esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Medicinal Health Food (MHF)  
The concept of \"Medicine and Food Homology\" originated from traditional Chinese medicine, implying that some foods not only have nutritional value but also are capable of preventing and treating diseases. People eat plants containing active compounds to treat illnesses and enhance immunity. The Chinese Ministry of Health has listed 114 kinds of items as medicinal health foods [123]. Among them, _Pogostemon cabin_, _Herba menthae_, _Flossophorea_, and _Ganoderma lucidum_ contain significant levels of terpenoids as their respective functional factors [123; 124; 125]. Nevertheless, it is worth mentioning that traditional Chinese medicine may contain multiple complex components, some of which might cause adverse reactions or allergies in certain individuals. Therefore, under the condition that the efficacy of terpenoids is known or the mechanism is clear, taking the specific active factor directly is better than taking the whole herb. For instance, artemisinin has shown significant effects in treating malaria, and extracting artemisinin for treatment might be more effective than consuming the whole _Artemisia annua herb_. With the development of synthetic biology and systems biology techniques, the biosynthetic pathways of more and more terpenoids are being elucidated, making it possible to produce these valuable terpenoids using microbial cell factories. For example, researchers achieved de novo synthesis of three terpenoid compounds in _S. cerevisiae_ by integrating the whole biosynthetic pathways for valarenic acid [126], oleanane-type ginsenosides [127], and (-)-borneol [52].
###### Abstract  
With larger surface areas and nanochannels for mass delivery and gas diffusion, three-dimensional tubular anodic aluminum oxide (AAO) films have practical advantages over two-dimensional AAO films for medical and energy applications. In this research, we have developed a process for batch production of tubular AAO films using a 6061 Al tube. The tubular AAO films have open nano-channels on both sides, with average pore dimensions of about 60 nm and pore densities of about \(10^{8}\) to \(10^{9}\) pore/cm\({}^{2}\). It was found that the porous AAO material with nano-channel structure exhibited dialysis behavior, allowing for liquid/gas exudation through diffusion between the inner and outer surfaces of the tubular AAO films. Ar gas bearing test and aeration test were conducted to find the pressure bearing capacity of tubular AAO films. It was demonstrated that the AAO film with a thickness of 100 \(\upmu\)m can resist an argon pressure up to 8 atm; however, 30 \(\upmu\)m AAO film can only withstand 3 atm of Ar gas. The tubular AAO films with exudation characteristics have the potential for applications in advanced technologies, such as liquid or gas filters, drug delivery, and energy applications.  
tubular anodic aluminum oxide (AAO); porous material; nanochannels; filtering Article
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Machine Learning Approaches
#### 4.2.3 Support Vector Machine  
SVMs are supervised learning models that analyze data used for classification and regression analysis. An SVM training technique assigns all new examples to a single category, thereby making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples in space that maps the examples of separate categories divided by a clear gap calculated to be as wide as possible. All new examples are mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to linear classification, SVMs can classify nonlinear data efficiently using a
## 4 Technological Advancements
### Manual to Automatic Non-Detachable Inter-Modular Connections
Since their initial stiffnesses were comparable to those of the welding connection, they were just as dependable as the welding IMC. The problem with these connections was that they only fixed two sides by locking sliders on two sides, leaving the other sides loose or unsupported, which made them weaker in earthquake-prone areas. Therefore, to achieve the fixity of the tubes and IMC in opposite and neighboring four sides, researchers developed pin-type joints, as shown in Figure 3c [72], and plug-in type joints, as shown in Figure 3d [73], inserted inside columns, which further improved their seismic performance and made them suitable for mid- to high-rise PFISBs. Other researchers, such as [PERSON] et al. [74] and [PERSON] [75], depicted in Figure 3b,g, built an installation-friendly self-locking fastener machine by using springs and nuts and bolts to provide support around the bolt shaft. Their seismic performance shows that they behave as semi-rigid to rigid in high-seismic zones, making them suitable for usage in high-rise PFISBs. However, these self-locking IMCs ignore construction or deformation tolerances, which require a high degree of precision. Additionally, these IMCs cannot be removed once locked, making it challenging to reuse the IMU in the event of accidental damage during servicing.
## 3 Home Area Network
### Smart Home HAN Requirements
#### 3.2.1 Secure Communication
In this sense, data encryption allows a high level of protection by masking the information whose reading requires a key for that purpose. The communication technology shall provide native mechanics with adequate strength and encryption method, as recommended for example in RFC3565 for AES128. Security Certification  
The HAN operated communication must support device security certification. Each device model must be security-certified by a recognized independent authority.
# Design Considerations of Multi-Phase Buck DC-DC Converter
## 2 Power Losses in the Multi-Phase Synchronous Buck Converter
Gate charge losses \(P_{GL}\) of the transistor \(Q2\) can be expressed by  
\[\Delta I_{L}=\frac{U_{0}(1-D)}{Lf_{SW}}, \tag{1}\]5  
where \(U_{GS}\) is the gate-source voltage and \(Q_{GL}\) is the gate charge of the low-side MOSFET. Output capacitance losses in the low-side MOSFET \(P_{\text{C}_{OSS\_L}}\) are given by  
\[\Delta I_{L}=\frac{U_{0}(1-D)}{Lf_{SW}}, \tag{1}\]6  
where \(\text{C}_{OSS\_L}\) is the output capacitance of the low-side MOSFET. Furthermore, the total losses in the LS transistor \(P_{QL}\), in the one buck stage, are the sum of the losses determined by (8), (9), (14), and (15), and can be found in [38]:  
\[\Delta I_{L}=\frac{U_{0}(1-D)}{Lf_{SW}}, \tag{1}\]7  
The total losses in the LS MOSFETs \(P_{QLN}\) in an \(N\)-phase synchronous buck converter is \(N\) times \(P_{QL}\). The conduction loss is caused by the DC resistance (DCR) of the winding forming the inductor [33]. The current amplitude and the switching frequency of the load determine the choice of inductors. The RMS current \(I_{L\_rms}\), through the inductance per phase, is given by  
\[\Delta I_{L}=\frac{U_{0}(1-D)}{Lf_{SW}}, \tag{1}\]8  
Hence, the conduction losses in the inductor per phase can be expressed by  
\[\Delta I_{L}=\frac{U_{0}(1-D)}{Lf_{SW}}, \tag{1}\]9  
where \(DCR1=DCR2=\ldots=DCRN=DCR\). Conduction losses \(P_{LN}\) in the inductors in an \(N\)-phase synchronous buck converter is \(N\) times \(P_{L}\). Power losses in the input capacitor \(P_{\text{C}in}\) become  
\[I_{IN\_rms}=\sqrt{\left(D-\frac{m}{N}\right)\left(\frac{1+m}{N}-D\right)I_{0}^{ 2}+\frac{N\Delta I_{L}^{2}}{12D^{2}}\left[(m+1)^{2}\left(D-\frac{m}{N}\right) ^{3}+m^{2}\left(\frac{1+m}{N}-D\right)^{3}\right]} \tag{2}\]0  
The current through the output capacitor \(Cout\) is the sum of the ripple currents of each individual stage. Moreover, the RMS value \(I_{Cout\_rms}\) is given by \[I_{IN\_rms}=\sqrt{\left(D-\frac{m}{N}\right)\left(\frac{1+m}{N}-D\right)I_{0}^{ 2}+\frac{N\Delta I_{L}^{2}}{12D^{2}}\left[(m+1)^{2}\left(D-\frac{m}{N}\right) ^{3}+m^{2}\left(\frac{1+m}{N}-D\right)^{3}\right]} \tag{2}\]1  
Thus, the power losses \(P_{Cout}\) in the output capacitor become  
\[I_{IN\_rms}=\sqrt{\left(D-\frac{m}{N}\right)\left(\frac{1+m}{N}-D\right)I_{0}^{ 2}+\frac{N\Delta I_{L}^{2}}{12D^{2}}\left[(m+1)^{2}\left(D-\frac{m}{N}\right) ^{3}+m^{2}\left(\frac{1+m}{N}-D\right)^{3}\right]} \tag{2}\]2  
The total losses \(P_{out}\) in the \(N\)-phase synchronous buck converter can be found using  
\[I_{IN\_rms}=\sqrt{\left(D-\frac{m}{N}\right)\left(\frac{1+m}{N}-D\right)I_{0}^{ 2}+\frac{N\Delta I_{L}^{2}}{12D^{2}}\left[(m+1)^{2}\left(D-\frac{m}{N}\right) ^{3}+m^{2}\left(\frac{1+m}{N}-D\right)^{3}\right]} \tag{2}\]3  
For the efficiency of a multi-phase synchronous buck converter, the following relation is used:  
\[I_{IN\_rms}=\sqrt{\left(D-\frac{m}{N}\right)\left(\frac{1+m}{N}-D\right)I_{0}^{ 2}+\frac{N\Delta I_{L}^{2}}{12D^{2}}\left[(m+1)^{2}\left(D-\frac{m}{N}\right) ^{3}+m^{2}\left(\frac{1+m}{N}-D\right)^{3}\right]} \tag{2}\]4
# Irrigation System for a Roller-Type Onion Pot Seeding Machine
## 3 Results and Discussion
### Verification of the Developed Irrigation System  
The verification test was conducted to investigate the effectiveness of the developed irrigation system. The seeding was conducted by using both the original pot-seeding machine (Group 1) and the pot-seeding machine with the developed irrigation system (Group 2), and the growth conditions of onion seedlings were compared for each group after 8 weeks of growth. The best operating condition which was determined from a factorial experiment (irrigation hole diameter 1.0 mm and valve closed) was applied for the irrigation system. The seeding was carried out simultaneously for both groups at 5 pm in a local nursery, and manual irrigation was performed daily at 10:00 and 17.00, from the following day of seeding for the both groups. The growth level was observed at intervals of one week (Figure 13).  
In the test, for the both groups, the same bed soil and seeds were used in the same growth environments (temperature, humidity, etc.) so that only the irrigation conditions became influential factors. Also, the results were analyzed using sufficient number of samples to ensure high reliability.  
The growth status of 1344 onion seedlings was analyzed for both Group 1 and Group 2. Germination rate, number of leaves, diameter and length of stem, and length of root were used as representative factors to determine the growth status [15]. Germination rate is defined as the ratio of the number of holes with the onion seedlings to the total number of holes on the pot-tray. Definition of other factors is shown in Figure 14. The average values of the factors are listed in Table 13. The germination rate, leaf number, stem diameter, stem length, and root length were higher in Group 2 than in Group 1 by 7.37%, 0.02 pieces, 0.21 mm, 25.7 mm, and 7.6 mm, respectively.  
Figure 12: Seed and bed soil conditions after irrigation: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.  
Figure 13: Growth status of onion seedlings: (**a**) Week 1; (**b**) Week 2; (**c**) Week 3; (**d**) Week 4; (**e**) Week 5; (**f**) Week 6; (**g**) Week 7; (**h**) Week 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Group 1** & **Group 2** \\ \hline \hline \begin{tabular}{c} Germination rate (\%) \\ Number of leaves (pieces) \\ Diameter of stem (mm) \\ Length of stem (mm) \\ Length of stem (mm) \\ Length of root (mm) \\ \end{tabular} & \
\begin{tabular}{c} 78.79 \\ 3.03 \\ 2.41 \\ 244.4 \\ 28.8 \\ \end{tabular} &
\begin{tabular}{c} 86.16 \\ 3.01 \\ 2.62 \\ 270.1 \\ 36.4 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 13: The onion seedling growth states for both groups.
# Removal of Contaminants in Water with Scallop Shell Waste
## 4 Conclusions  
For the first time, this study uses different techniques to determine the properties of scallop shell waste from northern Chile. This characterization allows us to define potential applications for this waste, especially in the manufacture of composite materials to promote a circular economy.  
These wastes can also be used as material for water treatment due to their morphology, chemical composition, thermal stability, and surface functional groups. It was determined that the SS granulometry (<250 \(\upmu\)m) of the scallop shell waste shows the best removal percentages of caffeic acid (100.0%) and arsenic III (23.0%) from water.  
Tests developed using SS granulometry showed that the best conditions for favorable removal of the phenolic compound from water were using a pH close to 4.6 and a concentration of 500 ppm of scallop shell waste from northern Chile.  
Properties such as morphology and chemical composition do not significantly affect the removal of target contaminants using scallop shell waste as a decontaminating material. Therefore, high removal percentages of such compounds can be largely attributed to the pore size distribution of the shell powder, being a key property for this process regardless of specific surface areas in each material.  
To sum up, the results show that it is viable to use scallop shell waste generated in northern Chile to remove contaminants present in wastewater of the wine industry (caffeic acid) and drinking water (arsenic III).  
Conceptualization, P.Z.S. and A.A.-A.; methodology, A.A.-A. and A.C.M.; software, M.V.F. and A.D.C.; validation, A.C.M., J.L.V.R. and A.A.-; formal analysis, A.C.M.; C.A.N.R. and A.A.-A.; investigation, P.Z.S.; writing--original draft preparation, P.Z.S. and M.V.F.; writing--review and editing, A.C.M. and A.A.-A.; supervision, A.C.M. and A.A.-A.; project administration,A.A.-A.; funding acquisition, A.C.M. and A.A.-A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the financial support of DIDULS/ULS, through the projects PR21172 and PR2153857. Acknowledges the financial support of DIDULS/ULS through the project FIULS 2030 grant number 18 ENI2-104235 funded by the CORFO program \"Nueva Ingenieria para el 2030 en Regions--Etapa de Implementacion\", Multidisciplinary R&D Project DIDULS FIULS 2030 N_ID1953851, FONDEQUIP EQM-160070 and FONDEQUIP 150109.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to publicly.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Study Population  
Taichung Veterans General Hospital (TVGH) is a tertiary teaching hospital with 112 adult ICU beds in central part of Taiwan. 24,518 adult ICU admissions between July 2015 and December 2019 in the TVGH EHRs database were extracted for analysis. The inclusion criteria is age over 20 years old adults. Exclusion criteria are (1) End stage renal disease with the International Classification of Diseases Tenth Revision (ICD-10) code assignment of N18.6. (2) Had been treated with renal replacement therapy before the index ICU admission. (3) Received renal replacement therapy within 24 h after index ICU admission. (4) Stayed less than 24 h in ICU. (5) Had an invalid or missing urine output (UO) record. After selection, 15,702 adult ICU admissions were included for final analysis.  
AKI was labelled according to Section 2.2, where 9589 AKI patients and 6067 non-AKI patients were identified, respectively. To conduct research up to 72 h before AKI occurrence, we set additional filtering criteria for both AKI patients and non-AKI patients to ensure EHRs are longer than 72 h of time frame. The advantage of such filtering is that it gave us more data and longer period to study patient conditions. Additionally, by choosing a minimal 72-h time frame in ICU for both AKI and non-AKI group, we alleviate the possible bias in non-AKI group selection. [PERSON] et al. (2015) performed AKI mortality prediction on MIMIC III data with the Simplified Acute Physiology Score (SAPS), also focused on patients who survived in ICU for more than 72 h using multi-variable regression models.  
We obtained a final set of 4278 patients from both groups after filtering with greater than or equal to 72 h admission time, containing 1631 AKI patients and 2647 non-AKI patients. Figure 1 shows the data cohort workflow.
## 7 System Performance Analysis
### Model Loading Time Analysis  
Figure 12 below is a comparison of model loading using the system with KUKA Sim Pro (version 1.1) and RobotStudio (version 6.08), the robot simulation communication software from KUKA Robotics GmbH and ABB Ltd., respectively.  
Under the condition of loading the same model, each platform loaded the model 25 times, and the loading times were recorded by the testing tool. The test results are shown in Figures 13 and 14.  
Figure 12: Robot model loading.  
Figure 13: Comparison diagram of loading time with KUKA Sim Pro model.  
According to the test results, our system's average loading time for the KUKA KR150 model is 2.953 s, compared with 5.426 s for KUKA Sim Pro, resulting in a loading efficiency improvement of approximately 45.58%. The average loading time for the ABB IRB2400 model is 4.9224 s, compared with 6.5384 s for RobotStudio, resulting in a loading efficiency improvement of about 24.72%.  
The significant improvement in model loading efficiency can be attributed to our system's smaller codebase, which excludes functionalities such as offline programming, signal analyzers, and sales tools, focusing solely on model simulation. This specialization allows for greater efficiency in the loading of the same models compared with traditional-brand robot simulation software.
## 4 Results and Discussion
### Characterization of Microstructure of GONF-Composite  
SEM and EDS were employed to investigate microstructure and chemical composition of cement hydration products when GONF is introduced into the cement matrix. The GONF-combined cement paste specimen with 0.05% GONF was particularly chosen for the characterization of microstructure since 0.05% GONF was found as an optimal content for strength improvement. Figure 10 shows the overall surface morphology of a GONF-cement paste specimen with 0.05% of GONF. To investigate the crystalline structure, the magnification was increased from 5000 X (Figure 10a) through 10 kX (Figure 10b). The yellow box indicates the enlarged area under greater magnification. In other words, the yellow box in Figure 10a is the image shown in Figure 10b. The image with the magnification of 5000 X (Figure 10a) reveals several needle like ettrin  
Figure 9: Compressive and flexural strength results of GONF-combined concrete with Dry-mix design. (**a**) Mechanical properties of CD (7 days); (**b**) Mechanical properties of CD (28 days).  
early age morphologies of amorphous C-S-H. Figure 9(b) reveals the structures of the grains and crystals with higher magnification. Needle-shaped ettringite can be clearly seen from Figure 9(b) protruding into the pores. Amorphous morphology of C-S-H is clearly evident from Figure 9(b).  
To verify the locations of GONF by detecting carbon element and their contribution to the cement hydration products (e.g., C-S-H), EDS was performed at different spots while SEM images were taken. Each number in Figure 9(b) indicating either the spot or area shot by the emitted electron beam from EDS to provide the localized elemental information. EDS data is presented in Table 6. As shown in Table 6, the results of two area shots (area shots 1 and 2) and one point shot (point shot of 5) show the presence of carbon and high percentage of oxygen in the hydration products. The data of all area shots show the presence of high amounts of carbon and oxygen including silicon and calcium. Moreover, these two areas which include carbon and high oxygen contents indicate either amorphous C-S-H or crystalline C-S-H which is Jennite. It is well known that C-S-H is the major hydration product which is primarily responsible for the strength in cement paste. On the other hand, the other two point shots (point shots 3 and 4) show that ettringite which is one of byproducts of cement hydration does not have a carbon element. This EDS result is meaningful because most of the GONF is found in C-S-H which is responsible for strength. This observation may draw a conclusion that GONF which is composed of more than 99% carbon may either act as an initiating point for C-S-H or reinforce C-S-H with its own properties (high tensile strength and modulus).
## 3 Results
### PTE Inhibits ERK, but Not JNK and p38 MAPK Phosphorylation  
Considering the fact that mitogen-activated protein kinases (MAPKs), including JNK, ERK, and p38, involve in inflammation-related signaling pathways, we studied the influence of PTE on the LTA-induced MAPK pathway. Figure 3 shows the induced expression of phosphorylated ERK1/2, JNK1/2 and p38 MAPK in RAW cells after exposure to LTA. Moreover, PTE had an inhibitory effect only on ERK activation when cells were pretreated at a higher concentration of PTE (40 \(\upmu\)M), but not on JNK and p38. These results indicated that PTE exposes its anti-inflammatory effects in LTA-induced RAW 264.7 cells via ERK signaling pathways.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 3 Adjustments, Testing and Results
### Statistical Testing
In case of the charging process, the battery sets 1 and 4 are clearly separable from the rest by both input variables, the temperature and the voltage range. These battery sets do not contain defected batteries, which is reflected by their smaller means of ranges. Figure 6: The analysis of variance of temperature (**left**) and voltage (**right**) ranges during the charging period. Figure 7: The analysis of variance of temperature (**left**) and voltage (**right**) ranges during discharging period.
###### Abstract  
To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information fusion. A pseudo-three-dimensional spatial feature extraction method is proposed by integrating generalized MUSIC with range-dimension information. The pseudo-WVD time-frequency feature is enhanced through the incorporation of prior knowledge. Additionally, the Doppler frequency shift distribution feature for underwater moving targets is derived and extracted. A multidimensional feature information fusion network model based on meta-learning is developed. Meta-knowledge is extracted separately from spatial, time-frequency, and Doppler feature spectra, to improve the generalization capability of single-feature task networks during small-sample training. Multidimensional feature information fusion is achieved via a feature fusion classifier. Finally, a sample library is constructed using simulation-enhanced data and experimental data for network training and testing. The results demonstrate that, in the few-sample scenario, the proposed method leverages the complementary nature of multidimensional features, effectively addressing the challenge of limited adaptability to relative horizontal orientation angles in target recognition, and achieving a recognition accuracy of up to 97.1%.  
underwater target recognition; feature extraction; feature information fusion; meta-learning; Doppler frequency shift distribution; spatial feature; time-frequency feature +
Footnote †: journal: Applied sciences  
Article