esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results  
With the purpose of studying the accuracy of the suggested method the simulation outcomes are further shown. Thus, for this purpose, the case study presented in Chapter 4 and shown in Figure 1 is utilized to analyze and study the proposed strategy. The electricity prices were taken from [53] while the vital information concerning the PV's insolation of is given by National Renewable Energy Laboratory (NREL) and can be found in [54]. Additionally, the theoretical load demand is related to a standard microgrid. Table 1 gives all the necessary characteristics of the ESS. With the intention of having a realistic estimation of the PV generation and demand, the seasonality effect is considered in their modeling. It is assumed that 3 MW is the rated power of the PV. The PV generation and demand are shown over the time horizon of 168 h in Figures 2 and 3, respectively.  
Figure 2: The representation of the power demand.  
which show the power transferred from the grid to demand, PV to grid, and PV to battery storage, respectively; there is not much of a difference between the risk averse and the risk neutral policy, to specify these variables. However Figure 9 demonstrates that power transferred from the grid to the battery has changed significantly. This shows that risk consideration on the price have a large effect on the performance of the battery storage. In Figure 11 the amount of power transferred from the PV to the battery storage is shown. In contrast to the neutral risk policy, there is no power delivered from the PV to the battery storage if a risk averse method is employed.  
Figure 8: Transferred power from the grid to the demand side.  
Figure 10: Transferred power from the PV to the grid.  
Figure 9: Transferred power from the grid to the battery storage.  
Figure 11: Transferred power from the PV to the storage.  
Figure 12: Transferred power from the storage to the demand side.  
Figure 13: Transferred power from the storage to the grid.  
Moreover, Figures 12 and 13 show the injected power from the battery storage to the engagement demand and confirm that power in the grid has increased considerably by using the risk averse policy in detriment to the neutral risk strategy, which shows that ESS is used more actively to optimize the cost function while reducing the risk measurement. This increases the system operational cost to some extent.  
However, it strengthens the system against the uncertainty in the electricity price. The confidence level \(\beta\) can control such types of amounts in a way that higher value for \(\beta\) imposes a higher operational cost on the system owner. Thus, in this way, the risk averse policy can protect the system against uncertainty in the electricity price more than the simple and neutral strategies could ever protect.
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 7 Discussion
### Scalability Analysis  
As the number of records in event logs increases, the resulting process maps often become more complex. More preprocessing iterations and effort are hence usually required. It is important to test the scalability of the CPA-PM method and scripts when the size of event logs increases. A scalability analysis was performed on subsets of real datasets that vary in size (by doubling the initial one 9 times). The same script with the same API function invocations was run on each of the datasets. The script was run three times on each dataset, and the average execution times are reported in Figure 7.  
Figure 7: Script execution performance (the \(X\) axis uses a logarithmic scale).  
In term of environment, the scalability analysis was performed with RStudio running on a laptop with Windows 10 (64-bit), 16 GB RAM, and an Intel(r) Core(tm) i5-8250U processor. No other application was running in parallel.  
As expected, the higher the number of events in the log, the higher the average runtime. This might cause issues in some contexts where this part of the analysis is time sensitive. What is interesting in Figure 7, however, is that the increase in average duration is less than linear with respect to the number of events in the log, likely because the initial filtering steps are efficiently removing many of the records up front.  
In some situations, the dataset might include more attributes (i.e., more columns), which would require more computations when merging the rows and hence result in longer run times. The impact of such additional attributes was not assessed explicitly here. However, in situations where more pattern iterations are executed, the number of rows would be reduced, which might in turn shorten the average runtime.  
Still, in general, having to wait 10-12 min to preprocess automatically a million events, in a repeatable way, is still much better than doing so manually, as the latter approach would take much longer and be more prone to errors.
## 3 Results and Discussion  
This section presents and discusses the preliminary results from the UPD and intelligent off-road vehicle localization tests. The UPD localization tests utilized both the GNSS and RSSI localization methods. Fifteen hundred events were recorded in the static and dynamic GNSS localization tests, and approximately 800 events were recorded in the RSSI localization tests. Of the 16,690 events that were recorded in five passes in the vehicle experiment, 610 were used to determine the level of accuracy. A greater amount of data was collected in this study than in previous studies [15,16]; however, this study was limited to a single manufacturer's implementation of a RTK-GNSS system.
## 5 Experimental Tests Results, and Discussion
### Effect of Input Current to TEC
The current was varied from 2 A to 8 A, and as a result, the COP increased from 0.35 to 2 with different current inputs. The best result was recorded at an input current of 4 A where a COP of nearly 2 was calculated. Figure 4: Bulk mean temperature for various input currents for the cold plate exchanger. Figure 5: Total heat removal rate at various input currents from the hot side of the TEC module. The COP of the system largely depends upon the heat removal rate and cooling effect generated by TEC modules. Additionally, Figure 8 presents the temperature distribution inside the closed room when the current varies. Figure 6: Total heat removal rate at various input currents from the cold side of the TEC module. Figure 7: COP of the system at various input currents to the TEC module. The TEC generates more cooling effect on the cold side as applied current increases, and the temperature on the hot side is also increased to maintain the temperature difference constant. Hence, this leads to generate a higher temperature on liquid inside the cold plate that removes heat from the hot side of TEC. Therefore, the thermoelectric liquid cooler releases more heat to the flowing liquid which ultimately increases the temperature inside the closed room.
## 2 Materials and Methods
### Transcriptome Analysis  
The quality control of transcriptome raw data was performed using fastp to filter out low-quality data [27]. The short-read matching tool bowtie2 was used to align the clean reads against the ribosome database and remove unmapped reads [28]. Global and local alignment searches were performed using HISAT2 to match spliced reads in RNA Seq data [29]. The fragments per kilobase of exon model per million mapped fragments (FPKM) method was applied to calculate gene expression levels [30; 31]. The differentially expressed genes (DEGs) were annotated with gene ontology (GO) terms, and the number of genes associated with each GO function was determined. The hypergeometric test was applied to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that showed significant enrichment among the DEGs.
# Progress in Electrodeposition of Zinc and Zinc Nickel Alloys Using Ionic Liquids
###### Abstract  
Zinc (Zn) and zinc-nickel (Zn-Ni) electrodeposition has been widely used in many industries, such as automotive and aerospace, for corrosion protection of steel components owing to their excellent corrosion resistance. Conventional zinc and zinc-nickel electrodeposition is performed in different types of aqueous baths (acid and alkaline). Such electrolytes suffer from certain drawbacks such as hydrogen gas evolution, low coulombic efficiencies, and environmental toxicity. Electrodeposition of Zn and Zn-Ni alloys from ionic liquids has gained significant attention in aerospace and automotive sectors owing to the different environments they provide for electrodeposition. This paper reviews the progress in deposition of zinc and zinc-nickel alloys in non-aqueous systems, especially ionic liquids. In addition, the challenges and technological developments associated with the Zn and Zn-Ni deposition on different substrates and the factors that need to be considered while electroplating at an industrial scale are discussed.  
ionic liquids; deep eutectic solvents; corrosion resistant coatings; aerospace; electrodeposition; zinc and zinc alloy; metallization; electroplating +
Footnote †: journal: Applied Sciences
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 2 Methodologies
### Algorithms and Program
#### 2.2.3 Integrated Prediction Model  
We developed a comprehensive prediction model. For each vehicle, its trajectory is reconstructed utilizing this model. At every timestep, the model initially forecasts the lane change maneuver. If the predicted maneuver yields a value of zero, then the LK-LSTM model is engaged to predict positions for the subsequent timestep. Conversely, if the maneuver does not equate to zero, then the LC-LSTM model is employed for prediction. To gauge the accuracy of the predicted trajectory, it is compared against the true trajectory using the root mean squared error as a metric.  
Regarding the algorithm's practical applications, it should be noted that the learning datasets employed in our study are derived from the ground truth NGSIM field datasets. It captures a broad spectrum of driving behaviors like acceleration, deceleration, and uniform speed across various scenarios. Despite utilizing offline data, our integrated approach is adept at learning from these ground truth behaviors, enabling the model to generate realistic trajectory predictions. In practical applications, our model is engineered to operate in real time. In practical applications, our model is engineered to operate in real time. It takes multiple timesteps of historical trajectory data as input and outputs multiple timestep prediction trajectories at each timestep. Leveraging advancements in vehicle localization and sensing technologies, the model can collect real-time data on historical trajectories as input. Furthermore, the computational time required for each timestep is minimal--far less than 0.1 s--making it feasible for real-time implementation.
## 4 Experimental Investigations
### Obtain the Measured Signals and Classification Results
\begin{tabular}{c c c c c c c} \hline \hline
**Rub-impact** & \multicolumn{6}{c}{**The Calculated Results in 8 Frequency Bands of Rub-Impact Fault**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SD & 0.010 & \(4.90\times 10^{-4}\) & \(1.30\times 10^{-4}\) & \(1.70\times 10^{-4}\) & \(3.55\times 10^{-5}\) & \(7.95\times 10^{-5}\) & \(1.10\times 10^{-4}\) & \(8.64\times 10^{-5}\) \\ Peak & 0.020 & \(1.44\times 10^{-3}\) & \(3.80\times 10^{-4}\) & \(5.70\times 10^{-4}\) & \(9.78\times 10^{-5}\) & \(2.30\times 10^{-4}\) & \(4.60\times 10^{-4}\) & \(2.20\times 10^{-4}\) \\ Kurtosis & 2.30 & 3.27 & 3.01 & 3.44 & 2.39 & 2.88 & 6.41 & 2.79 \\ CLF & 2.62 & 4.47 & 4.48 & 5.09 & 3.77 & 4.23 & 7.86 & 3.91 \\ IF & 2.31 & 3.78 & 3.78 & 4.31 & 3.29 & 3.59 & 6.25 & 3.21 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c c} \hline \hline
**Combination of** & \multicolumn{6}{c}{**The Calculated Results in 8 Frequency Bands of Compound Faults**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SD & \(1.19\times 10^{-2}\) & \(4.20\times 10^{-4}\) & \(1.30\times 10^{-4}\) & \(1.10\times 10^{-4}\) & \(3.40\times 10^{-5}\) & \(8.17\times 10^{-5}\) & \(1.10\times 10^{-4}\) & \(8.15\times 10^{-5}\) \\ Peak & \(2.26\times 10^{-2}\) & \(1.09\times 10^{-3}\) & \(3
## 2 Manufacturing the Composite Main Reflector  
### Deployable Reflector Antenna Requirements Derivation  
The composite main reflector applied to the SAR antenna was designed to meet the weight and stiffness requirements, as shown in Table 1. These requirements were determined based on the satellite's launch conditions and orbital operation conditions.  
The antenna's dimension requirements were defined considering the transmit-receive performance of the reconnaissance satellite antenna. When assuming the weight of the entire satellite would be between 500 and 1000 kg, the weight of the antenna, excluding the satellite body, was estimated to be in the 100 kg class.  
Satellites are launched using launch vehicles to carry out their missions. To prevent resonance between the launch vehicle and the satellite during the launch process, the stiffness of the antenna was selected to be 33 Hz or higher in the stowed condition, considering the loading requirements of certain launch vehicles.  
In order to perform reconnaissance missions in Spotlight mode, which involves repeatedly photographing the target area during satellite orbital operations, the satellite performs agile attitude control maneuvers to focus on the reconnaissance area. The stiffness of the antenna was selected to be 2.5 Hz or higher in the deployed condition, considering the stabilization time for the vibration behavior of the antenna reflector surface that occurs after attitude control [16].
## 4 Results and Discussion
### Growth of Vorticity and Pressure Drop  
Cases 2 and 8 in Figure 12, which are picked as the best cases for the flow uniformity, are compared for velocity and vorticity field in Figures 13 and 14. In Figure 13a,b, the flow is observed to be accelerated at the narrow gaps between each obstacle. The elliptic cylinders scatter the accelerated flow region far longer because its longitudinal axis distance is longer than that of circular ones. Figure 14a,b shows the vorticity, or the geometrical norm of curl \(V\), to see the rotational mixed flow: for Case 2 (circular), it is observed that a microscopic flow separation develops a weak vortex shedding at the wake direction while the vortices generated from the boundary layer in Case 8 (elliptic) is shown to be attached at the long streamwise surface to delay the flow separation. It is understood that this slight difference results in more stable uniform flow at the wake of elliptic cylinders than that of circular ones. The penetrated vorticity providing the rotation to the flow wake is considered as the most dominant cause to increase mixing in the laminar flow. The larger the vorticity and the longer the rotational region will improve the mixing characteristics.  
Figure 12: RMS error results by cases listed in Table 5.  
Figure 13: Velocity contour at the diffuser: (**a**) Case 2 and (**b**) Case 8.  
For a quantitative comparison, two lumped parameters are proposed as follows:  
\[I_{p}=\frac{\Delta p_{8}}{\Delta p_{2}} \tag{25}\]  
\[I_{\omega}=\frac{\int\omega_{8}dS}{\int\omega_{2}dS} \tag{26}\]  
where the subscripts 2 and 8 represent Cases 2 and 8, respectively. Equation (25) indicates the overall ratio of pressure drop where the symbol \(\Delta\) indicates the difference of values measured on the discharge of pipe and the outlet of diffuser, and Equation (26) is the ratio of integrated vorticity, or circulation for the estimation of flow rotation where \(dS\) stands for the area segment on the surface of all the obstacles. \(I_{p}=1.03\) means that the pressure drop after elliptic cylinder increases only 3% compared with that of the circular cylinder. However, the integrated vorticity increases as much as 49% since \(I_{\omega}=1.49\). Consequently, the choice of a 2:1 ellipse shape can be even better than a cylinder because it can increase the overall mixing performance in spite of very minor increase on the adverse effect of pressure loss.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 2 Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
### Multilevel Features Extraction  
We use ResNet-50 as a multilevel features extraction network. ResNet is a network with deep layers and a low risk of over-fitting. It is connected by multiple residual connection blocks, as shown in Figure 2. By suppressing the problem of gradient disappearance through the residual mechanism, it is possible to construct a deeper network and extract deeper features.  
In Figure 2, \(H(x)\) represents the combination of feature mapping \(F(x)\) and original input \(x\). \(F(x)\) represents the main feature learning module, which is usually a concatenation of convolutional layers and activation layers. Express the principle of residual connection as a mathematical form:  
\[H(x)=F(x)+x \tag{1}\]  
Unlike the original ResNet-50, we removed the global average pooling and fully connected layers. The \(7\times 7\) convolution kernel with a stride of 2 in Conv1 was replaced with three \(3\times 3\) convolution kernels with a stride of 1. This modification reduces the loss of original information in the first layer and makes the feature extraction more stable. The stride of the first \(1\times 1\) convolution kernel in Conv3-5 is 2, and the strides of the other \(1\times 1\) convolution kernels are 1. Downsampling is performed by convolution between each block to ensure that features at each level are extracted at different information scales. Redundant information is effectively removed, and the required calculations are reduced. The specific parameter setting is shown in Table 1.  
In order to achieve a good detection effect, we need to use the high-level feature information and low-level feature information extracted by ResNet-50. To select the best feature set for detection, experiments were performed using different levels of features extracted by the ResNet-50. Experimental results show that the detection using the features of the last three blocks is the best. When using the feature outputs from all five blocks, the detection accuracy does not improve significantly, and some non-target objects are detected as targets. When only the feature outputs of the last two blocks are used, some small-scale oceanic phenomena become undetectable, and some detection results are incomplete. This is because when too many low-level features are used, more noise is introduced. It does not help to improve the detection accuracy, but instead causes some interference targets. When too few low-level features are used, due to lack of detailed information, some small phenomena cannot be detected, and some detection results are incomplete. So after many experiments, this study mainly uses the feature outputs of the two blocks of Conv3 and Conv4 in ResNet-50 and the deep feature output of Conv5. The feature outputs of Conv3 and Conv4 are reserved for the feature fusion module, and the features of the deep feature output are used for further multi  
Figure 2: Basic bottleneck module in ResNet-50 [39].
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 6 Tsunamis  
Tsunamis are large waves in a body of water generated by different events, such as earthquakes, volcanic eruptions, landslides impacting the water, or underwater explosions. Tsunami prediction involves seismic event detection and sea water levels and wind-waves measurements. Other methods can also be used in a Tsunami EW system, such as hydro-acoustic waves measurement, pressure measurements or camera based methods. In tsunamis generated by seismic events, the initial warning could be given by earthquake measurements, to locate the epicenter and make a prediction on the tsunami time of arrival, then other measurements can take place, such as hydro-acoustic waves detection [75]: earthquakes or, more generally, movements in the sea bottom, generate hydro-acoustic waves that propagate at the speed of the sound in water (1500 m/s) and, since their propagation speed is much faster than that of the surface waves, they can be used to predict tsunamis deploying underwater acoustic sensors. Similarly, underwater pressure sensors have also been widely used to detect tsunami waves [76].  
Generally, Underwater Wireless Sensor Networks can be used to collect and record data from large geographical settings and forward it to a central hub for data processing and generation of an EW. UWSNs are used for many applications, and Internet of Underwater Things (IoUT) is a paradigm that allows detection and prediction of events that could lead to disasters. A UWSN usually consists of the following components: (i) underwater static or mobile nodes equipped with sensing units and acoustic modems; and (ii) sink nodes (on autonomous surface vehicles, buoys or ships) equipped with acoustic and radio modems [77], used to gather data from the underwater sensors and forward it to a remote server or monitoring center, usually through an IP network [78]. The use of acoustic modems instead of radio units is usually the better choice because of the propagation conditions in an underwater environment. Figure 4 illustrates a generic implementation of an underwater Tsunami Early Warning System based on the aforementioned basic UWSN architecture.
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
###### Abstract  
The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is the combination of the OFDM and MIMO technologies, which could improve the system capacity and make efficient utilization of the frequency spectrum. This paper utilizes space-time block coding (STBC) to achieve diversity gains and combat the channel fading. However, channel estimation is an essential block for space-time block decoding (STBD). Many channel estimation methods are utilized for the single antenna OFDM system, but they cannot be directly applied to the multiple antennas system due to the interference from other antennas. In this paper, orthogonal pilot sequences are designed to suppress the interference of pilot symbols from other transmit antennas. This paper also derives a minimum mean square error (MMSE) channel estimation method in MIMO-OFDM systems. The MMSE method involves the inverse operation of the channel autocorrelation matrix, which has a large calculation complexity. To further reduce the complexity of the MMSE method, the singular value decomposition (SVD) is used to decompose the channel autocorrelation matrix, which avoids the inverse operation. Simulation results verify that the SVD channel estimation method with comb-type pilots and STBC can be effectively adapted to multipath propagation conditions.  
channel estimation; multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM); space-time block coding (STBC); singular value decomposition (SVD); bit error rate (BER) +
Footnote †: journal: Applied Physics Letters  
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