esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
###### Abstract  
This paper explores empirical modeling of McKibben muscle in characterizing its hysteresis behavior and nonlinearities during quasi-static, quasi-rate, and historic dependencies. The unconventional materials-based actuating system called McKibben muscle has excellent properties of power-to-weight ratio, which could be used in rehabilitation orthosis application for condition monitoring, physical enhancement, and rehabilitation therapy. McKibben muscle is known to exhibit hysteresis behavior and it is rate-dependent (the level of hysteresis depends closely on rate of input excitation frequency). This behavior is undesirable and it must be considered in realizing high precision control application. In this paper, the nonlinearities of McKibben muscle is characterized using empirical modeling with multiple correction functions such as shape irregularity and slenderness. A particle swarm optimization (PSO) method is used to determine the best parametric values of the proposed empirical with modified dynamic friction model. The LabVIEW and MATLAB platforms are used for data analysis, modeling and simulation. The results confirm that this model able to significantly characterize the nonlinearities of McKibben muscle while considering all dependencies.  
Mckibben muscle; empirical modeling; particle swarm optimization Article
## 2 Materials and Methods
### Methods
#### 2.2.8 Bioactivity Study (Alizarin Red S Staining)  
To assess the mineralization potential of the electrospun membranes, Alizarin Red S (ARS) staining was performed to detect calcium-rich deposits formed by MG-63 osteoblast-like cells. Membrane samples were first sterilized using UV irradiation (30 min per side) and placed into 24-well plates. MG-63 cells were seeded onto the membranes at a density of 1 \(\times\) 10\({}^{5}\) cells/membrane and allowed to adhere for 24 h in standard culture medium.  
After cell attachment, the medium was replaced with osteogenic differentiation medium, consisting of DMEM supplemented with 10% fetal bovine serum (FBS), 10 mM \(\beta\)-glycerophosphate, 50 ug/mL ascorbic acid, and 100 nM dexamethasone. The medium was refreshed every 2-3 days. On day 21, samples were washed with PBS and fixed with 4% paraformaldehyde for 20 min at room temperature. Fixed samples were incubated with 2% (\(w/v\)) Alizarin Red S solution (pH 4.2) for 30 min to stain calcium deposits. Excess dye was removed by washing with distilled water until the background was clear. Stained samples were photographed under a bright-field microscope for qualitative assessment. For quantitative analysis, bound dye was solubilized using 10% cetylpyridinium chloride (Sigma-Aldrich, Poznan, Poland) for 60 min at room temperature. The resulting solution was transferred to a 96-well plate, and absorbance was measured at 540 nm using a microplate reader (Rayto RT6900, DRG MedTek, Warszawa, Poland).  
The general idea of the research pathways, biomaterial composition, and application is given in Figures 1 and 2.  
Figure 1: General research scheme.  
Figure 2: Main principle of novel biomaterials.
## 2 Results
### HD Analysis  
The EOs of _Iris pseudopallida_, _I. illlyrica_, and _I. adriatica_ were isolated by HD, resulting in the identification of 73 compounds, accounting for 89.31-99.50% of the total VOC content (Table 3). Fatty acids, accompanied by fatty acid esters, were the most abundant EO constituents for _I. pseudopallida_ B (37.11%), _I. pseudopallida_ T (99.20%), _I. illlyrica_ Z (81.82%), and _I. adriatica_ (85.74%). In _I. pseudopallida_ D, alkanes (48.56%) and norisoprenoids (29.44%) were the most abundant groups of compounds. The group of alkanes (55.74%), followed by fatty acids (38.67%), was also characteristic for _I. illlyrica_ V. The most common individual compound observed in the EO of _I. pseudopallida_ B, _I. pseudopallida_ T, and _I. illlyrica_ Z was tetradecanoic acid (31.92-97.01%), whereas (_Z_,_Z_)-octadeca-9,12-dienoic acid (40.69%) and hexadecanoic acid (35.48%) were the major compounds found solely in _I. adriatica_ EO. Additionally, dodecanoic acid (1.18-3.90%) and decanoic acid (0.34-2.82%) were detected in all studied samples except for _I. pseudopallida_ D. Furthermore, docosane dominated in _I. pseudopallida_ D and _I. illlyrica_ V EO (45.79% and 55.45%). In _I. pseudopallida_ D EO, _cis_-\(\alpha\)-irone was also found in a high percentage (24.70%), while other constituents occurred in percentages less than 10% in all investigated samples. Among them, several compounds were characteristic of _I. pseudopallida_ B EO, including myrtenol (9.60%), terpinen-4-ol (7.27%), \(\alpha\)-pinene (5.98%), \(\alpha\)-terpineol (3.38%), and 2-phenylethylanol (2.20%).  
Overall, HD isolated more fatty acids and their esters from _Iris_ rhizomes in comparison with HS-SPME, which was expected due to their lower volatility. The same method was more efficient in extraction of alkanes as well. Conversely, HS-SPME using the two different fibers was better for extracting norisoprenoids; aliphatic and aromatic compounds, including alcohols, aldehydes, and ketones; oxygenated monoterpenes; and sesquiterpene hydrocarbons (due to their higher volatility and headspace concentration). Compared with DVB/CAR/PDMS fiber, PDMS/DVB fiber extracted more monoterpene hydrocarbons, oxygenated monoterpenes, and sesquiterpene hydrocarbons, while fatty acids and fatty acid esters were more abundant when using the second fiber (Figure 2). Total ion chromatograms (TICs) of _I. pseudopallida_ B are presented in Supplementary Figure S1 as the sample chromatograms.
## 3 Ant Colony Optimization (ACO) Algorithm
If the ant is travelling from node \(r\) to node \(u\), the value \(\eta(r,\,u)\) is calculated as \(\eta(r,\,u)\ =\ 1/p_{i,j}\) where \(p_{i,j}\) is the processing time of the operation \(O_{ij}\) that corresponds to node \(u\), \(q\) is a uniformly distributed random number in [0,1], \(q_{0}\) is a predefined parameter with (\(0\leq q_{0}\leq 1\)), \(\beta\) is the controlled parameter relating the importance of processing times of the operations. \(J(r)\) is the set of nodes to which the ant can choose to travel from the current node. This set of possible nodes includes the nodes that have not been visited and follows the precedence constraints. The variable \(s\) is randomly chosen using the probability distribution given below. \[P(r,s)=\left\{ \begin{array}{rl}\frac{\left[\tau(r,s)\right]\cdot\left[\eta( r,s)^{\beta}\right]}{\sum_{u\in I(r)}\left[\tau(r,\,u)\right]\cdot\left[\eta(r,u) ^{\beta}\right]},&\text{if }s\in J(r)\\ 0,&\text{otherwise}\end{array} \right. \tag{2}\]  
While an ant travels from node \(r\) to node \(s\), it deposits an amount of pheromone along the path. This action is realized in the algorithm by the _local pheromone updating rule_ as follow. Figure 2: Disjunctive graph of Ant Colony Optimization (ACO) algorithm. \[\tau(r,s)\leftarrow(1-\rho)\cdot\tau(r,s)+\rho\cdot\tau_{0} \tag{3}\]  
where \(\rho\) is the local pheromone evaporation rate \((0<\rho<1)\). Once all the ants of the colony have completed their tours, the schedule to execute the operations can be built based on the sequence of nodes that each ant has visited during the tour. The operations are scheduled by following the sequence of the nodes as soon as the required machines are available. Afterwards, the time to complete all the operations of each ant, \(C_{max}\), is defined from the schedule. The best tour provides the minimum value of \(C_{max}\). Subsequently, the _global pheromone updating rule_ is performed as follow. \[\tau(r,s)\leftarrow(1-\alpha)\cdot\tau(r,s)+Q\cdot\alpha\cdot\Delta\tau(r,s) \tag{4}\]  
where  
\[\Delta\tau(r,s)=\left\{ \begin{array}{cc}\frac{1}{\min_{\text{Cmax}}},& \text{if }(r,s)\in\text{global}-\text{best}-\text{tour}\\ 0,&\text{otherwise}\end{array} \right. \tag{5}\]  
\(\alpha\) is the global pheromone evaporation rate which control the influence of the new best solution, \(Q\) is a tuning parameter. With the new updated values of pheromone on all the possible paths, the process, where all the ants build their tours until the global pheromone updating rule is performed, is iterated until the solution \((\text{C}_{max})_{min}\) converges and the scheduling optimization is completed.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## Appendix A Examples of Errors and Inconsistencies in the Original BIRD Dataset
### Summary of Identified Error Types in Original BIRD Dataset
**Question 416**  
What percentage of cards without power are in French? **Original Answer**  
SELECT CAST(SUM(CASE WHEN T2.language =  
'French' THEN 1 ELSE 0 END) AS REAL) * 100  
/ COUNT(T1.id) FROM cards AS T1 INNER JOIN  
foreign_data AS T2 ON T1.uuid = T2.uuid  
WHERE T1.power IS NULL OR T1.power LIKE  
'**Error Analysis**  
In Magic: The Gathering (MTG), a power value of '*' is not considered \"without power\" (highlighted in question); it indicates variable power. The SQL's condition (highlighted in original answer) incorrectly includes such cards, showing the dataset developers made a false domain-specific assumption. **Figure A7. Example of difficulty mislabeling from the BIRD dataset (Question 322, mislabeled as 299). The question, a simple lookup of an element by atom ID, is labeled 'challenging' despite its straightforward SQL solution. This overestimation of difficulty can skew evaluation metrics and reduce the dataset’s reliability.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 4 5G for Industrial Wireless Networks: Recent Architectural Trends
A variety of sensors, devices, machines, robots, actuators, and terminals are communicating to coordinate and share data. Some of these devices may be directly connected to a local type-b network and some may be connected via gateway(s) to have access to the industrial network functions in the enterprise network. In addition, also the integration of 5G networks with Time Sensitive Networks (TSN) is also considered to guarantee deterministic end-to-end industrial communications, as presented in [35]. TSN is a set of IEEE 802 Ethernet sub-standards that aim to achieve deterministic communication over Ethernet by using time synchronization and a schedule which is shared between all the components (i.e., end systems and switches) within the network.
## 4 Experimental Evaluation of Performances
### Evaluation Measures  
To evaluate the quality of the community anomaly detection model and check its validity on real and synthetic datasets, we perform a comparison between the obtained nodes' ranks of the model and the ground truth. The closer the rank is to the true label, the more efficient the community-based anomaly detection model is. The first measure used is Area Under The Curve (AUC). This is one of the most important performance measurements for classification models and anomaly ranking. It tests the model's capability to distinguish between two classes, and a higher AUC means a better model. In a machine learning classification problem, the model predicts classes compared with the actual classes, and the results fall into four areas. These are either true positive (predicted anomaly for an actual anomaly), false positive (predicted anomaly for an actual normal), true negative (predicted normal for an actual normal), or false negative (predicted normal for an actual anomaly). The proportion of positives that are correctly identified is called sensitivity, and the proportion of negatives that are correctly identified is called specificity. In the classification model, we define various thresholds and plot the Sensitivity (also called true positive rate) with the false positive rate, which is (1-Specificity), to plot the ROC curve. Therefore, the best model is the one that can reach the maximum value of sensitivity and specificity at a certain threshold value, which means all positives are correctly identified and all negatives correctly identified. The maximum value is the one located in the top-left corner of the ROC plot. As a result, the area under the curve measures the ROC curve's ability to reach the maximum value of sensitivity and specificity (top-left corner). The second measure used in this evaluation is the model's runtime, as it is important to check the model's capability to detect the community anomaly within a reasonable time. A lower runtime is preferable when it is combined with good-quality results.
## 4 Discussion
### Remote Collaboration  
In this study of multiple stakeholder design collaboration, each participant created new forms and modified existing ones simultaneously. As the participants could modify all forms that exist within the virtual environment, the forms created by other stakeholders were also subjected to changes without the intent of the creator stakeholder. This was considered problematic, especially by architect and property developer stakeholders, since ownership of a design also entitles responsibility for that very design during a work process. Collaboration of this type would resemble a multi-user CAD project where users remotely and simultaneously can edit parts of a CAD model [73]. Such a project design emphasizes the need for communication between the users [74] to transfer the participants' perspectives among users to have a general view on what other participants are currently working on and, not least, to resolve issues deriving from invading other participants \"design spaces\". Using a form of an avatar to indicate what other participants are currently engaged with can also help the flow of collaboration [73] while also increasing creativity [59].  
The Urban CoCreation platform implemented both these strategies, i.e., the possibility to adopt other participants' points of view and utilize avatars as an aid for collaboration. However, the issues of locking a model or isolating and/or locking modifiable design features were considered critical for claiming ownership and limiting and controlling what each stakeholder can engage with. This aspect would be essential for collaboration among stakeholders engaged with specific tasks, such as building engineers and architects. Still, consideration needs to be taken when locking and assigning levels of control; the purpose of the platform is to be able to take in many and diverse stakeholders, generating as much input as possible. For citizen participation, even though having one's design deleted and changed by others spontaneously might be problematic, allowing each citizen to claim a model and lock it would also create problems. Careful design is needed of communication channels between the stakeholders, of possibilities to adopt the point of view of others, of avatars to clearly understand who is working on which item, of ways to mark creatorship of each design and of mechanisms to lock some of the models.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods  
The CEEMDAN-FE-TFPF method introduced in this paper is an improvement of the traditional EMD and TFPF denoising methods. CEEMDAN can overcome the problem of modal aliasing in the traditional EMD method. Each IMF obtained by CEEMDAN can flexibly select different window lengths to overcome the TFPF drawback. The following is the basic principle of this method.
## 3 Preliminaries
### Mathematical Foundations  
This subsection describes in detail the concept of Chebyshev polynomial mapping and its two hard assumption problems that are used in our work.  
* Chebyshev polynomial: **Definition 1**.: _Assume \(n\) and \(P\) indicate an integer and a large prime number, respectively. \(x\) indicates a variable taking values over the interval \([-\infty,\infty]\). \(\mathcal{T}_{n}(.)\): a Chebyshev polynomial \([-\infty,\infty]\rightarrow[-\infty,\infty]\) of stage \(n\) is identified as_ \[\mathcal{T}_{n}(x)=cos(n*arccos(x))\] (1)  
Thereby, based on **Definition 1**, the recurrence formulation of the Chebyshev polynomial mapping \(\mathcal{T}_{n}:R\to R\) is as below:  
\[\mathcal{T}_{n}(x)\equiv 2x\mathcal{T}_{n-1}(x)-\mathcal{T}_{n-2}(x)(modP),(n \geqslant 2) \tag{2}\]  
where \(\mathcal{T}_{0}(x)=1\) and \(\mathcal{T}_{1}(x)=x\). In addition, some examples of the Chebyshev polynomial are  
\[\mathcal{T}_{2}(x) =2x^{2}-1,\] \[\mathcal{T}_{3}(x) =4x^{3}-3x,\] \[\mathcal{T}_{4}(x) =8x^{4}-8x^{2}+1\]  
The Chebyshev polynomial has two significant properties, namely chaotic and semigroup, respectively.  
* Chaotic property: When degree \(n>1\), it can identify a Chebyshev polynomial mapping as a chaotic mapping \(\mathcal{T}_{w}:[-\infty,\infty]\rightarrow[-\infty,\infty]\) with a constant density function \(f^{*}(y)=1/(\pi\sqrt{1-y^{2}})\).
* Semi-group property: \[\mathcal{T}_{w}(\mathcal{T}_{l}(x)) =cos(wcos^{-1}(cos(los^{-1}(x))))\] \[=cos(wlcos^{-1}(x))\] \[=\mathcal{T}_{wl}(x)\] \[=\mathcal{T}_{l}(\mathcal{T}_{w}(x))\] where \(l\) and \(w\) are integers of positive as well as \(x\in[-\infty,\infty]\)  
The two hard assumption problems are introduced.  
**Definition 2**.: _\"Chebyshev polynomial-based Diffie-Hellman problem (CPDHP)\": The main task of CPDHP is to estimate the \(\mathcal{T}_{wl}(x)\) for three given parts \(\mathcal{T}_{w}(x)\), \(\mathcal{T}_{l}(x)\) and \(x\)._  
**Definition 3**.: _\"Chebyshev polynomial-based Discrete Logarithm problem (CPDLP)\": The main work of CPDLP is to find the unknown value \(w\) such that \(\mathcal{T}_{w}(x)\equiv y\) for two given parts \(x\) and \(y\)._
## 4 High-Speed On/Off Valve Orifice Erosion Influence Factors Analysis
### Correlation Analysis of Influencing Factors  
The gray relational analysis was introduced into the analysis of the influencing factors of orifice erosion, and the correlation values of each influencing factor (spool taper angle, orifice pressure difference, oil contamination level, particle size, and filter pore size) and the maximum erosion rate were calculated. The most important factors affecting the maximum erosion rate of the orifice were determined according to the magnitude of the correlation [27]. The correlation values calculated according to the gray relational analysis are shown in Table 3.  
Figure 19: Distribution of erosion rate with different filter pore sizes (unit: kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)).  
Figure 18: The curve of the maximum erosion rate with a filter pore size (inlet pressure—5 MPa, outlet pressure—4 MPa, valve opening—0.2 mm, spool taper angle—120\({}^{\circ}\), oil pollution grade—10).  
The table shows that the influencing factor with the greatest correlation is the pressure difference at the valve port, indicating that the change in the pressure difference at the valve port is most likely to cause changes in the maximum erosion rate at the valve port. The two influencing factors with the second and third highest correlations are the particle size and filter aperture. From this, it is clear that reducing the proportion of large particle contaminants in the fluid, as well as the use of an appropriate aperture filter, can significantly improve the service life of high-speed on/off valves.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 7 Conclusions  
In this paper, an accurate range model and an efficient ground moving target imaging method have been proposed for airborne CSSAR. The main merits of the proposed range model are highlighted in two aspects: (1) its accuracy is even higher than that of the third-order Taylor approximated range equation; and (2) based on the proposed range model, the accurate analytical expression for the target's 2-D spectrum can be easily derived. Moreover, an efficient ground moving target imaging method has been proposed. The proposed method focuses the target via a phase multiplication in the 2-D frequency domain and utilizes the GA to accomplish an efficient search of the parameters of the proposed range model. Experimental results have validated the proposed imaging method.  
**Author Contributions:** The work presented in this manuscript was accomplished together with all of the authors. [PERSON] proposed the idea, carried out the theoretical derivations and analyses, conducted the experiments, and wrote the manuscript. [PERSON] conducted the theoretical derivations, carried out numerical simulations, and reviewed the manuscript. [PERSON] validated the proposed method and reviewed the manuscript. [PERSON] managed the project and reviewed the manuscript. [PERSON] conducted the simulations and reviewed the manuscript. [PERSON] reviewed the manuscript and wrote some codes. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was funded by the Postdoctoral Science Foundation of China under Grants BX20180261 and 2019M653739, by the Aeronautical Science Foundation of China under Grant ASFC-20200020053001, by the National Natural Science Foundation of China under Grant 61701413, by the Undergraduate Innovation and Entrepreneurship Training Program under Grant S202010699318, by the Natural Science Foundation of Shaanxi Province under Grant 2018 JQ6095, and by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2020 JQ-203.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank the [PERSON] for his help in preparing this paper. They would also like to thank the reviewers for their valuable comments, and [PERSON], [PERSON] and [PERSON] for proofreading the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Deviations of the optimal image contrasts obtained by 500 random experiments.