esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analyses  
### Data Groups and Acquisition  
The data obtained by the test system included carrier bus data, carrier GPS/BD reference data, target GPS/BD reference data, airborne radar measurement data, and pod measurement data.  
Among the aforementioned data, the carrier bus data included data generation time \(t_{air}\), carrier inertial navigation output position longitude \(L_{air\_INS}\) (rad), latitude \(B_{air\_INS}\) (rad), and height \(H_{air\_INS}\) (m). The carrier aircraft GPS/BD reference data included carrier GPS/BD time scale \(T_{air}\) (s), carrier longitude \(L_{air}\) (rad), latitude \(B_{air}\) (rad), height \(H_{air}\) (m), and carrier velocity \(V_{air}\) (m). The target GPS/BD reference data included target GPS/BD time stamp \(T_{aim}\) (s), target longitude \(L_{aim}\) (rad), latitude \(B_{aim}\) (rad), and height \(H_{aim}\) (m). The airborne radar measurement data included target longitude \(L_{aim\_rad}\) (rad), latitude \(B_{aim\_rad}\) (rad), height \(H_{aim\_rad}\) (m), and velocity \(V_{aim\_rad}\) (m). The pod measurement data included the target measurement information output by the seeker [20; 21; 22; 23].
# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
## 3 Method
### Preliminaries  
NeRF [5] strives to acquire knowledge about a three-dimensional scene using a given image from different viewpoints. Then it generates new perspectives by predicting the color \(c=(r,g,b)\) and volume density \(\sigma\) for a spatial location \(x=(x,y,z)\) in 3D space, considering the direction of the observation view \(d=(\theta,\Phi)\). An implicit function \(F:(x,d)\rightarrow(c,\sigma)\) can mathematically describe the process. During the rendering process, the color \(\hat{C}(r)\) of a pixel that is intersected by the ray \(r(t)=o+td\) from the camera center \(o\) may be computed by accumulating the color \(c\) along the ray:  
\[\hat{C}(r)=\int_{t_{n}}^{t_{f}}\sigma\big{(}r(t)\big{)}\cdot c\big{(}r(t),d \big{)}\cdot T(t)dt \tag{1}\]  
where \(t_{n}\) and \(t_{f}\) represent the lower and upper limits, \(T(t)\) is the cumulative transmittance from \(t_{n}\) to \(t\), \(s\) is the thickness of the acquired image, and \(r\) is the brightness:  
\[T(t)=exp\big{(}-\int_{t_{n}}^{t}\sigma\big{(}r(s)\big{)}ds\big{)} \tag{2}\]In NeRF-based on a hash grid [8], the coordinate x of the spatial point is encoded using a multi-level hash encoder H. Therefore, with the audio feature as the condition, the basic implicit function of the audio-driven portrait synthesis based on NeRF can be expressed as follows:  
\[\mathcal{F}^{\mathcal{H}}:(x,d,a)\rightarrow(c,\sigma) \tag{3}\]  
In this paper, we adopt the fundamental framework of previous studies based on NeRF [6; 11; 12; 34]. More precisely, we simulate the talking head from a video of a specific person delivering a speech that lasts many minutes. Meanwhile, we use a 3 DMM [37; 38; 39; 40; 41] model to estimate the camera parameter for each video frame based on head posture. We utilize the pre-trained DeepSpeech [42] to extract audio features. Following existing approaches, we use the semantic parsing method to segregate distinct components in each video frame for various applications.
## 4 System Design
### H\(\infty\) Controller  
After \(Q(s)\) is designed, the virtual plant of the outer loop is made certain, and then \(\Delta_{1}\) can be calculated, as is shown in Figure 13, in which \(W_{3}(s)\) is also drawn for comparison. In Figure 13, the black lines represent the possible multiplicative perturbation \(\Delta_{1}^{(i)}\) of the models in \(P_{0}(s)\) against the nominal model \(P_{\rm n}(s)\), which is compensated by the DOB. Compared with \(\Delta_{\rm L}\), the uncertainty is restrained significantly in the low-frequency region, while in the middle- and high-frequency regions, the uncertainty enlarges some, but it is still below \(W_{3}(s)\). This means the allowable bandwidth of the  
Figure 12: (**a**) Frequency magnitude responses of \(Q(s)\) and \(W_{3}^{-1}(s)\); (**b**) Frequency magnitude responses of \(1-Q(s)\) and \(W_{1}^{-1}(s)\).  
system changes little after being compensated by the DOB, while the system perturbation decreases greatly in the low-frequency region. The function \(W_{3}(s)\) can be a candidate weighting function to describe the uncertainty of the outer loop.  
The H\(\infty\) mixed sensitivity method is used to derive the performance controller \(G_{\mathrm{c}}(s)\). The sensitivity function \(S_{\mathrm{c}}\) and the complementary sensitivity function \(T_{\mathrm{C}}\) of the outer loop are deduced according to the definition of robust control theory as  
\[S_{\mathrm{C}}=\frac{1}{1+P_{\mathrm{n}}(s)G_{\mathrm{c}}(s)},\ T_{\mathrm{C} }=\frac{P_{\mathrm{n}}(s)G_{\mathrm{c}}(s)}{1+P_{\mathrm{n}}(s)G_{\mathrm{c}}( s)} \tag{29}\]  
The performance weighting function \(W_{\mathrm{lo}}(s)\) of the outer loop is set as  
\[W_{\mathrm{lo}}(s)=\frac{3.017\ \mathrm{s}+17500}{33.33\ \mathrm{s}+1} \tag{30}\]  
Set \(W_{3\mathrm{o}}(s)=W_{3}(s)\), \(W_{2\mathrm{o}}(s)=0.00001\), and then solving the standard H\(\infty\) mixed sensitivity problem, \(G_{\mathrm{c}}(s)\) is obtained as  
\[G_{\mathrm{c}}(s)=\frac{4809662.1287(s+38.82)(s^{2}+1759s+1579000)}{(s+426300)( s+0.03)(s^{2}+4509s+7399000)} \tag{31}\]  
The closed-loop characteristic curve of the nominal plant is displayed in Figure 14.  
Figure 14: Closed-loop characteristic curve of the nominal plant.  
Figure 13: System uncertainties for outer loop design.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 5 Data Analysis
In the majority of the instances, SSA is able to provide superior forecasts without the need for data transformations when compared with time series following varied frequencies. Figure 4: Effect plot: RMSFE \(\sim\) Tr + Freq + Tr \(\times\) Freq for forecast horizons \(h=6\) and \(h=12\).
# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 4 Solution Description
### AI and Human Validators  
The platform's architecture integrates a federated AI component that is formed by separated autonomous AI modules. These AI modules perform, in parallel, along with human actors, the analysis of the news and assign a trust score to each of them. Moreover, each of these modules can provide additional information as to why a certain trust score was given.  
The platform is designed to support a plugin-like architecture while also exposing well defined interfaces, so that anyone can develop an AI module and register it in the platform. The AI modules are subject to reputation and incentivization/penalization schemes, such as human validator components.  
An AI module usually employs a mix of NLP, Deep Learning and Neural Networks to extract the relevant entities from the news, to perform sentiment and bias analysis, to detect clickbait, to check for semantic similarities and to automatically classify the news items. A Cross-Lingual Semantic Textual Similarity can also be considered, which will allow the use pretrained models on multiple languages.  
The platform combines the AI modules and the human validators effort through a recommender system that selects which news will be validated by humans. This system must consider at least the following three factors:  
* Validator affinity: a human validator receives news on topics and areas that he is familiar with, based on previously validated news. This approach is similar to the behavior of classic recommender systems.
* Confidence scores of the AI validators: the fake news detection can be seen as a semi-supervised learning problem. Most news articles are unlabeled data, with few data being labeled by human validators. Uncertainty sampling will allow the recommender system to select the news for which the AI validators are least certain to be validated by humans.
* A random factor to reduce the assignments predictability and to prevent malicious actors from exploiting the system and to modify the verdict for certain news. An entity that spreads fake news might try to add bot-like human validators to our system to manipulate the news assessment.
## 3 Parameter Optimization of PRPS and CGPR
### Parameter Optimization of CGPR  
The grouting thickness is the major parameter of CGPR design because it is directly related to the development and the distribution of the plastic zone [28]. To obtain optimal grouting thickness, in the design stage, there were ten grouting schemes with different grouting thickness ranging from 0 to 5 m. Due to the importance of the rock pillar, increasing the grouting volume and multiple grouting were particularly used to improve the mechanical properties of the rock pillar [14; 15]. The material properties of the grouted rock mass and rock pillar are shown in Table 4. The grouting area was the surrounding rock around the excavation contour. Because of the disturbance of the complex grouting process, the primary lining was delayed with a lag distance of 4 m.  
According to the numerical results (Figure 13), we found that the PPR was more sensitive to grouting thickness than the MCS. It could be attributed to the larger reinforced zone and smaller stiffness of CGPR. The primary role of CGPR was improving mechanical properties and self-capacity of surrounding rock and protecting the rock pillar from excavation disturbance. Relatively, the stiffness of the grouted rock was much smaller than the pipe roof, which led to less effect of the CGPR in controlling crown settlement. Therefore, the PPR should be the major evaluation parameter in grouting thickness optimization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\rho\) **(kg/m3)** & \(E\) **(GPa)** & \(\
u\) & \(c\) **(kPa)** & \(\varphi\) (\({}^{\circ}\)) \\ \hline \hline Grouted rock mass & 2200 & 2.5 & 0.33 & 300 & 30 \\ Grouted rock pillar & 2200 & 8.0 & 0.28 & 900 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties of grouted rock mass and rock pillar.  
Figure 12: Effect of pipe arrangement angle on MCS and PPR.  
The PPR reduced by 78.1% when the grouting thickness increased from 0 to 5 m. When the grouting thickness was larger than 3 m, this decreasing trend became less noticeable. Moreover, the high cost and long construction period of the CGPR should also be taken into consideration. Thus, the reasonable grouting thickness was determined to be 3 m.
# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
## 3 VGG-16 and Multilayer Perceptron
### Network Architecture
#### 3.2.1 Fine-Tuning VGG-16  
The adaptation of learned knowledge of a pre-trained model from one problem to another problem is known as transfer learning. In deep learning, transfer learning is a popular technique because the model was pre-trained on a large dataset, thus requiring significantly lower computational resources. Mostly, the technique is applied by transferring the learned features of pre-trained models to solve downstream tasks. To ensure that the pre-trained model effectively adapts to the downstream tasks, fine tuning is required. During the fine-tuning process, the entire model or part of the model is unfrozen. The number of dense layers and classifier layers to be added to the network depends on the complexity of the downstream tasks.  
In this work, a pre-trained VGG-16 model [46] was fine-tuned. The VGG-16 model is an improved version of the AlexNet [3] model where the VGG-16 model consists of more convolutional layers and uses the smallest kernel size. The VGG-16 model was trained over the ImageNet dataset that contains 15 million images. The VGG-16 model is made up of 13 convolutional layers, 5 max-pooling layers, and 3 fully connected layers, where the convolutional and max-pooling layers were segmented into five sets. The first two sets contain two convolutional layers followed by a max-pooling layer. The subsequent three sets consist of three convolutional layers and a max-pooling layer. The kernel size of 3 \(\times\) 3 with stride 1 and padding 1 was used throughout the network that makes the model stand out among the other pre-trained models. The usage of the smallest kernel size greatly reduces the number of parameters. Moreover, the application of the smallest kernel size also avoided the network from becoming overfit. Max pooling was performed over a 2 \(\times\) 2-pixel window with stride 2. By doing so, the dimension of the feature maps is halved from the original size. The rectified linear unit (ReLU) activation function was applied on  
Figure 1: Sample GEIs obtained from different datasets, CASIA-B (**first row**), OU-ISIR D (**second row**), and OU-LP (**third row**).  
Figure 2: The architecture of the proposed VGG16-MLP model.  
all the convolutional layers, as it is computationally efficient and reduces the vanishing gradient problem.  
The pre-trained VGG-16 model is chosen due to its easy implementation. Furthermore, a smaller number of parameters are involved in the model, which resulted in a faster learning network.
## 4 Solution Approach
### Training Vehicles with Data  
We consider a dataset \(\mathcal{D}_{n}\) with \(n\) samples that comprises a \(d\)-dimensional feature space bounded and closed in \(\mathbb{R}^{d}\). The vehicle selection is assigned to the UAV according to a policy that varies with the UAV's battery power and trajectory coordinates. The non-optimal actions are iteratively removed from the action set. We introduce a trajectory storage vector \(T_{r}(x)\) that stores the difference between the two adjacent trajectory coordinates. We identify the trajectory predictors of \(T_{r}(x)\) using samples from \(\mathcal{D}_{n}\). To build estimators of \(T_{r}(x)\), we calculate an optimal minimax rate to estimate \(T_{r}(x)\) using \(\mathcal{D}_{n}\). The optimal actions are updated as \(V(s)=\max_{a}\{Q(s,a):a\in\mathcal{A}(s),\mathcal{A}(s)\subseteq\mathcal{A}\}\), where \(\mathcal{A}\) denotes the available actions for the vehicles and the UAV. The state value \(V^{*}(s_{t})\) is the total reward a vehicle can accumulate and an action value \(Q^{*}(s_{t},a_{t})\) is the cumulative reward. The Bellman equations are given by Equations (14) and (15) as  
\[V^{*}(s_{t}) = \max_{a_{t}}\{\mathcal{R}(s_{t},a_{t})+V^{*}(s_{t+1})\}\quad \forall t\in T \tag{14}\]  
where \(\mathcal{R}(s_{t},a_{t})\) is the accumulated reward for a state-action pair. The value \(Q_{\theta}\) is updated as in Equation (15):  
\[Q_{\theta}(s_{t},a_{t+1})=\min\Bigl{(}Q_{\theta_{1}}(s_{t},a_{t}),Q_{\theta_{2 }}(s_{t},a_{t})\Bigr{)}+\gamma_{d}\mathbb{E}_{a_{t}\sim\pi}\bigl{[}Q_{\theta}(s _{t},a_{t})+\varphi\mathcal{H}\bigl{(}\pi_{\phi}\bigr{)}\bigr{]} \tag{15}\]  
where \(\mathcal{H}\bigl{(}\pi_{\phi}\bigr{)}\) is the policy learned by the UAV at height \(\mathcal{H}\). At different heights, the available battery power is different, leading to different policies, \(\gamma_{d}\) is the discount factor; \(a_{(t+1)}\) is the action at the next time; and \(\varphi\) is the personalization parameter. The expectation \(\mathbb{E}_{a_{t}\sim\pi}\) is over state \(s\). The policy aims to maximize the cumulative reward and hence we parameterize the policy using past interactions. For UAV trajectory prediction, we propose a multidimensional time series. The UAV's action space is continuous and an actor-critic algorithm is used to train the trajectory predictor. This extracts the temporal dependencies among vehicles' data to forecast the data traffic of each vehicle when spatial correlations exist. This enables PFL to learn the long-term dependency among the local models of different clusters. As illustrated in Figure 4, the local model hyper-parameters are stored and communicated to the UAV. Using PFL, the redundant local model hyper-parameters are discarded to reduce the overall computational complexity of the proposed solution approach.  
Figure 3: A block diagram representing the PFL-based solution approach for UAV-assisted vehicular communications.
## 1 Introduction  
Nearly 25 years ago, Iijima began considering carbon nanotubes [1]. After this time, scientists began finding applications (including but not excluding: medicine, drug delivery, solar cells, electronics) for carbon nanotubes technologies in different configurations (single and multi-walled carbon nanotubes, functionalized carbon nanotubes) and for different purposes due to their properties including mechanical strength, aspect ratio and electrical and thermal conductivity. As recent literature has indicated, these nanomaterials are unconditionally considered as suitable adsorbents in the treatment of waters and in the recovery and separation of metal species from aqueous solutions [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]  
Referring to gold adsorption, little information is available using this nanotechnology, despite the fact of the importance and price (reaching USD1244 per ounce barrier as at 16 th July 2018) of this metal and its presence in aqueous solutions coming from the processing of ones (alkaline cyanide medium) or jewelry scraps and printed circuit boards (PCBs) (acidic chloride medium). The reference [13] mentioned in the manuscript title about \"prerequisites of carbon nanotubes to adsorb gold(III)\", though then in the text and conclusions not special mention to such requisites were given, the second reference on the topic is about the adsorption of Cr(VI) and Au(III) onto oxidized multiwalled carbon nanotubes [14].  
Due to this lack of recent information, in the present investigation, the adsorption of gold from these two aqueous media (alkaline cyanide or acidic chloride) by multi-walled carbon nanotubes was considered. The aim was to optimize various operational parameters and, thus, obtain efficient carbon nanotubes processing.
## 3 Results and Discussion
### Quality Evaluation
#### 3.1.3 \(Q^{\text{LSSIM}}\)  
\(Q^{\text{LSSIM}}\): A quality metric of fused image according to the structural similarity between the source images. The closer the \(Q^{\text{LSSIM}}\) value to 1, the better the fusion performance. \(Q^{\text{LSSIM}}\) is calculated from SSIM between fused image and two source images. SSIMM is defined as follow:  
\[Q^{\text{LSSIM}}(A,B,F)=\frac{1}{N}\sum{ISSIM}(A,B,F) \tag{16}\]  
where _ISSIM_ are obtained by using the method in [24], \(N\) is the size of \(ISSIM(A,B,F)\),and \(\gamma(w)\) is local weight coefficient which is calculated as \(\gamma(w)=\frac{\alpha_{wA}^{2}}{\alpha_{wA}^{2}+\alpha_{wB}^{2}}\).
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 2 Experiment to Acquire Infrasonic Signals during Sandstone Shear Destruction
### Experimental System
#### 2.1.1 Rock Shear Loading System  
The equipment used for rock shear loading was a YZ-50 direct shear apparatus with a maximum load of 1000 kN. The direct shear apparatus can complete rock shear loading under stable axial pressure, automatically collect data such as time, tangential force, and tangential displacement, and store data in the PC connected for later analysis.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 5 Results and Discussion
### Comparison with the State-of-the-Art  
Table 6 collects the metric values achieved by all evaluated approaches. The DL approaches described in [20, 31, 32, 33, 34] and random forest classifiers [30] are selected for purposes of comparison. They have been chosen not only because they are among the most efficient ML approaches in literature, but also because of their similarity to the proposed work, as they perform their crop classification on Campo Verde database. In addition, for the selected approaches, some parameters, such as the patch size, depth, and filter size must be carefully set to guarantee the best accuracy to be achieved for a given application. Table 7 collects computational complexities data where the number of _MAC_ operations is computed for compared DL models where the architecture details are provided. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Methodology**} & \multicolumn{3}{c}{**Image Sequence 1**} & \multicolumn{3}{c}{**Image Sequence 2**} \\ \cline{2-9}  & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** & \(f_{1}\)**(\%)** & \(AA\)**(\%)** & \(OA\)**(\%)** \\ \hline Proposed methodology & **73.67** & **92.23** & 84.31 & **78.11** & 85.21 & **77.98** \\ Proposed methodology - BP & 73.63 & 92.21 & 84.26 & 77.86 & **85.63** & 77.32 \\ FCNN by [34] & \(\approx\)70 & - & \(\approx\)**87** & \(\approx\) 70 & - & \(\approx\)75 \\ FCNN by [33] & \(\approx\)60 & - & \(\approx\)85 & \(\approx\) 60 & - & \(\approx\) 75 \\ FCNN by [32] & \(\approx\)60 & - & \(\approx\)78 & \(\approx\)68 & - & \(\approx\)72 \\ RNN-CNN by [20] & - & 64.20 & 77.24 & - & 64.46 & 68.06 \\ Dense-FCNN by [31] & - & 79.1 & 81.7 & - & 67.6 & 74.9 \\ Random Forest [30] & \(\approx\)42 & - & \(\approx\)65 & \(\approx\)52 & - & \(\approx\)62 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall accuracy from Campo Verde database and comparison with the state-of-the-art.The highest values achieved for each measurement are highlighted.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 4 PIC for Next-Generation PON
### Integrated Photonics Packaging
The referred receiving technologies are wide wavelength band, however, with a coherent detection scheme tunable reception can be achieved. Depending on the arrangement, a different number of associated PINs (from dual-polarization differential with 8 PINs to quasi-coherent single photodetector) simultaneous gain and filtering (stemming from the local oscillator beating with the signal) can be implemented [7; 77; 86]. To achieve the optical interface between the PIC and the fiber, there are several challenges to be surpassed, such as the mode adaptation between the WG in the PIC and the cylinder-shaped fiber core [87]. Spot size convert (SSC) modules in the PIC, lensed fibers connected through V-grooves, holders, and other techniques are used to achieve consistent solutions. The PIC packaging interfacing of the electrical connections between the PIC and the PCB is usually carried out by wire-bonding [34]. As bandwidth grows, the trend is to replace the bonding wires by flip-chip solutions to reach higher thermal and electrical performance. Furthermore, packaging should be a gateway to solve power dissipation, which is expected to increase significantly with density [7]. Packaging solutions based on holders with high thermal conductivity and inherent thermal monitoring is an attractive solution to overcome this constraint.