esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] [26] utilized an active absorber consisting of four electromagnetic poles to reduce the undesired resonant vibrations of a vertically supported Jeffcott rotor system. They introduced a control strategy relying on the push-pull control mechanism. Based on the acquired results, they concluded theoretically and experimentally that the introduced active vibration absorber can reduce the system's unwanted vibrations when its parameters are designed according to the non-linear model of the Jeffcott system. [PERSON] et al. [27, 28] introduced the PD-controller with four-pole magnetic actuator to suppress the non-linear oscillations of both the horizontally supported Jeffcott rotor [27], and the asymmetric Jeffcott system [28]. In addition, [PERSON] [29], [PERSON], and [PERSON] [30] studied the effect of the loop delays on dynamical behaviors of the four-pole actuator integrated with the PD-controller to suppress the non-linear vibrations of the Jeffcott system. They illustrated that the existence of time delays in the control loop may destabilize the stable motion of the rotor system. Relying on Refs [24, 25, 26, 27, 28, 29, 30], one can report that the eight-poles magnetic actuator, as well as the radial PD-control algorithm, have not been applied before to suppress the resonant vibrations of the non-linear Jeffcott system. So,the main purpose of the current article is to investigate the performance of radial PD-control strategy along with the eight-pole magnetic actuator as a novel control methodology to suppress the resonant vibrations of the non-linear rotating machines. In addition, this work is intended to explore the dynamic interactions between the non-linear Jeffcott system, the eight-pole actuator, and the radial PD-controller for the first time.
## 2 Modeling and Simulation Methods
### Multipactor Simulation for the SCLPF with Flat Dielectric Loaded  
We use SPARK3D to achieve the simulation of multipactor. SPARK3D is a module in CST that simulates the secondary electron multiplication discharge characteristics of passive devices, such as waveguides, microstrips, and antennas. The SPARK3D module employs the Monte Carlo method and 3D electron tracking model to solve for the electron number in the device-defined region. Therefore, SPARK3D can calculate the maximum operating power of the device without multipactor, which is defined as the multipactor threshold. Some reports of multipactor studies using SPARK3D [37; 38] and an introduction to the SPARK3D software (version number, CST2020) [39] are given in the literature and the Supplementary Materials.  
We built and calculated the device model shown in Figure 2 using CST Microwave Studio. We obtained the distributions of the electric field and magnetic field of the SCLPF at a working frequency of 1.5 GHz. The simulation area shown in Figure 2 is defined as the sensitive region. We set the SEY of each surface within the area and randomly injected 1500 initial seed electrons into the area to simulate the free electrons in the microwave  
Figure 3: Simulated frequency responses of SCLPFs with (**a**) alumina loaded and (**b**) PTFE loaded.  
Figure 2: (**a**) The 3D model of the designed SCLPF, (**b**) the cross-section image of the SCLPF.  
cavity of the spacecraft device. The initial simulation power is 500 W. When this power leads to secondary electron multiplication and breakdown, the software will reduce the input power by half and restart the simulation. On the contrary, if the input power does not result in breakdown, the average of this power and the nearest breakdown power is calculated, and the simulation is repeated until the final result gradually approaches the actual multipactor threshold.  
We employ the SEY data of the primitive alumina [40] and PTFE [41] sheets into SPARK3D. The SEY peak values of alumina and PTFE are 3.60 and 2.04, respectively. The simulation results are shown in Figure 4. As we can conclude from Figure 4, the multipactor threshold values of the SCLPFs with flat alumina loaded and PTFE loaded are 137.69 W and 347.63 W, respectively.
## 4 Results and Discussion
### Overall Assessment
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**S. No** & **Application Domain** & **No of Papers** & **Reference** \\ \hline
1 & Agriculture & 5 & [8,29,33,69,85] \\
2 & Business sector & 8 & [6,11,20,28,31,76,83,95] \\
3 & Computer field & 15 & [2, 4,12,25,27,36,56,72,77,78,81,97,98,101] \\
4 & General problem solving & 7 & [19,21,24,39,59,61,94] \\
5 & Law & 1 & [66] \\
6 & Industrial domain & 6 & [17,46,71,74,82,86] \\
7 & Media evaluation & 1 & [73] \\
8 & Medical field & 15 & [5,13,38,41,43,44,45,47,48,50,52,92,100,102,103] \\
9 & People safety & 2 & [22,35] \\
10 & Supply chain & 4 & [7,55,62,89] \\
11 & Sustainable computing & 1 & [80] \\
12 & Transportation & 10 & [23,49,60,63,64,90,93,96,99,104] \\
13 & University or Company & 1 & [88] \\
14 & Waste management & 3 & [9,57,87] \\
15 & Water resource management & 2 & [34,84] \\
16 & Geography & 2 & [58,65] \\
17 & Energy sector & 6 & [10,26,37,51,53,75] \\
18 & Disaster management & 4 & [54,67,79,91] \\
19 & Information about DSS & 2 & [1,68] \\
20 & Environmental side & 1 & [32] \\
21 & Hydrogeological sciences & 1 & [70] \\
22 & Refugees settlement & 1 & [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Details of references associated with each application domain. Figure 9: Applications of DSS in various application domains. To know the trends of papers based on the quality of contributions. Figure 10 represents the year-wise trends of the papers based on their quality. From Figure 10, it is evident that most of the contributions in recent years have high quality; there is also a trend towards a higher frequency of contributions. A somewhat similar trend can be seen for the good quality contributions and their frequency is higher as compared to the contribution having acceptable quality. In addition, it is important to note that paper belongs to 2020 are published in the earlier months of 2020 when the research was conducted.
## 4 Prediction of Side Wall Rock Mass Mechanical Parameters
### Estimation of Rock Mass Parameters
#### 4.2.2 The Hoek-Brown Criterion  
In 2002, [PERSON] et al. [21] introduced the geological strength index \(GSI\) and disturbance parameter \(D\) as modifications to the Hoek-Brown criterion. The modified Hoek-Brown criterion is expressed as follows:  
\[\sigma_{1}=\sigma_{3}+\sigma_{\rm ci}\left(m_{\rm b}\frac{\sigma_{3}}{\sigma_{ \rm ci}}+s\right)^{a} \tag{4}\]  
In the equation, \(\sigma_{1}\) and \(\sigma_{3}\) represent the maximum and minimum principal stresses at rock failure, respectively. \(\sigma_{\rm ci}\) is the uniaxial compressive strength of the rock. \(m_{\rm b}\), \(s\), and \(a\) are functions related to the geological strength index \(GSI\) and disturbance parameter \(D\). The geological strength index \(GSI\) can be expressed in terms of the rock's longitudinal wave velocity \(V_{\rm p}\). Therefore, the final expression [22] is:  
\[\begin{array}{l}m_{\rm b}=m_{\rm i}\exp\left(\frac{40V_{\rm p}-290}{84-24D} \right)\\ s=\exp\left(\frac{40V_{\rm p}-290}{27-90}\right)\\ a=\frac{1}{2}+\frac{1}{6}\left(\mathrm{e}^{-\left(8V_{\rm p}+2\right)}{9}- \mathrm{e}^{\frac{-20}{3}}\right)\\ \end{array} \tag{5}\]  
In the equation, \(m_{\rm i}\) represents the Hoek-Brown constant, which is derived by [PERSON] et al. [23]. \(V_{\rm p}\) is the longitudinal wave velocity of the rock, measured in km/s. \(D\) is the disturbance coefficient, which can be expressed as follows [22]:  
\[D=1-k_{\rm v} \tag{6}\]  
In the equation, \(K_{\rm v}\) represents the rock mass integrity index, which can be calculated using Equation (3).
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Safety Applications
#### 3.1.2 Emergency Electronic Brake Light Warning (EEBL)  
In this application, drivers are alerted of an emergency hard braking event performed by vehicles in front of them to be able to take the proper action to avoid an accident. The emergency hard braking event flag means that an event flag is incorporated within the data elements. Event flags' data elements are not included within the BSMs except if one of those flags are set to one [80]. EEBL application is considerably useful in situations where poor visibility exists, for instance, harsh weather conditions such as heavy rain or when the driver's sight is blocked by other vehicles [81; 82].  
Several studies have been directed to enhance and develop the EEBL application performance. For instance, authors in [83] present a comparison between two approaches carried out to evaluate the significance of electronic brake reports in EEBL systems. The firsttechnique was an analytical equation that depended on the minimum safety gap scheme. The second method was a machine learning tactic that was designed and proposed by the authors. In this context, reports regarding decelerated vehicles were being broadcast by their application to inform and alert drivers in need to conduct emergency braking. Based on the relevance of this report to the driver, vehicles decide whether to notify drivers or not. The lane and direction of the data were both common features to determine the relevance; however, false-positive warnings can occur, which may lead drivers to disregard these warnings or switch off the system, hence ignoring any safety benefits of the application. According to the analysis, the machine learning model outperformed the analytical technique in decreasing the number of false positive warnings by learning from the post actions taken by the driver upon obtaining the report. Both methodologies were compared using the MITSIM simulation software with a variety of traffic and communication specifications [83]. In another study, [PERSON] et al. [84] present the design and assessment of in-vehicle signs shown on the smartphone to applications including EEBL, electronic-vehicle warning (EVW), traffic-condition warning, and road-work warning. They evaluated their design using quantitative and qualitative techniques. A total of 44 participants were provided with 11 design options, and their display included both non-legend and legend designs. The results showed that EEBL and EVW signs based on the legend design were better understood and less vague, in comparison to non-legend-based signs. Displaying a legend design underneath the signs to alert drivers of any potential emergency braking that might occur ahead was effective in situations where poor visibility conditions exist. Hence, an improvement for safety-critical incidents can be achieved.
# Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
## 2 Theoretical Background  
### Stimulated Brillouin Scattering  
As we mentioned in the introduction, the Brillouin acoustic wave scatters the pump wave while the scattered light will be downshifted in frequency [6; 7]. Since the pump wave at frequency \(\omega_{p}\) is scattered from a retreating sound wave, the scattered radiation will be shifted downward in frequency to:  
\[\omega_{s}=\omega_{p}-\Omega_{B} \tag{1}\]  
where \(\omega_{s}\) is the backscattered frequency and \(\Omega_{B}\) is the acoustic wave frequency, called the Brillouin frequency. Since the Brillouin frequency is related to the acoustic wave vector, and since this wave is driven by the beating of the two counter-propagate pump and Stokes, it is possible to show that the Brillouin frequency is given by [7]:  
\[\Omega_{B}=\frac{2\
u_{a}}{c/n}\omega_{p} \tag{2}\]  
where \(\
u_{a}\) represents the velocity of sound, c is the velocity of light and n is the refractive index of the fiber. Since the acoustic velocity in optical fiber is about 5800 m/s [15], the typical value for Brillouin frequency for pump wave with a wavelength of 1550 nm is about 10 GHz.
## 7 Antenna Qualification and Acceptance Test  
Following the successful validation of the antenna, both in isolation and in deployment on the satellite, space qualification and acceptance tests are conducted. The qualification (QM unit) and acceptance (FM unit) levels will be those specified by ISI-LAUNCH, as Leader-S is scheduled for launch on a SpaceX Falcon 9 rocket [36]. Qualification tests are performed on the QM S-Band antenna. This model has been submitted to a comprehensive qualification test campaign, encompassing various stages (Figure 19), with specific qualification durations and levels designated for the environmental tests.  
Figure 17: AR measurement of S—band antenna isolated vs. placed on RF dummy, f = 2.03 GHz: (a) Port 1; (b) Port 2.  
Figure 18: AR measurement of S—band antenna isolated vs. placed on RF dummy, f = 2.205 GHz: (a) Port 1; (b) Port 2.
## 1 Introduction
The important thing is that a pseudo-flicker weight function with the frequency of the flicker noise was proposed in order to increase the effect of reducing the flicker noise. A similarity to the previous work is as follows: It reduces the electrical noise generated in the electrical driver circuit. The differences and similarities between previous work and proposed techniques are summarized in Table 2.  
This paper is organized as follows. In Section 2, the proposed scheme for reducing flicker noise is described along with the mathematical equations. In Section 3, the implemented experimental Li-Fi link is presented to verify the proposed technique. In Section 4, the experimental results, which are measured using the implemented experimental Li-Fi link, are shown. Finally, Section 5 summarizes the content of the paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & & **Differences** & **Similarities** \\ \hline \multirow{3}{*}{\(\bullet\) Previous work [21]} & \(\bullet\) & Reduction of the interference noise & \\  & & and shot noise produced by the & \\  & & electric circuits that generate & \\  & & various lights around Li-Fi link & \(\bullet\) & Reduction of the electrical noises \\ \hline \multirow{3}{*}{\(\bullet\) Proposed technique} & \(\bullet\) & Reduction of the flicker noise & \\  & & generated in the wireless Li-Fi & \\ \cline{1-1}  & & transmitter including the electric & \\ \cline{1-1}  & & circuit that drives white LED & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between previous work and the proposed technique.
## 4 Description of the SAVOA
### Multi-Guides Differential and Dynamic Rotation Transforms  
The dynamic decay factor \(\delta\) is first introduced in the exploitation phase:  
\[\delta=1-\left(\frac{iter}{iter_{Max}}\right)^{2} \tag{52}\]  
In the first exploitation phase, when \(P_{2}\geq rand_{P_{2}}\), to avoid over-reliance on a single leader individual, the positional differentials of the three bootstraps (optimal, suboptimal, and random individuals) are combined to strengthen the local exploitation capability while retaining some global search potential. The difference \(\Delta d\) is given by:  
\[\left\{\begin{array}{ll}\eta_{1}=U-0.5,\ U\sim U\Big{(}0,1\Big{)}^{d}\\ \Delta d=0.6(BestVulture_{1}(i)-P(i))\\ +0.3(BestVulture_{2}(i)-P(i))\\ +0.1\cdot\eta_{1}\cdot(ub-lb)\end{array}\right. \tag{53}\]  
where \(\eta_{1}\) is a randomized perturbation vector for adding small perturbations; \(\mathcal{U}(0,1)^{d}\) is a Gaussian distribution; \(BestVulture_{1}(i)\) and \(BestVulture_{2}(i)\) are the optimal and suboptimal solutions, respectively; \(P(i)\) represents the location of the current individual; \(ub\) and \(lb\) denote the upper and lower boundaries, and \(d\) is the spatial dimension; the coefficients for the three guides are 0.6, 0.3, and 0.1.  
When \(P_{2}<rand_{P_{2}}\), a rotational perturbation strategy that utilizes sinusoidal and cosinusoidal offset generation can produce periodic perturbation jumps to avoid over-exploitation. The strategy generates a perturbation amplitude control factor \(\xi\) and a randomized perturbation template \(\eta_{2}\):  
\[\left\{\begin{array}{ll}\xi=(0.8+0.4\cdot rand)\cdot(1+2\cdot|F|)\cdot\delta \\ \eta_{2}=U+0.5,\ U\sim U(0,1)^{d}\end{array}\right. \tag{54}\]  
\(\xi\) is used to prevent poor convergence due to too large a jump or too small a perturbation; \(\eta_{2}\) is used as the coefficient template of the random perturbation factor to scale the individual update amount; \(\delta\) is the dynamic attenuation factor in Equation (52).  
In summary, the positional equation for the first phase of SAVOA's exploitation is updated to:  
\[P^{\prime}(i+1)=\left\{\begin{array}{ll}\text{Equation}(\ref{eq:P1})&P_{2} \geq rand_{P_{2}}\\ \text{Equation}(\ref{eq:P1})&P_{2}<rand_{P_{2}}\end{array}\right. \tag{55}\]  
\[P^{\prime}(i+1)=D^{\prime}(i)\times(F+0.3\times rand_{4})-d^{\prime}(t) \tag{56}\]\[D^{\prime}(i)= \big{|}X^{\prime}\times R(i)-P(i)\big{|} \tag{57}\] \[d^{\prime}(t)=R(i)-P(i)-\Delta d\] (58) \[S^{\prime}_{1}=R(i)\cdot\left(\frac{rand_{5}\times P_{i}}{2\pi} \right)\cdot cos\big{(}2\cdot\pi\cdot rand_{\theta}\big{)}\cdot\xi\cdot\eta_{2}\] (59) \[S^{\prime}_{2}=R(i)\times\left(\frac{rand_{6}\times P_{i}}{2\pi} \right)\times sin\big{(}2\cdot\pi\cdot rand_{\theta}\big{)}\cdot\xi\cdot\eta_{2}\] (60) \[P^{\prime}(i+1)=R(i)-\big{(}S^{\prime}_{1}+S^{\prime}_{2}\big{)} \tag{61}\]  
where the range of \(X\) is changed to \([0,\ 1.5]\); \(d^{\prime}(t)\) requires the subtraction of the difference \(\Delta d\) from \(d(t)\); \(rand_{\theta}\) is a random number between \(0\) and \(1\); \(P^{\prime}(i+1)\) denotes the volume position in the next iteration.
# Multi-Source Satellite Imagery and Machine Learning
## 2 Related Work
Experimental results confirm the effectiveness of this approach for geological interpretation. [PERSON] et al. 's study used satellite remote sensing to monitor geological hazard areas [14]. In particular, they developed a new method combining a deep learning model (ECA U-Net) and InSAR to detect mining subsidence. Furthermore, theyapplied the SBAS InSAR technique to monitor the Xiaolangdi dam, highlighting significant deformations. The study also explores the potential of InSAR to protect architectural heritage and proposes a new method for temporal detection of landslides based on optical imaging. The results highlight the effectiveness of AI coupled with InSAR for improving the monitoring of geological risks.
###### Abstract  
To the best knowledge of the authors, in former studies in the field of measuring volume fraction of gas, oil, and water components in a three-phase flow using gamma radiation technique, the existence of a scale layer has not been considered. The formed scale layer usually has a higher density in comparison to the fluid flow inside the oil pipeline, which can lead to high photon attenuation and, consequently, reduce the measuring precision of three-phase flow meter. The purpose of this study is to present an intelligent gamma radiation-based, nondestructive technique with the ability to measure volume fraction of gas, oil, and water components in the annular regime of a three-phase flow independent of the scale layer. Since, in this problem, there are several unknown parameters, such as gas, oil, and water components with different amounts and densities and scale layers with different thicknesses, it is not possible to measure the volume fraction using a conventional gamma radiation system. In this study, a system including a \({}^{241}\)Am-\({}^{133}\)Ba dual energy source and two transmission detectors was used. The first detector was located diametrically in front of the source. For the second detector, at first, a sensitivity investigation was conducted in order to find the optimum position. The four extracted signals in both detectors (counts under photo peaks of both detectors) were used as inputs of neural network, and volume fractions of gas and oil components were utilized as the outputs. Using the proposed intelligent technique, volume fraction of each component was predicted independent of the barium sulfate scale layer, with a maximum MAE error of 3.66%.  
annular regime; scale layer-independent; petroleum pipeline; volume fraction; dual energy technique Article Application of Artificial Intelligence and Gamma Attenuation Techniques for Predicting Gas-Oil-Water Volume Fraction in Annular Regime of Three-Phase Flow Independent of Oil Pipeline's Scale Layer [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2021)
# Instance Segmentation Method of User Interface Component of Games
## 5 Conclusions  
In this study, we proposed a method for automatically segmenting UI components on the game screen using a Pix2 Pix. In addition, we proposed a novel methodology that could mass-produce paired datasets required for deep learning research by using the synthetic data. Our method can reduce data labeling cost by developing a tool that automatically extracts and labels only the UI area from the game screen for supervised learning. The UI segmentation network trained in this way has the advantage of being able to detect UI components in the games regardless of their position and it can detect UI areas in other similar games. Our study can be used to extract useful information from UIs in game vision-based deep learning research. Our system can obtain various metadata, such as HP, skill, and chat, which can be taken from UI areas within the game screen. The data obtained in this way can be annotated back to the corresponding images and used to create additional image-parameter pair data. These datasets can then be used for various derivative studies. Our study can be effective as a preprocessing tool for various systems that exploit supervised learning data and facilitate research on vision-based game deep learning.  
Our system has a number of limitations. First, for images with high complexity in the original image, UI components can be segmented into groups because our automatic UI extraction tool does not work normally. Second, the general UI assumes a square shape in numerous cases, so the difference from the background image is clear, but the difference in shape between UI components is negligible. Therefore, there may be limitations in recognizing the difference between each UI component. To solve this problem, the segmentation network should be trained to focus on textures rather than shapes. Finally, our experiment was only tested on the genre of mobile games based on touch interfaces and PC MMORPG games. There is a need to apply it to a wider variety of genres and games to check its stability.  
Our system has the potential to develop in two aspects in the future. The first is to extract the actual string by applying the text classifier to the extracted segmentation image. This makes it possible to generate more precise metadata. The second is to develop universal UI network segmentation that can extract all game UIs within a similar genre, not a segmentation network specialized for one game. Consequently, the usefulness of the network will increase significantly. To achieve this, it will be necessary to design and combine a multidomain segmentation network.  
Methodology, S.K.; software, J.-i.C. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Result of applying the network trained with Black Desert to WOW image. (Left: Original image, Middle: Ground truth, Right: Segmented image).  
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.NRF-2019R1A2C1002525) and a research grant from Seoul Women's University (2020-0392).