esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 2 Related Works
### Handover Schemes for Feeder Links  
Although scholars have carried out substantial work on the handover in satellite networks, they mainly focused on how the users maintain ongoing communication when switching the connected satellites. Efficient transmission between the LEOs and GSs is also an indispensable component for end-to-end communication [26]. In [18], multiple-input multiple-output (MIMO) technology was applied to the feeder link handover strategy to improve the achievable transmission rate of feeder links. An adaptive access selection algorithm for GS is proposed in [27]. The selection strategy of a backup access satellite and the concept of virtual destination address can reduce the routing overhead after switching.  
Although several studies have been proposed to optimize the feeder link handover, they ignore that the inter-satellite network near the GSs is also a potential bottleneck. In this article, we propose a handover scheme jointly considering the feeder links and the inter-satellite network.
## 3 Dynamic Response Analysis of RPC Columns Under Coupled Fire and Explosion
### Axial Pressure Ratio  
The axial pressure ratios of 0.2, 0.4, and 0.6 were selected to analyze the effect of the axial pressure ratio on the blast resistance performance of RPC columns. The simulation results analyzed show that when the explosive equivalent selected is smaller, the p-\(\Delta\) effect is not obvious enough, resulting in a smaller gap in the displacement of the RPC column under the explosive load. So, in this section, 100 kg TNT explosive equivalent was selected to analyze the blast resistance of the RPC column. Different axial pressure ratios under the column span displacement-time curve are shown in Figure 10; it can be seen that the amplitude of the RPC column explosion shock stage is significantly reduced. This is due to the high axial pressure ratio under the action of the column in the explosion load producing greater deformation; the vertical load of the component stress redistribution of span displacement has a greater impact on the column rebound difficulties. It can also be seen that the change in the axial pressure ratio has only a limited effect on the column shock phase period.  
Figure 10: Time course of horizontal displacement in span of RPC columns with different axial pressure ratios.  
Figure 9: Plot of horizontal displacement in RPC column span as a function of section size.  
The maximum mid-span deformation and residual deformation of RPC columns with different axial pressure ratios are shown in Figure 11. It can be seen that the maximum mid-span displacements of the RPC columns at axial pressure ratios of 0.2, 0.4, and 0.6 are 62 mm, 66.24 mm, and 71.56 mm, respectively, and the residual displacements are 52.11 mm, 58.06 mm, and 65.74 mm, respectively. The maximum mid-span displacements of the RPC columns at an axial pressure ratio of 0.2 were reduced by 6.4% and 13.35% compared to those at axial pressure ratios of 0.4 and 0.6, respectively. The residual displacements of the RPC columns with an axial pressure ratio of 0.2 were reduced by 10.24% and 20.7% compared to those with axial pressure ratios of 0.4 and 0.6, respectively. The above results show that the axial compression ratio has a significant effect on maximum mid-span and residual displacements.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 1 Introduction
### The Present Work  
Inspired by the research of spherical motor [10; 11], as well as the driving method of an intraocular micro robot OctoMag [12], a laparoscopic robot system is designed in this paper. Both OctoMag and the structure proposed in this paper use multiply electromagnets to generate controllable magnetic field to control the position and of the robot. However, the OctoMag is designed for microrobots used in retinal procedures, therefore, gravity compensation of the robot is relatively easy to achieve due to the liquid work environment and the light weight of the robot. However, for the structure proposed in this paper, electromagnets need to provide enough force to compensate the gravity of the laparoscopic robot, which is the prerequisite for position and orientation control of the robot.  
As shown in Figure 1, the system consists of an external driving device (i.e., the stator) and an inner laparoscopic robot (i.e., the rotor). The stator consists of five electromagnets. The rotor is a capsule-shaped cylinder, whose hemispherical dome is installed with IPMs. The distribution of electromagnets and IPMs are shown in Figure 2a.  
As shown in Figure 2b, under the action of the external magnetic field, the stator can keep the rotor away from the incision and then make room for other surgical instruments in SILS. Thus, the interferences between rotor and other surgical instruments can be avoided. Additionally, the stator can provide position and orientation control of the rotor to achieve adjustable field of views. Since the driving system is separated from the laparoscopic robot, the laparoscopic robot has a simpler structure, less energy consumption, and smaller size.  
In this paper, only the cylindrical shell and the hemispherical dome with IPMs of the rotor are fabricated. In order to test the carrying capacity of our design, the IMU unit, wireless unit, camera unit, and the power unit are replaced by a counterweight in practical experiments.  
In the following part, the structure and control principle of the robot system will be introduced in detail.
## 2 Design and Simulation Setup
### Design of the Unit Cell Structure  
The proposed design depicts a metal-dielectric-metal MMA structure with a patch and ground of tungsten (W) and a silicon dioxide (SiO\({}_{2}\)) dielectric substrate. The width and length of the unit cell that has a square shape are both defined to be 800 nm. The outer split ring resonator (SRR) is defined as W\({}_{1}\) = 680 nm and L\({}_{1}\) = 306 nm, and the inner split ring resonator (SRR) is defined as W\({}_{2}\) = 480 nm and L\({}_{2}\) = 205 nm. The parameters are correspondingly specified as H\({}_{1}\) = 100 nm, H\({}_{2}\) = 60 nm, and H\({}_{3}\) = 15 nm. The CRR's radius is R\({}_{1}\) = 140 nm and R\({}_{2}\) = 160 nm. The surface area, (\(\delta\)) = (2/2 fR0)1/2, must be larger than the thickness of the metallic layer to achieve near-zero transmission in an optical area. Figure 1 shows the unit cell's front, side, back, and perspective views. Tungsten (W) and silicon dioxide (SiO\({}_{2}\)) can be identified by their respective light blue and red colors. Table 1 provides a summary of the overall design parameters. An open add space on the z-axis unit cell boundary conditions have been imposed, as well as the x-axis and y-axis for simulation. The refractive index and excitation coefficient of tungsten and Silicon dioxide (SiO\({}_{2}\)) in the optical are illustrated in Figure 2.
# A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks
## 3 Methodology
### Representation of Users and Products Based on Single Behavior  
In a multi-behavior recommendation scenario, each interaction has its own features and semantic representation. For example, in an e-commerce commerce platform, users' browsing behavior is more likely to occur than purchasing behavior, and adding to cart and purchasing behavior may occur simultaneously with high probability. Therefore, the proposed module aims to capture personalized behavioral semantic signals. Based on the representation \(\widetilde{E}_{i}\) of each user \(u_{i}\) and the representation \(\widetilde{E}_{j}\) of each good \(v_{j}\) learned by the embedding module, this module designs a messaging strategy to capture the user-goods interaction graph \(G_{k}=\{V,e_{k}\}\) under a single behavior, where \(V\) denotes the set of user and goods nodes, \(e_{k}\) denotes the set of interaction edges in \(V\), and all the interactions are of type \(k\) at this point. The goal of this module is to learn different behavior-specific embedding vectors. The specific formula is as follows:  
\[\left\{\begin{array}{l}\overline{E}_{i,k}=\widetilde{E}_{i}+\sigma\left( \sum_{j\in N_{i}^{k}}\alpha_{i,j,k}\widetilde{E}_{j}\right)\\ \overline{E}_{j,k}=\widetilde{E}_{j}+\sigma\left(\sum_{i\in N_{i}^{k}}\alpha_{ i,j,k}\widetilde{E}_{i}\right)\end{array}\right. \tag{3}\]where \(\overline{E}_{i,k}\) and \(\overline{E}_{j,k}\) are the embeddings of user \(u_{i}\) and item \(v_{j}\) at behavior type \(k\). Define \(\alpha_{i,j,k}\) as standardization factor \(\sqrt{\mid N_{i}^{k}\parallel N_{j}^{k}\mid}\) where \(N_{i}^{k}\) denotes the set of goods that user \(u_{i}\) interacts with under \(k\) behavior types, and \(N_{j}^{k}\) denotes the set of users interacting with item \(v_{j}\) under behavior type \(k\).
## 3 Results and Discussion
### Phenolic Profile by HPLC-DAD-ESI/MS  
In the interest to provide a phenolic characterization of alcoholic and non-alcoholic beer, an HPLC-DAD-ESI/MS method was used. Figure 3 and Table 2 describe the phenolic content of the ethyl acetate extract, which turned out to be the most complex one. A total of 13 compounds were identified and tentatively known based on their retention times, MS data, and comparison together with the information that was earlier described in the literature [33; 34; 35]. HPLC-MS analysis shows the degradation of antioxidants, such as 3-Hydroxyphloretin 2\({}^{\prime}\)-O-glucose, in the finished product of non-alcoholic beers, depending on the fermentation process on which they are produced.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Peak N** & \(\mathbf{t_{R}}\) **(min)** & **MS** & **MS Fragments** & **Compound** & **Alcoholic Beer** & **Non-Alcoholic Beer** & **References** \\ \hline
**1** & 16.15 & 451- & - & 3-Hydroxyphloretin & & \\
**2** & 25.42 & 357+ & 193- & Ferulic acid-O-hexoside & x & x & [34; 35] \\
**3** & 26.24 & 387+ & 223- & Sinapic acid-O-hexoside & x & x & [34; 35] \\
**4** & 28.22 & 291+ & 245- & Catechin & x & x & [34; 35] \\
**5** & 28.38 & 291+ & 245- & Epicatechin & x & x & [34; 35] \\
**6** & 35.81 & 329- & 314- & 37-Dimethylquercetin & x & x & [34; 35] \\
**7** & 36.43 & 365- & Unknown & x & x & [34; 35] \\
**8** & 44.17 & 347- & 235- & Chumululone I & x & x & [34; 35] \\
**9** & 44.81 & 347- & 278- & Chumulone II & x & x & [34; 35] \\
**10** & 46.24 & 361- & 292- & \(n\)-Humulone & x & x & [34; 35] \\
**11** & 46.73 & 361- & 265- & Iso-a-ad/\(n\)-humulone & x & x & [34; 35] \\
**12** & 47.13 & 361- & 265- & Iso-a-ad/\(n\)-humulone & x & x & [34; 35] \\
**13** & 47.93 & 361- & 265- & Iso-a-ad/\(n\)-humulone & x & x & [34; 35] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of phenolic compounds in alcoholic and non-alcoholic beer samples by HPLC-MS.  
Figure 2: GC–MS chromatograms of alcoholic and non-alcoholic beer samples.  
The final beer product under different conditions was analyzed by GC in order to identify its aromatic compounds (Table 3).  
Fermentation, coupled with the thermal process, represents the easiest method to create alcohol-free beer, the application of heat between 35 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, until boiling, results in the elimination of alcohol and other volatile substances, such as esters and higher alcohols, consequently increasing the turbidity from 0.35 to 2.5 I  
Figure 3: Chromatogram of mass spectrometry in positive and negative modes of bioactive compounds in non-alcoholic (**A**) and alcoholic beers (**B**).  
degradation and diminution of the solid content of the word from 11.60 to 3.16 (wt%) shows that the quality of the non-alcoholic product is impacted by the production process.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 5 Conclusions  
This study proposes a method of full-field mode shape identification based on the subpixel edge-detection and tracking techniques. The Canny operator and the improved ZOM-based subpixel edge-detection technique were adopted to detect the precise structure edges. The detected edge points were then tracked to obtain the displacement time histories, which were subsequently used to determine the full-field mode shapes of the structure. The proposed method was verified on an aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The following conclusions are drawn:  
1. The proposed method uses the Canny operator to locate the rough pixel-level edge and applies the ZOM-based algorithm to relocate the precise subpixel-level edge. The detected edge is very complete and close to the true edge, which shows a much better performance in edge detection compared to the methods in the literature.
2. The methods in the literature can only identify the first two bending vibration frequencies of the cantilever beam, while the proposed method can clearly identify the first four bending vibration frequencies, with errors of less than 3% of the theoretical values.
3. The results of the cantilever beam and the Humen Bridge confirm that the proposed method is capable of identifying high-resolution full-field mode shapes without installing artificial targets. A higher resolution of the mode shapes can be identified as more edge points are selected. The identified high-resolution mode shape of the Humen Bridge is helpful to the determination of modal characteristics.  
However, this study has not considered the influence of complex environments in terms of, e.g., temperature, rain, fog, and atmospheric flow. The applicability under such conditions is the content of future research. In addition, since the actual displacements of the cantilever beam and Humen Bridge are not available, further tests will be conducted to verify the accuracy of the displacement identification using the laser displacement sensors.  
Conceptualization, X.K. and J.Y.; Methodology, X.K. and J.Y.; Investigation and result analysis, K.L. and J.Y.; Writing--original draft, X.K. and J.Y.; Supervision and writing--review and editing, X.K., K.L., X.W. and J.H.; Project administration, X.W. and J.H.; Funding acquisition, X.K., K.L. and J.H.; Resources and visualization, X.W. All authors have read and agreed to the published version of the manuscript.  
This research work was fully supported by National Natural Science Foundation of China (Grant No. 52008160 and 52108434), Science Foundation for Excellent Young Scholars of Hunan Province, China (Grant No. 2021 JJ20015), Postgraduate Scientific Research Innovation Project of Hunan Province, China (Project No: QL20220091).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: Identified mode shapes of girder: (**a**) 11 points; (**b**) 51 points.
## Materials and Methods
### Tower-Based Measurements
#### 2.1.4 Vegetation Indexes and FPAR Estimation  
Canopy reflectance in the range from 645 nm to 805 nm was calculated with the measurements of solar irradiance and vegetation-reflected radiance spectra. According to the study of Reference [42], the soil-adjusted vegetation index (SAVI) had a close connection with the leaf area index (LAI) and was insensitive to soil reflectance at a low LAI. In this study, the daily averaged SAVI was adopted to estimate the LAI of the wheat canopy at the XTS site in 2017. The SAVI was defined by Reference [43] as:  
\[\text{SAVI}=(1+\text{L})\cdot(\rho_{801}-\rho_{670})/(\rho_{801}+\rho_{670}+ \text{L}) \tag{3}\]  
where the constant L = 0.5 was adjusted to explain the first-order soil background variation. The reflectance at 801 nm was replaced by reflectance at 795 nm in this study due to the limitation in the spectrometer's wavelength range.  
The normalized difference vegetation index (NDVI) was used to estimate the diurnal FPAR. According to previous studies [44; 45], for a fixed observed geometry, the diurnal cycles of NDVI and FPAR had similar responses and the FPAR-NDVI relationship was relatively invariant to the SZA. The NDVI was defined as [46]:  
\[\text{NDVI}=(\rho_{801}-\rho_{670})/(\rho_{801}+\rho_{670}). \tag{4}\]  
Similarly, we used reflectance at 795 nm instead of 801 nm in this study. Based on the NDVI linear stretching model reported in Reference [47] and Reference [48], the diurnal FPAR can be expressed as:  
\[\text{FPAR}=\frac{\text{NDVI}-\text{NDVI}_{\rm min}}{\text{NDVI}_{\rm max}- \text{NDVI}_{\rm min}}.(\text{FPAR}_{\rm max}-\text{FPAR}_{\rm min}) \tag{5}\]  
where NDVI\({}_{\rm min}\) and NDVI\({}_{\rm max}\) were set at 0.3 and 0.925 for the crops in our study area and, generally, FPAR\({}_{\rm min}\) and FPAR\({}_{\rm max}\) were 0.001 and 0.95, respectively [47]. [PERSON] [19] compared the model-predicted FPAR with the measured FPAR data and demonstrated that this NDVI-based model reliably estimated FPAR and its diurnal variations. Then, the diurnal FPAR values for all measuring days were estimated using the measured spectral data. Accordingly, the absorbed PAR (APAR) values were calculated as the product of FPAR and PAR derived by the AWS. Similar to the calculation of SIF\({}_{\rm T30}\) in Equation (1), half-hour APAR data (APAR\({}_{\rm T30}\)) were derived. The daily APAR (APAR\({}_{\rm daily}\)) was calculated by integrating the APAR from sunrise to sunset for one day, which was similar to the calculation of SIF\({}_{\rm daily}\) in Equation (2) and has a time-integrated unit of MJ/m\({}^{2}\)/d.
## 2 Analysis of the Runout Interference Mechanism During Sampling
Thus, when runout is present, the tooth coordinates must satisfy the following condition:  
\[y_{k}(T)=0,\ x_{k}(T)>0 \tag{8}\]  
Consequently, when a tooth passes the \(x\)-axis under the effect of this compound motion, the following equation holds:  
\[R\sin(\theta(T)+\alpha(k-1))+d_{y}(T)=0 \tag{9}\]  
In the absence of runout, this becomes  
\[R\sin(\theta(T)+\alpha(k-1))=0 \tag{10}\]  
The relationship for the angle turned by a tooth is  
\[\theta(T)+\alpha(k-1)=n\pi(n\in\mathbb{Z}) \tag{11}\]  
Figure 2: Schematic illustrating the compound motion of the toothed disc involving rotation and runout. That is, in the absence of runout, the angle turned between adjacent teeth \(k\) and \(k+1\) is \(\alpha\). When runout is present, the actual angle turned incurs an error due to \(d_{y}(t)\). Let the \(x\)-axis crossing times for these teeth be \(k\) and \(k+1\). Using a small perturbation approximation,  
\[\theta(t_{k})+\alpha(k-1)\approx-\frac{d_{y}(t_{k})}{R} \tag{12}\]  
\[\theta(t_{k+1})+\alpha k\approx-\frac{d_{y}(t_{k+1})}{R} \tag{13}\]  
Subtracting the two equations yields the actual angle turned:  
\[\theta(t_{k+1})-\theta(t_{k})=\alpha-\frac{d_{y}(t_{k+1})-d_{y}(t_{k})}{R} \tag{14}\]  
The angular displacement error is  
\[\Delta\theta=-\frac{d_{y}(t_{k+1})-d_{y}(t_{k})}{R} \tag{15}\]  
Let the average rotational speed be \(\omega\). Without runout, the time interval is  
\[\Delta t_{0}=\frac{\alpha}{\omega} \tag{16}\]  
With runout, the actual angle turned is \(\alpha+\Delta\theta\), and the time interval is  
\[R\sin(\theta(T)+\alpha(k-1))+d_{y}(T)=0 \tag{9}\]0  
The error contained in the time interval is  
\[R\sin(\theta(T)+\alpha(k-1))+d_{y}(T)=0 \tag{9}\]1  
When \(d_{y}(t)\
eq 0\) is such that \(\Delta t_{\text{error}}\) is non-zero, the time interval measured by the sensor includes errors caused by runout. The deviation between the calculated rotational speed \(\omega_{\text{meas}}\) and the true rotational speed \(\omega\) is  
\[R\sin(\theta(T)+\alpha(k-1))+d_{y}(T)=0 \tag{9}\]2  
Evidently, when the runout component \(\Delta t_{\text{error}}\) is present and its value differs between two consecutive measurement instances that contribute to a speed calculation, it directly impairs the accuracy of the rotational speed measurement. In other words, toothed disc runout causes the tooth-passing times measured by the sensor to include uncertainties. This leads to deviations in the calculated rotational speed, which, in turn, affects the accuracy of assessing the system's torsional vibration characteristics.
## 2 A Review of Decision Model for the DGRP  
As this study aims to validate the effectiveness of using a saying to develop solvers for the DGRP, a brief review of the decision model for the problem is given in this section. Table 1 summarizes the notations used in this paper. The DGRP follows the double auction paradigm in which drivers and passengers submit bids based on their transport requirements subject to several constraints. The problem is to determine the winning bids of drivers and passengers to optimize the overall cost savings while meeting the transport requirements of drivers and passengers and satisfying the constraints. In a DGRP, passengers play the roles of buyers and drivers play the roles of sellers. Consider a DGRP with \(P\) passengers and \(D\) drivers. Let \(p\in\{1,2,3,\ldots.P\}\) be the index of a passenger and let \(d\in\{1,2,3,\ldots,D\}\) be the index of a driver. The bid of passenger \(p\) is denoted by \(PB_{p}=(s_{p1}^{1},s_{p2}^{1},s_{p3}^{1},\ldots,s_{pp}^{1},s_{p1}^{2},s_{p2}^{ 2},s_{p3}^{2},\ldots,s_{pp}^{2},f_{p})\), where \(s_{pk}^{1}\) is the number of seats requested by \(p\) at location \(k\), \(s_{pk}^{*}\) is the number of seats released at drop-off location \(k\) of passenger \(p\) and \(f_{p}\) is the passenger \(p^{\prime}\)'s original cost when he/she travels alone. A driver may submit multiple bids. The \(j-th\) bid of driver \(d\in\{1,2,\ldots,D\}\) is represented by \(DB_{dj}=(q_{dj1}^{1},q_{dj2}^{1},q_{dj3}^{1},\ldots,q_{djp}^{1},q_{dj1}^{2},q_ {dj2}^{2},q_{dj3}^{2},\ldots,q_{djp}^{2},o_{dj},c_{dj})\), where \(q_{djk}^{1}\) is the no. of