esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Canonical Analogue Black Holes
### Mode Decomposition of the Scalar Field  
In conclusion, one obtains the mode decomposition of the scalar field, as a superposition of modes with fixed angular momentum and frequency  
\[\theta_{1}\left(t,r,\Omega\right) = \int_{0}^{\infty}\mathrm{d}\omega\sum_{l=0}^{\infty}\sum_{m=-l}^{l }\left(f_{\omega lm}^{\mathrm{HC}}\left(t,r,\Omega\right)a_{\omega lm}^{ \mathrm{HC}}+\overline{f_{\omega lm}^{\mathrm{HC}}}\left(t,r,\Omega\right)a_{ \omega lm}^{\mathrm{HCt}}\right)+ \tag{57}\] \[+\int_{0}^{\infty}\mathrm{d}\omega\sum_{l=0}^{\infty}\sum_{m=-l}^ {l}\left(f_{\omega lm}^{\mathrm{ext}}\left(t,r,\Omega\right)a_{\omega lm}^{ \mathrm{ext}}+\overline{f_{\omega lm}^{\mathrm{ext}}}\left(t,r,\Omega\right)a_ {\omega lm}^{\mathrm{extt}}\right)+\] \[+\int_{0}^{\infty}\mathrm{d}\omega\sum_{l=0}^{\infty}\sum_{m=-l}^ {l}\left(f_{\omega lm}^{\mathrm{int}}\left(t,r,\Omega\right)a_{\omega lm}^{ \mathrm{int}}+\overline{f_{\omega lm}^{\mathrm{int}}}\left(t,r,\Omega\right)a _{\omega lm}^{\mathrm{int}}\right)\,\] \[\rho_{1}\left(t,r,\Omega\right) = -\frac{1}{\lambda}\left(\partial_{t}-V\left(r\right)\partial_{r} \right)\theta_{1}\left(t,r,\Omega\right). \tag{58}\]  
Depending on the radius, only one set of modes between \(f^{\mathrm{ext}}\) and \(f^{\mathrm{int}}\) appears in the definition of the field.  
The field operators follow the canonical commutation relations  
\[\left[\theta_{1}\left(t,r,\Omega\right),\theta_{1}\left(t,r^{ \prime},\Omega^{\prime}\right)\right] = 0\,, \tag{59}\] \[\left[\theta_{1}\left(t,r,\Omega\right),\rho_{1}\left(t,r^{ \prime},\Omega^{\prime}\right)\right] = \delta\left(\Omega,\Omega^{\prime}\right)\frac{\delta\left(r,r^{ \prime}\right)}{rr^{\prime}}\,, \tag{60}\]  
and the ladder operators relative to the modes are defined to follow the usual commutation relations in the quasi-particle Fock space  
\[\left[a_{\omega lm}^{\mathrm{HC}},a_{\omega^{\prime}l^{\prime}m^ {\prime}}^{\mathrm{HCt}}\right] = \delta_{ll^{\prime}}\delta_{mm^{\prime}}\delta\left(\omega,\omega ^{\prime}\right)\,, \tag{61}\] \[\left[a_{\omega lm}^{\mathrm{ext}},a_{\omega^{\prime}l^{\prime}m^ {\prime}}^{\mathrm{extt}}\right] = \delta_{ll^{\prime}}\delta_{mm^{\prime}}\delta\left(\omega,\omega ^{\prime}\right)\,,\] (62) \[\left[a_{\omega lm}^{\mathrm{int}},a_{\omega^{\prime}l^{\prime}m^ {\prime}}^{\mathrm{int}}\right] = \delta_{ll^{\prime}}\delta_{mm^{\prime}}\delta\left(\omega,\omega ^{\prime}\right)\,, \tag{63}\]  
and all the other commutation relations between ladder operators vanish identically.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 1 Introduction
The authors presented an investigation on wind turbine contribution to the frequency control of non-interconnected island systems in [18], and a novel frequency regulation by DFIG-based wind turbines was presented in terms of coordinating inertial control, rotor speed control and pitch angle control for the low, medium or high wind speed mode [19]. The main contributions of the paper include the following. 1. A very detailed Doubly Fed Induction Generator with a two-lumped mass Wind Turbine model is presented to analyze the impact of wind power generation on power system transient stability. 2. The factors including wind power generation level, the wind power location and wind speed are comprehensively investigated for system transient stability analysis in the paper. 3. The simulations result of the modified IEEE 14-bus system showed that (a) the system stability decreases with the growing penetration level of wind power generation; (b) The wind farm locations and wind speed can affect power system transient stability; (c) Installing capacitors will improve system transient stability. The paper is organized as follows. The basic theory of transient stability analysis and the conventional generator dynamic models are introduced in Section 2, and then the very detailed DFIG with wind turbine model is presented in Section 3. In Section 4, the factors possibly affecting the system transient stability level are investigated in detail with a conclusion drawn in the last section.
## 2 Study-Case  
The selected study case is a viaduct of the Perugia-A1 motorway in the municipality of Perugia, in central Italy, built in the 80's. An aerial view including the considered sector of the viaduct is given in Figure 1, along with the plan view with the selected pier (blue circle).  
The viaduct is composed of eight piers of pseudo-rectangular cross-sections with a dimension of 6 \(\times\) 2 m; their lengths vary between 6 and 12 m, and the axial distance between each other is 19 m. The deck is constituted of a prestressed reinforced concrete hollow section, with a total weight of 4880 kN. The five central piers are founded on caissons of 8.3 m in diameter and 9 m in depth, while the three lateral (shorter) piers are founded on piled rafts. Figure 2 shows the longitudinal section of the viaduct derived from the original design plot (courtesy of National Autonomous Roads Corporation, ANAS). The soil stratigraphical information was obtained from the original project. The foundation soil is constituted of two layers: a first clayey layer--from the surface down to 9 m, and a fractured Marly formation--from 9 m to the end of the borehole (50 m). The depth of the water level is assumed at about 2 m from the ground surface, with small oscillations during the year. The base of the caissons reaches the Marly formation.  
Figure 1: The figure shows: (**a**) the aerial view of the examined viaduct of the Perugia A1 motorway; and (**b**) the plan view of the considered sector where the blue circle indicates the considered pier (original project).
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Mobility and Handovers  
The permissible radio range of CM4 is \(\sim\)5 m (as indicated in the IEEE standard itself [10]). This is the motivation for dividing the coverage area into smaller partitions, i.e., zones of 5 \(\times\) 5 m\({}^{2}\). This inspiration of dividing the physical location into smaller regions comes from the cellular networks (e.g., micro or femto cells).  
The positions of the gateways are fixed, i.e., at the center of each zone, while the nodes are randomly deployed. All nodes (patients) can freely and randomly move in all directions across all zones, at a constant speed of 1 m per second. The gateways are installed in such a way that when a node crosses the coverage of one gateway, it enters into the other gateway coverage zone, and handover is taking place. A simple distance-based handover algorithm has been implemented to facilitate the movement of all nodes freely across all zones and providing seamless vital data transfer while moving from one access point (gateway) to another [24; 30].
## 3 Numerical Analysis and Experimental Verification
### Modulation Due to Improper Tension  
As the rotational vibration is closely related to belt tension, this section examines how the belt tension influences modulation signals. Figure 12 illustrates the spectra of the current and speed when \(P_{0}\) is reduced to 260 N. Compared with Figure 10, it can be observed that reducing \(P_{0}\) leads to a clear increase in the amplitude at \(2f_{r}\). However, the spectral amplitudes at other frequencies, such as \(f_{r}\) and \(f_{s}\), including their harmonics, showed little change.  
To monitor the condition of the machining system using the modulation sideband, it is necessary to ensure that the tension is within the appropriate range. As shown in Figure 13, the amplitude of \(2f_{r}\) is extracted when \(P_{0}\) is linearly adjusted and the driving pulley speed is constant. On increasing the tension force, the amplitude of the transverse vibrations  
Figure 11: Spectra for current and rotational speeds of the motor and spindle.  
Figure 12: Spectra of current and speed (\(P_{0}\) = 260 N).  
is decreased; however, the frequency of the vibrations remains unaltered. Similarly, low tension results in a low \(2f_{r}\) sideband amplitude, as power cannot be transferred to the belt drive because of the low tension. Additionally, the amplitude at \(2f_{r}\) did not change when the tension was increased to a certain extent, indicating that the belt was not tensioned appropriately.
## 6 Conclusions  
The stability of bedding slopes is analyzed based on the pseudo-dynamic method in this study, and the influences of slope parameters and seismic parameters on stability are also investigated. The feasibility of the pseudo-dynamic method is verified by comparison with the results of earlier examples and the finite element method. To sum up, the following conclusions are obtained:  
1. The slip surface of a bedding slope is the structural plane closest to the slope toe, and this conclusion has been confirmed by the pseudo-dynamic method and numerical simulation in this study. It has a great theoretical significance for understanding the slip mechanism of bedding slopes.
2. The safety factor obtained by the pseudo-dynamic method shows an apparent periodical characteristic with time. Hence, the minimum transient safety factor is defined as the dynamic safety factor under the seismic load. Without offering enough consideration of practical deformation, the pseudo-dynamic method makes the safety factor overly conservative.
3. The influences of slope parameters on the safety factor include both that the safety factor increases linearly with an increase in the internal friction angle and cohesion, but decreases with the increase in both slope angle and slope height; and that there seems to be an optimal value corresponding to the minimum safety factor for the structural plane angle and slope angle, and the ratio is approximately 0.5-0.6.
4. The influences of seismic parameters on the safety factor include the following: the influences of seismic acceleration coefficients on the safety factor are mainly shown by vibration time and direction, and the influence efficiency of \(k_{\text{h}}\) on the safety factor is significantly greater than \(k_{\text{v}}\); the effect of the seismic amplification coefficient on the safety factor is different at different vibration times, and it can effectively simulate the seismic inertia force at different positions compared with the pseudo-static method; and the input wave velocity only affects the changing frequency of the safety factor, but has no effect on its extreme value.  
Conceptualization, investigation, methodology, Y.L. and X.C.; data processing, J.H. and X.Z.; original draft, Y.L.; writing and editing the draft, Y.L. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hebei Natural Science Foundation (No. E2021512002) and the Key Laboratory of Building Collapse Mechanism and Disaster Prevention, China Earthquake Administration (No. FZ211104).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon request.  
Author [PERSON] was employed by the company China North Industries Norengeo Ltd. 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.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
## 3 Results and Discussion  
### Optimization Design of the Waveguide Parameters  
To realize low loss and efficient transmission of optical carrier, the dimensions of the waveguide cross section and bending radius of the waveguide are optimized. The ridge-type waveguide structure can effectively reduce the scattering loss as this structure can reduce the light interaction with the sidewalls. In this work, the proposed device adopts this structure. The total thickness of lithium niobate film is 600 nm, in which the thickness of residual layer is 250 nm and the ridge height is 350 nm. Figure 3a shows the single-mode condition of the waveguide. The critical points of single-mode of both TE and TM modes can be found at \(w\) = 1 \(\upmu\)m and 0.8 \(\upmu\)m, respectively. Figure 3b,c show that with gradually increasing waveguide width (\(w\)), the waveguide transmission loss decreases and the optical power propagated in LN layer increases. The higher proportion of light field confined in LN, the greater the opto-electric overlap factor will be, causing the modulator to have higher electro-optic modulation efficiency. Figure 3e shows that with the increase of the electrode spacing, the absorption of the waveguide gradually decreases, resulting in a gradual decrease of waveguide loss. Finally, the dimensions of the waveguide are also  
Figure 2: Comparison of resonant peaks of double ring resonator and single ring resonator at the same resonant wavelength. The ring length of the single ring resonator is the same as the inner ring length of the double ring resonator.  
determined. As shown in Figure 3a, the waveguide width is chosen to be 0.8 \(\upmu\)m in order to guarantee the single-mode condition. In this case, the effective refractive index (\(n_{\textit{eff}}\)) is 1.85, and the group refractive index (\(n_{g}\)) is 2.23. It can be seen from Figure 3c that with 0.8 \(\upmu\)m waveguide width, the optical power ratio in LN waveguide exceeds 80%. It can be noticed from Figure 3d that when the bending radius is greater than 100 \(\upmu\)m, the waveguide loss is approximately equal to that of the straight waveguide and tends to be stable. Therefore, the bending radius of 100 \(\upmu\)m is chosen in our design. The electrode spacing of 6 \(\upmu\)m is selected for a lower insertion loss.  
Figure 3: (**a**) The effective refractive index at different waveguide width; (**b**) the waveguide loss at different waveguide width; (**c**) the optical power ratio in lithium niobate at different waveguide width; (**d**) the waveguide loss at different bending radius; and (**e**) the transmission loss of the LN waveguide with different electrode spacing.
## 3 Surface Defect Detection Network for Automobile Wheel Hub
### High-Resolution Feature Extraction Module  
In CNN, the commonly used backbone networks include VGGNet and ResNet. These backbone networks use many pooling layers, which help classify defects, but loses low-level spatial information, which is not conducive to the pixel detection in defect areas. HRNet can maintain a high-resolution feature with accurate spatial information and fuse the semantic information in the low-resolution feature, exhibiting a good defect segmentation effect. The resolution of the final feature map output from ResNet-101 is \(1/32\) of the original image, while that of the final feature map output from HRNet is \(1/4\) of the original image, which is nearer the original image resolution. When the feature map is restored to the original resolution, HRNet only needs four upsampling iterations, while ResNet-101 requires 32 upsampling iterations, indicating that HRNet as the backbone network can obtain a more accurate segmentation map than ResNet-101 [9].  
HRNet is used on the image as the high-resolution feature extraction module. The structure of HRNet is shown in Figure 2.  
First, HRNet uses a convolution with a stride of 2 on the input image to obtain low-level feature \(F_{fine}\), which contains fine image spatial information. Then, a convolution with a stride of 2 is used to extract a feature map whose resolution is \(1/4\) that of the original image. Next, this feature map is input into branch1, whose resolution is the highest among all the subnetworks. Finally, according to the order of resolution from high to low, the  
Figure 1: Surface defect detection network for automobile wheel hub.  
features are gradually input into the subnetworks for further feature extraction. After the repeat fusion of features with different resolutions in the four subnetworks, the feature maps from three other subnetworks are fused into branch1 to obtain the feature (\(F_{high}\)) to be output. \(F_{high}\) has both high-resolution spatial and deep semantic classification information.
## 2 Methodology  
As illustrated in Figure 2, STFD comprises three key components. In the temporal domain, we propose TRC and RIA, which address challenges posed by complex scenarios and dim targets. In the spatial domain, we introduce the GELCM, which leverages directional contrast to capture gradient information for spatial target detection. Finally, we fuse the detection results from these three components to achieve robust and effective detection of dim and small targets in complex scenarios.
# Lateral-Torsional Buckling of Cantilever Steel Beams under 2 Types of Complex Loads
## 4 Deduction of the Closed-Form Solution of \(\boldsymbol{M_{\text{cr}}}\)
### Derivation of Closed-Form Solution of \(M_{\text{cr}}\) Based on Twist Angle Function  
With the assumption that torsional rotation is small, the second derivative of \(u\) with respect to \(z\) can be written as given below from [9].  
\[u^{\prime\prime}=-\frac{M_{\text{x}}\varphi}{EI_{y}} \tag{41}\]  
The moment distribution function \(\kappa(z)\), the twist angle function \(\varphi(z)\) and \(M_{\text{max}}\) are defined as follows.  
\[\kappa(z)=\frac{M_{\text{x}}}{M_{0}} \tag{42}\]  
\[\varphi(z)=B\varphi_{0}(z) \tag{43}\]  
\[M_{\text{max}}=-M_{0} \tag{44}\]  
where \(M_{0}\) is the moment at the fixed end of the cantilever beam. The value of \(M_{0}\) is less than 0. \(\varphi_{0}(z)\) is the basic function of twist angle function \(\varphi(z)\) of the cantilever, and \(B\) is an undetermined coefficient. According to the conclusions obtained in Table 7 and Figure 6, \(\varphi_{0}(z)\) could use the following formula.  
\[\varphi_{0}(z)=\sum_{n=1}^{10}B_{n}\bigg{[}1-\cos\frac{(2n-1)\pi z}{2l}\bigg{]} \tag{45}\]  
Some physical quantities are defined as follows.  
\[r_{1}=\int_{0}^{l}\kappa^{2}(z)\varphi_{0}{}^{2}(z)\text{d}z \tag{46}\]  
\[r_{2}=\int_{0}^{l}\left(\varphi_{0}{}^{\prime\prime}(z)\right)^{2}\text{d}z \tag{47}\]  
\[r_{3}=\int_{0}^{l}\left(\varphi_{0}{}^{\prime}(z)\right)^{2}\text{d}z \tag{48}\]  
\[r_{4}=\int_{0}^{l}\kappa(z)(\varphi_{0}{}^{\prime}(z))^{2}\text{d}z \tag{49}\]  
\[r_{5}=\int_{0}^{l}\varphi_{0}{}^{2}(z)\text{d}z \tag{50}\]  
\[r_{6}=\varphi_{0}{}^{2}(\lambda l) \tag{51}\]  
Then Equation(1) can be written as follows.  
\[\Pi=-\frac{M_{0}{}^{2}B^{2}}{2 EI_{y}}r_{1}+\frac{1}{2}EI_{\omega}B^{2}r_{2}+ \frac{1}{2}GI_{\text{t}}B^{2}r_{3}+\beta_{\text{x}}B^{2}M_{0}r_{4}+\frac{1}{2} a_{\text{q}_{y}}q_{y}B^{2}r_{5}+\frac{1}{2}a_{\text{p}_{y}}P_{y}B^{2}r_{6} \tag{52}\]\[M_{\rm cr}\]  
's expression can be obtained from \(\frac{3\Omega}{\partial B}=0\).  
\[M_{\rm cr}=\frac{\sqrt{EI_{y}}}{2r_{1}}\Big{[}-\sqrt{EI_{y}}(2r_{4}\beta_{\rm x} -\frac{2r_{5}}{(\alpha+2\lambda)l^{2}}a_{yy}-\frac{2r_{6}}{(\alpha+2\lambda)l }a_{yy})+\sqrt{EI_{y}(2r_{4}\beta_{\rm x}-\frac{2r_{5}}{(\alpha+2\lambda)l^{2} }a_{yy}-\frac{2r_{6}}{(\alpha+2\lambda)l^{2}}a_{yy})^{2}+4r_{1}(EI_{\rm av}r_{2 }+Glr_{3})}\Big{]} \tag{53}\]  
When the load case is CUDL, \(\alpha=1\) and \(a_{py}=a_{qy}\) are considered. Some physical quantities are defined as follows.  
\[R_{1}=2r_{1} \tag{54}\]  
\[R_{2}=-\frac{2r_{6}}{(\alpha+2\lambda)l}\text{ or }R_{2}=-\frac{2r_{5}+2r_{6}l}{( \alpha+2\lambda)l^{2}} \tag{55}\]  
\[R_{3}=2r_{4} \tag{56}\]  
\[R_{4}=r_{2} \tag{57}\]  
\[R_{5}=r_{3} \tag{58}\]  
The expression of \(M_{\rm cr}\) can be written as follows.  
\[M_{\rm cr}=\frac{\sqrt{EI_{y}}}{R_{1}}\Big{[}\sqrt{EI_{y}(R_{2}a+R_{3}\beta_{ \rm x})^{2}+2R_{1}(EI_{\rm av}R_{4}+G_{1}R_{5})}-\sqrt{EI_{y}}(R_{2}a+R_{3} \beta_{\rm x}) \tag{59}\]
## 3 Smart and Secure HIE Systems: SSHES
### Smart Contracts and Integration into HIE Systems  
A smart contract is an autonomous contract where the agreement terms are encoded directly into lines of code, enabling self-execution. It is an automated and decentralized technology that enables the running of transactions or agreements without the necessity of intermediaries. Smart contracts are built on blockchain platforms, most commonly associated with cryptocurrencies like Ethereum. Smart contracts are created to streamline, authenticate, and ensure the negotiation or execution of a contract. They operate on an \"if-then\" logic, where predefined conditions are set, and when those conditions are triggered, the contract automatically runs the agreed-upon tasks. These contracts remove the necessity for intermediaries like attorneys or notaries, as they provide transparency, security, and efficiency in executing contractual obligations.  
To integrate smart contracts into health care information exchange, several key steps need to be followed [30; 31; 32; 33; 34]:  
* Identify Use Cases: these include patient consent management, insurance claim processing, managing the flow of goods and services, and clinical trial information control.
* Define Contract Terms: identify the parties involved, their roles and responsibilities, the actions to be executed, and the conditions triggering those actions.
* Select a Blockchain Framework: choose a suitable blockchain framework that helps smart contracts and aligns with the requirements of health care information exchange.
* Develop Smart Contract Code: the code should accurately reflect the agreed-upon terms and incorporate appropriate logic for executing actions based on predefined conditions.
* Test and Deploy: simulate various scenarios to verify that the contract behaves as expected under different conditions.
* Integrate with Existing Systems: this step involves developing APIs or other interfaces to facilitate data exchange and communication between the smart contract and other systems.
* Ensure Compliance and Privacy: this step considers regulatory requirements and privacy concerns when integrating smart contracts into health care information exchange.
* Monitor and Maintain: continuously monitor the performance and security of the smart contract in production.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 3 Problem Description and Model Used
### Models Used: XGBoost and Random Forest  
XGBoost [20] and Random Forest [21] were implemented to make predictions based on the aforementioned features. Their performance when dealing with features of tabular data format, in addition to their built-in capability of ranking feature importances, were the reasons for choosing them.  
Both models are based on the combination of regression trees. That is, both are ensemble methods, whose final predictions are based on the combination of the predictions of single regression trees. A key difference exists between them, however, in the manner in which each ensemble is constructed [22]. XGBoost utilizes the concept of boosting, whereas Random Forest relies on the concept of bagging.  
According to bagging, all trees are grown in parallel, with each tree a standalone predictor of the quantity under estimation. The ensemble's estimation is the average of the estimations of all trees. Boosting, by comparison, relies on the sequential growth of trees. Each new tree is grown with respect to the previous tree's errors, and tries to compensate for these errors.