esa-sceva/satcom-chunk-collection huggingface dataset:
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 3 Biosynthetic Pathways of Rice Secondary Metabolites
### Biosynthesis of Flavonoids  
A rice flavonoid biosynthesis pathway has been suggested by several studies, and the identified genes and enzymes involved in the pathway are shown in Figure 10 [124].  
The biosynthesis of sakuranetin (**81**) has been given particular attention as this metabolite is an important phytoalexin in rice. In addition to the phytopathogenic infection induction [125], sakuranetin (**81**) can be induced by ultraviolet (UV) irradiation [43], treatment with CuCl\({}_{2}\)[126], JA [127], methionine [128], the herbicides pretilachlor and butachlor [129], the bacterial phytotoxin coronatine [130], the phytopathogenic stem nematode _Ditylenchus angustus_[131], and the insect pest white-backed planthopper (_Sogatella furcifera_) [132].  
Sakuranetin (**81**) has been revealed to be biosynthesized from naringenin by \(S\)-adenosyl-l-methionine-dependent naringenin \(7\)-\(O\)-methyltransferase (NOMT), which is a key enzyme for sakuranetin production. NOMT was successfully purified and identified [42]. As naringenin (**79**) is a biosynthetic intermediate for a variety of flavonoids, NOMT plays a key role in sakuranetin biosynthesis at a branch point in the common flavonoid biosynthetic pathway (Figure 11). OsMYC2, which is an essential factor for JA-induced sakuranetin production in rice, interacts with MYC2-like  
Figure 10: Proposed biosynthesis pathway of rice flavonoids [124]. Abbreviations: CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone \(3\)-hydroxylase; FLS, flavonoid synthase; F3\({}^{\prime}\)H, flavonoid \(3^{\prime}\)-hydroxylase; DFR, dihydroflavonol \(4\)-reductase; ANS, anthocyanin synthase; UGT, UDP-glucoseyl transferase; LAR, leucanthocyanidin reductase; ANR, anthocyanin reductase; FNSII, flavone synthase II; OMT, \(O\)-methyltransferase; F2H, flavanone \(2\)-hydroxylase; CGT, C-glucoseyl transferase; and DH, dehydatase; OsNOMT, rice naringenin \(7\)-\(O\)-methyltransferase.  
proteins that enhance its transactivation ability [133]. The biosynthetic pathway of sakuranetin (**81**) is shown in Figure 11[9].
## 3 Results and Discussion
MOFs 2-4 demonstrate consistent thermal stability up to 355-360 \({}^{\circ}\)C (Figures S4-S6), at which point they degrade to CuO consistent with prior observation by [PERSON] et al. [50]. MOF-5 was synthesized via a combination of iron (III) chloride hexahydrate, trimesic acid, and PEG 400 in addition to water and sodium hydroxide. MOF-5 was the only MOF synthesized here that did not contain a 100% surfactant reaction medium due to the need to adjust the pH using sodium hydroxide solution [53]. The IR spectrum of MOF-5 in Figure S7 is plotted with commercially available amorphous Fe-BTC (Basolite-F300). Both IR spectra in Figure S7 exhibit the same vibrational frequencies. The PXRD analysis of MOF-5 (Figure 5) also confirms the structural similarity to amorphous Fe-BTC (Figure 5) [53]. The SEM images of MOF-5 also confirm the amorphous quality of this product, with no observable crystalline structures (Figure 6). Additionally, TGA analysis of MOF-5 (Figure S8) is consistent with the known decomposition to Fe\({}_{2}\)O\({}_{3}\) at 336 \({}^{\circ}\)C [53]. Figure 3: Powder X-ray diffractograms (from top to bottom) of: commercially available Basolite C300 (Cu-BTC; HKUST-1) (black solid line), MOF-2 (black dashed-dot line), MOF-3 (black short dashed line), MOF-4 black (dotted line), and simulated (solid gray). Figure 4: SEM images of MOF-2. Increasing magnification from 1000\(\times\) (**a**) to 10,000\(\times\) (**b**). SEM images of MOF-3. Increasing magnification from 1000\(\times\) (**c**) to 10,000\(\times\) (**d**). SEM images of MOF-4. Increasing magnification from 1000\(\times\) (**e**) to 10,000\(\times\) (**f**). Figure 5: Powder X-ray diffractogram of MOF-5.
## 2 Material and Methods
### Sensory Analysis  
At each sampling date, portions of the SG, VP CSS and MAP CSS were collected and kept frozen at \(-80\)\({}^{\circ}\)C until analysis. For each product, a conventional sensory profiling test was conducted according to ISO 13299 [35]. The sensory evaluation was performed using an internal trained panel of 17 judges that were experienced in seafood, especially in salmon products [10; 36]. During the sessions, the panelists were asked to assess the global spoilage based on aspect and off-odors perception. Then, the products were described according to a list of relevant sensory descriptors for odor (fatty fish, acid, amine, smoke and dill), appearance (fatty and orange color), texture (greasy film, firm, melting and pasty texture) and flavor (acid, salty, amine, fish, smoked, sweet and dill). Both the global spoilage and descriptors were scored depending on their intensity on a continuous scale from 0 (low intensity) up to 10 (high intensity). A product was considered strongly spoiled and unfit for consumption when the global spoilage level exceeded a threshold value of 6.  
The day before the sensory evaluation, samples were thawed overnight at 4 \({}^{\circ}\)C. Sessions were performed in individual partitioned booths, as described in the procedure NF V-09-105 [37] and equipped with a computerized system (Fizz, Biosystems, Couternon, France). Each panelist received one slice of the SG and 15 g of MAP CSS dices, where both were presented in a covered plastic container. For the VP CSS, a half slice (around 15 g) was repacked and presented in aluminum foil. Samples were assigned with 3-digit numbers and randomized regarding the order of presentation for each panelist. For each matrix, odor and appearance were assessed for all sampling dates, while flavor and texture were assessed on 3 dates: days 1, 21 and 28 for the SG; days 1, 21 and 35 for the MAP CSS; and days 1, 28 and 35 for the VP CSS.  
Normalized principal component analyses (PCAs) were performed on the sensory descriptors' mean scores using the R package ggfortify [38]. In addition, a two-way analysis of variance (ANOVA) was applied to the panelists' descriptor scores using products (with time) and panelists as independent factors. Significant differences between means were determined using [PERSON]'s multiple range test (\(p\)-value < 0.05) (Fizz software, Biosystems, Couternon, France).
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 2 The Channel Model
### Gradient  
The gradient represents the spatial rate of change of a scalar field, encompassing all the information from its partial derivatives. Under the same conditions as the directional derivative, the vector \(\
abla P_{Rx}(\theta,R)\), known as the gradient of \(f\), is defined by Equation (19) [12]:  
\[\
abla P_{Rx}(\theta,R)=f_{\theta}(\theta,R)\,\mathbf{i}+f_{R}(\theta,R)\, \mathbf{j}. \tag{19}\]  
Figure 2: Graph of the theoretical power derivative with respect to distance vs. distance.  
The directional derivative is related to the gradient through the following equation:  
\[D_{\vec{u}}f=\
abla f(P)\cdot\vec{u}=\langle\frac{\partial f}{\partial\theta}(P), \frac{\partial f}{\partial R}(P)\rangle\cdot\vec{u} \tag{20}\]  
For this work, the unit vector representing the direction of movement is defined in polar coordinates, as shown in Equation (22):  
\[\vec{U}=(\phi_{2}-\phi_{1};R_{2}-R_{1}) \tag{21}\]  
\[\vec{U}=(\Delta\phi;\Delta R) \tag{22}\]  
Now, by expanding Equation (20) and adapting it to the parameters of interest--the angle \(\theta\) and the distance \(R\)--we incorporate the unit movement vector from Equation (22) to obtain the following expression (23), which represents the variation in power between consecutive points when the direction of movement is known:  
\[D_{v}P(\theta,R)=P_{\theta}(\theta_{1},R_{1})(\phi_{2}-\phi_{1})+P_{R}(\theta _{1},R_{1})(R_{2}-R_{1}) \tag{23}\]  
Another way to express this is through the following Equation (24):  
\[D_{v}P(\theta,R)=\frac{\partial P_{Rx}}{\partial\theta}\cdot\Delta\phi+\frac {\partial P_{Rx}}{\partial R}\cdot\Delta R \tag{24}\]  
In this way, the theoretical expressions (7) and (24) are derived to quantify the variations in the received power at the mobile terminal relative to its direction of movement within the coverage area of the cellular network cell.  
To estimate the received power at a consecutive point along the terminal's trajectory, it is first necessary to know the received power at a previous point. The power variation in the direction of movement is then computed using the directional derivative \(D_{u}P_{Rx}(R,\Theta)\) and added to the previous measurement to obtain the estimated power at the next location:  
\[P_{Rx_{2}}=P_{Rx_{1}}+D_{u}P_{Rx}(R,\Theta) \tag{25}\]  
where \(P_{Rx_{2}}\) is the estimated received power at the new location, \(P_{Rx_{1}}\) is the experimentally measured power at the previous point, and \(D_{u}P_{Rx}(R,\Theta)\) represents the variation in received power along the direction of terminal movement, defined by radial (\(R\)) and angular (\(\Theta\)) changes.  
This formulation enables the real-time estimation of the signal strength as the user terminal moves, providing an analytical mechanism to approximate the received power without the need for constant empirical sampling, thus supporting efficient network planning and mobility-aware signal prediction.
## 4 Experiment Results and Discussion  
### Noise and Vibration Experiment  
The experimental results obtained with and without the housing of the test motor are shown in Figure 5. Given that the test motor is a 2-pole 8-slot motor, the eighth-order component, which is the least common multiple of the number of poles and slots, is the primary rotational vibration component [6]. As shown in Figure 5, the eighth-order component and its harmonic components due to motor rotation have the greatest impact on noise and vibration. In other words, the eighth-order and its harmonics caused by motor rotation are more dominant than the other components, most likely generated by the housing. In addition, it can be seen that the magnitude of noise and vibration varies depending on the presence or absence of the housing, which appears to be different regarding the magnitude due to the overall boundary conditions, such as motor stiffness and change. Given that the rotation speed of the motor is changed by the worm gear in the housing, the frequency of the vibration may also change slightly. However, there is no difficulty in understanding overall noise and vibration trends. Therefore, it can be seen that there is no problem, even if the noise and vibration components are analyzed using the back EMF value measured with the housing removed.  
On the other hand, even if the same voltage is applied to the sample motor, the RPM of the sample motor changes, and it can be confirmed that the frequency components and magnitude of the noise and vibration in the CW (clockwise) and CCW (counterclockwise) change (Figure 6). Based on these results, it was hypothesized that the back EMF of CW and CCW would be different even if the sample motor was rotated at the same voltage. To  
Figure 5: FFT results of noise and vibration of the sample motor. The values measured by the microphone and accelerometer were acquired as raw data in SQUADRIGA II, and FFT was performed in MATLAB: (**a**) Noise experiment results including motor housing; (**b**) vibration experiment results including motor housing; (**c**) noise experiment result with motor housing removed; (**d**) vibration experiment result with motor housing removed.  
analyze the reason, the eighth order of the motor was fixed by rotating the motor at the same RPM, and a comparison of the magnitude of the back EMF is presented in Section 4.2.
## 4 Results and Discussion
### Comparison between Temperatures in Trochoidal and Side Milling  
Trochoidal milling has been reported less tool wear than side milling, when removing the same volume of material, therefore the tool life is longer. Since cutting temperature has a significant influence on tool wear, a temperature comparison between trochoidal and side milling is conducted here.  
The cutter, workpiece, cutting parameters, and trochoidal parameters described in Section 4.1 are adopted again. The maximum temperature \(T_{max}\) and the minimum temperature \(T_{min}\) out of six trochoidal cycle are calculated, and they are plotted in Figure 15. The maximum temperature \(T_{tromax}\) of each trochoidal cycle is marked with black dots in the figure. The six \(T_{tromax}\) are 454 \({}^{\circ}\)C, 456 \({}^{\circ}\)C, 456.7 \({}^{\circ}\)C, 457.3 \({}^{\circ}\)C, and 457.4 \({}^{\circ}\)C, respectively. Apparently, \(T_{tromax}\) is rising, but the variation is less than 0.75%; more importantly, the rate of the change is decreasing and tends to zero. From the fifth to the sixth trochoidal cycle, \(T_{tromax}\) only increases 0.1 \({}^{\circ}\)C. It is reasonable to predict that \(T_{tromax}\) will be stable in the vicinity of 457.4 \({}^{\circ}\)C even though more trochoidal cycles are calculated.  
For comparison, a side milling is simulated with our method. The cutter, workpiece and cutting parameters described in Section 4.1 are adopted. For comparison, radial depth of cut \(a_{e}\) is set to 23.201 mm to ensure an immersion angle of 83.82\({}^{\circ}\), same as the maximum immersion angle of the trochoidal cycle. The maximum temperature \(T^{s}{}_{max}\) and minimum temperature \(T^{s}{}_{min}\) of a cutting edge in the side milling are calculated and plotted in Figure 15. The maximum temperature rises dramatically for the first few seconds. After reaching 460.9 \({}^{\circ}\)C, at the moment \(t\) = 4.959 s, the temperature change slows down. At the moment \(t\) = 90 s, the rate of change is close to zero (about 0.0026%). This pattern is consistent with the reports by [PERSON] et al. (2016) and [PERSON] (2017).  
By comparison, \(T_{max}\) in trochoidal milling is found to be always lower than that \(T^{s}{}_{max}\) in side milling and so is \(T_{min}\). This indicates that the cutter cuts the material with trochoidal tool path, in general, in a lower cutting temperature than that in side milling. However, the maximum temperature \(T_{tromax}\) of each trochoidal cycle is just a litter less than the \(T^{s}{}_{max}\) in side milling, for example a discrepancy of 17.95 \({}^{\circ}\)C between trochoidal and side milling at the moment \(t\) = 80.57 s (see Figure 15). This may account for the extended tool life of trochoidal milling but should not be the only reason. Another probable cause of prolonged the tool life is further analyzed in the next section.  
Figure 15: The cutting temperature of trochoidal and side milling.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Incorporation of the Communication System
#### 3.3.1 RF Transmission System (Tx-RF/Rx-RF)  
Operating in the 915 MHz ISM band, this subsystem handles discrete data and alarm signaling through long-range LoRa modulation. It achieves point-to-point connectivity at distances up to 8 km in line-of-sight (LoS) scenarios. Technical specifications include:  
* Transmit power: +20 dBm (100 mW).
* Receiver sensitivity: \(-\)139 dBm
* Transmission rate: 0.3 to 62.5 kbps
* Latency: \(<\)10 ms
* Antennas: Dual 5 dBi omnidirectional, IP67-rated for outdoor operation  
This configuration ensures immediate actuation of security systems and discrete output relays connected to the operational control network, enabling automated incident response mechanisms in restricted areas.
## 2 Experimental Research and Analysis
### Finite Element Calculation of Distribution Parameters of Permanent Magnet Motor End Cable  
The solution of the distributed parameters of the three-phase cable mainly includes the distributed inductance of the cable, the ground capacitance of the cable, and the interphase capacitance between adjacent cables. The capacitance diagram of the three-phase cable is shown in Figure 4:  
Figure 1: The specific structure and layout of the cable.  
Figure 3: Equivalent circuit of high-frequency lossless transmission line.  
In Figure 4, \(C_{a0}\), \(C_{b0}\), and \(C_{c0}\) represent the ground capacitance of A, B, and C, respectively, and \(C_{a-b}\), \(C_{b-c}\), and \(C_{a-c}\) represent the interphase capacitance between AB, BC, and AC.  
The length of the cable is set to 4 m, and the thickness of the outer insulation layer is 3 mm. To obtain the distributed capacitance, the electrostatic field solver of ANSYS EM is used. The finite element method is used to divide the solution domain, and the element analysis is carried out for each unit. Finally, the capacitance of A to ground is 17.0 pF, the capacitance between two cables in parallel is 28.4 pF, the capacitance between AB phases is 28.4 pF, and the capacitance between AC phases is 1.6 pF.  
When calculating the distributed inductance, the electrostatic field solver of ANSYS EM is used to obtain the distributed inductance; the distributed inductance is 0.7 uH.
## 2 Materials and Methods
### Remote Sensing Data  
The moderate-resolution imaging spectroradiometer (MODIS) covers the entire surface of the Earth every 1-2 days, and other biophysical quantities, at resolutions between 250 m and 1 km. In this work, the NDVI was calculated based on MOD09A1 data obtained from the Land Processes Distributed Active Center (LPDAAC) [31]. Eight-day LST data with a 1-km resolution (MOD11A2) were obtained  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site Name** & **Loc\_Lat** & **Loc\_Long** & **Elevation** & **Vegetation Type** & **Active or Not** \\ \hline US-ARM & 36.6058 & \(-\)97.4888 & 314 & crop & Active \\ US-Aud & 31.5907 & \(-\)110.51 & 1469 & grass & Active \\ US-FR2 & 29.9495 & \(-\)97.9962 & 272 & grass & Active \\ US-FR3 & 29.94 & \(-\)97.99 & 232 & grass & Active \\ US-KFS & 39.0561 & \(-\)95.1907 & 310 & grass & Active \\ US-Kon & 39.0824 & \(-\)96.5603 & 330 & grass & Active \\ US-Ro3 & 44.7217 & \(-\)93.0893 & 260 & crop & Active \\ US-Var & 38.4133 & \(-\)120.951 & 129 & grass & Active \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flux sites used in this study.  
from the National Aeronautics and Space Administration's (NASA) earth observing system data and information system (EOSDIS) [32]. Seventeen tiles of MODIS data (h07v06, h08-h13, v04-v06) were used to cover the CONUS, and data from 89 to 297 days of each year were used to cover the primary growing season of most vegetation types. Eight-day images were composited into monthly data, weighted by the number of days recorded in each month, and based on the data generated after masking the cloud value pixels, using MODIS data quality flags.  
The Tropical Rainfall Measuring Mission (TRMM) 3B43 data, which were obtained from the NASA Data and Information Services Center (DISC) and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), which were obtained from the Vrije Universiteit Amsterdam [33] and NASA (VUA-NASA), were also used in this study.
## 4 Simulation Experiments: Security Analysis and Comparison
### Encryption Quality Analysis  
To analyze the encryption quality \(EQ\), the frequency of letters in both the plaintext \(P\) and ciphertext \(C\) are compared. This analysis determines the average number of letter changes, which can be mathematically expressed as [45]  
\[EQ=\frac{\sum\limits_{L=32}^{126}|H_{L}(C)-H_{L}(P)|}{95}, \tag{8}\]  
where the number of times that a letter with ASCII code \(L\) appears in \(C\) and \(P\) is denoted as \(H_{L}(C)\) and \(H_{L}(P)\), respectively. If the encryption quality of a cryptographic algorithm is high, then it is considered better. The highest possible encryption quality is achieved when there are no repeated letters between \(P\) and \(C\). The encryption quality can reach a maximum of \(2n/95\), where \(n\) represents the length of \(P\).  
Table 1 displays the results obtained from testing the encryption quality of the proposed scheme on various texts. Table 1 shows that for the text size ranging from 30 to 5995, the percentage (%) of \(EQ\) lies in the range [76; 90], which is closer to the optimal percentage. Thus, the proposed scheme can encrypt the text with good encryption quality. We also compared \(EQ\) of the proposed scheme with the state-of-the-art schemes [10; 11; 12; 13; 14; 15; 16; 17; 18], and the results are shown in Figure 4, which shows that the proposed scheme has better encryption quality than the schemes [10; 11; 12; 13; 14; 15; 16; 17; 18].
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 2 Application Scenario and System Model  
### Application Scenario Analysis  
The application of HWSNs in smart agriculture significantly enhances the efficiency of soil nutrient monitoring and management, environmental monitoring, weather forecasting, and crop growth monitoring. As illustrated in Figure 1, HWSNs deployed in agricultural fields utilize a variety of sensor nodes to continuously gather data on soil moisture, PH values, nutrient content, light exposure, and air temperature. Cluster heads (CHs) aggregate this monitoring data and employ an optimized algorithm to select the most efficient relay nodes for data transmission to the base station (BS). The collected data are subsequently uploaded to a smart cloud platform, enabling farmers to access and analyze the information via remote computers or mobile devices. This capability allows farmers to adjust irrigation and fertilization strategies in real time. Through the real-time data monitoring and precise management facilitated by HWSNs, farmers can more scientifically manage the agricultural environment, thereby improving crop yield and quality. This not only propels the development of smart agriculture but also provides technological support for agricultural modernization. However, HWSNs require regular maintenance and battery replacement, which increases maintenance costs. To enhance the energy efficiency of HWSNs, it is essential to implement optimization strategies, such as employing information transmission energy efficiency optimization algorithms to extend the network's operational lifetime. This forms the primary focus of the research presented in this paper.  
In the smart agriculture application scenario depicted in Figure 1, various types of sensor nodes need to be deployed across extensive agricultural areas and their continuous and stable operation ensured over extended periods. In practical applications, problems such as uneven energy consumption among heterogeneous nodes and premature energy depletion, leading to monitoring blind spots, may emerge. This necessitates the consideration of energy efficiency coordination management strategies for HWSNs. Given its lightweight and easy-to-implement characteristics, the DQN is particularly suitable for addressing energy efficiency coordination management issues in networks with relatively scarce hardware resources, such as HWSNs.