esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Theory of GS Algorithm  
The Gerchberg-Saxton (GS) algorithm is well known as an iterative algorithm that has received a lot of attention in the field of phase information retrieval [36], beam shaping [37], image optics and wave-front reconstruction [38]. In particular, the GS algorithm can also be used to compensate for turbulence distorted wavefronts in the OAM-based communication links, as proposed both theoretically and experimentally in the past years [39; 40]. In our simulation, we use GS algorithm to compute a pre-compensate phase. The iterative procedure of computing the phase compensation which should load on the spatial light modulator (SLM) plane is shown in Figure 2. The specific steps of loop iteration can be described as follows:  
**Step 1**. Initialization, take out the complex amplitude \(a_{0}\) of the original LG beam distorted by turbulence and a flat phase \(\varphi_{0}\) of Gaussian probe beam, and then perform Fast Fourier Transform (FFT) to obtain a spectral function on the image plane:  
\[F[a_{0}\times\exp(i\varphi_{0})]=A_{0}\times\exp(i\varphi_{1}), \tag{6}\]  
Where \(F\) represents FFT, \(A_{0}\) and \(\varphi_{1}\) are the amplitude and phase in the Fourier domain respectively.  
**Step 2.** Replace the amplitude \(A_{0}\) with the amplitude \(B_{0}\) of target LG beam, but reserve the phase \(\varphi_{1}\), the new spectral function can be obtained as:  
\[U=B_{0}\times\exp(i\varphi_{1}). \tag{7}\]  
Figure 1: Schematic demonstration of atmospheric turbulence channel with \(N\) random phase screens. The distance \(\Delta z\) between each screen is \(Z/N\). (\(Z\): transmission distance; \(N\): the number of sub-channels).  
**Step 3.** Perform the Inverse Fast Fourier Transform (IFFT) and obtain the amplitude and phase function:  
\[F^{-1}\{B_{0}\times\exp(i\varphi_{1})\}=b_{0}\times\exp(i\varphi_{2}), \tag{8}\]  
where \(F^{-1}\) represents IFFT, \(b_{0}\) and \(\varphi_{2}\) are the amplitude and phase on the SLM plane.  
**Step 4.** Continue to replace amplitude \(b_{0}\) with the amplitude \(a_{0}\) of the original distorted LG beam, but reserve the phase \(\varphi_{2}\), thus the optical field \(u\) on the SLM plane can be obtained as:  
\[u=a_{0}\times\exp(i\varphi_{2}). \tag{9}\]  
Then, go back to step 1 to continue the loop iteration. The updated Fourier transformed phase function is substituted into the error function. According to the error precision, it can be judged that the simulation continues or jumps out of the loop iteration process. If the error condition is satisfied, jump out of the process and the phase function in this loop is the output, from which the pre-compensation phase is obtained as \(\varphi_{3}=-\varphi_{2}\).
## 3 Materials and Methods  
### Non-Invasive Methods  
During the last mapping survey carried out by TNRICPT and DoA technicians, Me-Taw-Ya temple wall paintings were submitted to non-invasive in situ analyses.  
Energy Dispersive X-ray Fluorescence analysis was carried out using an Oxford Instruments X-Met 8000 energy dispersive portable spectrometer with a silicon drift detector SDD detector, X-Flash, and 6 mm diameter spot, with a rhodium (Rh) target X-ray tube operating both at 8 kV, 50 \(\upmu\)A and 40 kV, 8 \(\upmu\)A. The first operating condition is particularly sensitive to light elements (from about Al) and was used for detecting Ca, S, Si, K, Cl, Ti, Ni, and Fe elements. The second operating condition related to heavier ones was useful for the As, Hg, Mg, Zr, and Sr elements including Sn, Sb and Ba K-lines. The net areas under the x-ray characteristic K energy lines of Al, Si, S, Cl and Ca, which were collected at 8 kV, and the net areas under the X-ray K energy lines of Fe, Ni and As and L energy lines of Hg, which were collected at 40 kV, were measured by Python multichannel analyser (PyMCA) and the data were handled by principal component analysis (PCA). This approach helped us to collect sufficient intensity of different important elements which showed higher variance in the entire dataset.  
The BRAVO spectrometer uses a new patented technology called SSE(tm) (Sequentially Shifted Excitation, patent number US8570507B1) in order to mitigate fluorescence [22; 23]. During the acquisition, the laser was slightly wavelength shifted three times and three raw Raman spectra are recorded. A proper algorithm recognizes all the peaks that shift at different laser wavelengths as good Raman peaks, and other peaks, non-shifting, as fluorescence (or absorbance) peaks, removing them. Moreover, the BRAVO uses two different lasers (DuoLaserTM), ranging from 700 to 1100 nm, during the acquisition. The spectra are collected from 300 to 3.200 cm\({}^{-1}\). The applied laser power is always less than 100 mWfor both lasers. The spectra were acquired with acquisition time ranging from 500 ms to 2 s and accumulation ranging from 5 to 30. For all the measurements OPUS(tm) software (Version 7.7) has been used in order to select the appropriate acquisition parameters.
# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 3 Feature Extraction
### Local Ternary Cross Structure Pattern
Similarly, in order to improve the robustness to rotation, the _LBP_ feature map \(LBP(i,j)\) in \(LTCSP\) can be replaced by the \(LBP_{ri}\) feature map \(LBP_{ri}(i,j)\) to construct the rotation-invariant local ternary cross structure pattern (\(LTCSP_{ri}\)).
###### Abstract  
Currently, ceramics are widely used in various industry branches, especially in energy, chemistry, and aerospace, as well as in medicine. Yttria-stabilized zirconia (YSZ) having unique electrical, thermal, and mechanical properties is one of the most popular ceramics for such applications. In this study, the influence of sintering temperature and yttria percentage on the microstructure and mechanical behavior of YSZ ceramics have been investigated. Corresponding mixtures of ZrO\({}_{2}\) powder doped with 3 and 6 mol% Y\({}_{2}\)O\({}_{3}\) powders (hereinafter: 3 YSZ and 6 YSZ) were prepared, and a series of ceramic specimens were sintered in argon at 1450 \({}^{\circ}\)C, 1500 \({}^{\circ}\)C, and 1550 \({}^{\circ}\)C for 2 h. Changes in the morphology and size of microstructural components as well as their distribution were analyzed with respect to the sintering mode, phase composition, flexural strength, and fracture surface morphology. The 3 YSZ and 6 YSZ ceramics sintered for 2 h at 1550 \({}^{\circ}\)C and 1450 \({}^{\circ}\)C, respectively, exhibited the highest levels of strength due to the presence of agglomerates of fine tetragonal zirconia phase particles with high bond strength, as well as larger grains of the monoclinic zirconia phase. The dominant fracture micromechanisms in both the 3 YSZ and 6 YSZ ceramics related to their high strength are discussed.  
yttria-stabilized zirconia; microstructure; grain morphology; porosity; phase balance; strength; fracture behavior Article
# Decision Support Model for Allocating Maintenance Budgets for Bridges
## 2 Literature Review
### Research Gaps  
The shortcomings of the previous models, individually or collectively, can be summarized as follows:  
* The available maintenance budget is allocated only to those bridges with the highest rank, even if all the structural elements of the selected bridges do not require maintenance, since the bridge is considered as one unit. Thus, very limited bridges will be maintained.
* The budget is allocated on the basis of the maintenance program/plan score, which is a combination of maintenance strategies considered for each bridge (e.g., [23; 24; 26]).
* Other important criteria such as the location of bridge are not considered. For example, a bridge located in a critical intersection could be selected for maintenance, while another bridge with a moderate condition assessment may not be selected.
* The review of previous reported models shows a lack of a relationship between the considerations of prioritizing the bridges in the network level and the main goal of the study which is allocating the budget for a set of bridges.
* The previously reported models require many iterative comparisons between the maintenance strategies as input data (e.g., [16; 23; 24; 25]).
* Some models are simple and consider only one criterion, namely the condition assessment, as in the model reported by [PERSON] and [PERSON] [18].
* Some approaches are not able to handle large networks.
* The uncertainty inherent in the maintenance cost and condition assessment is not considered, despite the fact that the condition assessment is one of the most common criteria, as it depends on the subjective evaluation of the bridge engineer.
* The outputs of the previously developed models are deterministic and do not consider the uncertainties associated with maintenance cost.
* The previously developed models do not provide either the optimal solution or the risk associated with the selected optimal solution.  
This paper introduces a decision support model for bridge maintenance which aims to overcome the shortcomings of the previously reported models or systems. The developed model takes into consideration two main aspects: (1) uncertainties associated with the estimated maintenance cost and (2) optimization of the budget allocation while taking into account the risk using frontier analysis technique. This model also provides the most attritive solution in which the decision-maker will be more assured that the selected maintenance plan (a set of structural elements to be maintained) will be achieved with a certain level of confidence.
## 2 Methodology
### Numerical Model
#### 2.2.3 Data Collection  
The load scenarios created as described in Section 2.2.2 were loaded into the FEM, and FEA was performed. Strains and displacement measurements were obtained at 11 locations at uniform intervals of 3.6 m below the flange of each girder, as shown in Figure 7. The center point is included as a measurement point because the largest displacement generally occurs at the center of the girder. Both ends of the beam were included to confirm that the strain and displacement at the support points in the simple supported beam are zero. Thus, a total of 55 strain (11 points \(\times\) 5 girders, input) and displacement (11 points \(\times\) 5 girders, output) measurement points were set. That is, one data set contains 55 strains and 55 displacements. The FEA for 25 scenarios for each speed was performed using the load scenarios created as described in Section 2.2.2. The value of \(\Delta t\) was 0.01 s, and the vehicles were loaded for a total of 8 s so that many vehicles could completely pass across the bridge. Thus, 800 data sets per scenario was obtained. Table 2 shows the amounts of training and test data obtained.  
An ANN is significantly less predictive of data beyond the range of the data that were used for training. In other words, if the range of data to be predicted is outside the range of data used for training, the prediction accuracy of the ANN is lowered. Therefore, the range of the test data should be configured to be within the range of the training data [27]. In this study, one scenario was selected such that the strain and displacement values used in the testing for each speed were within the range of values used in the training, as shown in Table 3. A total of 57,600 data sets were used as training data, and a total of 2400 data sets were used as test data, with 800 data sets per speed. In general, the quantity of data used for training should be greater than the quantity of data used in the test [34]. The difference between the quantity of training data and test data in this study is considered to be appropriate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Data**} & \multicolumn{3}{c}{**Test Data**} \\ \cline{2-7}  & **40 km/h** & **60 km/h** & **80 km/h** & **40 km/h** & **60 km/h** & **80 km/h** \\ \hline Number of load scenarios & 24 & 24 & 24 & 1 & 1 & 1 \\ Number of data sets per scenario & 800 & 800 & 800 & 800 & 800 & 800 \\ Total number of data sets & \multicolumn{3}{c}{\(24\times 800\times 3=57\),600} & \multicolumn{3}{c}{\(1\times 800\times 3=2400\)} \\ \hline \hline \end{tabular}  
* One data set consists of 55 strains (input) and 55 displacements (output).  
\end{table}
Table 2: Number of training and test data sets.  
Figure 7: Measurement points on the girder (unit: mm).
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### New Reflectance-Based Stitching Algorithm
#### 2.2.3 Two-Step Reflectance Algorithm

Table 3: Run time comparison between exhaustive enumeration and two-step reflectance algorithms for two types of topography. All reflectance-based stitching algorithms are contained in MorphoStitch, an in-house stitching software developed by one of the authors of this article, based on Matlab(r). Furthermore, in MorphoStitch, elementary maps are processed in a domino fashion and the current elementary map is compared to the already placed and stitched elementary maps. Therefore, there is a dependency on the stitching order (which starts at the top-left of the stitching grid and ends at the bottom-right of the stitching grid, by processing elementary maps on the same line from left to right).
## 4 Discussion
### Technology Applications  
Blockchain technology offers a range of solutions tailored to the challenges of interoperability, security, and administrative inefficiency. By enabling seamless data-sharing across institutions through decentralized ledgers, blockchain addresses the fragmentation prevalent in the GCC's education systems. [PERSON] (2023), Use of Blockchain Technology in Education [14].  
In terms of security, blockchain's cryptographic foundations provide robust protection against unauthorized access and cyber threats. Decentralized storage of academic records ensures data integrity, thereby safeguarding sensitive information such as student credentials and institutional data. Studies from Europe and Asia highlight the feasibility of integrating blockchain into existing administrative processes to enhance data security and trust [8].  
Administrative efficiency can also be significantly improved through blockchain applications. Automated workflows for credential issuance, enrolment, and grading reduce human error and operational delays, streamlining processes and allowing institutions to focus on core academic objectives. Examples from North America and Europe demonstrate blockchain's transformative potential in improving administrative efficiency, a need that resonates strongly in the GCC context [15].
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 3 Numerical Model
### Net System
#### 3.2.1 Hydrodynamic Coefficients  
The nets of the project are made of nylon cables. As shown in Table 4 and Figure 6, the diameter of a single nylon cable is 5 mm, and the density is \(6.5\times 10^{-6}\) t/m. The length of the nylon cable is the same as the distance between the two ends of the opening area of the platform. The spacing between two adjacent nylon cables is 50 mm.  
The forces on the nets are calculated using the Morison Equation (13). According to the guideline issued by the CCS [43], the drag coefficient in the Morison equation can be estimated using the following formula:  
\[C_{d}=0.04+(-0.04+S_{n}-1.24S_{n}^{2}+13.7S_{n}^{3})\cos\alpha^{\prime} \tag{14}\]  
where \(\alpha^{\prime}=90-\alpha\), \(\alpha\) represents the angle of attack that is the angle between the net and the flow direction on the horizontal plane, and when the flow direction is perpendicular to the nets, the angle of attack is 0; \(S_{n}\) represents the solidity ratio of nets, which is the ratio of the project area of the nets to the whole contour area (Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Unit** & **Value** \\ \hline Diameter & mm & 5 \\ Density & t/m & \(6.5\times 10^{-6}\) \\ Spacing & mm & 50 \\ \hline \end{tabular}
\end{table}
Table 4: Detailed parameters of the nets.  
Figure 6: Division of flow field region.  
In this study, the solidity ratio of nets is \(Sn=0.22\), and the corresponding drag coefficient of the whole net is \(C_{d}=0.305\). The inertia coefficient is \(C_{m}=1.0\). According to the principle of force equivalence, the drag coefficient of the single net line is \(C_{d}^{\prime}=1.525\), and the inertia coefficient of the single net line is \(C_{m}^{\prime}=1.0\).
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 5 Example Application
When further comparing to a system where every stream has a keyframe every second to accommodate random access and packet losses, the main downside of the proposed technique is the encoding step to generate the companion stream.
# Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording
## 4 Discussion
For pair comparison, the user density, which is equal to AD \(\times\) code rate (R), should be considered. Because we investigate the performance using a 4/6 (=R) modulation code at an AD of 3 Tb/in\({}^{2}\)[18], the PRML detector alone (uncoded system) must be tested at an AD of 2 Tb/in\({}^{2}\). All models using modulation code at 3 Tb/in\({}^{2}\) exhibit better BERs performance after decoding than the PRML at 2 Tb/in\({}^{2}\). At the BER of 10\({}^{-4}\) and TMR of 0%, ensemble of the MLP (64, 64) detectors with the demodulator performs better by approximately 1.3 and 1.7 dB, compared with the PRML with the demodulator and the PRML detector alone, respectively. Even with increasing TMR, the ensemble of the MLP (64, 64) detectors with the demodulator exhibit better BER performance. Figure 8 presents the BER performance of detectors and demodulators according to MLP detectors. All of the MLP detectors consist of an input layer, 2 hidden layers, and an output layer and have 64 neurons in each hidden layer. Firstly, we have compared the performances of the MLP detectors (dotted lines). When the MLP detectors are used alone, the performances of the regression and proposed MLP detector are better than that of the classification MLP detector. The performance of the regression MLP detector is similar to that of the proposed MLP detector. Secondly, we have verified the performance of a Euclidean distance demodulator depending on MLP detectors (solid lines). The classification MLP detector with a demodulator shows the worst performance because the prediction values are similar to the hard decision value {0, 1} and it is difficult to achieve some decoding gain when conducting error correction coding and modulation coding of the data. The decoding performance of the proposed MLP detector with demodulator gives the best performance since the proposed MLP detector provides more reliable information to the demodulator. However, to use the proposed MLP detector, the trade-off between complexity and BER performance should be considered in the training process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Variance**} \\ \cline{2-5}  & \(-\)**1 bit** & **+1 bit** & \(-\)**1 bit** & **+1 bit** \\ \hline PRML & \(-\)3.59 & 3.59 & 2.07 & 2.07 \\ MLP detector alone & \(-\)3.52 & 3.38 & 1.24 & 1.32 \\ Ensemble of MLP detectors & \(-\)3.37 & 3.26 & 1.18 & 1.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and variance of the models. Figure 6: Probability density function of the output of PRML, MLP detector alone, and ensemble of MLP detectors at SNR of 14 dB.
# Methods of Atmospheric Coherence Length Measurement
## 2 Retrieval of \(r_{0}\) Based on \(C_{n}^{2}\)
### Scintillation Method
We should note that scintillometers are susceptible to the scintillation saturation effect [26; 31]. Therefore, the scintillometer would fail once it enters regions of strong turbulence (\(\beta_{I}^{2}\geq 1\)) since scintillation saturation occurs. Fortunately, new techniques have sprung up to address such issues [23; 32; 33].
## 4 Results and Discussion
### Effect of the Data Characteristics on the Co-Registration Results  
Since the number of linked trees is an indicator of matching point patterns, the probability of a successful co-registration increased with an increasing number of linked trees (Figure 8a). A saturation effect occurred for five links and more (average co-registration probability above 90%), since not every tree pair has to be investigated to decide if the tree patterns match. As desired (by choosing the given GNSS error distribution), the co-registration probability decreased with increasing horizontal displacements (Figure 8b).  
Figure 9 illustrates the effect of different forest types and stand characteristics on the co-registration probability. The Kruskal-Wallis rank sum test indicates significant differences of the probabilities between different tree species (\(p=0.00\)). The two-tailed Wilcoxon rank-sum test showed that coniferous trees reached significantly higher probabilities than deciduous trees (\(p=0.00\)), which confirms the findings of previous studies ([PERSON] et al., 2017; [PERSON] et al., 2017). Nevertheless, within the conifers, the larches reach lower probabilities than the other conifers. This effect can be explained by the different crown shapes. Cone-shaped crowns result in higher individual tree detection accuracies and higher location accuracies, which eases the co-registration compared to dome-shaped crowns.  
Figure 7: Feature importance derived by the random forest classifier.  
An increasing number of tree species (as an indicator of stand variability) results in a positive effect on the co-registration result. The ANOVA of the co-registration probability grouped by the number of species within a plot (Figure 9b) indicates a significant (linear) trend (\(p\) = 0.00).  
The ANOVA of the co-registration probability grouped by the height of the tallest tree (Figure 9c) also indicates a significant trend (\(p\) = 0.00). A saturation effect for tall trees can be observed. Additionally, the standard deviation of tree heights (Figure 9d) shows a significant linear trend (\(p\) = 0.00). With an increasing height of the tallest tree and with an increasing variability of the tree heights, the probability of a correct co-registration also increases, since large values of these variables facilitate the identification of matching tree pairs. These results go along with the findings of [PERSON] and [PERSON] (2017).  
Figure 8: Effect (**a**) of the number of linked trees and (**b**) of the horizontal displacement of the plot center on the co-registration probability. The numbers in brackets correspond to the number of plots. The whiskers extend to 1.5 times the interquartile range. Outliers are marked by circles.