esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
## 3 Methods
### Transformer-Based Model  
Traditionally, convolutional neural networks (CNNs) have been the preferred architecture for image segmentation tasks due to their ability to extract features from the input image. However, in recent years, transformer-based models have shown remarkable performance in a variety of natural language processing (NLP) tasks and have been extended to computer vision tasks, such as image segmentation.  
CNNs have a strong ability to extract local features, but they inherently fail in modeling the global context due to the limited receptive fields. The transformer can model the global context easily. Furthermore, it has become the most used technique in computer vision. Due to this, we used a transformer model for IMC segmentation. The proposed method used a pyramid vision transformer backbone to capture the global information, a pyramid feature aggregation (PFA) model to combine low- and high-level features and an efficient regression head with multi-scale dilated convolution (MDC) to predict the final results [20]. The input image is transformed into a 1D sequence first, then the output is fed into the transformer-based backbone. The pyramid transformer in [45] is adopted to capture the global context through various downsampling stages. The outputs of each stage are reshaped into 2D feature maps for pyramid feature aggregation. Finally, a simple regression head with multi-scale receptive fields regresses the final results. The proposed architecture is illustrated in Figure 2b.
## 4 Performance Evaluation  
In this section, we evaluate the performance of our proposed resilient routing protocol through simulations. We compare our protocol with existing routing protocols to demonstrate its effectiveness in terms of PDR, end-to-end delay, and energy consumption. The simulations are conducted under various network scenarios to assess the protocol's adaptability to different conditions.
## 4 Results and Discussion
### AI Applied to CFD with a Focus on the TES Field
#### 4.2.1 Analysis by Countries  
Figure 12 presents a map of the top ten countries with the highest number of publications in this research area. Countries that also appeared in the top ten of the first query are marked with yellow stars. The leading contributors in this second query are Iran, Germany, Canada, and Turkey, each with four publications. Unlike the first query, where a single country dominated the publication volume, the second query reflects a more globally distributed research effort.  
Interestingly, not all the top ten countries from the first query appear in Figure 12. If the European Union was considered a single territorial entity in this analysis, it would lead the ranking with a total of eight publications. As in the first query, France and Germany play a key role in driving innovation in this field. Additionally, Spain, recognized for its focus on renewable energy, emerges as a significant contributor to thermal energy storage research.  
To assess the collaborative capacity of the top ten publishing countries in this query, Figure 13a presents a co-authorship analysis. Iran and Australia, positioned at the center of the network, emerge as key players in research collaboration, having established connections with most of the other top ten countries. The figure also reveals two major clusters of collaboration, cluster 1, with Canada and European countries (excluding Spain), and cluster 2, with Turkey and the United States, which have also formed strong research partnerships.  
Additionally, Figure 13b provides a broader perspective, showcasing all countries with publications related to the second query. In contrast to Figure 4b, the second query exhibits significantly lower levels of international collaboration. Apart from the connections between some of the top ten publishing countries, no additional collaborative clusters are  
Figure 11: Annual publications between 200 and 2024 for the second query (orange), compared to the same period of the first query (blue).  
observed. Notably, Spain and South Korea do not show visible collaborative links with other countries.  
The graphs also illustrate the average publication year for each country, revealing that Iran, Turkey, Spain, India, and South Korea present opportunities for pioneering research in this field. Given this, the low level of international collaboration in this research area may be attributed to its relatively recent emergence as a novel topic of scientific inquiry.
# Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters
## 4 Finite State Machine Modeling
### Finite State Machines  
When current state in the state plane reaches the command radius \(R_{ref}\), the DC switches are turned off to control the amount of energy in the filter and generate the required grid current \(i_{ac}\). A finite set of states, inputs, and outputs of finite state machine for DC side bridge is defined as follows in Table 3:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**DC-Side Switching State** & **Pivot** & **Output** \\ \hline DC\_P1 & \(P_{1}\) & 1001 \\ DC\_P2 & \(P_{2}\) & 0110 \\ DC\_P3 & \(P_{3}\) & 0110 \\ DC\_P4 & \(P_{4}\) & 1001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Switching states-DC side.  
Figure 8: Initial trajectory transition in the \(m_{C}-j_{L}\)state plane in the first quadrant.  
The four output bits represent the gate signals S4, S3, S2, and S1, respectively. Inputs required for control are \(m_{C},\;j_{L}\) and \(R_{ref}\) to control the grid-side current. The switching state of the main MOSFET is determined by comparing the reference radius \(R_{ref}\) with the radius \(R\) on the state plane.  
The DC-side bridge operates in four distinct states based on the operating conditions. When the \(R\) reaches the command radius \(R_{ref}\), the system transitions to the next state, and the output gate signal of the DC-side bridge is updated accordingly. During steady-state operation, the state circulates DC_P\({}_{1}\)-DC_P\({}_{2}\)-DC_P\({}_{3}\)-DC_P\({}_{4}\) in the first and third quadrants, and the state circulates in the reverse order DC_P\({}_{4}\)-DC_P\({}_{3}\)-DC_P\({}_{2}\)-DC_P\({}_{1}\) in the second and fourth quadrants (Figure 9). Although the voltage and current waveforms have opposite polarities in the first and third quadrants, the finite state machine remains the same. If the \(R\) is smaller than \(R_{ref}\), the state variable cannot reach the desired values, so the system oscillates between the DC_P1 and DC_P3 states to build up the radius until it reaches the \(R_{ref}\).  
A finite set of states, inputs, and outputs of Finite state machine for DC side bridge is defined as follows in Table 4. Inputs required for control require grid voltage, filter inductor current, and filter capacitor voltage:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AC-Side Switching State** & **Output** \\ \hline AC\_1 & 1101 \\ AC\_2 & 0111 \\ AC\_3 & 1110 \\ AC\_4 & 1011 \\ \hline \hline \end{tabular}
\begin{tabular}{c} State \(\in\) [AC\_1, AC\_2, AC\_3, AC\_4] \\ Input \(\in\) [\(m_{ac},j_{L},m_{C}\)] \\ Output \(\in\) [\(1101\), \(0111\), \(1011\), \(1110\)] \\ \end{tabular}  When the grid voltage is positive, the state changes between the AC_1 and AC_2 states depending on the polarity of \(j_{L}\), and when the grid voltage is negative, it changes between the AC_3 and AC_4 states. These two operation patterns switch twice per grid voltage period in Figure 10.  
\end{table}
Table 4: Switching states—AC side.  
Figure 9: DC-side state diagram, first and third quadrants (solid line), second and fourth quadrants (dotted line).
## 3 Materials and Methods
### Design of the CNN-LSTM-Attention-Based Digital Twin Fault Prediction Model
#### 3.5.2 Fault Prediction Flow Based on the CNN-LSTM-Attention Model  
This chapter proposes a fault warning method for wind turbine gearboxes based on a CNN-LSTM-Attention network model. The entire process is divided into three stages, as shown in Figure 12.  
(1) Data Processing: First, the SCADA dataset undergoes outlier detection and missing value imputation to ensure data reliability and validity. Features with high correlation are selected using the Pearson correlation coefficient. These selected feature variables are  
Figure 11: Framework of the CNN-LSTM-Attention-based fault warning model for gear transmission system.  
then normalized to eliminate the impact of differing scales, ensuring the model treats each feature fairly. After preprocessing, the dataset is divided into training, validation, and test sets. Both the training and validation sets consist of SCADA monitoring data from normally operating turbines and are used for model training and validation. The test set comprises SCADA data from a faulty turbine.  
(2) Model Training: The CNN-LSTM-Attention network model is first constructed, and its associated hyperparameters are initialized. The training dataset is then used to train the model. During the training process, the model's performance is evaluated on the basis of the output from the validation set, and model parameters are adjusted accordingly. Concurrently, residual data is processed, and the 3\(\sigma\) criterion is applied to determine the threshold for fault determination.  
(3) Model Testing: The effectiveness of the prediction model is validated by testing it with the preprocessed test dataset.
# Cu&Si Core-Shell Nanowire Thin Film as High-Performance Anode Materials for Lithium Ion Batteries
## 3 Results and Discussion  
### Characterization of Cu@Si CSNWFs  
Figure 2a shows the XRD pattern of the as-received Cu nanowires, Cu nanowire coated Cu foil after thermal treatment, and Cu&Si CSNWFs. The diffraction patterns of the as-received Cu nanowire can be well assigned to Cu and Cu oxides (CuO and Cu\({}_{2}\)O) [20], suggesting the oxidation of the Cu nanowire surface during storage. After heat treatment at 280 \({}^{\circ}\)C for 2 h in the reductive atmosphere of 5 v.% H2 + 95 v.% Ar, diffraction peaks corresponding to the Cu oxides disappear completely, indicating that this heat treatment is an effective way to remove Cu oxides. After heat treatment and Si deposition, four new peaks are observed; these peaks match well with Si and Cu\({}_{3}\)Si [21; 22; 23; 24], respectively, demonstrating that the sputter-deposited Si is crystalline and there is formation of the crystalline Cu\({}_{3}\)Si phase. It is noteworthy that [PERSON] et al. also found the formation of Cu\({}_{3}\)Si during sputtering of a Cu layer on the surface of Si nanowires [25]. Figure 2b,c exhibit the SEM images of the Cu nanowire-coated Cu foil before and after Si deposition, respectively. It is clearly seen that the Cu nanowires connect either to each other or to the Cu foil substrate, forming a continuous electron-conductive pathway. After Si deposition, there is an apparent increase in diameter of the Cu nanowires, and meanwhile a large number of void spaces could be observed in between them (Figure 2c). Based on Figure 2b,c, the corresponding diameter distribution of the Cu nanowire before and after Si deposition was statistically analyzed and exhibited in Figure 2d,e. The diameter of the Cu nanowire ranges from 50 to 300 nm, mainly between 150 and 200 nm. After Si deposition, the diameter is increased to 300-650 nm, and mainly lies in the range of 400-500 nm, suggesting the formation of a Cu@Si core-shell nanowire. The formation of voids was ascribed to the shadowing effect of the Cu nanowires during Si deposition. Figure 2f exhibits the STEM image of an individual core-shell nanowire, in which the core and shell can be distinguished by the obvious contrast in the image. The brighter inner part is the Cu core, which is covered by a continuous uneven gray Si shell. In order to further confirm the elemental composition, we have performed an EDS test in the mode of linear scanning along the straight line from A to B, as shown in Figure 2g. It can be recognized that this single Cu@Si core-shell nanowire has a Cu core of \(\sim\)200 nm in diameter and a Si shell of 125 nm in thickness, with only trace amounts of oxygen. Furthermore, an obvious mixed layer of Cu and Si is also observed, corresponding to a thin layer of Cu\({}_{3}\)Si, as confirmed by the XRD in Figure 2a, which is  
beneficial for the enhancement of adhesion strength between the Cu core and Si shell. Based on the above, it is concluded that the Cu@Si CSNWFs were successfully fabricated.
# Comparative Aerodynamic Performance Analysis of Camber Morphing and Conventional Airfoils
## 3 Methodology
### Computational Method and Verification
In the ANSYS Fluent setup, we set the \(\alpha\) as a parameter and compute the corresponding \(L\) and \(D\) values. With each setup being a parametric study of the respective morphing or conventional airfoil cases, over a range of \(\alpha\) from \(0^{\circ}\) to \(15^{\circ}\), the \(L\) and \(D\) would need to be computed with respect to the \(\alpha\) the flow was being simulated for. However, ANSYS Fluent does not have the option to set the \(L\) and \(D\)'s direction vector as a parameter so the normal x and y components, F\({}_{\text{X}}\) and F\({}_{\text{Y}}\), were generated using ANSYS Fluent and those values were transformed using a rotation matrix to compute the \(L\) and \(D\). The \(L\), \(D\) in the matrix corresponds to the \(\alpha\), and the equations used are stated below:  
\[\left[ \begin{array}{c}L\\ D\end{array} \right]=\left[ \begin{array}{cc}\cos\ \alpha&\sin\ \alpha\\ -\sin\ \alpha&\cos\ \alpha\end{array} \right]\left[ \begin{array}{c}\text{F}_{ \text{X}}\\ \text{F}_{\text{Y}}\end{array} \right] \tag{4}\]  
To verify the reliability, the flow was simulated around a NACA 0012 airfoil at 6 million \(Re\) and the \(C_{L}\) and \(C_{D}\) were computed and compared with the validation case provided by NASA's Langley Research Center [45; 46] as shown in in Figure 5. Two different turbulence models, (1) Spalart-Allmaras (_S-A_) and (2) k-omega with SST (_k_-\(\omega\)) were tested. Although both can be seen to be in good agreement with the validation cases, the _S-A_ turbulence model was selected for the aerodynamic simulations as it has good accuracy, comes at a relatively low computational cost, and is recommended for low-\(Re\) flow simulations [43]. Figure 4: Mesh independence study (NACA 0012 airfoil, \(Re=6\) million, \(\alpha=0^{\circ}\)).
## 7 Experimental Data and Model Validation
### Validation Method Durbin-Watson Test and Adjusted R\({}^{2}\)
The adjusted R\({}^{2}\) for the lift forces indicates that the model represents 97.27% of the observed data. For the lift forces, the hypothesis test shows that there is a strong correlation between both results values, while the hypothesis H\({}_{0}\) cannot be rejected with a confidence level of 95%. The validation tests were conclusive, thus indicating that the CFD model can simulate lift and drag forces at a 95% confidence level. The drag force residuals appear to have an autocorrelation, possibly due to the non-linearity of the range of observed values. These experimental values have an uncertainty of \(\pm\)0.04N, estimated at a confidence level of 95%. The simulation model of the adaptive wing prototype was validated by the Durbin-Watson and adjusted R\({}^{2}\) tests, and it can be used to obtain accurate aerodynamics lift and drag forces.
### Contributions  
The following are the potential contributions of the proposed framework:  
* THBNL is a BC enabled framework that considers the acquisition of BNL features (e.g., localization accuracy, node losses and Packet Delivery Ratio (PDR)) and determines the verified and trusted beacon nodes. THBNL relaxes the parameter assumptions; thus, it can emulate realistic situations in UASN for safe localization through HB\({}^{2}\)NL algorithm.
* THBNL focuses on the heterogeneity of the verified localized beacon nodes that challenges the algorithm design of HB\({}^{2}\)NL. It achieves the localization accuracy in beacon node level identification for different acoustic environments (e.g., deep and shallow water). Moreover, the THBNL framework is extremely flexible and can easily be extended by supporting more selection features for better efficiency (where needed).
* The effectiveness of the HB\({}^{2}\)NL algorithm is validated via different load measurements (e.g., base load, interruptible load, uninterruptable load and on average load) of acoustic communication data traffic. Finally, we compare the accuracy of HB\({}^{2}\)NL algorithm with localization accuracy, node losses, PDR, survival nodes, residual energy and number of delivered/received packets.
* Scheduling and scalability are addressed for selecting verified beacon nodes (VBN) by meta-heuristic bio/nature inspired techniques by Levy Firefly Algorithm (LFA) and Birds Foraging Algorithm (BFA), respectively. Moreover, for the purpose of scheduling, we introduce the notion of 'beacon node coordination'. This aids the scheduler in selecting the verified beacon nodes (which are localized) and neighboring nodes without any localization interruptions.
* To achieve the system objectives and incorporate the cost reduction mechanism (CRM), we will schedule BNL as a _'knapsack problem'_ with a knapsack capacity (small items with largest values), which is solved using 'dynamic programming' [13]. This helps the CRM to draw inferences regarding newly encountered nodes and schedule them correctly.
* The beacon node mechanism inside HB\({}^{2}\)NL selects a group of validator nodes as VBN using the (\"TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank BC nodes. TOPSIS, a multi-criteria decision analysis method, estimates the shortest geometric distance from the ideal best value and the longest geometric distance from the ideal worst value) TOPSIS/multi-criteria analysis method. Throughthis criterion, the beacon node with the greatest trust value is chosen as a validator node for the localization process of a block in BC and responsible for smooth data forwarding.  
The remaining sections of the paper deal with related work, problem description and proposed framework in Sections 2-4, while meta-heuristics analysis and simulation results are discussed in Sections 5-7. Section 8 deals with the conclusion.
## 2 Materials and Methods
### What Are the Proposed Architectures That Support Cognitive Radio LPWAN Based IIoT?
#### 2.2.4 Architectures of NBIoT Cognitive Radio  
New 3 GPP technology standardized in LTE known as NBIoT has gained more attention for IIoT applications. NBIoT enables a wide range of services for M2M communication for stable connectivity among IIoT devices, which are desirable to access the medium at any time. The IIoT traffic involves small data packets that can be subject to excessive collision due to many devices. The use of cognitive radio with random access strategies is done to reduce congestion in the network [92]. In addition, the cognitive radio will promise to meet the requirements of future IIoT spectrum by identifying vacant channels dynamically, and the networks of CR-IIoT will offer collaborative sensing at a low rate and provide support needs of channels for information exchanges between cognitive radio users and the central node for the IIoT, as shown in Figure 9 below.  
Figure 8: Coexistence of U-LTE Cognitive Radio/IIoT [88; 90].  
In NBIoT cognitive radio, energy saving should be considered to reduce the system realization cost for cognitive radio users; this is due to comparing the energy detected on a channel with the local threshold. Matched filter detection can be used as an optical signal detection mechanism to maximize the SNR of the recovered primary user (PU) signal. In such a network, the received signal can be represented as y(n) affected by the noise u(n) thatis assumed to be a complex Gaussian distribution function with zero mean and known variance given by \(\mathrm{E}(\left|u(n)\right|^{2})=\sigma^{2}n\). The received signal at NBIoT cognitive radio device denoted by S(n) with zero mean and known variance can be given by \(\mathrm{E}(\left|s(n)\right|^{2})=\sigma^{2}s\)[32]. The shared signal-to-noise ratio \(\beta\) at a certain group of NBIoT cognitive radio devices is given by:  
\[\beta=\frac{\sigma^{2}s}{\sigma^{2}n}. \tag{5}\]  
In this system, all devices assumed are assigned to different groups of sensed frequencies based on their packet priority and sensing SNR, such that devices with the same sensing abilities were assigned to the same group. The decision variable of the energy detector for collected \(N\)s recovered signals can be written as follows.  
\[T(\beta)=\sum_{n=1}^{N\mathrm{s}}\left|\beta(n)\right|^{2} \tag{6}\]  
when \(T(\beta)\) follows Gaussian distribution Collaborative sensing, all devices in NBIoT cognitive radio will transmit their local detection information to the central node to take the final decision on the presence of the PU signal, and then the decision is communicated to all devices. The collaborative sensing system set to times is known as sensing time \((T_{S})\) and collaborative sensing time \((T_{CS})\) for sensing and communicating sensing information to and from the central node. This scheme will provide more accurate sensing decisions, especially under the low SNR scenario.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 1 Introduction
* The proposed approach reduces TTR and improves rendezvous success rates compared to existing algorithms, making it more suitable for real-time applications [8; 10; 11]. * We provide a comprehensive performance analysis, showing that the proposed method consistently achieves faster and more reliable rendezvous compared to the JS algorithm and the RL-based approach in [5; 8; 10]. The remainder of this paper is organized as follows. Section 2 reviews related work on CH schemes and RL approaches to the rendezvous problem, providing a detailed discussion of the strengths and weaknesses of existing methods [3; 5; 8; 9; 10]. Section 3 introduces the proposed RL-based rendezvous algorithm, including the integration of M-sequences and the learning framework used to optimize hopping patterns [10]. In Section 4, we evaluate the performance of our method through extensive simulations, comparing it with both traditional and RL-based CH schemes [5; 8]. Finally, Section 5 concludes the paper and outlines potential future research directions, including the exploration of more advanced RL techniques and the application of our approach to other types of dynamic spectrum access problems [9; 10].