esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Morphology, Cell Growth and Apoptosis of Cell Clusters of Ovarian Cancer Cell Lines  
As shown in Figure 1A, 2D cell monolayers of OVCAR-5 and OVCAR-4 showed compact cell colonies, and these cells formed small clusters when cultured in a non-adherent condition. SKOV-3 grew as a loose colony pattern in cell monolayers, but formed compact cell aggregates in a non-adherent condition.  
Next, the activity of two inhibitors, the EGFR inhibitor gefitinib and the dual EGFR/HER-2 inhibitor canertinib, was investigated on cell clusters of these three cell lines. The graphs in Figure 1B show that the anti-growth activity of gefitinib and canertinib was cell line-dependent. Gefitinib and canertinib significantly (\(p<0.05\)) reduced cell growth in OVCAR-5 clusters in a similar manner. Both the inhibitors significantly (\(p<0.05\)) reduced cell growth in SKOV-3 cell aggregates. However, canertinib showed (\(p<0.05\)) a greater decrease in growth in SKOV-3 than gefitinib. Both inhibitors had limited decrease in cell growth in OVCAR-4 clusters.  
The decrease in cell growth by both inhibitors may be attributed to the increase of apoptosis. As shown in Figure 1C, both gefitinib and canertinib marginally (\(p<0.05\)) increased apoptotic cells in OVCAR-5 cell clusters. However, there was a more prominent increase of cell apoptosis of SKOV-3 aggregates treated with canertinib, than those treated with gefitinib (\(p<0.05\)). OVCAR-4 did not have significantly increased apoptotic cells. As shown in Figure 1D, canertinib reduced the expression of cyclin D2, PCNA and cyclin B2 in SKOV-3 cell aggregates. However, gefitinib had a limited effect on the overall apoptosis of SKOV-3 and SKOV-3. However, the increase of the apoptosis of SKOV-3 and SKOV-3 is not significant.  
Figure 1: Effects of gefitinib and canertinib on three well established ovarian cancer cell lines cultured in 3D aggregates. Morphological appearances of OVCAR-5, SKOV-3, and OVCAR-4 are distinctive in monolayer. In 3D culture, both OVCAR-5 and OVCAR-4 form small clusters, but SKOV-3 cells form compact cell aggregates (**A**). Gefitinib and canertinib significantly reduce cell growth of OVCAR-5 cell clusters and SKOV-3 cell aggregates. OVCAR-4 cells do not show any growth response to inhibitors (**B**). Canertinib induces greater apoptotic cells in SKOV-3 cell aggregates than gefitinib (**C**). Proteins associated with the cell cycle, cyclin D2, PCNA, and cyclin B2, are reduced in SKOV-3 canertinib treated cell aggregates (**D**). Number of cells in control and treatment arms in Figure 1B were obtained by counting cells with a haemocytometer. Apoptotic cells were detected by Annexin V-FITC labelling cells and analysed with a flow cytometry. Representative analysis from a set of four independent experiments (Mean \(\pm\) SE; \(n=4\)) is presented and statistical significance was determined by a \(t\)-test (*, \(p<0.05\)).  
on the decrease in these proteins. OVCAR-5 and OVCAR-4 cell clusters did not change the level of these proteins with either gefitinib or canertinib treatment. These results suggest that the response to gefitinib and canertinib is cell line dependent.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Wavelet Transform Methods
In [108], the wavelet coefficients which have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain by HAAR wavelet and finally fault diagnosis of induction machines was realized. The wavelet packet decomposition (WPD) is a wavelet transform, in which the signal is passed through more filters. [PERSON] et al. [109] utilized envelope analysis to demodulate the wavelet packet coefficients which extracted from the selected wavelet packet node for the weak bearing fault signal detection. [PERSON] et al. [110] developed a noise suppression method through combining EMD and WPD for weak feature extraction of wind turbine bearings. The Morlet wavelet is a wavelet composed of a complex exponential Gaussian window (envelope). [PERSON] et al. [111] apply the optimal Morlet wavelet filter to preprocess the signal, and envelope detection was used to identify bearing failures at early stage. [PERSON] [112] proposed a feature extraction method combining the morphological analysis and complex shifted Morlet wavelets for bearing fault diagnosis. In [113], the signal was filtered using a band-pass filter determined by a Morlet wavelet which parameters were optimized based on maximum kurtosis. Then the envelope detection and the energy operator were used for gear fault diagnosis. [PERSON] et al. [114] utilized a complex Morlet wavelet filter which is determined by a simplex-simulated annealing algorithm to extract the weak bearing fault features. The abovementioned methods are summarized in Table 7.
## 6 Conclusions  
Dating clay cores of bronze states by TL is the only possibility of reaching a chronological determination for the casting of the statues, due to the impossibility of directly dating a metal. Here, it is shown how complex is the application of TL to clay cores. Nonetheless, important results can be achieved by accurate analysis of the experimental data and by a correct statistical management of these data.  
The sources of uncertainties can highly reduce the dating precision, either due to the materials, because the clay core is not a ceramic, and due to the difficulties in the calculation of the dose rate. The internal dose rate can be strongly affected by an unknown water content that reduces the dose rate in the centuries, while the external dose rate can be assessed sometimes with large uncertainty, due to the difficult reconstruction of the life of the statue in time. We re-considered three cases of clay-core dating from important bronzes in Rome. The traditionally reported Etruscan origin of _Lupa Capitolina_ is definitely ruled out, even if the precision in the dating is too low to allow precise proposals for the date of casting. The statistical comparison with radiocarbon results shows good agreement for a Medieval dating.  
A horse from Musei Capitolini is assigned to a casting in the Greek period, rather than a Roman casting.  
A third study relative to the statue of [PERSON] in the Vatican shows that, in particularly favourable situations, the dating of clay cores can be similar to the dating of ceramics. It is necessary to find materials in the clay core behaving like good dosimeters and the possibility of measuring all the parameters influencing the calculation of the dose rate is essential, such as in the case of the [PERSON] statue, which has very likely always been kept in the same radiation field.  
The basic idea of dating by TL can be applied, in principle, to the material remaining in the interior of a bronze statue after its casting, the so-called clay core. In favourable cases, this material behaves like a ceramic and the procedures used for dating ceramics can be also applied to clay cores. This is extremely important, considering that, with very few exceptions that are not treated in this paper, metal objects cannot be dated by absolute techniques. In this work, the application of TL dating to clay cores is introduced and the specific difficulties deriving from the characteristics of this material, together with the complex determination of the radiation environment, are commented on.  
Conceptualization, M.M.; writing--original draft preparation, M.M. and A.G.; writing--review and editing, M.M. and A.G.; supervision, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was also possible thanks to all researchers that have collaborated with us during the activity of our laboratory. We would like to mention [PERSON] particularly, for helping us with the elaboration of data in the case study of _Lupa Capitolina_.
# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 2 Methodology
### Target Segmentation from Pending Images  
To obtain a common target from the two images, we processed image segmentation. The segmentation for the infrared image is relatively easy. Because the infrared image itself is characterized by gray levels, in most cases, a reasonable threshold value such as the Otsu threshold method [23] can achieve the binary classification of the target and the background. The polarization parameter image is too rich in texture and lacks gray-level information. In polarized parametric images, such as the degree of polarization image and the angle of polarization image, the target and the background usually exhibit vastly different textures. Sorting out polarized texture regions to match gray-scale regions within infrared images is our goal of image segmentation, therefore, a texture-sensitive segmentation is needed.  
The Gabor filter is constructed by a sinusoidal function and Gauss function, so it is sensitive to periodic information. Since texture features can be considered as a combination of periodic signals of different angles and frequencies, we used a filter bank with multiple Gabor filters in different angles to filter the polarization parameter images for sub-band images. Figure 2 is a graphical representation of the Gabor filter bank. We took 4 angles evenly at 180 degrees, and the frequency difference was achieved by scale differences in filters.  
After filtering, the texture part in the image will be extracted and the smooth part will be removed. The texture of different directions will be divided by the filter of corresponding angles to realize the binary classification of each sub-band image. In the polarized parameter image, the region of interest in the figure appears as dense periodic stripe. To process the stripe into connected regions, an active contour method [24] is conducted to segment the result. It placed an initial circle on the image and changed the contour of the circle with the optimization process, the boundary achieved when  
\[\text{min}\big{(}\text{Q}_{\text{in}}-\overline{\text{Q}_{\text{in}}}\big{)} +\big{(}\text{Q}_{\text{out}}-\overline{\text{Q}_{\text{out}}}\big{)} \tag{1}\]  
where \(\text{Q}_{\text{in}}\) represents the gray value of each pixel inside the contour, \(\overline{\text{Q}_{\text{in}}}\) represents the average gray value of each pixel inside the contour, \(\text{Q}_{\text{out}}\) represents the gray value of each pixel outside the contour, and \(\overline{\text{Q}_{\text{out}}}\) represents the average gray value of each pixel outside the contour. The minimization was conducted by the gradient descend method. When the minimum value is obtained, the gray values inside and outside the boundary tended to be consistent with their average values. It was considered that the accurate contour was drawn, and finally, the polarization image target and background were segmented.
## 3 Methodology
### 3D Reconstruction Based on CasMVSNet  
Considering the time cost and hardware equipment requirements for the three-dimensional modeling of UAV images, this study uses CasMVSNet [26] for 3D reconstruction due to its faster processing speed and higher accuracy. CasMVSNet extends the regularization of cost volume using 3D CNN [32] in MVSNet, which can better capture spatial feature information and use a multi-layer cascade method from coarse to fine. The multi-scale feature maps extracted from the feature extraction part are matched to construct cost volume with different resolutions. In the previous stage, the rough depth information was obtained via the calculation of the small-resolution feature map, and this is used to adaptively narrow the range of depth calculated by the higher resolution feature map in the next stage.  
As shown in Figure 4, to obtain the multi-scale feature map and calculate the depth at different stages, the feature extraction part uses the feature pyramid network (FPN) method to extract the feature map with three scale resolutions, and their size is reduced by [33] times compared to the original image. Based on the above multi-scale feature map, the cost volume is constructed in stages from small to large. As shown in the green line in Figure 4, the depth information calculated by the cost volume with smaller resolution in the previous stage restricts the depth range of the homography transformation in the next stage to the depth value of this calculation. In fact, the depth interval of the previous stage is refined, and a smaller cost volume is constructed, which not only reduces the amount of calculation but also consumes explicit memory when calculating more accurate depth maps.  
\[H_{i}^{(k+1)}\Big{(}d^{(k)}+\Delta^{(k+1)}\Big{)}=\Big{(}d^{(k)}+\Delta^{(k+1 )}\Big{)}K_{i}T_{i}T_{0}^{-1} \tag{2}\]  
The differentiable homography [20] of CasMVSNet in different stages is as follows: The depth value of different stages is modified to \(d=d^{(k)}+\Delta^{(k+1)}\), where \(k\) refers to the number of stages. The depth range for the next stage is calculated by adding the depth result \(d^{(k)}\), calculated in the previous stage, to the residual depth \(\Delta^{(k+1)}\). As shown in part \(W\) of Figure 4, with the change in the feature map, the intrinsic parameters of the camera are scaled equally. Using these parameters, the feature maps of the auxiliary view are warped into the reference image view space, and the cost volume is constructed by aggregating the cost matching the auxiliary view feature maps and the reference image feature maps.
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 4 Learning Physical Properties
### Mass Ratio of Objects  
In this section, analytical methodology is provided to estimate the masses of the objects. In dynamic manipulation, the motion of the objects is determined based on both the dynamic characteristic of the objects and motion of the manipulator. Accordingly, it is important to infer mass properties of the objects to model their motion after impulsive actions. Like other physical properties, the masses ratio of objects are also estimated based on observed parameters and combining with analytical model. Substitution of Equation (17) into Equation (15) will yield:  
\[\begin{split}\Delta{v_{a}}=\frac{{1+e}({{\mathbf{v}_{a}}}-{{\mathbf{v}_{ b}}})+{{\mathbf{v}_{a}}}^{2}+{{({\mathbf{v}_{a}}{\mathbf{v}_{a}})}^{2}}/{{\mathbf{v}_{a}}^{2}}+{{({ \mathbf{v}_{b}}{\mathbf{v}_{a}})}^{2}}/{{\mathbf{v}_{b}}}^{2}}}{{\mathbf{v}_{a}}}\cdot{\mathbf{n}} +\\ {\mathbf{r}_{a}}\times\frac{{{(-1+e)({{\mathbf{v}_{a}}}-{{\mathbf{v}_{b}}})}+ {{\mathbf{v}_{a}}}}}{{{2{\mathbf{v}_{a}}^{2}}}{\left({1+{\frac{{{\mathbf{v}_{a}}}}{{{{\mathbf{ v}_{a}}}}}}}\right)}+{{({\mathbf{n}_{a}}{\mathbf{v}_{a}})}^{2}}+{{({\mathbf{r}_{b}}{\mathbf{v}_{b}} )}^{2}}+{{\mathbf{r}_{a}}^{2}}/{{\mathbf{v}_{b}}}^{2}}}{{\mathbf{v}_{a}}}\cdot{\mathbf{n}} \times{\mathbf{r}_{a}},\end{split} \tag{33}\]  
Figure 8: Coefficient of restitution: Average value and standard deviation. M stands for manipulator.  
Similarly, the substitution of Equation (17) into Equation (16) will yield:  
\[\begin{array}{l}\Delta v_{b}=\frac{-(1+e)(\overline{v}_{a}-\overline{v}_{b}) \cdot\mathbf{u}}{1+\frac{m_{b}}{m_{a}}+2(|\tau_{b}\mathbf{x}_{b}|^{2})/\tau_{b}^{2}} \frac{m_{b}}{m_{b}}+(n_{b}\mathbf{x}_{b})^{2}/\tau_{b}^{2}]}\cdot\mathbf{n}-\\ \mathbf{r}_{b}\times\frac{-(1+e)(\overline{v}_{a}-\overline{v}_{b})\cdot\mathbf{u}}{2 \tau_{b}^{*2}(1+\frac{m_{b}}{m_{a}})+|(\tau_{b}\mathbf{x}_{b})^{2}/\tau_{b}^{2}/ \tau_{a}^{2}\frac{m_{b}}{m_{a}}+(n_{b}\mathbf{x}_{b})^{2}]}\cdot\mathbf{n}\times\mathbf{r}_ {b},\end{array} \tag{34}\]  
where all parameters except the ratio of masses (\(\frac{m_{b}}{m_{a}}\), \(\frac{m_{b}}{m_{a}}\)) are already known. The velocities before/after collision are observed from the videos and the coefficient of restitutions are already estimated. Thus, it is inferred from the above equations that the change in velocities of two objects being involved in collision is associated with ratio of masses of two objects. That is, there exists infinite solutions for \(m_{a}\) and \(m_{b}\), which implies that the equations can be written in form of \([x\;\;y]\)(\(\begin{array}{c}m_{a}\\ m_{b}\end{array}\)) = 0, where x and y are known values. It is noted that the ratio of the masses is always the same no matter what equation is analyzed. That solution will be enough to model the object's trajectory after collision even though the true masses are unknown. Figure 9 denotes the average values and standard deviation of mass ratio of each object relative to other objects.
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 3 Results and Discussion
### MIMO Performances  
Therefore, some antenna elements (Ant 1 and Ant 2, Ant1 and Ant 3, Ant 1 and Ant 4, Ant 1 and Ant 5, Ant 1 and Ant 6, Ant 2 and Ant 3, Ant 2 and Ant 4, and Ant 3 and Ant 4) with similar positions and strong radiation interference, were simulated and tested. The ECC values between these antenna elements can be calculated according to the calculation Equation (1) [24]:  
\[\rho_{e}=\frac{\left|\iint_{4\pi}F_{1}^{-\ast^{\ast}}(\theta,\phi)\cdot F_{2} ^{-\ast^{\ast}}(\theta,\phi)d\Omega\right|^{2}}{\iint_{4\pi}\left|F_{1}^{- \ast^{\ast}}(\theta,\phi)\right|^{2}\cdot d\Omega\times\iint_{4\pi}\left|F_{2 }^{-\ast^{\ast}}(\theta,\phi)\right|^{2}d\Omega} \tag{1}\]  
where \(F_{i}\)\(\rightarrow\)*(0, \(\phi\)) denotes the \(i\)th antenna's complex three-dimensional (3-D) radiated far-field pattern.  
Figure 14: Simulated and measured antenna efficiencies.  
Figure 13: Simulated and measured. (**a**): Reflection coefficients and (**b**): transmission coefficients of MIMO antenna system.  
The results are shown in Figure 15. Clearly, the level of ECC is below 0.049 in the desired frequency band, which provides promising MIMO diversity performance.
## 3 Results and Discussion
### Eating Quality of Black Rice  
#### 3.4.1 Taste Analyzer  
Rice taste analyzers developed in Japan operate on the basis of correlations between near-infrared reflectance measurements of the crucial constituents and the \"taste\" scores of the cooked rice [7; 38]. It has been reported in the literature that the taste value of cooked rice is closely related to the physical and chemical parameters of rice [7; 39]. The principle of the rice taste analyzers is based on the correction between near-infrared spectroscopy measurements of key constituents (such as amylose, protein, fatty acids, and moisture) and Japanese preferred sensory scores [38]. The \"taste\" scores assigned by the rice taste analyzer were designed to predict the comprehensive taste quality value [24]. As shown in Table 2, the taste score was significantly influenced by milling treatment in black rice cultivars. The taste value of cooked black rice increased first and then decreased with processing from whole black rice (A) to refined rice (F). Water diffusion and starch leaching are important factors affecting cooked rice quality [34]. Slightly milled black rice (B) and refined black rice (F) showed the highest and lowest taste value, respectively. This indicated that a moderate milling process can destroy the bran layer, thus improving the cooking quality. However, excessive milling and exposure of the endosperm lead to water absorption and dissolution during cooking, thus losing the proper viscoelasticity of black rice. In addition, there were significant differences in the quality traits of cooked rice (appearance, tastiness, hardness, and stickiness) among the black rice with different milling degrees. However, no differences were observed in terms of elasticity.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Example 2  
To achieve the specified side lobe levels (\(-20\) dB, \(-30\) dB, \(-40\) dB, and \(-50\) dB), amplitude tapering among the elements is optimized using Taylor distributions. This optimization ensures that the radiation pattern meets the desired sidelobe levels. The main lobe width can be adjusted accordingly during the optimization process to meet any specific requirements. Finally, the array factor representing the radiation pattern is computed, followed by simulation and iterative optimization using MATLAB R2023a until meeting the specified design criteria [47].  
Figure 3 shows the [PERSON] antenna array with a Kaiser window, where the array size is \(N=16\), and the inter-element spacing is \(d=\frac{\lambda}{2}\). This configuration operates at an angle of \(60^{\circ}\), demonstrating the radiation pattern at this specific angle. Figure 4 presents a similar [PERSON] antenna array with the same parameters (\(N=16\), \(d=\frac{\lambda}{2}\)), but the array operates at \(90^{\circ}\). Finally, Figure 5 illustrates the [PERSON] antenna array with a Kaiser window under the same conditions, but operating at \(120^{\circ}\). These figures highlight how the antenna's radiation pattern varies with different operating angles while maintaining the same array size and element spacing.
# Analyzing the Corporate Business Intelligence Impact: A Case Study in the Financial Sector
## 2 Proposed Research Framework, Methodology, and Case Study
### ETL Processes
#### 2.4.2 Transform Phase  
* Data Cleaning
* Correction of Incomplete or Incorrect Data: Cheque query data received from the platform may be incomplete or incorrect. In this case, the accuracy of the data is ensured. For example, a cheque status may be \"unpaid,\" but the date information may be missing, in which case the missing date information can be completed.
* Conversion of Data Formats: Data usually come to CRB. In cases where the data are in different formats (JSON, XML, CSV, etc.), all data are converted to a standard format. It is ensured that cheque amounts and dates are displayed consistently.
* Data Normalization
* In cheque query data, it is necessary to take into account appropriate cultural formats to harmonize date formats. This ensures that users from different geographic regions see and understand the date data correctly. Correctly adapting date formats improves the user experience by eliminating data incompatibilities
and ensuring system accuracy. It also ensures that operations performed with date information produce accurate and error-free results.