esa-sceva/satcom-chunk-collection huggingface dataset:
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 4 Simulation and Results
### Controller Determination
#### 4.1.2 Goal PDF Is Multi-Gaussian Distribution  
The control design is carried out considering that the desired rotor speed PDF obeys the variable weight multi-Gaussian distribution. The goal PDF expression is:  
\[p_{trf}(x)=\sum_{z=1}^{4}w_{z}exp\left(-\frac{\left(x-\mu_{z}\right)^{2}}{{\sigma_ {z}}^{2}}\right) \tag{32}\]  
where the values of \(w_{z}\), \(\mu_{z}\), and \(\sigma_{z}\) are shown in Table 2.  
Similarly, when the order of the controller is taken as five, seven and ten, the tracking effect of rotor speed PDF shape is shown in Figure 5b.  
It can be found that the tracking accuracy become increasingly higher with the increase of controller order, and the control effect improves accordingly. The specific tracking error and the unknown coefficients in \(p_{trf}^{\approx}(x)\) are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & \(z=1\) & \(z=2\) & \(z=3\) & \(z=4\) \\ \hline \(w_{z}\) & 1.16800 & 1.61300 & 1.89800 & 0.95800 \\ \(\mu_{z}\) & 1.17100 & 0.77670 & 1.10300 & 0.89170 \\ \(\sigma_{z}\) & 0.08411 & 0.09418 & 0.10170 & 0.12660 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of \(p_{trf}(x)\) when the goal function is multi-Gaussian distribution.  
Figure 5: The tracking result under different rotor speed distributions: (**a**) when the goal function is unimodal Gaussian distribution; (**b**) when the goal function is variable weight multi-Gaussian distribution.  
\begin{table}
\begin{tabular}{c c c} \hline
**Controller Order** & **Coefficients in \(p_{trf}^{\approx}(x)\)** & **Tracking Error** \\ \hline
5 & \(C_{5}\) & 0.0342 \\
7 & \(C_{7}\) & 0.0071 \\
10 & \(C_{10}\) & 0.0010 \\ \hline \end{tabular}
\end{table}
Table 3: Coefficients and tracking errors of controllers under multi-Gaussian distribution.  
Considering the high system sensitivity and difficulty of realization caused by a too large order, the seven-order controller is selected, and its control law is:  
\[{T_{g}}^{*}={K_{opt}}{\omega_{g}}^{2}-J\left({c_{1}}+2{c_{2}}{\omega_{g}}+3{c_{3} }{\omega_{g}}^{2}+\cdots+8{c_{8}}{\omega_{g}}^{7}\right) \tag{33}\]  
where \({c_{1}},\;{c_{2}},\;\cdots,\;{c_{7}}\) and \({c_{8}}\) are the coefficients of the output PDF when the seven-order controller is applied.  
This design combines the OT control with the PDF control of the stochastic system, and fully considers the influence of the stochastic distribution of wind speed. The novel control design has a good tracking effect on the changing stochastic wind speed, which can effectively improve the wind energy capture efficiency of the wind turbine.
# Design and Impedance Control of a Traveling Wave Ultrasonic Motor with a Spherical Rotor
## 6 Position-Based Impedance Control
### Virtual Wall Experiment  
Furthermore, in order to test the haptic interaction performance of the device in simulating the force feedback during teleoperation, the virtual wall experiment was conducted. The virtual wall model is a simulation of the contact force between remote objects and environmental obstacles, as shown in Figure 11. When the handle rotates within the range, the expected interaction force of the operator is 0 N. Once the boundary of the virtual wall is reached and crossed, the virtual contact force will be calculated based on the crossed distance and stiffness of the virtual wall model, and input into the force controller as the reference.  
In this experiment, the boundary of the virtual wall was set to \(\pm\)45\({}^{\circ}\). Two cases with virtual stiffnesses of 1 mNm/\({}^{\circ}\) and 4 mNm/\({}^{\circ}\) were verified, and the results are shown in Figures 12 and 13, respectively. Figures 12a and 13a show the comparison between the measured and desired torques in the interaction, and Figures 12b and 13b show the measured and reference positions of the handle. From Figure 12, when the position of the handle was within the range of the virtual wall, the interaction torque was almost 0 mNm, and a similar case is seen in Figure 13. In Figure 12, when the handle position was outside the virtual wall, the interaction torque could track the desired torque well.  
Figure 11: Scheme of virtual wall.  
Figure 10: Force feedback based on impedance control: (**a**) comparison of the measured and desired torque, and (**b**) the calculated stiffness.  
The maximum angular displacement was about \(-140^{\circ}\), which corresponds to a virtual desired torque of about 95 mNm, which was less than the stalling torque of the USM, and the measured torque was very close to the desired value. Figure 13 shows that the interaction torque could track the desired torque well until the desired torque was over the stalling torque of the USM. If the position of the handle was at or over the angular displacement of \(\pm 75^{\circ}\), the feedback torque was maintained around 120 mNm, which was about the stalling torque of the USM.  
In addition, there was no obvious torque overshoot when the virtual wall was crossed, which meant that the impedance control method could work well in the haptic interaction system based on an ultrasonic motor.
High quality remote sensing based land use/cover maps serve as important spatial input data for the application of such grid-based hydrological models at the catchment scale and especially for developing and analyzing LULCC scenarios. At the catchment scale, information about LULCC is required especially when the focus is on studying the impact on related wetlands. Earth observation, however, is recognized as a means to provide consistent and comprehensive information to complement monitoring, management and ground-based knowledge [20; 21; 22]. The SSA region recorded an extensive conversion of natural vegetation to agriculture in the past decades [23]. This is particularly evident for the IGAD (Intergovernmental Authority on Development in Eastern Africa) region where over the last two decades various land cover change processes, mainly driven by demographic growth, caused high losses of natural vegetation and an increase in agricultural areas [24; 25]. In Tanzania, several studies have been conducted to explore LULCC and its impact at regional and local levels [26; 27; 28; 29]. They show that already some 10 years ago, LULCC was taking place largely driven by human activity and often with negative consequences for wildlife. The recent introduction of an agricultural green growth approach in the Southern Agricultural Growth Corridor (SAGCOT)--covering one third of the mainland of Tanzania including three key development clusters in the Kilombero, Ihemi and Mbarali region--has driven an international focus on the region and will doubtlessly have an impact on agricultural land use practices and hence on overall land use dynamics [30]. These key clusters are characterized by their great agricultural potential, extensive forests, wildlife, protected areas and by their poorly developed infrastructure. This paper focuses on the potential and challenges of satellite-based integrated research on water resources and related ecosystems in the Kilombero floodplain and its contributing catchment, representing one of the SAGCOT key development clusters in Tanzania [31]. The objectives of this study are to outline and discuss the prospects of these integrated scientific results for supporting national decision and policy making, reporting on Multilateral Environmental Agreements as well as required capacity building and awareness raising activities.
### Parametric Techniques for BOC Signals
Thus to make the parametric techniques feasible in numerous real applications especially for BOC signals, breakthroughs from two aspects are required, which are improving the robustness to the noise and reducing the computation complexity. To deal with the above problems, we present a modified frequency-domain technique to estimate multipath estimation parameters method for arbitrary BOC(\(m\),\(n\)) signals which is called averaging-FFT method or aFFT method for simplicity in this paper. By the processes of cross power spectral density (CPSD) estimation and spectrum division, the proposed method can estimate the multipath parameters quantitatively and then mitigate the multipath signals. The principal contribution of this paper is that, compared with other parametric techniques, the proposed method provides better anti-multipath capability with acceptable algorithm complexity, which makes it an interesting candidate for real applications. The performance analysis is implemented on BOC(1,1), BOC(14,2) and BOC(15,2.5) signals. Besides, the performance comparison between the direct-FFT method, MEDLL and CCRW techniques are also provided. Results show that the averaging-FFT method is feasible to perform multipath parameter estimation for BOC(\(m\),\(n\)) signals of arbitrary types with various numbers of multipath signals.
# Speech Enhancement Algorithms: A Systematic Literature Review
## 5 Conclusions  
This study presents a systematic review of current research on SEAs, with a focus on papers examining different topics in the field. Through a systematic review, we have aimed to highlight current research trends around SEAs and guide researchers to the most important topics published between 2015 and 2024. A total of 47 conference papers, articles, and workshop studies were retained for review from five research databases: Springer Link, Science Direct, IEEE Xplore, ACM Digital Library, and Google Scholar. Our research methodology included several stages: following a brief introduction and background on SEAs, we formulated our research questions, search strategy, and quality assessment items. Afterwards, we analyzed the selected studies based on their applicability to the research questions. Statistics on the most frequently used datasets, discrete transforms, and measurements in the reviewed papers are provided. The findings of this study illustrate that speech enhancement algorithms represent an emerging research field, with an increasing amount of work conducted over the years. Numerous studies focus on deep learning, along with a  
Figure 9: Main challenges in SEA research.  
number of papers focusing on statistical approaches. Several of these studies applied more than one SEA technique. The publication trends in SEA research are detailed along with the main challenges and current research gaps. Future directions of SEA research are explored as well. It is expected that this review will help researchers and provide a comprehensive systematic review of recently published SEA studies.  
Conceptualization, B.M.M.; methodology, S.T.Y. and B.M.M.; formal analysis, S.T.Y.; investigation, S.T.Y. and B.M.M.; resources, S.T.Y. and B.M.M.; writing--original draft preparation, S.T.Y. and B.M.M.; writing--review and editing, B.M.M. and S.T.Y.; visualization, B.M.M. and S.T.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the University of Baghdad for their general support.  
The authors declare no conflicts of interest.
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 4 SA with Incomplete Cost Evaluation Applied to EIT
### Formulation of the Forward Problem  
The EIT forward problem consists of calculating the potential distribution \(\phi\) within a domain \(\Omega\) given a conductivity distribution \(\sigma\) and given an applied current pattern \(J\). Since the frequencies of the applied current are low, the capacitive and inductive effects can be ignored, and so the 2D EIT problem satisfies the Laplace equation,  
\[\
abla\cdot[\sigma(x,y)\cdot\
abla\phi(x,y)]=0,(x,y)\in\Omega, \tag{5}\]  
where \(\sigma\) and \(\phi\) are the conductivity and potential distributions, respectively, and \(\Omega\) is the domain. The equation is set to zero because there are no current sources inside the domain [36].  
Solving the forward EIT problem consists of solving the Laplace equation in its weak form for \(\phi\), given the conductivity distribution \(\sigma\) and the injected current \(J\). The FEM is widely used to model forward problems, given its ability to model arbitrary geometry and boundary conditions [37; 38]. In this case, the formulation of the forward problem of EIT by the FEM reduces the problem to the resolution of a linear system  
\[K\cdot\Phi=J, \tag{6}\]  
where \(J\) is the vector of injected currents, \(\Phi\) is the vector of potentials, and \(K\in\mathbb{R}^{t\times t}\) is the conductance matrix [39].  
Figure 4: Given a domain with a conductivity distribution \(\sigma(x,y)\), EIT equipment is used to measure the electrical potential \(\Phi_{j}^{i}\) on all \(j\in[1,16]\) electrodes from the injection of the current pattern \(J_{i}\).
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 4 Discussion
### Optimisation of Neural Networks for a Class of Images  
The architecture of a CNN is often tailored to the type of image. For example, visible light images are typically represented as three-channel (RGB) inputs; thus, networks are designed to process them with three inputs. In contrast, X-ray, infrared, or thermal images are single-channel, meaning that the input layer of the network often has only one input. In addition, the specific characteristics of each image type, such as noise patterns, dynamic range, and resolution, all affect the choice of network depth, filter size, and normalisation strategy and need to be trained on separate datasets. Therefore, although the network architecture may remain similar, individual implementations utilize an optimised neural network for each type of image [14; 17].
# Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
###### Abstract  
This paper is the first to highlight the vibrations of a hemispherical shell structure interacting with both compressible and incompressible fluids. To precisely calculate the pressure of the shell vibrating in the air, a novel analytical approach has been established that has existed in very few publications to date. An analytical formulation that calculates pressure was developed by integrating both the'small-density method' and the 'Bessel function method'. It was considered that the hemispherical shell vibrates as a simple harmonic function, and the fluid is non-viscous. For comparison, the incompressible fluid model has been analyzed. Surprisingly, it is the first to report that the pressure of the shell surface is proportional to the vibration acceleration, and the velocity amplitude decreased at the rate of \(\frac{1}{r^{2}}\) when the fluid was incompressible. Otherwise, the surface pressure of the hemispherical shell was proportional to the vibration velocity, and the velocity amplitude decreased with the rate of \(\frac{1}{r}\) when the fluid was compressible. The compressibility of fluid played an important role in the dynamic pressure of the shell structure. Furthermore, the scale factor derived by the theoretical approach was the product of the density and the sound velocity of the fluid (\(\rho_{o}c\)) exactly. In this study, the analytical solutions were verified by the calibrated numerical simulations, and the analytical formulation were rigorously tested by extensive parametric studies. These new findings can be used to guide the optimal design of the spherical shell structure subjected to wind load, seismic load, etc.  
Keywords:incompressible fluid; compressibility; hemispherical shell; fluid-structure interaction +
Footnote †: journal: Applied Mechanics and Physics Letters
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 4 Research Methodology
### State Space  
State representation allows the agent to identify different situations and make decisions based on the environment. To create a complete observation, the UAV's front-mounted camera processes the segmentation map, outputting the target object's bounding box coordinates. These coordinates are then combined with a short video consisting of five consecutive depth map images from the same camera view, each delayed by 0.1 s.  
Depth map images are acquired directly from AirSim using its APIs ([[https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)]([https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)), accessed on 15 November 2022). Depth map images provide information about obstacles in the environment. They are more efficient than RGB images, as they contain distance information and they use a single channel, reducing the size to one-third compared to RGBs. The agent utilizes bounding box coordinates to identify the target object and depth map images to navigate around obstacles.  
A segmentation map is also acquired from AirSim APIs ([[https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)]([https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)), accessed on 15 November 2022). During the initial setup, a specific RGB value is assigned to the target object in the segmentation map, which has the same camera view as the depth images. This assignment enables easy identification of the target object and accurate extraction of the bounding box. All images returned by AirSim's APIs ([[https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)]([https://microsoft.github.io/AirSim/api_docs/html/](https://microsoft.github.io/AirSim/api_docs/html/)), accessed on 15 November 2022) have a resolution of \(128\times 72\). Examples of a segmentation map and a complete state representation can be seen in Figure 12 and Figure 7, respectively.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 1 Introduction
### Methodological Approach  
The research implements three distinct strategies to enhance the GOOSE optimization algorithm [15], developing the Improved GOOSE Optimization Algorithm (IGOOSE). Comparative simulation experiments were conducted between IGOOSE and other algorithms, including DBO [16], HBA [17], GWO [18], HHO [19], and the original GOOSE, using CEC2022 benchmark functions [20], leading to the establishment of the IGOOSE-XGBoost prediction model for bovine subclinical mastitis. The main innovations of this study include developing an XGBoost-based predictive model for subclinical mastitis in dairy cows, optimizing model parameters and feature selection through the improved GOOSE optimization algorithm, and developing the IGOOSE-XGBoost prediction model. Qualitative and quantitative evaluation results demonstrate the significant contribution of the IGOOSE-XGBoost model in diagnosing and predicting subclinical mastitis in dairy cows.
## 2 Materials and Methods
### Methods  
#### 2.3.1 The DINEOF Algorithm  
The principles of the DINEOF algorithm and implementation details used in this study can be described as follows:  
1. The original dataset was stored in an \(m\times n\) matrix, where \(m\) represented the number of spatial pixels and \(n\) denoted the number of images. First, a base-10 logarithmic transformation was applied and the spatiotemporal mean was substracted from the  
Figure 2: Spatial distribution of the pixels TMR in 2019 chl-a original dataset of the Bohai Sea region.  
matrix. Then, values at missing points were set to 0 (considered unbiased estimates), and the resulting matrix was designated as \(X\). From this, 1% of valid data were randomly selected as the cross-validation set \(X^{c}\), with the corresponding values in \(X\) also set to 0.
2. The Singular Value Decomposition (SVD) was performed once on \(X\) as Equation (1). Given a mode number \(P\), values at all missing points in \(X\) were reconstructed following Equation (2), generating the gap-filled matrix \(X^{re}\). The Root Mean Square Error (RMSE) between the original and reconstructed values at the cross-validation set was calculated and recorded as the iteration error \(R\) based on Equation (3): \[X=USV^{T}\] (1) \[X^{re}_{i,j}=\sum_{t=1}^{P}a_{t}(u_{t})_{i}\Big{(}v_{t}^{T}\Big{)}_{j}\] (2) \[R=\sqrt{\frac{\sum_{n=1}^{N}\big{(}x_{n}^{re}-x_{n}^{C}\big{)}^{2}}{N}}\] (3) where \(U_{m\times n}\) represents the spatial EOF modes, \(V_{m\times n}\) represents the temporal EOF modes, \(S_{n}\) is a diagonal matrix containing the singular values, and \(u_{t}\),\(v_{t}\), \(a_{t}\) denote the \(t\)-th column of \(U\), \(V\), and \(S\) respectively, with \(T\) indicating the matrix transpose. \(N\) is the number of cross-validation points in \(X^{c}\). The number of SVD decompositions was increased by one until the RMSE between consecutive iterations at \(X^{c}\) fell below a threshold \(\varepsilon\) or the iteration number reached a maximum \(iter\) in order to prevent dead loops and save computational time. 3. The procedure was repeated iteratively with \(P=1,2,\ldots,k_{max}\), and the optimal number of EOFs, denoted as \(P_{b}\), was determined when the \(R\) was at minimum. 4. Based on the optimal EOFs \(P_{b}\), the missing values were reconstructed and the gap-filled matrix \(X^{re}\) was generated via procedure 2. Then, we added back the spatiotemporal mean and applied an exponential transformation to obtain the final reconstruction results.