esa-sceva/satcom-chunk-collection huggingface dataset:
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 3 Results  
### Evaluation  
This paper employs three quantitative metrics--the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM)--to comprehensively evaluate the denoising effect of OCT images.  
The MSE (Mean Squared Error) is the average of the squares of the errors between the original image and the denoised image. It is one of the commonly used scales for comparing variable differences. A smaller MSE value indicates a higher degree of the restoration fidelity of the recovered image.  
\[MSE=\frac{1}{M\times N}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}[f(i,j)-g(i,j)]^{2} \tag{7}\]  
\(M\) and \(N\) represent the number of pixels along the length and width of an image, respectively. \(f(i,j)\) denotes the pixel value of the original image, while \(g(i,j)\) represents the pixel value of the denoised image.  
The PSNR (Peak Signal-to-Noise Ratio) measures the similarity between two images and evaluates the noise intensity of an image relative to its original. A higher PSNR value indicates a better denoising effect for the image.  
\[\text{PSNR}=10\log_{10}\left(\frac{255^{2}}{\sqrt{\text{MSE}}}\right) \tag{8}\]  
The SSIM (Structural Similarity Index Measure) measures the similarity between two images based on their luminance, contrast, and structural information. An SSIM value closer to one indicates a better denoising effect for the image.  
\[SSIM=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\delta_{xy}+C_{2}\right)} {\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\delta_{x}^{2}+\delta_{y}^{ 2}+C_{2}\right)} \tag{9}\]In this context, \(x\) and \(y\) represent the original image and the processed image, respectively. \(\mu_{x}\) and \(\mu_{y}\) are the mean pixel values of \(x\) and \(y\). \(\delta_{xy}\) denotes the covariance between x and y, while \(\mu_{x}^{2}\) and \(\delta_{y}^{2}\) are the pixel variances of \(x\) and \(y\). \(C_{1}\) and \(C_{2}\) are constants that ensure the denominators do not become zero.
## 2 Materials and Methods  
Records of the ECG signals have been taken from the standard MIT-BIH _Malignant Ventricular Arrhythmia Database_[25,26] and AHA (2000 series) [27], generating both the training and the test sets from them. In total, 24 continuous monitoring records (22 MIT-BIH records plus two additional AHA records) were used, with a sampling frequency of 125 Hz. All records have cardiac events already labeled. The additional AHA records aim to increase the number of Ventricular Tachycardia (\(VT\)) episodes to improve the balance between recorded time of \(VT\) and \(VF\) episodes. With the episodes labeled, four groups (classes) of signals were created: Ventricular Fibrillation (\(VF\)) corresponds to the class \(VF\) represents all the sections of registers in which there has been ventricular fibrillation and ventricular flutter. ECG signals with the presence of ventricular tachycardia were assigned to the class \(VT\), which, in many cases, appear as a prior stage to ventricular fibrillation (sometimes, \(VT\) sections have \(VF\)-like morphologies). Normal rhythms were assigned to the \(Normal\) class that constitutes the segments labeled with sinus rhythm. Finally, the rest of signal types not labelled as the previous classes (other arrhythmias, noise, etc.) have also been considered and assigned to the class \(Others\). In total, 20,040 s were generated for all ECG signal registers, 3600 s corresponded to the class \(VF\), 1380 s to \(VT\), 10,860 s to \(Normal\), and 4200 s to \(Others\).
## 4 Numerical Examples
### Explicit Performance Functions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & \(\mathbf{Z_{1}}\) & \(\mathbf{Z_{2}}\) & \(\mathbf{Z_{3}}\) & \(\mathbf{Z_{4}}\) \\ \hline \multirow{2}{*}{NMMSO} & mean & 0.6082 & 0.0900 & 1.1815 & 0.9949 \\  & std & (0.1643)+ & (0.2111)+ & (0.1177)+ & (0.0625)+ \\ \multirow{2}{*}{Multi\_AMO} & mean & 0.3026 & 0.2215 & 0.5006 & 1.2302 \\  & std & (0.2141)+ & (0.4230)+ & (0.0683)+ & (0.0514)+ \\ \multirow{2}{*}{DP-MSCC-ES} & mean & 0.9509 & 0.2657 & 1.3537 & 1.2122 \\  & std & (0.2898)+ & (0.1137)+ & (0.1625)+ & (0.0583)+ \\ \multirow{2}{*}{RSCMSAESII} & mean & 1.5406 & 0.3214 & 3.2956 & 1.9924 \\  & std & (0.1435)+ & (0.0352)+ & (0.1013)+ & (0.0539)+ \\ \multirow{2}{*}{NSPSO} & mean & 0.1776 & 0.0241 & 0.1961 & 0.4031 \\  & std & (0.2573) & (1.4025) & (0.0597) & (0.0474) \\ \multirow{2}{*}{+/\(-\)/\(-\)} & 4/0/0 & 4/0/0 & 4/0/0 & 4/0/0 & 4/0/0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of _IGD_S_ of different algorithms for four explicit performance functions. Figure 7: Optimal results of all algorithms. From Tables 3 and 4 it can be seen that, _IGD_S_ of NSPSO is far less than those of other algorithms, denoting a better diversity and convergency for NSPSO. As for \(EOR\), the far bigger values of NSPSO denotes that NSPSO can find much more effective training samples on the LSF for establishing surrogate models. Take F1 for example. Figure 8 shows the optimization history of all algorithms. Optimization history is the recorded 10,000 positions at which the performance function has been evaluated. In the five algorithms, NSPSO shows the clearest outline of the LSF, most sampled points were evenly distributed on and around the LSF. The movement trajectory of the sampled points shows a clear trend of the particles in searching the LSF with the shortest route and spreading around the LSF. The search route is attributed to the normal search pattern, while the spreading behavior should be attributed to the breeding strategy. Also, repulsion strategy makes sure that the whole LSF was covered and also extends the sampling range around LSF. In this way, the contribution of the function evaluations was maximized. On the other hand, results of the competitive algorithms either show a strong clustering trend or are evenly distributed in the variable space. Moreover, it is simple for NSPSO to control the sampling density by setting different repel distance \(d_{rep}\), as shown in the comparative results in Figure 9. This is a useful feature for reliability analysis. When the computational cost for each function evaluation--for example a single slope stability analysis in the slope reliability analysis--is determined, by altering the sampling density, the overall computational cost can be controlled while maximizing the calculation accuracy.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 5 Simulation Evaluation  
### Simulation Configuration  
Our simulation map is an urban area spanning about \(1\times 1\) km\({}^{2}\) in downtown, San Francisco, as shown in Figure 7. We imported the map into SUMO [35] and generated a certain number of vehicles randomly distributed on the map. During the 500-second simulation, each vehicle picks up a random point on the map as a destination and calculates a shortest path. Once it reaches the point, it will randomly choose the next destination and keep doing so until the end of simulation.  
We have chosen two points on the area as the centers of Regions of Interest, as shown Figure 7, where a Road Side Unit (RSU) is deployed. RSU can detect nearby traffic information and generate data in response to interest packets. In our simulation, the two RSUs are running NDN producer applications and replying data upon interest. A subset of vehicles are running NDN consumer applications by issuing sequential interest at a certain frequency (10 s per interest). Each interest is requesting traffic data generated by one of the RSUs and will be expired in 50 s. Other vehicles are playing the role of forwarders to forward the interest if needed. This configuration is to mimic the situation in VANETs that vehicles periodically request sequential traffic information in their interested area.  
Two variables will be used to simulate different network scenarios: number of cars and number of consumers. The first one mimics the network density. More cars means denser network. The second one simulate request load. More interests will be sent if there are more consumers.  
We fully implemented our proposed LBDB and compared protocols in a modified version of ndnSIM 2.0 [36; 37]. The broadcast schemes were added to NetDevice interface without making any changes to the default NDN Forwarding Daemon (NFD). All vehicles and RSUs are equipped with Wi-Fi ad-hoc interfaces and installed with NDN stack.  
Figure 6: Interest Broadcast Evaluation Result.
## 4 Discussion
Results of RF-based models can be applied to plan agricultural land use in order to increase the yield and make it sustainable, without environmental side effects. One of the most important and interesting results in our perspective is the quantification of the relative importance of explanatory variables, which best reflects the different edaphic and climatic needs of the observed crop species. For wheat, soil characteristics are the most important factors, while temperature and precipitation are less important [66]. In case of maize, soil parameters are still important but temperature and precipitation have more importance than in the case of wheat, highlighting that, even in a relatively small country like Hungary, climate tolerance of plants is a differentiating factor. This observation becomes more evident when studying sunflowers, where the importance of mean temperature and precipitation outweighs those of soil type and soil textures as earlier presented by [PERSON] et al. (2018) in case studies from Hungary. Nevertheless climatic variables, such as precipitation in October and November and temperature in January and February are also important for winter wheat [66]. Our results also show the importance of summer rainfall totals (May, June, July) for maize, while for sunflowers the most important parameters are spring and autumn temperatures. We have to emphasize that it is often difficult to compare our results with those of other researchers, because the bioclimatic variables of the study area differ. For example, the work of [PERSON] and [PERSON] (2018) from northern Belgium, investigating the importance of climatic variables in winter wheat yield estimation, found similar parameters to be important, but in a different order. While in Hungary, the mean temperature in January and the amount of precipitation in November are the most important, in Belgium, winter precipitation is the most important [67]. The model fit can be further improved by adding information on management factors such as nutrient levels and fertilizer inputs [52; 68]. Soil plays an important role in increasing crop production. The soil science community is trying to define the appropriate indicators. The presented analysis on the importance of variables in calculating productivity also provides a good basis for SDG indicators, as the related target of SDG is to improve land and soil quality progressively. Addressing soil health and soil quality are the main criteria for achieving sustainable agriculture. Climate change largely affects the minimum and maximum temperatures and the amount of precipitation per month [69; 70; 71]. Our results suggest that these variables are also important for winter wheat, maize and sunflowers, and that changes in these variables could change soil productivity in the future. We established a baseline prediction model for biomass productivity applicable for Hungarian croplands using Earth observation data and yield statistics, identified the importance of soil and climatic determinants of biomass productivity, and proposed a methodology for integrated monitoring of biomass productivity.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 4 Significance Analysis of Mental Workload and Feature Based on SVM Classifier
### Feature Extraction  
#### 4.2.1 Four Bands of EEG Data  
Numerous studies have shown that human brain states are related to the \(\alpha\), \(\beta\), \(\theta\), and \(\delta\) bands.  
\(\alpha\) band: The electromagnetic wave frequency is between 8-13 Hz, and the amplitude is between 30-50 \(\upmu\)V. This periodic wave is produced in the parietal lobe and occipital region of the brain in the state of consciousness, quietness, or rest.  
\(\beta\) band: The electromagnetic wave frequency is between 14-30 Hz, and the amplitude is between 5-20 \(\upmu\)V. This activity occurs in the frontal area when people are awake and alert.  
\(\theta\) band: An electromagnetic wave with a frequency between 4 and 7 Hz with an amplitude of less than 30 \(\upmu\)V. This activity occurs mainly in the parietal and temporal regions of the brain.  
\(\delta\) band: The electromagnetic wave frequency is between 0.5-3 Hz, and the amplitude is between 100-200 \(\upmu\)V. This activity occurs during deep sleep, unconsciousness, anesthesia, or hypoxia.  
Figure 8: Subject 12 uses ICA to remove artifacts after EEG topographic map.  
Figure 7: Subject 12 uses ICA to remove artifacts before EEG topographic map.
## 3 Fluid-Structure Interaction Analysis of Cross-Type Hydraulic Pipeline
### Influence of Fluid Pressure on Fluid-Structure Interaction Characteristics of Cross-Type Pipeline  
In order to consider the coupled vibration of the pipeline caused by fluid pressure pulsation, the fluid pressure values are set as 0 MPa, 1 MPa, 5 MPa, 10 MPa, 15 MPa, and 21 MPa, and the influence of fluid pressure on it is discussed by solving the fluid-structure  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pipeline** & \multicolumn{2}{c}{**Fluid**} \\ \hline Tube outer diameter (mm) & 50 & Density (kg/m\({}^{3}\)) & 850 \\ Wall thickness (mm) & 5 & Bulk modulus (GPa) & 1.666 \\ Elastic modulus (GPa) & 206 & \\ Density (kg/m\({}^{3}\)) & 7900 & \\ Poisson’s ratio & 0.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-type pipeline material characteristics.  
interaction steady-state parameters under six working conditions. The treatment method for the fluid is the same as that for using the finite element method for the single pipeline, with boundary conditions set. The inlet of the cross-type pipe is set as the velocity inlet, and the flow rate is 1 m/s. The outlet is set as the pressure outlet. The pressure values correspond to the six working conditions, and the fluid-solid interface is set as the wall. As the fluid pressure increases, the maximum response value increases and the maximum stress decreases.  
After the flow field calculation is completed, the fluid pressure is introduced into the solid inner wall of the cross-type pipeline for import calculation. After the calculation is completed, the pressure load and displacement of the flow field on the pipeline solid can be obtained, as shown in Figure 5, which is the deformation diagram and stress distribution diagram of the cross-type pipeline under the corresponding working condition of 21 MPa pressure.  
Plot the maximum response value and maximum stress of the pipeline under different fluid pressures as a curve, as shown in Figure 6. As the fluid pressure increases, the maximum deformation of the cross-type pipeline decreases in a small range, and the maximum stress shows a decreasing trend.  
Figure 5: Analysis results of 21 MPa cross-type pipeline fluid–structure interaction. (**a**) steady-state deformation of cross-type pipe; (**b**) steady state stress of cross-type pipe.  
Taking the cross-type hydraulic pipeline with the same boundary condition and different pipeline elastic modulus as the research object, the fluid-structure interaction analysis was carried out to study the influence of the pipeline elastic modulus on the steady-state characteristics of the branch pipeline. Taking into account the elastic moduli of all metal materials, the range of elastic moduli is determined to be 70-230 GPa. Calculations are conducted every 20 GPa to obtain the steady-state deformation and stress, and the curve is plotted as shown in Figure 7. By analyzing the curve trend, it can be concluded that as the elastic modulus of the pipeline increases, the maximum response value decreases, and the maximum stress shows a linear increase trend.
# A Programmable SRv6 Processor for SFC
## 2 Related Work
### Network Virtualization  
Network virtualization means that the connections between virtual network nodes are not connected using physical cables but instead rely on specific virtualized links to connect them [4]. Objectively speaking, it is the development of cloud computing that promotes the development of network virtualization [5]. It focuses on the abstraction of network resources (ports, broadband and IP addresses) and supports dynamic allocation and management according to tenants and applications. Network virtualization technology creates multiple virtual networks on a shared physical network resource, while each virtual network can be deployed and managed independently. As a branch of virtualization technology, network virtualization is essentially a resource-sharing technology. Common network virtualization applications include virtual local area networks (VLANs) [6], VXLAN [7], virtual private networks (VPNs) [8], and virtual network devices.  
Network slicing is an on-demand networking approach that allows operators to separate multiple virtual end-to-end networks in a unified infrastructure in order to accommodate various types of applications [9]. The core of network fragmentation technology is network function virtualization (NFV) [10]. The NFV separates the hardware and software parts from the traditional network. The hardware is deployed by a unified server and the software is undertaken by different virtual network functions (VNFs) to meet the requirements of flexible service assembly. These VNFs include the virtual evolved packet core (vEPC) [11], virtual radio access network (vRAN) [12], and applications from different parties.  
Segment routing (SR) is a protocol designed to forward data packets over a network based on the concept of source routing, which divides the network path into segments and assigns SIDs to these segments and network nodes. A forwarding path can be obtained by ordering the SIDs [13].
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Performance of Tamper Detection and Self-Recovery
#### 4.2.6 Large Area Tampering  
To test the performance of the proposed method for large area tampering, Figure 13 gives the tamper detection and image recovery results under large area tampering, where the tampering ratios are 50% in the left part and 80% in the middle, respectively. From the figures, it can be seen that when half of the image has been modified, the recovered image still has a good image quality subjectively and objectively; when the tampering ratio is up to 80%, the restored image becomes seriously blurred and its PSNR value is decreased to 19 dB. However, the basic content of the image is still recognizable, which proves that the proposed method is effective for large area tampering.
## 3 Results
### Remote Sensing Reflectance Model Considering Vertical Distribution Of Phytoplankton
\[r_{rs}(0^{-},\lambda)=R_{rs}(\lambda)/(0.52+1.7R_{rs}(\lambda)). \tag{21}\]  
The results showed that acceptable relative errors were acquired when validating using the field-measured data (Figure 13). The APD of model-derived \(r_{rs}(0^{-})\) and field-measured \(r_{rs}(0^{-})\) was 12.1%, 17.0%, and 27.8% at 490, 550, and 675 nm, respectively (Figure 13b). The uncertainties may come from the difficulty in optical closure between Ecolight simulation and field measurement of natural waters, the measurement of \(R_{rs}(\lambda)\), and the derivation of \(r_{rs}(0^{-})\) from measured \(R_{rs}(\lambda)\).
# Exploring Benefits of Using Blending Splines as Transition Curves
## 3 Method
### The Original Railway Curve  
The original railway curve consists of two arc segments, of different radii, connected by a clothoid transition curve. The example is extracted from the Norwegian railway line, Ofotbanen. Parameters for the segment are presented in Table 1, with the layout plotted in Figure 3.  
The clothoid is implemented by exploiting a power series expansion of the integrals in (5), given in [31] as,  
\[\begin{split} C(t)&=\frac{1}{a}\sum_{i=0}^{\infty} \frac{(-1)^{i}(\frac{\pi}{2})^{2i}\hat{t}^{4i+1}}{(2i)!(4i+1)},\\ S(t)&=\frac{1}{a}\sum_{i=0}^{\infty}\frac{(-1)^{i}( \frac{\pi}{2})^{2i+1}\hat{t}^{4i+3}}{(2i+1)!(4i+3)},\end{split} \tag{6}\]  
where the variable \(a\) is set up for the clothoid to join two arcs, instead of an arc and a line.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Arc 1** & **Clothoid** & **Arc 2** \\ \hline
**Start Radius (m)** & - & 401 & 315 \\
**End Radius (m)** & 401 & 315 & - \\
**Length (m)** & 135.335 & 60 & 103.481 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segment parameters.  
Figure 3: Layout of the original railway curve. Two circular arc segments (solid) connected by a clothoid segment (dotted).
## 2 Materials and Methods
### Flux Measurements  
Turbulent fluxes of carbon dioxide (CO\({}_{2}\)) were quantified using the eddy covariance (EC) method. The EC setup comprised an open-path infrared gas analyzer (LI-7500, LI-COR, Lincoln, NE, USA) to measure CO\({}_{2}\) exchange, paired with a Gill WindMaster 3D sonic anemometer (Gill Instruments, Lymington, UK) to capture three-dimensional wind velocity components. All sensors were installed at the top of a 32 m tall tower.  
Additional environmental variables were monitored at the same height. Air temperature was monitored with an HMP45 AC probe (Vaisala, Finland), while photosynthetically active radiation (PAR) was measured using a LI-190 quantum sensor (LI-COR, Lincoln, NE, USA). Net radiation was recorded by a CNR-1 radiometer (Kipp & Zonen, Delft, The Netherlands), and precipitation was captured using a TE-525 tipping-bucket rain gauge (Campbell Scientific Inc., Logan, UT, USA).  
Half-hourly net ecosystem exchange (NEE) of CO\({}_{2}\) was computed using EddyPro software version 7.0.9, LI-COR ([[https://www.licor.com/support/EddyPro/software.html](https://www.licor.com/support/EddyPro/software.html)]([https://www.licor.com/support/EddyPro/software.html](https://www.licor.com/support/EddyPro/software.html)), accessed on 20 November 2024). A friction velocity (u*) threshold of 0.3 m s\({}^{-1}\) was applied for filtering nighttime fluxes. Standard corrections and quality control procedures were implemented, including spike detection [22], planar fit coordinate rotation [23], time lag compensation, air density corrections [24], and frequency response adjustments for both high- [25] and low- [26] pass filtering. Data quality was classified on a scale from 0 (high) to 2 (low) [27], with values labeled as 2 excluded from further analysis.  
To fill data gaps and partition net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (RE), we utilized the REddyProc processing tool developed by the Max Planck Institute for Biogeochemistry ([[https://www.bgc-jena.mpg.de/5622399/REddyProc](https://www.bgc-jena.mpg.de/5622399/REddyProc)]([https://www.bgc-jena.mpg.de/5622399/REddyProc](https://www.bgc-jena.mpg.de/5622399/REddyProc)), accessed on 20 February 2025). Daily NEE was calculated by summing the 30 min values across each 24 h period. Annual totals for NEE, GPP, and RE were derived by aggregating the corresponding daily sums.