esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
## 1 Introduction
Additionally, the weight of the generator is closely linked to its material cost, which is an important criterion for the wind turbine design [14; 15]. There is research on the weight reduction of the AFPM generator. [PERSON] et al. [14] investigated the influence of the weight on the efficiency and material cost of the AFPM generator considering four shape design parameters. The electromagnetic analysis was conducted to evaluate the performance of the generator, and the obtained results showed that the weight was significantly reduced with only a small reduction in the efficiency of generator. However, in this case, the shape design parameters were considered on a limited basis. [PERSON] et al. [15] conducted a dimensioning optimization of the AFPM generator. This methodology allowed optimizing the generator design by establishing useful dimensioning options for a range of magnet radiuses and lengths. The application of this methodology demonstrated the viability of maximizing the efficiency and reducing the weight of the generator. However, the optimization was conducted by considering only a few shape design parameters that influenced the weight and performance of the generator, so it is necessary to identify additional design parameters that impact the performance of the generator. In this context, an optimal shape design for the AFPM generator is presented to minimize the weight of the generator under the constraint of the required target specification such as an active power. An outer-rotor type generator was proposed, which was designed for a rated capacity of 1 kW. The electromagnetic analysis based on the finite element method was carried out to evaluate the active power of the generator. To investigate the sensitivity of the shape design parameters that impacted the active power and weight of the proposed generator, response surface methodology (RSM) was employed based on the electromagnetic analysis results. Then, the optimal shape design parameters were determined using a desirability function via the obtained response's surface model and compared with other research.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
###### Abstract  
This research aims to investigate the impact of incorporating porous materials on reducing noise and vibration in wooden floor panels, and to analyze the vibroacoustic performance of the assembled panel under different types of excitation and boundary conditions, particularly in the lower frequency range. The study begins with an experimental investigation and numerical modeling to determine the mechanical properties of the orthotropic wood material used in the floor panels. Subsequently, a finite element formulation, based on a variational approach, is presented to study the vibroacoustic response of an elastic structure coupled with a porous material exhibiting realistic behavior. The porous material is characterized by two phases: solid and fluid, represented in the formulation through the displacement field for the solid phase and the pressure for the fluid phase. This formulation offers the advantage of reduced computation cost and simplifies the coupling between all domains. To calculate the acoustic radiation of the structure, the Rayleigh integral is employed. Utilizing the proposed numerical approach, a comprehensive study is conducted to analyze the reduction in vibration-acoustic response of the floor with the incorporated porous layer, taking into account different types of excitation and boundary conditions applied to the system.  
porous material; timber floor panel; vibroacoustic; numerical modeling; experimental material characterization
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## 5 Conclusions  
Analysis of a spaceborne GNSS-R-based signal response to varying flowrates over a section of the Pascagoula River has been conducted using data from a series of raw IF river overpasses during the period 18-25 April 2019. The width of the signal corresponding to the river overpasses was deduced by fitting a power series to a portion of the waveform bounded by limits of \(\sim\)20% of the peak signal at river center to either side of the peak. The signal width, referred to as the river's associated GNSS-R width (AGW), is then adjusted for the track angle of the satellite's specular point relative to a perpendicular line across the banks. The river AGW maps approximately monotonically and linearly to the observed flowrates contemporaneous with the data collections. While this finding indicates high potential for exploiting GNSS-R based signals for measuring river streamflow directly or as an input to remote-sensing-based flowrate retrieval algorithms, further work with more extensive datasets will be necessary to determine the robustness of the method and the limits on its applicable flowrate ranges.  
Conceptualization, C.R. and A.W.; Methodology, C.R. and A.W.; Software, C.R and A.W.; Validation, C.R. and A.W.; Formal analysis, C.R. and A.W.; Investigation, C.R. and A.W.; Resources, C.R. and A.W.; Data curation, A.W.; Writing--original draft preparation, C.W. and A.W.; Writing--review and editing, C.R. and A.W.; Visualization, C.R. and A.W.; Supervision, C.R. and A.W.; Project administration, C.R. and A.W.; Funding acquisition, C.R. and A.W.  
This research was funded by NASA Earth Science Research Program, Grant/Cooperative Agreement Number 80 NSSC18K0702 with SRI and NASA Science Mission Directorate contract NNL13 AQ00C with U-M.  
The authors would like to thank the NASA CYGNSS Science Team for facilitating the collection and processing of the raw IF data used in this research.  
The authors declare no conflict of interest.
###### Abstract  
In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network's objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods--Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)--in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.  
genetic algorithm; Particle Swarm Optimization; task allocation; wireless sensor networks +
Footnote †: journal: Applied sciences
# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in 3D-Printed Fabrics Design  
Some 3D-printed materials and structures have unique preprogrammed properties, e.g., lightness, mechanical resistance, or multifunctionality. These are obtained either by mimicking natural materials (e.g., porous) or by synthesizing from scratch structures that do not occur in nature. Currently, analyzing the relationship between geometry and mechanical properties is complex and costly, but ML has the potential to change this by contributing to the development of new classes of 3D-printed matter and structures with preprogrammed physical or chemical properties. The use of ML in the design of 3D-printed fabrics, such as chainmails for exoskeletons, enables automation and significant improvement in the efficiency of the entire process. This allows the creation of structures with programmed properties, such as variable stiffness or flexibility, depending on the direction of force. This allows for the adaptation of designs to the individual needs of users, taking into account different types and degrees of physical deficits [40]. An example of such an application is the hand exoskeleton, in which 3D-printed elements create a single-layer structure with an adjustable unidirectional or bidirectional bending module. The key innovation of this approach is the integration of real data from research on hand biomechanics with modern analysis methods using deep neural networks. These advanced algorithms allow for more precise adjustment of the mechanical parameters of the designed structures, such as stiffness or bending angles. This process ensures transparency, scalability, and the possibility of personalization of products from 3D-printed fabrics [40,41]. Thanks to this, exoskeletons can be precisely adapted to the therapeutic needs of a specific patient, supporting their rehabilitation. An additional advantage is the ability to selectively program the mechanical properties of such structures, which allows for their optimal operation in different directions of movement [42]. These solutions can be used not only in rehabilitation but also in innovative devices supporting the daily functioning of people with limited mobility. The use of advanced analytical tools in the design of 3D fabrics opens up new possibilities in the field of medicine and biomechanical engineering [43,44]. The example of the hand exoskeleton shows that 3D-printed chainmails can be created quickly and efficiently while maintaining high precision in adapting to the specific requirements of the user. In the future, such technologies can be used on a large scale, supporting not only rehabilitation but also the development of exoskeletons in other areas of application. Personalized 3D-printed structures have the potential to revolutionize the approach to creating modern assistive devices.
## 2 Material and Methods
#### 2.2.1 Data Preprocessing Based on BPNN  
Principles of Neural Network and BPNN  
The neural network is a computing model that is composed of a large number of interconnected neurons. In addition to the input neurons, each neuron represents a distinct output function, which is also known as the excitation function. The connection between each pair of nodes represents a weighted value of the signal passing through the connection, which is called the weight and is equivalent to the memory of the artificial neural network. The output of the network varies with the connection mode, weight value, and excitation function of the network. In general, neural networks can be classified into the forward type, feedback type, random type, and competitive type.  
BPNN is a kind of feedforward neural network. It includes a back-propagation algorithm in addition to the standard structure of the feedforward neural network, which is used to adjust the weights and thresholds of the network during training.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**First-Level Indicators** & **Second-Level Indicators** & **Third-Level Indicators** \\ \hline \multirow{7}{*}{A5: Talent Training and Team Building} & & C47: Academicians \\ \cline{3-3}  & & C48: Yangtze River scholars \\ \cline{3-3}  & B13: Training and & C49: Distinguished youths \\ \cline{3-3}  & Importing Talents & C50: National Overseas High-Level Talent Program \\ \cline{3-3}  & Team Building & C51: Talent plan for provincial and ministerial levels \\ \cline{3-3}  & & and above \\ \cline{3-3}  & B14: Personnel training & C52: Train students with master’s or above degrees \\ \cline{3-3}  & B15: New Innovation Team & C53: New Innovation Team \\ \cline{3-3}  & B16: New Major International Cooperation Studies & C55: New Major International Cooperation Studies \\ \cline{3-3}  & B17: To host (undertake) & \\ \cline{3-3}  & international academic conferences & C56: To host (undertake) international academic conferences \\ \cline{3-3}  & B18: New positions in & C57: Total numbers \\ \cline{3-3}  & international academic & C58: New positions in international academic institutions \\ \cline{3-3}  & academic journals & C59: New positions in international academic journals \\ \cline{3-3}  & B19: International exchange and & C60: Dispatch personnel \\ \cline{3-3}  & mutual visits of personnel & C61: Visitors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
Optimize Data Validity Using the BPNN Training Model  
The existence of noise in the training sample data may cause unpredictable deviations in the importance assessment. The training model based on BPNN can check and fill the data and simultaneously classify and rearrange the effective data against the evaluation index system to improve the validity of the data. The output data is then normalized to minimize the error between data and improve the accuracy of the results.
## 3 Methodology
### P(True)-Based Confidence Evaluation Mechanism
#### 3.5.1 P(True) Confidence Scoring Method  
Building upon the findings of [PERSON] et al. [22], we enhance the suffix-based confidence scoring method to enable a more intuitive evaluation of LLMs' confidence in their own outputs. Specifically, after LLMs generate a candidate tail entity \(t^{*}\), we append the following prompt to guide the model in performing a binary classification: _\"The possible correct triple is: (\"Avatar\", \"director\", \"[PERSON]\"). Is the Possible Triple: (A) True (B) False?\"_ The LLM is then instructed to choose between the two options. We extract the probability assigned to the option True as the confidence score for the candidate tail entity, which is computed as  
\[P(\text{True})=\frac{e^{\text{$\mathsf{s_{True}}$}}}{e^{\text{$\mathsf{s_{ True}}$}}+e^{\text{$\mathsf{s_{False}}$}}}, \tag{2}\]  
where \(\text{$\mathsf{s_{True}}$}\) and \(\text{$\mathsf{s_{False}}$}\) are the logits output by LLMs for selecting options (A) True and (B) False, respectively. The softmax operation is applied to normalize these logits, resulting in the final confidence score \(P(\text{True})\).
## 4 Chemical Methods for Synthesis of Magnetic Ferrite Nanoparticles
### Thermal Decomposition  
The thermal decomposition of organometallic compound acetylacetonates in hydrophobic organic solvents containing stabilizing surfactants is the primary method for synthesizing monodisperse magnetic ferrite nanocrystals with smaller sizes [197; 198]. The thermal decomposition method is widely used for synthesizing high-quality magnetic ferrite nanoparticles with precise control over size, shape, and composition. In this process, organometallic precursors, such as Fe(acac)\({}_{3}\) and Co(acac)\({}_{2}\), are decomposed at elevated temperatures (typically 200-320 \({}^{\circ}\)C) in the presence of surfactants like oleic acid or ole  
Figure 8: A schematic of the polyol synthesis method for magnetic ferrite nanoparticles of AFe\({}_{2}\)O\({}_{4}\) (A = Mn, Co, Ni, Zn, etc.) spinel ferrites. Reproduced with permission from Ref. [190].  
lamine, which act as stabilizing agents. By carefully controlling reaction conditions such as temperature, pH (adjusted to specific values as required), and synthesis time (ranging from a few hours to several days), the process allows for precise control over nanoparticle characteristics. The reaction occurs under an inert atmosphere to prevent oxidation [199; 200; 201; 202; 203; 204; 205; 206; 207; 208; 209]. [PERSON] et al. demonstrated that the synthesized CoFe\({}_{2}\)O\({}_{4}\) nanoparticles are spherical and highly monodisperse. They studied the effects of temperature, reaction time, and capping agents on these nanoparticles. Benzyl ether was chosen for its high boiling point (298 \({}^{\circ}\)C). The mixture was stirred and degassed at room temperature for 60 min, heated to 100 \({}^{\circ}\)C for 30 min to remove water, then to 200 \({}^{\circ}\)C for 30 min, and refluxed at 300 \({}^{\circ}\)C for 60 min. The cooled mixture was purified with ethanol and hexane, resulting in a black magnetic precipitate redispersed in 20 mL of hexane to form a CoFe\({}_{2}\)O\({}_{4}\) nanoparticle ferrofluid [141]. Surfactants act as a protective layer around nanoparticles, and the addition of various surfactant families, such as alcohol, polymer, and carboxylic acid, can enhance the microstructural, optical, and ferromagnetic properties of the material [210; 211]. High-boiling-point organic solvents, such as octadecene, ensure controlled decomposition. As the reaction proceeds under an inert atmosphere, the precursors break down, leading to the nucleation and growth of monodispersed ferrite nanoparticles. After the response, the nanoparticles are collected, purified, and dried. The thermal decomposition process of CoFe\({}_{2}\)O\({}_{4}\) nanoparticles is schematically demonstrated in (Figure 9) [141].  
This method offers several advantages, including producing highly crystalline and uniform nanoparticles. The key properties of magnetic ferrite nanoparticles synthesized through thermal decomposition include the ability to exhibit superparamagnetic behavior at 300 K and soft ferromagnetic behavior at 15 K [209].
## 1 Introduction
The most well-known is the semi-analytical finite element (SAFE) method, where FE is used to model the cross-section, and a complex exponential function is used to describe the displacement field in the direction of wave propagation [19]. However, the SAFE method cannot be used to model truly periodic structures [20] that consist of identical substructures joined to form a continuous structure. The scaled boundary finite element method (SBFEM) was also applied in a similar semi-analytical framework for guided wave simulations [21] but only in isotropic structures. The wave and finite element (WFE) method has been developed over the last decade [22]. It combines FE and periodic structure theory to model wave propagation in periodic structures. It has been used in a number of research areas like structural identification [23], damage detection [24], multi-scale wave propagation [20], transient wave propagation [25], and wave steering in composites [26]. The main issue associated with the existing group velocity calculation methods is that they require custom formulations, which are difficult to implement, and none of them are able to handle truly periodic structures. Moreover, the group velocity from WFE was approximated by the slope of the wavenumber-frequency dispersion curve using finite differentiation. The principal novelty of the work exhibited in this manuscript is the calculation of the group velocity curves for two dimensional complex periodic and/or composite structures using a wave and finite element scheme without involving any finite differentiation. The approach is therefore free from computational errors, requirement for convergence checks, and additional computational cost implied by finite differentiation schemes. The use of periodic structures such as textile composites has become increasingly common in automotive and aerospace industries [27]. In order to employ guided-wave-based damage localization and identification, it is necessary to calculate the dispersion properties of such structures. The WFE scheme based calculation of dispersion curves has the following advantages:  
* It is capable of handling both periodic and layered composite structures. * It is relatively easier to implement and does not require custom FE formulation. * It is computationally efficient and has minimal memory requirements. The novelty of this work is the form of a comprehensive algorithm to calculate the dispersion properties of arbitrary plate-like structures. The paper is organised as follows: A brief introduction to the WFE method is presented in Section 2, followed by the energy and power flow considerations necessary for dispersion curve calculation. At the end of Section 2, the main algorithm is presented. The symbols used in this section are listed in Table 1. The method is compared with existing dispersion properties calculation techniques in Section 3, highlighting the main advantages of this approach. The comparison with experimental results is described in Section 4. This is followed by our conclusions in Section 6.
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 2 CEL Method for One- and Two-Way Fluid-Structure Coupling Validation
### CEL Method
#### 2.1.3 Coupling  
As mentioned above, in the CEL (coupled Eulerian-Lagrangian) method, there exist motion equations in two different coordinate systems (Eulerian and Lagrangian). A common strategy to tackle this issue in coupling methods involves introducing interface coupling conditions to coordinate the exchange of information between the two systems. These interface conditions may encompass characterizing the forces exerted by the fluid on the solid and the influence of the solid on the fluid in terms of forces and displacements. By using the CEL method, the contact between the Eulerian domain and Lagrangian domain is discretized using the general contact algorithm, which is based on the penalty contact method [28]. The penalty contact method is less stringent when compared to the kinematic contact method. Seeds are created on the Lagrangian element edges and faces, while the anchor points are created on the Eulerian material surface. The penalty method approximates hard pressure-overclosure behavior. This method allows the small penetration of the Eulerian material into the Lagrangian domain. The contact force \(F_{\text{P}}\), which is enforced between seeds and anchor points, is proportional to the penetration distance \(d_{\text{P}}\).  
\[F_{\text{P}}=k_{\text{P}}d_{\text{P}} \tag{3}\]  
The factor \(k_{\text{P}}\) is the penalty stiffness which depends on the Lagrangian and Eulerian material properties.  
The detailed principles of the CEL method are described in Section S5 of Supplementary Materials. So far, the CEL method is mainly used to address significant object displacement problems, such as pile infill, landslides, collisions, and fluid dynamics, and has not been used in the field of SFTs. Therefore, careful verification is needed to determine whether the CEL method can be applied in fluid-solid coupling simulations. Accordingly, in this study, the CEL method was employed to simulate the classical hydrodynamic problem of circular cylinder flow to validate the accuracy of the CEL method in this context and provide data to support subsequent research.