esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 3 Numerical Simulation of the Platform Pose
### Finite Element Analysis  
Finite element analyses were conducted to evaluate the static behavior of the parallel platform. Three cases were considered depending on three different sets of input parameters and using \(E=1.1\cdot 10^{9}\) Pa as the material Young's modulus.  
Three cases will be studied:  
1. pull on Type (f) cables and null tension on Type (i) cables;
2. pull on Type (i) cables and null tension on Type (f) cables;
3. asymmetric pull: pull on one Type (f) cable and pull on two Type (i) cables;  
The fixed reference frame \((x,y,z)\) and the unit vectors \((\vec{u}_{x},\vec{u}_{y},\vec{u}_{z})\) attached to the mobile platform were positioned as in Figure 5a. This figure also shows the input displacement \(d_{1}\). Rotations \(\theta_{x}\), \(\theta_{y}\), and \(\theta_{z}\) were assumed to be about \(\vec{u}_{x}\), \(\vec{u}_{y}\), and \(\vec{u}_{z}\), respectively.  
In the first case, a displacement was imposed on each one of the Type (f) cables, equal to \(d_{1}=d_{2}=d_{3}=0.5\) mm, and null tensions on Type (i) cables, giving rise to null rotation angles \(\alpha_{1}=\alpha_{2}=\alpha_{3}=0\) of the three CSFH hinges. The deformed configuration is reported in Figure 5b, showing a platform displacement along the \(z\)-axis in the negative direction. No other significant displacements or rotations were registered.  
The second case corresponded to \(\alpha_{1}=\alpha_{2}=\alpha_{3}=20^{\circ}\) with \(d_{1}=d_{2}=d_{3}=0\) mm. The deformed configuration is reported in Figure 6a, showing a platform displacement along the \(z\)-axis in the positive direction. Analogous to the previous case, no other significant displacements or rotations were registered.  
The third case corresponded to a general actuation scheme with \(\alpha_{1}=0^{\circ},\alpha_{2}=5^{\circ},\alpha_{3}=20^{\circ}\), \(d_{1}=1\) mm, \(d_{2}=d_{3}=0\) mm. The deformed configuration is reported in Figure 6b, showing a platform displacement with components along the \(x-\) and \(y\)-axes and rotations about the \(x\)- and \(z\)-axes.  
The input parameters, the platform displacement, and the rotations are reported in Table 1 for the three analyzed cases.  
Figure 5: (**a**) Reference frames and nomenclature; (**b**) platform displacement along the \(z\)-axis, negative direction (Case 1).
## 5 Object Detection Application Domains
### Crowd Detection  
One of the most challenging tasks in object detection applications is crowd detection. In a crowded crisis, there are large crowds of confused people, resulting in pushing, mass-panic, crowd crushing, and loss of control [215]. To prevent these fatalities, automatic detection of critical and unusual situations in a dense crowd is necessary. As a result, that definitely will help to make emergency controls and appropriate decisions for security and safety [216]. This system can be used for detection and to count people, and also produce alarms in the presence of the dense crowd. Some of the applications of crowd detection are disaster management, safety control, public area management, and visual surveillance systems.  
[PERSON] [217] implemented crowd detection by using spatial-temporal information [218] and described it as a scanning window pedestrian detector. [PERSON] [219] implemented pedestrian detection in crowded scenes by combining local and global features in potential top-down segmentation. [PERSON] [220] implemented crowd detection through wavelet templates and vision-based technologies. As an example, to avoid crowd related disasters and ensure public safety automatic, detection of anomalies in crowded scenes [221] should be performed in real-time. [PERSON] et al. [113] implemented a detector trained with repulsion loss while detecting crowd occlusion scenarios.  
[PERSON] [222] used SIFT features to detect crowds and used SVM classification, which requires a proper training set. [PERSON] and [PERSON] [223] implemented abnormal crowd behavior detection using label distribution learning. Still there is much research to be done in the crowd detection area. Table 7 shows a comparison of various deep learning based methods to overcome challenges raised in object detection domains.  
The commonly used datasets for evaluating crowd detection performance are WIDERFACE [71], PASCAL-VOC [5], MS-COCO [77], and Open Images [86].
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
###### Abstract  
Steel defect diagnostics is considerably important for a steel-manufacturing industry as it is strongly related to the product quality and production efficiency. Product quality control suffers from a real-time diagnostic capability since it is less-automatic and is not reliable in detecting steel surface defects. In this study, we propose a relatively new approach for diagnosing steel defects using a deep structured neural network, e.g., convolutional neural network (CNN) with class activation maps. Rather than using a simple deep learning algorithm for the classification task, we extend the CNN diagnostic model for being used to analyze the localized defect regions within the images to support a real-time visual decision-making process. Based on the experimental results, the proposed approach achieves a near-perfect detection performance at 99.44% and 0.99 concerning the accuracy and F-1 score metric, respectively. The results are better than other shallow machine learning algorithms, i.e., support vector machine and logistic regression under the same validation technique.  
steel surface defect; deep learning; convolutional neural network; class activation map
## 4 Conclusions  
Non-proportional damping structures widely exist in practical engineering. In this paper, a modal perturbation method is proposed for evaluation of natural frequencies and damping ratios to significantly simplifies the solution process. Based on extensive analyses and numerical results, the following conclusions can be drawn:  
1. The modal perturbation method transforms the characteristic equation of state space into a set of nonlinear algebraic equations by using the real mode shapes of the corresponding undamped system. The method only requires the first few lower eigenvectors of the undamped system, the calculation time is shorter than the state space method, which will simplify the solution and be convenient to solve the large system.
2. With the increase of vibration modes of the corresponding undamped system, the eigenvalues and eigenvectors monotonically converge to exact solutions. Usually \(j+8\) is enough to obtain sufficiently accurate natural frequencies and damping ratios. When all of the mode shapes of the undamped system are used, the modal perturbation method will obtain the exact solution.
3. The equivalent proportionally damped system is close to the non-proportionally damped system to reduce the effect of the deviation damping matrix so that the method can deal with the highly non-proportionally damped system. The method is particularly suitable for finding a few individual orders of frequency and mode for system with highly non-proportional damping.  
Conceptualization, D.P.; methodology, D.P.; investigation, X.F.; writing--original draft preparation, X.F. and J.T.; writing--review and editing, P.L. and Q.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Foundation of State Key Laboratory of Disaster Reduction in Civil Engineering (SLDRCE15-01).  
The authors declare no conflict of interest.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 7 Algorithm Simulation and Experimental Verification
### Experimental Test  
Figure 23 illustrates the actual influences of different flywheel rotating speeds, wave periods, and pitch amplitude on output power. Under the same conditions, the output power improves as the wave period decreases, the flywheel rotating speed raises, and the pitch amplitude increases, which also verifies the previous simulation. From Figure 23a,b, it can be concluded that the modified MPPT control algorithm takes approximately 10 s to reach the vicinity of MPP and maintain a stable output. This also proves the feasibility and stability of the algorithm under different working conditions. It can be observed from the figure that the peak voltage of the power output is approximately 50 V, and the output period is the same as the wave. It is worth noting that there is still a certain gap between the output power and the theoretical power in each experimental state in the figure. The experimental power of Figure 23d is 13.8 W, which is only 0.92 W lower than the theoretical output, accounting for 93.8% of the maximum power. However, the actual output of Figure 23a is 6.1 W, which is 1.15 W smaller than the simulated value, and only reaches 84.2% of the simulated maximum power, which is the smallest proportion in Figure 23. The ratio of the actual power to the theoretical power value in Figure 23b is 91.1%, and the ratio in Figure 23c is 85.0%. The power difference between theory and reality is affected by factors such as the mechanical efficiency of the transmission mechanism, friction, and electrical losses. After comparing all the actual outputs and the simulated values, it can be concluded that the difference between the two values is almost the same under different working conditions.
## 3 Result and Discussion
### Performance of the Method in Estimating Fruit Properties Based on the Most Effective Wavelengths
#### 3.5.1 Properties of Starch  
As seen in Figure 11, the regression coefficients obtained using spectral data of 800 to 900 and effective spectral data are above 0.97, which means the starch content is predictable. Table 3 gives the mean and standard deviation of the ANN-BBO algorithm predicting the starch of apples in 100 iterations and the values of the various criteria in the best state of training using spectral data of 800 to 900 nm and the data of the most effective wavelength. Given the nearly identical performance in different iterations, it can be said that the proposed method has high reliability.  
Figure 11: Regression analysis of scatter plot at the best state of training of ANN-BBO to predict starch content of Fuji apple, (**a**) based on spectral data in the range of 800 to 900 nm, (**b**) based on spectral data of the most effective wavelengths.  
Figure 10: Box diagram of five criteria for evaluation of ANN-BBO performance in estimating TSS of Fuji apple on spectral data of the most effective wavelength, (**a**) coefficients of regression and determination, (**b**) error criteria.
## 3 Example and Discussion  
### Boundary Condition Verification  
The relevant parameters were as follows [4]: circular tunnel radius \(R_{1}\) = 3.0 m; lining thickness 0.3 m (\(R_{2}\) = 2.7 m); vertical geostress \(\sigma_{y}\) = 1 MPa; lateral pressure coefficient \(\lambda\) = 0.5; tunnel external water pressure \(p_{1}\) = 0.2 MPa, internal water pressure \(p_{2}\) = 0.5 MPa; elastic modulus of surrounding rock and lining \(E_{1}\) = 1 GPa, \(E_{2}\) = 36 GPa; Poisson ratio \(\mu_{1}\) = 0.25, \(\mu_{2}\) = 0.22; displacement release coefficient \(\eta\) = 0.2. The stress results in this paper still stipulate that tensile stress is positive and compressive stress is negative. Using the method in this paper, the stress at the boundary of \(L_{1}\) and \(L_{2}\) was obtained, and the results are shown in Figure 2:  
In Figure 2, the radial normal stress \(\sigma_{3}^{r}\) on the lining inner boundary \(L_{2}\) is \(-0.5\) MPa (internal water pressure) and shear stress \(\tau_{3}^{r\theta}\) is \(0\) MPa, complying with the stress conditions of the inner boundary of the lining. The radial normal stress \(\sigma_{1}^{r}\) of the surrounding rock on both sides of the contact surface \(L_{1}\) between the surrounding rock and the lining is parallel to the radial normal stress result \(\sigma_{2}^{r}\) of the lining and satisfies \(\sigma_{1}^{r}-\sigma_{2}^{r}=0.25\) MPa (being equal to external water pressure). In addition, the shear stresses \(\tau_{1}^{r\theta}\) and \(\tau_{2}^{r\theta}\) on both sides of the contact surface are equal to zero, meeting the characteristics of smooth contact. That is to say, the interaction relationship between surrounding rock and lining at the \(L_{1}\) boundary is satisfied through the preset requirements, and the load on the outer boundary of lining is the result of superposition of geotechnical pressure caused by tunnel excavation and external water pressure \(p_{1}\). To sum up, the stress results are consistent with the preset lining boundary conditions and also accord with the actual engineering situation, which to some extent verifies the correctness of the model established in this paper.  
Furthermore, based on the parameters employed in this example, it can be observed that under the combined action of tunnel excavation and internal and external water pressures (\(p_{1}\), \(p_{2}\)), the tangential stress of the lining remains in a compressive stress state throughout the domain (\(\sigma_{2}^{\theta}\), \(\sigma_{3}^{\theta}\) are less than \(0\)). This indicates that tunnel excavation predominantly influences the stress state. Additionally, the tangential stress distribution amplitude of the lining inner boundary and outer boundary is significant, presenting an approximately sinusoidal distribution within the ranges of [\(-6.25\), \(0\)] and [\(-5.75\), \(0\)] respectively. The mean values are approximately \(\overline{\sigma}_{3}^{\theta}=-3.125\) MPa (compressive stress), and \(\overline{\sigma}_{2}^{\theta}=-2.875\) MPa (compressive stress), which are slightly smaller than the results obtained for the inner boundary of the lining.
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 4 Discussion
### Performance Evaluation of Robot Combine Harvester
The results verify that the bias error between the CLAS and the RTK is less than 0.10 m. The bias error between the MADOCA and the RTK is less than 0.40 m. This bias error is larger than the bias error between the CLAS and the RTK because of the big oscillation of the MADOCA caused by the time dependency of this system, as concluded from the analysis of the data shown in Figure 9 and summarized in Table 6. These biases are also caused by the simultaneous change of position in the easting and northing coordinates while the robot combine harvester is running. The bias from the change of the position will always be visible even when the machine is controlled by using the RTK positioning augmentation system. This bias should be less notorious if the direction of the predesigned navigation map is parallel to one of the UTM coordinate system axis. This means the bias should be smaller if the robot combine harvest travels only in the northing or easting direction. However, this condition is unrealistic and cannot be implemented in practical applications. This raises the question of whether it would be possible to adjust the values obtained from the CLAS positioning augmentation system by estimating an almost constant bias in both easting and northing directions. However, this is a work still in progress and is out of the scope of this research.
## 4 Synthetic Time Series Analysis
The mean RMS values of the extracted CMEs by PCA and IPCA were 1.50 mm and 1.47 mm, of which the improvement of IPCA was 2.00%. Besides Group 1, two groups of eight stations named as Group 2 and 3 were also randomly selected from 25 stations, which are listed as follows:  
* Group 2: YNMH, YNGM, YNJP, YNYL, YNSD, YNJD, YNTC, YNLC
* Group 3: YNSM, YNRL, YNYM, YNHZ, YNTC, YNWS, YNYS, YNGM  
The eight stations of Group 2 and 3 were also employed for the simulation analysis of Case 1, Case 2 and Case 4. Table 4 presents the mean RMS values of the eight selected stations of extracted CME signals by PCA and IPCA, as well as the improvements of IPCA, with respect to PCA. In Case 2, the same true CME, but different formal errors were employed, where Up, North, East denoted the formal errors of Up (scaled by 0.3), North and East, respectively. The analysis of Up, in Case 2, was the same as with Case 1, which was anticipated given all the simulated parameters were the same. We can see that all the mean RMS values by IPCA were smaller than those by PCA. The relative reduction of mean RMS values ranged from 1.35% to 3.93%. It is reasonable to conclude that IPCA outperformed PCA in extracting CME, regardless of whether local effects were considered or not. \[RMS=\sqrt{\frac{\sum\left(\mathbf{S}_{0i}-\hat{\mathbf{S}}_{0i}\right)^{2}}{m_{i}}} \tag{15}\]1 RMS with local effects considered of YNDC.
# Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
## 6 Engineering Applications  
In order to form a closed loop from identification to maintenance of concrete defects, the concrete defect intelligent identification system was designed, which consists of an image acquisition module, a defect identification module and a subsequent maintenance module. Considering the convenience and ease of development on the Web side, this study encapsulated the improved Mask R-CNN model and built the system based on the Web side. To exclude possible artificial interference in the production of the dataset and to verify the feasibility of the method in actual engineering, the DJI Matrice 300 RTK UAV was used to collect defects images in bulk. The flow diagram of the concrete defect intelligent identification system is shown in Figure 16. The concrete defect intelligent identification system identification result is shown in Figure 17.  
As shown in Figure 16, the defect images are transmitted to the image database through the wireless network; the concrete defect intelligent identification system obtains the image from the image database by executing the defect identification operation. Then, the defect identification module is used to identify the acquired image, determine whether the image contains defects and if there is a relevant defect, obtain the defect information and back it up to the historical database. Finally, the defect information and historical database are transmitted to the bridge health monitoring system. The maintenance personnel will view the defect information, and carry out reconnaissance and subsequent bridge maintenance on the damaged parts of the corresponding bridges. This paper used the images of reinforced concrete bridge defects collected by the DJI Matrice 300 RTK UAV, which is located on the Wuhan-Huangshi expressway. The detection results are shown in Figure 18.  
Figure 18a shows the original image taken by the UAVs; this paper used it directly in model inference without any special processing of the image. Figure 18b shows the example of detection results; the detection results are consistent with the actual defect information, with fewer missed detection and false detection.  
The detection results are shown in Table 5. The improved Mask R-CNN proposed in this paper has higher detection accuracy for the images taken by the UAVs which is new  
Figure 17: The concrete defect intelligent identification system identification result.  
Figure 18: Example of detection results for the image taken by the UAVs.  
and untrained, where the AP values of cracks, exposed bars, spalling, efflorescence and void are 86.3%, 95.9%, 91.2%, 92.7% and 87.1%, respectively, and the mAP value is 90.6%. It is shown that the proposed method can effectively detect the five major types of concrete defects, which is suitable for actual engineering applications.
# Analysis of the Situation of Social Public Procurement of Works at the Valencian Region (Spain)
## 3 Materials and Methods  
### Measurement Technique: Content Analysis  
From the literature review, several methods have been used to measure the state and progress of social sustainability in construction public procurement: questionnaires [7; 39], interviews [10; 26] and tender analyses [10; 18; 39; 40]. Each method shows several advantages but also some important limitations.  
Interviews can offer good response rates, but the information gathered depends strongly on the judgment of the surveyor, and interviewees often exaggerate their answers [61; 62]. A low response rate in the use of questionnaires can influence the representativeness of the sample [13]. Finally, tender's analysis may be strongly influenced by the researcher's interpretation if the methods of analysis are not rigorous enough [63].  
The method selected to analyse the inclusion of social sustainability in construction public procurement at the regional level was based on the analysis of tendering documents, and therefore it is secondary research. Content analysis was the method used in this study, as it is a qualitative, objective and systematic method [64; 65]. The researchers analysed the public tender documents obtained and quantified the results by transforming and parameterizing them into useful indicators and metrics. Content analysis is a research method that is widely used for systematically and objectively identifying characteristics of large volumes of written material [66]. To reduce the subjectivity and maximize the objectivity, the method must be adequately structured, and can therefore be reproducible [67].  
The procedure was divided into five steps (Figure 2). The first step was the collection of the tender documents. After that, the second step consisted of the analysis of the content of the tender documents. This phase was divided into three sub-phases: the first sub-phase was the identification of the social criteria involved in the tenders. The second sub-phase was the analysis and classification of the social criteria, by category, subsector (building or civil engineering), contracting authority, geographical scope and project budget. The third sub-phase was the analysis of the weight of the social criteria and their classification according to subsector, geographical scope and project budget. In the third step, results were obtained. The fourth step included the discussion and comparison of the results with other studies, and, finally, the conclusions were presented in the fifth step.  
Figure 2: Employed methodology.
## 3 Experiment and Results
### Experimental Results and Analysis
This indicates that combined models can identify changes in wind speed and wind power._  
To verify the applicability of the combined model with five sub-models optimized by MODA for determining the wind speed and wind power series, the forecasting results for the fourth season are shown in Table 7 and Figure 5.  
For Site 1, in the fourth season, the combined model with five sub-models (CM5) obtained the best forecasting accuracy. For wind speed and wind power forecasting, the evaluation metrics of the CM5 were also better than those of combined models with less than five sub-models and for the sub-models individually. This means that the CM5 is more suitable for wind speed and wind power forecasting. For Site 2, the sub-models and combined models showed analogical forecasting abilities in terms of the values obtained for the forecasting evaluation metrics. To be more specific, for wind speed forecasting, the MAPE values of the five sub-models were 16.30%, 18.64%, 20.90%, 23.90% and 27.26%, respectively. In contrast, the MAPE value of the combined model (CM5) was 6.77%--an improvement of 9.53%, 11.87%, 14.13%, 17.13%, and 20.49%, respectively, compared with the five sub-models (shown in Table 4). As for Site 3, the combined model with five sub-models showed a superior performance to the other combined models based on the eight employed evaluation criteria with MAE, RMSE, STDAPE, DA, U1, U2, MAPE, and R\({}^{2}\) values for wind power forecasting of 26.4136, 48.6081, 31.34%, 81.83%, 0.0692, 0.9668, 14.28% and 0.9774, respectively. Among the remaining combined models, the ranking of methods in terms of their forecasting accuracy, from good to bad, was CM2, CM3, and CM4, with MAPE values of 19.27%, 17.61%, and 15.93%, respectively.