esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 3 Methods  
It is very important to use a suitable equalization algorithm to improve the system performance (Figure 4). Some algorithms provide very good accuracy in some areas, but cannot be used for equalization. Based on the literature, algorithms based on LMS and RLS provide very good accuracy for equalization; therefore, the LMS, NLMS, and QR-RLS algorithms were chosen for this article. The rest of this section deals with the basic mathematical description of the chosen methods. For a more detailed description, refer to the cited articles.  
Figure 4: Classification of adaptive algorithms. LMS, least mean squares.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 3 Experimental Setup
### Evaluation Metrics  
To quantitatively assess performance, several standard evaluation metrics were used for semantic segmentation, including the following:  
(1) Intersection over Union (_IoU_) [37]: Measures the overlap between the predicted crack regions and the ground truth annotations. It is calculated as the ratio of the intersection area to the union area between the predicted segmentation and the ground truth.  
\[IoU=\frac{\text{Intersection}}{\text{Union}}=\frac{|A\cap B|}{|A\cup B|} \tag{17}\]  
where \(A\) is the predicted crack area, and B is the actual labeled crack area.  
(2) F1 Score [38]: The harmonic means of precision and recall, which provides a balance between false positives and false negatives. It is particularly useful when the dataset is imbalanced.  
\[F1\ Score=2.\frac{Precision\cdot Recall}{Precision+Recall} \tag{18}\]  
where precision represents the proportion of pixels correctly predicted as cracks to all pixels predicted as cracks: \(Precision=\frac{TP}{TP+FP}\); recall represents the proportion of pixels correctly predicted as cracks to all actual crack pixels: \(Recall=\frac{TP}{TP+FN}\); TP (True Positive) is the true example of correctly predicting the number of pixels that are cracks, FP (false positive) is a false positive example that incorrectly predicts the number of pixels with cracks, FN (false negative) is a false negative example, indicating the number of pixels that were not predicted as cracks.  
(3) Pixel Accuracy [39]: The percentage of correctly classified pixels in the entire image.  
\[Pixel\ Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{19}\]  
where TN (true negative) is a true negative example, correctly predicting the number of pixels that are not cracks. TP + TN is the total number of correctly classified pixels.  
(4) In addition to these standard metrics, a Crack Detection Accuracy metric was introduced, which specifically measures the percentage of correctly detected crack pixels relative to the total number of crack pixels in the ground truth.  
\[Crack\ Detection\ Accuracy=\frac{TP}{TP+FN} \tag{20}\]  
This evaluated metric relevance through a structural risk assessment with civil engineers. Crack Detection Accuracy showed 92% correlation with crack severity scores (vs. 65%  
Figure 6: Chasing P200 Pro and collecting scenario.  
for IoU), confirming its operational value. When Crack Detection Accuracy > 78%, crack width measurement errors remained <0.15 mm--critical for fracture mechanics analysis.
## 3 Overview of Study Site
### Monitoring Data Analysis  
We took the average monthly displacement as the mother sequence, and the monthly rainfall and average monthly reservoir water level as the subsequence, to analyze the relative relationships between them using the aforementioned grey correlation analysis theory. The results are shown in Table 1.  
Figure 4: Relationship of the displacement rate of the landslide and its main influencing factors.  
The table shows that the correlation between displacement and rainfall is much bigger than that between displacement and reservoir water level during each year, and the average correlation of the former from 1998 to 2005 is 0.894, whereas the average correlation of the latter is only 0.338.  
From these correlations and the field survey, it is concluded that the deformation of the large-scale landslide is influenced by many factors. In addition, rain is always a key influencing factor, and the changes of the reservoir water level have obvious influences on the landslide only in the early impounding stage.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 3 Materials and Methods
### Dataset  
To allow the bench-marking of model performance, the Combined Aerial and Indoor Small Wind Turbine Blade dataset (CAI-SWTB) was leveraged in [9]. In [9], due to the lack of available faulty images, we created intentional synthetic damages to the blades of a small-scale turbine simulating the most common faults including leading edge erosion, cracks, and holes in operational commercial wind turbines. The selected fault types focus on the common damage types of commercial wind turbines. It is worth noting that other damages may exist in commercial turbine blades such as fiber splitting, buckling due to compressive load, gel-coat debonding, among other less common manufacturing errors [6]. However, such damages were not studied in our work. For the creation of this dataset, a Primus AirMax turbine (Ryse Energy, Houston, TX, USA), shown in Figure 4, was studied. This turbine provides output power within the range of 0-0.425 kW. The CAI-SWTB dataset was created via a handheld cell phone camera along with aerial footage from a DJI Mini 3 Pro drone (DJI Technology, Nanshan, Shenzhen, China). We investigated the binary classification of healthy versus faulty images of the blades in our previous work [9].  
Building upon our previous work, the object detection problem is investigated to provide additional positional data along with distinct fault instance classification. To allow for object localization, the dataset was hand-labeled for each of the three fault types, holes, erosion, and cracks. A sample from the training set of this dataset can be seen in Figure 5. Here, Figure 5a illustrates samples with the synthetic erosion and hole damage type simulating leading edge erosion and impact damage typical of commercial grade turbines. Figure 5b contains images with synthetic crack damage which simulates common delamination and stress fractures that present in turbines over time. Finally, Figure 5c illustrates the healthy examples from the dataset which contain no faults.  
The final dataset consists of a total of 6000 RGB images with a size of \(300\times 300\) with a training, validation, and testing split of 70%, 10%, and 20%, respectively. This is further visualized in Table 1. Additionally, the distribution of annotated faults provides details on the dataset and is shown in Table 2. Here, it can be seen the crack frequency is higher than the other classes but maintains a similar distribution between splits.
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 9 Conclusions  
ChatGPT has the potential to improve education by making it more personalized, accessible, and interactive for teachers and students alike. However, issues of privacy, data security, and ethical use must be addressed. This study emphasizes ChatGPT's function in assisting with theory-based queries, developing ideas for application-based questions, and incorporating technology into teaching. Teachers can also use it during workshops to review and evaluate AI-generated responses. Despite its benefits, ChatGPT has drawbacks, including the danger of academic dishonesty and reliance, which can impair critical thinking. It can be difficult for educators to distinguish between learners who actively engage with the curriculum and those who rely on automation. Notably, ChatGPT paraphrases comments in ways that bypass similarity detection technologies, prompting upgrades to plagiarism detection tools. Future research should focus on enhancing AI's ability to perform non-textual jobs, creating more powerful AI content identification tools, and resolving ethical concerns about academic integrity and data protection. Additionally, policies and teacher training programs are required to enable responsible AI incorporation. Collaboration between educators, legislators, and technology providers is essential for defining guidelines, improving professional development, and closing the digital gap. Finally, this review serves as a foundational resource for stakeholders navigating AI's role in education, advocating for proactive governance and further empirical research. Therefore, we provide the following key recommendations for stakeholders: (1) Educators should integrate AI literacy into curricula and use ChatGPT as a supplement, not a substitute. (2) Policymakers should consider developing standardized regulations to ensure equitable and responsible AI use. (3) Researchers are also expected to prioritize empirical studies on AI's long-term pedagogical impact and bias mitigation. By addressing these gaps, stakeholders can harness ChatGPT's potential while safeguarding educational integrity and inclusivity.  
The work presented here was carried out in collaboration among all authors. Conceptualization, Y.Y.M. and W.A.; formal analysis, Y.Y.M. and Y.B; methodology, A.M. and Y.Y.M.; supervision, M.A.; visualization, Y.Y.M. and W.A.; writing--original draft, M.A.; writing--review and editing, Y.B. and A.M., Fund Acquisition, W.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset can be available upon request.  
The authors declare no conflict of interest.
## 1 Introduction
Major contributions are summarized as follows:  
1. We propose a mobile video caching framework in vehicular networks, where vehicle users can access the high-resolution video from the MECS deployed on RSU directly without occupying the backhaul network to release the bandwidth resource and reduce the data transmission delay. 2. We designed an average download percentage (ADP) weighted mobile video mean opinion score (MV-MOS) model for vehicle users. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. 3. Based on a deep Q-learning network (DQN), we propose an intelligent caching algorithm to solve the problem while achieving a maximum ADP weighted MV-MOS. Furthermore, a P3 DVQC caching scheme in vehicular networks was proposed to improve the performance of algorithm by integrating the prioritized experience replay, dueling, and double DQN technologies. The remainder of this paper is organized as follows. The related work is presented in Section 2. We describe the system model in Section 3. In Section 4, we formulate intelligent caching for mobile video problems. Section 5 gives the solution of the proposed method. Numerical results and discussion are presented in Section 6. Finally, a conclusion is drawn in Section 7.
# EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs
## 3 Results and Discussion
### Experimentation of Stage 2
#### 3.6.2 ANOVA  
The \(p\) value is considered as the most important indicator of significance evaluation. The \(p\) value \(<0.05\) indicates that the factor significantly contributes to the results; otherwise, the factor's influence is insignificant. ANOVA for each response was conducted and the results in consolidated form are presented in Table 8. As it can be seen that for the case of machining time, the \(p\) value against relief angle and land thickness are greater than the threshold value of 0.05. Hence, it can be stated that the effect of relief angle (by varying relief from 5\({}^{\circ}\) to 10\({}^{\circ}\)) and land thickness (by varying land from 2 mm to 4 mm) is statistically non-significant on the machining time. Likewise, \(p\) values of both the relief angle and land thickness is found to be higher than qualifying value of 0.05 for all the remaining three responses. Thus, the effect of relief angle (within 5-10\({}^{\circ}\)) and land thickness (within 2-4 mm) is statistically non-significant. It means that whether the response values are different in different cases of relief angles and land thickness but the difference in values is statistically insignificant and do not seriously affect the process performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Design** & **Design** & **Relief** & **Land** & **Design** & **Machining** & **Taper Angle** & **Radial** & **Surface** \\
**Type** & **Name** & **Angle** & **Thickness** & **Symbol** & **Time** & **(deg)** & **Overcut** & **(\(\mu\)m)** \\ \hline
**Dc** & Conventional & 0 & 0 & \(\mathrm{D_{CR0}}\) & 22.25 & 2.34 & 464.50 & 13.70 \\ \hline \multirow{4}{*}{\(\mathbf{D_{RL}}\)} & \multirow{4}{*}{Relief angle with land} & \multirow{4}{*}{5} & 2 & \(\mathrm{D_{RL5-2}}\) & 16.28 & 1.85 & 190.50 & 10.00 \\  & & & 3 & \(\mathrm{D_{RL5-3}}\) & 19.02 & 2.27 & 227.70 & 10.50 \\ \cline{1-1}  & & & 4 & \(\mathrm{D_{RL5-4}}\) & 19.18 & 2.48 & 246.20 & 10.20 \\ \cline{1-1} \cline{2-7}  & & & 2 & \(\mathrm{D_{RL10-2}}\) & 13.93 & 2.09 & 199.50 & 8.65 \\ \cline{1-1}  & & & 3 & \(\mathrm{D_{RL10-3}}\) & 14.07 & 2.49 & 227.25 & 10.45 \\ \cline{1-1}  & & & 4 & \(\mathrm{D_{RL10-4}}\) & 16.46 & 3.09 & 293.25 & 9.95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of stage-2 experimentation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Response** & \multicolumn{5}{c}{**Statistical Measures**} \\ \cline{2-6}  & **Mean** & **SE Mean** & **StDev** & **Minimum** & **Maximum** \\ \hline Machining time; MT (min) & 17.14 & 1.13 & 2.78 & 13.93 & 20.37 \\ Hole taper angle; \(\omega\) (deg) & 2.382 & 0.173 & 0.423 & 1.854 & 3.091 \\ Radial overcut; ROC (\(\mu\)m) & 230.8 & 15.0 & 36.8 & 190.5 & 293.3 \\ \hline Surface roughness; SR (\(\mu\)m) & 9.958 & 0.277 & 0.679 & 8.650 & 10.500 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistics of machining responses.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Use Case
#### 3.1.3 Obstacle Detection  
This test analyzed the RPSC's ability to detect and respond to obstacles using the HC-SR04 ultrasonic sensor. Markers were placed on the ground at known distances from 20 to 200 cm, and cardboard prisms were used as obstacles. Their positions varied systematically while the RPSC remained stationary.
###### Abstract  
Migration from a monolithic architecture to a microservice architecture is a complex challenge, which consists of issues such as microservices identification, code decomposition, communication between microservices, independent deployment, etc. One of the key issues is data storage adaptation to a microservice architecture. A monolithic architecture interacts with a single database, while in microservice architecture, data storage is decentralized, each microservice works independently and has its own private data storage. A viable option to fulfil different microservice persistence requirements is polyglot persistence, which is data storage technology selected according to the characteristics of each microservice need. This research aims to propose and evaluate the approach of monolith database migration into multi-model polyglot persistence based on microservice architecture. The novelty and relevance of the proposed approach are double, that is, it provides a general approach of how to conduct database migration from monolith architecture into a microservice architecture and allows the data model to be transformed into multi-model polyglot persistence. Migration from a mainframe monolith database to a multi-model polyglot persistence was performed as a proof-of-concept for the proposed migration approach. Quality attributes defined in the ISO/IEC 25012:2008 standard were used to evaluate and compare the data quality of the microservice with the multi-model polyglot persistence and the existing monolith mainframe database. Results of the research showed that the proposed approach can be used to conduct data storage migration from a monolith to microservice architecture and improve the quality of the consistency, understandability, availability, and portability attributes. Moreover, we expect that our results could inspire researchers and practitioners toward further work aimed to improve and automate the proposed approach.  
Keywords:software migration; microservice architecture; polyglot persistence; SQL; NoSQL; cloud computing; software engineering +
Footnote †: journal: Applied
## 4 Conclusions  
In the current research, 6061-T6 aluminum alloy workpieces were successfully butt welded at 3, 4, and 5 Hz by using the DP-GMAW. The microstructure evolution, mechanical, and corrosion properties of the weld joints were investigated. It is found that the thermal frequency of 5 Hz should be preferred when AA6061-T6 aluminum alloy is welded by DP-GMAW because of excellent microstructures, good mechanical and corrosion properties. The exhaustive main conclusions obtained from this investigation are as follows:  
Figure 14: Potentiodynamic polarization curves of welded joint produced by different thermal frequency.  
Figure 13: Scanning electron microscope (SEM) fractographs of impact specimens.  
1. The welding processes using 3, 4, and 5 Hz are stable without spatter, and weld seams exhibits aesthetic ripples. The distance of adjacent ripples becomes wider, when the thermal frequency is lower.
2. The grain size and porosity of the fusion zone decrease with increasing thermal frequency, because the thermal frequency has an evident effect of improving the fluidity of the weld pool and inhibiting the growth of grains.
3. The Vickers microhardness of welded joint is higher, when the thermal frequency is higher.
4. When the thermal frequency changes from 3 Hz to 5 Hz, there is no significant difference in the tensile and impact result. The tensile and impact properties of welded joint are weaker than that of the base metal.
5. The samples welded at 3, 4, and 5 Hz can be bent to 180\({}^{\circ}\) and no separation, tearing, or crack is observed.
6. When the thermal frequency increases, the corrosion properties of welded joint also increase, due to the smaller grain size and less porosity in the fusion zone.  
This work was supported by the High-level Leading Talent Introduction Program of GDAS (No. 2016 GDASRC-0106), Industry-university- research of Guangdong province and Ministry of Education (No. 2013B090600098) and the National Natural Science Foundation of Guangdong Province (No. 2016A0303117).  
In this paper, [PERSON] gave the guidance and advice about how to analyze the result of the researches. [PERSON] did the main job of this paper. [PERSON] and [PERSON] provided a lot of help for the experiment.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Meteorological and CO\({}_{2}\) and H\({}_{2}\)O Flux Data Sets  
The archives of eddy covariance flux measurements and meteorological data integrated into global and regional networks [40], providing access to historical data on net radiation, CO\({}_{2}\) fluxes, and latent and sensible heat fluxes, were used to analyze the flux response to extreme weather conditions. The similar standardized equipment and data processing software of the FLUXNET stations allow comparable time-series data on turbulent atmospheric fluxes to be produced. To analyze the response of the daily NEE and LE fluxes to extreme weather events in temperate and boreal forest communities, we used several datasets, including the global FLUXNET [40], the regional AmeriFlux [43], the European Fluxes Database Cluster [44], and AsiaFlux [45]. Unstable power supply, adverse weather conditions, and technical problems with equipment may result in numerous gaps in the FLUXNET meteorological and flux data sets, particularly for temperature and precipitation. To obtain continuous time series of meteorological parameters at selected flux tower sites in our study, meteorological reanalysis data (the reanalysis produced by the European Centre for Medium-Range Weather Forecasts, ERA5 [46]) were used. Temperature at 2 m above ground with a temporal resolution of 3 h and precipitation amount with an hourly resolution were used. The spatial resolution of the reanalysis data sets was 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).  
A correlation analysis showed that there was strong agreement between the reanalysis and the gap-free FLUXNET data sets for air temperatures. The R-squared values for the temperature data sets exceeded 0.98 at 13 stations and ranged from 0.95 to 0.97 at 8 stations at \(p<0.05\) (Supplementary Materials, Figure S1). Considering the strong compatibility between the reanalysis and FLUXNET air temperature data, we used only the reanalysis data in our study to ensure the consistency of the temperature data at different locations. To determine the air temperature values at the FLUXNET station locations, the average temperature values between 4 adjacent grid points were used. The agreement between the precipitation rates obtained from the reanalysis and the monitoring stations was less favorable, with R-squared values ranging only from 0.30 to 0.79. Thus, for 21 flux stations with precipitation time series without gaps, we used in situ data, and only for 5 stations where precipitation measurements were missing or had numerous gaps, the precipitation time series were filled by the reanalysis product.
## 3 Materials and Methods
### Evaluation Indicators  
Accuracy is a widely used evaluation metric for assessing the performance of machine learning models, particularly in classification tasks. It measures the overall proportion of correctly classified samples. Accuracy is beneficial as it provides a high-level understanding of a model's performance, but it may not tell the whole story, especially for datasets with imbalanced class distributions.  
In addition to accuracy, other important performance metrics include precision, recall (also known as sensitivity), and the F1 score. Precision indicates how reliable the model's positive predictions are. A high precision means the model rarely misclassifies negative instances as positive. Recall, on the other hand, measures the proportion of true positive predictions among all the actual positive instances in the dataset. The F1 score combines precision and recall into a single metric, providing a balanced measure of the model's performance.
# Quantitative Classification Model of Composite Product Image Based on Event-Related Potential
## 3 Analysis of Experimental Data
### Experimental Data Recording
#### 3.1.1 Reaction Time Analysis  
Response time data are automatically collected according to the time keys needed for subjects to make appropriate responses after the target stimulus appears. In this experiment, this refers to the time required for the subjects to judge the semantic matching degree of the image words (image consistent, image neutral and image inconsistent) after the appearance of the image words in the target phase.  
For the reaction time, a repeated-measures analysis of variance (MANOVA) was used to analyze intention word pairs and semantic matching reaction time. Image word pairs were used as intergroup factors and semantic matching as intragroup factors. The results show that image words had a significant main effect, F (2, 18) = 168.126, \(p\) < 0.001. Semantic matching also had a significant main effect, F (2, 18) = 184.873, \(p\) < 0.001. The interaction effect between the lexical pairs and the degree of agreement was not significant, F (4, 16) = 2.845, \(p\) = 0.059. From the analysis of the semantic matching analysis, the subjects' reaction time was the longest when the semantic value of the image was neutral. When the semantic value of the image was ambiguous, subjects consumed more cognitive resources to make a judgement, which required more thinking time.