esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Use of Artificial Intelligence in Databases Instruction  
The analysis of the selected studies reveals that seven out of the twenty-four studies specifically investigate the use of generative AI in database education [14; 23; 26; 27; 28; 29; 30]. The emergence of AI, especially large language models (LLMs) such as ChatGPT, is changing teaching and learning paradigms, especially in online education [26; 27; 28; 30]. AI tools are being integrated into database education to enhance the learning experience and provide personalized support. These tools offer benefits such as personalized learning, increased student engagement, and reduced administrative burden for educators [23; 29; 30]. For example, AI-powered chatbots, such as MoodleBot [27] and ChatbotSQL [23], can assist students by providing 24/7 support, answering questions, and generating exercises. ChatGPT can help generate SQL queries, assess student work, and provide feedback on student solutions. AI can also support self-paced learning by providing immediate feedback and explanations [14; 23; 26; 27]. In addition, instructors can use AI to create course materials and design assignments that promote deeper understanding and problem-solving skills [29].  
However, the integration of AI in database instruction also poses challenges. Concerns include the accuracy and consistency of AI responses [28], the potential for bias and hallucinations [27], and the risk of over-reliance on AI [30], which may hinder critical thinking [29]. There are also challenges regarding data privacy, security, and the potential for cheating and misuse of AI tools [26, 30]. The effectiveness of AI responses can be sensitive to prompting, requiring careful design of questions and prompts. Moreover, students have reported issues with the accuracy of AI feedback, with correct answers being marked wrong and AI struggling with complex queries [28].  
To address these challenges, ongoing research and refinement of AI tools are necessary. This includes improving the accuracy and contextual understanding of AI, implementing automated fact-checking mechanisms, and incorporating interactive features and human support to enhance effectiveness [27, 28]. Educators are encouraged to design assignments that balance AI assistance with the development of critical thinking and problem-solving skills, and to train students in prompt engineering and ethical considerations in AI use [29]. Future research could explore broader applications of AI in various educational contexts and focus on creating more interactive and personalized learning experiences, such as through virtual tutor systems [30]. These systems should be designed to incorporate features such as implicit query execution, visual step-throughs, data personalization, pop quizzes, and learning outcomes reports to provide comprehensive support and enhance mastery of database concepts [14, 30].
# Structure and Photoelectrical Properties of Natural Photoactive Dyes for Solar Cells
## 3 Results and Discussion
### Electrochemical Properties  
The thermodynamic feasibility of electron injection from the excited dye molecule to the TiO\({}_{2}\) conduction band and dye regeneration utilizing electrolyte can be investigated through the research on the electrochemical properties of the dyes [64; 65]. Herein, the electrochemical properties of the six kinds of natural dyes were studied via cyclic voltammetry measurement in the ethanol solvent, adopting 0.1 M KNO\({}_{3}\) as the supporting electrolyte. The measured cyclic voltammetry curves of the six dyes are exhibited in Figure 6, and the onset oxidation potentials of the dyes obtained from the curves are listed in Table 4. As listed in Table 4, the onset oxidation potentials of the six dyes are 0.34 V, 0.19 V, \(-\)0.07 V, 0.07 V, 0.11 V and 0.10 V for D1, D2, D3, D4, D5 and D6, respectively. From the previous report [66], it can be known that the HOMO energy can be obtained by the onset oxidation potential of dye, when an Ag/AgCl electrode is used as the reference electrode; the HOMO energy can be calculated via the following equation: HOMO \(=-\mathrm{e}(\mathrm{E}_{\mathrm{OX}}+4.40)(\mathrm{eV})\), where \(\mathrm{E}_{\mathrm{OX}}\) stands for the onset oxidation potential of dye. The calculated HOMO energies of the six dyes are listed in Table 4, which shows that the HOMO energies of the six dyes are \(-4.74\) eV, \(-4.59\) eV, \(-4.33\) eV, \(-4.47\) eV, \(-4.51\) eV and \(-4.50\) eV for D1, D2, D3, D4, D5 and D6, respectively. In terms of the work of [PERSON] et al. [67], the dye with strong donating ability will exhibit the more negative HOMO level. Therefore, it can be concluded from the obtained HOMO energies of these dyes that the electron donating ability of these dyes in the order of D1 \(>\) D2 \(>\) D5 \(>\) D6 \(>\) D4 \(>\) D3. Their LUMOs are higher than that are higher than semiconductor band (\(-4.0\) eV), meaning possible occurrence of electron injection for studied systems. Furthermore, the lowest HOMO can enhance the ability of dye regeneration because the larger driving force of dye regeneration between dye and electrolytic, which is contributed to the \(J_{\mathrm{SC}}\).  
Figure 6: Cyclic voltammograms of the six natural dyes. (**a**) dyes D1–D3; (**b**) dyes D4–D6.
###### Abstract  
It is important to determine the soil-water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.  
large-scale landslide model experiment; soil-water characteristic curve; Bayesian updating; Markov chain Monte Carlo; artificial neural networks +
Footnote †: journal: Applied Sciences
###### Abstract  
A novel three-dimensional (3D) coupled train-track-soil interaction model is developed based on the multi-body simulation (MBS) principle and finite element modeling (FEM) theory using LS-DYNA. The novel model is capable of determining the highspeed effects of trains on track and foundation. The soils in this model are treated as saturated media. The wheel-rail dynamic interactions under the track irregularity are developed based on the Hertz contact theory. This model was validated by comparing its numerical results with experimental results obtained from field measurements and a good agreement was established. The one-layered saturated soil model is firstly developed to investigate the vibration responses of pore water pressures, effective and total stresses, and displacements of soils under different train speeds and soil moduli. The multi-layered soils with and without piles are then developed to highlight the influences of multi-layered soils and piles on the ground vibration responses. The effects of water on the train-track dynamic interactions are also presented. The original insight from this study provides a new and better understanding into saturated ground vibration responses in high-speed railway systems using slab tracks in practice. This insight will help track engineers to inspect, maintain, and improve soil conditions effectively, resulting in a seamless railway operation.  
s _article_2019, 9, 4991; doi:10.3390/appr9234991
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Enhancement in CoAP Security
#### 4.3.1 Enhanced DTLS Protocol  
The traditional DTLS initiates the process of authentication and authorization between a client and a server which is not secure as it is vulnerable to hacking attacks. An attacker acting as a client may send numerous ClientHello messages and hack the server in traditional DTLS, giving him access to private and sensitive data at server. The scheme proposed in [42], based on CoAP, presents an enhanced version of DTLS for end-to-end security for IoT in healthcare. The enhanced DTLS employs use of smart gateways for authentication and authorization, unlike traditional DTLS. After the process of authentication and authorization is complete, the gateway connects the client to the server using a session update. In the proposed approach, after the connection is established, the communication is performed between client and server via a smart gateway. With the help Advanced Encryption Standard-Counter with Cipher Block Chaining-Message Authentication Code (AESCCM) encryption and decryption algorithm, the data is securely transmitted between client and server. In the case when the user (client) may move out of the range of one smart gateway (say smart gateway-1), in order to maintain continuous communication, it uses neighbor solicitation and neighbor advertisement to identify another smart gateway (smart gateway-2) and shares all the necessary security information with it. Elliptic Curve Digital Signature Algorithm (ECDSA) is used to perform mutual authentication between smart gateway-1 and smart gateway-2. Afterwards, to authorize the end user to communicate with smart gateway-2, DTLS session update is carried out and continuous communication between client and server is realized securely.
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
## 4 Simulation Results  
In order to validate the proposed SST-based shipboard electric system, detailed simulations have been conducted for three different scenarios using MATLAB/Simulink platform. Table 3 reveals the design parameters of each component of the shipboard electric system. The following subsections present the three simulation scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Parameter** & **Value** & **Category** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{**GS-VSR**} & Rated power & 2 MVA & & Rated power & 1.5 MW \\ \cline{2-5}  & Line voltage & 6.6 kV (rms) & & Rated voltage & 6.6 kV \\ \cline{2-5}  & Frequency & 60 Hz & & Rated frequency & 50 Hz \\ \cline{2-5}  & Interface inductor & 4.44 mH & & & R\({}_{8}\) = 0.654 \(\Omega\) \\ \cline{2-5}  & Switching frequency & 20 kHz & & R\({}_{7}\) = 0.521 \(\Omega\) \\ \cline{2-5}  & HVDC link voltage & 10 kV & & L\({}_{8}\) = 13.8 mH \\ \cline{2-5}  & HVDC link capacitor & 5593 uH & & & L\({}_{tr}\) = 12.7 mH \\ \hline \multirow{6}{*}{**DAB**} & Rated power & 500 kVA & & L\({}_{m}\) = 656 mH \\ \cline{2-5}  & Voltage ratio & 10,000 V / 650 V & & & \\ \cline{2-5}  & Current ratio & & & \\ \cline{2-5}  & (prim/sec) & 50 A/769 A & & & \\ \cline{2-5}  & Switching frequency & 20 kHz & & & \\ \cline{2-5}  & DAB leakage & 1.25 mH & & & \\ \cline{2-5}  & inductance & & & & \\ \cline{2-5}  & HFT magnetizing & 62.8 mH & & & \\ \cline{2-5}  & Capacity & 29 kAhr & & Inertia & 50 \\ \cline{2-5}  & Voltage & 48 V & & Friction coeffcient & 0.01 \\ \cline{2-5}  & & & & Rated speed & 1500 rpm \\ \hline \multirow{2}{*}{**Battery**} & Capacity & 29 kAhr & \multirow{2}{*}{**PV**} & Power & 130 kW \\ \cline{2-5}  & Voltage & 48 V & & Voltage & 145 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall system parameters.  
Figure 7: Power management control algorithm.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 1 Introduction  
Radio- and television-transmitting stations usually deploy multiple equipment to complete multiple transmission tasks every day. Different equipment can be selected for each transmission task, but the effect of the execution on different equipment is different. On-duty personnel usually draw the transmission plan based on experience and control the equipment to complete the task according to the plan [1, 2]. However, this does not guarantee the optimal transmission effect of the prepared plan. In addition, when a temporary task is added and the transmission plan needs to be adjusted, the transmission plan cannot be adjusted according to the transmission effect in time to obtain a new transmission plan with the best effect. To solve the above problems and improve the field of radio and television coverage, it is urgent to complete the dynamic scheduling of transmission tasks (DST) through intelligent algorithms and improve the transmission effect.  
In previous research, we completed the static allocation of transmission tasks, that is, at the same time point, we assigned multiple tasks to multiple equipment with the goal of achieving an optimal comprehensive transmission effect [3]. The static-task-allocation problem can be summarized as a combinatorial optimization problem (COP), which is an NP-Hard problem. When there are fewer tasks and equipment, the enumeration algorithm is used to accurately calculate the global-optimal solution; when the number of tasks and equipment increases, the computing time of the enumeration algorithm increases exponentially. By introducing intelligent algorithms to solve this, the optimal solution can be calculated within an acceptable timeframe. Based on this, to calculate the taskallocation of multiple time periods, the problem can be broken down into independent allocations of multiple time periods. However, in practice, once the transmission task starts, the equipment cannot be interrupted and replaced before it ends. The calculation method of independent allocations gives priority to the previous tasks' optimal selection. The optimal allocation of the subsequent mission reduces the possibility of optimization, and the overall view is that the global-optimal solution cannot be obtained. Based on a global comprehensive evaluation, the scheduling problem of multi-period tasks for multiple equipment is thus solved thanks to the DSTT.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 3 RIS Field Trials
### RIS Performance Evaluation Parameters [28]
#### 3.1.3 Coverage  
Coverage is defined as the area where more than X% of users can achieve user throughput R. For example, coverage is often defined as the area where at least 95% of users have a throughput of 1 Mbps or higher.
###### Abstract  
Shear wave velocity prediction is critical for applications in petrophysics, reservoir characterization, and unconventional energy resource development. While empirical formulas and theoretical rock physics models offer solutions, they are often limited by geological complexity, high cost, and computational inefficiency. After the emergence of deep learning methods, a series of new approaches have been provided to tackle these problems. In this study, a novel Inception-attention-BiLSTM hybrid network is proposed to enhance shear wave prediction accuracy and stability by integrating the strengths of three components: Inception for multi-scale feature extraction, attention mechanisms for dynamically highlighting key temporal features, and BiLSTM for capturing long-term dependencies in logging data. The test dataset of this network comes from the Jurassic Badaowan Formation in the Junggar Basin, achieving superior performance compared to standalone Inception and BiLSTM networks. The proposed hybrid network demonstrated MAE and R\({}^{2}\) values of 0.211 and 0.994, respectively, outperforming Inception (MAE 0.671, R\({}^{2}\) 0.981) and BiLSTM (MAE 0.215, R\({}^{2}\) 0.991). These results underscore its robustness in handling complex logging data, providing a more accurate and generalizable framework for Vs prediction while addressing limitations of traditional methods. This work highlights the potential of hybrid deep learning architectures in advancing logging data analysis and reservoir characterization.  
Inception; BiLSTM; attention mechanism; shear wave velocity prediction; hybrid network +
Footnote †: journal: Applied Sciences
## 4 Experiment and Results
### ECA-ATCNet on MI-EEG Classification
#### 4.1.3 Ablation Analysis of Attention  
We performed an ablation analysis on the BCI-2a dataset to evaluate the performance of efficient spatial attention and efficient spectral attention in the Efficient Channel Attention Convolution (ECA-Conv) module. The effects of adding the corresponding attention layers in ECA-ATCNet are shown in Table 1. The attention layers were added before the training and testing procedures. The results show that efficient spatial attention improved the overall accuracy of the model by 2.02%, while the individual efficient spectral attention decreased the accuracy by 0.95%. However, the parallel collaboration of efficient spatial attention and efficient spectral attention increases the overall accuracy by 2.51%. Notably, the combination of efficient spatial attention and efficient spectral attention generates a synergistic effect, amplifying the advantages of each module and significantly improving the overall accuracy of the model. This is because Efficient Channel Attention (ECA) helps highlight valuable features in the spectral and spatial dimensions of the MI-EEG data.
## 3 Results  
### Analysis of WUE in Anhui Province by Time Scale  
#### 3.1.1 Characteristics of WUE Changes in Anhui Province on Annual, Quarterly, and Monthly Scales  
The analysis indicated significant differences in the yearly changes in the average WUE for Anhui Province over the period 2001 to 2022 (Figure 3). The lowest average WUE (1.367316 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)) was observed in 2003 and the highest (1.648538 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)) in 2014. The association between WUE and ET or the GPP showed increasing trends, with the GPP showing the fastest growth rate, indicating its importance in the growth of WUE.  
The seasonal statistics are shown in Figure 4. The GPP increased through all four seasons, with the fastest growth rate occurring in winter. Winter vegetation included evergreen vegetation (e.g., evergreen broad-leaved forests), which still performed photosynthesis during winter. With improvements in climatic conditions, the GPP of these plants may increase and contribute to the overall winter GPP. In addition to the declining trend of ET in autumn, growth rates increased in other seasons, being the fastest in summer. Summer presented vegetation growth peaks, with high temperatures, strong solar radiation, and vigorous vegetation transpiration. Soil evaporation increased the ET demand. The variation trend in WUE in different seasons was not clear.  
Figure 5 shows both the changes in WUE during different months and the interannual variation. Overall, an inverted \"V\"-shaped trend is observed on a monthly basis, with WUE reaching a maximum in August and a minimum in January. The statistics indicate that the maximum WUE has occurred in August over the last 20 years, with the highest overall observed in August 2002; however, the WUE was higher in July than August in 2020. August, usually a period of high precipitation and temperature, provides sufficient  
Figure 4: Average WUE over four seasons in Anhui Province.  
Figure 3: Changing WUE, GPP, and ET trends.  
moisture and suitable temperatures for crops [59]. Simultaneously, many major crops enter the late growth or maturity stage in August, when photosynthetic product accumulation reaches its peak, and the GPP of the crops at this stage is high, which in turn increases the WUE [60]. Furthermore, the photosynthetic efficiency of different crops (e.g., rice and maize) is significantly higher in August because of the different maturity levels, which is a driving force for the overall WUE value in August. In contrast, WUE is generally lower and less distinct during the winter months of January and December, when it is relatively concentrated.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis
### DoU Loss Function Parameter Experiment  
In DoU loss, there exists an alpha hyperparameter to control the proportion of boundary optimization. In order to obtain the optimal parameter values, we conducted an \(\alpha\) coefficient experiment, and the experimental results are shown in Figure 5.  
As shown in the above figure, when the value of \(\alpha\) was small, the penalty effect of the loss function was not good, resulting in lower ODS and OIS. When the value was 0.9, the optimization space given to the model was relatively small, and the effect was not as good that at 0.8. Therefore, this study selected 0.8 as the value of \(\alpha\).