esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.4 Random Noise and Signal Processing
However, more commonly in practice, the actual peak value of the signal lies within the interval between two discrete spectral lines. If the frequency point corresponding to the nearest spectral line to the peak is directly used as the result of the frequency estimation, it will produce a significant estimation error, with an error range of \([-\frac{f_{s}}{2N},\frac{f_{s}}{2N}]\). Figure 6: Actual signal waveform detected by the FMCW LiDAR. Improving SNR and resolution is achievable through two main strategies: directly modulating the emitted laser with an optical modulator to increase the echo signal's SNR [19]; and utilizing wavelet transform for echo signal-processing. Although modulating the emitted laser directly with an optical modulator improves the signal-to-noise ratio, it increases the design cost, leading many researchers to favor wavelet analysis [20] for noise reduction. Wavelet analysis involves applying appropriate translations and dilations to the original signal, decomposing it into sub-signals with distinct time-domain and frequency-domain characteristics. These sub-signals exhibit varying properties in terms of spatial resolution, frequency, and directionality. This method reveals specific local behaviors of the original signal and provides a basis for localized analysis in both time and frequency domains. Wavelet analysis is characterized by multi-resolution analysis capabilities, making it effective for studying and analyzing non-stationary signals and achieving optimal denoising results.
## 2 Experimental Procedure and Design
### Correlation Analysis  
To investigate the relationship between acoustic features and pronunciation proficiency, this study employs the Pearson correlation coefficient (r) to quantify the linear correlation between the following variables:  
Independent variable: Learners' vowel acoustic deviation (Bark value), calculated as the Euclidean distance between each vowel's F1-F2 and the standard group. Dependent variable: Learners' Mandarin proficiency level (elementary = 1; intermediate = 2; advanced = 3). Statistical analysis is performed using Python 3.10 and SciPy 1.13.0 (scipy.stats module), with a significance level set at \(\alpha\) = 0.05. The specific steps are as follows:  
1. Calculate the mean deviation for all vowels of each learner to generate a deviation dataset;
2. Convert Mandarin proficiency levels into ordinal numeric variables;
3. Call the pearsonr () function to compute the correlation coefficient (r) and \(p\)-value;
4. Control for multiple comparison errors using the Bonferroni correction.  
The analysis results show a significant negative correlation between acoustic deviation and pronunciation proficiency (r = -0.76, \(p\) < 0.001), indicating that the poorer the pronunciation ability, the more obvious the acoustic features deviate from standard values.
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
## 2 Materials and Methods
### Cavity Stabilization Scheme  
The relative position of the laser injection wavelength and cavity resonance can be controlled using the local thermal heater detailed above. This scheme makes the device robust to changes in either the laser wavelength or global thermal shifts of the cavity resonance position. In order to avoid dithering of the cavity resonance that will push the device out of the resonant operation window, this method makes use of the known spectral transfer function of the device. By measuring the cold cavity spectrum, the linear losses, resonator Q-factor and lineshape, and central resonant wavelength can be measured. Given the thermal refractive index coefficient of silicon, along with the absorption in the resonator and thermal dissipation, the transfer function can be calculated as a function of injection power [7]. Figure 4 shows simulated transmission spectra of a \(\pi\)-phase shifted grating with the parameters matching the fabricated device with injection power as parameter. The simulation was carried out using a transfer matrix method (TMM) solution to the coupled mode equations [19], where the refractive index is a function of the power in the resonator mode volume. In this work injection power refers to the optical power in the access waveguide coupled to the resonator, and transmission refers to the power in the post-resonator waveguide. The characteristic asymmetric resonance shape is visible with injection powers as low as 0.09 mW.  
A schematic of the stabilization arrangement is presented in Figure 5. Given the transfer function of the resonator and a measurement of the input power, the resonator response can be easily calculated using the simple TMM model, which can be stored in a microprocessor controller. To stabilize the device the user sets the device manually to the optimal working point then initiates the feedback control system. If the relative position of the resonator and laser injection wavelength changes this will create an error signal dependent on the shift away from the calculat  
Figure 4: Simulated transmission spectra of a \(\pi\)-phase shifted grating as a function of injection power.  
Figure 3: Schematic of the measurement setup. Obj.1 and 2 are objective lenses for imaging the device facet and top surface respectively, PD are photodiodes and Osc. is a digital oscilloscope. CCD is an InGaAs CCD camera.  
spectral model. This error signal can then be fed-back to the local thermal heating element to shift the position of the resonant line. Any injection power changes are captured by the power tap at the input of the system.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 1 Introduction
By selectively strengthening the channel weights and emphasizing the features of important channels, the SE module makes the network have a strong expression ability, leading to superior performance. The contributions of this paper can be summarized as follows. 1. We choose the convolutional denoising autoencoder as the noise reduction model for modulation signals. Residual connections are added not only inside the encoding (decoding) block but also between the encoding (decoding) blocks of different depths to form a double residual connection. 2. The SE module is improved and introduced into the denoising autoencoder to optimize the feature extraction of modulation signals. The improved SE module retains the advantages of selective enhancement for channel features, and the number of parameters is fewer, which makes the network model easier to train and achieve a better noise reduction effect. 3. In the decoding module of the denoising autoencoder, transpose convolution (TConv) is selected to complete upsampling. The parameters inside TConv can be initialized randomly and updated iteratively. At the same time, the SmoothL1 Loss(SLL) function is used as the loss function of the denoising autoencoder, which can better measure the error between the estimated value and the real value, reconstruct a more accurate modulation signal, and improve the accuracy of the model training. The remainder of this paper is organized as follows. Section 2 describes the communication system model. Section 3 analyzes the applicability of the neural network and introduces the principles and details of the proposed DRdA-CA. Section 4 describes the dataset generation and preprocessing. The simulation results and performance analysis are summarized in Section 5. Finally, Section 6 concludes the paper.
# Extended Reality (XR) Engines for Developing Gamified Apps and Serious Games: A Scoping Review
## 4 Discussion
### RQ4. What Learning Activities for Serious Games Are Supported in XR Game Engines?  
The learning activities referenced in this review were identified by [PERSON] [115]. The authors define a learning activity as a \"specific interaction among students using specific tools and resources, orientated toward specific outcomes\". [PERSON]'s classification of learning activities comprises the following categories: information transmission activities, individual activities, collaborative activities, and discussion and argumentation activities.  
* Information transmission activities allow students to reflect on information for learning [115; 116].
* Individual (constructivist) activities evidence that learning is more successful when students are actively involved rather than just trying to learn passively [114].
* Collaborative (constructivist) activities aid in the acquisition of knowledge and promote mediated and structured interactions [114].
* Discussion and argumentation activities encourage learning through discussions and questions of new information to evaluate, express, and discuss their ideas [114].  
Designing serious games and gamified apps with XR revolves around creating engaging learning activities that incorporate interactive gaming elements to enhance the overall learning experience [117]. Tables 8 and 9 show the learning activities that are supported in the reviewed proprietary engines and JavaScript-based engines, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proprietary XR Game** & **Information** & **Individual Activities** & **Collaborative Activities** & **Discussion and Argumentation Activities** \\ \hline Google ARCore & No & Yes & Yes & Yes \\ Apple ARKit & Yes & No & Yes & Yes \\ Unity 3D & Yes & Yes & Yes & Yes \\ Vuforia & Yes & Yes & No & Yes \\ Unreal Engine 5 & Yes & No & Yes & Yes \\ Nuke & Yes & No & Yes & Yes \\ HerodMirror & Yes & Yes & Yes & Yes \\ Godot & Yes & No & Yes & Yes \\ Verge3D & Yes & No & Yes & Yes \\ Amazon Sumerian & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 8: Learning activities supported in proprietary XR game engines.
# Methodology for Efficient Bitumen Storage with Reduced Energy Consumption
###### Abstract
Instead of maintaining bitumen temperature at 150 \({}^{\circ}\)C through its storage period, we increased the temperature to 160 \({}^{\circ}\)C and then stopped the energy supply, and let the temperature decrease to attain 140 \({}^{\circ}\)C. We repeated this operation during the entire storage period, creating a sort of thermal cycle. Thermal cycling has been studied before, focusing on thermal fluctuations that occur naturally during the \"in-service\" part of bitumen's lifetime. [PERSON] studied the effect of thermal cycling on the creep-recovery behavior of road 40/50 paving grade bitumen, where they used heating/cooling (25 \({}^{\circ}\)C/45 \({}^{\circ}\)C; 45 \({}^{\circ}\)C/65 \({}^{\circ}\)C) and freezing/thawing (\(-\)15 \({}^{\circ}\)C/20 \({}^{\circ}\)C) cycles (90 cycles each) to simulate climate changes in different regions and during different seasons. They found that these two phenomena have both affected bitumen properties in different ways. The first one decreased bitumen susceptibility and caused the hardening of the material, which reduces rutting but increases the risk of thermal cracking at low temperatures. The second one increased both bitumen susceptibility and the risk of rutting at high temperatures [18]. [PERSON] et al. studied the impact of thermal fatigue cycles on the rheological behavior of polymer-modified bitumen. They applied 100 cycles (representing 100 days) of freezing/thawing between \(-\)10 \({}^{\circ}\)C and 25 \({}^{\circ}\)C and found that these cycles make the modified binder softer and more deformable, cause an increase in the susceptibility and the risk of rutting, and produce a loss of consistency [19]. The second storage method that we studied is room temperature storage at 25 \({}^{\circ}\)C for the storage period. [PERSON] et al. studied PG 70/100, PG 40/60, and polymer-modified bitumen ageing at room temperature (24.6 \({}^{\circ}\)C) for 5, 10, 15, 20, and 25 days, focusing on the chemical and rheological changes [20]. Thus, this paper's novelty consists of optimizing energy consumption in the bitumen industry without adding any modifiers to our 35/50 bitumen while using fossil energy. It contains four sections, organized in this manner: the first section provides an accurate depiction of the studied problem; the second section provides a detailed description of the materials and methods used for the study and offers data and specifications; the third section discusses the obtained results and provides recommendations; finally, conclusions are summarized in the fourth section.
## 3 Proposed Methodology
### Phase 1: Initialization of Parameters  
The initial phase of our methodology involves the crucial step of parameter initialization, which lays the foundation for the subsequent stages. In this phase, we meticulously define and set the values of the parameters that govern the different algorithms and techniques of the proposed methodology. These parameters act as the guiding principles and variables that influence its behavior and performance. It is essential to establish appropriate initial values for these parameters, as they significantly impact the overall effectiveness and accuracy of the subsequent computations. Thus, by conscientiously determining the initial values, we ensure a solid starting point for our methodology, enabling reliable and meaningful results throughout the entire process. The parameters and symbols used are:  
1. \(D\): The dimensionality of the search space.
2. \(N\): The population size.
3. \(x_{\text{min}}^{(j)}\): The component \(x^{(j)}\) of vector \(\mathbf{x}\) is bounded below by \(x_{\text{min}}^{(j)}\).
4. \(x_{\text{max}}^{(j)}\): The component \(x^{(j)}\) of vector \(\mathbf{x}\) is bounded above by \(x_{\text{max}}^{(j)}\).
5. \(\mathcal{N}(\mu,\sigma)\): The normal distribution with mean \(\mu\) and variance \(\sigma\).
6. Beta(\(\alpha,\beta\)): The beta distribution, where \(\alpha\) and \(\beta\) are real numbers.
7. IterMax\({}_{1}\): The maximum number of iterations for the GA.
8. IterMax\({}_{2}\): The maximum number of iterations for the Nelder-Mead algorithm.
9. \(\rho\): The coefficients of reflection.
10. \(\chi\): The coefficients of expansion.
11. \(\gamma\): The coefficients of contraction.
12. \(\sigma\): The coefficients of shrinkage.
13. \(r_{c}\): The probability of performing crossover between pairs of selected individuals during reproduction.
14. \(r_{m}\): The probability of introducing random changes or mutations in the offspring to promote diversity.
## 4 Experimental Results and Discussion
### Accuracy Under a Real Noisy Environment  
The signals in the real power systems are contaminated with the high probability of containing noise. Therefore, an artificial test signal is constructed with different intensities of white Gaussian noise (AWGN). The intensity of the white Gaussian noise is given as the signal to noise ratio (SNR), which is defined in Equation (10), where Ps and Pn are the power of the signal and noise, respectively.  
Table 4 shows the performance of the proposed classifier under different SNRs between 20 and 50 dB, which is conducted on the real microgrid platform. The robustness of the proposed algorithm is tested for 500 test samples for each PQD class in the presence of noise. The type of PQD events and the number of PQD events are selected in random series during the test stage. Overall, the classification accuracy decreases as the SNR level decreases. Nevertheless, the classification accuracy still reaches greater than 94%, even if the SNR drops to 20 dB. The results demonstrate that the SVM classifier of the proposed algorithm is less sensitive in a noisy environment. The reason that the classifier is insensitive to noise is attributed to the satisfactory performance of the algorithm. Each mode has a central frequency with a narrow bandwidth since VMD decomposes the signal into band-limited modes by updating with Wiener filtering. In this method, the disturbance signal is effectively separated from noise, which benefits the procedure of feature extraction for the SVM classifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Assigned Class**} & \multicolumn{4}{c}{**Signal to Noise Ratio**} \\ \cline{2-5}  & **20 dB** & **30 dB** & **40 dB** & **50 dB** \\ \hline C1 & 100\% & 100\% & 100\% & 100\% \\ C2 & 95.4\% & 98.2\% & 98.8\% & 99.2\% \\ C3 & 99.2\% & 100\% & 100\% & 100\% \\ C4 & 100\% & 100\% & 100\% & 100\% \\ C5 & 94.2\% & 97.8\% & 97.8\% & 99.6\% \\ C6 & 94.4\% & 98.0\% & 99.6\% & 100\% \\ C7 & 97.6\% & 99.6\% & 99.6\% & 99.6\% \\ C8 & 100\% & 100\% & 100\% & 100\% \\ C9 & 100\% & 100\% & 100\% & 100\% \\ C10 & 98.0\% & 98.4\% & 99.8\% & 100\% \\ C11 & 95.4\% & 95.8\% & 97.2\% & 98.8\% \\ C12 & 96.2\% & 96.6\% & 97.6\% & 98.4\% \\ C13 & 95.6\% & 97.8\% & 98.6\% & 99.2\% \\ Overall & 97.38\% & 98.63\% & 99.15\% & 99.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of PQD classification accuracy under a noisy environment.  
Figure 12: Receiver operating characteristic curve for each PQD class.
###### Abstract  
We demonstrated extra-cavity gain narrowing pre-compensating in a regenerative amplifier with a multi-order triple-wavelength waveplate (WP). With a 1.117 mm thickness quartz waveplate, which was placed on the stretcher, the gain narrowing effect was reduced efficiently, the output spectral bandwidth was broadened from 25 nm to 40 nm, the broadest spectral pulse was compressed to 33.2 fs and the output energy was 1.5 mJ, the as same as with no waveplate. Then, the amplified pulses were used for cross polarized wave (XPW) generation, and the generated XPW spectra were broadened from 45 nm without WP to 68 nm with WP, with a conversion efficiency of 15%. This simple method can produce broader output spectra with a broader seed beam.  
waveplate; gain narrowing; regenerative amplification; cross polarized wave generation
###### Abstract  
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a Generative Adversarial Network (GAN) with cycle consistency loss function known as cycleGAN. The proposed method aims to generate synthetic data that could effectively replace real experimental data. The generative model is trained to transform wavelet images of simulated vibrational signals into authentic data obtained from machinery with damaged bearings. The utilization of Maximum Mean Discrepancy (MMD) and Frechet Inception Distance (FID) demonstrates a noteworthy resemblance between synthetic and real experimental data. Also, the generative model enables the synthesis of data that may have been entirely lacking from the experimental observation, indicating generative zero-shot learning capabilities. The efficacy of synthetic data in training diagnosis algorithms by means of Transfer Learning (TL) on Convolutional Neural Networks (CNNs) has been demonstrated to be comparable to that of real data. The study has been validated by means of the test rig for medium-sized industrial bearings accessible at the Politecnico di Torino.  
intelligent fault diagnosis; generative adversarial networks; cycleGANs; transfer learning; machine fault diagnosis; convolutional neural networks; maximum mean discrepancy; bearings; rotating machinery; condition monitoring
## 4 Data Analyses, Discussion and Findings
### Bim  
The degree of influence of BIM to the piping prefabrication is shown in Figure 5, in which 5 means the highest degree of influence. It can be seen in Figure 5 that BIM has a significant influence to the piping prefabrication. In most cases, the designer provides 2D drawings to the piping contractor. These drawings can only give a general information of the piping system. Some information that is important for procurement and construction is insufficient in these drawings. Some designers may provide a design model for the contractor (about 23%). However, the level of detail (LOD) for this design model is commonly LOD 300 [32], according to the definition of LOD by American Institute of Architects (AIA). In this circumstance, some piping contractors develop the BIM model for construction. In China, as the piping contractor is responsible for the procurement of pipes and fittings, the piping contractor will supplement more information such as the length of the pipes, the type of the fittings into the BIM model. In the process of developing this BIM model, some design errors can be fixed. However, the quality and precision of the BIM model are dependent on the quality of materials provided by the distributor. The contractors need to develop a BIM model above the level of LOD 400. As observed from interviews with some piping engineers, the main challenges when developing the BIM model for prefabrication are in two aspects. On one hand, it is difficult for the contractor to choose the optimum connection solution of pipes as the shortage of appropriate fittings. On the other hand, it is difficult for the contractor to set the parameters of components as lacking size of some devices. Figure 6 shows a case of a prefabricated fire sprinkler system that the writer has visited. In this project, the contractor developed a detailed BIM model. Then 2D drawings were exported from BIM model to workers on site.  
The distributor is involved in this project when developing the BIM model. Therefore, the accurate sizes of the fittings including elbows, branches, reducers, flanges and so on to provide a chance for pipe prefabrication.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 5 Simulation Results and Analysis
### Average Packet Loss Rate  
The packet loss rate (PLR) can reflect the advantages and disadvantages of the network in congestion avoidance. Figure 3 shows the comparison of the packet loss rate with the change of network size under different algorithms. It can be seen that compared with the other two algorithms, the packet loss rate of DS-EERA in this paper is always stable at a lower value with the change of network size. This is because both the MCRP and the FLEOR do not consider the queue length of the nodes when routing, and a large amount of data is concentrated in some \"hot spots\" areas at the same time, resulting in the nodes exceeding their own load capacity due to too much received data. Thus a large amount of data is discarded. Our algorithm adopts \"idleness degree\" as one of the attribute indexes of decision making, and uses the entropy weight method to periodically adjust the importance of each index according to the network operation, ensuring that the node selects the forward neighbor node with large idleness to become the next hop. It is more likely to be able to effectively alleviate congestion and make network traffic more balanced.