esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### UV-VIS Analysis and Determination of the Protective Properties Against UV Radiation  
Poly(ethylene terephthalate) is transparent for UV, undergoing itself a surface modification under deep UV irradiation (e.g., [126; 127]). The importance of protection against ultraviolet radiation (UV) is increasing daily. Investigations on poly(ethylene terephthalate) fibers' protection against solar ultraviolet radiation (UVR) have been frequently undertaken (e.g., [128; 129; 130]). The property of UVR protection exhibited by PETE-Cu\({}^{(0)}\) composites was investigated using a UV spectrophotometer by measuring the transmittance of UV-rays through the fabrics.  
Thus, the transmittance spectra [%T] of starting PETE knitted textile and PETE-Cu\({}^{(0)}\) composites in the range \(\lambda\) = 200-800 nm are presented in Figure 6.  
As shown in Figure 6, the transmittance of UV-rays of the PETE fabrics, PETE-Cu\({}^{(0)}\)-1 and PETE-Cu\({}^{(0)}\)-2 composites were different in the wavelength range examined. Thus, the PETE transmittance curve exhibited a complexed shape, with a sharp decrease in the 200-250 nm range from ca 14%T to ca 1.5 %T, then a slow increase from 1.5% T (310 nm) to 7% T (880 nm), a rapid decrease to 26%T at 420 nm and the plateau region to 600 nm and a further gradual increase to 29%T at 700 nm with the plateau to 800 nm. The PETE-Cu\({}^{(0)}\)-1 composite exhibited null transmittance in the wavelength range 200 to 720 nm, with an increase to 3%T at the 720-800 nm range. The PETE-Cu\({}^{(0)}\)-2 composite exhibited null transmittance in the  
Figure 6: Comparison of transmittance spectra [%T] in the range \(\lambda\) = 200–800 nm of unmodified PETE knitted textile and PETE-Cu\({}^{(0)}\) composites (PETE-Cu\({}^{(0)}\)-1, PETE-Cu\({}^{(0)}\)-2).  
full examined wavelength range 200 to 800 nm. As a result, the property of UVR protection of the both PETE-Cu(\({}^{0}\)) composites had improved significantly in almost the full wavelength range (excluding the 250-300 nm gap: 0%T for PETE-Cu(\({}^{0}\)) vs 1.5%T for PETE).  
The transmittance (%T) spectra of PETE and PETE-Cu(\({}^{0}\)) composites in the range \(\lambda\) = 290-400 nm (UV A and UV B) are presented in Figure 7.  
On the basis of these transmittance measurements obtained for PETE knitted textile and PETE-Cu(\({}^{0}\)) composites at \(\lambda\) = 290-400 nm, corresponding UPF values have been calculated [114]. UPF values of PETE knitted textile and PETE-Cu(\({}^{0}\)) composites, calculated on the basis of transmittance measurements for \(\lambda\) = 290-400 nm (Figure 6), have been listed in the Table 3.  
These data revealed that both PETE-Cu(\({}^{0}\)) composites possess UPF(PETE-Cu(0)) values \(>\) 50 in comparison with, UPF(PETE) values \(\sim\)37 indicating that the magnetron sputtering modification performed imparts proper barrier properties of PETE knitted textile against UV radiation.
## 4 Results and Discussions
### Sem
#### 4.2.4 Path Coefficient  
Upon execution of the PLS-SEM algorithm, estimations were derived for the associations within the structural model, specifically the path coefficients. The hypothesized relationships between the constructs are represented by these links. Based on Table 7, road accident statistics were shown to be significantly correlated with horizontal alignment at hotspot segments (\(\beta=-0.316\), \(t=2.922\), \(p<0.01\)), supporting Hypothesis 1 (H1): Horizontal alignment indicators are significantly related to traffic accidents. The statistical research revealed that vertical alignment had no discernible impact on traffic accidents at hotspot segments (\(\beta=-0.118\), \(t=1.311\), \(p=0.19\)). Therefore, H2 is unsupported. In Hypothesis 3, a significant effect is shown by H3 (\(\beta=0.594\), \(t=10.570\), \(p<0.001\)), which provides support for H3.
## 5 Future Research Opportunities  
### Further Investigations into the Performance of Biochar-Amended Green Roofs  
As previously discussed, the performance of biochar in GRs has a strong relationship with biochar-related variables such as particle size, processed forms of biochar, application methods, and amendment rates. Though some valuable findings in relation to biochar variables have been identified, definitive conclusions cannot be drawn due to the limited number of studies on biochar in the context of GRs. This review found a lack of comprehensive studies on biochar application methods. Thus, more research is necessary to determine the optimal application method of biochar in GRs. Furthermore, due to limited research, a thorough understanding of how biochar particle sizes and processed forms affect biochar-amended GRs is still lacking. Additionally, the effects of biochar amendment rates on nutrient retention and plant performance have not been fully covered. Therefore, further studies are needed to address these research gaps.  
The benefits of biochar extend beyond stormwater management, runoff quality, and plant performance. It has also shown positive results in thermal insulation capacity, plant growth, microbial diversity, carbon sequestration, and soil erosion control. However, apart from plant growth and microbial diversity, there has been limited research on other biochar benefits. This opens up numerous research opportunities. Future studies should focus on maximizing biochar's potential by addressing multiple benefits simultaneously. An ideal biochar-amended GR should simultaneously improve runoff and nutrient retentions, delay runoff outflow, reduce peak runoff, improve thermal insulation, boost plant performance and microbial diversity, limit biochar and substrate losses, and sequester carbon. To achieve these outcomes, research studies should explore several biochar benefits concurrently.
# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
## 6 Conclusions  
In real-time grape leaf disease detection, complex models often entail substantial computational costs and high resource demands, hindering widespread deployment. This paper proposes GCS-YOLO, a lightweight algorithm based on an improved YOLOv8 architecture. The algorithm replaces the original C2f module in YOLOv8 with a novel C2f-GR module  
Figure 11: Algorithm improvement contrast graph.  
that integrates Ghost Module and RepConv, effectively reducing the model's parameter count and computational complexity. Additionally, the CBAM attention mechanism is incorporated to enhance the model's focus on lesion regions, thereby improving detection accuracy. The detection head is further optimized using SSDetect, a lightweight head with cross-scale shared convolutions, to boost inference efficiency. The effectiveness of each module was validated through ablation studies, demonstrating the impact of individual improvements on model performance. The proposed model outperformed five other YOLO series variants, achieving a balanced performance with a mAP of 96.2%, 1.63 M parameters, 4.5 GFLOPs computational load, and a compact model size of 3.6 MB. In real-time performance testing, the model attained 136.9 FPS on an RTX 4060 Ti GPU, meeting the practical requirements for real-time processing in production environments. Although the GCS-YOLO algorithm proposed in this study achieved notable results in object detection, we recognize that its applicability and generalizability in real-world scenarios require further validation. Future work will focus on testing the model in more complex real-world environments, including varying lighting conditions and weather scenarios. Additionally, efforts will be made to further optimize the model's computational efficiency to facilitate practical application and deployment on diverse edge devices.  
Conceptualization, Q.H. and Y.Z.; methodology, Q.H.; software, Q.H.; validation, Q.H.; formal analysis, Q.H. and Y.Z.; investigation, Q.H.; resources, Q.H.; data curation, Q.H.; writing--original draft preparation, Q.H.; writing--review and editing, Q.H. and Y.Z.; visualization, Q.H.; supervision, Y.Z.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
## 9 Conclusions  
This paper presents a parallel overset grid assembler and flow solver tested on a generic helicopter configuration. Both modules were made to share the same partitioning layout as the solver was found to be more time consuming than the assembler by a large margin, and therefore, load balancing based on the assembler was redundant. An adaptive octree was used to maintain load balance. The most loaded bin of octree was refined in each refinement step and optimal load distribution was computed with weighted graph partitioner. Even though load (re)balancing was found to be the most time consuming task, it was shown that frequent load balancing reduces simulation time considerably. The main reason for the high cost of load balancing was the high number of octree bins resulting from refinement of octree in order to reduce imbalance under a threshold. Usage of stencil walk algorithm saves 5% percent of time of donor search compared to alternating digital tree (ADT) in every time step.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft, writing--review and editing, visualization, O.S.; Writing--review and editing, supervision, I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
* Reported data can be generated with the code provided in [[https://github.com/orxshi/tailor](https://github.com/orxshi/tailor)]([https://github.com/orxshi/tailor](https://github.com/orxshi/tailor)) (accessed on 29 May 2021).  
The authors declare no conflict of interest.  
Figure 14: Speed-up for combination of tasks hole cutting, donor search, and overlap minimization.
## 3 Results and Discussion
### Pareto Front Points
#### 3.1.2 Fluctuating Flow Field  
In cyclones, the inner vortex center does not coincide with the cyclone axis while it rotates. This rotating vortex undergoes lateral movements, adding to its precessing nature. The resulting phenomenon is referred to as the precessing vortex core (PVC) that causes significant enhancement in the levels of pressure and velocity fluctuations. These fluctuations are detrimental to the cyclone performance--collection efficiency, in particular--as they lead to the dispersion of the particles near the wall region. Hence, exploring the fluctuating field is of utmost importance, and in this study, it is represented by root mean square error (rmse) values that relate to the fluctuation levels of the velocity components.  
As a common observation (cf. contour plots in Figure 7, and radial profiles in Figure 8) the pressure and velocity fluctuations are largest in the core region, referred to as the coherent fluctuations. In the outer region, these fluctuations do not have direct influence, so the nature of the fluctuations is turbulent--the latter is much weaker compared to the former. Cyclone model A exhibits the lowest level of fluctuations, while model C has the largest fluctuations of both the scalar and vector fields. Seemingly, the models Std and B have a nearly similar fluctuation level. The same observation, i.e., a high level of fluctuations in the core region, is apparent from the radial profiles. However, in the outer vortex regime, there are observable differences--the pressure fluctuations are reluctant, while for the axial and tangential velocities, many variations in the turbulent fluctuation levels are observed.  
Figure 6: Radial profiles: From left to right at axial locations \(Z/D=1.0\), \(2.0\), and \(2.5\); from top to bottom: mean static pressure, mean axial velocity, and mean tangential velocity, respectively, in cyclone models Std, A, B, and C.  
Figure 7: Contour plots on \(Y=0\) plane: From left to right: cyclones Std, A, B, and C; from top to bottom: rmse static pressure, rmse axial velocity, and rmse tangential velocity, respectively.
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Traditional Handcrafted Features  
During the earlier developments of the video classification task, the traditional handcrafted features were combined with state-of-art machine learning algorithms to classify the videos. Some of the most popular handcrafted feature representation techniques used in the literature are spatiotemporal interest points (STIFs) [26], improved dense trajectories (iDT) [27], SIFT-3D [28], HOG3D [29], motion boundary histogram [30], actionbank [31], cuboids [32], 3D SURF [33], and dynamic-poselets [34]. These hand-designed representations use different feature encoding schemes such as the ones based on pyramids and histograms. iDT is one of these handcrafted representations that is widely considered the state-of-the-art. Many recent competitive studies demonstrated that handcrafted features [35, 36, 37, 38] and high-level [39, 40] and mid-level [41, 42] video representations have contributed towards the task of video classification with deep neural networks.
# Experimental Detection of Nonlinear Dynamics Using a Laser Profilometer
## 2 Experimental Test Measurements
### Testing Procedure  
The experimental setups, realised to analyse the effect of nonlinearity on the cantilever beam dynamic behaviour in case of magnets in attraction, non-symmetric constraint, and large initial displacement conditions, are reported in Figure 5.  
The excitation of the structure is performed with a wire which can be pulled from two different positions:  
* the hole near the lumped mass located at 493 mm from the clamp;
* the hole located at 295 mm from the clamp.  
In this way, it is possible to separately excite the first two typical bending vibration modes of the Euler-Bernoulli beam: in particular, pulling the wire from the first position, a static deformation equivalent to the 1 st bending mode shape is given to the system, while using the second one the static deformation typical of the 2 nd bending mode is assigned to the beam. To excite the structure with only a non-null initial displacement, the wire is set with to the desired initial condition (IC): the acquisition software is used to display the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Product Name** & \
\begin{tabular}{c} **Distance** \(D\) \\ **(mm)** \\ \end{tabular} & **Range \(d\) (mm)** &
\begin{tabular}{c} **Sensitivity** \(S\) \\ **(mV/mm)** \\ \end{tabular} \\ \hline L1 & LK-H152 & 150 & \(\pm\)40 & 250 \\ L2 & LK-H082 & 80 & \(\pm\)18 & 555.56 \\ L3 & LK-H082 & 80 & \(\pm\)18 & 555.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Point-laser characteristics.  
Figure 4: Laser beam generated by profilometer (**a**), and measured points (**b**).  
chosen IC. Then, the wire is locked and finally cut by a pair of scissors, which allows to have null initial velocity thanks to the instantaneity of the action. Moreover, this procedure has the advantage of being easily repeatable. Additionally, it is important to set the wire along the middle transversal plane of the beam, because, in this way, the torsional modes of the structure are not excited. For sake of clarity, Figure 6 shows how the initial conditions are set.  
A sampling frequency \(f_{\mathrm{s}}\) = 16,384 Hz is chosen for the single-point lasers, while the maximum sampling frequency of 1000 Hz is chosen for the profilometer acquisition system. Single-point lasers are addressed to high-frequency dynamics measurement, while the profilometer is conventionally adopted for static detections of manufactured products in the production line. Moreover, acquired time histories using the profilometer are limited to 20 s due to acquisition system limits.  
Figure 5: Overview of the experimental setups: clamped-free beam (**left**), beam with two pairs of permanent magnets acting in attraction or repulsion (**centre**), and beam with a non-holonomic constraint (**right**).  
Figure 6: View of the method to excite the system with the desired initial condition.  
From the good agreement of single-point lasers time data and profilometer corresponding point acquisitions comparison, the use of the latter as a reasonable new tool for dynamic tests is proved.
## 1 Introduction
Therefore, within the existing literature, it is hard to find an answer to the question of: \"Which machine learning model can most accurately impute missing meteorological data?\" or \"Under what conditions is method x suitable?\"  
In this study, a multidimensional meteorological data imputation framework (MMDIF) that uses machine-learning predictions to reconstruct missing meteorological data was designed. Based on MMDIF, the imputation performance of 20 typical machine-learning methods for the missing values of 124 pieces of ground meteorological observation data in six climatic regions was verified. Each method was automatically individually tuned to guarantee optimal performance.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 3 Results
### Multi-Site Averaged Time Series  
In order to further explore the temporal variation of each carbon flux, data from all stations were analysed together. Here, predictions from all sites at each timestamp in GCFD were averaged for the period of 1999 to 2018. Figure 7a shows the annual average changes predicted by the proposed model and the observed values. Moreover, according to the Koppen Climate Classification [37], the annual variation of the carbon fluxes in different climate zones is shown in Figure 7b-h. The predictions of annual changes were quite consistent with the observation from carbon flux towers in all climate zones. It shows that the variations of GPP and RECO were similar, which coincides with recent studies [38]. Meanwhile, the variations of NEE were relatively small in all climate zones, except in tropical and subtropical climate. GPP showed a significant increase in the dry climate zone in the last two decades, which can be due, in the main, to the CO\({}_{2}\) fertilization effect, as reported by previous studies ([PERSON] et al., 2021). On the other hand, GPP has been decreasing in tropical zones since 2005. This possibly happened due to a higher vapour pressure deficit in the warmer climate over the tropics, which could lead to a suppression of photosynthesis [39]. The comparison among different global carbon fluxes is discussed in Section 4.2.  
Figure 5: The prediction bias of (**a**) GPP, (**b**) RECO, and (**c**) NEE from all sites using RF, ANN, and CNN.
## 2 Materials and Methods
### Model Development
#### 2.3.4 Random Forest (RF)  
Built using Decision Trees, the Random Forest model is an ensemble of smaller Decision Trees that produces a predictive model [38]. A Random Forest classification model follows these steps [42]:  
1. Dataset \(D\) is bootstrapped \(b\) to produce samples of size \(n\) such that \(D_{b}=\{(x_{1b},y_{1b})\),..., \((x_{nb},y_{nb})\}\);
2. A Decision Tree \(f_{b}\) is trained with \(D_{b}\) to get \(f_{b}\);
3. The tree is grown to the largest extent;
4. Steps 1 to 3 are repeated to build bootstrapped Decision Trees \((\hat{f}_{b}\ldots\hat{f}_{B})\);
5. The different Decision Trees are combined using majority voting \(\hat{f}=mode(\hat{f}_{1}\ldots\hat{f}_{B})\).
## 3 Results
### Assessing Accuracy of Orthomosaic  
Table 4 shows the statistics, as detailed in Section 2.5, computed by measuring the VP at orthomosaics produced for the three study areas, and produced by carrying out the AT with the BBA_traditional and BBA_PPK methods. Similarly, as for BBA accuracy, the BBA_traditional method achieved the smallest difference in average in each coordinate, compared to the VPs. The differences range from \(-0.104\) m (Krnica, diff\({}_{\rm X}\)) to \(0.078\) m (Krnica, diff\({}_{\rm Y}\)) in the case of BBA_traditional, while the differences with BBA_PPK were higher; they range from \(-0.181\) m (Mangart, diff\({}_{\rm Y}\)) to \(-0.016\) m (Mangart, diff\({}_{\rm X}\)). The mean value of differences ranges from \(-0.017\) m (Kevec, diff\({}_{\rm Y}\)) to \(0.018\) m (Kevec, diff\({}_{\rm X}\)) in the case of BBA_traditional. The mean value of differences with BBA_PPK were higher, and range from \(-0.119\) m (Mangart, diff\({}_{\rm Y}\)) to \(-0.089\) m (Mangart, diff\({}_{\rm X}\)). The standard deviations of the differences were similar for both BBA methods for all coordinates when observing the Kevec (from 0.011 to 0.012 m) and Krnica site (from 0.022 to 0.027 m). At Mangart site, the BBA_PPK had larger standard deviations than BBA_traditional, namely reaching 0.026 m for diff\({}_{\rm X}\) and 0.045 m for diff\({}_{\rm Y}\), while in case of BBA_traditional, standard deviation values were the same for diff\({}_{\rm X}\) and diff\({}_{\rm Y}\) (0.012 m). The RMSE values per coordinate were similar between the both coordinates in the case of BBA_traditional with all rockfall sites (0.020-0.021 m for Kevec, 0.025-0.026 m for Krnica, and 0.013-0.016 for Mangart). For the BBA_PPK method the RMSE values range from 0.031 to 0.034 m at the Kevec and Krnica rockfall site, while at the Mangart rockfall site RMSE values per coordinate achieve values between 0.093 and 0.127 m.  
In Figure 7, the frequency distributions of the diff\({}_{\rm X}\) and diff\({}_{\rm Y}\) for both georeferencing methods and all sites can be observed. Similar as with the assessment of BBA, the lowest values of differences were achieved by BBA_traditional, which had the densest distribution in all cases, and differences are clustered around 0. BBA_traditional's differences did have an overlap with BBA_PPK, but the differences of BBA_PPK experienced larger variability. The location that stood out is Mangart, where the differences of X and Y retrieved from orthomosaic produced by BBA_PPK were larger and do mostly did not match with those of BBA_traditional. The overall accuracy of orthomosaic (RMSE\({}_{\rm XY}\)) was higher in the case of BBA_traditional (range from 0.020 to 0.036 m), while the RMSE\({}_{\rm XY}\) of orthomosaics produced by the BBA_PPK ranged from 0.039 to 0.157 m.  
Figure 7: Frequency distribution of the X and Y coordinate differences at all three rockfall sites for orthomosaic_traditional (produced by BBA_traditional) and orthomosaic_PPK (produced by BBA_PPK). Bar width is set to 0.01 m.