esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data  
#### 2.2.1 Remote Sensing Data  
The PS satellite constellation comprises more than 180 satellites in orbit, allowing it to provide high-resolution data with daily global coverage [69]. For this study, cloud-free data from the PS satellite for February 2022 were collected. The images were a level 3B product that had undergone pre-processing, including radiometric and geometric correction [58]. They had a spatial resolution of 3m and consisted of four bands in the visible (blue, green, and red) and near-infrared regions of the spectrum. An image mosaic was created for six scenes of the PS image using QGIS 3.28 to fully cover the study area. Then, it was projected to Adindan Universal Transvers Mercator (UTM) Zone 37N.  
Figure 1: Map of Fagita Lekoma District.  
Vegetation indices (Appendix A), known for their capabilities in detecting and mapping the distribution of plant species, were selected from the Index Database ([[https://www.indexdatabase.de/](https://www.indexdatabase.de/)]([https://www.indexdatabase.de/](https://www.indexdatabase.de/)) accessed on 17 March 2023). Indices that have proven effective in characterizing various vegetation types were selected, particularly those sensitive to reflectance in the visible and NIR regions. These regions have been recognized as effective in discriminating commercial forest species [70]. The four spectral bands of PS and the thirty-one vegetation indices derived were tested to identify Acacia _decurrens_ plantation forest species.
# Chinese Character Component Deformation Based on AHP
## 4 Control Point Selection and Component Deformation  
The general model for affine transformations is :  
\[\left\{\begin{array}{l}X=a_{1}x+b_{1}y+c_{1}\\ Y=a_{2}x+b_{2}y+c_{2}\end{array}\right. \tag{1}\]  
Some rules for determining control points in the transformation of Chinese character components are proposed in [8; 17]. We summarized them and consider the process of control point selection as a multicriteria decision-analysis problem [25]. An effective way to solve this type of problem is the Analytic Hierarchy Process (AHP) [26]. This is a method to model and quantify the human decision-making process. The profound theoretical foundation and simple expression make it favored by many researchers [27]. It usually consists of three steps:  
1. Establishing the hierarchical structure model;
2. Hierarchical single ranking and consistency test;
3. Hierarchical total ranking and consistency test.  
Figure 8: Information about the Feature Point neighborhood.  
Figure 9: ORB and GMS have difficulty in this case because the color and texture features of Chinese characters are not obvious. Our method can perform accurate feature point detection and eliminate false matches with neighborhood information.
## 4 Parameter Identification  
### Flexure Spring Stiffness  
The flexure spring stiffness was measured from load-deflection tests for the calculation of stiffness coefficients in the EOM. Figure 3 shows the static load-deflection tests of the flexure spring for the PP and DP: (a) test spring manufactured from SK5, (b) measured force-displacement curves during consecutive pushes and pulls, and (c) a test rig using a lathe. A load cell measures the force applied to the spring, and a laser displacement sensor measures the displacement of the spring.  
As a result, the test spring deflects with maximum bidirectional deformations of \(\pm 15\) mm with proper linearity and negligible hysteresis, satisfying the piston's design amplitude of 12 mm. The measured spring stiffness is 42 kN/m. Note that the spring was designed through finite element stress analyses, which are omitted for brevity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**PP Mass (kg)**} \\ \cline{3-5}  & & **3.3** & **3.6** & **3.9** \\ \hline PP stiffness & 42 & Case 1 & - & Case 2 \\ (kN/m) & 84 & - & Case 3 (ref.) & - \\
126 & Case 4 & - & Case 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental design with parameters of PP mass and PP spring stiffness (DP mass of 1.64 kg and DP stiffness of 84 kN/m for all cases).  
Figure 3: Static load-deflection tests of flexure spring: (a) manufactured spring, (b) measured force–displacement during consecutive pushes and pulls, and (c) the test rig using a lathe.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 2 Direct Visual Odometry
### Image Warping
Due to the smoothness constraints imposed by the semi-global matching scheme, occlusions are filled by some non-zero value. We handle occlusions by right-left consistency check: The disparity map \(\mathcal{D}^{\ell,r}\) is computed first, then the second disparity map \(\mathcal{D}^{r,\ell}\) by reflecting the left and right images along the \(v\)-axis and using the right image in place of the left, and the left in place of the right. The final disparity map \(\mathcal{D}\) is equal to \(\mathcal{D}^{\ell,r}\) when the absolute difference between \(\mathcal{D}^{\ell,r}\) and \(\mathcal{D}^{r,\ell}\), evaluated at a position vector \(\mathbf{p}\), does not exceed some threshold \(\delta\). Otherwise, \(\mathcal{D}(\mathbf{p})\) takes zero,  
\[\mathcal{D}(\mathbf{p})= \begin{cases}\mathcal{D}^{\ell,r}(\mathbf{p})&\text{ if }|\mathcal{D}^{\ell,r}(\mathbf{p})-\mathcal{D}^{r,\ell}(\mathbf{p})|\leq \delta\\ 0&\text{otherwise}\end{cases} ,\quad\forall\mathbf{p}\in\Omega\,. \tag{12}\]  
In this work, we use \(\delta=1\). We do not perform sub-pixel refinement to interpolate disparity values. We simply use positive integers to represent the disparity map, \(\mathcal{D}:\Omega\to\mathbb{N}\). The depth estimate, \(z\), for the pixel \(\mathbf{p}\) is given via triangulation,  
\[z(\mathbf{p})=\frac{fb}{\mathcal{D}(\mathbf{p})}\,, \tag{13}\]  
where \(f\) is the focal length and \(b\) is the baseline distance of the stereo rig in centimeters. This assumes that the stereo camera is calibrated and row-aligned. We follow methods presented in [53] for camera calibration.
## 7 Risk Assessment
### Bioassays  
In vitro bioassays are increasingly integrated into hazard characterization to address data gaps in NIAS risk assessments. For example, [PERSON] et al. [40] prioritized 16 NIAS found in epoxy coatings and evaluated their mutagenic and genotoxic potential. They synthesized an oligomer based on tetramethyl bisphenol F-based diglycidyl ether (TMBPF-DGE) resin and hydroquinone and tested it using two in vitro assays: a bacterial reverse mutation test (Ames test) and an in vitro micronucle test with human lymphocytes. The Ames test showed no mutagenic activity, and the micronucle test revealed no genotoxicity. These results suggested that the TMBPF-DGE+HQ oligomer did not pose a genotoxic or mutagenic risk, warranting further in vivo evaluations. Additionally, the study also assessed the overall migration of substances from the V70 coating using a modified Ames test, finding no mutagenicity in the migration products. The authors acknowledged the limitations of these bioassays, including their focus on a limited number of toxicological endpoints, challenges in testing complex mixtures, the lack of full validation for some assays, and the inability of in vitro assays to fully replicate in vivo conditions, as well as concerns regarding sensitivity and specificity, which may lead to false negatives or missed endpoints relevant to long-term exposure. Nevertheless, bioassays remain a valuable tool to identify potential hazards, prioritize substances for further testing, and complement chemical analysis in the safety evaluation of FCMs.  
These methods complement the TTC approach by providing mechanistic insights; however, extrapolating in vitro responses to actual human health risks remains a significant challenge. Overall, the risk assessment of NIAS remains a multifaceted challenge that requires balancing conservative default approaches like TTC with emerging toxicological evidence. While tools such as the Cramer classification and SMLs provide useful regulatory benchmarks, they have limitations, particularly in addressing genotoxicity, cumulative exposures, and the unique hazards posed by recycled and bio-based materials. Advancing NIAS risk assessments will require the development of dedicated toxicity databases, the integration of in vitro and in silico tools, and the implementation of probabilistic models to better capture exposure variability. Coordinated efforts between regulatory bodies, industry stakeholders, and academic researchers will be essential to bridging existing gaps and safeguarding public health in the context of increasingly complex FCM compositions.
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 4 Simulation Study
### Case II: Simulation for Multi-Frequency Constraints Control  
Then, a simulation for a multi-frequency AVC with an output constraint control is studied at sample time \(T_{s}=0.001s\). A disturbance signal that contains four frequency components has been chosen: \(f_{1}=22\) Hz, \(f_{2}=31\) Hz, \(f_{3}=55\) Hz, and \(f_{4}=120\) Hz. Their amplitude and initial phase are set as [1, 5, 5, 5] (unit: \(m/s^{2}\)) and [2*\(f_{1}\), 2*\(f_{2}\), 2*\(f_{3}\), 2*\(f_{4}\)] (unit: degree), respectively. A white noise (RMS = 0.1 \(m/s^{2}\)) is added to the disturbance signal, as previously. The adaption step size is set to \(\mu=0.5\times 10^{-5}\). The output constraint vector \(A_{u,\max}\) is set as \(A_{u,\max}=\) {10, 0.4, 0.2, 0} (unit: V). Thus, the goals of the simulation can be explained as: (a) rejecting the low-frequency component \(f_{1}=15\) by setting the constraint \(A_{u1,\max}=10\) V (means no constraint applied to \(f_{1}\)); (b) supposing that \(f_{2}=31\) Hz is a key frequency and allocating more of the actuator's power at \(f_{2}\) by setting \(A_{u2,\max}=0.4\) V; (c) supposing that \(f_{3}=55\) Hz has a small impact and allocating less power by setting \(A_{u3,\max}=0.2\) V; (d) supposing that the frequency component \(f_{4}=120\) Hz is not concerned and then leaving it alone by setting \(A_{u4,\max}=0\). To compare and illustrate the effect of compensations, the normal NFXLMS algorithm and the NFXLMS with compensation \(\mathbf{P}_{s,com}\) only are accomplished. The results are shown in Figures 8-11.  
Figures 8 and 9 shows the time curves of the residual vibration \(e(n)\) and output signal \(u(n)\), respectively. The effect of the amplitude compensation is illustrated by comparing (b) with (c) in view of the control result and output in Figures 8 and 9. In Figures 8d and 9d, the proposed algorithm has kept the output signal \(u(n)\) and residual vibration \(e(n)\) to a certain level. Figure 10 shows the time curves of all the four sub-frequency component's amplitudes. It can be found that four sub-frequency components achieved almost the same growth rate after compensating and that the constraints had been well implemented. Thus, the control goals according to \(A_{u,\max}\) have been achieved. Figure 11 shows the spectrum of the residual vibration \(e(n)\) after the AVC process at 90-100 s. In Figure 11b, there are still two small-scale frequency components, \(f_{2}=22\) Hz and \(f_{4}=120\) Hz, that have not been  
Figure 11: The spectrum of the residual vibration \(e(n)\) after the AVC process at time intervals of [90s, 100s] using different variants of NFXLMS algorithms.  
Figure 10: The convergence curve of each sub-frequency’s amplitude in vector \(A_{u}\).  
totally rejected due to the low convergence rate, while the NFXLMS with compensation has rejected all the frequencies to zero when compared with Figure 11c. In Figure 11d, almost no harmonics are introduced to the control system, as in the single frequency case.
## 6 Conclusions  
The features of the LBS distribution and certain associated inferential methods have been discussed in this paper. The PDF of the LBS distribution might take on many shapes due to the inclusion of the shape parameter. It has either a decreasing or a unimodal HF function. When there is no contamination in the data, all approaches perform well, according to the MC simulations study. The contamination in the data may have an impact on the shape and scale parameters, with the exception of two-tailed contamination, which has no impact on the scale parameter. Finally, based on the results of the real data analysis, it is concluded that the LBS distribution, as compared to the well-known distributions, may provide a better fit to real-life data. In practice, data contamination is not the only issue researchers encounter. Another significant research challenge that needs to be addressed in future studies is data censoring, since it negatively impacts estimation efficiency and robustness. Comparing the examined estimators to Bayesian estimation in terms of performance is the last study area that one may consider.  
Under the idea and supervision of the corresponding author [PERSON], [PERSON] carried out the numerical calculations. Both [PERSON] and [PERSON] contributed to the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Q1** & **Q2** & **Q3** & **Mean** & **Kurtosis** & **Skewness** & **SD** \\ \hline Sample & 54.7500 & 69.1896 & 112.7500 & 99.81944 & 5.6144 & 1.7962 & 81.1180 \\ \hline Exponential & 28.7163 & 70 & 138.3791 & 99.81944 & 9 & 2 & 99.8194 \\ \hline Rayleigh & 97.2947 & 151.0239 & 213.5801 & 113.6745 & 3.2451 & 0.6311 & 84.0330 \\ \hline Weibull & 45.1928 & 84.9660 & 139.7509 & 100.8287 & 4.8757 & 1.2081 & 73.3186 \\ \hline BS & 46.6880 & 77.5278 & 128.7388 & 99.9169 & 9.8454 & 2.0774 & 77.3171 \\ \hline LBS & 48.3869 & 76.0104 & 119.4038 & 97.5030 & 10.5492 & 5.6765 & 81.7657 \\ \hline \hline \end{tabular}
\end{table}
Table 6: EDA outcomes based on Cavia Porcellus data.  
Figure 10: The fitted PDFs based on Cavia Porcellus data.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: The authors express their sincere gratitude to the anonymous reviewers and the editors for providing useful suggestions on earlier versions of this manuscript which resulted in this improved version.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 3 Fault Handling Method and Theoretical Discussion
### Time (\(t_{3}<t<t_{4}\))  
At time \(t_{3}\), as the current in the power electronic branch is about to reach its carrying threshold, the microcontroller sends a signal to turn off the thyristor and MOSFET in the power electronic branch. Similar to the principle of the steady-state branch, after the MOSFET is turned off, the thyristor follows by turning off. The fault current surges into the buffer circuit's buffer capacitor in the SM, causing the capacitor to charge rapidly, as shown in Figure 3d. Ignoring \(R_{line}\) and \(R_{F}\), \(U_{L1}\) is the same as the previous level Equation (3). According to [PERSON]'s voltage law:  
\[L_{\sum 1}C\frac{d^{2}u_{c}}{dt^{2}}+u_{c}=U_{dc} \tag{5}\]  
In the equation: \(L_{\sum 1}=L_{line}+L_{s1}+L_{s2}+L_{1}\). \(C\) is the equivalent capacitance value of all the buffer capacitors in the sub-modules. Substituting the initial values \(u_{c}(t_{3})=0\) and \(i_{z}(t_{3})=I_{3}\) into Equation (5), we can obtain:  
\[\left\{\begin{array}{l}i_{c}(t)=\sqrt{\frac{C}{L_{\sum 1}}}U_{dc}\sin\frac{1}{\sqrt{L_{\sum 1}\zeta}}(t-t_{3})\\ +I_{3}\cos\frac{1}{\sqrt{L_{\sum 1}\zeta}}(t-t_{3})\\ u_{c}(t)=U_{dc}-U_{dc}\cos\frac{1}{\sqrt{L_{\sum 1}\zeta}}(t-t_{3})\\ +\sqrt{\frac{L_{\sum 1}\zeta}{\zeta}}I_{3}\sin\frac{1}{\sqrt{L_{\sum 1}\zeta}}(t-t_{3}) \end{array}\right. \tag{6}\]  
Analyzing Equation (6), it can be known that the fault current flowing through the capacitor is proportional to the capacitor capacity. The larger \(C_{s}\) is, the higher the amplitude of the fault current will be. At the same time, an increase in the capacitance value will limit the rise rate of the fault current and will also correspondingly extend the fault clearance time.
## 3 Model and Methods  
### Workflow  
To achieve high-resolution bathymetric inversion imagery, the bathymetric mapping in this study is divided into two parts. First, we employed a method that combines active and passive remote sensing data to fuse ICESat-2 data and Sentinel-2 data, resulting in bathymetric inversion at a spatial resolution of 10 m, which serves as prior data. Subsequently, to obtain high-resolution bathymetric results, it is necessary to combine the data with high-resolution fusion data from GF-1. After preprocessing the raw GF-1 data, the Nearest-neighbor diffusion-based pan-sharpening (NNDiffituse) method was employed to achieve a high-resolution fusion image at a resolution of 2 m. This image was then resampled to a spatial resolution of 10 m. A quadratic polynomial ratio model (QPRM) was fitted to derive the transformation equation, which was applied to the fused image based on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Lat Lon** & **Satellite ID** & **Image ID** & **Imaging Time** \\ \hline Vuthovutho Island & 16.09\({}^{\circ}\)S 179.56\({}^{\circ}\)W & GF-1B & 1228298519 & 20 May 2023 \\ Onotoa Island & 1.88\({}^{\circ}\)S 175.55\({}^{\circ}\)E & GF-1D & 1257241855 & 22 June 2023 \\ Ant Atoll & 6.78\({}^{\circ}\)N 157.96\({}^{\circ}\)E & GF-1C & 1022061238 & 10 May 2023 \\ Emae Island & 17.06\({}^{\circ}\)S 168.28\({}^{\circ}\)E & GF-1D & 1257142905 & 9 November 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GF-1 data information and geographic location (latitude and longitude) of the four study areas.  
the scale-invariant assumption, ultimately resulting in high-resolution bathymetric imagery at a spatial resolution of 2 m. The specific workflow diagram is shown in Figure 2:
## 6 Conclusions  
Conventional single-degree-of-freedom (SPS), dual-degree-of-freedom (DPS, EPS), and triple-degree-of-freedom (TPS) modulation strategies cannot simultaneously achieve ZVS and low current stress, leading to reduced efficiency in DAB converters. To address this limitation, this paper proposes an enhanced integrated optimization strategy based on TPS (EIOS-TPS). The theoretical analysis and experimental results demonstrate that, by establishing an optimization path with a tunable \(\lambda\) parameter, EIOS-TPS dynamically balances current peak suppression and full-switch ZVS operation. This strategy extends the ZVS range across wide voltage-gain conditions (both buck and boost modes) while ensuring low current stress over the full power range. Furthermore, in high-frequency applications utilizing wide-bandgap semiconductors, EIOS-TPS effectively co-optimizes switching and conduction losses, providing significant efficiency improvements and offering a practical solution for high-power-density power electronic systems.  
Conceptualization, L.C.; Methodology, L.C.; Visualization, L.C.; Project Administration, Y.Z.; Validation, X.W.; writing--review and editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key R&D Program of China (Grant No. 2022 YFF0706202).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Challenges, Limitations, and Future Prospects
Current challenges regarding the application of machine learning models along with hyperspectral imaging include the need for large datasets of high quality and well-labeled to develop robust models, which usually are complicated and time consuming to acquire. In addition, enhancing the visualization and interpretation of hyperspectral data remains a challenge due to the high dimensionality of spectral information. These could be achieved by developing data augmentation techniques to artificially increase the size of a dataset or by using transfer learning through the adaptation of a previously trained model. The development of more efficient dimensionality reduction and feature extraction algorithms is also another possible approach. These kind of ideas may be developed in close collaboration between academic researchers, food industries, and technology developers in these areas; machine learning experts and computational scientists have a key role in model improvements and the development of better visualization tools. Although there are some challenges in the application of this method and using it widely in the food industry, some remarkable patents have been registered in recent years. Different cases were investigated in order to optimize food processing. Detecting the fertility and gender of unhatched eggs [166] using AI-based HSI can represent the potential of this technology in food quality assessment and increasing the production efficiency of food industries. Furthermore, it has been stated that it is possible to measure the nutrition value of food using HSI and neural network models [167], enabling the better classification of food products. These advancements illustrate the potential of AI-based HSI to solve real-world problems and enhance production efficiency, reduce waste, and improve food safety. The literature review indicated a reliance on ML algorithms for the analysis of hyperspectral images [24; 26; 107; 119; 129; 165]. Studies on the use of deep learning algorithms in food products are still at a nascent stage and need further research to realize complete exploitation. Currently, machine learning algorithms in common practice operate in isolation, whereby for a given algorithm execution, one considers training datasets to develop models and does not retain knowledge for future use. Also, most of the studies reviewed have focused on the applications of machine learning in solid products rather than in solid-liquid and liquid products.
# An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion
## 2 CAN's Background and Types of Attacks  
### Background of CAN Bus  
Robert Bosh GmbH developed the CAN for vehicles in 1986 [32]. The CAN bus protocol transmits data and messages to all network nodes using a broadcast technique. CAN has various advantages that make it suitable for IVNs. The CAN bus has a flexible design and is suitable for fast and low-latency real-time communication in vehicles. Moreover, it has a high level of reliability, noise resistance, and fault tolerance. CAN utilizes a priority-based strategy that prevents communications interruptions from lower-priority messages for higher-priority messages within the bus. CAN is essential and irreplaceable as the core of automotive IVN communication in modern vehicles. Replacing it would require a complete redesign of the IVN architecture [33; 34; 35].  
A CAN packet has seven fields in its structure, which are explained in the following: Start of the Frame, which uses a single bit to indicate the start of CAN message transmission to all connected nodes; Arbitration Field, which is referred to as the CAN ID, and indicates the importance of the message received. The CAN ID message has a low value, indicating its high priority. Control Field, which contains a data length code (DLC), shows the data field's total bytes. Data Field: the actual data transfer is the responsibility of this field. Cyclic Redundancy Check (CRC) Field: utilized in the process of validating data packets. Acknowledge (ACK) Field verifies that the receiver nodes in the network have successfully received the CAN packets. End of the Frame shows the completion of the message of CAN. Figure 1 depicts the CAN bus's frame structure.