esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Speed Dynamic Projection Mapping onto Human Arm with Realistic Skin Deformation +
## 2 Related Work
### 3D Human Surface Estimation  
In computer vision, remarkable results have been achieved in the reconstruction of the human body surface based on parametric deformable surface models, such as SMPL [38], without using markers.  
Early works have been aimed to fit parametric models to 2D image observations through iterative optimization, implying time-consuming computations and requiring careful initialization. [PERSON] et al. [39] proposed the SMPLify framework to automatically estimate the SMPL parameters using human landmarks. Similarly, [PERSON] et al. [40] introduced a method for estimating the SMPL parameters using silhouettes, and [PERSON] et al. [41] proposed the Total Capture system to estimate the SMPL parameters using a multicamera system.  
Convolutional neural networks have been widely studied with the development of deep learning. [PERSON] et al. [42] presented an end-to-end adversarial learning method, HMR, to estimate the SMPL parameters in approximately 40 ms using a single GPU (NVIDIA GeForce GTX 1080 Ti; NVIDIA, Santa Clara, CA, USA). Similarly, [PERSON] et al. [43] introduced an end-to-end framework to estimate the SMPL parameters in 50 ms using a single GPU (NVIDIA Titan X). In these methods, the process is decomposed into two stages--first, regression of some types of 2D representations such as joint heatmap, mask, or 2D segmentation; second, estimation of model parameters from the intermediate results.  
As the performance of two-stage methods relies heavily on the accuracy of the intermediate results, input information is not fully utilized. Recent studies have reported the higher efficiency and effectiveness of estimating meshes representing human bodies in a single stage instead of two stages [44; 45; 46]. The concept of DenseBody proposed by [PERSON] et al. [46] achieves state-of-the-art performance in speed by introducing new representations for 3D objects, being more suitable for convolutional neural networks. Its runtime is approximately 5 ms for images of 256 \(\times\) 256 pixels using a single GPU (NVIDIA GeForce GTX 1080 Ti). Although a runtime of 5 ms may be appropriate, the accuracy of DenseBody is still a critical problem, especially for representing the limbs. The main reason for the limited accuracy of DenseBody is its low resolution, and the limbs only account for a small portion of input images of the whole body. Thus, the skin deformation accuracy on the limbs, including the arms, is low for dynamic projection mapping. Moreover, the reconstruction of realistic skin deformation without markers while achieving high projection accuracy and speed remains to be solved.
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 7 Suggestions for Improvement  
Through a numerical simulation of the compression retention volume loss in the fluid domain of a CO\({}_{2}\) piston pump and the liquid film leakage volume loss of three important friction pairs, the following design key points were noted:  
1. Reducing the clearance volume of the plunger cavity can reduce the retention volume loss and secondary compression loss of the plunger pump. Minimum bottom clearance between the plunger and the top of the cavity can be ensured through high-precision processing and strict tolerance matching. Replacing the plunger structure, designing the plunger as a ladder shape approaching the bottom dead center, and making the smaller-diameter part enter the top of the cavity can effectively reduce the loss of retention volume;
2. Groove structures (spiral grooves, hole grooves, annular grooves, etc.) set into the surface of moving parts of the friction pairs introduce hydrodynamic effects, which can inhibit leakage volume loss of the liquid film.  
Figure 12: Leakage volume loss of plunger side.  
Figure 13: Leakage volume loss of the sliding shoe pair.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 7 Economic Aspects of Submarine Networks
Results for the amplifiers' cost comparison showed that the use of MC-EYDFAs (over the EDFAs) in the 29-node, 16,400-km span Mediterranean network can achieve cost savings of around 37%. Fiber coating will be a cost-reduction factor in the near future. Specifically, in order to cope with future capacity needs, increasing the number of FPs and keeping the costs down are of important interest. As the space housing in a submarine cable is limited, a novel solution is to develop fibers with thinner coating variants of approximately 200 \(\upmu\)m, and also the fiber out diameter. Although during the first 30 years of single-mode fiber manufacturing, a coating diameter of 245-250 \(\upmu\)m was a standard in the industry, OFS in 2014 launched a 200-\(\upmu\)m fiber to deal with the need of the higher fiber density demands. Submarine fibers with these characteristics can give up to a 50% higher fiber density by a reduction on both the primary and secondary coatings and an existing fiber diameter at 125 \(\upmu\)m, which guarantees that the existing fiber-processing procedures can be used. In [85], [PERSON], et al. made a techno-economic analysis for the application of MCFs consisting of 2-4 weakly coupled cores and reduced diameter fibers (RDF) in high-capacity submarine cables simulating the transatlantic link length. Their research evaluated three system design methods in order to examine the use of MCF in submarine cable systems. They compared two different diameter SCFs with MCFs exploring cable capacity and cost/bit achieved. Their study's results showed that MCFs can provide higher cable capacity if FP limits are imposed, but likely not at lower cost/bit unless optimistic and best-case assumptions are considered with respect to MCF relative fiber cost. Moreover, their results showed that the reduced diameter SCFs can bring much of the density and cable cost reduction that motivates interest in MCF without the challenges of a new ecosystem as required by MCF. However, MCF may trigger the design of the highest cable capacities, such as 1 Pb/s, which cannot be possible with SCFs unless important cable changes are made. From Figure 34 we can conclude that there is one lowest cost/bit \"optimal\" design and capacity for each FP bringing out the SDM benefit. Also, an optimal design is below maximum capacity/FP, achievable with the given line card by ~1 dB. In the case of a power-limited submarine system (i.e., a very long distance requiring many FPs) \"optimal\" design has to be more \"linear\" in order to be more power efficient.
## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis
#### 3.3.3 Data Visualization in Internet of Emergency Services
about how to respond, ultimately saving lives and minimizing the impact of disasters and emergencies on communities. One specific example of the use of GIS in an emergency response is the response to Hurricane Katrina in 2005. GIS was used extensively to help manage the response effort, providing information on the location of affected areas, the extent of damage, and the location of emergency resources. This information was used to help coordinate the response effort, directing resources to where they were most needed and ensuring that critical supplies and services were delivered to those in need. Another example is the use of GIS in wildfire response. GIS can be used to create maps that show the location and extent of a wildfire, as well as the location of homes and other structures that may be at risk. This information can be used by firefighters to create a plan of attack, directing resources to where they are most needed and helping to protect homes and other structures from damage. Another application of data visualization in IoES is the use of dashboards and real-time data feeds to provide situational awareness to emergency responders and decision makers. By displaying data in an easy-to-understand format, emergency responders can quickly assess the situation, identify potential risks, and make informed decisions. Data visualization can also be used to analyze historical data and identify trends and patterns that can help emergency responders prepare for future incidents [110]. For example, by analyzing past emergency response data, emergency responders can identify areas that are prone to disasters and develop more effective response plans. Data visualization is a powerful tool in the IoES that can help emergency responders make more informed decisions, identify potential risks and hazards, and respond to disasters more effectively. As technology continues to evolve, the use of data visualization in emergency response and management will likely become even more critical.
## 5 Effect of RAS on Performance Properties of Asphalt Binders and Mixtures
### Moisture Damage Resistance of RAS-Modified Asphalt Mixtures  
The tensile strength ratio (TSR) is the most common parameter used to evaluate the moisture susceptibility of asphalt mixtures. [PERSON] et al. used 4.77% optimum virgin asphalt binder and noted a 54% TSR value for virgin asphalt mixtures without shingles. The addition of 3% and 6% RAS led to 81 and 87% TSR values, respectively [14]. [PERSON] et al. concluded that the addition of RAS had no effect on the moisture susceptibility of asphalt mixtures [40]. [PERSON] et al. suggested that the addition of 5% RAS and 15% RAP slightly affected the moisture resistance of modified mixtures, while 10% RAS and 25% RAP gave TSR values lower than the acceptance limit (80%) [42]. [PERSON] et al. used the Hamburg wheel track test to evaluate the rut depth as well as moisture damage resistance. They concluded that there was no stripping inflection point in both virgin and RAS-modifiedasphalt mixtures. Thus, RAS addition does not increase the moisture susceptibility of asphalt mixtures [43].  
[PERSON] et al. discovered that 25% RAP and 5% RAS did not affect the moisture damage resistance of modified asphalt mixtures [44]. [PERSON] et al. found that RAS/RAP-modified mixes had relatively higher moisture damage resistance. Moreover, WMA provided similar or better moisture damage resistance [32]. [PERSON] et al. used two different aggregate sources, but mostly RAS-modified test specimens had more than 80% TSR values, which is a common acceptance criterion [52]. [PERSON] et al. found that there was an inverse relationship between the RAS quantity and moisture damage resistance. However, in the Hamburg wheel track test, there was an improvement in the stripping inflection point and putting depth with an increase in the RAS quantity. The increase or decrease in RAS mixtures' moisture damage resistance is strictly related to the specifications [58].
## 4 Experimental Result of IGSAE
### Effect of the Coefficients and Classifiers  
#### 4.5.1 Coefficients Optimization on the Six Datasets  
In this experiment, two optimization techniques are used to find the optimum of the three coefficients \(\epsilon\), \(\lambda\), and \(\mu\). The first one is a grid search algorithm that searches all values within a specific range and the second is a Bayesian optimization algorithm that probabilistically finds the optimal parameter composed of a surrogate model (Gaussian process) and an acquisition function [46; 47; 48]. Figure 5 shows the experimental results for coefficients \(\epsilon\), \(\lambda\), and \(\mu\) using grid search, and the ranges are \(\{0.005\times\mathrm{e}|\mathrm{e}=1,\ 2,\ 3,\dots,\ 10\}\), \(\{0.01\times\mathrm{e}|\mathrm{e}=1,\ 2,\ 3,\dots,\ 10\}\) and \(\{0.001\times\mathrm{e}|\mathrm{e}=1,\ 2,\ 3,\dots,\ 10\}\). Since \(\lambda\) is relatively more influential than \(\epsilon\) and \(\mu\), we reduce the range of the sensitive coefficient by the results of a grid search and, additionally, test the coefficient using the Bayesian optimization algorithm to obtain a suitable value efficiently. The six datasets are split into two subsets: one accounting for about one-fifth of all samples as the test data, and the rest as the training data. We ran tests five times with randomly shuffled samples. As a classifier, SVM was chosen to optimize all coefficients. Figure 5 shows the results of the parameter experiment on the Heart dataset. As the Wasserstein regularization parameter increases, the classification accuracy decreases. It shows superior outputs for values between about 0.003 and 0.004. \(\lambda\) is the most important coefficient for extracting core features, and the optimal value should be obtained through experiments since the performance varies greatly depending on the data. If \(\lambda\) is too low, it causes a huge loss of the guiding feature and low accuracy of classification. If \(\lambda\) is too high, the weights are updated to improve the performance of guiding layers only. It brings a high reconstruction error of the IGSAE. The \(\mu\) is the role to prevent overfitting. If \(\mu\) is too low, the first guiding and second guiding layers are not interactive. In this case, the imbalanced guiding layers are updated only to reduce the reconstruction error, which causes overfitting easily. If \(\mu\) is too high, the first guiding layer and the second guiding layer become almost the same. This is too stiff to be optimal and leads to high reconstruction error. The \(\epsilon\) filters sparse nodes out. It may increase or decrease the performance depending on the data and hidden unit. In Figure 4, IGSAE shows better results when sparse regularization is not applied. Therefore, the parameter optimization process to obtain useful features is imperative in IGSAE. Figure 5-f are the optimization results of Wine, USPS, Fashion-MNIST, Yale, and COIL-100, respectively.
## 3 Results
### Case Study
#### 3.1.1 Load Data Pre-Processing  
In any process involving artificial neural networks, normalizing the input data before training is essential. This accelerates learning, leads to faster convergence, and reduces the risk of overfitting [49]. In this case, all measured data followed a Gaussian distribution. For instance, Figure 7 illustrates the standardization of energy demand data, which centers the values around a mean of just under 5 GWh/h and a standard deviation of approximately 1 GWh/h, providing the optimal scaling method for normalization. This remarkable natural behavior is due to the fact that residential load behavior dominates regional load demand.  
Figure 7: Energy demand Gaussian distribution representing the probability distribution of energy demand.  
Figure 8 illustrates GCRORI's daily hourly load profiles from 2016 to 2022. Note that a few profiles deviate from the majority's behavior and tend toward zero at the end of the day. These profiles are anomalous due to a malfunction in the measuring devices.  
By clustering similar load profiles, the training process can be optimized to identify more subtle temporal patterns, resulting in more accurate load demand forecasting. This approach not only enhances the model's predictive accuracy but also enables a more targeted strategy for addressing various types of load patterns that might otherwise be overlooked in a generic training method.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 4 Challenges and Research Issues  
Existing studies and experiments have provided fruitful results in terms of the network framework, key generation rate, communication distance, and routing protocol. However, there are still some challenges and issues that must be resolved. This study focusses on security issues (i.e., security assumptions and applications) to indicate important challenges and issues associated with QKD networks, describes these challenges and the feasible solutions and strategies for researchers to understand these challenges easily, and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Network** & **DARPA** & **SECOQC** & **UQCC** & **China QKD** \\ \hline Project year & 2002–2006 & 2004–2008 & 2010 & 2014–2017 \\ QKD Network type & Active optical switch + Trusted node & Trusted node & Trusted node & Trusted node \\ QKD protocol & BB84 protocol & 5 different QKD protocols & BB84 and BBM92 & BB84 \\ Max. key generating rate & 400 bps & 3.1 kbps & 304 kbps & 250 kbps \\ over 29 km & over 33 km & over 45 km & over 43 km \\ \hline \end{tabular}
\end{table}
Table 1: Summary of QKD network experiments.
## 2 Description of the Proposed Approach  
### Network Architecture  
The FLRAF routing protocol utilizes the network architecture depicted in Figure 1, which comprises ordinary sensor nodes, sink nodes, and a shore-based station. The ordinary sensor nodes, which are responsible for data collection, are dispersed randomly within a 3D space. The sink nodes, fixed on the ocean surface, gather the data collected by the ordinary sensor nodes. Consequently, after receiving the acoustic data transmitted from ordinary sensor nodes in a hop-by-hop manner, the sink nodes aggregate the information and relay it via electromagnetic waves to the shore station for subsequent processing and analysis.
## 4 Algorithm Analysis
### Message Flow between Nodes
#### 4.1.3 Message Flow during In-Cluster Node Position Adjustment  
In this process, the message flow between nodes takes place in three sub-processes. First, a cluster head node receives \(M_{CD}\) from its next-hop node and begins to calculate the position of its in-cluster nodes, such as \(p_{2}\) in Figure 6c. Second, after the position calculation of in-cluster nodes, it sends message to its in-cluster nodes. Last, it sends \(M_{CD}\) to one of its last-hop nodes if it cannot be adjusted. Otherwise, the node sends \(M_{AD}\) to its next-hop node. The message flows between them are respectively shown in Figure 9a-d.
## 5 Experiments and Results
### Deep Learning Algorithms  
As depicted in Table 3, the DNN model achieved 91% accuracy and F1-score, outperformed by RNN with a higher accuracy of 93%. The LSTM model achieved 98% accuracy, indicating potential overfitting due to the short sequences of the dataset. Figures 10-12 illustrate the confusion matrices for the tested algorithms, demonstrating the significant strength of RNNs at delivering better performance on text classification compared to classical machine learning algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision** & **Recall** & **F1-Measure** & **Accuracy** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline DT & 94 & 92 & 93 & 92 \\ \hline SVM & 93 & 93 & 92 & 92 \\ \hline NB & 91 & 90 & 91 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Text classification performances using machine learning algorithms.  
Figure 8: SVM model performances.  
Figure 9: Naive Bayes model performances.  
Figure 14: Training and loss accuracy for RNN model.  
Figure 12: LSTM model performances.  
Figure 13: Training and loss accuracy for DNN model.
## 2 Model and Methods
### Specific Attenuation and DSD  
The relationship between the specific attenuation (\(a\) in \(\mathrm{dB/km}\)) and the DSD of (1) is given by [35]  
\[a=4.343\times\int_{0}^{\infty}N(D)Q(D)dD, \tag{3}\]  
where \(Q(D)\) is the extinction cross section of the raindrop (in m\({}^{2}\)), which is determined by the raindrop shape, temperature, and frequency, direction and polarization of the microwave signal. Because large raindrops are in an oblate shape, their extinction cross sections will be different for vertically and horizontally polarized microwave signals with the same wavelength. Figure 1 shows a sketch of the raindrop and signals with different polarizations, where \(Q_{H}(D)\) and \(Q_{V}(D)\) are the extinction cross sections for horizontally and vertically polarized links, respectively. As a result, the specific attenuations in the rain field, namely \(a_{V}\) and \(a_{H}\), will also be different.  
The exact shape of a raindrop is usually complicated as it is determined by the surface tension of the water and the air flow around it as it falls. However, there are simplified models available to suit the requirement of calculating \(Q(D)\). In this paper, we adopt the widely used Pruppacher-Beard model [32] to characterize the raindrop shape. The axial ratio of the raindrop is given by  
\[\frac{y}{x}=1.03-0.062D, \tag{4}\]  
where \(x\) is the semiminor axis and \(y\) is the semimajor axis of the raindrop. This model yields a very good approximation of raindrop shapes according to their diameters up to 7 mm [36], which enables us to utilize the T-matrix method [33; 37] for calculating their extinction cross sections. The T-matrix method proposed by [PERSON] [38] is a powerful tool for accurately computing electromagnetic scattering by nonspherical particles. It is based on directly solving [PERSON]'s equations and yet its efficiency enables very fast computation. The fact that raindrops are approximately spheroids further simplifies the T-matrix calculation.
# Graph Neural Network for Senior High Student's Grade Prediction
## 2 Method
### Graph Neural Network
```
1:input: Graph \((E,V,\mathbf{u})\)
2:for\(k\in\{1\ldots N^{e}\}\)do
3:\(\mathbf{e}_{k}^{\prime}\leftarrow\phi^{e}(\mathbf{e}_{k},\mathbf{v}_{r_{k}},\mathbf{v}_{s_{k}},\mathbf{u})\) # Compute updated edge attributes
4:endfor
5:for\(i\in\{1\ldots N^{v}\}\)do
6: let \(E_{i}^{\prime}=\{(\mathbf{e}_{k}^{\prime},r_{k},s_{k})\}_{r_{k}=i,k=1:N^{v}}\)
7:\(\mathbf{e}_{i}^{\prime}\leftarrow\rho^{e\to v}(E_{i}^{\prime})\) # Aggregate edge attributes per node
8:\(\mathbf{v}_{i}^{\prime}\leftarrow\phi^{v}(\overline{\mathbf{e}}_{i}^{\prime}, \mathbf{v}_{i},\mathbf{u})\) # Compute updated node attributes
9:endfor
10: let \(V^{\prime}=\{\mathbf{v}^{\prime}\}_{i=1:N^{v}}\)
11: let \(E^{\prime}=\{(\mathbf{e}_{k}^{\prime},r_{k},s_{k})\}_{k=1:N^{v}}\)
12:\(\overline{\mathbf{e}}^{\prime}\leftarrow\rho^{e\to u}(E)\) # Aggregate edge attributes globally
13:\(\overline{\mathbf{v}}^{\prime}\leftarrow\rho^{v\to u}(V^{\prime})\) # Aggregate node attributes globally
14:\(\mathbf{u}^{\prime}\leftarrow\phi^{u}(\overline{\mathbf{e}}^{\prime},\overline{ \mathbf{v}}^{\prime},\mathbf{u})\) # Compute updated global attribute
15:return: update Graph \((E^{\prime},V^{\prime},\mathbf{u}^{\prime})\)
```  
**Algorithm 1** Computation steps of graph network block. Figure 2: Graph network block (used to represent the data in the graph structure). The third is to construct the graph neural network by composing the graph network block. Because the above graph network block is defined as always taking a graph comprised of edge, node, and global attributes as input, and returning a graph with the same constituent attributes as output, it ensures that the output of one graph network block can be passed as the input to another graph network block. Therefore, the graph network block can be composed to achieve the graph neural network. In the most basic form, two graph network blocks can be composed by passing the output of the first graph network block as the input of the second graph network block. It can be written as \(GN_{2}(GN_{1}(G))\), where \(GN_{2}\) represents the second graph network block, \(GN_{1}\) represents the first graph network block, and \(G\) means the graph structure data. Figure 4 presents the construction of the graph network blocks.