esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Transient Stability Analysis  
The full-order model of the grid-connected VSC system under asymmetrical faults is a nonlinear differential equation that cannot be solved analytically. Additionally, the full-order model is a high-order model with 42 state variables, which makes it almost impractical to use common methods such as [PERSON]'s direct method [12], linear matrix inequality (LMI) optimization method [13], or the sum-of-squares (SOS) programming method [12] to construct the largest estimated domain of attraction of the system for evaluating transient stability.  
Figure 10: Simulation results of the full-order model and the detailed switching model for Case 1.  
Figure 9: Detailed nonlinear model of grid-connected VSC during asymmetric faults.  
Thus, in this section, the phase trajectory method is used to analyze transient stability which can provide a simple and intuitive result of PLL-Syn transient stability. First, the phase trajectory method is briefly introduced in Section 4.1. Then, in Section 4.2, the transient stability of the system is analyzed based on the phase trajectory method. Finally, the conditions under which dynamics such as LC filter and AC network transient, current control dynamics, and the DSOGI//PNSC can be ignored are discussed in Section 4.3.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 3 Method: Data Modeling of Marine Casualty  
This section describes how the proposed data model was developed to represent marine casualty information in accordance with the S-100 standard. The model was designed to provide a structured and interoperable format for encoding casualty data into digital representations suitable for use in ENCs. The overall development process followed the standard modeling methodology defined by the S-100 framework and included key stages such as requirements analysis, schema design, and portrayal configuration [27]. Figure 2 provides a visual summary of this development process, illustrating how each step contributes to the construction of a complete S-100-compliant data model package.  
The application schema was developed using Enterprise Architect version 10, a UML-based modeling tool widely used for geospatial data standardization [25]. The FC and PC were authored directly in Extensible Markup Language (XML) format in accordance with the S-100 standard structure. The accompanying symbology was created as a set of Scalable Vector Graphics (SVG) image files consistent with the defined portrayal rules. All resulting model components--including the application schema, FC, PC, and symbology--have been published in a GitHub repository for reference and use (see Data Availability Statement).
## 6 Conclusions  
The present analysis showed the potential of the newly produced multispectral harmonized Landsat-8 and Sentinel-2 (HLS) data to retrieve time series of vegetation properties (i.e., biochemical and structural), which were further used as inputs to the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model to simulate the time series of gross primary production GPPSIM. The study led to the following conclusions:  
1. HLS data can provide the dense time series of surface reflectance at the desired locations because it combines data from two existing satellites: Landsat-8 and Sentinel-2. The results demonstrated that the HLS data are capable of preserving the needed information about vegetation properties. We investigated the retrieval only for one evergreen tree species. Nevertheless, our analysis has an important implication for the future use of HLS data for the dense time series retrieval of vegetation properties, which is needed for other tree species, such as deciduous species, showing strong temporal phenology.
2. We did not observe any improvement in GPPSIM using the time-varying vegetation properties retrieved from the HLS data. We observed the influence of the time-varying maximum rate of carboxylation (\(V_{\text{cmax}}\)) on GPPSIM. However, the empirical relationship used to estimate \(V_{\text{cmax}}\) from leaf chlorophyll content (\(C_{\text{ab}}\)) decreased the accuracy of GPPSIM. Future studies need to redefine this empirical relationship.  
**Supplementary Materials:** The following are available at [[http://www.mdpi.com/2072-4292/12/22/3773/s1](http://www.mdpi.com/2072-4292/12/22/3773/s1)]([http://www.mdpi.com/2072-4292/12/22/3773/s1](http://www.mdpi.com/2072-4292/12/22/3773/s1)).  
**Author Contributions:** Conceptualization, R.R., B.B., P.L., and L.H.; methodology, R.R.; software, R.R., B.B., and P.L.; investigation, R.R., B.B., and P.L.; data curation, R.R., L.H., and L.S.; Writing--Original draft preparation, R.R.; Writing--Review and editing, R.R., B.B., P.L., L.H., and L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by: (1) the Global Change Research Institute of the Czech Academy of Sciences and the Ministry of Education, Youth and Sports of CR within the CzeCOS program, grant number LM2018123; (2) the Czech Science Foundation project \"Radiation balance of forest stands of the Czech Republic\", grant number GJ17-05608Y. The co-author [PERSON] was supported by the Ministry of Education, Youth and Sports of CR within Mobility CzechGlobe2 (CZ.02.2.69/0.0/0.0/18_053/0016924).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work  
With the growing demand for efficient traffic scheduling and topology adjustment in satellite networks, existing research primarily encompasses protocol stack optimization, optimization algorithms, spatiotemporal models, machine learning, game theory, and multipath transmission. Although these methods have made progress in improving network latency, throughput, and resource utilization efficiency, they still face challenges such as insufficient adaptability, high computational complexity, and slow convergence rates in large-scale dynamic environments. Spatiotemporal models and machine learning rely heavily on extensive historical data, making real-time responses to rapidly changing network conditions difficult. Game theory and multipath transmission perform well for resource competition and network reliability, but still encounter high computational complexity and load-balancing issues in large-scale networks. Other approaches, such as congestion control, bandwidth allocation, and fault tolerance mechanisms, are effective in specific scenarios but must deal with the complexities and management overhead brought about by network interruptions, link failures, and rapidly changing demands in practical applications. Therefore, there is an urgent need for an innovative solution that can achieve maximum resource utilization and system stability in large-scale, high-dimensional environments, thereby comprehensively enhancing the performance and reliability of dynamic satellite networks.
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 3 Results
### Analytic Results
The resulting value showed excellent agreement with the experimental data, demonstrating that the analytical model, despite its simplifications, can reliably predict the dynamic behavior of the porous material. This analytical section primarily provides a brief overview of the Euler-Bernoulli beam theory as applied to cellular materials. While this method allows for estimating the fundamental resonance frequency, it does not capture the full dynamic behavior--specifically, the acceleration response observed in the two vibration modes recorded during experimental testing. The Euler-Bernoulli theory has inherent limitations when applied to porous and heterogeneous materials such as metallic foams, particularly under dynamic loading. These limitations stem from its assumptions of material homogeneity, neglect of shear deformation and rotary inertia, and inability to account for local microstructural effects. As a result, the theory may yield inaccurate predictions of dynamic behavior--especially resonance frequencies--since it does not incorporate scale-dependent properties, density variations, or the complex internal geometries characteristic of cellular materials.
## 2 Measurement System
### Configuration of the Experimental Modal Test
The stimulator consists of two components: a pneumatic spindle and an eccentric wheel (Figure 3). By regulating the air pressure, different vibration frequencies can be obtained. The type of pneumatic spindle is SRD 645, which is produced by BIAX Company (Greenville, WI, USA). Under a pressure of 8 bar, the spindle can reach 45,000 rpm. To provide a certain excitation force under different rotating speeds, two methods are used to achieve this requirement. One method changes the eccentric distance. The other method changes the weight of the eccentric wheel. As a result, two eccentric wheels of different weights are designed with two different eccentric distances. **PULSE vibration measuring system**  
In the experimental modal analysis, PULSE software is used to analyze the FRF with the data from the accelerometer and impact hammer. In this experiment, the software is used to monitor the instant vibration frequency, and obtain the required excitation frequency through regulating the pneumatic spindle. As shown in Figure 4, the PULSE software displays the data in three groups. The right graph shows the instant vibration signal from the accelerometer, which displays the signal in an instant period of one second. The upper left graph shows the fast Fourier transform of the vibration signal in one second. The lower left graph shows the 3D display of the amplitude of the vibration signal in the time and frequency domains. The x-axis is the frequency domain set from 0 to 200 Hz. The y-axis is the total measuring time, and the color contour provides the vibration amplitude. The domain vibration frequency is generated from the stimulator. It is therefore used to monitor the rotating frequency of the pneumatic spindle. \begin{table}
\begin{tabular}{c c} \hline \hline
**PONTOS System** & **Value** \\ \hline Focal length of lens & 35 mm \\ Measuring volume & 400 \(\times\) 400 mm \\ Frame rate & 500 Hz \\ Resolution & 1024 \(\times\) 1024 pixels \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings in the PONTOS system. **Force measuring system**  
This system includes one six-degrees-of-freedom force sensor (Figure 3) and a data acquisition system. The parameters of the force sensor are stated in Table 3. In this vibration measuring experiment, only the forces of three directions are measured with the ATI data acquisition (DAQ) system [23], which uses the DAQ F/T interface for data collection from the force sensor. The maximum data rate of this data acquisition system can reach 41.67 kHz [24]. The data sampling rate in the force measurement is set to 500 Hz.
## 5 Discussion
### The Particularities and Influencing Factors of High-Quality Reservoir Pore System
The gas content in shale reservoirs is mainly influenced by several factors, such as the TOC content and thermal maturity of the organic matter. The relevance analysis reveals a correlation between the TOC content and gas content of shale samples from the Wufeng-Longmaxi Formation, as shown in Figure 15. A positive correlation is observed between gas content and TOC content, with an R\({}^{2}\) value of 0.7277. Therefore, TOC content could potentially be the main influencing factor in shale gas content. As the organic matter goes through hydrocarbon generation during geological burial processes, modified by shale reservoir diagenesis, developed organic nanopores are formed that enhance the adsorption and storage capacity of the reservoir. Rich shale gas in the reservoir is also formed during this process. Overall, the accumulation of shale gas in the Wufeng-Longmaxi Formation reservoirs of the Z-3 well is influenced by multiple factors. TOC content is one of the most important factors among them. The bottom S\({}_{1}\)[1]-[1] sub-layer section of the shale reservoir has a high TOC content, a high level of organic matter pore development, and good porosity and permeability, which is conducive to the adsorption and storage of shale gas. The total gas content of shale samples in the bottom S\({}_{1}\)[1]-[1] sub-layer is greater than 2 m\({}^{3}\)/t, and the TOC content exceeds 4%, which indicates that the bottom S\({}_{1}\)[1]-[1] sub-layer should be regarded as a higher prioritized layer for exploration.
## 2 Review of the State of the Art about Packet Analysis and Related Work
### Network Forensics  
When considering cybersecurity, the main goal of conducting network forensics is to acquire evidence to be to able to reconstruct and gather information about an incident, crime or cyberattack. In this case, evidence can be obtained from inspection of the network flow, log files from firewalls or hosts, Intrusion Detection Systems (IDS), Intrusion Prevent Systems (IPS) and so on.  
Nonetheless, there are some challenges to forensics procedures when dealing with network security evidence [35]. Due to the high transmission rate in networks, data is generated very fast, enforcing the necessity to have large storage capacity devices. Another challenging consequence of this high rate of data generation is that evidence of an investigated event will correspond to smaller percentages of the dataset, thus forcing cybersecurity professionals to dig and mine large amount of data searching for strings, files or patterns that are not easily found.  
[PERSON] et al. [35] also cite data integrity as a network forensics challenge. Digital evidence may be easily manipulated, either accidentally or not, compromising the validity of the evidence. It can be avoided by copying the data and examining it as if it was the first obtained evidence, reducing the risks of data being corrupted or lost. In addition, it is easy to identify corrupted data by comparing its hash with the original's. Another feature of digital evidence that cooperate with an investigation is the fact that, depending on the type of supporting media being used, even if evidence is deleted, in some cases they can be recovered using appropriate tools.  
When investigating a network event, many sources of data must be inspected (e.g., firewall, IDS, IPS, raw packets), each generating new data at a high rate. Analyzing this voluminous and heterogeneous data can be a cumbersome task and delay the investigation. To support investigators in this scenario, [PERSON] et al. [36] proposed a platform named Visibility Across Space and Time (VAST), which provides both continuous ingestion of voluminous event streams and interactive query performance. It aims to achieve interactivity, scalability and expressiveness with its distributed architecture and powerful indexing technology, which allows the capture and analysis of the entire activity of the network. Their tests showed that VAST supports interactive investigation more efficiently than other current systems.  
Different network technologies require different forensics procedures. As an example, [PERSON] et al. [37] studies the emerging SDN technology and its forensics. The authors compare the implementation of SDN forensics with traditional network forensics, discussing the differences; highlighting potential locations for evidence acquisition in each layer of the technology architecture; and describing the challenges in SDN forensics.
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
###### Abstract  
As a novel non-destructive testing technique, capacitive imaging (CI) has been used to detect defects within the insulation layer and metal surface of an insulated metallic structure, that is, pipe or vessel. Due to the non-linearity of the probing field, the defects at different depths in the insulation layer are difficult to compare accurately using the conventional CI sensor with a single pair of electrodes. In addition, the conventional CI sensor cannot provide adequate information to discriminate the defects in the insulation layer and metal surface. In order to solve the above-mentioned problems, the multi-electrode sensor is introduced. The multi-electrode sensor uses multiple quasi-static fringing electric fields generated by an array of coplanar electrodes to obtain extra information about the defects in the specimen. In this work, the feasibility of multiple quasi-static electric fields detecting the defects was demonstrated and the Measurement Sensitivity Distributions (MSDs) of the multi-electrode sensor detecting the defects were acquired using the FEM models. The simulation and experimental results show that the Dynamic Change Rates (DCRs) of the measured values obtained at the center of the defects in the insulator layer and metal surface present different variation patterns, which can be used to discriminate these two different kinds of defects. The reasons for the different variation patterns of DCRs were explained by the changing trends of MSDs with increased electrode separation. In addition, it was demonstrated that the different depths of the defects in the insulator layer can be compared accurately by comprehensive analysis of the detection results from all the electrode pairs.  
capability imaging; multi-electrode sensor; defect discrimination; measurement sensitivity distribution; dynamic change rate Article
## 5 Conclusions  
The present study supported the hypotheses that the remote monitoring of tropical rangelands with satellite imagery can be used to calculate VIs, which are associated with the LWC and LW of cows in commercial grazing conditions. Machine learning models allow the prediction of the LWC in breeding cows from satellite VI, date, and rainfall data. The combination of satellite imagery and automatic remote weighing of animals offers a tool to monitor and manage animals in near-real time under extensive grazing conditions and a further investment and development of these tools is encouraged. Longer term data collection in different locations could help in developing robust tools and models with applications in diverse conditions. However, the present study provides a framework integrating multiple technologies for the remote monitoring of animals in extensive conditions. A further investigation into the suitability of predictive modelling in various regions to nowcast and forecast cattle live weight changes and other productivity indicators from satellite-derived vegetation data could provide tools to improve land and animal management.  
Conceptualization, L.A.G.; Data curation, C.P., P.F. and L.A.G.; Formal analysis, C.P., P.F. and L.A.G.; Funding acquisition, L.A.G.; Investigation, C.P.; Methodology, L.A.G.;Project administration, L.A.G.; Resources, C.P., P.F. and L.A.G.; Software, P.F. and L.A.G.; Supervision, P.F. and L.A.G.; Validation, C.P., P.F. and L.A.G.; Visualization, C.P.; Writing--original draft, C.P.; Writing--review and editing, P.F. and L.A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Meat and Livestock Australia Donor Company, the Commonwealth Government of Australia, and The University of Sydney (project number P.PSH.0817).  
Data Availability Statement: Data available on request from L.A.G. due to restrictions.  
We would like to gratefully acknowledge funding from the Meat and Livestock Australia Donor Company, the Commonwealth Government of Australia, and The University of Sydney. Their support was pivotal in the success of completing this study. We would also like to thank [PERSON] and [PERSON], managers of the station where the study took place, who far surpassed our hopes in their enthusiasm, commitment, and long-term dedication to this work; they are a credit to the livestock industry. Without their help and passion, this study would not have been possible.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript.
## 6 Computational Experiments
### Sensitivity Analysis  
#### 6.3.1 ANOVA Measures  
The two meta-heuristics MO-VNSSA and MO-VNSTS found optimal values for the first objective function \(F_{1}\). In order to show the insignificant difference for the second objective \(F_{2}\), a testing of hypothesis using analysis of variance ANOVA is performed for both meta-heuristics. The ANOVA test showed that both algorithms do not differ from the optimal value of \(F_{2}\) at 95% of confidence limit. The detailed results are presented in Table 5. As \(p\)-value \(>\) 0.05, there is not a significant difference between the meta-heuristics and the optimal values of CPLEX.
# Predicting the Onset of Freezing of Gait Using EEG Dynamics
## 4 Conclusions  
In this study, we investigated the application of CNN to an end-to-end classification of transitions to FOG, voluntary stopping, and normal walking. The model automatically learns the discriminative features for classifying normal walking, transitions to FOG, and voluntary stop. Furthermore, the convolutional neural network approach removed the need for feature extraction and selection. This research is the first of its kind, and the reported classification model could pave the way to detecting the onset of FOG precisely and effectively. As the transitions to FOG can be accurately distinguished from the transition to voluntary stopping with just a two-second window, this could enable appropriate interventions (e.g., cueing) to help the patient avoid freezing. Further, a larger dataset can improve the performance of the models, and future studies should investigate the real-time FOG detection performance. This work will expedite the development of future therapeutic interventions that can reliably predict freezing episodes in PD patients. Future interventions for FOG must diligently eliminate all false positives from the confounding voluntary stopping.  
Conceptualization, H.-T.C., K.E.M., M.G. ([PERSON]), M.G. ([PERSON]), H.T.N., S.J.G.L. and C.-T.L.; Methodology, A.R.J., K.E.M., M.G. ([PERSON]), M.G. ([PERSON]), S.J.G.L. and C.-T.L.; Formal analysis, A.R.J. and Z.C.; Investigation, K.E.M., M.G. ([PERSON]), H.T.N., S.J.G.L. and C.-T.L.; Data curation, K.E.M., M.G. ([PERSON]), M.G. ([PERSON]) and C.-T.L.; Writing--original draft, A.R.J.; Writing--review & editing, A.R.J., Z.C., H.-T.C., K.E.M., H.T.N., S.J.G.L. and C.-T.L.; Supervision, C.-T.L.; Project administration, K.E.M., H.T.N., S.J.G.L. and C.-T.L.; Funding acquisition, H.T.N., S.J.G.L. and C.-T.L. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Australian Research Council (ARC) under discovery grants DP180100670, DP180100656 and DP210101093. The research was also partially sponsored by the Australia Defence Innovation Hub under Contract No. P18-650825, US Office of Naval Research Global under Cooperative Agreement Number ONRG - NICOP - N62909-19-1- 058, and AFOSR - DST Australian Autonomy Initiative agreement ID10134, and AFOSR Grant No. FA2386-22-1-0042. We also thank the NSW Defence Innovation Network and the NSW State Government of Australia for financial support in part of this research through grant DINPP2019 S1-03/09.  
The University of Sydney Ethics Committee provided ethics approval for this study (HREC approval number: 2014/255).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data will be made available upon reasonable request to the corresponding author.  
The authors declare no conflict of interest.