esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art and Opportunities for Bioactive Pentacyclic Triterpenes from Native Mexican Plants
### General Overview of Pentacyclic Triterpenes  
#### 1.1.1 Structure, Classification, Distribution, and Biological Importance  
Pentacyclic triterpenes (PCTs) are subcategorized inside the triterpen cluster and are heterogeneous compounds of 30 carbon atoms [8] belonging to the general group of compounds called terpenes, whose basic subunit is isoprene (methyl-1,3-butadiene). In general, terpenes composed of two isoprene units are called monoterepenoids (10 C), those with three units are known as sesquiterpenoids (15 C), those with four units as diterpenes (20 C), and those with more than four subunits are classified as polyterpenes [9]. Triterpenes are considered polyterpenes because they present six isoprene units. Both sesquiterpenes and polyterpenes are produced in the plant's cytosol while the majority of diterpenes and tetraterpenes (40C) have their origin in plastids [10].  
On their part, PCTs are molecules with a backbone based on five six-member rings or four cyclohexanes and one cyclopentane. Ursane, oleanane, taraxastane, gammacerane, and friedelane are PCTs made from five six-member rings while lupane and hopane are groups based on four six-member rings and a final five-member ring (Figure 1) [6; 11; 12]. The classification criteria for the establishment of these triterpene groups are based on biogenetic principles [11]. PCTs are widely distributed in nature and are present in fruit peel, stem bark, and leaves [11; 13]. In addition, these can be found in their free form, as esters or as saponins (i.e., combined with other sugars) [11].  
In the last years, several beneficial activities of PCTs with medical importance have been described, including anti-inflammatory, antibacterial, antiviral, anti-parasitic, anticancerogenic, antidepressant, anti-anxiety, neuroprotective, cardioprotective, and immunodulatory effects [6; 14; 15; 16] Moreover, triterpenes from the lupane, oleanane, and unsane groups have received special attention because of their pharmacological effects, coupled with their low toxicity [13]. Naturally, PCTs are produced as a defense against metabolic oxidations and some other factors such as microorganisms or environmental stress [17]. The bioactivity of the triterpenes is highly affected by its backbone and the position and number of chemical groups (i.e., alkyl, hydroxyl, carbonyl, and aminoacids) in their structure [18]. It is important to point out that many of these PCTs, because of the complexity of their structure, have not yet been chemically synthesized.  
Figure 1: Pentacyclic triterpene backbone classification and representative subgroups: oleanane (**A**), ursane (**B**), and lupane (**C**).
## 4 Digital Twin and Control System Design
### Geometry and Kinematics Model
The kinematic state of a system is defined by position, velocity, and acceleration as functions of time. The standard kinematic equations for constant acceleration are as follows:  
\[ \begin{array}{l}s=v_{0}t+\frac{1}{2}at^{2}\\ v=v_{0}+at\\ v^{2}=v_{0}^{2}+2 as\end{array}  \tag{1}\]  
where \(s\) is the displacement, \(v\) is velocity, \(v_{0}\) is initial velocity, \(a\) is acceleration, and \(t\) is time. Figure 9: Kinematic modelling of the pneumatic cylinder in the MPS “Distribution” station. The model was converted to JT format and imported into the project database, where kinematics was assigned. Actuating the pneumatic cylinder extends the feeding mechanism, releasing the cylinder into the working position. In a homogeneous transformation matrix form, pure translation along an axis \(x\), \(y\), or \(z\) can be represented as  
\[ \begin{bmatrix}1&0&0&x\\ 0&1&0&y\\ 0&0&1&z\\ 0&0&0&1\end{bmatrix}  \tag{2}\]  
where \(x\), \(y\), and \(z\) represent linear displacements along the respective axes. Rotational motion involves movement about a fixed axis and is described by angular displacement (\(\theta\)), angular velocity (\(\omega\)), and angular acceleration (\(\alpha\)). The kinematic equations for rotational motion are analogous to those for linear motion:  
\[ \begin{array}{c}\theta=\omega_{0}t+\frac{1}{2}\alpha t^{2}\\ \omega=\omega_{0}+\alpha t\\ \omega^{2}=\omega_{0}^{2}+2\alpha\theta\end{array}  \tag{3}\]  
where \(\theta\) is angular displacement, \(\omega\) is angular velocity, \(\omega_{0}\) is initial angular velocity, and \(\alpha\) is angular acceleration. In matrix representation, a pure rotation about an axis is given by the following rotation matrix for a rotation about the \(z\)-axis:  
\[ \begin{bmatrix}cos\theta&-sin\theta&0&0\\ sin\theta&cos\theta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{4}\]  
Similar matrices exist for rotations about the \(x\)- and \(y\)-axes.
## 4 Data Acquisition and Analysis  
A total of 11 MT stations were obtained during the survey, including 10 observation stations in the northern Pingtung Plain, divided into two profiles trending NW-SE and NE-SW, and one remote reference station located in Meinong Hills, approximately 15-20 km away from the local stations. Survey locations are depicted in Figure 1. The data were collected using Earth Data EDR-210 Multichannel data logger, manufactured by Earth Data Division of Kenda Electronic Systems from Southampton, UK, which offers a wide sampling rate from 1 to 3000 Hz. Our data were sampled at 500 Hz. The \(H\) was measured using Bartington Mag-03 MSESL 100 and Mag639 fluxgate magnetometer (Bartington, Witney, UK), a low-noise level magnetometer that can be operated to measure the \(H\) in the range of \(\pm\)100 uT. We used a Lead-Lead-Chloride (Pb-PbCl2) non-polarized electrode to measure the potential difference. The electric field and the potential difference are related through the following equation:  
\[E=U/d, \tag{12}\]  
where \(E\) is the electric field, \(U\) is the potential difference, and \(d\) is the dipole length. Dipole length is the separation of each coupled porous pot sensor. Most of the stations are recorded using the cross-configuration with a 50 m dipole length, where a pair of electrodes are aligned north-south (\(x\)) and another pair aligned east-west (\(y\)). In the condition where space is limited, the configuration is adjusted into L configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age** & **Formation** & **Depth (m)** & **Thickness (m)** \\ \hline Recent & Alluvium & 0–73 & 73 \\ Pleistocene & Linkou Conglomerate & 73–737 & 664 \\ Pliocene (Upper) & Nanshilun Sandstone & 737–1341 & 604 \\ Pliocene (Lower) & Kaitzuliao Shale & 1341–2395 & 1081 \\ Upper Miocene & Wushan Formation & 2395–3003 & 608 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The PTG-1 stratigraphy well record in Pingtung Plain adapted from [38].
# Flavour Volatiles of Fermented Vegetable and Fruit Substrates: A Review
## 3 Results
### Phenols  
Nine out of thirty-five studies detected phenolic compounds, which were categorised into phenols or alcohols or others. Phenol, 4-vinylphenol-2-methoxy, 2,6-di-tert-butyl-4-methylphenol, 4-ethyl-2-methoxyphenol, 4-ethyl-phenol, 2-methoxy-phenol, 2,5-dimethyl-phenol, 2,4,5-trimethyl-phenol, and 2,6-dimethoxyphenol were the phenolic compounds most frequently reported. The concentration of 2-methoxy-4-vinylphenol increased after fermentation in 2 studies: (1) In goji juice fermented by a mixed bacterial culture, the 2-methoxy-4-vinylphenol concentration ranged from 532.5 to 678.6 ng/g, compared to 98.3 ng/g in the unfermented juice [25]; and (2) in apple juice fermented by either _L. plantarum_, _L. rhamnus_, _L. casei_, or _L. acidophilus_, the 2-methoxy-4-vinylphenol concentration was 2.4, 1.7, 2.1, and 2.1 ng/g, respectively, compared to 0.2 ng/g in the unfermented juice [21]. In addition, the concentration of phenol increased after fermentation in 3 studies:(1) In apple juice fermented by either _L. plantarum_ or _L. acidophilus_, the phenol concentration was 3.2 and 2.2 ng/g, respectively, compared to 0.1 ng/g in the unfermented juice [21]; (2) in papaya juice fermented by _L. acidophilus_, the phenol concentration was 1.6 times higher compared to the concentration in _L. plantarum_ fermented juice, where it was not detected in the unfermented juice [23]; and (3) in okara fermented by the fungi _R. oligosporus_ in combination with the yeast _Y. lipolytica_, the phenol concentration was 2.4 times higher compared to the concentration when fermented by _R. oligosporus_ in monoculture [38].
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 1 Introduction
(2019) ensembled two neural networks, namely 3D U-Net and DeepMedic, which were trained with different hyper-parameters so that one neural network focused on small metastases with high sensitivity while the other one addressed overall tumor segmentation with high specificity, yielding a good performance on segmentation within 305 patients, with a median dice score of 0.74 [28]. [PERSON] et al. (2019) trained the convoluted neural network with multiplanar slices, reducing false-positive predictions and yielding a dice score of 0.77 on the 248-patient dataset while maintaining competent 80% isodose coverage [29]. [PERSON] et al. (2021) implemented deep learning in the treatment planning process, decreasing the plethora of time consumed during the planning process as well as enhancing the prediction overlap with ground truth significantly especially in the subgroup of non-experts. However, the cohort size was rather small [11]. Heterogeneity could have been considered a major problem for machine learning decades ago; however, it should be considered a real-world situation. A heterogeneous dataset could help the generalizability and transferability of trained models [11; 23; 25; 30; 31; 32]. However, in the previously-mentioned studies, small sample sizes were important contributors to the lack of confidence to infer the generalization of deep-learning models in clinical practices with heterogeneous lesion types. For the technology to achieve satisfactory performance, we explored the behavior of deep-learning models in a realistic scenario. Therefore, we collected a relatively large dataset with 1688 patients and analyzed the performance of models with various types of settings and architectures. More specifically, we benchmarked the performance of different segmentation models previously proposed for other tasks and also compared the effectiveness of various sampling methods and the choice of loss functions. We used the BRATS dataset to evaluate whether our implementations of deep learning models were correct and comparable to their original implementations.
## 4 Communication Infrastructure
In the literature, several solutions have been proposed, including the use of traditional wired fieldbuses (e.g., Modbus RTU over RS-485 [37], CANopen over CAN-bus [38], and Modbus TCP over Ethernet [39]), used to connect electrical machines, wireless connection (e.g., IEEE 802.11 ac [40]), and Power Line Communication (PLC) with network management provided by Software Defined Networking (SDN) [41]. In addition, a time synchronization service is required by the plant level in order to be able to properly refer each measurement provided by the devices in the plant with respect to the Universal Time Coordinated (UTC). Since the time synchronization requirement is almost relaxed (i.e., time synchronization accuracy in the order of seconds is more than enough for a large part of the power applications), general purpose time synchronization services, such as the Network Time Protocol (NTP), are typically adopted [42]. Locally, the data aggregated by DER-ACs, each of them representing a single POD, are then transmitted to the local VPPM aggregator. The total amount of data transmitted over the local-level communication depends on the number of total DER-ACs from which the VPPM aggregates data, typically in the order of tens. Thus, the bandwidth requirement is well below 1 Mbps. The local level has also limited requirements both in terms of latency as well as in terms of time synchronization. The supervisory level, connecting the VPPM with the DSO/IO-DMS, shares a large part of these requirements. The main limits, in both cases, are represented by the installation and management costs of a communication infrastructure over the distribution grid. Therefore, these levels are deployed using the already existing communication infrastructure, such as telco network, integrated with technologies, such as PLC or Wireless, with limited installation costs. The performance of such an approach has been validated on a working pilot project on the distribution grid and the results are presented in [43]. In the current analysis, the focus is on the plant-level communication infrastructure. The characterization of a pilot plant adopting such approach is provided in [44]. As indicated in [44], a proper use of existing communication solutions makes the application of the proposed VPP approach technically feasible and economically sustainable.
## 3 Multi-Nondiffracting SPP-Beam Launching
We chose the two points of (\(-11\) um, 0) and (\(11\) um, 0) on the two main SPP intensity lobes under different circular polarization incidences as observation points to observe the directional launching ability of this device, and used the contrast ratio of the two observation points' SPP intensity as the evaluation criterion. Figure 8a shows the SPP intensity contrast ratio of the two points with LCP incident light in the wavelength range from 500 to 1000 nm. It can be seen that the SPP intensity contrast ratio is larger than 10 dB between 565 and 875 nm. The SPP intensity distributions on the metal/air interface when the incident wavelengths are 565 and 875 nm are shown in Figure 8b,c, respectively. It can be seen that, although the perpendicular distance \(S=150\) nm of the two columns designed for the wavelength 633 nm cannot perfectly match the \(\frac{\lambda_{ SPP}}{4}\) at these two incident wavelengths, the device can still realize SPP directional launching.
# A Physical Approach to Simulate the Corrosion of Ceramic-Coated Magnesium Implants
## 3 Results and Discussion
### Model Validation  
Figure 14 shows the corroded screws of bare and coated HP-Mg screws under the same conditions of the immersion test in a simulated body environment. Visual inspections show the superior performance of the MAO-coated screws in maintaining the head and thread geometry at the same duration.  
The FEA model results of the screws are shown in Figure 15. The bare screw model shows wider spread in the contour plot of the concentration near the screw surface than the coated screw model. This result reflects that more magnesium ions had diffused into the environment more than for the coated screw. The numerical mass loss data for bare screw were compared with the collected experimental mass loss data in Figure 16. The transport numerical model was found to best match the experimental data towards the end of the test period of 4 weeks. This result validates the ability of the model to predict the mass loss of the ceramic-coated HP-Mg implants to good accuracy on the long term.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Time (Days)**} & \multicolumn{2}{c}{**Mass Loss (g/cm\({}^{2}\))**} \\ \cline{2-3}  & **Bare HP-Mg** & **MAO Coated HP-Mg** \\ \hline
7 & 0.017 & 0.0066 \\
14 & 0.04362 & 0.0232 \\
21 & 0.05951 & - \\
28 & 0.0693 & 0.0459 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass loss data for bare and coated HP-Mg in immersion test.  
Figure 14: Immersion test effect on HP-Mg screws. (**a–e**) Bare Mg screws after 0–4 weeks of immersion; (**f–j**) show the MAO coated group. The MAO coated screws maintained showed less visual corrosion compared to the uncoated ones.
## 3 System Overview
### BLE Path Loss Model  
The signal power at the BLE receiver is given by [59]  
\[\mathrm{P_{rb}}=\mathrm{P_{0}d_{r}}^{-\upsilon} \tag{6}\]  
where \(\mathrm{P_{0}}\) is the received power at a reference distance from the transmitter, \(d_{r}\) is the communication distance, and \(\gamma\) is called the distance power gradient.  
Figure 3: Optical channel model for OCC.  
The path loss for BLE is expressed as [59]  
\[\mathrm{L_{P}}=10\log(\mathrm{P_{tr}})-10\log(\mathrm{P_{0}})+10\gamma\log( \mathrm{d_{r}}) \tag{7}\]  
where \(\mathrm{P_{tr}}\) signifies the transmitted power.
## 6 Results and Discussion
### Comparison with the Application's QoS Requirements
#### 6.2.2 Data Traffic Rate (DTR)
For two and four applications, as Figure 16(b),d show, all scenarios presented similar values for the network sizes up to 100 nodes. For larger network sizes, the control overhead is directly proportional to the DTR value. This means that Scen 6 with the highest DTR had the highest control overhead values, whereas Scen 1 had the lowest. For a network size of 196 nodes and four running applications, the control overhead values were 36,926.2 messages, 40,301.4 messages, and 47,097.8 messages for Scen 1, Scen 5, and Scen 6, respectively. This occurs since higher DTR requires more exchanged control messages (rescheduling request message, reschedul  
Figure 15: Data delivery rate, changing the DTR value. ing message, and new scheduling message), as an attempt to ensure the application's QoS requirements. Figure 18 shows that, in general, the DTR value did not significantly affect the energy consumption. However, higher DTR values led to a little increase in energy consumption in most cases. For instance, in the case of three running applications and a network size of 100 nodes, the energy consumption values were 58,482.5 mJ, 59,190.6 mJ, and 60,647.2 mJ for Scen 1, Scen 5, and Scen 6, respectively. Figure 19 shows that Scen 1 and Scen 5 have roughly similar control delivery rate values. Scen 6, however, presents the worst values for all cases. This confirms that the control delivery rate is inversely proportional to the DTR value. For four applications and a network size of 144 nodes, Figure 19d shows that the obtained values were 59.64% for Scen 6, compared with 79.16% and 75.34% for Scen 1 and Scen 5, respectively. This is due to the additional control traffic in the case of Scen 6, which increases the congestion during the control timeslots after the network convergence. The control delay is shown in Figure 20. Similarly to the control delivery rate, the obtained control delay values show that Scen 6 continues to present the worst values, whereas Scen 1 and Scen 5 present similar values for most cases. For two applications and a network size of 144 nodes, Figure 20b shows that the control delay values were 1.75 s for Scen 6, compared to 1.39 s and 1.47 s for Scen 1 and Scen 5 respectively. Table 6 summarizes the previous results to provide an overview of the AA approach's performance in terms of the data-delivery rate and data delay. All the data of Table 6 have adopted the obtained results for Scen 1 (with the default values of MCR and DTR, as Table 3 shows) and considered the first three applications (1 st App, 2 nd App, and 3 rd App), which have QoS requirements. Figure 16: Data delay, changing the DTR value. Figure 17: Control overhead, changing the DTR value. Figure 18: Energy consumption, changing the DTR value. Figure 19: Control delivery rate, changing the DTR value. Figure 20: Control delay, changing the DTR value.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 2 Typical Details concerning Deep Excavation  
### Geotechnical and Geological Conditions  
Beijing is situated on the alluvium of the Yongding River with complex geological and hydrogeological conditions. Commonly, the soil layers are continuous, and they alternate between different densities, ranging from medium to very dense. The adjacent layers adhere tightly to each other. The strata are principally diluvial and alluvium in the quaternary period. The soils that cover the area consist mainly of medium-coarse sediments, such as sand and gravel, as well as silty sand, silty clay, silt, backfill soil, etc. Table 1 displays the physical and mechanical characteristics of soils which were collected from various locations in the city through in situ sampling. Gravel and sand show favorable engineering characteristics in the natural state, including high shear strength and stability [21; 22]. Conversely, clay and backfill soil are easily disturbed, their strength rapidly decreases, and the void ratio and fractal dimensions of the pores and soil particles changes as well [23; 24]. Additionally, the underground water is approximately distributed from \(-5\) m to \(-26\) m.
## 5 Numerical Analysis Results and Discussion
### PLAXIS Analysis  
PLAXIS 2D was used to build slope simulation models, with the Mohr-Coulomb model used for soil layers. The soil parameters used in the model were the unsaturated unit weight \(\gamma_{\mathrm{unsat}}\), saturated unit weight \(\gamma_{\mathrm{sat}}\), [PERSON]'s modulus \(E\), [PERSON]'s ratio \(\
u\), cohesion \(c\), internal friction angle \(\varphi\), and dilatancy angle \(\psi\). The model consisted of grouted anchor bars, free-styled grill beams, RC curtain walls, free and fixed lengths of the ground anchor, and a typical retaining wall. The ground anchor design is based on the freeway's slope engineering design specifications [41]. The side friction resistance of the front and rear endsof grouted anchor bars is based on the calculation formula proposed by [PERSON] in 2007 [42]. The calculation formula is as follows:  
\[\tau=\frac{P}{A_{s}}=\frac{P}{\pi\times D\times L} \tag{6}\]  
where \(\tau\) is the side friction resistance (kN/m), \(A_{s}\) is the lateral area of ground anchor, \(P\) is the preforce (kN/m), \(D\) is the diameter of ground anchor (m), \(L\) is the length of ground anchor (m).