esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Limit Load Analysis of Straight Pipes with Local Wall-Thinning Defects under Single Loads
### Empirical Limit-Load Equations of Straight Pipes with Local Wall-Thinning Defects
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Defect Size**} & \multicolumn{5}{c}{**Limit Load**} \\ \hline \(a\) & \(b\) & \(c\) & \begin{tabular}{c} **Limit Internal** \\ **Pressure/MPa** \\ \end{tabular} & \begin{tabular}{c} **Limit Bending** \\ **Moment/kNm** \\ \end{tabular} & \
\begin{tabular}{c} **Limit** \\ **Torque/kNm** \\ \end{tabular} &
\begin{tabular}{c} **Limit Axial** \\ **Force/kN** \\ \end{tabular} \\ \hline
0.2 & 0.2 & 0.2 & 42.59 & 18.92 & 15.66 & 586.12 \\
0.2 & 0.4 & 0.4 & 40.36 & 17.04 & 11.93 & 538.00 \\
1.0 & 0.8 & 0.6 & 36.16 & 15.06 & 10.30 & 320.43 \\
0.2 & 0.6 & 0.6 & 37.31 & 15.48 & 8.61 & 421.57 \\
5.0 & 0.6 & 0.4 & 22.70 & 17.08 & 14.59 & 456.37 \\
0.2 & 0.8 & 0.8 & 34.58 & 12.85 & 6.31 & 243.84 \\
5.0 & 0.2 & 0.8 & 9.47 & 14.57 & 10.28 & 501.88 \\
3.0 & 0.8 & 0.4 & 27.09 & 16.38 & 12.93 & 416.90 \\
3.0 & 0.6 & 0.2 & 35.60 & 18.21 & 15.78 & 531.68 \\
5.0 & 0.8 & 0.2 & 35.09 & 17.61 & 15.55 & 513.44 \\
3.0 & 0.4 & 0.8 & 14.26 & 14.59 & 10.14 & 402.53 \\
1.0 & 0.4 & 0.2 & 39.71 & 18.01 & 16.19 & 557.07 \\
1.0 & 0.6 & 0.8 & 24.51 & 14.32 & 8.83 & 292.87 \\
1.0 & 0.2 & 0.4 & 34.91 & 16.88 & 12.85 & 561.34 \\
5.0 & 0.4 & 0.6 & 16.02 & 14.75 & 11.50 & 318.98 \\
3.0 & 0.2 & 0.6 & 21.91 & 15.70 & 12.37 & 519.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The calculation results of the limit load of straight pipes with local wall-thinning defects under single loads. \[n_{\rm LS} =-0.416G_{3}+1.045 \tag{8}\]  
where \(G_{3}\) denotes the geometric parameters of the local wall-thinning defect, \(G_{3}=a^{-0.1}b^{0.7}c\). The least square error \(R^{2}\) of the equation is 0.968. The limit load of SPLWT under axial force can be calculated by Equation (9):  
\[f_{\rm LS} =-1.242G_{4}^{2}-0.213G_{4}+0.985 \tag{9}\]  
where \(G_{4}\) denotes the geometric parameters of the local wall-thinning defect, \(G_{4}=a^{0.05}b^{1.5}c^{0.05}\). The least square error \(R^{2}\) of the equation is 0.984.
## 2 Numerical Approach and Boundary Treatment  
The development of LBM has been ongoing for several decades, and numerical studies are covering a wide range of fields. Without any doubts, it has already become a trustworthy approach used in computational fluid mechanics. The discrete lattice Boltzmann-Bhatnagar-Gross-Krook (LBGK) equation is given by  
\[f_{i}(\mathbf{s}+\mathbf{e}_{i}\Delta t,t+\Delta t)-f_{i}(\mathbf{s},t)= \Omega_{f}^{i}=-\frac{1}{\tau}[f_{i}^{eq}(\mathbf{s},\xi)-f_{i}(\mathbf{s}, \xi,t)] \tag{1}\]  
where \(i\) represents the direction of the discrete velocities, \(f_{i}(\mathbf{s}+\mathbf{e}_{i}\Delta t,t+\Delta t)\) and \(f_{i}(\mathbf{s},t)\) characterize the discrete post- and precollision distribution functions vectors, respectively. \(\Omega_{f}^{i}\) is the discrete collision operator on \(i\)th direction and \(\tau\) is the relaxation time defined as \(\tau=\frac{3\
u}{\Delta t}+0.5\), where \(\
u=\frac{\
u\Pi}{\mathcal{R}c}\) is the kinematic viscosity and \(\Delta t\) denotes the time step.  
Since the Navier-Stokes equations can be recovered from the discrete lattice Boltzmann equation [41; 42], flow density \(\rho\) and velocity \(\mathbf{u}\) can be calculated through distribution functions and defined as  
\[\begin{split}\rho=\sum_{i}f_{i}\\ \rho\mathbf{u}=\sum_{i}\mathbf{e}_{i}f_{i}\end{split} \tag{2}\]  
Speaking of LBM, the LBGK D2Q9 model [41] is popular and it is also employed in the present paper. Figure 1 shows the discrete velocities of the LBGK D2Q9 model.  
The equilibrium distribution functions, for the 9 discrete velocities, are determined by  
\[f_{i}^{eq}=\omega_{i}\rho\Bigg{[}1+\frac{\mathbf{e}_{i}\cdot\mathbf{u}}{C_{s} ^{2}}+\frac{(\mathbf{e}_{i}\cdot\mathbf{u})^{2}}{2C_{s}^{4}}-\frac{\mathbf{u}^ {2}}{2C_{s}^{2}}\Bigg{]};\qquad i=0,1,\ldots,8 \tag{3}\]  
where \(\omega_{i}\) represents the weight coefficients, \(i\) denotes the direction of discrete velocities, and \(C_{s}=\sqrt{RT}=\frac{\mathrm{C}}{\sqrt{3}}\) is defined as the lattice sound speed.  
In order to deal with the straight wall boundary conditions involved in the present numerical simulations, a popular scheme, the nonequilibrium extrapolation scheme [43] is employed, in which the distribution function of each direction \(f_{i}\) is classified as the nonequilibrium term \(f_{i}^{neq}\) and the equilibrium term \(f_{i}^{eq}\); (\(f_{i}=f_{i}^{eq}+f_{i}^{neq}\)). Both of these terms are calculated through the formulations presented in [43]. Flow velocity and density are calculated and defined as  
\[\begin{split} u_{x}=U_{lid};\quad u_{y}=0;\quad\frac{\partial \rho}{\partial y}=0\Bigg{|}_{Lid};\\ \mathbf{u}=0;\quad\frac{\partial\rho}{\partial\mathbf{n}}=0 \Bigg{|}_{Straight\ walls};\\ \mathbf{u}=0;\quad\rho=0.5(\rho_{H}+\rho_{V})\Bigg{|}_{Tilled\ walls}; \end{split} \tag{4}\]  
where \(\mathbf{n}\) is the normal vector perpendicular to the walls. \(\rho_{H}\) and \(\rho_{V}\) are the first layer fluid points adjacent to the wall boundary nodes, respectively on horizontal and vertical directions.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 1 Introduction
### Data Availability  
The experiments in this study were conducted using the ISIC 2020 Challenge dataset, provided by the International Skin Imaging Collaboration (ISIC). The dataset includes de-identified dermoscopic images, which were used in accordance with the guidelines provided by the ISIC.  
The dataset is publicly available and can be accessed by researchers for further experimentation through the following repositories:  
* ISIC Archive;
* ISIC 2020 Challenge.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion
### Consistency Test  
To evaluate the visual based transmission line positioning system, we performed consistency tests following the guidelines presented in [59]. Figure 14 shows the consistency of the estimation of the \(x\) and \(z\) coordinates of the drone flying over the transmission lines, in Figure 14a,b, respectively. As can be seen, the error in both coordinates remains bounded by two times its standard deviation. As ground truth (and only with the aim of performing the consistency tests) we used the GNSS positioning system described in Section 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Transmission Line 68 kV** \\ \hline Height & 12 m \\
3\(\phi\)-Separation & 2.4 m between left and right wire \\
3\(\phi\)-Separation & 1.6 m between left and center wire \\  & 0.8 m between right and center wire \\ Distribution & Horizontal \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural characteristics of the transmission grid.  
Figure 14: Consistent tests: (**a**) the consistency of the error in the \(x\) coordinate; and (**b**) the consistency of the error in the \(z\) coordinate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frames** & **True** & **False** & **Efficient-ency** & **RMSE X** & **RMSE Z** \\
**Positi-ves** & **Positi-ves** & **Positi-ves** & **Estitator m** & **Estitator m** \\ \hline
549 & 502 & 47 & 91.44\% & 0.2577 & 0.1026 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Field results: Statistical analysis of different developed experiments.
## 7 Optimization Techniques for Demand-Side Resources in Active Distribution Networks
### Optimization Strategies and Models Used in Demand-Side Resources  
Optimization techniques for demand-side resources in active distribution networks are essential for achieving a more resilient, efficient, and sustainable energy system [126]. Accurate modeling, load forecasting, demand response programs, and the integration of distributed energy resources are key components of effective DSR optimization. Multi-objective optimization and real-time control systems further enhance the capabilities of active distribution networks.  
The foundation of effective optimization techniques for DSRs lies in accurate modeling. To optimize DSRs in active distribution networks, accurate load forecasting is critical. There are different load-forecasting techniques, including statistical methods, machine learning algorithms, and hybrid approaches. The integration of weather data and historical consumption patterns enhances the accuracy of load forecasting, enabling better DSR optimization. Demand response programs play a pivotal role in optimizing DSRs. The demand response strategies include time-based pricing, direct load control, and incentive-based programs. The optimization techniques for DSRs should consider the integration of distributed energy resources (DERs) such as rooftop solar panels, energy storage systems, and electric vehicles. Exploring modeling approaches and optimization strategies enables the effective management of the interaction between DSRs and DERs [127]. To maximize the economic and environmental benefits of these resources, the coordination and control algorithms must be considered. Multi-objective optimization techniques are essential to balance conflicting objectives, such as minimizing costs and reducing carbon emissions.  
Different multi-objective optimization algorithms exist, which include genetic algorithms, particle swarm optimization, and Pareto-based methods [126]. Additionally, real-time control and decision support systems are crucial for the dynamic management of DSRs. Advanced control strategies are developed to enable real-time decision-making and control of DSRs. These include various controls, such as model predictive control (MPC), and the integration of advanced sensors and communication networks [128].
## 3 Results
### Determination of a Time Interval for Measurement Evaluation  
To determine conditions of the time interval for the evaluation of contact forces (CFs), and for the minimum acceptable difference in contact forces (CFs), the data from the m200 measurement were used, which were obtained when the moment of tensioning termination was identified (the maximum value for tensioning forces in the transport belt of 28,000 N was achieved). The subtracted instant values after 5, 10, 15, 20, 30, 60, and 90 s are listed in Table 4. After 90 s, the condition was considered stable. In Table 5, to record the dynamics of relaxation, differences in contact forces (CFs) were calculated after 10, 15,..., and 90 s and 5 s from restoring termination (ID/\(\text{CF}_{\text{108}}-\text{ID}/\text{CF}_{\text{58}}=0.2\) N, ID/\(\text{CF}_{\text{158}}-\text{ID}/\text{CF}_{\text{58}}=0.8\) N,..., and ID12\(\text{CF}_{\text{908}}-\text{ID}12\text{CF}_{\text{58}}=-9.3\) N). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time from Tensioning** & \multicolumn{5}{c}{**CF (N)**} \\
**Termination (s)** & **ID7** & **ID8** & **ID9** & **ID10** & **ID11** & **ID12** \\ \hline
5 & 73.6 & 46.1 & 155.4 & 242.8 & 243.3 & 215.1 \\
10 & 73.4 & 47.1 & 154.5 & 243.1 & 236.6 & 216.2 \\
15 & 72.8 & 46.5 & 153.0 & 241.4 & 235.5 & 217.0 \\
20 & 71.7 & 46.3 & 154.1 & 241.0 & 233.6 & 217.8 \\
30 & 70.5 & 45.6 & 152.5 & 241.1 & 230.3 & 220.4 \\
60 & 72.1 & 46.2 & 151.9 & 238.1 & 223.5 & 222.1 \\
90 & 71.8 & 46.2 & 151.8 & 237.4 & 219.2 & 224.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Contact forces (CFs) measured after 5, 10,..., and 90 s from tensioning termination. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time from Tensioning** & \multicolumn{5}{c}{**CF Difference (N)**} \\
**Termination (s)** & **ID7** & **ID8** & **ID9** & **ID10** & **ID11** & **ID12** \\ \hline
10 & 0.2 & \(-\)1.0 & 0.9 & \(-\)0.4 & 6.7 & \(-\)1.1 \\
15 & 0.8 & \(-\)0.3 & 2.4 & 1.3 & 7.8 & \(-\)2.0 \\
20 & 2.0 & \(-\)0.2 & 1.3 & 1.8 & 9.8 & \(-\)2.8 \\
30 & 3.1 & 0.5 & 2.9 & 1.6 & 13.0 & \(-\)5.3 \\
60 & 1.5 & \(-\)0.1 & 3.5 & 4.7 & 19.8 & \(-\)7.0 \\
90 & 1.9 & \(-\)0.1 & 3.8 & 5.4 & 24.2 & \(-\)9.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Calculated differences in contact forces (CFs) after 10, 15,..., and 90 s from contact forces (CFs) 5 s after tensioning termination. The largest difference in contact forces (CFs) for a steady state was at position ID11 CF90s \(-\) ID11 CF5s = 24.2 N, highlighted in yellow, i.e., 85 s from tensioning termination. The same calculation was performed (Table 6) to determine differences in each contact force (CF) at the position after 20, 30,..., and 90 s and 15 s from the tensioning termination (ID/CF20s \(-\) ID2 CF5s = 12 N, ID/CF30s \(-\) ID7 CF15s = 24 N,..., and ID12 CF90s \(-\) ID12 CF15s = \(-\)74 N). The largest difference in contact forces (CFs) for a steady state was at position ID11 CF90s \(-\) ID11 CF15s = 16.3 N, highlighted in yellow, i.e., 75 s from tensioning termination.
## 2 Proposed Framework for Probabilistic Stability and Performance Assessment  
The robustness assessment can be considered in two different levels: the converter level and the system level. This paper's primary goal was to present and discuss a systematic way to perform a probabilistic assessment at the converter level. A single grid-connected VSC was considered, and the impact of the power grid and operating point conditions and control-loop bandwidths were studied in relation to the system stability and performance. The proposed flow diagram for a probabilistic robustness assessment at the converter level is shown in Figure 1. A single VSC with known control parameters was connected to the power grid at PCC. It is worth remarking that the power grid was represented by a Thevenin equivalent model, and the grid SCR or grid equivalent inductance could vary over a wide range and it was unknown. Moreover, the inverter power level was dictated by the power profiles of the renewable sources or power flow calculations at the higher level. This might also have affected the system's stability and performance. In the proposed algorithm, at first, proper PDFs for uncertain parameters, such as grid SCR, were defined in order to consider its variations. A Monte Carlo sampling method was then employed to draw parameters from the PDFs. The sampled and fixed parameters were entered into the operating point calculation. After that, the stability model was built based on the available data and the linearized time-invariant state-space model. The eigenvalue analysis calculated the system's critical mode and its damping factor and ratio. This procedure was repeated \(N_{\mathcal{C}}\) times for different sampled parameters that were provided by MC.  
Finally, the statistical characteristics of the outputs, which were of utmost importance, were computed and plotted.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CH\({}_{4}\) Combustion
#### 3.2.4 Effect of Residence Time \(t\) on NO Production  
The research content of this section is about the influence of residence time in the high-temperature furnace on NO emission when the mixed fuel gas with 50% ammonia gas is burned at different temperatures and different excess air coefficients. The residence time of mixed gas in high-temperature furnace mainly affects the mixing degree of four gases and the combustion degree of fuel gas in the furnace. The experimental results are shown in Figure 6. The combustion temperature was set to 1500 \({}^{\circ}\)C; the excess air coefficients were 0.9, 1.0, and 1.1; and the residence time varied from 565 ms to 2258 ms.  
As shown in Figure 6, the amount of NO produced by combustion gradually decreased with the increase in residence time, which was in line with our general understanding: the longer the residence time, the more fully the gas is mixed in the high-temperature furnace, the deeper the combustion degree of the fuel gas in the furnace, the fewer the O/H free radicals generated during the combustion process, thus reducing the oxidation of amino NH\({}_{2}\) and reducing the emission of NO. It is worth noting that with the increase in residence time, the decrease in NO content produced by combustion also gradually decreased. Due to the key free radicals produced in the ammonia reaction not being sensitive to the residence time, the O/H free radicals in the combustion furnace reached a relative equilibrium state with the extension of the residence time after full reaction, and the content of NO tended to be stable.  
It can be seen from Section 3.2.1 that the combustion of doped gas produced more NO than the combustion of pure ammonia, which was due to the large increase in HNO, NH\({}_{i}\), N\({}_{2}\)O, and other free radicals generated by the reaction under the mixed combustion condition, and the increased selectivity of NH\({}_{2}\) free radical conversion to NO. This makes the amount of NO produced by NH\({}_{3}\)/CH\({}_{4}\) mixed combustion gas in any case more than that of pure ammonia combustion.  
Figure 6: NO produced by NH\({}_{3}\)/CH\({}_{4}\) combustion at different residence times at 1500 \({}^{\circ}\)C.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 6 Discussion  
This study demonstrates a significant advancement in addressing key challenges in the SHM of bridges, such as accuracy, energy efficiency, and deployment feasibility. The proposed WSN, integrating solar-powered MEMS accelerometers synchronized via GNSS, offers a scalable and sustainable alternative to conventional wired systems.  
The system's deployment efficiency surpasses that of traditional wired setups, which often require extensive infrastructure modifications and prolonged installation times. Unlike the node developed by [PERSON] et al. [15], which lacks synchronization, the proposed solution achieves a synchronization error as low as 16.8 \(\upmu\)s, a critical improvement for modal analysis. Additionally, while [PERSON] et al. [16]'s system faced limitations due to the low data rate of LoRa communication, our system's MQTT-based data transmission ensures higher data throughput and real-time performance.  
Regarding energy autonomy, the integration of photovoltaic (PV) panels aligns with the findings of [13]; however, our approach ensures longer node lifetimes and supports multiple daily acquisitions, even in varying seasonal conditions. The energy-efficient design was validated through field tests, demonstrating continuous operation over 50 days with minimal power depletion, a stark contrast to [PERSON] et al. [17]'s system, which relies solely on battery power for limited durations.  
The system's enhanced accuracy, demonstrated by a noise density of 20 \(\upmu\)g/\(\sqrt{\mathrm{Hz}}\), enables precise modal analysis, meeting the standards outlined by [PERSON] et al. [20] for effective OMA. The deployment on a real bridge confirmed the system's practical applicability, revealing natural frequencies and mode shapes crucial for assessing structural health. This reinforces its potential for widespread adoption in civil infrastructure monitoring. Future studies could explore optimizing the system for extreme environmental conditions, enhancing data security in wireless transmission. Broader applications, including other civil structures, could further establish the system's versatility in SHM.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
###### Abstract  
This paper proposes a cooperative medium access control (MAC) protocol for underwater wireless sensor networks (UWSNs) named UCMAC, which fundamentally benefits from cooperative communication. In UCMAC, a source identifies cooperators and provides its destination with a list of the cooperators while also delineating their proximity to the destination. For erroneous reception of data packets, the destination then requests retransmission to the cooperators in a closest-one-first manner. A designated cooperator transmits the buffered data packet it has successfully overheard from the source or other cooperators. A signaling procedure and the various waiting times of the nodes are carefully designed to address the overheads that stem from cooperation. Through computer simulation, this paper evaluates UCMAC in terms of system throughput, latency, single-hop packet delivery ratio (PDR), and energy efficiency. The results show that UCMAC performs better than existing schemes, including MACA-U and CD-MACA.  
underwater wireless sensor network; automatic repeat request; medium access control; cooperative communication; spatial diversity; cooperative region; cooperative ARQ; cooperative MAC
# An Approach of Automatic SPARQL Generation for BIM Data Extraction
## 2 Related Work
### Semantic BIM Data Extraction
The query results can be either rule-compliant or rule-incompliant BIM data, depending on the SPARQL programming. That is, rule compliance checking and data extraction of BIM have the same processing flow, and the differences are the origin and contents of the query filter conditions. For example, an ontology-based framework was proposed to carry out environmental monitoring and compliance checking cross BIM and different information systems, in which the regulation clauses were transformed into SPARQL codes [42]. Additionally, a semantic BIM Reasoner (SBIM-Reasoner) was developed for IFC model semantic validation, in which semantic technologies are used to build the semantic repository from the input IFC model, and in the same way, validation regulations were designed as SPARQL queries [43]. In general, although SPARQL has widely been used for data extraction and related research in AEC industry, SPARQL queries are typically generated/programmed manually [19; 37; 38; 39; 40; 41; 42; 43]. To bridge this gap, we develop herein an approach of automated SPARQL query generation for BIM data extraction. This approach can be utilized in various applications, such as data extraction, rule compliance checking, data retrieval in semantic enrichment of BIM models, and so on. In this paper, we only focus on SPARQL generation according to query keywords provided by users/engineers, so natural language processing (NLP) technologies (e.g., semantic parsing, syntactic structure, and dependency parsing) and the engineering applications of data extraction are beyond the scope of this article.
## 4 Experimental Results
### Calibration  
Owing to chip differences, assembly errors, and PCB processing tolerances, the amplitude and phase of each channel in the assembled array were not consistent, so the two polarization channels of the array required separate calibration before the overall array could be subjected to a performance test.  
During calibration, the array is facing the \(Ku\)-horn antenna. The port of the array and the horn are connected to two ports of the VNA, respectively. Firstly, the channels of the array are turned on sequentially, and the channels' phase and gain can be measured by \(S_{12}\) of the VNA. Secondly, the phase of each channel is set to 0\({}^{\circ}\) by adjusting the digital phase shifter, and the gain is set to a preset value by adjusting the digital attenuator. Finally, the \(S_{12}\) of each channel can be tested again separately to confirm that the amplitude and phase residual errors of the calibrated channels are small. The antenna was calibrated at each test frequency point to ensure that the entire array had good performance throughout the operating band. Both polarization channels are calibrated in the same way. Since the entire array is calibrated together, amplitude and phase differences between those 16 subarrays are also compensated.  
The residual amplitude and phase errors following calibration at 11.7 GHz are shown in Figure 15. The root mean square (RMS) amplitude errors of all of the array's channels were 0.52-0.57 dB for the V\(-\) and H\(-\) polarizations. The RMS phase errors were 2.13-2.19\({}^{\circ}\) after calibration, indicating that the array possessed ideal scanning patterns. These residual errors were primarily attributed to differences in the beamformer chips--specifically the accuracies of their phase shifters and attenuators.  
Figure 14: **(a)** Top view showing the antenna elements on a subarray. **(b)** Back view showing surface-mounted chips on a subarray. **(c)** Photograph of the fabricated RX phased array. **(d)** Experimental setup for measurement in an anechoic chamber.