esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure-Property Relation of Trimethyl Ammonium Ionic Liquids for Battery Applications
## 2 Materials and Methods  
### Synthesis of the Ionic Liquids  
The ionic liquids investigated in this work were prepared by alkylation of the amine and subsequent anion exchange. In the first step, an aqueous solution of trimethylamine was reacted with either alkyl-bromide or alkyl-ether-bromide to obtain the corresponding aprotic ammonium bromide salt. The high melting salts obtained were dissolved in water and the bromide anion exchanged with an amide anion by metathesis with an alkali metal salt of bis(fluorosulfonyl)imide or bis(trifluoromentanesulfonyl)imide. The resulting ionic liquids were further purified and dried in high vacuum. Identity and purity of the samples were confirmed by multinuclear NMR-spectroscopy. Absence of halides was confirmed by testing with AgNO\({}_{3}\)-solution. Water content after drying in oil pump vacuum for at least two days was below 100 ppm, as confirmed by Karl-Fischer-titration. Further details of the synthesis are given in the supporting information.  
The ionic liquids prepared as described above were dried on a vacuum line for two days prior to each physicochemical measurement and handled using Schlenk techniques or a Labmaster 130 glove box (MBraun, Garching, Germany) with oxygen and water levels below 1 ppm. Mixtures of the ionic liquids with lithium salts were prepared by weighting the dried lithium salt for the electrolyte of the required concentration in a volumetric flask, adding a stir bar and filling with ionic liquid below the graduation. After stirring until complete dissolution of the lithium salt, the stir bar was removed, and the volumetric flask was filled to the graduation and shaken for several minutes. The binary mixtures prepared this way were then degassed and dried again for two days and handled under inert conditions.
## 6 Conclusions  
The MODIS NPP model represents all forest vegetation within a 1 \(\times\) 1 km resolution, assumes fully stocked forest stands and cannot effectively detect management influence in FPAR and LAI. Thus, the effect of forest management, which changes the carbon allocation patterns and, as a consequence, the tree dimensions, is not well represented by MODIS driven NPP estimates. Terrestrial NPP, on the other hand, represents only NPP of trees bigger than the diameter threshold and captures the response of trees to management and, thus, the actual carbon allocation.  
Using daily local climate data with high spatial resolution improves the agreement between MODIS NPP and terrestrial NPP.  
Correcting the observed stand density related bias in MODIS NPP and thus combining the \"space-based\" MODIS productivity estimates and \"ground-based\" national forest inventory data provide consistent and continuous forest productivity estimates. The corrected MODIS NPP has more agreement with forest inventory data. A better agreement of MODIS and terrestrial NPP estimates allows using MODIS for large-scale forest resource estimates in areas with forest management.  
Forest productivity across large scales is of increasing interest as more demands are made on forest resources [55] as well as in the context of REDD (Reducing Emissions from Deforestation and Forest Degradation) [56]. This is of particular importance for areas where no or little terrestrial information on forest productivity is available. With this paper, we provide a conceptual outline of how realistic forest productivity estimates can be derived by combining satellite-driven NPP estimates, such as results from the MODIS NPP algorithm, with stand density estimates (using terrestrial or remote-sensing data) in order to enhance forest productivity predictions across multiple scales.
## 3 Results and Discussion
### Comparison of Extraction Results with Literature Data  
In Table 2 we have also included analysis results from the Swedish National Screening Programme 2010 [38] together with a selection of literature data describing concentrations of APIs in digested sludge. The top five compounds in the Swedish National Screening Programme were ketoconazole,citalopram, ciprofloxacin, metoprolol, and venlafaxine, the same top five compounds that we found in our extracted sludge. With a few exceptions, the absolute values are roughly the same, only the antibiotic compound erythromycin convinced a lower concentration in our study compared to the National Screening Programme, representing the only truly deviating value.  
In the Swedish report, we couldn't find what extraction technique and method used. If not as exhaustive as our PHWE method this may be one reason for not achieving such high sum concentrations of all investigated compounds. In fact, the two compounds contributing most to the difference is ketoconazole and citalopram, both adsorbing strongly to the sludge. In our work ketoconazole had a concentration of 3009 \(\upmu\)g/kg, while the other three sludges (Skovde, Stockholm and Umea) were in the range 510-1200 \(\upmu\)g/kg. Likewise, citalopram had a concentration in our study of 958 \(\upmu\)g/kg in our study but was in the range 570-760 \(\upmu\)g/kg in the Swedish report.
## 4 Simulation Results
### Total Consumed Energy  
The total consumed energy includes communication energy dissipation, computation energy dissipation, and sensing energy dissipation. In Figure 10, the total consumed energy is plotted against the number of data gathering rounds. As the number of rounds increases, the total consumed energy also increases. While the number of rounds increases, the total consumed energy of CBDAS increases the most steadily among those approaches. Since the energy consumption is evenly distributed among sensor nodes in CBDAS, the number of rounds can be over 3000. The total energy consumed increases abruptly when using Direct and PEGASIS though.  
Figure 10: Total consumed energy over time.  
Figure 9: The first round energy consumed.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 5 Conclusions  
This paper considers a decoding scheme to reduce the decoding complexity without compromising the error correction performance for JSCAC system. We have observed that the improvement of post-decoding BER of the global expanding window decoding in a JSCAC system with finite coupling length gradually decreases with the decoding delay, while the decoding complexity increases approximately linearly. Our key idea, called joint hybrid window decoding (JHWD), is to implement an adaptive local expanding window decoding within the sliding window region. We have also proposed an improved density evolution algorithm to analyze the asymptotic decoding complexity of the JHWD algorithm. The numerical analyses and simulation results have shown that the proposed JHWD algorithm can greatly reduce the decoding complexity while maintaining BER performance similar to that of prior-art decoding schemes, which could be a good alternative for the application of JSCAC systems in low complexity scenarios.  
Conceptualization, L.D. and Y.W.; methodology, L.D. and X.Y.; software, L.D. and Y.W.; validation, Z.S. and X.Y.; formal analysis, Y.L.G.; investigation, Y.W.; resources, Z.X.; data curation, X.Y.; writing--original draft preparation, L.D.; writing--review and editing, Z.S. and Y.L.G.; visualization, Z.X.; supervision, Y.L.G.; project administration, Z.S.; funding acquisition, L.D. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62371101 and 62461017; the National Key Laboratory of Wireless Communications Foundation under Grant 2023 KP01609; the National Key Research and Development Program under Grant 2022 YC0305702; the ZTE Industry-University-Institute Cooperation Funds under Grant IA2024000001; the Natural Science Foundation of Guangxi under Grant 2022 GXNSFBA035508 and 2023 GXNSFAA026018.  
Not applicable.  
The data presented in this study are available on request from the corresponding author  
The authors thank the anonymous reviewers and the Editor for their helpful comments and review of this article.  
Figure 7: BER performance comparisons with other kinds of JSCC schemes, \(R^{cc}=0.25\), \(R^{cc}=0.5\), \(N^{cc}=640\) bits, \(N^{cc}=320\) bits, and \(L=20\)[10, 16].
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 4 Applications
### Decision-Making Support  
Knowledge and experience required for shipbuilding is spread through several engineering fields. To overcome the complexity issue, shipbuilders are turning to computer-aided technology, which can then be used to form the basis for a DT. In addition, ship owners are increasingly demanding lifecycle projections and decision-making support, where the power generation system will have the greatest benefit through process and maintenance optimisation, especially throughout a ship's lifecycle.  
In [12], a DT model of a ship's hull was proposed for the purpose of decision-making to preserve structural integrity. Due to the recent implementation and slow dynamics of hull processes, the proposed concept has not been validated yet.  
Paper [23] proposed a DT of a cruise ship, which is among the most demanding ship types for shipbuilders due to the requirements of safety, comfort, entertainment capabilities, reliability, and effectiveness. The cruise ship's DT was proposed to provide decision-making support and alerts, as well as to predict changes in the ship over time.  
Paper [22] proposed a DT application for ship trajectory prediction utilising real-time visualisation of the predicted trajectory, thus augmenting the current scene of the environment on the computer screen in the wheelhouse. The proposed approach offers real-time decision-making support for navigating in hazardous navigation areas but needs to be further researched and validated.
## 3 Results and Discussion
### Validation of the Key Assumption of the Study  
#### 3.4.1 Appropriateness of the Use of GAM  
To validate the key assumption on the appropriateness of the GAM modelling technique in the reduction of the model space, we ran the extra 81 models through the ANN process. The best performer from the set of non-selected models had an \(R^{2}\) of 0.50. A summary of the performance of the non-GAM selected models in the test dataset is provided in Figure 13.  
Figure 12: Multi-class ROC plot of the best model as a drought phase classifier. The curves represent the pairwise comparison of the five classes. The overall area under the multi-class ROC is the average of the areas under each of the ROCs for the pairwise class comparisons.  
The results validate the assumption of the utility of GAM in modelling non-linearity as well as in their use in this study for model space reduction prior to the use of computationally intensive algorithms like artificial neural networks. The models that are not selected by the GAM process are not expected to perform any better in the ANN process than the GAM selected models. The GAM process is, in essence, more optimistic in performance ranking than the ANN process. This property is useful as it generally guarantees that good models are not filtered out of the ANN process.
## 4 Conclusions  
The effect of B4C and TiC nanoparticles on the microstructure and mechanical properties of the AA6061 aluminum alloy was investigated in the present work. Al/B4C and Al/TiC were manufactured using a powder metallurgy route. The images of field emission scanning electron microscopy (FESEM) showed a homogeneous dispersion of B4C and TiC into the AA6061 matrix. The results showed that Al/B4C specimens 92 (HRC) had higher micro-hardness with respect to Al/TiC specimens 87.4 (HRC). For the same concentration of B4C and TiC nanoparticles, the effect of B4C nanoparticles on the enhancement of microstructure and mechanical properties was higher than that for TiC ones. Additionally, it was indicated that the wear rate of Al/B4C and Al/TiC increased as the applied load increased. It was shown that the wear rates of Al/B4C and Al/TiC were 4.21 \(\times\) 10\({}^{-7}\) (g/cm) and 5.1 \(\times\) 10\({}^{-7}\) (g/cm), respectively. The increment of B4C nanoparticles percents increased the actual density, while the increment of TiC nanoparticles percents slightly decreased the actual density. Also, the increment of B4C nanoparticles percent decreased the porosity by (4.61%), while the increment of TiC nanoparticles percent increased the porosity by (6.2%). Finally, the powder metallurgy route to produce Al/B4C and Al/TiC nanocomposites is recommended to enhance the microstructure and wear properties of the AA6061 alloy.  
Methodology, L.K.H. and S.M.J.; investigation, L.K.H., S.M.J., K.D.S. and W.A.K.A.-M.; writing--original draft preparation, L.K.H., S.M.J., K.D.S. and W.A.K.A.-M.; writing--review and editing, L.K.H., S.M.J., K.D.S. and W.A.K.A.-M.; supervision, F.A. and B.E. All authors have read and agreed to the published version of the manuscript.  
All authors certify that they have no affiliations or involvement with any organization or entity with any financial or non-financial interest in the subject or material discussed in this manuscript. This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG--German Research Foundation) and the Open Access Publishing Fund of the Technical University of Darmstadt. The authors would like to also thank the University of Technology-Iraq.  
The authors declare no conflict of interest.  
Figure 8: Show relationships between microhardness and wt.% of B4C and TiC.
## 2 Cavitation  
Cavitation is a phase-change phenomenon in which the liquid phase is changed to a gas phase due to a decrease in liquid pressure to vapor pressure by increasing flow velocity [2], and it is called \"hydrodynamic cavitation\". Cavitation is also generated by ultrasonic vibration, which is named \"ultrasonic cavitation\". Bubbles are also formed by irradiating a pulse laser into water, in which the bubble behaves as a cavitation bubble [57]; this is called \"laser cavitation\". A schematic diagram of cavitation is illustrated in Figure 2a. When a cavitation nucleus such as a tiny air bubble is subjected to a low-pressure region, it becomes a cavitation, and it develops and shrinks, then collapses, producing microjet and shock wave during rebound. The microjet and the shock wave produce a severe impact, which can deform metals. After rebound and shrinking, bubbles remain in the water, which is called a residual bubble [58]. In the research area of cavitation, including bubble dynamics, spherical bubbles have mainly been investigated by numerical simulations [59] and experimental studies [57; 60; 61], and the effects of bubble shape and bubble interactions have also been studied [62].  
In experimental studies of severe cavitation erosion, it was found that vortex cavitation, as shown in Figure 3[63], produced severe impacts on fluid machineries such as pumps and valves [1; 64; 65]. A schematic diagram of vortex cavitation is shown in Figure 2b. Cavitation nuclei accumulate in a vortex, and they become vortex cavitation in a high-speed region, i.e., a low-pressure region. The vortex cavitation develops and shrinks, then collapses. Regarding a model test of vortex cavitation using a rotating device [66], a microjet was observed in the vortex cavitation. Thus, in order to use cavitation impacts for practical applications, the generation of vortex cavitation is very important.  
Figure 2: Schematic diagram of development and collapse of cavitation: (**a**) spherical bubble; (**b**) vortex cavitation.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 5 _Acrocomia aculeata_ Oil Extraction Using Supercritical Fluids
#### Oil Extraction Conditions from Acrocomia aculeata Pulp
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Solvents**} & \multirow{2}{*}{**Conditions**} & **Solvent Density** & **Oil Yield** & \(\beta\)**-Carotene** & **Flavonoids** & **Phystorelos** \\  & & & & **(kg/m\({}^{3}\))** & **(\%)** & **(mg/100 g)** & **(mg/100 g)** & **(mg/100 g)** \\ \hline \multirow{3}{*}{[25]} & Soxhlet & Petroleum & \multirow{3}{*}{60 \({}^{\circ}\)C (480 min)} & \multirow{3}{*}{n.d.} & \multirow{3}{*}{25.72} & \multirow{3}{*}{124.16} & \multirow{3}{*}{25.34} & \multirow{3}{*}{-} \\  & & ether & & & & & & \\ \cline{1-1} \cline{5-10}  & Low- & Ethyl acetate & 40 \({}^{\circ}\)C/40 rpm & n.d. & 22.97 & 308.38 & 16.97 & 82.45 \\  & pressure & n-hexane & (240 min) & n.d. & 23.39 & 334.04 & 13.78 & 76.85 \\  & isopropanol & n.d. & 27.43 & 348.30 & 14.78 & 104.15 \\ \hline \multirow{3}{*}{[14]} & Soxhlet & n-hexane & 80 \({}^{\circ}\)C (360 min) & n.d. & 30.09 & - & - & - \\  & Ethanol & 80 \({}^{\circ}\)C (360 min) & n.d. & 31.10 & - & - & - \\ \cline{1-1} \cline{5-10}  & Supercritical & \multirow{3}{*}{Carbon dioxide} & 40 \({}^{\circ}\)C/150 bar & 781.17 & 4.09 & - & - & - \\  & fluids & Carbon & 40 \({}^{\circ}\)C/200 bar & 840.61 & 9.64 & - & - & - \\  & (Nascimento, 2016) & & 55 \({}^{\circ}\)C/150 bar & 654.90 & 14.49 & - & - & - \\  & & & 55 \({}^{\circ}\)C/200 bar & 755.41 & 13.83 & - & - & - \\ \hline \multirow{3}{*}{[15]} & Soxhlet & n-hexane & 70 \({}^{\circ}\)C (480 min) & n.d. & 25.64 & - & - & - \\  & dichloromethane & 40 \({}^{\circ}\)C (480 min) & n.d. & 26.83 & - & - & - \\ \cline{1-1} \cline{5-10}  & Supercritical & Propane & 100 \({}^{\circ}\)C/40 bar & 110 & 9.77 & - & - & - \\ \cline{1-1}  & fluids & Propane & 100 \({}^{\circ}\)C/120 bar & 410 & 22.69 & - & - & - \\ \cline{1-1}  & ([PERSON], 2017) & Propane & 50 \({}^{\circ}\)C/40 bar & 440 & 22.86 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Oil yields obtained (dry basis) in the extraction of macaque pulp oil using different techniques and conditions.
## 2 Materials and Methods
### Time Series Analysis  
The analysis of the \({}^{220}\)Rn time series was performed by the well-proven hybrid method Multiple Linear Regression + Empirical Mode Decomposition + Support Vector Regression (MLR + EMD + SVR) [11; 24; 27], already used for the analysis of the \({}^{222}\)Rn signal in the retraced paper [1]. It is an effective methodology that aggregates multiple separate methods, combining the performance advantages of each one, optimizing algorithms and achieving greater accuracy, developed and tuned in the Matlab\({}^{\text{\textregistered}}\) environment [25; 36]. The signal trend extraction was performed by using the MLR + EMD method. The MLR technique creates a model describing the contribution of the recorded environmental parameters (i.e., temperature, relative humidity and pressure, which influence the signal) to the time series,via a multiple linear regression model based on the least-squares fit. The original time series was subtracted by the MLR model, in order to obtain the correct one. The procedure is fully described in [37]. On the correct time series, the EMD technique was applied to further filter the unknown seasonal-periodic modulations out of the signal, by decomposition. The signal was decomposed into a collection of components; they were obtained by iterative differences between the signal and the average of the upper and lower envelopes, which came from the spline interpolation among all the local maxima and minima, respectively. The components increased progressively in frequency, and the decomposition process ended with constancy or monotony of the signal. The last component was the representative trend of the time-series. The detailed analytical description is reported in [38, 39]. The EMD method is improved to deal with time series having missing data, thanks to the Self-Consistent Regression Estimator (SCRE) technique that recursively imputes missing values [40]. The residuals (anomalies) identification was performed by observing the raw signal outside the two 95% confidence intervals (CI) of the forecasted signal, obtained using the SVR technique applied to the sum of all components output of MLR + EMD hybrid method [1, 24]. The SVR method computes the forecasted signal through a regression model using the set of data as training, in order to predict them with a function having lowest deviation. The regression model was made by using the Gaussian kernel mapping function with the Lagrange dual formulation. A complete analytical description of the SVR method is available in [41, 42], with explanatory examples. The residuals (anomalies) were recognized if they were beyond one of the two forecasted time series at 95% CI that contained the same raw signal, and, then, were computed by subtracting the two forecasted time series from the raw one, in absolute terms. The anomalies identification process, with many exemplifying figures, is in [1, 24, 43, 44].
## 3 Research Methodology
### Hybrid Feature Selection (HFS)
#### 3.3.1 ReliefF  
Relief is a widely used filter-based FS method that identifies the best subset of features by measuring features' weights. Proposed by [PERSON] and [PERSON] [51], this algorithm assigns weights to features based on correlation between features and categories, and then selects all features with weight greater than an artificial threshold. Notably, the Relief algorithm is limited to binary classification problems. To address this problem, [PERSON] [52] proposed the ReliefF algorithm, which has the ability to work with multiclass problems. ReliefF is a distance-based feature selector that uses Manhattan distance to measure weights. The evaluation criterion of Relieff is presented in Equation (3), where: \(W(f_{0,i})\) stands for the weight of the \(i\)th feature before updating; \(W(f_{i})\) is the updated weight of the \(i\)th feature; \(A\) is the vector of features; \(k\) is the number of nearest neighbors; \(m\) is the number of cycles; \(f_{h(x_{i})}\) and \(f_{r(C)}\) are the value of \(k\) nearest neighbors of \(x_{i}\) in the same and different class, respectively; \(P(C)\) is the ratio of the target samples \(C\) to the total sample; \(P(class(x_{i}))\) is the ratio of the samples in the same class including \(x_{i}\) to the total samples; and \(diff\) () denotes the distance of two samples on each feature in \(A\).  
\[\begin{array}{ll}W(f_{i})=W(f_{0,i})&-\frac{\sum_{j=1}^{k}diff(A,\,x_{i},\,f _{h(x_{j})})}{m\times k}\\ &+\sum\limits_{C\
eq class(x_{i})}\frac{P(C)}{1-P(class(x_{i}))}\times\frac{ \sum_{j=1}^{k}diff(A,\,x_{i},\,f_{r(C)})}{m\times k}\end{array} \tag{3}\]  
This study uses Manhattan distance to measure the distance between two samples as shown in Equation (4), where \(diff(A,\,R1,\,R2)\) is the difference between the sample \(R1\) and \(R2\) in the vector of feature \(A\).  
\[diff(A,\,R1,\,R2)=\frac{|R1-R2|}{A_{max}-A_{min}} \tag{4}\]  
In this study, [PERSON] selected the most correlated CLP factors as its output. The factors selected by [PERSON] were then applied as inputs to the combination of SVM and GA.