esa-sceva/satcom-chunk-collection huggingface dataset:
# FEA for Optimizing Design and Fabrication of Frame Structure of Elevating Work Platforms
## 4 Conclusions  
This study highlights the effectiveness of Finite Element Analysis (FEA) in optimizing the material selection and design of telescopic arms for Elevating Work Platforms (EWPs) used in agricultural applications. The comparison of four materials--Aluminum Alloy (EN-AW 1200 and EN-AW 2014), HSLA Steel (Fe275 JR, and S700)--has led to the following key findings:  
1. HSLA Steel S700 emerged as the optimal material due to its superior strength, lower deformation, and high safety factor, making it ideal for demanding operational conditions requiring durability and load-bearing capacity.
2. Aluminum Alloy (EN-AW 2014) provides a viable lightweight alternative, reducing the overall weight of the platform by approximately 60%. This weight reduction enables the use of smaller, less powerful tractors, improving maneuverability, lowering the center of gravity, and enhancing stability and safety during operations on uneven or sloped terrain.
3. The use of FEA allowed for the identification of stress concentrations, deformation patterns, and failure points, enabling targeted improvements to the structural design without relying on time-consuming and costly physical tests.  
The results also support targeted optimization strategies, such as reinforcing critical regions with higher local stress or introducing variable wall thicknesses and stiffness, to improve safety and performance while maintaining weight advantages.  
Modal and fatigue analyses confirmed that the selected materials, especially HSLA steels, perform well under dynamic and cyclic loading. However, further refinement of design details at connection points is recommended to mitigate localized vibration and fatigue risks.  
This study demonstrated that integrating performance analysis, optimization strategies, and design improvements can result in safer, lighter, and more cost-effective EWPs that meet the demands of agricultural applications.  
The findings highlight the potential to strike a balance between performance, safety, and practicality. Future studies should not only focus on geometry optimization and hybrid materials but also incorporate advanced simulation techniques (e.g., parametric and topology optimization) to fine-tune design parameters for different operational scenarios, including varying wind loads and dynamic conditions.  
This work provides valuable insights for manufacturers and operators, supporting the development of safer, lighter, and more cost-effective EWPs tailored to the unique demands of agricultural environments.  
Conceptualization, A.T.; methodology, K.S.L.O.; software, A.B.; validation, C.D.D.; data curation, D.T.; writing--original draft preparation, A.B.; writing--review and editing, A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Implementations with DSD Reaction Networks
### Discussion  
The advantages of the two novel CRN-based biomolecular controllers proposed in this manuscript are reflected in the structural design. On the one hand, the positive autoregulation mechanism is utilized to improve the stability of the ultrasensitive response. The proposed controller is thought to have two distinct properties: ultrasensitivity, which is considered an essential component of many robust perfect adaptation (RPA) mechanisms [30; 37; 38] and is implemented by using covalent modification cycles; and bi-stability [39; 40], which is realized by utilizing the positive autoregulation mechanism, thereby enhancing the stability of biochemical control systems. On the other hand, the subtraction module is not involved, which circumvents the limitation of the dual rail representation when designing CRNs, reducing the complexity of DNA implementations. This is mainly because the dual rail representation uses the difference in concentration of two specific chemicals to represent the signal [8], which at least doubles the total number of abstract chemical reactions required for implementation [10; 11; 13]. If the dual rail representation can be avoided in controller designs, the number of CRN reactions and the complexity of DNA implementation can be reduced. From these two perspectives, the benefits of structural design ensure the efficacy of the controllers proposed in this work.  
In addition, [PERSON] et al. [30] examined potential biological applications of the Brink controller using three different implementation paths involving RNA molecules (aptamers and toehold switches) and proteins. The two novel biomolecular controllers proposed (including the BC-DPAR and BC-IPAR) differ structurally from the Brink controller; however, the above design approaches can be applied to build corresponding experiments to validate the results in the manuscript. With advances in protein [41] and RNA engineering [42], the proposed biomolecular controllers may be used to build adaptive biological systems.  
Positive autoregulation constructed by the covalent modification cycle can achieve unlimited ultrasensitivity. Embedding this ultrasensitivity mechanism into a negative feedback loop can result in a near-horizontal response to external network stimulus, thus enabling RPA. The proposed BC-IPAR controller can only exhibit two-way bistable switches; however, our proposed BC-DPAR controller can also exhibit one-way switches. Bistable switches are considered to play a central role in signalling events, such as apoptosis and motility. In these cases, unlike the two-way switches in the BC-IPAR controller, the additional one-way switches in the BC-DPAR controller may have a profound impact on the control of biochemical processes.
## 2 Background Theories  
### Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN)  
To enhance the performance of the EMD, some noise-assisted improvements have been proposed. The main idea of these improved versions is to add controllable noise to the signal so that the problems with the original EMD can be solved to some extent. Most recently, ICEEMDAN was proposed to obtain less noise and more physical meaning components with a lower computational cost [13]. The basic principle of ICEEMDAN is reformulated as follows.  
Given a univariate signal \(\mathbf{s}=[s_{1},s_{2},\ldots,s_{L}]^{T}\) of length \(L\), let \(\mathbf{w}^{(i)}\) be a realization of zero mean unit variance white noise vector of the same length, \(E_{k}(\cdot)\) be the operator which produces the \(k\)th mode obtained by EMD, M(\(\cdot\)) be the operator which produces the local mean of the signal that is applied to, and \(\langle\cdot\rangle\) be the operator of an ensemble averaging throughout the realizations. Then, the procedures of ICEEMDAN can be briefly described as follows:  
Step 1: Calculate the first residue \(\mathbf{r}_{1}=M\big{(}\mathbf{s}^{(i)}\big{)}\) by EMD with the local means of realizations \(\mathbf{s}^{(i)}=\mathbf{s}+\beta_{0}E_{1}\) (\(\mathbf{w}^{(i)}\)) (\(i=1,2,\ldots,I\));  
Step 2: Obtain the first mode as \(\mathbf{d}_{1}=\mathbf{s}-\mathbf{r}_{1}\);  
Step 3: Estimate the \(k\)th residue \(\mathbf{r}_{k}=M(\mathbf{r}_{k-1}+\beta_{k-1}E_{k}(\mathbf{w}^{(i)}))\) for \(k=2,\ldots,K\).  
Step 4: Compute the \(k\)th mode as \(\mathbf{d}_{k}=\mathbf{r}_{k-1}-\mathbf{r}_{k}\).  
Step 5: Go to step 4 for next \(k\) until \(K\).  
Through the ICEEMDAN algorithm, the original signal \(\mathbf{s}\) is adaptively decomposed into a set of meaningful intrinsic mode functions (IMFs) and a residual so that useful information can be obtained by further processing these components.
## 5 Discussion
On the other hand, the short-scale fluctuations of the scalar, perpendicular, and parallel temperature that we observe in the 2D histograms are positively correlated with the plasma density fluctuations. This positive correlation is possibly due to the polytropic behavior of the plasma protons within plasma streamlines. We derive the polytropic index \(\gamma\) of the protons by analyzing short subintervals (\(<\)15 min) in 1995. Our analysis derives an average value \(\overline{\gamma}\)\(\sim\)1.3 and a standard deviation \(\sigma_{\overline{\gamma}}\)-0.93. The analysis of [22] derives an average \(\gamma\sim\)1.45 for the 1995 observations. The difference is possibly due to the different filters applied to the selected data and the duration of the analyzed subintervals. Finally, we derive the polytropic index by fitting a linear model to the fluctuations of \(T_{\perp}\) and \(T_{\parallel}\). The average polytropic index values we find with these regressions are \(\overline{\gamma}_{\perp}\)\(\sim\)1.26 and \(\overline{\gamma}_{\parallel}\)-1.41, with standard deviations \(\sigma_{\overline{\gamma}_{\perp}}\)\(\sim\)1.08 and \(\sigma_{\overline{\gamma}_{\parallel}}\)\(\sim\)1.39, respectively. Therefore, according to our statistical study, we derive similar (within \(\sim\)0.1) average polytropic index values regardless of the temperature tensor element we use in our linear regression analysis. We infer that the anisotropic heating within the analyzed streamlines is not significant enough to prevent accurately calculating the average polytropic index of the solar wind plasma protons. We also find that \(\sigma_{\overline{\gamma}}<\sigma_{\overline{\gamma}_{\perp}}<\sigma_{ \overline{\gamma}_{\perp}}\), which is in agreement with the expected deviations from the linear model that we discuss above. We acknowledge that this result is based on the specific streamline selection criteria. For instance, if we analyze longer subintervals, we will accumulate larger anisotropy fluctuations within the analyzed data. That will reduce the accuracy of the polytropic indices that are calculated with either \(T_{\perp}\) or \(T_{\parallel}\) instead of \(T\). Finally, we emphasize that our study focuses on solar wind protons at 1 au. It will be of a great interest to examine the same mechanism in solar wind electrons, or even in other solar wind ion species through the heliosphere. The recent ESA's Solar Orbiter mission is determined to provide high-quality measurements of electron and heavy ion 3D VDFs. These measurements will be ideal to investigate the temperature anisotropy and the polytropic behavior of solar wind species. As has been recently shown by [22; 32; 33], studies of the polytropic behavior in space and astrophysical plasmas leads to important knowledge about the velocity distribution functions of the plasma particles and the potential energy acting on them.
## 2 Related Works
### Hybrid Approaches to Mitigate ADI  
To leverage the strengths of both traditional signal processing and modern deep learning techniques, [PERSON] et al. [17] proposed a hybrid ADI mitigation system tailored for Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing (QAM-OFDM)-based wireless networks. The architecture of the proposed system is implemented at the receiver side and is composed of seven integrated modules: an analog-to-digital converter, a deep learning-based error compensator, an OFDM demodulator, a deep learning-based interference cancelation unit, a channel equalizer, a forward error correction unit, and a maximum likelihood estimation unit.  
The deep learning components of the system incorporate four convolutional neural network (CNN) layers and four Long Short-Term Memory (LSTM) layers. These layers are responsible for capturing spatial and temporal dependencies within the received signal, enabling more precise detection and suppression of ADI-induced distortions. The model was trained using 400 frames and evaluated on 100 test frames. The experimental results demonstrate a significant improvement in communication reliability, with the symbol error rate (SER) being reduced from 0.37618 to 0.0003. This result highlights the potential of hybrid learning-based architectures for real-time and high-accuracy interference mitigation in modern wireless communication environments.
## 2 Materials and Methods
### Research Tools and Variables  
#### 2.3.1 Research Tools  
This study used the PASCO PS-3230 wireless dual-axis platform, manufactured in Roseville, California, USA. PASCO SCIENTIFIC is a voltage-sensing force plate with product parameters including four corner force elements with a range of \(\pm\)1100 N, and a vertical resultant force up to 4400 N. Each force element has an overload protection of 1700 N, with a total vertical overload protection force of up to 6600 N. The device's sampling frequency was set to 1000 Hz, allowing for 1000 force measurements per second. The PASCO force plate was used to analyze human jump dynamics, including vertical, lateral, and horizontal jump analysis [32].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Content** & **Reps/Set** \\ \hline Warm-up & Warm-up with 10 min of aerobic exercise. \\ \hline \multirow{3}{*}{Dead lift} & Barbell (10 kg) + weight plates (20 kg) = 30 kg, 15 reps (Set 1) \\  & Barbell (10 kg) + weight plates (30 kg) = 40 kg, 10 reps (Set 2) \\  & Barbell (10 kg) + weight plates (40 kg) = 50 kg, 1 to 5 reps (Set 3) \\ \hline \hline \multicolumn{2}{l}{Skater hops} & 15 reps each on the left and right (Set 1 to Set 3) \\ \hline \multirow{3}{*}{Lateral shuffle} & Dumbbell (6 kg) in each hand, 12 reps (Set 1) \\  & Dumbbell (8 kg) in each hand, 8 reps (Set 2) \\  & Dumbbell (10 kg) in each hand, 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{Rocket jump} & 15 reps (Set 1), 10 reps (Set 2), 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{Lateral box jump} & 6 inches to your side, 10 reps each side (Set 1 to Set 3) \\ \hline \multicolumn{2}{l}{\begin{tabular}{l} The modified single-leg squat \\ (MSLS) \\ \end{tabular} } & Holding a dumbbell in each hand + unilateral (1 leg at a time) training. \\ \multicolumn{2}{l}{} & Dumbbell (6 kg) in each hand, 12 reps (Set 1) \\  & Dumbbell (8 kg) in each hand, 8 reps (Set 2) \\  & Dumbbell (10 kg) in each hand, 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{Holding a dumbbell in each hand + laterally split squat (one lunge each to the left and right).} \\ \multicolumn{2}{l}{} & Dumbbell (6 kg) in each hand, 12 reps (Set 1) \\ \multicolumn{2}{l}{} & Dumbbell (8 kg) in each hand, 8 reps (Set 2) \\  & Dumbbell (10 kg) in each hand, 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{\
\begin{tabular}{l} The bilateral back squat \\ (BS) \\ \end{tabular} } & Barbell (10 kg) + weight plates (30 kg) = 40 kg, 12 reps (Set 1) \\  & Barbell (10 kg) + weight plates (40 kg) = 50 kg, 8 reps (Set 2) \\  & Barbell (10 kg) + weight plates (50 kg) = 60 kg, 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{
\begin{tabular}{l} Counter movement jumps \\ \end{tabular} } \\ \multicolumn{2}{l}{} & Box height: 18 inches, 12 reps (Set 1) \\  & Box height: 24 inches, 8 reps (Set 2) \\  & Box height: 32 inches, 1 to 5 reps (Set 3) \\ \hline \multicolumn{2}{l}{Cool-down} & Muscle relaxation can be performed with roller stretching or static stretching \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contents of the pyometric training program.
## 3 Results and Discussion  
### Structural Characterization  
The effect of Pd photodeposition on the surface morphology of ZnO films was examined via scanning electron microscopy (SEM). Figure 2 shows SEM images of the pristine ZnO and Pd-functionalized ZnO (Pd/ZnO) films, modified at varying UV photodeposition doses. The pristine ZnO displays a ganglia-like morphology, which is characteristic of sol-gel thin films produced from the ZnO deposition sol used in this study [33].  
Figure 1: Schematic illustration of the photocatalytic experimental setup used to evaluate the doxycycline removal efficiency of ZnO and Pd/ZnO thin films.  
After Pd photodeposition, the ZnO films exhibit a progressively coarser and grainier morphology, likely due to two factors: (i) the deposition of Pd particles via photoreduction of Pd\({}^{2+}\) under UV illumination, and (ii) partial dissolution of ZnO ganglia under the mildly acidic Pd(NO\({}_{3}\))\({}_{2}\) solution (pH-2.6).  
To confirm Pd photodeposition on the ZnO surface, energy-dispersive X-ray spectroscopy (EDS) analysis was performed on the Pd/ZnO samples, with representative spectra shown in Figure 3. Results indicate that Pd functionalization yields approximately 3 wt.% (-1 at.%) in ZnO films treated at 2.5 and 5 J/cm\({}^{2}\) UV doses. For films treated at the highest UV dose of 10 J/cm\({}^{2}\), Pd content slightly increases to 3.5 wt.% (1.2 at.%). The EDS spectra also show Si peaks, likely from the underlying glass substrates, which contributes to a higher oxygen peak intensity and may slightly alter the Zn/O ratio from the expected 1:1. The Si signal intensifies with longer photodeposition durations, suggesting some ZnO dissolution in the Pd(NO\({}_{3}\))\({}_{2}\) electrolyte over time. Aside from these observations, no additional elemental peaks were detected, verifying the purity of both pristine and Pd-functionalized ZnO films.
Our experimental results with random and tournament selection show the following: * Ranking by counting \(PO\) provides almost identical results as ranking by Pareto dominance; * Using probabilistic \(PO\) ranking allows us to increase the hypervolume of the resulting solutions for many-objective and most multi-objective optimization problems; * With the increase in the number of the objectives, probabilistic \(PO\) yields a set of solutions that are almost never dominated by the solutions of other algorithms; * In general, \(PO\)-\(prob\) and \(NSGA\)-\(III\) algorithms yield fewer extreme solutions when the number of objectives increases; * We demonstrate that probabilistic \(PO\) ranking is computationally much more efficient. It allows for reducing the time complexity of the sorting procedure from \(O(N^{2}M)\) to \(O(NMlog(N))\), where \(N\) is the population size and \(M\) is the number of objectives. 3. Our algorithms are implemented as an extension of the Python library for evolutionary computation DEAP ([[https://deap.readthedocs.io/en/master/](https://deap.readthedocs.io/en/master/)]([https://deap.readthedocs.io/en/master/](https://deap.readthedocs.io/en/master/)), accessed on 28 January 2022). They are available as open source ([[https://github.com/marharyta-aleksandrova/kPO](https://github.com/marharyta-aleksandrova/kPO)]([https://github.com/marharyta-aleksandrova/kPO](https://github.com/marharyta-aleksandrova/kPO)), accessed on 28 January 2022). * We also discuss how Pareto optimality can contribute to the problem of interpretability, see Section 6. The rest of the paper is organized as follows. In Section 2, we discuss the related state-of-the-art research work. Section 3 presents the proposed \(PO\)-based sorting and the related algorithms. In Section 4, we describe experimental setup, and in Section 5, we analyze the characteristics and the performance of the proposed algorithms via experiments. Finally, Sections 6 and 7 contain concluding remarks and a discussion of possible future work.
# Deflection Modeling and Curvature Manipulation of a Variable-Stiffness Flexible Needle
## 2 Materials and Methods  
### Design of the Stiffness-Tunable Needle  
The conventional flexible needle tip is deflected under external forces and moments in the radial direction, with the bending behavior being related to the geometry of the needle [20, 21, 22]:  
1. Within certain limits, a thicker tip on the flexible needle results in greater extrusion deformation of the soft tissue after cutting. That means a greater deflection force, which the soft tissue can provide.  
A sharper flexible needle tip with a larger bevel will displace more tissue and generate a greater reaction force on the tip.
3. If the body of the needle is too stiff or too thin, or if the tissue is unable to support the traversal of the body, it can slice through the tissue.  
The body of a flexible needle with appropriate structural parameters will follow the movement of the needle tip during puncture. Thus, by adjusting the stiffness of the needle tip, the bending radius of the flexible needle can be manipulated. The flexible needle with variable stiffness proposed by [PERSON] et al. [17] is shown in Figure 1.  
The stiffness-tunable flexible needle consists of two parts: a nitinol stylet with a variable diameter and a supporting nitinol tube. Specifically, the tip of the flexible needle features a conventional beveled design with a diameter matching the outer diameter of the nitinol tube, allowing it to compress the tissue fully and provide sufficient deflection force. Compared to the beveled tip, the diameter of the stylet is slightly thinner (not limited to the cylindrical structure shown in Figure 1 or the axial position) and is comparable to the inner diameter of the tube.  
The thinner stylet results in a smaller bending moment, making it easier for the flexible needle to produce a larger deflection when the tip is subjected to a constant cutting force.  
A variable-stiffness needle is a retractable cannula device in which the stiffness can be dynamically adjusted during tissue insertion. In order to increase the bending curvature of the needle, the stiffness of the needle tip (actually a portion of the stylet and the tube) is reduced by retracting the nitinol tube (i.e., increasing the stylet extension length). Conversely, advancing the nitinol tube (i.e., shortening the stylet extension) increases the stiffness and thereby reduces the curvature. When the stylet extension length is zero, the variable-stiffness flexible needle essentially becomes a conventional bevel-tip needle.
## 1 Introduction
### State-of-the-Art  
Multiple researchers have attempted to efficiently approximate the CAP solution using various optimization techniques [9], such as Linear Programming, Graph Theory, Heuristics, and Bio-inspired Evolutionary Algorithms. In linear programming, the goal is to simplify the formulation into binary or integer programming, relying on linear constraints [10]. In graph theory, each cell is represented as a vertex, and the edges symbolize connections between devices. This approach facilitates the formulation and resolution of the graph coloring problem, which involves finding the minimum assignment of distinct colors within the graph created by its nodes and edges [11; 12]. Due to the potentially significant computational costs associated with the two aforementioned approaches, alternative optimization strategies have been developed for identifying local optima in a wide range of real-world problems.  
On one hand, heuristics represent straightforward strategies that guide the discovery of local optima. These strategies rely on logical or procedural rules closely aligned with the problem's nature. Noteworthy among the successful heuristics employed in channel assignment are Taboo Search [13; 14], Simulated Annealing [15; 16], and Greedy Algorithms [17; 18; 19; 20]. On the other hand, a wide range of modern Bio-inspired Evolutionary Algorithms [21] have been developed for more difficult optimization problems. As examples, we find Genetic Algorithms (GA) [22; 23; 24; 25], Neural Networks (NN) [26; 27], Particle Swarm Optimization (PSO) [28; 29], Ant Colony Algorithms (ACO) [30], Grey Wolf algorithms (GWO) [31; 32], Artificial Bee Colony Algorithms (ABC) [33; 34], Firefly Algorithms (FA) [35; 36], Whale Algorithms (WOA) [37], Quantum-based Avian Navigation Algorithms (QANA) [38], Zebra Optimization Algorithms (ZOA) [39], and the more general Swarm Intelligence (SI) [40].