esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Biosynthesis of Tanshinones in _S. miltiorrhiza_  
Tanshinones are a group of abietane-type norditerpenoid quinone natural products that primarily accumulate in the roots of _S. miltiorrhiza_. The biosynthetic pathway of tanshinones could be divided into three steps: the formation of precursors for all terpenoids; the construction of skeletons of tanshinones; and the post-modification of the skeleton, such as oxidation, methylation, decarboxylation or cyclization to produce the various tanshinones. This pathway has been explored for more than two decades, and most genes involved in tanshinone biosynthesis have been cloned (Table 1 and Figure 1) [6,26,27]. Figure 1: Biosynthesis of tanshinones in _S. miltiorrhiza_. Solid arrows indicate the established relationships, and dashed arrows indicate hypothetical relationships. G3P: glyceraldehyde-3-phosphate, IPP: isopentenyl pyrophosphate, DMAPP: dimethylallyl pyrophosphate, IPI: IPP isomerase, GPP: geranyl diphosphate, GPPS: GPP synthase, FPP: farnesyl diphosphate, FPPS: FPP synthase, GGPP: geranylgeranyl diphosphate, GGPP: GGPP synthase, CPP: copalyl diphosphate, SmCPS: CPP synthase, SmKSL: kaurene synthase-like cyclase. \begin{table}
\begin{tabular}{c c c c} \hline
**Gene (Genbank No.)** & **Highest Expression Sites** & **Elicitor** & **Reference** \\ \hline _SmAACTI_ (EF635969) & root & YE, Ag\({}^{*}\) & [30] \\ _SmAACTI_ (AE55671) & stem & & [31] \\ _SmHMGS_ (F7185325) & leaf & SA, MJ, YE & [32] \\ _SmHMGRI_ (EU608985 GU67911) & flower and root & SA, JA, MJ, PEG, ABA & [31,33,34] \\ _SmHMGR2_ (F747636) & leaf, stem, root & MJ, Ag\({}^{*}\) & [22,31,35,36] \\ _SmHMGR3_ (JN831102) & leaf & MJ & [31,37] \\ _SmHMGR4_ (JN831103) & flower & & [31] \\ _SmMK_ (JN831104) & stem & MJ & [31] \\ _SmPMK_ (JN831095) & stem and root & MJ & [31] \\ _SmMDC_ (JN831105) & stem and root & MJ & [31] \\ _SmDXSI_ (U60744) & leaf & Ag\({}^{*}\) & [31,35] \\ _SmDXS2_ (F1643618) & root and leaf & MJ, PEG, ABA, MJ Act12, Ag\({}^{*}\) & [17,31,34,35,38] \\ _SmDXS3_ (JN831116) & leaf, stem, root cortex, and root stele & MJ & [31] \\ _SmDXS4_ (JN831117) & ubiquitous & & [31] \\ _SmDXS5_ (JN831118) & leaf and stem & & [31] \\ _SmDXR_ (F1768959, F1476255, DQ991431) & leaf & SA,YE, Hypertonic stress, Act12 & [34,35,38,39,40] \\ _SmCMT_ (JN831096) & leaf & PEG, ABA and MJ, Ag\({}^{*}\) & [31,35,38,39,40] \\ _SmCMK_ (EF534309) & leaf & MJ & [31,41] \\ _SmMCS_ (JX233816) & leaf and stem & Ag\({}^{*}\) & [31,42] \\ _SmHDS1_ (JN831098) & leaf & & [31,43] \\ _SmHDS2_ (KJ746807) & & & [44] \\ _SmHDR1_ (JX23317 JN831099) & leaf & MJ, Ag\({}^{*}\) & [31] \\ _SmHDR2_ (JN831100) & leaf & MJ, SA & [45] \\ _SmIPII_ (JN831106) & root & MJ, fungal elicitor & [22,46] \\ \hline \end{tabular}
\end{table} 
Table 1: The genes involved in isoprenoids metabolic pathway in _S. milliorrhiza_.
## 4 Discussion  
### Influence of Number of Angles on Compressive Beamformer in the Fourier Domain  
Beamformers in the Fourier domain based on [PERSON]'s f-k migration are compatible with multi-angle RF coherent compounding imaging method [21]. Then compressive beamformer in the Fourier domain is also available for multi-angle compounding imaging. Although the results of fkCS is better than DAS, the image quality will be influenced with a low compression rate. Multi-angle compounding imaging is a method to improve image quality from the signal transmitter. In this part, we discuss the influence of the number of angles on compressive beamforming framework in the wavenumber domain with different compression rates, as well as comparing with compressive beamforming in the temporal domain.  
Image quality was analyzed by lateral resolution and contrasts. As it shown in Figure 10, lateral resolution was measured by point scatterer located at (10, 40) mm. Contrasts of cyst and strong reflection region were measured with area A and area B in Figure 5. We discussed the influence of number of angles with [PERSON]'s f-k migration method and compressive method with compression rates of 30%, 50% and 80%, which was represented by fk, fkCS_80%, fkCS_50% and fkCS_30%, respectively. The simulated B mode images with 5 angles were shown in Figure 10d-g, respectively. It can be seen that when compression rate decreases to 30%, the strong reflection region will distort significantly.  
As shown in Figure 10, with the increase in the number of compounding angles, the lateral resolution and low echo contrast of images generated by different methods have improved, but the degree of improvement is limited. The main influence of number of angles on contrast is about low echo area. From Figure 10a, when the number of angles is less than 5, increasing the number of angles can significantly improve the cyst contrast. Additionally, the lower the compression rate, the more obvious the effect of the increase of the angle. With the decrease in compression rate, the contrast of cyst deteriorates, especially  
Figure 10: Influence of number of angles on fkCS. (**a**) gCNR of cyst, (**b**) gCNR of strong reflection area, (**c**) lateral resolution with different number of angles and compression rate, (**d**–**g**) are B mode images based on beamformer of Stolt’s f-k, and fkCS with compression of 80%, 50% and 30% with 40 dB dynamic range, respectively.  
the change of contrast is unstable with compression rate of 30%. For the lateral resolution shown in Figure 10c, when the number of angles is less than 12, the increase in the number of angles can improve the lateral resolution, but the number of angles has similar effects on images with different compression rates. When the number of angles is greater than 11, increasing the number of angles will not improve the lateral resolution area any more. At this time, the increase in the number of composite angles will not improve the image quality, but will reduce the frame rate.
## 4 Results and Discussion
Similarly, if the value of \(N\) and \(a_{pt}=\frac{\sigma_{m}^{2}}{N_{0}}\) are fixed, increasing \(\xi_{n}=\frac{1}{SNR}(a_{pt}+1)\) leads to an improvement in the BER performance. For the target BER value of \(10^{-6}\), there is \(\approx\)8 dB improvement in the proposed system model by doubling the number of meta surface elements \(N\) in IRS. The comparison of BER performance of ideal continuous and discrete phase shifters of 1-bit, 2-bits, and 3-bits for \(N=32\) is shown in Figure 11. It is inferred that, the 1-bit shifter at IRS results in poor performance for a bidirectional FD-MTC system. By increasing the bit level of the discrete phase shifter at IRS, the BER performance improves and it approaches the ideal continuous phase shifter at IRS. Figure 8: Outage probability of IRS-assisted bidirectional HD vs. FD MTC system at SI = 15 dB. Figure 9: Outage probability of IRS-assisted bidirectional HD vs. FD MTC system at SI = 20 dB. The comparison of outage performance of ideal continuous and discrete phase shifters of 1-bit, 2-bits, and 3-bits for \(N=32\) is shown in Figure 12. From the PDF plot of the discrete phase shifter in Figure 3 and the outage performance in Figure 12, the variance of the discrete phase shifter for the different bit level \(b\) is obtained as,  
\[Var[b]=\eta 2^{b} \tag{29}\]  
where \(\eta\) is the difference between the SNR-dB of the \(b\)-level phase shifter and the SNR-dB of the ideal continuous phase shifter at a particular outage probability value. Also, increasing the bit level of the discrete phase shifter at IRS improves the outage performance and it approaches the ideal continuous phase shifter at IRS. As seen in Figures 11 and 12, a 3-bit discrete phase shifter requires just \(~{}1\) dB more than an ideal continuous phase shifter at IRS to achieve the desired BER and outage. The achievable rate performance of various locations of the IRS-assisted bidirectional FD-MTC system is shown in Figure 5:0 It is inferred, when the IRS is placed closer to any of the source nodes, the achievable rate for that particular source node is better than the source node which is far away from the IRS. Figure 5:1 BER for IRS-assisted bidirectional FD-MTC system in continuous and discrete phase shifter. Figure 5:2 BER for IRS-assisted bidirectional FD-MTC system.
## 3 Results  
### Open Porosity and Density  
The physical properties of different mortar formulations incorporating washing fines are presented in Table 4. An increase in porosity is observed with the increasing incorporation of washing fines as a partial replacement for cement. Indeed, the porosity of the reference control mortar is 19.3%, while L10, L20 and L30 formulations have porosities of 23.2%, 24.1% and 25.8%, respectively. This tendency for porosity to increase with the rate of substitution can be explained by the less efficient filling effect of wash fines compared to cement in the cementitious matrix, leaving more voids [35].  
At the same time, mortar bulk density decreases with the incorporation of washing fines. The reference mortar has a density of 2008 kg/m\({}^{3}\), while the densities of the L10, L20 and L30 formulations are 1937, 1898 and 1835 kg/m\({}^{3}\), respectively. This decrease in density is consistent with the observed increase in porosity, and can also be attributed to the intrinsically lower density of wash fines compared to Portland cement.  
These results indicate that the incorporation of washing fines as a partial replacement for cement has a significant impact on the microstructure of mortars, increasing their total porosity and reducing their compactness.
## 5 Application Cases and Effect Analysis
### Field Application and Effect Analysis  
The active roof control technique of double-layer flexible thick anchorage fully utilizes the high load-bearing performance of deep rock masses to constrain the deformation of shallow rock masses, achieving the deformation linkage of deep and shallow rock masses. Figure 11 shows the supporting effect of the field application. The deformation of the surrounding rock was measured using the 'cross-section method' in the central area of the withdrawal roadway using steel measuring tape and laser rangefinders. The deformation includes two stages of excavation and mining. After strong mining, the maximum deformation of the roof, floor, non-mining rib, and the midpoint of the mining rib are 107 mm, 258 mm, 215 mm, and 524 mm, and their average deformation is 59 mm, 97 mm, 93 mm, and 209 mm, respectively. The cross-sectional dimensions of the roadway after deformation fully comply with the usage requirements and exhibit a relatively ideal deformation control effect.  
Figure 10: The support parameters of withdrawal roadway 21405.  
This technique effectively solves the problem of controlling large deformation in roadways under strong mining disturbance, greatly supporting and effectively ensuring the continuity of mine production. Moreover, this technique simplifies the support process, eliminating the heavy workload and safety risks associated with traditional stacked support transportation. As a result, it offers significant economic and technical advantages compared to the original technology.
## 5 Result and Discussion  
### Porous Transition Layer  
The thermal and physical properties of carbon aerogel and the dense SiC layer are well-known. However, the transition layer, with its complex porous structure of Si, C, and SiC, lacks accurate data. Therefore, estimating these properties through simulation is necessary. The microstructure of the transition layer, determined via high-resolution FIB-SEM (Focused Ion Beam-Scanning Electron Microscope) (see Figure 5a), resembles the partially sintered solid sphere model [30]. The voxel material with porosity ranging from 0.1-0.4 (see Figure 5b,c), is modeled as linear elastic and thermally isotropic, with a Poisson's ratio of 0.3, an elastic modulus of 180 GPa, thermal conductivity of 1 W/mK, and a thermal expansion coefficient of 3.8 \(\times\) 10\({}^{-6}\) K\({}^{-1}\). To predict the effective [PERSON]'s modulus and Poisson's ratio of the porous transition layer, uniaxial loads and thermal conductivity calculations are applied along three axes. The effective coefficient of thermal expansion is determined using coupled solid heat transfer and mechanics models.  
Porosity significantly affects Young's modulus, thermal conductivity, Poisson's ratio, and strength, as demonstrated by numerous empirical models and theoretical approaches. Models such as Spriggs and Rice [31; 32] relate porosity to Young's modulus, while inclusion-based models like Coble-Kingery and Hasselman provide refinements [33]. [PERSON]'s equation [34] offers a comprehensive approach for complex pore morphologies. [PERSON] et al. [35] identified a critical porosity (P\({}_{\text{crit}}\)) where structural collapse occurs, and [PERSON] et al. [36] used FEM to study the effects of pore shape on the elastic properties of porous ceramics. Experimental data and simulations show a general exponential decline in strength metrics like tensile, compressive, and fracture strength as porosity increases, while material behavior transitions from brittle to pseudo-plasticity at higher porosities. Similarly, Poisson's ratio varies with pore geometry and initial matrix properties, as highlighted by [PERSON] and [PERSON] [37; 38]. Effective thermal conductivity decreases sharply with porosity, bounded by the Voigt-Reuss limits, with different models predicting this behavior based on pore connectivity and distribution. Numerical simulations and models, such as those by [PERSON] and [PERSON] [39; 40], highlight pore dispersion and contact as critical factors. These findings, as illustrated in Figure 6, guide the parameter selection in our calculations and provide a foundation for evaluating the thermal and mechanical performance of porous materials under thermal shock conditions. Further explanation of Figure 6 can be found in Supplementary Materials.  
Figure 5: (a) FIB-SEM analysis of porous material; RVE models for the porous transition layer with porosity of (b) 0.4 and (c) 0.2.
## 2 Materials and Methods
### Physicochemical Analysis  
Muscle samples (of BF and SM) were trimmed of superficial connective and fat tissue and pulverized in liquid nitrogen with a laboratory mill (Grindomix GM200, Retsch GmbH and Co., Haan, Germany). The total nitrogen, non-protein nitrogen (NPN) and salt (NaCl) contents were chemically determined, as described by [PERSON] et al. (20). Briefly, the content of NaCl was determined by potentiometric titration using a DL53 general purpose titrator ([PERSON], Schwarzenbach, Switzerland), then the proteolysis index (PI) was calculated as a ratio between the previously determined NPN and the total nitrogen content, while intramuscular fat and moisture contents were determined by near-infrared spectral analysis (NIR Systems 6500 Monochromator, Foss NIR System, Silver Spring, MD, USA) using internal calibrations developed at the Agricultural Institute of Slovenia.  
Lipid oxidation was measured by assaying the 2-thiobarbituric acid-reactive substances (TBARS) in the muscle (BF only) using the method employed by [PERSON] et al. with slight modifications (20). Briefly, 2 g of the sample was weighed in a 50 mL PP test tube, and 10 mL of 5% aqueous trichloroacetic acid (TCA) and 5 mL of 0.8% butylated hydroxytoluene (BHT) in hexane was added to each tube. The contents were homogenized using the T10 basic Ultra Turrax homogenizer (IKA-Werke GmbH & Co. KG, Staufen, Germany) for 30 s at high speed, and then centrifuged for 5 min at \(2500\times g\) (Centric 322A, Tehntica Zelezniki d.o.o., Zelezniki, Slovenia). The upper hexane layer was discarded and the bottom aqueous layer was filtered (grade 391 filter paper; Munktell & Filtrak GmbH, Barenstein, Germany). A 2.5 mL aliquot was pipetted into a screw-capped PP tube, then 1.5 mL of 0.8% aqueous 2-thiobarbituric acid (TBA) was added and the contents were incubated for 30 min at 95 \({}^{\circ}\)C (Memmert GmbH, Schwabach, Germany). After incubation, the tubes were cooled under tap water, and their absorbance at 532 nm was determined (Helios \(\gamma\), Thermo Electron Ltd., Rugby, UK) against a blank containing 2.5 mL of TCA and 1.5 mL of 0.8% aqueous TBA. The content of malondialdehydehyde (MDA) was calculated from the standard curve using 1,1,3,3-tetrametoxypropane. The TBARS were measured in triplicate and were expressed as mg of MDA per kg of tissue.  
For pH measurement, a suspension of 10 g of muscle sample was mixed with 90 mL of distilled water, and the pH was measured with a pH meter (TESTO 230, Testo SE & Co., KGaA, Titisee-Neustadt, Germany). Two-point calibration with the Testo buffer pH 4/7 set was performed before the measurements were taken, according to the manufacturer's instructions. Water activity was measured with the HygroPalm AW1 SET instrument (Rotronic AG, Bassersdorf, Switzerland), using the Aw Quick mode, in sub-samples taken after coarse homogenisation of 80 g of the muscle. The Aw and pH analyses were performed in triplicate.
# Mid-Infrared Multispectral Gaseous Stimulated Raman Scattering Laser
## 3 Results and Discussion
### High-Power Mid-IR Laser at 4432 nm
Figure 2: Schematic representation of the mid-IR 4432 nm Raman laser generation by FWM. Furthermore, according to Equations (1) and (2), it could be found that the wave-vector mismatch \(\Delta\)\(k\) should be satisfied to generate a mid-IR laser by the FWM process. [PERSON]'s research concluded that phase-matching conditions would be influenced by focus conditions [23]. While a lens of a short focal length is used to focus the pump laser into Raman cells, at the focus, the numerical aperture is relatively large, so the maximum angle between the pump laser and Raman lasers may be large enough to satisfy the wave-vector match [24]. In this case, a mid-IR laser is easily generated by the FWM process. However, the power density would be so high that the laser pulses could ionize Raman media at the focus, and the laser-induced breakdown would deteriorate the conversion of Raman lasers [25]. On the other hand, while a lens of long focal length is used to focus the pump laser, at the focus, the numerical aperture is small, and the maximum angle between the pump and Stokes waves may be too small to satisfy the wave-vector match. In this case, the FWM process is limited, and it is hard to generate a mid-IR laser. In our study, by using a lens with a focal length longer than 2 m, the mid-IR laser at 4432 nm could not be observed. In summary, compared with cascade SRS, the FWM process could dramatically reduce the threshold of mid-IR Raman laser generation. By further SRS amplification, a mid-IR 4432 nm laser achieved high peak power output. The SRS process of the vibrational first Stokes Raman laser of H\({}_{2}\) pumped by a 1560 nm laser contributed to the amplification, and the SRS process of the vibrational first Stokes Raman laser of D\({}_{2}\) pumped by a 1907 nm laser was also conducive to it. The waveform of the 4432 nm laser was recorded and is shown in Figure 4a. Due to the pulse compression of the SRS process, the pulse width of the 4432 nm laser was smaller than the pump laser, which was only 2.8 ns. The output power of the mid-IR laser at 4432 nm is also shown in Figure 4b. It can be seen that the maximum output peak power reached 121 kW, pumped by the laser at 1064 nm with a pulse energy of 431 mJ and linewidth of 1 cm\({}^{-1}\). However, the output energy of the 4432 nm laser would decay due to the laser-induced breakdown mentioned above when the pump energy was beyond 440 mJ. Figure 3: Output power of Raman components of (**a**) mixed gas of 0.4 MPa H\({}_{2}\) and 2.0 MPa D\({}_{2}\); (**b**) mixed gas of 0.5 MPa H\({}_{2}\) and 2.0 MPa D\({}_{2}\).
## 6 Sensitivity Analysis
### Sensitivity Analysis under Decentralised Decision-Making
#### 6.2.1 Impact of Demand Sensitivity Factor for Logistics Services  
We analysed the impact of changes in the coefficient of elasticity of demand for logistics, \(k_{T}\), on the optimal decision-making in the supply chain, taking the value of \(k_{T}\) between 1 and 40. Only the value of \(k_{T}\) was changed, and the values of other parameters were consistent with the base setting. The graphs of the relationship between optimal profit, optimal demand, optimal decision variables, and the sensitivity coefficient of logistics services in supply chains with different channel types under decentralised decision-making were obtained.  
Analysing Figures 7 and 8, it can be concluded that in the dual-channel supply chain model under decentralised decision-making, the profit of the supply chain \(\tau_{C}\), the profit of the producer \(\tau_{S}\), the profit of the retailer \(\tau_{r}\), the market demand of the offline channel \(Q_{T}\), and the market demand of the online channel \(Q_{O}\), all increase with the increase in the coefficient of elasticity of demand of the logistic service \(k_{T}\). The optimal decision variables in the supply chain models with different channel modes under decentralised decision-making all increase with the increase in the elasticity coefficient of demand for logistics services \(k_{T}\). Meanwhile, observing the slope of the curve, it can be found that under decentralised decision-making, as the elasticity coefficient of demand for logistics services \(k_{T}\) increases, the optimal profit, optimal demand and optimal decision-making variables grow faster, which indicates that with the increase in consumers' attention to the level of logistics services, the overall profit enhancement of the supply chain under decentralised decision-making will be greater under a higher level of logistics services.  
Figure 6: Effect of \(k\) on optimal decision variables (CD).
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 2 Materials and Methods
### Use Case Diagrams
#### 2.1.4 Patient Application UC Diagram  
This diagram includes nine use cases aimed at the patient-user, who performs different tasks, such as consult of the menu and eating patterns, consult of informative videos, recommendations, alerts, laboratory results, registration of glucose results, among others (Figure 6).  
* UC-18 Consult food menu: The patient can consult the different food menus with the calories previously assigned by the nutritionist.
* UC-19 Consult eating pattern: The patient sees the eating pattern assigned by the nutritionist.
* UC-20 Consult equivalent foods: The patient can choose the food groups containing a list of foods that have the quantity and measure unit appropriate to intake.
* UC-21 Record questionnaire responses: the patient answers to the questionnaires in this UC to know the type and frequency of the food that he eats. The nutritionist will consult the answers in UC-11.
* UC-22 Consult recommendations: The patient can see the recommendations that the nutritionist sent before.
* UC-23 Record glucose results: This allows the patient to records glucose results depending on the type of blood glucose test performed.
* UC-24 Consult glucose history: The patient can check the history of all glucose reports.
* UC-25 Consult laboratory results: This allows the patient to review the results of laboratory tests that the physician previously recorded.  
Figure 5: Nutrition UC diagram.  
* UC-26 Consult health education videos: Through this UC, the patient can consult and view videos recommended by physicians in the health education section, and mark those the patient has seen.