esa-sceva/satcom-chunk-collection huggingface dataset:
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 4 Methodology
### AI-Based Procedures to Replace Human Processing
#### 4.3.5 Histogram Gradient Boosting Classifier  
Decision trees also inspired gradient boosting, one of the most useful algorithms for generating table structures and enabling predictive regression modelling, according to [PERSON] [85]. There are two variants that are based on the operating system implementation: Light Gradient Boosting (LGB) and GPU-accelerated XGBoost. LGB is a fast, distributed, high-performance gradient boosting framework based on the decision tree algorithm that can be used for ranking, classification and a variety of other ML tasks [86; 87]. This model reduces the learning process time by at least 20 times while maintaining the same precision. According to [PERSON] [88], the XGBoost algorithm boosts the GPU performance by using perfect shuffling of indexes and data in parallel sums and GPU-accelerated sorting, generating trees of all data concurrently for each iteration.  
XGBoost is an enhanced version of the gradient boosting algorithm that is more efficient and scalable. Automatic feature extraction is one of the characteristics that distinguishes XGBoost from other algorithms. XGBoost supports regularisation to prevent overfitting and has the capacity to learn from nonlinear datasets. In addition, the parallelisation feature enables XGBoost to utilise multiple CPU cores. It is one of the tree-basedadditive ensemble models that consists of a group of base learners. XGBoost can generally be represented by:  
\[F=(m_{1},m_{2},m_{3},m_{4}\ldots m_{n}),\hat{y}_{i}=\sum_{t=1}^{n}m_{t}(x_{i}) \tag{37}\]  
where \(\hat{y}_{i}\) is the final predictive model, which is the combination of all weak learners, and x is the input feature for each weak learner, i.e., m.  
From the paper [87], we extracted the objective function for XGBoost, as given below:  
\[\text{Obj}(\theta)=\sum_{i=1}^{m}L(z_{i},z_{i})+\sum_{t=1}^{T}\Omega(f_{t}). \tag{38}\]  
In Equation (38), note that the objective function has two parts; the first part denotes the loss function, i.e., \(L\) denotes the training loss of either the logistic or squared loss, and the second part represents the addition of each tree's complexity. \(z_{i}\) is the actual value and \(z_{i}\) is the predicted value, whereas \(\Omega\) is the regularisation term, \(T\) denotes the total number of trees, and f is the function.
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 2 Materials and Methods  
### Patients  
This was a retrospective investigation on a prospective CHO PET cohort which was approved by the Institutional Review Board of Peking Union Medical College Hospital (PUMCH) (ethics code ZS-2660), and informed consent was collected from all patients. Eligible criteria of the current study include those: (1) of age \(\geq\) 18 years and Karnofsky Performance Score (KPS) \(\geq\) 70; (2) suspected of having a primary brain tumor and being planned for surgery; (3) who underwent head CHO PET/CT prior to surgery; (4) with histopathological proof that the brain lesions are primary diffuse glioma; and (5) who had no anti-tumortreatment prior to PET/CT or surgery. Finally, 39 patients with pathological-confirmed primary diffuse glioma were enrolled for CHO PET/CT and included in the current study. The role of CHO PET quantitative parameters to distinguish the World Health Organization (WHO) grade and molecular markers were reported previously [17; 18], and the current study focused on the denoising of CHO PET image.
## 4 Discussion
### Practical Applications  
Sports scientists can use the ACWR in conjunction with other tools to predict the likelihood of non-contact injuries. Regarding the parameters that can be applied, these include TD, HSD, ACC, and DEC. Also, the duration of the ratio seemed not to affect the outcome, so sports scientists can use any duration of the ratio (1:2, 1:3, 1:4) that serves the team's periodization.
## 3 Methodology
### Complex Network Analysis and Girvan-Newman Method
#### 3.2.7 Girvan-Newman Method  
The _Girvan-Newman method_ uses betweenness centrality, which is defined in Section 3.2.5. The algorithm first calculates the betweenness centrality for the all edges. Then, remove an edge with the highest betweenness centrality value and calculate the modularity \(Q\) about the new network. Repeatedly perform this process in descending order of betweenness centrality. The structure of the network with the highest modularity \(Q\) means the most divided network.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Lossless Compression  
In this section, we show the compression ratio and compression throughput obtained in the tests for KMWCD and KMALL files. The compression ratio is defined as the original  
Figure 8: Relationship between ratios and instructions per byte for the KWCD and the KMWCD algorithms. Fugro files not included.  
file size divided by the compressed file size and the compression throughput as the raw data volume compressed per second. The lossless compression results are presented in three sections: the first one for.kmmwcd files (that is, MWC datagrams), the second one for.kmall files (that is, MRZ and SKM datagrams) and the third one for combined files (that is, MWC, MRZ and SKM datagrams). Regarding compression speed, it determines if a compressor is capable of performing real-time compression. In practice, real-time compression may be performed by an Autonomous Underwater Vehicle (AUV) with an ARM processor with much less computing power than our testing setup.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 3 Description of the Proposed System Architecture
### Receiver
After implementing an adaptive threshold, the resulting image resembles Figure 5a. Following this, the contours are detected, as depicted in Figure 5b. Nevertheless, a few of these outlines are not appropriate for our requirements. Consequently, a filter is implemented on the edges of the contours to generate contours around the LEDs and remove the noise, as depicted in Figure 5c. Ultimately, the unprocessed digital data can be readily obtained by utilizing the LED outlines, as illustrated in Figure 5d. Figure 4: LED segmentation based on (**a**) Basic thresholding method and (**b**) Adaptive thresholding. After the acquisition of the raw digital data, it becomes necessary to rapidly identify and assess the preamble pattern. Through the identification of the preamble pattern, the system possesses the capability to assess the authenticity and reliability of the provided raw digital data. Therefore, the original digital data extracted from a frame may be either precise or imprecise. Moreover, in cases where the packet data are precise, the process of SN identification is employed to distinguish between individual packets. The ensuing stages involve decoding and comprehensive data collection. The whole structure of the receiver-side processing approach is illustrated in Figure 6. On the other hand, in cases where the data are imprecise, the receiver will assess the next raw digital data. Figure 5: (**a**) LED segmentation based on adaptive thresholding method; (**b**) Recognition of contours before filter is used; (**c**) Recognition of contours LED after filter is used; (**d**) Raw digital data before downsampling. \[b_{x}=\sigma(t_{x})+k_{x} \tag{1}\]0 Receiving-side 2D MIMO processing algorithm
## 4 MATLAB Simulation Results
### Estimate the Number of Robots Required  
Next, we address how to estimate the number of robots required for covering a 3D workspace, considering the case where an obstacle inside the workspace is not known a priori. To estimate the number of robots, we simulate Algorithm 1, while setting no obstacles in the environment. We set no obstacles in the simulation, since we estimate the number of robots, in the case where an obstacle inside the workspace is not known a priori.  
Figure 4 shows the sensor configuration, as Algorithm 1 is used to simulate the robot maneuvers for covering the 3D workspace with a known size \([0,200]*[0,200]*[0,200]\) in meters. See that there is no obstacle in the simulated environment.  
In Figure 4, the path of a robot that is dropped from the ship at \((30,30,0)\) is marked with yellow asterisks. Also, the path of a robot that is dropped from the ship at \((150,150,0)\) is marked with blue asterisks. Using MATLAB simulations, 14 s are spent to build the complete network without sensing holes.  
32 robots are dropped in the simulations, and the position of a robot is depicted with a green circle in Figure 4. Using the simulation in the workspace without obstacles, we can estimate the number of required robots as 32.  
Figure 4: The sensor configuration after the robots are simulated to cover the 3D workspace without obstacles. 32 robots are dropped in total, and the position of a robot is depicted with a green circle. The path of a robot that is dropped from the ship at \((30,30,0)\) is marked with yellow asterisks. Also, the path of a robot that is dropped from the ship at \((150,150,0)\) is marked with blue asterisks. Using the simulation in the workspace without obstacles, we can estimate the number of required robots as 32.  
Figure 3: Considering the scenario in Figure 2, this figure shows the sensor network that is built under our 3D coverage methods. The path of a robot that is dropped from the ship at \((30,30,0)\) is marked with yellow asterisks. Furthermore, the path of a robot that is dropped from the ship at \((150,150,0)\) is marked with blue asterisks.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 4 Strategy of Communication Path Selection
### Combinatorial Optimization Based on the Genetic Algorithm
On the basis of the objective function and penalty function, the fitness of chromosomes can be calculated in the following formula, and \(popSize\) denotes the size of the current population:  
\[eval(v_{k})=\frac{1}{f(v_{k})\cdot g_{k}}\ \ \ k=1,2,\cdots,popSize \tag{18}\]  
Crossover is the process of generating new chromosomes, which involves taking two chromosomes from the previous generation, cutting them off at one point, and stitching them together to form a new chromosome. Among them, the selection of the parent chromosome is not random. Here, the roulette method is adopted for crossing. The fitness probability of each chromosome is calculated by Formula (18), and the chromosome with a larger value is more easily selected. \[selectionProbability(v_{k})=\frac{eval(v_{k})}{\sum eval(v_{k})}\ \ \ k=1,2,\cdots,popSize \tag{19}\]Mutation: A number of genes need to be selected randomly from the newly-generated chromosome and their values modified. In this way, new genes can be introduced into the existing chromosomes to break through the limitation of the current search and avoid falling into the local optimal solution, which is more conducive to the algorithm finding the global optimal solution. The value of the gene should change in the solution domain to ensure that the mutated chromosome still has the corresponding routing scheme. Reproduction: The chromosome with higher fitness can be copied directly, which can preserve the excellent chromosome of the previous generation and accelerate the process of searching for the optimal solution. In addition, the genetic algorithm is better every time it evolves, but it needs to find a balance between the accuracy of the results and the efficiency of execution in practical applications. Therefore, the algorithm can be stopped by limiting the number of evolutions \((S)\) or the error range of the solution \((\sigma)\). We take \(K\) alternative paths of each user as the input of the genetic algorithm and iterate according to the algorithm flow, so as to obtain the path combination with the global minimum energy consumption that meets the constraints.
# The Effect of 3D TVs on Eye Movement and Motor Performance
## 1 Introduction
A further in-depth investigation of eye movement and motor performance are needed to enhance the performance in 3D TV. Our previous studies investigated the effect of parallax on eye movement parameters in the projection-based stereoscopic display [8; 28; 29; 30]. Eye movement parameters which consisted of eye movement time, fixation duration, time to first fixation, number of fixations, and eye gaze accuracy were evaluated under three different levels of depth. The results revealed that depth had significant effects on all eye movement parameters in projection-based stereoscopic displays [28; 29]. The participants were found to have longer eye movement time, longer fixation duration, longer time to first fixation, larger number of fixations, and less eye gaze accuracy when the target was projected at 50 cm in front of the screen compared to projected at 20 cm in front of the screen or projected at the screen [28]. The purpose of this study was mainly intended to investigate the effects of 3D TV environments on eye movement and motor performance. Using a similar approach to our previous studies [8; 28; 29; 31; 32; 33], we utilized an eye tracker to explore a comprehensive analysis regarding the effect of 3D TVs on selected eye movement parameters and motor performance. We also discussed the effect of depth and index of difficulty, since both variables could influence eye movement and motor performance in a stereoscopic environment [34]. This study is one of the first studies that investigated the effect of 3D TVs on eye movement and motor performance simultaneously. The findings of this study could lead to better understanding of the visual and motor performance for 3D TVs.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 3 Design and Implementation of Elliptic-Curve Cryptosystem F256 on FPGA
### Nist Standard for 256-Bit Koblitz Curve  
The NIST curve (SECP256K1), used in this analysis, over prime fields \(F_{p}\), is defined as E: \(y^{2}=x^{3}+ax+b\) mod p, where \(a\) = 0 and \(b\) = 7 and \(p=2^{256}-2^{32}-2^{9}-2^{8}-2^{7}-2^{6}-2^{4}-1\)[31]. The two main field operations in the double-and-add-always algorithm, point doubling and point addition in Jacobian coordinates over curve E, used for this study, are described in [32]. Jacobian coordinates are preferred over affine coordinates because inversions can be avoided while performing the addition or doubling operation, which is not the case in the affine coordinate system.
## 3 Numerical Verification
### Periodically Varying System  
A shear building under consideration features periodically varying stiffness in the first story, and its modal damping ratios are described as follows:  
\[k_{1}=\begin{cases}\begin{matrix}1800&0\leq t\leq 8\\ 1800\begin{bmatrix}1-0.3\sin((t-8)\pi/5)\end{bmatrix}&t\geq 8\end{matrix}\quad \text{and}\\ \tilde{\xi}_{i}=\begin{cases}\begin{matrix}0.05&0\leq t\leq 8\\ 0.05\begin{bmatrix}1+0.4\sin((t-8)\pi/5)\end{bmatrix}&t\geq 8\end{matrix}&\cdot \text{(for $i=1,2,\ldots,6$)}.\end{cases}\end{cases}\]  
The first mode scalogram of the responses from the sixth floor (Figure 7b) illustrates that the building exhibits characteristics indicative of a time-varying system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode No.** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{2}{*}{\(f_{\text{n}}\) (Hz)} & 0.80 & 2.14 & 3.15 & 4.24 & 5.00 & 5.44 \\  & (0.80) & (2.14) & (3.15) & (4.25) & (5.04) & (5.37) \\ \hline \multirow{2}{*}{\(\xi\) (\%)} & 5.19 & 5.27 & 5.20 & 5.02 & 4.65 & 5.22 \\  & (5.00) & (5.00) & (5.00) & (5.00) & (5.00) & (5.00) \\ \hline MAC & 1.00 & 1.00 & 1.00 & 0.99 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identified results for the time-invariant six-story shear building.  
Figure 8: The stabilization diagrams of the identified frequencies and damping ratio using different values of \(s\): (**a**) using signals in \(U_{6}^{(1)}\); (**b**) using signals in \(U_{6}^{(2)}\); (**c**) using signals in \(U_{6}^{(4)}\); (**d**) using signals in \(U_{6}^{(3)}\).  
To process the noisy base excitations and responses of the first and sixth floors, the SWPT was applied. Responses in different subspaces at decomposition level 6 were utilized to ascertain the time-varying modal parameters. Modal parameter identification involved windowed responses with a window length of 2 \(L\), equal to 4 s. In Equation (25), the parameter '\(d\)' in the weighting function was set to 0.06 s, and adjacent windows had an overlay of 3.9 s.  
The identified frequencies and modal damping ratios for all modes are presented in Figure 9, where results without employing the weighting function (denoted by \(d=\infty\)) are also displayed. Notably, the identified results obtained using the weighting function with '\(d\)' set to 0.06 s exhibit excellent agreement with the analytical values. Maximum differences in frequency were less than 1% for all six modes. Regarding modal damping ratios, the maximum differences were less than 15% for the first five modes and 20% for the sixth mode. The results obtained using the weighting function with '\(d\)' set to 0.06 s significantly outperformed those obtained without its use.
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 3 Mechanical System
### Mobile Platform  
A mobile platform is used to move the robot between locations. In fruit and vegetable harvesting, platforms like four-wheel drives and rail carriages often transport the harvester or collected produce. However, mobile platform options for mushroom harvesting are more limited. This is mainly due to the design of Dutch shelves, which have a rectangular workspace, meaning that the target mushrooms are always on a specific horizontal plane [61; 62; 63; 64]. Consequently, the manipulator must move along this plane, and most mobile platforms are trolley- or cart-like mechanisms made of Cartesian manipulators installed on the shelves. There are a few exceptions where mobile vehicles carry articulated manipulators. The different mechanisms used as a mobile platform in automated mushroom harvesting are shown in Figure 5.  
For example, [PERSON] et al. employed a stepper motor-driven frame to move the system [26; 27], while [PERSON] et al. designed a horizontal rail system for the vertical manipulator to slide along [32]. Other systems use carts or trolleys with wheels that roll on slots in the Dutch shelves, such as round groove pulley wheels [37; 40; 58] or train wheels [51] that roll on I-channel profiled shelves. [PERSON] et al. used V-groove wheels to move the system on T-slot profiled shelves [33]. Some systems employ metal wheels on a specific shelf, and others use moving carts with round profile wheels [36; 38; 60]. These cart-like mobile platforms are usually part of the Cartesian manipulator with wheels, where the platform moves in one direction [30; 34; 40]. These types of on-the-shelf mobile platforms are very efficient on the shelf where they are installed. However, they may need additional transport mechanisms to switch the shelves.  
Figure 4: The different collection mechanisms used in mushroom harvesting: (**a**) horizontal on-the-shelf conveyer (reprinted from ref. [38]); (**b**) on-the-shelf rotating disc (reprinted from ref. [58]); (**c**) off-shelf vertical collector (reprinted from ref. [43]); and (**d**) off-shelf vertical baskets (reprinted with permission from ref. [51]). Copyright 2024 Elsevier.  
Off-the-shelf mobile platforms include robot chassis or vehicle-like mechanisms that move on the ground and carry manipulators or accessories like cameras for scanning [48]. For instance, TechBrew's commercial mushroom harvester has a mobile platform that moves the entire system around the shelves while remaining outside them [43]. Ground-based vehicles are commonly used to transport the articulated arms or harvesting systems, offering flexibility to reach multiple layers and navigate between shelves [44; 45]. These outside vehicles allow a single manipulator to access all areas, including inside multi-layered shelves.
###### Abstract  
As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model's accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (\(R^{2}\)) and the 1-Mean Absolute Percentage of Error (\(I\)-\(MAPE\)) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents.  
oil spill; transport behavior; oil spill transport state prediction model; river crossing pipeline