esa-sceva/satcom-chunk-collection huggingface dataset:
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture
### Convolutional Block Attention Module  
In the field of image processing, the feature map contains a variety of important information. The traditional convolutional neural network performs convolution in the same way on all channels, but the importance of information varies greatly depending on different channels, hence, treating each channel equally can decrease the precision of the network.  
To improve the performance of convolutional neural networks for feature extraction, [PERSON] et al. [25] put forward a convolutional attention mechanism named Convolutional Block Attention Module (CBAM) in 2018, which is a simple and effective attention module for feedforward convolutional neural networks and contains two independent sub-modules, namely, Channel Attention Module (CAM) and Spatial Attention Module (SAM), which perform Channel and Spatial Attention, respectively. They added a module that can be seamlessly integrated into any Convolutional Neural Network (CNN) architecture and trained end-to-end with the base CNN to classical networks such as ResNet and MobileNet. The analysis showed that the attention mechanism in the spatial and channel dimensions improved the performance of the network to a certain extent.  
Figure 4: (a) ShuffleNet V2 Unit1 (**b**) ShuffleNet V2 Unit2. ShuffleNet-V2 is an effective lightweight deep-learning network with only 2M parameters. It uses the idea of group convolution from AlexNet and channel shuffle, which not only greatly reduce the number of model parameters, but also improve the robustness of the model. (DWConv [24]: Depth Wise Convolution).  
Given an intermediate feature map, the CBAM module will sequentially compute attention maps along two independent dimensions (channel and space), and then multiply the attention map with the input feature map for adaptive feature optimization.  
It determines the attention region by evaluating the importance of both the channel and spatial orientation of the image to suppress irrelevant background information and strongly emphasize the information of the target to be detected.
# Communication Analysis and Privacy in CAI Based on Data Mining and Federated Learning
## 5 Case Study
### Practical Application of the System
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Loan Success** & \multirow{2}{*}{**Accuracy A**} & **Loan Success** & \multirow{2}{*}{**Accuracy B**} & \multirow{2}{*}{**Average Accuracy**} \\  & **Rate A** & & & **Rate B** & \\ \hline Local BLS & 51.7\% & 73.9\% & 57.5\% & 82.7\% & 76.9\% \\ \hline Longitudinal VBFLS & 59.3\% & 85.6\% & 61.6\% & 88.5\% & 82.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model accuracy. \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{Combinability} & Components are combined through interfaces. \\  & Middleware is used for composition as much \\  & as possible. Middleware combines functions \\  & through component input or external \\  & interfaces. \\ \hline \multirow{4}{*}{Deploy ability} & To make it deployable, the component needs to \\  & be self-contained, and it must be able to run as \\  & an independent entity on the component \\  & platform that provides its component model \\  & implementation. \\ \hline \multirow{2}{*}{Documentation} & The component document must be a complete \\  & component document produced through CBSE \\  & process validation. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Average Accuracy (\%)** & **Training Duration (s)** \\ \hline BLS & 76.9\% & 62.4 \\ \hline Vertical V BFLS & 82.7\% & 104.6 \\ \hline MLP & 75.3\% & 252.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of model training time. such systems and finally realize the comprehensive informatization of secondary vocational computer online-aided teaching systems. To summarize, the difference between CAI software and other common application software is that its application serves teaching and integrates with the whole teaching process to improve teaching quality and students' learning efficiency. This determines that the design of CAI software must obey the guidance of modern education theory and conform to the objective teaching laws.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 2 HV-PLC Channel
### Study Case  
Consider as study case the power system with the layout shown in Figure 1. The full length of the line is \(l=210\) km and the distance between line transpositions is \(l_{T}=70\) km. Figure 5 provides the transversal geometry of the transmission line and the electric data required to evaluate the corresponding frequency-dependent matrices \(\mathbf{Y}\) and \(\mathbf{Z}\) as in Equations (1) and (2) [21]. From these matrices, one can then evaluate matrices \(\mathbf{A}\) and \(\mathbf{B}\) as in (8) and (9) and, further, \(\mathbf{A_{1}}\), \(\mathbf{A_{2}}\), \(\mathbf{B_{1}}\) and \(\mathbf{B_{2}}\) in (12). The desired PLC-channel transfer function \(H\) in (13) is derived according to the connection diagram of Figure 1, from the latter four matrix functions, from the nodal representations of the coupling devices in Figures 4a-c and from the matrices \(\mathbf{Y}_{BUS_{1}}\) and \(\mathbf{Y}_{BUS_{2}}\) representing power systems 1 and 2. For the sake of simplicity, in the present study case, it is assumed that both power systems can be considered as semi-infinite power lines; thus, according to (6):  
\[\mathbf{Y}_{BUS_{1}}=\mathbf{Y}_{BUS_{2}}=\mathbf{Y}_{c}\]  
Table 1 provides the coupling schemes being considered here. Table 2 provides the data for the coupling devices in Figure 4. Figure 6 provides plots of the PLC-channel frequency-response (attenuation) for each coupling scheme. One can note from this figure that the best couplings are \(A_{c_{1}}\) and \(A_{c_{5}}\). One can also notice there that coupling \(A_{c_{4}}\) presents a large attenuation peak very likely due to the cancellation of line modes. In practice, coupling \(A_{c_{4}}\) should thus be avoided.  
Figure 5: Transversal geometry of the transmission line HV.
# Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial
## 3 Results and Discussion
### Properties of liposomal Vitamin C in Powder Form
In vitro pharmacokinetic models are often used to rapidly assess the oral bioavailability of bioactive ingredients before performing expensive and time-consuming in vivo studies, including clinical trials [60]. To assess the permeability and uptake of various ascorbic acid formulations, we utilized the Caco-2 cellular model. This standard model, derived from human colon adenocarcinoma cells, is employed to effectively mimic the conditions of the small intestine [46, 61] and is widely used for studying intestinal absorption and transport mechanisms of various nutrients and pharmaceuticals [62, 63, 64]. In this experiment, the bioavailability, expressed as the relative difference in active ingredient content in the basal layer of the Caco-2 model, was 22.28% higher for liposomal vitamin C compared to the non-liposomal form (\(p<0.05\)) (Figure 5A). Given that the Caco-2 model is a reliable predictor of human intestinal absorption, this 22.28% increase is practically significant. Enhanced bioavailability suggests more efficient absorption, potentially leading to higher and more sustained plasma vitamin C levels. This can improve clinical outcomes, particularly in immune support and oxidative stress reduction, while allowing for lower dosages to minimize side effects and improve patient compliance. Therefore, the promising results observed in the Caco-2 model prompted us to test liposomal vitamin C powder in a clinical trial to further assess the advantages of the liposomal form in terms of higher bioavailability. \begin{table}
\begin{tabular}{c c} \hline \hline
**Quantiles \({}^{\ast}\)** & \\ \hline Harmonic mean \({}^{\ast\ast}\) & 267.7 nm \\ D10 & \(\leq\)223 nm \\ D16 & \(\leq\)224 nm \\ D50 (SD \({}^{\ast\ast\ast}\)) & \(\leq\)262 nm (49.8 nm) \\ D84 & \(\leq\)334 nm \\ D90 & \(\leq\)354 nm \\ Span & 0.500 \\ \hline \hline \end{tabular} \({}^{\ast}\)The results presented in this table are the average values obtained from 3 independent measurements of the tested product batch. \({}^{\ast\ast}\)The harmonic mean represents the average size of the liposomes after excluding particles with exceptionally large and/or small sizes. In line with the results presented in this table, both the D50 values and the harmonic mean are around 260 nm, indicating the absence of abnormally sized particles in the sample, falsifying the observed result. \({}^{\ast\ast\ast\ast}\)SD—Standard deviation.  
\end{table} 
Table 2: Parameters generated from a particle size distribution analysis using STEP-Technology® for liposomes.
# High Throughput Data Relay in UAV Wireless Networks
## 5 Numerical Results
In the DS method, the UAV hovers for the longest time, and in the two non-DS schemes, it hovers for much less time. When the UAV flying speed is less than around \(40\) m/s, it consumes most power when remains in the hovering status [28]. This is why the UAV consumes most propulsion energy in the DS method. However, for a longer time period, when \(T=60\) s, the non-DS bandwidth contention scheme consumes most propulsion energy. This is because the UAV hovers at the position of each victim sequentially. The DS method consumes more energy than the non-DS fair bandwidth allocation scheme, but with higher max-min average data rate among all the victims. To better understand the relationship of propulsion power and the speed of UAV, we plot Figure 8 with the same parameters configured in the simulation. By observing the curve, the minimum power consumption is at a UAV speed of around \(20\) m/s rather than when hovering, at \(0\) m/s. Hence in order to reduce the propulsion energy consumption for the DS method, we replace the hovering status with a circular movement with a relative small radius at a speed of \(20\) m/s. By providing a small angular  
Figure 7: DS method optimal trajectories comparison. UAV movement, the propulsion energy needed to provide sufficient lift is appreciably reduced as shown in the last row of Table 2.
###### Abstract  
This study proposes an adaptive distributed Student's t extended Kalman filter (EKF) using Allan variance for ultrawide-band (UWB) localization. First of all, we model the state equation using the target's position and velocity in east and north directions and the measurement equation by using distance between the UWB base station (BS) and the target object. Then, the adaptive distributed filter employs a federation structure: A local t EKF is designed to estimate the target's position by fusing the distance between the UWB base station and the target object. The main filter fuses the local filter's outputs and computes the final output. For the local t EKF, in order to overcome the problem that noise in the Kalman method is assumed to be white noise and difficult to adapt to practical application environments, the t distribution is used to model noise. Meanwhile, Allan variance is calculated to assist the local filter, which improves the adaptive ability. Experimental results show that the proposed method effectively enhances navigation accuracy compared to the distributed EKF.  
t +
Footnote †: journal: Sensors  
0000-0002-4891-3399][PERSON]  
0000-0002-3232-1883
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.4 Weighted Average Results

Table 9: Computing time for Jeju data. trajectory plots of predicted and actual points. The weighted average provides an advantage when the number of data for each case is different. Experiments with more data are considered more important; thus weights are based on the number of data. The weighted average is calculated as follows:  
\[\sum\
olimits_{i=1}^{n}d_{i}r_{i}\Big{/}\ \sum\
olimits_{i=1}^{n}d_{i}, \tag{2}\]  
where \(d_{i}\) refers to the number of data of the \(i\)th case and \(r_{i}\) refers to the evaluation result of the \(i\)th case. One of the indicators covered in this section is the standard deviation. As described in Section 4.1, this experiment uses the method of building ten models, evaluating each of them, and then obtaining the average. The performance of each model may vary for each run. For practical use, we need to determine whether or not the performance deviation of each model is large. Table 10 shows the overall performance of each method. In this context, CMA-ES showed the highest performance on the Seosan data and LSTM on the Jeju data. The performance of DNN and RNN on the Jeju data was good. The amount of data has to be equalized in each case of using neural networks. Rankings were calculated separately for each method in terms of MAE and NCLS. RBFN was the best for MAE, and LSTM was the best for NCLS. Since NCLS is more popular measure for predictions of drifter trajectory than MAE, LSTM was the best method for this study. As expected, LSTM was superior to RNN. There is past information to be forgotten and current information to be remembered according to the direction of the drifters. The performance of DNN and RNN was good for Jeju, so if we get more data in the future, it will be possible improve their performance. Evolutionary methods showed good performance on both Seosan and Jeju data. Especially for Seosan, where the number of data in each case was large, these methods showed better performance than the other methods. Figure 8 shows the trajectory of a drifter predicted by CMA-ES, which showed the best performance for Seosan. Figure 9 exhibits the trajectory of a drifter predicted by LSTM, which had the best
## 3 Microbiological Degradation of EDCs  
In developed countries, where the problem of endocrine substances is the greatest, sewage treatment plants play a key role in their removal. Organisms involved in the removal of estrogens from the environment include bacteria, as well as fungi and microalgae (Figure 1) [11]. Bacteria that degrade these hormones belong to genera such as _Bacillus_, _Virgibacillus_, _Novosphingobium_, _Rhodococcus_, _Acinetobacter_, _Pseudomonas_, _Sphingomonas_, _Enterobacter_, _Klebsiella_, _Aerononas_, _Comomonas_, _Thauera_, _Deinococcus_, _Stenotropomonas_ and cyanobacteria [3; 4; 6; 7; 10; 12; 25; 28; 31; 33; 34; 35; 36; 37; 38; 39]. _Raphidocelis subcapitata_, _Chlorella vulgaris_, _Chlorococcus_ and _Scentedesmus quadrica_ belong to algae capable of removing hormones [11; 26; 40; 41]. Fungi from genus _Trichoderma_, _Trantes_, _Planerochaete_, _Pleutotus_, _Apergillus_, _Lentinula_, _Drechslera_, _Curvalaria_, _Penicillium_, _Acremonium_, _Fusarium_, _Gibberella_, _Beauvaria_, _Doratomyces_, _Scopulariopsis_, _Chaetomidium_, _Thielavia_, _Neurospora_, _Absidia_, _Cunningham_, _Actinomucor_, _Rhizopus_, _Phyomyces_, _Mortierella_, _Circinella_ and _Gandorma_ (Table 2) were also described as being capable of hormone biodegradation/biotransformation [22; 30; 42; 43; 44; 45; 46]. Figure 1: The main mechanisms for removing EDCs [3; 9; 26; 30].
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 3 Numerical Simulations
### Single-Pipe Model with Cavity Disease  
This model simulated two types of the cavity disease around the pipeline, which further verifies the accuracy of the conformal algorithm. The model had two layers with an air layer on top and a clay layer on the bottom. As shown in Figure 8, there were some irregular cavities above the concrete pipe with an outer diameter of 0.6 m and an inner diameter of 0.48 m. Figure 9 shows a concrete pipe with an outer diameter of 1.2 m and an inner diameter of 0.96 m with some irregular cavities underneath. The spatial steps and time steps were 0.005 m and 0.01 ns, respectively, and the simulation iterations were 5000. Table 2 shows the conductivity and relative permittivity of different materials in the model. The excitation source of the model was the same as above.  
Figure 8: Schematic diagram of model with cavity disease on the upper part of pipeline.  
Figure 7: GPR B-scan images of the two-layer cavity model: (**a**) Obtained by conformal ADI-FDTD method; (**b**) Obtained by conformal FDTD method; (**c**) Obtained by non-conformal ADI-FDTD method; (**d**) Obtained by non-conformal FDTD method.  
By analyzing Figure 10, the non-conformal model profile only shows one distinct reflected wave at the bottom of the pipe. The profile of the conformal model shows that there are two reflected waves on the upper and lower sides of the pipe, and the reflected waves of the irregular cavity disease above the pipe are incredibly apparent. As shown in Figure 11, both the non-conformal and conformal model profiles clearly show the reflected waves at the top and bottom of the pipe, but the non-conformal model profile only shows the reflected waves from the cavity damage on the left side, while the conformal model profile completely shows the reflected waves distributed on both sides of the cavity damage. As can be seen in Figures 10 and 11, the circular pipe grid points are more accurately processed using the conformal grid technique, and the conformal ADI FDTD algorithm is more accurate.
###### Abstract  
This paper follows up on a reference paper that inspired MDPI's topic \"Stochastic Geomechanics: From Experimentation to Forward Modeling\", in which global and local deformation effects on sand specimens were fully described from high-resolution boundary displacement fields. This paper is supported by that study's experimental database, which is open to the scientific community for further study. This paper focuses on the analysis of this experimental study to investigate strain localization effects on a subset of tests included in this database. Strain localization is defined here as associated with the non-homogeneous deformation process occurring in elastoplastic materials, including sands. Many experimental and numerical studies have been conducted during the last two decades to explore the characteristics of localization effects on sand, and to determine how these contribute to the failure mechanisms of specific sands. Under a triaxial compression condition, localization effects have been studied mainly with regard to particle kinematics and translational strain of the specimen's displacement fields. However, to the best of the authors' knowledge, there has been no 3D experimental kinematic analysis performed on sands to study the localization phenomena that can directly relate the impact of a specimen's initial and boundary conditions to a failure mechanism during a triaxial test. In this paper, we introduce a full set of 3D kinematic operators under cylindrical coordinates to assess the boundary localization effects of deforming sand specimens under triaxial loading conditions. Furthermore, a set of experiments were carried out under varying experimental conditions to study the impact of variability in these localization effects. Results show that patterns of kinematic effects are quantifiable and can be used to assess likely failure-influencing factors, such as confining pressure, initial density, sample geometry, and sample heterogeneity, in the development of specific failure mechanisms. Spatio-temporal interdependencies between localization effects, such as the interactions between shear, expansion, and compaction bands observed during the specimen's shearing process, were also studied. We therefore hypothesize that the proposed framework will serve as the basis for quantifying the uncertainty associated with the development of localization effects over the boundary of sand-deforming specimens.  
s +
Footnote †: journal: Applied sciences
###### Abstract  
Malolactic fermentation (MLF) converts malic acid into lactic acid by lactic acid bacteria (LAB). MLF may affect potential wine quality impact as global warming intensifies, and the alcohol in the wine increases, which threatens MLF. _Lactiplantobacillus plantarum_ is considered a new generation of MLF starter because of the ability of high ethanol tolerance and good enological characteristics. In this research, 132 LAB strains were isolated from the eastern foothills of Helan Mountain in Ningxia, China. Twenty-one higher ethanol tolerance isolates were obtained by 15% (\(\
icefrac{{r}}{{v}}\)) ethanol preliminary screening. They were identified by 165 rRNA sequencing and differentiated by randomly amplified polymorphic DNA (RAPD). Stress factors include ethanol, pH, and SO\({}_{2}\), and the combination of stresses was used to screen stress-tolerance strains. \(\beta\)-D-glucosidase activity, MLF performance, and biogenic amine content were tested to evaluate the enological characteristics. GC-MS detected the volatile components of the wine after MLF. The results showed that twenty strains were identified as _L. plantarum_, and one strain was _Lentilactobacillus bilgardii_. Especially, the strains of A7, A18, A23, A50, and B28 showed strong resistance to high ethanol, low pH, and high SO\({}_{2}\). A7, A50, and B28 showed better \(\beta\)-D-glucosidase activity and thus were inoculated into cabernet Sauvignon wines whose ethanol content was 14.75% (\(\
icefrac{{r}}{{v}}\)) to proceed MLF. A7 finished MLF in 36 d, while the control strains _Oenococcus oeni_ 31-DH and _L. plantarum_ BV-S2 finished MLF in 24 d and 28 d, respectively. Nevertheless, A50 and B28 did not finish MLF in 36 d. The data showed that A7 brought a more volatile aroma than control. Notably, the esters and terpen in the wine increased. These results demonstrated the potential applicability of _L. plantarum_ A7 as a new MLF starter culture, especially for high-ethanol wines.  
malactic fermentation; _Lactiplantobacillus plantarum_; enological potential