esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### MRI/fMRI-Based Hyperscanning Research
#### Motor Synchronization  
MRI and fMRI were the least commonly used methods for investigating motor synchronization. The first study investigated how attention is shared between pairs of subjects [164]. This study conducted three experiments which used an eye blink synchronization experimental task to measure shared attention between a pair of participants. Results from this experiment suggest that shared attention is represented and retained by pair-specific neural synchronization that cannot be reduced to the individual level. The second study [165] evaluated the neural substrates of cooperation using a grip-force synchronization task to evaluate the neural substrates of cooperation during a joint force-production task. Participants were positioned side by side in two separate fMRI machines to measure the neural synchrony while participants attempt to synchronize their grip strength force.  
_Setting, Orientation and Group Size._ Both studies were conducted in a lab setting and organized groups of participants into pairs. [PERSON] et al. [157] evaluated how information is shared and retained between two people by asking participants to interact face-to-face. This study featured an equal number of male and female subjects. On the other hand, [PERSON] et al. [165] looked into the neural substrates of cooperation during a joint force-production task, and had participants engage side-by-side. This study only included female subjects.
## 4 Results  
### Spatiotemporal Distribution of Downscaled SIF  
The RF-based method was employed to obtain the downscaled SIF data with a resolution of 1 km in Henan Province. Figure 3 provides a detailed illustration of the specific results for May 2018, serving as an example.  
In Figure 3, the SIF data at different resolutions were compared (GOSIF at 0.05 degrees and downscaled SIF at 1 km). High and low SIF values (W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) were represented by red and green colors, respectively. Relatively high SIF values were predominantly observed in forest areas, followed by grassland and farmland. The comparison figures revealed that the values of the downscaled SIF result had a similar trend to the original GOSIF data. However, the 1 km spatial resolution SIF result could provide more detailed ground information than the 0.05-degree resolution data.
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
## 2 DC Microgrid Structure with Integrated V2G  
The microgrid, which plays an important role in the production of a smart grid, which can produce the integrated operation of multiple power sources and loads, can coordinately control the public grid and can meet users' requirements for power quality, power reliability, and safety.  
A microgrid based on a DC common bus is considered due to its superior current control because there are no negative and zero sequence currents [30]. The design of an EV charging station including V2G is based on DC microgrid. As shown in Figure 1, the DC microgrid is composed of four parts: PV sources, a storage system, a public grid connection, and EVs.  
DC microgrid power flow is shown in Figure 2: \"PV\" represents PV sources, \"PG\" represents the public grid, \"S\" represents the storage system, \"EV\" represents EVs. The real-time power priority is shown by the arrow. For example, PV sources firstly support EVs, secondly charge storage, then thirdly inject into the public grid.  
According to the \"peak\" periods and \"valley\" periods of the public grid, the energy flow of the DC microgrid can be divided into two cases. During the \"valley\" periods of the public grid, the electricity produced by PV sources is first used to charge EVs. Storage is the second-ranked energy source for charging EVs; in addition, it is also used to absorb excess energy produced by PV sources. The public grid is the third-ranked energy source for charging EVs, and it can buy excessive energy from PV sources. During the \"peak\" periods of the public grid, the order of charging EVs is the same as the \"valley\" periods of the public grid, and the difference is that V2G is achievable during this period. If the energy produced by PV sources is still surplus after being supplied to the EVs, to adjust the peak-valley difference of the public grid, PV sources can inject excess energy into the public grid. Besides, the energy released by EVs can be injected into the public grid. Considering the energy cost, the order of injecting energy into the public grid is PV sources firstly, then EVs batteries.  
During the period of V2G, in order to ensure the power balance of the public grid, the power injected into the public grid, namely \(p_{inject\_G}\), should be limited. It may be assumed that the maximum power injected into the public grid cannot exceed the estimated PV power under standard test conditions (STC), namely \(P_{PV\_STC}\), which is shown in Equation (1), where \(p_{PV}\) is PV power that can  
Figure 1: DC microgrid with integrated vehicle-to-grid (V2G). PV: photovoltaic sources, EV: electric vehicles.  
Figure 2: DC microgrid power flow. PG: public grid, S: storage system.  
be injected into the public grid, \(p_{EV\_total}\) is the power that EVs can output. \(c_{PV}\) is the coefficient of PV power, and \(c_{EV}\) is the coefficient of the energy that EVs can release.  
\[p_{inject\_G}=P_{PV\_STC}=c_{PV}p_{PV}+c_{EV}p_{EV\_total}\,\ \text{with}\ c_{PV},c_{EV}\in[0,1] \tag{1}\]
## Abbreviations  
The following abbreviations are used in this manuscript:  
GA Genetic Algorithm  
RNN Recurrent Neural Network  
KNN K-Nearest Neighbour  
MAE Mean Absolute Error  
RMSE Root Mean Square Error  
MSE Mean Square Error  
rMAE relative Mean Square Error  
rRMSE relative Root Mean Square Error  
QRA Quantile Regression Averaging  
DM Diebold-Mariano  
RES Renewable Energy Source  
PV Photovoltaic  
NWP Numerical Weather Prediction  
ANN Artificial Neural Network  
SVM Support Vector Machine  
CARDS Combined Autoregressive Furthermore, Dynamic System  
PSO Particle Swarm Optimisation  
GAM Generalised Additive Model  
LASSO Least Absolute Shrinkakage Furthermore, Selection Operator  
NF Neuro-Fuzzy  
ARMA Autoregressive Moving Averaging  
NN Neural Network  
GHI Global Horizontal Irradiance  
SUI Solar Utility Index  
AAKR Auto-Associative Kernel Regression  
MLP Multilayer Perceptron  
PI Prediction Interval  
PINC Prediction Interval With Nominal Confidence  
PIW Prediction Interval Width  
PICP Prediction Interval Coverage Probability  
PINAW Prediction Interval Normalised Average Width  
GCV Generalised Cross Validation  
MD Murphy Diagram
## 6 Open Challenges, Future Prospects and Research Directions
### FSO Scalability and Reliability for 5G and Beyond Networks
#### 6.1.3 Resource Management  
Effective resource management is essential for the scalability and reliability of FSO systems. Dynamic resource allocation is an important aspect of resource management. In this regard, implementing AMC schemes can optimize the use of available bandwidth and maintain link quality under varying atmospheric conditions. Likewise, dynamic load balancing across multiple FSO links can enhance network performance and prevent congestion.  
Utilizing WDM technology can significantly increase the capacity of FSO links by transmitting multiple data streams on different wavelengths over a single optical path. Efficient channel allocation strategies can minimize interference and maximize the utilization of available optical spectrum.  
Furthermore, priority-based traffic management can facilitate network performance. For example, implementing QoS mechanisms that prioritize critical traffic can ensure reliable performance for latency-sensitive applications, such as real-time video and Internet of Things (IoT) services. Similarly, advanced error correction techniques can alleviate the impact of signal deterioration caused by atmospheric conditions, enhancing the reliability of FSO links.
## 6 Conclusions  
Relationships between the impact resistance and tensile properties PVA-FRCM were discussed by analyzing the experimental data of the drop-weight test and the uniaxial tension test performed in this study. The conclusions are as follows:  
(1) When the disc specimen with more PVA fibers fails under the impact hammer dropping, the fibers are pulled out, the diameter of impact crater is larger, more debris appears, and more energy is consumed. The blow number increases with the PVA fiber volume content for the disc specimens. And this increase of the blow number for the ultimate failure is faster than the one for the first visible crack.  
(2) In the uniaxial tension test, the slab specimen with more PVA fibers has better toughness performance and more cracks. The number of small curve segments in the tension stress-strain curves is roughly equal to the number of cracks. \(\sigma_{\rm c}\) is decisive on the first crack of specimens. \(\varepsilon_{\rm f}\) is more appropriate to describe the failure process of specimens.  
(3) Based on the analysis on the relationship between the impact resistance and tensile properties, the blow number for the first visible crack N\({}_{\rm c}\) depends largely on \(\sigma_{\rm c}\) of slab specimen and increases obviously with an increase of \(\sigma_{\rm c}\). The crater diameter and blow number at ultimate failure moment N\({}_{\rm f}\) increase with an increase of \(\varepsilon_{\rm f}\) of slab specimen. When both \(\sigma_{\rm c}\) and \(\varepsilon_{\rm f}\) of PVA-FRCM are larger, much more blows are needed to cause both the first visible crack and ultimate failure.  
(4) According to the presented relationships between impact resistance and tensile properties of PVA-FRCM in this study, impact resistance properties of PVA-FRCM could be conventionally evaluated or predesigned through its tensile properties without conducting extra dynamic test.  
[PERSON] designed and performed the experimental work; [PERSON] made positive suggestions about the work; [PERSON] wrote the original draft; [PERSON] supervised the experiments and interpreted the data; [PERSON] performed the experimental work.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51768051 and 51968056, the Inner Mongolia Natural Science Foundation, grant number 2017 MS0505, and the Inner Mongolia Science and Technology Innovation Guidance Project, grant number KCBJ2018016.  
Figure 14: Relation between \(N_{\rm f}\) and \(\varepsilon_{\rm f}\).  
**Acknowledgments:** The authors are grateful to [PERSON] and [PERSON] for their help in this investigation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Examples of Seismogram Combined Representation by Bigraph and \(R_{Q}\)-Envelope  
The bigraph and the R\({}_{Q}\)-envelope create a complementary representation of a seismogram. In the following, a few examples are given and briefly interpreted. All examples are taken from the PEER seismic database except the last one.
# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 3 Research Methodology  
This work constitutes applied research, focusing on the development of a manufacturing system prototype that integrates modeling and simulation to explore the practical application of the DT concept in production process representation. Additionally, the study adopts an exploratory approach, presenting an in-depth examination of DT architecture, simulation perspectives, and its application in manufacturing processes. This foundation is built on a review of the literature, aimed at establishing meaningful connections within this domain.  
The research included a case study conducted at the laboratory of the School of Mechanical Engineering at Unicamp's main campus. The laboratory provided essential support for this investigation, offering physical resources and access to its technical team to assist in developing the prototype used to test the DT concept.  
This study was organized into several stages, ranging from the theoretical phase to the structuring of the case study, which included the development and testing of the prototype according to the steps below:  
* Step 1: This phase involved defining DT concepts, understanding simulation applications, and positioning DT within the context of process simulation;
* Step 2: Selection of the simulation tool for the project. The simulation software Plant Simulation(r) version 16 by SIEMENS was used in this stage. Additionally, the programming language SimTalk was employed, as it is the standard language used within this software environment;
* Step 3: This stage focuses on comprehending the simulation environment and designing a real-time data exchange model between the manufacturing system and the simulation model;
* Stage 4: A simulation model was created, along with a mechanism capable of detecting failures in the production system and initiating optimization routines without human intervention;
* Step 5: Development of both the physical and virtual prototypes of the manufacturing system and establishment of the connection between them. Key activities included automating system operations and structuring computer simulations with self-reprogramming capabilities;
* Step 6: The final stage involved operating the prototype, monitoring information flows between the physical and virtual environments, and validating the execution of the reprogramming model.  
This work was developed based on the simplification of a production system. However, since it is a prototype, a simple operating mechanism was chosen, controlling only the time, number of parts and speed variables. Additionally, the speed variable is adjusted to regulate the system's work rhythm. Therefore, this study is limited to modifying a single operating variable.
###### Abstract  
In this paper, an aqueous solution of xanthan gum (XG) at a weight fraction as high as 0.2% was elected as the non-Newtonian base liquid, the multi-walled carbon nanotubes (MWCNTs) dispersed into non-Newtonian XG aqueous at different weight factions of MWCNTs was prepared. Convection heat transfer of non-Newtonian nanofluids in the shell side of helical baffled heat exchanger combined with elliptic tubes has been investigated experimentally and numerically using single-phase flow model. Results showed that the enhancement of the convective heat transfer coefficient increases with an increase in the Reynolds number and the nanoparticle concentration. For nanofluids with 0.2 wt %, 0.5 wt % and 1.0 wt % MWCNTs, the Nusselt number, respectively, increases by 11%, 21% and 35% on average at the same Reynolds number, while the comprehensive thermal performance factors are 3%-5%, 15%-17% and 24%-26% higher than that of base fluid at the same volume rate. A remarkable heat transfer enhancement can be obtained by adding MWCNTs into XG aqueous solution based on thermal resistance analysis. Correlations have been suggested for the shell-side Nusselt number and friction factor of non-Newtonian nanofluids in the helical baffled heat exchanger with elliptic tubes. Good agreements existed between corrections and experimental data.  
non-Newtonian fluid; nanofluids; elliptic tube; numerical simulation; heat transfer +
Footnote †: journal: Applied Sciences
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 2 Background
### TrnGs  
TRNGs are used in a wide variety of applications ranging from cryptographic protocols [14] to Monte Carlo simulations [15]. From a security point of view, TRNGs are particularly important because they are typically used to add freshness to cryptographic algorithms. For that reason, the increasing necessity of embedded hardware-based random number sources has spawned a proliferation of embedded TRNGs solutions such as [3; 4]. TRNGs that consist of sampling jittery clocks stand out among the different existing proposals for their flexibility and straightforward implementation.  
Jitter can be defined as the deviation from true periodicity of a presumably periodic signal. This deviation can come from deterministic sources or random jitter sources as thermal noise. The entropy extraction principle of this kind of generators is depicted in Figure 1. Several high-frequency oscillators are sampled by a reference clock. If at least one of these oscillators is sampled in the jitter zone, the addition of all the samples will be random. A higher number of oscillators will increase the possibilities of sampling at least one in the jitter zone.  
In this work, we have selected two representative TRNGs based on jitter sampling that use two different kinds of high-frequency oscillators. On the one hand, [PERSON] et al. proposal [12] uses Ring Oscillators (ROs) as high-frequency clocks. A RO is a circuit composed of an odd number of inverters in a ring, whose output oscillates. In Figure 2a a typical RO scheme is depicted. On the other hand, the Cherkaoui TRNG [13] generates the high-frequency signals using a Self-Timed-Ring (STR). An STR is a n-stage micropipelined architecture that implements a handshake protocol that guarantees the distribution of events through the different stages. Each stage consists of a Muller gate and an inverter. Figure 2b presents a STR architecture. We urge the reader to consult the original works [12; 13], for further details about the implementation specifications and stochastic models linked to each TRNG.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection
#### 4.2.1 \(Q\)-Squiare  
\(Q\)-SQUARE is a \(Q\)-learning-based UAV flight planning algorithm that improves the quality of experience (QoE) of video users proposed in [52]. A macro BS is considered to consist of several user clusters that require video streaming. Multiple UBSs are hovering over multiple clusters with prefetched or on-demand data depending on the QoE demand of the clusters without interfering with each other. The flying path is determined by the \(Q\)-learning algorithm, where the location of the cluster with a high QoE requirement, residual energy of the UBS, and flying time are considered. Paths to multiple recharge points are also considered for recharging the UBSs. While hovering over the cluster if the energy level approaches a certain threshold, the UBS will fly to the charging point to charge and comeback. UBS flies back to the macro BS if more video data need to be fetched. Here, the agent UBS follows the \(\epsilon\)-greedy policy to determine the flight trajectory [52].
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Laser Transmitter Module Feasibility Test  
In order to simulate the dusty and dark tunneling environment, this test was conducted on a foggy night as shown in Figure 22.  
The test sketch map is shown in Figure 23.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Position of** & \multicolumn{2}{c}{**System Detection Results**} & \multicolumn{2}{c}{**Position of**} & \multicolumn{2}{c}{**System Detection Results**} \\ \cline{2-5}
**Micro Motion** & & 1 \(\upmu\)**\(\upmu\)**\(\upmu\)**\(\upmu\)**\(\upmu\)**\(\upmu  
As shown in Figure 23, the laser transmitter module was located on a precision swivel table that was fixed on the micro motion platform. Two image acquisition modules and two reference monitoring image acquisition modules were placed on both sides of the laser transmitter module respectively, an _Ls_ represents the distance between the laser transmitter module and the image acquisition modules, \(\Delta\alpha\) represents the horizontal rotation angle of the laser transmitter module, and \(\Delta x\) represents the displacement along the \(x\)-axis of the laser transmitter module.  
In this test, the two reference monitoring modules and the two image acquisition modules were fixed, and the laser beam reference was shifted 12 mm and rotated 36\({}^{\prime\prime}\). According to Equation (9), the detected displacement can be compensated. The test data are shown in Table 5.  
According to Table 5, this compensation method can effectively reduce the error caused by small sudden vibrations of the laser transmitter module.
# The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
## 4 Results and Discussion
### Network Conclusion  
The previous experiments found a network architecture with the most efficient settings. We next compared our results with the networks with other researches based on raw signals or spectrograms without augmentations, as shown in Table 11.  
We summarized our experiment results as following:  
* Our proposed method is an end to end system achieving 81.55% of accuracy in ESC50.
* Our proposed 1D-2D network could properly extract features from raw audio signal, compared with the old works.
* Our proposed ParallelNet could efficiently raising the performance with multiple types of input features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aug Type** & **Original with Extra** & **Overlap with Raw** \\ \hline Avg. & 81.40\% & 81.35\% \\ Std. & 3.04\% & 3.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Test result of different kinds of models with open-datasets.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-4}  & & **ESC50** & **ESC10** \\ \hline Piczak’s CNN [15] & log-mel spectrogram & 64.5\% & 81.0\% \\ \hline m18 [19] & raw audio signal & 68.5\% [22] & 81.8\% [22] \\ \hline \multirow{2}{*}{EnvNet [21] *} & raw audio signal & \multirow{2}{*}{70.8\%} & \multirow{2}{*}{87.2\%} \\  & & \(\oplus\) & \\  & log-mel spectrogram & & \\ \hline SoundNet (5 layers) [18] & raw audio signal & 65.0\% & 82.3\% \\ \hline AlexNet [36] & spectrogram & 69\% & 86\% \\ \hline GoogLeNet [36] & spectrogram & 73\% & 91\% \\ \hline \multirow{2}{*}{EnvNet with BC [22] *} & raw audio signal & \multirow{2}{*}{75.9\%} & \multirow{2}{*}{88.7\%} \\  & & \(\oplus\) & \\  & log-mel spectrogram & & \\ \hline EnvNet–v2 [22] & raw audio signal & 74.4\% & 85.8\% \\ \hline
1D-2D network (ours) & raw audio signal & \multirow{2}{*}{73.55\%} & \multirow{2}{*}{90.00\%} \\  & & \(\delta\)log-mel spectrogram & & \\ \hline Human accuracy [30] & & & \\ \hline \hline \end{tabular}  
* Result combined before softmax.  
\end{table}
Table 10: Test accuracy of augments applied in full pre-trained network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aug Type** & **Original with Extra** & **Overlap with Raw** & **Overlap with PS/TS** \\ \hline Avg. & 74.05\% & 76.95\% & 75.20\% \\ Std. & 3.55\% & 3.59\% & 2.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test accuracy of augments applied in partial pre-pertained network.