esa-sceva/satcom-chunk-collection huggingface dataset:
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
###### Abstract  
Particle radiotherapy based on the medical accelerator is emerging as a major treatment for cancer. To enhance the clinical flexibility of particle radiotherapy and further promote the use of medical accelerators, the Shanghai Institute of Applied Physics (SINAP) has presented a new linear accelerator plan for medical application. The new plan utilizes a 200 MHz Radio Frequency Quadrupole (RFQ) as the injector. The RFQ is designed to accelerate ions with charge-to-mass ratios of 1/3 to 1/2 from 8 keV/u to 750 keV/u. For the beam dynamics design, a new design strategy is presented to enhance the suppression of space charge effects and improve beam capture efficiency by optimizing the modulation, synchronous phase, and focusing strength. The simulation results demonstrate that the multi-ion RFQ can operate at a maximum beam current of 3.2 mA while maintaining a transmission efficiency above 95% with a compact length of 2.5 m. Multi-particle simulations confirm the high reliability of the design. Additionally, input and mechanical error analyses evaluate the RFQ's tolerance and stability. The research results demonstrate the feasibility of a compact, high-efficiency RFQ for multi-ion acceleration in medical applications, contributing to the advancement of particle therapy.  
RFQ; beam dynamics; error analysis; particle radiotherapy; medical accelerator
## 4 Applications of PCEM
### Combination of PCEM and PCEF for Label-Free/Fluorescence-Labeled Imaging Simultaneously  
The PCEM is not limited to detection of optical scatters or absorbers, but is also capable of enhancing the emission and extraction from optical emitters (such as fluorescent dyes) in the evanescent field of the PC biosensor. Based on this principle, the label-free PCEM system can be slightly modified to include an additional illumination path from a laser that can excite fluorescent emitters. The ability to tune the illumination angle of the laser to match the resonant coupling  
Figure 7: Photonic Crystal Enhanced Fluorescence (PCEF) portion on a PCEM imaging system. (**A**) Schematic of the PCEF portion on modern PCEM detection instrumentation. Inset (top left): angle reflection spectrum; (**B**) Brightfield and PCEF images of membrane dye-stained 3T3 fibroblast cells: (a) brightfield, (b) off-resonance PCEF, (c) on-resonance PCEF, (d) enhancement factor image, (e) 3D surface plot image of the enhancement factor (Reprinted in part with permission from [96], © 2014 RSC Publishing).  
condition of the PC substantially enhances the electric field intensity that is used to excite fluorophores, resulting in higher intensity fluorescence microscope images. Photonic Crystal Enhanced Fluorescence (PCEF) represents an additional imaging modality within the PCEM that enables rapid switching between label-free and fluorescence-labeled imaging modes (Figure 3A) [76,83]. Figure 3B demonstrates the enhanced fluorescence image and the label-free image of the same microarray spots printed with cyanine-5-tagged streptavidin (Cy5-SA) proteins. Figure 7A depicts the current optical setup for the PCEF portion of a combined imaging system. Illumination from a fiber-coupled semiconductor laser diode is collimated and passed through a half waveplate to produce a polarization perpendicular to the PC grating lines. Figure 7A inset (top left) plots an angle reflection spectrum of the PC surface when illuminated with a collimated semiconductor laser at 637 nm over a range of illumination angles. Maximum reflection intensity occurs at the on-resonance condition at an incident angle of \(\pm 1.14^{\circ}\) from normal direction. The off-resonance condition refers to the laser illumination at an incidence angle of \(5^{\circ}\). Figure 7B illustrates the corresponding enhanced fluorescence images for membrane dye-stained 3T3 fibroblast cells [96]. The combination of both modalities extends the PC-enhanced imaging system to be multi-functional and capable of imaging in numerous bio-applications.
## 4 Conclusions  
FMCW radars often suffer from nonlinear frequency modulation, which reduces the range resolution and image quality when used in a synthetic aperture radar. Previously reported methods of linearizing the modulation for SAR applications resulted in: (1) additional hardware; (2) an increase in the computational load; and (3) are only effective over a limited range extent.  
The technique presented here results in little to no computational cost when the Range Migration Algorithm is used because the correction is a simple sum before the interpolation process needed by the Stolt transform. Once found, the phase correction applies to the full range extent that is expected from the Stolt transform. Given the fact that the phase error is found from experimental data using a calibration target, the anytime the modulation characteristics of the VCO, such as temperature or power supply voltage, the calibration will no longer be valid. The extent of the degradation will depend on the VCO's response to these changes and the severity of the modulation error.  
Figure 8: Range compressed data of a point target before and after correction.  
Figure 9: Radar image after correction.  
**Author Contributions:** Conceptualization, T.G.; methodology, T.G.; software, T.G.; validation, T.G. and C.O.; formal analysis, T.G. and C.O.; investigation, T.G. and C.O.; resources, T.G.; data curation, T.G.; writing--original draft preparation, T.G.; writing--review and editing, C.O.; visualization, T.G.; Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 4 Experiments
### Implementation Details
The constants \(c_{1}\) and \(c_{2}\) are included to avoid instability when the denominator is close to zero. **Masked SSIM**: To specifically evaluate the inpainted regions, we compute SSIM on only the masked areas, as follows:  
\[\text{SSIM}_{\text{masked}}(x,y,m)=\text{SSIM}(x\odot m,y\odot m) \tag{3}\]  
where \(m\) is the binary mask indicating the inpainted region, and \(\odot\) represents element-wise multiplication. **Histogram Similarity**: To assess the color consistency of the inpainted results, we compute the correlation between color histograms in hue, saturation, value (HSV) color space, as follows:  
\[\text{HistSim}(x,y)=\frac{\sum_{i}(H_{\text{x}}(i)-\bar{H_{\text{x}}})(H_{y}(i )-\bar{H_{y}})}{\sqrt{\sum_{i}(H_{\text{x}}(i)-\bar{H_{\text{x}}})^{2}\sum_{i}( H_{y}(i)-\bar{H_{y}})^{2}}} \tag{4}\]  
where \(H_{\text{x}}\) and \(H_{y}\) are the normalized hue histograms of images \(x\) and \(y\), respectively, and \(\bar{H_{\text{x}}}\) and \(\bar{H_{y}}\) are their mean values. This metric ranges from \(-1\) to \(1\), with \(1\) indicating perfect correlation. **Combined Metric**: We utilize a weighted combination of these metrics for comprehensive evaluation, as follows:  
\[\text{Score}_{\text{final}}=0.2\cdot\text{SSIM}+0.4\cdot\text{SSIM}_{\text{ masked}}+0.4\cdot\text{HistSim} \tag{5}\]  
This weighted combination emphasizes the quality of the inpainted region (Masked SSIM) and color consistency (Histogram Similarity), while also considering the overall structural similarity (SSIM).
# On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning
## 2 Path Planning  
Mobile robots, unmanned aircraft, and autonomous vehicles use path-planning algorithms to find the safest, most effective, collision-free, and most minor expensive routes between two points. Point-to-point navigation can be made safe and efficient by selecting the correct path-planning algorithm. However, the best algorithm depends on the robot geometry and computing constraints, including statically/holonomically and dynamically/non-holonomically constrained systems, and it necessitates a thorough understanding of current solutions.  
A continuous path connecting a robot from an initial to a final goal configuration must be found in path planning, a non-deterministic polynomial-time (\"NP\") hard problem [12]. As the robot's degrees of freedom rise, the problem becomes more difficult. Then, the best course of action (the optimal path) will be chosen based on restrictions and requirements, such as the shortest distance between two points or the shortest time to travel without colliding. Sometimes, goals and limits coexist, such as when attempting to reduce energy use without increasing journey time past a certain point. Figure 2 delineates the path planning with obstacle avoidance along with the computational geometric planners.  
Four requirements must be met for a path-planning algorithm to be effective. First, in realistic static situations, the motion-planning technique must always be able to identify the best path. Second, it also needs to be scalable to dynamic contexts. Third, it must continue to be beneficial to and compatible with the selected self-referencing strategy. Finally, the complexity, data storage, and computing time must all be kept to a minimum [13]. The most prevalent path-planning algorithms for robots and AVs are presented in this paper, and we examine which method is best suited for static and dynamic environments. A taxonomy of different CG-based algorithms applied to mobile robot path planning is illustrated in Figure 3.  
Figure 2: Pictorial view of robotic path planning and obstacle avoidance using computational geometry.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 5 Case Study
### Case 1: Comparison of Power Generation Capacity  
In Case 1, the wind speed and \(P_{\mathrm{dem}}\) were set to 12 m/s and 18 MW. All WTs were operating in the normal condition. PDS does not consider the wake effect and only distributes the power demand  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Rated Power & 5 MW \\ Rotor Diameter & 126 m \\ Min. and Max. Rotor Speed & 6.9 rpm, 12.1 rpm \\ Cut-in, Rated, Cut-out Wind Speed & 3 m/s, 11.4 m/s, 25 m/s \\ Gearbox Ratio & 97:1 \\ Synchronous Frequency & 50 Hz \\ Electrical Generator Efficiency & 94.4 \% \\ Number of Pole-pairs & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of NREL 5-MW wind turbine.  
Figure 10: Control diagram of power dispatch.  
proportionally according to the available power of WT. If we only focus on power, when the power demand is lower than the maximum output of the WF in this strategy, this strategy is feasible. However, when the power demand is higher than the maximum output of the WF in this strategy, the WF cannot produce more power. In ODS, although the optimization algorithm does not directly consider the wake effect, the power demand appears in the objective function. The PSO algorithm searches the optimal solution to track the power demand as close as possible.  
The simulation result is shown in Table 2 and Figure 11.  
It can be seen that the WF can only produce 17.88 MW in PDS, but the power demand was 18 MW. The red bar shows the power difference between the power reference and the power output. However, through PSO, ODS can improve the power generation capacity of the WF and follow the power demand.
## 3 Method
### Anomaly Detection  
The image of the difference between the reconstructed data and the original data is shown in Figure 5. When the difference result exceeds the threshold, it is judged as abnormal.  
In the process of difference data calculation, the exponential weighted average method was used to smooth the difference sequence so as to avoid the noise generated by controlling torque gyroscope telemetry data mutation and other factors in the process of difference calculation being mistaken as abnormal, resulting in false positives and resulting in accuracy degradation.  
At the same time, the setting of the threshold of the working condition segment is dynamic, and the setting of the threshold of each working condition segment is determined using the following formula:  
\[C^{i}_{Threshold}=\mu_{i}\cdot\left(\lambda_{stable}+\frac{\sigma_{i}}{ \sigma_{0}}\cdot\lambda_{Dynamic}\right) \tag{6}\]  
where, \(C^{i}_{Threshold}\) represents the abnormal judgment threshold of the ith working condition category, \(\mu_{i}\) represents the mean of the difference between the piecewise fitting reconstruction data and the original telemetry data of the working condition, \(\sigma_{i}\) and \(\sigma_{0}\) respectively represents the standard deviation of the piecewise difference and the standard deviation of the whole life cycle error of the working condition, \(\lambda_{Stable}\) and \(\lambda_{Dynamic}\) is a pair of constants set in advance, 5% and 20% respectively. Respectively, they represent the lowest acceptable anomaly detection threshold and the increase of anomaly detection threshold determined by the volatility of segmented data in the current working conditions.
## 4 Substructure Pseudo-Dynamic Test of Soil-Pile-Structure Interaction
### Numerical Substructure  
#### 4.2.1 Soil Parameters  
The soil was divided into three layers, consisting of type II site soil. The first layer was clay with a thickness of 4 m, the second layer was silty clay with a thickness of 4 m, and the third layer was silt with a thickness of 8 m. The material parameters of the soil are shown in Table 1. In the MATLAB program, each layer was divided to a height of 2.5 m. [PERSON] et al. [73] studied the dynamic shear modulus ratio (\(G/G_{max}\)) and damping ratio (\(D\)) of different kinds of soils, including clay, silty clay, silt, sand, muck, and mucky soil, in more than 10 different areas of China by free vibration testing using the resonant column apparatus. The change curves of the dynamic shear modulus ratio \(G/G_{max}\) and damping ratio \(D\) with shear strain \(\gamma\) for various soils were obtained. The curves of the dynamic shear modulus ratio \(G/G_{max}\) and damping ratio \(D\) changing with shear strain were selected from the literature for clay, silty clay, and silt in this paper. The data extracted from the curves are shown in Table 2. The parameters in Equations (7) and (9) were fitted according to the data in Table 2 and used in the calculations in the improved Penzien model, as shown in Table 3. The fitted \(G/G_{max}\)-\(\gamma_{d}\) and \(D\)-\(\gamma_{d}\) curves are shown in Figures 6-8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**Parameter**} & \multicolumn{8}{c}{**Shear Strain \(\gamma\) (10\({}^{-4}\))**} \\ \cline{3-10}  & & **0.05** & **0.1** & **0.5** & **1** & **5** & **10** & **50** & **100** \\ \hline \multirow{2}{*}{Clay} & \(G/G_{max}\) & 0.9954 & 0.9897 & 0.9465 & 0.8975 & 0.6347 & 0.4647 & 0.1478 & 0.0798 \\  & \(D\) & 0.0342 & 0.0408 & 0.0608 & 0.0716 & 0.1284 & 0.1703 & 0.2402 & 0.2543 \\ \hline \multirow{2}{*}{Silty clay} & \(G/G_{max}\) & 0.9936 & 0.9858 & 0.9274 & 0.8634 & 0.5563 & 0.3851 & 0.1112 & 0.0589 \\  & \(D\) & 0.0223 & 0.0276 & 0.0447 & 0.0549 & 0.1131 & 0.1383 & 0.1735 & 0.1798 \\ \hline \multirow{2}{*}{Silty} & \(G/G_{max}\) & 0.9919 & 0.9839 & 0.9242 & 0.8591 & 0.5495 & 0.3788 & 0.1087 & 0.0575 \\  & \(D\) & 0.0129 & 0.0174 & 0.0342 & 0.0445 & 0.0934 & 0.1220 & 0.1620 & 0.1689 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of the curves of the dynamic shear modulus ratio \(G/G_{max}\) and damping ratio \(D\) changing with shear strain \(\gamma\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**\(A\)**} & \multirow{2}{*}{\(B\)} & \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{\(D_{min}\)} & \multirow{2}{*}{\(D_{0}\)} & \multirow{2}{*}{\(\beta\)} \\ \hline Clay & 1.01712 & 0.49852 & 8.47 \(\times 10^{-4}\) & 0.03699 & 0.23288 & 0.88074 \\ Silty clay & 1.02237 & 0.49812 & 6.07 \(\times 10^{-4}\) & 0.01833 & 0.16941 & 0.72872 \\ Silty & 1.00003 & 0.49998 & 6.10 \(\times 10^{-4}\) & 0.01235 & 0.16416 & 0.84408 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitted values of parameters in the Davidenkov model.
## 3 Simulation and Results  
The aim of this section is to compare the performances of all the proposed digital filters and recommend the best fit filter. We use R Programming to simulate the original datasets that has a suite of noise added to it. After completing the simulations, we applied a variety of loss estimations, _i.e._,Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R\({}^{2}\), AIC, BIC and Accuracy count which is a sum of the upward and downward movements of all the underlying local signals after they had transited the reversion points. See flowchart of simulation and results in Figure 5.
# A Field Investigation on Vortex-Induced Vibrations of Stay Cables in a Cable-Stayed Bridge
###### Abstract  
A field study was conducted to identify the vortex-induced vibrations (VIVs) of stay cables in a cable-stayed bridge. A full-scale health-monitoring system was established to observe the wind effects of the selected cables. The vibration amplitudes in the twenty selected stay cables were first studied. The results indicate that only cable CAC20 has large amplitudes with a multimode and high-frequency vibration in the investigated period. The correlation between the wind and cable vibration was subsequently investigated. The large vibration amplitudes are primarily located in the mean speed scope of 4 to 6 m/s, simultaneously close to the reduced velocity of five when the wind was almost perpendicular to the bridge axis and had a smaller turbulence intensity. Moreover, the relationship between the maximum vibration amplitude with the mean wind speed was fitted by a function that was validated by the measured data. Finally, an estimation method was presented to predict the participative vibration modes that would happen in the VIVs of the stay cables, according to the known wind and cable parameters. The measured cable vibrations were employed to validate this estimation method. The results indicate the estimated vibration modes are close to the measured vibration modes.  
stay cable; vortex-induced vibration; field monitoring; wind; multimode +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Limitation of the Study  
Despite the findings from numerical analyses, the analytical and experimental results concur well. There are various restrictions to consider before utilizing the model in the field. Several proposed methods are available to determine the uplift and lateral capacity of a helical pile. In this study, methods proposed by [PERSON] et al. [9] and [PERSON] and [PERSON] [24] were used to calculate the uplift capacity and the method proposed by [PERSON] and [PERSON] [22] was used to calculate the lateral capacity. The results are compared with the helical pile capacity determined using the predicted results of the numerical model, also some experimental tests are carried out to validate the FEA results. Figure 14 shows the agreement ratio between all the methods used to calculate capacity and compares it with the numerical results. Even though the prediction is good, it is best to only use this model after including the right correlation factors from field testing results. This means taking into account the effects of installation, the sensitivity of the soil, the consistency of the soil at the field, and the adhesion factor. The significant response of a helical pile is greatly affected by combined loads and is primarily dependent on various parameters such as the diameter of the helical pile, the length of the pile, and the varying soil conditions, which are not discussed in this study.  
Figure 18: Variation of displacement contours with different number of plates under combined loading: (**a**) SHP-1-12; (**b**) SHP-2-8.75; (**c**) SHP-3-8.75, and (**d**) SHP-4-8.75.  
The installation effect is considered negligence as the model is verified for a pile installed in a remolded clay sample. In practice, however, the reduction in soil strength caused by pile installation is more severe in soils near the helices edge than in soil beyond the helix's edges. As a result, the critical spacing ratio for multi-helix piles installed in natural clay deposits may be exceeded.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 2 Related Work
The second heuristic, online, called DESRA, defines the flow rules to add to the switches when a new flow arrives this is done selecting the path with the minimum number of links thus consuming the minimum energy. For wireless sensor SDN in which the energy is often a scarce resource, since most of this equipment is battery powered, [PERSON] et al. [13] proposed an algorithm to route packets on a software-defined wireless sensor network. The objective is to increase the network lifetime and improve the average packet delay. A novel routing protocol called Energy-aware Software-Defined Network (EASDN) is used, and the controller must know all the network topology, also for a single controller scenario. A more comprehensive approach to energy consumption on SDN, which accounts also for the control-plane consumption, was developed by [PERSON] et al. [10] using a load balancing between many controllers to lower the demand on links by coalescing traffic turning off switches and controllers reducing energy on all planes. The algorithm is executed before the network is operational; it selects the most energy-efficient among various known routing algorithms and picks the most energy-efficient for that particular topology. One of the most recent works on SDN energy-efficiency was by [PERSON] and [PERSON] [9], who presented a new metric for energy efficiency on SDN concerning the link utilization on the data-plane. Using this metric, the authors proposed Maximizing Ratio for Energy Saving in SDN (MaxRESDN) an algorithm to minimize the number of hops, thus turning off links reducing energy. The algorithm is executed by the single controller, which needs full knowledge of the network topology. Table 1 summarizes some of the most recent and recognized works on energy efficiency for software-defined networks. The column Multi-Controller indicates if the approach applies to networks with more than one controller; Controller's Energy points if the work considered the impact of controller energy on overall energy consumption; Fault-Tolerant registers if the approach considers some redundancy on control-plane; and Partial  
As presented in Table 1, the main characteristics of our approach improve control-plane energy efficiency and can be associated with current state-of-the-art and commercial data-plane solutions, improving SDN overall energy efficiency. We use an innovative strategy based on parallel processing to achieve better energy efficiency in the SDN's control plane with no conflict with common data-plane strategies. Using a parallel implementation, we can employ more processor cores with lower frequencies to any number of controllers on the network, filling the gap on multi-controllers scenarios. This approach also allows for easy fault-tolerance since the controllers do not need to have full knowledge (knowing a partial topology) of the network to benefit from the trade-off between operation frequency and performance.