esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Cost Function Parameter Definition  
The cost function parameters (\(\lambda\) and \(\mu_{s}\)) definition is one of the most challenging tasks of for the objective function analysis. In fact, it is highly computational expansive and it requires the final solution analysis.  
For the analysed problem, the definition of \(\lambda\) is easier because this is a single parameter that combines two performance indexes. In order to analyse the behaviour of the optimization process with different values of \(\lambda\), a set of optimization trials have been performed.  
Seven values of \(\lambda\) have been tested: for each of them, 25 independent optimization trials have been done with 50,000 objective function calls. For each trial, the values of the radiation pattern and scanning errors have been stored. Figure 6 shows the results of this analysis: each plot corresponds to a different scan angle. The left axis of all of them shows the values of RE\({}_{s}\): the blue line is the average of all the trials, while the light blue area represents the confidence interval of 90%. Similarly, the right axis represents the same values for the \(\Delta\theta_{s}\).  
The optimal value of the \(\lambda\) parameter is the one for which both the errors are minimum in all the scan angles. The selected value in this application is \(\lambda=10^{2}\), which has been able to provide more reliable results for \(\theta_{s}=40^{\circ}\).  
The analysis of the second scalarization level, characterized by the values of \(\mu_{s}\), is harder because it is possible to perform neither full nor a one-at-time analysis due to the high computational cost of the antenna optimization.  
The parameter selection has been done with a trial-and-error procedure aimed to have similar performances for all the four scan angles. These values have been selected starting from the physical knowledge of the problem: the radiation pattern error is calculated in a more accurate way for low values of \(\theta_{scan}\); in addition, a higher scan angle implies a harder optimization problem, because they are characterized by a lower value of the directivity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Box Condition** & **Mean** & **Standard Deviation** & **Best Result** \\ \hline \multicolumn{1}{c}{Impenetrable wall} & **191.41** & **19.75** & 164.14 \\ \multicolumn{1}{c}{Elastic wall} & 201.08 & 52.76 & **159.1** \\ \multicolumn{1}{c}{Eliminating wall} & 2566.66 & 527.95 & 1452.67 \\ \multicolumn{1}{c}{Closed space} & 291.09 & 62.12 & 219.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the results with the four feasibility function tested. The results are obtained with 12 independent trials and with 50,000 objective function calls. In bold the best values.
# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 2 Materials and Methods
### Mesh Refinement  
High quality results can be achieved with relatively lower processing time when appropriate element sizes are selected for a given geometry. The authors in [30] noted that voxelization--which refers to a geometry discretization, when used in topology optimization--decreases the time necessary for iterative analysis of the addition or removal of material from regions where they are either weak or excessively strong. Hence, the mesh was refined by systematically adjusting the mesh settings in ANSYS. The mesh quality was analyzed using the maximum equivalent (von-Mises) stress.  
Beginning with the default meshing values, which produced a rather coarse mesh, we performed a mesh convergence study with six levels of refinement to identify an appropriate mesh for the subsequent structural analysis calculations. Successive levels of refinement produced finer meshes relative to the preceding levels. The convergence criteria used in this study were set at <0.5% of allowable change in the equivalent stress of each succeeding refinement level.  
For the purpose of this convergence study, structural steel was assumed for the bracket model, having principal dimensions equal to 107.8 \(\times\) 50 \(\times\) 67.3 mm, a plate thickness of 10 mm, and a volume of 77,444 mm\({}^{3}\), which corresponds to a weight of 0.608 kg when stainless steel is considered. This weight corresponds to approximately 0.059 kg for the unoptimized printed specimen in our study; note that PLA with a printing infill density of 50% was used--see Section 2.4. In Figure 1, image A illustrates the original bracket geometry; B depicts the applied compressive force of 1.5 kN on the bottom face of the bracket; while image C highlights the fixed support applied on the upper face of the bracket (yellow area).  
For both the level-set and density-based methods, four optimization scenarios were considered. For case 1, the default option was used for the region to be optimized, which is determined by the boundary conditions only--i.e., the boundary conditions define the region of the design domain that is excluded from the optimization process. For the remaining cases, 2, 3, and 4, we specified the optimization regions using the geometry selection tool, which allows the designer to specify regions to be excluded from the optimization in addition to the ones defined by the boundary conditions. The purpose of cases 2, 3, and 4  
Figure 1: Boundary conditions for analysis; **(A)**: bracket model, **(B)**: force application, and **(C)**: additional boundary conditions.  
was to investigate whether varying the optimization regions would significantly affect the results of the optimized component. The excluded regions for each case are red highlighted in Figure 2, below.
## 2 Sensor Calibration
### Light Plane Calibration  
The image point \(P_{\text{S}}(u,v)\), after being mapped through the oblique camera imaging model, can obtain the normalized coordinates \(\left(X_{\text{C}}^{\prime},Y_{\text{C}}^{\prime},1\right)\) of point \(P_{W}\) in the coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\). However, this coordinate lacks depth information. Furthermore, by using the constraint that the object point \(P_{W}\) lies on the light plane, the equation of the light plane in the coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\), \(A_{pl}X_{\text{C}}+B_{pl}Y_{\text{C}}+D_{pl}Z_{\text{C}}+E_{pl}=0\), can be used as a supplementary condition to obtain depth information. The transformed form of the equation is expressed as follows:  
\[A_{pl}X_{\text{C}}^{\prime}+B_{pl}Y_{\text{C}}^{\prime}+D_{pl}+\frac{E_{pl}}{ Z_{\text{C}}}=0 \tag{10}\]  
where \(A_{pl}\), \(B_{pl}\), \(D_{pl}\), and \(E_{pl}\) are the parameters of the light plane.  
According to Equation (10), when the normalized coordinates \(\left(X_{\text{C}}^{\prime},Y_{\text{C}}^{\prime},1\right)\) and the light plane parameters are known, the value of \(Z_{\text{C}}\) can be obtained, thereby yielding the complete coordinates \(\left(X_{\text{C}},Y_{\text{C}},Z_{\text{C}}\right)\) of the object point \(P_{W}\) in the coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\).  
To obtain the light plane parameters, the coordinates \(\left(X_{\text{C}},e,Y_{\text{C}},e,Z_{\text{C},e}\right)\) of (h) non-collinear feature points \(q_{pl,e}\), on the light plane in the coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\) can be utilized. This information constructs an objective function that minimizes the sum of distances from all points to the plane, which can be solved using methods such as SVD decomposition [29], as shown below:  
\[G\left(A_{pl},B_{pl},D_{pl},E_{pl}\right)=min\sum_{e=1}^{h}\frac{\left|A_{pl}X _{\text{C},e}+B_{pl}Y_{\text{C},e}+D_{pl}Z_{\text{C},e}+E_{pl}\right|}{\sqrt{ A_{pl}^{2}+B_{pl}^{2}+D_{pl}^{2}}} \tag{11}\]
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 4 Methods  
In navigation tasks, neural networks for dense depth estimation can reconstruct precise obstacle locations within a scene. However, for navigation purposes, reconstructing excessive surface details of obstacles is time-consuming and unnecessary, and it consumes substantial computational resources. This is particularly problematic in outdoor environments, where roads often occupy a large portion of the field of view. Furthermore, neural-network-based depth estimation tends to produce significant errors in areas with low texture, such as road surfaces. To address this problem, we employ semantic segmentation to extract ground features from the input image, fit a plane using the identified points, and designate the resulting plane as the ground. This plane is then rasterized, and the ground grid in the global coordinate system is reconstructed through back-projection [31]. Consequently, we generate a 2D occupancy grid map for navigation based on 3D point cloud data. The primary reason for choosing this map representation is that a 2D grid map transforms continuous space into a structured 2D array or matrix, providing directly interpretable input data (e.g., array indices) for path-planning algorithms.  
The algorithm framework for the grid map construction module is shown in Figure 2. First, keyframes are extracted from the red-green-blue (RGB) image sequence using ORB-SLAM3. Semantic segmentation is then applied to generate a semantic mask for each keyframe, from which map points corresponding to the ground semantic region are identified. These map points are used to estimate the position of the road surface. Finally, the fitted road surface is rasterized, and the occupancy probability of each grid cell is determined through back-projection. This probability is continuously updated as the robot moves.  
The ground to be fitted during map construction is derived from the point cloud in keyframes. However, in practical applications, it is unnecessary to wait for the tracking thread to extract keyframes before performing semantic segmentation. Therefore, an independent thread is designed to perform semantic segmentation on the input images, extracting the semantic masks corresponding to keyframes. This approach improves the real-time performance of the algorithm.
## 3 Experimental Results
### Fragment Size Distribution
Within the range, DT8 had the smallest particle size in the same accumulated mass passing. Table 3 shows that DT1 had the smallest \(x_{0.1}\) with a particle size of 0.24 mm among the eight experiments, and DT8 had the smallest \(x_{1}\), \(x_{50}\), and \(x_{100}\) with particle sizes of 1.8 mm, 57.1 mm, and 111 mm, respectively. \(x_{50}\) is commonly used in engineering to evaluate the blasting quality [51]. The histogram of \(x_{50}\) and delay time is shown in Figure 18.  
Figure 17: Fitted curves by using the extended Swebrec function: (**a**) fitted curves of DT1-DT4; (**b**) fitted curves of DT5-DT8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen No.**} & **Delay Time** & \(x_{0.1}\) & \(x_{1}\) & \(x_{50}\) & \(x_{100}\) \\  & **(\(\upmu\)s)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline DT1 & 13.69 & 0.24 & 3.37 & 122.1 & 152.3 \\ DT2 & 27.36 & 0.87 & 4.65 & 127.6 & 161.5 \\ DT3 & 54.72 & 1.27 & 4.44 & 140.1 & 189.4 \\ DT4 & 60 & 0.39 & 5.67 & 123.3 & 142.3 \\ DT5 & 90 & 0.37 & 10.10 & 179.9 & 237.7 \\ DT6 & 120 & 1.06 & 3.13 & 100.1 & 133.6 \\ DT7 & 180 & 0.67 & 2.25 & 95.7 & 126.2 \\ DT8 & 339.6 & 0.65 & 1.80 & 57.1 & 111.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from fitted curves of particle sizes at 0.1%, 1%, 50%, and 100% mass passing. In these bench blasting experiments, the minimum value of \(x_{50}\) appeared in DT8. The post-blast picture shows that the DT8 specimen, with the longest delay time, had the smallest excavation. Thus, DT8 had the worst blasting quality. Among the other seven blasting experiments, DT7 had the smallest mean fragment size of 98.7 mm. [PERSON] [1] pointed out that good fragmentation requires as many small fragments as possible, and fewer boulders and fine particles are also important. The fragment size distribution of DT7 is consistent with the option. In summary, the DT7 model with 180 \(\upmu\)s delay time obtained the best blasting excavation quality.
## 1 Introduction
[PERSON] et al. [20] applied LR for classification and prediction of occupational injury risk in the shipbuilding industry and proved its efficiency in estimating risk probability from working conditions predictors of incident type, situation, and condition involved in the incident. Understanding injury patterns and the underlying mechanisms of incidents with respect to a specific industry may produce effective insights to boost policymaking, training, and incident/injury intervention and prevention efforts [19]. Agriculture-related industries are routinely among the most hazardous work environments [23] despite growing improvement in technology, coordinated prevention measures, improved training, and higher education of the workforce [9]. The focus of this study was to perform an injury analysis on agribusiness industries with the highest frequency of occupational incidents. The study specifically looked at occupational injury analysis in agribusiness industries, including grain elevator operations, refined fuels, feed milling for livestock, food distribution operations, fertilizer blending/distributions, poultry hatchery/grower/processor, and grain milling operations. Every year, thousands of occupational incidents occur in agribusiness industries [24]. Agribusiness is defined as the operations involved in manufacturing and distribution of farm supplies, production operations on the farm, and the storage, processing, and distribution of farm commodities [25]. Agribusiness industries refer to the organizations, business administrations, and industries engaged in agriculture and agriculture-related products and services [26]. In agribusiness industries, similar to other occupational factors, incidents happen through a combination of activities, causes, different machines, and equipment in diverse scenarios in which workers suffer injuries with various severity levels [9]. Despite the benefits of using workers' compensation claims data to analyze and predict injuries, little research has effectively utilized such data to provide detailed information for the predictors of work-related injuries in agribusiness industries with high rates of occupational incidents. Using the records of occupational incidents between 2008 and 2016 in selected agribusiness industries, the objectives of this study were to:  
1. Evaluate the effectiveness of LR in identifying significant predictors of the post-incident state of occupational injuries. 2. Determine the contribution of occupational incident factors to the likelihood of medical versus disability post-incident states. 3. Discuss the application of results in reducing the incidence of injury in the agribusiness industry.
###### Abstract  
Wireless communication represents the basis for the next generation of vehicle safety systems, whereas visible light communication (VLC) is one of the most suitable technologies for this purpose. In this context, this work introduces a novel VLC receiver architecture that integrates a field-of-view (FoV) adaptation mechanism in accordance with the optical noise generated by the sun. In order to demonstrate the benefits of this concept, a VLC prototype was experimentally tested in an infrastructure-to-vehicle (I2V) VLC configuration, which uses an LED traffic light as the transmitter. At the receiver side, an automatic FoV adaptation mechanism was designed based on a mechanical iris placed in front of a photodetector. Adjustments were made based on the values recorded by a multi-angle light sensor, built with an array of IR photodiodes covering an elevation from \(0^{\circ}\) to \(30^{\circ}\) and an azimuth from \(-30^{\circ}\) to \(30^{\circ}\). Depending on the incidence of solar light, the mechanical iris can adjust the FoV from \(\pm 1^{\circ}\) to \(\pm 22^{\circ}\), taking into account both the light irradiance and the sun's position relative to the VLC receiver. For experimental testing, two identical VLC receivers were used: one with an automatic FoV adjustment, and the other with a \(\pm 22^{\circ}\) fixed FoV. The test results performed at a distance of 50 m, in the presence of solar irradiance reaching up to 67,000 \(\upmu\)W/cm\({}^{2}\), showed that the receiver with a fixed FoV saturated and lost the communication link most of the time, whereas the receiver with an adjustable FoV maintained an active link throughout the entire period, with a bit error rate (BER) of less than \(10^{-7}\).  
field of view (FoV); noise-adaptive; optical noise mitigation; visible light communication (VLC)
## 1 Introduction  
Rubber tree (_Havea brasiliensis_ Muell. Arg., Euphoriaceae, Malpighiales) is a valuable source of natural rubber (NR) which is an indispensable commercial source material for the manufacturing of more than 5000 products worldwide [1]. The rubber tree genome contains a diploid set of 36 chromosomes, and the first draft genome was released in 2013 [2]. The production of natural rubber is highly hampered by different pathogens that infect the rubber tree [3, 4, 5, 6, 7]. Recently, a novel capillovirus was identified infecting the rubber tree in China. The RNA genome of the novel capillovirus was isolated, sequenced and was tentatively assigned the nomenclature of rubber tree virus 1 (RTV1). RTV1 has emerged as a deleterious pathogen of the genus _Capillovirus_ in the family _Betaflexiviidae_. RTV1 consists of a monopartite, linear, non-enveloped, +ssRNA molecule of 6811 nucleotides [6].  
Considering the RNA nature of RTV1, RNA-based molecular approaches, RNA interference (RNAi) and host-derived microRNA (miRNA) silencing are emerging as the potential tools, used to cleave the viral mRNA in the infected host cells. RNA silencing is a conserved, sequence-specific gene silencing mechanism controlled by the siRNAs [8]. It is an important line of defense against invading viruses in the host cell [9]. The Dicer and Argonaute genes are the key components of the RNAi machinery. The cleavage of the precursor dsRNA results in short 21-24 nucleotides siRNA that inhibit protein translation during infection [10]. The artificial microRNA (amiRNA)-mediated RNAi has high silencing specificity and develops a single 21-nucleotide amiRNA to target the corresponding sequence [11]. The plant miRNAs are small endogenous, noncoding, regulatory bigwigs of 20-24 nucleotides in length which are encoded by _MIR_ genes [12]. They can regulate complex biological processes including gene expression and regulations in plants [13]. The rubber tree has evolved diverse molecular mechanisms and has inherited mature miRNAs that provide immunity against biotic and abiotic stresses [14,15].  
The amiRNA-mediated silencing technology has emerged as a novel approach to control plant viruses. In a previous study, the amiRNA-based construct was designed and transformed in Arabidopsis to create resistance against _Tymovirus_ and _Potyvirus_[11]. In the rubber tree genome, 30 mature miRNAs have been found [16]; a subset of these mature miRNAs in the rubber tree should have targets in the RTV1 genome. This current computational biology approach was based on a comprehensive bioinformatics analysis of the RTV1 genome using the rubber tree miRNAs. The current study aims to implement computational algorithms for the prediction of the rubber tree genome-encoded miRNAs targeting RTV1. The predicted rubber tree miRNAs can be utilized for the development of the amiRNA-based constructs to transform in the rubber tree to control the RTV1 in the future.
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 5 Simulation Setup and Results  
We implemented an OFDM transceiver model in MATLAB 2019b together with a UW channel realization. Table 3 shows the configurations for the transceiver used in our simulations, whereas Table 4 shows the UW channel parameters used. We present the PAPR performance as well as the BER performance of the proposed algorithm in the following two subsections.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
###### Abstract  
Back-to-back geosynthetic-reinforced soil walls (BBGRSWs) are commonly used in embankments approaching bridges and narrow spaces. However, the available literature and design guidelines for BBGRSWs are limited. The aims of this research were to develop a greater understanding of the working performance of BBGRSWs and to optimize the design method of a BBGRSW to ensure the cost-efficiency as well as the stability of the structure. On the basis of a monitored BBGRSW structure located in China, we established a numerical model. The parameters of the materials used in the actual project were determined through triaxial and tensile tests. The numerical results were compared with the measured results in the field to verify the correctness of the selected parameters. Two parameters were investigated by the FEM method: the reinforcement length and the arrangement. The FEM analysis indicated that post-construction deformations such as displacement and settlement could be reduced by reinforcing the same layer on both sides. Longer reinforcements were needed to achieve the same performance if the reinforcements were cross-arranged. Thus, BBGRSWs can have a superior performance if the reinforcements are connected in the middle from both sides. Even with longer reinforcements, the safety factor of the wall with a cross-arranged reinforcement was smaller than that with same-layered reinforcements.  
geogrid-reinforced soil wall; field test; FEM simulation; parameter study
## 3 Network Model Construction
### Attention Mechanism  
In neural networks, attention mechanisms enhance critical components of input data while suppressing irrelevant details, enabling focus on subtle yet important features.  
The CBAM, a hybrid attention mechanism integrating both channel and spatial attention, has its structure illustrated in Figure 3. The channel attention module first processes input feature maps, followed by the spatial attention module, thereby achieving seamless fusion of the two mechanisms.  
For the channel attention module, input features are subjected to parallel global maximum pooling and global average pooling to compress the spatial dimensions. The resulting outputs are processed through a shared multi-layer perceptron (MLP)--with the number of neurons matching the number of input channels--to generate channel-wise attention weights via sigmoid activation. These weights refine features by emphasizing critical amplitude-related channels. The calculation of channel attention is as follows:  
\[M_{\text{C}}(F)=\sigma(MLP(F_{\text{Agg}}^{\text{C}})+MLP(F_{\text{Max}}^{ \text{C}})) \tag{1}\]  
For the spatial attention module, channel-wise global average pooling and maximum pooling generate two 1 \(\times\) H \(\times\) W feature maps, which are concatenated and processed via 1 \(\times\) 1 convolution and sigmoid activation to produce spatial attention weights. These  
Figure 3: Structural diagram of Convolutional Block Attention Module.  
Figure 2: Residual network structure diagram.  
weights highlight key temporal segments (e.g., P-wave/S-wave arrival times) within the waveform. The two-dimensional feature maps generated via channel-wise pooling are as follows:  
\[\begin{split} F^{\text{S}}_{Avg}&\in R^{1\times H \times W}\\ F^{\text{S}}_{Max}&\in R^{1\times H\times W}\end{split} \tag{2}\]  
The formula for calculating spatial attention is  
\[M_{S}(F)=\sigma(f^{1\times 1}(F^{\text{C}}_{Avg};F^{\text{C}}_{Max})) \tag{3}\]  
By sequentially applying channel and spatial attention, the CBAM enables the network to precisely capture task-relevant features in microseismic waveforms. Unlike conventional mechanisms (e.g., SENet) that focus solely on channels, the CBAM retains both inter-channel interactions and spatial dependencies, addressing limitations in waveform feature extraction by optimizing parameters from multi-dimensional perspectives.