esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 1 Introduction
Recent studies--described in the present review and based on numerical calculations--have shown for elastodynamic problems that the accuracy of the Kirchhoff theory should depend on the parameter \(\sigma^{2}/L\) (\(\sigma\) and \(L\) being the RMS surface height and the height correlation length). [PERSON] is considered accurate when this parameter is smaller than an upper bound \(c\) depending on the wavelength: (\(\sigma^{2}/L<c\)). [PERSON] accuracy is also dependent on incidence and scattering angles. In the recent years new developments or comparisons of the previous methods have also been proposed in the literature and for different applications, such as non-destructive evaluation (NDE), underwater acoustics or even medicine. Section 1 of the current paper is devoted to the parametrization of rough surfaces, Gaussian height distribution being usually assumed in modeling. The two historical approximations (Kirchhoff and the perturbation theory) are then described (Section 2) as well as their use in the scientific literature, their validity and an application example for non-destructive testing. Then, more modern approaches with larger domains of validity (notably suitable for grazing observation angles and generally having applications for underwater acoustics) are presented in Section 3.
## 3 Proposed Methodology  
The proposed framework applies certain schemes to make balanced, robust, and improved NIDS. It includes certain steps to perform class balancing and features-selection-based NIDS that are shown in Figure 1.  
The proposed framework includes dataset cleaning and the removal of unnecessary features from the given dataset. The meaningful features are then further selected before classification. However, at first, the minority class is balanced with the GAN method, and instances are increased with the appropriate method. The detailed discussion in coming sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Year** & **Methods** & **Dataset** & **Results** \\ \hline
[21] & 2019 & SMOTE to solve class imbalance and classical classification method & KDD 99 Cup & Highest Accuracy = 92.57\% \\ \hline
[22] & 2020 & SMOTE, GMM, and CNN & UNSW-NB15 & Binary-Accuracy = 98.8\%, \(\text{F1}=95.53\%\) \\ \hline
[22] & 2020 & SMOTE, GMM, and CNN & UNSW-NB15 & Multi-Accuracy = 96.54\%, \(\text{F1}=97.26\%\) \\ \hline
[22] & 2020 & SMOTE, GMM, and CNN & CICIDS2017 & Multi-Accuracy = 99.85\(\text{F1}=99.86\%\) \\ \hline
[25] & 2020 & Focal-Loss based DNN and CNN & NSL-KDD & DNN Bi.-F1 = 83.92\%, \(\text{M}\)-F1 = 47.33\% CNN & \(\text{Bi.-F1}=84.87\%\), \(\text{M}\)-F1 = 51.96\% \\ \hline
[25] & 2020 & Focal-Loss based DNN and CNN & UNSW-NB15 & DNN Bi.-F1 = 79.24\%, \(\text{M}\)-F1 = 98.90\% CNN & \(\text{Bi.-F1}=95.57\%\), \(\text{M}\)-F1 = 95.51\% \\ \hline
[23] & 2022 & Bi-LSTM and ADASYN method of assigning attention weights for class imbalance & NSL-KDD & Accuracy = 90.73\%, \(\text{F1}\)-Score = 89.65\% \\ \hline
[24] & 2022 & SMOTE-KNN for class imbalance and random forest for classification & NSL-KDD & Testing-Accuracy = 78.47\% \\ \hline
[26] & 2022 & Modified cross entropy applied for class imbalance and Neural Network applied for Classification & NSL-KDD & Accuracy = 85.56\% \\ \hline
[26] & 2022 & Modified cross entropy applied for class imbalance and Neural Network applied for Classification & UNSW-NB15 & Accuracy = 90.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recently applied NIDS studies using class imbalance problem solving.
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 3 Method of Adaptive Sampling Grid Partitioning
### DM Code Image Preprocessing  
After obtaining the coarsely positioned DM code image, the image is scaled to a pixel space of 256 \(\times\) 256 by bilinear interpolation to ensure that the algorithm has similar performance on all images and can be better compared and analyzed.  
Since this algorithm needs to calculate the gray value deviation between each pixel of the DM code image and its adjacent pixels and accumulate the gray value deviation, it has a certain sensitivity to image noise; but since this algorithm judges the overall waveform, it has good anti-blur performance. Therefore, filtering and binarization can be performed on the DM code image before dividing the adaptive sampling grid.  
Bilateral filtering [38] is a nonlinear filtering method that can reduce noise while maintaining clear edges by combining information from the spatial domain and gray value domain. The OTSU13 method is based on the analysis of the image grayscale histogram and finds an optimal threshold to divide the image into two parts: background and foreground. This algorithm uses bilateral filtering and the OTSU method to carry out filtering and binarization operations on DM code images.
## 3 Proposed Technique
### Implementation of the Proposed Technique  
The proposed technique is obtained by combining the two parts in the previous subsections. The flowchart of the proposed technique is shown in Figure 1 with detailed steps summarized as follows.  
1. The initialization parameters are set to \(k=1\) and \(\hat{\psi}=0\). The array output signal is obtained using Equation (4) and the local carrier replica is generated as Equation (10).
2. The ICR and POW can be calculated according to Equations (14) and (15), and the \(d_{\text{ICR}}\) and \(d_{\text{POW}}\) can be obtained through Equations (16) and (17).
3. If \(d_{\text{ICR}}>T_{1}\mid\mid d_{\text{POW}}>T_{\text{P}}\) at the \(K\)-th correlation epoch, then the phase bias estimate is reset to \(\hat{\psi}=0\) to start phase bias estimation. Otherwise, move to step (5).
4. If \(\text{ICR}>T_{\text{J}}\), then the NCO is tuned by the prediction model using \(\hat{\mathbf{X}}_{k}^{-}\) (see Equation (11)) in \([K,K+L-1]\) correlation epochs. The phase bias is estimated using robust estimation in Equations (19)-(24). After that, set \(k=k+L-1\), and move to step (6). Otherwise, move to step (5).
5. The NCO is tuned using \(\hat{\mathbf{X}}_{k}=\hat{\mathbf{X}}_{k}^{-}+\mathbf{K}_{k}\delta\tilde{ \varphi}_{k}\) in Equation (13).
6. Set \(k=k+1\) and return to step (2).  
From the above, it can be seen that the proposed technique inserts the phase bias detection and estimation processing (steps (2)-(4)) into the conventional receiver processing (steps (1), (5), and (6)). The proposed technique operates in real time, with no processing delays, and requires only modest changes to existing receivers.
## Data and Methods
### Data Processing  
The processing of the collected data from the Insta Pro2 camera is essential in order to retrieve multimedia and metadata in a proper format. As we mentioned in the previous paragraph, Insta Pro2 consists of 6 individual cameras. The footage from one camera is processed by the StreetScouting platform, and usually, the camera that is directed towards the front road is chosen. To process the multimedia stream, the video frames are extracted at a specific FPS which is set to 10 by default. Moreover, the frames are clipped to specific dimensions to eliminate any distortion from the ultra-wide lens. Then, the GPS data of the video is extracted from the metadata. The GPS data are synchronized with the extracted frames and any missing data are filled with linear interpolation. In the case of the video input from a conventional camera and external GPS data file in GPX format, the processing pipeline is similar. The only difference is that the frames are not clipped but resized to particular dimensions preserving the aspect ratio of the image.
# Surface Plasmon Nanolaser: Principle, Structure, Characteristics and Applications
## 3 Structures and Characteristics of Plasmonic Nanolasers
### Plasmonic Nanolaser Based on Single Perovskite Nanowires  
In 2001, [PERSON] et al. first studied the application of ZnO nanowires in deep-ultraviolet laser emission [58]. A series of subsequent studies show that material loss plays an important role in the threshold of plasmonic nanolasers. Although nanowires have many advantages as a gain media of laser emission, their application is limited by their high lasing threshold, so it is necessary to find more ideal optical gain materials. In recent years, metal halide perovskite materials with excellent optical conversion properties have attracted abundant attention. Metal halide perovskite material has a long carrier lifetime (10-100 ns) and a long carrier diffusion length (micron scale), and also has a high fluorescence quantum efficiency, which indicates that it has great potential as a gain medium.  
In 2016, [PERSON] et al. of the University of California first reported the application of all-inorganic perovskite nanowires in a laser field [59]. They successfully prepared CsPbBr\({}_{3}\) and CsPbCl\({}_{3}\) nanowires by the liquid-phase cryogenic solution method and used them as gain media, as shown in Figure 7. Compared to the synthesis of semiconductor nanowires mentioned above, the preparation process and conditions of perovskite nanowires are much simpler. The nanowires prepared by the low-temperature solution method have a length of 2 to 40 \(\upmu\)m and a width of 0.2 to 0.3 \(\upmu\)m, which is an ideal gain medium. Under pulsed laser excitation, the lasing threshold of CsPbBr\({}_{3}\) nanowires is 5 \(\upmu\)Jcm\({}^{-2}\), and the maximum quality factor is about 1009. The time-resolved fluorescence spectroscopy results show that the radiative recombination lifetime of CsPbBr\({}_{3}\) nanowires is less than 30 ps under stimulated emission, which is much smaller than the radiation lifetime under normal conditions. The minimal recombination lifetime causes the carriers to deplete rapidly and be unable to participate in spontaneous emission, meaning that high-quality factor lasers can be obtained. Different from the double exciton recombination in the excitation process of semiconductor nanowires, the author attributes the excitation emission of all inorganic perovskite nanowires to the electron-hole plasma mechanism, which refers to two different gain mechanisms. Electron-hole plasma is formed when the carrier concentration exceeds the Mott density, which makes it possible to stimulate emission. The formation of the electron-hole plasma will reduce the refractive index and lead to the blue shift of the resonant mode. The experimental results provide evidence for the correctness of the electron-hole plasma mechanism. Compared with the previously reported organic-inorganic hybrid perovskite [60], all-inorganic perovskite exhibits better stability. Under the excitation of a pulsed laser, CsPbBr\({}_{3}\) nanowires can normally emit a laser for 109 cycles, equivalent to 1 h.
# Load Frequency Control for Multi-Area Power Plants with Integrated Wind Resources
## 3 Wind Plant Model  
A typical variable speed wind turbine generator system (VS-WTGS) is evaluated. For the VS-WTGSs, the wind power generates mechanical torque via the turbine generator shaft; thus, electrical torque is produced. The mechanical system acceleration, deceleration or constant speed depend on the change in mechanical and electrical torque. Thus, the net power output is related to the mechanical power.  
Building the dynamic equations of the wind plant is based on the relationship of mechanical output power and wind velocity. The output power is therefore given as [35]  
\[P_{m}=C_{p}(\lambda,\beta)\frac{1}{2}\rho AV^{3}\underset{wind}{ \overset{wind}{\overset{wind}{\overset{\right.\] \ }}{\overset{\right.\] \ }}{\overset{\right.\] \ }}{\overset{\right.\] \ }{\overset{\right.\ }{\overset{\right.\ }{\overset{\right.\ }{\overset{\right. \ }{\overset{\right.\ }{\overset{\right.\ }{\overset{\right. \ }{\overset{\right.\ }{\overset{\right. \ }{\overset{\right. \}{\overset{\right. \}{\overset{\right. \}{\left.\}{\left.{\overset{Performance coefficient (\(C_{p}\)) of the turbine is given by.  
\[C_{p}(\lambda,\beta)=c_{1}\bigg{(}\frac{c_{2}}{\lambda_{i}}-c_{3}\beta-c_{4} \bigg{)}e^{\frac{-c_{3}}{\lambda_{i}}}+c_{6}\lambda \tag{9}\]  
with  
\[\frac{1}{\lambda_{i}}=\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1} \tag{10}\]  
where the coefficients \(c_{1}\) to \(c_{6}\) are dependent on the wind plant.  
Finally, we can present the equation of the wind plant via a per-unit (p.u) system, as given below.  
\[P_{m_{\_}pu}=k_{p}\times C_{p\_}pu\times V_{wind\_}pu^{3} \tag{11}\]  
\(P_{m\_}pu\) is the mechanical power in per unit, \(k_{p}\leq 1\) is the amplified power factor, \(C_{p\_}pu\) is the power factor and \(V_{wind\_}pu\) is the wind speed.  
To design the new LFC for the MAIPS, we derive the following basic assumption. A lemma is also adopted to accompany the progress of the work.  
**Assumption 1**.: _Load uncertainty \(\Delta P_{\_}di(t)\) and the differential of \(\Delta P_{\_}di(t)\) is bounded such that \(\|\Delta P_{\_}di(t)\|\leq\tau_{\_}i\) and \(\|\Delta P_{\_}di(t)\|\leq\tau_{\_}i\), where \(\gamma_{\_}i\) and \(\overline{\gamma}_{\_}i\) are known scalars and \(\|.\|\) is a matrix norm._  
**Assumption 2**.: _The eigenvalues of the matrix \(A_{\_}i-T_{\_}iC_{\_}i\) can be chosen arbitrarily by appropriate choice of the observer gain \(T_{\_}i\) when the pair \([A_{\_}i,C_{\_}i]\) is observable._  
**Lemma 1**[24].: _Let \(\mathbf{X}\) and \(\mathbf{Y}\) be a real matrix of suitable dimension then, for any scalar \(\mu>0\), the below matrix inequality holds:_  
\[\mathbf{X}^{T}\mathbf{Y}+\mathbf{Y}^{T}\mathbf{X}\leq\mu\mathbf{X}^{T}\mathbf{ X}+\mu^{-1}\mathbf{Y}^{T}\mathbf{Y}. \tag{12}\]
## 4 Discussion
### Selection of Regularization Parameters  
In the regularization term, we need to introduce different regularization factors for each of the five parameters to be solved. Different combinations of regularization factors have a significant impact on the inversion results. Trying each combination one by one would obviously be extremely inefficient. So, we introduced the Bayesian optimization  
Figure 6: The normalized sensitivity of each parameter.  
method in the step of selecting regularization parameters. The Bayesian optimization method is a hyperparameter optimization approach based on sequential decision-making. Its core has two aspects: First, it uses a surrogate model to output the expected value of the objective function, guiding the search direction; and second, it evaluates and updates the surrogate model through an acquisition function, enabling iterative optimization.  
Our surrogate model employs the commonly used Gaussian Process Regression (GPR). This model assumes that the objective function follows a Gaussian distribution. Then, the approximation of the objective function can be expressed as follows:  
\[f(x)\,\sim GP\Big{(}\mu(x),\sigma^{2}(x)\Big{)} \tag{15}\]  
where \(f(x)\) is the objective function, \(GP()\) denotes a Gaussian distribution, \(\mu(x)\) represents the predicted mean (expected performance), and \(\sigma(x)\) stands for the predicted standard deviation (uncertainty). Based on the output of the surrogate model, we calculated the value of the acquisition function \(\alpha(x)\) at all candidate points \(x\). We then select the point that maximizes \(\alpha(x)\) as the next sampling point:  
\[f(x)\,\sim GP\Big{(}\mu(x),\sigma^{2}(x)\Big{)} \tag{16}\]  
We used the commonly adopted Expected Improvement (EI) method as the acquisition function:  
\[\alpha(x)=\Bigg{\{}\begin{array}{ll}(\mu(x)-f(x^{+})-\xi)\Phi(Z)+\sigma(x) \phi(Z),&\text{if }\sigma(x)>0\\ 0,&\text{if }\sigma(x)=0\end{array} \tag{17}\]  
where \(Z=\frac{\mu(x)-f\big{(}x^{+}\big{)}-\xi}{\sigma(x)}\), \(\xi\) is the exploration parameter, \(\Phi\) is the Cumulative Distribution Function (CDF) of the standard normal distribution, and \(\phi\) is the Probability Density Function (PDF) of the standard normal distribution.  
The process of conducting Bayesian optimization using the above method is shown in Figure 7. A total of 200 iterations were carried out. According to the figure, convergence was basically achieved after the 100 th iteration. The obtained optimal combination of regularization factors is \(\lambda_{1}=1.76\times 10^{-15},\lambda_{2}=9.64\times 10^{-15},\lambda_{3}=9.5 3\times 10^{-14},\lambda_{4}=8.77\times 10^{-15}\).  
Figure 7: Bayesian optimization process.
# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 2 Related Work
### Audio Classification Based on Attention Mechanism  
As computer performance enhanced, accurate recognition was achieved using deep neural networks for audio classification tasks. In comparison with traditional artificial features, convolutional neural networks can extract time-frequency features directly from the original audio spectrogram. Additionally, convolutional neural networks have local modeling and translation invariance, and these inherent a priori inductive biases to help learn representations. However, they have disadvantages such as poor modeling of contextual information and poor understanding of global information. Therefore, a recent trend to capture long-range global context more effectively is to introduce an attention module into CNN. These approaches have been successful in a variety of audio tasks, such as speech emotion recognition [42; 43] and audio event classification [44; 45]. Recently, with the development of pure attention-based models in the visual domain, it has become a popular direction to introduce Transformer structure [35] in audio classification. [PERSON] et al. [25] transferred Vision Transformer [46] to audio classification and improved the recognition accuracy through a pure self-attention mechanism and ImageNet [28] pretrained model. Due to a large number of AST parameters, it takes up a lot of GPU resources and takes a long time to train on the full AudioSet [47]. [PERSON] et al. [40] improved global attention in AST by replacing it with sliding-window attention. The continuous reduction of time-frequency dimensional information as the network deepened reduced the algorithmic complexity of the model's self-conscious computational process, but also lost some ability to model the audio spectrogram globally as a result. Unlike the mixed calculation of the time-frequency dimension, [PERSON] et al. [41] designed a separable Transformer (SepTr). It calculates attention separately on the frequency and time axes, first processing tokens within the same time period and then within the same frequency. Although this method occupies less memory, it loses a certain recognition accuracy in the task of environmental sound classification [48].
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 4 Systems Description and Configuration
### Baseline ASR Architectures
#### 4.2.2 Quartznet Q5\(\times\)5 Based System  
This ASR engine was presented as the third contrastive system to the Albayzin-RTVE 2020 Speech to Text Transcription Challenge, and we selected it as the second baseline system in this work in order to compare its performance with the previously presented Quartznet Q15\(\times\)5 based system.  
The Quartznet Q5\(\times\)5 architecture is similar to the Q15\(\times\)5 architecture explained in Section 4.1.2, but, in this case, each group of block is composed of a block \(B_{i}\) repeated only one time, instead of three, thus decreasing the total number of parameters from 18.9 to 6.7 M.  
In contrast to the Q15\(\times\)5 based system, the training of the Q5\(\times\)5 was performed for 100 epochs, with a cosine annealing learning rate using a batch-size of 40.  
The decoding was realised using the same 5-gram language model described above. The parameters of the Beam Search CTC decoder corresponded to a _beam-width_ of 1000, \(\alpha=1.2\) and \(\beta=0\).
## 2 Characterization of Bacterial Biofilms
### Stages of Bacterial Biofilm Formation  
Biofilm formation is a complex process during which intercellular and intracellular signaling occur. This process consists of five stages (Figure 1b). The first is the microorganism's attachment to a living or nonliving surface with the use van der Waals forces, as well as by employing fimbiae and flagella. In the next step, bacteria form a monolayer and anchor themselves by producing an extracellular matrix. Next, the process of multiplication and division of microbial cells starts, initiated through particular chemical signaling within the EPSs. This action leads to the formation of microcolonies and gives rise to three-dimensional structures [9]. In the following phase, a thin biofilm is formed from layered cells and small clusters. As a result, the maturation and formation of the architecture of the biofilm take place. Clusters develop into large microcolonies, and many cells displace from the substratum to form channels and voids [15]. In the last stage of biofilm formation, microbial cells upregulate the expression of genes coding for proteins related to flagella formation, allowing the bacteria to move to a new location. Bacteria within the biofilm start to multiplicate quickly, detach, and disperse. This process enables conversion of bacteria to a motile form, with subsequent spreading and colonization of new surfaces [9]. The release of cells from a biofilm may be caused by different events, including environmental cues and microorganism-derived signals. On the one hand, the active bacterial escape from the protective biofilm environment is associated with nutrient deprivation, steepening concentration gradients of oxygen and waste products, and extracellular signaling compounds over the course of biofilm development that lead to the stress response and accumulation of molecules that are capable of inducing dispersion. Importantly, sensing and relay of signal is performed via a series of post-transcriptional modifications that result in modulation of cyclic di-guanosine monophosphate (c-di-GMP) level. A low concentration of this intracellular signaling molecule enhances motility and the planktonic mode of growth [16]. In turn, the passive release of cells or their loss is induced by mechanical, physical, or frictional forces. Notably, layers of biofilm may be broken off due to (i) natural collision of biofilm cells with environmental particles (abrasion), (ii) feeding activity of eukaryotic organisms (grazing), or (iii) frictional forces caused by the velocity of the liquids in the aqueous environment (erosion and sloughing) [16; 17].
## 3 Simulation Results and Discussion
### Equilibrium Simulation and Results  
The condition of route equilibrium will have a significant effect on network performance. Figure 9 shows the equilibrium condition for different node numbers.  
We can see from the Figure 9 that the route equilibrium of both TSR and TSBR decrease with the increasing number of nodes, but TSBR performs better than TSR.  
Figure 8: Route establishment time under different node numbers.  
Figure 9: Equilibrium degree for different node numbers.