esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Relative Quantification of N-glycans by MALDI-ToF MS  
An additional goal of this research was to identify a sensitive, specific, and rapid method to determine relative quantities of \(N\)-glycans to facilitate assessment of enzyme activity. Spectrophotometric methods involving phenol-sulfuric acid have been used, but lack selectivity for certain saccharide types and tend to be affected by other compounds present in the sample, not only the \(N\)-glycan products of this reaction [20; 21; 22]. Additionally, the diverse array of monosaccharides present in \(N\)-glycans adds a layer  
Figure 1: Protein immobilization yield for amino, adsorption, and epoxy immobilization methods, measured fluorometrically by Qubit Protein Assay Kit. Error bars represent one standard deviation and means followed by different letters (a, b, and c) are statistically different at \(p<0.05\).  
of complexity leading to inconsistent responses in these assays [21]. As an alternative, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry MALDI-ToF MS has been used for relative quantification of various carbohydrates [23; 24]. It is known that MALDI-ToF, albeit rapid and effective in providing results, may suffer from low reproducibility due to inconsistent crystallization of the samples on the target plate, leading to shot-to-shot variation. These disadvantages can be overcome by using an internal standard [25]. Additionally, MALDI has been used to study enzymatic reactions including both large and small substrates/products [26; 27; 28].  
Linearity of the 3-FL (3'-fucosyllatose)-spiked released \(N\)-glycan system was evaluated. An example of a typical annotated mass spectrum of \(N\)-glycans can be seen in Figure 2. The theoretical _m/z_ for sodiated 3-FL, Man 5, Man 6, Man 7, and Man 8 are 511.16, 1054.34, 1216.40, 1378.45, and 1540.50 respectively, accounting for one fewer \(N\)-acetylglucosamine residue according to cleavage by Endo BI-1. The most abundant glycan released from RNase B was Man5 (_m/z_ 1054), with larger glycans being less abundant, which was consistent with previous literature [29]. Bovine RNase B was chosen as a model glycoprotein due to its single \(N\)-glycosylation site and straightforward glycan composition.  
Over the examined order of magnitude range, the normalized response plotted against \(N\)-glycan/LS (internal standard) ratio was linear (R\({}^{2}\) = 0.995, Figure S2). A high R\({}^{2}\) value indicates that analysis of released \(N\)-glycans in presence of LS. analyzed by MALDI-ToF MS is a suitable tool for rapid relative quantification of \(N\)-glycans needed for evaluating kinetic parameters over a substantial range of concentrations and amounts of reactant.
# A Pulse Shaping Based Optical Transmission System of 128 QAM for DWDM with N \(\times\) 904 Gbps
## 3 System Architecture and Transceiver Units
The received signals are described by:  
\[S(k)=C(k)\cdot\mathrm{e}^{j\cdot(2\pi\cdot\Delta f\cdot k\cdot T+\phi_{k})}+n(k) \tag{11}\]  
where \(C_{k}\) are symbols, \(T\) is the symbol period, \(\Delta f\) is the carrier frequency offset, \(\phi_{k}\) is the carrier phase, and \(n(k)\) are zero-mean Gaussian-random-variables. We can then deduce the frequency offset estimate (FOE) which is based on the maximization of the period-gram of \(S^{\prime}(k)\) as shown below [24]:  
\[\left\{ \begin{array}{l}S^{\prime}(k)=\frac{1}{\gamma}\cdot\arg\{\max[\left|Z (f)\right|]\}\\ Z(f)=\frac{1}{N}\sum\limits_{k=0}^{N-1}S^{\prime}(k)\cdot\mathrm{e}^{j\cdot(2\pi \cdot\Delta f\cdot k\cdot T+\phi_{k})}\end{array} \right. \tag{12}\]The blind phase search (BPS) algorithm [25] is utilized to recover and subsequently to remove the remaining phase mismatch between the signal and the local oscillator. We adopted the BPS algorithm for the carrier phase estimation (CPE).
# Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures
## 2 Kohonen Neural Networks for Clustering Problem
### Proposed Algorithms
The SOM algorithm was better than its greedy version in the vast majority of cases. The accuracy of the clustering in batch mode is similar to the situation with the objective function. Applying a greedy heuristic to the BVQ algorithm improves the clustering accuracy. However, the greedy version of the SOM algorithm exhibits less clustering accuracy. Figure 5: Coefficient of variation of the objective function value for the four-batch mixed lot. ## 5 Conclusions

In our work, we proposed algorithms for products clustering based on a Kohonen network and self-organizing Kohonen maps using the greedy agglomerative heuristic procedure in online and batch modes. We performed experiments with different distance measures (Euclidean, squared Euclidean, Manhattan, Chebyshev, Mahalanobis), different ways of neuron weights initialization (average, random, k-means) and different numbers of extra neurons in the greedy heuristic procedure. The studies have shown that the used distance measure, in most cases, does not significantly affect the clustering accuracy. The way of neuron weights initialization plays a role for the stability of the objective function: the coefficient of variation for any type of mixed lot composition was higher (worse) with k-means initialization. In batch mode, in the vast majority of cases, the minimal objective function value was demonstrated by the SOM algorithm without the influence of the initialization method or distance measure. The computational experiments showed that the use of the greedy agglomerative heuristic in online mode, in most cases, improves the accuracy of homogeneous batch separation. In batch mode, the greedy heuristic improves the accuracy for the vector quantization algorithm and, on the contrary, reduces the accuracy for self-organized maps. The study of the batch mode and online mode of algorithms for clustering products using the greedy agglomerative heuristic procedure on a large number of homogeneous batches is an interesting area for further research. Conceptualization, G.S., L.K. and E.T. ; methodology, G.S. and L.K. ; software, G.S. and L.V. ; validation, G.S., N.R. and E.T. ; formal analysis, L.K. ; investigation, L.V. ; resources, L.V. and L.K. ; data curation, L.K. ; writing--original draft preparation, G.S. and E.T. ; writing--review and editing, L.K., G.S., N.R. and E.T. ; visualization, G.S. ; supervision, L.K. ; project administration, L.K. ; funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Higher Education of the Russian Federation, project no. FEFE-2020-0013. Not applicable. The authors declare no conflict of interest.
## 5 Discussion
### On Technology Advancement  
Immersive VR technology is under constant change, with new hardware and software being released at a rapid pace. The technology employed in Four Season can be considered obsolete in 2024, as the latest HMDs, such as Varjo XR-4, Meta Quest 3, Apple VisionPro, and HTC VIVE XR Elite, offer built-in high-precision hand tracking, high-resolution displays, and other sensors to track the user's gestures and actions. We expect that future immersive experiences will happen in mixed reality with various degrees of overlay between the real world and the virtual world. At the same time, artificial intelligence (AI) technologies, such as voice cloning, natural language processing, and generative AI models, enable the provision of natural interaction with AI-driven virtual characters.  
The objective of this study was to investigate the user experience of Four Seasons and, therefore, identify potential presence factors that can facilitate the design of future immersive VR experiences. Despite the use of old technology, or perhaps due to it, we identified 23 potential presence factors that can inform the design of immersive VR experiences with recent technology. For example, tracking errors of Leap Motion helped us to identify presence factors related to hand gesture synchronization, hand animation simulation, and natural hand movement; although such errors are not likely to occur so much with the latest hardware, it is important to acknowledge their effect on the user experience. Moreover, potential presence factors related to the content (e.g., aesthetics, realism of environment, interactions with the animals) are relevant to any immersive VR hardware configuration and especially important for experiences presented via standalone HMDs that have limited rendering capacity.
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 5 Analysis and Results
Table 2 shows the dependency between quantized parameters and their effects on NICS and accuracy. Quantizing the input, which corresponds to the I/Q signal used to train the model, does not significantly affect accuracy. For instance, the correlation coefficient has values of 0.295 and 0.275, corresponding to NICS and accuracy, respectively, representing low dependency,where the highest correlation coefficient is one. Regarding activations, the correlation shows that they have a mid-strong relationship with accuracy, higher than NICS. This means that quantizing the activations impacts the accuracy (e.g., does not heavily reduce the accuracy) but barely affects the inference cost. On the contrary, quantizing the weights correlates strongly with NICS and, to a lesser extent, with accuracy. With a correlation of 0.955, it is clear that the parameters that, when quantified, have the most significant impact in reducing inference cost are the weights. Moreover, it can also be seen that the accuracy is barely affected, having a value of 0.790, which is considered to be high. This indicates that we can quantize the weights to obtain similar accuracy at a reduced inference cost regarding the reference model. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Total BOPS** & **NICS** & **Accuracy** \\ \hline
**Input** & 0.351 & 0.295 & 0.275 \\
**Activations** & 0.470 & 0.336 & 0.442 \\
**Weights** & **0.893** & **0.955** & **0.790** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Degree of correlation between the quantified parameters (input, activations, weights) and the total BOPS, accuracy, and NICS. The weight is the parameter with the highest correlation. Figure 4: Effect of quantization in the VGG10 1D-CNN architecture regarding the accuracy and inference cost. Inputs, activations, and weights are quantized in a range of 2 to 8 bits in the first stage (in aquamarine). Points in different colors represent the degree of quantization in the weights (see the second-stage methodology). In blue, the points with the best solutions obtained thus far with the Pareto front are highlighted.
## 3 Methodology
### Feature Selection Based on MC Analysis  
The feature selection module was executed using MC analysis. This process allows for the identification of essential features to propose a simpler yet accurate model. We assessed collinearity among the selected features from health examinations, nutrition, and basic information concerning diabetes using MC in regression analysis [32]. MC is a statistical term used when two or more input attributes exhibit strong correlation [33]. Certain attributes were removed when highly correlated variables were present. We evaluated the results in terms of tolerance and the variance inflation factor (VIF) using MC analysis. An MC problem arises when the VIF value exceeds 10 and the tolerance falls below 0.10. After this analysis, we retained 36 features that were used as inputs for subsequent analysis of the two datasets. The comprehensive results of the correlation and MC analyses of the target dataset are presented in Table 2. The highest VIF was observed for \"Attempted suicide for 1 year\" at 5.171, followed by \"Counseling for Mental Issues for 1 Year\" (4.261), which was notable as it diverges from the common symptoms associated with diabetes. Predictors such as \"glycated hemoglobin\" and \"fasting blood sugar\" had the highest VIF scores of 3.546 and 3.173, respectively. A VIF ranging from 1 to 5 indicated that the predictors were not highly correlated, and could be considered when building a diabetes prediction model.
# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 4 Performance Evaluation
### Evaluation of Confidence Using Novelty Detection  
As mentioned in Section 3.2, in order to further improve the accuracy of the localization system, we introduced the novelty detection test. This test evaluates whether a sample belongs to a certain class (KNN predicted class/position). This approach still uses the threshold \(\alpha\), but uses the novelty detection to improve even more the evaluation of confidence.  
Similar to the previous section, Figure 10 shows the relationship among the value of the threshold \(\alpha\) (\(x\)-axis), the percentage of accepted estimations (in blue), and the final accuracy of the accepted classifications (red curve in Figure 10a), the average localization error (red curve in Figure 10b), and the room accuracy (red curve in Figure 10c).  
Figure 9: Cumulative distribution function of the localization error using prediction probability with thresholds \(\alpha=25\%\), 45%, and 75%: it shows the percentage of estimations (\(y\)-axis) with an error smaller than the \(x\)-axis.  
As we can see, the novelty detection was indeed able to improve both the localization accuracy and the average error. However, it was at the cost of a lower number of accepted predictions. For instance, compared to the prediction probability in Section 4.2, as we can see in Figure 10a, for a threshold of \(\alpha=40\%\), the classification accuracy is near \(45\%\), increased from \(40\%\). As for the average localization error, shown in Figure 10b, it decreased from \(1.8\) m to \(1.7\) m. However, in both cases, the accepted predictions went from \(85\%\) to \(68\%\). In the case of room accuracy, it increased from \(96.4\%\) to \(97.7\%\), while the accepted predictions decreased from \(99.6\%\) to \(87\%\).  
On the other hand, for a threshold \(\alpha=80\%\) the results do not change significantly since, in most cases, the estimations with high prediction probabilities also passed the test for novelty detection.  
Finally, Figure 11 shows the cumulative distribution function for the thresholds \(\alpha=25\%\), \(45\%\), and \(75\%\). When compared to the previous results, we can notice some improvements in the localization errors. For instance, when using a threshold \(\alpha=25\%\) and considering the estimations with errors lower than or equal to \(2\) m, the number of estimations increased from \(68\%\) to \(72\%\).  
As we can see, even though the novelty detection is able to improve accuracy, it was at the cost of accepted predictions. In the next section, we will show the results for our hybrid approach for the evaluation of confidence, that tries to minimize this problem.
## 2 Materials and Data
### Dataset Construction  
LabelImg was used as the annotation tool for creating the seedling detection dataset. To avoid missing any seedlings, each image was manually annotated to obtain the positional information of seedlings in both qualified and floating states, completing the construction of the seedling planting condition dataset. The dataset was divided randomly into training, validation, and test sets in a ratio of 7:1:2. This division helps address overfitting and underfitting issues. The dataset consists of 1913 training images, 273 validation images, and 546 test images.
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
## 4 Improved System Description
### Improved IEEE 9 Bus System Modeling with Different SCR Value
The maximum reactive power output of STATCOM is independent of the bus voltage level, while that of SVC is strongly affected by the bus voltage level.
# Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product
## 2 Methods
### fMRI Data for Mental Arithmetic Task
#### 2.5.5 Functional Connectivity Analysis: Derivation of Correlation Matrices  
For the functional connectivity analysis, the functional images preprocessed using SPM12 were further processed using the CONN toolbox [32]. In detail, nuisance regression was performed using an anatomical component-based noise correction method (aCompCor) [33], in which the first five principal components of signals from the white matter and cerebrospinal fluid masks, head-motion parameters, their first-order temporal derivatives, and the main task effect from the task block (modeled as a canonical hemodynamic-response-function-convolved response) and its first order derivatives were regressed to eliminate physiological noise and potential confounding effects of the task responses.  
To calculate functional connectivity during the tasks, the preprocessed images were parcellated into 116 regions, including 90 cerebrum regions and 26 cerebellum regions defined via automated anatomical labeling (AAL); the mean Blood-Oxygen-Level-Dependent (BOLD) time course was then calculated for each region. Subsequently, Pearson correlations among BOLD time courses of the 116 regions were calculated and then Fisher-z transformed. As a result, a \(116\times 116\) correlation matrix was constructed for each participant. In this study, we chose 90 cerebrum regions as the ROIs and used the corresponding \(90\times 90\) correlation matrices for the analysis because the WM system is considered associated with the cerebrum regions [34].
# Evaluating the Impact of Artificial Saliva Formulations on Stainless Steel Integrity
## 3 Results and Discussion
### Hardness Analysis  
The hardness analysis of the stainless steel samples was conducted at three points for each sample. For the 316 stainless steel blank sample, we obtained an indentation with the following diagonal dimensions D1 = 100 \(\upmu\)m and D2 = 99.51 \(\upmu\)m and the hardness obtained is 186.3 HV; the Rockwell conversion is approximately 55.3 HRA which is within the range of typical values for stainless steel. Table 4 presents the results of the Vickers hardness (HV) and Rockwell hardness (HRA) tests for samples stored in artificial saliva by Afnor, Fletcher, and Fusayama/Meyer after 7 and 28 days. All of the samples showed an increase in Vickers hardness (HV) and Rockwell hardness (HRA) after 28 days compared to 7 days. In the case of the Afnor sample, the increase was from 196.1 HV to 234.9 HV, suggesting moderate hardening, possibly due to surface passivation or precipitation. The Fusayama/Meyer sample showed a smaller hardness increase (from 191.1 HV to 204.7 HV), which may be linked to corrosion product layer formation with a limited strengthening effect. In the case of the Fletcher saliva sample, this showed the most pronounced increase (from 191.3 HV to 256.9 HV), indicating the possible deposition of hard crystalline corrosion products or sub-surface hardening due to localized attack.  
Figure 6: FTIR spectra of 316L stainless steel initially and stored for 7 and 28 days in artificial saliva by Afnor, Fletcher, and Fusayama/Meyer.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Wavenumber (cm\({}^{-1}\))** & **Possible Assignment** & **Interpretation** \\ \hline \(\sim\)3414, 3438 & O–H stretching (H\({}_{2}\)O, hydroxyls) & Surface hydration or hydroxylation, strongest in \\  & & Fusayama/Meyer for 28 days \\
2916, 2848 & C–H stretching & Possible organic residue adsorption from artificial saliva \\
1652, 1543 & Amide I/II, H–O–H bending & Associated with water or organic layer accumulation \\
1463, 1386 & C–H bending, COO\({}^{-}\) & Presence of degraded organics or adsorbed molecules \\
1230, 1078 & P=O, P–O–C, S=O & Phosphate/sulfate species, intense in Fusayama/Meyer \\
718, 669 & Metal–O, Cl\({}^{-}\) & Cr–O, Fe–O, or chloride species, evident in Fletcher \\
429, 671 & Metal oxide lattice & Broadening indicates surface oxide changes over time \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interpretation of FTIR spectra for samples stored in artificial saliva.  
These trends correlate with the SEM and EDX observations--the fluoride-rich Fletcher environment promoted crystalline surface alteration.
## 2 Methods and Materials
### Statistical Machine Learning Models and SHAP Method
#### 2.4.6 XGBoost and LightGBM  
XGBoost falls into the category of tree boosting, a highly effective and widely used machine learning method. Specifically, XGBoost is a scalable, end-to-end tree boosting system that has gained significant popularity among data scientists for delivering state-of-the-art results in numerous machine learning challenges [45]. LightGBM, which stands for Light Gradient-Boosting Machine, is a high-performance, open-source framework for gradient boosting that leverages tree-based learning algorithms. Originally developed by Microsoft, LightGBM is widely used for tasks such as ranking, classification, and other machine learning applications. The framework implements various boosting techniques, including Gradient Boosting Tree (GBT), Gradient Boosting Decision Tree (GBDT), Gradient Boosted Regression Tree (GBRT), Gradient Boosting Machine (GBM), and Multiple Additive Regression Tree (MART). Designed for distributed and efficient computation, LightGBM offers several advantages, including faster training speeds, lower memory usage, enhanced accuracy, and support for parallel, distributed, and GPU-accelerated learning. Furthermore, its capability to handle large-scale datasets makes it an excellent choice for modern machine learning challenges.
## 6 Results
### Total RE Installed Capacity vs. Different Policies
#### 6.3.3 Results of the Total RE Installed Capacity vs. Different Policies Analysis for the USA  
The USA regression model can be depicted as illustrated in Table 15:  
\[y=\ 128.413\ +\ 7.487\ x_{1}-39.515\ x_{2}+6.831\ x_{3} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Coefficients** & **Standardized Coefficients** & **Correlation** & **F stats** & **Adjusted R2** \\ \hline Constant & 46.774 * & - & - & \\ \hline EI & 6.523 * & 0.542 & 0.942 * & \\ \hline IE & 12.899 * & 0.307 & 0.84 * & 68.919 * & 0.923 \\ \hline PS+RI & 1.837 * & 0.208 & 0.891 * & \\ \hline \hline \end{tabular} Note: * \(p\) value \(\leq\) 0.01, and * \(p\) value \(>\) 0.1. Dependent variable = total RE capacity; independent variables = EI, IE, and PS + RI.  
\end{table}
Table 14: Statistics of the model of Brazil.  
Figure 7: Homoscedasticity of the China model.  
The coefficients of \(x_{1}\) and \(x_{3}\) are positive while \(x_{2}\) has a negative coefficient. This means that the EI, RD&D, PS, and RI policies have a positive effect on the installed total RE capacity, while the remaining policy (IE + VA) has a negative effect. The coefficients of Equation (4) have \(p\)-values equal to 0.007, 014, and 0.078, respectively, which point to a statistical significance level \(<\)0.1. Additionally, the correlation factors are 0.814, 0.723, and 0.795, respectively, which indicate the existence of linear relations between the total RE capacity and the policy variables. Moreover, \(\text{R}^{2}_{\text{adj}}\) is found to be 0.889, which indicates that 88.9% of the total RE capacity variation is explained by the policy variables and 11.1% is due to random errors. Additionally, it is indicated that the significance level of F-stats is less than 0.01, which shows a good fit for the model. It is worthy to indicate that the first category (EI + RD&D) has the largest weight; therefore, it is a more effective policy for the total RE capacity in the United States. This is attributed to allocating $30 billion to produce 3.1 GW from a geothermal project using the public investment and loans [21]. Additionally, subsidies and grants were given to RE projects to produce 188 MW from concentrated solar power and 95 MW from hydropower [21]. In addition, over $80 billion was provided to support clean energy RD&D [25].