esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 3 Verification and Comparison
### Compare with Different Advance Ratios  
The thrust coefficient and ten times the torque coefficient with different turbulent models under the deliberately selected \(J\) (avoid excessive numerical simulations) are compared with open-water data from [PERSON] (1971), which are shown in Figure 8a,b. Both the LES-LDKSGS model and the RANS-kEpsilon model get results that are in line with the physical experiment. However, the LES model requires a higher calculation cost than the RANS model, so the LES model has no advantage in calculating the average thrust and torque of the propeller as already stated. Coefficients of thrust, torque and efficiency at uniform inlet velocity \(U_{x}=4.4945\) m/s and at constant rotating speed \(n=25\) r/s for different advance ratios \(J\in[0.1,1]\) are shown in Figure 8c. The difference of them is due to the different Reynold numbers. As the \(Re\) increases, the gap between them becomes smaller. There is a critical Reynolds number, when the actual Reynolds number exceeds this value, the influence of different Reynolds numbers can be ignored.  
Figure 7: Comparison of normalized axial velocity profiles between LES-LDKSGS (solid lines) and PIV (square dots) at radial and streamwise locations: (**a**) At \(r/R=0.7\), \(0.8\), \(0.9\). (**b**) At \(x/D=0.06\), \(0.08\), \(0.1\).
###### Abstract
Additionally, as the PPG signal is typically captured from the fingertip, it provides a more stable measurement point compared to multi-channel PPG signal acquisition, making it particularly suitable for extended blood pressure monitoring sessions. To better grasp the long-term correlation between PPG signals and blood pressure values, and to enhance the precision and robustness of continuous blood pressure estimation on large-scale public datasets, this paper introduces a method that integrates a CBAM into a TCN for continuous blood pressure estimation. Our approach employs one-dimensional convolutional modules alongside the CBAM for comprehensive signal feature extraction and fusion. Additionally, we use dilated and causal convolutions to decipher the temporal dependencies between blood pressure and PPG waveforms. Experimental results on public datasets indicate that our method notably improves blood pressure estimation accuracy when compared to traditional machine learning models and deep learning models rooted in the CNN-RNN framework. Moreover, it excels at capturing abrupt changes in blood pressure. The primary contributions of this study are outlined below:  
1. This paper introduces dilated convolution and causal convolution from TCN to effectively learn the temporal dependencies between blood pressure and PPG waveforms. 2. Integration of CBAM module: To further enhance the performance of feature extraction, this method incorporates the CBAM module into the one-dimensional convolutional module. 3. Experimental validation on large-scale public datasets: This study employs large-scale public datasets for experimental evaluation, aiming to ensure the robustness of continuous blood pressure estimation across diverse samples and scenarios.
# A Review of Modular Multilevel Converters for Stationary Applications
## 4 MMC Modulation Techniques
It is advantageous to select the switching vectors and arrange the switching sequences with more freedom in SVM-PWM. SVM-PWM has demonstrated good performance with superior harmonic features and DC-link utilization in the two-level converter [126]. However, when it comes to multilevel modulation, the SVM-PWM algorithm is challenging to implement with the significantly increased computational burden and complexity [1]. For the case of \(N\) levels, the figures of switching vectors and triangles soar to \(N^{3}\) and \(6(N-1)^{2}\), respectively [3]. To simplify multilevel SVM-PWM applied in the MMC, an improved scheme is proposed by decomposing the multilevel space vector into several two-level space vectors [127]. This method can easily be extended to multilevel converters with a reduced computational cost. Another low switching frequency modulation approach referred to sampled average modulation (SAM) has a similar process to SVM-PWM but with less computation complexity and simple implementation to multilevel converters with any number of submodules [128]. In SAM-PWM, the command voltage is produced by averaging the two nearest voltage levels and avoiding zero vectors in each sampling interval. In terms of fundamental switching frequency modulation, a quite popular method is nearest level modulation (NLM) [129, 130]. In this approach, the switching states and dwell time are directly derived from the command voltage eliminating the use of carrier signals. It is easy and practical to implement, especially in the case of MMC involving a large number of submodules. Selective harmonic elimination (SHE) also belongs to the category of FSF-PWM. SHE-PWM [131] can provide a high performance of output voltages. However, this method is difficult to implement due to the heavy calculation of substantial switching angles with multilevel voltages. An overview and comparison of different modulation techniques regarding modulation categories, switching frequency, application with the employed number of submodules, output harmonic performance, and implementation effort are presented in Table 2.  
Figure 16: High switching frequency pulse width modulation (PWM) techniques: (**a**) phase-shifted carrier-based pulse width modulation (PSC-PWM) with triangular carriers; (**b**) PSC-PWM with sawtooth carriers; (**c**) phase-disposition pulse width modulation (PD-PWM); (**d**) phase-opposition-disposition pulse width modulation (POD-PWM); (**e**) POD-PWM.
## 7 Conclusions  
We propose a data acquisition model using stable matching of bipartite graph in cooperative vehicle-infrastructure systems, namely DAS, aiming for combining the two criteria, i.e., the access ratio and the access delay, in vehicular applications. In DAS, a bipartite graph with the contents as the left vertices and the storage cells of roadside units as the right vertices is constructed. Furthermore, the preference of content and the priority of a storage cell are modeled based on the response factor and the encountering latency factor, respectively, and the rankings on two sides are obtained. In this way, the data distribution problem is formulated as a stable matching problem, which is solved in polynomial time by the Gale-Shapley algorithm together with the stable improvement cycles algorithm. In order to deal with the contents having more than one replica, a multi-replica content preprocessing algorithm is designed to pave the way for one-to-one mapping in stable matching. In addition, vehicles execute the supplementary storage so as to satisfy those failed requests due to bandwidth competition at roadside units via V2V communications. Moreover, an incentive strategy is designed to motivate vehicles to carry and forward assistant contents. Finally, the experiments show that DAS has a high access ratio and a small storage consumption with an acceptable access latency.  
However, there is still much profound research to be done to enhance the performance of our proposal. In simple simulated scenarios, the lower bounds of the metrics in theory could be helpful. Additionally, since the wireless channel at a roadside unit is a bottleneck, a solution to the traffic scheduling problem on the radio segment will further enhance the overall performance [38]. In addition, considering the cooperative downloading improves the information acquisition through inter-vehicle data transfers, the integration of our model with a cooperative downloading algorithm [39] has bright prospects.  
Figure 14: Results in real vehicular scenario. **(a)** access ratio; (**b**) average access delay; (**c**) number of replicas; (**d**) transmission overhead.  
**Acknowledgments:** We gratefully acknowledge the support from China's Natural Science Foundations (61502320, 61373161 and 61173009), the Science and Technology Project of Beijing Municipal Commission of Education in China (KM201410028015), the Youth Backbone Project of the Beijing Outstanding Talent Training Project (2014000020124G133), the Beijing Advanced Innovation Center for Imaging Technology, and the Cultivation Object of [PERSON] Scholar of Capital Normal University. The authors would like to thank [PERSON] for help with preparing taxi trajectory data, and [PERSON] for proofreading our manuscript.  
**Author Contributions:** [PERSON] conceived and designed the study; [PERSON] performed the experiments and wrote the article; [PERSON] analyzed the data and revised the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Field Study on Earth Pressure of Finite Soil Considering Soil Displacement
## 1 Introduction  
Due to the limited space for urban land use, the comprehensive development of urban underground space has become a hotspot in recent years [1; 2; 3; 4; 5]. In this context, complex foundation pit groups came into being. The \"group pit coupling effect\" caused by the superposition of the soil stress field and displacement field caused by the cross construction of adjacent foundation pits is more complicated than that of single foundation pits [6; 7]. When the pit spacing is small, the lateral earth pressure has a greater influence on the envelope structure so it is necessary to study it. At present, the more widely used earth pressure theories are Rankine earth pressure and Coulomb earth pressure, but they are aimed at semi-infinite space and are the basis of earth pressure theory. Nowadays, scholars' research theories consider either the nonlinear distribution of earth pressure [8; 9; 10; 11; 12] or the soil arch effect [13; 14; 15].  
For earth pressure of finite soil in foundation pit engineering the exertion mechanism and variation law of earth pressure are not clear due to very few experiments, and the relevant research and discussion are rare and mainly focused on the theoretical aspects [16; 17; 18; 19]. For example, [PERSON] used the upper limit theory to study the earth pressure in a limited range and analyzed the difference between it and the Rankine soil pressure theory [20]. [PERSON] et al. derived the analytical solution of active earth pressure through the finite soil calculation model [21]. [PERSON] et al. proposed a solution that could more accurately predict earth pressure [22]. However, these studies ignore the effect of displacement on earth pressure and the calculations are mostly limit states. In addition, there are no field measured data for comparison and verification.  
In this paper, high-precision optical fibers were adopted to measure the deformation of the on-site envelope structure, and earth pressure cells were equipped to measure the earth pressure during the excavation. Moreover, a simplified calculation formula of earth pressure in finite soil is proposed, which could provide reference for the research of similar projects in the future.
## 5 Wood-Based Panel Emissions
### Wood Panels Bonded with an Adhesive
#### 5.1.2 VOC Emissions from Wood-Based Panels with Bio-Based Adhesive  
[PERSON] et al. [143] produced biobased panels from birch. They extracted suberinic acid from the wood, mixed it with birch particles, and thermocompressed it at 226 \({}^{\circ}\)C for 9.5 min. They then studied the molecules emitted by these panels by SPME-GC-ToF-MS and detected 49 compounds, including furfural, acetic acid, and 5-methylfurfural as the majority compounds with relative peak areas of 21, 14, and 13%, respectively.  
[PERSON] et al. [48] elaborated plywood panels bonded with phenolic resins (PF) based on bio-oil, obtained by the pyrolysis of birch co-products. Emissions of formaldehyde and other VOCs by these panels were then measured according to the percentage replacement of phenol by bio-oil. We observed an increase in formaldehyde emissions with the percentage of bio-oil. The panels also emitted various compounds such as furfural, acetic acid, and hexanal. In addition, some toxic VOCs were detected, such as \(N\),\(N\)-dimethylformamide, furfural, and phenol, which are carcinogenic mutagenic reprotoxic (CMR) compounds, or benzofuran, suspected of being carcinogenic.  
Panels made with biobased binders therefore emit compounds linked to the thermal degradation of raw material components, such as furfural or acetic acid. In the case of panels bonded with bio-based resins with the phenol-formaldehyde adhesive system, compounds typical of these resins have been identified, notably formaldehyde.  
Bio-based panels therefore offer an alternative to petrochemical resins, but the result does not seem entirely satisfactory, since they may emit higher levels of compounds such as formaldehyde [48] than industrial panels marketed today.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Communication Model
#### 3.1.2 HAP Drone-LEO Satellite Communication Model  
Because both HAP drones and LEO satellites are located at altitudes of at least 20 km above the ground, we consider the wireless channel between HAP drones and LEO satellites to be line-of-sight (LoS) channels, which comply with the free-space path loss model. According to 3 GPP TR 38.811 [33], we can model this channel as follows:  
\[g_{n}^{m}=32.45+20\log_{10}(d_{n}^{m})+20\log_{10}(f_{c}^{H}), \tag{3}\]  
where \(d_{n}^{m}\) is the distance between the HAP drone \(n\) and LEO satellite \(m\). \(f_{c}^{H}\) represents the carrier frequency of the wireless signal transmitted from the HAP drone to the LEO satellite, and we set it as \(f_{c}^{H}=31\) GHz.  
The corresponding data rate between the HAP drone \(n\) and LEO satellite \(m\) can be formulated as follows [34]:  
\[R_{n}^{m}\leq C_{n}^{m}=b_{n}^{m}\log_{2}(1+\frac{p_{n}^{m}G_{ts}G_{tr}g_{n}^{ m}L_{ss}}{N_{0}b_{n}^{m}}), \tag{4}\]  
where \(G_{ts}\) is the transmit antenna gain of the HAP drone, and \(G_{ts}\) is the receive antenna gain of the LEO satellite. \(L_{ss}\) represents the path loss caused by environmental and atmospheric effects. \(b_{n}^{m}\) is the wireless channel bandwidth between the HAP drone \(n\) and LEO satellite \(m\).
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 1 Introduction
Using the improved IEEE 33-node system, the paper examines the optimization role of an SOP when introduced in various operational scenarios, impacting the reverse power flow capacity and overall operational efficiency of the distribution network.
## 3 Experimental Testing of Search Performance on IABCBAS Algorithms  
### Test Functions and Environment  
Four swarm intelligence algorithms from different periods are selected to conduct comparative experiments on nine test benchmark functions to verify the performance and feasibility of the proposed IABCBAS algorithms. All experiments are conducted on the same experimental platform. The comparison algorithms [16] and related parameters are shown in Table 1.  
Benchmark functions with different dimensions and numbers of peaks are employed to verify the algorithms' convergence speed and their ability to escape local optima. The selected benchmark functions [30] are listed in Table 2, and part of the test function space is illustrated in Figure 3. The Sphere test function is a continuous unimodal convex function in a high-dimensional space with multiple local minima, enabling it to test the search algorithm's ability to escape local minima. The Foxholes test function is a low-dimensional multimodal function with numerous canyons and obstacles, making the testing of algorithms more challenging.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameter Setting** & **Parameter Meaning** \\ \hline PSO & \(\begin{array}{c}c_{1}=c_{2}=2\\ \omega\in[0.5,0.8]\end{array}\) & \(\begin{array}{c}c_{1},\ c_{2}\text{ are learning factor, }\omega\text{ is inertia weight, adjustment of}\\ \text{ particle speed}\end{array}\) \\ \hline ACO & \(\alpha=1\ \ \beta=3\ \ p=0.6\) & \(\begin{array}{c}\alpha\text{ is pheromone heuristic factor, }\beta\text{ is heuristic function factor, }p\text{ is}\\ \text{ pheromone evaporation coefficient}\end{array}\) \\ \hline SSA & \(P_{dp}=0.2\ \ G_{c}=1.8\ \ sf=18\) & \(\begin{array}{c}P_{dp}\text{ is sparrows exploring new areas, }G_{c}\text{ is sparrows that warn}\\ \text{ of danger, }sf\text{ is alert threshold}\end{array}\) \\ \hline WOA & \(\alpha_{s}=0.15\ \ \varepsilon=0.01\ \ b=2\) & \(\begin{array}{c}\alpha_{s}\text{ is linear control parameter, }\varepsilon\text{ is hunting probability factor, }b\text{ is}\\ \text{ bubble spiral sparsity}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison algorithms and related parameters.
## 2 Application of NPs in the Diagnosis of CKD  
An early prediction of the onset and progression of renal disease is crucial. In this context, specific biomarkers have been considered as valuable tools for diagnosis. Successful diagnosis could be followed by an effective therapeutical intervention to prevent CKD and diminish other possible complications such as hypertension, infection, anemia, and heart failure. The application of the conventional diagnostic technique is currently hindered by several limitations such as inconvenience and lack of sensitivity. Thus, NPs might be crucial in the early detection of CKD since they can provide high sensitivity. Table 1 represents the characteristic features and targets of different NPs used so far in CKD treatment.  
The breath assessment of volatile organic compounds has been previously used to detect CKD [16]. Carbon-nanotube-based sensors and breath sample analysis have been utilized as non-invasive tools in diagnosing end-stage renal disease (ESRD) in vivo [17]. Accordingly, gold NP-based sensors have been used to detect CKD in breath samples. This sensor can accurately distinguish between the exhaled breath of CKD patients and healthy individuals. Moreover, the sensor has shown a capability to diagnose early and late-stage CKD [18].  
Magnetically assisted hemodialysis (MAHD) has been posed as an effective approach in ESRD management. In this context, biocompatible ferromagnetic NPs bind to targeted binding substances such as proteins, antibodies, and immunoglobulins with a high affinity for toxins in serum. The iron constituents of NPs can be magnetically manipulated and then target different body tissues. In this system, before the hemodialysis NP infusion is performed, a sufficient time for binding the toxic substance to NP is allowed. In vitro studies have shown the adsorbance and clearance of homocysteine [19]. Moreover, a single circulation of MAHD leaves only an immeasurably small magnetic signal, which is equivalent to 8 circulations of hemodialysis.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 3 Proposed Protocol  
Considering the challenges, and the harsh ocean environment, a self-organizing protocol is proposed to achieve scalability, robustness and fault tolerant system known as Self Organizing and Scalable Routing Protocol (SOSRP). The SOSRP is designed to conserve the energy and ensure the packet delivery to sink through a power control and hop count-based techniques, including a fault-tolerance algorithm. The protocol enables a node to find the neighboring nodes, forming a connectivity matrix. The packet routing is based on the smallest distance and hop count between the source and sink. Figure 2 depicts the working principle of proposed protocol. The working methodology consists of four phases, chronologically: Network initialization, Neighbor Discovery, Path Selection Criteria and Packet Transmission.
###### Abstract  
To gain insight into riser motions and associated fatigue damage due to vortex-induced vibration (VIV), data loggers such as strain sensors and/or accelerometers are sometimes deployed on risers to monitor their motion in different current velocity conditions. Accurate reconstruction of the riser response and empirical estimation of fatigue damage rates over the entire riser length using measurements from a limited number of sensors can help in efficient utilization of the costly measurements recorded. Several different empirical procedures are described here for analysis of the VIV response of a long flexible cylinder subjected to uniform and sheared current profiles. The methods include weighted waveform analysis (WWA), proper orthogonal decomposition (POD), modal phase reconstruction (MPR), a modified WWA procedure, and a hybrid method which combines MPR and the modified WWA method. Fatigue damage rates estimated using these different empirical methods are compared and cross-validated against measurements. Detailed formulations for each method are presented and discussed with examples. Results suggest that all the empirical methods, despite different underlying assumptions in each of them, can be employed to estimate fatigue damage rates quite well from limited strain measurements.  
marine riser; vortex-induced vibration; fatigue damage prediction, empirical method 2018
## 6 Experimental Evaluation
### Methodology
#### 6.1.5 Parameter Setting
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{_Average Fitness_} \\ \hline \(p\)s & 30.37.30.1 & 30.37.50.1 & 30.37.70.1 & 30.37.90.1 & 30.37.100.1 \\ \hline
15 & 251.96 & 267.15 & 286.97 & 295.06 & 296.51 \\ \hline
25 & 253.78 & 266.50 & 288.01 & 295.45 & 297.48 \\ \hline
50 & 253.70 & 271.89 & 287.39 & 295.37 & 297.19 \\ \hline \multicolumn{5}{c}{_Friedman Rank (\(p\)-Value \(<10^{-3}\))_} \\ \hline \(p\)s & 30.37.30.1 & 30.37.50.1 & 30.37.70.1 & 30.37.90.1 & 30.37.100.1 \\ \hline
15 & 3 & 2 & 3 & 3 & 3 \\
**25** & **1** & **3** & **1** & **1** & **1** \\
50 & 2 & 1 & 2 & 2 & 2 \\ \hline \multicolumn{5}{c}{_Best Fitness_} \\ \hline \(p\)s & 30.37.30.1 & 30.37.50.1 & 30.37.70.1 & 30.37.90.1 & 30.37.100.1 \\ \hline
15 & 260.71 & 276.41 & 291.07 & 296.69 & 298.31 \\ \hline
25 & **260.94** & 275.33 & 291.72 & 297.95 & 298.46 \\ \hline
50 & 262.18 & 273.62 & 290.06 & 296.09 & 298.22 \\ \hline \multicolumn{5}{c}{_Friedman Rank (\(p\)-Value \(<10^{-3}\))_} \\ \hline \(ps\) & 30.37.30.1 & 30.37.50.1 & 30.37.70.1 & 30.37.90.1 & 30.37.100.1 \\ \hline
15 & 3 & 1 & 2 & 2 & 2 \\
**25** & **2** & **2** & **1** & **1** & **1** \\
50 & 1 & 3 & 3 & 3 & 3 \\ \hline \end{tabular}
\end{table} 
Table 2: Analysis of the population size. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{_Fitness_} \\ \hline \(id\) & \(p_{R}\) & \(p_{M}\) & 30.37.30.1 & 30.37.50.1 & 30.37.70.1 & 30.37.90.1 & 30.37.100.1 \\ \hline C1 & 0.5 & 0.010 & 253.18 & 263.90 & 285.82 & 292.03 & 294.97 \\ C2 & 0.5 & 0.005 & 263.30 & 274.21 & 293.15 & 298.19 & 298.79 \\ C3 & 0.5 & 0.001 & 253.99 & 267.70 & 287.67 & 295.56 & 297.06 \\ C4 & 0.75 & 0.010 & 250.67 & 268.65 & 286.70 & 293.12 & 294.50 \\ C5 & 0.75 & 0.005 & 262.91 & 276.65 & 291.69 & 296.48 & 298.25 \\ C6 & 0.75 & 0.001 & 250.32 & 270.05 & 284.09 & 292.87 & 294.89 \\ C7 & 0.9 & 0.010 & 255.12 & 269.66 & 289.43 & 293.91 & 296.06 \\ C8 & 0.9 & 0.005 & 260.94 & 275.33 & 291.72 & 297.95 & 298.46 \\ C9 & 0.9 & 0.001 & 256.80 & 269.87 & 288.57 & 296.71 & 297.45 \\ \hline \end{tabular}
\end{table} 
Table 3: Analysis of the recombination and mutation probabilities in the proposed EA.