esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
In this section, we provide the results of the clustering validation index tests with K-medians, K-means, and K-spatialmedians clustering. Results for the synthetic datasets are combined in Table 11, where each cell includes the results for all three methods with 'cb' referring to the city-block distance (\(p=q=1\)),'se' to the squared Euclidean distance (\(p=q=2\)), and 'ec' to the Euclidean distance (\(p=2,\ q=1\)). In addition, the convergence properties of the clustering algorithms are compared for varying \(K\) values.
## 3 Results and Discussion
### The Piezoelectric and Dielectric Properties of PZT-BYS(\(x\)) Ceramics
Above 0.1 moles of Sb\({}_{2}\)O\({}_{3}\), the grain size decreases from 2.2 \(\upmu\)m to around 1.7\(\sim\)1.4 \(\upmu\)m. As a result, the clamping effect caused by internal stress below a critical grain size increases the \(E_{\rm c}\). By contrast, the increasing behavior of \(P_{\rm r}\) with increasing Sb\({}_{2}\)O\({}_{3}\) content can be explained by the presence of second phases, which is similar to the behavior of \(d_{33}\). It is believed that, at the higher sintering temperature, the second phases are dissociated. According to the similarities of ionic radius, the dissociated phase produces cation vacancies in PZT-BYS(\(x\)) as explained for the behavior of \(d_{33}\) as a function of Sb\({}_{2}\)O\({}_{3}\) content (see Figure 6a). As a result of cation vacancies, the mobility of the domain wall increases [26] due to the sufficient displacement, which finally increases the \(P_{\rm r}\). Therefore, \(P_{\rm r}\) increases at higher Sb\({}_{2}\)O\({}_{3}\) content due to the higher concentration of the second phase in the PZT-BYS(\(x\)) system. These results are in agreement with a previous analysis of piezoelectric/dielectric properties. The dielectric constant (\(\varepsilon_{33}\)T) of PZT-BYS(\(x\)) ceramics as a function of temperature is shown in Figure 8. The \(\varepsilon_{33}\)T was almost constant up to 300 \({}^{\circ}\)C, and then increased gradually to a maximum value \(\varepsilon_{33}\)T\({}_{\rm(max)}\) at Curie temperature (\(T_{\rm C}\)). The \(T_{\rm C}\) of pure PZT is about \(\sim\)360 \({}^{\circ}\)C. It is well known that  
Figure 7: (a) Polarization-electric (\(P\)-\(E\)) hysteresis loops, (b) \(P_{\rm r}\) and \(E_{\rm c}\) measured from their corresponding hysteresis loops of PZT-BYS(\(x\)) ceramics. cubic phase addition to PZT reduces \(T_{\rm C}\), but in our recent work where BY was added into the PZT system, \(T_{\rm C}\) increased [13]. This anomaly was explained in terms of internal stress, which retarded the ferroelectric and paraelectric transition. In the present case, \(\rm{Sb_{2}O_{3}}\)-doping has been shown to produce a linear reduction in \(T_{\rm C}\). In Figure 8, it is clear that the \(\varepsilon_{33}{}^{\rm T}\)(max) sharply decreases as the content of \(\rm{Sb_{2}O_{3}}\) increases and the dielectric peaks broaden around \(T_{\rm C}\), which is similar to previous observations [13; 37]. Similarly, [PERSON] et al. also explained that the broadening of the dielectric peak and variation of \(\varepsilon_{33}{}^{\rm T}\)(max) could be attributed to the variation of grain size and some kinds of structural disorder [20]. It has already been established that, with decreasing grain size, \(\varepsilon_{33}{}^{\rm T}\)(max) decreases, which results in the broadening of the dielectric peak, and shifts in ferroelectrics transition to higher or lower temperature sides [20; 29; 38].
## 6 Final Remarks
### Limitations and Future Work  
In addition, for future work, it will also be essential to analyze the automatic reading of container seal numbers, a critical component in the definition of a smart gate. These are crucial for guaranteeing the integrity and security of the cargo being transported and are used to check whether a container has been opened and/or modified during transport. From what we can gather from technology providers, reading the container's seal number is still a work in progress, as cameras have difficulty accurately reading seal numbers that are small, not at an ideal angle, or in sub-optimal lighting conditions.  
This study focussed on process management and the integration of technology as an opportunity to improve processes. As this study progressed, the need arose to study the critical role of people in any innovation process such as this, an essential component of the trilogy mentioned above--technology, processes, and people [23]. One of the biggest challenges of digital transformation is resistance to change on the part of people, and this is one of the problems that currently exist at the terminal under study (a problem due to lack of knowledge/training/awareness). The intention, therefore, was to implement a strategy in the short term at the current gate facilities of the terminal under study to reinforce the need for and benefits of using technologies and the new digitized process by means of an awareness-raising study. Implementing this measure will reap immediate benefits and will contribute to the success of the future motorway gate. Therefore, for it to serve as a holistic and replicable model for other ports seeking modernization and operational optimization in line with global digitalization and sustainability trends, a paradigm shift should be encouraged, which could involve awareness-raising actions with new practices for using and providing services at the Portuguese port under study.  
Conceptualization, J.B.-R.; methodology, J.B.-R. and L.T.; validation, L.T.; formal analysis, J.B.-R., C.P. and L.T.; writing--original draft preparation, J.B.-R.; writing--review and editing, J.B.-R., C.P. and L.T.; supervision, C.P. and L.T.; funding acquisition, J.B.-R. and L.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the PRR--Recovery and Resilience Plan and by the NextGenerationEU funds at Universidade de Aveiro, through the scope of the Agenda for Business Innovation \"NEXUS: Pacto de Inovacao--Transicao Verde e Digital para Transportes, Logistica e Mobilidade\" (Project no. 53 with the application C645112083-00000059). This work was also supported by the research unit IESTA (UIDB/00127/2020), funded by national funds through FCT--Fundacao para a Ciencia e a Tecnologia.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflicts of interest.
###### Abstract  
PupposeTo develop and test an innovative semi-automatic method for quantifying the three-dimensional morphology of the peripapillary retinal pigment epithelium-basement membrane (ppRPE/BM) layer, with application to lumbar puncture (LP) patients. **Methods:** Nineteen patients undergoing LP were recruited. The optic nerve head images of both eyes were acquired in 12 radial directions using optical coherence tomography (OCT) before and after LP. For each OCT image, the ppRPE/BM layer was automatically segmented with manual corrections by independent graders when necessary. The linear regression model of the ppRPE/BM layer was fitted using the least squares approach, and the ppRPE/BM layer angle was measured as the slope of the linear regression line. The Bland-Altman plots and intraclass correlations (ICC) were used to assess the inter-observer reliabilities in measuring the ppRPE/BM layer angle. The repeated measures ANOVA analysis was applied to determine whether the ppRPE/BM layer angle changes following LP varied across the radial directions. **Results:** The percentages of scans that required manual corrections were 24% and 32% in the right eyes and left eyes, respectively. The Bland-Altman plots and ICC demonstrated excellent inter-observer reliability. The ppRPE/BM layer angle varied significantly across the 12 radial OCT scanning directions both before and after LP. However, the LP-induced changes in the ppRPE/BM layer angle across different radial directions were not statistically significant (\(p\)-value \(>\) 0.01). **Conclusions:** The three-dimensional quantifications of the ppRPE/BM layer angles, enabled by the semi-automatic method, provided enhanced information of the optic nerve head structure. For LP patients, the ppRPE/BM layer angle changes following the LP did not vary significantly across various radial directions, indicating that it could be evaluated in any radial direction.  
Optic nerve head (ONH); OCT; 3D ppRPE/BM; angle; lumbar puncture +
Footnote †: journal: Applied Sciences
## 7 Recently Proposed Intelligent Techniques for Power Systems
### Wavelet with Artificial Neural Network  
[PERSON] et al. [149] suggested a wavelet-based ANN technique for safety in transmission systems. In the suggested method, the relaying point's input signals were fed into a DWT to extract distinguishing characteristics, which were then supplied to an ANN system for fault classification. [PERSON] et al. [150] have suggested a method for wavelet and probabilistic neural network (PNN)-based online fault detection in a power system. Multi-resolution analysis (MRA) wavelet transform divides the signal into several resolutions, which the authors utilized to analyze the signal behavior in various spectral bands. PNN is used to classify and locate faults using the characteristics from wavelet analysis. A wavelet-assisted neural network-based distance relaying system has been suggested [151; 152]. The suggested plan uses a discrete wavelet transform (DWT) and neural network in Matlab, as well as an electromagnetic transient software to simulate a transmission line model. The authors employ DWT to extract fault signal characteristics and a backpropagation neural network classifier to determine the kind and location of the problem. Wavelet transforms and neural networks were proposed by [PERSON] et al. [153], who suggested a system for faulty phase selection on double-circuit transmission lines. For the goal of training a neural network, the authors utilized the Levenberg-Marquardt method. The suggested technique employs wavelet transform to extract distinguishing characteristics from the input signal and feed them to the neural network for classification. [PERSON] et al. [143] suggested using a DWT with a backpropagation neural network (BPNN) method for fault detection on single-circuit transmission lines. The authors extracted the fault current's high-frequency component using DWT, and the high-frequency component of signals was divided up using the Daubechies 4 (db4) mother wavelet. After a DWT investigation of the defect, a decision-making algorithm called BPNN was created. Wavelet entropy and neural networks were used in a novel method by [154] for classifying and locating transmission line failures. The entropies of the wavelet decompositions were supplied to a neural network for fault classification and placement, and the authors employed the Db4 mother wavelet to analyze defective voltage signals. The suggested approach uses an Elman backpropagation architecture for fault identification and a probabilistic neural network (PNN) architecture for fault classification.
## 2 Materials and Methods  
### Multi-Microwave Sources Heating Model  
The microwave heating model utilizes a rectangular cavity with six microwave sources distributed symmetrically on the three faces of the cavity. The cavity and WR340 standard waveguide are made of copper. The internal medium is air, and the initial temperature is 25 \({}^{\circ}\)C at a microwave frequency of 2.45 GHz. Numbers 0-5 in the subfigure (a) represent six microwave sources, the specific model parameters are sourced from reference [16]. Establishing a numerical heating model for multi-microwave sources using finite element analysis in COMSOL, as shown in Figure 1.  
The cavity walls and waveguides are copper and can be considered perfect cavities with electromagnetic boundary conditions, \(n\times B=0,n\times E=0\). The natural thermal convection between the heated medium and air is expressed as:  
\[h\times(T-T_{air})=-k\frac{\partial T}{\partial n} \tag{1}\]  
where \(h\) is the heat transfer coefficient of air, and \(n\) is the unit normal vector of the cavity wall surface, \(T\) is the temperature for the medium. \(E\) and \(B\) denote the electric field intensity and the magnetic induction intensity, respectively. \(T_{air}\) denotes the temperature of the air medium inside the reaction chamber. \(\frac{\partial T}{\partial n}\) denotes the temperature gradient perpendicular to the surface of the temperature domain.  
During microwave heating, the temperature of the medium is related to the dielectric loss \(Q_{e}=\frac{1}{2}\omega e_{0}e^{\prime\prime}\left|E\right|^{2}\) and hysteresis loss \(Q_{m}=\frac{1}{2}\omega\mu_{0}\mu^{\prime\prime}\left|H\right|^{2}\)[16], where \(\varepsilon_{0}\) is the vacuum permittivity and the \(\omega\) denotes the frequency of the electromagnetic wave.  
\[Q=Q_{e}+Q_{m} \tag{2}\]  
where \(Q\), \(e^{\prime\prime}\) and \(\mu^{\prime\prime}\) denote microwave dissipation power, the imaginary part of dielectric constant, and the imaginary part of magnetic permeability, respectively.  
The heat conduction equation for medium temperature is as follows:  
\[\rho C_{p}\frac{\partial T}{\partial t}-K\
abla^{2}T=Q \tag{3}\]  
Figure 1: 3D geometrical model of the cavity with the multi-microwave sources distributions.  
where \(\rho\) is the medium's density, and another \(K\) and \(C_{p}\) denote the thermal conductivity and specific heat capacity for the medium, respectively. By combining Equations (2) and (3) with \(Q_{m}=0\), it can be concluded that:  
\[\rho C_{p}\frac{\partial T}{\partial t}-k\
abla^{2}T=\frac{1}{2}\omega\varepsilon _{0}\varepsilon^{\prime\prime}|E|^{2} \tag{4}\]  
From Equation (4), adjusting the electric field strength \(E\) and the electromagnetic wave frequency \(\omega\) can optimize the temperature distribution. The \(E\) can vary based on microwave source power.
## 4 Results and Discussion
### Case 4: Minimization of Fuel Cost, Power Losses, and Voltage Drops
and high power losses of 7.059 MW, but for a slightly lower cost of USD 827.2878/h. MVO has a well-balanced performance of USD 829.1404/h cost for a power loss of 7.22 MW with a higher voltage drop of 0.2478 p.u. HHO is not as cost-efficient, at USD 838.8745/h, and it has a high voltage drop of 0.2681 p.u. with a power loss of 6.38 MW. The overall observation shows that all the variants of ESDO are cost-efficient and powerful in voltage stability; however, they come at higher power losses when compared to other algorithms like HO and PSO, which have better power loss metrics but come at higher costs. Based on the results shown in Tables 4-11 and the convergence curves for the four cases shown in Figures 4, 6, 8 and 10, the performance of ESDO was compared with other well-known algorithms, namely PSO, MVO, GOA, HHO, and HO. A comparison of key characteristics of these algorithms is presented below in Table 12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**PSO**} & \multirow{2}{*}{**MVO**} & \multirow{2}{*}{**GOA**} & \multirow{2}{*}{**HHO**} & \multirow{2}{*}{**HO**} & \multicolumn{2}{c}{**ESDO**} \\ \cline{5-6}  & & & & & & **Alpha Dog** & **Beta Dog** \\ \hline Cost (USD/h) & 873.1963 & 829.1404 & 827.2878 & 838.8745 & 824.1647 & 825.7937 & 819.8483 \\ \hline \(P_{\text{loss}}\) (MW) & 5.55 & 7.22 & 7.059 & 6.38 & 8.13 & 7.65 & 8.01 \\ \hline V.D. (p.u) & 0.1899 & 0.2478 & 0.4756 & 0.2681 & 0.2004 & 0.1851 & 0.2041 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Multi-objective comparison. Figure 10: Case 4 convergenve curve. Table 12 shows the advantages of ESDO over PSO, MVO, GOA, HHO, and HO as follows: The ESDO algorithm was inspired by Egyptian stray dogs for fast convergence with excellent dynamic balance between exploration and exploitation, thus being highly effective to avoid local optima. On the contrary, PSO presents fast convergence, yet it usually suffers from stagnation in convergence during the final stages because of weaker exploration. MVO maintains a strong balance between exploration and exploitation with medium-order convergence speed, whereas GOA is effective in global exploration but converges more slowly compared to ESDO. HHO includes fast convergence along with adaptive, dynamic balance. The mechanisms involved in ESDO are finer because they are based on behavior. Finally, HO provides good adaptability; however, without offering dynamic balance, as provided by ESDO. All the algorithms of this class, including ESDO, grade out as a medium concerning computational complexity. There is an important difference, though: ESDO can maintain a strong balance between exploration and exploitation and avoid local optima. Its application range is especially fitting for complex optimization problems, hence versatile and efficient compared to its counterparts.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 3 Results  
### Response of the SPR-Bare Platforms to the Refraction Index Variation  
The response of the SPR platforms, not derivatized with MIP (SPR-bare), here considered, has been checked as previously proposed [36]. In particular, the resonance wavelengths of the platform in liquid media of different RI, measured by an Abbe refractometer, were registered. As an example, the spectra of water-glycerol solutions with different RI are reported in Figure 2a. The response curve \(\Delta\lambda\) (referring to pure water) against RI is shown in Figure 2b.  
The sensitivity (\(\delta\Delta\lambda/\delta\)RI) is significantly different from zero in the whole RI range, and it increases at increasing RI (Figure 2b). Nevertheless, for short RI ranges, the curve can be assumed to be linear. At increasing RI, the peak is shifted to higher wavelength values and becomes larger and flatter, making the acquisition of the minimum less accurate and precise. For this reason, the best RI of the dielectric layer in contact with the resonant surface must be a compromise between the highest sensitivity and precision.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 3 Principles and Methods  
### Principle of Hht  
The steel wire rope inspection system could be viewed as a linear time invariant system, and the Hilbert transform of the input signals containing multi-source noise could be described as a convolution operation with the impulse response, namely,  
\[\hat{\mathbf{x}}(\mathbf{t})=\mathrm{H}[\mathbf{x}(\mathbf{t})]=\mathbf{x}( \mathbf{t})*\mathrm{h}(\mathbf{t})=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{ \mathbf{x}(\mathbf{\tau})}{\mathbf{t}-\mathbf{\tau}}\mathrm{d}\mathbf{\tau} \tag{2}\]  
where, \(\mathbf{t}\) is the time, \(\mathbf{\tau}\) is the time shift, \(\mathbf{x}(\mathbf{t})\) and \(\mathrm{h}(\mathbf{t})\) represent the input signals of wire rope inspection and the system impulse response, and the * means the convolution operation in Equation (2). In other words, the Hilbert transform could also be regarded as a quadrature filter, which processes the signals mainly in the perspective of phase domain and makes up for the deficiency of time and frequency domain in consideration of the mixed features of multi-source noises and the indistinguishable frequency distribution. The Fourier transform of the system impulse response is defined as,  
\[\mathrm{H}(\omega)=\left\{\begin{array}{ll}-\mathrm{j}&(\omega\geq 0)\\ +\mathrm{j}&(\omega<0)\end{array}\right. \tag{3}\]  
where \(\mathrm{j}\) is the imaginary unit, and \(\omega\) is the angular frequency in Equation (3). Apparently, according to the Hilbert transform, the phase of the steel wire rope testing signal will move \(-\pi/2\) and \(\pi/2\) for these positive and negative frequency signal components in phase domain, separately, while the signal amplitude remains unchanged. Combining the spiral structures of wire rope and the strand signals, the approximate periodic interference of strand signals could be eliminated after further being processed by the differentiating techniques, namely, the output signals could be expressed as,  
\[\mathrm{x_{o}}=|\hat{\mathbf{x}}(\mathbf{t})|-\mathbf{x}(\mathbf{t}) \tag{4}\]  
Figure 3: STFT of different weak signals. (**a**) STFT of signal 1; (**b**) STFT of signal 2; (**c**) STFT of signal 3; (**d**) STFT of signal 4; (**e**) STFT of signal 5; (**f**) STFT of signal 6.  
where t is the time in Equation (4). Due to the complexity of the weak signals of steel wire rope in big liftoff distance defect detection, nonlinear and linear signal processing methods such as the median filtering and moving average filtering as well as the wavelet denoising methods are also applied and compared. Although the linear method is effective for additive Gaussian noise and features fast calculating speed, the nonlinear filtering method could overcome its shortcomings of blurring the signal edge.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods
### Statistical Methods
#### 2.3.2 Modeling EVI Magnitude of Change
The two perpendicular dashed lines divide the study area into four quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the interpretation of results. Map projection is NAD83 Statistics Canada Lambert, EPSG: 3347. Figure 4: Spatial distribution of EVI trend types in the AGPE from 2003 to 2022. These trends were produced with the bfastclassify algorithm. The trend types are those proposed by [PERSON] et al. [57]. Abbreviations—MIG: monotonic increasing, greening trend (\(n\) = 33,683); MDB: monotonic decreasing, brownning trend (\(n\) = 4981); IInb: interruption, increasing trend with a negative break (\(n\) = 11,637); RBG: reversal, brownning to greening trend (\(n\) = 11,654). (Trends MIGpb, MDBnb, IDpb, and RGB are not shown given their low percentages). The dashed black line shows the study area footprint. The solid black line shows Algonquin Provincial Park’s footprint. The two perpendicular dashed lines divide the study area into four quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the interpretation of results. Map projection across all figures is NAD83 Statistics Canada Lambert, EPSG: 3347. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Group** & **\# of Forest Pixels** & \multicolumn{2}{c}{**\# of Breaks in 2003–2022**} & \multicolumn{2}{c}{**\# of Pixels with Breaks in 2003–2022**} \\  & & & **(\% of Breaks)** & & & **[\% of Forest Pixels]** & \\ \hline  & & All & Negative & Positive & All & Negative & Positive \\ \hline AGPE & 230,806 & 15,904 & 11,969 & 3935 & 12,348 & 9720 & 3033 \\  & & & (75.3\%) & (24.7\%) & [5.3\%] & [4.2\%] & [1.3\%] \\ \hline Non-protected forests & 194,046 & 15,178 & 11,499 & 3679 & 11,805 & 9366 & 2840 \\  & & & (75.8\%) & (24.2\%) & [6.1\%] & [4.8\%] & [1.5\%] \\ \hline Protected forests & 36,760 & 726 & 470 & 256 & 543 & 354 & 193 \\  & & & (64.7\%) & (35.3\%) & [1.5\%] & [1.0\%] & [0.5\%] \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of EVI breaks (abrupt change) from 2003 to 2022 broken down by break type and forest protection status. The values were derived from the output of the bfast algorithm. Percentages in brackets are derived from the overall number of breaks while percentages in square brackets are derived from the overall number of forest pixels. A pixel can have one or more breaks. Therefore, the values in the negative and positive columns (last two columns) do not add up to the values in the ‘All’ column (third last column).