esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.10. Navigation III  
In [68], the authors developed a robot system capable of navigating in unknown environments by using RL and semantic photo segmentation. The authors aimed to create a model that governs the robot's movement using a monocular camera, enabling it to navigate without reliance on pre-existing maps. They also desired to connect simulated training settings with real-world settings by constructing a strong semantic model capable of properly identifying areas on the floor that the robot can navigate using its camera input.  
The authors in [68] used the PPO RL algorithm to train an agent that controls the robot's speed and direction in response to its sensory input, as shown in Figure 7. An ROS serves as middleware for incorporating the RL and semantic segmentation models into an operational real-world environment. This work emphasizes the role of segmenting images to improve the resilience of robotic perception. It demonstrates how segmenting complex color images enhances the robot's ability to generalize robot behavior from simulated to real-world contexts.  
The above-mentioned work presents a technique for mobile robots to self-navigate in unknown indoor environments. They show how RL with semantic image segmentation can help robots understand and adapt to their surroundings in real-world environments. This approach has shown promising results; however, it needs to be improved in complex environments such as varying lighting conditions.
## 4 Modeling of the System Components
### Scroll Expander  
The scroll expander is an open-drive, lubricated machine converted from a refrigerant compressor. The test bench is not equipped with a dedicated oil separator circuit; hence, the lubricant oil is mixed with the refrigerant and circulates in the system. It is modeled using a semi-empirical model developed by some authors of this work, which simulates the expansion process as a polytropic one. Moreover, a more detailed suction pressure drop model is included compared to what is commonly used in literature. The mechanical losses are modeled using a lumped loss torque, which is correlated to the expander shaft speed. The inputs to the model are the expander suction pressure and temperature; expander shaft speed; expander discharge pressure; expander geometrical parameters, BVR and SV, known from the manufacturer datasheet; and the empirical coefficients. The model outputs are the expander shaft power, the isentropic efficiency, the volumetric efficiency  
Figure 20: Results of the evaporator hydrothermal model vs. experimental data: refrigerant discharge temperature (**top-left**), diathermic oil discharge temperature (**top-right**), thermal load in the refrigerant side (**bottom-left**), and temperature profile for one of the experimental points (**bottom-right**).  
(FF), and the expander mass flow rate. The expander performances were mapped using the model and are presented in Figure 21. Further details on the expander paper can be found in [17].
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 2 Scanning Methods
### Scanning Trajectory Pattern and Density
Fortunately, it is possible to make theoretical predictions of \(H_{max}\) and \(D_{max}\) for the sinusoidal Lissajous, which are provided below [25]:  
\[H_{max}=\frac{2A_{x}A_{y}sin\frac{\pi}{N_{x}}sin\frac{\pi}{N_{y}}}{\sqrt{ \left(A_{x}sin\frac{\pi}{N_{y}}\right)^{2}+\left(A_{y}sin\frac{\pi}{N_{x}} \right)^{2}}} \tag{1}\]  
\[D_{max}=\sqrt{\left(A_{x}sin\frac{\pi}{N_{y}}\right)^{2}+\left(A_{y}sin\frac{ \pi}{N_{x}}\right)^{2}} \tag{2}\]  
where \(A_{x}\) and \(A_{y}\) are the width and height of the FOV, while \(N_{x}\) and \(N_{y}\) represent the number of lobes or stripes along the x and y axes, respectively. Equations (1) and (2) above clearly show that if the FOV is fixed, \(H_{max}\) and \(D_{max}\) depend on the number of lobes \(N_{x}\) and \(N_{y}\). As a result, given a fixed FOV, increasing the lobe count will result in a denser pattern, and, hence, a higher image resolution. An earlier investigation into the sinusoidal Lissajous trajectory revealed that a configuration of \(N_{x}=N_{p}=N_{y}+1\) will guarantee the highest density of the forming pattern [26]. Here, \(N_{p}\) is the ratio of two frequencies of the x and y axes, \(f_{x}\) and \(f_{y}\), and it is proportional to the scanning density or image resolution (see Table 1 for more details). Trajectory-scanning patterns are usually generated from the conjunction of two signals. The waveforms are typically sinusoidal signals with variable frequencies (\(f_{x}\) and \(f_{y}\)) and amplitudes (A and B). The shape and density of the scanning trajectory can vary depending on the value of \(N_{P}\). Table 1 provides a summary of the full definitions for these parameters and coefficients. Although there are theoretical formulas for \(H_{max}\) and \(D_{max}\) for the sinusoidal Lissajous trajectory, there is not one for \(D_{min}\)[25]. However, our work indicates that the values of \(D_{min}\) and \(D_{max}\) are required to determine \(H_{max}\). Therefore, in the majority of situations, these numbers will need to be determined numerically. In terms of the selection of trajectories, this work will concentrate on the four primary trajectories: bidirectional Cartesian (BC), triangular Lissajous (TL), sinusoidal Lissajous (SL), and radial Lissajous (RL), which are frequently utilized for MPI research. The formulation and characteristics of these trajectories are presented in Table 1 ((3)-(10)).
# Color-Space-Based Visual-MIMO for V2X Communication +
## 2 Experimental Section
### Mobile Networking through Visual-MIMO
The image processing based division of the communication area can also significantly reduce the complexity in  
Figure 5: Multi-node V2X communication. Figure 6: The divided ROI for multi-node V2X communication. the object detection process. This allows a flexible change from time to time to cope with the various situations of the V2X communication. Figure 7 shows an example of target color transition in a color-space-based constellation diagram, where the target color of traffic light is denoted by a star symbol. Note that 'E' denotes the white color in CIE1931 color space. In particular, we consider a case in which the target color of the traffic light changes from green to yellow and from yellow to red. For example, traffic lights can send out their timing and phase information to prevent red light violations and potential accidents and this traffic information is transmitted by symbols (or colors) corresponding to constellation points as explained in Figure 2.
## 1 Introduction
### Contribution and Paper Organization  
This work presents a novel approach using ML models to predict the structural dynamics of a wind turbine blade based on the flow conditions and the turbine's control actions. Three datasets are generated for the NREL 5 MW wind turbine using the OpenFAST tool for aeroelastic analysis under different turbulence classes. The datasets are used to train and build a regression model that can predict the blade's structural dynamic measures. The two major contributions of this work are as follows:  
1. Ten different ML models, including linear, nonlinear, and ensemble models, are trained to predict the blade tip deflections and root shear forces in the flapwise and edgewise directions.
2. The most accurate ML is tested for generalization by training it on one dataset and testing it for the two remaining datasets.  
The paper is organized into four sections and two appendices. Section 1 provides an introduction and background on the state-of-the-art research related to this work, as well as the major contributions and novelty. Section 2 describes the wind turbine adopted for the simulations, the methodology followed to generate the dataset and exploratory data analyses, and the main quality metrics used to assess the ML models. Section 3 shows the key findings and discussion of the outcomes and observations of this work. Section 4 concludes the work and shows trends for future research. Appendix A shows extra results for generalizing the random forest ML model in predicting the blade's tip deflections. Finally, Appendix B shows the generalized results of the random forest ML model in predicting the blade root shear forces.
# Outlier Detection and Prediction in Evolving Communities
## 4 COTILES for Outlier Detection
### Prediction  
In the final stage of our proposed framework, prediction for each node outlier behavior takes place based on each node's outlier score. We argue that our proposed outlier score indicates the outlierness of each node in terms of structure and content, and as such can provide a valid indication of the nodes' fluctuations in future connectivity. In particular, we argue that a high outlier score at a current time point describes a node that is more likely to become a community outlier or even be eliminated from the network at subsequent time points.  
Thus, we model the problem of predicting outlier behavior as a classification problem and propose using the nodes' outlier score as a predictive feature. A reasonable outcome of our predictions would be a high proportion of nodes that become outliers in later time instances if their outlier score at a specific time point is high.  
A node, even one that is a member of a community, might reach a high outlier score because of a possible low connection with the community members (community focus) or low similarity with the community's content. In such cases, our method leads to predicting outlying behavior of the node in the subsequent time slices, not necessarily the immediate next one, thereby detecting the outlying tendencies of nodes even earlier.  
In addition, the community and outlier detection process in our framework takes place in an online way without taking into account previous stages or snapshots of the network. For prediction purposes, however, the recent history of the node's outlierness can be used in the form of chronological chains which demonstrate a node's evolution over time. A chronological chain can be a series of a specific node's outlier scores in a few consecutive observation window points. If a node has a history of increasing outlier scores, it is more likely that it will become an outlier, leaving all communities or even the network in the future; on the other hand, a history of decreasing outlier scores, even those that are not members of any community, could indicate nodes that are gradually becoming more embedded in the network's communities and are less likely to leave.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
###### Abstract  
The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with adjustable clustering coefficients is proposed. Subsequently, we use the SIS (susceptible-infectious-susceptible) model to describe the information propagation process in the aggregation-phenomenon hypernetwork. In addition, we establish the relationship between the density of informed nodes and the structural parameters of the hypernetwork in a steady state using the mean field theory. Notably, modifications to the clustering coefficients do not impact the hyperdegree distribution; however, an increase in the clustering coefficients results in a reduced speed of information dissemination. It is further observed that the model can degenerate to a BA (Barabasi-Albert) hypernetwork by setting the clustering coefficient to zero. Thus, the aggregation-phenomenon hypernetwork is an extension of the BA hypernetwork with stronger applicability.  
hypernetwork; social networks; information dissemination; clustering coefficient; SIS model
## 3 Profile Fitting and Geometric Modeling
### Coordinate System Transformation and Curve Fitting  
#### 3.2.1 Coordinate System Transformation  
After performing key point detection, the feature points are displayed within the pixel coordinate system with the coordinate origin in the upper left corner of the image, the u-axis along the image width direction, and the v-axis along the image height direction, as shown in Figure 7a. To facilitate fitting the fusion line, the key point coordinates need to be converted to a weld coordinate system with the origin at the center of the weld path, as shown in Figure 7b. The procedure for locating the origin coordinates and transforming the key point coordinates for the \(i\) weld is as follows:  
* The coordinate sequence of the feature points detected by the HRnet network in the pixel coordinate system is \((u_{i},v_{j}),0<i<10;0<j<10\).
* The horizontal coordinates of the five groups of feature points are differenced and averaged to obtain the horizontal coordinates of the origin \(O_{i}\) in the pixel coordinate system as: \[u_{o}=\frac{\sum\limits_{i=1}^{5}u_{L_{i}}+\sum\limits_{i=1}^{5}u_{R_{i}}}{10}.\]
* The maximum value of the vertical coordinate of the 5 groups of key points is calculated, and the vertical coordinate of the origin \(O_{i}\) in the pixel coordinate system is obtained as \[v_{o}=\max(\max(v_{T_{i}}),\max(v_{R_{i}})),0<i<5.\]
* The horizontal and vertical coordinates of the feature point in the pixel coordinate system are subtracted from the horizontal and vertical coordinates of the point \(O_{i}\) to  
Figure 6: HRnet network structure diagram.  
obtain the horizontal coordinates \(x_{i}=u_{o}-u_{i}\), \((0<i<10)\), and vertical coordinates \(y_{i}=v_{o}-v_{i}\), \((0<i<10)\) of the feature point in the weld coordinate system. The coordinates of point A in the pixel coordinate system are \((u,v)\); the coordinates of the weld coordinate system are \((x,y)\).
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
In view of this, this paper proposes a data-driven state estimation method based on TL for low-observable distribution networks, and the main contributions are summarized as follows: (i) the topology of the distribution network is modeled by an adjacency matrix; then, a singular value decomposition is performed on the adjacency matrix to obtain the corresponding singular value sequence, and a clustering algorithm is used to cluster the obtained singular value sequence to obtain the typical topology set of the distribution networks; (ii) sample migration technology is used to transfer samples in distribution networks with high observability to those with low observability in the same typical topology set to solve the challenging problem of state estimation in low-observable distribution networks; (iii) the proposed method enables accurate state estimation in low-observable distribution networks and improves the model's generalization performance. The remainder of this paper is organized as follows. Section 2 provides the background knowledge of deep transfer learning. Section 3 introduces the model of state estimation based on sample migration. A case study on practical distribution systems is demonstrated in Section 4. Conclusions are drawn in Section 5.
## 5 Discussion
The deformations of the ground on the city area have continuous character. There are no significant increases in the value of displacements in the surroundings of tectonic faults. However, it should be noted that possible movements on tectonic faults can be suppressed by 250 m thick cover of Cenozoic deposits. The sediments in the majority of the research area are alternating layers of clays, sands and boulder clays, which may undergo compaction processes. The displacements can also be affected by complex ground-water conditions. Cenozoic sediments include the 10-20 m thick sands layers that constitute confined aquifers whose condition is not monitored. Historical data on vertical displacements in the area of Wroclaw [42] for the period of 1930-1998 (Figure 5) and changes in Polish precise levelling lines [41] for the period of 1956-2000 show noticeable differences in displacements between the southwest and the northeast parts of the city. The past 20 years saw an intensive development of new road infrastructure and residential housing projects in the city outskirts. Corine Land Cover change [54] database for the periods of 1990-2000, 2000-2006, 2006-2012 and 2012-2018 was used to identify potential causes of man-made displacements that may influence results of SAR data processing (Figure 12). The CORINE Land Cover (CLC) is a systematized data set. It comprises the borders of areas classified into five main classes and three levels of hierarchy. In urban areas, ground surface deformations are significantly influenced by new construction projects that cause land coverage to be changed into artificial surfaces. The classes of anthropogenic areas in the CLC database are shown in Table 2.  
Land use change in the area of Wroclaw has been predominately the result of development and expansion of housing. Land cover changes in the period 1990-2018 within the city limits occurred only on the area of about 7%. Construction of the Wroclaw Motorway Bypass was realized in the 2010-2013 period and could not affect the calculated displacement velocities. SAR images of the required quality are not available for the 2009-2013 period. Operation of the ERS-2 and Envisat satellites came to an end in 2011 and 2012 respectively, while Sentinel-1A was launched in April 2014. \begin{table}
\begin{tabular}{l l l} \hline
**Level 1** & **Level 2** & **Level 3** \\ \hline
1 Artificial surfaces & 11 Urban fabric & 111 Continuous urban fabric \\  & & 112 Discontinuous urban fabric \\ \hline  & 12 Industrial, commercial and & 121 Industrial or commercial units \\  & transport units & 122 Road and rail networks and \\  & & associated land \\  & & 123 Port areas \\  & & 124 Airports \\ \hline  & 13 Mine, dump and construction sites & 131 Mineral extraction sites \\  & & 132 Dump sites \\  & & 133 Construction sites \\ \hline  & 14 Artificial, non-agricultural & 141 Green urban areas \\  & vegetated areas & 142 Sport and leisure facilities \\ \hline \end{tabular}
\end{table} 
Table 2: Classes of anthropogenic areas with codes according to CORINE Land Cover. Figure 12: Corine Land Cover (CLC) and land cover change in 2000–2018 on the Wroclaw area.
# Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations
## 3 The \"Social Space Identification\" Problem
### Main Notation and Definitions
With the above concepts, we can formulate the optimization problem in the following general terms. **Problem 1. **_Input_: (1) _A graph \(G=(V,E)\), with \(V=T\cup X\cup R\), where \(T\) is the set of RD patients, \(X\) is the set of EX patients, and \(R\) is the set of HSP centers. (2) _A set of service \(S\) and a labeling \(h\), such that \(h:T\cup X\cup R\to S\), i.e., \(h\) labels expert patients, recent patients, and services. (3) The target patient \(\hat{t}\in T\). Output: A set \(V^{\prime}\subseteq V\), such that \(\hat{t}\subseteq V^{\prime}\) and \(G[V^{\prime}]\) is a two-club having both maximum size and the largest number of feasible pairs._  
**4. Approximate Solutions**  
Problem 1 is a computational variant of the max \(s\)-club problem, whose complexity is NP-hard for \(s\geq 1\)[39]. Similarly, this result also applies to Problem 1. In order to find approximate solutions at a reasonable cost (time), in this paper, we designed a genetic-based heuristic by defining specific operators that allow obtaining fast solutions.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 2 Materials and Methods
### Particle Swarm Optimization (PSO)  
First presented by [PERSON] and [PERSON] in 1995, a population-based optimization technique called particle swarm optimization simulates the actions of biological swarms [22]. PSO is based on the observation that social animals, such as flocks of birds and fish, interact while foraging for food. When one of them finds food, the others, without breaking away from the flock, change their position in the direction of the food and update their speed accordingly. PSO is based on the mathematical modeling of these natural movements [23]. The algorithm is used in many fields as an effective and efficient optimization method. The basic principle of PSO consists of a group of particles moving through a solution space. Each particle represents a potential solution, and its movement is influenced by its knowledge and the best performances of its neighbors. Particles move through the search space by updating their velocity and position. This process is given in Equations (1) and (2).  
\[V_{j}(t+1)=wv_{j}(t)+c_{1}n_{1}\big{(}p_{j}-a_{j}(t)\big{)}+c_{2}n_{2}(g-a_{j}( t)) \tag{1}\]  
\[a_{j}(t+1)=a_{j}(t)+V_{j}(t+1) \tag{2}\]  
In this equation, \(V_{j}(t),j\): particle \(t\). velocity at iteration \(t\), \(a_{j}(t),j\): particle's position at iteration \(t\). \(c_{1}\) and \(c_{2}\) are the learning coefficients and usually take values between 0 and 2. \(n_{1}\) and \(n_{2}\) are randomly generated numbers ranging from 0 to 1. \(p_{j}\) is the best location of the \(j\). particle and \(g\) is the best location among all particles globally.