esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
###### Abstract
However, it still cannot provide inertial support for the traction power supply system, and due to the heavy load and dynamics of the traction load, its anti-disturbance performance is also limited due to the lack of damping support. In recent years, the research on Energy Storage (ES) based on MMC topology is becoming mature. However, most ES units are combined with three-phase MMC. ES units are distributed connection schemes; that is, they are connected parallel to the DC link of the MMC submodule directly or through DC/DC converters [17,18]. DC/DC converters are either isolated or non-isolated. In this paper, a distributed ES scheme is adopted, in which super-capacitors (SCs) are used as ES units, and they are connected in parallel to the DC side of the sub-module in one bridge-leg of the MMC through the non-isolated DC/DC converter [19]. Since the SC charges and discharges rapidly, it is more suitable to be used as an intermittent power buffer rather than a continuous power supply [20]. Therefore, it is necessary to use DC/DC converter to improve the voltage level of the ES unit. At the same time, the DC/DC converter can decouple the SC from the DC link support capacitor of the submodule and reduce the DC filtering demand. The research focus of this paper is to achieve the comprehensive management of power quality, realize the energy conservation of traction power supply systems through multi-functional power electronic equipment, and improve the stability and anti-interference ability of the system through new controls. Therefore, this paper applies an ES-type MMC-RPC system to realize the management of power quality and energy utilization, and proposes new controls for the MMC-RPC system to improve the system stability. Firstly, this paper established the mathematical model of the MMC-RPC and analyzed its dynamic characteristics. A two-stage circulation suppression is adopted to suppress the double frequency circulating current component of the MMC-RPC, ensuring the stability of the MMC-RPC. A VSG control is adopted to provide the support of inertia and damping, and a small signal model is established to verify the stability of the system. Furthermore, for the SC-type ES system, a virtual DC motor (VDCM) control for the bidirectional DC/DCconverter is adopted to enhance the stability of the system [21; 22]. The joint VSG-VDCM control can provide better steady-state and dynamic performance compared to other controls [23]. Finally, the effectiveness of the controls of the MMC-RPC is verified by the simulation on the Matlab/Simulink platform.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis  
#### 5.3.1 Packet Loss Ratio  
Figure 7 depicts the packet loss ratio of the WLQRP, VBF, and HH-VBF protocols as the number of nodes increases. It can be inferred from the graph that the packet loss ratio of the WLQRP, VBF, and HH-VBF protocols decreases with the augmentation of node quantity. This phenomenon is attributed to the elevated likelihood of discovering available neighboring nodes by these protocols as node density escalates, thereby diminishing the probability of encountering voids resulting in packet loss. Additionally, the graph illustrates that under low node density conditions, WLQRP exhibits superior performance compared to VBF and HH-VBF. This performance advantage can be attributed to the relay selection mechanism of WLQRP, enabling it to identify dependable next-hop nodes even within sparsely connected node networks, thereby mitigating the packet loss ratio. As the network becomes denser, WLQRP's performance further improves, with the packet loss ratio potentially declining to less than 0.05%.
###### Abstract  
The formation of ice on nacelle causes the reduction or loss of aerodynamic performance, fuel consumption increases, reduced thrust, and the ingestion of ice, which can damage the engine. The piccolo tube anti-icing employed as an active ice protection system has limitations in terms of performance losses and energy costs. Furthermore, according to the FAA regulation, it cannot be activated during takeoff and initial flight phases in order to avoid engine thrust reduction. This work reports on an icing wind tunnel test campaign performed at initial flight phases conditions on the M28 PZL nacelle before and after the application of a superhydrophobic coating in order to study the effect of wettability on ice accretion. Results highlighted that an ice thickness reduction of \(-49\%\) has been recorded at \(-12\)\({}^{\circ}\)C, matched to an increase in the impingement length of \(0.5\%\). At \(95\) m/s and at \(420\) s of exposure time, the ice thickness was reduced by \(-27\%\) and \(-14\%\), respectively, whereas the impingement length reductions were \(-9.6\%\) and \(-7.6\%\). Finally, an ice thickness reduction of \(-8\%\) was observed at a liquid water content of \(1\) g/m\({}^{3}\), matched to an increase in the impingement length of \(3.7\%\) and to a reduction in length and number of the frozen rivulets.  
superhydrophobic coatings (SHC); nacelle lip-skin; passive ice protection system; icing wind tunnel tests +
Footnote †: journal: Applied  
Article  
Icing Wind Tunnel Test Campaign on a Nacelle Lip-Skin to to Assess the Effect of a Superhydrophobic Coating on Ice Accretion
## 5 Rockburst Warning Based on a Mechanical Model
### 3S Theory of Stress Adjustment Process  
The formation of a rockburst is a complex mechanical process, and a microfracture is a manifestation of the geostress disturbance. In studies on earthquake formation, stress buildup, stress shadow, and stress transference (3S) are the three stress states [66]. Similar to the earthquake principle, in the process of rockburst formation, stress adjustment requires a certain period. From statistical data on moderate and above moderate rockbursts, it can be found that microseismicity has a certain periodicity and regularity before and after a rockburst. From this regularity, the 3S phenomenon in seismology can be related to the process of stress adjustment.  
After accumulating to a certain extent in a certain area, the microseismic events peak, indicating that the rock mass is in the stress accumulation stage. Based on the monitoring data and geological conditions, the corresponding early warning should be made in time. If a rockburst occurs during the peak period, the stress accumulated in the rock mass is released (corresponding to the stress shadow). The rockburst may change the state of the stress field around the source. After the rockburst occurs, the stress can be generally divided into two cases: complete release and incomplete release. If the stress and energy in the rock in this area are completely released, the frequency of microseismicity decreases rapidly, and little or no microseismic events occur. For the latter, a part of the stress is  
Figure 6: Two-body interaction model (**a**) and constitutive curve (**b**).  
released, while the other part of the stress is transferred, which is reflected in the migration form of the microseismicity. The stress tends to transfer to the weak structural plane of the rock mass or other locations where stress concentration can easily occur. The next rockburst is likely to occur in these stress concentration areas. Therefore, the next possible rockburst area can be predicted with the help of the spatial migration of the microseismicity.  
Before the initial support of the surrounding rock is completed, if a rockburst risk is observed, measures such as stress relief drilling and hydrofracturing can be adopted to reduce the strength of the rock mass and release part of the elastic strain energy artificially. This corresponds to the stress release process in the 3S model. After the initial support of the surrounding rock is completed, the stress accumulation and rockburst risk should be judged again. For the tunnel constructed using the drilling and blasting method, when a rockburst risk is observed, rockburst prevention and control measures can be adopted, such as borehole stress release and installing anchor bolts. The risk of a rockburst is judged again after adopting the measures. For the tunnel constructed using the TBM method, it is difficult to adopt stress relief or strengthening measures after the completion of the support. Therefore, when a rockburst risk is observed, steel arches are recommended to reduce the impact of the rockburst.
## 6 Proposed Joint Mode Selection and Resource Allocation Scheme (SD-Scheme)
### Proposed RL-Based Power Allocation Scheme
```
1: Initialize: \(N\), \(M\), \(P_{max}\), \(P_{min}\), \(\Pi_{n,k}\), \(\mathcal{G}(s,a)\) table, \(\mathcal{Y}\), \(\varepsilon\), \(\alpha\)
2: For episode \(\in\{1,\ldots,EP\}\) do
3: Reset \(s\), \(t=0\)
4: Select the level of power between \(P_{max}\) and \(P_{min}\), utilizing policy derived Q (\(\varepsilon\)-greedy)
5: For \(t\in\{0,\ldots,T-1\}\) do
6: Every agent performs an action \(\alpha\in A\) as well as observes \(\mathcal{R}\) and \(\delta_{t+1}\)
7: Check (11a), (11f), and (11g)
8: If the conditions are satisfied, then
9: Establish action \(\alpha\in A\), \(\mathcal{R}\), \(\delta_{t+1}\)
10: End if
11: Each agent takes an action \(e_{t+1}\in A\) and observes \(\mathcal{R}\) and \(\delta_{t+1}\)
12: Update the Q-table
13: \(S\leftarrow\ \delta_{t+1}\), \(A\leftarrow\ \alpha_{t+1}\)
14: End until all D2D pairs connect or the total iteration numbers is reached
15: End for
16: Output: optimum power for each D2D pair
```  
**Algorithm 2** SARSA algorithm for power allocation issue
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 4 Data Analysis
### Model Evaluation and Performance Metrics
#### 4.4.1 ARIMA Model  
The ARIMA model was trained using data up to 2022 and then applied to predict demand for 2023. The model achieved the following results for 2023 predictions:  
* Mean Absolute Error (MAE): 4.5%
* Root Mean Squared Error (RMSE): 6.2%  
These results indicate that the ARIMA model effectively captures seasonal and temporal demand trends. However, short-term fluctuations pose challenges, such as temperature and humidity, which are known to influence cold chain logistics.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 3 Methodology
### Signal Processing  
ICE noise primarily consists of combustion, mechanical and aerodynamic noise. Combustion noise is important in the engine noise at low and medium rotating speeds, while mechanical noise dominates the engine noise at higher speeds [35; 36]. Combustion noise is intrinsically transient in nature, which is heavily influenced by the injection parameters, spark timing and AFR. Therefore, it is essential to evaluate these signals with the help of time-frequency analysis. Several time-frequency analysis methods are commonly available, such as Gabor transform, STFT, Wigner-Ville transform and wavelet transform. Among these, WPT is of particular interest due to its ability to detect rapidly  
Figure 4: Workflow of the methodology and the comparisons for engine fault diagnosis.  
changing amplitude and phase characteristics [37]. It is a form of wavelet transform, which can decompose a temporal signal into several independent time-frequency vectors termed packets.  
In brief, the wavelets are created from scaling and shifting of the mother wavelet. The decomposition results in discrete approximation coefficients low frequency components and detail coefficients high frequency components, using high and low pass filters. There are a number of mother number wavelets that can be used to analyze a signal, the selection of which depends on the signal type and the nature of the application. Among them, Daubechies wavelets are a group of orthogonal wavelets commonly used for discrete wavelet transform which are useful for analyzing transient signals [13]. In this case, Db4 (Daubechies 4) WPT with four layers and a filtering frequency of 12 kHz are used for further analysis. This procedure enables selecting only the optimal components frequency ranges for analysis of different fault conditions.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 5 Discussion  
Numerical calculation was carried out to study the dimpled heat exchange tube. The flow and heat transfer characteristics of the fluid in tube and shell sides of the dimpled tube exchanger were analyzed, and the conclusions are as follows:  
(1) The concave dimple in the outer wall creates a dead zone in the flow, which reduces the local heat transfer coefficient to some extent, but also increases the heat transfer area. The vortex formed in the dimple affects the near wall boundary layer and enhances the heat transfer capacity. The inner wall dimples do not produce flow dead zone, and can change the direction of flow velocity in the near wall boundary layer, which destroys the flow state of the fluid in the near wall area in the dimpled tube, and enhances the degree of turbulence. Therefore, the heat transfer performance of both tube side and shell side can be enhanced.  
(2) The size of the dimple can affect the effect on the heat transfer ability significantly. In the Reynolds number range of 10,000-60,000, the larger the size of both the concave dimple and the protrusion dimple, the stronger the heat transfer ability, but meanwhile, the larger the resistance.  
(3) Non-dimensional performance evaluation criteria showed clear advantage of dimpled tube over the smooth tube. Within the five types of dimple size calculated in this paper, the comprehensive heat transfer factor of 10-15 dimple combination is the largest, up to 1.83, and its comprehensive heat transfer capacity is the strongest. It is worth noting that the combined heat transfer coefficient is better under the condition of low Reynolds number. This indicates that the turbulence intensity increases with the increase of Reynolds number, and the disturbance effect generated by dimple is weakened. The heat transfer capacity should be further optimized by adjusting the cell arrangement, density, and other parameters.  
Conceptualization, overall planning, Z.W.; methodology, modeling, Y.W.; software, numerical calculation, J.Z.; validation, data processing, S.L. and Y.X.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and J.Z.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 6 Sensitivity Analysis on Parameters
### Influence of the Corrosive Environment  
For better illustration, \(I_{p}\) can be written in the form of \(I_{p}=k\times I_{pi}\) where \(I_{pi}\) stands for the original pitting current that is \(1.05\times 10^{-8}\) C/s, and \(k\) is the pitting current magnify coefficient ranging from 0.7 to 3.4. The crack depth was plotted against time, as shown in Figure 14.  
Figure 14: Crack development under various pitting currents: **(a)** HPS 70W and **(b)** 14 MnNbq.  
Figure 13: Impact of the stress ratio on the C-F life: **(a)** Initiation life and **(b)** propagation life.  
According to the result, when the pitting current magnify coefficient increased from 0.7 to 3.4, the whole C-F life was reduced by 70.2% in the HPS 70W steel and 59.6% in the 14 MnNbq steel. However, even under the higher reduction rate, the C-F life in the HPS 70W was still far longer than that in the 14 MnNbq under various pitting currents.  
For further analysis, the influence of pitting current on the initiation life and the propagation life are shown in Figure 15a,b, respectively.  
It could be found that with the increases in the pitting current, the initiation life was reduced by 74.9% in the HPS 70W and 69.2% in the 14 MnNbq steel. Meanwhile, with the same change in the pitting current, the propagation life was reduced by 47.7% in the HPS 70W and 41.5% in the 14 MnNbq steel. It could also be found that the difference in the initiation life between two types of steel was extremely high under the low pitting current. As the current increased, the difference gradually reduced to a relative stable value after the magnify coefficient reached 3.0. A similar trend could also be observed in terms of the propagation life. Meanwhile, it is worth noting that pitting corrosion could still dominate the C-F process for a while after the pitting depth reached the critical size, especially under the high pitting current. Therefore, it indicates that the high pitting current could lead to the result that the corrosion-induced growth rate was higher than the fatigue-induced rate when crack size was small.
## 2 Operation Principle/System Design  
In the present paper, we consider a cylindrical concentric waveguide combiner with multiplication of pupils along one direction. The described system is an optical waveguide with two DOEs, in-coupling and out-coupling (Figure 1). In this case, the system is constructed in such a way that the projector's field of view (FOV) is located symmetrically with respect to the normal to the center of the in-coupling DOE, and the user's eye is located opposite the center of the out-coupling DOE. The described arrangement of the projector and the user's eye relative to a waveguide provides an axial optical system, which allows the achievement of minimal aberrations during transmission of an image.  
For aberration-free transmission of an image through the optical combiner under consideration, its entire FOV must propagate without aberration inside a waveguide, as well as be in-coupled and out-coupled without aberration. That is why, in the present paper, we propose the following methodology for calculating a waveguide combiner, which consists of the three following stages:  
1. formulation of conditions for a beam of rays inside a concentric curved waveguide, which provide its propagation without aberration;
2. calculation of parameters of an in-coupling DOE providing the formation of a beam inside a waveguide, which meets the conditions of item (1), taking into account a FOV, a size of the projector's exit pupil, a location of the projector relative to a waveguide, a quality of an image, etc.;
3. calculation of parameters of an out-coupling DOE providing the formation of an eye-motion box (EMB) of a given size for a given FOV with a given quality of an image.
# A Machine Learning Approach to Simulation of Mallard Movements
## 2 Materials and Methods
### Second Approach of Prediction of Movement Patterns--Time-Series Analysis  
In the second approach to time-series prediction, a test set was incorporated. The existing training set was further divided into a pure training set and a validation set, maintaining the same division as in the previous analysis case. The notable change involved the treatment of wild mallards' data: the model was trained using two of the mallards, while the third served as a test set encompassing the complete time series from 28 September 2012 at 16:00 to 2 October 2012 at 01:00. This procedure was applied to each of the three wild mallards, effectively acting as a form of cross-validation. For the farmed mallards, one time series was designated as the test set and the other thirteen as the training and validation sets. This selection was randomly made for three mallards from the farmed mallard dataset.  
This second approach enabled both single-step and twelve-step predictions. An illustration of the twelve-step prediction, using a sliding window of 12 data samples (equivalent to 12 h), is presented in Figure 11.  
Figure 11: An example of the 12 h prediction model. In this example, this was a predictive model with a sliding window of 12 h.  
Figure 10: The picture shows ML-model II. This model has as input features three time series from three different mallards and the “part of the day” feature (part of day data). The model predicts the next distance (_ds_) for each time series; see Figure 8. An overall MAE is calculated for the complete dataset and used for evaluation.  
In this second approach, a test set from a different mallard was used to measure performance, contrasting with the first approach. Table 6 displays the various models along with their respective hyperparameters. Additional hyperparameter values were tested, but these did not result in significant changes. The primary objective of this study was to determine whether any model could outperform the baseline models \"last\" or \"12-h repeat\".  
The baseline or naive models either used the last observed value for prediction or repeated the last 12 values. In contrast, the linear and dense models made predictions about future values based solely on the present value. The convolutional neural network (CNN) and LSTM models, however, based their future value predictions on the past twelve values. The AutoRegressive (AR)-LSTM model operated differently, making predictions in 1 h increments. These predictions were recursively fed back into the model, allowing it to make subsequent predictions conditioned on the previous one. This process was repeated 12 times, enabling the model to forecast up to 12 h into the future. For a comprehensive view of the different models used in this analysis, see Table 6.  
The linear model was implemented using the Keras dense class [38] but without an activation function. The dense models were also implemented with the Keras dense class but with the rectified linear unit (relu) activation function. The relu activation function was also used in the CNN model.