esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### Malonyl-CoA Pathway  
The 3-hydroxypropionic acid cycle was discovered in _Chloroflexus aurantiacus_ as an autotrophic carbon fixation pathway ([PERSON], 2012). In subsequent studies, researchers used a part of the reactions from the 3-hydroxypropionic acid cycle (from acetyl-CoA to 3-HP) for 3-HP production and named it the malonyl-CoA pathway ([PERSON], 2012). In the malonyl-CoA pathway, acetyl-CoA is converted into malonyl-CoA by acetyl-CoA carboxylase (ACC), which fixes one molecule of CO\({}_{2}\) at the expense of one molecule of ATP, after which the malonyl-CoA is converted into 3-HP by a two-step reaction catalyzed by NADPH-dependent malonyl-CoA reductase (MCR). The MCR consists of two different functional domains: MCR-C catalyzes the reduction of malonyl-CoA to malonyl semialdehyde (MSA) and MCR-N catalyzes the reduction of MSA to 3-HP, and the overall reaction consumes a total of two molecules of NADPH. In addition to glucose, it is also possible to use ethanol, fatty acids, acetic acid, and other less common carbon sources to produce 3-HP via the malonyl-CoA pathway, since acetyl-CoA is a common metabolic intermediate.
## 4 Advantages and Uses of the Digital Twin  
A digital twin can be designed and enacted when the physical system has the capabilities that help to provide complete data through which the digital twin can create and maintain a virtual system that simulates the physical system in real time and provides feedback for the received data [8]. The digital copy enables the analysis of the behavior of the real world system and thus, modifications and improvements can be made to the target activities within the project. At the same time, real events and operational issues, as well as future variables, can also be predicted through the virtual simulation [9].  
When the digital twin achieves the flexibility of exchanging data and decisions in both directions, it can then correctly predict and mitigate complex system behaviors and technical malfunctions, in addition to external influences on the system, in due time [10].  
Digital twins have a considerable number of advantages and uses:
* They can ensure and verify the integrity of a system model by conducting tests using data from the operating environment and then realizing evaluations, improvements and predictions;
* They can assist decision-makers in supporting their decisions based on reports provided by the digital system regarding the project to be implemented in the real environment;
* They can predict future changes in the physical system as the digital system provides the ability to analyze and simulate operations so that proper plans can be prepared;
* Through simulation, they can discover applied opportunities to be added to the physical system, and to any improved versions, as the twin can reveal extended visions and significant gains for the results of the real-world system.  
Considering the rapid spread of information technology and the Internet of Things (IoT) and the availability of sensors, communication networks and analytics software, it is now possible to develop digital twin systems in most areas of interest within various fields and industries, including construction project management.
## 3 Results and Discussion
### Trends in Spectral Line Intensity Changes  
To determine the characteristic spectrum of fireproof samples, we selected a galvanized steel sample coated with fireproof paint for the experiment. Initial tests were conducted using LIBS, with the laser energy set at 85.5 mJ, a frequency of 1 Hz, a delay time of 2.5 us, atmospheric pressure at 101 kPa, and an ambient temperature of 19 \({}^{\circ}\)C. Since the fireproof paint was applied to the surface of the galvanized steel, we also performed LIBS on the galvanized steel sample to distinguish between the two materials. The spectrograms for the galvanized steel and fireproof paint are shown in Figure 3.  
Based on the NIST database for line selection, as shown in Figure 5, the coating on galvanized steel is a zinc layer. Therefore, the Zn spectral lines were chosen as the characteristic spectral lines for galvanized steel materials, which include the elemental spectral lines Zn I 334.56 nm and Zn I 472.22 nm, among others. The spectral graph of the fireproof coating presents characteristic elemental spectral lines such as Ti II 334.94 nm, Ca II 393.37 nm, and Ti I 453.48 nm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Quality Score (\%)** \\ \hline C & 40.57 \\ O & 37.29 \\ Na & 0.49 \\ Al & 1.07 \\ Si & 1.73 \\ P & 11.54 \\ Ca & 1.20 \\ Ti & 4.49 \\ Mo & 1.38 \\ Cl & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fireproof coating EDS test results.  
Figure 4: Microstructure of the test sample.  
The fireproof coatings Ti II 334.94 nm and Ti I 453.48 nm were selected to study the effect of air pressure on the intensity of spectral lines. The pressure range was set at a range of 35 kPa to 100 kPa, with a laser energy of 75.9 mJ, a delay time of 2.5 us, and the temperature during testing was the ambient temperature of 19 \({}^{\circ}\)C.  
As shown in Figure 6, the intensity of the Ti II 334.94 nm and Ti I 453.48 nm spectral lines generally decreases as the air pressure drops. The decline is more gradual from 100 to 70 kPa, and more rapid from 70 to 35 kPa. This trend can be attributed to the weakening of the environmental gas confinement effect with decreasing pressure, which accelerates the expansion rate of the plasma. As a result, the number of particles per unit volume in the plasma decreases, reducing the likelihood of collisions between particles and weakening avalanche ionization, ultimately leading to a decrease in spectral line intensity.
## 5 Modified Chirp Scaling and Azimuth Compression for GTLR-BiSAR System
### Azimuth Compression in Azimuth Frequency Domain  
For the traditional monostatic stripmap workmode, the azimuth compression is achieved by compensating the azimuth nonlinear phase in RD domain and changing the data through azimuth IFFT. Then the azimuth compression is completed in time domain. However, the data of GTLR-BiSAR operating in sliding spotlight or TOPS mode is aliasing in azimuth time domain after the deramping pre-processing ( Section 2). Combined with the idea of SPECAN processing, we focus the point target in the azimuth frequency domain as follows.  
After the range compression, bulk RCM correction, residual phase compensation and azimuth phase removal (referring to Equation (38) in the former subsection), we construct a transfer function as follows:  
\[H_{tf}=exp(j\pi\frac{\lambda R_{r0p}}{V_{r}^{2}}{f_{a}}^{2}-j\pi\frac{\lambda R _{r0,ref}}{V_{r}^{2}}{f_{a}}^{2}) \tag{39}\]  
Then we obtain the signal expressions:  
\[S_{tf}(f_{a};P)=S_{2D}(f_{\tau},f_{a};P)\cdot H_{az}^{*}\cdot H_{RCM}^{*}\cdot H _{ran}^{*}\cdot H_{RP}^{*}\cdot H_{tf} \tag{40}\]where \(*\) represents the conjugate operation. The \(S_{tf}(f_{a};P)\) (\(f_{\tau}\) is removed in the operation above) is no longer aliased in azimuth time domain, so we convert it to azimuth time domain by azimuth IFFT, and the signal becomes:  
\[S(t_{a};P)=exp(j\pi\frac{V_{r}^{2}}{\lambda(R_{r0\_ref}-R_{r0\_p})}{t_{a}}^{2}) \tag{41}\]  
Compensate the above phase in time domain and transfer it to azimuth frequency domain, we finally obtain the focused image.
###### Abstract  
Mesh quality can affect both the accuracy and efficiency of numerical solutions. This paper first proposes a geometry-based smoothing and untangling method for 2D meshes based on explicit element geometric transformation and element stitching. A new explicit element geometric transformation (EEGT) operation for polygonal elements is firstly presented. The transformation, if applied iteratively to an arbitrary polygon (even inverted), will improve its regularity and quality. Then a well-designed element stitching scheme is introduced, which is achieved by carefully choosing appropriate element weights to average the temporary nodes obtained by the above individual element transformation. Based on the explicit element geometric transformation and element stitching, a new mesh smoothing and untangling approach for 2D meshes is proposed. The proper choice of averaging weights for element stitching ensures that the elements can be transitioned smoothly and uniformly throughout the calculation domain. Numerical results show that the proposed method is able to produce high-quality meshes with no inverted elements for highly tangled meshes. Besides, the inherent regularity and fine-grained parallelism make it suitable for implementation on Graphic Processor Unit (GPU).  
m mseh smoothing; mesh untangling; explicit element geometric transformation; parallel algorithm
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Moment-Resisting Reinforced Concrete Frames
General observations from studies indicate that PEF can lead to the rapid collapse of structures already weakened by an earthquake. The fire resistance of RC frames exposed to PEF is substantially lower than that of undamaged frames [47]. Experimental tests on RC frames subjected to PEF have shown that damage patterns and failure modes are influenced by factors such as earthquake intensity, axial load levels, and detailing for ductility [15]. These findings consistently highlight that PEF poses a significant threat to the integrity of RC structures. They emphasize the need for comprehensive analysis and design approaches that explicitly address the combined effects of earthquake damage and fire exposure. While existing studies provide valuable insights into the behavior of moment-resisting RC frames under PEF conditions, further research--particularly through full-scale experimental investigations--is essential to deepen our understanding of this complex phenomenon. Such research will be critical in developing more robust design guidelines and mitigation strategies to enhance the resilience of RC structures in earthquake-prone regions.
###### Abstract  
The equatorial plasma bubbles (EPBs) are depleted plasma density regions in the ionosphere occurring during the post-sunset hours, associated with the signal fading and scintillation signatures in the trans-ionospheric radio signals. Severe scintillations may critically affect the performance of dynamic systems relying on global navigation satellite system (GNSS)-based services. Furthermore, the occurrence of scintillations in the equatorial and low latitudes can be triggered or inhibited during space weather events. In the present study, the possible presence of the EPBs during the geomagnetic storm periods under the 25 th solar cycle is investigated using the GNSS-derived total electron content (TEC) depletion characteristics at a low-latitude equatorial ionization anomaly location, i.e., KL University, Guntur (Geographic 16\({}^{\circ}\)26\({}^{\prime}\)N, 80\({}^{\circ}\)37\({}^{\prime}\)E and dip 22\({}^{\circ}\)32\({}^{\prime}\)) in India. The detrended TEC with a specific window size is used to capture the characteristic depletion signatures, indicating the possible presence of the EPBs. Moreover, the TEC depletions, amplitude (S4) and phase scintillation (\(\sigma_{\varphi}\)) indices from multi-constellation GNSS signals are probed to verify the vulnerability of the signals towards the scintillation effects over the region. Observations confirm that all GNSS constellations witness TEC depletions between 15:00 UT and 18:00 UT, which is in good agreement with the recorded scintillation indices. We report characteristic depletion depths (22 to 45 TECU) and depletion times (28 to 48 min) across different constellations confirming the triggering of EPBs during the geomagnetic storm event on 23 April 2023. Unlikely, but the other storm events evidently inhibited TEC depletion, confirming suppressed EPBs. The results suggest that TEC depletions from the traditional geodetic GNSS stations could be used to substantiate the EPB characteristics for developing regional as well as global scintillation mitigation strategies.  
equatorial plasma bubble; multi-GNSS constellations; ionospheric irregularities and scintillation; TEC depletion; geomagnetic storm 2023, 15, 4944. [[https://doi.org/10.3390/rs15204944](https://doi.org/10.3390/rs15204944)]([https://doi.org/10.3390/rs15204944](https://doi.org/10.3390/rs15204944))
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### MAC Sub-Protocol for Periodic Communication  
In NC-NET, PC service is the core transmission demand with the characters in terms of delay sensitivity, packet loss sensitivity, and high communication frequency. In this section, we propose a collision-free MAC sub-protocol, which adopts DiffServ-based clustering topology and adapt-TDMA, to handle these periodic communication requests.
# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 1 Introduction
Existing studies have revealed the complexity of urban waterlogging formation, indicating that urbanization processes, rainfall intensity, drainage system capacity, and microtopographic features are key factors influencing waterlogging. Despite significant progress in research on urban waterlogging, current analyses still face numerous challenges. For instance, the dynamic characteristics of waterlogging involve complex interactions between natural and social systems, and traditional models have limitations when handling high-resolution data and dynamically coupled scenarios. Furthermore, how to efficiently integrate multidisciplinary technologies (such as hydrological modeling, artificial intelligence, and remote sensing) to meet the prediction demands of extreme rainfall events remains a critical issue that needs to be addressed. The research objectives include the following: (i) Reviewing the current theoretical research results on urban waterlogging, analyzing the formation mechanisms of waterlogging, and revealing the impact of multi-factor coupling on waterlogging. (ii) Reviewing experimental studies on urban waterlogging, analyzing experimental methods related to waterlogging, systematically summarizing the application status of physical models, scaled experiments, and hydrodynamic experiments, and discussing the critical role of experimental research in validating numerical models, optimizing drainage system design, and revealing the mechanisms of waterlogging damage. (iii) Evaluating numerical simulation methods for urban waterlogging: clarifying the theoretical support role of numerical simulation in urban waterlogging risk assessment and management, and proposing model frameworks to address increasingly complex waterlogging issues. (iv) Exploring the application of artificial intelligence in urban waterlogging research, particularly the use of algorithms such as deep learning and reinforcement learning for data analysis and risk assessment. (v) Discussing potential strategies to enhance urban disaster resilience through the integration of existing technologies. The structure of this paper is as follows: The second Section presents the research methodology. The third Section introduces the relevant theories of urban waterlogging, the current status of experimental research on urban waterlogging and the latest advancements in numerical simulation in complex waterlogging scenarios. The fourth Section analyzes the innovative applications of artificial intelligence technologies in waterlogging research, highlighting the advantages of methods such as deep learning, random forests, and reinforcement learning in waterlogging prediction and risk assessment. Finally, the fifth Section concludes the research findings and proposes directions for future research. The flowchart is shown in Figure 1.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## The Model of Room Monitoring System
### Proposed Person Detection Scheme  
This section presents the proposed person detection scheme that combines the Mask R-CNN scheme and tile technique. This scheme includes three main steps. First, the tile images of identical size are generated from the upper part of the input image. Second, the object detection and filtration are implemented on each tile image and the full-frame image. The resulting bounding boxes are collected as the initial results. Finally, the initial results are merged according to the intersection of bounding boxes to get the final results. Algorithms 1 and 2 are proposed for merging bounding boxes. Furthermore, we also introduce a new concept in the merging process. Figure 3 details the process of this scheme.
## 3 Methodology
### Determination of Accident and Failure Probabilities  
As mentioned, we want to differentiate between accidents and (technical) failures. Therefore, we assume that an accident depends on the driver, while a failure depends on the car, that is, the team. If a team changed its name from one season to the next, for example, when Sauber became Alfa Romeo Racing, we treat it under its original name to ensure that the failure probabilities are determined correctly. The accident and failure probabilities are determined by applying Bayesian inference, as suggested by [PERSON] [7]. For Bayesian inference, a prior distribution and a likelihood function are required. As with [PERSON] [7], the Beta distribution is used as prior distribution, and the Bernoulli distribution as likelihood function (the possible race outcomes are: \"finished\" or \"did not finish\"). The prior distribution parameters \(\hat{\alpha}\) and \(\hat{\beta}\) are determined to [7] p. 11  
\[\hat{\alpha} =\left(\frac{1-\hat{\mu}}{\hat{\sigma}^{2}}-\frac{1}{\hat{\mu}} \right)\hat{\mu}^{2}\quad\text{and} \tag{11}\] \[\hat{\beta} =\hat{\alpha}\left(\frac{1}{\hat{\mu}}-1\right). \tag{12}\]  
\(\hat{\mu}\) and \(\hat{\sigma}\) stand for mean and standard deviation of the prior distribution. They are determined using the total accident fraction per driver, and the total failure fraction per team. Hereby, only drivers and teams with at least 30 races in the database are considered. The two prior distributions for accidents and failures then represent our knowledge about the respective probabilities on the entire database.  
Thereafter, driver-, team- and season-specific posterior distributions are calculated taking into account the corresponding accident and failure fractions within the particular season. This proceeding combines the overall knowledge with the specific influence factors of driver, team, and season. For the chosen combination of prior distribution and likelihood function, the posterior distributions are also a Beta(\(\alpha+z,\beta+N-z\)) distribution [7] p. 12. \(z\) is the number of accidents or failures in the respective season, and \(N\) stands for the number of attended races in that season. Figure 6 shows the resulting probability density functions of the accident prior distribution and three driver-specific accident posterior distributions.  
Figure 6: Probability density functions (PDF) of the prior accident probability distribution in comparison to three driver-specific distributions of the 2018 season. Driver abbreviations: HAM–[PERSON], GRO–[PERSON], VET–[PERSON].  
Finally, the mean values of the posterior distributions are used as accident \(P_{\text{accident}}\) and failure probabilities \(P_{\text{failure}}\) for the simulation. The parameters of the drivers and teams of the 2019 season are given in the Tables A1 and A2 in Appendix A.
###### Abstract  
Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.  
ad hoc distribution; rollback; graphical analysis; distributed simulation +
Footnote †: journal: Applied Sciences
## 3 Results  
### Characteristics of the Included Studies  
The article selection process is summarized in Figure 1.  
A total of 295 articles were obtained from the Pubmed database research and only one was obtained from external reviews.  
After removing the duplicate articles, 229 articles were examined, and 162 of them were discarded after a first reading of the titles and abstracts.  
A total of 11 studies of the 67 remaining articles met the inclusion criteria [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. The characteristics of the included studies are listed in Table 1. Among these studies, nine of them [21; 22; 23; 25; 26; 27; 29; 30; 31] were double-blind placebo-controlled RCTs and two studies [24; 28] were placebo-controlled RCTs.  
Figure 1: Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) flow diagram.  
The publication date of the articles ranged from 2018 to 2020.  
The examined patients had a clinical status of systemic health and a stage II, III, or IV (Chicago Work Shop-2017) diagnosis of periodontal disease, and only two studies included smoking subjects.  
All of them received NSPT treatment associated with domiciary probioticic treatment or placebo therapy. Two studies [24; 29] evaluate the additional effects of antibiotics or laser therapy by analyzing a third patient group, and one study [31] also includes a third patient group where oral probiotics, such as chewing gum were administered.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{8}{c}{**Main Characteristics of the Included Studies**} \\ \hline
**Study** & **Study Design** & **(T/C)** & **Intervent** & **Probiotic** & **Application** & **Outcomes** & **Follow-Up (Months)** \\ \hline [PERSON] et al., & Double-blind, & & & & & & \\
2020 & placebocontrolled & 15/15 & NSPT+ &
\begin{tabular}{} \end{tabular  
Patients were followed for a range of 1 to 12 months. Six studies [23; 26; 27; 28; 30; 31] used _Lactobacillus reuterii_ probiotic; patients evaluated in two studies [21; 22] were treated with _Bifidobacterium animalis subsp. lactis_; three studies [24; 25; 31] examined _Lactobacillus brevis_ and _Lactobacillus Plantarum_; lastly, another study [29] used _Lactobacillus rhamn_. Six studies [21; 22; 24; 27; 29; 31] have reported microbiological outcomes.  
The domiciliary probiotics protocol of all studies evaluated oral administration of the probiotic via tablets or sachets, while only 3 of the 10 studies [24; 25; 27] applied subgignically, in gel or drops, during the in-office session as an additional probiotic solution.  
No adverse events were reported in all studies.  
In addition, comparing treatment groups, both experimental and control groups showed a partial or complete positive change with a significant reduction of the clinical parameters, while the additional use of probiotics offered a more significant clinical benefit than the placebo groups.  
However, in some studies, similar benefits of the additional use of probiotics were found compared to the placebo treatment [23; 24; 25; 28; 30].