esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Constant Amplitude Test  
Due to the general increase in fatigue life, the stress amplitudes selected for the LPM at 260 Hz are also significantly higher compared to those at 80 Hz. The changes in temperature  
Figure 5: Cyclic deformation curves of load increase tests (LIT) for specimens of SAE 1045 +N steel starting at \(\sigma_{a,start}\) = 180 MPa with \(\Delta\sigma_{a}\) = 5 MPa and \(\Delta N\) = 1.8 \(\times\) 10\({}^{5}\) for (**a**) f = 80 Hz and (**b**) f = 260 Hz.  
and electrical resistance are detected as analog to the LITs. The cyclic deformation curves of the CATs are exemplary illustrated for the change in temperature in Figure 6a and for the direct measured electrical resistance \(R\) and \(R_{corr}\) in Figure 6b. A significantly more pronounced material response in the case of 260 Hz is confirmed in both figures. It should be noted that the data points at 0.5\(\cdot\)N\({}_{f}\) are highlighted since they are used within the calculations according to StressLife\({}_{\text{HCF}}\).  
In all fatigue tests, the slope of the increase in \(R_{corr}\) is lower than the directly measured electrical resistance.  
The point where the maximum cyclic hardening occurs highly depends on the test frequency. While maximum hardening at 80 Hz occurs in a range of approx. 50 % of the fatigue life, it is observed at approx. 76 % of the fatigue life concerning a test frequency of 260 Hz. An explanation for this frequency-related shift has already been given in Section 3.1.  
In general, competing processes must be taken into account. On the one hand, more energy per time unit is induced in the specimen due to the increased test frequency, leading to a higher amount of dissipated thermal energy. However, this energy cannot radiate across the specimen quickly enough, and the portion of internal stored heat increases. Due to this, dislocation movement is more facilitated, which tends to shorten the lifetime of the specimens [48]. On the other hand, due to the higher test frequency, it takes considerably longer until the necessary cumulative shear stresses are induced in the microstructure to start the dislocation movements, which tends to extend the lifetime [49]. Depending on the frequency, one of these two effects can dominate, resulting in different cyclic deformation behaviors for different test frequencies. For example, the tests at 80 Hz exhibited a cyclic hardening that extends over a significantly longer cycle range (approx. 10\({}^{5}\) cycles) than at 260 Hz (approx. 3 \(\times\) 10\({}^{4}\) cycles). As already explained for the LITs, no temperature correction of the change in electrical resistance is possible for the CAT with a stress amplitude of 285 MPa for a few cycles (Figure 6b), as the change in temperature exceeds 180 K.  
Figure 6: Cyclic deformation curves of constant amplitude tests (CAT) for specimens of SAE 1045 +N steel based on (**a**) change in temperature and (**b**) change in electrical resistance and change in temperature-corrected electrical resistance.
## 4 Experimental Results
### Comparison between GRASPSPP and Reactive GRASP Algorithm  
This section shows the comparison results between the GRASPSPP and reactive GRASP algorithm proposed by [PERSON] et al. in 2008 [PERSON] et al. (2008). The comparisons only have the best height and average values because the author prioritizes the results of the instances and not the computational times. Therefore, we cannot guarantee a fair equivalence regarding quality and efficiency; hence, we included these comparisons merely as a complement using a time limit of 60 s.  
Figure 14 shows the percentage error (see Equation (11)) comparison between GRASP-SPP (0.03 average, 0.01 standard deviation) and reactive GRASP (0.84 average, 0.57 standard deviation) algorithm from instance C1-C7 from the set Htu/ht (see Table 2. The reactive GRASP produces, in general, high average errors. The Wilcoxon test shows that the GRASPSPP algorithm outperforms statistically the reactive GRASP with a \(p\)-value of 0.046.  
Figure 12: GAP comparison between GRASPSPP and NB&B algorithm.  
Figure 13: Time comparison between GRASPSPP and NB&B.  
Figures 15 and 16 shows the comparison of best values between the GRASPSPP (958.94 average, 1847.64 standard deviation) and reactive GRASP (948.59 average, 1786.24 standard deviation) algorithms. It is important to highlight that the author does not provide the rest of the average errors for these instances. Instead, they choose to use the best value, and, according to Figure 14, we believe that their algorithm produces solutions with a high variance.  
As we can see, the differences between both algorithms are minimal except for a handful of instances.  
The Wilcoxon test shows that the reactive GRASP has statistically better performance than GRASPSPP with a \(p\)-value of 0.000.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 1 Introduction  
### _Theme Background and Significance_  
In modern society, road traffic holds significant importance in both the economy and daily life. However, with the rapid development of the automotive industry, there is a lack of sufficient awareness regarding the limitations and scarcity of traffic resources. The surge in travel demands has placed immense pressure on transportation systems. Although the construction of new roads can alleviate congestion, most cities have turned to advanced technologies due to limitations in urban space and costs. In recent years, many countries worldwide have invested substantial resources into the research and development of intelligent transportation systems (ITSs), which have played a critical role in alleviating congestion and enhancing the traffic flow efficiency.  
As a key component of smart cities, ITSs play a vital role in improving the environmental quality and enhancing the quality of life of residents. ITSs utilize real-time monitoring, intelligent traffic signal control, and other technological means to directly reduce trafficcongestion, lower accident rates, and relieve urban traffic pressures. Additionally, ITSs can decrease vehicle idling, thereby reducing exhaust emissions and indirectly improving the urban environmental quality. Most importantly, through services such as intelligent navigation systems and real-time public transportation information, ITSs enhance the convenience and comfort of residents' travel [1; 2].  
In summary, ITSs play an irreplaceable role in achieving the development goals of smart cities. However, an ITS is a complex management system, encompassing multiple subsystems, such as rail transit management, intelligent traffic signal management, and vehicle navigation. To ensure the efficient operation of the ITS, traffic flow prediction technology is of paramount importance.  
Traffic forecasting technology is the cornerstone of intelligent transportation systems. Its core principle is to model urban road networks, highways, and rail transit systems and, by integrating historical traffic data, predict traffic conditions for specific future time periods. By accurately predicting traffic flow data, traffic management authorities can more effectively guide vehicles, mitigate urban traffic congestion, and improve the vehicle throughput efficiency. Accurate traffic flow forecasting also helps to schedule vehicle operations in rail systems, preventing public safety issues that may arise from large crowds gathering for extended periods, thus ensuring the safety of citizens' travel. Furthermore, precise traffic forecasting can reduce the energy consumption associated with travel, contributing to the construction of low-carbon and environmentally friendly cities. Therefore, the further enhancement of traffic flow forecasting technologies is imperative.
## 3 Results and Discussion
### Evaluation of the Mechanical Performance of the Selected Asphalt Mixtures
#### 3.5.3 Resistance to Permanent Deformation of the Selected Asphalt Mixtures  
The wheel tracking test (WTT) was then carried out to characterize the resistance to permanent deformation of the two mixtures with 75% SSA and the equivalent conventional mixtures. The evolution of the average rut depth of the two slabs of each asphalt mixture can be observed in Figure 8, while the results of the permanent deformation parameters obtained from this test are presented in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Asphalt Mixtures**} & \multicolumn{2}{c}{**Indirect Tensile Strength**} & \multicolumn{2}{c}{**ITSR**} & \multicolumn{2}{c}{**Density (kg/m\({}^{3}\))**} & \multicolumn{1}{c}{**Va**} \\ \cline{2-7}  & **ITSd (kPa)** & **ITSw (kPa)** & **(\%)** & **MD** & **Bulk** & **(\%)** \\ \hline \multirow{3}{*}{AC 14 surf} & 75\% SSA & 2631 & 2535 & 96 & 2907 & 2681 & 7.8 \\  & 100\% NA & 3218 & 2471 & 77 & 2449 & 2355 & 3.8 \\ \cline{1-1} \cline{2-7}  & 75\% SSA & 2809 & 2064 & 74 & 2913 & 2770 & 4.9 \\ \cline{1-1} \cline{2-7}
20 bin/base & 100\% NA & 3074 & 1974 & 64 & 2452 & 2385 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Water sensitivity test results for mixtures with 75% SSA and natural aggregates.  
Figure 8: Evolution of the rut depth of the different asphalt mixtures in the WTT test.  
The results obtained demonstrate that the mixtures with SSA are much less susceptible to deformation than the equivalent conventional mixtures. Both asphalt mixtures with SSA have wheel tracking slopes (WTS\({}_{\text{AIR}}\)) lower than 0.1 mm/10\({}^{3}\) cycles, less than half of the identical asphalt mixtures with NA. The irregular and rough shape of the SSA may have contributed to this excellent performance at higher temperatures. Figure 9 shows images of the rut depth obtained in the AC 14 mixtures to exemplify the differences between the studied asphalt mixtures (100% natural aggregates and 75% SSA).
[19] developed a dynamic model of a rotor system supported by roller bearings and a floating ring squeeze film damper (FSFD), revealing that FSFDs significantly reduce bi-stable responses in the system. [PERSON] et al. [20] established a flexible asymmetric rotor system model incorporating nonlinear support interactions between rollers, bearings, and SFDs. Their results highlighted significant nonlinear features such as bi-stable phenomena, amplitude hopping, and desynchronized vibrations arising from the nonlinear coupling among the rotor, bearings, and SFDs. Existing research has addressed nonlinear phenomena caused by SFDs and critical speed calculations of rotors, yet it remains unclear how the imbalance response of rotors differs under the influence of nonlinear forces versus linear ones. Due to the difficulty in modeling the nonlinear dynamic characteristics of SFDs in support structures, establishing a precise analytical model to reveal the parametric influence laws of complex vibration behaviors remains challenging. This paper proposes a finite element model to investigate the impact of nonlinear forces on the dynamic characteristics of rotors. This paper employs the Euler-Bernoulli beam rotor model and establishes a dynamic model of the rotor-support system using the finite element method (FEM). Two nonlinear forces are applied to the model as external loads, allowing for their adjustment and removal. The Newmark \(\beta\) method is introduced to solve the time-domain response of the rotor system, facilitating the investigation of the nonlinear forces' impact on the system's dynamic characteristics. Initially, the nonlinear forces are neglected to compute the rotor response under no external excitation. Subsequently, the SFD nonlinear oil film force and the support nonlinear restoring force are added separately to analyze their individual effects on the rotor response. Finally, both nonlinear forces are coupled and applied to study their synergistic influence on the system's behavior.
## 3 Preliminaries: Network Model and Problem Definition
### Multi-Agent System Based on Actor-Critic Framework  
This section proposes a multi-agent system architecture suitable for MEC networks based on the classic strategy learning framework Actor-Critic. The multi-agent system that was designed is shown in Figure 5.  
The system adopts the region partitioning method, dividing the entire network into multiple subnets based on the hop distance. If an undirected graph is used to represent a node network, the system will select the node with the highest degree as the commentator node for each subnet in the network graph every time the network topology changes. The commentator node will act as the sole agent in the subnet, making decisions for all nodes within the subnet. Ordinary nodes only need to submit their observed information to the commentator node as a decision basis and execute the action values issued by the commentator node. The commentator node maintains multiple policy networks and a value network internally, with each policy network corresponding to the offloading strategy of each node in the subnet. The intelligent agent can make decisions and execute actions based on the output values of the policy network. Subsequently, the intelligent agent will use the value network to evaluate this action based on environmental feedback rewards and new observation information. The policy network and value network will update their parameters accordingly.  
Figure 4: Offloading and scheduling when user moves across cell (The numbers 1 –6 represent the order in which the scheduling rules are executed).  
Figure 5: Multi-agent system based on the Actor-Critic framework.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
###### Abstract  
As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input-target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).  
remote sensing; big data interpretation; synthetic aperture radar (SAR); speckle noise; self-supervised learning; Bernoulli sampling
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 5 Conclusions  
Our findings show the efficacy and advantages of the S-ANC method for the intelligent control and management of hybrid MGs. The optimization of S-MPC improves energy management and power flow control, resulting in more efficient use of resources. The integration of AR and RNN-LSTM models improves the accuracy of predictions, allowing the control system to adapt to dynamic system conditions and to optimize the operation of the MG. The successful implementation of S-ANC significantly affects the dependability, sustainability, and cost-effectiveness of hybrid MG systems. We can achieve efficient control and management of complex energy systems by leveraging the capabilities of advanced modeling techniques within the S-MPC framework. These findings support the incorporation of hybrid MGs in future energy systems and contribute to developing intelligent control strategies. By combining the AR-LSTM, the computational time of the model was reduced by approximately 47.2%. In addition, this study assessed the accuracy of our predictive model. The R-squared error, which quantifies the amount of variance in the dependent variable that can be predicted from the independent variables, was 0.951. Our model predicted 95.1% of the variance in the data, indicating a high level of predictive ability. The MAE and MSE values of 0.571 indicated precise forecasts, with minimal deviations from the actual values. The focus of future research and development should be on validating larger-scale systems and incorporating additional advanced models. These developments will enhance the performance and applicability of the S-ANC methodology and contribute to the efficient operation and integration of hybrid MGs in future energy systems. Conceptualization, M.C. and Y.F.U. ; methodology, M.C., A.A. and D.G. ; software, M.C. and A.A.; validation, M.C., H.A. and A.A.; investigation, M.C. ; writing--original draft preparation, M.C. and Y.F.U. ; writing--review and editing, M.C., Y.F.U., H.A., A.A., K.A. and D.G. ; visualization, M.C., Y.F.U. and H.A. ; supervision, A.A., K.A. and D.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study
### Industrial Problem Description
In this context, this case study focuses on the case of a Portuguese commercial aircraft that was faced with the need to perform repairs that were not described in the aircraft's maintenance manual. These repairs have been systematically identified for the Airbus A320 family, especially for aircraft older than 20 years. Currently, the repairs are being carried out by a third-party company to restore the airworthiness of the damaged components, which has a huge economic and logistical impact on this company. This company has the DOA certificate to perform design activities, but does not have a structured approach to evaluate its ability to perform these activities. Therefore, the extended RPI model proposed in this paper is used to access this capability so that the company can make informed decisions about its project portfolio and its investment in human and technical resources.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 2 The RCS Calculation Model for Dihedral CR
### The RCS Feature Comparison of Triangular and Dihedral CRs  
To evaluate the relationship between side length and corner reflector RCS, Figure 2a shows the RCS curve with the panel side length change of the dihedral CR (Equation (4)) and the triangular CR [27] under a C-band SAR sensor. The scattering energy of the dihedral CR is about 8 dBsm higher than the triangular CR under the same side length. Due to the return orbit design characteristics of most spaceborne SARs, the heading angle in a certain area is maintained at a fixed angle. If the azimuth alignment accuracy of the dihedral CR can be guaranteed during the installation, the RCS of the dihedral CR with the same side length will be significantly better than that of the triangular CR.  
Figure 2b illuminates the RCS reduction curve caused by the deviation angle. The side lengths of the dihedral CR with the square panel and the triangular CR are set as 0.7 m. Referring to Equation (4), we calculated the RCS reduction of these two corner reflectors as the deviation angle increases. The RCS reduction of the dihedral CR reaches 3 dB with a deviation angle of 16\({}^{\circ}\), while the triangular reflector is approximately 20\({}^{\circ}\). The deviation angles of the dihedral CR in our study are about 1-3\({}^{\circ}\) and 9-14\({}^{\circ}\) under Sentinel data. The maximum deviation angle of the same reflector reaches 12\({}^{\circ}\), which leads to an RCS reduction of 2.4 dBsm.
# Design and Experiment of Variable-Spray System Based on Deep Learning
## 3 Results and Discussion
### Variable-Spray Experiment Results
#### 3.3.2 Analysis of Spray Droplet Deposition Effect  
The results of the variable-spray droplet-coverage test at different speeds of variable spray are shown in Figure 13a. Taking water-sensitive papers No. 2 and No. 4 as sample  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spraying Modes** & **VMD/\(\mu\)m** & \
\begin{tabular}{c} **Droplet Density** \\ **/(Droplets/cm\({}^{2}\))** \\ \end{tabular} & **Coverage/\%** &
\begin{tabular}{c} **Deposition Rate** \\ **/(\(\mu\)L-cm\({}^{-2}\))** \\ \end{tabular} \\ \hline Constant spray & 284.49 & 102.56 & 48.52 & 2.36 \\ Variable spray & 146.36 & 141.43 & 46.35 & 2.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Droplet parameters under two types of spray methods.  
Figure 11: Constant- and variable-spray effect images. (**a**) Constant spray; (**b**) variable spray.  
Figure 12: Comparison chart of actual flow rate versus theoretical flow rate.  
data, it could be concluded that at the speed of 1 m/s, the average droplet coverage in areas with sparse weed density decreased by 13.98% compared to areas with dense weeds. Taking water-sensitive paper No. 4 as an example, it showed that under the same plant density, the average coverage rate increased by 2.91% at a forward speed of 0.5 m/s compared to 1 m/s, and was 6.59% higher than the average coverage at 1.5 m/s. There was no significant difference in the coverage rate of droplets between constant and variable sprays in the target area, indicating that variable spraying with less spray volume can achieve good coverage. Variable spraying provides superior spraying performance and can avoid excessive pesticide spraying. Similar research results were also obtained by [PERSON] et al. [32] for the variable spraying of grapevines, but the drop-coverage loss in the variable-spray system proposed in this study is 2.17%.  
As shown in Figure 13b, the droplet density of the variable spray is significantly higher than that of the constant spray. The average droplet-coverage density of the variable spray is 141.43 droplets/cm\({}^{2}\). The similarity of sprayed droplets in the process of variable spraying was high according to the field analysis. The droplets undergo secondary collisions in the air. Therefore, finer and more evenly distributed droplets are produced, which leads to an increase in droplet density. These results indicate that the variable-spray system can improve droplet size and spatial distribution uniformity and realize variable spraying at different speeds and different weed densities, with good spraying performance.