esa-sceva/satcom-chunk-collection huggingface dataset:
# Scattering of Plane Waves by Cylindrical Cavity in Unsaturated Poroelastic Medium +
## 3 Numerical Results and Discussion  
From the perspective of engineering seismic resistance, the main goal of the research is to study the dynamic response at the surface of the cavity, which is focused on the dynamic stress concentration factor. Take P\({}_{1}\) wave incidence for example, the influence of the saturation on the hoop stress concentration factor (\(\sigma_{\theta\theta}^{S}/\sigma_{0}\)) of the unsaturated porous medium at the cavity surface will be studied under both the impermeable and permeable conditions next. \(\sigma_{0}\) is the maximum amplitude of hoop stress of the solid phase, owing to the incident wave at the same position. The material parameters are set per [PERSON] (2005) [21] and listed in Table 1. A dimensionless frequency \(k_{0}a\) determined by the wavelength and the cavity radius is introduced. (1)  
The influence of saturation \(S_{\rm r}\) on the dynamic response of the cavity  
Taking the second set of parameters in Table 1 with [PERSON]'s ratio \(v=0.3\) and the different saturations \(S_{\rm r}=0.2\), \(0.6\), \(0.95\), \(1.0\) under different frequencies \(k_{0}a=0.5\), \(1.0\), \(3.0\), Figures 2 and 3 show the distributions of \(\sigma_{\theta\theta}^{\rm S}/\sigma_{0}\) around the surface of the cavity for the P\({}_{1}\) wave incidence under impermeable and permeable conditions, respectively. Considering that the P\({}_{1}\) wave velocity of the unsaturated porous medium varies greatly with the saturation, here, \(k_{0}\) is taken as the wave number of the S wave for \(S_{\rm r}=1.0\). As shown in the figure, for both the impermeable and permeable conditions, the stress distribution around the cavity is symmetrical on both sides of the incident wave. The response of the stress around the cavity for \(S_{\rm r}=1.0\) is significantly different from that for \(S_{\rm r}=1.0\). When \(S_{\rm r}<0\), the saturation has little effect on the distribution of stress around the cavity, especially at low-frequency incidence. It means that the existence of the gas phase significantly affects the dynamic response of the cavity in the unsaturated medium. The main reason for the effect is that the existence of the gas phase will significantly affect the wave velocity of the P wave in the unsaturated medium [14]. The stress distribution around the cavity changes significantly with the change of the frequency of the incident wave. At low-frequency incidence (\(k_{0}a=0.2\)), the maximum appears slightly deviated from the orthogonal direction (\(\theta=80^{\circ}\) and \(280^{\circ}\)). With the increase in the incident frequency, the direction in which the maximum value occurs gradually approaches the direction of
## 7 Use of Biomaterials in the Release of Extracellular Vesicles for Treatment of Cutaneous Wounds
### Pluronic F127 Hydrogels  
Pluronic F127 (PF127) allows the manufacture of thermosensitive injectable hydrogels, because at certain concentrations the increase in temperature induces its gelation. Furthermore, the use of PF127 has been approved by the U.S. Food and Drug Administration (FDA) for the controlled release of drugs, due to its good biocompatibility and absorbability [115]. Thus, the fabrication of an injectable adhesive, with thermosensitive multifunctional polysaccharide-based hydrogel scaffolds has been described. It is generated by a reversible Schiff base reaction between PF127 grafting aldehyde pullulan and polyethyleneimine or PF127, oxidative hyaluronic acid and Poly-\(\varepsilon\)-L-lysine. Gelification is reached at temperatures above 30 \({}^{\circ}\)C. It exhibits antibacterial activity, good UV-shielding performance, fast hemostatic ability, tissue-adhesive and self-healing behavior. In addition, these biomaterials can be loaded with MSC-EV, through electrostatic interactions, and released in response to changes in pH [116; 117]. Hydrogels were generated by incorporation of adipose-tissue MSC-derived EV, termed FEP@exo and FHE@exo. They showed increased proliferation, migration and formation of tubular structures in HUVEC, in vitro. In a diabetic full-thickness cutaneous-wound mouse model, the application of FEP@exo or FHE@exo accelerated wound healing. That promoted increased granulation tissue formation and collagen deposition, enhancing extracellular matrix remodeling, and favoring the appearance of skin appendages. Interestingly, unlike what happens when MSC-EV are directly applied to wounds, MSC-EV in these hydrogels were progressively released throughout the healing process for three weeks, maintaining their biological activity [116; 117].  
Type I and III collagens are the major components of the dermal extracellular matrix. While collagen I is the most abundant in normal skin, collagen III is produced during healing. Abundant deposition of the latter in the early stages of wound healing facilitates the generation of scarless skin [118]. In the case of PF-127 hydrogels loaded with EV derived from adipose-tissue MSC, increased production of Collagen III has been observed during healing, favoring the formation of scar-free wounds [119]. Also, these hydrogels decrease the inflammatory response by downregulating _TNF-\(\alpha\)_ and _IL-6_, as well as promoting polarization from M1 into M2 macrophages [119]. EV derived from hUCMSC have also been encapsulated in PF127-based hydrogels. They were evaluated in a streptozotocin-induced diabetic rat model of scarring. The PF127 hydrogel protected the biological activity of MSC-EV, increasing angiogenesis and granulation-tissue formation. That accelerated wound healing, relative to treatment with MSC-EV or hydrogel alone [120].
## 2 Experimental Equipment and Signal Processing
### Acquisition Signal Processing  
#### 2.4.1 Kalman Filtering  
According to the Kalman filter, the magnetic induction data of each channel are denoised. The whole noise reduction process is divided into two processes: the prediction stage and the update stage. The prediction stage is mainly divided into two steps: state prediction and covariance prediction. State prediction is the first step of Kalman filtering, which uses the state and control input of the previous moment to predict the current state. Then, the uncertainty of the current moment is updated by covariance prediction. The updating stage is mainly divided into three stages: calculating Kalman gain, updating motion state, and updating the covariance matrix. In the updating stage, the Kalman filter uses the new measured value to update the previous state prediction. By calculating the Kalman gain, the filter balances the uncertainty between prediction and measurement, resulting in a more accurate state estimate [29; 30; 31]. After the Kalman filter completes the state prediction and update, the two processes are repeated continuously until the data processing is completed [32; 33; 34]. The main process is shown in Figure 3.
## 3 Results
### Proposed Calibration Models
#### 3.2.2 Two-Step Calibration  
The identification of the direction of the marker axes through Vuforia has an angular nature. That is, the larger this angle, the greater the divergence between the idealized virtual plane and the virtual plane created by the system. Despite the good quality of computer vision algorithms for marker recognition, distortions are still present, and small divergences in the identification of these angles can generate large inconsistencies as the user wearing the device moves away from the origin. To mitigate this axis angular problem, we proposed a two-step calibration system. This calibration system consists of adding a second marker with a fixed and known distance and orientation relative to the first marker. This second marker aims to determine angles forming the axes more precisely. In other words, unlike simple calibration, which uses Vuforia to recognize the first marker, instantiate a hologram based on the orientation and proportions of the marker, and use the position and direction of this hologram to create the axes, two-step calibration uses the first marker to determine the position of the hologram and the second marker to rotate the hologram. Only after recognizing the second marker will the axes be generated based on the hologram. It is important that the two markers are facing each other at approximately 1.5 m.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 3 Results
### Accuracy Assessment
\begin{table}
\begin{tabular}{c|c c c c c c c c|c c} \hline \hline  & \multicolumn{2}{c}{**Ground**} & \multicolumn{2}{c}{**Truth**} & \multicolumn{2}{c}{**(Pixels)**} & & & & & & & \\ \hline
**Class** & **MinLWS** & **MLWS** & **MLW** & **MLWN** & **MSL** & **MHWN** & **MHW** & **MHWS** & **Total** & **User Acc.** \\ \hline
**Water** & 120 & 378 & 82 & 25 & 5 & 3 & 0 & 0 & 613 & \\
**\textless{5\%**} & 11 & 105 & 26 & 1 & 1 & 1 & 1 & 0 & 146 & 79\% \\
**5–25\%** & 62 & 395 & 117 & 25 & 16 & 3 & 2 & 2 & 622 & 86\% \\
**25–50\%** & 27 & 122 & 196 & 186 & 99 & 1 & 6 & 5 & 642 & 44\% \\
**50–75\%** & 6 & 51 & 83 & 88 & 406 & 52 & 45 & 27 & 758 & 60\% \\
**75–95\%** & 0 & 9 & 13 & 4 & 36 & 49 & 27 & 32 & 170 & 64\% \\
**95–99\%** & 0 & 0 & 0 & 1 & 3 & 15 & 17 & 5 & 41 & 54\% \\
**S1 Land** & 0 & 1 & 7 & 7 & 17 & 108 & 211 & 99 & 450 & 22\% \\
**DEM > 50 cm** & (0) & (0) & (3) & (4) & (4) & (4) & (4) & (4) & (4) & (4) \\
**Total** & 226 & 1061 & 524 & 337 & 583 & 232 & 309 & 170 & 3442 & \\
**Prod. Acc.** & 32\% & 47\% & 22\% & 63\% & 87\% & 44\% & 9\% & 80\% & & \\
**ITZ area acc.** & 47\% & 64\% & 84\% & 93\% & 99\% & 99\% & 100\% & 100\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation confusion matrix of the ITZ AtmExp. product versus GPS tracks along the different tidal reference levels. Note that the classes do not correspond to each other as the tidal level lines correspond to the border between classes. Gray cells indicate correct classification in regard to the individual tidal reference lines. User and producer accuracies are indicated. The red squares indicate the correct classification into low-, mid-, and high-tide regions. The last line indicates the accuracy to map a certain reference line as part of the ITZ area.
# Automatic Age and Gender Recognition Using Ensemble Learning
## 3 Dataset and Methods
### Ensemble Learning
#### 3.3.2 Stacking  
Stacking is an ensemble learning approach in which the results of the base classifiers forming the ensemble are combined with a meta-classifier. This approach consists of two stages called Level 0 and Level 1, and its schematic diagram is as shown in Figure 1. In the first stage, a set of base-level classifiers is generated from the initial training dataset. Then, the predictions of these classifiers are stacked and the predictions are applied to the meta-classifier to produce the final predictions of the ensemble model. The training set of the meta-classifier is created according to the leave-one-out or cross-validation procedure. Assuming that there are \(L\) base-level classifiers and a training set consisting of \(m\) samples, in the leave-one-out procedure, one sample from the training set, \(s_{i}\), is reserved for testing and the base-level classifiers are trained with the remaining \(m-1\) samples. Then, the predictions of the base-level classifiers are generated for the sample, \(s_{i}\). This process is repeated for all samples, and the training set of the meta-classifier is created by stacking the correct classes of the samples with the predictions of the \(L\) base-level classifiers. If the leave-one-out method is used to train base-level classifiers, \(L\) classifier must be trained \(m\) times. This process will be quite time-consuming for a large dataset. In this case, the \(k-\)fold cross-validation method was used instead of leave-one-out. The training dataset was divided into \(k\) parts; one of these parts was used for testing the classifier, and the other \(k-1\) part was used for training the classifier. Thus, the \(L\) base-level classifier was trained \(k\) times instead of \(m\). Since \(k\) will be considerably smaller than \(m\), the use of this method in training base-level classifiers will significantly reduce the computational cost. In this research, the five-fold CV approach was used to generate the training set of the meta-classifier. Any machine learning method can be used as a meta-classifier, but experimental studies have shown that the use of complex models causes overfitting problems [29]. For this reason, simple models such as linear regression are preferred as meta-classifiers [30].
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Mechanisms by Which Walnut Contributes to the Management of NAFLD
#### 3.2.4 Effects of Walnut on Stemness  
Hepatic stem/progenitor cells are bipotent progenitor cells that can differentiate into either hepatocytes or cholangiocarcinomaytes. They are quiescent during physiologic conditions but are activated in response to acute and chronic liver injuries and play a critical role in regeneration and repair. Hepatic stem/progenitor cell activation appears to be a physiologic response of the liver to oxidative stress in NAFLD [75].  
Even though there is no direct evidence regarding the effects of walnut on hepatic stem/progenitor cells, the results of two animal studies indicate a beneficial effect. In an _Adenomatous Polyprosis Coli_ (_APC_)-deficient animal model of colon cancer, walnuts seems to preserve intestinal stem cell function in high-fat diet conditions [87]. To assess the intestinal stem cell function, crypts were isolated from the small intestine of mice fed either a low-fat, high-fat, or high-fat diet plus walnut for four weeks. An ex vivo 3D intestinal organoid assay revealed that four weeks of a high-fat diet significantly reduced the ability of intestinal stem cells to generate organoid-like structures, but walnut consumption reversed the high-fat diet-induced decline in intestinal stem cell function. Consumption of walnut--containing a high concentration of \(\alpha\)-linolenic acid--was associated with increased hepatic docosahexanoic acid levels in mice; this compound has been shown to induce neuronal differentiation of gliogenic neural stem/progenitor cells [88]. Further studies are needed to determine the effects of walnut or walnut components on hepatic stem/progenitor cells to enable the use of walnut for hepatic regeneration from the hepatic cell death and fibrosis in NAFLD.
## 1 Introduction
[PERSON] et al. [28] combined the genetic algorithm with particle swarm optimization to enhance neural network performance, resulting in improved robot localization accuracy. [PERSON] et al. [29] used the sparrow search algorithm to optimize the BP neural network, effectively reducing robot localization errors. Although these optimization algorithms have improved robot localization accuracy to some extent, issues such as low search efficiency and suboptimal optimization effects persist and require further improvement. To address the aforementioned challenges, this paper proposes an error compensation method based on an ISBOA optimized BP neural network. The main contributions of this paper are as follows:  
1. Through improvements to the SBOA, the ISBOA is proposed. By employing the good point set strategy to initialize the population, the diversity of the initial population is increased, and the convergence speed of the algorithm is improved. 2. The ISBOA is utilized to optimize the initial weights and biases of the neural network, effectively addressing the issue of the BP neural network's tendency to fall into local optima. 3. A neural network mapping relationship is established between the joint values of the robotic arm and the end-positioning error. This enables effective error compensation and significantly improves the positioning accuracy of the rock-drilling robotic arm. The remainder of this paper is organized as follows: Section 2 presents the forward kinematic modeling and error calculation for the robotic arm. Section 3 describes the method for obtaining the BP neural network's weights and biases using the ISBOA and introduces the ISBOA-BP neural network-based error compensation method for rock drilling robotic arm positioning. Section 4 validates the effectiveness of the proposed method through experimental results.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.3 METEOR Score Analysis  
Figure 11 presents the comparison of METEOR scores between the Fine-Tune and RAG phases for four models: mT5, GPT-2, mBART, and GPT-Neo. The graph highlights the differences in METEOR scores when the models are trained using the RAG method compared to the Fine-Tune method. All four models show improved METEOR scores when using RAG, as opposed to fine-tuning. This indicates that the RAG method leads to more accurate translations. mT5 demonstrates the most significant improvement in METEOR score when switching from Fine-Tune to RAG. The METEOR score for mT5 increases considerably in the RAG phase, suggesting that RAG had a strong positive effect on the model's performance. This increase implies that RAG enabled mT5 to generate more accurate translations, particularly for Swahili language tasks. GPT-2 also showed an increase in METEOR score with RAG, but the improvement was smaller compared to mT5. While the change wa less dramatic, the results indicate that RAG still enhanced GPT-2's performance. mBART and GPT-Neo both show improvements in METEOR scores as well, though the changes were less pronounced than those observed in mT5. Nevertheless, RAG still produced better results for these models, resulting in more fluent and accurate translations compared to fine-tuning. The graph confirms that RAG consistently improved the METEOR scores for all models tested. This suggests that RAG enhanced the models' ability to produce more accurate and coherent translations. The observed improvements in METEOR scores were likely due to RAG's ability to retrieve and incorporate relevant external information, improving the overall translation quality for Swahili NLP tasks [55].  
Figure 10: BLEU score comparison between Fine-Tune and RAG models.
## 3 Extended Kalman Filter with Maximum Correntropy
Therefore, the objective is to increase the objective function \(J_{m}\) provided by  
\[J_{m}=G_{\sigma}\biggl{(}\biggl{|}\biggl{|}\biggl{|}\vec{x}_{k}-\vec{h}(\hat{ \vec{x}}_{k|k-1})\biggr{|}\biggl{|}\biggl{|}\vec{x}_{k^{-1}}\biggr{)}+G_{\sigma }\biggl{(}\biggl{|}\biggl{|}\biggl{|}\hat{\vec{  
Taking the derivative of the objective function \(J_{m}\) with respect to \(\overset{\wedge}{\mathbf{x}}_{k|k}\) and setting its value to zero  
\[\frac{\partial J_{m}}{\partial\overset{\wedge}{\mathbf{x}}_{k|k}}=0 \tag{22}\]  
and therefore the following equation can be obtained  
\[\overset{\wedge}{\mathbf{x}}_{k|k}=\mathbf{f}(\overset{\wedge}{\mathbf{x}}_{k -1|k-1})+\frac{G_{\sigma}(\left|\left|\left|\mathbf{z}_{k}-\mathbf{h}(\overset{ \wedge}{\mathbf{x}}_{k|k-1})\right|\right|\right|_{\mathbf{r}_{k}^{-1}})}{G_{ \sigma}(\left|\left|\overset{\wedge}{\mathbf{x}}_{k|k}-\mathbf{f}(\overset{ \wedge}{\mathbf{x}}_{k-1|k-1})\right|\right|_{\mathbf{r}_{k|k-1}^{-1}})} \mathbf{H}_{k}^{\mathrm{T}}(\mathbf{z}_{k}-\mathbf{h}(\overset{\wedge}{ \mathbf{x}}_{k|k-1})) \tag{23}\]  
From Equation (22), we have  
\[-\frac{1}{\sigma^{2}}G_{\sigma}(\left|\left|\left|\mathbf{z}_{k}-\mathbf{h}( \overset{\wedge}{\mathbf{x}}_{k|k-1})\right|\right|\right|_{\mathbf{R}_{k}^{- 1}})\mathbf{H}_{k}^{\mathrm{T}}\mathbf{R}_{k}^{-1}(\mathbf{z}_{k}-\mathbf{h}( \overset{\wedge}{\mathbf{x}}_{k|k-1}))+\frac{1}{\sigma^{2}}G_{\sigma}(\left| \left|\overset{\wedge}{\mathbf{x}}_{k|k}-\mathbf{f}(\overset{\wedge}{\mathbf{ x}}_{k-1|k-1})\right|\right|_{\mathbf{r}_{k|k-1}^{-1}})\mathbf{P}_{k|k-1}^{-1}( \overset{\wedge}{\mathbf{x}}_{k|k}-\mathbf{f}(\overset{\wedge}{\mathbf{x}}_{ k-1|k-1}))=0 \tag{24}\]  
which can be written as  
\[\mathbf{P}_{k|k-1}^{-1}\overset{\wedge}{\mathbf{x}}_{k|k}-\mathbf{P}_{k|k-1}^ {-1}\mathbf{f}(\overset{\wedge}{\mathbf{x}}_{k-1|k-1})=\mathbf{L}_{k}\mathbf{ H}_{k}^{\mathrm{T}}\mathbf{R}_{k}^{-1}(\mathbf{z}_{k}-\mathbf{h}(\overset{ \wedge}{\mathbf{x}}_{k|k-1})) \tag{25}\]  
where  
\[V_{\sigma}(X,Y)=E[k_{\sigma}(X,Y)]=\iint k_{\sigma}(x,y)f_{XY}(x,y)dxdy \tag{16}\]0  
Since \(G_{\sigma}(\left|\left|\left|\overset{\wedge}{\mathbf{x}}_{k|k}-\mathbf{f}( \overset{\wedge}{\mathbf{x}}_{k-1|k-1})\right|\right|\right|_{\mathbf{P}_{k|k -1}^{-1}})\approx\left.G_{\sigma}(\left|\left|\overset{\wedge}{\mathbf{x}}_{k| k-1}-\mathbf{f}(\overset{\wedge}{\mathbf{x}}_{k-1|k-1})\right|\right|_{\mathbf{P}_{k|k -1}^{-1}})=1\), we have  
\[V_{\sigma}(X,Y)=E[k_{\sigma}(X,Y)]=\iint k_{\sigma}(x,y)f_{XY}(x,y)dxdy \tag{16}\]1  
The resulting estimator is referred to as the maximum correntropy criterion-based EKF, which has the new gain given by  
\[V_{\sigma}(X,Y)=E[k_{\sigma}(X,Y)]=\iint k_{\sigma}(x,y)f_{XY}(x,y)dxdy \tag{16}\]2  
which can also be written as  
\[\mathbf{K}_{k}=\mathbf{L}_{k}\mathbf{P}_{k|k-1}\mathbf{H}_{k}^{\mathrm{T}} \big{
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 8 Quantum Communication
Furthermore, the distinguishability theorem states that non-orthogonal states cannot be distinguished clearly. That is to say, it is inevitable that constancy of the quantum states will be disturbed when [PERSON] is attempting to gain  
Figure 20: (**a**) The four states that applied and (**b**) the schematic of a polarization based BB84 protocol. (WCS source: weak coherent state source; PC: polarization controller; SMF: single mode fiber; IF: input fiber; L1: collimator; BSS: beam steering system; WP: wave plate; HWP: half-wave plate; BS: beam splitter; PBS: polarization beam splitter; L2: focusing lens; PD: photodetector). information on transmitted bits and this will be detected by [PERSON]. The CV-QKD applies the uncertainty principle and states that the in-phase and quadrature components of the coherent state cannot be measured completely and accurately at the same time. Recently, the development of QKD has greatly improved not only theoretically but also experimentally; an LED-based decoy-state QKD was proposed recently by [PERSON] et al. in 2019 [146]. By replacing the WCS laser source with an LED, they lower the cost of the QKD system, providing a tolerable price and sufficient security.
# A Study on the Impact of DPF Failure on Diesel Vehicles Emissions of Particulate Matter
## 3 Results
### Measurement of Smoke Concentration by DPF Damage Type  
The DPF samples were installed in the experimental vehicle, and KD-147 driving mode tests were conducted. The smoke concentrations of each of the DPF types were measured using the OPA-102, a smoke concentration measurement device, and the results are depicted in Figure 5. In Korea, the smoke concentration is required to be 10% or less, and it was confirmed that this regulation was satisfied for all DPF damage types. However, in the case of the normal and crack-damaged DPFs, the smoke concentration was the lowest at 2%, whereas, in cases of the melt-, hollow-, and plug-damaged DPFs, the smoke concentrations were relatively high at 7% or more. In the case of the melt-damaged DPF, the smoke concentration was 7.3%, and the filtering performance was believed to have deteriorated because the substrate channel had melted. In the case of the hollow-damaged DPF, the smoke concentration was 7.7%, and this was attributed to the reduced filtration performance owing to the center of the inner substrate being hollow and empty. In the case of the plug-damaged DPF, the smoke concentration was 7%, and the deterioration in filtration performance was thought to be caused by damage to the plug that performed the filtering function in the internal substrate [19; 20].