esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 4 Performance Evaluation
### The Affection of the Parameters  
The closed-loop controller via the VMM includes several parameters, the values of which are significant to the performance of the robotic fish. This subsection analyzes the relations between the parameters and the efficiency to provide references for the parameter setting. The experiment fixes other parameters and changes each parameter to test how it affects efficiency.  
In Figure 16, when \(K_{2}=16\), after reaching a maximum value at \(K_{1}=5\), the efficiency of the robotic fish decreases to a minimum at \(K_{1}=25\). Therefore, 5 is a good choice for \(K_{1}\)  
Figure 14: The angle of the first joint in Karman vortex.  
Figure 15: The efficiency in Karman Vortex.  
The results also show the changing of the efficiency along with the variation of \(K_{2}\). The value of \(\eta\) fluctuates with the change of \(K_{2}\). We found that if the \(K_{2}\) is too large during the simulation experiments, the curve will show a large overshoot, so we finally chose \(K_{2}=16\).  
Figure 17 depicts how the intensity of the feedback of the virtual spindle affects efficiency. The efficiency increases with the increase of \(H_{1}\). \(H_{1}\) is set to 1 for the intensity of the virtual spindle in the first joint. The relation between the efficiency and \(H_{2}\) is an upward convex curve, which gets its maximum at \(H_{2}=9\).  
The virtual spindle model in VMM is the part to percept the force of the flow field onto the robotic fish and to add the adaptive response into the torques. The values of \(H_{1}\) and \(H_{2}\) have an obvious effect on the efficiency.  
In Figure 18, the efficiency curve of the robotic fish fluctuates and decreases with the increase of \(K_{v1}\). When \(K_{v1}\) is less than 0.07, though the efficiency is high, the robotic fish cannot follow the given speed. The gain is so small that the closed-loop control cannot eliminate the following deviation of the speed. To balance the swimming efficiency and the following of the closed-loop control, we chose \(K_{v1}=0.1\). The efficiency to \(K_{v2}\) is an upward projecting curve; it rises quickly before \(K_{v2}=0.1\) and declines slowly after this point. At \(K_{v2}=0.1\), the efficiency reaches its maximum value. Simultaneously, the swimming speed of the robotic fish is also in good condition.  
Figure 16: The efficiency of the robotic fish to \(K_{1}\) and \(K_{2}\). (**a**) When \(K2=16\), the efficiency varies with \(K1\). (**b**) The changing of the efficiency along with the variation of \(K_{2}\).  
Figure 17: The efficiency of the robotic fish to \(H_{1}\) and \(H_{2}\). (**a**) The changing of efficiency along with the variation of \(H_{1}\) when \(H_{2}=9\). (**b**) The changing of efficiency along with the variation of \(H_{2}\) when \(H_{1}=1\).
# Identification of Factors Influencing Fluoride Content in Tea Infusions: A Systematic Review
## 3 Results
### Main Study Outcomes  
#### 3.3.1 Variability in Fluoride Concentration Across Samples  
Numerous studies demonstrated substantial variability in fluoride concentrations across different tea samples, with values spanning over three orders of magnitude--even under comparable brewing conditions (see Figure 2). For instance, [PERSON] et al. [19] reported values ranging from 0.04 to 6.76 mg/L, while [PERSON] et al. [58] observed a similar spread among black and green teas (approximately 0.06 to 5.99 mg/L). [PERSON] et al. [51] and [PERSON] et al. [47] documented particularly high levels in Chinese teas, reaching up to 11.05 mg/L and 7.25 mg/L, respectively. [PERSON] et al. [55], [PERSON] et al. [52], and [PERSON] et al. [56] highlighted the impact of tea type (e.g., black, green, herbal), form (powder, leaves, bags), and geographical origin on fluoride release. [PERSON] et al. [70] reported high fluoride exposure from brick tea, especially among Tibetan populations. [PERSON] et al. [54] demonstrated that using highly fluoridated water during brewing can significantly increase final fluoride content in the infusion. Beyond composition and origin, technological and chemical factors also influence fluoride levels. [PERSON] et al. [63] found that fermentation increases fluoride content in black tea, while [PERSON] et al. [28] reported higher levels in granulated and bagged teas compared to loose-leaf forms. In addition, [PERSON] et al. [34] observed an inverse correlation between fluoride concentration and antioxidant potential, suggesting a trade-off between fluoride exposure and potential health benefits. [PERSON] et al. [60] and [PERSON] et al. [48] further emphasized the role of tea matrix composition, reinforcing the multifactorial nature of fluoride variability across samples. (see Figure 3) A detailed description of the included studies is presented in Supplementary Table S1.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Microalgae-Based Aquaculture Feed  
#### 3.5.1 Algal Species with Commercial Potential  
In aquaculture practice, microalgae are important nutrition sources of fish or shrimp either by direct consumption or as indirectly prepared feed. Advantages of microalgae feed over traditional feed in aquaculture include the abundance of nutrients and the maintenance of water quality. Microalgae are rich in proteins, lipids, and carbohydrates, which are essential nutrients to aquatic animals. In addition, determined by the regulatory gene and growth condition, microalgae could intensively synthesize a variety of value-added components, such as antioxidants and pigments [81; 82]. For the purpose of aquaculture use, microalgae biomass with value-added components is highly needed. Therefore, in addition to screening robust algal strains for wastewater remediation, we need to obtain value-added algal strains with commercial potential in aquaculture. Table 4 listed some algal strains with great potential to be used for wastewater remediation and value-added biomass production. Generally, for aquaculture practice, the value-added components in microalgae could be classified into three categories. First, microalgae rich in protein and carbohydrate can be used to partly replace traditional feed, reducing the aquaculture cost. Second, antioxidants in microalgae could be exploited to enhance the immunity of aquatic animals, overcoming the problems of antibiotics abuse. Third, some components play important roles in the growth of special fish. For example, astaxanthin, which determines the skin and flesh color of some fish, is an essential pigment in salmon production industry.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
* \({}^{\text{a}}\) “NA” is short for “Not Available”.  
\end{table}
Table 4: Nutrition profile of microalgae biomass.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
###### Abstract  
Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions.  
interferometric synthetic aperture radar; error budget; systematic and random error 202420246354. [[https://doi.org/10.3390/rs16020354](https://doi.org/10.3390/rs16020354)]([https://doi.org/10.3390/rs16020354](https://doi.org/10.3390/rs16020354))  
16 January 2024
## 4 Test Results and Analysis  
### Response Surface Test Results  
According to the field test flow and evaluation index calculation method, a field test was performed. The response surface test table and the corresponding test results are shown in Table 3. The average _PLR_, _PIR_, and _PBR_ over 17 treatments were 1.36%, 1.10%, and 0.99%, respectively.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{3}{c}{**Levels**} \\ \cline{2-4}  & \(-\)**1** & **0** & **1** \\ \hline Drum rotation speed (\(A\), r/min) & 500 & 600 & 700 \\ Operating speed (\(B\), km/h) & 2.5 & 3.5 & 4.5 \\ Spiral angle of straw guide board (C, \({}^{\circ}\)) & 15 & 25 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coding of trial factors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multicolumn{3}{c}{**Experimental Factor**} & \multicolumn{3}{c}{**Evaluation Indicators/\%**} \\ \cline{2-5}  & \(A\)**/(r/min)** & \(B\)**/(km/h)** & \(C\)**/(\({}^{\circ}\))** & _PLR_ & _PIR_ & _PBR_ \\ \hline
1 & 500 & 3.5 & 35 & 1.64 & 0.65 & 0.52 \\
2 & 700 & 3.5 & 35 & 1.34 & 1.30 & 1.18 \\
3 & 700 & 2.5 & 25 & 1.23 & 1.47 & 1.52 \\
4 & 600 & 3.5 & 25 & 1.11 & 0.89 & 0.73 \\
5 & 600 & 2.5 & 35 & 1.34 & 1.02 & 0.62 \\
6 & 600 & 4.5 & 35 & 2.04 & 1.25 & 1.10 \\
7 & 600 & 3.5 & 25 & 1.14 & 0.94 & 0.68 \\
8 & 500 & 3.5 & 15 & 1.49 & 0.85 & 0.74 \\
9 & 600 & 4.5 & 15 & 1.58 & 1.34 & 1.23 \\
10 & 600 & 3.5 & 25 & 1.03 & 0.97 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of field trials.
# Biosynthesis of Camphane Volatile terpenes in _Annomum_ _villosum_ Lour: Involved Genes and Enzymes
###### Abstract  
_Anomum villosum (A. villosum)_ Lour., a medicinal species of the _Zingiberaceae_ family, is used for medical purposes. Bornyl acetate, camphor, and bormoel are the main bioactive ingredients in _A. villosum_ fruit, and the amount of bornyl acetate is regarded as a measure of the fruit's quality. In order to explore the major effective genes regulating the biosynthesis of camphane volatile terpen in _A. villosum_, some DEGs involved in camphane volatile terpene biosynthesis and transcription factors were analyzed and summarized in this study. The result showed that the content of bornyl acetate was altered in the different growth stages. In particular, the significant change occurred from 7 to 30 DAP (days after pollination). The content of bornyl acetate at 30 DAP was 169.3% more than that at 7 DAP. In total, 4782 up-regulated and 5284 down-regulated unigenes were found in G2 vs. G1, as well as 3324 up-regulated and 5036 down-regulated unigenes in G3 vs. G1, and 3332 up-regulated and 4490 down-regulated unigenes in G3 vs. G2. A total of 323 up-regulated and 820 down-regulated unigenes were shared in three growth stage comparisons. We screened the genes that encode the enzymes most likely to inhibit bornyl diphosphate synthase, bornel dehydrogenase, and BAHD acyltransferases. Interestingly, we found that borneol dehydrogenase and bornyl diphosphate synthase displayed bi-substrate features, suggesting that a substrate of catalyzation is promiscuity in the biosynthesis downstream pathway, and the unknown bornyl pyrophosphate hydrolase may not be the specific enzyme for borneol formation. Additionally, the DXR, HDS, and IDS found in the PPI network would assist in the understanding of molecular regulation. The results of this study constructed DGE libraries and identified key genes related to camphane volatile terpenes, which laid a foundation for a deep investigation of the mechanism of volatile terpene biosynthesis, and provided a reference for mining other key genes in _A. villosum_ fruits.  
biosynthesis pathway; camphane volatile terpene; transcriptome +
Footnote †: journal: plants  
0000-0002-2341-1767
# Examining the Optimal Use of WBG Devices in Induction Cookers
## 2 Power Semiconductor Selection for Series Resonant Half-Bridge Converter
### WBG-Based Power Semiconductors Selection  
Wide-band gap power semiconductors have been popular for modern power electronics applications such as EV (Electrical Vehicle) charges, renewable energy systems, traction inverters, PFC (Power Factor Correction) circuits, and motor drivers. While the requirements of modern systems have been getting more challenging, Si-based power semiconductors, which are already within their theoretical limits, are not able to live up to those demands. The advantageous characteristics of wide band gap power semiconductors, such as high PCB (Printed Circuit Board) density, lack of bulky heatsinks, and cooling fans, are just a few of them. As Si-based power semiconductors get closer to their theoretical limits, new searches focus on wide band gap semiconductors. Table 2 compares the basic chemical characteristics of Si and WBG semiconductors.  
In series resonant half-bridge converters, the maximum voltage stress of power switches is 325 V. Therefore, a power semiconductor whose voltage rating is 650 V can be selected. To compare with Si-based power semiconductors, GS66516B from GaN Systems is selected in order to examine the usage of WBG power semiconductors in domestic induction heating applications. It can switch on and off at tremendous speeds that Si-based devices cannot reach. Its voltage strength is 650 V, which is very well suited for grid-connected series resonant half-bridge converters. The electrical characteristics of selected power semiconductors are shown in Figure 6a-c.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **Unit** & **Si** & **6H-SiC** & **4H-SiC** & **GaN** & **Diamond** \\ \hline Thermal Conductivity Band Gap & [W/cmK] & 1.5 & 4.9 & 4.9 & 1.3 & 22 \\  & [eV] & 1.12 & 3.03 & 3.26 & 3.45 & 5.5 \\ Breakdown Field Dielectric Constant & [MV/cm] & 0.3 & 2.5 & 2.2 & 3.3 & 20 \\  & 11.9 & 9.66 & 9.7 & 8.5–10.4 & 5.5 \\  & [cm\({}^{2}\)/Vs] & 1500 & 400 & 800 & 2000 & 1060 \\  & [10\({}^{7}\) cm/s] & 1.02 & 2.0 & 2.0 & 2.2 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Semiconductor Material Comparison.  
Figure 6: _Cont._
## 4 Spectrogram
The scan parameter of d-scan is the thickness \(d\) (or the dispersion) of the wedges, the measured trace is \(S_{\text{d-scan}}(\omega,\,d)\). To get an effective trace, the inserted medium should introduce a sufficiently large dispersion to influence the SHG spectrum, so d-scan is suitable for pulses those with broad bandwidth, especially few-cycle pulses. Learned from spatial ptychography, [PERSON] [52] developed a pulse measurement technique: TDP. TDP uses a correlation setup similar to SHG-FROG but one of the arms is spectrally filtered. All the spectrogram techniques (both FROGs and generalized spectrograms) use a similar method: firstly, the test pulse \(E(t)\) is spectrally modulated with a scan parameter \(\xi\), yields the modulated field  
\[E_{\text{m}}(t,\xi)=\mathcal{F}\ ^{-1}[\mathcal{M}_{\xi}[\mathcal{F}[E(t)]]], \tag{8}\]  
where \(\mathcal{M}_{\xi}\) is the spectral modulation operator, \(\mathcal{F}\) and \(\mathcal{F}^{-1}\) represent the Fourier and inverse Fourier transforms. For example, the delayed pulse in SHG-FROG can be expressed as \(E_{\text{m}}(t,\xi)=\mathcal{F}^{-1}[\exp(\text{i}\omega\tau)\mathcal{F}[E(t)]]\). Secondly, a nonlinear effect is applied to \(E_{\text{m}}(t,\xi)\) and \(E(t)\), generate a new field  
\[E_{\text{nl}}(t,\xi)=\mathcal{N}[E_{\text{m}},E], \tag{9}\]  
Figure 3: Schematic of the GRENOUILLE beam geometry. Top view: Time-to-space encoded scanning geometry. Side view: sum-frequency spectrum generation by angle-independent phase-matching. where \(\mathcal{N}\) is the nonlinear operator. In the viewpoint of frequency-domain, the time-domain nonlinear is equivalent to a spectral convolution. Finally, make a spectral measurement to \(E_{\text{nl}}(t,\xi)\), obtain the trace  
\[S(\omega,\xi)=|\mathcal{F}[E_{\text{nl}}(t,\xi)]|^{2}. \tag{10}\]  
The retrieval algorithm is to get \(E(t)\) by solving \(S(\omega,\xi)\). Basing on this common characteristic, [PERSON] et al. [56] developed a common pulse retrieval algorithm for this kind of methods.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 4 Thermal Analysis
### Parameters of Working Condition in Thermal Analysis
#### 4.2.1 Condition I, High Temperature Condition  
* Solar constant is at its peak, 1412 W/m\({}^{2}\) (at December solstice)
* MLI made of polyimide, at the end of its life, \(\alpha\)s/\(\varepsilon\) = 0.64/0.69 where \(\alpha\)s means solar absorptance, \(\varepsilon\) means emissivity;
* At the end of the life of the radiation region coatings, \(\alpha\)s/\(\varepsilon\) = 0.36/0.87;
* All instruments onboard are working at their maximum power consumption;
* Active thermal control is working well to meet the design requirement.
## 2 Materials and Methods
### Antioxidant Capacity
#### 2.7.3 Assessment of TAC via FRAP Assay  
To evaluate TAC, the FRAP assay was applied [28; 29; 30]. This assay relies on the reduction of TPTZ-Fe\({}^{3+}\) to TPTZ-Fe\({}^{2+}\). In a 96-well plate, 80 \(\mu\)L of FRAP Reagent and 20 \(\mu\)L of the sample solution were combined. The 96-well plate was kept in the dark for 30 min, and the absorbances were measured at 595 nm using a spectrophotometer (SPARK, TECAN, Mannedorf, Switzerland). The TAC was determined using a standard FeSO\({}_{4}\) curve, and the results were expressed in millimoles of Fe\({}^{2+}\) per gram of dry weight of sample (mmol Fe\({}^{2+}\)/g dw). The analysis was conducted in triplicate.  
2.7.4. In Vitro Digestion Analysis  
An adapted static in vitro digestion model, based on the [PERSON] et al. [29] method as previously described by [PERSON] et al. [31], was performed to simulate the gastrointestinal digestion process and to predict the bioavailability of total antioxidants and total phenolics. Specifically, 2 mL of the sample solution was dispensed into 6-well plates, along with 100 uL of porcine pepsin solution (0.2 g of pepsin, 5 mL of HCl 0.1 M). The samples were incubated in a shaking incubator (Shaking Incubator SKI-4, China) at 37 \({}^{\circ}\)C for 2 h. After incubation, the appropriate membrane was introduced into each well, followed by the addition of 2 mL of piperazine-N,N'-bis(2-ethanesulfonic acid) (PIPES) buffer reagent (0.15 M), adjusting the pH of the sample to 6.3. Subsequently, the samples were further incubated (1 h, 37 \({}^{\circ}\)C). Afterwards, 0.5 mL of a pancreatin and bile salts solution (0.02 g of pancreatin, 0.12 g of bile salts, 10 mL of 0.1 M NaHCO3) was added to each well, followed by additional incubation (2 h, 37 \({}^{\circ}\)C). The supernatant fraction that was released during the simulated digestion and contained the high-molecular-weight compounds was centrifuged at 4500 rpm (15 min, 4 \({}^{\circ}\)C). The TAC and TPC were determined using the FRAP and Folin-Ciocalteau assays, respectively, as previously described. Finally, the predicted bioavailability of the TAC and the TPC were calculated according to Equation (1):  
\[\%\text{ predicted bioavailability}=(\text{C}_{\text{f}}/\text{C}_{\text{i}})\times 100 \tag{1}\]  
where C\({}_{\text{f}}\): post-digestion concentration of a compound in the supernatant fraction, and C\({}_{\text{i}}\): pre-digestion, initial concentration of the same compound, as suggested by [PERSON] et al. [32].