esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Graph Models for Traffic Prediction
## 2 Related Work
### Deep Learning Method  
Since deep learning methods can extract deep semantic information and have powerful feature learning capabilities, deep learning-based methods have made breakthroughs in computer vision, natural language processing, and traffic prediction [19; 20]. Convolutional neural networks use convolution operations to aggregate features of adjacent nodes to extract rich spatial-temporal information. [PERSON] et al. [21] and [PERSON] et al. [22] divided the traffic network according to geographic location and then fused the domain information through a convolutional neural network. However, the performance of CNN learning-based methods will drop sharply on non-Euclidean data structures. Due to the irregular distribution of sensors in the traffic network, the nodes of the traffic network are more suitable to be regarded as a non-Euclidean structure. Since the graph-based method can effectively represent the topological information between nodes, it has gradually become an alternative method and has received extensive attention. [PERSON] et al. [23] and [PERSON] et al. [24] used a graph to represent road spatial information and demonstrated the effectiveness of the proposed method. [PERSON] et al. [25] proposed to set up different hops and consider the information of long-range adjacent nodes to provide rich spatial information. As shown in Figure 1b, although the distance between node \(O\) and node \(D\) is large, they have a strong correlation. Previous methods construct the topological information of the network distance-based or similarity-based between nodes, which cannot discover the potential interactivity.  
Many works have started to reflect other factors apart from distance information [26; 27]. [PERSON] et al. [26] modified the distance map by adding additional information, such as inflow/outflow and reachability. [PERSON] et al. [28] considered multi-graph convolutions to provide rich spatial-temporal information for traffic prediction. In order to achieve better prediction performance, [PERSON] et al. [29] proposed to dynamically generate the topology structure between nodes during the model training process to mine the potential information between nodes. However, these methods do not utilize static and dynamic topology information at the same time, which can also lead to information loss. In the temporal dimension, the information of adjacent moments is extracted by means of 1D CNNs or RNNs in the early stage. [PERSON] et al. [30] and [PERSON] et al. [31] adopted CNNs to construct nonlinear temporal dependencies for different time steps. Recurrent neural networks such as long short-term memory or gated recurrent units have been successfully applied to traffic prediction due to their ability to model long-range temporal information. However, recurrent neural networks for sequence learning require iterative training, which may introduce error accumulation and cause a vanishing gradient.
## 4 Experiment and Results
### Overcome the Aperture Effect  
In order to conduct far-field simulation experiments to verify the structure, 27 GHz is used as the center frequency of the RF signal and the bandwidth of the RF signal is 2 GHz.  
Figure 5: Schematic diagram of the measurement setup. TLS: tunable laser source; MZM: Mach–Zehnder modulator; PC: polarization controller; OSP: optical spectroscope; VNA: vector network analyzer; EDFA: erbium-doped fiber amplifier; AWGR: arrayed waveguide gratings router; PD: photodiode; VGA: variable gain amplifier; PS: phase shifter.  
Figure 6: Schematic diagram of the measurement setup.  
Figure 7: Phase response of different wavelength.  
The antenna array is ideally designed for the center frequency. The ideal antenna consists of four linear antenna arrays with equal spacing of \(\lambda/2\), \(\lambda=27\) GHz/c. In Figure 8, the digital phase shifter is given a different amount of phase shift by the beam-forming principle. Simulation results show, without OTTD, beams shift to \(-15.84^{\circ}\) at 26 GHz and \(-20.16^{\circ}\) at 28 GHz. A beam pointing error of \(4.32^{\circ}\) occurs in the beam over a bandwidth of 2 GHz. The experimental results were similar, the beam points at \(-14.76^{\circ}\) and \(-22^{\circ}\) at 26 GHz and 28 GHz, respectively. This has a large pointing error. As shown in Figure 9, with the introduction of half of the OTTD in the link, the simulation results show that the beam is no longer shifted by large angles and the total beam error is less than \(1^{\circ}\). The phase response of each path was remeasured using the experimental setup. The \(S_{21}\) phase response of each path was remeasured using the experimental equipment. After re-calculating each phase for beam synthesis, the beam is no longer shifted at 26 GHz, 27 GHz, and 28 GHz. In the AWGR-assisted phased array system proposed in this paper, signals with a transmission bandwidth of 2 GHz will no longer have a beam squint.  
Figure 8: Beam squint with non-true time delay.  
Figure 9: Non-beam squint with optical true time delay.
# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
###### Abstract  
Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propose a product evaluation prediction model based on multi-level deep feature fusion of online reviews. It mines product satisfaction from the massive reviews published by users on e-commerce websites, and uses this model to analyze the relationship between design attributes and customer satisfaction, design products based on customer satisfaction. Our proposed model can be divided into the following four parts: First, the DSCNN (Depthwise Separable Convolutions) layer and pooling layer are used to combine extracting shallow features from the primordial data. Secondly, CBAM (Convolutional Block Attention Module) is used to realize the dimension separation of features, enhance the expressive ability of key features in the two dimensions of space and channel, and suppress the influence of redundant information. Thirdly, BiLSTM (Bidirectional Long Short-Term Memory) is used to overcome the complexity and nonlinearity of product evaluation prediction, output the predicted result through the fully connected layer. Finally, using the global optimization capability of the genetic algorithm, the hyperparameter optimization of the model constructed above is carried out. The final forecasting model consists of a series of decision rules that avoid model redundancy and achieve the best forecasting effect. It has been verified that the method proposed in this paper is better than the above-mentioned models in five evaluation indicators such as MSE, MAE, RMSE, MAPE and SMAPE, compared with Support Vector Regression (SVR), DSCNN, BiLSTM and DSCNN-BiLSTM. By predicting customer emotional satisfaction, it can provide accurate decision-making suggestions for enterprises to design new products.  
product evaluation prediction; Depthwise Separable Convolutions; Bidirectional Long Short-Term Memories; attention mechanism; genetic algorithm Article
## 5 Experiments
### Experimental Results
#### 5.2.1 Summary of the Results of Earlier Experiments  
As already mentioned above when explaining the two gene models and details of GLEAM and its operators, we found the second gene model in conjunction with phenotypic repair, the usage of the OX crossover as an additional crossover operator, and the fifth auxiliary criterion to be the best choice. These results are based on all four benchmark classes using 50, 100, and 200 grid jobs at 10 resources and 200 grid jobs at 20 resources [20,36]. The effect of seeding the initial population was also investigated and it was found that it improves the results significantly [20]. In the same paper the results of the three heuristics for grid job sequencing were compared using the three RAS each. The _earliest due time_ heuristic yields by far the best results independently of the RAS used. As the other two heuristics are also successful in some cases and their calculation requires only little time, they are still used for seeding the start population. Another reason of their continued use is their relatively high  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Benchmark** & \multicolumn{3}{c}{**Time benchmark reserve (\%)**} & \multicolumn{3}{c}{**Cost benchmark reserve (\%)**} \\ \cline{2-7}
**class** & min(\(t_{\textit{res},\textit{hm}}\)) & \(\overline{t_{\textit{res}}}\) & max(\(t_{\textit{res},\textit{hm}}\)) & min(\(\overline{c_{\textit{res},\textit{hm}}}\)) & \(\overline{c_{\textit{res}}}\) & max(\(\overline{c_{\textit{res},\textit{hm}}}\)) \\ \hline IRID & 47.2 & 48.9 & 50.1 & 27.3 & 27.7 & 28.1 \\ sRID & 53.7 & 55.9 & 58.3 & 25.2 & 25.9 & 26.7 \\ lRsD & 57.2 & 59.2 & 62.1 & 22.3 & 23.3 & 24.1 \\ sRsD & 63.4 & 64.4 & 65.5 & 20.8 & 21.5 & 22.0 \\ \hline \end{tabular}
\end{table}
Table 2: Averages \(\overline{t_{\textit{res}}}\) and \(\overline{c_{\textit{res}}}\) of the time and cost benchmark reserves \(\overline{t_{\textit{res},\textit{hm}}}\) and \(\overline{c_{\textit{res},\textit{hm}}}\) together with their minima and maxima per benchmark class, see also [58]. The reserve values are given as percentages of the corresponding budgets and time frames.  
fitness values before the penalty functions are applied. The idea is that their results may serve as good starting material for the evolutionary search.  
Based on the results for planning from scratch summarized in the last paragraph, the replanning task was tackled by the investigation of 32 replanning scenarios with varying fractions of finished grid jobs, which were replaced by new ones [59, 60]. The most important results are:  
* The usage of the old plan as described in Section 4.1 is beneficial especially for small amounts of 10 or 20% finished and new grid jobs.
* Both sets of heuristics introduced in Section 4.1 contribute well, complement each other and GLEAM.
* It is clearly situation-dependent which heuristic or GLEAM performs best.
# Hierarchical Concept Learning by Fuzzy Semantic Cells
## 4 Experiments
### Experimental Results

Table 3: The classification accuracy in percentage (mean \(\pm\) standard deviation) under different numbers of prototypes on Pendigits dataset (30% as training set). different prototype initialization methods. When prototypes are initialized by category, the results are consistent after repeated experiments, and the standard deviation is low. When the prototypes are initialized with a global method, the overall classification accuracy is not significantly worse, though the result is slightly different and the standard deviation is relatively larger. In the last prototype initialization with 0.3 and 0.5 as the training ratio, the average training accuracy is slightly lower than the testing accuracy. However, when considering the deviation, such difference is not significant. We attribute this phenomenon to randomness. Using Adam [17] is based on the following empirical findings. The learning problem of our method is an unconstrained non-convex optimization, and the loss is estimated within batches of samples. It has been shown Adam converges faster than other first-order optimization methods do [17], like SGD and RMSprop [50]. For second-order optimization methods, Newton and Quasi-Newton require expensive computation and memory cost. Besides, L-BFGS is an efficient Quasi-Newton approximation. We conduct experiments on SGD, RMSprop and L-BFGS. We use the fourth initialization method of prototypes and compares the test accuracy in Table 5. The L-BFGS introduce instability when the ratio is 0.1. The performances of other methods are worse than Adam in this scenario. Evolutionary optimization and particle swarm optimization are also used in related works [26; 27], but we believe numerical optimization methods are more reproducible. In the final experiment, we compared the differences between the proposed method and the six conventional classification methods. We conduct this experiment with packages in scikit-learn [51]. SVM uses the polynomial kernel function, and AdaBoost's learning rate is set to 0.3. The Neural Network is a multi-layer perceptron model of two hidden layers, which have five and two neurons, respectively. The nonlinear activation is ReLU, and the out neurons are softmaxed to give a probability distribution. To optimize the model, a cross-entropy loss and an L-BFGS optimizer are used. Other settings are the default. We test typical classification methods (DecisionTree, NaiveBayes, KNN, SVM, AdaBoost, Neural Network) in classification accuracy for five different datasets. For each dataset, we select 10% as the training set and the remaining 90% as the test set to compare the classification effects in the case of a small size of the training set. Because fewer samples are employed for training, most algorithms can achieve outstanding performances on the training set, but algorithms perform variedly on the test set. The classification accuracy on the test set is shown in Table 6.
## 3 Experimental Results
### Misting System  
#### 3.2.1 Two-Fluid Nozzle Misting  
In the experiments using the two-fluid nozzle for misting in a 6 m long cultivation bed, the feasibility of achieving a uniform mist distribution was assessed. The introduction of an external air injection misting method in an enclosed space aimed to enhance the efficiency of water, nutrients, and oxygen supply. However, difficulties were encountered in achieving uniform mist distribution across the entire cultivation bed. The primary issue arose from the backflow phenomenon, caused by injecting external air into a closed space. The two-fluid nozzle misting technique involves drawing in external air to finely atomize water into microscopic particles. With all four sides of the cultivation area sealed, continuous misting led to air blockage at the opposite end, as depicted in Figure S11, resulting in reduced misting efficiency and visibly wilting plants farther from the mist source.  
Additionally, technical issues were identified with the compressor that supplied the air necessary for the continuous operation of the two-fluid nozzle. It was observed that oil leaking from the air compressor mixed with the air and was sprayed along with the mist onto the plant roots, as evidenced in Figure S12. This contamination significantly degrades the quality of the mist and poses a serious concern, being potentially harmful to plant health.  
These findings underscore the critical design parameters and operational considerations necessary for misting system implementation and design. Specifically, managing contaminants within the system that could affect misting efficiency and plant health is highlighted as a vital area of research for optimizing cultivation systems.
## 2 Materials and Methods
### Research Methods  
#### 2.2.1 Geostatistics and Fractal Method  
Geostatistics is an effective method to study the spatial distribution structure characteristics of regional variables. Its basic tool is a semivariate function that can be estimated by the following formula [33]:  
\[\gamma(h)=\frac{1}{2N(h)}\sum_{i=1}^{N(h)}[Z(x_{i})-Z(x_{i}+h)]^{2} \tag{1}\]  
where \(\gamma(h)\) is the variation function, \(Z(x)\) is the value of the regionalised variable at sampling point \(x\), \(N(h)\) is the number of pairs with interval \(h\) and \(h\) is the interval, which is called the lag distance. Variograms can reflect and describe many properties of regionalised variables, and it is an important tool to analyse their spatial variation.  
The fractal dimension, \(D\), is a measure of complexity. Due to the complexity of soil structure and the local micro differences of internal soil factors, the soil property parameters are irregular and random, like random fractional Brownian motion [34]. The semivariate function in one dimension is:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Property** & **Symbol** & **Particle Size** & **Test Method** \\ \hline Sand & \(\backslash\) & 0.075–2 nm & Sieve analysis \\ Silt & \(\backslash\) & 0.005–0.075 mm & Densimeter method \\ Clay & \(\backslash\) & \(<\)0.005 mm & Densimeter method \\ Bulk density & \(\gamma\) & \(\backslash\) & Ring knife method \\ Saturated water content & \(\theta_{\mathrm{s}}\) & \(\backslash\) & Drying method \\ Saturated hydraulic & \(\mathrm{K_{s}}\) & \(\backslash\) & Variable head test \\ conductivity & \(\mathrm{K_{s}}\) & \(\backslash\) & method \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test methods of soil properties.  
Figure 2: Grid division of the study area.  
\[2\gamma(h)=E\Big{[}[Z(x)-Z(x+h)]^{2}\Big{]}=h^{2H} \tag{2}\]  
where \(Z(x)\) and \(Z(x+h)\) are the measured values at \(x\) and \(x+h\), respectively; \(h\) is the interval (lag); and \(H\) is the power index.  
For Brownian motion, the power index \(H\) is 0.5, while for the variation in soil properties, the \(H\) value ranges from 0 to 1.0. With the increase in \(H\), the variation in soil properties gradually weaken. The fractal dimensions of these Brownian motions are given by the formula \(D=2-H\).  
\(\gamma(h)\) and \(h\) are drawn in double logarithmic space according to Formula (2). The fractal dimension \(D\) is calculated from the slope m of the regression line.  
\[D=(4-m)/2 \tag{3}\]  
The fractal dimension, \(D\), represents the structure between samples. The smaller the value of \(D\), the greater the difference in soil properties between samples; that is, the worse the degree of uniformity; on the contrary, the larger the value of \(D\), the smaller the difference in soil properties between samples, and the better the degree of uniformity.
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 3 Methodology
### Dataset  
The dataset of chest X-rays utilized in this research work is available in Kaggle and was utilized in other research studies [41; 42]. The database emerged from a small number of COVID-19 cases [42] and was constantly updated. The current, updated version of the database contains the largest number of COVID-19 chest X-ray images yet to be experimented upon in any research work to the best of the authors' knowledge. The current updated version of the database contains 3616 COVID-19 cases, 6012 lung opacity cases,and 10,192 normal (healthy) images. Lung opacity represents the set where the opacity is formed by pneumonia and differentiation of COVID-19 from this class and the healthy (normal) class is provided in the subject study.  
The database was developed by researchers from Qatar University (Doha, Qatar), University of Dhaka (Banglades), Pakistan and Malaysia, and with the cooperation from medical doctors. The database development was initiated from sources including the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 database, COVID-19 positive chest x-ray images from different articles [41], The Radiopedia, COVID-19 chest imaging is mentioned in the initial database research article. The current distribution also contains an additional class, lung opacity, which was recently added from the Radiological Society of North America (RSNA) CXR dataset with number of images, 6012 [23].  
Samples of chest X-ray images for normal, COVID-19 and lung opacity cases used in the training of the deep learning models in our research work are shown in Figure 1.
###### Abstract  
Recombination of charge carriers at silicon surfaces is one of the biggest loss mechanisms in crystalline silicon (c-Si) solar cells. Hafraium oxide (HfO\({}_{2}\)) has attracted much attention as a passivation layer for n-type c-Si because of its positive fixed charges and thermal stability. In this study, HfO\({}_{2}\) films are deposited on n-type c-Si using remote plasma atomic layer deposition (RP-ALD). Post-annealing is performed using a rapid thermal processing system at different temperatures in nitrogen ambient for 10 min. The effects of post-annealing temperature on the passivation properties of the HfO\({}_{2}\) films on c-Si are investigated. Personal computer one dimension numerical simulation for the passivated emitter and rear contact (PERC) solar cells with the HfO\({}_{2}\) passivation layer is also presented. By means of modeling and numerical computer simulation, the influence of different front surface recombination velocity (SRV) and rear SRV on n-type silicon solar cell performance was investigated. Simulation results show that the n-type PERC solar cell with HfO\({}_{2}\) single layer can have a conversion efficiency of 22.1%. The PERC using silicon nitride/HfO\({}_{2}\) stacked passivation layer can further increase efficiency to 23.02% with an open-circuit voltage of 689 mV.  
hafraium oxide; atomic layer deposition; crystalline silicon solar cell; annealing +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Research Highlights and Future Implications
Additionally, it increased the organic-bound fraction of Pb by 61.3% at the 6% application rate and doubled the organic-bound fraction of Cd at both the 4 and 6% application rates [110]. In conclusion, the suitability of _Paulownia_ for the phytoremediation of the contaminated DS is well supported by plant's ability to accumulate a wide range of elements, including rare and noble metals. The incorporation of biochar into the system enhances remediation efficiency and accelerates economic viability, aligning with both circular economy principles and global climate goals. Thus, phytoremediation using _Paulownia_ supported by biochar presents a viable and economically profitable approach for the ex situ remediation of contaminated sediments. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/su16209080/sl](https://www.mdpi.com/article/10.3390/su16209080/sl)]([https://www.mdpi.com/article/10.3390/su16209080/sl](https://www.mdpi.com/article/10.3390/su16209080/sl)), Figure S1: Experimental parameters of chlorophyll fluorescence of _P. tomentosa_ grown in biochar-amended sediments. Presented values normalised referring to control; Figure S2: Comprehensive bio-concentration index representing overall ability of _P. tomentosa_ to accumulate EEs in biochar-amended sediments; Figure S3: Translocation factor values of _P. tomentosa_ grown in biochar-amended sediments; Table S1: Elemental composition of sediments amended with different doses of biochar (mg kg\({}^{-1}\)); Table S2: Influence of biochar incorporation on leaf length and width (cm); Table S3: The experimental, specific, and phenomenological parameters of chlorophyll fluorescence of _P. tomentosa_ grown in biochar-amended sediments. Numbers in brackets are the normalised values relative to the control; Table S4: BCF values calculated for _P. tomentosa_ grown in biochar-amended sediments; Table S5: Correlation matrix for plant physiological parameters; Table S6: Correlation matrix for plant chlorophyll fluorescence indicators; Table S7: Correlation matrix for bioconcentration factor values of aboveground biomass. **Author Contributions:** Conceptualization, A.M.; methodology, A.M., P.H., V.P. and P.S. ; software, A.M.; validation, A.M.; formal analysis, A.M., P.H. and P.S. ; investigation, A.M.; resources, V.P. ; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, A.M., V.P. and A.N. ; visualisation, A.M.; supervision, A.M.; project administration, V.P. ; funding acquisition, A.M. and V.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Ministry of Science and Higher Education, Republic of Kazakhstan (grant No. AP14973042) and NATO SPS MYP (grant No. G6094). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data available upon request. **Conflicts of Interest:** The authors declare no conflicts of interest.