esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 1 Introduction
The uniform distribution method and \"5 + 2\" method distribute the multi blade tip-timing sensors evenly around the casing and improve the sampling frequency of the corresponding vibration signal [20]. Because the blade tip-timing vibration measurement is a seriously under-sampling method, the blade resonance frequency is usually higher than the sampling frequency of the BTT data acquisition system, which leads to large identification error of blade vibration parameters. This has aroused people's desire for the new measurement and signal processing methods [21; 22; 23; 24; 25; 26; 27]. Ref. [28] proposes a new identification approach based on nonlinear free vibration decay. The nonlinear free response of the system was determined by the coupling of generalized variation iteration and the modified differential transformation methods. The waveform of the blade tip-timing sensor can be used as an additional information source for blade vibration analysis, which enriches the traditional blade tip-timing data. In [29; 30; 31], the importance of sensor waveform analysis and the need for new signal processing technology are pointed out. Ref. [30] proposed a method for blade vibration identification based on the whole sensor signal waveform, which significantly improved the available data volume of BTT sensor. However, the error between the identified resonance frequency and the real resonance frequency is large. The method needs further improvement to improve its identification accuracy. In Ref. [32], a method for identifying blade vibration parameters of turbomachinery based on blade tip-timing and tip clearance sensor waveforms is proposed. Since there is noise in the measured waveform signal, the iterative convergence analysis method, increasing the number of optimized waveforms, is needed to identify the blade vibration parameters, which is time-consuming and is not suitable for real-time monitoring of blade vibration. Aiming at the shortcomings, a method of blade vibration parameter identification is proposed based on the mapping of sensor static waveform and dynamic waveform. The chapters of this paper are as follows: Section 2 presents the theory of waveform mapping method; the influence of different parameters on the waveform shape is observed in Section 3; Section 4 analyzes the influence of number of sensors on the identification accuracy of waveform mapping method; Section 5 summarizes the main conclusions.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
###### Abstract  
To reduce emissions worldwide, decarbonization has also affected for power engineering and its conventional methods of power generation, distribution, and transmission. This fact affects not only the production of power and supply itself, but also other parts related to it, namely systems for the protection of electricity generation facilities and the location of faults in distribution networks and transmission systems. Is this new technology of renewable energy sources (RES) mastered enough to be able to replace traditional methods of energy generation reliably and safely? If we can answer the previous question, we could proceed to the next phase of putting these technologies into practice, considering the actual state of distribution networks and transmission systems. The aim of the article will be to evaluate the current state of systems and production sources of electricity in the Slovak Republic and to provide a better insight and suggestions for rebuilding current networks on a smart grid. The proposals will also include models of some RES in the Matlab/Simulink software.  
smart grid; microgrid; protection system; modeling renewable energy sources +
Footnote †: 2021: 11, 5252.
## 2 Theoretical Analysis and Experimental Details
### Mechanical Mechanism of Flat Digging Based on the DEM  
#### 2.2.1 Particle Model of the DEM  
The analysis object of the DEM is a series of discrete objects, and each unit is a particle. In the DEM, the motion parameters and motion characteristics of each particle are calculated, and a discrete simulation of the material is carried out. In the DEM, the mechanical characteristics of the particles need to be analyzed and specified. Through the vibration equation, the contact between the particles and the force between the particles can be obtained. Figure 7 presents the vibration model of the particle [19; 20; 21].  
In this study, the DEM is used to analyze the contact process of the material particles. Through the output of the particle vibration model, the normal vibration motion equation of the particles can be obtained [22; 23]:  
\[m_{1,2}d^{2}u_{n}/dt^{2}+c_{n}d\omega_{n}/dt+K_{n}u_{n}=F_{n} \tag{9}\]  
In the same way, the tangential vibration equation of the particle can be obtained:  
\[m_{1,2}d^{2}u_{s}/dt^{2}+c_{s}d\omega_{s}/dt+K_{s}u_{s}=F_{s} \tag{10}\]  
Figure 6: Partition diagram of material unit body.  
Figure 7: Vibration model of the particle.  
\[h_{1,2}\partial^{2}\theta/dt^{2}+(c_{5}du_{5}/dt+K_{5}u_{5})s=M \tag{11}\]  
In the above formulae, \(m_{1,2}\) are the masses of particles i and j, \(h_{1,2}\) are the moments of inertia of the particles, \(s\) is the radius of rotation, \(u_{n}\) and \(u_{5}\) are the particle normal and tangential displacements, respectively, \(\theta\) is the particle rotation angle, \(F_{n}\) and \(F_{5}\) are the particle normal component and tangential component, respectively, \(M\) is the external moment of the particle, \(K_{n}\) and \(K_{5}\) are the particle normal and tangential elastic coefficients, and \(c_{n}\) and \(c_{5}\) are the particle normal and tangential damping coefficients, respectively.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 1 Introduction  
In recent decades, biomedical signals have been used for communication in Human-Computer Interfaces (HCI) for medical applications; an instance of these signals are the myoelectric signals (MES), which are generated in the muscles of the human body as unidimensional patterns. Because of this, the methods and algorithms developed for pattern recognition in signals can be applied for their analyses once these signals have been sampled and turned into electromyographic (EMG) signals. Additionally, in recent years, many researchers have dedicated their efforts to studying prosthetic control by means of EMG signal classification, that is, by logging a set of MES in a proper range of frequencies to classify the corresponding EMG signals.  
The EMG signals are obtained from sensors placed on the skin surface and can help retrieve muscular information during contractions when flexing or extending an articulation. There are also implants placed under the skin that facilitate the signal acquisition, but these are not commonly used.  
Regarding the pattern recognition problem for myoelectric control systems, its success depends mostly on the classification accuracy [1] because myoelectric control algorithms are capable of detecting movement intention; therefore, they are mainly used to actuate prostheses for amputees [2].  
With the aim of carrying out the pattern recognition for myoelectric applications, a series of features is extracted from the myoelectric signal for classification purposes. The feature classification can be carried out on the time domain or by using other domains such as the frequency domain (also known as the spectral domain), time scale, and time-frequency, amongst others [3].  
One of the main methods used for pattern recognition in myoelectric signals is the Support Vector Machines (SVM) technique whose primary function is to identify an \(n\)-dimensional hyperplane to separate a set of input feature points into different classes. This technique has the potential torecognize complex patterns [4] and on several occasions it has proven its worth when compared to other classifiers such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA) and Particle Swarm Optimization (PSO) [5; 6; 7; 8]. The key concepts underlying the SVM are: (a) the hyperplane separator; (b) the kernel function; (c) the optimal separation hyperplane; and (d) a soft margin (hyperplane tolerance).  
A compilation of the most outstanding works that combine different techniques based on SVM is presented in this paper. It also includes a list of those features most commonly used in the time, frequency, time-frequency, and spatial domains for pattern recognition. Finally, other applications of the SVM-based classifier are included in the last section.
### Scope and Contributions  
In this paper, we present smooth, nonlinear reduced-attitude controllers for fixed-wing UAVs, in a coordinate-free manner, using a global, singularity-free attitude representation on \(\mathbb{S}^{2}\). The method applies to UAVs with fully actuated rotational dynamics, e.g., those that are equipped with a full set of control surfaces, such as ailerons, elevator, and rudder. The chosen reduced-attitude representation is independent of the yaw angle and thus enables traditional banked-turn maneuvers. A consequence of this is that the presented approach can be deployed in conjunction with state-of-the-art hierarchical flight control architectures that rely on roll and pitch control in the inner loop, such as [1], and those implemented in open-source autopilots such as ArduPilot [43] and PX4 [44]. Furthermore, no lateral/longitudinal decoupling assumptions are used in the design, allowing the attitude controller to compensate for coupling effects that arise when such assumptions are violated.  
The reduced-attitude representation allows for a convenient decomposition of the dynamics and a natural corresponding decoupling of the control objective into two parts: (1) reduced-attitude (roll/pitch) control, and (2) control of the angular velocity about the inertial \(z\)-axis (turn rate control). Using Lyapunov theory, almost global asymptotic stability is established for three controllers: one constructed based on an energy-like Lyapunov function, a variation of this based on a backstepping procedure, and lastly an adaptive version of the latter that estimates the net aerodynamic moment caused by the translational dynamics (flow angles). This alleviates the need for expensive flow angle measurement equipment, as well as the knowledge of an accurate aerodynamic model. Furthermore, we show that only a rough estimate of the input matrix is needed to achieve ultimate boundedness. The suitability of the proposed attitude control algorithm is demonstrated in realistic software-in-the-loop simulations.
## 2 Theory and Experiments  
### The KZK Equation  
The KZK equation is an extended form of Burgers model, and an approximation of the Westervelt equation, written as [41]  
\[\frac{\partial^{2}p}{\partial z\partial\tau}=\frac{c_{0}}{2}\Delta_{\perp}p+ \frac{\delta}{2c_{0}^{3}}\frac{\partial^{3}p}{\partial\tau^{3}}+\frac{\beta} {2\rho_{0}c_{0}^{3}}\frac{\partial^{2}p^{2}}{\partial\tau^{2}}, \tag{1}\]  
in which \(p\) is the acoustic pressure, \(c_{0}\) is the sound velocity, \(\delta\) is the sound diffusivity, \(\beta\) is the nonlinearity coefficient, \(\rho_{0}\) is the ambient density of the medium, \(\Delta_{\perp}\) is the transverse Laplace operator (defined as \(\Delta_{\perp}=\frac{1}{r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial}{ \partial\tau}\bigg{)}+\frac{1}{r^{2}}\frac{\partial^{2}}{\partial\theta^{2}}\) in cylindrical coordinates), and \(\tau=t-z/c_{0}\) is the time delay at the axial distance of \(z\) with \(t\) being the time. The three terms on the right side of Equation (1) represent the diffraction, attenuation and nonlinearity, respectively. The attenuation caused by viscosity and thermal conduction is hence proportional to the square of the angular frequency \(\omega\), i.e., \(\alpha(\omega)=\omega^{2}\delta/(2c_{0}^{3})\). However, in biological tissues, the attenuation factor is usually a non-integer less than 2, the attenuation term is hence only valid for describing wave propagation in fresh water (attenuation factor \(y=2\)). If the attenuation parameter is directly set for fresh water rather than considering the actual properties of the media, the accuracy of calculation would certainly be undermined. Furthermore, the sound velocity \(c_{0}\) here is regarded as a constant for all harmonic components, which is in conflict with the inherent Kramers-Kronig dispersion relations where the phase velocity varies with increasing frequency.
# A Framework to Understand Attitudes towards Immigration through Twitter
## 4 Methodology
### Theory-Informed Profile Labelling  
Here we define the two attitudes we classify profiles into, and how to label profiles according to these attitudes for classification. In the spectrum of attitudes towards immigration there are two that define two opposite extremes. Usually, these types of categories are named _positive_/_negative_, _in-favour_/_against_, or similar. Here we rely on the social psychology theories described in Section 2: the Intergroup Contact Theory (ICT), and the Integrated Threat Theory (ITT). Based on those theories, we name the two attitudes we are classifying as _empathy_, due to the empathy toward immigrants, and _threat_, due to perceptions of threats regarding migration.  
Given the potential size of the discussion under analysis, manually labelling the user profiles (or their tweets) into these two categories is expensive and impractical. In view  
Figure 3: Schematic diagram of the proposed methodology.  
Figure 2: Weekly volume of content in the dataset, disaggregated by type of content/interaction.  
of this difficulty, we predict attitudes using a classifier trained on a labelled subset of the dataset. This subset is labelled automatically from a list of seed patterns and keywords for each attitude, as words are an effective mechanism to predict the community a user belongs to [18; 69; 70]. To identify seeds, we iteratively explore the dataset to seek for features that could be mapped to the _empathy_ and _threat_ attitudes. In _empathy_, we look for features that indicate that immigrants are welcome and will be received in equal conditions (e.g., \"we are all immigrants\"). In _threat_, we look for features that show that immigrants are not welcome or qualify them or their arrival negatively (e.g., \"illegal immigrants take our jobs\"). The labelled terms are not necessarily frequent, instead, they are discriminative, i.e., it is likely that someone in its corresponding category would use the term, and not from the other. The list is built iteratively in the sense of running the first steps from this section up to the classification step, and then exploring the usage of discriminative terms by people in each group to look for other potential seeds. See example seeds for each attitude in Table 1.  
Next, we label users who match these patterns. Those who are labelled in both attitudes have their labels removed. Note that we assume that in the period under study attitudes do not change. Additionally, we manually label accounts of institutions (such as the International Migration Office and the Jesuit Service for Migrants), opinion leaders, journalists, and politicians that have explicitly expressed their attitude on the issue.
## 4 Trust Evaluation in VANET
### Trust Level Computation
#### 4.1.2 Direct Trust  
The RSU computes the trust level of each new node based on their previous direct interactions. In our work, the RSU evaluates its interactions with the \(j^{th}\) node according to a scale of n levels numbered from 1 (the largest successful interaction) to \(n\) (the lowest successful interaction). We assumed \(a_{j}^{i}(t)\) is the number of interactions of type \(t\) between the \(i^{th}\) RSU and the \(j^{th}\) node. The RSU computes the trust for the \(j^{th}\) node as follows:  
\[S(j)=\frac{\sum_{x=1}^{m}w_{x}\sum_{h=1}^{a_{j}^{i}(x)}v_{h}}{\sum_{x=1}^{m}a _{j}^{i}(x)} \tag{10}\]  
where \(w_{x}\) denotes the importance of the completed transaction, and \(v_{h}\) reflects the degree of cooperation of the \(j^{th}\) node with the RSU. The RSU must distinguish between transactions carried out by the \(j^{th}\) node. Transactions of the same type have varying values in our work, reflecting each transaction's importance. This value is represented by \(v_{h}\).
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Node Behavior Design
#### 3.2.2 Intermediate Nodes Behavior  
When an intermediate node receives a packet, it identifies the packet as part of the SR backup path. The node forwards the packet along the path specified by the label. Specifically, the node determines the outgoing port and label operation code based on the label. It then pops the top adjacency label from the label stack in the packet header and forwards the packet accordingly.
## 2 Sensors
### Light Detection and Ranging (LiDAR)  
LiDAR is one of the most important components of most MMSs. LiDAR is able to efficiently collect 3D information from the surrounding environment. Current applications of LiDAR are vast, since it provides highly accurate distance measurements. Employing LiDAR in different architectures, such as mirror-based configurations [90] or arrays of rotating sensors [91], enables 2D and 3D measurements. The sensor principle behind measuring the position of a 3D point by LiDAR is based on emitting laser beams toward a target and measuring the time it takes for the light to travel and bounce back after hitting a surface, providing precise distance measurement [18]. These 3D points could be combined to form detailed point clouds. A scanned area could be represented in high resolution with digital twins such as the LiDAR point clouds [92].  
LiDAR systems can operate at various wavelengths, typically in the near-infrared spectrum for terrestrial mapping [93], but green (532 nm) wavelengths are also used for bathymetric LiDAR [94], which penetrates water bodies. The first demonstrated LiDAR application was in meteorology [95]. It has applications across multiple domains, such as autonomous vehicle navigation [96], where it aids in object detection and environment mapping, and topographic and forest canopy studies [97; 98]; it is also used in many other applications, such as urban planning [99], enabling infrastructure assessment and site analysis [100; 101]. The accuracy of LiDAR data is influenced by factors such as the laser pulse frequency, angle of incidence, atmospheric conditions, and the reflectivity of the scanned surfaces, which necessitates advanced calibration and processing techniques to maximize its utility in complex environments [102].  
Generating a photogrammetric point cloud usually needs considerable computation power and requires a certain amount of post-processing time. In general, LiDAR fills the computational-cost gap, since the collection of dense point clouds does not require high computational power. Despite this advantage, LiDAR in no possible way could be considered a replacement for the camera systems that exist in MMSs. Still, the cameras provide a practical form of information that is much better in many applications, such as object detection and visual presentation. If relative orientations and translations of LiDAR become known with respect to the cameras, and an accurate synchronization between LiDAR points and camera shots exists, a data fusion approach could be achieved to bring the power of both data sources under a unique 2D-3D presentation umbrella, where every image point is connected to its 3D object point. In the post-processing phase, many sophisticated geometric operations are applied to a point cloud, e.g., the number of points is efficiently reduced [103], or the point clouds are converted into mesh forms that are more suitable for visualization [104].
## 3 Results and Discussion
### Energy of bR Protein  
The total energy measured in the analysis comprises the non-bonding interaction energies (van der Waals and electrostatics) between graphene and the bR protein. The total energy value of bR protein in the presence of graphene decreases initially but then increases up to \(\sim\)3150 kcal/mol at \(\sim\)60 ns. After that, it remains stable around an average value of 2900 kcal/mol (Figure 4a). On the other hand, for bacteriorhodopsin without graphene, the total energy decreases gradually to 2600 kcal/mol. Comparing the two cases, the increase of \(\sim\)500 kcal/mol in the presence of graphene is a clear indication of the adsorption of bR onto the surface of graphene. In regards to electrostatic energy, for bR-graphene it was \(\sim\)400 kcal/mol lower than for the control, indicating that the adsorption of bR onto graphene is largely due to the VDW interactions (Figure 4b). In the case of VDW energy, there are two adsorption events that are observed at \(\sim\)10 ns and \(\sim\)70 ns, where the VDW energies are found to be \(\sim\)90 kcal/mol and \(\sim\)110 kcal/mol (Figure 4c). These energies together indicate changes in the conformational arrangements that are occurring in the bR protein. Such behavior in energy is also apparent in the key dihedral C14-C15 = NZ-CE, which is a double bond between the carbon C15 of retinal and the nitrogen NZ of the amine group of the LYS216 residue of bR (Figure 4d). In Figure 4d, the increased dihedral energy of bR-graphene suggests that it undergoes structural change due to the adsorption of bacteriorhodopsin onto graphene. Details of these dihedral analyses are given in the following Section 3.6.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
###### Abstract  
Graph clustering has received considerable attention recently, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources. This study proposes an approach to partitioning and overlapping clustering of undirected unweighted graphs, allowing users to specify the required density of resultant clusters. This required density is achieved by means of 'Relative Density'. The proposed algorithm adopts an edge-based approach, commencing with the determination of the edge degree for each edge. The main clustering process is then initiated by an edge with an average degree. A cluster is expanded by considering adjacent edges that can be included while monitoring the relative density of the cluster. Eight empirical networks with diverse characteristics are used to validate the proposed algorithm for both partitioning and overlapping clustering. Their results are assessed using an appropriate metric known as the mean relative density deviation coefficient (MRDDC). This is the first work that attempts to carry out partitioning and overlapping graph clustering, which allows user-specified density.  
edge degree; graph clustering; mean relative density deviation coefficient (MRDDC); overlapping clustering; partitioning clustering; relative density