esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Volatile Compounds  
#### 2.4.1 HS-SPME-GC-MS Analysis  
HS-SPME-GC-MS analysis of dried citrus peel samples was conducted according to the method of [PERSON] et al. [27] with some modifications. A certain amount of samples (2 g) was finely ground in high-speed universal grinder (FW100, Tianjin Tester Instrument Co., Tianjin, China.) and mixed with 100 \(\upmu\)L of phenyl ethyl acetate (20 \(\upmu\)L/mL in methanol, an internal standard), then immediately sealed in a 20 mL vial covered with an aluminum seal with a PTFE septum. The sample vial was then equilibrated at 90 \({}^{\circ}\)C for 5 min on a heating platform. The extraction was conducted by inserting the preconditioned SPME fiber (DVB/PDMS) (75 \(\upmu\)m) into the head space of the vial for 30 min at 90 \({}^{\circ}\)C. At the end of extraction, the fiber was desorbed into the injection port of GC for 5 min.  
GC-MS analysis was performed on Agilent 8890/7000D (Agilent technologies Inc., Santa Clara, CA, USA). Volatiles were separated using a HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent technologies Inc., Santa Clara, CA, USA). The carrier gas was helium (>99.999%) at a constant flow rate of 1 mL/min. The oven temperature was initially held at 70 \({}^{\circ}\)C for 2 min, then increased to 210 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, and held for 10 min. The injector temperature was maintained at 250 \({}^{\circ}\)C with a split ratio of 1:20. The transfer line to the mass spectrometer was maintained at 250 \({}^{\circ}\)C and the ion source temperature was 230 \({}^{\circ}\)C. The mass detector was operated in the electron impact mode with ionization energy of 70 eV and a scanning rage of 35-500 a.m.u. Compounds were identified by comparing their mass spectra and linear retention index (LRI) [28] with those contained in the NIST mass spectrum database, as well as the available reference standards. The content of the volatiles was approximately semi-quantified by comparing the peak area with that of the internal standard (phenyl ethyl acetate) in the total ion chromatogram. The peak area normalization method was also used to obtain the relative percentage content of each compound in the samples.
## 4 Discussions  
From these studies, we can summarize the following observations.  
* PINN embeds physical constraints into the loss function of the neural network by using automatic differentiation. The imposition of the \"hard\" boundary makes the approximate solution automatically meet the Dirichlet boundary, which accelerates the convergence speed and improves the prediction accuracy [1].
* In addition to the advantage of being mesh-free, PINN can also generalize the construction process of various PDEs. On top of that, classical methods, such as FEM, can only obtain the solution on discrete points, while further interpolation is required for other points. This property makes the solution of the state variables at the interpolated points less accurate. For PINN, when considering the points that do not appear in the training set, there is no need to conduct an interpolation scheme to obtain the solutions.
* With the help of automatic differentiation, the derivative of each state variable can be easily calculated. Hence, the derivative distribution is smooth. However, for the first-order FEM, the first derivative of the state variable inside of an element is constant, which makes the derivative distribution discontinuous.
* Based on the experimental results, the convergence speed of PINN gradually increases as the size of the neural network goes up at the beginning. However, when it reaches a certain point, the improvement in computation time stagnates or even becomes worse. In addition, when studying the number of training samples influence on the convergence speed, except the case where the sampling points are too few, the convergence speed increases as the number of training samples increases; however, when it reaches a certain number, the improvement in convergence speed stagnates or even becomes worse.
* According to our experiments, although the PINN offers unique advantages for solving PDEs, its computational efficiency is an obvious disadvantage compared to FEM. Therefore, figuring out how to accelerate the training of PINN is an important research topic. Ref. [40] introduced an efficient approach based on adaptive sampling strategy, which speeds up the computation of the PINN. In [19], parallel calculation of the PINN was successfully implemented, which can easily handle any complex regional problems, but the improvement in computing time is still on the way. [PERSON] et al. [41] realized speeding up convergence for high-frequency wavefields solutions by using the information from a pre-trained model instead of initializing the PINN randomly.
* PINN can be conveniently utilized to generate a surrogate model in the parametric analysis. In [42], the authors conducted a sensitivity analysis experiment. They first trained the PINN with merely a few values of a specific parameter and then utilized the trained neural network to predict the solution to this parameter within a range. The result is less accurate but still useful for the specific condition, which shows the possibility of PINN in tackling this kind of issue; we will work on this subject in our future study.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Frequency Sampling Methods
#### 3.1.2 Viholainen's Method  
Similar to the design in [118], **Condition 1** is also considered in [PERSON]'s method [51], but **Condition 2** is relaxed and it is assumed that \(H_{1}=\chi\). When \(K=3\) and \(K=4\), the filter weighting coefficients are  
\[\left\{\begin{array}{l}K=3:\ H_{0}=1;\ H_{1}=\chi;\ H_{2}=\sqrt{1-\chi^{2}}, \\ K=4:\ H_{0}=1;\ H_{1}=\chi;\ H_{2}=1/\sqrt{2};\ H_{3}=\sqrt{1-\chi^{2}}.\end{array}\right. \tag{24}\]  
According to a specific objective function, the optimal solution can be obtained by a simple global search algorithm.  
In [51], the LS criterion and the minimax criterion are selected for comparison with the method in [118]. The frequency responses of prototype filters in [51, 118] based on the LS criterion are shown in Figure 1a, where the number of subcarriers is 16. As can be seen, the side-lobes of the prototype filterswhen \(K=4\) are lower than those with \(K=3\), that is, better stopband performance is benefited by a larger value of \(K\). Furthermore, the first side-lobe of the prototype filter in [51] is a little lower than that in [118]. Figure 1b shows the frequency responses of prototype filters in [51; 118] based on the minimax criterion. Similarly, the prototype filters in [118] have higher first side-lobe levels. It is also observed from Figure 1, the prototype filters based on the minimax criterion improve the stopband performance at the cost of sacrificing the main-lobe width.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 3 Problem Formulation  
In this article, our objective is to maximize the sum rate of GUs in urban areas by jointly optimizing the UAV trajectory, RIS phase shift, and transmit power from UAV-RIS to GUs. The sum rate maximization problem can be written as  
\[\max_{\{q,\theta_{u},p\}}\sum_{n=1}^{U}\Theta_{u} \tag{16}\] \[\textbf{s.t}:\Theta_{u}\geqslant\Theta_{\min}\forall t\] (16a) \[\sum_{u=1}^{U}P_{u}^{\text{UR}}\leq P_{\max}^{\text{UR}}\] (16b) \[\textbf{0}\leq\theta_{f}\leq\textbf{2}\tau;f=\textbf{1},\ldots.F\] (16c) \[x_{\min}\leq x^{\text{UR}}\leq x_{\max};\] \[y_{\min}\leq y^{\text{UR}}\leq y_{\max};\] (16d) \[h_{\min}\leq h^{\text{UR}}\leq h_{\max}\]  
Constraint (16a) provides a minimum achievable rate for all GUs while guaranteeing their QoS. Constraint (16b) ensures that the overall power transmitted by the UAV-RIS does not exceed the maximum power transmission limit. Constraints in (16c) specify that the RIS reflecting matrices are phase shift matrices, ensuring that all transmitted signals are reflected without any power loss. Constraint (16d) limits UAV-RIS's capability to fly within a specific area, aiming to provide seamless connectivity to GUs and achieve a significant enhancement in throughput.  
The optimization problem mentioned in (16) is non-convex and difficult to solve directly. Therefore, in the subsequent sections, we divide the original problem into two subproblems to determine the optimal user partitioning and the best locations of UAVs, along with the orientation of the RIS phase shift. The proposed methodology consists of two main stages. First, users are partitioned into clusters based on both distance and SINR levels using a modified K-means algorithm to ensure QoS satisfaction. Second, a DDPG-based reinforcement learning model is employed to jointly optimize UAV horizontal trajectory and RIS phase shifts, maximizing the system sum-rate while reducing HOFs and maintaining EE under user mobility.
## 4 Pattern Recognition
### Fisher's Linear Discriminant Analysis  
Linear Discriminant Analysis (LDA), also known as Fisher's LDA, uses a linear hyperplane to separate the data representing each of the two classes (see Figure 5).  
The hyperplane is obtained by projecting high-dimensional data onto a line. The objective of this projection is to maximize the distance between the means of the two classes while minimizing the variance within each class. This defines the [PERSON] criterion, which is maximized over all linear projections, \(w\):  
\[J(w)=\frac{(\tilde{\mu}_{1}-\tilde{\mu}_{2})^{2}}{S_{1}^{2}+S_{2}^{2}} \tag{4}\]  
where  
projected samples \(y_{i}=w^{T}x_{i}\). Based on these equalities, we can rewrite [PERSON]'s criterion as a function of \(w\) in the following way:  
\[J(w)=\frac{w^{T}S_{B}w}{w^{T}S_{w}w} \tag{5}\]  
where \(S_{B}\) and \(S_{W}\) measure the separation between means of both classes and the within-class scattering, respectively. Given the previous equation, we can find its maximum by solving the generalized eigenvalue problem as follows:  
\[S_{B}w=\lambda S_{w}w \tag{6}\]  
Solving this problem will result in a collection of eigenvectors \(w\) and their corresponding eigenvalues \(\lambda\). Then, these eigenvectors must be sorted according to their eigenvalues from biggest to smallest, and finally, a set of \(k\) eigenvectors is chosen to create a weight matrix \(w\), which is the representation of the new space in which the data are going to be projected. Algorithm 2 gives the basic steps for LDA.  
```
Input network:  Training set \(S=\{(x_{1},t_{1}),(x_{2},t_{2}),\ldots,(x_{s},t_{s})\}\) Train network:  Calculate the means \(\mu\)  Calculate \(S_{B}\) and \(S_{W}\)  Get the eigenvectors and values: \((e_{1},e_{2},\ldots,e_{n}),\ (\lambda_{1},\lambda_{2},\ldots,\lambda_{n})\)  Obtain the matrix \(S_{x}\)  Sort the eigenvectors and chose the \(k\) ones with the bigger eigenvalues  Form a matrix \(W(n\times k)\) Output network: Returns Matrix \(W\)
```  
**Algorithm 2** LDA Pseudo-code.
## 2 Materials and Methods
### Quantification of Total Flavonoid Content  
The total flavonoid content of the extracts was estimated using the aluminum chloride (AlCl\({}_{3}\) anhydrous crystallized \(\geq\) 99.0%, Sigma-Aldrich Germany) colorimetric assay. Crude extracts were dissolved in methanol. Then 200 uL of each extract were added to 1 mL of a 2% AlCl\({}_{3}\) methanolic solution, followed by the addition of 3.8 mL of methanol to make a final volume of 5 mL. Solutions were vortexed and incubated for 15 min in the dark. Absorbance was read at 430 nm against a blank solution using methanol. A calibration curve was constructed following the above steps, using ruin dissolved in methanol at concentrations ranging from 200-1700 ug/mL. Results were expressed as mg of ruin equivalents (RE) per gram of dry plant material using the equation derived from the plot: y = 0.0539x + 0.0585 (r = 0.986, \(n\) =3).  
Rutin was chosen as a standard solution, as indicated by the European Pharmacopoeia, Section II, hypericin herba [25], and according to preliminary experiments described as follows: For the total flavonoids assay, preliminary experiments with quercetin and ruin standard solutions (ExtraSynthese, Genay, France) after complexing with AlCl\({}_{3}\) showed a maximum absorbance for quercetin at 458 nm, while the maximum absorbance for ruin was read at 435 nm after reaction with AlCl\({}_{3}\) [Figure 1]. Afterwards, the reaction of the flavonoids with the AlCl\({}_{3}\) complex was performed on the extracts. The results showed that, in the presence of the extracts, absorbance was near 430 nm. According to the literature data, most studies use a range of 410-430 nm to measure TFC [26]. Therefore, for the reasons mentioned above, ruin was chosen for the quantification of TFC, and the selected wavelength was 430 nm.
## 5 Performance Assessment  
The performances of D-ST were assessed using OMNeT++ [57; 58], a simulation environment widely used both in academia [59; 60] and in industry. The Network Simulator for Time-Sensitive Networking (NeSiNg) [61], which extends the Internet Networking (INET) framework, was used to develop the simulation model.  
The performance metrics adopted are the frame end-to-end delay (_e2 eDelay_) and the absolute jitter (AbsJitter).  
Figure 2: Ethernet port forwarding architecture according to D-ST.  
The _e2 eDelay_ of the \(i\)-th frame of the flow \(\mathsf{f}\left(e2 eDelay_{i,f}\right)\) is the time interval between the frame generation at the source node (\(GenTime\)) and the complete delivery at the destination node (\(RxTime\)) measured at the application level, given by Equation (6).  
\[e2 eDelay_{i,f}=RxTime_{i,f}-GenTime_{i,f}. \tag{6}\]  
The absolute jitter of the flow \(\mathsf{f}\left(AbsJitter_{f}\right)\) represents the widest span of _e2 eDelay_ values, defined as the difference between the maximum and the minimum _e2 eDelay_ measured for the flow \(\mathsf{f}\), as shown in Equation (7).  
\[AbsJitter_{f}=max(e2 eDelay_{f})-min(e2 eDelay_{f}). \tag{7}\]  
The following subsections present the simulative assessment of D-ST in a realistic automotive scenario.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{5}{c}{**Deposition Time**} \\ \cline{2-6}  & **10 min** & **20 min** & **40 min** & **60 min** & **90 min** \\ \hline \multicolumn{6}{c}{Sulfacyl sodium} \\ \hline
1 & 2.04 & 2.28 & 1.45 & 1.16 & 1.43 \\ \hline
2 & 1.0 & 1.12 & 1.03 & 1.0 & 1.73 \\ \hline
3 & 1.58 & 1.25 & 1.71 & 2.03 & 1.28 \\ \hline
4 & 1.99 & 1.0 & 1.67 & 1.02 & 2.82 \\ \hline \multicolumn{6}{c}{Clonidine} \\ \hline
1 & 1.38 & 3.68 & 2.25 & 1.46 & 1.0 \\ \hline
2 & 1.04 & 1.06 & 1.0 & 1.0 & 1.0 \\ \hline
3 & 1.15 & 1.0 & 1.07 & 1.16 & 1.09 \\ \hline
4 & 1.16 & 1.13 & 1.26 & 1.1 & 1.45 \\ \hline \multicolumn{6}{c}{Vitamin B12} \\ \hline
1 & 1.99 & 1.0 & 1.14 & 1.0 & 1.05 \\ \hline
2 & 1.0 & 1.01 & 1.1 & 1.04 & 1.13 \\ \hline
3 & 1.04 & 1.02 & 1.06 & 1.0 & 1.01 \\ \hline
4 & 1.0 & 1.0 & 1.0 & 1.5 & 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Light-to-dark ratio of organic-carbon hybrids with different organics on various substrates. reaches values of 1.5-2.0 at certain times of deposition on different substrates. In the case of this experimental work, the best substrate, in terms of electrical parameters, as well as in terms of morphological quality, is substrate 1. This corresponds to the efficiency of solar hybrids of these compounds on silicon [2]. Back to the composition of substrates, Table 2, magnetite is included only in substrate 2. Moreover, this substrate does not contribute to the self-organization of heterocyclic amines and their photosensitivity. In addition, substrate 1 contains activated carbon, and this substrate exhibits both the highest ability for the self-organization of heterocyclic amines and better electrical properties. Analysis of dimensionless sensitivity of CVCs, presented in Figures 6b and 7b, demonstrates Ohmic dependences of all illuminated curves, DS = 1.0. At the same time, some dark CVCs behave as injection characters with a maximum value DS = 1.5. This characterizes the structure as a potential p-type layer in a future solar cell structure. In a two-layer structure with n-type layers, the structures with larger light-to-dark ratios will be able to provide greater efficiency in converting solar energy into electrical. On the basis of CVCs, the possibilities of structures under investigation for the formation of two or more layered hybrids in future solar cells were analyzed. It is necessary to note that the next step of studies will focus on clonidine-on-carbon-on-copper hybrid that shows photovoltaic properties; this will be the subject of some further research reports.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 3 Results and Discussion
### The Parameter Distribution of Coaxial Jets
#### 3.1.3 Reynolds Shear Stress and Turbulence Intensity  
The Reynolds shear stress corresponds to the momentum exchange caused by the pulsation of fluid particles. For convenience, the simplified instantaneous Reynolds shear stress expression was -u'v'. The radial distribution of the dimensionless Reynolds shear stress is shown in Figure 5a-c. At x/D\({}_{\rm{oa}}\) = 0.12, the position of the left peak of the Reynolds shear stress curve corresponded to the boundary between the central jet and the annular jet, and the right peak corresponded to the boundary between the annular jet and the ambient air, indicating that the momentum exchange there was large. With the development of the mainstream, the inner shear layer expands outward and the velocity gradient became smaller, and the momentum  
layer to become more intense. At the same time, the velocity pulsation in the inner shear layer also increased, but since the time average velocity significantly increased (Figure 3, y/D\({}_{\text{oa}}\) = 0.07), the left peak of the turbulence intensity barely changed.
## 4 Discussions
### Light and Water Limitations Identified by MTVDI  
The direction and mechanism of canopy seasonality over Amazon tropical forests at a large scale is a complicated and debated question. Some studies [67; 81; 82] used TWS and CWD data to detect the spatial pattern of canopy dry-season green-up in the Amazon. Meanwhile, in some other studies, VPD, which indicates how the dryness of air temperature affects the closure of plant stomata and thus controls physiological processes such as plant transpiration and photosynthesis [83], is used as a robust trigger of tropical forests canopy seasonality [5]. Actually, the above studies indicate two different viewpoints of Amazonian dry-season \"green up\". The first is that it is more likely that Amazon tropical forests encounter soil water deficit during the dry season, while the second is that it is more likely that VPD triggers growth of new leaves and enhances canopy rejuvenation [5]. [PERSON] et al. [8] shows significant canopy rejuvenation due to more new leaves growing in the dry season. [PERSON] et al. [66] also pointed out that in most parts of the Amazon tropical forests, the largest incident radiation was observed from Aug to Oct and induced an enhanced photosynthesis activity that occurs mainly from Sep to Dec.  
Different clusters (Figure 4b) identified by monthly Ts, QSCAT, and NIR data represented different light and water constraints on tropical canopy phenology [79]. The water availability significantly dominated the canopy phenology in the southern parts (e.g., Figure 4b SA1 and SA2) and the canopy phenology of the northeastern parts (e.g.,Figure 4b NA2), where there is a significant dry-season green-up, was strongly limited by light availability, and there was a southwest-to-northeast transition from predominantly water-adapted to light-adapted canopy seasonality. The MTVDI clusters (Figure 4a) showed a similar pattern with this previous study (Figure 4b), distinguishing the difference between water-controlled canopy seasonality and light-controlled canopy seasonality.
###### Abstract
Therefore, the main purpose of this paper is to propose a new niche adaptive elite evolutionary algorithm (NAEEA) to reduce the network energy consumption of AUSCNs [22; 23]. In NAEEA, the new niche strategy can evolve the population in groups, which greatly expands the search space of the population and the diversity of the population solution, effectively improving the convergence speed of the NAEEA. The new adaptive strategy and elite strategy can effectively prevent NAEEAs from falling into premature convergence and causing evolutionary stagnation by adaptively changing the parameters of the algorithm and retaining the local optimal solution [24]. Compared with the above heuristic algorithms, the most attractive features of this algorithm can be summarized as its fast convergence speed, high group diversity and strong global search ability. NAEEAs not only select appropriate head nodes, but also aim to solve combinatorial clustering problems [25]. In addition, NAEEAs can solve clustering problems more effectively than original mathematical methods. Simulation results show that the newly designed NAEEA can greatly reduce the energy consumption of AUSCNs. The main contributions of this article are as follows:  
1. A novel designed niche adaptive elite evolutionary algorithm (NAEEA) is proposed, which integrates the merits of elite selection and adaptive adjusting to obtain high performance. The NAEEA can choose suitable head nodes at a fast rate to reduce energy consumption in AUSCNs. Furthermore, this algorithm has an excellent convergence speed which can better deal with clustering optimization in AUSCNs. 2. New elite and niche operators are designed to effectively speed up the convergence rate and avoid falling into the local optima. Moreover, a novel adaptive operator is designed, which provides a dynamic self-adapting adjustment mechanism to effectively select head nodes. 3. Lots of simulations were performed for clustering in AUSCNs. The results show that the NAEEA significantly reduces the network energy consumption compared with the evolutionary algorithm (EA), simulated annealing algorithm (SA) and leapfrog algorithm (SFLA), under three different conditions (1. Different ratio of head nodes; 2. Different number of sensor nodes; 3. Different size of area.). The paper structure is as follows: In Section 2, the related works are discussed, and in Section 3, an energy consumption model is introduced. Then, Section 4 shows the process of the NAEEA. Next, in Section 5, simulations are carried out for clustering in AUSCNs by using the NAEEA, EA, SA and SFLA. Furthermore, the discussion of the results is shown in Section 5. In the end, the conclusion part is given in Section 6.