esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Basic Strategy to Solve Design Challenges
Figure 10: Example of REPs that the server selects to send a bid invitation for a data request (for \(\theta_{n}=17\)). Figure 9: Example of neighbor existence area. 2. Mitigating the effects of misbehaving REPs that fail to fulfill the role of REP  
We mitigate the effects of misbehaving REPs by making additional mobile hosts among the one-hop neighbors of a P-REP work as REPs and having the server send the same bid invitations to those REPs. For this purpose, one or more S-REPs volunteer to serve as a REP triggered by the appearance of a P-REP. When a mobile host volunteers to serve as a P-REP, the P-REP broadcasts a notification message, and the one-hop neighbors of the P-REP know the P-REP has volunteered. S-REPs volunteer among the one-hop neighbors of the P-REP. The server sends the same bid invitations to S-REPs that it sent to the P-REP corresponding to S-REPs. An S-REP sends a bid invitation to the one-hop neighbors of the P-REP and receives bids from them via D2D communication (Figure 12). After sending a Bids message to the server, an S-REP finishes its role as S-REP because the corresponding P-REP also stops being a P-REP after sending a Bids message to the server. If at least one REP among the P-REP and S-REPs is non-misbehaving, each mobile host can receive a bid invitation and send its bid to the server. Thus, the effects of misbehaving REPs can be mitigated. Note that if a mobile host does not broadcast a notification message when it becomes a P-REP, another mobile host becomes a P-REP, and its one-hop neighbors can receive a bid invitation and send their bids to the server. Note that we assume the following to avoid the situation where a REP may tamper with the bidding message or upload the wrong bidding results to increase its profits. We assume that a sensing service provider distributes applications to mobile hosts such as smartphones and those applications execute MH's processing in the proposed method on mobile hosts. We also assume that the sensing service provider detects unintended changes to the applications by some method such as remote attestation and prevents the modified applications from participating in MCS. Thus, the sensing service provider can prevent the  
Figure 11: Example of mobile hosts that are included in the NEA of different P-REPs.  
Figure 12: S-REP sending a bid invitation and receiving bids. situation where a REP may tamper with the bidding message or upload the wrong bidding results to increase its profits.
## 3 Processing and Extraction
### Conventional Extraction Methods  
Conventional, liquid-liquid and solid-liquid extraction methods are the most commonly used at a laboratory scale. In these processes, usually organic solvents, like methanol, ethanol, hexane and acetone, are used, but aqueous solvents might also be employed. The mechanism of the process involves removing a soluble fraction from an insoluble solid. The concentration of compounds released from plant tissues to the solvent reaches equilibrium with the concentrations of unreleased substances as described by the equilibrium distribution constant. [PERSON]'s second law of diffusion describes how fast the compounds are able to dissolve and reach equilibrium [83]. Factors that may improve extraction efficiency are increased solvent concentration and reduced particles of solids. Increasing solvent concentration enhances the gradient of concentrations in two phases and reducing the size of particles decreases the diffusion distance of a solution within the solid and, additionally, increases the concentration gradient, too. Temperature also helps to increase diffusivity, according to the [PERSON] equation. However, due to solvents' toxicity with respect to the environment or human health, conventional methods are perceived as inappropriate in the food processing industry. What is more, not all of the phenolic or lipid compounds are possible to extract using these methods. Increased temperature may also lead to damage of some thermolabile structures [83]. The efficiency of solvent extraction is determined mainly by the type of solvent and the material/solvent ratio [84]. Tables 4 and 5 compare the conditions and effectiveness (in the case of oils, yield and fatty acid profiles; in the case of phenolic extracts, antioxidant composition and antioxidant capacity of extracts) of conventional extractions of antioxidants and oils from berry by-products.
# Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions
## 2 Dynamic Modeling
\begin{table}
\begin{tabular}{c c} \hline
**Specification** & **Value** \\ \hline Car body mass (kg) & 4.08 \(\times\) 10\({}^{4}\) \\ Bogie frame mass (kg) & 3188 \\ Wheelest mass (kg) & 1640 \\ Axle box mass (kg) & 85.367 \\ Gearwheel mass (kg) & 53.15 \\ Pinion mass (kg) & 5.15 \\ Gearbox mass (kg) & 149.75 \\ Rotor mass (kg) & 178..36 \\ Motor mass (kg) & 422.82 \\ Rotational inertia of car body \(x/y/z\) (t-m\({}^{2}\)) & 75.06/2277.4/2277.4 \\ Rotational inertia of bogie frame \(x/y/z\) (kg-m\({}^{2}\)) & 2040/2710/3460 \\ Rotational inertia of wheelset \(x/y/z\) (kg-m\({}^{2}\)) & 725/100/725 \\ Rotational inertia of axle box \(x/y/z\) (kg-m\({}^{2}\)) & 1.455/2.448/2.011 \\ Rotational inertia of gearwheel mass \(x/y/z\) (kg-m\({}^{2}\)) & 4.55/4.85/4.555 \\ Rotational inertia of pinion \(x/y/z\) (kg-m\({}^{2}\)) & 0.006/0.007/0.006 \\ Rotational inertia of gearbox \(x/y/z\) (kg-m\({}^{2}\)) & 4.22/10.45/8.56 \\ Rotational inertia of rotor \(x/y/z\) (kg-m\({}^{2}\)) & 24.5/1.9/24.5 \\ Rotational inertia of motor \(x/y/z\) (kg-m\({}^{2}\)) & 77.5/24.7/75.2 \\ Stiffness of primary suspension \(x/y/z\) (N/m) & 9.2 \(\times\) 10\({}^{6}\)/8 \(\times\) 10\({}^{6}\)/1.5 \(\times\) 10\({}^{6}\) \\ Stiffness of secondary suspension \(x/y/z\) (N/m) & 2.06 \(\times\) 10\({}^{5}\)/2.06 \(\times\) 10\({}^{5}\)/4.41 \(\times\) 10\({}^{5}\) \\ Stiffness between motor and bogie frame \(x/y/z\) (N/m) & 3 \(\times\) 10\({}^{7}\)/1 \(\times\) 10\({}^{7}\)/3 \(\times\) 10\({}^{7}\) \\ Stiffness between gearbox and bogie frame \(x/y/z\) (N/m) & 3 \(\times\) 10\({}^{6}\)/3 \(\times\) 10\({}^{6}\)/3 \(\times\) 10\({}^{6}\) \\ Damping coefficient of primary suspension \(x/y/z\) & 5560/5560/1800 \\ (N-s/m) & 6 \(\times\) 10\({}^{4}\) \\ Damping coefficient between motor and bogie frame & 1 \(\times\) 10\({}^{3}\)/1 \(\times\) 10\({}^{3}\)/1 \(\times\) 10\({}^{3}\) \\ \(x/y/z\) (N-s/m) & 3 \(\times\) 10\({}^{5}\)/2 \(\times\) 10\({}^{5}\)/3 \(\times\) 10\({}^{5}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Main parameters of the urban rail train dynamics model. The marshaling train model is shown in Figure 1c, which adopts the marshaling form of \"four motor cars + two trailers\": M1-M2-T1-T2-M3-M4 (where M1, M2, M3 and M4 are motor cars and T1 and T2 are trailers). Here, the \(x\) axis is longitudinal, the \(y\) axis is lateral and the \(z\) axis is vertical. Each car is connected by a coupler, and the coupler is defined by force element No. 4.
# Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
## 4 Experimental Results and Analysis
### Analysis and Discussion
There are several limitations of using AI in the automation of the healthcare sector, including the following:Data quality and availability: The quality and availability of data are a major challenge for AI in healthcare. Health data are often siloed and not in a format that can be easily analyzed by AI algorithms. * Privacy and security: The handling of sensitive health data is a concern for privacy and security. There is a risk of data breaches and unauthorized access to patient information. * Ethical and legal issues: The use of AI in healthcare raises ethical and legal questions, such as issues related to bias, accountability, and informed consent. * Lack of regulatory framework: There is currently a lack of a clear regulatory framework for AI in healthcare, which makes it difficult to ensure the safety and efficacy of AI-powered medical devices and systems. * Technical limitations: AI algorithms have a limited ability to handle complex medical conditions and perform well when there are limited data available. The development of AI algorithms for healthcare also requires domain-specific expertise. * Resistance to change: The healthcare sector is often slow to adopt new technologies, and there may be resistance to the use of AI from healthcare professionals and patients. Despite these limitations, AI has the potential to significantly improve the efficiency, accuracy, and cost-effectiveness of healthcare delivery. However, careful consideration of these limitations is necessary to ensure the responsible and ethical use of AI in healthcare. Overall, ML-based systems for diabetes prediction have the potential to improve the lives of millions of people, but technology transfer is an essential step to ensure that these systems are implemented in a way that maximizes their potential benefits. Finally, the following summarizes the contribution of this work to society:  
Automatic prediction of diabetes in healthcare systems using machine learning and predictive methods can improve early detection and intervention of the disease, leading to better management and cost-effectiveness. By identifying individuals at risk before symptoms appear, healthcare providers can focus their efforts on high-risk patients, reducing health disparities. Implementing an efficient and effective system for diabetes management can improve overall population health and reduce the burden of chronic disease.
# Expert Recommendation for Answering Questions on Social Media
## 5 Conclusions  
We proposed a new expert recommendation method by considering the user interest, human relation, and answer quality on social media. The user interest was determined based on keywords and trust extracted by analyzing the user activities. The human relation was determined by the PageRank algorithm in the reconstructed interest network. The answer quality was determined by assessing how quickly and precisely the user had responded. The expertise score was determined by adding the user interest, human relation, and answer quality. To recommend an expert who can answer a user's question well, a keyword included in the user's question was extracted, and a matching expert was recommended using the hierarchical relationship of words. As a result of the experimental evaluation, it was proved that the proposed method had improved recall, precision, and F-measure compared with the existing methods. When the proposed method is applied to social Q&A services such as Quora, it is possible to construct an expert pool and improve the quality of answers through experts.  
Conceptualization, K.B., H.S., D.C., J.L., D.P., and J.Y.; methodology, K.B., H.S., D.P., and J.Y.; software, H.S.; validation, K.B., H.S. and D.P.; formal analysis, K.B., H.S., D.C., J.L., and J.Y.; data curation, K.B. and H.S.; writing--original draft preparation, K.B. and H.S.; writing--review and editing, K.B. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT). (No. 2019R1A2C2084257, 2020R1F1A1075529), by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government(MSIT) (No.2014-3-00123, Development of High-Performance Visual BigData Discovery Platform for Large-Scale Real-time Data Analysis), and the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-2020-0-01462) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
Figure 11: F-measure according to the number of expert recommendations.
## 2 Radiative Transfer
### Approximations  
By means of the following approximations, we are reducing the complexity of the final result which further enhances the computational efficiency. Note that this also considerably reduces the complexity of the (analytic) partial derivatives needed to compute the Jacobian (used by the retrieval).  
Due to the high accuracy requirements for the retrieval of greenhouse gases, we are primarily interested in scenarios where scattering at aerosols and clouds is minimal, even if the retrieval algorithm is, in principle, capable of reducing scattering related errors.  
Additionally, we are primarily interested in accurate greenhouse gas concentrations; inaccuracies in the retrieved scattering properties are less important. For these reasons, we make an approximation for small extinction optical thicknesses.  
Further, we assume that the spectral signal produced by absorption within the scattering layer cannot easily be disentangled from an albedo and scattering signal. For some cases, it is even identical; e.g., when the single scattering albedo (\(\omega=\tau_{s}/\tau_{e}\)) becomes zero, the absorption and the albedo signal become identical. Therefore, we are not aiming to explicitly retrieve the absorption within the scattering layer and approximate that \(\tau_{a}=0\) (i.e., \(\tau_{e}=\tau_{s}\)). As a result, the retrieved albedo and the amount of scattered radiation may be slightly off, which does not pose a problem as long as the retrieved greenhouse gas concentrations are not affected.  
Additionally, we assume that the light is scattered in same parts into the upper and lower hemisphere at the scattering layer (\(f=b=1/2\)), which is reasonable especially for an optically thin scattering layer.  
First order Taylor series approximation of Equations (4) and (3) gives  
\[S_{I}(\tau_{s},\zeta) \approx \zeta\ \tau_{s}\ \text{and} \tag{22}\] \[T_{I}^{s}(\tau_{s},\zeta) \approx 1-S_{I}(\tau_{s},\zeta). \tag{23}\]  
The amount of diffuse scattered radiant flux (Equation (11)) simplifies to  
\[S_{F}(\tau_{s},\tau_{\downarrow}) \approx \frac{E_{2}(\tau_{\downarrow})}{E_{3}(\tau_{\downarrow})}\ \tau_{s}. \tag{24}\]  
Here \(E_{2}\) is the second exponential integral and \(E_{2}(\tau_{\downarrow})/E_{3}(\tau_{\downarrow})\) a number always between 1 and 2.  
Substituting Equations (22)-(24) into Equations (17)-(21) and subsequently first order Taylor series approximation of Equation (1) at \(\tau_{s}=0\) as done in Appendix A yields:  
\[\begin{array}{l}I\approx\frac{F_{0}}{\pi\zeta_{0}}\ T_{I}^{g}(\tau_{ \downarrow},\zeta_{0}+\zeta)\ \left[\frac{1}{2}\ \zeta_{0}\ \tau_{s}+\right.\\ \alpha\ \left(T_{I}^{g}(\tau_{\downarrow},\zeta_{0}+\zeta)\ \left[1-(\zeta_{0}+\zeta)\ \tau_{s}+2\ \alpha\ E_{2}(\tau_{\downarrow})\ E_{3}(\tau_{ \downarrow})\ \tau_{s}\right]+\right.\\ \left.T_{I}^{g}(\tau_{\downarrow},\zeta_{0})\ E_{2}(\tau_{\downarrow})\ \tau_{s}+\ T_{I}^{g}(\tau_{\downarrow},\zeta)\ E_{3}(\tau_{ \downarrow})\ \zeta_{0}\ \tau_{s}\right)\right]+\\ \frac{F_{0i}^{0}}{\pi}\ T_{I}^{g}(\tau_{\downarrow}+\tau_{\uparrow},\zeta)\ \left[1-\zeta\ \tau_{s}\right].\end{array} \tag{25}\]
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 4 Dynamic Model of Balloon-Borne UAVs  
This section illustrates the dynamic model of balloon-borne UAVs. The longitudinal motion equation of the large wingspan UAV can be written as follows:  
\[\dot{\mathbf{x}}=Ax+Bu \tag{1}\]  
where \(x=[V,\alpha,q,\theta]^{T}\) and \(u=[\delta_{e},\delta_{p}]^{T}\) are the state matrix and control matrix,respectively. \(V\) is the velocity, \(\alpha\) is the attack angle, \(\theta\) is pitch rate, and \(q\) is pitch angle. The matrix of \(A\) and \(B\) can be described as follows:  
\[\mathbf{A}=\left[\begin{array}{cccc}X_{V}&X_{\alpha}+g&0&-g\\ -Z_{V}&-Z_{\alpha}&1&0\\ M_{V}-M_{\alpha}Z_{V}&M_{\alpha}-M_{\alpha}Z_{\alpha}&M_{q}+M_{\alpha}&0\\ 0&0&1&0\end{array}\right],\]  
\[\mathbf{B}=\left[\begin{array}{cccc}X_{\delta_{e}}&X_{\delta_{p}}\\ -Z_{\delta_{e}}&-Z_{\delta_{p}}\\ M_{\delta_{e}}-M_{\alpha}Z_{\delta_{e}}&M_{\delta_{p}}-M_{\alpha}Z_{\delta_{p}} \\ 0&0\end{array}\right],\]  
where \(X_{V}=Q/(0.5*m*V)*C_{D}*S\), \(X_{\alpha}=-CD_{\alpha}*Q*S/m\), \(Z_{V}=(CL_{V}+2*CL)*Q*S/(m*V^{2})\), \(Z_{\alpha}=(CD+CL_{\alpha})*Q*S/(m*V)\), \(M_{V}=Cm_{V}*Q*S*c/(V*I_{Z})\), \(M_{\alpha}=Cm_{\alpha}*Q*S*c/Z_{\alpha}\), \(X_{\delta_{p}}=0\), \(Z_{\delta_{e}}=CL_{\delta_{e}}*Q*S/(m*V)\), \(Z_{\delta_{p}}=0\), \(M_{q}=Cm_{q}*C^{2}*Q*S/(2*V*I_{Z})\), \(M_{\delta_{p}}=Cm_{\delta_{e}}*Q*S*c/Z_{\alpha}\), \(M_{\delta_{p}}=0\), and \(M_{\delta}=0\). They are all dependent derivative formulas. In the above equations, \(m\) indicates mass. \(J_{Z}\) is the moment of inertia. \(CL\), \(CD\), and \(Cm\) denote the lift coefficient, drag coefficient, and pitching moment coefficient. \(Q\) represents the dynamic pressure, and \(S\) is the longitudinal reference area. \(c\) stands for the longitudinal reference length. \(\delta_{e}\), \(\delta_{p}\), and \(g\) illustrate the elevator control input, engine control input, and gravitational acceleration, respectively.  
Once these equations are obtained, calculating the forces and moments that act on the UAV is necessary. The total aerodynamic force acting on the aircraft includes \(F_{wing}\), which is generated by the wing, the aerodynamic force acting on the fuselage, \(F_{fus}\), and aerodynamic forces on the horizontal stabilizer, \(F_{bf}\). In addition, gravity affects the aircraft (\(mg\)). Each force is illustrated in Figure 4.
## 3 Results and Discussions
### Results and Analysis of SSC Prediction Test
#### 3.2.3 Establishment and Comparison of SSC Prediction Models  
We used PLSR to establish multivariate linear regression models. The measured and predicted values of the modeling set and test set are shown in Figure 12.  
Figure 11: Mahalanobis distance between the samples.  
The model performance indices are shown in Table 2. The main evaluation indicators are \(\text{R}_{\text{p}}\)\({}^{2}\) and RMSE of the test set, and the auxiliary evaluation indicators are \(\text{R}_{\text{m}}\)\({}^{2}\) and RMSEM of the modeling set. It can be concluded that in terms of accuracy, the ranking  
Figure 12: The prediction results of the four regression models. (**a**) SPA. (**b**) MCUVE. (**c**) SPA-MCUVE. (**d**) MCUVE-SPA. (**e**) No selection. (**f**) 1-Der without selection.  
of the models from high to low is as follows: MCUVE, MCUVE-SPA, SPA, no selection, SPA-MCUVE, 1-Der without selection.  
It can be seen from Table 2 that among all the models, the model established using MCUVE to select the characteristic wavelengths had the best performance, and the \(\text{R}_{\text{p}}\text{${}^{2}$}\) of the test set was 0.8755, which showed good versatility and stability. The models without characteristic wavelengths selection had overfitting. The selection of characteristic wavelengths should not be too many or too few. In addition, it can also be seen that using pretreatment methods such as first derivative can lead to a decrease in model performance.  
After comprehensive consideration, we chose the model established using 35 wavelengths selected with MCUVE. The equation of the model is shown in Figure 11, where \(Y\) is the value of the grape SSC (unit: Brix%), and \(X_{k}\) is the standard reflectance of the grape at wavelength \(k\).  
There has been some research on the selection of characteristic wavelengths for different grape varieties, for example, Red Globe [12; 36], Wink [13], and various wine grapes (Cabernet Sauvignon, Nebbiolo, Merlot, Shiraz, etc.) [37; 38; 39]. By comparing these studies, it can be generally concluded that the wavelengths related to SSC are as follows: 450-460 nm, 670-680 nm, 720-730 nm, 950-980 nm.
## 2 Materials and Methods
### Anti-microbial Activity of Mangiferin and Time-Kill Assay  
A disc diffusion assay was used to evaluate the antibacterial effect of varying dilutions of mangiferin on cariogenic _S. mutans_. The following concentrations of mangiferin 125 \(\upmu\)m, 250 \(\upmu\)m, 500 \(\upmu\)m and 1000 \(\upmu\)m were used for the screening of its antimicrobial effect while DMSO and ciprofloxacin (20 \(\upmu\)g) were the negative and positive controls respectively. The method described earlier [29] was used for the time kill assay of mangiferin against _S. mutans_. A bacterial suspension made up of \(4\times 10^{6}\) CFU/mL of _S. mutans_ was prepared in tubes containing 8 mLs of BHI broth to which mangiferin concentrations that ranged between 62.5-1000 \(\upmu\)m were added. A vehicle control was also set up and this consisted of DMSO and bacterial inoculum. All tubes were incubated at a temperature of 37 \({}^{\circ}\)C in a shaking incubator for 42 h. At time intervals of 0, 24, 30, 36, and 42 h, 1 mL of the bacterial suspension was drawn out, serially diluted in BHI broth, and then plated out on Muller Hilton agar. All plates were incubated for 24 h at a temperature of 37 \({}^{\circ}\)C, after which colony-forming units (CFU) were counted.
## 3 Results
### Effects of KRG-P on IL-6, TNF-\(\alpha\), and NOS2 Gene Expression in RAW264.7 Cells  
Macrophases activate the expression of the IL-6, TNF-\(\alpha\), and NOS2 genes through the MAPK and NF-\(\kappa\)B pathways, thereby releasing cytokines and NO. The NO produced by iNOS encoded by the _nitric oxide synthase 2_ (_NOS2_) gene, which can kill bacteria and viruses, plays an important role in the immune response. To determine the effect of cell signaling pathways activated by KRG-P on expressed genes, we evaluated its effect on the expression of IL-6, TNF-\(\alpha\), and NOS2 in RAW264.7 cells. RAW264.7 cells were treated with KRG or KRG-P at concentrations of 10-50 \(\upmu\)g/mL for 6 h. RNA was evaluated and the results are shown in Figure 4. The expression of the IL-6 gene was significantly increased by KRG-P compared with the control group and the expression of TNF-\(\alpha\) and NOS2 were also  
Figure 3: Activation of MAPK and NF-\(\kappa\)B pathways by treatment with KRG-P in RAW264.7 macrophage cells. (a) RAW264.7 cells (3.0 \(\times\) 10\({}^{6}\) cells/well) were treated with KRG-P at a series of concentrations (5–50 \(\upmu\)g/mL) or with LPS (500 ng/mL) in 6-well flat-bottom plate for 30 min. CTL; control, LPS; lipopolysaccharideacharide. Western blots show the expression of the representative proteins. The bar graphs respectively show density ratios of p-p38 to total p38 (b), pERK to total ERK (c), pJNK to total JNK (d) and p-p65 to total p65 (e) as analyzed by ImageJ Software. Data are presented as the means \(\pm\) SD. \(\mathbf{***}\)\(p<\) 0.001 vs. the control group.  
increased by KRG-P. The results indicate that the expression of _IL-6_, _TNF-\(\alpha\)_, and _INOS_ were enhanced by KRG-P in RAW264.7 cells (Figure 4). Although the gene expression results did not show a concomitant concentration-dependent increase with the production of cytokines following KRG-P treatment for 6 h, it still showed an increased trend compared with that of the control group, which may be related to the time of KRG-P treatment. Overall, these results demonstrate the production of NO and cytokines (IL-6 and TNF-\(\alpha\)) by KRG-P exposure.
## 3 Method
### Reinforcement Learning to Optimize Fusion Parameters
#### 3.3.2 Action Definition  
The action space comprises operations that directly impact the model fusion process. These actions encompass adjusting the weights of the base models to control their contribution to the final output, selecting fusion methods (TIES [13] or Slerp [30]) for model combination, and optimizing hyperparameters, such as learning rates, to refine the fusion process. Additionally, the agent balances exploration (searching for new configurations) with exploitation (optimizing existing configurations) to enhance the fusion results.  
An action represents the various model fusion parameter tuning strategies. The following are the available actions and their corresponding instructions: