esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 1 Introduction
Intuitively, assigning more power to texture videos than depth maps will cause an inevitable geometric distortion in virtual views, whereas less power allocation to texture videos would incur additional distortions in reference views. Hence, under limited resources and joint texture/depth power allocation, the problem becomes one of achieving the optimal reconstruction quality. In another case, given a predefined quality at the receiver end, the problem is one of achieving the minimal resource (bandwidth and power) consumption. This paper proposes incorporating a joint texture-depth power allocation and resource optimization for 3D video transmission, as shown in Figure 1. The optimal power allocation ratio (PAR) between texture and depth is estimated, assuming equal power among all reference viewpoints. Then, the intricate resource control problem of 3D video transmission is considered from the point of view of two optimization problems: minimum distortion and target distortion. Resource allocation algorithms are designed to find the optimal solutions to the formulated problems. The main contributions of this work are as follows:* The optimal solutions for minimum and target distortion problems are obtained in terms of texture and depth using proposed resource allocation algorithms, and the trade-off relationship between power and bandwidth usage is analyzed. * The proposed resource control scheme for 3D transmission integrates the optimal PAR with resource allocation algorithms, ensuring balanced power allocation between texture and depth to improve the transmission results. The simulation results demonstrate a significant reduction in resource usage in terms of texture and depth, while achieving a satisfactory or desired video quality. The remainder of the paper is organized as follows. SoftCast is reviewed in Section 2. The minimum distortion optimization is presented in Section 3. Section 4 discusses the target distortion optimization. Sections 5 and 6 show the simulation results and conclude the paper.
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform
### Assessment Subsystem
#### 2.3.2 Overall Performance Evaluation Based on Frequency Variation  
Under a variety of cyclic loads, the natural frequency of an offshore platform will change. Therefore, dynamic characteristics such as the natural frequency of the structure could be used to comprehensively checkout its overall performance. Modal identification is a process of extracting the characteristic natural vibration parameters of the structure--such as frequency, vibration mode, and damping--through real-time monitoring of response data. Overall performance evaluation based on frequency changes: it is an important index to evaluate the integrity and safety of the structure. The health of the structure can be investigated through changes in frequency.  
Two modal parameters, frequency and strain modal shape, can be identified by strain sensor for damage identification in general. So, the natural frequency of the structure can be easily obtained through strain monitoring. Once the structural component is damaged, the health status of the structure can be evaluated by the natural frequency of the structure before and after the damage. The frequency change rate of the structure before and after damage can be defined as:  
Figure 3: Stress model of a jacket leg.  
\[\frac{df_{i}}{f_{i}}=\frac{|f_{ui}-f_{di}|}{f_{ui}} \tag{7}\]  
where \(f_{ui}\) and \(f_{di}\) respectively represent the \(i\)-th order natural frequency value of the structure after and before damage.
## 4 Experiments
### 24-h-Ahead Forecasting Analysis
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**MAPE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAXAE**} \\ \cline{2-7}  & **Mean** & **Std.Dev** & **Mean** & **Std.Dev** & **Mean** & **Std.Dev** \\ \hline
**LSTM** & 32.3 & 41.6 & 66.5 & 122.6 & 182.5 & 220.1 \\ \hline
**ETS** & 28.8 & 9.2 & 48.6 & 61.7 & 130.74 & 164.6 \\ \hline
**AR** & 19.7 & 10.3 & 36.9 & 49.6 & 116.3 & 149.8 \\ \hline
**CNN** & 12.0 & 9.1 & 20.5 & 26.7 & 68.8 & 90.88 \\ \hline
**MLP** & 11.6 & 7.4 & 21.5 & 27.4 & 72.5 & 93.1 \\ \hline
**Naive** & 9.0 & 2.4 & 17.7 & 20.7 & 91.8 & 132.5 \\ \hline
**Prophet** & 8.8 & 4.8 & 58.8 & 98.5 & 134.6 & 173.2 \\ \hline
**CNN-LSTM** & 7.9 & 3.7 & 14.9 & 28.6 & 32.6 & 54.79 \\ \hline
**ARIMA** & 7.7 & 3.9 & 13.9 & 15.9 & 57.0 & 69.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and standard deviation for each of the three performance measures across all the considered cells sorted by MAPE error. Figure 12: The distribution of MAPE across cells in a 24-h-ahead forecasting scenario. Figure 13: The distribution of MAXAE in a 24-h-ahead scenario.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results  
In this section, we present the Monte Carlo simulations to observe the effect of system parameters on satellite-ground FSO uplink and downlink communications. In the simulations, we describe the effect of the FSO channel while changing three important parameters, such as nominal refractive index structure, zenith angle, and Fried parameter, and also show the effect on the relevant parameters as presented in Table 2.
# A Secondary Reconfigurable Inverter and Its Control Strategy
## 2 Materials and Methods
### Analysis of the Fault Tolerant Inverter Operating Principle  
#### 2.2.1 Analysis of the Switching Mode  
When the secondary reconfigurable inverter is running in the faulty state, the faulty power switch is removed. The remaining power switches of the two bridges can be reconfigured to keep operating. Each switching mode of the TPFS inverter has the corresponding switching mode as the TPSS inverter in a normal state. Varieties of switching modes and current paths of the TPSS inverter circuit and the reconfigured TPFS inverter circuit are shown in Figure 4.  
In switching mode (a\({}_{1}\)), there are two current paths, \(i_{41}\) and \(i_{42}\), which both flow from the upper-half source of the DC-side source, which is equivalent to the condition that the b-phase lower-half bridge is connected and the upper bridge arm is cut off in the TPSS inverter circuit. At the moment, two current paths, \(i_{61}\) and \(i_{62}\), also exist in the TPSS inverter circuit.  
According to the switching mode (\(a_{1}\)) in Figure 4, the two current paths \(i_{41}\) and \(i_{42}\) satisfy the following equations, respectively.  
\[i_{41}=\frac{\frac{u_{d4}}{2Z}}{2Z}=\frac{u_{d4}}{4Z} \tag{1}\]  
\[i_{42}=\frac{\frac{u_{d4}}{2Z}}{2Z}=\frac{u_{d4}}{4Z} \tag{2}\]  
Therefore the three-phase output voltages of the reconfigurable inverter circuit are  
\[u_{an4}=i_{41}Z=\frac{u_{d4}}{4} \tag{3}\]  
\[u_{cn4}=i_{42}Z=\frac{u_{d4}}{4} \tag{4}\]  
\[u_{bn4}=(i_{41}+i_{42})Z=\frac{u_{d4}}{2} \tag{5}\]  
In switching mode (a\({}_{2}\)), the two current paths \(i_{61}\) and \(i_{62}\) of the normal inverter circuit satisfy the following equations, respectively.  
\[i_{61}=\frac{u_{d6}}{2Z} \tag{6}\]  
\[i_{62}=\frac{u_{d6}}{2Z} \tag{7}\]  
Then, the three-phase output voltages of the normal inverter circuit are defined by the following equations respectively.  
\[u_{an6}=i_{61}Z=\frac{u_{d6}}{2} \tag{8}\]  
\[u_{cn6}=i_{62}Z=\frac{u_{d6}}{2} \tag{9}\]  
\[u_{bn6}=(i_{61}+i_{62})Z=u_{d6} \tag{10}\]  
Let the three-phase output voltages of the reconfigurable inverter circuit be equal to the three-phase output voltages of the normal inverter circuit, respectively. Hence,  
\[\left\{\begin{array}{l}u_{an4}=u_{an6}\\ u_{bn4}=u_{bn6}\\ u_{cn4}=u_{cn6}\end{array}\right. \tag{11}\]  
That is to say,  
\[u_{dc4}=2u_{dc6} \tag{12}\]  
Connecting Equation (12) with the Equations (1), (2), (6) and (7), it can be derived that the output current of the inverter satisfies the following equations  
\[i_{41}=i_{61} \tag{13}\]  
\[i_{42}=i_{62} \tag{14}\]  
Similar results can be obtained in other switching modes, which are not repeated here. By setting the DC side voltage value of the reconfigurable inverter to be twice the DC-side voltage in the normal state, we can control the inverter according to the switching state shown in Table 1, no matter if the inverter is running in a faulty state or in a normal state. The normal output voltage and output current can be obtained in both states.
# Advanced Deep Learning Methods to Generate and Discriminate Fake Images of Egyptian Monuments
## 4 Results  
### Discriminator Architecture Comparison  
The investigation of discriminator network designs demonstrated major performance differences as superior designs surpassed the baseline framework (Table 2).  
The results demonstrate that SE blocks contributed the largest individual improvement (_FID_ reduction of 2.9 points), followed by noise injection (2.5 points), improved Minibatch-StdLayer (2.2 points), and skip connections (1.49 points). Skip connections improved the training gradient flow, subsequently decreasing training time by about 15% while still contributing to the overall performance improvements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \cline{2-5}  & **FID**\(\downarrow\) & **IS**\(\uparrow\) & **Precision**\(\uparrow\) & **Recall**\(\uparrow\) \\ \hline
1 & 33.3 & 4.98 & 0.34 & 0.13 \\ \hline
2 + Noise Injection & 30.8 & 5.12 & 0.36 & 0.15 \\ \hline
2 + SE Blocks & 27.9 & 5.35 & 0.39 & 0.17 \\ \hline
2 + Improved MinibatchStdLayer & 25.7 & 5.48 & 0.40 & 0.18 \\ \hline
2 + Skip Connections & 24.21 & 5.58 & 0.41 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results.  
Adding features of the noise injection, the squeeze-and-excitation blocks, and the improved Mini-batchStdLayer to the discriminator-enhanced architecture allows us to generate realistic Egyptian monuments while maintaining the diversity of outputs.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design  
In this section, we describe our heuristic 5G Core Slice Provisioning algorithm based on network Resource and Topology attributes called RT-CSP in detail. Accordingly, we first present a method for ranking network nodes by using these attributes. Then, we elaborate our two-stage algorithm consisting of algorithms for heuristic slice node provisioning and \(k\)-shortest path based slice link provisioning. Finally, we analyze the time complexity of the RT-CSP algorithm and prove that it can run in polynomial time.
# Effects of 450 MeV Kr Swift Heavy Ion Irradiation on GaN-Based Terahertz Schottky Barrier Diodes
## 4 Conclusions  
In summary, this work systematically investigated the effects of 450 MeV Kr SHI irradiation on the electrical characteristics and induced defects in GaN-based THz SBDs. It was found that the Kr SHI irradiation degraded the high-frequency performance of GaN-based THz SBDs due to irradiation-induced defects in the GaN epitaxial layer. After Kr SHI irradiation, decreases in \(f_{c}\) and \(\varphi_{B}\) were observed, accompanied by increases in \(I_{R}\) and \(n\). As confirmed by SRIM calculations, Kr SHI irradiation introduced displacements and vacancies, which significantly degraded the high-frequency performance of GaN-based THz SBDs. The trap states with an energy level approximately 0.62 eV below the conduction band were identified through LFN spectra. Furthermore, the analysis results based on RL and YL spectra confirm the presence of irradiation-induced defects caused by Kr SHI irradiation. These findings demonstrate that the high-frequency performance of GaN-based THz SBDs in the THz band is highly sensitive to heavy ion irradiation. Under prolonged irradiation conditions, irradiation-induced defects gradually accumulate, leading to continuous performance degradation and reduced durability of GaN-based THz SBDs.  
The work presented in this paper was a collaboration of all authors. Experiments and analysis, Y.R., Y.Y., C.P., Y.L. ([PERSON]), Z.F. and H.L.; software, Y.R., Y.L. ([PERSON]) and Y.Y.; resources, Y.R., S.Z., Y.L. ([PERSON]), Z.L., Y.N. and Z.F.; writing--original draft preparation,  
Figure 8: (**a**) The RL and YL spectra of (**a**) the pristine GaN epitaxial wafer and Kr SHI irradiated GaN epitaxial wafers with irradiation fluences of (**b**) \(5\times 10^{6}\) ions/cm\({}^{2}\), (**c**) \(1\times 10^{7}\) ions/cm\({}^{2}\) and (**d**) \(2.5\times 10^{7}\) ions/cm\({}^{2}\).  
Y.R. and H.L.; writing--review and editing, H.L., Y.L. ([PERSON]), X.S. and. H.Z.; supervision, S.Z., Y.N. and H.L.; funding acquisition, Y.N., S.Z. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Guangzhou Basic and Applied Basic Research Foundation (grant numbers 2023A04J0324 and 2023A04J0325).  
The data that support the finding of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Aggregatable Subvector Commitment with Efficient Updates
###### Abstract  
An aggregatable subvector commitment scheme extends a vector commitment scheme by enabling the aggregation of multiple proofs into a single compact subvector proof. However, the existing schemes have to recompute proofs for each position when inserting an element into the vector, incurring significant computational overhead. In this paper, we propose a novel aggregatable subvector commitment scheme based on Newton interpolation, which efficiently supports the addition of new elements. Specifically, the proposed scheme allows incremental updates to the commitment and proofs for each position, avoiding the requirement for full recomputation and thereby significantly reducing computational overhead. Additionally, we employ the Karatsuba algorithm to efficiently perform large-integer multiplication, which improves the aggregation and verification of proofs. Finally, we implement the proposed scheme and conduct a detailed comparison with aSVC. Experimental results demonstrate that the proposed scheme achieves a 48\(\times\) speedup when adding an element to a 16-length vector, as well as 2.13\(\times\) and 1.73\(\times\) speedups for aggregating eight proofs and performing verification, respectively.  
vector commitment; Newton interpolation; aggregatable
# Intra Prediction-Based Hologram Phase Component Coding Using Modified Phase Unwrapping
###### Abstract
For example, in the case of HEVC, if the Intra mode is used, QP and CU size can be selected. In this way, some results may differ due to the difference in the setting of the preferred parameter. Research on compressing a hologram can be classified into a method of compressing the hologram itself and a method of compressing the information of the surface diffracted (restored) at the position after propagating the hologram over a certain distance [13; 14; 15; 16]. Most of the methods discussed so far have been attempted to compress the hologram itself. Since the hologram has a shape similar to noise, it is very difficult to find a spatial correlation between pixels, so it is difficult to show a good energy concentration even if a frequency transform is performed. Therefore, the compression method in the reconstruction domain is to compress the hologram after transforming it into a form of data with higher spatial correlation. We focus on the study of compressing the hologram itself. Holograms can be expressed in various ways. If the hologram was photographed using a CCD by optical method, it would correspond to an intensity-based hologram, and in the case of a phase-shifted hologram, the difference between each hologram (Phase-shifted distances-based representation) is used for compression. The most common hologram is expressed as a complex object wavefield-based representation. Complex waves can be expressed as real and imaginary numbers, or as amplitude and phase. If information on the same hologram is represented by different expression methods, the result of compression may be different. In general, the real and imaginary representation shows better compression efficiency than the amplitude and phase pair. This is because the phase component has too strong randomness and the compression efficiency is very low. Therefore, most previous studies prefer to compress real and imaginary information. We propose a new method for compressing phase information. The proposed method assumes the use of HEVC as an anchor codec, and proposes a method for efficiently compressing the phase information of a complex hologram using a phase unwrapping technique [17] and a prediction mode of HEVC Intra. This paper is organized as follows. Section 2 discusses the characteristics of the phase information of a full-complex hologram. Section 3 describes the proposed phase component compression method. Section 4 shows the experimental results, and Section 5 concludes this paper.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 2 Theoretical Model
### Vibration Theory of a Simply Supported Curved-Plate Structure in a Thermal Environment  
[PERSON]'s [29] theory provides vibration equations based on the thin-shell assumption. However, because the theory considers the related shear and inertial forces as well as the loads in all directions, the equations are complicated and inconvenient to calculate. In this study, based on the simplified theory of [PERSON] [30], the dynamic equation neglects the shear forces, plane displacement, and plane inertia. In addition, the effects of sound pressure fluctuations and thermal stress are considered; therefore, the vibration control equation of the curved plate is as follows:  
\[D\
abla^{4}w+\
abla_{1}^{2}f+\rho h\frac{\partial^{2}w}{\partial t^{2}}=j \omega(\rho_{1}\Phi_{1}-\rho_{2}\Phi_{2})+N_{\theta}\frac{\partial^{2}w}{R^{2 }\partial\theta^{2}}+N_{L}\frac{\partial^{2}w}{\partial L^{2}}+2N_{\theta L} \frac{\partial^{4}w}{R^{2}\partial\theta^{2}\partial L^{2}}, \tag{12}\]  
where \(j\) is the imaginary part, \(w\) is the deflection, \(\rho_{1}\) and \(\rho_{2}\) are the air densities on the incident and radiating sides of the curved plate, respectively, \(\rho\) is the plate density, and and \(\Phi_{2}\) are the acoustic velocity potential functions on the incident side and the radiating side of the curved plate, respectively. \(f\) is the Airy stress function replacing the film stress and satisfying the coordination equation [30].  
\[Eh\
abla_{1}^{2}w-\
abla^{4}f=0. \tag{13}\]  
The plate's bending stiffness (\(D\)) is as follows:  
\[D=\frac{Eh^{3}(1+j\eta)}{12(1-p^{2})}, \tag{14}\]  
where \(\eta\) is the loss factor of the plate. \(\
abla\) is the Laplace operator, denoted as follows:  
\[\
abla^{2}=\frac{\partial^{2}}{\partial L^{2}}+\frac{1}{R^{2}}\frac{\partial^ {2}}{\partial\theta^{2}}. \tag{15}\]  
\(\
abla_{1}\) is the variational Laplace operator, denoted as follows:  
\[\
abla_{1}^{2}=\frac{1}{R}\frac{\partial^{2}}{\partial L^{2}}. \tag{16}\]  
Joining Equations (12)-(16) results in the vibration control equation of the curved plate in a thermal environment.  
\[\begin{array}{l}D\
abla^{8}w+Eh\
abla_{1}^{4}w+\rho h\
abla^{4}\frac{ \partial^{2}w}{\partial L^{2}}=\\ \
abla^{4}\Big{(}j\omega(\rho_{1}\Phi_{1}-\rho_{2}\Phi_{2})+N_{\theta}\frac{ \partial^{2}w}{R^{2}\partial\theta^{2}}+N_{L}\frac{\partial^{2}w}{\partial L ^{2}}+2N_{\theta L}\frac{\partial^{4}w}{R^{2}\partial\theta^{2}\partial L^{2} }\Big{)}.\end{array} \tag{17}\]
## 3 Reconstruction Method and Protection Algorithm
### Characteristics of Current Trajectory Images
#### 3.2.2 Internal Failure and CT Saturation  
When an internal fault occurs, as shown in Figure 10, due to the control strategy, the amplitude of \(I_{p}\) is much smaller than \(I_{g}\). The current trajectory is close to the Y-axis or in the first and third quadrants. With CT saturation, the waveforms become irregularly distorted.  
As evidenced by Figures 9 and 10, the two-dimensional reconstructed current trajectory images exhibit marked dissimilarities between the internal and external fault conditions, while pronounced waveform discrepancies are also observed between faulted phases and normal phases. So, deep learning can be used to identify the trajectory and then realize fault location.  
Figure 8: Current characteristic trajectory reconstruction.  
Figure 9: Current trajectory images (A-ground external faults). (**a**) PV side; (**b**) system side.
# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the PKU Dataset
In Figure 5, we report the mean license plate recognition accuracy on the PKU dataset. Our proposed LPR method comprehensively beats the compared methods in terms of mean recognition accuracy. As shown in Figure 5, our developed license plate recognition method yields 99.63% accuracy on the PKU dataset. Similarly, the work proposed in [34]  
Figure 4: The LP recognition on different images of the PKU dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **G1** & **G2** & **G3** & **G4** & **G5** \\ \hline
[32] & 96 & 97.80 & 92.60 & 80.00 & 72.00 \\ \hline
[33] & 92.00 & 90.50 & 90.10 & 89.60 & 86.40 \\ \hline
[34] & 98.00 & 98.50 & 90.00 & 86.01 & 81.10 \\ \hline
**Proposed** & **100** & **100** & **100** & **99** & **99.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Category-wise LP recognition accuracy comparison (%) on PKU dataset. ranks second, yielding 90.72% accuracy. On the aforementioned dataset, the work proposed in [32] yields the lowest license plate recognition accuracy of 79.28%. To the best of our knowledge, on the PKU vehicle dataset, the proposed method has almost solved the LPR accuracy problem.
# Use of Social Media Data in Disaster Management: A Survey
## 4 Data Sources for Social Media Data Analysis
### Sensors
#### 4.1.1 Physical Sensors  
Earth Observation (EO) and ancillary data generated from physical sensors can enhance the effectiveness of social media data analysis for disaster management. There are several types of sensors that generate EO and ancillary data. An in situ sensor is a basic sensor that is deployed in the place of interest to observe and measure the physical phenomena directly. Examples of in situ sensors include temperature sensors, rain gauges, and soil moisture sensors. In situ sensing is suitable for analytics that require high-accuracy observation. Furthermore, a wireless sensor network (WSN) [18] is an advanced in situ sensor system that consists of spatially distributed sensors called nodes. Each node is usually equipped with wireless connectivity, a microcontroller, a power source, and multi-type sensors. Based on this, data observed by each node can be exchanged among nodes within the system. With computing capabilities, a WSN can be applied in many applications, including industrial process monitoring and control, machine health monitoring, and natural hazard or fire detection. Even though the in situ sensing method can provide highly accurate data, the deployment of in situ sensors to cover a wide area is difficult and extremely expensive. Remote sensing technologies (e.g., radar, satellite, and airborne) have thus been used to remotely sense physical and environmental conditions and to generate observation data that cover a wide area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Repository** & **Number of Publications** \\ \hline IEEE Xplore & 12 \\ ACM Digital Library & 9 \\ ScienceDirect & 7 \\ SpringerLink & 4 \\ Others (via Google Scholar) & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numbers of publications selected from repositories and search engines.