esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
During the last two decades, the number of macrocell mobile telephony base station antennas emitting radiofrequency (RF) electromagnetic radiation (EMR) in residential areas has increased significantly, and therefore much more attention is being paid to RF EMR and its effects on human health. Scientific field measurements of public exposure to RF EMR (specifically to radio frequency radiation) from macrocell mobile telephony base station antennas and RF electromagnetic field (EMF) intensity parameters in the environment are discussed in this article. The research methodology is applied according to the requirements of safety norms and Lithuanian Standards in English (LST EN). The article presents and analyses RF EMFs generated by mobile telephony base station antennas in areas accessible to the general public. Measurements of the RF electric field strength and RF EMF power density were conducted in the near- and far-fields of the mobile telephony base station antenna. Broadband and frequency-selective measurements were performed outside (on the roof and on the ground) and in a residential area. The tests performed on the roof in front of the mobile telephony base station antennas in the near-field revealed the presence of a dynamic energy interaction within the antenna electric field, which changes rapidly with distance. The RF EMF power density values on the ground at distances of 50, 100, 200, 300, 400, and 500 m from the base station are very low and are scattered within intervals of 0.002 to 0.05 \(\upmu\)W/cm\({}^{2}\). The results were compared with international exposure guidelines (ICNIRP).  
electric field strength; power density; non-ionizing electromagnetic radiation; mobile telephony base station antenna +
Footnote †: journal: Journal of  
_Article_  
A Technical Approach to the Evaluation of Radiofrequency Radiation Emissions from Mobile Telephony Base Stations
# Bit Independence Criterion Extended to Stream Ciphers
## 2 Preliminaries  
A stream cipher can be viewed as a function \(f:\mathbb{F}_{2}^{n}\rightarrow\mathbb{F}_{2}^{m}\) that transforms a binary input vector \(X=(x_{1},\ldots,x_{n})\) of \(n\) bits into a binary output vector \(Y=f(X)=(y_{1},\ldots,y_{m})\) of \(m\) bits, where \(n,m\in\mathbb{N}\). In [13], the difference between the outputs \(Y=f(X)\) and \(Y^{i}=f(X^{i})\), corresponding to the inputs \(X\) and \(X^{i}\), is called the avalanche vector and denoted by \(V^{i}=Y\oplus Y^{i}\), where \(X^{i}=X\oplus e_{i}\), with \(1\leq i\leq n\) and \(e_{i}\) the unit vector with \(1\) in the \(i\)-th component. In \(V^{i}=Y\oplus Y^{i}=(v^{i}_{1},v^{i}_{2},\ldots,v^{i}_{m})\) each \(v^{i}_{j}\in\mathbb{F}_{2}\), with \(1\leq j\leq m\), is called an avalanche variable (see Table 1, Appendix A).  
Given the set \(D=\{X_{1},\ldots,X_{l}\}\) of \(l\) inputs \(X_{r}\) of \(n\) bits, with \(1\leq r\leq l\), a binary matrix \(H^{i}\) is constructed for each \(e_{i}\), \(1\leq i\leq n\). To construct the matrix \(H^{i}\), the avalanche vectors \(V^{i}_{r}=Y_{r}\oplus Y^{i}_{r}=(v^{i}_{r1},v^{i}_{r2},\ldots,v^{i}_{rm})\) are calculated, with \(Y_{r}=f(X_{r})\), \(Y^{i}_{r}=f(X_{r}\oplus e_{i})\). It is said that \(f\) satisfies the BIC if, by changing any bit \(i\) in the \(l\) inputs \(X_{r}\in D\), it is satisfied that every pair of avalanche variables \(v^{i}_{.j}\) and \(v^{i}_{.k}\) are independent, with \(1\leq j,\ k\leq m\). The matrix \(H^{i}\) will be called the SAC matrix associated with the vector \(e_{i}\) and is shown in Table 1.  
To measure the degree of independence between the pairs of avalanche variables, [PERSON] and [PERSON] [13] used [PERSON]'s correlation coefficient. In [29], the maximum value of these coefficients was used as a test statistic, denoted here by  
\[BIC_{Pearson}(f)=\max_{\begin{array}{c}1\leq i\leq n\\ 1\leq j,k\leq m\\ j\
eq k\end{array}}\rho(v^{i}_{.j},\ v^{i}_{.k}). \tag{1}\]  
If all pairs of avalanche variables \(v^{i}_{.j}\) and \(v^{i}_{.k}\) are independent, then ideally, \(BIC_{Pearson}(f)=0\). Therefore, in practice, when \(BIC_{Pearson}(f)\approx 0\), it is concluded that \(f\) satisfies the BIC.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & [28] & [29] & [30] & [31] & **This Work I (Split)** & \begin{tabular}{c} **This Work II** \\ **(Stack)** \\ \end{tabular} \\ \hline Process & GaN 0.25 \(\upupmu\)m & GaN 0.25 \(\upmu\)m & GaN 0.25 \(\upmu\)m & GaN 0.25 \(\upmu\)m & GaN 0.25 \(\upmu\)m GaAs & GaN 0.25 \(\upmu\)m GaAs \\  & & device ** & & & & IPD & IPD \\ \hline \multirow{2}{*}{Type} & One-stage Cont. & \multirow{2}{*}{Two-stage} & \multirow{2}{*}{Discrete two-stage} & \multirow{2}{*}{Two-stage} & Quasi-MMIC two-stage Cont. & Quasi-MMIC two-stage Cont. \\  & & & & & & Class-J \\ \hline Freq. (GHz) & 4.2–7.0 & 5.2–6.2 & 3.8–8.8 & 2.7-3.5 & 2.85–4.8 & 2.84–4.47 \\ \hline BW (\%) & 50 & 17.5 & 79.4 & 25.8 & 44.5 & 44.6 \\ \hline Gain (dB) & 12.4 & 20 & 17 & 19.3 & 21.7 & 21.6 \\ \hline \(P_{sat}\) (dBm) & 36.4 & 43 & 37.7 & 34.6 & 40.3 & 40.0 \\ \hline PAE\({}_{\text{peak}}\) (\%) \({}^{*}\) & 49 & 41 & 38 & 56.6 & 39.3 & 36.5 \\ \hline Chip Size (mm\({}^{2}\)) & 2.16 & 15.75 & NA & 3.55 & \
\begin{tabular}{c} 2.87 (GaN) 2.83 \\ (GaAs) \\ \end{tabular} &
\begin{tabular}{c} 2.1 (GaN) 12.11 \\ (GaAs) \\ \end{tabular} \\ \hline \hline \multicolumn{6}{c}{* PAE @ saturated output power. ** Wolfspeed CGHV1F006S.} \\ \end{tabular}
\end{table} 
Table 1: Comparison of various S/C band GaN MMIC and quasi-MMIC power amplifiers.
## 4 Results and Discussion
### pH Analysis
Regarding the results from the literature, some of the works listed in Table 1 are not comparable with ours due to differences in the anthocyanin quantification procedures. The RMSE values presented in our work outperformed those obtained [11]. However, the authors of [13] obtained better RMSEs than those obtained here, indicating that the use of neural networks might be more effective for predicting the anthocyanin concentration. The results of [12] also showed a better performance for the SVR approach, indicating that the use of a genetic algorithm can be a good alternative for predicting the anthocyanin concentration. In addition, Table 5 displays the results for RPD and RER, and from which it is possible to conclude that, overall, moderately successful predictions were achieved, with RDP and RER obtaining values larger than 2.5 and 10, respectively [39]. Comparing these values with those reported in the literature, the authors of [10] reported RPD and RER values of 3.89 and 10.49, respectively, for a Syrah variety and of 5.38 and 12.78, respectively, for a Cabernet Sauvignon variety, but the authors used a large number of berries per sample, while [11] only stated that the models were moderately successful with average R\({}^{2}\) but low RPD values. The remaining works from Table 1 did not report values for RPD or RER for the anthocyanin parameter. As EN was the best method for estimating the anthocyanin concentration and RR appeared on the top of the best methods for the enological parameters considered in this work, both were selected to identify the relevant spectral regions, regarding the anthocyanin property. From the analysis of Figure 13, which presents the regression coefficients obtained for the 30 runs of the Monte Carlo double cross-validation, it is possible to observe an agreement regarding the relevant regions, covering 400-520 nm and 700-900 nm ranges. Nevertheless, spectral regions between 400-480 and 900-1000 nm tend to be noisier for the RR method, but presented much less noise for the EN method. Thus, 420, 450, 490, 520, 730, and 850 nm peaks might be related to the anthocyanins. Additionally, some coefficients were very important in some runs, but were very close to 0 in others (Figure 13b), confirming the instability due to the collinearity that is prevalent in the spectral datasets. In some runs of Monte Carlo double cross-validation, a single wavelength was selected and led to better results, whereas nearby regions were ignored (their coefficients were set to zero, which is a property of the LASSO L1-norm penalty). On other runs, the coefficients converged to a scenario where nearby spectral regions had a similar contribution (a property of the RR L2-norm penalty). Again, this example shows the benefits of Monte Carlo double cross-validation as it allows for a more comprehensive study
# A Review of Deep Learning Applications for Railway Safety
## 2 Overview of Deep Learning Approaches
### Tasks
#### 2.2.4 Feature Extraction  
Feature extraction transforms raw data into numerical features more beneficial for the main task (e.g., classification, object detection). This task often affects model performance, helping reduce the dimensionality of the model and better represent latent patterns. Principle component analysis (PCA) is one of the traditional algorithms for feature extraction. PCA is a method of reducing multidimensional data by selecting the axis with the largest variance as the first principal component, then selecting the larger axis as the second principal component, and linearly converting the data when each variable (feature) is projected onto one axis. Other known techniques include linear discriminant analysis (LDA) [29], canonical correlation analysis (CCA) [30], singular value decomposition [31], isometric feature mapping (ISOMAP) [32], and locally linear embedding (LLE) [33].  
Auto encoder (AE) is a deep neural network that can be used for feature extraction [34]. AE is used for anomaly detection, which determines whether a sample is normal or abnormal, or for denoising operations that extract the original data by removing the noise added to the data. AE is unsupervised learning that learns to output the same results as the input data. However, since the dimension of the hidden layer is designed to be lower than that of the input and output layers, AE learns in the direction of exploring representation information that can effectively indicate input data. The restricted Boltzmann machine (RBM) is also a deep learning model for feature learning that works through the process of finding better representations of input values [35]. The RBM consists of a visible layer, which is an input layer, and a hidden layer in which feature values are learned. The deep belief network (DBN) is a probabilistic generative model built by layers of pre-trained RBMs [36].
# Topology Optimization Based Material Design for 3D Domains Using MATLAB
## 4 The MATLAB Code Implementation
### The Seven Components of the UCOpt3D Function
#### 4.1.3 Homogenization Section  
In order to perform the homogenization section of the optimization procedure, a modified variant of the homo3D function, developed by [PERSON] et al. [13], is utilized. In the proposed variant, the lame parameters \(\mu\) and \(\lambda\) are replaced by Poisson ratio \(v\) and [PERSON]'s modulus \(E\) while the material indicator matrix \(voxel\) is not used in the proposed MATLAB code. In the original code, matrix \(voxel\) was utilized to determine the unit cell dimensions and the active degrees of freedom. In the current implementation, the unit cell dimensions are determined by the [PERSON]'s modulus matrix \(E\) and the active degrees of freedom using a minimum [PERSON]'s modulus value \(E_{min}\) as a threshold. Finally, the derivation process for [PERSON]'s modulus with respect to the material densities \(dE\) is added in order to compute the derivative of the homogenized elasticity tensor \(DCH\). Thus, the final form of the input function is as follows:  
* function[CH,DCH] = homo3D(lx,ly,lx,e,dE,nu,Emin)  
_Line_ 11 of the original homo3D function is changed. In the current implementation matrix, \(E\) is used to obtain the number of elements along \(x\), \(y\), and \(z\) directions, instead of matrix \(voxel\) as follows:  
* [nelx, nely,nelz] = size(E);For implementing the computation part of the elemental forces \(fe\) and stiffness matrix \(ke\), a modified version of the hexahedron function from that of the original MATLAB code [13] is utilized. In addition to the element dimensions, the Poisson ratio is provided as an input argument to the function as well. Function hexahedron is split into two functions to enable its usage for the computation of the element stiffness matrix. In addition, \(Line\) 45 of the original homo3D function is removed, since matrix \(E\) already contains the correct distribution for the computations required. \(Line\) 47 of the original homo3D function is replaced with the following expression:  
```
sK=ke(:)*E(:).';
```  
where [PERSON]'s modulus is used to compute \(sK\) instead of the lame parameters. In order to specify the active degrees of freedom, \(Line\) 58 of the original function is modified as follows:  
```
activedofs=edof(E>Emin,:);activedofs=sort(unique(activedofs(:)));
```  
where a small value \(E_{min}\) for [PERSON]'s modulus is utilized instead of the \(voxel\) matrix. Finally, for implementing the calculation part of the derivative for the homogenized elasticity tensor \(DCH\), a cell array of the unit cell dimensions is created first and then initialized as follows:  
```
DCH=cell(nelx,nelx,nelz); DCH(:,::)={zeros(6)};
```  
Subsequently, inside the loop of \(Line\) 85 to 96 of the original function, an additional iterative procedure is implemented for computing each variable of the \(6\times 6\) derivative of the homogenized elasticity tensor for each finite element as follows:  
```
fS=dE.*sum_Y; fork=1:nely forl=1:nelx forh=1:nelz DCH[k,l,h](i,j)=1/volume*fS(k,l,h); end end end
```
###### Abstract  
Active methods are proposed to improve the measurement accuracy of a compact laser diode-based (LD-based) system, which is designed to measure the geometric errors of machine tools. The LD has some advantages, such as a small size, low cost and high efficiency. However, the laser spot of the LD is elliptical and the stability in the output power of the LD is low, which limits the accuracy of the measurement system, where the LD is used as the laser source. An active shaping method is proposed to shape the elliptical laser spot of the LD without adding additional optical elements. In addition, the laser beam drifts, including the linear drift and angular drift, are compensated in real-time by a proposed improved active error compensator, which consists of two drift feedback units and a Backpropagation Neural Networks-based PID controller, during the long-distance measurement. A series of experiments were conducted to verify the effectiveness of the proposed methods and the capability of the constructed LD-based system.  
laser diode; geometric errors measurement; laser spot shaping; linear drift; angular drift
## 3 Blast Hole Pressure Measurement Experiment  
### Explosion Pressure Sensor Calibration  
In this study, it was necessary to calibrate the pressure sensor to measure the explosive pressure inside the blast hole. Therefore, prior to this experiment, explosive pressure sensor calibration using water pressure was performed. The explosive pressure sensor was calibrated under the same installation conditions as those of the blasting site.  
Calibration of the explosion pressure sensor was performed by filling the nylon tube connected to the explosion pressure sensor with water, connecting it with a water pump, and pressurizing the pressure port of the explosion pressure sensor under the conditions of 0 MPa, 25 MPa, and 50 MPa three times each. The average and standard deviation of the results for three calibration tests was 0.984 (\(\pm\)0.0033) Voltage at 0 MPa, 2.98 (\(\pm\)0.0082) Voltage at 25 MPa, and 4.98 Voltage (\(\pm\)0.0144) at 50 MPa. In addition, Nonlinearity was 0.137% FS (Full Scale), and Accuracy was 0.86% FS. The results of the experiments performed are specified in Table 1, and the Voltage to pressure (MPa) correction equation is shown in Figure 5.  
Figure 5: Explosive pressure bar calibration results.
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 1 Introduction
In this representation, lines were integrated within the SLAM machinery as if they were points and were hence able to be processed by reusing the ORB-SLAM [10] architecture. The line segment detector (LSD) method [13] was applied to extract line segments, as it has high precision and repeatability. For stereo matching and frame-to-frame tracking, line segments are augmented with a binary descriptor provided by the line band descriptor (LBD) method [14], which is useful to find correspondences among lines based on their local appearance. The characteristics of LBD were used in the closed-loop detection mechanism outlined below. In this paper, a visual-based underwater location approach is presented. In Section 2, the PL-SLAM method is presented for real-time underwater localization, including estimation of the real-time position of the AUV, closed loop detection and a closed-loop optimization algorithm based on point-line features. Section 3 is the experimental section, in which the errors of positioning for linear and arbitrary trajectory motions are evaluated. Section 4 summarizes the experimental results and analyzes the problems.
## 2 Framework of Training Network for an Imbalanced Point Cloud
### Balanced Focus Loss Function  
In this study, we considered three loss functions: the normal categorical cross-entropy loss function, a weighted loss function, and a focus loss function. The normal categorical cross-entropy loss function treats all classes and all misclassifications equally, and there is no additional reward for identifying the minority class over the majority class. This can lead to the misclassification of the point clouds of minority classes. The categorical cross entropy loss function is defined as follows:  
\[CE(\theta)=-\sum_{i=1}^{n}y_{i}\log(\hat{y}_{i}(x_{i},\theta)), \tag{1}\]  
where \(\hat{y}(x,\theta)\) is the posterior probability obtained by applying the softmax function to the network output layer, and \(\theta\) denotes the trainable parameters of the network; \(x\) is the input sample, and \(y\) is the ground truth.  
One common strategy that helps to increase the true positive rate is to penalize misclassifications of the minority class more heavily than those of the majority class. Thus, a weighted loss function is used to assign each class a ratio that is smaller for the background than for small objects. Unfortunately, the highly sensitive process of assigning an appropriate ratio set can be time-consuming. Any given set of ratios with a fixed value cannot be effective for all cases. Furthermore, the ratio of class instances varies according to the situation. The weighted loss function is formulated as follows:  
\[WCE(\theta)=-\sum_{i=1}^{n}\alpha_{i}y_{i}\log(\hat{y}_{i}(x_{i},\theta)), \tag{2}\]  
where \(\alpha\) is the penalization weight for false negative errors, which is usually selected manually and based on the experience of experts.  
The balanced focus loss function presented in this study to integrate categorical cross-entropy and weighted loss functions is defined as follows:  
\[E_{BL}(\theta)=-\sum_{i=1}^{n}(1-\hat{y}(x,\theta))^{\gamma}\alpha_{i}y_{i} \log(\hat{y}_{i}(x_{i},\theta)), \tag{3}\]  
where \((1-\hat{y}(x,\theta))^{\gamma}\) is the penalization weight for the class, \(\gamma>0\) is a tunable focusing parameter, and \(\alpha\) is the factor emphasizing the minority classes according to the ratio of each the classes in that particular, formulated as follows:  
\[\alpha(c)=\frac{1}{dist\_prob(c)}, \tag{4}\]  
where \(dist\_prob(c)\) is the distribution probability of class \(c\) in training batch, calculated as  
\[dist\_prob(c)=\frac{1}{length(y)}\sum(y==c). \tag{5}\]
At the beginning of training, a large sampling ratio is first used to lead the network to learn more about the minority classes. Note that the sampling ratio should not be so high that it causes excessive fluctuations in the training metric curve. The influence of the balanced focus loss function is not particularly pronounced; therefore, the sampling ratio is decreased gradually to shift the attention to the background. Eventually, when the sampling process halts, the network is trained using only the original dataset with the balanced focus loss function.
## 2 Materials and Methodologies
### Throughput Analysis Tool
\(\rho\) is density, \(p\) is pressure; \(w_{x}\), \(w_{r}\),\(w_{u}\) are scalar values for relative velocity in \(x\), \(r\) and \(\varphi\) direction, respectively; \(e\) and \(i\) are the relative total energy and total enthalpy per unit individually and they take on the form:  
\[e=c_{\text{p}}T+\frac{1}{2}\Big{(}{w_{x}}^{2}+{w_{r}}^{2}+{w_{u}}^{2}\Big{)} \tag{5}\]  
\[i=e+\frac{p}{\rho} \tag{6}\]  
where \(c_{\text{p}}\) is the specific heat at a constant volume. In Equation (4), the first row represents the continuity equation, rows 2-4 depict the momentum equations, and the last row describes the energy balance. A time-marching finite volume method is used to solve the equation. The [PERSON] LDFSS [16] scheme is applied to discretize the convective fluxes. The calculation of the viscous fluxes requires the evaluation of the primitive variables and their first derivatives at the edge middle of the considered cell. The Green-Gauss theorem is also used to estimate the gradient of the variables. An explicit Runge-Kutta method is chosen for the time discretization as it is straightforward to implement. Meanwhile, local time step and implicit residue averaging are used to accelerate the convergence.