esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Structural Models
### 1D Beam Model  
[PERSON]'s beam theory is applied to the 1D beam model, which can apply the torsional moment and warping distorsion in consideration of the characteristics of a container ship with a very large opening section. It is a very difficult task to calculate the sectional properties of the hull section, and it is generally calculated through the 2D sectional analysis of the specific cross-section. For the sectional properties of the 1D beam, the grid point was set at the position of the transverse bulkhead in the longitudinal direction, and the information was extracted from the middle of each node. Figure 8 shows the sectional property of each element in the analysis. In order to confirm the dynamic characteristics of the idealized 1D beam model and the 3D wire model, normal mode analysis was performed and the results were compared. As shown in Table 2, it can be seen that the torsion mode (T) and longitudinal bending mode (VB) of the two models are almost similar in dry conditions. This result shows that the idealized 1D beam model reflects the information of the 3D model well.  
\begin{table}
\begin{tabular}{l l l} \hline Length overall, LoA (\(m\)) & 347.0 \\ \hline Length between perpendiculars, Lpp (\(m\)) & 342.5 \\ \hline Breadth, B (\(m\)) & 48.2 \\ \hline Depth, D (\(m\)) & 29.8 \\ \hline Design draught, Td (\(m\)) & 14.5 \\ \hline Displacement, (\(t\)) & 168,658.0 \\ \hline Longitudinal center of gravity, L.C.G (m) & 170.12 \\ \hline Max. speed (knots) & 24.0 \\ \hline Gyration radius in roll and pitch (m) & 17.10, 88.24 \\ \hline \end{tabular}
\end{table}
Table 1: Principle dimension.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1D FE Model** & **3D FE Model** & **Dry Mode Error** & **3D FE Model** \\  & **(Dry)** & **(Dry)** & **(\%)** & **(Wet)** \\ \hline VBM (Hz) & 0.604 & 0.614 & 1.8 & 0.471 \\ \hline TM (Hz) & 0.387 & 0.362 & 6.6 & 0.352 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modal analysis.  
Figure 7: Body plan.
# Imaging Characteristics of Disturbance Flow Field Surrounding a Hypersonic Target
## 2 Methods
### Phase Difference and Imaging Blurring Derivation  
As light propagates through the disturbance flow field, the optical distance through which it passes can be expressed by the optical path length [23]  
\[\text{OPL}=\sum\limits_{i}n_{i}h \tag{14}\]  
where \(n_{i}\) is the refractive index at \(\mathbf{r}_{i}\) and \(h\) is the step length of ray tracing. The optical path difference of the light can be used to evaluate the phase difference and can be expressed as [23]  
\[\text{OPD}=\text{OPL}-\text{OPL}_{0} \tag{15}\]  
where \(\text{OPL}_{0}\) is the optical path length that the light propagates in free space. Thus, the phase difference can be expressed as [23]  
\[\varphi(x,y)=\frac{2\pi}{\lambda}\text{OPD} \tag{16}\]  
where \(\lambda\) is the wavelength of light.  
The pupil function can be determined by calculating the phase difference generated by all rays on the image plane and can be expressed as [24]  
\[P(x,y)=\left\{\begin{array}{cc}A(x,y)e^{i\varphi(x,y)}&x^{2}+y^{2}\leq(D/2) ^{2}\\ 0&x^{2}+y^{2}\geq(D/2)^{2}\end{array}\right. \tag{17}\]  
where \(A(x,y)\) is the amplitude distribution and \(D\) is the diameter of the pupil.  
According to the [PERSON] principle, the far-field approximation of the amplitude distribution of light waves within the pupil of the light on the image plane is expressed as [24]  
\[U(x^{\prime},y^{\prime})=\iint P(x,y)e^{-i\frac{2\pi}{\lambda y^{\prime}}(xx^ {\prime}+yy^{\prime})}\text{d}x\text{d}y \tag{18}\]  
where \(f^{\prime}\) is the focal length of the optical system. Apparently, \(U(x^{\prime},y^{\prime})\) is the Fourier transform of \(P(x,y)\). The point spread function can be expressed as [24]  
\[\text{PSF}(x^{\prime},y^{\prime})=|U(x^{\prime},y^{\prime})|^{2} \tag{19}\]  
Applying the Fourier transform to the point spread function, the optical transfer function OTF can be obtained [24]  
\[\text{OTF}\Big{(}f_{x^{\prime}},f_{y^{\prime}}\Big{)}=\iint\text{PSF}\big{(} x^{\prime},y^{\prime}\big{)}e^{-i2\pi\big{(}f_{x^{\prime}}x^{\prime}+f_{y^{ \prime}}y^{\prime}\big{)}}\text{d}x^{\prime}\text{d}y^{\prime} \tag{20}\]The amplitude distribution of the image plane can be expressed as [24]  
\[I(x_{0},y_{0})=O(x_{0},y_{0})*\mathrm{PSF}(x^{\prime},y^{\prime}) \tag{21}\]  
where \(O(x_{0},y_{0})\) is the amplitude distribution of the object plane, \(*\) represents the convolution.  
The peak signal-to-noise ratio is used to evaluate the quality between blurred image and original image and can be expressed as [25]  
\[\mathrm{PSNR}(i,j)=10\log_{10}\left[\frac{(L-1)^{2}}{\mathrm{MSE}(i,j)}\right] \tag{22}\]  
where \(L\) is the maximum valid value for a pixel, \(\mathrm{MSE}\) is the mean squared error of the image and can be expressed as [25]  
\[\mathrm{MSE}(i,j)=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\left[f(i,j)-\hat{f} (i,j)\right]^{2} \tag{23}\]  
where \(\hat{f}(i,j)\) and \(f(i,j)\) represent the blurred image and the original image, respectively. \(M\) and \(N\) represent the length and width of the image, respectively.
## 2 The Excimer Laser Micromachining System and the Optical Diffraction Effect
Furthermore, the hole-area opening ratio ranges from 0.2 to 0.7 due to the line-width limitation of the Cr/glass photo-masks preparation system. As expected, the machining rate increases with an increasing hole-area opening ratio. However, the trend is non-linear. This result is reasonable since the projected energy is determined not only by the average optical transmission coefficient (i.e., the hole-area opening ratio) of the photo-mask but also by the optical diffraction effect, which is governed by the feature size of the mask pattern. As can be seen in Figure 4, for a hole-area opening ratio of 0.2, the machining rate is equal to zero, irrespective of the element size. In other words, given a very small hole-area opening ratio, the diffraction effect is so pronounced that the projected energy intensity is below the energy intensity threshold of the PC material. Curve fitting lines for the experimental data obtained with three different elements sizes are shown in Figure 4.  
Figure 3: Laser-machined cross-section profiles using hole-arrayed mask patterns with a hole-area opening ratio of 0.65 and a square element size of (**a**) 10 \(\upmu\)m, (**b**) 15 \(\upmu\)m, and (**c**) 20 \(\upmu\)m.  
displayed in Figure 4. In subsequent works, mask pattern designs will all use a square element size of 10 \(\upmu\)m for a stronger optical diffraction effect, achieving smoother energy distribution for better machined surface roughness. The solid fitting line shown in Figure 4 for the case of a 10 \(\upmu\)m element size will be used for subsequent mask pattern designs of 3D micromachining.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 2 Enabling 6G Technologies  
### Multiband Technology  
MB technology enables to significantly enhance the capacity of optical networks by simultaneously transmitting data in multiple frequency bands [25]. In particular, MB enables to expand the bandwidth of conventional standard single-mode fibres (SSMFs) by considering additional bands beyond C-band such as O-, E-, S-, C-, L-, and U-bands. This translates to an available optical bandwidth of 51.88 THz [26]. MB can provide cost-effective upgrades of optical networks by maximizing the utilization of already existing fibre infrastructure. The need for extensive new fibres can effectively be postponed while optimizing the return on investment. On the other hand, due to the frequency varying fibre characteristics (i.e., in terms of fibre attenuation/loss, chromatic dispersion (CD) and effective area), different performance is exhibited when transmitting in the different bands. This enables specific classes of applications and services with different requirements to be allocated accordingly within the differently performing bands. An optimal and dynamic resource allocation of the different frequency bands can be considered according to the varying network condition and traffic demands. However, MB transmission presents some challenges that should be considered. Specifically, the influence of stimulated Raman scattering (SRS) leads to a power transfer between wavelengths with a 100 nm spacing, as a result of the interplay of wavelength division multiplexing (WDM) channels across the various bands [17; 27]. Additionally, the best technology option for amplification, filtering, modulation, and detection should be used according to the band of interest [28]. For example, regarding amplification, praseodymium-doped fibre amplifier (PDFA), neodymium DFA (NDFA), thulium DFA (TDFA), and erbium DFA (EDFA) could be considered for the O-band, E-band, S-band, and C+L-bands, respectively, [29]. Alternative solutions based on bismuth DFA, Raman amplifiers, or semiconductor optical amplifiers (SOA) can also be exploited [30]. All this poses new considerations and requirements in the design and implementation of novel MB transmission systems.
## 3 Experimental Results and Analysis
### Experimental Results  
The proposed CPFRA is measured in a microwave anechoic chamber. The reflection characteristics of the antenna are measured by using a vector network analyzer (VNA). In addition, a coaxial-to-waveguide adaptor also needs to connect the test system and the feed horn. Figure 12 shows the measured normalized radiation patterns of the CPFRA at 26 GHz. It can be seen from the figure that the radiation pattern is consistent, the sidelobe is less than \(-13\) dB, and the cross polarization is less than \(-15\) dB. The curves of gain and aperture efficiency varying with frequency are shown in Figure 13. The difference between the simulation and test results mainly lies in the machining error and assembly errors. The FRA achieves a maximum gain of 21.2 dBi and an aperture efficiency of 27.2% at 26 GHz. In addition, the 3 dB gain bandwidth of the proposed FRA is 27.4% (22-29 GHz), and the 3 dB axial ratio (AR) bandwidth is 34%.  
Figure 11: The photographs of the fabricated CPFRA. (**a**) Top view of the sub-reflector, (**b**) top view of the primary reflector, and (**c**) 3D view of the FRA.  
The performances of the proposed CPFRA together with other published works [15; 19; 20; 21; 22] are shown in Table 2. As can be seen from the table, compared with the non-folded circularly polarized reflectarray antenna [19; 20; 21; 22], the profile of the proposed circularly polarized folded reflectarray antenna in this work is reduced to half of that without folding. In terms of performance, compared with [21], although the proposed antenna has a lower aperture efficiency, it can still achieve a higher 3 dB axial ratio (AR) bandwidth. Compared with the circularly polarized folded reflectarray antenna design with a linearly polarized source in [15], the circularly polarized folded reflectarray antenna based on the transmissive linear-to-circular polarization converter proposed in this work has an approximately equal aperture efficiency while having a wider 3 dB gain bandwidth and 3 dB AR bandwidth.  
Figure 12: Simulated and measured normalized radiation patterns.  
Figure 13: Measured and simulated gain and axial ratio curves versus frequency.
## 2 Background on SSI
### SSI Architecture  
Reminiscent of the layered Open Systems Interconnection (OSI) model or transmission control protocol (TCP)/Internet protocol (IP) architecture, the components of an SSI system are organized in a similarly layered architecture. Most often, the architecture described by the Hyperledger Aries project [14] is used. This architecture consists of four layers, as shown in Figure 1. The first, bottom layer defines the technology used to store and express credentials. In the case of the Hyperledger Aries project, this layer is implemented on the Sovrin Ledger [14], but, in general, a variety of blockchains, distributed ledgers, or other decentralized databases can be used. This layer is necessary for specifying identifiers (DIDs) and associating keys to them. On top of that, the second layer adds communication protocols to securely exchange messages between agents, wallets, and hubs.  
Providing the technical premises, layers one and two are used as the foundation to build organizational and human trust at layers three and four. Layer three defines the process of creating, exchanging, and presenting verifiable credentials. Allowing those credentials to be used in more than one specific setting is the goal of layer four. It spans a governance structure, helping organizations to cooperate and accept each other's credentials.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 2 Fundamentals of Finite Element Modeling  
### Basics of Finite Element Method  
The FEM is a robust numerical technique used to address a variety of engineering problems. It involves dividing the domain of interest into smaller, finite-sized elements that approximate the geometry of the structure being analyzed. These elements are interconnected at specific points known as nodes, creating a mesh. By applying appropriate mathematical formulations, such as the principle of virtual work [34] or the Galerkin method [35], the governing equations of the problem can be discretized within each element. This process generates a set of algebraic equations that can be solved to find the solution to the problem. In the context of fatigue crack propagation, FEM offers a flexible framework for modeling and analyzing the behavior of cracked structures under cyclic loading conditions. Many textbooks covering the basic principles of the FEM can be found in the literature, such as [36; 37; 38; 39]. These textbooks provide a comprehensive overview of the fundamental concepts and mathematical foundations of the finite element method, which is a widely used numerical method for solving partial differential equations in engineering and scientific applications. The accuracy of FEM solutions is influenced by several factors, including the type of element used, the size of the elements, and the selected numerical integration scheme.
# Software for Monitoring the In-Service Efficiency of Hydraulic Pumps
## 4 The Laboratory Validation of the Software Module for the Pump's Full Operating Range
The results provided by the software module for OP9 are: \(Q=17.968\times 10^{-3}\) m\({}^{3}\)/s, with a percentage error of \(-3.61\%\) compared to \(Q_{ms}=18.64\times 10^{-3}\) m\({}^{3}\)/s, \(\eta_{P}=53.49\) %, \(H=47.63\) m, \(P_{m}=15.1\) kW. The negative value of the percentage error shows that the flow rate value, determined by the software module, is lower than the value measured by the flowmeter. The abnormal operation diagnostic (AO) \(\underline{\text{RED}}\)  supplied is correctly determined. The input variables for operating point number 10 (OP10) set on partial flow rate during normal regime (NO) (see Figure 7c) are: static pressure measured at suction \(p_{s}=-18,\)530.6 Pa, static pressure measured at discharge \(p_{d}=\) 408,535.72 Pa and the value of the volumetric flow rate measured by the flowmeter \(Q_{ms}=24.23\times 10^{-3}\) m\({}^{3}\)/s. The results provided by the software module for OP10 are: \(Q=23.21\times 10^{-3}\) m\({}^{3}\)/s, with a percentage error of \(-4.192\%\) compared to \(Q_{ms}=24.23\times 10^{-3}\) m\({}^{3}\)/s, \(\eta_{P}=62.02\) %, \(H=45.427\) m, \(P_{m}=16.98\) kW. The normal operation diagnostic (NO) [6]  
hydraulic quantity that must be determined while the pump is in service. Determining the flow rate in situ is a challenge and a requirement for the water treatment technological process. The relative error of the flow rate estimated by the software module compared to the value measured by the flowmeter fits within the limit of \(-5.6\%\) for the operating points with a flow rate below \(25\%\) of the value of the operating point with maximum efficiency. In exchange, the limit of the relative error is between \(-4.192\%\) and \(+0.113\%\) for all the operating points of the pump, except those with a flow rate below \(25\%\) of the value associated with the operating point with maximum efficiency. Based on the validated results, we can conclude that the software module allows estimating the flow rate of the pump's operating point when there is no flowmeter installed (or where there is no possibility to install one). The estimation of the flow rate using the software module is an additional gain to the assessment of the operating regime of the pump.
###### Abstract  
A number of studies realized operation of power systems are unstable in developing countries due to misconfiguration of distribution systems, limited power transfer capability, inconsistency of renewable resources integration, paucity of control and protection measures, timeworn technologies, and disproportionately topology. This study underlines an Afghanistan case study with 40% power losses that is mainly pertinent from old distribution systems. The long length of distribution systems, low-power transfer capability, insufficient control and protection strategy, peak-demand elimination, and unstable operation (low energy quality and excessive voltage deviations) are perceived pre-eminent challenges of Afghanistan distribution systems. Some attainable solutions that fit challenges are remodeling (network reduction), networks reinforcement, optimum compensation strategy, reconfiguration options, improving, and transfer capability. This paper attempts to propose a viable solution using multiobjective optimization method of auto-tap-changer pole transformer (ATCTr). The proposed methodology in terms of optimal numbers and placement of ATCTr can be known as a novel two-dimensional solution. For this purpose, a real case of Kabul City distribution system is evaluated. Simulation results indicate the effectiveness of the proposed method in reducing system losses and improving system overall performance. This approach tends to regulate the voltage deviation in a proper and statutory range with minimum number and optimum placement of ATCTr. The proposed method is simulated using MATLAB(c) environment to compare and evaluate performance of the proposed network under different situations and scenarios.  
auto-tap-changer pole transformer (ATCTr); distribution network; genetic algorithm (GA); multiobjective optimization; voltage deviation control; voltage regulation; voltage stability
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 4 Case Studies
### Comparison of State Estimation Results under the Bad Data Condition
The mean absolute error of the bus voltage amplitude was less than 0.6%, while the minimum mean absolute error of the bus voltage amplitude was less than 0.1%. The maximum relative error of the voltage phase was less than 0.4\({}^{\circ}\). At the time of 3 min, 6 min, 7.5 min, and 9 min, the bad data were created by adding a disturbance into the measurements, and the proposed IIGGIII-LSE performed well, which indicated the practicability and effectiveness of the method in this paper. Figure 11: State trace at bus 30 of the 33-bus system under the bad data condition: (**a**) voltage amplitude curve, and (**b**) voltage angle curve. Figure 10: State trace at bus 6 of 33-bus system under the bad data condition: (**a**) voltage amplitude curve, and (**b**) voltage angle curve.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 1 Introduction
Here, we present the further development of these efforts, which include processing chain enhancements, development of an operational blue-sky or actual albedo, and further emphasis on the impact of retrieval conditions on the accuracy of the operational product.
## 5 Discussion
### Limitations  
The classification accuracy of the RF-CNN method is 98.14%, which is considered to be within the appropriate range [77], indicating that debris-covered glaciers can be extracted. However, this classification accuracy is affected--to a certain extent--by the model parameters and the accuracy of the sample set. The model parameters used in the experiment and the accuracy of the sample set are not discussed in detail. It is necessary to gradually explore the best glacier extraction model, the optimum parameters, and the best sample set in future work. Further, owing to the insufficient sample size in the Eastern Pamir (continental glaciers) and the Nyaingentanglha Mountains (maritime glaciers), the model trained using this deep learning framework is not suitable for complex mountain-shaded glaciers. A large number of compound glacier samples can be added to train the RF-CNN model to improve the accuracy for identifying mountain-shaded glaciers (continental glaciers and maritime glaciers). Because the glacier data set used in this experiment is not sufficiently large, the superiority of deep learning in comparison to traditional machine learning methods (such as RF and CNN) is not clear when processing large-scale sample sets. However, RF-CNN also has the advantage of high accuracy, where its accuracy does not depend on the choice of threshold. This means that it is a simple and feasible automatic method for glacier extraction.
# Robustness Analysis on Graph Neural Networks Model for Event Detection
## 5 Experiments
### Model Performance on the Adversarial Data
#### 5.3.2 Model Robustness to Word-Level Transformations  
Both Tense and SwapSyn are word-level transformations because they transform one word into another. The experimental results show that the model's robustness to Tense and SwapSyn are both relatively strong, while the former is a little stronger than the latter. We have conducted the following analysis:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adversarial Data**} & \multicolumn{3}{c}{**A-MDL-EEGCN**} & \multicolumn{3}{c}{**EE-GCN**} & \multicolumn{3}{c}{**MOGANED**} \\ \cline{2-10}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Keyboard & **72.1** & 58.1 & 64.3 & 70.9 & **59.5** & **64.7** & 70.8 & 48.4 & 57.5 \\ Ocr & 69.6 & **52.6** & **59.9** & **73.2** & 47.9 & 57.9 & 71.5 & 42.7 & 53.5 \\ SpellingError & 71.1 & **56.7** & 63.1 & 73.4 & 55.1 & 62.9 & 69.2 & 47.5 & 56.3 \\ Typos & 71.7 & **49.9** & **58.8** & 71.0 & 47.7 & 57.0 & **71.9** & 40.7 & 52.0 \\ EntTypos & **74.5** & **77.5** & **75.8** & 71.8 & 75.9 & 73.8 & 71.8 & 65.3 & 68.4 \\ Tense & 71.1 & **77.2** & **74.0** & 71.3 & 74.9 & 73.1 & **72.2** & 63.9 & 67.8 \\ SwapSyn & 73.2 & **72.4** & **72.8** & 69.6 & 68.5 & 69.1 & **73.7** & 60.0 & 66.2 \\ Tense + Typos & **70.5** & **51.9** & **59.8** & 69.6 & 49.0 & 57.5 & 66.2 & 39.5 & 49.5 \\ SwapSyn + Typos & 68.9 & **41.9** & **52.1** & **70.3** & 40.3 & 51.2 & 67.4 & 34.3 & 45.5 \\ Length \(\leq 50\) & 79.2 & **79.1** & **79.1** & 78.0 & 78.6 & 78.3 & **79.7** & 72.6 & 76.1 \\ Length \(\geq 50\) & 63.6 & **71.8** & **67.5** & 59.0 & 59.0 & 59.0 & **73.4** & 56.3 & 63.7 \\ Perplexity-0-50\% & 69.8 & **77.0** & **73.2** & 68.8 & 74.4 & 71.5 & **73.6** & 66.1 & 69.7 \\ Perplexity-0-20\% & 65.8 & **75.0** & **70.1** & 67.0 & 70.9 & 68.9 & **69.6** & 64.9 & 67.1 \\ \hline \hline \end{tabular} Bold indicates the highest value.  
\end{table}
Table 4: Robustness evaluation results.  
* Transforming all verb tenses basically does not change the meaning of the sentence, and the semantic difference between verbs in different tenses is small; the corresponding word vectors should be very similar, thus Tense causes little perturbation to the original sentence.
* Replacing words with synonyms slightly changes the meaning of the sentence (e.g., the degree of emotion); although word vectors of synonyms should also be similar, SwapSyn causes perturbation to the original sentence a little more than Tense.  
It can be seen from the above analysis that the GNN-based ED model can cope with the slight change in lexical features well; that is, the model can handle sentences with different expressions but nearly the same meaning. A-MDL-EEGCN is more robust to word-level transformations than EE-GCN and MOGANED.