esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Model Training
#### 3.2.3 Simulation Results  
The established motion trajectory was in the northeast direction and each type of target moved along the same path. We recorded the actual position and the observed distance and orientation information in unit time and transformed them from the polar coordinate system \((r,\theta)\) into the Cartesian coordinate system \((x,y)\). The initial position information of the target and its velocity in the axis direction were expressed as \((x,y)=(-100\;\text{m},200\;\text{m})\). We set the initial position of the moving target as \((x,y)=(-100\;\text{m},200\;\text{m})\), the initial speed as \((v_{x},v_{y})=(2\;\text{m}/\text{s},20\;\text{m}/\text{s})\), the process noise variance as \(0.01\times diag[0.5,1,0.5,1]\), and the observation noise variance as \(10\times eye(2)\). During the observation scanning period \(T=1\;\text{s}\), the total number of samples was 80.  
In the simulation experiment, the real path was compared to the tracking results from the Kalman filtering method and the LSTM-Kalman filtering method. Figure 10 shows the final target tracking results.  
The above figure shows that the target estimation error of the traditional Kalman filtering method fluctuated with great scope, while the Kalman filter that was corrected  
Figure 10: Comparison of the real path, the tracking trajectory that was obtained using the Kalman filtering method, and the tracking trajectory that was obtained using the LSTM–Kalman filtering method.  
Figure 9: MAE of the LSTM-NN model after 1000 epochs of training.  
by the LSTM network was more in line with the actual result and the prediction error was small. We compared the two trajectories and the results are shown in Figure 11.  
Compared to the actual target trajectory, the traditional Kalman filter deviated by less than 50 m, but the fluctuation range was large and the average deviation was 19.58 m. After the Kalman filtering method was corrected using the LSTM model, the deviation was less than 20 m, the average deviation was 7.79 m, and the overall error was reduced by 60%.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas
### Other Tuning Techniques
#### 3.3.1 Static vs. Dynamic  
One of the advantages of the liquid dielectric antennas is their rapid reconfigurability as the liquid can be quickly removed when the antenna is not is use which lower the weight of the antenna and ease its transportation. It also means that a dynamic type antennas can be realized. For example, in Reference [51], a shunt excited monopole antenna was positioned vertically on the sea surface and the bottom end of the cylinder was connected to a water pump. When the pump was on, it would take the sea water, guide it through the cylinder, and thus activate the antenna. It was found that the water stream coming out of the top of the cylinder increases the bandwidth of the antenna. However, such design proved only two states (on and off) and no frequency tuning.  
A variation of this dynamic type antenna was presented in Reference [104], where this monopole antenna was tilted with respect to the vertical axis such that the sea water stream coming out of the top of the cylinder forms a sinusoidal curve. Again, no frequency tuning was demonstrated, however, it is possible that by changing the tilt angle the resonance frequency may shift. In Reference [105], a similar tilted design was demonstrated to receive a broadcast signal.
## 2 Materials and Methodology
### Network Architecture of CCS-YOLOv8
#### 2.2.3. Carafe  
Conventional up-sampling techniques often fail to incorporate semantic information, leading to the production of feature maps that may lack realism, naturalness, and fidelity to the original image content. We include the CARAFE [36] lightweight up-sampling operator into the YOLOv8n baseline model's feature fusion network. The CARAFE operator adaptively recombines the up-sampled feature maps, increasing the perceptual range based on the content and structure of the feature maps, helping the model better capture global information in the image, thereby improving the accuracy of the reconstruction and alleviating the inherent limitations of traditional up-sampling techniques.  
As illustrated in Figure 5, the CARAFE operator accomplishes efficient feature up-sampling and reorganization through two principal steps: First, the up-sampling kernel  
Figure 4: Details of the C2 fCBAM.  
Figure 3: Overview of the CBAM.  
prediction module determines the attention weight of each up-sampled site based on the mapping relationship between the up-sampled sites and the down-sampled feature map. The input feature map is rearranged in the second stage by the content-aware reorganization module using the resulting up-sampling kernel to maintain contextual information and spatial details while enhancing multi-scale target identification performance.  
Define \(N(\mathcal{X}_{l},k)\) as the \(k\times k\) neighborhood region of \(\mathcal{X}\) centered at position \(l\). The kernel prediction module predicts a location kernel \(\mathcal{W}_{l^{\prime}}\) based on \(N(\mathcal{X}_{l},kup)\), represented as:  
\[\mathcal{W}_{l^{\prime}}=\psi(N(\mathcal{X}_{l},k_{up})), \tag{3}\]  
where \(\psi\) is the kernel prediction module.  
Subsequently, the content-aware reorganization module recombines the neighborhood region \(N(X_{l},k_{up})\) of \(\mathcal{X}_{l}\) with the predicted kernel \(\mathcal{W}_{l^{\prime}}\) to obtain the up-sampled target feature \(\mathcal{X}_{l^{\prime}}^{\prime}\):  
\[\mathcal{X}_{l^{\prime}}^{\prime}=\phi(N(X_{l},k_{up}),\mathcal{W}_{l^{ \prime}}), \tag{4}\]  
where \(\phi\) is the content-aware reorganization module.  
The CARAFE operator offers multiple advantages over traditional up-sampling methods and other decomposition methods. It possesses a large sensory field, which can better capture the image's semantic information during the feature fusion process. Furthermore, the CARAFE operator provides a potent tool for multi-scale image processing, which is crucial for enhancing the precision and efficiency of the target detection task. It does this by amplifying the multi-scale target detection effect following multi-level feature fusion without adding excessive parameters or calculations. Experiments have demonstrated that the CARAFE operator can reduce unnecessary interference information and works effectively with datasets that contain background noise.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 3 Numerical Example
### Model Updating  
As explained in Section 2.2, the model updating process was practically similar to previous model updating methods. The difference was that the mode shape of the node DOF of Equation (35) was not used as it was, but was transformed into the measured values as shown in Equations (40) and (41) when the residual for the mode shape was adopted as the objective function.  
Model updating can be done for the measurement mode obtained from the strain sensors or for the measurement mode obtained from the acceleration sensors or for the mixed measurement mode from both types of sensors. Since model updating for the mixed measurement mode falls out of the scope of the present study, this case was not considered here.  
Under the assumption that the initial elastic modulus of all the elements was 20 GPa, the model updating using the eigenvalue \(\lambda_{mea}\) of the strain gauges, the measured mode \(\phi_{e,maa}\), and the transform matrix \(\overline{B}\) gives an elastic modulus of 25 GPa for all the elements. This value corresponds to that of the model and demonstrates the feasibility of the proposed approach. Figure 4a shows the model updating process. The upper graph plots the change of the residual norm and the lower graph plots the changing norm for the difference between the final updated parameter and the parameter at each iteration. Only four iterations are needed to see that convergence was reached. In addition, the model updating using the eigenvalue \(\lambda_{mea}\) of the accelerometers, the measured mode \(\phi_{a,maa}\), and the transform matrix \(\overline{N}_{a}\) provided similar results. The convergence process in Figure 4b shows a similar pattern to the case where the strain was measured.  
The proposed approach was seen to allow model updating not only by means of the acceleration measurement but also by means of the strain measurement. There was also no need to limit the location of the sensors to the location of the nodes in the model. This means that the sensors can be installed at arbitrary locations inside the structure. Consequently, it was then possible to model the structure regardless of the position of the sensors and to select the location of the sensors independently from the model.
# Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design
## 2 Simulation-Based Reliability Design Optimization Method (SBRDOM)
### SORA and Its Associated Mathematical Formulation  
It has been practically testified that SORA [27] is an efficient algorithm to solve reliability design optimization problems, which has been widely used due to its high efficiency. In SORA, the optimization solution process is decomposed into a deterministic optimization and an uncertainty analysis, which are executed alternately. The detailed process of SORA is shown in Figure 3 and explained as follows.  
Step 5.1: Initial value setting. The starting points including the design variables and the parameters were set. Let the shift vector \(\mathbf{s}=0\), and set the iteration number \(k=1\). Set \(\mathbf{P}_{\text{iMPP}}=\mathbf{\mu}_{\mathbf{p}}^{(0)},\mathbf{x}_{\text{ iMPP}}^{(0)}=\mathbf{\mu}_{\mathbf{x}}^{(0)}\), where the superscript (0) stands for the initial iteration, and \(\mathbf{P}_{\text{iMPP}}\) and \(\mathbf{x}_{\text{iMPP}}\) stand for the inverse most probable point (iMPP) of the random parameter and the iMPP of the random variable, respectively.  
Step 5.2: Current shift vector \(\mathbf{s}\) calculation. The shift vector \(\mathbf{s}\) is calculated by  
\[\mathbf{s}_{i}=\mathbf{\mu}_{\mathbf{x}}^{(k)}-\mathbf{x}_{\text{iMPP}}^{(k)} \tag{3}\]  
where \(\mathbf{s}_{i}\) is the vector of the shift vector corresponding to the \(i\)th performance constraint function, \(k\) stands for the \(k\)th iteration, \(\mathbf{x}_{\text{iMPP}}^{(k)}\) is the iMPP corresponding to the \(i\)th performance constraint function.  
Step 5.3: Deterministic design optimization. In this step, the deterministic design optimization was conducted, whose model is given as follows.  
\[\begin{array}{c}\min f(\mathbf{\mu}_{\mathbf{x}})\\ \text{s.t. }g_{i}(\mathbf{\mu}_{\mathbf{x}}-\mathbf{s}_{i},\mathbf{p}_{\text{ iMPP}})<0\\ \mu_{\mathbf{x}}^{L}\leq\mathbf{\mu}_{\mathbf{x}}\leq\mathbf{\mu}_{\mathbf{x}}^{L }\\ i=1,2,\cdots,n\end{array} \tag{4}\]  
Figure 3: Detailed process of SORA.  
Step 5.4: Reliability assessment. \(\mathbf{p_{iMPP}}\) and \(\mathbf{x_{iMPP}}\) could be determined by using the reliability assessment, which was conducted through the following optimization model.  
\[\begin{array}{ll}\max&G_{i}(\mathbf{u})\\ s.t.&\left\|\mathbf{u}\right\|=\Phi^{-1}(R_{t})\end{array} \tag{5}\]  
where \(G_{i}\) is the \(i\)th performance function in standard normal space (u-space), and \(\mathbf{u}\) is the vector of standard normal random variables and parameters, that is \(\mathbf{u}=\left[\mathbf{u_{p}},\mathbf{u_{x}}\right]\). \(\mathbf{u_{p}}\) and \(\mathbf{u_{x}}\) are transformed from the original vector of random variables and random parameters, respectively. For the transformation formulas, please refer to Ref. [28].  
Step 5.5: Convergence judgment. Judging whether the objective function achieved convergence and whether the constraints were respected. If the convergence criterion os met, then go to step 6, otherwise let \(k=k+1\) and proceed to the next iteration.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### Gpu
#### 3.1.9 Other Applications  
The rest of the applications appear with less than five references with the use of GPU has been used (electrical vehicles [217; 218; 219; 220], probabilistic power flow [221; 222; 223], power system visualization [232; 233], electricity market [240], small signal analysis [241], transient stability-constrained optimal power flow [229], and short circuit analysis [234]).  
GPU has helped with the following tasks: to optimize the large-scale design of electric vehicles [217], to accelerate probabilistic power flow computation based on the Monte-Carlo simulation with simple random sampling [223], to visualize real-time power system contouring based on a power grid digital elevation model [233], to forecast power system demand improving the data training of an artificial neural network with a multi-layer perceptron architecture using the Levenberg-Marquardt learning method [240], to approximate the solution of large differential-algebraic equations for small-signal stability with four methods (Chebyshev discretization, time integration operator discretization, linear multistep, and Pade approximants) [241], and to speed up the short-circuit current calculation of large-scale power systems with a batch solution where the admittance matrix inverse, short-circuit current of the specified node, node voltages, and branch currents are calculated in parallel with the SIMD technique [234].  
Regarding TSCOPF, ref. [229] presented the parallelization of an OPF evaluating steady-state and transient-state constraints where fuel costs were optimized. The transient-state constraints for each contingency were computed in the GPU. The rest of the algorithm ran in the CPU. For SCOPF application, no reference was found where a hybrid CPU-GPU algorithm had been implemented to reduce the execution time of either power flow simulations or the contingency evaluation.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 2 Development and Application of Multimodal Interaction Technology in Remote Education
### Key Technologies in Remote Interaction  
Speech- and Text-Based Interaction: Speech-based communicative modes, as well as natural language processing, are essential elements for a multi-channel interaction systems. These technologies have been integrated into different digital platforms, thus facilitating language-based interactions in a more natural and intuitive way [11; 17].  
Visual- and Gesture-Based Interaction: More recently, computer vision technology has been used with cameras to sense gestures as well as facial expressions, enabling the simultaneous recognition of the movements and emotions of the user in real time [13; 14].  
Haptic- and Motion-Based Interaction: Employing devices with tactile feedback, for instance, gloves that are sensitive to force and touch-sensitive interfaces, will improve users' sense of physical involvement in the interaction. Therefore, these technologies are extremely important in the field of smart manufacturing, remote collaboration, and online teaching [18].  
Multimodal Data Fusion: Deep learning methods implement different fusion techniques, either early or late, or hybrid fusion models, in order to enrich speech, text, and visual data to generate multimodal data that in the end will lead to the development of advanced artificial intelligent systems with more elaborate context adaptations [19; 20].
## 5 Jerusalem Artichoke Supplementation Effects in Monogastric Animals on Productive Performances
### Effects of Jerusalem Artichoke in Reducing Skatole Levels  
Feeding J.A. to male pigs resulted in a dose-dependent decrease in skatole levels in the hindgut and adipose tissue. This reduction in skatole levels may be attributed to the decrease in Clostridium perfringens and the increase in short-chain fatty acids (SCFAs), with a subsequent reduction in pH [77]. Adding 4% of dried Jerusalem artichoke tubers for 40 days to diet of piglets (28 days of age) decreased skatole levels in the colon and feces, as well as a decreased the dry matter content and pH in the colon [81]. Additionally, there was a reduction in enterobacteria levels in the colon with increasing levels of J.A. [79].  
[PERSON] et al. [62] supplemented different levels of J.A. (4.1%, 8.1%, or 12.2%) in pig diets for 13 days before slaughter to reduce skatole and indole levels responsible for boar titant, which can affect the taste and odor of pork. The group fed with 12.2% J.A. showed a  
\begin{table}
\begin{tabular}{l c} \hline \multicolumn{3}{c}{**Jerusalem Artichoke Tubers**} \\ \hline \multicolumn{3}{c}{**Ileal Digestibility, g/kg**} \\ \hline Dry matter & 465 \\ \hline Ash & 385 \\ \hline Organic matter & 485 \\ \hline Nitrogen & 475 \\ \hline Crude fiber & 69 \\ \hline Energy & 489 \\ \hline \multicolumn{3}{c}{**Faecal Digestibility, g/kg**} \\ \hline Dry matter & 859 \\ \hline Ash & 706 \\ \hline Organic matter & 860 \\ \hline Nitrogen & 682 \\ \hline Crude fiber & 665 \\ \hline Energy & 864 \\ \hline \end{tabular}
\end{table}
Table 2: The ileal and fecal digestibility of J.A. tubers in pigs.  
decrease in skatole concentration in adipose tissue (\(p\) = 0.003) and reduced _E. coli_ levels in feces (\(p\leq\) 0.001). A significant correlation was found between J.A. concentration, _E. coli_ levels, and skatole levels in adipose tissue.  
The inclusion of J.A. in pig diets presents opportunities for reducing skatole levels and modifying the intestinal microbiota, but careful attention to diet composition and supplementation levels is necessary for achieving optimal results.
## 3 Results and Discussion
### Analysis of Phenolic Compounds

Table 6: Phenolic compounds analysis of the two studied plants *. Samples of EPT and EPF exhibited a diverse range of compounds. Notably, dimeric caffeoylquinic acid (752.8 and 1093.0 ppm) and quercetin-3-O-hexoside-7-O-rhammoside from the phenolic acids class (604.4 and 1092.7 ppm) were predominant, registering the highest concentrations in samples EPT and EPF, respectively. Other noteworthy compounds included quercetin-3-O-glucosylrutinoside (278.0 ppm), catechhin dimer (248.4 ppm), kaempfer 3,7-di-o-hexoside (473.4 ppm), and kaempfer-O-rhammodihexoside (356.6 ppm) from the flavonoids class, observed at elevated concentrations in the EPT samples. Conversely, sample EPF revealed the sagerinic acid isomer from the phenolic acids class (791.5 ppm) and kaempfer-rhammose-hexose-rhammose (782.2 ppm), as well as rutin (545.6 ppm), which are classified as flavonoids, were among the most abundant compounds. The findings of this study are consistent with previously published data [6; 28; 29]. Moreover, these results indicate that phenolic acids and flavonoids are the main phenolic compounds in both species. These plant-derived compounds possess biological activities including antioxidant, antidiabetic, anti-inflammatory, and anticancer effects [30]. Additionally, studies showed that flavonoids and phenolic acids present in white penoy tea [31], _Staphylea bumalda_ and _Staphylea holocarpa_ plants [32], and tea leaves [33] have a key role in flavor formation.
## 3 Reconstruction of Rainfall Field by OELs Network
### Simulation Experiment
We suppose the \(V\) has a sinusoidal variation from 244 to 246 dB which simulates the effect of other atmospheric factors [46; 47]. Based on Equation (4), the true \(SNR\) is obtained. In practice, the signal-to-noise ratio is estimated by receiver system and denoted as \(S\hat{N}R\). A maximum likelihood estimator is deployed in signal process terminal to estimate \(SNR\)[48]. The expected value and variance of \(S\hat{N}R\) are given by:  
\[\left\{ \begin{array}{l}S\hat{N}R=\frac{2W}{2W-3}\Big{(}SNR+\frac{1}{2W}\Big{)} \\ \mathrm{var}(S\hat{N}R)=\frac{2\cdot(2W)^{2}}{(2W-3)^{2}(2W-5)^{2}}\Big{[}SNR^ {2}+2\cdot SNR\cdot\Big{(}1-\frac{1}{W}\Big{)}+\frac{1}{2W}\Big{(}1-\frac{1}{W }\Big{)}\Big{]}\end{array} \right. \tag{20}\]  
where \(W\) is the number of data symbols used for \(SNR\) estimation. The bit rate of received signal is set as 10 Mbps [49]. Considering the bias of estimated signal-to-noise ratio and the relative error of measured rain attenuation, a Gaussian random variable \(n\) is added to true rain attenuation \(A_{R}\). Therefore, the measured attenuation \(\hat{A_{R}}\) is expressed as:  
\[\left\{ \begin{array}{l}\hat{A_{R}}=A_{R}+n\\ n\ \sim N(0,\ \mathrm{var}(S\hat{N}R)\ \pm\ 0.01\cdot A_{R})\end{array} \right. \tag{21}\]  
where 0.01 \(A_{R}\) represents 1% relative error in measured rain attenuation. Therefore, the path-average rain intensity of OELs can be calculated, and then the rainfall field will be reconstructed by using the ordinary kriging (OK) model. The simulation process of rainfall field reconstruction is shown in Figure 3.
## 4 Feature Extraction
### Time Domain Features
#### 4.2.2. Comparison of Features  
For accelerometer and RCOU signals, the root mean square (RMS), standard deviation (STD), average (AVG), and sample entropy were applied to abstract the feature from the original signal. Additionally, the sample entropy was applied to the accelerometer because it is suitable for analysis of complex signals and identifying obscure patterns. In Figure 5, a simple demonstration of different kinds of states shows the discrepancy in magnitude between the features. It can be seen that the magnitudes of AVG and STD of the damaged propeller were larger than the others. However, it is difficult to distinguish non-fault and motor screw loosening using AVG and RMS, in addition to the STD features of both the PWM signal and accelerometer, because of the smaller difference. Nonetheless, the sample entropy is a powerful characteristic in this circumstance, and enables classification of slight differences. In Figure 5b, the difference between the three kinds of states can be clearly distinguished.