esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors
### Attitude Determination with Three GNSS Receivers
#### Analysis of Benefit of Magnetometer-Based Attitude Information for GNSS Integer Ambiguity Fixing  
In this section, we analyze the benefit of magnetometer-based attitude information for GNSS integer ambiguity fixing. Galileo double difference carrier phase and pseudorange measurements are simulated according to Equations (8) and (9) with the settings of Table 1.  
Figure 1: Integer ambiguity fixing: sequential tree search with attitude constraints.  
The integer ambiguity estimation is performed in three steps:  
* Estimation of float solution of baselines and ambiguities by least-squares estimation using single epoch measurements,
* Normalization of baseline estimates with prior information on baseline length and respective adjustment of float ambiguities,
* Integer ambiguity fixing with sequential tree search and integer decorrelation using magnetometer-based attitude information and baseline length prior information.  
Figure 2 shows the probability of incorrect integer ambiguity fixing as a function of the accuracy of the magnetometer-based heading and pitch angle accuracies. We can observe that the probability of incorrect integer ambiguity fixing is reduced by **several orders of magnitude** by the magnetometer based heading/pitch angle with an accuracy of \(10^{\circ}\).  
Figure 3 shows the probability of incorrect single epoch integer ambiguity fixing with attitude prior information over time. One can observe that the probability varies with the change of the satellite constellation, and can be significantly reduced by the magnetometer-based attitude information.  
\begin{table}
\begin{tabular}{l l} \hline \hline simulated measurements & single frequency double difference \\  & pseudoranges and carrier phase measurements \\  & on L1 (\(f_{c}=1575.42\) MHz) of 27 Galileo satellites \\  & using nominal Walker constellation [12] \\  & (satellite altitude: 23, 222 km, orbital inclination: 56\({}^{\circ}\)) \\ \hline receiver position & longitude \(\lambda=11.568578^{\circ}\) E, latitude \(\varphi=48.150889^{\circ}\) N \\ baseline vector & length of 1 m, random attitude angles \\ noise statistics & phase noise: \(\sigma_{\varphi}=2\) mm \\  & code noise: \(\sigma_{\rho}=2\) m including multipath \\ \hline accuracy of prior information on baseline length & \(\sigma_{l_{tr}}=2\) cm \\ \hline accuracy of magnetometer based attitude information & variable accuracies for both heading and pitch angles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated Galileo double difference measurements.  
Figure 2: Benefit of magnetometer-based prior attitude information for single epoch integer ambiguity fixing: the accuracy of the prior heading information is shown on the \(x\)-axis and the accuracy of the prior pitch information is provided in the legend.
## 3 Experiments
### Tests on Real Ultrasound Images
The merits of our method can be summarized, as follows: (1) Our method employs statistic-based local processing, but does not destroy the local correlations of the details, as superpixel segmentation can effectively maintain local structures [31]. This suggests a new way for local statistic methods to provide reasonable smoothing for further processing. (2) In contrast to those traditional local methods, which may over-smooth details, a HVS-inspired image processing strategy is specifically designed in our model, in order to further compensate for and recover the image details. In particular, the center-surround opponent RF structure can effectively filter out the noise and preserve the wanted details. In addition, the ON and OFF pathways separately process the bright and dark parts, which helps to simultaneously recover the details in both the bright and dark regions of the UIs. Please note that, differing from normal noise reduction tasks, some \"speckle noise\" in the ultrasound images are meaningful and are useful in clinical diagnoses. For example, liver fibrosis due to viral infection or alcohol abuse will cause the lobule to form a fibrous septum. This will lead to inhomogeneous ultrasound waves when travelling through the abnormal liver parenchyma, which will form a clinically meaningful speckle pattern in the UIs [32]. So, the compensation strategy we introduced is very important for effective speckle reduction in real applications. Considering that superpixel segmentation is a key step in our proposed flowchart, our future works will be focused on improving its effect when applied to the specific task of UI despeckling. For example, besides the median filtering we adopted in this work before superpixel segmentation, other de-noising methods can be used, in order to achieve robust superpixel segmentation in UIs containing high-level noise. In addition, among many other superpixel segmentation methods, we can replace the SLIC method with modified superpixel segmentation methods, which may show better speckle noise tolerance abilities [31]. Conceptualization, Y.C., M.Z., and K.-F.Y. ; Funding acquisition, H.-M.Y. and Y.-J.L. ; Methodology, Y.C. and M.Z. ; Software, M.Z. and K.-F.Y. ; Supervision, H.-M.Y. and Y.-J.L. ; Validation, Y.C. and K.-F.Y. ; Writing--original draft, Y.C. and M.Z. ; Writing--review and editing, H.-M.Y., Y.-J.L., and K.-F.Y. This research was funded by the National Natural Science Foundations of China under grants 61773094 and 61703075. The work was also supported by the Sichuan Province Science and Technology Support Project under grants 2017 SZDZX0019 and 2017Y0249. The authors declare no conflict of interest.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### The Parameters for Calculating the Capabilities of the Ground Station
#### 4.2.4 The Communication Capability of the Ground Station System  
For the ground station to successfully decode the signal, the power received by the ground station (\(P_{r}\)) must be greater than the sensitivity. The calculation of the \(P_{r}\) depends on several factors, as shown in Figure 4, which can be presented in the following equation:  
\[P_{r}=EIRP+G_{r}-L_{p}-L_{a}-L_{ta}-L_{ra} \tag{7}\]  
where \(EIRP\) is the effective isotropically radiated power, \(G_{r}\) is the gain at the receiver, \(L_{a}\) is an attenuation in the atmosphere, \(L_{ta}\) are losses associated with the transmitting antenna, \(L_{ra}\) are losses associated with the receiving antenna, and \(L_{p}\) is the free space path loss. Additionally, the system may incur other losses during signal transmission. The equation can specify other loss values than those shown here.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 5 Case Study
The distribution of the costs can be calculated using the generated failure times of the bootstrap approach in the general method. Therefore, the \(P_{\text{Is}}\) of the EoL test can be shown with regard to the test costs; see Figure 18.  
It can be seen that a sample size of \(n=48\) has \(P_{\text{Is}}=80.35\) % and costs EUR 10.98 million for the uncensored EoL test. Therefore, it is the most efficient test in terms of reliability demonstration. Alternatively, in order to use the whole budget, an EoL test using \(n=56\)  
Figure 13.0 \(P_{\text{Is}}\) of the SR test using a lifetime ratio for reliability demonstration of the battery and the corresponding required sample size \(n\). Figure 13.1 \(P_{\text{Is}}\) of the uncensored EoL test with regard to the median of test costs, calculated using the general method. samples yields an even greater value of \(P_{\rm{bs}}=84.5\,\%\), while costing EUR 12.81 million. Due to the high value of the Weibull shape parameter of \(b=9.6\), the total test costs do not scatter to a great extent for the uncensored EoL test with a standard deviation of \(\sigma=\) EUR 195,000 (\(n=48\)) and \(\sigma=\) EUR 215,000 (\(n=56\)). Here, the SR test is unsuitable due to a value of \(P_{\rm{bs}}<50\,\%\), however, it costs significantly less than the EoL tests, with a total test cost of EUR 7.29 million. Only through objective assessment using the \(P_{\rm{ts}}\) can the risk of a failing test be acounted for in the decision-making process of reliability test planning. In 62 % of cases, the EUR 7.29 million of the SR test would not suffice for reliability demonstration, as failures would occur during testing.
## 3 Results
### Statistical Analysis  
The results obtained using a PCA explained 78.19% of the total variance, with the F1 axis accounting for 53.10% and the F2 axis for 25.09% of the total variance (Figure 8). All variables were positively correlated with the positive F1 axis except the ratio Phaeo/Chl-\(a\) and, to a lesser extent, \(\alpha\)PSII that was negatively correlated with F1. Cell mL\({}^{-1}\), time, and OD variables were positioned in the first quadrant, while Chl-\(a\), cell size, Fv/Fm, and Treatment showed a negative correlation with F2 axis, being positioned in the second quadrant. The Phaeo/Chl-\(a\) ratio was instead positioned in the third quadrant, almost overlapping the negative F2 axis, while \(\alpha\)PSII was positioned in the fourth quadrant with a higher correlation with the positive F2 axis. To investigate the influence of bicarbonate addition on the considered variables, we performed a PCA, coloring the observation by treatment, with ellipses of confidence of 95%. The light blue point and line represent the 1 g L\({}^{-1}\) treatment; the yellow color represents 2 g L\({}^{-1}\) treatment, the purple 3 g L\({}^{-1}\) treatment, and the dark blue the control one. The biplot graph shows clear differences between the control and all bicarbonate treatments, with ellipses highlighting the effect of bicarbonate addition already at low concentrations and control treatment, clearly different from others. Moreover, the amplitude of the ellipses increased with bicarbonate addition.  
Figure 7: Microbial contamination reduction in _Chlorella sorokiniana_ cultured with 0 or 3 g L\({}^{-1}\) of sodium bicarbonate. Analysis performed three times, after 0, 3, and 48 h after bicarbonate addition.
## 4 Proposed Scheme
### Message Signing and Verification Phase  
After completing the login phase, vehicle \(V_{i}\) first computes signing keys. Then, vehicle \(V_{i}\) sends its pseudonym ID, the message, and the respective message signature to its neighboring vehicles. Upon receiving the message-signature tuple, the receiver must check its message signature before accepting the messages. The message signing and verification processes are explained separately in subsequent subsections.
## 5 Limitations and Future Direction of the Research  
There are some limitations in this study. The first is the accuracy of CNES real-time orbit and clock products. If the accuracy of real-time orbit and clock products can be improved, the positioning accuracy of real-time PPP and the accuracy of water vapor inversion can be further improved. Secondly, in this study, the IF combination is used for the real-time PPP algorithm. Hong Kong is located in low latitudes. In low latitudes, the ionospheric content is higher than that in high latitudes. When the IF combination is adopted, it is difficult to eliminate the influence of the higher-order term of the ionospheric delay, which increases the convergence time of real-time PPP and has a certain impact on the positioning accuracy. In future research, higher-precision real-time orbit and clock products will be studied to improve the positioning performance of real-time PPP. A real-time PPP algorithm suitable for low-latitude areas is explored to avoid the influence of ionospheric delay on real-time PPP.
## 3 Results and Discussion  
### Synthesis of PS-Bound Lithium Salts  
As outlined in Figure 1, the synthesis began with the preparation of crosslinked polystyrene nanoparticles following the method of [PERSON] et al. [22]. Chlorosulfonation of the PS nanoparticles was successfully carried out with a minor modification of a reported procedure [22]. The new TFSI-like PS-bound lithium salt, PSTFSILi, was synthesized in a single-step by reacting the chlorosulfonated PS nanoparticles with CF\({}_{3}\)SO\({}_{2}\)NH\({}_{2}\) in the presence of LiOH. The incorporation of a similar lithium salt moiety (-SO\({}_{2}\)NLiSO\({}_{2}\)CF\({}_{3}\)) on a benzene ring has been previously reported, but involved three steps starting from the benzene sulfonyl chloride analog [23].  
The aforementioned one-step synthesis of the lithium sulfonamide derivative was successfully employed to prepare the other new PS-bound lithium salt, PSPhSILi (Figure 2). The special feature of this solid-liquid phase synthesis included a very simple work-up (filtration and washing).  
Figure 1: Synthesis of PSTFSILi.  
The final new PS-bound lithium salt, PSDTTOLi, was prepared in three steps: (1) coupling of 1,3-dithiane with PS-SO\({}_{2}\)Cl using the standard BuLi activation protocol; (2) oxidation of thioether groups to sulfones using hydrogen peroxide; and (3) lithiation using LiOH (Figure 3).
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 5 Kubernetes Vulnerabilities and Kubernetes Penetration Testing
### Vulnerability Databases  
Vulnerability databases are crucial in contemporary cybersecurity frameworks, especially for cloud-native environments like Kubernetes. These archives methodically document known security vulnerabilities, frequently outlining the technical characteristics of each defect and its possible effects on systems. They also suggest remediation measures such as patches or configuration adjustments. Each item is generally allocated a unique identity, such as a CVE (Common Vulnerabilities and Exposures) ID, to guarantee standardized monitoring throughout organizations and tools [30]. Notable platforms comprise the National Vulnerability Database (NVD), overseen by NIST, which enhances CVE entries with scoring data and metadata via the Common Vulnerability Scoring System (CVSS) [31].  
Vulnerability databases are essential for Kubernetes and other containerized systems, aiding in patch priority, risk assessment, and regulatory compliance. Nevertheless, dependence on these databases presents its difficulties. Research has revealed disparities among databases such as NVD, OSVDB, and others, particularly in severity scoring and affected software versions [32]. These discrepancies can result in differing risk evaluations and influence mitigation strategies. Furthermore, the imperative for patch creation, especially in high-severity situations, may engender new vulnerabilities if not rigorously verified, further complicating security management [33]. Access to comprehensive vulnerability data may differ based on user responsibilities or corporate security clearances, affecting repair tactics and response times.  
Notwithstanding these issues, vulnerability databases are fundamental to proactive security tactics. They consolidate and distribute threat knowledge, optimize security automation, and facilitate manual and AI-assisted vulnerability triage procedures. Their ongoing advancement--via enhancements in data quality, machine-readable formats, and AI-augmented analysis--is essential for safeguarding Kubernetes deployments and broader software ecosystems [30; 34].
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 3 Results
### Comparison of Algorithms' Performance  
All the parameters gathered in Section 3.3 were utilized to train the optimal ML model for the fault diagnosis of RPM equipment. The training dataset, along with these parameters, was used in the training of the optimal machine learning model. Following the construction of the model, it was assessed on a testing dataset using the confusion matrix approach, which compared the actual and predicted conditions of the railway point machine. Figure 8 depicts the performance of the optimal model in terms of each algorithm.  
According to the results in Figure 8 and using Equation (1), the accuracy of the optimal ML model was 84% for the kNN algorithm, 87% for the LR algorithm, 94% for the SVM and ANN algorithms, and 100% for the DT and RF algorithms. The LR and kNN algorithms achieved testing accuracy values of less than 90%, whereas the SVM, DT, RF, and ANN algorithms achieved testing accuracy values higher than 90%, indicating that they performed well in the fault diagnosis of railway point machines, especially the DT and RF algorithms, which achieved testing accuracy values of 100%.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ML Algorithm** & **Hyperparameters** & **Train Accuracy** & **Validation Accuracy** \\ \hline LR & C = 7 & 88\% & 88\% \\ kNN & n neighbors = 1 & 100\% & 84\% \\ SVM & kernel = rbf, C = 7, gamma = 0.1 & 100\% & 93\% \\ DT & criterion = gini, maximum depth = 2 & 100\% & 100\% \\ RF & criterion = gini, n estimators = 4 & 100\% & 100\% \\ ANN & configuration for layers and neurons = [10 7 3], batch size = 32, epochs = 1414 & 93\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of grid search 5-fold cross-validation for the six ML algorithms.  
Figure 8: Confusion matrices for the six ML algorithms: (**a**) LR, (**b**) kNN, (**c**) SVM, (**d**) DT, (**e**) RF, (**f**) ANN.
## 3 Wide Tearing Modes
### Island Width Estimate  
The DBS data from Figure 6 were used to establish the area of tearing mode development and estimate the magnetic island width. The probing beam trajectories and magnetic flux surfaces for discharge #40515 are presented in Figure 7. The TM was detected using channels with probing frequencies of 39, 50, 55 and 60 GHz. The area between the cut-offs of the channels where the TM was observed is colored blue in Figure 7. Additionally, the area between the 60 and 65 GHz frequency cut-offs, as well as outside the 39 GHz frequency is colored yellow to indicate the area where the TM could potentially have developed;  
Figure 5: The Globus-M2 plasma parameters for discharge #40515: (**a**) plasma current \(\rm I_{p}\), (**b**) electron density \(\rm n_{e}\), (**c**) \(\rm D_{x}\) signal, (**d**) magnetic probe signal \(\rm U_{MP}\).  
however, no DBS data were available to confirm this for discharge #40515. The magnetic surface \(q=2\) is introduced to Figure 7 in bright red; it is located in the center of the blue area where the TM was detected using DBS.  
As the TM was found on several DBS channels, it has been dubbed the wide tearing mode. We may assume that the island width is limited by the cut-off positions of the 65 and 39 GHz frequencies. Using the cut-off radii values we can estimate the island width to be 8-10 cm.  
Figure 6: Spectrograms of the DBS phase derivative for discharge #40515.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 2 The Electric System of El Hierro
### The Electric Demand  
The hourly electrical demand data of El Hierro was obtained from the website of the Spanish electricity market operator (Red Electrica de Espana--REE) [45]. The data for the island is only available from 1 January 2014, onwards on this site. However, if older annual demand data is considered [24] (Figure 1), a preliminary analysis reveals that the island's electrical demand has remained quite stable at about 40-50 GWh a year for nearly two decades, even though a slight upward trend could be considered, from less than 40 GWh in 2005 to nearly 50 GWh. The sharpest increase took place between 1985 and 2005, when electricity demand went from less than 10 GWh in 1985 to stabilization at around 40 GWh in 2005. This tendency is also evident in the electrical energy output curve shown in Figure 1, in which the evolution from 2010 to 2022 shows no significant variations.  
El Hierro has the mildest climate among the Canary Islands, with temperatures remaining relatively stable throughout the year. Winter temperatures generally range from 13-19 \({}^{\circ}\)C, while summer temperatures range from 24 to 25 \({}^{\circ}\)C. The climate does not significantly affect electricity demand, but demand patterns are strongly influenced by tourism (Figure 2a). Winter months tend to have the lowest demand, while summer months (especially July, August, and September) experience the highest demand, particularly in the afternoons and evenings. The demand curve typically shows two peaks: one about  
Figure 1: Historical evolution of the electric demand curve of the El Hierro island.  
midday and another towards sunset, with lower consumption at night and more stable levels during the day. These behaviors show the seasonal fluctuations caused by the great weight of the island's vacation sector, with higher summer demand and lower winter demand (Figure 2b).  
As displayed in Table 1, the renewable energy contribution grew sharply from practically zero to approximately 50%, driven by the full operation of the Gorona del Viento plant. The lower renewable generation in 2014 and 2015 was due to the plant not being completely operational. From 2016-2017 onwards, its contribution has become stable at around 50%, though these quantities are significantly below the renewable contribution percentages defined in the project (73.4%).  
Figure 2: El Hierro demand curves for year 2023: (**a**) two-dimensional graph (days and hours) of demand for the 12 months; (**b**) hourly demand profiles for representative days in each season.  
If the hourly demands shown in Figure 2a are grouped in a histogram (Figure 3), it appears that the demands are between approximately 3 and 8 MW, with a maximum demand of 7.71 MW. However, the greater predominance of values is between about 5 and 7 MW. The range with the highest absolute frequency is between 6.25 and 6.5 MW with around 1200 h, although the range 6.0-6.25 is very close, while the average demand is around 5.75 MW.