esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Stage 1: Research Questions  
This initial stage is an imperative stepping stone towards comprehending the wider scope of AR, as it is used to guide the definition and outlining of the research inquiries. The aim here is not just to raise questions but to construct a clear and methodical road map to navigate the complex landscape of AR literature. Through this, it is possible to identify the key developments, applications, challenges, and trends that AR encompasses across various domains. Furthermore, an additional goal is to delve into the unique benefits and potential drawbacks that AR implementation offers in a variety of sectors such as education, healthcare, manufacturing, retail, and urban planning. Additionally, the Research Question (RQ) and Sub-Questions (SQ) must be formulated to comprehend the current technological advancements and emerging trends in AR, assessing how these developments shape the future of AR applications and their potential impact on the respective domains. This initial stage sets the foundation for this systematic investigation, fostering a comprehensive understanding of AR, its potential, and its limitations.  
* RQ: What are the key developments, applications, challenges, and trends in augmented reality across various domains, and what are the implications of these findings for future research and practical implementation?
* SQ1: How has augmented reality been implemented in different domains, such as education, healthcare, manufacturing, retail, and urban planning, and what are the unique benefits and challenges in each domain?
* SQ2: What are the current technological advancements and emerging trends in augmented reality, and how do these developments shape the future of AR applications and their potential impact on various domains?
* SQ3: What are the primary challenges and limitations associated with the application of augmented reality across different fields, and how can these challenges be addressed to improve the effectiveness and user-friendliness of AR solutions?  
In order to answer these questions correctly, AR technology will be addressed through three main sections: types of AR applications (by functionality), AR devices, and AR usage by domain, thus having a broad understanding of the landscape of recent years in this type of technology, being aware of its applications in various domains and its limitations in each of its forms, for example, limitations of devices, whether by hardware or software, limitations by sectors and even by the type of application and the way in which said AR technology operates and is related with the users and the environment.
## 4 Discussion
The results demonstrated that anisotropy was an important factor affecting the variation in the self-potential of a rock mass under loading; the influence of anisotropy on the compressive strength of a rock mass may be directly related to the variation amplitude of the self-potential during rock damage and failure. This topic requires further study. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\(\mathbf{R^{2}*}\)} & \multicolumn{1}{c}{**CH25**} & \multicolumn{1}{c}{**CH26**} & \multicolumn{1}{c}{**CH27**} & \multicolumn{1}{c}{**CH28**} & \multicolumn{1}{c}{**CH29**} & \multicolumn{1}{c}{**CH30**} & \multicolumn{1}{c}{**CH31**} & \multicolumn{1}{c}{**CH32**} & \multicolumn{1}{c}{**Loading**} & \multicolumn{1}{c}{**Stress**} \\  & & & & & & & & & & & & **Rate (mm/s)** & **Interval (MPa)** \\ \hline R\({}^{2}\) (A1) & 0.527 & 0.421 & 0.055 & 0.0006 & 0.087 & 0.606 & 0.931 & 0.124 & 0.001 & \(\leq\)4.899 \\ R\({}^{2}\) (A2) & 0.465 & 0.658 & 0.817 & 0.778 & 0.707 & 0.320 & 0.249 & 0.882 & 0.001 & \(\leq\)5.523 \\ R\({}^{2}\) (A3) & 0.017 & 0.280 & 0.388 & 0.902 & 0.956 & 0.853 & 0.958 & 0.783 & 0.002 & \(\leq\)11.352 \\ R\({}^{2}\) (A4) & 0.055 & 0.490 & 0.183 & 0.041 & 0.908 & 0.932 & 0.941 & 0.882 & 0.002 & \(\leq\)3.853 \\ R\({}^{2}\) (A5) & 0.362 & 0.153 & 0.247 & 0.002 & 0.007 & 0.016 & 0.126 & 0.124 & 0.005 & \(\leq\)2.833 \\ R\({}^{2}\) (A6) & 0.304 & 0.447 & 0.778 & 0.769 & 0.780 & 0.638 & 0.168 & 0.942 & 0.002 & \(\leq\)4.585 \\ R\({}^{2}\) (B1) & 0.091 & 0.786 & 0.954 & 0.935 & 0.580 & 0.631 & 0.942 & 0.964 & 0.001 & \(\leq\)31.251 \\ R\({}^{2}\) (B2) & 0.347 & 0.977 & 0.910 & 0.664 & 0.159 & 0.586 & 0.194 & 0.582 & 0.005 & \(\leq\)20.303 \\ R\({}^{2}\) (B3) & 0.981 & 0.902 & 0.186 & 0.632 & 0.890 & 0.620 & 0.798 & 0.848 & 0.005 & \(\leq\)14.706 \\ R\({}^{2}\) (B4) & 0.894 & 0.658 & 0.030 & 0.933 & 0.89 & 0.903 & 0.145 & 0.650 & 0.005 & \(\leq\)18.660 \\ R\({}^{2}\) (B5) & 0.001 & 0.199 & 0.061 & 0.021 & 0.47 & 0.658 & 0.727 & 0.784 & 0.002 & \(\leq\)17.518 \\ R\({}^{2}\) (B6) & 0.388 & 0.958 & 0.960 & 0.125 & 0.874 & 0.820 & 0.065 & 0.821 & 0.002 & \(\leq\)20.486 \\ \hline \hline \end{tabular} \({}^{*}\) Correlation coefficients R\({}^{2}\) marked in red were greater than 0.5. Samples A1 to A6 were drilled parallel to bedding and samples B1 to B6 were drilled perpendicular to bedding.  
\end{table} 
Table 2: Correlation coefficient parameters of self-potential and stress of all studied bituminous coal samples. Figure 11: Scatter diagram of the relationship between self-potential and stress of bituminous coal sample A3 at the initial stage of uniaxial compression.
## 5 Simulation Results
### DC Load Asymmetrical Step-Change  
The first considered event is a DC load asymmetrical step-change from 20 kW to 40 kW on port 5+, occurring at time 0.3 s. The corresponding voltages \(v_{5}\)+, \(v_{5}\)- are reported in Figure 4a,b. Currents \(i_{5}\)+ is reported in Figure 5a,b. One can notice that voltage and current transients exhibit the expected behaviour: initially, the load step-change causes a fast change in the current reference, which causes a violation of controllability condition (10) and, consequently, a temporary loss of sliding condition. By roughly one millisecond, condition (10) is satisfied again, reaching phase is established, and sliding condition is enforced. Once sliding condition is reached, voltage error is forced to zero following the assigned dynamic, such that nominal voltage is restored in about 25 ms (\(\cong\)five time constants). In addition, one can note that the voltage of the negative half on port five is not affected by any perturbation (Figure 4b).  
To verify the effectiveness of the equilibrator control in balancing internal voltages, the internal voltages \(v_{in}\)+ and \(v_{in}\)- are reported in Figures 6a and 6b, respectively. One can observe that, except from a negligible transient in correspondence of the DC load step change, voltages track their respective references correctly and no voltage unbalance due to load unbalance is present.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rated Values** & \multicolumn{4}{c}{**Components**} \\ \hline Internal Voltage & \(\pm\)450 V & L\({}_{i}\) & 1 mH & L\({}_{\text{eq}}\) & 2 mH \\ Switching Frequency & 10 kHz & R\({}_{i}\) & 10 m\(\Omega\) & Re\({}_{\text{eq}}\) & 20 m\(\Omega\) \\ Maximum Port Current & 250 A & C\({}_{1,3}\) & 400 \(\mu\)F & C\({}_{4,5,6}\) & 6.8 mF \\ Control Time Constant & 5 ms & C\({}_{\text{in}}\) & 1.1 mF & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three-wire, capacitive-coupled MC parameters.  
Figure 4: Port 5: (**a**) voltage \(v_{5}\)+ reference (black) and actual value (red); and (**b**) voltage \(v_{5}\)– reference (black) and actual value (red) during DC load transient.
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
###### Abstract  
Field experiments were carried out at the Regional Centre of Experimentation in Organic agriculture at Auch (near Toulouse, South west of France). Due to the high potential applications for its oil components such as fatty acids and phytosterols, safflower (_Carthamus tinctorius_ L.) is considered as an emerging crop. Safflower plants, as many other oil crops, are submitted to environmental stresses that modify seed composition. Nevertheless, few reports are available about the effects of environmental conditions on fatty acid and phytosterol compositions in safflower. Different rainfall supplies can be managed by delaying the sowing dates. In this study, fatty acid and phytosterol contents have been evaluated in safflower seeds cultivated at two sowing dates (conventional and late) that led to a differential of rainfall during seed development. At harvest, seeds were used for oil extraction. Fatty acid composition was performed by using GC-FID. A set of seeds was dehulled to separate the almond (embryo) and hull to release the extraction and measurement of sterol contents in the two compartments by GC-FID. A delay of sowing increased the content of all sterol categories but induced a significant decrease in fatty acids. The ratio of saturated to unsaturated fatty acids increased under a delaying sowing. The repartition of phytosterols was \(\frac{3}{4}\) and \(\frac{1}{4}\) of total sterols in the embryo and the hull, respectively. These results could make the use of hull (considered as waste) possible, help breeders to improve safflower oil composition and develop new industrial applications.  
stiffower (_Carthamus tinctorius_ L.); delay of sowing; sterols distribution; lipids accumulation; hull; seed Article
## 3 Results
### Biological Pretreatment Performance
#### 3.1.3 TSS Removal  
In reference to Table 1, the high values obtained at the inlet feed were attributed to solids' presence and entrapment in the animal fat which is normally observed during pick processing times [19]. Furthermore, it was established that the presence of high carcass debris, might have been a contributing factor to the organic matter found in the raw PSW culminating in the high concentrations of TSS due to high FOG [20]. However, when Eco-flushTM was added, some FOG hydrolysis ensued, which consequently resulted in the break-down of hydrocarbons, some of which were hypothesized to be converted into useful nutrients in the form of soluble fatty acids [21] for the bacterial population, releasing some solids which can be removed through filtration or a screening device. For this study a 53 \(\upmu\)m screen was used, thus a significant portion of large particles of less soluble matter were filtered out as this will assist in the reduction of particles that could clog the DEGBR in the subsequent stage [5; 18].  
Overall, the biological pre-treatment unit reduced COD from 9946 to 5896 mg/L, TSS from 5399 to 2392 mg/L and FOG from 1765 to 269 mg/L, respectively. A clear indication of the aesthetics of the raw PSW in comparison to the pre-treated PSW samples is shown in Figure 7, suggesting an initial satisfactory performance of the biological pre-treatment stage.
## 2 UTM Monitoring Framework
### Network Remote ID  
A network Remote ID is one of the features in the Remote ID specification that transmits UAV information via an internet protocol. Its standard is established in the American Society for Testing and Materials (ASTM) F3411-19 document [27], but not included in Federal Aviation Regulation (FAR) 89 due to some objections from the public [28]. The transmitted information includes a unique ID as its identification that can be traced to the UAV and its operator. The other information is flight parameters, such as position, height, and speed, that can be used for UTM monitoring.  
There are two types of service providers involved in this data transmission, which are the network Remote ID service providers and network Remote ID display providers, as shown in Figure 1. The network Remote ID service providers develop the hardware in the UAV that transmits the information and the cloud database to receive the transmitted information. The hardware could be a built-in module as part of UAV components or an add-on module that is not part of the UAV. The network Remote ID display providers develop an application for the user to visualize the UAV information collected in the cloud database. Conflict detection monitoring can be developed as a feature in the network Remote ID display provider. Especially for UAS operators, this feature is very important for their safe operation.
## 6 Analysis of Retrofit Effect of SSDB Systems
### Story Displacement Response
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**MS DS**} & \multirow{2}{*}{**Story**} & \multicolumn{3}{c}{**AS1**} & \multicolumn{3}{c}{**AS2**} & \multicolumn{3}{c}{**AS3**} & \multicolumn{3}{c}{**AS4**} \\ \cline{3-14}  & & **LS-1** & **LS-2** & **LS-3** & **LS-1** & **LS-2** & **LS-3** & **LS-1** & **LS-2** & **LS-3** & **LS-1** & **LS-2** & **LS-3** \\ \hline \multirow{4}{*}{DS1(MS1)} & 1 & **93.8** & 72.5 & 76.5 & **93.9** & 64.0 & 76.6 & **89.9** & 45.2 & 59.2 & **91.0** & 49.1 & 42.1 \\  & 2 & 66.7 & **76.4** & 79.1 & 60.7 & **70.7** & 78.6 & 27.3 & **49.2** & 64.7 & 41.5 & **56.4** & 51.8 \\  & 3 & 55.7 & 72.6 & **79.9** & 50.5 & 60.6 & **79.4** & 3.4 & 34.7 & **65.2** & 23.6 & 44.3 & **55.2** \\  & Mean & 72.1 & 73.8 & 78.5 & 68.4 & 65.1 & 78.2 & 40.2 & 43.0 & 63.0 & 52.0 & 49.9 & 49.7 \\  & 1 & **94.8** & 72.2 & 75.2 & **94.3** & 69.4 & 79.7 & **87.2** & 44.7 & 60.3 & **90.3** & 45.6 & 52.8 \\  & 2 & 67.5 & **76.3** & 78.3 & 74.1 & **75.6** & 81.6 & 20.5 & **49.3** & 64.7 & 45.8 & **51.9** & 63.4 \\  & 3 & 56.2 & 71.8 & **79.2** & 65.1 & 67.1 & **82.1** & \(-\)**2.3** & 33.0 & **65.6** & 30.8 & 35.1 & **66.8** \\  & Mean & 72.8 & 73.4 & 77.6 & 77.8 & 70.7 & 81.1 & 35.1 & 42.3 & 63.5 & 55.6 & 44.2 & 61.0 \\  & 1 & **75.2** & 44.1 & 41.1 & **93.6** & 82.5 & 81.8 & **93.8** & 27.0 & 25.9 & **88.3** & 38.5 & 31.2 \\  & 2 & 41.0 & **45.3** & 44.5 & 76.8 & **83.1** & 82.2 & 42.0 & **32.2** & 33.1 & 39.9 & **46.0** & 43.1 \\  & 3 & 26.0 & 40.1 & **46.7** & 68.9 & 80.9 & **82.5** & 21.6 & 25.5 & **35.8** & 18.7 & 42.4 & **47.7** \\  & Mean & 47.4 & 43.2 & 44.1 & 79.8 & 82.2 & 82.2 & 52.5 & 28.2 & 31.6 & 49.0 & 42.3 & 40.7 \\  & 1 & **79.7** & 50.8 & 52.1 & **92.1** & 77.9 & 77.2 & **90.6** & 34.6 & 49.4 & **74.7** & 26.6 & 18.6 \\  & 2 & 47.8 & **51.7** & 54.5 & 73.3 & **79.0** & 78.0 & 33.5 & **40.7** & 54.5 & 6.7 & **34.8** & 33.8 \\  & 3 & 33.7 & 46.2 & **56.5** & 65.1 & 76.5 & **78.5** & 10.5 & 34.1 & **56.7** & \(-\)**24.0** & 22.8 & **38.2** \\  & Mean & 53.7 & 49.6 & 54.4 & 76.8 & 77.8 & 77.9 & 44.9 & 36.5 & 53.5 & 19.1 & 28.1 & 30.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Peak displacement degradation percentage (PDDP) (%) of MD-RC frames under different retrofitting schemes. Figure 10: Displacement time histories of DS1 (**a**) and DS2 (**b**) damage states MD-RC frame due to MS1 mainshock before and after retrofitting subjected to AS1 and AS3 aftershocks. Figure 11: Displacement time histories of DS1 (**a**) and DS2 (**b**) damage states MD-RC frame due to MS2 mainshock before and after retrofitting subjected to AS1 and AS3 aftershocks.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods
### Artificial Neural Networks  
Artificial Neural Networks (ANNs) represent a simplified emulation of biological neural networks, which offer valuable insights into the potential operations of biological neural networks. ANNs possess the capability to learn and undergo training to derive solutions, identify patterns, classify data, and make predictions about future events. Broadly, ANNs  
Figure 1: The structure of a random forest regression prediction model.  
can be categorized as either static or dynamic learning methods. Numerous studies have focused on static feed-forward neural networks (FFNNs). These networks react instantly to inputs since they do not incorporate time delay units. The emergence of dynamic neural networks (feedback neural networks with loops) has made possible increased computing capacity with respect to static ANNs. This paper aims to compare static and dynamic neural networks in terms of their prediction performances.
## 2 Data Collection
### Static GNSS Data Collection  
The static smartphone raw GNSS data acquisition was made on the roof of Wuhan University School of Geodesy and Geomatics building, with the antennas setting depicted in Figure 1. One geodetic-quality Trimble Zephyr Geodetic Model 2 antenna connecting to a Septentrio PolaRx5 receiver was used as a reference station. A NovAtel GPS-704-WB antenna connecting to a Septentrio PolaRx5s receiver, mounted on the other measuring pillar, was used as a rover station. The distance between the two geodetic-quality antennas was less than 5 m. An android smartphone Xiaomi Mi 8 was located next to the NovAtel GPS-704-WB antenna on the measuring pillar. After the Xiaomi Mi 8 data collection, a microstrip antenna connected to a u-blox M8T receiver was placed at the same spot occupied by the Xiaomi Mi 8, in order to obtain the precise position of the Xiaomi Mi 8 by RTK with the base station.  
The raw Android GNSS observations were collected on 24 January 2019 with a data rate of 1 Hz for approximately 8 h through the GNSSLogger App released by Google [10]. The observed L1/E1 and L5/E5 sky plots and availability of some of the satellites for Xiaomi Mi 8 are shown in Figure 2, plotted by RTKLIB [24]. The short red line is the AccumulatedDeltaRangeState (ADRS) field provided by Android smartphone, which indicates the validity of the carrier measurement. Cycle slips are reflected in the ADRS [25]. It is seen that about 25 satellites were viewed on L1/E1 frequency and 11 satellites on L5/E5 frequency. In addition, for this static data, most carrier phase cycle slips occur at low satellite elevation, and the cycle slips may not appear on L1/E1 and L5/E5 simultaneously, such as satellites J03.  
Figure 1: Static data collecting environments on the roof of Wuhan University School of Geodesy and Geomatics building.  
Figure 2: _Cont._
## 3 Results and Discussion
### Antioxidant Capacity  
Regarding the antioxidant activity of ginger's extracts determined through the ABTS\(\bullet\)+ technique, the results were from 12.23 mg to 22.53 mg Trolox/100 g. The highest value corresponded to the extracts of ginger obtained at 80% of amplitude and 30 \({}^{\circ}\)C; these conditions coincide with the ones observed in the DPPH assay. The antioxidant capacity of the extracts analyzed by DPPH assay (109.65-157.15 mg Trolox/100 g) were similar to the results found by [PERSON] and [PERSON] (147.6 mg Trolox/100 g) [14], [PERSON]  
Figure 1: Representation of the response surfaces for surface models for Y\({}_{1}\), DPPH in mg Trolox/100 g (**A**); Y\({}_{2}\), ABTS in mg Trolox/100 g (**B**); Y\({}_{3}\), FRAP in mg Fe\({}^{2+}\)/100 g (**C**); Y\({}_{4}\), total phenolic content in mg GA/100 g (**D**) and of overlaid contours at upper (**E**), and lowest values of responses (**F**).  
et al. (115.1 mg Trolox/100g) [35], and [PERSON] et al. (119.3 mg Trolox/100 g) [36] using ethanol-water, water, and methanol as extractants agents, respectively. In the cited researches, the release of antioxidant compounds was mainly dependent of temperature and time of extraction but in our case, amplitude was the most important factor.  
Despite UAE being reported as an effective extraction method for the release of thermosensitive and antioxidant compounds [31; 32; 33], in our work, the antioxidant activity values measured by DPPH were lower than those reported by [PERSON] et al. [37]. This difference is attributed to the conditions used for the extraction. In that research, authors used methanol-water mixture as extractant under agitation for 24 h. It has been demonstrated than organic solvents as methanol, can allow a higher extraction of compounds with antioxidant properties [38]. In the present research, we avoided the use of organic solvents since the purpose was to evaluate the antioxidant capacity of aqueous finger extracts, which can be used directly in the food industry.  
With regard to the antioxidant capacity determined by FRAP assay, the values observed were lower (8.18-17.11 gallic acid/100 g) than those reported previously (147-1237 mg Fe\({}^{2+}\)/100 g) [16; 39]. In FRAP assay, the capacity to reduce Fe(III) can be attributed to two factors: (1) the donation of hydrogen from phenolic compounds, or (2) the number and position of the hydroxyl group of phenolic compounds [3] so, a low content of phenolic compounds will leads to a low antioxidant capacity measure. Additionally, several factors involving extraction step such as media, method (conventional, and assisted by ultrasound or microwave), time and ginger variety affect the yield of antioxidant compounds released [14; 16; 31; 32; 39].
## 5 Bioactivities
### Anti-Inflammatory Activity
#### 5.2.2 Changes Exerted by Targeting Different Metabolites  
Other compounds from jackfruit significantly inhibited the release of \(\beta\)-glucuronidase and histamine from P-methoxy-N-methylphenylethylamine (dihydroisocycyloartomunin), inhibited lysozyme release (artocarpanone), and the formation of superoxide anion (cycloheterophyll, artonins B and antocarpanone) in rat neutrophils stimulated with formyl-Met-Leu-Phe (fMLP), as well as the inhibition of nitric oxide production and iNOS expression in RAW 264.7 cells (artocarpanone) [95].  
Figure 3: Main bioactive compounds present in lesser-consumed tropical fruits (lychee, mamey, passion fruit, acai and jackfruit) known to exert significant anti-inflammatory effects.  
Passion fruit peel flour treatment (8 mg/mL in the drinking water) has shown an anti-inflammatory effect on the intestine of female C57 BL/6] mice by attenuating colitis-induced damage. Biochemical and molecular analyses revealed the inhibition of the expression of pro-inflammatory cytokines and an improved intestinal protective barrier. In addition to these effects, increases in the formation of short-chain fatty acids were observed, which are known to play an important role in the maintenance of colonic homeostasis; any imbalance in the microbiota homeostasis can up-regulate the immune response leading to mucosal damage and intestinal inflammation, thus supporting a prebiotic effect of passion fruit peel flour [20].  
Different treatments can reduce or inhibit several inflammation cascades based on the bioactive compounds found in the lesser-consumed tropical fruits and their by-products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Compound** & **Classification** & **Activity** & **Reference** \\ \hline \multirow{3}{*}{Açai} & \multirow{3}{*}{Velutin} & \multirow{3}{*}{Flavone} & Inhibit SEAP secretion & \multirow{3}{*}{[91,96]} \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{Açai pulp} & Anthocyanins & \multirow{3}{*}{Anthocyanins} & \multirow{3}{*}{\(\downarrow\) IL-6 and IFN-\(\gamma\)} & \multirow{3}{*}{[47]} \\  & cyanidin-3-glutoside & & & \\ \hline \multirow{3}{*}{Açai seed extract} & Catechin, epicatechin, and & \multirow{3}{*}{Polyphenols} & \multirow{3}{*}{\(\downarrow\) NF-\(\kappa\)B and IL-6} & \multirow{3}{*}{[25]} \\  & polymeric procyanidins & & & \\ \hline \multirow{3}{*}{Lychee} & Catechin-type monomers and oligomers of proanthocyanidins & Flavanols and proanthocyanidins & Suppression of NF-\(\kappa\)B activation and \(\downarrow\) IL-6 and TNF-\(\alpha\) & \multirow{3}{*}{[97]} \\ \hline \multirow{3}{*}{Lychee seed extract} & 21 compounds, including & \multirow{3}{*}{\(3\),5-dihydroxyroxbenzoic acid, 3,4-dihydroxyroxbenzaldehyde, 3,6-imidazolD, 3,6  
Nevertheless, further research is still needed to identify, isolate, and quantify their phytochemical content and determine their efficacy on human health.