esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 7 Relay Routing  
Our approach to mobility and routing in UAV swarms is explicitly homogeneous. Each node in the network balances the two needs of exploration (area coverage) and communication (providing routes within the network and to the BS). A possible alternative, of course, is to assign roles to nodes within the network, allowing some nodes to become solely responsible for providing routing (\"relays\"), while others continue to explore the environment. As an example, relay UAVs might be deployed as a separate layer to provide continuous BS connectivity in a hierarchical network [37; 38].  
We believe that a homogeneous approach has a number of important advantages. Although a relay-based scheme prioritizes ensuring a stable communication path by focusing on maintaining a specific route, it may create bottleneck nodes that suffer from congestion or can become critical points of failure to the communications structure. Moreover, these nodes may be more vulnerable to antagonistic interference (e.g., on a battlefield) [39], since differences in the nodes' behavior may indicate their current role. Finally, in low-density networks, the use of some nodes for data communication can significantly degrade area coverage, without an obvious mechanism to trade off between the two.  
In order to explore the scale of these trade-offs with our approach, we implement a simple relay-based routing scheme (called Relay) and compare its performance with our proposed schemes. Initially, all UAVs in the network follow the BS-CAP mobility model. When a UAV finds a target, it finds the shortest route to the BS using AODV routing. The nodes along this route to the BS are reassigned as relay nodes. Relay UAVs on the established route start flying in a nearly circular trajectory within or around their current grid cell to maintain the established route. Thus, a stable and permanent route is established between each target UAV and BS for data communication. When a relay node fails, the above process is repeated to establish a new shortest route. Of course, in the presence of multiple targets, this process of selecting the shortest routes to the BS may lead to multiple routes sharing links in common, which will result in congestion, especially at high data rates. Relay UAVs act as a separate layer from the rest of the UAVs, which continue to perform area search and coverage while maintaining connectivity using the BS-CAP mobility model. Again, the more nodes that are selected to act as relays, the fewer nodes that are available for search area coverage, degrading the swarm's coverage performance.
## 5 Conclusions  
This study proposed the LSTM-MNF prediction model based on a neural network using the water flow data of three DMAs in County Y. The flow data were collected from the SCADA system via TM/TC. Then, machine learning approaches such as LSTM and MLP were implemented to predict the water consumption of the next day with the training from the previous 7 days. MNF data were then derived from the predicted flow.  
When evaluating the performance of the LSTM-MNF and MLP-MNF models, the results showed that the LSTM-MNF model proposed in this study showed better prediction than the MLP-MNF model. This is because of its characteristic of remembering patterns in time series for long durations of time. LSTM networks are specific types of Recurrent Neural Network architecture that can use long-term data to predict future. The LSTM model can better take into account the time-dependent structure of water data.  
The study not only provides prediction flow, but also acts as an important support for decision-making in terms of repair and replacement, identifying the time at which water leakages start to occur. It is judged that it will be of great help in detecting and reducing the amount of water leakage through the prediction of the minimum flow at night. It is also possible to replace the missing flow data with the prediction flow data in a timely manner  
Due to the difficulty of acquiring data for more than three places, we could only apply the model for MNF prediction to the YD 2, 3, and 4 DMAs, which cannot reflect the entire network of County Y, which includes 21 DMAs. Additionally, the method proposed in this study may not be effective for customers who have big water tanks, such as fire stations, hospitals, night markets or apartments complexes, which may use a lot of water at night. This pattern of water demand can make it difficult to estimate the MNF and the exact amount of leakage if a lot of water is used at night for business activities. We suggest that this problem can be solved by installing many flow meters in sub-DMAs. Flow meters are mainly installed in a DMA to measure the water of 500-1500 households. In order to establish more accurate predictions, we suggest that many flow meters be installed to monitor the water flow, e.g., installing flow meters for every 100 households. In addition, the proposed model could not predict MNF after one week; the performance of the model will be improved by training data for long periods in the future. More accurate MNF can be predicted in the future by considering additional data, such as weather information.  
[PERSON] provided the problem of prediction design with LSTM and the machine learning algorithm. [PERSON]-[PERSON] proposed the data analysis. [PERSON]-[PERSON] advised the method of the proposed algorithm and contributed to sentence correction. All authors provided substantive comments. All authors have read and agreed to the published version of the manuscript.  
Kyungpook National University Research Grant.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
Through inspecting our simulation data for the particle of \(d=0.28D\) (i.e., the smallest particle moving with the LD mode for both \(h_{s}\)) at \(h_{s}=0.01R\) and \(N=32\), the smallest gap distance between the particle and the cylinder is around \(0.02R\), which is beyond the critical distance (\(h_{s}=0.01R\)) to activate the lubrication force saturation. Thus, the particle motion is not affected by setting of \(h_{s}=0.01R\) and \(0.005R\). Figure 5: Particle trajectories for (**a**) \(d=0.34D\); (**b**) \(d=0.4D\); (**c**) \(d=0.44D\); and (**d**) \(d=0.48D\) at different lubrication force saturation distances \(h_{s}\) till \(t=30D/U_{m}\) in the DLD with the periodicity of \(N=16\). For a larger row shift fraction \(\varepsilon\) (i.e., smaller periodicity \(N\)), the particle critical diameter is larger, and the short-range hydrodynamic interaction between the particle and the cylinder is stronger; thus, generally, we expect a stronger effect of the lubrication force saturation on the particle motion mode, as evidenced by scattered experimental data on the critical diameter at large row shift fractions [5; 10]. The effect of the lubrication force saturation due to the surface roughness may be an important reason for the discrepancy between the critical diameters of different groups, as shown in Hochstetter [5].
## 3 Design of Experiments
### Surrogate Modelling Approach  
The aerodynamic and aeroelastic responses of the fifteen sample points (g\(i\); \(i=1\),... 15), whose geometry was introduced above, are numerically simulated using CFD techniques. Surrogate models, often called by other names such as approximation models, metamodels or response surface models, are widely used numerical approaches which provide approximate results for design points inside the design space. Surrogate modelling involves training the model to generate analytical relationships between the inputs and outputs, thereby enabling the model to reproduce and replicate the results for any point inside the design space.  
This approach, however, cannot completely replace experiments or a high-fidelity CFD methodology, as the surrogate model requires the initial population, or the initial data, to frame the analytical relationship between the inputs and the outputs. Therefore, in the present study, the initial dataset of aerodynamic and aeroelastic outputs is generated using the CFD-based methodology for the complete set of 15 samples, which is first verified and afterwards validated using experimental data for a subset of three samples. The realizations for the complete set of 15 samples are then fed into the surrogate model to populate the data over the entire design space. The combination of the high-fidelity time-consuming CFD techniques, along with the cheap and quick low-fidelity surrogate techniques, is sometimes also referred to as hybrid modelling.  
In the present work, the surrogate model aims to evaluate the response function for the force coefficients and the flutter derivatives of the twin-box deck by assuming a smooth and continuous variation over the entire design space. The surrogate technique adopted in the present study is the radial basis function, whose formulation was introduced in Section 2.4, which is trained based on the outputs from the set of 15 samples introduced in Section 3.2. Consequently, for the definition of the surrogate model for the force coefficients, 45 CFD simulations will be completed for the static twin-box deck, corresponding to 15 different geometries, whose aerodynamic response is numerically evaluated at three different angles of attack. Similarly, for the evaluation of the flutter derivatives, the set of 15 different deck geometries are considered as samples, and the aeroelastic responses are obtained for two different degrees of freedom, heave and pitch at five different reduced velocities, which yields a total of 150 forced-oscillation simulations. The overall number of CFD simulations to complete is therefore 195, which is burdensome, although manageable when adopting a relatively inexpensive 2D URANS approach, but this would be intractable with a higher-fidelity CFD model such as 3D LES (Large Eddy Simulation), due to the remarkably larger computational demands.  
Figure 4: Geometrical definition for the left box of the 15 samples considered for the surrogate model definition (note the symmetry axis, providing the reference for the associated gap distance).
## 5 Implementation and Results  
Experimental implementation and classification effectiveness achieved based on the dataset obtained in Section 3.2 is described in detail in this section. To validate our results a comparison of the proposed approach on GoogleNet, MobileNet, and a custom CNN, is performed.
## 2 Materials and Methods
### Machine Learning Regression
#### 2.3.5 Random Forest Regression (RFR)  
RFR is an ensemble machine learning technique based on the concept of bootstrap aggregation or bagging. It consists of a combination of decision tree predictor models in which random subsets of the training dataset are provided to the individual decision trees by bootstrapping for training [5; 76; 77]. For example, let \(\text{D}_{\text{tr}}\) represent a set of given training  
Figure 8: Basic concept of a non-linear SVR.  
data consisting of 'N' evidence, such as {(X1, Y1), (X2, Y2) (X3, Y3)... (XN, YN)}, where each piece of evidence consists of a set of input vectors 'X' with'm' input features represented as {x1, x2, x3,... xm}, and a target variable 'Y'. During bootstrapping, 'n' random subsamples (dtr) are drawn with replacement from 'Dtr' and the input data gets split at each node by the decision tree algorithm [78]. The root node initiates the splitting process, where each succeed node applies its own function to divide the new input. Splitting progresses repetitively until a terminal node, referred to as 'leaves', is attained. When the training is complete, a prediction function such as the one shown in Equation (13) is constructed corresponding to each \(i\)th tree. The outputs of 'n' trees are then averaged in the case of regression problems. This process is called aggregation and is obtained using Equation (14) for a random forest regression model.  
\[{\rm y_{i}}^{{}^{\prime}}={\rm f(X_{i},D_{tr})}, \tag{13}\]  
\[\hat{y}=\frac{1}{n}\sum_{i=1}^{n}f(X_{i},D_{tr}) \tag{14}\]  
However, sometimes one or more of the'm' features get chosen in many of the 'n' trees if they are good predictors of the output variable (Y), leading to their correlation. To avoid such correlation of different trees and provide immunity to noise, the modified tree learning algorithm used by random forests chooses a random subset of the features at each root node split during the learning process by feature bagging. Accordingly, with several de-correlated trees, the diversity of trees can be increased, and the noise sensitivity of the model gets largely decreased, making it robust against slight variations in the input data. With RFR, the trees grow without pruning, and this makes the algorithm computationally less expensive. Usually, the more trees that are grown in a forest, the more reliable and accurate a prediction can be. In open-pit bench blasting, the Random Forest method has been used by different researchers for the prediction of back-break, flyrock, and air overpressure in bench blasting [77; 79; 80; 81]. Figure 9 shows a basic architecture of an RFR algorithm.
Parameters like \(CT\), \(NN\), and \(SS\) have proven to be especially effective for forwarding decisions in dense network scenarios, while \(E_{\text{res}}\) is particularly relevant in energy-aware contexts. Building on the limitations observed in our prior work, this paper presents a comprehensive exploration of two advanced methods for overcoming the static nature of traditional broadcasting schemes in MANETs:* Reinforcement learning-driven adaptive fuzzy logic broadcasting: This method extends our previous research [2] by integrating reinforcement learning (RL) with dynamically adjustable fuzzy logic membership functions. Key network parameters, including node density, Connection Time, and RSSI, are continuously monitored, and their fuzzy membership function boundaries are adaptively recalibrated based on real-time network conditions. The RL module then iteratively optimizes the broadcasting decision by adjusting the proportion of nodes that are broadcast (BC) versus those marked as do-no-broadcast (DNB), employing an \(\epsilon\)-greedy policy to balance exploration and exploitation. This iterative learning process progressively refines the fuzzy rule base, enhancing network coverage, reducing Redundancy, and minimizing packet collisions. * Parameter-based dynamic membership function adaptation: This novel approach introduces dynamic adaptability by leveraging multiple predefined profiles, each tailored for specific network conditions. Unlike Method A, Method B decouples the broadcasting ratio optimization from the adaptation of fuzzy logic membership functions. Instead, the method selects the optimal profile dynamically based on real-time macro-level network conditions. For example, energy-aware topology utilizes Eesidual Energy (\(E_{\text{res}}\)) and the Number of Neighbors (\(NN\)). In this research, we explore the impact of the network parameters used for the membership function or broadcasting decision-making to see if creating a model that changes its impact parameters depending on network conditions can be a valid candidate for providing an efficient method of network broadcasting. Through extensive simulations and comparative analyses, this paper demonstrates that both methods significantly outperform static fuzzy logic-based broadcasting schemes, achieving superior adaptability, reduced network overhead, and enhanced delivery reliability. The reinforcement learning-driven approach excels in situations demanding iterative optimization, while the parameter-based method offers a more direct response to changing network conditions.
## 2 Related Work  
### Solar Dynamics Observatory  
The objective of SDO is to help understand the Sun's influence on Earth and near-Earth space by studying the solar atmosphere on small scales of space and time and in many wavelengths simultaneously [17]. SDO sends solar observation images to Earth at a continuous science data downlink speed of 130 Mbps, which are then stored. The SDO images captured at multiple wavelengths can be used to observe various events such as flares, active regions, and coronal holes by employing the web-based Helliviewer ([[https://heliviewer.org](https://heliviewer.org)]([https://heliviewer.org](https://heliviewer.org)), accessed on 1 September 2021). Furthermore, JHelioviewer ([[http://www.jhelioviewer.org/](http://www.jhelioviewer.org/)]([http://www.jhelioviewer.org/](http://www.jhelioviewer.org/)), accessed on 1 September 2021), the open source data visualization tool, has improved usability, focusing on space weather analysis [18]. However, the entry barrier is too high for non-experts to operate easily. When SDO images are visualized, the placement and resizing of the images are also limited by the monitor size. NASA provides SDO Solarium ([[https://www.nasa.gov/solarium](https://www.nasa.gov/solarium)]([https://www.nasa.gov/solarium](https://www.nasa.gov/solarium)), accessed on 1 September 2021) (Figure 2a) and the video wall of the NASA Museum Sun Video Wall ([[https://www.nasa.gov/content/goddard/sdo/national-air-and-space-museum-debuts-must-see-sun-video-wall](https://www.nasa.gov/content/goddard/sdo/national-air-and-space-museum-debuts-must-see-sun-video-wall)]([https://www.nasa.gov/content/goddard/sdo/national-air-and-space-museum-debuts-must-see-sun-video-wall](https://www.nasa.gov/content/goddard/sdo/national-air-and-space-museum-debuts-must-see-sun-video-wall)), accessed on 1 September 2021) (Figure 2b) for the general public to easily experience the SDO images, which are again used only by experts. Because large displays are utilized for exhibition and education, SDO images can be experienced with greater immersion than when using a dedicated viewer.  
Figure 1: Solar Dynamics Observatory (SDO): Investigating the causes of solar variability and how space weather results from that variability.
## 2 Materials and Methods  
Because of the widespread use of the fixed thresholding and STA/LTA triggering algorithms, these two have become the most popular benchmarks for probing new signal detectors and phase pickers. Therefore, they were chosen for the comparative analysis in the present work. Besides, the optimised method by [PERSON] et al. [57] is also tested for comparison. This method was chosen for three reasons: (i) it has been designed to work with continuous, broadband streaming data; (ii) it uses a characteristic function based on a combination of the signal and its time derivative at successive samples, and (iii) it has been extensively documented for implementation, and therefore seemed suitable for the purpose of this study [58]. As will become clear, this method is similar in a sense to our algorithm, which is footed on the synergy of the time-frequency signal decomposition and the abrupt change in the PSD when the transient signal appears in the background noise. In the proposed method, the original AE stream is transformed into a time-dependent parameter \(\Phi\), which was defined in [59] and used for increasing the signal-to-noise ratio and finding the weak events in seismic time series. It then was shown effective for finding AE events in a noisy time series recorded during micro-indentation testing [60]. Unlike the previous reports, the present work deals with signal detection, and its key novelty lies in the proposed strategy pairing the \(\phi\)-parameter with a decision function, which is then tuned for precise AE arrival time picking and signal end detection in a robust and reproducible way. The new steps implemented in the algorithm are detailed and highlighted in the next section.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
###### Abstract  
Dynamic optimization has wide applications in scientific and industrial researches. Multiresolution techniques provide an efficient way to solve dynamic optimization problems but have some disadvantages. An improved multiresolution technique is developed in this paper to overcome these disadvantages. The proposed technique consists of local collocation methods and a multiresolution-based mesh refinement method. New, generalized dyadic meshes are proposed to overcome the dyadic limitation, and the mesh refinement method is improved so that it can start with the coarsest generalized dyadic mesh. Additionally, the proposed technique involves a mesh refinement algorithm to remove the redundant mesh points in the constant control regions by analyzing the control slopes. The technique is applied to three chemical process control optimization problems and compared with other methods to demonstrate its effectiveness. Numerical results show that the proposed technique can solve chemical process control optimization problems accurately and efficiently and has advantages over other methods.  
dynamic optimization; collocation methods; multiresolution; chemical process Article
## 4 Materials and Methods
### RNA Isolation and Quantitative PCR (qPCR)  
Total RNA was isolated using TRI reagent (Sigma-Aldrich) according to the manufacturer's protocol followed by LiCl precipitation. cDNA was synthesized with the M-MLV reverse transcriptase (Promega, Madison, WI, USA) and qPCR was performed on the Rotor-Gene Q instrument (Qiagen, Hiliden, Germany) using the GoTaq qPCR Mastermix (Promega). The qPCR temperature program is shown in Table S1. The qPCR primer with the following nucleotide sequences were used: RPLP0 fwd: 5'-GGCACCATTGAAATCCTGAG TGAGTG-3', RPLP0 rv: 5'-TTGCGGAACCCTCCCAGGAAGC-3', GLII fwd: 5'-TTCGGACTACCACCCTCCTG-3', GLII rv: 5'-ACTGCAGCTCCCCAAATTTTTTCTG-3', PTCH1 fwd: 5'-TCCTCGTGGTGTCTTCTTC-3', PTCH1 rv: 5'-GCTGAAAAGCCAAAGCCGA-3', HHIP fwd: 5'-ACTTGCCGAGCCATATTCCAGGT-3', HHIP rv: 5'-ATCCACTACTGCAGGCCAAAC-3', CSNK1D fwd: 5'-TTTTCCGCCTTCGTGGTTTGC-3', CSNK1D rv: 5'-GTGTAGGGGTAGGGGTGTGTG-3'.