esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wind-Induced Fragility Analysis of Corroded Transmission Tower  
### Finite Element Model of Corroded Transmission Tower  
A typical 110 KV cathead linear tower in Ningbo Zhenhai (industrial-marine atmospheric environment) was taken as the research object, with a tower height of 35.5 m, a nominal height of 30 m and a bottom root opening of 6.509 m, and its single line diagram is shown in Figure 16a. Using ANSYS to establish its finite element model, the tower main material, slant material, cross-partition material and auxiliary material were used to establish the BEAM188 unit. Among them, the main material was Q345 steel, and the rest of the material was Q235 steel; the steel density was 7850 kg/m\({}^{3}\), the Poisson's ratio was 0.3 and the elastic modulus was 206 GPa. Notably, Q345 is a kind of low-alloy steel, as defined in the Chinese steel specification, where \"Q\" means the yield strength, and \"345\" means the yield strength of this steel is 345 MPa. Q235 is a common carbon structural steel, and \"235\" represents the yield value of this steel, which is around 235 MPa. The finite element model is shown in Figure 16b. The first two fundamental mode shapes of the transmission tower without rust are depicted in Figure 16c,d, where it can be seen that the first two modes are dominated by the sway component.  
In actual engineering, steel transmission towers will receive anticorrosive treatment during the early stage of construction, and the galvanized layer is usually painted on the surface. However, with the increase of the service life, the galvanized layer gradually loses its protective ability due to external erosion. Therefore, this paper took the time when the transmission tower loses the protection of the galvanized layer as the initial year and established models of the steel transmission tower under 30/60/90 corrosion years, respectively. The effect of steel corrosion on the deterioration of the wind resistance of the transmission tower was simulated by weakening the cross-sectional dimensions of the members. It should be noted that only the uniform corrosion of the whole tower was considered in this paper.  
Figure 16: Schematic diagram of the transmission tower. (**a**) Single line diagram (unit: mm); (**b**) finite element model; (**c**) 1 st mode f\({}_{1}\) = 3.33 Hz; (**d**) 2 nd mode f\({}_{2}\) = 3.40 Hz.  
Figure 17 shows the cross-section of the corroded angle. \(D\) represents the initial section thickness; \(D^{\prime}\) represents the residual depth of corrosion. Based on the prediction results of Figure 8, the corrosion depth of carbon steel under the I-M atmospheric environment for 30, 60 and 90 years of corrosion can be calculated. By subtracting the corrosion depth value from the initial section thickness \(D\) of each angle member, the residual corrosion depth \(D^{\prime}\) of the angle sections can be obtained, as shown in Table 3. By integrating \(D^{\prime}\) into ANSYS, finite element models of the steel transmission tower under 30/60/90 corrosion years were finally established.
# Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
## 3 Experiments
### Influence of the Input Noise Level  
In this section, we evaluate the sensitivity of SDCNN by varying the input noise levels while maintaining a constant simulated ground level of noise. In practical applications, accurately estimating the noise-level map from observed noise can be challenging, often resulting in a mismatch between the estimated input and the actual noise levels. When the input noise level is lower than the real noise level, complete noise removal becomes unfeasible. Consequently, users typically opt for higher noise levels to enhance noise reduction, although this may inadvertently lead to the removal of some image details along with the noise. In real-world scenarios, it is essential for denoising models to exhibit tolerance towards such mismatches in noise levels. To address this, we utilized Equation (16) to train the model by adjusting parameters \(\theta\) and \(\sigma\) and enabling control over the input noise level. The testing results of this training are presented in Table 1, offering the following key observations.  
The testing results of this training are presented in Table 1. Various trained models, each with a distinct input noise level (e.g., 'SDCNN-1-25' signifying SDCNN with a fixed input noise level of 1-25), are assessed using simulated images characterized by noise levels ranging from 0.5-0 to 4-100. The multiplicative noise coefficient of the input noise image was determined, and different additive noise coefficients were applied during training. The sensitivity of the trained models to additive noise is evaluated using a variety of noise speckle images. The results reveal that SDCNN exhibits robust adaptability to denoise different additive noise speckle images. Similarly, when the multiplicative coefficient is set to one, SDCNN produces better results in reducing noise and maintaining structural similarity.  
The comparative results indicate that the SMELoss model, overall, exhibits slightly lower denoising effectiveness than the SmoothL1 Loss model. However, as the input noise level increases, the difference between the two becomes negligible, particularly when the noise level reaches 60. Furthermore, the SMELoss model notably lags behind the SmoothL1 Loss model in preserving image details, a trend that persists even at higher input noise levels.
## 3 Results and Discussion
### Numerical Model Validation  
As mentioned, this research investigates varying crude oil grade effect on slug characteristics and induced stresses. Due to the fact, and to the authors best knowledge, there is no published research that investigates the effect of crude oil slug flow on induced stresses in horizontal pipes. Hence the results of 'water-air' slug flow published by [19] was used to validate this model which could be considered as reasonably acceptable validation; taking into consideration that the software solvers (CFD and FE) used in this research solve the same governing equations for both crude oil-natural gas and 'water-air' multiphase flow. Past research also performed their validation and verification models of crude oil against 'water-air' multiphase published results [18; 30].
## 4 Simulation Scenarios
### Communication Simulation Settings  
Simulation parameters for the communication network were set according to the ETSI EN 302 663 [34]. Transmit power of all communication modules was set to 20 dBm, receiver sensitivity to \(-89\) dBm, and we assumed the power of thermal noise in the communication channel to be \(-110\) dBm. The maximum channel data rate was set to 6 Mbps to use the robust Quadrature Phase Shift Keying (QPSK) modulation. The signal path loss was modeled by the free-space path loss model.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis
### Preparation of RNA with Homogeneous 3\({}^{\prime}\)-End Using Modified DNA Template  
The alternative method used for reducing RNA 3\({}^{\prime}\)-end heterogeneity is the modification of the DNA template for the in vitro transcription reaction. It requires incorporating one or two C2\({}^{\prime}\)-methoxy groups into the 5\({}^{\prime}\)-end of the antisense strand of the DNA template (Figure 8A) [78]. It was shown that C2\({}^{\prime}\)-methoxy modified DNA significantly reduced the N+1 activity of RNA polymerase and increased the amount of proper RNA product compared to transcription carried out with an unmodified template [78; 138]. Since the modifications are located on the sugar moiety, there is a low risk of incorporating nucleotides other than those encoded in the sequence. Such 2\({}^{\prime}\)-methoxy-modified phosphoramidites are commercially available. The DNA oligomer can be synthesized using an in-house  
Figure 7: Assessment of ribozyme cleavage efficiency monitored by PAGE. (**A–C**) line 1, RNA-HH 1 to 6 constructs; (**C**) lane 2, construct HH-RNA-HH; (**D**) construct HH-RNA-HP (see text for details); (**E**) RNA-HDV 1 (see text for details); (**F**) RNA-HDV 2.  
synthesizer or purchased from external companies. For longer DNA templates, 2\({}^{\prime}\)-methoxy modifications can be incorporated during the PCR by using a 2\({}^{\prime}\)-methoxy-modified reverse primer [78; 138]. Recent reports suggest that the reduction of the side products can be further improved by the addition of DMSO (dimethyl sulphide) [139; 140]. This modification has been used for synthesis of 5\({}^{\prime}\) leader of the HIV-1 genome for NMR studies [141].  
We designed and synthesized a DNA template with two 2\({}^{\prime}\)-methoxy-modified nucleotides at the 5\({}^{\prime}\)-end of the antisense strand. Next, we performed in vitro transcription of modified and unmodified DNA templates in order to compare the homogeneity of the RNA products. HPLC analysis showed a significant reduction in RNA heterogeneity. With the modified DNA template, only two peaks were observed, and with the unmodified template, RNA products of different lengths were detected (Figure 8B,C).  
Using the same method, we obtained RNAs with either a 5\({}^{\prime}\) OH or monophosphate group at the 5\({}^{\prime}\)-end by conducting in vitro transcription in the presence of guanosine or GMP, respectively. The HPLC analysis showed no difference in chromatograms compared to the control transcription reactions (Figure 8D,E). The presence of a monophosphate at the 5\({}^{\prime}\)-end was further confirmed by the ligation reaction (data not shown).
## 6 Future Work and Limitations  
This study considers machining temperature as a response rather than as a dynamic input. Additionally, the model relies on continuous variables. Consequently, it does not have the ability to capture the changes that are related to the temperature and affect the surface quality. Future work will process the acquired measurements so that they can be implemented into the model. The aim is to develop a dynamic prediction system that can be linked to a digital twin.  
Conceptualization, A.T. and P.K.; Methodology, A.T.; Software, A.T.; Validation, A.T., D.N., S.-N.M. and P.K.; Formal analysis, A.T., D.N., S.-N.M. and P.K.; Investigation, A.T.;  
Figure 10: The Pareto front of solutions (**a**) and the corresponding solutions with respect to the input parameters (**b**).  
Resources, P.K.; Data curation, A.T., D.N., S.-N.M. and P.K.; Writing--original draft, A.T.; Writing--review & editing, A.T., D.N., S.-N.M. and P.K.; Visualization, A.T.; Supervision, A.T. and P.K.; Project administration, A.T. and P.K.; Funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data presented in this study are available in this article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ANFIS Adaptive Neuro-Fuzzy Inference System  
CFRP Carbon-Fiber-Reinforced Polymer  
GA Genetic Algorithm  
MF Membership Function  
NSGA-II Non-Dominated Sorting Genetic Algorithm 2  
PETG Polyethylene Terephthalate Glycol
# SDN-Based Survivability Analysis for V2I Communications
###### Abstract  
In vehicle-to-infrastructure (V2I) communications, various failures in the dynamic movement pose serious link interruptions. To study the continuous service quality for the V2I network when this issue happens, this paper proposes a survivability analysis and establishes the communication architecture for software-defined network (SDN)-based V2I communications. With the controllable advantages of SDN centralized management, the multi-path transmission control protocol is used to seamlessly switch the transmission information between the V2I links of each vehicle node. Specifically, according to the analysis of specific fault types for V2I links, the definitions of SDN-based V2I survivability is provided to establish the corresponding survivability mode. To further verify the survivability model, a full-state search is adopted by means of probability model checker PRISM. In addition, multi-directional probability and expected reward evaluation analyses are carried out from the point of view of time. The simulation results show that, with the failure of multiple V2I links, the network quality of service (QoS) correspondingly declines, but the network still survives, due to the multi-path transmission control protocol (MPTCP) action. Moreover, with a high fault repair rate, the service performance and survivability of the network is improved rapidly.  
multi-path transmission control protocol; probability model checker; software-defined network; survivability; vehicle-to-infrastructure (V2I) +
Footnote †: journal: Sensors
# Visible Light Communications through Diffusive Illumination of Sculptures in a Real Museum
###### Abstract  
The recent, massive diffusion of LED-based illumination devices makes Visible Light Communications (VLC) a widely recognised wireless communication technology with large potential impact in many indoor and outdoor applications. In the indoor scenario, one of the most promising VLC implementations is foreseen in museums, exhibitions and cultural heritage sites. In this context, digital data can be transmitted by the specific lighting system of each artwork and received by the nearby standing visitors, allowing a complete set of dedicated services such as augmented reality (AR) and real-time indoor positioning, exploiting the directionality of the optical channel. In this work, we achieve, for the first time, VLC transmission through diffusive LED illumination of three-dimensional artworks (wooden and marble sculptures) in a real museum, exploiting the available LED illumination system, demonstrating the feasibility of VLC technology also when complex three-dimensional artworks, such as sculptures or bas-reliefs, are involved. In our experimental campaign, performed inside the Basilica of Santa Maria Novella in Florence, we perform extensive Packet Error Rate (PER) and Signal-to-Noise Ratio (SNR) tests on two important wooden and marble sculptures (_Crucifix_ by [PERSON] and the _Holy Water Font_ by [PERSON], respectively), for different distances, view angles and configurations, in order to mimic a wide set of situations that visitors may encounter in a realistic scenario. We achieve successful VLC transmission for distances up to 8 m from artworks, at baud rate of 28 kBaud. We also provide detailed results on the characterization of the transmission Field of View (FoV) for our prototype, as well as the effect of side shifts of the observer's position on the quality of VLC transmission, providing essential information for future implementations of positioning protocols and dedicated services in realistic, indoor scenarios. Our work represents an important step forward towards the deployment of VLC technology in museums and, more in general, it opens for far-reaching developments in a wide set of real indoor environments, including the cultural heritage sector, where diffusive VLC links exploiting illumination of three-dimensional objects could represent a ground-breaking innovation.  
visible light communication; VLC; wireless communication; indoor localization; digital cultural heritage; museums; Internet of Things +
Footnote †: 7 July 2021
## 3 Results  
### Marshall Stability vs. CBA Mixture  
Marshall stability refers to the highest load that a specimen can withstand during a test conducted at a temperature of 60 \({}^{\circ}\)C and a loading rate of 50.8 mm/min before it fails. Figure 3 depicts the relationship between the Marshall's stability value and the CBA mixture for varying binder concentrations. According to the data, the stability improved as the content of bitumen increased, reaching its optimum value before decreasing with further increases in the bitumen content. The results also indicate that the stability of the CBA mixture was consistently worse than that of the control mixture. The findings demonstrate that the control mixture met the minimum stability criteria of 6.2 kN, as stipulated in the JKR standards for SMA20 [49]. However, in the case of the CBA mixture, only the CBA-10 mixtures containing 5% and 5.5% bitumen content satisfied the specified requirement. Previous studies have shown that stability and flow tests are not the most reliable methods for evaluating the strength of SMA mixtures. Therefore, the results of these tests should be regarded as supporting data rather than as the primary basis for accepting or rejecting a SMA design [58; 59].  
The decrease in the stability values is attributed to the rough, gritty surface texture of coal bottom ash particles leading to weaker adhesion between the materials. The porous, angular, and irregular nature of coal bottom ash and its sharp edges increase the likelihood of breakage during compaction. This breakage can cause the bonding between the bitumen and aggregate to deteriorate.  
Figure 3: Marshall stability vs CBA mixtures.
# Fast Path Planning of Autonomous Vehicles in 3D Environments
## 4 Simulation Results
### Cluttered Environments  
We present the MATLAB simulation results of the proposed path planner (Algorithm 1). Using MATLAB 2016, we spend 61 s to run 20 MC simulations under Algorithm 1. The mean of \([L^{1},L^{2},\ldots,L^{20}]\) is 268. Furthermore, the variance of \([L^{1},L^{2},\ldots,L^{20}]\) is 95.  
Figure 4 depicts the path constructed utilizing one MC simulation of Algorithm 1. The obstacles are shown as spheres, and the generated path is depicted with red line segments. In Algorithm 1, the AV moves toward the goal directly if possible. This direct movement is depicted in Figure 4. Since the radius of the virtual agent is \(r\) distance units in the simulations, the constructed path is a safe path for the AV that can be approximated as a sphere with radius \(r\) distance units.  
Comparison with the 3D RRT-Star Planner  
We compare the performance of the proposed planner with the 3D RRT-star planner in [24]. The 3D RRT-star planner ends when the distance between a sample point and the goal position is shorter than 10 distance units. Two sample points are neighbors if the relative distance between them is shorter than 10 distance units. The step size (expansion of the tree within one time step) in the 3D RRT-star planner is set as 10 distance units.  
Once a path is generated, we can further improve the generated path by smoothing it or removing vertices on the path. The Open Motion Planning Library (OMPL) library has a path simplifier and a B-Spline smoother for these tasks. See the following website for the OMPL library: [[https://ompl.kavrakilab.org/index.html](https://ompl.kavrakilab.org/index.html)]([https://ompl.kavrakilab.org/index.html](https://ompl.kavrakilab.org/index.html)), accessed on 10 April 2022.  
For fair comparison with the 3D RRT-star planner, we do not apply a path simplifier or a B-Spline smoother.  
Using MATLAB 2016, one spends 644 s to run 20 MC simulations under the 3D RRT-star planner. Under the 3D RRT-star planner, the mean of \([L^{1},L^{2},\ldots,L^{20}]\) is 384. Furthermore, the variance of \([L^{1},L^{2},\ldots,L^{20}]\) is 1099.  
See that Algorithm 1 runs much faster than the RRT-star planner. Also, Algorithm 1 generates a shorter path than the 3D RRT-star planner. This shows that Algorithm 1 outperforms the 3D RRT-star planner.  
Figure 5 depicts the path constructed utilizing one MC simulation of the 3D RRT-star planner. The obstacles are shown as spheres. The generated path is depicted with red line segments.  
Figure 4: The path constructed utilizing one MC simulation of Algorithm 1. The obstacles are shown as spheres. The generated path is depicted with red line segments.
# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 3 YOLO-ESL Network
### Improved Non-Maximum Suppression (NMS)  
During the prediction phase of object detection, the YOLO model generates multiple candidate bounding boxes after inference. These boxes frequently display significant overlap and redundancy, which is addressed using non-maximum suppression (NMS) during the post-processing phase. YOLOv7 initially utilized IoU-NMS, which relies on the intersection over union (IoU) metric [28]. The IoU quantifies the proportion of the overlapping area to the total area covered by both the predicted and ground truth boxes. The IoU-NMS method aims to eliminate redundant boxes by establishing an IoU threshold, discarding any boxes whose IoU with the box of highest confidence exceeds this threshold.  
However, traditional IoUNMS has its limitations. It primarily relies on IoU to assess the similarity between boxes, which considers only the positional information of the bounding boxes, overlooking factors such as scale and shape. Therefore, in some cases, it may not accurately evaluate the similarity between boxes. Moreover, IoUNMS's sequential processing mode can lead to increased computational complexity and lower efficiency.  
To address these issues, the original NMS of YOLOv7 is replaced with CIoUNMS [29], which stands for center-IoU-based non-maximum suppression. CIoU is a metric that measures the distance between target boxes, considering factors such as their position, scale, and shape. Thus, CIoUNMS can more accurately assess the similarity between boxes, thereby improving the precision of object detection. The formula for CIoUNMS is given by Equations (1)-(5), where Si represents the classification confidence, \(\epsilon\) is the NMS threshold, M is the box with the highest confidence, IoU represents the intersection over union between the bounding box and the ground truth, \(\rho^{2}(b,b^{gt})\) represents the distance between the center ends of the bounding box and the ground truth, \(c^{2}\) represents the diagonal length of the minimum enclosing rectangle of the two boxes, B is the anchor point number corresponding to each grid, \(\alpha\) is the weight function, and \(\
u\) is used to measure the similarity of aspect ratios. \(w^{gt}\)and \(h^{gt}\) are the width and height of the ground truth box G, and \(w\) and h are the width and height of the predicted box B. Improving IoUNMS to CIoUNMS can better enhance detection accuracy, alleviate occlusion issues, and improve recall while keeping the NMS threshold constant.  
\[s_{i}=\begin{cases}s_{i}&\text{if }IoU-R_{CIoU}(M,B_{i})<\epsilon,\\ 0&\text{if }IoU-R_{CIoU}(M,B_{i})\geq\epsilon.\end{cases} \tag{1}\]  
\[IoU=\frac{\left|B\cap B^{gt}\right|}{\left|B\cup B^{gt}\right|} \tag{2}\]  
\[R_{IoU}=\frac{\rho^{2}(b,b^{gt})}{c^{2}}+av \tag{3}\]  
Figure 6: LGA module structure.  
\[v=\frac{4}{\pi^{2}}(\arctan\frac{w^{gt}}{h^{gt}}-\arctan\frac{w}{h})^{2} \tag{4}\]  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{5}\]