esa-sceva/satcom-chunk-collection huggingface dataset:
marine monoterepenes were not identified until 1973 when the isolation of polyhalogenated compounds from _Aplyasia californica_ [PERSON] was done [13; 14]. During the last decades, following these initial discoveries, new marine monoterepenes have been reported including degraded monoterepenes and monoterepenes of mixed biogenesis [15; 16; 17; 18].  
This review highlights the occurrence of monoterepenes in macroalgae with emphasis on the cyclic and halogenated monoterepenes since they were the most active. Macroalgal monoterepenes extraction techniques, methods for the analysis, and structure elucidation are presented, as well as their bioactivity. Available literature, including reviews on biochemistry, biosynthesis, and bioactivity have been focused mainly on monotereponids from terrestrial plants [19; 20; 21; 22; 23; 24; 25; 26]. Several reviews on marine volatile halogenated metabolites (including monoterepenes) exist [7; 27; 28; 29; 30; 31]. Neither of these reviews represented comprehensively all domains which are presented in this paper. [PERSON] et al. [27] not only put an emphasis on monoterepenes, but also on other volatile metabolites present in red algae with short description of their bioactivities. [PERSON] et al. [28] reported only few halogenated monoterepenes with antiplasmodial activity. [PERSON] et al. [30] in the review mentioned only the insecticidal activity of few monoterepenes. [PERSON] [7] covered all bioactive compounds present in the algae and, among them, emphasized monoterepenes as anticancer agents. Monoterepenes were also reviewed with other nutrients and secondary metabolites of the macroalgae [29]. [PERSON] et al. [31] represented that brown algae of the genus _Dicytopteris_ also contained some monoterepenes but in much lower concentration when compared to red algae. In distinction to the published reviews, the present review presents several novelties: (a) exclusively monoterepenes (acyclic, monocyclic, and bicyclic) from red macroalgae, as the main source of monoterepes, were targeted; (b) the methods of their isolation, analysis, and structure elucidation were systematically summarized in the tables; (c) the bioactivity (anticancer, antiplasmodial, and insecticidal) of macroalgal monoterepenes was systematically presented; (d) the literature references were updated.
## 4 Results
### Development of Broad Principles and Vision for EASY Concept  
#### 4.2.1 Broad Principles for EASY Concept Design  
In order to develop some broad principles for the EASY concept design, workshop participants were asked what extension tasks the system could be used for, who would use the system, and what the overall objectives of such a system should be.  
Participants envisioned that the EASY system would provide a web-based application where communities of extension staff, landholders, researchers and program managers could collaborate through the publishing and sharing of spatial and related information for agricultural planning andmanagement. It was suggested by several participants that the system should be applicable across Australia at various spatial scales, and usable by a variety of individuals, governments and non-government organisations. Another important principle was that the basic EASY system should be freely available.  
The importance of the EASY system as an \"information conduit,\" \"facilitator for effective knowledge transfer,\" and for \"two-way information sharing\" between landholders and researchers/extension staff was a consistent theme among workshop participants; lack of such an ability was also noted as a barrier to DSS adoption in the literature cited in the Introduction. It was suggested that researchers and agricultural and natural resource managers could publish spatial datasets, models and other textual information using EASY. Further to this, landholders and extension staff should be able to easily access EASY to find, view, analyse and download published information for improving the productivity and sustainability of agricultural land management practices. The ability for researchers, extension staff and catchment management organisations to compile monitoring information was also considered to be an important feature.
###### Abstract  
A secure smart city integrates advanced technologies to enhance efficiency, sustainability, and safety while safeguarding citizens' privacy and data. Through interconnected Internet of Things (IoT) devices, real-time monitoring, and data analytics, a city can optimize transportation, healthcare, energy usage, waste management, and public services. Robust encryption protocols and cybersecurity measures are implemented to protect against cyber threats and unauthorized access to sensitive information [1].  
In a secure smart healthcare system, cutting-edge technologies converge to optimize patient care while safeguarding sensitive data. Advanced encryption protocols ensure the confidentiality of patient information, while robust authentication mechanisms guarantee secure access to medical records. Interconnected IoT devices monitor patients remotely, enabling real-time health data collection and analysis [2]. Artificial intelligence algorithms process these data to predict health trends and provide personalized treatment recommendations [3, 4]. Additionally, blockchain technology maintains immutable records of medical transactions, enhancing transparency and accountability across the healthcare ecosystem.  
Secure smart transportation systems form the backbone of efficient urban mobility in a smart city. Integrated with advanced technologies such as IoT sensors, AI-driven traffic management systems, and encrypted communication networks, these transportation networks ensure seamless and safe journeys for citizens [5, 6]. Robust authentication protocols and encryption mechanisms safeguard user data and transactions, mitigating cybersecurity threats.  
Secure smart energy systems integrate advanced technologies to ensure the reliable, efficient, and resilient delivery of energy while addressing cybersecurity challenges. These systems leverage interconnected devices, data analytics, and automation to optimize energy generation, distribution, and consumption. Advanced authentication mechanisms, such as biometrics and multifactor authentication, mitigate unauthorized access, while continuous monitoring and response strategies enable real-time threat detection and mitigation.  
In the context of illuminating recent advancements, technologies, and case studies related to sustainability, smart cities, smart health, data-driven technology, and the IoT, this Special Issue entitled \"Secure, Sustainable Smart Cities, and the IoT\" (available at [[https://www.mdpi.com/journal/sustainability/special_issues/7](https://www.mdpi.com/journal/sustainability/special_issues/7) SKG529 PVX]([https://www.mdpi.com/journal/sustainability/special_issues/7](https://www.mdpi.com/journal/sustainability/special_issues/7) SKG529 PVX), accessed on 3 February 2024) comprehensively explores a multitude of research endeavors in this domain. We express our gratitude to all of the authors who contributed to the success of this Special Issue. In this Editorial, perspectives, challenges, and future directions for secure, sustainable smart cities and the IoT are explored.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.1 Combustion Optimization Using CFD and AI
Therefore, despite their high effectiveness in controlled laboratory conditions, the use of neural networks in industry requires careful validation and resistance tests to operational variables. In Ref. [15], an ANN was used to predict pyrolysis and incineration of dairy waste. The model accurately reproduced the experimental data of TGA, enabling the prediction of thermal process kinetics. In Ref. [16], an ANN made it possible to accurately predict hydrogen production from the reaction of aluminum and NaOH, with a correlation coefficient of 0.998. In Ref. [17], ANN + PCA were used to predict the distribution of solid nanoparticles (PMs) from the GDI engine. The results were highly consistent with the experimental data. It should also be noted that Ref. [18] developed a method to optimize the biomass gasification process by combining Monte Carlo simulations, a kinetic model,and a random forest (RF) algorithm. Randomly generated process data predicted which parameters (e.g., water content, reaction temperature) had the greatest impact on syngas yield. A high agreement of the predictions with the experimental results was obtained.
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## Appendix B The Taylor Expansion of \(P_{b}\)  
The derived expression of \(P_{b}\) is composed of \(N\) variable terms which are polynomials. If we have the Taylor series for \(f(x)\) and \(g(x)\) which are polynomial, we can easily figure out the Taylor series for \(f(x)+g(x)\), \(f(x)g(x)\), and \(f(x)/g(x)\) by just adding, multiplying, and dividing the series.  
The coefficient \(A_{ij}\) can be considered with a more simple modified form for computation, given in [15; 19]. It can be rewritten as follows  
\[A_{i,K_{i}}=\prod_{t=1,t\
eq i}^{N}\left(1-\frac{\gamma_{i}^{-1}}{\gamma_{t}^{ -1}}\right)^{-K_{t}}=\prod_{t=1,t\
eq i}^{N}\left(1-\frac{d_{i}^{\alpha}}{d_ {t}^{\alpha}}\right)^{-K_{t}}, \tag{13}\]  
and for \(j=K_{i}-1\), down to \(1\), \(A_  
which denotes the coefficient of the \(r\)-th term in the \(j\)-th polynomial in (A5). Then, (A5) can be simply collected in a single indeterminate \(\rho\) as follows  
\[\sum_{j=1}^{K_{i}}\sum_{r=1}^{\infty}\beta_{i}(r,j)\rho^{-(r+j-1)}=\sum_{r=1}^{K _{i}}\sum_{t=1}^{r}\beta_{i}\left(t,r+1-t\right)\rho^{-r}+\sum_{r=K_{i}+1}^{ \infty}\left(\sum_{t=r-K_{i}+1}^{r}\beta_{i}\left(t,r+1-t\right)\rho^{-r} \right).\] (A7)  
Let \(\beta_{i,r}\) be the coefficient of \(\rho^{-r}\) for the \(i\)-th variable term, which is given by  
\[\beta_{i,r}=\begin{cases}\sum\limits_{t=1}^{r}\beta_{i}\left(t,r+1-t\right)& \text{if }1<r\leq K_{i}\\ \sum\limits_{t=r-K_{i}+1}^{r}\beta_{i}\left(t,r+1-t\right)&\text{if }r>K_{i} \end{cases}.\] (A8)  
Since the derived expression of \(P_{b}\) is composed of \(N\) polynomials, the sum of the Taylor series for all the polynomials is considered, of which all the coefficient of \(\rho^{-r}\) for \(r<K\) are added to be zero.  
\[\sum_{i=1}^{N}\beta_{i,r}=0\quad\text{for }r<K.\] (A9)  
Consequently, \(K\)-th term remains as the first-order term as follows.  
\[P_{b} =\sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}P_{b}(i,j)\] \[=\sum_{j=1}^{K_{1}}A_{1,j}P_{b}(1,j)+\sum_{j=1}^{K_{2}}A_{2,j}P_{ b}(2,j)+\ldots\sum_{j=1}^{K_{N}}A_{N_{j}}P_{b}(N,j)\] \[\approx\left(\sum_{i=1}^{N}\sum_{t=K-K_{i}+1}^{K}\beta_{i}\left( t,K+1-t\right)\right)\,\rho^{-K}+N\mathcal{O}(\rho^{-(K+1)})\] \[=\left(\frac{(\frac{2K-1}{K})}{2^{K}}\prod_{i=1}^{N}d_{i}{}^{ \alpha K_{i}}\right)\,\rho^{-K}+N\mathcal{O}(\rho^{-(K+1)})\] \[=\left(\frac{\Psi}{2^{K}}\prod_{i=1}^{N}d_{i}{}^{\alpha K_{i}} \right)\,\rho^{-K}+N\mathcal{O}(\rho^{-(K+1)}),\] (A10)  
where \(K=\sum_{i=1}^{N}K_{i}\).
## 4 Measurement Results  
In the measurement setup (Figure 13), the Verilog design of the proposed CNN ANC model (Figure 12) is, first, downloaded from a personal computer to the FPGA, along with the 3232 trained parameters (16-bit floating point numbers). Then, a reset switch starts the ANC operation in the FPGA; it accepts a mono audio input from the reference microphone, performs the CNN ANC operation on the mono audio input, generates an anti-noise signal, and sends it to the speaker of the in-ear headphone, to cancel noise. The FPGA includes a USB 2.0 link, to communicate with a smartphone through a USB 2.0 PHY chip. The smartphone monitors the output data, from the reference and error microphones of the in-ear headphones.  
Frequency spectra (Figure 14) of the output data were obtained from the reference and the error microphones, at noise incidence directions of 0\({}^{\circ}\) (Figure 14a) and 90\({}^{\circ}\) (Figure 14b); a band-limited (100\(\sim\)2000 Hz) pink noise was applied to a loud speaker. Without the ANC operation, the error microphone output shows a high frequency attenuation, beyond 300 Hz, at both 0\({}^{\circ}\) and 90\({}^{\circ}\), due to the mechanical structure of the in-ear headphone. With the ANC operating, the error microphone output demonstrates a large attenuation at low frequency (>20 dB at f < 300 Hz), and total power reduction of 14.8 dB at 0\({}^{\circ}\) and 14.3 dB at 90\({}^{\circ}\), compared to the case in which the ANC is not used. With the ANC operating,  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Operation** & **Measured Cycles** & **Measured Time (\%)** \\ \hline
1 & Dilated 1-D CONV & 1490 & 12.4 \\
2 & ReLU & 90 & 0.7 \\
3 & FC & 930 & 7.8 \\
4 & Residual/skip add & 140 & 1.2 \\
5 & 512-tap FIR & 2653 & 22.1 \\ - & Others & 399 & 3.3 \\ - & Total & 5702 & 47.5 \\ \hline \end{tabular}
\end{table}
Table 1: Operation cycles of the proposed CNN model, to process three samples.  
Figure 13: Measurement setup (**a**) block diagram and (**b**) photo.  
the attenuation bandwidth was 2000 Hz at both 0\({}^{\circ}\) and 90\({}^{\circ}\); the attenuation bandwidth is defined to be the cross-over frequency of the error microphone spectrum, between the ANC on and off cases.  
Since the dilated CNN model of this work predicts the future signal well, by observing a long previous data, it does not need to satisfy the causal constraint; this work observes 327 ms-long previous data, whereas the FxLMS algorithm [15] observes only 250 \(\upmu\)s-long previous data. The in-ear headphone presented here achieved greater power reduction, attenuation bandwidth, and maximum noise reduction, than previous noise-cancellers that used the FxLMS algorithms (Table 3), and it, especially, achieved an excellent total power reduction of 14.8 dB at 0\({}^{\circ}\) and 14.3 dB at 90\({}^{\circ}\), whereas they were 12.2 dB at 0\({}^{\circ}\) and 5.6 dB at 90\({}^{\circ}\) in [20].
## 2 Theory And Model Building
### [PERSON]'s Acoustic Analogy Theory  
The [PERSON] acoustic analogy theory approximates the complex sound source formed by fluid motion as a static medium equivalent source to solve difficult problems to accurately describe the sound source. The theory assumes that the coupling between the fluid and sound field exists only in the near field, and considers the far field as the acoustic radiation region, whose flow has almost no effect on the sound field, where \(\rho_{a}=\rho-\rho_{0}\), \(P_{a}=P-P_{0}\), \(\rho_{0}\) and \(P_{0}\) represents the density and sound pressure. Then, the equation can be expressed as follows:  
\[\frac{\partial^{2}\rho_{a}}{\partial t^{2}}-c_{0}^{2}\frac{\partial^{2}\rho_ {a}}{\partial x_{i}x_{j}}=\frac{\partial^{2}T_{ij}}{\partial x_{i}x_{j}} \tag{4}\]  
\[T_{ij}=\rho v_{i}v_{j}+\delta_{ij}\Big{[}(P-P_{0})-c^{2}\Big{(}\rho-\rho^{2} \Big{)}\Big{]}-\tau_{ij} \tag{5}\]  
where \(c_{0}\) is the velocity of sound under isentropic conditions; \(T_{ij}\) is the Lighthill stress tensor; \(\delta_{ij}\) is the elastic constant; \(\tau_{ij}\) is the viscous stress.  
As the viscous stress is a small amount of Reynolds stress and can be neglected, the final equation can be simplified as follows:  
\[\frac{\partial^{2}\rho_{a}}{\partial t^{2}}-c_{0}^{2}\frac{\partial^{2}\rho_ {a}}{\partial x_{i}x_{j}}=\frac{\partial^{2}T_{ij}}{\partial x_{i}x_{j}} \tag{6}\]  
\[T_{ij}=\rho_{0}v_{i}v_{j} \tag{7}\]  
This equation is the basic equation of [PERSON]'s acoustic analog theory, which can be solved for turbulent pulsating pressure direct radiation noise.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Radio Optimization
#### 2.1.4 Directional Antennas  
Directional antennas are used to allow signals to be sent and received in more than one direction simultaneously, thus enhancing transmission range and throughput. Directional antennas may require positioning techniques to be oriented, but multiple communications can take place in close proximity, which results in spatial reuse of bandwidth. Many researchers have proposed the use of directional antennas to improve the energy efficiency of the network [36].  
Given that omnidirectional antennas transmit a signal equally to all directions and, in most of the applications, communication is unicast, we can infer that significant amount of energy can be saved using directional antennas. In [37] the authors study the effect of directional antennas on energy efficiency of the IEEE 802.15.4 standard in outdoor WSNs. In [38] we can read a review of energy efficiency using directional antennas in mobile ad hoc networks and [39] is a very recent review of directional antennas for WSN applications, where a little section is dedicated to contention reduction and energy efficiency.  
The use of directional antennas can also reduce the interference between radio streams and improve the SNR. Therefore, directional antennas can improve the reliability and reduce the number of retransmissions. However, use of directional antennas necessitates to find the right direction and parameters correctly and quickly. A recent work in this area [40] presents an energy-efficient localization of sensor nodes in WSNs using beacons from a rotating directional antenna.  
Finally, we must note that the most active research area in recent years in order to take advantage of the properties of directional antennas is the proposal of new MAC protocols. The work in [41] contains a review of this topic but there are interesting subsequent works [42; 43; 44; 45; 46; 47].
# Parametric Study of Three Types of Timber Connections with Metal Fasteners Using Eurocode 5
## 4 Parametric Study of the Connections
### Design Parameters
In W-S-W connections with side members smaller than 45 mm, the failure model represents the bending of the fastener, but when the wooden thickness increases, the failure model is represented in the wood embedment in combination with the bending of the fastener. The failure mode is independent of the wood member thickness. This fact is evidenced by the failure mode determined with the minimum value of the characteristic load-carrying capacity per shear plane and fastener given by Equations (3), which varies according to the dowel diameter, wood strength, and density. In W-W-W connections, the calculation of the number of dowels does not depend on the thickness of the wooden elements, when considering thicknesses equal to or greater than 45 mm. This fact is supported by the same failure mode, identified from the minimum characteristic load-carrying capacity. The failure mode starts with the simultaneous plastic hinge formation on the fastener inside the wood central member and bearing failure of all members, in connections with larger diameters and thicknesses of the elements equal  
to 45 mm. For elements with thickness higher than 45 mm the failure mode appears simultaneously with a plastic hinge formation in the fastener within all members and bearing failure of all members. However, considering elements with thicknesses lower than 45 mm, the failure mode is influenced by the thicknesses of both wood members and represented by the bearing failure of the central member. In S-W-S connections, the number of dowels is independent of the thickness of the wooden elements and has the same failure mode, for thicknesses equal to or greater than 45 mm. This failure mode represents the embedment of the wood, bending of the fastener and failure, both together. However, using wooden members with a thickness of less than 45 mm, the number of dowels depends on the wooden thickness, as shown in the calculated results. The failure mode represents the wood embedment, regardless of the dowel diameter or wood material. The results show that the W-S-W connections need the least number of dowels according to the design equations, that is, the GL32H connection with lesser member thickness needs the least number of dowels, when using a diameter equal to 12 mm. On the other hand, S-W-S connections require a greater number of dowels when compared to the other connection types, especially the connection GL20H with a smaller dowel diameter. As the diameter of the dowel increases, the hole size in the wooden members also increases, which can produce a concentration of stress around the hole.
# The Impact of the Stringent Response on TRAF GTPases and Prokaryotic Ribosome Assembly
## 3 Domain Structure of RA-GTPases  
All P-loop GTPases are characterised by the signature central GTPase (G) domain, consisting of either four or five highly conserved G-motifs (G1-G5: Figure 2) that confer binding specificity and functionality to the active site [4; 47]. G1, also known as the Walker A motif or P-loop, is present in the majority of identified nucleotide triphosphate (NTP) binding proteins (whether GTP or ATP binding). The consensus sequence GxxxxGKT/S enables the orientation of the \(\alpha\) and \(\beta\) phosphate of NTPs to allow nucleophilic attack of the tertiary phosphate via the primary amino group of the conserved lysine [48]. The G2 motif, also known as switch I, is characterised by a conserved threonine in TRAF GTPaseS. This threonine residue coordinates the essential Mg\({}^{2+}\) cofactor responsible for stabilising the \(\gamma\)-phosphate of the bound NTP, while also activating a water molecule in preparation for the hydrolytic S\({}_{\text{N}}\)2 nucleophilic attack [49]. The adjacent G3 motif, otherwise known as the Walker B motif or switch II, carries out a similar function to G2 and has a consensus of DxxG. During GTP hydrolysis, the Switch I and II regions undergo large conformational changes, which are thought to contribute to the molecular'switching' behaviour of GTPases by transducing a signal to the accessory domains to alter activity or binding specificity [5]. G4 is characterized by the presence of four bulky hydrophobic amino acids followed by (N/T)(K/Q)xD, within which the conserved lysine/glutamine stack specifically against guanine nucleotide bases, while the aspartic acid residue forms specific bifurcated hydrogen bonds with the guanine secondary amines to act as a selectivity barrier against ATP [50]. The G5 motif interacts with the guanine base via water-mediated hydrogen bonds and is not strictly ubiquitous.  
Figure 1: Schematic overview of rRNA production in prokaryotes using _E. coli_ as an example. Production initiates with the transcription of a typical \(rm\)-encoded rRNA operon by RNA polymerase into a single polycistronic transcript encoding 16S, tRNA, 23S and 5S (the presence of tRNA depends on the operon). Processing begins concurrently with transcription, with r-proteins and secondary rRNA structures beginning to assemble while chemical modifications occur. RNase III cleaves the primary transcript into the pre-16S (17S), pre-23S (25S), pre-tRNA and pre-5S through recognition of helical stem loops. Subsequent processing to generate mature rRNA is carried out by a range of ribonucleases as indicated, which differ depending on the organism. Enzymes involved in the 3′ processing of tRNA differ depending on the tRNA family and 3′ immature region in question [46].