esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Rock-Like Specimens with Single Flaw under Hydro-Mechanical Coupling
## 3 Results
### Mechanical Properties
#### 3.1.2 Peak Deviator Stress  
The peak deviator stress is the highest value of the stress-strain curve and equal to the triaxial compression strength minus the confining pressure value. Figure 6 shows the peak deviator stress of the specimens under different confining pressure, water pressure, and flaw inclination angle. As shown in Figure 6a, the strength increased with the confining pressure only, except for the 30-6 specimen. In the hydraulic coupled triaxial test, the strength was much lower than that of the triaxial compression test (Figure 6b-d). The dotted line shows the average strength under the same conditions. It can be seen whether the confining pressure (CP) was 2 MPa, 4 MPa or 6 MPa; the peak deviator stress decreased with the increase of water pressure (WP). That indicates the water pressure greatly reduced the strength, in other words, water pressure promoted the failure trend of the specimens. This means that rock mass in a stable state can be destabilized by the injection of water.  
Figure 7 shows the linear fitted contour map of peak deviator stress; different colors indicate the magnitude of peak deviator stress. It can be seen that the confining pressure increased the peak deviator stress and water pressure weakened it. When the inclination angle was between 30\({}^{\circ}\) and 60\({}^{\circ}\), the strength was the smallest, while the strength of 0\({}^{\circ}\) and 90\({}^{\circ}\) specimens was the largest and most similar to that of intact specimens. It is worth noting that the 15\({}^{\circ}\) flaw inclination angle interval is still relatively large and this trend needs further research.  
Figure 5: Sketch diagram of stress–strain curve types in triaxial tests. (**a**) Triaxial compression test; (**b**) hydraulic coupled triaxial test.
## 2 Experimental Section
### Post-Processing Approach  
Raw data acquired in a GNSS static survey can be post-processed with commercial or scientific software, using both real data obtained from CORSs and synthetic data (Virtual Rinex, hereinafter VR) created by the network software. Great importance is given to the distance between master and rover [1, 6, 17]; some studies show the behaviour of mass-market receivers in real time and in post-processing positioning.  
The correct fixing of the phase ambiguity for single-frequency receivers has been studied for many years, [18, 19] and for this reason the master-rover distances are usually limited to five to ten km. In mass-market receivers, the problem is even more complex, due to the noise of the code and carrier phase data [6].  
Figure 1: GNSS Regione Piemonte GBAS NRTK Network [20].  
For these reasons, different tests were made for the function of master-rover distance: we compared the solutions obtained by NVS and u-blox receivers when a master station is five, ten, and 25 km from the rover for a session length of five, ten, and 15 min. At the same time, the session length plays an important role in post-processing performances; it is important to know the time that the user must wait before completing a survey in order to have a fixed ambiguity phase on an L1 frequency to obtain centimetric accuracy.  
The data files were processed by the commercial software Leica Geomatics Office(tm v.8.3) by Leica Geosystems(r), which is based on the double differences approach, and for generating the VR, we used Leica Spidernet(r) as a network software: the network configuration is shown in Figure 1.
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 5 Implementation  
In this section, we describe in detail the implementation of the SDV prototype, as well as its performance evaluation. The SDV controller is implemented using Java Servlet on Apache Tomcat version 8. It is hosted in a virtual machine under MIMOS cloud platform. The virtual machine runs on Ubuntu 14.04 as the operating system, with 4 GB memory. The SDV API with the implementation of the multi-server SSE scheme, SSE\({}_{SDV}\), is developed using Java and C++. The web interface for the SDV App, on the other hand, is based on JSP. MySQL is deployed as the underlying database. We incorporated a local storage server and Google drive as the cloud storage servers for our current deployment. We note that the number of servers is configurable.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm  
This section describes the cluster head selection algorithm based on fuzzy relevance. The efficient formation of clusters plays an important role in the processing rate, performance improvement, and network stability.
## 4 Results and Discussion
### Pile Load-Sharing Ratio  
As shown in Figure 14, when the pile spacing is 4D, the load-sharing ratio of the continuously solidified part and pile lateral friction resistance remain basically unchanged with the increasing burial depth. Among them, the ratio of the continuously solidified part was about 33%, and the ratio of pile lateral friction resistance was about 63%. When the pile spacing was 6D, with the increasing burial depth of the continuously solidified part, the ratio of pile lateral friction resistance gradually decreased, while the load-sharing ratio of the continuously solidified part gradually increased with the increasing burying depth and even exceeded the pile lateral friction resistance when the burying depth reached 18D. At the same time, no matter whether the pile spacing was 4D or 6D, the bearing capacity of the pile end was no more than 5% after setting up the continuously solidified part, which indicated that the bearing capacity of the SCS group pile is mainly borne by the continuously solidified part and the pile lateral friction resistance.  
Figure 13: Lateral friction resistance distribution under 6D pile spacing.
# Energy Management Scheme for an EV Smart
## 4 EMS for a Smart Charger  
Utilizing EV battery capacities to provide active power ride-through for short-term renewables transients such as for photovoltaics during cloudy weather and wind turbines during variable wind speed is an appealing prospect. Moreover, making the EV charger capable of reactive power support for voltage regulation simultaneously further enhances its functionality.  
In this study, we have proposed a comprehensive EMS, which calculates the \(PQ\) references instantaneously during grid disturbances for a three-phase fast charging station. The charger termed as a smart charger is composed of two main entities: a four-quadrant converter and the EMS to drive its operation (Figure 10).  
Figure 8: Phase locked loop block diagram.  
Figure 9: Four-quadrant converter control strategy block diagram.  
The EMS is the key to designate the active and reactive power flows between the EV charger and the grid. The voltages, phase angles and currents are measured from the grid and the PCC and fed to the EMS. Furthermore, it takes the information from EVs regarding their SoC and battery capacities and distributes the energy accordingly among them. The EMS sends the reference signals to the grid-side three-phase four-quadrant converter controller and the EV side DC-DC converter controllers. The process through which the EMS derives the specific references for its various functionalities is detailed ahead.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 7 Challenges of 6G
### Device Capability  
The technical capabilities of mobile devices should be compatible with the new 6G features, given the extensive use of the 5G mobile network [122]. As mobile devices that support 5G are not compatible with some of the new 6G features, improving the technical capabilities of mobile devices for 6G may contribute to higher costs. However, supporting 1 Tbps throughput, AI, XR, and integrated sensing with communication features using individual devices is challenging. Several other important points that increase the device capability challenges of 6G deployment are:  
* Hardware for the research and development of 6G is still not ready. Having equipment and modern labs helps the developers and devices designers to test and develop the devices and produce compatibility with 6G technology.
* The industry standard is still not finalized. There are no standards for the devices that can work with 6G, which complicates things for the device's manufacturer. Furthermore, organizations such as 3 GPP, which provides mobile technology standardization, have still not defined the standards of 6G technology.
* Circuit design for 6G device at THz requires plenty of validation works in the R&D stage. At the current stage, there are plenty of research works focusing on characterizations and modeling at the THz level, especially the first work on THz [123] and those from NYU Wireless [124].
# Computer Vision Techniques for Growth Prediction:
## 4 Discussion
### Implications for Future Research  
As shown in Figure 4, there was a significant increase in DL in 2019 and 2020. We did not find many changes in the field of non-ML from 2017 to 2022. There were 26 publications related to non-ML studies, whereas only 21 were related to ML. This indicates that non-ML publications have dominated the ML publications in terms of growth prediction. There was no increment in ML publications between 2017 and 2018, where only a couple of publications were noted. However, we found a change in this trend after 2019 only. It was doubled in 2019 and sharply increased in 2020 and 2021, resulting in six publications in total in 2021.  
Holes and scars on plants are either filled in or remain intact as the plant grows. Considering wounds as geometric features, it is reasonable to use STN, which can make predictions while preserving geometric features [35; 59]. Although the examples of STN [35] are limited to datasets taken indoors from above, it is rather suitable for predicting environments where leaves are easily scratched, such as outdoors. Conversely, for the prediction of animal antlers, the entire image's affine transformation is unsuitable for cases such as adult deer, whose physical characteristics do not change significantly except for the antlers. A possible application using partial STN would be to predict the growth of a specific part of the image (e.g., the growth of antlers at the end of a branch).  
A study by [PERSON] et al. [47] demonstrated the suitability of an unsupervised learning-based GAN technique for generating realistic images of future plant growth stages. In contrast, the STN study presented in this paper is classified as supervised machine learning, as it relies on pregrowth and actual postgrowth images. Since tracking the sequential growth of wild animals in images is challenging, especially considering our goal of individual animal identification, the paper's findings on growth prediction by unsupervised learning pave the way for future research.  
To improve the accuracy of plant growth prediction, [PERSON] et al. [35] proposed a novel loss function for combining STN loss with RGB and shape loss. Meanwhile, other studies have introduced loss functions that minimize the geodesic distance between the sample and the regressed curve and metrics produced from simple distance spaces [32]. As complex geometry data become more prevalent, the adaptation of machine learning models to handle such data and the derivation of complex losses by combining existing ones will continue to evolve.  
There are models that can make predictions based on data types other than images, such as signal or land data, and there are also models that can output 3D images as well as just 2D images [31; 38]. Research on sensor fusion or image fusion, which involves obtaining images from various data sources, is increasing every year [90], and we can expect models that can make predictions using diverse data sources to continue developing.
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 4 Simulation Results  
This section discusses the performance of the proposed C-DL-NOMA system, based on different chaotic maps, through simulation results. This simulation was performed using MATLAB. The impacts of various chaotic maps assigned to Users 1, 2, and 3 on SISO DL power-domain NOMA performance, in terms of BER and some security analyses, were demonstrated via the simulation results. The proposed model for evaluating the quality of the system performance was simulated based on the parameters listed in Table 1.
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 2 Material and Methods
### Measurement Methodology
The receiving of satellite signals was characterized by frequent short-term outages;
* Evaluation of signal-to-noise ratio (SNR) as a function of the influence of vegetation density on the quality of the received GNSS signal;* Evaluation of the position accuracy of a moving vehicle, determined by a GNNS receiver and compared with the coordinates of a pre-targeted vehicle route.
## 3 Main Results
### Algorithm Feasibility and Stability Analysis
If \(\psi_{v}(k)+\epsilon_{v}(k)\geq\frac{\epsilon}{2}\), we have \[\kappa_{v}(k)=\frac{1}{(1-\phi_{v}(k)\epsilon_{v}(k))(1+\phi_{v}(k)\epsilon_{ v}(k))}\leq\frac{1}{(1+\phi_{v}(k)\epsilon_{v}(k))}\leq\frac{2}{\phi_{v}(k)\epsilon}\] Similarly, if \(\psi_{d}(k)+\epsilon_{d}(k)\geq\frac{\epsilon}{2}\), we have \[\kappa_{d}(k)\leq\frac{2}{\phi_{d}(k)\epsilon}\] (22) Furthermore, we have \[-\kappa_{v}(k)\epsilon_{v}(k)\leq\frac{2}{\epsilon\phi_{v}(k)^{2}}\text{ or }-\kappa_{d}(k)\epsilon_{d}(k)\leq\frac{2}{\epsilon\phi_{d}(k)^{2}}.\] Therefore, we have \[0\leq F(k)=\min\Bigl{\{}-\kappa_{v}(k)\epsilon_{v}(k),-\kappa_{d}^{1}(k) \epsilon_{d}(k)-\kappa_{d}^{2}(k)\epsilon_{v}(k)\Bigr{\}}\leq\frac{2}{\epsilon \phi_{d}(k)^{2}}+\frac{2}{\epsilon\phi_{v}(k)^{2}}\geq C_{k_{0}}\]
###### Abstract  
Erecting underground structures in regions with unidentified weak layers, cavities, and faults is highly dangerous and potentially disastrous. An efficient and accurate near-surface exploration method is thus of great significance for guiding construction. In near-surface detection, imaging methods suffer from artifacts that the complex structure caused and a lack of efficiency. In order to realize a rapid, accurate, robust near-surface seismic imaging, a minimum variance spatial smoothing (MVSS) beamforming method is proposed for the seismic detection and imaging of underground geological structures under a homogeneous assumption. Algorithms such as minimum variance (MV) and spatial smoothing (SS), the coherence factor (CF) matrix, and the diagonal loading (DL) methods were used to improve imaging quality. Furthermore, it was found that a signal advance correction helped improve the focusing effect in near-surface situations. The feasibility and imaging quality of MVSS beamforming are verified in cave models, layer models, and cave-layer models by numerical simulations, confirming that the MVSS beamforming method can be adapted for seismic imaging. The performance of MVSS beamforming is evaluated in the comparison with Kirchhoff migration, the DAS beamforming method, and reverse time migration. MVSS beamforming has a high computational efficiency and a higher imaging resolution. MVSS beamforming also significantly suppresses the unnecessary components in seismic signals such as S-waves, surface waves, and white noise. Moreover, compared with basic delay and sum (DAS) beamforming, MVSS beamforming has a higher vertical resolution and adaptively suppresses interferences. The results show that the MVSS beamforming imaging method might be helpful for detecting near-surface underground structures and for guiding engineering construction.  
near-surface; seismic imaging; beamforming; underground structure; reflection seismic +
Footnote †: journal: Applied Sciences
# Several Cases for the Validation of Turbulence Models Implementation
## 2 Physical Model
### Mass, Momentum and Energy Conservation Equations  
For an arbitrary finite volume, \(\Omega\), bounded by a closed surface, \(\partial\Omega\), a small surface, \(dS\), with a unit normal vector, \(\hat{n}\), pointing outward from the control volume, the mass conservation equation is  
\[\frac{\partial}{\partial t}\int_{\Omega}\rho d\Omega+\oint_{\partial\Omega} \rho(\vec{v}\cdot\hat{n})dS=0 \tag{1}\]  
where \(\rho\) is the density and \(\vec{v}\) is the velocity vector.  
The momentum equation is  
\[\frac{\partial}{\partial t}\int_{\Omega}\rho\vec{v}d\Omega+\oint_{\partial \Omega}\rho\vec{v}(\vec{v}\cdot\hat{n})dS=\oint(-p\mathbf{I}+\mathbf{\tau})\cdot \hat{n}dS \tag{2}\]  
where the scalar \(p\) is the pressure, \(\mu\) is the dynamic viscosity coefficient, \(\mathbf{I}\) is the identity tensor, and \(\mathbf{\tau}\) is the viscous stress tensor.  
The energy conservation equation for a calorically perfect gas is  
\[\frac{\partial}{\partial t}\int_{\Omega}\rho Ed\Omega+\oint_{\partial\Omega} \rho H(\vec{v}\cdot\hat{n})dS=\oint_{\partial\Omega}k(\
abla T\cdot\hat{n})+( \mathbf{\tau}\cdot\vec{v})\hat{n}dS \tag{3}\]  
where \(E\) is the total energy, \(H\) is the total enthalpy, \(T\) is temperature, and \(k\) is thermal conductivity.
# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
###### Abstract  
Formation of a hierarchy within an organization is a natural way of assigning the duties, delegating responsibilities and optimizing the flow of information. Only for the smallest companies the lack of the hierarchy, that is, a flat one, is possible. Yet, if they grow, the introduction of a hierarchy is inevitable. Most often, its existence results in different nature of the tasks and duties of its members located at various organizational levels or in distant parts of it. On the other hand, employees often send dozens of emails each day, and by doing so, and also by being engaged in other activities, they naturally form an informal social network where nodes are individuals and edges are the actions linking them. At first, such a social network seems distinct from the organizational one. However, the analysis of this network may lead to reproducing the organizational hierarchy of companies. This is due to the fact that that people holding a similar position in the hierarchy possibly share also a similar way of behaving and communicating attributed to their role. The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network. As a technique for answering this research question, machine learning apparatus was employed. Here, for the classification task, Decision Trees, Random Forest, Neural Networks and Support Vector Machines have been evaluated, as well as a collective classification algorithm, which is also proposed in this paper. The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm. The results demonstrate that the social network built using the metadata on communication highly exposes the organizational structure.  
social network analysis; classification; organizational hierarchy; machine learning Article
## 3 Design of a CMG for SWT
### SWT and CMGT Topology Definition  
An existing SWT, which is a 500 W counter-rotating wind turbine from Ilmunandar and Bramantya [16], was used for the installation of the CMGT. The CMGT shown in Figure 1 is a 2-DOF mechanism; two independent inputs are required to obtain a predictable output. When the outer rotor and carrier of the CMGT are assigned as the two input links and connected to the counter-rotating blades, the inner rotor will be the output link connected to the generator. Based on Equation (2), the inner rotor obtains the highest rotational speed, as shown in Equation (3):  
\[\omega_{i}=\frac{N_{c}}{p_{i}}\cdot\omega_{c}-\frac{p_{o}}{p_{i}}\cdot\omega_{ o}. \tag{3}\]  
In this situation, the CMGT is a speed multiplier with the highest speed ratio, defined as the output shaft speed ratio to the input shaft speed. When the two blades rotate with identical rotational speed, but in the opposite direction for a counter-rotating wind turbine, the speed ratio of the CMGT is equal to \((N_{c}+p_{o})/p_{i}\). The detailed design specifications of the CMGT are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Items** & **Values** \\ \hline
1. & Available space & \(140\times 140\times 30\) mm\({}^{3}\) \\
2. & Speed ratio 1  & 12 \\
3. & Maximum transmitted torque & 48 Nm \\
4. & Permanent magnet material & NdFeB 35 \\
5. & Pole-pieces material & Steel 1008 \\
6. & Remanence of permanent magnet & 1.18 T \\
7. & Total Weight & Less than 6 kg \\ \hline \end{tabular}
\end{table}
Table 1: Design specifications of the CMGT.  
Figure 2: Design process of CMGT used for SWT.
## 3 Results
### Compared with Other Methods
#### 3.2.1 Positioning Error  
The positioning error results of various feature point mismatch removal methods are shown in Table 2. From the experimental results, it can be seen that in easy scenarios such as \(MH01-MH03\), \(V101-V102\), and \(V201-V202\), the RANSAC method can effectively eliminate mismatched points and achieve relatively high positioning accuracy. However, in scenes such as \(MH04-MH05\) in a dark environment, \(V103\) with varying illumination, and \(V203\) with weak texture and poor image quality, RANSAC performs poorly, with an accuracy of only 0.125 m for \(MH04-MH05\), and only 0.161 m for \(V203\). Our proposed method further improves the accuracy under the influence of illumination changes and image blurring by combining the RANSAC method. The reverse optical flow exhibits an upper middle level in various scenes. Compared to the RANSAC method used by VINS Mono, our proposed method has improved the positioning effect in various scenarios, especially in the light changes of \(V103\) and fuzzy weak texture scenarios of \(V203\). It is indicating that the method in this paper has better accuracy and robustness for feature point mismatch removal.
## 3 Results and Discussion  
### Simulations  
To test the capability of the system in identifying the presence of a perturbation in different conditions, we performed a tomographic reconstruction on simulations changing, one parameter at a time. The reference system and the optodes scheme is depicted in Figure 2.  
We studied three different variables, that are (1) real \(\Delta\mu_{a}\), (2) radial distance (along \(y\) axis) from the injection source, and (3) depth along the \(z\) axis.