esa-sceva/satcom-chunk-collection huggingface dataset:
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 3 Biosynthetic Pathways of Rice Secondary Metabolites
### Biosynthesis of Terpenoids  
The biosynthesis of rice diterpenoid phytoalexins has been relatively detailed studied. The biosynthesis of the other terpenoids such as abscisic acid (ABA) and GAs in plants was discussed in reviews published elsewhere [134; 135]. The production of rice diterpenoid phytoalexins can be induced by a series of stresses. For examples, phytocassanes can be induced by ultraviolet (UV) irradiation [72], and momilactone A (**148**) can be induced by the bacterial phytotoxins coronatine [130] and methionine [128].  
The biosynthetic genes of diterpenoids are organized on the chromosome in functional gene clusters, comprising diterpene cyclase, dehydrogenase, and cytochrome P450 monooxygenase genes. Most of them in the rice genome are present in two gene clusters on chromosomes 2 and 4 (termed the Os02g cluster and the Os04g cluster, respectively) [136; 137; 138]. Their functions have been studied extensively using in vitro enzyme assay systems. Specifically, P450 genes (_CYP71Z6_, _Z7_, _CYP76M5_, _M6_, _M7_, _M8_) on rice chromosome 2 have multifunctional activities associated with _ent_-copalyl diphosphate-related diterpene hydrocarbons. Rice diterpenoids mainly contain phytohormones (i.e., gibberellins), phytoalexins (i.e., oryzaeins and phytocassanes), and allelbochemicals (i.e., momilactones). They are biosynthesized via the MEP pathway in rice plants and have been well reviewed [139; 140; 141; 142]. Gibberellins are considered phytohormones and are a large family of diterpenoids that possess the tetracyclic _ent_-gibberellane carbon skeletal structure arranged in either four or five ring systems, where the variable fifth ring is a lactone. Gibberellins biosynthesis and metabolism were well reviewed [143].  
Rice diterpenoids are labdane-related. Their biosynthetic pathways in cultivated rice _O. sativa_ are shown in Figure 12[140; 141].  
Figure 11: Biosynthesis pathway of sakuranetin in rice [9]. Abbreviations: SAM, S-adenosyl-L-methionine; SAH, S-adenosyl-L-homocysteine.  
Bioactive rice diterpenoids are commonly elaborated by the addition of at least two spatially separated hydroxyl groups. For example, orzyalexin D (**166**) is simply 3\(\alpha\),7\(\beta\)-dihydroxylated _ent_-sandaracopimaradiene, while orzyalexin E (**167**) is the 3\(\alpha\),9\(\beta\)-dihydroxy derivative. Notably, the production of these phytoalexins appears to proceed via the initial C3\(\alpha\) hydroxylation of _ent_-sandaracopimaradiene catalyzed by OsCYP701A8, while OsCYP76M6 and OsCYP76M8 catalyze the subsequent hydroxylation at C9\(\beta\) or C7\(\beta\), leading to the production of oryzaelas D (**166**) and E (**167**), respectively (Figure 13). These final biosynthetic steps represent the first complete pathways in the production of rice diterpenoid phytoalexins [144].  
Figure 12: Biosynthesis pathways of the labdane-related diterpenoids in rice [141].  
Figure 13: Biosynthesis pathways showing _ent_-sandaracopimaradiene dihydroylation into oryzaelas D and E [144].
## 2 Study Area  
The Echaurren Norte Glacier (33 deg34'S-70 deg07''W) is a small mountain glacier (0.17 km\({}^{2}\) in 2015) located in the upper Maipo basin in the Central Chilean Andes, 50 km east of Santiago (Figure 1). The glacier lies in the headwaters of the Laguna Negra in the Yeso sub-basin, which is one of the highest mountain sub-basin within the upper Maipo basin. The Yeso sub-basin, which includes the Yeso reservoirs, are the main source of freshwater for more than 7 million inhabitants of Santiago and the large-scale agriculture production in the region [34].  
The glacier mass balance measurements at Echaurren Norte Glacier are the longest continuous time series in South America and in the Southern Hemisphere (1975-present). Measurements have been sponsored by the Direccion General de Aguas (DGA) using the direct glaciological method [35]. Computations use the combined system based on the stratigraphic and fixed-date systems, calculated from measurements on fixed calendar rates [36; 37]. The hydrological year spans from the 1 st of April to the 31 st of March, with accumulation and ablation periods defined from April to September and October to March, respectively. Accumulation was calculated by means of a snow pit dug near the center of the glacier. Within the pit, measurements of snow temperature, density and grain size were taken every 20 cm. The data from the snow pit was used as an estimation of the mean accumulation on the glacier [38]. Measurements were normally taken at the beginning of spring,  
Figure 1: **(a)** Administrative regional distribution of Chile. **(b)** Metropolitan Region, Santiago capital of Chile. El Yeso sub-basin (black line). Background Shuttle Radar Topography Mission (SRTM) digital elevation model (USGS). **(c)** Location of the Echaurren Norte Glacier in the Central Andes of Chile. Glacier area corresponds to 2015. The background is a SPOT satellite image from 30 January 2015 (obtained from the Direccion General de Aguas, DGA). **(d)** Panoramic photography from west to east in March 1999 (Photo taken by [PERSON], DGA).  
close to the 1 st of October. However, due to logistic constraints, extrapolation factors were applied to accumulation values when field measurements did not coincide with the beginning of the melt season [39]. The annual accumulation was calculated using the mean snow density and depth to the previous year firn or ice layer at the snow pit. At the beginning of the melt season, an ablation stake network was installed on the glacier, which was measured regularly until the end of summer. At the beginning of the mass balance measurements, 40 stakes were distributed over the glacier surface; however, due to glacier shrinkage in the last decade, especially since 2006, only 18 stakes were placed every year to measure the glacier ablation [39].
## 5 Model Evaluation
### Detection of Possible Strategic Goals According to SWOT Analysis Results  
To choose the possible strategic goals, we first need to obtain the SWOT analysis results [37]. The results are shown in Figure 7. Ontology is populated with the results of the SWOT analysis. We queried the ontology using the Controlled Natural Language Protege plugin ACE View [38]. ACE View is an ontology and rule editor that uses Attempto Controlled English (ACE) to create, view, edit, and query ontology web language (OWL) ontologies and semantic web rule language (SWRL) rules.  
The results obtained from ACE View word usage in SPO ontology for variable Iskandar Puter are: _Johor_Balru Have_cities Iskandar_Puteri, Iskandar_Puteri is a city, Iskandar_Puteri Have_strengths Construction_industry, Iskandar_Puteri Have_opportunities Public_transport, Iskadar_Puteri HaveLow Employment_rate.  
The results show that the city of Iskandar Puteri has the opportunity to develop public transport and has strength in the construction industry. In this case, we select the construction industry as a possible strategic goal.  
Figure 7: Populated ontology.  
Simulation of the Economic Impact of Chosen Possible Strategic Goals with a Budget of 3 Million MYR for Increasing Final Demand  
To simulate the economic impact of the identified possible strategic goals (we chose the construction industry), we used I/O multipliers from the ontology shown in Figure 7 and the ontology plugin ACE View. In this phase, we query ontology using ACE View to obtain multiplier values and calculate the economic impact for the possible strategic goal chosen in the previous step of the construction industry. The employment multiplier E1 (Figure 7) gives the number of direct jobs related to the specific industry and indirect and induced jobs in the support industry, and output multiplier O1 (Figure 7) provides direct and indirect output impacts on the industry.  
The results obtained from ACE View word usage in the SPO ontology for variable construction_industry are: _Construction_industry haveMultipliers O1_, _Construction_industry haveMultipliers E1_, _O1_ is an Output_, _O1_ is 1.4, _E1_ is Employment_, and _E1_ is 11.3.  
Multiplying 3 million (defined in the strategic planning case) with a multiplier value obtained from an ontology, we can conclude that the total direct and indirect output impacts on industry of region will be 3 \(\times\) 1.4 = 4.2 mil. MYR. _Output indicator value = 4.2 mil_.  
Multiplying 3 million (increase in final demand) with a given employment multiplier, we can conclude that the total direct and indirect impacts on employment changes resulting from this additional output in the construction industry will be 3 \(\times\) 11.3 = 33.9, and approximately 34 new jobs will be created from this investment. _The employment indicator value is 34._
## 2 Materials and Methods
### Sorption Isotherm Measurements  
The sorption of phosphorus (added as KH\({}_{2}\)PO\({}_{4}\)) was studied by shanking aqueous suspensions with 1.00 g of the sample placed in 100 mL Erlenmeyer flasks containing 50 mL of KH\({}_{2}\)PO\({}_{4}\) solution. The initial P concentrations were 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 mg\(\cdot\)L\({}^{-1}\), respectively. Three drops of chloroform were added to inhibit microbial activity. After 24 h of equilibration with an oscillation frequency of 200 rpm at 25 \(\pm\) 1 \({}^{\circ}\)C, the solution was centrifuged at a rotational speed of 4000 rpm for 10 min. The P concentration in the supernatant was determined, and the amount of phosphorus adsorbed by the soil was calculated. All the tests were repeated three times, with results presented as the mean.  
The Langmuir isotherm and Freundlich isotherm models were applied to evaluate adsorption, as shown in the following equations:  
\[q_{eL}=\frac{q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{2}\]  
\[q_{eK}=K_{F}C_{e}^{1/n} \tag{3}\]  
where \(q_{e}\) is the amount of phosphorus adsorption (mg\(\cdot\)g\({}^{-1}\)) at equilibrium, \(C_{e}\) is the dissolved phosphorus concentration in the solution (mg\(\cdot\)L\({}^{-1}\)) at equilibrium, \(q_{m}\) is the maximum amount of phosphorus adsorption (mg\(\cdot\)g\({}^{-1}\)), \(K_{L}\) is the Langmuir adsorption coefficient, \(K_{F}\) is the Freundlich adsorption coefficient, and \(n\) is a constant determined via experiments.
## 2 Materials and Methods
### Total Phenolic Content Analysis of Raw Materials and Bread Samples  
Total phenolic content (TPC) was determined using the Folin-Ciocalteu method as described by [PERSON] et al. [26] with slight modifications. Specifically, 0.5 g of each sample (semolina, MPP, CTR, MPP-5, and MPP-10) was added to 8 mL of methanol/water solution (80:20 \(v/v\)). The mixtures were kept in an ultrasonic water bath and sonicated for 45 min. The extracts were filtered through 0.45 \(\upmu\)m PTFE filters (Whatman, Milan, Italy). Then, 100 microliters of filtrate solution were added to 625 \(\upmu\)L of Folin-Ciocalteu reagent and 120 \(\upmu\)L of 7% (\(w/v\)) Na\({}_{2}\)CO\({}_{3}\) solution. The mixtures were incubated in the dark at 25 \({}^{\circ}\)C for 60 min. The reaction formed a blue-coloured complex, with colour intensity proportional to the phenolic compounds present. The resulting colorimetric reaction was measured at 765 nm using a UV-Vis spectrophotometer (UV-1600 PC, VWR, Radnor, PA, USA) with methanol as the blank. TPC was calculated by interpolating from a calibration curve of gallic acid (GA), used as the standard (ranging from 0.01 to 0.5 mg/mL). The final results are expressed as milligrams of gallic acid equivalents per gram of the sample [mg gallic acid equivalents (GAE)/g]. Experiments were performed in triplicate.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 6 Performance Evaluation  
In this section, we show the performance evaluation results of the proposed protocol based on security analysis, computational delay and communication overhead.
# Innovative System for BIM/GIS Integration in the Context of Urban Sustainability
## 3 Materials and Methods
### BIM and GIS Integration through Autodesk Infrawworks  
Autodesk Infrawworks software (version 2023)is generally used to observe, model, and comprehend the project in its geographical context in order to integrate the information about the building model (BIM) and the geospatial information of the GIS environment [42; 43]. By using Infrawworks, which has the capacity to exchange and dynamically transmit data, it is feasible to build three-dimensional models of up to 200 km\({}^{2}\) of the study area. The suggested approach adds data layers as set features straight to Autodesk Infrawworks models using Autodesk Connector for ArcGIS Pro 3.0. By using the ArcGIS login credentials, this utility allowed for the program to be connected to the ArcGIS data.  
In this case, it is chosen to incorporate more detailed 3D models of the structures in the Infrawworks project, in this region of interest, in order to meet any requirements pertaining to the territorial UP. Ultimately, positioning the model and defining its coordinate system, scale, and rotation allowed for its importation into Revit (Figure 4). The ability to georeference digital data and 3D capabilities provides an interesting multi-scale environment that connects BIM and GIS.  
Figure 3: Shared coordinate positioning: Revit aligns the site plan with the model based on shared coordinates.  
From a design point of view, the advantages of using Infrawworks for the interoperability of BIM and GIS data are evident: this software allows for combining data from different origins, like BIM models, allowing for a complete and integrated analysis and enabling in-depth spatial analysis on a specific area by identifying the best design solutions. However, as is known, the main drawback of this proposed solution is that even if the building is georeferenced, the information associated with it is in any case almost nil and the necessity to use two separate software platforms persists.
## 1 Introduction
### Equivalent Inelastic SDOF System for Steel Frames with Hysteretic Dampers
#### 2.2.2. Inelastic Spring Equivalent to Steel Frame  
With respect to the first mode, the period \(T_{f}\) of the bare frame can be obtained from EVA. Additionally, this period is also calculated as follows:  
\[T_{f}=2\pi\sqrt{\frac{f\overline{M_{1}}}{k_{f}}} \tag{6}\]  
where \(k_{f}\) denotes the effective elastic stiffness of the bare frame and \(f\overline{M_{1}}\) is the effective mass of the bare frame that is defined using Equation (4) with \({}_{s}\phi_{1,i}^{E}\) replaced by \(f\phi_{1,i}^{E}\), obtained from EVA of the bare frame.  
Equation (6) is used to represent the elastic stiffness \(k_{f}\) of the bare frame, as follows:  
\[k_{f}=\frac{4\cdot\pi^{2}\cdot_{f}\overline{M_{1}}}{\left(T_{f}\right)^{2}} \tag{7}\]  
It is assumed that the period calculated by the effective elastic stiffness \({}_{s}k_{f}\) of the spring, which is represented as the only bare frame in the equivalent SDOF system with multi-springs, and the effective mass \({}_{s}\overline{M_{1}}\) is equal to the period of the equivalent SDOF system of the bare frame, as shown in Equation (6). This assumption is represented as follows:  
\[T_{f}=2\pi\sqrt{\frac{\overline{M_{1}}}{sk_{f}}}=2\pi\sqrt{\frac{f\overline{M _{1}}}{k_{f}}} \tag{8}\]  
Based on Equations (7) and (8), the effective elastic stiffness of the spring representing the bare frame in the equivalent SDOF system with multi-springs is defined as follows:  
\[{}_{s}k_{f}=\frac{4\cdot\pi^{2}\cdot_{s}\overline{M_{1}}}{\left(T_{f}\right)^ {2}} \tag{9}\]  
In order to consider the post-elastic behavior of the bare frame, the stiffness reduction factors (\(\gamma 1\) and \(\gamma 2\)) after the elastic behavior are calculated based on the same method used in a previous study [26], in which a tri-linear skeleton curve is assumed. Further details on the post-yield stiffness can be found in a previous study [26].  
The first and second yield drifts on the skeleton curve for the bare frame can be defined as follows:  
\[\delta=1\frac{\theta_{roof,1}\cdot H_{roof}}{\Gamma_{1}^{E}\cdot_{s}\phi_{1, roof}^{E}},\ \delta 2=\frac{\theta_{roof,2}\cdot H_{roof}}{\Gamma_{1}^{E}\cdot_{s}\phi_{1, roof}^{E}} \tag{10}\]  
where \(\theta_{roof,1}\) and \(\theta_{roof,2}\) are the first and second yield drift ratios on the skeleton curve [26], respectively; and \(H_{roof}\) and \({}_{s}\phi_{1,roof}^{E}\) denote the height of the roof above the base and the first mode vector of the roof of the steel frame with dampers, respectively.  
The stiffness reduction factors and yield drifts are used to calculate the yield forces \(Q1\) and \(Q2\), as follows:  
\[Q1={}_{s}k_{f}\cdot\delta 1,\ Q2={}_{s}k_{f}[\delta 1+\gamma 1(\delta 2- \delta 1)] \tag{11}\]  
Subsequently, the skeleton curve of the inelastic spring that is equivalent to the bare frame is determined by Equations (10) and (11).
## 2 Delay-Free Phase Noise QRNG Scheme
### Post-Processing Method
Pictured in (**b**) is the incorrect waveform when the difference of center wavelength is too large to maintain a regular shape, hence the signal after mixer is also very unstable. \[H_{\infty}(X)=-\log_{2}(\max_{x\in[0,1]^{n}}Pr(X=x)). \tag{12}\]  
The output sequence of Toeplitz hashing, based on the input, is almost unique, for it has a collision probability of only \(2^{-M+1}N\) for a different input to share the same output. According to information theory, the security parameter \(\varepsilon\) should satisfy:  
\[M=N\cdot H_{\infty}-2\log_{2}(1/\varepsilon) \tag{13}\]  
where \(\varepsilon\) also indicated the distance between the output sequence and ideal uniform sequence. Therefore, by designing a Toeplitz matrix with ratio \(M/N\) slightly smaller than the min-entropy and adopting different matrix size, the security parameter \(\varepsilon\) could be arbitrarily close to zero. Noticing that, the seeds consumed in generating Toeplitz matrix is \(N+M-1\). Since the data size is huge in QRNG systems, post-processing should be run by block: \(N=Bn,M=Bm\), where \(m\times n\) is the size of practical Toeplitz matrix, and \(B\) is the number of blocks. After discarding the unoccupied bits in raw data, the min-entropy in our system is 6.60 bits/sample, hence we set our Toeplitz matrix at a moderate size of \(1536\times 3072\) and run the post-processing method by block, which means \(3072\div 12=256\) consecutive samples are collected and process in one Toeplitz hashing operation. The security parameter is \(\varepsilon=7.6\times 10^{-24}\) in our implementation. Since the extracting ratio decided by the Toeplitz matrix is also 50%, the generation rate of adopting Toeplitz hashing method is also 600 Mb/s.
## 2 Calorimetric Sensor Design  
The calorimetric sensor is shown in Figure 1. It was manufactured by using a surface micro-machining technique. Three parallel micro-wires depict the sensitive section. Micro-wires are free from the substrate. A silicon oxide micro-bridge mechanically supports the sensitive section over a cavity. The three parallel micro-wires are designed perpendicularly to the flow. For the sensitive section, heating and wall shear stress measurement are combined in the central wire. The right and the left wires use the principle of the calorimetric phenomena. As a result, they represent a well solution to detect the flow direction. Owing to the flow, the right wire and the left wire are cooled differently. Therefore, the upstream wire becomes cooler compared to the downstream wire. As a result, the flow direction is known by their temperature difference. Table 1 shows the geometric sizes of each wire. They measure 1-mm-long, 3-\(\upmu\)m-wide and less than 0.5-\(\upmu\)m-thick. The wires are isolated in a 20-\(\upmu\)m deep cavity in order to increase their convective heat transfer and to avoid heat losses. Therefore, the wires are within the flow. A high temperature gradient is guaranteed by the high aspect ratio of the wires in the flow direction. A homogeneous temperature is ensured along the single wire. The mechanical robustness of the wires despite their length over the flow is guaranteed by their maintenance with periodic silicon oxide bridges.  
Three layers depict the central wire, as shown in Figure 2. The first is an Au/Ti layer, which is the heater. This material was chosen because of its high temperature stability. The second is a Ni/Pt/Ni/Pt/Ni multilayer, which is the measurement wire. It reaches about 130-nm-height. We chose this material because of its high temperature-dependent resistivity. The last is a SiO\({}_{2}\) insulator layer to separate  
\begin{table}
\begin{tabular}{c c c c} \hline
**Size** & **Heater** & **Sensing Wires** & **Bridges** \\ \hline Width (\(\upmu\)m) & 3 & 3 & 7 \\ Length (mm) & 1 & 1 & 0.03 \\ Thickness (nm) & 200 & 120 & 450 \\ \hline \end{tabular}
\end{table}
Table 1: Calorimetric sensor geometric sizes.  
Figure 1: Overview of the calorimetric sensor design.  
the first two layers. The external power supply attacks only the heater layer. Therefore, the heater layer heats the whole sensor. To improve the signal to noise ratio, heating and measure are electrically uncoupled. The 4-point measurement technique is used to measure the current. A current of 100-\(\upmu\)A crosses the measurement wires. Therefore, the resistances measurements are realized without heating the wires.