esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Bidirectionally Pretrained Taggers as Feature Generators
## 2 Materials and Methods
For TreeTagger and NLTK (using the multinomial Naive Bayes), probabilities were readily available, and for the three RNN-based taggers, RNNTagger, spaCy, and Stanza, a Softmax (normalized exponential) function was used. \[\text{Softmax}(x_{i})=\frac{\exp{(x_{i})}}{\sum_{j}\exp{(x_{j})}} \tag{1}\]  
where \(x_{i}\) is an input vector and \(x_{j}\) is and output vector. This was applied to the output tensors to procure the actual probabilities. Using this method, a vector of probabilities was output by all taggers for each token in the tagged test set. To test how unique each tagger's outputs were, we used these vectors to calculate the average Cross-Entropy Loss [CEL] using the formula  
\[\text{CEL}(g,y)=-\sum\hat{g}\text{log}\,y \tag{2}\]  
where \(\hat{g}\) is the predicted vector and \(y\) is the target vector for every token for every pair of taggers. This was used to detect pairs giving results that were too similar to justify the inclusion of both taggers in the composite system. These cross-results are shown in the upper half of Table 1.  
For each tagger, we also calculated the total average CEL, the CEL against correct annotations (test set CEL), and the difference between the two (uniqueness score) as well as counting unique correct annotations (UCA). Each time a tagger correctly predicted a POS tag for a token that no other tagger predicted correctly, it was awarded one UCA point. These additional metrics are shown in Table 1 (bottom half) and indicate how good and how unique each of these taggers is and which of them satisfy the composite system requirements. Suitable taggers had to have a high average CEL and a low test set CEL, yielding a high uniqueness score and a number of UCA points, the higher the better. NLTK was immediately identified as an outlier with a negative uniqueness score (\(-0.63\)), despite a very high average CEL (\(8.42\)), and most importantly, it gained no UCA points. RNNTagger, Stanza, and spaCy were all built upon the same RNN technology and trained on the same dataset, so their results were bound to be similar, as demonstrated by the smallest CEL between these taggers shown in Table 1. However, the RNNTagger was the only tagger other than NLTK with a negative uniqueness score (\(-0.84\)), with only seventeen UCA in the set of over 30,000 tokens. Further inspection showed that most of its correct annotations were also correctly annotated using the spaCy or Stanza taggers but not vice-versa. Upon reflecting on the results, we decided to drop NLTK and RNNTagger from further experiments and focus on bidirectional training of the TreeTagger, spaCy, and Stanza taggers and their integration into the composite environment.
# Driver Clustering Based on Individual Curve Path Selection Preference
## 3 Results
### Proposed Workflow  
In the previous sections, a proof of the personalization concept of lane centering systems has been introduced. Architecture-wise, a two-step approach is proposed:  
1. Data of reference drivers \(\mathcal{D}^{\text{ref}}\) is collected, the LDM parameters of these drivers are calculated offline, using (20) to form the driver clusters \(c=\begin{bmatrix}c_{1}&c_{2}&\cdots&c_{K}\end{bmatrix}\). This is called the referencing phase.
2. Then, new test drivers, who are not part of the reference group, are measured, and their parameter vector is calculated online using the EKF algorithm given in Section 2.4. Using the identified parameter vector, the new driver is classified into one of the driving clusters (27) and (28). This is called the personalization phase.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Classification Results**} \\ \hline Driver group match & 27 \\ Driver group mismatch & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results of all drivers into driving style groups. Match means that classification based on the estimated parameters resulted in the same driving group as classification based on the reference parameters.  
Figure 7: Dependency of the estimation error on the initial covariance value \(\sigma_{0}\). The estimation accuracy is not impacted significantly by choosing different initial covariance.  
This two-step approach is illustrated in Figure 8. In general, the proposed workflow can cover multiple curve driving styles, out of which in this study we identified two: dynamic and cautious. With the simple solution of EKF and the classification based on the parameter vector error to the cluster centroid parameters, implementation is possible even in environments with low available memory.
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 2 Basic Theory and Algorithm
### Feature Selection
#### 2.2.2 Maximum Information Coefficient (MIC)  
[PERSON] et al. [39] proposed the MIC theory and solution method, focusing on the linear and nonlinear metric relationships between variables, and further exploring the non-function dependencies between variables through this metric relationship. The MIC mainly uses mutual information as an indicator of the degree of correlation between variables and meshing methods are used for calculation.  
Given variable \(A=\{a_{i},i=1,2,\cdots n\}\) and variable \(B=\{b_{i},i=1,2,\cdots n\}\), where \(n\) is the number of samples, the mutual information \((MI)\) is defined as follows:  
\[MI(A,B)=\sum_{a\in A}\;\sum_{b\in B}p(a,b)\log\frac{p(a,b)}{p(a)p(b)},\]  
where \(P(a,b)\) is the joint probability density of \(A\) and \(B\), and \(P(a)\) and \(P(b)\) are the boundary probability densities of \(A\) and \(B\), respectively.  
Suppose set \(D=\{(a_{i},b_{i}),i=1,2,\cdots n\}\) is a set of finite ordered pairs. It defines a division \(G\), which is used to divide the value range of variable \(A\) into \(x\) segments and divide the value range of variable \(B\) into \(y\) segments. \(G\) is a grid with a size of \(x\times y\). Calculate \(MI(A,B)\) within each grid partition obtained, since the same grid can be divided several ways. The maximum value of \(MI(A,B)\) under different division methods is chosen as the \(MI\) value of a division \(G\).  
The maximum mutual information formula of \(D\) under a division is defined as \(MI^{*}(D,x,y)=\max MI\left(D\left|G\right.\right)\), where \(D|G\) denotes data \(D\) are divided by \(G\). The maximum information coefficient \((MIC)\) uses \(MI\) to indicate the quality of the grid; a feature matrix is formed by maximum normalized \(MI\) values under different divisions. The feature matrix is defined as \(M(D)_{x,y}\) and the formula is \(M(D)_{x,y}=\frac{MI^{*}(D,x,y)}{\log\min\{x,y\}}\).  
\(MIC\) is defined as: \(MIC(D)=\max_{xy<B(n)}\{M(D)_{x,y}\}\), where \(n\) is the sample size of the sample and \(B(n)\) is a function of sample size and represents the upper limit of the grid \(x\times y\). Generally, \(\omega\left(1\right)\leq B\left(n\right)\leq o\left(n^{1-\varepsilon}\right)\), \(0<\varepsilon<1\). We set \(B\left(n\right)=n^{0.6}\) in the experiment [39].  
Suppose feature set \(F=\{f_{1},f_{2},\cdots,f_{m},R\}\). The number of features is \(m\) and the real RUL values are \(R\). \(MIC\) is used to define the correlation between feature \(f_{i}\) and real RUL values \(R\) as \(MIC(f_{i},R)\). Similarly, \(MIC\) is used to define the correlation between feature \(f_{i}\) and feature \(f_{j}\) as \(MIC(f_{i},f_{j})\). We prefer to select larger \(MIC(f_{i},R)\) and smaller \(MIC(f_{i},f_{j})\) features to form a set of bearing degradation indicators.  
To reduce the dimension of the bearing degradation indicator set feature data, we propose the following three-stage bearing degradation indicator set construction framework based on feature subsets fusion method.
# Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods
## 1 Introduction  
Machine learning (ML) uses algorithms to identify and predict useful patterns from data. Although it has found success in many areas, the results of multimedia mining are not satisfactory. ML in multimedia application extracts relevant data from multimedia files, such as audio, video, and still images, to perform similar searches, identify associations, and perform entity identification and classification. CNN emerged as a new breakthrough in the fields of data mining and AI, and has proven useful in both data analysis and application. In addition, CNN has made great progress in the area of multimedia. CNN is a field of machine learning that is applied in smart phones for face recognition and voice commands. Additionally, CNN technology contributes to the development of algorithms for the safety and security of multimedia data and the development of new applications.  
This Special Issue will share the achievements of key researchers and practitioners in academia, as well as in the industry, dealing with a wide range of theoretical and applied problems in the field of multimedia.
## 5 Conclusions  
The microstructure evolution and deformation behavior of the Al-1.2 Mg-0.7 Si-1.0 Cu-0.1 Sc-0.2 Zr alloy after the homogenization annealing and multidirectional forging (MDF) to a cumulative strain of \(\sum\)e = 14.4 in a temperature range of 150-350 \({}^{\circ}\)C were studied. The main conclusions are drawn as follows:  
* Two Sc-bearing phases were formed in the studied alloy during solidification: the Cu-rich W(AlCuSc)-phase with dissolved Si atoms and the Cu-free V(AlSc\({}_{2}\)Si\({}_{2}\)) phase were suggested. A minor amount of particles of these phases with a size in a range of 0.2-2 \(\upmu\)m were also observed after homogenization annealing and MDF. The dispersions of the L1\({}_{2}\)-Al\({}_{3}\)(Sc,Zr) phase with a mean size of 10 \(\pm\) 1 nm were precipitated during homogenization annealing.
* Due to the Zener pinning effect of nanoscale L1\({}_{2}\) phase dispersoids and a large cumulative strain of 14.4, the fine-grained structure with a mean grain size of 1.2-2.1 \(\upmu\)m was formed after MDF.
* Fine-grained as-forged at 350 \({}^{\circ}\)C alloy exhibited the maximum elongation to failure of \(\sim\)300% at 460 \({}^{\circ}\)C in a strain-rate range of 2 \(\times\) 10\({}^{-3}\)-1 \(\times\) 10\({}^{-2}\) s\({}^{-1}\); at room temperature, the alloy exhibited yield strength of 326 \(\pm\) 5 MPa, ultimate tensile strength of 366 \(\pm\) 5 MPa, and elongation at fracture of 10 \(\pm\) 3%.
* The hot deformation behavior was described using the Arrhenius type constitutive model. The developed model demonstrated a high predictability accuracy with a maximum average absolute relative error of 6.6%.  
Conceptualization, A.G.M. and A.V.M.; methodology, L.Y.K. and V.V.P.; software, A.O.M.; validation, A.G.M. and V.V.P.; formal analysis, A.O.M.; investigation, A.G.M. and L.Y.K.; resources, A.G.M.; data curation, A.O.M.; writing--original draft preparation, A.G.M.; writing--review and editing, A.G.M. and V.V.P.; visualization, A.O.M.; supervision, A.V.M.; project administration A.G.M.; funding acquisition, A.G.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Russian Science Foundation under grant number 22-79-00253.  
Not applicable.  
The data presented are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors are also grateful for TEM studies carried out by the Collective Use Equipment Center \"Material Science and Metallurgy\" working due to the equipment modernization program No. 075-15-2021-696 represented by the Ministry of Higher Education and Science of the Russian Federation.  
The authors declare no conflict of interest.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 6 Performance Evaluation
### Scenario 1. Reference Point Group Mobility  
Figures 5 and 6 illustrate performance in terms of model accuracy and training loss in vanilla FL and mobile FL, respectively, in the RPGM mobility scenario. The figures also show the impacts of different noise levels applied to them for privacy. The graphs clearly demonstrate the trade-off between accuracy and privacy. We initially set the performance of models with the noise multiplier of 0.5 as the target performance. The (\(\delta\), \(\varepsilon\)) values are set to (2, \(1\times 10^{5}\)), which are widely accepted. Then, each model adjusts both the number of vehicles participating in learning per round and the noise value until it achieves the target performance for privacy. Our experiments find that 36 participating vehicles and a noisemultiplier value of 1.6 (while keeping the training steps at 200) ensure the required level of privacy and accuracy performance for the models.  
Table 1 enumerates and compares the accuracy and loss values of the four training models (vanilla FL, mobile FL, and their two privacy-enhanced models). Mobile FL shows the best performance. The privacy-enhanced mobile FL model performs with 80.28% accuracy, which is degraded by 3.11% when compared to mobile FL. It is interesting to observe that it performs better than vanilla FL; that is, it is able to protect private data and predict results more correctly than conventional FL models. The accuracy performance of the two privacy-enhanced FL models degrades by 3.26% on average compared to non-privacy FL models, but this loss is fully compensated for by their capabilities in protecting vehicles against inference attacks.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Backbone Feature Extraction Network  
The original PSPNet [20] network utilizes ResNet50 as the backbone feature extraction network. ResNet50 is a deep residual network capable of learning more complex feature representations, thereby enhancing performance. Additionally, the deep design of ResNet50 may require more time and computational resources during training. However, in the study area the intra-class differences are relatively small, and this paper focuses on binary  
Figure 1: Flowchart of the methodology for detecting changes in agricultural areas.  
classification. Hence, the advantages of deep network structures cannot be fully utilized. Moreover, deep network structures suffer from issues such as a large number of parameters and susceptibility to overfitting.  
In this paper, an improved MobileNetV2 is used to replace the original network as the backbone network, which can improve the detection accuracy and stability while maintaining high efficiency without significantly expanding the model's complexity and computational volume. Firstly, MobileNetV2 adopts inverted residuals to produce the 3 \(\times\) 3 depthwise separable convolution after 1 \(\times\) 1 point-by-point convolution upscaling (Depthwise Convolution, DW). Then, the CBAM [21] module is embedded before the activation layer in the 3 \(\times\) 3 network structure. The CBAM module can significantly improve the model's ability to detect the agricultural areas in remote sensing images and increase the weight of the main features to enhance the segmentation performance, especially in the case where the agrarian areas are similar to the non-agricultural areas. Finally, MobileNetV2 undergoes 1 \(\times\) 1 point-by-point convolution to reduce dimension. Overall, this paper uses a single convolution kernel to apply a filter per each input channel and utilize the linear combination between channel features, which can obtain more information containing new features and enhance the efficiency and accuracy compared with the standard convolution. This expansion-contraction linear bottleneck structure of MobileNetV2 is capable of solving the problem of the small convolution kernel processed by DW as well as the gradient vanishing due to the ReLU6 activation function (expression as in Equation (1)) that makes the neuron output zero. The structure is shown in Figure 2.  
\[\text{ReLU6(x)}=\text{min (max (0, x), 6)}, \tag{1}\]  
Therefore, to maintain good performance while reducing computational costs, this study opts to use MobileNetV2 instead of ResNet50 as the backbone feature extraction network.  
Figure 2: MobileNetV2 backbone network structure: (**a**) inverse residual module, (**b**) network block diagram.
## 2 Materials and Methods
### Summary of the Adopted Method  
Figure 10 illustrates the algorithmic scheme of the applied techniques. To implement the harmonic extrapolation algorithm, it was ensured that some of the critical assumptions stated above hold for the seismic data used. First, the bandlimited seismic was correlated to two wells to understand the characteristics of the wavelet response. This was necessary because we extrapolated from the known (bandlimited) frequency to the unknown (extrapolated band). By comparing the synthetic to the seismic data, we ascertained the signal-to-noise ratio of the data; noisy data would amount to noise boosting. The sample rate of the data is 2 ms, which was judged to be sufficient and would give a good output frequency band. To ensure that noise was not introduced into the data, an acceptable regularization parameter of 0.01 was selected by comparing the well resolution to the seismic. For each seismic trace, an analysis window was slid along the trace to compute the Fourier transform at every window location. We divided the spectra by the wavelet spectrum to deconvolve the reflectivity spectrum. Furthermore, a basis pursuit atomic algorithm was applied to decompose the real and imaginary parts of the spectrum into summations of cosines and sines. Finally, we extrapolated the decomposed sinusoids to the band of interest, multiplied by the desired output wavelet spectrum, and inversed the Fourier transform to get a time reflectivity series associated with each window position. The addition of the time series overall window positions formed the bandwidth-extended trace.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety
### Indirect Connection of Boron with 1,3,4-Oxadiazole Ring via Heteracyclic Linker
#### 3.3.1 Boronic Acid Derivatives of (1,3,4-Oxadiazol-2-yl)pyridines
A single example describes a 6-(1,3,4-oxadiazol-2-yl)pyridin-3-ylboronic acid pinacol ester, synthesized using Pd(dppf)Cl\({}_{2}\)-DCM and potassium acetate in DMSO at 80 \({}^{\circ}\)C. A 35% yield was obtained after 2 h of heating and standard workup (Table 5, Entry 5). The second method for preparing boronic acids linked to a 1,3,4-oxadiazol ring via a pyridine moiety is based on a lithium borylation mechanism (method A). This method, applicable to 5-bromo-2-(1,3,4-oxadiazol-2-yl)pyridine, employs \(n\)-butyllithium in hexane and triisopropyl borate (B(O-\(i\)-Pr)\({}_{3}\)) (Scheme 18). The reaction is conducted in THF or a 4:1 mixture of toluene and THF at -78 \({}^{\circ}\)C. The reaction can be quenched with water, followed by boronic ester hydrolysis using 50% HCl and subsequent boronic acid precipitation. Alternatively, quenching with 2\(N\) aqueous HCl, extraction, and crystallization can be employed to isolate the 6-(1,3,4-oxadiazol-2-yl)pyridin-3-ylboronic acid [15]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Entry** & **Product** & **Catalyst** & **Solvent** & **Base** & **Temp./Time** & **Yield** & **Ref.** \\ \hline
1 & **71a**, R\({}_{1}\) = NH\({}_{2}\) & Pd(dppf)Cl\({}_{2}\)-DCM & 1,4-dioxane 1 & AcOK & 120 \({}^{\circ}\)C/45 min & na & [88] \\ \hline
2 & **71b**, R\({}_{1}\) = H & Pd(dppf)Cl\({}_{2}\) & ACN 1 & AcOK & 160 \({}^{\circ}\)C/15 min & na & [89] \\ \hline
3 & **71b**, R\({}_{1}\) = H & Pd(dppf)Cl\({}_{2}\) & DMSO & AcOK & 80 \({}^{\circ}\)C/24h & 15\% & [69] \\ \hline
4 & **72** & Pd(PPh\({}_{3}\))\({}_{2}\)Cl\({}_{2}\) & 1,4-dioxane & AcOK & 70 \({}^{\circ}\)C/12.5h & 76\% & [90] \\ \hline
5 & **73** & Pd(dppf)Cl\({}_{2}\)-DCM & DMSO & AcOK, Et\({}_{3}\)N & 80 \({}^{\circ}\)C/2h & 35\% & [91] \\ \hline \hline \end{tabular}  
\({}^{1}\) Microwave irradiation. na—not available.  
\end{table} 
Table 5: Miyaura borylation conditions for (1,3,4-oxadiazol-2-yl)pyridines (**71**–**73**). Figure 9: The 1,3,4-oxadiazol and pinacol boronic ester conjugates connected by pyridine linkers: 5-(1,3,4-oxadiazol-2-yl)-pyridin-3-ylboronic acid pinacol ester (**71**), 4-(1,3,4-oxadiazol-2-yl)pyridin-3-ylboronic acid pinacol ester (**72**), 6-(1,3,4-oxadiazol-2-yl)pyridin-3-ylboronic acid pinacol ester (**73**).
# ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
## 3 Experimental Results
### Application of ADAfinder Tool and Results  
To obtain the ADA from the deformation maps shown in Figures 2 and 3, the ADAfinder tool was applied, as explained in Section 2.3. Figures 4 and 5 show the extracted ADA for both the orbits for the two case study areas. The polygons, colored in red for a better visualization, and the \"active\" PSs based on the velocity color scale, are represented. The definition of \"active\" depends on the stability threshold imposed on the V\({}_{\rm m}\), that is area-dependent. In general, the stability threshold was set at 2\(\sigma\), in accordance with what was explained in Section 2.3. Moreover, the ADA radius was set at 25 m and the minimum number of PSs to be included in an ADA was fixed to 3. It can be observed that the number of PSs over the areas was greatly reduced with respect to the input datasets (of Figures 2 and 3). For the area of _Eixample_, only 75% of ASC and the 37% of DES PSs was preserved, while for the _Zona Franca_, the cut was even more drastic, with a loss of more than the 80% of PSs in both datasets. This largest cut was justified by the presence of different gradients of deformation in the considered portion of the _Zona Franca_, previously commented, with a greater variability of V\({}_{\rm m}\) values. In Tables 4 and 5, the statistics of the V\({}_{\rm m}\) of the active PSs are also reported.  
In Table 6, a summary of the number of buildings included in ADA for both ASC and DES datasets is reported. In _Example_ area, a percentage greater than 50% was included in ADA, while in _Zona Franca_, this percentage was about 10%. The difference is due to the different velocity threshold, which is very low in the first area (because of its stability) and higher in the second area, where a significant sorting is done on buildings that are really deforming.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & _Eixample_ & _Zona Franca_ \\ \hline
**Total number of buildings** & 540 & 1358 \\
**\% of buildings included in an ASC ADA** & 54\% & 13\% \\
**\% of buildings included in a DES ADA** & 53\% & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the number of buildings in the case study areas.  
Figure 5: ADA extraction from the active PSs of the selected area of _Zona Franca_ from (**a**) ascending and (**b**) descending datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Count** & **Minimum V\({}_{\text{m}}\)** & **Maximum V\({}_{\text{m}}\)** & **Average V\({}_{\text{m}}\)** & \(\sigma\) \\ \hline
**Ascending** & 4800 & \(-\)7.30 & 6.90 & 0.12 & 1.06 \\
**Descending** & 1624 & \(-\)6.20 & 7.30 & 0.15 & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the mean velocity V\({}_{\text{m}}\) (mm/y) of the active PSs of the selected area of _Eixample_.