esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Hop Cone Analysis
#### 2.3.1 GC-MS Hop Oil Analysis
DC., where plants subjected to different nitrogen levels and plant spacing exhibited significant differences in \(\alpha\)-humulene, \(\beta\)-caryophyllene and \(\alpha\)-pinene content [26]. This study also highlighted a greater amount of \(\alpha\)-pinene when plants were treated with either 60 or 120 t/ha of nitrogen and with 100 \(\times\) 100 cm spacing, while amounts of \(\alpha\)-humulene and \(\beta\)-caryophyllene were higher with a supply of 60 t/ha of N and 50 \(\times\) 50 cm spacing. Similarly, in our study the presence of \(\beta\)-pinene and \(\beta\)-myrcene was higher in T1, T2, T3 and T4, where there was a greater quantity of nitrogen added. In contrast, the presence of \(\beta\)-caryophylline, \(\alpha\)-humulene, muurolene, \(\beta\)-selinee, \(\alpha\)-selinene and \(\tau\)-cadineine was higher in C (no N supply) [26]. Similar results have been found for sage (_Salvia officinalis_ L.) subjected to different nitrogen levels, where \(\alpha\)-humulene percentages were higher in samples with no nitrogen added [27]. The compounds \(\beta\)-pinene and \(\beta\)-myrcene (monoterpenes) together with \(\beta\)-caryophylline, \(\alpha\)-humulene, muurolene, \(\beta\)-selinene, \(\alpha\)-selinene and \(\tau\)-cadineine (sesquiterpenes) result from two different metabolic pathways: monoterpenes are synthesised by the mevalonate (MVA) pathway in the cytoplasm, whereas sesquiterpenes derive from the 2-C-methyl-D-erythrid 4-phosphate (MEP) pathway in plastids. According to [PERSON] et al. [28] fertilisation could favour one pathway while hampering the other. In a study on aromatic plants, [PERSON] et al. [29] did not observe compost supply effects on the _Rosmarinus officinalis_ terpenene concentration in glandular trichomes, even if an increase in biomass production was detected. This might suggest that applied treatments do not contribute to the development of glandular trichomes to such a large degree in Mediterranean plants, since they need to avoid related energy costs as a plant maintenance strategy in arid periods. However, this finding is not in line with our study, where an increase in monoterpenes and a decrease in monoterpenes were observed. The level of sesquiterpenes in our samples, in particular, seems to have been reduced by high levels of N. A possible explanation, which is in accordance with the \"excess carbon hypothesis\", could be that carbon-based secondary compounds decrease as a result of increased carbon allocation to growth in response to high nutrient availability [30]. In a study on _Phoebe bournei_ ([PERSON]) [31], a reduction in the sesquiterpene content was observed when com
## 3 Results
### Microwave-Assisted Extraction Optimization Results
#### 3.1.1 Extraction Solvent  
For the development of the extraction method and the selection of the most appropriate solvent, experiments were performed using ACN, AcOEt, water, and MeOH as extraction solvents at 60 \({}^{\circ}\)C for 5 min. ACN and AcOEt were found to lead to satisfactory recoveries for a larger number of compounds compared to water and MeOH, according to the graph presented in Figure 1. The obtained RE% for all the analytes using different extraction solvents are presented in the Supplementary Materials, Table S2. According to the calculated RE%, the extraction using ACN resulted in acceptable recoveries (70-120%) for 80% of the analytes, while the use of AcOEt reduced the percentage of acceptable recovered compounds to 57%. Under the same parameters, water extraction was successful in extracting only 26% of the analytes, while the RE% obtained with MeOH as an extraction solvent were relatively low.  
In general, ACN exhibits increased microwave absorption capacity and has already been successfully used in an MAE technique to extract pesticides from food matrices [27]. This solvent was also used to extract aflat from cereals and was found to be most suitable compared to MeOH and AcOEt [24]. The use of AcOEt resulted in lower RE%, for all the analytes, except for the mycotoxins citrinin (CIT) and alternarol (AOH), for which the RE% increased when using AcOEt. The RE% of alternarol monomethyl ether (AME) were found to be at the same level as those obtained when ACN was used as the extraction solvent, while in the case of patulin the RE% when using AcOEt were low. These differences are due to the different solubility of each compound in different free extracts, which affects their extraction capacity.
## 4 Discussion on Mechanisms
### Adsorption Mechanisms on Biosorbents
The complexity of the adsorption process may rise due to the potential interaction between the surface chemistry of these biosorbents and the positively charged MB molecules, as illustrated in Figure 11. The hydroxyl groups (-OH, 3600-3100 cm\({}^{-1}\) in FTIR results, Figure 1) of these biosorbents cause a dipole-dipole hydrogen bond between the H acceptor in MB molecules and hydroxyl groups (as H-donor) on the biosorbent structures (Figure 11) [118]. Additionally, positively charged MB molecules are attracted by negatively charged carboxylate ions (-COO\(-\)) over the surface of biosorbents, which was clearly demonstrated at the peaks 1428 and 1592 cm\({}^{-1}\) (symmetric and asymmetric stretching of -COO\(-\)) in FTIR analysis (Figure 1) [119]. Similarly, the positively charged MB molecules are attracted to the negatively charged -COO (indicating the peaks 1100 and 1027 cm\({}^{-1}\) in FTIR analysis). A higher MB adsorption was observed for LD, CH, HH, and RS at higher pH levels due to strong electrostatic attractions between the negatively charged biosorbents and positively charged MB molecules [114]. The other potential interaction is a \"n-n interaction\", which could be between Si-O-Si groups of the biosorbent surface and aromatic structure of MB [95] or potentially the oxygen in the acid groups that donate electrons to the aromatic rings in the MB molecules [120]. Furthermore, MB adsorption can partially be enhanced by the \"n-n interaction\" between the hexagonal structure of hemicellulose, cellulose, and lignin and aromatic rings of MB molecules [95]. Although these interactions are potentially explanations for how MB absorption occurs for the biosorbents used in this study (LD, CH, HH, and RS), a detailed, comprehensive, and quantitative surface characterisations of these biosorbents are required in order to accurately propose the MB adsorption mechanisms at play.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials  
Biomaterials play a crucial role in cardiac regeneration by providing a supportive environment for cell growth, facilitating tissue repair, and promoting functional recovery of the heart.  
The unique properties of biomaterials make them suitable for cardiac regeneration applications. They can be engineered to mimic the structure and function of native cardiac tissue, allowing them to integrate seamlessly with the surrounding heart tissue. Biomaterials can be designed to possess specific mechanical properties, such as elasticity and stiffness, which are essential for proper cardiac function [175, 176].  
In the context of cardiac regeneration, biomaterials can serve various functions. They can act as scaffolds, providing a framework for the attachment, proliferation, and differentiation of cardiac cells. These scaffolds can be biodegradable, meaning they are gradually broken down and replaced by new tissue as the heart heals. Additionally, biomaterials can release bioactive molecules or drugs to enhance cell survival, promote angiogenesis, and modulate the immune response [177, 178].  
Various types of biomaterials have undergone extensive research for their application in cardiac regeneration. These include a diverse range of natural polymers, such as collagen, fibrin, gelatin, alginate, chitosan, silk, hyaluronic acid, and decellular extracellular matrix [179]. Furthermore, synthetic polymers like polyurethane, polyethylene glycol, poly(\(\varepsilon\)-caprolactone), poly(lactic-co-glycolic acid), poly(l-lactide), and poly(glycerol sebacate) have also been meticulously investigated for their potential in this field. These materials can be processed into various forms, such as hydrogels, films, or fibres, depending on the specific application [180].  
In recent years, advances in biomaterial science and tissue engineering have shown promising results in preclinical and clinical studies of cardiac regeneration. The use of biomaterials has demonstrated improvements in cardiac function, scar reduction, and tissue remodeling, leading to a better quality of life for patients with heart disease [181, 182].  
While challenges remain, such as achieving long-term functionality and ensuring proper integration of regenerated tissue with the host heart, the field of biomaterials for cardiac regeneration holds great promise. Continued research and innovation in this area have the potential to revolutionize the treatment of heart disease, providing new opportunities for restoring damaged hearts and improving patient outcomes.  
Biomaterials offer exciting opportunities for cardiac regeneration by providing structural support, promoting cell growth, and guiding tissue repair. With ongoing advancements in biomaterial science, the development of innovative strategies, and collaboration between scientists, engineers, and clinicians, we are inching closer to harnessing the full regenerative potential of biomaterials for treating cardiac diseases and enhancing cardiac function.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### Network Infrastructure
#### 3.3.3 Broadcast Topology  
In a broadcast device, data can be transmitted to other devices without the overhead of setting up a network. The communication is unidirectional and the destination address is also not required for this communication. The broadcast is initiated by sending the primitive broadcast using a beacon at the beginning of the superframe (Figure 4). The rest of the superframe comprises of GTS downlink timeslots. Similar to star topology, passive  
Figure 3: Frame structure—star topology.  
Figure 2: Frame structure—peer-to-peer topology.  
scanning is used to discover the devices in the vicinity. The number of timeslots in every broadcasting contention free period is not clearly defined in the standard.
# A Review of Medication Adherence Monitoring Technologies +
## 5 Challenges and Future Trends
### Challenges
#### Acceptability and User's Comfort  
The user's perception of a monitoring system has a great impact on its adoption and success. First, technological barriers such as battery energy consumption, mobility support, and others play a significant role as barriers to the wide acceptance of technology-based systems for medication adherence monitoring. Second, ethical challenges such as privacy and confidentiality also exist. Users are concerned about behaviors being monitored beyond medication taking and the potential of unintended users accessing the information collected [161]. In addition, users, and especially the older ones, tend to have social, physical, demographic, and cultural barriers towards using technology and, as a result, barring the user's acceptance of modern technology [65; 113; 162].  
In this regard, we can compare active sensing versus passive sensing systems. By active sensing, we mean that the sensing device is directly attached to the patient body and should be in place and active during the medication taking activity. An example of such a system can be a smartwatch. On the other hand, passive sensing is performed by using an off the patient body system. Nonetheless, portability is a leading specification that should be supported by medication adherence systems in order to earn the user's perception by maintaining comfort. However, as we discussed before, most portable systems, such as the wearable sensors, requires the user's attention by wearing it contentiously and charging or replacing its battery frequently [163]. Meanwhile, passive sensing systems may be less accurate and not portable, but not sensitive to the battery replacement issue. Managing this trade-off implication requires a system that is portable but energy efficient and accurate [62].
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 4 Configurations  
In terms of emitter types, OPAs can be sorted into EF OPAs, WGA-based OPAs, and OPAs based on other emitter structures. The configurations of OPAs based on emitters will be discussed here.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 1 Introduction
According to the network architecture of substation automation systems and the characteristics of power grid service traffic, a design scheme for smart substation Ethernet networking based on TSN characteristics has been proposed [13; 14; 15; 16]. In the three-layer and two-network substation network architecture, traditional Ethernet switches are replaced with TSN switches, and Qbv and Qbu technologies are utilized to provide low-latency and low-jitter services for some time-sensitive flows. The application of TSN can not only ensure the safety of large smart substations in network congestion but can also increase the scalability of small network-scale substations, facilitating subsequent access to more equipment and services. However, if only the Qbv scheduling technique of TSN switches is available and the end-side devices do not have TSN characteristics and cannot collaborate with the TSN switches for traffic scheduling optimization, the upper bound of the end-to-end delay of the data streams will not be optimal. In the three-layer two-network smart substation architecture that introduces TSN switches, this paper proposes a design scheme for a smart substation end-side device based on TSN technology. In terms of implementation, the device utilizes Linux's Earliest TxTime First (ETF) Qdisc and the Intel 210 NIC to achieve the time-aware capability of hardware-timed message transmission, which, together with the Qbv technology of the TSN switch, is able to provide a bounded delay for time-sensitive streams and offers a new solution for Quality of Service (QoS) enhancement in smart substation automation systems. This scheme can not only optimize network resources, reduce redundant communication and improve network efficiency in large-scale substation networks but it can also be applied in small-scale substations to facilitate subsequent system expansion. To summarize, the main contributions of the paper are as follows: (1) By using a TSN switch in smart substation networks, security problems arising from network congestion can be solved, and the scalability of small-scale network substations can be improved. (2) By using the ETF queue scheduling mechanism and NIC cards that support hardware timing, the proposed end-side devices can collaborate with TSN switches to provide bounded delay transmission for time-sensitive data streams. (3) The results show that the delay for time-sensitive messages is significantly reduced, and jitter is stable enough to achieve deterministic transmission.
# Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes
## 3 The Desktop-Simulated Testing Environment (DSTE)
### Visual Realism in the DSTE  
We decided to keep the DSTE as essential as possible without using a photorealistic rendering. Virtual reproductions of objects not relevant to the experiment were not included, such as picking bins and workplace facilities. The virtual objects that simulate their corresponding real ones were then limited to the LEGO bricks and a 26 \(\times\) 26 green LEGO Duplo plate on which users place them. Even though the DSTE does not have a high degree of fidelity with a real testing environment, it does not invalidate the results of the experiments. We were careful that there was no altering of the information provided through visual assets or how they combine with the simulated real objects. Virtual LEGO bricks that simulate real ones are rendered in \"opaque\" mode, whereas CAD models used as visual assets are rendered in \"transparent\" mode with a semi-transparent shade (alpha 150 in the range from 0 to 255), as made in [17; 38; 39] (see Figure 1a). We took from [36] visual assets that can be tested with the proposed DSTE, and they can be either world-fixed (see Figure 2) or screen-fixed (see Figure 2). Screen-fixed visual assets are placed in the top-right corner of the interface. We also paid attention to the world-fixed ones to reproduce the effect of registration error through a slight misalignment between the simulated real bricks and the corresponding visual assets (see Figure 1b).  
Figure 1: Visual realism in the DSTE: (**a**) the blue LEGO brick, which simulates the real one, is rendered in “opaque” mode, whereas the red product model used as a visual asset is rendered in “transparent” mode; (**b**) the misalignment between the simulated real brick and the corresponding visual asset to reproduce the effect of registration error.
## 6 Results and Discussion
### Optimal Position of the Relay without and with Obstacle  
Capacity analysis is performed for three different positions of relay for evaluating the optimal position. The capacity gain is achieved for the optimal position by using the following cases:  
* Case 1: Capacity gain of Position A over B
* Case 2: Capacity gain of Position A over C
* Case 3: Capacity gain of Position B over C  
In this setup, no obstacle is placed in between the source and the destination and relay path. The performance is analyzed by varying the position of the relay at position A, B, and C. Capacity gain is compared among all the three position of the relay.  
Figure 9 shows the results of evaluation for the optimal position of the relay without obstacle. The relay position B performance is better than Position A and Position C. While the relay position A performance is better than C.  
From Table 5, the average capacity gain of relay position B over A and C are 1.35% and 1.90%, respectively. On the other hand average capacity gain of relay position at A is 0.54% over C. The resulted analysis shows that optimal position of the relay is at point B. This means if relay is placed equally distance from the source and destination it provides more capacity gain.  
In this setup, when the obstacle is introduced between source and destination, performance is analyzed for all three position A, B, and C. Capacity gain is calculated for all the above three cases. In Figure 10 shows the results of evaluation for the optimal position of the relay with obstacle. The relay position at B performance is better than A and C. While the relay position at A performance is better  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tx Antenna Gain (dB)**} & \multicolumn{3}{c}{**Capacity (Mbits/s/Hz)**} & \multicolumn{3}{c}{**Capacity Gain**} \\ \cline{2-7}  & **Position A** & **Position B** & **Position C** & **Case 1** & **Case 2** & **Case 3** \\ \hline
0 & 2.33 & 2.36 & 2.32 & 1.28 & 1.72 & 0.43 \\
10 & 2.41 & 2.45 & 2.40 & 1.65 & 2.08 & 0.41 \\
15 & 2.46 & 2.5 & 2.45 & 1.62 & 2.04 & 0.40 \\
20 & 2.49 & 2.51 & 2.46 & 0.80 & 2.03 & 1.21 \\
25 & 2.51 & 2.55 & 2.50 & 1.59 & 2.00 & 0.40 \\
30 & 2.56 & 2.59 & 2.55 & 1.17 & 1.56 & 0.39 \\ \hline \multicolumn{7}{c}{Average Gain} & \multicolumn{3}{c}{1.35} & 1.90 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal position of the relay without obstacle.  
Figure 9: Optimal position without obstacle.  
than C. From Table 6, the average capacity gain of relay position at B is 1.22% and 1.70% over A and C. On the other hand, the average capacity gain of relay position at A is 0.47% over B position.
## 3 Lowest-Frequency Modes and Evolution
ENM studies indicate that selection guides sequence evolution to favor dynamical properties required for function, such as allosteric behavior or protein-protein interactions [113; 114]. An exhaustive study by the Bahar group on nearly 27 K proteins representing 116 CATH superfamilies [72] characterized the cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members, developing an integrated pipeline to evaluate the signature dynamics of families based on ENMs (SignDy). They confirmed that global lowest-frequency modes of motion are conserved within a family, but there is a subset of motions that sharply distinguishes subfamilies at low-to-intermediate frequencies and is responsible for functional differentiation. Then, modulation of robust/conserved global dynamics via low-to-intermediate frequency fluctuations could be a versatile mechanism ensuring fold adaptability and subfamily specificity, subject to both positive and negative selection. Finally, taking one step further with this \"selectionist\" view, recent works have attempted to predict functional dynamics directly from sequence evolutionary couplings, skipping structures altogether [115].
## 4 Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) Methodology
### LSTM-RNN Model Structure for the Problem  
Model structures with multiple training hidden layers are intended to approximate the roots (Eigenvalues) of the transcendental equation of the prismatic cantilever beam. The first hidden layer is a 200-neuron LSTM-RNN layer, while the second layer consists of 100 nodes of a completely interconnected opaque layer. Furthermore, for learning parameters, a tangent hyperbolic sigmoid (tanh sigmoid) is adapted as an activation function to be computed and is less liable to saturate zero gradients for the network [41]. Input data for the LSTM-RNN model include features like natural frequency \(\big{\langle}f_{n}\big{\rangle}\) and beam geometry characteristics such as a Young's modulus (\(E\)), moment of inertia (\(I\)), density (\(\rho\)), number of modes (\(n\)) and cross-sectional area (\(A\)). In the meantime, the output layer uses the evaluated roots (\(\beta_{n}l\)) as target data output. In addition, an ADAM optimization algorithm which combines the heuristics of both Momentum and RMSProp is adapted to enhance the learning procedure of the network [42]. Similarly, for better accuracy of the predicted values of target data, mean squared error (MSE) and mean absolute error (MAE) functions are applied with value of 0.001 identity regression.