esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In engineering applications, we need to convert the control force and moment into the pitch angle of the air duct propeller and rudder angle of the air rudder to control the ACV, respectively. Unfortunately, very few studies are available in this regard. An adaptive terminal sliding model control scheme is proposed to address the servo system uncertainties in dynamic positioning [26]. In 1983, [PERSON] proposed Nussbaum functions to solve the system control coefficient uncertainty and model parameter uncertainty [27]. A robust Nussbaum gain-based approach is designed for adaptive fuzzy asymptotic control of multiple input and multiple output nonlinear systems [28]. Then, in [29], the author studied the application of Nussbaum functions in system stabilization. The unknown control direction is resolved effectively by the Nussbaum gain function, and the boundedness of uncertain time-varying parameters is guaranteed by using the continuous projection algorithm [24]. The Nussbaum analysis tool was adopted to study the motion control problem of robotic systems with the generalized actuator [30]. To the best of our knowledge, this study is the first to introduce the Nussbaum function into the study of ACV to solve the problem of actuator effective input parameters. Motivated by the above analyses, a novel control method is designed to solve the trajectory tracking of ACVs under the consideration of unknown effective input parameters, the turning rate error within the safety limit and wind disturbance. The main contributions of this paper are expressed as follows:  
* A four-DOF vector mathematical model of an ACV is proposed to simplify the controller design. Based on the vector model, RBFNN is adopted to provide the estimation of the model uncertainties and external wind disturbance. Command filters and auxiliary systems are integrated with the control law such that the complicated computation of the virtual control derivative can be avoided. * A Nussbaum function is first introduced into the study on the ACV to solve the unknown nonlinear relationships between the actuator's input and output. * The yaw rate error constraint of the ACV is approached by introducing a BLF in combination with an adaptive Nussbaum function to prevent the tail swing phenomenon caused by the large yaw rate. * The stability analysis shows that the proposed control algorithm can accurately track the set trajectory and ensure that the yaw rate error and the roll angle are within a safe range. All the error signals of the whole closed-loop control system can converge into a small neighbourhood around zero. The comparative simulation results illustrate the effectiveness and superiority of the proposed trajectory tracking control scheme. The rest of the paper is organized as follows. In Section 2, the preliminaries and a mathematical model are provided. The trajectory tracking controller is designed in Section 3. Numerical simulation results are drawn in Section 4. Finally, Section 5 summarizes the conclusion.
## 3 Materials and Methods
### Combination Empowerment
#### 3.3.1 Subjective Weight Definition (PSO-AHP)
The algorithm flow is shown in Figure 3.
* Determine the parameters of the PSO algorithm, namely, the number of populations \(n\), the maximum number of iterations \(N\), and the learning factors \(c_{1}\) and \(c_{2}\); the variation range of \(v_{m}\) is the inertia coefficient \([-v_{max},v_{max}]\). In this article, \(n\) = 100, \(N\) = 500, \(c_{1}=c_{2}\) = 2, \(-v_{max}\) = -0.001, and \(v_{max}\) = 0.001. * Normalize the weights to generate the initial solution of the particles. The feasible solution is substituted into Equation (5) to calculate the fitness of the initial particles, and the global optimal particles are selected from them. Update iterates over particles with Equations (10) and (11). \[v_{in}^{k+1}=v_{in}^{k}+c_{1}r_{1}\Big{(}p_{in}^{k}-x_{in}^{k}\Big{)}+c_{2}r_{2 }\Big{(}p_{gn}^{k}-x_{in}^{k}\Big{)}\] (10) \[x_{in}^{k+1}=x_{in}^{k}+v_{in}^{k}+1\ (i=1,2\ldots m;n=1,2,\ldots N)\] (11)
* After updating the fitness of the particles, compare and select the optimal position of the particles and the global. If the iteration termination condition is met, the iteration terminates, outputs the optimal solution obtained by the model, and moves on to the next step. If the iteration termination condition is not met, jump back to the first step until the iteration termination condition is met. * Substitute the obtained optimal value into the objective function and calculate the consistency ratio value corresponding to the judgment matrix. If the consistency requirement is met, the next step is conducted; otherwise, adjust the judgment matrix and re-execute the loop. Finally, the global optimal position and corresponding weight value, and consistency indicator function value \(MinF_{CI}\) are output.
## 1 Introduction
In this paper, the Segment vehicle, Link quality and Degree of connectivity based Geographic Distance Routing (SLD-GEDIR) protocol is proposed. The protocol chooses a small region at the boundary of the circle formed by transmission range as radius and the source node as the center of the circle. The small region has been referred to as segment area. Vehicles present in the segment area are called segment vehicles. The selection of NHV depends on segment vehicle, one-hop link quality, and degree of connectivity. The key components of the present study are as follows: (1) determining the number of segment vehicles, which means finding all the vehicles that belong to the segment area of current forwarding vehicle; (2) prediction of one-hop link quality using packet error rate; (3) determining the degree of connectivity of NHV with segment vehicles. The NHV with the highest degree of connectivity has been considered as most reliable forwarder. These components of the proposed protocol have been analyzed and validated through analytical results. The proposed protocol has been simulated in ns-2. A comparative study has been carried out between SLD-GEIDR and other state-of-the-art protocols: V-GEDIR, J-GEDIR and P-GEDIR. The rest of the paper has been organized as follows: in Section 2, early and recent research works on position-based and geographic routing protocols have been summarized. A description of the mathematical formulations and details of the conceptualization of SLD-GEDIR protocol is provided in Section 3. The analytical and simulation results have been discussed in Section 4. The conclusions of the study are presented in Section 5.
## 7 Verification of Our Computational Model  
Our numerical model is verified by using the experimentally validated formulas of the topics _sum frequency generation via non-linear wave mixing_ and _optical amplification via non-linear wave mixing_ as described in the examples below. **Example 1. **_Sum frequency generation via non-linear wave mixing (frequency up-conversion)_  
_This example is about the generation of a higher frequency component \((\omega_{3})\), by mixing of two monochromatic waves with frequencies \(\omega_{1}\) and \(\omega_{2}\) via non-linear coupling, such that \(\omega_{3}=\omega_{2}+\omega_{1}\)._  
The pump wave \(\mathbf{E_{2}}\) is originated at x = 2.5 \(\upmu\)m, with an amplitude of \(A_{2}\) V/m and a frequency of 180 THz. \[\mathbf{E_{2}}(x=2.5\mu m,t)=A_{2}\times\sin\!\left(2\pi\!\left(1.8\times 10^{14} \right)\!t+\varphi_{2}\right)\ V/m\]  
The input wave \(\mathbf{E_{1}}\) is originated at x = 2.5 \(\upmu\)m, with an amplitude of \(A_{1}\) V/m and a frequency of 120 THz (see Figure 8). \[\mathbf{E_{1}}(x=2.5\mu m,t)=A_{1}\times\sin\!\left(2\pi\!\left(1.2\times 10^{14} \right)\!t+\varphi_{1}\right)\ V/m\ \left(\text{assume }\varphi_{1}=0,\ \varphi_{2}=0\ \right)\]  
\[\text{\it Spatial and temporal simulation parameters }0\leq x\leq 10\mu m,\ \ 0\leq t\leq 60 ps\]  
\[\text{\it Resonance frequency of the medium}:\ \ f_{0}=1.1\times 10^{15}Hz\]  
\[\text{\it Damping rate of the medium}:\ \ \gamma=1\times 10^{12}Hz\]  
\[\text{\it Dielectric coefficient of the medium}\ (\varepsilon_{\infty})=1+\chi=10\ \ \ \ \ (\mu_{r}=1)\]  
\[\text{\it Left PML (left absorption layer)}\ \text{is from }x=0\text{ to }x=2.25\mu m\]  
\[\text{\it Right PML (right absorption layer)}\ \text{is from }x=7.75\mu m\text{ to }x=10\mu m\]  
The experimentally verified theoretical formula for frequency up-conversion efficiency is given as [1,4]  
\[ \begin{array}{c}\mathfrak{n}_{\text{\it thoretical}}\ =\frac{\omega_{3}}{ \omega_{2}}(\ \sin\sqrt{2d^{2}n^{3}\omega_{3}{}^{2}(cn\varepsilon_{0}A_{2}{}^{2})L^{2}}\ )^{2}\ \ )^{2}=\frac{\omega_{3}}{\omega_{2}}(\ \sin\sqrt{2d^{2}n^{4} \omega_{3}{}^{2}c\varepsilon_{0}A_{2}{}^{2}L^{2}}\ \ )^{2}\\ \omega_{2}=\text{\it Frequency of the pump wave}\ \ \,\ \ \ \omega_{1}=\text{\it Frequency of the input wave}\\ d=\ \text{\it Material non -linearity coefficient},\ n=\ \text{\it Refractive index}\\ A_{2}=\text{\it Pump wave amplitude }A_{1}=\text{\it Input wave amplitude }L=\text{\it Length of the non - linear media}\\ \omega_{3}=\omega_{1}+\omega_{2}=\text{\it Frequency of the upconverted wave}\\ \end{array}  \tag{20}\]  
Figure 8: Configuration for the frequency up-conversion in example 1. Our computational model, which is based on the non-linear electron motion equation, is implemented via FDTD discretization.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 3 Results
### Nationwide Annual Flood Mapping  
After better understanding the characteristics of the 2015 flood from the in situ and hydrological data, the satellite-derived water index (SfWi) was employed to generate an annual flood extent map. The characteristics of the hydrograph during the 2015 flood (Figure 3) represent a clear relationship between the observed water levels and the SfWi's values, an index designed to show progress in flood extent. The resultant annual flood extent map revealed a remarkable change in the detected extent of floodwaters in 2015, compared with an average annual flood. Figure 5 shows the maximum flood inundation extent with different periods of flood duration at different locations. The SfWi-derived flood map was produced by counting how many times the floodwater pixel appeared at a certain  
Figure 4: Cross-validated observations of in situ and high-resolution SAR data: (**a**) Comparisons of histogram of distributed MLSWI values before (black bar) and during (blue bar) the 2015 flood in sampling site, Sirajganj district; (**b**) Comparisons of PDF histogram before (dash line) and during (blue line) the 2015 flood; (**c**) sampling sites, UAV-assisted field investigation (circle of H) and handheld measurement (black points) for ground validation based on the pixel-based classification of MLSWI.  
location in the 46 images. The map shows that the annual SfWi increases over the optimal threshold (over 0.7 of MLSWI) in the part where SfWi = 1 for all MLSWI > 0.7 (yellow zone in Figure 3), which started when the water level reached the flood danger level on 21 August 2015. The red pixels, derived from SfWi, indicate the locations with 5-10 water-pixel appearances based on the eight-day composite images, which estimate the maximum flood duration of about 80 days. The light blue pixels, also derived from SfWi, indicate the locations with 1-4 water-pixel appearances, or a flood duration of about a month. The flood propagation was in good agreement with the timing of the water level exceeding the flood danger level, which is 13.35 m above mean sea level (MSL) in the case of the 10-year return period flood event--particularly at the Sirajganj station--compared with historical records in Table 2[33; 34]. Moreover, the SfWi-indicated flood extent is represented in Figure 5 with strong relationship between hydrological responses highlighted by yellow and red point stations as the overflow vulnerability index. In the case of the monsoon regional flood, I have found that the clear relationship between SfWi-indicated flood area and flood danger level is a key flood indicator. In particular, after the peak water level on 6 September 2015, the maximum flood inundation extent represented larger flooded area than before the peak water level. In Figure 5, the two maps show that the annual maximum inundation area from SfWi is estimated at 32% (29,900 km\({}^{2}\)) of the total area of Bangladesh. All these results confirmed that the flood plain area near the Brahmaputra River is the most vulnerable to flood hazard and risk in Bangladesh.
## 2 Remarks on the Topology of Microgrid  
A microgrid is a local energy system integrating distributed generation, energy storage, and controllable loads within a defined electrical network. Microgrids stand out among low-power generation systems for their ability to operate independently of the primary grid and manage the energy sources that comprise them. Typically, energy management integrates an algorithm to optimize operation [3]. These networks could be classified according to their connection and mode of operation. According to the connection, there are isolated and grid-connected microgrids. Isolated microgrids operate independently of a primary power grid, rendering them suitable for remote areas without access to conventional electrical infrastructure. On the other hand, grid-connected microgrids operate synchronously with a primary power grid, allowing the import and export of energy as needed [12; 22]. Microgrids can operate in AC, DC, or a hybrid combination. AC microgrids are the most common due to their compatibility with existing electrical infrastructure and conventional devices. DC microgrids, although less common, are gaining popularity for specific applications. Hybrid configurations combine AC and DC, offering greater flexibility and efficiency by optimizing the operation of different types of loads and generation sources [23]. Microgrids, with their ability to manage generation intermittency, represent a promising solution for integrating RES. Their significant role in improving local energy efficiency offers feasibility for a more sustainable future.  
Researchers have suggested categorizations for microgrids given the above scenarios. Figure 3 highlights some commonly used configurations, modes of operation, and applications in this field. The following sections address these classifications. Section 2.1 presentsthe alternative microgrid operation modes. Section 2.2 provides comments on integrating RES. Then, Section 2.3 focuses on microgrid applications. Finally, Section 2.4 provides a general framework for the cost of microgrids in planning.
## 4 Bigram and Traditional Results
### Advanced Leak Detection in Water Networks  
Water leak detection is crucial for effective water management, waste reduction, and preventing damage to infrastructure and property. Various methods are used to detect leaks, such as acoustic leak detection, visual inspection, tracer gas leak detection, pressure testing, data loggers and flow meters, smart water networks, and pipe inspection robots and cameras. Each method has its benefits and limitations, and choosing the most suitable method depends on factors like pipe material, leak size, and pipeline location. Often, a combination of methods is used to ensure accurate and efficient leak detection.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Derivation of Variogram Formula Based on Magnetic Dipole  
The magnetic map is the main tool with which to describe the distribution of the Earth's magnetic field so as to reflect the distribution of the characteristics of the magnetic field in the target area. In general, coordinate points with the same numerical magnetic elements are connected with smooth curves to form a magnetic map, which is essentially a regular spatial arrangement of magnetic data points. The magnetic field can be simulated by the spherical harmonic model, but for a large proportion of the magnetic field with high precision, the magnetic dipole model is usually used to simulate the magnetic field distribution of the target equivalent when the detection  
Figure 1: Schematic diagram of Kriging interpolation algorithm. Red dots \(Z_{i}(i=1,2,3,4)\) is a set of measured magnetic sample data with coordinate position \((x_{i},y_{i})(i=1,2,3,4)\); green dot \(\hat{Z}_{0}\) is the estimation of magnetic data. distance reaches 2.5\(\sim\)3 times the size of the target [29]. Especially in engineering practice, the target is more equivalent to the magnetic dipole model, which can also effectively approximate the far-field distribution of the target magnetic field [30]. When the magnetic dipole is located at the origin, the magnetic field of the magnetic dipole at any point \(r\) in space is as follows:  
\[B_{r}=\frac{\mu_{0}}{4\pi}\left[\frac{3\langle M,r\rangle}{|r|^{5}}-\frac{M}{|r |^{3}}\right], \tag{7}\]  
where \(\mu_{0}=4\pi\times 10^{-7}\) represents the vacuum permeability and \(M\) represents the equivalent magnetic moment vector. According to Equation (2), the variogram of two points in the simulated magnetic field based on magnetic dipoles is expressed as follows:  
\[\gamma_{ij} =\tfrac{1}{2}E[\left(z_{i}-z_{j}\right)^{2}]=\tfrac{1}{2}E[\left|B _{i}-B_{j}\right|^{2}] \tag{8}\]  
It can be seen from the above formula that the magnetic map constructed by magnetic dipoles is not only affected by the magnetic moment in space but also has a complex relationship with the positions of two points. In order to reduce complexity, point \(i\) is located directly above the magnetic dipole, point \(j\) is located in the \(y\)-axis direction of the plane where the two points are located, and the plane distance between the two points is \(l\).
## 3 GOA-GPR Optimization Algorithm  
### Basic Concept  
The main strategy of this method is as follows. First, the GOA is used in several rounds of stochastic optimization within the model space. Then, taking the current optimal grasshopper operator as the centre, the \(3\times N\) historical operators closest to the current optimal operator (\(N\) is the population number of the GOA algorithm) are obtained to construct the optimization experience dataset. GPR is used to fit the dataset, and the functional relationship between the decision variables and objective function values is constructed to obtain the corresponding surrogate model. Then, the Newton method is used to optimize the surrogate model and obtain the optimal operator of the local region prediction. Because the surrogate model is used to replace the real performance function, the real performance function is not used for fitness evaluation, and the efficient Newton method is used to replace the GOA for local optimization, so the fitness evaluation times of the objective function are significantly reduced and the overall efficiency of the algorithm is improved. Finally, the predictive optimal operator found by the Newton method is substituted into the real objective function to obtain the corresponding fitness value, and the value is compared with the fitness value of the current optimal operator. If it is better than the current optimal operator, the current optimal operator is replaced, and the next round of optimization is conducted. The iterative process does not stop until the convergence criterion is satisfied.
# Virtual Replication of IoT Hubs in the Cloud:
## 3 Architecture
### Operational Scenarios
#### 3.2.2 Observing Resources  
The Observe option [24] is a CoAP option which allows resource observing. According to this specification, a CoAP client can send a single request, including an Observe option with a value of 0 (register), in order to register its interest in receiving updates related to the targeted resource. The CoAP server sends a response each time the resource value is updated. In this case, multiple responses are sent after a single request. The Observer option allows to implement a notification-based communication model, thus reducing network traffic. The observing scenario is shown in Figure 4. If the targeted smart object is a CoAP server which implements the Observe option, it can receive requests to access its hosted resources, which may be either observable or not. In the latter case, requests are handled as in the polling case. In the former case, instead, the message flow resembles that of the polling scenario, but (i) the IoT Hub observes the resource and (ii) the external client observes the cached resource on the replica. When the resource is updated, (i) the smart object will send a notification to the IoT Hub; (ii) the IoT Hub will synchronize the resource with its replica; and, finally, (iii) the replica will send a notification to the external observing client.
###### Abstract
For instance, modal aliasing can occur, where distinct frequency components are combined within a single intrinsic mode function (IMF), or a single-frequency component is distributed across multiple IMFs. This phenomenon can weaken the physical significance of the IMFs, thereby affecting the accuracy of the decomposition results. Another issue, proposed by [PERSON] et al. [21], is the endpoint effect: EEMD is prone to generating endpoint effects at the signal ends, meaning that false components may be generated at the signal endpoints during the decomposition process. This effect is more pronounced when dealing with finite-length signals, affecting the IMFs' accuracy. Additionally, other drawbacks, as described in [22,23], exist when processing actual engineering signals: the noise introduced by EEMD may mix with the noise in the original signal, leading to signal contamination and affecting the authenticity and accuracy of the decomposition results. In bridge damage detection, although contact point response has been proven superior to dynamic response in terms of accuracy and effectiveness, research in this area is still relatively scarce. Therefore, this study proposes a new bridge damage detection method based on vehicle scanning, variational modal decomposition-continuous wavelet transform, to achieve more effective and accurate bridge damage identification. First, we detail the theoretical derivation process of the proposed vehicle scanning method in bridge damage detection. Then, the effectiveness of the proposed method is tested through numerical simulation studies. Finally, this paper presents a parameter study examining how factors such as the vehicle speed, scale factor, number of bridge spans, and pavement roughness influence the effectiveness of the damage identification method.