esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
## 1 Introduction
### The State of the Art  
In recent decades, numerous studies have focused on enhancing helicopter TE efficiency and reliability. The development of helicopter TE surrounding control algorithms relies on classical regulation approaches, such as PID controllers [12; 13] and linear control models [14; 15], which are commonly used in aviation. However, despite their effectiveness in static and predictable conditions, these systems often struggle with abrupt changes during operations, including sudden variations in load, altitude, or ambient temperature. Such limitations restrict their application in dynamic flight scenarios, particularly for helicopters that must quickly adapt to changing conditions.  
Contemporary research has begun to concentrate on adaptive control systems capable of automatically modifying parameters in real time based on actual sensor data [16; 17]. Examples include control methods utilizing [PERSON]'s adaptive control theory [18], stabilizing engine performance under various deviations from normal operating conditions. The implementation of these systems has shown improvements in helicopter TE reliability and stability; however, challenges remain regarding the accuracy of predicting dynamic changes and accounting for random factors.  
One promising direction involves applying machine learning methods [19; 20] and artificial intelligence [10; 11; 21; 22] to predict engine behavior while adapting control systems based on these predictions. Models based on neural networks [23; 24; 25] can predict engine performance across various modes, accumulating data for continuous learning, thereby enhancing management effectiveness. However, much research in this area focuses on isolated aspects, such as fault diagnosis or optimization in specific modes, rather than creating a comprehensive adaptive management system.  
Based on the above, a new mathematical model for adaptive control law emerges as necessary, accounting for real-time changes in helicopter TE operating conditions. This model should utilize data gathered from onboard sensors, adjusting engine control parameters to maintain optimal performance. A critical aspect involves the mathematical modeling of processes occurring within the engine, enabling predictions regarding its behavior in unstable conditions and timely responses to changes.  
Integrating artificial intelligence into this model will facilitate control adaptation and future system states' predictions based on the accumulated data. This approach will help anticipate failures, reduce component wear, and enhance overall reliability during helicopter operation. Implementing such technologies will represent a significant advancement in adaptive control systems, markedly improving safety and efficiency within aviation.
## 3 Results and Discussion
### Correlation of Various Volatiles with Sensor Arrays  
From Figure 10, it is evident that at a temperature of 4 \({}^{\circ}\)C, aldehydes and nitrogen-containing compounds exhibit a strong linear relationship with the sensor response, whereas other volatile substances in the 'others' category demonstrate relatively weak linear correlations with the sensor response. Aldehyde compounds display high correlation coefficients with sensors 1, 2, 3, 4, 5, 9, and 10. In contrast, nitrogen-containing compounds show significant correlations with sensors 3, 7, and 8. Furthermore, from Table 3, it can be observed that phenolic substances at a temperature of 12 \({}^{\circ}\)C exhibit substantial and robust correlations with sensors 1 and 2.  
After a comprehensive analysis of the correlation between volatile substances and the sensor array at various temperatures, the results indicated that sensors 1, 2, and 6 exhibited a higher association with amine substances. As shown in Table 1, these three sensors effectively detect nitrogen\(-\)containing compounds, demonstrating a high degree of consistency. Furthermore, sensors 1 and 2 displayed a significant correlation with sulfur-containing substances, suggesting that their target gases include sulfides such as hydrogen sulfide. Alcohol substances were found to have a strong correlation with sensors 7, 8, and 9, which consistently detected organic solvents like alcohol. Phenolic substances primarily correlated with sensors 1, 2, and 6, aligning with their high association with nitrogen-containing compounds; this indicates that this type of gas is not confined to a single category but exists in various forms. Aldehyde and ketone substances demonstrated significant correlations with sensor 9, which targets components such as organic solvents, showcasing good adaptability in this context. Hydrocarbon substances primarily involved sensors 1, 5, and 7; these sensors detected target gases including propane, methane, and iso-butane, illustrating strong compatibility in this regard. Additionally, a clear correlation was observed between ester compounds and sensors 6 and 8, while ether compounds were more closely related to channels 3, 5, and 6. Other types of compounds relied more on detection from channels 2, 9, and 10.
## 3 Results
### Selectivity and Cod Length Structure
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Haul No.** & **UC + DD + DK [\%]** & **Herring Coded [\%]** & **Difference [\% Points]** \\ \hline
1 & 3.03 & 59.19 & 56.16 \\ \hline
2 & 4.92 & 65.43 & 60.51 \\ \hline
3 & 0 & 68.52 & 68.52 \\ \hline
4 & 0 & 61.60 & 61.60 \\ \hline
5 & 4.20 & 67.93 & 63.73 \\ \hline
6 & 4.84 & 65.20 & 60.36 \\ \hline
7 & 4.65 & 84.26 & 79.61 \\ \hline
8 & 4.13 & 55.43 & 51.30 \\ \hline
9 & 4.43 & 55.20 & 50.77 \\ \hline
10 & 3.72 & 67.75 & 64.03 \\ \hline
**Mean** & & **61.66** \\ \hline
**SD** & & **8.42** \\ \hline
**Pearson's coefficient of UC + DD + DK \% and herring \%** & **0.09** \\ \hline
**Spearman’s coefficient of UC + DD + DK \% and herring \%** & **0.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentages of undersized cod caught with UC + DD + DK coded and non-selective herring coded by double bottom trawling in 2023. Figure 20a,b additionally shows the results of Baltic cod length measurements made in 2023 from hauls with a herring codon without any selectivity (10,587 individuals measured). The x-axis represents 57 classes of cod length (from 8 to 65 cm long, clustered every 1 cm); the y-axis represents the class share [%] in the caught cod mass. Figure 19: Number of occurrences for differences between percentages of undersized cod caught with UC + DD + DK codend and non-selective herring codend by double bottom trawling in 2023 allocated into classes. The distribution of caught cod classes fits well a Gaussian model equivalent to a normal PDF given by Equation (1):  
\[y=ae^{-\frac{1}{2}(\frac{x-b}{c})^{2}}=\frac{1}{c\sqrt{2\pi}}e^{-\frac{1}{2}( \frac{x-b}{c})^{2}} \tag{1}\]  
where \(y\) is the class share [%] in catch mass, \(x\) is the length class [cm], \(a\) = 5.8% is the amplitude of the share, \(b\) = 30.9 cm is the centroid of the class (mean cod length), and \(c\) = 6.9 cm is the standard deviation (SD) of classes related to their share. The \(R^{2}\) coefficient of this fit goodness is 0.93 (the square of the correlation between the measured values and the modelled values). According to the percentage of classes and fitted PDF, approximately 68% of the caught sample of cod were in sizes from 24 cm to 38 cm (mean \(\pm\) SD), 66% were in classes below 35 cm, and 34% were in classes equal to 35 cm and over. It should be noted, however, that there is a distinct drop in distribution of cod sizes in the range of 27 to 30 cm.
# Study on the Design of the Fear Pair and Flow Characteristics of Circular-Arc Gear Pumps
## 4 Conclusions  
For the design of circular gear pairs, the theoretical aspects were first analyzed in this study, and the parameters affecting the flow rate characteristics of gear pumps were obtained and verified through fluid simulation. Then, the control variable method was used to analyze the influence of different design parameters on the flow characteristics. The results show the following:  
The number of teeth, module, pressure angle, and spiral angle are the design parameters that affect the flow characteristics of the gear pump. The spiral angle ensures that the gear pump is \"free of flow pulsation\". For the circular gear pump to be free of flow pulsation, a specific helix angle is required so that the angle at both end faces of the gear is staggered by an integral multiple of \(\pi/Z\).  
In terms of flow rate, the number of teeth and the module have the greatest influence on the displacement of the gear pump. At a rotating speed of 10,000 r/min, the flow rate of the gear pump increases by 6-7 L/min for each additional tooth. For each 0.2 increase in the module, the flow rate of the gear pump increases by 4-5 L/min. However, the pressure angle has little influence on the flow rate. The flow change is less than 1 L/min for every 2\({}^{\circ}\) change in the pressure angle. From the perspective of flow pulsation, the module has a great influence on the flow pulsation of the gear pump, and an increase in the module increases the flow pulsation of the gear pump. Next is the pressure angle. The influence of the pressure angle on flow pulsation is nonlinear, showing a trend of first decreasing and then increasing.  
The method of controlling variables was used in this work to study the influence of a single tooth profile parameter on flow characteristics, without analyzing the impact of multiple parameters working together. Therefore, the application scenarios of this design method are limited. In the future, the performance of gear pumps under the joint effect of multiple parameters can be studied to better guide the design of such circular-arc helical gear pumps.  
**Author Contributions:** Methodology, G.L.; Writing--original draft, Y.L.; Formal analysis, W.H.; Resources, D.L.; Validation, S.W. and Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research is supported by the Henan Provincial Key Research and Development Special Project (25111220200); The research is supported by the Natural Science Foundation of Henan Province Project (252300420446).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The detailed data are available upon request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The author declares that there are no conflicts of interest. And the author and co-authors have collaborated previously, so there are no conflicts of interest.
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 8 Simulation
### Experimental Results  
First, we discuss the deterministic case. The simulation covers a wide range of randomly generated SP graphs. The example in Figure 7 shows a case where a number of links is pruned where \(m=10\). An instance is the connection between nodes \(A\) and \(B\) where the link of \(g(LCM\{1,2\})\) activation function would completely cover the pruned link of \(g(8)\) activation function. The left plot of Figure 8 shows how the pruning rate deteriorates as the range of the frequencies \([1,m]\) becomes wider. This is due to the fact that as the frequencies range becomes wider, the possibility of getting a link with a \(\tau\) value that is divisible by the \(\tau\) value of its parallel link (or combination of links) becomes lower. The figure presents a comparison between different percentages of added shortcuts.  
Figure 8: The pruning rate in the deterministic case where \(L=1.5\), \(p=50\%\). For the left plot \(n=20\), for the right plot \(m=10\).  
Figure 6: The topologies used in the probabilistic example. (**a**) is the low-clustered topology. (**b**) is the high-clustered topology.  
Figure 7: An example of pruning in the deterministic case.  
The right plot of Figure 8 shows how the pruning rate slightly increases as the size of the network (represented by the number of link insertions \(n\)) becomes larger. This is due to the fact that the average pruning rate of a larger network would not be worse than the average pruning rates of the smaller sub-networks that compromise it. However, combining those small networks to form a larger one would give the chance of having intermediate connecting links that have a chance to be pruned. Eliminating the shortcuts from the network would reduce the pruning rate slightly as those shortcuts typically cover more cases where the links are pruned. For example, having a shortcut with \(\tau=1\) makes the whole network parallel to it pruned. Furthermore, the left plot of Figure 9 shows how the expected average degree of parallelism \(L\) improves the pruning rate drastically as it would produce a large number of parallel redundant links that would be pruned. The right plot of Figure 9 shows how increasing the number of expected parallel connections in the network with respect to the series connections would increase the pruning rate as most of the pruned links would typically be in a parallel connection.  
Now, we discuss the results of the simulation in the probabilistic case.
# Iterative Learning for K-Approval Votes in Crowdsourcing Systems
## 6 Experiments
### Relationship between Reliability and \(y\)-Message  
The inference of workers' relative reliability in the course of iterations is the most important aspect of our algorithm. Now, we define the estimated reliability \(\hat{p_{j}}\) for each worker \(j\) as follows:  
\[\hat{p_{j}}=\frac{\#\,of\,responses\,including\,correct\,answer}{r_{j}}.\]  
After \(k_{max}\) iterations, we can find reliable workers by the value of the worker message \(y_{j}\) since this value is proportional to \(\hat{p_{j}}\), which is influenced by \(p_{j}\). The relative reliability \(y_{j}\) is calculated by the following equation in Algorithm 1:  
\[y_{j}\leftarrow\sum_{i\in\partial j}\frac{1}{K_{i}}\Big{(}\vec{A}^{ij}-\Big{(} \frac{K_{i}}{D_{i}}\Big{)}\vec{1}\Big{)}\cdot\vec{x}^{(k_{max}-1)}_{i\to j}\]  
Figure 6: Comparisons of probabilities of error between different algorithms varying \(l\) (**first row**) and \(q\) (**second row**) values (\(m=n=2000\), \(l=r=25\)).  
Figure 7 shows that there are strong correlations between \(y_{j}\) and \(\hat{p_{j}}\). In one simulation, the correlation coefficients (Pearson product-moment correlation coefficient (PPMCC) was used for evaluation) between \(y_{j}\) and \(\hat{p_{j}}\) were measured as 0.991, 0.992, 0.904, and 0.954 for \((D,K)=(3,1),(4,1),(5,2)\) and \((6,2)\), respectively. We can also check that the line approximately passes the point of \((\frac{K}{D},0)\), where \(p_{j}=\frac{K}{D}\) is close to the reliability of a non-informative worker who gives random responses, as expected in Section 5.
# Defining Breast Tumor Location Using a Four-Element Wearable Circular UWB MIMO Antenna Array
## 4 Results and Discussion
### Diversity Performance  
The MIMO performance of the suggested antenna is justified by calculating the diversity performance parameters (ECC, DG) [31; 32]. Based on the S-parameters calculation, the ECC can be obtained using Equation (7).  
\[ECC\overline{\epsilon}=\frac{\left|\mathcal{S}_{m}^{*}S_{nm}+\mathcal{S}_{nm} ^{*}S_{mm}\right|^{2}}{\left(\left(1-\left(\left|S_{nm}\right|^{2}+\left|S_{ nm}\right|^{2}\right.\right)\right)\left(1-\left(\left|S_{nm}\right|^{2}+\left|S_{ nm}\right|^{2}\right)\right)\right)} \tag{7}\]  
Also, the ECC can be calculated from the following:  
\[ECC\overline{\epsilon}=\frac{\left|\iint_{4\pi}\left(G_{i}(\theta,\varphi) \right)\times\left(G_{j}(\theta,\varphi)\right)d\Omega\right|^{2}}{\iint_{4\pi }\left|G_{i}(\theta,\varphi)\right|^{2}d\Omega\iint_{4\pi}\left|G_{j}(\theta, \varphi)\right|^{2}d\Omega} \tag{8}\]  
where \(G_{i}(\theta,\ \varphi)\) denotes the radiation pattern in three dimensions that results from the excitation of the ith antenna, \(G_{j}(\theta,\ \varphi)\) denotes the radiation pattern in three dimensions that results from the excitation of the jth antenna, and \(\Omega\) denotes the solid angle. To obtain a good MIMO performance, the \(ECC\) value of the antenna should be less than 0.5 as per the International Telecommunication Union (ITU) standard. For the proposed MIMO antenna, the value of correlation is less than 0.00375 for the entire operating frequency bands, as demonstrated in Figure 14a, which makes it a suitable MIMO antenna under the designated bands. The diversity gain is also calculated using Equation (9). The accepted diversity gain value is very close to 10. From Figure 14b, it is observed that the proposed MIMO antenna has a diversity gain of almost 10 for the operating frequency bands.  
\[DG=10\sqrt{1-\left|ECC\right|^{2}} \tag{9}\]  
Figure 13: The maximum spatial average SAR value using 1 g at 2.8 GHz; (**a**) 1 g, (**b**) 10 g.
## 2 Algorithm for MBS  
Unlike the space-born SAR, the imaging of MBS is much different because of the complex Earth-Moon motion relationship, as well as atmospheric and ionosphere disturbances. The FBP algorithm in this paper mainly focus on the spatial relationship and the establishment of the algorithm model, as seen in the detailed flow chart displayed in Figure 2.  
Box 1 shows the processing of echo generated from the range history between MBS and EGT; since the Moon revolves around Earth with Earth's rotation, the ex-poser time for MBS' achievable EGT changes significantly. Here, the range history is calculated based on a real scenario, e.g., the JPL ephemeris and EOP at different time epochs, and other MBS parameters, such as carrier frequency, bandwidth, and look angle (here, an angle between a vector, i.e., from MBS to EGT, and another vector, i.e., from MBS to Earth centroid, is calculated as the look angle) mainly contribute to the beam shape on Earth, which is related to the MBS's resolution. The detailed introduction of how to calculate the range history is displayed in Section 2.2. Box 2 shows the progression of the final image generated from the echo in Box 1. The sub-aperture division and sub-imaging area are the keys to the FBP algorithm. In the traditional BP algorithm, the sampling echo is projected on all pixels of the image area; this coherent method incurs huge computational time costs, especially when sampling points are very large. Therefore, for reducing time consumption without a loss of generality, the echo projection method used in FBP is changed to a centerline based on the sub-aperture and sub-imaging area division within acceptable error; the range  
Figure 1: A back-projection imaging processing of MBS (the figure was created using the Adobe Illustrator, a vector graphics software).  
interpolation on the projected centerline contributes to a reduction of range error; the final image will be generated after the superposition processing. The detailed introduction of the core processing will be displayed in Section 2.1.
## 1 Introduction
### Our Contribution  
This paper gives a more space-efficient data structure for answering any \(\epsilon\)-approximate frequent item set query. Our data structure uses \(O(\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\log\log W)\) words, which is significantly smaller than the one given by [PERSON] _et al_. [1] (see Table 1). Furthermore, this space complexity is larger than the best synchronous solution by only a factor of \(O(\log W\log\log W)\), which is close to the expected gap of \(O(\log W)\). Similar to existing data structures for this problem, it takes time linear to the data structure's size to answer an \(\epsilon\)-approximate frequent item set query. Furthermore, it takes \(O(\log(\frac{\epsilon B}{\log W})(\log\frac{1}{\epsilon}+\log\log W))\) time to modify the data structure for a new data item. Occasionally, we might need to clean up some old data items that are no longer significant to the approximation; in the worst case, this takes time linear to the size of the data structure, and thus is no bigger than the query time. As a remark, the solution of [PERSON] _et al_. [1] requires \(O(\log(\frac{\epsilon B}{\log W})\log W\log\log|U|)\) time for an update.  
In the asynchronous model, if a data item has a delay more than \(W\) time units, it can be discarded immediately when it arrives. In many applications, the delay is usually small. This motivates us to extend the asynchronous model to consider data items that have a bounded delay. We say that an asynchronous data stream has tardiness \(d_{\max}\) if a data item created at time \(t\) must arrive at the stream no later than time \(t+d_{\max}\). If we set \(d_{\max}=0\), the model becomes the synchronous model. If we allow \(d_{\max}\geq W\), this is in essence the asynchronous model studied above. We adapt our data structure to take advantage of small tardiness such that when \(d_{\max}\) is small, it uses smaller space (see Table 1) and support faster update time (which is \(O(\log(\frac{\epsilon B}{\log d_{\max}})(\log\frac{1}{\epsilon}+\log\log d_{ \max}))\)). In particular, when \(d_{\max}=\Theta(1)\), the size and update time of our data structure match those of the best data structure for synchronous data stream.  
_Remark._ This paper is a corrected version of a paper with the same title in WAOA 2009 [13]; in particular, the error bound on the estimates was given incorrectly before and is fixed in this version.