esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
###### Abstract  
The dynamic modeling, control, and simulation of renewable energy sources connected to the electrical grid are investigated in this study. Photovoltaic (PV) systems and wind systems connected to the power grid via the point of common connection (PCC) were the only two systems included in our study. Simulation and control methodologies are provided. For both PV arrays, the method of extracting maximum power point tracking (MPPT) is utilized to obtain the highest power under standard test conditions (STC: 1000 W/m\({}^{2}\), 25 \({}^{\circ}\)C). A power electronics converter that can transform DC voltage into three-phase AC voltage is required to connect a PV system to the grid. Insulated gate bipolar transistors (IGBTs) are utilized in a three-level voltage source converter (VSC). The distribution network is connected to this three-phase VSC by way of a step-up transformer and filter. During synchronous rotation in the \(d-q\) reference frame, the suggested control for the three-level solar power system that is connected to the grid is constructed. To obtain a power factor as near to one as possible, the phase-locked loop (PLL) is employed to align the angle of the power grid voltage with the angle of the current coming from the inverter. Squirrel-cage induction generators (SCIGs), which are utilized as fixed speed generators and are linked directly to the power network, are the foundation of the wind system. Additionally, a pitch angle control approach is suggested to keep the wind turbine's rotor speed stable. MATLAB/Simulink software is utilized to model and simulate the suggested hybrid system. Under fault scenarios such as the line to line to line to ground fault (LLLG fault), the suggested hybrid system's dynamic performance is examined. The simulation results prove the ability to manage the small hybrid system that combines solar and wind power, as well as its dynamic performance.  
PV; wind; hybrid system; MPPT; VSC; PLL; pitch angle control; LLLG fault +
Footnote †: journal: Applied Soft. 2023, 13, 1743.  
_Applied_  
_sciences_
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.3 Annealing Process  
The effective deployment of newly synthesized TL material for radiation dosimetry requires a well-identified annealing procedure to resensitize the dosimeter by eliminating the irradiation history. Therefore, the annealing procedure was optimized by applying different thermal treatments by varying the annealing time and temperature. Figure 8 presents an annealing temperature dependence of S35654 (MgB4O7:Zn glass with 0.4 mol% ZnO). The annealing temperature was restricted to 400 \({}^{\circ}\)C to avoid thermal damage to the glass, which could potentially alter the intrinsic defects that could affect the dosimeter sensitivity. The annealing time dependence of the TL response is shown in Figure 9. The synthesized glass was observed to be significantly resensitized, producing a significant TL response with acceptable standard deviation, when subjected to 250 \({}^{\circ}\)C for 45 min. The identified annealing process is practically and economically feasible for routine clinical radiation dosimetry, as compared to the dual-step annealing process of standard TLD-100 that requires annealing the dosimeter at 400 \({}^{\circ}\)C for 1 h followed by 80 \({}^{\circ}\)C for 24 h [18]. Moreover, the sensitivity of standard TLD-100 was reported to be vulnerable to the annealing process [36]. Therefore, a well-defined and simple annealing process is always desirable to resensitize the dosimeter without affecting its sensitivity.  
Figure 8: Effect of annealing temperature on TL response of MgB4O7:Zn glass S35654.  
Figure 9: Effect of annealing time on TL response of MgB4O7:Zn glass S35654.
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms
### Remote Sensing
#### 3.3.1 Passive Remote Sensing
The CubeSat mission SWEET (Sweet Water Earth Education Technologies) [54] and SeaHawk ([[https://uncw.edu/socon/seahawk.html](https://uncw.edu/socon/seahawk.html)]([https://uncw.edu/socon/seahawk.html](https://uncw.edu/socon/seahawk.html)), accessed on July 2021), addressing the gaps of lack of dedicated missions and high costs for vessels, is also a promising new technology for the monitoring of water quality with a revisit time of 3 to 5 days. Complementing these systems are a number of geostationary ocean color sensors (e.g. GOCI--Geostationary Ocean Color Imager) and meteorological sensors that demonstrated potentiality to observe turbidity dynamics (e.g. SEVIRI--Spinning Enhanced Visible and InfraRed Imager), and have the advantage of obtaining hourly to daily data allowing the investigation of ocean properties variations over a day [55; 56; 57; 58]. Integrating data from different sensors and missions is imperative to resolve the physical and biological complexity of most inland and coastal waters because of the combinedrequirements for high spatial, temporal, and spectral resolution data [59]. The observational characteristics of satellite data are highly complementary to those of in situ monitoring programs. They also enable indicative monitoring for a larger number of lakes where no other data sources are available. For example, satellite sensors are often the only means of acquiring regular water quality information in regions where issues of accessibility and lack of resources limit in situ monitoring, and make these regions scarcely sampled compared with more economically developed and more densely populated regions. Providing this synoptic perspective is critical to resource planning, decision making, and improved model predictions of future change [60; 61]. Yet, satellite observations alone cannot provide all information needed by policy makers and environmental managers to address inland and coastal water quality issues [62; 63; 64; 65]. The limitations of satellite remote sensing for water quality monitoring are the range of derived parameters and the inability to retrieve information from depth [66]. The performance of atmospheric correction models and algorithms in the optically complex waters also remains an issue for some applications where highly accurate products are required. Other much-quoted technical limitations concern spatial resolution and sampling gaps due to cloud coverage [67]. Slow adoption of satellite remote sensing data for water quality monitoring is also because of practical reasons, such as data continuity, and retrieval accuracy requirements [3]. Validation of satellite data products for inland and coastal waters is a challenge for scientists and concern for stakeholders. There are two aspects to this challenge for scientists: (1) acquiring and/or accessing suitable in situ data for calibration and validation of satellite measures and (2) minimize the propagation of errors from in situ data to satellite measures.
## 2 Results and Discussion
### Sensorial Analysis  
With regard to the aroma, the sensorial test revealed a wide range of odor impressions in the 17 varieties of chili peppers examined.  
The chili peppers belonging to the _C. chitinense_ species were mainly characterized by exotic, fruity, and/or sweet notes (Figure 7 and Figure S3 from Supplementary Materials), and their aromas were the most intense among all samples investigated. Such notes are due to the presence of numerous esters, especially 4-methylpentyl 3-methylbutanoate and 4-methylpentyl 2-methylbutanoate [20], Hexyl 3-methylbutyrate, (Z)-3-Hexenyl 3-methylbutyrate, and (Z)-3-Hexenyl 2-methylbutyrate. The dairy, buttery, and creamy notes found in some peppers, such as Naga morich, Habanero fatali, Naga chocolate, Trinidad scorpio moruga yellow, Habanero red savina, Scotch bonnet, and Habanero chocolate, are due to the presence of some ketones such as acetoin [21; 22], and especially medium-short chain fatty acids such as _n_-decanoic, which characterizes the base note of the Habanero fatali.  
Figure 6: Hierarchical cluster analysis based on relative percentage areas of the 118 most abundant identified volatiles. Original values are ln(x + 1)-transformed. Rows are centered; Pareto scaling is applied to rows. Imputation is used for missing value estimation. Columns are clustered using correlation distance and average linkage. There is a total of 44 rows and 20 columns.  
The sweet note of vanilla perceived in the Scotch Bonnet is confirmed from an analytical point of view by the presence of guaiacol, a compound that characterizes the vanilla beans [21; 22].  
Similarly, the note of \"wintergreen\" (_Gaultheria procumbens_) found in the Scotch Bonnet and Habanero Fatalii is confirmed by the presence of methyl salicate in the volatile profile [21; 22].  
In contrast, _C. annuum_ and _C. bacatum_ showed different intensity and aroma profiles with a medium-intense mixture of fruity and vegetable-like notes (Figure 7 and Figures S4 and S5 from Supplementary Materials) due to the presence of (_E_)-2-hexenal in both species, and also depending on isopentyl alcohol and _n_-hexanol in _C. annuum and C. bacatum_, respectively.  
The persisting final perceived note of cheese in _C. annuum_ is dependent on _n_-isovaleric acid [21; 22]. Additionally, in some chili peppers belonging to _C. bacatum_, such as the Banana pepper and Cayenna impala, dairy and blue cheese notes were found due to the presence of _n_-hexanoic acid, but they do not really affect the perceived aroma on the olfactory level.  
In the peppers belonging to the _C. bacatum_ species, the fruity notes are predominant, in particular the green and yellow apple typical of (_E_)-2-hexenal and pentyl isovalerate [21; 22], respectively, and the perceived aroma is generally light.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
###### Abstract  
Color distortion, low contrast, and blurry details are the main features of underwater images, which can have adverse effects on their quality. To address these issues, a novel enhancement method based on color correction and multiscale fusion is proposed to improve underwater image quality, achieving color correction, contrast enhancement, and detail sharpening at different stages. The method consists of three main steps: color correction using a simple and effective histogram equalization-based method to correct color distortion, decomposition of the V channel of the color-corrected image into low- and high-frequency components using a guided filter, enhancement of the low-frequency component using a dual-interval histogram based on a benign separation threshold strategy, and a complementary pair of gamma functions; the fusion of the two versions of the low-frequency component to enhance image contrast; and finally, the design of an enhancement function to highlight image details. Comparative analysis with existing methods demonstrates that the proposed method achieves high-quality underwater images and favorable qualitative and quantitative evaluations. Compared to the method with the highest score, the average UIQM score of our method exceeds 6%, and the average UCIQE score exceeds 2%.  
color correction; contrast enhancement; underwater image enhancement Article
## 3 LDPC Coding--Preliminaries
### Quasi-Cyclic LDPC Codes  
Many communication systems use the quasi-cyclic (QC) subclass of LDPC codes [15]. These codes have properties that make them suitable for efficient encoder and decoder designs, as well as for industrial implementations [17; 24; 35; 36]. The PCM of QC-LDPC codes has a distinct structure composed of circulant permutation matrices (CPMs):  
\[\mathbf{H}=\begin{bmatrix}\mathbf{P}^{s_{1},1}&\mathbf{P}^{s_{1},2}&\cdots& \mathbf{P}^{s_{1},C}\\ \mathbf{P}^{s_{2},1}&\mathbf{P}^{s_{2},2}&\cdots&\mathbf{P}^{s_{2},C}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{P}^{s_{R},1}&\mathbf{P}^{s_{R},2}&\cdots&\mathbf{P}^{s_{R},C}\end{bmatrix}, \tag{2}\]  
where \(\mathbf{P}^{s_{r,c}}\) is either a \(P\times P\) all-zero submatrix (by convention, it is indicated by \(s_{r,c}=\infty\)) or an \(s_{r,c}\)-CPM of size \(P\times P\), that is, a matrix obtained by cyclically shifting every row of an identity matrix \(\mathbf{I}_{P\times P}\) by \(s_{r,c}\) positions to the right [37]. The particular code can be unambiguously defined by its size and the values of \(s_{r,c}\in\{0,1,\ldots,P-1,\infty\}\), for macrorows \(r=1,\ldots,R\), and macrocolumns \(c=1,\ldots,C\).  
The design of QC-LDPC codes can be treated as an important special case of the protograph-based LDPC code construction process [38]. In essence, this construction process is based on the copying and edge permuting of a smaller graph (protograph, base graph) to achieve a larger Tanner graph of a QC-LDPC code.
## 4 Applications  
Soft strain sensors can be applied to wearable personal health monitoring devices to assist in injury prevention or disease diagnosis. Compared to membrane-based strain sensors, the fabric-based strain sensors are more convenient in monitoring human health.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 3 Experiment Results
### Accuracy Verification of the Static and Kinematic PPP
#### 3.3.2 The Kinematic PPP Tests  
In order to verify the effectiveness of the improved model in real-time kinematic PPP, two IGS stations (BJFS and CEDU) are used. The raw data is the 24-h observation of day 125, 2016 and the sampling interval is 30 s. The IGS final product of the orbit and clock are used for kinematic PPP solution and clock prediction. We assume that 1-h interruption in the real-time clock caused by an unknown reason occurs at the end of 5 h on day 125, 2016, and the real-time estimated clock is replaced by the prediction clock to realize the real-time kinematic PPP. The residuals of each station in E, N, U directions are calculated from the precision position published on the IGS website.  
Figures 9 and 10 show the time series variation of the kinematic PPP residuals of BJFS and CEDU. The accuracy of the first 30 min prediction is obviously better than that of the latter 30 min prediction, and many peaks stand out in the figure. The effect of data interruption on the kinematic PPP solution will continue for some time after the recovery of the real-time clock estimation. However, from Table 6, we can find that the absolute value of residuals in the E, N and U directions is better than 0.3 m, and the std. is better than 0.2 m. It can also be found that the N direction changed the most during the period of prediction. Compared with results of non-prediction, this result proves that the improved model can overcome the problems of interruption and delay in data broadcast in real-time clock estimation and can meet the requirements of real-time PPP.  
Figure 8: Precise point positioning (PPP) precision of different clock prediction products.
## 7 Conclusions  
The application of the HASP constitutive model for the numerical prediction of the soft overconsolidated clay behavior due to embankment loading is presented. A fully coupled flow-deformation analysis was performed in the PLAXIS 2D FEM software package. Additional numerical predictions were conducted using the Modified Cam Clay model for soft overconsolidated clay layers. The obtained results were compared to the field measurements, consisting of settlements, excess pore water pressure recordings, as well as ground surface settlement profiles.  
Taking into account the state of the soil and the overconsolidation ratio as important determinants of a soil's mechanical response, the hardening coefficient of the HASP model is introduced. There is no pure elastic domain, but the hardening coefficient reduces the plastic strains and controls all elements of the mechanical behavior of overconsolidated clays. Without explicitly taking into account anisotropy, destructuration and creep, excellent agreement is obtained between computed values and field observations.  
During the construction of the embankment, the HASP model can provide satisfactory settlement predictions, which are qualitatively and quantitatively consistent with the field measurements, while the MCC model fails to provide reasonable predictions. For these two constitutive models, significant differences in the predicted strain magnitudes are observed. Due to the overconsolidation of the surface layers, the MCC model predicts only elastic strains, while the hardening coefficient of the HASP model adequately controls the whole process of clay deformation. Thus, the overconsolidation ratio of the soft soil is the key parameter controlling the amount of deformation of the deposit under the embankment loading. On the other hand, deviations in predicting the development of pore water pressures are observed for Embankment B.  
Reliable numerical prediction of the mechanical behavior of embankments for supporting roads, railroads, or for flood protection is an integral part of the infrastructure management policy. The estimation of construction and postconstruction settlement of an  
Figure 24: Comparison of the settlement of the ground surface for Embankment B—day 1046.  
embankment is a very important serviceability issue, considering the need to maintain the embankment elevation at the designed level. Given that the parameters of the HASP model are obtained by standard laboratory tests, and that reliable predictions of soil deformations are achieved, the advantage over more complex models is clearly observed. From the results of this study, it can be concluded that the simple HASP soil model can be used for routine geotechnical analyses and conventional design procedures.  
Conceptualization, N.O., S.J. and M.V.; methodology, N.O., S.J. and M.V.; software, N.O.; investigation, N.O.; writing--original draft preparation, N.O.; writing--review and editing, S.J. and M.V.; visualization, N.O. and S.J.; supervision, S.J. and M.V. All authors have read and agreed to the published version of the manuscript.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Frequency Exclusion List Management  
Interference detection can be performed using real-time spectrum analysis. Frequencies exhibiting high noise levels or consistent transmission errors are added to a frequency exclusion list \(F_{\text{excl}}\). The system updates the set of available frequencies \(F_{\text{avail}}=F\setminus F_{\text{excl}}\) dynamically, where the operator \"\(\backslash\)\" denotes the set difference between two sets. This means that \(F_{\text{avail}}\) consists of all frequencies in \(F\) that are not in \(F_{\text{excl}}\). By removing the excluded frequencies from the full set, we ensure that only reliable frequencies are used for communication. To maintain randomness, the RPU can be informed of \(F_{\text{avail}}\) so that it only maps random numbers to permissible frequencies without altering the underlying entropy of the sequence.  
Interference Detection and Frequency Update Algorithm  
To ensure that randomness is preserved across the adjusted set of available frequencies, the mapping function within the Random Processing Unit (RPU) is modified to account for the updated set of frequencies, \(F_{\text{avail}}\), as described in Algorithm 3. This adjustment process involves normalizing the generated random numbers to match the range of \(F_{\text{avail}}\), thus maintaining a uniform probability distribution over the entire frequency set. The mapping is performed by selecting a frequency \(f_{o}\) from \(F_{\text{avail}}\) in a manner that corresponds to each random number \(R_{o}\), ensuring uniform randomness across the set. This process can be described as follows:  
\[f_{o}=F_{\text{avail}}\bigg{(}\frac{R_{o}}{R_{\text{max}}}\times|F_{\text{avail }}|\bigg{)} \tag{11}\]  
where:  
* \(R_{o}\) is the raw random number generated by the RPU,
* \(R_{\text{max}}\) is the maximum possible value of \(R_{o}\),
* \(|F_{\text{avail}}|\) is the cardinality (size) of the available frequency set \(F_{\text{avail}}\),
* and \(f_{o}\) is the selected frequency within \(F_{\text{avail}}\).  
This approach scales each random number \(R_{o}\) to a valid index within \(F_{\text{avail}}\), effectively normalizing the random output for uniform selection across the updated frequency set. This normalization ensures the distribution of frequency selections remains unbiased and uniformly spread over \(F_{\text{avail}}\).  
```
1:while Communication is Active do
2:for all Frequencies \(f\in F\)do
3: Measure interference level \(I(f)\)
4:if\(I(f)>I_{\text{threshold}}\)then
5: Add \(f\) to \(F_{\text{excl}}\)
6:else
7: Remove \(f\) from \(F_{\text{excl}}\) if present
8:endif
9:endfor
10: Update available frequencies \(F_{\text{avail}}\gets F\setminus F_{\text{excl}}\)
11: Inform RPU of updated \(F_{\text{avail}}\)
12:endwhile
```  
**Algorithm 3** Interference Detection and Frequency Exclusion
## 1 Introduction  
There has long been a tradition of using plants as medicines [1; 2]. In the latest generations, extensive investigation has been performed on the plants utilized historically or described in ancient literature. Plants grown for ornamental purposes include those used as households, cut flowers, specimen displays, and gardens and landscape design projects. Crops planted for ornamental and floral purposes can offer nutritional and therapeutic benefits. To improve the healing process that takes place in healthcare settings, there has been a resurgence of interest in the use of therapeutic garden surroundings with medicinal value. Several populations, particularly in underdeveloped nations, still depend on conventional therapeutic plant-based therapy in modern times. This is mostly owing to the scarcity and high cost of traditional medications in underdeveloped nations. Approximately 80% of people from developed nations use conventional medicine, which includes chemicals obtained from medical plants [3]. As a result, the use of bioactive components for pharmaceutical applications has gradually expanded worldwide. Flowering trees are now used in herbal medicine, and it is well known that they have healing properties.  
_Elaodendron_ is a genus in the Celastraceae family [4]. The Celastraceae belongs to the order Celastrales and consists of approximately 96 genera and 1350 species of herbs, vines, shrubs, and small trees [5]. The Celastrales is a flowering plant order that may be found in the tropical and subtropical regions, with just a few genera expanding into temperate areas. _Elaodendron_ is a genus of approximately thirty to forty species native to Africa, Bermuda, the Mexican coast, Madagascar (particularly Mascarene), Australia, Melanesia, and India [5,6]. This genus includes evergreen and, on rare occasions, deciduous shrubs and trees. The lenticies are frequently evident in the yellow pigment layers found in the bark. The leaves are either subopposite or opposite or rotate on occasion. The petals are cream to greenish, and the stamens are upright. The fruits are smooth-surfaced, drupaceous, spherical, white to yellow, and drupaceous. Reddish-brown seeds with succulent cotyledons are squashed [4,5].  
Flavonoids [4], terpenoids [4,7] cardiac glycosides [8], and cardenolides [9] have been isolated from these species, which are mainly shrubs and deciduous trees. Plants of this genus have been shown to have antiviral, anti-HIV, anticancer, antiproliferative, antioxidant, anti-inflammation, anti-plasmodial, cytotoxic, antifungal, anti-arthritic, and antibacterial properties in earlier research [10,11].  
The purpose of this article is to compile and analyze the scientific data that is currently available regarding pharmacological properties, bioactive chemical components, and traditional usage of _Elaodendron_ species based on online scholarly databases such as Scopus, SciFinder, PubMed, Google Scholar, and published books and proceedings. The existing gaps in the research on the _Elaodendron_ species are also reviewed, along with suggestions for the future.