esa-sceva/satcom-chunk-collection huggingface dataset:
# _Applied sciences_
## 5 Conclusions  
We have shown the usefulness of UR-Net for color recognition and segmentation when there is not enough annotated image data. Our network model has been shown to improve the semantic segmentation performance when compared to traditional networks and unsupervised feature extraction techniques. Our research adds the improved residual blocks of ResNet to the U-Net architecture to optimize the model and constructs a UR-Net for the semantic segmentation of color paintings. The experiment was conducted with earth red and ultramarine blue and can theoretically identify any pigments in the color painting. First, residual blocks were constructed and improved to make them suitable for the U-Net architecture. Second, the improved residual blocks were added to the U-Net architecture to construct UR-Net. Then, training samples were trained using UR-Net to obtain a semantic segmentation model applicable to painted colors. Finally, the proposed model was used to segment the painted images to derive semantic maps of the corresponding colors. The segmentation accuracy of the proposed UR-Net is examined by calculating the parameters of PA, recall, precision, F1-score, and IoU and comparing the three methods of K-means, ResNet, and U-Net. The results show that UR-Net has the advantages of a short training period, accurate pixel recognition, and high segmentation accuracy, in addition to achieving color consistency without the need for a large number of samples for training, which is very suitable for color-rich painted images. When segmenting color painting images with the proposed method, only the color pixels in a color painting need to be labeled as samples for training beforehand, and then the rest of the samples can be segmented, improving efficiency and ensuring segmentation accuracy.  
Conceptualization, S.F. and Z.L.; methodology, S.F.; software, S.F.; validation, S.F., L.L. and S.L.; formal analysis, Z.L.; investigation, S.F.; resources, S.F.; data curation, S.F.; writing--original draft preparation, S.F.; writing--review and editing, S.F.; visualization, S.F.; supervision, S.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
National Social Science Fund (Grant No. 21 CTQ041); Natural Science Foundation of Shandong Province (Grant No. ZR2024 MG044); and Ministry of Education of Humanities and Social Science Project (Grant No. 24 YJAZH095).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author would like to thank their mentor for his help and the reviewers for their insightful comments.  
Figure 18: Painted images segmented by UR-Net.
## 1 Introduction
By adjusting laser pulse parameters, it is possible to reverse the motion of the electrons, which is preliminarily speculated to have significant potential for electron trapping and trajectory control. This could exhibit even greater effects and benefits in future research. This experiment has a high practical application value and provides a theoretical analysis for the special trajectory electrons generated in practical nonlinear Thomson scattering experiments. This paper explains the pulse laser parameters and mechanisms that lead to the generation of special trajectory electrons, offering corresponding theoretical parameters for electron trajectory control. It also presents an innovative analytical model and approach for analyzing the interaction between free electrons and pulse lasers in a coaxial collision scenario. Furthermore, this paper extends the theoretical analysis of the interaction between free electrons with different initial positions and velocity directions and chirped pulse lasers. Other directions, such as off-axis collisions (i.e., non-coaxial), multi-electron systems, and plasma-laser interactions, still hold significant research value, and this study can serve as a reference for these extended areas. By combining the interaction and cross-analysis of single electrons, plasma, and lasers from various angles, initial positions, and velocity directions, the simulation of actual experimental scenarios can be significantly improved in terms of rationality and accuracy. In the future, this can be further developed into a computerized simulation laboratory for electron-laser pulse interactions, achieving standardized and scaled control of simulated experiments and improving the reliability and effectiveness of experiments and data collection.
## 3 Results and Discussion
### Determination of Selenium Content in Yeast
Research presented by [PERSON] et al. [39] showed that _Rhodotorula glutinis_ X-20 yeast grown in 30 mg Se\({}^{4+}\)/L supplemented medium is able to accumulate selenium at a level of 5349.6 ug/g after 36 h of cultivation. An interesting course of changes in the bioaccumulation of selenium in the cell biomass of _Enterococcus durans_ LAB18s was presented by [PERSON] et al. [40]. The authors showed that bacteria are able to bind selenium in the range from 8449 to 61524 ug/g when the concentration of sodium selenite added to the culture medium was 15 to 120 mg/L, respectively. In a recent study, [PERSON] et al. [41] showed that the accumulation of selenium by _Candida utilis_ ATCC 9950 and _Saccharomyces cerevisiae_  
Figure 2: Changes in the content of selenium (μg/g\({}_{\text{d.w.}}\)) in the cell biomass of _C. utilis_ yeast after cultivation in the control (YPD) and in the experimental media enriched with selenium in the form of Na\({}_{2}\)SeO\({}_{3}\). a\({}^{-1}\) Means with the same letter did not differ significantly (\(p<0.05\)). ATCC MYA-2200 growing in medium with 60 mg Se\({}^{4+}\)/L after a 72-h culture reached a selenium value of 5470 and 5640 ug/g, respectively. It should be noted that in this study, selenium was determined by spectrophotometry using the Variamine Blue reagent. This phenomenon indicates the accumulation of the elemental form of selenium in yeast cell structures. [PERSON] and [PERSON] [PERSON] and [PERSON] (2010) and [PERSON] et al. [PERSON] _et al._ (2010) found large quantities of selenium occurring in the reduced form in the vesicular structures of yeast cell biomass, thereby reducing its value as a source of selenium in a highly absorbed form. For selenium concentrations below 30 mg/L, no significant phenomenon related to the occurrence of amorphous elemental selenium was observed after 72 h of culturing (Figure 3). Selenium concentrations above 30 mg/L in the experimental media resulted in the yeast cell biomass turning red in color just after the first day of culturing. The observed color change indicates that to a large extent the resulting yeast biomass contains selenium in an inorganic form, which was also characterized by low bioavailability. It can be concluded that the occurrence of discoloration of the culture and the yeast biomass shows that, to a large extent, the cells contained selenium in an inorganic form in their cellular structures, which is biologically useless [PERSON] _et al._ (2010); [PERSON] _et al._ (2010). In this situation, experimental media supplemented with selenium at doses of 20 and 30 mg/L may be used for the production of fodder yeast biomass enriched with this element, which occurs mostly in an organic form. The results of this work show that yeast enriched with selenium can be an interesting alternative to the production of supplements in dietary or pharmaceutical preparations to supplement deficiencies of this element in the diet. In addition, the results obtained can find promising use in the production of animal feed preparations.
## 2 Design Principle and Simulation Method
structure without the square ring merely covers an angle range of 300\({}^{\circ}\) (from \(-63.9^{\circ}\) to \(-365^{\circ}\)) under \(x\)-polarized incidence. For the cases under \(y\)-polarized incidence (red lines), both the amplitude and phase are independent on dx. In addition, it is easy to understand that the dependence of phase shift on dy is opposite, due to the structural symmetry. Therefore, these results clearly demonstrate that the metal square ring can effectively extend the ability of the proposed structure to manipulate the reflective phase. Note that, the reflective phase covers only 320\({}^{\circ}\), and it cannot reach a perfect coverage of 360\({}^{\circ}\). To minimize the deviation during the metasurface design, we can choose the cell with a relative phase of 320\({}^{\circ}\) and 360\({}^{\circ}\) (0\({}^{\circ}\)) to approximate that with a phase of 320\({}^{\circ}\)-340\({}^{\circ}\) and 340\({}^{\circ}\)-360\({}^{\circ}\), respectively. According to the analysis above, it can be concluded that the anisotropic metasurface cell can be used to manipulate the wavefront of the \(x\)- and \(y\)-polarized waves independently, by changing the geometric parameters of dx and dy, respectively. In the following, we will manifest the proposed metasurface as a good candidate for polarization-dependent wavefront shaping by investigating two proof-of-concept examples, i.e., a reflective polarization beam splitter and a focusing lens.
## 3 Experiments and Results
### Experimental Flow  
The research started by collecting panoramic radiograph datasets from two main data sources, including CBNUH and AIHub, which contain 87 and 1044 valid images, respectively. Data augmentation was applied to only the CBNUH dataset due to having a small amount of data. Furthermore, this dataset was used to generate two types of masks,  
Figure 3: Integrated model framework using deep learning algorithms for periodontitis stage classification.  
which are PBL and CEJ masks, which acted as the training input for the two U-Net models. The purpose of these models is semantic segmentation, which is to detect the boundary of the PBL and CEJ area on the panoramic radiograph. At the same time, the dataset from AIHub was combined with its corresponding JSON label to create a required format for training the YOLOv5 model. The purpose of this YOLOv5 model is to detect and assign a number to each individual tooth that exists in the panoramic radiograph, as well as to calculate the length of that tooth. To this step, we had three models, which were two U-Net models and a YOLOv5 model. From these three models' results, a periodontal disease stage for each tooth was classified with the following steps:  
* Get the intersection of the PBL's upper side and the tooth's middle axis.
* Get the intersection of the CEJ's upper side and the tooth's middle axis.
* Calculate the length from those two intersections (known as the RBL length).
* Calculate the RBL percentage from the RBL length and tooth root.  
With this percentage of RBL, we can classify periodontal stages, which will be detailed in Section 3.3.
## 6 Experimental Results and Discussion
### Result and Discussion
#### 6.1.4 F-Score  
Based on Equation (41), the F-score of each feature can be computed (shown in Tables 7 and 8). As shown in Figure 11, when we add more GLCM features based on their F-score, the accuracy increases and reaches a peak at the 36 th feature at 77.25% accuracy and then slightly decreases at the end. Meanwhile, SVM achieves 87.125% accuracy at the 87 th feature (including 34 features) before dropping rapidly from the 63 rd feature to the end of GLRLM features, as shown in Figure 12. In this case, we can also obtain 86.625% accuracy with only 16 features at the 61 st feature of GLRLM. When using all features, the trend of accuracy is quite similar to the case of applying GLRLM features. SVM can obtain a classification result up to the peak accuracy of 88.875% before dropping at the end, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Without SBS** & **With SBS** & **Number of Selected Features** \\ \hline Accuracy rate with GLCM features & 75.75\% & 75.5\% & 48 \\ Accuracy rate with GLRLM features & 54.53\% & 88.25\% & 28 \\ Accuracy rate with GLCM and GLRLM features & 61\% & 88.87\% & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy rate when applying the sequential backward selection method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Without SFS** & **With SFS** & **Number of Selected Features** \\ \hline Accuracy rate with GLCM features & 75.75\% & 78\% & 5 \\ Accuracy rate with GLRLM features & 54.53\% & 88.13\% & 7 \\ Accuracy rate with GLCM and GLRLM features & 61\% & 89.25\% & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy rate when applying the sequential forward selection method.  
Figure 11: Results of GLCM features and SVM.  
Figure 12: Results of GLRLM features and SVM.  
Figure 13: Results of all features and SVM.  
The other method to apply F-score is to select several thresholds where the gap between low F-score and high F-score is considerable. We selected four thresholds for each kind of feature and six thresholds for all features. The results are shown in Table 9.  
As presented in Table 9, the threshold method achieves results as good as those by searching all features.
## 4 Research Execution and Analysis
### Delphi Consultation
#### 4.2.2 Delphi Expert Questionnaire Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(N\) = 20 & \multicolumn{3}{c}{**First Round Delphi Data**} & \multicolumn{3}{c}{**Second Round Delphi Data**} \\ \hline Indicator & M & SD & CV & M & SD & CV \\ \hline Learnability & 4.50 & 0.61 & 0.13 & 4.45 & 0.61 & 0.14 \\ \hline Efficiency & 4.70 & 0.47 & 0.10 & 4.60 & 0.50 & 0.11 \\ Memorability & 4.40 & 0.75 & 0.17 & 4.30 & 0.80 & 0.19 \\ \hline Error Prevention & 4.40 & 0.60 & 0.14 & 3.80 & 0.77 & 0.20 \\ \hline Satisfaction & 4.15 & 0.67 & 0.16 & 3.90 & 0.72 & 0.18 \\ \hline Flexibility & 4.75 & 0.44 & 0.09 & 4.60 & 0.50 & 0.11 \\ \hline Attitude & 1.65 & 0.67 & 0.41 & — & — & — \\ \hline User-Centricity & 2.70 & 1.46 & 0.54 & — & — & — \\ \hline Simplicity in Use & 4.75 & 0.44 & 0.09 & 4.55 & 0.51 & 0.11 \\ \hline Immediate Feedback & 4.75 & 0.44 & 0.09 & 4.65 & 0.49 & 0.11 \\ \hline Consistency & 4.60 & 0.50 & 0.11 & 3.90 & 0.64 & 0.16 \\ \hline Clarity & 1.60 & 0.75 & 0.47 & — & — & — \\ \hline Terminology & 1.60 & 0.82 & 0.51 & — & — & — \\ \hline Help & 3.80 & 0.70 & 0.18 & 3.30 & 0.66 & 0.20 \\ \hline Privacy Protection & 4.60 & 0.50 & 0.11 & 4.55 & 0.51 & 0.11 \\ \hline Property Protection & 4.85 & 0.37 & 0.08 & 4.95 & 0.22 & 0.05 \\ \hline Graphic Layout & 3.85 & 0.81 & 0.21 & 1.85 & 0.93 & 0.50 \\ \hline Interface Simplicity & 4.45 & 0.51 & 0.11 & 4.30 & 0.57 & 0.13 \\ \hline Findability & 4.65 & 0.49 & 0.11 & 4.10 & 0.64 & 0.16 \\ \hline Accessibility & 4.35 & 0.59 & 0.13 & 4.20 & 0.70 & 0.17 \\ \hline Credibility & 1.90 & 1.07 & 0.56 & — & — & — \\ \hline Value Realization & 3.95 & 0.76 & 0.19 & 3.95 & 0.76 & 0.19 \\ \hline Convenience & 4.75 & 0.44 & 0.09 & 4.40 & 0.68 & 0.15 \\ \hline Pleasure & 4.10 & 0.91 & 0.22 & 3.80 & 0.89 & 0.24 \\ \hline Social & 3.90 & 0.85 & 0.22 & 4.00 & 0.73 & 0.18 \\ \hline System Security & 4.85 & 0.37 & 0.08 & 4.85 & 0.37 & 0.08 \\ \hline Page Information Architecture & 4.80 & 0.52 & 0.11 & 4.65 & 0.49 & 0.11 \\ \hline Navigation & 4.40 & 0.50 & 0.11 & 3.90 & 0.79 & 0.20 \\ \hline Error Feedback & 4.20 & 0.95 & 0.23 & 3.95 & 0.89 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Delphi survey results.
## 3 Standards for Cyber-Physical Energy Systems (CPES) Wireless Networking  
While Ethernet is the key to wired networks for CPES, there are numerous proprietary and standardized technologies for wireless technologies. The wireless networking of CPES requires machine-to-machine (m2m) radio technologies with a low data rate; the requirements for reliability, real-time capability, and range depend on the specific application. In the \"last mile\" of wireless sensor networks (WSN) applications [17], there are numerous available standards and a growing market of \"smart\" wireless components. Many of these products possess proprietary technologies for radio transmission, whereby the term \"proprietary\" is also valid if the protocols are known, but the provider has authority over a transmission medium or cloud storage.  
IoT radio networks, known as low-power wide area networks (LPWAN) [18], exist for ranges of several kilometers in combined indoor and outdoor use. This class of networks is specially designed for M2M and CPES communications. Instead of a high data rate, the requirements for such LPWANs are in other areas:  
* _Coverage_: a single radio base station must cover a smart microgrid. As its maximum radius is not definable, one may assume several kilometers. Public LPWAN infrastructures cover entire countries or continents.
* _Sensor energy consumption_: battery-powered sensor nodes should be able to work with one battery for several years.
* _Capacity_: a base station must be able to supply several thousand IoT devices.
* _Adaptively_: An LPWAN standard should consider multiple user profiles for different deployment scenarios. For instance, [19] contains four different categories with application examples that describe different requirements for hardware complexity and use of radio channels.
* _Reliability_: the radio link must also function within buildings through numerous walls and floors. The protocols should detect transmission errors and then be able to take appropriate action.
* _Security_: the network protocols must provide security mechanisms at all protocol levels to ensure privacy, authenticity, and integrity.
* _Economic efficiency_: the hardware required for networking must be cost-effective in order to make use of a large number of sensors or even disposable objects economically feasible.  
With LPWANs, we distinguish between commercial providers for network as a service (NaaS) and infrastructures that can be set up independently. For the latter, the Long Range (LoRa) Wide AreaNetwork (WAN) technology is currently without an alternative. Furthermore, NaaS has no relevance for short-range WSN.
## 5 Application of Our Proposed Approach
### Case Study-1: Demand Data for Transformer Tanks
#### 5.1.2 Determination of the Initial Values  
Once the optimum values of the trio (\(\alpha\), \(\beta\) and \(\gamma\)) are obtained, the level (\(L_{T}\)), the growth rate (\(b_{T}\)), the seasonal parameter (\(SN_{T}\)), the forecasted demand (\(Y_{T}\)), and the error (\(E_{T(L)}\)) are computed respectively by using Equations (1)-(6). The computed values are presented in Table 1.  
Figure 4: Convergence of mean absolute deviation (_MAD_) at (**a**) constant \(\alpha=1\); (**b**) constant \(\beta=0\); (**c**) constant \(\gamma=0.2\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Parameter**} & \multicolumn{10}{c}{**Months**} \\ \cline{3-14}  & & **January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** &
## 3 Results and Discussion
### Model Evaluation Metrics  
In this study, the performance of the model in detecting maize seed surface mildew was evaluated from the following aspects:  
(1) _Precision_ refers to the proportion of true positive predictions out of all the instances that were predicted as positive. The formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
where True Positives (_TP_) refer to instances where the model correctly predicts the positive class. False Positives (_FP_) refer to instances where the model incorrectly predicts the positive class.  
(2) _Recall_ measures the proportion of actual positive cases that the model correctly identifies.  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
where False Negatives (_FN_) refer to instances where the model incorrectly predicts the negative class.  
(3) _mAP50_ provides an overall measure of a model precision and recall across different classes and Intersection over Union (IoU) thresholds. The formula is as follows:  
\[mAP50=\frac{TP_{1}+TP_{2}+\cdots TP_{n}}{TP_{1}+TP_{2}+\cdots TP_{n}+FP_{1}+ FP_{2}+\cdots FP_{n}} \tag{5}\]  
where \(TP_{i}\) represents the number of correct predictions for the \(i\)-th category, and \(FP_{i}\) represents the number of incorrect predictions for the \(i\)-th category.  
(4) Five-fold cross-validation is a pretty common method for assessing models. It helps improve a model generalization ability and reduce overfitting. To ensure the final model is stable, the following steps were applied: As shown in Figure 10, the entire data set was randomly divided into five distinct subsets. Subsequently, the models were trained and tested five times, with four parts of the data set utilized as the Train and the remaining part as the Val each time. Ultimately, the results were averaged to obtain the final evaluation.  
Figure 10: Five-fold cross-validation.
## 4 Discussion
### Impact of Doppler Shift  
To investigate the effect of the Doppler shift on the forward model, we compared the SVD results of the no-Doppler-shift spectral dataset before and after frequency shift correction. Since the differences between the two sets of singular vectors mainly reside in the seventh and eighth singular vectors, we adopted different strategies in Figure 12 to display the first eight singular vectors.  
The frequency shift correction reduces the redundancy of information in the no-Doppler-shift spectral dataset, resulting in richer information contained in the first few singular vectors of the no-shift spectrum set. For instance, the second singular vector carries additional slope information of the satellite spectrum, and the prominence of peaks in the second to fourth singular vectors is significantly enhanced in correlation with the depth of the Fraunhofer lines. Overall, the differences in the first six singular vectors are minimal. The seventh and eighth singular vectors of the original spectral dataset exhibit  
Figure 11: Slope and GOF of the linear fit between the SIF retrieval results from the combined retrieval window (O\({}_{2}\) absorption and FLs bands) and FLs band alone with GPP/EVI.  
strong high-frequency features, concentrated near the Fraunhofer lines, which are absent in the corresponding singular vectors of the no-shift spectral dataset. Therefore, we attribute the seventh and eighth singular vectors mainly to the Doppler effect. These two singular vectors are not suitable for constructing the forward model, and thus the Doppler effect cannot be corrected through singular vectors.  
By correcting the Doppler shift in the satellite spectra, both the slope and the goodness-of-fit between the retrieval results and GPP and VI are further improved, as shown in Figure 13. This is because the frequency shift correction eliminates the frequency shift between the forward model and the satellite spectra, allowing the depths of the Fraunhofer lines to be fully utilized, thereby further enhancing the sensitivity of the data-driven method to the SIF signal.  
Figure 12: The first six singular vectors and the seventh to eighth singular vectors obtained from the spectra of the training set before and after frequency shift correction.  
Figure 13: Scatter plots and linear fitting results of retrieval outcomes with GPP and EVI before and after satellite spectral frequency shift correction in January 2019.