esa-sceva/satcom-chunk-collection huggingface dataset:
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 3 System under Consideration
### Converters' Controllers  
In the considered grid-tied wind energy system, there are two main current controlled converters, which are the diode rectifier-boost converter at the machine side and the three-phase voltage source inverter at the grid side. Four control variables need to be tightly controlled, which are the generator active power, the DC-link voltage and grid active and reactive powers. This is done by the MSC and GSC controllers as shown in Figure 5.  
* The rectified voltage produced by the diode rectifier \(V_{r}\) varies with generator speed. However, the DC-link voltage must be regulated at a constant value in order to maintain energy balance at the DC-link; thus, active power transfer to grid is guaranteed. DC-link voltage control is carried out by GSC during MPPT mode, then it is swapped to the MSC during LVRT mode, as will be discussed later.
* Generator power is controlled via the MSC PI controller, which forces the boost chopper current \(I_{b}\) to follow a command value \(I_{b}^{*}\). This reference signal depends on system operating mode, i.e., at MPPT mode it is determined to satisfy maximum power extraction from the wind turbine, whereas during low voltage it is chosen to extract the active power adequate to just regulate the DC-link voltage.
* Grid synchronization during different operation modes is achieved via the GSC PI controllers, which control the active and reactive powers injected into the grid by controlling the grid current \(d\) and \(q\) components. This control strategy is the \(dq\) synchronous reference frame control, which uses a reference frame transformation, from \(abc\to dq\) to transform the grid current and voltage waveforms into a reference frame that rotates synchronously with the grid voltage using the [PERSON]'s and Clark's transformations. Transforming the AC quantities into DC values leads to easier filtering and control. However, these transformations require the instantaneous grid  
Figure 4: Operation modes of system under consideration.  
voltage phase-angle \(\theta_{u}\), which is detected by synchronizing the PLL rotating reference frame to the utility-voltage vector [44].
## 4 Pancreatic Cancer
### Microflora in Pancreatic Adenocarcinoma
Since then, a number of studies have examined the correlation between oral microbiota and pancreatic cancer. The abundances of specific salivary bacteria, such as the ratio between _Lepotrichia_ and _Porphylomonas_ numbers, were believed to be useful biomarkers for early-stage pancreatic cancer; the oral microbiota of PDAC patients was reported to be enriched in _Lepotrichia_ and poor in _Porphylomonas_ and _Neisseria_[80]. _Leptotrichia_ has been isolated from patients suffering from colorectal cancer, gastrointestinal ulcers, and systemic infections; however, its role in diseases is not clear [84]. Remarkably, antibodies to _Porphylomonas gingivalis_ have been directly associated with pancreatic cancer development. [PERSON] et al. also suggested that a decrease in _Porphylomonas_ in the oral microbiota of PDAC patients might result from an extensive immune response against those bacteria [83]. _Porphylomonas gingivalis_, _Tannerella forystthia_, and _Treponema denticola_, collectively called the red complex, are major pathogens responsible for chronic periodontitis. Those bacteria are believed to promote PDAC development via peptidylarginine deaminase secretion, which might lead to p53 and K-ras point mutations by degrading arginine [85]. Moreover, _A. actinomycetemcomitans_, another periodontal pathogen related to pancreatic cancer, was reported to initiate the Toll-like receptor signalling pathways. TLR activation has been shown to be tightly correlated to pancreatic carcinogenesis development in animal models [86]. Compared with healthy subjects, oral microbiota samples obtained from PDAC patients exhibited a significant decrease in commensal bacterial genera such as Fusobacteria _Corynebacterium_, as well as species such as _Neisseria elongata_ and _Streptococcus mitis_, and an increased abundance of _A. actinomycetemcomitans_ (aggressive periodontitis) and _Granulicatella_adiacens_ (bacteremia and infective endocarditis), as well as _Leptotrichia_, whose number normally increases in cancer patients, and _Bacteroides_ genus [84,86] (Table 4). Oral microbiota and its microbiome can influence pancreatic cancer development. Bacteria influence neoplastic changes by production of metabolites that lead to production of tumorigenic agents, induction of immune system, and displacement of commensal bacteria. Changes in gut microbiome composition are being reported in pancreatic cancer patients, however, the exact impact of dysbiosis in oral microbiome needs further investigation [84,86].
## 3 Results
### Simulated Datasets
#### 3.2.1 Relationships among the PK Parameters  
In order to identify the relationships among the PK parameters, PCA was performed on the simulated BE datasets. In order to facilitate the clarity of the plots, PK parameters of secondary importance, like Cavg and Cmax/Cavg, were excluded from this analysis. Figure 6 shows the biplots for the three sampling schemes: sparse (Figure 6a), typical (Figure 6b), and dense (Figure 6c). In all three cases, almost similar results were obtained. Additionally, the findings from the simulated datasets are in line with those derived from the actual donepezil data (Figure 2). AS and Cmax/Tmax show similar performances (they are superimposed) and are almost the opposite of Tmax (the angle formed between them is close to 180\({}^{\circ}\)). This attribute remains constant regardless of the type of sampling scheme and verifies the opposite behavior between AS (or Cmax/Tmax) and Tmax. The  
Figure 5: Simulated concentration vs. time profiles of donepezil. **(a)** A typical sampling scheme is used for the three different absorption kinetics (slow (0.5\(\times\)), typical (1\(\times\)), and fast (2\(\times\))). **(b)** Three different sampling schedules (sparse, typical, and dense) are shown in the case of typical (1\(\times\)) absorption kinetics. The terms 0.5\(\times\), 1\(\times\), and 2\(\times\) refer to the number of times the absorption rate constant used in simulations is greater or less than the one observed in actual data. All other pharmacokinetic parameters were kept equal to those found from population pharmacokinetic modeling. A number of 1000 subjects was simulated in each case.  
latter underlines the desired characteristics of the two newly proposed metrics (AS and Cmax/Tmax) for the rate of absorption. The descriptive ability of the PCA models is equal to 75.24% for the sparse case (Figure 6a), 73.30% for the typical case (Figure 6b), and 78.02% for the sparse design (Figure 6c). Two principal components were identified in all three cases, and the scree plots are depicted in Figure A1 in the Appendix A.  
Figure 6: Principal component analysis for the pharmacokinetic parameters estimated from the simulated bioequivalence studies The results refer to the three simulated sampling schemes: (**a**) sparse, (**b**) typical, and (**c**) dense. Left panel: Biplot of the two principal components displaying the individual scores and the loadings (blue lines). Right panel: Loading values in the case of the first and second principal components (PC1 and PC2). A description of the pharmacokinetic parameters is provided in Table 1.  
To focus on the role of the sampling scheme, which may have the greatest influence on the calculation of Cmax and Tmax, the simulated data were divided into three categories: sparse, typical, and dense. In each category, the simulated data of the scenarios with different absorption kinetics were merged. The PCA results presented above refer to each pre-referred dataset. Furthermore, in Figures 11-12 of the Appendix A, PCA analyses are shown separately for each absorption scenario.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services
### Network Support  
The present-day 3 GPP releases are offering next-generation (5G) communication-based network support for addressing V2X services. In fact, the 5G design and architecture allow huge network efficiency and traffic capacity with the lowest latency in comparison to the 4G communication technology. Since, the 5G system includes a 5G core (5 GC) and 5G access network (5G-AN), the 5G network support is divided into core network support and access network support. We now focus on the important parts of the core and access network support to equip the readers with the latest information regarding the support for V2X services.
## 2 Materials and Methods
### Genomic DNA Analysis  
To determine the plasma-induced changes in the genome of _A. oryzae_, the whole genome was sequenced post plasma treatment. The fungal spores (300 mL, 10\({}^{6}\) fungal spores per mL) were treated with plasma for 5 and 20 min or left untreated (control). The spore suspension (50 mL) was transferred to a 100 mL Erlenmeyer flask and incubated at 30 \({}^{\circ}\)C while shaken for 48 h. The fungal mycelia were harvested, washed twice with DI water, and stored at \(-80\)\({}^{\circ}\)C until use. The genomic DNA was extracted using a high-salt cetyltrimethylammonium bromide (CTAB) extraction protocol [26], and its concentration was measured using a NanoDrop(tm) system (Biotek Instruments, Winooski, VT, USA).  
DNA library construction and sequencing were carried out by Macrogen (Seoul, Korea). For library construction, the genomic DNA was randomly fragmented, and 5\({}^{\prime}\) and 3\({}^{\prime}\) adapters were ligated to the DNA fragments. Adapter-ligated DNA fragments were amplified using PCR and purified on gel. The DNA library was loaded into a flow cell and captured using oligos complementary to the library adapters on the surface of the flow cell. Each DNA fragment was amplified into clonal clusters using bridge PCR amplification. Sequencing was performed using Illumina SBS (sequencing by synthesis) technology (next-generation sequencing) (Macrogen, Seoul, Korea). The sequence reads were aligned to each other to identify variants.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Genes**} & **Primer sequences** \\ \hline
**Vesicle trafficking and transport** & \\ GTPase (ER to Golgi), SAR1 homolog & Forward-5\({}^{\prime}\)CGAAGTGAGCGGTATCGTTTG\({}^{\prime}\) \\  & Reverse-5\({}^{\prime}\)CCCTTCTGTGCTGGTA3\({}^{\prime}\) \\  & Forward-5\({}^{\prime}\)TGATGGAAGAGTGAAGC3\({}^{\prime}\) \\  & Reverse-5\({}^{\prime}\)TTGACACCCTCAGTGGCATA3\({}^{\prime}\) \\
**Reference gene** & \\
18S ribosomal RNA & Forward-5\({}^{\prime}\)GGAAACTACACAGGTTCCAGA3\({}^{\prime}\) \\  & Reverse-5\({}^{\prime}\)AGCCGATAGTCCCCTAAGA3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of primers used in the quantitative polymerase chain reaction (qPCR).
## 3 Results and Discussion
### Spectral Characteristics Overview for Samples after 23 Days of Storage Measured with FT-FIR
The \"three-legged\" molecules can fit together in different ways, such as double-chain packing and triple-chain packing. The different polymorphs are the most apparent from a top view of the planes, which show the subcell structure [3], and in this way, three polymorphic forms, i.e., \(\alpha\), \(\beta^{\prime}\), and \(\beta\) type, can be defined. The current study is novel due to the following reasons:  
1. Terahertz spectroscopy, which is much safer for operators than X-ray examination, can be a novel emerging tool to monitor cocoa butter crystallization. Previous research studies identified the types of \(\alpha\), \(\beta^{\prime}\) (III), and \(\beta\) (V) based on XRD [8; 39; 40; 42; 43; 45], the information based on identifying polymorphs cocoa butter [\(\alpha\), \(\beta^{\prime}\) (III), and \(\beta\) (V) type] using terahertz spectroscopy has not been comprehensively exploited. This study may provide a useful fingerprint of crystallization with different phases. 2. Previous studies elaborated the general information of how each phase of crystallization formed: rapidly freezing the melted cocoa butter and storing it for several minutes to an hour at 0 \({}^{\circ}\)C may obtain \(\alpha\) type products [8]. The transient metastable \(\gamma\) and \(\alpha\) phases were generated if the temperature was below \(-15\)\({}^{\circ}\)C [39]. This study, for the first time, provides the specific processing temperature and cooling rate that respond to each type of crystallization, which is of practical use for the tempering control for chocolate manufacturers. 3. Due to the attractive transmission capability, chocolate even packed in films or paper with high transmittance of terahertz wave, can still be utilized by using terahertz spectroscopy [50]. As stated above, the employment of terahertz spectroscopy in chocolate mostly emphasized detecting foreign bodies and quality in the packed chocolates [34; 35; 36; 37; 38]. It is for the first time to detect cocoa butter polymorphs of \(\alpha\), \(\beta^{\prime}\) (III), and \(\beta\) (V) by terahertz measurement, moreover, as the XRD spectrograph acquired in 4-5 min per sample during measurement whilst terahertz spectroscopy method needs at most 3 min per sample to identify the different polymorphs, those offer rapid detection and useful information to control cocoa butter crystallization, which is essential to chocolate factories.
# Analysis and Design of a Minimalist Step Climbing Robot
## 3 Design and Modeling
### Locus of the Center of Wheel While Ascending/Descending the Stairs
#### 3.2.1 Case 1: Radius of Wheel Is Greater than Height of Step  
Figure 4 shows the schematic of the condition where the radius of the wheel is greater than the height of the obstacle.  
\[OA = R_{w}-h_{s}\] \[AB = \sqrt{OB^{2}-OA^{2}}\] \[= \sqrt{R_{w}^{2}-(R_{w}-h_{s})^{2}}\]  
\(R_{w}=\) Radius of wheel  
\(h_{s}=\) Height of step  
\(\omega=\) Angular velocity of the wheel  
Let \((x_{o},y_{o})\) be the coordinates of point \(O\) (center of wheel) and \((x_{i},y_{i})\) be the coordinates of the vertex of the step, where \(i\) is the step number on which the wheel is climbing.  
\[y_{o}=\begin{cases}R_{w},\text{if }AB>x_{o}-x_{i}\\ \sqrt{R_{w}^{2}-(x_{o}-x_{i})^{2}},\text{if }AB\leq x_{o}-x_{i}\end{cases} \tag{2}\]  
Equation (2) illustrates that the center of the wheel moves in a straight line with zero slope until the horizontal distance between the wheel center and vertex of step is less than \(AB\), i.e., when the wheel touches edge of the step. After that center of the wheel moves in a circular path, with \((x_{i},y_{i})\) as its center and \(R_{w}\) as the radius of the circular path.  
Once the wheel starts to move in a circular path, the step count is incremented to the next step and the cycle continues until the last step.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Satellite Imagery  
RapidEye is a constellation of Earth-observing satellites that provide sun synchronous imagery from an orbit height of 680 km with a swath width of 77 km and a 5 day revisit time. RapidEye imagery has a spatial resolution of 5 m and provides data in 5 broad bands (blue = 440-510 nm, green = 520-590 nm, red = 630-685 nm, red edge = 690-730, and near-infrared 760-850 nm) (Figure 1). Four RapidEye (Planet, San Francisco, CA, USA) satellite images that included the study site were acquired from the 28 November 2015, 10 December 2015, 20 December 2015, and 20 February 2016. These were the only suitable cloud-free images available during the duration of the experiment. Level 3A RapidEye images were used as an input with a pixel size of 5.0 m. The images were radiometrically and geometrically corrected by the supplier. A noise filter and top-of-atmosphere (TOA) dark object subtraction were applied to each image. The residual geographic error was corrected by manually geo-registering all images to a reference UAV image using a polynomial warping method (ENVI 4.5,Harris, Boulder, CO, USA). The total RMS Error after applying the correction across the entire scene was 0.29 m.
## 5 Conclusions  
In this paper, we implement several experiments to investigate the physical layer security of a relay transmission model with different relay behaviors. Experimental results are compared with a theoretical analysis. We find a gap between the ideal theoretical simulations and the non-ideal real experiments:  
1. The relays need more power to achieve the maximum-security performance in a real environment. In the experiment, the optimal relay power is larger than the source power in both cooperative and selfish behavior relay scenarios because of the non-ideal source to destination (S-D) channel;
2. In the malicious behavior relay case, the experimental value of \(P(C_{S}>0)\) is different from the ideal theoretical simulation result when the relay power is larger than the critical power (\(P_{r}/P_{S}=5\) dB), which indicates that when s malicious relay forwards high enough noise power, the eavesdropper channel is deteriorated more seriously than the main channel;
3. Optimal relay location in both cooperative and selfish behavior of relay is in the middle of source and destination (\(d_{SR}/d_{SD}=0.5\)) in the ideal theoretical simulation. However, in the experiment, the optimal relay location is nearer to the destination because of the non-ideal source to relay and to destination (S-R-D) channel.
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model
#### 3.3.2 Ellipsoidal Informed Sampling and Goal Node Configuration  
In the BIT* algorithm, the ellipsoidal informed sampling region is dynamically adjusted based on the positions of the start and goal nodes. The semi-major axis of the ellipsoid is determined by the current best path cost \(c_{i}\), while the semi-minor axis is calculated from the minimum cost \(c_{min}\), between the start and goal nodes. This dynamic adjustment mechanism ensures that the sampling region remains focused on areas likely to contain the optimal path, thereby enhancing search efficiency.
## 4 Materials and Methods
### Methods
#### 4.3.2 Model Validation  
We randomly selected 80% of the measured samples for modeling analysis and 20% for model verification. This study used the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess model accuracy. R2 indicates the proximity of the observed values to the fitted regression line or the proportion of variance explained by the predictors. RMSE calculates the error between predicted and measured values. MAE calculates the average absolute differences between predicted and measured values. The closer R2 is to 1, and the smaller the RMSE and MAE, the stronger the model's estimation ability and the higher its accuracy and stability. The calculation formula for _MAE_ is as follows:  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|Y_{i}-Y_{i}^{\prime}\right|} \tag{1}\]  
where \(Y_{i}\) is the measured grassland AGB. \(Y_{i}^{\prime}\) is the simulated grassland AGB. \(\overline{Y}\) is the average value of measured grassland AGB. \(N\) is the total number of sample points.
# Antioxidant Production in _Dunaliella_
## 4 Tuning Antioxidant Enzyme Activity in _Dunaliella_
### Temperature  
Temperature affects the microalgal growth rate and the biochemical compositions as the cells' ability to assimilate nutrients are reduced [134], which can cause intracellular stress.  
Cultivation at low temperature (13 \({}^{\circ}\)C) together with low light irradiation (20 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\)) was found to enhance SOD, APX, MDHAR, DHAR activity in _D. salina_ compared to the unstressed controls [135], whilst cultivation at a very low temperature (5 \({}^{\circ}\)C) decreased CAT activity [136]. Less carotenoid and ascorbate contents was also observed when the cells were cultivated at extremely low temperatures [135]. _D. salina_ exposed to low temperature and cultivated under a high light intensity (100-1200 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\)) was found to increase SOD, MDHAR, GR, APX and POD activity relative to unstressed cells. High light intensities provide saturated CO\({}_{2}\), absorption and the low temperature decreased the rate of CO\({}_{2}\) absorption, which induced photo-oxidative stress and resulted in increased enzymatic activity [35]. SOD, APX, DHAR, activity were increased in _D. salina_ (IR-1) when cells were grown for 2 days under high temperature (28 \({}^{\circ}\)C) under light irradiation ranging between 100-1200 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) compared to a different strain (Gh-U) [35]. The different oxidative stress responses between two strains could result from variations in resistance to low temperature and high light. Only a few studies describe the effect of temperature on antioxidant enzyme responses in _Dunaliella_, and further investigations are required to explore the effect of temperature on antioxidant enzyme activity.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 4 Simulations
### Usable Harvested Energy  
In mobile scenarios, the node may leave the RF charging station before the capacitor voltage reaches \(V_{max}\) required to boot the node, and any residual charge will start to gradually dissipate until the node visits another RF station. In the most extreme scenario, when the node always leaves the RF station before the capacitor is fully charged, all harvested energy will be dissipated between successive partial charges.  
We define usable harvested energy as the total amount of harvested energy less the amount of energy spent on charging a capacitor prior to node startups, ignoring the energy harvested while \(V_{cap}<V_{max}\). In other words, usable harvested energy is the total amount of harvested energy that is actually available at an application layer, when the device is already active and ready to operate and until the capacitor voltage stays above \(V_{cap}>V_{min}\). In the experiment below, we show that usable harvested energy may constitute only a fraction of total harvested energy due to losses, which increase with the capacitor size.  
The first experiment demonstrates the impact of capacitor size on the total usable harvested energy. The hybrid configuration combined a small 10-mF capacitor with a larger capacitor in the 10-100 mF range. Figure 4 demonstrates that increasing the capacitor size from 10 mF to 100 mF reduces the amount of usable harvested energy by 40%. The hybrid approach in contrast shows consistent performance for various capacitor sizes. The usable harvested energy reduces slightly due to the time needed for an initial capacitor charge when the node switches to a large capacitor near a home station.