esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Method Validation  
#### 3.5.1 Standard Calibration, Linearity and Sensitivity  
In order to develop the calibration curve of selected analytes in the present study, seven concentration levels were chosen (0.05, 0.1, 0.5, 1, 5, 10 and 20 \(\mu\)g/L) for the P&T-GC-QqQ-MS/MS detection method and for the HS-SPME-GC-QqQ-MS/MS detection method under the MRM mode, respectively.  
Therefore, so as to ensure the accuracy of the calibration curve, triplicate detections were performed for each water sample, the average value was chosen to plot the standard calibration curves of these analytes. Before GC-QqQ-MS/MS detection, all these water samples were pretreated by P&T or SPME as described in Sections 3.3 and 3.4. The coefficients of linear regression (\(r^{2}\)) of the calibration curves of these selected analytes in P&T-GC-QqQ-MS/MS detection system and HS-SPME-GC-QqQ-MS/MS detection system were described in Tables S2 and S3, with \(r^{2}\) ranged from 0.9961 to 0.9999.  
It is remarkable that two internal standards, MTBE-\(d_{3}\) and fluorobenzene, were added into water samples during each operation of the calibration standard solutions detection, eliminating the influence of real water matrix. The concentrations of MTBE-\(d_{3}\) and fluorobenzene were fixed at 0.5 \(\mu\)g/L and 1 \(\mu\)g/L respectively. In the GC-QqQ-MS/MS detection system, LODs are described as the lowest concentrations of the analyte that can be detected to satisfy the statistical differences between water sample and blank sample [35]. In the GC-QqQ-MS/MS detection system of this study, LODs and LOQs of these selected oxygenate compounds were discovered to be 0.52\(\sim\)32 ng/L in the P&T-GC-QqQ-MS/MS detection system method and 0.11-151 ng/L in HS-SPME-GC-QqQ-MS/MS detection system as shown in Tables S2 and S3.
## 2 Description of Deep Learning Model
### Hybrid GRU-MLP Model  
The prediction of gas production from stimulated shale reservoirs is inherently a multivariate problem, as it depends not only on historical production data but also on various physical constraints. These constraints include a wide spectrum of factors, ranging from geological attributes to engineering parameters such as formation properties, fracture geometries, and fracturing treatment specifics. Consequently, the development of forecasting models for well gas production requires the incorporation of numerous input parameters derived from diverse domains.  
Given that the selected parameters in this study are static, MLP networks are robust models for addressing such non-sequential data. The hybrid GRU-MLP network capitalizes on the advantages of two distinct neural network architectures. The GRU component can capture long-term dependencies and temporal patterns present in the historical production data, while the MLP component can handle non-sequential features and learn intricate nonlinear relationships within the dataset. This hybrid approach allows for a more comprehensive representation and understanding of the input data, leading to improved accuracy in the prediction of well gas production.  
Figure 4 illustrates the architectural composition of the hybrid GRU-MLP neural network [29]. Initially, a multi-layer GRU neural network is employed to capture the relationship between historical production data (x\({}_{1}\), x\({}_{2}\), \(\ldots\), x\({}_{t}\)) and projected forthcoming value (h\({}_{t}\)). The output of the GRU network is then passed through a linear activation layer and subsequently combined with the physical constraints (c\({}_{1}\), c\({}_{2}\), \(\ldots\), c\({}_{n}\)) as inputs to the MLP component, capturing the non-linear relationship between the production data and the constraints. Finally, the hybrid neural network generates the production prediction (x\({}_{t+1}\)) at the time step (t + 1).
## 3 Results  
### Reliabilities, Factorial Components and Global Descriptives of the Scales  
The reliability analysis of all six survey scales provides good internal consistency values ([PERSON]'s \(\alpha\) over 0.80).  
Table 3 shows results obtained from scale reliability analyses, percentages of categorized BARS, and descriptive information from all.  
A two-factor solution resulted from factor analysis of the organizational resilience scale. The two factors are comparable to those found in previous research [55; 56], except for the item 5, with factorial loading values within adaptive capacity instead of planning. To ensure a more accurate comparison between our results and those of previous studies, and considering the high internal consistency in both indicators, analyses within our study maintained the definitions for the two factors within the organizational resilience scale as proposed by the authors. Table 4 includes the results of factorial analyses of the organizational resilience scale.  
Analyses for nuclear power plants show that the average for the six scales is above the midpoint. The highest scores are obtained in the safety scale, with an average of 5.89. As for the risk perception scale, it has the lowest scores with an average of 4.24.  
Within-subjects comparisons between the six scales (upper part of Table 5) show magnitude differences of \(\delta>0.20\) in all cases except between the hazard perception and adaptive capacity scales (\(\delta=-0.13\)) and the SCWE and adaptive capacity (\(\delta=0.19\)) scales. It is worth mentioning the significant differences between the hazard perception and safety scales (\(\delta=-0.95\)) and the planning and adaptive capacity scales of resilience (\(\delta=0.92\)). Comparisons by pairs for the BARS (lower part of Table 5) show magnitudes of large effect (\(\delta>0.80\)).  
The BARS with the lowest score is resources allocation (BARS resources allocation) with a 3.39 average (Table 3, Figure 2). The result of this scale shows that perception of organizational resources allocation (both in terms of personnel, equipment, time, and economic budget) is positively valued by 53% of workers. On the contrary, 26.37% of them have a negative opinion (Figure 3). This is the study scale with the lowest scores (in both surveys and BARS).
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
###### Abstract
classified maize varieties in the visible light band, reduced the dimension of data by PCA, and established a CNN model to realize the non-destructive testing of corn varieties [19]. [PERSON] et al. used the Fourier transform near-infrared spectroscopy combined with discriminant analysis to classify sweet corn seeds, they used KNN to build the model, and the accuracy was 97.56% [20]. Although these studies have achieved high classification accuracy, the premise is that there is a large data set. If the number of experimental samples is small, or even smaller than the data dimension, it is difficult to ensure the classification accuracy, resulting in results that do not meet the requirements of the single seed sowing method for seed authenticity. Moreover, with the development of commercial breeding technology, there are more and more varieties of maize. There are a lot of hybrids with the same parents, and the differences between individual seeds are small. There are few studies on the classification of hybrids with the same parents. In this paper, five different kinds of hybrid maize seeds were chosen as the research objects. The hyperspectral imaging system was used to acquire the hyperspectral images in the spectral range of 881-1715 nm with 256 bands of the seeds. Multiple Scattering Correction (MSC) was used to pretreat the spectrum, and Principal Component Analysis (PCA) was used to reduce the dimension of the data. The seven characteristic wavelengths with the highest weight were used as the input to build the classification models of maize based on PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Sigmoid, and the impact of different algorithms on the classifier performance is obtained through the accuracy assessment of the classification results. Through this study, we can provide an experimental basis for applying hyperspectral imaging technology in the quality inspection of seeds and provide a more effective method for purity detection in maize seeds.
## 3 Results
### Validation of the Model and Optimal Extraction Condition of ED-F1 Oligosaccharide Fraction  
Based on the response surfaces, the yield of ED-F1 is highly influenced by the hydrolysis parameters. Hence, it is important to get the most desirable parameter conditions to obtain the optimum ED-F1 yield. Since HMF production is directly proportional to the reduction of ED-F1, the goal is set to reduce the production of HMF in the extraction liquor. The reduced quadratic models were used to generate optimal hydrolysis conditions for ED-F1 oligosaccharidesacharide fraction yield and the values were 121 \({}^{\circ}\)C, 21 min and 0.12 M H\({}_{2}\)SO\({}_{4}\) concentration (Table 3). The desirability value was close to 1 and calculated to be 0.844. All the hydrolysis parameters were set in range while the ED-F1 fraction yield and HMF concentration were both set at maximum and at minimum, respectively. The experimental validation of the mathematical model was performed in triplicate. The ED-F1 yield obtained through the optimization procedure was 11.15 \(\pm\) 0.03 g/100 g dw. Both ED-F1 yield and HMF by-product produced values were close to the predicted by the mathematical model in optimal conditions with a low percentage of relative error (3.15% and 4.72%, respectively) reflects the adequacy of the developed quadratic models [52]. The optimization procedure increased the yield of ED-F1 by 31.41% compared to initial extraction and hydrolysis methods.  
Figure 4: Three-dimensional response surface plots showing the effects of temperature, time and H\({}_{2}\)SO\({}_{4}\) on ED-F1 yield (**a–c**) and HMF (**d**,**e**).
## 2 Materials and Methods  
### Description of the Samples  
Investigated samples were commercial natural inorganic pigments for painting, i.e., Raw Sienna, Burnt Sienna, Raw Umber, Burnt Umber, Yellow Ochre, French Ochre, Green Earth from Verona, Terra Pozzuoli, Titanium Orange, and Titanium White, purchased from different factories. In total, fifty samples were analyzed and divided into ten groups (G#, # = 1, 2, \(\ldots\), 10) (five samples per group) according to their chemical nature (see Table 1). Each sample from the group was analyzed by using High-Purity Germanium (HPGe) \(\gamma\)-ray spectrometry, and then the values were averaged per group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group ID**} & \multicolumn{2}{c}{**Chemical Characterization**} & \multirow{2}{*}{**Origin**} & \multicolumn{1}{c}{\multirow{2}{*}{**Number of Samples**}} \\ \cline{3-3} \cline{5-5}  & & \(\text{Fe}_{2}\text{O}_{3}\cdot\text{nH}_{2}\text{O}+\text{Al}_{2}\text{O}_{3} \cdot\text{MnO}_{2}\) & & \\ \hline G1 & Raw Sienna & \(\text{Fe}_{2}\text{O}_{3}\cdot\text{nH}_{2}\text{O}+\text{Al}_{2}\text{O}_{3} \cdot\text{MnO}_{2}\) & Italy & 5 \\ \hline G2 & Burnt Sienna & \(\text{Fe}_{2}\text{O}_{3}\cdot\text{nH}_{2}\text{O}+\text{Al}_{2}\text{O}_{3} \cdot\text{MnO}_{2}\) & Italy & 5 \\ \hline G3 & Raw Umber & \(\text{CaSO}_{4}\cdot\text{2H}_{2}\text{O}+\text{CaSO}_{4}+\text{CaSO}_{3}+ \text{FeO(OH)}+\text{clay}\) & Italy & 5 \\ \hline G4 & Burnt Umber & \(\text{CaSO}_{4}\cdot\text{2H}_{2}\text{O}+\text{CaSO}_{4}+\text{Fe}_{2}\text{O}_{3} +\text{CaCO}_{3}+\text{clay}\) & Italy & 5 \\ \hline G5 & Yellow Ochre & \(\text{Fe}_{2}\text{O}_{3}\cdot\text{nH}_{2}\text{O}\) & Italy & 5 \\ \hline G6 & French Ochre & \(\text{SiO}_{2}+\text{Al}_{2}\text{O}_{3}+\text{Fe}_{2}\text{O}_{3}\) & Germany & 5 \\ \hline G7 & Green Earth & \(\text{K}(\text{Mg}_{2}\text{Fe}^{2+})(\text{Fe}^{3+},\) & & \\  & from Verona & \(\text{Al})[\text{Si}_{4}\text{O}_{10}](\text{OH})_{2}+\text{Germany}\) & Germany & 5 \\  & & \((\text{K}\text{Na})(\text{Fe}^{3+},\text{Al}_{3}\text{Mg}_{2})\) & & \\  & & \((\text{Si}_{4}\text{Al}_{2}\text{O}_{10}(\text{OH})_{2}\) & & \\ \hline G8 & Terra Pozzuoli & \(\text{Fe}_{2}\text{O}_{3}\cdot\text{nH}_{2}\text{O}\) & Germany & 5 \\ \hline G9 & Titanium Orange & Ti-Sb-Cr-O Rutilie & Germany & 5 \\ \hline G10 & Titanium White & \(\text{TiO}_{2}\) & Germany & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Investigated pigments, together with their identification code (Group ID), chemical characterization, and number of analyzed samples for each group.  
Photos of representative samples of the investigated pigments (one sample per group) are reported in Figure 1.
## 2 The TASIPPS Conceptual Framework
### The TASIPPS Framework Components  
#### 2.1.1 The Middleware Server Module  
Technical interoperability pertains to the seamless communication and interaction between hardware/software components, systems, and platforms, facilitating machine-to  
Figure 1: The TASIPPS Framework showing the components of the TASIPPS framework.  
machine connectivity [3]. This form of interoperability involves the utilization of standardized communication protocols and the necessary infrastructure to support these protocols' functioning. The focus of technical interoperability is ensuring efficient data exchange, allowing diverse machines and systems to interact cohesively within a connected ecosystem. Figure 2 shows the various components that make up the Middleware server module of the TASIPPS framework.  
Figure 2 presents the application standards, communication protocols, and the common data format that will be utilized within this framework. The components are explained below.  
Application standards: The application standards defined within this framework set parameters and associated values to constrain the connecting systems technology in terms of performance or other features. The suggested AES 256 encryption is used for a range of encryption needs, including wireless networks and secure online transactions. The importance and suitability of this encryption model, because it is difficult for hackers to circumvent, were discussed by [39].  
The extensive hash value of SHA-512 enhances its resistance to attacks, surpassing other hash functions in terms of security; as a result, SHA-512 is acknowledged as a potent, resilient, and swift hashing algorithm [40].  
OAuth 2.0 permits restricted user data access, including access upon the expiration of authorization tokens. This capability enables data-sharing among users without the necessity of disclosing personal details. Furthermore, its implementation is simplified while also delivering enhanced authentication strength, as reiterated by [23].  
The framework incorporates REST (Representational State Transfer) APIs, which have been recommended as the best way to facilitate interoperability due to their flexibility, allowing for the use of a variety of data formats [41]. A notable aspect of REST APIs is their flexibility [42]. REST APIs liberate data from being bound to resources or methods, allowing them to adeptly manage a variety of call types, offer diverse data formats, and seamlessly evolve in structure through the skillful incorporation of hypermedia. This makes REST APIs very suitable for the proposed framework.
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 3 Design of Miniaturized Dual-Band Broadband Printed Antennas
### Antenna Structure Design  
In Figure 3, the structure chart of printed monopole antenna is presented. The antenna is printed on the FR4 antenna substrate with a relative dielectric constant of 4.4. The size of the substrate is Ws \(\times\) Ls, the thickness is 1.6 mm. The ground plane of the antenna is printed on the back ground of substrate, whose length and width are represented by Lg and Ws, respectively. In addition, the main part of the antenna is printed on the front of the substrate. The antenna is fed by a microstrip line with a characteristic impedance of 50 \(\Omega\), and the size of microstrip line is Wf \(\times\) Lf. The radiating element of the antenna is composed of two rectangular patches and a T-shaped folded patch. The length of the rectangular patch is L2, and the width is L3. The length of the vertical portion of the T-shaped is L4, the length of horizontal section is L5, and the folding part is L6.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 2 Related Work
### DRL-Based Methods  
In recent times, DRL has achieved remarkable advancements in addressing a wide range of routing and scheduling problems. A notable milestone in this field is the attention model (AM) proposed by [PERSON] et al. [24], which leverages a transformer-based encoder-decoder framework to learn decision-making strategies for VRPs. Building upon this foundation, [PERSON] et al. [25] introduced policy optimization with multiple optima (POMO), an approach designed to enhance solution diversity through multiple rollouts and data augmentation, achieving state-of-the-art performance across VRPs of varying scales and node distributions. Further refining DRL-based routing models, [PERSON] et al. [26] brought in a feature embedding refiner (FER) block within a novel encoder-refiner-decoder architecture. As a model-agnostic and plug-and-play module, FER dynamically enhances node embeddings and adjusts probability distributions over a broader search space, leading to notable improvements in conventional VRP-PD solutions. Additionally, [PERSON] et al. [27] developed a meta-learning-based framework for multi-objective optimization, demonstrating adaptability and efficiency in addressing improved VRP-PDs and other complex routing problems. Despite these advancements, DRL-based methods for VRP-PDs remain an open research area with substantial gaps. For instance, [PERSON] et al. [28] proposed an unsupervised competitive neural network to adaptively organize feature maps for linehaul and backhaul nodes. However, their approach did not fully leverage edge information among customer nodes, and solution quality for improved VRP-PDs remained suboptimal, underscoring the need for further exploration in this domain.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.3 Analysis of STAGCN-IEALL
Experiment 3: Intuitive comparison of prediction effect. In order to observe the predicted results and true values of the model and the performance of the comparison model more intuitively, Figure 12 shows the predicted performance of three models: STAGCN-IEALL, IEALL, and ST-GCN when the prediction duration is set to 230:  
As can be seen from Figure 12, the STAGCN-IEALL model proposed in this paper is better than the IEALL model and the classic spatio-temporal prediction model ST-GCN, which can accurately predict the trend of dam deformation and effectively reduce the prediction error. Experiment 4: Analysis of ablation study. The STAGCN-IEALL model proposed in this paper is made up of four major components: a spatial attention mechanism, a temporal attention mechanism, a spatial feature extraction module, and a temporal feature extraction module. This section removes a part from the STAGCN-IEALL model in turn, and tests the performance of each model through experiments, in order to further analyze the effectiveness of each module. The following are variant models: (1)-SA: Removing spatial attention module; (2)-TA: the temporal attention module is removed on the basis of model (1); (3)-GCN: the GCN spatial feature extraction module is removed on the basis of (2); (4)-IEALL: based on the model (3), the IEALL time feature extraction module is removed, which is a common encoder-decoder model. The STAGCN-IEALL model's prediction table, after each module has been removed one at a time, is shown in Figure 13. Calculations show that the RMSE, MAPE, and MAE of the encoder-decoder model are lowered by 8.43%, 8.12%, and 11.86%, respectively, after  
Figure 12: Comparison of prediction effects. the addition of the IEALL time feature extraction module. The prediction accuracy of the model is effectively improved after the time dimension features were successfully extracted using the IEALL model. On this basis, the RMSE, MAPE, and MAE of the model are decreased by 15.97%, 17.38%, and 22.19%, respectively. It can be concluded that GCN's spatial feature extraction of deformation data from multiple measurement points improves the model's spatial dimension expression capacity. The model's predictability has significantly increased. Due to the temporal attention module, the three indexes of the model are lowered by 6.12%, 9.37%, and 12.39%, respectively. This shows that by taking into account the dynamic correlation between various moments, the model's prediction accuracy can be significantly increased. Finally, the three indexes increased by 7.78%, 5.93%, and 7.91% after the spatial attention module was implemented. The dynamic correlation of different measurement points was considered by the spatial attention module, which effectively improved the prediction performance. Therefore, it can be concluded that the cooperation between the modules of STAGCN-IEALL model effectively improves the prediction accuracy of the model and achieves more accurate dam deformation prediction.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 7 Simulation--Real Topology
### The Influence of Cable Type on Transfer Function and Throughput  
According to simulation results from the previous chapter, the different cable types achieve very different results of throughputs. Therefore, the simulation of influence of cable types on transfer function and throughput was conducted. The route from n1 to n7 (31 m) was chosen due to a power line without branches and a line with a short distance, to achieve ideal conditions.  
The transfer functions of different cables are shown in Figure 10. The overhead power line AlFe shows the highest attenuation. The AlFe cable is considered as an old type of cable with isolation in the form of air. This environment is not good for signal transmission on a frequency of 50/60 Hz. The successor of this cable is the AES cable with isolation in the form of polyethylene.  
The underground power lines NAVY and AYKY have the same attenuation, and this attenuation is significantly lower than overhead power lines.  
Table 2 shows throughout for different cables. Similarly, as a transfer function, the throughput of the underground power line is significantly higher than overhead power lines. The throughputs for different underground power lines are similar.  
The results of transfer functions (attenuation and notches) of underground power lines from our simulation are in agreement with the results in a previous study [62].  
The reason for significantly lower values of throughput and higher values of attenuation for overhead power lines are caused by multipath signal propagation, higher interferences and attenuation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cable Type** & **Throughput on Physical Layer [Mbps]** \\ \hline NAVY 150 SE & 47.94 \\ ALFE 4 \(\times\) 50 & 6.30 \\ AYKY 3 \(\times\) 120 + 70 & 47.45 \\ AYKY 4 \(\times\) 50 & 46.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Possibility of communication—without repeaters.  
Figure 10: Throughout for different types of cables.  
(higher background noise), lower power transmission, higher influence of branches and distance, and the issue of isolation [63].