esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Allan Variance Based Semi-Parameter Model to Calibrate Pointing Errors of Alt-az Telescopes
## 5 Conclusions  
To improve the LOS pointing precision of alt-az telescopes, offline modeling and online calibration techniques are researched in this article from both theoretical and practical aspects.  
A mount model was established to demonstrate and eliminate the impacts of geometric errors on the LOS pointing performance of alt-az telescopes, which was based on comprehensive analysis of geometric error sources and modeled with D-H transformation.  
An improved model called the AVSPM was proposed to compensate for residual nonlinear errors excluded in the mount model. An experiment was carried out to evaluate properties of the proposed methods. As a result, the LOS pointing precision of telescope has been remarkably improved, and the AVSPM was proven to outperform the kinematic mount model. The AVSPM is also applicable to analogous two degrees-of-freedom equipment.  
Comments and suggestions from [PERSON] and [PERSON] during this research are very much appreciated.  
[PERSON] is the head of the research group that conducted this study. He contributed to the research with his original idea and he carried out the experiment and wrote this paper. [PERSON] participated in analyzing the theory and writing this paper. [PERSON] contributed to the research through her theoretical guidance and advice.  
The authors declare no conflict of interest.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 1 Introduction
### The SWARMs Approach  
Ontologies provide a formal specification of conceptualization in a well-defined and unambiguous manner; thus, they facilitate knowledge sharing and reusing. They are widely used to formally represent information in many domains, such as semantic web [6], smart home [7], and healthcare [8]. The SWARMs project also adopts ontologies to model all information that is necessarily exchanged between vehicles. A networked ontology is developed as a part of the SWARMs semantic middleware. As shown in Figure 1, the semantic middleware is defined to provide interactions between vehicles and the Mission Management Tool (MMT), which is located onshore or on a vessel and is responsible for the generation of missions, assignment of tasks to robots, and supervising the mission. More importantly, the semantic middleware can offer a variety of common services (e.g., security, context awareness, and publishing/subscription) for vehicles and MMT in the SWARMs project.  
The networked ontology, encased in the SWARMS Ontology component, acts as a common information model to represent different domain-specific information and enables knowledge sharing within the SWARMs system. Specifically, it abstracts information, which is classified into four domains, including robotic vehicles, mission and planning, environment recognition and sensing, and communication and networking. In addition, it also provides valuable inputs for the context reasoner component to execute effective context reasoning. The context reasoner employs a hybrid context reasoning method [9], which includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning. It is worth noting that information that is obtained by sensors or other sensing instruments is prone to be uncertain in the underwater environment [5] due to partial views, imperfect instruments, challenging communications, data loss, etc. Thus, uncertainty, as an inherent characteristic of information, also needs to be formally annotated in the SWARMs ontology for the sake of completeness in knowledge representation and also to provide support for uncertainty reasoning defined in the context reasoner component. The PR-OWL ontology, proposed by [PERSON] et al. [10], provides a principled formalism to represent uncertainty complying with the MEBN theory. It defines a set of ontology constructs that can be used to annotate probabilities about uncertain information and provide support for the hybrid context reasoning in the context reasoner. Thus, incorporated with PR-OWL ontology constructs, the SWARMs ontology can provide a complete and formal representation of information exchanged between vehicles, enable information sharing and reuse, and support for the ease of performing hybrid context reasoning.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 4 Landing Site Detection
### Place Recognition  
The most appropriate landing site can be determined according to the smooth area in the LIDAR point cloud. However, some areas that are smooth on the surface may not actually be suitable for landing. For example, a shallow water area may appear as a fairly regular plane in the LIDAR system, but landing there is a potential risk. Similarly, a road may be suitable for UAV landing, but it could cause a traffic accident. Therefore, a visual analysis of the semantic information of the scene is necessary to further ensure a safe UAV landing.
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
###### Abstract  
The production of postbiotics for food applications has been growing in recent years owing to their biological potential and superior technological performance over probiotics. Their production involves the use of synthetic culture media, and in this work, we propose using sugarcane straw as a source of sugar and biological components and a sustainable alternative. Thus, this by-product was used as a substrate to produce a postbiotic extract using _Saccharomyces cerevisiae_ as a fermentative microorganism. Sugarcane straw underwent a precedent saccharification step to release the fermentable sugars. The final extracts were characterized for their total content of sugars, phenolic compounds, organic acids, and their respective chromatographic profiles. Seventeen different polyphenols were identified with the predominance of three classes, the hydroxybenzoic acids, hydroxycinnamic acids, and flavonoids, where ferulic acid, 3,4-dihydroxybenzaldehyde, and 2,5-dihydroxybenzoic acid were most prevalent. The current work evaluated the potential use of this postbiotic extract for food applications, its antioxidant activity, gut microbiota modulatory effect, and intestinal anti-inflammatory potential. The resultant extracts showed considerable antioxidant activity and the ability to lower the pro-inflammatory mediators (i.e., interleukin 6, 8, and tumor necrosis factor-alpha) in Caco-2 cells. During the fecal fermentability assay, no modulatory effect was observed on the main beneficial bacteria, such as _Lactobacillus_ and _Bifidobacterium_. Nevertheless, a significant increase in short-chain fatty acids, namely, acetate, propionate, butyrate, and valerate was observed. Moreover, the extract also demonstrated capacity to inhibit the proliferation of putrefractive bacteria such as _Enterobacteriaceae_. Finally, sustainable postbiotic extracts produced by _S. cerevisiae_ fermentation using sugarcane straw as a substrate exhibited relevant biological properties with potential use as food and nutraceutical ingredients.  
postbiotics; _Sacharum officinarum_, L.; straw; _Saccharomyces cerevisiae_; in vitro fermentation; short-chain fatty acids
# Sound Synthesis of Objects Swinging through Air Using Physical Models +
## 3 Method  
### Aeroacoustics  
When sound is generated by airflows or air interacting with objects, the process is labelled aeroacoustics. This falls under the wider body of research known as fluid dynamics, which describes the physical processes controlling the flow of fluids and enables the prediction of pressures, noises, strains on objects, etc. Understanding these processes enables better design of a wide number of objects including aircraft, cars, trains, ships, buildings, space vehicles and bridges.  
Today, computers are able to solve the highly complex equations that govern these processes using techniques like finite difference or finite volume techniques and mapping out the domain of interest with complex mesh structures. Even with the advances in the computational power available, these processes can take hours and even days to complete depending on the level of detail required.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Reference** & **Synthesis Method** & **Parameters** & **Comments** \\ \hline
[1] & Physically derived & Length, diameter, length of swing and speed of swing & Operates in real time \\ \hline
[2] & Frequency domain & Amplitude control over analysis and synthesis filters & Operates in real time \\ \hline  & Granular & Accelerometer speed & Mapped to playback speed \\ \cline{2-3}  & Sample-based & Accelerometer speed & Triggered by threshold speeds \\ \cline{2-3}
[4] & Noise shaping & Accelerometer speed & Mapped to bandpass centre frequency \\ \cline{2-3}  & Physically inspired & Accelerometer speed & Mapped to the amplitude of frequency modes \\ \hline
[6] & Computational fluid dynamics & Length, diameter and swing speed & Real-time operation, but requires initial offline computations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table highlighting different synthesis methods for swing sounds.  
Prior to the availability of such processing power, engineers and scientists derived and defined simpler equations to allow them to calculate the approximate acoustic characteristics. These are labelled semi-empirical equations, where assumptions and generalisations have been made to simplify calculations or to yield results in accordance with observations. Although many of these equations may at first appear complicated, once all the relevant parameters are known, they produce exact results with errors only due to the approximations made during the equation derivation.  
There is a number of fundamental aeroacoustic sounds that constitute the main focus of research. Figure 1 illustrates a number of these fundamental tones and gives examples of the types of objects that produce them. Each tone is generated by distinct fluid dynamics processes.
# Analysis and Improvement of Oversize Goaf Backfill Engineering Based on Fuzzy Theory
## 3 Fuzzy Analysis of Backfill Quality
### Fuzzy Analysis of the Backfill Quality  
The backfill process is a complex engineering system. Similar to many other studies [42; 43; 44; 45], understanding and managing the different factors also affects the backfill system. Generally, filling material is similar to cementitious materials, and their strength is greatly affected by the amount of cement and the water-cement ratio. For concrete with different particle sizes, their strength also varies greatly. Therefore, when analyzing the mechanical properties of filling materials with less binder material, some analysis methods of ordinary concrete can still be used for reference. In order to determine the importance of the factors affecting the quality of the backfill body at #17R, such as the lime-sand proportion control \(A_{1}\), slurry concentration control \(A_{2}\), slurry launching point \(A_{3}\), coalmine dehydration drainage \(A_{4}\), and particle size grading of the tailings \(A_{5}\), eight experts in the filling field were invited to evaluate these factors using fuzzy math methods.  
The evaluation factor set was defined as \(U=\{u_{1}\), \(u_{2}\), \(u_{3}\), \(u_{4}\), \(u_{5}\}\), where \(u_{1}\) is the technical level, \(u_{2}\) is the operating difficulty,  
The membership matrices \(R_{2}\)-\(R_{5}\) for \(A_{2}\)-\(A_{5}\) and the corresponding priority levels \(N_{2}\)-\(N_{5}\) were calculated using the same method, based on the membership degree \(r_{11}\) of each evaluation scale as follows:  
\[R_{2}=\left[\begin{array}{ccccc}0.25&0.375&0.25&0.125&0\\ 0.125&0.625&0.25&0&0\\ 0.5&0.25&0.25&0&0\\ 0.375&0.375&0&0.25&0\\ 0.5&0.125&0.25&0.125&0\\ 0.5&0.375&0.125&0&0\\ 0.25&0.625&0.125&0&0\\ 0.5&0.25&0.125&0.125&0\\ 0.25&0.75&0&0&0\\ 0.625&0.25&0&0.125&0\\ 0.25&0.375&0.375&0&0\\ 0.625&0.25&0&0.125&0\\ 0.5&0.125&0&0&0\\ 0.375&0.5&0.125&0&0\\ 0.625&0.25&0.125&0&0\\ 0.625&0.25&0.125&0&0\\ 0.625&0.375&0&0&0\\ 0.5&0.5&0&0&0\\ 0.875&0.125&0&0&0\\ 0.75&0.125&0.125&0&0\\ \end{array}\right]\]  
\[S_{2}=W^{0}R_{2}=(0.34375&0.3375&0.2625&0.11875&0)\]  
\[N_{2}=S_{2}^{0}V^{T}=0.625\]  
\[S_{3}=W^{0}R_{3}=(0.44375&0.50625&0.06875&0.04375&0)\]  
\[N_{3}=S_{3}^{0}V^{T}=0.7037554_{4}=W^{0}R_{4}=(0.40625&0.36875&0.1625&0.0875&0 )\]  
\[N_{4}=S_{4}^{0}V^{T}=0.639375\]  
\[S_{5}=W^{0}R_{5}=(0.69375&0.2125&0.06875&0\;0\;0)\]  
\[N_{5}=S_{5}^{0}V^{T}=0.689375\]  
Therefore, the importance of these five factors can be ranked as follows to provide useful decision information for technical improvement.  
\[N_{3}>N_{5}>N_{4}>N_{2}>N_{1}\]  
The importance ranking of the factors from high to low is as follows: slurry launching point \(A_{3}\), particle size grading of the tailings \(A_{5}\), coalmine dehydration drainage \(A_{4}\), slurry concentration control \(A_{2}\), and lime-sand proportion control \(A_{1}\).
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 3 Route Guidance Models
### Static Routing Guidance
This hypothesis is tested using the Wilcoxon rank-sum, Moses, and Kolmogorov-Smirnov tests. Results show that majority of the acceptable parameter sets have a reasonably short travel time mean absolute error ratio (MAER), which ranges from 0.007 to 0.067, compared to travel times derived from uncalibrated models, which, on average, have MAER of 0.215. Nevertheless, to find out whether the findings presented in this research are true in other contexts, the model should be applied to other network configurations and other network types (such as freeways). One of the major drawbacks of traditional static assignment methods is that they use assumptions, resulting in infeasible traffic flow predictions, flows exceeding the capacity of the road, and congestion inside bottleneck links instead of upstream bottlenecks. As a consequence, this results in implausible travel times. Flow and density constraints mandate more dynamic information, which may negatively impact computational efficiency, convergence and scalability properties. There have been several attempts to develop static models by including capacity constraints. However, the resulting models commonly do not consider realistic link exit capacities, and consequently still mainly produce unrealistic bottleneck locations and travel times, because all links are forced into a free-flow regime. Furthermore, model application on both urban roads and motorways would demand different requirements. A traffic assignment model should allow for multi-user-class assignment, where each vehicle class has its own route choice parameters, free-flow speed and set of network restrictions, making the model applicable for all vehicle classes [32].
###### Abstract
Compared with these arrays structures, the coprime array structure has less mutual coupling due to its sparse array structure and higher DOF. CSA consists of two ULAs having \(M\) and \(N\) antenna elements with \(Nd\) and \(Md\) inter-element spacing, respectively, and \(O(MN)\) DOF. CSA has been become more attractive for researchers due to its less mutual coupling and higher achievable DOF [19]. Therefore, this paper addressed the coprime array structure for far-field sources estimation to enhance the DOF in radar antenna array signal processing. After receiving the signal through antenna arrays, estimation algorithms play an important role in array signal processing for parameters estimation. In most of the literature, the two most dominant algorithms are used in array signal processing for parameter estimation. One of them is deterministic algorithms, and the other is the heuristic approach. Deterministic algorithms are mostly sub-space based methods like multiple signal classification (MUSIC) [20; 21], signal parameter via rotational invariance Technique (ESPRIT) [22], root MUSIC [23], weighted subspace fitting (WSF) [24], etc. Although these algorithms perform very well in estimation accuracy, the major issue to implementing them is computational complexity. In this modern era of technology, this issue is resolved using meta-heuristic algorithms. Nobody can deny the significance of heuristic techniques such as genetic algorithm (GA) [25], particle swarm optimization (PSO) [26], differential evolution (DE) [27], simulated annealing (SA) [28], ant colony optimization (ACO) [29], bee colony optimization (BCO) [30], flower pollination algorithm (FPA) [31], cuckoo search algorithm [32] and so on. These techniques are population based, and these methods are efficient and robust. Recently in the research community, cuckoo search has become more popular due to its easy implementation and few parameters required to execute the algorithm compared with other heuristic approaches. Following our literature review, no one has implemented the cuckoo search technique through coprime arrays for DOA estimation. Therefore, a novel approach has been executed by employing coprime antenna arrays in radar technology with an intelligent metaheuristic optimization algorithm. The structure of the paper is organized as follows. Section 2 discusses the analytical representation of the proposed CSAs, including fitness function. The presented methodology is investigated in Section 3 with the currently used PSO system. Section 4 contains the material related to results and discussion of the simulation analysis of the proposed CSA technique for DOA. The conclusion of the presented framework is summarized in Section 5.
# A New Approximation for the Perimeter of an Ellipse
## 1 Introduction  
It can be argued that the idea of establishing \"precision machine learning\" would not be considered a new endeavor for many scientists, as it has indeed been the aim of mathematicians over many centuries to give the best possible approximations of objects when there is no closed-form formula. For instance, precisely calculating elliptical perimeters without complex integrals has been a long-standing problem in mathematics. The accuracy of these approximations can be critical in astronomy, manufacturing, medical imaging, and geophysics calculations, such as the orbital mechanics of celestial bodies, where small errors can lead to significant deviations in downstream calculations or decisions, affecting everything from scientific research to industrial processes.  
Beyond the motivation for greater precision for the theoretical sake of future unknown applications, a note of particular importance is the wide range of scales on which perimeter approximations are necessary. Applications scale from the astronomical order in orbital bodies to atomical in elliptical lens design. Approximations at these scales are also subject to data accuracy, for instance, when visually eliciting ellipse characteristics from images with limited resolution. As hardware improves over time, the viability of applying different potential approaches could also increase, and reassessment of available methods is necessary to maintain the state of the art.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
The complete MMKP CubeSat design problem is then mathematically expressed as Equation (6). \[ \begin{split}\underset{\mathcal{G}}{\min}f(\mathcal{G})& =\frac{-AC_{D}}{m_{T}c_{T}}\\ \text{s.t.}&\left\{\begin{array}{l}g_{1}(\mathcal{G} )\equiv m_{T}-4\leq 0\text{ kg}\\ g_{2}(\mathcal{G})\equiv l_{x}-105\leq 0\text{ mm}\\ g_{3}(\mathcal{G})\equiv l_{y}-105\leq 0\text{ mm}\\ g_{4}(\mathcal{G})\equiv l_{z}-340\leq 0\text{ mm}\\ g_{5}(\mathcal{G})\equiv P_{arg}\geq P_{T}\equiv P_{T}-P_{arg}\leq 0\text{ }W\\ g_{6}(\mathcal{G})\equiv SOC\geq 0.2\equiv 0.2-SOC\leq 0\\ g_{7}(\mathcal{G})\equiv Pointing\ Accuracy-1^{\circ}\leq 0\\ g_{8}(\mathcal{G})\equiv f_{OBC}\geq 730\equiv 730-f_{OBC}\leq 0\text{ MHz}\\ g_{9}(\mathcal{G})\equiv Storage\ Capacity\geq 1.25\equiv 1.25-Storage\ Capacity\leq 0\text{ Gb}\\ g_{10}(\mathcal{G})\equiv f_{trans}^{(L)}+\frac{BW}{2}\leq f_{att}\leq f_{ trans}^{(U)}-\frac{BW}{2}\end{array}\right.\end{split}  \tag{6}\]  
Now, to calculate the total downloadable data, \(D\), average solar power generated, \(P_{arg}\), state of charge of the batteries, \(SOC\), and pointing accuracy of the ADCS system, a simulation environment is created that models orbit dynamics, attitude dynamics, solar power generation, battery state of charge, and communication disciplines as shown in Figure 5. Every individual of the population is an input to the COTS database that produces the CubeSat design. The design is then an input to the simulation environment, and then the outputs of the simulation environment along with mass, volume, power, and other properties of the design are used to determine the fitness (combination of objective function and the constraints). Details of the modeled disciplines are provided below. Figure 5: Simulation environment for CubeSat design optimization.
## 4 Results and Discussions
### Scenario I: Gap Detection
The equivalent weld seam length is provided on top (in purple), which indicates that the weld seam is progressed for 0.2 mm in 1 ms. The results are obtained by conducting repeated learning-testing validations for 50 iterations based on Scenario I, where the segments are classified into three classes (Zero Gap, Gap, and Noise). Top row (**a**): average accuracy with error bars (standard deviation) and bottom row (**b**): average confusion matrices for selected segment duration. The channels mentioned in the results are the structure-borne ultrasound sensor at the beginning (SB1) and the airborne ultrasound sensor (AB). A noticeable change can be observed in the cluster separability for both sensor signals when the results of 50 ms segments are compared to those of 5 ms. With 5 ms segments, the Zero Gap cluster is _closer_ to the Gap cluster, causing a larger overlap of these two classes. Indeed, the average accuracy of distinguishing Zero Gap and Gap segments is significantly reduced, as shown in Figure 5b. This may be attributed to the fact that the actual gap size of Zero Gap patches is not zero, making the distinction of smaller gaps from Zero Gap difficult. This trend continues when segments become further shorter to 1 ms, which is equivalent to 0.2 mm weld seam, where the clusters of all classes are closer and the separation of one another is ambiguous. It is also worth noticing that the size of the Zero Gap and the Gap clusters resembles that of the noise cluster for shorter segments, implying that the effect of noise becomes larger with decreasing duration.