esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 5 Performance Analysis
### Number of Control Messages  
The simulation results of the number of control message vs. the UE moving speed, cache size, and CDF are shown in Figure 15a-c, respectively. As shown in Figure 15a, the the number of control message increases if the more number of handover events occurred. Figure 15a shows that the number of control messages is 10 using the PMIPv6 with FMC under the case of the UE moving speed is 5 (m/s). However, the number of control messages of our proposed scheme with FMCL are 15, 18, 33, and 48 if the cache hit rate is 0, 10%, 40%, and 70%, under the case of the UE moving speed is 5 m/s, respectively. Figure 15a shows that the number of control messages of PMIPv6 with FMC was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 10%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 40%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different UE moving speed. Figure 15b shows simulation result of the number of control messages vs. the different cache  
Figure 14: Probability of packet loss vs. (**a**) UE moving speed and (**b**) cache size, and (**c**) CDF vs. probability of packet loss.  
size (is ranging from 100 M to 200 M). The higher cache size is, the higher the number of control message will be. This is because that as the high cache size also implies that the high cache hit rate, so more control messages to needed to communicate with UE and F_AP. Figure 15b shows that the number of control messages is 4 using the PMIPv6 with FMC under the case of the cache size is 150 (M). However, the probability of packet loss of our proposed scheme with FMCL are 7, 18, 24, and 30 if the cache hit rate is 0, 10%, 40%, and 70%, under the case of the cache size is 150 (M), respectively. Figure 15b illustrates that the the number of control messages of PMIPv6 with FMC was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(<\) that of our proposed scheme with FMCL (the cache hit rate = 10%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 40%) was \(<\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different cache size. Finally, Figure 15c provides the simulation result of CDF vs. the number of control messages.  
Figure 15: _Cont._
## 4 Experiments and Discussion  
In this section, we conduct ablation and comparative experiments on a public optical remote sensing dataset DIOR [2] with 20 categories to validate the proposed YOLO-DSD, considering the accuracy, deployability and speed indictors. Another optical remote sensing dataset RSOD [50] with 4 categories was utilized to further verify the effectiveness of the proposed YOLO-DSD compared with YOLOv4.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 3 Proposed Algorithm  
Only using MMSE as a criterion, it is difficult to make a balance between speech distortion and noise reduction. This section derives a more generalized fullband loss function.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 3 Modeling of QoS and Energy-Efficient Routing
### Modeling of QoS Routing
\[Loss(P)=1-\Pi_{(i,j)\in P}\Big{(}1-l_{(i,j)}\Big{)} \tag{6}\]  
Since the packet rate calculation cannot be multiplied, it needs to be converted into a transmission success rate before processing, where \(l_{(i,j)}\) is the packet loss rate on link \((i,j)\).
## 3 Methodology  
The development of the SCADA system with 3D visualization for the Lerma Basin water supply network involved a structured methodology, divided into four main phases: (1) creation of 3D models, (2) application development, (3) technological integration, and (4) testing and optimization (see Figure 1). Each phase was designed to ensure the system's functionality, scalability, and usability in real-world conditions.
## 2 Theoretical Model
### BRCS DDM Modeling  
The BRCS DDM is calculated using the method discussed in [11]. Using [11] (Equation (49)), the BRCS DDM is expressed as  
\[\langle\sigma(i,j)\rangle=\frac{1}{N}\sum_{n=1}^{N}\int_{\Sigma_{0}}\sigma_{0 }\Big{\langle}\big{|}\chi(\delta\tau_{i},\delta f_{j})\big{|}^{2}\Big{\rangle} \mathrm{d}\vec{\sigma} \tag{20}\]where \(N\) is the number of coherent integration intervals in a single incoherent averaging of the DDM, \(\chi\) is the Woodward ambiguity function (WAF), and \(\Sigma_{0}\) is the integration area, which needs to be over a planar or locally planar surface. The quantities \(i\) and \(j\) denote delay and Doppler indices, respectively. The variable \(\delta\tau_{i}\) is the delay of bin \(i\) relative to the nominal specular point (SP) delay, and the variable \(\delta f_{j}\) is the Doppler of bin \(j\) relative to the nominal SP Doppler.  
A good approximation of the GPS L1 C/A WAF is in [11] (Equation (50)). The WAFs of other GNSS waveforms, such as GPS L5, Galileo E1A, and Galileo E6A/B/C, can be calculated using the method presented in [23].
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 3 Experiments
### Experimental Results of the Natural Image Dataset  
The UGAN was used to generate the mixed images by adding a yellow haze interference on the clear shoe image source. Next, the generated results were input into the PAGAN to train them to separate the clear shoe image source from the mixed source.  
As shown in Figure 4, we see that NMF and FastICA cannot separate the image from the interference source. The single-image reflection removal algorithm proposed by [PERSON] et al. also cannot separate the images. NES can separate the image, but the color is not clear. In contrast, the separation effect of our method is better than those of the other methods.  
To further evaluate the separation performance, we carried out experiments on a dataset synthesized from two images. As mentioned before, the purpose of the experiment was to separate the image of the larger weight from the synthetic image. Figure 5 shows three groups of results for the synthetic images. NMF and FastICA were ineffective at separating the two image sources, whereas the NES and single-image reflection removal methods could not clearly separate the image. In contrast, our method achieved better results.  
Table 1 lists the objective measurement results for each set of experiments. In Table 1, we can see the PSNR (dB)/SSIM scores of several image separation methods on these two datasets. The separated image is said to be closer to its ground truth if it has a higher PSNR value, while a higher SSIM score means that the result is more similar to its reference image in terms of image brightness, contrast and structure. It can be observed from Table 1 that the proposed method achieves the best performance on two of the datasets, and outperforms NMF, FastICA, NES, and [PERSON] et al.'s method with respect to both PSNR and SSIM. This substantiates the flexibility and generality of our proposed method in diverse mixing types  
Figure 4: Results of image separation for the yellow haze images.
# The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT
## 3 The Distributed Task Offloading Scheme for Massive IoT
### System Model
#### 3.1.1 Transmission Delay  
Define \(b_{j,m}(t)\in\{0,1\},m=\{0,1,2,\cdots,N\}\) to indicate the connection relationship between the services and the servers, where \(b_{j,m}(t)=1,m=0\) indicates that the service \(j\) is offloaded to the cloud server at time \(t\), and \(b_{j,m}(t)=1,1\leq m\leq N)\) indicates that the service \(j\) is offloaded to the \(m^{th}\) edge server at time \(t\).  
When the service is offloaded to the edge servers, that is \(b_{j,m}(t)=1,1\leq m\leq N)\), the channel gain is denoted as \(h_{j,m}(t)\), and the transmitting power is \(p_{j,m}(t)\). Assuming that  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Decision-Making Mechanism** & **Scalability** & \begin{tabular}{c} **Training** \\ **Overhead** \\ \end{tabular} & \
\begin{tabular}{c} **Processing** \\ **Delay** \\ \end{tabular} &
\begin{tabular}{c} **Output** \\ **Policy** \\ \end{tabular} \\ \hline Centralized training and & No & High & High & Optimal \\ centralized training and & Yes & High & Low & Suboptimal \\ distributed decision-making & Yes & Low & Low & Suboptimal \\ \hline \hline \end{tabular}
\end{table}
Table 4: The differences of the centralized and distributed decision-making.  
the transmitting channels are orthogonal and the noise obeys the Gaussian distribution with \(\mathcal{N}(0,\sigma^{2})\), we can obtain the transmitting rate which can be denoted as follows:  
\[r_{j,m}(t)=W\log_{2}(1+\frac{b_{j,m}(t)p_{j,m}(t)h_{j,m}(t)}{\sigma^{2}}) \tag{1}\]  
where \(W\) is the allocated bandwidth, which is assumed as the same. Therefore, the transmitting time can be denoted as follows:  
\[TR_{j,m}(t)=\frac{D_{j}(t)}{r_{j,m}(t)} \tag{2}\]  
When the service is offloaded to the cloud server, that is \(b_{j,m}(t)=1,m=0\), we assume that the service is offloaded to the cloud server through the edge server, which can be denoted as  
\[TR_{j,m}(t)=\min\{\frac{D_{j}(t)}{r_{j,m^{\prime}(1\leq m^{\prime}\leq N)}(t)} \}+Tr \tag{3}\]  
where \(Tr\) is the needed time transmitting from the edge servers to the cloud servers.
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 7 Conclusions
### Limitations  
A conceptual reference framework is a simplified representation of reality in that it reduces the complexity of reality to present a topic in a comprehensible and communicable way. Consequently, it does not include all the attributes of the actual phenomenon but only those that seem relevant. It is unavoidable in the design cycles that subjective influences come into play, especially in identifying meta-features and constructing the dimensions used. Since the focus in developing the application-oriented DTF was to consider practitioners' collected input and perspectives, there are corresponding constraints. For example, the three-dimensional representation of the scientific DTF was omitted to meet the demand for a more straightforward, intuitively understandable representation. Through this omission, interrelationships in the individual dimensions are lost. Therefore, the application-oriented DTF can claim less of a complete mapping of the dimensions of value creation with DTs. On the other hand, the application-oriented DTF could be supplemented with elements that increase its usefulness in practice, as they enable a more operationalized representation. Furthermore, the application to the use case has shown that a representation chosen for the application-oriented DTF can easily be supplemented with additional elements or specifications if required. Such additions could be, for example, the connectivity solutions used or additional levels in the system hierarchy. Conducting an individual case study has the advantage that a use case can be examined in depth and detail. However, this also results in limitations. The most critical limitation is the constrained generalizability of the findings and the created instance of the application-oriented DTF. Even if it is assumed that the basic mechanisms of value creation with DTs are transferable to other cases, it is unclear how similar these use cases must be. Furthermore, no statements have been made about the economic viability of the project or the commercialization of the created value. Therefore, whether an equivalent development of DTs is worthwhile for other companies from an economic point of view cannot be answered.
[23] investigated the detection and classification method for single and mixed power quality disturbances by utilizing VMD and a decision tree. [PERSON] et al. [24] adopted VMD and ST to extract the PQD features and then used sequential forward selection and sequential backward selection to eliminate the redundant features. The results demonstrated the satisfactory performance of VMD in terms of speed and accuracy. There is still a lack of analysis of the feature extraction effect with classifier accuracy, despite the numerous studies performed in the field of PQD detection. Few studies have focused on feature selection during PQD detection. Feature selection also needs to be taken seriously since not all features are involved in PQD detection. Features with useful information the increase the accuracy of PQD classification. Meanwhile, redundant features may mislead the classifier and reduce the computational efficiency. In this paper, a novel methodology is developed for the detection and classification of power quality disturbances in a microgrid system. Different from the method that is introduced in the reviewed studies, this proposed approach takes advantage of VMD, permutation entropy, and Fisher scoring to obtain the relevant features for classification. Specifically, VMD is utilized to decompose the obtained signal into a cluster of sub-intrinsic mode functions. Afterwards, the statistical features, such as RMS, standard deviation, and multiscale moments, are extracted from each IMF under noise-free and noisy environments. Furthermore, the permutation entropy and Fisher scoring are used to effectively identify the relevant features. Both of the two analysis methods are essential and constitute a two-stage heuristic feature selection method. Finally, an SVM classifier is implemented to identify the different types of the PQ disturbance signals through the optimized feature vectors. Experimental investigations are performed while using the proposed methodology in both a simulation model and real microgrid platform. The main contributions of the proposed approach include the following aspects:  
1. VMD is more capable of PQD signal decomposition when compared with wavelet packet decomposition due to its adaptive and robust characteristics, especially in dealing with recursive calculation and mode mixing problems. Additionally, the start and end points of a PQD event can be detected by using VMD and permutation entropy. 2. The two-stage heuristic feature selection technique consists of permutation entropy, and the Fisher score is utilized to eliminate any irrelevant features, which not only enhances the generalization capability and detection accuracy of the classifier, but also releases the computational complexity in terms of efficiency. 3. The proposed algorithm has sufficient flexibility under different levels of noisy environments, either for the detected sensitivity or specificity of the PQD.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 4 Result Calculation and Analysis
### Analysis of Oil Film Pressure and Thickness for Multiple Rolling Elements  
The computational results using the parameters from Table 2 (cylindrical roller bearing) and Table 3 (operational conditions) are shown in Figures 7 and 8, which illustrate the pressure and film thickness distributions of elastohydrodynamic lubrication under the line contact conditions for each cylindrical roller presented in Table 3, where the horizontal coordinate x/b denotes the non-dimensional coordinate along the x direction in the contact zone.  
As shown in Figure 6, the oil film exhibits a horseshoe-shaped profile upon entering the contact zone. As the rollers transition from mesh-in to mesh-out positions, the film thickness gradually decreases with necking phenomena. At the central contact region, the film adopts a parallel configuration.  
With an increase in roller number, both the central film thickness and minimum film thickness exhibit a marginally ascending trend, which is fundamentally attributed to the diminishing contact load between the rollers and bearing rings as the numbering increases.  
Figure 6: Calculation flowchart.  
Figure 7: Film thickness of rollers and bearing rings at different positions.  
Notably, while the load reduction facilitates film thickness growth, the lubricant within the Hertzian contact zone demonstrates viscosity attenuation due to the pressure-viscosity effect, leading to progressive intensification of the curvature in the film thickness decay curve. When considering only the viscosity variation, reduced viscosity inherently decreases the film thickness. Consequently, in the studied case, the diminished lubricant viscosity parameter enhances the decay rates of both the central and minimum film thicknesses, resulting in nonlinear evolutionary characteristics in their distribution profiles.  
As shown by the numerical analysis results in Figure 7, the pressure distribution in the contact zone closely aligns with the Hertzian theoretical pressure distribution. As the roller moves into the engagement phase within the contact zone, the pressure curve exhibits a characteristic pattern of an initial rise, followed by a gradual decrease. This phenomenon occurs because the lubricant entering the contact zone enhances the hydrodynamic effects through extrusion between rollers, leading to pressure elevation. When the roller transitions to the separation phase, the expansion of the lubricant flow space results in pressure reduction. Furthermore, the pressure gradient variations at the inlet and outlet regions of the contact zone tend to flatten.  
Additionally, as the contact load progressively decreases from roller No. 1 to No. 4, the overall magnitude of oil film pressure shows a gradual diminution, accompanied by a corresponding decrease in the size of secondary pressure peaks with reducing loads.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 6 Actuators Used in the Active Seat Suspension System  
Certain types of actuators have been implemented to drive the active seat suspension in different directions. These actuators are divided into three categories, namely: hydraulic, pneumatic and servo actuators. Some moving machinery vehicles are driven by hydraulic power. Therefore, it is advantageous to adopt a hydraulic actuator for the active suspension system, while pneumatic and servo actuators are used for the other systems. The main problem related to the pneumatic actuators is that they need more energy to keep the enough air pressure in the source. Furthermore, they require much longer response time to the control signals due to the air compressibility. In addition, the mathematical model complexity of the system makes the pneumatic actuators less reliable and less convenient [65].  
Air springs have been extensively used in the seat suspension and vehicle suspension systems of heavy trucks because the pressure inside the spring is changeable and would provide an extra comfort and ride quality [66; 67]. Recently, it was found that the air spring has a significant advantage over the coil spring as the spring rate varies with the supplied air pressure and makes the seat suspension stiffness consistent and reliable.  
Plenty of studies have used rotatory motors to drive the active seating suspension system in the vertical direction by varying the motor torque though a control signal. They have been shown to have sophisticated advantages over linear motors in terms of the low cost and low energy consumption. Additionally, the rotary motor can be integrated with a gear reducer to provide a required high torque to drive the active seating suspension system.
# Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study
## 4 Discussion  
A small sample of subjects controlling an exoskeleton was tested on different textured pavements while their neural activity was recorded. To the best of our knowledge, this is the first study to investigate the neural activity associated with different pavement textures while subjects controlled an exoskeleton. The analysis of the neural activity associated with tactile processing revealed an overall pattern of increases and decreases in power within the delta frequency bands of the C3 and C4 electrodes when the exoskeleton was used. However, no clear pattern was observed with respect to the different textured pavements. When utilizing four different classifiers, the results showed high levels of decoding accuracy, with the LDA classifier demonstrating the best performance. Collectively, these findings suggest that the use of an exoskeleton is associated with changes in the neural activity in electrodes C3 and C4, as well as that LDA is effective in classifying these differences.  
Exoskeleton control was associated with changes in neural activity. The pilot analysis of the power in the C3 and C4 electrodes revealed that the use of the exoskeleton in different pavements was associated with changes occurring mostly in the delta band. This finding from the pilot analysis was further supported by the the classifiers' performance outcomes. Our results are in line with previous studies highlighting the role of the delta frequency band in upper and lower limb exoskeleton control [38, 39, 40, 41, 42]. An alternative explanation for these findings could be the increases in attention, perception, and decision making [43, 44, 45]. This alternative explanation also fits with self-reports of subjects indicating the need to pay attention to the exoskeleton movements and balance during the task. It will be relevant in future studies to determine if the continuous use of the exoskeleton results in a decrease in the power of the delta frequency band for this electrode.  
In summary, based on the presented results, significant changes were observed in the delta frequency bands of the C3 and C4 electrodes within both the Exo and No Exo (control) conditions while walking on the four different pavements. Among the four classifiers used, LDA achieved the best performance. Additionally, it is worth noting that, although the neural patterns could not be inferred during the pilot analysis, all implemented machine-learning models were able to distinguish between all experimental conditions, albeit with varying degrees of performance.
# Study on Solar Radiation and the Extreme Thermal Effect on Concrete Box Girder Bridges
###### Abstract  
Thermal effect is an essential factor in the durability and safety of concrete bridges. Therefore, this paper mainly studied the concrete bridge box girder temperature distribution and thermal effect under solar radiation and the thermal load. With a concrete rigid frame bridge as the engineering background, the temperature distribution of the box girder on a clear summer day was observed. Then, according to the solar physics and heat transfer theory, the different surfaces of the box girder cross-section are classified based on the heat transfer conditions, and the variation of solar radiation on different surfaces is investigated. The temperature field of the box girder is simulated by ANSYS. To obtain the extreme thermal condition, the meteorological data of the bridge site from 1990 to 2020 are collected. The data are fitted by generalized extreme value distribution to obtain the extreme temperature and average wind factors in the bridge design lifetime. Combined with the solar radiation, temperature, and wind factors, the extreme thermal condition of the concrete box girder is obtained. Lastly, the thermal effect of the box girder under the extreme condition is analyzed, and the thermal stress is compared with the allowable stress in the design code. The results show that the girder temperature difference is closely related to the solar radiation intensity and heat transfer conditions, and the solar radiation intensity is the more critical factor. The tensile stress caused by the extreme thermal load is more significant than the design strength value in the girder cross-section. The results also provide a method to obtain the extreme thermal condition and evaluate the impact of the thermal effect on concrete box girder bridges.  
concrete box girder bridge; solar radiation; temperature difference; generalized extreme value distribution; thermal effect +
Footnote †: journal: Applied Sciences  
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