esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Matrix Techniques Based on Averages
## 2 Averaged Power Method for Finding Eigenvalues and Eigenvectors of a Matrix

Table 1: Approximations of an eigenvector and successive Euclidean distances. _The eigenvalue computed is \(\lambda=<AX_{7},X_{7}>/<X_{7},X_{7}>=2\). The stopping criterion was \(||X_{n}-X_{n-1}||<10^{-6}\)._  
**Example 4**. : _Let us consider the following matrix:_  
\[A= \begin{pmatrix}-2&4&1\\ 0&3&0\\ 5&-4&2\end{pmatrix} .\]  
_The set of eigenvalues of \(A\) is \(\sigma(A)=\{3,-3\}\). The algorithm described with \(\alpha_{n}=\beta_{n}=\gamma_{n}=1/2\) starting at \(X_{0}=(2,-3,2)\) with Euclidean scaling produces the sequence of approximations and distances as between those collected in Table 2._  
_The eigenvalue computed is \(\lambda=<AX_{3},X_{3}>/<X_{3},X_{3}>=3\). The stopping criterion was \(||X_{n}-X_{n-1}||<10^{-6}\)._  
**Remark 1**. : _Let us notice that the matrices of these examples do not have a dominant eigenvalue but, nevertheless, the N-iterated power algorithm is convergent._  
_Averaged Power Method for Finding Eigenvalues and Eigenfunctions of a Linear Operator_  
The algorithm proposed in this section can be applied to find eigenvalues and eigenvectors/eigenfunctions of a linear operator defined in an infinite-dimensional normed linear space \(K:E\to E\), in case of existence (see, for instance, Theorem 2). Starting at \(f_{0}\in E\), we would follow the steps  
\[g_{n}=(1-\alpha_{n})f_{n}+\alpha_{n}Kf_{n},\] \[h_{n}=(1-\beta_{n})f_{n}+\beta_{n}g_{n},\] \[f_{n+1}=(1-\gamma_{n})f_{n}+\gamma_{n}Kf_{n},\] \[f_{n+1}:=f_{n+1}/||f_{n+1}||,\] \[\lambda_{n+1}=<Kf_{n+1},f_{n+1}>\]  
for \(\alpha_{n},\beta_{n},\gamma_{n}\in[0,1]\), such that \(0<\inf\alpha_{n}\leq\sup\alpha_{n}<1\), \(0<\inf\gamma_{n}\leq\sup\gamma_{n}<1\), and \(f_{0}\
eq\emptyset\). Let us note that a structure of inner product space in \(E\) for computing the eigenvalue by means of the Rayleigh quotient is required. Otherwise, we should solve the equation \(Kf_{n}=\lambda_{n}f_{n}\). The stop criteria of the algorithm are similar to the given for matrices, using the norm of the space \(E\) or the modulus in the case of employing the approximations of the eigenvalue. **Example 5**. : _Let us compute an eigenvalue of a Fredholm integral operator on \(\mathcal{L}^{2}([0,1])\) defined, for \(f\in\mathcal{L}^{2}([0,1])\), as_  
\[Kf(x):=\int_{0}^{1}(2x^{2}+3y^{3})f(y)dy,\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & \((\mathbf{n})\) & \(\mathbf{X_{n}}\) & \(||\mathbf{X_{n}}-\mathbf{X_{n-1}}||\) \\ \hline
1 & \((-0.212899,\,-0.479022,\,0.851594)\) & \(3.545573\) \\ \hline
2 & \((-0.212899,\,-0.479022,\,0.851594)\) & \(0.000001\) \\ \hline
3 & \((-0.212899,\,-0.479022,\,0.851594)\) & \(0.000000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Approximations of an eigenvector with successive distances.
## 2 Methodology
### PACF Based Input Selection Method  
Due to the different fluctuation characteristics of the DD at different periods, the correlations of the K VMFs obtained by the above method also vary. Therefore, before predicting each component, we need to analyze the correlation of each VMF, and then the optimal input variables for the ELMs can be selected. Here, the PACF method is used to evaluate the correlations and selection results of the components [50].  
Figure 2: Specific process of sample entropy (SE).  
Assume that \(X_{t}\) is the output variable, and if the lag autocorrelation length value of \(X_{t-a}\) falls at the 95% confidence interval \(\left[-\frac{1.96}{\sqrt{n}},\frac{1.96}{\sqrt{n}}\right]\) for the first time, and there are no obvious outliers after it. At this time, \((a-1)d\) is selected as the delay time value of the corresponding time series. PACF is described below.  
For DD time series, the covariance \(\hat{\gamma}_{a}\) at lag \(a\) is expressed as  
\[\hat{\gamma}_{a}=\frac{1}{n}\sum_{t=1}^{n-a}(x_{t}-\bar{x})(x_{t+a}-\overline{ x}),\ (a=1,2,\cdots,M) \tag{10}\]  
where \(\overline{x}\) is the mean value of the time series and \(M\) is the largest lag coefficient, \(a\) is the lag length of the autocorrelation function, and \(\hat{\rho}_{a}\) can be estimated as follows  
\[\hat{\rho}_{a}=\hat{\gamma}_{a}/\hat{\gamma}_{0} \tag{11}\]  
For PACF at lag \(a\), the \(f_{aa}\) is presented as follows  
\[\left\{\begin{array}{c}\hat{f}_{11}=\hat{\rho}_{1}\\ f_{a+1,a+1}=\left(\hat{\rho}_{a+1}-\sum_{j=1}^{a}\hat{\rho}_{a+1-j}f_{a}\right) \middle/\left(1-\sum_{j=1}^{a}\hat{\rho}_{j}f_{a}\right)\\ \hat{f}_{a+1,j}=\hat{f}_{aj}-\hat{f}_{a+1,a+1}\hat{f}_{a,a-j+1}\end{array} \right.,\ \ \ (j=1,2,\cdots,a) \tag{12}\]  
where \(1\leq a\leq M\).
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Determination of Total Polyphenols and Flavonoids  
TPC (total phenolic content) was determined using a modified microplate Folin-Ciocalteu reagent method [18]. Briefly, 20 \(\upmu\)L sample extracted in Section 2.1 was mixed with 20 \(\upmu\)L Folin-Ciocalteu reagent (Sigma-Aldrich, St. Louis, USA), shaken (medium speed) for 2 min using a microplate reader (SpectraMax(r) iD3, Molecular Devices, San Jose, CA, USA), and left undisturbed for 6 min. Subsequently, 200 \(\upmu\)L Na\({}_{2}\)CO\({}_{3}\) solution (7%, _w/v_; Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) and 10 \(\upmu\)L purified water were added, followed by shaking for 1 min. After incubation for 120 min in the dark (25 \(\pm\) 2 \({}^{\circ}\)C), absorbance was measured at 750 nm. Results were expressed as mg gallic acid equivalents (mg GAE)/g based on a gallic acid standard curve (Shanghai Yuanye Bio-Technology, Shanghai, China).  
TFC (total flavonoid content) is determined based on the reaction with the aluminum trichloride (AlCl\({}_{3}\)) reagent method [19]. Briefly, 50 \(\upmu\)L of sample extracted in Section 2.1 was mixed with 100 \(\upmu\)L methanol (Honeywell Research Chemicals, Morris Plains, NJ, USA) and 20 \(\upmu\)L of 10% (_w/v_) AlCl\({}_{3}\) solution (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China). After gentle mixing, the mixture was incubated at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) for 3 min. Subsequently, 20 \(\upmu\)L of 1 M CH\({}_{3}\)COONa solution (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) and 60 \(\upmu\)L methanol were sequentially added, followed by incubation for 40 min in dark (25 \(\pm\) 2 \({}^{\circ}\)C). Absorbance was measured at 430 nm. TFC was expressed as mg quercetin equivalents (mg QE)/g based on a quercetin standard curve (Shanghai Yuanye Bio-Technology, Shanghai, China).
## 5 Results and Discussion
### Relative Importance of Composite Weights  
As illustrated in Figure 9, after conducting an analysis on the relative importance of composite weights by multiplying the weights of 23 secondary indicators with their respective higher-level indicator weights, several key findings emerged. The most critical  
Figure 8: Bar chart of weights for B1 indicator layer.  
Figure 7: Bar chart of weights for B3 indicator layer.  
components identified are as follows: under the Security and Privacy category, Asset Property Protection (0.2245), focusing on the safeguarding of user financial security; within the Information Architecture category, Page Information Architecture (0.1193), ensuring users' clear comprehension of page layouts and content; and, again in Security and Privacy, Privacy Protection (0.0986) aimed at preventing the leakage of users' personal data. Indicators with relatively lower importance predominantly fall within the Emotional Design category, including Pleasure (0.0030), where the aim is to elicit users' emotional values through textual and visual content; Value Realization (0.0031), which is achieved through the software fulfilling anticipated goals; and in the Interaction Experience category, Help (0.004) offers detailed instructions for product use and answers to common questions. These findings offer valuable insights for future assessments of the usability of second-hand trading platform apps.
## 2 Radiation in Space and Its Effects on Electronic Components  
Once on board the spacecraft, humans and electronic components receive radiation of various types and origins that causes different types of damage. In this section, we first present a summary of all radiations and their effects, and then focus on the different ways in which expected radiation doses can be estimated during the lifespan of space missions.
## 3 Proposed Algorithm
### Variational Autoencoders (VAE)
The specific parameters for each component of VAE are detailed in Table 1. Figure 8 presents a graph of the training and validation loss, where the VAE was trained on the normal dataset, using the test dataset as validation data for early stopping. A specific set of hyperparameters was selected, including a batch size of 32, a minimum change of 0.00075, a patience value of 5, ReLU activation in the intermediate layers, and a sigmoid activation function in the final layer. The VAE is used to encode visual gun features extracted from image patches within video frames, compensating for information loss caused by the skeletonization of the human subject. These deep features, denoted as \(\overline{v}\), are incorporated into a unified feature vector that combines object-specific visual data extracted from the hands by the imaging device with pose information obtained from HPE. This comprehensive representation enhances the model's ability to detect firearms by preserving both visual and contextual human pose information. A comprehensive analysis of proposed approach is provided in the forthcoming results and evaluation Section 4. \begin{table}
\begin{tabular}{c c c c} \hline
**Network Layer** & **Network Structure** & **Input** & **Output** \\ \hline Encoder layer 1 & Fully Connected & 1024 & 512 \\ \hline Encoder layer 2 & Fully Connected & 512 & 256 \\ \hline Encoder layer 3 & Fully Connected & 256 & 128 \\ \hline Encoder layer 4 & Fully Connected & 128 & 64 \\ \hline Decoder layer 1 & Fully Connected & 64 & 128 \\ \hline Decoder layer 2 & Fully Connected & 128 & 256 \\ \hline Decoder layer 3 & Fully Connected & 256 & 512 \\ \hline Decoder layer 4 & Fully Connected & 512 & 1024 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of the VAE used to extract deep latent features from hand patches.
## 2 Materials and Methods
### BSA-FITC Conjugation  
For protein corona studies, a model protein--bovine serum albumin (BSA)--was used. The choice of the protein model was based on the fact that serum albumin is the most abundant protein in the serum/plasma, and multiple mass spectrometry based studies have demonstrated that, usually, for metallic and polymeric nanoparticles, it is the protein that contributes the most to the protein corona formation [20; 21]. BSA was labeled with FITC using the protocol described by [PERSON] et al. [22]. FITC was dissolved in dimethyl sulfoxide (DMSO) (5 mg/mL) and was added to 1 mL of BSA 10 mg/mL dissolved in carbonate buffer (50 mM, pH = 9.2) at a ratio of 25 dye molecules per 1 BSA molecule. The solution was stirred for 4 h at room temperature, and the excess dye was removed using a size exclusion column (PD-10 desalting column, molecular weight cut off (MWCO) = 5000 Da) resulting in a purified fluorescently-labeled protein (BSA-FITC).  
The concentration of the FITC-labeled protein was determined using Quick Start(tm) Bradford Protein Assay (Bio-Rad Laboratories, Hercules, CA, USA) using a calibration curve with BSA as standard in a working range of 1-12 ug/mL. The samples absorbance was read at 595 nm using a CLARIOstar Plus microplate reader (BMG Labtech, Ortenberg, Germany). The fluorescence emission spectra and fluorescence emission intensity dependence of BSA-FITC vs. concentration are determined using a CLARIOstar Plus microplate reader.  
For the calibration curve, the fluorescence intensity of 12 serial dilutions of BSA-FITC (0.125-120 ng) in phosphate-buffered saline (pH = 7.4) mixed with KCN (final concentration 50 mM) was measured. The effect of AuNP dissolution on the recovery was evaluated by adding three known amounts (1, 50 or 100 ng) of BSA-FITC to a solution containing \(1\times 10^{10}\) AuNPs/mL. BSA-FITC % recovery was calculated as the % of BSA-FITC recovered from the added amount after AuNP dissolution.
## 3 Estrogen Receptors
### Estrogen Receptor Mutation in Humans  
The involvement of estrogen in bone homeostasis started with the clinical observations of male patients with anomalies in the estrogen synthesis and signaling pathway (described earlier in this review). More specifically, the mutation in the ESR1 (gene encoding ER\(\alpha\)) and the CYP19A1 gene encoding the aromatase enzyme. While mutations in ESR1 are rare in primary tumors (less than 1%) recently has been reported that the prevalence of ESR1 mutations is higher in diagnosed metastatic patients (6-55%), including in patients with bone metastasis. These mutations in the ligand-binding domain (LBD) region of ER result in ligand-independent constitutively activated receptors. The most common ESR1 hotspot mutations are D538G [64], L536R, and Y537S/N/C [65; 66; 67; 68]. Mutations in the ESR2 (gene encoding for ER\(\beta\)) have been primarily involved in breast cancer development. It was not until recently that bone abnormalities involving a mutation in the ER\(\beta\) were reported. In 2018, two papers were published reporting ESR2 mutations in four unrelated patients with syndromic and non-syndromic 46, XY disorder of sex development (DSD) [69; 70]. 46, XY DSD patients have one X chromosome and one Y chromosome, a pattern usually found in males. At birth, it is not clear whether these patients are male or female because patients have genitalia but have an undeveloped or complete absence of female reproductive organs.  
Clinically these patients suffer from absent breast development, primary amenorrhea, abnormal or absent gonadal development, and delayed bone age. Several gene mutations in nuclear receptors have been linked to this disorder but they still do not account for all the cases. Here four cases of DNA sequences analysis are described. Case#1: one of the patients revealed a homozygous 3-bp deletion in exon 9, c.541_543 del, which leads to in-frame deletion, p.(Asn181 del), affecting a highly conserved amino acid located in the DNA Binding Domain (DBD). Two other patients showed heterozygous ERS2 missense variants: case#2 had a c.251G>T found in exon 2 leading to a missense change p.(Gly84 Val). This mutation affects a highly conserved amino acid located in the N-terminal where the first activation function (AF-1) is located. Case#3: the patient had c1277T>G in exon 8 of ESR2 leading to a missense change p.(Leu426 Arg) in the ligand-binding domain [70]. Case #4: the patient had a heterozygous missense mutation, a transition from A to G in the fifth codon of ESR2 that translates in a Lys314 Arg, and based on molecular modeling, this mutation is predicted to impair interaction with nuclear coactivator 1 (NCoA1) [69].
## 3 Results
### RQ3: How Should Psychometrics Assess Emotional Experience?
#### 3.5.1 Statistical Analysis: Parametric Methods  
Even though most studies did not specify the execution of tests for ensuring parametric conditions, most of them determined the use of parametric tests to analyze and correlate electrophysiological signals with emotions. Table 5 summarizes the parametric tests used in the revised literature.  
Repeated measures ANOVA was the most used statistical test to analyze the relation between stimuli effect and electrophysiological signals for ER or recognition [25; 39; 41; 45; 50; 52]. Moreover, this test was used to assess the effect of emotion recognition or regulation on different measurement locations [47; 51; 53; 58; 65]. It was consistently used to inquire about differences between emotional tasks in the selected sample. Furthermore, it was used to correlate emotions with physical or mental conditions [46; 58]. Two-way ANOVA was used to analyze the relation between groups and emotional conditions [36] during ER [55]. Three-way ANOVA was used to analyze the relation between groups, electrodes, and emotions with electrophysiological data [28; 49]. [PERSON] et al. decided to add two more variables: age and gender [63]. The mixed-design ANOVA was employed to notice the relation between groups and stimuli with electrophysiological signals. Finally, three different versions of ANOVA were reported: (1) multivariate-ANOVA (MANOVA) to assess differences in tasks [42], (2) mass univariate ANOVA for signal perturbations [57], and (3) analysis of co-variance (ANCOVA) for regulation strategy [51]. \(t\)-test was frequently used as post  
Figure 5: Presentation time regarding types of stimuli. Numbers indicate the number of studies. Note that some studies may have been counted in various cells.  
hoc test after ANOVA analysis [41; 42; 43; 45; 47]. In one study, it was used to comprehend the relation between ER and EEG frequency power density [37]. [PERSON]'s test was used as a post hoc test to analyze valence and emotion recognition with electrophysiological data and compare between groups [30]. It was also used to analyze ERP data [65]. The Pearson test was another parametric test that was used to associate the regulation effect in valence and arousal ratings for different stimuli [53] with EEG activity [56].
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
###### Abstract
[15], and [PERSON] and [PERSON] [16] studied the stabilization of subgrade soils using the granulated blast furnace slag at different proportions and temperatures. These studies were conducted for a period of one year and it was found that the addition of slag increased the overall strength. When more slag was added, it was shown to significantly reduce the risk of sulfate attack, alkali-silica reaction, and chloride penetration [17, 18, 19, 20, 21, 22]. Some researchers have even suggested that the material chosen for the chemical injection should possess certain properties [23, 24, 25, 26, 27, 28]. Most studies on the long-term curing of chemicals have been conducted for a period of 6 months or 1 year. Very few have been conducted for more than a year. These studies were mainly based on actual field data or long-term experimental data from the laboratory. Data acquisition from an actual experiment or the field involves a tedious process. In addition, if the process is hindered and the data cannot be acquired, it will be a devastating loss of time and resources. Therefore, in this study, the available time-series long-term sustainability of ultrafine grouting materials used in the chemical injection method was verified. Three different prediction models were studied, and the best-fitting prediction model was determined. Since the scientific society nowadays puts its best effort in the decision-making of uncertain matters by various analysis tools considering sustainability [29, 30], the main objective of this study is to obtain useful information for establishing and suggesting a reliable model for time-series predictions of the granulated blast furnace slag. Since the prediction pattern depends upon the adopted model and input data characteristics, the result of this study can be used as a reference for other data with similar patterns.
## 2 Materials and Methods
### DE-CT Imaging Protocol  
DE-CT was performed using a third-generation dual-source CT scanner (SOMATOM Force; Siemens AG, Erlangen, Germany) operating in the dual-energy scan mode, with a tube A voltage of 150 kV, yielding a reference of 208 mA and a tube B voltage of 80 kV, yielding a reference of 374 mA. A 10 mL iodine-containing contrast medium (370 mg I/mL) diluted in half using saline was injected as a test injection to determine the scan delay. The contrast medium was injected for 10 s at a rate of 22 mgI/kg/s. A region of interest (ROI) was mapped out in the main pulmonary artery, and the time-density curve within the ROI was recorded. The early phase DE-CT scan was acquired 4 s after the test injection-mediated enhancement had peaked from the lung's base to its apex. Composite images were created by fusing the high- and low-voltage images, and color-coded lung PBV images were reconstructed at 5 mm intervals using the dual-energy application software _syngo_ CT Workplace, VA44A (Siemens AG, Erlangen, Germany).
## 3 Results and Discussion
### Test Pulse Characterization
#### 3.2.1 Characterization of Single-Colored Femtosecond Pulses  
The temporal profiles of pulses A and B in the test pulse can be retrieved separately by analyzing each of the two FROG signals (Figure 6a). The FROG signal for pulse A (Figure 6c) is symmetric, whereas that for pulse B is tilted with respect to the time delay (Figure 6d). The negative slope of the latter indicates a positive frequency chirp in pulse B. The FROG errors in the analyses of pulses A and B were 0.44% and 0.62%, respectively, yielding the temporal profiles shown in Figure 7. The pulse durations of pulses A and B were calculated to be 51 fs and 55 fs, respectively. To check the reproducibility in the characterization of the pulse, two sets of data were measured independently under the same experimental conditions. In Figure 8, the retrieved temporal intensity profiles obtained from the two XFROG traces are compared. The pulse durations for pulse A (Figure 8a) were 46 fs and 50 fs. Although there is a small difference at the shoulder of the main peak for pulse A, the two temporal profiles are almost identical. The retrieved temporal intensity profiles for pulse B (Figure 8b) suggest pulse durations of 55 fs and 54 fs.  
Figure 8: The two sets of retrieved temporal intensities (solid line) and phase (broken line) profiles of (**a**) pulse A and (**b**) pulse B retrieved from the two XFROG traces.  
Figure 6: (**a**) Measured XFROG trace; (**b**) Retrieved XFROG trace from (**a**); (**c**,**d**) The expanded views of the cross-correlation signals corresponding to the spectral components at 800 nm and 1180 nm in the test pulse, respectively.  
Figure 7: Retrieved temporal intensity (solid line) and phase (broken line) profiles of (**a**) pulse A and (**b**) pulse B.