esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principle of Particle Swarm Optimization  
### Basic Particle Swarm Optimization  
The basic PSO algorithm is a swarm intelligence algorithm based on a biological population system and solves computational problems by simulating biological population behavior. It is supposed that there is only one piece of food in an area and that birds would search for food in the area. However, no birds know where the food is, but only the distance between themselves and the food. According to these characteristics, those birds transmit information to each other in the search process, and the simplest and most effective way to find food is to search within the surrounding area of the bird closest to the food. Regarding every bird in the search space as a particle, the particle is the potential solution in the PSO method. A single particle position is adjusted according to a previous one, and the optimal solution is obtained through iterations. In the early stage, the PSO method was used to deal with optimization problems, which is widely used in topology, size, and shape optimization of structures [29].  
The basic PSO method adopts velocity to represent the positional change of each particle in the population range, and each particle records the best position it reaches. In the iteration, the particles change their position by tracking two optimal solutions, which are the individual optimal location and the global optimal location. The individual optimal position is the optimal solution found by the particle itself, while the global optimal location is the optimal solution currently found by the population.  
Suppose that there is a group of particles in \(D\)-dimensional space moving at a certain speed. The state of particle \(i\) at time \(t\) is set as follows:  
For position,  
\[x_{i}^{t}=\left(x_{i1}^{t},x_{i2}^{t},\ldots,x_{id}^{t}\right)^{T}, \tag{28}\]  
where \(x_{id}^{t}\in\left[L_{d},U_{d}\right]\). \(L_{d}\) and \(U_{d}\) stand for the upper and lower limits of the search space, respectively.  
For velocity,  
\[v_{i}^{t}=\left(v_{i1}^{t},v_{i2}^{t},\ldots,v_{id}^{t}\right)^{T}, \tag{29}\]  
where \(v_{id}^{t}\in\left[v_{\min,d},v_{\max,d}\right]\). \(v_{\min,d}\) and \(v_{\max,d}\) stand for the minimum and maximum speeds, respectively.  
For the individual optimal position,  
\[p_{i}^{t}=\left(p_{i1}^{t},p_{i2}^{t},\ldots p_{iD}^{t}\right)^{T}, \tag{30}\]  
For the global optimal location,  
\[p_{g}^{t}=\left(p_{g1}^{t},p_{g2}^{t},\ldots p_{gD}^{t}\right)^{T}, \tag{31}\]where \(1\leq d\leq D\), \(1\leq i\leq M\).  
Hence, at time \(t+1\), the particle would be updated by  
\[v_{id}^{t+1}=v_{id}^{t}+c_{1}r_{1}\big{(}p_{id}^{t}-x_{id}^{t}\big{)}+c_{2}r_{2} \Big{(}p_{gd}^{t}-x_{gd}^{t}\big{)}, \tag{32}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1}, \tag{33}\]  
where \(r_{1}\) and \(r_{2}\) represent random numbers uniformly distributed in \((0,1)\) interval. \(c_{1}\) and \(c_{2}\) represent learning factors, and \(c_{1}=c_{2}=2\) in general.  
A basic flowchart of the PSO method is shown in Figure 4.
# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 3 Results and Discussions  
Modeling studies are very important in the evaluation of green and dry alfalfa leaf resistance. A statistical summary of some mechanical property data of dry and green alfalfa leaves used for the modeling study, including the means and standard deviations, is shown in Table 1. The differences between the mechanical properties of dry and green alfalfa are shown in Table 1. The leaf breaking force, leaf breaking resistance and leaf breaking tension of dry alfalfa are much lower than the values obtained in green alfalfa, which indicates that the leaf losses will be much higher in dry alfalfa. While the leaf breaking force of green alfalfa was 0.087 N, this value was found to be 0.031 N in dry alfalfa, and it is seen that there is a 64.36% decrease between these two values. This decrease was 61.65% in leaf breaking stress and 64.42% in leaf breaking energy. These values clearly show that the mechanical strength of alfalfa leaves decreases as they dry and so leaf losses will increase rapidly. Predicting the breaking resistance of alfalfa leaves in advance will make it possible to minimize the leaf losses that may occur during and after harvest.  
In this study, five different machine learning methods, Extra Trees, Random Forest, Gradient Boost, Extreme Gradient Boosting, and Cat Boost, were used. The performances  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plan Type** & **Variables** & **Mean \(\pm\) SD** \\ \hline \multirow{4}{*}{Dried} & Leaf stem diameter (mm) & 0.561 \(\pm\) 0.157 \\  & Leaf petiole area (mm\({}^{2}\)) & 0.261 \(\pm\) 0.152 \\  & Leaf breaking force (N) & 0.031 \(\pm\) 0.023 \\  & Leaf breaking energy (J) & 0.037 \(\pm\) 0.028 \\  & Leaf breaking stress (N mm\({}^{-2}\)) & 0.158 \(\pm\) 0.204 \\ \hline \multirow{4}{*}{Green} & Leaf stem diameter (mm) & 0.580 \(\pm\) 0.157 \\  & Leaf petiole area (mm\({}^{2}\)) & 0.280 \(\pm\) 0.152 \\ \cline{1-1}  & Leaf breaking force (N) & 0.087 \(\pm\) 0.055 \\ \cline{1-1}  & Leaf breaking energy (J) & 0.104 \(\pm\) 0.066 \\ \cline{1-1}  & Leaf breaking stress (N mm\({}^{-2}\)) & 0.412 \(\pm\) 0.371 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimentally measured values of green and dry alfalfa.  
of the models were interpreted based on the evaluation metrics obtained as a result of the established modeling. The results of the models established to predict the breaking stress of the green alfalfa plant and dry alfalfa plant are shown in Table 2.
## 5 Conclusions  
This study evaluated the performance of three convolutional neural network architectures--U-Net, NablaNet, and SegNet--for automatic adrenal gland segmentation on multiphase CT scans. Our findings demonstrate that while U-Net achieved the highest segmentation accuracy, with Dice scores of 0.63 for the right and 0.66 for the left adrenal gland, these values remain lower than those commonly reported for larger abdominal organs. This performance gap underscores the significant anatomical and technical challenges inherent in segmenting small, low-contrast structures such as the adrenal glands.  
Despite these challenges, all evaluated models demonstrated clinically acceptable inference times, with a clear performance hierarchy establishing U-Net as the optimal architecture, offering the best balance between segmentation accuracy and computational requirements. Our study also introduced and evaluated key performance indicators relevant for clinical implementation, including time-to-model-integration and preprocessing requirements, providing valuable practical insights for potential deployment.  
Although certain evaluation metrics such as precision, sensitivity, specificity, and Hausdorff distance were approximated based on established relationships with Dice scores, our findings provide important contributions to understanding the capabilities and limitations of current deep learning approaches for adrenal segmentation. Further research employing larger, more diverse datasets and exploring emerging network architectures is necessary to improve segmentation quality and ensure robust generalization in clinical settings.  
In conclusion, this work establishes the potential of AI-based methods for adrenal gland segmentation while providing a comprehensive assessment of current technical constraints and opportunities for advancement. These findings lay the groundwork for continued development of automated tools to support radiological workflows in adrenal imaging.  
Conceptualization, V.-O.B.; Formal analysis, M.G.C.; Methodology, O.N.-C.; Project administration, L.S.C.M.; Software, O.N.-C.; Supervision, C.M. Validation, A.M. and V.J.; Visualization, V.J.; Writing--original draft, V.-O.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Publication of this paper was supported by the University of Medicine and Pharmacy Carol Davila, through the Doctoral School. Publication of this paper was supported by the University of Medicine and Pharmacy Carol Davila, through the institutional program Publish not Perish.  
The authors declare no conflicts of interest.
## 4 Experimental Results and Analysis  
### Experimental Validation of LAWDS Module  
To assess the effectiveness of our LAWDS module for small target detection tasks, we compared the performance of the YOLOv8n model with the YOLOv8-LAWDS model using various metrics, including precision (P), recall (R), mAP50, mAP50-95, parameters, LAWDS, and GFLOPS. Additionally, we conducted experiments to determine the optimal placement of the LAWDS module, evaluating its impact at different locations within the YOLOv8 base network architecture--specifically, the backbone layer, neck layer, and backbone + neck layer. The results for each metric are presented in Table 2.  
The results presented in the table indicate that we introduced the LAWDS module in the backbone layer, neck layer, and the combination of both within the YOLOv8 base network structure for experimental comparison. The experimental findings reveal that incorporating the LAWDS module in the backbone layer yields the most significant enhancement in mAP, reduces the number of parameters, and achieves the fastest computation speed while utilizing the fewest network layers. Furthermore, when we compared the YOLOv8-LAWDS (backbone) model with the baseline model, we observed that our improved model demonstrates superior performance in terms of accuracy, mAP50, mAP50-95, and GFLOPs. Notably, our model also reduces the number of parameters by 10% compared to the baseline model, achieving a lightweight network structure while simultaneously enhancing accuracy.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
###### Abstract  
The assembled corrugated steel initial support structure is a new prefabricated structure in highway tunnel engineering, achieving a balance between economy and safety. This study proposes a simplified calculation method and elucidates the mechanical mechanisms of assembled corrugated steel initial support structures. Firstly, the stiffness characteristics of corrugated steel plates were studied based on full-scale tests. A general equivalent stiffness coefficient table was established. Numerical simulations of corrugated steel flange joints were conducted to explore their bending mechanical properties. A two-stage rotational stiffness model for corrugated steel flange joints was proposed. Finally, a plane strain-spring simplified calculation method for the assembled corrugated steel initial support structure was developed, and the monitoring data from the Qipanshan Tunnel validated the correctness and reliability of the proposed method. The results demonstrate that (1) the plane strain-spring simplified model consists of the planar strain equivalent calculation method for corrugated steel plates and the two-line stiffness equivalent spring of the corrugated steel flange joint. The simplified model was validated as effective by monitoring data. (2) Corrugated steel plates exhibit two stages under loading, namely gap elimination and elastic stages. The elastic stage stiffness of corrugated steel plates decreases with increasing ratio of depth to pitch (RDP), positively correlating with plate thickness when the RDP exceeds 0.333 and otherwise negatively correlated. (3) Corrugated steel lining flange joints exhibit distinct elastic and plastic stages in their linear moment-rotation curves under loading.  
highway tunnel; corrugated steel; initial support; full-scale test; numerical simulation; calculation method +
Footnote †: journal: Applied Sciences
## 3 Workflow Description  
This section describes the customized but rather general workflow developed in this work for CFD simulations, with a special focus on user/software interaction. As discussed in the previous section, the T-WorkFlow role is mainly concentrated in the early stages of the project, when the designer needs to evaluate and select from various radiator models or explore potential modifications before requesting their production. To streamline this process, automation of the computational tool is mandatory, enabling the designer to rapidly compare different configurations and identify the best solution within the tight deadlines characteristic of the design phase. Once the model is selected, the company can focus on more detailed flow and thermal analysis. For this reason, the tool has been designed to require as little input as possible, only using the data immediately available to the designer also for the automatic generation of the computational mesh.  
The first input required from the user is the radiator geometry, provided as (i) the CAD file of the radiator in.stp format; (ii) the.stl files for the solid walls and the inlet and outlet surfaces, as shown in Figure 1. The latter is essential for generating the mesh and applying boundary conditions, while the.stp file is essential for identifying some \"critical\" points of the model by using FreeCAD.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 2 Optical Principle of VBDA Method
For example, one can insert a parallel flat glass as shown in Figure 7, which alters the ABCD matrix. The ABCD matrix transmitted from the waist of the incident Gaussian beam to the front surface of the plate is \([1,\ b;\ 0,\ 1]\). The ABCD matrix transmitted from the front surface of the plate to the back surface of the plate is \([1,\ d/n;\ 0,\ 1]\). The ABCD matrix transmitted from the back surface of the plate to the front surface of the lens is \([1,\ l-b-d;\ 0,\ 1]\). Figure 6: **(a)** The divergence angle \(\theta_{0}^{\prime}\) versus \(F\) and \(l\). **(b)** The divergence angle \(\theta_{0}^{\prime}\) versus \(l\) with different values of \(F\). Figure 7: Schematic diagram of the method for changing the optical path length between the light source and the collimating lens. Therefore, the ABCD transmission matrix from the beam waist position of the incident Gaussian beam to the rear surface of the lens is changed to:  
\[ \begin{array}{ll}\left[\begin{array}{cc}A&B\\ C&D\end{array}\right]&=\left[\begin{array}{cc}1&0\\ -1/F&1\end{array}\right]\left[\begin{array}{cc}1&l-b-d\\ 0&1\end{array}\right]\left[\begin{array}{cc}1&d/n\\ 0&1\end{array}\right]\\ &=\left[\begin{array}{cc}1&l-\left(1-\frac{1}{n}\right)d\\ -1/F&(-1/F)\left[l-\left(1-\frac{1}{n}\right)d\right]+1\end{array}\right] \end{array}  \tag{8}\]  
Substituting this into Equation (3), we obtain:  
\[q_{F}=\frac{i\frac{\pi w_{0}^{2}}{\lambda}+l-\left(1-\frac{1}{n}\right)d}{- \frac{1}{k}i\frac{i\pi w_{0}^{2}}{\lambda}+\left(-\frac{1}{k}\right)\left[l- \left(1-\frac{1}{n}\right)d\right]+1}=i\frac{\pi w_{0}^{\prime\;2}}{\lambda} -l^{\prime}, \tag{9}\]  
The solution is:  
\[w_{0}^{\prime\;2}=\frac{F^{2}w_{0}^{2}}{\left(F-a\right)^{2}+\left(\frac{\pi w _{0}^{2}}{\lambda}\right)^{2}},\left[a=l-\left(1-\frac{1}{n}\right)d\right], \tag{10}\]  
Substituting this into \(\theta=2\lambda/(\pi w_{0})\), we obtain:  
\[\theta_{0}^{\prime}=\frac{2\lambda}{\pi w_{0}^{\prime}}=\frac{2\lambda}{\pi Fw _{0}}\sqrt{\left[F-l+\left(1-\frac{1}{n}\right)d\right]^{2}+\left(\frac{\pi w _{0}^{2}}{\lambda}\right)^{2}}, \tag{11}\]  
According to Equation (11), altering the refractive index \(n\) or thickness \(d\) of the parallel flat glass can change the divergence angle \(\theta_{0}^{\prime}\) of the outgoing beam, as shown in Figure 8.  
As mentioned above, the divergence angle of the outgoing beam can be modified by adjusting the focal length \(F\) of the collimating lens or the optical path length between the beam waist and the collimating lens. Based on these two categories, the existing techniques for achieving VBDA are classified and depicted in Figure 9. The following sections provide a detailed description of these methods. Figure 8: (**a**) The divergence angle \(\theta_{0}^{\prime}\) versus \(n\) and \(d\). (**b**) The divergence angle \(\theta_{0}^{\prime}\) versus \(d\) with different values of \(n\).
# AI-Based RPA's Work Automation Operation to Respond to
## 4 Experiments and Results
### Results and Performance Analysis
#### 4.2.1 Performance Comparison Results of the Automated Threat Response System
Error Rates: Error rates in the Existing System range from 5% to 35%, primarily due to the reliance on manual operations, where human error plays a significant role in security failures. The AI-only System reduces error rates to between 2% and 15%, as AI automates some of the decision-making processes. However, the Improved System eliminates errors entirely, achieving a 0% error rate across all stages. This is a statistically significant improvement, with error variance approaching zero. Mathematically, this improvement in accuracy can be represented as follows:  
\[\sigma_{\text{Error Rate}}\approx 0\;\text{(Improved System)}\]  
This result underscores the dramatic enhancement in reliability and accuracy that complete automation provides, ensuring that human errors no longer jeopardize system security. Information Sharing with Related Institutions: Another critical area of comparison is the ability to share threat information with related organizations. The Existing System lacks this capability, severely limiting inter-agency collaboration. The AI-only System achieves moderate success in this area, with a 68% to 72% information-sharing rate, primarily due to automated reporting. However, the Improved System excels with a 98% to 99% sharing rate, enabling real-time collaboration between organizations and significantly enhancing coordinated responses to cyber threats. Administrative Delay Rate: The administrative delay rate in the Existing System is high, primarily due to manual processing. Each administrative task introduces delays that hinder timely responses to ongoing cyber threats. The AI-only System improves this metric, reducing delays to between 18% and 25%. However, the Improved System eliminates administrative delays almost entirely, achieving near-zero delay rates. In conclusion, the comparative performance analysis unequivocally demonstrates that the Improved System, which integrates AI for advanced threat detection and RPA for automating repetitive tasks, provides the most effective solution across all performance metrics. The drastic reductions in processing time and operational costs, combined with the complete elimination of errors and improved inter-agency collaboration, position the Improved System as the optimal solution for modern cybersecurity challenges. Statistically, the integration of AI and RPA reduces human error variability and enhances system responsiveness. From a mathematical perspective, the improvements observed across all metrics reflect a systemic optimization that offers scalability and adaptability in real-world applications, making this a robust, future-proof solution for the increasing complexities of cyber threats.
## 3 Numerical Model for the Tunnel with Rock Bolts  
The transverse section of the tunnel at DL34+600 was selected for the numerical simulation. The width of the tunnel, \(D\), is equal to 10.2 m. Thirty rock bolts with a length of 9 m were arranged in the circumferential direction every 12\({}^{\circ}\). An axisymmetric numerical model with a height of 100 m and a width of 50 m was constructed in ABAQUS software (Figure 4a). The Y-axis was set to be the axis of the symmetrical domain, and the tunnel section was set at the mid-height of the model. Hence, the boundaries of the numerical model are far from those of the tunnel and will not impose a boundary effect to ensure that simulation is efficient enough. The four-node plane strain element (CPE4) was used to simulate the surrounding rock mass, and the grid division is as shown in Figure 4b. In this paper, the key analysis portions are densified to improve the calculation accuracy. Away from the tunnel wall, the grid size was set as 2 m, and at the tunnel wall, it was set to 0.5 m. The parameters for the rheological constitutive model of the surrounding rock mass are listed in Table 2. They were taken from [37], in which the studied rock type was the same as that of the present case. During the numerical simulation, an asymmetric boundary constraint was imposed along the X-axis of the model. A symmetric boundary constraint was set along the Y-axis. A uniformly distributed load was applied at the top boundary of the model to represent the in situ ground stress according to different burial depths. The lateral pressure coefficient was set as 0.7 to compute the in situ ground stress. During the numerical simulations, the beam elements in ABAQUS were used to simulate the rock bolts.  
To validate of the model, the analysis results were compared to the displacement obtained from the monitoring on site. See Figure 5 for the layout of the monitoring sensors. A comparison of both arrangements can be seen in Figure 6. Their agreement is non-trivial, because parameters of the model were taken from an independent study reported in [37]. This underlines the usefulness of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(E_{0}\) & \multirow{2}{*}{\(\mu\)} & \(C\) & \(\varphi\) & \(E_{1}\) & \(\eta_{1}\) & \(E_{2}\) & \(\eta_{2}\) & \(\sigma_{f}\) & \(\sigma_{s}\) & \(A\) & \(T\) (h) \\ (**GPa**) & & **(MPa)** & **(\({}^{\circ}\))** & **(GPa)** & **(GPa-h)** & **(GPa)** & **(GPa-h)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
3.5 & 0.35 & 1 & 33 & 8.32 & 11,200 & 9.16 & 10,400 & 0.3 & 0.6 & 3.8 & 1600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters of the surrounding rock mass in section DL34+600 [37].  
Figure 4: Numerical model: (**a**) cross–section calculation; (**b**) meshing diagram.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
###### Abstract  
With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users' privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users' privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users' flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.  
mobile online social networks; privacy-preserving; location sharing; access control policy +
Footnote †: journal: Applied Soft. 2020, 10, 8402; doi:10.3390/app10238402