esa-sceva/satcom-chunk-collection huggingface dataset:
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 3 Methodology
### Implementation
#### 3.2.4 Slab-Based Filtering  
To limit the number of visible data, a slab-based filtering mechanism is employed. The goal is to retain only those data points that lie close to the viewing space--within a given distance slab_thickness from a hyperplane defined by the current rotation.  
The filtering uses a normal vector to the viewing space to compute signed distances from each point. Depending on whether the filtering is applied before or after transforming the data by the view matrix, different normal vectors are used:  
* If filtering is performed in the original spatial coordinates (X_spatial), the normal vector can be taken as the last row of the rotation matrix, which defines the orientation of the view;
* If filtering is done after applying the rotation (X_view), the normal vector can be defined in canonical coordinates as \([0,0,0,1]\), which corresponds to the last axis.  
Each point is projected onto the normal vector, and only those within the allowed threshold are retained.  
Eigen::VectorXd slab_values = normal_vector.transpose() * X_view; auto mask = slab_values.cwiseAbs().array() < slab_threshold; Eigen::MatrixXd X_visible = skip_masked(X_view, mask);  
Points that satisfy the slab condition--meaning they are sufficiently close to the viewing hyperplane--are passed to the next stage of the pipeline. This filtering effectively creates a thick slice through the multidimensional space (Figure 4).
## 4 Methods
### Deep Learning Methods
#### 4.2.1 Artificial Neural Network (ANN)  
The Artificial Neural Network (ANN) is the most basic neural network, also called a multilayer perceptron. It is characterized by modeling and predicting through the process of learning training data without the need for complex mathematical models. Based on the fact that ANN can approximate arbitrary nonlinear mapping relations, it can be applied to the field of time series analysis for experiments [43].  
As shown in Figure 5, an ANN network model containing 3 hidden layers was built for the dynamic journal evaluation task in this study. Both the ANN model and the machine learning method inputs were one-dimensional vectors obtained by the one-dimensional stitching of journal metric values in 3 timesteps, so the input layer had 54 neurons, the 3 hidden layers had 64, 128, and 64 nodes, and the output layer had only 1 node to output the predicted results of dynamic journal evaluation. Each layer is set with a corresponding partial coefficient. The network was optimized using the Adam algorithm, using the MSE metric as the loss function, and we set the initial learning rate of the model to 0.1, the decay rate of the first-order moment estimation to 0.99, the decay rate of the second-order moment estimation to 0.999, and the batch size to 32. In total, 90% of the data in the training set were taken for training, and 10% of the data were used to validate the network.  
The ANN model is essentially a nonlinear mapping transformation of input samples to output samples, and the ANN model built in this study has the function of mapping past time series observations of journal metrics (each journal metric in the past three years) to future predicted values (possible future impact factors of journals). In this study, the ANN network model was constructed as a typical method of time series prediction in deep learning to perform one-step prediction and complete the task of the dynamic evaluation of journals.
## 3 Results and Discussion  
The model proposed in the paper was applied to a 1.5 MW VSVP wind turbine and a 2.0 MW VSVP wind turbine to predict the bending moments at the blade root. The corresponding results were compared with those computed by FOCUS [12] to validate the model. The particular constraint conditions used in the PSO-ELPM is introduced firstly.  
(1) For the 1.5 MW VSVP wind turbine, the constraint conditions were  
\[\left\{\begin{array}{ll}\beta_{m}=\text{max}(\beta_{r},\beta_{v})&(V_{1}\leq V _{o})\\ \beta_{m}=\beta_{v}&(V_{1}>V_{o})\end{array}\right. \tag{31}\]  
where \(\beta_{v}\) satisfies the following expressions.  
\[\beta_{v}=-1\quad(V_{1}\leq 11\;\text{m/s}) \tag{32}\]  
\[\beta_{v}=22.674-10.86+5(V_{1}-25)\quad(V_{1}>25\;\text{m/s}) \tag{33}\]  
and \(\beta_{r}\) satisfies the following expressions.  
\[\beta_{r}=-1\quad(\Omega<1.82\;\text{rad/s}) \tag{34}\]  
\[\beta_{r}=1.9969\Omega-35.681\quad(\Omega\geq 1.82\;\text{rad/s}) \tag{35}\]  
The other variables satisfy the following inequalities.  
\[\left\{\begin{array}{ll}0.94\;\text{rad/s}\leq\Omega\leq 2.84\;\text{rad/s}\\ 3.00\;\text{m/s}\leq V_{1}\leq 38.26\;\text{m/s}\\ \beta_{m}\leq\beta_{2}\leq 90.0^{\lx@math@degree}\\ -8.0^{\lx@math@degree}\leq\gamma\leq 8.0^{\lx@math@degree}\\ 0.0^{\lx@math@degree}\leq\psi\leq 360.0^{\lx@math@degree}\end{array}\right. \tag{36}\]  
(2) For the 2.0 MW VSVP wind turbine, the constraint conditions were  
\[\left\{\begin{array}{ll}\beta_{m}=\text{max}(\beta_{r},\beta_{v})&(V_{1}\leq V _{o})\\ \beta_{m}=\beta_{v}&(V_{1}>V_{o})\end{array}\right. \tag{37}\]  
where \(\beta_{v}\) can be formulated as:  
\[\beta_{v}=0\quad(V_{1}\leq 10.5\;\text{m/s}) \tag{38}\]  
\[\left\{\begin{array}{ll}\beta_{v}=-0.0476{(V_{1})}^{2}+2.9958V_{1}-22.756-8. 0\\ \beta_{v}=0\;\;\;(\beta_{v}<0^{\circ})\end{array}\right.,\;(10.5\;\text{m/s}< V_{1}\leq 25\;\text{m/s}) \tag{39}\]  
\[\beta_{v}=22.389-8.0+5(V_{1}-25)\quad(V_{1}\geq 25\;\text{m/s}) \tag{40}\]  
and \(\beta_{r}\) is:  
\[\beta_{r}=0\quad(\Omega<1.81\;\text{rad/s}) \tag{41}\]  
\[\beta_{r}=3.5594\Omega-60.153\quad(\Omega\geq 1.81\;\text{rad/s}) \tag{42}\]The other variables can be defined as:  
\[\left\{\begin{array}{rl}&0.94\;\text{rad}/\text{s}\leq\Omega\leq 2.45\;\text{rad}/ \text{s}\\ &3.00\;\text{m}/\text{s}\leq V_{1}\leq 38.87\;\text{m}/\text{s}\\ &\beta_{m}\leq\beta_{2}\leq 90.0^{\circ}\\ &-8.0^{\circ}\leq\gamma\leq 8.0^{\circ}\\ &0.0^{\circ}\leq\psi\leq 360.0^{\circ}\end{array}\right. \tag{43}\]  
Then, in order to compare with the results from FOCUS, the load factor \(\gamma_{F}\) will be considered. According to the GL standard, \(\gamma_{F}\) is set to be 1.35.  
Moreover, the related parameters in the improved PSO algorithm are prefixed as follows:  
1. Number of individuals: 30;
2. Maximum number of iterations: 100;
3. Probability of selection _Ps_: 0.0333;
4. Maximum inertial weight: 0.6253;
5. Minimum inertial weight: 0.0562
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.6. PPPM Analysis of DP-Naive Bayes (Q2.1.1)  
Result Utility Quality  
Figure 14 shows the DP-Naive Bayes model (test) accuracy metric with respect to the epsilon parameter values behavior on our real-life event logs. As seen from the results, there exists a decreasing trend in accuracy values according to increase in the privacy level for all event logs as expected. However, even if differential privacy has a nondeterministic nature, random deviations between different epsilon privacy budget parameters do not occur for all event logs. For supervised learning algorithms, the instance count is an important consideration for the model's accuracy. In our experiments, while the Road Traffic Fines event log has the highest number of events, there is a dramatic reduction (around 85%) in the model's accuracy after differential privacy protection. On the other hand, given that the Sepsis event log exhibits the highest percentage of trace uniqueness, it experiences the second most significant decline in accuracy of approximately 50%.  
Time Utility Quality  
Table 5 includes the runtime(s) performance metric results of the applied DP-Naive Bayes model on our real-life event logs. During the experimentation with the DP-Naive Bayes algorithm, slight fluctuations in the model runtime were observed as a consequence of specific epsilon parameter modifications. A proportional decrease in runtime was observed with increasing privacy levels, attributable to reduced model complexity. How  
\begin{table}
\begin{tabular}{c c c} \hline
**Event Log** & **Accuracy** & **Time** \\ \hline Sepsis & 0.79 & 0.02299785614013672 \\ BPIC13 & 0.75 & 0.029001474380493164 \\ CoSeLog & 0.35 & 0.015794992446899414 \\ Road Traffic Fines & 0.98 & 0.36087751388549805 \\ \hline \end{tabular}
\end{table}
Table 4: Discovered (non-private) DP-Naive Bayes model quality and runtime metric(s) results.  
Figure 14: Discovered DP-Naive Bayes model quality results.  
ever, these fluctuations are generally minimal and do not constitute significant changes in algorithmic efficiency.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 3 Methodology
### The Spherically Symmetric Refractivity Profile  
Similar to the atmospheric density, the atmospheric refractivity decreases exponentially with altitude. A new variable \(N(r)\), which is the spherically symmetric refractivity, is calculated to depict a reference change of refractivity with altitude. It is defined by the global mean refractivity at the same distance (\(r\)) from the perigee point of RO observations (\(N(i(r))\)) to the earth's center:  
\[\overline{N}(r)=\overline{N(i(r))} \tag{4}\]  
where \"\(i(r)\)\" represents the \(i\)th observation point whose distance to the earth's center is \(r\). In the following, we will subtract this spherically symmetric refractivity from the total refractivity (\(N\)) to examine the vertical gradients of refractivity. In other words, the vertical gradient of \(N-N(r)\), instead of \(N\), can be compared among different altitudes.
Based on the theoretic prediction of the wave-damage interaction, a damage quantitation method is proposed to determine the sizes of the defect in Section 3. The characteristics of the wave-damage interaction and the efficacy of the proposed damage quantification method are then confirmed by numerical simulations in Section 4. Experiments are conducted to quantify the sizes of the defects on two representative steel rods with the developed method in Section 5. Conclusions are finally drawn in Section 6.
## 3 Results
### External Validation and Generalization Performance
Their predictions deviate significantly from the expected trend, leading to high residual dispersion, particularly for PWV values above 10 m/s. In contrast, PR and NN maintain robust predictive performance, with \(R^{2}\) values remaining above 0.90 and residuals distributed more homogeneously around zero.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 4 Experimental Results
### TIMIT Database  
We have carried out speech recognition experiments on the TIMIT corpus [25], which is designed to provide speech data for studies of acoustic-phonetic and speech recognition systems. The speech data was recorded at Texas Instruments (TI), transcribed at the Massachusetts Institute of Technology (MIT), and prepared for CD-ROM production by the National Institute of Standards and Technology (NIST). The corpus includes the speech waveforms with phonetic transcriptions. TIMIT corpus has 6300 sentences, spoken by  
Figure 7: Various types of the consonant group models with additional autocorrelation features. (**a**) input features are concatenated. (**b**) BLSTM outputs are concatenated. (**c**) another BLSTM layer is added for the concatenated BLSTM outputs.  
630 speakers, who spoke exactly 10 sentences each. There are three types of sentences. Dialect sentences (SA) were recorded from speakers with dialectal diversity. Phonetically-compact (SX) and phonetically-diverse sentences (SI) were created to model different situations of phoneme distributions. Phonetic transcripts for all sentences are provided in the TIMIT corpus distribution. The full phoneme transcript of an example sentence is shown in Table 4. All the numbers are in samples, so the length of the audio file in the example is 46,797/16,000 \(\text{kHz}=2.924\) s.  
The whole corpus is divided into non-overlapping training and testing sets. We use core test set [25] in an evaluating performance of phoneme recognition. The core test data has a total of 24 speakers, more specifically two male and one female from each of the eight dialect regions. The number of phoneme tokens are 177,080 in the training set and 9098 in the testing set. The number of tokens for each of the five phoneme groups is shown in Table 5.
## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 The Improved U-Net++ Network
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **34-Layer** & **101-Layer** \\ \hline Conv1 & \(112\times 112\) & \(7\times 7\), 64, stride = 2 \\ \hline Conv2 & \(56\times 56\) & \(3\times 3\)max pool, stride = 2 \\ \cline{3-3}  & & \(\left[\begin{array}{cc}3\times 3&64\\ 3\times 3&64\end{array}\right]\times 3\) & \(\left[\begin{array}{cc}1\times 1&64\\ 3\times 3&64\end{array}\right]\times 3\) \\ \hline Conv3 & \(28\times 28\) & \(\left[\begin{array}{cc}3\times 3&128\\ 3\times 3&128\end{array}\right]\times 4\) & \(\left[\begin{array}{cc}1\times 1&128\\ 3\times 3&128\\ 1\times 1&512\end{array}\right]\times 4\) \\ \hline Conv4 & \(14\times 14\) & \(\left[\begin{array}{cc}3\times 3&256\\ 3\times 3&256\end{array}\right]\times 6\) & \(\left[\begin{array}{cc}1\times 1&256\\ 3\times 3&256\\ 1\times 1&1024\end{array}\right]\times 23\) \\ \hline Conv5 & \(7\times 7\) & \(\left[\begin{array}{cc}3\times 3&512\\ 3\times 3&512\end{array}\right]\times 3\) & \(\left[\begin{array}{cc}1\times 1&512\\ 3\times 3&512\\ 1\times 1&512\end{array}\right]\times 3\) \\ \hline Ending & \(1\times 1\) & Average pool, 1000-\(d\) fc, softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ResNet34 and 101-layer network architecture table. ResNeSt solves the above problem by segmenting the network structure. Taking ResNet-50 as an example, as shown in Figure 6c: 1 divide the network structure into three parts, four main stages (Conv2-5), one start (Conv1), and an ending phase (ending); 2 each stage in the four main stages can contain several Blocks, and each stage is divided into three parts: one start ResBlock, several middle ResBlocks, and one end ResBlock. According to the different positions of the stage, each ResBlock has a different design. (2) Improved projection shortcut. In the original ResNet architecture, as shown in Figure 7a, if the superior input \(x\) and output dimensions do not match, a \(1\times 1\) convolution with a step size of 2 is used to adjust the dimensions, and thus both channel and spatial matching information can be achieved by the \(1\times 1\) convolution. Figure 5: Improved U-Net++ network. Figure 6: Comparison of ResBlock: (**a**) original (inside the red frame); (**b**) pre-activation (inside the blue frame); (**c**) the improved ResStage (inside the green frame). However, the architecture will lose 75% of important information, and the remaining 25% of the information will not have meaningful screening criteria. It also introduces noise and causes information loss, which will negatively affect the main channel flow information. As shown in Figure 7b, ResNeSt considers all information from feature mapping. ResNeSt first uses the \(3\times 3\) max pooling layer of stride = 2, then uses \(1\times 1\) convolution of stride = 1 for channel projection, connecting with BN and finally selecting the element with the highest activation degree in the next step in order to reduce information loss.
## 5 Numerical Analysis
### Channel Capacity of the Link
On the other hand, the turbulent outer scale mainly leads to the random deflection of the beam propagation path, and the value of the random deflection is directly proportional to the size of the turbulent outer scale. As a result, the transmission channel with large outer scale of turbulence will cause a large random deflection of light rays, resulting in large random optical path difference between the sub-beams of OAM mode, resulting in large wavefront distortion of the OAM mode and large OAM crosstalk. Of course, the AIC loss of the link increases as a result. Figure 9: The capacity of the POV carrier link versus propagation distance for wavelength and image refractive index under (**a**) 4-QAM, \(z=100\) m, (**b**) 256-QAM, \(z=150\) m.  
It can be seen from Figure 9-11, the influence of the absorption coefficient of seawater, \(\tilde{w}\), the inner scale and outer scale of turbulence on the average information capacity of OAM links with the low-order QAM modulation is greater than that of OAM links modulated by high-order QAM.
## 3 Dynamic Algorithm of Resource Re-allocation  
Due to the highly dynamic environment of vehicular networks, a computationally efficient online resource allocation scheme has to be designed. A stochastic optimization problem can be transformed into a series of deterministic optimization problems using the Lyapunov optimization and can then be used to decrease the computational complexity for the resource allocation [32]. Therefore, the problem in Equation (25) is solved by the Lyapunov optimization.  
The time-averaged expected service rate of member \(n\) in the platoon is denoted as \(\overline{\mu_{n}}\), which satisfies:  
\[\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T}\mathbb{E}\left\{\mu_{n}(t) \right\}=\overline{\mu_{n}}. \tag{26}\]  
Based on the stability conditions of the queue, as stated in [32], the sufficient condition for stability occurs if \(a_{n}\leq\overline{\mu_{n}}\).
# Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation
## 3 Results
### The Color Doppler Results
#### 3.2.2 Scale  
The scale is a parameter that controls the range of velocity displayed in the image. In this experiment, color Doppler imaging was performed with scale settings of 4 kHz and 0.6 kHz. Figure 5a,c display the images obtained with a scale setting of 4 kHz. Figure 5a represents the image with the color box set to the left, while Figure 5c represents the image with the color box set to the right. The Nyquist limit for the scale setting of 4 kHz in Figure 5a,c was 25. As the maximum velocity of the blood flow did not exceed this Nyquist limit, no aliasing artifacts were observed.  
Figure 5b,d show the images obtained with a scale setting of 0.6 kHz. Figure 5b represents the image with the color box steered to the left, while Figure 5d represents the image with the color box steered to the right. The Nyquist limit corresponding to a scale of 0.6 kHz was 3.8. As the maximum velocity of the blood flow exceeded the Nyquist limit of 3.8, aliasing artifacts were observed. These aliasing artifacts resulted in the appearance of various colors within the vessels, affecting the visualization of the blood flow information.
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 3 Simulation
### Array Performance Evaluation
#### 3.2.2 Array Performance Indicator  
The array performance indicator (API) was introduced to quantify the performance of the array and image reconstruction algorithm [28]. If a point reflector is assumed in the wave field, a reflection surface of the B-scan will look like a Gaussian distribution in the three dimensional space. Since the image reconstruction algorithm with better performance focuses reflection at a smaller area, the reflection surface will be sharper. Thus, the concept of the API aims at estimating the narrowness of the reflection hill by calculating the intersecting area between the reflection surface and a flat surface down from the maximum value. The smaller the API, the better the performance of the array and algorithm. The equation of API is expressed in Equation (8).  
\[API=\frac{A}{\lambda_{c}^{2}}, \tag{8}\]  
where \(A\) is the intersection area between reflection surface of the B-scan and the flat surface down from the maximum reflection point; and \(\lambda_{c}\) is the wavelength of the input pulse. In this research, the flat surface is down-shifted by \(-6\) dB from the max value.  
The API is consistent regardless of wave frequency as the area value is divided by the square of the wavelength. The reflection surfaces of the B-scan by the TFM and the PPD-TFM on the simulated data are depicted in Figure 6. The reflection values are normalized to the maximum values. With the peak delay calibration, not only did the accuracy of the  
Figure 4: (**a**) Potential damage curve and (**b**) corresponding B-scan for a single pair of transducers.  
Figure 5: Potential damage curve for TFM (**left**) and PPD-TFM (**right**).  
algorithm improve, but the performance of the B-scan also increased in terms of the API by decreasing 61.2%.
## Appendix A  
This section takes the LADRC in Section 3.2.1 as an example to prove the stability of the closed-loop system. Firstly, according to the LESO in Equation (20), the estimation error vector is defined as \(\varepsilon=\left[\varepsilon_{1},\varepsilon_{2},\varepsilon_{3}\right]^{T}\), with \(\varepsilon_{i}=x_{1i}-\varepsilon_{1i}\). Then, the following Theorem A1 can be obtained.  
Assuming that \(h=f_{h}\) is bounded, that is, \(|h|\leq M_{1}\), \((M_{1}>0)\), then the observer error \(\varepsilon\) of the LESO is also bounded, that is, \(|\varepsilon|\leq M_{2}\), \(M_{2}>0\).  
With Equations (19) and (20), the state space equation about \(\varepsilon\) can be derived as:  
\[\dot{\varepsilon}=A_{1}\varepsilon+Bh\] (A1)  
Furthermore, Equation (A9) can be expressed in another form:  
\[\dot{\xi}=A_{2}\xi+A_{3}\varepsilon\] (A10)  
with \(A_{2}=\left[\begin{array}{cc}0&1\\ -k_{p1}&-k_{d1}\end{array}\right]\), and \(A_{3}=\left[\begin{array}{cc}0&0&0\\ -k_{p1}&-k_{d1}&-1\end{array}\right]\).  
Similarly, Equation (A10) has the following solution,  
\[\xi(t)=e^{A_{2}t}\xi(0)+\int_{0}^{t}A_{3}\varepsilon e^{A_{2}(t-\tau)}d\tau\] (A11)  
\(A_{2}\) is also a Hurwitz matrix, thus satisfying \(\left\|e^{A_{2}t}\right\|_{m_{\infty}}\leq\beta e^{-\lambda_{1}t}\). As a result, there is the following inequality about \(\xi(t)\),  
\[\begin{split}\|\xi(t)\|&=\left\|\xi^{A_{2}t}\xi(0)+\int_{0}^{t }A_{3}\varepsilon e^{A_{2}(t-\tau)}d\tau\right\|\\ &\leq\left\|\xi^{A_{2}t}\xi(0)\right\|+\left\|\int_{0}^{t}A_{3} \varepsilon e^{A_{2}(t-\tau)}d\tau\right\|\\ &\leq\beta e^{-\lambda_{1}t}\|\xi(0)\|+\|\varepsilon\|\|A_{3}\| \beta\int_{0}^{t}e^{-\lambda_{1}(t-\tau)}d\tau\\ &\leq\beta e^{-\lambda_{1}t}\|\xi(0)\|+\frac{M_{2}\|A_{3}\|\beta}{ \lambda_{1}}\Big{(}1-e^{-\lambda_{1}t}\Big{)}\end{split}\] (A12)  
In accordance with the assumption presented in Equation (A7), finding the limit of \(\xi\) in Equation (A12) leads to the following result,  
\[\lim_{t\to\infty}\|\xi(t)\|=0\] (A13)  
\(\qed\)  
**Remark A1**.: _Theorem A1 proves the stability of the linear extended state observer under the premise that the disturbance is bounded, which shows that the observation errors are bounded._  
**Remark A2**.: _Theorem A2 further establishes that with the utilization of the expanded state observer and PD control law, the tracking error of the closed-loop system is bounded, thus ensuring the stability of the closed-loop system._