esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
\[\Psi_{0}\ =\ A_{1}y\ +\ A_{2}y^{3}+A_{3}{\rm sinh}(m_{e}y),\]  
\[u_{0}\ =\ A_{1}\ +\ 3A_{2}y^{2}+A_{3}m_{e}\cosh(m_{e}y)\]  
\[\frac{\partial p_{0}}{\partial x}\ =\ 3A_{4}+A_{5}\cosh(m_{e}y),\]  
\[\theta_{0}\ =\ B_{1}+B_{2}y^{2}+B_{3}y^{4}+B_{4}y\cosh(m_{e}y)+B_{5}y\cosh(m_{e}y)^{3 }+\ B_{6}y\sinh(m_{e}y)+\]  
\[B_{7}{\rm sinh}(m_{e}y)^{3}+B_{8}y\cosh(m_{e}y)^{2}{\rm sinh}(m_{e}y)+B_{9}y \cosh(m_{e}y){\rm sinh}(m_{e}y)^{2}+\]  
\[B_{10}{\rm sinh}(m_{e}y)+B_{11}{\rm sinh}(2m_{e}y)+B_{12}{\rm sinh}(m_{e}y)^ {3}+B_{13}{\rm sinh}(2m_{e}y)^{3}+\]  
\[B_{14}\cosh(2m_{e}y)^{3}\sinh(2m_{e}y)+B_{15}\cosh(m_{e}y){\rm sinh}(m_{e}y)^ {2}\]  
\[\Omega_{0}\ =\ C_{1}+C_{2}y^{2}+C_{3}y^{4}+C_{4}y\cosh(m_{e}y)+C_{5}y\cosh(m_{e}y)^{3 }+\ C_{6}y\sinh(m_{e}y)+\]  
\[C_{7}y{\rm sinh}(m_{e}y)^{3}+C_{8}y\cosh(m_{e}y)^{2}{\rm sinh}(m_{e}y)+C_{9}y \cosh(m_{e}y){\rm sinh}(m_{e}y)^{2}+\]  
\[C_{10}\cosh(m_{e}y)+C_{11}\cosh(2m_{e}y)+C_{12}{\rm sinh}(m_{e}y)+C_{13}{\rm sinh }(2m_{e}y)+C_{14}\cosh(m_{e}y)^{3}+\]  
\[C_{15}\cosh(2m_{e}y)^{3}+C_{16}{\rm sinh}(m_{e}y)^{3}+C_{17}{\rm sinh}(2m_{e}y) ^{3}+C_{18}\cosh(m_{e}y)^{2}{\rm sinh}(m_{e}y)+\]  
\[C_{19}\cosh(m_{e}y){\rm sinh}(m_{e}y)^{2}+C_{20}\cosh(2m_{e}y)^{2}{\rm sinh}(2m _{e}y)+C_{21}{\rm sinh}(2m_{e}y)^{2}\cosh(2m_{e}y).\]\[\begin{array}{ll}\psi_{1}=&D_{1}y+D_{2}y^{3}+D_{3}y^{5}+D_{4}y^{2}\text{sinh}(m_{ e}y)+D_{5}y\cosh(m_{e}y)+D_{6}y\cosh(2m_{e}y)\\ &\qquad+D_{7}\text{sinh}(m_{e}y)+D_{8}\text{sinh}(2m_{e}y)+D_{9}\text{sinh}(3m_{ e}y)\\ &\\ u_{1}=&3D_{2}y^{2}+5D_{3}y^{4}+m_{e}D_{4}y^{2}\cosh(m_{e}y)+2D_{4}y\text{sinh}(m_{ e}y)+m_{e}D_{5}y\text{sinh}(m_{e}y)\\ &\qquad+D_{5}\text{cosh}(m_{e}y)+2m_{De}D_{6}y^{3}\text{sinh}(2m_{e}y)+D_{6} \cosh(2m_{e}y)\\ &\qquad+m_{e}D_{7}\text{cosh}(m_{e}y)+2D_{8}m_{e}\cosh(2m_{e}y)+3D_{9}m_{e} \cosh(3m_{e}y)\\ \end{array}\]  
\[\begin{array}{ll}\frac{\partial p_{1}}{\partial x}=&D_{11}+D_{12}y^{2}+D_{13 }y^{2}\cosh(m_{e}y)+D_{14}y\text{sinh}(m_{e}y)+D_{15}y\text{sinh}(2m_{e}y)\\ &\qquad+D_{16}y\cosh(m_{e}y)\text{sinh}(m_{e}y)+D_{17}\cosh(m_{e}y)+D_{18} \cosh(2m_{e}y)\\ &\qquad+D_{19}\cosh(3m_{e}y)+D_{20}\cosh(m_{e}y)\text{sinh}(m_{e}y)^{2}+D_{21 }\text{sinh}(m_{e}y)^{2}\\ &\qquad+D_{19}\text{sinh}(3m_{e}y)+D_{20}\cosh(m_{e}y)\text{sinh}(m_{e}y)^{2}+ D_{21}\text{sinh}(m_{e}y)^{2}\\ &\qquad+D_{18}\text{sinh}(3m_{e}y)+D_{14}\text{cosh}(2m_{e}y)+D_{19}\text{sinh }(2m_{e}y)\\ &\qquad+E_{10}y\text{sinh}(3m_{e}y)+E_{11}\text{cosh}(m_{e}y)+E_{12}\text{cosh }(2m_{e}y)\\ &\qquad+E_{13}\text{cosh}(3m_{e}y)+E_{14}\text{cosh}(4m_{e}y)\\ \end{array}\]  
\[\begin{array}{ll}\Omega_{1}=&F_{1}+F_{2}y^{2}+F_{3}y^{4}+F_{4}y^{6}+F_{5}y ^{3}\text{sinh}(m_{e}y)+F_{6}y^{2}\cosh(m_{e}y)\\ &+F_{7}y^{2}\cosh(2m_{e}y)+F_{8}+F_{8}y\text{sinh}(m_{e}y)+F_{9}y\text{sinh}(2 m_{e}y)\\ &\qquad+F_{10}y\text{sinh}(3m_{e}y)+F_{11}\text{cosh}(m_{e}y)+F_{12}\text{cosh }(2m_{e}y)\\ &\qquad+F_{13}\text{cosh}(3m_{e}y)+F_{14}\text{cosh}(4m_{e}y)\\ \end{array}\]  
where \(A_{1}-A_{5},B_{1}-B_{15},C_{1}-C_{21},D_{1}-D_{21},E_{1}-E_{14}\) and \(F_{1}-F_{14}\) are evaluated through Mathematica software package.
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 3 Experimental Evaluation
### Comparison
#### 3.2.5 Classification Algorithms  
In general, although by a small margin, the CNN classifier outperforms the SVM classifier. One of the main reasons for this performance difference is that the CNN classifier can consider the color space features and segmentation properties, which the HOG-SVM cannot. We utilized HOG-SVM to accelerate the process and focus only on vital details. This technique ignores region-based segmentation and only works with what is judged as necessary throughout the HOG transformation. However, it is crucial to note that the SVM classifier performed as well or slightly better than the CNN when DS2 was used as the training set.  
By combining the results of 123 experiments, we confirmed that color spaces are important in image feature detection and classification. In particular, hue, lightness, and saturation performed better than other color spaces. Nonetheless, a color space combination, such as HLG, can sometimes perform better than HSL. Thus, color space transformation is necessary when dealing with data with a high emphasis on color intensity and different lighting conditions, and combining color features can be more efficient. Furthermore, not only do color spaces matter, but they also affect the classification better when combined with region-based segmentation because this technique helps to split the foreground, background, and necessary and unnecessary information. The results indicate that the CNN is a better-fitting algorithm than the SVM for this case study.
# NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
## 3 The NSGA-PINN Framework
### NSGA-PINN Main Loop  
The main loop of the proposed NSGA-PINN method is described in Algorithm 3. The algorithm first initializes the number of PINNs to be used (\(N\)) and sets the maximum number of generations (\(\alpha\)) to terminate the algorithm. Then, the PINN pool is created with \(N\) PINNs. For each loss function in a PINN, \(N\) loss values are obtained from the network pool. When there are three loss functions in a PINN, \(3N\) loss values are used as the parent population. The population is sorted based on non-domination, and each solution is assigned a fitness (or rank) equal to its non-domination level [21]. The density of each solution is estimated using crowding-distance sorting. Then, by performing a crowded binary tournament selection, PINNs with lower front values and higher density values are selected to be put into the mating pool. In the mating pool, the ADAM optimizer is used to further reduce the loss value. The NSGA-II algorithm selects the PINN with the lowest loss value as the starting point for the ADAM optimizer. By repeating this process many times, the proposed method helps the ADAM optimizer escape the local minima. Figure 2 shows the main process of the proposed NSGA-PINN framework.  
```
Hyper-parameters: parent population N and max generation number \(\alpha\); \(count=0\) ;  Initialize the parent set \(S\): the parent set has a number of N neural networks.; while\(count<\alpha\)do \(f1\leftarrow\mathit{res}(nn)\) for nn in S; \(f2\leftarrow\mathit{ics}(nn)\) for nn in S; \(f3\leftarrow\mathit{data}(nn)\) for nn in S; \(R_{t}=P_{t}\cup Q_{t}\) ; \(F_{i}\) = non dominated sorting(f1,f2,f3) ;  crowding distance sorting(\(F_{i}\));  mating pool \(\leftarrow\) crowded binary selection(\(F_{i}\), \(\prec_{n}\)); \(Q_{t}\leftarrow\) ADAM optimizer (mating pool(i)); \(count=count+1\);
```  
**Algorithm 3**Training PINN by NSGA-PINN method.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.3 Sandia Voltage Shift Method (SVS)  
This approach's fundamental idea is comparable to the SFS method, which depends on amplifying positive feedback to the voltage at the PCC. There are no noticeable changes when the main grid is connected. However, a voltage shift that is noticed and magnified by the positive feedback exceeds the threshold value when the main grid is unplugged, causing the O/UV relay to trip the system [19].  
The NDZ of this straightforward approach is modest. However, this technology has the potential to reduce the quality of the power.
## 3 Results and Discussion
### Removal of Subsurface Impurity Defects by HF Acid Etching  
The simulation results of temperature and thermal stress caused by subsurface impurity defects presented in Sections 3.1 and 3.2 indicate that the impurity defects included in the SSD lasers play a crucial role in lowering the laser damage resistance of polished fused silica. In order to improve the laser damage resistance of silica optics, HF acid etching assisted with buffered oxide etch (BOE) is applied to remove the SSD layers. Then, the etching rate, surface quality and impurity contents are all investigated, and the laser damage threshold is tested to validate the effect of impurity defects on the laser damage resistance of fused silica optics.
###### Abstract  
In recent years, the in situ resource utilization of CO\({}_{2}\) on Mars for oxygen and carbon monoxide production has attracted increasing attention. Dielectric barrier discharges (DBDs) have great potential for large-scale industrial application of CO\({}_{2}\) decomposition, and the nonlinear behaviors of DBDs are directly related to the discharge stability. In this paper, a fluid model is built to investigate the influence of gap width on temporal nonlinear behaviors in CO\({}_{2}\) DBDs driven by tailored voltages under Martian conditions (the pressure and temperature are 4.5 Torr and 210 K, respectively). The simulation results show that, with the increase in the gap width, the discharge evolves from period-one state into period-two state, then changes into chaos, and finally undergoes an inverse period-doubling bifurcation from reverse period-two discharge to period-one discharge. After the CO\({}_{2}\) discharge is extinguished, the electron density drops rapidly, and the dominant charged particles in the discharge region are heavy CO\({}_{3}^{-}\) and CO\({}_{2}^{+}\) ions. As the gap width increases, the heavy ions produced by the previous discharge cannot be completely dissipated and stay in the sheath region, which makes the subsequent discharge easy to be ignited and reduces the breakdown voltage, leading to the evolution from period-one discharge to period-two discharge. When the gap width is increased to 5 mm, a lot of charged particles stay in the discharge gap, and these charged particles, especially electrons, are driven to the electrodes by the applied voltage, forming a reverse electric field, which inhibits the development of positive discharge and facilitates the formation of negative discharge. Then, as the gap width continues to increase, the density and spatial distribution of residual ions in the sheath region at the beginning of the negative discharge for two consecutive voltage periods are gradually equal, resulting in the discharge evolution from reverse period-two state to reverse period-one state. This study could deepen the understanding of the underpinning physics of nonlinear behaviors, and provide a groundwork for actively regulating the evolution of nonlinear behaviors.  
electrode gap; nonlinear behavior; CO\({}_{2}\) discharges; Martian pressure Article  
Influence of Gap Width on Temporal Nonlinear Behaviors in CO\({}_{2}\) Dielectric Barrier Discharges under Martian Conditions.  
2022
## 2 Materials and Methods
### Working Range, Method Detection (MDL) and Quantification Limits (MQL), and Expanded Uncertainty  
Standard addition calibration curves at six concentration levels ranging from 0.75 \(\upmu\)g/L to 94 \(\upmu\)g/L were used to determine the working range of the method. The working range was evaluated using the coefficient of determination (R2), the coefficient of variation of the method (CVm, %), and [PERSON]'s test for nonlinearity [27]. The acceptance criteria of these statistical tests were R2 \(\geq\) 0.995, CVm \(\leq\) 5.0%, and PG \(\leq\) F(0.05; N-1; N-3), respectively.  
The instrumental limit of quantification was determined based on the standard deviation of the method (_Sm_), namely, LOQ = 10 \(\times\) _Sm_. Sm is the ratio between the relative standard deviation of the standard addition calibration curves (S/\(\upmu\)z) and its slope (\(b\)).  
The method detection limit (MDL) and method quantification limit (MQL) of the SPE-LC-MS/MS method were based on the limit of quantification (LOQ) of LC-MS/MS (first calibration point of calibration curve) using the following equations:  
\[MQL\ (\upmu\text{g/L})=\frac{LOQ}{CF}\times\frac{100}{Rec} \tag{1}\]  
\[MDL\ (\upmu\text{g/L})=\frac{MQL}{3.3} \tag{2}\]  
where, \(LOQ\) = concentration equivalent to the first calibration point, expressed in \(\upmu\)g/L; \(CF\) = concentration factor from the SPE; \(Rec\) = recovery of PhAC, expressed in %.  
The recovery assays were performed daily with a spike concentration equivalent to the second concentration level of the standard addiction calibration curve.  
The expanded uncertainty of the method was evaluated using \"top-bottom\" methodology. Using this approach, we combined the components of uncertainty related with the precision and trueness studies [28]. Therefore, this estimation of uncertainty was based on the quality control of results obtained during all assays.
## 3 Interoperability for Wide-Area Applications
### R-GOOSE for Wide-Area Protection  
IEC 61850-90-5 also defines R-GOOSE so that it can transport the data required to achieve wide-area protection applications, as explained in [22; 23]. Wide-area protection applications can either be designed based on Synchronized Measurement Technology (i.e., PMU-based) or on generic protection IEDs. Conventional protection schemes are largely local with communication only taking place inside the substation area. In substations, protection IEDs receive local measurement data and exchange protection data (tripping, interlocking, blocking, etc.) in the form of GOOSE communication (IEC61850-8-1) over a substation LAN. The use of these data will be expanded in wide-area protection applications to build new distributed protection systems [24; 25]. In such systems, the protection decision is made based on other means than just utilizing local measurements. Moreover, the control action is optimized via messages exchanging among the IEDs scattered across the power system. This introduces the important concept of communication-based protection automation that uses local intelligence along with inter-IED communication to acquire the real-time data required for making intelligent protection decisions. Examples of communication-based protection automation can be found under GOOSE-based Logic Selectivity [26] and communication-based Loss-of-Main (LOM) protection [27] in which tripping/blocking messages exchange among IEDs and affect decision-making of the protection algorithm. IEC 61850-90-5 R-GOOSE [2] is a good candidate for use in communication-based protection applications because tripping/blocking messages can be defined in standardized format. In addition, secured WAN communication is enabled for these messages since R-GOOSE includes new fields used for routing and cybersecurity. Therefore, R-GOOSE provides interoperable and secure communication for wide-area and communication-based protection applications.
## 1 Introduction
Hence, Sentinel-2 data on the GEE platform, 459 sample plots, and three algorithms were used in this study to achieve the following objectives: (1) to identify and select the most important variables of the Sentinel-2 images for FSV estimation using plot-level tree measurements from a large number of in situ sites in Hunan Province, southern China, where forest species and stand structures are complex; (2) to assess and understand the performance of machine learning algorithms and the typical MLR models for FSV estimation; and (3) to map the FSV in Hunan Province. This study will help move towards the overall goal of developing and improving GEE-based remote sensing approaches to estimate the FSV on local, regional and national scales.
# Phase Transition and Controlled Zirconia Implant Patterning Using Laser-Induced Shockwaves
It also exhibits high corrosion resistance and is hypoallergenic, making it suitable for patients with metal allergies [16; 17; 18]. However, zirconia typically shows lower osseointegration compared to titanium, meaning that it does not integrate with bone as effectively, which can affect the long-term stability of the implant [19; 20]. Zirconia implants typically show lower osseointegration compared to titanium implants due to differences in their surface properties [21]. Titanium's inherent roughness and ability to form a stable oxide layer provide a favorable environment for bone growth and integration [22]. In contrast, zirconia surfaces are smoother and chemically inert, which can limit cellular attachment and bone integration [23]. This limitation underscores the importance of surface modification techniques, such as laser-assisted template imprinting, to enhance zirconia's bioactivity and improve its osseointegration potential. To enhance the biocompatibility of zirconia, we propose using laser-assisted template imprinting. This technique involves imprinting the shape of a grid template onto the surface via a shockwave produced from laser-material interaction. Studies have shown that topographical surface modifications can substantially alter a material's interaction with biological tissues by enhancing cell adhesion, protein adsorption, and bone-to-implant contact [24]. Increased surface area, surface roughness, and specific patterns promote better integration with the bone tissue [25; 26; 27]. Compared with other surface modification techniques, laser-assisted template imprinting offers precise control over surface characteristics, leading to enhanced osteoblast activity [28; 29].
## 5 Conclusions  
In this paper, we have presented a simulation and verification platform for avionics systems, which adopts the FACE architecture to provide an extensible development template and model scheduling strategy for components, supporting the scheduling operation of periodic and non-periodic tasks. The communication employs memory database technology to ensure the consistency and transmission characteristics of the interactive data. In addition, the platform is equipped with relevant tools to provide users with a good man-machine interface. Finally, the simulation and verification platform is evaluated through the function test and performance test. In the future, we hope to deeply understand the concept of comprehensive, modular, and open architecture. On this basis, through using multi-task scheduling optimization, airborne network management, reliability enhancement, and other advanced technologies, we will vigorously improve the level of avionics technology, which is the common responsibility of the majority of scientific and technological workers in the avionics industry.  
Conceptualization, J.C. and C.D.; Data curation, X.D.; Investigation, M.Y. and W.Y.; Methodology, J.C., X.D. and W.Y.; Project administration, C.D.; Software, X.D. and M.Y.; Validation, M.Y.; Visualization, C.D.; Writing--original draft, X.D. and W.Y.; Writing--review & editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China No. 62106202, the National Natural Science Foundation of China No. 62102316, and the Aeronautical Science Foundation of China No. 2020Z023053004.  
Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
Data Availability Statement:Data available on request from the authors.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Interaction Modules** & **Bytes** & **Min** & **Max** & **Aveg** & **Stdev** \\ \hline \multirow{3}{*}{component interaction} & 32 & 379 & 504 & 426.70 & 43.07 \\  & 64 & 480 & 649 & 547.10 & 51.99 \\  & 128 & 986 & 1213 & 1061.20 & 75.90 \\  & 256 & 1196 & 1539 & 1355.10 & 97.00 \\ \hline \multirow{3}{*}{board interaction} & 32 & 152 & 233 & 182.90 & 21.62 \\  & 64 & 269 & 343 & 292.20 & 21.24 \\ \cline{1-1}  & 128 & 456 & 518 & 489.30 & 20.42 \\ \cline{1-1}  & 256 & 638 & 759 & 693.80 & 31.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance analysis of data interaction (microseconds).  
Figure 13: The communication delay tests when the size of the packet is 64 bytes and 128 bytes, respectively.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Data Integrity and Availability  
It is vital to maintain data integrity and data availability, while ensuring data safety and security. Otherwise, it would become challenging to detect or be aware of any data tampering and any form of data alteration taking place during data transmission or data storage. Moreover, the stored data should be easily accessible, in a timely and reliable manner, when and where it is needed. Who can access, their level of access, and the visibility of control over data is a different security concern altogether, which should be managed and monitored by the access control mechanisms and techniques.
## 2 Materials and Methods
### Modeling Methodology  
The main purpose was to design a comprehensive process model for methanol synthesis from pyrolysis oil gasification. The model was then used as a tool to identify the influence of the operating conditions on the methanol synthesis yield. Operating parameters such as gasification temperature, moisture content in pyrolysis oil, Rectisol pressure and temperature, but also methanol synthesis reactor conditions (temperature and pressure) were varied over wide ranges.  
Pyrolysis oil gasification was applied to produce syngas. In many cases, syngas produced from biomass through gasification is CO\({}_{2}\) rich or H\({}_{2}\) deficient. However it can be adjusted using processes like water gas shift reaction, methane reforming, or CO\({}_{2}\) removal [29]. Supplying external H\({}_{2}\) is also a solution to adjust its composition before feeding into the fuel synthesis loop [30]. In this study, Rectisol process was applied to clean up syngas and adjust syngas composition to satisfy the methanol synthesis requirements. Then post-treatment syngas was compressed at high pressure and introduced into the methanol synthesis reactor to produce the desired compound. The process flow diagram in Figure 1 shows the Aspen Plus blocks illustrating the model used to simulate the methanol synthesis production from pyrolysis oil. The model consists of three main parts: gasification, syngas post-treatment, and methanol synthesis. The pyrolysis oil is specified as a nonconventional ingredient in Aspen Plus and defined in the block by properties presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Proximate Analysis (wt.\%)**} \\ \hline Moisture content & 32.5–43.7 \\ \hline \multicolumn{2}{c}{**Ultimate analysis (\%)**} \\ \hline Carbon & 30.4–37.7 \\ Hydrogen & 7.6–7.9 \\ Nitrogen & \textless{}0.27 \\ Oxygen & 54.4–61.7 \\ Sulfur & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Beech wood pyrolysis oil composition.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Scalability and Robustness  
#### 5.4.1 Robustness Across Different \(k\)  
In Figure 4, we observe that at \(k\geq 250\), Adaptive-DecayRank consistently outperforms baseline methods in terms of precision, demonstrating its robustness as the number of detected anomalies increases. Table 5 further highlights this, showing that Adaptive-DecayRank achieves _perfect precision_ (1.000) for **Top**-50 anomalies across all datasets. Even at **Top**-100 and **Top**-200, its precision remains notably high, outperforming AnomRank, which drops significantly, especially on **RTM-30**. This suggests that Adaptive-DecayRank is more effective at identifying the most critical anomalies while maintaining a higher precision threshold.  
As \(k\) increases, the performance gap between the two methods becomes more evident. At **Top**-300 anomalies, Adaptive-DecayRank maintains **0.683** on DARPA, **0.907** on CTU-13, and **0.880** on RTM-30, whereas AnomRank struggles at 0.563, 0.407, and 0.280, respectively. At **Top**-500, the difference becomes even more pronounced, particularly in CTU-13 and RTM-30, where Adaptive-DecayRank maintains a robust detection performance compared to the declining precision of AnomRank. These results demonstrate our model's superior ability to detect micro-changes and structural anomalies across diverse real-world dynamic graph datasets while preserving high detection quality, even as the number of anomalies increases.