esa-sceva/satcom-chunk-collection huggingface dataset:
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 1 Introduction
Simultaneously, the overarching network employs a parallel structure to maintain the parallelism of low-resolution and high-resolution feature maps, continuously executing multi-scale fusion operations to acquire multi-scale information and perpetually exchanging information between different resolutions. Through incessant information exchange, the model more effectively integrates multi-scale semantic information, considering the semantic information of high and low features, thereby augmenting the expressive capacity of the network model. In comparison to existing models such as U-Net, ResNet, DeepLabV3, ResUnet, and HRNet, the HRU-Net model's unique parallel connection structure and continuous multi-scale feature integration and information exchange not only preserve detailed information but also capture global features, achieving more accurate road recognition. The content arrangement of this paper in subsequent sections is as follows: Section 2 provides detailed information regarding the road detection network proposed in this paper; Section 3 presents the experimental results, encompassing data introduction, experimental settings, and result analysis; Section 4 constitutes the discussion segment; Section 5 offers concluding remarks.
# Review of Functionally Graded Thermal Sprayed Coatings
## 4 Major Applications of Functionally Graded Coatings
### Other Applications  
A rapid development of industrial technologies in the industrial sectors of aerospace, automobile, shipbuilding, etc., resulted in many applications of non-ferrous metals. Consequently, the joining of such metals became an important issue. The conventional welding processes are not adapted for joining the non-ferrous metals. A possible solution is an application of an additional interlayer. The popular techniques of interlayers deposition are hot dipping and galvanizing [350; 351; 352]. An interesting alternative is cold spraying technique. [PERSON] et al. [353] used LPGS to deposit composite coatings of Al + Al\({}_{2}\)O\({}_{3}\), Al + Ni + Al\({}_{2}\)O\({}_{3}\) and Ni + Al\({}_{2}\)O\({}_{3}\). The authors found that the microstructure of Ni + Al\({}_{2}\)O\({}_{3}\) interlayer after the resistance spot welding (RSW) remains unchanged (Figure 14) and that the shear strength of the joint was comparable to that of joint of Al + Al. Another opportunity to use the cold spray technology was a deposition of interlayers on non-metallic substrates made by [PERSON] et al. [354]. The authors used metallic (Al and Cu) and MMC composite (Al + Al\({}_{2}\)O\({}_{3}\) and Cu + Al\({}_{2}\)O\({}_{3}\)) interlayers in soldering and concluded that the interlayers deposited by LCPS effectively limited formation of the reaction zones at the interface of interlayer with the soldered joint. Consequently, the mechanical properties of such joint could were improved [354]. [PERSON] et al. [355] used cold spraying to obtain Sn-Cu coatings onto aluminum and on copper substrates. The results showed the improvement in soldering. The deposition of graphite-copper composite coatings onto aluminum alloy 6060 with the use of LPCS were analyzed in [356]. The authors added aluminum, aluminum with alumina and copper to exclude erosion of the graphite at cold spraying of composite coating.  
MMC coatings are used frequently to improve resistance against erosion and wear. Most of thermal spraying processes are done at high temperature and, consequently, are associated with the phase transformation, oxidation and decarbarization of ceramic reinforcement or soft metal matrix. Therefore, a low temperature process of cold spraying can be useful. The drawback of cold sprayed MMCs is low strength and ductility. The post-spray heat treatment process may help in eliminating these negative effects. Consequently, the post-spray friction stir processing (FSP) of MMC coatings deposited by cold spray was tested [357; 358; 359; 360; 361; 362]. Such post-treatment resulted in reduction of interparticles distance and in refinement of the reinforcing particles. [PERSON] et al. [361] showed that FSP post-spray treated cold sprayed Al + Al\({}_{2}\)O\({}_{3}\) composite coating had satisfactory erosion resistance.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### CPU Clusters, Fog and Cloud Computing, and FPGA
#### 3.2.5 Optimal Power Flow and Security Constrained Optimal Power Flow  
The next applications with usage of PHC technologies are OPF refs. [156; 157; 158; 159; 160; 161; 162; 163] and SCOPF refs. [225; 226; 227; 228]. The authors in ref. [156] presented a parallel OPF solution developed in a network of workstations (CPU-cluster). The power system and the OPF problem was divided into geographical regions. Transmission lines that interconnect regions were divided into two lines connected to a dummy bus. Active and reactive power flows and voltage magnitudes and angles were defined as variables in the OPF problem for each area. The objective function for each area neglects the rest of the system and includes the cost of each generator that belongs to the corresponding area. Variables corresponding to dummy buses are modeled with dummy generators. Each OPF is defined with an interior point method. The solution is implemented in a network of seven Sun UltraSparc workstations.  
In ref. [227], the decomposition of a SCOPF problem using the Bender decomposition was implemented. The Bender decomposition divides the SCOPF into one master problem (OPF) and N subproblems where a total of N contingencies are evaluated. Subproblems check the solution feasibility for the master problem. The master solution corresponds to the overall problem solution if the subproblems are feasible, and all control provided by the master problem does not violate any constraint for the post-contingency state.  
Since subproblems are independent, they are solved in parallel and minimize required adjustments of preventive controls. When the subproblem is infeasible, instead of preventive control adjustments, it passes cut constraints to the master problem. The feasibility cut is added to the master problem. If there is any violation in the master problem under any contingency, the control variables for the violation are sent to the subproblems to retrieve the corresponding Bender cut.
## 3 Results  
### Variations in Environmental Factors and LAI  
The variations in the monthly average abiotic factors and LAI during the kiwifruit growing season of 2018-2019 are shown in Figure 2. T\({}_{\alpha}\), T\({}_{s}\), PAR, CH\({}_{2}\)O, RH, VPD, and LAI increased firstly and peaked in July or August, followed by a gradually declined trend. The average T\({}_{\alpha}\) was 21.5 \({}^{\circ}\)C and 20.9 \({}^{\circ}\)C during the growing seasons of 2018 and 2019, and T\({}_{\mathrm{s}}\) closely followed the variations of T\({}_{\alpha}\), with a maximum difference of less than 4 \({}^{\circ}\)C. The monthly average PAR steadily increased from March to August, with its maximum being 80.50 and 87.66 W m\({}^{-2}\) d\({}^{-1}\) in 2018 and 2019, respectively. CCO\({}_{2}\) and CH\({}_{2}\)O showed an opposite trend during the growth season. SWC and RH were relatively lower in the kiwifruit initial growing stages but increased sharply during the month of April-May. The averaged SWC and RH were 34.81% and 86.91% during the two growing seasons. The average monthly VPD in 2019 was higher than that in 2018, with the maximum of the former and latter being 2.08 and 1.69 kPa, respectively. As time elapsed, the monthly average LAI slightly decreased in late April or early May due to spring pruning, followed by a steady increase; it then decreased again in the late growing stage due to leaf senescence and shedding.
## 2 Anomaly Detection from Event Log  
Anomalies are data patterns that deviate from the defined normal data patterns [19]. Anomaly Detection is therefore intended to find data that show unexpected patterns. In the early years, Anomaly Detection involved specifying data thresholds and using inductive learning [20]. Later, methods to classify normality and anomalous behavior using classification techniques such as support vector machine (SVM) and neural network were used [21; 22; 23]. In the field of business process management, Anomaly Detection is used to identify abnormal flows by comparing the defined process model or the process model derived from the event log with the actual event log. For this purpose, conformance checking was used [24; 25]. Furthermore, studies to derive high-frequency patterns and detect anomalies by using data such as object or material flows and trajectories have been conducted in manufacturing, transportation, and logistics as well as in the field of business process management [26; 27].
## 1 Introduction
These metallic MEMS mirror actuators have an anchor region, a curled hinge region, and a flat mirror region. For all these metallic and hybrid MEMS shutter and micro-mirror arrays for optical applications [22; 23; 24; 35], the closing time or the actuation time is crucial. Since the shutter dynamics is also related to the magnitude of the applied voltage, also the pull-in voltage is involved and of high interest. Therefore, the main motivation of our paper is to simulate how pull-in voltage and actuation speed depend on geometrical shape and metallic material parameters and to provide support in material selection and optimization. Our studies are contributing to design rules by identifying the trends, in which way dynamic properties depend on different metallic materials, geometrical shapes and material parameters. The knowledge of these trends is helpful in the design process of electrostatic MEMS actuators. Our studies further reveal that expensive materials can be avoided in the design of metallic MEMS devices since low-cost materials deliver even better performance. The structure of this paper is given in the following. First, the pull-in voltage and actuation time of electrostatically actuated metallic MEMS cantilevers are studied as a function of geometrical and material parameters. This will be completed twice, analytically and numerically. For simple idealized parallel plate MEMS actuators, an analytical formula can be given for the pull-in voltage (in Section 2) and for the eigenfrequency, charging time and the temporal step-response function (in Section 3). Basic knowledge is recalled in Sections 2 and 3 as well as reconsidered in Section 4 for qualitative explanation of the numerical simulation results. The analytical formulas displayed below in Sections 2 and 3 are intended to simplify the access to Section 4 and to support a better intuitive understanding of the MEMS cantilever device studied in Section 4. For the latter device, no analytical formulas are given. In Section 4, COMSOL Multiphysics is used for numerical simulations based on a self-consistent solution of electro-mechanical equations concerning all these properties, but with the focus on pull-in voltage and actuation speed for different metalsand varying geometries. In Section 5, the results of Section 4 are compared with trends from analytical formula: in Section 5.1 for a parallel plate MEMS actuator and in Section 5.2 for a cantilever MEMS actuator. Section 6 proposes the workflow of optimization processes.
## 3 Comparison of Dynamic Response Characteristics under Fault-Free State
### Dynamic Response of Gear Transmission of Ordinary Rotating Machinery  
In Figures 3 and 4, G1 and G2 represent the driving and driven gears, respectively; \(F_{n}\) represents the normal meshing force; AX, AY and AZ represent the vibration acceleration of the gear rotation center in the x, y, and z directions, respectively; FFTMAG(Fn) and FFTMAG(AX), respectively, represent the Fourier transform of normal meshing force magnitude and vibration magnitude in the x-direction.  
Figure 3a,b shows the displacement variation of the rotation center of the driving and driven gears in the x and y directions. The driving gear and driven gear are set in the same coordinate system; the rotation centers of the driving gear and the driven gear are, respectively, set at the origin of coordinates and the position with the coordinate value of 330.0024. That is, the position of the blue line in Figure 3b. The disorganized trajectories are caused by the large clearance of the bearing and meshing impact caused by eccentric force, especially in the start-up stage. There is a violent fluctuation process within the range of 0.05 mm, but it is much smaller than the bearing clearance of 0.195 mm. During stable operation, the rotation center position of the driving gear is far away from the initial installation position and the increase of the center distance leads to tooth backlash.  
Figure 3: Dynamic response of ordinary rotating machinery: position curve of (**a**) driving gear center and (**b**) driven gear center.  
Figure 4a shows that the normal meshing force fluctuates within 0-5000 N. The gear transmission is relatively stable, and no obvious impact can be seen except for position 1, resulting from tooth backlash and a slight difference in the speed of the two gears. Similarly, the vibration acceleration can also reflect a slight impact. After removing the trend part during time-frequency transformation, the main frequency components of ordinary rotating machinery gears are gear meshing frequency, i.e., GMF, and its multiples, of which the amplitude of 2 GMF is the largest. The vibration features in the y and z directions are basically consistent with that in the x direction, so only vibration acceleration in the x direction is shown.
# An Analytical Design of Simplified Decoupling Smith Predictors for Multivariable Processes
## 2 Materials and Methods
### IMC-PI/PID Tuning Rules for Typical Processes without Time Delay  
In this work, three free-delay process models were considered:  
_Case 1_: The first-order system:  
\[q_{i0}(s)=\frac{K_{i}}{\tau_{i}s+1}. \tag{29}\]  
The proposed IMC filter structure  
\[f_{i}=\frac{1}{\lambda_{i}s+1}. \tag{30}\]The ideal feedback controller is obtained by  
\[g_{ci}(s)=\frac{\tau_{i}s+1}{K_{i}\lambda_{i}s}=\frac{\tau_{i}}{K_{i}\lambda_{i}} \bigg{(}1+\frac{1}{\tau_{i}s}\bigg{)}. \tag{31}\]  
Therefore, in this case, the PI controller settings are obtained:  
\[K_{ci}=\frac{\tau_{i}}{K_{i}\lambda_{i}} \tag{32}\]  
\[\tau_{li}=\tau_{i}. \tag{33}\]  
Note that, in this case, \(\tau_{Di}=\tau_{Fi}=0\).  
_Case 2_: The second-order system:  
\[q_{ii0}(s)=\frac{K_{i}}{(\tau_{ii}s+1)(\tau_{il}s+1)}. \tag{34}\]  
The proposed IMC filter structure  
\[f_{i}=\frac{1}{(\lambda_{i}s+1)^{2}}. \tag{35}\]  
The ideal feedback controller is obtained by  
\[g_{ci}(s)=\frac{(\tau_{ii}s+1)(\tau_{il}s+1)}{K_{i}\lambda_{i}s(\lambda_{i}s+2 \lambda)}. \tag{36}\]  
Rewriting Equation (36) into the form of Equation (21), the controller is obtained as Equation (37):  
\[g_{ci}(s)=\frac{\tau_{ii}+\tau_{il}}{2K_{i}\lambda_{i}}\bigg{(}1+\frac{1}{( \tau_{ii}+\tau_{il})s}+\frac{\tau_{ii}\tau_{il}}{\tau_{ii}+\tau_{il}}s\bigg{)} \frac{1}{(\lambda_{i}/2)s+1}. \tag{37}\]  
Therefore, the setting rules of the controller are obtained:  
\[K_{ci}=\frac{\tau_{ii}+\tau_{il}}{2K_{i}\lambda_{i}} \tag{38}\]  
\[\tau_{li}=\tau_{ii}+\tau_{il} \tag{39}\]  
\[\tau_{Di}=\frac{\tau_{ii}\tau_{il}}{\tau_{ii}+\tau_{il}} \tag{40}\]  
\[\tau_{Fi}=\frac{\lambda_{i}}{2}. \tag{41}\]  
_Case 3_: The second-order with negative zero:  
\[q_{ii0}(s)=\frac{K_{i}(\tau_{i2}s+1)}{(\tau_{ii}s+1)(\tau_{il}s+1)}. \tag{42}\]  
The proposed IMC filter structure  
\[f_{i}=\frac{1}{(\lambda_{i}s+1)^{3}}. \tag{43}\]  
The ideal feedback controller is obtained by  
\[g_{ci}(s)=\frac{(\tau_{ii}s+1)(\tau_{il}s+1)}{K_{i}\lambda_{i}s(\tau_{il}s+1) (\lambda_{i}2^{s}+3\lambda_{i}s+3)}). \tag{44}\]The ideal controller (44) does not have the standard form of Equation (21). Therefore, it was necessary to use the approximate techniques according to Equation (28), and the setting rules were derived as follows:  
\[K_{ci}=-p_{i}(0)\left[\frac{p_{i}^{{}^{\prime\prime\prime}}(0)}{3p_{i}^{{}^{ \prime\prime}}(0)}\right]+p_{i}^{\prime}(0) \tag{45}\]  
\[\tau_{li}=-\frac{p_{i}^{{}^{\prime\prime\prime}}(0)}{3p_{i}^{{}^{\prime\prime} }(0)}+\frac{p_{i}^{\prime}(0)}{p_{i}(0)} \tag{46}\]  
\[\tau_{Di}=\frac{1}{2}\Bigg{[}\frac{2p_{i}^{{}^{\prime\prime\prime}}(0)p_{i}^{ \prime}(0)-3\big{(}p_{i}^{{}^{\prime\prime}}(0)\big{)}^{2}}{p_{i}^{{}^{\prime \prime\prime}}(0)p_{i}(0)-3p_{i}^{{}^{\prime\prime}}(0)p_{i}^{\prime}(0)} \Bigg{]} \tag{47}\]  
\[\tau_{Fi}=-\frac{1}{3}\Bigg{[}\frac{p_{i}^{{}^{\prime\prime}}(0)}{p_{i}^{{}^{ \prime\prime}}(0)}\Bigg{]}. \tag{48}\]  
The tuning rules for different types of process models are summarized in Table 3.
## 3 Stability Analysis under Weak Grid
### Closed-Loop Stability of Integral-Resonant State Feedback LQR Control under Grid Impedance Change
The disturbance rejection response for the augmented integral-resonant state feedback LQR controller with \(L_{1}\) = 1.7 mH, \(L_{2}\) = 1.0 mH, and \(C_{f}\) = 4.5 uF from stiff grid to very weak grid is shown in Figure 13a. The disturbance rejection response also exposes the reason of the system instability when the grid impedance increases. As seen from Figure 13a, the bandwidth of the harmonic compensators is significantly reduced when the grid is weaker. Moreover, the high peak exceeds 0 dB when \(L_{g}\) reaches 21 mH, which causes the instability in system. The interpretation from the frequency response shows the maximum stability margin of \(L_{g}\) is 14 mH. Similar conclusions are inferred from Figure 13b,c, in which the maximum stability margins in the critical and low regions are 7 mH and 4 mH, respectively. The theoretical analysis will be validated by the simulation and experiment in the next section. To summarize the stability analysis based on the closed-loop poles' location in the discrete-time domain, Figure 14 represents the stability comparison of two controllers under grid impedance variation for different LCL parameter sets. It is inferred from these results that as the resonance frequency is reduced, the stable boundary according to the grid impedance variation is also reduced. In the direct grid current controller in Figure 14a, the stability region is gradually reduced as the designed LCL resonance frequency is decreased from high to low region. The inverter system is stable in the region of \(L_{g}\) < 4 mH in Case 1, and in the region of \(L_{g}\) < 2 mH in Case 2. In Case 3, the direct grid current controller cannot  
Figure 13: Frequency responses for the augmented integral-resonant state feedback LQR controller under grid impedance variation: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.  
stabilize the inverter system. On the other hand, the integral-resonant state feedback current controller in Figure 14b greatly extends the stability limit for three LCL filter cases. The inverter system is stable in the region of \(L_{g}<14\) mH in Case 1, in the region of \(L_{g}<7\) mH in Case 2, and in the region of \(L_{g}<4\) mH in Case 3. As a result, a relative stability is enhanced in the LCL filter design having high resonance frequency. Moreover, integral-resonant state feedback control has an improved relative stability than direct grid current control in view of the harmonic compensation and effective resonance damping.
## 2 Theoretical Foundation
### Fluid Governing Equation  
The fluid flow process follows the three basic physical conservation laws of mass, energy and momentum. When the fluid is mixed with other components, it also follows the conservation law of the components. Generally, compressible fluids can be described by the following governing equations:  
Mass conservation equation:  
\[\frac{\partial\rho_{f}}{\partial t}+\
abla\cdot\left(\rho_{f}v\right)=0 \tag{1}\]  
Momentum conservation equation:  
\[\frac{\partial\rho_{f}v}{\partial t}+\
abla\cdot\left(\rho_{f}vv-\tau_{f} \right)=f_{f} \tag{2}\]  
where, \(t\) represents time, \(f_{f}\) represents volume force vector, \(\rho_{f}\) represents the density of the fluid, \(v\) represents the velocity vector of the fluid and \(\tau_{f}\) represents the shear force tensor, which can be expressed as:  
\[\tau_{f}=(-p+\mu\
abla\cdot v)I+2\mu e \tag{3}\]  
where, \(p\) represents the pressure of the fluid, \(\mu\) represents the dynamic viscosity and \(e\) represents the velocity stress tensor of the fluid, \(e=\frac{1}{2}\big{(}\
abla v+\
abla v^{T}\big{)}\).  
If the energy equation is considered, the energy equation in the form of total enthalpy can be described as:  
\[\frac{\partial(\rho h_{tot})}{\partial t}-\frac{\partial p}{\partial t}+ \
abla\cdot\left(\rho_{f}vh_{tot}\right)=\
abla\cdot\left(\lambda\
abla T \right)+\
abla\cdot\left(v\cdot\tau\right)+v\cdot\rho f_{f}+S_{E} \tag{4}\]  
where, \(\lambda\) represents the heat absorption coefficient, \(h_{tot}\) represents the total enthalpy and \(S_{E}\) represents the energy source term.
## 3 Results and Discussion
### Waveform Analysis
#### 3.3.3 Multifractal Feature Analysis  
To verify whether the strong dynamic load signals exhibit multifractal characteristics, the graphs in Figure 11 illustrate the relationships between the fluctuation function \(F\left(q,s\right)\) and \(q\) (Figure 11a), the generalized Hurst exponent \(h(q)\) and \(q\) (Figure 11b), and the mass exponent \(\tau(q)\) and \(q\) for the F1 event, with \(q\) ranging from \(-10\) to \(10\) and a step size of \(0.1\) (Figure 11c). Additionally, Figure 11d displays the relationship between \(f(\alpha)\) and \(\alpha\). Due to the involvement of 200 curves at a step size of \(0.1\), only a portion of the curves is shown in the graph for better visualization.  
From Figure 11, it can be inferred that the \(q\)-order fluctuation function \(F(q,s)\) of the F1 event decays following a power-law relationship with the time scale s. The generalized Hurst exponent \(h(q)\) exhibits a nonlinear decreasing relationship with \(q\), while the mass exponent \(\tau(q)\) shows a nonlinear relationship. Therefore, the M1 event demonstrates a certain level of invariance in the time series and exhibits multifractal characteristics. The calculated multifractal parameters \(\Delta\alpha\) and \(\Delta f(\alpha)\) for the nine events investigated in this study are presented in Table 7. It can be seen from Table 7 that the three types of strong dynamic carriers are all less than \(0\), indicating that the proportion of large peak amplitudes in the waveform is large, which reflects the general characteristics of dynamic carriers. The average spectral width of the fault-type strong dynamic carrier is \(1.312\), the roof-type is \(1.543\), and the coal pillar-type is \(1.622\).