esa-sceva/satcom-chunk-collection huggingface dataset:
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 4 Big Data Technologies
### Management of Spectral Files  
Figure 8 shows how to manage spectral files in a big data management system. Spectral files are managed in units of one spectral dataset. Different file directories represent different datasets, distinguished by dataset ID. Spectral datasets can be distinguished according to different application scenarios. For example, dataset A is used for red wine testing and dataset B is used for white spirits testing. The spectral files under a given dataset are at the same level. When creating a new spectral dataset, the system generates a dataset ID, then creates a directory in the data node to represent the dataset and saves all the spectral files to the appropriate spectral dataset directory. When reading a file, the system firstly determines the dataset ID and then reads the file from the directory. In this manner, the spectral files are managed separately according to the dataset to which they belong. In contrast, if spectral data is managed in a single spectral file, all spectral files are at the same level. Although this method results in a simpler file hierarchy, it is less efficient to read and write.
## 4 Discussion
QBA exhibits multi-target and multi-pathway properties in delaying aging, potentially acting through the FOXOO signaling pathway, lifespan regulation pathway, prolactin signaling pathway, AGE-RAGE signaling pathway, and insulin resistance pathway. This represents an encouraging discovery in the anti-aging field. As a functional factor derived from food, QBA is deemed safe, signifying its potential applications in food, pharmaceuticals, cosmetics, and other sectors. Employing network pharmacology, we have elucidated the molecular mechanisms behind QBA's anti-aging effects from both genetic and metabolic perspectives, confirming the reliability of predictions through molecular docking simulation. However, our study had limitations. Although we identified QBA and its anti-aging targets through extensive database searches, these findings may be insufficient due to the exclusion of recent research not yet reflected in the databases. Additionally, these targets and key pathways require further experimental validation. This informs our future research plans, focusing on exploring QBA's anti-aging mechanisms through in vitro, in vivo, and comparative analyses of its regulation across diverse proteins and pathways. Overall, our research offers a theoretical framework for discovering and exploiting the efficacy of novel functional factors, laying the groundwork for developing QBA-based products. The identified targets and pathways lay a foundation for experimental validation, guiding future studies to prioritize TNF and AKT1 as potential biomarkers. The KEGG results also suggest QBA may act through hormesis-like mechanisms, balancing pro- and anti-aging pathways. This comprehensive dataset fills a critical gap in understanding QBA's molecular actions, supporting its development as a therapeutic candidate for age-related diseases. While this study advances the understanding of QBA's anti-aging mechanisms, several limitations exist. First, target prediction relies on incomplete pharmacological databases, potentially missing rare or tissue-specific targets. Second, PPI network analysis uses confidence thresholds, risking false positives/negatives. Third, enrichment analysis overlooks post-translational modifications critical for aging signaling. To address these,future work should validate prioritized targets experimentally, integrate single-cell transcriptomic data, and employ dynamic docking simulations. These steps would refine QBA's anti-aging mechanisms and enhance translational potential.
## 3 Results
### The Improvement of Disaster Resistance Ability of Cropland after HSF Construction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Region** & **Time** & **Disaster Situation** \\ \hline \multirow{4}{*}{2011} & Northern Henan, east of & & \\  & middle Henan (Sanmenxia, & \\  & Luoyang, Zhengzhou, & \\  & Puyang, Pingdingshan, & \\  & Zhumadian, Nanyang) & & \\  & Some areas are dry, Xinyang & \\  & was seriously dry & \\  & Different degrees of drought & \\  & with Xinyang affected by the & \\  & most serious drought & \\  & 2014 & Different degrees of drought & \\ \hline \hline \end{tabular}
\begin{tabular}{c} \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the drought disasters in Henan Province in the period 2011–2014. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cropland Productivity in**} & \multicolumn{2}{c}{**Change Rate of**} & \multicolumn{2}{c}{**Cropland Productivity in**} & \multicolumn{2}{c}{**Change Rate of**} & \multicolumn{1}{c}{**Productivity**} \\ \cline{2-7} \multicolumn{1}{c}{**Project**} & \multicolumn{1}{c}{**Project Region (kg/mu)**} & \multicolumn{1}{c}{**Cropland Productivity**} & \multicolumn{1}{c}{**Non-Project Region (kg/mu)**} & \multicolumn{1}{c}{**Cropland Productivity**} & \multicolumn{1}{c}{**Stability Improvement**} \\ \cline{2-7} \multicolumn{1}{c}{} & **Drought** & **Normal** & **under Drought Stress** & **Drought** & **Normal** & **under Drought Stress in** & **provement** \\  & **Year** & **Year** & **in Project Region (\%)** & **Year** & **Year** & **Non-Project Region (\%)** & **Level (\%)** \\ \hline Project A & 950 & 889 & 6.42 & 741 & 753 & \(-1.62\) & 8.04 \\ Project B & 983 & 931 & 5.29 & 784 & 865 & \(-10.33\) & 15.62 \\ Project C & 880 & 897 & \(-1.93\) & 663 & 758 & \(-14.33\) & 12.40 \\ Project D & 928 & 803 & 13.47 & 702 & 737 & \(-4.99\) & 18.46 \\ Project E & 867 & 807 & 6.92 & 685 & 727 & \(-6.13\) & 13.05 \\ Project F & 974 & 794 & 18.48 & 687 & 724 & \(-5.39\) & 23.87 \\ Project G & 888 & 793 & 10.70 & 711 & 762 & \(-7.17\) & 17.87 \\ Project H & 871 & 917 & \(-5.28\) & 694 & 722 & \(-4.03\) & \(-1.25\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of productivity stability improvement in sampling project region. Figure 7: Dynamic change in the cropland productivity of HSF construction in drought and normal years.
## 4 Performance Evaluation of the Proposed Framework
### Cluster Setup Performance  
Figures 3 and 4 illustrate the time consumed by various cluster setups over a one hour and three hour time periods, respectively, for processing 20 GB of data. The time consumed represents the aggregated time consumption of each workload. It can be noticed in Figure 4 that the seven-slave cluster setup on the AWS cloud takes much longer to process the data, when compared to the fourteen-slave cluster or the auto-scaling cluster setups. However, this situation happens for the heavy data loads, and this is demonstrated by the increased time consumed starting at minute 91 and decreasing by minute 156 as seen in the graph. While the data load on the cluster is exceeded, the processing jobs of input data queue in a job tracker waiting to be assigned when the old jobs are completed. More precisely, submitted jobs in a job queue wait a long period of time when there is an increase in the data load.  
The fixed cluster setups of three, seven and fourteen slave nodes therefore restrict capacity to process the map reducing jobs on the streaming data load on the Hadoop cluster. Moreover, the results show that the time consumption of each workload for the auto-scaling cluster is almost the same as the fourteen-slave cluster setup. However, it can be noticed in Figure 4 that the auto-scaling cluster presents some fluctuations starting at 1.5 h. This is not the case for the fourteen-slaves cluster setup. The sharper fluctuations indicate the scaling-up of the resources on the cluster in order to finish the jobs on time. The scale-up performance involves adding task/slave nodes on the cluster to make sure that the jobs complete on time.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 4 Mission Elements
### Data and Products
Aside from these data products directly delivered by the MERLIN mission, level 4 (L4) data products are methane surface fluxes at various temporal and spatial scales, obtained through assimilation in transport models. Level 4 data products will be provided by scientists at various scientific research institutions using lower level MERLIN data. In the context of the overall mission objective, additional geophysical products can be derived from MERLIN mission using the above mentioned L1a products. They might provide information about the surface (topography, estimates of the vegetation height and occasionally information on the vertical structure, Lidar retro-reflectance) and the atmosphere (cloud boundaries, including cloud base for small-to-moderate cloud optical thickness). Their retrieval will be left to individual scientific groups worldwide.
## 5 Ethical Considerations
### Ensuring Accuracy and Fairness in 3D Object Modeling  
Maintaining fairness and balance in data representation is crucial in the application of 3D modeling techniques. Special attention must be given to avoid overrepresentation or underrepresentation of specific regions, buildings, or roads during the modeling process. To prevent data loss and distortion, a comparative analysis of multi-resolution source data was conducted.  
Using only high-resolution data may lead to excessive computational overhead, while relying solely on low-resolution data can reduce the level of spatial detail. Therefore, to ensure both accuracy and fairness, this study adopted an optimal data resolution tailored to the requirements of LoD1-level modeling, maintaining a balanced approach to 3D object representation.
## 3 Results and Discussion
### Surface Potential and Current
#### 3.1.3 Computational Efficiency  
To evaluate the computational efficiency of the proposed explicit surface potential approximation, the models are implemented in Verilog-A and the runtime is measured with HSPICE simulations. Each runtime evaluation is performed by measuring the difference between the CPU time of 50,000 surface potential calculations and that of a surface potential calculation in order to reduce the impact of read-in, error check, setup, and output times. Table 4 compares the computational time of different surface potential models using a computational efficient charge density model (CE CDM) and accurate charge density model, respectively.  
To achieve the same accuracy for surface potential, the proposed solution with just one iteration achieves up to 1.8\(\times\) runtime speedup compared to the iterative solution. The proposed solution has almost the same computational efficiency as [13], but higher accuracy. The same method is used to evaluate the computational efficiency of current calculations. Figure 6 shows the computational time of surface potential and current  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & [4] & [13] & **Proposed** & \
\begin{tabular}{c} **Proposed +** \\ **Iteration** \\ \end{tabular} \\ \hline \multirow{2}{*}{CE CDM} & Time (s) & 170.05 & 81.77 & 82.11 & 96.19 \\  & Speedup & (1\(\times\)) & (2.1\(\times\)) & (2.1\(\times\)) & (1.8\(\times\)) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Accurate \\ CDM \\ \end{tabular} } & Time (s) & 190.71 & 87.33 & 86.68 & 102.5 \\  & Speedup & (1\(\times\)) & (2.2\(\times\)) & (2.2\(\times\)) & (1.9\(\times\)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational time of surface potential calculation.  
Figure 5: (**a**) Drain current versus front gate bias at different values of drain voltage; (**b**) Drain current versus drain voltage at different values of front\(-\)gate bias for a n\(-\)type short\(-\)channel device with \(L=20\) nm, \(W=80\) nm, \(T_{OX1}=1.33\) nm, \(T_{OX2}=20\) nm and \(T_{Si}=6\) nm.  
calculations, respectively. The analytical solution in [13] is represented by blue line. The solid lines with symbols and dashed lines in Figure 6 indicate the computational time using the accurate charge density model and the computational efficient charge density model, respectively.
## 3 Results and Analysis
### Evaluation of Soil Nutrient Status by LightGBM Model
#### 3.3.3 Nutrient Status Evaluation  
A total of 290 tobacco planting soil samples including nine nutrient factors in Debao County were taken as the test sample sets to conduct automatic classification of nutrient status evaluation in the LightGBM model. The classification level was consistent with that of model training and was divided into five classes (Table 5). As shown in Table 5, Grade I indicates that the nutrient status of tobacco planting soil is at a very high level,  
Figure 5: Eigenvalue score of LightGBM model.  
accounting for 10%. Grade II and III indicate that the nutrient status is high and relatively high, accounting for 7.24% and 17.59%. Grade IV indicates that the nutrient status of tobacco planting soil is at the medium level, accounting for 46.2%, which is the largest part of the nutrient status of tobacco planting soil in the town. Grade V indicates that the nutrient status of tobacco planting soil is at the general level, accounting for 18.97%.  
Based on the evaluation results of nutrient status applied by the LightGBM model, the distribution of soil nutrient status in seven tobacco planting towns in Debao County can be obtained using the kriging analysis method (Figure 6). In Table 5 and Figure 6, the nutrient status of tobacco planting soil in Yantong Town and Jingde Town among the seven tobacco planting towns in Debao County are at a extremely high level, accounting for 0.69% and 9.31%. The nutrient status of Yantong Town, Du'an Town, Dongling Town and Jingde Town in the county are at a medium level or above, while Longguang Town, Najia Town and Zurong Town are at medium and low level. In general, the soil nutrient status of the seven tobacco planting towns in Debao County is basically at the medium level or above, accounting for 81.03%, and the low level of nutrient status accounts for 18.97%.
The system model of HAP drones and LEO satellite collaborative networks is introduced in Section 3. In Section 4, we formulate the problem. We introduce the task offloading and resource allocation algorithm proposed in this paper in detail in Section 5. Numerical results are presented to verify the convergence and performance of the proposed algorithm in Section 6. Finally, we conclude the paper in Section 7.
# Influence of Electrohydrodynamics on the Drying Characteristics and Volatile Components of Ginger
## 3 Results and Discussion
### Protein Secondary Structure of Ginger  
The secondary structure of ginger protein was detected by Fourier infrared spectroscopy (FTIR). The secondary structure of the protein has different types, which can be specifically classified into \(\alpha\)-helix, \(\beta\)-folding, \(\beta\)-turning, and \(\beta\)-antiparallel folding, and the spectral band region of its conformation is the amide I band at 1600-700 cm\({}^{-1}\)[40]. The amide I band shows bands for \(\beta\)-folded structure (1610-1642 cm\({}^{-1}\)), \(\alpha\)-helix structure (1650-1660 cm\({}^{-1}\)), \(\beta\)-turned corner structure (1660-1680 cm\({}^{-1}\)), and \(\beta\)-antiparallel folded structure (1680-1700 cm\({}^{-1}\)). \(\alpha\)-helix, \(\beta\)-folding, and \(\beta\)-antiparallel folding are ordered structures, while \(\beta\)-turning is a disordered structure. The transmission spectra in Figure 5a were analyzed through baseline adjustment and the application of second-order derivatives using the appropriate software (Peak Fit 4.12). The statistical analysis of the various structural components within the protein's secondary structure yielded results presented in Figure 5b. Upon observing Figure 5b, it becomes evident that \(\alpha\)-helix and \(\beta\)-sheet constitute the primary forms of the secondary structure in ginger sheet proteins. Figure 5b shows that the contents of ordered structures treated at 0, 15, 20, 25, and 30 kV drying voltage were 87%, 86%, 83%, 86%, and 84%, respectively, whereas the contents of disordered structures were 13%, 14%, 17%, 14%, and 16%. When the voltage is increased, electrohydrodynamic (EHD) drying discharge weakens the hydrogen bonding inside amino acids, causing ruptures in the \(\alpha\)-helix and \(\beta\)-folding. That is, electrohydrodynamic (EHD) drying causes the protein secondary structure to change from ordered to disordered. [PERSON] et al. [41] found that this unfolds or denatures the high-level structure of proteins and reorganizes the peptide chains, altering the structural and functional properties of material proteins. As such, the EHD drying process has a significant impact on the internal structure of materials, which in turn affects product quality.
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 4 Results and Discussion
Figure 9 depicts the behavior of NLI, LDs and PAPR for the proposed WDM model and the BER outcomes with the increase in number of WDM channels. As the number of channels are increased, the crosstalk due to inter-channel interference affects the performance for smaller channel spacings. Figure 6: Normalized signal to noise ratio against BER for 0, 2.5, 5 and 8 deg PEs at 10 km of path cover and 60 GHz of RF. Figure 7: BER versus transmission range for modulation signals with 16 QAM (Non-OFDM), 64 QAM (Non-OFDM), 16 QAM-OFDM and 64 QAM-OFDM. Such effects become more visible after transmission of 10 km of fiber distance as shown in Figure 9. In addition, with decrease in channel spacing, the BER threshold cannot be achieved with 12.5 GHz spacing using 14 or more channels even with the treatment of NLIs and LDs. The presented model is further investigated using constellation visualizer diagrams, optical spectrum analyzer and RF spectrum analyzer, which are explained in Figures 10a-d, 11a-c and 12a-c. Figure 10a shows the performance of WDM-RoF model at 10 km of transmission range without mitigating NLIs, LDs and PAPR. The constellation diagram shows that received symbols are difficult to be recovered with such performance. Figure 10b shows the performance improvement in terms  
Figure 8: BER versus input launch power at 10 km optical fiber length for evaluating nonlinear impairments (NLIs), peak to average power ratio (PAPR) and linear distortions (LDs). Figure 9: Channels as a function of BER at 12.5, 25 and 50 GHz channel spacing, length = 10 km and RF = 5 GHz. of the recovered constellation for 16-QAM and Figure 10d for 64-QAM. Figure 11 shows the optical spectrum of the first channel from the WDM system. The optical spectrum shown in Figure 11b has been taken from the output of the FBG at the RAU. The noise level can be seen due to the NLIs and LDs in green color, which spreads across the whole band. The proposed mitigation lowers it down to a considerable extent as can be seen from Figure 11c. The RF spectrum is shown in Figure 12 where it can be seen how NLIs and DLs cause the amplitude variations (Figure 12b) and are treated (Figure 12c). Figure 11: Optical spectrum analyzer; (**a**) input transmitted signal for 1 st channel, (**b**) without managing NLIs, LDs and PAPR at 10 km of fiber length, (**c**) output signals with managed of NLIs, LDs and PAPR at 10 km of length. Figure 2:0 Constellation visualizer diagrams; (**a**) constellation diagram of 16 QAM-OFDM at 10 km distance and 5 GHz RF without addressing NLs, LDs and phase noise, (**b**) constellation diagram of 16 QAM-OFDM at 10 km distance and 5 GHz RF after the proposed DSP receiver, (**c**) constellation diagram of 64 QAM-OFDM at 10 km distance and 5 GHz RF without addressing NLs, LDs and phase noise and (**d**) constellation diagram of 64 QAM-OFDM at 10 km distance and 5 GHz RF after after the proposed DSP receiver.
###### Abstract  
Enzyme-induced carbonate precipitation (EICP) is an emerging biogeochemical technique that uses free urease to improve soil. Despite its advantages of eliminating complex microbial cultures and reducing reaction byproducts, its efficiency is considered lower than that of microbial induced calcite precipitation (MICP) due to the lack of nucleation sites that induce calcium carbonate deposition. To enhance the strengthening efficiency of EICP for fine-grained soils, an improved EICP method that involves adding an appropriate mass concentration of organic materials (skim milk powder, glutinous rice powder, and brown sugar) into urease solution was proposed and applied to reinforce silt in the Yellow River flood area of China. The preferred concentration and ratio of cementation solution and the optimum concentration of each of the organic materials were determined. Then, the reinforcement effect of the improved EICP at the optimum concentration was compared with the control group, and the reinforcement mechanism for this method was discussed. The results show that after the organic material inclusions, soil strength can be enhanced by 33% compared with EICP-treated soil and is nearly four times higher than that of untreated soil. The superiority of this method over traditional EICP and MICP mainly stems from its ability to provide templates and nucleation sites for calcium carbonate deposition and to improve the size, morphology, and structure of calcium carbonate crystals.  
biogeotechnical; soil improvement; enzyme-induced carbonate precipitation (EICP); organic materials; silt soil; nucleation site; template +
Footnote †: journal: Applied Soft. 2020, 10, 7678; doi:10.3390/app10217678
## 3 Results
### Special Role of PRI in the SIF-GPP Relationship for Corn and Soybean  
#### 3.2.1 Linear Relationship of SIF to GPP for Corn and Soybean  
For corn and soybean, we examined the association between \(SIF_{t}\) and GPP. At the half-hourly and daily timescales, \(SIF_{t}\) exhibited a strong connection with GPP for both corn and soybean (R\({}^{2}\) = 0.48-0.62, \(p\) < 0.01; Table 1). It is worthy to note that the slope of the linear \(SIF_{t}\)-GPP relationship for corn ranged from 8.38 to 8.87 (\(\mathrm{\mu mol\,CO_{2}\cdot m^{-2}\cdot s^{-1}}\)), whereas the slopes for soybean ranged from 6.45 to 6.57 (\(\mathrm{\mu mol\,CO_{2}\cdot m^{-2}\cdot s^{-1}}\)).  
From Figure 4, it can be seen that the slopes of the \(SIF_{t}\)-GPP relationship generally have a lower value at the lower PRI than that at the higher PRI. It is critical to combine information from \(SIF_{t}\) and PRI in order to calculate GPP accurately.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
###### Abstract  
The aim of this study was to evaluate the potential use of remote and proximal sensing techniques to identify homogeneous zones in a high density irrigated olive (_Olea europaea_ L.) orchard subjected to three irrigation regimes (full irrigation, deficit irrigation and rainfted conditions). An unmanned aerial vehicle equipped with a multispectral camera was used to measure the canopy NDVI and two different proximal soil sensors to map soil spatial variability at high resolution. We identified two clusters of trees showing differences in fruit yield (17.259 and 14.003 kg per tree in Cluster 1 and 2, respectively) and annual TCSA increment (0.26 and 0.24 dm\({}^{2}\), respectively). The higher tree productivity measured in Cluster 1 also resulted in a higher water use efficiency for fruit (WUE\({}_{\ell}\) of 0.90 g dry weight L\({}^{-1}\) H\({}_{2}\)O) and oil (WUE\({}_{\ell}\) of 0.32 g oil L\({}^{-1}\) H\({}_{2}\)O) compared to Cluster 2 (0.67 and 0.27 for WUE\({}_{\ell}\) and WUE\({}_{\infty}\), respectively). Remote and proximal sensing technologies allowed to determine that: (i) the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard; (ii) tree vigour played a major role in determining the final fruit yield under optimal soil water availability, whereas soil features prevailed under rainfted conditions.  
NDVI; _Olea europaea_ L.; precision orchard management; soil apparent electrical conductivity; tree water status; unmanned aerial vehicle; water use efficiency