esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Technologies and the Position of the Present Research
### Technologies for Avoiding Contention in Multi-Access to the Transmission Medium  
#### 2.2.1 CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance)  
CSMA/CA is a collision avoidance protocol adopted in a wireless communication specification, IEEE 802.11 [9]. When a terminal senses that other terminals are not communicating, it begins to communicate after a waiting time consisting of the IFS (interframe space) and a random back off time. The IFS can be adjusted based on the processing performance of the devices involved and the priority of the particular message. There are different types of IFS: SIFS (short interframe space), which is applied to high-priority messages, and DIFS (DFC interframe space), which is used to send ordinary data. The value of SIFS is smaller than that of DIFS. CSMA/CA avoids a collision by allowing a terminal to initiate communication after an elapse of the back off time. This can be used in both access point mode and ad hoc mode communication. However, the propagation delay is so long in ultrasonic communication that carrier sensing often fails to detect the starts of communication by other terminals. This means that many terminals (known as hidden terminals) go unnoticed, resulting in channel contention and packet loss.
# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## Appendix A
### Razor Network  
Razor Network is an oracle service inspired by oracle protocols such as Augur and Kleros. It is specifically made to be resistant to social attacks, such as collusion, takeover, griefing, bribing, etc. The Razor oracle is made of stakers who process queries and provide the results to the client. Stakers are those who deposit Razor Token in the platform as a stake. The job manager then orders the queries in relation to the paid fees and delays (not discard) those with lower fees.  
Any application can be a client for the Razor Network, as it is a permissionless platform; however, only one query and feed can be executed at a time. Since working in the background, the final user of the DeFi platform is not aware when the Razor oracle is being used. Depending on the request made by the client, the oracle may perform an automated round or a manual round. The automated round resolves relatively quickly and, the staker only submits an URL that provides the required data. Since the URL may be unreachable or fails to load, the required stake, called \"validity bond\", is also lower than the amount required for the manual round.  
On the other hand, the manual round requires more fees, and the stakers manually provide the feed. The manual round may take few days to resolve. In both cases, the result can be disputed, but again, the dispute is quicker for the automated round. The Razor Network employs a Commit/Reveal scheme in which stakers encrypt their reply until it is received by the network. That way, other stakers or oracles cannot copy the answers. Unlike Tellor, there is no minimum stake to participate in the network, and the contract randomly chose validators among the stakers. However, chances, as well as rewards, are proportional to the staked amount. If a result is successfully disputed, 100% of the stake is slashed, and of this amount, 50% is burned, while the other half is distributed among the disputers. Proposing a faulty data feed led to the severest punishment, but other behavior, such as failure to commit a result or delay in providing a feed, also results in a slash of 5% and 1%, respectively. Every oracle round takes the name of \"Epoch\". Figure 10 describes the steps followed in the Razor Network epoch. Finally, it is interesting how the Razor Network exploits economic incentives to prevent social attacks. If the value of the data feeds is worth more than any incentive mechanism offered by the platform, the oracle may deliberately provide false data for its self-interest [38]. For that reason, the Razor Network only works for applications whose total market cap equals half of the total staked amount [34].
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 3 Overview of Spectrum Sensing and Adopted Deep Learning Algorithms
### One-Dimensional Convolutional Neural Network  
Convolutional neural networks are a type of DNN used to process data in multiple arrays, such as images. The key feature is the use of convolutional layers, which apply a set of filters to the input data, extracting relevant features and patterns. This allows imagespecific features to be incorporated into the network design, improving the performance for image-based tasks and reducing the configuration parameters. CNNs are based on four key concepts that exploit the properties of natural signals: local connections, shared weights, pooling and multiple layers [30]. CNNs are renowned for their significant computational demands, necessitating specialized hardware, particularly during the learning process. As a result, real-time applications on mobile devices and devices with limited power and memory resources should refrain from employing 2 DCNNs. Furthermore, achieving a satisfactory level of generalization in deep CNNs requires a sufficiently large dataset for training. However, in many real-world applications involving 1D signals, acquiring labeled data can be challenging, making this approach less feasible. A modified version of a CNN, called 1 DCNN, has recently been developed. Studies have shown that for some applications, 1 DCNNs are advantageous and therefore preferable to their 2D counterparts for 1D signal processing [32; 55]. In particular, 1 DCNNs use three main types of layers: convolutional layers, pooling layers and fully connected layers. Figure 1 shows a streamlined 1 DCNN architecture for classification.  
The initial step in 1D filter kernels involves a series of convolutions, whose sum passes through the activation function, followed by a pooling operation that reduces the output of each convolutional layer. This sets apart 1 DCNNs from 2 DCNNs, where 2D matrices are used for kernels and feature maps. In the subsequent stage, the CNN layers work on the raw 1D data and \"learn to extract\" the features required for classification, which is accomplished by the fully connected (FC) layers. They establish connections between each neuron and the neurons in the preceding layer, enabling intricate interactions and the integration of information from various features. FC layers perform a weighted sum of the inputs, followed by an activation function, resulting in the final output. As a result, the process of feature extraction and classification is combined into a single backpropagation process, allowing for optimization to enhance the classification accuracy. This integration is a key advantage of 1 DCNNs. Additionally, 1 DCNNs can minimize the computational complexity compared to 2 DCNNs since the primary operation involves a sequence of 1D convolutions, which inherently have lower dimensionality. A 1 DCNN involves simpler filter operations with fewer parameters and lower memory usage.
## 4 Experimental Validation and Result Analysis  
### Processing of Experimental Data  
The superiority of the proposed method is demonstrated on the IEEE PHM2012 dataset. The experimental platform is shown in Figure 7. A sampling frequency of 25.6 KHz is adopted; that is, 2560 samples are collected every 10 s. The dataset can be found in the literature [54]; in this paper, the training and test sets are shown in Table 2. Horizontal vibration signals contain more degradation information, and therefore they are used in the conducted experiments.  
Figure 7: Data acquisition platform.
## 3 Materials and Methods
### Feature Fusion and Classification
#### 3.7.2 Classification Algorithms  
After feature extraction and dimensionality reduction, the fused features were fed into three different machine learning classifiers: Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). These classifiers were chosen based on their effectiveness in handling high-dimensional feature spaces and their proven performance in agricultural image classification tasks.  
SVM is a widely used supervised learning algorithm that finds an optimal hyperplane that separates data points from different classes [29]. Given a training dataset \(\{(x_{i},y_{i})\}_{i=1}^{N}\), where \(x_{i}\) represents feature vectors and \(y_{i}\in\{-1,1\}\) denotes class labels, the decision function of an SVM is given by:  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}K(x_{i},x)+b \tag{5}\]  
where \(K(x_{i},x)\) is the kernel function and \(\alpha_{i}\) are the Lagrange multipliers. The Radial Basis Function (RBF) kernel was used in this study due to its ability to handle non-linearly separable data.  
RF is an ensemble learning method that constructs multiple decision trees and aggregates their predictions to improve classification accuracy [30]. The classification decision is made based on majority voting:  
\[\hat{y}=\arg\max_{c}\sum_{t=1}^{T}I(h_{t}(x)=c) \tag{6}\]  
where \(h_{t}(x)\) represents the prediction of the \(t\)-th decision tree. RF was selected for its robustness against overfitting and ability to capture complex patterns in high-dimensional feature spaces.  
ANN consists of multiple layers of neurons, each applying a nonlinear activation function to process input features. The output of a neuron in a hidden layer is computed as:  
\[z_{i}=f\left(\sum_{j=1}^{n}w_{ij}x_{j}+b_{i}\right) \tag{7}\]  
where \(w_{ij}\) are the weights, \(x_{j}\) are the inputs, and \(b_{i}\) is the bias term. The Rectified Linear Unit (ReLU) activation function was used in hidden layers, defined as:  
\[f(x)=\max(0,x) \tag{8}\]  
A feedforward ANN with multiple hidden layers was trained using the Adam optimization algorithm and categorical cross-entropy loss function. ANN was chosen for its ability to model complex feature interactions and learn hierarchical representations.  
After training, the final classification decision for each maize leaf image was made based on the highest probability score assigned by the classifiers.
## 1 Introduction
Furthermore, a time lag was found in the convection process, and simultaneous vortices were identified that were dominated by thermocapillary and solutocapillary effects, respectively. [PERSON] and [PERSON] [43] employed numerical simulation techniques to examine the impact of buoyancy on the thermo-solutocapillary convection of non-Newtonian fluids. Their results indicated that as buoyancy increased, and the influence of surface tension on heat and mass transfer decreased. Furthermore, they observed an increase in both the average Nusselt number and Sherwood number with a rise in temperature, which was attributed to the effects of the Marangoni number and thermal Rayleigh number. In summary, temperature difference in the thermo-solutocapillary flow refers to the presence of a temperature gradient on the liquid bridge surface, driving the flow of fluid near the surface and affecting the crystal growth process. Heat loss represents the energy loss that occurs on the liquid bridge surface, which may impact the temperature and concentration fields driving the flow, leading to changes in the oscillation characteristics of the flow. The oscillation characteristics of thermo-solutocapillary flow refer to the periodic or stochastic fluctuation of temperature, concentration, velocity, and free surface deformation within the liquid bridge. These oscillations are due to the interaction between temperature-concentration convection, gravity, surface tension, and buoyancy, among other factors. However, studies on the coupled thermo-solutocapillary convection with different temperature differences and heat losses within the float zone are still scarce, particularly considering dynamic interface deformation. Numerical simulations with accuracy and stability can be difficult to achieve under these circumstances. Therefore, the purpose of this study is to investigate these issues, taking into account the effects of the dynamic free surface deformation, and offer assistance in the crystal growth process using the float zone method. Section 2 of the study introduces the physical and mathematical models of thermo solutocapillary convection and the level set method. Section 3 discusses the influence of different temperature differences on the convection structure and interface oscillation characteristics of thermo-solutocapillary flow. Subsequently, we discuss the impact of heat loss on the convection structure and oscillation characteristics of thermo-solutocapillary flow. Finally, the effects of different variables on the flow and oscillation at the liquid bridge interface are summarized.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 4 Implementation Results
### NIST Statistical Test Suite
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 FiRO-1 GaRO**} & \multicolumn{2}{c}{**2 FiRO-2 GaRO**} & \multicolumn{2}{c}{**4 FiRO-4 GaRO**} \\
**Test Name** & _p_**-Value** & **Proportion** & _p_**-Value** & **Proportion** & _p_**-Value** & **Proportion** \\ \hline Frequency & 0.794626 & 0.996 & 0.739918 & 0.996 & 0.034455 & 0.993 \\ BlockFrequency & 0.103676 & 1 & 0.320988 & 0.993 & 0.199580 & 0.987 \\ CumulativeSums * & 0.880335 & 0.996 & 0.701879 & 0.990 & 0.355569 & 0.993 \\ Runs & 0.971267 & 0.981 & 0.695458 & 0.987 & 0.109597 & 0.990 \\ LongestRun & 0.365877 & 0.981 & 0.990440 & 0.987 & 0.708280 & 0.987 \\ Rank & 0.490727 & 0.990 & 0.011333 & 0.993 & 0.081137 & 0.993 \\ FFT & 0.880335 & 0.981 & 0.235285 & 0.990 & 0.235285 & 0.987 \\ NonOverlappingTemplate * & 0.969045 & 0.978 & 0.177264 & 0.978 & 0.392456 & 0.978 \\ OverlappingTemplate & 0.746157 & 0.990 & 0.250878 & 0.981 & 0.460664 & 0.987 \\ Universal & 0.288780 & 0.978 & 0.048716 & 0.975 & 0.414525 & 0.993 \\ ApproximateEntropy & 0.521600 & 0.993 & 0.371101 & 0.993 & 0.213309 & 0.990 \\ RandomExcursions * & 0.120558 & 0.985 & 0.825505 & 0.980 & 0.247472 & 0.985 \\ RandomExcursions Variant * & 0.673507 & 0.980 & 0.334538 & 0.990 & 0.144153 & 0.985 \\ Serial * & 0.546791 & 0.990 & 0.297739 & 0.990 & 0.202944 & 0.981 \\ LinearComplexity & 0.502986 & 0.984 & 0.148968 & 0.987 & 0.159799 & 0.990 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: NIST 800.22 Statistical Test Result with 100 MHz of Sampling Rate. * Worst case reported for tests with multiple outcomes.
## 2 Results and Discussion
### Volatile Profiling

Table 3: Volatile compounds that showed significant correlations to the five quality proxies (Pearson’s product correlation coefficient r with df = 25, \(p<0.05\)). lower eucalypttol levels for older vintages (up to 10 years old). This latter trend is suggested by our findings of a negative correlation to wine age for eucalypttol (C40). Three C6 compounds, namely 1-hexanol (C24), ethyl hexanoate (C35) and hexyl acetate (C36) all show a positive correlation with vintage (Table 3). For seven volatiles, namely, 1-butanol (C8), acetoin (C9), furfurral (C20), ethyl-3-methyl butanoate (C23), p-cymene (C38), isopentyl hexanoate (C56), and ethyl hexadecanoate (C64), significant regional differences were found (Tables 3 and 4). However, ester content is heavily influenced during winemaking by the starting grape material (e.g., sugar levels, nitrogen content) and yeast strains [47,53]. Furfural is an aging-related compound [47], while p-cymene was reported after heated acid hydrolysis of grape-derived precursors [50]. It seems that these correlations are more a result of the different winemaking regimes exercised by the different wineries in the different regions. Only if all other parameters (winemaking, grape-growing, storage, _etc._) are properly controlled could differences in volatile composition be attributed to different geographical regions.
## 2 Photogrammetric Processing Chain
### PhotoScan Overview  
PhotoScan(r)Professional (version 1.2.3) is a commercial product developed by AgiSoft(r). The procedure for deriving orthophotographs and the DEM follows the workflow presented in Figure 2. Some parameters can be adjusted at each of these steps:  
1. Image orientation by bundle adjustment. Homologous keypoints are detected and matched on overlapping photographs so as to compute the external camera parameters for each picture. The \"High\" accuracy parameter is selected (the software works with original size photos) to obtain a more accurate estimation of camera exterior orientation. The number of tie points for every image can be limited to optimize performance. The default value of this parameter (4000) is kept. Tie point accuracy depends on the scale at which they were detected. Camera calibration parameters are refined, including GCPs (ground and image) positions and modelling the distortion of the lens with [PERSON]'s distortion model [30].
2. Creation of the dense point cloud by dense image matching using the estimated camera external and internal parameters. The quality of the reconstruction is set to \"High\" to obtain a more detailed and accurate geometry.
3. DEM computation by rasterizing the dense point cloud data on a regular grid.
4. Orthophotograph generation based on DEM data.  
The intermediate results can be checked and saved at each step. At the end of the process, the DEM and the orthophotograph are exported in GeoTiff format, without any additional post-processing (optimization, filtering, etc.). The software is user-friendly, but the adjustment of parameters is only limited to pre-defined values. Nevertheless, with the versions upgrading, more parameters are adjustable and more detailed quality reports are available.
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 4 Theoretical Studies of Combined Influence of Kolmogorov and Non-Kolmogorov Turbulence
To summarize, it is an indisputable fact that the atmospheric turbulence in Earth's atmosphere consists of Kolmogorov turbulence and non-Kolmogorov turbulence. It is a scientific problem used to study the effect of combined atmospheric turbulence on optical wave propagation according to the actual situation of atmospheric turbulence. It is an important area for the future investigations of science.
## 1 Introduction
In work [21], the authors analyzed the integration of advanced technologies such as MR, robot vision, intuitive and immersive teleoperation, and artificial intelligence (AI) to build an interactive telerobotic welding system. This paradigm enables efficient human-centered collaboration between remote welding platforms and operators through multi-channel communication. A teleoperated wall-climbing robotic welding system was developed to demonstrate the application of various technologies in an innovative robotic interaction system to best achieve natural human-robot interaction. However, the mobile wall-climbing welding robot presented in this system has a simple structure and does not have a flexible robot manipulator to mimic welders' human-level manipulation and make dexterous welding adjustments. Natural human movement signals were not used to improve the system intuitiveness and control the robot for tele-welding tasks.
The algorithms presented in [29; 30; 31] has no wait and data holding mechanism, and the data are dropped when an MS is not in the coverage of the node. To address the above-mentioned limitations of the existing algorithms, this paper presents an algorithm with sink mobility exploiting cooperation for UA-WSNs. First, the network is partitioned into four equal squares. Four MSs move continuously in these squares to report data from the nodes. The direct link is established for data exchanging between an MS and a node in the case of MS presence in the coverage area of the node. Otherwise, the node holds data and waits for the MS to reach it. For the coverage of all of the network, a triangular path is determined for the MS. Furthermore, the MS not only reports data from specific regions' nodes, but also, it can collect data from any region that it covers. The data are determined for acceptance at the MS if they hold the specific criteria of the data correction. Otherwise, the MS demands the retransmission of the packet. Upon the demand, the relay cooperates with the MS for successful data delivery. Our contribution is summarized as:* The proposed SiM-RPO algorithm tends to perform persistent operation of the network and achieves high throughput with minimum energy cost and latency. For the sake of MSs' path determination and their collision avoidance with each other, the network is partitioned into four regions having equal area. The MSs move in their own regions on the determined path to pick the information from the nodes directly. The path is determined for all the MSs in such a way to so as cover all the network and pick the data even from a single node. * Furthermore, to reduce the latency and energy expenditure, the data are delivered to the MS directly instead of multi-hoping. The data are delivered directly to the MS when a node finds the MS in its coverage area. Otherwise, the node waits for the MS and holds the data. * In SiM-RPO, delivery over a single path does not ensure successful transfer of the information. Therefore, the addition of cooperation to the SiM-RPO, titled CoSiM-RPO, boosts the reliability and data reception probability. In CoSiM-RPO, the data are analyzed for errors at the MS for satisfied data reception. The data are considered correct if they hold the specified criteria of the BER. Otherwise, the MS demands data retransmission from the relay. The data is transferred again by the relay to the MS. The arrangement of the paper is as follows. The main idea of the existing approaches is described in Section 2. The proposed algorithms SiM-RPO and CoSiM-RPO are presented with the necessary detail in Sections 4 and 5, respectively. In Section 6, the MATLAB simulation of both the proposed schemes compared with the three existing algorithms is given with the full description. The last section presents the conclusions and future insights.
## 3 Proposed System Model  
Simulation models for 5G radio channels have been developed through a body of research to model the amplitude and phase variation of the radio signal. They are used to determine the performance of different aspects of the system, such as the signal processing algorithms that contribute to the improvement of the radio link. These models are usually rather complicated and must be used appropriately with a high level of skill. The proposed simulation model can efficiently produce realistic signal strength metrics using well-known radio propagation models to simplify this task. Data science communities and researchers can easily use this model to develop large datasets for the training and testing of RBS detection procedures. Furthermore, to the best of our knowledge, there are no simulations of this type that do not require deep radio signal expertise. This is beneficial for the broader 5G security community.  
The literature review above indicates the importance of detecting RBS activity to prevent the unauthorised capture of a UE channel. The work presented here is therefore motivated by the need to generate synthetic data that are reasonably representative of the measurement report data that would be observed by a vehicle driving through a 5G coverage area serviced by several base stations. By generating realistic synthetic data, it is possible to quickly create a large dataset that can be used to investigate new mechanisms to prevent handover to an RBS.