esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 3 Hydrodynamics  
The dynamic equation of the AUV floating body under the action of various marine environments can be expressed as:  
\[[M+A(\omega)]\ddot{X}+B(\omega)+KX=F_{E}+F_{L} \tag{10}\]  
Among them, \(M\) is the mass matrix of the floating body, \(A\) is the additional mass matrix generated by the water dragging the floating body during its motion, \(B\) is the radiation damping and viscous damping matrix, and \(K\) is the hydrostatic stiffness.  
\(F_{E}\) is the generalized excitation force transmitted by the wave structure, obtained by Haskind expression [37]. All the hydrodynamic parameters are evaluated by ANSYS/AQWA due to the complexity and strong coupling of wave conditions. \(F_{L}\) is the interaction force generated by the gyroscope. \(X\) contains the six degrees of freedom (surge, sway, heave, roll, pitch, and yaw) that need to be considered for the motion of the floating body.  
The maximum power output can be obtained when the GWEC resonates with the incident wave. The mass distribution and shape of the floating body have been optimizedto ensure the device has pitch resonance at a wave period of 7 s. Figure 2 shows the response amplitude operator (RAO) in the pitch direction when the float is subjected to wave impact. The results show that the float has the largest pitch response when the wave period is 7 s. The RAO will decay extremely rapidly as the wave period moves away from the rated period, so the device must be placed in a suitable sea area.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 4 Data Gathering with the Help of Multi-Sink  
### Leader Selection Strategy in DGA  
In LDGM, it is assumed that one or more \"lead nodes\" exist in each DGA which receive data from other nodes in the same DGA and upload them to the mobile Sinks. During the network lifetime, the mobile Sinks only need to communicate with these leaders, enhancing the efficiency of data gathering.  
For the DGA consisting of three DGUs, the node located at the triple coverage region (named as \"region \(C\)\" for short) could be selected as the leader. Other nodes in the same DGA transmit data to the leader hop by hop. To prolong the network lifetime, node with the maximum residual energy \(E_{r}\) in region \(C\) is selected as the leader, as \(S_{1}\) shown in Figure 6. The value of \(E_{r}\) must be greater than the threshold, \(E_{th}\). When the Sinks are far away from the DGA, all of the nodes carry out their sensing tasks and upload data to their leaders.  
However, according to the definition of DGA, the area of region \(C\) is relatively small. So there may be no nodes deployed in this region. In this case, the node with the maximum value of \(W\) in each DGU will be selected as the leader, as node \(S_{2}\), \(S_{3}\) and \(S_{4}\) shown in Figure 6. The value of \(W\) could be calculated as follows: In Formula (5), \(d_{io}\) is the Euclidean distance between node \(i\) and center of region \(C\). \(\alpha\) and \(\beta\) are the adjustable parameters and \(\alpha+\beta=1\).  
\[W=\alpha E_{r}+\beta\frac{1}{d_{io}{}^{2}} \tag{5}\]  
Moreover, for the DGA consisting of less than three DGUs, the leaders will be selected directly in each of its DGUs according to Formula (5), as \(S_{5}\), \(S_{6}\) and \(S_{7}\) shown in Figure 6. If there are just only two DGUs in a DGA, \(d_{io}\) is just the Euclidean distance between node \(i\) and the midpoint \(O\) of the line connecting the centers of the two DGUs.  
Figure 5: Distribution of DGAs (_Case 4_).
## 4 The Compressive Strength and Resistivity Prediction Model of the CLSM
### The Prediction Model of the Surface Resistivity
Where \(f_{c}^{\prime}\) refers to the compressive strength at any curing age, \(f_{c0}^{\prime}\) refers to the compressive strength is given by tested results, \(E_{r}\) is the surface resistivity at any curing age, \(E_{r0}\) is the surface resistivity is given by tested results, \(t\) refers to the curing age (1 to 91 days), DS refers to the replacement rate of desulfurization slag, Where \(a_{e}\) and \(b_{e}\) are exponential function coefficients of the surface resistivity prediction model based on the DS content. Among them, the coefficients \(a_{e}\) and \(b_{e}\) are shown in Table 5 and the relationship with curing age \(t\) has a significant nonlinear increase. When Equation (5) is passed back to normal scale coordinates, it can be rewritten as Equation (6):  
\[y=log_{10}E_{r}=f(t)=\frac{t}{a_{e}+b_{e}t} \tag{5}\]  
\[E_{r}=exp\Big{(}2.303\times\frac{t}{a_{e}+b_{e}t}\Big{)} \tag{6}\]  
\[a_{e}=a_{e1}+a_{e2}*DS+a_{e3}*DS^{2} \tag{7}\]  
\[b_{e}=b_{e1}+b*DS \tag{8}\]  
\[\Big{(}f_{c}^{\prime}-f_{c0}^{\prime}\Big{)}=\frac{(E_{r}-E_{r0})}{a_{er}+b_{ er}(E_{r}-E_{r0})} \tag{9}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**w/b**} & \multirow{2}{*}{**DS**} & \multicolumn{3}{c}{**Coefficients of Exponential Function**} \\ \cline{3-5}  & & \(a_{e}\) & \(b_{e}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{5}{*}{1.5} & 0 & 1.7948 & 1.1629 & 0.9173 \\  & 0.1 & 1.3136 & 1.1705 & 0.8551 \\ \cline{1-1}  & 0.2 & 1.7087 & 1.2452 & 0.9403 \\ \cline{1-1}  & 0.3 & 2.2119 & 1.4247 & 0.8837 \\ \cline{1-1}  & 0.4 & 3.6567 & 1.4697 & 0.8670 \\ \cline{1-1}  & 0.5 & 3.7226 & 1.5226 & 0.8825 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Exponential function coefficients of the surface resistivity prediction model based on the DS content. Under the same conditions, as a result of the test of resistivity, the coefficient \(a_{e}\) increases as the content of the DS increases. Therefore, a quadratic increment function between \(a_{e}\) and DS is used, which indicates that \(a_{e}\) has a relationship between a parabolic curve and a change in the content of the DS, as shown in Figure 12 and Equation (7). The coefficient \(b_{e}\) shows a linear relationship with the increase of the DS, as shown in Figure 13 and Equation (8). Figure 11: (**a**) The relationship between the curing age (\(t\)) and surface resistivity (\(E_{r}\)) of the CLSM (under normal scale coordinates). (**b**) The relationship between the curing age (\(t\)) and surface resistivity (\(E_{r}\)) of the CLSM (after transfer to semi-logarithm scale coordinates). Figure 12: Characteristics of the coefficient \(a_{e}\) of the surface resistivity prediction model versus the DS content.
## 1 Introduction
It is now known that (p)ppGpp and DKsA dock on the secondary channel of the \(\beta\)' subunit, and the aspartic acid residue in the helical structure approaches the RNAP active site, strengthening the signaling of (p)ppGpp during the translation [22; 23]. Transcriptome results have shown that DKsA regulates more than 7% of genes in _Escherichia coli_[24] and over 20% of the genes in _Xanthomonas citri_[25]. These genes are involved in various physiological processes, such as the type 3 secretion system (T3 SS), motility, biofilm formation, ribosome synthesis, and others. Fatty acid degradation is a fundamental metabolism in bacteria, which not only generates a large amount of energy and materials for the cell, but also maintains the homeostasis of cell membrane lipids in growth and stationary phases [26]. Several studies have reported the association of (p)ppGpp with bacterial fatty acid metabolism. For example, fatty acid starvation is an important trigger of stringent responses. During fatty acid starvation, the acyl carrier protein (ACP) is not charged, and the uncharged ACP binds to SpoT, a member of the bifunctional RelA-SpoT homologue family enzymes in most beta- and gamma-proteobacteria, to induce the synthesis of (p)ppGpp [27]. Furthermore, it has been reported that YttK interacts with the N-terminal region of SpoT to trigger a stringent response [28]. (p)ppGpp also has a significant regulatory effect on fatty acid metabolism pathways. [PERSON] et al. reported that lack of (p)ppGpp would cause significant changes in the expression levels of 17 fatty acid synthesis genes and 6 fatty acid \(\beta\)-oxidation genes [29]. In addition, (p)ppGpp can bind and inhibit PIsB and PgsA, which are responsible for lipid and phospholipid biosynthesis, leading to the inhibition of the carboxyltransferase complex activities [30; 31]. These studies collectively indicate that (p)ppGpp affects fatty acid metabolism in multiple ways, but the effect of DKsA on fatty acid metabolism remains unclear. This study aims to characterize the functions of DKsA in the fatty acid metabolism and cell membrane integrity of _Y. enterocolitica_. Additionally, the effect of DKsA on the bactericidal mechanism of polymysin B in _Y. enterocolitica_ was identified. Results from this study will provide insights into the regulatory role of DKsA in the cell membrane structure in _Y. enterocolitica_ and assist in identifying a valuable target for controlling _Y. enterocolitica_ in food.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 5 Experiments
### Ablation Studies  
To validate the effectiveness of each module, this study conducted ablation experiments on the ODConv, BRA, and GL modules, analyzing their impact on the overall model performance using the SubCATHR-DET dataset. The experimental results are presented in Table 5.  
The ablation results demonstrate that the AugNet model with the ODConv module achieved a 6.2% improvement in mAP, indicating that ODConv enables the network to adaptively process different target features, particularly for few-shot or weak-feature data,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mAP (\%)** & **Training Time (h)** \\ \hline AugNet & 70.1 & 0.3 \\ SPQ & 74.9 & 0.7 \\ HiHPQ & 64.3 & 1.4 \\ CIBHash & 58.4 & 0.9 \\ MeCoQ & 72.3 & 1.0 \\ AugGDNet_BRA (Ours) & 78.7 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training time comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **ODConv** & **BRA** & **GL** & **mAP (\%)** \\ \hline AugNet & - & - & - & 70.1 \\ AugNet+ODConv & ✓ & - & - & 76.3 \\ AugNet+BRA & - & ✓ & - & 73.5 \\ AugNet+GL & - & - & ✓ & 71.2 \\ AugGDNet_BRA (Ours) & ✓ & ✓ & ✓ & 78.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results.  
Figure 9: Retrieval results on CUB-200-2011.  
by finely adjusting convolutional kernels to better capture targets. The model incorporating the BRA module showed a 3.4% increase in mAP, suggesting that BRA significantly enhances the ability to capture key features and improves the discriminability of weak features, thereby boosting retrieval accuracy and generalization. Replacing the loss function with GL leads to a 1.1% mAP gain, demonstrating that this loss function helps the model extract more robust and discriminative features under few-shot and weak-feature conditions, thereby further optimizing performance.
# A Performance Evaluation for Software Defined Networks with P4
## 7 Discussion
The statistics such as increased bps and throughput, reduced delay jitter, packet loss, delay and buffering time have led to a higher quality of application at the receiver's end. Improvement of the quality of the video at the receiver end for a video streaming application (Figure 11) has also been presented. We investigated whether the type of traffic has a bearing on performance for _SDN+P4_ in comparison to _SDN+OvS_. Given that the majority of network traffic will be accumulated by multimedia applications in the future, a fair switching mechanism that enhances the performance of such applications will provide a beneficial resource for service providers. As the majority of the downloaded traffic in a modern context consists of UDP traffic (multimedia applications) and the demand for such applications is expected to grow exponentially in the future, the employment of _SDN+P4_ is a viable solution for service providers looking to provide an improved service. Protocols such as Google Quick UDP Internet Connections (GQUIC) in mobile telecommunication will also benefit from the employment of _SDN+P4_. The employment of _SDN+P4_ will also benefit non-media applications where a connection-less protocol is employed by a service provider. For example, communication between a Radio Device and eNB, Multi-access Edge Clouds (MEC) or between Radio Access Networks (RAN) and Fog nodes [60] each can benefit through the use of _SDN+P4_. To meet the requirements for faster convergence and faster processing of packets, _SDN+P4_ will, we believe, serve as an inventive and improved solution.
###### Abstract  
It is important to create a comfortable wind environment around high-rise buildings for outdoor activities. To predict the wind environment, Computational Fluid Dynamics (CFD) has been widely used by designers and engineers. However, the simulation results of different CFD turbulence models might significantly vary. This paper researched the wind environment around a typical high-rise building and verified the accuracy of the CFD simulations based on polyhedral meshes. The differences between the simulation results of the k-\(\varepsilon\) turbulence models and those of the wind tunnel experiments were compared from the perspectives of wind speed and turbulence energy. The results show that the modified \(k\)-\(\varepsilon\) models could still not perfectly match the wind tunnel experiment results. Specifically, in the low-wind-speed areas, the simulation results of the Realizable Two-Layer K-Epsilon (RTLKE) model were the closest to the experimental results of the wind tunnels, while in the high-wind-speed areas the simulation results of the Standard Two-Layer K-Epsilon (STLKE) model were the closest to the experimental results of the wind tunnels. Therefore, it is recommended that these two k-\(\varepsilon\) turbulence models are applied under different conditions--the RTLKE model should be used to simulate low-wind areas around high-rise buildings (e.g., defining the size of the static-wind area around high-rise buildings, predicting the diffusion time of pollutants around high-rise buildings, etc.); STLKE should be used to simulate high-wind-speed areas around high-rise buildings (e.g., the high speed wind area around high-rise buildings during a typhoon, the maximum wind speed area around high-rise buildings, etc.). It is expected that findings from this research study supplement some existing high-rise building design guidance.  
high-rise building; polyhedral mesh; wind; k-\(\varepsilon\) turbulence model; accuracy Article
## 2 Basic Equations and Problem Formulation
### Theoretical Verification  
In this subsection we validate the formulation developed in this paper with results from the published literature. The purpose of the comparison is to demonstrate the accuracy of the dispersion equations of the model.  
To demonstrate the accuracy of the statements of the sound field and the establishment of the dispersion equations, the porosity of porous medium \(\beta\) was set to zero. By Equations (11) and (12), we can conclude that the fast longitudinal wave velocity \(c_{Plf}\) and the shear wave velocity \(c_{Pls}\) are the longitudinal wave velocity and shear wave velocity of the solid skeleton in porous medium, and the slow longitudinal wave velocity \(c_{Pl}\) is 0. The slow longitudinal wave does not exist, and so the liquid participation coefficients \(\eta_{1}\), \(\eta_{2}\) and \(\eta_{3}\) are all 0, and the liquid phase potential function is 0. When the porosity is 0, \(K_{PS}=K_{Pb}(K_{PS},K_{Pb}\) are the bulk modulus of the solid skeleton and the bulk modulus of the solid matrix), \(N=\mu_{S}\). From Equations (13)-(15), \(A=K_{PS}-\frac{2}{3}N=\lambda_{S}\), \(R=0\), \(Q=0\), and \(\lambda_{S}\) and \(\mu_{S}\) are the same constants of the solid in porous medium. It can be seen that the expressions of displacement and stress, and the boundary conditions, correspond to those in a situation wherein the medium surrounding the liquid-filled pipe is a completely homogeneous elastic medium, and the dispersion equation also satisfies this condition [36; 37].  
All these prove that when the porous medium becomes a completely homogeneous elastic medium, the statements of the sound field and the dispersion equations satisfy this condition.  
In the cylindrical structure with porous media, the dispersion function of the guided wave is a complex function. Equations (34) and (35) represent the relationship between frequencies and wavenumbers, leading to a theoretical zero-problem. By solving Equations (34) and (35), the velocities of the guided wave can be obtained. The real part represents the wave mode information that can be used to plot the phase velocity dispersion curves, and the imaginary part corresponds to the attenuation coefficient.
## 4 Dispersing the Light
### Echelle Gratings  
#### 4.2.1 The Concept  
Another photonic technique based on a conventional spectrograph is photonic echelle grating (also called planar concave grating) spectrograph. A sample schematic is shown in Figure 5. The light from a single mode fiber is coupled with the input waveguide which then illuminates the grating through the free propagation region. The grating teeth are designed to have reflectivity, which creates constructive interference fringes for different wavelengths in a spectral order at different locations in the focal plane. The gratings can be designed to focus most of their light in a particular order by controlling the blazing angle. The geometry of echelle gratings can be designed in many ways, the most common being the Rowland circle method [7], the two-point stigation method [74], and perfectly chirped gratings [75]. The high-efficiency reflection can also be achieved in a few different ways, such as by coating the vertical surface of grating teeth with reflective material like chromium or silver [76; 77; 78] or using distributed Bragg reflectors (DBRs) [79].  
Photonic echelle gratings (PEGs) are particularly well suited for high-finesse (i.e., cross-talk \(\lesssim\!-25\) dB) spectrographs, thanks to the much larger number of grating teeth that can be incorporated compared to the number of arrayed waveguides in an AWG. In addition, due to their compactness, multiple PEGs can be etched on a single compact chip in a tandem echelle design [7] without losing material uniformity. A small footprint also implies smaller propagation loss.
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
## 4 Discussion  
### Uncertainty Correction of the Ground Motion Attenuation Relationship  
The PSHA method is a method that fully considers the temporal and spatial heterogeneity of seismic activity, including the contributions of all potential hypocenter regions and the average activity rate associated with the seismic zone (e.g., [42; 43]). In the process of developing the probabilistic seismic hazard analysis methods in recent years, engineering geological studies have proposed uncertainty corrections for the ground motion attenuation relationship (e.g., [44; 45; 46; 47]). Since the ground motion attenuation relationship has a certain discreteness, the attenuation formula applied in the calculation process gives only the expected value of ground motion. [PERSON] et al. [48] suggested that if the seismic input comes from a probabilistic seismic scenario, the nonlinear response of the PGA to the site effect and topographic effect should be considered. Therefore, to make the results safer and more reliable, we made an uncertainty correction for the decay relationship using the following equation:  
\[P(A>a)=\frac{1}{\sqrt{2\pi}\sigma_{i}}\int_{-3\sigma_{lnA}}^{3_{lnA}}P(A>ae^{ -x})exp[-\frac{1}{2}(\frac{X}{\sigma_{lnA}})^{2}]dx \tag{8}\]  
where \(P(A>a)\) is the corrected transcendence probability, \(P(I>I-x)\) and \(P(A>ae^{-x})\) are the uncorrected transcendence probabilities, \(x\) is a random variable, and \(\sigma\) is the standard deviation of the decay relation.  
In addition, because the earthquake isoseismal is elliptical, in addition to the earthquake magnitude and distance, the isoseismic long-axis orientation plays a certain role in the seismic hazard risk evaluation of the site especially in the near field. The orientation of the long axis of the isoseismal line is represented by the directional function \(f(\theta)\), which is related to the tectonic trend of the corresponding potential source area, and its expression is:  
\[f(\theta)=P_{1}\delta(\theta_{1})+P_{2}\delta(\theta_{2}) \tag{9}\]  
Figure 4: Response spectrum curve of the bedrock horizontal acceleration at the engineering site of Najin Lhasa.  
In the formula, \(\theta_{1}\) and \(\theta_{2}\) are the possible main rupture surface strikes in the potential source region, and \(P_{1}\) and \(P_{2}\) are the corresponding orientation probabilities.
## 4 FEM Verification and Analysis
### Validation Analysis of Electromagnetic Torque and Magnetic Levitation Force Models  
#### 4.2.1 FEM Validation Analysis of Torque Model  
Under the condition of zero radial displacement, and within the range of 0-6 A, only torque current was applied to the six-phase winding with a step size of 1.5 A. Figure 6 shows the dynamic waveform of electromagnetic torque over time (or rotor position angle), and Figure 7 shows the comparison curve between the model calculation results and the FEM analysis results for electromagnetic torque. Based on Figures 6 and 7, the following research results were obtained:  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pole-pair number of the rotor & 1 \\ Number of stator slots & 12 \\ Stator core outer diameter (mm) & 80 \\ Stator core inner diameter (mm) & 38 \\ Rotor core outer diameter (mm) & 32 \\ Thickness of PM, \(L_{\mathrm{m}}\) (mm) & 2 \\ Air gap length, \(g\) (mm) & 1 \\ \(L_{1}\) (mH) & 5.45 \\ \(L_{2}\) (mH) & 2.72 \\ \(M_{12}\) (H/m) & 0.6829 \\ \(\Psi_{f}\) (Wb) & 0.133 \\ Axial length (mm) & 40 \\ Residual magnetic density of PM (\(T\)) & 1.23 \\ Coercivity of PM (kA/M) & \(-\)890 \\ Turn number of coils & 100 \\ Core materials of stator and rotor & DW465\_50 \\ Permanent magnet materials & NdFe35 \\ Relative permeability, \(\mu_{r}\) & 1.09978 \\ Boundary condition & Natural boundary condition \\ Rated power (kw) & 1.2 \\ Rated speed (rpm) & 3000 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the six-phase DSW-12/2-BPMSM.  
Figure 5: Distribution of magnetic lines of force after applying levitation current.  
(1) The steady-state mean value of the torque is basically proportional to the torque current, and the model calculation value of the torque closely matches the FEM analysis value, with the maximum error between them not exceeding 6.86%. The reason for the error is that the torque model does not take into account the effects of the magnetic pressure drop and magnetic saturation of the iron core, as well as the edge effect of the permanent magnet field and other factors.  
(2) Under different torque current excitations, the dynamic waveforms of torque over time (or position angle) are smooth, and the peak-to-peak fluctuation rate is generally maintained at about 9.26%. The dynamic waveform of torque is smooth, and the control performance is good. This is an advantage of multiphase motors, providing a reliable foundation for the stable rotation control of the six-phase DSW-12/2-BPMSM.
## 4 Implementation Procedure
### Implementation of Proposed Model
#### 4.2.2 Defining the Existing Sites or Nodes  
* SVM model is trained by performing fitness evaluation on each subset and evaluating its accuracy on a validation set.
* The algorithm continues for a specified number of iterations, gradually improving the fitness of the population and finding the best subset of features.
* Once the algorithm completes, the dataset is fragmented into subsets based on the selected features from the best solution found by the RFO-SVM algorithm.
* Each fragment represents a smaller portion of the original dataset, possibly containing a subset of features and corresponding target values.
* Accuracy is used as a metric for efficient optimization of SVM parameters.