esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Research Design
#### 3.1.3 Construction of Spatial Econometric Model  
In order to investigate the impact of GPSTPS policy on regional S&T innovation ability, this paper constructs the spatial econometric model by taking the evaluation score of GPSTPS policy as the core explanatory variable and the regional S&T innovation ability as the explained variable. Due to the uncertainty of specific forms related to S&T innovation ability, this paper constructs the spatial lag model (SLM) that includes dependent variables, the spatial error model (SEM) that includes error terms and the spatial Durbin model (SDM) that includes both spatial lag endogenous variables and spatial error exogenous variables.  
(1) Spatial lag model (SLM)  
\[y_{it}=\rho\sum\limits_{i=1}^{n}w_{ij}y_{it}+\gamma_{1}pol_{it}+\beta X_{it}+ \mu_{i}+\lambda_{t}+\varepsilon_{it} \tag{3}\]  
(2) Spatial error model (SEM)  
\[\begin{split} y_{it}=\gamma_{2}pol_{it}+\beta X_{it}+\mu_{i}+ \lambda_{t}+\varphi_{it}\\ \varphi_{it}=\delta\sum\limits_{t=1}^{n}w_{ij}\varphi_{it}+ \varepsilon_{it}\end{split} \tag{4}\]  
(3) Spatial Durbin model (SDM)  
\[y_{it}=\rho\sum\limits_{i=1}^{n}w_{ij}y_{it}+\gamma_{3}pol_{it}+\beta X_{it}+ \eta w_{ij}pol_{jit}+\sum\limits_{i=1}^{n}w_{ij}\theta X_{ijt}+\mu_{i}+\lambda _{t}+\varepsilon_{ij} \tag{5}\]  
where \(i\) represents the province and \(t\) represents the year. \(y_{it}\) refers to the S&T innovation ability of the \(i\)th province in the \(j\)th year. \(pol_{it}\) refers to the evaluation score of GPTSPS policy of the \(i\)th Province in the \(j\)th year. \(w_{ij}\) is the spatial weight matrix. \(X_{it}\) is the matrix of other control variables of the \(i\)th province in the \(j\)th year. \(\gamma_{1},\gamma_{2},\gamma_{3}\) are the estimation coefficients of the core explanatory variable, which is the evaluation score of GPTSPS policy. \(\beta\) is the coefficient matrix of other control variables. \(\rho\) is the spatial autocorrelation coefficient. \(\mu_{i},\lambda_{t}\) refer to the individual fixed effect and time fixed effect respectively. \(\varepsilon_{it}\) is the random error term, which obeys normal distribution. \(w_{ij}y_{it},w_{ij}pol_{jit},w_{ij}X_{jit}\) are the spatial lag terms of S&T innovation ability, GPSTPS policy and other control variables, respectively. \(\eta\) is the spatial lag coefficient of GPTSPS policy. \(\theta\) is the matrix of other variables' spatial lag coefficient.  
Considering the hysteresis of GPSTPS policy on regional S&T innovation ability and the endogenous problem of the model, we control the time of explanatory variable and control variable in the model to be one year later than the time of the explained variable.
# Region-of-Interest Based Coding Scheme for Live Videos
## 3 Proposed Approach
### Fast Object Detection  
To meet the real-time constraints of object detection in live streaming and ensure each frame is encoded within the time limit, we leverage reference frame relationships to propagate and track detected objects.  
In our approach, as depicted in Figure 5, a target area is initially detected using mm-detection in a preceding frame. If there exists a corresponding area with modifications in the subsequent frame, these Coding Units (CUs) are concatenated to produce a new region, which serves as the targeted detection outcome for that particular frame. Concurrently, to  
Figure 4: Model Training Data Generationg.  
accommodate potential new targets entering the scene in later time frames, a fresh round of detection is executed on the entire screen every n frames.  
Given the low latency requirements of live streaming scenarios where videos are typically encoded as a Group of Pictures (GOP) per encoding cycle, empirical validation has shown that setting n equal to 8 achieves the optimal balance between detection quality and speed, effectively boosting the algorithm's efficiency by a factor of 8. This strategy ensures both timely detections and seamless video encoding without compromising visual fidelity or stream stability.
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 2 Materials and Methods
### Study Area  
Figure 2 shows the part of the apartment complex of Jeonju-si located in Jeollabuk-do, Korea that was selected in this study as the target site. The area is composed of vegetation areas, such as trees and grass lands, as well as non-vegetation areas, such as basketball court, building, road, and parking lot.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 3 Methodology for Cross-Layer Coordination
Both algorithms consider \(N\) neighboring vehicles, \(P\) protocols to be coordinated, and \(C\) communication parameters over which the protocols can act. In Algorithm 2, \(\Delta_{k}^{avg}\) represents the average difference between the communication parameters used by vehicles within 2 hops and the minimum parameters of their ICS. \(a_{k}\) and \(m_{k}\) represent the additive and multiplicative factors of communication parameter \(k\) (\(1\leq k\leq C\)), respectively. ```
Input : \(c_{kj}^{min}\) and \(c_{kj}^{max}\) for each protocol \(j\) and parameter \(k\) Output : \(c_{k}^{min}\) and \(c_{k}^{max}\) of the ICS for the vehicle executing COMPASS for each parameter \(k\)
1. Initialization : \(c_{k}^{min}=\ 0\) and \(c_{k}^{max}=\infty\) for \(1\leq k\leq C\)
2.For each protocol coordinated \(j\) (\(1\leq j\leq P\))do
3.For each communication parameter \(k\) (\(1\leq k\leq C\))do
4.if\(c_{k}^{min}\) is lower than the \(c_{kj}^{min}\) for protocol \(j\)do
5.\(c_{k}^{min}=c_{kj}^{min}\) reported for protocol \(j\)
6.End If
7.if\(c_{k}^{max}\) is higher than the \(c_{kj}^{max}\) for protocol \(j\)do
8.\(c_{k}^{max}=c_{kj}^{max}\) for protocol \(j\)
9.End If
10.End For
11.End For
```  
**Algorithm 1** Derive own ICS
## 5 Conclusions
### Limitations and Future Lines of Research  
This study has some limitations that could be improved for future research. The sample was restricted to the Basque Autonomous Community; thus, the generalizability of the findings to other geographical regions is limited. Future research could include diverse regions or countries to clarify whether the obtained conclusions differ depending on cultural, economic, and organizational contexts. This research relied on a convenience sample, which may not fully represent the broader population. A larger and more representative sample could provide a more comprehensive understanding of how knowledge transfer behaviors function across different types of organizations and sectors. Data was collected through self-reported questionnaires, which could be susceptible to biases (subjectivity, personal interpretation). As the research results are based on a single-source questionnaire and primarily rely on correlations, attention must be given to the potential presence of common method bias. Future studies could address this limitation by incorporating direct observations or carrying out a longitudinal study to track actual changes in knowledge transfer behaviors and their impact on organizational performance over time. Future research would benefit from a more robust methodological framework, such as longitudinal tracking, triangulation of data sources, or experimental validation. Thus, future research could adopt a mixed-methods design, combining quantitative data with qualitative insights.  
This would provide an in-depth exploration of factors influencing employees' willingness to share knowledge and the specific elements of protocols that contribute to their effectiveness. Interviews, focus groups, or case studies could provide valuable context and detail. Addressing these limitations in future research could enrich our understanding of knowledge transfer behaviors and their relationship with organizational factors, further refining strategies to enhance organizational outcomes and sustainability.
## 3 Results and Discussion
### Case Study in Multi-Layered Soils
#### 3.3.1 Geological Parameters and Numerical Model Construction  
The stratigraphic profile and geotechnical properties of the target site are presented in Figure 21, revealing alternating layers of sand (LM-1/3/4/6/8) and clay (LM-2/5/7). The CEL numerical model (Figure 21) was configured with the following parameters: a refined mesh zone (1.5 \(L\times\) 1.5 \(L\)) surrounding the spudcan with an element size of 0.03 \(L\); radial mesh gradient expansion (0.03 \(L\)-0.15 \(L\)) in peripheral regions; penetration rate \(v_{p}\) = 0.2 m/s; and a penalty-based friction model for spudcan-soil interface interactions.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 2 Dynamics Analysis of the Variable-Sweep Wing UAV
### Dynamic Characteristics Analysis of the L-30A UAV
**(a)** Schematic of the rotation mechanism. **(b)** Schematic of the wing–fuselage junction detail. different flight states. The 16\({}^{\circ}\) sweep angle improves lift and stability during maneuvers, while the 60\({}^{\circ}\) sweep angle reduces drag in high-speed dives. This optimization enhances the UAV's adaptability and response in dynamic environments, providing robust support for control under challenging flight conditions. The findings highlight the clear advantages of the variable-sweep wing design over fixed-wing configurations. The precise dynamic response of the variable-sweep wing UAV has been revealed through the above dynamic modeling. This model establishes a solid theoretical foundation for applying MARL algorithms in UAV formation control. Building on this foundation, the discussion will now focus on the design and application of the MADDPG-VSC algorithm to enhance the efficiency and stability of cooperative UAV control.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)
### Working of a CAN-Enabled GNSS Receiver
In most of the previously proposed models, the signal characteristics (i.e., the number of visible satellites, DOP and signal strength/CNR) along with machine-learning techniques, fuzzy inference systems, stochastic modeling and Hidden Markov models [36; 37; 38; 39; 40; 41; 42] were used to propose environment-detection models, which is not the case in the proposed CAN algorithm. The main advantage of using CAN is its simplicity in terms of implementation design, which can be integrated into a GNSS receiver without many hardware modifications. Additionally, the efficient design of the proposed CAN, which includes the selection of optimal GNSS parameters along with context definitions for environment characterization makes it more practically realizable compared to complex methods, which would become practically impossible to implement in the receiver.
Therefore, the frequency domain research content of rock slope can be further improved by incorporating the modal analysis into the frequency domain research of rock slope and fully considering the mutual verification and supplement of Fourier spectrum and modal analysis results. Therefore, the frequency-domain analysis should be paid much more attention to further study the dynamic response characteristics of slopes. In this work, the time-domain and frequency-domain analyses are fully considered to reveal the seismic response characteristics of a layered rock mass slope from multiple perspectives. Taking a bedded rock slope as an example, two-dimensional dynamic analyses are performed on the slope, using FLAC3D. Two aspects in this research were used to investigate the seismic response of the slope, including the time-domain analysis using dynamic acceleration response and frequency-domain analysis using modal analysis and Fourier spectrum analysis. The wave propagation characteristics through the rock mass slope and topographical dynamic amplification effect of the slope were analyzed. The impact of weak structural planes on the seismic response characteristics of the bedded slope was also identified. In particular, the relationship between the natural frequencies of the slope and its dynamic deformation characteristics was clarified under earthquakes. Moreover, the dynamic failure mechanism of the slope was discussed, based on the time- and frequency-domain analyses.
## 2 Methods and Data
### Model
#### 2.3.3 Random Forest  
Random forest is a classification algorithm combining multiple decision tree models to obtain higher prediction accuracy (than a single decision tree), and it possesses stronger generalization ability [31]. The schematic of random forest is shown in Figure 3.  
In random forests, each tree in the ensemble is built from a sample drawn with replacement (i.e., a bootstrap sample) from the training set. Furthermore, when splitting each node during the construction of a tree, the best split is found either from all or a random subset of the input features. Using two methods, i.e., \"Bootstrap method\" and \"Random selection of eigenvalues\", a decision tree with diversity can be trained.
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Real-Time Simulation Mechanism  
Figure 9 shows the architecture of the studied C-\(\imath\)grid in a real-time simulation environment. When the whole C-\(\imath\)grid is simulated with only one personal computer (PC) it may face a worse-solution efficiency condition, i.e., it takes too much time to complete one simulation. This is due to MATLAB/Simulink limited calculation capability. In order to overcome this problem and perform time-series simulation analysis with a higher efficiency, the real-time simulation mechanism (RTSM) is used in this work [19]. The RTSM includes two parts: the host PC and target cluster. In the former, a desktop with Intel 4 Cores 3.30 GHz CPUs and 8.0 GB RAM is used, and in the latter, the OP5600 HIL box with two Intel Xeon 6 Cores 3.46 GHz CPUs and 4.0 GB RAM is used, which provides multi-cluster nodes for parallel simulations. Data exchange between different cluster nodes is done though the shared memory that has ultra-low latency in the same order of the CPU system memory and thus allows the parallel simulation of electric power systems at a certain microsecond time step. The Ethernet protocol with a 100-Mbps data rate has been established to carry out the communication and data conversion between the target cluster and the host PC. The target cluster runs on a Redhat real-time operating system (RTOS), which is mostly emphasized for computational purposes. The host PC runs on a Windows 7.0 operating system where MATLAB/Simulink, SPS toolbox and Real-Time Workshop (RTW) are installed for system modelling, compilation, code loading and monitoring outputs. The real-time interfacing software RT-LAB, which builds parallel tasks from the original Simulink models and runs them on each cluster node of the OP5600 HIL box, is also required for the host PC.  
A fixed-step solver may be required for the real-time simulators due to its computational efficiency. Despite the fact that SPS supports a fixed-step solver based on the Tustin method, the SPS toolbox alone cannot satisfy the demands in real-time simulation due to some limitations [20]. A software toolkit added to the SPS toolbox called the Advanced Real-Time Electro-Mechanical Simulator (ARTEMIS)  
Figure 8: Controller design of a single-phase DC/AC inverter.  
Figure 7: A single-phase DC/AC inverter model.  
is thus introduced and offers an alternative to the Tustin method of the SPS toolbox, which provides enhanced algorithms to support real-time implementation of power system simulations that improve the computation speed and increase numerical stability while preserving accuracy. With the use of RTSM, a complicated C-\(u\)grid model, as indicated in Figure 3, may be separated into five subsystem models, i.e., 5 cluster nodes work on 5 CPU cores, 1 subsystem master (SM_PVM) and 4 subsystem slaves (SS_PVC, SS_COV, SS_INV and SS_AC), and then the computation of each subsystem model can be performed in parallel on an OP5600 HIL box. This higher efficiency computation mechanism is thus used to decrease the computation burden and raise the simulation speed.
## 6 Experimental Analysis
### Verification of Aircraft Experimental Platform  
In order to verify the feasibility of the controller on the actual quadrotor aircraft hardware platform, this paper adopts the model-based design (MBD) method to convert the designed controller into embedded programming language, compile and burn it to the Pixhawk4 flight control platform for execution. Pixhawk4 is an open source flight control platform designed by the Holybro company and Px4 team; stm32f765 is used as the main control chip, and sensors for flight control, such as gyroscope and accelerometer, are integrated. The aircraft experimental platform built in this paper is shown in Figure 11:  
In the experiment, the tracking performance of the pitch angle of the quadrotor aircraft is tested, and the experimental results are shown in Figure 12.  
It can be seen that the response of pitch angle can track the desired curve well, the response speed is fast, and the stable attitude tracking control can be realized. However, due to the excessive rate of expected angle change, there is a slight overshoot at the peak and valley of the expected curve, and the overshoot is within the allowable range. It shows that the designed controller can be implemented on the actual aircraft platform, and it has the performance of fast attitude tracking and a small overshoot.  
In order to verify the disturbance rejection performance of the controller, the experiment tested the attitude tracking performance of the pitch angle by hanging a 380 mL  
Figure 11: The experiment platform of quadrotor UAV.  
Figure 12: The tracking curve of pitch angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Max Deviation (Rad)** & **RMSE** \\ \hline ADRC & 0.0442 & 0.0111 \\ PSO-ADRC & 0.0425 & 0.0093 \\ AGAPSO-ADRC & 0.0384 & 0.0081 \\ FPSO-ADRC & 0.0249 & 0.0077 \\ FOFPSO-ADRC & 0.0210 & 0.0071 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Attitude tracking performance.  
water bottle on the end of any arm. The experimental environment is shown in Figure 13, and the experimental results are shown in Figure 14. The suspended water bottle will swing irregularly under the wind of the rotor, leading to an uneven force on the rotor arm. Therefore, the water bottle suspended on the arm can be considered as additional random interference--this added interference is relatively strong and with high uncertainty.  
It can be seen that, under the effect of external reinforcement interference, the pitch angle of the quadrotor aircraft can still track the desired attitude curve and realize stable real-time control. Due to the swing of the water bottle driven by the wind of the rotor, there is a slight error in the attitude curve at the peak and valley of the expected attitude angle. With the sharp change of the attitude angle and the swing amplitude of the water bottle, there is a large angle error at about 27 s. The overall tracking curve is within the allowable error range and can be modified quickly, which further proves that the designed controller has a strong disturbance rejection ability and fast correction ability, and the robustness of the control system is high.
# Comparison of Analytical Methods for Determining Methylesterification and Acetylation of Pectin
## 2 Materials and Methods
### Determination of DM and DAc by HPLC  
To determine the DM and DAc levels in esterified pectin samples, the samples were saponified with NaOH to produce methanol and acetic acid, which were then detected and quantified by HPLC using a C18 column (Grace Prevail, 5 \(\upmu\)m, 4.6 \(\times\) 250 mm), essentially as previously reported [17] with some modifications. The pectin sample (5 mg) was suspended in 0.2 M NaOH (0.5 mL) and stirred for 2 h at 4 \({}^{\circ}\)C. The reaction solution was then neutralized with 0.2 M H\({}_{2}\)SO\({}_{4}\) (0.5 mL) and filtered through 0.45 \(\upmu\)m Milliporembranes and injected (20 \(\upmu\)L solution) onto the HPLC.  
Methanol and acetic acid were used as external standards to generate the calibration curve for quantifying samples. Standard solutions of methanol and acetic acid with concentrations of 5 mM, 10 mM, 15 mM, 20 mM, and 25 mM were prepared and injected onto the HPLC. Using a refractive index detector at 25 \({}^{\circ}\)C, the elution was performed with 4 mM H\({}_{2}\)SO\({}_{4}\) at a rate of 0.7 mL min\({}^{-1}\). For each standard and CP pectin, three independent samples were taken and their HPLC elution curves were recorded.  
The DM and DAc content of pectin samples were calculated as follows:  
\[\mathbf{DM}=\frac{\mathbf{C}(\mathbf{MeOH})\times\mathbf{176}\times\mathbf{10 ^{-3}}}{\mathbf{m}\times\mathbf{GalA}\%}\mathbf{DAc}=\frac{\mathbf{C}( \mathbf{HOAc})\times\mathbf{176}\times\mathbf{10^{-3}}}{\mathbf{m}\times \mathbf{GalA}\%} \tag{2}\]  
Here, C(MeOH) and C(HOAc) are the concentrations of MeOH and HOAc; \"176\" is the molecular weight of GalA in pectin; \"m\" represents the weight of HG pectin to be determined, and the GalA content was determined by monosaccharide composition analysis.
## 2 Challenges with Sharing LCA Data
Details such as intended application, upstream input providers, intended impact methods, scope, etc., are typically insufficiently described in a single unit process metadata. As a consequence, an LCI typically cannot be recreated from a unit process or collection of unit processes without providing additional information to a modeler. This additional information includes (but is not limited to) how processes exchange inputs, how co-products are managed, which impacts the process is designed for, and the algorithm for calculating results. Data vendors developing ISO compliant exchange formats take different approaches to adding this necessary information, which creates inconsistencies between formats and information models. The open source LCA modeling software openLCA produces a JSON-based serialization of its native data structure that is fully ISO 14048 compliant with extended metadata and documentation fields. The Federal LCA Commons adopted the openLCA data model for managing and publishing LCA data for three reasons:  
1. The data model includes a comprehensive representation of ISO 14048,
2. OpenLCA json supports associating collections of unit processes into product systems which includes modeling decisions significant for re-use, and
3. OpenLCA is a free open source modeling tool, enabling researchers to use the same data model and software for LCA modeling and data documentation and publication. OpenLCA serializes its data modeling into a relational, hierarchical JSON-LD format. Independent classes of objects receive unique identifiers and full descriptions with formatted metadata and attributes. OpenLCA references instances of independent objects by their IDs. For example, openLCA treats a process as an independent object. This class of independent object has process specific metadata (as described above), and includes references to other independent objects, such as flows (describing inputs and outputs), units of measure, people, and source documents. Flows as independent objects also contain references to material such as units of measure and source information. Other data formats use this hierarchical approach as well, most notably ILCD and ESv2. However, the complexity of these relationships makes it very difficult to generate these files without the help of software designed for the task.