esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In the previous sections, we introduced the integration of the CVM and KMP to provide a comprehensive obstacle prediction system. The fusion prediction results are then used as a reference for trajectory replanning, where we design an appropriate cost function for online trajectory optimization to avoid moving obstacles.  
We believe that motion prediction plays a critical role in motion planning. Therefore, we explored the integration of prediction and planning to ensure both the safety and intelligence of the system. In [31], the CVM is evaluated as a simple yet effective baseline method, while in [32], the KMP is introduced as a powerful imitation learning approach. Based on these insights, we combined the strengths of both methods to offer a fusion prediction approach. However, it is important to recognize that motion prediction is a complex task, and accurate prediction under all conditions remains challenging. Improving prediction accuracy requires incorporating more environmental clues, which is a direction we intend to explore further.  
Regarding gradient-based trajectory optimization, this method has demonstrated high effectiveness [17; 18; 19]. In this study, we focus specifically on online trajectory replanning for fixed-wing UAVs. We designed a cost function that integrates the motion prediction results into a collision cost function. After experimenting with various forms of cost functions, we selected the current exponential function due to its smoothness characteristics. Additionally, we developed a dynamic feasibility cost function and a kinematic constraint cost function tailored to fixed-wing UAVs. To our knowledge, the kinematic constraint cost function we proposed has not been addressed in the previous literature, though the reference trajectory cost function and smoothness cost function are widely used.  
The UAV parameters presented in this paper include minimum and maximum speeds of 15 m/s and 25 m/s, respectively, and a maximum turn rate of 1 rad/s. These parameters correspond to a small fixed-wing UAV with a wing area of 0.5 m\({}^{2}\) and a mass of 2 kg, powered by an electric motor with approximately 500 W, which provides no less than 6 N of thrust. This configuration is designed for light payloads and short-range missions, ensuring stable flight performance as well as reliable takeoff and landing capabilities.  
In terms of safety distance, we believe that 10 m is a reasonable setting, especially in open areas or when supported by adequate perception and control systems. The safety distance is intended to maintain a minimum buffer between the UAV and other objects (such as the ground, obstacles, or other aircraft) to avoid collisions, turbulence, or other potential risks. For a fixed-wing UAV flying at the specified speeds, both its flight path and reaction time require some buffer space. And the 10-meter safety distance serves as a buffer against sudden obstacle appearances and aids in trajectory planning, rather than being relied upon solely for a full stop.
# Digital-Twin-Based Monitoring System for Slab Production Process
## 2 Related Work
### Intelligent Application of Steelmaking Process  
In recent years, intelligent research on the steelmaking process has garnered widespread attention and development. The steelmaking industry is gradually achieving automation control and intelligent optimization [27; 28; 29]. [PERSON] et al. [27] applied multivariate data analysis (MVDA) to develop a dynamic system for vibration and acoustic measurements of converters to predict the tilt of the converter. [PERSON] et al. [28] validated the proposed method's impact on the steelmaking process. [PERSON] et al. [29] evaluated the influence of process conditions on product performance using various techniques, such as full steel and slag chemical measurements, inclusions analysis, process analysis, and thermodynamics. [PERSON] et al. [30] introduced big data and Internet of Things (IoT) technologies into the field of steelmaking, enabling early detection of smelting equipment failures. [PERSON] et al. [31] created an instrument for measuring the thickness of thin slab continuous casting and rolling (TSCF) using various devices, such as measuring rods, mechanical devices, andhigh-definition industrial cameras. [PERSON] et al. [PERSON] et al. (2019) proposed a method based on flame image processing and pattern classification for end-point prediction in the basic oxygen furnace (BOF). [PERSON] et al. [PERSON] et al. (2019) suggested a method using region-scanning charge-coupled device (CCD) laser stripe scanning to measure large field-of-view visualizations based on MV in modern steel production lines. Additionally, scholars have discussed the application of intelligent logistics systems to achieve accurate tracking and optimization of raw materials, thereby reducing costs and improving production efficiency [PERSON] et al. (2019); [PERSON] et al. (2019). Some research has explored novel slag processing technologies and energy efficiency improvements to reduce carbon emissions, aiming to minimize waste and pollution [PERSON] et al. (2019); [PERSON] et al. (2019).  
The steelmaking industry has gradually introduced numerous intelligent applications to enhance production efficiency and safety and reduce raw material waste and production costs. The above research emphasizes the practicality of automation, data-driven approaches, visual analysis, and sustainability-related technologies in the smelting process. However, these technologies have not been integrated or applied to the slab production process by combining various aspects, such as vision, data-driven methods, and management. By comprehensively applying these technologies, the steelmaking industry is poised to improve production efficiency and product quality and achieve more environmentally friendly production.
## 5 Results
### Classification Performance  
Table 1 presents a direct comparison of the classification performance of the two models. The results for the original model (ViT + Siamese network with BiomedCLIP) are reproduced from our previous work [2]. The results for the new model (ViT + Siamese network with SigLIP 2) are obtained from our experiments using the methodology described in the Methodology.  
As shown in Table 1, the new model using SigLIP 2 features consistently outperforms the original model across all classes and in terms of overall accuracy. The overall accuracy improved from 82.76% to 86.67%. Improvements were also observed in all individual class metrics. For CP, the F1 score increased from 86.00% to 87.54%; for UCLP, it increased from 82.00% to 85.00%; and for BCLP, it increased from 80.00% to 85.33%. These results strongly support our hypothesis that SigLIP 2's enhanced feature representations lead to improved CL/P classification performance.
## 2 Related Works
### Denoising Algorithms
The median filter is effective in removing a complex form of noise present in the image (e.g., non-linear noise or impulse noise in which the value of a pixel changes rapidly) before performing advanced image processing such as edge (or outline or boundary) detection. The median filter is also useful for reducing speckle noise or noise in the form of small spots and for preventing blurring in contours through the contour protection property of the filter [33; 34]. To smooth the image by removing the white Gaussian noise presented in an image, the non-local means (NLM) filter based on the mean of a set of pixels surrounding a target pixel has been developed. Specifically, the NLM filter can smooth the pixel values inside the contour while maintaining the contour of the image by using the intensity and relative distance of adjacent pixels in an image. The NLM algorithm performs well when enlarging the image since it is effective when there are large patches (i.e., a set of pixels) in an image of similar colors, but the calculation speed is somewhat slow due to a large amount of computation [30; 35].
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 5 Discussion
### Mountain Geomorphological Mapping Future Implications  
Geomorphological maps are useful for planning and natural resources management, providing basic information of landscape biophysical and human uses [89]. Modern geomorphological maps are valuable resources for environmental applications such as landscape studies, due to their flexibility in using new technologies [90]. Hence, the use of classic geomorphology cartography supported by technologies such as GIS, aerial photos, automatic morphologies determination (i.e., geomorphons), topographic surveys, satellite images, LiDAR, and drones, will be the key to the future in a science that is the basis for environmental studies, natural risks, geoheritage, and land-use planning in mountain landscapes.
## 5 Discussion and Conclusions
### Magnetic Field Design  
In all sections except Section 4.2, we assume a uniform field within the magnetic chamber (\(B_{0}=0.1\) T). The SIMION software, on the other hand, models a more realistic magnetic field by solving a Poisson equation for the magnetic field: \(\mathbf{B}=-\mu\
abla\Omega\), where \(\Omega\) is the magnetic scalar potential and \(\mu\) is the constant magnetic permeability. Thus, SIMION models more realistically the magnetic field generated by the two magnets in the absence of currents within the magnetic chamber. This is sufficient for a first investigation at this point but not for a more detailed instrument design. The path of the particles inside the instrument is governed by the instantaneous magnetic field vector at its current position. Furthermore, the magnetic circuit must be designed in detail to keep the field inside the gap as homogeneous and unidirectional as possible and the stray field as low as possible. This work would benefit from the heritage of the MagEIS instrument in which case the magnet design was a key point of the success for the instrument [16]. Indeed, in the Low and Medium units, the field's variations were kept below 0.5% of the mean field value [16] to ensure uniformity of the field. The stronger field inside the High chamber (0.48 T) of the MagEIS suite was realized with variations of around 5% of the mean value. Following the detailed design phase of the magnets for MPA, a file containing the established magnetic field vectors inside the chamber can be used in SIMION to simulate the real response of the instrument to different specific particle populations.
## 5 Experiments and Results
### Rotation Recovery Results  
An important test in this proposal is the rotational invariance of the natural scenes. Since the images can be taken from a drone, light aircraft, helicopter, satellite, etc., the images can be acquired rotated at a given angle. Thus, using the scenes from the proposed databases, In Figure 9, it can be seen that the query image is within the most similar image. Although the five most similar images are sought, the image firstly recovered is the image sought. The confusion matrix result for the rotation tests is given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(Scene_{i}\)/\(Scene_{j}\) & **Forest** & **Sky** & **Coast** & **Mount** & **Prier** & **River** \\ \hline Forest & 100 & 0 & 0 & 0 & 0 & 0 \\ Sky & 0 & 100 & 0 & 0 & 0 & 0 \\ Coast & 0 & 0 & 100 & 0 & 0 & 0 \\ Mount & 0 & 0 & 0 & 100 & 0 & 0 \\ Prier & 0 & 0 & 0 & 0 & 0 & 100 \\ River & 0 & 0 & 0 & 0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix using the full image base.  
Figure 8: Test with scaled images down to 50% and 75%.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 4 Results  
Bathymetric inversion around Ganquan Island was carried out based on the LiDAR waveform data and the QuickBird image. We compared the depth detected by the multi-Gaussian function and the LiDAR processing software. Moreover, the inversion capability from combining active and passive remote sensing was evaluated from the aspects of overall accuracy and specific depth range accuracy.
## 3 Methods and Model Setup
### Methods
#### 3.1.4 Rock Density and Rheology  
The rock density (\(\rho\)) depends on the pressure (\(P\)), temperature (\(T\)), mineralogical composition (\(M\)), and chemical composition (\(C\)). Using the rules \(e^{a}\approx 1+a\) and \(e^{-a}\approx 1-a\) (when \(a\ll\)1), the equations can be simplified to the following:  
\[\rho=\rho_{r}[1-\alpha(T-T_{r})]\times[1+\beta(P-P_{r})] \tag{7}\]  
where \(\rho_{r}\) is the density of a given material at reference pressure \(P_{r}\) (\(P_{r}=10^{5}\) Pa in this paper) and temperature \(T_{r}\) (\(T_{r}=273\) K in this paper), \(\alpha\) is the thermal expansion (1/K), and \(\beta\) is the compressibility (1/Pa).  
Rheology is the physical property characterizing the flow and deformation behavior of materials [62; 63]. In nature, rocks are generally viscous-elastic-plastic. The rheological behavior is based on the minimum creep viscosity by comparing the ductile and brittle/plastic deformation fields [64]. Dry olivine flow laws were used for the asthenospheric mantle and lithospheric mantle, wet quartz flow laws were used for the upper continental crust, and plagioclase An75 flow laws were used for the lower continental crust. The equation used in the calculation was as follows (from the compilation by [64]):  
\[\dot{e}_{II}=A_{D}(\sigma_{II})^{n}exp\ (\frac{-(E_{a}+V_{a}P)}{RT}) \tag{8}\]  
\(\dot{e}_{II}\) is the strain rate, \(A_{D}\) is the material constant, \(\sigma_{II}\) is the deviatoric stress, \(E_{a}\) is activation energy.  
The plastic yield strength for a given rock \(\sigma_{yield}\) is based on [65]:  
\[\sigma_{yield}=C+\sin(\varphi)P \tag{9}\]
## 4 Discussion
### Predictors of the Terms of the Light Use Efficiency Equation  
Understanding the causes of variability of the parameters of LUE equations (\(\mathrm{LUE_{p}}\), \(\mathrm{LUE_{f}}\), and \(\mathrm{Fesc_{fw}}\) is fundamental to exploit remote sensing information such as \(\mathrm{F_{760}}\) for modeling spatio-temporal patterns of GPP [20]. We show that Ts is the main predictor of \(\mathrm{LUE_{p}}\), and together with %graminoids is one of the two main predictors of \(\mathrm{LUE_{f}}\). Ts is a good indicator of water stress and strongly related to phenology and green fraction of vegetation [59; 60], which ultimately relates to temporal variability of \(\mathrm{LUE_{p}}\). However, the fact that variables normalized by APAR such as \(\mathrm{LUE_{p}}\) and \(\mathrm{LUE_{f}}\) are driven by Ts indicates that it is not only a seasonal effect but rather physiological. In fact, Ts contains also information related to the activation of the xanthophyll cycle responsible for NPQ processes (Figure S17) that ultimately is related to \(\mathrm{LUE_{p}}\) and \(\mathrm{LUE_{f}}\)[18]. Finally, many variables that have the potential to influence \(\mathrm{LUE_{p}}\), such as photorespiration and chlororespiration, are influenced by leaf temperature [61], potentially explaining why Ts is being selected. Our results reinforce the idea that Ts should be used as additional input of LUE models aimed at the prediction of GPP [62].  
The %graminoids is by far the best predictor of \(\mathrm{Fesc_{fw}}\), independently by the method used for the calculation of Fesc. Graminoids are mainly erectophiles [29], because of this particular LAD, most of the fluorescence is emitted laterally and therefore scattered by the vegetation [8]. In this work, we tested different formulations of \(\mathrm{Fesc_{fw}}\) with consistent results, in particular between the model-based (\(\mathrm{Fesc_{fw}}\)) and the data-driven (\(\mathrm{Fesc_{emp}}\)) estimates. The fact that %graminoids is a good proxy for the effect of structure on \(\mathrm{F_{760}}\) and Fesc also opens interesting perspective to use \(\mathrm{F_{760}}\) as well as Fesc to assess taxonomic diversity, when diversity is somehow represented by changes in canopy architecture [63].  
N% is an additional predictor selected for \(\mathrm{LUE_{f}}\) and \(\mathrm{LUE_{p}}\), although the additional explained variance seems marginal (Figure 5). N% is positively related to Vcmax [24; 64], and under light saturated conditions a higher Vcmax leads to an increase of \(\mathrm{LUE_{p}}\) and, to less extent, to increase of \(\mathrm{LUE_{f}}\)[65]. As hypothesized, from this analysis, it appears that the effect of N% on \(\mathrm{F_{760}}\) and LUE equation terms is not direct and, in Section 4.3, we discuss the relationships between N%, canopy structure, and the observed variables.
# Extreme Droughs in the Peruvian Amazon Region (2000-2024)
## 3 Results
### Spatial Distribution of Drought Regions
The results are shown in Figure 9. There is no consensus across datasets about the most extended of the extreme droughs, but it can be asserted that the lower threshold (1) (MCWD \(\geq\) 30 mm) attained around 80% of the area of the region for all datasets and the higher threshold (4) of (MCWD \(\geq\) 200 mm) reached around 10-20%. According to ERA5 and CHIRPS, the most extended maximum threshold corresponded to 2023-24, but according to MSWEP. It corresponded to 2004-05 and 2009-10. Figure 7: _Cont._  
Figure 8: Climatology of the spatial distribution of MCWD for the Peruvian Amazon region from different datasets. Left: ERA5. Center: MSWEP-ERA5 Right: CHIRPS-ERA5. Figure 7: Spatial distribution of MCWD for the Peruvian Amazon region for the extreme drought hydrological years using three different datasets. Row 1: Precipitation and evaporation: ERA5. Row 2. Precipitation: MSWEP; evaporation: ERA5. Row 3: Precipitation: CHIRPS, evaporation: ERA5. Column 1: HY 2004-05. Column 2: HY: 2009-10. Column 3: HY 2022-23. Column 4. HY 2023-24. Annual Cycles of Energy Fluxes According to ERA5 in Extreme Drought Conditions in the Peruvian Amazon  
The surface radiation fluxes are tightly related to the precipitation regime and to the occurrence of drought in the tropical climate. To investigate the consistency of the annual cycle of three of the main energy flux components with the occurrence of drought, their average annual cycle was plotted for extreme drought HYs in contrast with the climatological cycle. Figure 10 shows the plots of the average annual cycle of the sensible and latent heat flux and the net solar radiation for LOR1, UCA, and MD, as obtained  
Figure 9: Percentages of the Peruvian Amazon basin area covered by the different drought threshold intervals for the extreme droughts of 2004-05, 2009-10, 2022-23, and 2023-24. (**A**). According to ERA5. (**B**). According to MSWEP. (**C**). According to CHIRPS. Thresholds: 1: MCWD \(\geq\) 30 mm; 2: MCWD \(\geq\) 110 mm; 3: MCWD \(\geq\) 140 mm; 4: MCWD \(\geq\) 200 mm. from the ERA5 data. The net solar radiation for the extreme drought years is greater than climatology, but the general form of the cycle is similar, showing a minimum for the dry season. The climatological cycles of sensible and latent heat show similar behavior for the three regions, and the bulk Bowen ratio (Bowen ratio for monthly totals) shows values within the rank characteristic of tropical forest, though they are lower for LOR1 and higher for MD, which is coincident with the higher precipitation averages in LOR1. The extreme drought plots show relevant absolute maxima in August for UCA and MD, clearly different from climatology, which are present in individual HY records. Consequently, the bulk Bowen ratio plots for extreme droughs are clearly different, showing extreme values of less than 0.3 for LOR1 and of almost 0.5 for UCA and MD, all of them more than 0.1 greater than climatology, which are consistent with the measurements of [58] for the dry season in Brazil.
# Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers
## 4 Conclusions  
A program for structural health monitoring of cable-stayed bridges using seismic accelerometers was developed. Various response indices in monitoring structural safety and serviceability of the bridges were discussed. Additional studies including a long term monitoring of field systems and statistical analyses were needed to better define the limit states of response indices for condition assessments. A systematic approach to process the raw data for generating appropriate response indices was proposed. The proposed system was composed of major components of a health monitoring software. The data matrix adopted in the system represents the interrelationship between the measured data from the seismic accelerometers and the response indices. A whole set of data in this system was organized in the form of 2D matrix. This form of data set was devised to pursue efficiencies in database accommodation and expandability to other structures. Through the reference application, it is shown that the proposed system of data process and the format of data organization could be used for the condition assessment of cable stayed bridges.  
Conceptualization, S.-H.J., J.-W.N. and H.A.; methodology, S.-H.J.; software, W.-S.J.; validation, D.-J.K.; formal analysis, J.-W.N. and H.A.; investigation, W.-S.J.; resources, J.-W.N. and H.A.; data curation, W.-S.J.; writing--original draft preparation, S.-H.J.; writing--review and editing, S.-H.J.; visualization, W.-S.J.; supervision, D.-J.K.; project administration, D.-J.K.; funding acquisition, J.-W.N. and H.A. All authors have read and agreed to the published version of the manuscript.  
The research described in this paper was financially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03036358) and Inha University Research Grant.  
The authors declare no conflict of interest.
## 4 Results and Discussion
Figure 7.3 Comparison of achievable data rate: proposed model vs. [28]. investigated model is coding and modulation. Modulation methods and coding techniques are important components of wireless communication systems; they were purposefully left out of our inquiry model. The proposed model's aims were customized to solve specific difficulties connected to fulfilling the system requirements by dynamically determining the optimum transmission distance with the suitable transmission power based on real-time system performance for enhancing energy efficiency and achievable data rate. Modulation and coding are crucial parts of autonomous vehicle communication, and future research efforts may dive deeper into these areas.