esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Sixth-generation (6G) announcements promise the best performance not only for latency but also for the number of connected objects. These characteristics particularly suit intelligent transport system (ITS) applications involving a large number of moving vehicles with stringent latency constraints. Moreover, in the 6G era, these applications will often operate while relying on direct cooperation and exchanges between vehicles, in addition to centralized services through a telecommunication infrastructure. Therefore, addressing collaborative intelligence for ad hoc routing protocols that ensure efficient management of multihop vehicle-to-vehicle communications is mandatory. Among the numerous organization models proposed in the literature, the chain-branch-leaf (CBL), a virtual backbone-like model, has demonstrated best performance regarding latency against the state-of-the-art approaches. However, its structure, which lacks redundancy, may lead to higher data loss in the case of the failure of one of the relaying branch nodes. This study investigated how the multipoint relay (MPR) technique--which is intrinsically redundant--used in the optimized link state routing (OLSR) protocol can be efficiently adapted to the road traffic context, especially by restricting MPR selection to a single traffic flow direction (TFD-OLSR). The simulation results confirmed that CBL-OLSR obtains the least end-to-end delay for various types of application traffic due to its efficient reduction in the number of relays and the amount of routing traffic. However, despite higher routing traffic, TFD-OLSR improves the delivery rate, especially for more than two-hop communications, thus demonstrating the benefits of its redundancy property.  
ad hoc routing protocol; multi-hop communications; 6G wireless communications; redundancy; clustering; vehicular ad hoc networks +
Footnote †: journal: Electronics Electronics
## 4 Discussion
### Contact Area  
According to one study using ProFile instruments (Maillefer, Ballaigues, Switzerland), the contact areas were 1-11 mm from the tip in step-back and crown-down techniques, except for the first three instruments in the crown-down technique, in which 2-3 mm at the apex were devoid of contact [32]; therefore, use of the step-back technique was associated with larger contact areas, generating higher forces and torque compared with the crown-down technique [32]. Conversely, another study found that a larger contact area was correlated with high torque but not with high forces [33].  
Available evidence indicates that the contact area varied depending on the length of the instrument inserted in the canals, and thus, the contact area of instruments with various sizes should be examined at uniform lengths using canalss with varying diameter and size because instrument size, canal size, and contact area may be confounding factors.
# FrameAugment: A Simple Data Augmentation Method for Encoder-Decoder Speech Recognition
## 4 Results
### Comparison with the Existing Data Augmentation Methods  
SpecAugment and speed perturbation are widely used as a data augmentation method. We compared the performance of the previous data augmentation and the proposed method. For SpecAugment, we set the parameters to \(W\) = 5, \(F\) = 30, \(T\) = 40, and n_mask = 2. Tables 6 and 7 show the performance of the existing data augmentation methods and the proposed method in WSJ and LibriSpeech, respectively.  
Table 8 shows the performance of incrementally combining the existing data augmentation methods and the proposed method in LibriSpeech. The combination of SpecAugment and speed perturbation with the proposed method of 6.01% improved performance by up to 13.6% over the baseline of 6.96%. In addition, the proposed method was shown to further reduce WER from 6.26 to 6.01% compared with the combination of the two previous data augmentation methods (SpecAugment and speed perturbation), which means a relative performance improvement of 4%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation** & **Dev93** & **Eval92** \\ \hline None & 7.88 & 5.02 \\ SpecAugment & 7.23 & 4.87 \\ Speed perturbation & 7.24 & 4.93 \\ Proposed method & 6.89 & 4.68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: WER (%) results of WSJ database according to data augmentation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation** & **Dev-Clean** & **Test-Clean** \\ \hline None & 6.62 & 7.33 \\ SpecAugment & 6.23 & 6.96 \\ Speed perturbation & 6.10 & 6.74 \\ Proposed method & 6.14 & 6.63 \\ \hline \hline \end{tabular}
\end{table}
Table 7: WER (%) results of LibriSpeech database according to data augmentation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation** & **Dev-Clean** & **Test-Clean** \\ \hline SpecAugment & 6.23 & 6.96 \\ +Speed perturbation & 5.83 & 6.26 \\ +Proposed method & 5.53 & 6.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: WER (%) results of LibriSpeech database when incrementally combined with the previous data augmentation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & \(N\) & **Dev93/Dev-Clean** & **Eval92/Test-Clean** \\ \hline \multirow{2}{*}{WSJ} & 0 & 7.88 & 5.02 \\  & Fixed \((N=500)\) & 7.24 & 4.94 \\  & Flexible \((N=L\times 0.7)\) & 6.89 & 4.68 \\ \hline \multirow{2}{*}{LibriSpeech} & 0 & 6.62 & 7.33 \\  & Fixed \((N=1200)\) & 6.06 & 6.68 \\ \cline{1-1}  & Flexible \((N=L\times 0.7)\) & 6.14 & 6.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: WER (%) results according to the maximum augmentation range. For the flexible \(N\) case, the ratio was 0.7 and the frame rate range was 0.5–1.5 in the proposed method.
## 4 Numerical Experiments
### Small Network  
The network's structure and characteristics are presented in Figure 4. It consisted of seven links, five nodes, and two O-D pairs (O-D pairings (1-4) and (1-5)). To compute the travel time, the BPR link performance function (\(\alpha_{a}=0.15\) and \(\beta_{a}=4.00\)) was used, and the travel time weight parameter (\(p^{m}\)) was considered to be \(1.0\).  
Figure 3: Solution procedure strategy for the multi-class TAP with asymmetric vehicle interactions.  
To begin, we assessed the network's overall travel time as a result of multi-class TAP in Figure 5. As expected, when the percentage of CAVs increased, overall travel time decreased due to increased capacity. However, overall travel time was reduced nonlinearly, although the percentage of CAVs was raised linearly. Increases in the proportion of CAVs at an early stage (0 percent to 10% of CAVs) were more effective than increases at a later stage (90 percent to 100 percent of CAVs). Additionally, when the percentage of CAVs was 100%, capacity improved by 50%, as shown in Figure 2b, while overall travel time improved by 60%. As a result of the findings, we could infer that the network was highly congested when only HV demand was used.  
Figure 4: Network topology and features of the small network. (**a**) Test network; (**b**) link characteristics; (**c**) route set, (**d**) O-D demand with % of CAVs.  
Figure 5: Total travel time improvement as the percentage of CAVs.  
Second, by increasing the percentage of CAVs, the trajectories of the link capacity were explored and presented in Figure 6. When the percentage of CAVs was increased from 0% to 60%, the capacities of links 2, 3, 6, and 7 increased, while the capacities of the other links (i.e., links 1, 4, and 5) increased above 60% of CAVs. This result indicated that more CAVs were allocated to routes 3 and 6 until 60% of CAVs was reached, while CAV flows were increased in routes 1 and 4 after 60% of CAVs was reached. This was because the links in routes 3 and 6 were approaching their maximum capacity, and the excess CAV flows were diverted to other underutilized links to increase capacity and decrease travel time accordingly. As could be seen, capacity expansion also did not follow a linear trend.
## 3 CGST Codec Simulation for Processing Images
### CGST-Compressed Image Postprocessing Using Neural Networks
We used a set number of 100 epochs in training for all simulations. The different NNs' results are shown in Table 2.  
To visually evaluate the performance of the applied NNs, the examples of the restored image with varying values of CW-SSIM are shown in Figure 7.  
Figure 6: Neural network structure to restore the CGST-compressed image. Based on the results presented in Table 1, we have concluded that using an NN helped address the two issues of blurred images and changing spatial frequency. The most significant improvement was observed in the CW-SSIM indicator, which is responsible for the visual correspondence between the restored image and the original one. We found that using a fully connected NN did not lead to any improvements. However, the recurrent NN and CNN helped increase the CW-SSIM by 0.15 and 0.27, respectively, at the maximum response time (140 ms). This led to a significant improvement in the MSE and MN criteria. The CNN in Fourier space demonstrated the best results with a CW-SSIM increase of 0.3. However, there was no significant improvement in the MSE and MN values compared to traditional NNs. At the same time, even the least efficient NN (recurrent one), with a relatively simple architecture and NN response time as low as 20 ms, provided more effective image quality restoration than traditional filtering. At the same time, traditional JPEG compression of the source images provided an average image compression of 2.55 with SSIM equal to 0.975, an MSE of 0.140, and an MN of 12.423, which are higher than our result. It is worth noting that we previously mentioned that JPEG compression and most existing lossy image compression methods belong to the transform-based approach. In contrast, our proposed method is not transform-based and operates on several quantization levels. These two approaches can be combined for better results.
# A Novel Deep Learning Approach for Deepfake Image Detection
## 2 Related Literature
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & **Approach** & **Dataset** & **Accuracy Score (\%)** & **Research Aim** \\ \hline
[10] & 2021 & XGBoost & CelebDF and FaceForencis++ & 90 & The video deepfake detection using a deep learning-based methodology was proposed. \\ \hline
[12] & 2020 & Mobile Net and Xception & FaceForencis++ & 91 & Automatic deepfake video classification using deep learning was proposed. \\ \hline
[13] & 2020 & DeepVision & Static deepfakes eye blinking images dataset & 87 & The deepfake recognition based on a human eye blinking pattern using deep learning was proposed. \\ \hline
[14] & 2020 & Multimodal network & Facebook deepfake challenge dataset & 61 & The deepfake detection based on spectral, spatial, and temporal inconsistencies using multimodal deep learning techniques was proposed. \\ \hline
[15] & 2019 & Dense Net and fake feature network & CelebA & 90 & Deepfake image detection using pairwise deep learning was proposed. \\ \hline
[16] & 2022 & Dense Net & annotated CT-GAN & 80 & Medical deepfake image detection based on machine learning and deep learning was proposed. \\ \hline
**Proposed** & **2022** & **Novel DFP** & **Photoshopped real and fake faces** & **94** & **A novel DFP approach based on a hybrid of VGG16 and convolutional neural network architecture is proposed for deepfake image detection.** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The deepfake detection-related literature summary and comparative analysis. posed optimized model is efficient in the data processing. In conclusion, our novel research study has many valuable applications in cybersecurity for deepfake detection.
# Adaptive Emergency Call Service for Disaster Management
## 4 Adaptive Multi-Hop Emergency Call Protocol: 5G-SOS
### 5G-SOS Adaptive Parameters
#### 4.1.1 Waiting Time for Relaying, \(T_{r}\)
\begin{table}
\begin{tabular}{c c c} \hline \hline \(n_{k}\) & \(n_{c}\) & \(T_{\max}\) \\ \hline \(\downarrow\) & \(\downarrow\) & \(\downarrow\) \\ \(\downarrow\) & \(\uparrow\) & \(\uparrow\) \\ \(\uparrow\) & \(\uparrow\) & \(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Expected behavior of \(T_{\max}\) with \(n_{c}\) and \(n_{k}\). To compute the waiting time \(T_{r}\), \(r\) uses its coverage state \(\mathrm{I}_{\mathrm{C}r}\), its localization accuracy \(\mathrm{LocAcc}_{r}\), and its residual energy \(SoC_{r}\). However, (3) and (4) show that the protocol 5G-SOS introduces the use of RSSI in the computation of the waiting time parameter. The ratio between the \(RSSI_{\mathrm{tx}}\) of the transmitter device that sent the call request, and the \(RSSI_{r}\) of the \(r\) itself is computed. This ratio is higher than 1 when the transmitter of the emergency call is in better radio conditions than the receiver (farther from the gNBs). In this case, the ratio contributes to increasing the value of \(T_{r}^{AM-HLP}\) in order to penalize the receiver in its selection for relaying the emergency call. Otherwise, when the ratio is less than 1, the receiver is in better radio conditions (closer to the gNB), and the waiting time is decreased to give priority to the receiver. The RSSI ratio is used to improve the latency fairness between UEs. Indeed, a relay device waits less time when the request is sent by a farther device than when the request is sent by a closer device, as depicted in Figure 5. Therefore, when two requests are received at the same time, the request of the farthest device from the gNB is transmitted first, which contributes to balancing the emergency call transfer delays. Figure 4: Illustration of the standard Rosenbrock function defined by two variables and given by \(f(x,y)=(a-x)^{2}+b(y-x^{2})^{2}\). Here, we assume a = 1, b = 100, and the minimum value of zero is at (1,1). The adaptive \(T_{\mathrm{max}}\) given in (5) is based on the Rosenbrock function. Figure 5: A relay UE gives higher priority for a far emergency UE than a near UE using the RSSI ratio. The RSSI ratio in (4) is lower for a far UE than a near UE. The relay UE waits a lesser time, hence relays faster the transmissions of the far UE emergency call in the direction of a functioning gNB.
###### Abstract
Based on the above issues, this paper focuses on three aspects: (1) verify the feasibility of a PCA algorithm in 3D wind field reconstruction; (2) explore the feasibility of a 3D pressure field reconstruction in a large wind field; and (3) attempt to develop an optimal placement algorithm based on a mathematical matrix operation, which is different from a greedy algorithm, to solve the time-consuming problem while ensuring the accuracy of the reconstruction. For these proposals, sparse sensing and QR pivoting are employed in the reconstruction. Sparse sensing is expert in reconstructing complete signals from a small measurement subset, and QR pivoting is applied in the optimization process [22]. The reconstruction calculation is divided into two parts: First, the combination of PCA and sparse sensing is synergistic, in which the PCA is employed to extract features from CFD simulation results. Combining the extracted features with sparse measurements, the 3D velocity field and pressure field distribution can be obtained by using the developed inverse process algorithm. In addition, QR pivoting and the reconstruction algorithm are combined to determine the optimal sparse sensor placements. A simulation and an experiment are carried out to demonstrate that drastic reductions of sensors and improved reconstruction accuracy can be achieved using the proposed optimized algorithm. In general, the key contribution of the innovative approach can be reflected in two aspects: First, the combination of PCA, CFD results and sparse sensing can creatively utilize rapid wind field reconstruction to achieve short-term wind forecasting, addressing the instability of the statistical model and large computational load of CFD calculation. Second, unlike a traditional brute-force combinatorial search, the proposed optimization algorithm is developed by QR pivoting and PCA, which bring a significant improvement of the reconstruction accuracy and the associated speedup of optimized sensing. In the wind power industry, this innovative approach can be used in a variety of situations; where sensors come at a high cost, or when ultra-short reconstruction time is required, the reduction in the number of sensors can be significant.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Solution Development
#### 4.1.3 Collaborative System Utility  
An improved Lyapunov Drift function \(\Delta_{V}(t)\) is defined by collaborating system utility \(\mathcal{H}(t)\), expressed as  
\[\Delta_{V}(t)=\mathbb{E}[\Delta(t)-V\mathcal{H}(t)|\Pi(t)], \tag{14}\]  
where \(V\) is a positive constant to tradeoff system utility and stability. Compared with the Lyapunov drift function, minimizing \(\Delta_{V}(t)\) can simultaneously maximize system utility and achieve system stability.  
Since it is not easy to obtain the minimum value of \(\Delta_{V}(t)\) directly, we turn to minimize the maximum of \(\Delta_{V}(t)\) as obtained in Theorem 1.  
Under the condition of modeled queues and system utility \(\mathcal{H}(t)\), the maximum value of \(\Delta_{V}(t)\) is expressed by  
\[\Delta_{V}(t)\leq\mathbb{E}[\Gamma_{V}(t)|\Pi(t)]+\Lambda, \tag{15}\]  
where \(\Lambda\) is a constant that can be denoted as  
\[\Lambda=\frac{M}{2}\Big{[}(\tau\xi_{\max})^{2}+(\tau\varphi_{\max})^{2}+(\tau \rho_{\max})^{2}+(E_{\max}^{c})^{2}\Big{]}. \tag{16}\]  
As shown in (17), \(\Gamma_{V}(t)\) is a function of the variables in \(\Pi(t)\) and the process of proof is expressed in Appendix A.  
\[\begin{split}\Gamma_{V}(t)=&\underbrace{\sum_{i=1 }^{M}\rho_{i}(t)(\tau-\omega_{i}(t))(E_{i}(t)-\mathcal{B})}_{\text{BMO}}\\ &+\underbrace{\sum_{i=1}^{M}\tau\big{[}\mathcal{D}_{i}(t)-\frac {P_{r}}{\varphi_{\max}}(E_{i}(t)-\mathcal{B})\big{]}\varphi_{i}(t)-V\mathcal{H }(t)}_{\text{DACO}}\\ &+\underbrace{\sum_{i=1}^{M}\sum_{j=1}^{N}\tau\Big{[}\frac{P_{t }}{\xi_{\max}}(\mathcal{B}-E_{i}(t))-\mathcal{D}_{i}(t)\Big{]}\delta_{ij}(t) \gamma_{i}(t)}_{\text{LTO}}.\end{split} \tag{17}\]  
Since \(\Lambda\) is a constant, \(\Delta_{V}(t)\) can be minimized by minimizing \(\Gamma_{V}(t)\). According to the system stochastic variables, \(\Gamma_{V}(t)\) is divided into three parts in (17). Thus, we can achieve the initial optimization goal by optimizing the energy harvesting rate \(\rho_{i}(t)\), data acquisition rate \(\varphi_{i}(t)\), ISL contact plan \(\delta_{ij}(t)\), and inter-satellite transmission rate \(\gamma_{i}(t)\).
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 5 Results
### Multi-Temporal Data Acquisition--The Influence of the Vegetation  
As the study area is in a natural forest with mainly deciduous trees, the aspect of leaf-off and leaf-on conditions has an influence on the point cloud density, on the range of the reflected laser pulses and on the accuracy of the point cloud position. Furthermore, this affects the number of satellites available for GNSS trajectory determination. The comparison of the number of visible satellites shows that under leaf-off conditions more satellites tended to be visible than under leaf-on conditions (Figure 13). The peaks depict larger canopy gaps.  
The comparison of multi-temporal TLS and MLS data from a 3 m wide transect under leaf-off and leaf-on conditions shows that the presence of leaves in the summer prevents the laser pulse from reaching the more distant parts of the crown, resulting in less reflections (Figure 14). Furthermore, the weight of the foliage leads to a lowering of the position of the branches in summer.  
Figure 14: Comparison of the multi-temporal TLS and MLS point clouds of plot 1 on the example of a 3 m wide profile perpendicular to the scan trajectory of the MLS system.  
Figure 13: Number of satellites during the MLS recording. (**a**) leaf-off; (**b**) leaf-on.
# Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules
## 4 UML Profile for Smart Contracts  
Unified modeling language allows for the enrichment of its semantics through the use of extension mechanisms [44]. There are three types of such elements: stereotypes, tagged values, and constraints. At the design level, the author has used stereotypes as one of the UML extensibility mechanisms. UML profiles the prepared new modeling constructs. The author has proposed a set of stereotypes for designing smart contracts. The set can be divided into two groups. The first one encompasses blockchain platform-independent (PI) stereotypes. The second group consists of blockchain platform-specific (PS) stereotypes. Platform-independent stereotypes are generic for modeling smart contracts in various blockchain frameworks.  
The profile defines the following set of generic stereotypes for smart contracts design:  
* \(\ll\)AbstractSContract\(\gg\)--used for marking an abstract, generic class of smart contract. The class is a superclass for all types of concrete smart contracts;
* \(\ll\)AbstractVRule\(\gg\)--used for marking an abstract, generic interface of verification rule. The interface must be implemented by concrete verification rules;
* \(\ll\)SContract\(\gg\)--used for marking concrete smart contract classes. This means the agreement that regulates the cooperation of blockchain nodes;
* \(\ll\)VRule\(\gg\)--used for marking concrete verification rules classes. This means a concrete condition that must be met in the smart contract.  
The \(\ll\)VRule\(\gg\) stereotype can mark various kinds of verification rules, e.g., technical and business. The profile does not contain a hierarchy for verification rule types.  
At the platform-specific level, the profile defines stereotypes for concrete blockchain environments. Currently, the profile specifies stereotypes for the R3 Corda framework smart contracts:  
* \(\ll\)State\(\gg\)--an object that is stored in blockchain nodes. Comprises the quantity exchanged and references to both parties: producer and buyer.
* \(\ll\)Flow\(\gg\)--the procedure of reconciliation transactions and cooperation among nodes.  
Figure 3 presents the UML Profile diagram that shows declared generic stereotypes. The \(\ll\)GenericSContractObject\(\gg\) stereotype is introduced only for the classification of those stereotypes as generic ones.  
Figure 3: The UML profile diagram with platform-independent stereotypes.  
Figure 4 presents the UML profile diagram that shows declared stereotypes for the R3 Corda smart contracts.  
Table 2 contains a summary of platform-independent and platform-specific stereotypes in the profile with indicated UML classifiers, which are extended. The pattern layer, in which the stereotype is used, is also specified.  
The Visual Paradigm modeling tool was used to design the _UML Profile for Smart Contracts_. All the above-mentioned stereotypes are included in the profile. The actual version of the profile is available at the GitHub repository [42].