esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Spatial Distribution Characteristics of TCWV in China  
#### 3.1.1 Spatial Distribution Characteristics of Annual mean TCWV in China  
We selected the data of TCWV in China from September 2002 to February 2022, provided by the AIRS dataset, and calculated the average value of TCWV over the China region during these 20 years. Figure 1a shows the variation trend of TCWV with longitude, showing a \"V\" shape as a whole. In the regions with longitude ranging from 70\({}^{\circ}\) E to 100\({}^{\circ}\) E, the TCW decreases with the increase of longitude; in the regions with longitude ranging from 100\({}^{\circ}\) E to 140\({}^{\circ}\) E, the TCWV increases with the increase of longitude. The main reason is that the areas of 70\({}^{\circ}\) E-100\({}^{\circ}\) E is mainly the Tibetan Plateau, which is the Third Pole in the world. Figure 1b shows the variation trend of the TCW with latitude. It can be seen that the TCWV in China decreases with the increase of latitude. This is mostly related to the temperature, with higher temperatures in lower latitudes, making it easier for moisture to evaporate, thus allowing more water vapor to enter the air. The 20-year average spatial distribution of the TCWV in China is shown in Figure 1c. The 20-year average value of the TCWV over the China region ranges from 2.27 kg/m\({}^{2}\) to 47.29 kg/m\({}^{2}\), with the maximum value located in South China and the minimum value located at the junction of southwest and northwest China. It can be seen from Figure 1c that the TCWV in China has an obvious southeast-northwest distribution, that is, the TCWV in the southeast is higher than that in the northwest. This is mainly because the southeastern region is closer to the ocean, while the northwest China region is deep inland and far away from the ocean, making it difficult for the humid air brought by the monsoon from the ocean to reach the region. On the other hand, it is also because most of the northwest region is desert, which is arid and short of water, so therefore less water can enter the air by evaporation. The other special area is the Tibetan Plateau. It can be seen from Figure 1c that the latitude of the Tibetan Plateau is lower than that of northwest China, but the TCWV is also lower, which is obviously related to the altitude of the Tibetan Plateau. On the one hand, high mountains have a certain influence on the transportation of the TCWV; on the other hand, the higher the altitude, the shorter the column of air above the water vapor, and thus the TCWV is lower. Therefore, generally speaking, the distribution of TCWV in China is related to latitude, distance from the ocean, and altitude. These conclusions are consistent with the work of [37].
## 4 Materials and Methods
### Reactive Oxygen Species (ROS) Measurements  
In order to check if the presence of nootkatone activates an ROS-mediated plant defense, an ROS burst was evaluated using a luminol-based assay with leaf discs and a 96-well plate luminometer (Tecan infinite 200 Pro; Tecan Group Ltd., Mannedorf, Switzerland), as described previously [50]. Briefly, leaf discs of 14-day-old _Arabidopsis_ plants were incubated overnight in sterile double-distilled water, then transferred to cavities of a 96-well plate filled with 5, 10 or 20-ppm nootkatone or with 0.5% of DMSO or with sterilized double-distilled water as negative controls or with the bacterial peptide fig22 (1 \(\upmu\)M) as a positive control [50]. Light emission was measured in relative units over a 75-min-long incubation period and analyzed using the Tecan i-control software. Data was tabulated and plotted. Four leaf discs were used per treatment as technical replicates. The experiment was repeated 3 times.
## 5 Experimental Setup
### Experiments Design
#### 5.3.3 Cross-Validation and DRL Impact Assessment  
The final phase implemented cross-validation testing to evaluate pattern recognition accuracy and quantify the impact of DRL on prediction performance. This phase analyzed 1000 candlesticks across both daily and hourly intervals, utilizing ten years of historical data for DRL training. The analysis focused on two primary pattern types: incomplete (1-2-3-4) and complete (1-2-3-4-5) impulsive waves.  
The validation criteria were tailored to each pattern type. For incomplete waves (1-2-3-4), predictions were evaluated based on the directional accuracy of subsequent price movements over a period calculated as 1.62\(\times\) the length of the first wave. This multiplier, derived from Fibonacci relationships integral to Elliott wave theory, provided a standardized timeframe for validation. For complete waves (1-2-3-4-5), the validation process examined the correlation between wave 5 and the subsequent corrective wave A, focusing on both directional alignment and magnitude comparison. Incorporated comparative analysis between standard pattern recognition and DRL-enhanced prediction, enabling assessment of the DRL component's contribution to prediction accuracy.
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 2 Proposed Prediction Model  
### NSNP Systems  
The spiking neural P (SNP) system [19] is a brand-new batch of parallel computational models based on the structure and attributes of biological neurons. In the past, researchers have proposed a variety of variants [21; 22; 23; 24; 25]. The nonlinear spiking neural P (NSNP) system [20], regarded as one of the nonlinear variants of the SNP system, can process the firing of neurons through nonlinear spiking rules, which means that it can be used to develop a new recurrent-like model.  
Definition 1. An NSNP system is acquired as  
\[\Pi=(O,\sigma_{1},\sigma_{2},\cdots,\sigma_{m},syn,x,y) \tag{1}\]  
where,  
1. \(O=\{a\}\) denotes a singleton alphabet (a indicates the spike).  
\(\sigma_{i}=(u_{i},r_{i})\) is the \(i\)th neuron, \(1\leq i\leq m\), wherein 1. \(u_{i}\) denotes the primary state of \(\sigma_{i}\). 2. \(r_{i}\) denotes the nonlinear firing rule, and the modality is \(a^{g(u)}\to a^{f(u)}\), wherein \(u=u_{i}+x\), and both the functions \(g(\cdot)\) and \(f(\cdot)\) are nonlinear. 3. \(syn=1,2,\cdots,m\times 1,2,\cdots,m\) with \(i,j=1,2,\cdots,m,i\
eq j\) (synapses). 4. \(x\) denotes the external input of the model. 5. \(y\) denotes the external output of the model. An NSNP system can be expressed by a directed graph of \(m\) neurons. In an NSNP system, every neuron is composed of a state unit and a nonlinear firing rule. The rule is in the form of \(a^{g(u)}\to a^{f(u)}\), and the state of the neuron is evolved by the nonlinear spiking rule. Therefore, the nonlinear spiking rule characterizes the dynamic behaviors of the NSNP system. In the case of solving practical problems, the NSNP system can be considered as a multiple input, multiple output (MIMO) system, where x and y are the external input and output of the model. Suppose that \(u_{i}(t)\) and \(u_{i}(t-1)\) represent the states of neuron \(\sigma_{i}\) at time \(t\) and time \(t-1;x_{i}(t)\) is the input of neuron \(\sigma_{i}\) at time \(t\). According to the nonlinear spiking mechanism, the state and input-output equations for neuron \(\sigma_{i}\) is shown as follows: \[u_{i}(t)=u_{i}(t-1)-g(u_{i}(t-1)+x_{i}(t))+n\] (2) \[y_{i}(t)=f(u_{i}(t-1)+x_{i}(t))\] (3) where \(n\) is the value of spikes gained from other neurons.
## 1 Introduction
Transitions were analyzed in detail, through flow topologies, phase trajectories, Poincare map and perturbation decay rate. The chaotic mechanism inside the classic lid-driven and two-sided wall-driven cavity [39] were discussed along with the development of vortical structures. Regarding the flow over square and circular cylinders [39; 40], Hopf bifurcation was observed and analyzed. With a splitter plate positioned in front of a square cylinder, both Karman vortex street and Kelvin-Helmholtz instability were captured. The impact of the plate on the flow transitions was discussed for different values of plate thickness, plate position, plate length, and velocity ratios. In the present paper, steady solutions of right-angled isosceles triangular cavity flow are initially studied, and the critical Reynolds number between steady and unsteady solutions is obtained and discussed. The flow topology evolution as a function of the Reynolds number is visualized through stream functions. The behavior of the dominant primary and secondary vortices are discussed in detail. For the right-angled isosceles trapezoidal enclosures, a geometrical parameter \(\lambda\) (the ratio of the top-line over base-line) is introduced in order to distinguish the different geometries. We investigate the influence of \(\lambda\) on steady solutions at Reynolds numbers 1000 and 7000 for both bowl-shaped and pyramid-shaped trapezoidal cavities. For these cavities and for the different \(\lambda\) studied, the Hopf bifurcation is obtained and presented for the first time. We found that the geometrical parameter \(\lambda\) plays an important role on flow stability. Regardless of the shape of the trapezoidal cavity tested, the flow inside the domain behaves more stable than that in a triangular enclosure. Moreover, the influence mechanism of parameter \(\lambda\) on transitions is concluded for both bowl-shaped and pyramid-shaped trapezoids. The rest of the paper is designed as follows, in Section 2, the framework of the numerical approach is introduced. In Section 3, the code validation is presented. The new results and flow analysis are to be found in Section 4. The conclusions are presented in Section 5.
# Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson's Disease
## 3 Mathematical Background for Cross Wavelet Analysis
### Extraction of the Global Wavelet Spectra Information and Cross Wavelets Profiles  
After performing multiple XWT within the groups, 63 cross wavelet spectra were obtained (21 for each combination, corresponding to each electroencephalographic channel) for the CL <60 group, 126 for the CL >60 group, 21 for the PD patients <60 group and 210 for the PD patients >60. From each cross-wavelet spectrum, only the information from the GWS was extracted. To obtain the global wavelet information per channel within each group, the arithmetic average of the global wavelet curves of each crossing corresponding to the same channel were obtained. Thus, one array for each group with 21 time series, corresponding to 21 electroencephalographic channels, was obtained.  
From the last arrays, four general wavelet profiles were constructed for each group: (1) The global wavelet curves profile was made by plotting the 21 mean global wavelet curves per channel (grey lines) of each group as well as the total mean curve of these curves (black line). This profile shows information about the main brainwave per channel, as well as the most relevant brainwave for the group. Vertical dotted lines on this profile limit the frequency range of each brainwave. (2) The main frequencies profile was made using the frequency value with the highest power observed in the mean global wavelet curves per channel. These profiles showed the most common brainwave on the brain and the frequency values of each channel, as well as their distribution. (3) The power anomalies profile was constructed from the 21 mean global wavelet curves per group and presented by bar graphs per channel to observe the power profile with respect to the total mean curve of each group. Positive anomalies (black bars) indicated an increased power with respect to the total mean curve, and negative anomalies (grey bars) corresponded to decreased power. A linear regression was performed on the power anomalies values to quantify the anteroposterior power gradient of each group. (4) The brainwave power profile was obtained from the total mean curves of each group with respect to the brainwave frequency range used in the research. The brainwave power values were normalized with respect to the highest power for each group.  
Figure 4: Cross wavelet spectrum from the Cz channel of the crossing between CL01 and CL03 <60.
## 5 Convergence of ML and Blockchain in IoV
### Prospects of ML and BC Convergence for IoV System
#### 5.1.3 Privacy Protection  
[PERSON] et al. [143] proposed a BPFL scheme for preserving the privacy of sensitive data in the IoV by means of FL. In this study, Multi-Krum technology was applied to achieve distributed verification and filtering at the ciphertext level. A consortium-based smart contract was used to facilitate the privacy protection by incorporating model verification and a filtering mechanism. A total of 500 clients were selected for the experiment and simulation was performed on the Go (v-1.10.3) platform. The authors reported model performance based on model accuracy, the computation overhead of the verification, and the effectiveness of the incentive mechanism. The proposed model, through its distributed parallel verification mechanism, improved the accuracy of the model, lowered the runtime overhead of the verification contract and effectively managed nodes allocation and honest behaviors of the FL nodes joining the system.  
[PERSON] et al. [151] proposed a fully decentralized ML system combined with FL and then integrated it with a blockchain for the privacy preservation of CAVs. The authors developed a Bift model that enabled a distributed CAVs to train ML models locally their own driven data, using their own while a Proof of Federated Learning (PoFL) was used as the consensus algorithm to resist a security breach in the system. Their results showed that Bift model achieved superior performance in terms of privacy preservation, storage system, and consensus algorithm. Other privacy-preserving approaches have been explored integrating ML and BC for an efficient learning, and data-preserving applications have been reported [152; 153; 154], with all showing varying success in the different applications.
## 4 Model Validation  
### Impact Hazard Prediction Effectiveness Analysis  
In order to verify the prediction performance of the BO-LSTM model, it is compared with two other models: a GRU (a recurrent neural network model) and a 1 DCNN (a one-dimensional convolutional neural network model). It can be seen from Figures 6-8, the BO-LSTM model demonstrates the most accurate prediction of impact risk, with precise predictions for the four events of medium and strong impact risk. The GRU model is the second-best performer, with two accurate predictions for the four events of medium impact risk and strong impact risk. The 1 DCNN prediction effect is the least accurate, with only one accurate prediction made for the four events of medium impact risk and strong impact risk.  
In order to make a more accurate evaluation of the performance of the three neural network models, MAE, MAPE, VAF, and MSE were used to evaluate the prediction results of the impact risk score.  
The results of the evaluation are presented in Table 2. The mean absolute error (MAE) and mean absolute percentage error (MAPE) values of the bidirectional long short-term memory (BLSTM) model are the smallest, with values that are 0.038369 and 0.195484 lower than those of the gated recurrent unit (GRU) model and 0.052916 and 0.310299 lower than those of the one-dimensional convolutional neural network (1 DCNN) model, respectively. Furthermore, the variance accounted for is closest to 1, indicating that the predicted value of the impact risk and the true evaluation value are well fitted. In the MSE value evaluation, the GRU model outperforms the 1 DCNN in the first three evaluation indicators due to the greater susceptibility of the mean square error (MSE) to large errors and the greater sensitivity of the GRU model to outliers. The MSE values of the two models are comparable, yet the evaluation results are inferior to those of the BO-LSTM model. Consequently, the
## 2 Background
### Opposition-Based Learning
#### 2.3.1 Advantages of OBL  
Some advantages of OBL are:  
1. **Improved solution quality:** By considering contrasting information through opposite solutions, OBL can enhance the exploration of the search space, potentially leading to improved solution quality and diversity.  
2. **Enhanced convergence properties:** OBL can help optimization algorithms converge faster by introducing additional diversity and promoting the search in unexplored regions of the search space.
3. **Robustness:** OBL has been shown to improve the robustness of optimization algorithms by reducing the risk of becoming trapped in local optimums.
4. **Widely applicable:** OBL can be applied to various optimization algorithms and problem domains, making it a versatile approach to improving optimization performance.
5. **Simple implementation:** OBL is relatively easy to implement, as it involves generating opposite solutions by incorporating contrasting information.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 3 Results
### CFD Simulations
#### 3.1.2 Validation of the CFD Model  
A contour plot was used to compare the CFD simulation and the experimental values and to analyze the airflow distribution over the paddy rice (Figure 8). The simulated results were consistent with Figure 7. Thus, the airflow velocity was higher at the left side. It can be seen that the air velocity profile at 0.08 m decreased in the heating area from 5.0 to 0.5 m s\({}^{-1}\), and the velocity in the drying area was around 0.1 m s\({}^{-1}\).  
Figure 9 shows the computational and experimental velocities over the ISD's length. The results indicate that air velocity decreased along the dryer length and that air velocity magnitude in the x-direction at 0.08 m above the paddy rice verifies the computational results with the experimental data. The comparison with the computed and experimental data revealed a good correlation, with a R\({}^{2}\) of 0.78 and a RMSE of 0.72 m s\({}^{-1}\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Dry Season** & **Rainy Season** \\ \hline Moisture content w.b., \% & 18.0 & 22.5 \\ Humidity ratio, kg kg\({}^{-1}\) & 0.015 & 0.020 \\ Drying time, h & 32 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial values for the ISD model.  
Figure 7: 3D streamlines of airflow in the ISD: (**a**) top view and (**b**) side view with zoomed view of the heating area.
## 7 Use of Biomaterials in the Release of Extracellular Vesicles for Treatment of Cutaneous Wounds
### Complex Multifunctional Scaffolds  
In order to optimize the functionality of dressing on wound healing, complex biomaterials are being developed. They combine EV with other biologically-active compounds or factors, that also contribute to improving and accelerating the healing process. Among these complex scaffolds are those based on polyurethanes. These constitute a family of polymers with a high spectrum of physicochemical properties. They are synthesized from reactions between polyols and diisocyanates. Currently, they have been developed with new biobased macromolecular architectures. That has allowed us to improve their biocompatibility for biomedical applications, including the development of wound dressings [126].  
In this regard, the fabrication of a wound dressing called OxOband has recently been described. It is composed of oxygen-releasing antioxidant polyurethane (PUAO-CPO) scaffolds, embedded with exosomes derived from adipose-derived stem cells from rat fattissue [127]. This dressing is characterized by: (i) its antioxidant activity, as a consequence of the ascorbic acid incorporated in it [128], releasing O\({}_{2}\) for a period of more than 10 days, due to the incorporation of calcium peroxide [129]; and (ii) its regenerative capacity, including MSC-EV. Due to these properties, this biomaterial has the potential to decrease oxidative stress in wounds. Oxygen supply facilitates vascularization, further reducing the possibility of infection. Thus, in a diabetic animal healing model, treatment with OxOband, compared to control animals, accelerated wound closure and decreased inflammation and favored reepithelialization. Increased collagen formation and angiogenesis, promoting the formation of a mature epithelium with hair follicles, similar to that of healthy skin. These effects were also observed when wounds in the model were infected with _Staphylococcus aureus_ and _Pseudomonas aeruginosa_[127].  
Another possibility to enhance or complement the regenerative effects of MSC-EV is the incorporation of plant extracts, with positive effects on healing, into wound dressing. For example, the combination of MSC-EV and astragalus extract in gel improved the speed of skin healing in a murine model. This is partly enhanced by the anti-inflammatory effect of astragalus extract [130].
## 4 Performance Evaluation
### Perceived Direction Performance  
Figure 11 shows the experimental setup for evaluating listeners' directional perception. To record stereo signals, a stereo microphone array was placed with 3-cm spacing, and a sound source was played through one loudspeaker from the center of the microphone array at a distance of 1.5 m. For the evaluation, each listener was sitting in an anechoic room of dimensions 2130 mm \(\times\) 3370 mm \(\times\) 3000 mm, in which two loudspeakers had been placed as shown in the figure. Note that here the model of all the loudspeakers was Genelec 6010A. In this experiment, we prepared five audio clips that were excerpted from the sound quality assessment material (SQAM) [30]; Table 1 describes the genre and musician of each audio clip. Note that since all audio clips were sampled at 44.1 kHz, we upsampled the audio clips to 48 kHz to ensure a consistent experimental environment. Then, we recorded five audio clips in three different environments where the loudspeaker was rotated from 0\({}^{\circ}\) to 90\({}^{\circ}\) at a 15\({}^{\circ}\) step towards the right direction, resulting in seven different directions. After that, the recorded signals were processed by the DS beamformer and the proposed method (some audio samples can be found at [31]). After the processed clips were played at sound pressure level (SPL) 90 dB, eight participants (four males and four females) with no auditory diseases were asked to indicate their perceived directions for the original and processed signals. Note that the participants were allowed head movement.  
Figure 12 compares the perceived azimuths averaged over five audio clips and eight participants for each source direction in three different recording environments, where the dashed line indicates the target direction, and the vertical bar on each bar chart corresponds to the standard deviation. As shown in the figure, the originally recorded signals were all perceived at around 0\({}^{\circ}\)-10\({}^{\circ}\) for all environments, even though the actual source angles were above 15\({}^{\circ}\). By applying the DS beamformer to enhance  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Track** & **Genre** & **Description** \\ \hline
49 & Speech & Female English speech \\
66 & Orchestra & Wind ensemble, [PERSON] \\
67 & Orchestra & Wind ensemble, [PERSON] \\
69 & Pop music & Abba \\
70 & Pop music & [PERSON] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information on five audio clips used for the evaluation of perceived direction.  
Figure 11: Experimental setup for evaluating the perceived directions.  
the stereophonic image, the perceived angles increased. However, errors between the actual angle (dashed straight line) and the perceived angle increased as the actual angle increased, especially in anechoic and real reverberant environments. The proposed method provided smaller perceived errors than the DS beamformer, which implies that it could enhance stereophonic images for all recording environments compared to the conventional method.