esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 3 Simulations with the Numerical Method ELES
### Turbulence Generation and Structure  
Three cases with typical flow patterns listed in Table 1 were simulated with ELES. Before processing the validation of the ELES model, some work needs to be completed such as checking the turbulence generation and structure.  
By vorticity post-processing, Figure 5a shows that the resolved turbulences were well-generated from the upstream interfaces in the three reference cases. Here, the vorticity (omega) has projections in the Cartesian coordinate system as seen in Equation (25):  
\[\Omega_{x}=\frac{\partial V_{z}}{\partial y}-\frac{\partial V_{y}}{\partial z},\Omega_{y}=\frac{\partial V_{x}}{\partial z}-\frac{\partial V_{z}}{\partial x },\Omega_{z}=\frac{\partial V_{y}}{\partial x}-\frac{\partial V_{x}}{\partial y} \tag{25}\]  
Additionally, the turbulence structures in the current ELES simulations need to be checked. Figure 5b shows iso-surfaces of the \(Q\)-criterion colored with \(V_{z}\) in three cases. Turbulence structures are depicted to be well-developed and show no damping or disruption downstream. The definition of Q is defined as Equation (26):  
\[Q=\frac{1}{2}\Big{(}\Omega^{2}-S^{2}\Big{)} \tag{26}\]  
where \(S\) is the strain rate and \(\Omega\) is the vorticity, and both are absolute values. The iso-surfaces of the \(Q\)-criterion shown in Figure 5b are equal to 200 [s\({}^{-2}\)]. The above analysis reveals that the current ELES simulations provide high resolution on turbulence structures in LES zones with the vortex method adopted at the upstream interface.
## 5 Discussion  
This study explores the crucial factors influencing LC location decisions in the evolving logistics sectors of Pakistan and China. The analysis highlights key regions, such as the Gwadar Port Area in Baluchistan and Tianjin in China, as pivotal logistics hubs due to strategic locations, connectivity, and ongoing infrastructure development. In Pakistan, the prominence of Gwadar, Ormara, Port Qasim, and other locations stems from their geographical advantage, proximity to major cities, and well-developed transportation networks. The Gwadar Port Area, which is situated along the Arabian Sea, stands out as a crucial node in the CPEC, emphasizing its role as a regional trade hub. Ongoing infrastructure projects, including road networks and free trade zones, further underline Pakistan's potential as a logistics powerhouse. The decision-making process incorporates a comprehensive analysis considering economic, regulatory, and environmental factors. The fuzzy-TOPSIS method is employed, ranking criteria such as freight demand, transportation expenditures, and proximity to customers. The results emphasize the significance of freight demand and transportation costs in LC selection, guiding economic growth policies and infrastructure development. Comparative assessments between CPEC locations and Taiwan offer insights into cost, location, security, and management considerations. Peshawar emerges as a top-performing location, excelling in management, while Port Qasim demonstrates a balanced performance. Tianjin in China holds significance for its excellent management and security ratings. Furthermore, this study evaluates alternatives using a weight-normalized fuzzy decision matrix, considering linguistic variables transformed into fuzzy ratings. The positive ideal and negative ideal options are determined, leading to the ranking of locations based on their proximity to the ideal solutions. Strategic recommendations derived from the findings emphasize the vital roles of key locations. Peshawar is recognized as a central transportation hub promoting regional connectivity, while Gwadar's strategic coastline linkages contribute to its significance. Tianjin's importance in China is underscored by its proximity to Beijing and its role as a regional logistics hub. The discussion elucidates the nuanced dynamics of LC location decisions, shedding light on the multifaceted considerations that contribute to the strategic positioning of logistics hubs in Pakistan and China. The integration of fuzzy-TOPSIS methodology, comparative analyses, and strategic recommendations enriches the understanding of the complexities involved in logistics center location planning.
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 2 Method and Geological Conditions
### Geological Conditions  
Wangtaipu coal deposit formed during the Carboniferous period and is located in Jincheng city, Shanxi Province, China. It covers a field area of 33.7 km\({}^{2}\). There are fourteen layers of coal, and at present, No. 15 coal, which has a stable and simple structure, is being mined at 1.8 to 3.5 m and an average of 2.5 m. The geological conditions of the XV2214 working face (trial area) are as follows: the average buried depth is 175 m, with a dip angle of 1-3\({}^{\circ}\). The average inclination length is about 280 m, and average trend length, 70 m. The surface buildings above are brick-concrete structures. They all require grade III protection (based on Specification Requirements). Based on the KS theory and borehole columnar section, the parameters of overburden are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Lithology**} & \multicolumn{1}{c}{**Volumetric**} & **Elastic Modulus** & \multirow{2}{*}{**Poisson’s Ratio (–)**} & **Uniaxial Tensile** \\  & **Weight (kN/m\({}^{3}\))** & **(GPa)** & & **Strength (MPa)** \\ \hline Coal seam & 14 & 2.6 & 0.29 & 1.60 \\ Limestone & 29 & 42.5 & 0.23 & 7.98 \\ Mudstone & 25 & 12.7 & 0.24 & 3.73 \\ Fine sandstone & 27 & 28.3 & 0.22 & 3.65 \\ Sandy mudstone & 26 & 13.2 & 0.26 & 3.83 \\ Medium sandstone & 28 & 35.8 & 0.21 & 6.63 \\ Silstone & 26 & 8.7 & 0.25 & 4.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of overlying strata and KS location.  
According to Equations (1) and (2), the KS location could be calculated as follows:  
\[\left(\text{KS}\,1\right)\left\{\begin{array}{c}q_{1}\,=253.5\,\text{ kPa}\\ \left(q_{2}\right)_{1}\,=298.6\,\text{ kPa}\\ \vdots\\ \left(q_{14}\right)_{1}\,=893.8\,\text{ kPa}\\ \left(q_{15}\right)_{1}\,=358.7\,\text{ kPa}\end{array}\right.\left\{\begin{array}{c}q_{15}\,=5 515.8\,\text{ kPa}\\ \left(q_{16}\right)_{15}\,=571.1\,\text{ kPa}\\ \vdots\\ \left(q_{21}\right)_{15}\,=893.8\,\text{ kPa}\\ \left(q_{22}\right)_{15}\,=675.3\,\text{ kPa}\end{array}\right.\left\{\begin{array}{c}q_{15}\,=5 515.8\,\text{ kPa}\\ \left(q_{23}\right)_{22}\,=656.8\,\text{ kPa}\\ \vdots\\ \left(q_{24}\right)_{22}\,=748.2\,\text{ kPa}\\ \left(q_{25}\right)_{22}\,=488.6\,\text{ kPa}\end{array}\right.\left\{\begin{array}{c}q_{25}\,=5 01.8\,\text{ kPa}\\ \left(q_{26}\right)_{25}\,=656.8\,\text{ kPa}\\ \vdots\\ \left(q_{24}\right)_{22}\,=748.2\,\text{ kPa}\\ \left(q_{25}\right)_{22}\,=488.6\,\text{ kPa}\end{array}\right.\left\}\right.\]  
It can be seen from the above that there are four KS in the overburden: namely, the first layer (limestone), 15 th layer (sandy mudstone), 22 th layer (sandy mudstone), and 25 th layer (medium sandstone). A schematic diagram of the coalmine and the KS locations are shown in Figure 1.
## 2 Methodology
### Steady-State Performance Analysis  
#### 2.3.1 Stability Analysis  
The error vector of the filter parameter estimation is the difference between the estimated value and the optimal value of adaptive filter parameters, which can be expressed as  
\[\tilde{\Phi}_{k}=\Phi_{o}-\Phi_{k}. \tag{15}\]  
It is known that  
\[\Phi_{k+1}=\Phi_{k}+\mu\phi_{GMACC}(\varepsilon_{k})\varepsilon_{k}\Gamma_{k}. \tag{16}\]  
Combining (15) and (16), we can get  
\[\tilde{\Phi}_{k+1}=\tilde{\Phi}_{k}-\mu\phi_{GMACC}(\varepsilon_{k}) \varepsilon_{k}\Gamma_{k}. \tag{17}\]  
According to the energy conservation relation (ECR) [34; 35; 36], we can get  
\[E\Big{[}\big{\|}\tilde{\Phi}_{k+1}\big{\|}^{2}\Big{]} =E\Big{[}\big{\|}\tilde{\Phi}_{k}\big{\|}^{2}\Big{]} \tag{18}\] \[-2\mu E\Big{[}\tilde{\Phi}_{k}^{T}\phi_{GMACC}(\varepsilon_{k}) \varepsilon_{k}\Gamma_{k}\Big{]}\cdot\] \[+\mu^{2}E\Big{[}\big{\|}\phi_{GMACC}(\varepsilon_{k})\varepsilon _{k}\Gamma_{k}\big{\|}^{2}\Big{]}\]  
In order to ensure the algorithm finally come to the steady state, the error vector norm must satisfy:  
\[E\Big{[}\big{\|}\tilde{\Phi}_{k+1}\big{\|}^{2}\Big{]}\leq E\Big{[}\big{\|} \tilde{\Phi}_{k}\big{\|}^{2}\Big{]}. \tag{19}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Algorithm** & **Number of Additions and Subtractions** & **Number of Multiplications and Divisions** & **Number of Exponentiations** \\ \hline LMS & n + 1 & n + 1 & 0 \\ GMACC & n + 2 & n + 5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity (per iteration) of LMS and GMACC algorithms.  
Figure 2: The weighting functions of different algorithms.  
So, we can derive that  
\[\mu^{2}\mathbf{E}\Big{[}\|\phi_{GMACC}(\varepsilon_{k})\varepsilon_{k}\Gamma_{k}\|^{ 2}\Big{]}\leq 2\mu\mathbf{E}\Big{[}\tilde{\Phi}_{k}^{T}\phi_{GMACC}(\varepsilon_{k}) \varepsilon_{k}\Gamma_{k}\Big{]}. \tag{20}\]  
Then, we can deduce that the step size \(\mu\) should satisfy the following condition  
\[\mu\leq\frac{2\mathbf{E}\Big{[}\tilde{\Phi}_{k}^{T}\phi_{GMACC}(\varepsilon_{k}) \varepsilon_{k}\Gamma_{k}\Big{]}}{\mathbf{E}\Big{[}\|\phi_{GMACC}(\varepsilon_{k}) \varepsilon_{k}\Gamma_{k}\|^{2}\Big{]}}. \tag{21}\]  
so that the expectation sequence of the power of the estimation error \(\mathbf{E}\Big{[}\|\tilde{\Phi}_{k}\|^{2}\Big{]}\) monotonously decreases, hence the adaptive filter converges to the steady state after some iterations.  
As one can see from (21), the threshold value of the step size depends on many factors such as the error size, the variance of the system noise, the kernel width, and the shape parameter. Therefore, the threshold value is hard to tell before the experiment, because the steady-state error is previously unknown. Usually, a small step size can help the algorithm to converge to a low steady-state MSD. Thus, one can just set a small step size at the beginning of the simulation and carefully try a larger step size to get a faster convergence speed. By running a simulation, we can provide an empirical value of the threshold. For example, in experiment 1, the empirical value of the step size was less than 2.5.
###### Abstract  
The atmospheric stability and ground topography play an important role in shaping wind-speed profiles. However, the commonly used power-law wind-speed extrapolation method is usually applied, ignoring atmospheric stability effects. In the present work, a new power-law wind-speed extrapolation method based on atmospheric stability classification is proposed and evaluated for flows over different types of terrain. The method uses the wind shear exponent estimated in different stability conditions rather than its average value in all stability conditions. Four stability classification methods, namely the Richardson Gradient (RG) method, the Wind Direction Standard Deviation (WDSD) method, the Wind Speed Ratio (WSR) method and the Monin-Obukhov (MO) method are applied in the wind speed extrapolation method for three different types of terrain. Ta applicability is analyzed by comparing the errors between the measured data and the calculated results at the hub height. It is indicated that the WSR classification method is effective for all the terrains investigated while the WDSD method is more suitable in plain areas. Moreover, the RG and MO methods perform better in complex terrains than the other methods, if two-level temperature data are available.  
wind speed extrapolation; atmospheric stability; wind shear; wind resource assessment 2018 8 1429 1
## 4 Results
### Algorithm Accuracy Verification
The corner reflector as illustrated in Figure 9 is the international standard benchmark model. The sonar TS result of the corner reflector at a 15 KHz frequency based on GPU is shown in Figure 9, compared with the result that is calculated by the PEM method. It shows that the trend of the corner reflector TS curve based on the SBR algorithm is consistent with the PEM. Moreover, the TS of the bow and the stern is significantly less than the TS of the hull and is consistent with the corner reflector echo characteristics. As illustrated above, the numerical results verify the accuracy of calculating the TS by the conventional SBR algorithm. Figure 8: The curve of TS with \(ka\) (\(\theta=90^{\circ}\)) by conventional SBR algorithm based on GPU. (**a**) A rigid sphere with radius 1 m. (**b**) A rigid cylinder with length 6 m and radius 1 m.  
Figure 7: The TS value of the target at 15 KHz by conventional SBR algorithm based on GPU. (**a**) A rigid sphere with radius 1 m. (**b**) A rigid cylinder with length 6 m and radius 1 m.  
In SBR algorithm, the ray-tracing process considers the multiple reflection of sound waves and improves the spatial and frequency domain information integrity of the underwater targets. Due to the memory limitation on the GPU, we divide the virtual aperture plane equally into several portions of the conventional SBR. Based on the principle of reducing the number of ray tubes and saving running memory, an accelerated multiresolution SBR algorithm based on GPU to predict the TS value is proposed. The comparison of the results between the GPU-based multiresolution SBR and the GPU-based SBR is shown in Figure 10. It is observed that the results of the GPU-based multiresolution SBR has good agreement with the GPU-based conventional SBR. Figure 10: The comparison of the GPU-based multiresolution SBR result and the GPU-based conventional SBR result of the corner reflector. Figure 9: The comparison of our GPU-based SBR result and the PEM result of the corner reflector.
## 1 Introduction  
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide. As of 2019, approximately 59 million people globally were affected by AF [1]. A Chinese study involving over 10 million participants estimated the lifetime risk of developing AF to be around 20% [2]. Over a 50-year follow-up, the age-standardized prevalence of AF increased four-fold [3].  
Patients with AF experience rapid and irregular electrical activity in the atria, contributing to complications such as heart failure, myocardial infarction, and thromboembolism [4]. Thromboembolism occurs due to reduced atrial contractility, which slows blood flow within the heart [5]. If a thrombus dislodges, it can travel through the circulatory system to cerebral vessels, potentially causing an ischemic stroke [6]. The left atrial appendage (LAA) is the most common site for thrombus formation in AF patients, and these thrombi can lead to strokes if they enter the bloodstream [7]. The risk of stroke in AF patients is increased five-fold [8], with AF being the cause of 25% of strokes among the elderly [8]. Furthermore, strokes related to AF typically result in more severe clinical outcomes compared to other types. AF patients are twice as likely to become bedridden due to stroke complications and have a higher mortality risk [9, 10].  
The current management of AF patients is primarily based on assessing the risk of stroke and bleeding [11, 12]. However, these assessments rely solely on demographic and clinical data. Numerous studies [7, 13, 14, 15] have demonstrated that including hemodynamic and geometrical parameters in stroke risk assessment methods can significantly enhance their accuracy. The study of hemodynamic mechanisms in AF patients who experience strokes should focus on the processes of thrombus formation and detachment. Despite its importance, there is currently a lack of research specifically analyzing thrombus detachment. Besides medical imaging techniques, existing research methodologies include both numerical and experimental approaches. Numerical methods comprise traditional computational fluid dynamics (CFD) [16; 17; 18] as well as advanced artificial intelligence (AI) [19; 20; 21] techniques. This work provides a comprehensive overview of the current status, fundamental principles, and related technologies in this field.
# Named Entity Recognition Networks Based on Syntactically Constrained Attention
## 2 Related Work
### Application of Syntactic Information to NLP Tasks  
Recent approaches have focused on learning context representations that are sensitive to syntactic structures, by utilizing syntactic dependency analysis. Syntactic dependency trees facilitate the creation of a tree structure, based on the linguistic relationships between words in a context, which can guide the model in capturing the dependencies between words in a sentence. Prior studies have incorporated information garnered from the analysis of syntactic dependency trees, by transforming dependencies into vectors. For instance, studies such as [27; 28; 29; 30] leverage the structural information regarding the dependency syntactic tree, in order to construct the adjacency matrix, as well as use graph neural networks (GNNs) to conduct emotion analysis. [PERSON] et al. [31] extracted sequence features and region features of the context using the dependency structure, and constructed a complete word graph, in order to extract implicit features between all word pairs, leading to significant improvements in the overlapping relational triad extraction task. Meanwhile, [PERSON] et al. [32] utilized multi-headed attention mechanisms, in order to compute the relationship weights between any two words, allowing the model to exploit different dependencies accordingly. Instead of updating the representation of adjacent words using GNNs, our model prunes attention using syntactic information, in order to constrain the relevant tokens, which is a more linguistically motivated approach than simply adding dependent features.
## 1 Introduction  
The European Union (EU) has set ambitious goals to reduce its dependency on fossil fuels as part of its broader strategy to combat climate change. These goals have been supported by a range of policies and regulations, including a cap-and-trade system designed to limit carbon dioxide emissions from power plants and other major sources [1]. Such initiatives are crucial components of the EU's drive toward a more sustainable and low-carbon economy. Within this context, renewable energy sources like solar and wind power are gaining prominence as vital, eco-friendly methods for energy production. However, the efficiency of these renewable sources is heavily influenced by climatic factors, which introduces significant challenges in predicting energy production.  
RES, such as solar and wind, are critical components in the fight against climate change and the transition to a low-carbon economy. However, the strategic deployment and optimization of these energy sources present several challenges due to the inherent variability of renewable resources. The efficiency of renewable energy systems is highly dependent on environmental factors, such as sunlight, wind speed, and temperature, which can vary significantly across different geographical locations and over time. This variability adds complexity to the tasks of predicting energy output and efficiently placing renewable energy systems to achieve maximum energy production.  
To overcome these challenges, the integration of advanced predictive models and optimization techniques is essential. Traditional energy planning methods are often limited in their ability to handle the spatial and temporal complexities of renewable energy. As a result, there is a growing need for sophisticated, data-driven frameworks that can effectively analyze large volumes of meteorological data, optimize the placement of RES, and make informed decisions that enhance both sustainability and energy output. Machine learning (ML) and AI have emerged as powerful tools in this regard, offering the potential to analyze complex datasets and provide insights that can significantly improve the strategic planning and deployment of renewable energy technologies.
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 4 Results and Discussion
### Effect of Driving Strength
#### 4.3.3 Mean Flow Fields  
The streaming velocity can be calculated based on the average mass transport velocities [27]:  
\[u_{st}\ =\ \frac{\left<\rho u\right>}{\left<\rho\right>},\ \ \ v_{st}\ =\ \frac{\left<\rho v \right>}{\left<\rho\right>} \tag{13}\]  
where, \(u_{\rm st}\) and \(v_{\rm st}\) are the axial and radial components of the streaming velocity, respectively, and the symbol \(\left<\cdot\right>\) means time averaging. The secondary flow (mean flow), which superposes on the resonant oscillation and forms the fluid motion, can be described by the streaming velocities \(u_{\rm st}\) and \(v_{\rm st}\).  
The mean flow fields for the \(m=2.2\) exponential resonator under five cases are shown in Figure 11, where the color contours indicate the distribution of the axial streaming velocity \(u_{\rm st}\) (m/s). Only the upper half of the flow fields is depicted because the mean flow fields are all nearly axisymmetric. We can find from Figure 11 that, along with increasing driving strength, the \"vortex fission\" phenomenon occurs near the small end of the resonator, but little changes can be found for the vortex structure near the driving end from Cases A-D. However, the big vortex ring near the large end also split into two vortices for Case E, so that the streaming rolls in Figure 11e spread to the middle region from the two ends and divide into secondary vortices of various scales.  
In brief, Figure 11 indicates that vortices only appear at the two ends of the resonator in Figure 11a-d, but irregular and unsteady vortices of various scales occur throughout the resonator in Figure 11e. An earlier study [32] argued that the refrigeration performance can be enhanced by a so-called bi-cellular structure, which looks like that shown in Figure 11a-d. [PERSON] et al. [22] also observed a better performance of the tube when the flow was less uniformly distributed. Therefore, to avoid detrimental effects of secondary streaming, an optimal driving strength must be found. All information indicates that this optimal driving strength is \(l\leq 400\)\(\mu\)m.  
Figure 11: Time average flow fields (secondary flow) in the \(m=2.2\) exponential resonator under different cases: (**a**) Case A, (**b**) Case B, (**c**) Case C, (**d**) Case D, and (**e**) Case E. The color contours indicate the distribution of the axial streaming velocity \(u_{\text{st}}\) (m/s).
## 5 Experiment and Analysis
### Simulation Experiments
#### 5.1.2 Simulation Comparison Test between Fuzzy PID and a Traditional PID System  
The model for building a conventional PID and fuzzy PID controller is shown in Figure 13.  
Based on the traditional PID and fuzzy PID controllers, we built the simulation model of the harvester's adaptive traveling speed under the action of the two controllers, as shown in Figure 14.  
The simulation test received a traveling speed step signal of 1 m/s as input, and we set the simulation time to 30 s. Figure 15 shows the adaptive simulation results of the traveling speed under two control algorithms. The results show the harvester's traveling speed variation rise time under the conventional PID algorithm \(t_{r}\) = 3.2 s, peak time \(t_{m}\) = 5.2 s. The time to reach the steady state was slower at about 20 s, with a significant overshoot. The rise time corresponding to the fuzzy PID \(t_{r}\) = 1.8 s, peak time \(t_{m}\) = 3.2 s, had a shorter time to steady state, at about 5.5 s, with almost no oscillation.  
Figure 14: Simulation model of the harvester traveling speed adaptive regulation comparison test system.  
Figure 13: (**a**) Traditional PID simulation model; (**b**) fuzzy PID simulation model.