esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
## 4 Conclusions  
This research investigated the impacts of UV attenuation at different phenological stages on the concentration of free terpenes in Shiraz grapes at harvest. Results showed a small reduction in total monoterpenes and increases in selected norisoprenoids and sesquiterpenes in UV-attenuated treatments. The application stage of UV shading also have an impact on the final concentration of terpenes in grapes at harvest, where UV attenuation after veraison tended to modify the concentration of \(\gamma\)-terpinene, cynmee, linalool, geranylacetone and \(\beta\)-damascenone. These impacts could be due to the UV modulation of relevant terpene synthase and/or through the impacts on relevant \(\beta\)-D-glucosyltransferases responsible for the conversion between free and bound monoterpenes and norisoprenoids. Two representative sesquiterpenes from the germacrene D pathway, \(\alpha\)-muurolene and \(\gamma\)-cadinene, were likely to be upregulated by UV attenuation from intermediate ripeness to harvest. This may be due to UV regulation of their biosynthesis or conversion into other metabolites. Future research should look into the impacts of UV radiation on the expression of relevant terpene synthase to further confirm the findings of the current study.  
The following are available online at [[http://www.mdpi.com/2073-4395/10/9/1431/s1](http://www.mdpi.com/2073-4395/10/9/1431/s1)]([http://www.mdpi.com/2073-4395/10/9/1431/s1](http://www.mdpi.com/2073-4395/10/9/1431/s1)), Table S1: Weather conditions during vintages 2016 and 2017 (obtained from the website of the Australian Government Bureau of Meteorology using the Arxat Prison Observation Station data), Table S2: Free terpene concentrations in Shiraz grape at harvest under different UV attenuation treatments in vintage 2016, Table S3: Free terpene concentrations in Shiraz grape at harvest under different UV attenuation treatments in vintage 2017.  
Conceptualization, K.H. and P.Z.; Data curation, W.M., J.L. ([PERSON]) and J.L. ([PERSON]); Formal analysis, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; Investigation, [PERSON] ([PERSON]) and P.Z.; Methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON] and P.Z.; Project administration, [PERSON]; Supervision, [PERSON] and P.Z.; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON], K.H., and P.Z. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, and Veski through its Veski Sustainable Agricultural Fellow program awarded to [PERSON].  
The authors gratefully thank Mount Langi Ghiran for donating grape samples to support this study and [PERSON], [PERSON] and [PERSON] for their expert technical support.  
The authors declare no conflict of interest.
## 3 Results
### Experimental Data and Result Visualization
#### 3.4.2 Experiments Based on the Massachusetts Roads Dataset  
Table 2 compares the evaluation results on the Massachusetts Roads dataset. The proportion of positive samples is only half of the German-Street dataset, making class imbalance more pronounced. With a larger sample size, the IoU and Recall for the background class are 4% higher on average than in Table 1, allowing better road region identification. Our method achieves the highest IoU and Recall for the road class, with an IoU improvement of 3.21% over Deeplabv3+, demonstrating superior performance in capturing road region details and completeness. Overall, PSPNet and PSANet perform poorly, while SegFormer, with higher accuracy in recognizing non-road areas, shows better accuracy. Our method effectively balances segmentation performance for both background and road classes under class imbalance, boosting the F1-score by 3%, significantly surpassing other models.  
Figure 5 presents a visual comparison of results from different models on the Massachusetts Roads dataset. The Massachusetts Roads dataset includes roads from urban, suburban, and rural areas, with relatively dispersed distributions, emphasizing the importance of maintaining road continuity in recognition. Visual results show that other methods often produce fragmented roads and incomplete edges in dense areas or under challenges like tree shadows and similarities with exposed soil. In contrast, our method achieves superior continuity and classification performance.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**Background**} & \multicolumn{2}{c}{**Road**} & \multicolumn{2}{c}{**Overall**} \\ \cline{2-9}  & **IoU (\%)** & **Recall (\%)** & **IoU (\%)** & **Recall (\%)** & **mIoU (\%)** & **mPA (\%)** & **Accuracy (\%)** & **f1-Score (\%)** \\ \hline PSPNet [39] & 92.26 & 96.91 & 47.84 & 59.58 & 70.03 & 78.25 & 92.74 & 84.88 \\ PSANet [40] & 92.22 & 97.11 & 47.05 & 57.83 & 69.63 & _77.47_ & 92.72 & 84.41 \\ CCNet [27] & 92.32 & **97.33** & 46.95 & 56.91 & 69.64 & _77.12_ & 92.81 & 84.24 \\ Deeplabv3+ [12] & 91.95 & 96.49 & 47.51 & 60.75 & 69.73 & 78.62 & 92.49 & 84.99 \\ Unet [13] & 92.52 & 96.76 & 50.57 & 63.57 & 71.54 & 80.17 & 93.05 & 86.13 \\ Segformer [41] & 92.48 & 96.83 & 50.05 & 62.66 & 71.72 & 79.75 & 93.01 & 85.87 \\ Our method & **92.85** & 96.68 & **53.24** & **67.29** & **73.05** & **81.98** & **93.39** & **87.31** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the results on the German-Street dataset.  
Figure 4: Visual comparison of various methods on the German-Street dataset. Red boxes highlight areas with ground truth misidentifications.
## 2 Materials and Methods
### Color, Firmness and Soluble Solids Analysis  
Portable colorimeter, which has been previously calibrated using calibration plate has been used to measure the surface color of strawberry fruits (CR-10 Chroma; Minolta, Osaka, Japan). Color coordinates L\({}^{*}\), a\({}^{*}\) and b\({}^{*}\) had been recorded: L\({}^{*}\) for perceptual lightness, a\({}^{*}\) for red-green (a\({}^{*}\) negative green and a\({}^{*}\) positive red) and b\({}^{*}\) blue-yellow (b\({}^{*}\) negative demonstrate blue, b\({}^{*}\) positive yellow). Fruit firmness, recorded on 10-berry samples for each treatment, was measured on the fruit shoulder through the skin using a penetrometer (T.R. Turoni srl, Forli, Italy) with a 3 mm-diameter needle and expressed in N (Newton, N) immediately after harvesting. Total soluble solids were determined using a digital handheld refractometer (30 PX, Mettler Toledo, Columbus, OH, USA) and the results expressed in \({}^{\circ}\)Brix.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 4 Experiments
### (2) Ablation Study on Historical Observation Window Size  
In this experiment, we set BatchSize = 30 as the baseline configuration based on the real-time frame rate (approximately 30 frames per second) of ORB-SLAM3 on our testing platform and compared it with BatchSize = 15 and BatchSize = 60 to evaluate the impact of different historical observation window sizes on system performance. As shown in Table 8, BatchSize = 30 demonstrates significant advantages in both accuracy and efficiency. On the MH01 dataset, BatchSize = 30 achieves an RMSE of 0.007, outperforming BatchSize = 15 with an RMSE of 0.028 and showing comparable performance to BatchSize = 60 with an RMSE of 0.008 while reducing computation time by approximately 12% compared with BatchSize = 60 (220 s vs. 249 s). Similarly, on the MH02 dataset, BatchSize = 30 maintains an RMSE of 0.010, slightly lower than BatchSize = 60's 0.009, while decreasing processing time from 185 s to 163 s.  
In the V202 dataset tests, BatchSize = 30 and BatchSize = 60 achieved identical RMSE values of 0.019, yet BatchSize = 30 required significantly less processing time (123 s vs. 135 s). For the Corridor2 dataset, while BatchSize = 60 showed a marginally better RMSE of 0.012 compared with BatchSize = 30's 0.014, this came at a substantial computational cost, with processing time increasing from 373 s to 426 s. Similar patterns were observed in the Corridor5 dataset, where BatchSize = 30 matched the accuracy of BatchSize = 60 (both with RMSE of 0.047) while reducing processing time by approximately 9.4% (349 s vs. 385 s).  
In tests with the Room5 dataset, BatchSize = 30 maintained the same accuracy as BatchSize = 60 (both with RMSE of 0.009) while significantly reducing computational overhead (156 s vs. 186 s). This trend is evident across most test scenarios, indicating that larger BatchSize values often lead to diminishing returns in accuracy while substantially increasing computational demands, whereas BatchSize = 30 provides an optimal trade-off between precision and efficiency.  
In conclusion, BatchSize = 30, which corresponds to the system's real-time operational frame rate, achieves the optimal balance between accuracy and efficiency, making it the ideal configuration for our system.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
###### Abstract  
Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.  
5G networks; air-to-ground communications; machine-learning; unmanned aerial vehicles (UAVs); cellular networks +
Footnote †: journal:  
_Review_  
A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Results
The displacement reference value is obtained by fitting the measured in-plane displacements according to the rules. Then, the errors of the measured displacements, relative to the reference value are calculated, as shown in Figure 8(c1,c2). The root-mean-square (RMS) errors of in-plane displacements in the x-axis direction and y-axis direction are 0.071 \(\upmu\)m, and 0.072 \(\upmu\)m respectively, which exhibits high accuracy. Because the displacement, shown in Figure 8, is produced by an in-plane rotation, the contours of the absolute in-plane displacement are essentially a set of concentric circles around the rotation center. The synthetic in-plane displacement vectors are illustrated in Figure 9, with the contours of the absolute in-plane displacement. The vectors are tangent to the corresponding displacement contours, satisfying the law of displacement introduced by rigid body rotation. The direction of rotation can be easily distinguished from the directions of the in-plane displacement vectors. Because only one interferogram is needed to measure the phase in this experiment, the interferogram can be continuously acquired during the displacement of the object. Thereby, the displacement at any acquisition time can be evaluated. Therefore, dynamic measurements can be made, and the measurement speed depends on the frame rate of the camera.
## 8 Conclusions  
The integration of FRP and FRCC represents a significant advancement in concrete technology, addressing the limitations of traditional RC, especially issues like steel corrosion, low tensile strength, and brittleness. A systematic overview and discussion regarding the structural performance of FRCC members reinforced with FRP bars has been presented here. Based on the findings from this review, the following conclusions can be drawn:  
1. The bond behavior between FRP bars and FRCC is complex and influenced by factors such as bar diameter, surface treatment, and the characteristics of the FRCC matrix. The bond strength decreases when the bar diameter and embedded length increase. It improves as both the amount of fiber and the size of the cross-section of FRCC increases. Further research is needed to develop reliable models for predicting bond strength and performance, as existing studies show variability in results based on different experimental setups and material properties.
2. FRCC members reinforced with FRP bars exhibit superior load-carrying capacities and ductility compared to conventional RC, highlighting their potential for improved structural performance. Type of FRP, reinforcement ratio, and environmental conditions are some of the factors that highly affect the flexural and ductility performance of these members.
3. The shear capacity of FRCC members reinforced with FRP bars increases with low longitudinal reinforcement ratio, higher shear reinforcement ratio, or low shear span ratio.
4. It was found that the torsional resistance of FRCC members reinforced with FRP bars is more dependent on the height/width ratio than the reinforcement ratio.  
In conclusion, the findings aim to inform the development of design guidelines and standards for the effective use of these innovative materials in construction, ultimately contributing to enhanced structural performance and sustainability in civil engineering applications.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Application of NDVI and TBI to Wavelength and Water Status
#### 3.5.3 Characteristic Wavelength Bands
It is known that obvious high reflectance occurs at 1300 nm for a leaf with sufficient moisture [37]. However, few studies have adopted 1300 nm as the characteristic wavelength band in a model, except [PERSON] et al. [38], who reported the plant WC parameter and the remote sensing leaf water index by using 1300 nm and 1450 nm wavelengths. Furthermore, many studies have focused on estimating the leaf water status using a leaf dehydration dataset. The difference in leaf water status can assist in easily selecting the wavelength bands that correspond to strong absorption by water. However, in the present experiment, all leaf samples were obtained under normal conditions with sufficient moisture but without water stress. The fewer differences in leaf water status among the samples resulted in the decreased influence of water absorption on wavelength, duo to which the wavelengths of 1300 nm and 1350 nm, both of which are not water absorption bands, were chosen in this experiment. Meanwhile, the smaller leaf water status difference makes modeling much more difficult. In this experiment, the best model for MC assessment was attained using NDVI with RAW and 1300 nm and 1310 nm wavelengths. Previous studies have shown that the NIR plateau between 800 nm and 1300 nm, as well as water absorption bands above 1300 nm, are common characteristics of reflectance spectra of all healthy green plants. The high reflectivity in the NIR region of 800 nm to 1300 nm is due to the porous plant leaf structure (tissues and cells), whereas the low reflectivity above 1300 nm is caused by water absorption [3; 39]. The 1300 nm wavelength is the dividing line of the histological structures and the specific components of plant leaves, both of which are the main factors affecting the reflectance spectral characteristics of the leaves. That causes the reflectance of tomato leaf to start changing from the reflectivity peak to the absorption valley at 1300 nm. This may also have resulted in 1300 nm and 1310 nm being adopted in modeling as the characteristic wavelength bands. In addition, a higher autocorrelation coefficient exists in the 1300 nm and 1310 nm wavelengths (Figure 8). Although this combination is not suitable for a model, non-linear stretching by using NDVI enhances the contrast ratio of these two bands, which may assist in modeling.
## 4 Design and Optimization of Hardware Prototype System
### Hardware Prototype Optimization
#### 4.2.3 INS Aiding Information Delay  
If the INS aiding information cannot be provided to the receiver tracking loop and reflect the vehicle dynamics in time, its contribution will be greatly degraded, especially with strong vehicle dynamics. To ensure the performance of the deeply-coupled system, the time delay of the aiding Doppler should be short enough.  
Data transmission is carried out between the FPGA and DSP in the integrated system. On the one hand, GNSS data and IMU data use the same transmission channel (EMIF), which causes basically the same delay. On the other hand, the EMIF can make communication efficient and seamless. Therefore, the processing time difference between loop filter output and INS aiding information is mainly caused by the different processing time of the discriminator, loop filter and INS mechanization, as well as Doppler estimation. In order to reduce the INS aiding information delay, the Doppler estimation is firstly executed once the INS mechanization is completed. Figure 13 shows the test result of adding data delay. While the operation time is about 382 \(\upmu\)s, the transmission time is about 12 \(\upmu\)s, which is negligible. Assume the dynamics of the platform is 100 m/s\({}^{2}\); the Doppler change rate will be 525 Hz/s. Therefore, a delay of 0.4 ms causes a 0.21-Hz Doppler error, which has very little effect to the tracking loop.  
Figure 12: Sequence diagram of the IMU data sampling. PPS means pulse per second.  
Based on the design and optimization of the hardware prototype, the scalar-based deeply-coupled system was implemented on the platform in real time. With the guidance of the proposed steady-state error model, the INS-aided second-order PLL is designed on the embedded hardware platform. Next, the tracking performance evaluations of the second-order PLL before and after INS aiding will be carried out on the hardware prototype.
## 5 Discussion and Future Research Directions
### Scalability  
Scalability refers to the capability to maintain the QoS in case of increasing network traffic. This challenge has received significant attention from researchers. SDN's centralized controller can be affected by the congestion resulting from the growing amount of traffic [66; 67]. Furthermore, this issue is addressed in NFV as it yields more entities to manage.
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 3 Results
### Effect of Platforms and the Function of the Additional Materials
The positive contribution of increasing the internal area of the wing to thrust is greater than the other two types of increasing the external area of the wing. It is worth noting that the elliptical planform of the flapping wing can only produce less thrust. According to the theory of fixed wings, the reduction of the induced drag of elliptical wings is not enough to compensate for the increment brought by the high lift coefficient. Through the comparison of solid and dashed lines, it can be found that the additional materials can have a beneficial impact on thrust. Numerically, the thrust increases by at least 20%. The pitching moment at the root of the wing is shown in Figure 11c,f,i. Among these eight flapping wings, the elliptical planform results in the largest downward force moment. The downward force moment of the external area of the wing that is increased with an oblique rod setback is higher than that of the wing without an oblique rod setback, and the planform of increasing the internal area of the wing is lower. It can also be concluded that the attachment of additional materials can reduce the downward force moment to some extent. Considering the influence of the above planform and additional materials on the lift, thrust, and pitching moment of the flapping wings (i.e., Figure 9d), increasing the internal area of the wing and attaching additional materials (weighing 163 g) are chosen for subsequent study.
## 3 Results
### Multi-Block Analysis
#### 3.3.2 SO-CovSel-LDA Analysis  
Similarly to the procedure described in Section 3.3.1, the SO-CovSel-LDA model was built using both predictors blocks preprocessed by 1 st derivative. The optimal number of selected variables (defined on the basis of cross-validation) was 1 for X\({}_{1}\) and 20 for X\({}_{2}\). The cross-validated calibration model provided sensitivities of 96.1% and 96.0% for Class Sorrento and Class Non-Sorrento, respectively. The application of this model to the test samples led to the correct classification of 58 over 60 validation objects. This outcome is good, comparable to those obtained by PLS-DA, but less satisfying than SO-PLS-LDA analysis.
## 3 Procedure
### Laser Application  
Each patient in this study underwent a laser application on one of the mini-screws and a placebo irradiation on the other in the following manner: first at the day of the implantation (T0) followed by every other day for five days at T0 + 2 days, T0 + 4 days, and at each orthodontic visit (every three weeks) for a period of three months at T0 + 3 weeks, T0 + 6 weeks, T0 + 9 weeks, and T0 + 12 weeks.  
For the L group, the laser device employed in the treatment was an 810-nm diode laser (Picasso Lite; AMD lasers, Indianapolis IN, USA). The laser energy was applied in continuous wave mode with an output power of 1 W for 50 s and was used in a sweeping movement over the mini-screws. The diode laser delivery system was an optical fibre with a detachable non-initiated 400 m quartz tip presenting a Gaussian energy distribution. The aiming beam was a low-power visible red semiconductor diode laser (632-nm, power \(<\) 5 mW) and was transmitted coaxially along with the optical fibre. The laser beam was positioned over the mini-screw head at 22 mm height, covering an area of 0.8 cm\({}^{2}\), with a power density of 1.2 W/cm\({}^{2}\), a total energy of 50 joules, and an energy density with a movement of 8.3 J/cm\({}^{2}\). The speed of movement was 1 mm/s.  
As for the P group, the irradiation through the placebo laser (0 W, 0 J/cm\({}^{2}\)) was performed for 50 s.  
The precision of the laser therapy parameter was secured by the Pronto-250 power meter (Gentec Electro-Optics, Inc., Quebec, QC, Canada). Adverse events due to a possible undesirable thermal effect were avoided by monitoring the irradiation with a thermal camera FLIR ONE Pro-iOS (FLIR Systems, Inc. designs, Portland, OR, USA) (dynamic range: \(-20\)\({}^{\circ}\)C/+400 \({}^{\circ}\)C; resolution 0.1 \({}^{\circ}\)C). All laser safety measurements were respected in accordance with the American National Standards Institute (ANSI) guideline [18; 20].