esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 5 Hardware Infrastructure
### Uav  
The UAV is an important component of the system and will function based on the data that is received from the sensor nodes in the field. If the computer at the control room determines that there is an area that is likely a disease to manifest then the UAV is sent to that area to take pictures. Once the UAV has returned the pictures are forwarded to the system administrator to decide any further action needed. If the picture quality or the sensor data are not sufficient the system administrator can send the UAV back over the node to take more pictures.  
In the introduced system, the quadcopter AR Drone 2.0 was used [31]. The drone is shown in Figure 10 along with some important specifications in Table 6.  
Figure 10: AR Drone 2.0 [31].  
Figure 9: Power consumption of different wireless technologies (**a**) for the monitoring node and (**b**) for the relay node.  
The drone is also equipped with GPS. This increases energy consumption and decreases the available flying time. To expend the available flying time, different configurations were examined. At the default configuration, the drone uses its camera and forwards a live video to the controller device, while it also uses the GPS. However, with this configuration, the flying time is 15 min. Without using the Wi-Fi network and just store the video at the SD card, the flying time is 18 min. A third configuration was designed. In this configuration, when the drone leaves the control room, it deactivates both the camera and the wireless transmission. Only when the drone is over the node which reported the problem, the drone turns on the camera and takes the picture. The drone returns to the base station and after it lands successfully it enables its wireless transmission to forward the data to the computer. This configuration can achieve a maximum of 22 min, hence not only the drone is active for more time but it can also cover a greater area. The energy consumption of the three configurations is shown in Figure 10(a).  
The current consumption of the drone was also examined, under the three different configurations, when it needs to cover a distance of 1 km. The drone can reach the speed of 5 m/s (11 mph), while it needs 20 s to receive the coordinates and take off from the control room, and 15 s to take pictures from the node that reported problems. The results are shown in Figure 10(b).
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 5 Chatbots as Statistical Consultants in Infectious Disease Settings: Potential and Reliability
### Enhancing the Reliability of Chatbots: A Hybrid Approach  
To mitigate the risks associated with chatbot recommendations, a human-in-the-loop approach is advisable, especially in high-risk settings such as infectious disease management. In this context, chatbots provide preliminary recommendations that can be verified and refined by human experts. This hybrid approach ensures that the scalability and efficiency of AI is balanced with the critical judgment and domain-specific expertise provided by human statisticians. In addition, human experts can also review the chatbot's suggestions, providing additional insights or corrections that can improve the quality of the analysis. In addition, integrating real-time feedback systems that allow users to report inaccuracies or clarify uncertainties can help improve the chatbot's performance over time, increasing both accuracy and user confidence. Regular updates of the chatbot's training data can also ensure that it remains current with the latest developments in statistical methods and public health practices.  
Future developments could focus on creating domain-specific chatbots trained on datasets directly relevant to public health and epidemiology. By incorporating knowledge about recent infectious disease outbreaks and advances in statistical methods adapted to these contexts, chatbots could become more reliable tools for public health professionals. This would also involve incorporating more sophisticated models for error detection, allowing the chatbot to flag potential risks in its recommendations and alert users to seek expert advice. Furthermore, collaboration with interdisciplinary teams that include statisticians, epidemiologists, and data scientists can help refine the chatbot's capabilities. This collaboration can improve the chatbot's ability to provide personalized recommendations that closely align with the specific needs of public health practitioners.  
Artificial intelligence-based chatbots such as ChatGPT offer significant potential as statistical consultants in infectious disease settings, providing quick and accessible guidance on the use of appropriate analytical techniques. However, their recommendations should not be considered a substitute for expert judgment. Although these systems can suggest general methods, their limitations--such as lack of deep understanding, potential for error, and inability to explain complex statistical nuances--mean they should be used with caution. The future of chatbots in this role likely lies in hybrid models that combine the scalability of AI with the critical oversight of human experts, ensuring more reliable and contextually appropriate statistical advice. As public health continues to evolve in an increasingly data-driven world, the integration of these technologies can improve decision-making processes and, ultimately, health outcomes.
## 10 Future Directions  
### Enhanced Privacy-Preserving Techniques  
In light of the ongoing advancements and the challenges faced in the field of FL defense techniques, several future directions can be explored to enhance the robustness and efficiency of these systems.  
**Homomorphic Encryption:** Future work should focus on making homomorphic encryption more efficient and scalable for real-time applications in FL. Homomorphic encryption enables computations on encrypted data without requiring decryption, thus preserving data privacy. However, the computational cost is currently prohibitive for large-scale applications. Optimizing this encryption method could significantly improve privacy-preserving FL systems.  
**Differential Privacy:** Integrating differential privacy into FL remains a significant research area. There is a need to balance the trade-off between data utility and privacy. Developing adaptive mechanisms that can dynamically adjust privacy parameters based on the sensitivity of the data could lead to more effective privacy preservation without compromising model performance.
## 3 Results  
### Extraction and Chemical Characteristics of SGP  
The extraction and fractionation procedure for preparing SGP is shown in Figure 1A. Sevag reagent and Diaion HP-20 resin were used to enrich carbohydrates and to avoid contamination with other plant-derived impurities such as proteins and phenolic compounds [30]. SGP yield was 25% (\(w\)/\(w\)). To examine whether SGP was a macromolecular extract of polysaccharides, its molecular weight distribution was assessed using HP-GPC and pullual molecular weight markers (Figure 1B). SGP showed a broad molecular weight distribution ranging from 5.9 to 788 kDa. Two distinct peaks occurred at 12.6 and 14.2 min retention time. The respective molecular weights were 245.7 and 67.6 kDa. These results imply that SGP is a macromolecular of heat-processed ginseng extract.  
To examine whether SGP was a polysaccharide-rich fraction, we first determined its chemical composition using colorimetric assays. As shown in Figure 2A and Table 1, total carbohydrate, uronic acid, and protein levels (%, \(w\)/\(w\)) in SGP were 84.3% \(\pm\) 2.1%, 5.5% \(\pm\) 0.6%, and 1.50% \(\pm\) 0.02%, respectively. These data suggest that SGP was mainly composed of carbohydrates. The monosaccharide composition of SGP was determined using PMP-derivatization and HPLC analysis. Component monosaccharides were liberated from polymers by hydrolysis of glycosidic linkages. PMP-derivatized monosaccharides were analyzed using reverse-phase HPLC and a UV detector. Representative chromatograms are shown in Figure 2B and Table 1. The content (%, \(w\)/\(w\)) of monosaccharides was determined using standard calibration curves. Glucose (67.3%) was the most dominant monosaccharide in SGP, followed by fucose (4.4%), arabinose (3.2%), galactose (2.8%), galacturonic acid
## 1 Introduction  
In recent years, Yb-doped fiber lasers (YDFLs) have been widely applied to the fields of scientific research and industrial production, owing to its advantages, which include a high conversion efficiency, a high beam quality, and excellent heat dissipation [1]. Benefiting from the development of large-mode-area (LMA) fibers and high-brightness semiconductor-based pump sources, the range of the output power achieved by the fiber lasers has been obviously improved [2; 3; 4]. Unfortunately, as they are limited by stimulated Brillouin scattering and undesirable thermo-optics, it is difficult to scale up the output power of a single fiber laser to higher power levels, and this would hardly meet the increasing demands of modern and technologically advanced industries [5; 6; 7; 8; 9].  
Filter-based spectral beam combining (FSBC) is a promising approach to significantly increasing beam brightness, with the basic principle of multiplexing a certain number of single beams in the near and far fields through the use of a steep-edge filter [10; 11; 12]. As the key element in the FSBC system, the filter needs to suffer exposure to all of the laser radiation from each incident channel,and the optical performance of the edge filter has a significant influence on combining efficiency and beam quality. An edge filter with high reflectivity and a steep rising edge would not only achieve a high combining efficiency, but it would reduce the negative influences on the laser beam-combining system that are induced by the scattering light. Meanwhile, the steep rising edge of the filter is conducive to supporting the combination of more channels, so as to produce a higher combined output power. However, previous research has mainly focused on the effect of spectral broadening, and the relationship between the combining efficiency and the reflectance curve of the combining element has not been sufficiently discussed [11]. Hence, it is essential to conduct a comprehensive analysis on the filter's optical characteristics, especially for the filter used in a high-energy spectral beam-combining system.  
In this contribution, we introduce a robust spectral beam by using a steep-edge filter to combine two high-power, broad-linewidth single beams. As for the combining element, the optical properties of the filter are closely related to the combining efficiency and the beam quality. By quoting and extending the uncorrelated surface roughness model as proposed by [PERSON] [13], a comprehensive discussion has been made, to analyze the effects of surface roughness, thickness error, and the angle of incidence on the filter's reflectivity and combining efficiency. The simulation results could predict the trends of the combination efficiency, and they can aid in the optimization of the FSBC system. Moreover, based on the angular spectrum theory, a preliminary study was undertaken, to establish the relationship between the surface roughness and the combined beam quality, which will be beneficial for the future fabrication of high-performance optical filters, using a 10-kW-class laser beam-combining system.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 4 Examples
### Monthly and Seasonal Averages  
Most datasets do not come with their monthly averages or climatology. To avoid the confusion with the WMO standard definition for climatology, which is an average of a variable for over 30 years [40],  
Figure 4: The average MERRA-2 atmospheric conditions at 850 hPa for the MJO event in Figure 3: (**a**) wind (in m/s); (**b**) air temperature (in Kelvin); (**c**) geopotential height (in m); and (**d**) specific humidity (in kg/kg).  
only monthly or seasonal averages are used here. As mentioned, for satellite-based datasets, it is quite a complicated process to develop a consistent dataset or climatology for climate studies. For example, corrections are often needed to remove systematic differences across multiple satellite missions or satellites in a constellation. With Giovanni, users can generate monthly or seasonal averages from a list of variables for a quick evaluation. Users can also define the length of the averaging period. Figure 5 contains two maps of the monthly and seasonal averages of IMERG Final Run. Figure 5a is the seasonal average map for June, July and August (JJA) during the period 2000-2019 and Figure 5b shows the similar average map for the month of July only. Users can visit the NASA GPM mission website for more seasonal information including animation [54]. Figure 5c shows their difference (July minus JJA) map, generated with the data downloaded from Giovanni and imported in Panoply for analysis and visualization. It is seen that large differences are found in some regions such as the Indian subcontinent, especially along the mountain range of the Western Ghats (Figure 5c).  
Figure 5: Monthly and seasonal IMERG Final Run precipitation maps (in mm/day) averaged during the period 2000–2019 from Giovanni: (**a**) seasonal averaged map for June, July and August (JJA); (**b**) monthly averaged map for July; and (**c**) their differences (July minus JJA) in mm/day.  
Likewise, similar average maps can be readily produced for other variables in Giovanni (e.g., in Figure 6). Figure 6a shows the surface skin temperatures in JJA (2000-2019) over the region between 30\({}^{\circ}\) N-S with smaller temperature gradients found over oceans than over land. Cloud top pressure in Figure 6b consistently shows the most convective activities occur north of the Equator during the boreal summer season (JJA). Surface wind speeds in JJA are shown in Figure 6c, showing the well-known Somali Jet [4]. Figure 6d is the dust column mass density map for JJA, showing several regions (e.g., the Sahara) with high dust density.  
Figure 6: Similar to Figure 5 for June, July and August except from MERRA-2: (**a**) surface skin temperature (in Kelvin); (**b**) cloud top pressure (in hPa); (**c**) surface wind speed (in m/s); and (**d**) dust column mass density (in kg/m\({}^{-2}\)).
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL
### Results
#### 5.3.3 Comparison of Average Relay Power of Proposed Scheme MRS-ST-BL with MRS-ST-FB and MRS-ST-WFB
From the comparison of Figure 7(a)-c, we can see that the transmission power of relay increases for all the schemes with the increase in social trust among users in the network (figures from left to right). Figure 8 illustrates similar trend in change of relay transmission power with the increase in search radius for different social trust scenarios, when node density in network is low. From the above analysis, we can see that our proposed scheme MRS-ST-BL selects relays with higher transmission power compared to MRS-ST-WFB. This increment in transmission power of  
Figure 8: Average transmission power comparison of various schemes in different social trust scenarios when network width is 100 m.  
relay contributed to improve average throughput of MRS-ST-BL in Figure 2(b), Figure 2(b), Figure 3(b), Figure 3(b), Figure 3(b), Figure 3(b) and Figure 3(b).
## 3 Proposed Approach
### For Multiple DGs Interconnection  
In multiple DGs case, all possible combinations or sequences of interconnected DG buses are generated and obtained. As an example, for the distribution system shown in Figure 4, the possible sequences of locations for interconnecting two DGs (if the slack bus is excluded) are: (Bus2, Bus3), (Bus2, Bus4), (Bus3, Bus4). For each obtained sequence, the system must be divided into regions in which the rules discussed before are satisfied. Then, the loads can be divided to be feed from the DGs and the substation in order to minimize the resistance in which each load current passes from it.  
Notes:  
1. If the DG is located in a bus at any sub-branch the number of the regions in the system is increased by one.
2. If the number of DGs added increases by one, the number of the regions in the system is increased by one.  
Figure 6 represents a flow chart which can be followed to determine the optimal penetration levels and optimal power factors for desired DG locations. After assessing these values for each sequence, the optimal location that minimizes the total power losses in the network can be evaluated.  
Figure 5: Sample of distribution network with sub-branch.
## 4 Numerical Results
### Scattering of the Natural Waves
Figure 11 shows normalized radiation patterns (in amplitude) for a relatively small width of the waveguide, \(h=10\)\(\upmu\)m. Here, the width of the variation interval of the main lobe is about \(46^{\circ}\) for \(\mu_{c}=0.6\ldots 1\) eV. For such value of the slab width, the decrease in the amplitude of the main lobe is not significant near resonance \(G_{1}\). Resonance \(G_{1}\) rises for \(\mu_{c}\approx 0.77\) eV. Finally, in Figure 12 we presented normalized radiation patterns (in amplitude) for the case of the natural wave \(H_{1}\) incidence. As the attenuation of this wave is much lower than that of wave \(P_{1}\), it makes sense to choose a larger number of strips, \(N=20\). The propagation constant of the wave \(H_{1}\) slightly depends on the chemical potential of graphene plane. As a result, the elevation angle of the main lobe is nearly independent of the chemical potential. Our study shows that the influence of the temperature \(T\) on the scattering characteristics is negligible.
## 4 Results  
### Simulation Steps and Conditions  
Before optimization, validation tests were conducted using varying parameters: cluster diameter \(D\) = 30 km, cluster circle positioning spacing \(\Delta\)d = 0 km and \(D\) = 40 km, and cluster \(\Delta\)d = 5 km. The optimal path was identified using sequential clustering and ant colony optimization algorithms. This path length was measured from the UAV's starting point to each aircraft's designated final target. The results are graphically illustrated in the accompanying Figures 3 and 4 and Table 1.  
Figure 4: UAVs’ path plan before optimization. The green circle represents the target, and the pink star represents the start point.  
For the scenario where \(D=30\) km, \(\Delta\)d = 0 km, a total of 18 UAVs are required to search 100 targets in a cluster formation. In this configuration, the longest path ranged from 34.52 km to 116.53 km, with an average of 70.50 km and a standard deviation of 23.58 km. When \(D=40\) km and \(\Delta\)d = 5 km, the mission necessitates 13 UAVs to search the same number of targets collectively. In this setup, the path lengths vary between 26.49 km and 141.87 km, averaging 86.55 km and a standard deviation of 33.97 km. The data show that the chosen values for cluster diameter \(D\) and cluster circle positioning spacing \(\Delta\)d significantly impact the number of UAVs deployed in the cluster and the extent of the longest path, indicating a substantial scope for optimization.
# Robust Template-Based Watermarking for DIBR 3D Images
## 5 Conclusions  
Due to the advent of new 3D applications, DIBR has taken on an important role in 3D content. To protect the copyright of such content, this paper proposed a template-based DIBR watermarking system. Ensuring robustness against geometric attacks in rendered images requires that watermarks should be robust to both DIBR and geometric attacks. In order to have robustness to a combination of linear and non-linear attacks, a watermarking system was designed by combining two methods: template and message watermark. Inserting a template into the curvelet domain robust to DIBR allowed this method to restore the geometrically-distorted image. Then, the message was extracted using a 1D-DCT message watermarking method that was invariant to the DIBR. In the experimental results, the proposed method showed high image quality. In terms of robustness, it had low BER to DIBR configuration adjustment, as well as standard configured DIBR. Additionally, the results showed that the proposed method is very robust against noise addition and JPEG compression. For geometric distortion, such as scaling, rotation, translation and shearing, good performance was also demonstrated. However, this method still does not consider the robustness of video coding, which is often used in 3D video, such as high efficiency video coding (HEVC). Therefore, future work will focus on extending this method to 3D videos. In addition, since the proposed method does not use message encryption, we could further improve the security of the message by using encryption. In the future, we will investigate various other types of attack, such as copy-and-paste or transplantation.  
Conceptualization, W.-H.K. Investigation, J.-U.H. Methodology, W.-H.K. and J.-U.H. Resources, H.J. Software, W.-H.K. Supervision, H.-K.L. Validation, H.-U.J. Writing of the original draft, W.-H.K.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2016R1A2B2009595). The authors declare no conflict of interest.
## 5 Component Reliability Importance  
For critical components that can be swapped, the reliability importance of a specific component or multiple components needs to be measured in the way described in Section 3.2. Components are defined based on their locations in the original system. The results of reliability importance analysis can help rank critical components, and allocate more resources (reliability and maintenance) to weakness than others.