esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 1 Introduction
In addition to considering noises caused by distance, as proposed in [24], we contribute to depth camera noise modeling by also accounting for noises caused by the materials of detected objects. This addition is significant as it has been demonstrated to have a substantial impact on depth camera noise [25]. Regarding the aerodynamic drag model, while Airsim currently uses only the velocity and rotation matrix to calculate the aerodynamic drag force [26], we enhance the modeling by incorporating thrust values. This enhancement has shown a substantial improvement in performance compared to the embedded model in Airsim. Furthermore, in addition to proposing these models, to the best of our knowledge, we are the first to implement these improved models in a drone simulator such as Airsim. This implementation is expected to make a significant contribution to drone research by providing a more effective platform for testing drone algorithms and training reinforcement learning methods. Specifically, the depth noise is modeled as Gaussian noise. To identify and verify the parameters of the depth camera noise model, depth measurements were collected using  
Figure 1: (**a**) Simulation in Gazebo, (**b**) simulation in Unreal Engine. the Intel RealSense D435 camera at distances ranging from 0.5 m to 8 m and on various materials. Additionally, simulations and experiments were conducted at various flight speeds to compare the accuracy of the proposed drag force model with the embedded model in Airsim. Experimental results demonstrated that the depth camera model becomes more accurate and realistic with varying distances and materials. Furthermore, the proposed drag force model was shown to reduce acceleration errors by up to 51% when compared to the Airsim-provided model during high-speed flights at 10 m/s in a real flight scenario. In short, our contribution includes:
* Development of an improved model of the depth model by embedding a Gaussian noise model, which was verified through experiments, into the depth camera model. This modification makes the depth camera model more accurate and realistic across various distances and materials. * Development of an improved rotor air drag force model. By including thrust values in addition to drone velocity and rotation, we create a force model that more accurately represents real-world air drag forces. * Providing an improved simulation environment that is more reliable for testing the real-world behavior of autonomous drones compared with the existing Airsim simulator, especially in high-speed drone flight experiments. This paper is composed as follows. Section 2 presents depth camera modeling with simulation implementation. Section 3 shows rotor drag force model implementation with test results, and finally, Section 4 provides a conclusion.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Impact of Training Dataset Size on Accuracy
#### 4.6.1 Accuracy in Function of Number of Datasets  
The first analysis explores the model's accuracy as a function of the number of measurement datasets utilized for training. The test results are evaluated by concatenating all device datasets. The outcomes, presented as the cumulative distribution of absolute error, are displayed in Figure 19. Figure 19a,b demonstrate that models trained on one or two datasets show noticeably worse performance compared to the other cases, which exhibit similar curves across the entire error range. The variations are substantial, with the 90%-threshold varying from \(0.22\,\mathrm{dB}\) to \(0.36\,\mathrm{dB}\), and the 99%-threshold ranging from \(0.35\,\mathrm{dB}\) to \(0.58\,\mathrm{dB}\). Figure 19c shows these error thresholds as a function of the number of datasets. It indicates that accuracy improves consistently up to three datasets, after which it stabilizes with only minor gains as the number increases to six. Based on these results, it can be concluded that incorporating data from multiple devices during training is essential  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Gain** & **Time [s]** & **Total Row Count** & **Time per Row [ms]** \\ \hline EDFA-17 & LOW & 2.70 & 21,318 & 0.13 \\ \hline EDFA-35 & HIGH & 15.04 & 113,696 & 0.13 \\ \hline EDFA-35 & LOW & 35.08 & 195,712 & 0.18 \\ \hline EDFA-ILA-L & LOW & 16.58 & 102,624 & 0.16 \\ \hline JUNIPER & HIGH & 12.05 & 74,928 & 0.16 \\ \hline JUNIPER & LOW & 17.83 & 116,928 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation time for training the noise figure polynomial models.  
to account for errors introduced by the manufacturing process. However, the number of devices included can remain relatively low.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Architecture of Value Network  
As shown in Figure 4a, we employ an FCN to construct the value network architecture, aiming to enhance the action space of reinforcement learning agents and improve decision-making capabilities in complex environments. This architecture implements high-dimensional state-action value function mapping through an encoder-decoder framework, drawing design inspiration from multi-scale feature fusion techniques in point cloud completion [23], semantic segmentation domains [24], and correspondence learning [25]. Specifically, the fully connected layers in traditional CNNs are replaced with convolutional layers, enabling the network to accept arbitrary-sized inputs and output resolution-matched value distribution maps through full convolutionalization.  
The encoder is built upon the ResNet-50 [26] backbone network, extracting multi-level feature maps through four-stage downsampling operations from input states. Among them, Figure 4b shows the residual block structure. This multi-scale feature extraction inherits the advantages of the FCN in semantic segmentation tasks, effectively capturing composite features across spatial and semantic dimensions while representing both local details and global scene information. In decoder design, a Feature Pyramid Network (FPN) architecture is introduced to achieve cross-level feature fusion via lateral connections and a top-down pathway. Low-level features are fused with upsampled high-level semantic features through skip connections, progressively restoring spatial resolution while preserving semantic consistency. The final output value distribution map maintains pixel-level alignment with the input state, where each spatial location corresponds to the Q value of its action.  
Figure 4: Value network. (**a**) Overall architecture of value network. (**b**) Structure of residual block.
# Design of a Power Converter for Solar Energy Storage System
## 4 Conclusions  
This paper presents a single-stage, three-energy-port, isolated-type energy converter that integrates an interleaved synchronous boost rectifier and a bidirectional full-bridge circuit into a single-stage energy converter topology. The proposed energy converter enables bidirectional energy transfer between the battery, DC grid, and renewable energy sources. In this study, a 1-kW three-port converter was constructed. The paper provides a detailed and comprehensive analysis of various power conversion modes, and experimental measurements show that the energy conversion efficiency of the converter can reach 94%. Additionally, the converter can operate simultaneously in all three energy ports. The following are the advantages of the proposed converter:  
* The interleaved synchronous boost rectifier can increase the voltage of the renewable energy unit to match that of the battery unit. In addition, the converter can increase the voltage of the renewable energy and process it through a full-bridge circuit to transfer energy to the DC grid.
* The bidirectional full-bridge topology includes a battery storage unit and a DC grid with a bidirectional energy propagation and electrical isolation capability, thereby effectively enhancing electricity usage security and increasing the power capacity of the converter.  
Figure 21: Efficiency versus voltage curve for in the R2D mode.  
Figure 20: Load regulation curve in the R2D mode. (\(V_{\textit{rpg}}=140\) V).  
With the bidirectional function of the converter, the battery unit can continuously supply energy to the DC grid when the renewable energy unit fails to do so, thereby providing an uninterruptible power supply.
* The power conversion mode of the developed converter has a G2B mode, which allows the battery port to be charged by the DC Grid port when there is no input from renewable energy sources. This makes the battery port a small energy storage system that can regulate electrical energy.  
For future studies, the number of ports on the converter in this study could be expanded to develop a multi-functional port DC converter with a miniature solar power generation system. This converter would enable the conversion of electrical energy between the solar power generation port, solar cell port, DC power port, and electric vehicle charging port. By allowing for the mutual exchange of power between these energy ports, the converter would enhance its ability to regulate and schedule stored solar energy. Additionally, to ensure maximum efficiency of the converter under variable solar conditions, the maximum power tracking function of the converter will have to be strengthened.  
Conceptualization, Y.-N.C. and Y.-Y.W.; methodology, Y.-H.Y. and Y.-N.C.; software, Y.-H.Y. and Y.-Y.W.; writing--original draft preparation, Y.-H.Y. and Y.-Y.W.; writing--review and editing, Y.-H.Y. and Y.-N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Efficient Internet-Wide Scan Approach Based on Location Awareness
## 3 Considered Scenario and Motivation
### Considered Scenario  
In this study, we focus on Internet-wide scanning. In an Internet environment, devices typically rely on the TCP/IP protocol for data transmission. The TCP/IP protocol is the foundation of modern network communication and is widely used for communication between various devices. As shown in Figure 1, network administrators often use specialised network scanning tools, such as Nmap, Zmap, and Masscan, to perform vulnerability scanning and security assessments in order to identify potential security risks and vulnerabilities in a timely manner.  
Currently, there are various scanning methods available, one of which is TCP Connect Scan (full open scan). This paper primarily investigates the TCP Connect Scan method, which simulates a normal TCP connection process by sending an SYN packet to the target port. The status of the port is determined based on the received SYN-ACK or RST response. If an SYN-ACK response is received, the target port is open; if an RST response is received, the target port is closed. Additionally, there is a \"filtered\" status, which indicates that the state of the target port cannot be determined, as the probe packet may have been intercepted by a firewall, network device, or network congestion, preventing a response. In other words, the probe packet fails to receive the expected response (such as SYN-ACK or RST), leading to a timeout in the scanning tool's waiting period. This situation directly affects the performance of the scan.
# Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
## 2 Experiment
### Contacts  
After the growth and deposition, Au contacts of two different types were used to connect the GaN NWs in order to study the influence of the applied voltage on their structure. The first contacting approach was similar to our previous work [22]. The position and orientation of the NWs were random with respect to the main contacts on the substrate after their deposition. We developed a sample holder, which provided the electrical connection to the selected single NWs. The free-lying GaN NWs were connected from two sides to the power supply via pads and solid wires using different contacting methods (see Supplementary Materials). The 220 nm thick metallic contacts were deposited by electron beam lithography and thermal evaporation of Ti and Au. First, a 20 nm thick layer of Ti was deposited on \(SiO_{2}\) and GaN NW to provide good adhesion between Au and GaN, and then 200 nm thick layer of Au on top of Ti layer. The SEM images of the 350 nm and 200 nm NWs contacted using this method are shown in Figure 2a,b. In comparison to our previous experiment [22], these Au contacts had larger width and were thicker. The contacts were covering a larger part of the NWs at the bottom, while the tip of the NWs was covered less. These variations in contacting geometry define the possible difference in properties of the electronic system \"GaN NW + Au contacts\".  
The second type of contacts was manufactured differently. The Au contacts were deposited on the \(SiO_{2}\) layer using the similar lithography technique from two sides leaving a gap between them. Further, GaN NWs were positioned on the top of these contacts and were slightly melted into the contacts by the increased temperature of 400 \({}^{\circ}\)C for 20 min. The SEM image of this type of contacts is shown in Figure 2c.  
We performed electrical measurements before the experiment. The resistance of the system \"GaN NW + Au contacts\" was about \(10^{12}\) Ohm. Therefore, the current through the NWs was relatively low, and additional cooling of the sample was not necessary.
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
###### Abstract  
As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.  
Keywords:singular point detection; deep learning; semantic segmentation; fingerprint recognition +
Footnote †: journal: Applied Sciences
# CPM-OFDM Performance over Underwater Acoustic Channels
## 3 System Architecture
### CPM-OFDM Transmitter  
The proposed OFDM transceiver is shown in Figure 1. The OFDM transmitter is implemented as an \(N\)-point inverse discrete Fourier transform (IDFT) on a block of \(N\) information symbols. Practically, the IDFT operation is implemented using an inverse fast Fourier transform (IFFT) because it is computationally efficient.  
Let \(\{s_{k},k=0,1,\ldots,N-1\}\) represent a block of \(N\) complex data symbols which is the output of the CPM mapper--explained in the next subsection. The IDFT of the data block is  
\[S_{n}=\sum_{k=0}^{N-1}s_{k}\exp\left\{\frac{j2\pi nk}{N}\right\},\;n=0,1,\ldots,N-1 \tag{1}\]  
outputting the time-domain sequence \(S_{n}\). Each OFDM symbol of \(N\) IFFT coefficients is preceded by \(N_{S}\) samples--called cyclic prefix (CP) or a guard interval. The number of \(N_{S}\) samples is either equal to or greater than the channel length \(N_{h}\) in samples, where \(N_{h}=\frac{T_{h}}{I_{s}}N\), \(T_{h}\) is the length of the channel in seconds, and \(T_{s}\) is the duration of an OFDM symbol in seconds. The CP samples are a repetition of the last \(N_{S}\) samples of the OFDM symbol. Hence, \(N+N_{S}\) becomes the total number of samples of each OFDM symbol.
# Sustainable Vaporization of Wine Lees: From Waste to Value-Added Products
## 4 Applications of Wine Lees in Various Industries
### Nutritional Supplement  
The aging wine lees process involves yeast cells from the bottom of barrels interacting with wine during fermentation [87]. This procedure causes yeasts to release polysaccharides, proteins, peptides, amino acids, and lipids, which improves the organoleptic qualities and quality of wine [33]. Aging wine lees show benefits due to yeast-cell-released manoproteins, which promote tartaric and protein stabilization [31; 88]. [PERSON] et al., 2024, suggest in their study that post-alcoholic-fermentation wine lees can be used as a nutritional supplement for grape must fermentation, improving fermentation timeframes and altering volatile compound production [89]. [PERSON] et al. found in their study that wine lees have a major impact of different doses of fermentation lees on the oxygen consumption and antioxidant activity of wine, which are crucial for the final product's quality and consumer satisfaction. The study analyzed wines of the Gruner Veltliner variety with different wine lees proportions. Higher dosages of fermentation lees resulted in a greater rate of dissolved oxygen consumption; however, as early as the second day following bottling, a notable decrease was noted. Total polyphenol content and antioxidant activity also increased with the dose of yeast lees. The major negative parameter for the highest addition of lees was the concentration of volatile acids, which increased from 0.34 g L\({}^{-1}\) to 0.45 g L\({}^{-1}\). The addition of this substance significantly impacts the chemical composition and specific parameters of wine, specifically the quantity of reducing sugars, volatile acids, and glycerol [88; 90]. White wines, sparkling wines, and occasionally red wines are aged using WLs. An enological process called \"aging on lees\" involves putting wines on their lees, which are leftover grapes and dead yeast cells. The wine's flavor, structure, and color stability are all improved by aging on lees, raising the wine's overall quality [35].  
[PERSON] et al.'s study examines the molecular fingerprints and oxidative stability of Chardonnay Champagne base wines that have been stored on lees for a year in new oak barrels after two consecutive vintages. New oak barrels improve the wines' oxidative stability, and the antioxidant metabolome of the wines--which is composed of nucleophilic and antiradical compounds--seems to be vintage- and barrel-aging dependent [91]. Yeast lees autolysis releases mannoproteins, which can interact with phenolic chemicals [33], improving the quality of the final product by reducing the astringency and bitterness, and led to a better structure and the color stability [8].
# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
###### Abstract  
To address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, we proposed an aerial dense small object detection algorithm called Global Normalization Attention Mechanism You Only Look Once (GNYL) based on the Global Normalization Attention Mechanism. In the backbone network of GNYL, we embedded a GNAM (Global Normalization Attention Mechanism) that explores channel attention features and spatial attention features from input features in a concatenated manner. It utilizes batch normalization's scale factors to suppress irrelevant channels or pixels. Furthermore, the spatial attention sub-module introduces a three-dimensional arrangement with a multi-layer perceptron to reduce information loss and amplify global interaction representation. Finally, the computed attention weights are weighted to form the global normalized attention weights, which increases the utilization of effective information in input feature channels and spatial dimensions. We have optimized the backbone network, feature enhancement network, and detection heads to improve detection accuracy while ensuring a lightweight detection network. Specifically, we have added a small object detection layer to enhance the localization accuracy for the abundant small objects in aerial imagery. The algorithm's performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv8l, GNYL achieved a 7.2% improvement in mAP\({}_{0.5}\) and a 5.0% improvement in mAP\({}_{0.95}\). Compared to CDNet, GNYL showed a 14.5% improvement in mAP\({}_{0.5}\) and a 9.1% improvement in mAP\({}_{0.95}\). These experimental results demonstrate the strong practicality of the GNYL object detection network for detecting dense small objects in the aerial imagery captured by unmanned aerial vehicles.  
computer vision; small object detection; YOLOv8; attention mechanism; unmanned aerial vehicle