esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 4 Experimental Arrangement
### Experimental System  
The experimental system set in this work is shown in Figure 7. The excitation source, an infrared camera, a control circuit, and a computer were the main components of the system. We chose two halogen lamps whose power are 1000 W each as the excitation source, and we laid them symmetrically on the non-defective side of the specimen. We used the TiX660 infrared camera (Fluke, USA), whose resolution is 640 \(\times\) 480 pixel. The control circuit contained time relay and intermediate relay; the function of the former was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Thermal Conductivity \(\lambda\)** & **Specific Heat Capacity \(C\)** & **Density \(\rho\)** \\  & **W/(m\(\cdot\)K)** & **J/(kg\(\cdot\)K)** & **kg/m\({}^{3}\)** \\ \hline PMMA & 0.18 & 1464 & 1190 \\ AIR & 0.0267 & 1005 & 1.293 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The thermal parameters of PMMA and AIR.  
Figure 5: The process of depth determination based on GRU.  
Figure 6: The dimension diagram of the specimen.  
to control the excitation time, and the latter was to protect the circuit. The computer was connected with the infrared camera by gigabit ethernet to receive and process the datasets collected; the raw datasets were acquired by SmartView software.  
In the experiment, the excitation source and the infrared camera were arranged at the same side of the specimen. The non-defective side of the specimen was heated by halogen lamps for a certain period of time, and then we chilled down the specimen at room temperature. The distance between the specimen and the excitation source was 450 mm, while the distance between the specimen and the infrared camera was 550 mm. The essential parameters of the experiment are listed in Table 2. We collected 90 frames to construct the thermal sequences; the thermal image that represents the specimen surface temperature distribution after 11 s heat excitation is shown in Figure 8a, while Figure 8b represents the thermal image that cooled for 10.5 s at room temperature. From the Figure 8, the surface restricted within respective blue box area shows the visual defects that have the same defect depth, and the defects with different depths had diverse temperature field distributions on the surface.
# Performance of Ore Sand as Aggregate for Interlocking Blocks
## 3 Results
### Transportation Impacts
These values were obtained by establishing linear functions (Equations (2) and (3), for train and truck transportation, respectively) connecting the transportation distance for the ore sand to the Score Indicator. This result means that the ore-sand block is a most sustainable alternative in comparison to traditional blocks only when OS is transported less than 1147 km by train or 217 km by truck. It is important to highlight that both distances entail the same diesel consumption (162.2 L), but trucks are less efficient than trains in terms of the distance travelled per liter. [PERSON] et al. [5] verified this same trend, where transporting ore sand by train causes a lower environmental impact in greenhouse gas emissions compared to road transport. Each liter of diesel used for transportation contributes to the following environmental impacts (per unit): 1.63 \(\times\) 10\({}^{-7}\) kg Sb eq. (abiotic depletion); 4.26 \(\times\) 10\({}^{1}\) MJ (abiotic depletion--fossil fuels); 3.06 kg CO\({}_{2}\) eq. (global warming); 5.46 \(\times\) 10\({}^{-7}\) kg CFC-11 eq. (ozone-layer depletion); 1.90 \(\times\) 10\({}^{-1}\) kg 1,4-DB eq. (human toxicity); 3.20 \(\times\) 10\({}^{-2}\) kg 1,4-DB eq. (freshwater aquatic ecotox. ); 1.31 \(\times\) 10\({}^{2}\) kg 1,4-DB eq. (marine aquatic ecotox. ); 5.01 \(\times\) 10\({}^{-4}\) kg 1,4-DB eq. (terrestrial ecotox. ); 4.64 \(\times\) 10\({}^{-4}\) kg C\({}_{2}\)H\({}_{4}\) eq. (photochemical oxidation); 2.36 \(\times\) 10\({}^{-2}\) kg SO\({}_{2}\) eq. (acidification); and 5.29 \(\times\) 10\({}^{-3}\) kg PO\({}_{4}\) eq. (europhication) [35; 39; 41]. \[SI_{train}=0.237\times 10^{-3}(d)+0.728\left(R^{2}=1\right) \tag{2}\]  
\[SI_{truck}=1.25\times 10^{-3}(d)+0.728\left(R^{2}=1\right) \tag{3}\]
# Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
## 5 Simulation Experiments
### Critical Slip State Experiments
(**a**) X-directional position–time curve in PID experiment. (**b**) Y-directional position–time curve in PID experiment. (**c**) Orientation–time curve in PID experiment. (**d**) Error of X-directional position–time curve in PID experiment. (**e**) Error of Y-directional position–time curve in PID experiment. (**f**) Error of Orientation–time curve in PID experiment. The tracking accuracy penalty matrix of the velocity loop is  
\[\textbf{Q}_{v}= \begin{bmatrix}270,000&0&0\\ 0&270,000&0\\ 0&0&900\end{bmatrix} \]  
The control smoothness penalty matrix for the velocity loop is  
\[\textbf{R}_{v}= \begin{bmatrix}0.01&0&0\\ 0&0.01&0\\ 0&0&10,000\end{bmatrix} \]  
The relaxation factor coefficient of the velocity loop is  
\[\rho_{v}=0\]  
The safety coefficient vector \(\textbf{S}_{f},\textbf{S}_{w}\) and \(\textbf{S}_{v}\) are all taken as \( \begin{bmatrix}0.95&0.95&0.95\end{bmatrix} ^{T}\). The results of the simulation experiment are shown in Figure 13. The experimental measurement trajectories in the experimental results overlap with the reference trajectory. Compared with the PID algorithm, the NMPC algorithm reduces the impact of time delay on control accuracy. The NMPC controller achieves accurate and smooth trajectory tracking control of the robot during the wall motion. Figure 13: The result of NMPC controller in the simulation experiment. (**a**) X-directional position–time curve in NMPC experiment. (**b**) Y-directional position–time curve in NMPC experiment. (**c**) Orientation–time curve in NMPC experiment. (**d**) Error of X-directional position–time curve in NMPC experiment. (**e**) Error of Y-directional position–time curve in NMPC experiment. (**f**) Error of Orientation–time curve in NMPC experiment. In order to further quantify and compare the tracking accuracy of the PID algorithm and NMPC algorithm, we use the root mean square error (RMSE) between the experimentally measured trajectory position quantity and the reference trajectory position quantity as the evaluation index. The comparison results are shown in Table 1.  
From the results in Table 1, the RMSE of PID experimental results in \(X\)-direction and \(Y\)-direction position quantities are approximately 74.02 times and 9.596 times of NMPC experimental results, respectively, while the ratio of RMSE of PID experimental results to NMPC experimental results in terms of orientation is 2.516. Thus, the results indicate that the deviation of the actual trajectory and the reference trajectory of the NMPC algorithm is smaller than that of the PID algorithm in the joint dynamics simulation environment. Therefore, the NMPC algorithm outperforms the PID algorithm in terms of trajectory tracking accuracy in the application scenario of this paper.
## 7 Conclusions  
This article described a novel communication protocol that combines multiple technologies to enable decentralized and opportunistic communications among IoT devices, testing the performance and reliability of the proposed protocol by conducting tests that focused on latency and packet loss in various indoor and outdoor environments.  
The results showed that the protocol works effectively in environments with minimal signal obstruction, maintaining constant latency and a reduced jitter, as in the home experiments. However, in other locations like industrial environments, with more barriers between the transmitting and receiving nodes, the performance was less stable, with higher latency variability and occasional spikes and packet drops. These findings imply that physical barriers and obstacles, such as large metallic objects, walls, and furniture, have a significant impact on the performance of the opportunistic protocol, particularly on signal strength and reliability.  
Consequently, this paper demonstrated that the proposed protocol is a viable solution for implementing opportunistic systems based on Bluetooth 5 and libp2p in both industrial and home automation environments. The results highlight that the protocol achieves low latency, with minimum values around 8 s (depending on the environment). In addition, it can be stated that the protocol performs well in terms of reliability, with packet reception rates between 95% and 99% in the evaluated scenarios. These findings suggest that the protocol can be effectively deployed in diverse scenarios, although its performance can vary depending on environmental factors. Thus, although this article provides clear guidelines on how to make use of the developed OEC communications protocol (which is available as an open-source project), future researchers may need to optimize its operation by adapting it to the specific conditions of each application, including adjustments to the network topology or the incorporation of fault-tolerance mechanisms.  
**Author Contributions:** Conceptualization, T.M.F.-C.; methodology, P.F.-L. and T.M.F.-C.; investigation, A.N.-M., I.F.-M., P.F.-L. and T.M.F.-C.; writing--original draft preparation, A.N.-M., I.F.-M. and T.M.F.-C.; writing--review and editing, A.N.-M., I.F.-M., P.F.-L. and T.M.F.-C.; supervision, P.F.-L. and T.M.F.-C.; project administration, T.M.F.-C.; funding acquisition, T.M.F.-C. All authors read and agreed to the published version of the manuscript.  
This work has been supported by Centro Mixto de Investigacion UDC-NAVANTIA (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021-2027. In addition, this work has been supported by the grant PID2020-118857 RA-100 (ORBALLO) funded by MCIN/AEI/10.13039/501100011033.  
Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
We would like to thank the Navantia personnel who collaborated with us both during the tests carried out in the shipyard workshops. A special mention should be addressed to the support provided by [PERSON] and [PERSON].  
The authors declare no conflicts of interest.
# Geometric Nonlinear Analysis of the Catenary Cable Element Based on UPFs of ANSYS
## 4 Verification and Numerical Examples
### Verification and Analysis of Tensioned Cable Structures  
A calculation model for the construction phase of an asymmetric cable-stayed bridge is shown in Figure 6, with a cable-stayed cable with rigid arms on each side of the tower, elements 5 and 6, for a total of eight nodes and nine elements. 1-4 in Figure 6 indicate main beam elements 1-4, 5-6 in Figure 6 indicate cable-stayed cable elements 5-6, 7 in Figure 6 indicates lower tower column element 7. 8-9 in Figure 6 indicate upper tower column elements 8-9. The modulus of elasticity E = 1.097\(\times\)107 MPa and other basic calculation parameters are shown in Table 2.  
Figure 5: Conf.  
Figure 5: Calculated results for a single cable structure: **(a)** Vertical displacement, units in m; **(b)** Lateral displacement, units in m; **(c)** Axial force diagram, units in m; **(d)** Load-displacement curve.  
The calculation is divided into two working conditions:  
1. The two cables are synchronized for initial tensioning at the end of the tower, with the initial tension of 27,154 kN for the left-hand cable, that is, element No. 5, and 33,037 kN for the right-hand cable, that is, element No. 6, anchored after tensioning is completed;
2. A concentrated force couple of 8 \(\times\) 10\({}^{6}\) kN\(\cdot\)m is applied simultaneously at each of the two cantilevered ends of the main beam.  
Figure 7 and Table 4 show that the displacement results of the catenary cable element user101 developed in this paper based on ANSYS secondary development and the cable element developed by the program prepared in the literature [34] differ by 0.06%. The bending moment results only differ by 0.02% under the action of working condition one. The displacement results only differ by 0.08%, and the bending moment results only differ by 0.24% under working condition two, thus verifying the accuracy of the cable element developed in this paper. The accuracy of the cable element developed in this paper is thus confirmed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Tensioning**} & \multicolumn{3}{c}{**Concentration Couple**} \\ \cline{2-6}  & \(\Delta\mathbf{v_{1}}\)*/m & \(\mathbf{M_{2}}\)/kN\(\cdot\)m & \(\mathbf{M_{3}}\)/kN\(\cdot\)m & \(\Delta\mathbf{v_{1}}\)/m & \(\mathbf{M_{2}}\)/kN\(\cdot\)m & \(\mathbf{M_{3}}\)/kN\(\  
Figure 7: Cont.  
Figure 7: Calculation results of development element user101: (**a**) vertical displacement under condition 1; (**b**) bending moment under condition 1; (**c**) vertical displacement under condition 2 (**d**) bending moment under condition 2.
# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 1 Introduction
The decreased high-frequency band resolution reduces the time-frequency data size while maintaining a high degree of whistle similarity. In addition, since the dolphin whistles exit only at specific times and frequencies in the spectrogram of the recorded data, the proposed WhistleGAN is trained with a bilateral filter that preserves an accurate contour. To evaluate the performance of the proposed method, the complexities of the conventional generative models were compared. A mean opinion score (MOS) test was conducted to evaluate how similar the whistles generated by the proposed WhistleGAN were to the real whistle sounds. Through the MOS test, we verified that the new dolphin whistles generated by the proposed method resemble real dolphin sounds to the human ear, even though the frequency contours differ. The bit error rate (BER) performance of whistles generated by WhistleGAN were tested to evaluate the communication performance of the generated whistles. The simulation results verified that the BER performances of the proposed method are the same as actual whistles with the lower complexity of the whistle generation. The contributions of this paper are threefold:  
1. We propose the WhistleGAN to generate the new whistles with high mimicking performance with less computation by reducing the frequency data size using the Mel filter bank, which considers human hearing characteristics. 2. We conducted the MOS test to evaluate the mimicking performance of the newly created whistles. We verified that the whistle sound generated by the proposed WhistleGAN resembles real dolphin sounds. 3. We compared the BER performance of the whistles generated by the proposed WhistleGAN and the real dolphin with the conventional biomimetic UWA covert communication's modulation method. We demonstrated that the BER performance of the whistles generated by the proposed method was the same as that of the real whistles.
## 4 Results
### Freshness of Fish  
As a real data example, hyperspectral signatures of frozen fish were analyzed. The data acquisitions took place on several different days after a fish was captured. In our analysis, a subset of 3012 = 90,601 signatures were analyzed in each acquisition. This is a subset of the full HSI cube that consists of 912,082 spectral signature curves, chosen due to the upper limit of 130 GB of RAM available in the computer used to perform our experiments. An example taken from one fish at day 0 is given in Figure 5(a) and signatures for two different acquisitions for the same fish at day 4 are given in Figure 5(b),c, respectively. Comparison of the signatures of Figure 5(a) with Figure 5(b),c results in the significance maps in Figure 7. There the four panels show the results using two different parameters, the mean and the median. Changes are detected with the mean and the median, but with the standard deviation and the range, no statistically significant changes were detected. A careful examination of the curves in Figure 6 reveals that location parameters are expected to detect changes best in this case and this is indeed what happens. Typical FPR results are reported in Figure 8. From the results here we see that the Hannig-Marron critical value gives better performance. The difference is, however, not very clear in this example.  
Due to computational challenges, results for the method of [PERSON] et al. in [7] and PCA could not be obtained for the full data sets. In comparisons using only smaller subsets of the data, all three methods performed similarly in detecting changes while the their FPRs were similar to Figure 8.  
Figure 6: _Cont._  
Figure 6: (**a**) Plot of HSI-curves from acquisition number one from frozen fish at day 0. (**b**) Plot of HSI-curves from acquisition number two from frozen fish at day 4. (**c**) Plot of HSI-curves from acquisition number four from frozen fish at day 4.  
Figure 8: False positive rate (FPR) in the fish freshness example. (**a**) FPR from a leave-one-out test using Bonferroni correction to account for multiple testing. (**b**) FPR from a leave-one-out test and using the Hannig-Marron global rejection threshold.  
Figure 7: (**a**) Significance map for the comparison of the day 0 frozen fish acquisitions with day 4 acquisition number two using the median and the mean. (**b**) Significance map for the comparison of the day 0 frozen fish acquisitions with day 4 acquisition number four using the median and the mean.The Hannig-Marron rejection threshold was used in all maps.
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM and IFCM  
IFCM includes the relationship between \(X_{p}\) and its neighbor \(X_{p}\), which enhances the performance of the traditional FCM. As noted, CGAN generates new training data that not only lower the effect of imbalanced classes in SVD and VOICED but also enhance the performance of the detection model. Table 6 summarizes the performance of four cases of the detection models using CGAN-IFCM and IFCM. The results reveal that CGAN-IFCM lowers the difference between TNR and TPR by 58.4% (an average of four cases, 2.09% versus 5.02%). Further, the proposed CGAN-IFCM outperforms stand-alone IFCM by 10-12.6% and 5.8-16.2% in the TNR and TPR, respectively. This demonstrates the dual benefits of CGAN, which (i) reduces the effect of the imbalanced class (and thus lowers the difference between TNR and TPR) and (ii) generates more data to enhance detection performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Control Weighting Factors**} & \multicolumn{8}{c}{**TNR (\%)/TPR (\%) with Varying \(N_{e}\)**} \\ \hline
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
## 4 In Situ Testing at Xiaping Landfill
### Results and Discussions  
Figure 10 shows the field testing of methane concentration using TDLAS. By placing six mirrors around a point, the methane concentration in six different directions can be measured. The flat mirror was made of glass coated with Al film and had a diameter of 15 cm. The mirror was placed to be perpendicular to the emitted laser. The testing distance was up to 10 m by this method. For a point, the measured methane concentration in six different directions is shown in Figure 11. Within 40 seconds of the testing time, there were some fluctuations in methane concentration due to the change of wind direction. However, the percentage change of methane concentration within testing time was basically less than 20%, which indicated the stability of the test data. The average methane concentration in six different directions was 56 ppm, 57 ppm, 59 ppm, 59 ppm, 46 ppm, and 40 ppm. The close values in different directions show the accuracy of the testing method.  
Figure 9: Plan view of the Xiaping municipal solid waste (MSW) landfill.  
Figure 12 shows the methane concentration distribution above the surface of the Xiaping landfill. There were about 170 measured points in total. All the tests were carried out in one day. The measurements were conducted from 8 a.m. to 6 p.m. There was no rain, and the wind speed was lower than 3 m/s during the testing. According to the  
Figure 11: Measured methane concentrations in six different directions: (**a**) direction 1, (**b**) direction 2, (**c**) direction 3, (**d**) direction 4, (**e**) direction 5, and (**f**) direction 6.  
Figure 10: Field testing of methane concentration in six different directions using TDLAS.  
data of the local environmental protection bureau, the background methane concentration at 2 km away from the landfill was 3.7 ppm. The methane concentrations in most areas were below 100 ppm. This showed that the air-tightness of the final cover and temporary cover was good. However, the local methane concentrations were too high in three areas. The methane concentration was 617 ppm in the working area (i.e., uncovered area). The methane concentration was 701 ppm, where there were holes in the geomembrane. The methane concentration was 1959 ppm, where the gas collection pipe is disconnected. The testing results of methane concentration were consistent with the field observations. The TDLAS method can be used to accurately detect the spatial distribution of methane emissions in landfills.
## 5 DT Workshop Planning Model-Based Decision Support Methodology
### Resource Bottleneck Identification  
Resource bottlenecks are an important factor affecting the continuity of operations. Dynamic resource bottleneck identification methods and optimization of manufacturing resources [43] have been explored in many studies, and the feasibility and practicality of bottleneck identification analysis are further enhanced with the support of the Digital Twin. In the event that the sensor identifies fluctuations in the amount of material in the operation area or buffer, the bottleneck identification method timely pushes the operation blocking node to the DT system and triggers the rapid adjustment of MPS and ERP.  
The bottleneck identification calculation mainly includes resource state, device state, and buffer state characteristics. In addition, further consideration is given to the interactions between operational links. The real-time bottleneck calculation methodology can be summarized as follows:  
\[RBI(i)=\omega f_{res}(i)+\mu f_{eq}(i)+\lambda f_{buffer}(i) \tag{42}\]  
\[f(i)=\frac{v_{measure}}{max\{v_{measure}^{(1)},v_{measure}^{(2)},\cdots,v_{measure }^{(t)}\}} \tag{43}\]\[\omega+\mu+\lambda=1 \tag{44}\]  
where \(i\) is the workstation number, \(f_{res}(i)\) is the resource state function, \(f_{eq}(i)\) represents the device state function, and the buffer state function is \(f_{buffer}(i)\). The state calculation function is shown specifically in Equation (43) as the ratio of the real-time measured value to the maximum value in time \(t\). \(\omega\), \(\mu\), and \(\lambda\) are the weight coefficients, whose sum is 1 as shown in Equation (44).  
Demand forecasting and bottleneck identification are the two most significant aspects of workshop resource management, which are intimately related to the planning model. Based on the real-time update of resource information by the DT system, managers are able to analyze the operation plan and resource plan collaboratively and construct an effective \"Plan-Resource\" management framework, as shown in Figure 9.