esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Opportunistic Transmission Model with Selfish Nodes
Node cooperation type III (defined as \(i_{3}\)): In integrated UAV-ICV networks, this paper adds voluntary nodes to improve the performance of on-board information distribution [17]. This paper assumes that the number of voluntary nodes is \(N_{\text{pub}}\). For type III, the node cooperation level \(C_{\text{III}}\) can be expressed as  
\[C_{\text{III}}=1-\frac{N_{\text{sel}}}{N_{\text{non-sel}}+N_{\text{sel}}+N_{ \text{pub}}}. \tag{3}\]  
Therefore, when the relay type is type III, if the communication node carrying the source node's message copy contains information relevant to the relay node, the message is forwarded. In this situation, the number of hops experienced by the message copy is increased by one. In addition, if the communication node carrying the source node's message copy contains information unrelated to the relay node, the message is not forwarded, and the number of hops experienced by the message copy remains unchanged.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 3 Datasets Used for Diabetic Retinopathy Segmentation/Lesion Detection
The MESSIDOR dataset was part of the MESSIDOR project and has been publicly available since 2008 [101]. The DDR dataset was collected from 9598 patients and 757 images with DR have been annotated for DR lesions [102]. Out of these datasets, DRIVE and STARE datasets are the highly applied datasets for retinal blood vessel segmentation. However, since manual annotation of the retinal blood vessels is time consuming and tedious, these datasets have very few images. Datasets like IDRiD, E-Optha MA, DIARET DB0, and DIARET DB1 have been annotated for MA detection. E-Optha EX, IDRiD, DIARET DB0, and DIARET DB1 have been annotated for EX detection. Whereas the annotation for OD is available for MESSIDOR and DIARET DB1 datasets. It was noted that only very few datasets are annotated for retinal blood vessel segmentation and for lesion detection. This may be because marking retinal blood vessels manually will require a lot of time, as well as effort, and annotating lesions demands intra-annotator agreement and high annotation precision. Due to this reason, the majority of the studies utilized more than one dataset for training as well as validation.
###### Abstract  
A new gallium nitride (GaN) high electron mobile transistor (HEMT) small-signal model is proposed considering source via effects. In general, GaN HEMTs adopt a source via structure to reduce device degradation due to self-heating. In this paper, the modified drain-source capacitance (C\({}_{\text{ds}}\)) circuit considering the source via structure is proposed. GaN HEMTs fabricated using a commercial 0.15 \(\upmu\)m GaN HEMT process are measured with a 67 GHz vector network analyzer (VNA). The fabricated device is an individual source via (ISV) type. As a result, it is difficult to predict the measured S12 in the conventional small-signal model equivalent circuit. This causes errors in maximum stable gain/maximum available gain (MSG/MAG) and stability factor (K), which are important for circuit design. This paper proposes a small-signal equivalent circuit that adds the drain-source inductance to the drain-source capacitance considering the source via structure. The proposed equivalent circuit better reproduces the measured S12 without compromising the accuracy of other S-parameters up to 67 GHz and improves the accuracy of MGG/MAG and K. It is expected that the proposed model can be utilized in a large-signal model for 5G millimeter-wave GaN HEMT power amplifier design in the future.  
GaN HEMT; source via effect; 5G millimeter-wave; small-signal model; power amplifier +
Footnote †: : 2021: 11, 9120. [[https://doi.org/10.3390/app11199120](https://doi.org/10.3390/app11199120)]([https://doi.org/10.3390/app11199120](https://doi.org/10.3390/app11199120))
# The Effects of Musical Experience and Hearing Loss on Solving an Audio-Based Gaming Task
## 4 Results
### Subjective Evaluation
#### 4.2.1 Fondness  
The post-activity questionnaire asked participants about the game in terms of enjoyment. The results of rated fondness derived from \"how entertaining\" the Music Puzzle is and preferences towards material are shown in Figure 3a,b respectively. Questions were answered as follows:  
_How entertaining was the game?_ Hearing participants with low reported musical activity (_NEX_) gave the lowest evaluation. Overall, 3 of 12 found it to be \"boring\" and only half found it to be entertaining. In the other groups, 28 out of 36 gave ratings that the game was entertaining, and only 1 person found it boring.  
_Which material do you like the best?_ The three groups showed a difference in their preferred sound mode. The preference for the Music mode was greatest in the _HI_ group, while _NEX_ clearly preferred the Mixed mode. None of the groups rated the Speech condition highly.  
_Would you use the game if it was free?_ With similar distribution across all groups, 69% answered they would use the Music Puzzle if it was free. The game is not currently available in the Android or iOS app stores, and there are no plans to charge for use when it is publicly released.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & **HI** & **NEX** & **EXP** \\ \hline \hline (**a**) & _Number of game sessions_ & Total & 81 & 79 & 104 \\ \hline \multirow{2}{*}{(**b**)} & \multirow{2}{*}{_Game sessions ratio_} & Speech & 0.37 & 0.37 & 0.34 \\  & & Music & 0.40 & 0.32 & 0.34 \\  & & Mixed & 0.23 & 0.32 & 0.33 \\ \hline \multirow{2}{*}{(**c**)} & \multirow{2}{*}{_Achieved sessions ratio_} & Speech & 0.35 & 0.93 & 1.00 \\  & & Music & 0.25 & 0.83 & 0.85 \\  & & Mixed & 0.23 & 0.95 & 1.00 \\ \hline \multirow{2}{*}{(**d**)} & \multirow{2}{*}{_Evaluation of difficulty_} & Speech & 0.53 & 0.83 & 0.92 \\  & & Music & 0.44 & 0.24 & 0.66 \\ \cline{1-1}  & & Mixed & 0.47 & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of game sessions with (**a**) the total number of sessions played for each of the three groups hearing impaired (HI), and normal hearing with low (NEX) and high (EXP) music experience, (**b**) the ratio of material played for each mode, (**c**) the ratio of completed sessions for each mode, and (**d**) the ratio of puzzles that were evaluated as easy (ratio = 1) for each participant group and mode.  
Figure 3: Ratings of fondness of playing the game. (**a**) Answers to “how entertaining was the Music Puzzle?” (**b**) Preferred material by each participant group.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 4 Results
### Ablation Experiment  
#### 4.2.1 Comparison of Backbone Networks  
This subsection compares the performance and parameters of ResNet series networks as the encoder in the proposed model, with the purpose of selecting the appropriate ResNet model for subsequent research.  
Figure 5 plots the progression of F1-score values when ResNet series models are used as the backbone of the encoder during the training process. Table 1 provides summary statistics for performance evaluated with the metrics of F1-score and the number of parameters. Through experiments, it is found that the performance of the network improves with the increase of parameters. The ResNet with 101 parameter layers performs best in terms of F1-score, 85.14% obtained, while the Resnet-18 performs the worst for its lowest number of parameter layers, which is 3.35% lower than the former. Sequential performance was achieved by ResNet-50 and ResNet-34, reaching F1-scores of 84.51% and 84.92%, respectively. The above four models have similar results except for ResNet-18. However, it cannot be ignored that ResNet-50 and ResNet-101 have several times the number of parameters compared with ResNet-34, while it gives trivial performance gain. Since road extraction is a simple binary segmentation problem that does not need to model complex background information, the parameters in ResNet-34 are powerful enough for road extraction tasks. Finally, we choose the ResNet34 model as the backbone of the encoder based on performance and parameter considerations.  
Figure 5: Progression of F1-score values for four ResNet series models as the backbone of the encoder during training. The ResNet models are with 18 layers (ResNet18), 34 layers (ResNet34), 50 layers (ResNet50) and 101 layers (ResNet101).
# Fractional View Analysis of Acoustic Wave Equations, Using Fractional-Order Differential Equations
## 6 Conclusions  
In this article, the fractional view of acoustic wave equation is discussed by using a modified analytical technique. The solution graphs are plotted to provide clear pictures and analysis of the obtained results. The graphical representation has suggested the greatest rate of convergence as compared to other analytical methods. The fractional-order analysis of the acoustic wave equation is important to investigate the behaviour of the dynamics as compared to the classical one. Therefore, in the present application scenario, the proposed method has played a significant role to describe sophisticated solutions of fractional-order partial differential equations arising in different areas of sciences and engineering. Moreover, the present method uses the variational parameters which reduces the calculations' complexity. Also, the He's polynomials have been used to obtain the solutions in an accurate way as compared to Adomian polynomials. The rate of convergence of the suggested method is found to be higher than other existing methods. Hence, it is concluded that the present method can be extended to solve other fractional non-linear partial differential equations.  
Conceptualization, I.A. and H.K.; Methodology, R.S.; Software, H.K.; Validation, R.S. and I.A.; Formal Analysis, R.S.; Investigation, R.S. and D.B.; Resources, H.K. and P.K.; Writing--Original Draft Preparation, R.S.; Writing--Review and Editing, H.K., M.A. and P.K.; Visualization, M.A.; Supervision, D.B., P.K.; Project Administration, P.K.; Funding Acquisition, P.K. All authors have read and agreed to the published version of the manuscript.  
Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science, King Mongkuts University of Technology Thonburi (KMUTT).  
Figure 8: Variational homotopy perturbation transform method solution of example 4.4 at different fractional order \(\beta=1,0.8,0.6,0.4\).
## 2 Problem Formulation and Preliminaries  
### Preliminaries  
The interaction topologies in a multi-agent system are modeled by an undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\), where \(\mathcal{V}=\{v_{1},\,\dots,\,v_{n}\}\) is the set of vertices, \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) is the set of the edge, and \(\mathcal{A}=[a_{ij}]\), \(a_{ij}\geq 0\) is the weighted adjacency matrix of the graph \(\mathcal{G}\). If \((v_{i},\,v_{j})\in\mathcal{E}\), \(a_{ij}=a_{ji}>0\), otherwise, \(a_{ij}=a_{ji}=0\). Each edge \((v_{i},\,\,v_{j})\) represents information that can be transmitted between the \(i\)th agent and the \(j\)th agent. In addition, we assume that \(a_{ii}=0\). The neighboring set of agent, \(v_{i}\), is \(\mathbf{N}_{i}=\left\{j:\ (v_{i},\ v_{j})\in\mathcal{E}\right\}\). The Laplacian matrix \(\mathcal{L}=[l_{ij}]\in\mathbb{R}^{n\times n}\) of graph \(\mathcal{G}\) is denoted as \(\mathcal{L}=\mathcal{D}-\mathcal{A}\), where \(\mathcal{D}\triangleq\text{diag}\{d_{1},\ \cdots,\ d_{n}\}\) with \(d_{i}=\sum_{j=1}^{n}a_{ij}\). A path in the undirected graph \(\mathcal{G}\) is denoted as a finite sequence \(\left\{v_{i_{1}},v_{i_{2}},\cdots,v_{i_{j}}\right\}\) and \((v_{i_{t}},v_{i_{t+1}})\in\mathcal{E},l=1,2,\cdots,j-1\).
## 3 Model Development and Training
### G-CTRNN Time Constant Training  
Training the time constant in the G-CTRNN using the BPTT is challenging, as this parameter has a physical meaning and some restrictions, such as that it should be a positive number within a specific range. The regularization technique is one option to solve this problem by limiting the training time constant to account for this constraint. In this method, the parameter of interest, the time constant here, will be added to the loss function to force the training to minimize those parameters [31]. However, this method will not guarantee that the parameter (time constant) does not exceed a specific value. Alternatively, TensorFlow has a non-popular functionality to override the constraints of the parameters of each cell to have values from a specific distribution [32]. In this work, we adapted this method to limit the time constant value during the training. This constraint will use the MINMAX Norm, which will constrain the values of the weights of any layers to have the norm between a lower bound and an upper bound.  
Finally, it is worth mentioning that our implementation adopts a single time constant per neuron to maintain architectural simplicity and facilitate hardware integration. While this approach effectively stabilizes training and ensures feasibility under physical constraints, modeling temporal complexity in some applications, such as healthcare, may benefit from extended architectures. Specifically, incorporating multiple time constants per neuron or introducing hierarchical time scales would allow simultaneous encoding of rapid signals (e.g., tremors) and slower trends (e.g., postural transitions). These architectural variations present a promising direction for future research to generalize G-CTRNN across multi-scale scenarios.  
Figure 4: Forward and backward steps of implementing the CTRNN with time constant as a learnable parameter using the TensorFlow library.
## 2 State-of-the-Art Smart Transport Infrastructure
### Road Side Infrastructure  
Roadside Infrastructure (RSI) is an important aspect of the entire STI. It comprises vehicle counters, speed cameras, monitoring cameras, accident prediction systems, infrared sensors, vehicle flow managers, and advanced computer vision for tracking cars. To effectively and efficiently operate the overall system, these systems communicate vital information to other systems and vehicles passing through them. The constraints for those systems include security, privacy protection, and energy-efficient operations of the components that reside within the RSU domain.
# Mid-State Kalman Filter for Nonlinear Problems
## 4 Nonlinear Filter Performance Evaluation  
In order to better verify the performance of nonlinear filters, it is necessary to analyze and evaluate the filtering accuracy, credibility, stability, and other aspects of the filter which largely depend on reasonable evaluation metrics. In addition to the common evaluation metrics of state accuracy, such as root mean square error, this paper also deduces and gives the Cramer-Rao Low Bound iteration formula for nonlinear systems and analyzes the consistency of a filter by using a normalized error square.  
Figure 2: The algorithm flow of the MSKF.
## 3 Data Processing and Kinematic Analysis
### Clustering and Object Detection  
The Euclidean distance required for the clustering process was set to 0.4 m (minimum) \(\sim\)2 m (maximum) that correspond to the averages of a human (worker) chest width and vehicle size, respectively. Then, the only objects detected within this range were considered for clustering. In this way, the objects that are smaller than a human and bigger than a vehicle were ignored. As the next step, a bounding box was assigned to fit into a single cluster that indicates a detected object. The bounding box generates the center point of  
Figure 2: Procedure of extracting and processing point cloud data.  
each detected object in terms of \(x,y,\) and \(z\) coordinates. This center point was used for a tracking filter to estimate and predict the object states.
# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 1 Introduction
In [11], the subspace theory was used to enhance the calculation effect, but, because the application model of the solution algorithm is easy (applying total variation (TV) as regularization), and the correlation of the whole image is not fully utilized, it is not good when dealing with large-format image motion blur with spatial variation. To solve the above problems, this paper proposes a restoration method named the dual-channel block-cluster deblur algorithm (DCBCD), for the problem of space-variant blurred image restoration, which combines the characteristics of the traditional multi-channel restoration image algorithm and the single-channel blind deconvolution restoration algorithm. The main content of this paper has three parts: (1) the improved model of the dual-channel input deblur method, named DCBCD is proposed; (2) the relative side prior information (RSPI) and non-dimensional weight-balanced Gaussian measure (WG-NGM), which uses kernel estimation, are outlined; (3) a clustering-based algorithm is proposed, to restore the space-variant blurred images.
## 3 Fundamental Equations  
### Equations of Motion  
The analysis includes the behavior of bent and eccentrically compressed reinforced concrete elements. The elements are modeled using a system of plane bars and takes into account the initial curvature. The bar system is statically loaded with a short-term longitudinal force, bending moment, uniformly distributed load, and concentrated forces acting on the plane perpendicular to the longitudinal axis of the element.  
This reinforced concrete element considers specific geometrical factors including the variable cross-sectional distribution of concrete and reinforcing steel areas, the initial curvature, and the boundary conditions that result from the support and external load.  
Figure 3: Limit curve for the plane stress state.  
The equations of dynamic motion for a reinforced concrete bar element that is characterized by the unit mass (\(\mu\)), the unit mass of inertia \(j\), and the unit damping coefficients for the displacements \(c\) and rotations \(c_{f}\) were derived. The problem of the damping coefficient estimation was detailed in the paper [33].  
The differential equilibrium equations were defined in the global Cartesian coordinate system \(\{x(u),\,z(w)\}\), as shown in Figure 4, where \(ds\) is the length of the deformed element, and \(\theta\) is the slope angle. They have the form shown below.  
\[\left\{\begin{array}{c}-\frac{\partial(N\cos\theta)}{\partial s}+\frac{ \partial(Q\sin\theta)}{\partial s}+p_{x}(s)-\mu\ddot{u}-c\dot{u}=0\\ -\frac{\partial(N\sin\theta)}{\partial s}-\frac{\partial(Q\cos\theta)}{ \partial s}-p_{z}(s)+\mu\ddot{w}+c\dot{w}=0\\ \frac{\partial M}{\partial s}-Q-j\ddot{\phi}-c\dot{\phi}=0\end{array}\right. \tag{9}\]  
where \(N\) is the internal longitudinal force, \(Q\) is the transversal force, \(M\) is the bending moment, \(\{p_{x},\,p_{y}\}\) are the external loads, and \(\{\mu\ddot{u},\,\mu\ddot{w},\,j\ddot{\phi}\}\) are the inertial longitudinal, transverse, and rotational forces.  
The linear geometrical relationships that relate to the longitudinal deformation of the central axis \(e(s)\), to the change of the average rotation angle in the cross-section \(\kappa(s)\), and to the average non-dilatational strain angle \(\gamma(s)\) are defined below.  
\[\left\{\begin{array}{c}e(s)=\frac{ds^{0}-ds}{ds}\\ \kappa(s)=\frac{dt^{0}}{ds}\\ \gamma(s)=\phi+\Phi,\quad\Phi=\theta^{0}-\theta\end{array}\right. \tag{10}\]  
where \(\phi\) is the average rotation angle of the cross-section, and \(\Phi\) is the rotation angle of the bar's central axis. In Equation (10), the index \((^{0})\) indicates the initial position of the un-deformed bar structure. We determine the value of \(e(s)\) according to Equation (10). We assumed that the positive longitudinal force is compressive.
# Analysis of Trends in Mega-Sized Container Ships Using the K-Means Clustering Algorithm
## 2 Materials and Methods
### Target Data  
Due to ship-to-ship communication limitations and the network size of the shipping industry, studies involving big data analysis have been limited due to lack of data [21]. Recently, due to the development of the very-small-aperture terminal and other devices, communication difficulties between ships have been resolved, and amount of data have been collected. Operational data provided by navigation devices, such as Global Positioning System (GPS), AIS, and the Electronic Chart Display and Information System, are valuable for forecasting, decision-making, and preventing accidents in the ship and shipping industry [22]. In particular, AIS data consist of dynamic information, such as GPS position, heading, and speed over ground, and static information, such as the Maritime Mobile Service Identity, dimensions of the ship, type of cargo, and GT. These data can be used for various studies, such as safe navigation range selection, berthing step classification through density-based spatial clustering, and navigation route extraction [23; 24; 25]. In this study, analysis was performed using the ship's AIS information obtained through the IHS Markit Economics and Country Risk, Inc. world fleet of existing ships (December 2020). This includes information on all ships with the IMO number (LR Number) registered.
## 3 Test Results and Discussion
### Damaged Process and Mechanism
#### 3.1.1 Flexure-Dominant Failure  
Figure 8 shows the crack patterns of HPLW500-1 at the failure stage. Several horizontal cracks appeared at the root of web at a drift ratio of 0.62% with a lateral load of 81.5 kN for positive loading direction. Shear cracks firstly formed at a drift ratio of 0.71%. The web boundary longitudinal reinforcing bars yielded at lateral loads of (+107.1 and \(-\)148.1 kN) at drift ratios of (0.80% and \(-\)1.00%). Peak lateral loads of 181.2 and \(-\)229.1 kN were reached at drift ratios of (3.11% and \(-\)2.43%). Extensive horizontal and shear cracks formed at the web, and modest spalling of the cover concrete occurred at the free web boundary at a drift ratio of 2.01%. During the drift ratio of \(-\)2.80%, cove concrete spalling at the wall boundaries extended up to approximately 200 mm from the wall-foundation interface. Wall lateral strength dropped to \(-\)170.8 kN or 74.6% of the peak strength under negative loading at a drift ratio of \(-\)3.0%, due to concrete crushing and buckling of boundary longitudinal reinforcements. However, minor concrete spalling and no vertical splitting were observed at the flange. Evidently, HPLW500-1 failed with an expected flexure-dominant behavior.
## 3 Experimental and Analysis
### Comparison Experiments  
#### 3.5.1 Comparison Experiment with Feature Fusion Structures  
To validate the effectiveness of the E-BiFPN structure, this study compared it with PANet, BiFPN, and AFPN [18] feature fusion structures. To ensure the fairness of the experiment, the backbone network was fixed, and only the performance of different feature fusion structures was compared. As shown in Table 6, the E-BiFPN structure proposed in this paper achieved a better balance between model complexity and detection accuracy, making it more suitable for engineering-vehicle detection tasks.