esa-sceva/satcom-chunk-collection huggingface dataset:
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results
### Changes in CF Spectral Responses to Herbicides  
Three CF parameters, \(\mathrm{F_{v}/F_{m}}\), \(\mathrm{\Phi PSII}\), and \(\mathrm{F_{d}/F_{m}}\), demonstrated differential changes over time after herbicide treatment depending on the herbicide mode of action (Figure 4). Except penoxysulam, which did not show any herbicidal activity against crabgrass, all the other herbicides, namely paraquat (PSI), glufosinate (GS), difenacil (PPO), glyphosate (EPSPS), and isoxaflutole (HPPD), resulted in significantly reduced CF parameters compared to the untreated control. Herbicide treatment primarily resulted in a decrease in all the CF parameters, with variations depending on the herbicide mode of action. Except for penoxysulam (ALS), the CF spectral responses to the herbicides were more immediate and pronounced compared to the other spectral responses such as the RGB and IR thermals. Among the herbicides, paraquat showed the fastest and greatest reduction in all the CF parameters, even at 3 HAT, resulting in a significant reduction in \(\mathrm{F_{v}/F_{m}}\), \(\mathrm{\Phi PSII}\), and \(\mathrm{F_{d}/F_{m}}\) by up to 45.9%, 56.7%, and 31.8% of the untreated control, respectively, and it reached 0% at 6 HAT. Tiafenacil (PPO) showed the second fastest and greatest significant reduction in all the CF parameters from 24 HAT to almost zero at 72 HAT. Glufosinate (GS) and glyphosate (EPSPS) displayed a sudden decrease in \(\mathrm{\Phi PSII}\) at 24 HAT, but their decreases in \(\mathrm{F_{v}/F_{m}}\) and \(\mathrm{F_{d}/F_{m}}\) were not that great when compared to \(\mathrm{\Phi PSII}\). Isooxaflutole (HPPD) did not exhibit a significant change in all the CF parameters until 48 HAT, but a sudden decrease in \(\mathrm{\Phi PSII}\) was observed at 120 HAT, where no changes in the RGB and IR thermal spectral responses were observed (Figures 3 and 5).  
Figure 3: Changes in (**A**) mNDI and (**B**) ExG values over time after treatment of herbicides with different modes of action.  
Figure 4: Changes in chlorophyll fluorescence parameters, including (**A**) \(\rm F_{v}/F_{m}\), (**B**) \(\rm\Phi P\)SII, and (**C**) \(\rm F_{d}/F_{m}\), over time after treatment of herbicides with different modes of action.  
Figure 5: Changes in temperature difference over time after treatment of herbicides with different modes of action.
###### Abstract  
The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication.  
quantum-resistant algorithms; digital signatures; computational efficiency standardization +
Footnote †: journal:  
0000-0001-111-1111-1111  
Of the algorithms chosen, Crystals-Dilithium, Falcon, and Sphincs+ pertain to digital signatures. Crystals-Dilithium and Falcon are lattice-based schemes, with considerations for both depending on the context of application--Dilithium is easier to implement, while Falcon yields shorter signatures [9]. Sphincs+, although slower and larger, is significant because it is based on a different mathematical approach, i.e., hash functions. This diversification offers a fail-safe against unforeseen vulnerabilities in lattice schemes. Furthermore, a \"hybrid mode\" is permitted, enabling the use of quantum-resistant algorithms alongside already approved NIST algorithms without compromising FIPS validation [10].  
While the NIST PQC process provides a roadmap, it is crucial to also consider implementation, integration into existing systems, and potential adversarial strategies. From a research and development perspective, this demands interdisciplinary expertise in quantum computing, cybersecurity, and algorithmic theory.
## 4 Discussion
### The Impact of Variable Selection on the Accuracy of Carbon Storage Estimation  
A common limitation of optimal sensors is that they cannot accurately capture changes in aboveground biomass and carbon storage. They can measure three-dimensional forest structure, but cannot obtain three-dimensional forest structure information. In this study,GEE was used to acquire pre-processed Landsat 9 images. Images with less than 2% cloud cover in the vegetation growing season (March to May) in 2022 were obtained. Previous studies have shown that the application of different feature combinations can effectively avoid the loss of information caused when using a single variable model and it also can determine the range of AGB saturation points based on different data [45]. To analyze the influence of different variables selection on carbon storage estimation results, we conducted a feature optimization of variables extracted from the GEDI and Landsat 9 images, as seen in Figure 7d,e. The estimation results of this study were similar to [PERSON]'s (who used regression with log transformations model to integrate the Sentinel-2 and GEDI image feature variables, wherein the estimated R\({}^{2}\) was 0.72 and the RMSE was 17.93 t/hm\({}^{2}\)) [46]. The results illustrated that the accuracy of carbon storage inversion using optical images or spaceborne LiDAR variables alone was lower than that of estimation by integrating multiple data sources.  
According to the results of the estimation model, combined with the characteristics of optical information and the calculation method of sample carbon storage, it can be seen that optical remote sensing can only obtain the reflection or radiation information of the vegetation surface. It had some limitations in estimating carbon storage. But the spaceborne LiDAR had the ability to acquire forest vertical structure information, resulting in better estimation accuracy for _Quercus aquifolioides_ carbon storage compared to optical remote sensing data. Hence, the combination of the two information sources reflected the characteristics of carbon storage from both canopy and vertical structures, so as to achieve complementary information and improve the estimation accuracy of carbon storage [47]. In consideration of the feature variables, we just chose one vegetation index (EVI) and one band reflectance (B1) to build the model, because the research suggested that vegetation indices sensitivity is reduced in areas with dense vegetation [48]. Consequently, the correlation between vegetation indices and _Quercus aquifolioides_ carbon storage was lower than other feature variables. Moreover, this study discovered a strong correlation between different vegetation indices, which results in multicollinearity between variables and affected both the correlation between vegetation indices and biomass, as well as the results of random forest feature selection. Therefore, a collinearity diagnosis of the vegetation index extracted from Landsat 9 in SPSS 26 was performed, and some vegetation indices variables with strong collinearity were eliminated to reduce estimation errors.
# Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis
## 3 Uncertainty Flow Framework
### III. Model Uncertainty  
In order to craft model uncertainty, we rely on the quantitative analysis of posterior predictive distribution under Bayesian neural networks. As the production of posterior predictive distribution entails the computation of the intractable parameter posterior, the common way is to approximate it via minimising the KL-divergence [23; 31] (\(KL(q||p)=\sum_{\theta}q(\theta)log\frac{q(\theta)}{p(x|\theta)}\)) between approximated variational distribution, i.e., \(q(\theta)\) and true posterior, i.e., \(p(\theta|x)\), therefore solving for optimal posterior, i.e., \(q^{*}(\theta)\), becomes:  
\[q^{*}(\theta)=argmin_{q(\theta)\in Q}KL\big{(}q(\theta)||p(\theta|x)\big{)} \tag{13}\]  
As we cannot compute the KL-divergence directly, the common approach is to resort on optimising an alternative objective, i.e., maximising the ELBO(evidence lower bound), derived as  
\[ELBO(q^{*})=\mathbb{E}[logp(x|\theta)]-KL(q(\theta)||p(\theta)) \tag{14}\]  
Whereas \(ELBO\) can be seen as a sum of the expected log likelihood of the data with the negative divergence between the variational variance and the model priors. Then it is customary to use the mean-field variational family to complete the specification of the above-mentioned optimisation. The mean-field variational family for each latent model parameter, i.e., \(\theta\), can be defined:  
\[q(\theta)=\Pi_{j=1}^{m}q_{j}(\theta_{j}) \tag{15}\]  
Hence, finding the intractable posterior degrades to a coordinate ascent optimisation in obtaining the optimal \(\theta^{*}\) in maximising \(ELBO\) (cf. Algorithm 1 in [23] for detailed review). The learned optimal parameter posterior, e.g., \((\theta_{s}|x)\) serves as a surrogate to be used in the parameter posterior in target BNN, i.e., \((\theta_{t}|x)(\theta_{s}|x)\). The model uncertainty, which is distilled from the source task, is now flowed to the target task.
## 2 Analytical Derivation
### Effects of Self-Steepening  
SPM is the lowest-order nonlinear phenomenon based on the optical Kerr effect that provides symmetric spectral broadening. The next higher-order nonlinear phenomenon is self-steepening, which gives asymmetric spectral broadening. Here, we consider self-steepening together with TOD, while GDD is neglected for simplicity.  
A nonlinearly chirped pulse including TOD is expressed in the time domain as,  
\[E_{6}(t)=E_{0}\exp\!\left(-at^{2}-\frac{2\Phi^{\mathbf{\sigma}}}{3\omega_{0}}a^{2}t ^{3}\right) \tag{20}\]  
The optical intensity corresponding to the electric field is  
\[\left|E_{6}(t)\right|^{2}=E_{0}^{2}\exp\!\left(-2 at^{2}-\frac{4\Phi^{\mathbf{ \sigma}}}{3\omega_{0}}a^{2}t^{3}\right) \tag{21}\]  
From Equations (20) and (21)  
\[\frac{\partial}{\partial t}\!\left(\left|E_{6}(t)\right|^{2}E_{6}(t)\right)=-6 at\!\left(1+\frac{\Phi^{\mathbf{\sigma}}}{\omega_{0}}at^{2}\right)\left|E_{6}(t) \right|^{2}E_{6}(t) \tag{22}\]  
and the temporal profile including the self-steepening can be represented by  
\[\begin{split} E_{7}(t)=E_{0}\exp\!\left[-a(1-2 iB)t^{2}-\left\{ \frac{\Phi^{\mathbf{\sigma}}}{3}(1-2 iB)+6B\right\}\frac{2a^{2}t^{3}}{\omega_{0}} \right.\\ \left.-\frac{8B\Phi^{\mathbf{\sigma}}}{\omega_{0}^{2}}\!\left(1+\frac {a\Phi^{\mathbf{\sigma}}t}{\omega_{0}}\right)a^{3}t^{4}\right]\end{split} \tag{23}\]  
Neglecting \(t^{4}\) and higher-order terms, the Fourier transform of Equation (23) results in  
\[\begin{split}\tilde{E}_{7}(\omega)=\frac{E_{0}}{\sqrt{2a(1-2 iB)} }\exp\!\left[-\frac{\omega^{2}}{4a(1-2 iB)}+\frac{2(1-4B^{2})\Phi^{\mathbf{\sigma} }+9B(3-4B^{2})}{3a\omega_{0}(1+4B^{2})^{3}}B\omega^{3}\right.\\ \left.-i\frac{(1-16B^{4})\Phi^{\mathbf{\sigma}}+18B(1-12B^{2})}{6a \omega_{0}(1+4B^{2})^{3}}\omega^{3}\right]\end{split} \tag{24}\]  
The TOD of the output pulse resulting from SPM and self-steeping can be obtained from the imaginary part of the exponential function as  
\[\Phi^{\mathbf{\sigma}}_{OUT}=\frac{1-4B^{2}}{\left(1+4B^{2}\right)^{2}}\Phi^\mathbf{{\sigma}}+\frac{18B(1-12B^{3})}{\left(1+4B^{2}\right)^{3}} \tag{25}\]
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 3 Proposed Method
### Architecture of SR-SLNN  
Because SR-ILLNN has hierarchical network structure due to the two inputs, we propose a SR-SLNN to reduce the network complexity of SR-ILLNN. As depicted in Figure 3, the SR-SLNN was modified to remove the HR feature layers and the shared feature layers of SR-ILLNN. In addition, it has seven convolution layers and two deconvolution layers, where two convolution layers between deconvolution layers are also removed. Table 2 presents the structural analysis of network parameters in SR-SLNN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Kernel** & **Num. of** & **Padding** & **Output Feature Map** & **Num. of** \\
**Name** & **Size** & **Kernels** & **Size** & **(W \(\times\) H \(\times\) C)** & **Parameters** \\ \hline Conv1 & \(3\times 3\times 1\) & 64 & \(1\) & N \(\times\) M \(\times\) 64 & 640 \\ Conv2 & \(3\times 3\times 64\) & 64 & \(1\) & N \(\times\) M \(\times\) 64 & 36,928 \\ Conv3 & \(3\times 3\times 128\) & 64 & \(1\) & N \(\times\) M \(\times\) 64 & 73,792 \\ Conv4 & \(3\times 3\times 192\) & 64 & \(1\) & N \(\times\) M \(\times\) 64 & 110,656 \\ Conv5 & \(1\times 1\times 64\) & 32 & \(0\) & N \(\times\) M \(\times\) 32 & 2080 \\ Deconv1 & \(4\times 4\times 32\) & 32 & \(1\) & 2N \(\times\) 2M \(\times\) 32 & 16,416 \\ Conv6, 7 & \(3\times 3\times 32\) & 32 & \(1\) & 2N \(\times\) 2M \(\times\) 32 & 9248 \\ Deconv2 & \(4\times 4\times 32\) & 32 & \(1\) & 4N \(\times\) 4M \(\times\) 32 & 16,416 \\ Conv8 & \(3\times 3\times 32\) & 16 & \(1\) & 4N \(\times\) 4M \(\times\) 16 & 4624 \\ Conv9 & \(5\times 5\times 1\) & 64 & \(2\) & 4N \(\times\) 4M \(\times\) 64 & 1664 \\ Conv10 & \(3\times 3\times 64\) & 64 & \(1\) & 4N \(\times\) 4M \(\times\) 64 & 36,928 \\ Conv11 & \(3\times 3\times 128\) & 64 & \(1\) & 4N \(\times\) 4M \(\times\) 64 & 73,792 \\ Conv12 & \(3\times 3\times 192\) & 16 & \(1\) & 4N \(\times\) 4M \(\times\) 16 & 27,664 \\ Conv13, 14 & \(3\times 3\times 32\) & 32 & \(1\) & 4N \(\times\) 4M \(\times\) 32 & 9248 \\ Conv15 & \(5\times 5\times 32\) & 1 & \(2\) & 4N \(\times\) 4M \(\times\) 1 & 801 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of network parameters in SR-ILLNN.  
Figure 3: The framework of the proposed SR-SLNN.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Other Projects for Energy Management through ICT Tools
#### 4.4.4 EDISON  
ICT components and tools have been implemented in the \"EDISON\" project, in which a smart energy platform has been created that accommodates smart meters, sensors, actuators and power electronic devices. The project entails participants from five countries. Focus has been given on realising an efficient lighting system, in which sensors indicate when the lights should turn on and off in public buildings [95].
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 3 Results and Discussion
### Flow Field Prediction Analysis
To further illustrate the capability of the proposed model to capture different input features, the CFD calculated and model predicted fields are compared under different AOAs. Figure 12 shows the pressure and velocity fields for airfoil NACA9812 at the same Reynolds number with AOAs of 0\({}^{\circ}\), 4\({}^{\circ}\) and 6\({}^{\circ}\). We are not surprised to see that the predicted fields correspond closely to the calculated ones in both Figures, in which the error bands of velocity fields almost coincide with the wake. Reproduction of the calculated fields indicates that the proposed model is able to take advantage of the parameterized information within the input matrix and figure out the mapping between different element features and the input flow field, benefiting from the SDF representation of geometry features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **True (CFD)** &
\begin{tabular}{c} **Prediction** \\ **(CNN-DCNN)** \\ \end{tabular} & **Relative Error (\%)** \\ \hline (a) & 0.00908 & 0.00898 & 1.1\% \\ (b) & 0.00394 & 0.00389 & 1.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Drag coefficients for two cases in Figure 11. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Pressure Field** & **Velocity Field** \\ \hline Accuracy (\%) & 98.2302 & 99.6016 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean accuracy of the physical field prediction for the test data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data Preparation** & **Train Cost** & **Prediction Cost** \\ \hline CFD & 86.1 h & - & 3 min \\ CNN-DCNN (GPU) & - & 25 min & 24 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of time cost for the physical field prediction by the CNN-DCNN model and CFD simulation. The GPU is RTX 3080. Figure 11: The true (CFD) and predicted velocity gradient fields for three airfoils under \(Re=7\times 10^{6}\): (**a**) NACA3112 with \(6^{\circ}\) AOA and (**b**) NACA3312 with \(1^{\circ}\) AOA. Figure 10: The true (CFD) and predicted physical fields for three airfoils under \(Re=5\times 10^{6}\): (**a**) NACA2412 with \(5^{\circ}\) AOA; (**b**) NACA7412 with \(6^{\circ}\) AOA; (**c**) NACA9612 with \(1^{\circ}\) AOA. A more rigorous evaluation of the prediction quality is provided in Figure 13, where the contour lines of flow fields are directly compared, with different airfoil geometries, AOA, and Reynolds numbers. We could find out that the contours of the flow fields predicted by the CNN-DCNN model are consistent, if not coincident with those from the CFD simulations. The good match around the flow separation regions indicate that the proposed model has the potential to deal with highly non-linear problems. Figure 12: The true (CFD) and predicted physical fields for NACA9812 airfoil under \(Re=7\times 10^{6}\) across three AOAs: (**a**) \(0^{\circ}\); (**b**) \(4^{\circ}\); (**c**) \(6^{\circ}\).
# Effectiveness of Laser-Assisted Teeth Bleaching: A Systematic Review
## 3 Results
### General Characteristics of the Included Studies  
The selected studies assessed the change in the color of teeth and dental materials as a result of carbamide peroxide (CP) and hydrogen peroxide (HP) whitening activated by laser light. To assess color, researchers most often used a spectrophotometer and its parameters using the CIE L**a***b*** system. It is a color space created by the International Commission on Illumination (CIE) for the purpose of the standardization and repeatability of measurements. The L* axis contains shades of gray, numerically ranging from 0 to 100, where 0 is black and 100 is white. The a* axis is referred to as \"red-green\". At its negative end there are shades of red, and at its positive end there are shades of green. The b* axis, called \"blue-yellow\", is created analogously. The blue color is described by negative numbers, while the positive numbers are reserved for the yellow color. The tested color is described as a point in three-dimensional space. It is defined by three numerical values that change linearly. If two colors are written in the CIE L**a***b*** color space, you can calculate the number \(\Delta\)E (the number of the difference between the colors), which is the distance between these colors in the three-dimensional CIE L**a***b** color space, using the formula \(\Delta\)E*** = (\(\Delta\)L***2** + \(\Delta\)**a***2** + \(\Delta\)**b***2**)1/2 [30; 31]. Some authors also calculated the Whitening Indexfor Dentistry (WI\({}_{\text{D}}\)) using the formula WI\({}_{\text{D}}\) = 0.511 L* \(-\) 2.324 a* \(-\) 1.100 b* [1; 3]. Another system used is Vita Classical Shade Guide, where we deal with 16 shades arranged from the lightest B1 (1) to the darkest D4 (16). To calculate the difference in color change, the authors used numerical values corresponding to individual shades and called them Shade Guide Units (SGU) [32].
# Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
###### Abstract
They did not use any data-balancing techniques [6]. [PERSON] et al. (2022) built a framework for diabetes prediction called the intelligent diabetes mellitus prediction framework (IDMPF) with an accuracy of 83%. They employed a dataset called the Pima Indian Diabetes (PID) dataset. The result of their model is still not good enough, and there is scope to improve it [7]. [PERSON] et al. (2020) proposed a model for the prediction of type 2 diabetes in the future, and they achieved a 95.94% accuracy [8]. The collected dataset used in their study was from the San Antonio Heart Study, a widely prescribed investigation. The study was successful, but the number of features used was only 11, which is an insufficient number to build and validate a machine learning model. [PERSON] et al. (2020) assembled classifiers to propose a model with an AUC of 0.95. In this work, they utilized the Pima Indian Diabetes (PID) dataset [9]. [PERSON] et al. (2021) proposed an ensemble Weighted Voting LRRFs ML model with an AUC of 0.884 for type 2 diabetes prediction [10]. The collected dataset in their study was from the English Longitudinal Study of Ageing (ELSA) database. In this study, there were not any feature analysis techniques. [PERSON] et al. (2022) proposed a machine learning (ML) based model of the Fused Model for Diabetes Prediction (FMDP) and obtained an accuracy of 94.87% [11]. For this study, they used a dataset collected from the hospital of Sylhet, Bangladesh. Their study method was well designed, but they did not show any feature impact on the model and conducted no feature analysis. [PERSON] et al. (2020) introduced a model combining logistic regression (LR) feature selection and random forest (RF), which gave an accuracy of 94.25% and an AUC of 0.95 [12]. The National Health and Nutrition Examination Survey conducted from 2009 to 2012 was used in their research. The dataset has only 14 features. [PERSON] et al., in 2010, conducted a study for diabetes mellitus prediction using a support vector machine (SVM) with an accuracy of 94%, a sensitivity of 93%, and a specificity of 94% [13]. In this study, they did not introduce any feature analysis techniques. Therefore, in this research, we proposed a machine learning (ML)-based model for diabetes prediction at an early stage. In recent years, ML has proven to be a very efficient technique for disease prediction. Nowadays, ML plays an essential role in the biomedical sector to overcome traditional methods of diagnosis, disease prediction, and treatment. Consequently, there is no doubt about using an ML-based prediction model to predict diabetes. Our contributions are mentioned as follows:  
* Building an ML model that will predict diabetes using socio-demographic characteristics rather than clinical attributes. This is relevant because not all people, especially those from lower-income countries, have access to their clinical features. * Revealing significant risk factors that indicate diabetes. * Proposing a best fit and clinically usable framework to predict diabetes at an early stage.