esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Independent Structural Images for Principal Component Thermography
## 5 Conclusions  
Traditional Principal Component images extracted from raw thermographic data provide clear images which can be used for visualization of structural features such as defects, inclusions, surface heating non-uniformities and so on. This becomes possible because most variance of an entire data set becomes concentrated in a few low order principal components, while high-order components only contain uncorrelated noisy patterns. Extraction of these images forms the core of Principal Component thermography (PCT). Among the drawbacks of PCT images is its inability to provide independent images of individual structural features of the sample under inspection. In particular, the requirement of orthogonality of extracted images (columns of matrix \(U\) in Expression (1)) leads to the fact that a number of principal images may share contours of the same features (e.g., intersecting defects).  
The approach proposed in this work allows for use of the principal images extracted by a conventional PCT approach as a basis for constructing a new set of images corresponding to independent structural patterns. Construction of such patterns requires appropriate sorting of a set of random combinations of meaningful PC images and finding those pixels which would exhibit similar sorting type. In this work, the approach has been demonstrated as capable of extracting nearly unmixed patterns of defects, internal structure and surface heating. It may be that the approach proposed could be valuable when it is necessary to extract unbiased images of particular structural features, i.e., with minimal inclusion of noise and patterns associated with other structural features.  
For effective work of the algorithm, manual selection of a few points where no independent images overlap is required. Automatic selection of such points would be the next development step.  
The authors express their gratitude to Bombardier Aerospace for the samples of C-series composite structures provided.  
The presented work was completed under the supervision of [PERSON], based on the work of [PERSON].  
The authors declare no conflict of interest.  
Figure 12: Examples of reconstructed structural images (archetypes) for a damaged composite plate: (**a**) points chosen for “defect #1”; (**b**) points chosen for “defect #2”; (**c**) reconstructed “defect #1”; (**d**) reconstructed “defect #2”.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
7:Step 2:CalculateArray Coefficients
8: Compute the excitation coefficients for each element using the selected window. 9:Step 3:Compute Element Weights and Positions
10:for\(i=1\)to\(N\)do
11: Calculate weight Weight\([i]\) = WindowFunction(\(i,N\)). 12: Calculate element position Position\([i]\) = \(d\times(i-(N+1)/2)\), where \(d\) is the element spacing. 13:endfor
14:Step 4:EvaluatePerformance
15: Compute the radiation pattern and evaluate sidelobe levels, beamwidth, and directivity. 16:Step 5:Adjust and Finalize
17:ifPerformance is satisfactory then
18: Finalize the array design and export the parameters. 19:else
20: Adjust window parameters or element spacing and repeat from Step 2. 21:endif
22:endfunction
```  
**Algorithm 1** Synthesize Taylor-Kaiser antenna array. This Taylor series can be truncated after the first order if a linear approximation is sufficient for the spatial distribution of the electromagnetic field around the reference point. The amplitude distribution for the Taylor one-parameter distribution is given by the following:  
\[w_{n}(\alpha)=\frac{J_{0}(\alpha\sqrt{1-\rho^{2}})}{J_{0}(\alpha)} \tag{6}\]  
where the following applies:  
* \(w_{n}(\alpha)\) is the amplitude weighting coefficient for the \(n\)-th element of the array. * \(J_{0}\) is the zeroth-order Bessel function of the first kind. * \(\alpha\) is the design parameter that controls the characteristics of the radiation pattern. * \(\rho=\frac{2\pi d}{\lambda}\sin(\theta)\) is the normalized distance between array elements, where
* \(d\) is the spacing between elements;
* \(\lambda\) is the wavelength;
* \(\theta\) is the angle of radiation. The Bessel function of the first kind of order zero, \(J_{0}(x)\), is defined by the infinite series:  
\[J_{0}(x)=\sum_{k=0}^{\infty}\frac{(-1)^{k}}{(k!)^{2}}\Big{(}\frac{x}{2}\Big{)} ^{2k} \tag{7}\]  
It satisfies the recurrence relation:  
\[J_{0}(x)=\frac{2}{x}J_{1}(x)-J_{-1}(x) \tag{8}\]and its asymptotic behavior for large \(x\) is approximated by  
\[J_{0}(x)\approx\sqrt{\frac{2}{\pi x}}\cos\left(x-\frac{\pi}{4}\right) \tag{9}\]  
This function frequently appears in applications such as wave propagation in cylindrical waveguides, heat conduction in cylindrical coordinates, and vibration analysis in cylindrical structures.
## 1 Introduction
In this respect, to improve the ANN performance of implemented models, some researchers, such as those in [23; 24; 25; 26], have combined ANN models with a principal component analysis (PCA) to create new features based on the original variables in order to reduce the dimensions, resulting in a favorable prediction performance of the ANN in their respective applications. The concentration measurement of TZ, AR, and B1 mixture dyes in aqueous solutions cannot be carried out directly by UV-VIS spectrophotometry due to the overlapping of their absorption spectra. Although, in recent years, various direct spectrophotometric determinations of dyes in aqueous mixture methods have been reported, the use of an ANN and PCA coupled model has not been used for this purpose. This study presents a new framework in which the use of a combined ANN and principal component analysis (PCA) model serves as an alternative for the concentration determination of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. This paper is organized as follows: Section 2 introduces the proposed method, data collection, and PCA and ANN models. In Section 3, we show the results and a discussion. Finally, Section 4 presents the conclusions.
## 1 Introduction  
The correct molar position and occlusal relationship have been described by [PERSON] as one of the six keys essential to achieving a successful orthodontic treatment [1, 2]. In 1969, [PERSON] described the following method to assess the proper distal rotations of the upper first molars in evaluating mesial drift in the malocclusion: in a parabolic dental arch form, the normal position of the upper first molar can be assessed when a line is drawn through the tips of the molar's disto-buccal and the meso-lingual cusps cross the distal surface of the opposite canine [3]. Due to its rhomboidal shape with a wider buccolingual diameter than mesiodistal, a rotated upper first molar occupies more space, creating a situation unreceptive to obtain a normal occlusion with more crowding between adjacent teeth [2, 4]. This rotation is caused by the mesiolingual cusp of the upper first molar, which firmly occludes in the central fossa of the lower first molar and acts as a center of rotation, causing a mesial movement of the mesibuccal cusp [4]. The tooth occupies excessive space in the dental arch and its buccal cusps occlude with a tendency of Class II molar relationship [5].  
In the literature, there are plenty of investigations focused on this topic [5; 6; 7; 8; 9]. The prevalence of the mesial rotation in subjects with the presence of Class II malocclusion is about 86%. [PERSON] et al. [6] found a correlation between the severity of Class II division 1 malocclusion and the amount of maxillary first molars' mesiopalatal rotation. Furthermore, [PERSON] et al. reported that there is an association between upper first molar mesial rotation and anterior dental crowding in permanent dentition, while subjects in mixed dentition with mesial rotation should be considered at a higher risk of dental crowding [7]. In 2011, [PERSON] et al. [8] investigated the upper first molar mesial rotation in Class II division 1 malocclusion subjects in mixed dentition. They performed dental cast measurements in a sample of 120 Class II division 1 subjects and in a sample of 58 Class I subjects, and they found that the amount of upper molar rotation was significantly greater in the Class II group than in the Class I group. In a recent case report, [PERSON] et al. highlighted how maxillary molar derotation represented a strategic step to allow mesial mandibular shift and gain space in the arch, thus contributing to malocclusion correction [9].  
However, in the literature there is a lack of information about the correction of upper first molar mesial rotation with clear aligners (CA).  
The aim of the present study was to analyse the derotation movement of the maxillary mesio-rotated first permanent molars in subjects with Class II edge-to-edge dental malocclusion in mixed dentition treated with Invisalign Clear Aligners (CA).
# Effect of Aging on Class G High Sulfate-Resistant Oil Well Cement Under High Relative Air Humidity
## 3 Results and Discussion
### Influence of Aging on the Particle Size of Oil Well Cement  
When the estimated aging time was reached, the aged cement was found to be caked, with the extent of caking increasing with increasing aging time, notably, A28 cement was bonded to the stainless steel plate. The particle size distributions of samples A0 and A28 were characterized using a Hydro 2000 M/MU laser particle size analyzer from Malvern, UK, as shown in Figure 4 and Table 5. The specific surface areas of samples A0 and A28 were measured with an FBT-9 Blaine air permeability apparatus (Shanghai Yichang), as listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cement Sample** & **Rheological Model** & **Fitting Function** & \(R^{2}\) & \(\mu_{p}\) (Pa-s) & \(\tau_{0}\) & **Fluidity (cm, 25 \({}^{\circ}\)C)** \\ \hline A0 & Bingham fluid & \(\tau=0.068\gamma+0.920\) & 0.995 & 0.068 & 0.920 & 19.0 \\ A7 & Bingham fluid & \(\tau=0.077\gamma+1.333\) & 0.999 & 0.077 & 1.333 & 19.0 \\ A14 & Bingham fluid & \(\tau=0.091\gamma+2.467\) & 0.998 & 0.091 & 2.467 & 18.0 \\ A28 & Bingham fluid & \(\tau=0.176\gamma+12.864\) & 0.947 & 0.176 & 12.864 & 16.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The rheological curve fitting results of cement pastes after different aging time at 80 \({}^{\circ}\)C.  
Figure 3: The rheological curves at 80 \({}^{\circ}\)C of cement pastes after different aging time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cement Sample** & \multicolumn{3}{c}{**Partical Size (\(\mu\)m)**} & **Specific Surface Area (m\({}^{2}\) kg\({}^{-1}\))** \\ \cline{2-5}  & **d\({}_{10}\)** & **d\({}_{50}\)** & **d\({}_{90}\)** & **\\ \hline A0 & 2.54 & 15.15 & 36.69 & 375 \\ A28 & 3.66 & 16.56 & 37.55 & 310 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The specific particle size distributions of cement samples A0 and A28.  
According to laser particle size analysis, compared with those of the A0 sample, the d\({}_{10}\), d\({}_{50}\), and d\({}_{90}\) values of the A28 sample increased by 1 \(\upmu\)m, but this difference was not significant. In terms of the specific surface area, the A28 cement showed a reduction of 17% when measured by the Blaine method. Notably, the content of fines with particle size below 1 \(\upmu\)m in A0 and A28 samples showed little change, indicating that aging had less significant impact on the particle size distribution of cement but more significant influence on its specific surface area.  
The decrease in the specific surface area indicated that with increasing aging time, the surface energy of the cement powder decreased, which was usually accompanied by a decrease in the cement hydration reaction rate and compressive strength.
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 1 Introduction
Through the mathematical model, this paper successfully proposes the \(EMP\), which can be applied in any \(TM\) driven by an \(SS\). Figure 1: Electric injection molding machine with a mold. If there are some foreign bodies in the mold without protection, the mold will be damaged when the mold closes. Generally, the mold is expensive, and the maintenance is tedious. 3. The proposed method was implemented in a 140-ton electric injection molding machine (\(EIMM\)), and decreased the detected distance of \(MP\) by 22% compared with general one. An extended Kalman filter (\(EKF\)) \(EKF\) linearizes with an estimate of the current mean and covariance, which is a nonlinear kind of the Kalman filter [25,26]. It is widely used in various fields. [PERSON] et al. [27] presented a hybrid FLC-EKF scheme for temperature control of a refinery debutanizer column. [PERSON] et al. [28] used neural networks and EKF to estimate the charging state for Lithium-ion batteries. [PERSON] et al. [29] applied an EKF-based filter fusion method in a high-efficiency motion tracking system. [PERSON] proposed a two-stage EKF to build an observer for a sensorless control [30]. One key point for using the EKF is the choice of the elements of the covariance matrices, which affects both the performance and the convergence [31]. Many studies [32, 33, 34] have provided practical approaches and accounts. More details can be found in the surveys of [35, 36, 37]. However, none of these studies are involved in mold protection problems. The remainder of this paper is structured as follows: Section 2 introduces the \(TM\) system, \(SS\) system, and \(MP\) problem (\(MP\)); Section 3 illustrates the \(TM\) model driven by \(SS\) and \(EMP\); Section 4 presents the effect validation of \(EMP\); and the last section concludes our work.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 5 Experimental Results
### The Display  
The display covers five seconds of ECG, and the trace dynamically rolls from left to right. The nominal refresh rate, which corresponds to the frequency with which the sensor generates data frames, is 25 refresh events per second.  
The configuration using the local by-pass server is useful to test the JavaScript application: given that there is no data loss, the browser has to display the ECG at full speed. In that case, we observe a limited stuttering that is probably due to the browser.  
In the case of moderate (4%) data loss, the display may stop the visualization and later resumes consistently.  
When data loss approaches 40% the trace is frequently unreadable.  
Regarding transmission delays, they should be limited to allow the doctor to instruct the patient about electrodes positioning, for instance on a phone call while taking the ECG. In normal conditions, latency is unnoticeable, and has been measured around 0.1 s with the European server; however, it strongly depends on the quality of the Internet connection.  
We added to the display application three utility functions only to show that our design allows ECG processing on doctor's side:  
* a moving average filter that significantly attenuates the residual 50 Hz noise,
* the dynamic evaluation of the heartbeat rate,
* the possibility to put on hold the screen to analyze the ECG.
## 5 Case Study Analysis
### Real Region Case
The corresponding system inertia levels of the three schemes are shown in Figure 6, where the black line is the demand inertia of the system and the red line is the actual inertia level of each scheme in different scenarios, where scenario 1 contains 1 h to 24 h and scenario 2 contains 25 h to 48 h. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Scheme I** & **Scheme II** & **Scheme III** \\ \hline Unit retirement capacity/MW & 4000 & 3800 & 3800 \\ \hline Unit retrofit capacity/MW & 2690 & 2890 & 2890 \\ \hline New capacity of coal-fired & & & \\ power unit/MW & 1300 & 1840 & 2020 \\ \hline New capacity of wind & & & \\ turbine/MW & 6300 & 6300 & 6000 \\ \hline New capacity of photovoltaic & & & \\ unit/MW & 7200 & 7300 & 8000 \\ \hline Renewable penetration/\% & 30.9 & 30.7 & 30.9 \\ \hline Renewable energy & & & \\ accommodation/\% & & & \\ \hline New lines & (4–3) (54–2) & (55–5) & (55–5) & (55–5) \\ (starting point-end point) & (24–59) (59–20) & (24–59) (59–20) & (59–20) & (59–20) (41–46) (47–66) & (47–66) \\  & (41–46) (47–66) & (70–25) & (47–66) & (47–66) \\ \hline Investment cost/billion (CNY) & 69 & 72 & 73 \\ \hline Operation cost/billion (CNY) & 588 & 593 & 595 \\ \hline Total cost/billion (CNY) & 657 & 665 & 668 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various planning results of 24-node case. Figure 6: System inertia of real-region case. Scheme 1 does not take into account system frequency security; therefore the inertia level is lower than the demand inertia in both Scenario 1 and Scenario 2. Schemes 2 and 3 consider frequency security and the system inertia level is higher than the demand inertia in both scenarios 1 and 2. Compared to Scheme 2, Scheme 3 considers the node inertia level and the overall system inertia level changes less in Scenario 2. Nodes 6, 29, 43, and 65 are selected to facilitate the comparison of the node inertia. The variations in inertia levels at key nodes are given in Figures 7 and 8. Figure 7: Key node inertia in Scenario 1.
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 2 Materials and Methods
### Presumptive Background of HPOM
#### 2.6.1 ORCP Inference
Finally, the ORCP estimation is completed through the process of finding intersections of all the obtained straight lines. In order to find the intersections of all straight lines, these line equations are divided into a coefficient term, an unknown term, and a constant term. They can be arranged, as shown in Equation (13), as a determinant. Since the unknown term is the position on the xy-plane of the ORCP that is to be estimated, the ORCP can finally be calculated by solving Equation (14) after organizing for the unknown term by finding the inverse matrix for the constant term. The coefficient matrix that is expressed in Equation (13) is not a square matrix because of the number of lines for ORCP estimation is always greater than 2. Thus, the inverse matrix cannot be calculated using a general method. However, the pseudo-inverse matrix with similar functions to the inverse matrix can be considered. A pseudo-inverse calculation is an operation that can be used for all types of matrices and generalizes the inverse matrix operation of the invertible matrix. It can be applied to an invertible matrix and can be carried out using a Singular Value Decomposition (SVD) [32]. The coefficient matrix in Equation (13) is an invertible matrix because of every line for estimating the ORCP are crossed at a point. In conclusion, the ORCP can be calculated using a pseudo-inverse. \[\left(\mathrm{x_{P}-x_{ORCP}}\right)^{2}+\left(\mathrm{y_{P}-y_{ORCP}}\right) ^{2}+\left(\mathrm{z_{P}-z_{ORCP}}\right)^{2}-\mathrm{R}^{2}=0. \tag{6}\]  
\[\mathrm{D}=\sqrt{\left(\mathrm{x_{P}-x_{ORCP}}\right)^{2}+\left(\mathrm{y_{P} -y_{ORCP}}\right)^{2}}=\mathrm{R}\times\sin\theta,\ \ \left(\theta=\mathrm{ RAE}\right) \tag{7}\]  
\[\mathrm{z_{P}-z_{ORCP}}=\mathrm{R}\times\cos\theta=\frac{\mathrm{D}}{\sin \theta}\times\cos\theta=\frac{\mathrm{D}}{\tan\theta},\ \ \left(\theta=\mathrm{ RAE}\right) \tag{8}\]  
\[\mathrm{K}\times\mathrm{D}^{2}-\mathrm{R}^{2}=0,\ \ \left(\mathrm{K}=1+\frac{1}{\tan \theta^{2}},\ \theta=\mathrm{RAE}\right) \tag{9}\]  
\[\mathrm{K_{n}\times{D_{n}}^{2}-K_{m}{D_{m}}^{2}=0,\ \left(n\
eq m\right)} \tag{10}\]  
\[\mathrm{Ax}+\mathrm{By}=\mathrm{C} \tag{11}\]  
\[\mathrm{k}\times\mathrm{D}^{2}=0,\ \left(\mathrm{k}=\mathrm{K_{n}-K_{m}}\right) \tag{12}\]  
\[ \begin{pmatrix}\mathrm{A_{1}}&\mathrm{B_{1}}\\ \mathrm{A_{2}}&\mathrm{B_{2}}\\ \vdots&\vdots\\ \mathrm{A_{n}}&\mathrm{B_{n}}\end{pmatrix} \mathrm{(x}\ \ \mathrm{y}\ \mathrm{)}= \begin{pmatrix}\mathrm{C_{1}}\\ \mathrm{C_{2}}\\ \vdots\\ \mathrm{C_{n}}\end{pmatrix} ,\ \ \left(\mathrm{n}=\text{the number of straight line}\right) \tag{13}\]  
\[\left(\mathrm{x}\ \ \mathrm{y}\ \mathrm{)}= \begin{pmatrix}\mathrm{A_{1}}&\mathrm{B_{ 1}}\\ \mathrm{A_{2}}&\mathrm{B_{2}}\\ \vdots&\vdots\\ \mathrm{A_{n}}&\mathrm{B_{n}}\end{pmatrix} ^{-1} \begin{pmatrix}\mathrm{C_{1}} \\ \mathrm{C_{2}}\\ \vdots\\ \mathrm{C_{n}}\end{pmatrix}  \tag{14}\]
## 2 Methodology
### Orbital Configuration and Sensor Type
#### 2.2.4 Orbital Parameters  
Sensor IDs 1-3 (Table 1) have orbital configurations that are polar and sun-synchronous, which means the local overpass time at the equator will be similar from one day to the next. A consistent overpass time is critical for microwave-based remote sensing, be it active or passive in nature, because the presence (or absence) of liquid water can drastically change the snow dielectric constant that could in turn affect the observed signals and, therefore, introduce additional errors or uncertainties in the snow retrievals. To further reduce the diurnal variations of the observations in sensor IDs 1-3, only one overpass direction representing the nighttime overpass is used here in order to minimize wet snow effects. By contrast, both ascending and descending observations are used for sensor IDs 4, 5, and 6 since the snow depth measured by LiDAR is less impacted (related to microwave sensors) by snow wetness or snow temperature.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup  
This section begins with an introduction to the experimental setup, as detailed in Sections 4.1-4.4. Following that, we delve into the configuration of the benchmark schemes in Section 4.5. Additionally, Section 4.6 outlines the performance evaluation metrics.
###### Abstract  
Background and Objectives: Korean red ginseng (KRG) is known as an immune-enhancing health food and has been approved by the Korea Food and Drug Administration. We analyzed the immune-enhancing activity of KRG and its polysaccharide (KRG-P) using RAW264.7 murine macrophage cells. Materials and Methods: The protein and mRNA expression levels of IL-6 and TNF-\(\alpha\) were measured using ELISA and qRT-PCR, respectively. Nitric oxide levels were measured using the Griess reagent. The phosphorylation and total protein levels of ERK, p38, JNK, P65, and GAPDH were determined by immunoblotoid assay. Results: The polysaccharide (KRG-P), but not KRG, produced nitric oxide, interleukin 6 (IL-6), and tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) in RAW264.7 cells. KRG-P increased nitric oxide synthase 2 (NOS2), IL-6, and TNF-\(\alpha\) expression in RAW264.7 cells. KRG-P also increased phosphorylation of MAPKs (mitogen-activate protein kinases) including ERK, p38, JNK, and NF-\(\kappa\)B (nuclear factor kappa-light-chain-enhancer of activated B cells) in a concentration-dependent manner in RAW264.7 cells. Conclusions: The polysaccharide KRG-P is the active component responsible for the immune-enhancing activity of Korean red ginseng and may modulate the systemic immune system in vivo.  
Keywords:Korean red ginseng; polysaccharide; macrophages; innate immune responses +
Footnote †: journal: Applied Sciences