esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Molecular Docking Studies  
Multiple molecular targets that mediate antioxidant activity were retrieved from the Protein Data Bank (PDB) as.pdb files, including NADPH oxidase enzyme (PDB: 2 CDU), cytochrome P450 (CP450) enzyme (PDB: 1 OG5), lipoxygenase (LOX) enzyme (PDB: 1N8Q), myeloperoxidase (MP) enzyme (PDB: 1 DNU) and xanthine oxidase (XO) enzyme (PDB: 3 NRZ) and were subjected to protein preparation [30]. Specifically, all water molecules were removed, missing residues and hydrogens were added, and restrained energy minimization was followed using an OPLS2005 force field.  
A total of 22 phenolic compounds (Figure 1), present in most of the studied flowers [5; 31; 32], were sketched and prepared at pH = 7.0 \(\pm\) 0.5, using LigPrep of MAESTRO [33].  
Validation was performed based on the RMSD values, expressing the similarity between the overlapping crystallographic and docking poses of the co-crystallized ligands. Figure S1 (Supplementary Materials) presents the superimposition of the crystallographic and predicted poses together with their RMSD values. Additionally, dextromethorphan, 5-fluorouracilacil, zileuton, melatonin and febuvostat, molecules with known activity against NADPH oxidase, CP450, LOX, MP and XO enzymes, respectively, were also rendered as positive controls, reinforcing the validation process. Through the validation process, the critical protein-ligand interactions, which may positively contribute to antioxidant capacity were identified, in accordance with previous studies [23; 24].  
Subsequently, molecular docking simulations were performed for all compounds by applying Glide in standard precision (SP) mode [34] to identify their favorable binding poses. The maximum number of docking poses was set equal to 10 and each of them was visually inspected and analyzed.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Radio link budgets
#### Weather Station Specifications  
Solar radiation: Range: 0 to 1750 W/m\({}^{2}\); resolution: 1 W/m\({}^{2}\); accuracy: \(\pm\)5% of typical measurement.  
Precipitation: Range: 0 to 400 mm/h; resolution: 0.017 mm; accuracy: \(\pm\)5% of measurement from 0 to 50 mm/h.  
Vapor Pressure: Range: 0 to 47 kPa; resolution: 0.01 kPa; accuracy: varies with temperature and humidity, \(\pm\)0.2 kPa typically below 40 \({}^{\circ}\)C.  
Relative Humidity: Range: 0 to 100% RH (0.00-1.00); resolution: 0.1% RH; accuracy: varies with temperature and humidity, \(\pm\)3% typical RH.  
Air temperature: Range: \(-\)50 to 60 \({}^{\circ}\)C; resolution: 0.1 \({}^{\circ}\)C; accuracy: \(\pm\)0.6 \({}^{\circ}\)C.  
Humidity sensor temperature: Range: \(-\)40 to 50 \({}^{\circ}\)C; resolution: 0.1 \({}^{\circ}\)C; accuracy: \(\pm\)1.0 \({}^{\circ}\)C.  
Barometric pressure: Range: 50 to 110 kPa; resolution: 0.01 kPa; accuracy: \(\pm\)0.1 kPa from \(-\)10 to 50 \({}^{\circ}\)C, \(\pm\)0.5 kPa from \(-\)40 to 60 \({}^{\circ}\)C.  
Horizontal wind speed: Range: 0 to 30 m/s; resolution: 0.01 m/s; accuracy: greater than 0.3 m/s or 3% of measurement.  
Wind gust: Range: 0 to 30 m/s; resolution: 0.01 m/s; accuracy: greater than 0.3 m/s or 3% of measurement.  
Wind direction: Range: 0\({}^{\circ}\) to 359\({}^{\circ}\); resolution: 1\({}^{\circ}\); Accuracy: \(\pm\)5\({}^{\circ}\)  
Tilt: Range: \(-\)90\({}^{\circ}\) to +90\({}^{\circ}\); resolution: 0.1\({}^{\circ}\); accuracy: \(\pm\)1\({}^{\circ}\).  
Lightning strike count: Range: 0 to 65,535 strikes; resolution: 1 strike; accuracy: variable with distance, \(>\)25% detection at \(<\) 10 km typical.  
Lightning average distance: Range: 0 to 40 km; resolution: 3 km; accuracy: variable [20].  
Drifter--A free-floating sensor that takes local measurements and tracks the flow in water systems. Drifters compile water measurements to estimate the flow of a hydrodynamic system; however, it requires communication capabilities and a method to integrate the gathered data into an appropriate data structure.  
The drifter architecture (Figure 4) includes a radio to communicate location information to the fixed node and RiverDrone (sniffer), thereby increasing the possibilities of finding and recovering them.  
Figure 3: Weather station hardware block diagram.  
It also incorporates an SD card module to store the location, speed, and potentially, any other data that are registered. Importantly, it can be equipped with additional modules or sensors. The system recovery method is explained in Section 3.2.  
The sniffer architecture (Figure 5) allows the device to work as a sniffer looking for drifters. Equipped with matching radio, cellular communication, and GPS, drifters can report their location to the EWIN platform, where the information is forwarded and displayed on a web page.  
Figure 4: Drifter hardware block diagram.  
Figure 5: Sniffer hardware block diagram.
## 3 Research Design  
### Search Strategies and Data Sources  
For the patent analysis, the researchers sourced data from the database of the United States Patent and Trademark Office (USPTO). The main reason for this selection was the fact that it is a rich database with the earliest data traced back to 1975. Additionally, because the US is the world's largest commercial trading market, most inventors apply for patents for their products in the US while simultaneously doing so in other countries. Therefore, when researchers measure innovative activities and innovative performance at the global level, they typically use data from the USPTO database [21; 32]. For the present study, the patent data consisted of US patents approved between 1 January 2014 and 30 June 2019. Additionally, the researchers used a more precise search method--Derwent Smart Search--to conduct the patent search. Smart Search is a search tool developed by hundreds of experts, who first completed the reading of official patent disclosures in the database. Subsequently, procedures such as translation, abstract rewriting, content debugging, and patentee information normalization were performed. Lastly, the modified and normalized data were stored in the database. In short, Smart Search is a keyword search tool established through reading and organization by people. The search criteria of the current study were as follows: (SSTO/5 th-Generation) and (SSTO/optical network); a total of 124 patents were retrieved. In terms of technology classification, the Cooperative Patent Classification (CPC) system has been implemented since early 2013. Therefore, the researchers adopted the CPC system as their analytical framework. Considering that a patent may involve multiple CPC technologies and that individual patents may overlap in certain CPC technology fields, the more frequently a set of different CPC technologies is applied in a same patent, the higher the level of technological interrelatedness between them. Therefore, as when determining the relationships between members of a social network, the relationship between CPC technologies (nodes) and their application frequency (sides) in a same patent can be used for the construction of a technology network. A node, or player, situated at the center of a technology network is a key player, or key technology [33].
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 4 Simulation Results
### Comparison of Torque Ripple  
This chapter compares the relative torque ripple \(\hat{w}_{u}\) of both PWM schemes, which is defined as the ratio of torque ripple and its average value \(\overline{T}\), i.e.,  
\[\hat{w}_{u}=\frac{T_{max}-T_{min}}{2\cdot\overline{T}} \tag{20}\]  
Figure 12 shows the relative torque ripple as a function of the whole operating range. To avoid a division by zero, the torque ripple is only shown for loads of 0.5, 1, 1.5 and 2 Nm. Moreover, standstill is omitted because only one half bridge is used at this operation.  
In Figure 12, the relative torque ripple rises with a decrease in the torque reference point, whereas the absolute ripple is approximately the same. Furthermore, the relative torque ripple is independent from speed. This behavior is the same for both PWM schemes. Considering (20), also the absolute torque ripple is more or less constant over the whole speed range. Due to a higher maximum switching frequency, the relative as well as the absolute torque ripple is smaller by using \(\Delta\Sigma\)-PWM. Consequently, the different average switching frequencies influence the results. Figure 13 illustrates the difference in average switching frequency for both PWM schemes.  
It is confirmed that the average switching frequency of both PWM schemes is mainly dependent on the speed and corresponds to the voltage magnitude. The average switching frequency increases almost linearly with the speed and with the voltage magnitude in the low speed range. Above 700 rpm, the frequency shows a further increase in case of \(\Delta\Sigma\)-PWM, whereas it is reduced with DTC. However, the average switching frequency is always higher than with DTC. Due to the EMF, the magnitude of the reference space vector increases with the speed, when using the \(\Delta\Sigma\)-PWM, \(N_{1}\) is exceeded more often and consequently the switching frequency increases [37]. With PMSM, the flux moves synchronously with the rotor. As a result, at higher speeds the flux space vector is less frequently slowed down by a zero vector. This leads to the decrease of the switching frequency of the DTC.  
Figure 12: Torque ripple at various operating points.  
To analyze the effects of these differences, the following comparison in torque ripple is based on modified boundary conditions. The hysteresis parameters of \(\Delta\Sigma\)-PWM are adopted, so that the average switching frequency of both schemes matches.  
The result is shown in Figure 13(b) and the corresponding torque ripple for both PWM schemes is illustrated in Figure 13(a). The diagram is based on a load of 1.5 Nm and presents the relative torque ripple at various speeds. In contrast to Figure 12 it can be seen, that DTC exhibits a smaller torque ripple within the whole working area. Except for speeds higher than 800 rpm the torque ripple stays rather constant at 2%, whereas \(\Delta\Sigma\)-PWM shows a torque ripple of 3.9%.
## 4 Consequences and Discussion
### Experimental Results on the MaF Test Functions  
The results of the IGD metric on the MaF test functions are shown in Table 5. For the MaF1 and MaF2 test functions, the CMPSO algorithm outperforms the other algorithms. Our approach comes directly after. For the Maf3, MaF4, MaF5 and MaF6 test functions, the CMGBO algorithm dominates its competitors, which proves the high competitiveness of our approach with respect to convergence for this set of test functions. Good convergence of the proposed CMGBO algorithm can be clearly observed in Figures 8-10. On the other hand, the results for the HV metric are listed in Table 6. It is evident that CMGBO obtains the best statistical results in most cases, which means that the proposed algorithm benefits from a good equilibrium between convergence and diversity compared to the selected methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Best** & **Worst** & **Median** & **Average** & **Std.
## 3 Numerical Simulation of the Effect of Deep Excavation on Adjacent Tunnels
### Simulation Conditions  
In numerical simulations, the factors of excavation depth (\(H_{\text{e}}\)), the maximum retaining wall deflection (\(\delta_{\text{hm}}\)), and tunnel position (\(H_{\text{t}}\), \(L_{\text{t}}\)) are considered as three variables to study the effect of deep excavation on the adjacent tunnel.  
For the excavation depth, \(H_{\text{e}}=18\) m, 15 m, 12 m, and 9 m are adopted, totaling 4 conditions. It is known from the statistics that the number of deep excavations with \(H_{\text{e}}\leq 18\) m accounts for 81.0%. Therefore, it is considered that the values of \(H_{\text{e}}\) adopted in this study are representative of engineering.  
For the maximum retaining wall deflection, \(\delta_{\text{hm}}=0.18\%H_{\text{e}}\), \(0.3\%H_{\text{e}}\), and \(0.7\%H_{\text{e}}\) are adopted according to the technical code CCES 03-2016 [38], totaling 3 conditions. It reflects the different control levels of retaining wall deflection in construction.  
For the tunnel position, the spacing between each tunnel is 3 m when \(L_{\text{t}}/\leq H_{\text{e}}\) and \(H_{\text{t}}\leq H_{\text{e}}\) and that is 6 m when \(L_{\text{t}}>H_{\text{e}}\) and \(H_{\text{t}}>H_{\text{e}}\), and the maximum value of \(L_{\text{t}}=3.0\)\(H_{\text{e}}\) and \(H_{\text{t}}=3.0\)\(H_{\text{e}}\). It is known from the statistics that the tunnels with \(L_{\text{t}}/H_{\text{e}}\leq 3.0\) account for 87.2%, and tunnels with \(H_{\text{t}}/H_{\text{e}}\leq 3.0\) account for 100%. Therefore, the \(L_{\text{t}}\) and \(H_{\text{t}}\) adopted are widely representative in practical engineering. Since construction is not allowed within 3 m from the edge of the tunnel, and the buried depth of the tunnel crown should not be less than 1.0 times the tunnel diameter, the minimum value of \(L_{\text{t}}\) is 6 m, and the minimum value of \(H_{\text{t}}\) is 9 m.  
After the above three factors were combined together, totaling 732 numerical models were established, as listed in Table 2.
## 3 Assessment of Spire Data Characteristics
### Stability
Below a 30 km impact height, there is no noticeable difference in the standard deviations for these five SNR groups. The COSMIC-2 SNR level slightly affects the mean bias at a low level. A higher COSMIC-2 SNR introduced a more negligible bias in the height range close to the surface; however, the standard deviations relative to the mean BA fractional Spire-COSMIC-2 differences for all five groups are all around 4.0% from the surface to a 40 km impact height, which is larger than those of the Spire-RO pairs (see Table 3).
## 5 Predefined Path-Based Techniques
### Ring-Based Dual Paths (RDP)
#### 5.3.3 Advantages and Disadvantages  
Like other predefined path-based techniques such as ODP and DVP, the RDP approach significantly reduces redundant unicast traffic in HSR networks compared with the standard HSR protocol by forwarding the unicast traffic through two predefined paths. Like DVP, the RDP approach establishes dual paths by sending and receiving control messages, such as PREQ and PREP messages. However, unlike the ODP and DVP approaches that discover and establish node-based dual paths for each connection pair of terminal nodes, the RDP approach discovers and builds dual paths for each connection pair of DANH rings (terminal rings), therefore, the RDP significantly reduces the number of connection pairs, which in turn reduces additional control overhead for discovering and establishing dual paths compared with the ODP and RDP approaches. However, because of using control messages to discover and build dual paths, the RDP approach still has additional control overhead to setup dual paths in HSR networks.
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
## 6 Validating the Framework
### Quantitative Results
#### 6.1.2 Quantitative Results: UX Artifacts  
Table 7 summarizes the survey results for dimensions D1 and D2 of the UX artifacts proposed in the first version of the FRAMUX-EV. A brief analysis of the descriptive statistics is provided below.  
**(D1) Usefulness.** The UX artifact that obtained the best rating from the practitioners was \"UX backlog\", with a score of 4.26 out of 5.00, followed by \"UX evaluation repository\", with 4.18 out of 5.00. On the other hand, the UX artifacts with the worst usefulness perceived by the participants were \"UX personas\", with a score of 3.82 out of 5.00, and \"UX responsibilities and roles\", with a score of 3.29 out of 5.00. However, although these UX artifacts were the worst evaluated, none were rated as \"not very useful\" (score 2) or \"very unhelpful\" (score 1).  
**(D2) Ease of integration.** The UX artifact considered the easiest to integrate by the practitioners was \"UX responsibilities and roles\" with 3.91 out of 5.00. Another UX artifact considered easy to integrate by the participants was \"UX evaluation methods\", with a score of 3.85 out of 5.00. On the other hand, the UX artifacts with the worst rating assigned by the participants were \"UX personas\", with a score of 3.50 out of 5.00, and \"UX backlog\", with a score of 3.26 out of 5.00. Although these UX artifacts were the worst evaluated, none were rated as \"difficult\" (score 2) or \"very difficult\" (score 1). In addition, none of the UX artifacts were classified as \"ease to integrate\" (score 4).  
Initially, no UX artifacts should be discarded from this initial version of FRAMUX-EV, as all received a usefulness rating above 3. However, it is necessary to provide more detailed explanations of how and when all the proposed UX artifacts can be used, as, surprisingly, none received a score higher than 4. The decision to discard UX artifacts or  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**D1—Usefulness**} & \multicolumn{2}{c}{**D2—Ease of Integration**} \\ \cline{2-5}
**UX Artifacts** & **Mean** & **SD** & **Mean** & **SD** \\ \hline UX evaluation methods & 4.15 & 0.74 & 3.85 & 0.86 \\ UX design system & 4.12 & 1.07 & 3.74 & 0.79 \\ UX persona & 3.82 & 0.97 & 3.50 & 0.93 \\ UX responsibilities and roles & 3.29 & 1.14 & 3.91 & 0.79 \\ UX evaluation repository & 4.18 & 0.80 & 3.59 & 0.78 \\ UX backlog & 4.26 & 0.90 & 3.26 & 0.90 \\ UX sprint backlog & 4.12 & 1.01 & 3.59 & 0.86 \\
**Mean** & **3.99** & & **3.63** & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Survey results for dimensions D1 and D2 of UX artifacts.  
provide detailed guidance will be revisited after analyzing the qualitative feedback from the experiment.
## 2 Results and Discussion
### Comparison of the PPy@MIL-101(Cr)-Coated Fiber with Commercial Fibers  
Physicochemical characteristics of fiber are the most important criteria in SPME sampling, as a well-known fact. Accordingly, many SPME development studies are concerned with the preparation of new fiber coatings for wider applications diversity and more effective and selective trapping of analytes of interest. In this research, the PPy@MIL-101(Cr) coated fiber was prepared to achieve more efficient extraction of MTBE as a semi-polar compound (with relative polarity of 0.124 and eluant strength of 0.20. It was anticipated that the PPy@MIL-101(Cr) fiber would have higher extraction performance due to the larger sorptive surface area and more strong interactions with the analyte, in comparison with the commercial fibers. On the other hand, incorporation of MIL-101(Cr) with PPy into a single sorbent was expected to show much higher extraction efficiency, because of significant adsorption features of MILs. Therefore, to assess these hypotheses and to evaluate the reliability of the prepared PPy@MIL-101(Cr) fiber, it was compared with PPy handmade fiber, as well as with PA, PDMS, and DVB/CAR/PDMS commercial SPME fibers. As is clear in Figure 6, under the same conditions, the PPy@MIL-101(Cr)-coated fiber showed higher extraction efficiency for the target analyte, in comparison with all examined fibers. It is noticeable that the amount of MTBE extracted by the new fiber was 64% higher than PDMS, as a general fiber for volatile and semi-volatile compounds. It was also much higher than that of DVB/CAR/PDMS commercial fiber (28%), while DVB/CAR/PDMS has been distinguished as a very suitable sorbent for many non-polar and semi-polar analytes in fiber [24].  
Figure 5: Study of desorption time and desorption temperature of the HS-SPME–GC-FID procedure for analysis of MTBE in solid samples (analyte concentration: 2 \(\upmu\)g\(\cdot\)g\({}^{-1}\), extraction time: 15 min, extraction temperature: 60 \({}^{\circ}\)C).
# Recent Developments on Biomineralization for Erosion Control
## 4 Suggestions for Future Research  
Although MICP technology has been applied to control erosion and liquefaction in ocean engineering, from units, models, to a small number of fields, there are still issues that should be considered, such as low reinforcement efficiency, insufficient reinforcement durability, and possible foreign microbial invasion concerns. In addition, current experimental studies have basically ignored the mutual coupling effects of MICP reaction and common ocean dynamic environmental elements such as waves and currents. To ensure the responsible and sustainable application of MICP in marine environments, future research should prioritize practical and urgent needs. In the short term, efforts should focus on conducting life cycle assessments (LCAs) to evaluate the overall environmental footprint, assessing potential ecosystem impacts to avoid unintended consequences, and performing durability testing to ensure long-term performance under harsh marine conditions. Screening and utilizing indigenous urease-producing bacteria is essential to reduce ecological risks associated with non-native microbial introduction. Additionally, developing ecological monitoring strategies--such as environmental DNA (eDNA) tracking, microbial community analysis, and water quality monitoring (e.g., ammonia, pH)--will help track changes in the environment during and after MICP application. Multi-scale experiments (e.g., in tidal tanks) can simulate real ocean conditions, while the integration of real-time sensors can enable in situ monitoring and adaptive control of treatment processes. In the long term, advanced approaches such as gene editing may be used to enhance microbial performance in a controlled and safe manner, while AI-based optimization could help tailor treatment plans to site-specific conditions. Furthermore, addressing practical challenges in large-scale deployment--including injection logistics, treatment scalability, and meeting regulatory standards--is crucial for successful field application. This balanced and forward-looking approach aims to bridge scientific innovation with environmental responsibility and real-world engineering needs.  
Figure 6: Liquefaction mitigation of seabed: (**a**) the influence of MICP on the dynamic response of seabed, and (**b**) the influence of wave height on the maximum instability depth [133].