esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 2 Methodology
### Coastal Type Classification  
A further key element of the MapItaly program is to provide SAR images taken from slightly different observation directions, thus allowing SAR interferometric (InSAR) analyses, based on the coherent nature of SAR imaging.  
InSAR analysis has been applied in this study, since the estimation of the interferometric coherence through ergodic estimators can provide an additional information layer to be used for remote classification of the coastal morphology. This allows in turn: (i) the automatic identification of rocky and sandy coastal areas, (ii) the adaptive and optimal configuration of the segmentation algorithm, thus ensuring satisfactory performances regardless of the coastal type of the investigated areas.  
A further preliminary step consists in the coastal type classification, which is recommended since it can enhance the land-sea boundary extraction. The next processing step, consisting in the segmentation of the SAR images, must be indeed properly and differently configured for sandy and rocky segments of the coast.  
The COSMO-Beach processing chain automatically detects the shoreline type, by taking into account both amplitude and interferometric coherence information: high coherence and signal amplitude values allow to classify image pixels as \"rock\" or \"coastal structure\", while the pixels with lower values are recognized as \"sand\". The selected SAR dataset is first co-registered with sub-pixel accuracy [65] and an average coherence map is hence computed from multiple interferometric pairs. Optimal co-registration results are obtained by properly selecting a reference image named Master. More specifically, the perpendicular baselines and dates of the available acquisitions are analyzed in order to identify the acquisition closer to the \"mass center\" of the point cloud representing the various images in the space of temporal and perpendicular baselines. This selection is performed automatically although it can be changed by the user in those cases in which the automatic result is not adequate due to the presence of strong artifacts (e.g., atmospheric or focusing artifacts).  
Furthermore, SAR amplitude images are radiometrically calibrated and despeckled. In order to mask out ambiguities and artifacts, apodization filters are also recommended [66].  
Available orophotos are, hence, used for the tuning of the amplitude and coherence thresholds to be configured in the coastal classification process.
# Optimizing Pretrained Autonomous Driving Models Using Deep Reinforcement Learning
## 5 Experiments and Results
### Limitations  
Although CILRLv3 delivers clear gains in sample efficiency and Catastrophic Forgetting resistance, it inherits several constraints that should be acknowledged. First, it freezes the weights of the original Transformer encoder included in the Pretrained Policy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Score** & **CIL++** & **CILRL++** & **CILRLv3** \\ \hline Avg. Driving Score (\%) & 2.593 & 10.019 & **12.726** \\ Avg. Route Completion (\%) & 10.932 & 14.484 & **16.843** \\ Avg. Infraction Penalty & 0.404 & **0.654** & 0.646 \\ Collisions with Vehicles & **0.0** & **0.0** & **0.0** \\ Collisions with Pedestrians & 256.214 & 52.842 & **47.692** \\ Collisions with Layout & 411.010 & 255.232 & **229.47** \\ Red Light Infractions & 9.340 & 7.975 & **6.864** \\ Stop Sign Infractions & 5.767 & **0.0** & **0.0** \\ Off-Road Infractions & 253.666 & 186.474 & **181.377** \\ Route Deviations & **0.0** & 76.737 & 65.212 \\ Route Timeouts & **0.0** & 0.0 & **0.0** \\ Agent Blocked & 398.996 & 222.645 & **211.751** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation Table for CIL++ and CILRL++ [7], as well as the proposed CILRLv3, using the CARLA Leaderboard metrics.  
Figure 9: Visualization of the learning curve of the fusion coefficient \(\alpha\) during five different runs.  
Branch. Consequently, gradient-based interpretability techniques, such as Grad-CAM, cannot reveal improved task-specific attention patterns beyond those already present in the CIL++ model. By contrast, CILRL++, which fine-tunes the entire policy, naturally updates transformer representations and therefore yields better visual explanations. A straightforward method to address this issue would be to equip the Encoder Branch with its own Transformer encoder. However, this modification would increase both wall-clock training time and GPU memory requirements, eliminating a key advantage of CILRLv3.  
A second practical limitation is that, while each optimization step is faster than in CILRL++, the agent still demands billions of environment steps to attain peak performance in complex driving tasks. Such large-scale interaction remains costly in realistic simulators like CARLA, so additional sample efficiency enhancements are still required.
## 4 Experimental Validation
### CASE 2: Fault Diagnosis of a Roller Bearing Unit
#### 4.2.2 Comparative Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Fault (Yes/No)** & **Fault Location** &
\begin{tabular}{c} **Fault Diameter** \\ **(Inches)** \\ \end{tabular} & **Abbreviation** \\ \hline Class 1 & No & / & / & Nor \\ Class 2 & Yes & Inner ring & 0.007 & IR7 \\ Class 3 & Yes & Rolling element & 0.007 & RE7 \\ Class 4 & Yes & Outer ring & 0.007 & OR7 \\ Class 5 & Yes & Inner ring & 0.014 & IR14 \\ Class 6 & Yes & Rolling element & 0.014 & RE14 \\ Class 7 & Yes & Outer ring & 0.014 & OR14 \\ Class 8 & Yes & Inner ring & 0.021 & IR21 \\ Class 9 & Yes & Rolling element & 0.021 & RE21 \\ Class 10 & Yes & Outer ring & 0.021 & OR21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ten working conditions of rolling bearings. Figure 9: Time-domain waveforms of the nine types of working fault. As shown in Table 5, under the operating conditions of a motor load of 2 horsepower and a motor speed of 1750 rpm, the proposed method achieves the highest accuracy across all metrics, demonstrating its superior diagnostic performance. From the results of Method 1 and Method 2, it is evident that the number of EMD layers has a significant impact on diagnostic accuracy, with diagnostic performance improving as the number of decomposition layers increases. Notably, when the decomposition layers are insufficient, traditional feature extraction methods yield suboptimal diagnostic results. For instance, with only 3 decomposition layers, the average classification accuracy of Method 1 and Method 4 fails to reach 90%, making these methods impractical for real-world applications. The results of Method 2, Method 3, and Method 4 also show that when extracting detailed features using fuzzy entropy, an excessive number of EMD layers can lead to overfitting. For Method 3 and Method 4, the average accuracy drops by 0.12%, with the highest and lowest accuracy remaining largely unchanged. This suggests that attempting further feature extraction on entropy features at high decomposition levels increases the risk of overfitting. To address this, the proposed approach introduces IMFE as the feature extraction method, combined with LDA for dimensionality reduction, under low decomposition-layer conditions. Experimental results show that with three EMD layers, the proposed method achieves high classification accuracy, confirming its feasibility and effectiveness in practical applications.
# A Transformer-Less Buck-Boost Grid-Tied Inverter with Low Leakage-Current and High Voltage-Gain
## 2 Circuit Configuration  
The schematic of the investigated transformer-less buck-boost grid-tied inverter is illustrated in Figure 3. Two dual-switch buck-boost DC-DC converters with coupled inductors are connected in parallel and output unipolar half-waves respectively during positive and negative half-cycles. The dual-switch buck-boost converter for a positive half-cycle is mainly composed of the power switches \(S_{Bu1}\), \(S_{Bo1}\), the diodes \(D_{1}\), \(D_{3}\), and the coupled inductors \(L_{1}\), \(L_{3}\). The power switches \(S_{Bu2}\), \(S_{Bo2}\), the diodes \(D_{2}\), \(D_{4}\), and the coupled inductors \(L_{2}\), \(L_{4}\) form the dual-switch buck-boost converter for the negative half-cycle. The capacitors \(C_{f}\), \(C_{s}\) and the inductor \(L_{s}\) are the output low-pass filter. Since this paper focuses on the steady-state characteristics and feasibility of the proposed circuit structure, the input source is represented by an ideal voltage source to simplify the circuit.  
During the positive (negative) half-cycle and in the step-down mode, the switch \(S_{Bu1}\) (\(S_{Bu2}\)) performs high-frequency switching, and the power switch \(S_{Bo1}\) (\(S_{Bo2}\)) is maintained in the off state. When \(S_{Bu1}\) (\(S_{Bu2}\)) is turned on, energy is able to be directly delivered to the  
Figure 3: The schematic of the investigated transformer-less buck-boost grid-tied inverter.  
output terminal via the switch \(S_{1}\) (\(S_{2}\)). Its working principle is similar to a buck converter, resulting in greatly improving conversion efficiency.  
During the positive (negative) half-cycle and in the step-up mode, the switch \(S_{Bo1}\) (\(S_{Bo2}\)) performs high-frequency switching, and the power switch \(S_{Bu1}\) (\(S_{Bu2}\)) remains on. When \(S_{Bo1}\) (\(S_{Bo2}\)) is switched off, energy is able to be directly delivered to the output terminal via the diode \(D_{3}\) (\(D_{4}\)) and the switch \(S_{1}\) (\(S_{2}\)). Its working principle is similar to a boost converter, which can greatly improve conversion efficiency.  
In addition, the switch \(S_{1}\) (\(S_{2}\)) can provide a path for connecting the mains and the negative end of the input source, so that the leakage current of the PV array can be suppressed. The proposed transformer-less buck-boost inverter is appropriate for the PV grid-tied systems with wide-range voltage changes or high AC output-voltage because of its step-up and step-down capabilities. In addition, high-frequency switching is performed in only one MOSFET in each state, leading to low switching loss and high conversion efficiency.
## 2 Engineering Background and in Situ Monitoring Methods
### In Situ Monitoring Method of Surrounding Rock Pressure
#### 2.2.3 Principles for in Situ Monitoring  
During the in situ monitoring of the surrounding rock pressure, the following requirements must be met:  
1. Sensor installation. The pressure membrane of the pressure sensor must be in close contact with the rock surface. If the rock surface is uneven, leveling pretreatment should be carried out prior to installation. The steel frame should establish good contact with the pressure box to ensure proper stress transfer. The installation effect is shown in Figure 5.
2. Calibration and protection. After the installation of the pressure sensor, its identification numbers should be calibrated. Additionally, protective measures must be implemented for the data transmission lines to ensure uninterrupted data transmission, as illustrated in Figure 6.
3. Pre-installation debugging. Prior to installation, the pressure sensor should be thoroughly calibrated and de bugged. Following installation, the first set of pressure data should be recorded to confirm the system's functionality.
4. Data collection protocol. During data collection, special attention should be given to ensuring that the transmission wire is properly inserted into the acquisition instrument interface. Data should only be read once the values have stabilized, ensuring accuracy and reliability.
5. Data integrity. Data collection should occur once daily to maintain continuity and ensure the integrity of the dataset.
# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 3 Material and Methods
### Classification Algorithms  
In order to explore the best classification model, different ML approaches were evaluated: \(k\)-Nearest Neighbors (\(k\)-NN) [45], Support Vector Machine (SVM) [45], Random Forest (RF) [46], and Extreme Gradient Boosting (XGB) [47]. These algorithms were selected due to high predictive performance and broad use in related papers. Linear SVMs were used in Reference [22; 24; 25], while \(k\)-NN was used in Reference [48]. Considering ensemble methods, RF and XGB were evaluated in Reference [24; 49; 50].  
Most hyperparameters were kept with default values for the used implementation, which were scikit-learn[51] and xgboost[47] Python packages. Only the indispensable hyperparameters were chosen, such as \(k\) for \(k\)-NN and the kernel of SVM. For \(k\)-NN, the \(k\) value was set to 10 after an exhaustive grid search [52], while the distance metric was kept as the default Euclidean distance. On SVM, a linear kernel was used to evaluate the linear separability of the samples, and the other hyperparameters were kept with default values. Moreover, for RF and XGB models, where both algorithms make use of an ensemble approach, the number of subtrees that the model generates was set to 500 to get stable results.  
After extracting the feature vectors and removing the outliers, the four classification algorithms were tested through a \(k\)-fold cross-validation. This process consists in randomly splitting the dataset into \(k\) groups, holding out the first group for validation, and fitting the model on remaining \(k-1\) set [53]. A \(k=5\) was used, so for each iteration, 80% of the randomly sampled dataset was used for training and 20% for validation. This process was repeated 20 times, totaling 100 results for each algorithm.
## 3 Results
#### 3.4.1. [PERSON] et al. Occupational Effective Dose Estimation Algorithm [24; 25]  
This study used the [PERSON] et al. algorithm (based on double dosimetry) to estimate the effective occupational dose for staff during various steps of the \({}^{99m}\)Tc-MDP bone scintigraphy, and the results are presented in Table 2. The table outlines three critical metrics for each step: Hp(0.07) and Hp(10) dose equivalents and the corresponding estimated occupational effective dose derived from these values. For most steps of the bone scintigraphy procedure, including labeling, quality control, syringe preparation, patient administration,  
Figure 6: Comparison of actual occupational effective dose and Netherlands Commission on Radiation Dosimetry (NCS) algorithm. Results are presented as mean \(\pm\) standard deviation (\(n\) = 30), and results marked with * indicate a statistically significant difference (\(p<0.05\)), NCVS, [40].  
and patient discharge, the estimated occupational effective dose remains consistently low at 0.02 eV. The Padovani et al. algorithm offers a systematic method for estimating the occupational effective dose based on dosimeter readings, and the low effective dose across all stages (with a maximum of 0.03 eV) indicates that personnel involved in \({}^{99\text{m}}\)Tc-MDP bone scintigraphy are exposed to very low levels of radiation, well within the safety limits recommended by the ICRP.  
Overall, the Padovani et al. algorithm confirms that the effective occupational dose for personnel during \({}^{99\text{m}}\)Tc-MDP bone scintigraphy is minimal throughout the procedure and concurs with the actual occupational effective doses. The reliability and effectiveness of the Padovani et al. algorithm in estimating occupational radiation exposure are key findings of our study and support the conclusion that current safety measures are sufficient to protect staff. However, the slight increase in occupational exposure observed during the imaging step highlights the need for additional precautions during this phase to further mitigate potential risks.
## 2 Materials and Methods
### Modeling Methods and Model Evaluation  
In this study, 600 samples were divided into a calibration set and a prediction set at a ratio of 2:1 according to the Kennard-Stone method [41]. Therefore, the calibration set included 400 samples, and the prediction set sample included 200. Because the visible and near-infrared spectra constitute a wide-band response [42], the qualitative analysis of substances should be carried out using chemometrics and discriminant analysis of models (LDA, PCA-SVM, and PCA-GRNN) [43]. Different granulation levels of samples were set from none to greater than 75% granulation, and designated A, B, C, D, and E. The classification accuracy represents the percentage of the samples classified correctly of all samples, and this was used to estimate model performance [44]. The accuracy rate of a model (_ARM_) was defined as follows [12]:  
\[ARM(\%)=(\frac{\textit{number of accurate classified samples}}{\textit{number of total samples}})\times 100, \tag{12}\]  
When the numbers of positive and negative samples differ, confusion matrices can be used to evaluate different classification models, using information about correct classification and predicted categories [45]. For example, if Class A is identified as positive, then Class B, Class C, Class D, and Class E are defined as negative. The true positives (_TP_) are the samples that are positive and correctly classified as Class A. The true negatives (_TN_) are the samples that are negative and correctly assigned as negative. The false positives (_FP_) are the negative samples incorrectly classified as Class A. The false negatives (_FN_) are negative samples incorrectly assigned to other negative categories, such as if samples that should have been classified as Class B were incorrectly classified as Class C or Class D. Based on the model's confusion matrices, predictive performance includes classification accuracy (_CA_), classification sensitivity (_CS_), and classification specificity (_CSP_), as defined by Equations (13)-(15), respectively. Values of _CA_, _CS_, and _CSP_ that are closer to 1 suggest more accurate classification.  
\[CA=\frac{\textit{TP}+\textit{TN}}{\textit{TP}+\textit{TN}+\textit{FP}+\textit{ FN}}, \tag{13}\]  
\[CS=\frac{\textit{TP}}{\textit{TP}+\textit{FN}}, \tag{14}\]  
\[CSP=\frac{\textit{TN}}{\textit{TN}+\textit{FP}}, \tag{15}\]
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 1 Introduction
[PERSON] proposed a tight integrated vision/GNSS navigation system and introduced an integrity monitoring reset mechanism, which can eliminate the influence of undetected faults on the new reset detector [20]. [PERSON] et al. used ultra-wideband (UWB) technology to make up for navigation performance when GNSS is unreliable and discussed how to apply FDI technology to avoid fault anchors when employing UWB in disturbance environments [21]. [PERSON] et al. proposed a sensor verification method, which introduces the fault detection of a residual into the filter update in theory and improves the integrity of the integrated navigation system [22]. [PERSON] et al. studied the use of multiple sensors in different environments to ensure the required navigation performance and put forward a general method for integrity problems based on measurement rejection and position error representation [23]. However, the former methods mainly judge whether the measurement in the integrated navigation system is faulty. There is no effective fault detection and isolation method for the resilient architecture of graph optimization. An INS is a kind of autonomous navigation sensor, which is generally free from external interference. GNSS is vulnerable to electromagnetic interference, and the measurement may contain fault errors. To solve the fault problem of navigation equipment in the complex environment, a fault detection and isolation algorithm based on pseudo-range measurements is proposed. Compared with the traditional FDI method, this method can discern which satellite has a problem in pseudo-range and has better isolation efficiency. The contents of this paper are organized as follows. The system scheme is defined in Section 2.1. The factor graph fused method is introduced in Section 2.2. The integrity detection method is shown in Section 2.3. Simulation experiments and actual data experiments are presented in Section 3. Section 4 presents the discussions. Section 5 is the conclusion.
## 3 Optical Error Analysis
### Simulation Experiment Results
#### 4.2.5 Reflectivity Variation Error of FPI
The  
Figure 21: **(a1–d1)** The FPI reflectance spectra generated by curves 1–4 under PVs of 0%, 2%, 6%, and 10%, respectively. **(a2–a5)** Correspond to curve 1, **(b2–b5)** to curve 2, **(c2–c5)** to curve 3, and **(d2–d5)** to curve 4, illustrating the changes in the response functions of \(m=0\), 1, 2, 3 frequency-shift components due to reflectance variations. resulting super-resolved spectrum may exhibit distortions due to the reflectivity curve, but these can be corrected during spectral radiance calibration. The primary errors in FTIS include collimation errors, beam splitter reflectivity spectral errors, and mirror tilt errors. These errors do not affect the linear relationship and do not deviate from the fundamental MJJ-HI model. However, they introduce certain inaccuracies in the final calculated spectrum. Collimation errors cause wavenumber shifts and reduced spectral resolution, beam splitter errors lead to spectral distortions, and mirror tilt errors result in reduced coherence intensity. Notably, the mirror tilt errors in FTIS may interact with the non-parallelism errors in FPI, introducing an equivalent tilt angle for each frequency-shifted component in \(I_{2}\). This interaction not only disrupts the linear relationship in MJJ-HI but also causes the interferogram \(I_{2}\) to lose symmetry. Through simulation analysis, it has been determined that MJJ-HI maintains high inversion accuracy when the FWHM of the FTIS tilt error broadening function and the FPI non-parallelism error broadening function are both less than \(\lambda_{\text{min}}/4\). Under these conditions, the effects of these errors can be neglected.