esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Effect of Drying Temperature on the Volatile Components of LBT
Eleven differential volatile components (\(p\) < 0.05 and VIP > 1) were identified through the OPLS-DA (Figure 2A), including \(\beta\)-myrcene, cis-\(\beta\)-ocimene, trans-\(\beta\)-ocimene, (Z)-linalool oxide (furanoid), (E)-linalool oxide (furanoid), (E)-linalool oxide (furanoid), (E)-linalool, methyl salicate, geraniol, phenylethyl alcohol, and limonene. The thermographic analysis (Figure 2B) revealed that the contents of these compounds increased initiallyand then decreased with an increasing drying temperature, with most of them reaching the highest content in the temperature range of 80 to 100 \({}^{\circ}\)C. The joint analysis of eleven differential volatile components and eleven aroma compounds with an OAV \(>\) 1 identified eight key differential aroma components, and their correlations with aroma indices and aroma scores were further examined. As depicted in Figure S3, linalool, (Z)-linal oxide (furanoid), (E)-linaloid oxide (furanoid), cis-\(\beta\)-ocimene, and methyl salicate exhibited significant positive correlations with a sweet aroma, floral aroma, and the overall aroma score. Linalool and its oxides have been established as crucial contributors to the floral and fruity aroma of black tea [29], while cis-\(\beta\)-ocimene is known to impart a fruity scent [30]. This may explain the reason for the higher sensory evaluation rates of floral and fruity aromas in samples dried at low temperatures than those at high temperatures. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Volatile Components**} & \multicolumn{2}{c}{**Threshold**} & \multicolumn{5}{c}{**OAV**} \\  & \(\upmu\)kg & **70**\({}^{\circ}\)C & **80**\({}^{\circ}\)C & **90**\({}^{\circ}\)C & **100**\({}^{\circ}\)C & **110**\({}^{\circ}\)C & **120**\({}^{\circ}\)C & **130**\({}^{\circ}\)C \\ \hline \(\beta\)-Myrcene & 15 & 21.60 & 40.15 & 25.66 & 21.13 & 21.59 & 14.95 & 18.93 \\ cis-\(\beta\)-Ocimene & 10 & 13.84 & 17.43 & 13.91 & 14.99 & 12.00 & 10.77 & 8.89 \\ trans-\(\beta\)-Ocimene & 34 & 5.45 & 10.27 & 7.82 & 4.93 & 5.01 & 3.23 & 3.26 \\ (Z)-Linalool oxide (furanoid) & 190 & 1.11 & 1.17 & 1.36 & 1.58 & 1.09 & 0.28 & 0.27 \\ (E)-Linalool oxide (furanoid) & 190 & 2.00 & 2.02 & 2.21 & 2.66 & 1.87 & 0.43 & 0.42 \\ Linalool & 0.22 & 1688.39 & 2620.19 & 2417.25 & 1858.59 & 1911.51 & 1429.72 & 1520.22 \\ Nonanal & 1.1 & 5.34 & 10.97 & 6.31 & 0.98 & 2.68 & 2.67 & 2.12 \\ Methyl salicate & 40 & 1.59 & 2.63 & 1.94 & 1.70 & 1.58 & 0.83 & 0.83 \\ Geranid & 7.5 & 47.58 & 77.02 & 48.66 & 37.79 & 50.54 & 28.63 & 28.66 \\ \(\alpha\)-Cubebene & 0.8 & 3.88 & 12.38 & 5.87 & 4.46 & 5.59 & 3.96 & 4.24 \\ cis-Jasmone & 7 & 0.59 & 0.95 & 0.62 & 0.69 & 1.11 & 0.40 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: OAVs for the volatile components in the LBT at different drying temperatures. Figure 2: OPLS-DA score plot (**A**) and heat map (**B**) of the differential volatile components of the LBT at different drying temperatures.
## 8 Future Perspectives  
In the future, the development of these technologies can follow various directions. One of these paths is SIoV that applies social network algorithms and notions to IoV, focusing on the interactions between vehicles, devices, and infrastructure, to allow for more efficient communication strategies and AI implementation [28].  
Another direction is the integration with SDNs, which reduce smaller devices' resource consumption, deviating responsibility to an SDN controller, for example [78]. This integration brings out benefits such as enhanced network and Blockchain management and improvements in terms of the lower complexity of smaller nodes in vehicles and other devices, with the removal of the control plane.  
While reputation-based systems in IoV networks are usually used to incentivize data dissemination, like in [PERSON] et al. [104], where, with the help of machine learning and artificial immune systems, nodes in the network are incentivized to disseminate or cache data, these reputation-based systems can also serve other purposes. [PERSON] et al. [46] present a system architecture that substitutes the consensus algorithm of Blockchain altogether by utilizing a reputation-based system as a basis for their lightweight Blockchain-based solution for data sharing in the IoV, where each node stores a list of reputations of connected nodes with which they interact. These types of implementations can also be looked upon with great interest, as they prove to be viable takes on consensus and trust management in these environments.  
Concerning smart cities, in the near future, smaller-scale implementations are likely to start being implemented, since they are less complex and easier to gather information, fix or exchange parts, for testing on different approaches. This would allow researchers to test various approaches on how to implement these smart systems and how to operate them efficiently. Some years later, with this knowledge at hand, a large-scale implementation in a large city might be possible, with the correct standards and regulations required.
## 2 Shear Crack Propagation Theory (SCPT)
The center of the rotation varies within the shear failure process corresponding to the propagation of the crack tip with the increasing load. The calculated local deformations and strains, in turn, serve as the input values for the constitutive relations. Figure 1: Overview of the Shear Crack Propagation Theory (SCPT) algorithm: (**a**) elementary effects of the shear behavior, (**b**) idealized crack geometry and fracture kinematics, (**c**) constitutive relations and (**d**) shear contributions and equilibrium. In each zone, the application of constitutive relations allows calculating the contribution of the different shear transfer actions along the propagating crack (e.g., residual tensile stress transfer, aggregate interlock and friction along the sliding crack interfaces and crack bridging, including dowel action). Furthermore, the integration of horizontal normal stresses and shear stresses in uncracked regions of the control section allows to determine all transferred normal and shear forces in the continuous concrete (Figure 1c). The further surrounding sections of the member are assumed as rigid. In summary, such an idealization of the shear crack provides the necessary and sufficient conditions of the equilibrium (Figure 1d) and kinematic compatibility to devise an incremental algorithm that allows to calculate the shear crack extension (crack propagation angle and crack propagation length), deformations and strains, as well as all internal forces (e.g., the overall shear force \(\mathrm{V_{i}}\), including all the shear contributions) corresponding to a prescribed load increment. The position of the governing (critical) shear crack along the beam axis has to be determined iteratively. To this end, various crack positions defined through \(\lambda_{\mathrm{cs}}\times\mathrm{d}\) (Figure 1d), which is the distance between the center line of the support and the root of the shear crack, have to be checked. The crack position delivering the minimum shear capacity is the critical crack position. With regard to the experimental results presented in this paper, the critical shear crack position is marked in Figure 5. A detailed description of the mechanical background of the SCPT can be consulted in reference [25]. In the future, the SCPT algorithm will also be extended to the application of shear reinforcement. Accordingly, the equilibrium conditions will be extended, the changes in kinematics will be considered and the additional failure mode of a compression strut failure will be implemented. Furthermore, the activation of shear reinforcement and, thus, the effective steel stress will be in the focus of future studies.
## 4 Improve Image Colorization Discriminant Network
### Attention Mechanism  
Convolutional Block Attention Module (CBAM) is a simple and effective attention module [28], which consists of two parts: channel attention and spatial attention. CBAM calculates the attention weights corresponding to the two dimensions. It uses the attention weights to weigh the input features so that the feature map can highlight the important features more. Each channel is a feature, and channel attention allows the network to focus more on meaningful features. Not all regions of a feature map are necessarily useful information, and each region has a different level of importance. Therefore, spatial attention lets the network find important regions to focus on.  
The structure diagram of channel attention is shown in Figure 7.  
The operation process of CBAM is divided into two steps, the first step is to calculate the channel attention \(Mc\), and then to calculate the spatial attention \(Ms\). As shown in Figure 8, F denotes the input feature map; the input feature map is pooled by maximum pooling and mean pooling, and after pooling, the summation operation is performed through the fully connected layer, and finally, the channel attention \(Mc\) is obtained through the sigmod function. The feature map \(F\) is subjected to the operation of \(Mc\), resulting in a further feature map. The feature map is then subjected to maximum pooling and mean pooling, and the features are stitched together after pooling, and then obtain the spatial attention \(Ms\) by convolution and sigmod. Finally, \(Ms\) is multiplied with the feature map  
Figure 6: The pattern of PatchGAN for patch scoring.  
to get the features that pass through the attention channel. The whole process can be formulated as follows:  
\[F^{\prime}=Mc(F)\otimes F \tag{3}\]  
\[F^{\prime}=Ms\big{(}F^{\prime}\big{)}\otimes F^{\prime} \tag{4}\]
## 2 Literature Review
### Multicriteria Decision-Making  
[PERSON] (1943) works represent the starting point of the scientific treatment of the individual decision-making process problems [12]. The decision-making process can be done by applying different methods and tools, as well as using different objectives [13]. The use of multicriteria decision-making (MCDM) methods constitutes an efficient tool for reducing subjectivity and systematizing the decision-making process [14]. They can be used in different stages of the process, either to decide on the importance of the criteria for each alternative, to select the most suitable alternative or to establish a ranking of alternatives. Thus, MCDM methods can be used to select the best alternatives when there are several conflicting criteria in a context of uncertainty [7]. MCDM methods have had a fast-growing in many disciplines [15]. In a decision-making problem there are always several elements: Decision criteria \(C\) = {\(C_{1}\), \(C_{2}\),... \(C_{n}\)}, conditions which allow us to differentiate alternatives and to establish the preferences of the decision-maker; Weight or measurements of the importance of the criteria for the decision-maker, being each criteria vector associated to a weight vector [\(w\)] = (\(w_{1}\),... \(w_{n}\)). Weights can be established by direct allocation methods or by other methods as Simos method [16], Delphi method [17] or by paired comparisons, analytic hierarchy process (AHP) [18] among others. Alternatives, different solutions to be adopted in a decision-making problem, which are assigned as, As = {A\({}_{1}\), \(A_{2}\),... \(A_{m}\)} (I = 1, 2,... m) are the possible alternatives. And last, the assessment or decision matrix, by which, for all of the criteria taken into account and for each alternative of the choice ensemble, the decision-maker is able to give a numeric or symbolic \(a_{ij}\) value that expresses an assessment or opinion of the alternative \(A_{i}\) regarding criteria \(C_{j}\)[19]. The MCDM methods can be classified into different groups according to similar characteristics [13]. These groups are: (1) Scoring methods, their basis consists of assessing the alternatives using basic arithmetical operations, including the simple additive weighting (SAW) and the complex proportional assessment (COPRAS) methods obtain the sum of the weighted normalized values of all the criteria [20]; (2) distance-based methods, the basic principle of them is obtaining the distance among each alternative and a specific point (a hypothetical best alternative), including technique for order preference by similarity to an ideal solution (TOPSIS) and the viekwriterijumsko kompromisno rangiranje (VIKOR) methods [21; 22; 23]. This group also includes others based on Euclidean distance measurement [24; 25]. (3) Pairwise comparison methods are widely used for their ease of calculation when selecting different alternatives in case there are quantitative and qualitative criteria. They allow evaluating the different alternatives according to qualitative criteria when comparing them in pairs and they are sometimes used to weigh the selection criteria as it
###### Abstract  
The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time-frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence-arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.  
DEAP dataset; feature extraction; feature selection; frequency domain; time domain; time-frequency domain; location or spatial domain; emotion recognition; EEG signals Article
# Predator-Prey Model Based Asymmetry Resource Allocation in Satellite-Terrestrial Network
## 2 Related Works
Based on the above considerations, in reference [22], the authors considered the link congestion problem caused by satellite density and proposed an explicit load balancing algorithm, that is, adjacent satellites display and exchange their congestion states to avoid congestion and improve the data transmission rate and throughput. In reference [23], the authors proposed a load routing aware protocol, which can better achieve low latency load balancing by limiting the state synchronization range, reducing the discovery path time and controlling the overhead. In reference [24], the authors proposed a load balancing algorithm based on the idea of intelligent optimization algorithm and linear programming and found the optimal solution through dynamic alternation of two algorithms. In reference [25], the authors considered the satellite network in a polar orbit scenario and proposed that a mobile agent combined with a fixed agent to collect routing information and data transmission cost to dynamically update routing terms in the case of queuing delay and transmission delay can achieve efficient complex equalization. In reference [26], the authors proposed an adaptive distributed load balancing mechanism based on historical information, which made routing selection by jointly analyzing the current and historical status of inter satellite links. In reference [27], the authors proposed a hybrid load balancing algorithm for cascading congestion. Based on the existing dis-tributed mechanism and prior geographic information, a long-distance traffic bypass strategy was designed in which a long-distance traffic bypass mechanism was designed to achieve more efficient traffic load balancing. In reference [28], the authors considered the congestion state of local nodes and relay nodes and updated the routing items by combining offline calculation and online dynamic adjustment strategy, as to provide load balancing efficiency. However, most of the existing studies in satellite communication only consider load balancing based on the on-board routing strategy, which does not consider the power allocation of the satellite-terrestrial transceivers [29; 30; 31] and cannot satisfy the development trends and business requirements of 6G mobile communication. Furthermore, by considering the ground communication and on-board communication to build a load balancing model, how to realize efficient data flow and business processing is still a problem that needs to be solved. In this paper, we use the Lotka-Volterra based predator-prey model to analyze the load balancing problem among these satellites in two groups, which is a dynamic control process to realize efficient data flow and business processing.
## 4 Comparative Analysis of Test Results
### Comparative Analysis of the Central Yunnan Water Diversion Project
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Test Section** \\ **Length (m)** \\ \end{tabular} } & \multicolumn{2}{c}{**Permeability Coefficient of Slug Test (cm/s)**} & \multicolumn{2}{c}{**The Mean of Permeability**} \\ \cline{3-5}  & & **Kipp Model** & **CBP Model** & **HWS Model** & **Coefficient (cm/s)** \\ \hline CYWD-2 & 69 & \(4.73\times 10^{-5}\) & \(3.32\times 10^{-5}\) & \(3.01\times 10^{-5}\) & \(3.69\times 10^{-5}\) \\ CYWD-3 & 403.9 & \(6.49\times 10^{-6}\) & \(4.61\times 10^{-6}\) & \(4.42\times 10^{-6}\) & \(5.18\times 10^{-6}\) \\ CYWD-4 & 133.7 & \(5.42\times 10^{-5}\) & \(3.81\times 10^{-5}\) & \(3.68\times 10^{-5}\) & \(4.30\times 10^{-5}\) \\ CYWD-5 & 136.6 & \(4.80\times 10^{-4}\) & \(4.04\times 10^{-4}\) & \(3.43\times 10^{-4}\) & \(4.09\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the calculation results of the slug test in boreholes CYWD-2–CYWD-5. Figure 11: Comparison of the calculation results water injection test and different slug test theory models in borehole CYWD-1. According to Table 2 and Figure 12, during the full borehole test, the calculated results of the Kipp model are still the largest among the three models. The results calculated by the CBP model and HWS model are close to and consistent with the average value, and those of the HWS model are relatively smaller. Nevertheless, the calculated results of the three models are basically consistent, and the ratio of the maximum and minimum permeability coefficients calculated by different methods is about 1.5 times. Meanwhile, combined with the qualitative analysis results in Figure 8, the quantitative calculation results in Table 2 also show that the permeability of the rock mass in the boreholes CYWD-2, CYWD-3 and CYWD-4 is low, and the permeability of the rock mass in borehole CYWD-5 is high. The qualitative and quantitative analysis results are completely consistent, which shows that the slug test has a good field operation stability, and the results calculated by the three models are reliable. Comparative Analysis of the Water Diversion Project from the Three Gorges Reservoir to Hanjiang River
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 3 Performance Evaluation and Results
### Transmission Distance and the AOC  
Figure 10 shows the BER performance of all three data-embedding mechanisms with respect to \(D\) and \(AOC\). For the experiment, \(\alpha\) for M1 was set to 30, and that for M2 and M3 was set to 0.2. To investigate the BER performance with distance, \(D\) was increased from 10 to 30 cm. The \(AOC\) was set to 90\({}^{\circ}\), representing a perfect angular alignment between the display and camera. Figure 10a shows that the BER performance is degraded with increasing \(D\). This is attributable to image blurring and an out-of-focus effect introduced when capturing images  
Figure 8: BER performance with respect to PSNR [dB] for images captured via an optical wireless channel. (a) \(m=250\) and (b) \(m=500\).  
Figure 9: Achievable data rate (ADR) [bps] against the PSNR [dB] performance of the proposed system. (a) \(m=250\) and (b) \(m=500\).  
over a large distance. Blurring introduces phase noise, which impairs the transmitted data; blur is usually modeled as additive white Gaussian noise (AWGN) in wireless communication scenarios. Similarly, Figure 10b shows the effect of the \(AOC\) on the BER performance of the D2C system. To observe the effect of the \(AOC\) on communication performance, we varied the angle from \(30^{\circ}\) to \(150^{\circ}\) at a constant transmission distance of 15 cm. We can observe an excellent BER performance for an \(AOC\) of \(90^{\circ}\), but errors occurred as the \(AOC\) was significantly varied around \(90^{\circ}\). BER performance is completely dependent on the transmission distance and angle at which the camera captures the display content. Therefore, to optimize D2C communication performance, the display and camera must be close, and the \(AOC\) should be set as close to \(90^{\circ}\) as possible.