esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Numerical Model: The Mohr-Coulomb Constitutive Model  
The Mohr-Coulomb constitutive model allows one to calculate a fixed average stiffness or a stiffness that increases linearly with depth for each soil layer, enabling rapid estimation of deformation [30]. It is widely used for assessing rock slopes' stability, with the factor of safety determined using fundamental shear strength parameters, such as cohesion (c) and the internal friction angle (\(\blacklozenge\)). The simplicity of these parameters makes the results easy to comprehend [31].  
A soil nail support system was designed using the Mohr-Coulomb model based on an idealized soil profile. The material parameters for the structural elements of the slope model were determined after establishing boundary conditions (Table 3). In Section 1-1, soil nail lengths at all levels were set to 6.00 m, while the slope inclination was 65\({}^{\circ}\), and the nail spacing was 1.60 m. In Section 2-2, the length of the soil nails for the upper three stages was 6.00 m, while that in the last two stages was 4.00 m. The slope inclination was 85\({}^{\circ}\), with a nail spacing of 1.40 m. All nails in both sections had a 15\({}^{\circ}\) inclination. The borehole diameter was 127 mm, and the nail reinforcement consisted of 28 mm diameter ribbed construction iron. The shotcrete surface, created using Q188/188-class steel mesh, was 200 mm thick.  
The soil profile was classified as completely weathered sandstone from a depth of 0.00 to 7.50 m and moderately weathered sandstone below 7.50 m (Table 4).  
Mohr-Coulomb model analysis conducted along Section 1-1\({}^{\prime}\) revealed the maximum horizontal displacement in the coating to be 2.78 cm, with a maximum horizontal displacement of 2.22 cm at the position of the inclinometer behind the wall (Figure 9).  
Figure 8: Variation in internal parameters: (**a**) angle of internal friction; (**b**) effective cohesion.  
Similarly, for Section 2-2\({}^{\prime}\), the maximum horizontal displacement in the coating was 1.62 cm, while it was 1.53 cm at the position of the inclinometer behind the wall (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Symbol** & **Unit** & **W3 Sandstone** & **W4 Sandstone** \\ \hline Modulus of Elasticity & E & MPa & 417 & 40 \\ Unit Weight & \(\gamma\) & kN/m\({}^{3}\) & 26 & 21 \\ Cohesion & c & kPa & 87 & 5 \\ Internal Friction Angle & \(\Phi\) & \(\circ\) & 32 & 26 \\ Poisson’s Ratio & \(\upsilon\) & - & 0.25 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 4: The Mohr–Coulomb soil model structure parameters used in the project-based design.  
Figure 10: Calculated horizontal displacement values from the Mohr–Coulomb model (2–2\({}^{\prime}\) Section).  
Figure 9: Calculated horizontal displacement values from the Mohr–Coulomb model (1–1\({}^{\prime}\) Section).
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 3 Problem Formulation  
### Problem Description  
The 2E-EVRPTW-BSS system considers a depot, multiple satellites, a number of customers with deterministic demands, and a group of BSSs that have no restrictions on the capacity of EVs. In addition, the positions of all these nodes have been given and fixed.  
Heavy loaded ICVs are employed in the first echelon to transport demands from the depot to each satellite, while EVs are considered in the second echelon to transport demands from satellites to each customer, as depicted in Figure 1. EVs are supposed to have a limited driving range and have to visit BSSs to swap their batteries before they run out of battery power. In addition, to promise the timeliness of delivery, this work also considers the time windows of customers, which in turn require the latest arrival time of ICVs at the satellites, i.e., there is also a time window for the arrival time of ICVs at each satellite. The 2E-EVRPTW-BSS aims to minimize the total costs, including the transportation cost of two echelons, fixed cost of the vehicles, and battery swapping cost.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 2 Mission and Payload Operations
### Time Schedule of a Quick-Response Capture  
As stated in **mission Goal 1**, the data latency should be under 1 h. This is important when supporting field campaigns, autonomous agents in the field, or providing data on oceanographic events such as algal blooms or HABs, during which a quick response is needed.  
An example of setting up a quick-response plan, covering the tasks of choosing targets, scheduling and generating scripts, uploading scripts and acquiring data from the satellite (steps 1 to 4 of the operational workflow), is shown in Table 3. The image capture process, from image capture planning until the image capture, takes less than 1.5 h. The downlinking time for the raw data, however, depends on ground station availability, and the size of the observation. This causes the full planning, capture, and downlink process to be performed in about 5-6 h. When the onboard processing pipeline can produce operational data of a much smaller size, the desired time constraint can be met. For targets near the equator, there is normally only a single possible capture pass per day when taking the elevation requirement into account, while targets with higher latitudes (north or south) can be imaged multiple times per day during polar summer.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 5 Open Research Problems
For example, a vehicle running on a road must not be able to access the other vehicle's infotainment system in V2V communication. Similarly, a vehicle must not be able to include its data in EV charging machine or RSUs. Only some controlled access must be allowed. Some decentralized mechanisms of access control and their verifiability must be researched. * _Heterogeneity_: In vehicular networks, there are different kinds of OBUs, cellular transmitters, sensors, digital audio systems, GPS, etc. Therefore, the data is heterogeneous. A standard security model or the benchmark for VANET security is a missing link. It will be very much beneficial for VANETs to have such a baseline security attempting to detect the anomalies and adapting itself to increase the knowledge base for analysis of new vulnerabilities. * _Attacks solution_: From the literature, we have observed that DoS/DDoS attacks are the major consideration in VANET security. This is true, as these attacks are executed in all the layers of VANETs and may take various forms. However, the other categories of attacks need to be explored for developing optimized security method. * _IoE consideration_: The future of VANETs is closely connected with IoE that is connected with power generation units with various resources. The connection of vulnerabilities between VANETs and IoEs must be explored in all possible directions. Appropriate solutions must be developed to mitigate the risk of attacking energy infrastructure through VANET components. * _Blockchain aspects_: The decentralization and distribute computing is one of the major enablers in the present technologies. Blockchain ensures these features efficiently. Therefore, such blockchain-based solutions can be beneficial for VANET security. The VANET infrastructural components can work in a transparent and decentralized way to account the transactions of data. However, methods should be developed to enhance this feature along with maintaining the required security services.
## 3 Results and Discussion
### Verification of the New Method  
The plot shown in Figure 7a indicates that the number of feasible cycle numbers for the new CV step pattern charging method with a 20% capacity degradation in the 3.2 Ah battery increased by 48.9% when compared with the conventional CCCV step charging method, while those of the adaptive step rapid charging method increased by 61.7%. In addition, the conventional step charging procedure resulted in a sharp drop in the available capacity at the beginning of the cycle, while the proposed novel adaptive charging pattern gave no drop in capacity.  
In addition, the plot given in Figure 7b shows the increase in the rate of internal resistance upon standard charging, conventional CCCV step rapid charging, CV step rapid charging, and adaptive step rapid charging. In this context, it must be considered that internal resistance is related to the battery output, and is an important indicator for potential electric vehicle batteries. In all charging patterns indicated, no increase in the internal resistance was observed at the beginning of the cycle, although an increase took place when the capacity began to drop.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 4 Simulation Results
### Evaluate the System Reconfiguration Time  
To evaluate the system reconfiguration time of the proposed DSA-PSC method, Figure 28 shows the simulation of reconfigurations for a six-cell converter using the CPSC method (with modified updating rule) as well as the proposed DSA-PSC one. At the beginning, the system starts with six interleaving cells involving six carriers with inter-carrier phase-shift \(\Delta\theta_{base}=60^{\circ}\). Then, at time 0.5 ms (at time step 0.5 \(\times\) 104), the cell 3 is removed, and the five remaining cells adjust their own inter-carrier phase-shifts to reach \(\Delta\theta_{base}=72^{\circ}\). Next, at time 1 ms (at time step 1 \(\times\) 104), the cell 5 is removed, and the system is interleaved by self adjusting the phase-shifts to \(\Delta\theta_{base}=90^{\circ}\). Then, the cell 5 is reinserted at time 1.5 ms (at time step 1.5 \(\times\) 104), resulting in a new inter-carrier phase-shift \(\Delta\theta_{base}=72^{\circ}\). Finally, the cell 3 is re-activated at time 2 ms (at time step 2 \(\times\) 104), and the system returns to the original configuration.  
The reconfiguration time study of two methods shown in Figure 28 approves the advantage of the proposed DSA-PSC method in terms of lower reconfiguration time with zero inter-carrier phase-shifts relative error, which is summarized in Table 6.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 2 Materials and Methods  
### Unsupervised Classification Algorithm FBCCA  
The FBCCA algorithm does not need training and can directly conduct unsupervised classification [17]. Traditional CCA requires pre-training of models with EEG data before online classification [18; 19].  
As shown in Figure 1, the main steps of FBCCA are as follows: (1) filter bank analysis; (2) canonical correlation analysis between SSVEP subband components and sinusoidal reference signals; and (3) target classification and recognition.  
Filter bank analysis in FBCCA can deconstruct SSVEP into several subband components, allowing the discriminant information contained in SSVEP harmonic components to be extracted. As a result, FBCCA delivers more extensive and robust harmonic information to recognize SSVEP targets and has a higher classification recognition effect.
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 1 Introduction
After briefly introducing aspects of the workflow that clinical AF detection follows (how cardiologists detect AF), relevant contributions in automatic AF detection over the last years will be addressed. With the emergence of remote health and heart monitoring paradigms, simple univariate algorithms for AF arrhythmia detection continue capturing both the interest of research and commercial actors in the study of the heart condition. However, as robust and powerful cloud infrastructures make their way into real products and use case scenarios for remote health monitoring while device computational capabilities increase, multivariate analysis methods that can be pretrained and hosted in the cloud may be experiencing a wave of renewed interest. In the following sections of this work, significant contributions of univariate and multivariate data analysis applied to automatic AF monitoring are reviewed.
## 6 Experimental Results and Discussion
### Mapping Result  
Figure 11 shows the results of mapping. Our SLAM solution used a stereo camera, which could obtain the scale of the points. The point cloud map showed that our method accurately constructed the structure of the warehouse, with very few outliers added to the map. Then, from the point cloud map, the octree map was generated to provide a basis for subsequent path planning and obstacle avoidance. The point clouds exceeding the specified height would not affect path planning and obstacle avoidance; only point clouds within the specified height range were selected to generate the octree map.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### 1-Neighbor-Selection Algorithms  
Throughout this section, we assume that a forwarder peer has a set of \(\mathcal{N}\) neighbors (e.g., actions) noted \(n_{1},\ldots,n_{m}\). For a given search query \(q_{t}\), the forwarder runs a learning algorithm (e.g., an agent) to select one neighbor \(n_{i}(q_{t})\) then receives a reward \(r_{n_{i}}(q_{t})\in\{0,1\}\) from the selected neighbor \(n_{i}(q_{t})\) (i.e., \(r_{n_{i}}(q_{t})=1\) if \(n_{i}\) returns pertinent documents to the query \(q_{t}\), otherwise \(r_{n_{i}}(q_{t})=0\)). The goal of the agent is to learn a 1-neighbor-Selection (1-NS) strategy that maximizes the cumulative rewards, i.e., the sum of rewards over all processed queries during learning. We note that the agent doesn't have prior knowledge about the reward of each neighbor. Therefore, an efficient 1-NS strategy must balance between exploration (i.e., pulling different neighbors to learn new information) and exploitation (i.e., pulling the neighbor with the highest estimated reward based on previous queries). This will serve to enhance the routing performance continuously and, therefore, goes quickly through the cold-start phase [7; 17; 18; 19].  
In the following, we introduce an adaptation of three pioneering MAB algorithms for resolving the conflicting goals of exploration and exploitation.
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 4 Analysis of Results
### Energy Analysis as a Comparison Metric
We make this comparison to base the consumption on the energy model proposed for the sensors, detailed in Table 3. Each of the energies described here are total energies for 30 s of simulation time and based on the grid described in Figure 1. We expose the two techniques of jamming detection when the jammer node is in the middle of the topology, jamming is reactive, and the percentage of packet loss per link is between 0.5 and 2.5%. We note that the Extended Mechanism improves energy consumption by about 20% against Connected Mechanism. This table checks the energy consumption of the nodes under the reactive jamming, which is the most aggressive of all the cases for the scenarios we have proposed. We analyze each one of the energy types of the model to visualize where the greatest energy expenditure is made at the moment of a network under attack conditions. The types of energy that have the greatest impact are the energy of the CSMA algorithm, the receiving energy and the transmission energy. This situation is logical because, under attack, the amount of overhead in the network increases and, therefore, increase packet retransmissions, retries to listen to the communication channel and collisions.
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 2 Methods
### The Measurement Campaign
#### 2.3.2 Doppler Spectra Measurements Using Signal Carrier Transmission  
The measurements of the Doppler spectrum were performed using the same measuring station as for the measurements using impulse transmission, with one difference: the carrier signal was transmitted in a continuous mode. Therefore, there was no need to perform time synchronization of the transmitting and receiving station, while the carrier frequency was disciplined on both sides: in the signal generator (on the transmitting side--using the rubidium oscillator) and in the spectrum analyzer (on the receiving side--using GPS option). The measurement studies described in this section were aimed at modeling the radio channel to characterize the signal propagation in the presence of a wind farm. Having achieved that, the radio signal propagation channel in the presence of wind turbines can be modeled with the Doppler spectrum associated with each path to account for variability due to blade rotation.  
Doppler spectrum measurements were used to determine the frequency and shape of the Doppler spectrum. The Doppler frequency depends on the relative positions of the transmitting antenna, wind turbine and receiving antenna \(\phi_{r}\) (shown in Figure 3), the maximum rotation frequency of the wind turbine \(\omega_{max}\) (rad/s), and the blade length \(l\), as per Equation (9). In the case under consideration, \(f_{B~{}max}\) corresponds to the angle \(\phi_{r}=0^{\circ}\) and the blades rotating in the plane transmitting antenna--wind turbine--receiving antenna. Substituting the parameters of the Vestas 126-3.45 MW turbines, utilized in the measurement campaign site (see next section):  
* Maximum rotation speed: 16 rpm;
* Blade length: 61.7 m.  
We obtain a maximum Doppler frequency of 111.5 Hz. Doppler power spectral density, in accordance with ITU-R BT.1893-1 (Annex 2), can be determined depending on the maximum Doppler frequency and wind conditions (low, medium, and high variability) translated into different rotational speed and orientation of turbine rotors.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 3 Results
### Selection of IoU Threshold  
The model's stable performance within the wide range of IoU threshold is presented in Figure 9. Above 0.7 IoU threshold level, a slight decline of recall can be observed. However, above 0.8 IoU, a significant decline in the model performance can be noticed. This deterioration results from the fact that it is hard for the model to locate objects in the images with high precision.
## 2 Saturable Absorber Fabrication and Characterization
### Nonlinear Transmission of the m-SWCNT Saturable Absorber  
The free-standing m-SWCNT films were transferred to fiber connector tips, as shown in Figure 2a. To fabricate a fiberized saturable absorber, two fiber connectors with a 150 nm m-SWCNT thin film on each (a total of 300 nm thick material layer) were connected with a fiber adapter. Nonlinear optical transmission of the absorber was characterized in a fiber Z-scan style setup, incorporating an electronically controlled variable optical attenuator [41]. The measurement was conducted at 1560 nm (Figure 2b) and 1950 nm  
Figure 1: SEM image of the surface of m-SWCNT film with thicknesses of 150 nm (**a**) and 300 nm (**b**); scale bar represents 600 nm, Vis-IR absorbance spectra (**c**) and Raman spectra (**d**) of the films shown on SEM images. The m-SWCNT films were transferred on the Si/SiO\({}_{2}\) substrate and on soda–time glass for the Raman and absorbance measurements, respectively.  
(Figure 2c). The data were well fitted using a fast saturable absorber model [42] with the saturable absorption parameters indicated on corresponding graphs.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 4 Findings
### Movement Dynamics (Speed, Acceleration, Distance)  
The movement of the tracker was analyzed using metrics derived from GPS telemetry, including speed, acceleration, and distance traveled. These metrics provided insights into the dynamic behavior of plastic waste as it moved through the Saigon River, highlighting patterns influenced by environmental factors.  
The tracker's speed ranged from 0.0 m/s to a maximum of 2.55 m/s, with an average of 0.50 m/s. This average speed reflects the generally slow-moving nature of the river and the tracker's dependence on tidal flows and currents for movement. Resting states, identified by a speed of 0.0 m/s, accounted for 8.79% of the tracker's journey. The standard deviation of 0.21 m/s indicated moderate variability, with occasional spikes in speed linked to dynamic external forces, such as boat wakes or rapid currents.  
Acceleration was calculated as the change in speed over time. Most of the tracker's journey was characterized by low or negligible acceleration values, consistent with steady movement through calm water. However, periods of significant acceleration were identified during speed spikes, where external forces influenced the tracker's behavior. These bursts of activity provided additional context for the tracker's interaction with its environment.  
The straight-line distance from the launch point to the endpoint was 26.7 km, while the total distance traveled by the tracker was approximately 205.7 km, as shown in Figure 6.  
This substantial distance demonstrates the capability of the tracker to cover long stretches of the river, providing valuable insights into the transport of plastic waste. The mean distance traveled between consecutive GPS points was 4.90 m, reflecting steady, incremental movement influenced by the river's currents. Notably, the maximum distance recorded between two points was 767.74 m, an outlier that occurred during a speed spike event, suggesting an interaction with a strong external force or rapid current.