esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The presence of SS or CM amendments in the soil resulted in higher GSL levels in collard plants compared to those grown under controlled conditions. This finding suggests that collard leaves cultivated in soil amended with SS or CM could be used as an alternative method for managing soil-borne diseases in agricultural systems. Furthermore, [PERSON] et al. [18] found that different ratios of far-red and red light reflected by colored plastic mules used for water conservation irrigation systems affected turnip plants (_Brassica rapa_ L. cv. Purple Top). Turnip roots grown in blue plastic muld exhibited the highest total GSL concentrations among the various colored mulches. Although turnip roots grown with white  
Figure 1: Chemical structure of glucosinolate (GSL) molecule as potassium salt. Note that every GSL molecule has a central carbon (C) atom bonded to a sulfur atom in the glucose group and via a N atom to an O-sulfated thiohydroximate. The side group R differs among diverse GSLs’ structures and biological activities. plastic much were larger in size than those grown with blue or green plastic mulch, they showed lower GSL concentrations on a fresh weight basis. Another study [14] highlighted two approaches to increase ITC concentrations: growing Brassica plant varieties with high GSL content or disrupting the cells of Brassica plants when incorporating their tissues into the soil. This disruption causes the GSLs to break down, resulting in a straightforward increase in ITC hydrolysis. In this current study, the aim is to investigate the impact of various organic and inorganic fertilizers on the internal composition of Brassica plants, including GSLs, vitamin C, total phenols, and soluble sugars. Additionally, the pyrolysis process, which involves burning material without oxygen, produces biochar. The potential benefits of biochar in improving agricultural soil quality have been investigated. It has been observed that biochar enhances the soil's capacity to retain nutrients and water, potentially leading to accelerated plant growth [19]. Furthermore, biochar can serve as a habitat for soil microorganisms, aiding in the breakdown of organic matter in the soil [20]. The present investigation aimed to: (1) Assess the composition of animal manures used for growing turnips, arguula, and mustard. (2) Assess the variation in total GSLs concentration among varieties of turnip, _Brassica rapa_ (Tokyo Cross, Scarlet Queen Red, and Purple Top White Globe. (3) Assess the impact of fourteen (14) soil treatments on the concentration of GSL, one of the unique phytochemicals in Brassica family. (4) Test the impact of animal manure (SS, CM, and HM) as organic fertilizers on the concentrations of phytochemicals (vitamin C, total phenols, and soluble sugars in three varieties of field-grown turnips, _Brassica rapa_; arguula, _Eruca sative_; and mustard, _Brassica juncea_ greens.
## 5 Simulation Results and Discussion
### Plot Performance of Proposed Risk Mitigation Methods  
Figures 16 and 17 represent voltage changes at the PCC as a result of both 3 LG temporary and permanent failures at location A with a 900 ms delay. The responses show that delayed minimization techniques worked well and enhanced the power performance of the system. In terms of improving hybrid grid power quality, the results also suggest that the fuzzy-logic-controlled strategy beat the modified predictor method. The proposed approach's efficacy in limiting the negative impacts of delay and enhancing the system power quality was proved. Therefore, the fuzzy-logic-controlled technique beat the improved logic-controlled predictor method. Figure 18 shows the fluctuation in the active power of the generator in the test system (G1-G6). When the three-phase failure occurred, at \(t_{1}=1\) the two generators immediately dropped out of the synchronization process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Delay** \\ **Value** \\ \end{tabular} } & \multirow{2}{*}{**Fault Type**} & \multirow{2}{*}{**Fault**} & \multicolumn{2}{c}{**Voltage Indices at PCC**} \\ \cline{3-5}  & & & & **No Compensation** & **Predictor** & **Fuzzy** \\  & & & & **Method** & **Method** \\ \hline \multirow{6}{*}{900 ms} & & A & 0.2128 & 0.1898 & 0.1698 \\  & 3 LG & B & 0.2055 & 0.1966 & 0.1776 \\  & Temp & C & 0.2128 & 0.1976 & 0.1776 \\  & & A & 0.1090 & 0.1060 & 0.0789 \\  & 3 LG & B & 0.1651 & 0.1551 & 0.1351 \\  & Perm & C & 0.1828 & 0.1091 & 0.0891 \\  & & A & 0.2126 & 0.2084 & 0.1894 \\  & 3 LG & B & 0.2043 & 0.1978 & 0.1778 \\  & Temp & C & 0.2265 & 0.1992 & 0.1802 \\  & & A & 0.1059 & 0.1093 & 0.0893 \\  & 3 LG & B & 0.1659 & 0.1560 & 0.1352 \\  & Perm & C & 0.1827 & 0.1093 & 0.0893 \\  & & A & 0.2128 & 0.2097 & 0.1897 \\  & 3 LG & B & 0.2044 & 0.2004 & 0.1814 \\  & 3 LG & B & 0.2044 & 0.2004 & 0.1814 \\  & Temp & C & 0.2128 & 0.2035 & 0.1845 \\
500 ms & & A & 0.1061 & 0.1095 & 0.0895 \\  & 3 LG & B & 0.1669 & 0.1406 & 0.1216 \\  & Perm & C & 0.1819 & 0.1095 & 0.0895 \\  & & A & 0.2032 & 0.2059 & 0.1876 \\  & 3 LG & B & 0.2042 & 0.2053 & 0.1844 \\
300 ms & Temp & C & 0.2085 & 0.2056 & 0.1846 \\  & & A & 0.1059 & 0.1091 & 0.0891 \\  & 3 LG & B & 0.1655 & 0.1594 & 0.1384 \\  & Perm & C & 0.1678 & 0.1095 & 0.0895 \\  & & A & 0.2089 & 0.2090 & 0.1872 \\  & 3 LG & B & 0.2110 & 0.2066 & 0.1756 \\
100 ms & Temp & C & 0.2135 & 0.2080 & 0.1915 \\  & & A & 0.1062 & 0.1094 & 0.0894 \\  & 3 LG & B & 0.1664 & 0.1597 & 0.1297 \\  & Perm & C & 0.0111 & 0.0109 & 0.0074 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Voltage indices of PV plant.  
Figure 16: Inter-area voltage for 3 LG permanent fault at location B.
## 3 Formulation of the Mathematical Model
### Modified Effective Pore Diameter with Effects of Adsorption/Desorption and Stress-Sensitivity  
Both adsorption/desorption and stress-sensitivity induced by dynamic reservoir pressure changes have impacts on the effective pore diameters of shale reservoirs. To accurately evaluate gas flow within a shale matrix, it is important to obtain the effective pore diameter size for shale reservoirs under different reservoir pressure conditions while considering the effects of adsorption/desorption and stress-sensitivity.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
###### Abstract  
Drought events have been reported in all Brazilian regions every year, evolving slowly over time and large areas, and largely impacting agriculture, hydropower production, and water supplies. In the last two decades, major drought events have occurred over the country, such as the 2010 and 2015 events in the Amazon, the 2012 event in the Pampa, and the 2014 event in the Cerrado biome. This research aimed to understand drought propagation and patterns over these biomes through joint analysis of hydrological, climatic, and vegetation indices based on remote sensing data. To understand the drought cascade propagation patterns, we assessed precipitation, evapotranspiration, soil moisture (at surface and sub-surface), terrestrial water storage, land surface temperature, enhanced vegetation index, and gross primary productivity. Similar drought patterns were observed in the 2015 Amazon and 2012 Pampa droughts, with meteorological and agricultural droughts followed by a hydrological drought, while the 2014 event in the Cerrado was more associated with a hydrological drought. Moreover, the 2015 Amazon drought showed a different pattern than that of 2010, with higher anomalies in precipitation and lower anomalies in evapotranspiration. Thus, drought propagation behaves differently in distinct Brazilian biomes. Our results highlight that terrestrial water storage anomalies were able to represent the hydrological drought patterns over the country. Our findings reveal important aspects of drought propagation using remote sensing in a heterogenous country largely affected by such events.  
A 202315454[[https://doi.org/10.3390/rs15020454](https://doi.org/10.3390/rs15020454)]([https://doi.org/10.3390/rs15020454](https://doi.org/10.3390/rs15020454))
###### Abstract  
In terms of optimization, one of the core challenges in Wireless Sensor Networks is determining the locations of nodes. While simulating this problem in a 3D environment instead of the traditional 2D increases problem complexity, it is crucial for accurately representing real-world scenarios. Furthermore, the success of locating moving nodes in a 3D space is closely linked to the overall efficiency of the network. This study proposes a solution that can detect the locations of target nodes at various levels using a single anchor node. The method employs the Improved Adaptive Artificial Bee Colony (iaABC) algorithm, a model of the classical ABC algorithm. This improvement updates the control parameter values during the scanning, allowing the algorithm to focus its search direction on better exploitation. The performance of the search and convergence of this method was tested on CEC 2022 test suits. The CEC 2022 benchmark functions have more up-to-date content and are fairer because they utilize the same initial solutions for each competing algorithm. Subsequently, the approach was used to determine node locations. The results demonstrated that iaABC can locate 100 target nodes with a single anchor in a 3D environment.  
Keywords:wireless sensor networks; node location; umbrella projection; improved adaptive ABC +
Footnote †: journal: Applied sciences
## 2 Spoilage Characteristics and Shelf Life of Fresh Fruit and Vegetables
### Microbial Growth  
All fruits and vegetables are nutrient-rich and high in water activity, providing a suitable habitat for various microorganisms. The main spoilage flora is composed of saprophytic microorganisms derived from the surroundings in the field or orchard (e.g., soil, air, insects, irrigation water, hands of personnel) [26]. Microbial spoilage can occur for any product and often is the limiting factor for its shelf life, because of altered quality parameters such as smell, flavour, firmness, texture, and colour. For vegetables, bacterial spoilage is common [27]. Organisms such as Erwinia carotivora and Pseudomonas spp. frequently cause spoilage in onions, crucifers, peas, beans, carrots, potatoes, asparagus, celery, and lettuce [27]. Due to their acidity, fruit are more likely to be infected by fungi [27]. Berries in particular are very susceptible to fungal infections and subsequent spoilage. Main spoilage organisms are Rhizous stolonifer and Botrytis cinerea, both leading to high market losses [26]. Likewise, citrus fruit are frequently spoiled by fungi, especially by the species Penicillium italcium and _Penicillium digitatum_[26]. In addition, fruit and vegetables can be associated with pathogens naturally occurring in the plant environment or by contamination via various sources. Recent outbreaks in Europe have for instance been reported for Norovirus in radish sprouts and _Salmonella enteritidis_ in cucumbers and tomatoes [28; 29]. Growth of microorganisms is strongly dependent on ambient parameters such as temperature, RH, and gas atmosphere [30]. Therefore, close control and monitoring of these parameters is essential for delaying microbial spoilage and preventing the growth of pathogens.
## 3 Results
### Multi-Incidence and Multi-Polarization Estimates in C-Band  
In the C-band, independent statistical algorithms are trained and validated by distinguishing the polarization states and, as previously conducted, for buffers ranging from 5 to 30 m (Figure 6). The evolution of statistical performance with the size of the buffer shows a similar behavior whatever the considered polarization state. A significant increase in the coefficient of correlation is observed for the first four intra-plot studied spatial scales (with correlation values increasing from 0.29 to 0.71, and a decrease in RMSE of more than 2.3 dB, on average), with the gain of accuracy being more moderate for the buffers of 25 and 30 m. In comparison with the X-band, the coefficient of correlation associated with the first spatial scale is very low in the C-band. Despite the increase in performances accompanying the change in the intra-plot spatial scale, they remain lower than those obtained previously for the X-band.  
The level of accuracy obtained for the C-band is acceptable and fully consistent with past studies [1; 2; 3; 5; 6; 7], where several types of modeling were evaluated and classified into two categories: physical-based [11; 12; 13; 32; 33] and empirical (or semi-empirical) models [6; 8; 9; 10]. In the present study, for a buffer with a radius of 30 m, the values of correlation and RMSE are close for modeled co-polarized backscattering coefficients (r close to 0.75 and RMSE of 1.67 dB), while cross-polarized backscattering coefficients are estimated with higher correlation and RMSE (r of 0.80 and RMSE of 1.86 dB) values derived from the validation subset of 399 samples (Figure 7). As observed in the X-band, the values of the RMSE and correlation obtained with a buffer of 30 m (or even with a buffer of 25 m for some polarization states) are higher than those obtained with smaller buffers and at plot scale (as observed and explained for the X-band, Figure 6). The phenomenon of under sampling of surface conditions observed in the X-band is also observed in the C-band. It causes bad simulations for low and high values of the backscattering coefficient (whatever the polarization: VV, HH, and VH) due to smooth and dry or rough and wet surface conditions. As for the X-band, more ground data are requested for these conditions.  
Figure 5: Comparison between simulated and observed backscattering coefficients in X-band for a buffer with a radius of 30 m. The gray or black colors represent the training or validation datasets, respectively.  
Figure 6: Evolution of the statistical performances (coefficients of correlation and root mean square errors, bars and dots, respectively) with the buffer’s size and at the plot spatial scale (PS, [24]) for the training (gray) or validation (black) subsets of samples. Results obtained in C-band in three polarization states: (**a**) HH, (**b**) VV, and (**c**) VH.  
Figure 7: _Cont_.
# Intra-oral 3D Scanning for the Digital Evaluation of Dental Arch Parameters
## 2 Methods  
### Data Acquisition  
The digital dental model construction, processing and analysis are based upon data acquired by the widely used Trios system. This intra-oral scanning device, introduced by the 3 shape company in 2010, uses advanced parallel confocal imaging and point-and-stitch methods to reconstruct surfaces from data points. Figure 1 presents the use of the wand for data acquisition (with a heating system to prevent lens fogging) and the fundamental steps of data processing. In principle, the device [29; 30; 31] captures sets of two-dimensional (2D) images of the oral cavity with its lower and upper jaws and combines them into the 3D objects. This system is based on ultrafast optical sectioning technology [19] to construct the 3D model of the surface geometry with the possibility of using function tolerances to control and evaluate error in geometrical modelling [32]. The Trios software is also able to detect areas with missing data that must be rescanned. A comparison of the Trios system with further intra-oral scanners in [33] presents its very high resolution of 23,500 triangles and line deviation bellow 25 \(\upmu\)m.  
The 3 shape opened its Trios intra-oral scanner system for stereolithography (STL) file export in 2017. The present paper uses these data to construct the digital model that can be visualised and processed by the appropriate mathematical tools. Results obtained in the MATLAB (R2018a, The MathWorks, Inc., Natick, MA, USA, 2018) environment are presented in Figure 2. The processing steps of the point cloud data acquired by the intra-oral scanner include:  
1. Visualisation of the STL data allowing the surface model rotation and its detailed study from different view-points,
2. The STL data transform and the spatial model construction in the selected coordinate system enabling application of specific mathematical methods for data processing,
3. The detailed 3D model and contour plot construction of all teeth allowing detection of specific surface areas for further processing and evaluation of dental arch parameters.  
The proposed experiments included the 3D intra-oral scanning of the lower and the upper dental arch of one individual (female, 25 years old) during one day by different specialists, and analysis of 20 experiments to show the reproducibility of selected dental arch parameters and the reliability of electronic models to replace the plaster casts that are frequently still in use in statontology [1].  
Figure 2: The processing steps of the point cloud data acquired by the intra oral scanner including visualisation of the STL data, their transform to the spatial model in the selected coordinate system and the detailed molar contour plot allowing detection of specific surface areas: (**a**) STL model; (**b**) 3D spatial model; (**c**) Molar contour plot.
## 6 Key Challenges and Mitigation Strategies
### Scalability and On-Demand Deployment  
Finally, achieving efficient scalability and supporting on-demand deployment is crucial for applying FL-trained LLMs across massive and dynamic IoT populations [42]. Managing the training process and subsequent inference efficiently requires optimized edge infrastructure, including techniques like caching and optimized model serving [43]. Scalable FL orchestration is also essential, employing architectures like hierarchical, decentralized, or asynchronous FL, each presenting different trade-offs in coordination complexity, robustness to failures or stragglers, and communication latency [101]. Furthermore, effective resource-aware management, incorporating adaptive scheduling, intelligent client selection strategies, and potentially incentive mechanisms, is needed to handle the dynamic nature of device availability and network conditions [125].
## 3 Results and Discussion
### Reaction Mechanism  
The reaction mechanism for obtaining MIPs is proposed based on the literature [45; 62] and the results of FTIR spectrometry, and is shown in the example of MIPs with DVB in Figure 6. The suggested mechanism firstly shows the formation of a pre-polymerization complex, in which the monomers and template are involved. Donor-acceptor bonds are formed between the pyridine group of the monomer 4-VP and template ions, and hydrogen bonds are formed between the monomer MAA and template molecules. Furthermore, the pre-polymerization complex is polymerized due to the disclosure of the double bonds of MAA and 4-VP. The polymerization process occurs in the presence of the initiator AIBN, which initiates the polymerization, and then, due to the presence of a crosslinker, the mesh structure is formed. Finally, during the process of dialysis, the template molecule is washed off or removed, and a polymer with molecular imprints is obtained. Molecular imprints are formed as complementary cavities.  
Figure 5: FTIR spectra of MIP(DVB).
# Map Matching Based on Seq2 Seq with Topology Information
## 4 Experiment
### Experimental Result  
#### 4.4.1 Visualization of Matching Effect  
Two simulated noise tracks were selected to match the road network through the model, and the results were visualized as shown in Figure 9.  
It can be seen that the results obtained by the model match the topology characteristics of the road network and are consistent with the real trajectory. Vehicle track data is strictly restricted to the road network, so the noise track is confined to the road network with a certain distribution law of data. The simulation trajectory generated by the data expansion method in this paper can almost look like the actual driving trajectory. However, in order to ensure the rigor of the experiment, it is still necessary to use the real driving track for actual verification. Therefore, this paper collected a section of Yibin City driving routes  
Figure 9: Matching result visualization.  
for test verification. The software that collects track information is the Android App (GPS tracking Route Map). The track is approximately 31.31 km long and contains 1467 GPS track points.  
The overall trajectory is shown in Figure 10 below. The green area represents the recorded track points, and the red is the route formed by the points.  
The matching results of conventional sections are shown in Figure 11.  
According to Figure 12 depicting matching results, it can be seen that the matching effect is very good whether it is in the conventional road section or the complex intersection road section. There is no wrong lane matching, large deviation matching, or other types of wrong matching. Some mismatched sections are shown in Figure 13 below.  
Figure 11: Conventional road section matching results. The green line represents the wrong trajectory and the red line represents the corrected trajectory.  
Figure 10: The red line represents the actual trajectory and the green part represents the generated noise points.  
The left side of Figure 13 shows the turntable area, and the matching results produced an error loop. Our estimation is that there was a traffic jam when the trajectory was collected, which resulted in excessive positioning in a short time, resulting in a deviation of the model prediction. The right side of Figure 13 shows the intersecting multi-road section, and the correct route should be the route in the yellow area.
## 2 Materials and Methods
### PCR Amplification, Sequence Processing and Analysis  
Sample DNA was diluted to 1 ng/uL with sterile water. The V3-V4 region of 16S rRNA was amplified using bacterial specific primers 515F (5\({}^{\prime}\)-GTGCCAGCMGCCCGGTTAA-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACTHVGGGWTCTAAT-3\({}^{\prime}\)). The PCR reaction mixture contained 15 ulL 2\(\times\) Phusion Master Mix, 3 ul each of forward and reverse primers (2 umol/L), 7 ul genomic DNA (1 ng/uL), and 2 ul ddH\({}_{2}\)O. PCR reaction conditions were the following: 98 \({}^{\circ}\)C for 1 min; 30 cycles of 98 \({}^{\circ}\)C for 10 s, 50 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s; and final extension at 72 \({}^{\circ}\)C for 5 min. Equal amounts of PCR productions were mixed, homogenized, and purified. Paired-end sequencing of 16S rRNA hypervariable regions was performed using the Illumina NovaSeq platform (Illumina, San Diego, CA, USA).  
Raw sequencing data were analyzed using the QIIME2.0 DADA2 and USEARCH pipelines. After assembly, filtering, and quality control, amplicon sequence variants (ASVs) were defined at 100% similarity. Bacterial sequences were classified using the SILVA database (Release 138, [[http://www.arb-silva.de](http://www.arb-silva.de)]([http://www.arb-silva.de](http://www.arb-silva.de)) accessed on 12 May 2022). ASVs and basic analysis results were obtained for each sample, with composition structure statistics and diversity analysis conducted at various taxonomic levels.