esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.1.1 Antinflammatory Drugs  
There is a group of papers concerning the improvement of the production technology of different model drugs using polymer additives. The first direction for utilization of polymers is preventing the nucleation of crystalline drugs or precipitation of amorphous drugs. [PERSON] et al. tested homopolymers and copolymers of \(N\)-ethylmorpholine methacrylamide (EMA) and \(N\),\(N\)-dimethylacrylamide (DMA) hydrogels as matrices for aqueous ibuprofen (nonsteroidal anti-inflammatory drug) release to prevent possible damage of the mucous membrane of the stomach [45]. The most promising results were obtained for EMA hydrogels which were able to prevent crystallization of ibuprofen at all pH values. Crystallization of ibuprofen with the addition of poly(acrylic acid) (PAA), PVP or poly(4-vinylphenol) (PVPh) as polymeric additives was also studied by [PERSON] et al., showing that PVP was the most effective additive that prevented the crystal nucleation and allowed the sustained release of ibuprofen [46]. In another paper, the effect of functionalized poly(chloromethylstyrene-_co_-styrene) on the stability in amorphous solid dispersions of drugs was studied [47]. The authors found that hydrogen bonding between functionalized polymers and nabumetone (non-steroidal anti-inflammatory drug) can improve stability against crystallization. They also found that supersaturation maintenance for ethemzamide (analgesic and anti-inflammatory drug) was improved by increasing the hydrophobicity of dissolved poly(\(N\)-hydroxyethyl acrylamide) in an aqueous solution [48].
## 2 Nutritional Characteristics
### Phenolic Compounds  
Artichoke represents a great source of bioactive compounds such as phenolic compounds. These are secondary metabolites present in plants with a common structure: one or more phenol groups attached to an aromatic or aliphatic structure. These compounds are produced under stressful conditions such as UV radiation, injury and infection. Its main function is to protect against pathogen attack and to intervene in the growth and maturity of the plant [67]. However, the importance of these compounds resides mainly in their function as antioxidants [68].  
The most abundant phenolic compounds in artichoke are hydroxycinnamic acids derived from caffe acid. Most of the caffe acid present is conjugated with quinic acid, forming caffeoylquinic derivatives. The most relevant are cynarin (1,3-O-dicaffeoylquinic acid), chlorogenic acid (3-O-caffeoylquinic acid), neochlorogenic acid (5-O-caffeoylquinic acid) and 1,5-O-dicaffeoylquinic acid [9]. Although these caffe acid derivatives are found mainly in artichoke head [20], higher levels of 5-O-caffeoylquinic acid and 1,5-O-dicaffeoylquinic acid have been reported in the leaves and stems [55].  
The second most abundant class of natural polyphenols in artichokes are flavonoids, especially flavones. The most abundant are apigenin, luteolin and their respective glycosides and rutosides [69]. Flavonoids in artichoke are mainly concentrated in the leaves and head and are absent in the flower stalk [70]. These compounds have an anti-inflammatory effect [71] as well as the ability to protect cells from oxidative damage. Other important flavonoids in artichoke are anthocyanins, including peonidin, delphinidin and cyanidin and its glycosides (cyanidin-3-glucose and cyanidin-3,5-diglucoside) [56]. These compounds have been shown to downregulate the production of the inducible nitric oxide synthase (iNOS) isomer, which has a pro-inflammatory effect in the vascular system [72]. In addition, these pigments are responsible for the color of artichoke capitula, which ranges from green to violet [9].  
Finally, the amount of these polyphenols varies depending on the harvest [55], genotype [20] and fertilization [73]. [PERSON] et al. [55] reported a higher amount of phenolic compounds, especially caffeoylquinic acid derivatives, in artichokes harvested between February and April. The processing of the vegetable also plays an important role, as [PERSON] [74] reported a significant increase in the phenolic acids level after boiling artichoke in water. The high water solubility of many phenolic compounds in artichoke can lead to the lixiviation of these compounds, which may be extracted from the plant and become part of the cooking water.
## 3 Proposed System Architecture
### Abnormal and Normal Event Analysis
#### 3.3.3 Half Width Value (\(\
u_{\text{hw}}\))  
In order to more precisely detect the periods of abnormal events, we here consider a parameter called half width value (\(\
u_{\text{l}n}\)) on the curve of the modified difference. The starting point (\(f_{1}\)) and the ending point (\(f_{2}\)) are set at the half of the largest curve which can represent the irregular event. Then,  
Figure 8: Analysis for possibility of abnormal event by using modified difference. (**a**) Calculation of modified difference on moving average of aspect ratio. (**b**) Calculation of modified difference on moving average of point distance.  
Figure 7: Analysis for possibility of abnormal event by using moving average. (**a**) Moving average calculation on aspect ratio. (**b**) Moving average calculation on point distance.  
the periods for abnormal and normal events are obtained by calculating the distance of \(f_{1}\) and \(f_{2}\), as in Equation (10), and the consideration of \(v_{hw}\) is described in Figure 9.  
\[v_{hw}=\
ot{v}_{1}-f_{2}| \tag{10}\]  
where \(v_{hw}\) is the half width value of \(MD\),\(f_{1}\) and \(f_{2}\) mean the estimated starting and ending periods of a fall event, respectively.
## 6 Conclusions  
In this comparative study, we have assessed the alignment between data collected by radiation dosimeters in various space experiments and the predictive models used in aerospace engineering. The results highlight significant discrepancies between measurements and computational model predictions and their propensity to overestimate radiation levels, which can impact mission design and component selection.  
Computational models continue to play a pivotal role in space missions, particularly those with stringent safety requirements and manned missions. They provide a worst-case scenario-based approach and offer a substantial safety margin. However, it is crucial to acknowledge their tendency to overestimate radiation levels, which can have implications for mission design and component choices.  
In 2019, the European Space Agency initiated a series of comparative studies aimed at the analysis of automotive components adhering to AEC-Q standards. These studies sought to establish parallels with ECSS standards and identify additional tests necessary to qualify these components for use in space applications, with radiation-related considerations excluded.  
Based on the data presented in this work, components subjected to total ionizing dose testing have demonstrated robustness in operating within the radiation environment of a low Earth orbit. While certain components may exhibit heightened sensitivity, this challenge can be effectively addressed through a preliminary selection process aimed at identifying potential vulnerabilities to TID. The availability of a pre-qualified list of commercial off-the-shelf components suitable for deployment in space environments (similar to a Preferred Part List, but for COTS) may prove invaluable in expediting the adoption of this technology in low Earth orbit missions.  
In conclusion, the findings underscore the potential for integrating COTS components into space missions, particularly in LEO missions, where TID testing has shown promise. The establishment of stringent selection criteria for such components can mitigate any sensitivity concerns and facilitate their seamless integration into space technology, thereby advancing innovation and exploration in the realm of space science and technology.  
Conceptualization, O.G. and M.P.; methodology, A.P.-E.; validation, A.P.-E., A.R. and M.P.; investigation, O.G., M.P. and A.R.; resources, M.B.; writing--original draft preparation, D.G.; writing--review and editing, A.P.-E.; visualization, A.P.-E.; supervision, M.P.; project administration, M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministerio de Ciencia e Innovacion grant number PID2019-104863 RB-100/AEI/10.13039/501100011033 and FEDER MCIU Agencia Estatal de Investigacion Projects grant number ESP 2105 68266 R and ESP 2017 88436 R.  
Not applicable.  
The authors acknowledge the Spanish Ministerio de Ciencia e Innovacion for their support on the development of the Solar Orbiter ICU.  
The authors declare no conflict of interest.
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 4 Experiment
### Effectiveness of the Proposed BSEGAN-SI Method  
To confirm the effectiveness of the proposed BSEGAN-SI method in speaker-independent spectral enhancement for BC speech tasks, the methods used in this paper use speaker-independent BC data sets for model training. We selected the GMM [34], BLSTM [35], StarGAN [36], and CycleGAN model [16] as the comparison benchmark methods in this task.  
Figure 5 is the spectrum diagram of BC speech and its corresponding AC speech. From the BC speech spectrum enhanced by multiple methods in Figures 6-8, it can be seen that the BSEGAN-SI method proposed in this paper is superior to the other benchmark methods. The traditional statistical method GMM has the worst enhancement effect on BC speech, with the least voiceprint loss and high-frequency component recovery in the speech spectrogram, and the spectrogram is fuzzy; compared with GMM, the spectrogram of BC speech enhanced by the BLSTM and CycleGAN models is better, but some voiceprint details are still missing in the middle and high-frequency component of the speech. After the StarGAN model is enhanced, the voice print of BC speech is lost, and the last noise area appears in the spectrogram. Our method can better recover the missing components of BC. The spectral details are clearer and most similar to the target AC speech spectrum. The enhanced speech quality has been better improved, which shows the effectiveness of BSEGAN-SI in speech enhancement tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Module** & **Kernel Size** & **Stride Size** & **Channels** \\ \hline \multirow{8}{*}{Discriminator} & Gated-Conv2D & \(3\times 3\) & 1 & 64 \\  & Conv2D & \(5\times 5\) & 1 & 128 \\  & Conv2D & \(3\times 3\) & 1 & 128 \\  & Conv2D & \(5\times 5\) & 1 & 256 \\  & Conv2D & \(3\times 3\) & 1 & 256 \\  & Conv2D & \(5\times 5\) & 1 & 512 \\  & Conv2D & \(3\times 3\) & 1 & 512 \\  & Conv2D & \(5\times 5\) & 1 & 1024 \\  & Conv2D & \(1\times 3\) & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting of BSEGAN-SI Discriminator.  
Figure 5: (a) AC speech spectrogram. (b) BC speech spectrogram.  
Figure 6: (a) Enhanced BC speech spectrogram by GMM. (b) Enhanced BC speech spectrogram by BLSTM.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 2 Model Design  
Due to the inherent complexity and variability of the physical mechanisms involved in wave generation, propagation, and interaction, traditional physical models often struggle to accurately capture the highly uncertain and dynamic nature of these environments. Consequently, identifying efficient methods to model and represent these complex relationships has emerged as a significant challenge in the field. To overcome this challenge, this study introduced a multimodal hybrid model that used deep learning techniques. This model was able to effectively capture the characteristics of the underlying data and provide accurate wave height predictions by separately modeling the seasonality, trend, and remainder components of the data. The general design of the model and the comprehensive analysis of its components are described and discussed below.  
The model adopts multilevel data decomposition and multi-model fusion methods to make class-specific predictions based on the seasonality, trend, and remainder components of wave data. According to the classification results, the most appropriate model is selected for each type of feature data for prediction. The BiLSTM model can effectively capture long-term dependencies and nonlinear trends, and has strong noise resistance, making it very suitable for trend prediction. The SARIMAX model can capture well the periodicity of the remaining data and is very suitable for the prediction of seasonal data. The 1D-CNN model can effectively identify multidimensional anomalies in the remainder, providing strong support for the prediction of the remainder. As shown in Figure 1, the process includes multiple steps such as data division, model selection, and application.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 2 Background and Rationale
### Rationale of the Study  
The objective of the paper is to quantitatively assess the impact of the orbital drift on the temporal consistency of Proba-V TOC directional reflectances, which are provided by ESA as part of standard user products [18]. There are a number of difficulties in addressing this objective, since the temporal stability of satellite-based surface reflectances is impacted by a range of concomitant factors, such as calibration drifts, directional effects, as well as natural variability of surface anisotropy and atmospheric conditions [23]. In order to disentangle sensor-related effects from the underlying natural variability, the problem is addressed from two complementary viewpoints.  
At first, the impact of the drift is characterized using a prognostic approach, namely, the temporal evolution of LTDN is used as input to estimate the changes of surface reflectances over a sample vegetated site and for a range of sun and viewing angles. The results of the simulation allow to investigate the impact of the drift in a controlled experiment with known input data, and therefore it is used as a benchmark to verify the temporal patterns observed in real observations.  
Secondly, the temporal drifts in operational TOC products are estimated over a large ensemble of terrestrial sites, spanning a wide range of land cover classes and climatic conditions. The orbital effect on real data is quantified as statistical difference between the drift estimated for TOC products and BRDF-normalized surface reflectances. The assumption is that BRDF-corrected data, normalized to a fixed sun-viewing geometry, are free of spurious trends induced by the drifting orbit.
## 2 Proposed Energy Cost Model for Offshore Wind Farms
### Annual Energy Production
#### 2.2.1 Power Curve Modeling of the Offshore Turbines  
Figure 1 shows the four control regions in wind turbines based on wind speed. In Region 1, where the wind speed is below cut-in speed (\(v_{c}\)), the wind turbine is in standby mode. In Region 2, where the wind speed is between \(v_{c}\) and the rated speed (\(v_{r}\)), the turbine is able to generate partial power. In Region 3, where the wind speed is between \(v_{r}\) and the cut-off speed (\(v_{f}\)), the wind turbine is limited to the rated power. In Region 4, where the wind speed is above the \(v_{f}\), the turbine is shut down or, better, set aside to avoid component over-loads. Thus, the power curve model can be expressed analytically as:  
\[P(v)=\begin{cases}0&(v<v_{c})\\ P_{f}(v)&(v_{c}\leq v\leq v_{r})\\ P_{r}&(v_{r}\leq v\leq v_{f})\\ 0&(v_{f}<v)\end{cases}, \tag{9}\]  
Figure 1: Four regions of the wind turbine power curve.  
where \(P_{f}(v)\) is the active power when the wind passes through a wind turbine and is exploited by it. The mathematical formula of \(P_{f}(v)\) is given by:  
\[P_{f}(v)=\rho\pi R^{2}C_{p}v^{3}/2, \tag{10}\]  
where \(\rho\) and \(R\) denote the air density and the rotor radius, respectively; \(C_{p}\) denotes the coefficient of power that depends on the wind speed, which is assumed as constant and equal to 0.42 for simplicity [26]. Then, the turbine rated power \(P_{r}\) can be calculated as  
\[P_{r}=\rho\pi R^{2}C_{p}v_{r}{}^{3}/2 \tag{11}\]
## 2 Literature Review  
### Likelihood-Based (LB) Method  
AMC is treated as a hypothesis-testing problem in the LB method. The algorithm based on the LB method can be efficient from a Bayesian perspective, and it is beneficial for reducing the likelihood of a hypothesis problem occurring. High computational complexity often affects accurate decisions, which can be difficult to obtain in actual sys  
Figure 1: Block diagram of a communication link with automatic modulation classification.  
tems. The LB method can reduce the probability of misclassification and can obtain the best classification accuracy, as such methods maximize the chance of correct classification with perfect channel situations. Furthermore, in real-world scenarios, uncertainty factors must be considered, and the likelihood function is ineffective in handling any unknown parameters. The unknown parameters problem is replaced with the essential component of their probability density function (PDF) in the average-likelihood ratio test (ALRT) [8]. However, as the number of missing factors grow, the likelihood function in ALRT becomes more sophisticated, resulting in a significant processing cost. To solve the complexity, the generalized likelihood ratio test (GLRT) was developed. The parameters in GLRT are estimated by using a maximum likelihood (ML) estimator [9]. This biased classifier affects the performance of nested modulations such as 16-quadrature amplitude modulation (QAM) and 64-QAM. The hybrid likelihood ratio test (HLRT) improves the performance of the likelihood function with respect to the unknown function. It first evaluates the likelihood of the data symbols as discrete random variables, consistently allocated across the alphabet set, and considers the carrier phase as a predetermined variable [10]. Since this method requires prior information about the signal, including its carrier frequency and other channel parameters, its implementation becomes difficult in the presence of complex and unknown parameters. Despite their ability to provide optimal solutions, they may not be appropriate in practical scenarios [11].
# Bio-Based Polymers with Potential for Biodegradability
## 2 Bio-Based Polymers
### Cellulose and Chitosan  
Cellulose and chitin are the most important polysaccharides [6]. Bacterial xanthan gum has been utilized as a bio-based resin [78]. However, not all common polysaccharides are useful as bioreenwable polymers. Various approaches have been investigated for the preparation of composites consisting of cellulose particles embedded in a cellulose matrix [79]. Invariably, successful composites depend on the solubility of matrix and reinforcement [80], which can be tuned through pretreatment processing [81; 82]. Cellulosic matrices have also been reinforced with metal particles for antimicrobial and magnetic applications [83]. Indeed, Fe\({}_{2}\)O\({}_{3}\) nanoparticles embedded in a sodium carboxymethyl cellulose matrix successfully responded to a magnetic field [84]. These materials may be excellent candidates for biomedical applications due to the reduced risks of bacteria-born infections. The development of these antimicrobial materials may also represent a new direction in the synthesis of bio-based structures with improved biological resistance for increased durability in a specific environment.  
There exists a great variety of applications for chitosan [85; 86], including self-healing composite anti-corrosion coatings [87], absorbent biopolymer membranes [88; 89], and several biomedical uses due to their physical properties, chemical stability, biocompatibility, biodegradability, and ability to undergo chemical modification [90; 91; 92; 93]. More specifically, chitosan has been reinforced with calcium phosphate nanocrystals for tissue engineering with strong adherence to natural bone [94]. Also, chitosan nano- and micro-particles have been used for drug delivery [95; 96; 97]. Chitosan has also been reinforced with cellulose nanocrystals [98], and nanofibers [99], leading to materials with satisfactory mechanical and water vapor barrier properties for food packaging [99]. The addition of glycerol as a plasticizer to such systems has also been investigated [100; 101]. Other reinforcement materials for chitosan-based matrices are multi-walled carbon nanotubes [102], and gold nanoparticles [103].
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 3 Biomass Generalities
### Pyrolysis of Lignocellulosic Materials  
Pyrolysis involves the decomposition by heat in the absence of air of an organic substance. It can be achieved under a vacuum or in the presence of an inert gas.  
The pyrolysis products of lignocellulosic materials are a solid residue, charcoal, while the other products constitute a volatile fraction containing condensable and non-condensable materials (Figure 2a) [20]. The quality and quantity of the pyrolysis products and the composition of the volatiles depend on several parameters, among which are the characteristics of the treated material and the operating conditions. Indeed, three parameters are inseparable--the temperature, the heating rate, and the residence time. They greatly condition the yield and the distribution of solid, liquid, and gaseous products (Figure 2b). Pyrolysis processes are associated with either slow heating rates or fast heating rates. Fast pyrolysis processes at low temperatures are of great interest for oil production. Indeed, Figure 2b [20] indicates that the best oil yield is achieved at 500 \({}^{\circ}\)C under flash or fast pyrolysis conditions.  
Fast pyrolysis at a high temperature is of great interest when one is interested in fluidized bed gasification processes, where the temperature is around 1173 K and the heating rate is high [21]. Fast pyrolysis offers several advantages, including maximizing gas production. Fast pyrolysis can also produce large quantities of gas, which can then be reformed to produce syngas. However, it is important to note that syngas itself is not directly used by many energy sources as it is considered a secondary energy source [22]. The resulting syngas, composed primarily of hydrogen (H\({}_{2}\)) and carbon monoxide (CO), can be used in a variety of other energy systems and applications. It can be used as a versatile feedstock for other processes such as methanol synthesis, methane production or Fischer-Tropsch synthesis [23]. These downstream processes convert syngas into final energy products that can be used by various energy utilizations, including power plants, industrial applications or transportation systems [22,24].