esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Use Cases and Applications
### Agriculture  
The use of drones in agriculture has gained a lot of popularity in recent years. Farmers are seeking efficient ways to monitor their crops, and specially designed drones offer a wide range of applications that are guaranteed to help them do just that [159]. One of the key advantages of such drones is the creation of high-quality images using NDVI (normalized difference vegetation index) technology. By combining these images with machine learning and computer vision, farmers are able to monitor plant health and even predict crop yields. Drones proved to be an attractive solution during the COVID-19 pandemic, particularly when physical contact was limited.  
Drones used in the field include various models tailored to specific agricultural tasks. Bayer drones [160], for instance, are multipropeller drones powered by rechargeable batteries. Equipped with up to a 10-liter tank, these drones can cover a hectare of land in 10-15 min. Their autonomy allows them to detect and avoid obstacles or automatically fill the tank. Moreover, they can operate collaboratively in a group of drones, thereby enhancing their efficiency.  
Similarly, the DJI Agras MG-1 drone [161] is designed specifically for the application of pesticides and fertilizers on crops. With a payload capacity of 10 kg, it can efficiently cover 1 hectare of area in about 20 min. The drone's efficiency and cost minimization are achieved through automated spray adjustment, which depends on the current speed of the drone, thus ensuring a uniform application of the product.  
Expanding beyond aerial solutions, autonomous vehicles on the ground have also found application in modern agriculture. In the realm of autonomous ground vehicles, technology has been leveraged to optimize tasks such as planting, harvesting, and monitoring [162]. These vehicles offer a promising alternative, especially in scenarios where aerial drones may face limitations.  
Autonomous ground vehicles, much like their airborne counterparts, leverage advanced technologies such as machine learning and computer vision to navigate through fields, assess crop health, and perform targeted interventions. These vehicles can operate efficiently in a variety of terrains, thus offering farmers a versatile tool for precision agriculture [163].  
In the context of autonomous vehicles on the ground, one notable example is their application in autonomous farming vehicles for soil mapping. In regions, drones like those developed by Drones FAO are recommended to farmers when different operations, such as pesticide spraying or fertilizer application, are needed. These ground-based vehicles provide accurate information to help farmers achieve greater efficiency and quality in their crops, thereby showcasing the interdisciplinary nature of autonomous technologies in modern agriculture.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 2 The Four Major Specialized Metabolome Pathways
### Phenolics
Some, such as secoisolariciresinol, can serve as antioxidants; however, many others, such as justicidin, are the product of many oxidative reactions and so no antioxidant activity remains [74]. Undirected, non-stereospecific radical-radical coupling between monolignols in the cell wall leads also to the formation of the lignin polymer [75]. Depending on the plant species, the monolignols \(p\)-couamyl alcohol (yielding the H-unit in lignin), coniferyl alcohol (G-unit) and sinapyl alcohol (S-unit), and other minor monomers belonging to different phenolic classes (hydroxyaryl-propanols, hydroxycinnamyl esters, hydroxycinnamaldehydeydes, hydroxycinnamatic acids, hydroxycinnamides, hydroxybenzaldehydeydes, hydroxybenzoic acids, hydroxystilbenes and the flavone, tricin), can be incorporated in the lignin polymer, leading to a high, naturally occurring structural variation [69; 70]. As lignins from more species and tissues continue to be examined, this structural variability is likely to continue growing. The flexibility of the lignin structure is further shown in mutants and transgenic plants. Upon monolignol biosynthesis pathway perturbation, intermediates accumulate and are converted into a wide range of derivatives, which are partially incorporated into the lignin polymer [7; 69; 76; 77]. Lignin provides the plant with strength to grow upright, and with hydrophobicity for water transport, distributing nutrients from the soil within the plant. Although the incorporated monomers will affect the structure and physico-chemical properties of lignin, a large compositional variation in lignin is allowed without affecting its functionality [69; 70; 78]. The phenylpropanoyl CoA esters can also serve as starter units for chain extension with malonyl CoA units. Chain extension involving three malonyl CoA molecules occurs most commonly and produces stilbenes (e.g., resveratrolol) and chalcones (e.g., naringenin chalcone). Isomerization of the chalcones yields the flavanones (e.g., naringenin). This occurs by a Michael-type nucleophilic attack, yielding a six-membered heterocyclic ring. The flavanones can be converted to a wide range of flavonoids, i.e., flavones, flavonols, flavanols, proanthocyanidins, anthocyanins and catechins, among others, through hydroxylation, reduction and dehydration reactions of the heterocyclic ring. Flavonoids contribute to plant colours necessary for the attraction of pollinators [79]. Chalcones and flavonoids account for the yellow colors, whereas anthocyanidins are responsible for red and blue colors and combinations thereof. In addition, flavonoids are involved in plant development regulation, UV protection, plant-microorganism signaling and plant defense [80].
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 4 Discussion  
In the study, we used RPCs instead of rigorous sensor modeling. This is for easier and efficient processing as well as compatibility. However, satellite image providers may use the same approach with their physical sensor model. Regarding image matching, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Max Errors**} \\ \cline{2-5}  & **Line** & **Sample** & **Line** & **Sample** \\ \hline Shift & 1.44 & 3.22 & 3.01 & 5.96 \\ Linear & 1.46 & 2.79 & 3.23 & 5.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of mosaic strip RPCs after the bias compensation (unit: pixels).  
Figure 12: GCP distribution with the number.  
matching window size and search area can be better optimized considering the area of interest and satellite data specification. For example, fewer features would require a larger matching window size, and satellites with precise sensor models would require a smaller search area. In addition, feature-based image matching methods can be used instead [19]. The discrepancy patterns between image strips in line and sample coordinates would be different for satellite data. The data with stable ephemeris would show rather regular discrepancy patterns along the image lines. However, in any case, image compensation should not be carried out for each image line because there are no redundant matching pairs on a single image line. The sensor modeling of the mosaic tends to be more accurate compared to each image strip due to the averaging effects. Therefore, a mosaic of more image strips would produce better positional accuracy [20].  
As shown in the resulting mosaic, the three strips' radiometric differences are observed due to the differences in the acquisition date and angles. The focus of the study is on minimizing the geometric discrepancy and the generation of single sensor model information. Therefore, we have not treated the radiometry in this study, and future research will include the sensor-level radiometric adjustment between the input image strips.  
Note that the proposed method is different from the conventional image mosaic carried out with orthorectified images. The proposed sensor-level mosaic is carried out before the photogrammetric processes, including the sensor modeling and orthorectification. Therefore, users can perform their photogrammetric function with the mosaic and the sensor model information.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 2 System Components
### Hydrogen Storage
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Power (MW)**} & \multirow{2}{*}{**Hydrogen Volume** **(m\({}^{3}\)/day)**} & **Cylinder Radius (m)** & **Cylinder Height (m)** \\ \hline
25 & 1386 & 9 & 6 \\
100 & 5544 & 13 & 11 \\
500 & 27,720 & 25 & 15 \\
1000 & 55,440 & 30 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Possible approximate dimensions for gaseous storage facilities. The most cost-effective and practical way of storing large quantities of hydrogen as a gas is using underground natural structures. Aquifers are not as well sealed as salt caverns, which leads to increased leakage [15; 19]. While some leakage might be reasonable when storing natural gas, due to the small size of the hydrogen molecule the leakage rate increases significantly; for this reason, aquifers are not adequate to store hydrogen. Salt caverns are the best underground storage structures for several reasons, including low construction costs, low leakage rates, fast withdrawal and injection rates, and a harsh environment for bacteria, which decreases unwanted bacterial activity [2; 15; 19]. Initial research shows no significant difference between storing hydrogen compared to natural gas in these structures, and pure hydrogen is already stored using this approach in Teeside, the UK, and Texas, USA [2; 19]. The second approach consists of storing liquid hydrogen in metal tanks, a process similar to what is widely used for Liquified Natural Gas (LNG). The main advantage is the high density in the liquid state of 70 kg/m\({}^{3}\)[15], which is almost 10 times the density of hydrogen in a gas state at a pressure of 100 bar. However, the liquefaction of hydrogen is a very energy-intensive process, with anywhere from 6 to 10 kWh of electricity needed to liquefy 1 kg [15; 20] of hydrogen. The current installed liquefaction capacity is around 355 tons per day [15], below the necessary capacity needed for a 1 GW plant. The main reasons for the low liquefaction capacity are the high initial investment associated with liquefaction plants and the high energy consumption to liquefy the hydrogen.
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## Materials and Methods
### Finite Element Analysis-Simulations  
The simulations were conducted with finite element analysis (FEA) for the above-mentioned mechanical tests except for balloon expansion. The stent design (Figure 2c) created using SolidWorks(r), Bursa, Turkey was exported to HyperMesh(r), Bursa, Turkey for meshing. A quad-shape shell mesh with 100-micron thickness was defined, dividing each cell wall of the stent into two elements. In their previous work, [PERSON] et al. [51] demonstrated mesh convergence, and a comparison between tests and simulations for tensile testing validated that two-element over thickness provides high accuracy. Since the auxetic stent displayed a highly deformable structure and strain hardening, damaging, and failure stages involve material nonlinearities, implicit analysis was executed running simulations using RADIOSS(r), Bursa, Turkey solver accompanied by HyperMesh. Tabulated piecewise linear and quadratic elastoplastic law (Law 36 in RADIOSS, Bursa, Turkey) for material and failure modeling and stress-strain data from tensile testing of the base material (316 L stainless steel strips) [51] was entered into the program to identify parameters in this law. Boundary conditions and loadings were determined based on testing conditions indicated in the previous section. Type 7 contact (a multipurpose contact in RADIOSS) between pins and stents was defined for the three-point bending simulations. Applying a downwardimposed velocity of 3 mm/min to the top cylinders' center analysis was conducted to the point where the loading pin touches supporting pins. Fixing one edge of the stent in all directions, then applying an imposed twist with 0.05 rad/s velocity to the other edge, torsion simulations were run. For longitudinal buckling and lateral compression, a rigid plate was modeled and placed flat on the top surface of the stent, and setting the bottom of the stent fixed in all directions, and imposed velocity of 3 mm/min defined for the rigid plate.
## 5 Conclusions  
The influence of the cosine wavy shape on the far-field aerodynamic noise reduction of a cylinder in an aeroacoustic wind tunnel has been reported. The noise can be reduced significantly for the wavy cylinder with a large spanwise wavelength and large aspect ratio. Due to the cylinder tone noise, the maximum far-field dSPL between the wavy cylinder and the baseline cylinder is up to 37 dB, occurring at the frequency of 200-300 Hz. Among all the aspect ratios tested, the far-field SPL of the wavy cylinder would be the lowest when the aspect ratio is 30. Compared with the baseline cylinder, the OASPL for the wavy cylinders showed a large decreasing trend in the specified frequency range. Interestingly, a scaling law is found between the noise reduction, the aspect ratio of the wavy cylinder geometrical parameter and the incoming flow speed, which may be used to predict the noise reduction effect of the wavy cylinders under other conditions. The noise reduction mechanisms of the wavy cylinder have also been investigated via large eddy simulation. The numerical analysis shows that, the cosine wavy shape makes the periodic vortex shedding tend to be banded instead of alternate oscitation. Moreover, the wavy cylinder with a large spanwise wavelength and large aspect ratio has a significant function of reducing the turbulence intensity and velocity fluctuation, compared with the baseline cylinder. Therefore, the wavy shape changed the local flow field around the wave crest and wave trough of the wavy cylinder, resulting in the far-field noise reduction. Figure 25: Velocity fluctuation profile along the streamwise direction and along the transverse direction. **Author Contributions:** Conceptualization, C.X. ; methodology, C.X. and F.T. ; software, F.T. ; validation, C.X. and F.T. ; formal analysis, C.X. and F.T. ; investigation, C.X. ; resources, C.X. ; writing--original draft preparation, C.X. and F.T. ; writing--review and editing, F.T. ; visualization, C.X. ; supervision, C.X. ; project administration, C.X. ; funding acquisition, C.X. and F.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by National Natural Science Foundation of China, funding number No.11572338, No.12102451 and No.12261131502. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data is unavailable due to privacy. **Acknowledgments:** The authors would like to acknowledge the China Aerodynamics Research and Development Centre for allowing the use of the aeroacoustic wind tunnel. We sincerely thank [PERSON] for his help during the experiment and [PERSON] for the inspiration. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 3 Methods
### AICA Concept  
The North Atlantic Treaty Organization (NATO) task group on \"Intelligent, Autonomous and Trusted Agents for Cyber Defense and Resilience\" outlined an AICA architecture [3,4]. Members of this group envisioned a future where both defensive and offensive autonomous agents battle for supremacy in cyberspace. Figure 1 illustrates their proposed architecture for an AICA and which parts of that architecture are represented in this research. As the AICA setup is nearly identical to the agent-environment interaction loop of RL, it seems natural to apply this approach to the problem.  
The outer bounding box represents an environment. For our use case, the environment is the intra-vehicle control network. The inner bounding box is the agent itself, consisting of software and the required hardware to run it, operating within that environment. Using sensors, the agent receives observations to build a view of the environment state based on relevant parameters that the agent has access to. In our case, this is direct access to the decoded CAN bus messages in the form of a two-dimensional (2D) grayscale image. In the AICA architecture, these data are stored in databases and used for various services, e.g., to keep track of current and historical states or world models to help with planning and learning. Rather than explicit planning via world models, model-free RL algorithms inherently incorporate much of this functionality within the trained agent.  
The goal of a vehicle platform AICA is to maintain the greatest degree of cyber resilience for a vehicle platform. In this context, cyber resilience refers to the vehicle's ability to continue operation and achieve its KPPs while under cyberattack. From a vehicle control network standpoint, this equates to assuring the control network remains available  
Figure 1: The autonomous intelligent cyber-defense agent reference architecture, as used in our prototype, is color-coded to show which pieces are represented.  
for communication, legitimate nodes communicate their legitimate messages successfully, and illegitimate messages are blocked.  
When the state of the world is different than the desired state, the agent must generate plans for attaining the desired state, select a plan, and execute it with feedback. An autonomous intelligent agent can adapt its response over time to attain the desired state more efficiently and effectively. This intelligent and goal-oriented action also allows it to respond to novel attacks in novel ways, unlike manually configured automated defenses. While this is an important long-term goal of this research area, we currently limit our research to initial training and assessment of an AICA without considering the additional desired capabilities obtained through online learning.  
AICA components for inter-agent communication and negotiation, stealth and security, and self-assurance are marked in gray in Figure 1. These are important to the overarching AICA concept in which agents collaborate, potentially with competing goals, to thwart offensive agents. These components are beyond the scope of this research.
## 1 Introduction
There have been quite extensive expansion and application of RPCA, where RPCA via the principal component pursuit (PCP) method has been used to reduce the amount of calculation, with numerous extensions and expansions [28], including stable principal component pursuit (SPCP) [28], quantization based principal component pursuit (QPCP) [29], block based principal component pursuit (BPCP) [30], and local principal component pursuit (LPCP) [31]. Additionally, other methods for solving low-rank matrix and sparse matrix also include the subspace tracking series method [32], matrix completion series method [33], and nonnegative matrix factorization series method [34]. Of the discussions on these various methods, so far, studies have provided different pros to decompose matrices [28; 35; 36]. This study attempts to fuse the images of different focal distances by decomposing low-rank matrix and sparse matrix, not only taking into consideration decomposition and recombination of a single image [37; 38] but also considering simultaneously decomposing and fusing more than two images [6; 7] and even expanding to multiple images. Among those studies to date, there has not yet been a correct image fusion rating standard, and different fields result in different conclusions. Nevertheless, the rating standard currently provides evidential fusion results and field applicability related studies [39; 40; 41]. Thus, the indicators provided in the study by [PERSON] [42] et al. were adopted to carry out fusion rating. The program for fusion rating standard used is provided by the website below: [[https://github.com/zhengliu6699/imageFusionMetrics](https://github.com/zhengliu6699/imageFusionMetrics)]([https://github.com/zhengliu6699/imageFusionMetrics](https://github.com/zhengliu6699/imageFusionMetrics)), which discusses feasibility of applying deep semi- nonnegative matrix factorization (NMF) model [34] method in autofocusing and image fusion.
## 3 Results
### Integration Strategies for Nanosensors in Bridge Maintenance
#### 3.6.3 Data Management and Analysis  
Implementing advanced data management and analytical strategies guarantees the conversion of raw data into actionable insights, thereby facilitating informed decision-making.  
* Data Pre-Processing and Calibration: Employ automated data pre-processing and calibration to manage noise and ensure accuracy in the data.  
* Machine Learning and AI: Integrate machine learning algorithms and AI to derive predictive insights from the data, facilitating preemptive maintenance strategies.
* Visualization Tools: Develop intuitive visualization tools and dashboards that convey real-time and historical data in an accessible manner to various stakeholders.
## 1 Introduction
[14] simulated the aircraft braking process by building an experimental platform, measured the system temperature, and used a one-dimensional thermal model and inverse method to identify the frictional heat flux, providing an important basis for research on the thermal performance of aircraft braking systems. This method relies on experimental data for parameter setting. However, how to set parameters during the brake design stage remains to be explored. Meanwhile, most studies are confined to specific scales or idealized conditions, with limited research on full-scale simulations under diverse conditions. This is particularly true for medium-sized aircraft brake discs (e.g., 100-seat, 50-ton class), where related research remains insufficient. This study focuses on medium-sized aircraft brake discs, conducting full-scale numerical simulations to investigate their heat transfer characteristics during braking, with the aim to analyze the thermal performance of the braking system during design phase. A multiphysics coupling model was developed, accounting for key heat transfer mechanisms such as frictional heat generation, inter-disc thermal conduction, and surface heat dissipation via convection and radiation. The steps of parameter setting are described in detail. The model comprehensively captures the generation, transfer, and dissipation of heat under dynamic conditions, revealing the spatiotemporal distribution of the temperature field and the influence of varying operating conditions on heat transfer characteristics. Simulation results enable the accurate prediction of temperature distributions and the evaluation of the thermal load's potential impact on the disc and surrounding components in the design phase. This research provides scientific insights for optimizing brake disc design, enhancing heat dissipation performance, and ensuring braking system reliability, contributing significantly to the operational safety and performance of medium-sized aircraft.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 3 Results
### SOS and EOS Estimates from Other Vegetation Indices  
The RMSEs obtained per species for SOS and EOS are reported in Table 1. This includes RMSEs for SOS and EOS derived from MODIS GPP (Figures 7 and 8), but also from MODIS LAI, EVI and NDVI as well as Landsat 7 EVI and NDVI (Figures S1-S15). The results show large differences across the different vegetation indices. While lower RMSE values were obtained for EVI compared to NDVI (both MODIS and Landsat 7), these errors  
Figure 8: Modelled vs. observed EOS per species, all stations together. The red lines are identity (1:1) lines for each panel.  
Figure 7: Modelled vs. observed SOS per species, all stations together. The red lines are identity (1:1) lines for each panel.  
as well as those obtained with MODIS LAI were much higher than those obtained from MODIS GPP data.