esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
The importance of OWTG support structure dynamics has been identified by IEA OC3 and OC4 projects over the past decade. The substructure dynamics should be treated as a part of full system dynamics when OWTG installed in the sea. For clearly exploring the interaction of substructures and OWTGs, various types of support structures were investigated in the individual phases of OC3 and OC4 projects. The environmental impacts for OWTGs are varied according to the OWTG installed location. In order to establish the OWTG design technologies of Taiwan, a low cost and simplified analysis method has been proposed in this work. The main results are summarized as follows:  
* Time consumption for analyses is much reduced. In this work, totally 1512 (1296 + 216) cases have been investigated. For one case (a ten minutes simulation), the dynamic analysis of an equivalent model can be accomplished within 20 min but a detailed analysis by software _Bladed_ takes at least 6 h. The proposed analysis method is much fast than the standard method.
* No expensive software is included. All of the involved software is freeware or low-cost computer program.  
Figure 30: Joint Strength Check (Case 6).  
Figure 29: Joint Strength Check (Case 5).  
* The decoupled method is helpful for the initial design phase. Since the analysis of OWTG system by the proposed method does not take much time, many DLCs can be performed to check the system design.
* A method for equivalent monopile model construction has been proposed. The aerodynamic analyses of wind turbines are often investigated with the aid of FAST. However, complex support structures cannot be included in FAST simulations. With the aid of software BModes 3.0, an equivalent monopile for the jacket substructure can be built and enclosed in the OWTG analysis under FAST.
* According to the analysis results of strength check for members and joints, the loadings are highly determined by the environmental variations. As a result, the DLC and the operational situation should be investigated as much as possible.
* More attention should be devoted to the bottom of jacket substructure due to the loading of axial forces and moments.
* API standard may be more suitable for support structure design under rigorous environment.  
This research was sponsored by the Ministry of Science and Technology in Taiwan under Grants MOST 104-3113-E-042A-003 and MOST 103-2218-E-167-002.  
[PERSON] and [PERSON] performed the analyses; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Methodology
### Establishment of SVM Model  
The machine learning model--the support vector machine (SVM)--has been widely used for the pattern recognition or classification of the EMG signal [64; 65]. The basic idea behind the SVM is to construct an optimal hyperplane [66], which can be used to classify linear separable patterns. Non-linear separable patterns are mapped into higher-dimension space through a kernel function, so that the patterns become linearly separable [67]. The kernel function selected in this paper was the radial basis function (RBF) kernel, which has a good classification performance for multi-classifiers with a small margin between each of the two classes [68].  
Figure 4: A sketch for depicting part of the extracted EMG and GRF features. (**a**) Sketch for GRF-based features; (**b**) sketch for EMG-based features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **The Meaning of the Features** \\ \hline \multicolumn{3}{c}{**EMG-Based**} \\ \hline
**TA Peak** & The peak value of the TA linear envelope \\
**TA MAV** & MAV of the TA EMG, \(MAV=\frac{1}{N}\sum_{i=1}^{N}|x_{i}|\) \\
**TA STD** & STD of the TA EMG; \(STD=\sqrt{\frac{N+1}{N}(x-\frac{x}{N})^{2}}\) \\
**TA RMS** & RMS of the TA EMG; \(RMS=\sqrt{\frac{N}{N+1}x^{2}}\) \\
**TA WL** & WL of TA EMG; \(WL=\sum_{i=1}^{N}|Ax_{i}|\), where \(Ax_{i}=x_{i}-x_{i-1}\) \\
**TA 80** & The value of the TA linear envelope at 80\% gait \\
**SL Peak** & The peak value of the SL linear envelope \\
**SL MAV** & MAV of the SL EMG, \(MAV=\frac{1}{N}\sum_{i=1}^{N}|x_{i}|\) \\
**SL STD** & STD of the SL EMG; \(STD=\sqrt{\frac{N+1}{N}(x_{i}-x)^{2}}\) \\
**SL RMS** & RMS of the SL EMG; \(RMS=\sqrt{\frac{N}{N+1}x^{2}}\) \\
**SL WL** & WL of SL EMG; \(WL=\sum_{i=2}^{N}|Ax_{i}|\), where \(\Delta x_{i}=x_{i}-x_{i-1}\) \\
**SL 25** & The value of the SL linear envelope at 25\% gait \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The establishment of the SVM model in this paper is described as follows: The features and labels of the selected steps were used as the input vector of the model, and a ten-fold cross-validation method [64; 69] was applied to train the SVM model. In this method, all of the samples were split equally into 10 groups while ensuring the proportion of each class in each group remained unchanged. Then, one group was used for testing and the other nine groups were used for training. The process above was iterated until all groups had been treated as the testing set.
###### Abstract
Furthermore, there are many cities in eastern China, which make it difficult to find an observatory on a uniform underlying surface for satellite validation. There are many deserts, such as the Gobi Desert, and plateau with lower anthropogenic emissions in western China. It is expected that ground-based observations in the barren Gobi Desert with a uniform underlying surface can be good practice for satellite payload validation and intercomparison due to the smaller spatial differences [15; 27]. Therefore, ground-based XCO\({}_{2}\) and XCH\({}_{4}\) were observed in the Gobi Desert in western China using a portable EM27/SUN Fourier Transform Spectrometer under a payload validation campaign. The purpose of this study is to (1) obtain the occurrence of surface and column CO\({}_{2}\) and CH\({}_{4}\) mole fractions in desert regions; (2) compare ground-based observations with satellite retrievals; (3) compare surface and column observations at the same site.
## 4 FSO System Design
The optical signal-to-noise ratio (OSNR) at the output of the post-amplifier is  
\[A_{Sat.-Sat.}=\frac{D_{T}^{2}D_{R}^{2}}{L^{2}\lambda^{2}}\eta_{T}T_{T}(1-L_{P} )T_{R}\eta_{R}, \tag{13}\]3  
where \(B_{opt}\) = 12.5 GHz is the reference bandwidth. From Equations (13)-(15), it follows that for the given transmitter output power, the uplink has the lowest OSNR and sat-to-sat link has the highest OSNR.
## 3 Results and Discussion
### Statistical Analysis of Factor Effects on CR
According to fundamental principles of corrosion chemistry, the CR of carbon steel typically decreases as pH increases [21; 22]. Previous research has identified pH as an influencing factor for CR. [PERSON] et al. [23] reported that lowering the pH level of seawater from 8.5 to 7.0 increased the CR by 42.52%. Similarly, [PERSON] et al. [24] found that CR accelerates in acidic environments, indicating that lower pH levels lead to more aggressive corrosion behavior. However, the results of this study indicate that in the presence of temperature and flow velocity factors in a 3.5% NaCl solution, the influence of pH is not significant.
## 2 Problem Description and Formulation
### Problem Description  
The satellite can establish a downlink with the ground station to transmit the data stored on itself to the ground when it can gain access to a ground station. According to the visible time of different satellites and ground stations conflict with each other, ground station scheduling is needed to complete the data transmission task of each satellite. The MGSS is the last step in the satellite scheduling workflow, which has a great impact on user satisfaction and data timeliness. This problem is mainly caused by the insufficient number and the limited geographical distribution of ground stations. Due to the limitation of the curvature of the earth and the communication capability of the satellite antenna, it is impossible for the satellite and the ground station to maintain a connection state at any time, but can only communicate and transmit data within a specific time window. Additionally, the ground station usually can keep downlink with only one specific satellite simultaneously. As shown in Figure 1, the GS-1 can access to SAT-2, SAT-3 simultaneously, and GS-2 can access to SAT-1 and SAT-2. SAT-2 needs to choose between ground station 1 and ground station 2 as the downlink target. As shown in Figure 2, there are overlaps in the same ground station with different satellites.  
After the observation scheduling proposed by the user is completed, the executing satellite of each target, the start time of observation, the time of task completion, and the consumption rate of on-board storage can be calculated. Additionally, then, the problem is transformed into multi-satellite downlink scheduling, and a download plan is arranged for each datum. A downlink task can be described as (1), each task \(t\) contains a unique _id_, _sat_ is the satellite number of the current downlink task, _tArrival_ is the specific time of the current task, _tDur_ is the time required for data transmission, and _priority_ stands for the importance of the task.  
\[t=(id,sat,tArrival,tDur,priority) \tag{1}\]  
Task set \(T\) contains all downlink task for all satellites. A subset \(T^{\prime}=(t_{1},t_{2},\cdots,t_{n})\in T\) is selected after executing the downlink scheduling. The exact execution time of each \(t\) is determined to ensure that the downlink task has the shortest waiting time while meeting all the constraints of the tasks in \(T^{\prime}\). The access time conflict should also be solved.
###### Abstract  
As one of the main errors that affects Global Navigation Satellite System (GNSS) positioning accuracy, ionospheric delay also affects the improvement of smartphone positioning accuracy. The current ionospheric error correction model used in smartphones has a certain time delay and low accuracy, which is difficult to meet the needs of real-time positioning of smartphones. This article proposes a method to use the real-time regional ionospheric model retrieved from the regional Continuously Operating Reference Stations (CORS) observation data to correct the GNSS positioning error of the smartphone. To verify the accuracy of the model, using the posterior grid as the standard, the electron content error of the regional ionospheric model is less than 5 Total Electron Content Unit (TECU), which is about 50% higher than the Klobuchar model, and to further evaluate the impact of the regional ionosphere model on the real-time positioning accuracy of smartphones, carrier-smoothing pseudorange and single-frequency Precise Point Positioning (PPP) tests were carried out. The results show that the real-time regional ionospheric model can significantly improve the positioning accuracy of smartphones, especially in the elevation direction. Compared with the Klobuchar model, the improvement effect is more than 34%, and the real-time regional ionospheric model also shortens the convergence time of the elevation direction to 1 min. (The convergence condition is that the range of continuous 20 s is less than 0.5 m).  
GNSS; ionospheric delay; regional ionospheric model; Android smartphone +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 5 Simulation Results and Discussion of the Developed Model
If the condition is approved, the residential load and the battery are supplied by the power generation, and if not, only the residential load is supplied. When the power generation falls below the residential load, the EMS control automatically disconnects all the loads (shut-down loads). In this case, no load is supplied. The loads are momentarily disconnected and the available power  
Figure 6:8 Transition state from Stateflow chart. Figure 6:9 EMS control output results when priority is supplied. generation is used to charge the battery until its SOC meets the requirement or the power generation becomes active. The result of this scenario is shown in Figure 8:0  
The simulation results of the developed DC microgrid and energy management algorithm model are presented in this section. The model was designed and developed using MATLAB/Simulink software. In this paper, it was a question of focusing on the energy management control of the DC microgrid system to finally maintain its proper operation. The method used to design and develop this energy management system algorithm was the Stateflow logical programming environment in MATLAB/Simulink software. Based on all these scenarios, the developed energy management system algorithm has shown the ability to ensure the reliability, resiliency, robustness, and proper operation of the system. The developed controllers reveal a promising approach towards effectively integrating renewable energy sources into DC microgrid systems. One notable aspect of the results is the robustness analysis conducted to assess the performance of the developed energy management strategy under various scenarios and conditions. During the robustness analysis, the energy management strategy was evaluated against a range of factors, including changes in renewable energy availability, and variations in load demand. This analysis provided valuable insights into the resilience and stability of the developed strategy in real-world operating conditions. The results demonstrate that the energy management strategy exhibits robust performance across different scenarios, effectively balancing energy generation and consumption while ensuring the reliable operation of the microgrid system. Despite fluctuations in renewable energy generation and load demand, the strategy demonstrates adaptability and resilience, maintaining stable operation and minimizing disruptions. Furthermore, the robustness analysis highlights the flexibility of the energy management strategy in accommodating changes and uncertainties in the microgrid environment. By optimizing energy flow, the strategy enhances the overall efficiency and reliability of the microgrid system, while maximizing the integration of renewable energy sources.
## 2 The Feasibility Analysis of the SVR Model Optimized by the AOA Algorithm  
UHPC involves multiple variables and complex nonlinear relationships. It requires considering not only the composition of the concrete but also the interactions between these components and their combined effects on the final strength, making UHPC strength prediction challenging. Traditional prediction methods, such as linear regression and empirical formulas, are based on simplified assumptions and struggle to accurately describe complex nonlinear relationships. Additionally, predicting carbon emissions further complicates the process, as it involves considering the energy consumption of raw material production, environmental impacts during production, and the lifespan of the materials, encompassing multiple dimensions.  
To address these challenges, methods capable of handling highly nonlinear problems, such as ANNs and SVM, are needed. These methods improve prediction accuracy by learning complex patterns and relationships within data and can self-optimize as new data becomes available. Particularly, support vector regression (SVR) has been widely used to solve complex nonlinear problems due to its unique design, which effectively captures and models nonlinear relationships in data. However, the performance of ANNs is heavily influenced by hyperparameter settings, which must be determined before training, making the process complex and subjective.  
To overcome these limitations and improve hyperparameter optimization efficiency and model accuracy, the arithmetic optimization algorithm (AOA) is introduced. AOA explores different regions of the solution space through multiplication and division operations and fine-tunes the search direction and step size using addition and subtraction operations to find hyperparameter configurations that minimize network error. The application of AOA not only automates and accelerates the hyperparameter search process but also increases the likelihood of finding optimal hyperparameter combinations. This is because AOA effectively balances exploration (global search) and exploitation (local search), ensuring the algorithm can escape local optima and progress toward a global optimal solution.  
In summary, applying AOA for hyperparameter optimization, combined with the nonlinear modeling capabilities of the SVR model, provides an efficient and accurate solution for complex problems. This integration is expected to enhance the performance of AI models across various fields. In this study, we propose the AOA-SVR model by combining AOA's parameter optimization with the SVR model's ability in order to extract nonlinear information to capture the relationships between factors affecting mechanical performance and to predict trends in mechanical properties. The process flowchart is illustrated in Figure 1.  
Figure 1: Working program for the establishment of the machine learning optimization model.
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 3 Star Identification
### Impact of Position Errors on Identification Rate  
The centroid positions of stars in the star image are calculated using the centroid algorithm, and a position error is generally caused by the imaging quality of the star sensor and the signal-to-noise ratio (SNR) of the star image. During the process of star identification, a position error affects the accuracy of the angular distance by altering the observation vector, which can result in redundant and false matching when compared to the navigation feature library. To assess the impact of position errors on the identification rate of the proposed algorithm, different position errors are added to the centroid positions of stars from 1000 frames of actual star images. The average identification rate under different position error conditions is then recorded and compared with other subgraph isomorphism algorithms. The experimental results are shown in Figure 8.  
The results in Figure 8 demonstrate that, under the same experimental conditions, the average identification rate of the proposed algorithm is significantly higher than that of the other algorithms. As the position error increases, the average identification rate of all algorithms declines, but the proposed algorithm has the slowest declining speed. Moreover, under the same position error conditions, the average identification rate of the proposed algorithm remains higher than that of the others. The analysis of the experimental results is explained below. The Mortari algorithm performs star identification across the entire celestial sphere, making it more susceptible to position errors, and thus exhibits weaker robustness. The You, Liu, and Wang algorithms perform coarse identification across the entire celestial sphere and fine identification in localized areas, providing some robustness to position errors. However, the proposed algorithm matches only within the basic unit of the navigation feature library, significantly reducing the probability of redundant and false matching compared to algorithms that operate on the entire celestial sphere. Consequently, the proposed algorithm demonstrates stronger robustness to position errors.
## 3 Results and Discussion
### Algorithm Performance for Ice-Risk Forecasts
#### 3.2.1 The Case-Study at the Pedemontana Motorway  
For the first case-study, the analysis is carried out by comparing the observed road-conditions data, measured by the LUFFT sensors, with the ice alert code forecasted values using the WRF model as forcing input into the algorithm. As mentioned before, since the dataset covers twenty-one months from April 2017 to December 2018 including warmer seasons where no ice risk is expected, only days with hourly air temperature below 4 \({}^{\circ}\)C are considered to calculate the contingency tables.  
Figures 9 and 10 present the contingency tables during clear and cloud sky days, respectively, for the three station points: SP2, Gorla, and Ponte Olona. It is worth mentioning that the total number of ice risk events during cloudy days is very low, which is consistent with the typical meteorological conditions of this region in northern Italy: in fact, the presence and risk of ice generally is high during clear nights only. This explains equal results in the three neighboring station points under overcast sky conditions.  
All the skill scores are afterwards summarized in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Station Point** & **Accuracy** & **BIAS** & **False Alarm Ratio** & **Miss Alarm Ratio** & **Hit Rates** \\ \hline \multirow{3}{*}{**Cloudy days**} & SP2, & & & & & \\  & Ponte Olona, & 0.93 & 0.80 & 0.00 & 0.20 & 0.80 \\  & Gorla & & & & & \\ \hline \multirow{3}{*}{**Clear days**} & SP2 & 0.77 & 0.81 & 0.14 & 0.31 & 0.69 \\  & Ponte Olona & 0.59 & 5.60 & 0.82 & 0.00 & 1.00 \\  & Gorla & 0.61 & 0.84 & 0.29 & 0.41 & 0.59 \\ \hline \multirow{3}{*}{**Total**} & SP2 & 0.81 & 0.80 & 0.12 & 0.29 & 0.72 \\  & Ponte Olona & 0.66 & 3.20 & 0.72 & 0.10 & 0.85 \\ \cline{1-1}  & Gorla & 0.67 & 0.83 & 0.26 & 0.38 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Evaluation of algorithm performance for the ice alert code forecast for Pedemontana motorway: values assumed by the indices.  
Figure 10: Contingency tables for cloudy days.  
Figure 9: Contingency tables for clear sky days.  
It clearly appears how the false alarm forecasts at Ponte Olona are quite high. This is mainly due to peculiar microclimate conditions where the weather station is located. In fact, the Ponte Olona station is situated in a track of the Pedemontana motorway on a bridge of the Olona river creek which is subjected to a canyon effect with stronger wind conditions than the other two weather stations located alongside the lane. Notwithstanding this, the obtained results for the other two measurement points (SP2 and Gorla) underline a satisfactory performance, especially during cloudy days as well as highlighted in the LST forecasts; the BIAS index between 0.80 and 0.84 indicates a slight tendency of the MEC algorithm to underestimate the presence of ice.