esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Research on the Temperature Distribution of the Arrester
### The Temperature Characteristics of the Arrester under Typical Overvoltage
#### 4.6.3 Steep Impulse Overvoltage  
After the steep impulse overvoltage waveform shown in Figure 8c was imported into the simulation model, the results of the current and temperature distribution are shown in Figure 18.  
As shown in Figure 18, under the action of steep impulse overvoltage, the current amplitude of the arrester is 30.02 kA, which is much larger than the nominal discharge current of 10 kA, and the internal temperature of the arrester rises sharply in a short time--it can reach 241 \({}^{\circ}\)C.
# Polarization-Discriminated RSOA-EAM for Colorless Transmitter in WDM-PON
## 4 Conclusions  
In summary, we have proposed a PD RSOA-EAM to discriminately processes the down- and upstream signals at a different polarization state. Influences of the rotation angle on both the down- and upstream signals have been studied. An increased rotation angle enhances the downstream signal erasing, hence improving the dynamic ER of the upstream signal eye-diagram at a cost of the low upstream output signal power. An optimum rotation angle always exists at which the upstream signal output power and integrity are both guaranteed. With the polarization state exploited as an extra dimension, the proposed device can simultaneously saturate the downstream signal and linearly amplify the upstream signal in a fairly wide power operation range. As evidenced by the simulation result, our proposed device can significantly improve the eye-diagram quality and raise the output power of the upstream signal. Hence, the proposed PD RSOA-EAM is promising as a colorless transmitter solution to ONUs in the WDM-PON.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/24/9049/s1](http://www.mdpi.com/2076-3417/10/24/9049/s1)]([http://www.mdpi.com/2076-3417/10/24/9049/s1](http://www.mdpi.com/2076-3417/10/24/9049/s1)), 1. Theoretical Models for the proposed PD RSOA-EAM. 2. Numerical Implementation. 3. Numerical Simulation Results. Figure S1: Schematic diagram of the simulation setup for polarization-discriminated RSOA-EAM. Figure S2a: Static gain of the single SOA section (at an input power of \(-25\) dBm) and the SOA saturation input power as a function of the input polarization angle. Figure S2b: Static single-pass extinction curve of the EAM section with different powers injected into the EAM section. Figure S3: ER of the suppressed downstream signal as functions of the downstream signal wavelength. Figure S4: Output power and Q-factor of the upstream signal as functions of the downstream signal wavelength.  
**Author Contributions:** Supervision, X.L.; Writing--original draft preparation, C.Z.; Writing--review and editing. C.Z. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: (**a**) Normalized _FOM_ as functions of the rotation angle at different downstream signal input powers. (**b**) The optimum rotation angle as a function of the downstream signal input power. (**c**) The Q-factors obtained with an adaptive \(\theta_{FR}\), a constant \(\theta_{FR}\) at \(87^{\circ}\), a constant \(\theta_{FR}\) at \(80^{\circ}\), and a constant \(\theta_{FR}\) at \(0^{\circ}\), respectively, as a function of the downstream signal input power. The ER of the downstream input signal is \(6\) dB.
## 4 Comparison with Existing Vivaldi Antennas  
The proposed design is compared to other Vivaldi design in terms of size, as a function of wavelength, calculated at 18 GHz, operating frequency bands, Bandwidths, efficiencies, number of elements, and measured gain. It can be noticed that the size of the proposed antenna is normal compared to other Vivaldi antennas. The proposed antenna covers portions of Ku, K and Ka bands. It also covers the important segment of the mmW spectrum, allocated for 5G communication at 28 and 38 GHz. The proposed eight elements AVA is most efficient and has higher gain compared to others. The comparison with relevant antennas is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref No.** & **Year** & \begin{tabular}{c} **Size (\(\lambda\times\lambda\times\lambda\))** \\ **(\(\lambda\) Is Taken in mm)** \\ \end{tabular} & \begin{tabular}{c} **Operating Frequency** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **BW** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Efficiencies** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Number** \\ **of Elements** \\ \end{tabular} &
\begin{tabular}{c} **Measured** \\ **Gain (dBi)** \\ \end{tabular} \\ \hline
[38] & 2020 & 6.93 \(\times\) 4.15 \(\times\) 0.27 & 24.8–34.52 & 9.72 & N.M & 01 & 7.27 \\ \hline
[39] & 2020 & 0.56 \(\times\) 0.82 \(\times\) 0.046 & 23–39 & 16 & N.M & 01 & 7.2 \\ \hline
[40] & 2015 & 0.7 \(\times\) 0.7 \(\times\) 0.011 & 1–35 & 34 & \(>\)77 & 01 & 6.8 \\ \hline
[41] & 2018 & 2.087 \(\times\) 1.52 \(\times\) 0.09 & 10–40 & 30 & N.M & 01 & 8.5 \\ \hline
[42] & 2018 & 4.51 \(\times\) 2.136 \(\times\) 0.033 & 27–31 & 04 & 84.9 & 01 & 8.51 \\ \hline
[43] & 2011 & 3.3 \(\times\) 3.43 \(\times\) 0.015 & 4–50 & 46 & N.M & 01 & 3–12 \\ \hline \multicolumn{1}{c}{} & 1.83 \(\times\) 0.47 \(\times\) 0.05 & 14.44–20.98 & 6.54 & & & 3.4 \\ \multicolumn{1}{c}{} & 24.34–29 & 4.66 & \(>\)86 & 01 & 3.6 \\ \multicolumn{1}{c}{} & 33–40 & 7 & & & 7.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of proposed 5G AVA to other Vivaldi antennas.  
Figure 14: Simulated and measured efficiencies verses frequency of 8-element AVA.
## 2 Methodology
### The WCDPSO Algorithm
#### 2.1.1 Improving Inertia Weight  
The inertia weight \(w\) linearly descends according to research results in [30], which gives particles strong searching capability at the early iteration stage and more large solution space. The convergence speed is faster at the late iteration stage. However, if the optimal particles are not obtained at the initial iteration stage, although the local search capability of particles is increased, it is easy to fall into the local optimal value. Reference [31] proposed the differential descending method to compensate for the deficiency of the PSO algorithm to some extent; as a result, the performance of the PSO algorithm could be improved.  
Reference [32] proposed a nonlinear descending method by introducing exponential factor \(\lambda\) and iterative threshold value \(\text{max}gen_{o}\) to prevent particles falling into the local optimal value. The specific formulas are described as follows.  
\[w(i)=w_{\text{max}}-\left(\frac{i-1}{\text{max}gen_{0}-1}\right)^{\lambda}(w_{ \text{max}}-w_{\text{min}}) \tag{1}\]  
where, \(w_{\text{max}}\) and \(w_{\text{min}}\) are the maximum inertia weight and the minimum inertia weight, \(i\) is the current iteration number, \(\lambda\) is the exponential factor, and \(\text{max}gen_{0}\) is the iteration threshold. The nonlinear decreasing method effectively prevents particles from falling into the local optimal value and speeds up its convergence.  
The adjusting inertia weight method is improved according to the above-described methods; the inertia weight \(w\) decreases with the increase in iteration number \(i\), so it can improve the convergence speed of particles and the accuracy of the PSO algorithm. Thus, the differential descending method is proposed in the paper, and the specific formulas are described as follows.  
\[w(i)=(w_{\text{max}}-w_{\text{min}})\bigg{(}\frac{i}{\text{max}gen}-1\bigg{)}^ {2}+0.4 \tag{2}\]  
where max\(gen\) is the maximum iteration number and the most proper offset is thought to be 0.4 after multiple simulations. The PSO algorithm is improved by adjusting inertia weight \(w\), then it is renamed the WPSO algorithm.
## 2 Internet of Vehicles and Smart Cities
### The UAV and the Smart City Paradigm  
#### 2.5.1 UAV  
UAV are a type of aerial vehicle that does not require a pilot and can move and interact with the environment independently. They are also a part of the unmanned aircraft system (UAS), which requires a UAV, a ground control system (GCS), and a communication link [36]. UAVs can fly autonomously with pre-arranged flight plans or create their own plan mid-flight. The UAV consists of two parts: the machines themselves and the payload (what modules and other technologies it carries/can carry to provide further services for users and connected devices).  
Studies are being made on the usage of UAV as drone base stations (DBS) [37; 38] which complement other base stations and expand the already existing networks by increasing coverage, availability, reducing latency, network congestion, or even in the spread of 5G in hard to reach zones [39], as seen in Figure 6. [PERSON] et al. [40] list the benefits of UAV usage as base stations:  
1. Drones can move immediately, in real-time, to provide dynamic coverage according to necessity. This makes the overall connectivity robust as they can change as the environment does too.
2. As the deployment is through the air, it is not necessary to rent sites. Moreover, costs related to cables, towers will diminish or simply be non-existent.
3. Line-of-Sight (LoS) is one reliable communication among the possible types of connections since both transmitter and receiver are aligned, and the connection is direct. LoS' connections are much easier through open space where drones are supposed to be. So, reliable connections, at least at a certain level, would be possible even inside cities.
4. UAV swarms is a novel multi-purpose technique to provide services. A connected swarm can guarantee ubiquitous connectivity to end-users on the ground. In a nutshell, end-users benefit from higher data rates with lower latency, and providers can lower their costs and raise profits simultaneously.
## 5 Conclusions
; writing--review and editing, Y.Y. ; visualization, T.M. and Y.Y. ; data curation, Y.Y., L.D., G.Z., T.Z. and X.W. ; formal analysis, T.M., Y.Y., L.D. and S.Z. ; supervision, Y.Y. ; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Key Research & Development Program of China (Grant No. 2018 YFB1502801), the National Natural Science Foundation of China (Grant No. 42305092), the CREEI (Grant No. ZY-KJHB-20220005), the Gansu Science and Technology Plan Project (Grant No. 23 JRRA662), and the Large-scale instruments functional Development project of the Chinese Academy of Sciences (Grant No. 2024g109). The data used in this study are from the \"Integrated land-surface-atmosphere boundary layer observation dataset for onshore wind farms (April 2020)\", the dataset has been posted on the National Cryosphere Desert Data Center ([[http://www.ncdc.ac.cn/portal/metadata/abb7](http://www.ncdc.ac.cn/portal/metadata/abb7) ee11-b01c-48 ca-8a3b-caf17a7 fa584]([http://www.ncdc.ac.cn/portal/metadata/abb7](http://www.ncdc.ac.cn/portal/metadata/abb7) ee11-b01c-48 ca-8a3b-caf17a7 fa584), accessed on 2 October 2023) and is available via offline application. We thank all the colleagues who participated in the field experiment. We are grateful to Long Yuan (Beijing) Wind Power Engineering & Consulting Co., Ltd. for providing site support. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
LidarLight detection and ranging  
CNRCarrier-to-noise ratio  
VADVelocity-azimuth display  
DBSDoppler beam swinging  
VVPVolume-velocity processing  
PPIPlan-position indicator  
RHIRange-height indicator  
CIConfidence Index  
GOFGoodness of Fit  
RAERelative absolute error  
ZeStandardized residual  
AGLAbove the ground level  
R2 Coefficient of determination  
RMSERoot mean square error  
MAEMean absolute error  
CSIClear-sky index  
LDRLongwave downward radiation
## 3 Optical Properties of Laccase
### Raman Spectroscopy  
Raman spectroscopy is used to determine the chemical composition of a sample, without any preparation in many cases, and allows information to be obtained from a biochemical point of view without being affected by interference from water molecules.  
A study in which the resonant Raman spectra of lacase from _Rlus verniciifera_ in the 350-450 cm\({}^{-1}\) region have been obtained by exciting the sample at 647 nm is reported by [PERSON] et al. [83]. Measurements have been made both at room temperature and in a frozen state (200 K). The spectra reported in Figure 8 show the characteristic Raman features of T1 copper sites [84; 85; 86], which can be ascribed to the mixing of the Cu-S(Cys) stretching vibration with multiple heavy atom bending modes of the ligand and adjacent residues. The intensity of the band at 379 cm\({}^{-1}\) is observed to increase when the temperature decreases, while the signal at 403 cm\({}^{-1}\) remains unchanged [83].  
Lacase from _Trametes hirsuta_ in solution and immobilized in the cubic phase of monoolein have been studied using Raman spectroscopy. Strong bands are observed at 387 and 408 cm\({}^{-1}\) along with other bands with weak signals; see Ref. [87].  
The resonant Raman spectrum of _Pleurotus ostreatus_ lacase has been reported by [PERSON] et al. [65], with an excitation of 633 nm in resonance with the T1-absorption feature. The resulting spectrum for the range 300-500 cm\({}^{-1}\) is shown in Figure 9. In this case, peaks at 372, 382, 393, 404 and 414 cm\({}^{-1}\) together with some shoulders at around 355, 427 and 434 cm\({}^{-1}\) can be observed, recalling the characteristic Raman features of the T1 site.  
Figure 7: FT-IR spectra of yellow lacase (red line), blue lacase (blue line) and white lacase (black line) (reprinted from Ref. [53] under open access conditions).
## 3 Results and Analysis
### Mathematical Morphological Processing  
The most basic operations in mathematical morphology include dilation and erosion. Expanding the resulting binarized image enlarges the boundary points of the object in the target image. All background points in contact with the object merge into it, and the result expands outward and fills in holes and edge depressions in the image. Conversely, erosion is used to remove object boundary points by inwardly contracting the boundary and eliminating small and insignificant foreground areas [27]. Closed and open operations are combinations of dilation and erosion. Morphological processing involves the combination of dilation, erosion, and opening operations. The image processing results are shown in Figure 8.  
Figure 7 shows that the landslide area after the OTSU and filtering algorithms is more defined, and the shape of the landslide is clearer. As shown in the figure, misclassified areas and voids are still observed on the landslide surface due to the constraints of classification and slope terrain factors. The loss of landslide features caused by vegetation cover is also noted. Morphological operations in Figure 8 eliminate misclassifications and improve the voids in the landslide area, which makes the landslide area complete and consistent.
# Solving a Simple Geduldspiele Cube with a Robotic Gripper via Sim-to-Real Transfer
## 2 Approach to Solving Geduldspiele Cubes via Sim-to-Real Transfer
### Dynamic Models: Ball-Plane Model and Ball-Hole Model
#### 2.2.1 Ball-Plane Model  
By following [40, 41], let \(K_{p}\) and \(K_{b}\) be the kinetic energy of the plane and the iron ball, respectively. \(K_{p}\) and \(K_{b}\) can be defined as  
\[\begin{split} K_{p}&=\frac{1}{2}J_{p}\dot{\theta}^{2} \\ K_{b}&=\frac{1}{2}J_{b}\dot{\theta}^{2}+\frac{1}{2}m_ {b}v_{b}^{2}\end{split} \tag{1}\]  
where \(J_{p}\) is the moment of inertia of the plane and \(\dot{\theta}\) is the angular velocity of the plane. For the iron ball, \(J_{b}\), \(\dot{\beta}\), \(m_{b}\), and \(v_{b}\) represent the moment of inertia, the angular velocity, the mass, and the linear velocity, respectively. From Figure 2b, we can easily find that \(\dot{\beta}\) and \(v_{b}\) can be expressed by  
\[\dot{\beta}=\frac{\dot{p}}{r_{b}}\quad\text{and}\quad v_{b}^{2}=\dot{p}^{2}+p^{ 2}\dot{\theta}^{2} \tag{2}\]  
where \(r_{b}\) is the radius of the iron ball. Substituting (2) into \(K_{b}\) in (1) yields  
\[K_{b}=\frac{1}{2}\Bigg{(}\frac{J_{b}}{r_{b}^{2}}+m_{b}\Bigg{)}\dot{p}^{2}+ \frac{1}{2}m_{b}p^{2}\dot{\theta}^{2} \tag{3}\]  
Let \(U_{p}\) and \(U_{b}\) be the potential energy of the plane and the iron ball, respectively. \(U_{p}\) and \(U_{b}\) are defined by  
\[\begin{split} U_{p}&=m_{p}g\frac{l}{2}(1+\sin \theta)\\ U_{b}&=m_{b}gp\sin\theta\end{split} \tag{4}\]  
Figure 2: Coordinate setting: (**a**) the robotic gripper and plane coordinate and wrist joint actuators, (**b**) actuator A and corresponding iron ball action along \(y\)-axis, and (**c**) actuator B and corresponding iron ball action along \(x\)-axis.  
where \(l\) and \(m_{p}\) are the length and the mass of the plane, and \(g\) is a gravitational constant. Now we can define the Lagrangian \(L\) for the ball-plane system:  
\[\begin{split} L&=(K_{p}+K_{b})-(U_{p}+U_{b})\\ &=\frac{1}{2}\left(\frac{I_{b}}{r_{b}^{2}}+m_{b}\right)\dot{p}^{2} +\frac{1}{2}(J_{p}+m_{b})p^{2}\dot{\theta}^{2}-m_{p}g\frac{l}{2}\sin(1+\theta )-m_{b}gp\sin\theta\end{split} \tag{5}\]  
Let the generalized coordinate be \(q(t)=[p(t),\theta(t)]^{T}\). By the Euler-Lagrange equation of the first kind,  
\[\frac{d}{dt}\left(\frac{\partial L}{\partial\dot{p}}\right)-\frac{\partial L} {\partial p}=F_{\mu}, \tag{6}\]  
hence, we have  
\[\left(\frac{I_{b}}{r_{b}^{2}}+m_{b}\right)\ddot{p}+m_{b}g\sin\theta-m_{b}p \dot{\theta}^{2}=F_{\mu} \tag{7}\]  
where \(F_{\mu}\) is the friction force defined by  
\[F_{\mu}=-\mu N\text{sign}(\dot{p})=-\mu m_{b}g\cos\theta\text{sign}(\dot{p}). \tag{8}\]  
Similarly, by the Euler-Lagrange equation of the second kind,  
\[\frac{d}{dt}\left(\frac{\partial L}{\partial\dot{\theta}}\right)-\frac{ \partial L}{\partial\theta}=\tau, \tag{9}\]  
we obtain  
\[(m_{b}p^{2}+J_{p})\ddot{\theta}+2m_{b}p\dot{p}\dot{\theta}+m_{b}gp\cos\theta+ m_{p}g\frac{l}{2}\cos\theta=\tau \tag{10}\]  
where \(\tau\) is a torque generated by the actuator.
## 1 Introduction
The cost of CA is mainly related to the complexity of the production process, the current uncertainties about the regulatory elements controlling the CA biosynthetic gene cluster and the intellectual property associated with its production [6]. Despite the significant number of studies related to CA production in _S. clavuligerus_ submerged cultivations, low titers (~1 g\(\cdot\)L\({}^{-1}\)) are still obtained when using a wild type strain. The productivity of CA production bioprocess is also compromised by the downstream processing: CA separation from fermentation broths and precipitation as clavulanate salt. This review presents a holistic overview of CA production process: CA biosynthesis; CA production in _S. clavuligerus_ submerged cultivation; recent advances in strain engineering; and elucidation of regulatory elements controlling CA production, systems biology approaches, and downstream processing (Figure 2). Figure 1: Clavulanic acid (CA) structure. Red and blue C atoms correspond to those coming from C-3 and C-5 precursors, respectively. * Stereochemical centers on CA structure.
###### Abstract  
Circuit design techniques for enhancing an efficiency of the high power feedback-type GaN-HFET (Gallium Nitride Heterojunction Field Effect Transistor) oscillator for use in the microwave oven are shown in this paper, focusing primarily on the harmonic terminations for high efficiency of power amplifiers, the coupling factor to feedback circuits and the insertion phase adjustment of feedback loops. With the use of these circuit design techniques, an output power of 263 W and an efficiency of 61.3% have been successfully achieved at 2.44 GHz.  
microwave ovens; high power microwave generation; high power amplifiers; feedback circuits; imbalanced coupling resonators +
Footnote †: journal: Applied Sciences  
_Applied Sciences_  
output signal and then takes back to the input of the power amplifier (B) via the imbalanced coupling resonator and the phase shifter. The insertion characteristics of the feedback circuit and power amplifier are defined as \(\beta\)(s) and A(s), respectively. To achieve high efficiency of the feedback-type oscillator, it becomes crucial how to achieve high efficiency of the power amplifier by harmonic terminations in addition to making the feedback amount as small as possible. To achieve high efficiency of the power amplifier, GaN-HFETs in place of the conventional LDMOS are employed because of high gm and ft. In addition, the harmonic termination is utilized in the design of the power amplifier (B). On the other hand, the value of C\({}_{1}\) of the imbalanced coupling capacitor has to be small to achieve a weak coupling for improving an insertion loss of the feedback circuit. On the contrary, the value of C\({}_{2}\) has to be large to achieve a strong coupling to the power amplifier circuit for low insertion loss and high efficiency [8; 9]. The oscillation conditions are based on Barkhausen Criteria [10] as follows:  
\[\text{A(s)}\cdot\beta\text{(s)}\geq 1 \tag{1}\]  
\[\sum\Phi=2\pi\times\text{n}\left(\text{n}=1,2,\dots\right) \tag{2}\]  
Equations (1) and (2) define its criteria for amplitude and phase, respectively.
## 3 Experiment Description and GNSS Data Processing
### GNSS Data Processing Strategy
\begin{table}
\begin{tabular}{l l} \hline \hline
**Satellite System** & **GPS: L1/L2; GLONASS: G1/G2; BDS: B1/B2** \\ \hline Sampling rate & 30 s \\ \hline Elevation angle mask & \(10^{\circ}\) \\ \hline \hline \multicolumn{3}{c}{Satellite orbit: Precision Orbit (5 min), 10 order lagrange interpolation} \\ \hline \multicolumn{3}{c}{Satellite clock: Precision clock error (30s), Linear interpolation} \\ \hline \multicolumn{3}{c}{Relativistic effect: Modeling correction} \\ \hline \multicolumn{3}{c}{Troposphere GPT (Global Pressure and Temperature) model [41] and VMF} \\ Error model & (Vienna Mapping Functions) mapping function were applied [42], Not considering the tropospheric gradient \\ \hline \multicolumn{3}{c}{Ionosphere: The first order of the ionospheric delays is eliminated by IF} \\ \multicolumn{3}{c}{(Ionosphere-Free) combination; Not} \\ \hline \multicolumn{3}{c}{considering the higher-order delays [43]} \\ \hline \multicolumn{3}{c}{Phase wind-up: Modeling correction [44]} \\ \hline \multicolumn{3}{c}{Phase wind-up: Modeling correction [44]} \\ \hline \multicolumn{3}{c}{Tidal correction: Solid tides, ocean tide loading and polar tides (IERS 2010 [45])} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific processing strategy for multi-GNSS observations based on the PPP technique.