esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comparative Analysis of Sequential Performance  
Here, we analyse the performance of the adaptive scheme compared to the backward Euler algorithm with uniform time step \(\tau=\tau_{0}\). Figure 2 shows the computation times of the sequential algorithms with the LU factorization solver. We see that the adaptive time stepping scheme has a clear advantage in the case of a smaller time step \(\tau_{0}=4.8828125\times 10^{-7}\). This experimental result is fully consistent with the theoretical conclusion in Section 4 that the adaptive method has better computational complexity when \(m<N\). The number of steps generated by the adaptive algorithm is presented in Figure 3. We observe that although the minimum step size is reduced by two orders of magnitude, the amount of adaptive time steps is increased by only a factor of approximately two. In other words, the discussed adaptive strategy leads to a logarithmic increase in the total number of time steps with respect to \(\tau_{0}\), that is, \(m=O(|\log\tau_{0}|)\). This explains the much better computational times of the adaptive method when \(\tau_{0}\) is small enough.  
Let us recall that the adaptive forward-backward Euler method developed is explicit. We do not need inner iterations to fit the error estimator. Avoiding inner iterations is an  
Figure 3: Number of adaptive steps used for both minimum step sizes.  
Figure 2: Sequential computational times of the uniform and adaptive schemes with LU factorization from the MKL package.  
important sought-after property of the algorithm, taking into account the non-locality of the fractional diffusion and the fact that the stiffness matrix is dense.  
The following Figure 4 plays a key role in the experimental proof-of-concept of the approach proposed in this paper. Here, we present a comparison of the computational times of the adaptive algorithm with LU-based and HSS-based solvers. For both variants of the minimum time step \(\tau_{0}\), hierarchical compression performs much better. It should be noted that there is little difference between the results with \(\tau_{0}=1.5625\times 10^{-5}\) and \(\tau_{0}=4.8828125\times 10^{-7}\). The reason for this is that most of the steps are relatively larger, except for the locally refined ones around the jump points of \(f(x,t)\). Figure 5 illustrates this behaviour of the adaptive step sizes over the entire time interval. We also see the monotonically increasing time steps in the subintervals where the right-hand side does not change. This is consistent with the evolving smoothness of the solution.
## 4 Experimental Results
### Synchronization Design  
In order to apply the audio watermarking technique in real situations, integrity information should be embedded repeatedly in the audio signal within each block unit. Each block must be further segmented to the synchronization codes part and the watermark information part. Synchronization is an effective way to accurately identify the watermark location.  
In our algorithm, we can use a certain number of consecutive frames as the synchronization codes parts by embedding a fixed watermark sequence. In the extraction process, by checking whether the extracted consecutive watermark bits are same as the synchronization codes or a certain proportion is correct, we can determine the start of a block.  
As we used the correlation between the signal and a delayed version of itself to extract the watermark, finding the exact start sample of each watermarked frame was not necessary because the correlation values \(\overline{D}\) around the start point were similar. Figure 15 shows the \(\overline{D}\) values calculated for each of the 5000 samples of \"Drama\" audio after HE-AAC v1 24 kbps compression, where the frame length was 500 samples and the watermark bits [1, 0, 1, 0, 1, 0, 1, 0, 1, 0] were embedded. As we mentioned in Section 3.2, the \(\overline{D}\) value was calculated to determine the extracted watermark bit. From the figure, we can see that the \(\overline{D}\) values changed slowly and were not likely to have an abrupt change within several samples. Hence, we could efficiently find the start of a block by skipping a number of samples each time when determining if the synchronization codes are found instead of brute-force searching through each sample. The selection for number of skipping samples is affected by frame length, and a larger number of skipping samples could be chosen with a longer frame length.  
To ensure extracting complete information, according to the BER and the length of watermark information, an appropriate error correcting code should be applied.
## 2 Materials and Equipment
### Experimental Equipment and Procedure  
The High-Temperature and High-Pressure Sandstone Impregnation Device is a device independently developed by Chongqing University for supercritical CO2 impregnation of sandstone. The equipment consists of a high-temperature control system, a high-temperature and high-pressure reaction kettle, pressure sensors, temperature sensors, a vacuum pump, and a high-precision plunger pump, among other components.  
Uniaxial testing utilizes the MTS815 Rock Mechanics Testing System. The MTS815 is a hydraulic servo mechanical system produced by MTS Corporation in the United States primarily used for testing the mechanical properties and permeability of rocks, concrete, and coal bodies under complex stress conditions. The system has high precision and stable performance, and can perform high- and low-speed data collection. It employs control methods such as force, displacement, axial strain, and transverse strain, with a maximum axial loading capacity of 2800 kN and a maximum confining pressure of 80 MPa. The experimental steps are as follows:  
After acid treatment, supercritical CO2 treatment, and acid-supercritical CO2 combined treatment, the samples were subjected to XRD (scanning angle of 0-90\({}^{\circ}\)) and NMR tests. First, the samples were immersed in distilled water to leach out the residual acid within the samples, followed by water saturation treatment at 5 MPa pressure for 12 h (Figure 3). NMR was used to test the saturated samples and analyze the effect of acid treatment on pore development. The NMR test parameters were set as follows: waiting time of 1.5 s, echo spacing of 0.2 ms, and echo number of 8000. Uniaxial compressive strength tests were conducted on the samples under different treatment conditions using an MTS815 testing machine. The samples were placed on the testing platform, and a small axial load was applied to ensure complete contact between the indenter and the sample. The loading rate of the uniaxial compression test was set to 0.1 mm/min, and circumferential extensometers were used to measure radial strain.  
Figure 3: Experimental system procedure.
# Proof of Adjourn (PoAj): A Novel Approach to Mitigate Blockchain Attacks
## 7 Evaluation
### Transaction Confirmation Delay  
The average block generation time of bitcoin blockchain is 10 min. In most cases, in the bitcoin blockchain a transaction is considered confirmed after 6 confirmation waiting time, while additional confirmations strengthen the security.  
Bitcoin blockchain has a problem that results in large-sized transactions, with lower fees, to be held in the mempool for a longer period. This is because miners tend to include lower-sized transactions so that they could fit many transactions within the limited block size and get more rewards. Once large-sized lower fees transactions get added to a block after an immense waiting time, the participants still require to wait for at least 6 confirmation time to consider their transaction secure. Besides that, the current PoW consensus rules do not guarantee any specific time for a transaction to be included in the block. The transactions are not picked by order; hence, a transaction may remain in the mempool for a few days or more.  
The proposed PoAj solves this problem by its novel sorting approach. Figure 8 shows that 3 blocks entered the sorting period. Block C picked its transactions according to the higher fees offered by users to speed up their transactions. Furthermore, Block C also picked the smallest-sized transactions so that it could fit the maximum number of transactions to boost its profit. However, Miners of Block A and B were encouraged to pick their transactions based on the PoAj block selection criteria. Once the novel sorting and comparison approach is applied, Block B wins the race among 3 blocks for including the  
Figure 7: PoAj stopping the 51% attack.  
maximum large-sized transaction. It should be noted that the transaction fees do not reflect the PoAj block selection process.
###### Abstract  
The leave-one-out cross validation (LOO-CV), which is a model-independent evaluate method, cannot always select the best of several models when the sample size is small. We modify the LOO-CV method by moving a validation point around random normal distributions--rather than leaving it out--naming it the move-one-away cross validation (MOA-CV), which is a model-dependent method. The key point of this method is to improve the accuracy rate of model selection that is unreliable in LOO-CV without enough samples. Errors from LOO-CV and MOA-CV, i.e., LOO-CVerror and MOA-CVerror, respectively, are employed to select the best one of four typical surrogate models through four standard mathematical functions and one engineering problem. The coefficient of determination (\(R\)-square, \(R^{2}\)) is used to be a calibration of MOA-CVerror and LOO-CVerror. Results show that: (i) in terms of selecting the best models, MOA-CV and LOO-CV become better as sample size increases; (ii) MOA-CV has a better performance in selecting best models than LOO-CV; (iii) in the engineering problem, both the MOA-CV and LOO-CV can choose the worst models, and in most cases, MOA-CV has a higher probability to select the best model than LOO-CV.  
Keywords:cross validation; model selection; surrogate model +
Footnote †: journal: Applied Sciences
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### GNSS Code and Phase Observations
#### 3.1.1 Ionospheric-Free (IF) Code and Phase  
As previously introduced in Section 2.1 and represented by (1), the IF combination offers a solution to eliminate first-order ionospheric delays at the cost of increased noise levels.
## 3 Phytochemistry
### Essential Oil  
The main components of the leaf EO were 1,8-cineole, \(\alpha\)-terpinyl acetate, and \(\alpha\)-terpineol (Table 1). Bicyclic monoterpenes such as \(\alpha\)- and \(\beta\)-pinene and sabinene were also identified. The acyclic monoterpenes linalool and myrcenol were present in smaller amounts, as were the sesquiterpenenes. In addition, _cis_- and _trans_-thuj-2-en-4-ol, which are monoterpene alcohols, were identified in the EO of _L. notilis_[35]. The distillation process, which is utilized to extract essential oils, can impact their chemical composition. In the case of bay leaves, steam distillation yielded 73 identified compounds, whereas hydrodistillation only yielded 54 compounds in the EO [36].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CompoundsOrigin** & **Turkey [37]** & **Italy [11]** & **Iran [38]** & **Greece [39]** & **Georgia [39]** & **Bulgaria [40]** \\ \hline
1,8-Cineole & 58.6\% & 32\% & 41\% & 30.8\% & 29.2\% & 33.3\% \\ \(\alpha\)-Terpinyl acetate & 8.9\% & 5.9\% & 14.4\% & 14.9\% & 22.6\% & 10.3\% \\ Sabinene & 3.3\% & 12\% & 8.8\% & 7.9\% & 12.2\% & 8.8\% \\ Methyleugenol & 0.05\% & 3.3\% & 6\% & 3.6\% & 8.1\% & 6.6\% \\ Linalool & 0.2\% & 0.1\% & 4.9\% & - & 3.8\% & 3.8\% \\ \(\alpha\)-Terpineol & 1.4\% & 3.3\% & 3.1\% & 8\% & - & 3.1\% \\ Terpinen-4-ol & 4.2\% & - & 2.3\% & 6\% & 1.8\% & 3.3\% \\ \(\alpha\)-Pinene & 3.4\% & 5.8\% & 2.6\% & 5.3\% & 5.5\% & 11\% \\ \(\beta\)-Pinene & 3.2\% & 1.4\% & 2.5\% & 3.6\% & 3.7\% & 0.3\% \\ Eugenol & 0.2\% & 1.6\% & 1.5\% & 2.7\% & 0.8\% & 0.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the EO of laurel leaves from different countries.  
As the above table shows, the major component of the leaf EO, regardless of country, is 1,8-cineole (29.2-58.6%). In the EO from Turkey, the three main constituents are 1,8-cineole, \(\alpha\)-terpinyel acetate, and terpinen-4-ol, whereas in those from Italy, Iran, and Georgia, they are 1,8-cineole, \(\alpha\)-terpinyel acetate, and sabinene. In the EO from Bulgaria, 1,8-cineole, \(\alpha\)-terpinyel acetate, and \(\alpha\)-pinene are the main constituents, and in that from Greece, 1,8-cineole, \(\alpha\)-terpinyel acetate, and \(\alpha\)-terpineol, are the main compounds.
## 5 Proposed Method
### Method of Selecting Nodes That Connect to More Adjacent Nodes  
If relay nodes are connected to more nodes, many node pairs may be connected with each other in one hop. This may result in an efficient network where each node can be connected with fewer hops. Therefore, we propose a method to select nodes connected to more neighboring nodes (hereafter, \"more adjacent nodes\") to operate the relay function.  
1. In the node \(i\), the number of adjacent nodes \(Na_{i}\) can be obtained from the source address of the received HELLO message.
2. Each node sends the number of adjacent nodes to the adjacent nodes by the HELLO messages. The \(M\) denotes the set of \(Na_{i}\)s that are received from adjacent nodes.
3. As shown in Figure 6, each node selects the nodes among its adjacent nodes that have more adjacent nodes and notifies the selected nodes that they should activate the relay function. Each node is set as \"the number of relay nodes (\(N\))\" in advance. The node \(i\) whose \(Na_{i}\) satisfies the conditions of Equation (3) is selected as the relay node.  
\[max_{N}(M) \tag{3}\]
# Mechanical and Metallurgical Properties of CO\({}_{2}\) Laser Beam INCONEL 625 Welded Joints
## 4 Conclusions  
Laser beam welding of Inconel 625 was investigated. Melt run trials were first carried out to optimize the process parameters. Almost defect-free specimens were then deeply analyzed via metallurgical investigation, residual stress and deformation measurements, and mechanical tests. The main outcomes can be summarized as follows  
1. Full penetration of 5 mm thick Inconel 625 plates butt joint was obtained using a laser power of both 3 and 3.3 kW and a welding speed of 1.0 m/min. These values correspond to a heat input of 180 and 198 J/mm, respectively, which are much lower compared to those used in conventional fusion welding processes for the same material and geometry.
2. Metallurgical investigation showed a typical epitaxial growth in the fusion zone with eventually some porosity and an extremely narrow heat-affected zone.
3. The microhardness values in the weld bead of all specimens were found slightly greater than those measured on parent metal; this effect was attributed to the fusion zone finer microstructure compared to that of the parent metal.
4. Despite the presence of some porosity in the fusion zone, the tensile strength of the joints was comparable to that of the parent metal, indicating excellent laser weldability of such kind of alloy,
5. The higher the heat input, the higher the residual stress and distortion.
6. The results obtained in this paper will be used, in future work, for calibrating a laser beam welding process numerical model.  
Conceptualization, H.V., R.K.B. and E.G.; methodology, H.V., S.A. and R.K.B.; investigation, H.V., E.G. and A.C.U.M.R.; resources, H.V. and E.G.; writing--original draft preparation, H.V., A.C.U.M.R. and P.F.; writing--review and editing, H.V., P.F. and F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors are gratefully acknowledged for the provision of equipment from the Science and Engineering Research Board, Department of Science and Technology, New Delhi,  
Figure 13: Residual stress distribution of the top and bottom surface of the final welds: (**a**) joint 1 and (**b**) joint 3.  
under the Young Scientist Scheme (Ref. No. SB/FTP/ETA-0190/2014). The authors are also thankful to the management of the Vellore Institute of Technology (VIT), Vellore for enabling us to conduct Residual stress measurements using Pulstec Residual stress analyzer test in Advanced Material Processing and Testing Lab.  
The authors declare no conflict of interest.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
###### Abstract  
Drowsy driving is one of the common causes of road accidents resulting in injuries, even death, and significant economic losses to drivers, road users, families, and society. There have been many studies carried out in an attempt to detect drowsiness for alert systems. However, a majority of the studies focused on determining eyelid and mouth movements, which have revealed many limitations for drowsiness detection. Besides, physiological measures-based studies may not be feasible in practice because the measuring devices are often not available on vehicles and often uncomfortable for drivers. In this research, we therefore propose two efficient methods with three scenarios for daze alert systems. The former applies facial landmarks to detect blinks and yawns based on appropriate thresholds for each driver. The latter uses deep learning techniques with two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method analyzes the videos and detects driver's activities in every frame to learn all features automatically. We leverage the advantage of the transfer learning technique to train the proposed networks on our training dataset. This solves the problem of limited training datasets, provides fast training time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the effectiveness of our methods compared with other methods. Empirical results demonstrate that the proposed method using deep learning techniques can achieve a high accuracy of 97%. This study provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by drowsiness.  
driver drowsiness; eyelid movements; deep learning; drowsiness detection +
Footnote †: journal: Applied Sciences
## 2 Modelling Approach and Governing Equation
### Establishment of the Cardan Joint Perturbation Equation  
In a proper examination of the proportions of the driveshaft fluctuations and estimation of the maximum driven shaft vibrations caused by the variations in the [PERSON]'s joint including the angular speed, the motion equations governing the Cardan joint must be established. Such a set of equations is derived in this work using some geometrical and trigonometrical relations between the input and output angular speed \(\theta_{2}\) and \(\theta_{1}\) of the Cardan joint. Assuming that there is no friction between the cross pins of the driving and  
Figure 1: Sketch of the partially immersed Cardan shaft system in a viscous fluid.  
Figure 2: Deformed configuration of the shaft attached with discs 1 and 2, respectively.  
driven shafts, the vector expressing the motion transmitted between the two shafts can then be written as  
\[\theta_{2}=(1-\mu)\theta_{1} \tag{1}\]  
where \(\mu\) is a small perturbation parameter that depends on \(\theta_{1}\). The kinematic relationship between the output motion \(\theta_{2}\) and input \(\theta_{1}\) is given by [PERSON] et al., [15]  
\[\tan\theta_{2}=\cos\beta\tan\theta_{1} \tag{2}\]  
where \(\beta\) is the angle of inclination of the secondary shaft. Combining Equations (1) and (2), yields  
\[\tan(1-\mu)\theta_{1}=\gamma\tan\theta_{1} \tag{3}\]  
After a series of trigonometric manipulations and for infinitesimal small-angle approximation, the Cardan joint fluctuation leads to  
\[\cos\beta\tan\theta_{1}=\frac{\tan\theta_{1}-\tan(\mu(\theta_{1}))}{1+\tan \theta_{1}\tan(\mu(\theta_{1}))} \tag{4}\]  
Making the perturbation function \(\mu(\theta_{1})\) in Equation (4) the subject yields to  
\[\mu(\theta_{1})=\left\{\begin{array}{ll}1-\cos\beta&\quad\quad\quad\quad if \ \theta_{1}=0\\ \frac{(1-\cos\beta)\tan\theta_{1}}{\theta_{1}[\cos\beta\tan^{2}\theta_{1}+1]} &\quad if\ 0<\theta_{1}\wedge\text{mod}\pi,\theta_{1}\
eq 0\end{array}\right. \tag{5}\]  
For a limited range of \(\mu(\theta_{1})\), Equation (5) will be finite and periodically convergent. Recalling that the equation for Hooke's joint fluctuation coupling is a function of the driveshaft angular displacement \(\theta_{1}\) and driven shaft curbing \(\beta\); therefore, the first derivative with respect to time gives the output driven shaft angular velocity using the chain rule as follows  
\[\dot{\theta}_{2}=\frac{d}{dt}\bigg{(}\theta_{1}-\frac{a\tan\theta_{1}}{\big{(} (1-a)\tan^{2}\theta_{1}+1\big{)}}\bigg{)}=\dot{\theta}_{1}\bigg{(}1-\frac{an} {(1-a)n^{2}+1}\bigg{(}\frac{n^{2}+1}{n}-\frac{2(1-a)n\big{(}n^{2}+1\big{)}}{(1 -a)n^{2}+1}\bigg{)}\bigg{)} \tag{6}\]  
where \(n=\tan\theta_{1}\) and \(a=1-\cos\beta\). Equation (6) demonstrates that the single Hooke's joint will produce a periodic nonuniform output velocity even though the input velocity is constant. Considering the perturbation function, the discrete representation of the coupled rotor model is reduced to a system with five degrees of freedom.
## 3 Optimized Visual Geometry Group Model
### CNN Architecture  
The optimized CNN model has a VGG-16 convolutional base (conv_base) [19] and two fully connected (FC) layers, with the FC layers reconstructed, as shown in Table 1. In the training process, images input into the CNN model are resized to 150\(\times\)150 grey images and do not need any preprocessing. Then, the image passes through a VGG-16 conv_base, which has 14,714,688 parameters previously saved after training in ImageNet. After that, there are two FC layers.  
Figure 2: Three different types of welding area images: (**a**) Normal, (**b**) Porosity, and (**c**) Level Misalignment.  
In this study, we remold and retrain the VGG-16 model for welding area quality inspection. We keep the convolutional layers of VGG-16 unchanged and replace the three FC layers with two new FC layers based on the theory of transfer learning. The convolutional layers include a large number of parameters and weights trained in ImageNet. They also have a strong ability to extract features of image edges and contours [24]. The optimized CNN architecture is capable of extracting welding area features distinctively and robustly because the neural network is sufficiently deep. Moreover, it is less likely to overfit. The first FC layer, with 256 channels, is followed by a dropout layer, which is used to decrease overfitting. Regarding the activation function, rectified linear units (ReLUs) are at a potential disadvantage during optimization because the gradient is zero whether the unit is active or not, and this leads to the scenario in which a unit never activates. Like the vanishing gradient problem, we might expect learning to be slow when training ReLU networks with constant zero gradients. Hence, in our experiment, we replace the ReLU activation function with Leaky Rectified Linear Unit (Leaky_ReLU) [25] in the FC layers to solve this problem.  
Particularly, the last FC layer of this optimized model represents an \(N\) classes predictor, where \(N\) is the number of labels in the database [19]. In our case, the last FC layer has two or three channels to represent three-classifications of welding area image mentioned in Section 2.2, and the Q-D two-classifications, respectively. The final layer is the softmax layer, which is used to provide the final classification results. Specifically, softmax is a generalization of the logistic function that maps a length-p vector of real values to a length-K vector of values. Cross-entropy loss together with softmax used in this optimized model is arguably one of the most commonly used supervision components in CNNs. Despite its simplicity, softmax has high popularity and excellent performance in terms of the discriminative learning of features [26].