esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A FSO communication system employing a QPD-based PAT and a common optical path scheme is reported. The precise LOS maintenance and full-duplex 1.25 Gbps high-rate data communication were performed simultaneously at 50 m. The 4.25 times faster tracking performance was experimentally demonstrated when the incident beam size on the QPD became approximately equal to the size of the QPD. The improved tracking performance could help to quickly establish a reliable mobile FSO communication link. We believe that the proposed full-duplex Gbps FSO communication system with a common optical path and a simple and compact structure has the potential to be used for future FSO-based fronthaul/backhaul in 5G wireless networks and application services using UAV.  
Conceptualization, S.P., C.I.Y., Y.S.H. and J.H.R.; methodology, S.P.; software, S.P. and J.H.R.; validation, S.P., D.-S.L. and J.-H.J.; formal analysis, S.P. and H.S.K.; investigation, S.P., C.I.Y. and Y.S.H.; resources, S.P.; data curation, S.P., C.I.Y. and Y.S.H.; writing--original draft preparation, S.P.; writing--review and editing, S.P., C.I.Y., Y.S.H., H.S.K., D.-S.L. and J.-H.J.; visualization, S.P., C.I.Y. and Y.S.H.; supervision, H.S.K., D.-S.L. and J.-H.J.; project administration, H.S.K.; funding acquisition, H.S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean Government. [22 ZK1100, Honam Region Regional Industry-based ICT Convergence Technology Advanced Support Project].  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Type of Data** & **Optical Path for** & **Link Rate** & **Beamforming** & **PAT** & **Number of Optical** & **Ref.** \\
**Transmission** & **Tx/Rx/Beacon** & **(Gbps)** & **(\(\theta_{\text{div}}\))** & **Type** & **Components** & **[18]** \\ \hline Simplex & & & Hybrid & & & \\ (UAV-Ground) & Separate & 0.5 & SLM [1] & (MRR + FSM + & \(\leq\)19 & \\  & & & & QPD + Camera) & (Ground) & \\ Simplex & & & Fixed & Hybrid & \(\geq\)23 & \\ (Airborne platform & Separate & 1.25 & (2.26 mrad) & (FSM + Camera) & (TOGS [2]) & [24] \\ -TOGS) & & & & & Hybrid & \(\leq\)10 & \\ Full-duplex & & & & (FSM + 5-cell detector) & (Ground) & \\ (UAV-Ground) & & & & Hybrid & & \\ Simplex & & - & - & (FSM + Camera + & \(\leq\)12 & \\ (UAV-Ground) & & & & QPD) & & [26] \\ Full-duplex & & & EMS & & & \\ (OAT-OGT) & Share & 1.25 & (0–8.84 mrad) & QPD & (OGT) & work \\ \hline \end{tabular}  
* SLM: spatial light modulation, [2] TOGS: transportable optical ground station.  
\end{table}
Table 1: FSO communication system for UAVs with PAT system.
###### Abstract  
As large-scale renewable energy sources are increasingly integrated into AC grids via voltage source converters (VSCs), the transient synchronization stability of phase-locked loop (PLL)-based VSCs during fault ride-through is gaining more attention. Most existing studies assume that the positive and negative sequence separation and current control dynamics are much faster than the PLL dynamics, thereby neglecting their impact on the transient synchronization stability of the system. However, when the PLL bandwidth is relatively large, ignoring the positive and negative sequence separation and current control dynamics may result in incorrect stability assessments. To address this issue, this paper first considers the multiple control loop coupling, including positive and negative sequence separation, current control, and PLL, to construct a full-order nonlinear mathematical model of the VSC grid-connected system under asymmetric fault conditions. Based on this, the phase trajectory method is employed to analyze the transient synchronization stability of the system. Additionally, this full-order mathematical model is used to determine the PLL bandwidth boundary beyond which the effects of positive and negative sequence separation and current control dynamics cannot be neglected. Finally, PSCAD/EMTDC simulation results validate the effectiveness of the theoretical analysis presented in this paper.  
grid-connected VSC; asymmetric faults; multiple control loop coupling; transient synchronizing stability +
Footnote †: journal: Applied  
_aciences_
## 2 Materials and Methods
### Antenna III  
The miniaturization technique used for Antenna III is different from the Antenna I and II designs. In patch antennas, one effective way to reduce the antenna's area and improve the bandwidth is to introduce slots in the structure of the antenna. In this method, by having slots and defects in the antenna's shape, we can cause curves and bendings in the current paths and consequently have a smaller area, without the need to introduce vertices in the antenna's structure itself. Additionally, by adding periodic slots in Antenna III, we have periodic capacitances between the adjacent branches, which decreases the resonance frequency and hence further reduces the area of the antenna. It is also worth noting that since there is no opposite going current paths in this design, miniaturization has the least negative effect on the radiation pattern. Finally, similar to the previous designs, the inverted F method is used to tune the input impedance, and additional branches at high-current nodes are introduced to achieve resonance frequencies of 2.4 GHz and 5.7 GHz bands. This configuration has the best BW among the three designs. Unlike the other geometries, in the comb antenna, the input port is located at the beginning of the structure. The dimensions are 1.4 mm \(\times\) 12 mm \(\times\) 12 mm.  
Figure 2: _Cont._  
The step-by-step process for designing a multiple-band implantable antenna is shown in Figure 3. In the next section, the results and performance of the mentioned implantable antennas in the equivalent phantom are compared [29].
## 6 Conclusions  
In this work, we successfully modeled the C-ITS infrastructure of the Budapest Ring Road (expressway M0) and simulated V2X messaging of Day1 and Day2 cooperative services using real-world traffic data measurements in our implementation. The traffic data were processed and fed to the simulation engine, which used a detailed implementation of the overall V2X communication chain from the access and networking layers through the facilities and toward the applications/services.  
We could see from the results that the V2X channel load remained in the acceptable range in every case. In the case of 20% penetration--which could represent the V2X penetration in the foreseeable future--the CBR value was about 0.02 in the combined CAM and CPM case and only about 0.01 in the case of single CA or CP services (both from the infrastructure's and the vehicle's point of view).  
However, as the V2X penetration grows and the number/bandwidth requirements of C-ITS services increases, the ITS-G5 channel will definitely see more considerable challenges: it might quickly be saturated in Day2 and beyond scenarios. Multi-Channel Operation (MCO) techniques can be applied to mitigate this problem.  
Our future work is to implement a complete list of ITS services with all the possible Day1/2/3 applications in the created C-ITS environment and analyze the precise conditions of channel saturation during the modeled realistic traffic situations.  
Conceptualization, L.B.; Writing - original draft, A.W., T.A.T. and L.B.; Writing - review & editing, T.A.T. and M.V. All authors have read and agreed to the published version of the manuscript.  
C-ITS deployment and services evaluation was co-funded by the European Commission through the Connecting Europe Facility (C-Roads Hungary project, Action no. 2016-HU-TMC-0216-M and C-Roads 2 Hungary project, Action no. 2018-HU-TM-0092-M).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The methods based on ML are common and use a simple approach, which needs to maximize a log-likelihood function. However, these approaches might not work for denoising problems [24]. The Bayesian-oriented methods make the optimal estimation based on the known probability distribution and observed data, which is often used to solve the optimization and approximation problems of nonlinear signal models or processing systems [25]. MAP approaches use an assumed prior distribution of the related signal to regularize the estimation process, and the basis of MAP is Bayesian formula. These methods usually have deduced probabilistic models and the pdfs of the related signals play an important role in these methods. Furthermore, most of these methods need to iteratively estimate the parameters of the models deduced. But, generally speaking, these methods have high computational efficiency. In this paper, we propose an effective wavelet-based method with low computational complexity to fast extract the fault feature components from noisy bearing fault signals. This method only needs one prior, the pdfs of related signals. The analysis of simulation and experimental noisy fault vibration signals prove that our proposed method can efficiently estimate the repetitive transient component and extract fault feature frequency. The proposed method uses the MAP estimator and derives the general model of estimating the transient components based on the pdfs of the transient components of faulty bearings and noise, which means that our method has a scientific explanation of probability theory. From the point of view of timeliness, the proposed method in this paper only requires a small amount of iterative parameter estimation of the model derived using the expectation-maximization (EM) algorithm. At the same time, the computational cost of the discrete wavelet transform is also very low. Therefore, compared with some other popular fault feature extraction methods, the computational complexity of our proposed method is much lower and the running time also proves the high computation efficiency. Our method can not only get the result at a fast speed, but also ensure the accuracy of fault feature extraction. This paper is organized as follows. In Section 2, the derivation of the model to estimate the transient components and the algorithm to estimate parameters are presented in detail. In Section 3, the proposed method is used to analyze a simulated signal. In Section 4, three experimental bearing fault signals are analyzed using the proposed method respectively. Lastly, Section 5 presents the conclusions of the study.
## 1 Introduction
* **Security**: a multi-UAV system must be encrypted, guarding against hacking, jamming, and other types of interference to the drones and their communications [18; 34; 35]. * **Interference**: in highly dynamic surroundings, drones may encounter interference from other wireless devices, which can affect communication or navigation [18]. * **Energy consumption**: to ensure that the drones can operate for long periods of time, energy management is crucial. Drones must be able to manage their power consumption and plan their routes to optimize battery life [36; 37; 38; 39; 40; 41; 42]. * **Interoperability**: the system must guarantee the exchange of information between different types of drones regardless of their communication protocols. Unfortunately, there is still no common protocol for drones to communicate with each other. One key challenge in the design and implementation of multi-UAV systems is ensuring that the UAVs work together effectively in a distributed environment. This requires the development of robust communication and coordination architectures, as well as the integration of sensors and other equipment to allow the UAVs to sense and adapt to their environment. One of the main problems with swarms is the coordination of multiple UAVs carrying out a task in a distributed area. This problem can be addressed by using distributed algorithms. Algorithms such as consensus algorithms [43], leader-follower algorithms [44], and distributed optimization algorithms [45] can be used to coordinate multiple drones in a distributed area. These algorithms allow the UAVs to communicate and coordinate with each other so that they can complete the task in an efficient manner. Furthermore, these algorithms can also be used to ensure safety and prevent collisions between UAVs (see Section 10.1). Overall, these challenges require sophisticated algorithms and technologies, and the development of new solutions to these challenges is an active area of research in the field of drone swarm technology. In this research, we explore the problem of target detection in a distributed environment. We focus on a review of communication architectures and routing protocols, as well as the integration of routing protocols to enable effective coordination among the UAVs. In the second part, we show how to elaborate a centralized and decentralized communication architecture to achieve a collaborative mission. With this work, we aim to contribute to the progress of multi-UAV systems and their potential use in a wide range of applications.
###### Abstract  
Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine's sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.  
Industry 4.0; prognostics; cyber-physical system; smart maintenance; machine workload Article
# The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
## 4 Results
### User Engagement Scale
Overall, the paired sample \(t\)-test showed that there is no significant increase in the overall engagement score of the ARUI compared to the TUI, (\(t\)(23) = \(-\)0.21, \(p\) = 0.42, CI [\(-\)0.73, 0.57]), indicating that the ARUI did not increase user engagement; however, FA was significantly increased by the ARUI. Table 5 shows the UES result of Group B, who experienced the PHOENIX solution with the mobile app of the TUI for three months. Since the sample size for this group was too small (\(n=5\)) to conduct inferential statistical analysis, descriptive statistics (M and SD) were calculated to check the central tendency and dispersion of Group B's data. We found within Group B that the TUI's overall engagement score is higher than that of the ARUI due to the higher scores for the TUI in AE, PU, and RW dimensions. However, FA score of the ARUI is higher than the TUI, which is similar to Group A's result. Figure 6 depicts the distribution of UI preference of the participants in Group B based on the mean of AE, FA, PU, and RW. The result showed that the ARUI was selected by four participants (80%), while the TUI was chosen more frequently (60%) in other dimensions, such as AE, PU, and RW. The 'Equal' refers to a case when both UIs received the same score from a participant. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dimension** & \(M_{d}\) & \(SD_{d}\) & **SE** & \(p\) & \(t\) & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-8}   AE & \(-\)0.03 & 0.78 & 0.16 & 0.42 & \(-\)0.21 & \(-\)0.31 & 0.24 \\ FA & 0.28 & 0.55 & 0.11 & 0.01 & 2.49 & 0.09 & 0.47 \\ PU & \(-\)0.29 & 0.57 & 0.12 & 0.01 & \(-\)2.52 & \(-\)0.49 & \(-\)0.09 \\ RW & \(-\)0.03 & 0.40 & 0.08 & 0.34 & \(-\)0.41 & \(-\)0.17 & 0.11 \\ Overall & \(-\)0.08 & 1.85 & 0.38 & 0.42 & \(-\)0.21 & \(-\)0.73 & 0.57 \\ \hline  
The Q-Q plot was used to confirm the normality of the differences, and the paired sample \(t\)-test results indicated that there is no statistically significant difference in the right-tailed test of SUS scores, \(t(23)=-2.35\), \(p=0.01\), CI [\(-10.45\), \(-1.64\)]. Table 7 shows Group B's SUS scores for the TUI and ARUI. Three participants (P26, P27, and P28) perceived the mobile app with TUI had better usability than the app with ARUI. However, the average SUS score of the mobile app with the TUI was 69.00 (SD = 26.80), whereas the app with the ARUI was rated 72.00 (SD = 14.83) due to the large difference in SUS scores by P29. The data of P29 could be an outlier in a statistical analysis. However, since there was a difference in the period of time for experiencing each UI and the small sample size of Group B, conducting an inferential statistical analysis was inappropriate due to the low power of a statistical test with Group B's data. Therefore, we kept the P29's data to gain insight into what caused this difference between the two UIs.
## 2 Basic Theory of Nonlinear Lamb Waves
### Pulse Inversion Method of Frequency-Mixing Excitation  
Pulse inversion technique was originally used for extracting the second harmonic in medical field [27], which can effectively extract second harmonic time-domain signal, enhance the amplitude of second harmonic signal, and suppress odd harmonic components produced mainly by the experimental system [28].The method is used for further processing of the output mixing-wave signal in Equation (3). When the initial phase of the exciting ultrasonic signal is 0\({}^{\circ}\), the output of the nonlinear response is as follows:  
\[\begin{array}{ll}y_{1}(t)=&\alpha A_{1}\sin(2\pi f_{1}t+k_{1}x)+\alpha A_{2} \sin(2\pi f_{2}t+k_{2}x)-\\ &\beta\frac{A_{1}^{2}}{2}\cos[2\pi(2f_{1})t+2k_{1}x]-\beta\frac{A_{2}^{2}}{2} \cos[2\pi(2f_{2})t+2k_{1}x]+\\ &\beta A_{1}A_{2}\cos[2\pi(f_{2}-f_{1})t+(k_{2}-k_{1})x]-\beta A_{1}A_{2}\cos [2\pi(f_{2}+f_{1})t+(k_{2}+k_{1})x].\end{array} \tag{6}\]  
If the initial phase of the exciting ultrasonic signal is 180\({}^{\circ}\), the output of the nonlinear response becomes:  
\[\begin{array}{ll}y_{2}(t)=&-\alpha A_{1}\sin(2\pi f_{1}t+k_{1}x)-\alpha A_{2 }\sin(2\pi f_{2}t+k_{2}x)-\\ &\beta\frac{A_{1}^{2}}{2}\cos[2\pi(2f_{1})t+2k_{1}x]-\beta\frac{A_{2}^{2}}{2} \cos[2\pi(2f_{2})t+2k_{2}x]+\\ &\beta A_{1}A_{2}\cos[2\pi(f_{2}-f_{1})t+(k_{2}-k_{1})x]-\beta A_{1}A_{2}\cos [2\pi(f_{2}+f_{1})t+(k_{2}+k_{1})x].\end{array} \tag{7}\]  
Adding the response signals \(y_{1}(t)\) and \(y_{2}(t)\) yields:  
\[\begin{array}{ll}y_{3}(t)=&-\beta A_{1}^{2}\cos[2\pi(2f_{1})t+2k_{1}x]- \beta A_{2}^{2}\cos[2\pi(2f_{2})t+2k_{2}x]+\\ &2\beta A_{1}A_{2}\cos[2\pi(f_{2}-f_{1})t+(k_{2}-k_{1})x]-2\beta A_{1}A_{2} \cos[2\pi(f_{2}+f_{1})t+(k_{2}+k_{1})x].\end{array} \tag{8}\]  
and the Fourier transform converts Equation (8) into:  
\[\begin{array}{ll}Y_{3}(f)=&-\beta A_{1}^{2}[\delta(f+2f_{1})+\delta(f-2f_{ 1})]e^{j2k_{1}x}-\beta A_{2}^{2}[\delta(f+2f_{2})+\delta(f-2f_{2})]e^{j2k_{2}x} \\ &+2\beta A_{1}A_{2}[\delta(f+f_{2}-f_{1})+\delta(f-f_{2}+f_{1})]e^{j(k_{2}-k_ {1})x}-\\ &2\beta A_{1}A_{2}[\delta(f+f_{2}+f_{1})+\delta(f-f_{2}-f_{1})]e^{j(k_{2}+k_{1 })x}.\end{array} \tag{9}\]  
Equation (9) reveals that the fundamental frequency component of the original response signal is canceled out in the signal processed by pulse inversion, whereas the amplitudes of the corresponding second-order, difference frequency, and sum frequency components were increased. In other words, after pulse inversion processing, the fundamental frequency is effectively eliminated, and the amplitudes of the difference frequency and sum frequency components in the response signal become twice as large as that of the original response signal.
## 3 Biosurfactants  
### History of Biosurfactants  
Surfactants are characterized by an amphiphilic structure of molecules, with both a hydrophilic part (head) and hydrophobic (tail). Thus, these structures are able to accumulate at the surface separating two immiscible physical phases. Due to migration, the concentration of surfactant particles at the phase boundary is greater than in solution.  
By reducing the free energy of a given physical system, decrease in surface tension (ST) between two immiscible phases occurs. A good surfactant can lower ST of water from 72 to 35 mN/m [66]. The most commonly used surfactants are sodium lauryl sulfate (SLS) and ammonium lauryl sulfate (ALS). The hydrophobic tail of surfactants is composed of long-chain fatty acids. According to the ability of the hydrophilic head to dissociate in aqueous solutions, there are non-ionic and ionic (cationic, anionic, and amphoteric) surfactants. Most chemical surfactants are made from petroleum-based raw materials [67]. Biosurfactants are produced by plants, bacteria, yeast, and filamentous fungi [2]. Biosurfactants, as amphiphilic molecules, also possess a hydrophilic part (e.g., amino acids, proteins, mono and polysaccharides, phosphates, and alcohols) and a hydrophobic part (e.g., long-chain fatty acids, hydroxyl fatty acids, and \(\alpha\)-alkyl-\(\beta\)-hydroxy fatty acids) [66]. Biosurfactants are classified according to the producer microorganism, molecular weight, properties, and mechanism of action (Table 4). According to the chemical structure, biosurfactants are mainly divided into glycolipids (composed of carbohydrates and long-chain aliphatic or hydroxylialphatic acids); lipoproteins (composed of the amino acids and carboxyl and hydroxyl groups of a 14-carbon fatty-acid chain); and polymeric biosurfactants (lipid-protein and lipid-polysaccharide complexes) [67; 68]. Differences in the structure of biosurfactants may depend on the type of medium used to grow the microorganisms (type of substrates, amount of macro, micro, and trace elements), and the growth conditions (i.e., temperature and aeration) [69]. The content of carbohydrates in the microbial medium plays the greatest role in biosurfactant production, because they participate in the formation of both the hydrophilic and hydrophobic part of the compound [67; 68]. Studies have also shown a positive relationship between the efficiency of the biosurfactant synthesis process and the presence of hydrophobic carbon sources [70]. The first known biosurfactants were rhamnids synthesized by _Pseudomonas aeruginosa_, lipopeptide synthesized by _Bacillus subtilis_, and mannose/lethitritol produced by _Candida_. In recent years, the main interest of research were rhamnids and sophorolipids obtained from bacteria and yeast, while the use of fungi as a source of biosurfactants is negligible [67].