esa-sceva/satcom-chunk-collection huggingface dataset:
# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 3 Materials and Methods
### Classification Models  
CNN Models for Image Classification  
The CNN model used in this study was ResNet-50. In previous studies, ResNet-50 showed high performance in classifying COVID-19 CXR images.  
ResNet was proposed by [PERSON] et al. [38] in 2015. Contrary to the assumption that prediction performance improves as the neural networks of AlexNet and VGGNet deepen, performance degradation occurs owing to gradient vanishing and gradient exploding. To solve this problem, ResNet uses the skip/shortcut connection method. In the learning of the existing neural network, \(H(X)=X\) was learned, but using residual functions, \(H(X)=F(X)+X\) was learned, and residual learning was conducted so that \(F(X)=0\). Therefore, the problem of gradient loss was solved by suggesting a solution to approximate \(H(X)=X\) (Equations (3)-(5), Figure 4). In the formula, \(X\) represents input, \(Y\) represents output, and \(F(X)\) represents the residual function. ResNet is relatively more efficient than the existing CNN model because it uses an element-wise sum rather than the existing  
Figure 4: Fine-tuning strategy for domain extension transfer learning.  
element-wise product. Figure 5 describes the learning method of ResNet, which is called \"Residual learning\".  
\[F(X):=H(X)-X \tag{3}\]  
\[H(X)=F(X)+X \tag{4}\]  
\[Y=F(X,w_{i})+X \tag{5}\]  
To implement the proposed methodology, each image classification model was selected as a comparison group, and a comparative experiment was conducted to determine the effectiveness of ResNet-50. When the proposed methodology was applied to ResNet-50, the highest COVID-19 CXR classification accuracy was achieved. There was a high-performance difference in classification accuracy and precision, sensitivity, specificity, and the F1-score. Table 4 shows the results of applying the proposed methodology to each CNN model pretrained using natural images. The classification models used for the control group were VGG16 [39], DenseNet-121 [40], Inception-V3 [41], MobileNetV2 [42], and EfficientNet-B0 [43]. Figure 6 shows accuracy, inference time, MADDs, and parameters for each model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F1-Score** &
\begin{tabular}{c} **Inference** \\ **Time (MS)** \\ \end{tabular} & **MADDs(G)** & **Prams** \\ \hline ResNet-50 & 96.7\% & 96.2\% & 96.1\% & 97.2\% & 96.1\% & 45 & 4.08 & 23.5 M \\ VGG16 & 95.8\% & 95.2\% & 94.9\% & 96.5\% & 95.1\% & 225 & 15.46 & 134 M \\ DenseNet-121 & 95.6\% & 93.5\% & 96.2\% & 95.0\% & 94.8\% & 28 & 2.80 & 7.9 M \\ Inception-V3 & 95.9\% & 94.8\% & 95.6\% & 96.1\% & 95.2\% & 35 & 2.80 & 21 M \\ MobileNetV2 & 95.5\% & 94.3\% & 95.1\% & 95.8\% & 94.7\% & 82 & 0.3 & 3.5 M \\ EfficientNet-B0 & 96.3\% & 95.4\% & 95.9\% & 96.6\% & 95.6\% & 47 & 0.385 & 5.2 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of classification models after applying the proposed method on COVID-19 X-rays.  
Figure 5: Residual learning.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Length of the Contention-Free Interval
#### 6.1.1 Opening-Edge Overlapping  
As mentioned, we consider \(Q_{k}^{h}\) as a targeted TT queue for latency calculations in any node \(h\). Then, to include the overlapping at the opening edge of the \(Q_{k}^{h}\) window, we assume that when its gate (\(G_{k}^{h}\)) changes from closed to open in the \(i\)-th gating cycle (at \(l_{k}^{h,o,i}\)), there is another TT gate open (\(G_{m}^{h}\Big{(}\begin{smallmatrix}t_{h}^{h,o,i}\end{smallmatrix}\Big{)}=1\)), as depicted in Figure 7. The length of the overlapping interval between \(Q_{k}^{h}\) and any other TT queue (\(Q_{m}^{h}\)) open windows can be found easily as  
\[L_{k,m}^{h,B,i}=\left\{\begin{array}{ccc  
In the worst case, servicing \(Q_{k}^{h}\) frames is not guaranteed in a time slot if there is any higher-priority window open at the same time slot. It contributes to reducing the contention-free interval, which starts at the closing time of the higher-priority window that has the largest overlapping. Thus, the starting time of the guaranteed service window can be given by  
\[t_{H}^{h,B,i}=t_{k}^{h,\rho,i}+L_{k,H}^{h,B,i}=t_{k}^{h,\rho,i}+OR_{k,H}^{h,B,i} \cdot W_{k}^{h,i} \tag{8}\]  
Lower-priority overlapping at the opening edge: Similarly, based on the window overlap, as shown in Figure 7, the largest overlapping ratio between \(Q_{k}^{h}\) and \(Q_{k^{+}}^{h}\) windows (\(k+1\leq k^{+}\leq N_{q}\)) at the opening edge is defined as  
\[OR_{k,L}^{h,B,i}=\frac{max}{\frac{k+1\leq k^{+}\leq N_{q}}{W_{k}^{h,i}}}=\frac {L_{k,L}^{h,B,i}}{W_{k}^{h,i}} \tag{9}\]  
Although the \(Q_{k}^{h}\) frame has the priority to be transmitted before \(Q_{k^{+}}^{h}\) frames, it has to wait until completing the transmission of \(Q_{k^{+}}^{h}\) frames at the transmission status if the non-preemption technique is considered between TT transmissions. Accordingly, the \(Q_{k}^{h}\) frame cannot be transmitted until passing the maximum size of lower-priority frames (\(f_{k^{+}}^{h,max}\)). Thus, the \(i\)-th contention-free interval is reduced at the opening edge by the value \((d_{k^{+}}^{h,np,i})\), which can be given as  
\[d_{k^{+}}^{h,np,i}=\left\{\begin{array}{cc}min\Big{\{}f_{k^{+}}^{h,max},L_{k, k^{+}}^{h,B,i}\Big{\}}&\cdot,t_{k^{+}}^{h,\rho,i}<t_{k}^{h,\rho,i}<t_{k^{+}}^{h,c,i} \\ 0&\cdot,Otherwise\end{array}\right. \tag{10}\]  
Under the worst case, the maximum delay is considered as  
\[d_{L}^{h,np,i}=\underset{k+1\leq k^{+}\leq N_{q}}{max}\Big{\{}d_{k^{+}}^{h,np, i}\Big{\}} \tag{11}\]  
By just considering the maximum influence of lower-priority overlapping on the \(i\)-th \(Q_{k}^{h}\) open window \(W_{k}^{h,i}\), we can define the starting time of the \(i\)-th contention-free interval (\(\overline{W}_{k}^{h,i}\)) as  
\[t_{k,L}^{h,B,i}=d_{L}^{h,np,i}+t_{k}^{h,\rho,i} \tag{12}\]
## 1 Introduction
Understanding the relationship between land use and water quality would help assess water quality in unmonitored basins, as monitoring is often costly and time-consuming. Additionally, this knowledge would provide guidelines for watershed managers and policymakers to prioritize future land use development [8]. Thus, conducting an analysis of the relation between water quality and different types of land use and cover is an important step in the management of natural resources, developing conversion strategies, and guiding the sustainable use of these resources. In this regard, studies have analyzed the influence of land use and cover on water quality through multiple approaches; utilizing correlation coefficients between quality and land use [11], comparative analysis between input and output [12], through the use of regression equations [7], and multivariate analysis [6]. Although many approaches are being evaluated to analyze the influence of land use on water quality, most studies rely on limited time series data, often from only a single year or a single monitoring point, or few parameters. The MRW has a monitoring network with 28 stations distributed across the main rivers of the basin, as well as a quality and consistent historical data series available to the general public. However, few studies are reported with an emphasis on this specific area. Overall, there are still few studies on the capacity of removal/retention of substances in natural wetland areas of tropical and subtropical regions, especially in South America [13]. The fact is that the number of parameters and the monitoring frequency have been continuously expanded, generating water quality data from an important transition region between the Pantanal and Cerrado Biomes, as it is also located in the transition region from the Plateau to the Pantanal Plain. Therefore, analyzing the relationship between land use and water quality by jointly considering the temporal dimension with the spatial dimension is essential to understand the dynamics of water quality and land use in the region's natural wetlands. The objective of this study was to analyze the correlation between land use and land cover and the quality of surface waters in a natural wetland area in the MRW, in Mato Grosso do Sul, Brazil. For that purpose, the most relevant quality parameters were identified (key factors), along with the dynamics of substance concentrations along the main watercourses of the basin. Additionally, the study aimed to analyze the influence of natural wetland areas on water quality, considering how these areas are capable of improving water quality.
## 5 Conclusions  
In this contribution a comparative study of modern heuristics on the school timetabling problem is presented. Two new heuristic population based algorithms, a particle swarm optimization (PSO) and an artificial fish swarm (AFS) one, are introduced and applied to the school timetabling problem. In order to demonstrate their efficiency and good performance both algorithms have been applied to the Beligiannis school timetabling data set and their results are compared with the results of four other heuristic algorithms published in the respective literature. Experimental results showed that the proposed PSO algorithm achieves the best results ever found for 5/6 instances of the Beligiannis data  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & **Proposed PSO** & **Proposed AFS** & **EA [25]** & **SA [19]** &
\begin{tabular}{c} **Hybrid PSO** \\ **[20]** \\ \end{tabular} & **GASPHH [26]** \\ \hline
1 & 11 & 16 & 70 & 29 & 15 & 60 \\
2 & 18 & 21 & 76 & 37 & 17 & 66 \\
3 & 5 & 7 & 20 & 8 & 7 & 17 \\
4 & 5 & 9 & 41 & 28 & 8 & 43 \\
5 & 0 & 0 & 10 & 3 & 0 & 15 \\
7 & 23 & 32 & 109 & 40 & 32 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing the performance of both proposed algorithms with the algorithms presented in [19, 20, 25, 26].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{6}{c}{**Proposed PSO**} & \multicolumn{6}{c}{**Proposed AFS**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Fitness**} & \multicolumn{3}{c}{**Execution Time**} & \multicolumn{3}{c}{**Fitness**} & \multicolumn{3}{c}{**Execution Time**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Best**} & **Worst** & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** \\ \hline
1 & 11 & 21 & 16 & 2.5 & 36.6 & 3.4 & 16 & 31 & 22 & 3.8 & 120.8 & 2.4 \\
2 & 18 & 28 & 22.1 & 2.5 & 47.8 & 0.8 & 21 & 34 & 28.6 & 3.7 & 147.2 & 1.1 \\
3 & 5 & 9 & 7.4 & 0.8 & 3.9 & 0.4 & 7 & 13 & 10.4 & 1.7 & 28.5 & 0.9 \\
4 & 5 & 13 & 8.8 & 2.2 & 13.2 & 0.3 & 9 & 23 & 14.5 & 3.2 & 59.4 & 2.6 \\
5 & 0 & 0 & 0 & 0 & 3 & 0.1 & 0 & 5 & 2.4 & 1.5 & 24 & 0.1 \\
7 & 23 & 49 & 35.8 & 6.8 & 57.9 & 0.4 & 32 & 88 & 56.1 & 14.1 & 168 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the proposed stochastic algorithms.  
set. On the other hand, the proposed AFS algorithm, which comprises the first attempt to apply an AFS algorithm to the school timetabling problem in the literature, exhibits also very satisfactory results, since it overcomes three other heuristic approaches. This demonstrates that the AFS algorithm can be effectively applied to solve the school timetabling problem. The application of the proposed PSO algorithm to problems belonging to other timetabling or scheduling domains, as well as the investigation of the best set of parameter values for the proposed AFS approach, will be the main issues of our future work.
## 1 Introduction
Feature extraction techniques are the most commonly used to transform data with attribute dependencies into data that have no dependencies. [PERSON] and [PERSON] [9] applied class-conditional independent component analysis (CC-ICA) to improve the classification performance of the NBC. [PERSON] et al. [10] proposed an ICA-based NBC (ICA-NBC) to improve the classification performance of the NBC. [PERSON] and [PERSON] [11] evaluated the classification performance of the NBC built using three feature extraction methods, namely principal component analysis (PCA), ICA, and CC-ICA. Experimental results have shown that PCA, ICA, and CC-ICA can slightly increase the testing accuracies of the NBC on the selected data sets. [PERSON] and [PERSON] [12] trained an NBC based on website attributes extracted by PCA to perform web link spam detection. [PERSON] et al. [13] applied PCA to extract key attributes in network data and then trained the NBC to conduct network intrusion detection. Although the aforementioned methods can improve the classification performance of the NBC for specific application scenarios, these methods do not provide a good solution for handling mixed attributes (continuous and categorical attributes). Some studies [14; 15] revealed that the discretization of continuous attributes can lead to losing precious information about the original data set, while the determination of an optimal Bayesian network structure is an NP-hard problem [16]. To address the above limitations, this paper proposes a new strategy to enhance the generalization capability of the NBC for the mixed-attribute classification problem. The proposed approach not only retains as much information as possible about the original data but also keeps the model structure as simple as possible. The contributions of this paper are summarized as follows. A new mixed attribute fusion-based NBC (MAF-NBC) is proposed for mixed-attribute classification problems. To relax the attribute independence assumption, an autoencoder neural network (ANN) based on the minimization of both encoding error and attribute dependence is iteratively trained to transform the original mixed attributes into a series of independent and encoded at tributes. Extensive experiments conducted on multiple data sets demonstrate the feasibility, rationality, and effectiveness of the MAF-NBC. The remainder of this paper is organized as follows. Section 2 reviews the principles of the NBC. Section 3 presents the proposed MAF-NBC. Section 4 reports results from experiments to assess the performance of MAF-NBC. Finally, Section 5 concludes this paper and describes future studies.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Definition of State, Action and Reward in RL
#### 3.2.2 Hybrid Action Space  
Considering the discrete and continuous variables involved in problem (8), a hybrid action space is used in this problem. That is, the discrete action,  
\[\mathbf{a}_{1}=\{x_{nm},n\in\mathcal{N},m\in\mathcal{M}\}, \tag{17}\]  
which represents the association between devices and BSs; the continuous action  
\[\mathbf{a}_{2}=\{b_{nm},n\in\mathcal{N},m\in\mathcal{M}\}, \tag{18}\]  
which represents the bandwidth assigned by BS.  
Since a BS only allocates bandwidth resources to its associating devices, we have that only when \(x_{mn}=1\), then \(b_{mn}>0\). Hence, a hierarchical policy network is designed, where we divide the action selection into two stages to significantly reduce the action space, as illustrated in Figure 2, association action \(\mathbf{a}_{1}\) is selected at Stage-1 based on the state \(\mathbf{s}\), and then at Stage-2, bandwidth allocation action \(\mathbf{a}_{2}\) is chosen based on the set \(\{\mathbf{s},\mathbf{a}_{1}\}\).  
In \(t\)-th time episode, we denote the action for the whole system as  
\[\mathbf{a}^{(t)}=\{\mathbf{a}^{(t)}_{1},\mathbf{a}^{(t)}_{2}\}. \tag{19}\]  
Figure 2: The structure of two-stage action selection.
## 3 Improvement of Particle Swarm Optimization Algorithm
### Improvement of the Particle Swarm Algorithm  
The particle swarm algorithm suffers from the problem of a premature convergence and tends to fall into local optimality for the multimodal function as the objective function. In order to solve this problem, researchers have proposed numerous different improvement schemes in which one idea is applied to adjust the parameter settings of the algorithm. In the particle swarm algorithm, the parameter settings of the inertia weight \(\omega\) and the accelerating factors \(c_{1}\) and \(c_{2}\) have an important influence on the performance of the algorithm. The global search ability of the algorithm can be improved by increasing the value of \(\omega\); otherwise, the local search ability can be enhanced. Because the \(c_{1}\) and \(c_{2}\) represent the individual cognition and group cognition of the particles, respectively, the algorithm has a better local search ability when \(c_{1}\) is smaller and \(c_{2}\) is larger; otherwise, the global search ability is better.  
In addition, researchers have proposed some improved hybrid particle swarm algorithms by referring to ideas from other optimization algorithms. For example, the idea of chaos optimization can be introduced into the particle swarm algorithm, which improves to the chaotic particle swarm optimization algorithm (CPSO) [37]. On the one hand, the position and velocity of the particles were initialized by the chaotic sequence; on the other hand, the chaotic sequence was generated based on the optimal position of the group, while one particle in the current particle swarm was randomly replaced by the optimal particle.  
Herein, the logistic equation was applied to the chaotic system in the optimization:  
\[z_{n+1}=\mu z_{n}(1-z_{n}),n=0,1,2,\cdots,Z_{n+1} \tag{23}\]  
where \(\mu\) is the control parameter, \(\mu=4\), and the initial value \(z_{0}\) satisfies \(0\leq z_{0}\leq 1\).  
The algorithmic flow of the improved particle swarm optimization algorithm applied is shown in Figure 5:
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 3 Results  
### RGB Imagery Classification  
Figure 4a shows the result of the classified image and Figure 4b the respective validation with 150 points randomly generated by the software.  
Table 2 presents the results of the validation of the kappa coefficient, and the estimated precision for the identification of soil, crops, and weeds in the 150 points randomly generated.  
For kappa validation, coefficient matrices were generated for each image, \"crossing\" the estimation made by the image classification method with the actual data of the RGB image using the sample observation. The final kappa coefficient calculated for the image classification method was calculated with a precision of 78% (k1), and assumes the value of 0.48 (k), framing the classification in the category of moderate performance [17]. According to the kappa coefficient, the classification can distinguish soil from the image with an accuracy of 99%, and weeds with an accuracy of 45%.  
Figure 5 represents the spatial distribution of broadleaf weeds before the herbicide application, the prescription cartography per plot, and the percentage of broadleaf weeds in the plant population after the herbicide treatment.  
Considering the multiplication of the total area of Plots II and III by the applied amounts of herbicide, an average reduction of 0.22 l ha\({}^{-1}\) was achieved using the VRA in Plot II. These results mean a 30% reduction in the amount of herbicide used in the fixed dose parcel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{N=150}\) & \multicolumn{2}{c}{**Precision (\%)**} \\ \hline k1 & 0.78 & \multicolumn{2}{c}{Soil} & 99 \\ k2 & 0.58 & Estimation & Crop & 20 \\ k3 & 0.48 & \multicolumn{2}{c}{Weed} & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The validation of the kappa coefficient (left), and the estimated precision for the identification of soil, crops, and weeds (right).  
Figure 4: (**a**) Classified image and (**b**) respective validation.
## 3 Materials and Methods
### Methods
#### 3.2.5 LCA Assumptions and Limitations  
The main assumptions and limitations of the LCA performed in this study are the following:  
* According to the cut-off criteria of EN 15,804 + A2 [40], no additives are included in the LCA analyses because they never exceed 5% of total mass.
* For processes modeling, secondary (generic) data are retrieved from the internationally recognized databases ecoinvent v.3 and ELCD (European Platform on LCA).
* The factories producing waste used as aggregates are considered in their real locations.
* No environmental impacts are associated with sieving processes.
* The transports distance is always considered to be one-way delivering.
* Concerning transport distances from the gate to the site (A4), two assessment scenarios are assumed: 50 km (scenario A) and 500 km (scenario B).
## 1 Introduction
[14] established a numerical model of a specimen with multiple nonpersistent joints by PFC3D and investigated the effects of joint geometry parameters and particle parameters on the deformability and damage mode of the specimen under uniaxial compression. Apart from the PFC, numerical methods such as the finite element method (FEM), realistic failure process analysis (RFPA), and fast Lagrangian analysis of continua (FLAC) have also been adopted by scholars, and most studies show good agreement between numerical simulations and experimental results. [PERSON] et al. [15] performed 3D numerical simulations in FLAC3D using hyperfine meshes to completely simulate the brittle failure process of single fractured rock masses under uniaxial and biaxial compression. [PERSON] et al. [16] used FLAC3D to simulate the loading failure of nonpersistent backfill-jointed rocks and investigated the effects of factors such as joint length, dip angle, internal friction angle, and cohesion of the backfill on the mechanical properties of uniaxial compression. Based on extended nonordinary state-based peridynamics, [PERSON] et al. [17] proposed a new method of simulation to model the crack evolution of jointed rocks under compressive loads. By using RFPA2D, [PERSON] et al. [18] investigated the splitting fracture process of brittle rocks with different joint parameters under uniaxial compression, and the effect of joint length on the extension of wing-shaped cracks was discussed. Although a significant amount of research has been conducted on the failure behaviour of nonpersistent joints, only a few studies have been conducted to investigate the failure mechanism of nonpersistent joints considering the influence of joint inclination and per sistence, especially for nonpersistent joints in three dimensions. However, in practical engineering, most rock nonpersistent joints are 3D nonpersistent joints with different inclinations (Figure 1). Consequently, the failure behaviour of 3D nonpersistent joints should be studied further. In this research, the discrete element software PFC3D is used to establish a nonpersistent fracture joint specimen model and conduct uniaxial numerical simulation tests to explore the effects of joint persistence and inclination on the strength and failure process. The relation between peak strength and joint parameters was explored, and the crack initiation, propagation and fracture pattern of the specimen were studied. Moreover, the shear stress on the joint plane was monitored by using the measuring circle, and its evolution law was also discussed.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 2 Related Work
As a result, the model calculation took 2 \(\upmu\)s to perform the entire kinematics algorithm, which represented 200 clock cycles. In this context, it is possible to realize that the use of FPGA-based computing can accelerate haptic device control algorithms. Unlike traditional hardware that processes information sequentially, FPGA enables parallel information processing. However, most studies from the literature have developed partially parallel implementations, that is, implementations in which parts of the used algorithms are executed sequentially. Unlike the research previously mentioned, this study presents a new approach in which the execution of the robotic manipulation algorithms are performed in a full-parallel hardware implementation. This proposed implementation provides a latency reduction for the tactile devices and enables tactile internet applications.