esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Rice Virus-Induced Gene Silencing System Mediated by Wheat Dwarf Virus
###### Abstract  
The virus-induced gene silencing (VIGS) technique can effectively inhibit systemic viral infection by down-regulating plant endogenous gene expression, and it has become an important tool to study plant gene function. However, few studies have reported that wheat dwarf virus (WDV), which enables high-throughput gene silencing, could be used in a rice VIGS system. In this study, a VIGS vector system was constructed based on WDV, and successfully silenced the _Pitytoene desaturase_ gene and the rice blast resistance gene _Piz1_ in rice. _Piz1_-silenced plants showed significantly increased resistance to rice blast, significantly reduced lesion area, and did not show high disease symptoms (grade 8-9). In addition, the WDV vector has the advantages of rapid infection, high proliferation, and an unconformity genome, and has little influence on rice growth and development. This study validates the effectiveness of the WDV-VIGS system in rice gene function studies and provides a new gene silencing tool for blast resistance breeding.  
wheat dwarf virus; _Piz1_; virus-induced gene silencing; rice; rice blast resistance
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 3 System Model
### Distance Measurement Model  
#### 3.2.1 Node-State Analysis  
Because the 3D underwater environment is considered in this paper, the force analysis of anchor nodes is carried out from the vertical direction and horizontal plane. In the vertical direction, anchor nodes are subject to gravity, buoyant force, and upward fluid resistance force. During the diving process, the mobile anchor node first performs an acceleration motion and then performs a constant-speed linear motion. In this paper, we assume that the acceleration process of the anchor node is ignored, and the anchor node moves in a straight line at a constant speed in the vertical direction. Moreover, we assume that anchor nodes in the horizontal plane are affected by water flow and conform to the following motion model [19]:  
\[\left\{\begin{array}{l}x_{t}=x_{t-1}+v_{cx}\\ y_{t}=y_{t-1}+d_{t}v_{cy}\end{array}\right. \tag{1}\]  
Here, \([x_{t},y_{t}]\) is the planar coordinate of the sensor node at a time, \(t\). Furthermore, we assume that the water flow moves mainly along the \(x\)-axis direction; \(d_{t}\) is the movement direction of the node on the \(y\)-axis.  
\[d_{t}=\left\{\begin{array}{l}-1,\text{ if }d_{t-1}=1\text{ and }y_{t-1}>l_{cy}\\ 1,\text{ if }d_{t-1}=-1\text{ and }y_{t-1}<-l_{cy}\end{array}\right. \tag{2}\]  
The constant speed, \(v_{cx}\), is randomly chosen from \([0,v_{\max}]\). Moreover, on the \(y\)-axis, we assume that nodes can oscillate by relatively small amounts \(\left(l_{cy}\right)\). Furthermore, for ordinary sensor nodes in UWSNs, we assume that the depth fluctuation of ordinary sensor nodes underwater is negligible, that is, the depth remains unchanged. Besides this, its motion in the horizontal plane is consistent with that of the anchor node. In this paper, therefore, it can be regarded as the overall drift of nodes in UWSNs.  
Figure 2: The vertical plane of the 3D UWSNs.
## 3 Results
### The Performance is Improved Due to the Addition of CO\({}_{2}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multicolumn{2}{c}{**SIF-GPP Model**} & \multicolumn{2}{c}{**SIF-CO\({}_{2}\)-GPP Model**} & \multicolumn{2}{c}{**Increase Rate**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Fitted Formula**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & **Fitted Formula** & \multicolumn{2}{c}{**R\({}^{2}\)**} & **(\%)** \\ \hline US-Srr & GPP = \(4.97\)\(\times\) SIF - 2.67 & 0.53 & GPP = \(11.52\times\) SIF - \(0.16\times\) CO\({}_{2}\) + 65.37 & 0.70 & 32\% \\ US-WCr & GPP = \(21.06\times\) SIF - 1.78 & 0.80 & GPP = \(19.80\times\) SIF - \(0.05\times\) CO\({}_{2}\) + 18.49 & 0.83 & 4\% \\ US-Syr & GPP = \(20.50\times\) SIF - 1.98 & 0.61 & GPP = \(9.97\times\) SIF - \(0.53\times\) CO\({}_{2}\) + 214.29 & 0.86 & 41\% \\ US-PFA & GPP = \(15.86\times\) SIF - 6.64 & 0.72 & GPP = \(15.24\times\) SIF - \(0.16\times\) CO\({}_{2}\) + 65.37 & 0.88 & 19\% \\ US-TP3 & GPP = \(30.05\times\) SIF + 2.97 & 0.47 & GPP = \(29.42\times\) SIF - \(0.41\times\) CO\({}_{2}\) + 150.73 & 0.63 & 34\% \\ US-TP4 & GPP = \(29.20\times\) SIF + 2.90 & 0.41 & GPP = \(9.42\times\) SIF - \(0.47\times\) CO\({}_{2}\) + 179.32 & 0.67 & 33\% \\ CA-SCC & GPP = \(27.20\times\) SIF - 0.80 & 0.68 & GPP = \(33.81\times\) SIF - \(0.05\times\) CO\({}_{2}\) + 22.64 & 0.73 & 7\% \\ US-EML & GPP = \(18.56\times\) SIF + 0.73 & 0.73 & GPP = \(24.88\times\) SIF - \(0.07\times\) CO\({}_{2}\) + 27.53 & 0.80 & 10\% \\ US-TPD & GPP = \(27.70\times\) SIF + 7.02 & 0.40 & GPP = \(32.30\times\) SIF + \(0.11\times\) CO\({}_{2}\) - 37.61 & 0.50 & 25\% \\ US-Ro4 & GPP = \(42.92\times\) SIF - 1.09 & 0.87 & GPP = \(49.09\times\) SIF + \(0.08\times\) CO\({}_{2}\) - 26.03 & 0.90 & 3\% \\ US-Los & GPP = \(17.82\times\) SIF - 0.64 & 0.75 & GPP = \(17.74\times\) SIF - \(0.001\times\) CO\({}_{2}\) - 0.40 & 0.77 & 3\% \\ US-Los & GPP = \(8.77\times\) SIF - 0.01 & 0.64 & GPP = \(8.256\times\) SIF - \(0.07\times\) CO\({}_{2}\) + 29.57 & 0.69 & 9\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the SIF-GPP model and the SIF-CO\({}_{2}\)-GPP model. Increase rate is the difference between the R\({}^{2}\) of both models. The CO\({}_{2}\) data comes from the EC flux tower.
###### Abstract  
The spacecraft tracking telemetering and command (TT&C) system plays an essential role in celestial and terrestrial networks, requiring relative ranging and communication, particularly in satellite formation flying networks and distributed spacecraft networks. To achieve precious ranging and high-data-rate communication in a Master/Slave satellite architecture, an integrated communication-ranging system (ICRS) is introduced. ICRS is based on the inter-satellite spread spectrum ranging and spread/non-spread spectrum communication modulated by unbalanced quadrature phase shift keying (UQPSK). In both uplink and downlink, the in-phase (I) branches and the quadrature (Q) branches undertake the tasks of ranging and communication, respectively. In addition, a global navigation satellite system (GNSS) like signal is adopted in I branches for the sake of better ranging accuracy, and binary phase shift keying (BPSK) modulation is employed in Q branches for a higher data rate. Therefore, the key point of the ICRS design is the power resource allocation between two branches via the selection of a suitable power distribution factor (PWDF). Simulation results demonstrate the good performance of the proposed approach in ranging error and bit error rate (BER). In addition, a reasonable PWDF is recommended. Furthermore, the influence of clock offset is also taken into consideration.  
ranging; communication; unbalanced quadrature phase shift keying (UQPSK); tracking telemetering and command (TT&C); distributed spacecraft networking +
Footnote †: journal: Sensors
## 2 The Network Model and Analytical Methods  
### A Bank-Asset Bilateral Network Model  
A complex network can be regarded as a set of non-empty finite point \(V\) and a binary relation \(E\), where \(E\) refers to the edge set formed by the specific relationship between nodes. For a bank-asset bipartite network model, commercial banks and assets are regarded as 2 groups of nodes with different properties. The directional connection represents the relationship between banks holding assets. As shown in Figure 1, if commercial bank \(i\) holds asset \(j\), a link from bank \(i\) to asset \(j\) is generated.  
When establishing complex networks of real systems, we take the proportion of asset investment as the weight of the edges. First, we establish a matrix \(A=(a_{ij})\), in which the element \(a_{ij}\) represents the value of the asset \(j\) held by bank \(i\). Then, we divide each \(a_{ij}\) by the sum of the elements in the \(i\)-th row in matrix \(A\) and derive a quantity \(e_{ij}\). \(e_{ij}\) represents the percentage of the total value of each asset held by the bank \(i\). After that, we establish an investment matrix \(E\), whose \((i,j)\)-th entry equals \(e_{ij}\) and where the sum of the elements in each row of \(E\) is equal to 1. The element \(e_{ij}\) is used to represent the weight of the edge between bank node \(i\) and asset node \(j\). According to matrix \(E\), the bank-asset bipartite network model can be constructed. For the bipartite network, the edges are directional and only exist between bank nodes and asset nodes. Since the two group nodes can be effectively connected, the relationship between the banks and the assets in the financial system can be displayed more truly and intuitively.
# Laser Scanning versus Wide-Field--Choosing the Appropriate Microscope in Life Sciences
## 1 Introduction  
Optical microscopy is a rapidly developing and widely used technique in life sciences such as biochemistry, biophysics, toxicology, genetics, and immunology. It is subdivided into two principal fields: Wide-field and laser scanning microscopy (LSM) [1, 2]. While in the first case, whole images of a sample are recorded simultaneously, in the second case, the sample is scanned point by point with an image being composed of all the individual data points. Current techniques and applications are focused on 3D microscopy of the samples whose size exceeds the focal depth of a microscope objective lens, as well as on high resolution, contrast enhancement, and low light exposure, to avoid the photobleaching and photo-toxicity of living specimens. Label-free methods and methods that use markers or (genetically generated) fluorescent proteins are distinguished. However, most of the methods with fluorescent labelling are restricted to in vitro applications. Methods are subdivided into transmission, scattering, and fluorescence microscopy, with fluorescence microscopy playing a predominant role in the life sciences. Fluorescence microscopy is, therefore, the focus of this manuscript including Structured Illumination Microscopy (SIM), Light Sheet Fluorescence Microscopy (LSFM), Total Internal Reflection Fluorescence (TIRF) Microscopy, and single molecule-based techniques (all wide-field methods), as well as confocal (point-scan, Airy scan, spinning disk) or multiphoton laser scanning microscopy. In addition, Stimulated Emission Depletion (STED) or MINFLUX microscopy with a high super-resolution potential are emphasized. Further techniques increasing either resolution or specificity include axial tomography, spectral or fluorescence lifetime imaging, and Forster Resonance Energy Transfer (FRET). Clinical applications of an operation microscope, however, are not considered in this manuscript.
## 4 Optimization Process
### 3D Optimization Stage
#### 4.4.2 Control Points and Deformation Function  
The first step of the adjoint optimization method consisted in determining the control points and the surfaces to be deformed. In order to ensure some stability in the process, as the adjoint method is really sensitive to the selection of the mobile surfaces, some simplifications were taken. Those parts that were close to the boundary conditions or to geometric limitations remained immobile and the adjoint did not act over them. Then, those mobile parts were fulfilled with control points whose coordinates served as inputs to the deformation function. The distance of the points with respect to the surface was adjusted by trial and error, but those trials were not included in the present paper due to their lack of interest, as they led to nonsensical geometries. In Figure 10, the mixer is divided in fixed parts, shown in green, and in mobile parts, shown in red. The control points and their initial position are also included, reflecting their chaotic distribution.  
For the deformation function, a first-order gradient descent was proposed. This ordinary approximation permitted to compute the new position of the control points by evaluating the deformation function from the cumulative deformation and the new position  
Figure 9: Flowchart of the implemented adjoint method.  
of the control points scaled by a relaxation factor. This factor should be positive, as the optimization process minimized the cost function:  
\[F_{\delta}=F_{\Delta}-5\cdot 10^{-6}\cdot r_{points} \tag{10}\]
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 3 The Motivation for RehabHand
Instead, rehabilitation platforms like RehabHand enable therapists to connect with patients over the internet and control rehabilitation aid remotely. Imagine helping someone in low or middle-income countries without proper rehabilitation facilities, or someone with movement problems not able to visit the rehabilitation center. Apart from this, therapists need to save exercises, observe previous treatments, and similar functionalities for dislocated patients. Today these functionalities can be achieved with cloud computing. We believe such technology can provide a way to easier access to rehabilitation services. Besides, it promotes well-being for all ages.
## 3 Proposed Algorithmic Approach
### Route Generator Application  
Figure 2 shows the application in charge of generating new routes by default; it has been developed in the PyCharm integrated development environment using the Python 3.9.0 programming language and the QT Designer tool.  
The application was designed to obtain and send data from a controller node using a serial communication interface (USB to UART CP210x). The application integrates the codes corresponding to the Kruskal and Dijkstra algorithms. Based on graph theory, these  
Figure 1: Network prototype implemented.  
algorithms have been adapted to receive information from the wireless network. They require vertices, edges, and weights. Therefore, an adaptation is carried out where the nodes represent the vertices, the wireless connections between nodes represent the edges, and the combination of the battery level with the received signal level represents the weights. The information the application receives is displayed on the screen and processed to execute the Kruskal or Dijkstra algorithm. The number of vertices and edges processed will be displayed. Once the algorithms have been implemented, the new default route will be presented in a new window, which can be sent to the wireless network through the communication interface and the controller node.  
For the development of the application, the flow diagram represented in Figure 3 is used. In this diagram, the interaction between the route-generating application and the ATZB-256 RFR2 nodes is observed, especially with the node that functions as a controller. The application listens to the COM communication port to receive data from some node (_neighbors_Table_). This completes the application's _neighborsTableList_. Once the list above contains sufficient data, the application processes it to verify the information necessary to apply an algorithm. Subsequently, it converts this data into numerical values that can be used to execute the algorithms. The results are stored in the _nodesAndWeightsList_. If there is no necessary data, the application restarts the lists. When the required data is available, the execution of the Kruskal or Dijkstra algorithm should be selected. By choosing any of the algorithms, the application executes them and generates a new route, which is stored in its respective list (_defaultRouteKruskal_ or _defaultRouteDijkstra_). With the generated route, the application sends this information to the controller node, which is responsible for transmitting it to the corresponding nodes. Once the new route has been sent, the application empties the data from all the lists, thus avoiding conflicts when executing the algorithms again. For a detailed view of the application and node configuration, readers can access the respective repositories [30; 31].  
Figure 2: Route generator application with several nodes’ voltage and signal level data.
# The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys
## 2 Scope of Analysis  
### Aluminium Alloys for Structural Applications  
Aluminium as a material is characterised by a large number of different alloys, which can be both heat-treated (HT) and non-heat-treated (NHT). Many of those alloys are used in civil engineering practice for various applications [19; 20; 21]. Load-bearing and secondary aluminium elements can be made from 3 xxx, 5 xxx, 6 xxx, and 7 xxx alloy series, alloyed primarily with manganese (Mn), magnesium (Mg), magnesium and silicon (MgSi), and zinc (Zn), respectively. Many of the relevant aluminium properties, such as strength, ductility, corrosion resistance, and weldability, are strongly influenced by chemical composition. Additionally, treatments (tempers) of aluminium alloys greatly affect the properties of aluminium products as well [22]. Most commonly, wrought aluminium alloys are used. Cast aluminium products must be in accordance with EN 586 [23] but are rarely used as they contain a higher percentage of alloying elements and are more prone to manufacturing defects [24]. European standards for aluminium products divide wrought aluminium products into following groups: sheet (SH), strip (ST), plate (PL), extruded tube (ET), extruded profile (EP), extruded rod/bar (ER/B), drawn tube (DT), and forgings (FO). All aluminium products which are to be used in Europe must be in accordance with EN 485 [25] for plates, sheets, and strips or with EN 755 [26] for extruded rods, bars, tubes, and profiles. Wrought aluminium alloys for structural applications according to EN 1999-1-1 are shown in Table 1.
## 2 State of the Art
### Fuzzy Inference System (FIS) and Fuzzy Petri Nets
[PERSON] and [PERSON] (2019) mentioned that \"use the Mamdani max-min inference method to calculate a 'risk reliability (R-R) score based on a fuzzy definition of frequency of hazard occurrence, the severity of hazard outcomes, and system reliability. The application of the proposed model is presented in the context of a complex-human-in-the-loop system using the MATLAB fuzzy logic toolbox\" [48]. According to [PERSON] et al. (2012), \"For this purpose, an easy-to-use program, \"MamLand,' was developed for the construction of a Mamdani fuzzy inference system and employed in MATLAB. Using this newly developed program, it is possible to construct a landslide susceptibility map based on expert opinion\" [49]. According to [PERSON] et al. (2020), \"The Mamdani fuzzy system is known as a linguistic model where the semantic meaning of the fuzzy rules is an intrinsic characteristic that must be retained during the learning process while seeking for high accuracy\" [50]. [PERSON] and [PERSON] (2009) mentioned that \"A fuzzy logic approach is adopted to handle the uncertainty conditions. To meet the requirement of real-time decision-making, the fuzzy project programs were coded and compiled into DLL files\" [51]. [PERSON] et al. (2018) stated that \"Mamdani fuzzy logic system interface and Sugeno type fuzzy system. Both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slight differences in developing each type of the fuzzy system such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant\" [52]. [PERSON] et al. (2020) stated that \"A Computer-Based Risk Analysis Model (CBRAM) was designed to analyse the risk influencing factors using a fuzzy logic theory to consider any uncertainty that is associated with stakeholders' judgments and data scarcity. The CBRAM has confirmed the most critical risk influencing factors, in which this study has explained the effective methods to manage them\" [53]. [PERSON] et al. (2016) stated that \"The Mamdani-type FIS requires defuzzification, whereas the Sugeno-type FIS applies a constant weighted-average technique avoiding defuzzification. The results for the two field cases evaluated convincingly demonstrate that the Sugeno-type FIS is superior to the Mamdani-type FIS for WOB prediction using the same input data and membership functions\" [54]. About this type of approach, the research developed by [PERSON] et al. [55; 56; 57] must be considered as very relevant, regardless of to be focused on mainly in energy management,namely for a grid-connected microgrid with renewable energy sources and energy storage system, including the design of fuzzy logic-based controllers to be embedded in a grid-connected microgrid with renewable and energy storage capability. From the many approaches done by many researchers and the authors' research, it can consider that fuzzy Petri nets have a very high potential to help solve complex reliability problems inside the systems.
## 3 Results  
### Verification of the Effectiveness of the WUE Estimated from the GLASS Products  
To verify the effectiveness of the GLASS-WUE, we compared the two WUE datasets derived from the GLASS GPP and ET products and from the MODIS GPP and ET datasets for the TNR (Figure 2). We found a high degree of consistency between the MODIS-WUE and GLASS-WUE (RMSE = 0.21, R\({}^{2}\) = 0.72, \(p\) < 0.001). However, the GLASS-WUE was slightly overestimated compared to the MODIS-WUE for the whole study area. Upon comparing the GLASS-WUE and MODIS-WUE values in the different regions (Figure 2b-e), there was good consistency between the values in regions I to III; all R squared values were greater than 0.6.  
Figure 2: Comparison of the GLASS-WUE with the MODIS-WUE (Unit, g C kg\({}^{-1}\) H\({}_{2}\)O). The x-axis and y-axis are the WUE values calculated by MODIS and GLASS, respectively. The red dashed line represents the regression line, and the round, triangle, square, and diamond symbols represent regions I to IV, with different colors for croplands, forests and grasslands, respectively. The error bar indicates the standard error of the mean. (**a–e**) Comparison of the GLASS-WUE with the MODIS-WUE in the four regions. The four regions are named regions I, II, III and IV.  
Upon comparing the results of this study with other estimates in the literature, the spatial distribution of the GLASS-WUE was consistent with the results of the model estimations and remote sensing calculations [21; 64; 65]. The reported ranges of the WUE in China were approximately 0.37-3.10 g C kg\({}^{-1}\) H\({}_{2}\)O in the period from 2003 to 2010 [64], 0-3.8 g C kg\({}^{-1}\) H\({}_{2}\)O between 1980 and 2010 [65], approximately 0.36-3.89 g C kg\({}^{-1}\) H\({}_{2}\)O in the period of 2001 to 2010 [21], and 0-5 g C kg\({}^{-1}\) H\({}_{2}\)O between 1979 and 2012 [18], all of which were similar to our results, which ranged from 0 to 4 g C kg\({}^{-1}\) H\({}_{2}\)O.  
Therefore, the WUE values calculated using the GLASS products in this study were consistent with the results of previous studies, supporting the use of the GLASS products to study WUE. It was feasible and effective to use GLASS products to study the ecosystem WUE, especially for long-term research.