esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Experimental Results and Analysis  
#### 4.4.1 Experimental Environment and Configuration  
The experimental environment for remote sensing image object classification is 64-bit Ubuntu 16.04 operating system, and the graphics GPU is NVIDIA GeForce GTX 1080 Ti. In the process of model training, the improved remote sensing image classification network is solved iteratively by using the stochastic gradient descent optimization algorithm. The epoch is set to 30, and the learning rate is set to 0.001. When the loss value of the network continuously changes several times, the learning rate is reduced by 0.1 times. The training is stopped when the network error is less than the set value, or the number of iterations reaches the required level. The model with the best classification effect is selected from all the models as the final multi-objective classification model.
## 3 Methods
### Framework for Spatiotemporal Feature Extraction Based on Dual-Stream Networks
#### 3.1.3 Multi-CSAM Fusion Module  
To comprehensively capture information, enhance expressiveness, improve robustness, and mitigate overfitting risks, we performed a secondary fusion of features extracted from different stages and scales, as depicted in the Multi-CSAM Fusion Module (MCFM) in Figure 1.  
Specifically, we started by downsampling the CSAM1, CSAM2, and CSAM3, fused by the CSAM module at different stages, to match the scale of CSAM4. Subsequently, we applied the same spatial dimension reduction operation to each scale of features, using spatiotemporal average pooling. The dimension-reduced features from all scales were then concatenated to form a single feature vector. A dropout layer was also introduced after the final average pooling layer to prevent overfitting. Finally, the concatenated feature vector was input into fully connected layers to output the ultimate prediction for driver intent recognition.  
This process of making different-scale features consistent facilitates parameter sharing, improving computational efficiency, ensuring dimension consistency, and avoiding information loss. It simplifies the model's learning task, reduces the risk of overfitting, and enhances the model's generalization ability and performance across various tasks.  
By fusing features from different scales and stages for driver intent recognition, the model comprehensively captures different levels and details of input data, improving its global understanding of data features. Fusing features from different scales enhances the model's expressive power, enabling it to better learn and represent complex data patterns. Furthermore, the fusion of features from multiple scales helps improve the model's robustness to scale and structural variations, making it more resilient in different environments. Multi-scale feature fusion also aids in reducing the model's parameter count, lowering the risk of overfitting, and enhancing its generalization performance. In summary, the multi-scale feature fusion followed by classification through fully connected layers is well-suited for driver intent recognition.  
Figure 4: Cross-Spatial Attention Module (CSAM).
## 4 Simulation and Experiment
### Test on Typical Simulated Time Series
#### 4.1.2 Test Results  
In the experiment, 200 sets of data were tested using the proposed and contrast methods. The general prediction outputs are shown with curve graphs, for which the results of the MG system are shown in Figure 7, and the MSO system's results are shown in Figure 8.  
Figures 7 and 8 show the performance of different methods. In total, 200 sets of data in the MS and MSO systems were tested. ARIMA performs poorly for the losing track in the MS system and shifting in the MSO system. Other methods can trace the general trend, but their approximate degree varies. The LSTM network fluctuates more acutely, and the GRU develops a little. The fluctuation of the transformer is relatively more severe. CI-GRUs and A-CI-GRUs are closer to the true value, and their deviations are smaller than others. For an intuitional comparison, the absolute errors in all methods are drawn in Figures 9 and 10.  
Figure 7: Prediction results of data in the MG system.  
The error in each point in Figures 9 and 10 shows the same performance corresponding to Figures 7 and 8. The general descending sort of all methods is A-CI-GRUs, CI-GRUs, GRU, transformer, LSTM, and ARIMA. A conclusion can be drawn following the error criteria in Table 4. From the perspective of the different methods, their errors decrease progressively from ARIMA to the A-CI-GRUs, which is similar to the graph trend in Figures 7-10. From the perspective of step length, the error rises when the step increases. The increment is dramatic in ARIMA, LSTM, and transformer. Multiple models are affected little by the step length, and errors in the A-CI-GRUs are relatively stable.
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 4 Test Results
### Deformation of Bearing  
Figure 9 illustrates that the bearing deformation in the excitation of NP ground motions is the largest. When the connecting direction of the two abutments is the same as the EW  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{\(\rm X_{P}\)-Directional Excitation} & \multicolumn{3}{c}{\(\rm X_{P}+Y_{S}\)-Directional Excitation} & \multicolumn{3}{c}{\(\rm Y_{P}\)-Directional Excitation} & \multicolumn{3}{c}{\(\rm X_{S}+Y_{P}\)-Directional Excitation} \\ \cline{2-13}  & **NP** & **NN** & **FF** & **NP** & **NN** & **FF** & **NP** & **NN** & **FF** & **NP** & **NN** & **FF** \\ \hline
1\# beam end & 11.58 & 6.51 & 6.38 & 15.94 & 8.30 & 7.06 & 8.23 & 5.16 & 4.68 & 11.62 & 7.54 & 7.16 \\
4\# beam end & 10.05 & 5.55 & 5.43 & 13.10 & 9.61 & 8.97 & 8.99 & 7.29 & 6.60 & 12.43 & 9.35 & 8.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak value of beam end displacement (mm).  
direction (Figure 9a), the rotation generated by the curved bridge in the excitation of X\({}_{\text{P}}\) makes the bearing deformation of pier 1 larger. Compared with FF ground motions, NP and NN ground motions increase by 185.5% and 18.0%, respectively. In the excitation of X\({}_{\text{P}}\) + Y\({}_{\text{S}}\), the bearing deformation of pier 1 is the largest under NP ground motion excitation, while the bearing deformation is the largest at pier 4 under NF and FF ground motion excitation. The reason is that the curved bridge rotates under the X\({}_{\text{P}}\) + Y\({}_{\text{S}}\) excitation of NP ground motions, and it mainly moves within the plane under the excitation of NN and FF ground motions. Compared with FF ground motion excitation, NP and NN ground motion excitations increase by 140.1% and 20.7%, respectively.  
When the connecting direction of the two abutments is the same as the NS-direction (Figure 9b), the curved bridge is mainly translational. The higher the pier is, the greater the amplifying effect of bearing deformation is, resulting in the maximum bearing deformation at pier 4. Compared with FF ground motion excitation, NP and NN seismic records increase by 81.0% and 20.4%, respectively, under unidirectional seismic excitation and by 142.0% and 33.8%, respectively, under bidirectional seismic excitation.  
In summary, bearing deformation is closely related to the motion state of the curved bridge. Owing to the rotation of the curved bridge, the bearings at the lower pier produce a large deformation, which increases the risk of bearing shedding.
## 1 Introduction
### Application Examples  
With the high degree of deployment flexibility, applications of WSN are vast and can be broadly classified into the monitoring and tracking categories. Monitoring applications include environmental monitoring such as forest fire detection, biocomplexity mapping of the environment, flood detection, precision agriculture; health monitoring contains tele-monitoring of human physiological data, monitoring doctors and patients conditions and drug administration in hospitals; inventory location monitoring; factory, machine, chemical and structural monitoring. Military monitoring examples can be found in monitoring friendly forces, equipment and ammunition, battlefield and terrain surveillance, reconnaissance of opposing forces, targeting, battle damage assessment, nuclear, biological and chemical attack detection. Tracking applications include objects, animals, humans, vehicles, and military enemy tracking. These applications are made possible due to the fact that WSN has a short system setup time and sensors can be disposed with acceptable operation cost.
## 2 Materials and Methods
### Quantitative Analysis of Phenolic Acids  
Fifteen plants per plot were selected randomly and mixed. Stock solutions of rosmarinic acid and salvianic acid B (purchased from China National Institutes for Food and Drug Control, Beijing, China) were dissolved in 70% aqueous methanol and diluted to an appropriate concentration range to establish the calibration curves. The dried roots of the plant materials were ground to a powder as described above. Individual samples (40 mesh, 100 mg) were accurately weighed, mixed with 20 mL 70% aqueous methanol and sonicated for 30 min at room temperature. The extract solution was filtered through a 0.45 \(\upmu\)m filter, and 5 \(\upmu\)L was injected for HPLC (Waters Corp., Milford, MA, USA) analysis. HPLC separation was performed with a mobile phase containing a gradient of solvents A and B, where A was a deionized water-phosphoric acid (A; 100:0.020, \(v/v\)) solution and B was acetonitrile. The initial condition was A-B (95:5, \(v/v\)), which changed linearly to A-B (88:12, \(v/v\)) in 12 min. The percentage of mobile phase B increased abruptly by 20% at 12.1 min, followed by linear increments to 25% over the next 20 min. The detection wavelength was set at 280 nm for analyses, and the column temperature was set at 25 \({}^{\circ}\)C; the flow rate was 1.0 mL/min.
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.3 Mode Shapes  
Mode shapes are quite an advantageous condition assessment tool, being less influenced by environmental effects than frequencies [44], while also containing spatial information that can be used for damage localisation (Level 2 as per [29]). Many techniques have been developed over the years such as the Modal Assurance Criterion (MAC) by [PERSON] and [PERSON] [45], which detects mode shape changes over the entire structure by taking advantage of the orthogonality of eigenvectors. [PERSON] et al. [46] furthered the MAC in the development of the Coordinate Modal Assurance Criterion (COMAC) method, which uses modal node displacement to detect and locate damage. Equation (1) shows how COMAC can be applied to a node \(i\), by measuring the normalised difference of mode shape vectors of the undamaged (\(\varphi_{ij,i}^{u}\)) and damaged (\(\varphi_{ij}^{d}\)) conditions. The application of MAC and COMAC in bridge structures by [PERSON] and [PERSON] [47] found that the methods could detect most structural changes and locations, but also identified spurious damage. This was also observed when applied to beam structures, by [PERSON] [48], who showed that COMAC was prone to indicating false damage detections. COMAC remains a popular damage detection methodology across many engineering disciplines, with more recent, civil-based, applications of COMAC focusing on its incorporation with other methodologies to improve performance [49; 50].  
\[\text{COMAC}_{i,j}=\frac{\left[\sum_{j=1}^{m}\,\varphi_{i,j}^{u}\varphi_{i,j} ^{d}\right]^{2}}{\sum_{j=1}^{m}\,\left(\varphi_{i,j}^{u}\right)^{2}\,\sum_{j =1}^{m}\,\left(\varphi_{i,j}^{d}\right)^{2}} \tag{1}\]
## 4 Experiment Results and Analysis
### Implementation at the School for Visually Impaired
As a result of implementation of the research work in the specialized boarding school for the visually impaired, using salient object extraction method to access visual information, not only did blind people recognize geometric shapes that are included in the education process, but they also perceived the surrounding visual information using a tactile graphic and display, which can help them to adapt quickly to learning, independent movement, and social life.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 5 Conclusions  
Collaborative UVs approaches have the potential to be the enabler for implementing unmanned IMR operations within the OWT industries. Hence, this paper reviewed the collaborative strategies that can be implemented using several types of UVs for the OWT IMR operations. This review also highlighted the potential solutions and improvements a collaborative UVs approach may bring. To conclude, this review shows that:  
* Unmanned vehicles such as UAVs, UUVs, or USVs have a very limited capability to perform unmanned IMR operations for OWT when on their own, with the exception of carrying out visual inspections and simple NDT inspections.
* A swarm of drones or homogeneous UVs may accomplish complex IMR tasks but they still depend on USV as a power charging station, to change tools, as a communication hub, etc.
* Many collaborative strategies integrating the UAVs, USVs, UUVs, and crawler robots can be implemented as presented in Table 3, which depends on the degree of task complexity.
* USVs as sources of accurate global positioning (GNSS) may provide reliable and better localization systems for UAVs and UUVs to improve their existing localization system.
* UWB collaborative localization system is currently the best approach for UAVs to work close to an OWT, providing the OWT is shut down temporarily. However, the LiDAR-based SLAM technique has the potential to be implemented while the WT is fully operational.
* The implementation of a USV within a collaborative unmanned network has the potential to solve a number of issues surrounding the unmanned IMR OWT operations, including performing complex tasks, achieving better localization and navigation, prolonging operation time, and establishing better communication.
* Satellite communication system is currently the best communication platform for OWT sectors due to its geolocation. However, the introduction of 5G networks at offshore wind farms is slowly opening up the opportunity for a better communication system.
* At present, there is no redundancy strategy available for the collaborative UV; however, the redundant systems that are currently being deployed for a single UV can be further adapted for the collaborative unmanned systems. Collaborative approaches may also be used to address failure modes in designing redundant systems.
## 5 Structural Interpretation--Discussion
### Remarks on Comparing the Methods  
The detailed re-examination of these fault-slip data with the TRM application, which uses additional constraints induced using Slip Preference Analysis, likewise defines TRM site stress tensors with less than 10 fault-slip data [32] and TRM bulk stress tensors with more than 10 fault-slip data (except for T1\({}_{\text{TRM}}\), which was calculated from eight fault-slip data). However, the TRM site and bulk stress tensors are quite similar and not diverse, in terms of stress axis orientation and stress ratio, as those calculated from the best-fit stress inversion methods. It also indicates that the TRM defines stress tensors that are not as sensitive to the input fault-slip data as the best-fit stress tensors are. Thus, we can argue that the best-fit stress inversion methods suffer from the 'overfitting' modelling error, which is more limited in the TRM application due to the additional SPA geological constraints [24, 25, 26].  
More importantly, the resolved bulk stress tensors obtained from the TRM were determined exactly from the same original fault-slip dataset. These TRM bulk stress tensors were defined with much more than 10 fault-slip data, indicating that these solutions can hardly be considered a result of random fault-slip data (see Table 4) [41]. TRM bulk stress tensors also explain more fault-slip data recorded in more SSs. As a result, the basic assumption of the inverse problem, i.e., that the faults slip independently, does not seem to be violated when the 'point' no longer occupies an infinitesimal size, as defined in physics, or the SS of limited spatial scale, as defined in geology, but refers to a geological region covering several tens of km\({}^{2}\). In the case of bulk stress tensors, the large number of fault-slip data recorded in different SSs, as well as the fact that the dataset, as a whole, represents a wide variation, both in the orientation and size of the measured faults, eliminate, as much as possible, any influence imposed by the fault structures themselves. Likewise, it eliminates any deviation between the local and regional kinematic field, e.g., in a thrust belt [89].
# Evaluation of Early-Age Compressive Strength in Winter Prefabrication: A Comparative Study
## 3 Experimental Methods
### Calorimetry Test
#### 3.4.4 Significance and Implications of Findings  
Through the calorimetry analysis, the study aims to derive a comprehensive profile of the thermal characteristics of cement hydration under varied conditions. The data acquired not only reveal the state of the material's heat output at an early age, but also enable concrete mixes and curing processes to be optimized. By understanding the factors influencing the rate and magnitude of heat release during cement hydration, concrete technologists can tailor mix formulations and curing environments to achieve desired performance characteristics, such as setting time and early strength development.
## 3 Results
### TMG Parameters
#### 3.1.1 Right Leg  
Table 3 shows the multivariate analysis of the right leg. All factors except muscle \(\times\) group and time \(\times\) muscle \(\times\) group have a significant difference with a large effect size.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sample**} & \multicolumn{2}{c}{**CG**} & \multicolumn{2}{c}{**EG**} \\  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \(n\) & 34 & & 15 & & 19 & \\ Age (years) & 24.3 & 4.64 & 21.07 & 1.73 & 26.68 & 4.69 \\ Height (cm) & 179 & 7.07 & 178 & 8.68 & 179 & 5.7 \\ Weight (kg) & 74 & 6.16 & 72.79 & 7.23 & 74.89 & 5.27 \\ \hline \end{tabular} Note: \(n\), number; SD, standard deviation; CG, control group; EG, experimental group.  
\end{table}
Table 1: Baseline characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & **Values** & \\
**Leg** & **Muscle** & **Var** & **Group** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Right & BF & Tc (ms) & CG & 26.02 & 8.54 & 25.57 & 18.47 \\  & & & EG & 29.92 & 7.68 & 37.75 & 14.89 \\  & & Dm (mm) & CG & 4.23 & 2.29 & 4.15 & 1.68 \\  & & & EG & 5.35 & 1.79 & 4.79 & 1.91 \\ \cline{2-6}  & ST & Tc (ms) & CG & 48.27 & 6.65 & 30.42 & 10.39 \\  & & & EG & 47.42 & 9.05 & 44.43 & 11.36 \\  & & Dm (mm) & CG & 7.80 & 2.11 & 5.88 & 2.28 \\  & & & EG & 9.30 & 2.81 & 6.24 & 2.96 \\ \hline Left & BF & Tc (ms) & CG & 43.61 & 18.89 & 30.85 & 19.14 \\  & & & EG & 38.07 & 16.27 & 36.34 & 11.40 \\  & & & CG & 6.83 & 2.87 & 6.34 & 1.85 \\  & & & EG & 6.17 & 3.11 & 5.44 & 2.76 \\ \hline ST & Tc (ms) & CG & 46.96 & 6.44 & 35.58 & 9.38 \\  & & & EG & 49.54 & 7.34 & 41.11 & 12.32 \\  & & & CG & 8.54 & 1.67 & 6.45 & 1.57 \\  & & & EG & 7.82 & 2.58 & 6.41 & 3.18 \\ \hline \end{tabular} Note: BF, biceps femoris; ST, semitendinosus; Tc, time contraction; Dm, maximal radial muscle-belly displacement; Pre, first assessment moment; Post, second assessment moment; Var, variable; CG, control group; EG, experimental group.  
\end{table}
Table 2: Descriptive values of TMG in muscular response for both legs, muscles and groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **F-Value** & **Df** & \(p\)**-Value** & \(\mathbf{n_{p}^{2}}\) & \(\mathbf{1-\beta}\) \\ \hline Time & 5.540 & 2,31 & 0.009 & 0.263 & 0.817 \\ Time \(\times\) Group & 5.348 & 2,31 & 0.010 & 0.257 & 0.803 \\ Muscle & 19.109 & 2,31 & \textless{}0.001 & 0.552 & 1 \\ Muscle \(\times\) Group & 1.632 & 2,31 & 0.212 & 0.095 & 0.318 \\ Time \(\times\) muscle & 16.160 & 2,31 & \textless{}0.001 & 0.510 & 0.99 \\ Time \(\times\) muscle \(\times\) group & 0.092 & 2,31 & 0.912 & 0.184 & 0.063 \\ \hline \end{tabular} Note: Df = Degree of freedom, \(\mathbf{n_{p}^{2}}\) = Eta partial Square, \(1-\beta\) = power statistic.  
\end{table}
Table 3: Multivariate analysis values of right leg.
## 3 Results and Discussion
### Discussion  
Since existing real-data from solar irradiance was used in these existing mathematical models, one can assume adequate results. The uncertainty derived from the input data on the model should then be assessed before the model output is assessed.  
Particular technical challenges need to be considered to implement the optimal tilt and orientation angles while installing PVs, such as physical space availability and ideal installation conditions with shadow-free.  
Although the presented models and others could present accurate results for tilt and orientation angles in fixed flat surfaces, it is crucial to note that some input parameters could have a strong influence on the irradiance data. For example, other parameters, such as relative humidity, cloud coverage, and cloud base, could impact irradiance data, which could be taken into account in future works.  
Observe also that anisotropic methods could be unable to mimic the anisotropy of real skies, especially partly cloudy skies or those observed under elevated turbidity conditions. Thus, various accurate solutions exist to model spectral/broadband irradiance on sloped surfaces, such as the Unisky Simulator [41]. This tool provides the maps of zenith-normalized spectral sky radiances and can model random distributions, as well as statistical averaging over random distributions [42].  
Optimum tilt angle strongly depends on momentary cloudiness and turbidity conditions. Aerosols can change the angular distribution of scattered electromagnetic radiation significantly. For instance, a forward-lobed scattering is typical for large particles, while the ratio of side-to-forward scattering increases as the particles become smaller in size. Therefore, knowledge of particle size distribution (or at least PM1.0) may be crucial to understanding diffuse light distribution dynamics.  
For a country, such as Ecuador, where there are significant changes in weather conditions across cities due to geographic conditions, it is not representative for the entire country to estimate a single average optimum tilt angle.  
Overall, the results obtained indicate that, in Ecuador, the optimum inclination of a flat surface should be between \(12^{\circ}\) and \(19^{\circ}\) concerning the horizontal and the orientation between \(20^{\circ}\) and \(60^{\circ}\) concerning the North. However, although the results are congruent, the results would be more reliable with data measured in all cities since the TMY data could generate inaccuracies in the results. Additionally, in this work, the authors do not model hypothetically idealized skies.