esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 2 System Design  
The system is considered to be a rigid multi-body system as the links' and the joints' flexibility is not considered. As shown in Figure 2 the set-up involves a hyper-redundant manipulator named M, alongside two rigid bodies, B1 and B2. B1 represent the satellite or the base and B2 represent a free-floating object. To keep track of the system's movements, we use an inertial frame denoted as \(\mathcal{F}_{e}\). Within this system, there are a total of 24 rigid bodies and 24 joints. Although these bodies and joints can be arbitrarily labeled, for purposes of clarity, each joint is assigned the same number as its connected outboard body (as shown in Figure 2). Each body Bj (where j = 1, 2, \(\ldots\), 24) is equipped with its own fixed frame \(\mathcal{F}_{i}\), aiding in the analysis of its movement and orientation.  
Figure 1: Scenario demonstrating the application of the HRSM.  
Figure 2: Illustration of the spacecraft platform equipped with an HRSM.  
Given that the mass of spacecraft bodies is generally large, we adopt the assumption of a fixed base position; however, as explained later, the attitude of the base is controlled. This rationale allows us to simplify the system analysis by assuming the base does not significantly move or deform, thereby concentrating on the dynamics and control of the manipulator itself. Under this premise, there are N revolute joints in the system. We designate the angular velocity of each revolute joint as the generalized velocity, described in the local coordinate systems of each joint. Consequently, the generalized velocity of the hyper-redundant space robotic manipulator system can be expressed as follows:  
\[\mathrm{u}=\begin{bmatrix}\dot{\mathrm{o}}_{1}\\ \vdots\\ \dot{\mathrm{o}}_{n}\end{bmatrix}, \tag{1}\]  
For a revolute joint \(\dot{\mathrm{o}}_{j}=\dot{\mathrm{a}}_{j}\), where \(\dot{\mathrm{o}}_{j}\) is the relative rotational angle, the generalized velocity according to [PERSON]'s method can be written as follows:  
\[\dot{\mathrm{q}}=\begin{bmatrix}\dot{\mathrm{q}}_{1}^{\top},\dot{\mathrm{q}}_{ 2}^{\top},\dots,\dot{\mathrm{q}}_{24}^{\top}\end{bmatrix}^{\top}, \tag{2}\]  
where \(\dot{\mathrm{q}}_{i}\) is the generalized speed of Bj. It describes the relative motion between Bj and its inboard body \(\mathrm{Bc}(j)\), which is located near the base body and is connected to Bj by joint hj. The superscript \"T\" denotes the transpose of a matrix.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Vitamins
#### 4.1.5 Vitamin E and C Interaction  
The interaction between vitamins E and C exemplifies the cooperative function of hydrophilic and hydrophobic antioxidants. This synergy is particularly evident in the regeneration of the tocopheryl radical by ascorbate. Ascorbic acid facilitates the conversion of the tocopheryl radical back totocopherol, thereby extending its half-life. This interaction occurs at the aqueous phase boundary due to the spatial orientation of the chroman group within the tocopheryl molecule, enabling efficient interaction with ascorbate [53].  
Within cellular systems, vitamins C and E interact synergistically to provide robust antioxidant protection. In cellular membranes, vitamin E acts as a lipid-soluble antioxidant, becoming oxidized as it neutralizes peroxyl free radicals. The oxidized form of vitamin E is subsequently reduced and regenerated by intracellular vitamin C, owing to its lower redox potential. This regeneration process eliminates the need for continuous replacement of vitamin E within the membrane. Clinical evidence highlights the efficacy of combined supplementation with vitamins C and E in protecting against UV-induced erythema in humans. High oral doses of these vitamins provide significant photoproduction, whereas either vitamin alone proves insufficient. The topical application of L-ascorbic acid (15%) in conjunction with \(\alpha\)-tocopherol (1%) enhances photoproduction, offering a four-fold reduction in UV-induced erythema. This combined treatment also significantly reduces sunburn cell formation and thymine dimer production in porcine skin, compared to the two-fold protection achieved by either vitamin individually. The photoprotective effects of vitamins C and E are further potentiated when used in combination with melatonin, as demonstrated in human studies. Notably, the incorporation of these hydrophilic and lipophilic antioxidants into topical formulations stabilizes their activity, resulting in enhanced efficacy and a cosmetically elegant product [54].
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 4 Results and Discussion  
This comparison analysis assesses the efficacy of three regression models: Linear Regression, Lasso Regression and RFR, utilized for wind power predicting data. The performance of each model, the selection, and the comparison are evaluated using standard statistical metrics, such as R2, Adjusted R2, MSE, RMSE, and MAE, and criteria, such as AIC and BIC. Furthermore, cross-validation was conducted to assess the models' stability and generalizability. The following is a comprehensive comparison of the three regression techniques based on the data acquired. Initially, the Random Forest Regression and Linear Regression attain exceptionally high R2 and Adjusted R2 values, ranging from 0.98 to 0.99. Subsequent to preprocessing, it was observed that the ML algorithms exhibit overfitting issues. In conclusion, preprocessing reduces the risk of overfitting, ensuring accurate predictions on both training and new data. The final results are presented in Table 4.
# Design of WPT System Based on Interleaved Boost Converter
## 4 Stress Analysis and Parameter Design
Figure 6: Changes of primary side current with primary and secondary side self-inductance before and after misalignment. (**a**) Alignment; (**b**) Offset to farthest distance. Since the WPT system proposed in this paper is a closed-loop control system, in addition to the stress of passive components, the possibility of closed-loop system implementation should also be considered when designing parameters. According to Equations (4) and (5), the relationship between the relative voltage gain and the self-inductance of the primary and secondary sides can be obtained as:  
\[G_{0}=\frac{\omega k\pi^{2}U_{\text{O}}\sqrt{L_{\text{P}}L_{\text{S}}}}{8 RU_{ \text{In}}} \tag{20}\]  
According to Figure 2, it can be found that when the duty cycle is around 0.5, that is, when the relative voltage gain \(G_{0}\) is around 2, the smaller the dead zone of the output voltage of the interleaved boost converter, the better the overall performance. According to the analysis of system efficiency improvement in Section 2, when \(G_{0}\) is around 2, the system efficiency improvement can also be guaranteed. According to Equation (20), the contour diagram of the relative voltage gain and the self-inductance of the primary and secondary sides can be drawn before and after the offset, as shown in Figure 5:0 When the self-inductance of the primary and secondary sides is within the black circle, the stress limit of the passive devices is satisfied, and the relative voltage gain is maintained at around 2. From the result of the simulation, the final value of the primary and secondary side self-inductance is at point A, where the primary side self-inductance \(L_{\text{P}}\) is 1177 \(\mu\)H, and the secondary side self-inductance \(L_{\text{S}}\) is 282 \(\mu\)H.  
Figure 5:1 Changes of \(U_{\text{C2}}\) with primary and secondary side self-inductance before and after misalignment in simulation. Figure 5:2 Changes of \(U_{\text{C1}}\) with primary and secondary side self-inductance before and after misalignment. **(a)** Alignment; **(b)** Offset to farthest distance.
## 4 Discussion  
The development of a voice-controlled robotic system for early education, as presented in this study, marks a step forward in leveraging technology to enhance learning experiences for young children. By employing advanced artificial intelligence technologies, including large language models and sophisticated object detection techniques, the system enables children as young as four to interact with robots intuitively through voice instructions. This approach not only simplifies the interaction process but also opens up a wide range of educational possibilities that were previously inaccessible. The complexities of programming languages and robot theory, often too advanced for young learners to grasp, can no longer be barriers to exploration and learning. Moreover, this type of system enhances accessibility, making educational robotics more inclusive and adaptable to diverse learners' needs.  
Our findings demonstrate the system's effectiveness in recognizing and executing commands from children. However, the study also underscores several technological challenges. The voice recognition and action recognition systems need to be significantly faster to prevent distractions among young learners, which could limit their practical use in classrooms. Additionally, these systems must reduce their hardware requirements for broader adoption, making the system more accessible to schools of any size and budget. This represents significant challenges on the technological front to achieve widespread implementation.  
Future research should explore more open-ended interaction scenarios, enabling children to perform various tasks and engage more deeply with the robot. Moreover,  
Figure 7: Children engaging with the robot in the workshop session.  
comprehensive investigations are necessary from an educational perspective to assess learning gains across multiple domains, such as computational thinking, collaborative work, and subjects like science or mathematics, to substantiate the educational value of this type of robot. Integrating learning subjects could be particularly effective given the robot's versatile nature. This aspect is crucial not only for the educational adoption of the technology but also for mitigating concerns about the use of technology in early education, which is often perceived as more detrimental than beneficial.  
Additionally, it is worth noting that the potential of these interfaces and robots extends far beyond education. The key lies in making technology more intuitive to use, fostering true human-robot collaboration, and ensuring its positive application across all areas of human activity.
## 3 Results and Discussion
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{\mathscr{N}}\) & **Composition** & \(\mathbf{\mathrm{g}}\mathbf{\mathrm{t}}\) **(min)** & \multicolumn{5}{c}{**Relative Peak Area \%**} & \\  & & **NB** & **N-free** & **TSD** & **LB** & **MRS** \\ \hline
1 & Butanoic acid, 3-methyl- & 3.410 & & & 3.19 & 19.03 \\
2 & Butanoic acid, 2-methyl- & 3.690 & & & & 2.61 \\
3 & p-Xylene & 3.741 & 1.31 & 2.22 & & & 0.25 \\
4 & Phenylethyl Alcohol & 7.139 & & & & 0.15 \\
5 & Benzoic acid & 7.844 & & & & 1.19 \\
6 & Benzeneacetic acid, methyl ester & 8.057 & & & & 1.62 \\
7 & Benzeneacetic acid & 9.085 & & & & 0.13 \\
8 & Indole & 9.756 & & & & 0.30 \\
9 & Benzenpropanoic acid, alpha-hydroxy-, methyl ester & 10.775 & & & & 0.30 \\
10 & Ocriol & 10.852 & 6.62 & & & \\
11 & 1,3-Benzenzediol, 4,5-dimethyl- & 11.412 & 11.48 & & & \\
12 & 13-Methyltetradecanal & 11.999 & & & 1.13 & & \\
13 & 2,2’-Isopropylidenehs(5 methylfuran) & 14.343 & 11.13 & & & \\
14 & 2-Tridecan-1,0-(E) & 14.403 & & & 0.44 & & \\
15 & Pyrrolo[1,2-alpyrazine-1,4 dione, hexahydro-15.337 & & & & 0.33 & \\
16 & Methyl 13-methyltetradecanoate & 15.609 & & & 0.68 & 0.64 & \\
17 & Methyl 9-methyltetradecanoate & 15.609 & & & & 0.71 \\
18 & Diethylithylight & 17.206 & & & & 2.92 \\
19 & Pyrrolo[1,2-alpyrazine-1,4 dione, hexahydro-3-(2 methylpropyl)- & & & & 4.45 & \\
20 & Hexadecanoic acid, methyl ester & 17.223 & & 7.33 & & \\
21 & Pyrrolo[1,2-alpyrazine-1,4 dione, hexahydro-3-(2-methylpropyl)- & & & 17.512 & 1.46 & & 2.41 \\
22 & Dbutyl phthalate & 17.775 & & & 1.48 & 1.72 & 1.29 \\
23 & Tetradecanamide & 18.684 & & & 0.55 & & \\
24 & 9-Cudadecenamide, (Z)- & 18.684 & & & & 0.78 & \\
25 & 9-Hexadecen-1-ol, (Z)- & 18.965 & & & & 0.26 \\
2-[2-[2-[2-[2-[2-(Hydrocyclohexylethoxy)ethoxy]]] & & & & & 0.30 \\ ethoxylethoxy]ethoxy]ethoxy]ethoxy] ethoxylethoxy] & & & & & 0.82 \\
27 & 1-Octadecene acid (Z)-, methyl ester & 19.517 & 1.38 & 16.05 & & 0.39 & 1.17 \\
28 & 9-Octadecenoic acid (Z)-, methyl ester & 19.593 & & & & 0.30 \\
29 & Cis-13-Octadecenoic acid, methyl ester & 19.848 & & & & 0.29 \\
30 & Heptadecanoic acid, 16-methyl-, methyl ester & 19.848 & & & & 0.18 \\
31 & Methyl 17,12-octadecanoate & 20.112 & & & & 0.15 \\
32 & Methyl 10-\(t\)mrs,12-\(cis\) octadecionate & 20.111 & & & & & 0.65 \\
33 & Hexadecanamide & 20.604 & & & & & 1.06 \\
34 & 1,4,7,10,13,16-Hexadecadecadecadecane & 23.068 & & & & & 0.65 \\
35 & Pyrrolo[1,2-alpyrazine-1,4 dione, hexahydro-3-(phenylmethyl)- & & & & & 1.06 \\
36 & hexahydro-3-(phenylmethyl)- & & & & & & 1.06 \\
37 & 9-Octadecenamide, (Z)- & & & & & & 1.23 \\
38 & octahydro-4a-methyl-7-(1-methylethyl)-, & & & & & & 0.29 \\
39 & Eicosane & 30.595 & & & & & 1.17 \\ \hline \hline \end{tabular} Note: NB— nutrient broth; N-free—N-free broth; TSD—tryptic soy dextrose; LB—LB broth; MRS—De Man, Rogosa, and Sharpe broth.  
\end{table} 
Table 9: The volatile chemical composition of the most active endophytic bacteria, _B. haltolerans_ XJB-35.
## 4 Discussion
### Parameter Importance Analysis  
In this study, GOA-RF (Pop = 10) is the best model for the \(GS\) prediction. Typically, the out-of-bag (OOB) data error rate is used as the basis for assessing correlation in the RF model, which also represents the contribution degree of variables to the results [96].  
Figure 10 illustrates the importance of each relevant parameter for the \(GS\) prediction based on the GOA-RF (Pop = 10) model. The results clearly illustrate that \(MC\), \(AR\), \(Sm\), and \(GP\) have achieved higher importance scores (2.586, 2.277, 2.078, and 1.875) than \(E\), \(EP\), and \(H\) with relatively lower importance scores of 1.55, 1.078, and 1.052, respectively. Therefore, \(MC\), \(AR\), \(Sm\), and \(GP\) are more important for the \(GS\) prediction than \(E\), \(EP\), and \(H\). Although these seven parameters are different from the \(GS\), the difference in importance scores indicates that neither parameter can be discarded for predicting \(GS\). In future research, additional geological, design, and operational parameters should be considered to improve the model efficiency and precision.  
Figure 9: Comprehensive information on different hybrid model evaluation indicators: (**a**) Comparative analysis of train set; (**b**) Comparative analysis of test set.
## 4 Conclusions  
Bio-based eco-friendly composites were designed and elaborated by combining a vegetable oil-based epoxide, ELO, with spruce bark wood, SB, and its hydrochar, HC, in the presence of DDSA anhydride as a hardener. The reactivity of formulations containing from 0 to 30% HC and SB fillers was studied with the help of DSC, with the results showing a high catalytic effect of both fillers on the ELO crosslinking reaction. Indeed, the enthalpy of reaction is 6.6 times higher for the formulation with 30% HC compared with that of the neat resin (i.e., 608 vs. 92 J\(\cdot\)g\({}^{-1}\)) and almost four times higher in the case of formulation with 30% SB (364 vs. 92 J\(\cdot\)g\({}^{-1}\)). The onset of the reactions' temperatures had also been influenced, being decreased for the both HC and SB systems. These results are a clear sign that both HC and SB contribute to the chemical mechanism of ELO crosslinking. Not only were the chemical reactivities influenced by the two fillers, but also the final properties of  
Figure 10: SEM micrograph of the ELO-based formulations filled with SB at (**a**) 100 \(\upmu\)m, (**b–d**) 10 \(\upmu\)m, (**e**) 2 \(\upmu\)m, and (**f**) 1 \(\upmu\)m.  
Figure 9: SEM micrograph of the ELO-based formulations filled with HC at (**a**) 100 \(\upmu\)m, (**b**) and (**c**) 10 \(\upmu\)m, (**d**) 2 \(\upmu\)m, (**e,f**) 1 \(\upmu\)m.  
the produced composites. The storage modulus in the rubbery plateau increases with HC% in composites, a sign of a reinforcement of the material's stiffness.  
Based on the Shore hardness test, the inclusion of both SB and HC in the epoxy-based matrix improved the hardness of the materials from 63 SD (neat resin) to 84-85 SD for the composites with 30 wt.% filler. By tensile test, an improvement of the mechanical properties was also observed, with the Young's modulus increasing from 180 MPa (neat resin) to 300 MPa for the sample with 15 wt.% HC and 270 MPa for the one with 20 wt.% SB. The influence of the natural fillers on the water absorption behaviour at the saturation level (after 11 days of immersion) was also investigated, revealing a low water retention with values ranged between 0.7 and 1.7% for HC materials and between 0.7 and 6.3% for SB composites. Depending on this parameter, the materials with HC and low SB quantity can be easily used in outdoor applications, while materials with higher concentrations of SB that have a slightly higher WA% can be intended for indoor use. SEM analysis revealed a very good interaction between the matrix and fibres and also a good distribution and homogeneity of the particles in the system.  
R.D.: formal analysis; investigation; data curation; writing--original draft preparation; methodology; I.B.: formal analysis; investigation; data curation; I.V.: validation; supervision; A.M.: Conceptualization; methodology; validation; supervision; resources; writing--review and editing; project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Overview of 31P qNMR for Phosphorus Compounds
### Internal Standard Method for qNMR  
In the internal standard method of quantitative nuclear magnetic resonance (qNMR), a compound with a precisely known concentration is added to the sample as an internal standard. By measuring the ratio of the integrated signal areas between the internal standard and the analyte, the concentration of the analyte can be accurately determined. This approach is widely used in qNMR due to its precision and reliability. For instance, [PERSON] et al. employed the internal standard method to determine the purity of a thiopeptin reference standard using sulfadoxine as the internal standard [25]. Similarly, [PERSON] et al. developed a quantitative proton NMR method to analyze pregnolone, utilizing an internal standard for accurate quantification [26]. The concentration of the analyte can be calculated using the following equation [15]:  
\[m_{x}=\frac{I_{x}}{I_{std}}\ \frac{N_{std}}{N_{x}}\ \frac{M_{x}}{M_{std}}\ p_{std}\ m_{std} \tag{1}\]  
where \(\rm I_{x}\) is the integral of the analyte signal, \(\rm I_{std}\) is the integral of the internal standard signal, \(\rm N_{x}\) is the number of nuclei contributing to the analyte signal, \(\rm N_{std}\) is the number of nuclei contributing to the internal standard signal, \(\rm M_{x}\) is the molar mass of the analyte, \(\rm M_{std}\) is the molar mass of the internal standard, \(\rm P_{std}\) is the purity of the internal standard, and \(\rm m_{std}\) is the mass of the internal standard. By employing quantitative nuclear magnetic resonance (qNMR) with an internal standard, the necessity for calibration curves is eliminated. This technique involves adding a reference material of known concentration directly to the analyte solution or using a coaxial insert. The internal standard method is a well-established primary ratio analytical approach, extensively utilized in organic chemical analysis [27].
## 4 Convergence Analysis of the Revised CIM-PNMCC
### Mean Convergence
\(\text{E}\left[m\left(n\right)\right]\) is bounded [41] and every element of \(\hat{\mathbf{w}}^{\prime}\left(n+1\right)\) is independent. Hence, \(\hat{\mathbf{w}}^{\prime}\left(n+1\right)\) converges to zero if and only if \(\left|1-\chi_{1}g_{k}\lambda_{k}\right|<1\). As a result, our CIM-PNMCC algorithm converges only if it converges for the \(\lambda_{\text{max}}\). Since \(\lambda_{\text{max}}\leq\text{tr}\left[\mathbf{R}\right]\), where \(\text{tr}\left[\cdot\right]\) is matrix trace operation, \(\hat{\mathbf{w}}\left(n\right)\) converges to zero when \(\chi_{1}\) meets  
\[0<\chi_{1}<\frac{2}{\text{tr}\left[\frac{\mathbf{G}\left(n\right)\mathbf{R}}{N \sigma_{x}^{2}+\varepsilon_{\text{CIM}}}\right]}. \tag{36}\]  
Using A1 and A3, we have \(\mathbf{R}=\text{E}\left[f\left(e\left(n\right)\right)\right]\sigma_{x}^{2} \mathbf{I}\). Moreover, considering \(\text{tr}\left[\mathbf{A}\mathbf{B}\right]\leq\text{tr}\left[\mathbf{A}\right] \text{tr}\left[\mathbf{B}\right]\), and \(\text{tr}\left[\mathbf{G}\left(n\right)\right]=N\), we obtain  
\[0<\chi_{1}<\frac{2 tr\left(N\sigma_{x}^{2}+\varepsilon_{\text{CIM}}\right)}{N \sigma_{x}^{2}\text{E}\left[f\left(e\left(n\right)\right)\right]}. \tag{37}\]
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
## 2 Preliminaries for Non-Binary Polar Codes
### MR-Based Non-Binary Polar Codes  
We extend binary polar codes to non-binary polar codes by considering the following \(2\times 2\) kernel \(\mathbf{G}_{2}\) over GF(\(2^{q}\)), which is given by  
\[\mathbf{G}_{2}=\left[\begin{array}{cc}1&0\\ a^{t}&1\end{array}\right], \tag{6}\]  
where \(\alpha\) is the primitive element of GF(\(2^{q}\)) and \(0\leq t<2^{q}-1\). Note that, in the case of \(\alpha^{t}=1\), \(\mathbf{G}_{2}\) has the same form as the Ankan kernel \(\mathbf{F}_{2}\). In the case of \(\alpha^{t}\
eq 1\), we call \(\mathbf{G}_{2}\) an MR-based matrix for convenience. In this paper, the generator matrix with code length \(N=2^{n}\) symbols is given by  
\[\mathbf{G}_{N}=\mathbf{G}_{2}^{\otimes n}=\left[\begin{array}{cc}\mathbf{G }_{2}^{\otimes(n-1)}&\mathbf{0}\\ \alpha_{n}\mathbf{G}_{2}^{\otimes(n-1)}&\mathbf{G}_{2}^{\otimes(n-1)}\end{array} \right],n\geq 2, \tag{7}\]  
where \(\alpha_{n}=\alpha^{2^{n-1}}\) and \(t=1\) in the matrix \(\mathbf{G}_{2}\).  
The encoding of non-binary polar codes can be described as  
\[x_{1}^{N}=v_{1}^{N}\mathbf{G}_{N}=v_{1}^{N}\mathbf{G}_{2}^{\otimes n}\mathbf{ B}_{N}, \tag{8}\]  
where \(v_{1}^{N}\) denotes the \(N\) input symbols of GF(\(2^{q}\)). Let \(u_{1}^{N_{b}}=u_{1}^{N\times q}\) represent the binary representation of \(v_{1}^{N}\). The factor graph of the \(\mathbf{G}_{8}\) MR-based polar codes over GF(4) is shown in Figure 1.  
\[\begin{split} W_{l}^{(2j-1)}\Big{(}y_{1}^{2j},v_{1}^{2j-2}\,|\,v_ {2j-1}\Big{)}=&\sum_{v_{2j}}\frac{1}{2^{q}}\Big{\{}W_{l-1}^{(j)} \Big{(}y_{1}^{2l-1},v_{1,\,\text{odd}}^{2j-2}\,+\alpha_{k}v_{1,\,\text{even} }^{2j-2}\,\,|\,\,  
are equal, for the conventional symbol-level construction with the information symbol set \(\mathcal{A}\) and frozen symbol set \(\mathcal{F}\), the SC decoder generates its decision \(\hat{v}_{i}\) by  
\[\hat{v}_{i}=\begin{cases}\arg\max_{\beta\in\text{GF}(2^{\text{f}})}W_{n}^{i}(y_{ 1}^{N},v_{1}^{i-1}\mid\beta),&i\in\mathcal{A}\\ \text{value of the frozen symbol }v_{i},&i\in\mathcal{F}.\end{cases} \tag{11}\]