esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Datasets  
Our_flame_smoke and FLAME are applied to compare the segmentation performance of Flame-SeaFormer with that of other methods. The training, validation, and testing sets of the two datasets are divided at the ratio of 8:1:1, as shown in Table 2.  
* Our_flame_smoke: This dataset comprises 4392 fire images acquired by self-designed fire experiments, each with a resolution of 1920 \(\times\) 1080. The experimental apparatus is depicted in Figure 3a. All the images in this dataset depict indoor fire scenarios, providing a comprehensive representation of the entire burning process of flames. The dataset exhibits variations in flame scale and shape, offering a diverse range of flame instances for evaluation.
* FLAME ([[https://ieee-dataport.org/open-access/flame-dataset-aerial-imagery-pile-burn-detection-using-drones-uavs](https://ieee-dataport.org/open-access/flame-dataset-aerial-imagery-pile-burn-detection-using-drones-uavs)]([https://ieee-dataport.org/open-access/flame-dataset-aerial-imagery-pile-burn-detection-using-drones-uavs](https://ieee-dataport.org/open-access/flame-dataset-aerial-imagery-pile-burn-detection-using-drones-uavs)), accessed on 24 March 2023): This dataset consists of fire images collected by drones during a pileup debris burn in an Arizona pine forest. The dataset includes 2003 flame images, each having a resolution of 3840 \(\times\) 2160 pixels. Several images of the dataset are shown in the first row of Figure 8. It is worth noting that the scale of the flame objects in this dataset is relatively small as all the images were captured from an overhead view using drones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Training Set** & **Validation Set** & **Testing Set** & **Total** \\ \hline Our\_flame\_smoke & 3512 & 440 & 440 & 4392 \\ FLAME & 1601 & 201 & 201 & 2003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of two datasets.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.2 ADS-B In and Out  
ADS-B (Automatic Dependent Surveillance-Broadcast) is a DAA system that relies upon the unmanned aircraft having a transponder on board that broadcasts its position, registration (or identity if the unmanned aircraft is not registered), direction of travel, and speed over an encrypted data link [46]. The ADS-B Out function is when the aircraft sends its own data, while ADS-B In will provide information about other ADS-B equipped aircraft [47]. ADS-B In is commonly used on unmanned aircraft operations, as it is relatively low-cost and can be used with small, unmanned aircraft as an aid to situational awareness [33]. ADS-B Out is more complicated, as the required power output and potential for interference with other onboard systems mean that only larger unmanned aircraft could have a conventional ADS-B Out system. However, there are lower power alternatives to the ones used on manned aircraft that can be applied to smaller unmanned aircraft, albeit with reduced broadcasting ranges [48; 49; 50]. The suitability of ADS-B systems as a DAA tool depends upon the airspace where the aircraft will be used. If it will be used withincontrolled airspace, this would bring the unmanned aircraft up to similar standards applied to manned aircraft, and ATC will be able to ensure separation between the unmanned aircraft and any other aircraft within the operating area. Even in remote areas, having a lower powered system is an additional safety layer that, if a manned aircraft that has a transponder on board was approaching the unmanned aircraft, could trigger some form of alert or warning. However, if the other aircraft does not have the ADS-B transponder, it will not be picked up with this tool alone.
## 3 Methods
### Data Sources and Processing
#### 3.3.1 Data Screening  
User-generated content (UGC) was collected from 12 urban parks in Macau between 2020 and 2024, resulting in over 26,000 valid text entries. As the collected data involved multiple languages, all texts were initially translated using the online version of Google Translate, accessed on 30 December 2024. Subsequently, manual proofreading was performed to enhance translation accuracy and ensure contextual appropriateness. Subsequently, the data underwent rigorous screening. First, comments unrelated to cultural ecosystem services (CES), such as those about transportation or dining, were removed. Second, duplicate entries, irrelevant content, and promotional comments were excluded to improve data quality and relevance. Additionally, textual data were preprocessed by removing special symbols, formatting tags, and meaningless characters. This step was conducted to further clean the data and ensure consistency in data formatting. Finally, the screened texts were classified systematically to ensure the accuracy and reliability of this study's findings.  
Figure 2: Map of locations of Macau parks. The star indicates Beijing, the capital of China.
# Effective Scheme for Inductive Wireless Power Coil Design Using Scan-and-Zoom Optimization
## 5 Hardware Verification
### Evaluation of Proposed Coil Design  
According to the proposed design in Section 4.3, \(R_{out}\), \(R_{in}\), and N values in the proposed coil design approach are set equal to 173 mm, 139 mm, and 10, respectively, as shown in Figure 17. Tables 6 and 7 summarize the FEA and measured results, as well as the capacitance results. Table 8 summarizes the experimental results. The \(P_{out}\), \(V_{out}\), and \(f_{o}\) values are determined following the IPT system design specifications. The efficiency is measured when the system is operated at resonance frequency \(f_{o}\) where the current and voltage phases are in phase as shown in Figure 18. The proposed coil design method shows higher efficiency than the conventional coil design method by 0.401%. Furthermore, the proposed method has fewer FEA executions than the conventional method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameters** & **Calculated Results** & \
\begin{tabular}{c} **Measured Results** \\ **(Error)** \\ \end{tabular} \\ \hline \(C_{Tx}\) &
\begin{tabular}{c} Transmitter \\ capacitance \\ \end{tabular} & 15.89 nF & 15.92 nF \((-0.19\%)\) \\ \(C_{Rx}\) & Receiver capacitance & 16.18 nF & 16.07 nF \((-0.68\%)\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculated and measured capacitance results of proposed and conventional coil design method.  
Figure 16: Transmitter and receiver coils using coil radius of the conventional coil design method.  
Figure 17: Transmitter and receiver coils using coil radius of the proposed coil design method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **FEA Results** & **Measured Results (Error)** \\ \hline \(L_{Tx}\) & 117.28 \(\mu\)H & 117.3 \(\mu\)H (\(+0.02\%\)) \\ \(L_{Rx}\) & 117.28 \(\mu\)H & 115.32 \(\mu\)H (\(-1.67\%\)) \\ \(R_{Tx}\) & 70.19 \(\mathrm{m}\Omega\) & 92.83 \(\mathrm{m}\Omega\) (\(+32.26\%\)) \\ \(R_{Rx}\) & 70.19 \(\mathrm{m}\Omega\) & 89.86 \(\mathrm{m}\Omega\) (\(+28.0251\%\)) \\ \(k\) & 0.349 & 0.29 (\(-5.73\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 7: FEA and measured results using coil radius (\(R_{out}\) = 173 mm and \(R_{in}\) = 139 mm) of the proposed coil design method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameters** & **Conventional** & **Proposed** \\ \hline \(P_{out}\) & Output power & 100 W & 100 W \\ \(f_{0}\) & Operating frequency & 100.6 kHz & 101.3 kHz \\ \(V_{out}\) & Output voltage & 49.5 V & 49.4 V \\ \(R_{L}\) & Load resistance & 24.89 \(\Omega\) & 24.93 \(\Omega\) \\ \(\eta_{t}\) & Power transfer efficiency & 97.734\% & 98.135\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The experimental results.  
Figure 18: _Cont._  
Figure 17: Transmitter and receiver coils using coil radius of the proposed coil design method.
## 5 Conclusions  
1. In order to create a multiphase structure of 38 CrNi3 MoV steel containing RA (7.5-10.8%), isothermal hardening at the (\(\gamma\)+\(\alpha\))-area is proposed. When holding 38 CrNi3 MoV steel at the temperature of 750 \({}^{\circ}\)C for 20 min, no primary ferrite was formed. Upon subsequent cooling, structural components of the eutetoid and quasi-eutetoid types were formed. A significant effect of isothermal holding temperature on the structure and hardness of alloyed steel has been established. The bainite of steel contained particles of carbides, predominantly of cementite type, 0.2-1.0 microns in size, which were formed during the isothermal treatment of 38 CrNi3 MoV steel.
2. Using X-ray microanalysis, it was shown that quasi-eutetoid and bainitic components were formed mainly in the inter-dendritic sections of the steel structure, with high contents of nickel, chromium, and molybdenum.
3. The use of DCT, according to the proposed mode of hardening of 38 CrNi3 MoV steel, resulted in a significant transformation of RA into martensite, increasing microhardness by 22.3%.
4. Isothermal hardening of 38 CrNi3 MoV steel at the (\(\gamma\)+\(\alpha\))-area contributed to an increase in wear resistance compared to traditional heat treatment modes.
5. Application of DCT to 38 CrNi3 MoV steel for 10 min led to the transformation of austenite into martensite, increasing hardness and wear resistance. In the experimental samples of 38 CrNi3 MoV steel, the block sizes decreased and microdistortions of the crystal lattice increased.
6. The experimental mode of hardening and DCT with a holding time of 1 h provided the greatest increase (by 58%) in the hardness and wear resistance of 38 CrNi3 MoV steel, and can be recommended for implementing as a hardening technology for critical metal parts.
7. An increase in the DCT time to 22 h did not lead to an increase in the wear resistance of 38 CrNi3 MoV steel, presumably due to an increase in hardness and brittleness in the absence of RA. Samples of 38 CrNi3 MoV steel with high hardness, hardened by conventional technology, also had reduced wear resistance.  
**Author Contributions:** Conceptualization, S.B. and E.P.; methodology, O.B.; validation, P.K., T.G. and O.B.; formal analysis, T.G.; investigation, O.B.; data curation, S.B.; writing--original draft preparation, S.B.; writing--review and editing, E.P. and P.K.; visualization, T.G.; supervision, P.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are unavailable due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Study Area and Data  
### Study Area  
The study area was the southern region of the Yellow Sea (see Figure 1), where the north wind prevails in winter, and the south wind prevails in summer. Due to wind effects, the area's sea surface current shows the same mode, flowing from south to north in summer [32]. This area experiences semi-diurnal tides, with a greater tidal range in the east than in the west. At the same time, the semi-diurnal tidal currents in this region are pronounced, and the flow velocity in the east is greater than that in the west [33; 34]. The western part of this area includes the Subei radiation shoal, which due to unique geological and tidal conditions, provides a favorable environment for the growth of Porphyra yezoensis. This area has become a well-known area for the aquaculture of Porphyra yezoensis [35]. Porphyra rafts offer conditions for the adhesion and growth of green tide algae [36; 37]. During the harvesting of Porphyra yezoensis in late April and early May, green tide algae are abandoned into the sea from the culture rafts and become the primary source of the green tide. They continue to grow and drift northward under favorable oceanographic conditions (temperature, wind and current) for the transportation and rapid growth of the Enteromorpha prolifera bloom [3; 38; 39]. Eventually, the green tide reaches the waters of Shandong Province (to the north of the study area) and sinks to the bottom of the sea off southern Shandong Province in late June or early July, causing damage to local fisheries, water sports, tourism, and marine ecology.  
Figure 1: Schematic diagram of study area.
## 2 Materials and Methods
### Terrestrial Measurements and Data Acquisition  
The terrestrial measurements were linked to the reference systems of the Czech Republic (S--JTSK, Bpv) using three temporary points determined by a GNSS RTK Trimble R2 receiver with corrections from the CZEPOS virtual reference station system. These temporary points were subsequently georeferenced using the Trimble S9 HP total station, which was employed to determine ground control points for the laser scanners. In this way, an identical coordinate system was ensured for point clouds from both scanners, facilitating their comparison.  
The lower structure of the bridge was scanned with a Leica ScanStation P40 from 15 scanning positions at a density of 6 mm/10 m using a total of 7 GCPs (Leica GZT21 black and white 4.5-inch diameter targets; Figure 3, points 1-7) georeferenced using a Trimble S9 HP total station. In addition, 10 highly reflective 0.5 \(\times\) 0.5 m targets (Figure 4, points 1001-1010) were georeferenced in the same way to enable the transformation of the clouds from the Zeb Horizon scanner into the required coordinate system. All measurements were made from two positions of the total station.  
The point cloud obtained by the Leica ScanStation P40 laser scanner was considered as the reference cloud (Figure 4).  
Two point clouds of the lower bridge structure (GeoSLAM 1 and GeoSLAM 2) were acquired using the Zeb Horizon scanner (the data from GeoSLAM 1 are shown in Figure 5)  
Figure 4: The reference cloud obtained using Leica ScanStation P40 colored according to the intensity of the reflected signal.  
Figure 3: Overview of the (ground) control points. Here, 1–7 indicate vertical black and white targets serving as ground control points (GCPs) for the Leica P40 scanner (red), and 1001–1010 are horizontal targets used as GCPs for SLAM cloud transformation into the Czech coordinate systems (white).  
using the same trail (each cloud was created during a separate round trip) leading approximately 10 m from the construction. The collection of photographic data was turned off during data acquisition. To further improve the accuracy, ten stops were performed during each pass on so-called alignment points in accordance with the manufacturer's instructions.
# Leading-Edge Vortex Characteristics of Low-Aspect-Ratio Sweptback Plates at Low Reynolds Number
###### Abstract  
A sweptback angle can directly regulate a leading-edge vortex on various aerodynamic devices as well as on the wings of biological flyers, but the effect of a sweptback angle has not yet been sufficiently investigated. Here, we thoroughly investigated the effect of the sweptback angle on aerodynamic characteristics of low-aspect-ratio flat plates at a Reynolds number of 2.85 \(\times\) 10\({}^{4}\). Direct force/moment measurements and surface oil-flow visualizations were conducted in the wind-tunnel B at the Technical University of Munich. It was found that while the maximum lift at an aspect ratio of 2.03 remains unchanged, two other aspect ratios of 3.13 and 4.50 show a gradual increment in the maximum lift with an increasing sweptback angle. The largest leading-edge vortex contribution was found at the aspect ratio of 3.13, resulting in a superior lift production at a sufficient sweptback angle. This is similar to that of a revolving/flapping wing, where an aspect ratio around three shows a superior lift production. In the oil-flow patterns, it was observed that while the leading-edge vortices at aspect ratios of 2.03 and 3.13 fully covered the surfaces, the vortex at an aspect ratio of 4.50 only covered up the surface approximately three times the chord, similar to that of a revolving/flapping wing. Based on the pattern at the aspect ratio of 4.50, a critical length of the leading-edge vortex of a sweptback plate was measured as \(\sim\)3.1 times the chord.  
separated flow; leading-edge vortex; sweptback wing; revolving/flapping wing Article  
[PERSON], Chair of Aerodynamics and Fluid Mechanics, Department of Mechanical Engineering, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany; [EMAIL_ADDRESS]
Electronics_**2015**, \(4\), 380-423; doi:10.3390/electronics4030380  
_electronics_ ISSN 2079-9292  
www.mdpi.com/journal/electronics  
_Article_  
Security of Cooperative Intelligent Transport Systems: Standards, Threats Analysis and Cryptographic Countermeasures  
**[PERSON] *, [PERSON] and [PERSON]**  
Qatar Mobility Innovations Center (QMIC), Qatar Science and Technology Park (QSTP), Doha, P.O. Box 210531, Qatar; E-Mails: [EMAIL_ADDRESS] (H.N.); [EMAIL_ADDRESS] (W.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]_ADDRESS];  
Tel.: +974-4459-5082.  
Academic Editor: [PERSON]  
_Received: 31 May 2015 / Accepted: 24 June 2015 / Published: 6 July 2015_  
**Abstract:** Due to the growing number of vehicles on the roads worldwide, road traffic accidents are currently recognized as a major public safety problem. In this context, connected vehicles are considered as the key enabling technology to improve road safety and to foster the emergence of next generation cooperative intelligent transport systems (ITS). Through the use of wireless communication technologies, the deployment of ITS will enable vehicles to autonomously communicate with other nearby vehicles and roadside infrastructures and will open the door for a wide range of novel road safety and driver assistive applications. However, connecting wireless-enabled vehicles to external entities can make ITS applications vulnerable to various security threats, thus impacting the safety of drivers. This article reviews the current research challenges and opportunities related to the development of secure and safe ITS applications. It first explores the architecture and main characteristics of ITS systems and surveys the key enabling standards and projects. Then, various ITS security threats are analyzed and classified, along with their corresponding cryptographic countermeasures. Finally, a detailed ITS safety application case study is analyzed and evaluated in light of the European ETSI TC ITS standard. An experimental test-bed is presented, and several elliptic curve digital signature algorithms (ECDSA) are benchmarked for signing and verifying ITS safety messages. To conclude, lessons learned, open research challenges and opportunities are discussed.  
keywords:cooperative intelligent transport systems (ITS); V2X communications; threats analysis; cryptographic countermeasures; ETSI TC ITS standard; elliptic curve digital signature algorithm (ECDSA); test-bed; experimental performance evaluation
## 6 Conclusions and Future Work  
In underwater wireless sensor networks, due to the unpredictable and harsh aquatic environment, the reliable delivery of information is a challenging task, because the channel unfavorable behavior affects the reliable data delivery. Therefore, this paper presents two protocols: DNAR and Co-DNAR, to cope with these challenges. The entire network is divided into five equal zones, where five minor sinks are deployed. The splitting of the network and deployment of minor sinks ensure the reception of the maximum packet at the final destination. In DNAR, the destination node is chosen when it has the lowest depth and also lowest noise along the source-destination channel. Cooperative routing is added to the DNAR protocol to make Co-DNAR protocol for further reliability in packets transfer, which involves relay nodes with the source-destination pair to achieve spatial diversity. Simulation results proved the promising behavior of the proposed schemes in consumed energy, packets advancement to destination and network stability.  
In the future, the opportunistic routing can be used in order to overcome the high data traffic on the relay and the destination nodes. This type of routing consists of a set of nodes that forwards the packets to the destination rather than burdening the data traffic on a single destination node.  
**Author Contributions:** [PERSON] implemented the main idea under the supervision of [PERSON]. [PERSON] and [PERSON]-[PERSON] responded to reviewers' comments and streamlined the flow of the manuscript.  
**Funding:** This research was funded by SEP-CONACyT Research Project under grant 255387, the School of Engineering and Sciences and the Telecommunications Research Group at Tecnologico de Monterrey, Monterrey, Mexico.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
AF Amplify and forward  
BER Bit error rate  
dB Decibel  
Fuzzy logic relay selection  
MS Mobile sinks  
MAC Medium access control  
MRC Maximal combining ratio  
PSD Power spectral density  
UWSNs Underwater wireless sensor networks  
Figure 15: Average end-to-end delay.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 3 Morphological Semantics Memory Model for Visual Stimuli
### Sensory Memory: Feature Extraction by CNN-Autoencoder  
HTM neural model assumes that the operation of visual information processing in the brain is based on the activated pattern of neurons. Therefore, the features of a physical image must first be effectively extracted to convert an image into an activated pattern of neurons. Convolutional Neural Networks (CNN) [23] is widely used as an effective method for extracting image features in the field of machine learning. If features can be effectively extracted from physical images, it is easy to convert them to SDRs. Therefore, CNN-based technology is adopted in this model.  
In addition, the supervised learning technique for feature extraction requires a high cost for constructing training data. Using unsupervised learning structure such as autoencoder [24] is beneficial to reduce this possible cost. Therefore, in this model, features are extracted from the image using a CNN-based autoencoder [25] that can effectively extract feature values with relatively low cost.  
We believe that the better features in this stage make better results in the later steps, but the main purpose of this paper focuses on investigating memory and recall model based on a biological neuron activation scheme. For this reason, we employ a simple vanilla-level CNN-based autoencoder in this paper. In the implementation of the proposed memory model, the ground-truth label of the image is of no interest, but the ground-truth label of the data set is used in the experimental stage only for the performance evaluations.  
Figure 9 shows the internal structure of the CNN-based autoencoder implemented to extract features from images. In this model, an autoencoder with three convolution layers with 16, 32, and 64, 3 \(\times\) 3 filters respectively and two fully connected layers is used for feature extraction based on unsupervised learning. The extracted feature vector (\(Z\)) obtained by encoding part of the autoencoder is transferred to the working memory step, and additional data processing is performed for implementing memory model.  
Figure 8: Memory model remembering the morphological semantics of visual stimuli.