esa-sceva/satcom-chunk-collection huggingface dataset:
## Approach based on non-overlapping community  
This type of method is based on non-overlapping community division, focusing on two attributes of nodes, that is, the connection of nodes within the community and the outside world.  
[PERSON] et al. [11] first proposed the Commn centrality index, which considers the in-degree and out-degree of a node in the community. In-degree and out-degree respectively represent the number of edges directly connected to the node in the community and out of the community. For the nodes in the community C, the calculation process of Commn centrality is as follows:  
\[CC(i)=(1+\mu_{C})*(\frac{k_{i}^{in}}{max(k_{j}^{in}y\in C)}*R)+(1-\mu_{C})*(\frac {k_{i}^{out}}{max(k_{j}^{out}\forall\in C)}*R)^{2} \tag{2}\]  
where \(R\) is an arbitrary positive integer and its function is to make the obtained in-degree and out-degree values within the same value range, i.e., \([0,R]\). The author proposes \(R\) to take the largest in-degree in the community \(C\), i.e., \(max(k_{j}^{in}y\in C)\). \(\mu_{C}\)is the ratio of outgoing connections to the total number of connections in the community C and can be calculated as:  
\[\mu_{C}=\frac{\sum_{i\in C}\frac{k_{i}^{out}}{k_{i}}}{size(C)} \tag{3}\]Another metric, gateway local rank (GLR) [12], is based on the idea that nodes that have shortest path to core nodes of community can spread information more efficiently. First, a local critical node and a gateway node are defined In each community. The GLR value of node \(v\) is calculated as follows:  
\[GLR(v)=[\alpha_{1}\sum_{u\in\Gamma_{k}}d(v,u)+\alpha_{2}\sum_{p\in\Gamma_{G}}d(v,u)]^{-1} \tag{4}\]  
where \(\Gamma_{k}\) is the set of local cores, \(\Gamma_{G}\) is the set of gateway nodes, and \(d(v,u)\) is the shortest path between node \(v\) and \(u\). Parameters \(\alpha_{1}\) and \(\alpha_{2}\) are set to weight the different parts.  
In terms of centrality based on community structure, there are some other related works such as community hub-bridge centrality [13], modular centrality [14], etc. Another feasible idea is the method proposed by [PERSON] et al. [15], which is based on the modularity of the network about the community, and measures the importance of the node in maintaining the community structure by calculating the change of the corresponding modularity after removing the node.  
The algorithms based on non-overlapping community structure focus on the connection between communities, and most of them have the same idea, that is, to comprehensively consider the connection status of nodes inside and outside the community and then make an evaluation. Therefore, some related indicators, such as in-degree, out-degree, community size, etc., are used in the intermediate process of calculating such indicators. As a cost, such methods ignore the topology features of nodes in the global network, and rely on computationally expensive community detection algorithms.
## 3 Materials and Methods
### Defects Classification
#### 3.6.1 Data Processing  
There is an insufficient amount of data available for specific defects compared to other more commonly found defects. For this reason, we compiled similar defects into broader categories, resulting in a reduced number of classes (9). However, there is still class imbalance which can lead to overfitting of the classification model, thus reducing generalization. To address this issue, data augmentation techniques were employed, namely translations, rotations, flips, blurring, contrast, and sharpness adjustments. Furthermore, we used a weighted loss function to assign higher weights to the underrepresented classes and lower weights to the overrepresented classes.
## 8 Conclusions  
In this paper, a novel distributed clustering method to determine the optimal CH for every cluster in WSNs is proposed so as to maintain the trade-off between average delay and energy. The nodes join clusters where CHs are selected based on costs related to trust, buffers, waiting time, traffic load, energy, and distance. Further, a novel cost function is offered for an inter-cluster multi-hop routing protocol depending on the proposed delay model. A multi-hop routing protocol is offered to support efficient transmission from CHs to the sink involving lower energy cost, which is linked to the limitations of end-to-end delay. Further, to reduce the delay and energy overheads, XOR encoding and decoding, which enhances the network lifetime and reduces energy consumption, delay, and overheads, is included. Priority-based data delivery is included in the proposed scheme to avoid network congestion. The performance of the proposed protocol is compared with that of the existing TBSEER, CTRF, and TAGA protocols in terms of PDR, throughput, average delay, overhead, detection ratio of malicious nodes, packet loss, and energy. It is seen that the proposed HTCCR protocol outperforms the benchmarked protocols considered in this paper. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lesser PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times less routing overheads in comparison to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the 'C1' and 'C2' RT packets, and the average throughput of RT is 10.4% better when compared with NRT.  
Conceptualization, methodology, validation,and simulation results, S.N.B.; supervision, formal analysis, A.C.S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData can be made available upon reasonable request by contacting email/contact information.  
The authors declare no conflicts of interest related to this study.  
Figure 15: Throughput for Varying Traffic Rates.
## 1 Introduction  
Dry eye disease (DED) is a multifactorial condition characterized by a loss of homeostasis of the tear film accompanied by ocular symptoms, in which several ocular signs play etiological roles [1]. This condition shows a high prevalence, ranging from 5% to 50% of the adult population. Such high variability could be due to differences in age, world region (e.g., climate conditions, ethnic factors... ), and/or diagnostic criteria [2].  
The diagnosis of DED is frequently based on the presence of ocular symptoms and the detection of associated ocular signs, such as a tear film instability or a deficient tear volume, among others. Indeed, the Tear Film and Ocular Surface Society Dry Eye Workshop II (TFOS DEWS II) established tear break-up time evaluation as one of the most important parameters for diagnosing and monitoring DED. Traditionally, ocular signs for the diagnosis of DED have been measured by means of anterior segment biomicroscopy. However, these measurements present a subjective character that involve high variability [3, 4, 5]. In recent years, the development of automatic instruments is leading to the achievement of higher accuracy and objective measurements [6]. Specifically, several dry eye modules of corneal topography systems allow for measuring some parameters, such as non-invasive tear break-up time (NIBUT) and tear meniscus height (TMH), even providing a clinical grading scale to evaluate anterior segment alterations (blebaritis, meibomian gland dysfunction, etc.).  
The Medmont E300 (Medmont International Pty Ltd., Melbourne, Australia) and the Visionix VX120+ (Visionix-Luneau Technologies, Chartres, France) systems are corneal topographic devices that have incorporated a software to assist in dry eye diagnosis. Both instruments allow measuring NIBUT and TMH, showing acceptable repeatability outcomes [7,8]. However, the measurements procedures and algorithms followed by the instruments to calculate these parameters are slightly different. Consequently, the aim of the present study was to analyze the agreement between Medmont E300 and Visionix VX120+ platforms in the assessment of the NIBUT and TMH.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 2 Methodology
### Dense GCP Acquisition  
The error analysis of Kompsat-3A images demands extensive GCP information across the entire image space. GCP information comprises 3D ground coordinates such as latitude, longitude, and ellipsoidal height, along with corresponding 2D satellite image coordinates. Traditionally, acquiring GCPs involves a manual approach by a human operator, which is time consuming and labor intensive. To address this limitation, we employed an automated procedure, as outlined in Figure 2. Numerous GCP image chips were automatically extracted from aerial orthoimages and linked with a DSM (digital surface model) for elevation data, ensuring accurate 3D ground coordinate information.  
The subsequent step involved the automated retrieval of corresponding Kompsat-3A image coordinates. This was achieved using robust image-matching techniques between the GCP image chips and the targeted Kompsat-3A images. Before image matching, the GCP chips were projected onto each Kompsat-3A image to calculate the associated image coordinates, such as \((l,s)\) in Equation (1). However, due to inherent errors in the associated RPC information, the computed image coordinates \((l,s)\) were biased. Hence, the  
Figure 2: Automated process for dense GCP information acquisition.  
image-matching process aimed to determine the correct image coordinates of the chips, represented as \((l^{\prime},s^{\prime})\) in Equation (2). Finally, a comparison between the computed image coordinates \((l,s)\) and the correct image coordinates \((l^{\prime},s^{\prime})\) was conducted to analyze locational errors. This study utilized a hybrid method of NCC (normalized cross-correlation) and RECC (relative edge cross-correlation) for image matching [13].
## 2 The Numerical Framework
### High-Order Mesh Generation  
An accurate treatment of curved boundaries is mandatory for the application of finite element methods such as DG methods to industrial CFD computations. In particular, high-order coarse meshes are needed to fully exploit the potential of high-order DG methods in delivering accurate solutions, while saving the overall number of cells needed to represent the geometry.  
Figure 1: Comparison between a fixed (**left**) and variable (**right**) order approach to represent the solution.  
In this work, the generation of the curved high-order meshes, suitable for the DG method, is performed using the GridPro [40] software. Different structured meshes with high-order hexahedral elements are generated for each geometry. The software elevates the linear meshes into 2 nd- (quadratic edges), 3 rd- (cubic edges) or 4 th- (quartic edges) order meshes. A cubic representation of the edges is adopted for the simulations presented in the following as a good trade-off between accuracy and computing time.  
The \"original\" DrivAer fastback model [11] has been slightly simplified, removing the handles and the rear-view mirrors, smoothing the underbody and closing the front radiator grid. Figure 3 shows the geometry of the car body used for the simulations in Section 3.2, while Figures 4 and 5 show the corresponding high-order mesh. The mesh consists of 406,304 cubic-edges hexahedral elements.  
Figure 3: DrivAer car model adopted for the simulations presented in this work.  
Figure 2: Flowchart of the \(p\)-adaptation strategy.
## 3 Results and Discussion
### Comparison of Different Feature Fusion Methods  
To validate the superiority of the proposed MS-FPN, the YOLOv8n algorithm with PAN-FPN was used as the baseline and compared with other advanced feature pyramid structures such as the BiFPN, HS-FPN [30], and AFPN [31] on the SBDS dataset. The experimental results are presented in Table 2. After incorporating the BiFPN, HS-FPN, and AFPN into the baseline model, improvements in the mAP metrics were observed. Among them, the model using the BiFPN structure achieved the highest improvement of 0.6% in mAP\({}_{0.5}\). Although the MS-FPN showed a slight increase in the number of parameters and FLOPs, its mAP\({}_{0.5}\), mAP\({}_{0.75}\), and mAP\({}_{0.50.95}\) increased by 1.1%, 0.8%, and 0.7%, respectively, demonstrating an outstanding performance in strawberry ripeness detection. Compared to other advanced FPN models, MS-FPN exhibited a significant enhancement in overall detection accuracy. In summary, these results fully demonstrated the capability of MS-FPN in detecting fine-grained features in strawberry images and highlight its advanced nature in feature fusion methods.  
Figure 10: Original image (**a**) and heat maps generated under the following different attention mechanisms: (**b**) no attention, (**c**) SE, (**d**) CBAM, (**e**) CA, (**f**) ELA, (**g**) Transformer, and (**h**) PHAM.
## 3 Results
### On-Site Practical Result  
This paper relies on the slope of a highway construction project in Yunnan, China to verify the improved method of surface displacement monitoring on five large-scale slopes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **UAV** & **TLS** & **UAV + TLS** \\ \hline Number & 10 & 7 & 10 \\ Horizontal error & 10 mm & - & - \\ Inclined error & 20–30 mm & 10 mm & - \\ \(RMSE\) & 1.2 mm & 0.48 mm & 0.73 mm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the three methods in the number of monitoring points, horizontal error, inclined error and root mean square error.  
Figure 20: The displacement of 10 monitoring points. Monitoring point No 5, 6 and 8 are horizontal monitoring points and the rest are inclined monitoring points.  
The on-site practical experiments implemented DJI M600 Pro and Trimble TX8 TLS as the slope image and point clouds data acquisition devices. The TLS is erected at the point 28.2 m away from the slope toe line of the vertical slope.  
Besides the monitoring points set on slope, there are some squares can be mobilized to manual stimulate large displacement situation. After data collection and UAV image distortion correction, the complement point clouds made up the block area which TLS view cannot reach. The registration of two point clouds formed a complete datasets, thus, the surface displacement was calculated. In order to improve the visual display, the orthophoto was generated based on the registered point cloud, as shown in the Figure 21.  
Because the actual deformation of the slope is relatively small, so as to test the calculation accuracy of this method for different degrees of displacement in this paper, the square formwork sprayed with red paint was artificially moved to simulate the situation of large displacement, in addition to using the slope frame beam and the monitoring points on the platform. The displacement measurement results of monitoring points are shown in Table 6. In the measured data, the maximum displacement calculated by registration point clouds is 20.7 cm. In the project site, the RTK is used for verification, and the maximum displacement measured is 20.7 cm and the minimum value is 0.5 cm. The maximum displacement difference of each monitoring point between the two measurement methods is 1.1 cm. Among them, No. 2 monitoring point and No. 10 monitoring point are artificially moved red square formwork, moving 20.7 cm and 17.8 cm, respectively, which verifies the reliability of this method for large displacement monitoring.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & \(|\mathbf{\Delta x}|\) & \(|\mathbf{\Delta y}|\) & \(|\mathbf{\Delta z}|\) & \(|\mathbf{\Delta d}|\) & \(|\mathbf{\Delta d_{RTK}}|\) & \(|\mathbf{\Delta d}|-|\mathbf{\Delta d_{RTK}}|\) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Displacement processing result of monitoring points (cm).  
Figure 21: Point cloud model after registration and orthophoto.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
###### Abstract  
Ultra-wide-field of view (UWFOV) imaging systems are affected by various aberrations, most of which are highly angle-dependent. A description of UWFOV imaging systems, such as microscopy optics, security camera systems and other special space-variant imaging systems, is a difficult task that can be achieved by estimating the Point Spread Function (PSF) of the system. This paper proposes a novel method for modeling the space-variant PSF of an imaging system using the Zernike polynomials wavefront description. The PSF estimation algorithm is based on obtaining field-dependent expansion coefficients of the Zernike polynomials by fitting real image data of the analyzed imaging system using an iterative approach in an initial estimate of the fitting parameters to ensure convergence robustness. The method is promising as an alternative to the standard approach based on Shack-Hartmann interferometry, since the estimate of the aberration coefficients is processed directly in the image plane. This approach is tested on simulated and laboratory-acquired image data that generally show good agreement. The resulting data are compared with the results of other modeling methods. The proposed PSF estimation method provides around 5% accuracy of the optical system model.  
space-variant point spread function; optical system modeling; Zernike polynomials Article
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 4 Discussion
Thirdly, only a small number of studies (such as [90]) used hybrid techniques to address issues with intelligent intersection control connected to efficiency and safety. Rule-based and optimization-based techniques are combined in hybrid approaches. Hybrid approaches take less time to compute than optimization-based methods, since they are somewhat based on rules, which reduces their computational complexity. In contrast to rule-based systems, the optimization component of hybrid methods enhances their adaptivity. However, a distinct blend of rule-based and optimization-based techniques can provide noticeably different results. It is not easy to integrate the rule-based strategy and the optimization-based method. Balancing numerous aims and assuring performance is another frequent issue with the current approaches. Additionally, considering the techniques' validation environment highlighted shortcomings and inadequacies. First, the validation process's consideration of traffic circumstances was overly simplistic compared to actual traffic patterns at crossings. A few suggested approaches were only evaluated in predetermined traffic scenarios with set traffic flow rates. The volume of traffic, however, fluctuates according to the time of day, the day of the week, the weather, and other factors. For instance, the methods described in [77] are more efficient and effective when there is little traffic than when there is significant traffic. Several techniques, such as [59], were validated by considering various traffic scenarios and circumstances. In some publications, balanced traffic at the crossroads, interchanges, and roundabouts was considered, but the types and quantities of traffic coming from various directions tend to change. Next, the majority of the car details and driving styles were improbable. Deterministic vehicle features have been employed in previous investigations (for instance, [84]) and car-following behavior parameters, While the driver-behavior parameters for human-driven automobiles in real-world traffic (such as duration, headway, standstill distance, and so on) are stochastic. Additionally, different automakers outfit their creations with sensors of varying caliber, and they have access to a variety of algorithms for automatic movement. Furthermore, the controllers for various vehicle types with variances in size and weight (such as trucks, passenger cars, vans, and so on) may change. Fourthly, many techniques have been verified in simulation settings (for instance, [27]). Platforms for simulation might be unable to correctly represent real-world circumstances, including geometry restrictions, weather, and pedestrian movement. It continues to be challenging to create approaches for taking V2X communication technology restrictions into account in simulations.
## 4 Methodology
### Hybrid ANN-PSO  
As mentioned previously, the training process of ANN leads to a minimization problem that can be solved by classic or metaheuristic algorithms. In a hybrid ANN-PSO model, PSO is involved to minimize the errors of the ANN by determining the optimum values for the weights and biases of the model [96]. Therefore, in this problem, variables are the weights and biases, and the feasible space of the problem depends on the interval at which these variables vary. The cost function (fitness function) of the \(i\)th particle can be defined in the term of root mean squared error as follows [97]:  
\[E(w_{i},\ b_{i})=\sqrt{\frac{1}{S}\sum_{k=1}^{S}\left[\sum_{l=1}^{O}\{T_{kl}-P _{kl}(w_{i},\ b_{i})\}^{2}\right]} \tag{5}\]  
where \(E\) is the cost (fitness) value, \(T_{kl}\) is the target value, \(P_{kl}\) is the predicted output based on \(w_{i}\) (weights) and \(b_{i}\) (biases), \(S\) is the number of training samples, and \(O\) is the number of neurons.  
To have a hybrid ANN-PSO model, these steps can be followed:  
1. Considering a number of neurons in the hidden layer, develop a neural network with initial weights and biases.
2. Reform the weights and biases in a way where they can represent the location of a particle in the \(D\) -dimensional space of the problem, where \(D\) is the total number of weights and biases.
3. For each of the particles in every iteration, we can predict output values and then calculate the value of the presented cost function in Equation (5).
4. Update the location of particles by the PSO algorithm for a specific number of populations and iterations until the target is achieved (i.e., the cost function is minimized).