esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we introduced an augmented version of the Fast Marching Tree (FMT*) algorithm, meticulously engineered for the sophisticated task of Cartesian space trajectory planning in robotic arms, with a special focus on evading both physical obstacles and the mathematical quagmires known as singularities. This enhancement is primarily anchored in the strategic application of Sobel sampling--a technique renowned for its capability to yield a uniform distribution of sampling points across the space. By integrating this technique into a novel two-stage sampling point generation strategy, we have significantly advanced the efficiency and effectiveness of trajectory planning in complex environments.  
A pivotal innovation of our work is the refinement of the FMT* algorithm's path connection strategy through the incorporation of a geometric perception singularity index. This modification imbues the algorithm with the dual capability to skirt around obstacles while meticulously avoiding singular regions, ensuring a safer and more reliable path for robotic arm movement. The path selection process is equally robust, employing a comprehensive approach that leverages collision detection and path cost analysis to identify the most optimal trajectory. The application of quasi-uniform cubic \(B\)-splines further enhances the trajectory, ensuring smoothness and continuity essential for the precise execution of tasks by robotic arms.  
Notwithstanding these advancements, our improved FMT* algorithm is not devoid of limitations. A notable challenge is the potential discrepancy between the optimal path in Cartesian space and the efficiency of that path when translated into joint space--a critical consideration for the practical application of robotic arms. Moreover, the reliance on sampling for path generation introduces a degree of variability influenced by initial sampling parameters. This sensitivity can result in significant variations in outcomes, emphasizing the impact of the stochastic nature of sampling-based methods on the predictability and consistency of the optimal path.  
Future endeavors to ameliorate these limitations could explore adaptive sampling techniques that dynamically refine based on environmental complexity or investigate more sophisticated metrics for joint space efficiency. Additionally, integrating predictive models or machine learning algorithms to optimize initial sampling parameters may offer a pathway to reducing variability and enhancing the consistency of the algorithm's outcomes.  
Conceptualization, B.W.; Writing--Original Draft, X.W.; Writing--Review and Editing, N.H.; Funding acquisition X.H. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by The Liaoning Provincial Research Foundation for Applied Basic Research, China (No. 2023 JH2/101300205).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
All authors would like to express their gratitude to everyone who contributed to this study for their insightful remarks.  
The authors declare no conflicts of interest.
# Development of a Power and Communication Bus Using HIL and Computational Intelligence
## 5 HIL-Based Optimization Method for Bus Prototyping
The proposed scheme of the optimization method is present in Figure 8. The HIL part of the system is composed of \(n\) modules (one master unit and \(n-1\) clients), where all modules are connected with one twisted pair of cables and a wireless network to remove the influence of auto-addressing signals. The optimization process is managed by the software created employing MATLAB and Raspberry Pi platforms.The authors decided to use the Global Optimization Toolbox for the implementation of evolutionary and particle swarm algorithms. More details are given and described in Section 5.2. Different optimization algorithms can be utilized for solving the problem, which has been declared in the form of (2) or (10). Hard computing optimization methods, for example, gradient-based approaches, cannot be adopted in this paper, mainly due to the stochastic nature of the objectives and because there are continuous and discrete decision variables in the vector p. On the other hand, pure stochastic optimization methods, for example, Monte Carlo techniques, will not be able to find an accurate solution, in this case guaranteeing polynomial-time convergence. Instead, soft computing optimization methods may be used to find the global minimum of the function. In this study, heuristic algorithms are applied such as SOAE, MOAE (Single or Multi-Objective Evolutionary Algorithm) and PSO (Particle Swarm Optimization).
## 3 Case Study
### Data Collection and Estimation of the Fraction of Time for Each POS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**POS** & **Description** & **Unit 1** & **Unit 2** & **Unit 3** & **Unit 4** & **Unit 5** & **Unit 6** \\ \hline \multirow{2}{*}{At-Power} & At-power operation (including & & & & & \\  & full-power and low-power) & 89.4\% & 92.5\% & 85.3\% & 75.6\% & 93.0\% & 92.4\% \\ \hline POS 2 & Coddown with SGs & 0.2\% & 0.2\% & 0.2\% & 0.2\% & 0.2\% & 0.2\% \\ \hline POS 3 & Coddown with SCS & 0.3\% & 0.3\% & 0.3\% & 0.3\% & 0.3\% \\ \hline \multirow{2}{*}{POS 4A} & RCS draindown & & & & & & \\  & (pressurizer manway closed) & 0.0\% & 0.0\% & 0.0\% & 0.0\% & 0.0\% \\ \hline \multirow{2}{*}{POS 4B} & RCS draindown & & & & & & \\  & (pressurizer manway open) & 0.1\% & 0.1\% & 0.1\% & 0.1\% & 0.1\% & 0.1\% \\ \hline POS 5 & 1 st mid-loop operation & 0.1\% & 0.1\% & 0.1\% & 0.1\% & 0.1\% \\ \hline POS 6 & Fill for refueling & 0.4\% & 0.3\% & 0.3\% & 0.3\% & 0.4\% & 0.4\% \\ \hline POS 7 & Offload & 0.8\% & 0.7\% & 0.7\% & 0.7\% & 0.8\% & 0.8\% \\ \hline POS 8 & Maintenance while defueled & 6.4\% & 3.9\% & 11.0\% & 20.8\% & 2.8\% & 3.4\% \\ \hline POS 9 & Onload & 0.7\% & 0.6\% & 0.6\% & 0.6\% & 0.7\% & 0.7\% \\ \hline POS 10 & RCS draindown after refueling & 0.5\% & 0.4\% & 0.4\% & 0.4\% & 0.5\% & 0.5\% \\ \hline \multirow{2}{*}{POS 11} & 2 nd mid-loop operation & 0.2\% & 0.1\% & 0.1\% & 0.1\% & 0.2\% & 0.2\% \\ \hline \multirow{2}{*}{POS 12A} & RCS refill & & & & & & \\  & (pressurizer manway open) & 0.1\% & 0.0\% & 0.0\% & 0.0\% & 0.1\% & 0.1\% \\ \hline \multirow{2}{*}{POS 12B} & RCS refill & & & & & & \\  & (pressurizer manway closed) & 0.3\% & 0.3\% & 0.3\% & 0.3\% & 0.3\% & 0.3\% \\ \hline POS 13 & RCS heaup with SCS isolation & 0.2\% & 0.2\% & 0.2\% & 0.2\% & 0.2\% \\ \hline POS 14 & RCS heaup with SGs & 0.4\% & 0.3\% & 0.3\% & 0.3\% & 0.4\% & 0.4\% \\ \hline Total & - & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ \hline \end{tabular}
\end{table} 
Table 3: Estimated fraction of time spent in each POS of Hanul Units 1-6. In the case of Units 3 and 4, it can be seen that the fraction of time for the at-power state is relatively low compared to the other units, while the fraction of time for POS 8 is relatively high because these two units replaced steam generators during the period as shown in Figure 7.
## Abstracts
### A Framework for Multi-f0 Modeling in SATB Choirs  
[PERSON], [PERSON], and [PERSON]  
Fundamental frequency (f0) modeling is an important but relatively unexplored aspect of choir singing. Performance evaluation as well as auditory analysis of singing, whether individually or in a choir, often depend on extracting f0 contours for the singing voice. However, due to the large number of singers singing at a similar frequency range, extracting the exact individual pitch contours from choir recordings is a challenging task. In this paper, we address this task and develop a methodology for modeling pitch contours of SATB choir recordings. A typical SATB choir consists of four parts, each covering a distinct range of pitches and often with multiple singers each. We first evaluate some state-of-the-art multi-f0 estimation systems for the particular case of choirs with a single singer per part, and observe that the pitch of individual singers can be estimated to a relatively high degree of accuracy. We observe, however, that the scenario of multiple singers for each choir part (i.e., unison singing) is far more challenging. In this work, we propose a methodology based on combining a multi-f0 estimation methodology based on deep learning followed by a set of traditional DSP techniques to model f0 and its dispersion instead of a single f0 trajectory for each choir part. We present and discuss our observations and test our framework with different singer configurations.
## 4 Results and Discussion  
### Grid Independence Verification  
The selection of an appropriate meshing level is crucial for accurate and efficient thermal simulation. In this study, three distinct meshing levels were carefully evaluated, with the number of meshes being 271,781 (level 1), 452,629 (level 2), and 876,692 (level 3), respectively. The meshing diagram of the water tank in Figure 7 shows a tetrahedral grid structure and topical encryption. To verify the grid independence of the model, a reference point located 200 mm from the bottom of the tank section was chosen, and its temperature variations over nondimensional time were analyzed. As shown in Figure 8, the temperature curves obtained from the three meshing levels exhibit remarkable consistency, indicating that the choice of grid count minimally impacts the temperature calculation results. Given the need to optimize computational efficiency without compromising accuracy, a model with 271,781 grids was chosen for this study. This selection ensures accurate simulations while minimizing computational time, thereby facilitating efficient and reliable thermal engineering analyses.  
Figure 7: Grid division of elbow-type thermal storage tanks (with local encryption of inlet and outlet). (**a**) Overall grid division; (**b**) center cross-section grid division.
## 3 Results and Discussion
### Label Analysis  
Seaweeds offer a variety of nutrients in concentrations that can meet nutritional needs, even allowing for some nutritional claims. However, there are no data on the daily intake of seaweed for the Portuguese population or other European countries. In contrast, the average daily intake of seaweed in Asian countries is about 8 g of seaweed dry matter [37; 60]. Table 3 shows a label with the contribution of analysed nutrients in the macroalgae to the recommended daily intake for an adult. Firstly, all seaweeds are extremely low in calories, with 8 g a day providing just 1.02-1.13% of the recommended daily intake (RDI). Algae are also very low in fat, with 8 g representing 0.01 to 0.02% of the RDI [76]. As for carbohydrates, proteins, and fibres, the concentrations are equally low in all taxa. Polysaccharides act as dietary fibres, promoting gut health by serving as prebiotics for beneficial gut bacteria. Despite representing a relatively small proportion of the daily nutrient intake, seaweed contains a considerable amount of minerals, largely due to the high availability and diversity of these elements in the ocean [69] and a healthy, low concentration of salt. In spite of the variations between the taxa analysed, as far as trace elements are concerned, values of Mg, Ca, and Fe are exceptionally high, representing very interesting dietary sources of these elements. However, the high concentrations of sodium and copper in all taxa could be worrying in terms of cardiovascular health risks in the case of the former and bioaccumulation of heavy metals in the case of the latter [11]. As stated above, _Sargassum muticum_ from Buarcos has higher levels of Ca, Fe, Na, and Cu than the biomass harvested from San Marcos beach. For instance, 8 g of dried seaweed
## 5 Evaluation
### Threats to the Validity
#### 5.3.1 Internal Validity  
Internal threats to the validity of our experiments are largely related to the selection and grouping of software developers participating as evaluators. We conducted the assessment with ten software developers who design and implement public transportation systems at an industrial scale, which could limit the generalizability of the findings due to the small sample size and lack of diversity. However, the participating developers' significant experience with leading companies in the public transportation domain enhances the reliability of their feedback and the evaluation of DSML4 PT. Their expertise is considered a valuable asset to this study. Using a single evaluator group poses a risk, as evaluators may benefit from their previous experience with DSML4 PT when developing the same business application without it, and vice versa. In our previous work on the usability of DSLs/DSMLs for other application areas [45; 92; 94; 95; 100; 101; 102; 103], both single and dual evaluator groups have been used. Despite the risk, a single group was chosen to ensure a more efficient qualitative evaluation based on user feedback, as ensuring homogeneity in two separate groups in terms of domain knowledge, experience, and skills is challenging. This threat was mitigated by selecting qualified professionals for the development and evaluation tasks and by randomizing the order of the evaluator groups and the applied case studies: half of the evaluators followed MDE methodology with DSML4 PT at first and then switched back to their classical methodologies, while the other half did the reverse. Additionally, developers might become more proficient with the tools as they use them,potentially skewing the results. To balance any learning effects across both development approaches, the order in which developers used DSML4 PT and traditional methods was randomized. By carefully selecting experienced professionals, randomizing the order of methodologies, and considering the unique advantages and challenges of using a single group, we aimed to minimize internal validity threats and ensure robust and reliable evaluation results.
## 4 Results
### Hypotheses Testing
#### 4.2.2 Effect of Information Disclosure Nudges on Information Disclosure (H2 and H3)  
To test whether the information disclosure differed in the presence of nudges (H2a, H2b, and H3), we looked at the effect of the nudges on the response rate per question sensitivity. When hypothesized that when a low sensitivity signal is present (vs. absent) for less sensitive questions, disclosure increases (H2a) and that when a high sensitivity signal is present (vs. absent) for more sensitive questions, disclosure decreases (H2b). We also hypothesized that social proof moderates the relationship between question sensitivity and users' information disclosure, such as greater social proof, leads to more disclosure and less social proof leads to less disclosure (H3). Table 9 presents a summary of the results.  
The above results show that the response rate to low-sensitivity questions increases (estimate = 1.47, \(p\)-value = 0.0334) when the low-sensitivity signal is present. Thus, H2a is supported.  
The results show that, for high-sensitivity questions, the response rate decreases (estimate = \(-\)1.03) when the high-sensitivity signal is present compared to when absent, however, this result is not statistically significant (\(p\)-value = 0.1142). H2b is not supported.  
For the social proof nudge, the results go in the same direction as the hypothesis, where disclosure decreases with social proof is low compared to when social proof is high for both question sensitivity levels (estimates = \(-\)1.29 and \(-\)1.32) but these results are not statistically significant (\(p\)-values = 0.1301 and 0.1269). Thus, H3 is not supported.  
The comparison between the two social proof levels when present vs. when absent (no social proof) was also tested, but found not to be significant. Moreover, the interaction between the effect of the two nudges on disclosure was also tested, but also found to be not significant.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
###### Abstract  
In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems.  
s +
Footnote †: journal: Mathematics  
**MSC:** 68T20
## 3 Results and Discussion
### Analysis of Real Samples  
#### 3.2.1 Samples from a Former MDMA Lab  
To gain information about the capabilities of the tested techniques under real conditions, practical measurements were performed at a former clandestine MDMA laboratory. The clandestine laboratory and parts of the utilised equipment can be seen in Figure 2. Three different samples were acquired and analysed. In addition to two spots from an actively used Buchner funnel and drying cabinet, one sample was obtained from the wall behind the equipment. This sample is particularly interesting because the laboratory was equipped with professional equipment allowing a less MDMA contaminating work flow during production, compared to amateur productions. In Figure 2A, all sample spots, in the clandestine laboratory, are marked with red rectangles. Visual results of the IDT are shown in Figure 2B. While the drying cabinet and the Buchner funnel exhibit similar colour intensities, a lower colour intensity, and therefore lower concentration of MDMA was found for the wall sample. Detection results of MDMA for IMS are plotted in Figure 2C. Therein, the peak at around 9.8 ms is related to MDMA and exhibit similar concentrations for the wall sample and the drying cabinet. A higher concentration of MDMA was found for the Buchner funnel. This discrepancy can be explained due to local variations in concentration at the surface of the drying cabinet and funnel. These variations are most likely based on the use of multiple sample swabs, applied to areas close together, which were necessary for the applied test procedure. In Figure 2D, the positive CI mass spectra of the investigated surfaces are plotted. The measured concentration are in good agreement with the IDT results. The highest concentration of MDMA was found for the Buchner funnel, followed by the drying cabinet. The lowest concentration observed correlates to the wall sample.  
Table 2 summaries the results for the investigation of sample swabs from all three areas for the three investigated techniques. The measured concentration differs for IMS and APLD-MS. Observed signal differences could be based on sampling variations, such as pressure applied to the sample swab or sampled area, MDMA surface concentration inhomogeneity or differences in sample transfer efficiency between pad and investigated surface. While the concentration of MDMA differs between the sample spots, positive detection was possible for all samples with all investigated methods. Therefore, the LOD of all techniques seems to be sufficient for this practical scenario. No amounts or concentrations of the tested sample are given, as not all techniques enables access to this information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **IDT** & **IMS** & **APLD-MS** \\ \hline Drying cabinet & + & + & + \\ Büchner funnel & + & + & + \\ Wall & + & + & + \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sampling swabs applied at different spots of a clandestine MDMA laboratory. All sampling swabs are examined for MDMA. Plus means positive detection.
# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 3 Machine Learning Method
### Machine Learning Models
#### 3.1.2 Random Forest (RF)  
Random forest is an extended variant of Bagging, which belongs to the ensemble learning method [26; 27; 28]. The idea is that, given a dataset containing m samples, the sample may still be selected in the next sampling so that the dataset still contains m samples after m random sampling operations. Some samples of the initial training dataset appear multiple times in the sampling set, while the others never appear again. About 63.2% of the samples in the initial training dataset are included in the sampling set, as suggested by Equation (2).  
\[\lim_{m\rightarrow\infty}\left(1-\frac{1}{m}\right)^{m}\rightarrow\frac{1}{e }\approx 0.368 \tag{2}\]  
According to this approach, there can be T sample sets containing m training samples. Then, a base learner is trained based on each sample set before the trained base learners are combined. Random forest is evolved from the construction of the Bagging integration of decision tree learners, and further introduces random attribute selection in the training process of decision trees. The decision tree selects an optimal attribute from the attribute set of the current node when selecting the partition attribute. For each node in the random forest, a subset with k attributes is first randomly selected from the attribute set of the node before an optimal attribute of the subset is picked for division. The diversity of base learners in the random forest owes to sample disturbances and attribute disturbances. As a result, the generalization performance of the final integration can be further improved by increasing the difference between individual learners.