esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Results of Continuous Musical Gesture Recognition  
In previous experiments, each gesture was separated from each other and only one gesture was input at a time. However, in this experiment, we used actual gestures in a performance where all the gestures were connected and continuous. For the continuous gestures, the experiment was divided into two parts. In the first part, we used continuous gestures at five different BPM and calculated the mean squared error between the computed BPM and the actual ones. Table 6 lists the results of the average squared errors in the gestures of different BPM.  
The mean squared error shows the average error that the user makes when making the gestures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speed of the Gesture** & **Number of Gestures** & **Mean Squared Error** \\ \hline
60 BPM & 75 & 0.3 BPM \\
90 BPM & 97 & 0.4 BPM \\
120 BPM & 136 & 0.6 BPM \\
150 BPM & 173 & 0.9 BPM \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean squared error results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tempo** & \begin{tabular}{c} **Number of** \\ **Testing Data** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Correctly** \\ **Classified Data** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Misclassified Data** \\ \end{tabular} & **Accuracy** \\ \hline
60 BPM & 1050 & 1050 & 0 & 100\% \\
90 BPM & 1050 & 1050 & 0 & 100\% \\
120 BPM & 1050 & 1050 & 0 & 100\% \\
150 BPM & 1050 & 948 & 102 & 90.29\% \\
200 BPM & 1050 & 564 & 486 & 53.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of musical gestures classified by BPM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Angles** & \begin{tabular}{c} **Number of** \\ **Gestures Made** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Correctly** \\ **Classified Data** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Misclassified Data** \\ \end{tabular} & **Accuracy** \\ \hline
45\({}^{\circ}\) Upward & 800 & 713 & 87 & 89.13\% \\
45\({}^{\circ}\) Downward & 800 & 716 & 84 & 89.50\% \\
0\({}^{\circ}\) Facing Camera & 800 & 724 & 76 & 90.50\% \\ \(-\)45\({}^{\circ}\) Left & 800 & 715 & 85 & 89.38\% \\ \(-\)90\({}^{\circ}\) Left & 800 & 702 & 98 & 87.75\% \\
45\({}^{\circ}\) Right & 800 & 710 & 90 & 88.75\% \\
90\({}^{\circ}\) Right & 800 & 698 & 102 & 87.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of musical conducting gesture recognition at different viewing angles.  
In the second part of our experiment, we chose three musical pieces for the actual performance. Unlike the previous experiments, in this part of the experiment, the musical conductors changed their gesture styles in different parts of the music to convey its emotion. Table 7 shows the results of musical gesturing along with the music.  
The experimental result shows that our system can recognize the gestures accurately with variable BPM.
## 5 Case Study
### Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Change Schemes** & \multicolumn{2}{c}{**Solution**} & **Cost (\(\times 10^{5}\) S)** & **Robustness Degree** \\ \hline \multirow{3}{*}{C1-R1} & \(n_{1-3}=1\), \(n_{1,5}=2\), \(n_{2,4}=1\), \(n_{2,6}=1\), \(n_{5-10}=1\), & \multirow{3}{*}{189.0} & \multirow{3}{*}{99.98\%} \\  & \(n_{6-10}=1\), \(n_{7,8}=2\), \(n_{10-12}=1\), \(n_{14-16}=1\), \(n_{2,8}=1\) & & \\ \cline{1-1} \cline{3-4}  & \(n_{1,16-25}=2\), \(n_{7,28}=2\), \(n_{1,2-7}=3\), \(n_{2,28}=3\) & & \\ \hline \multirow{3}{*}{C2-R1} & \(n_{1-3}=1\), \(n_{2,4}=1\), \(n_{5-10}=4\), \(n_{6-10}=1\), \(n_{7,8}=1\), & \multirow{3}{*}{197.0} & \multirow{3}{*}{99.99\%} \\  & \(n_{10-12}=1\), \(n_{14-16}=1\), \(n_{16-25}=2\), \(n_{7,26}=2\), \(n_{7,26}=2\), & & \\ \cline{1-1} \cline{3-4}  & \(n_{1-27}=2\), \(n_{2,28}=2\), \(n_{7,5-29}=3\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Robustness analysis for C1 and C2. Figure 8: (**a**) Feasible planning schemes of C1; (**b**) Feasible planning schemes of C2. As shown in Figure 9, with the increase in the number of wind farms, the investment costs of robust planning schemes increase monotonically in three cases whereas the economic planning schemes all keep a reasonable investment cost. The increase of cost-saving ratio further indicates that the proposed TNEP method can provide more reasonable and economic planning schemes when the robust planning design is overly conservative under large uncertainties.
# A Deep Learning Method for the Prediction of Pollutant Emissions from Internal Combustion Engines
## 3 Developing and Optimization of the Artificial Neural Network  
### Back Propagation Structure  
The back propagation artificial neural network (BPANN) is a fundamental type of ANN primarily used in supervised learning tasks. It is designed to learn from data by minimizing the discrepancy between actual and predicted results through the backpropagation process. The network (Figure 7) consists of layers, including an input layer that receives raw data, one or more hidden layers that process these data by applying weights and activation functions, and an output layer that produces the network's prediction. Neurons in these layers take inputs, multiply them by weights, apply an activation function (for example, Sigmoid function, ReLU function,...) to introduce non-linearity, and pass the result to the next layer [27].  
Figure 6: Engine speed (red curves) and torque (blue curves) variations for the 5 dynamic cycles.  
The network operates through a forward pass, where data flow through the network layer by layer and an output is generated based on the processing in the hidden layers. After the forward pass, the network's output is compared to the actual target using a loss function, which measures the error between the predicted output and the target. The process of Back Propagation follows, during which the error is transmitted from the output layer back to the input layer. Gradients of the loss function with respect to each weight are calculated, and the weights are updated in the direction that minimizes the loss, typically using optimization algorithms like gradient descent [28].  
This process is repeated over many iterations, known as _epochs_, allowing the network to gradually learn the optimal weights that minimize the error and thus improve its performance on the training data. Key concepts include the following:  
* Learning rate, which controls the pace of learning.
* Overfitting, in which the network exhibits strong performance on training data but struggles with unseen data, a challenge that can be mitigated using techniques such as regularization.
* Convergence, where training stabilizes as the network learns optimal weights.
## 4 Results and Discussion
### Performance Evaluation Matrices
#### 4.2.3 Specificity  
Specificity is the opposite of recall. It calculates the ratio between TN values over the sum of TN and FP values. The mathematical formula for specificity is described in Equation (6):  
\[\text{Specificity}\,=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{6}\]  
Specificity of tier-I, tier-II, and tier-III was 91.65%, 94.36%, 96.34%, respectively. However, the global specificity was 97.26%.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Evaluation
#### 6.1.4 Transmission Loss  
Transmission loss is dependent on; number of transmissions that a packet undergo during multi-hop communication, attenuation loss of the signal, and bandwidth efficiency. It is calculated using thorp attenuation formula given in Equation (2). Major contribution to transmission loss is the spreading of the acoustic wave as it propagates away from the source for longer distances in  
Figure 8: Throughput.  
Figure 9: PDR.  
underwater and cause greater transmission losses. Transmission loss for all comparing schemes is calculated and samples from whole lifetime taken in ascending order are plotted in Figure 10. ESDR performs best among all comparing protocol in terms of transmission loss as well. This is due the fact that a major part of data generated is transferred to patrolling sinks and is not suffered to long distances spreading losses. Moreover premature death of intermediate nodes in previous schemes cause increase in transmission loss for their respective protocols.
## 6 A Method to Calculate \(\boldsymbol{k_{b/m}}\)
### The Three Inequations to Be Fulfilled by \(k_{b/m}\)
The parameters of Equation (50) that can be calculated during operation time are: the number of I+P frames received from the best path (\(n_{rb}\)) and the number of I+P frames sent through the best path (\(n_{sb}\)) to compute \(\hat{n}_{b}=\frac{n_{rb}}{n_{sb}}\); the number of I+P frames received from the medium-quality path (\(n_{rm}\)) and the number of I+P frames sent through the medium-quality path (\(n_{sm}\)) to compute \(\hat{n}_{m}=\frac{n_{rm}}{n_{sm}}\); the jitter delay through the best and the medium-quality paths (\(J_{b}\) and \(J_{m}\), respectively) computed from the RTCP packets; and the MOS of the best and the medium-quality paths, computed with Equation (B1) presented in the Appendix B. The only variable in Equation (50) that is not defined yet is \(\frac{\partial p_{i}^{*}(k_{b/m})}{\partial k_{b/m}}\). To design a proper value for \(\frac{\partial p_{i}^{*}(k_{b/m})}{\partial k_{b/m}}\), we carried out a high amount of simulations under different network conditions and with different values of \(\frac{\partial p_{i}^{*}(k_{b/m})}{\partial k_{b/m}}\) and we noticed that with a value of \(\frac{\partial p_{i}^{*}(k_{b/m})}{\partial k_{b/m}}=-0.2\), the variation of \(p_{i}^{*}\) throughout time was soft without sharp changes. From Equation (50), we see that we have two possible values for \(k_{b/m}\), one of them is higher than \(K_{b/m}\) and the other one is lower than \(K_{b/m}\), so we take the one which belongs to the range \(]K_{b/m},+\infty)\). To conclude with, Algorithm 1 summarizes the methodology to compute the best response probability \(p_{i}^{*}\) for player \(i\) to send his/her I+P frames through the best path. Figure 7: Best response probability \(p_{i}^{*}\) as a function of \(k_{b/m}\), see Equation (26). ```
0: Obtain updated QoS values from the periodically received RTCP packets. 1: Obtain the values of (\(MOS_{b}\), \(MOS_{m}\), \(J_{b}\), \(J_{m}\), \(\hat{n}_{b}\), \(\hat{n}_{m}\))
2: Compute the \(k_{b/m}\) parameter designed that fulfills the requirements. \(k_{b/m}=\frac{\pm\sqrt{\frac{\frac{MOS_{b}\cdot MOS_{b}}{J_{b}}\cdot(h_{b}+h_{m }-2)}{\frac{2\cdot\left(\frac{h_{b/m}\cdot MOS_{b}}{J_{b}}+\frac{MOS_{m}}{J_{m} }\right)}{2\cdot\left(\frac{h_{b/m}\cdot MOS_{b}}{J_{b}}+\frac{MOS_{m}}{J_{m} }\right)}}}{\frac{MOS_{b}}{J_{b}}}\)
3: Calculate the probability \(p_{i}^{*}\) to send I+P frames through the best path, that maximizes the utility function \(U_{i}\)

\(p_{i}^{*}(k_{b/m})=\frac{\left(\frac{k_{b/m}\cdot MOS_{b}}{J_{b}}\right)\cdot \hat{n}_{b}+\left(\frac{MOS_{m}}{J_{m}}\right)\cdot(2-\hat{n}_{m})}{2\cdot \left(\frac{k_{b/m}\cdot MOS_{b}}{J_{b}}+\frac{MOS_{m}}{J_{m}}\right)}\)
```  
**Algorithm 1** Calculation of \(p_{i}^{*}\), the best response probability for player \(i\) that maximizes his/her utility function \(U_{i}\).
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 2 Materials and Methods
### Databases and Search Strategy  
The literature search was conducted across three major databases: PubMed, Scopus, and Google Scholar, selected for their coverage of biomedical, nutritional, and interdisciplinary environmental health research. The search included peer-reviewed studies published in English between January 2000 and February 2025.  
A comprehensive Boolean query was formulated to ensure a sensitive yet specific retrieval of relevant studies, integrating controlled vocabulary (e.g., MeSH terms) and free-text keywords. The final search string was as follows:  
((\"_plant-based diet\"[MeSH Terms] OR \"vegan diet\"[MeSH Terms] OR \"vegetarian diet\"[MeSH Terms] OR \"sustainable diet\"[MeSH Terms] OR \"EAT-Lancet diet\"[MeSH Terms] OR (\"plant-based diet\" OR \"vegan diet\"OR \"vegetarian diet\"\"OR \"sustainable diet\"\"_) OR \"EAT Lancet diet\")) AND ((\"food allergy\"[MeSH Terms] OR \"hypersensitivity\"[MeSH Terms] OR \"allergy\"[MeSH Terms] OR (\"food allergy\" OR \"allergic sensitization\" OR \"lgE-mediated allergy\" OR \"allergy symptoms\" OR \"oral allergy syndrome\")) AND ((\"gut microbiota\"[MeSH Terms] OR \"gastrointestinal microbiome\"[MeSH Terms] OR \"short-chain fatty acids\"[MeSH Terms] OR \"epithelial barrier\"[MeSH Terms] OR \"immune tolerance\"[MeSH Terms] OR \"T lymphocytes, regulatory\"[MeSH Terms] OR \"GPR43\" OR \"polyphenols\"[MeSH Terms] OR \"mast cells\"[MeSH Terms]) AND ((\"biodiversity\"[MeSH Terms] OR \"dietary diversity\"[MeSH Terms] OR \"food supply\"[MeSH Terms]) OR (\"food diversity\" OR \"dietary variety\" OR \"seasonal diet\" OR \"minimally processed food\" OR \"food processing\")). The last search was performed on 28 February 2025. Two independent reviewers conducted the screening of titles and abstracts, followed by full-text assessment. Disagreements were resolved by discussion until consensus was reached. A third reviewer was not involved, as conflicts were infrequent and resolved.
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 6 Polyphenols as Antimicrobial Agents
### Resveratrol  
Resveratrol (3,4',5-trihydroxystilbene), first isolated in 1939 by [PERSON] from _Veratrum grandiflora_ [PERSON] (root of the white helebore) [107], is a member of the stilbene family [4], and it is commonly found in red fruits and berries, specifically in grape pulp, skin, seeds, and stems [108], being produced in response to environmental stress [109]. Its basic structure is composed of two phenolic rings linked together by a double styrene bond, with the molecular formula C\({}_{14}\)H\({}_{12}\)O\({}_{3}\), which results in 3,5,4'-trihydroxystilbene. This double bond is responsible for resveratrol isomeric cis- and trans-forms. It is worth noting that the trans-isomer is the more stable form from a steric point of view [110]. This polyphenol is widely explored due to its biological properties, such as antioxidant, antimicrobial (antiviral, antibacterial, antifungal), anti-inflammatory, immunomodulatory, antidiabetic, wound healing, and neuroprotective activities [109; 111; 112; 4]. Furthermore, resveratrol's anticancer effects have been extensively explored since the initial report in the prestigious journal 'Science' in 1997 [113]. The accumulated results indicate that it might be a potential agent for the prevention and treatment of numerous cancers, including breast, cervical, uterus, skin, stomach, kidney, and liver cancers [114; 115; 116; 112]. Moreover, this polyphenol is considered safe and well-tolerated [117; 111].  
Regarding resveratrol's antimicrobial activity, its antifungal effect is generally stronger than its antibacterial action [117]. For example, it inhibited the fungal species _C. albicans_, _Saccharomyces cerevisiae_, and _Trichosporon beigelii_, with MICs ranging from 10 to 20 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL [118]. For some bacteria species, resveratrol only has a growth inhibitory effect at doses > 100 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL. For instance, Gram-positive pathogens such as _S. aureus_[119; 120; 121; 122] and _Enterococcus faecalis_[121; 120] present MICs around 100-200 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL, while Gram-negative ones, such as _E. coli_, _Klebsiella pneumoniae_, and _P. aeruginosa_[121; 122; 123], being less susceptible, present MICs superior to 200 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL.
## 3 Numerical Example  
The performance evaluation of SFT needs to be carried out using a realistic model. Therefore, the prototype of the proposed SFT model, as shown in Figure 2[8; 9], which is planned to be built in Qiandao Lake, China, is evaluated in this study. The factored load combinations used by [8] were used for both static and hydrodynamic analyses in the present study.  
Figure 2a shows the side view of the SFT model; the SFT is supported at Locations A, B and C by mooring cables. Figure 2b shows three different types of mooring cables arrangements. Figure 2c shows the material cross-section of the SFT. Three different SFT cases are analyzed based on three different mooring cable arrangements defined in Table 1, where the circled numbers represent the cable arrangement shown in Figure 2b. The input parameters for SFT tunnel, hydrodynamic and mooring cables are given in Table 2; the hydrodynamic parameters are those measured at Qindao Lake [8].  
The assumptions made in this study and criteria are given as follows:  
* The cross-section of the SFT is hollow circular, consisting of an external aluminum layer followed by a sandwiched concrete layer and internal steel layer. The material's thicknesses and tunnel diameters are shown in Figure 2c. A uniformly-distributed load of 125 kN/m, per unit length, was used for the tunnel. The Archimedes buoyancy was 160 kN/m (\(\rho_{w}gA_{Ttunnel}\) = 1050 \(\times\) 10 \(\times\)\(\pi\)\(\times\) 4.42/4 = 160 kN/m), and the net residual buoyancy was 35 kN/m (160 \(-\) 125 = 35 kN/m). A uniformly-distributed live load of 10 kN/m was assumed to be acting on the tunnel;
* The mooring cable connections at the anchor point (seabed) and tunnel were treated as pins; and
* The tunnel displacements were restrained at one end, while the axial displacement was left free at the other end. The flexural rotations were allowed at both ends of the tunnel.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mooring configuration** & **Section A** & **Section B** & **Section C** \\ \hline Configuration 1 (C1) & 1 & 1 & 1 \\ Configuration 2 (C2) & 1 & 2 & 1 \\ Configuration 3 (C3) & 1 & 3 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Three cable configurations used to support the SFT.  
Figure 2: Schematic layout of the submerged floating tunnel (SFT): (**a**) side view of the SFT and (**b**) mooring cables arrangements; and (**c**) material cross-section of the SFT.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Parameter** & **Unit** & **Value** \\ \hline \multirow{4}{*}{Tunnel} & Tunnel equivalent density & kg/m\({}^{3}\) & 2451 \\  & Elastic modulus & N/m\({}^{2}\) & \(3\times 10^{10}\) \\  & Area & m\({}^{2}\) & 5.1 \\  & Moment of inertia & m\({}^{4}\) & 12.3 \\  & Length of tunnel & m & 100 \\ \hline \multirow{4}{*}{Mooring cables} & Elastic modulus & N/m\({}^{2}\) & \(1.4\times 10^{11}\) \\  & Diameter of cable & m & 0.06 \\ \cline{1-1}  & Moment of inertia & m\({}^{4}\) & \(6\times 10^{-7}\) \\ \cline{1-1}  & Cable density & kg/m\({}^{3}\) & 7850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the SFT tunnel, mooring cables and hydrodynamics.
## 3 Results and Discussion
### Field Emission Scanning Electron Microscopy Analysis of Al-BiSn Composites  
Figure 5a-d represents the field emission scanning electron microscopy (FE-SEM) micrographs which reveal the morphology of the extruded Al-BiSn (0-15 wt.%) composites. In pure Al, a clear dark matrix with no reinforcement is observed (Figure 5a). A homogeneous distribution of the BiSn particles (indicated by arrow marks) in Al matrix is noticed for all Al-BiSn composites (Figure 5b-d). Moreover, good interfacial integrity of the bright BiSn particles in the dark Al matrix also confirms the incorporation of BiSn in Al matrix. It is pertinent to mention here that a partial agglomeration of the BiSn particles is also observed in the developed Al-(15 wt.% BiSn) composites. The energy dispersive X-ray (EDX) analysis of Al-BiSn composites containing 10 and 15 wt.% BiSn is presented in Figure 5e,f, which confirms the existence of BiSn particles in Al matrix.  
Figure 4: X-ray diffraction patterns of extruded Al-BiSn composites.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 2 Numerical Method
### Numerical Model  
The terrain area with a diameter of 13 km is selected to establish the island terrain model. The scale of the model is 1:1300 due to the low altitude of the island. At this scale, the diameter of the island model is 10 m, and the calculation domain is consistent with the size of the low-speed wind tunnel of Central South University: 18 m long, 12 m wide and 3.5 m high. The bottom of the terrain model is sea level (0 m), so there is no need to add transition segments at the terrain boundary. The inlet boundary condition adopts velocity inlet, and the outlet boundary condition adopts pressure outlet. The top surface, bottom surface and side surfaces of the computational domain adopt non-slip wall boundary. The distance from the center of the island terrain model to both sides is 6.0 m, and the distances from the entrance and exit are 7.0 m and 11.0 m, respectively. The wind speed was set to 8 m/s with a uniform profile.  
The research purpose of this paper focuses on the comparison of wind characteristics in different regions. Because the trees on the island are low, the roughness is simplified here, and only the differences caused by topographic relief are considered. In order to comprehensively evaluate the wind resources at different locations and provide reference for wind tunnel test, monitoring points are arranged on flat land, hillside, valley and mountaintop to monitor the specific value of wind characteristics. The location of wind speed monitoring points is shown in Figure 2.