esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### MOO Evaluation
#### 4.1.4 Convergence Graphs  
For additional evaluation, we present a boxplot convergence curve of each of the objectives with respect to the generation from the starting point of the optimization until the end. We can observe from the figures that SC-MOPSO had fewer outliers than the other two algorithms, namely, NAGA-II and NSGA-III. This was generalizable to the trust results in Figure 7.  
Contrary to expectations, the energy consumption profile decreased as the number of iterations increased from 10 to 100 for all three algorithms. The energy consumption for each algorithm started at 0.05 and decreased as the number of iterations increased. A similar pattern can be observed for all three algorithms in Figure 8. However, both NSGA-II and NSGA-III had more outliers than SC-MOPSO-DG.  
Figure 6: Comparing SC-MOPSO with benchmarks in terms of the number of nondominated solutions.  
Figure 7: Boxplot representation of trust starting from first generation until the end. The + sign indicates the outliers.  
Contrary to expectations, the cost profile decreased as the number of iterations increased from 10 to 100 for all three algorithms. The cost for each algorithm started at 0.05 and decreased as the number of iterations increased. A similar pattern can be observed for all three algorithms in Figure 9. However, both NSGA-II and NSGA-III had more outliers than SC-MOPSO-DG.  
The outcomes of our study are depicted in Figure 10, where we compare the performance of our newly developed SC-MOPSO-DG algorithm with that of two existing benchmarks. The results demonstrate that the median value of SC-MOPSO-DG significantly improved and reached a level below 1500, which is comparable to that of the other two benchmarks. Moreover, we observed that NSGA-II, one of the benchmark algorithms, had a higher number of outliers and incurred a greater cost than the other two algorithms. Overall, these findings indicate that SC-MOPSO-DG is a promising algorithm that can achieve comparable or better results than the existing benchmarks while being less susceptible to outliers and reducing the overall cost of the optimization process.  
Figure 8: Boxplot representation of energy starting from first generation until the end. The + sign indicates the outliers.  
Figure 9: Boxplot representation of cost starting from first generation until the end. The + sign indicates the outliers.  
The final metric that we analyzed was noncoverage, which is shown in Figure 11. The results indicate that the noncoverage of SC-MOPSO-DG significantly decreased to 40%, which is much lower than that of NSGA-II (48%) and NSGA-III (62%). This finding suggests that SC-MOPSO-DG outperforms the other two algorithms in terms of noncoverage, which is a desirable characteristic in multiobjective optimization. Therefore, our results demonstrate the superiority of SC-MOPSO-DG over the other two benchmarks and highlight its potential for addressing complex optimization problems that involve multiple objectives.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 2 Methodology  
There are a variety of methods and metrics to evaluate the quality of mobile broadband services. Hence, each country has used a subset of these methods and metrics for evaluations. In the future, these countries and the ITU are expected to harmonize mobile broadband service evaluations such that consumers realize that the service provided in their country is of the highest standard. One such effort is being made by the Body of European Regulators for Electronic Communication (BEREC) [9]. However, to uniformly summarize the quality assessment from the regulatory body of each country, this report follows the structure detailed below.  
The summary of the regulatory report of each country is organized by:  
* **Regular**: The body responsible for mobile broadband service quality evaluation, the year the regulator started the assessment, the year regulators published the most recent report, and which year that report is about. For instance, Germany's last report was published in 2019 about the year 2018.
* **Evaluated service**: The mobile broadband service among 2G, 3G, or 4G is evaluated in the report. For instance, Singapore does not report on services such as 2G because it is no longer provided, whereas countries such as Japan discuss mobile broadband service in general rather than separately.
* **Operators**: The most prominent operators are mentioned in this part.
* **Method**: The method used to obtain the data and if statistical correction was used to account for errors is discussed here. Multiple means of obtaining data can be used.
* **Metrics**: As mentioned earlier, each country used different sets of metrics to evaluate the quality of their mobile broadband service. However, most of them provided some means of measuring coverage.
* **Results**: This topic is used to discuss the results obtained from each report and the policies outlined to improve the quality of the mobile broadband service.
* **Policies**: Policies outlined to improve the quality of mobile broadband service are presented.
## 1 Introduction
Therefore, this study primarily focuses on enhancing wake processing in high-resolution spaceborne SAR, leveraging the differences in the linear characteristics of wake echoes and the motion characteristics of random ocean background clutter. The research investigates methods for enhancing wake features during imaging processing in sliding spotlight mode, aiming to fundamentally improve the quality of wake images and enhance wake detection performance. Thus, the goal of this study is not to improve traditional wake detection or deep learning algorithms. The research content of this paper does not focus on the image domain implemented by traditional algorithms, but rather on wake feature enhancement through echo signal optimization processing during high-resolution spaceborne SAR imaging. This paper first combs the current research status and existing problems of the study on the enhancement of ship wake, and then, the image characteristics of ship wake are analyzed based the mechanism of ship wake of spaceborne SAR. This study proposes a method for enhancing ship wakes based on sub-aperture image sequences, specifically tailored for high-resolution SAR imaging. By employing equivalent multi-channel processing, this method preserves and enhances wake and wave information within sub-aperture images, resulting in clear images with enhanced wake features. Again, evaluation methods for assessing the effectiveness of ship wake image enhancement are studied. Finally, experiments are conducted using the real data of AS01 satellite and validate that the proposed method can enhance wake detection capabilities, thereby improving the applicability of spaceborne SAR in marine target detection.
This control algorithm has already been applied to PMSM and achieved favorable control effects [34; 35]. By incorporating fuzzy control algorithms with vector control algorithms and designing an appropriate control system for marine equipment, the issue of speed control for propulsion systems driven by electricity can be effectively addressed. However, another important issue lies in the existing research field of system dynamics modeling, where the dynamic characteristics and energy consumption characteristics of systems are often studied separately [36]. On the one hand, existing mechanical system dynamics models excessively focus on studying the transmission characteristics of the system [37; 38; 39]. On the other hand, research into energy consumption characteristics often focuses more on energy consumption monitoring and management during system operation [40; 41; 42]. This issue is mainly due to the diversity and complexity of the energy consumption of the actual system. Power bond graph theory provides a feasible solution to this problem, which was first proposed by Professor [PERSON] in the United States. With years of evolution and refinement, it has gained practical application across numerous research fields, particularly in mechatronics systems, unveiling considerable prospects for application. This methodology allows for the extraction of pivotal system parameters based on system attributes, which facilitates a more intuitive derivation of the system state equation [43; 44; 45], making this theory an instrumental tool for resolving dynamic analysis of complex systems [46; 47; 48]. In response to the prevalent issues in the hydraulic helicopter traction system, a novel electromotive helicopter traction system has been devised. The system employs a synchronous motor as its power source and integrates the vector control algorithm with the fuzzy adaptive PID control algorithm to function as a velocity-tracking controller, thereby allowing for the regulation of the speed of the helicopter traction system. This strategy ensures efficiency while minimizing force during traction. Furthermore, this article integrates power bond graph theory to establish a dynamics model and dynamic energy consumption model for the helicopter traction system, then conducts systematic simulation. The simulation results demonstrate that the newly designed system effectively elevates energy efficiency, ensuring pre-existing operational requirements while also exhibiting superb speed control efficacy. Simultaneously, this modeling approach is intuitive, efficient, and capable of accurately reflecting the dynamic and energy consumption characteristics of the system, enhancing the preliminary design analysis or postoperative energy optimization of marine equipment.
## 2 Materials and Methods
### Preparation of Calibration Standards and Quality Control Samples  
Calibration was performed by the method of internal standard. Two different calibration approaches, solvent-based and matrix-matched, were used. Separate stock solutions were used for the preparation of calibration standards and quality controls. For solvent-based calibration, 10 mg/mL AA working standard was diluted with mobile phase B to prepare calibration standards with concentrations 0.5, 1.0, 2.0, 4.0, and 5.0 mg/mL. To each calibration standard, 20 mL of IS working solution (500 mg/mL) was added, giving a final IS concentration of 50 mg/mL.  
Matrix-matched calibration was performed by spiking both surrogate matrix and authentic matrix (pooled human serum) with working AA standards to achieve the following final AA concentrations: endogenous unspiked, and endogenous spiked with 0.5, 1.0, 2.0, and 5.0 mg/mL AA. Working IS solution was added to each serum calibrator to achieve a concentration of 50 mg/mL. All calibration standards were prepared in triplicate.  
Three calibration curves, solvent-based, surrogate matrix- and authentic matrix-matched, were generated. Solvent-based and surrogate matrix-matched calibration curves were constructed with a blank, a zero-blank, and calibration standards in concentrations of 0.5, 1.0, 2.0, and 5.0 mg/mL AA. An authentic matrix-matched calibration curve includes a zero-blank and calibration standards prepared by spiking the matrix with the same concentrations of AA. Parameters such as regression equation, slope, intercept, and linearity, evaluated by calculating the Pearson's determination coefficient R\({}^{2}\), were estimated for each calibration curve.  
Due to variations in endogenous AA levels, two types of quality control (QC) samples, QC1 and QC2, were prepared daily and run with each batch. QC1 samples were prepared at three AA concentration levels: unspiked endogenous AA, and endogenous AA spiked with 1.0, and 5.0 mg/mL AA using the same pooled human serum as for the authentic matrix matched calibration. QC2 samples were prepared daily at concentrations of unspiked endogenous AA and endogenous AA spiked with 2.0 \(\upmu\)g/mL AA using pooled serum of all samples analyzed for the day. The choice of this concentration is based on literature data [21; 22; 23].
# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
###### Abstract  
Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station's high load utilization rate and the core network's bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0-1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.  
5G network; edge server; cluster method; mobile hotspot; greedy algorithm; placement of base station +
Footnote †: journal: Applied sciences
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 3 Probabilistic Evaluation of the Leakage Current Density through a Holiday in the Coating
### Example of Application to a Real Case of 50 Hz Interference  
In this paragraph, one considers a 50 Hz interference case between a 380 kV power line carrying, under normal operating conditions, a balanced current of 630 A and a nearby pipeline 28.5 km long having a coating thickness of 3 mm; the soil resistivity is 100 \(\Omega\)m; the layouts of the two plants are shown in Figure 11 where one can notice a close approaching between them for almost the whole pipeline route.  
Figure 11: Layouts of inducing and induced plants.  
Figure 10: Per cent probability of exceeding 30 A/m\({}^{2}\) versus induced potential and for different values of soil resistivity and coating thickness; \(\rho_{p}=\rho/10\).  
By means of a two-step calculation method based on treating both power line and pipeline by means of equivalent transmission line models one can obtain the 50 Hz induced voltage along the pipeline versus the pipeline abscissa \(s\) (see Figure 12).  
By applying (10), one can determine the probability that the limit of 30 A/m\({}^{2}\) for the induced current density is exceeded in a generic point of abscissa s along the pipeline provided that a holiday exists in the insulating coating in the same point. As far as the pore resistivity is concerned, the two hypotheses previously presented have been considered i.e., \(\rho_{p}=\rho\) and \(\rho_{p}=\rho/10\). The results are shown in Figure 13.  
As one can expect, the probability profiles of Figure 13 are like the induced voltage profile of Figure 12 just because, according to Formula (4), the current density J is directly  
Figure 12: Induced voltage profile along the pipeline.  
Figure 13: Per cent probability profile along the pipeline.  
proportional to the voltage V. According to the expectations, the plot in Figure 13 corresponding to the case \(\rho_{p}=\rho\) is always lower than the one corresponding to the case \(\rho_{p}=\rho/10\) and in certain intervals, along the pipeline route it is zero.
# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 3 The Proposed Method  
### The Basic Structure and Variants of GAN  
The principle of GAN involves a minimum-maximum zero-sum game. The players in the game are networks called generators and discriminators, which are usually deep neural networks. Figure 1 shows the basic structure of the original GAN.  
The function of the generator is to generate artificial samples in the potential space using random Gaussian noise, while the function of the discriminator is to judge whether the given sample is a real sample from the original database or an artificial sample from the generator. The resource of a given sample can be shown from the discriminator output, which represents the probability that the given sample is from the original dataset (namely a true sample).  
During the training stage, the parameters of the generator and discriminator networks are adjusted. The discriminator will be more capable of distinguishing between true and false samples, and provide an optimization gradient for the generator so that the generator can generate artificial samples that have closer distributions to those of real samples. Formula (1) shows the objective function of the original GAN:  
\[\min_{G}\max_{D}L\big{(}X_{r},X_{g}\big{)}=\mathbb{E}_{x_{r}\sim X_{r}}[\log(D (x_{r}))]+\mathbb{E}_{x_{g}\sim X_{g}}[\log\big{(}1-D\big{(}x_{g}\big{)}\big{)}] \tag{1}\]  
There are many variants of GAN, including the principles of WGAN and CGAN mentioned in Section 2.2.1, which can be easily transplanted to various network structures based on GAN, and are widely used in data augmentation.  
* WGAN: A major problem of the original GAN is its instability during generative adversarial training resulting from gradient disappearance caused by the discontinuity of the Jensen-Shannon divergence. WGAN formalizes adversarial training by minimizing the Wasserstein distance rather than the Jensen-Shannon divergence between the distribution of generated data and real data so that it can continuously provide useful gradients for the parameter optimization of the generator. Formula (2) shows the objective function of the WGAN: \[\min_{G}\max_{D}L\big{(}X_{r},X_{g}\big{)}=\mathbb{E}_{x_{r}\sim X_{r}}[D(x_{r })]-\mathbb{E}_{x_{g}\sim X_{g}}[D\big{(}x_{g}\big{)}]\] (2)
* CWGAN: CGAN specifies the types of generated data for the generation network by connecting labels and random noise at the input, so as to ensure the generation of data within a specific range. It can be simply combined with WGAN to form CWGAN. Formula (3) shows the objective function of the CWGAN: \[\min_{G}\max_{D}L\big{(}X_{r},X_{g},Y\big{)}=\mathbb{E}_{x_{r}\sim X_{r},y\sim Y }[D(x_{r}|y)]-\mathbb{E}_{x_{g}\sim X_{g},y\sim Y}[D\big{(}x_{g}\big{|}y\big{)}]\] (3) The proposed method is designed based on the CWGAN.
###### Abstract  
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.  
fault diagnosis; rolling bearing; nonlinear entropy; improved complete ensemble empirical mode decomposition with adaptive noise; ensemble SVM; rotating machines +
Footnote †: journal: Applied Sciences
## 6 Experimental Setup and Results
### Numerical Results  
In this section, we illustrate the numerical results that allowed us to draw conclusions regarding the compared algorithms. Results of our experiment are provided as supplementary materials, see the relevant section.  
Table 1 lists the main common hyperparameters of the algorithms that generated the bests results in our experiments. The Kubernetes cluster was configured by using a downscale stabilization of 60 s, in order to be more reactive with respect to the default configuration of 5 min. In fact, in a Kubernetes cluster this parameter is used to prevent the flapping of replicas, in case of fluctuating metrics. Given the analysis in Section 4, combining all the delay contributions determining the value of \(T\) in Equation (4), we opted for a value that slightly overcame the maximum \(T\) value.  
The state features included the parameters affecting the evolution of the system. They modeled the environment in the global sense, including the technical parameters of the infrastructure, the process parameters of the incoming service requests, and the operative parameters of the running system logic. The state of the environment was defined through the following quantities:  
* Average latency of the response to a request made to the function;
* Number of function replicas within the cluster;
* Average CPU usage in percent;
* Average RAM usage in percent;
* Number of requests in one minute;
* Total CPU usage by all instantiated replicas in one minute;
* Total RAM usage by all instantiated replicas in one minute;
* Success rate;
* HPA CPU threshold value in percent;
* Cosine of the angle associated with a certain minute of the day;
* Sine of the angle associated with a certain minute of the day.  
The last two values in the list were used to introduce periodic time values, to represent the daily cycle.  
The Kubernetes manifest file that instantiates the deployment associated with the function instances was configured with the following values of requests and limits:  
* requests: memory: \"100 Mi\", cpu: \"100 m\";
* limits: memory: \"500 Mi\", cpu: \"500 m\".  
For all algorithms, the first test carried out consisted in using a constant and high frequency of function invocations, which was equal to 1000 invocations per minute. In this way, both the convergence capacity of the algorithms and the training stabilization time were analyzed.  
What immediately emerged in our tests was a significant difference between the performance obtained with DQN and those relating to the other algorithms. For this reason, this section is organized into the following two subsections.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 7 Comparison with Other Mobility Models
### Random Waypoint Mobility Model  
In this model, the motion of the mobile sink is divided into pause and movement rounds. Initially, the mobile sink will be in a pause round for a specific time. When the pause round expires, the movement round is initiated, and the mobile sink will randomly select a new location. It will start moving to that location at a specific speed. Upon arrival at the new location, a new pause round is started, and the mobile sink will stay in its location for a specified period, and so on [35; 37].  
This model was chosen for the comparison because its behavior is similar to the proposed model in this work. In other words, in both models, the movement of the mobile sink is divided into pause and movement rounds.