esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 3 Method
### Copula Function  
Copula is a very convenient tool for constructing joint distribution and nonlinear correlation analyses with known marginal distributions [42]. It is also called the connection function. Any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula that describes the dependence structure among variables. Suppose \(x_{1},\ x_{2},\ \cdots,\ x_{n}\) are \(N\) random variables, their marginal distributions are \(F_{x_{1}}(x_{1}),\ F_{x_{2}}(x_{2}),\ \cdots,\ F_{x_{n}}(x_{n})\), and their joint distributions are \(H(x_{1},\ x_{2},\ \cdots,\ x_{n})\); then there is a function \(C(u_{1},\ u_{2},\ \cdots,\ u_{n})\) to connect the marginal distribution and the joint distribution, such that \(H(x_{1},\ x_{2},\ \cdots,\ x_{n})=C[F_{x_{1}}(x_{1}),\ F_{x_{2}}(x_{2}),\ \cdots,\ F_{x_{n}}(x_{n})]\). In this study, the overall joint distribution was obtained from the marginal distribution of each element. When calculating the marginal distribution of variables, it is often difficult to assume the distribution of a sequence per pixel; therefore, the kernel density estimation method [24] was used in this study to calculate the marginal distributions of the annual GPP anomaly, Tmax, and drought index for each pixel.  
Copula functions are categorized into many types according to their construction methods and specific uses. The bivariate normal copula function and the bivariate t-copula function have wide ranges of applications for correlation analysis. Single-parameter Archimedes-type copula functions have simple structures and are easy to solve. Commonly used single-parameter Archimedes-type copula functions include the Frank, Clayton, and Gumbel copula functions, among which the Clayton and Gumbel copula functions can only be used to calculate the joint distribution between positively correlated variables, while the Frank copula function is applicable to both positive and negative correlations. In this study, bivariate normal, t-, and Frank copula functions of the Archimedes type were used to construct 2D joint distributions between the annual GPP anomaly and drought index and between the annual GPP anomaly and Tmax. The Euclidean distance (D\({}^{2}\)) between the empirical and theoretical copula, Akaike and Bayesian information criteria was used to evaluate the accuracy of the model's fit and to select the optimal model. Smaller D\({}^{2}\) values and Akaike and Bayesian information criterion values indicate better model fits than larger values. The three-element normal and t-copula functions were used to construct the 3D joint distribution between the annual GPP anomaly, drought indices, and Tmax, and the optimal model was selected according to the D\({}^{2}\) value.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism
### Mechanical Analysis
The preloading of spring pins must be greater than the friction in the corresponding joints caused by the compression of the helical refueling pipe and the precompression caused by machining accuracy and assembly errors, as shown in Figure 12. Thus, the resetting conditions of the corresponding spring pins can be obtained using the following equation:  
\[F_{si}>f_{rl}+f_{mij},i=1\dots 5\]  
Figure 11: Free-body diagram of “contact and alignment I”: (**a**) the mechanical analysis of the drogue (the green line represents the probe), (**b**) the orthogonal decomposition of the contact forces. where \(i\) represents the five joints for alignment, \(F_{s}\) is the preloading of the spring pin, \(f_{r}\) is the friction generated by the compression of the refueling pipe acting on the kinematic pair, and \(f_{m}\) is the friction of the kinematic pair caused by machining accuracy and assembly errors. The conclusions reached through the mechanical analysis of each process are summarized below:  
1. The spring force of the helical refueling pipe exists throughout the docking process and cannot be ignored as one of the resistances of the driving force. 2. The driving force is related to the contact force, which is related to the preloading and axial stiffness of the spring pins. 3. The preloading and axial stiffness designs of the spring pins are based on the preloading condition (Equation (1)) and the resetting condition (Equation (5)), which are related to the axial force of the helical refueling pipe. 4. As a precondition for satisfying the preloading and resetting conditions, the axial stiffness of the spring pins should be as low as possible to prevent an increased con-tact force. In summary, decreasing the axial force of the helical refueling pipe can significantly decrease the driving force of the docking mechanism. Thus, the structural and mechanical property design of helical refueling pipes is a significant area of research. In addition, the design space for the refueling pipe is irregular because of the compactness of the on-orbit mechanisms. Therefore, an elliptical-helical pipe with bias loading may be a better choice for the refueling pipe, as its envelope is larger, which means the axial stiffness is lower, and the mechanical properties of the pipe are analyzed in Section 3.
## 4 Experimental Setup  
The propagation model for the short and tall grass was investigated by performing an extensive experiment campaign using the LoRa 433 MHz WSN at a smart farm in Nakornchaisri. The smart farm located in the West suburbs of Bangkok has GPS coordinates of 13.77242 and 100.17602. The LoRa device is a non-cellular, long-range and low-power wireless technology widely used in the IoT to communicate between nodes and gateways. Table 1 presents the WSN specification and measurement parameters. The equipment characteristics influenced the RSSI (e.g., spreading factor (SF) and bandwidth) [19]. The first measurement step was conducted in normal short grass. The other measurements were implemented for the uncertainty's phenomena of the surrounding environments. The measurement showed varying RSS data for each antenna height. Natural propagation phenomena known as the diffraction, refraction, and reflection of the transmitted signal caused by the surroundings of the rural open environment were also observed. Some of the excess attenuations were possibly caused by the uncertainty's phenomena, such as the grass height, weeds, uneven ground, and animal movement. The measurements were performed in both the dry and rain seasons; however, the effects of rainfall for frequencies below 10 GHz showed insignificant effects on the signal propagation [28]. The SF and bandwidth were fixed to 7 and 125 kHz, respectively, for the distance between 0 m and 40 m to concentrate on the signal fluctuation in the interesting surroundings.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)  
[PERSON]  
1 State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China; [EMAIL_ADDRESS]
2 School of Automotive Engineering, Chongqing University, Chongqing 400044, China; [EMAIL_ADDRESS] (S.X.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (J.Z.)  
[PERSON]  
2 School of Automotive Engineering, Chongqing University, Chongqing 400044, China; [EMAIL_ADDRESS] (S.X.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (J.Z.)  
[PERSON]  
2 School of Automotive Engineering, Chongqing University, Chongqing 400044, China; [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.S.); 20183202001@cqu.  
it was subject to interference by false edges such as disturbances in the road or obstacles on either side. [PERSON] [9] proposed the use of the first frame of the lane detection images to initialize the ROI; detection would then be performed according to the ROI; however, the accuracy of the approach needs to be improved.  
When it comes to lane models, except for the widely applied mathematical models, (including the straight line, parabola, hyperbolic and line-parabola models), the three-dimensional traffic model was established by analyzing the lane space structure [10]. Because the straight line model cannot be used to fit the lane line in complicated environments, models such as parabola and hyperbola need to be utilized to match the lane line [11]. A simple parabola model [12; 13; 14] which combines the position of the lane line, the angle and the changed curvature, was presented, but this model cannot fit a transitional connection between a straight lane line and a curved lane line. In order to adapt to the constant transformation of lane lines, parabolic models need to be replaced by a higher order model [15]. An improved model, combining straight and curved lanes and geometric constraints, was proposed in which the second derivative of a quadratic curve was used as the basis to estimate the condition of the road [16]. Although this method enhances flexibility, it is still difficult to use it to search for the connection point between the straight line and the curve.  
In view of the above works, a novel lane detection method based on the DROI is proposed in this paper; firstly, to fulfill the accurate selection of the ROI, the lane line equation and the vehicle speed of the last frame are utilized to solve the curvature of the points on the lane edge and the maximum safe distance. Then, the feature points of the lane line are detected using DROI global searching. Lastly, the lane line is fitted by the polar equation and the fitting effect is good.
## 3 Results and Discussion
### Optimisation of HS-SPME Procedure by BBD

Table 2: Results obtained for the mass, length, diameter and firmness of the studied raspberry cultivars expressed as average quantities \(\pm\) standard deviations (\(n=3\)). These results could be explained by the effect of the temperature applied during the HS-SPME, which modified the raspberry cell walls, mainly due to pectin and hemicellulose depolymerisation that enhanced the extractability of the studied compounds [23]. Increasing the extraction temperature has been reported to be a good way of improving the extraction recovery, but high temperatures are also associated with the unwanted generation of artefacts such as BB interactions, which have been shown to have negative effects [5]. The significant and positive effects of the AC interaction underlined the fact that a higher sample weight and extraction time improves the recovery. However, the negative effect of the quadratic interaction of the sample weight (AA) could be related to the saturation of the closed vial headspace, increasing the competition between target and interfering compounds for absorption in the fibre coating and, consequently, their extraction. The following equation expresses the mathematical model representing the studied response as a function of the independent variables within the region under investigation:  
\[\mathrm{Y}=-4.22696\mathrm{E}7-2.1645\mathrm{S}\mathrm{E}8 \times\mathrm{A}+1.06789\mathrm{E}7\times\mathrm{B}-6.6675\mathrm{E}6\times \mathrm{C}-6.1836\mathrm{E}6\times\mathrm{A}^{2}+\] \[2.79469\mathrm{E}6\times\mathrm{AB}+4.01389\mathrm{E}6\times \mathrm{AC}-100127\times\mathrm{B}^{2}+32128.9\times\mathrm{BC}+45266.1\times \mathrm{C}^{2}\]  
The R-squared statistics indicate that the model, as fitted, explains 88.8% of the variability in the response, demonstrating a good correlation between the actual and predicted values since R square is close to unity. Additionally, the lack-of-fit was not significant (F-value = 3.16) relative to the pure error, with a \(p\)-value higher than 0.05 (\(p\)-value = 0.1848). Using the model quoted above, the optimal HS-SPME conditions for obtaining the highest response of \(2.82\times 10^{8}\) are sample weight = 1.36 g, extraction temperature = 60 \({}^{\circ}\)C and extraction time = 45 min. Triplicate HS-SPME-GC-MS determinations were carried out, obtaining a response of \(2.68\times 10^{8}\) with a DER of 4.1% intra-day by performing three extractions under optimal conditions in a single day, and a DER of 7.2% inter-day based on three extractions under optimal conditions per day over three consecutive days (\(n\) = 9).
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 2 Materials and Methods
### Data Gathering and Processing  
#### 2.3.1 Response Variable  
The primary source for producing the EUNIS forest habitat classification is a dataset extracted by the European Vegetation Archive [17; 66]. The archive collects records of vegetation plots, which include: a full list of vascular plant species; the estimates of cover abundance for each species; georeferenced point location; and, additional information on vegetation structure and environmental plot features. Vegetation plots that are larger than 200 m\({}^{2}\) and that only reported species composition without cover abundance information have been excluded.  
The final forest vegetation dataset (see Table 3) was obtained according to the following stepwise procedure:  
1. resampling data: to perform the habitat classification only on the natural woodland areas, a forest mask was generated to resample the vegetation plots. The mask was obtained by combining Copernicus Land Monitoring Service products, specifically the High-Resolution Layers Tree Cover Density (TCD, [67]), the Imperviousness [68], and the CORINE Land Cover [69]. All of the records outside the forest mask were excluded;
2. clustering data: from the whole dataset only forest habitats were extracted and clustered according to the EUNIS hierarchical classification nomenclature, following the EUNIS-ESy definitions [22]; and,
3. filtering data: all data assigned at more than one EUNIS codes (e.g., bias plots assignment of mixed forest that linked at two different EUNIS groups) and all data recorded with the same geographic coordinates (i.e., plots of re-surveying monitoring research activities) were also excluded. Finally, a visualization data test was performed to identify spatial mismatches errors and/or spatial bias occurrences.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 4 Proposed Parallel Phase Demodulation Using Dual Frequencies  
The dual-frequency modulation scheme based on parallel phase demodulation is described in this section. The main advantage of the proposed method is that the demodulation processing time is nearly identical to one integration time regardless of the number of samples per distance measurement. This is possible because the proposed method uses parallel phase demodulation, meaning that all samples are calculated in parallel.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 6: Calculating the Resonant Device Parameters  
After the quantities \(Q_{1}\), \(k_{1}\), \(g_{1}\), and \(m_{1}\) are determined, the parameters of the associated resonant devices with device parameters \(R_{\alpha\epsilon}\), \(C_{r1}\), \(L_{r1}\), \(L_{m}\), \(C_{r2}\), and \(L_{r2}\) can be obtained by (14) to (19).  
\[R_{\alpha\epsilon}=\frac{8N^{2}}{\pi^{2}}\cdot\frac{V_{Bat}}{I_{Bat}}=\frac{8 \cdot 1.2^{2}}{\pi^{2}}\cdot\frac{403}{2.5}\cong 188.16\Omega \tag{14}\]  
\[C_{r1}=\frac{1}{2\cdot\pi\cdot f_{r1\_F}\cdot Q_{1}\cdot R_{\alpha\epsilon}}= \frac{1}{2\cdot\pi\cdot 100\cdot 0.2\cdot 188.16}\cong 42.29 nF \tag{15}\]  
\[L_{r1}=\frac{1}{\left(2\pi f_{r1,F}\right)^{2}\cdot C_{r1}}=\frac{1}{\left(2 \cdot\pi\cdot 100\right)^{2}\cdot 42.29n}\cong 59.90\mu H \tag{16}\]  
\[L_{m}=k_{1}\cdot L_{r1}=3.5\cdot 59.9\mu=209.65\mu H \tag{17}\]  
\[C_{r2}=g_{1}\cdot N^{2}\cdot C_{r1}=1\cdot 1.2^{2}\cdot 42.29n\cong 60.90 nF \tag{18}\]  
\[L_{r2}=\frac{L_{r1}\cdot m_{1}}{N^{2}}=\frac{59.89\cdot 1}{1.2^{2}}\cong 41.60 uH \tag{19}\]
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 4 Deployment Scenario  
The presented C-ITS infrastructure in this paper is being deployed along several motorways in Portugal, covering the region around the capital city of Lisbon and some areas close to the borders with Spain, in order to also test cross-border interoperability aspects. The locations of the RSUs already installed by A-to-Be are depicted in blue in Figure 6, as well as the corridors covered in the Portuguese C-Roads project. Other RSU sites are also being deployed by distinct project partners managing different motorways in the country (markers in orange).  
Figure 6: Deployment scenario of the C-ITS infrastructure in Portugal. Each dot corresponds to a deployed RSU.  
The project tested \"Day 1\" and \"Day 1.5\" services over a length of 460 km of roads, focusing mostly on Infrastructure-to-Vehicle (I2V) pilot use cases, namely Vehicle Data Collection, Animal or person on the road, Weather Condition Warning, Stationary vehicle, Traffic Jam Ahead, Emergency Vehicle Approaching, Accident Zone, Lane closure, Dynamic Speed Limit Information, and Embedded Variable Message Sign (VMS) \"Free Text\".  
The same designed architecture was also implemented in the scope of STEROID project covering Aveiro region in central Portugal. Figure 7 presents an overview of the installed RSUs and the C-ITS platform application. Green squares represent the installed RSUs, while the blue circles represent the perceived objects (vehicles) by the RSUs with traffic radars. The coverage areas include a motorway and an urban environment. The present deployment serves as a testbed for vehicular services.
## 2 Materials and Methods
### Procedure  
The model was implemented in the Matlab 2022a environment. The data were divided into 64 to 70% datasets for training, while the remaining 36 to 30% were used for testing. The ANFIS model was first run with a classic training algorithm to get basic results. After importing data into the neuro-fuzzy designer, a fuzzy inference system (FIS) was generated using a Sugeno fuzzy inference system, and a subtractive clustering approach (with default parameters) was chosen, as the number of input parameters was relatively high. The set FIS was trained using a hybrid learning strategy that combines gradient descent and linear and least squares methods. The maximum number of epochs was set to 100 (refer to Section 3.1 for further details). After this stage, the FIS was tested for generalization capability. Second, the ANFIS model was optimized with particle swarm optimization (refer to Section 3.2 for details), and finally with a genetic algorithm (refer to Section 3.3 for details) for comparison purposes.
## 1 Introduction
Among the above mentioned models, the CENTRUY model considered the effect of grazing on NPP in terrestrial ecosystem, but only simplified the effect into four types (no effect, mild, moderate, and severe), and failed to reflect the effect of different grazing intensity on carbon dynamics. Biome-BGC model is a process-based computer program that can be used to estimate carbon, nitrogen, water, energy states, and has already been proved useful for large-scale terrestrial ecosystems. [PERSON] et al. [20] employed the Biome-BGC model to analyze the evapotranspiration and gross primary productivity within Mediterranean macchia, and they indicated that Biome-BGC can be applied to estimate water and carbon flux with good accuracy. [PERSON] et al. [14] utilized the modified Biome-BGC model to simulate the grazing process in arid grassland in Xinjiang, China, and their simulated results agreed well with field data. [PERSON] et al. [21] made use of Biome-BGC model to simulate carbon and water storage for crops over china, which also approved the feasibility of the model. In summary, Biome-BGC model allowed fully coupled investigations on the ecosystem sustainability, and obtaining feedback between climate change and carbon cycle. Numerous studies have focused on the carbon stock and fluxes in Central Asia [4; 22; 23]. However, these studies have failed to investigate the different responses of the various vegetation types to climate change. Therefore, in this study, we examined the temporal-spatial variations of carbon dynamics under four vegetation types during 1979-2011 in Central Asia using the Biome-BGC model. This study aims (1) to evaluate the spatial and temporal trends of carbon dynamics in Central Asia for the four vegetation types and (2) assess and compare the carbon responses of the four vegetation types subjected to different climate factors. The results of this study will provide a scientific basis for sustainable ecosystem restoration at the regional scale and a deep understanding of relationships among climate change, vegetation type, and carbon budget. The paper is structured as follows: Section 2 describes the input data for Biome-BGC model and how the outputs are analyzed; Section 3 illustrated the climate conditions during 1979-2011 and the corresponding response of carbon dynamics; Section 4 compare results from this research with previous studies and discuss the underlying implications for future study; and Section 5 concludes the entire research.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 3 Results
### Sensitivity of the MPLT Assay  
The sensitivity of the MPLT assay was examined by testing serial ten-fold dilutions of the nine target bacterial DNA samples, and distilled water was used as negative control. The detection limits in the MPLT assay are demonstrated in Figure 3A-I. The results showed that the limit of detection was \(10^{2}\) CFU/mL for _S. pneumoniae_, _M. catarrhalis_, _S. aureus_ and _K. pneumoniae_ and \(10^{3}\) CFU/mL for _S. pyogenes_, _H. influenzae_, _Legionella_ spp. and _P. aeruginosa_. Finally, the detection limit for _M. pneumoniae_ was \(10^{-3}\) ng/\(\mu\)L.  
Figure 1: Results of the two mPCR reactions for nine target pathogens. Agarose gel electrophoresis showing the results of the four-plex PCR amplification and five-plex PCR amplification. Each DNA sample was successfully amplified and lanes are as follows: M, DNA Marker 100; 1, _S. pneumoniae_ 350 bp; 2, _M. catarrhalis_ 140 bp; 3, _S. aureus_ 270 bp; 4, _S. pyogenes_ 93 bp; B, negative control (distilled water); M, DNA Marker 100; 5, _H. influenzae_ 296 bp; 6, _M. pneumoniae_ 209 bp; 7, _Legionella_ spp. 650 bp; 8, _P. aeruginosa_ 249 bp; 9, _K. pneumoniae_ 364 bp; B, negative control (distilled water).  
Figure 2: Results of the MPLT assay for nine target pathogens. **(A)** Detection of target products by the four-plex PCR-Luminex showed a high specificity. Distilled water was used as the negative control (NC). Each bar represents the average median fluorescence intensity (MFI) of triplicate samples. The error bars indicate standard deviations. SP, _S. pneumoniae_; MC, _M. catarrhalis_; SA, _S. aureus_; SPY, _S. pyogenes_; **(B)** Detection of target products by the five-plex PCR-Luminex showed a high specificity. Distilled water was used as the negative control (NC). Each bar represents the average MFI of triplicate samples. The error bars indicate standard deviations. HI, _H. influenzae_; MP, _M. pneumoniae_; LP, _Legionella_ spp.; PA, _P. aeruginosa_; KP, _K. pneumoniae_.