esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis  
A simulation of the theory was first conducted in a three-phase, open-loop configuration for the trapezoidal reference signal. A closed-loop simulation of a hybrid active filter for an MC interfaced three-phase load was analyzed. Figure 8 depicts a three-phase voltage source supplying a three-phase MC interfaced load. The HAPF consisted of the parallel combination of a shunt active power filter (SAPF) and a passive L-C filter. Table 5 shows the parameters selected for the simulations of the MC-HAPF in MATLAB/Simulink environment based on equations from an earlier work by the same authors [39]. Irrespectively, non-ideal conditions were included in the simulation to approximate practical implementation for future work.  
The open-loop setup consisted of a three-phase supply and the MC coupled load as seen in Figure 4 above. The open-loop reference trapezoidal signal was compared to the VSC output current signal in the presence of the hysteresis band. The trapezoidal signal was selected because its rate of change in current closely mimics that of the matrix converters. Table 6 shows the parameters for the trapezoidal reference simulation. The effects of the reference signal's frequency and hysteresis current bandwidth on the switching frequency of the VSC were tabulated and analyzed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{S_{MC}}\)/kW/6 kHz & \multicolumn{3}{c}{**10**} \\ \hline \(\varepsilon\)/A & 0.2 & 0.5 & 0.7 & 1 \\ \(f_{inst}\)/\(MHz\) & 6.72 & 2.68 & 1.92 & 1.319 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationship between hysteresis error current and the VSC switching frequency at \(\mathbf{S_{MC}}\) (10 kW).  
Figure 8: Schematic diagram of closed-loop active filter schematic diagram. Source: [39].  
The simulation results for the open-loop and closed-loop configurations are shown below. The open-loop current shape and its maximum switching frequency as well as that for the closed-loop hysteresis-controlled MC setup are shown in Figures 9 and 10, respectively.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Supply voltage (V\({}_{\text{L-L}}\)) & 400 V, 50 Hz \\ MC rated power, \(S_{\text{MC}}\) & 50 kW \\ MC switching frequency & 6 kHz \\ DC capacitor voltage & 653 \(V_{min},700\)\(V_{max}\) \\ Coupling inductance & 115 \(\upmu\)H \\ Reference signal frequency & 50 Hz, 6 kHz \\ \hline \end{tabular}
\end{table}
Table 6: Parameters selected for the closed-loop configurations.  
Figure 9: HCC simulation results for reference tracking, error current and maximum-switching frequency for trapezoidal wave reference signal.
## 5 Results
### RQ3. Results of the Text-Image-Code Experiment  
The fourth row of Table 5 per each project corresponds to the results of the Text-Image-Code experiment.  
First, when compared to the Text Only model, all of the Precision, Recall, and F1-score values of the MulDIC\({}_{TIC}\) model are higher than those of the Text Only model. The MulDIC\({}_{TIC}\) models showed Precision improvements of 11.95%, 14.34%, 9.15%, and 5.03% for VS Code, Kubernetes, Flutter, and Roslyn. MulDIC\({}_{TIC}\) demonstrated Recall improvements of 11.43%, 13.90%, 8.00%, and 5.07% for VS Code, Kubernetes, Flutter, and Roslyn. MulDIC\({}_{TIC}\) made F1-score improvements of 11.69%, 14.12%, 8.57%, and 5.05% for VS Code, Kubernetes, Flutter, and Roslyn.  
Second, when compared to the MulDIC\({}_{TI}\) model, all of the Precision, Recall, and F1-score values of the MulDIC\({}_{TIC}\) model are still higher than those of MulDIC\({}_{TI}\). The Precision improvements of MulDIC\({}_{TIC}\) are 0.80%, 12.90%, 7.06%, and 1.82% for VS Code, Kubernetes, Flutter, and Roslyn. The Recall improvements are 1.78%, 11.67%, 6.25%, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{VS Code} & Text Only & **68.45** & **68.27** & **68.36** \\  & MulDIC\({}_{TC}\) & 68.11 & 67.77 & 67.94 \\ \hline \multirow{2}{*}{Kubernetes} & Text Only & 66.91 & 66.10 & 66.50 \\  & MulDIC\({}_{TC}\) & **79.97** & **78.33** & **79.14** \\ \hline \multirow{2}{*}{Flutter} & Text Only & 67.70 & 67.66 & 67.68 \\  & MulDIC\({}_{TC}\) & **68.68** & **68.42** & **68.55** \\ \hline \multirow{2}{*}{Roslyn} & Text Only & 62.92 & 62.86 & 62.89 \\  & MulDIC\({}_{TC}\) & **66.06** & **66.04** & **66.05** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results that compared MulDIC\({}_{TC}\) with the Text Only model.  
1.89% for VS Code, Kubernetes, Flutter, and Roslyn. The F1-score improvements are 1.30%, 12.28%, 6.65%, and 1.86% for VS Code, Kubernetes, Flutter, and Roslyn.  
Last, when compared to the MulDIC\({}_{TC}\) model, the Precision, Recall, and F1-score values of the MulDIC\({}_{TIC}\) model are higher than those of MulDIC\({}_{TC}\) across all projects, VS Code, Kubernetes, Flutter, and Roslyn. The Precision improvements of MulDIC\({}_{TIC}\) are 12.29%, 1.28%, 8.17%, and 1.89% for VS Code, Kubernetes, Flutter, and Roslyn. The Recall improvements are 11.93%, 1.67%, 7.24%, and 1.89% for VS Code, Kubernetes, Flutter, and Roslyn.  
These findings indicate that incorporating multiple modalities leads to significant performance improvements. The results of this experiment demonstrate the synergistic effect of using heterogeneous modalities in classifying issue reports.
# Technology Acceptance in Healthcare: A Systematic Review
###### Abstract  
Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.  
technology acceptance; technology adoption; healthcare; systematic review +
Footnote †: journal: Medical  
acceptance models and theories. This is because those theories and models offer a better understanding of the users' behaviors toward a specific technology or service through the factors underpinning them [11]. It is believed that the identification of these factors would enhance the effectiveness of healthcare technologies by allowing scholars to investigate the technical, social, and cultural aspects and understand the correlation between those factors and users' readiness to use healthcare systems. Therefore, this study aims to systematically review the studies that empirically evaluated the different technologies in healthcare in relation to technology acceptance models and theories. Stemming from this aim, the authors intend to answer the following research questions:  
RQ1. What are the prevailing technology acceptance models and theories explored in the healthcare domain?  
RQ2. What are the key factors affecting technology acceptance in the healthcare domain?  
RQ3. What are the primary confirmed relationships among the influential factors in the past studies?  
RQ4. What are the leading information technologies studied and their relationships with countries and participants?  
RQ5. How are the reviewed studies distributed across the regions and countries of technology implementation?  
RQ6. What is the progress of technology acceptance studies in healthcare?
###### Abstract  
Rapid advances in text-to-image (T2I) generative models have significantly enhanced visual content creation. However, evaluating these models remains challenging, particularly when assessing their ability to handle complex textual content. The primary aim of this research is to develop a systematic evaluation framework for assessing T2I models' capabilities in generating specialized content, with emphasis on measuring text rendering accuracy and identifying model limitations across diverse domains. The framework utilizes carefully crafted prompts that require precise formatting, semantic alignment, and compositional reasoning to evaluate model performance. Our evaluation methodology encompasses a comprehensive assessment across many critical domains: mathematical equations, chemical diagrams, programming code, flowcharts, multi-line text, and paragraphs, with each domain tested through specifically designed challenge sets. GPT-4 serves as an automated evaluator, assessing outputs based on key metrics such as text accuracy, readability, formatting consistency, visual design, contextual relevance, and error recovery. Weighted scores generated by GPT-4 are compared with human evaluations to measure alignment and reliability. The results reveal that current T2I models face significant challenges with tasks requiring structural precision and domain-specific accuracy. Notable difficulties include symbol alignment in equations, bond angles in chemical diagrams, syntactical correctness in code, and the generation of coherent multi-line text and paragraphs. This study advances our understanding of fundamental limitations in T2I model architectures while establishing a novel framework for the systematic evaluation of text rendering capabilities. Despite these limitations, the proposed benchmark provides a clear pathway for evaluating and tracking improvements in T2I models, establishing a standardized framework for assessing their ability to generate accurate and reliable structured content for specialized applications.  
Keywords:text-to-image models; visual text rendering; human-AI alignment; complex textual content +
Footnote †: journal: Applied sciences
## 4 Discussion
### Machine-Learning Frameworks  
We used an SAE to transform raw signals into extracted features, and the sparse representations learned by the SAE were used as inputs to train SVMs as classifiers to predict emotion states. The combination of the SAE and SVMs leveraged the feature extraction capabilities of the SAE, reduced the difficulty of handling high-dimensional data, and enhanced the overall model performance. The advantages of combining SAE and SVM are as follows.  
Effective feature extraction: The SAE, an unsupervised learning method, automatically learns useful feature representations from raw data. This is particularly beneficial for emotion recognition tasks because physiological signals related to emotions (e.g., RSP signals) often contain redundant and irrelevant information. Using the SAE, we can automatically extract features closely related to emotional states, thereby reducing the complexity of subsequent classification tasks.  
Dimensionality reduction: The SAE maps the original high-dimensional data to a lower-dimensional sparse representation through a multilayer encoding and decodingprocess. This helps mitigate the \"curse of dimensionality\" problem faced by SVMs when dealing with high-dimensional data, which can lead to reduced generalization capability. By providing low-dimensional features extracted by the SAE, the SVM can be classified more effectively.  
Robust feature learning: The SAE can learn robust representations, even from limited datasets. This implies that even with a small amount of training data, the features extracted by the SAE can help the SVM achieve better performance.  
Nonlinear mapping: The SAE employs nonlinear activation functions to map raw data to the feature space, enabling the model to capture nonlinear relationships within the data. This is crucial for emotion recognition tasks because emotions often have complex nonlinear relationships with physiological signals.  
By combining the SAE and SVM, we achieved a more efficient and effective approach to emotion recognition. The SAE reduces the complexity and dimensionality of the input data, making it easier for the SVM to handle and classify the data, thus improving the overall performance of the model in recognizing emotions based on physiological signals.
## 2 State of the Art: Tolerancing Information Sharing throughout the Product Life Cycle
### Representation Models for Tolerancing Information  
The efficient modelling and representation of tolerancing information has been identified as an important enabler for the seamless integration of tolerancing tasks in the virtual product development process. In this regard, a dimension and tolerance data model has been proposed in [10], which is to allow the communication between tolerance-related software tools. In the course of this, a layered tolerance representation model was proposed in [11] and the need for integrating tolerancing information in product life cycle management tools to allow tolerancing data traceability led to a functional architecture for tolerancing data in [12]. More recently, OWL-based (Web Ontology Language) domain ontology models for the representation of tolerancing information were proposed. Summarizing these research works, an in-depth analysis of existing representation models for tolerance information is provided in [13], concluding that future research is required regarding the automatic update of tolerance information representation models and the integration of these models into commercial computer-aided design tools.
## 3 Resonant Scattering of Nanocylinders
### Scattering Characteristics of Three-Nanocylinders-Combination  
Next, we study the scattering characteristics and resonances of the three identical nanocylinders combination in Figure 10. By constructing trimeric silicon nanocylinders, more complex interaction can be introduced. Figure 10 shows the scattering performance of trimeric silicon nanocylinders with the gap from 100 nm to 10 nm. It is clear that the scattering performance is consistent with that of a single nanocylinder when g is 100 nm, showing a weaker interaction among three nanocylinders. As the spacing becomes smaller, the interaction becomes stronger and the obvious magnetic resonance splitting emerges as the gap is 50 nm. The newly \"MD2\" is more pronounced than that in the dimer. When \(g=10\) nm, the \"MD1\" seems to be disappeared, the \"ED\" and \"MD2\" modes become dominant scattering resonance. Because of the intense interaction, the splitting resonant modes result in a distinct dip, which is disadvantageous in broadband application. Furthermore, the scattering characteristics of three-different-nanocylinders-combination are studied in Figure 11. Two resonant peaks at short wavelength are close to each other and are mixed to form an electromagnetic resonant mixing mode with decreasing spacing. The magnetic resonance mode is becoming more and more significant when the spacing is decreased among three nanocylinders.  
Figure 10: Scattering of three- identical-nanocylinders-combination with decreasing spacing from 100 nm in air. The height and diameter of three same nanocylinders are both 100 nm.  
Figure 9: Scattering spectra of two-different-nanocylinders-combination with the decreasing spacing from 100 nm in air. The height of cylinders is still 100 nm, while the diameter of the left nanocylinder is 100 nm, and the diameter of the right nanocylinder is 140 nm.
## 3 Results and Discussion
### Environmental Risk Assessment  
In this study, the MECs and PNECs of the target compounds for algae, invertebrates, plants, and fish were utilized to conduct a risk assessment. According to the most sensitive PNEC values, the RQs were calculated using the maximum MECs, and the results of the risk assessment are presented in Table S5 and Figure 6. The concentration levels of the compounds detected in this study do not pose a toxic effect on either plants or fish because all their RQs were far lower than 0.1. However, invertebrates and algae are sensitive to PhACs in aquatic environments. The RQ value of tetracycline to invertebrates was as high as 35.97, indicating that tetracycline may cause very high ecological risks to relevant invertebrates. The situation is also not pleasant in terms of algae; The RQ values of tetracycline and sulfamethoxazole to algae were 16.67 and 0.13, respectively, refering to high and medium risks.  
Algae and invertebrates are an important component to the bottom of the food chain, and minor changes in the population structure will have a significant impact on the entire ecosystem [70]. Therefore, we should conduct long-term monitoring and effective management of PhACs in the environment. The actual ecological risk of PhACs in snow may be higher than the reported data. There are two reasons for this: First, different types of PhACs in the marine environment do not exist alone. Due to the limitations of basic toxicological data, this study discussed the RQ values of only PhACs, and it is not possible to accurately estimate the cumulative toxicity risk of various PhACs. Second, because most of the PhACs evaluated in this study were new types of marine organic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**PhACs**} & \multicolumn{2}{c}{**Component**} & \multirow{2}{*}{**3**} \\ \cline{2-3} \cline{5-5}  & **1** & **2** & **3** \\ \hline Sulfamonomethane & 0.853 & \(-\)0.239 & \(-\)0.114 \\ Sulfameter & 0.891 & \(-\)0.089 & 0.259 \\ Nitzazepam & 0.735 & 0.207 & 0.275 \\ Rondiazole & \(-\)0.533 & \(-\)0.162 & 0.33 \\ Salbutamol & \(-\)0.575 & \(-\)0.492 & 0.291 \\ Desacetyllecofotaxime & 0.755 & 0.245 & \(-\)0.223 \\ Ceftiofur & 0.858 & \(-\)0.02 & 0.248 \\ Flunixin & 0.02 & \(-\)0.675 & 0.198 \\ Sulfamethoxazole & 0.012 & 0.836 & 0.33 \\ Tetracycline & 0.101 & 0.832 & 0.227 \\
5-chloro-1-methyl-4-nitroimidazole & \(-\)0.053 & 0.029 & \(-\)0.76 \\
5-nitrobenzimidazole & \(-\)0.12 & \(-\)0.136 & \(-\)0.726 \\ Oxolinic acid & \(-\)0.376 & \(-\)0.197 & 0.632 \\ Roxithromycinycin & 0.066 & 0.232 & 0.478 \\ Eigenvalues & 4.34 & 2.52 & 2.09 \\ \% Variance & 30.98 & 17.97 & 14.94 \\ Cumulative \% & 30.98 & 48.95 & 63.89 \\ \hline \multicolumn{4}{c}{Extraction Method: Principal component analysis.} \\ \multicolumn{4}{c}{Rotation Method: Varimax with Kaiser normalization.} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Loadings of PhACs on 3 components.  
pollutants, some of the target toxicity data were unknown; as a result, we are temporarily ignoring their ecological risk.
# Study on the Time Domain Semi Analytical Method for Horizontal Vibration of Pile in Saturated Clay
## Appendix A  
In Equation (26), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) and \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(\varepsilon_{3}\) are all known functions about \(s\), with the specific form as follows:  
\[\beta_{1}=\frac{d_{1}+\sqrt{d_{1}^{2}-4d_{2}}}{2};\ \beta_{2}=\frac{d_{1}- \sqrt{d_{1}^{2}-4d_{2}}}{2};\ \beta_{3}^{2}=\frac{-(\
u\gamma_{k}^{2}-\rho s^{2})(\rho_{ fS}s/k_{f}+\rho_{fS}z^{2}/n)-\rho_{f}^{2}s^{4}}{\mu(\rho_{fS}s/k_{f}+\rho_{fS}z^{2}/n)};\] \[\varepsilon_{1}=\frac{-\alpha M\beta_{1}^{2}+\rho_{f}s^{2}}{M \beta_{1}^{2}-(\rho_{f}s^{2}/k_{f}+\rho_{f}s^{2}/n)};\ \varepsilon_{2}=\frac{-sM\beta_{2}^{2}+\rho_{f}s^{2}}{M \beta_{2}^{2}-\big{(}\rho_{f}s/k_{f}+\rho_{f}s^{2}/n\big{)}};\ \varepsilon_{3}=\frac{-s}{s/k_{f}+s/n};\]  
The expressions of \(d_{1}\), \(d_{2}\) in the above equation are as follows:  
\[d_{1}=\frac{\big{(}\lambda+2\mu+\alpha^{2}M\big{)}\big{(}\rho_{ f}s^{2}/k_{f}+\rho_{f}s^{2}/n\big{)}-\big{(}\
u\gamma_{k}^{2}-\rho s^{2} \big{)}M-2\rho_{f}sMs^{2}}{M(\lambda+2\mu)};\] \[d_{2}=\frac{\big{(}\rho s^{2}-\
u\gamma_{k}^{2}\big{)}(\rho_{f}s ^{2}/k_{f}+\rho_{f}s^{2}/n)-\rho_{f}^{2}s^{4}}{M(\lambda+2\mu)};\]  
In Equation (31), \(\kappa_{1}\), \(\kappa_{2}\) are known coefficients with expressions as follows:  
\[\kappa_{1}=\frac{a_{2}b_{1}-a_{1}b_{1}}{a_{2}b_{3}-a_{3}b_{2}};\ \kappa_{2}=\frac{a_{2}b_{1}-a_{1}b_{2}}{a_{3}b_{2}-a_{2}b_{3}};\] \[a_{1}=\beta_{1}K_{2}(\beta_{1}r_{0});\ a_{2}=\beta_{2}K_{2}( \beta_{2}r_{0});\ a_{3}=-\beta_{3}K_{2}(\beta_{3}r_{0});\] \[b_{1}=-\varepsilon_{1}\beta_{1}[K_{0}(\beta_{11}r_{1})+K_{2}( \beta_{11}r_{1})]/2;b_{2}=-\varepsilon_{2}\beta_{2}[K_{0}(\beta_{2}r_{0})+K_{ 2}(\beta_{2}r_{0})]/2;\ b_{3}=\varepsilon_{3}K_{1}(\beta_{3}r_{0})/r_{0};\]  
In Equations (36) and (37), the expressions of \(C_{1}\), \(C_{2}\), \(Q_{1}\), and \(Q_{2}\) are as follows:  
\[C_{1}=-\rho_{p}s^{2}\frac{E_{p}+C_{p}}{E_{p}C_{p}};\ C_{2}=\rho_{ p}s^{2}\bigg{(}\frac{\rho_{p}s^{2}}{E_{p}C_{p}}+\frac{S}{E_{p}I_{p}}\bigg{)};\] \[\chi_{1}=\sqrt{\frac{c_{1}+\sqrt{c_{1}^{2}-4C_{2}}}{2}};\ \chi_{2}=\sqrt{\frac{-C_{1}+\sqrt{c_{1}^{2}-4C_{2}}}{2}};Q_{1}= \widetilde{q}\bigg{[}\frac{\gamma_{k}^{2}}{C_{p}s^{2}}-\frac{1}{E_{p}I_{p}} \bigg{(}\frac{\rho_{p}I_{p}s^{2}}{C_{p}s^{2}}+1\bigg{)}\bigg{]}\bigg{/}\Lambda; \ Q_{2}=\widetilde{q}\gamma_{k}/\big{(}E_{p}I_{p}\Lambda\big{)};\]  
In Equation (38), \(\eta_{1}\)-\(\eta_{4}\) arenkon coefficients, the expressions of \(C_{1}\), \(C_{2}\), \(Q_{1}\), and \(Q_{2}\) are as follows:  
\[\eta_{1}=\frac{\gamma_{k}\cos(\chi_{1}H)\text{sh}(\gamma_{k}H)}{ \gamma_{k}^{2}+\chi_{1}^{2}};\ \eta_{2}=\frac{\gamma_{k}\sin(\chi_{1}H)\text{sh}(\gamma_{k}H)+\chi_{1}}{\gamma_{ k}^{2}+\chi_{1}^{2}};\] \[\eta_{3}=\frac{\gamma_{k}\text{ch}(\chi_{2}H)\text{sh}(\gamma_{k}H )}{\gamma_{k}^{2}-\chi_{2}^{2}};\ \eta_{4}=\frac{\gamma_{k}\text{sh}(\chi_{2}H)\text{sh}(\gamma_{k}H)+\chi_{2}}{ \gamma_{k}^{2}-\chi_{2}^{2}}\]
## 1 Introduction
In scenarios like smart cities, smart agriculture, smart grids, smart environmental monitoring, smart homes and healthcare, and smart industry, many applications today heavily depend on sensor data for effective analysis and informed, intelligent decision-making. Therefore, to ensure the credibility of the sensed data, implementing sensor fault analysis approaches is crucial in real-world applications. The remainder of this paper is organized as follows. A brief summary of recent research focused on sensor fault analysis using an RNN and similar models is presented in Section 2. The proposed methodology is described extensively in Section 3. Experimental results and discussions are presented in Section 4, and Section 5 concludes the paper.