esa-sceva/satcom-chunk-collection huggingface dataset:
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 3 Proposed Framework
According to the embedding strategy given by Equations (13) and (14), the histogram of the watermarked region is modified as shown in Figure 7, ensuring the same distortion as that of the algorithms reported previously [23; 24]. In both embedding strategies, the bits \(w_{i}\) = 0 and \(w_{i}\) = 1 of the binary watermark are embedded imperceptibly into the pixels with low and high intensity respectively, and with respect to the optimal mean value \(\mu_{R}\). In Equation (13), the pixels with low intensity are far from the optimal mean value in \(-\Delta\), as is shown in Figure 7a; whereas in Equation (14), the pixels with high intensity are far from the mean value in \(+\Delta\), as is shown in Figure 7b. 4. Finally, the watermarked image \(I_{w}\) = {\(I_{w}(p_{j})\) 1 0 \(\leq\)\(I_{w}(p_{j})\) \(\leq\) 255, \(i\) = 1, 2, 3,..., \(w_{Iw}\) \(\times\)\(h_{Iw}\)} with size \(w_{Iw}\)\(\times\)\(h_{Iw}\) is obtained through the reconstruction of the watermarked region \(R_{w}\). According to the embedding procedure survey in Table 1 and considering that \(n\) non-overlapping homogeneous regions can be found into an image, these steps can be performed iteratively until they have embedded \(n\) binary watermark patterns imperceptibly to the HVS. Although the proposed algorithm is designed to embed a watermark with its original size, it is not limited to leverage the robustness of the QR codes and the characteristics of owner logotypes to resize it if the original image does not have homogeneous regions with the same size as the watermark. \begin{table}
\begin{tabular}{c c} \hline
**Procedures** & **Details** \\ \hline Input & Original image \(I_{k}\) and binary watermark \(W\) pattern \\ \hline Step 1 & The largest homogeneous region \(R\) with the same size of the watermark pattern is selected from the original color image. \\ Step 2 & The mean values \(\mu_{RR}\), \(\mu_{RG}\), and \(\mu_{RB}\) of each \(RGB\) color channel are obtained, as formulated by Equation (10). \\ Step 3 & According to Equation (11), the optimal mean value \(\mu_{R}\) is obtained. Moreover, the minimum or maximum mean value selected as the optimal one \(\mu_{R}\), also determines the appropriate color channel to embed the binary watermark pattern \(W\). \\ Step 4 & Ensuring the homogeneity of the embedding region \(R\), the embedding strength JNDL-based is obtained with respect to the optimal mean value \(\mu_{R}\), as shown in Equation (12). \\ Step 5 & According to Equation (13) or Equation (14), the binary watermark pattern \(W\) is embedded into the appropriate embedding region. \\ Step 6 & Finally, once the watermark pattern \(W\) is embedded, the watermarked region is reconstructed, and the watermarked image \(I_{w}\) is obtained. \\ \hline Output & Watermarked image \(I_{w}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Embedding procedure survey. Figure 7: Histogram distortion of the watermarked region, (**a**) Histogram distortion induced by Equation (13), (**b**) Histogram distortion induced by Equation (14).
# Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies
## 2 Materials and Methods
### Textural Analysis  
Texture analysis is a type of image processing that examines image patterns and structural features. It calculates the various statistical measurements of the grey-level intensity values of the pixels in the image and can provide information on the spatial distribution and arrangement of those pixels. Textural analysis can be used to extract contrast, entropy, and homogeneity, which express subtle changes in the X-ray image and reflects different bone tissue organization [19].  
The textural analysis for each ROI was also carried out using qMaZda software, which provides a plethora of different textural parameters. The qMaZda software computes multiple textural features, including histogram-based, co-occurrence-matrix-based, run-length-matrix-based, gradient-map-based, autoregressive model-based, and Haar wavelet transform-based features. All these parameters can be calculated directly from the original image histogram or based on a normalized histogram by setting different (4 to 12) bits per pixel [18].  
Initially, more than 10,000 parameters were determined for each ROI. Subsequently, the parameters that contained NaN values were eliminated. As a result, 10 datasets were generated, corresponding to each ROI, containing a total of 6836 features.
## 4 Stability Analysis  
**Theorem 2**. : _For system (11), by using the control law (43) with unknown function approximators (33), (39) and (46), the pair of disturbance observers (37), adaptive mechanisms (36), (40), and (47), and differentiators (27) and (28), the ultimately uniformly bounded tracking performance is ensured in the presence of unknown dynamics and both mismatched and matched disturbances._  
Proof of Theorem 2.: Consider the candidate Lyapunov function as  
\[ \begin{split} V&=\frac{1}{2}\sigma^{2}+\frac{1}{ 2\gamma_{f_{1}}}\hat{\mathbf{W}}_{f_{1}}^{\mathrm{T}}\hat{\mathbf{W}}_{f_{1}} +\frac{1}{2\gamma_{df_{1}}}\hat{\mathbf{W}}_{df_{1}}^{\mathrm{T}}\hat{ \mathbf{W}}_{df_{1}}+\frac{1}{2\gamma_{G_{1}}}\hat{\mathbf{W}}_{G_{1}}^{ \mathrm{T}}\hat{\mathbf{W}}_{G_{1}}+\frac{1}{2\gamma_{G_{2}}}\hat{\mathbf{W}}_ {G_{2}}^{\mathrm{T}}\hat{\mathbf{W}}_{G_{2}}\\ &+\frac{1}{2\gamma_{f_{2}}}\hat{\mathbf{W}}_{f_{2}}^{\mathrm{T}} \hat{\mathbf{W}}_{f_{2}}+\frac{1}{2\gamma_{g_{2}}}\hat{\mathbf{W}}_{g_{2}}^{ \mathrm{T}}\hat{\mathbf{W}}_{g_{2}}+\frac{1}{2}d_{1}^{2}+\frac{1}{2}d_{2}^{2} \end{split}  \tag{48}\]  
Taking the time derivative of (48), one obtains  
\[ \begin{split}\dot{V}&=\sigma\dot{\sigma}+\frac{1}{ \gamma_{f_{1}}}\hat{\mathbf{W}}_{f_{1}}^{\mathrm{T}}\hat{\mathbf{W}}_{f_{1}} +\frac{1}{\gamma_{df_{1}}}\hat{\mathbf{W}}_{df_{1}}^{\mathrm{T}}\hat{\mathbf{W }}_{df_{1}}+\frac{1}{\gamma_{G_{1}}}\hat{\mathbf{W}}_{G_{1}}^{\mathrm{T}}\hat{ \mathbf{W}}_{G_{1}}+\frac{1}{\gamma_{G_{2}}}\hat{\mathbf{W}}_{G_{2}}^{\mathrm{ T}}\hat{\mathbf{W}}_{G_{2}}\\ &+\frac{1}{\gamma_{f_{2}}}\hat{\mathbf{W}}_{f_{2}}^{\mathrm{T}} \hat{\mathbf{W}}_{f_{2}}+\frac{1}{\gamma_{g_{2}}}\hat{\mathbf{W}}_{g_{2}}^{ \mathrm{T}}\hat{\mathbf{W}}_{g_{2}}+\tilde{d}_{1}\tilde{d}_{1}+\tilde{d}_{2} \tilde{d}_{2}\end{split}  \tag{49}\]Based on (18), (37), (42), and (43), (49) can be transformed into  
\[ \begin{split}\dot{V}&=\sigma\begin{pmatrix}\tilde{f}_{ 2}+\frac{1}{2}\tilde{\mathbf{W}}_{g2}^{T}\mathbf{h}_{g_{2}}u+\frac{1}{2}\tilde{ G}_{1}+\frac{1}{2}\tilde{G}_{2}+\tilde{d}_{2}\\ +\tilde{f}_{1}+d_{1}+\left(\lambda+k-\frac{1}{\kappa_{1}}\right)\left(\tilde{f }_{1}+\tilde{d}_{1}\right)\\ -\eta_{1}\text{sgn}(\sigma)-\eta_{2}\sigma\end{pmatrix}-\frac{1}{\gamma_{f_{ 1}}}\tilde{\mathbf{W}}_{f_{1}}^{T}\hat{\mathbf{W}}_{f_{1}}-\frac{1}{\gamma_{4f _{1}}}\tilde{\mathbf{W}}_{df_{1}}^{T}\hat{\mathbf{W}}_{df_{1}}\\ &-\frac{1}{\gamma_{f_{2}}}\tilde{\mathbf{W}}_{f_{2}}^{T}\hat{ \mathbf{W}}_{f_{2}}-\frac{1}{\gamma_{g_{2}}}\tilde{\mathbf{W}}_{g2}^{T}\hat{ \mathbf{W}}_{g_{2}}-\frac{1}{\gamma_{G_{1}}}\tilde{\mathbf{W}}_{G_{1}}^{T} \hat{\mathbf{W}}_{G_{1}}-\frac{1}{\gamma_{G_{2}}}\tilde{\mathbf{W}}_{G_{2}}^{ T}\hat{\mathbf{W}}_{G_{2}}\\ &+\tilde{d}_{1}\biggl{(}-\frac{1}{\kappa_{1}}\left(\tilde{f}_{1}+ \tilde{d}_{1}\right)+\tilde{d}_{1}\biggr{)}+\tilde{d}_{2}\biggl{(}-\frac{1}{ \kappa_{2}}\left(\tilde{f}_{2}+\tilde{G}_{1}+\tilde{d}_{2}\right)+\tilde{d}_{2 }\biggr{)}\end{split}  \tag{50}\]  
Applying adaptation mechanisms (33) and (40), (47) and (50) become  
\[
# Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation
## 4 Conclusions  
In this paper, the optimal- and recursive-form FIR filter was proposed by employing the Kalman filtering technique, moving the horizon estimation strategy for discrete time-varying state-space models. The initial state and its error covariance were optimally estimated in the maximum likelihood sense over the horizon, then they initiated the finite horizon Kalman filter. The proposed recursive optimal FIR filter was designed without assumption of nonsingular transition system matrix and any a priori initial information. In addition, it was also proved that the proposed ROFIR filter is the best linear estimator on the finite estimation horizon. Furthermore, by applying the modified sequential noise statistics estimation method to the ROFIR filter, an AFIR filter was also proposed as an application of the ROFIR filter, which shows that many useful techniques of Kalman filtering could be applied to the proposed ROFIR filter for improving the estimation performance of FIR filters. To validate the proposed filters, computer simulation was performed and it was shown that the proposed filters were more accurate and robust than other conventional Kalman filters and adaptive Kalman filters.  
Conceptualization, B.K.; methodology, B.K.; software, B.K.; validation, B.K. and S.-i.K.; formal analysis, B.K.; investigation, B.K. and S.-i.K.; resources, B.K. and S.-i.K.; data curation, B.K. and S.-i.K.; writing--original draft preparation, B.K.; writing--review and editing, S.-i.K.; visualization, B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IIR & Infinite Impulse Response \\ FIR & Finite Impulse Response \\ BIBO & Bounded Input Bounded Output \\ RHK & Receding Horizon Kalman \\ KUFIR & Kalman-like Unbiased Finite Impulse Response \\ ROFIR & Recursive Optimal Finite Impulse Response \\ AFIR & Additive Finite Impulse Response \\ SHAK & Sage-Husa Adaptive Kalman \\ LMAK & Limited Memory Adaptive Kalman \\ \end{tabular}
## 3 Results
### Outcomes and Impacts
#### 3.5.1 Improved Physical Awareness and Decreased Physiological Stress  
Aging can lead to declines in spatial navigation and memory, often resulting in reduced confidence in leaving home, which may increase loneliness and lower quality of life [37]. One study combined fruit and meat olfactory stimuli with HMD and Xiaomi glasses to enhance spatial orientation in older adults using wheelchairs in a virtual supermarket setting [37]. Participants were randomized into a control group, viewing only the virtual supermarket, and an experimental group, which experienced added olfactory and auditory stimuli. The experimental group showed improved spatial orientation, shape perception, and memory scores compared to the control group, particularly in identifying locations and sketching sequences [37]. These findings suggest that multisensory stimuli, including aromatherapy, can strengthen the link between memory and physical awareness in older adults.  
In terms of perceived stress, two quantitative studies reported differing outcomes. One study showed improvement in perceived stress levels with weekly 2 h sessions over 9 weeks [21], while another study found no improvement with shorter 5 min sessions conducted over 4 weeks [38]. These findings suggest that both the duration and frequency of sessions may impact stress reduction, with longer and more frequent sessions potentially yielding better effects on perceived stress. Although shorter sessions did not result in improvements in perceived stress, physiological stress indicators, such as heart rate and blood pressure, showed a noticeable reduction [38]. These findings align with other studies that used longer sessions, suggesting that while perceived stress may not improve in shorter sessions, physiological stress responses can still be effectively reduced with brief interventions.
# A Comprehensive Failure Risk Assessment Method of
## 2 Comprehensive Risk Assessment of the Machining Center Component Based on Topological Analysis  
The failure risk of the component of the machining center is not only related to the independent risk of itself but is also related to its position in the topology and the impact of the failure of the adjacent component (that is, the risk of propagation). Therefore, this paper considers the independent risk attributes of the component and the impact of the propagation risk to comprehensively assess the failure risk of the machining center. The comprehensive failure risk assessment framework of machining center component based on topological analysis is shown in Figure 1.  
On the basis of failure mode and cause analysis, considering the correlation of failure modes, ANP is used to calculate the influence degree of failure modes and to calculate the independent risk value of components in combination with the component failure mode frequency ratio and the failure rate function. The influence of the topological characteristics of the machining center on the propagation of failure cannot be ignored. Therefore, based on the machining center ANP model, starting from the component level, introducing the centrality measurement of network theory (the degree centrality, betweenness centrality, and closeness centrality of the component nodes) and using the analytic hierarchy process (AHP) and entropy weight method (EWM) to carry out the subjective and objective combination weighting of the indicators to calculate the structural importance of the machining center components to characterize the topological properties of the model. The coupling degree function is introduced to calculate the importance of the connection edges, combining it with the failure probability to calculate the failure propagation influence degree and thus to realize the calculation of the component propagation fa  
Figure 1: Comprehensive failure risk assessment framework diagram of machining center component.  
independent failure risk and the propagation failure risk of the component are integrated to evaluate the comprehensive failure risk of the component. The method proposed in this paper provides a reference for the formulation of the fault risk control plan for the user of the machining center and is of great significance for reducing the fault risk of the machining center.
## 3 Stability Analysis and Experimental Validation
### Experimental Validation of Stability Lobes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode \(z\)** & \(f\) [Hz] & \(k\) [N/m] & \(\xi\) \\ \hline
1 & 93 & 6.59E5 & 0.003 \\
2 & 304 & 4.89E6 & 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured modal parameters of the workpiece. Figure 5: Experimental setup used for stability verification. An accelerometer is attached in the workpiece to detect vibrations, while the optical sensor is used to measure the spindle rotation. sampling (red circles) denotes vibration amplitude when the cutting tool hits the workpiece. Poincare diagrams (Figure 7g-i) were constructed with the acceleration sampling data versus 20-delayed samples. The power spectral density (PSD) in Figure 7j-l describes the power density from the acceleration data in the frequency domain. Point A (Figure 7a,d,g,j) exhibits unstable period-doubling chatter, meaning that each tooth passing the excitation period is half of the dominant period in the acceleration signal. Even the PSD plot (Figure 7j) exhibits several chatter frequencies, while the doming frequency almost coincides with the cross marks, which confirms the period-doubling chatter. Due to all these chatter frequencies, the Poincare diagram in Figure 7g draws two semi-elliptical trajectories. This behavior contrasts with a pure period-double chatter experiment (data not shown) that draws two concentration of samples; in other words, the workpiece vibrates with a dominant frequency that is half that of the tool passing excitation frequency. Point B (Figure 7b,e,h,k) shows stable cutting conditions. In this case, the dominant frequency in the CWT is related to the tool passing frequency (circle marks) during several revolutions. Notice as well that for each period \(\tau\), two stains appear corresponding to one vibration cycle: one peak and one valley. As a result, the peak frequencies in the PSD plot coincide with multiples of the tool passing frequency. A Poincare diagram for stable cutting (Figure 7h) shows the concentration of sampled acceleration data, meaning the vibration amplitude is similar every time a flute hits the workpiece. Point C describes Hopf instabilities; this instability draws in the PSD plot-dominant quasi-periodic chatter frequencies. Notice that the frequency intensity in the CWT (Figure 7c) is below normalized frequency \(f_{n}=1\), and those values change in time. In contrast with Point A and B, the PSD (Figure 7l) shows multiple chatter frequencies, which are responsible for several loops in the Poincare diagram. Figure 6: Predicted stability lobes obtained from Equation (11) together with experimental data in a down-milling operation. (\(\Circle\) stable cut, \(\square\) quasi-periodic chatter, \(\Delta\) double periodic chatter, and \(\Diamond\) border cutting parameters). Figure 7: _Cont._
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 4 Comparison and Results
As shown in these figures, using LE, CSI, PI, and TVSI in SVS monitoring is not reliable for time 2 s. The \(TVI\) index has an average performance in SVS monitoring. It needs at least 1.5 s for correct unstable cases detection. The \(VSRI\)'s error at short-term voltage instability detection is minimum and near 3% as shown in Figure 10(b). Furthermore, SVS monitoring using \(VSRI\) can be done with time equal to or less than 1 s for detection of instability, which makes this index superior in comparison to others. This research shows that \(VSRI\) has better performance in online SVS monitoring compared to the other indices. Figure 11: Linear approximation of index error in SVS assessment  
As an in-depth comparison between the introduced and modified indices in this paper, a qualitative comparative study is introduced in Table 3. It is clear that all the introduced indices except the proposed one highly depends on the initial values used for running their algorithms. Such feature makes the \(VSRI\) more practical and easy to implement the index in reality. Furthermore, the proposed \(VSRI\) index requires less time and has better performance in detecting the unstable cases. Moreover, the proposed index is very suitable for online SVS monitoring applications compared to other indices.
## 3 Results and Discussion
### GPP Estimation of LUE Models, SVM, and RF on Forest Sites of FLUXNET2015  
We evaluated each LUE model and machine learning-based fusion models for all sites using the \(R^{2}\), _RMSE_, and _RPE_ metrics, as shown in Table S3 in the supporting information file. Table S3 shows that EC-LUE and VPM outperformed other LUE models in most sites, and the RF outperformed all models involved in this study. Specifically, we picked out four typical DBF, EBF, ENF, and MF flux sites to illuminate the ability of simulating seasonal GPP variation by using the best LUE model (i.e., EC-LUE) and machine learning model (i.e., RF) compared to FLUNNET2015's GPP product. These representative sites were from Europe, the United States, and Australia for each forest type, and 6 years of data were extracted in long-term observation, as shown in Figure 8. Our results indicate that the difference between FLUXNET2015's GPP product and the RF estimation was dramatically less than the difference between FLUXNET2015's GPP and that of EC-LUE. This means that compared to EC-LUE, the RF could explain more seasonal variation in GPP.  
Figure 7: The _AIC_ (**a**) and _BIC_ (**b**) of the LUE models, MODIS, SVM, and RF across the DBF, EBF, ENF, and MF.  
We drew a boxplot of \(R^{2}\), _RMSE_, and _AIC_ for each LUE and machine learning model across all selected FLUXNET2015 sites. A flat box and narrow range of outliers means reliable model performance in a boxplot. Figure 9 shows that EC-LUE and VPM outperformed other LUE models, while the SVM and RF could improve GPP estimation accuracy significantly. The RF had flatter boxes and a narrower outlier range than the SVM, especially an obvious distribution of a higher \(R^{2}\) and a lower _RMSE_ and _AIC_ compared to that of the SVM, which indicated the RF was a better machine learning method to simulate forest GPP in this study.  
Figure 8: Daily FLUXNET2015’s GPP (black dots), the best LUE model-estimated GPP (EC-LUE, line in blue), and the best fusion method-estimated GPP (RF, line in orange) at 4 sites: DE-Hai of DBF (**a**), AU-Tun of EBF (**b**), US-Blo of ENF (**c**), and BE-Bra of MF (**d**).
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Random Sampling Approaches
#### 2.1.3 Random Hyperuniform Spatial Arrangements  
Another important category of random distributions is random hyperuniform spatial arrangements which have been observed in different physical systems ranging from disordered ground state to jammed particle packing [36; 37; 38]. Hyperuniform systems are exotic states of matter which exploit designed disorder laying between a crystal and a liquid. A statistically homogeneous hyperuniform points configuration in \(d\)-dimensions is one in which the number variance of \(N\) points within a spherical observation window of radius \(R\) grows more slowly than \(R^{d}\), i.e.,  
\[\sigma_{N}^{2}(R)\sim R^{d-1} \tag{5}\]  
This is equivalent to having a structure factor that tends to zero as the wavenumber tends to zero, implying that single scattering of incident radiation at infinite wavelengths is completely suppressed, i.e., they do not have Bragg peaks. From an array sampling point perspective, these types of distributions can eliminate grating lobes. Due to the random nature of these distributions, we would limit our discussions on these hyperuniform arrangements and focus on LDS techniques and refer the interested reader to [36; 37; 38; 39; 40].