esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Minutes after an injury to the spinal cord, structural and functional reorganization of the connected brain areas may be initiated. Exercise enhances this neuroplasticity in the further course of the condition, which might modulate the connectivity patterns in brain regions responsible for movement execution and imagination. However, connectivity patterns have not been analyzed as a correlate for activity effects on neuroplasticity after spinal cord injury (SCI). We hypothesize that wheelchair sport has a modulating effect on the cortical connectivity in patients with SCI, such that distinguished activity patterns can be observed between sportive and non-sportive individuals with SCI and healthy participants. Methods: Sportive (\(n=16\)) and non-sportive (\(n=7\)) patients with SCI as well as sportive (\(n=16\)) and non-sportive (\(n=14\)) healthy participants were instructed to either observe, imagine, or conduct an observed movement while high-density EEG (HD-EEG) was recorded. Functional connectivity was computed from the recorded signals, and the coefficients were compared between groups and conditions using a non-parametric repeated measures analysis. Results: We found that depending on being sportive or not, patients with SCI and controls would react differently to the conditions, but the effects depended on the location in the brain as well as the analyzed frequency range (\(p<0.05\)). Further analysis indicates that non-sportive patients showed higher connectivity received by the right posterior parietal cortex and a lower connectivity received by the left M1 compared to sportive patients. These effects were mainly observed during movement imagination, not during movement. Sportive and non-sportive participants in the healthy control group showed smaller differences than the patients. Conclusions: The results suggest a modulative effect of sports on connectivity patterns during movement imagination and to some extent during movement. This effect was predominantly found in patients with SCI, and to a lesser extent in healthy participants with opposing connectivity patterns. We suggest that this might be due to increased cortical excitability and the elevated brain derived neurotrophic factor (BDNF) level in patients with SCI that is enhanced by exercise.  
spinal cord injury; wheelchair sports; exercise; cortical connectivity; HD-EEG 4.0.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### Kurtosis and Information Entropy  
Kurtosis is introduced to explain the contour and non-Gaussian properties of the probability density distribution. It takes the normal distribution as the standard to determine the tail distribution of a specific PDF. A greater value of kurtosis indicates a thicker tail and a wider range of the curve. Kurtosis is used to study the properties of the signal amplitude distribution under different scintillation intensities. For a fourth-order random variable, the kurtosis is given as [43]  
\[C_{k}=\frac{E(x-E(x))^{4}}{\left[E(x-E(x))^{2}\right]^{2}}-3 \tag{15}\]  
where \(x\) is sample values.  
Information entropy was originally used in the field of informatics to measure the disorder of information and reflect the amount of information at the same time. Currently, it is used to quantitatively describe the degree of chaos and uncertainty of the system. The higher the value is, the higher the disorder degree of the system. Information entropy is expressed as  
\[H=-\sum_{i=1}^{m}P_{i}\log_{2}P_{i} \tag{16}\]  
where \(P_{i}\) indicates the probability. The equation fails when \(P=0\). Since ionospheric scintillation is a random event with high disorder and uncertainty, information entropy is used here to discuss the confidence level of the amplitude distribution under different scintillation intensities. A similar usage of information entropy in scintillation studies is referred to in [PERSON] et al. 2017 [29].
## 2 Configuration
### Roll Measurement  
The roll measurement method is based on double parallel beam measurement, using two RRs as sensitive elements. In Figure 5, O1 and O2 indicate the centers of the incident surfaces of RR1 and RR2, respectively. A is the incident point of the measuring beam passing though PBS1, and B is the exit point on RR1. C is the incident point of the measuring beam on RR2, after being reflected by M2 and M3. D is the exit point on RR2, which is finally received by the CMOS1 camera. When there is no roll, the positions of B and C are the same in the y direction. Similarly, the reflected light from PBS2 is incident on RR2, which is finally received by the CMOS2 camera. E and F are the incident and exit points, respectively.  
Figure 4: Measurement principle of horizontal straightness error.  
When the moving unit produces roll, the position of beam spots will change on RR1 and RR2 except for incident point A. The distance between the center points of the incident surfaces of the two RRs will not change. According to the triangle sine theorem, we can obtain  
\[sin\,\gamma=\frac{\Delta Y_{2}-\Delta Y_{1}}{L}=\frac{\Delta Y_{CMOS1}}{2L}, \tag{5}\]  
where \(L\) is the distance between the center points of the incident surfaces of the two RRs; \(\Delta Y_{2}\) and \(\Delta Y_{1}\) are the position changes of O1 and O2 in the y direction; and \(\Delta Y_{CMOS1}\) denotes the displacement of output laser beams in the y direction, which is spotted on the CMOS1 cameras.  
Similarly, according to another opposite light path, the beam is received by the CMOS2 camera, and we can obtain  
\[sin\,\gamma=\frac{\Delta Y_{2}-\Delta Y_{1}}{L}=\frac{\Delta Y_{CMOS2}}{2L}, \tag{6}\]  
where \(\Delta Y_{CMOS2}\) denotes the displacement of output laser beams in the y direction, which is spotted on the CMOS2 cameras.  
Considering that \(\gamma\) is a small angle, the roll can be expressed as  
\[\gamma=\frac{\Delta Y_{CMOS2}-\Delta Y_{CMOS1}}{4L}. \tag{7}\]  
In other similar methods, a measuring beam passes through an RR only once. However, in the provided system, each beam passes through the RRs twice, which can improve the measurement resolution of roll and horizontal straightness. It is specifically expressed by '4' in Formulas (3) and (6).
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
### Design of Collectors and Relays
It can be obtained e.g., by knowing the density of the user in the area, coverage area size, and the number of acknowledgment message received by UAVs (during hovering time cycle) from the users.
## 6 Quantitative Evaluation  
In this section, we present the quantitative evaluation metrics to assess the scalability and cost-effectiveness of the proposed SSFXG model. The following system environment assumptions are considered to emulate the network operations and interactions designed for each phase in the proposed SSFXG model. The blockchain environment is assumed to use an Ethereum-compatible test network for simulating public blockchain transactions and smart contract deployment. Ganache-CLI is utilized to emulate the blockchain operations, providing an in-memory blockchain environment capable of hosting the system's smart contracts on a local machine. The smart contracts are developed using Solidity and interact with the blockchain through the Web3.py Python library. The SCs are deployed in Docker containers, hosted on virtual machines (VMs) with the following specifications: x86_64 architecture, Intel(R) Core(TM) i7-9750H CPU @ 2.60 GHz, and 4 GB of RAM.  
The evaluation metrics used in this study are System Throughput, Blockchain Transaction Latency, Computation Cost, and Total Blockchain Cost. The first two metrics assess the scalability of the SSFXG model, measuring the system's ability to handle increasing subscriber requests and the impact of concurrent requests on transaction latency. The latter two metrics evaluate the computational overhead and gas fees incurred during identity issuance, service provisioning, and voice call operations and the cost-effectiveness of a competitive bidding mechanism among network providers.
## 2 Materials and Methods
### System Design and Test
#### 2.2.3 Design and Test Process  
The process of executing the design and test workflows is shown in Figure 8. This manual process is characterized by decisions at each stage based on intuition or experience [40].  
First, in Figure 8, the system is specified in the MBSE system model. The system specification includes the collection and definition of requirements and the derivation of a suitable functional and solution architecture. From this, simulation models are derived and linked in design and test workflows. The design workflow uses these models and other calculations (norms, standards, and guidelines) to determine the parameters of the solution architecture. In the test workflow, parameter values are calculated and checked for requirement satisfaction. If requirements are not met, the design variables are changed, and the design and test workflows are executed again. If the requirements are met, the design can be approved and detailed. There may arise the case that no technical variant  
Figure 7: Test workflow for the electric coolant pump. The implementations of _calculateMechPower_ and _calculateEEnergy_ are described in detail in Section 3.4.  
can fulfill the requirements. Then requirements can be relaxed, or they can be returned to a previous development phase to adapt the system architecture. These two cases are not considered in this paper.  
In the case of the electric coolant pump, there are any number of technical variants that meet the exemplary requirements (Figure 6). However, during the design and test activities, the pump installation space and electric energy consumption arise as conflicting objectives. This means that the requirements for the highest efficiency and smallest installation space are met to varying degrees by different technical variants.  
With increasing system complexity, it is difficult to estimate which design variable changes are suitable for finding a good variant or any variant at all. Furthermore, it is unclear which design variables are particularly suitable for responding to requirement changes.
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 3 Approach
### Navigation Stack  
The algorithm developed in the original program of the work presented in [1] has been adapted in this work to make it usable on a real robot by using the Robotic Operating System (ROS), which is an open-source framework for robotic applications. A representation of the ROS navigation stack is presented in Figure 4, with the main ROS components and the waypoint generator integrated before the motion planner. The stage simulator [29] integrated into the ROS was used, which can realistically simulate the robots' dynamics and kinematics as well as their sensors and the world's physics. Two types of agents were developed: active agents, which use the S-DOVS algorithm for navigation, and passive agents, which act as dynamic obstacles.  
Figure 3: Graphical representation of the DOVS model and its action space: (**a**) DOVS with forbidden velocities and (**b**) representation of the action space.  
Figure 2: The figure shows how the robot is reduced to a point (red point); the obstacle is enlarged with the robot radius (gray), its collision band is drawn for its current trajectory, and the intersection points between two possible trajectories (green) and the collision band are computed in blue.  
The modified simulator detects collisions and allows agents to be teleported to start a new episode without restarting the simulation. The launching of agents and the environment is performed with an automatic script. This makes conducting qualitative experiments and comparative navigation systems possible in maps such as the ones shown in Figure 5a,b. The proposed modifications enabled the program to be usable on a real robot, making it more practical and realistic.  
To navigate in a scenario, a robot needs to know its location; however, unlike in simulations using a centralized program, in the real world the robot's position is not known. Therefore, the active agents in our study use sensor data such as 2D LIDAR and wheel encoders to estimate their location. To achieve localization, we employ Adaptive Monte Carlo Localization (AMCL) [30] from the ROS navigation stack, which involves updating and sampling a large number of particles based on laser scans and the odometry estimation to converge on the robot's current position. The AMCL requires a static map of the environment, laser scans, an initial position, and encoder measurements as inputs, and outputs the robot's position in the map frame. An example of the AMCL estimated pose of the robot is seen in Figure 5c.
## 3 Magnetic Field Analysis and Calculation of the Eddy Current Braking Force
### The Eddy Current Density  
As the demagnetization effect of the eddy current in the cooling jacket is ignored, Equation (4) can be regarded as the expression of the flux density in the cooling jacket. In Figure 2, the magnetic vector potential in the cooling jacket also satisfies the following equation  
\[\frac{\partial^{2}A_{\rm{z}}}{\partial x^{2}}+\frac{\partial^{2}A_{\rm{z}}}{ \partial y^{2}}=j\omega\mu\sigma A_{\rm{z}} \tag{6}\]  
where \(\omega\) is the angular frequency, \(\mu\) is the permeability of the cooling plate, \(\sigma\) is the conductivity of the cooling plate.  
Transforming (6) to  
In the cooling jacket, the magnetic vector potential, magnetic field density, and eddy current density meet the following relationship  
\[\left\{\begin{array}{l}H_{x}=\frac{1}{\mu}\frac{\partial A_{x}}{\partial y}\\ H_{\mathrm{y}}=-\frac{1}{\mu}\frac{\partial A_{x}}{\partial x}\\ J_{\mathrm{z}}=\frac{\partial H_{\mathrm{y}}}{\partial x}-\frac{\partial H_{ \mathrm{x}}}{\partial y}\end{array}\right. \tag{11}\]  
By substituting (9) into (11), the expression of the eddy current density can be written as  
\[\begin{array}{l}J_{\mathrm{z}}=-\frac{2C_{1}\alpha\beta}{\mu}\left[e^{- \alpha y}\mathrm{cos}\big{(}\omega t-\frac{\pi}{\tau}x-\beta y+\theta_{1} \big{)}\right.\\ \left.+e^{\alpha y}\mathrm{cos}\big{(}\omega t-\frac{\pi}{\tau}x+\beta y+ \theta_{1}\big{)}\right]\end{array} \tag{12}\]
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 5 Discussion and Conclusions
**Nomenclature**  
**Acronym** **Full Name**  
ODG **Obstacle-Dependent Gaussian**  
MPC **Model Prediction Control**  
ADAS **Advanced Driver Assistance Systems**  
GPS **Global Positioning Systems**  
RRT **Rapidly exploring Random Tree**  
APF **Artificial Potential Field**  
PF **Potential Field**  
LRF **Laser Range Finder**  
ERF **ERror Function**  
ST **SafeTy**  
ISO **International Organization for Standardization**  
RMS **Root Mean Square**  
**Variable** **Definition**  
\(d_{max}\) **The maximum detection range of sensor**  
\(l_{f}\) **Distance from C.G to front**  
\(l_{r}\) **Distance from C.G to rear**  
\(E\) **Ego vehicle state**  
\(S_{k}\) **Surrounding vehicle state**  
\(W_{E}\) **Ego vehicle width**  
\(W_{S_{k}}\) **Surrounding vehicle width**  
\(W_{R}\) **Road width**  
\(W_{L}\) **Line width**  
\(\mathbf{x}\) **State of the vehicle vector**  
\(\mathbf{u}\) **Acceleration and steering vector**  
\(A_{d}\) **Discrete system matrix**  
\(B_{d}\) **Discrete input matrix**  
\(C_{d}\) **Discrete output matrix**  
\(T_{s}\) **Sampling time**  
\(p_{E_{X}}\) **Longitudinal position of ego vehicle**  
\(v_{E_{Y}}\) **Longitudinal velocity of ego vehicle**  
\(p_{S_{X}}\) **Longitudinal position of obstacle**  
\(v_{S_{X}}\) **Longitudinal velocity of obstacle**  
\(p_{s_{Y}}\) **Lateral position of obstacle**  
\(v_{S_{Y}}\) **Lateral velocity of obstacle**  
\(\Delta p_{s_{Y}}\) **Lateral displacement of \(S_{k}\) in the sampling time**  
\(\omega\) **Risk at the obstacle location**
# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Detecting Fake Sources Using GCNs
[PERSON] et al. [37] focused on detecting rumors and proposed a GCN-based method for the identification of multiple rumor sources. They use knowledge from infection and influence models in order to model the problem of rumors that spread in the social network. The input to their mode is a set of different infection states of a given network and the output is the actual rumor sources. Their method is based on the fact that rumor sources are surrounded by more infected nodes than normal sources, and non-infected nodes are usually far from the rumor sources. In order to capture the features of multi-order neighbor nodes, they stack multiple GCN layers that use RELU and a single dense layer that uses the sigmoid function. They consequently modify GCN to be directly applied to the supervised learning task of detecting rumor sources. They evaluate their method on popular benchmark social networks in which they define infection sources that the method has to detect and apply different propagation models. The problem formulation in this case begins with a set of social media users (accounts) \(U=\{u_{1},...,u_{n}\}\), who connect to each other with social links and have social and textual metadata associated with them based on the posts they make, their profile information and the social features (e.g., connectivity metrics) they have. As shown in Figure 4, in the pre-processing step, the context of user profile and user posts is processed with language models (e.g., transformers) to produce the textual information embeddings, which are concatenated with the numerical and categorical social features. The vectors are attached to the nodes \(v\in V\) of the directed graph \(\mathcal{G}=V,E\), which has users as nodes \(u\in V\) and their social links as edges \(e\in E\). Once again, the concatenated textual embeddings and social features constitute the feature vector \(R\) of each node, and the vectors of all nodes form the input matrix \(X\in\mathbb{R}^{nxd}\), where \(n\) is the number of nodes and \(d\) is the dimensionality of the node vectors. The adjacency matrix \(A\in\mathbb{R}^{nxn}\) is used to represent the set of edges \(E\) and the same graph convolution and max pooling steps are applied, as in the case of news classification, to classify
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 5 Analysis of Results
### First Test: Verification of Dimension Recognition for the Printing Code  
This test is purely digital, and its purpose is to verify whether the dimensions of any construction element designed in BIM (in this case in RevitTM) are indeed being respected in the control code. To do this, a series of digital designs were built in RevitTM with different geometries that allow checking the effectiveness of the code. The designs were then transferred to DynamoTM.  
In Figures 6 and 7, it is possible to appreciate that the printing code develops not only develops the exact replica of the dimensions of the designed element but also automatically traces the spiral-shaped printing path, so it can be deduced that, digitally, the tests are successful. The above is not trivial. The fact that this test has been verified is important for future research because it will facilitate the exploration of 3D concrete printing processes with different shapes without the need of creating a printing code for each of them.  
Figure 6a shows a 200 mm \(\times\) 400 mm curved beam designed in RevitTM. Figure 6b represents the same beam but with its printing path using the code designed in DynamoTM. The dimensions of this element were modified from RevitTM so that, at the time of printing, it matches the desired measurements.  
Figure 7a presents a circular column with a diameter of 300 mm and a height of 1000 mm designed in RevitTM. Figure 7b represents the same column but with its printing path built with the code designed in DynamoTM. As done with the previous element (Figure 6), the dimensions of this element were modified from RevitTM so that, at the time of printing, it coincided with the desired measurements.
## 3 Results  
### Plasma-Deposited Oxidants Decreased Melanoma Cell Viability and Motility  
Cold physical plasma introduces oxidants into liquids surrounding cells, with hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) being among the long-lasting products of redox chemistry [40]. In pure PBS (Figure 1a) and fully supplemented cell culture medium (Figure 1b), H\({}_{2}\)O\({}_{2}\) accumulated in a dose-dependent manner, validating the introduction of plasma-derived species in culture systems. The concentration of H\({}_{2}\)O\({}_{2}\) differed only modestly between complex cell culture medium compared to buffered saline (PBS), suggesting that FCS in cell culture medium has a negligible effect on H\({}_{2}\)O\({}_{2}\) pre-cursor species from the kINPen. Furthermore, the effect of FCS as a growth factor for B16F10 was measured via metabolic activity using different FCS concentrations. As expected, the melanoma cell metabolic activity increased with increasing FCS concentration (Figure 1c). Following this, the effect of plasma treatment on melanoma growth with and without FCS was analyzed and not found to be substantially different at 4 h (Figure 1d). This suggests a growth-supporting but not a dramatic antioxidative effect caused by the proteins of FCS with plasma treatment. To see whether FCS presence or absence during plasma treatment affects the migratory capability in melanoma cells, scratch assays were performed (Figure 1e). Results showed that the presence FCS had only a minor impact on cellular migration, in contrast to plasma treatment, which reduced cell regrowth with increasing treatment times (Figure 1f). Importantly, FCS-containing medium was added after treatment to both conditions to focus on short-term effects following plasma treatment. This suggests that within our experimental system, FCS (composed of various proteins) in solution only played a minor role in mediating or protecting from plasma effects on tumor cells. Altogether, plasma treatment slowed melanoma cells' metabolic activity and motility, regardless of FCS presence or absence.