esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 PMSM Control
### Vector Control of PMSM  
Figure 2 shows a block diagram for the dynamic simulation of the PMSM. As shown in the figure, the overall system includes two closed loops, an inner current control loop and an outer speed control loop.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Number of slots/poles & 9/8 & \\ Outer radius of stator & 47 & mm \\ Outer radius of rotor & 43 & mm \\ Thickness of PM & 5 & mm \\ Axial length & 30 & mm \\ Magnet remanence & 1.27 & T \\ Pole arc ratio & 0.9 & \\ Rated speed & 1000 & rpm \\ Rated torque & 2 & N \\ Rated power & 200 & W \\ PWM method & SPWM & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design specifications.  
Whenever a reference speed is specified, the system automatically compares it with the actual speed. The speed error directly affects the load torque profile based on the motor motion equation as follows [12]:  
\[T_{e}=J_{r}\frac{dw_{r}}{dt}+B_{f}w_{r}+T_{d} \tag{6}\]  
where \(T_{e}\) and \(T_{d}\) are the electromagnetic and load torques, respectively; \(J_{r}\) and \(B_{f}\) are the inertias of the rotor and friction coefficients, respectively; and \(i_{u}\), \(i_{b}\), and \(i_{c}\) are the measured phase currents. The Clarke transformation was applied to them to determine the stator current in the stationary \(d\)-\(q\) frame. The Park transformation was applied to obtain this projection in a synchronous \(d\)-\(q\) frame with rotor position information. When the actual speed or load torque changed, the reference \(d\)-\(q\) currents immediately adjusted the speed and torque. Once the appropriate adjustment was accomplished, the motor speed became the reference speed, and the motor quickly achieved a steady-state operation. Owing to the current loop, the actual current tracked the commanded current.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 3 LEO/INS Alignment Architecture  
### KF-Based Alignment Method  
The receiver's error model consists of attitude, position, and velocity errors, as well as errors of the gyroscope and the accelerometer. The error model is given by Equation (24).  
\[\dot{X}(t)=F(t)X(t)+w(t), \tag{24}\]  
There are 15 states for the matrix \(X(t)\), which defined \(\delta\varphi\), \(\delta\theta\), and \(\delta\psi\) as errors of attitude; \(\delta v_{N}\), \(\delta v_{E}\), and \(\delta v_{D}\) as errors of velocity; \(\delta L\), \(\delta l\), and \(\delta h\) as errors of position; \(b_{gx}\), \(b_{gy}\), and \(b_{gz}\) as bias errors of the gyroscope; and finally \(b_{ax}\), \(b_{ay}\), and \(b_{az}\) as bias errors of the accelerometer (see Equation (25)). Moreover, the \(w(t)\) is the white Gaussian noise vector defined for the sensor biases. The evolution of the biases was modeled with a random walk process, where \(\dot{b}_{g}=\mu_{g}\) and \(\dot{b}_{a}=\mu_{a}\) with a zero expected value and a covariance of \(\sigma_{\mu_{g}}^{2}\) and \(\sigma_{\mu_{a}}^{2}\), respectively. The completed states of the model are shown in the vector \(X(t)\).  
\[X(t)=\left[\delta\Omega^{T}\,\delta v^{T}\,\delta p^{T}\,b_{g}^{T}\,b_{a}^{T} \right]^{T}, \tag{25}\]  
where \(\delta\Omega=[\delta\phi\,\delta\,\delta\psi]^{T}\), \(\delta v=[\delta v_{N}\,\delta v_{E}\,\delta v_{D}]^{T}\), \(\delta p=[\delta L\,\delta l\,\delta h]^{T}\), \(b_{g}=\left[b_{gx}\,b_{gy}\,b_{gz}\right]^{T}\), and \(b_{a}=\left[b_{ax}\,b_{ay}\,b_{az}\right]^{T}\). Moreover, the matrix \(F(t)\) is defined in Appendix A. The measurement model is \(Y(t)=HX(t)+n(t)\), where \(n(t)\) is the measurement noise with the white Gaussian model. As described before, the measurements were obtained from the comparison of master and slave positioning systems, which are based on two different sources, namely, LEO downlinks and the IMU data. The measurement matrix \(H\) was given by Equation (26). The measurement vector was defined as \(Y=\left[\delta\Omega\,\delta v\,\delta p\right]^{T}\), where its elements were given by Equation (27).  
\[H\;=\;\left[\begin{array}{cc}I_{9\times 9}&0_{9\times 6}\end{array}\right], \tag{26}\]  
\[\delta\Omega=\Omega_{c}-\Omega_{s},\;\;\delta v=v_{c}-v_{s},\;\;\;\delta p=p_{ c}-p_{s}, \tag{27}\]  
It should be mentioned that the \(\Omega_{c}\) and \(\Omega_{s}\) were obtained from their quaternion form using the quaternion to Euler transform equation. After discretizing the continuous error model, a linear Kalman filter was implemented to estimate the error states. The Kalman-filter-updated equations had the same process as Equations (20)-(23).
## 5. Drought Rate Evaluation Discussion
### URANS-Based DR Evaluation  
URANS simulations are the first step on the way of the velocity fluctuations accounting. After time-averaging, URANS data provide the 3D fields of mean velocity magnitude, \(<\)\(V\)\(>\), and turbulent kinetic energy, \(k\). \(Tu\) could be computed from \(k\) using the following relation: \(Tu=(2\)\(<\)\(k\)\(>\)\(/3)^{0.5}\)\(<\)\(V\)\(>\). Equation (1) could be applied to the isothermal URANS data after some temperature level is set. The \(DR\) distributions computed with the indoor air temperature taken as 27 \({}^{\circ}\)C are presented in Figure 9. The \(DR\) fields are plotted at the same horizontal cross-sections: \(z=0.1\) m, \(z=0.6\) m, \(z=1.1\) m, and \(z=1.7\) m. The integral \(DR\) value area-averaged over four \(z\)-planes under consideration is about 10%, but thermal comfort evaluation based on the averaged \(DR\) value is incomplete. First, it does not take into account the \(DR\) stratification visible in the occupied zone. Second, it does not take into account strong \(DR\) non-uniformity at each section, both spatial and temporal. The latter was not considered in the RANS simulation, but in the URANS fields, per the unsteady behavior of velocity distribution, discussed in Section 4.1, the \(DR\) fields are substantially unsteady.  
Figure 9: Unsteady RANS (URANS)-obtained instantaneous distributions of \(DR\) values at four cross-sections of the room (data for \(\theta=25^{\circ}\), case #2).  
Due to pronounced unsteady effects, the instantaneous distributions given in Figure 9 differ from the RANS-obtained fields presented in Figure 8a. In general, the URANS solution reports stronger draft discomfort than the RANS data. Thus, the spots with local values of \(DR\) exceeding 20% at two \(z\)-coordinate levels: \(z=1.1\) m and \(z=1.7\) m, change their locations from one instant to another, and the resulting high-\(DR\) zones cover a larger area as compared with the RANS data (category III in EN 16798-1:2019 [28]). The \(DR\) values exceed 10% at \(z=0.1\) m and \(z=0.6\) m (category II in EN 16798-1:2019 [28] instead of category I, resulted from the RANS data).
### Research Questions and Objectives  
This study aims to enhance the detection and classification of propaganda in COVID-19-related social media data, addressing critical gaps in existing methodologies and leveraging the diffusion of innovations theory to facilitate the adoption of advanced techniques. The research is guided by the following research questions (RQ):  
* **RQ1:** How can fine-tuned DL models, such as RoBERTa, address the limitations of traditional methods like BERT, CNNs, and RNNs in detecting propaganda in COVID-19-related social media data?
* **RQ2:** What are the unique challenges of multi-label classification in propaganda detection, and how can novel DL-based approaches mitigate these challenges?
* **RQ3:** How do fine-tuned DL models compare with traditional NLP techniques in terms of accuracy, reliability, and robustness for propaganda detection?
* **RQ4:** What is the impact of addressing data uncertainty and incorporating augmentation techniques on the performance of ML-MC classification systems in COVID-19-related contexts?  
The primary objective of this research is to design, implement, and evaluate fine-tuned DL models for propaganda detection in social media discussions. Specifically, this involves the following:  
* Developing a novel ML-MC classification approach leveraging advanced DL architectures such as RoBERTa to improve accuracy and interpretability.
* Addressing challenges in multi-label classification by incorporating innovative data augmentation and optimization techniques.
* Conducting comparative analyses of traditional NLP methods and fine-tuned DL models to highlight the relative advantages of the latter.
* Investigating the effects of data uncertainty and augmentation on classification performance, thereby enhancing model robustness in real-world scenarios.  
These objectives align with the principles of diffusion of innovations theory by focusing on developing solutions with demonstrable relative advantage, compatibility with existing methods, and reduced complexity, thus fostering their adoption within the IS community.
# Fast Implicit Surface Reconstruction for the Radial Basis Functions Interpolant
## 2 Related Works  
We briefly review the two bodies of work that are close to ours, namely radial basis functions and implicit surface reconstruction.  
There are many implicit functions (e.g., Poisson function [15; 16], the Discrete Smooth Interpolation function [17; 18]) used to represent implicit surfaces. For the radial basis functions interpolant, the evaluation process is to compute the sums of the linear combinations of kernel functions. The common method for fast evaluation is the fast multipole method (FMM). The basic idea is to expand the radial basis function (RBF) kernels via low-rank approximations, and neglect the far field expansion within a given precision. At present, a number of evaluation methods have been proposed to expand different types of kernels in an analytic way, such as thin-plate splines [19], Gaussian kernel [20] and polyharmonic splines [21]. Recently, some general FMM methods are developed to evaluate kernels without the implementation of multipole expansions, including the kernel independent FMM [22] and the black-box FMM [23]. Besides the global evaluation methods, the local evaluation methods using compactly supported radial basis functions (CSRBF) are also a feasible way of acceleration. To balance the efficiency and effect, the local evaluation methods require the selection of adaptive supported radii for different interpolation centers. Based upon the comparison of a quantitative measure of approximation error, [PERSON] et al. [24] proposed an adaptive radial basis functions interpolation method using an error indicator.  
The reconstruction process usually has nothing to do with the evaluation process. Some of the implicit surface reconstruction methods only need to know the implicit function values at any given position, such as the well-known marching cubes and marching tetrahedra algorithms. At present, many extensions of marching cubes have been proposed, such as dual marching cubes [25], adaptive marching cubes [26], extended marching cubes [27] and multiple material marching cubes [28]. To track the zero level sets of the implicit surface directly, some surface following methods are developed based on a greedy growing idea. [PERSON] et al. [11] presented the growing-cube algorithm to track the neighboring cells with zero values based on the marching cubes algorithm.  
Note that the combination of some additional conditions (e.g., the Hermite data of the implicit function) is useful to improve the mesh quality and minimize the number of evaluations.  
[PERSON] et al. [29] presented the regularized marching tetrahedra algorithm to reconstruct meshes with fewer triangles and better aspect ratios. On purely the reconstruction process, the surface following method with mesh optimization strategy is an optimum scheme for implicit surface reconstruction. In this work, we focus on improving the reconstruction efficiency, combining both the evaluation and reconstruction processes.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Wavelet Transform Techniques  
Wavelet transform is a powerful approach for multiresolution analysis of the ECG signal, allowing its decomposition into different frequency components. This technique is essential for identifying and isolating meaningful features in the signal, being particularly useful in the context of ventricular fibrillation (VF) detection and prediction.
## 6 Matching User Preferences to Network Response
### Combining Evaluation Metrics for Comparison  
Scoring the evaluation metrics is integral to the eventual decision. This involves standardising the disparate criteria into like variables which can then be combined. While scoring the criteria can be a domain specific problem resources are available to guide a scoring structure [37]. Two techniques are generally available for attributing a score to individual criteria: local and global scaling. Local scaling involves setting up an arbitrary scale, from 0-1 or 1-100, and attributing scores for the criteria within this scale. This is generally used for qualitative data. Global scaling involves bounding the scale between two extreme possible values of the criteria. For example, in a test set the lowest latency for an experiment is given as 0.02 s while the highest latency is 0.34 s. These values become the bounds on the scale. If a solution returns a value of 0.02 s it is given a standard score of 100 (lets say), while a solution returning a value of 0.34 s will be given a standard score of 0 and a value of 0.16 s is given a score of 50.  
A specific scoring structure is created for the evaluation metrics considered during testing in Section 7. A form of global scaling is used in the scoring mechanism. The scoring structure is based around the mean of the criteria over the expected performance results from all communications solutions. To create a standard score for a communication solutions reliability value, the mean of the predicted reliability values for each of the communications solutions is calculated. The criteria score for the communications solution is taken as the percentage difference from the mean. This is formulatedin Equation (2). This produces a positive score for criteria with a value better than the mean and a negative score for criteria with a value lower than the mean.  
\[\text{Criteria Score}=\frac{x}{\hat{\tau}}\times 100-100 \tag{2}\]  
Scoring the evaluation metrics as relative to the mean is an effective way to present likewise disparity of values over disparate scales. It is still necessary, however, to apply additional scaling and weighting to the criteria scores before they can be summed to create a solution score.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 6 Result
### Performance of System
#### 6.2.1 Throughput  
Throughput is the most important performance index in this system. We test the system throughput in three modes by controlling the byte size of the packet (from 64 bytes to 1024 bytes). The result is shown in Figure 6. As the byte size of the packet increases, the throughput increases in the three modes. When the byte size reaches 1024 bytes, the baseline, S-VNFs, and VNFs (w/opt.) throughputs reach 1550 Mb/s, 1060 Mb/s, and 1250 Mb/s respectively. Compared to baseline, S-VNFs decreased about 31.3% more in terms of throughput. The throughput of optimized S-VNFs (w/opt.) is about 19.7% lower than the baseline. The results prove that the use of DPDK, batch processing, and shared memory can improve the throughput of the system to a certain extent. In addition, the throughput reduction caused by the multi-enclave-based system architecture proposed in this paper is within a certain acceptable range. The reason for this decline is mainly due to the fact that the SGX consumes more CPU and memory, as well as the transmission of packet addresses between multiple enclaves.
## 3 Case Study  
Aiming to show the applicability and the effectiveness of the framework depicted in Figure 1, a case study is described here, related to a working task carried out in a laboratory, where a simple assembly task has been defined and performed. Figure 2 shows the working scenario reproduced in a simulation environment.  
As described in Figure 3, which shows both the real experiment and the Digital Twin, a female worker manually performs an assembly task of two components (labeled \"1\" and \"2\") made of steel. After picked up and positioned the components, these are joined by performing four screwings; then, the assembly is placed in a cart. A detailed description of each operation in reported in Table 1.  
The assumed cycle time is 30 s, which includes 2 s of recovery. In addition, the work-shift duration has been assumed equal to 8 h, including 30 min breaks (10 min per break), equally distributed along the whole shift. A 60 min lunch-break has been scheduled at the middle of the shift.  
Going into detail about the characteristics of the workstation, the components 1 and 2 have weight equal to 6 kg and 2 kg respectively, while the weight of the screwdriver is 2.5 kg.  
About the screwings, the joints are made with M10 threaded bolts and the tightening torque is equal to 30 Nm.  
The shelf where the two components are placed is 1400 mm high, while the height of the workbench is 900 mm. Finally, the assembly is positioned in the cart on a support plane 500 mm high.  
The worker is P40 of the Italian female population [36], with a stature of 1550 mm and a weight of 45 kg.  
In the following sections, the application of the methodological framework depicted in Figure 1 is applied.  
Figure 2: Workstation layout, reproduced in a simulation scenario.
### Related Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Country/Location** & **Used Technique** & **Period** & **Results** & **Advantages** & **Shortcomings** \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{Saudi Arabia} & ARIMA time-series model for predicting the number of people who were newly infected & 2 March 2020 to 20 April 2020 & Prediction with a horizon of 4 weeks & Useful models particularly in the beginning of the pandemic in Saudi Arabia & The results were not sufficiently accurate \\ \hline \multirow{4}{*}{[18]} & \multirow{4}{*}{Saudi Arabia} & Generalized & \multirow{4}{*}{2 March 2020 to 10 October 2020} & Forecasting of the number of cumulative infection and the pandemic (\(R^{2}=0.9953\)) & The end of the first wave of the disease in Saudi & An increasing bias was observed \\  & & & & & \\ \cline{1-1} \cline{3-6}  & & SIR and ML & \multirow{4}{*}{2 March 2020 to 21 February 7021} & Prediction of infections, recoveries, and deaths (MAPE = 7%) & Data processed forward and no effect of the neurons’ historical states \\ \hline \multirow{4}{*}{[12]} & \multirow{4}{*}{Saudi Arabia} & Abbreviations: ARIMA (Autoregressive Integrated Moving Average), Bi-LSTM (Bidirectional LSTM), CNN (Convolutional Neural Network), DL (Deep Learning), DRL (Deep Reinforcement Learning), GCN-LSTM (Graph Convolutional Network LSTM), LSTM (Long Short-Term Memory), MAPE (Mean Absolute Percentage Error), MCNN (Multivariate CNN), MLP (Multi-Layer Perceptron), RMSE (Root Mean Square Error), SLSTM (Stacked LSTM), SIRVD (SIR–Vaccinated–Deceased), VAE (Variational Auto-Encoder). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._* We used a prediction window of the 14 previous days to predict new infections one day ahead, which corresponds to the incubation period of COVID-19. Intuitively, any new infection may result from a person who was infected during the previous 14 days or less. * Through our study, the simple-structure DL techniques in use (RNN, GRU, and LSTM) are found to yield good forecasts although the available datasets are of limited size (due to the fact that COVID-19 data are released daily). Therefore, it is not necessary to train complex-structure DL algorithms for small (not guaranteed) improvements in accuracy. Simple DL algorithms are sufficient in the case of COVID-19. * To the best of the authors' knowledge, our study is among a small number of works covering a dataset that includes the periods before and after the appearance of vaccines. For this reason, our study is expected to capture the effect of vaccination on the disease spread. * The case studies considered possess different features in terms of demographic patterns, climatic conditions, cultures, quality of health systems, economic situation, etc. This may help evaluate the predictability of new COVID-19 infections using the same DL tools as in various case studies. The rest of this paper is structured as follows. In Section 2, the study material and methods are provided. In Section 3, the results obtained and their discussion are presented. Finally, the conclusion and suggestions regarding future works are detailed in Section 4.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 5 Discussion
### User Experience Evaluation  
Following the auditory localization tests, participants provided informal verbal feedback, offering valuable insights into their experiences with the VR environment and gamification elements.  
The feedback indicated that most participants were highly engaged and enthusiastic, noting that the experiment generated significant interest and involvement. This engagement with the virtual environments contributed to a noticeable reduction in the tests' adjustment period compared to previous listening tests conducted at the LATe lab. The quicker adaptation allowed for a shorter overall test duration, making the process more efficient and less tiring for subjects.  
Participants highlighted the rapid learning curve, which reduced training time and improved self-consistency in results. Many participants reported feeling more comfortable during the second part of the test, where the wetland visual environment and Ambisonics field recordings contributed to a more immersive experience. Although this increased comfort did not directly translate into statistical improvements, it is likely that it reduced fatigue and helped participants maintain attention throughout the test.  
The VR environment, especially when combined with wetland field recordings, offered participants a sense of landscape familiarity. Interactive features, such as stimulus control and progress tracking, helped keep participants engaged throughout the test.  
Moreover, it was clear that the subjective component played a significant role in this experiment. The user experience and interaction with the virtual environment had a noticeable impact, underscoring the complexity of factors influencing auditory perception in VR settings. In future stages, qualitative analyses of the user experience and their interaction with the virtual environment are planned in order to deepen the understanding of these variables and identify possible trends or patterns.  
These findings highlighted the positive impact of the VR environment and gamification tools in enhancing participants' engagement and comfort during the tests, suggesting that these features can make localization listening tests more efficient and consistent. However, there is no evidence that these factors had a noticeable impact on results in statistical terms. This suggests that although the VR setup enhanced the user experience, its role in facilitating accurate spatial perception is complex and multifaceted, influenced by factors such as visual dominance, loudspeaker configuration, and users' prior experience with HMDs.