esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Spatial and Temporal WUE Variation Characteristics  
Based on the WUE dataset division in Anhui Province on time scales such as years, seasons, and months, combined with the spatial scale analysis of natural areas and major soil types, the spatiotemporal variation characteristics of WUE were investigated. WUE fluctuated greatly in the interannual variation: in 2003, the WUE was the lowest (1.367316 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)), the overall GPP was low, and the ET was high, especially in spring, autumn, and winter [63]. Except for July and August, the WUE of the other months was lower than the average for the same month in previous years. This downward  
Figure 18: Influence weights of each influencing factor. (**a**) Influence factors of five topographic regions. (**b**) Influence factors of eight soils.  
trend is mainly attributable to a general decrease in the GPP across the five natural terrain regions [64]. The WUE in 2014 reached the highest value (1.648538 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)). Although the WUE in the four seasons of 2014 was consistent with the annual average, the GPP in the five natural topography regions increased significantly with little change in ET; in particular, the WUE in January, July, and August was higher than that in the same calendar year [64; 65]. In addition, forest ecosystems generally have a high WUE. In the geomorphic regions of Anhui Province, the highest WUE is found in the southern hills of Anhui Province and the Dabie Mountains of western Anhui Province, which are rich in forest resources [11; 66]. Brown soil in the Dabie Mountain area of western Anhui has good water retention and moderate drainage characteristics, which can effectively support crop growth and increase WUE under proper management. The water retention and nutrient absorption of brown soil can be optimized by applying organic matter and regulating the pH value, and the WUE can be further improved [67; 68].
## 4 Conclusions  
In this paper, a deep learning method with CNNs using skip connections from input data is proposed to alleviate the problems of gradual extinction of the input data information and very narrow distribution of the data in hidden layer units when the ReLU operation is repeatedly applied to residual-type data. The proposed method divides whole layers into several layer groups and uses skip connections with different MFs to the outputs of layer groups.  
The operation of ReLU units in the proposed method is exactly the same as the VDSR. Compared to the VDSR method, the data processed in intermediate hidden layers in the proposed method are distributed in a wider range with greater similarity to a normalized distribution. This effect is obtained by the repetitive skip connections with MFs from input data to the layer groups in the proposed method.  
Figure 6: Super-resolution image reconstruction results. (**a**) Ground truth image. (**b**) The result of the VDSR method (PSNR = 29.34 dB). (**c**) The result of the proposed method (PSNR = 30.31 dB).  
Figure 7: Super-resolution image reconstruction results. (**a**) Ground truth image. (**b**) The result of the VDSR method (PSNR = 23.93 dB). (**c**) The result of the proposed method (PSNR = 24.20 dB).  
The proposed method shows greater PSNR performance and better subjective quality in super-resolution image reconstruction with a similar amount of computation. In future work, the proposed method can be applied to other residual-type deep learning networks for other applications to improve performance with relatively low computational complexity.  
Conceptualization, H.A. and C.Y.; methodology, H.A.; software, H.A.; validation, H.A.; formal analysis, H.A. and C.Y.; investigation, H.A. and C.Y.; resources, H.A. and C.Y.; data curation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, C.Y.; visualization, H.A.; supervision, C.Y.; project administration, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Konkuk University grant number 2018-A019-0691.  
This paper was supported by Konkuk University in 2018.  
The authors declare no conflict of interest. The funders had no role in the design of the study; collection, analyses, or interpretation of data; the writing of the manuscript; or the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SISR & single image super-resolution \\ SRCNN & super-resolution convolutional neural network \\ VDSR & very deep super-resolution \\ CNN & convolutional neural network \\ ReLU & rectified linear unit \\ ResNet & residual network \\ MF & multiplication factor \\ PSNR & peak signal-to-noise ratio \\ \end{tabular}
## 2 Materials and Methods
### In Vitro Gastrointestinal Digestion  
Simulated complete digestion of the two SF samples (SFOH and SFCONV) was performed according to the method described by [PERSON] et al. [PERSON] et al. (2018) with slight modifications. This procedure mainly comprised sequential phases simulating different conditions along the gastrointestinal tract. A schematic representation of the main steps performed and the fractions obtained, along with simulated gastrointestinal digestion, are presented in Figure 1, providing information on the process and its conditions. Briefly, 2 g of each sample were mixed with 20 mL of PBS solution, and \(\alpha\)-amylase (100 U/mL) was added to the simulated mouth step, and the mixture was incubated for 2 min at 37 \({}^{\circ}\)C under agitation (200 rpm). Subsequently, a pepsin (Sigma-Aldrich Chemistry, St. Louis, MO, USA) solution, 25 mg/mL, was added with a ratio of 0.05 mL/mL of a sample at pH 2.0 (simulated gastric solution, stomach) were added following incubation for 2 h, 37 \({}^{\circ}\)C at 130 rpm orbital agitation for the gastric phase. Finally, a solution composed of 2.0 g/L pancreatin (Sigma-Aldrich Chemistry, St. Louis, MO, USA) and bile salts, 12 g/L, (OxoidTM, Hampshire, UK) at pH 6.0 was added (0.25 mL/mL) and incubated for 2 h at 37 \({}^{\circ}\)C with 45 rpm on an orbital incubator to simulate the small intestinal phase. Then the samples were placed in a dialysis membrane, closed in vials with a solution of PBS to simulate the colon (nonabsorbable sample), and the liquid outside the membrane or blood, basolateral part. Both volumes, inside and outside the membrane, were measured. After the gastrointestinal simulation, the basolateral fraction was freeze-dried for subsequent analysis.  
During the gastrointestinal stimulation, samples were collected in each step--mouth, stomach, small intestine, colon, and basolateral fraction--to analyze total phenolic compounds, total carotenoids, individual compounds, and metabolites (phenolic, carotenoids, and volatile compounds) by HPLC and UPLC-qTOF MS, and antioxidant activity. All analyses were performed in triplicate.  
Figure 1: Gastrointestinal simulation and biological analysis throughout each step of digestion.
## 3 Results and Discussion
### Effects of Resin Treatment on Other Wine Parameters  
To assess other effects produced by the treatments with resins, free SO\({}_{2}\), total SO\({}_{2}\), pH, total acidity, TPI and colour of all the samples were analysed before and after the treatment (Table 6).  
For the SLB scavenger, a higher concentration of free sulphur dioxide (16%) is obtained after incubation, showing that the process has worked properly, and the equilibrium has been displaced. In the test with the same wine spiked with SO2, only a 3% increase of free SO2 was found, which may indicate that the breakdown of the hydroxyalkylsulfonate adducts due to the removal of the aldehyde is not favourable at high adduct concentrations (see Table 6). The other scavenger, ETDM, seems to have been less effective if this parameter is taken into account, however, almost the same difference is again observed between the wine with the native content of SO2 or the one with higher amount.  
Regarding ion exchange resins, the ones that react directly with the adduct, it was expected that the free and total sulphur dioxide would decrease as the process progressed. Indeed, this is effectively what has been obtained and is reflected in Table 6. Having an initial wine with a higher sulphur dioxide content increases the percentage of SO2 removal due to the treatment, especially with the pure ion exchange resins DOW and IRA.  
Regarding TPI and colour, the resins behaved in the same way regardless of the level of SO2 in the initial wine. The decrease in these parameters may mainly be due to the elimination of catechins and hydroxycinamic acids [36]. [PERSON] et al. established in their work the selection of ion exchange resins, a maximum colour elimination criterion of 35% in order not to compromise the rest of the sensory properties [36]. As can be seen in Table 6, only the scavengers would fulfil this premise, obtaining a marked decrease in colour (higher than 63%) for the rest of the resins, except for BEC.  
Regarding the pH and total acidity, only the treatment with ETDM and SXAW resins provided a notable variation. In these two cases, the pH increased and the total acidity decreased, which is totally undesirable at an oenological level, since it compromises the microbial stability of the wine.  
It must be considered that the concentration of phenylacetaldehyde increased after the treatment with scavenger resins. Moreover, one of the two resins of this type sharply decreased the total acidity of the wine. Therefore, the use of scavengers in this context can be ruled out. Thus, reaching a compromise, it can be concluded that the resins with the best behaviour are the mixed ion exchange SXA, both for the elimination of aldehydes, except isobutyraldehyde, and for maintaining the pH and total acidity to a great extent, closely followed by MAX resin. However, the colour decrease is greater than 75% in both cases. Perhaps by using a lower dose than the one studied in this work (10 g/L), the process would be more viable from a sensory and economic point of view.
## 3 Establishment of a Single-Degree-of-Freedom Eddy Current Damping Vibration Suppression Model  
### Establishment of Eddy Current Damping Vibration Suppression Model  
In automotive theory, because the body mass of the automobile is much greater than the human body mass, it is believed that there is no feedback on the above two links, and the vibration of the body floor can be used as the input of the \"seat-human body\" system. Usually, the \"seat-human body\" is simplified as a single degree of freedom vibration system [25]. Due to the more significant vertical excitation effect caused by road surface smoothness, A vertical single-degree-of-freedom model can meet the requirements. Figure 3 depicts the motion model of a single-degree-of-freedom eddy current damping vibration suppression structure. In this model, \(\mathbf{m}\) is the mass of the main system, \(\mathbf{k}\) is the stiffness of the main system, \(\mathbf{c_{0}}\) denotes the inherent damping of the main system, and \(\mathbf{c}\) is the adjustable eddy current damping. Among them, \(\mathbf{c_{0}}\) is the sum of the damping of the supporting spring and other frictional damping, and \(\mathbf{c}\) is the adjustable eddy current damping of the eddy current damping module. The functions \(\mathbf{x_{1}}(\mathbf{t})\) and \(\mathbf{x_{0}}(\mathbf{t})\) correspond to the displacement response of the main system and the displacement excitation from the vibrating surface, respectively. A vertical coordinate system with axes labeled \(\mathbf{x_{1}}\) and \(\mathbf{x_{0}}\) was established. The origin of this coordinate system corresponds to the static equilibrium position of the system, with the positive direction defined as upward. Based on [PERSON]'s second law and the model shown in Figure 3, the system's differential equation is derived and expressed as follows:  
\[m\ddot{x}_{1}+(c+c_{0})\big{(}\dot{x_{1}}-\dot{x_{0}}\big{)}+k(x_{1}-x_{0})=0 \tag{4}\]  
When dealing with an inhomogeneous second-order linear differential equation, if the inhomogeneous term appears as a constant value, it will add only one constant term to the coordinate solution. Such changes do not have a substantial impact on the vibrational  
Figure 3: Model of an eddy current damping structure with single degree of freedom. properties, such as the period and amplitude attenuation. Therefore, ignoring this item directly does not interfere with the overall analysis. To facilitate the solution of Equation (4), the following transformation are made. Based on mechanical vibration theory, the following variables are defined as follows:  
\[\left\{ \begin{array}{l}x_{1}=A_{1}e^{(\omega t-\phi_{1})i}\\ x_{0}=A_{0}e^{(\omega t-\phi_{0})i}\end{array} \right. \tag{5}\]  
\(\mathbf{A_{1}}\) and \(\mathbf{A_{0}}\) are amplitudes of the main system displacement response and excitation displacement, respectively. \(\mathbf{\omega}\) is the frequency. \(\mathbf{\varphi_{1}}\) and \(\mathbf{\varphi_{0}}\) are the initial phases of the main system and the excitation surface.
# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 4 Results
### Model Assessment
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Hypotheses** & **Path Coefficients** & **T Statistics** & _p_**-Value** & **Supported?** \\ \hline H1. RES -\textgreater{} EFF & 0.460 & 6.530 & 0.000 & Yes \\ \hline H2. RES -\textgreater{} EASE & 0.459 & 6.080 & 0.000 & Yes \\ \hline H3. EFF -\textgreater{} USE & 0.380 & 4.299 & 0.000 & Yes \\ \hline H4. FEDQ -\textgreater{} EASE & 0.282 & 3.735 & 0.000 & Yes \\ \hline H5. EASE -\textgreater{} USE & 0.369 & 3.585 & 0.000 & Yes \\ \hline H6. EASE -\textgreater{} SAT & 0.277 & 2.389 & 0.017 & Yes \\ \hline H7. USE -\textgreater{} SAT & 0.465 & 4.766 & 0.000 & Yes \\ \hline H8. SEC -\textgreater{} SAT & 0.228 & 2.337 & 0.019 & Yes \\ \hline H9. SAT -\textgreater{} BEH & 0.634 & 10.666 & 0.000 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of hypotheses testing. \begin{table}
**BEH** & & & & & & \\ \hline
**EASE** & & & & & & \\ \hline
**EFF** & & & & & & \\ \hline
**FEDQ** & 0.104 & & & & & \\ \hline
**RES** & 0.277 & 0.268 & & & \\ \hline
**SAT** & 0.671 & & & & \\ \hline
**SEC** & & & & 0.115 & \\ \hline
**USE** & & & & 0.348 & \\ \hline \hline \end{tabular} Note that endogenous constructs are in the columns while exogenous constructs are in the rows.  
\end{table} 
Table 8: Results of testing the effect size. compared to the naive LM benchmark, this suggests that model has a medium predictive power; (d) if none of the items has higher RMSE (or MAE) values compared to the naive LM benchmark, the model has high predictive power. Visual inspection of error histograms uncovered that the distribution of prediction errors is highly non-symmetric. Hence, we based predictive power evaluation on MAE. As shown in the fourth column of Table 9, the majority of endogenous construct items have higher PLS-SEM_MAE values when compared to the naive LM_MAE benchmark, which suggests that the proposed model has low predictive power. Changes in \(Q^{2}_{predict}\) reflect the relative impact (\(q^{2}\)) of exogenous constructs in predicting the observed measures of endogenous construct in the structural model. According to [53], \(q^{2}\) values of 0.02, 0.15, or 0.35 signify that a particular exogenous construct has weak, moderate, or substantial relevance in predicting an endogenous construct, respectively. Values of \(q^{2}\) are calculated as follows [72]:  
\[\frac{Q^{2}_{predict-1}-Q^{2}_{predict-E}}{1-Q^{2}_{predict-1}}\]  
\(Q^{2}_{predict-1}\) refers to the \(Q^{2}_{predict}\) value of an endogenous construct when the observed exogenous construct is included in the model estimation while \(Q^{2}_{predict-E}\) refers to the \(Q^{2}_{predict}\) value of an endogenous construct when the observed exogenous construct is excluded from the model estimation. Study results provided in Table 10 suggest that customer satisfaction (\(q^{2}\) = 0.227) has moderate relevance in predicting behavioral intention related to continued interaction with mobile banking applications. While responsiveness (\(q^{2}\) = 0.252)
## 3 Materials and Methods
### Models Overview
#### 3.7.4 Support Vector Classifier (SVC)  
Support Vector Classifier (SVC), introduced by [PERSON] [36], is a powerful supervised learning method designed to find the optimal hyperplane that separates data points into distinct classes. In this research, SVC was applied to classify human activities using sensor data from smartphones, with a focus on optimizing its performance through nested cross-validation.  
To effectively capture the non-linear relationships in the activity data, the Radial Basis Function (RBF) kernel was employed. The RBF kernel transforms input data into a higher-dimensional space, enabling the model to separate complex patterns in human movements. The kernel is controlled by the parameter \(\sigma\), which defines its width, and was optimized through grid search.  
The RBF kernel is mathematically expressed as:  
\[K(x_{i},x_{j})=\exp\Biggl{(}-\frac{\|x_{i}-x_{j}\|^{2}}{2\sigma^{2}}\Biggr{)}, \tag{15}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Sensor Configuration**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Max Depth**} & \multicolumn{1}{c}{\begin{tabular}{c} **Min Samples** \\ **Leaf** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Min Samples** \\ **Split** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **n\_estimators** \\ \end{tabular} } \\ \hline
2 Sensors—Non-overlapping & Gini & None & 2 & 5 & 100 \\
2 Sensors—50\% overlapping & Gini & 5 & 1 & 5 & 100 \\
4 Sensors—Non-overlapping & Entropy & 5 & 1 & 2 & 100 \\
4 Sensors—50\% overlapping & Gini & 15 & 2 & 2 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal Hyperparameters for Random Forest in Different Sensor Configurations.  
where \(x_{i}\) and \(x_{j}\) are the input feature vectors, and \(\sigma\) controls the kernel's width, influencing the decision boundary.  
The model's performance is highly sensitive to its regularization parameter \(C\) and kernel width \(\sigma\), both of which were fine-tuned for each sensor configuration (non-overlapping and 50% overlapping). For two-sensor setups, the RBF kernel with \(C=10\) and automatic \(\gamma\) yielded the best results, while the four-sensor non-overlapping configuration favored a linear kernel for its ability to handle simpler decision boundaries. The 50%-overlapping configuration returned to the RBF kernel to manage more complex, overlapping data relationships.Table 10 provides a summary of the optimal hyperparameters identified for each configuration.  
The use of nested cross-validation ensured a reliable evaluation of model performance by optimizing hyperparameters. The RBF kernel, particularly, demonstrated strength in capturing non-linearities within overlapping data, while the linear kernel proved efficient for non-overlapping configurations. These findings highlight the flexibility of SVC in handling various sensor setups and classification tasks.
# Roadmap Modeling and Assessment Approach for Defense Technology System of Systems
## 2 Literature Review
As mentioned above, various interactive elements, such as knowledge, technologies, products, and businesses, are required to investigate R&D planning. Consequently, one can use the multi-layer network model to depict technology roadmaps [8]. [PERSON] et al. [23] constructed a dual-layer citation network model involving scientific papers and patents based on open access data. Through analyzing the network structure, one can identify focal knowledge sources and technology fronts. [PERSON] et al. [24] investigated a triple-layer weighted network involving domains, researchers, and knowledge, and a novel method was designed to predict the potential links based on structural similarities between pairs of nodes. Meanwhile, the structure and dynamics of multi-layer networks has become a highlighted research area. Scholars have published many achievements on the theories and approaches on the node centralities, clustering, spreading process, and synchronization [25], which will promote the development of technology roadmap research. The foregoing literature provides theoretical guidance and useful tools for the research of defense technology roadmaps. Nevertheless, practical modeling and assessment approaches are still insufficient to identify the requirements from various sources and investigate the evolution mechanisms of the focal system of systems accurately. Therefore, this article will focus on three interrelated objectives, including model framework integrating data analysis and expertise, the contribution rate to satisfy external requirements, and the importance of knowledge diffusion. In particular, this article proposes a framework to collect expertise, taking into consideration the viewpoints of different stakeholders to reinforce the quantitative system evaluation based on data analysis of the roadmap model. Thus, the assessment results can be utilized to support decision-making with the assistance of practical experience of R&D managers. The corresponding approaches are proposed in the next section.
# Ammonia Gas Sensors: Comparison of Solid-State and Optical Methods
## 2 Solid-State Ammonia Sensors
high sensitivity, short response time, and fast reversibility for NH\({}_{3}\) gas concentrations ranging from 2 ppm to 1000 ppm.
Even though most of the spectrum is unused in this region, the Third-Generation Partnership Project (3 GPP) already mentioned in its release 15 and 16 that it is not harmful to utilize for wireless communication [63; 64]. The 3 GPP defines an NR 5G spectrum with two different sets of frequency bands. The first is frequency range 1 (<6 GHz), where the frequency using this region for 5G is approximately 3.5 GHz, and the maximum channel bandwidth available is 100 MHz [65]. As stated by the FCC, most of the spectrum used in this range is for 4G and its progenitor technologies [66]. By contrast, frequency range 2 (>24 GHz, i.e., mmWave frequency band) is where the maximum channel bandwidth is defined to be as low as 50 MHz to a maximum of 400 MHz [67]. The goal of 6G is to meet the needs of the information society ten years later (2030-); thus, the 6G vision must address the needs that 5G cannot meet and the need for further up-gradation [68]. 6G must be developed in response to the increasingly distributed Radio Access Network (RAN) and the desire to maximize the Terahertz (THz) spectrum for increasing capacity and reducing latency [69]. The 6G wireless system must have the following key factors: enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), massive Machine-Type Communication (mMTC), AI-integrated communication, tactile internet, low backhaul, and access network congestion and enhanced data security [70; 71; 72; 73]. In summary, the future 6G is a set of technologies that can deliver the optimum results in terms of throughput, network capacity, spectral efficiency, energy efficiency, lower power consumption, and latency, ensuring fairness among all the users, especially the cell-edge users [74].
## 3 Results
### Validation of the Model
Figure 18: Validation of the proposed model against experimental results. (—: model results, +: experiment results). On the other hand, as can be seen from the error analysis listed in Table 4, the model tends to slightly underestimate the acoustic end correction length in comparison with some of the experimental data; this model underestimation is more significant for plates with a y = 12 mm, where there are holes very close to cavity walls. As a result, a noticeable hole-to-wall interaction effect will probably contribute to the error.