esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we conducted a comprehensive investigation into the impact of radial installation deviations on the pressure pulsations caused by the rotor-stator interaction of the giant turbine. The following are the conclusions drawn from our study:  
1. In analyzing the pressure distribution, we found that total pressure readings should be used near rotating components, while static pressure readings are suitable for more distant regions. However, overall, whether static or total pressure is considered, the distribution remains consistent. Dynamic pressure weakens clearance effects on external surfaces but accentuates them on the internal surfaces due to wall velocity. Initially, static pressure minimally impacts clearances, resulting in a consistent overall distribution, but dynamic pressure exacerbates this influence.
2. The clearance size's minimum point relates closely to high- or low-pressure zones. Smaller clearances do not necessarily lead to higher pressures; there is an optimal size (0.5 mm). Beyond this, fluid cannot flow in, creating a low-pressure zone.
3. Radial forces most affect the lower ring and blades due to their larger projected areas. Influence patterns on the upper crown and runner are less stable than on the lower ring. In practice, it is advisable to focus on the upper crown and lower ring. Runner radial forces vary significantly in direction but are small in magnitude, making the upper crown more critical.
4. While we computed extreme cases with deviations of up to 1.5 mm to understand patterns and fit curves, it is crucial to note that such deviations can be dangerous. For example, at 1.5 mm, the radial force exceeds 700 t and surpasses 1000 t later, posing a severe risk to the turbine unit in practical engineering.  
Conceptualization, Y.W. and K.J.; methodology, X.H.; investigation, Y.W., K.J. and F.G., validation, X.H. and X.L.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and X.H.; supervision, Z.W. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received the research project: \"Multi-field coupling study on dynamic characteristics of 1000 MW hydogenerator unit\" supported by China Three Gorges Construction Group Co., LTD. (Contract No.: JGJD0322003).  
Not applicable.  
Author [PERSON], [PERSON] and [PERSON] are employed by the company Baihetan Engineering Construction Department. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. And The authors declare that this study received funding from China Three Gorges Construction Group Co., LTD. (Contract No.: JGJD0322003). The funder had the following involvement with the study: Providing the basic model and the referable boundary conditions.
# Finding Top-\(k\) Nodes for Temporal Closeness in Large Temporal Graphs
## 5 Experimental Results
### Ranking and Finding Top-\(K\) Nodes
#### 5.3.2 Computing Top-K  
Given an integer \(k\), we show how apx-\(h\) behaves when finding the top-\(k\) central nodes, observing where the top-\(k\) nodes in the ranking induced by exact appear in the ranking induced by apx-\(h\). In particular, let \(r\) be the exact ranking, such that \(r(i)\) is the the vertex in the \(i\)-th position (smaller \(i\) corresponds to higher centrality), and let \(q\) be the ranking obtained by apx-\(h\), and \(q^{-1}\) its inverse. Given \(k\), we compute the maximum ranking \(q^{-1}(v)\) for the first \(k\) nodes \(v\) in \(r\), namely \(\gamma(k)=\max_{1\leq i\leq k}q^{-1}(r(i))\). Hence, if \(k=1\), we are computing the position of the real top central node in the approximated ranking, while, for larger values of \(k\), we are considering the worst-case positioning among the real top-\(k\) nodes. Figure 7 reports these values in the case \(k=20\) and for different values of \(h\), for the graphs come, fbwa, and melb. In particular, for each \(h\), it reports the median of the \(\gamma(20)\) values found among 50 experiments (together with the minimum, maximum, first, and third quartile). As can be seen, despite the fact that the variability is relatively high for small sample sizes (namely, for \(h=32\) and \(h=64\)), already with \(h=128\) it significantly reduces. In particular, by setting \(h=512\), we have that the top-20 nodes are always in the first 512 positions of the ranking found by apx-512. This suggests that, in order to find the exact top-20, it is enough to run apx-512 and then compute the exact closeness of the top-512 found (in \(O(m)\) time each). We have verified this hypothesis for all the graphs in our dataset (apart from twit) and for different values of \(k\) (see Table 2 for the graphs in our sample dataset and Table A4 in the Appendix A for the entire dataset). As a matter of fact, in the case of large graphs (that is, with more than 20,000 nodes), we can compute the top-20 nodes by executing apx-\(h\) with \(h=1024\) and then compute the exact closeness of the top-\(h\) found: the time  
Figure 6: Average Kendall’s \(\tau\) values for undirected (left) and directed (right) graphs as a function of the sample size \(h\): the average Kendall’s \(\tau\) of apx–\(h\) (over 50 experiments) is computed by referring to the ranking computed by exact, except for the twit graph plot, where we refer to the ranking computed by apx-1024 (for this reason, its plot stops at \(h=512\)).  
complexity of this approach would then be \(O(2048\times m)\) (which is between 10 and 100 times better than the exact approach, when applied to the large graphs in our dataset). In the case of smaller graphs, the experimental results, described in Table 10 of the Appendix A, show that a smaller value of \(h\) (that is, \(h=256\)) is almost always sufficient, thus giving a similar speed-up. Even more impressive is the fact that, in the case of large graphs, the same value of \(h\) (that is, \(h=1024\)) can be actually be used for finding the top-100 nodes.
# Game-Theory-Based Multimode Routing Protocol for Internet of Things
## 3 Proposed Mechanism
### Assumptions and Features
#### 3.2.6 Nodes' Energy Level  
Each node has a finite amount of energy. Due to their heterogeneous nature, nodes' energy levels may differ from one another. If a node has enough energy to be spent on data packet transmission, processing, and reception, it is said to be alive. The dead nodes cannot be considered as part of the network. At any point in time if a node fails due to any reason--such as breakage or battery failure--then the node can be considered a dead node.  
\(E(n_{i})\) and \(E_{Initial}(n_{i})\) are the current and initial energies of a node \(n_{i}\), respectively. The ratio of a node's energy consumption over time \(t\) is defined as, \(\Delta E^{t}(n_{i})=E^{T-t}(n_{i})-E^{T}(n_{i}\ )\). After a certain amount of time, the energy fluctuation must fulfill the equation \(E(n_{i})>\Delta E(n_{i})\). The value \(\Delta E\) may differ for each node due to the heterogeneous environment. \(E^{t}_{Rnm}(n_{i})\) is the remaining energy ratio of a node, \(n_{i}\) at time \(t\), as computed in Equation (10).  
\[E^{t}_{Rnm}(n_{i})=\frac{E^{t}(n_{i})}{E_{Initial}(n_{i})} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Destination IP** & **Next Hop** & **Metric** & **Dest. Seq. Number** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Routing Table for \(ClassB\) (Reactive).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Designation IP** & **Next Hop** & **Metric** & **Dest. Seq. Number** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Routing table for \(ClassA\) (Proactive).  
In Equation (11), \(\lambda E^{t}(n_{i}\) ) represents the current energy to energy consumption ratio at time \(t\) of a node \(n_{i}\).  
\[\lambda E^{t}(n_{i})=\frac{E^{t}(n_{i})}{\Delta E^{t}(n_{i})} \tag{11}\]  
The median of the known nodes' \(\lambda E^{t}(n_{j})\) can be used to calculate the energy threshold value, \(ThE\), as shown in Equation (11). Each smart node can send a request to its known nodes to get these values.  
\[ThE=Med\big{(}\lambda E^{t}\big{(}n_{j}\big{)}\big{)} \tag{12}\]
## 1 Introduction
First, we use a continuous wave laser field and determine for various QD \(-\) MoS\({}_{2}\) layer distances the field strength that leads to acceptable fidelity levels. The effect of the quality of the MoS\({}_{2}\) material, as well as the effect of the host dielectric environment permittivity, on the fidelity of spin initialization, are examined. We also study the interaction of the QD with a laser pulse and apply numerical optimal control, using a state-of-the-art optimal control solver, to obtain the time-dependent field strength, which leads to maximum final fidelity for short time intervals. The latter approach gives beneficial results in comparison to the continuous wave field excitation. We also find that the resulting optimal pulse is robust to positioning error of the QD \(-\) MoS\({}_{2}\) monolayer structure and to additive constant control error. The paper has the following structure. In the coming section, we present the density matrix equations for the interaction of the QD in the Voigt geometry with a resonant optical field near the MoS\({}_{2}\) monolayer. Section 3 contains the basic results of the paper for the creation of rapid spin initialization with high fidelity using the interaction of the QD with laser fields with constant and time-dependent field strengths. Finally, in Section 4, we summarize our findings. Figure 1: (**a**) A structure of the energy levels for a QD in the Voigt configuration. (**b**) A schematic of the system under study, where the QD is placed at distance \(z=R\) on top of a MoS\({}_{2}\) monolayer. Density Matrix Equations for the QD Near a MoS\({}_{2}\) Monolayer under Interaction with an Optical Field  
As we have already mentioned above, we study a singly-charged self-assembled QD. The growth axis of the QD was the \(y\)-axis, and an external magnetic field with the direction in the \(x\)-axis was applied to the QD. This created the QD in the Voigt geometry and lifted the degeneracy of the electron/hole levels by Zeeman splitting. Then, the ground spin energy levels were \(|1\rangle=|\downarrow_{x}\rangle\) and \(|2\rangle=|\uparrow_{x}\rangle\), and the excited trion energy levels were \(|3\rangle=|\downarrow_{x}\uparrow_{x}\uparrow_{x}\rangle\) and \(|4\rangle=|\downarrow_{x}\uparrow_{x}\Downarrow_{x}\rangle\) (see Figure 1a for the actual level structure). With \(\uparrow(\downarrow)\) and \(\Uparrow(\Downarrow)\), we denote electron spins and heavy-holes, respectively. Here, the transitions \(|1\rangle\leftrightarrow|4\rangle\) and \(|2\rangle\leftrightarrow|3\rangle\) have \(x\)-polarized dipole moments, and the transitions \(|1\rangle\leftrightarrow|3\rangle\) and \(|2\rangle\leftrightarrow|4\rangle\) have \(z\)-polarized dipole moments. The goal of this paper is to create spin state \(|2\rangle\). Therefore, we considered the interaction of the QD with a \(x\)-polarized optical field, which drives at exact resonance the transition \(|1\rangle\leftrightarrow|4\rangle\), and then electron spin state \(|2\rangle\) is created by optical pumping. The dynamics of the interaction of the QD with the optical field is studied by the density matrix equations.
## 5 Conclusions  
This paper presented a detailed comparative analysis of five peak detection techniques tailored for FMCW radar systems under noisy and phase-distorted environments. A novel Learned Thresholded Subspace Projection (LTSP) method was proposed, combining subspace projection and signal reconstruction to enhance peak localization in low SNR and phase noise-affected conditions. Extensive simulations incorporating phase noise models demonstrated that LTSP outperforms FFT and MPM methods in robustness, while CA-CFAR remains the most resilient across all noise levels. Experimental measurements confirmed the simulated trends, with LTSP and CA-CFAR exhibiting high detection probability across target distances from 1.2 m to 2.0 m.  
In addition to performance comparisons, the computational complexity and FPGA implementation feasibility of each technique were evaluated. Results show that while LTSP and SVD-based methods offer high accuracy, their hardware cost is significantly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Complexity** & **DSP Usage** & **BRAM** & **LUT/FF Usage** \\ \hline FFT Threshold & \(O(N\log N)\) & Medium * & Low–Medium & Moderate \\ CA-CFAR & \(O(N)\) & Low & Low & Low–Medium \\ MPM (Simplified) & \(O(N)\) & Low & Low & Moderate \\ SVD-Based & \(O(N^{3})\) & High & High & Very High \\ LTSP (SVD + FFT) & \(O(N^{3})\) & High & High & Very High \\ \hline \hline \end{tabular}  
* FFT used dedicated DSP48 slices via Xilinx FFT IP Core. Estimates assumed \(N=256\) to \(1024\).  
\end{table}
Table 3: Computational complexity and FPGA resource estimates for peak detection techniques.  
higher than that of FFT and CA-CFAR, making them more suitable for software-based or co-designed radar platforms.  
Future work will focus on enhancing LTSP through adaptive rank selection and exploring hybrid methods that integrate the robustness of CFAR with subspace-enhanced detection. Furthermore, deployment of these algorithms in embedded systems will be explored using Vitis HLS and Zynq-based hardware for real-time radar applications.  
The concept, theoretical development, and measurements: A.E.-A. and M.K.; numerical analysis, F.Z., A.E.-A., M.K., and T.K.; the writing of the manuscript, A.E.-A., M.K., and T.K. All authors together analyzed and discussed the simulation and measurement results and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work received funding by the German Federal Ministry of Education and Research (BMBF) in the course of the 6 GEM research hub under grant number 16 KISK038. The Article Processing Charges (APC) were covered by the Open Access Publication Fund provided by the University of Duisburg-Essen.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
The thickness of the hot component in a turbine engine is usually small. Therefore, the traditional prediction model of anisotropic thermal conductivity (ATC) based on the periodic hypothesis may be improper for use in the thermal analysis of ceramic matrix composite (CMC) components with a thin-wall structure. Thus, the prediction model for the ATC of a 2.5-D braided CMC was investigated, taking into account the actual thickness of the CMC thin-wall structure. An RVE (Representative Volume Element) model with a periodic boundary and a full-size model with the actual thickness were built to study the temperature field, the heat flux field, and the effective thermal conductivity of the CMC. A validation experiment was carried out to verify the accuracy of the two prediction models. The effect of the composite's thickness on the ATC and the critical thickness suitable for the RVE model were also studied. The results showed that in the thermal analysis of the thin-wall structure, the RVE model had a large deviation in the estimation of the effective thermal conductivity in the thickness direction. The relative error between the numerical data based on the RVE model and the experimental data reached 10.93%, while the relative error was only 3.53% for the full-size model. Additionally, with increasing thickness, the effective thermal conductivities, based on the RVE model and the full-size model, were close to each other. For the critical thickness for the RVE model, which would be suitable for the prediction of the ATC, if the material's thermal properties such as the absolute value ratio and the level of anisotropy changed, the corresponding critical thickness was also different. For the ATC of the SiC/SiC composites used in this study, the critical thickness was found to be 18.4 mm, nearly 31 times larger than the RVE model's thickness.  
ceramic matrix composite; anisotropic thermal conductivity; 2.5-D braided composite; representative volume element; thin-wall structure +
Footnote †: journal: Applied Physics Letters
# Ensuring Stable Operation of Wind Farms Connected to Distribution Networks
## 2 Analysis of the Requirements for Stable Operation of Wind Farms
The studies in [35; 36; 37] provide LVRT adjustment methods based on the use of reactive power injection devices, such as static compensators (STATCOMs), dynamic voltage restorers (DVRs), static synchronous series compensators (SSSCs), and thyristor-controlled series compensators (TCSCs). In [38; 39], the focus is on  
Figure 3: Simplified single-line schemes of wind farm connection to the distribution network: (a) “input–output”; (b) “a branch from power transmission line” with “line-transformer” block; and (c) “line-transformer” block with connection to the substation busbars. the use of combined methods for controlling virtual resistance and demagnetization, which implement the disturbance suppression algorithms. In [40], the LVRT characteristics of a WT with a squirrel cage induction generator (SCIG) and a full-scale converter (FSC) were studied. The article shows that the main problem lies in the phase-locked loop (PLL) grid side converter (GSC) and considers the possibilities for adjusting the control algorithms and filter parameters. The authors of [41] investigate the LVRT characteristic of the PMSG-WT, analyze the influence of the DC link bandwidth on the WT performance after a typical disturbance, and demonstrate the application of the active damping method to improve the efficiency of the PMSG. In [42], the proposed a hybrid PMSG-WT LVRT method that combines crowbar and stored energy in the rotor inertia methods, which has the advantages of both. The study presented in [43] emphasizes that both external devices and modified control algorithms for wind turbine controllers can be used to adjust the characteristics of the WT LVRT in accordance with the requirements of grid codes. The use of external devices can be simple and technically efficient, but costly. This article considers the use of modified control algorithms for wind turbine controllers and FACTS devices. The analysis of the literature shows that in the countries where the form of the voltage-time LVRT characteristic for wind turbines has not been approved, it is essential when designing a wind farm to check the wind turbine LVRT settings under typical disturbances in the distribution network, to opt for methods capable of preventing unnecessary shutdowns of wind turbines, and to assess their effectiveness. A verification algorithm needs to be developed for a reasonable choice of effective methods to prevent unnecessary shutdowns of wind turbines.
###### Abstract  
Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.  
deep learning; automatic crack identification; convolutional neural network (CNN); 3D shadow modeling; structural health monitoring (SHM) +
Footnote †: journal: Applied Sciences
## 3 Methodology  
To conduct our study, we have used a commercial simulator APSYS to simulate our devices [36]. The energy band is computed as [37]:  
\[E_{g}(A_{1-x}B_{x})=(1-x)E_{g}(A)+xE_{g}(B)-x(1-x)C \tag{1}\]  
where \(E_{g}\) is the bandgap, \(A\) and \(B\) are the two binary elements, the bowing parameter \(C\) account for deviations from the linear behavior and is given in Ref. [38]. The current densities \(\overset{\rightarrow}{J_{n}}\) and \(\overset{\rightarrow}{J_{p}}\) of both the electrons and holes are originated by the electrostatic field \(\overset{\rightarrow}{F}\), as well as by the gradient of concentration of electrons \(\
abla n\) and holes \(\
abla p\)  
\[\overset{\rightarrow}{J_{n}}=q{n_{n}}n\overset{\rightarrow}{F}+qD_{n}\
abla n \tag{2}\]  
\[\overset{\rightarrow}{J_{p}}=q{\mu_{p}}p\overset{\rightarrow}{F}+qD_{p}\
abla p \tag{3}\]  
Figure 1: Schematic of the (**a**) device; (**b**) R-LED and W-LED indium composition in the quantum well.  
where \(q\) is elementary charge, \(n\) and \(p\) represent carrier densities, and \(\mu_{n,p}\) are their corresponding mobilities, respectively. The heterointerface charge density is estimated using reported nonlinear model [39; 40].  
\[P_{sp}^{Al_{x}Ga_{1-x}N}=(0.019\cdot x\cdot(1-x))-(0.034\cdot(1-x))-(0.090\cdot x) \tag{4}\]  
\[P_{sp}^{In_{x}Ga_{1-x}N}=(0.038\cdot x\cdot(1-x))-(0.034\cdot(1-x))-(0.042\cdot x) \tag{5}\]  
\[P_{GaN}^{pz}=-0.918e_{t}+9.541e_{t}^{2} \tag{6}\]  
\[P_{InN}^{pz}=-1.373e_{t}+7.559e_{t}^{2} \tag{7}\]  
\[P_{AIN}^{pz}=-1.808e_{t}-7.888e_{t}^{2}\ (e_{t}>0) \tag{8}\]  
\[P_{AIN}^{pz}=-1.808e_{t}-5.624e_{t}^{2}\ (e_{t}<0) \tag{9}\]  
The spontaneous, Shockley-Read-Hall, and Auger recombination rates, represented by \(R_{sp}\), \(R_{SRH}\) and \(R_{Aug}\) respectively, are given by [41]:  
\[R_{sp}=B\Big{(}np-n_{i}^{2}\Big{)} \tag{10}\]  
\[R_{SRH}=\frac{np-n_{i}^{2}}{\tau_{p}^{SRH}(n+n_{i})+\tau_{n}^{SRH}(p+n_{i})} \tag{11}\]  
\[R_{Aug}=\big{(}C_{n}n+C_{p}p\big{)}\Big{(}np-n_{i}^{2}\Big{)} \tag{12}\]  
where nonradiative SRH lifetimes for electrons (\(n\)), and holes (\(p\)) is given by \(\tau_{n,p}^{SRH}\), \(n_{i}\) is intrinsic density is \(C_{n}\) and \(C_{p}\) are Auger coefficients, depending on the type of material. For both the electrons as well as holes, \(\tau_{n,p}^{SRH}\) is assumed to be same. Fermi statistics as well as thermionic emission of carriers at heterointerfaces is estimated, as given in [42]. A typical value of offset ratio \(\Delta E_{c}/\Delta E_{v}\) is assumed to be 0.7 in our work [43; 44]. Other details are given in [45]. The internal quantum efficiency (IQE), \(\eta_{i}\), is estimated as [46]:  
\[\eta_{i}=\frac{\int R_{sp}dv}{\int Rdv}. \tag{13}\]