esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 6 Conclusions  
In this paper, we propose an improved MATD3 algorithm with an LSTM model incorporated into the MATD3 model to address the coverage problem of UAVs in the context of ocean data monitoring. The LSTM model is used to consider time series information during the UAV path-planning process. Through comparative experiments between the three algorithms, we have demonstrated that the improved MATD3 algorithm can effectively reduce the redundancy of coverage while ensuring high coverage rates of the target area. Future work can explore online reinforcement learning methods and multiagent communication mechanisms to continuously adapt to dynamic environments during execution. This will advance the applications of drone technology in various real-world settings, making a substantial contribution to the broader field of autonomous navigation systems.  
[PERSON] wrote the main manuscript text, including the study design, experiments, and data analysis. [PERSON] prepared the figures. [PERSON] reviewed and edited the English grammar, and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities 2023 YJS104.  
The data supporting this article are from previously reported studies and datasets, which have been cited.  
Figure 10: Repeated coverage of three algorithms with different numbers of UAVs.  
**Acknowledgments:** The authors express their gratitude to the editor and reviewers for their work and valuable comments on the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### Motor Housing
#### 3.1.2 Coolant Flow Status  
The velocity and temperature of coolant are important for heat transmission. Moreover, the latent heat of phase transitions in the coolant can also be utilized. [PERSON] et al. [30] conducted experiments on the relationship between the parameters of a water-cooled structure and its spindle temperature. The spindle temperature decreased as the cooling water velocity increased. Changing the initial cooling water temperature was more effective in suppression of the spindle temperature. Spindle temperature gradually tended to stable as cooling water flow increases [31]. [PERSON] et al. [32] investigated the effect of water runner diameter and flow rate on spindle cooling during the cooling process. In a certain area, larger flow channel diameter and flow rate can reduce the temperature and radial heat elongation effectively.  
To improve cooling efficiency, [PERSON] et al. [33] exploited the latent heat of coolant for motor cooling, as shown in Figure 10. The liquid coolant entered via the housing and subsequently exchanged heat in the spiral grooves of the housing. The coolant was then sprayed into the motor for a secondary heat exchange while the coolant underwent a vaporized phase change and the latent heat of the coolant was used for cooling. The results showed that by utilizing the latent heat of the coolant, the same mass flow rate of coolant could provide more cooling capacity.  
Figure 10: Schematic structure of gas–liquid phase change cooling in the motor [33].  
Figure 9: An optimization method to equalize cooling capacity based on energy saving [29]. (**a**) Structure before application; (**b**) Uniformly cooled water-cooled housing; (**c**) Overall structure of the motor spindle after assembly.  
Proper mating of cooling input power and heat evacuation can improve cooling efficiency. To ensure motor cooling efficiency and reduce spindle thermal errors, [PERSON] et al. [34] controlled circulating coolant supply power by matching the power difference between heat generation and heat evacuation. To generate different temperature coolant supplies and reduce thermal errors in the motorized spindle, [PERSON] et al. [35] equipped spindle coolant channels with a differentiated water-cooling circulation system and real-time spindle temperature detection.
## 4 The Study on Gas Control Technology of High Gas-Drainage Roadway  
### Theoretical Analysis of High Gas-Drainage Roadway Layout Range  
#### 4.1.1 Determination of Vertical Position Range of High Gas-Drainage Roadway  
The high gas-drainage roadway should be located within the fracture zone. On one hand, the fracture zone is less affected by mining-induced stress, making the roadway more stable and less prone to damage. On the other hand, the fractures within this zone are well developed, allowing for gas accumulation and enabling the stable extraction of high-concentration gas [42].  
The vertical distance between high gas-drainage roadway and coal roof can be calculated using the following formula:  
\[H_{fr}\geq H\geq H_{fa}+(1.0\sim 2.0)\cdot M \tag{1}\]  
where \(H_{fa}\) is the height of the falling zone, m; \(M\) is the coal mining height, m; \(H_{fr}\) is the height of the gas conductive fracture zone.  
According to the numerical simulation results of overlying strata failure, the height of the falling zone in the 91-105 working face is 14.4 m above the coal seam roof, the height of the gas-conductive fracture zone is 40.7 m, and the mining height is 6.15 m. Therefore, the vertical distance from the high gas-drainage roadway to the coal seam roof should satisfy the following condition:  
\[20.5\;\mathrm{m}\leq H\leq 40.7\;\mathrm{m}\]
## 3 Results  
### The Mean WUE in Central Asia  
Overall, the mean GPP in Central Asia showed a high consistency with ET during 2000 to 2014 (Figure S1a,b). The highest GPP and ET were both found in the Ili Valley, Xinjiang; high-altitude mountainous areas of Kyrgyzstan; and mountainous areas in the northeast regions of Kazakhstan. The lowest GPP and ET were observed in some severely arid areas, that is, the margin of the Gurbantunggut Desert in northern Xinjiang, the southern regions of Kazakhstan, and the southeastern regions of Uzbekistan and Turkmenistan. For the period 2000 to 2014, the mean annual WUE in Central Asia was 2.56 g C/kg\(\cdot\)H\({}_{2}\)O, which exhibited a remarkable spatial heterogeneity (Figure 1a). Some arid regions in southeastern Uzbekistan and Turkmenistan, and southern Kazakhstan had a high WUE, which ranged from 2.5 to 5.1 g C/kg\(\cdot\)H\({}_{2}\)O. The regions with a low WUE (<0.5 g C/kg\(\cdot\)H\({}_{2}\)O) were found in the high-altitude mountains in Kyrgyzstan and Tajikistan, and the Tianshan Mountains in Xinjiang. At the biome level (Figure 1b), closed shrulands showed the highest WUE (4.49 g C/kg\(\cdot\)H\({}_{2}\)O), and forests and croplands had the lowest WUE. According to the drought classification based on the TVDI (Figure 1c), severe drought ecosystems had the highest WUE (3.73 g C/kg\(\cdot\)H\({}_{2}\)O), followed by drought and slight drought ecosystems, and the lowest WUE (0.53 g C/kg\(\cdot\)H\({}_{2}\)O) was observed in humid ecosystems. This result is consistent with previous studies, which showed that humid regions are characterized by a low WUE [3,14]. The comparison of the WUE across the different countries revealed that UZB (2.86 g C/kg\(\cdot\)H\({}_{2}\)O) and TKM (2.79 g C/kg\(\cdot\)H\({}_{2}\)O) had a higher WUE than KZA (2.51 g C/kg\(\cdot\)H\({}_{2}\)O), TJK (1.77 g C/kg\(\cdot\)H\({}_{2}\)O), CHN (XJ) (1.46 g C/kg\(\cdot\)H\({}_{2}\)O), and KGZ (1.43 g C/kg\(\cdot\)H\({}_{2}\)O).
## 4 System Architecture
### Applying Inner Join Principle in Diagnosis  
Figure 1 shows the use of an inner join in a database to find a match between two tables. The first table, Table A (which also represents all disease tables in the database), contains a list of symptoms for each disease that was collected to create a knowledge base, as illustrated in Figure 2.  
The second table, Table B, contains the symptoms of a patient, which were input by the user and stored in the database. This database is dynamic and allows the use of SQL  
Figure 1: Diagram of Inner Join. A INNER Join B = \(\{(a,b)|(a,b)\in A\times B\) and \(a.column1=b.column1\}\).  
Figure 2: CVD database architecture. Underlined text depicts the source of keywords.  
commands such as UPDATE and DELETE, meaning that it can change with each user and is reset when the application is closed.  
However, during the diagnosis process, Table B is assumed to be static and cannot be changed. This is to ensure that the diagnosis remains accurate and consistent. To find a match between the two tables, the current user's symptoms in Table B are compared with the symptoms in the disease table (Table A) using an inner join. The inner join function returns only the rows that match the two tables.  
To further analyze the match between the two tables, the \"count\" function is used to count the number of symptoms that match. These values are stored in a table of \"disease_count\" using the UPDATE command. There is one query for each major CVD and to handle the counting process for each disease (see Figure 3).  
Utilizing the database of CVD symptoms, we align corresponding symptom descriptions from patient data. The maximum correlation identifies the most likely disease entity. To ascertain the most probable disease, the'max' function is employed to identify the highest count value among all diseases. This maximum value corresponds to the disease with the highest number of matching symptoms and is therefore the most likely diagnosis for the patient. This process of inner joining, counting, and finding the maximum value allows an accurate prediction of a disease based on the symptoms of the patient, as shown in Figure 4.  
Figure 3: Inner join principle and count of our proposed approach. An asterisk (*) depicts the foreign key.
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
###### Abstract  
A computational fluid dynamics investigation of a turbulent supersonic jet impacting a solid flat plate is conducted utilizing the OpenFOAM software. The research focuses on simulating the three-dimensional mean compressible flow for jet impingement on an inclined plate by analyzing the complex flow field and the surface distribution of pressure. Various simulations are carried out at a jet Mach number of 2.2 maintaining a constant nozzle-to-plate distance while varying the angle of inclination of the plate. In contrast to earlier numerical studies, this work employs a modern turbulence modeling technique known as detached eddy simulation (DES), along with a traditional unsteady Reynolds-averaged Navier-Stokes model. Making a comparison with experimental findings, the current analysis reveals that both turbulence modeling techniques effectively predict the mean pressure distribution on the plate. However, the DES approach offers deeper insights into the turbulent flow field, showing notable consistency with the experiments. The complex compressible flow patterns are simulated with higher accuracy compared to the traditional approach. Enhanced turbulence resolution is attained by utilizing the same computational grid with a limited increase in computational complexity.  
applied computational fluid dynamics; turbulent supersonic jet; Reynolds-averaged Navier-Stokes modeling; detached-eddy simulation +
Footnote †: journal: Applied Sciences
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 3 Theoretical Background
### Sae  
An SAE [28], also known as a deep autoencoder, is organized as shown in Figure 2. It has a structure in which multiple hidden layers are contained in the encoder and decoder, and its structure is symmetrical in relation to the latent layer. The latent layer is located between the encoder and the decoder, as in an autoencoder, and it stores the feature data that are acquired from the encoder.  
The SAE model is trained using the greedy layer-wise training methodology [29]. This methodology was proposed to determine the optimal parameters of an SAE, and it has been proven to be effective in learning an SAE with multiple hidden layers. This methodology can also reduce the network size of the SAE model and increase the training speed. Furthermore, it has the advantage of potentially reducing the risk of overfitting.
# Software Defined Networks in Wireless Sensor Architectures
## 3 Wireless Sensor Networks
### Wireless Sensor Network Challenges
Nowadays, privacy is the most important factor in communications. The risk of being spied is elevated since there are many reasons to do it, but, at the same time, a set of social rules and laws do not allow it to happen. Robustness to communication can affect the performance of the network through denial of service attacks [59]. This challenge tackles the employment of spread-spectrum communications. Secure routing is also an essential service of WSNs, as the existing routing protocols experience vulnerabilities due to the high sensibility of capturing network nodes [60]. Consequently, the resilience to node capture is also a challenge in order to maintain the security. Solutions to these problems are tamper evident methods included in sensor nodes, collecting data of the environment and crosscheck for consistency among others. WSNs open a wide range of possibilities in terms of building new applications, creating or improving protocols, introducing new design concepts and developing new algorithms. [PERSON] et al. [61] present a new protocol design that aims to reduce energy consumption, but, at the same time, it provides high scalability preventing collisions. This protocol is composed of three main components among others: a component that is listening and sleeping periodically, a component for preventing collision and eavesdropping, and, finally, a component for sending messages. Periodic listening and a sleeping component tries to decrease the listening time to a periodic value. Then, if the time of listening is cut in half, the energy saved is also reduced by half. To prevent collision and overhearing, it uses the duration field within each packet, which indicates the remaining time for the next transmission, and, then, it avoids packet collision. The proposed message handling divides the message into small fragments that will be sent in burst. Finally, physical attacks regarding capturing network nodes are also a big problem. In contrast to traditional IP networks that were inaccessible for possible attackers, WSN are placed in environments that are not totally guaranteed.
## 5 Performance Evaluation Using OPNET Testbed
### Small Network
#### 5.1.1 Time Evolution Performance  
Figure 16 shows the highly varying end-to-end delay, which can be attributed to the wireless interference and the high speed of the vehicular nodes in the highway. One can see that the controller is always active in reducing the end-to-end delay (blue solid curve) but not always to the minimal point. This is due to the coupling effect of the car speed and periodic feedback so that some packets go \"unnoticed\", thereby losing it effectiveness.  
MOS value for the highway scenario is shown in Figure 15. The score reflects the effect of fluctuations in end-to-end delay on the quality of video seen by the user. The controller tends to keep the visual experience at a constant rate, and improves when the end-to-end delay is reduced substantially.  
Figure 17 shows the effect of the controller on the queue size. By controlling congestion in the network, our controller is able to reduce the occasions of buffer saturations and therefore the loss of video quality due to packet loss.  
The mean end-to-end delay performance for the highway scenario is shown in Figure 18. As expected, as the service rate increases, the end-to-end delay reduces. Increasing the service rate reduces the end-to-end delay and increasing the buffer size increases the end-to-end delay.  
Figure 16: End-to-end delay for a highway scenario.  
Figure 17: Instantaneous queue size for highway scenario.
## 4 Conclusions  
The increasing demand for automated aircraft inspection and maintenance has led to significant advancements in Non-Destructive Testing (NDT) techniques, machine learning-based defect detection, and automated defect characterisation. This review investigated the latest research developments, emphasising how deep learning, advanced sensor fusion, and real-time automation transform aircraft maintenance practices.  
Recent studies demonstrate that AI-driven inspection systems achieve higher accuracy, improved defect classification, and reduced inspection time, mainly through hybrid models, transfer learning, and sensor fusion techniques. Advances in robotics, UAV-based inspection, and deep-learning-enhanced thermography have further improved the efficiency and reliability of damage assessment in metallic and composite aircraft structures. However, challenges remain in areas such as data limitations, environmental factors affecting imaging accuracy, and the need for standardised defect detection datasets.  
Future research should focus on expanding high-quality datasets, integrating real-time AI-powered analysis, and improving explainability in deep learning models to enhance trust in automated inspections. Developing self-adaptive defect monitoring systems could further streamline predictive maintenance and aircraft structural health monitoring. The aviation industry is progressing toward more efficient, reliable, and scalable aircraft maintenance solutions by bridging the gap between AI, advanced imaging, and aerospace engineering.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
###### Abstract
Since these supplies are not constant due to high-current switching through the pre-driver's PDN, therefore, the IBIS model fails to accurately predict the timing distortion originating from \(V_{DD}\), the voltage noise which affects the output eye jitter. Moreover, previous works presented an extended equivalent circuit behavioral model for SiPI simulation in the pre-driver and the driver's last stage [10, 11, 12]. Moreover, ground supply noise has not been considered [12]. For these reasons, this paper explores the study of the separate effect of the jitter distortion induced by both stages powered by distinct P/G supplies. For instance, the methodology consists of analyzing the jitter distortion induced by the IO buffer's circuit stages under three PVT corners with different circuit P/G supply configurations or scenarios which cover most of the practical IO buffer design with a separate PDN design. The analysis carried out in this work is based on transient simulation of the IO buffer transistor level (TL) space reference model, at three main PVT corners with different configurations of the PGSV variations connected at the pre-driver and last stage. The rest of the paper is organized as follows. Section 2 describes power supply induced jitter (PSIJ) prediction methodology followed in this paper. Section 3 presents the IO buffer circuit along with corner definition and circuit configuration to be simulated and analyzed. Moreover, it describes the jitter analysis of the pre-driver and last stage contributions, along with the shared vs. decoupled PGSV configuration. Section 4 investigates the PSIJ transfer function dependency on the frequency and amplitude variations of the driver's IO stages. Conclusions and future work are presented in Section 5.
# A Proposal for a New Python Library Implementing Stepwise Procedure
## 2 Background
### The Problem of Multicollinearity  
Multicollinearity occurs when there are very high correlations between the model's explanatory variables, which can be caused by the presence of variables with the same trend or the use of databases with few observations [32].  
As forms of multicollinearity diagnosis, we highlight the statistics _Tolerance_ and Variance Inflation Factor (\(VIF\)) based on estimating auxiliary regressions [40], according to Equations (4) and (5):  
\[Tolerance=1-R_{K}^{2} \tag{4}\]  
\[VIF=\frac{1}{Tolerance} \tag{5}\]  
\(R_{k}^{2}\) is the adjustment coefficient of each of the estimated \(k\) auxiliary regressions. According to [32], if _Tolerance_ is too low (which implies a high \(VIF\) statistic), there is evidence of multicollinearity problems. In this case, the explanatory variable depends on this auxiliary regression, sharing a high percentage of variance with the other explanatory variables [6].  
As reference values for diagnosing multicollinearity problems, many authors establish a \(VIF\) threshold above 10. However, [PERSON] and [PERSON] [32] warn that a \(VIF\) value equal to 4 will result in an \(R_{k}^{2}\) of 0.75 for a given auxiliary regression, a relatively high percentage of shared variance. If the VIF exceeds the established limits, it indicates the presence of multicollinearity in the proposed model. Therefore, the use of stepwise is recommended so that only statistically significant variables remain in the model, thus avoiding the problem of multicollinearity.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
###### Abstract  
On board of communication satellites, there are numerous RF filters in the payload used for guiding wanted signals as desired and for rejecting unwanted signals such as not to interfere with wanted signals. At present, satellites have a long lifetime (15 years at least for a typical geostationary communication satellite) and there is also time elapsing between its design and the start of in-orbit service (2 years at least). On the other hand, conventional RF filters for satellite applications are fixed frequency. Therefore, there is a growing demand for in-orbit tunable filters to be able to cope with changes in the market situation during that long time. Also, there are communication systems evolving that rely on tunable devices used for adaptive filtering or steering. This article reports on in-orbit tunable bandpass filters employing liquid crystal material and operating at 20 GHz, and especially on design aspects dealing with the harsh space environment, on the operating principles for commanding the filters on board the satellite, and on a qualification campaign passed successfully. A demonstrator unit is in manufacturing for in-orbit verification.  
liquid crystal; RF bandpass filter; container for liquid crystal; biasing circuit configuration; space application; harsh environment; qualification testing; tunable filter +
Footnote †: journal: Physics Letters B