esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## Literature Review
### The Determinants of Adoption: Awareness, Intention to Use, Satisfaction and Demographic Impacts
User satisfaction is another factor being utilized to assess the continual usage of e-government services and the success or failure of e-government projects [31]. The factor of Satisfaction is used in many studies to ascertain how the degree of satisfaction to e-Services will impact citizens' adoption rates. Improved quality of e-government will increase citizens' satisfaction, which, in turn, will increase the acceptance rate [32]. [PERSON] et al. have justified that a higher level of customer satisfaction will increase the rate of e-government adoption [33]. The case study of Romania has proven a positive impact of Satisfaction on e-government adoption based on empirical evidence [34]. From a user-centric perspective, [PERSON] and [PERSON] have argued that user satisfaction does indeed have a decisive influence on large-scale adoption and the use of e-government services by stating the process of citizens' reaction to e-services from the awareness stage to the satisfaction stage [35]. Moreover, some scholars have theorized that satisfaction would affect not only adoption but citizen trust and confidence in government [36; 37; 38]. Based on the literature above, we can conclude that e-government researchers have endeavored to identify determinants of traditional e-government services adoption whereas less attention has been paid to government SNS applications with regard to these factors. Therefore, more theoretical and empirical studies should be undertaking regarding emerging service platforms to the extent that they can offer a broader understanding of e-services.
## 2 Methodology  
In this study, we first determined drought events from precipitation data in the region through the SPI. Focusing on key timescales, a set of model structures with the SPI12 and SPI3 values were created, which effectively represent hydrological and meteorological droughts, respectively. We selected Support Vector Machines (SVM) with Adaptive Neuro Fuzzy Inference System (ANFIS) as the ML and DL algorithms to predict droughts and optimized them with ABC, GWO, GA, and PSO. Root Mean Square Error (RMSE), the correlation coefficient (r), and Nash Sutcliffe Efficiency (NSE) utilized to evaluate the model performance statistically while RMSE-Standard Deviation Ratio (RSR) used to interpret model performance both graphically and statistically. MATLAB software was used for all necessary calculations.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 2 Pull-Out Test
### Test Setup and Instrumentation  
As shown in Figure 4, the pull-out specimens of perfobond shear connectors were loaded to failure by using twin hydraulic lacks. The pull-out force between steel and concrete was applied by pushing up the steel H-beam. As a result, the perfobond shear connector embedded in concrete would be pulled out. The specimens CPT-1 and NPT-1 were tested under monotonic loading, while the other specimens were tested under uniaxial cyclic loading. Two displacement gauges were installed at the front and back of the specimens to measure the separation between steel and concrete. The applied pull-out load and the relative separations were continuously and automatically recorded. Therefore, the failure modes, pull-out resistance, and load-separation behaviors of perfobond shear connectors could be obtained, and the reliability of finite element analysis could be verified by comparison with the pull-out test results.
## 4 Numerical Results and Discussion
From Figure 5, it can be observed that when \(b_{S}=1\), the ABER is inversely proportional to \(b_{J}\). And when \(b_{S}=0.1\) and \(b_{J}=1.0\), the jamming power accounts for the main component of the Rx and, therefore, has a relatively large impact on the ABER. Figure 7 presents a comparison analysis of the effect of different jamming probabilities on the ABER under BPSK and 16-QAM modulation. As shown in Figure 7, under the same jamming probability, the ABER performance of the BPSK modulation is superior to that of the 16-QAM modulation. This is because, although the QAM modulation can effectively increase the transmission rate, it demands a higher channel quality compared to BPSK. Additionally, it is evident that the presence of jamming significantly influences the system's ABER. Additionally, the impact of jamming probability on the ABER varies between the two modulation schemes. When the probability of jamming is identical, a higher SJR has a more significant impact on the performance of 16-QAM compared to BPSK. For example, when SJR = 30 dB and \(\rho=0.5\), the ABER values for 16-QAM and BPSK are \(6.6\times 10^{-7}\) and \(7.5\times 10^{-9}\), respectively, differing by two orders of magnitude. Figure 5: ABER versus SJR (dB) curves for a wireless system with a varying \(g_{S}\) and \(g_{J}\). Figure 6: ABER versus SJR (dB) curves for a wireless communication system with a varying \(b_{S}\) and \(b_{J}\). Figure 8 illustrates an analysis of the influence that the positioning of the legitimate RIS has on the ABER across distinct modulation schemes, specifically 16-QAM and BPSK. Figure 8 analyzes the impact of the position of the legitimate RIS on the ABER under different modulation schemes (16-QAM and BPSK). From Figures 3 and 7, it can be concluded that regardless of whether the system employs QAM modulation or BPSK modulation, the closer the legitimate RIS is to the Tx, the better the system's ABER performance. As observed in Figure 8, the ABER performance of BPSK modulation is superior to that of 16-QAM.
## 2 Materials and Methods
### Data Preprocessing  
#### 2.2.1 Video Editing  
To synchronize data between the GoPro camera system and the optical motion capture system, the video data from both the GoPro and VICON systems were edited with Lossless Cut [42] to begin with the first frame following the LED illumination. Approximately 25 s of video content was used per trial after the video editing was complete.
## 3 Results and Discussion
### T-Peel Strength Tests  
T-peel strength is regarded as the capability of an adhesive to join 2 surfaces together, which can be assessed by measuring the cohesive energy (cohesive in the adhesive bulk) and adhesive strength (adhesive to PVC). The crystallinity of soft segments and molar ratio of soft/hard segments are the main factors affecting both cohesive energy and adhesive strength. The T-peel strength of adhesive bonds of WPUs were determined (Table 1). The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CO\({}_{0\%}\)-WPU** & **CO\({}_{2.50\%}\)-WPU** & **CO\({}_{5.54\%}\)-WPU** & **CO\({}_{7.40\%}\)-WPU** & **CO\({}_{8.65\%}\)-WPU** \\ \hline \(\sigma_{\text{m}}\)(MPa) & 24.1 & 18.7 & 17.8 & 17.0 & 15.3 \\ \(\epsilon_{\text{b}}\) (\%) & 5.0 & 11.0 & 18.9 & 24.1 & 391 \\ \(\sigma_{\text{b}}\) (MPa) & - & 10.8 & 11.5 & 11.3 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical property of WPUs.  
Figure 7: Tensile stress–strain curves of WPUs with different content of CO.  
T-peel strength of CO\({}_{90\%}\)-WPU was very low because of the high crystallinity of the linear structure of PCL-WPU without any CO. As a result, the cohesive failure occurred when the bond with the substrate exceeded the internal strength of the adhesives, thus leading to a poor contact and adhesive strength. In contrast, the adhesive strengths was remarkably enhanced with increasing the CO content, which converted the WPU linear structure to crosslinked structure. The adhesive strength markedly enhanced at the maximum CO content, followed by a slight reduction. It can be seen that the CO\({}_{7.40\%}\)-WPU had the highest adhesion strength, which indicated that better adhesion was achieved when higher cohesion and crosslinked structure were available. Nevertheless, a decrease in adhesion strength was observed with increasing CO content, leading to high tackiness and plasticization, and ultimately lowering the bond strength.  
Figure 9 presents the water contact angle and water absorption of the WPU films as a function of CO content. As observed, the water contact angle of the WPU films increased with the CO content, whereas the water absorption of the WPU films exhibited a decrease. As a result, the films could not be wetted easily. The water absorption of a polymer is an important parameter impacting its practical applications. On enhancing the CO content from 0 to 9.66 wt%, the water absorption was observed to decrease from 23.2 to 13.9%. It indicated that the CO acting as a crosslinker introduced in the PU backbone chain improved the water resistance of the WPU films.  
Figure 8: SEM micrographs of the tensile fracture surfaces of (**a**) CO\({}_{90\%}\)–WPU, (**b**) CO\({}_{2.50\%}\)–WPU, (**c**) CO\({}_{5.04\%}\)–WPU, (**d**) CO\({}_{7.44\%}\)–WPU, and (**e**) CO\({}_{9.66\%}\)–WPU.  
Figure 9: Hydrophilic angle and water absorption of WPU films as a function of TMPM content.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 6 Integration Mechanism for Fault Prediction and Scheduling Optimization
### Implementation Challenges  
While the integration of deep learning and SMT brings numerous benefits, it also presents several challenges that must be addressed to ensure system effectiveness and reliability:* Scalability: As the number of machines and jobs increases, the complexity of the scheduling optimization problem grows exponentially. To maintain real-time performance, efficient algorithms and robust hardware support are essential, such as GPUs for deep learning computations.
* Data Reliability: The accuracy of fault predictions \(P(M_{i},J_{j},t)\) depends on the quality of the real-time machine state data. Noisy, incomplete, or missing data can significantly degrade prediction performance, and consequently scheduling effectiveness. Implementing robust data preprocessing and handling techniques is crucial to mitigating these issues.
* Solver Performance: The SMT solver must efficiently handle large-scale optimization problems to provide timely solutions. In cases where exact solutions are computationally expensive, integrating heuristic or approximation algorithms with the SMT solver may be necessary in order to achieve acceptable performance within required time frames.
## 3 Results and Discussion
### Influence of Different Wave Numbers  
In this part of simulation, the wave number is, respectively, set as 3, 4, 5 and 6 in rear section of 1/4 cylinder. As TWW propagated downstream, the amplitude ratio \(\hat{A}/D\) equals to 0.02 and the velocity ratio \(c/U_{\infty}\) equals to 2.0. The lift and drag coefficient time histories under different wave numbers are shown in Figure 12. The results indicate that the TWW with different wave numbers can suppress the cylinder wake. On the other hand, the mean and fluctuating lift coefficients keep almost unchanged with the increase in wave number. In the meantime, as wave number changes from 3 to 6, there is little change in fluctuating drag coefficient, while the mean drag coefficient decreases to negative values.  
Figure 9: The vorticity contours of TWW cylinder with “Corotating-Clockwise” propagation. (**a**) \(t=2.16\) s. (**b**) \(t=2.31\) s. (**c**) \(t=2.46\) s. (**d**) \(t=2.61\) s. (**e**) \(t=2.97\) s.  
Figure 10: Lift and drag coefficients of TWW cylinder under various wave amplitudes. **(a)**\(\hat{A}/D=0.01\). **(b)**\(\hat{A}/D=0.02\). **(c)**\(\hat{A}/D=0.03\). **(d)**\(\hat{A}/D=0.04\). **(e)**\(\hat{A}/D\)= 0.05.  
Figure 13 shows the characteristic values of lift and drag coefficient under different wave numbers, including the mean and RMS values of lift and drag coefficient. In Figure 13a, with the increase in \(N\), \(\overline{C}_{l}\) decreases slightly from \(-0.019\) to \(-0.039\). \(C_{l}^{\prime}\) almost hardly changes, whose values are between 0 and 0.01. It is observed that the fluctuating  
Figure 11: Characteristic values of lift and drag coefficient under different wave amplitudes. (a) \(C_{l}^{\prime}\) & \(\overline{C}_{l}\). (b) \(C_{d}^{\prime}\) & \(\overline{C}_{d}\).  
lift coefficient has been tremendously suppressed, however, different wave numbers have little impact on fluctuating lift coefficient after activating TWW, in comparison with the standard circular cylinder. Figure 13b shows that \(\overline{C}_{d}\) decreases to negative values and \(C_{d}^{\prime}\) changes from 0.166 to 0.195 with the increase in \(N\). It indicates that the cylinder's reverse thrust increases gradually with the increase of \(N\), and the wave number has some effect on \(\overline{C}_{d}\).
# RNA Modification Related Diseases and Sensing Methods
## 3 RNA Modification and Cancer
The demethylation from m6A reduce the expression of ankyrin repeat, SOCS box containing 2 (ASB2), and RARA. This led to leukemogenesis (Figure 2). The development of therapeutic medicines and the clarification of disease mechanisms have generally resulted from these results [65]. Moreover, in glioma studies, it has been demonstrated that the knockdown of METTL3 or METTL14 changes the m6A enrichment of mRNA and the mRNA expression of target genes such as ADAM Metallopeptidase Domain 19 (ADAM19), which is crucial for glioblastoma stem cells (Figure 2) [66]. Additionally, blocking the m6A demethylase, FTO, slows tumor growth and lengthens survival in mice receiving glioblastoma stem cell transplants [65]. This study showed that m6A is crucial for the self-renewal and carcinogenesis of glioblastoma stem cells, indicating that m6A alteration is a possible therapeutic target for glioblastoma. Other modifications besides the modification of m6A have reportedly been linked to cancer. In 5-azacytidine (5-AZA)-resistant leukemia cell lines and clinically in samples from patients with 5-AZA-resistant myelodysplastic syndrome and acute myeloid leukemia, the cytosine-modifying enzyme DNMT2 has been linked to AML [67]. DNMT2 may, therefore, be useful in the management of leukemia. Additionally, the cytosine acetylase NAT10 has  
Figure 2: **RNA modifications associated with cancer malignancy**. RNA modifications reported to be associated with malignant transformation of each cancer type (Leukemia, Glioblastoma, Breast cancer, Lung cancer, Hepatocellular carcinoma, Pancreatic cancer, and Colorectal cancer) and their structural formulas are shown. The red part of the structural formula indicates the structure added by the modification. been discovered to play a role in the development of numerous cancers and malignancies, such as glioblastoma, HCC, colon cancer, breast cancer, and leukemia. METTL1 is an enzyme that converts guanine into N7-methylguanine (m7G), which is overexpressed in hepatocellular carcinoma and has carcinogenic activity via the PTEN/AKT serine/threonine pathway (Figure 2). High METTL1 expression has been associated with worse prognosis, larger tumor size, higher serum alpha-fetoprotein levels, and tumor vascular penetration in two separate cohorts of 892 patients with hepatocellular carcinoma [68]. These findings suggest that the METTL1/PTEN axis has therapeutic potential for the treatment of HCC and that METTL1 is a useful predictive biomarker [69]. Additionally, METTL1 has been linked to colorectal cancer drug sensitivity, and through regulation, METTL1 overexpression is connected to the hsa-miR-149-3p/S100A4/p53 axis and sensitizes cisplatin-resistant colorectal cancer cells (Figure 2) [69]. As described above, RNA modifications are altered by various modification species in different cancer types and are associated with malignant transformation. Therefore, accurate measurement of changes in RNA modifications can be useful in detecting these cancers and may have therapeutic applications.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### How Do Researchers Combine Audio Features with Other Data Sources? (RQ3)
#### 3.4.2 Deep Learning Techniques  
Deep learning (DL) approaches, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers, typically learn representations of audio directly from spectrograms or even raw waveforms. This reduces reliance on handcrafted features but often comes at the cost of increased model opacity, given the layered and non-linear nature of these architectures.  
In educational contexts, CNNs and LSTMs are frequently applied to tasks such as classroom sound classification and speech transcription under noisy conditions. For example, [PERSON] et al. [94] trains both CNN and LSTM architectures on spectrogram inputs to categorize classroom events, while [PERSON] et al. [36] designs a custom network to handle the interruptions and acoustic challenges typical of early childhood environments. Transformers, particularly BERT, are used in studies involving transcribed classroom discourse. For example, [PERSON] and [PERSON] [64] investigates the impact of removing specific word categories on classification accuracy, while [PERSON] et al. [95] employs BERT to differentiate between funneling and focusing questions.  
More complex architectures are also explored. [PERSON] et al. [46], for instance, combines CNNs, LSTMs, and transformers in an ensemble to predict emotional states from classroom audio, reflecting a growing interest in modeling subtle affective cues. Multimodal integration is another emerging trend, as discussed in Section 3.3: [PERSON] et al. [9] fuses audio and video streams to model classroom climate, showcasing how DL architectures naturally accommodate multimodal inputs.  
Despite these technical advances, interpretability remains a major limitation. Most DL studies prioritize performance metrics (e.g., accuracy and F1-score) over explainability. When attempts are made, they are typically superficial, such as inspecting attention weights in transformers or correlating predictions with input features. Rigorous explainability frameworks, such as SHAP or integrated gradients, are rarely used.  
This lack of transparency is particularly problematic in educational settings, where stakeholders must understand and trust system outputs to make informed decisions. Without interpretable outputs, DL models risk becoming \"black boxes\" that may perform well technically but lack pedagogical legitimacy.
## 2 Material and Method
### Characterization of Modified Asphalt Samples  
The characterization of asphalt-rubber mixture samples included penetration testing (ASTM D5), softening point (ASTM D36), weight loss (ASTM D6/D6M), penetration after TFOT (ASTM D5), and Marshall stability (ASTM D6927). Marshall stability testing uses the aggregate composition shown in Table 2 with the optimum bitumen content, as seen in Table 4. The modified asphalt characterization test results are shown in Table 4 and the Marshall stability test shown in Table 5. Furthermore, the dynamic shear rheometer (DSR) test describes asphalt's viscoelastic behavior after short-term aging using RTFOT. Evaluation of modified asphalt performance after the addition of crepe rubber and TMQ carried out DSR testing for asphalt samples of pen grade 60/70, asphalt with optimum crepe rubber without TMQ, and asphalt with the addition of crepe rubber and TMQ at optimum conditions. DSR testing is carried out on these samples before and after RTFOT (short term oven aging). The Fourier-transform infrared spectroscopy (FTIR) test was carried out to determine modified asphalt performance after long-term aging.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{\begin{tabular}{c} **Optimum Asphalt** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**Penetration (dmm)**} & \multirow{2}{*}{**Softening Point (\({}^{\circ}\)C)**} & \multirow{2}{*}{\begin{tabular}{c} **Weight of Loss by** \\ **RTOT (\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Penetration after** \\ **RTOT (\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Decreased** \\ **RTOT (\%)** \\ \end{tabular} } \\ \hline Pen grade 60/70 & 6.00 & 70.20 & 48.00 & 0.365 & 64.70 & 7.83 \\ A1 & 5.45 & 61.30 & 58.70 & 0.066 & 50.80 & 17.13 \\ A2 & 5.45 & 63.10 & 57.15 & 0.096 & 53.50 & 15.21 \\ A3 & 5.45 & 71.60 & 52.65 & 0.096 & 56.40 & 21.23 \\ B1 & 5.50 & 64.70 & 55.80 & 0.063 & 45.90 & 29.06 \\ B2 & 5.50 & 68.70 & 55.45 & 0.059 & 59.60 & 13.25 \\ B3 & 5.50 & 74.40 & 53.65 & 0.063 & 53.00 & 28.76 \\ C1 & 5.35 & 66.20 & 55.80 & 0.125 & 54.20 & 18.13 \\ C2 & 5.35 & 69.70 & 55.60 & 0.065 & 62.50 & 10.33 \\ C3 & 5.35 & 78.30 & 47.65 & 0.125 & 57.60 & 26.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of modified asphalt characteristics.
# Model Updating of Bridges Using Measured Influence Lines
## 3 Results
### Updated FE Model
#### 3.2.4 Strain IL-Based EDMF  
Figure 22 shows the strain IL-based EDMF. The bottom thresholds for strains were set to 0, representing the perfect match between the measured and modelled strain IL. The upper thresholds were set iteratively, such that three candidates were obtained. All candidates had RMSE E_MG1 and RMSE E_MG2 values lower than 96% for all FE models.  
The final candidates produced by the strain-based EDMF had \(\alpha_{\text{MG1}}\) and \(\alpha_{\text{MG2}}\) values in the narrow range of [1.30, 1.40] and 1.50, respectively. They are both comparable with the results from the Ravbarkomanda viaduct study [34], where the stiffness of the main girders increased by up to 50%. The values of \(\alpha_{\text{OTHER}}\) were in the range [1.20, 1,50].