esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 2 FD-SDWMN Architecture
### Local Functionalities  
The local modules in charge of network topology discovery, QoS monitoring, etc., manage flow prioritization, so according to network parameters the controller computes routes for the priority flow. Also, the modules react to network state (link broken, high latency, bandwidth degradation, etc.) dynamically by stopping and/or redirecting traffic according to its criticality. This work is different from our previous work [21] that presented a hierarchical architecture.  
* The aggregated-DB: is a central database in which a controller stores its local domain and other domains knowledge on topology, QoS monitoring and ongoing data flow. Other modules and agents use this information to reach the ultimate goals, i.e., taking suitable action on flows within a local domain and entire network.
* The QoS monitoring module: gathers QoS information such as bandwidth, delay, jitter and packet loss of domain switches and links in between using customized LLDP packets, and other domains' QoS information is aggregated.  
Figure 2: FD-SDWMN controller architecture. AMQP: Advanced Message Queuing Protocol.  
* The path computation module: computing routes from source to destination in the local domain and entire network using the Dijkstra algorithm taking into account QoS metrics of links in between. This module consults the aggregated-DB for network topology and QoS monitoring information.
* The service manager module: supports end-to-end provisioning by receiving requests from northbound APIs for efficient network SLA (Service Level Agreement) management. It verifies SLA feasibility and respect for consulting other modules.
* The virtual manager module: resides on top of the other modules. It offers a Graphical User Interface (GUI) through network virtualization to interact with other modules. It gathers information from many down modules and displays them to the network operator. Also, it provides them with essential network parameters such as flow priorities, new routes, etc.
* The coordinator: this module establishes a control channel between controllers. It manages status information that frequently exchanges (such as link state, host presence) and requests between controllers. Hence, this communication channel should be reliable to guarantee the messaging process. For delivery security and reliability, AMQP is used for Coordinator implementation because it ensures Reliability of message deliveries, Rapid and ensured delivery of messages and Message acknowledgements.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 5 Radio Propagation Experiments in an LOS Environment
It can be seen from Figure 26 that the median value in the case of 90\({}^{\circ}\) intervals from 45\({}^{\circ}\) is the same as that of the dipole antenna. However, the CDF curve in the case of 90\({}^{\circ}\) intervals from 45\({}^{\circ}\) is on the right-hand side from that of the dipole antenna, so the received level of the proposed antenna is improved. Nevertheless, it is on the left-hand side from that of the other division patterns. This is because the directivity of the beam directed at 0\({}^{\circ}\), as illustrated in Figure 17a, is smaller than the others. Therefore, the expected result in free space in the case of 90\({}^{\circ}\) intervals from 45\({}^{\circ}\) is smaller than that of the dipole antenna in the 2.5 to 3 m moving distance section. On the other hand, in the proposed antenna, the CDF curves for the other division patterns, except for the case of 90\({}^{\circ}\) intervals from 45\({}^{\circ}\), are improved by about 6 dB compared to those of the dipole antenna. Especially in the case of 45\({}^{\circ}\) intervals and the case of 22.5\({}^{\circ}\) intervals, the expected results in free space, as illustrated by the broken curves in Figure 25 and the CDF curves shown in Figure 26, reveal great effectiveness. Considering the simplicity of the beam steering system, it is reasonable to form beams at 45\({}^{\circ}\) intervals. It is concluded from these results that the introduction of the proposed antenna can either realize energy saving by reducing the radiated power at the sensor device or expand the coverage area of the proposed smart gateway. Figure 26: CDF characteristics of received power of each division pattern.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
## 3 Modeling
### Productivity
#### 3.3.2 Time  
The total manufacturing time is the time required to produce the above-mentioned output. Unlike the process time, where only the time to produce a single rim is considered, the manufacturing time takes into account the number of rims that corresponds to the oven capacity \(C_{\text{oven}}\). The manufacturing times for each process can be written as follows:  
\[\begin{array}{ll}t_{\text{winding,total}}&=(\frac{t_{\text{winding}}}{N_{ \text{rim}}}+t_{\text{conv,winding}})C_{\text{oven}}/N_{\text{machines}}\\ t_{\text{curing,total}}&=t_{\text{curing}}+t_{\text{conv,curing}}\\ t_{\text{turning,total}}&=(\frac{t_{\text{turning}}}{N_{\text{rim}}}+t_{\text{ conv,turning}})C_{\text{oven}}/N_{\text{machines}}\\ t_{\text{pressing,total}}&=(\frac{t_{\text{pressing}}}{N_{\text{rim}}}+t_{ \text{conv,pressing}})C_{\text{oven}}/N_{\text{machines}}\end{array} \tag{12}\]  
The expression \(\frac{t_{\text{winding}}}{N_{\text{rim}}}\) can be interpreted as the average operation time needed for a single rim. Eventually, the overall manufacturing time is calculated by determining the maximum  
\[t_{\text{manu}}=\text{max}\Big{(}t_{\text{winding,total}},t_{\text{curing, total}},t_{\text{turning,total}},t_{\text{pressing,total}}\Big{)}. \tag{13}\]  
Since one process may be repeated several times to produce one complete rotor, it should be noted that the most time-consuming process is not necessarily the process determining the overall manufacturing time. However, the process with the highest manufacturing time does not define the output. To gain a better understanding of this aspect, consider the following example.  
The average winding process time is 3 h and the oven capacity is three rims. The curing time is assumed to be 7 h. Therefore, curing is the most time-consuming process. First, it is assumed that the most time-consuming process, curing, defines the output. Since the oven capacity is three rims and assuming three rims are needed to build a single flywheel, the output is one (see Equation (11)). The manufacturing time is then determined by the winding process, as 9 h are required to wind three rims, resulting in a productivity of 1/9 flywheels/hour. During this process, the curing oven is not used for 2 h, since the entire winding process takes longer than curing. Therefore, it is examined if the results are better when the winding process determines the output. Instead of winding three rims in a row, only two rims are wound. The output is then 2/3 flywheels. This time, the curing oven defines the total manufacturing time as the 7-h curing process takes longer than 6 h of winding. Thus, the productivity is 2/21 flywheels/h, which is lower than the previous productivity. Although the oven has no downtime during this process, it does not work to capacity, resulting in lower performance of the second approach. In general, a manufacturing process is always optimal if accepted that the most time-consuming process defines the output.
## 6 Results and Discussion
### Comparative Analysis of FOPI-FOPI and PI-PI Controllers
#### 6.2.3 Case 2: Performance Evaluation under Balanced Three-Phase Swell  
Balanced three-phase voltage swells are temporary increases in the voltage level of an electrical power system, typically lasting from a few milliseconds to several seconds due to the sudden decrease in the charging demand of EV charging stations. Figure 14 compares the performance of the PI-PI controller with the FOPI-FOPI controller, both optimized using the WSO-WOA algorithm. During a period between 0.3 and 0.5 s, the microgrid encounters a rapid decrease in charging demand, resulting in a pronounced voltage rise. By 0.5 s, the load demand normalizes. The figure highlights the clear advantage of the FOPI-FOPI controller in maintaining voltage stability.  
The results in Table 6 highlight the performance differences between the PI-PI and FOPI-FOPI controllers. The FOPI-FOPI controller achieved a significantly shorter settling time of 0.068 s, compared to the PI-PI controller's 0.155 s. This suggests that the FOPI-FOPI controller is more effective at achieving a rapid response. However, both controllers exhibited similar levels of maximum overshoot at 12.35%. Figure 15 provides a detailed view of the improvement in load voltage at bus B4 following the implementation of the DSTATCOM with the FOPI-FOPI controller.  
Figure 13: The outcomes of the sag scenario of phase (**a**) using the DSTATCON using the FOPI-FOPI: (**a**) the load voltage without enhancement, (**b**) the load voltage with enhancement, and (**c**) the injected voltage and current.
## 3 Experimental Setup
### Implementation  
The image processing for locating the flow transition is implemented in the script language _Python_. The algorithm is divided in four sub-routines which are listed and explained in detail below. A flow chart of the image processing is depicted in Figure 6.  
1. Pre-evaluation of the thermographic image
2. Obtaining all local gradient maxima positions representing the natural and premature flow transition
3. Selection of all gradient maxima with plausibility and outliers analysis
4. Creating a final actual and natural flow transition line from the selected gradient maxima
5. Calculation of the laminar flow reduction (LFR)  
Figure 6: Flow chart of the image processing method, divided in five main parts: (1) A pre-evaluation of the thermographic image. (2) The localization of local gradient maxima. (3) Selection of gradients representing the flow transition. (4) Construction of the actual and natural flow transition line. (5) Calculation of the laminar flow reduction  
1. Pre-Evaluation  
The first step is the deletion of outliers in the thermographic image due to pixel errors on the camera chip with the Navier-Stokes image inpainting method of the python library _openCV_[29] with a convergence interval of 5\(\sigma\) around the mean temperature. Afterwards, the image is normalized to an 8 bit intensity value range between 0 and 255. In order to spatially limit the subsequent evaluation steps, the image area representing the rotor blade surface is separated from the background by column-wisely locating the two absolute maximum temperature gradients. The locations of these gradient maxima are saved as the position of the rotor blade's leading and trailing edge in the respective image column. All following evaluations steps are carried out within these borders.  
2. Obtaining Positions of Local Gradient Maxima  
The second sub-routine determines the positions of the local gradient maxima which meet the criteria of having a specific minimum magnitude above a given threshold and a specific direction corresponding to the known orientation of the flow transition segments. In order to achieve this, the two measurement approaches introduced in Section 2 are implemented.
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 2 Background
OMG DDS adopts the publish/subscribe distribution paradigm and the data-centric approach defined in [16]; the underlying data distribution model uses a Global Data Space (GDS) to identify data circulating in the distribution support. GDS also provides built-in data isolation mechanisms, called domains and partitions, designed to improve system scalability through view separation: DDS middleware nodes can be organized into physical (domains) and logical (partitions) groups; these groups are hierarchical and promote multiple views of the same GDS and physical DDS deployment. Moreover, the DDS specification defines high-level standardized interfaces and functions, by dividing them into two layers, namely Data Local Reconstruction Layer (DLRL) and Data Centric Publish/Subscribe (DCPS), to differentiate and isolate application problems from low-level implementation details. In particular, DLRL is an optional high-level Application Programming Interface (API) to provide application/service developers with a highly interoperable object-oriented view of the data exchanged by the (lower) DCPS layer. In addition, DCPS is a mandatory low-level layer with the primary goal of efficient delivery of data injected by publishers to subscribers; in the following, we only focus on DCPS since it is the most commonly supported in available DDS implementations, thus guaranteeing both good interoperability and DDS-vendor-independent portability. While DCPS describes distributed DDS components, a separate standard, the Real-Time Publish-Subscribe (RTPS) DDS Interoperability Wire Protocol, specifies the network protocols used to dynamically discover DDS entities and to exchange data between publishers and subscribers [17]. In particular, RTPS defines all low-level mechanisms to optimize communication aspects, such as caching. For instance, each topic data sample, pushed by publishers in the GDS, is delivered by the DDS middleware via RTPS to every subscriber that maintains a copy of it in its local cache. In other words, DDS can keep at each subscriber a local repository with all the latest values of each topic to have the possibility to locally perform query operations without introducing any additional network traffic in a transparent and interoperable way. Let us note that, in particular for local deployment environments, some DCPS-compliant DDS solutions, such as OpenSplice, may employ proprietary data delivery protocols. Additional details about DCPS and RTPS, out of the scope of this paper and not included here for the sake of briefness and to focus primarily on adaptability and interoperability advantages of DDS-based named data for UAS, can be found in the OMG specification [15; 17].
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
###### Abstract  
This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either 'leak' or 'non-leak'. Experimental validation on real-world pipeline data demonstrated the framework's efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management.  
acoustic emission; continuous wavelet transform; pipeline leakage; convolutional neural networks; deep learning Article  
Academic Editors: [PERSON] and [PERSON]  
Received: 7 November 2024  
Revised: 21 December 2024  
Accepted: 22 December 2024  
Published: 28 December 2024  
**Citation:** [PERSON]; [PERSON]; [PERSON] Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals. _Appl. Sci._**2025**, 15, 185.  
[[https://doi.org/10.3390/appl/15010185](https://doi.org/10.3390/appl/15010185)]([https://doi.org/10.3390/appl/15010185](https://doi.org/10.3390/appl/15010185))  
Copyright: 02 2024 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 3 Localization System
### Anchor Device Modeling  
The anchor described in the previous subsection and used in [16] has been modeled in the proposed analysis method.  
The radiation patterns of the eight patch antennas implemented in the SBA are considered identical and modeled by means of a cardioid. The latter is obtained via a best-fitting procedure carried on the average value calculated starting from the eight effective antennas radiation patterns [16]. The results for both dual frequencies are shown in Figure 3. Considering dual-band operation, the n-th beam pattern pointing toward a generic direction (\(\theta_{n}\)) at the \(i\)-th frequency can be written as:  
\[G_{n}^{(i)}(\theta)=G_{n}^{(i)}\bigg{[}\frac{1}{2}(1+\cos(\theta-\theta_{n})) \bigg{]}^{p_{n}^{(i)}} \tag{1}\]  
The exponents \(p_{n}^{(i)}\) control the beamwidth for the \(i\)-th frequency, while the coefficients \(G_{n}^{(i)}\) are the maximum gains. For fixed frequencies, these are nominally identical. However, fabrication tolerance, mutual coupling, channel imbalance, and mechanical arrangement tolerance can affect the beam-set uniformity.  
The simulation environment also demands the modeling of all the other functional components that constitute the anchor node. A descriptive approach based on the idealization of their features results sufficiently suitable to the aims of the analysis that we want to  
Figure 2: SBA of the proposed anchor device.  
carry out. On the contrary, the description of the SBA is a critical point since the latter is the core of the system.
# High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
## 3 Experimental Setup
### Hyperparameter Tuning  
For hyperparameter tuning, we used Optuna, a state-of-the-art automatic hyperparameter optimization software framework [2]. Optuna offers a define-by-run-style user API where one can dynamically construct the search space, and an efficient sampling algorithm and pruning algorithm. Moreover, our experience has shown it to be fairly easy to set up. Optuna formulates the hyperparameter optimization problem as a process of minimizing or maximizing an objective function that takes a set of hyperparameters as an input and returns its (validation) score. We used the default tree-structured Parzen estimator (TPE) Bayesian sampling algorithm. Optuna also provides pruning, i.e., the automatic early stopping of unpromising trials [2].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Regression**} \\ \hline
**Algorithm** & **Hyperparameter** & **Values** \\ \hline \multirow{5}{*}{BayesianRidge} & n\_iter & [10, 1000] (log) \\  & alpha\_1 & [\(1\times 10^{-7}\), \(1\times 10^{-5}\)] (log) \\  & lambda\_1 & [\(1\times 10^{-7}\), \(1\times 10^{-5}\)] (log) \\  & tol & [\(1\times 10^{-5}\), 0.1] (log) \\ \hline \multirow{5}{*}{DecisionTreeRegressor} & max\_depth & [2, 10] \\  & min\_impurity\_decrease & [0.0, 0.5] \\  & criterion & [squared\_error, friendman\_mse, absolute\_error] \\ \hline \multirow{5}{*}{GradientBoostingRegressor} & n\_estimators & [10, 1000] (log) \\  & learning\_rate & [0.01, 0.3] \\  & subsample & [0.1, 1] \\ \hline \multirow{5}{*}{KNeighborsRegressor} & weights & \{uniform, distance\} \\  & algorithm & \{auto, ball\_tree, kd\_tree, brute\} \\  & n\_neighbors & [2, 20] \\ \hline \multirow{5}{*}{KernelRidge} & kernel & \{linear, poly, rbf, sigmoid\} \\  & alpha & [0.1, 10] (log) \\  & gamma & [0.1, 10] (log) \\ \hline \multirow{5}{*}{LGBMRegressor} & lambda\_1l & [\(1\times 10^{-8}\), 10.0] (log) \\  & lambda\_l2 & [\(1\times 10^{-8}\), 10.0] (log) \\  & num\_leaves & [2, 256] \\ \hline \multirow{5}{*}{LinearRegression} & fit\_intercept & \{True, False\} \\  & normalize & \{True, False\} \\ \hline \multirow{5}{*}{LinearSVR} & loss & \{epsilon\_insensitive, \\  & tol & squared\_epsilon\_insensitive, \\  & & tol & [\(1\times 10^{-5}\), 0.1] (log) \\  & C & [0.01, 10] (log) \\ \hline \multirow{5}{*}{PassiveAggressiveRegressor} & C & [0.01, 10] (log) \\  & fit\_intercept & \{True, False\} \\  & max\_iter & [10, 1000] (log) \\ \hline \multirow{5}{*}{RandomForestRegressor} & n\_estimators & [10, 1000] (log) \\  & min\_weight\_fraction\_leaf & [0.0, 0.5] \\  & max\_features & [auto, sqrt, log2] \\ \hline \multirow{5}{*}{SGDRegressor} & alpha & [\(1\times 10^{-5}\), 1] (log) \\  & penalty & [\(12\), 11, elasticnet] \\ \hline \multirow{5}{*}{XGBRegressor} & n\_estimators & [10, 1000] (log) \\  & learning\_rate & [0.01, 0.2] \\ \cline{1-1}  & gamma & [0.0, 0.4] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
###### Abstract  
With the acceleration of urbanization, surface deformation monitoring has become crucial. Existing monitoring systems face several challenges, such as data singularity, the poor nighttime monitoring quality of video surveillance, and fragmented visual data. To address these issues, this paper presents a 3D real-scene (3 DRS)-enhanced GNSS/intelligent vision surface deformation monitoring system. The system integrates GNSS monitoring terminals and multi-source meteorological sensors to accurately capture minute displacements at monitoring points and multi-source Internet of Things (IoT) data, which are then automatically stored in MySQL databases. To enhance the functionality of the system, the visual sensor data are fused with 3D models through streaming media technology, enabling 3D real-scene augmented reality to support dynamic deformation monitoring and visual analysis. WebSocket-based remote lighting control is implemented to enhance the quality of video data at night. The spatiotemporal fusion of UAV aerial data with 3D models is achieved through Blender image-based rendering, while edge detection is employed to extract crack parameters from intelligent inspection vehicle data. The 3 DRS model is constructed through UAV oblique photography, 3D laser scanning, and the combined use of SVSGeoModeler and SketchUp. A visualization platform for surface deformation monitoring is built on the 3 DRS foundation, adopting an \"edge collection-cloud fusion-terminal interaction\" approach. This platform dynamically superimposes GNSS and multi-source IoT monitoring data onto the 3D spatial base, enabling spatiotemporal correlation analysis of millimeter-level displacements and early risk warning.  
deformation monitoring system; GNSS; multi-source sensor; intelligent vision Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System. _Appl. Sci._**2025**, 15, 4983. [[https://doi.org/10.3390/appl15094983](https://doi.org/10.3390/appl15094983)]([https://doi.org/10.3390/appl15094983](https://doi.org/10.3390/appl15094983))