esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Influencing Factors of Crossing Cable
### Ratio of Crossing Cable Area Between Mid Tower and Side Tower  
In Section 4.1, the anchor span ratio was obtained for the minimum vertical deflection of the deck when the area of crossing cables is equal. On this basis, the steel volume used for a pair of crossing cables is kept unchanged, and then the ratio of the crossing cable area between the mid tower and the side tower is adjusted, and the relationship between the ratio of the crossing cable area and its restraining effect is analyzed. The steel volume of a pair of crossing cables in cable-stayed bridges with main spans of 1000 m and 1500 m is 8.90 m\({}^{3}\) and 13.56 m\({}^{3}\), respectively. When the crossing cable area ratio of the mid tower and the side tower changes from 0.2 to 3, the crossing cable area of the mid tower changes from 0.0032 m\({}^{2}\) and 0.0031 m\({}^{2}\) to 0.0106 m\({}^{2}\) and 0.0109 m\({}^{2}\), respectively, and the crossing cable area of the side tower changes from 0.0162 m\({}^{2}\) and 0.0155 m\({}^{2}\) to 0.0035 m\({}^{2}\) and 0.0036 m\({}^{2}\), respectively.  
Figure 13 shows the maximum vertical deflection of the deck corresponding to different crossing cable area ratios under a 40 kN/m uniform load. It is noted that the maximum vertical deflection of the deck gradually decreases with the increase in the crossing cable area ratio. The finite element results are different from the change trend obtained in Figures 6 and 7. This is because after increasing the crossing cable area ratio between the mid tower and the side tower, the axial stiffness of the crossing cable of the mid tower increases, and the axial stiffness of the side tower crossing cable decreases. However, the ordinary cable adjacent to the side tower crossing cable can make up for the reduction in the stiffness of the side tower crossing cable. Therefore, when the steel volume of the crossing cable is constant, the crossing cable of the mid tower adopts a larger cross-sectional area to help improve the restraining effect of the crossing cable.  
Figure 12: Bending moment of mid-tower bottom under uniform load.  
Figure 13: The displacement of the deck with different crossing cable area ratios.  
When different crossing cable area ratios are used, the bending moment at the mid-tower bottom is shown in Figure 14. It is noted that the bending moment of the mid-tower bottom decreases with the increase in the crossing cable area ratio. It can be seen that increasing the cross-sectional area of the crossing cable of the mid tower can improve the structural stiffness and reduce the bending moment of the mid-tower bottom.
## 3 Results and Discussion
### Analysis of Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c} \hline
**RT 1\({}^{,}\)2** & **Compounds** & **RI** & **RI lit** & **\%** \\ \hline
7.286 & Ethyl acetate & 881.4193 & 884 & 1.21 \\
7.458 & Diethyl acetal & 890.7367 & 898 & 5.03 \\
9.355 & Ethyl 2-methylpropanoate & 964.9116 & 960 & 1.57 \\
9.689 & Isobutanal diethyl acetal & 977.4727 & 976 & 0.24 \\
10.853 & \(\alpha\)-pinene & 1020.207 & 1020 & 20.09 \\
11.273 & Ethyl butanoate & 1035.229 & 1040 & 2.28 \\
11.787 & Ethyl 2-methylbutanoate & 1053.612 & 1057 & 0.59 \\
12.232 & Ethyl 3-methylbutanoate & 1069.528 & 1072 & 0.34 \\
13.346 & \(\beta\)-pinene & 1108.049 & 1108 & 6.79 \\
14.647 & 3-carene & 1148.018 & 1146 & 6.20 \\
15.08 & \(\beta\)-myrcene & 1161.321 & 1160 & 0.39 \\
15.161 & \(\alpha\)-phellandrene & 1163.81 & 1164 & 0.76 \\
15.615 & \(\alpha\)-Terpinene & 1177.757 & 1176 & 0.38 \\
16.206 & D-limonene & 1195.914 & 1196 & 7.60 \\
16.514 & \(\beta\)-phellandrene & 1205.686 & 1201 & 0.87 \\
17.623 & \(\gamma\)-terpinene & 1241.715 & 1243 & 0.92 \\
17.739 & cis-\(\beta\)-Ocimene & 1245.484 & 1245 & 1.19 \\
17.88 & styrene & 1250.065 & 1250 & 0.16 \\
18.356 & \(\alpha\)-cymene & 1265.53 & 1268 & 6.34 \\
23.147 & acetic acid & 1434.643 & 1433 & 0.28 \\
23.747 & \(\alpha\)-cubebene & 1457.535 & 1458 & 0.67 \\
24.479 & ylangene & 1485.464 & 1485 & 1.38 \\
24.727 & copaene & 1494.926 & 1493 & 6.93 \\
25.301 & benzaldehyde & 1517.978 & 1520 & 0.38 \\
25.438 & (-)-\(\beta\)-bourbonene & 1523.563 & 1517 & 1.60 \\
27.442 & caryophyllene & 1605.558 & 1604 & 9.76 \\
27.699 & aromandendrene & 1616.631 & 1610 & 4.64 \\
28.628 & alloaromadendrene & 1656.657 & 1662 & 0.83 \\
29.181 & humulene & 1680.483 & 1681 & 1.39 \\
29.536 & \(\gamma\)-muurolene & 1695.778 & 1695 & 3.99 \\
30.315 & \(\alpha\)-muurolene & 1730.814 & 1727 & 2.51 \\
31.189 & \(\gamma\)-cadinene & 1770.362 & 1767 & 2.10 \\
34.567 & \(\alpha\)-calcone & 1929.507 & 1936 & 0.59 \\ \hline \end{tabular} \({}^{\dagger}\) R.T. means retention time. R.I. means retention index. R.I. lit means retention index in literature. \({}^{2}\) Relative standard deviation (RSD) for all compounds ranged from 2.7 to 8.25%.  
\end{table} 
Table 3: Chemical composition (%) of volatile compounds identified in propolisis residue. with those reported in the literature; in fact, studies on the volatile profile of propolis reported that \(\alpha\)-pinene is present at a percentage between 1.2 and 46.5%, cryophyllene 6.2-21.7%, limonene 11.6%, copaene 8.2-22.19%, and \(\beta\)-pinene 2.0-21.8% [55; 56; 57; 58]. The wide range of percentages found in the literature on propolis may be related to different geographical origins and biomes [1; 59], isolation, and analytical techniques [60]. Propolis volatiles may be used as markers in the identification of the propolis botanical origin and its characterization [11]. Anyway, [PERSON] et al. found that the most abundant compound in the essential oil of dried residue of propolis was \(\alpha\)-pinene (16.25%), followed by ethyl benzoate (7.80%) and \(\beta\)-pinene (5.15%) [2].
## 3 The Software of TLE Observation System
### Software Modules  
The software modules of the TLE observation algorithm consist of the low-level hardware IP cores and the high-level management module. Two main hardware IP cores are the detector IP core and the camera IP core, written in Verilog-HDL for the sake of low latency and fast processing speed. The detector IP core implements three discriminators for TLE detection, based on the MaPMT data retrieved from the DDC264 controller IP. It should be emphasized that the analog-to-digital conversion process by the DDC264 controller IP is synchronized with a single start conversion signal, thus the 64 channels data exhibit concurrent event for 8 \(\times\) 8 cells of the MaPMT. The detector IP core stores the MaPMT's 64 cell data on an on-chip FIFO memory uninterruptedly. When an event is detected by the discriminators, a data packet of 128 consecutive frames are extracted from the FIFO memory for being sent to the Nios-II softcore as the mission log data. At the same time, the detector IP core triggers the camera IP core for acquisition of image pixels over the ROI of the estimated location of the TLE event. As a result, the camera IP starts to transfer images pixels to the 64 Mb SDRAM memory so that a total of twenty five successive images at 40 fps are stored in the memory. Finally, it follows that the camera IP raises an interrupt flag to the Nios-II softcore to notify that a cycle of mission operation is completed. Figure 9 shows the details of command and data flow between the software modules.  
Figure 9: Data flow diagram of TLE observation system. Hardware IPs on the SoC interacts each other via Avalon Memory-Mapped Bus. Embedded software is implemented on the Nios-II to manipulate the mission data from low-level hardware IPs.  
Next, the high-level data processing software modules are implemented on the Nios-II softcore. The Nios-II exchanges commands and data with the C&DHS via the UART serial link. Upon receiving mission-related commands from the C&DHS, the Nios-II softcore manages the TLE observation system by starting or stopping both the detector IP and the camera IP. At the end of the data collection session, the Nios-II softcore retrieves the obtained TLE images in conjunction with the MaPMT cell data to pack in a binary format with the collected mission log information such as the status of detector, the number of TLE detection, and the estimated location of the TLE. In addition, the Nios-II softcore controls the output of the high voltage supply to adjust the gain of the MaPMT by utilizing the 16-bit SPI (serial peripheral interface) bus which is connected to an external DAC.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 5 Results
### Validation of the Fog Effect Modeling on the Point Cloud Level  
To validate the fog effect modeling on the point cloud level, we consider the same KPIs as given in Section 5.2. Figure 15 shows the DR of the real and the virtual LiDAR sensor for different visibility distances \(V\).  
It shows that as the visibility distances decreases, the DR of the real and the virtual LiDAR sensors also decreases. The size of the fog droplets ranges from 5 to 20, and 1  of air contains \(10^{6}\) fog droplets, which is \(10^{3}\) times more than the number of raindrops in 1  of air [31]. Therefore, it is easier for LiDAR rays to penetrate through raindrops suspended in the air than fog droplets. That is why the LiDAR signal attenuation is higher in dense fog than in heavy rain. Furthermore, the simulation and the real measurements show a good correlation. The MAPE for the DR is 7.5%. Figure 16 shows the FDR of the real and the virtual LiDAR sensor due to fog.  
Figure 16: The FDR of the LiDAR sensor for a real and virtual 3%-reflective Lambertian plate with different visibility distances \(V\). The FDR increases with a decrease in the visibility distances. It should be noted that FDR 300% or 500% shows that the LiDAR reflections received from the fog droplets are 3 or 5 times more than those obtained from the OOI (see Equation (39)).  
Figure 15: The DR of the LiDAR sensor for the real and virtual 3%-reflective Lambertian plate with different visibility distances \(V\). The simulation and real measurements show good correlations.  
The results show that as the visibility distance \(V\) decreases, the FDR of the real and virtual LiDAR sensors increases. The results also show that the mismatch between the simulation and the real measurements for the FDR increases at low visibility distances. It should be noted that the backscattering from the fog droplets is a random process, and it is, therefore, impossible to model the exact behavior of real-world fog in the simulation. Furthermore, we have not measured nor modeled the real fog distribution due to limited resources. This could be another reason for the mismatch between the simulation and the real measurements. The MAPE for the FDR is 48.2%.  
Figure 17 shows the distance error \(d_{error}\) due to fog droplets. The results show that the distance error also increases as the visibility distance \(V\) decreases, but still, the distance error does not exceed the maximum permissible error (MPE) specified by the manufacturer, which is 2 cm in this case. Because the size of fog droplets is several magnitudes smaller than the size of raindrops, LiDAR rays are less misaligned by the scattering from the fog droplets than raindrops. That is why the distance error due to the fog droplets is smaller than the one caused by raindrops.
# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 2 Techniques and Approaches  
### Object Detection Algorithms  
The object detection algorithms are fundamental and widely studied topics in computer vision, aiming to automatically detect objects of interest in images or videos and label their positions [23,24]. In recent years, target detection technology has been widely applied in various domains, including traffic monitoring for vehicle tracking, robot vision, unmanned driving, drone inspection, and safety engineering status monitoring.  
[PERSON] et al. [25] introduced the R-CNN algorithm in 2014, which first applied convolutional neural networks to object detection. However, the detection speed was slow, and real-time detection was not possible. In 2015, the emergence of Fast R-CNN [26] improved objected detection by introducing ROI (Region Of Interest) pooling layers. Short after, Faster R-CNN [27] significantly reduced computation time by introducing the RPN (Region Proposal Network) module, thereby improving detection efficiency. In 2016, [PERSON] et al. [28] proposed the YOLO algorithm, which divided the image into grid cells and directly predicted the target categories and bounding boxes in each cell using a convolutional neural network. It transformed the object detection task into a regression problem and had a concise and efficient framework, with fast detection speed and wide applicability. Subsequently, the YOLO series of object detection algorithms flourished [29]. In addition to the R-CNN and YOLO series, there have been other deep learning object detection algorithms including SSD (Single Shot Multi-Box Detector) [30] and RetinaNet [31] have also achieved good detection results. In recent years, researchers have been dedicated to further optimizing the efficiency and accuracy of deep learning-based object detection algorithms. Some works focus on improving network architectures, such as Efficient-Det [32] and CenterNet [33]. Some works aim to address challenges such as small object detection and occlusion, such as Cascade R-CNN [34] and Corner Net [35]. To sum up, the advantages and disadvantages of the target detection algorithm mentioned above are summarized in Table 1.
## 3 Results and Discussion
### Estimation of Volatiles over Extraction Method  
Although the SE and UAE techniques provided different oil yields, the analysis of the volatile compounds by HS-SPME-GC-MS showed similar results. In both techniques, a solvent is used for extraction, which permeates the oily cells to engage with the lipid compounds [41]. The main difference between these techniques was the time and temperature of extraction. In SE, the avocado fruit is repeatedly brought into contact with an organic solvent in a relatively mid-high temperature of 50 \({}^{\circ}\)C for 6 h, whereas in UAE, the avocado fruit has been used for accelerated extraction [30] in a relatively controlled mid-low temperature of 25 \({}^{\circ}\)C for 15 min. The extraction techniques did not have statistically significant differences between their qualification and quantification results. Only three components including octan-2-one, (62)-7,11-dimethyl-3-methylenedoca-1,6,10-triene (\(\beta\)-farnesene), and (15_,_25_,_AR_)-1-ethenyl-1-methyl-2,4-bis(prop-1-en-2-yl)cyclohexane (\(\beta\)-elemene) could potentially vary between the SE and UAE methods. However, an analysis of the variance indicated that the \(p\)-values were higher than 0.05 for all the volatile compounds. It is evident that the mass transfer and molecular affinity amongst petroleum ether and the targeted compounds exerted a higher influence on acquiring and retaining the volatiles. In this context, comparing the advantages and drawbacks between SE and UAE, the latter appeared more cost-effective and environmentally advantageous. Figure 2: A characteristic gas chromatogram of avocado oil (_Perea americana_ Mill., Greek “Zutano” variety) from UAE. (**P1**) 2,6,6-trimethylbicyclo[3.1.1]hept-2-ene (\(\alpha\)-pinene); (**P2**) 1-methyl-4-(prop-1-en-2-yl)cyclohex-1-ene (D-limonene); (**P3**) 4,10-dimethyl-7-propan-2-yltricyclo[4.4.0.01\({}^{\circ}\)5]dec-3-ene (\(\alpha\)-cubebene); (**P4**) (15,65,75,85)-1,3-dimethyl-8-propan-2-yltricyclo[4.4.0.02\({}^{\circ}\)7]dec-3-ene (a-copaene); (**P5**) (1\(R\),_9S_,_Z_)-4,11,11-trimethyl-8-methylenebicyclo[7.2.0]undec-4-ene (\(\beta\)-caryophyllene); (**P6**) 2,6-dimethyl-6-(4-methylpent-3-enyl)bicyclo[3.1.1]hept-2-ene (\(\alpha\)-bergamotene); (**P7**) (1\(E\),_AE_,_8_E_)-2,6,6,9-tetramethylcycloundeca-1,4,8-triene (humulene); (**L5**) Internal Standard.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 4 Results and Discussion
### SVM Model  
The SVM classification model also achieves an impressive accuracy of 99.9% for 64 \(\times\) 64 and 32 \(\times\) 32 image sizes, unlike the KNN model, which has different accuracies for the two sizes. Looking beyond accuracy, the model using the 64 \(\times\) 64 resized images has a training time of 43 s and a prediction time of 53 s, whereas the model using the 32 \(\times\) 32 resized images has a training time of 6 s and a prediction time of 18 s. That means that the 32 \(\times\) 32 SVM model is significantly faster than the 64 \(\times\) 64 SVM model, with an 86% faster training time, a 66% faster prediction time, and overall, 75% faster in combined computational time.
# Lightning Protection of Photovoltaic Systems: Computation of the Developed Potentials
## 4 Simulation Results
A 50 kA, 10/350 us lightning strike (Figure 4) on the air termination conductor (as shown in Figure 2a,b) was simulated considering the two-layer soil structures indicated in \[\mathrm{V(t)}=\frac{1}{2\pi}\int_{-\infty}^{+\infty}\mathrm{V( \omega)e^{i\omega t}d\omega}= \frac{1}{2\pi}\int_{-\infty}^{+\infty}\mathrm{V_{o}(\omega)I( \omega)e^{i\omega t}d\omega} \tag{1}\]2 In the case of the two-layer soil structure, \(\rho_{1}\) is the resistivity of the first layer (starting from the soil surface), \(\rho_{2}\) is the resistivity of the second soil layer and \(h_{1}\) is the thickness of the upper soil layer. The second layer thickness is assumed to be infinite. The calculated grounding resistance (steady state) for the alternative soil structures is shown in the last column of \[\mathrm{V(t)}=\frac{1}{2\pi}\int_{-\infty}^{+\infty}\mathrm{V( \omega)e^{i\omega t}d\omega}= \frac{1}{2\pi}\int_{-\infty}^{+\infty}\mathrm{V_{o}(\omega)I( \omega)e^{i\omega t}d\omega} \tag{1}\]3 The separation distance between the non-attached air termination rod (Figure 2a) and PV panels is set at 0.1, 0.5, 0.75, and 1 m.  
Figures 5 and 6 depict the calculated maximum values of the soil surface potential as a function of the distance between the air termination rod and the PV panels (system of Figure 2a) for the aforementioned soil structures. The potential was calculated on the ground surface 1.5 m away from PV installation (Figure 5) and at a 1 m distance from the air terminal struck by the lightning (Figure 6). Touch voltages (Figure 7) were computed considering a 70 kg human body standing on the ground surface at a 1 m distance from the air termination rod, touching the exposed metallic parts connected to the grounding system. As illustrated in Figures 5 and 6 the separation distance affects soil structures C and D (greater upper layer resistivity) more than A and B. The influence of the increasing depth of the grounding electrode A connected to the air termination rod on the developed potential diminished for a rod height greater than 4 m (results of gs3 and gs4 were almost identical). Furthermore, the level of calculated potential was lower in the case of four grounding electrodes at the edges of perimeter grounding (gs6) than in the case of lengthening the mounting poles of the support construction (gs5), which is rather impractical as PV support constructions are manufactured in certain dimensions.
## 4 Evacuation Model Design
### Pedestrian Evacuation Modeling (Trip Generation)
[53] used expressed preference questionnaires to gather departure timings and evacuation decisions from 500 participants and discovered that more than 50% of the participants left within 30 min and nearly all by 3 h. This research also worked on the factors defining the prediction of the periods affecting the evacuation time and found out that having a handicap, a bigger family, and having a lower sense of the emergency's risk all contributed to longer evacuation durations [53]. Being informed of an evacuation alert and the necessity to do more trips before leaving the area led to a faster evacuation. In wildfires [54] and hurricanes [55], researchers estimated the time or the period for each family or individual to evacuate, using dual logical choice models as functions of social and environmental factors [15]. The decision to evacuate or not is demonstrated as chains of binary choices, one of which should be chosen at each time step. Each incident is modeled as an instance when a new evacuation order or a new clue from fire is perceived by each individual [56]. Exogenous response curves reflecting the proportion of departures in each time interval can be used to estimate departure timing and household-level prediction methodologies. For hurricane occurrences, response curves are frequently designed for each place, for example, dividing the area into different zones and evacuating the zones based on the risk priority. [PERSON] et al. [15] observed that numerous distributions had been used for those departure response curves, including instantaneous, uniform, Poisson, Rayleigh, Weibull, and sigmoid. For example, the sigmoid departure response curve has two considerations: one determines the curve's slope, and the other designates the curve's midpoint [15]. Users frequently guess those values based on personal judgment, according to [PERSON] et al. [31]. While studies have shown that such curves would be acceptable for other catastrophes [57], it is vital to remember that hurricanes and wildfires have different evacuation time scales [45]. The hurricane evacuation study also aims at the elements that influence mobilization time, or the period between deciding to evacuate and doing so [58]. While the duration differences between wildfires and hurricanes may make it difficult to apply findings from one catastrophe to the next, the methodology used in these studies might be used for evacuation research under the threat of wildfire.