esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion
### Compression  
**The backbone size can be compressed by at least 50% at the second block, and by at least 70% at the first block.** The compressed backbones and uncompressed backbones follow a similar architecture. The compressed parameter percentage of each block is shown based on the corresponding whole heavy backbone in Figure 7. According to Figure 6, the accuracy by the second block of ST-GCN 12 is competitive compared with the whole ST-GCN 20. Since the rank of parameter size for each ST-GCN backbone is ST-GCN 20 \(>\) ST-GCN 12 \(>\) ST-GCN 8, if taking the second block of ST-GCN 12, the compressed percentage is 80% compared with the whole ST-GCN 20, including the compression from decreasing backbone layers and decreasing the number of blocks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **ST 20** & **ST 12** & **ST 8** & **Sup. ST 20** & **Sup. ST 12** & **Sup. ST 8** \\ \hline _pos_ & 81.37 & 77.49 & 21.46 & 75.19 & 74.97 & 79.47 \\ _mov_ & **85.91** & 82.34 & 18.75 & **82.32** & **81.99** & 80.28 \\ _mov_ + _pos_ & 85.79 & **84.63** & **27.84** & 82.19 & 81.34 & **80.34** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy performed on NTU xview. ST 20 means ST-GCN with 20 layers; ST 12 and ST 8 follow similar explanations. The supervised models are noted with Sup. and only used labels for training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **ST 20** & **ST 12** & **ST 8** & **Sup. ST 20** & **Sup. ST 12** & **Sup. ST 8** \\ \hline _pos_ & 77.36 & 62.14 & 21.46 & 71.34 & 71.48 & 69.09 \\ _mov_ & 80.25 & 77.47 & 17.41 & **75.35** & **76.25** & 75.81 \\ _mov_ + _pos_ & **80.61** & **78.67** & **26.29** & 74.83 & 74.63 & **75.96** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy performed on NTU xsub. ST 20 means ST-GCN with 20 layers; ST 12 and ST 8 follow similar explanations. The supervised models are noted with Sup. and only used labels for training.  
Figure 6: The accuracy increments of different blocks in HAR task, where kd_b1, kd_b2 and kd_b3 are the result from different blocks after self-distillation; sup indicates the results by supervision. The integers on the \(x\)-axis denote the number of layers of ST-GCN, and the _pos/mov/mov_ + _pos_ indicate different input data.  
The real-time running time can be accelerated by up to at least 1.42\(\times\) at the second block, and by at least 2.33\(\times\) at the first block.  
The details are listed in Table 3.
# Quantum Dot-Based White Organic Light-Emitting Diodes Excited by a Blue OLED
## 1 Introduction
QDs exhibit pure and saturated colors in the visible spectrum, as well as narrow bandgaps and high photoluminescence efficiency due to quantum confinement. Furthermore, modifying the particle surface can greatly enhance the fluorescence efficiency and, in particular, the structural stability of the nanocrystals. By combining these characteristics with the distinctive properties of organic materials, such as flexibility and ease of processing, low-cost hybrid white-emitting devices with improved lifetimes and color stability can be developed [18; 19]. Recently, quantum dot materials, such as CdSe/ZnS, have also been used in LED backlights and displays. CdSe/ZnS colloidal QDs possess a high quantum yield and good photostability at room temperature. This makes them an excellent choice for the lighting industry, and they have been extensively studied [20; 21; 22]. In many optoelectronic devices, quantum dots (QDs) are used as phosphors to absorb light in order to convert it into low-energy light in long wavelengths, since they exhibit excellent optical properties. The performance of QD-OLEDs depends on an efficient injection of charge carriers into the QDs from the electrodes. The choice of organic or inorganic semiconductors should be based on their energy levels in comparison to QDs. In the fabrication of OLEDs with quantum dots (QDs), alternate fabrication technologies have been suggested in order to achieve high efficiency, high stability, high durability, and cost-effectiveness [23; 24; 25]. The advantages of organic materials in light-emitting devices, including displays, comprise their ability to be deposited on glass and flexible substrates and their dazzling brightness. However, if organic materials were combined with quantum dots' unique properties, a more effective device could be created. QD-OLEDs based on QDs' CdSe/ZnS have been the subject of numerous attempts to increase performance by optimizing the device architecture. In this study, the electrical and optical properties of BOLEDs and WOLEDs were investigated using current density-voltage, luminance-voltage, photoluminescence (PL), and electroluminescence measurements. To develop a quantum dot-based WOLED, the following three approaches were carried out: (a) fabrication of a blue single-emitting layer OLED [ITO/HATCN (20 nm)/TAPC (30 nm)/MADN: v-DABA (40 nm)/TPBi (30 nm)/LiF (0.8 nm)/Al (150 nm)], (b) nanoimprinting into QD photoresists, and (c) green and red QD (CdSe/ZnS) photoresists as color conversion layers (CCL) excited by blue OLEDs. The red and green QD (CdSe/ZnS) photoresists were used as a color conversion layer and excited by a blue OLED to form white light. The study also strived to establish the exquisite architecture (BOLED\(\rightarrow\)RQD\(\rightarrow\)GQD) for the WOLED.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 2 System Model
### UWA Channel Model
The changes in path gain \(h_{p}\) and path delay \(\tau_{p}\) caused by the temporal change of the position of the UWA node can be expressed as a function of the path length which is expressed as \(d_{p}=\bar{d}_{p}+\Delta d_{p}\), where \(\Delta d_{p}\) is a random offset distance. The path gain \(h_{p}\) in Equation (7) can be characterized as a function of \(\bar{h}_{p}\) in Equation (6) as  
\[h_{p}=\bar{h}_{p}\frac{1}{\sqrt{\left(1+\frac{\Delta d_{p}}{\bar{d}_{p}} \right)^{k}\alpha_{0}^{\Delta d_{p}}}} \tag{8}\]  
where \(\alpha_{0}\) is the normalized absorption coefficient. For the further step, we consider the widely existed scattering in UWA channel, which is caused by placement of scattering points within a scattering field. If scattering occurs along a path, the path will be split to a number of micro-paths. Then the channel transfer function can be expressed as  
\[H(f)=\bar{H}_{0}(f)\sum_{p}h_{p}\beta_{p}(f)e^{-j2\pi f\tau_{p}} \tag{9}\]  
where \(\beta_{p}\) is a small-scale fading coefficient, which can be expressed as  
\[\beta_{p}(f)=\frac{1}{h_{p}}\sum_{i\geq 0}h_{p,i}e^{-j2\pi f\bar{d}_{\tau_{p},i}} \tag{10}\]where \(h_{p,i}\) is the random gain within the multi-path cluster, \(\tau_{p,i}=\tau_{p}+\delta_{\tau_{p,i}}\) is the random delay within the multi-path cluster, \(h_{p,i}\) and \(\delta_{p,i}\) characterize the random scattering in the UWA channel. Finally, the instantaneous channel gain \(\tilde{G}(t)\) of time-varying UWA channel is expressed as  
\[\tilde{G}(t)=\frac{1}{B}\int_{f_{0}}^{f_{0}+B}|H(f,t)|^{2}df \tag{11}\]  
where \(H(f,t)\) is the total channel transfer function including both large and small scale channel variations, which is related to the large-scale path propagation loss, interface reflection, small-scale scattering and other factors.
## 2 Related Work and Background  
Utilizing idle user resources is beneficial for creating a win-win situation. Consequently, it has gained widespread attention and research in both the industrial and academic sectors. Currently, the utilization of idle bandwidth resources of users is mostly achieved at the application layer. Therefore, we will separately introduce the research status of application layer technology solutions and network layer technology solutions.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 5 Evaluation Methodology
### Performance Analysis  
The following metrics are used to measure the performance of the proposed vehicular VLC network. The first metric is the service availability which is calculated by using the following equation:  
\[\text{Ratio}=\frac{\left(\text{service time}-\text{interrupted time}\right)}{\text{ service time}} \tag{7}\]  
Availability is measured as the ratio of time that the vehicular node's communication service is available. The node requires to receive and transmit data successfully to be considered with communication service. The monitoring period is set to 5 ms, and if the user receives at least one frame, the node is successfully connected in this interval. Otherwise, the system assumes that the node has lost connection during this period. The service time starts when the user has received its first data frame. Repeated frames are ignored because it means that the node is not able to reply to the communication.  
The second metric is the End-to-End Frame Error Rate (EFER) measurement. It represents the reliability of the system after the re-transmission and failure detection mechanism of the MAC layer. A frame can be lost if the re-transmission limit time is exceeded and the AP is not able to reach the node. Therefore, the data packet transmission performance can be characterized as the ratio of the incoming data frames from upper layers in a particular AP, referred to as \"total frames\", and the transmitted frames which have been received correctly in the MAC layer of the vehicular node, referred as \"delivered frames\", i.e.,  
\[\text{EFER}=1-\frac{\text{delivered frames}}{\text{total frames}} \tag{8}\]  
The third metric is the Frame Error Rate on the PHY layer (PFER). This measure considers all the AP transmission attempts, referred to as \"the number of transmissions\", including the transmitted frames which were not correctly delivered. This will reflect the total spent resources during the communication. In a similar fashion to EFER, PFER uses the complement of the ratio between delivered frames and the total number of transmission frames,  
\[\text{PFER}=1-\frac{\text{delivered frames}}{\text{number of transmissions}}. \tag{9}\]  
PFER and EFER require a bidirectional communication to increase the number of \"delivered frames\", including the successful transmission with an acknowledgment frame.  
Finally, the average data rate of the vehicular node is also measured to evaluate the performance of the network service quality. The instantaneous data rate metric, \(R_{ins}\), is defined as  
\[R_{ins}=\frac{\sum_{i=1}^{rf}P_{load}(i)}{t_{m}} \tag{10}\]  
where \(R_{ins}\) is the data rate measured during the time window \(t_{m}=5\) ms with \(P_{load}(i)\) as the payload of the \(i\)-th received frame. Then, the average data rate, \(R_{avg}\), during the total simulation time, \(t_{sim}\), is given as  
\[R_{avg}=\sum_{j=1}^{t_{s}/t_{m}}\frac{t_{m}R_{ins}^{j}}{t_{s}} \tag{11}\]
# Automatic Bug Triaging via Deep Reinforcement Learning
## 4 Bug Triaging via Deep Reinforcement (BT-RL) Model
### ODM Model
#### 4.3.1 MDP  
The MDP is used to describe the RL problem. We regard the bug triaging problem as an MDP. The framework of our model is shown in Figure 3. In general, the RL task corresponds to a four-tuple: \(E=\langle S,A,P,R\rangle\), where \(S\) is the state space, \(A\) is the action space, \(P\) is the state transition probability, and \(R\) is the reward function. This will be explained in detail in the following paragraphs.  
1. States (S)  
\(S\) represents the state space, which includes a series of bug triaging tasks, \(s,s\in S\), and \(s_{i}\) represent the current bug report text, the subproject and component, and the fixed time of developer when making a decision on the \(i-\)th bug of all tasks.
2. Action (\(A\))  
\(A\) is the action space, which is a set of developers who can perform the tasks. Specifically, it is a developer sequence corresponding to the first three months of bug reports with the same subprojects and components as the current bug report before time \(t\). For each bug \(s\), each developer that can be selected is defined as \(a,a\in A(s)\).
3. Transition probability (\(P^{a}_{ss^{\prime}}\))  
We define the state transition probability as \(P^{a}_{ss^{\prime}}\), and it can be expressed as:  
\[P^{a}_{ss^{\prime}}=P(s^{\prime}\big{|}s,a)=P(S_{t+1}=s^{\prime}\big{|}S_{t}=s,A_{t}=a), \tag{6}\]  
which represents the probability that the agent will move to the next bug, \(s^{\prime}\), at time \(t+1\) when it executes a developer, \(a\in A(s)\), in bug \(s\) at time \(t\).
4. Reward Function (\(R\))  
\(R\) is the reward function that provides scalar feedback regarding the performance of an agent. It is the learning direction of the model and determines the skills and efficiency of the model. When an agent selects \(a\) for bug \(s\), the reward function provides feedback to the agent to perform corresponding rewards and punishments for different results. Based on the model requirements, we represent the reward function in a sparse form as follows:  
\[r_{t}=\left\{\begin{array}{l}reach,if\;i\in\mathit{Topk}\\ r^{\mathit{crash}},if\;\mathit{otherwise}\end{array}\right., \tag{7}\]  
The value of \(R\) can take two values. Suppose the target developer is in the recommended top-k sequence. In that case, the reward is defined as \(r^{\mathit{reach}}\), typically a positive value, and encourages the model to develop in a favorable direction. In contrast, we define \(r^{\mathit{crash}}\). \(i\) represent the ground-truth developer in the dataset.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 5 Conclusions  
This study has made significant advancements in the modeling and control of variable-sweep wing UAVs, particularly through the refinement of longitudinal dynamic equations and the incorporation of maneuver-specific parameters from the L-30A model. These developments enabled accurate calculations of aerodynamic properties, such as optimal lift-to-drag ratios, and established a reliable model to represent the wing morphing process.  
Additionally, the introduction of the MADDPG-VSC algorithm offers a novel approach for optimizing sweep angles in response to real-time flight states. This methodology enhances multi-UAV cooperative formation control, which is crucial for complex, real-world missions. Flight experiments and simulations show that the algorithm maintains precision, coordination, and stability in dynamic conditions, with minimal computational demands.  
The MADDPG-VSC algorithm effectively addresses core challenges in UAV formation control, such as managing continuous action spaces and adapting to dynamic, unpredictable environments. By enhancing flight precision and formation stability, the algorithm shows strong potential for broader application in multi-UAV systems, particularly for tasks requiring high adaptability and scalability. This creates a robust framework for future use in increasingly complex flight environments.  
In future work, real-world flight tests will be conducted under complex conditions, including varying weather and the presence of dynamic obstacles, to validate the algorithm's robustness in environments significantly different from the training data. An evaluation framework will be established to assess the impacts of unexpected changes, including sudden airflow shifts and target movements, on formation stability and control. In addition, future simulations will incorporate more complex and irregular obstacles to better reflect real-world challenges, such as sensor noise and communication delays. Efforts will also focus on improving the system's scalability and adaptability to support larger UAV formations and enhancing real-time decision making to further improve performance and operational reliability.  
Conceptualization, Z.C. and G.C.; methodology, G.C.; software, Z.C.; validation, Z.C. and G.C.; formal analysis, Z.C.; investigation, Z.C.; resources, Z.C.; data curation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, G.C.; visualization, Z.C.; supervision, G.C.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant Nos. 92371201 and 52192633, and the Natural Science Foundation of Shaanxi Province, Grant No. 2022 JC-03.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
[PERSON] worked for Xi'an ASN UAV Technology Co., Ltd. [PERSON] declares no conflict of interest.
## 2 The Rise of the Federated Learning Approach
### Delving into Decentralized Federated Learning (DFL)  
In this section, we focus on purely distributed or otherwise decentralized federated learning (DFL) [26; 37; 38]. For all the works that follow, the major competitor is the efficient FedAVG [8] and its proven convergence rate. Unfortunately, in DFL, it is more difficult to reach convergence, either due to the heterogeneity of data distribution or the model deployed on the participating devices, which delays the learning process, or due to the difficulties that wireless network topologies cause in communication integrity. The main objectives in DFL, in order to become a viable learning scheme, as depicted in Figure 3, are the following:  
* The direct communication among devices lessens the network latency; however, the throughput of the messages must be lessened too, in order to avoid network _flooding_.
* To address the resource limitations of edge devices, such as energy, one strategy is to minimize communication between nodes in the network, focusing only on those that contribute actively to the federation process.
* In real-time scenarios, we cannot guarantee that all cooperating devices follow the same specifications (e.g., flash memory, energy capacity, etc.) or equally contribute to the learning process. Thus, there is a need for stipulating methods that reinforce the lower-contribution nodes in order to ensure accuracy _uniformity_.
* Last but not least, wirelessly interconnected devices, used for, e.g., surveillance and wearability, are usually mobile devices that are not stably positioned in a specific place, impeding the configuration of a strong topology.  
Figure 3: Objectives in DFL.
# Mdpi
## 4 Simulation Results  
We evaluated the performance of the proposed IN mitigation algorithm by establishing transmitters for use in a wavelet-OFDM PLC system in accordance with IEEE 1901 specifications [11]. The frequency band was 1.8 MHz to 28 MHz, and the modulation was assumed to be 2-PAM. Table 1 lists the parameters of a four-path channel model [25] and the corresponding magnitude and impulse responses are illustrated in Figure 5.  
Figure 5: (**a**) Magnitude and (**b**) impulse responses for the four-path channel model.  
Using the BG IN model described in Equation (3), we considered two IN environments: (1) strongly-disturbed IN (\(p=0.1\) and \(\Gamma=0.01\)) and (2) weakly-disturbed IN (\(p=0.01\) and \(\Gamma=0.1\)). The performance metric was the BER performance. The effectiveness of the proposed iterative IN mitigation method is confirmed in Figure 6. In the strongly-disturbed case, the \(E_{b}/N_{0}\) required to achieve BER of \(10^{-2}\) were as follows: iteration 1 (36 dB), iteration 2 (32 dB), and iteration 3 (28 dB). However, in the weakly-disturbed case, the improvements in \(E_{b}/N_{0}\) were marginal. Note that \(E_{b}/N_{0}\), which denotes the ration of energy per bit (\(E_{b}\)) to noise power spectral density (\(N_{0}\)), is an essential parameter in digital communication systems. It can be treated as a normalized signal-to-noise ratio (SNR) measurement, i.e., SNR per bit. Therefore, for a target BER, we prefer that the required \(E_{b}/N_{0}\) is as small as possible; on the other hand, for a fixed value of \(E_{b}/N_{0}\), we favor that the resulting BER is as low as possible.  
Figure 6: Performance of proposed algorithm with different iteration in (**a**) strongly-disturbed and (**b**) weakly-disturbed cases.  
As shown in Figure 7, the conventional blanking method [28] (with blanking device threshold optimized empirically) resulted in poor BER performance. Specifically, only subtle improvements (in terms of the BER ) were obtained when \(E_{b}/N_{0}\) was below 24 dB. When \(E_{b}/N_{0}\) exceeded 24 dB, no blanking was performed. In contrast, the proposed method with estimated channel impulse response \(\mathbf{\hat{h}}[n]\) achieved BER performance on par with what can be obtained when the channel information is known exactly. Compared to the case with no IN, the loss in \(E_{b}/N_{0}\) at \(\text{BER}=10^{-3}\) were as follows: strongly-disturbed IN (2.5 dB) and weakly-disturbed IN (0.5 dB).  
Figure 7: Performance comparison of the proposed algorithm (iteration = 3) with other related works in (**a**) strongly-disturbed and (**b**) weakly-disturbed cases.
## 3 Materials and Methods
### WAI Reconstruction Algorithm Based on Finite Difference
#### 3.2.1 Sampling of WAI Normals
Using the axial components of the vectors \(\hat{\mathbf{v}}_{k}^{\mathrm{P}}\), \(\hat{\mathbf{v}}_{k}\) and \(\hat{\mathbf{N}}_{k}\), Equation (4) can be converted into the dot product form,  
\[ \begin{bmatrix}0&-c_{k}^{\mathrm{P}}+c_{k}&b_{k}^{\mathrm{P}}-b_{k}\\ c_{k}^{\mathrm{P}}-c_{k}&0&-a_{k}^{\mathrm{P}}+a_{k}\\ -b_{k}^{\mathrm{P}}+b_{k}&a_{k}^{\mathrm{P}}-a_{k}&0\end{bmatrix} \cdot \begin{bmatrix} N_{x}\\ N_{y}\\ N_{z}\end{bmatrix} =0, \tag{5}\]  
where \(a_{k}^{\mathrm{P}}\), \(b_{k}^{\mathrm{P}}\), \(c_{k}^{\mathrm{P}}\) are the \(x\), \(y\) and \(z\) components of the vector \(\hat{\mathbf{v}}_{k}^{\mathrm{P}}\), respectively. \(a_{k}\), \(b_{k}\), \(c_{k}\) are the axial components of the vector \(\hat{\mathbf{v}}_{k}\). \(N_{x}\), \(N_{y}\), \(N_{z}\) are the axial components of the vector \(\hat{\mathbf{N}}_{k}\). Equation (5) also can be phrased in matrix form  
\[\mathbf{A}\hat{\mathbf{N}}_{k}=0, \tag{6}\]  
where  
\[\mathbf{A}= \begin{bmatrix}0&-c_{k}^{\mathrm{P}}+c_{k}&b_{k}^{\mathrm{P}}-b_{k }\\ c_{k}^{\mathrm{P}}-c_{k}&0&-a_{k}^{\mathrm{P}}+a_{k}\\ -b_{k}^{\mathrm{P}}+b_{k}&a_{k}^{\mathrm{P}}-a_{k}&0\end{bmatrix} . \tag{7}\]  
The WAI normal \(\hat{\mathbf{N}}_{k}\) is estimated by solving Equation (6): it is the null subspace of \(\mathbf{A}\). This process is repeated for each sampled point located at \(\mathbf{q}_{k}\). It yields a set of sampled vectors \(\{\hat{\mathbf{N}}_{k}\}\) corresponding to the set \(\{\mathbf{q}_{k}\}\).
###### Abstract
[23] investigated the effect of six gases, He, H\({}_{2}\), CO\({}_{2}\), N\({}_{2}\), CO, and CH\({}_{4}\), on the IFT of n-hexadecane using equilibrium molecular dynamics (EMD) and analyzed and summarized the effect of gas concentration and system temperature on the IFT of binary mixtures. [PERSON] et al. [24] used MD simulations to investigate the wettability of the interface between crude oil and CH\({}_{4}\). The effects of temperature and pressure on the physicochemical properties of the interface between CH\({}_{4}\) and crude oil were then determined. It is to be noted that the investigation of the effects of crude oil components and gas composition on the microscopic properties of the CH\({}_{4}\)+ alkane interface has been rare, both of which are critical for understanding the CH\({}_{4}\) oil drive mechanism. In this study, MD simulations determined the IFTs of several types of CH\({}_{4}\)+alkane systems, and the microscopic mechanisms affecting IFT were disclosed based on the molecular details provided by the simulations. Section 2 introduces the molecular models and simulation details used in this study. Section 3 investigates the effects of temperature, pressure, injected gas components, and crude oil components on IFT. Then, it is explained how these factors affect interfacial properties using parameters such as number density distribution, mean square displacement, and radial distribution function.