esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 4 Experimental Results and Analysis
### Global Model Training Efficiency  
To evaluate the global model training efficiency of the proposed ETFL framework, in the following experiments, 0%, 5%, and 20% malicious nodes will be randomly set up in the ETFL framework along with the mainstream Vanilla FL framework and FedAvg framework, respectively, and the prediction accuracies of the global models under the FEMNIST dataset are recorded at the end of each iteration after 100 rounds of iterations.  
As shown in Figures 9 and 10, after 100 rounds of iterations, the prediction accuracy ACC of the global models of several FL frameworks under the FEMNIST dataset at the end of each iteration is recorded.  
Figure 9: Comparison of global model accuracy in case of 0% malicious nodes.  
As shown in Figures 9 and 10, the global models trained by the three FL frameworks exhibit good prediction performance when all participating nodes are legitimate. At the end of the 70 th iteration, the global models of all three frameworks achieve more than 95% prediction accuracy, but the three have different convergence efficiencies. For ETFL, since all nodes in the network participate in model training and the trust evaluation method used in this scheme consumes less arithmetic, it has the best learning performance in the absence of malicious nodes. However, for the case of containing 5% and 20% malicious nodes, ETFL's scheme can still maintain higher security and accuracy in the presence of malicious nodes due to a trust scoring mechanism contained in the scheme. Thanks to the trust scoring mechanism, it can be observed from the above figure that as the number of training rounds increases, ETFL shows better stability because ETFL can effectively identify malicious nodes in the first 10-20 rounds as a way to reduce the negative impact of malicious nodes on the global model, and when the number of malicious nodes increases, the scheme's trust scoring validation mechanism can be more effective in maintaining the global model's training efficiency and robustness.
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 1 Introduction
CNIA was built upon the popular inductive relation prediction framework but improves on the following aspects: (1) In order to fully leverage the neighboring relations of target nodes, we constructed a _neighbor-enriched subgraph_ so as to include the useful neighboring information in addition to the enclosing subgraph; (2) To account for the structure of KGs, a dual attention mechanism was employed. This mechanism combined edge-aware attention, which addresses variations in the influence of different edges on target nodes, as well as relation-aware attention, which addresses the variations in the influence of diverse relationships on predicted rela  
Figure 1: An explanatory case in inductive relation prediction, which learned from a (**a**) training graph, and generalizes to be (**b**) without any shared entities for inference. A red dashed line denotes the relation to be predicted. tionships. Together, these two types of attention enable more accurate predictions within KGs. (3) A commonsense-based re-ranking strategy was used to update the score function of the triples to filter out less convincing prediction results. By integrating the aforementioned strategies during training, CNIA ensures the complete and effective retention of neighboring relation and path information, thereby enhancing the accuracy and rationality of KG relation predictions. The contributions of this work can be summarized into three aspects:  
* We put forward a commonsense-guided inductive relation prediction method with a dual attention mechanism, CNIA, which can enhance the representation learning and the accuracy of results;
* We propose to construct a neighbor-enriched subgraph to retain more useful neighboring information to aid the prediction;
* We compare the CNIA with state-of-the-art models on benchmark datasets, and the results demonstrate the superior performance of our model. Section 2 provides an overview of the related works. Section 3 introduces the module. Section 4 describes the experimental design and analyzes the results, and this is followed by the conclusions in Section 5.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 3 Study Methods
### Determination of Absorbed Photosynthetically Active Radiation
#### 3.2.2 Improved Calculation of fPAR  
For winter wheat, fPAR depends on the growing season and the growing period. References ([PERSON], 1998; [PERSON], 1998; [PERSON], 1998) showed the NDVI correctly represents the vegetation type and growth situation, and it is linear in fPAR. In the present study, the maximum and minimum NDVI and fPAR can be determined by HJ-1A/B images to calculate fPAR(x, t) as follows:  
\[\text{fPAR}(x,t)=\frac{\left[\text{NDVI}(x,t)-\text{NDVI}_{\text{min}} \right](\text{fPAR}_{\text{max}}-\text{fPAR}_{\text{min}})}{\left(\text{NDVI}_ {\text{max}}-\text{NDVI}_{\text{min}}\right)}+\text{fPAR}_{\text{min}}, \tag{8}\]In addition, [PERSON] et al. (2016) found that fPAR is linear in SR:  
\[\mathrm{fPAR}(x,t)=\frac{\left[\mathrm{SR}(x,t)-\mathrm{SR}_{\mathrm{min}} \right]\left(\mathrm{fPAR}_{\mathrm{max}}-\mathrm{fPAR}_{\mathrm{min}}\right)}{ \left(\mathrm{SR}_{\mathrm{max}}-\mathrm{SR}_{\mathrm{min}}\right)}+\mathrm{ fPAR}_{\mathrm{min}}, \tag{9}\]  
\[\mathrm{SR}=\frac{1+\mathrm{NDVI}(x,t)}{1-\mathrm{NDVI}(x,t)}, \tag{10}\]  
where \(\mathrm{fPAR}_{\mathrm{max}}=0.950\) and \(\mathrm{fPAR}_{\mathrm{min}}=0.001\) are the constant maximum and minimum values of fPAR.  
The constants \(\mathrm{NDVI}_{\mathrm{max}}\) and \(\mathrm{NDVI}_{\mathrm{min}}\) are the maximum and minimum NDVI for winter wheat. The determination of NDVI maximum and minimum has always been controversial. After making a comparative analysis, the 95% lower-side value and 5% lower-side value of the winter wheat NDVI maximum and minimum probability distribution for \(\mathrm{NDVI}_{\mathrm{max}}\) and \(\mathrm{NDVI}_{\mathrm{min}}\) was used in this study, respectively ([PERSON] and [PERSON], 2016). However, the present study covers the entire growing season of winter wheat, which translates into 39 scenes of HJ-1A/B data and 15 scenes of MOD17A2H data, with more processed images than usual for such studies. Thus, the available HJ-1A/B images for each month with the NDVIs for winter wheat were applied, which may differ greatly between months during the growing season of winter wheat, to calculate the monthly maximum and minimum NDVI by the calculation method mentioned above. \(\mathrm{SR}_{\mathrm{max}}\) and \(\mathrm{SR}_{\mathrm{min}}\) are determined from \(\mathrm{NDVI}_{\mathrm{max}}\) and \(\mathrm{NDVI}_{\mathrm{min}}\), respectively.  
With the improved CASA model proposed herein, both vegetation indices NDVI and SR can be used to estimate fPAR. According to previous research, the calculation of fPAR based on NDVI is often greater than the NDVI based on measurements, and the calculation of fPAR based on SR is often lower than the NDVI based on measurements. Therefore, the same weight \(\alpha\) = 0.5 was given to the two calculation methods:  
\[\mathrm{fPAR}(x,t)=\alpha\mathrm{fPAR}_{\mathrm{NDVI}}+(1-\alpha)\mathrm{fPAR }_{\mathrm{SR}}, \tag{11}\]
## 3 Numerical Validation Examples
### Six-Story Shear Building
#### 3.1.1 FE Model Updating Using Different Numbers of Modes  
Only incomplete modes are available in practice due to the difficulty of identifying higher modes and a limited number of sensors, because higher modes are usually noise-contaminated (e.g., low signal-to-noise ratio) [63; 64], resulting in a relatively inaccurate identification, compared to lower modes. Therefore, it is necessary to demonstrate the capability of dealing with incomplete modes (only using lower mode information) using the proposed approach. For mimicking the field conditions using incomplete modes, the proposed BMUA is used to update the six-story shear building model with a different number of modes in case of a healthy condition. The initial value for each mass and stiffness parameter is identically taken twice of unity, which is significantly overestimated by 100% with exact values.  
Table 1 shows the frequencies updated by the proposed approach with a different number of modes. It is seen that the updated frequencies match well with the actual counterparts. The first four modes are graphically compared in Figure 3 between actual and updated mode shapes. The updated mode shapes obtained from different mode numbers coincide with the actual ones, indicating the proposed approach's robustness with incomplete measured data. Table 2 lists the results of updating mass and stiffness and their standard derivations (S.D.). The proposed BMUA can accurately identify mass and stiffness parameters (only an error of less than 2% can be found). Additionally, the S.D. representing the uncertainties tends to be reduced as the number of modes used to update the model increases.  
Figure 2: Six-story shear building: (**a**) original system; (**b**) modified system with curved dampers.
## 5 Results
### Comparing Deterministic and Stochastic Solutions  
For the comparison between deterministic and stochastic solutions, a deterministic optimization is performed first. Afterwards, the generated deterministic solutions (\(f=PD\) and \(f=MS\)) are tested with stochastic values of the scenarios \(|N|\). \(f=M\_PD\), \(f=STD\_PD\), \(f=M\_MS\), and \(f=STD\_MS\) are used as objective functions to evaluate the stochastic influences. The maximum number of generations is \(gen=100\). The deterministic optimization aborts after \(gen=20\) generations with an unchanged objective value. \(Exp_{amt}=2\cdot cv_{lognorm}\cdot 8M\cdot 6f\cdot 10W=960\) experiments were performed. To evaluate the correlation, the Pearson correlation coefficient \(r(x,y)\) is calculated. For this purpose, the correlation between the deterministic solution \((DET)\) and mean value \((M)\) of the stochastic solution, the correlation between the deterministic solution and standard deviation \((STD)\) of the stochastic solution, and the correlation between the mean value \((M)\) and standard deviation \((STD)\) of the stochastic solution are calculated. The results are shown in Figure 5 as a boxplot diagram and in Table 1 combined with the mean value and confidence interval \(ki_{0.95}\).  
A high correlation exists between the deterministic solution \((DET)\) and the mean value of the stochastic solution \((M)\). Furthermore, there is a significant correlation between the mean value \((M)\) and the standard deviation of the stochastic solution \((STD)\). A low correlation exists between the deterministic solution \((DET)\) and the standard deviation of the stochastic solution \((STD)\). This reinforces  
\begin{table}
\begin{tabular}{c c c} \hline _r(x,y)_ & \(\overline{x}\) & \(ki_{0.95}\) \\ \hline \(DET\), \(M\) & 0.47 & \(\pm 0.08\) \\ \(DET\), \(STD\) & 0.18 & \(\pm 0.07\) \\ \(STD\), \(M\) & 0.34 & \(\pm 0.08\) \\ \hline \end{tabular}
\end{table}
Table 1: Statistical analysis coefficient of correlation.  
the statement that deterministic solutions can be suitable as initial solutions for stochastic optimization. Therefore, setting the parameter initial copy rate \(ICR\) is of interest.
# Study on Oscillatory and Undulatory Motion of Robotic Fish
## 2 Methodology
### The Numerical Meshes and Boundary Conditions  
The use of an overset mesh, also known as \"chimera\" or \"overlapping\" mesh, can streamline and expedite simulations involving moving components. Complex fluid flow simulations often encounter challenges when representing certain geometries with a single contiguous mesh. In such cases, different mesh types may be best-suited for distinct geometric features, making the preparation of a unified mesh time-consuming and intricate. This can lead to the creation of large poorly structured meshes that demand excessive solving time and compromise accuracy [19].  
Ansys Fluent offers support for overset mesh, which proves valuable in optimizing simulations encompassing various aspects, including structured mesh around individual parts, part swapping, moving cell zones (eliminating the need for remeshing or smoothing), and design exploration studies (as shown in Figure 6). Overset mesh is particularly advantageous when dealing with significant relative motion between components. In comparison to remeshing, it provides enhanced control over local mesh characteristics as the geometry moves within the domain, as individual mesh zones do not need to deform to accommodate moving geometry. When applied appropriately, overset mesh can facilitate the optimization of local cell types and quality, reduce cell count (thus decreasing computation time), and simplify model setup. This technology, complemented by other robust mesh-related features in Ansys Fluent, forms a comprehensive toolkit for simulating fluid dynamics in the presence of intricate moving geometries [16].  
Overset mesh limitations include several constraints to consider. Firstly, the overset interface cannot encompass solid cell zones. Secondly, component meshes cannot establish connections with a non-conformal interface. Additionally, background meshes should not feature non-conformal interfaces between them, and component zones are restricted from having periodic boundaries. It is worth noting that for all overset mesh problems, it is recommended to utilize double precision. These limitations underscore the importance of careful planning and adherence to guidelines when working with overset mesh techniques in simulations. The boundary conditions are shown in Figure 7 and Table 1.  
This model was calibrated using numerical simulations in conjunction with the least-square error technique. All simulations were performed using the commercial software Ansys Fluent 19.0, which employs the finite volume method based on finite elements to address the research problem.
## 2 Results and Discussion
### Analysis of Volatile Content  
Volatile constituents of fresh and dried _C. alata_, which were extracted using HS-SPME and analysed with GC-MS, revealed the presence of 50 volatile compounds. Table 4 presents the identified volatile compounds and the respective concentrations. The total concentration of volatiles in fresh _C. alata_ was 254.79 mg 100 g\({}^{-1}\) db, and the major volatiles were 2-hexenal (60.28 mg 100 g\({}^{-1}\) db), 1-hexanol (18.70 mg 100 g\({}^{-1}\) db) and salicylic acid (15.05 mg 100 g\({}^{-1}\) db). The analysis of volatile compounds showed that high losses of volatile content followed after drying; however, some drying methods could retain higher volatile content. Majority of volatile compounds were present in the dried sample but differed in their concentrations, with respect to the different drying methods and conditions. In the case of FD, losses of volatiles were higher than expected; however, the loss of volatile constituents during FD was linked to the sublimation of water during the process. The pressure in the chamber should be increased slightly to reduce the loss of volatiles during FD, as reported by [PERSON] et al. (2011) who stated that losses of volatiles is a result of the reduction in the pressure of drying chamber [32]. With regard to CD, huge differences in volatile content were observed when hot-air temperature was varied. A low application of temperature (CD at 40 \({}^{\circ}\)C) led to a high retention of volatiles. The total concentration of volatiles decreased from 61.85 mg 100 g\({}^{-1}\) db to 34.55 mg 100 g\({}^{-1}\) db when temperature was increased from 40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. This trend is noted considering individual volatile compounds--with concentrations being high when low temperature was applied. However, the concentration of some volatiles, such as \(\beta\)-cubebene, 2-undecanone and citronellol, failed to follow this trend. The respective concentrations of these volatiles at 60 \({}^{\circ}\)C were higher in comparison with 50 \({}^{\circ}\)C. With VMD, increasing the microwave wattage from 6 W/g to 12 W/g in VMD resulted in the increase of volatile content from 11.91 mg 100 g\({}^{-1}\) db to 55.67 mg 100 g\({}^{-1}\) db. A high microwave wattage was beneficial because the increased drying intensity effectively reduced the total drying time, and volatiles were less exposed to the high degradative temperature. Figure 3: Chemical structures of citronellol, fenchone and linalool.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
###### Abstract  
Path planning for autonomous vehicles is a core component of intelligent transportation systems, playing a key role in ensuring driving safety, improving driving efficiency, and optimizing the user experience. To address the challenges of safety, smoothness, and search efficiency in path planning for autonomous vehicles, this study proposes an improved hybrid A* algorithm based on the lemming optimization algorithm (LOA). Firstly, this study introduces a penalized graph search method, improves the distance heuristic function, and incorporates the Reeds-Shepp algorithm in order to overcome the insufficient safety and smoothness in path planning originating from the hybrid A* algorithm. The penalized graph search method guides the search away from dangerous areas through penalty terms in the cost function. Secondly, the distance heuristic function improves the distance function to reflect the actual distance, which makes the search target clearer and reduces the computational overhead. Finally, the Reeds-Shepp algorithm generates a path that meets the minimum turning radius requirement. By prioritizing paths with fewer reversals during movement, it effectively reduces the number of unnecessary reversals, thereby optimizing the quality of the path. Additionally, the lemming optimization algorithm (LOA) is combined with a two-layer nested optimization framework to dynamically adjust the key parameters of the hybrid A* algorithm (minimum turning radius, step length, and angle change penalty coefficient). Leveraging the LOA's global search capabilities avoids local optima in the hybrid A* algorithm. By combining the improved hybrid A* algorithm with kinematic constraints within a local range, smooth paths that align with the actual movement capabilities are generated, ultimately enhancing the path search capabilities of the hybrid A* algorithm. Finally, simulation experiments are conducted in two scenarios to validate the algorithm's feasibility. The simulation results demonstrate that the proposed method can efficiently avoid obstacles, and its performance is better than that of the traditional hybrid A* algorithm in terms of the computational time and average path length. In a simple scenario, the search time is shortened by 33.2% and the path length is reduced by 11.1%; at the same time, in a complex scenario, the search time is shortened by 23.5% and the path length is reduced by 13.6%.  
autonomous vehicles; path planning; graph search methods; hybrid A* algorithm; global optimization 2020
# Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources
## 3 Results and Discussion
### PV Panel Power Source
Analyzing Table 6 can provide insights into the cost-effectiveness of producing hydrogen using specific methods or under various operational conditions. It allows researchers and stakeholders to assess the economic viability of hydrogen production and make informed decisions regarding the most cost-efficient approaches. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Month** & **Input Power kWh** & \begin{tabular}{l} **Hydrogen Energy** \\ **Conventional System kWh** \\ \end{tabular}  & \ \begin{tabular}{l} **Electolysis Efficiency of** \\ **Conventional System** \\ \end{tabular}  & **CF** & \begin{tabular}{l} **kg H\({}_{2}\)** \\ **USD/kg H\({}_{2}\)** \\ \end{tabular}  \\ \hline January & 20.363 & 12.629 & 62\% & 2\% & 0.38 & 3 \\ \hline February & 21.498 & 13.333 & 62\% & 4\% & 0.40 & 2.5 \\ \hline March & 25.917 & 16.074 & 62\% & 4\% & 0. The simulation results clearly demonstrate significant differences in both the quantity and cost of hydrogen generation between the two power sources, the wind turbine and the PV panel. In Figure 30, it is evident that the wind turbine has a much more substantial effect on hydrogen production compared to the PV panel throughout the entire year. This indicates that the wind turbine is a more reliable and efficient power source for hydrogen generation in the given location. Figure 31, which analyzes the hydrogen cost based on the capacity ratio (CR), reinforces the idea that using the wind turbine as the power source is the most cost-effective option. The cost of hydrogen production is notably lower when the wind turbine is employed, and it is close to half the cost when the PV panel is used for most months of the year. In summary, the data suggest that in the Markham zone, the most effective and cost-efficient power source for hydrogen production through alkaline electrolysis is the wind turbine. This is attributed to the consistent availability and stability of wind energy  
Figure 30: Hydrogen production comparison for turbine and PV power sources. Figure 31: Hydrogen cost comparison for turbine and PV power sources. throughout the year, in contrast to the variability of solar radiation. Additionally, the higher efficiency of the wind turbine compared to the PV panel contributes to its cost-effectiveness in hydrogen generation. These findings provide valuable insights for selecting the optimal power source for hydrogen production in this specific location.
## 3 The Proposed Approach
### Prediction Model Training
#### 3.2.3 Training of the Proposed Network  
SAEs are serially trained autoencoders. The encoded features of an autoencoder are considered as the inputs of the next one as shown in Figure 3.  
Both of the unsupervised and supervised training in our proposed approach carried with fixed sizes of mini batches to verify the objective function of TD error in Equation (10) and to avoid a mismatch dimensions error for programming reasons. The results of the mapping of the SAEs are directly used for fine-tuning the RUL prediction network, the update of supervised and unsupervised training models is simultaneously achieved.  
During the training of the SAEs, we have to change the targets in the above-proposed equations to be the same as the input [13]. Therefore, Equations (1), (2), and (10) must be changed to fit the training process of the SAEs.  
Figure 3: Proposed NN (Neural Networks) for RUL prediction.  
Our main contribution to modify the autoencoder in this work is that the basic ELM theories for training the autoencoder show that: \"After training the Autoencoder, we can encode features using the transpose of output weights matrix [13], Equation (14)\". However, mathematically it is proven that the best encoding can be obtained by using the inverse matrix of the transpose of output weights as shown in Equation (15). The proposed formula is tested experimentally and proved its accuracy [27].  
\[X=H\beta\Rightarrow H=X\beta^{T}, \tag{14}\]  
\[H=(\beta^{T})^{-1}X, \tag{15}\]  
An important note that we should take into consideration during the training of the SAEs is that after the initialization phase, we are no longer in need of the randomly generated input weights and we will use the formula in Equation (16) instead of the one in Equation (1).  
\[H_{k+1}=G((\beta_{k}^{T})^{-1},BI,X_{k+1}), \tag{16}\]  
where \(X_{k+1}\) are the \(k^{th+1}\) mini batch of inputs of the training set.