1}^{m}p_{ik}\wedge p_{kj}, \tag{14}\]  
where \(M^{2}=\{p_{ij}^{2}\}_{i,j=1}^{m}\). The minimum of two possibilities \((p_{ik}\wedge p_{kj})\) must be either \(p_{ik}\) or \(p_{kj}\) and it does not depend on the indistinguishability operator used ((11) or (12)). Therefore, the composition of the matrices given by Equation (14) is also independent of the indistinguishability operator, as well as the values of the power matrix \(M^{\tau}\). From the matrix formulation of the system's evolution (see Equation (9)) and the convergence condition given in Section 5.1, it can be seen that the number of steps until convergence does not depend on the indistinguishability operator nor its parameters. Future works will analyse, on one hand, if there are other indistinguishability operators that do not exhibit this property and, on the other hand, if this property is preserved when the matrix composition is based on the use of triangular conorm-triangular norm algebras different from the max-min one.  
These simulations also show that only a 50% of the 500 environments could converge to a stationary state when probabilistic Markov chains are used. In contrast with fuzzy Markov chains, all the experiments converge and they required less than 25 steps. This shows that fuzzy Markov chains with indistinguishability operators always outperform their probability counterpart. Figure 3 shows the mentioned percentage of experiments that, using probabilistic Markov chains, do converge with 100 randomly placed tasks.  
Figure 3: Percentage of experiments that converge with 100 tasks using probabilistic Markov process.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 2 Literature Review
### Multi-Task and Multi-View Speech Reconstruction  
[PERSON] et al. [34] introduced LipSound [34] and its successor, LipSound2 [35], which use visual information for speech synthesis and speech recognition for transcription. By capturing mouth movements, they reconstructed speech as mel-spectrograms, forming the basis for recognition. Speaker-dependent evaluations show that LipSound's mel-spectrograms exhibit a 0.843% character error rate and 2.525% word error rate with the GRID Dataset. LipSound2 achieves an average PESQ score of 1.72 with the GRID and TCD-TIMIT datasets.  
Subsequent research [38; 39; 40; 42] focused on multi-view speech reconstruction, aiming to enhance speech quality through the integration of visual inputs from multiple angles. [PERSON] et al. [39] employed multiple mouth angles to estimate the LSP representation of LPC coefficients for audio. Notably, they achieved an Average PESQ score of 2.5291 for Speaker 1 (Male) and 2.6255 for Speaker 1 (Female) from the OuluVS2 database. [PERSON] et al. [42] extended this to multi-view speaker-independent techniques by using multi-view speaker videos and lip pose angles to identify optimal views. A decision network selected the best view combination and model for speech signal reconstruction. Their results showed a PESQ score of 1.623 and a Corr2d score of 0.816 with the OuluVS2 database.  
These studies aimed to enhance speech quality through text information output and multi-view utilisation. However, a key limitation is the lack of consideration for speaker independence. Additionally, their system structures are complex, demanding substantial data, resulting in high computational cost.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Attacks
#### 5.3.3 A3: Man-in-the-Middle (MitM) Attack  
Man-in-the-Middle (MitM) attacks involve intercepting and altering communication between WoT devices. This threat affects both the network layer and the web layer, exploiting vulnerabilities such as the lack of mutual authentication and weak encryption. In a MitM attack, the attacker can intercept, modify, and fabricate messages between devices, leading to data tampering and unauthorized control. The consequences are severe, as attackers can inject malicious commands, steal sensitive data, or alter device behavior. Misuses include unauthorized transactions, misinformation, and potential sabotage of device functions [56; 57].
## 3 Results and Discussion
### Oxidative Stability by Rancimat and PDSC Method  
Oxidative stability is a serious oil safety factor, used to estimate shelf life. The Rancimat test is often used for its determination. The studied amelina oils were characterized by oxidative induction time (OIT) at a temperature of 100 \({}^{\circ}\)C from 4.26 to 6.18 h (Table 1). Under the same measurement conditions, [PERSON] et al. [22] obtained results within 4.79-7.51 h, and [PERSON] et al. [20] from 4.58 to 5.63 h. [PERSON] and [PERSON] [1], when testing amelina oils at 110 \({}^{\circ}\)C, obtained OIT equal to 4.8 h. The oxidative induction time of the tested amelina oils was longer than that of linsed oil (from 3.67 to 4.65 h) [23], but shorter than that for cold-pressed rapeseed oils (12.96-13.98 h) [40].  
The oxidative stability of oils can also be well characterized by the induction time \(\tau_{\max}\) determined on the basis of PDSC technique [20; 23; 30; 50]. In the studied oils, PDSC \(\tau_{\max}\) ranged from 146.7 to 165.2 min (Table 1). Similar values under the same measurement conditions are presented in [PERSON] et al. [20] (151.9-172.3 min). [PERSON] et al. [23], when testing amelina oils at 120 \({}^{\circ}\)C, obtained substantially lower values (17.8-28.8 min).
# Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene
## 4 Experiments
### Validation on LFW Dataset
#### 4.1.1 Ten-Fold Cross-Validation  
The standard LFW evaluation protocol defines a verification experiment under ten-fold cross-validation with 300 genuine comparisons and 300 impostor comparisons per fold. Through the feature extraction network designed in Figure 6, we obtain 512 dimensions that correspond with output feature pairs. Each pair of feature representations receives a corresponding similarity score by calculating a distance. Furthermore, we classify all distances by choosing appropriate thresholds. Finally, we compare the actual classification to acquire the final accuracy on the test set. Using a direct calculation of the distance, we perform ten-fold cross-validation. We take turns to use 9 folders for training, these folders are used to determine the optimal value of hyperparameter _threshold_. In the last folder, we use the best _threshold_ to achieve accuracy and use the average of the results for 10 times to estimate the algorithm accuracy. Figure 9 shows the feature distances and labels of 6000 image pairs. We can set a _threshold_. If the distance of the image pairs is smaller than the _threshold_, the image pairs will be set to the same category. If the distance of the image pairs is larger than the _threshold_, the image pairs will be set to different categories. Then, we can calculate the accuracy. Evidently, the maximum accuracy is achieved when the _threshold_ value is around 1.5.  
From Table 1, we can see that the average accuracy of the algorithm in this study is 99.43%, and the deviation is \(\pm\)0.16%, which is better than the comparison algorithm. The results show that the network of the algorithm in this study is very stable and has a strong generalization ability. The advantage of this algorithm is mainly attributed to the stable basic network, which can steadily extract the basic features with strong expression ability. However, the PTFRM module has good transformation mapping ability under the optimization of pose loss, which can transform the feature of the multi-pose into a front feature and enhance the discrimination of those misclassified features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **LFW (Accuracy \(\pm\) Std)** \\ \hline DeepFace [23] & 95.92\% \(\pm\) 0.29\% \\ COTS matcher [24] & 98.2\% \(\pm\) 0.6\% \\ DeepID-2+ [25] & 98.70\% \(\pm\) 0.15\% \\ [PERSON] et al. [26] & 99.16\% \(\pm\) 0.31\% \\
**PTFRM\_PYR** & **99.43\% \(\pm\) 0.16\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of related algorithms on the LFW test set.  
Figure 9: The histogram of LFW verification pairs.
## 2 System and Channel Model
### System Model  
Exploring the RIS-enhanced electric vehicle charging network downlink procedure depicted in Figure 1, we observe base stations (BSs) acting as transmitters, equipped with N antennas, and one RIS composed of \(M\) element units for support, alongside \(L\) single-antenna electric vehicle users (VUs). The communication landscape is challenging, characterized by traffic congestion, buildings, and various obstacles, leading to blocked direct signal links from BSs to electric vehicle users. Consequently, a two-hop communication system is established, necessitating a BS to relay signals through an RIS to reach the users. For user \(k\), the received signal at time \(t\) is presented as:  
\[y_{k}(t)=(\mathbf{h}_{BV}+\mathbf{h}_{RV,k}(t)^{H}\Phi_{k}(t)\mathbf{H}_{BR,k}( t))\mathbf{x}(t)+n_{k}(t), \tag{1}\]  
In this scenario, the transmitted signal on the \(k\)-th subcarrier is represented as \(\mathbf{x}(t)\in\mathbb{C}^{M\times 1}\), the received signal is denoted by \(y_{k}(t)\), and the additive white noise is represented as \(n_{k}(t)\), following a normal distribution \(\mathcal{CN}(0,\sigma_{k}^{2})\). At time \(t\), the line-of-sight channel gain is presented as \(\mathbf{h}_{B}V\), and the non-line-of-sight channel gain is presented as the channel gain matrices from the base station to the RIS relay and from the RIS relay to the vehicle as \(\mathbf{H}_{BR,k}(t)\in\mathbb{C}^{N\times M}\) and \(\mathbf{h}_{RV,k}(t)\in\mathbb{C}^{1\times N}\), respectively. As Figure 1 shows, the direct link is blocked by buildings or other obstacles, so the communication between the base station and vehicle users is through the non-line-of-sight channel. Additionally, for user \(k\) at time \(t\), the RIS comprises \(M\times M\) reflecting elements, represented by the diagonal matrix \(\mathbf{\Phi}_{k}(t)\), indicating their corresponding phases. Specifically, it is defined as \(\mathbf{\Phi}_{k}(t)=\mathrm{diag}[e^{\beta\mathbf{i}_{1k}(t)},e^{\beta \mathbf{i}_{2k}(t)},\ldots,e^{\beta\mathbf{i}_{Mk}(t)}]\in\mathbb{C}^{M\times M}\). Considering the transmit power \(p_{k}(t)\) from the base station to user \(k\), the transmitted data \(s_{k}(t)\), and the beamforming vector \(\mathbf{q}_{k}(t)\)at the base station antennas, the term \(x(t)\) is expressed as \(\sum_{k=1}^{K}\sqrt{p_{k}(t)}\mathbf{q}_{k}(t)s_{k}(t)\), which is the transmitted signal \(\mathbf{x}(t)\) at time \(t\). The following constraints are applied to the transmit power at the base station:  
\[E[|\mathbf{x}(\mathbf{t})|^{2}]=tr(\mathbf{P}(t)\mathbf{W}^{H}(t)\mathbf{W}(t) )\leq P_{max}, \tag{2}\]  
where \(\mathbf{P}(t)=\text{diag}[\mathbf{p}_{1}(t),\dots,\mathbf{p}_{L}(t)]\in\mathbb{ C}^{K\times K}\), \(\mathbf{W}(t)\) is represented as \(\mathbf{W}(t)=[\mathbf{w}_{1}(t),\dots,\mathbf{w}_{K}(t)]\in\mathbb{C}^{M \times K}\), and \(P_{\max}\) represents the maximum transmit power.
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
###### Abstract  
Influences of temperature (25-35 \({}^{\circ}\)C) and substrate particulate content (3.0-9.4 g total suspended solids (TSS)/L) on granular sludge bed anaerobic digestion (AD) were analyzed in lab-scale reactors using manure as a substrate and through modeling. Two particle levels were tested using raw (RF) and centrifuged (CF) swine manure slurries, fed into a 1.3-L lab-scale up-flow anaerobic sludge bed reactor (UASB) at temperatures of 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Biogas production increased with temperature in both high- and low-particle-content substrates; however, the temperature effect was stronger on high-particle-content substrate. RF and CF produced a comparable amount of biogas at 25 \({}^{\circ}\)C, suggesting that biogas at this temperature came mainly from the digestion of small particles and soluble components present in similar quantities in both substrates. At 35 \({}^{\circ}\)C, RF showed significantly higher biogas production than CF, which was attributed to increased (temperature-dependent) disintegration of larger solid particulates. Anaerobic Digestion Model No.1 (ADM1) based modeling was carried out by separating particulates into fast and slow disintegrating fractions and introducing temperature-dependent disintegration constants. Simulations gave a better fit for the experimental data than the conventional ADM1 model.  
ADM1; anaerobic digestion; particle disintegration; temperature +
Footnote †: journal: Applied sciences
## 3 Earthquake Modelling
### Generalized Akaike's Bayesian Information Criterion (gABIC)
The covariance matrix \(\mathbf{Q}\) is expressed as follows (_blkdiag_ means block diagonal matrix):  
\[\mathbf{Q}=blkdiag\left[\mathbf{\tilde{Q}}_{GPS}\quad\left(\frac{\sigma_{Blk \mathbf{a}}^{2}}{\sigma_{GPS}^{2}}\right)\cdot\mathbf{\tilde{Q}}_{Bench} \quad\left(\frac{\sigma_{Blk\mathbf{a}}^{2}}{\sigma_{GPS}^{2}}\right)\cdot \mathbf{\tilde{Q}}_{lev}\quad\left(\frac{\sigma_{Blk\mathbf{a}}^{2}}{\sigma_{ GPS}^{2}}\right)\cdot\mathbf{\tilde{Q}}_{InSAR}\quad\left(\frac{\sigma_{ SPOT}^{2}}{\sigma_{GPS}^{2}}\right)\cdot\mathbf{\tilde{Q}}_{SPOT}\right]_{NxN}\]  
The ABIC function was first introduced by Reference [32], using only static GPS observations and Laplacian spatial constraint, as:  
\[ \begin{split} ABIC=N\cdot log\ s(\mathbf{a}^{*})-M\cdot log\ \alpha^{2}+log||\mathbf{H}^{T}\mathbf{E}^{-1}\mathbf{H}+\alpha^{2}\mathbf{G}|| +log||\mathbf{E}||-log||\mathbf{S}^{T}\mathbf{S}||\\ +N(log(2\pi)-logN+1).\end{split}  \tag{3}\]  
gABIC is the generalized version, it searches for the hyperparameters (\(\sigma_{i}^{2}\)) that minimize the ABIC value as presented in Reference [29]:  
\[ \begin{split} ABIC=N\cdot log\ s(\mathbf{a}^{*})-M\cdot log\ \alpha^{2}+log||\mathbf{H}^{T}\mathbf{E}(\sigma_{i}^{2})^{-1}\mathbf{H}+\alpha ^{2}(\mathbf{S}^{T}\mathbf{S})\Delta(\epsilon_{i}^{2})^{-1}||+log||\mathbf{E} (\sigma_{i}^{2})||\\ -log||(\mathbf{S}^{T}\mathbf{S})\Delta(\epsilon_{i}^{2})^{-1}||+N (log(2\pi)-logN+1),\end{split}  \tag{4}\]  
where:  
* \(\alpha^{2}=\left(\frac{\sigma_{GPS}^{2}}{\sigma_{GPS}^{2}}\right)\) is the smoothness parameter;\(\bullet\)\(\epsilon_{i}\) is a hyperparameter representing the weight of the constraint;
* \(s(\mathbf{a})=(\mathbf{d}-\mathbf{H}\mathbf{a})^{T}\mathbf{Q}^{-1}(\mathbf{d}- \mathbf{H}\mathbf{a})+\alpha^{2}\mathbf{a}^{T}(\mathbf{S}^{T}\mathbf{S})\Delta^{ -1}\mathbf{a}\).
## 2 Literature Review
### Statistical Techniques for Crash Severity
#### 2.2.2 Spatial Correlation in Crash Analysis  
Currently, spatial statistical techniques have been highly praised. For example, the risk factors associated with freeway crashes were explored by developing a spatial generalized ordered logit model [43]. The results showed that there were several important factors affecting freeway crash severities, such as vehicle type, season, traffic volume, crash type, and driver experience. Further, the spatial correlation effect was accommodated using a spatial structure error term to investigate the risk factors of crash frequency [44]. The results showed that the model simultaneously considering spatial correlation and unobserved heterogeneity outperforms the model considering only unobserved heterogeneity. The same conclusion was obtained by [PERSON] et al. [45], who used a spatial random intercept model to explore bicycle-motor vehicle crashes. In addition, there are many other studies to determine the risk factors of injury outcomes by establishing statistical functions with spatial error term [46; 47; 48]. That research could provide inspiration for this research.  
The existing spatial crash severity functions evolved from traditional statistical techniques by constructing a spatial structure term, including the MN-logit model, RI-logit model, and ordered logit model, etc. The spatial statistical function developed from a random coefficient model is limited. Numerous research studies have demonstrated that random coefficient regression functions are superior statistical techniques to fixed coefficient functions [2]. Hence, constructing a spatial structure in a random coefficient function is expected to exhibit a superior regression performance. Lately, some inspiring spatial random parameters functions have been established, including spatial random parameters Poisson-lognormal regression and spatial random parameters Tobit regression [49; 50]. These models were used to investigate crash frequency and crash rate, respectively. Based on an in-depth review of crash severity studies, few spatial random coefficient models were found that could fit crash severity. This is a methodological gap in traffic safety analysis and corresponding research should be conducted to enrich statistical theory.
## 5 Fragility and Leakage Risk Assessment of the NCS
### Fragility Assessment Results
#### 5.2.2 Fragility Curves of the NCS  
The fragility assessment of the NCS under internal pressure is essentially the conditional failure probability of the NCS at a given internal pressure. On the basis that the pressure capacity adheres to a lognormal distribution, the fragility curve can be determined in the following equation:  
\[F_{R}(p)=\Phi\bigg{(}\frac{\ln(p/P_{\rm m})}{\beta_{C}}\bigg{)}=\int\limits_{- \infty}^{p}\frac{1}{\sqrt{2\pi}\cdot\beta_{C}\cdot p}\exp\left[-\frac{1}{2} \bigg{(}\frac{\ln(p/P_{\rm m})}{\beta_{C}}\bigg{)}^{2}\right]dp \tag{8}\]  
where \(p\) is the internal pressure; \(P_{\rm m}\) is the median pressure capacity of the NCS; \(\beta_{C}\) is the composite logarithmic standard deviation, which can be calculated by the combination rule of the square root of the sum of the squares (SRSS) [37]:  
\[\beta_{C}=\sqrt{\beta_{R}+\beta_{M}} \tag{9}\]  
where \(\beta_{R}\) is the logarithmic standard deviation of the pressure capacity caused by the uncertainty of material properties; \(\beta_{M}\) is the logarithmic standard deviation of the pressure capacity caused by the modeling uncertainty, which can be taken as 0.12, as suggested by [PERSON] et al. [38].  
The fragility parameters \(P_{\rm m}\) and \(\beta_{R}\) can be determined by the moment estimate value from the 30 samples with the following expressions:  
\[P_{\rm m}=\exp\left(\frac{1}{n}\sum\limits_{i=1}^{n}\ln(P_{i})\right) \tag{10}\]  
\[\beta_{R}=\sqrt{\frac{1}{k-1}\sum\limits_{i=1}^{k}(\ln(P_{i}/P_{\rm m}))^{2}} \tag{11}\]  
where \(P_{i}\) is the pressure capacity of sample \(i\); \(k\) is the total number of the samples.  
Figure 14 illustrates the fragility curves of the gusset region of the NCS at different degrees of liner corrosion. It can be seen that the fragility curve gradually moves to the left with an increase in corrosion degrees, which indicates that the risk of containment leakage increases under the same internal pressure. Meanwhile, the statistical results of the fragility parameters are summarized in Table 3. Compared to the uncorroded case, the median pressure capacity of the NCS with 10%, 20%, and 30% corrosion degrees of the liner is reduced by 6.26%, 11.72%, and 16.02%, respectively. Moreover, the logarithmic standard deviations in the corroded case are all larger than those in the uncorroded case. In general, the NCS under liner corrosion conditions is susceptible to functional failure at lower levels of accident pressure, and the variability of the pressure capacity is greater in the corroded case than in the uncorroded case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\boldsymbol{\eta}\) (\%) & \(\boldsymbol{P_{\rm m}}\) (MPa) & \(\beta_{R}\) & **Decrease Rate of \(\boldsymbol{P_{\rm m}}\) (\%)** \\ \hline
0 & 1.654 & 0.022 & 0.00 \\
10 & 1.551 & 0.029 & 6.26 \\
20 & 1.46 & 0.033 & 11.72 \\
30 & 1.389 & 0.035 & 16.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pressure capacity of the NCS at different degrees of liner corrosion.