esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Removal of Subsurface Impurity Defects by HF Acid Etching
#### 3.3.2 Surface Quality and Impurity Content of Fused Silica Etched by HF Acid
It means that 56.5% of the CeO\({}_{2}\) impurity defects could be removed by HF acid etching and the heat absorption caused by impurity defects would be greatly mitigated to improve the laser damage resistance of fused silica. The evolution of Ce defect content with respect to the etching time is exhibited in Figure 13c. It can be seen that the content of Ce defect decreases gradually and becomes roughly stable when the fused silica optics are etched for a sufficient time. This phenomenon can be explained as follows. In the initial etching stage, the defect content decreases dramatically due to the removal of re-deposition layers, among which a majority of impurity defects are located and  
Figure 12: The evolution of surface morphology of etched fused silica under various etching periods. The etched surfaces are tested with a 3D stereo microscope for etching periods of (**a**) 0 min; (**b**) 30 min and (**c**) 60 min The surface morphologies of etched surfaces are also tested with white light interferometer for etching periods of (**d**) 10 min; (**e**) 20 min and (**f**) 40 min. distributed. After etching for 10 min, when the re-deposition layers are totally removed, the subsurface scratches and cracks appear and some of the impurity defects hidden inside or adsorbed to the cracks could be removed and washed away. The defect content therefore shows gradual decrease for etching time from 10 min to 50 min. When the silica optics are etched for longer than 50 min, the defect content keeps almost constant since the impurity defects adsorbed to the cracks are then difficult to be removed by ordinary etchant washing. From the discussions above, it can be concluded that the impurity defects can be effectively removed by HF-based acid etching. The acid etching shows the best effectiveness in removing the impurity defects, when the re-deposition layers are being etched. The mechanical properties and laser damage resistance of fused silica would be greatly improved by controlling the heat absorption caused by these impurity defects. Figure 13: Tested results of impurity defects on polished silica surfaces before and after acid etching: (**a**) impurity contents before HF acid etching; (**b**) impurity contents after HF acid etching for 90 min; (**c**) Variation of Ce defect content for various etching times.
## 4 Results
### Evaluation of the Improvement in Yield Prediction Accuracy
In these areas (see Figure 9), when the fraction of incomplete Landsat series across the growing season of 2015 falls below 42%, using \(L_{LM}\) reduced the field-level yield prediction errors by 0.15 t/ha for Western Australia and 0.28 t/ha for eastern Australia. More specifically, the bias of 598 kilotons in grain production over the cropping area of 39,882 km\({}^{2}\) in Western Australia and of 1672 kilotons in grain production over the cropping area of 59,716 km\({}^{2}\) in eastern Australia can be decreased when using the threshold determined herein to decide the areas where the blended data can improve the prediction accuracy.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 3 Signal Model
### Its-G5  
The physical layer of ITS-G5 is based on IEEE 802.11p, a modified version of the IEEE 802.11a standard. The main difference is that the subcarrier spacing and bandwidth are halved, which results in a symbol duration twice as long. The cyclic prefix duration is also doubled, which allows us to compensate for larger delay spreads and makes it more suitable for vehicular environments [24].  
The IEEE 802.11p frame consists of three main fields. The first field lasts 32 us and is called the preamble. It is used for channel assessment before transmission and for signal detection at the receiver side. The second element of the frame is the signal field and consists of one OFDM symbol. It is intended to indicate the data rate, packet length and modulation scheme of the transmitted signal. The last element is the data field, which has a variable number of OFDM symbols. It contains data, tail and padding bits [2].  
For OFDM transmission, a total of 64 subcarriers is used. The 0 th and the central 11 subcarriers are null. Those with indices 7, 10, 44 and 58 are occupied by pilot symbols, and the remaining 48 are used for carrying data [25]. The OFDM symbol lasts 8 us and the subcarrier spacing is 156.25 kHz, leading to a raw bandwidth of 10 MHz. ITS-G5 supports a wide range of modulation schemes, from Binary Phase Shift Keying (BPSK) to 64-QAM [26].
## 1 Introduction
The SEP algorithm determines the weighted probability for SNs to be elected as cluster heads (CHs) based on their energy levels. Since advanced nodes have higher initial energy levels, they have better chances to become CHs. Typically, the distance between a SN and a base station (BS) is a key factor that directly affects the cost of communication in a WSN. There has been a surge in interest in the deployment of mobile BSs recently, maybe due to the notion that doing so could better conserve the network's usage of energy [19]. Our paper's major objective is determining the optimal mobile BS location in a sensor field by computing the total distances between SNs and virtual BS points. Then, at some specific rounds, based on the current state of the network, the BS may be relocated to a more favorable point depending on the current operating SNs. Moreover, the impact of implementing a relay node (RN) in a large-area network is explored to improve scalability. The remainder of this paper is organized as follows: Section 2 discusses pertinent literature on WSNs. Section 3 presents the system description and the implemented energy model. Section 4 discusses the optimization of the BS location and presents the used algorithms. The simulation results and their comparisons with traditional SEP protocols are presented in Section 5. Finally, the conclusions of this study are discussed in Section 6.
## 2 Methods  
The signal-to-noise ratio for an MRI image depends on a number of scanning parameters (pulse sequence, image matrix, resonance coil type) and substance properties (proton density, relaxation processes, diffusion). The use of various algorithms to reconstruct MRI images allows for one to adjust the contribution of a scan parameter to the intensity of the recorded signal to obtain optimal contrast between normal and altered tissues on an MRI image. It is possible to reduce random noise in MRI images by increasing the number of measurements, which leads to an increase in operating costs and an increase in the duration of a procedure. At the same time, reducing the scan time allows for one to eliminate measurement errors that are caused by a change in the position of the investigated object in space, but it requires the additional computer processing of images to eliminate random noise in MRI images.  
The main methods for eliminating random noise in MRI images are rank and median filtering, Wiener filtering, correcting the initial k-space data, wavelet analysis and Fourier transform, and the statistical processing of experimental data. New ways to solve the problem of eliminating random noise in MRI images have become possible thanks to the use of the DICOM 3.0 protocol, which allows for efficient computer-based image correction due to the open graphic standard for digital data representation. Applying a model for eliminating mixed noise from images as an additional method of computer data processing allows us to improve the quality of MRI images of the internal structure of objects, which is an important scientific problem, while reducing the research time.  
The following approximation system was used in order to simplify the software calculation algorithm and identify the main features of the studied approach:  
1. the electronic matrix of MRI scanners have different shapes and geometric dimensions, however, for ease of implementation of the model, a square shape with piezoelectric elements of the same size and orientation was selected;
2. in addition to a useful image, the recorded data also contain fluctuation noise and pulsed flows of various intensities with a regular or chaotic structure (Gaussian and Poisson noise); and,
3. pulsed noise is understood to be a distortion of the recorded images by large pulsed emissions of arbitrary polarity and short duration, caused by both external pulsed electromagnetic interference and malfunctions of the system.  
The model for eliminating the mixed noise from MRI images is a software algorithm for suppressing impulse noise, fluctuations, and geometric noise in the recorded signal. It is used for computer processing of MRI images in the format of DICOM files after an object is scanned.  
The developed software algorithm for suppressing impulse noise, fluctuation, and geometric noise in the recorded image must have the property of universality.
## 1 Introduction
Thus, devices with positioning capabilities could choose the better suited positioning strategy (from a pool) considering the user's current context; for example, choosing a positioning strategy that is less energy consuming than using the de facto strategy. The positioning information of these nodes could then be shared with neighboring devices with limited or no positioning capabilities, allowing them to estimate their position collaboratively. The positioning model was evaluated using simulated outdoor environments, which were implemented over well-known communication and mobility models. The obtained results show that devices without positioning capabilities are able to gather positioning information from access points (e.g., command posts, communication antennas, and emergency vehicles parked in the field) and other devices in their vicinity, and then use this information to estimate their own positions, although not as accurately as with a direct positioning strategy like GPS. Moreover, the devices are able to determine which positioning strategies can be used in a certain environment, and choose the one that involves the least energy consumption. The next section shows the related work. The proposed context-aware positioning model is detailed in Section 3. The simulation scenario and the experiments are specified in Section 4, and the results are shown and discussed in Section 5. Section 6 establishes the limitations of this proposal. Section 7 presents the conclusions and future work.
## 2 Materials and Methods
### Data of the Seismic Sequence  
In the numerical dynamic simulations, seismic events registered in Mascioni in the Province of L'Aquila, Italy, were used as kinematic loadings of the structure [18]. The Richter magnitude (ML) of the first shock equal to 5.4, whereas the ML of the second and third shock equal to 5.3 and 5.1, respectively. Time histories of accelerations of the sequence of the seismic events in three directions are presented in Figures 9-11. The peak ground accelerations (PGA) of the kinematic loadings are delivered in Table 3. The accelerations were applied in three directions to the FE model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Seismic Event**} & \multicolumn{2}{c}{**The Peak Ground Accelerations (PGA) [cm/s\({}^{2}\)]**} \\ \cline{3-4}  & & **E-W** & **N–S** & **Vertical** \\ \hline
2017-01-18 10:14:12 & 1 st shock & 203.92 & 247.21 & 125.18 \\
2017-01-18 10:25:26 & 2 nd shock & 208.57 & 265.97 & 157.55 \\
2017-01-18 13:33:37 & 3 rd shock & 123.40 & 153.85 & 64.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The peak ground accelerations (PGA) of the seismic shocks.  
Figure 9: Time histories of acceleration in three directions of the 1 st shock.
## 2 Simulation Model and Method
### Boundary Condition  
Unstructured meshes are used in numerical simulation, which can make the boundary layer meshes denser. Figure 2 is the comparison of the average heat flux of the lower surface under three different mesh numbers. When the number of iterations reaches 1000, the heat flux on the lower surface begins to converge. When the number of meshes increases from 4.73 million to 6.96 million, the heat flux changes by 3%. When the number of them increases from 6.96 million to 8.52 million, the heat flux only changes by 0.28%. Finally, the number of simulation grids of the model is 6.96 million, as shown in Figure 3.  
Since the Reynolds number range studied is mainly in the turbulent region, the numerical simulation adopts a realizable k-\(\epsilon\) model, which has a high calculation accuracy in the turbulent region. Moreover, the realizable k-\(\epsilon\) model can be extended to the calculation of transition zone. The discrete format used in this calculation is the second-order upwind discrete format, and the algorithm is simple algorithm, because they converge fast. The growth rate of the boundary layer in the simulation is 1.2. The residual standard of pressure, temperature, heat flux convergence is \(1\times 10^{-5}\).  
The air inlet is the mass flow inlet, and the temperature of inlet air flow \(T_{0}\) is 300 K. The air is an incompressible ideal gas. The air outlet is the pressure outlet. The upper and lower surface and the surface of pin fins are heated by constant temperature \(T_{\text{k}}\) is 340 K. Other walls are adiabatic and have no-slip boundary conditions.  
Figure 2: Comparison of the average heat flux of the lower surface under three different mesh numbers.
## 5 Case Study and Analysis  
Using the IEEE 9-bus system as the test system example, connect the WT model to the line L3 of the system, as depicted in Figure 8. The studies in this paper were all obtained by simulating the operation of the WT at below rated power under different conditions. In order to ensure the method's applicability in diverse scenarios, the impact of different wind speed, wind power proportion, and load disturbance on the power system's frequency response is considered. Wind speed is varied from 4 m/s to 10 m/s in increments of 1 m/s, for a total of seven cases. Wind power proportion is set to range from 5% to 60%, increasing by 5% at each interval, for a total of 12 cases. Load disturbance ranges from 1.005 to 1.25, increasing by 0.05 at each step, for a total of 50 cases, resulting in 4200 total cases. Furthermore, a comparison of the proposed method's performance with that of other frequency regulation control techniques is conducted in typical scenarios.  
Figure 8: WT in IEEE 9-bus system.  
Figure 7: Flow chart of combined inertial intelligent control of WT based on CAE and DNN.
## 3 Data and Methods
### Calculation of Seasonal Movement  
Based on a preliminary analysis, we assumed strong variations in seasonal activity. To quantify this effect and assess its spatio-temporal distribution, we selected the orbit which shows the highest LOS displacement rates. This is usually the orbit, where the LOS has nearly the same direction as the movement of the observed feature along the slope, i.e., an ascending orbit for east and north-east facing landforms and descending orbit for west and north-west facing landforms. We selected ascending orbit 117 for Kaiserbergtal and ascending orbit 15 for Nair, descending orbit 66 for Gianda Grischa and Davains and descending orbit 168 for the Muragl site. For these selected orbits, we calculated the pixelwise median LOS 6d-displacement rate for each month based on the LOS displacement time series. The median of June and July was selected as the displacement rate before the seasonal increase and the median of September as the displacement rate at the maximum seasonal activity (Equations (4) and (5)). The difference between these two is defined as the absolute seasonal increase in the displacement rate (Equation (6)):  
\[med_{jun\_jul}=med\{disp_{jun\_1},\ldots,disp_{jun\_n},disp_{jul\_1},\ldots disp _{jul\_n}\}, \tag{4}\]  
\[med_{september}=med\{disp_{sep_1},\ldots,disp_{sep_n}\}, \tag{5}\]  
\[seasonality_{abs}=med_{september}-med_{jun\_jul},\quad[mm/6d], \tag{6}\]  
To quantify the proportion of the seasonal acceleration on the maximum velocity in summer, we additionally calculated the relative seasonality with Equation (7):  
\[seasonality_{rel}=\frac{seasonality_{abs}}{med_{september}},\quad[0\%;\ldots;100 \%], \tag{7}\]
## 5 Discussion
### Relationship between Model Performance and Land Cover  
The physical characteristics of the land cover affect the prediction accuracy of the developed LSTM model. This effect originates from the physical heterogeneity of the observed area.  
In the ISMN, every station is provided with its land cover type. The corresponding land covers are based on the ESA CCI land cover product [58]. In a total of 103 stations, 34 croplands, 20 grasslands, 18 shrulands, 23 trees/forests, and 6 mosaics (mixture of trees, shrubs, herbaceous, and cropland), and two urban sites exist. However, we did not investigate the urban sites due to the insufficient number of samples.  
Figure 6 presents the model's prediction capability for different land covers. The smallest MAE (\(\sim\)0.02) was achieved for shrubland class. The model shows similar performance for cropland, grassland, and tree covers with a mean MAE of approximately \(\sim\)0.03. However, the variance of MAE for the cropland cover is higher than the others. The worst MAE, (\(\sim\)0.05), is obtained for the mosaic cover due to the complexity of the surface. This can be explained by the scattering mechanism of SAR imagery in the presence of vegetation and forest. Because the shrubland land cover class is sparsely vegetated area, radar signals can interact with the soil more than vegetation or forest canopy.  
Figure 5: The scatter plot (top left and right) and distribution graph (bottom left and right) of (**a**) training and (**b**) testing data of windows size 5.
# Acceptance Model for Mobile Building Information Modeling (BIM)
###### Abstract  
Mobile Building Information Modeling (BIM) is noted for tools that enable the systematic interchange of information and contribute to enhancing collaborative performance through BIM. BIM programs, which are continuously available in the mobile environment, have been developed. Moreover, in some sites, mobile BIM is applied to generate benefits in projects. Various efforts are being made to use mobile BIM; however, its utilization is low. Also, mobile BIM has lacked an analysis of the factors that affect actual users' acceptance of mobile BIM. Therefore, this study analyzes the factors that affect the acceptance of mobile BIM by construction practitioners and presents the association of factors as a model to activate mobile BIM use. To this end, this study analyzed a literature review for suggesting the factors that were expected to affect mobile BIM acceptance. The assessment items were decided based on the analysis result. Second, 111 copies were received by surveying the construction practitioners. Third, it identified factors that significantly affected the acceptance of mobile BIM and proposed models through factor analysis and structural equation models. Finally, based on the analysis, it presented the findings. This study expects to contribute to enhanced acceptance of mobile BIM technology by managing the significant factors properly. Also, it is expected that the result can be used to develop a variety of mobile BIM that is more easily acceptable to them. This study presented a model for accepting mobile BIM based on the survey results of Korean practitioners; therefore, it is necessary to explore ways to generalize the model in the future.  
BIM; open BIM; mobile BIM; mobile application; technology acceptance model (TAM) +
Footnote †: journal: Applied Soft 2019, 9, 3668
## 3 The Proposed Energy-Balanced Data Collection (EBDC) Mechanism  
This section presents the details of the proposed _EBDC_ mechanism which is executed by the mobile sink for constructing the trajectory \(J_{min}^{t_{B-T}}\). At a conceptual level, the _EBDC_ mechanism is composed of three major phases: _Initialization Phase_, _Energy Estimation Phase_, and _Trajectory Construction Phase_. In the _Initialization Phase_, the number of sensors \(|S_{i}|\) in each track \(k_{i}\) will be evaluated while the _Energy Estimation Phase_ mainly measures the energy consumption of each sensor when the mobile sink traverses any track for one sweep repetition. In the _Trajectory Construction Phase_, the trajectory \(J_{min}^{t_{B-T}}\) can be planned by the information obtained in the previous two phases. These three phases are executed by the mobile sink. After determining the trajectory \(J_{min}^{t_{B-T}}\), the mobile sink will flood its movement plan, including the movement velocity in each track, the number of sweep repetitions in each track, the starting location and the starting time, to all sensors in the monitoring region. Each sensor can therefore derive the track \(k_{collect}\) the mobile sink is currently visiting. The following presents the details of the three phases.
## 4 Experimental Analysis
### RUL Prediction
Using MSCAE-HI for RUL prediction, the predicted RUL results are closer to the true values, and the predicted HI is more consistent with the true degradation trend. Combining the above analysis, the MSCAE-HI method proposed in this paper is more suitable for the RUL prediction of bearings. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline The number of neurons in the input layer & 360 \\ The number of neurons in the hidden layer & 29 \\ The number of neurons in the output layer & 1 \\ Learning rate \(lr\) & 0.07 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter configuration of LSTM. Figure 12: RUL prediction results of MSCAE-HI. (**a**) Bearing1_1. (**b**) Bearing1_3. Figure 13: RUL prediction results of RCNN-HI. (**a**) Bearing1_1. (**b**) Bearing1_3. To further validate the superiority of MSCAE-HI, five metrics for evaluating the prediction accuracy of RUL were used to compare the prediction performance of the three methods. These five evaluation metrics are score scoring function, mean absolute error (MAE), normalized root mean square error (NRMSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), which are defined in the literature [23]. Three methods were used to conduct five prediction experiments for Bearing1_1 and Bearing1_3, and the final evaluation results are shown in Table 4. In Table 4, the MAE, NRMSE, RMSE and MAPE of CNN-HI are the maximum among three methods. This phenomenon can be explained that the fluctuation of CNN-HI is very obvious at the late degradation stage, and the HI predicted by the LSTM is easily smaller than the degradation threshold, resulting in a large deviation of the predicted from the true RUL. RUL prediction using MSCAE-HI obtained the maximum score values and the minimum MAE, MRMSE, RMSE, and MAPE values on both Bearing1_1 and Bearing1_3, which once again confirmed the superiority of the HI indicator extraction method of MSCAE proposed in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Score**} \\ \hline  & MSCAE-HI & CRNN-HI & CNN-HI \\ Bearing1\_1 & **0.3315** & 0.2334 & 0.1379 \\ Bearing1\_3 & **0.4979** & 0.1366 & 0.0712 \\ \hline  & \multicolumn{3}{c}{**MAE**} \\ \hline  & MSCAE-HI & CRNN-HI & CNN-HI \\ Bearing1\_1 & **152** & 276 & 602 \\ Bearing1\_3 & **214** & 356 & 808 \\ \hline  & \multicolumn{3}{c}{**NRMSE**} \\ \hline  & MSCAE-HI & CRNN-HI & CNN-HI \\ Bearing1\_1 & **0.1516** & 0.2601 & 1.5480 \\ Bearing1\_3 & **0.2938** & 0.3596 & 4.2882 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of RUL prediction evaluation for 3 HI. Figure 14: RUL prediction results of CNN-HI. (**a**) Bearing1_1. (**b**) Bearing1_3.