esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 4 Control Strategy of the MCAT
### MCAT AC-Side Short-Circuit Fault Recovery Strategy  
When an AC side short-circuit fault occurs at the internal AC bus of the MCAT, the voltage of the AC bus drops rapidly, causing the decrease of the power transmitted by AC bus and generating a large fault current. For this, MCAT are quickly blocked and AC circuit breakers are opened. After the transient fault is cleared, it is necessary to close the circuit breakers and unblock the MCAT to recover the power transmission. In order to maintain the voltages of DC-isolation capacitors stable, MMCs adopting passive control inthe MCAT should be switched to DC voltage control with reference value \(U_{\text{dc}}^{*}\) and reactive power control with reference value 0 pu when recovering, that is:  
\[U_{\text{dc}}^{*}=\frac{\left(E_{\text{high}}-E_{\text{low}}\right)}{M} \tag{28}\]  
After the DC voltages of these MMCs are stabilized, they are switched to passive control again, and the system resumes steady-state operation.
## 3 Materials and Methods
### Vegetation Index  
Using spectral vegetation indices to estimate the canopy FPAR is a frequently-used approach in the remote sensing community [45; 46; 47; 48]. NDVI, derived from the red and near-infrared reflectance [49], is the most-used vegetation index for estimation of the canopy FPAR [13; 16]. However, it is well known that NDVI has several limitations, including saturation in a closed canopy and sensitivity to both atmospheric aerosols and the soil background [39; 50]. Therefore, a number of derivatives and alternatives to NDVI have been proposed in the scientific literature to address these limitations, including the soil-adjusted vegetation index (SAVI), the enhanced vegetation index (EVI), EVI2 and so on. SAVI minimizes soil brightness influences from spectral vegetation indices [37]. EVI is an optimized vegetation index, and it enhances the vegetation signal with improved sensitivity in high biomass regions and vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences [38]. Different from NDVI and SAVI, EVI is calculated based on three bands. A blue band is needed in addition to the red and near-infrared bands, which make EVI difficult to generate long-term EVI time series as NDVI counterpart. Therefore, EVI2 without a blue band, which has the best similarity with the 3-band EVI, is developed by [PERSON] et al. [51]. These vegetation indices are calculated according to following equations respectively.  
\[NDVI=\frac{R_{NIR}-R_{RED}}{R_{NIR}+R_{RED}}\]  
\[EVI=2.5\times\frac{R_{NIR}-R_{RED}}{1+R_{NIR}+6\times R_{RED}-7.5\times R_{ BLUE}}\]  
\[EVI2=\frac{R_{NIR}-R_{RED}}{R_{NIR}+2.4\times R_{RED}+1}\]  
\[SAVI=(1+L)\times\frac{R_{NIR}-R_{RED}}{R_{NIR}+R_{RED}+L}\]  
where \(R_{NIR}\), \(R_{RED}\), \(R_{BLUE}\) denote the spectral reflectance measurements acquired in the red, near-infrared, blue regions, respectively; \(L\) is a canopy background adjustment factor and was set as 0.5.  
Potentially, these vegetation indices vary from \(-1\) to 1.
## 2 Market Model
### Agent's Utility  
Standard DA markets assume that agents have simple quasi-linear utility: \(\sum_{j\in B}(s_{i,j}-v_{i})\pi_{i,j}\) for seller \(i\) and \(\sum_{i\in S}(v_{j}-b_{i,j})\pi_{i,j}\) for buyer \(j\). However, to represent the characteristics of a wholesale market for perishable goods, we define the idiosyncratic utility for sellers and buyers.  
For seller \(i\), when \(\pi^{t}_{i,j}\) units of goods are sold to buyer \(j\) at price \(s^{t}_{i,j}\) in round \(t\) within period \(p\), seller \(i\) obtains income \(s^{t}_{i,j}\). Because the production cost of the seller is \(v^{p}_{i}\), the seller's surplus is \((s^{t}_{i,j}-v^{p}_{i})\pi^{t}_{i,j}\). If a unit of goods perishes in round \(t\) without being traded, seller \(i\) loses valuation \(v^{p}_{i}\). Therefore, seller \(i\)'s utility is defined as follows.  
**Definition 2** (Seller \(i\)'s utility at time round \(t\)).: \[U_{i}(\hat{\theta}^{\leq t})=\sum_{\{p|a_{i}^{t}\leq t\}}\sum_{t^{\prime}\in[ \hat{a}_{i}^{p},\hat{d}_{i}^{p}]}\sum_{j\in B\wedge\pi^{t}_{i,j}>0}s^{t^{ \prime}}_{i,j}(\hat{\theta}^{\leq t})\pi^{t^{\prime}}_{i,j}(\hat{\theta}^{ \leq t})-\sum_{\{p|\hat{a}_{  
**Definition 3** (Buyer \(j\)'s utility at time round \(t\)).: \[U_{j}(\hat{\theta}^{\leq t})=\sum_{\{p\}|d_{j}^{r}\leq t\}\sum_{t^{\prime}\in[d_ {j}^{r},d_{j}^{r}]}\sum_{i\in S\wedge\pi_{ij}^{\prime}>0}\left(v_{j}^{p}-b_{ij}^ {t^{\prime}}(\hat{\theta}^{\leq t})+\gamma_{j}^{p}v_{j}^{p}\right)\pi_{ij}^{t^{ \prime}}(\hat{\theta}^{\leq t}).\] (3)  
Agents are modeled as risk neutral and utility maximizing. Equation (2) shows that the seller's bidding strategy is intricate because she can enhance her utility by either raising the market price with higher valuation bidding or preventing the goods from perishing with lower valuation bidding. Equation (3) reveals that the buyer also has difficulty finding an optimal bidding strategy because she can improve her utility by either bringing down the market price with lower valuation bidding or increasing successful trades and retail opportunities with higher valuation bidding.
# Changes in Propeller Shaft Behavior by Fluctuating Propeller Forces during Ship Turning
## 2 Approaches Adopted
### Measurement and Data Analysis Procedures
#### 2.3.3 Processing of Raw Signals  
The measurement data received from the strain gauge were used as basic information to obtain the amplitude and direction (bending angle) of bending moments that were subjected to the engine loads in the shaft cross-section where the gauges were installed. The bending moments at the position of the strain gauge were obtained by using the measured strain (\(\epsilon\)) as described in Figure 8.  
In Figure 8, the signal received from the displacement sensor includes the tachocomponent for detecting the rotating angle as well as the rotating speed of the shaft. The laser maker is located at 40\({}^{\circ}\) before the position of the strain gauge. As a result, the extracted signal was required to undergo a smoothing process to acquire proper displacement values as shown in Figure 9.  
Figure 8: Example of a measured bending moment at the position of the strain gauge and the vertical displacement before processing at the NCR going straight ahead.  
Figure 9: Example of a measured bending moment at the position of the strain gauge and the smoothed vertical displacement after processing at the NCR going straight ahead (filter type: moving average filter; span: 10).  
Figure 9 shows the waveform of the local bending moment, \(M_{s}\), at the position of the strain gauge. The magnitude of the nominal bending moment for the cross-section (it is now shortened to the bending moment) can be simply determined as follows:  
* Read the local bending moment directly from the measured strain during one revolution;
* Determine the maximum and minimum values;
* Obtain the bending moment as half of the peak-to-peak amplitude (\((\max-\min)/2\)) as described in Equation (5).  
The above algorithm is very convenient and precise for a pure sine wave, or a wave that is close to a sine wave, such as in a static slow shaft turning test (using turning gear). The accuracy is sufficient in the case of a complex signal in a dynamic test. In fact, the signal waveform is the result of the summation of many separate sine waves. By using the fast Fourier transform (FFT), it is possible to distinguish the vibrations of separate orders. The magnitude of the bending moment, \(M_{b}\), and the bending angle, \(\lambda\), are determined as the magnitude and the phase of the first-order vibration. In this way, the bending moment can be obtained for every revolution. Therefore, the bending moments of the separate vertical and horizontal components can be obtained using Equations (6) and (7), respectively. This process enables us to understand the general trends in the bending direction of the shaft.  
Therefore, through the use of these two methods, it is possible to cross-validate the behavior of the shaft occurring during the ship's operation.
## 3 NadamSPGD Algorithm in SLAO  
### Fitness in SLAO  
It is necessary to establish a connection between the fitness of optimization algorithms in SLAO and system evaluation indicators of the CFSOC system. First, we assume that the initial wavefront aberration of the laser carrier signal through the atmospheric channel is \(\varphi_{0}(r,\theta)\), and solutions of the algorithm are 97-dimension vectors \(u=\{u_{1},u_{2},\ldots,u_{97}\}\); every component in the vectors respects the control voltage of each actuator in CSDM. In SLAO, the solutions are continuously updated and the compensation phase \(\varphi(r,\theta)\) is generated according to Equation (2). Therefore, the residual phase aberration can be given by \(\mathcal{E}(r,\theta)=\varphi_{0}(r,\theta)-\varphi(r,\theta)\). On the basis of the analysis in Section 2.3, this study takes the SR of \(\mathcal{E}(r,\theta)\) as the fitness \(J\) in our algorithm to simplify the calculation [33; 36; 37]. In the following sections, the aim of the SLAO system is to optimize \(J\) to its ideal value. When the maximum \(J\) is obtained through the algorithm, we obtain the optimum voltage signals \(u\) and the best ME of the CFSOC system.
## 3 Four Kinds of Machine Performance and Comparison
### Comparison of Electromagnetic Properties
Under two times the rated load, the three layers of ferrite undergo different degrees of irreversible demagnetization, with the third layer suffering the most serious irreversible demagnetization. This paper mainly considers the condition of the four motors running under the rated load, so the design of the ferrite-assisted synchronous reluctance motor is reasonable. In Figure 17, the mechanical characteristics and torque output capability of the four motors are described. It can be seen from the diagram that the torque-speed characteristics and torque output capacity of the SynRM are weaker than those of the other three motors. The torque-speed characteristics of the IM and the IPM are similar. In the low-load region, the torque-speed characteristics and torque output capacity of the IPM and the IM are better than those of the ferrite-assisted SynRM. In the high-load area, the torque-speed characteristics and torque output capacity of the IPM are weaker than those of the ferrite-assisted SynRM. When the load is high, the influence of the permanent magnets on the IPM is small, while the salient pole ratio of the ferrite-assisted SynRM is high. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name** & **IM** & **SynRM** &
\begin{tabular}{c} **Ferrite-Assisted** \\ **SynRM** \\ \end{tabular} & **IPM** \\ \hline Rated output power (kW) & 37 & 37 & 37 & 37 \\ Average output torque (Nm) & 235.6 & 236.2 & 236.4 & 236.6 \\ Torque ripple (\%) & 11.23 & 14.52 & 11.4 & 14.6 \\ Efficiency (\%) & 91.76 & 94 & 94.3 & 95 \\ Power factor & 0.88 & 0.8 & 0.9 & 0.97 \\ Core loss (W) & 701 & 246 & 309 & 355 \\ Cooper loss (W) & 931 & 921 & 717.4 & 598 \\ Eddy current loss (W) & \(-\) & \(-\) & \(-\) & 0.3 \\ Current density (A/mm\({}^{2}\)) & 4.08 & 3.83 & 3.25 & 2.94 \\ Electric load (A/cm) & 287.2 & 270 & 238.7 & 215.9 \\ Thermal load (A\({}^{2}\)/mm\({}^{3}\)) & 1171.8 & 1074.8 & 775.6 & 635 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the electromagnetic performance of the four types of machines. Figure 16: Demagnetization of the ferrites: (**a**) rated load; (**b**) under 1.5 times the load; (**c**) under 2 times the load.
## 2 Materials and Methods
### Electronic Systems Design
#### 2.6.1 Tilt Angle Measurement  
The electronic subsystem that is responsible for the measurement of the tilt angle is based in a microcontroller board. Figure 9 shows the layout of the system, while Figure 10 represents its electrical schematic. The components included in this subsystem are described below:  
* Microcontroller. An Arduino UNO device (Arduino LLC, Boston, MA, USA) has been included, with Input/Output (I/O) ports for the connection of additional electronic components in a modular way. The programming language that has been used is C.
* Inertial Measurement Unit (IMU). A MPU6050 has been chosen for this function, since it contains an accelerometer and a gyroscope. This device can sense static and dynamic accelerations and is in charge of measuring the tilt angle. A complementary filter (CF), based in a Kalman filter, has been implemented to filter the noise in the measurement of both the accelerometer and the gyroscope, according to [17]. The measurement of the tilt angle, \(\theta_{\text{m,n}}\), with respect to the rotation axis of the mobile platform of the test bench, that corresponds to the instant of time n, has been calculated from Equation (4): \[\theta_{m,n}=A\cdot(\theta_{m,n-1}+a_{gyro}\cdot\Delta t)+B\cdot\beta_{accd}\] (4) where \(\beta_{accd}\) and \(a_{gyro}\) are obtained from the measurement of the accelerometer and the gyroscope, respectively. Parameters A and B are weighted coefficients for the application of the CF, where \[\text{A}+\text{B}=1\] (5)  
Figure 10: Rollover angle sensor system schematic.  
On the other hand, \(\theta_{m,n-1}\) is the previous tilt angle calculation to the instant of time, when the present measurement is performed.
* 4-digit 7-segment display. A display, model 5461 AS, has been chosen for showing the tilt angle that has been calculated from the measurements of the IMU. The values of the tilt angle are represented in real time, with a precision of a tenth of a degree, using an algorithm specifically developed for this project.
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 3 Experimental Studies and Modeling
### Experimental Results  
Figure 1(a) shows the experimental results of the rate of heat of hydration. The rate of hydration heat was normalized by the mass of the binder. First, for plain paste, the rate of hydration showed three peaks. The first peak occurred at the starting time, which was due to the rapid dissolution of C\({}_{3}\)A. The second peak occurred at about 12 h, which was due to the hydration of C\({}_{3}\)S of cement. The third peak occurred at about 17 h, which was due to the renewed dissolution of the aluminum of cement. After 3 days, the rate of hydration heat was low because diffusion became the controlling process of hydration. Second, as the replacement percentage of slag increased from 0% to 30% and 50%, the rate of hydration heat decreased. This was due to the dilution effect of slag addition. For specimens with 30% and 50% slag, the time corresponding to the second peak (C\({}_{3}\)S peak) was similar to that of plain concrete. Meanwhile, the third peak was much higher and sharper than the second peak. This was due to the aluminum phase in slag being much higher than that in cement. Third, Figure 1(b) shows the hydration heat normalized by the mass of cement. From mixing to the time of 9 h, the three curves of plain, 30% slag, and 50% slag merged together. This means that the slag reaction did not start at an early stage. In other words, in the early stage, the slag reaction was in the dormant period. After this stage, the difference in hydration heat between the control specimen and the slag-blended specimen was the result of the slag reaction.  
Figure 3 shows the strength development. At 3, 7, and 14 days, as the slag replacement percentage increased, the compressive strength of the concrete was reduced. This was due to the dilution effect of slag. Meanwhile, after curing for 28 days, the compressive strength of the slag composite specimen was higher than that of the plain specimen due to the reaction of the slag. In other words, strength crossover phenomena occurred between the slag-blended specimens and the plain specimens.  
Figure 2: Hydration heat of paste.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.15 Enhanced LEACH  
In [90], the LEACH protocol is modified to select the CH based on the lowest degree of distance from the BS, decreasing energy consumption in the network. When normal nodes select appropriate CHs located at a minimum distance from the \(BS\), it was proved that there is reduced battery power consumption. The goal is to determine how sensitive the network is to power consumption and how long the network can last.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.3 Calculation of the Rated Relative Inductive and Resistive Voltage Drops  
The rated relative inductive voltage drop determines the short-circuit current value of the thyristor valve. It is defined as:  
\[d_{\text{xRN}}=\frac{3}{\pi}\frac{X_{\text{t}}I_{\text{dN}}}{U_{\text{dioN}}} \approx(u_{\text{k}}+\text{relative voltage drop in PLC filter reactors})/2 \tag{36}\]  
where \(X_{\text{t}}\) is the commutation reactance.  
In practical engineering, the rated relative resistive voltage drop \(d_{\text{rRN}}\) is generally taken as 0.3%.
## 6 Challenges and Future Directions
### TSN-Grade Wireless Performance  
To achieve TSN-grade performance over wireless communication channels, one must overcome several difficulties due to the special properties of wireless media and communication protocols. Due to interference and the stochastic nature of the channel, wireless systems typically have a much higher packet error rate (PER) than cable- or optical fiber-based communication systems. TSN features like bandwidth scheduling and reservation are very good at delivering low latency/ jitter over Ethernet. These capabilities must take into account wireless link characteristics, such as achievable data rates and PER, which tend to change over time due to variations in the radio environment and interference. The 3 GPP Rel-15-defined ultra-reliable and low-latency communications (URLLC) mode is a key 5G capability that enables TSN-grade performance. The URLLC mode, as explained in [71], offers low latency with high dependability for short packets in conjunction with the flexible 5G frame structure concept. Additionally, it includes established QoS improvements for multiple concurrently active configurable grants and semi-persistent scheduling. Also, the extension of 802.1 TSN protocols over 802.11 is, by default, in line with the overall TSN reference architecture because 802.11 is one of the 802 LAN transport alternatives. However, in order for extensions of the 802.1 TSN protocols to function effectively, they must support the 802.11 MAC/PHY.  
Existing TSN services (such as scheduling implemented in network administration and configuration) and wireless networks (such as 802.11 and 5G) will require the definition of new abstractions and interfaces. To ensure predictable and dependable time-aware delivery of scheduled traffic across wired and wireless domains enabled by various wireless technologies, clear inputs, outputs, tasks, and responsibilities are required. The adoption and expansion of TSN networks over wireless networks will be facilitated by a standard interface to interact with wireless domains that abstracts the underlying wireless connectivity technology. This work might be conducted in cooperation between the Avnu Alliance and IEEE 802.1 standards groups and could potentially include the definition of new wireless-specific parameters (for example, as an extension to the current 802.1 Qbv YANG model). This work could also influence wireless standard organizations, such as IEEE 802.11 and 3 GPP.
## 1 Introduction
### Literature Review
Researchers in [19] assessed the problem of bringing production units into orbit, taking into account the uncertainty of wind and solar and load consumption, which are intended to be optimized, and reduce costs and pollution. In this study, several valid scenarios for the uncertainty of wind, solar, and load sources are considered. Under different scenarios, an algorithm for cost and pollution minimization (their mathematical hope) is presented. In [20], an optimal method for designing multiple microgrids is considered, which examines the reliability and security of the distribution system to collect the distribution network for microgrids considering network reliability. Researchers in [21] proposed a multi-objective economic method considering the probabilistic nature of distributed renewable generation resources to design a single microgrid. The design of microgrids based on self-healing against possible accidents in the distribution network was investigated in [22]. In this reference, network partitioning was evaluated with the aim of studying the least loading of the system. The purpose [23] of zoning distribution networks was to improve the reliability of the entire system. Researchers in [24] is suggested an optimal method for producing distributed renewable sources. The design of microgrids using graph theories was proposed in [25]. In this paper, the structure design of microgrids using graph partitioning theory as a circular structure with system reliability was investigated.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 1 Introduction
Therefore, in this paper, our work is done under the Rician fading channel. In this paper, a two phase buffer aided multi-UAV relaying network is studied, in which we choose two different UAVs to serve the source and destination at the same time, respectively. According to the buffer state and channel state information (CSI), the network will decide which UAV receives the data from the source and which UAV serves the destination. In particular, the UAV will never be chosen if its buffer is empty. In this paper, our goal is to maximize the end-to-end average throughput by jointly optimizing the relay selection, UAV transmit power control, and UAV trajectory based on the buffer constraints. However, the relay selection, UAV transmit power control, and UAV trajectory design are tightly coupled with each other in this formulated problem, which makes it difficult to solve directly via general optimization methods. To tackle the above problem, we relax the binary variables for relay selection into continuous variables and utilize the block coordinate descent and the successive convex optimization techniques to solve the considered problem with an efficient iterative algorithm [20]. The convergence of this iterative algorithm is proven analytically at the end of this paper. The remainder of this paper is organized as follows. In Section 2, the buffer aided multi-UAV relaying system model and the problem formulation are introduced. In Section 3, the solution approach based on the block coordinate descent and the successive convex optimization techniques is proposed. In Section 4, the simulation results are presented to verify the convergence and performance of the proposed network. Finally, we summarize the conclusions of this paper in Section 5.