esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 3 CAN Network Elements
### Soft-ROADMs
#### 3.2.1 Operating Principle
This means far cheaper optical and electrical components with low-bandwidth can be used (particularly ADCs with a lower sampling rate) in the ROTs to significantly reduce the overall cost of the CAN deployment. On the other hand, in the add functional element, similar to the procedure reported in [22], the add operation at a specific wavelength can be performed by passive optical coupling in an optical coupler (OC), as illustrated in Figure 7. Such an operation can be expressed as:  
\[|s_{out}(t)|^{2}=|s_{in}(t)|^{2}+|s_{add}(t)|^{2} \tag{3}\]  
where \(S_{in}(t)\) and \(S_{out}(t)\) are the input and output optical signals of the add function element, and \(S_{add}(t)\) is the optical sub-band/sub-wavelength signal to be added. In the sub-wavelength-level add operation, a sufficient spacing [23] between the wavelengths of the two channels should be considered to avoid the optical beat interference (OBI) effect within the bandwidth of the receiver. In Equation (3) it is assumed that there is no OBI effect present. In the sub-wavelength and sub-band level add operation, to achieve the contention-free add operation, \(S_{add}(t)\) has to be located in a free location within the digital filtering space. This can be easily realized by selecting a free digital filter type (\(I\) and/or \(Q\)) for a given central \(RF\) frequency and/or shifting the central \(RF\) frequency of a digital filter having a specific type in the digital domain prior to performing the E-O conversion in the corresponding ROT [9]. The aforementioned soft-ROADM operations are fully controlled by a ROADM-embedded DSP controller, as shown in Figure 7, which determines the required set of dynamic operating parameters through regular communication with the centralised SDN controller via an extended OpenFlow protocol [24; 25]. The dynamic parameter set may include digital filter characteristics employed by the targeted sub-bands/sub-wavelengths, characteristics of the drop RF signal when the drop operation is required and information on free filter locations in the digital filtering space when the add operation is required. As the parameter set can be dynamically generated and finely adjusted using DSP algorithms according to the prevailing tasks, traffic status and network characteristics, the operations of the proposed soft-ROADMs are thus flexible, scalable, adaptive and physical-layer network characteristic-transparent. Here it is worth mentioning the following two aspects: (i) For a given wavelength in a network node, the dropped signals at specific sub-wavelengths and/or sub-bands are not erased from the original input wavelength before the add operation, as shown in Figure 7. As such, when the add operation is performed at the same wavelength, this may result in possible contention, whose probability, however, can be very low due as a relatively large digital filtering space would be adopted in practice; (ii) directionless operations can be achieved when the proposed ROADM is equipped with a typical CDC structure.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 1 Introduction
Because the source distortion is not related to the pixel value, and the total end-to-end distortion can be represented by the sum of single lost distortion, and the overall distortion optimization process is simplified as a separate optimization process for each frame. Motivated by the work mentioned in [8; 9; 10; 11; 12; 13], a video error-resilient scheme is proposed for error-resilient transmission of screen content coding (SCC) in this paper. In the proposed scheme, additional code stream or redundant information is considered to improve error-resilient performance with redundant coding and acceptable video quality is obtained in the case of frame transmission error. In addition, selective allocation redundancy is conducted to take the importance of the video frame ROI area into account in the co-encoding process. Consequently, the redundancy insertion efficiency and reliability are improved in return. The corresponding experiments validate the effectiveness of the schemes proposed in this paper. The main contribution of this paper is concluded as follows. 1. This paper provides an adaptive error prevention coding mechanism for frame-level-based video transmission security, where the total end-to-end rate-distortion cost can be optimized by adjusting the number of coded redundant frames. 2. To further optimize the bit rate and redundancy allocation, the abrupt frames are distinguished according to the characteristics of SCC, and the region of interest is considered as a redundant allocation. The rest of this paper is organized as follows. Section 2 discusses the related work. Section 3 provides the main contribution of this paper. Simulation experiments and result analyses are accomplished in Section 4. Finally, Section 5 concludes this paper.
# UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net
## 2 The Modeling Method of UAV Cluster
### The Modeling Method of Interaction Event  
The UAV cluster can be regarded as a typical cyber physical system including physical space and information space. The interaction of a UAV mission between information and physics can be viewed as the interaction between the perception events and control events. Perception events include the perception of key attributes such as speed, altitude, pitch angle, and roll angle. Control events include maneuvers such as flat flight, acceleration and deceleration, dives, jumps, and combat turns.
## 3 Preliminaries
### Construction of Snapshot Matrix
The hexahedral structured mesh and boundary layer for the wind tunnel is shown in Figure 6. After the structured grid division is completed, the y+ of the hemispherical model and the sinusoidal model are calculated, with the y+ values of the hemispherical model varying from 2.41 to 84.62 and the sinusoidal model varying from 4.88 to 74.63. The calculation result of y+ proves the rationality of grid division and lays the foundation for the following fluent calculation. ANSYS Fluent is used to generate the velocity and relative pressure distributions to construct the database. The solution parameters of Fluent are set as follows: The 'Pressure-Based' method and 'Steady' are selected as the Solver. For the boundary conditions, the type of inlet is selected as'velocity-inlet' and the outlet is set as 'pressure-outlet'. In the inlet, the turbulence intensity is 5% and viscosity ratio is 10%. Standard K-\(\varepsilon\) is chosen as the viscous model because of its advantages such as low calculation demand, strong calculation stability and fast convergence speed. The Solution Method and Gradient are chosen as 'SIMPLE' and 'Least Squares Cell Based', respectively; the Momentum and Turbulent Kinetic Energy is selected as 'Second-Order Upwind', and the convergence residual is set to \(1\times 10^{-5}\). Finally, the mesh independency test is carried out, where 2,628,578 cells are considered as the standard. Four groups of dense to sparse cells are designed for comparison: 986,523, 415,243 and 195,489. The 12,500 velocity values of five planes calculated from 2,628,578 cells are used as the criterion, and then the results of the other three groups are compared with this criterion. Finally the relative error is obtained by using Equation (11). The relative errors compared to 2,628,578 cells are shown in Table 1. It is obvious that when the number of cells reaches approximately 400,000, the relative error decreases to approximately 0.2%, which can be considered to be within the acceptable range considering the calculation cost. Consequently, 415,243 cells are chosen for the following calculation.
## 2 Data and Methods  
### Study Area  
Northeast China was chosen as the study area, located between 38\({}^{\circ}\) north (N) and 53\({}^{\circ}\) N and between 115\({}^{\circ}\) east (E) and 135\({}^{\circ}\) E, covering an area of about 1,240,000 km\({}^{2}\) (Figure 1). It includes Heilongjiang Province, Jilin Province, Liaoning Province, and the east of the Inner Mongolia Autonomous Region. Northeast China belongs to a temperate continental monsoon climate, characterized by four distinct seasons with short, warm summers and long, cold winters. The precipitation is mainly concentrated in the summer, and the annual precipitation decreases from southeast to northwest. The study area is surrounded by mountain ranges along three directions, including the Greater Khingan Mountains in the northwest, the Lesser Khingan Mountains in the northeast, and the Changbai Mountains in the southeast. The Liao River Plain, Songnen Plain, and Sanjiang Plain are located in the central and southern parts and the northeastern corner, respectively. The Hulun Buir Plateau is located in the western tip of the region. There are hills and tabledands situated between mountains and plains. Phaeozem is the main type of soil. The main vegetation types include deciduous broadleaf forests, deciduous coniferous forests, coniferous, and broadleaf mixed forests and grassland. Throughout the past few decades, northeast China suffered from eco-environmental problems, such as serious soil erosion, decrease of available cropland, serious flood disaster, deterioration of ecological environment, and decrease of grain yield and quality [26]. Several ecological function protection areas were established in the study area, including (1) the Greater Khingan Mountains (GKM), (2) Songnen Plain Wetland (SNPW), (3) Sanjiang Plain Wetland (SJPW), (4) the Changbai Mountains (CM), (5) Liaohe River Delta Wetland (LRDW), (6) Horqin Sandy Land (HSL), and (7) the Source Region of West Liao River (WLR). Monitoring and evaluating the changes of ecosystem SWUE under different hydro-climatic conditions in northeast China is significant to understand the response of ecological function to climate change.  
Figure 1: The geographical location of northeast China.
# Enhanced Curvature-Based Fabric Defect Detection:
## 2 HIL (Hardware in the Loop) Implementation
### Data Acquisition  
In a vision system, data acquisition is a critical step that involves capturing and collecting visual information from the environment. This process plays a pivotal role in  
Figure 1: (**a**) Experimental Setup. (**b**) Close-Up View of Experimental Setup.  
enabling the system to analyze and interpret visual data for various applications. In this case, it is fundamental to provide an accurate fabric defect detection system.  
Data acquisition serves as the raw input to the vision system, capturing images or video frames from cameras or other sensors, and forming the foundation for subsequent processing and analysis. The quality and accuracy of the acquired data directly influence the overall performance of the system. High-quality, clear, and properly calibrated images lead to more precise and reliable results during analysis and decision-making processes. In rotating systems, issues such as blurring and illumination variations arise due to machine speed. While the rotation speed can be controlled in the experimental setup, the camera on the knitting machine must adapt its capture frequency and adjust to varying lighting conditions.  
For an effective defect detection system in knitting machines, the vision system must dynamically adjust to diverse environmental conditions, addressing key factors such as variations in lighting, blurring, and the presence of contaminants like dust and oil on the lens. Continuous and varied data acquisition is essential for training the system to manage these challenges, allowing it to learn and adapt accordingly. For applications requiring real-time analysis, efficient data acquisition is paramount, ensuring that the system can capture and process information in a timely manner to enable rapid responses and decision-making. Accurate and up-to-date visual data are essential for identifying the precise location of defects on the fabric.  
In conclusion, data acquisition is a fundamental component of a vision system's operation. The quality, quantity, and diversity of the acquired data significantly affect the system's ability to perform tasks accurately, adapt to changing conditions, and provide valuable insights for a range of applications.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 4 Results and Analysis
### Correlation Analysis of Contact Angle  
The variation in water saturation within the pore medium of the hydrate sediment layer as a function of time under the two flow rate conditions was established separately and fitted with a negative exponential function, as shown in Equation (13) [38]; fitting results are shown in Figure 12.  
\[S_{w}=Ae^{-t/B}+C \tag{13}\]  
Both displacement rates are sensitive to the contact angle of porous media. The changes in water saturation under the three wettability conditions are similar when the replacement rate is small, and the changes in water saturation under gas-driven water conditions in pore media with strong hydrophilicity exhibit more linear behavior. The curvature of the data curves changes significantly at large repulsive velocities, especially in weakly hydrophilic media. Porous media that are weakly hydrophilic are more sensitive to changes in water saturation initially, becoming more sensitive to time, changing more drastically over time, and becoming less sensitive to changes in time after the breakthrough at the tail end.  
To further characterize the correlation between the values of the fitted function parameters in different wetted pore media, we compared the relationship between the values of the fitted parameters \(A\) and \(B\) with the cosine of the contact angle, respectively, as illustrated in Figure 13. The values of the fitted parameter \(A\) exhibit a consistent trend under both replacement velocity conditions, decreasing with the weakening of hydrophilicity. They also demonstrate a linear correlation with the cosine value of the contact angle. However, the slope of the fitted parameter \(A\) decreases by a factor of 0.53 when the replacement velocity is increased by a factor of 5. Moreover, the value of the fitted parameter \(A\) is more responsive to changes in the contact angle under conditions of low flow velocity. The trend of the fitted parameter \(B\) value remains consistent under both driving speed conditions, decreasing with reduced hydrophilicity. It demonstrates a linear correlation with the cosine value of the contact angle. However, the slope of the fitted parameter \(B\) value decreases by 0.2 times when the driving speed is increased by a factor of 5. Additionally, the fitted parameter \(B\) value shows insensitivity to changes in the contact angle under conditions of high flow rate. Therefore, under the working conditions described in this paper, the curvature of the water saturation versus the time curve is primarily determined by the value of the fitting parameter \(A\).  
Figure 12: Relationship between water saturation changes and methane release in porous media with varying wettability under two different flow rates. (**a**) The entrance interface moves at a speed of 3.5 mm/s. (**b**) The entrance interface moves at a speed of 17.5 mm/s.
# ATLAS: Latest Advancements and First Observations
## 2 Materials and Methods  
### ATLAS: Overview and Current Status  
ATLAS was designed to consolidate national resources in preparation for the establishment of an SST sensor network. In 2019, Instituto de Telecomunicacoes initiated a major retrofit of its 9-m Cassegrain Antenna located in Pampilhosa da Serra Space Observatory (PASO), Portugal, to attach ATLAS, a complete radar system operating in the C-band (5.56 GHz) (Figure 1). A comprehensive technical overview of the system was presented in [5], accompanied by simulation studies on its expected operational performance. At the time of publication [5] in 2021, the generation and processing of the baseband signals were being thoroughly tested in a laboratory. These experiments led to several studies on the design of waveforms and signal processing chains for the system [5] (Section 4) [7]. The studies culminated in the first real-life scenario tests, where the whole system (except the antenna and power amplifiers) was tested in a setup with a major reflector at a well-defined distance [8].  
After completing the preliminary functional tests in the lab and the field, we proceeded to the deployment phase. The deployment consisted of connecting the output of the power amplifier to the antenna feed through a polarizer. The polarizer underwent a matching procedure to maximize the isolation between the input/output ports and minimize reflections [9]. As part of the developments in 2022, we also presented the ATLAS Cloud Service API (ACSA) in [9]. ACSA enables monitoring and controlling of the radar through a web-based API, allowing the system to be accessed by multiple clients remotely. PASO  
Figure 1: Antenna located in PASO, with the radar system inside the hub.  
houses many other sensors and systems [6; 10], allowing ATLAS to act as a complete SST ground station with the following features:  
* Monitoring and prediction of weather and sky conditions [11];
* Generation of schedules for optimal observation of a given catalog of space objects;
* Data fusion of optical and radar observations [12];
## 2 Data and Methods
### Data Processing Method
#### 2.2.3 Data Acquisition and Preparation
Shuttle Radar Topography Mission (SRTM) 30 m DEM was downloaded from an online portal ([[https://dwtkins.com/srtm30m/](https://dwtkins.com/srtm30m/)]([https://dwtkins.com/srtm30m/](https://dwtkins.com/srtm30m/)), accessed on 15 June 2019), whereas the latitude data was derived from the DEM data based on its y-coordinates. Finally, the measured aboveground biomass (AGB) was utilized to verify the BEPS model (Figure 1). The observations of AGB data were collected from 205 sample points through 41 stations across the country covering the year 2003-2018, provided by the Information and Research Institute of Meteorology, Hydrology, and Environment (IRIMHE) \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S.N.**} & \multirow{2}{*}{**Complete Name**} & \multirow{2}{*}{**Format**} & \multirow{2}{*}{**Units**} & \multirow{2}{*}{**Source**} & **Spatial** & **Temporal** & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Reference**} \\  & & & & & & **Resolution** & **Resolution** & \\ \hline
1 & Precipitation & img & mm & ECMWF & 12.5 km & Twice per day & [52] \\
2 & Net solar radiation & img & W-m\({}^{-2}\) & ECMWF & 12.5 km & Twice per day & [52] \\
3 & Net thermal radiation & img & W-m\({}^{-2}\) & ECMWF & 12.5 km & Twice per day & [52] \\
4 & Temperature & img & \({}^{\circ}\)C & ECMWF & 12.5 km & Twice per day & [52] \\
5 & Snow depth & img & mm & ECMWF & 12.5 km & Twice per day & [52] \\
6 & Net solar radiation Downward & img & W-m\({}^{-2}\) & ECMWF & 12.5 km & Twice per day & [52] \\
7 & Dew point temperature & img & \({}^{\circ}\)C & ECMWF & 12.5 km & Twice per day & [52] \\
8 & Wind speed & img & ms\({}^{-1}\) & ECMWF & 12.5 km & Twice per day & [52] \\
10 & Leaf area index & tiff & NA & MODIS & 500 m & Twice per day & [53] \\
11 & Vegetation type (C\({}_{3}\)) & tiff & NA & ESA & 300 m & Annual & [54] \\
12 & Land cover & tiff & NA & ESA & 300 m & Annual & [54] \\
13 & DEM & tiff & m & SRTM & 30 m & One & [55] \\
14 & Latitude & tiff & \({}^{\circ}\)(Degree) & SRTM & 30 m & One & [55] \\
15 & Tree cover & tiff & \% & MODIS & 250 m & Yearly & [56] \\
16 & Field capacity & tiff & KPA kg\({}^{-1}\) & DAAC & 8 km & One & [57] \\
17 & Wilting point & tiff & KPA kg\({}^{-1}\) & DAAC & 8 km & One & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of major parameters of BEPS model, their source, and spatiotemporal extent. ECMWF, European Centre for Medium-Range Weather Forecasts; MODIS, Moderate Resolution Imaging Spectroradiometer; ESA, European Space Agency; SRTM, Shuttle Radar Topography Mission; DAAC, Distributed Active Archive Center. of Mongolia [10]. A monthly (every 25 th day) measurement was made in the growing season during the study period. The observed AGB provided in the unit of dry matter (g/m\({}^{-2}\)), was converted to NPP value (g-C m\({}^{-2}\)year\({}^{-1}\)) by applying a carbon conversion factor of 0.475 for grass and foliage components as C per gram dry mass [58].
## 3 Proposed Method  
EFCANet is applicable to a single image, which is different from several other methods that require multiple images of the same scene to be fused. First, we use the Exposure Image Generator (EIG) to generate exposure-corrected images corresponding to low-light images. The Exposure Image Generator (EIG) increases the global brightness of the image. To maintain color and luminance balance, the exposure correction image and the original low-light image are converted from the RGB color space to the YCbCr color space, so that blending occurs in the YCbCr color space. Then, we use the Cross-Attention Fusion Model (CAFM) to fuse the original low-light image with the exposure-corrected image. CAFM yields an intermediate enhancement result with a color space of YCbCr. Then, CAFM is iteratively used three more times, and the input of the latter CAFM is the output of the previous CAFM and the exposure-corrected image with YCbCr color space. Finally, the enhanced image is transferred from the YCbCr color space back to the RGB color space to obtain the final enhanced image. The flow chart of the method is shown in Figure 2.
## 2 Analysis of PV PCC Harmonics
The curves illustrated in Figure 7 indicate that the phase sequences of the inter-harmonics (\(\varphi_{f4}\) and \(\varphi_{f6}\)) are symmetric, such as the same phase difference and opposite phase sequence. Further analysis shows that the amplitudes of the two inter-harmonic voltages are very close, and all the voltage root mean square (RMS) are about 24.00 V. The correlation of frequency, phase sequence and amplitude show that the inter-harmonics at PCC are not independent from each other and come from the same source. Therefore, it is inspiring to analyze the harmonics in the output circuits of PV grid-tied inverters.
## 3 Experimental Setup and Result Analysis
### Evaluation Criteria  
The model performance was comprehensively evaluated using accuracy, precision, recall, and the F1 value. Given that the focus is on subtype classification, the macro average was employed to calculate the final results. This approach ensures that each category is given equal weight in the calculation of the mean value. Consequently, the final result was the arithmetic mean of the corresponding metrics for each category.