esa-sceva/satcom-chunk-collection huggingface dataset:
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 3 The Chemical Composition of Volatile Compounds in Berries
### Cranberry  
Cranberries (_Vaccinium_ spp.) are native to North America, and production has been highly increased due to their well-known health benefits of cranberries [107]. The unique cranberry aroma is developed during ripening [75]. In 1981, [PERSON] et al. detected 70 volatile compounds from European (_Vaccinium oxysporum_, L.) and American cranberries (_Vaccinium macrocaron_, Ait.) [82]. In this study, benzyl alcohol accounted for 29.2% and 21.6% in European and American cranberries, respectively. \(\alpha\)-terpineol was 13% and 9.7% of total volatiles in European and American cranberries, respectively. [PERSON] et al. identified 21 volatiles from fresh cranberries [77]. Common volatile compounds detected from wild (_Vaccinium oxysporum_ L.) and different cultivars of cranberries (_Vaccinium macrocaron_ Ait., 'Early Black', 'Ben Lear', 'Steven', Bergman' and 'Pilgrim') were 4-penten-2-ol, 3-cis-hexenyl formate, benzaldehyde, \(\alpha\)-1-terpineol, butyric acid, and benzyl alcohol. [PERSON] et al. analyzed the cranberry (_Vaccinium macrocaron_ Ait.) volatile composition of four cultivars ('Early Black', 'Howes', 'Searles', and 'McFarlin') by using GC-MS and GC-olfactometry (GC-O) [75]. A total of 33-36 volatiles were detected as odor-active compounds by GC-O. Hexanal, pentanal, (E)-2-heptanal, (E)-2-hexenal, (E)-2-octanel, (E)-2-nonenal, ethyl 2-methylbutyric, \(\beta\)-ionone, 2-methylbutyric acid, and octanal were mainly contributing to the cranberry aroma. [PERSON] et al. detected 54 aromatic compounds in cranberry juice [84]. Twenty-three alcohols (41.2% of total concentration) were predominant in cranberry juice, followed by eight acids (40.7%), ten aldehydes (1.7%), five ketones (2.2%), five ethers (1.4%), three lactones, and each heterocyclic and unidentified compound (less than 1% each). Among alcohols, benzyl alcohol was the major volatile, accounting for 23.1% of total volatile concentration. [PERSON] et al. detected 23 cranberry volatiles by using GC-MS [83]. In terms of total volatile concentration, Monoterpene (84%) was predominantly contained in cranberries, followed by aldehyde (8%) and alcohols (3%). The major volatile compounds were \(\alpha\)-terpineol, linalool oxide, eucalypt, trans-2-decanal, and 2-octanal. In cranberry volatile extract, 35 volatiles were found: 16 monoterenes, 8 alcohols, 6 aldehydes, 2 esters, 2 ketones, and an acid [81]. Monoterpenes (60%) were predominant in total volatile concentration. \(\alpha\)-terpineol, eucalypt, 2-methylbutyric acid, ethyl benzoate, citronellol, and linalool were the major individual volatile compounds in cranberry.
## 3 Operating Principle and Performance of DCM-CRM Cuk PFC Converter Utilizing VI Control
### Operating Frequency Analysis  
From Figure 6, the switching frequency of the proposed converter can be derived as  
\[f_{\text{S}}=\frac{1}{T_{s}}=\frac{1}{t_{\text{on}}+T_{off}}=\frac{(V_{\text{ C1}}-V_{o})\cdot V_{o}}{2I_{o}L_{2}V_{\text{C1}}} \tag{28}\]  
Based on (28), under certain output load conditions, the frequency change curve of the conventional Cuk PFC converter can be plotted as shown in Figure 12. As illustrated in Figure 13, the switching frequency of the conventional converter is solely dependent on the inductor \(L_{2}\). For a constant value of the input voltage \(V_{\text{in\_RMS}}\), an increase in \(L_{2}\) leads to a decrease in the switching frequency. Conversely, when \(L_{2}\) is fixed, the switching frequency increases with a rise in \(V_{\text{in\_RMS}}\). Too high a switching frequency can lead to high power losses, while too low a switching frequency can increase the size of the magnetic core. In this paper, \(L_{2}\) is set at 350 \(\mu\)H, and the switching frequency range of the conventional converter is 46 kHz to 60 kHz.  
According to Equations (27) and (28) and Figure 12, with the condition \(L_{2}=350\)\(\mu\)H and \(L_{1}=L_{\text{initial}}=170\)\(\mu\)H, the relationship curves of \(f_{\text{s}}\) and \(V_{\text{in\_RMS}}\) for both the conventional converter and the proposed converter are shown in Figure 13. It is evident that the proposed converter significantly reduces the switching frequency compared to the conventional converter. This reduction in frequency leads to decreased turn-off and turn-on losses in the switch, which contributes to an improvement in overall system efficiency.  
Figure 11: The relationship curves between RMS input voltage \(V_{\text{in\_RMS}}\) and \(V_{\text{C1}}\).
## 2 Model and Assumptions
### Assumptions  
1. The lifetime of each individual product set in normal condition follows the generalized inverted exponential distribution independently and identically, with the functions provided in (1).  
Figure 3: An illustration depicting the general progressive Type-II censoring.  
(2) The HRF of any product set in accelerated condition is given as \(H_{2}(x)=\beta H_{1}(x)\), in which \(\beta>1\) represents the acceleration factor.  
Then, based on the functions described in (1) and the proposed assumptions, PDF, CDF, SF and HRF of the lifetime of each unit under the accelerated condition can be expressed as shown below:  
\[\begin{split} f_{2}(x)=&\beta\alpha x^{-2}e^{- \frac{\lambda}{4}}\Big{(}1-e^{-\frac{\lambda}{4}}\Big{)}^{\beta\kappa-1},\\ F_{2}(x)=& 1-\Big{(}1-e^{-\frac{\lambda}{4}}\Big{)}^{ \beta\kappa},\\ S_{2}(x)=&\Big{(}1-e^{-\frac{\lambda}{4}}\Big{)}^{ \beta\kappa},\\ H_{2}(x)=&\beta\alpha\lambda x^{-2}e^{-\frac{ \lambda}{4}}\Big{(}1-e^{-\frac{\lambda}{4}}\Big{)}^{-1},\end{split} \tag{2}\]  
where \(x>0\); \(\alpha\), \(\lambda>0\); \(\beta>1\). Therefore, if the lifetime of the \(n_{j},j=1,2\) units follows the GIED with \(f_{j}(x)\) as PDF and \(F_{j}(x)\) as CDF, then according to [15], Page 10, the joint density function for \((X_{j,r_{j}+1},X_{j,r_{j}+2},\cdots,X_{j,m_{j}})\) with CS \((R_{j,r_{j}+1},R_{j,r_{j}+2},\cdots,R_{j,m_{j}})\) can be given by:  
\[L=\prod_{j=1}^{2}A_{j}\Big{[}F_{j}\Big{(}x_{j,r_{j}+1}\Big{)}\Big{]}^{r_{j}} \prod_{i=r_{j+1}}^{m_{j}}f_{j}\big{(}x_{ji}\big{)}\big{[}1-F_{j}\big{(}x_{ji} \big{)}\big{]}^{R_{ji}}, \tag{3}\]  
where \(x_{ji},j=1,2\), \(i=r_{j}+1,r_{j}+2,\cdots,m_{j}\), is the observed \(m_{j}-r_{j}\) failure times, and  
\[A_{j}=\frac{n_{j}!}{r_{j}!(n_{j}-r_{j}-1)!}\Big{(}n_{j}-R_{j,r_{j}+1}-r_{j}-1 \Big{)}\cdots\Big{(}n_{j}-R_{j,r_{j}+1}\cdots-R_{j,m_{j}-1}-m_{j}+1\Big{)}.\]
### Isoprenoids
#### 2.1.1 Biosynthesis of Isoprenoids
The next step of UQ synthesis is the formation of polyisoprenoid tail of polyprenyl pyrophosphate (PPPP) in mitochondria by solanesyl diphosphate synthase (SPS3), which is trans-polyphenyl diphosphate synthase, also called Coq1. This is one of the most important enzymes in UQ synthesis, since Arabidopsis AtSPS3 knockout mutants were embryo-lethal [20]. The most likely substrate for SPS3 in plants is farnesyl diphosphate (FPP), as evidenced by the reduced UQ level in loss-of-function FPP synthase mutants FPS1 and FPS2 [43]. The aromatic ring precursor for UQ biosynthesis is 4-hydroxybezoic acid (4-HB, Figure 1C), which is derived from either L-phenylalanine or L-tyrosine. Phenylalanine is converted to p-coumaric acid through the phenylpropanol pathway, which also precedes the synthesis of flavonoids in cytoplasm. Formed in this way, flavonoid kaempfer serves as one of the 4-HB sources [44]. The other pathway involves the conversion of p-coumaric acid into 4-HB by \(\beta\)-oxidative metabolism, in which p-coumaric acid is imported into peroxisomes [45]. In peroxisomes, 4-coumarate CoA ligases (AT4G19010 and 4 CL8) catalyze the formation of p-coumaroyl-CoA with subsequent 4-HB formation. Coq2, 4-hydroxybenzoate polytyphenyl diphosphate transferase (PPT1), is another important enzyme in UQ biosynthesis, which provides the catalysis of its rate-limiting stage [15]. PPT1 transfers the polyisoprenoid chain of PPPP to the 4-HB ring, generating the first lipophilic UQ intermediate, polyprenyl-hydroxybenzoate (PPHB) [21]. Further UQ biosynthesis requires subsequent hydroxylation of C1, C5, and C6 positions of the aromatic ring in PPHB structure catalyzed by flavin-dependent monooxygenes (Coq6 and CoqF) and S-adenosyl-l-methionine-dependent methyltransferases (Coq3 and Coq5) [46]. The co-expression of the genes involved in UQ biosynthesis in mitochondria as well as of the genes involved in 4-HB biosynthesis and the MVA pathway was shown in Arabidopsis [20; 47]. In the context of plants, both the regulation of UQ biosynthesis and the response of the genes encoding UQ biosynthetic enzymes to environmental stimuli are less studied than in yeasts and mammals [46].
## 2 Materials and Methods
### Image Segmentation  
Following previous work [24; 25] a segment-based image analysis approach was used as this was shown to negate single-pixel misclassification errors observed when carrying out a pixel-based classification approach, driven by speckle noise within SAR imagery. Specifically, ALOS-2 PALSAR-2 ScanSAR data is susceptible to speckle noise due to a relatively low number of ground target looks during data acquisition.  
The image segmentation procedure aggregates single pixels into clumps of neighbouring pixels with a similar backscatter response, thereby eliminating isolated pixels with spuriously high or low backscatter values that might be incorrectly classified as Inundated Forest [48].  
The Inundated Forest, Open Water, and other landcover training data masks for each tile (1794 in total) within the Central Amazon region underwent image segmentation using the Shepherd Segmentation Algorithm [48] within the Python library RSGISLib [50].  
When applying the Shepherd segmentation to the input ALOS-2 PALSAR-2 tiles, pixels within areas of open water tend to cluster into relatively large clumps due to the homogenous nature of backscatter characteristics of open water. This resulted in relatively few segments that could be used for training data generation. Furthermore, the pixel statistics from these clumps (e.g., the mean and standard deviation for HH and HV bands, etc.) are not statistically representative of the backscatter properties of open water, which tend to be much more variable (in the HH band). As such, this training data would under-classify the extent of open water. To negate this, the HV-band imagery was thresholded to create a low HV backscatter image and a high HV backscatter image. These images subsequently underwent image segmentation independently, forcing the segmentation algorithm to create many clumps within the low HV backscatter image and better replicating how the training data was derived. The low and high backscatter segmented images were then merged to create a single segmented image to be classified.  
For each segment, the mean and standard deviation pixel value were calculated for (a) HH, (b) HV, (c) NPDI, (d) Incidence Angle, (e) Slope, and (f) Height Above Nearest Drainage (MERIT), and added to a Raster Attribute Table as classifier predictor variables. The Raster Attribute Table for each training dataset was exported as a pandas dataframe [50]. The dataframes for each of the ALOS-2 PALSAR-2 tiles were concatenated to generate three training datasets, one for each class. This process generated 12.3 million training samples for Inundated Forest, 3 million for Open Water, and 21 million for other landcover. All training data samples were normalised using SciKit Learn Standard Scaler [51; 52] to ensure that all values for each predictor variable were within the same order of magnitude.
## 3 Pharmacokinetics and Pharmacodynamics of Xylazine  
The intramuscular or subcutaneous injection of xylazine in horses leads to rapid absorption and produces a fast onset of action with a short duration [78]. Xylazine is quickly metabolized by liver cytochrome P450, specifically cytochrome P4503A (CYP3A) [66; 79], and eliminated by the kidneys with a biological half-life ranging between a few minutes to three hours [40; 60; 80; 81]. The severity and duration of sedative or analgesic effects depend on the dose and the examined species [28; 82; 83; 84; 85; 86; 87]. Xylazine is 10-20 times more potent in ruminants than in other species [88]. Specifically, cattle are the most sensitive [81; 89]; the dose used is about 1/10 of that adopted in horses or dogs to achieve the same sedative or analgesic level [82]. The rapid absorption of xylazine and spread in the body lead to the accumulation in the brain and kidneys a few minutes after the intravenous administration compared to other organs. The usual dose in animals is 0.5-2.0 mg/kg [90]. The sedative effect may appear immediately (5 min) and last up to 4 h [49; 91]. Xylazine is also often used in animals for anesthetic premedication [92; 93; 94; 95]. Intramuscular administration of xylazine (0.6-1.4 mg/kg) in large animals, such as sheep, leads to the achievement of the maximum plasma concentration within 0.2\(\sim\)0.3 h [96]. The rate of excretion depends on the species considered. [PERSON] et al. (1981) [82] reported that less than 1% is excreted unchanged in cow urine and approximately 8% in rat urine. When injected into the subarachnoid space, xylazine causes prolonged analgesia with a long-lasting duration of action and antinociceptive effect [97], even though side effects may occur [98]. The analgesic effect is mediated by \(\alpha\)2-adrenergic receptors in the dorsal horn of the spinal cord [99; 100]. The agonism of \(\alpha\)2-adrenergic receptors present at the presynaptic axon terminals, exerted by xylazine, blocks the neurotransmitters release into the synaptic cleft [101]; thus, reducing the release, for instance, of norepinephrine and serotonin into the synaptic cleft and, consequently, the injection of xylazine produces sedation and relaxation in the CNS [11]. Furthermore, norepinephrine is involved in arousal and, most importantly, in regulating blood pressure through binding to \(\alpha\)-1 receptors on blood vessels, causing vasoconstriction [102]. This implies that a xylazine injection potentially impairs arterial blood pressure [103]. In humans, the clinical effects of \(\alpha\)-2 agonism are hypotension (sometimes preceded by hypertension), lethargy, drowsiness, hyperglycemia, bradycardia, and potential apnea requiring intubation. Table 1 summarizes the available pharmacological parameters of xylazine in humans [66].
## 3 Results
### Intraobserver Reliability  
All intraobserver reliability variables fulfilled the assumption of normality. Intraobserver reliability (ICCtotal = 0.93) was significantly (\(p<0.001\), \(\eta_{\mathrm{p}}{}^{2}=0.399\)) higher than interobserver reliability (ICCtotal = 0.79). When considering the total intraobserver ICC, excellent values were observed for 94% (29/31) of parameters, 3% (1/31) were fair to good and 3% (1/31) were poor (Table 1). Based on the lower limit of the 95% CI, 87% (27/31) of the parameters showed an ICC value of at least 0.75 (excellent reliability). For intraobserver reliability, the calculated standard errors (\(\sigma\)repeated) and SEM were smaller than those for interobserver reliability (\(\sigma\)repeated) (Table 3, Tabel A2); however, these values were not compared statistically. The highest error for intraobserver reliability existed for ankle dorsiflexion during the swing phase (% gait cycle) (\(\sigma\)repeated = 4.2%), as well as plantar flexion during the swing phase (mean \(\sigma\)observer = 2.5\({}^{\circ}\)).  
Intraobserver reliability across laboratories (between-laboratory intraobserver) was excellent in 29% (9/31) of all parameters, fair to good for 48% (15/31), and poor for 26% (8/31) (Table 1). The highest ICC (0.90) was observed for hip flexion during the stance phase (% gait cycle). The worst ICC (0) was calculated for knee abduction angle in the swing phase.
## 2 Materials and Methods
### Goodness-of-Fits: Fit Residuals  
The results of fitting the measured, simulated and calculated correlation functions across the different experimental phantoms and MC models were quantified by using a non-negative scalar value \(\chi^{2}\), as a measure of the goodness-of-fit. This was calculated as \(\chi^{2}=\sum_{m=1}^{M}\bigl{[}g_{2}^{meas}(\tau_{m})-g_{2}^{fit}(\tau_{m}) \bigr{]}^{2}\), where \(g_{2}^{meas}\) was the intensity correlation (measured or simulated) and \(g_{2}^{fit}\) was the fitted intensity correlation (using Equation (2) or simulated). A perfect fit would have \(\chi^{2}=0\), and thus the lower the \(\chi^{2}\) the better the goodness-of-fit. The index \(m\) corresponds to discrete correlation-time bins (as output by the hardware correlator using a multi-tau data accumulation scheme). For analysis here, there were a total of \(M=178\) logarithmically spaced bins (i.e., the correlation times were evenly spaced on a log scale), matching experimental data with \(\tau_{1}=1\) us and \(\tau_{M}=1\) s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer \#** & \(\mu_{a}\) (1/cm) & \(\mu_{s}\) (1/cm) & \(g\) & **Refractive Index, \(n\)** & \(z_{1}\) (cm) & \(\langle\Delta r^{2}(\tau)\rangle\) \\ \hline Layer 1 & & & & & \(h\) & \(6D_{B}\tau\) \\ \hline Layer 2 & 0.075 & 120 & 0.9 & 1.35 & 0.15 & \(6D_{B}\tau\) or \\ \hline Layer 3 & & & & & & \(V^{2}\tau^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optical coefficients used for simulations of three-layered tissue models.
# Kinetics of Hyaluronidazoleae Inhibition by Rice (_Oryza sativa_ L.) Protein Hydrolysate
## 1 Introduction  
Protein is among the 10 trends that most strongly drive the current functional food market. Protein is an essential amino acid (EAA) source and thus a functional component of health [1]. Enzymatic protein hydrolysis requires mild conditions, elicits few side reactions, and increases the extraction rate, ultimately providing free amino acids and peptides in a variety of sizes. Owing to their functional and nutritional properties, enzymatic protein hydrolysates may have clinical and dietary (e.g., protein supplementation) application [2].  
Rice protein hydrolysates and their mixtures used in many studies can be obtained by subjecting flours and protein concentrates or isolates to microbial fermentation or chemical or enzymatic hydrolysis [3]. In the food industry, enzymatic hydrolysis is preferred because during processing, no toxic chemical residues are produced. The sources of proteolytic enzymes include not only animals and plants but also microorganisms. Their varied specificities render each proteolytic enzyme able to cleave proteins at only certain peptide bonds [4]. The sequence and biological properties of the resulting peptides are also affected by other factors, such as protein source and reaction temperature, time, and pH. After the completion of hydrolysis, the subsequent steps applied are ultrafiltration and chromatography--all to obtain peptides or peptide fractions that are highly pure and have distinct molecular weights, hydrophobicities, and ionization states [5; 6]. Studies [7; 8; 9] havereported on the biological functions of grain and vegetable protein hydrolysates and derived peptides; the biological functions include antitumor, antithrombotic, antihypertensive, cholesterol-lowering, and antioxidant activities, and the grains and vegetables have included soybeans, corn gluten, rice, rice, rice bran, and potatoes. Being excellent sources of not only proteins but also biologically active substances, plant protein hydrolysates have potential utility as differing providers of protein and beneficial-to-health elements in some nutritional products [10].  
Hyaluronic acid (HA)--a glycosaminoglycan with high molecular weight--can increase skin hydration and lubrication and maintain skin plasticity, smoothness, and moistness [11]. Moreover, to obtain skin with high vitality, maintaining the skin's moisture and the stratum corneum's extracellular matrix is crucial. However, in the skin, hyaluronic constantly breaks down HA to result in drying and eventually sagging of the skin. Youthful skin can be thus achieved by inhibiting hyaluronic and facilitating the synthesis of matrix proteins [12].  
The use of protein hydrolysates from rice--which is the principal food crop for at least 50% of people globally--as a hyaluronic inhibitor has not been reported thus far. Therefore, here, a rice protein hydrolysate's utility for hyaluronic activity inhibition was evaluated, followed by the development of alternative natural cosmetic materials.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 5 Practical Test on USV
### Safe Distance Test  
The USV is tested in a small lake of approximately 104 \(\times\)140 square meters. We manually plan a path with four special goals for the USV on the Google Earth shown in Figure 22. The safe distance constant is set to 2 m, which means that the USV should keep  
Figure 21: Communication architecture of USV.  
Figure 20: USV and components.  
at least 2 m away from obstacles during the autonomous navigation. In addition, we adjust the output power of the two propellers and set the USV's velocity to 1 m/s.  
We set the start waypoint at the north riverbank of the lake. There are steps down the north riverbank that allow us to launch the USV. We set the second waypoint (_Goal1_) in the middle of the lake and the third waypoint (_Goal2_) at the side of the west riverbank. From Figure 22, we find that there are no obstacles along the straight lines between the first three waypoints. Finally, we set the final waypoint at the northwest of the lake, resulting in a 220.52 m navigation path. There is a long riverbank composed of stones between the third waypoint (_Goal2_) and the final waypoint. The USV must avoid this huge obstacle (riverbank) and keep a safe distance to the riverbank during the navigation. In addition, there is a bridge across the river. The USV must pass through the bridge before it can reach the final waypoint. The purple dots in Figure 22 represent the actual path that the USV sails. We find that the USV moves along the riverbank with a safe distance. During the navigation, the live video is transferred from the camera on the USV to the app. Figure 23a shows a picture on the app that illustrates that the USV is moving along the west riverbank of the lake. Figure 23b shows that the USV is passing through the bridge to reach the final waypoint of the planned path. The USV's GPS positions, speed and heading are all shown on the app. Users can also adjust the velocity by moving the slider on the bottom left of the screen. Furthermore, users can manually control the heading of the USV by changing the direction of the red arrow in the white circle. The Supplementary Videos (Video S2) show the entire procedure.