esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 6 Influence of Solar Radiation on Temperature Distribution on Bridge Grider Cross-Sections
### T-Shaped Girder Bridge
#### 6.3.2 Influence of Solar Radiation on Temperature Distribution on Cross-Section of T-Shaped Girder  
During the monitoring period, 23 August 2012 was a sunny day with the high solar radiation. Comparisons of temperature-time curves obtained experimentally or from finite element model with or without the consideration of solar radiation are illustrated in Figure 21 (23 August 2012). The hollow points denote the measured data, the solid line denotes the calculated curves with the consideration of solar radiation, and the dash line denotes the calculated curves without the consideration of solar radiation. The temperature contour plots obtained from finite element model with or without the consideration of solar radiation are compared in Figure 22 (15:00, 23 August 2012). In Figure 21, it can be observed that the variation trends and the times at which the highest temperatures occurred are similar for measured and calculated results on 23 August 2012. The differences between the highest measured and calculated temperatures when considering the effect of solar radiation were very small (maximum 1.7 \({}^{\circ}\)C for top flanges and 1.0 \({}^{\circ}\)C for webs). However, the highest calculated temperatures without the consideration of solar radiation were lower (maximum 7.6 \({}^{\circ}\)C for top flanges and 3.1 \({}^{\circ}\)C for webs) and had a time delay (2 h for top flanges). For bottom flanges, the differences between the highest measured and calculated temperatures with and without the consideration of solar radiation were very small (maximum 0.3 \({}^{\circ}\)C). The accuracy of the solar radiation calculated by using the estimation models can meet the engineering requirements. In Figure 22, it can be observed that the differences between the highest calculated temperatures with or without the consideration of solar radiation were significant at 15:00 on 23 August 2012 (maximum 13.5 \({}^{\circ}\)C). The vertical temperature variation on the cross-section of T-shaped girder when considering the effect of solar radiation (19.7 \({}^{\circ}\)C) was significantly larger than that without the consideration of solar radiation (6.5 \({}^{\circ}\)C) in summer. Therefore, the influence of solar radiation should be considered in the analyses of the temperature distribution on T-shaped girders, especially for top flanges and webs.  
Figure 20: Finite element model of cross-section of T-shaped girder.  
Figure 21: Influence of solar radiation on temperature distribution on cross-section of T-shaped girder in summer: (**a**) top flange (T-1); (**b**) web (1-W); (**c**) web (2-W); and (**d**) bottom flange (4-B).  
Figure 22: Temperature contour plots of T-shaped girder at 15:00 on 23 August 2012: (**a**) with solar radiation; and (**b**) without solar radiation.
# A Spring Search Algorithm Applied to Engineering Optimization Problems
## 7 Experimental Results and Discussion
### Algorithms Used for Comparison  
In order to prove the potency of the SSA, it is also compared to eight optimization algorithms on unimodal, multimodal, fixed-dimension multimodal and composite optimization. They were assessed by solving a set of minimization problems introduced in the Constrained Single Objective Real-Parameter Optimization Technical Report, 'CEC'2015' [86]. To validate the performance of the _SSA_, the eight optimization algorithms included: GA [87], PSO [29], GSA [73], TLBO [88], GOA [53], GWO [52], SHO [34], and EPO [58]. The parameter values of optimization algorithms are given in the next table. **Parameter definition**  
```
1:GA
2:Population size N = 80
3:Crossover 0.9
4:Mutation 0.05
5:PSO
6:Suxm size S = 50
7:Inertia weight decreases linearly from 0.9 to 0.4
8:CI(individual-best acceleration factor) increases linearly from 0.5 to 2.5
9:C2(global-best acceleration factor) decreases linearly from 2.5 to 0.5
10:GSA
11:Objects number N = 50
12:Acceleration coefficient (a = 20)
13:Initial gravitational constant (G0 = 100)
14:TLBO
15:Suxm size S = 50
16:GOA
17:Search Agents N = 100
18:Cmax = 1
19:Cmin = 4 x 10^-5
20:I = 1.5 and f = 0.5
21:GWO
22:Wolces number = 50

#### 7.2.1 Evaluation of Unimodal Test Function with High Dimensions

Functions F\({}_{1}\) to F\({}_{7}\) are unimodal. The mean results of 20 independent runs of the algorithm are displayed in Table 1. These results show that the SSA has a better performance in all F\({}_{1}\) to F\({}_{7}\) functions than other algorithms. #### 7.2.2 Evaluation of Multimodal Test Functions with High Dimensions

In multimodal functions F\({}_{8}\) to F\({}_{13}\), by increasing the function dimensions, the number of local responses increased exponentially. Therefore, arriving at the minimum response of these functions is hard to achieve. In these types of functions, arriving at a response close to the ideal response denotes the algorithm's high capability in avoiding wrong local responses. The results obtained from assessing F\({}_{8}\) to F\({}_{13}\) after 20 runs of the SSA as well as the other algorithms compared are presented in Table 2. The SSA demonstrated the best performance of all the functions analyzed. #### 7.2.3 Evaluation of Multimodal Test Functions with Low Dimensions

Functions F\({}_{14}\) to F\({}_{23}\) have both low dimensions in addition to low local responses. Results obtained from 20 runs of each algorithm, are shown in Table 3. These results indicate that the SSA has a suitable performance in all functions F\({}_{14}\) to F\({}_{23}\). #### 7.2.4 Evaluation of CEC 2015 Test Functions

This section is devoted to real approaches and techniques for solving single objective optimization problems. All of these test functions are minimization problems. Table 4 shows the performance of the SSA as well as the other algorithms on the CEC 2015 test. Table 4 exhibits how the SSA is the most efficient optimizer of all of the benchmark test functions studied.
# System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam
## 2 Method
### System Design of Robot Grinding and Monitoring  
The Robot grinding and monitoring system consists of 4 parts: a 6-dof industrial robot, a line laser scanner, a force-controlled grinding tool, and a workpiece. The scanner is mounted on the end of the robot. The grinding tool is connected to the robot through a quick-change tool device. The reason for using the line laser sensor is that it can achieve high-precision measurement and complete the point cloud reconstruction of the weld seam. In contrast, the authors have tested a structured light-based 3D camera. Due to its insufficient measurement resolution, the weld seam measurement was almost ineffective, and there were many holes in the measurement results. When the weld seam is scanned, the grinding tool was unloaded to avoid blocking laser, and then the robot moves along the direction perpendicular to the light plane of the scanner. Combined with the recorded robot poses, a 3D reconstruction of the weld seam can be achieved. System working status and the grinding tool are shown in Figure 2.  
The design of the grinding tool is shown in Figure 3. The tool includes both electrical and mechanical systems. The electrical system has an interactive panel, a power module, a communication module, a force sensor module, an industrial personal computer (IPC), and two motor drivers. Almost all of the electrical parts are integrated into the grinding tool, only additional electrical  
Figure 1: Two types of weld seams.  
Figure 2: System working status and the grinding tool.  
power and communication are needed externally. The mechanical system consists of a driving part, a tension part, and a compensation part. Among the three parts, the driving part and tension part both provide the power for grinding. The driving part has a servo motor, a synchronous belt, and a wheel that drives an abrasive belt. The tension part has a low friction pneumatic cylinder and a contact wheel. Its function is to tension the belt. The cylinder is controlled by constant pressure, and its air pressure value is controlled by an external precision pressure reducing valve. The compensation part has a servo-electric cylinder, a contact wheel, and a force sensor. The force sensor and the contact wheel are connected through the shafting so that the force sensor can reflect the contact force. The direction of the measured force is perpendicular to the surface of the workpiece. The stroke of the electric cylinder is 40 mm. Since the robot positioning may not be accurate, when the contact wheel is far away from the weld seam or collides with the weld seam, the electric cylinder will expand or contract according to the force situation. During the grinding process, the force and the displacement of the electric cylinder are recorded simultaneously for real-time monitoring.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 4 Experimental Results and Analysis
### Necessity Validation of the Initial Registration
\(\rhd\)Transform \(\leftarrow\) computeTransform(\(p_{2}\), \(d_{2}\) - \(d_{1}\)) \(\rhd\)Update global transformation \(\rhd\)GlobalTransform \(\leftarrow\) globalTransform \(\ast\) pairTransform \(\ast\) transform_ return \(C\) ```  
**Algorithm 2** Align Point Clouds (\(C\), \(L\), \(P\), \(D\)). \begin{table}
\begin{tabular}{l} \hline \hline
**Algorithm 2** Align Point Clouds (\(C\), \(L\), \(P\), \(D\)). \\ \hline
**Input:** Point cloud of current frame \(C\), point cloud of previous frame \(L\), IMU data set \(P\), encoder data set \(C\) Output: Point cloud of current frame that transformed to the global coordinate \(\rhd\)Initial registration \(\Table 5 presents the details of measurement and calculation of deformation. The deviation between the measured value in the point cloud and the actual value is in the millimeter range and \(\tau\) is close to 1. These show that the translation and rotation deformation of terrain point clouds is small, and it can provide accurate information of the actual unstructured terrain. Therefore, our two-step registration method can be applied to the fast point cloud reconstruction of various unstructured terrains and is fast enough so that the information can be acquired quickly. Figure 7: Results of field unstructured terrain point cloud reconstruction. (**a**) Small gully terrain; (**b**) Small gully terrain point cloud; (**c**) Deep pit terrain; (**d**) Deep pit terrain point cloud; (**e**) Big bulge terrain; (**f**) Big bulge terrain point cloud.
## 4 Methodology
### Obtaining a Configuration Based on the Genetic Strategy
* Termination conditions: The most common alternatives for GA to terminate are: an upper limit for the number of generations, an upper limit for the conformance function (1 in our case), when the likelihood of achieving significant improvements in the next generation is very low or when a given number of generation does not obtain any improvements [27]. Algorithm 2 describes the basic GA strategy. The main inputs are a configurable process model and an event log. The initialization of chromosomes is made in _initialPopulation()_, then all chromosomes of the initial population _pop_ are evaluated using the _conformance_ function in _bestIndividual(pop)_, in order to obtain the best individual. The population evolves over several generations until a termination condition is reached. In each generation, the algorithm selects qualified individuals through elitism in _selectParents(pop)_. Later, the function _crossover(parents)_ recombines pairs of parents to create new individuals; in this way, a new population _pop_' is obtained. Mutation is then applied randomly to this new population, obtaining the new generation _pop_''. The best individual in the population _pop_'' is then compared to the best configuration obtained so far. At the end, the algorithm returns the best individual (configuration) obtained using this evolutionary approach. For the sake of generality,  
Figure 7: Overview of the genetic strategy to derive a process tree. The internal configuration representation allows this evolutionary algorithm to use crossover and mutation operations to generate new candidate configurations to be assessed. Algorithm 2 describes the most generic GA strategy. More sophisticated techniques for each step of the algorithm are also possible (e.g., tournament, elitism, among others). Please refer to [20; 27] for more details. ```
1:procedureGenetic(Log L, CPT \(Q^{\alpha}\), Weights\(W\))
2:\(pop\leftarrow\mathit{initialPopulation}(Q^{\alpha})\)
3:\(best\leftarrow\mathit{bestIndividual}(pop)\)
4:whilenot\(\mathit{TerminationCondition}()\)do
5:\(parents\leftarrow\mathit{selectParents}(pop)\)
6:\(pop^{{}^{\prime}}_{n}\leftarrow\mathit{crossover}(parents)\)
7:\(pop\leftarrow\mathit{mutate}(pop^{{}^{\prime}})\)
8:\(m\leftarrow\mathit{derive}(Q^{\alpha},\mathit{bestIndividual}(pop^{{}^{ \prime\prime}}))\)
9:if\(\mathit{conformance}(m,L,W)>\mathit{conformance}(\mathit{derive}(Q^{\alpha}, \mathit{best}),L,W)\)then
10:\(best\leftarrow\mathit{bestIndividual}(pop^{{}^{\prime\prime}})\)
11:endif
12:endwhile
13:return\(\mathit{best}\)
14:endprocedure
```  
**Algorithm 2** Obtain a configuration based on the genetic strategy.
## 4 Results and Discussion
### Estimation of Water Demand between 2020 and 2029
#### 4.6.3 Water Demand for Agriculture and Forest Uses  
The water demand for agriculture and forest uses was estimated based on the evapotranspiration coefficient and reference evapotranspiration [50]. The area of each agriculture and forest type between 2020 and 2029 and water demand are summarized in Table 23. According to the results, the total agriculture area will decrease from 185.76 km\({}^{2}\) in 2020 to 167.57 km\({}^{2}\) in 2029, with an average of 176.67 km\({}^{2}\). Water demand for agriculture use will decrease from 264.84 million m\({}^{3}\) in 2020 to 237.35 million m\({}^{3}\) in 2029. The average water demand for agriculture use is 250.93 million m\({}^{3}\). Furthermore, the total forest area will decrease from 124.49 km\({}^{2}\) in 2020 to 113.00 km\({}^{2}\) in 2029, with an average area of 118.78 km\({}^{2}\). Water demand for forest use will decrease from 159.58 million m\({}^{3}\) in 2020 to 142.74 million m\({}^{3}\) in 2029. The average water demand for forest use is 151.10 million m\({}^{3}\). This finding indicates that agriculture and forest land will decrease between 2020 and 2029.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM
### Remote Sensing Data as Proxies of Soil Properties Controlling Factors in the SCORPAN DSM Model
#### 4.2.3 Relief, Topography, and Landforms  
Relief, topography, and landforms are among the main controlling factors in the SCORPAN model [11; 286]. The covariates used in DSM are generally computed derivatives of Digital Elevation Models (DEMs). For global mapping, [PERSON] et al. [287] and [PERSON] et al. [286] recommended using the Shuttle Radar Topographic Mission (SRTM) DEM at 90 m resolution. The main reason for recommending SRTM was its availability and reasonable resolution considering the number of pixels generated (about 18 billions). Of course, there are now more accurate DEMs available from other RS products, including Lidar for the most accurate ones (e.g., [288; 289]). DEM derivatives have proven to be very efficient as covariates in DSM at different resolutions and area sizes, from field to national scales in France (e.g., [138; 139; 140; 141; 145; 146; 147; 259; 272; 290; 291; 292; 293]). The integration of DEM derivatives at various resolutions has also proven to be useful in capturing different scales of variability (e.g., [141; 144; 272]). Note that in some cases, elevation replaces temperature or rainfall/snowfall. It is clear that relief attributes directly reflect or control many factors in soil evolution (e.g., temperature, slope, soil depth, erosion and deposition, and alluvial plains). Conversely, some changes in soil surface topography can be useful covariates to help detect fill erosion [224], landslides [294; 295], or the swelling/shrinkage of clay soils (e.g., [194; 296; 297]).  
Note that, although French examples are not dominant in the literature (e.g., [76; 129; 144; 201; 202]), the automatic classification of landforms from DEM and other satellite imagery is one of the classical foundations of spatial segmentation, which is a technique often used for large-scale DSM (e.g., [142; 298; 299; 300; 301]).
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 2 Materials and Methods
### Algorithm Description and Analysis  
After interference cancellation, the reference signal and multi-path interferences are eliminated. However, the target echo signal is still hidden in the noise background, which means that it is difficult to achieve target detection. Therefore, further processing is needed to improve the target SNR. Traditional target detection methods use R-D processing to improve the output SNR of the target echo signal.
## 3 Data Assimilation Strategy
### Model Performance  
Pearson's correlation coefficient (\(r\)), standard deviation (SD), root-mean-square difference (RMSD), and centered RMSD (CRMSD) are used to quantify model accuracy and agreement using six flux tower sites. In this study we consider the flux-derived GPP as the \"ground truth\", though we acknowledge that the flux tower measurements may contain instabilities in observation caused by the contamination of atmospheric and cloud, and instrument errors.  
\[r=\sum_{i=1}^{n}\bigl{[}\bigl{(}GPP_{model}-\overline{GPP_{model}}\bigr{)} \cdot\bigl{(}GPP_{EC}-\overline{GPP_{EC}}\bigr{)}\bigr{]}\Big{/}(n\cdot SD_{ model}\cdot SD_{EC}), \tag{16}\]  
\[SD=\sqrt{\sum_{i=1}^{n}\bigl{(}GPP-\overline{GPP}\bigr{)}^{2}/n}\, \tag{17}\]  
\[RMSD=\sqrt{\sum_{i=1}^{n}\bigl{(}GPP_{model}-GPP_{EC}\bigr{)}^{2}/n}\, \tag{18}\]  
\[CRMSD=\sqrt{\sum_{i=1}^{n}\bigl{[}\bigl{(}GPP_{model}-\overline{GPP}_{model} \bigr{)}-\bigl{(}GPP_{EC}-\overline{GPP_{EC}}\bigr{)}\bigr{]}^{2}/n}\, \tag{19}\]  
Here, \(GPP_{model}\) and \(GPP_{EC}\) represent the estimated and observed GPP values, respectively; \(\overline{GPP_{model}}\) and \(\overline{GPP_{EC}}\) are the mean values of estimated and observed GPP values, respectively; and \(n\) is the number of observations.  
The Taylor diagram [60] provides a concise and graphical statistical summary of \(r\), \(CRMSD\), and \(SD\) of the observed and estimated patterns on 2D plots. In this paper, we normalized \(SD\) and \(CRMSD\) (\(NSD\) and \(NCRMSD\), respectively), to display and compare the performances of all flux towers in one diagram [61].  
\[NSD=SD_{model}/SD_{EC}, \tag{20}\]  
\[NCRMSD=CRMSD/SD_{EC}, \tag{21}\]  
Here, \(r\), \(NSD\), and \(NCRMSD\) are not independent, but are related by the following formula:  
\[NCRMSD=NSD^{2}+1-2\times SD\times r, \tag{22}\]  
In the Taylor diagram, \(NSD\) is expressed as a radial distance from the origin and the correlation with tower-based data as an angle in the polar plot. The distance to the observation point is proportional to \(NCRMSD\) between the estimated and observed values. The simulated points having good agreement with observations lie nearest to the point identified as \"observed\" on the \(x\)-axis. This diagram is especially practical and visual in evaluating multiple aspects of various models.  
In addition to the aforementioned indices, the relative \(RMSD\) (\(RMSD_{r}\)) is calculated as a proportion of the mean estimated value [62] as:  
\[RMSD_{r}=\bigl{(}RMSD/\overline{GPP_{model}}\bigr{)}\times 100\%, \tag{23}\]
###### Abstract  
In this paper, we present a study on a broadband transparent tapered slot antenna. In general, the objective of achieving optical transparency is to enable antennas to seamlessly integrate into windows, offering an aesthetically pleasing and inconspicuous appearance. The aim of our research is to develop antennas that possess the ability to adjust horizontal plane beams across a wide frequency range, from 24 to 28 GHz, for 5G applications. This structure combines three antennas into a single unit, providing an advantage in terms of saving space. Furthermore, this structure offers the possibility of choosing between using a single antenna to obtain a directional beam in the \(-90^{\circ}\), \(0^{\circ}\), or \(+90^{\circ}\) directions (depending on the activated antenna) corresponding to three states, or the combination between two states to obtain another three additional states. The combination of the three states also allows for the acquisition of another state. At this point, the total number of states is \(2^{3}-1\). Only three PIN diodes are employed to switch between all states. Additionally, by adjusting the bias values of the PIN diodes, which function as variable resistors, the antenna beamwidth can be adjusted in order to achieve a coverage of \(300^{\circ}\), offering more radiation pattern reconfigurability. The proposed method offers several advantages, including simplicity and feasibility in controlling the beamwidth and the beam direction electronically. This structure can be easily integrated into the development of fifth-generation communication systems.  
optical transparency; Vivaldi antenna; active antenna; reconfigurability; wideband frequency
## 2 Methods and Results
### Applications of Optimized Side-Band Frequency  
As shown in Figure 3, by taking into account the side-band frequency as a reference signal of the FM mode, we set the optimal frequency shift to be 2 kHz and acquired the TGS images using EFM set-up using DDS. Comparison with conventional AM mode was performed in our set-up because it is difficult to find the same probing spot using standard EFM. In this experiment, the resonance frequency of the cantilever was 66.7 kHz and the spring constant was about 3 N/m. Therefore, the generated side-band frequencies for the FM mode were 64.7 kHz and 62.7 kHz, respectively. In this case, the SNR produced by the optimized frequency also showed 1.73 times higher than that seen with the image of 4 kHz frequency shift, and 1.35 times higher than the AM method.  
We also implemented the DDS method with a stiff cantilever (PPP-NCHR, NANOSENSORS(tm), Neuchatel, Switzerland, \(K\sim\) 42 N/m, 330 kHz) using the DDS method because this allows the generation of the optimized side-band frequency (2 kHz shift). Gold film with a thickness of 15 nm was coated onto a silicon probe using an e-gun evaporator (VI-43N, Anelva, Kanagawa, Japan) to impart conductivity to the tip. With the deposition of Au, the resonance frequency of the cantilever changed slightly (\(\omega_{o}\sim\) 314 kHz).  
Surface dipole moment of PZT can be altered by direct contact EFM. Thus, the PZT was chosen to make narrow patterns showing electric contrasts. As shown in Figure 4a, we applied positive bias at 10 V to the dark lines and negative bias at \(-\)5 V to the gray line in order to accomplish the domain switching of polarization via an external electric field.  
When we measured the surface profiles of the PZT film using the AM mode and optimized side-band mode, the SNR values were found to be 6.0295 and 12.104, respectively. For the TGS sample, the SNR values were calculated to be 4.701 and 7.377. That is, the resolution of the optimized side-band EFM images is improved from 1.57 to nearly twofold compared to that of the AM mode images. Therefore, the frequency-detection method also can be applied to a high-frequency cantilever to improve the image quality.  
Figure 2: EFM images of the triglycine sulfate (TGS) surface with a scan size of 17 \(\upmu\)m \(\times\) 17 \(\upmu\)m, showing spontaneous polarization: (**a**) 1 kHz, (**b**) 2 kHz, (**c**) 3 kHz, and (**d**) 5 kHz side-band frequency shift images using the DDS method and plots of the dashed line. (**e**) Signal-to-noise ratio (SNR) graph with different side-band frequencies.