esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Analysis
### Discussion  
The experimental results indicate that the DMR-GCN framework outperforms the baseline model and other existing methods across various traffic scenarios. Comparisons  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train On** & \multicolumn{3}{c}{**KITTI**} \\ \hline
**Test On** & **Apollo** & **Indian** & **Ours** \\ \hline CLAL & 0.88 & 0.87 & 0.89 \\ CLAR & 0.87 & 0.86 & 0.88 \\ LCL & 0.86 & 0.85 & 0.87 \\ LCR & 0.85 & 0.84 & 0.86 \\ OVT & 0.83 & 0.82 & 0.84 \\ Map & 0.94 & 0.94 & 0.94 \\ Recall & 0.92 & 0.92 & 0.92 \\ F1 & 0.93 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the transfer learning experiment.  
Figure 9: Comparative line graph of experimental results. (**a**) DMR-GCN framework ablation experiments, (**b**) Pairwise comparison results of different models for Apollo, KITTI and Indian datasets, (**c**) Results of the transfer learning experiment.  
across the Apollo, KITTI, and Indian datasets revealed that the model achieved accuracy scores of 0.89 and 0.88 for continuous left-lane alteration (CLAL) and continuous right-lane alteration (CLAR), respectively, while the baseline model scored only 0.80 and 0.79. This improvement underscores the effectiveness of the dynamic multi-level relational graph (DRG) and the enhanced temporal interaction graph generation method (ETIG).  
Specifically, the MR-GCN framework demonstrated increases in mean accuracy (Map) and F1 scores from 0.91 and 0.90 to 0.94 and 0.93, respectively, highlighting the contributions of these components in capturing complex traffic dynamics. In transfer learning experiments, the model achieved CLAL and CLAR scores of 0.87 and 0.86 on the unseen Indian dataset, in contrast to the lower performance of models trained solely on the KITTI dataset, further validating the model's generalization capabilities.  
However, the limitations of this study include the necessity for improved adaptability to non-standard vehicles and extreme traffic conditions. Future research could address these issues by expanding the dataset to encompass various vehicle types and more complex traffic scenarios, enhancing the model's robustness. Additionally, incorporating richer external knowledge graphs may further improve the model's generalization under complex conditions.  
In summary, the results of this study demonstrate that the DMR-GCN framework achieves high accuracy and adaptability in predicting complex traffic behaviors, providing a valuable foundation for the advancement of autonomous driving technologies.
## 4 Proposed Approach and Requirements
### Distributed versus Centralized Approach
* Scalability: centralized database approaches are not scalable when the number of involved entities is scaling up, due to the huge information exchange overhead to keepthe database up to date. The number of exchanged messages that are needed to keep centralized databases up to date will further explode when dynamic entities are involved, which is the case with NGSO-SATs, ESIMs, and mobile users communicating with terrestrial systems. An increase in the dynamicity of the system may lead to outdated information stored in centralized databases. On the other hand, in distributed approaches, information exchange overhead between entities of satellite/terrestrial networks does not increase with the increase of network density. In this approach, information sharing between entities is event-based, information is exchanged only when required, which reduces control overhead and does not lead to performance degradation when network density increases.
# Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
## 2 Materials and Methods
### Wasserstein Generative Adversarial Networks  
The fundamental part of our research is the implementation of WGANs as a novel approach in BE studies. Generative adversarial networks are based on a game between two  
Figure 1: Proposed method for addressing the assessment of HVD in BE studies through the implementation of WGANs. The process begins with an input (dataset of a classic BE study), which is then processed through the WGAN generator and critic networks. The generator creates new data representations, while the critic evaluates the generated outputs to improve decision outcomes, by augmenting the dataset. In the example shown, the input refers to a “failed” BE assessment where the 90% confidence interval is outside the acceptance region, while the GAN augments the input, leading to a “Pass” output within the acceptance region.  
machine learning models, which are commonly implemented with two neural networks. Those are named generator and discriminator. The generator must learn to transform random noise into realistic samples and the discriminator must evaluate the sample to estimate whether or not they are real. If it originates from training data, then it is real; if it originates from the generator, then it is fake (Figure A1) [23; 24]. The two primary components of WGANs that provide them an edge over GANs are Wasserstein distance and loss. The work needed to convert one distribution into another is known as the Wasserstein distance between two distributions. The goal of the Wasserstein distance cost or loss function is to improve the gap between the generated and actual data [25; 26].  
Figure 1 solely refers to the proposed approach of our study and how HVDs that falsely failed to demonstrate BE, with the implementation of WGANs and data augmentation, can provide a more informed decision on the BE. On the other hand, Figure A1 provides a depiction of the GAN architecture, to present how the AI model works, by providing the generator with random noise and the generating data. The generated data are then added to the critic and by comparing them to real data, the model can decide if the generated data are real or fake. The whole process is repeated many times, and the critic learns each time to better distinguish real samples from fake ones.  
Training stability was ensured by tracking both generator and critic losses during training. By visually inspecting the curves, an indicator of stable training are smooth converging loss curves. Also, in our study where a small number of samples were used, we implemented an early stopping to prevent overfitting. Furthermore, consistent model initialization and fixed seeds throughout the code helped reduce variance across runs.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 5 Performance Analysis  
In this section, the TSSMM simulation model of Section 4 is utilized to conduct performance analysis of BBWNs. The analysis is achieved by evaluating the network performance measurement parameters for the two scenarios and the two activities, in terms of channel capacity and outage probability.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.5 Knowledge Plane  
In traditional networking, decisions are made not based on knowledge at all. Such networks are based on strict rules or policies enforced by network administrators. In SDN, knowledge is also not generated; however, the centralized controller uses information collected from the network, such as device statistics, status, etc., to develop a global view of the network to help in arriving at control decisions based on policies enforced by applications. In KDN, based on data collected from the network, machine learning or a heuristic model-based approach will be used to generate knowledge, which will be used in deriving rules with the aid of knowledge composition or by providing knowledge to the control plane to use in arriving at control decisions. Furthermore, the knowledge/rules can be provided to the application plane to update policies and to the management plane to aid in network monitoring.
## 4 Experiments Section
### Experimental Setup
#### 4.1.2 Model Implementation  
The proposed LSTM structure was implemented with Keras libraries [48], written in Python and utilizing Tensorflow [50] as the backend. All the LSTM models with different modalities were trained in a supervised manner. Each layer's weights and biases were initialized with randomly selected values. The LSTM parameters were optimized through minimizing the binary cross-entropy loss function. The binary cross-entropy loss function measures the distance between the predicted probability and the actual class. The binary cross-entropy loss for a batch of N samples was defined as:  
\[L=-\sum_{i=1}^{N}y_{i}log(\hat{g}_{i})+(1-y_{i})log(1-\hat{g}_{i}) \tag{8}\]  
where \(y_{i}\) denotes the truth value, taking the value 1 or 0, and \(\hat{g}_{i}\) is the predicted Sigmoid probability.  
An optimizer used to update the network parameters to minimize the loss function needed to be selected as well. We chose the Adam optimizer, since it was shown that it had the best fitting effect for the purpose of human activity recognition [41]. A learning rate of 0.0001 was used to control the extent of the parameter updates.
## 1 Introduction
[17] systematically investigated B\({}_{2}\)O\({}_{3}\) as a sintering aid and found it could reduce the sintering temperature of ZnTiNb\({}_{2}\)O\({}_{8}\) ceramics from 1250 \({}^{\circ}\)C to 1050 \({}^{\circ}\)C without negatively impacting microwave dielectric properties. [PERSON] et al. [18] used a high-temperature melting method to prepare ZnO-B\({}_{2}\)O\({}_{3}\) (ZB) glass, demonstrating that ZB glass effectively reduces the sintering temperature of LZT-CT composite ceramics from 1175 \({}^{\circ}\)C to 875 \({}^{\circ}\)C while promoting the densification of the LZT-CT composite ceramics. Few studies have explored the effects of boron oxide on high-temperature sintering in silica ceramics, with most research focusing on other ceramic systems or different sintering aids. Existing studies highlight the advantages of sintering additives like alumina or mullite fibers, but these often require higher sintering temperatures to achieve comparable densification, leading to increased energy consumption [19; 20]. This study stands out by systematically investigating how varying boron oxide content influences the microstructure, porosity, shrinkage rate, and mechanical properties of silica ceramics during high-temperature sintering.
# Workload Balancing on Identical Parallel Machines: Theoretical and Computational Analysis
## 5 Computational Analysis
### Results
#### 5.2.1 Dataset DM  
Table 2 lists the numerical results from solving the DM dataset by using three models (MIL, MIQ1 and MIQ2). With the linear model MIL, CPLEX solved 500 out of 1900 to optimality, i.e., 26%. The quadratic model MIQ1 yields 1269 instances solved 67% to optimality. With linear cuts, the number of opt for MIQ2 is 717, which is 38% of the total instances. The best average solving times of CPLEX was given by MIQ1, followed by MIQ2 and lastly MILP. While combining the solutions found by all the models, we increased the number of instances solved optimally to 1370, which is 72% out of 1900 instances. For this dataset, [18] found 945 optimal solutions by solving the quadratic model by [PERSON].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**M**} & \multirow{2}{*}{**N**} & \multicolumn{4}{c}{**Max Time**} & \multicolumn{4}{c}{**Min Time**} & \multicolumn{4}{c}{**Avg Time**} & \multicolumn{4}{c}{**Opt**} \\ \cline{3-14}  &  
_Solving difficult instances of DM dataset (10, 25), (15, 25) and (15, 50):_ While the MIQ1 had the best performance overall, it encountered difficulties when dealing with the difficult-to-solve instances: (10, 25), (15, 25) and (15, 50). The same phenomenon was also frequently observed in the literature, especially in the most recent work of [18]. [PERSON] and [PERSON] [18] did not find an optimal solution for all of the mentioned combinations. Interestingly, the linear formulation performed very well in those combinations: 44 (88%) instances solved to optimality for the problem size of (10, 25), and 32 (64%) instances solved to optimality for the problem size of (15, 25). The configuration (15, 50) remains problematic for our three models.
## 3 System Model and Problem Formulation
### Link Quality of Service (LQoS) Model  
According to the Shannon formula, the channel capacity \(C_{a,j}\) between the SN \(N_{a}\) and the neighbor node \(N_{j}\) is  
\[C_{a,j}=B\text{log}_{2}\left[1+\frac{P\left(L_{a,j}\right)^{-\gamma}|h_{a,j}|^ {2}}{n_{0}B}\right], \tag{2}\]  
where \(B\) represents the bandwidth, \(P\) represents the transmitting power of the SN, \(j\) represents the ID number of the neighbor node, \(L_{a,j}\) represents the distance between the SN and the neighbor node, \(n_{0}\) represents the noise power spectral density, \(\gamma\) represents the path loss factor, and \(h_{a,j}\) represents the small scale fading between \(N_{a}\) and \(N_{j}\), which follows the exponential distribution [30], i.e., \(|h_{a,j}|^{2}\sim E\left(\lambda\right)\). The probability density function of the exponential distribution can be expressed as  
\[f\left(|h_{a,j}|^{2}\right)=\lambda e^{-\lambda|h_{a,j}|^{2}}, \tag{3}\]  
where \(\lambda\) represents the rate parameter.  
According to (2) and (3), we can get the probability of successful transmission \(P_{a,j}\) from \(N_{a}\) to \(N_{j}\) is  
\[P_{a,j} =P\left(C_{a,j}\geq R\right)=P\left(|h_{a,j}|^{2}\geq\frac{(2^{R/ \beta}-1)n_{0}B}{P\left(L_{a,j}\right)^{-\gamma}}=\omega\right) \tag{4}\] \[=\int_{\omega}^{\infty}f\left(|h_{a,j}|^{2}\right)d\left(|h_{a,j} |^{2}\right),\]  
where \(R\) represents the actual transmission rate of the SN \(N_{a}\), and \(d\left(\cdot\right)\) represents the variable of integration. When \(C_{a,j}\geq R\), the message can be successfully forwarded.  
In addition, the mobility of nodes can affect the LQoS in UAV-assisted VANETs. As discussed in [8], the LQoS model from the SN \(N_{a}\) to the RN \(N_{j}\) can be defined as  
\[I_{a,j}=\frac{C_{a,j}}{T_{a,j}}=\frac{C_{a,j}}{T_{S_{a,j}}+T_{P_{a,j}}+T_{P_{a,j}}+T_{Q_{a,j}}}, \tag{5}\]  
where \(I_{a,j}\), \(T_{S_{a,j}}\), \(T_{P_{a,j}}\), \(T^{\prime}{}_{P_{a,j}}\) and \(T_{Q_{a,j}}\) represent the LQoS, the sending delay, propagation delay, processing delay, queuing delay from the SN \(N_{a}\) to the RN \(N_{j}\), respectively.  
Figure 1: Unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs) architecture.
## 2 Materials and Methods
### BEPS Approach  
The BEPS model used in our study is an hourly process-based diagnostic model [13; 14; 76] that computes the canopy level GPP as the sum of the sunlit and shaded leaf groups using the Farquhar, von Caemmerer and Berry (FvCB) photosynthesis model [18]. Although the BEPS was initially developed for boreal ecosystems, it has been expanded and used for temperate and tropical ecosystems in Asia [77; 78], which laid the foundation for its application in croplands.  
The BEPS adopts a sunlit-shaded leaves separation modeling mechanism that upscales photosynthesis from the leaf level to the canopy level. This kind of \"two-leaf\" model has been proven to be more effective in modeling canopy photosynthesis than the conventional \"big-leaf\" model [13; 79]. The photosynthetic rate of the canopy, \(A_{\mathrm{c}}\), is modeled as the sum of the sunlit and shaded leaf groups as  
\[A_{\mathrm{c}}=A_{\mathrm{sunlit}}LAI_{\mathrm{sunlit}}+A_{\mathrm{shaded}}LAI_{ \mathrm{shaded}} \tag{12}\]  
\[LAI_{\mathrm{sunlit}}=2\cos\theta(1-e^{-0.5\Omega LAI/\cos\theta}) \tag{13}\]  
\[LAI_{\mathrm{shaded}}=LAI-LAI_{\mathrm{sunlit}} \tag{14}\]  
where sunlit and shaded denote the sunlit and shaded leaf groups, respectively; \(\theta\) is the solar zenith angle; and \(\Omega\) is the clumping index.  
The photosynthesis model in the BEPS model is suitable for C3 carbon fixation pathways. However, maize is a C4 species according to its photosynthetic pathway. In this study, the BEPS model was modified to be suitable for C4 species estimation according to the theory of [PERSON] et al. [19]. The photosynthetic rate of the leaves for C4 species, \(A_{\mathrm{n}}\), is assumed to be limited by the phosphoenolpyruvate carboxylase (PEP-carboxylate)-limited rate of assimilation (\(W_{\mathrm{c}}\)), the light-limited rate of assimilation (\(W_{\mathrm{e}}\)), and the CO\({}_{2}\)-limited rate of assimilation (\(W_{\mathrm{s}}\)) [80]  
\[A_{n}=\min(W_{\mathrm{c}},W_{\mathrm{e}},W_{\mathrm{s}})-R_{\mathrm{d}} \tag{15}\]  
\[W_{\mathrm{c}}=V_{\mathrm{cmax}} \tag{16}\]  
\[W_{\mathrm{e}}=PAR\times(1-\omega)\times\epsilon \tag{17}\]  
\[W_{\mathrm{s}}=2\times 10^{4}V_{\mathrm{cmax}}C_{\mathrm{i}}/p \tag{18}\]  
\[R_{\mathrm{d}}=0.0025V_{\mathrm{cmax}} \tag{19}\]  
where \(V_{\mathrm{cmax}}\) is defined as the maximum carboxylation rate (mol m\({}^{-2}\) s\({}^{-1}\)), \(\omega\) is the leaf-scattering coefficient for PAR, \(\varepsilon\) represents the intrinsic quantum efficiency of CO\({}_{2}\) uptake (mol mol\({}^{-1}\)), and \(R_{\mathrm{d}}\) is the leaf dark respiration rate (mol m\({}^{-2}\) s\({}^{-1}\)).  
The C4 photosynthesis model was integrated into the BEPS model to couple with the Ball-Berry stomatal conductance model to generate the photosynthesis rate using an iteration calculating procedure [19; 81].
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 4 Modified Generative Adversarial Networks
### Conversion Process  
First, we feed the source speech into the CSM analyzer to extract the contF0, MVF, and spectral envelope. Then, we build an acoustic feature vector by stacking those parametric features. To have zero mean and unit variance in the training dataset, we normalize the acoustic features over all the speakers. Next, both generator and discriminator are optimized in an iterative way in the training time (number of epochs), where one module is being updated while the model parameters of another are fixed. It can be stated that the contF0 and MVF are converted frame by frame using the statistical distribution (mean-variance transformations) of the source speaker to target one. For example, to convert the contF0:  
\[contF0^{\prime}_{i}=\frac{\sigma_{y}}{\sigma_{x}}(contF0_{i}-\mu_{x})+\mu_{y} \tag{21}\]  
\[\mu_{contF0}=\frac{\sum_{i=1}^{N}w_{i}contF0_{i}}{w_{i}} \tag{22}\]  
\[\sigma_{contF0}=\sum_{i=1}^{N}w_{i}(contF0_{i}-\mu_{contF0})^{2} \tag{23}\]  
where \(contF0^{\prime}_{f}\) is the log-scaled contF0 after conversion at frame \(i\); \(\mu_{x}\) and \(\sigma_{x}\) are the source mean and variance of the contF0, respectively; \(\mu_{y}\) and \(\sigma_{y}\) are the target mean and variance of the contF0, respectively; and \(\sum_{i}^{N}w_{i}=1\) is the weight.  
For the spectral envelope, we use low-dimensional representation in a 36-MGC domain to reduce complexity. Thus, the MGC features of the source speaker are converted into those of the target speaker using the trained model described in Section 4. Once the training process is completed, we use the CSM synthesizer to generate speech from the converted features. The converted speech will present both the linguistic contents of the source utterance as well as the speaker attributes (identity, gender, and accent). An algorithm is presented below for summarizing the process of our acoustic modeling.
## 2 Materials and Methods
### Characterization Methods  
SEM-EDS surface and cross-section analysis was performed using a Hitachi SU5000 Schottky Field Emission Scanning Electron Microscope, equipped with both secondary and backscattered electrons detectors (BSE), together with energy dispersive X-ray spectrometry (EDS) module for chemical elemental analysis. Sample surface charging was overcome by employing a Low Vacuum and Variable Pressure module set at 30 Pa, 25 kV accelerating voltage and 125 \(\mu\)A emission current. 3D models of the coated samples' surfaces were generated by using each of the four channels of the BSE detector and the Hitachi Map 3D software. For cross-section PEO thin film analysis, the samples were cut, embedded in conductive resin, gradually polished with abrasive papers having different grain size and finally polished for one hour using IM4000 Ar\({}^{+}\) ion beam milling system.  
X-ray diffraction (XRD) patterns of the substrate and coated samples were recorded on a Rigaku Ultima IV diffractometer in Bragg-Brentano geometry, using CuK\(\alpha\) radiation (\(\lambda\) = 0.154 A, 45 kV and 40 mA) and a D/teX Ultra one-dimensional detector with graphite monochromator. For the phase analysis of the coated samples, XRD patterns were acquired in the 2\(\theta\) range of [15\({}^{\circ}\)-80\({}^{\circ}\)], with a step of 0.05\({}^{\circ}\) and a scanning speed of 3\({}^{\circ}\)/min. The ICDD PDF4+ 2022 database was used for crystalline phase identification.  
Microstructural parameters and quantitative phase analysis were performed by whole powder pattern fitting (Rietveld analysis) [29], using Rigaku's PDXL2 XRD software. Reliable XRPD (X-Ray powder diffraction) microstructural analysis required the detachment of the PEO coatings from the substrate using a procedure adapted from [30]: the coated samples were mechanically polished on the edges and then submitted in a H\({}_{2}\)SO\({}_{4}\) 3M solution. The sulfuric acid reacts preferentially with the magnesium alloy substrate producing MgSO4 and hydrogen gas, and so in a manner of minutes, the coating was detached from the substrate. After ultrasonically cleaning in acetone, the detached coatings were dried, fine gr grinded and submitted to analysis. The XRD patterns for Rietveld analysis were acquired in the 2\(\theta\) range of [15\({}^{\circ}\)-103\({}^{\circ}\)], with a step of 0.02\({}^{\circ}\) and a scanning speed of 0.5\({}^{\circ}\)/min. The micro-hardness of the coatings was measured using a FALCON 500 micro-Vickers hardness tester from INNOVATEST.  
The corrosion resistance of the uncoated and coated samples was tested in 3.5 wt.% NaCl solution at room temperature by measuring the polarization curves with a PARSTAT 4000 electrochemical system (Princeton Applied Research, Oak Ridge, TN, USA) over a voltage range of \(-\)0.25 to \(-\)1.5 V at 1.0 mV/s in a conventional three-electrode cell.