esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Strain Parameters of Basic Expressions  
As described in Section 2.1, facial images making six basic expressions were obtained. To analyze facial deformation patterns captured by CCD cameras, we focus on specific regions within the face images. An irregular region is selected from the cropped image for analysis. The eye and nose regions are excluded due to the absence of speckles, which prevent accurate deformation tracking. Additionally, the nose region is disregarded, as its high curvature and minimal deformation could introduce significant errors in the displacement calculations. Figure 3a-c depict the horizontal, vertical, and normal displacement distributions across the entire face during a happy facial expression, represented within the local coordinate system. The results reveal substantial movement at the corners of the mouth and cheeks, which is consistent with cheek bulging and mouth widening during laughter.  
Since the measured displacement fields encompass both facial deformation and head movements (e.g., translation and rotation), a strain analysis is conducted to isolate the pure deformation effects. According to Equations (1)-(3), the strain distributions corresponding to the six basic facial expressions are evaluated. Figure 3d-f illustrates the \(\varepsilon_{x}\), \(\varepsilon_{y}\), and \(\varepsilon_{xy}\) strain distributions for the happy expression. The strain around the eyes is predominantly in the y-direction, while the x-direction strain and shear strain are comparatively lower. In contrast, the mouth region exhibits significant variations in both linear and shear strain. To further achieve a consistent comparison of facial deformation, the principal strains and their corresponding directions are computed. Figure 4 presents the maximum and minimum principal strain distributions for the happy expression. Considering that wrinkle formation is predominantly associated with compressive deformation in biological tissues, our analysis primarily focuses on the direction of the minimum principal strain (\(\alpha_{2}\)). The principal strain distributions on the left and right sides of the face exhibit a general symmetry, though slight variations in strain magnitudes are observed. The greatest tensile strain occurs in the philtrum and lower jaw regions. The skin above the philtrum undergoes horizontal stretching. Conversely, the largest compressive strain is observed in the outer eye regions, corresponding to vertical uniaxial compression. Additionally, the principal  
Figure 3: Displacement and strain distributions during happy expression: (**a**) \(u_{x}\), (**b**) \(u_{y}\), (**c**) \(u_{z}\), (**d**) \(\varepsilon_{x}\), (**e**) \(\varepsilon_{y}\), and (**f**) \(\varepsilon_{xy}\). The color gradients in these figures indicate the magnitude of displacement values.  
strain and direction distributions for the remaining basic facial expressions are provided in Appendix A for further reference.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.2 Multivariate Models  
To exploit the potential of multivariate modeling, we used several classifiers of increasing complexity: Logistic Regression, Support Vector Machine with a linear kernel [38], Support Vector Machine with a radial basis kernel [39], Random Forest [40] (All above-mentioned models were implemented in the Scikit-learn v. 0.24.2 package [41]), and Extreme Gradient Boosting (XGBoost) [42] (Implemented in the xgboost Python package v. 1.3.3). All the models are multivariate and, thus, are able to benefit from the set of prepared features, namely the set of spatial features developed with the method described in Section 3.2.5, along with ship and wind-related features. All models selected for the experiment are highly robust. Therefore, the potential mistakes in human labeling, if present in reasonable amounts, should still allow for the models' proper training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **No Plume** & **Plume** \\ \hline Number of pixels & 68,646 & 6980 \\ Number of images & 208 & 535 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of measurement points per class in the dataset.  
Figure 8: Classwise distribution of the two main features of the dataset: NO\({}_{2}\) and Moran’s \(I\).  
The first feature of the model is enhanced by _Moran's 1_ values of the pixels that were translated into a one-dimensional feature vector. As mentioned in Section 3.2.3, the application of Moran's 1 may result in the creation of additional high-value pixels resulting from the enhancement of clusters of low-value pixels. To mitigate the negative impact of this side effect, apart from Moran's 1, the feature set was composed of the corresponding value of NO\({}_{2}\). This way, a supervised learning model will be able to differentiate between high- and low-value enhanced NO\({}_{2}\) clusters. Other features used by the model are _Wind Speed_, _Wind Direction_ (encoded into its sine and cosine components, in order to enable a continuous feature space for various wind directions), _Ship Speed_, and _Ship Length_. Finally, the position of an analyzed pixel within the _normalized sector_ in terms of _levels_ and _sub-sectors_ was translated into the feature vectors using one-hot encoding. The resulting feature set was composed of 17 features in total. For the full feature list, see Figure in Section 4.1. The used binary label indicates whether the given pixel is a part of the ship plume or not.
## 3 Proposed Collaborative Control Algorithm
### Independent Frequency Regulation Algorithm  
According to the GB/T15945 \"Power Quality Power System Frequency Permissible Deviation\", in China, the power grid frequency is normally 50 Hz. For grid capacity of 3 million kW and above, the upper and lower limits of grid frequency deviation are 0.2 Hz and \(-\)0.2 Hz, respectively. For those with a capacity of less than 3 million kW, the upper and lower limits of grid frequency deviation are 0.5 Hz and \(-\)0.5 Hz, respectively.  
Frequency regulation is the process of controlling the frequency within the normal range. Since the BESSs charging and discharging at different positions has almost the same influence on the grid frequency, therefore, the nodes with small sensitivity coefficients are preferred for the frequency regulation.  
The control center calculates the regulated power by frequency and grid information, including the following formula:  
\[\Delta f=f_{n}-f_{0} \tag{18}\]  
\[K_{H}=\frac{\Delta P_{f}}{\Delta f} \tag{19}\]  
\[\Delta P_{L}=K_{H}\cdot\Delta f \tag{20}\]  
Formula (19) and (20) can be obtained according to the frequency characteristics of the power system load. Where \(f_{0}\) is the rated frequency 50 Hz, \(f_{n}\) is the current frequency value; \(\Delta f\) (Hz) is the  
Figure 4: Battery energy storage systems (BESS) voltage regulation block diagram.  
system frequency deviation; \(\Delta P_{L}\) (MW) is the active load variation; \(K_{H}\) (MW/Hz) is the load frequency regulating effect coefficient, which is obtained by:  
\[K_{H}=\frac{d(P_{H}/P_{H0})}{d(f/f_{0})}=\frac{\Delta P_{H}/P_{H0}}{\Delta f/f_{ 0}} \tag{21}\]  
where \(P_{H0}\) is the total load at the rated frequency, and \(P_{H}\) is the total load at the \(f_{n}\) value. The power of frequency regulation can be obtained by Formula (22):  
\[\Delta P_{f}=\Delta\omega\cdot K_{H}=2\pi\cdot\Delta f\cdot K_{H} \tag{22}\]  
The block diagram of the frequency regulating algorithm is shown in Figure 5.  
The frequency regulation algorithm of each node is divided into five steps:  
Step 1: Obtain the frequency regulation power (\(\Delta P_{fi}\)) of this node from the control center.  
Step 2: In order to prevent the BESSs suffering damage while grid frequency being too high or too low, it is necessary to perform grid frequency detection. If the frequency is between 47.5 and 49.8 Hz, the BESS is ready to be discharged. On the other hand, if the frequency is between 50.2 and 52.0 Hz, the BESS is ready to be charged. When the grid frequency is less than 47.5 Hz or greater than 52.0 Hz, the BESS is disconnected from the system.  
Step 3: Perform the SOC control as shown in Figure 3. And then evenly distributed total regulation amount to the \(N\) BESSs.  
Step 4: BESSs perform charging or discharging operations.  
Step 5: Perform grid frequency detection again. If f is not between 49.8 and 50.2 Hz, steps 1-4 are repeated.
## 2 Results
### Analysis of the Conformational Changes at the N-Terminal Region of Cry1 Ab Toxin by FRET Assays
replace S39 located at helix \(\alpha\)-1 and T239 at helix \(\alpha\)-7 by Cys residues in the double mutant Cry1 Ab S39C-T239C, since these residues have a distance of 4.2 A between their \(\beta\)-carbons. In addition, W73 and I97 amino acids were replaced with Cys residues in the double mutant Cry1 Ab W73C-I97C, since these amino acids located in helices \(\alpha\)-2b and \(\alpha\)-3 have a molecular distance of 4.8 A between their \(\beta\)-carbons. As controls, we constructed all of the single Cys mutations of these selected residues (Figure 3A). The double mutants Cry1 Ab S39C-T239C and Cry1 Ab W73C-I97C produced similar parasporal crystal inclusions to Cry1 Ab. The toxicity data against _M. sexta_ larvae revealed that mutant Cry1 Ab S39C-T239C showed a comparable toxicity to Cry1 Ab (Table 1). In contrast, the Cry1 Ab W73C-I97C mutant was severely affected in toxicity (>1104 folds higher LC\({}_{50}\) value). We also analyzed the crystal production and toxicity of the control single Cys mutants. With the exception of the Cry1 Ab T239C mutant, which was affected in its crystal production in the Bt transformant strain, all of the other single Cys mutants formed crystal inclusions in the Bt strain and showed similar insecticidal activity to Cry1 Ab wild type (Table 1). To determine if cross-linked Cry1 Ab mutants by the introduced disulfide bridges are affected in their oligomer assembly, proteins were incubated with BBMVs isolated from _M. sexta_ and oligomer formation was analyzed by western blot using anti-Cry1 Ab antibody to recognize the high molecular band that corresponds to the oligomer structure. The buffers used for oligomerization and SDS-PAGE assays were prepared in the absence and in the presence of reducing agents to analyze the effect of the potential formation of disulfide bonds. Figure 3 shows that Cry1 Ab and the Cry1 Ab S39C-T239C mutant were able to form oligomer structures with or without reducing agents (Figure 3B). In contrast, the Cry1 Ab W73C-I97C mutant was severely affected in the formation of oligomers in the absence of reducing agents, but the addition of dithiothreitol (DTT) or 2-mercaptoethanolhanol (2-ME) during the incubation with BBMVs reconstituted the formation of oligomer structures (Figure 3B).
# A High Capacity Watermarking Technique for the Printed Document
## 4 Experimental Results and Discussion
### Visual Effect  
Depending on the definition of the quantization function, the pixel flipping range \(\delta\) of the character will affect the visual effect. Here we select an English letter picture for watermark embedding test, as shown in Figure 2(a), where the letters are Arial four-font. Figure 2(b) shows the case where a 42-bit watermark is embedded in the original picture at \(\delta=0.05\). Figure 2(c) shows the case where a 42-bitwatermark is embedded in the original picture at \(\delta=0.1\). Figure 3d shows the case where a 42-bit watermark is embedded in the original picture at \(\delta=0.2\). It can be seen that the larger \(\delta\) is, the worse the visual effect is after embedding the same watermark information. Figure 4 shows the enlarged view of the letter k in each case. Contrary to the visual effect, a larger \(\delta\) results in better robustness and greater correct rates of watermark information extraction after printing and scanning. Therefore, it is necessary to balance the visual effect and the robustness, which in turn depends on the error range of the printing scanning equipment. According to the analysis of Figure 2, the error range of the printing scanning equipment (Pantum M7300 FDW, resolution 600 DPI) used in this experiment was less than 0.015, so \(\delta\) needs to be no less than 0.15. With the improvement of the precision of the printing and scanning equipment, for example, the error range of the printing scanning equipment used in the literature [22] was less than 0.005, thus, a smaller \(\delta\) value can be selected for that case.  
This paper uses peak signal to noise ratio (PSNR) and similarity (SIM) percentage to judge the visual effect of the image after embedding watermark. Their calculation methods are shown in Equations (13)-(15). In Equation (13), \(X\) is the image to be evaluated, \(Y\) is the reference image, \(H\times W\) is the size of the image, and \(MSE\) is the mean square error of the two images. In Equation (14),  
Figure 4: Letter k enlarged view.  
Figure 3: The comparation of the visual effect with different \(\delta\).  
\(n\) represents the pixel bit width, and the unit of \(PSNR\) is dB. The larger \(PSNR\) indicates the better visual effect. \(SIM\) in Equation (15) represents the similar percentage of the two images.  
\[MSE=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}[X(i,j)-Y(i,j)]^{2} \tag{13}\]  
\[PSNR=10 log10\left(\frac{\left(2^{n}-1\right)^{2}}{MSE}\right) \tag{14}\]  
\[SIM=\left(1-\frac{\sqrt{MSE}}{2^{n}-1}\right)\times 100\% \tag{15}\]  
According to the calculation method of \(PSNR\) and \(SIM\), Table 2 shows the results of calculating \(PSNR\) and \(SIM\) separately for the subgraphs obtained by embedding the same watermark information under different \(\delta\) in Figure 3. It can be seen that the smaller \(\delta\) indicates the better visual effect and the higher similarity.
# A High-Level Representation of the Navigation Behavior of Website Visitors
## 2 Identification of Rules in Each Class of Visitors
### Representation of Each Class of Visitors as a Sequence of Symbols
#### 2.1.1 Identification of Relevant Pages  
We are interested in the navigation behavior that could be meaningful for marketing experts. Therefore, we identified relevant pages, which are described as follows:  
1. Filtering of pages of interest: Our marketing partner prepared a list of 298 web pages that they were interested in analyzing. We removed from sessions all web pages that were not pages of interest. We eliminated the sessions that did not visit any page of interest (26%). The dataset was reduced to 37,400 sessions (74%).
2. Removing of pages that are automatically loaded: Those pages are not meaningful for marketing experts because they do not represent the intentional navigation behavior of visitors (for example, Java resources necessary for the proper functioning of the site).
3. Removing the subsequent repetition of the same page: Most sessions had pages subsequently repeated n times. For example, Home \(\rightarrow\) Home \(\rightarrow\) Home \(\rightarrow\) Login \(\rightarrow\) Control panel \(\rightarrow\) Control panel \(\rightarrow\) Logout. Our information technology partner indicated to us that, in most cases, it is due to the functionality of the website, and not related to the navigation behavior of visitors. For example, if the visitor fulfils a form, the same page could be automatically reloaded whenever the visitor clicks on a different field. Therefore, we reduced the subsequent occurrences of the same page to one occurrence. The previous example would be reduced to Home \(\rightarrow\) Login\(\rightarrow\) Control panel \(\rightarrow\) Logout.  
The process to keep only relevant pages entailed some information loss. That information could be useful for some traffic analytics; for example, to measure the number of pages sent to the visitor, the amount of data transmitted, or the frequency of clicks. Nevertheless, that loss does not affect the objective of describing the navigation behavior of visitors. We only needed web pages intentionally visited.
## 2 Materials and Methods
### Workflow for Predicting Cardiovascular Diseases
#### 2.2.4 Quality Metrics for Synthetic Data
Furthermore, to evaluate if the oversampling methods capture adequately the relationships between features, the Pairwise Correlation Difference (PCD) [57] and the Log-Cluster Metric (LCM) [58] were considered. * PCD quantifies the difference between the correlation matrices of \(\mathcal{X}_{R}\) and \(\mathcal{X}_{S}\)[57], thus measuring the correlation among features that the different methods of oversampling can capture. It is defined as: \[PCD(X_{R},X_{S})=\|Corr(X_{R})-Corr(X_{S})\|.\] \(Corr(\cdot)\) represents the correlation among features for \(\mathcal{X}_{R}\) and \(\mathcal{X}_{S}\), and \(\|\cdot\|\) is a matrix norm that measures the similarity between two matrices (a scalar). Since we handled categorical features, [PERSON]'s V correlation [59] was used. Small PCD values indicate that relationships between features are preserved, meaning that \(\mathcal{X}_{R}\) is more similar to \(\mathcal{X}_{S}\) in terms of linear correlations across the features. * LCM [58] measures the similarity of the underlying latent structure of \(\mathcal{X}_{R}\) and \(\mathcal{X}_{S}\) in terms of clustering. This metric relies on two steps. Firstly, \(\mathcal{X}_{R}\) and \(\mathcal{X}_{S}\) were merged into one single dataset, and then a cluster analysis using the k-means algorithm [60] was performed on this dataset with a fixed number of clusters \(k\)[57]. LCM is defined as follows: \[LCM(X_{R},X_{S})=log\left(\frac{1}{k}\sum_{i=1}^{k}\Biggl{[}\frac{n_{j}^{R}} {n_{j}}-c\Biggr{]}^{2}\right),\] \(n_{i}\) being the number of samples in the \(i\)-th cluster, \(n_{i}^{R}\) the number of samples from the real dataset in the \(i\)-th cluster, and \(c=n^{R}/\left(n^{R}+n^{S}\right)\) (where \(n^{R}\) and \(n^{S}\) are the numbers of real and synthetic samples, respectively) [57]. High values of LCM denote disparities in the cluster memberships, indicating high differences in the distribution of \(\mathcal{X}_{R}\) and \(\mathcal{X}_{S}\)[57].
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### Time Complexity  
Finally, to make the comparison complete, the time complexity of the extraction methods was explored. Rather than embarking on another analysis of the computational complexities, however, the time consumption levels for the datasets were simply measured and compared. In the interest of brevity, the processing CPU times for each dataset is the time consumed by repeating the feature extraction several times and then averaging it. Table 3 presents a numerical comparison of the processing CPU times. Here, the times recorded are the required CPU times on a laptop computer with a CPU of 2.60 GHz and a RAM of 16.0 GB, and that is operating on a Window 64-bit platform.  
In Table 3, it can be observed that the extractor of the CNN-features requires a much shorter processing CPU time than the traditional nonlinear algorithms for the datasets used. Particularly, this demonstrates that the extractor can dramatically reduce the processing  
Figure 3: Plot comparing the complexity measures obtained in PCA, kPCA, AE, CNN-sum, and CNN-O2P from RealPatch. For ease of comparison, each of the complexity values measured was adjusted so that the maximum value was 1. In addition, CNN-sum and CNN-O2P were clearly compared only in F1. Thus, to clarify the focus of the paper, the two were analyzed only with a CNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **PCA** & **kPCA** & **AE** & **CNN** \\ \hline Train1 & 8.3 & 7775.0 & 9560.8 & 35.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The processing CPU times (in seconds).  
time compared to other kernel- and network-based methods (e.g., kPCA and AE). However, it should be noted that extracting the CNN-features requires a pretrained model. In Table 3, Train1 was used for the seismic data to obtain the pretrained model (# of epochs: 100). The training time for the model was excluded when counting the processing time in the table.
## 2 Materials and Methods
### Antioxidant Capacity  
The ferric-reducing antioxidant power (FRAP) assay, developed by ([PERSON] et al., 2017), was used to determine antioxidant activity. FRAP reagent was prepared by mixing three solutions (300 mM acetate buffer, 10 mM 2,4,6-tripyridyl-striazine (TPTZ) dissolved in 40 mM HCl and 20 mM FeCl\({}_{3}\cdot\)6H\({}_{2}\)O) at the ratio 10:1:1. Aliquots of samples (0.1 mL) were mixed with freshly prepared FRAP reagent (1.9 mL), shaken and incubated for 15 min at 37 \({}^{\circ}\)C. Measurements were carried out at 593 nm using the Helios Gamma spectrometer (Thermo, USA). The calibration curve was prepared using Trolox, and results were expressed as Trolox equivalents (TE) per one gram of mushroom dry weight.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Species** & **Stems [\%]** & **Caps [\%]** & **S:C Ratio** & **Description of the Fruiting Bodies** \\ \hline _A. bisporus_ (white) & 23 & 77 & 1:3.35 & closed caps, \\ _A. bisporus_ (brown) & 21 & 62 & 1:2.95 & closed caps, \\ _L. edodes_ & 27.8 & 72.2 & 1:2.59 & open caps, \\ _P. nameko_ & 32 & 68 & 1:2.12 & closed caps, \\ _P. arggii_ & 67.4 & 32.6 & 1:0.48 & caps diameter 4.5–7 cm \\ _P. ostreatus_ & 24.5 & 75.5 & 1:3.09 & open caps, \\  & & & & caps length 3.5–9 cm \\ \hline \end{tabular}
\end{table}
Table 1: Percentage of stems’ and caps’ dry weight for individual species, and the ratio between these parts of the fruiting bodies.  
The capacity to scavenge DPPH (1,1-diphenyl-2-picrylhydrazyl) radicals was determined according to [19]. The extracts (0.1 mL) were mixed with ethanolic solution of DPPH (0.2 mM). After 15 min of incubation at room temperature (in the dark), the mixtures were measured spectrophotometrically at 517 nm (Helios Gamma, Thermo, USA). The results were expressed as Trolox equivalents per one gram of mushroom dry weight.
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
### Design of 60 GHz I/Q Signal Generator  
The double-balanced I/Q passive mixers are used in the receiver, and therefore, the I/Q signal generator should have a balun to convert single-ended signal to differential and a 90\({}^{\circ}\) tandem coupler to generate accurate I and Q signals as shown in Figure 5. The passive couplers with the reduced transmission lines are used in the 60 GHz differential I/Q signal generator to achieve the wideband and accurate performance in phase and amplitude with the compact size. The tandem coupler is used in the 90-degree coupler to achieve the compact size because it can be implemented with only two inductors and capacitors. The capacitively loaded microstrip line is used to reduce the size of the coupler. The capacitors of C\({}_{5}\) and C\({}_{6}\) are used to improve the phase and the amplitude mismatches in the coupler. The full EM simulation is performed to make high accurate phase and amplitude at 60 GHz.  
Figure 4: Schematic of 60 GHz LO buffer amplifier.  
Figure 3: Schematic of 60 GHz single to differential amplifier.  
The amplitude and the phase mismatches are 2 dB and \(<\)7\({}^{\circ}\) at 57-62 GHz. The size of the coupler is 0.1 \(\times\) 0.43 mm\({}^{2}\). The Marchand balun with the broadside coupled transmission line structure is designed to enhance the coupled coefficient and balanced output. The capacitor C\({}_{7}\) is used for input matching, and capacitor C\({}_{8}\) is used for output port matching. The custom MIM capacitor is used, which has a high-quality factor at 60 GHz. These capacitors also provide phase compensation at respective ports.