esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Environmental Factors
#### 3.4.3 Other Environmental Factors  
Besides temperature and humidity, other environmental factors may also have some influence on sticking behavior. These factors include exposure to light, air quality, and atmospheric gases. Light exposure, particularly in the presence of certain sensitizers, can induce photochemical reactions that alter the surface chemistry of MXene flakes, potentially affecting sticking. Air quality and atmospheric gases, such as oxygen or pollutants, can also interact with MXene flakes and impact their surface chemistry, thereby contributing to sticking issues. For example, it was reported that the aging process of aerated aqueous MXene dispersions has led to the oxidation of MXene flakes, initially commencing from the edges where the formation of TiO\({}_{2}\) crystals resembling branches was observed [71].  
In Figure 15a, a non-oxidized MXene flake is shown. The TEM image in Figure 15b illustrates evident signs of oxidation on the MXene flakes. The image in the inset of Figure 15b further confirms the presence of TiO\({}_{2}\) crystals growing in a branch-like manner at the edges. During this phase, the MXene flakes maintained their 2D morphology, indicating partial oxidation reactions. However, continued aging of the MXene dispersion resulted in the agglomeration of C atoms, leading to the formation of amorphous carbon and TiO\({}_{2}\)-rich regions, representing a fully oxidized MXene dispersion (Figure 15c). At this stage, the sheet-like morphology of MXene flakes underwent a transformation into agglomerated TiO\({}_{2}\) nanocrystals.  
Understanding the impact of environmental factors on sticking behavior is essential for designing robust MXene-based supercapacitor electrodes. By controlling and optimizing the temperature and humidity conditions during electrode processing, as well as consideringother relevant environmental factors, researchers can reduce sticking, enhance dispersion stability, and improve the overall performance and reliability of MXene electrodes.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 3 Area of Interest  
Tyhoon Goni made landfall at Catanduanes island on the 31 October 2020 with ground speeds of 225 km/h [65]. Its course covered the provinces of Camarines del Sur, Albay and Quezon towards the east. The affected region is not only home to 24 M people but it also contains an important share of the country's coconut trees. According to the Food and Agriculture Organization of the United Nations the Philippines are the world's second largest coconut producer only after Indonesia and produced 17.8 Mt of coconut in 2019. The top-3 provinces in terms of coconut trees are Quezon, Northern Samar, Camarines Sur, all of which are located very close to Goni's path. Figure 1 shows the estimated number of coconut trees per province and Goni's path. For our analysis, we included all the provinces located within 500 km from the centre line of the typhoon's path (marked with white boundaries in Figure 1). This strip covers 15.7 Mha and 47 of the 87 provinces in the Philippines.
# Predictors of Engagement in Virtual Reality Storytelling Environments about Migration
## 3 Hypotheses Development
### Presence  
Presence can be seen as the sense of being there or being in one place [50]. In the literature on VR experiences, the concept of presence is also tackled as spatial presence, which can be defined as the \"cognitive perception that a person's body is occupying a space in which they feel in sync with the actions of the virtual experience\" ([51], p. 2). [PERSON] ([52], p. 432) defines presence as \"the illusion of being there, notwithstanding that you know for sure that you are not. It is a perceptual but not a cognitive illusion\". Additionally, a high level of presence in VR might indicate that the virtual scenario is viewed as real and provide a sense that virtual things may happen in the real world [53].  
[PERSON] et al. [22] agree that presence is an essential element in VR scenarios. They found that presence positively influences cognitive engagement. In the same vein, [PERSON] and [PERSON] [21] found that spatial presence leads to a higher emotional engagement. Another study reported that the higher level of presence made users focus more on the VR interactions rather than on self-thoughts about their own performance, which alleviated the sense of frustration in a training scenario [54].  
In contrast to those experiences, [PERSON] et al. [55] identified that higher levels of realism or spatial presence can reduce user engagement. However, most of the studies support the idea that the boundaries and features of VR scenarios make presence a factor that positively influences engagement [51]. Moreover, the type of display used in the VR experience is important and affects the variables of presence and engagement [56].  
**Hypothesis (H2).**_The sense of presence in the virtual reality storytelling environment has a positive and significant effect on engagement._
## Appendix A
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reverb Time** & **0.31 s** & **0.91 s** & **1.51 s** \\ \hline \multicolumn{4}{c}{**Energy (LUFS)**} \\ \hline Reference BRIR & \(-22.4044\) & \(-19.436\) & \(-19.3806\) \\ \hline BRIR with FER removal & \(-24.4902\) & \(-22.4524\) & \(-20.9696\) \\ \hline reference-FER removal & \(2.0858\) & \(3.0164\) & \(1.589\) \\ \hline BRIR with FER removal & \(-21.0237\) & \(-20.0485\) & \(-20.0019\) \\ \hline reference-RER removal & \(-1.3807\) & \(0.6125\) & \(0.6213\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the energy corresponding to the reference BRIR versus the BRIR with early reflections removed at different reverberation times (the amount of early reflection removal corresponds to the measured threshold in this paper).
## 3 Results and Discussion
### Plant Growth Phases and Cumulative Growing Degree Days  
As demonstrated in [42], thermal time is a pivotal factor in the regulation of the phenological development of sugarcane. During the trial, the evaluated genotypes exhibited comparable growth cycle durations. The complete cycle, from transplanting to harvest, lasted 306 days, which is consistent with the findings of [PERSON] et al. [43]. According to these authors, the transplantation period typically extends from March to early June. The stem elongation phase extends from early June to late September, followed by sugar accumulation in October, maturation from November to December, and harvesting from late December to March of the subsequent year. Growing degree days (GDDs) have been shown to provide a quantitative measure of the heat accumulation required for sugarcane development. The cumulative GDDs recorded during this study are illustrated in Figure 2.  
Figure 2: Cumulative growing degree days for sugarcane plants grown in Sicily.  
At the experimental location, the cumulative growing degree days (GDD) recorded from transplanting to harvest amounted to 2800 \({}^{\circ}\)C, a value considered fully adequate for optimal sugarcane development, as noted by [PERSON] et al. [7]. The initial phase, which is associated with the sprouting of cuttings, corresponds to a GDD accumulation between 0 and 250 \({}^{\circ}\)C [44]. The subsequent vegetative state requires cumulative GDD values ranging from 250 to 600 \({}^{\circ}\)C [42]. The third developmental phase, encompassing tillering, stem elongation, and biomass accumulation, occurs between 600 and 900 \({}^{\circ}\)C [45]. The maturation stage, during which sucrose accumulation occurs, is characterized by GDD values ranging from 900 to 1400 \({}^{\circ}\)C [46].
## 3 Methodology
### Performance Evaluation  
Performance evaluation of a fine-tuned 3D U-Net model for medical image classification can be performed in several ways. Two common metrics employed to calculate the performance of a binary classification task are training and testing accuracy and confusion matrix.  
Training and Testing Accuracy:  
The training and testing accuracies measure classifier capability for correct predictions of true labels for training and testing data samples, respectively. These metrics can be calculated as follows:  
\[\text{TrainingAccuracy}=\frac{\text{Correctlyclassifiedsamplecount}}{\text{Totaltrainingsamplecount}} \tag{20}\]  
\[\text{TestingAccuracy}=\frac{\text{Correctlyclassifiedsamplecount}}{\text{Totaltestingsamplecount}} \tag{21}\]  
Confusion Matrix:  
The confusion chart is a matrix that summarizes the classifier performance with the comparison of true labels and model predictions. It provides insights into the predictions made by the classification model. The correctly classified samples of the positive and negative classes are represented as true positive (TP) and true negative (TN), respectively, while the wrongly classified samples of the positive and negative classes are represented as false negative (FN) and false positive (FP), respectively. The values of the confusion matrix can be calculated as follows:  
TP = Number of samples correctly classified as positive  
TN = Number of samples correctly classified as negative  
FP = Number of samples incorrectly classified as positive  
FN = Number of samples incorrectly classified as negative
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
## 2 Methodology
### Risk Assessment of Buildings
The flood hazard caused by climate-induced sea level rise is estimated according to IPCC Fifth Assessment Report (AR5) [39] and Strategy for climate change adaptation for The Republic of Croatia [40], considering changes in the mean sea level. However, the EU Flood Directive [41] requires an analysis of a high, moderate and low probability scenario in the flood hazard assessment. The sea level in the Adriatic Sea is dominantly caused by sea tides and the effect of the barometric pressure [42]. The tidal component can be represented by the set of periodic functions [43], and the residual sea level can be represented with a probability distribution due to its randomness and stochastic behavior. In order to fulfil the Flood Directive requirements, probability scenarios are estimated from the particular probability distribution, corresponding to the return periods of 25, 100 and 250 years, respectively [44]. Finally, sea level is estimated as a superposition of mean sea level, maximum estimated tide and each probabilistic scenario. The distribution of the critical zones most prone to flood due to the impacts of climate change on sea level rise for the most critical scenario for year 2100 is shown in Figure 7 [45]. The extreme coastal waves hazard is determined based on the evaluation of wave heights and their propagation toward the coast (Figure 8). The methodology for computing the wave heights by using values of wind speeds in critical wind directions for the investigated area has been developed [46], where probability distribution function is used to fit wind speed histograms and to evaluate return period values. Figure 6: Vulnerability index of buildings for: (**a**) sea floods; (**b**) extreme sea waves. Vulnerability indexes of buildings for individual natural disasters coupled by the corresponding hazards are the basis for the multi-hazard risk assessment of the area [47].
## 3 Materials and Method
### SVM-Assisted Recognizing of Sensitive and Insensitive Regions
#### 3.3.1 Support Vector Machine (SVM)  
Support vector machine (SVM) is a supervised learning algorithm used for classification and regression tasks. It finds the best possible line or hyperplane that separates the different classes in the input data by maximizing the margin between the decision boundary and the nearest data points of each class. SVM can also handle non-linear classification problems by mapping the input data to a higher-dimensional feature space using a kernel function. SVM is effective even when the number of features is much larger than the number of training examples, and it is relatively insensitive to overfitting. However, SVM can be computationally expensive for large datasets and sensitive to the choice of kernel function and the regularization parameter [52].  
The goal of the SVM technique is to create the best decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct class. This decision boundary is called a hyperplane. SVM chooses the utmost vectors (points) that are used in creating the hyperplane. These utmost cases are called support vectors, and hence the algorithm is termed the support vector machine.
###### Abstract  
Under the pressure of limited arable land and increasing demand for food, improving the quality of existing arable land has become a priority to ensure food security. The Chinese government gives great importance to improving cropland productivity by focusing on the construction of high-standard farmland (HSF). The government puts forward the goal of constructing 1.2 billion mu (100 mu \(\approx\) 6.67 hectares) of HSF by 2030. Therefore, how to apply remote sensing to monitor the ability to increase and stabilize yields in HSF project regions has become an essential task for proving the efficiency of HSF construction. Based on HSF project distribution data, Moderate Resolution Imaging Spectroradiometer (MODIS) data and Landsat-8 Operational Land Imager (Landsat8-OLI) data, this study develops a method to monitor cropland productivity improvement by measuring cropland productivity level (CPL), disaster resistance ability (DRA) and homogeneous yield degree (HYD) in the HSF project region. Taking China's largest grain production province (Henan Province) as a case study area, research shows that a light use efficiency model that includes multiple cropping data can effectively detect changes in cropland productivity before and after HSF construction. Furthermore, integrated Landsat-OLI and MODIS data can detect changes in DRA and HYD before and after HSF construction with higher temporal and spatial resolution. In 109 HSF project regions concentrated and distributed in contiguous regions in Henan Province, the average cropland productivity increased by 145 kg/mu; among the eight sample project regions, DRA was improved in seven sample project regions; and the HYD in all eight sample project regions was greatly improved (the degree of increase is more than 75%). This evidence from satellites proves that the Chinese HSF project has significantly improved the CPL, DRA and HYD of cropland, while this study also verifies the practicability of the three indices to monitor the efficiency of HSF construction.  
high-standard farmland; cropland productivity; disaster resistance ability; homogeneous yield degree +
Footnote †: journal: remote sensing  
0022
# Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
## 3 Result Analysis and Discussion
### Performance Evaluation of ML Models
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm Accuracy** & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** & **Kappa Statistics** & **MCC** \\ \hline MLP & 93.59\% & 0.9423 & 0.9608 & 0.9514 & 0.8889 & 0.9608 & 0.8571 & 0.8875 \\ SVM & 93.59\% & 0.951 & 0.951 & 0.951 & 0.9074 & 0.951 & 0.8584 & 0.8584 \\ DT & 94.87\% & 0.9796 & 0.9412 & 0.96 & 0.9629 & 0.9412 & 0.8886 & 0.89 \\ LGBM & 98.08  
The five-fold CV results on the balanced dataset for the ML approaches are described in Table 5. According to Table 5, RF and LGBM have the highest maximum accuracy of 93.23%. Moreover, RF and LGBM show the highest precision value of 0.9574. The highest recall and specificity value is 0.9091, which is generated by DT, RF, XGBoost, and LGBM classifiers. Both RF and LGBM show a maximum sensitivity score of 0.9570. RF and LGBM have shown maximum values of f1-score, kappa statistics, and MCC of 0.9326, 0.8647, and 0.8658, respectively. Figure 4 shows the ROC curve and precision-recall (PR) curve for six different ML techniques that have been applied in this study. The results for the balanced dataset are shown in Figure 4A,B. On the other hand, Figure 4C,D show the results of the imbalanced dataset. For a balanced dataset, the highest AUC score is 1.00 for RF, XGBoost, and LGBM, as shown in the figure. At the same time, RF, XGBoost, and LGBM show the highest AUCPR value, which is also 1.00. On the contrary, the maximum AUC score is 0.999, as shown by RF. It is worth noting that RF also shows the highest AUCPR value of 0.999 for an imbalanced dataset. Figure 4: ROC curve and PR curve analysis. (**A**) ROC curve for the balanced dataset, (**B**) PR curve for the balanced dataset, (**C**) ROC curve for the imbalanced dataset, and (**D**) PR curve for the imbalanced dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** & **Kappa Statistics** & **MCC** \\ \hline DT & 91.67\% & 0.9278 & 0.9091 & 0.9184 & 0.9247 & 0.9091 & 0.8333 & 0.8334 \\ SVM & 87.50\% & 0.8947 & 0.8586 & 0.8763 & 0.8925 & 0.8586 & 0.7501 & 0.7507 \\ XGB & 92.71\% & 0.9474 & 0.9091 & 0.9278 & 0.9462 & 0.9091 & 0.8542 & 0.8549 \\ LGBM & 93.23\% & 0.9574 & 0.9091 & 0.9326 & 0.957 & 0.9091 & 0.8647 & 0.8658 \\ MLP & 91.14\% & 0.9271 & 0.9889 & 0.9128 & 0.9247 & 0.8989 & 0.8229 & 0.8233 \\
**RF** & **93.23\%** & **0.9574** & **0.9091** & **0.9326** & **0.957** & **0.9091** & **0.8647** & **0.8658** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation on imbalance dataset for five-fold CV method.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
###### Abstract  
Globally, there are now more than 19 billion connected Internet of Things (IoT) devices, which are fostering innovation across various sectors, including industry, healthcare, education, energy, and agriculture. With the rapid expansion of IoT devices, there is an increasing demand for sustainable, self-powered, eco-friendly solutions for next-generation IoT devices. Harvesting and converting radio frequency (RF) energy through rectennas is being explored as a potential solution for next-generation self-powered wireless devices. This paper presents a methodology for designing, optimizing, and fabricating a flexible rectenna for RF energy harvesting in the 5G lower mid-band and ISM 2.45 GHz band. The antenna element has a tree form based on a fractal structure, which provides a small size for the rectenna. Furthermore, to reduce the rectenna's environmental impact, we fabricated the rectenna on a substrate from biodegradable materials--natural rubber filled with rice husk ash. The rectifier circuit was also designed and fabricated on the flexible substrate, facilitating the seamless integration of the rectenna in next-generation low-power IoT devices. The numerical analysis of the parameters and characteristics of rectenna elements, based on the finite-difference time-domain method, demonstrates a high degree of agreement with the experimental results.  
rectenna; eco-friendly substrate; flexible antenna; RF energy Article
## 6 Empirical Results and Analysis
### Results of Mediating Effect Model Regression  
According to the setting of the mediating effect model constructed by the classic three-step method, the collaborative agglomeration of manufacturing and producer services is introduced to test the relationship between high-speed railway, industrial collaborative agglomeration, and green technology innovation. Table 4 reports the regression results of the mediation effect model. From the results of columns (1) to (3), the coefficient of the high-speed rail service frequency is always positive and exceeds the significance test of 5%. Column (1) demonstrates the results of baseline regression, while column (2) shows that the supply of high-speed rail services significantly enhances the degree of urban industrial collaborative agglomeration. It is clear that when the frequency of urban high-speed rail services increases by 1%, the city's industrial collaborative aggregation increases by 4.8%. Additionally, it can be found that there is a gap in the coefficient of the high-speed rail service frequency between column (1) and column (3), which is reduced from 0.091 to 0.089. Meanwhile, by observing the results of column (3), the coefficient of collaborative agglomeration for manufacturing and producer services is considerably positive at the confidence level of 5%, which is 0.046. This indicates that there is a remarkable and optimistic relationship between industrial collaborative agglomeration and green technology innovation. Hence, in this article, we can determine that industrial collaborative agglomeration plays a mediating role, and the facilitation effect of high-speed rail on green technology innovation can be realized by promoting the industrial collaborative agglomeration of manufacturing and producer services.
## 4 Retrieved XCO\({}_{2}\) Product Inter-Comparison
### Retrieved XCO\({}_{2}\) Inter-Comparison Result
The standard deviation of OCO-2 retrieved ABO2 albedo \(\sigma_{albedo}\) within the GOSAT IFOV can be defined as follows:  
\[\sigma_{\text{albedo}}=\frac{\text{STDV}(\alpha_{\text{OCO-2 IFOV}})}{\sum_{\text{N}}^{\text{IFOV}}(\alpha_{\text{OCO-2 IFOV}})/\text{N}} \tag{5}\]  
Figure 7: Correlation plots of GOSAT/ACOS XCO\({}_{2}\) and OCO-2 XCO\({}_{2}\) **(a)** over land with high gain; **(b)** over ocean with high gain and **(c)** over land with medium gain. The Pearson correlation coefficients, r, are in the upper left of each figure; **(d)** Histogram of \(\Delta\)XCO\({}_{2}\) for each observation mode. The bin size of the histogram is 0.5 ppm. The mean biases and standard deviations are **(a)** \(-\)0.57 \(\pm\) 3.33 ppm, **(b)** \(-\)0.17 \(\pm\) 1.48 ppm and **(c)** \(-\)0.19 \(\pm\) 2.79 ppm. where \(\alpha_{\text{\tiny{coo-2 IFOV}}}\) and \(N\) are the retrieved OCO-2 albedo within the GOSAT IFOV and the number of OCO-2 footprints within the GOSAT IFOV. There is no strong correlation between \(\Delta\)XCO\({}_{2}\) and AOD, whereas \(\Delta\)XCO\({}_{2}\) standard deviation becomes larger with lower retrieved Psurf and larger \(\sigma_{\text{\tiny{dledo}}}\). The largest disagreements in \(\Delta\)XCO\({}_{2}\) occur predominately at high altitude and in regions of large surface inhomogeneity, as evidenced by the lower retrieved Psurf and larger \(\sigma_{\text{\tiny{dledo}}}\) values. To investigate this, we checked individual match-up points. The areas with lower bias and fluctuation near 600 hPa correspond to in North Tibet in China. The areas with higher biases at 700 hPa are the Andean Range in South America and Tian Shan Range in North China. South Tibet in North-east India and forest in British-Colombia in Canada have larger bias and fluctuation, with retrieved ABO2 albedo deviation greater than 0.25. These complicated surface conditions prompt the surface flatness and BRDF uncertainty and a possible cause of the difference in the retrievals. The above results can explain why land data has a larger \(\Delta\)XCO\({}_{2}\) deviation than ocean glint data even if these are not the only causes. Figure 8: The upper three panels show the time series of \(\Delta\)XCO\({}_{2}\) (**a**) over land with high gain; (**b**) over ocean with high gain and (**c**) over land with medium gain. The lower panels (**d–f**) show the time series of \(\Delta\)Psurf with the same conditions as the above panels. The error bars indicate the standard deviation of \(\Delta\)XCO\({}_{2}\) and \(\Delta\)Psurf within GOSAT IFOV. If we filter out GOSAT/ACOS and OCO-2 Lite products with lower XCO\({}_{2}\) quality flags, the number of match-up data and the \(\Delta\)XCO\({}_{2}\) are significantly reduced. The mean biases, standard deviations and the number of match-up data are \(-\)0.05 \(\pm\) 1.47 ppm and 158 over land with high gain, \(-\)0.27 \(\pm\) 1.06 ppm and 283 over ocean with high gain and \(-\)0.03 \(\pm\) 2.93 ppm and 193 over land with medium gain.