esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Once the C++ application is implemented, the code is optimized after different iterations, checking to ensure that the results of the different algorithm's steps are the same. The applied techniques to vectorize the code and use a heterogeneous platform with a CPU-GPU allow accelerating the original MATLAB by around 10 times (\(\sim\!\times 10\)). Table 2 shows the execution times for each step when 15 iterations are used as an input parameter.  
While most of the code and functions of the algorithm in MATLAB were translated into C++ and optimized, others could not be translated directly. The reason is that there is no information about the internal calculations of some of the functions in MATLAB. This implies that the results obtained in both implementations are slightly different. These differences are mainly evident in the implementation of the griddata function. For this application, it has been used with the \"bicubic splines\" interpolation method, incorporated in the FastDDM solver belonging to the ALGLIB library. Therefore, the differences between the results with the MATLAB version are minor, but not negligible.  
Figures 8 and 9 show the results when comparing the tomographic reconstructions obtained in both versions of the algorithm and the absolute relative error of the projection reconstruction. In the first, it can be seen that the error between the algorithms for the projection reconstruction is less than 5%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function**} & **MATLAB for 15** & \multirow{2}{*}{**C++ for 15 Iterations (ms)**} & \multirow{2}{*}{**Gain (ms)**} \\  & **Iterations (ms)** & & \\ \hline Initialization for & & & \\ smoothing on the open & 3183.0 & 125.6 & 3057.4 \\ magnetic curves & & & \\ Load projections & 3.0 & 5.0 & \(-\)2.0 \\ Load geometry & 165.0 & 103.3 & 61.7 \\ Backprojections & 10.0 & 0.6 & 9.5 \\ Uncertainty preparations & 20.0 & 0.5 & 19.5 \\ Projection lines & 38,400.0 & 19.5 & 38,380.5 \\ approach loop & & & \\ Smooth the closed and & 2400.0 & 54.5 & 2345.6 \\ open surfaces & & & \\ Smooth LCF inside & 4902.0 & 2703.5 & 2198.6 \\ and outside & 27.0 & 3.3 & 23.7 \\ Evaluation of emissivity & 5454.0 & 2953.1 & 2500.9 \\ Uncertainty estimation & 630.0 & 12.8 & 617.3 \\ Evaluation of the & & & \\ reconstruction projection & 1.2 & 1.2 & 0.0 \\ Compute profile & 7624.0 & 264 & 7360.0 \\ radiation and noise & & & \\ \hline TOTAL & 62,819.2 & 6246.7 & 56,572.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution times (in ms) obtained MATLAB vs. C++ application.  
Figure 8: Comparison of the projection reconstruction for 15 iterations between the original MATLAB code and the C++ implementation.  
Figure 9: Comparison between the final reconstruction (15 iterations) obtained using the original MATLAB code and the C++ implementation, respectively.
## 4 Variational Inference Model for Non-Linear Total Variability Embedding
### Maximum Likelihood Training  
Once the GMM supervector \(\mathbf{\hat{m}}(\mathbf{X})\) is generated at the output layer of the decoder, the log-likelihood conditioned on the latent variable \(\mathbf{z}(\mathbf{X})\) can be defined in a similar manner with (4) as:  
\[\log P(\mathbf{X}|\phi,\theta,\mathbf{z}(\mathbf{X}))=\sum_{c=1}^{ C}(n_{c}(\mathbf{X})\log\frac{1}{(2\pi)^{F/2}|\Sigma_{c}|^{1/2}}\] \[-\frac{1}{2}\sum_{l=1}^{L}\gamma_{l}(c)(\mathbf{x}_{l}-\mathbf{ \hat{m}}_{c}(\mathbf{X}))^{t}\Sigma_{c}^{-1}(\mathbf{x}_{l}-\mathbf{\hat{m}}_ {c}(\mathbf{X}))), \tag{13}\]  
where \(\mathbf{\hat{m}}_{c}(\mathbf{X})\) denotes the \(c^{\text{th}}\) component of \(\mathbf{\hat{m}}(\mathbf{X})\). Using [PERSON]'s inequality, the lower bound of the marginal log-likelihood can be obtained as follows:  
\[\log P(\mathbf{X}|\phi,\theta)= \text{log}\mathbb{E}_{\mathbf{z}}[P(\mathbf{X}|\phi,\theta, \mathbf{z})]\] \[\geq \mathbb{E}_{\mathbf{z}}[\log P(\mathbf{X}|\phi,\theta,\mathbf{z} )]. \tag{14}\]  
The marginal log-likelihood can be indirectly maximized by maximizing the expectation of the conditioned log-likelihood (13) with respect to the latent variable \(\mathbf{z}\). The reparameterization trick in [19] can be utilized to compute the Monte Carlo estimate of the log-likelihood lower bound as given by  
\[\mathbb{E}_{\mathbf{z}}[\log P(\mathbf{X}|\phi,\theta,\mathbf{z})]\simeq \frac{1}{S}\sum_{s=1}^{S}\text{log}P(\mathbf{X}|\phi,\theta,\mathbf{z}_{s}( \mathbf{X})), \tag{15}\]  
where \(S\) is the number of samples used for estimation and \(\mathbf{z}_{s}(\mathbf{X})\) is the reparameterized latent variable defined as follows:  
\[\mathbf{z}_{s}(\mathbf{X})=\mu(\mathbf{X})+\sigma(\mathbf{X})\epsilon_{s}. \tag{16}\]  
In (16), \(\epsilon_{s}\sim\mathcal{N}(0,\mathbf{I})\) is an auxiliary noise variable, and \(\mu(\mathbf{X})\) and \(\sigma(\mathbf{X})\) are respectively the mean and standard deviation of the latent variable \(\mathbf{z}(\mathbf{X})\) generated from the encoder network. By replacing  
Figure 1: Proposed variational autoencoder (VAE) for non-linear feature extraction. Blue shows the loss terms. Red shows the sampling operations. GMM: Gaussian mixture model; UBM: universal background model.  
the reconstruction error term of the VAE objective function (11) with the estimated log-likelihood lower bound, the objective function of the proposed system can be written as:  
\[\begin{split} E_{Prop}(\mathbf{X})=& D_{KL}(q_{\phi}( \mathbf{z}|\mathbf{X})\|p_{\theta}(\mathbf{z}))\\ &-\frac{1}{S}\sum_{s=1}^{S}\text{log}P(\mathbf{X}|\phi,\theta, \mathbf{z}_{s}(\mathbf{X})).\end{split} \tag{17}\]  
From (17), it is seen that the proposed VAE is trained not only to maximize the similarity between the prior and posterior distributions of the latent variable, but also to maximize the log-likelihood of the generated GMM by minimizing \(E_{Prop}\) via error back-propagation. Moreover, we assume that the prior distribution for \(\mathbf{z}\) is \(p_{\theta}(\mathbf{z})=\mathcal{N}(\mathbf{z}|0,\mathbf{I})\) analogous to the prior for \(\mathbf{w}\) in the i-vector framework.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 4 Markov Decision Process Modelling
### Reward Function Design  
For reinforcement learning, the agent observes the current environment and selects an action, and then the environment moves to the next state and the agent receives a reward value simultaneously. The reward is very important, because the reward is the quantitative description of the optimization goal for the reinforcement learning mode, which can guide the agent to learn a better policy.  
The optimization goal of the proposed scheduling algorithm in 5G-TSN architecture is to provide a latency guarantee for time-triggered flows as well as to improve throughput of video flows. Hence, the reward function is designed as follows.  
\[\psi_{t}(\gamma_{i})=\left\{\begin{array}{cl}(1+\lambda_{1})*\gamma_{i}&i \in\mathbf{F}_{tt}\text{ and }\text{GCL}(t)=1\\ (1-\lambda_{2})*\gamma_{i}&i\in\mathbf{F}_{tt}\text{ and }\text{GCL}(t)=0\\ \gamma_{i}&i\in\mathbf{F}_{vi}\end{array}\right. \tag{15}\]  
\[\gamma_{i}=\left\{\begin{array}{cl}\frac{1}{d_{i}^{obs}-d_{i}^{obs}+\Delta }&\overline{R}_{i}\geq R_{i}^{\min},\;i\in\mathbf{F}_{vi}\;\;\text{or}\;i\in \mathbf{F}_{tt}\\ 0&\overline{R}_{i}<R_{i}^{\min},i\in\mathbf{F}_{vi}\end{array}\right. \tag{16}\]  
In Equation (15), \(\psi_{t}(\gamma_{i})\) is the reward function, which adopts the formulation of the potential function; it takes the GCL state at the \(t\)th TTI into consideration for time-triggered applications. As shown in Figure 4, in order to reflect the relationship between queueing delay of time-triggered flow in gNB and reward, \(\lambda\) is introduced as a self-adaption factor to adjust the reward. First of all, the rewards are different for the gated open and closed states. Therefore, \(\lambda_{1}\) and \(\lambda_{2}\) are introduced to adjust the reward value in different states. In addition, when the \(d_{i}^{sph}\) of the time-triggered flow at the base station is close to \(d_{i}^{loss}\), the reward value of scheduling the flow will be larger. Therefore, \(\lambda_{1}\) will increase with an increase in \(d_{i}^{sph}\), and \(\lambda_{2}\) is the opposite. The purpose of the reward function setting has two aspects: the first is encouraging time-triggered applications to be scheduled near the latency deadline, which leaves more opportunities for video flow transmission. The second is trying to schedule time-triggered flows when the GCL state of the time-triggered queue in DS-TT is \"ON\" as much as possible.  
Equation (16) defines the basic reward expression for both time-triggered and video applications. We can see that when the queueing delay is closer to the EMLR, more reward value may be achieved. Furthermore, the throughput constraint of video flows is also considered in Equation (16); if the throughput of video flow cannot satisfy the minimum threshold, the reward value is set to zero, which aims to train the agent toavoid making scheduling decisions leading to exceeding latency requirements during the training procedure.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Acid Solutions  
Acid solutions are commonly known for their ability to dissolve cellulose. Diluted acids can be applied for cellulose swelling, but in order to dissolve cellulose completely, more concentrated acids are needed. The treatment of lignocellulosic biomass with diluted acid solutions (lower than 5%) under a moderate temperature and pressure causes the degradation of hemicellulose to monomers. It is a possible way to separate hemicellulose from other components [53]. Sulfuric and hydrochloric acids are the most frequently used agents for cellulose dissolution. Concentrated sulfuric acid (approximately 70%) can break hydrogen bonds in cellulose. Additionally, it has to be taken into account that the esterification of hydroxyl groups in cellulose can occur, which results in replacing hydroxyl groups with sulfate. These processes lead to cellulose depolymerization [54]. Since [PERSON]'s lignin is insoluble in sulfuric acid, this method can be used in its separation from plant biomass [55]. Acidic biomass treatment also causes the further transformation of sugars into furfural and 5-hydroxymethylfurfural. Additionally, in the presence of strong acids, polymeric, insoluble polymers called humins are produced, which can significantly decrease the yield of valuable products produced in biomass transformations (e.g., glucose, fructose, levulinic acid, etc.) [56; 57].  
Organic acids, e.g., trifluoroacetic acid, dichloroacetic acid, and formic acid, can also be applied in biomass processing. This leads to the dissolution of cellulose via derivativization of hydroxyl groups to acyl groups [58].
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 4 Corrosion Detection Approaches
### Computed Tomography  
Computed tomography is a computerized imaging technique which generates cross-sectional images from radiation rays that are aimed and rotated around the object. The cross-sectional images or slices are also called tomographic images. Geometry processing generates 2D and 3D images of an object which contain important information such as shape and dimension. Several slices are stacked together to reconstruct a 3D image for corrosion detection. Computed tomography is a non-invasive technique in computer vision and image processing.  
The thermal spraying method [50] was developed in order to assess the corrosion mechanism and coatings. X-rays and Raman spectroscopy were used to protect the steel of anodic metals such as aluminum and zinc. This method was compared with electrochemical experiments such as open circuit potential and electrochemical impedance spectroscopy (EIS). The results showed that the corrosion resistance property was improved with X-ray computed tomography. The corner steel bar in concrete can also be detected with X-rays [51]. Moreover, novel tomographic acoustic microimaging (TAMI) [52] presented an approach to assess the depth of corrosion and pitted areas in images obtained from scanning acoustic microscopy (SAM). These images were processed with binarization under the C-mode to identify the corrosion pits under the rust. The results of the corrosion pit morphology were validated with optical microscopy and the 2D and 3D surface methodology, which achieved a 40% accuracy with regard to pit regions. Synchrotron radiation computed tomography (SRCT) [53] was tested using electrochemical reaction visualizations, composite failure analysis, and corrosion rate measurements. This tomography technique used a resolution of less than 1 \(\upmu\)m to create the 3D material microstructures. This technique was further improved by adding a fourth temporal dimension to analyze the under-load material response. The reconstructed 4D images managed to enhance the textural details of corrosion detection. On the other hand, neutron tomography [54] was mobilized to provide the details of the internal structure of heritage objects. Nevertheless, this technique can also be used for detecting the degree of degradation of heritage objects. The internal defects and spatial distribution information were obtained using modern mathematical algorithms and neutron attenuation coefficients. The 3D structural volume data and images were obtained, which provided opportunities for the future implementation and improvement of corrosion detection and prediction. Magnetic Resonance Imaging (MRI) can also be implemented for corrosion analysis, as presented in [55; 56; 57].
# Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
## 5 Results
### VST Formation Assembly  
This subsection demonstrates the application effect of VST in formation assembly. First, the initial positions of four USVs were set randomly. The initial coordinates of USV1 were (1,40), with a maximum velocity amplitude of 25 m/s. The initial coordinates of USV2 were (0,0), with a maximum velocity amplitude of 23 m/s. The initial coordinates of USV3 were (30,0), with a maximum velocity amplitude of 24 m/s. The initial coordinates of USV4 were (60,0), with a maximum velocity amplitude of 20 m/s. High-speed vehicles treated low-speed vehicles as obstacles.  
The disturbance coefficient \(\rho_{k}\) for obstacles was set to 3, and the deviation coefficient \(\sigma_{k}\) was set to 2. The disturbance coefficient \(\rho\) for low-speed vehicles was set to 1. The centers of the obstacles were located at (110,80), (40,50), (165,150), and (80,120). The radii of the obstacles were 13, 12, 11, and 10 m. The formation shape and assembly trajectories are shown in Figure 15a. The virtual formation velocity amplitude was 5 m/s.  
Figure 14: Comparison of the trap-area problem in (**a**) traditional IPDS and (**b**) VST.  
Figure 15: VST formation assembly: (**a**) Formation assembly trajectory (blue dots correspond to USV1, USV4, USV3, and USV2 from left to right); (**b**) Convergence of vehicle velocities during formation assembly; (**c**) Distances between vehicles during formation assembly.  
As shown in Figure 15a, all four USVs could quickly assemble in the corresponding formation positions. Figure 15b shows that in the initial stage, USVs pursued the virtual target points at maximum velocity amplitudes; their velocities converged to those of the VSPs within a reasonable range to maintain the formation shape. Figure 15c demonstrates that there were no collisions between vehicles during assembly. Overall, VST met the requirements for formation assembly and performed excellently.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Different Types of BackCom
#### 3.3.1 Dedicated BackCom  
Figure 7 delineates the concept of Ded-BackCom, which considers a dedicated power source for a tag. This type is used to obtain the high throughput and the least interference compared to other BackCom configurations. Ded-BackCom is widely used in a variety of applications. [PERSON] et al. consider a passive WiFi system with a dedicated WiFi source in order to transmit RF carrier signals [35]. Moreover, the paper also discusses the frequency range outside the desired WiFi channel band for BackCom, which allows a WiFi receiver to suppress the resulting direct link (out-of-band) carrier interference.
## 3 Data and Methods
### Linear Regression Model  
The correlation coefficient (R), also called the Pearson correlation coefficient, provides an index of the degree of correlation between two datasets. In the present work, R\({}^{2}\) was used to indicate the strength of the correlation between the modeled data set and the observations. The present work also employed the root mean square error (RMSE) to analyze the error between predictions and observations. The RMSE was used to evaluate the error of the predicted and the uncertainty relative to the TANSO-observed XCO\({}_{2}\). The RMSE and R\({}^{2}\), provide information on the regional performance and uncertainty of the modeled CO\({}_{2}\) concentration.
## 3 Evaluation of Surface Quality, Material Utilization, and Efficiency  
### Evaluation of Surface Quality  
Generally, the key deposition parameters affecting the bead geometry in Step 1 mainly include wire-feed rate (\(W_{\text{FR}}\)), travel speed (\(T_{\text{S}}\)), and welding voltage (\(W_{\text{V}}\)). The key milling parameters affecting the surface quality in Step 2 and Step 3 mainly include spindle speed (\(S_{\text{S}}\)), tool-feed rate (\(T_{\text{FR}}\)), and cutting depth (\(C_{\text{D}}\)). Only the side surface's quality in Step 3 is concerned in this study because the top surface will be covered by subsequent layers. Unlike other independent milling processes, the cutting depth in Step 3 is directly determined by the bead geometry produced in Step 1, as shown in Figure 3d. The larger the bead width (\(B_{W}\)) than the target width (\(T_{\text{W}}\)), the larger the cutting depth. The bead width is determined by the deposition parameters and the target width is a constant value for a specific stiffened panel. Therefore, the surface quality (represented by surface roughness, \(R_{\text{a}}\)) achieved by HWMP is a result of both the deposition and the milling parameters, as seen in Figure 4.  
Figure 3: (**a–c**) Work principle of HWMP for fabricating stiffened panels; and (**d**) the relation between cutting depth and bead width.  
Figure 2: Two-robot cooperative platform for implementing HWMP.  
To model the surface roughness in an efficient way, two cascaded regression models are developed, the first model relating the bead width to the deposition parameters and the second one relating the surface roughness to the milling parameters, as seen in Figure 4. The output of the first model, i.e., bead width, determines the input of the second model, i.e., cutting depth. Then the two models are synthesized to obtain the surface roughness model by establishing a link between cutting depth and bead width. The central composite rotatable design (CCRD) method is applied to obtain each regression model, which has been demonstrated to be an efficient experiment design method with a relatively small number of experiments without losing its accuracy [25; 26]. To apply CCRD, the following procedure should be obeyed: (1) identifying predominant factors affecting the response; (2) determining their upper and lower limits; (3) generating experimental design matrix; (4) conducting experiments according to the experimental design matrix; (5) developing the regression model; and (6) validating the adequacy of the developed model by analysis of variance (ANOVA).
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
## 4 Results
### Breast Cancer Data
\begin{table}
\begin{tabular}{l l l l l} \hline
**Metrics** & **5\%** & **10\%** & **20\%** & **30\%** \\ \hline
**Accuracy** & 0.798 & 0.809 & 0.774 & 0.732 \\ \hline
**Sen (Basal)** & 0.929 & 0.929 & 0.893 & 0.750 \\
**Sen (Her 2)** & 0.625 & 0.500 & 0.250 & 0.500 \\
**Sen (Lum A)** & 0.907 & 0.938 & 0.866 & 0.887 \\
**Sen (Lum B)** & 0.429 & 0.429 & 0.543 & 0.343 \\ \hline
**Spc (Basal)** & 0.993 & 1.000 & 0.993 & 0.993 \\
**Spc (Her 2)** & 0.994 & 0.994 & 0.988 & 0.969 \\
**Spc (Lum A)** & 0.690 & 0.690 & 0.704 & 0.634 \\
**Spc (Lum B)** & 0.925 & 0.932 & 0.895 & 0.902 \\ \hline
**PPV (Basal)** & 0.963 & 1.000 & 0.962 & 0.955 \\
**PPV (Her 2)** & 0.833 & 0.800 & 0.500 & 0.444 \\
**PPV (Lum A)** & 0.800 & 0.805 & 0.800 & 0.767 \\
**PPV (Lum B)** & 0.600 & 0.625 & 0.576 & 0.480 \\ \hline
**NPV (Basal)** & 0.986 & 0.986 & 0.979 & 0.952 \\
**NPV (Her 2)** & 0.981 & 0.975 & 0.963 & 0.975 \\
**NPV (Lum A)** & 0.845 & 0.891 & 0.794 & 0.804 \\
**NPV (Lum B)** & 0.860 & 0.861 & 0.882 & 0.839 \\ \hline
**F1 (Basal)** & 0.945 & 0.963 & 0.926 & 0.840 \\
**F1 (Her 2)** & 0.714 & 0.615 & 0.333 & 0.470 \\
**F1 (Lum A)** & 0.850 & 0.867 & 0.832 & 0.823 \\
**F1 (Lum B)** & 0.499 & 0.508 & 0.559 & 0.400 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance metrics in block-wise missing scenarios in breast cancer data. Accuracy and per class Sensitivity (Sen), Specificity (Spc), Positive Predictive Value (PPV), Negative Predictive Value (NPV), and F1.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### Dataset  
Our proposed method is based on a single RGB image whose labels are required for supervision. Traditional depth-based hand pose estimation datasets, such as MSRA [5] and the NYU Hand Pose Dataset by [PERSON] et al. [4], are not suitable for our method. Thus, we chose two open datasets: a real-world dataset from the Stereo Hand Pose Tracking Benchmark (STB) [10] and a generated dataset called the Rendered Hand Pose (RHD) [16] dataset, both of which contain RGB images of human hands and the position coordinates of 3D keypoints. Twenty-one hand keypoints are included in both datasets, including the palm center (not the wrist or hand center) and four keypoints per finger. Each sample in both datasets includes an RGB image, a handmask image, the rotation of the camera, and the 2D and 3D coordinates of each keypoint.  
The RHD dataset is a generated dataset that consists of 39 different actions performed by 20 different persons. The dataset contains 41,258 training samples and 2728 testing samples. An image pixel in the dataset is \(320\times 320\). The STB dataset is a real image dataset collected from different cameras. It contains 36,000 images and can be divided into six scenarios. Each scene contains two RGB images and a depth image of the same action in different positions. There are 30,000 training images and 6000 testing samples in the \(640\times 480\) dataset. The two datasets include images of the hands of different people.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 1 Introduction
Compared with TEC, the variation of IED with altitude is affected by photochemistry and electrodynamics, and the variation is more complicated, which makes it more important to discuss its optimization in depth. In this paper, based on the study of [PERSON] et al. [26], we adopt their finally derived optimal temporal and spatial interpolation schemes to compare and evaluate the accuracy of electron density and NmF2 output from IRI-2016 driven by IG12 and IG-up, respectively, using COSMIC data and ionosonde data. In addition, since the Shubin model performs better than AMTB for calculating hmF2 in the China region [27], the Shubin model is directly used to calculate the peak electron density heights. In this paper, a detailed accuracy assessment of the electron density and NmF2 output from the IRI-2016 model driven by IG12 and IG-up values is presented, respectively. It can provide a reference and insight for improving the accuracy of the IRI model's IED in different regions, and, at the same time, it can provide a more accurate initial reference value for other modeling techniques, such as ionospheric tomography and data assimilation.