esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Zigzag Length Determination Based on Image Processing  
Within the scope of zigzag length determination, only the GPS module and the monitoring camera were used.  
The GPS data are acquired at a rate of 1 Hz, and when using an Internet connection, the application can additionally identify where it is currently located on Google Earth maps. This is accomplished by creating shared variables that ensure real-time communication between files. The LabVIEW application can save GPS information (latitude and longitude) to documents for post-processing.  
The monitoring camera operates at a rate of 30 frames per second; thus, the time at which the frame is acquired differs from the time at which the other parameters are purchased. Due to the image frame dimensions, 300-frame.avi files are saved periodically. In the overall process of imagine post-processing, the time acquisition frame is preserved in a text file, and additional information is added to the acquired image, as illustrated in Figure 5.  
Figure 5: _Cont._  
The remaining three parameters--height to the pantograph-catenary contact point, air temperature, and humidity--were measured at a rate of 10 Hz.  
Based on these observations, the first operation that must be carried out, regardless of the method chosen, is to synchronize the acquired data to the same clock. All parameter values were synchronized using a 10 Hz base frequency and a linear interpolation technique.  
Based on the requirement that the zigzag length be determined as a function of geographical position, the second operation involves mathematically determining the zigzag length at equal distances between the rail line's axes.  
The measurements were taken between two railway stations connected by a double railroad line. Because rail transport operates in a single direction via wire, we refer to the route on which trains go from station 1 to station 2 as \"Line 1\" and the route on which trains go from station 2 to 1 as \"Line 2\". Measurements were taken in both directions. As will be discussed later, the exact same region was selected for investigation in both directions, between kilometers 505.2 and 510.5, a distance of 5.3 km.
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
###### Abstract  
The quasi-orthogonal space-time block code (QO-STBC) was introduced to achieve a full transmission rate for the four antennas system. In this paper, a decoding method for the QO-STBC is proposed to improve the bit-error-rate (BER) and to solve a rank-deficient problem. The proposed algorithm is based on the minimum mean-square-error (MMSE) technique. To overcome the implementation problem from the MMSE, an estimation method of the noise variance is developed in this paper. The proposed algorithm is implemented without matrix inversion, therefore, the proposed algorithm achieves a better BER than the conventional algorithms, as it has a low computational complexity. The simulation results show the low BER of the proposed algorithm in a Rayleigh fading channel.  
multiple-input/multiple-output system; noise variance estimation; MMSE decoder; quadrature phase shift keying; wireless communications +
Footnote †: journal: Electronics
## 2 Background
### Risks of Reducing Crew on Crew and Vessel Safety
#### 2.4.3 Mariner Experience and Situational Awareness  
Knowledge, discipline, training, aptitude, and experience are all important factors in shaping a successful mariner [45]. Onboard, operational decision making is shaped by years of professional training and sea-going experience, which is hard to replicate. The specific movements of a vessel, understanding weather, knowing how to react in extreme situations, and personnel management skills are acquired and refined over time. The preservation and inclusion of this experience should be considered when moving ship operators shoreside.  
Situational Awareness (SA) is the art of managing all available resources, particularly during a watch. It is necessary for the safe operation of a vessel according to the STCW [46]. A ship, being in a constant dynamic environment, must have crew that can accurately assess its ever-changing situation. Its crew is responsible for quick and diligent action to avoid potential dangers and preserve the safety of the ship and all personnel onboard. Loss of SA reduces vigilance and leads to breakdowns in communication, which can lead a vessel into a dangerous situation. This includes failure to observe or monitor critical data, failure to plan or to perceive the current situation accurately, and failure to project situations in the future [47]. Losing SA has proven to be a large contributor to maritime accidents [42].  
With autonomous vessels, ship operators are removed from the immediate scenario and are located potentially hundreds or thousands of miles away from the vessel being operated. How operators will maintain SA without physically being onboard must be addressed. In recent years there has been significant advances in automation technology within navigation systems, such as Electronic Chart Display and Information Systems (ECIDS) and Dynamic Positioning (DP), yet onboard crew is still needed to observe, monitor, and control these systems. To address SA, both the Rolls-Royce Autonomous Waterborne Applications (AAWA) initiatives project and the Lloyds Register concluded that a vessel with appropriate sensors could have SA that is as good as an onboard human mariner, but challenges with conveying this information exists [48].
## 3 Proposed RL-Based Real-Time Control Framework
### Design of Episode, Reward, State Space, and Action Space
#### 3.3.1 Episode  
An episode can start from any quasi-steady-state system operating condition that EMS snapshots can capture. For training corrective control RL agents, no actions need to be taken without any security violations if fixing security violations is the only control objective. However, due to variations in system loads, renewable generation, and unexpected contingencies, once voltage or line flow violations occur, the RL agent starts to take actions selected from a feasible action space to fix the security issues. For each iteration of applied control actions, the control performance is evaluated using reward values. To train effective agents, extensive representative operating conditions need to be collected or created, including random load changes, variations in renewable generation, generation dispatch schedules, and significant topology changes due to maintenance and contingencies. In this work, the actual system operating conditions captured by the EMS system every few minutes are taken as episodes for training RL agents.
# High-Gain Waveguide-Fed Circularly Polarized Spidron Fractal Aperture Antenna
## 1 Introduction  
Rectangular aperture antennas with a waveguide feeder are widely utilized in satellite communications and radar systems. These antennas can be flush-mounted onto the surfaces of aircraft or spacecraft, and their openings can be covered with a dielectric material to prevent exposure to environmental conditions that would otherwise cause them damage. It has been found [1] that instead of using an open-ended waveguide, resonant apertures should be used to attain a lower reflection coefficient. Among the currently available resonant apertures, the fractal aperture is a good candidate. With space-filling and self-similarity properties, fractal geometries have been applied extensively in antenna engineering [2; 3]. Therefore, fractal aperture antennas with a waveguide feeder have also attracted much attention [4]. However, the waveguide-fed fractal aperture antennas reported in earlier work [4] are mostly linearly polarized antennas. In most practical satellite communications and radar systems, circularly polarized (CP) antennas are preferred for their ability to alleviate multipath and fading effects and to provide a more flexible orientation between RF (Radio Frequency) transmitting and receiving antennas [5; 6; 7; 8]. Therefore, it is of interest to design waveguide-fed fractal aperture antennas with circular polarization.  
Moreover, antennas with high-gain characteristics are desirable for wireless communications over longer distances. A very simple method for achieving high-gain performance is the placement of a superstrate over an antenna [10; 11; 12]. By properly choosing the distance between the superstrate and theantenna and by carefully specifying the thickness of the superstrate, a very high gain can be attained. In addition, a superstrate with multilayer dielectric slabs separated by air gaps of approximately one-quarter of a wavelength was also investigated [12], but the prototype of this design was very bulky in size.  
In this paper, we propose the design of a rectangular waveguide-fed fractal aperture CP antenna with high-gain characteristics. In the design, two Spidron fractal slots are merged to form a Spidron fractal aperture to realize circular polarization operation. To improve the broadside gain, a single-layer dielectric substrate that acts as a superstrate is placed over the antenna at an appropriate distance. As a result, an enhancement of more than 5.4 dBic in the broadside gain is achieved. The results of the simulation were verified by measurements, and the proposed rectangular waveguide-fed Spidron fractal antenna with the superstrate exhibits CP operation of around 10.85 GHz with a maximum realized gain of 9.59 dBic. Details of the antenna design and the experimental results are presented and discussed in the following sections.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\[1:T_{\sigma}\] Scatter index in terms of stress \[1:T_{\Delta\overline{W}^{(\sigma)}}\] Scatter index in terms of strain energy density \[2\alpha\] Notch opening angle \[\lambda_{1}\] Williams' eigenvalue for mode I loading \[v\] Poisson's ratio \[\rho\] Notch root radius \[\sigma_{max}\] Maximum stress \[\sigma_{min}\] Minimum stress \[\sigma_{tip}\] Stress at the notch tip \[\sigma_{UTS}\] Ultimate tensile strength \[\sigma_{YS}\] Yield strength \[\Delta_{signed}\] Signed deviation \[\Delta_{unsigned}\] Unsigned deviation \[\Delta\varepsilon\] Strain range \[\Delta\sigma\] Stress range \[\Delta\sigma_{0}\] Plain material fatigue strength \[\Delta\sigma_{0,R=-1}\] Fatigue strength of the plain material tested at \[R=-1\] \[\Delta\sigma_{S}\] Stress range in the gross-section of the simulation models \[\Delta\sigma_{i}\] Stress range to be assessed \[\Delta\sigma_{Exp,Failure}\] Stress range, where burst failure occurred in the experiment \[\Delta\sigma_{Exp,Run-out}\] Stress range, where a run-out occurred in the experiment \[\Delta\sigma_{Exp,Run-out,i}\] Stress range, where a run-out occurred in the i-th experiment \[\Delta\sigma_{LLF,Defect}\] Fatigue strength of the defect-afflicted component \[\Delta\sigma_{LLF,Defect,i}\] Numerically evaluated fatigue strength of the i-th defect-afflicted component \[\Delta\sigma_{LLF,Defect,TCD}\] Fatigue strength of the imperfective component evaluated by TCD \[\Delta K\] Stress intensity factor range \[\Delta K_{hh,lc}\] Long-crack growth threshold stress intensity factor range of a V-notched component \[\Delta K\]\[\Delta W^{(\varepsilon)}\] Linear-elastic strain energy density range \[\Delta\overline{W}^{(\varepsilon)}\] Average linear-elastic strain energy density range \[\Delta\overline{W}^{(\varepsilon)}_{\overline{g},\overline{\psi}}\] Representative fatigue parameter \[\Delta\overline{W}^{(\varepsilon)}_{DEF,97.5\%}\] Average elastic strain energy density range for an exceedance limit of \[97.5\%\] \[\Delta\overline{W}^{(\varepsilon)}_{i}\] Linear-elastic strain energy density range averaged over \[\Omega_{i}\] \[\Omega\] Control volume \[\Omega_{i}\] Control volume at node \[P_{i}\] \[a_{0}\] Material intrinsic length \[c_{up}\] Mean stress correction coefficient \[da/dN\] Crack propagation rate \[c_{1}\] Geometry parameter \[n\] Number of results \[r_{0}\] Distance between V-notch apex and origin of coordinate system \[E\] Young's modulus \[F(2\alpha)\] Geometry parameter dependent on \[2\alpha\] \[H(2\alpha,R_{c}/\rho)\] Geometry parameter dependent on \[2\alpha\] and \[R_{c}/\rho\] \[N\] Number of cycles \[P_{i}\] Node on the defect contour in the finite element model \[P_{S}\] Probability of survival \[R\] Stress ratio \[R_{c}\] Control radius \[\overline{W}^{(\varepsilon)}\] Average linear-elastic strain energy density \[FKM\] Forschungskuratorium Maschinenbau \[\text{SED}\] Strain energy density \[\text{SENB}\] Single-edge-notched bending \[\text{TCD}\] Theory of critical distances
## 4 Results
### Comparison with Base Paper Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Dataset** & **Test Size** & **Paper Technique** & **Proposed Technique** \\ \hline [PERSON] et al. [6] & Cambridge hand gesture dataset & 20\% & 96.66\% (CNN Classification) & - 98.789\% (Hand coordinates + XGBoost) \\  & & & & - 99.565\% (CNN features on hand edges + PCA + Random Forest) - 99.533\% (Hybrid ANN) \\ \hline [PERSON] & ASL finger-spelling dataset & leave-one-out-accuracy & - 88.70\% (Single PCANet model + SVM) - 90.230\% (Hand coordinates + XGBoost) - 84.50\% (User-specific PCANet model + SVM) & - 99.230\% (Hand coordinates + ANN) \\ \hline [PERSON] [13] & Self-generated ISL digits & 250 & - 90.4\% (ORB + Nu-SVC) - 90.4\% (ORB + Nu-SVC) - 98.800\% (CNN features on hand edges + PCA + Random Forest) - 98.800\% (CNN features on hand edges + PCA + XGBoost) - 100.000\% (Hybrid ANN) - 80.000\% (Hand coordinates + Random Forest) - 80.6\% (ORB + KNN) - 80.000\% (Hand coordinates + XGBoost) - 80.000\% (CNN) - 97.200\% (Hand coordinates + Random Forest) - 85.6\% (ORB + KNN) - 97.200\% (Hand coordinates + XGBoost) - 90.800\% (CNN) \\ \hline \hline \end{tabular}  
* In the proposed technique, the ISL digits mentioned in the data availability statement have been used instead of a self-generated dataset.  
\end{table} 
Table 14: Results comparison. Since the NUS I dataset (having minimal segregation between individual classes) and the NUS II dataset (having highly intricate backgrounds) are complex in type, it is clear from Table 14 that hand coordinates are the ideal feature to classify the images contained within it as they deal with hand region location in a 3D space. Moreover, the utilization of a CNN is ideal in this scenario compared to ORB as specified in [PERSON], [PERSON] [13] because with the help of convolutional filters, a CNN is able to identify both the low-level and high-level features necessary for distinguishing labels of such complicated datasets, whereas a representative ORB keypoint of the same features can be the same for multiple classes.
# Transfer Learning-Based Fault Diagnosis under Data Deficiency
## 3 Application
### Training Neural Networks of Target Domain by Transfer Learning
In Figure 8a,b, the lower bound (fifth percentile) and the IQR between 25 th and 75 th percentiles are shown, which indicate the lower confidence limit of accuracy and the degree of statistical dispersion, respectively [18]. When the IR is 0.1, the lower bound of TL2 is 69.2, which is greater than 57.5 (TL1) and 53.3 (ML), and the IQR is 5.83, which is only about 60% of 9.58 (TL1) and 10.41 (ML). The higher the bound and the lower the IQR, the better the performance. This can be identified more clearly in Figure 9, where the histograms of diagnosis accuracy and its lower bound are displayed for TL1, TL2, and ML, respectively. While the histograms of TL1 and ML are similar and show much larger variance, TL2 shows smaller variance with greater lower confidence limit. However, as the IR increases greater than 0.1, the lower bound and IQR of TL2 are not superior to the others. The authors believe that these results are due to the negative transfer [10], which happens when the fault characteristics between the source and target domain are quite different. In that case, adding the fault data in the target domain does not help to improve the TL performance. Overall observations are summarized as follows: (1) TL1 and ML behave similarly, whereas TL2 is different from the others. That is, the initialization of NN parameters is of more importance than the adoption of source domain architecture in making the difference of TL from ordinary ML in the target domain. (2) TL2 is superior to ML and TL1 only when the number of fault data is very small such as when IR is 0.1 or less as is evident in Figure 8, in which the lower bound of accuracy for TL2 is higher and the IQR for TL2 is shorter than the other two. This is why TL is necessary for the case when the data are too few to train by their own. (3) It is of surprise that TL2 is soon found less effective than the other two as the IR gets greater than 0.1. Nevertheless, it should not be overlooked that the proposed approach is truly useful in the case of significant data deficiency, which is the highlight of the study. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**IR** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1.0** \\ \hline The number of nodes in hidden layer & 10 & 18 & 14 & 10 & 16 & 10 & 12 & 16 & 12 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of nodes in hidden layer determined in the target domain. Figure 8: Two measures of diagnosis accuracy: (**a**) lower bound; (**b**) interquartile range (IQR). Figure 7: Diagnosis accuracy of the target domain: (**a**) transfer learning (TL)1; (**b**) TL2; (**c**) machine learning (ML).
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 3 The Proposed Method
### The Embedment Algorithm of Smooth Blocks  
Let \(C_{i}=(a_{i},\ b_{i},\ B_{i})\) be the AMBTC compressed code of a smooth block \(I_{i}\). Following gives the embedment algorithm of a smooth block \(I_{i}\).  
* Step 1: Divide \(B_{i}\) into \(\left\{B_{i,j}\right\}_{j=0}^{1}\) and \(\left\{B_{i,j}\right\}_{j=2}^{15}\), which are employed to generate and carry \(ac_{i}\), respectively.
* Step 2: Use the bitmap \(\left\{B_{i,j}\right\}_{j=0}^{1}\), low quantized value \(a_{i}\), high quantized value \(b_{i}\), and position information \(i\) to generate \(ac_{i}\) using the following equation: \[ac_{i}=\text{hash}_{6}(\left\{B_{i,j}\right\}_{j=0}^{1},\ a_{i},b_{i,i}),\] (3) where \(\text{hash}_{6}(x)\) is the function that hashes \(x\) using the MD5 [32] and reduces the hashed results to 6-bit \(ac_{i}\) using the xor operation.
* Step 3: The 6-bit \(ac_{i}\) is divided into 2 groups of 3 bits denoted as \(ac_{i}^{0}\) and \(ac_{i}^{1}\). The matrix encoding described in Section 2.3 is then employed to embed \(ac_{i}^{0}\) and \(ac_{i}^{1}\) into \(\left\{B_{i,j}\right\}_{j=2}^{8}\) and \(\left\{B_{i,j}\right\}_{j=9}^{15}\), and we obtain \(\left\{\hat{B}_{i,j}\right\}_{j=2}^{8}\) and \(\left\{\hat{B}_{i,j}\right\}_{j=9}^{15}\), respectively.
* Step 4: Concatenate \(\left\{B_{i,j}\right\}_{j=0}^{1}\), \(\left\{\hat{B}_{i,j}\right\}_{j=2}^{8}\), and \(\left\{\hat{B}_{i,j}\right\}_{j=9}^{15}\), and we have the marked bitmap \(\hat{B}_{i}\). Finally, the marked compressed code \(\hat{C}_{i}=(\hat{a}_{i},\ \hat{b}_{i},\ \hat{B}_{i})\) is outputted, where \((\hat{a}_{i},\ \hat{b}_{i})=(a_{i},\ b_{i})\).  
A simple example is given to illustrate the procedure of generating an authentication code using the hash function \(\text{hash}_{6}(x)\). Suppose the hashed result of \(x\) is a 32-bit string '0011011011010100110000001010100'. Then the first 16 bits are xor-ed with the last 16 bits to create a 16 bits '11110010011000'. Repeat this xor-ed procedure one more time, and we obtain an 8 bits '01101010'. Since our method only requires 6 bits, the last 2 bits are discarded. Therefore, the authentication code '011010' is obtained.
## 4 Registration Algorithms Based on Feature
### Feature Extraction
RPMNet performs admirably in the presence of noise and partial overlap, but the model's initial input calls for additional label data, so it is not suitable for dense point cloud input. Similar to PRNet, RPMNet necessitates the computation of features again and over in iterations, raising the computational cost. [PERSON] et al. [119] suggested a multi-scale architecture and self-supervised fine-tuning (MS-SV Conv) based on FCGF. MS-SV Conv continues the FCGF's characteristics like fast operation speed and robustness to rotation, as well as greatly enhances the generalization performance. [PERSON] et al. [120] proposed a new neural network structure SpinNet. A 3D cylindrical convolution operation [137] procedure was applied to extract the final features for each voxel after the initial features for each voxel were retrieved using shared-weight MLPs and a maximum pooling aggregation function. SpinNet guarantees that features are rotationally invariant and perform well in general. [PERSON] et al. [138] supplemented the previous key point extraction methods. Thereafter, they pointed out that the prerequisite for key point extraction is to ensure that key points should be within the overlapping area of the two pieces of point clouds. [PERSON] and [PERSON] [121] introduced deep weighted consensus (DWC) for global registration. DWC is an unsupervised network that employs two DGCNNs [139] to extract the global and local RI in turn after first extracting rotation-invariant (RI) features in the local neighborhood of the point. The final features are obtained through the global-local fusion network. It is typical to learn characteristics jointly with crucial points' saliency. Since the saliency is frequently derived from unique features, this selection method relies on the encoding of the feature learning module, and the adaptive selection by the network is more crucial for the registration task at hand. In addition, proper exchange of information between two pieces of point clouds can make the detection of key points more accurate, for the reason that the alignment process tends to concentrate more on the overlapping regions.
## 5 The Principle of Intelligence Techniques
### Development of the PM\({}_{10}\) Concentration Predictive Models  
To establish the PM\({}_{10}\) concentration predictive models, a database including 245 drilling operations was randomly divided into two phases. In the first phase, \(\sim\)80% of the data (approximately 221 observations) was used for the development of the models. The remaining \(\sim\)20% (around 24 observations) in the second phase was used as new data for validating the performance of the developed models. ArcGIS software was used to manage databases with big data query capabilities [91]. The preparation of data for this study is illustrated in Figure 7.  
To validate/evaluate the models performances, two performance indicators, the root-mean-squared error (RMSE) and the determination coefficient (R\({}^{2}\)), were calculated (Equations (11) and (12)):  
\[\text{RMSE}\ =\ \sqrt{\frac{1}{n}\sum_{i\ =\ 1}^{n}\left(y_{i}-\hat{y_{i}} \right)^{2}} \tag{11}\]\[\text{R}^{2}\ =\ 1-\frac{\sum\limits_{i}{(y_{i}-\hat{y}_{i})^{2}}}{\sum\limits_{i}{ (y_{i}-\overline{y})^{2}}} \tag{12}\]  
where \(n\) is a total number of data; \(y_{i}\), \(\hat{y}_{i}\) and \(\overline{y}\) are measured, predicted, and mean of \(y_{i}\) values, respectively. For an optimal model, \(\text{R}^{2}=1\) and RMSE = 0.