esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Estimation of the Rotation
where \(w_{r}=N_{r}/N_{sum}\), \(N_{sum}\) is the number of pixels for all radii and \(N_{r}\) is the number of pixels for the specific radius \(r\), respectively  
In addition, two neighbors of the shift value \(k\left(r\right)\) at radius \(r\), \(k\left(r\right)-\ 1\) and \(k\left(r\right)+1\), and the corresponding correlation coefficients, \(\delta_{r}(k\left(r\right)-1)\) and \(\delta_{r}(k\left(r\right)+1)\), are input into the fitting model to refine the accuracy of the rotation angle. Computing the best fitting parabola equation involves solving a system equation, which is written as:  
\[\delta(x)=ax^{2}+bx+c \tag{17}\]  
where \(x=k\left(r\right)-1\), \(k\left(r\right)\), \(k\left(r\right)+1\). Three unknown parameters \(a\), \(b\) and \(c\) are calculated using Cramer's rule. When the parabola parameters are determined, the optimal shift value is calculated by:  
\[k_{opt}^{*}\left(r\right)=-\frac{b}{2a} \tag{18}\]  
Therefore, the greatest accuracy for the rotation angle \(\theta_{opt}^{*}\) is refined as:  
\[\theta_{opt}^{*}=\sum_{r=R_{\min}}^{R_{\max}}w_{r}\cdot k_{opt}^{*}\left(r \right)\cdot\Phi_{opt,l}(r) \tag{19}\]
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Support Vector Machine (SVM) Classifier  
SVMs [79] are supervised learning algorithms that construct an optimal hyperplane or a set of hyperplanes to linearly discriminate two groups of samples. The optimal hyperplane maximizes a distance between two planes (referred to as support vectors) that are a part of the convex hulls (margins) of those groups in a feature space. SVM learning is an iterative procedure to select a classifier featuring the optimal hyperplane that minimizes misclassifications. If samples are incorrectly labeled in a training dataset and consequently fall onto the wrong side of a hyperplane, then they are down-weighted to reduce their influence. In the case of a non-linear relationship between classes and samples in the feature space, these samples are projected by means of kernel techniques to a (usually) higher-dimensional space where the separation of classes becomes linear. The choice of optimal type (e.g., linear, Radial Basis Function (RBF), etc.) and the parameters of a kernel greatly influence the classification quality. SVMs perform a multi-class classification by fitting binary sub-classifiers to each class and selecting the one that occurs in the most cases. From a numerical perspective, optimization of SVMs is a quadratic problem not suitable for large datasets. The advantage of this method is related to reliable classification results in the case of sparse and noisy training datasets [87].
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
###### Abstract  
In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances.  
unmanned surface vehicle; smart collision avoidance system for unmanned ship; nonlinear optimal control system; fuzzy indicators system +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data  
#### 2.2.1 GPP Datasets  
MOD17 GPP outputs were used in global carbon cycle analysis, ecosystem status assessment, and environmental change monitoring. The MOD17 algorithm is based on the original radiation use efficiency logic of Monteith. The GPP can be estimated using the LUE model as follows:  
\[\text{GPP}=\varepsilon\times\text{(IPAR}\times\text{FPAR)} \tag{1}\]  
where IPAR is the incident photosynthetically active radiation (MJ m\({}^{-2}\)), FPAR is the fraction of photosynthetically active radiation absorbed by the vegetation canopy, and \(\varepsilon\) is the efficiency coefficient, which varies widely with different vegetation types.  
The MOD17A2H 006 product exhibited a long temporal cover, which includes data every eight days starting from 2000 to the present with a spatial resolution of 500 m, so we used it to analyze the spatiotemporal distribution and variation of GPP in BTH [21].  
Aiming to exploring inter-annual variation of GPP, we calculated accumulated GPP during the growing season from April to October [22], the spring season from March to May, the summer season from June to August, and the autumn season from September to November of each year.
## 3 Results and Discussion
### Simulation Environment  
The scenario for the evaluation of our proposal is shown in Figure 6. This scenario consists of a grid topology where the transmitted data traffic is bidirectional. That is, home users (smart meters and other home devices available at the HANs) transmit different applications (traffic types) to the data concentrator, such as periodic billing data (meter reading), Electric Vehicle (EV) charging information, and home energy, among other applications. On the other hand, the data concentrator is capable of transmitting demand response information to home users, with the aim, for example, of adjusting the energy consumption during peak hours. In the simulations, the number of nodes in the grid is a variable parameter, the data concentrator is located in the bottom left corner, and all applications are running simultaneously in every HAN.  
Figure 5: Possible loop creation for low priority packets.  
Figure 6: Scenario under consideration.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Differential ISB estimation between BDS-3/GPS/Galileo BIC-L1-E1 and B2a-L5-E5a signals  
In this section, we estimate phase and code differential ISBs between BDS-3 B1C/B2a and GPS/Galileo L1-E1/L5-E5a signals using short baseline (separated by approximately 3.85 m) data collected from 3-4 June 2018 at SGG of Wuhan University with the iGMAS and M300 Pro receivers. According to Equation (4), the phase and code ISBs are estimated epoch-by-epoch with known receiver positions under 10\({}^{\circ}\) elevation cut-off angle.  
Figure 7 shows estimated phase and code differential ISBs between identical receiver types (two iGMAS receivers) for GPS/BDS-3 L1-B1C/L5-B2a and Galileo/BDS-3 E1-B1C/E5a-B2a signals, respectively. As expected, the ISB series are indeed stable in the time domain and the average ISB values are close to zero. For GPS/BDS-3 L1-B1C/L5-B2a signals, the phase and code ISBs are \(-\)0.004/\(-\)0.001 cycles and \(-\)0.25/0.02 m, respectively, while for Galileo/BDS-3 E1-B1C and E5a-B2a signals, the values are 0/0 cycles and \(-\)0.25/0.03 m, respectively. Similar results are also observed for ISBs between two M300 Pro receivers, although not shown here. The STDs of the mean phase ISB obtained in the single-epoch solutions varied from \(\sim\)0.015 to 0.018 cycle, which is at the same level of phase noise. With respect to the code ISBs, the STDs for L1-B1C and E1-B1C are present with, to some extent, relatively large values (0.83/0.9 m), which is much larger than L5-B2a and E5a-B2a (0.27/0.24 m). This is reasonable considering that the L1-E1-B1C code observations are susceptible to be affected by larger multipath effect and noise as shown in Section 3. Such results allow for neglecting these ISBs in the tightly combined RTK with BDS-3/GPS/Galileo observations.  
Figure 8 shows the estimated phase and code ISBs with different receiver types (iGMAS and M300 Pro receivers) for L1-B1C/L5-B2a and E1-B1C/E5a-B2a signals, respectively. We observe that the ISB series are indeed present with non-zero values but stable in the time domain. Compared with the above identical receiver case, the STDs for the phase and code ISBs are slightly larger than that in Figure 7. For GPS/BDS-3 L1-B1C/L5-B2a signals, the average phase and code ISBs are \(-\)0.272/0.500 cycles and 14.51/\(-\)90.28 m, respectively, while for Galileo/BDS-3 E1-B1C/E5a-B2a signals, the values are  
Initial Assessment of Instantaneous Tightly Combined RTK with GPS, Galileo, and BDS-3 Operational Satellites  
In this section, we present initial assessment of the tightly combined BDS-3/GPS/Galileo B1C-L1-E1/B2a-L5-E5a RTK using short baseline data in terms of the ADOP, ratio values, the empirical success rate, and positioning accuracy. The performance of the TCM with ISBs calibration and correction (cf., Equation (5)) will be evaluated in comparison with the LCM solution (cf., Equation (3)).
## 1 Introduction  
Plate girders are prone to buckling under various load combinations because they are made of thin plate elements. The web part of these members carries the shear loads; therefore, shear buckling is a critical phenomenon affecting the web plates of I-shaped plate girders. The out-of-plane shear buckling resistance of plate girders is often increased by applying transverse stiffeners or corrugated webs. While the web of the plate girders is the primary element resisting the shear buckling, the flanges are the primary elements carrying the bending and torsional loads. Therefore, in addition to the shear buckling analysis, the design of plate girders also involves the lateral-torsional buckling of structural members. Figure 1 illustrates an example of lateral plate girder buckling during the process of installation on a bridge.  
The design of plate girders with the highest possible load-carrying capacity within cost and material restraints consists of finding the optimal combination of plate thicknesses, web plate slenderness, and stiffener spacing [1; 2; 3]. Furthermore, to obtain an adequate dimension of steel I-girders, both lateral-torsional and shear buckling should be considered in all construction stages.  
In this study, the flange and web dimensions of a doubly symmetric I-shaped plate girder are optimised. For the optimisation, a novel meta-heuristic optimisation algorithm called the harmony search algorithm is applied. Optimised cross-sectional dimensions were obtained with a satisfactory amount of computational overhead leading to a significant reduction in the cross-sectional area.
## 4 Results
### Mesh and Model Selection
#### 4.2.2 Pressure Comparison  
The interFoam solver failed to achieve mesh-independent results across the four tested meshes. Figure 5a illustrates that within the jet stagnation point, twoPhaseEulerFoam demonstrated superior performance, yielding numerical results closer to experimental data, while interFoam consistently overestimated pressures. However, for pressures far from the jet stagnation point \(r/D_{i}>0.69\), as shown in Figure 5b, interFoam provided good results. Notably, twoPhaseEulerFoam reached experimental pressure values with coarser meshes, reducing computational costs, as both solvers required the same time to compute one second of simulation duration.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 4 Results and Discussion
### Predicted Data  
Predicted data on the time series are also compared as an additional validation. Figure 12 illustrates the prediction curves generated by six models over the forecasting portion, allowing for a detailed comparative assessment of their predictive behaviours under varying data dynamics. The visual comparison reveals that most models demonstrate a stable prediction performance with limited deviations from the original data, the so-called experimental data.  
Notably, the MLP, LSTM, and GRU models exhibit smoother prediction curves, reflecting their robustness in capturing the underlying temporal structure of the dataset. The NAR and ANFIS models also maintain consistent predictions but show occasional small fluctuations in regions with rapid changes in configuration values. On the other hand, the  
Figure 12: Prediction curves of AL models.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Metrics** & **NAR** & **LSTM** & **ANFIS** & **GRU** & **CNN-RNN** & **MLP** \\ \hline Total data count & 45,654 & 45,654 & 45,654 & 45,654 & 45,654 & 45,654 \\ Training data count & 9130 & 9130 & 9130 & 9130 & 9130 & 9130 \\ MAE (unit) * & 2.592 & 5.081 & 2.805 & 4.124 & 5.063 & 2.122 \\ MAPE (\%) & 0.895 & 5.081 & 0.862 & 2.848 & 2.948 & 0.876 \\ ME (unit) & 1.279 & 3.061 & 1.296 & 2.488 & 3.632 & 1.145 \\ MSE (unit*2) & 1.225 & 17.297 & 1.985 & 8.243 & 30.814 & 2.011 \\ RMSE (unit) & 1.107 & 4.159 & 1.409 & 2.871 & 5.551 & 1.418 \\ Calculation duration (s) & 4.847 & 1826.525 & 796.826 & 1724.109 & 1004.711 & 10.762 \\ \(R^{2}_{\mathit{test}}\) & 0.9867 & 0.9166 & 0.9875 & 0.9656 & 0.9675 & 0.9858 \\ Number of iteration & 9 & 100 & 1000 & 100 & 100 & 9 \\ \hline \hline \end{tabular} (*) “unit” is the same as the data unit.  
\end{table}
Table 5: Models’ performance metrics on the “configuration” data.  
CNN-RNN model displays a relatively higher level of noise, which may suggest sensitivity to short-term volatility or overfitting in localised intervals.  
The presence of transient spikes in the predictions, as observed around timestamps 37,600 s and 41,700 s, further highlights the importance of model generalisation in regions with abrupt changes. Such artefacts are less pronounced in models like MLP and LSTM, implying their superior adaptability to both stationary and non-stationary patterns.  
In conclusion, the prediction curves support the quantitative findings reported earlier, confirming the higher temporal consistency and accuracy of the MLP, LSTM, and GRU models. These results emphasise their suitability for time prediction tasks characterised by complex temporal dependencies and moderate noise levels.  
Although the LSTM and GRU models provide a general curve shape, there is a distinct shift as reported in previous studies [32].
# Passive Control of Base Pressure: A Review
## 2 Various Types of Passive Controls
### Passive Control in the Form of Splitter Plate
A reduction in drag of around 28% is achieved by placing the model's front splitter plate. This research demonstrates the advantage of position adaptation and orientation of passive control in the presence of lateral wind. An experimental investigation was conducted by [PERSON] et al. [30] at high-speed supersonic flow at Mach number 2.49 with a splitter plate. The splitter plate is placed at the recirculation zone behind a blunt axisymmetric body. Triangular splitters that divide the near wake into one-half, one-third, and one-fourth cylinder regions were designed to exploit this flow's specific stability characteristics, impact the immediate wake flow, change the base pressure, and eventually affect the base drag. A base drag reduction of as high as 39% is achieved by employing the splitter plate. Integrating the no-control pressure distribution resulted in a resultant base drag factor of C\({}_{\text{Dnet}}\) is 0.1028. The resulting net base drag coefficients were C\({}_{\text{Dnet}}\) is 0.1033, 0.1071, and 0.1051, respectively, for the one-half, one-third, and one-fourth-cylinder configurations. Compared to the non-control geometries, the splitter plates generated drag coefficient changes of 0.5, 4.2, and 2.2 percent, which is within or too near to the usual measurement uncertainty.
## 6 Conclusions  
In this paper, we present a network traffic prediction method utilizing machine learning and GRU techniques with adaptive time stepping. In scenarios where the time-stepping size can adapt dynamically based on real-time fluctuations in network traffic, our method demonstrates enhanced accuracy in predicting network traffic compared to conventional approaches that rely on fixed time-stepping sizes. In 6G satellite networks, the ATS-GRU algorithm dynamically adjusts sampling intervals for accurate real-time traffic prediction, even in challenging scenarios like high-speed mobile connections and network congestion. It enhances network management by optimizing resource allocation based on precise predictions, reducing costs, and maintaining network performance, which is crucial for cost-effective broadband satellite communication. The ATS-GRU algorithm not only offers a novel approach to traffic prediction in the realms of communication networks, smart cities, and the Internet of Things, but also has the potential to monitor medical devices and patient health, thereby enhancing the efficiency and quality of healthcare services. Additionally, it can be employed to forecast financial market data flows, financial transactions, and investment trends, facilitating more precise decision making and improved risk management.  
Methodology, X.Z.; validation, Y.Z.; writing--original draft, X.Z.; writing--review and editing, P.Y. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Plan [Grant number: 2021 YFB2800805].  
**Data Availability Statement:** Data sharing is applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 4 Discussion and Limitations
### Limitations  
Despite the promising findings, this study has several limitations:  
* Simulation-based data: All models were trained and tested exclusively on simulation outputs. Real-world validation is required to assess robustness under diverse road conditions and vehicle aging effects.
* Simplified vehicle dynamics: Tire and suspension models were simplified; tire wear, road surface variations (e.g., wet or icy conditions), and load shifts were not explicitly modeled.
* Limited vehicle diversity: Although five vehicle types were analyzed, further expansion to include buses, tankers, and specialty vehicles is necessary for broader applicability.
* Temporal window constraints: The 10-time-step sequence window may be insufficient for certain high-speed maneuvers. Dynamic window sizing could be explored.
* Generalizability to different environments: Current models assume standard dry road conditions. Performance under adverse weather or non-uniform terrain remains unknown.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 7 S-ALOHA Evaluation in a Real Deployment
### The Environments  
We tested our custom sensor node in different deployments and applications. The results presented in this paper are acquired placing the network indoor, allowing an easy setup and configuration management for all the tests presented in the following chapters. We tested our S-LoRaWAN in CINECA (Figure 11), the Tier0 supercomputing center for scientific research in Italy, where our end-nodes were used to monitor in a distributed fashion the temperature evolution of the Computer Air Conditioners with the goal to improve the cooling efficiency, as an improvement to other WSNs implementations [38].  
We demonstrated [27] a packet collision reduction of 3.4\(\times\) in comparison with a standard LoRaWAN WSN with 20 end-nodes. Moreover, we tested our S-ALOHA in a structural monitoring application with a custom sensor node developed to measure the width of cracks in building walls [25]. This application would require hundreds of sensors, which generate thousands of bytes per day. This traffic can overcrowd a standard LoRaWAN network; indeed, our S-ALOHA enables the possibility to double the data exchanged on the network or increase the overall number of sensors.  
Figure 10: End device HW. (**a**) Hardware block diagram; (**b**) picture of the custom board.
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
## 4 Results and Discussion
### Relationship between Is(50) and Uniaxial Compressive Strength  
Based on the reasonable calculation method of Is(50), optimal loading rate, and stable dimensional range derived in the previous paper, the relationship between Is(50) and uniaxial compressive strength is analyzed in order to obtain the uniaxial compressive strength of the rock more accurately. Previous studies have shown [2; 33; 34] that the linear zero-intercept function is generally recognized by domestic and foreign scholars for predicting the physical significance of the model in a more reasonable way. The uniaxial compressive strength formulation was solved jointly with the linear zero-intercept function.  
The system of equations is:  
\[R_{\rm c}=P/A \tag{3}\]  
\[R_{\rm c}=K_{i}I_{\rm s(50)} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Rock Samples**} & \multirow{2}{*}{**Size (mm\({}^{3}\))**} & \multirow{2}{*}{**Strength Value**} & \multicolumn{2}{c}{**Is(50)\({}_{2}\) Difference between**} & \multirow{2}{*}{**Average of Is(50)\({}_{2}\) Difference**} \\ \multicolumn{1}{c}{**Loading Rate(kN/s)**} & & & & \\ \cline{3-5} \cline{5-  
where \(R_{\rm c}\) is the uniaxial compressive strength (MPa), \(P\) is the breaking load (N), \(A\) is the cross-sectional area of the rock sample perpendicular to the loading direction (mm\({}^{2}\)), and \(K_{i}\) is the strength conversion factor  
For the three rock samples in this paper, Table 9 lists the empirical conversion relationship equations given by relevant scholars at home and abroad, whose strength conversion coefficients range from 11.08 to 25.00. The strength conversion coefficients vary for different rocks, and even for the same rock; the strength conversion relationship varies greatly due to the different geographical conditions in which it is located. By substituting the uniaxial compressive strength obtained from the test with I\({}_{\rm s(50)2}\) into Equations (3) and (4), the empirical conversion equations for these three rocks were obtained, as shown in Table 10.  
The results show that the conversion coefficients for fine-crystalline granite, coarse-crystalline granite, and siltstone are 16.80, 15.32, and 14.60, respectively, which indicates that the conversion coefficients of rocks with high strength are higher compared to those with low strength. This phenomenon is consistent with that found in the literature [42; 43], which also indicates that the results of this study are scientifically reasonable.