esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Standardization ISO 9283--Path Accuracy
### Mathematical Background of CRX-10 iA
The greatest change was noted in the sixth joint. In planning the path, its calculation is sampled with a period defined by the speed of the inverse kinematic task calculation. In addition, path planning also accounts for sudden changes in movement so that permissible velocities and accelerations of individual drives are not exceeded. A path calculated in this way differs from the ideal one. Inaccuracy is influenced by several other factors, such as individual drives' positioning precision, robot rigidity etc., which have an effect on the resulting accuracy. Measurement can identify real deviation from the ideal path [31]. Figure 11: The course of acceleration analysis is plotted on the graph. Figure 9: The course of position measurement is plotted on the graph. Of basic importance to the entire line of matrix methods of kinematic and dynamic analysis and synthesis are homogenous transformations that we use to describe the position and orientation of parts. Utilizing visual systems' needs, homogenous transformations put the global position of parts into the context. The left-hand side of Figure 12 shows the first point in the measuring cycle, C8, and the right-hand side shows the last measuring point, C2. The resulting transformation matrix between adjacent coordinate systems is obtained by multiplying individual partial transformation matrices in the order in which the movements were made. The result of multiplication, describing the position of the first measuring point, is as follows:  
\[A^{i}_{i-1}= \begin{bmatrix}0.577&-0.707&0.408&323.758\\ -0.577&-0.707&-0.408&102.47\\ 0.577&0&-0.816&7.295\\ 0&0&0&1\end{bmatrix}  \tag{12}\]  
The homogenous transformation matrix for the last point measured:  
\[A^{i}_{i-1}= \begin{bmatrix}0.577&-0.707&0.408&773.758\\ -0.577&-0.707&-0.408&-347.530\\ 0.577&0&-0.816&457.295\\ 0&0&0&1\end{bmatrix}  \tag{13}\]
# Composition Diversity and Expression Specificity of the _Tps_ Gene Family among 24 _Ficus_ Species
## 3 Discussion
### Positive Selection of FTPSs  
In this study, the branch model and branch-site model methods were used to check for the positive selection of homologous _TPS_ genes. Except for five genes (_FTPS10_ and _FTPS12_ in the branch model test and _FTPS3_, _FTPS16_, and _FTPS20_ in the branch-site model test)that showed significantly positive selection signals in some specific species (Tables 2 and 3), a majority of the _FTPSs_ were still highly conserved among species and under purification selective pressure, suggesting functional stability of the _TPS_ genes. The five _FTPS_ genes encode four chemical compounds, linalool synthase, myrcene synthase, elemene synthase, and _E_-beta-caryophyllene synthase (Table S3). Notably, these compounds are all common in _Ficus_ species rather than species-specific ones [50; 54]. [PERSON] et al. [50] confirmed that beta-caryophyllene, linalool, myrcene, and beta-elemene can be identified from fig odors of 18, 10, six, and five _Ficus_ species, respectively. [PERSON] et al. [54] revealed that (_E_)-caryophyllene and beta-element were all highly abundant compounds released by both _F. hirta_ and _F. triloba_. These results imply that the _FTPS_ evolution may prefer to select those common genes shared by several _Ficus_ species rather than recruit some specific or rare new genes. In addition, in this study, two genes (_FTPS4_ and _FTPS8_) with three and one positive selection sites in species of _F. hirta_ and _F. triloba_, and _F. erecta var. becheyama_ were also identified under branch-site model, respectively. Although these sites were not located in the conserved protein domains of plants _TPS_ genes, these hidden interspecies divergences may prompt the diversification and complexity on the structure and function of the _FTPS_ proteins, and then enforce VOC specificity. Further in-depth functional verification should be performed.
# A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study
### Leveraging Data to Predict Maintenance
In online monitoring, equipment sensors allow data to be collected in real time, maybe stored on the cloud, with no time or effort from humans. The whole data collection process works automatically and remotely, including data processing and analysis by a platform based on artificial intelligence tools. The output is sent to managers who may have information and insights on the equipment's health status. So, equipment monitoring is possible by remote networked digital devices. Another reason to prefer online monitoring is because providing engine status information continuously is the best way to catch sudden failures in time, avoiding catastrophic accidents (see the Baltimore bridge collapse after cargo-ship power loss in March 2024). This paper contributes to online monitoring methods by a framework based on predictive algorithms. This framework consists in an easy-to-implement procedure supporting an initial-stage CBM. Namely, to perform online monitoring in an effective way, the first necessary step is to choose the most suitable predictive algorithm. As highlighted in the following sections, the motivation for the choice of the predictive algorithm is often omitted or underestimated or is not the consequence of a comparative analysis. So, to the best of the authors' knowledge, the novelty of the present study is a framework for an initial and quick benchmarking among three different machine learning (ML) algorithms that can be easily developed and implemented. The analysis compares the three ML algorithms based on their time response and predictive error, which, in the authors' opinion, are the most important variables to assess the algorithmic performance. Namely, the response time is crucial in maritime applications because giving failure information on time is necessary to guarantee the safety of the machinery, passengers, and crew. On the other hand, the predictive error must be low to have a reliable prediction. The paper is divided as follows. Section 2 gives an overview of the literature on the predictive maintenance approach based on efficient and profitable use of data. Section 3 shows the proposed framework to select the best algorithm for CBM. Section 4 synthetically describes the simulation model of the engine, in particular of the significant oil filter circuit, used as a case study for applying the framework strategy. Section 5 shows and briefly discusses the simulation results. Finally, Section 6 draws the conclusions.
## 4 Conclusions  
In this study, the aroma composition of hot-pressed yellow horn seed oil was assayed by using the seed of yellow horn roasted at different temperatures. VOCs were identified using HS-GC-IMS and HS-SPME/GC-O-MS. GC-IMS allowed for the determination of 97 VOCs, while GC-O-MS allowed for the determination of 77 VOCs, and both methods allowed for the identification of 24 common VOCs. In addition, the OAV values were calculated, and 35 VOCs were screened by OAVs \(\geq 1\) as the standard. Subsequently, twenty-three aroma compounds were confirmed through the use of olfactory probe recordings.  
2,5-dimethylpyrazine, hexanal, heptanal, 2-pentylfuran, hexanol and 1-octen-3-ol were determined as the key aroma compounds in yellow horn seed oil by VOCs with VIP \(>1\) in GC-IMS and aroma compounds with combined OAVs \(\geq 1\) and olfactory in GC-O-MS. Combining the result of OPLS-DA and PLS-DA, it was concluded that the aroma compounds of the samples were similar at 120 \({}^{\circ}\)C and 130 \({}^{\circ}\)C, while those of the samples were similar at 140 \({}^{\circ}\)C, 150 \({}^{\circ}\)C and 160 \({}^{\circ}\)C. According to the results of GC-O-MS, the VOC content of the sample at 160 \({}^{\circ}\)C is higher. The results of the present study supplemented the understanding of the composition and aroma of VOCs in yellow horn seed oil.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12173174/s1](https://www.mdpi.com/article/10.3390/foods12173174/s1)]([https://www.mdpi.com/article/10.3390/foods12173174/s1](https://www.mdpi.com/article/10.3390/foods12173174/s1)), Figure S1: Rose and pie chart of VOCs detected by GC-IMS (A), (B) and GC-O-MS(C), (D) in yellow horn seed oil; Figure S2: Pie chart(A), heat map (B), and VIP map (C) of 24 VOCs detected jointly by GC-MS and GC-IMS; Table S1: The VOCs identified by GC-IMS in yellow horn seed oil; Table S3: The VOCs identified by the combination of olfactory time and GC-MS retention time.  
Conceptualization, H.G. and J.S.; methodology, H.G.; software, H.G., M.L., H.L. and S.Z.; validation, H.G., M.L. and X.C.; formal analysis, H.G.; investigation, L.Z., Z.Z. and T.Z.; resources, J.S.; data curation, H.G. and S.L.; writing, original draft preparation, H.G. and M.L.; writing, review and editing, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Project supported by Qingdao Natural Science Foundation: 23-2-1-180-zyyd-jch; Key R&D Program of Shandong Province, China: 2021 TZXD10; The Two Hundred Talents project of Yantai City in 2020; Innovation Ability Improvement Project of Science and Technology smes in Shandong Province: 2022 TSGC2520, 2023 TSGC0892; Qingdao people's Liveblood Science and Technology Plan Project: 23-2-8-xdny-6-nsh, 23-3-8-xdny-1-nsh.  
Data Availability Statement: The data used to support the findings of this study can be made available by the corresponding author upon request.  
The authors declare no conflict of interest.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Session Key Security  
Given \(T_{r_{1}}(x_{0})\)\(mod\)\(p\) (\(=T_{r_{1}r_{2}}(x)\)\(mod\)\(p\)) and \(T_{r_{1}+1}(x_{0})\)\(mod\)\(p\) (\(=T_{r_{1}r_{1}r_{3}}(x)\)\(mod\)\(p\)), where \(x_{0}\) denotes \(T_{r_{s}}(x)\)\(mod\)\(p\), \(Y_{i}=T=T_{r_{\mathbf{nr}_{i+1}}}(x_{0})\)\(mod\)\(p\) (\(=T_{r_{1}r_{1}r_{s}}(x)\)\(mod\)\(p\)) cannot be determined, because of the ECM-DH problem. The values of \(r_{1}\),\(r_{2}\),..., \(r_{n}\) and \(r_{s}\) are randomly selected and mutually independent in each protocol execution, so the secret \(sk_{i,m}\) and the session key \(SK_{m}\) fail to be determined without knowledge of \(r_{s}\) and \(r_{i}\) for \(I\leq i\leq n\), where \(sk_{i,m}=T_{r_{1}r_{2}\cdot r_{5}}(x)\cdot T_{r_{2}\cdot r_{3}\cdot r_{5}}(x) \cdot\ldots\cdot T_{r_{n}\tau_{1}\cdot r_{5}}(x)\text{mod}p\) and \(SK_{m}=h(GID_{im}||sk_{i,m})\) for the group \(G_{im}\). Hence, the session key security is based on the ECM-DH problem and is therefore considered not computationally feasible.
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline
**Workpackage** & **Workshop Title** & **Report Title** \\ \hline \multirow{7}{*}{\begin{tabular}{l} Human Hazard \& \\ Exposure (WP1) \\ \end{tabular} } & \multicolumn{3}{l}{Minimal analytical characterisation of engineered nanomaterials need for hazard assessment in biological matrices [4];} \\  & Standardization of materials and protocols & First approaches to standard protocols and reference materials for the assessment of potential hazards associated with nanomaterials [5];} \\  & & Best practice documents for handling, testing safety \\  & Methods to describe and measure & Nanoparticle metrics in the air, exposure scenarios \\  & exposure routes/QA & and exposure routes [6] \\  & Protocols for assessment of biological & Protocols for assessment of biological hazards of \\  & hazards Biological responses & engineered nanomaterials [7] \\  & Development of strategies to assess & Strategies for assessing occupational health effects of \\  & occupational health effects & engineered nanomaterials [8,9] \\ \hline \multirow{7}{*}{\
\begin{tabular}{l} Environmental \\ Hazard \& \\ exposure (WP2) \\ \end{tabular} } & \multicolumn{3}{l}{Nanomaterials for environmental studies:} \\  & Strategies to standardize nanomaterials & Classification, reference material issues, and \\  & for environmental and ecotoxicological & strategies for physico-chemical characterisation [10]; \\  & research & Recommendations of protocols and approaches to \\  & & study environmental impacts of nanomaterials [11] \\  & & Development of standardized protocols \\  & & Environmental fate and behaviour of \\  & & nanoparticles—beyond listing of limitations [12] \\  & & Optimization of current standard protocols \\  & to allow assessment of nanomaterial & assessment of nanomaterial in a range of \\  & hazard in a range of organisms & organisms [13] \\ \hline \multirow{7}{*}{
\begin{tabular}{l} Life Cycle \\ Assessment \\ (WP3) \\ \end{tabular} } & \multicolumn{3}{l}{Life cycle assessment of} & \multicolumn{3}{l}{The importance of life cycle concepts for the} \\  & \multicolumn{3}{l}{nanomaterial-containing products} & \multicolumn{3}{l}{development of safe nanoproducts [14]} \\ \cline{1-1}  & \multicolumn{3}{l}{Risk assessment of nanomaterials} & \multicolumn{3}{l}{Consensus Report - Risk Assessment of} \\ \cline{1-1}  & \multicolumn{3}{l}{Nanomaterials: _In vitro–in vivo_ extrapolation [15]} \\ \cline{1-1}  & \multicolumn{3}{l}{Impact assessment of nanomaterials} & \multicolumn{3}{l}{Final report with recommendations for Impact} \\ \cline{1-1}  & \multicolumn{3}{l}{Assessment of Nanomaterials [16]} \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the NanoImpactNet Workshops and resulting reports.
# A Stair-Climbing Wheelchair with Novel Spoke Wheels for Smooth Motion
## 3 Theoretical Analysis  
### Spring Configuration  
#### 3.1.1 Spoke Deformation Analysis  
Let the number of spokes be \(n\), and the distance from the center of rotation to the center of the spherical foot pad is denoted as \(l\). The angle between the spokes and the ground is represented by \(\theta\). As the Flexwheel rotates, \(\theta\) varies within the range:  
\[\frac{\pi}{2}-\frac{\pi}{n}\leq\theta\leq\frac{\pi}{2} \tag{1}\]  
As shown in Figure 3, the supporting force \(F_{\mathrm{N}}\) acting on the foot pad can be decomposed into two components, one along the spoke direction (\(F_{1}\)) and the other perpendicular to it (\(F_{2}\)):  
\[\begin{cases}F_{1}=mg\sin\theta\\ F_{2}=mg\cos\theta\end{cases} \tag{2}\]  
In practical applications, friction \(f\) is generated due to the relative motion between the upper and lower spokes, given by:  
\[f=\mu_{0}\cdot F_{2} \tag{3}\]  
where \(\mu_{0}\) is the friction coefficient between 304 stainless-steel surfaces.  
Figure 3: Force analysis diagram.  
Figure 2: Wheelchair mechanism diagram.  
When the spring constant \(k\) approaches infinity, the compression spring undergoes negligible deformation. In this case, when the spoke is perpendicular to the ground, the maximum height of the wheelchair body (which coincides with the hub center height) is:  
\[h_{\text{BMAX}}=l \tag{4}\]  
The target height of the body's center of mass (CoM), denoted as \(h_{\text{B}}\), should satisfy:  
\[h_{\text{B}}=j\cdot l \tag{5}\]  
where \(j\) is the body height ratio. To maintain a stable body height while ensuring obstacle-crossing capability, the range of \(j\) is:  
\[0.4\leq j\leq\sin\Bigl{(}\frac{\pi}{2}-\frac{\pi}{n}\Bigr{)} \tag{6}\]  
During Flexwheel rotation, in order to maintain the CoM height at \(h_{\text{B}}\), the deformation \(\Delta l\) of the compression spring must satisfy:  
\[(l-\Delta l)\sin\theta=h_{\text{B}} \tag{7}\]  
Solving for \(\Delta l\):  
\[\Delta l=l-\frac{h_{\text{B}}}{\sin\theta} \tag{8}\]  
When the spring deforms by \(\Delta l\), it generates an elastic force \(F_{\text{E}}\), which follows [PERSON]'s law:  
\[F_{\text{E}}=k\Delta l \tag{9}\]  
Applying [PERSON]'s second law and considering force equilibrium along the spoke direction, we obtain:  
\[F=f+F_{\text{E}} \tag{10}\]  
By substituting Equations (2), (3) and (9) into (10), the relationship between body height \(h_{\text{B}}\), angle \(\theta\), and spring constant \(k\) is derived:  
\[mg\sin\theta=\mu_{0}mg\text{cos}\,\theta+k\Bigl{(}l-\frac{h_{\text{B}}}{\sin \theta}\Bigr{)} \tag{11}\]  
During Flexwheel rotation, the actual spoke length \(l_{\text{A}}\) can be expressed as \(l_{\text{A}}=h_{\text{B}}/\sin\theta\). Rearranging, the required spring constant \(k\) is given by:  
\[k=\frac{mg(\sin\theta-\mu_{0}\text{cos}\,\theta)}{l-l_{\text{A}}} \tag{12}\]  
Further transformation yields the final expression for the body height:  
\[h_{\text{B}}=l\sin\theta-\frac{mg\bigl{(}\sin^{2}\theta-\mu_{0}\text{sin}\, \theta\text{cos}\,\theta\bigr{)}}{k} \tag{13}\]
# Computation of Optimized Electrode Arrays for 3-D Electrical Resistivity Tomography Surveys
## 5 Field Data
### Elounda (Agios Nikolaos)  
The field data were collected from the bay of Elounta (Crete, Greece). The selected grid was 30 \(\times\) 47 m where 31 parallel survey lines (1 m apart) were used to collect the geoelectrical data in the marine environment. For each line, 48 submerged electrodes were used with 1 m probe spacing in depth varying from 0.07 m to 2.86 m and the resistivity of water was measured 0.2 ohm.m. Each line collected 1298 (pole-dipole forward and reverse), producing an overall complete data set of 40,204. All data were used to produce the comprehensive FULL data set.  
The Optim-3D data set with 21,184 measurements were created from the 3-D Jacobian optimization procedure. The amount of optimum data that was selected from 3-D algorithm from the comprehensive data set was 50%. All data were inverted with the same inversion parameters and are presented with a common resistivity scale from 0.2 to 7 ohm.m. The estimated RMS error for all inversion models is from 1.56 to 2.21% after five iterations. The deepest depth layer reaches 3.0 m.  
Both models (Figures 18 and 19) present similar results revealing a linear resistive area which extends from the depth of 0.5 m up to 2.5 m located between 23 and 38 m in the x direction and 18 to 21 m in the y direction. This area is correlated with the visible archaeological remains of a possible wall or tower. The relatively resistive area that extends from 29 to 41 m in the x direction across the y direction could be related to collapse parts of the aforementioned structure or with a poorly preserved wall. The area (0-25 m in x, 0-30 m in y) is dominated by the conductive sandy seafloor. A resistive target (1-7 m in x, 3-11 m in y) inside that area could be either beach rock, also visible in other parts of the bay, or some other archaeological relics.  
Figure 18: Inversion results from the comprehensive Elounda field data set.
## 2 Theoretical Frameworks  
The proposed hybrid model consists of techniques organised in different phases. The first one is a problem-structuring phase in terms of analysing causal factors. This includes a brainstorming exercise of different factors using the FTA modelling approach. The outcome of this phase is to systematise the review of the case study by classifying the factors into direct and contributing (indirect) factors, as well as expanding them into sublevels of root causes. The second phase aims at establishing the relationships between different factors and an assessment of vulnerability scenarios within the whole system. This is achieved through mapping the FTA into a RBD using a set of mapping rules. This phase produces a cut set analysis. The third phase is involves risk assessment, including the construction of FMECA focused on the direct causes and using the root causes from the FTA model. The aim of this phase is to determine the Risk Priority Numbers (RPNs) of different failure modes. The reason for focussing only on direct causes here is that it is more practical to quantify the components of RPN for direct compared to indirect causes. The final phase prioritises recommendations in terms of assessment of safety impact, probability of occurrence, and costs. It also prioritises the changes needed in order to become a High Reliability Organisation (HRO). This is achieved by constructing two models based on the Analytic Hierarchy Process (AHP) for direct factors and contributing (indirect) causes.  
The proposed framework aims at disaster risk reduction. Specifically, the framework demonstrates the main objective of this paper to show how a combination of integrated techniques in the form of a hybrid model can be applied to a case study in such a way to demonstrate the benefits obtained from every technique. The design of the framework also provides a societal impact through interdisciplinary studies, as it combines techniques inspired from reliability (through the use of FTA, RBD, and FMECA), operational research (through the use of AHP), and organisational resilience (through the incorporation of HRO) disciplines.  
A summary of the theoretical framework is shown in Figure 1 below.  
Note that in Figure 1, the FMECA can be obtained either directly from the FTA or from a combination of FTA and RBD. When selecting different techniques for the hybrid model, we ensured that the output of every technique fed as an input to the subsequent used technique. Each of the mentioned modelling techniques above is explained in the following subsections.
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 2 Materials and Methods
### Scenarios  
Since the objective of this work is to provide a performance analysis of WBAN channels under real conditions, all the characterized scenarios represent usual everyday activities and postures. First, two static scenarios are analyzed to observe the effect of frequency, node placement and body posture on WBAN channels, for an unmoving subject. In these scenarios, the signal paths are comprised a particular configuration of body tissue and free space that remains unchanged for the duration of the experiments.  
Next, two dynamic activities are studied to analyze the effect of body movements on the WBAN links condition, particularly in terms of nodes being in LOS or NLOS. The periodicity of these activities' motions is also extracted and analyzed.
# Design and Experiment of a Passive Vibration Isolator for Small Unmanned Aerial Vehicles
## 2 Design of the Vibration Isolator and Test of Rubber Stiffness
### Comparative Verification of Vibration Isolator Stiffiness through Experiment and FEM Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Hardness**} & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Calculated** \\  & **at 1 kg (mm)** & **at 2 kg (mm)** & **at 3 kg (mm)** & **at 4 kg (mm)** & **Stiffness (N/m)** \\ \hline
30 & 0.45 & 0.96 & 1.41 & 1.72 & 1796 \\
50 & 0.36 & 0.64 & 0.92 & 1.21 & 2902 \\
70 & 0.19 & 0.37 & 0.54 & 0.72 & 4652 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stiffness measurements of rubber by hardness level. Figure 5: Stiffness measurement experiment: (**a**) schematic diagram; (**b**) experiment setup; (**c**) measurement of shrinkage according to the stiffness of rubber. Figure 6: Vibration isolator static structural analysis model. analysis was also performed to calculate the displacement due to the load, and the results of the experiment displacement and static structural analysis displacement are organized into Table 3.  
In Table 3, the simulation results showed an error rate of less than 4.5%, and the difference from the actual experiment was identified as stemming from the geometric non-linearity of the rubber. As the load increases, the contact area with the metal plate increases, leading to changes in stiffness, which is why there is a discrepancy between the simulation represented by the spring constant and the actual experimental value. However, with a marginal error of less than 4.5%, the data were deemed usable for application. This study builds upon the earlier work by comparing and validating FEM analysis with experimental environments, thereby ensuring system reliability. Simulation of the Drone's First Natural Frequency Using a Fixed-Fixed Beam: Numerical and FEM Analysis