esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Existing Surveys on RPL
[133] conducted a thorough study of the RPL and P2P-RPL protocols. Their study included a detailed evaluation of the routing performance of these protocols, addressing major research challenges, and categorising key research directions while introducing potential technological enhancements. The paper presented the routing specifications of RPL and outlined an implementation framework using NS-3. Experimental evaluations of RPL and P2P-RPL protocols have been conducted, providing insights into their performance. The paper also identified active research areas and challenges related to RPL and addressed these challenges with enabling techniques. [PERSON] and [PERSON] [15] discussed the impact of attacks on RPL network parameters. They presented a survey of existing detection systems and focused on research areas for RPL attack solutions. The authors emphasised the need for research on countering RPL network attacks, particularly those that have not yet been evaluated. The authors proposed mechanisms to counter attacks such as Wormhole, Sybil, clone ID, and black-hole. They have expressed a need for the deployment of detection and prevention mechanisms that are based on detection systems or other suitable alternatives. The implementation of these mechanisms would contribute to a more secure system, ensuring the confidentiality and integrity of RPL. [PERSON] and [PERSON] [134] conducted a detailed study of the design objectives and network architecture of RPL. They outline the protocol control headers, network construction operations, and network management mechanisms, including fault tolerance, quality of service (QoS), and security aspects. The researchers also perform an empirical analysis to evaluate the performance of RPL in practical settings. They investigate the impact of various RPL attributes on network behaviour and compare it with other routing protocols. This comparative analysis provides valuable insights into the strengths and potential areas of improvement for RPL.
## Methodology  
### Schwarz Primitive Lattice Structures  
Schwarz primitive lattice structures are obtained by calculating isosurfaces of the real-valued function \(F:\mathbb{R}^{3}\to\mathbb{R}\) in Equation (1):  
\[F(x,y,z)=cos\left(\frac{2\pi}{L}x\right)+cos\left(\frac{2\pi}{L}y\right)+cos \left(\frac{2\pi}{L}z\right), \tag{1}\]  
where \(L\) is the desired length of the cell [25].  
Schwarz primitive lattice structures are employed in topology optimization for AM. The result of some common methods in topology optimization is a density map that is impossible to manufacture. The problem of converting that density map into a manufacturable domain does not have an exact solution. The mathematical structure of Schwarz primitive lattice allows for finding approximate solutions to that problem, providing manufacturable designs with smooth transitions in the connections of multiple cells, preventing stress concentration [7]. Moreover, Schwarz primitive lattice structures are stiffer than other lattice structures (such as the gyroid) [26]. These properties make Schwarz primitive structures attractive for engineering and biomedical applications [7; 8; 26].  
In order to show the geometry of the Schwarz primitive cell, we obtained the isosurfaces for the isovalues \(t=-0.87,0.0,0.87\); that is, we found the surfaces that solved the equation \(F=t\). The corresponding relative densities (\(\rho\); i.e., the ratio of the volume of the cell and \(L^{3}\)) of the cells were \(\rho=0.25,0.5,0.75\), respectively. Figure 1 displays the cells along with their corresponding isovalues and relative densities.
However, many authors have been using metaheuristic algorithms just as in the SHE method. For instance, in reference [15], a comparison is made between the THD by applying Particle Swarm Optimization (PSO), showing a good performance of this algorithm, but only simulation results are shown. In reference [14], the authors use the PSO in a five-level multilevel CHBMLI to obtain a low THD; the results were compared with the SHE method. The authors of reference [16] achieve the minimization of the THD in a seven-level CHBMLI of three phases by using the GA; however, no experimental results were shown. In reference [13], the Artificial Bee Colony (ABC) algorithm is explored to improve the voltage output THD in a five- and seven-level CHBMILI; the results were compared with the fsolve method included in the Matlab 2018b software. Previous studies predominantly focused on commonly employed metaheuristic methods. Nevertheless, the present investigation identified an overlooked aspect: the absence of prior examinations of the Moth-Flame Optimization (MFO) algorithm in addressing this issue. The principal contribution of this paper lies in venturing into this uncharted territory by utilizing the MFO algorithm to enhance the Total Harmonic Distortion (THD) for both five- and seven-level configurations of a CHBMILI. This paper employs the OMTHD method for the inverter modulation, obtaining the switching sequences and excluding the consideration of the SHE method or other modulation techniques. The OMTHD algorithm was obtained using the Matlab software; these sequences were then implemented in a microcontroller that controls a laboratory prototype for the CHBMILI. The CHBMILI prototype's THD for five and seven levels was obtained using a Power Quality Analyzer, showing the levels of the harmonics in a bar graph. These obtained THD were compared with previous studies that aimed to reduce the THD using the same objective function (OMTHD) but with different metaheuristic algorithms. The comparisons demonstrate that the MFO algorithm consistently outperforms the other algorithms by achieving lower THD values in most reported cases. The structure of this paper is outlined in the following way: Section 2 shows the explanation of the MFO algorithm, as well as the multilevel inverter topology, and then the OMTHD objective function used for optimization is established, which includes a detailed explanation. Section 3 presents the outcomes obtained from the MFO along with the practical application of those outcomes in a laboratory prototype. The results discussion is presented in Section 4, where it can be observed that the MFO algorithm successfully obtains a low THD in both cases presented; in addition, in many cases, this algorithm demonstrates superior THD in the output voltage compared to the alternative algorithms documented in the existing literature. Lastly, Section 5 presents the conclusions.
## 3 Methods
### Dimensionality Reduction and Visualization of Features  
The dimensions of the final temporal-spatial fusion feature input to the classification layer of the attTFBS model were 64. To realize feature visualization of the extracted features, the t-SNE method was used for dimensionality reduction and 64-dimensional features were converted into 2-D data. The effectiveness of the extracted features for rice classification was analyzed based on 2-D data obtained using the t-SNE method. The t-SNE method was developed based on the classical SNE method [40; 41]. The goal of t-SNE is to reduce data with higher feature dimensions to lower dimensions (such as 2-D or 3-D) for analysis or visualization. If two data points are similar in a high-dimensional space (e.g., they belong to the same category), after reducing their dimensionality to a low-dimensional space, their distance will then be relatively close, and vice versa. The Kullback-Leibler (K-L) divergence was employed by t-SNE to quantitatively calculate the similarity between the distances of points in high-dimensional space and the distances of points in low-dimensional space. t-SNE minimizes the sum of the K-L divergences of all point pairs through the gradient descent algorithm to reduce the dimension of high-dimensional space points to low dimensions while ensuring the similarity of the distances of point pairs.  
In this study, 10,000 rice pixels and 10,000 non-rice pixels from each study area were randomly selected from the feature images for feature dimensionality reduction and visual analysis. The pre-trained model and the fine-tuned model (Model C) were used to extract the hidden spatiotemporal features from the time-series SAR images of Shuangcheng District to compare the effectiveness of the features obtained by each model. At the same time, to further analyze the difference between model inter-continental transfer and model close-range transfer, this study also used a pre-trained model to extract the features of the Mississippipi Alluvial Plain (Region A, which was used as the source domain) and Sacramento Valley (Region B, which is approximately 2800 km from Region A). The t-SNE method was used in all of these areas to perform feature reduction and visualization.
## 2 Materials and Methods
### Recovery Time of \"FIX\" State after Its Loss  
Due to the frequent observation of the state of inability of the RTK receivers to successfully process the correction signal (given the low \(SSR\) ratio), these periods were further investigated. These observations (\(o\)) were searched in both the static and dynamic measurements, and only those with known failure time and recovery time boundaries were used. Continuous logging of the NMEA messages between these boundaries was a necessary condition. The number and duration of these outages were evaluated:  
* \(n_{o}\)--number of detected outages from the \"FIX\" state (-),
* \(min(t_{o})\)--the shortest duration of the outage (s)
* \(max(t_{o})\)--the longest duration of the outage (s),
* \(\mu(t_{o})\)--the mean duration of the outage (s) and
* \(Me(t_{o})\)--the median duration of the outage (s).
## 4 Experiment and Evaluation
### Odometry Trajectory Evaluation  
Figure 19 shows the comparison of three different odometry methods, T265 odometry, proposed GPO odometry, and EKF of T265 and GPO. For the evaluation of the EKF method, the T265 odometry and the GPO results are loosely coupled. To do this, the odometry outputs of the first two methods are filtered using the EKF algorithm in ROS. As shown in the figure, the EKF results best follow the ground truth trajectories. Table 4 is the translation error of three methods compared with the ground truth. In the table, GPO is better than the T265 odometry, and the EKF performs best. In addition, we also compare the GPO performance using two different ground plane images. In this table, using an ortho-rectified GPI is better than using the original GPI. This implies that ortho-rectified GPI can ideally generate the virtual top-view image from the fisheye camera.  
Figure 18: Comparison of median vector and mean vector method in GPO. From top, trajectories of Test1, Test2, and Test3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Test1** & **Test2** & **Test3** & **Average** \\  & & **(3.8832)** & **(2.2896)** & **(5.4559)** & **Average** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} GPO \\ (Mean Vector) \\ \end{tabular} } & min & 0.0018 & 0.0115 & 0.0533 & 0.0222 \\  & max & 0.1694 & 0.1736 & 0.1194 & 0.1541 \\  & avg & 0.0435 & 0.0876 & 0.0886 & 0.0732 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} GPO \\ (Median \\ Vector) \\ \end{tabular} } & min & 0.0019 & 0.0040 & 0.0182 & 0.0080 \\  & max & 0.0787 & 0.0329 & 0.0731 & 0.0615 \\  & avg & 0.0337 & 0.0165 & 0.0485 & 0.0329 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The GPO’s translation errors in meter. (Path length is in parenthesis).
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods  
This study focuses on the use of spherical cameras as primary sensors for point cloud generation in the context of a graffiti underpass case study. The selection and evaluation of the most suitable spherical cameras was conducted based on their technical features. To evaluate the accuracy of the resulting spherical 3D point cloud, a laser scanner was used as a reference point cloud. Additionally, to compare the spherical camera point clouds with other optical sensors, a DSLR camera was also used. The network design and placement of imaging stations for spherical cameras were determined based on the principles of close-range photogrammetry. Pre-processing, including data organization, was performed on the acquired data before the point cloud was generated from the spherical camera images. This resulted in obtaining the desired results for processing and fisheye camera calibration of both the spherical and DSLR cameras. The photogrammetric reconstruction of the spherical camera images ultimately led to the generation of a three-dimensional point cloud from these images. The point cloud was evaluated in relation to the correspondence point clouds of the laser scanner and DSLR camera. The evaluation of the spherical camera point clouds was also based on the geometric features and the accuracies observed during the 3D reconstruction.
## 3 Multi-Objective Optimization
### Multi-Objective Particle Swarm Optimization Algorithm  
The PSO is the optimization method form the group of EAs, which is inspired by the behavior of birds in the swarm [25]. The PSO algorithm consists of \(N_{P}\) particles in the swarm, which can be represented with the set \(P^{(G)}=\{\,\mathbf{x}_{i}\mid\mathbf{x}_{i}\in\mathbb{F}\},i\in\{1,2,\ldots, N_{P}\}\), where \(G\) denotes the generation index. Each particle in the swarm represents a candidate solution to the considered optimization problem. Two parameters, namely current position \(\mathbf{x}_{i}^{(G)}\) and velocity \(\mathbf{s}_{i}^{(G)}\) are associated with each particle. The position of the particle is influenced by the best position previously visited by the particle, denoted as \(\mathbf{x}_{best,j}^{(G)}\) and the best position discovered by the entire swarm of particles \(\mathbf{x}_{best}^{(G)}\). In this regard, at each iteration of the PSO algorithm the particle updates its velocity and then the position according to the expression  
\[\mathbf{s}_{i}^{(G+1)} =\mathbf{s}_{i}^{(G)}+c_{1}rand_{1}\Big{(}\mathbf{x}_{best,j}^{( G)}-\mathbf{x}_{i}^{(G)}\Big{)}+c_{2}rand_{2}\Big{(}\mathbf{x}_{best}^{(G)}- \mathbf{x}_{i}^{(G)}\Big{)}, \tag{37}\] \[\mathbf{x}_{i}^{(G+1)} =\mathbf{x}_{i}^{(G)}+\mathbf{s}_{i}^{(G+1)}, \tag{38}\]  
where \(c_{1}\) denotes the cognitive coefficient, \(c_{2}\) is social coefficient, \(rand_{1}\) and \(rand_{2}\) denote distinct random numbers in the range \(rand_{i}\in(0,1)\), \(i=1,2\). For solving the MOO problems, the procedure of the PSO algorithm need to be modified, in terms of mechanism for updating the personal best and global best positions [41]. The personal best position represents the best solution found by individual particle in the previous generations. For the MOO problem the personal best position for each particle is changed if the current personal best solution is dominated by the current position, which is written with the following equation  
\[\mathbf{x}_{pbest,i}^{(G+1)}=\begin{cases}\mathbf{x}_{pbest,i}^{(G)},&\text{if }\mathbf{x}_{pbest,i}^{(G)}\prec\mathbf{x}_{i}^{(G)}\\ \mathbf{x}_{i}^{(G)},&\text{otherwise}\end{cases}. \tag{39}\]  
On the other hand, the global best position \(\mathbf{x}_{gbest}^{(G)}\) represents the best solution found in the entire swarm up to current iteration. Unlike with SOO problems, in MOO problems due to the contradictory nature of the multiple objectives it is impossible to choose single global best solution for the \(\mathbf{x}_{gbest}^{(G)}\). Therefore, an external archive is introduced into the PSO algorithm, which captures a set of non-dominated solutions obtained in the optimization process. In each iteration, to evaluate the diversity of the particles in the external archive the crowding distance is calculated, and then the \(\mathbf{x}_{gbest}^{(G)}\) is selected by applying the binary tournament [22; 41].
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 2 Proposed MIMO Antenna Design  
Figure 1 shows the geometry of the proposed MIMO antenna system with the metal frame for the 5G smartphone. The ground is 155 \(\times\) 76 mm\({}^{2}\) and the thickness of the smartphone is 7.5 mm. The distances between the etched antenna slot on the long bezels and the corners of the entire body edge are 55.3 mm. The distances between the etched antenna slot on the short bezels and the corners of the entire body edge are 15.8 mm. Obviously, there is still design room for 2G/3G/4G terminal antennas and other equipment jacks. In this design, an FR-4 substrate (relative permittivity 4.4, loss tangent 0.02) with a thickness of 0.8 mm is used for the body construction of the smartphone. At the same time, we used copper (finite conductivity) with very stable conductivity (conductivity \(5.8\times 10^{7}\) S/m) and little effect on impedance matching as the radiator. The simulation results of the proposed antenna system were obtained by HFSS Software. During HFSS calculation, the adaptive meshing was performed on a single frequency point (4.75 GHz). After the meshing was completed, the solutions of other frequency points under the same solution setting item were based on the previous setting. The meshing was performed at the fixed frequency point (the maximum number of passes was 20). Radiation boundaries in all directions were more than \(\lambda_{0}/4\) far from the radiator (\(\lambda_{0}\) corresponds to a frequency of 4.75 GHz).
# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 5 Discussion
This result points out the crucial role of data augmentation in enabling extended training durations to achieve robust and accurate fine-grained classification. The YOLOv8m model indicated a notable improvement in performance after applying data augmentation techniques. Key metrics such as box precision (BoxP) and recall showed significant gains after 50 epochs, with further stabilization observed after 100 epochs. For instance, the mAP50 for Chilli-Pepper increased from 0.648 to 0.790 with augmentation, while the recall for Bode peppers improved from 0.436 to 0.564. The precision and recall values across the eight pepper varieties indicate the model's capacity to differentiate effectively between them. These results underscore the crucial role of data augmentation in enhancing the performance of CNN models in agricultural contexts, laying a solid foundation for future advancements in automated crop identification and management systems. Future research should explore advanced architectural enhancements, such as integrating attention mechanisms (e.g., SE-Net, CBAM), to improve spatial and channel feature refinement. Alternatively, employing ensemble approaches that combine predictions from multiple models could increase robustness across heterogeneous field conditions. Investigating lightweight architectures (e.g., MobileNet, YOLO-NAS) may also support deployment in edge devices used in precision agriculture.
## 1 Introduction
The aforementioned reports of Cryo-FIBID employ a liquid nitrogen-based cooling system to achieve the required low temperatures (in the order of \(-100\)\({}^{\circ}\)C). In such an approach, cooled gaseous nitrogen is made to flow through rubber tubes near the sample stage, cooling it down along with the sample. While effective for reaching the desired temperatures, the coolant flow introduces unavoidable vibrations in the sample, which, depending on the work dimensions and the required patterning resolution, can compromise the quality of the final deposit. The relatively high cost of a cryogenic module might also represent a deterrent for the technique. In this work, a coolant-free approach to the Cryo-FIBID procedure for the deposition of W(CO)\({}_{6}\) is presented. The control of the sample temperature is achieved using a commercial Peltier effect cooling stage, fundamentally consisting of a thermoelectric plate. This system does not require any cooling agents to operate and thus does not introduce the associated disturbances during growth. With a deposition temperature of \(-60\)\({}^{\circ}\)C (40 \({}^{\circ}\)C above the previously reported value of \(-100\)\({}^{\circ}\)C using liquid nitrogen), tungsten-carbon nanostructures have been grown and characterized.
## 5 Experimental Scenarios  
This work analyzed the impact of an entropy-based approach in detecting DDoS attacks using a statistical traffic analyzer. The analyzer calculates entropy that depends on one feature of the packet header features within a specific time \(t\) (window size), which is compared with a predefined threshold via preparation and configuration of multiple simulation scenarios and later evaluated using various evaluation metrics.  
The primary purpose of analyzing the impact of the entropy-based technique is to evaluate and detect various DDoS attack traffic rates (high or low attack traffic rates) against an SDN controller with high DRs and low FPRs. Therefore, this section presents the simulation scenarios employed to assess the entropy-based approach's performance and analyze the entropy method's impact on detecting a low DDoS attack traffic rate compared with a high DDoS attack traffic rate.  
The entropy-based detection approach's performance assessment employs eight simulation scenarios with low and high attack traffic rates [26]. Based on this, there are different assumptions regarding the single host's attack scenarios and the multiple hosts' attack scenarios against a controller targeting a single or multiple victim(s).  
Figure 2: Detection approach process.  
Each simulation scenario run lasts 30 min, with the average result recorded every 5 min. Thus, the window size is divided into n slots, and each slot is equal to m seconds. The computation of the time slot is shown in Equation (3) [3].  
\[Time\;slots(n)=(5*60)/m=300/5=60 \tag{3}\]  
In addition, the network traffic is aggregated every \(m\) seconds, where \(m\) is a configurable variable. Hence, the entropy-based detection method is run every 5 s to check for the existence of DDoS attacks in the aggregated traffic. This work determined the parameter \(m=5\) s through trial-and-error experiments.  
In the scenarios, a single or multiple hosts' attacks triggered low-rate and high-rate DDoS attacks against the controller targeting multiple or single victim hosts. The attacker used a unique source IP address to launch a DDoS attack against the victim(s). Based on that, a low DDoS attack traffic rate was sent at a rate of 0.2 s per packet, with a total number of attack packets sent in 1 s equal to 5 packets for a single host's attack and 15 packets for multiple hosts' attack. Meanwhile, a high DDoS attack traffic rate was sent at a rate of 0.03 s per packet, meaning that the total number of attack packets sent in 1 s equals 33 packets triggered by a single host's attack, while multiple hosts' DDoS attacks will send 166 packets to the victim (S). Table 2 shows the distributed number of packets for high- and low-rate DDoS attack traffic and normal traffic during 1 s.  
The experiment was conducted on a workstation running a 4.20 GHz Intel(r) Core i7-7700 CPU with 16 GB of RAM and a 250 GB SSD hard drive running the Ubuntu Linux 14.04 operating system. The network topology consisted of 64 hosts, an OpenFlow switch, and a POX controller. Additionally, Mininet version 3.7 (32-bit) virtualized the prototype network scenarios to emulate network elements.
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 2 System Model and Problem Statement
### Problem Statement  
Based on the above discussion, we formulate a new optimization problem that minimizes the total delay of the system by optimizing the optimal flying altitude of the UAV and the optimal task allocation ratio. Specifically, the optimization problem can be expressed as follows  
\[\min_{\{H,\alpha\}}T \tag{14a}\] \[\rm{s.t.0}\leq\alpha\leq 1,\] (14b) \[H_{\rm min}\leq H\leq H_{\rm max}, \tag{14c}\]  
where inequality (14b) represents the constraint of task allocation ratio, and inequality (14c) is the flying altitude constraint of the UAV.