esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Structural Characterisation  
The morphological analysis of CQDs utilized both TEM and HRTEM, as illustrated in Figure 2. The TEM image in Figure 2a reveals quasi-spherical CQDs with an average size of 4.4 nm and a narrow size distribution, as emphasized in the inset. Figure 2b provides HRTEM images, further confirming the uniformity and excellent crystalline structure of the CQDs. The majority of CQDs observed in HRTEM align with sizes from the TEM analysis,indicating consistent dimensions. The high-magnification HRTEM image displays distinct lattice fringes measuring 0.22 nm, similar to the (100) facet of sp\({}^{2}\) graphitic carbon [29], highlighting a well-defined crystalline structure within the CQDs. The combined TEM and HRTEM analyses offer a comprehensive view of the morphology of CQDs. The quasi-spherical shape, narrow size distribution, and good crystallinity affirm the precision and quality of the synthesized CQDs. These findings are crucial for understanding the structural characteristics of CQDs and informing their potential applications in various scientific and technological fields.  
Figure 3 presents the FTIR spectrum of CQDs. The peak at 3250 cm\({}^{-1}\) in the synthesized CQDs is attributed to the adsorption of O-H stretching vibration [30]. Peaks in the range of 1000-1131 cm\({}^{-1}\) are associated with C-H stretching [30]. Peaks at 1641 cm\({}^{-1}\) and 1412 cm\({}^{-1}\) can be attributed to the C=O and -C-OH groups, indicating plasma treatment that leads to the formation of sp\({}^{2}\) domains. These groups originate from small precursor molecules like sugar and citric acid. As a result, there are oxygen, hydroxyl, and carboxyl groups present in the sp\({}^{3}\) carbon area [31]. These functional groups contribute to the aqueous solubility of CQDs after carbonization by facilitating the formation of bonds with surrounding oxygen groups [32; 33].  
Figure 3: FTIR spectrum of CQDs.  
Figure 2: (**a**) TEM and (**b**) HRTEM images of CQDs (inset of Figure 2a: histogram showing the size distribution of CQDs).
## 3 Results and Discussion  
### Results for Mass Loss, Volume Loss and \(\mu\)  
Figures 7 and 8 show mass and volume losses as a function of load, grit size, sliding speed, and sliding distance. From Figures 8 and 9, it can be seen that increasing the load increased the mass and volume losses, but decreased when the sliding distance and sliding speed increased. Maximum mass and volume loss were obtained at 9 N, 150 mesh, 45 m, and 0.08 ms\({}^{-1}\), while minimum mass and volume losses occurred at 3 N, 1000 mesh, 55 m, and 0.14 ms\({}^{-1}\). This observable fact indicates that different wear mechanisms are perhaps involved. At higher loads, the abrasive particles penetrate deeply into the materials, causing ploughing and separation of the fibres from the matrix.  
Based on Table 4 and Figure 11, the optimum process variables for obtaining low M\({}_{\rm L}\) using the Taguchi technique were found to be L = 3 N, G = 1000 mesh, SD = 25 m, and SS = 0.04 ms\({}^{-1}\). The corresponding level values are presented in bold to facilitate understanding of the mean SNRs response table. This predicted combination of optimum parameters for M\({}_{\rm L}\) was coded as L1G1 SD1 SS1. According to Table 5 and Figure 12, the estimated optimum settings for achieving minimum V\({}_{\rm L}\) was determined to be L = 3 N, G = 1000 mesh, SD = 55 m, and SS = 0.04 ms\({}^{-1}\). This combination of optimum settings is coded as L1G3 SD3 SS1. From Figure 13 and Table 6, it was observed that the maximum mean SNRs achieved for \(\mu\) were L at 9 N, G at 1000 mesh, and SD at 25 m, and SS at 0.14 ms\({}^{-1}\). Thus, the estimated optimum process parameter settings for achieving a minimum \(\mu\) via the Taguchi method can be coded as L3G1 SD1 SS3. As seen in Figure 14 and Table 7, the highest mean SNRs obtained for T\({}_{\rm s}\) were L at 9 N, G at 1000 mesh, and SD at 55 m, and SS at 0.04 ms\({}^{-1}\). Therefore, using the Taguchi method, the predicted optimum parameter settings are styled as L3G1 SD3 SS1.  
ANOVA depicts the parameters that significantly influence the abrasive behaviors. The ANOVA for M\({}_{\rm L}\) showed that G was the most significant parameter, followed by L, SD, and SS as 97.26%, 1.12%, 0.96%, and 0.65%, respectively. With regard to the V\({}_{\rm L}\), the ANOVA indicated that G with 90.29% was the most influential parameter affecting the V\({}_{\rm L}\), followed by L (5.96%), SS (3.04%), and SD (0.71%). The analysis for \(\mu\) found G as the most significant parameter, having a percentage contribution of 37.24%, followed by L with 33.92%, SD with 17.62%, and SS with 11.20%. Equally, G was found to be the most influential parameter contributing to minimum T\({}_{\rm s}\), showing 65.00%, while L showed 65%, SD 22.57%, and SS 1.72%Figure 14: Main effect plot for SNRs of T\({}_{\rm s}\).  
Figure 12: Main effect plot for SNRs of V\({}_{\rm L}\).  
Figure 13: Main effect plot for SNRs of mean \(\mu\).
## 2 Numerical Modeling of the AGF Process including Phase Transition Conditions
### Coupling Calculation Model of Shield Machine Heating and Freezing Cooling
#### 2.2.2 The Finite Element Model  
Considering the above factors and to eliminate the boundary effect, the plane of the model face is 30 m \(\times\) 40 m. Considering the dynamic jacking process of the shield, the shield jacking route is partitioned along the tunnel axis. According to the engineering experience, the shield machine can complete the assembly of approximately five rings of segments every day under normal conditions. Therefore, the model takes a length of 6 m for each five-ring segment as the tunneling progress. The length of the freezing pipe in the other inner ring is 2.8 m, so the geometric size of the soil is designed as horizontal width (\(X\)-axis direction) \(\times\) longitudinal length (\(Y\)-axis direction) \(\times\) vertical depth (\(Z\)-axis direction) = 30 m \(\times\) 38.9 m \(\times\) 40 m. After the trial calculation, the range of this model is much larger than that of the freezing affected area, which is more reasonable and eliminates the error caused by the asymmetry of the freezing pipe, as shown in Figures 3 and 4.  
Figure 2: Layout diagram of freezing pipes (unit: mm).  
The temperature boundary is used for the boundary condition of the freezing pipe cooling, and the calculation equation is shown with:  
\[T=T_{0} \tag{5}\]  
The side, lower, and rear parts of the soil are semi-infinite spaces. An adiabatic boundary is used, and the calculation equation is shown with:  
\[\begin{split} q=0\end{split} \tag{6}\]  
Figure 4: Shield freezing system model.  
Figure 3: Model diagram.  
The upper surface of the soil and the entrance of the shield are external air. The second boundary considering air thermal convection is used. The calculation equation is shown with:  
\[q_{0}=h\cdot(T_{ext}-T) \tag{7}\]  
In this Equation (7), \(T_{ext}\) represents outside temperature, \(h\) represents the convection coefficient. Soil is used as a phase change material, and the phase change temperature is considered to be 0 \({}^{\circ}\)C.
## 2 Vision-Based Measurement System  
### EMM Vision System  
The EMM is a redundant seven-degree-of-freedom manipulator symmetrical about the elbow joint [37], with an identical EEF at each end, one of which acts as a base and can be connected to the module alone or to the CMM in a macro-micro way, while the other end is used to operate the payload. Each EEF is equipped with a vision system (short for EVS) primarily used to measure the attitude of the cooperative target and provide live image data. The EVS includes three separate optical systems called hand camera (HC), load-operated camera (LC), and vertical camera (VC). HC is located at the bottom for VS when the EFF is unloaded; LC is located at the top for the VS of small or medium-sized payloads when the hand camera is obscured; VC is mounted in a direction orthogonal to the other two cameras for VS of lateral payloads on the exposed platform. In addition, when the manipulator operates large payloads, such as pallets, a payload camera (PC) is mounted at the front of the pallet, as shown in Figure 2. The LC and VC use the same visual marker (VM), the others have different visual markers for the corresponding camera due to the working distance.  
The architecture of the EVS is shown in Figure 3 with redundant backup for each unit. The HC is the interface and control centre of the vision system, the external camera image data interface with PC and compressed image data interface with the manipulator controller are implemented here. Each of the cameras has an independent lighting system but they share one processing and compression unit. The imaging of HC and LC share the same image processing unit. The VC and PC have the independent imaging units which are located in their own camera. The image processing item in Figure 3 is to perform a measure function to recognize the marker and calculate the relative pose. In addition, the image processor has an internal 1553B bus function that allows it to receive and forward various control commands from the central control; the information received includes image data, temperature data, and control signal, such as lighting control and heating control. The interaction of the camera information with the manipulator controller via an internal 1553B bus at a rate of 8 Hz, through which the manipulator controller sends commands to the EVS and receives the measurements. The camera imaging and process are based on FPGA. Image compression is based on DSP and transmission via the Ethernet at 25 frames per second.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 1 Introduction
* The SAE network uses a four-layer network, which uses the dimension information of the measurement value, and the encoding matrix is obtained based on the training of the dataset, and the decoding matrix is obtained by using the group inverse instead of obtaining it through training. * The Unet network uses three different sizes of convolutional kernels to obtain target features of different sizes and uses sub-pixel convolution and attention mechanism to improve the reconstruction quality. * We apply the proposed methods in a single-pixel imaging system and verify the effectiveness and feasibility of the proposed methods.
## 3 Results
### Total Precipitation  
Precipitation is a value that describes the height, in millimeters, of a water layer that would form on the surface of the Earth without external influence. The first measurements of this parameter were made for the purpose of storm analysis, rain distribution, and rainfall forecasting. The first satellite measurements of precipitation were made using radar to produce three-dimensional maps of storm structure and to calculate the altitudes where the phase transition from snow to rain occurs. Since one of the most important climatic processes is the water cycle on Earth and in the atmosphere, such data allowed qualitatively to complement and improve models of global atmospheric circulation. The most common methods of precipitation measuring nowadays are either microwave sensors or ground-based observatories. Microwave sensors measure the energy emitted by the atmosphere or the ground, and extract quantitative characteristics of water vapor, water in clouds, and the intensity of precipitation in the atmosphere from the signal. There are sensors that additionally allow estimating both the rate of precipitation and the geometric characteristics of water particles.  
The MERRA-2 reanalysis mainly contains data from ground stations, collected in the Global Historical Climatological Network (GHCN) database, and in the Climate Anomaly Monitoring System (CAMS) database. In these catalogs, there is a basic division of information by ground and ocean stations, which is compared in parallel with similar published data sets from satellite missions.  
In the reanalysis, there is a separate processing block to account for inter-annual variability in ocean evaporation [46; 47].  
It is also worth noting that MERRA-2 initially uses precipitation data based on ground-based observations, which are further archived as an output variable and input parametrically to values obtained from satellite data. A description of the satellite instruments  
Figure 13: Mean values of air temperature (\({}^{\circ}\)C) at 2 m for January according to MERRA-2 data for the period 1980–2021.  
obtaining atmospheric precipitation values is given above in Section 2.2. In addition, some observation areas use CMAP satellite sensor results due to limitations in available observations [48].  
Consequently, the results are provided in the following variations: maximum and minimum precipitation rates for the period, total precipitation, convective precipitation, large-scale precipitation, snow, total precipitation from the atmospheric model, evaporation totals, and corrected large-scale and total precipitation. The adjusted total precipitation data were used in Figure 14. This map clearly identifies the zone with the maximum amount of precipitation, belonging to the zone of the Ural Mountains, and the minimum values are confined to the water area of the Kara Sea.
# Novel RNA Viruses Discovered in Weeds in Rice Fields
## 3 Results
### Characterization of Negative-Sense RNA Viruses  
The order _Bunyavirales_ was one of the largest groups of segmented negative-sense single-stranded RNA viruses, which include many pathogenic viruses that infect arthropods, protozoa, plants and animals [54]. In this study, 24 novel negative-sense RNA viruses were identified in eight weed samples in Sanya, Xiaogan and Guiyang. These were Guiyang bunya-like virus 1 (GBIV1), Sanya bunya-like virus (SBLV) 1 to 20, Xiaogan peribunya-like virus 1 (XPeV1), Sanya leish-like virus 1 (SLIV1) and Xiaogan phenui-like virus 1 (XPhV1), with genome lengths ranging from 2540 to 11,361 bp. These viruses encode conserved RdRp protein and displayed 27.5% to 72.1% sequence similarities in aa levels with the closely related viruses (Table S5). It is notable that each of the viruses (SBLV1 to -8) was detected in at least two weed samples in Sanya (Table S5); for example, SBLV1 was detected in the weeds _Leersia hexandra_ and _Leptochlo chinensis_. A large number of negative-sense RNA viruses in this study were identified from weeds in Sanya, and 71% of the viruses were found in weed _Leptochlo chinensis_ in Sanya. Of the identified viruses, only three clustered within known viral families (Figure 5): XPeV1 was assigned into the family _Peribunyaviridae_, XPhV1 clustered within the family _Phenuivriidae_ and SLIV1 clustered within the family _Leishbuviridae_. We were unable to classify the other 21 viruses into the present families; these viruses exhibited limited similarity (\(\leq\)72.1%) to any well-defined family in the order _Bunyavirales_, and they only contained the L segment encoding RdRp (Figures 5 and S13, Table S5). In particular, the 21 newly identified viruses occupy topological positions belonging to two families, thus filling major phylogenetic gaps. Hence, we deemed that the branches may represent two new families of the _Bunyavirales_ order. We named these branches _Alphabunyaviridae_ and _Betabunyaviridae_ (Figure 5).
## 4 Algorithm Design
### \(N\)-Ary Number Encoded ADENSGA for Node Placement Problem  
Different any number \(N\) may have different effects on the multi-objective problem than the single-objective problem. In order to apply degressive any number encoding in a multi-objective problem, the optimization effect of multi-objective \(N\)-ary number encoding ADENSGA(N-ADENSGA) under different any numbers \(N\) should first be tested. The process of N-ADENSGA is exactly the same as D-ADENSGA in Algorithm 2, with \(N(k)=N\) during the whole process of evolution. In this section, experiments are carried out on N-ADENSGA with different values of \(N\). In this experiment, set \(N=[2,10,18,26,34,42,50]\), the maximum iteration of the algorithm \(k_{max}=100\). The parameters used in the experiment are shown in Table 1.  
To comprehensively evaluate the optimization effect of the algorithms on the node placement problem, two auxiliary evaluation index _Score_ and \(D\) is introduced [10]. _Score_ represents the comprehensive performance of the algorithm. The larger the value of _Score_, the better the algorithm performance. \(D\) represents the total moving distance of all the nodes. The smaller the value of \(D\) is, the better the algorithm performance. The equations of _Score_ and \(D\) are shown in Equations (7) and (8). \[Score=f_{1}\cdot f_{2} \tag{7}\]  
\[D=\sum_{i=1}^{n}dis(i) \tag{8}\]  
where \(f_{1}\), \(f_{2}\) are the optimization objectives of the problem shown in Equation (3), \(dis(i)\) is the moving distance of the \(i\)th node, \(n\) is the number of sensor nodes in the monitoring area. The influences of any number \(N\) for two different random node placements are shown in Figures 2 and 3. Each figure is composed of (a), (b) and (c), respectively, corresponding to three evaluation indexes, average Coverage, average Rest Energy, and average _Score_. Figures 1(a) and 2(a) show that on the objective of coverage, low any number encoding is significantly better than high any number encoding in the final optimization effect, while high any number encoding is more likely to optimize the objective faster in the initial stage of optimization (before 20 generations). Figures 1(b) and 2(b) show that on the objective of rest energy, high any number encoding is better than low any number encoding in the final optimization effect, and can optimize the objective faster in the initial stage of optimization. Figures 1(c) and 2(c) show that considering the two optimization objectives comprehensively, high any number encoding can close to the optimization objectives faster in the initial stage of evolution, while low any number encoding can get better optimization results in the middle and late stages of evolution. Since the value any number \(N\) can determine the size of the search space, it may affect the running time of the algorithm and the accuracy of the solution. In order to find out the relationship between the running time, \(D\) and \(N\), the results for 10 random initial placements are calculated and shown in Table 2.
## 2 Methods and Data
### Experimental Design
#### 2.3.4 Accuracy Indices for Evaluation  
The mean absolute error (MAE) and mean relative absolute error (MRAE) values were computed as quantitative accuracy measures for both datasets. MRAE, defined as the ratio of the MAE to the mean of actual values, was considered to highlight the relative difference in errors for small actual reflectance values. Furthermore, the coefficient of determination (\(R^{2}\)) of regression modeling and error information of input data were used as supplementary information to compare and interpret the accuracy measures.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 3 Results and Discussion
### The Parameter Distribution of Coaxial Jets
#### 3.1.1 Velocity and Vorticity  
Figures 2-5 present the experimental results for the flow field of nozzle A in case 1. It can be clearly seen from Figure 2a,b that under the influence of the actuators, both the inner and outer shear layers become thicker with the development of the flow, and the velocity gradient becomes smaller along the radial direction. In order to facilitate the comparison between different cases, the distance from the position of the central jet with 16 m/s to the inner duct outlet was defined as the length of the potential core, which is 90 mm in Figure 2a.  
Figure 2: Time average velocity (**a**,**b**) and vorticity (**c**,**d**) distribution of nozzle A without (**a**,**c**) and with (**b**,**d**) actuators.  
Figure 4: The radial distribution of time averaged streamwise velocity (**a**–**c**) and radial velocity (**d**–**f**) at different axial positions of nozzle A.  
Figure 3: Velocity distribution along axial direction at different sections of nozzle A.  
Vorticity is mainly distributed in the shear layer. It can be clearly seen in Figure 2c that the coaxial jet has two major vortices, and the vorticity of vortices in the inner shear layer is more concentrated. [PERSON] (1975) named it the primary vortices, and named another in the outer shear layer as the secondary vortices. In Figure 2d, the vorticity showed significant diffusion along the axial and radial directions, indicating that the influence of the vortices expanded and more air from jets and ambient entered the shear layers to mix.  
In order to study the effect of the synthetic jet on the shear layer, three positions were selected on the X-Y plane in the radial direction, and the velocity distribution along the axial direction was measured. In Figure 3, y/D\({}_{\rm OA}\) = 0 was the center of the nozzle, the velocity value did not change significantly with actuators as a whole, indicating that the airflow at the center was not directly affected by the synthetic jet. y/D\({}_{\rm OA}\) = 0.07 was located in the inner shear layer. The velocity curve slightly fluctuated and the value in the entire section improved by actuators, with an average increase of 11%. This is because more high velocity air from the central jet was entrained into the inner shear layer, causing its disturbance to increase. y/D\({}_{\rm OA}\) = 0.54 was located in the outer shear layer, the actuators increase the upstream velocity and reduce the downstream velocity. This indicates that the annular jet expanded radially in the upstream and more ambient air was entrained into the outer shear layer at downstream by synthetic jets, which enhanced the mixing of the annular jet and the ambient air.  
Figure 5: The radial distribution of Reynolds shear stress (**a–c**) and turbulence intensity (**d–f**) at different axial positions of nozzle A.