esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Multiscale Back-Projection Feature Fusion
Then, the difference between the upsampled \(F\) and \(F^{3}\) is computed, resulting in the error feature \(e_{3}\). This error feature, \(e_{3}\), is then downsampled once to reach the original size of \(F\) (i.e., \(32\times\ 32\times 128\)). The down-sampled \(e_{3}\) is added to the original \(F\), resulting in a new feature: \(F^{\prime}\), which represents the output of one back-projection feature fusion block, as shown in Figure 3. In order to fuse with \(F^{2}\), the feature \(F^{\prime}\) from the previous step is upsampled twice using convolution operations, achieving a size of \(128\times 128\). Similar to the previous step, the error is computed between the upsampled \(F^{\prime}\) and \(F_{2}\), and the back-projection process is used to update the feature. The same process is repeated for \(F_{3}\), adjusting the upsampling and downsampling operations accordingly to ensure that the output size matches the input size after each convolution and deconvolution step. This back-projection feature fusion process allows encoder \(EB^{4}\) to merge the features from different encoders while maintaining consistent dimensions. In order to adapt DBPN for multiscale feature fusion, we reduced the number of transformations (upsampling and downsampling operations) from three times to twice. The feature maps from neighboring layers were fused by upsampling or downsampling the current layers using deconvolution or convolution. Omissions can avoid large computational costs for feature fusion. In contrast to DBPNs for super resolution, underwater image enhancement restores images of the same size. Therefore, our network consists of encoders and decoders, which have different structures, as illustrated in Sections 3.5 and 3.6.
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 5 Experiments and Results  
### Dataset and Experimental Environment  
As mentioned in Section 2.1, the key to machine learning-based security patch identification work is a modest dataset labelled with ground truth. Therefore, to increase code reliability and reproducibility, we employ PatchDB datasets to conduct experiments. Moreover, we will use PatchRNN on the same dataset to conduct experiments as a comparison model. The dataset contains a large number of security patches and non-security patches written in C/C++ language, not only the security patches with vulnerability numbers indexed by the National Vulnerability Database (NVD), but also a large number of security patches obtained from the wild. Among them, the security patch dataset obtained from the wild is collected from commits on GitHub. The dataset is manually validated by three security experts on the labels of security patches and non-security patches, and the authenticity of labels is ensured by cross-checking. Therefore, this paper has reason to believe that the labels of this dataset are convincing, and conduct relevant experiments on this dataset.  
Our experiment is conducted using Python 3.9.6, while the TMVDPatch is designed based on Pytorch 1.9.1. Our model is carried out in the Ubuntu 20.04.1 LTS environment running in AMD Ryzen Threadripper 3960X 24-Core Processor CPU with 125-GB RAM. We realize the neural network training by employing the CUDA 11.4 toolkit with 2 NVIDIA GeForce RTX 3090 GPUs.  
We use the classical parameter optimizer as the SGD optimizer. For other hyperparameter settings, the batch size is set to 64, and the number of epochs is set to 20.
## 3 Research Methods
### GWO-LSTM Prediction Model  
In order to solve the time sequence problem of land surface settlement prediction, the problems of local optimality and slow convergence of the LSTM model are considered, and an LSTM model combined with the Grey Wolf Optimizer (GWO) algorithm is proposed in this study (Figure 7). GWO is an emerging swarm intelligence optimization algorithm. It emulates the hierarchical order and predatory conduct witnessed in grey wolf populations, which guide the group to conduct a global search through alpha, beta, and delta wolves. This also reinforces the global search capability, which minimizes the likelihood of converging towards a suboptimal solution.  
The introduction of the Grey Wolf Optimization algorithm optimizes the weight and bias parameters of the LSTM neural network, which applies the global search ability of the GWO algorithm in the iterative process. This contributes to enhancing both the rate at which the network converges and the overall precision of predictions made by the model. In addition, the GWO algorithm can also adapt to changing problem scenarios, which enhances the robustness of the model in dynamic environments.  
In the process of modeling, the traditional LSTM model is used to establish a preliminary surface settlement prediction model. Then, the GWO algorithm is employed for the optimization of the initial parameters in LSTM. In each iteration of the GWO, it evaluates the fitness of all individuals (solutions) in the current population, and selects the most fit individual as the lead wolf to guide the group toward a better solution. Through continuous iteration, the optimal solution set as the optimal weight and bias parameters of the LSTM network is finally obtained, and a more accurate prediction model of surface settlement is constructed.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 6 Performance Validation  
The performance validation is based on assumptions 11:  
11. The pitch angle is fixed at \(\beta=-1.0^{\circ}\) in the conventional OT method.  
FAST code is used to simulate the aerodynamics and the mechanical aspects of wind turbines, while MATLAB/SIMULINK is used to simulate the DFIG system. This section compares the simulation results of a 5 MW wind turbine using the novel MPPT method with that using the conventional OT method. The parameters used are shown in Table 1.  
The C\({}_{\text{P}}\)(\(\lambda\), \(\beta\)) of the 5 MW wind turbine calculated by AeroDyn shows the \(C_{\text{pmax}}\) equals 0.4648 when \(\lambda=\lambda_{\text{opt}}=7.057\) and \(\beta=\beta_{\text{opt}}=-1.0^{\circ}\). Thus, the pitch angle is fixed at \(\beta=-1.0^{\circ}\) in the conventional OT method corresponding to assumption 11.  
To avoid short-term overloading of the generator and the gearbox, the electromagnetic torque is saturated to a maximum of 47,402.91 N-m, and a torque rate limit of 15,000 N-m-s\({}^{-1}\) is also imposed [23].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol** & **Value** & **Unit** \\ \hline Rotor radius & \(R\) & 63 & m \\ Gearbox ratio & \(n\) & 97 & \\ The moment inertia of rotor & \(J_{\text{r}}\) & 35,444,067 & kg\(\cdot\)m\({}^{2}\) \\ The moment inertia of generator & \(J_{\text{g}}\) & 534.116 & kg\(\cdot\)m\({}^{2}\) \\ The equivalent damping & \(k_{\text{eq}}\) & 92,214 & N\(\cdot\)m\(\cdot\)rad\({}^{-1}\) \\ The equivalent stiffness & \(c_{\text{eq}}\) & 5 & N\(\cdot\)m\(\cdot\)s\(\cdot\)rad\({}^{-1}\) \\ The stator resistance & \(R_{\text{s}}\) & 0.00706 & pu \\ The rotor resistance \(\text{Rr}\) & \(R_{\text{r}}\) & 0.005 & pu \\ Self-inductance of the equivalent stator winding & \(L_{\text{s}}\) & 3.071 & pu \\ Self-inductance of the equivalent rotor winding & \(L_{\text{r}}\) & 3.056 & pu \\ Mutual inductance & \(L_{\text{m}}\) & 2.9 & pu \\ Air density & \(\rho\) & 1.225 & kg\(\cdot\)m\({}^{-3}\) \\ Rated wind speed & \(v_{\text{rat}}\) & 11.4 & m\(\cdot\)s\({}^{-1}\) \\ Rated rotor speed & \(\omega_{\text{rat}}\) & 12.1 & rpm \\ Optimal TSR & \(\lambda_{\text{opt}}\) & 7.057 & \\ Optimal power coefficient & \(C_{\text{pmax}}\) & 0.4648 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of th ewind turbine.
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 3 Circuit Design
### DFE Adaptive Circuit  
DFE is a nonlinear equalizer, which is composed of a feedback filter and a Slicer, which can eliminate the ISI of the current symbol to the subsequent symbols, and has a better compensation effect on the reflection and fluctuation of the channel. However, the power consumption of DFE is larger than that of FFE under the same number of taps. In this paper, only one-tap DFE is used as equalization to reduce the error signal. Due to the high transmission rate, this paper adopts a pre-decision DFE to solve the problem of tight timing in the feedback loop. The traditional DFE structure needs to complete the calculation and decision process in the feedback loop, while the pre-judgment DFE only selects the MUX unit in the feedback loop [11]. After the pipeline structure is adopted in the whole data path, the pre-determined DFE is more suitable for high-speed circuits and parallel data chains.  
The structure of the DFE is shown in Figure 7. After the decision value of the decider passes through the feedback filter, it is differentiated from the input signal and directly eliminates the ISI from the input signal. The decision value is output by the Slicer.  
Similar to FFE tap update, the DFE tap update can be expressed as [10]:  
\[W(0)=W^{-1}(0)+2\mu*[ek(1)Y(0)+ek(2)Y(1)\cdots+ek(63)Y(62)] \tag{6}\]  
The overall equalization intensity of the DFE is relatively small, and it provides about 5 dB of equalization intensity in the overall equalizer, so the convergence speed can be relaxed under the premise of ensuring its convergence accuracy. Therefore, each time the DFE tap coefficients are updated, only one set of numbers needs to be randomly  
Figure 6: 16-tap parallel FFE structure.  
selected as the parameters required for the result equalization. Its update algorithm can be expressed as:  
\[W(0)=W^{-1}(0)+2\mu*[ek(i+1)Y(i)]\quad i\in 0,1\cdot\cdot\cdot\cdot\cdot 62 \tag{7}\]
# Forest Resistance and Resilience to 2002 Drought in Northern China
###### Abstract  
Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive flussite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling.  
d 2021 13 June 2021 22 July 2021 25 July 2021  
dynamics of photosynthesis [17; 18]. However, machine learning methods have been less involved in studying ecosystem stability.  
Evidence showed that dry and hot extremes in northern China have been increasing in recent decades [19]. However, the ecosystem stability of forests to drought in this area is still unclear. In this study, we used a machine-learning simulated gross primary productivity (MTE-GPP), two remote sensing-based vegetation products (MODIS-EVI and MODIS-GPP), and the state-of-the-art drought index SPEI, to explore the ecosystem stability of forests to 2002 drought in northern China. With this study, we tried to answer three questions: (1) how did the forests respond to drought during and after the event? (2) was there any difference between the spatio-temporal characteristics of ecosystem stability of needleleaf forests and broadleaf forests in the northern China? (3) what are the similarities and differences in the performance of the three data sources: measured tree-ring-width, machine learning-simulated GPP, and remote sensing-based vegetation data? Results from this study show diverse resistance and resilience of two forest types to drought in 2002, as well as the results from MTE-GPP and MODIS-GPP, which were the most consistent. These findings might be suggestive for forest conservation and vegetation modeling, respectively.
# Development of the Virtual Reality Application:
## 1 Introduction
### The Ships of Navarino  
Requirements and constraints, such as the time duration of the VR visit, the inclusion of people not accustomed to watching VR, and the device specifications set some early design directions for the general approach.  
Specifically, considering the time limitation, unnecessary interaction with the VR environment or any gamification elements that would extend the duration of the guide were not included in the final experience. Design decisions were aimed towards a more friendly approach for people new to VR, such as the transition between ships. The VR hands were included, providing a better understanding of the controller locations and to facilitate actions such as pointing. Moreover, the introduction scene was designed to resemble a cabin (Figure 1), as this is the only ship room that is, scale-wise, closer to the human size, offering a more friendly transition from the real world to the virtual one.  
Although interacting with the virtual world provides many possibilities for viewers to experience historical events and artifacts, the available technology comes with significant limitations regarding performance. While the Oculus Rift S targets 1-2 million triangles and 500-1000 draw calls per frame, the Oculus Quest targets 300,000-500,000 triangles and 150-175 draw calls per frame [17]. Put simply, this means that, considering performance, a very serious reduction was needed to be made to the number of polygons of the 3D models, and the number of materials, texture sizes and lighting had to be optimized.  
On the other hand, the aspiration of the project was to provide a realistic representation of the four historic warships, enhanced with important information about the decisive Battle of Navarino. From the beginning of development, it was clear to us that a reduction of complexity in the 3D models or the quality of textures and materials was not an option: the project's depiction of the warships should be as accurate as possible.  
Optimization methodologies adopted from the video game field were the answer to this problem.  
* The high polygon count 3D models, derived from detailed drawings and NURBS surfaces (.3 DM) were turned into a more common type of mesh, then remeshed and retopologized in order to reduce polygon count.
* In order to reduce draw calls, unwrapping and texturing was carried out in a manner that focused on the reusability of textures and materials on different meshes, resulting in a reduced number of materials. In addition, a tool was used to bake the lights in Unity3d.  
* Occlusion culling was also implemented in order to reduce triangles and draw calls per frame.  
In the following parts, the methodology employed for the development of the VR experience \"The Ships of Navarino\", will be presented and analyzed in detail.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 7 Discussion  
In the future, it will be necessary to adjust the protection system for renewable energy source, while the protection system will be managed centrally for the local microgrid, to speed up the process of locating the fault and fault elimination. The protection system depends on the operating conditions and the type of resources used for this reason, it is necessary to consider this system as a whole and not separate. The size of the microgrid system will be decisive in this issue, and for Slovakia we could consider the size of the microgrid system according to the current regions or larger parts with the binding of several microgrid systems cooperating with each other. The mix of energy sources of microgrid power supplies must comply with the year-round load diagram and for individual days, as the size of consumption varies from month to month and from the weather the system sources must be flexible enough.  
Figure 30 shows perspective places on the territory of the Slovak Republic for the location of photovoltaic stations, where the greatest perspective is in the southern part of Slovakia, while we can get the most electricity from photovoltaic stations in the vicinity of Komarno and Nitra.  
Figure 29: Microgrid model with renewable energy sources.  
Figure 31 shows actual hydropower plants places on the territory of the Slovak Republic, where the most power plants are placed on the Vah River. The hydropower plants in Slovakia are currently around 108. The largest hydroelectric power plants in Slovakia are Gabcikovo on the Dunaj River and Pumped-storage hydropower plants the Cierny Vah and the Liptovska Mara.  
Figure 31. Location of hydropower plants of Slovak Republic [24].  
Figure 32 shows actual biogas and biomass power plants places on the territory of the Slovak Republic. The biogas power plants of Slovakia are currently around 90. The biomass power plants of Slovakia are currently around 167.  
Figure 30. Photovoltaic power potential of Slovak Republic [28].  
For this reason, it is appropriate to place battery systems or other power sources in the northern parts of Slovakia. The balanced distribution of power plants as shown in Table 1 is not ideal due to the uneven distribution of power plants in Slovakia, because the western part of Slovakia has several times higher capacity of installed power plants than the eastern part.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 4 Optimization Problem of the Proposed System
### Mathematical Model of the Constraints
#### 4.2.4 The Heat Storage Constraints  
These constraints are expressed using the following equations:  
* State of Charge Constraint \[W_{HS-min}^{t}\leq W_{HS}^{t}\leq W_{HS-max}^{t}\] (43)
* Charging and Discharging Power \[U_{HS-ch}^{t}\cdot H_{SB-ch,min}^{t}\leq H_{HS-ch}^{t}\leq U_{HS-ch}^{t}\cdot P _{HS-ch,max}^{t}\] (44) \[U_{HS-dis}^{t}\cdot P_{HS-dis,min}^{t}\leq P_{HS-dis}^{t}\leq U_{HS-dis}^{t} \cdot P_{HS-dis,max}^{t}\] (45) \[U_{HS-ch}^{t}+U_{HS-dis}^{t}\leq 1\] (46) where \(U_{HS-ch}^{t}\) and \(U_{HS-dis}^{t}\) are zero/one variables that are utilized to prevent the HS from charging and discharging at the same time.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 3 Results and Discussion
### Calculation Conditions
#### 3.1.2 Background Simulation  
The spectral properties of different cloud marine backgrounds were simulated. In this study, MODTRAN 5 was used to simulate the atmospheric radiative transfer process and obtain the background radiance. The background types were sea, cumulus cloud, altostratus cloud and cirrus cloud in the equatorial region [42; 43; 44], and the calculation conditions were divided into daytime and nighttime. The simulation results are shown in Figure 6. It can be seen that the cloud background was strongly affected by sunlight during the daytime, which was the main factor affecting the detection.  
Figure 4: Flight speed and altitude of the airline, the cruising phase is highlighted with the red box.  
Figure 5: A commercial aircraft’s radiant intensity (W/Sr). (**a**) Cruise (daytime), (**b**) cruise (nighttime).  
Figure 6: Background spectral radiance. (**a**) Daytime, (**b**) nighttime.
## 3 Simulations
### Study of the Distance between Buoys  
The distance between both buoys and its variation are observed while the ROV moves forward at a constant speed (Figure 4). The coordinates of the centers of the buoys are used to calculate the distance and the variation. In this simulation, the ROV starts at a horizontal distance of 4 m from the remote station and a depth of 0.5 m. A length of 10 m of cable is unwound and then, at 11 s, the ROV starts to move 7 s forward at a controlled speed of 0.4 m/s. The desired speed is reached after 1.3 s. During the simulation, the difference between the length of the deployed cable and the distance of the ROV from the remote station is greater than 3 m. The time evolution of the distance between buoys shows that the system returns to its rest position in 5.5 s after the ROV stops. The 5% response time is 3.8 s. Thus, the system behaves like a damper. The impact of the 120 ms delay of the flex sensor, due to filtering post-processing, is negligible, and the acquisition/control frequency of 25 Hz is sufficient. Despite the damping effect, the variations in ROV speed immediately affect the distance between the buoys, which supports the interest in using this parameter to control the automatic distribution of the cable.  
To observe the behavior of the damped system, the relationship between the distance between the buoys and the ROV forward speed at 0.5 m depth is shown in Figure 5. The figure displays the curve obtained from simulation and the curves obtained from the theoretical model for values of \(T_{1c}\) equal to 0 N, 0.01 N, 0.03 N, 0.05 N, and 0.1 N. The drag forces were considered quadratic. Despite some slight offset, the curve issued from the steady-state model with an order of magnitude of 0.03 N for \(T_{1c}\) is close to the curve obtained in the simulation. The value of \(T_{1c}\) depends on the design of the feeder built for the simulation. The inter-buoy distance is close to its maximum when the ROV reaches a speed of 0.8 m/s. This value represents the speed limit for optimal behavior of the system. The distance versus speed relationship can be used to define a target inter-buoy distance as a function of the ROV speed to help the system converge to the steady-state mode. this is not possible since labels are not compatible with tex symbols  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Physical Parameters** & **Fathom Slim Tether** & **Unit** \\ \hline Axial stiffness & \(1.10^{12}\) * & N \\ Axial damping & \(8,2.10^{14}\) * & kg.m/s \\ Bending stiffness & \(5,7.10^{-4}\) & N.m\({}^{2}\)/rad \\ Bending damping & \(2,9.10^{-4}\) * & N.m\({}^{2}\)/rad \\ Torsion stiffness & \(8,8.10^{-4}\) & N.m\({}^{2}\)/rad \\ Torsion damping & \(8,8.10^{-5}\) * & N.m\({}^{2}\)/rad \\ Linear density & \(2.10^{-3}\) & kg/m \\ Drag coefficient & \(1,8\) * & - \\ Radius & \(2.10^{-3}\) & m \\ Breaking strength & 1520 & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for simulation of the Fathom Slim tether. Values marked with an asterisk (*) are approximate estimates.