esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiducial Lower Confidence Limit of Reliability for a Power Distribution System
## 4 Discussion  
Cold standby systems, hot standby systems and k-out-of-n systems are the most commonly used redundancy systems in reliability. Specifically, the hot standby system is a particular case of a k-out-of-n system where \(k=1\) and \(n=2\). For comparison, the authors of [25] derived the LCL of reliability for a k-out-of-n system. In this paper, a reliability model was developed for a typical satellite intelligent PDS, which used hot standby systems (one-out-of-two systems) in the DC/DC and SSPC subsystems. Experienced engineers recommend using a cold standby TM/TC as a redundancy of the main TM/TC. According to the results obtained in Section 3, the LCL of reliability for this typical satellite intelligent PDS was 0.998. When the TM/TC cold standby subsystem was replaced by a hot standby subsystem, the reliability of the TM/TC subsystem could be calculated from Equation (16) instead of Equation (15) such that  
\[R_{TT}(t)=1-\Big{(}1-e^{-\lambda_{T}t}\Big{)}^{2}. \tag{19}\]  
The reliability of the entire satellite intelligent PDS with 20 SSPC subsystems at time \(t\) was  
\[\begin{split} R_{20}(t)&=R_{DD}(t)R_{TT}(t)[R_{SS}( t)]^{20}\\ &=\Big{[}1-\big{(}1-e^{-\lambda_{D}t}\big{)}^{2}\Big{]}\Big{[}1- \big{(}1-e^{-\lambda_{T}t}\big{)}^{2}\Big{]}\\ &\cdot\Bigg{\{}1-\Bigg{[}1-\exp\Bigg{\{}-\Big{(}t\exp\Big{\{} \frac{E_{1}}{E_{2}}S-\overline{X}\Big{\}}\Big{)}^{\frac{E_{2}}{3}}-\frac{t}{2 T}E-\sum\limits_{i=3}^{6}\lambda_{Si}t\Bigg{\}}\Bigg{]}^{2}\Bigg{\}}^{2} \Bigg{\}}^{2}.\end{split} \tag{20}\]  
By using the generalized fiducial method, the LCL of the reliability would fall to 0.997, which was less than 0.998.  
It is widely known that the reliability of a cold standby system is always higher than that of a hot standby system. After calculation and analysis, the same conclusion applies the LCL of the reliability. It is suggested that engineers can substitute a cold standby subsystem for a hot standby subsystem (one-out-of-two system), which can increase the LCL of the reliability for the whole system.
## 4 Materials and Methods
### Model Selection Approach
#### 4.3.4 Epsilon-Support Vector Regression--SVR  
Similarly to Huber regressor, the Epsilon-Support Vector Regression, or SVR, has shown good resistance to outliers. Based on a classification algorithm of Support Vector Machines (SVM), SVR uses a kernel trick to perform regression in higher dimensions. As a result, SVR tends to generalize well without its computational complexity depending on dimensionality of the problem [110]. This generalization is mainly the result of SVR using an \(\epsilon\)-insensitive region (also known as an \(\epsilon\)-tube), that is often used to better approximate functions that have continuous values. With this property in mind, as well as the fact that SVR is known to perform well on smaller sets of data, our theory is that the overall prediction scores can be improved by applying the SVR algorithm in our experiments.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 5 Experiments with Real Data
### Data Processing
#### 5.1.3 Feature Point Extraction and Matching  
The orb feature descriptor was used to match the Frame 1 and Frame 2 feature points, and a total of 563 pairs of matching points were obtained. For Frame 1 and Frame 3, a total of 458 pairs of feature points were obtained, as Figures 13 and 14 show.  
The points of Frames 1 and 2 and Frames 1 and 3 were triangulated to obtain the corresponding map points, as Figure 15 shows.  
Figure 14: \(I_{1}\), \(I_{3}\) feature point matching map.  
Figure 12: Aberration correction before and after comparison.  
Figure 13: \(I_{1}\), \(I_{2}\) feature point matching map.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 6 Analytical Work
### Comparison and Validation  
As shown in Figure 21, the calculated pull-out resistances were compared to the push-out tests and parametric results to validate the previous and proposed equations. The equations suggested by [PERSON] et al. [13] and [PERSON] et al. [15] both overestimated the pull-out resistance of notched perfobond shear connectors. This was possibly because the contributions of the concrete dowel and the perforating rebar were overestimated, while the influence of the perfobond rib was neglected in Equations (6),(7). The calculated pull-out resistances from the proposed equation agreed reasonably well with the experimental and numerical results. Therefore, Equation (8) could be used to estimate the pull-out resistance of notched perfobond shear connectors in steel and concrete composite structures.
## 4 The Application of the Proposed Model
### Case Study
#### 4.1.1 Application of the ANP Method
Therefore, based on the calculation of the eigenvalue vector, the unweighted super matrix and weighted super matrix were obtained, as illustrated in Tables 6 and 7. Specifically, the unweighted super matrix shown in Table 6 illustrates the priorities and the relative importance of the fifteen sub-criteria within each main cluster. Based on the results of Table 6 and the priorities of the main criteria, the weighted super matrix of all sub-criteria is obtained. As shown in Table 7, the priorities of each sub-criteria indicating the relative importance of all elements are determined. The final weights of the identified criteria and sub-criteria were computed based on the weights of each criteria group, and therefore, the ranking results of all criteria were calculated as shown in Table 8. It can be concluded that the \"safety\" criterion was the most significant criterion among the main criteria with a value of 0.462. Meanwhile, the ranking of the remaining main criteria was the \"comfort\" criterion, the \"awareness\" criterion, and the \"other criteria\". The ranking result of the main criteria indicates that safety is still the most critical factor that needs to be considered during PPE selection. Specifically, among all the sub-criteria, sub-criterion C4 \"Procedure to monitor the usage of PPE\" achieved the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Description**} & **Number of** & \multirow{2}{*}{**Percentage (\%)**} \\  & & **Respondents** & \\ \hline \multirow{4}{*}{Occupational background} & Superintendents & 6 & 26.67 \\  & Project managers & 6 & 26.67 \\  & Site engineers & 6 & 26.67 \\  & Workers & 5 & 20.00 \\ \hline \multirow{4}{*}{Age} & 21–30 & 2 & 6.67 \\  & 31–40 & 3 & 13.33 \\  & 41–50 & 9 & 40.00 \\  & 51–60 & 6 & 26.67 \\  & Above 60 & 3 & 13.33 \\ \hline \multirow{2}{*}{Gender} & Female & 3 & 13.33 \\  & Male & 20 & 52.00 \\ \hline \multirow{4}{*}{Working experience in the construction field} & \textless{}3 years & 2 & 6.67 \\  & 3–10 years & 3 & 13.33 \\ \cline{1-1}  & 10–15 years & 6 & 26.67 \\ \cline{1-1}  & 15–20 years & 7 & 30.43 \\ \cline{1-1}  & above 20 years & 5 & 20.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Demographics of the participating respondents (\(n=23\)). largest weight with a value of 0.1932, while sub-criteria C2 (Availability and accessibility of the selected PPE) and C5 (Flexibility of movement) ranked the second and third with values of 0.1164 and 0.1140, respectively. This indicates that effective management and adequate supervision during the PPE utilization are significant to prevent fall accidents.
## 2 Related Work
### Fifth-Generation Radio Access Network (RAN) Architecture  
The 5G RAN architecture supports three main deployment options--enhanced Mobile Broadband (eMBB), massive Machine-Type Communications (mMTCs), and Ultra-Reliable Low-Latency Communications (URLLCs), to meet the diverse IoT application needs in next-generation networks [13]. This includes RAN configurations where Distributed Units (DUs) and Central Units (CUs) are either located together or distributed across the network to enable flexible resource management [3]. Radio Access Network (RAN) slicing, which partitions baseband functions into virtualised segments, enhances scalability and efficient Radio Resource Management (RRM) by granularly allocating resources to specific services in real-time. However, this introduces new network management challenges related to the virtualisation of baseband functions, and the efficient management of these functions on distributed units whilst meeting the required QoS [14]. Traditionally, 5G RAN relies on Next-Generation Node B-Distributed Units (gNB-DUs) for real-time data pre-processing(e.g., filtering and format conversion) to support URLLC [15]. In contrast, our proposed framework extends data pre-processing to connected vehicles in 5G VANETs through the integration of NFV and SDN to enhance QoS and dynamic resource allocation at the gNB for critical vehicular applications.
## 2 RNA Structural Requirements for Editing
### Structural Requirements for Editing the HDV Genotype 1 amber/W Site
To more directly determine whether the loss of editing activity in the truncated HDV RNAs shown in Figure 4 was due to the removal of these base-paired regions, we created three site directed mutations, each containing 3 nt bulges introduced into different base-paired segments in the region between 63 and 100 nt 3' of the site. All of these mutations reduced editing to less than one  
Figure 3: RNA secondary structure around the HDV type 1 amber/W site. The upper schematic indicates the location and orientation of the HDAg gene and, for reference, the position of the antigenomic ribozyme (scissors). A segment of the unbranched rod structure surrounding the amber/W site is shown below; the editing site is bolded and indicated by an asterisk. Site-directed mutagenesis has shown that the A-C mismatch and the eight base pairs immediately surrounding the site are necessary for editing [9,30]. third of the wild type RNA (RC, unpublished). Based on these results, we favor a model in which the dsRNA binding domains of ADAR1 recognize the base paired region between 64 and 100 nt 3' of the amber/W site and position the deaminase domain at the editing site _via_ a long range interaction that could involve bending of the RNA. The apparent contradiction between our recent results and those of [PERSON] _et al._[34] could be related to overexpression of ADAR in transfected cells in the [PERSON] study. Several reports have indicated that such overexpression can alter the behavior of the enzyme. We have found that overexpression of ADAR1 not only increased editing at the amber/W site, but also led to high levels of promiscuous editing at other sites in the RNA [11]. Moreover, [PERSON] and [PERSON] observed that, when overexpressed, a form of ADAR1 lacking the double-stranded RNA binding domains exhibited levels of activity on the GluR-B R/G site similar to that of the wild-type protein [35]. We have observed a similar result for the type 1 amber/W site--overexpression of ADAR1 constructs lacking the DRBMs edited this site with efficiency approximately half that of wild type ADAR1 (RC, unpublished). Thus, the contribution of the region 64 to 100 nt 3' of the amber/W site to editing activity might have been missed in the Sato study because overexpression of ADAR1 decreases the requirement for the DRBMs.
## 4 Discussion
Because of the relatively high and stable vegetation cover across Europe from 2001 to 2016 (Figure 4a), the FGM was generally able to separate the greening effect from the LULCC effect in Europe. Prior to this study, MODIS LAI data also detected widespread greening across Europe during the period of 2001 to 2015 [15]. Although different sources of LAI data were used, we obtained similar findings. We further quantified the contribution of land surface greening to GPP across Europe by different vegetation types. Figure 9: Interannual dynamics of LAI for land cover change areas and stable areas. Stable and change areas in land cover are identified by stacking 16 epochs of MCD12Q1 data together. Pixels were labelled as stable areas only if they were always classified as the same land cover type for 16 years; otherwise, pixels were labelled as change areas. Previous studies on decoupling GPP dynamics from climate change, CO2 fertilization, and land cover change were based mainly on light use efficiency models and process-based models and, in most cases, without the inclusion of spatial and temporal dynamics in V\({}_{\text{cmax}}\)[25], hampering reliable simulations of photosynthesis-climate interactions with a fine spatial resolution or on a long-term scale. Many studies have demonstrated that the V\({}_{\text{cmax}}\) changes with both space and time (Figure 4b). Our previous work showed that, compared with GPP using dynamic information on V\({}_{\text{cmax}}\) derived from leaf chlorophyll content, the conventional method of fixing V\({}_{\text{cmax}}\) using the calibrated V\({}_{\text{cmax}}\) or TRY-based V\({}_{\text{cmax}}\) overestimated the annual GPP by 0.5 to 2.9 Pg C yr\({}^{-1}\) or 5 to 31% and overestimated the interannual increasing GPP trend by 0.007 to 0.01 Pg C yr\({}^{-2}\) or 14 to 20%, respectively [28]. Changes in photosynthetic capacity occur since plants acclimate their leaf chemistry in response to climate change, such as global warming and elevated CO2 concentrations [60]. For the limited period of 16 years, the interannual dynamics in V\({}_{\text{cmax}}\) across Europe had minor impacts on GPP (Figure 4b). However, V\({}_{\text{cmax}}\) may continue to change on longer time scales due to plant adaptation, especially in light of continuous rising CO2 concentrations and global warming [61]. In this study, the V\({}_{\text{cmax}}\) change effect on GPP was only based on one typical V\({}_{\text{cmax}}\) product derived from leaf chlorophyll content. Recently, several global-scale V\({}_{\text{cmax}}\) products with different time spans and based on different remotely-sensed metrics (e.g., solar-induced chlorophyll fluorescence, SIF) have been distributed [24; 25; 26]; however, these products have contrasting patterns, which highlights the need for further validation of V\({}_{\text{cmax}}\) based on time-series experimental studies and the development of mechanistic V\({}_{\text{cmax}}\) models.
## 3 Results and Discussion
### Measurement of Properties
#### 3.2.2 Determination of Surface Charge of Nanoparticles in Test Medium  
It is shown in Figure 3 that the surface charge of ZnO changed from positive to negative with the addition of NOM, and the overall increase in the absolute value indicates an excellent dispersion stability. The NOM with negative charge increasingly tended to coat the surface of ZnO with positive charge, improving the dispersion stability [43], and, generally, remained stable above the absolute value of 30 mV [44]. pH was found to range from 7.53 to 7.99, with and without NOMs in the OECD medium, ISO medium, and dechlorinated tap water test medium, suggesting that the range was acceptable for ecotoxicity tests without further pH adjustment.  
Figure 2: Transmission electron microscopy (TEM) image of ZnO. Top (without NOM), down (with NOM).
###### Abstract  
This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF-GPP relationship was not evaluated previously--namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF-precipitation lead-lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50-75 gC/m\({}^{2}\)/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF-GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.  
solar-induced chlorophyll fluorescence; precipitation; soil moisture; gross primary production; satellite observations; multiple linear regression Article Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data
## 4 Data Acquisition and Experiment
### Validation
#### 4.3.2 Accuracy of the Detection Method  
In order to verify the accuracy the method, we used the detection indicator related to the supporting block (the slope of the supporting block) to test. The single supporting block model is manufactured according to the design drawings. The slope of the model supporting block relative to the bottom of the track slab is measured 10 times by the contact measurement system with accuracy of (0.9 + L/400) um, and the average measurement value was obtained. Similarly, the slope of the model supporting block was measured 10 times by the method presented in this paper, and the average measured value was obtained. The dimension of the area measured to extract the slope is shown in Figure 13.
## 2 Common-Mode Noise Suppression Filter Design
### Design Concept of Filter  
The very tight layout of the components of the PCB design often causes the path of the differential signal to be asymmetrical, which generates a lot of CM noise. A CM noise suppression filter is required to solve this problem. The MDCRP structure filter's design concept utilizes the differential signaling principle, the stepped impedance characteristics, and the CM return current path. Adding a mushroom structure to the defective corrugated reference plane structure creates multiple different CM current return paths.  
The differential signal has a virtual reference ground, which does not affect the differential signal transmission when the reference ground is damaged. However, the CM signal has a great impact because the CM signal current uses the ground plane to return to the source. The slot in the first ground plane blocks the CM current and allows it to flow smoothly through the via to the second ground plane. The mushroom structure of the first ground plane also allows the CM current to flow smoothly to the second ground plane. The second ground plane is the DGS structure, which changes the CM current return path. This results in several resonance points and many approach transmission zeros, which generate a wider suppression bandwidth, and maintains the signal integrity when the transmission is in DM.  
The side view of the CM current return path of the differential pair transmission of the PCB is shown in Figure 3. The differential pair is in layer 1 of the PCB, and the ground planes are in layers 2 and 3, respectively. The CM current return path passes through the first ground plane on the right (GL1b), through the four vias on the right to the second ground plane (GL2), and then through the four vias on the left to reach the first ground plane on the left (GL1a), forming a current return path. There is another current return path through the mushroom structure that passes through two vias to the second ground plane (GL2) and then passes through the four vias on the left to the first ground plane on the left (GL1a). Using the MDCRP structure creates more resonance points, which generate a broadband CM noise suppression capability to avoid EMI problems.  
Figure 2: The proposed MDCRP structure filter. (**a**) The first ground plane. (**b**) The second ground plane.
# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
## 6 Conclusions  
The decision to purchase real estate is undeniably very essential in the life of most adults. Thus, the appraisal and prediction of real estate can provide useful information to help facilitate real estate transactions. Real estate prices vary due to a wide variety of attributes. Machine learning models, including least squares support vector regression, classification and regression tree, general regression neural networks, and backpropagation neural networks, were used in this investigation to forecast real estate transaction prices with actual transaction data in Taichung, Taiwan. Genetic algorithms were employed to determine parameters of machine learning models. Empirical results revealed that attribute selection for machine learning models in this study does improve performances of four forecasting models in forecasting accuracy. With attribute selection, three machine learning models offer highly accurate predictions, and one machine learning model presents good prediction. Thus, four machines model with features selection used in this study are appropriate for forecasting real estate prices. Furthermore, the least squares support vector regression outperformed the other three forecasting models and obtains more accurate results than some previous studies in terms of MAPE. Thus, the least squares support vector regression with genetic algorithms is a feasible and promising machine learning technique in forecasting real estate prices.  
For future work, diverse data types such as comments of real estate attributes, prices from social media, images from Google maps, and economic indicators are possible sources added as inputs for machine learning models to improve forecasting accuracy. In this study, general regression neural networks and backpropagation neural networks seems not to generate satisfying results when compared with results provided by the least squares support vector regression and the classification and regression tree. Thus, another potential opportunity for future research might be the use of deep learning techniques to forecast real estate prices.  
Conceptualization, P.-F.P.; data curation; W.-C.W.; formal analysis, P.-F.P. and W.-C.W.; funding acquisition, P.-F.P.; methodology, P.-F.P. and W.-C.W.; software, W.-C.W.; visualization, P.-F.P.; writing-original draft, review and editing, P.-F.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan under the Contract Number MOST 109-2410-H-260-023.  
The authors declare no conflict of interest.