esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Initial MWCNT Research  
Figure 1 shows cross-sectional SEM images of the radially-oriented MWCNTs (a) and MWCNTs (b,c) synthesized from ferrocene and toluene obtained under selected conditions after grinding in a rotary-type disperser. Nanotubes are close-packed in an array radially-oriented to the substrate surface--the inner surface of the quartz cylinder.  
The SEM studies revealed the presence of MWCNTs with different diameters (Figure 1c). According to TEM data (Figure 1c), the average outer diameter of the synthesized carbon nanotubes is about 80 nm and their lengths ranging from hundreds of micrometers to several millimeters. According to high-resolution transmission electron microscopy (HRTEM) image, the MWCNT lateral surface is formed by layers of graphene with average distances between them equal to 0.339 nm (Figure 1e). Defects in the form of residual graphene layers on the MWCNT outer surface are shown in Figure 1e,f. The MWCNT internal channel diameter is, on average, 10 nm. HRTEM images also show the presence of nanotube partial filling with \(\gamma\)-Fe nanoparticles (Figure 1e,f). The crystalline iron particle fast Fourier transform (FFT) is shown in the insert (Figure 1f). According to the EDS results, the atomic composition of the obtained MWCNTs after transfer into air mainly consists of carbon (95%) and oxygen (5%). This indicates the presence of a small amount of oxides on the nanotube surface.  
The initial MWCNT X-ray powder diffraction analysis (Figure 2a) demonstrates that the main features correspond to the graphite peaks (002), (100), (101) and (004). Moreover, diffraction peaks (111), (200) prove the residual iron catalyst (\(\gamma\)-Fe) presence.  
Figure 1: Cross-sectional scanning electron microscopy (SEM) images of the radially-oriented aligned multi-walled carbon nanotubes (MWCNTs) (**a**), the initial MWCNTs after grinding in a rotary-type disperser (**b**,**c**). The transmission electron microscopy (TEM) image of MWCNTs (**d**) and high-resolution transmission electron microscopy (HRTEM) images of an iron catalyst (\(\gamma\)-Fe)-filled nanotube (**e**,**f**). The MWCNT dimensions and carbon interplanar spacing 3.39 Å are marked in (**e**). The (**f**) image shows the fast Fourier transform insert, that indicates the crystallinity of the filling (\(\gamma\)-Fe interplanar spacing 0.207 nm).
## 3 Methods
### Feature Extraction Techniques
#### 3.4.1 Time Domain Parameters  
Time Domain parameters (TDP) is a technique that is performed by calculating the time-varying power of first k derivatives in the following equation:  
\[p_{i}(t)=\left(\frac{d^{i}x(t)}{d_{t}{}^{i}}\right),\ldots\ldots i=0,1,2, \ldots\ldots,k \tag{4}\]  
The values that are obtained can be smoothened by utilising an exponential moving average window filter. Even though the features of the TDP are defined in the time domain, they can also be inferred as frequency domain filters. Other spectral approaches that are available are Fourier transform, wavelet analysis, and autoregressive spectrums, which can define the rest of the spectral density function. However, this technique has the limitation that classification does not occur when there are too many parameters. Therefore, the training requires lots of data that lead to the possibility of overfitting [23].
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 3 Measurement and Test
### Functional Test Results  
Various commands related to locomotive control have been sent from master to slave locomotives and the control result was displayed in the DU installed in each locomotive. The test items are shown in the Table 3. There are six basic functions for operating that pantograph function, main circuit breaker function, air compressor control, EB, SB, safety circuit. As shown in the Table 3, the WiFi-based WDPS applied heavy haul freight proved that control commands related to train operation and safety were correctly transmitted and executed from the master to the slave locomotive.
## 3 Impact of external errors
### Impact of roll angle knowledge  
The roll angle plays a major role in the height estimation process (see I.1): a roll error linearly impacts the height measurement thanks to the across track distance in the swath x according to:  
Figure 12: (a) Example of the spatial distribution of the FR ionospheric errors (differences between the nadir and the off-nadir delays) in an intermediate case at midnight in April 2000 (b) Maximum ionospheric errors in the swath (Middle Range and Far Range) in different cases.  
Figure 13: (a) Zonal variations of the Far Range mean tropospheric error in October 2003 (b) Worst cases tropospheric errors during fall 2003 with evidences of the Hurricane Juan (orange arrows).
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 4 Experimental Analysis
### Case Study 1: Generalization Experiment under Different Working Conditions
However, under other conditions, the proposed WMSCCN algorithm has the highest accuracy. In terms of average accuracy, the proposed WMSCCN algorithm is the highest at 96.44%, exceeding 92.76% of WDCNN. Such comparison results fully demonstrate that the proposed WMSCCN algorithm can effectively identify fault characteristics under different loads, and the generalization performance is good. WMSCCN-AdaBN refers to the application of the AdaBN algorithm on the basis of the WMSCCN algorithm to further strengthen the model's domain adaptation and generalization capabilities. Experimental results indicate that after applying the AdaBN algorithm, the average accuracy of the model has improved by 1%.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Distortion Correction  
Due to the limitations of the equipment and various problems in the assembly of the system, the cross laser-pattern images collected by the industrial cameras in the position and attitude measurement system will produce various distortions. According to the characteristics of the project, perspective distortion [18] and radial distortion [19] are the two most important factors affecting the result, so this paper mainly considers perspective distortion and radial distortion, and corrects them in real time.  
Before distortion correction, a thin Zhang Zhengyou [20] checkerboard grid was placed on the interior surface of the screen, and an image of the checkerboard grid was acquired by the industrial camera. This acquired checkerboard grid image was used to obtain distortion calibration parameters, and with these parameters distortion corrections can be realized in real time.  
Figure 5: Flowchart of the proposed algorithm.
# Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
## 2 General Concept  
A framework of the proposed inverse method for damage identification is shown in Figure 1. It is composed of three building blocks: (i) dataset computation, (ii) supervised learning, and (iii) inverse method.  
For the problem of guided wave-based damage identification, it is infeasible to collect a large experimental dataset that would cover various damage case scenarios, because it would require manufacturing and damaging multiple samples. Therefore, a possible alternative is a dataset computed numerically by using, e.g., a finite element solver. In particular, the dataset that is employed in the current research was generated by using the time domain spectral element method [15]. The dataset focuses on delamination as the most dangerous type of damage occurring in composite laminates such as carbon fibre-reinforced polymers (CFRP). Delamination is a separation between piles of composite laminate and can be a result of impact or manufacturing process. Delamination is often not visible from the outside and can grow during the operation of the structure. The process of generating this dataset involved modelling a square plate with delaminations of varyingshapes and positions into the parametric unstructured mesh. Following this, a forward solver was utilised to capture the interactions between the propagating guided waves with the delamination and the boundaries of the plate. It resulted in 512 frames for each delamination scenario. The dataset has been made available online [31] so that the results can be easily replicated.  
The dataset was used for the supervised training of the DL model. The idea was to input to the DL model a binary image in which ones (white pixels) represent the delamination region and zeros (black pixels) represent the healthy region for the respective delamination cases. Once the model is trained, it can be used in the inverse procedure as a surrogate model instead of a computationally expensive p-FEM or SEM forward solver.  
It should be noted that the PSO algorithm was used in the inverse method due to its efficiency in handling more general formulations of objective functions (as opposed to stricter algebraic constructs), but other algorithms can be used as well. On one hand, the inputs are initial particles represented by binary images in Figure 1. These are fed to the trained DL model, which, in turn, is predicting the full wavefield of propagating waves for respective delamination scenarios. On the other hand, full wavefield measurements are acquired. An objective function is built upon the computation of the mean square error (MSE) between the predicted wavefield and the measured wavefield. Next, the particle positions and their velocities are updated accordingly until the termination criterion is met. Finally, the identified delamination is visualised for the best match. It should be added that the proposed method was validated on synthetic data only.  
The next sections describe each building block of the proposed method in detail.
## 7 Field Test
### Online Monitoring System Based on the Pulse Current Method  
The online monitoring system based on the pulse current method is composed of a high-frequency current transformer (HFCT), a signal conditioning module, a high-frequency acquisition module and an industrial computer. The schematic diagram of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SNR Value of White Noise (dB)** & **SNR (dB)** & **NCC** & **RMSE** \\ \hline \multirow{3}{*}{S\_VMD} & \(-3\) & 4.6468 & 0.8414 & 0.0860 \\  & \(0\) & 7.3335 & 0.9022 & 0.0631 \\  & \(3\) & 8.4326 & 0.9261 & 0.0556 \\  & \(-3\) & 5.6942 & 0.8610 & 0.0763 \\ IIE-MVMD & \(0\) & 7.6029 & 0.9089 & 0.0612 \\  & \(3\) & 9.0524 & 0.9351 & 0.0520 \\  & \(-3\) & 7.6087 & 0.9129 & 0.0612 \\ SVD-EWT & \(0\) & 9.5816 & 0.9431 & 0.0487 \\  & \(3\) & 10.0402 & 0.9488 & 0.0462 \\  & \(-3\) & 11.2429 & 0.9639 & 0.0403 \\ ISVD-MVMD & \(0\) & 13.5069 & 0.9787 & 0.0310 \\  & \(3\) & 15.1637 & 0.9861 & 0.0256 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Noise reduction performance evaluation under different SNRs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluating Indicator** & **S\_VMD** & **IIE-MVMD** & **SVD-EWT** & **ISVD-MVMD** \\ \hline Time/s & 5.762 & 0.855 & 1.238 & 1.395 \\ \(E_{s}/s^{-1}\) & 15.89 & 115.90 & 106.22 & 280.95 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of duration and operating efficiency indicators of Simulated PD signals for different algorithms.  
the system is shown in Figure 15. By installing an HFCT on the cable grounding line to collect the partial discharge current pulse signal, the signal output via an HFCT is transmitted to the signal conditioning module through the high-frequency double-shielded coaxial cable for filtering and amplification. The signal is sent to the industrial computer through the high-speed data acquisition card for signal processing and analysis. Among them, the current transformer uses a broadband high-frequency current transformer produced by a company in Xi'an, with a maximum bandwidth of 100 Mhz. The acquisition card uses a QT7126 board, and the upper limit of the sampling rate is 3.2 GS/s. Some field devices are shown in Figure 16.
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
## 1 Introduction
Due to the errors of the redatumed Green's functions, it will be difficult to maintain reasonable accuracy in the local full waveform inversion [14]. Likewise, since the observations in the target-oriented FWI are also retrieved by Marchenko redatuming, this would lead to the unsatisfactory lateral resolution of the inversion results, as well as low sensitivity to steep structures [14]. In this study, we extend the local full waveform inversion using Marchenko methods with vertical seismic-profile (VSP) data to address the deficiencies in the lateral resolution and the poor reconstruction of the steep structure of local FWI method. [PERSON] and [PERSON] obtained virtual seismic data with virtual sources and receivers in the subsurface using a method called source-receiver Marchenko redatuming [25]. In their method, receiver-redatumed Green's functions are calculated by the standard Marchenko method to redatum the source to another location in the subsurface (virtual source point). [PERSON] et al. extended the source-receiver Marchenko redatuming method by replacing the receiver-redatumed Green's functions with VSP responses. The output of this method is an estimate of the wavefield from a subsurface virtual source, which potentially contains the information about lateral propagation of the wavefield. [26]. We, therefore, introduce the VSP data into Marchenko redatuming to add a new set of virtual shot records to the local FWI and reorganize the objective function. In this paper, we first introduce the local FWI method in the frequency domain based on standard Marchenko redatuming. It includes the derivation of the local forward modeling operator and the establishment of the objective function. Then, we use the source-receiver Marchenko redatuming method with VSP data to estimate the Green's functions of the virtual receivers (along the boundary of the target) with the virtual sources (downhole physical receivers) in the wellbore. Compared to the standard Marchenko method, the data reconstructed by the Marchenko method with VSP have a higher accuracy by accounting for the laterally propagating wavefield signals. Finally, we incorporate the retrieved wavefields into the local FWI method. The inversion results demonstrate a good improvement in the lateral resolution. The proposed method is applied to a synthetic benchmark model for a carbonate reservoir in the Tarim Basin with strike-slip faults and fracture-vuggy karsts. The FWI result verifies the effectiveness of this method.
## 5 Numerical Studies
### Effect of Machiting Error (ME) on Transmission Error (TE)  
Thermal deformation and pitch error are not considered as uncertainties in this example. To study the influence of the machining error on the transmission error, the mean and standard deviation of the transmission error are analyzed under unloaded and loaded conditions.  
The mean and standard deviation of the transmission error under the unloaded condition are shown in Figure 10. The performance of the spur gear pair under the unloaded condition is affected by the shape of the tooth profile. When \(\epsilon=0\), the tooth profile is an ideal involute curve, and the spur gear pair has no transmission error. As \(\epsilon\) increases, the fluctuation of the transmission error increases, which can be confirmed as shown in Figure 10. Figure 11 shows the mean and standard deviation of the transmission error under the loaded condition. The plastic spur gear pair has larger fluctuations than the steel spur gear pair in both the mean and the standard deviation of the transmission error because plastic has lower stiffness than steel. Furthermore, there is a difference in the tendency of the transmission error. The transmission error of the steel spur gear pair is similar to the transmission error under the unloaded condition, whereas that of the plastic spur gear pair is not. This implies that plastic is susceptible to applied force. As \(\epsilon\) increases, the standard deviation of the peak-to-peak transmission error (PPTE) increases. From Figure 12, the range of the PPTE of the designed spur gear pair can be predicted. The machining error under 2D simplification can be compared with total profile deviation, which is one of the factors in ISO precision grade. It is possible to calculate the level of machining error required to satisfy the transmission error of the desired gear quality.  
Figure 10: (**a**) Mean and (**b**) standard deviation (std) of transmission error under unloaded condition. When the machining error (\(\epsilon\)) increases, there is no thermal deformation or pitch error (\(\eta=0,e_{p}=0\)).
## 4 Discussion  
In this analysis of hydrological and meteorological conditions in the southern part of the Baltic Sea with a view to the use of LNG as bunkering fuel, the boundary values of wind speed, sea state, visibility, air temperature, precipitation, barometric pressure, icing, humidity, LNG temperature, mist and fog, salinity, sunshine duration, waves, and sea currents provided have been determined in compliance with international standards and guidelines issued by the International Maritime Organization (IMO), the International Organization for Standardization (ISO), and guidelines of the Baltic ports, including, without limitation, Gdansk, Gdynia, Helsinki, and Tallinn (Table 11). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather Component** & **Boundary Value** & **Value in the Baltic Sea** & \begin{tabular}{c} **Compliance with** \\ **Regulations** \\ \end{tabular}  & **Remarks** \\ \hline
**Wind speed** & Max. 15–20 m/s & Max. 15–20 m/s & Yes & \begin{tabular}{c} Additionally: \\ guidelines of the Baltic \\ ports. \\ \end{tabular}  \\ \hline
**Sea state** & Max. 3–4 Beaufort scale (wave height 1.25–2.5 m) & Max. 3–4 Beaufort scale (wave height 1.25–2.5 m) & Yes & \begin{tabular}{c} Additionally: \\ guidelines of the Baltic \\ ports (due to frequent \\ storms and short waves \\ specific to the Baltic). \\ \end{tabular}  \\ \hline
**Visibility** & Min. 500 m–1 km & Minimum 500 m–1 km & Yes & \begin{tabular}{c} Additionally: port \\ guidelines. \\ \end{tabular}  \\ \hline
**Air temperature** & From \(-20\ ^{\circ}\)C to +35 \({}^{\circ}\)C & From \(-20\ ^{\circ}\)C to +35 \({}^{\circ}\)C & Yes & \begin{tabular}{c} Specific to LNG: low \\ temperatures may \\ affect equipment \\ performance and pose \\ a risk of the freezing of \\ valves. \\ \end{tabular}  \\ \hline
**Precipitation** & No heavy rain or snow & No heavy rain or snow & Yes & \begin{tabular}{c} Provided that the risk \\ of equipment failure \\ and restricted visibility \\ are minimized. \\ \end{tabular}  \\ \hline
**Figure 9**: Total number of ships and forecast number of LNG-powered ships navigating the Baltic Sea [units] [150]. Although the boundary values for key meteorological components, such as wind speed, wave height, visibility, air temperature, icing potential, and sea currents, generally comply with international standards, the operational environment of the Baltic Sea is characterized by considerable variability. Conditions like wind speeds exceeding 15-20 m/s, visibility falling below 500 m due to fog, and seasonal ice formation can cause temporary interruptions to LNG transfer operations. Additionally, the Baltic's shallow depth and minimal tidal range and the frequent occurrence of short, steep waves during storms further complicate maneuvering and berthing, particularly for larger LNG-fueled vessels. From an infrastructure perspective, these environmental challenges necessitate the implementation of robust technical measures. For example, LNG bunkering terminals should be equipped with heated loading arms and anti-icing systems to ensure operational
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Use Cases of Human DTC
#### 5.5.2 Towards Personalized Healthcare with Augmented Reality and Digital Twins  
Digital technology has the potential to transform medicine and the healthcare industry in a sustainable way. At the same time, digital technology could equalize the relationship between medical professionals and patients. Many techniques and technologies have been proposed over the years for the diagnosis and treatment of diseases such as cancer. To add to these, technological breakthroughs in the fields of information and communication technologies (ICT), Augmented Reality (AR), and Artificial Intelligence (AI) might be of aid throughout the diagnosis and prediction process. Using AI algorithms and AR as technological pillars, the authors in [52] propose a conceptual framework for the display of magnetic resonance imaging (MRI) scans, data acquisition, and analysis from patients. Additionally, preliminary development is given, and future advancements are explored in light of the implications encountered. The graphical user interfaces (GUIs) (Figure 11a,b) and an information and communication technology (ICT) flowchart are presented in Figure 11c.  
Figure 11: (**a**,**b**) Graphical user interface for human digital twin application; (**c**) information and communication technology (ICT) flowchart [52].
## 3 Proposed APF-IRRT\({}^{*}\) Algorithm
### Description of Hybrid Algorithm  
An algorithm example using the proposed hybrid algorithm is presented in Algorithm 2. It is similar to an IRRT* algorithm, which adds the virtual force of APF into an IRRT* algorithm to generate new nodes (line 9). This results in increasing the guidance of the IRRT* algorithm, which will avoid random sampling all over the state space and reduce unnecessary extended nodes in invalid regions. It greatly reduces computation while improving the IRRT* algorithm in real time.  
Figure 3: The flow chart of hybrid APF-IRRT* algorithm.  
```
1:\(V\leftarrow\{x_{start}\}\) ;
2:\(E\leftarrow\varnothing\) ;
3:\(X_{soln}\leftarrow\varnothing\) ;
4:\(T=(V,E)\) ;
5:foriteration\(=1\) to n do
6:\(c_{best}\leftarrow min_{x_{soln}\in X_{soln}}\{Cost(X_{soln})\}\) ;
7:\(x_{rand}\leftarrow Sample(x_{start},x_{tar},c_{best})\) ;
8:\(x_{near}\gets Nearnest(T,x_{rand})\) ;
9:\(x_{new}\leftarrow\textbf{Steer}(x_{rand},x_{near},x_{tar},\textbf{map},\textbf{ stepsize})\) ;
10:ifCollisionFree(\(x_{nearest},x_{new}\)) then
11:\(X_{near}\gets Nearest(T,x_{new},\tau_{RT^{*}})\) ;
12:\(x_{parent}\gets Parent(x_{near})\) ;
13:ifInellipseRegion(\(x_{new}\)) then
14:\(X_{soln}\gets X_{soln}\cup\{x_{new}\}\) ;
15:\(T.addNode(x_{new})\) ;
16:Return\(T\) ;
```  
**Algorithm 2**\(\textbf{APF}-\textbf{IRRT}^{*}(\textbf{x}_{start},\textbf{x}_{tar})\)  
The key steer function algorithm (line 9) can be realized with three steps, as shown in Figure 4. Step one is to calculate the sum vector \(\alpha\) of \(x_{new}\) and \(x_{rand}\) (Figure 3(a)). Step two uses APF to calculate \(F_{att}\) and \(F_{rep}\), and then calculates the sum vector \(\beta\) (Figure 3(b)). The last step is to calculate the sum vector \(\theta\) of \(\alpha\) and \(\beta\) (Figure 3(c)).  
The steer function Algorithm 3 is described as:  
```
1:if\(x_{rand}\in C_{free}\)then;
2:\(\alpha\gets atan2(x_{near},x_{rand})\) ;
3:\(angle\leftarrow angle(x_{near},x_{tar},map,length(map))\) ;
4:\(Calculation\beta()\);
5:\(F_{a}\gets APF_{attract}(x_{near},x_{tar},angle,AFP_a,AFP_max)\) ;
6:\(F_{r}\gets APF_{repulsion}(x_{near},map,angle,AFP_r,AFP_po)\);
7:\(\beta\gets atan2(F_{a},F_{r})\) ;
8:\(x_{new}\gets Steer(\alpha,\beta,stepsize)\) ;
9:else
10:\(x_{rand}\ \sim U(X)\) ;
11:Return\(T\);
```  
**Algorithm 3**\(\textbf{Steer}(x_{rand},x_{near},x_{tar},map,stepsize)\)  
Figure 4: Realization of the steer algorithm in three steps.