esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Tent Chaotic Mapping Artificial Bee Colony Algorithm  
The artificial bee colony (ABC) algorithm is an effective optimization algorithm that is a natural heuristic algorithm proposed by [PERSON] et al. in 2005 [37]. Functioning akin to the organic processes observed within bee colonies, the ABC categorizes bees into three distinct groups: worker bees, onlooker bees, and scout bees.  
However, despite its efficacy, the algorithm for the ABC remains subject to continual refinement. The conventional approach for the ABC is to create worker bee placements in the solution space at random to start the bee population. We found that the traditional ABC algorithm's randomly initialized bee colonies may be too concentrated, leading to poor performance. Therefore, in our research, we use tent chaotic mapping as the bee colony initialization operator, which effectively solves the inherent inefficiency problem of this traditional method [38].  
Our proposed initialization operator markedly reduces exploration time by systematically distributing worker bees across the solution space. This strategy results in shorter distances for worker bee exploration, consequently enhancing search speed and efficiency. Equation (1) provides the representation of tent chaotic mapping, where \(\mu\) is typically 2. We iteratively generate a series of chaotic numbers through Equation (2) and map the generated chaotic sequence \(x_{0},x_{1},\ldots,x_{n}\) to the search space of the problem in Equation (3), where \(x_{ij}\) represents the jth component of the current solution vector for worker bee i and \([a,b]\) is the solution space. The symbol \(\bigsqcup\) denotes downward rounding.  
\[T(x)=\begin{cases}\mu x&,0\leq x<\mu\\ \mu(1-x)&,\mu\leq x\leq 1.\end{cases} \tag{1}\]  
\[x_{n+1}=T(x_{n}) \tag{2}\]  
\[x_{ij}=a_{j}+\bigsqcup[x_{i}*(b_{j}-a_{j})\bigrfloor \tag{3}\]  
We use the mapping results obtained from the above steps as the initial population for the artificial bee colony algorithm. The fitness value of the objective function \(fit\) is calculated by Equation (4), where \(f_{i}\) is the objective function value and \(fit_{i}\) is the fitness of bee \(i\).  
\[fit_{i}=\frac{1}{1+f_{i}} \tag{4}\]  
We use the roulette method to calculate the probability \(p_{i}\) that a worker bee is selected by an onlooker bee, which is obtained from Equation (5):  
\[p_{i}=\frac{f_{i}}{\sum_{i=1}^{n}fit_{i}} \tag{5}\]  
Onlooker bees searching for new solutions are calculated using Equation (6), where \(x_{kj}\) is the solution vector of a randomly selected other worker bee \(k\).  
\[x^{\prime}_{ij}=x_{ij}+rand[-1,1](x_{ij}-x_{kj}) \tag{6}\]  
Scouter bees use Equation (7) to randomly generate new solutions to avoid falling into local optima, where \(x^{\prime\prime}_{ij}\) is the position of the scouter bee and \(x^{min}_{j}\) and \(x^{max}_{j}\) represent the minimum and maximum of the \(j\)th component, respectively.  
\[x^{\prime\prime}_{ij}=x^{min}_{j}+rand[-1,1](x^{max}_{j}-x^{min}_{j}) \tag{7}\]
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 5 Experimental Results
### Efficiency Comparison Based on the Impact of Enhanced Population Size Variation  
This test demonstrates the impact of enhanced population size on the models' performance in coping with different node distribution patterns. VRP-RIDFU25, VRP-RIDFU50, and VRP-RIDFU75 are models that have enhanced population ratios of 25%, 50%, and 75%, respectively. The VRP-PSM model uses PSM to optimally adjust the enhanced population size based on the node distribution pattern. Optimal refers to the shortest path solution for the problem. Additionally, each model is tested over 20 runs to evaluate its robustness against uncertainty fluctuations ranging from 1 to 10 occurrences, as depicted in Figures 7 and 8.  
Figure 7 shows the testing results of the models on problem sets A and B. In contrast, Figure 8 shows the testing results for problem sets E and M. Each problem set has a distinct number of nodes and distribution patterns. The x-axis represents the number of uncertainty occurrences, and the y-axis shows the average waiting time. The performance testing indicates that the total waiting time obtained from the VRP-RIDFU models is less than optimal in all scenarios. The graph from the VRP-RIDFU models exhibits notable stability, particularly for the models utilizing the PSM approach. Additionally, the boxplot demonstrates minimal variation in the results, emphasizing the models' resilience to uncertainties in the developed routing plans. However, the optimal boxplot graph is non-distributed because it is the shortest path, so the results are the same every time it is run.  
Figure 7: Graphs of the performance response to the number of times uncertainty occurs for problem sets A and B.  
Moreover, most of the outliers in the VRP-RIDFU's boxplot graph are few and far from the median of the results, represented by rhombuses. Outliers identify situations where the model significantly underperforms or outperforms compared to expected results.
## 2 System Model
### Received Signal and NOMA Decoding  
The BS is equipped with a uniform linear array (ULA) consisting of \(N\) transmit antennas, and we consider a uniform rectangular array (URA) at the STAR-RIS. The BS sends the superposition coding to those two users according to the working principle of NOMA. As a result, each user's received signal can be represented as  
\[\begin{split}& y_{k}=\mathbf{F}_{k}^{H}\mathbf{\Theta}_{k}\mathbf{G} \mathbf{x}+n_{k},\\ & k\in\Big{\{}t,r\Big{\}}.\end{split} \tag{6}\]  
where \(n_{k}\) denotes the additive white Gaussian noise (AWGN) satisfying \(n_{k}\sim\mathcal{CN}\big{(}0,\sigma_{k}^{2}\big{)}\), and \(\mathbf{x}\) is the transmitted signal from the BS which is shown as  
\[\mathbf{x}=\sqrt{a_{t}}\mathbf{w}_{t}s_{t}+\sqrt{a_{r}}\mathbf{w}_{r}s_{r}, \tag{7}\]  
where \(s_{k},k\in\{t,r\}\) is the transmitted symbol sent to U1 and U2, respectively, with \(\mathbb{E}\Big{\{}\big{|}s_{k}\big{|}^{2}\Big{\}}=1\), \(\mathbf{w}_{k}\in\mathbb{C}^{N\times 1},k\in\{t,r\}\) denoting the beamforming vector for each user. Moreover, \(a_{r}\) and \(a_{t}\in(0,\,1)\) with \(a_{r}+a_{t}=1\) denote the power coefficient allocated to the U1 and U2, respectively. Because of the constraint imposed by (1d), the UAV is always closer to U1. To balance the achievable rates between the two users and mitigate significant rate disparity, a larger power allocation coefficient is assigned to U2. Therefore, \(a_{t}\) is set to be greater than \(a_{r}\) (i.e., \(a_{t}>a_{r}\)). At U1 and U2, SIC is used, and the signals are decoded in decreasing order of their channel gains.  
The achievable rate of U1 and U2 is given by  
\[R_{r} =\log_{2}\!\left(1+\frac{\left|\mathbf{F}_{r}^{H}\mathbf{\Theta}_ {r}\mathbf{G}\mathbf{w}_{r}\right|^{2}}{\left|\mathbf{F}_{t}^{H}\mathbf{ \Theta}_{t}\mathbf{G}\mathbf{w}_{r}\right|^{2}+\sigma_{r}^{2}}\right), \tag{8a}\] \[R_{t} =\log_{2}\!\left(1+\frac{\left|\mathbf{F}_{t}^{H}\mathbf{\Theta}_ {t}\mathbf{G}\mathbf{w}_{t}\right|^{2}}{\sigma_{t}^{2}}\right)\!. \tag{8b}\]
## 4 Experimental Results and Analyses
### Comparison with Other Methods
#### 4.2.2 Experiments on the ShanghaiTech PartA Dataset  
On the ShanghaiTech PartA dataset, we conducted a comparison between MS-WFFNet and mainstream crowd counting methods, with detailed results presented in Table 4. The results indicate that our method achieves the lowest MAE and RMSE, outperforming the second-best approach by 2.8% and 10.7%, respectively. Notably, SCPNet [49] incorporates a scale-aware module and a context-aware module to effectively capture multi-scale information and suppress background interference. Furthermore, in addition to the commonly used MSE loss and SSIM loss, it introduces a hierarchical scale loss to provide stricter positive supervision for the model. This unique design enables SCPNet to achieve excellent performance in MAE, achieving an MAE of 57.3 and an RMSE of 102.1. The design principles behind SCPNet have also served as valuable references and inspiration for our subsequent research efforts.  
As illustrated in Figure 8, we selected four representative crowd images from the dataset to compare the visualization results of MS-WFFNet with several classic crowd counting methods. The first two columns display the original images and their corresponding GT density maps, while the subsequent five columns present the predicted density maps from MS-WFFNet, ASANet, SANet, CSRNet, and MCNN. Overall, the density maps predicted by MS-WFFNet and ASANet exhibit high quality and authenticity, accurately reflecting crowd distribution. However, from a localized perspective, the limited receptive fields of MCNN and SANet hinder their ability to effectively capture large-scale head features, as observed in the second row, resulting in increased missed and false detections. In contrast, MS-WFFNet outperforms ASANet in capturing extra-large-scale head features, benefiting from the large kernel convolution design in the LSKAB module. Moreover, unlike MS-WFFNet and ASANet, the other three counting methods apply uniform attention weights across pixel values. This limitation becomes apparent in the third and fourth rows, where leaves in the images introduce varying degrees of interference to their predictions. These observations further validate the robustness of MS-WFFNet in handling complex scenes.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 6 Results and Discussion  
### Results  
The overall plate's recognition accuracy for both the old and improved ALPR system is calculated from:  
\[\textit{Accuracy}=\frac{\textit{Number of correctly recognized plates}}{\textit{Total number of plates}}\times 100\% \tag{4}\]  
A comparison between the accuracy of the old and improved ALPR systems is shown in Figure 8. As illustrated, the improved system demonstrated significant accuracy improvement when compared to the previously reported ALPR system [3]. For the entire dataset of images, the obtained results demonstrated a recognition accuracy of 79.5% for the improved system compared to 41.8% for the old system. Similarly, the recognition accuracy of American- and European-standard plates are respectively found to be 74.8% and 84% compared to 36.9% and 43.6% for the old ALPR system. The obtained results are also compared to other Jordanian plate-recognition systems reported in [5; 6; 20]. In [5] the authors used a dataset consisting of only 46 images and reported an overall system accuracy of 70%. In [6], the accuracy was 89% for a dataset of 100 images. In [20], an improved accuracy of 87% was reported for a dataset of 187,200 images that was used to train a deep neural network. It should be emphasized here that due to the lack of a publicly available dataset for the Jordanian plates; these investigations were conducted utilizing different datasets.  
It should be noted here that the accuracy of each stage is highly dependent on the results of the preceding stage. Therefore, the entire system is established as an integrative or chain relationship. Based on the results of the final stage in recognition, the system may succeed or fail to recognize the correct characters of the license plate; the failure can occur when any character is incorrectly interpreted. Samples of input images and corresponding detection, segmentation and recognition results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Image** & **Detection Output** & **Segmentation** & **Recognition Output** \\ \hline \hline
**Output** & 16\(\cdot\)10154 & 16\(\cdot\)10154 & 10154 \\ \hline
**Output** & 21\(\cdot\)10154 & 59767 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample input images, and corresponding detection, segmentation, and recognition results.  
Figure 8: Comparison between the accuracy of the Existing and Improved ALPR systems.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 3 Adaptive Beamforming Model Based on CNN
### Acquisition of Training Data Sets  
In order to provide enough training and validation data for the CNN, this paper utilizes the theory of unimodal Lamb wave propagation to simulate and calculate the generation of relevant datasets required for CNN training. Assuming that there exists a point damage \(P(x,y)\) in the damage detection region, the distance from the excitation point to the damage point and the distance from the damage point to the reception point are \(d_{t}\) and \(d_{n}\), respectively, which can be expressed by the following equation:  
\[d_{t} =\sqrt{(x_{0}-x_{P})^{2}+(y_{0}-y_{P})^{2}} \tag{9}\] \[d_{n} =\sqrt{(x_{n}-x_{P})^{2}+(y_{n}-y_{P})^{2}} \tag{10}\]where \(x_{0}\), \(y_{0}\), \(x_{n}\), and \(y_{n}\) are the coordinates of the excitation and receiving array elements; \(x_{P}\) and \(y_{P}\) are the coordinates of the damage. The signal scattered at the damage can be calculated using the following equation:  
\[s(t)=\frac{A}{\sqrt{d_{t}}\sqrt{d_{n}}}f(t-\Delta t_{tn}) \tag{11}\]
## 2 Overview of Network Architecture and Networking Technologies
### Network Architecture Based on Virtual Resource Allocation  
As a nationwide academic infrastructure which covers more than 700 universities and research institutions including all national universities, SINET3 provides a wide range of network services to support a variety of research and educational activities, as well as general activities using the Internet. For research activities, virtual private network (VPN) services, which form closed user groups for specific research areas, have become increasingly important and popular for collaborations between different organizations. The total number of interfaces for VPN services is steadily increasing and currently exceeds three hundred. As networking technologies progress, VPN usage is shifting from Layer-3 VPNs to Layer-2 VPNs or virtual private LAN service (VPLS). SINET3 accommodates these different VPN services, as well as Internet service, by forming virtual service networks (Figure 1). Each virtual service network corresponds to a VPN service for a number of research projects, e.g., L3 VPNs for nuclear fusion science, and GIRD computing; L2 VPNs for high-energy physics, earthquake research, and small virtual laboratories. The virtual service network corresponding to IPv4/IPv6 dual stack services provides IPv6 services with multicast capabilities as well as Internet access. SINET3 is interconnected with commercial Internet service providers via two major domestic Internet Exchange Points, called JPIX and JPNAP. As for the layer-1 services, which will be described later, we have another virtual service network that accommodates L1 VPNs for research groups, such as eVLBI [4], high-quality remote backup [8], and t-Room [9] projects.  
Each virtual service network applies its own routing and signaling protocols and has its own high-availability functions for protection, rerouting, _etc._ Although this enables us to control and manage each virtual service network independently, we also have to let these networks share network resources, such as nodes and links, and we have to flexibly assign the network resources depending on the service demand. Layer-1 and layer-2/3 services in our network use the same links, and they change the assigned bandwidths by using next-generation SDH technologies [11; 12; 13]. The layer-2/3 services, in particular, also have QoS controls that identify specific VPNs and IP flows.  
Figure 1: Virtual service networks and VPNs in SINET3.
## 5 Multiobjective Optimization of Main Beam Structure Based on RSM
### Multiobjective Optimization of Main Beam Structure
#### 5.2.1 Selection of Optimal Design Variables  
Through the analysis in Section 4.4, it can be seen that the stress of the main beam under the lateral eccentric load condition was the largest, and the yield failure was most likely to occur. Therefore, it was necessary to optimize the structure of the main beam under this working condition. The response surface optimization tool provided in the Design Exploration module of ANSYS Workbench was selected as the solution tool. Figure 25 shows the multiobjective optimization module built in ANSYS Workbench.  
The four thickness parameters of the beam in SmTS were selected as design variables, as shown in Figure 26, P1, P2, P3, and P4. The range of value variation was set to be no more than 10% of its initial value. The initial value, upper and lower limit values, and physical meaning of each design variable are shown in Table 7. We imported the model into the analysis software tool to establish the finite element model.  
Figure 26: Design variables of main beam.  
Figure 25: Multiobjective optimization module. (**A**) Set the parameters of “Geomotry”, (**B**) Set the parameters of “Static Structural”, (**C**) Set the parameters of “Model”, (**D**) Set the parameters of “Response Surface Optimization”.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 3 McMurchie-Davidson Method
As such, in this study, in order to realize the efficient and tailored computation paths across a diverse set of molecular configurations, we improved the evaluation of the term \(E_{t}^{i,j}\) by not only expanding the recurrence relations but also by creating the device functions tailored to each combination of the tuple \((i,j,t)\); as such, the overall computation load is anticipated to decrease [43]. Central to the MD method is the computation of the following recurrence relations:  
\[ \begin{cases}R_{t,u,v}^{n}=(t-1)R_{t-2,u,v}^{n+1}+(P_{x}-P_{x}^{ \prime})R_{t-1,u,v}^{n+1}\\ R_{t,u,v}^{n}=(u-1)R_{t,u-2,v}^{n+1}+(P_{y}-P_{y}^{\prime})R_{t,u-1,v}^{n+1}\\ R_{t,u,v}^{n}=(v-1)R_{t,u,v-2}^{n+1}+(P_{z}-P_{z}^{\prime})R_{t,u,v-1}^{n+1}\\ R_{0,0,0}^{n}=(-2\omega)^{n}F_{n}(\omega|\mathbf{P}-\mathbf{P}^{\prime}|^{2})\\ R_{t,u,v}^{n}=0\text{ if $t<0$ or $u<0$ or $v<0$}\end{cases}  \tag{22}\]  
where  
\[\mathbf{P}=\bigl{(}P_{x},P_{y},P_{z}\bigr{)}=\frac{\alpha\mathbf{A}+\beta\mathbf{B}}{\alpha +\beta}, \tag{23}\]\[\mathbf{P}^{\prime}=\left(P_{x}^{\prime},P_{y}^{\prime},P_{z}^{\prime}\right)=\frac{ \gamma\mathbf{C}+\delta\mathbf{D}}{\gamma+\delta}, \tag{24}\]  
\[\omega=\frac{pp^{\prime}}{p+p^{\prime}}. \tag{25}\]  
Thus, \(\mathbf{P}=(P_{x},P_{y},P_{z})\) is the point dividing \(A\) and \(B\) in the ratio \(\beta:\alpha\), where \(P_{x},P_{y},\) and \(P_{z}\) represent the \(x\)-, \(y\)-, and \(z\)-coordinates of \(\mathbf{P}\), respectively. Furthermore, the term \(F_{n}(\omega|\mathbf{P}-\mathbf{P}^{\prime}|^{2})\) in Equation (22), in which \(|\mathbf{P}-\mathbf{P}^{\prime}|\) is the norm of the vector \(\mathbf{P}-\mathbf{P}^{\prime}\), denotes the Boys function [8], defined as follows:  
\[F_{n}(x)=\int_{0}^{1}u^{2n}\exp\Big{(}-xu^{2}\Big{)}du, \tag{26}\]  
The equations from Equation (14) to Equation (26) follow the formulations in [10], but have been rewritten in a different form to improve readability. Although the above-mentioned Boys function is widely used in frameworks aiming at reducing the computation of complex integrals into amenable forms, the closed-form analytical solutions are nonexistent/intractable. In this study, to realize the highly accurate estimations with utmost efficiency frontiers, we leveraged the numerical approach rendered from GPU acceleration schemes proposed by [PERSON] et al. [44]. Although the computational burden behind evaluating the Boys function is high, it is possible to attain enhanced efficiency frontiers by devising strategies to reduce the number of evaluations in special scenarios. For instance, a special case occurs when \(x=0\); thus, the Boys function becomes \(F_{n}(0)=\int_{0}^{1}u^{2n}du=\frac{1}{2n+1}\); as such, the computational burden is expected to significantly reduce. The reader may note that the scenario \(x=0\) often occurs when the Cartesian centers \(\mathbf{A},\mathbf{B},\mathbf{C}\), and \(\mathbf{D}\) of the four GTOs used in the GTO-ERI are identical.
## Materials and Methods
### Investigated Soils  
Soil samples were taken from two locations in Austria close to the experimental sites, Zwerbach (ZW) (48\({}^{\circ}\)8\({}^{\prime}\)22\" N, 15\({}^{\circ}\)14\({}^{\prime}\)46\" E) and Mistelbach (MI) (48\({}^{\circ}\)35\({}^{\prime}\)3\" N, 16\({}^{\circ}\)35\({}^{\prime}\)16\" E). In the Czech Republic, soil samples were taken within the Central Bohemian Region in Bilkovice (BK) (49\({}^{\circ}\)45\({}^{\prime}\)41.5\" N, 14\({}^{\circ}\)50\({}^{\prime}\)20.0\" E). The locations of three sites are marked in Figure 1. The samples were collected from agricultural land in the first 10 cm during April 2017, after seeded preparation. Soil was dried and sieved through a 10 mm sieve and distributed to the three experimental sites. Particle size distribution was determined with a combined wet sieving and sedimentation method, as defined in the Austrian Norm for soil physical analysis [50; 51]. Accordingly, soil textures were determined using the Austrian soil texture triangle [52]. Soil pH was obtained with electrometric pH meter. Total organic carbon was measured according to the Austrian Norm for the determination of soil organic carbon [53], and the aggregate stability of soils was determined with the modified Kemper and Rosenau method [54]. The physical and chemical properties of the soils obtained in the laboratory analysis are listed in Table 1.
## 2 Literature Review  
The literature review of the various codes developed and studied previously is presented in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Codes** & \multicolumn{2}{c}{**Benefits**} & \multicolumn{2}{c}{**Drawbacks**} \\ \hline
**1D-FCC** & - & Short Code Length & - & AND Subtraction (Two Photo-detectors) \\  & - & Non-Zero Cross Correlation \(\lambda_{c}\geq 1\) \\  & - & Code Weight \(W\geq 2\) (Even Number) \\  & - & Support Low Number of Users \\  & & - & Low Data Rate \\ \hline
**1D-MDW** & - & Short Code Length & - & AND Subtraction (Two Photo-detectors) \\  & - & Short Code Length & - & Non-Zero Cross Correlation \(\lambda_{c}=1\) \\  & - & Code Weight \(W>2\) \\  & & - & Support Low Number of Users \\  & & - & Low Data Rate \\ \hline
**1D-DCS** & - & Short Code Length & - & AND Subtraction (Two Photo-detectors) \\  & - & Non-Zero Cross Correlation \(\lambda_{c}=1\) \\  & - & - & Code Weight \(W\geq 1\) (Any Number) \\  & & - & Support Low Number of Users \\  & & - & Low Data Rate \\ \hline
**1D-DEU** & - & Short Code Length & - & XOR Subtraction Detection Technique \\  & - & Short Code Length & - & Non Zero Cross Correlation \(\lambda_{c}\leq 1\) \\  & - & Short Code Weight \(W\geq 1\) (Any Number) \\  & - & Short Code Length & - & Code Weight \(W\geq 1\) (Any Number) \\  & - & Short Code Length \(W\geq 1\) (Any Number) \\ \hline
**1D-MD** & - & Support Large Number of Users & \\  & - & High Data Rate & \\  & - & Zero Cross Correlation & \\  & - & PIIN = 0 & - & Long Code Length \\  & - & Absence Multi Access Interferences (MAI) & - & Needs Narrow Filters \\  & - & Direct Detection Technique & \\  & - & Zero Cross Correlation \(\lambda_{c}=0\) & \\  & - & Code Weight \(W\geq 1\) (Any Number) & \\ \hline
**1D-ZCC** & - & Support Large Number of Users & \\  & - & High Data Rate & \\  & - & Zero Cross Correlation & \\  & - & PIIN = 0 & - & Compatible with Even Number of Users \\  & - & Absence Multi Access Interferences (MAI) & - & Long Code Length \\  & - & Direct Detection Technique & \\  & - & Zero Cross Correlation \(\lambda_{c}=0\) & \\  & - & Code Weight \(W\geq 1\) (Any Number) & \\ \hline
**2D-PD** & - & High Data Rate & - & High PIN Noise \\  & - & Short Code Length Compared To 1D & - & Presence MAI \\  & - & Two Photo-detectors at The Receiver \\ \hline
**2D-DCS** & - & High Data Rate & - & High PIN Noise \\  & - & Short Code Length Compared To 1D & - & Presence MAI \\  & - & Two Photo-detectors at The Receiver \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review of the various codes.