esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Modelling
### BP Neural Network
#### 3.3.3 Bayesian Regularization (BR)  
A neural network can be considered as a conditional distribution model \(P(Y\mid X,W)\), The meaning of this is the probability distribution of the predicted value of the outcome as \(Y\) under the condition that \(X\) is the input and \(W\) is the weight of the network. Bayesian regularization is a regularization method based on Bayesian statistical principles. It avoids overfitting by incorporating prior knowledge into the probability distribution of the model parameters. In Bayesian regularization, model parameters are treated as random variables that obey some prior distribution. We then update the posterior distribution of these parameters by observing the data. The specific formula is as follows:  
\[G=\varphi L(w,b)+\eta e_{\omega} \tag{10}\]  
\[e_{\omega}=\frac{1}{n}\sum_{i=1}^{N_{w}}W_{i}^{2} \tag{11}\]  
where \(n\) is the total number of samples in the selected training set; \(e_{\omega}\) is the network weights sum of squares mean, i.e., the penalty term; \(N_{\omega}\) is the number of weights; \(W_{i}\) is the ith weight; \(\varphi\) and \(\eta\) are the penalty factor. The parameters of which are calculated as follows:  
\[\varphi=\frac{N_{\omega}-\gamma}{2L(\omega,b)} \tag{12}\]  
\[\eta=\frac{\gamma}{2e_{\omega}} \tag{13}\]  
\[\gamma=N_{\omega}-2\eta\cdot\text{tr}(H)^{-1} \tag{14}\]  
\(\gamma\) is the number of effective weights, and \(H\) is the loss function Hessian matrix. In the training of the BP neural network, the Bayesian regularization algorithm can adaptively adjust \(\varphi\) and \(\eta\). If \(\varphi>\eta\), then the E term in the objective function G plays a greater role and the algorithm will maximize the increase in training accuracy; if \(\varphi<\eta\), then the algorithm will make the effective weight of the network as little as possible through the penalty term and small so as to simplify the model and prevent overfitting.  
The BP neural network optimization process of this paper is described below:  
**Step1.**: Based on the input and output of the data, determine the optimal number of hidden layer neurons for the BP neural network based on an empirical formula.
**Step2.**: For the overfitting problem of the neural network caused by the default use of the LM algorithm, compare the experimental results of the LM training algorithm and the BR training algorithm, and choose the better performing algorithm to continue the optimization.
**Step3.**: On the basis of the optimization of the training algorithm in the previous step, the four neural network activation functions are experimented and compared, then the activation function with better experimental effect is selected to continue the optimization.  
**Step4.**: According to the shortcomings of previous experiments, such as more iterations and unstable accuracy, a genetic algorithm is designed to optimize the weights and thresholds of the BP neural network, and finally the optimization results are compared. Specific experimental comparisons are presented in Section 4.
## 3 Results and Discussion  
### Petrographic Thin-Section Study (PTS)  
Figure 2a-d provides several microphotographs of the analyzed samples (PAAZ-01, PAAZ-02, PAAZ-03 and PAAZ-04). The mineral composition consists of mordenite, altered hornblende crystals, plagioclase, muscovite, interstitial glass, opaque minerals (possibly ilmenite) as well as sercite. The texture is varied, highlighting the porphyry, glomerulyphic, intersertal, granular-xenomorphic, granular-hypidiomorphic, inequigranular and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Nomenclature of \\ specimens \\ \end{tabular} } & \multicolumn{5}{c}{Mortar components} \\ \cline{2-5}  & NA  & DW 3  \\  & (\%) & (\%) & (\%) & (\%) \\ \hline PCS 4  & 100 & - & - & 100 & 225 \\ \hline PC/PAAZ-015  & 100 & 75:25 & 70:30 & - & 225 \\ \hline PC/PAAZ-02 & 100 & 75:25 & 70:30 & - & 225 \\ \hline PC/PAAZ-03 & 100 & 75:25 & 70:30 & - & 225 \\ \hline PC/PAAZ-04 & 100 & 75:25 & 70:30 & - & 225 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formulation of mortar mixtures made with different proportions of PC/PAAZ (70:30% and 75:25%).  
merocrystalline textures. In all cases, an advanced zeolitization process can be observed that has affected both the matrix and the original minerals. In Figure 2a,b,d, it is possible to observe how the secondary mordenite has penetrated the interior of the hornblende crystals following the exfoliation lines. Another feature to highlight is the dark halo originating at the edges of the relict crystals due to the reaction of the crystal with the fluids forming mordenite and smectite. In the process of alteration of pyroxenic andesite by zeolite, the protominerals have been completely replaced by mordenite, as indicated in Figure 2a-c, in which the total replacement of a pyroxene crystal by mordenite within a devitrified matrix can be observed.  
The microphotographs shown in Figure 3a,b show the mineral composition of the samples of unaltered pyroxenic andesite (UPA-01 and UPA-02) that have not undergone the zeolitization process and that are considered as unaltered samples. Both samples are composed of plagioclase, pyroxene, amphibole, glass, opaques, sercite and iron oxide. The texture is porphyry, poikilitic, pilotaxitic, glomero-porphyry, fluid, inequigranular, merocrystalline and pseudomorphic.  
Figure 2: Microphotographs of the thin sections (**a–d**) with Obj x.2.5 and parallel (N//) (**a,b,d**) and crossed nicols (Nx) (**c**).
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 4 Discussion
### The Effect of Elastic Modulus of Rail on The Propensity of Squeal Noise
This phenomenon can be analyzed from a mechanical standpoint: when the rail's elastic modulus is higher, its resistance to deformation increases, and the rail surface approaches a rigid plane configuration. This, in turn, amplifies the transverse creep between the wheel and rail, rendering the system more susceptible to instability. In summary, the matching of stiffness between the wheel and rail has a substantial impact on system stability. It is evident that the ideal elastic modulus ratio between the rail and wheel hovers around 1.2, as it strikes an appropriate balance between stability and the mechanical properties required for safe train operation and rail durability.
## 4 Description of Different Deployed VPN Solutions
### SSTP Road Warriors Scenario  
SSTP, the successor to PPTP-based VPNs, was introduced by Microsoft with the Windows Vista system. This VPN service is still trusted in Windows 7, 8, 10, and 11 versions. SSTP uses 256-bit AES encryption, thus providing reasonable security in data transit while also providing a reasonable speed for encrypted tunnel communications. It is mainly used in the Microsoft environment, but implementations are available on Linux (client/server mode), on OpenWRT (client mode) and on the Mikrotik platform (client/server mode). Figure 11 shows the comparison of the average throughput and standard deviation in Wireguard Road Warriors scenario, wired (on the left) and wireless (on the right). As seen from the figure, OpenWRT as an SSTP client, performs less than  
Figure 10: Comparison of the average Throughput in OpenConnect Road Warriors scenario.  
Figure 9: Comparison of the average Throughput in IPsec IKEv2 X509 Road Warriors scenario.  
Windows 10, but it still has almost double performance compared to Mikrotik in a virtual machine; both use a dedicated kernel module to establish SSTP connections. Finally, the Raspberry Pi 2 also has noteworthy performance. Windows 10, of course, has the best performance with native support for this technology. As for the wireless clients, the iPhones are incompatible with this technology unless one purchases specific apps.
## 2 Method  
The presented method is based on an ANN architecture called predictor-evaluator network (PEN), which was developed by the authors for this purpose. The predictor is the trainable part of the PEN and its task is to generate--based on input data--optimized geometries.  
As mentioned, unlike the state-of-the-art methods, no conventionally topology-optimized or computationally prepared data are used in the training. The geometries used for the training are created by the predictor itself on the basis of randomly generated input data and evaluated by the remaining components of the PEN, called evaluators.  
The evaluators perform mathematical operations. Other than the predictor, the operations performed by the evaluators are pre-defined and do not change during the training. It would also be possible to use evaluators based on an ANN.  
Each evaluator assesses the outputs of the predictor with respect to a certain criterion and returns a corresponding scalar value as a measure of the criterion's fulfillment. This fulfillment is the loss or the error of this evaluator. A scalar function of the evaluator outputs (objective function \(J\), see Section 2.7) combines the individual losses.  
During the training, the objective function computed for a set of geometries (batch) is minimized by changing the predictor's trainable parameters; see Section 2.2. In this way, the predictor learns how to produce optimized geometries.  
The predictor, the individual evaluators, their tasks and their way of operation are explained in detail in the following sections.
## 3 Antenna Simulation and Measurement
### Gain and Measurement  
The gain of a chip antenna is an important indicator of its performance. A high-gain antenna can concentrate more energy in a specific direction, thereby achieving a greater communication distance. However, increasing directionality may reduce coverage in other directions. Figure 17 shows the gain plot, with a peak gain of \(-4\) dBi at 98 GHz. Figure 18 illustrates the gain pattern of the antenna on different surfaces, while Figure 19 provides a 3D representation of the gain pattern, indicating a gain of \(-4.5\) dBi. Figure 20 shows a microscopic image of the antenna during the measurement, with visible GSG markings. Figure 21 shows the measurement setup, covering the range 70-140 GHz and utilising the GSG probe.  
Figure 16: S-parameter plot of the simulated and measured responses.  
Figure 17: Antenna gain graph.  
Figure 19: Three-dimensional radiation pattern of the antenna.  
Figure 18: (**a**) The 2D radiation pattern of the Vivaldi antenna (XY plane); (**b**) 2D radiation pattern of the Vivaldi antenna (YZ plane); and (**c**) 2D radiation pattern of the Vivaldi antenna (XZ plane).  
Figure 20: Microscopic image of the antenna.  
Table 2 presents a comparison between the antenna proposed in this study and various other types of antennas on the chip. Due to the wide bandwidth characteristics of the Vivaldi antenna, it outperforms [5, 6, 17, 19, 24] in terms of bandwidth. Additionally, in terms of the Vivaldi antenna size, the one proposed in this document measures 1.2 \(\times\) 1.2 mm, showing no significant difference from other types of antennas.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### Modal Identification with the LSCF and LSFD Estimators (1 st Step)
#### 2.1.2 The LSFD Estimator  
The LSFD is a technique commonly used in frequency-domain modal analysis to estimate the modal residues and to model the influence of the out-of-band modes in the frequency band of interest. When the poles are known a priori, these parameters can be estimated in a LS sense with the following pole-residue modal model with upper and lower residual terms  
\[H(\omega)=\sum_{m=1}^{N_{m}}\frac{[\mathit{Res}]_{m}}{j\omega-\lambda_{m}}+ \frac{[\mathit{Res}]_{m}^{*}}{j\omega-\lambda_{m}^{*}}+a(\omega)\left[LR \right]+b(\omega)\left[UR\right] \tag{4}\]  
where \(H(\omega)\in\mathbb{C}^{N_{h}\times N_{i}}\) is the modelled FRF matrix; \(N_{o}\), \(N_{i}\) and \(N_{m}\) denote, respectively, the number of outputs, the number of inputs and the number of vibration modes; \(\lambda_{m}\in\mathbb{C}\) and \([\mathit{Res}]_{m}\in\mathbb{C}^{N_{o}\times N_{i}}\) stand for the pole and modal residue corresponding to the \(m\)th vibration mode; \(LR\), \(UR\in\mathbb{C}^{N_{o}\times N_{i}}\) are, respectively, the lower and upper residuals used to model the influence of the out of band modes in the considered frequency band and \(a\) and \(b\) are scalars defined according to the measured quantity (i.e., displacement, velocity or acceleration) and to the type of analysis performed (i.e., Experimental or Operational Modal Analysis (EMA or EMA)) in Table 1[4]; and the operator \((\bullet)^{*}\) denotes the complex conjugate and \(j=\sqrt{-1}\) the imaginary unit. The poles \(\lambda_{m}\) occur in complex-conjugated pairs and are related to the eigenfrequencies \(\omega_{n_{m}}\) and damping ratios \(\xi_{n_{m}}\) as  
\[\lambda_{m},\lambda_{m}^{*}=-\omega_{n_{m}}\xi_{n_{m}}\pm j\sqrt{1-\xi_{n_{m} }^{2}}\omega_{n_{m}} \tag{5}\]  
where \(\omega_{n_{m}}=2\pi f_{n_{m}}\), with \(f_{n_{m}}\) representing the natural frequencies of each mode. An extended description of the LSFD estimator formulated in Pole-residue Modal Model is found in Appendix A.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Output Quantity**} & \multicolumn{2}{c}{**FRFs**} & \multicolumn{2}{c}{**Full Spectra**} & \multicolumn{2}{c}{**Half Spectra**} \\ \cline{2-7}  & \(\mathbf{a(\omega)}\) & \(\mathbf{b(\omega)}\) & \(\mathbf{a(\omega)}\) & \(\mathbf{b(\omega)}\) & \(\mathbf{a(\omega)}\) & \(\mathbf{b(\omega)}\) \\ \hline Displacement & \(\frac{1}{(j\omega)^{2}}\) & \(1\) & \(\frac{1}{(j\omega)^{4}}\) & \(1\) & \(\frac{1}{j\omega}\) & \(j\omega\) \\ \hline Velocity & \(\frac{1}{j\omega}\
## 4 Conclusions  
The present study shows the effect of the media pH in the growth of different bacterial species, putting in evidence for the important role of the type of acid used in media acidification in the survival of the microorganism. We observed that tolerance to the low pH is quite dependent on the type of acid used to acidify the growing medium, being that all studied bacteria were more susceptible to organic than inorganic acids. This suggests that bacteria use different regulatory circuits of acid resistance/tolerance to cope with different types of acidic environments. Moreover, this work also reveals that additional appropriate pH controls are mandatory when evaluating antibacterial activity under acidic conditions to avoid obtaining biased results not considering the effect of the type of acid. Such strategy will be particularly relevant, for instance, in experiments examining the effects of acidic foods and food constituents in the prevention of foodborne bacterial pathogens.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010305/s1](https://www.mdpi.com/article/10.3390/app13010305/s1)]([https://www.mdpi.com/article/10.3390/app13010305/s1](https://www.mdpi.com/article/10.3390/app13010305/s1)), Figure S1: Different mechanisms used by bacteria to cope with acidic environments.  
Conceptualization, P.P.-F., A.M.V., and J.S.C.; methodology, P.P.-F. and A.M.V.; formal analysis, P.P.-F. and A.M.V.; investigation, P.P.-F. and A.M.V.; resources, P.P.-F. and A.M.V.; data curation, P.P.-F., J.A.M.P., A.M.V., and J.S.C.; writing--original draft preparation, P.P.-F., J.A.M.P., A.M.V., and J.S.C.; writing--review and editing, P.P.-F., J.A.M.P., and J.S.C.; supervision, A.M.V. and J.S.C.; project administration, J.S.C.; funding acquisition, J.S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by FCT-Fundacao para a Ciencia e a Tecnologia through the CQM Base Fund-UIDB/00674/2020, and Programmatic Fund-UIDP/00674/2020, and a PhD Grant SFRH/BD/129630/2017 given to [PERSON], Portuguese Government funds, and by ARDITI-Agencia Regional para o Desenvolvimento da Investigacao Tecnologia e Inovacao, through the project M1420-01-0145-FEDER-00005-Centro de Quimica da Madeira-CQM+(Madeira 14-20 Program) and Project M1420-09-5369-FSE-00001 for the Post-Doctoral fellowship granted to [PERSON].  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflict of interest.  
Figure 3: Possible mechanisms of bacterial acidification by weak (organic) and strong (inorganic) acids. Weak acids can easily diffuse into bacteria cytoplasm where they are dissociated and acidify the internal medium. In contrast, strong acids are usually dissociated and will not reach the bacterial cytoplasm as easily as weak acids. A\({}^{-}\)–anion, H\({}^{+}\)–proton, HA–acid.
## 3 The Proposed Framework
### Task Statement  
The meanings of the symbols used throughout this paper are provided in Table 1. Following [6], an AVE is defined as an event in a video clip that is both visible and audible. Performing the AVE localization task involves both predicting the temporal boundaries  
Figure 2: The pipeline of our proposal. The whole method is divided into two parts. We first train the audio-visual representation network using the proposed AV-BT. Then, the fixed representations produced by audio and visual encoders are sent to the proposed TACMA for late information fusion.  
and identifying the content information. Specifically, given a synchronous audio-visual video sequence \(S=(S_{a},S_{v})\), where \(S_{a}\) and \(S_{v}\) denote the auditory portion and visual portion, respectively. \(S\) is continuously divided into \(T\) nonoverlapping segments of equal duration. As in [19; 50], the annotation of the \(t\)-th segment \(S^{t}=(S_{a}^{t},S_{v}^{t})\) is defined as \(\mathbf{y}^{t}=\left\{y_{c}^{t}|y_{c}^{t}\in\{0,\,1\},\sum_{c}y_{c}^{t}=1\right\} \in\mathbb{R}^{C}\), where \(C\) denotes the number of categories. If no event happens in a video segment, it will be labeled as _background_. Note that in our method, we directly classify the _background_ as one of the categories and use no additional supervision to help the network distinguish it from events. Compared to an AVE, it is noticeably more challenging to identify a _background_ because its audio and visual content are not correlated with each other.
## 2 Materials and Methods
### Different Possible Connections Computation Approach
For the scanning algorithm [25], which is a method by which the entire connection space is considered one by one, increasing the number of rows means increasing the computational cost, and the solution of the problem is no longer applicable in real time, because the time in real time is limited to determine the new configuration that can adapt to changing irradiance conditions. It is clear that a calculation that can take hours or even days is not applicable. Instead of scanning the entire space one point by point, it would be much more effective to use metaheuristic algorithm-based approaches that allow reaching the global maximum in a short time, that is, finding the most suitable match that will maximize efficiency, well below the real applicable times. The GA, which has a metaheuristic-based \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Array (m)**} & \multicolumn{2}{c}{**Probability Calculation of All Connections**} \\ \cline{2-3}  & **Single-Adaptive PV Array \(\rightarrow\)\(P(\binom{2^{m}}{m})\)** & **Dual-Adaptive PV Array \(\rightarrow\)\(P(\binom{2^{m}}{m})\)** \\ \hline
3 & \(P(\binom{2^{3}}{3})=P(\binom{8}{3})=336\) & \(P(\binom{2^{23}}{3})=P(\binom{6}{3})=249,984\) \\
4 & \(P(\binom{2^{4}}{3})=P(\binom{16}{4})=43,680\) & \(P(\binom{2^{24}}{4})=P(\binom{25}{4})=P(\binom{256}{4})=4,195,023,360\) \\
5 & \(P(\frac{2^{8}}{5})=P(\frac{3^{2}}{5})=24,165,120\) & \(P(\frac{2^{10}}{5})=P(\frac{1024}{5})=1,114,942,319,124,480\) \\
6 & \(P(\frac{2^{8}}{6})=P(\binom{6^{6}}{6})=751,408,320\) & \(P(\frac{2^{2}}{6})=P(\binom{4096}{6})=4,705,096,570,216,277,114,880\) \\
7 & \(P(\frac{2^{7}}{7})=P(\frac{128}{7})=476,410,007,808,000\) & \(P(\frac{2^{24}}{7})=316,506,657,532,245,270,854,601,277,440\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculation of all possible different connection possibilities of fixed and adaptive panels depending on the row numbers in single and dual-adaptive structures. algorithm approach, can converge to the optimum value in a shorter time thanks to its mutation and crossover features and depending on some parameters of the algorithm, without having to consider all the possibilities in the space. Moreover, the reconfiguration process for the four-row structure, which exceeds the real-time usage time of the scanning algorithm and takes days to calculate, can be performed in real-time with the GA.
###### Abstract  
Accurate prediction of the remaining useful life (RUL) of rolling bearings was crucial for ensuring the safe operation of machinery and reducing maintenance losses. However, due to the high nonlinearity and complexity of mechanical systems, traditional methods failed to meet the requirements of medium- and long-term prediction tasks. To address this issue, this paper proposed a recurrent neural network with a dual attention model. By employing path weight selection methods, Discrete Fourier transform, and selection mechanisms, the prediction accuracy and generalization ability in complex time series analysis were significantly improved. Evaluation results based on mean absolute error (MAE) and root mean square error (RMSE) indicated that the dual attention mechanism effectively focused on key features, optimized feature extraction, and improved prediction performance. An end-to-end RUL prediction model was established based on the MS-DAN network, and the effectiveness of the method was validated using the IEEE PHM 2012 Data Challenge dataset, providing more accurate decision support for equipment maintenance engineers.  
reliability assessment; deep learning; dual attention mechanism; multi-scale partitioning 2020 ac academic Editor: [PERSON]