esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Response surface methodology (RSM) has been employed to enhance the efficiency of MAE in extracting bioactive phytochemicals from various plant materials. For example, RSM has been successfully used to enhance the extraction of phenolics and flavonoids from plant resources like avocado [9], chestnut shells [10], and duku [11], where it helped in determining the best extraction conditions to maximize yield and quality. Polyphenol-rich crude extracts are valuable due to their complex mixture of bioactive compounds that work synergistically, enhancing their overall therapeutic effects. Recently,polyphenols have attracted significant attention in the nutraceutical industry due to their potential efficacy in combating oxidative stress-related disorders, aligning with emerging consumer trends favoring natural food items devoid of artificial ingredients and those promoting health, sustainability, and social responsibility [12]. Known for their strong antioxidant properties, polyphenols help prevent cellular damage and provide additional well-being benefits, such as anti-inflammatory, anticancer, and cardiovascular protective effects. The two main classes of polyphenols are flavonoids (such as isoflav, quercetin, cyanidins, and catechins) and phenolic acids (such as caffeic and ferulic acids) [13]. Consequently, extracting polyphenol-rich crude extracts from plant sources is essential in enhancing the bioactive potential of natural products used in functional foods, natural medicines, and cosmetics [14]. To the best of our knowledge, there is a significant knowledge gap regarding the optimization of microwave-assisted extraction (MAE) conditions specifically tailored toward maximizing the production of polyphenol-rich crude extracts from _Cinnamomum inters_ leaves. The present investigation employed response surface methodology (RSM) to optimize the MAE process for producing a polyphenol-rich crude extract from cinnamon leaves by determining the effects and interactions among the MAE time, microwave power, and solid-to-liquid ratio. This method allowed for the simultaneous optimization of all MAE factors, minimizing the number of experiments needed while predicting the most efficient conditions. RSM was used to model the impact of these factors on polyphenol extraction from _Cinnamomum inters_ leaves. TPC, TFC, and the DPPH assay were used to evaluate the potential of the model, ensuring maximum extraction efficiency in polyphenol extraction and assessing their antioxidant properties.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.4 Resistance to Insider Attacks  
During insider attacks, the corrupt insider party, i.e., \(RGC\) or \(SVR\), tries to obtain the password \(PWD\) of the \(USR\). The \(USR\) sends the message \(\{l_{U},\ R_{U}\}\) to the \(RGC\). From \(R_{U}\), the \(RGC\) is unable to extract the password since \(R_{U}=(PWD\oplus P_{U})\) and \(P_{U}=vG\). \(RGC\) cannot obtain \(v\), since it is privately generated by the \(USR\). Hence, the \(RGC\) cannot obtain the password of the \(USR\). Moreover,  
Figure 7: Resistance to user impersonation in ECSSP-SC.  
the \(SVR\) cannot also attain the password \(PWD\) of the \(USR\) from the contents of the message \(\{c,T,w,\ T_{U}\}\), since the anonymity has been preserved in this message. Therefore, the proposed ECSSP-SC successfully provides defense from the insider attack.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 3 System Model and Problem Formulation
### Computation Model  
In our model, the tasks of the vehicles are offloaded to RSUs equipped with computational capabilities to process traffic volumes. The computation task is related to both energy consumption (in joules) and processing (in seconds). There are three stages to accomplish task computation: (i) task uploading, (ii) task computing, and (iii) task downloading. Depending on the computation and communication demands, the vehicles will experience latencies influenced by the computing and communication capacities of the RSUs. The uplink and downlink bandwidths influence the communication latency. We neglect the downloading latency due to the small size of the computed tasks and the high data rates of the RSUs. The task of vehicle \(v\) is represented by the communication demand \(D_{v}\) (in bits) and the computation demand \(\Phi_{v}\) (in CPU cycles per bit). We denote the task offloading time by \(L_{n,v}\), which includes the uploading time \(\ell_{n,v}^{u}\) and the processing time \(\ell_{n,v}^{p}\). We calculate \(L_{n,v}\) as  
\[L_{n,v}=\ell_{n,v}^{u}+\ell_{n,v}^{p}, \tag{6}\]where  
\[\ell_{n,v}^{u}=D_{v}\left(U_{n,v}\right)^{-1}, \tag{7}\]  
and  
\[\ell_{n,v}^{p}=D_{v}\,\Phi_{v}\,(f_{n,v})^{-1}. \tag{8}\]  
\(U_{n,v}\) is the communication capacity (throughput or data transmission rate from vehicle \(v\) to RSU \(n\) in bits per second). \(U_{n,v}\) is calculated from the SINR mapping table for the throughput of 5G New Radio (NR) with a bandwidth of 100 MHz and a subcarrier spacing of 60 kHz [43].  
\(f_{n,v}\) is the computing power assigned from RSU \(n\) to vehicle \(v\) in CPU cycles per second (computation capacity). The energy consumption of the computation task \(E_{n,v}\) includes the consumptions resulting from offloading and execution. We denote the transmission energy consumption with \(\epsilon_{n,v}^{u}\) and the processing energy consumption with \(\epsilon_{n,v}^{p}\). We can calculate \(E_{n,v}\) as  
\[E_{n,v}=\epsilon_{n,v}^{u}+\epsilon_{n,v}^{p}, \tag{9}\]  
where  
\[\epsilon_{n,v}^{u}=P_{v}\,D_{v}\,(U_{n,v})^{-1}, \tag{10}\]  
and  
\[\epsilon_{n,v}^{p}=\psi_{n}^{V}\,D_{v}\,\Phi_{v}\,(f_{n,v})^{2}. \tag{11}\]  
\(P_{v}\) is the transmit power of vehicles \(v\). \(\psi_{n}^{V}\) is the energy coefficient associated with the chip architecture of the RSU server (effective capacitance coefficient for each CPU cycle of the RSU).
## 2 Materials and Methods  
### Experimental Design  
According to the analysis of rainfall data in the Longling area, three rainfall intensity levels were selected for the experiments: 50, 80, and 110 mm/h (50, 80, and 110 mm/h are the average value of the maximum single-point rainstorm in the past 10 years, the calculated single-point rainstorm value in 10 years and the single-point rainstorm value in 100 years. As well, the rainfall device can be set at a level of 20-240 mm/h). Under each of these three rainfall intensity levels, four slope gradients (10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\)) were experimentally evaluated resulting in a total of 12 test configurations. During each experiment, the erosion process of the simulated slope surface was monitored in real-time using a digital pan-tilt camera until the slope was damaged. The soil moisture contents and bulk densities were measured by drying methods before filling. The soil dry densities were controlled between 1.3 and 1.5 g/cm\({}^{3}\), and the soil moisture contents were approximately 10% (Near natural state values). The test soil was naturally dried and passed through a 10 mm sieve to remove stones and weeds. The filling thickness was 50 cm, paved, and compacted to reduce the influence of the boundary effect. After the filling was complete, the soil surface was scraped with a wooden board to create a certain surface roughness. The soil bulk density of the slope was measured using the ring knife method to verify that it reached the experimental design level. Each experiment lasted 1 h.
## 2 Sensors
### Trends and Challenges  
With the deepening of the research, sensor technology is becoming mature with its application scope expanding on a daily basis. It's developing towards the direction of high wearability and low power consumption. As a BSN is characterized by long-term real-time monitoring, measures must be taken to reduce the size of sensors and eliminate any possible physical and chemical harm to the human body for the sake of long-time use. Many studies have been carried out on the improvement of wearability in previous research. However, despite the fact a small portion of sensors show good wearability, many others still need to be improved. Especially for sensors monitoring human activities, there are still a lot of work to do in order not to affect everyday life and sports training. Similarly, solving the problem of power consumption of sensors is still a long-term goal. Nowadays, the functions of sensor nodes are becoming more and more complicated, and the demands of data processing quality increase continuously as well, which makes prolonging the service life of sensor nodes another major problem for the future, and meanwhile low-power design methods are being studied. In addition, with new physical parameters of interest being discovered continuously, researchers should design more and more new types of sensors. Meantime, research into new materials and new energy in other fields will promote the progress of BSN sensors.  
There are still some difficulties in the research of BSN sensors, such as extension of sensor life, improvement of measurement accuracy and design of implantable sensor antennas. In practical applications, replacement of sensor node cells may not be timely, and sometimes impossible, especially for implantable sensor nodes. How to supply energy is a great challenge to achieve long-term automatic monitoring. Some researchers propose acquiring energy from the external environment, but this technology is still not mature. Besides, there exists another challenge on how to improve the accuracy of signal measurement, when accuracy depends on the position of sensors and the denoising ability of sensors, so finding new denoising methods is also a challenge at present. As for implantable sensors, it is hard to design the antenna of a wireless communication module, as the antenna material must be non-corrosive and biologically compatible, such as platinum and titanium, but the wireless communication capability of these materials is less than that of copper, while their cost is too high to be widely used [54]. Meanwhile, the radio frequency (RF) environment will change with the variation of wearer's age, weight, and posture. All of these factors above will affect the quality of wireless communication, which needs to be considered carefully by designers [55].
###### Abstract  
The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as 'no-till' practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.  
AI; end user; grassland management; land-use; machine learning; pasture biomass; satellite; species composition; sustainability; unmanned aerial vehicle 2023
## 2 Mathematical Modeling of PMSG-WECS
### Wind Turbine Mathematical Modeling
The optimal mechanical torque can be obtained from the optimal mechanical power given in Equation (7) as follows:  
\[T_{m_{opt}}=\frac{P_{m_{opt}}}{\Omega_{\ell}}=\frac{\rho\pi R_{t}^{5}\Omega_{ \ell}^{2}C_{p_{max}}(\lambda)}{2\lambda_{opt}^{3}} \tag{8}\]  
Tables 1 and 2 describe different significant parameters of the overall PMSG-WECS along with its control system. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Name** & **Quantity** & **Value** \\ \hline \multirow{7}{*}{Wind Turbine} & Air density (at 10 °C, at sea level), \(\rho\) & 1.25 kg/m\({}^{3}\) \\  & Turbine blade radius, \(R_{t}\) & 2.50 m \\ \cline{1-1}  & Transmission (or gear) ratio, \(g_{t}\) & 7 \\ \cline{1-1}  & Average wind speed, \(v_{m_{opt}}\) & 7 m/s \\ \cline{1-1}  & Optimal tip speed ratio, \(\lambda_{opt}\) & 7 \\ \cline{1-1}  & Maximum power conversion coefficient, \(C_{p_{max}}\) & 0.4762 \\ \hline \multirow{7}{*}{PMSG} & Stator resistance, \(R_{s}\) & 3.30 \(\Omega\) \\  & Stator \(d\)-axis inductance, \(L_{d}\) & 41.56 mH \\ \cline{1-1}  & Stator \(q\)-axis inductance, \(L_{q}\) & 41.56 mH \\ \cline{1-1}  & Magnetic flux, \(\Phi_{m}\) & 438.20 mWb \\ \cline{1-1}  & Pole pairs, \(p\) & 3 \\ \cline{1-1}  & High-speed shaft moment of inertia, \(J_{h}\) & 0.0552 kg m\({}^{2}\) \\ \cline{1-1}  & Load inductance, \(L_{ch}\) & 0.008 H \\ \hline \multirow{7}{*}{FDTSMC} & Gain, \(p_{3}\) & 3 \\ \cline{1-1}  & Gain, \(q_{3}\) & 1 \\ \cline{1-1}  & Gain, \(c_{2}\) & 100 \\ \cline{1-1}  & Gain, \(c_{3}\) & 7000 \\ \cline{1-1}  & Gain, \(\beta_{3}\) & 200 \\ \cline{1-1}  & Gain, \(\mathcal{K}_{1}\) & 4000 \\ \cline{1-1}  & Gain, \(\mathcal{K}_{2}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PMSG-WECS and the proposed maximum power point tracking (MPPT) controller parameters. Figure 2: Wind turbine mechanical power output under different wind speeds.
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space
### Science
#### 3.1.1 Remote Sensing (LIDAR)
The BepiColombo mission, launched in 2017 and set to build on the achievements of the MESSENGER mission, incorporates a space-qualified miniaturized laser operating at 1064 nm and a repetition rate of up to 10 Hz, based on a Q-switched Nd:YAG rod oscillator pumped by two semiconductor diode lasers [56]. A 1064 nm Nd:Cr:YAG laser rod pumped by a gallium arsenide laser diode array was used as one of the principal components for the Near-Earth Asteroid Rendezvous (NEAR) laser ranging investigation launched in 1996 by the NASA, which observed the asteroid 433 Eros between 2000 and 2001. A laser pulse width of 12 ns was achieved at 1064 nm using an Nd:YAG laser crystal, with an energy per pulse of 15 mJ [57]. In 2003, Hayabusa 1 was launched by JAXA to study the Itokawa asteroid. The LIDAR system was used in rendezvous and approach phases of the mission, but also provided important surface information of the asteroid itself. The spacecraft was equipped with a laser altimeter system based on a diode-pumped, Q-switched, single-mode Nd:Cr:YAG emitting 8 mJ energy pulses at 1064 nm, with pulse widths of 14 ns and a repetition rate of 1 Hz through the use of Lithium Niobate Pockels cell [58]. The laser for the following Hayabusa 2 mission was developed based on the lasers for Selene and Hayabusa 1, but the output pulse energy was increased to 15 mJ and the pulse width reduced to 7 ns [59]. Hayabusa 2 was launched in 2014 and rendezvoused in 2018 with the asteroid Ryugu. NASA's Origins Spectral Interpretation Resource Identification Security-Regolith Explorer (OSIRIS-REx) mission was launched in 2016 to study the asteroid Bennu. It was equipped with the also called OSIRIS-REx Laser Altimeter system which is currently performing LIDAR measurements to provide high resolution topographical information of the asteroid. Two different Nd:YAG lasers operate at 1064 nm: a low-energy source operating at 10 kHz with a 10 \(\upmu\)J per pulse that can be used for rapid time-of-flight imaging down to 225 m and a higher-energy source emitting 1 mJ pulses at 100 Hz repetition rate to scan the asteroid at distances between 7.5 km and 1 km from the surface [60]. The OSIRIS-REx mission also plans to bring small samples from Bennu back to Earth. Finally, NASA's JUICE mission, which will be launched in 2022 to study the icy moons of Jupiter (Ganymede, Callisto, and Europa), will also use two cold-redundant, transversely-pumped, actively Q-switched Nd:YAG rod laser emitting 17 mJ pulses at a repetition rate of 30 to 50 Hz with a pulse width of 5.5 ns and a wavelength of 1064 nm [61]. The design of the whole LIDAR system is based on the designs of the Kaguya and Hayabusa missions, as well as that of the BepiColombo Laser altimeter. The examples above illustrate how a demonstrated and qualified laser configuration can be replicated with small updates to adapt the device to the specific requirements of following space missions in order to guarantee their success.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis
#### 4.2.3 The Influence of the Spreading Seed  
Fixed parameters of \(m_{0}=6\), \(m_{1}=3\), \(m_{2}=3\), \(m=3\), \(\beta=0.1\), \(\gamma=0.1\), and \(p=0.6\) were utilized to investigate the effect of the initial spreading seed on the process of propagation. The node with the largest, the average, and the smallest hyperdegree in the hypernetwork were chosen as the initial I-state nodes. The corresponding propagation curves are illustrated in Figure 7. The results show that the spreading seed with the largest hyperdegree is chosen to have the shortest time to reach the steady state. It is noteworthy that the three curves converge to the same value in the steady state. Therefore, the more extensive the socialization of individuals, the faster the information spreads.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 4 Bacteria Used for Hydrogen Production During Dark Fermentation
### Clostridium
#### 4.1.2 Clostridium beijerinckii  
_C. beijerinckii_ has been extensively studied for the production of H\({}_{2}\) and butanol. This species has shown good tolerance to pH changes and various inhibitors and is known to utilize a variety of substrates, such as glucose, xylose, and cellulosic biomass [88]. During the dark fermentation process, the substrates are converted to pyruvate by glycolysis via the Embden-Meyerhof-Parnas (EMP) pathway. The pyruvate is further converted to acetyl-CoA by the enzyme PFOR, which produces reduced ferredoxin that donates electrons to hydrogenase enzymes and catalyzes the production of H\({}_{2}\) gas [89]. Table 2 presents a list of _C. beijerinckii_ strains used to produce H\({}_{2}\) with different substrates and operational conditions.  
It was observed from Table 2 that when _C. beijerinckii_ was inoculated in batch mode of operation, it showed a maximum H\({}_{2}\) production of 9.39 mol H\({}_{2}\)/mol sucrose consumed. These bacteria have mostly been studied with glucose as the substrate, with a pH range from 6 to 7 and a temperature of 37 \({}^{\circ}\)C. _C. beijerinckii_ NCIMB-8052 yielded an H\({}_{2}\) yield of 2.47 mol H\({}_{2}\)/mol glucose using a 10% \(v/v\) inoculum, pH 6.5, 32 \(\pm\) 1 \({}^{\circ}\)C, 120 rpm, over 120 h under light illumination. However, when the same strain was fed a mixture of glucose and xylose as the substrate at 30 \({}^{\circ}\)C, it produced a lower H\({}_{2}\) yield of 0.086 mol H\({}_{2}\)/mol substrate, indicating that using only glucose as the substrate provided a better H\({}_{2}\) yield.
## 2 Materials and Methods
### Assessment of Models Performance  
#### 2.5.1 F-Score  
After the band optimization and accuracy assessment, the models were further assessed using F-score. Accuracy is an empirical measure that calculates only correct predictions to total predictions (Equation (12)), whereas F-score combines sensitivity and precision into a specific accuracy using an equal weighting for precision and sensitivity [43] as in the equations below:  
\[Accuracy=\frac{tp+tn}{tp+fp+tn+fn} \tag{12}\]  
\[Sensitivity=\frac{tp}{tp+fn}=recall \tag{13}\]  
\[Precision=\frac{tp}{tp+fp} \tag{14}\]  
\[F-measure=\frac{\left(\beta^{2}+1\right)\times precision\times recall}{\beta^{2 }\times precision\times recall} \tag{15}\]  
where \(tp\) is a true positive, \(tn\) is a true negative, \(fp\) is a false positive, and \(fn\) is a false negative. Sensitivity is determined by the number of correctly classified examples (true positive) and the number of misclassified examples (false negative). In addition, precision is determined by the number of true positives and examples misclassified as positives (false positive). In the equation, \(\beta\) is a parameter that can be tuned to favor precision or sensitivity, where F-score is evenly balanced when \(\beta=1\). It favors precision over sensitivity when \(\beta>1\) and vice versa [44].