esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Finite Element Simulations
#### 2.1.2 STBF Test  
In order to simplify the model and speed up the calculation, differently from [23], it was decided to directly apply the forces on the gear instead of modelling the anvil-gear contacts. This assumption could be made considering that the aim of the simulation is to study the stress state in the root region only and slight deformations of the flank will not affect the results. Moreover, only a section of the system, relying on its symmetry was modelled. The same tooth root meshing techniques adopted by [PERSON] et al. [18] were used to increase the mesh quality in the fillet region.  
STBF tests are widely exploited in the technical and scientific literature thanks to their simplicity and low cost. Many references can be found in the literature dealing with STBF tests [8, 11, 18, 19, 25, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]. The results of STBF tests should be used in combination with standards (e.g., [13]) for the design of new gears. However, the inverse application of the standards to convert the experimental data into material properties to be used for designing new gears \(\sigma_{Film}\), foresees the necessity to re-define some geometrical parameters; the standard, in fact, is applicable to meshing gears only. STBF tests differ from meshing gears for the following reasons [23]: (1) the point of application of the load did not change; (2) the load did not oscillate from zero to the maximum value, but in the range 10-100% of the maximum. In STBF (Figure 4) tests, in fact, to avoid undesired displacement of the gear, a minimum compressive load is necessarily present. This reflects on the stress history. In the STBF tests, the loads act with a fixed direction varying in a sinusoidal manner with a constant amplitude. This reflects on the angle between the force and the normal to the loaded tooth axis \(\alpha_{Fen}\), which did not change during the tests and can potentially be different from the one in the Outer Point of Single pair tooth Contact considered in the standard. These differences influence the share between pure bending and pure compressive stresses (neglected by the standard). In running gears, despite the angle between the force and the loaded tooth axis, the position of the contact also moves along the tooth flank during engagement. These circumstances lead to very different stress histories. The most reliable and effective method to take into account all these differences, is to use advanced fatigue criteria. This procedure was already used by the authors for determining the relation between STBF tests and real gears [23, 56, 57, 58, 59].  
Figure 3: Residual stresses \(\sigma_{res}\) in the radial direction \(d\) starting from the surface.
# Investigation of Fault Effect on Blast-Induced Vibration
## 4 Discussion of the Results Based on Directions
It has been observed that the fault zone has a significant effect on the propagation of blast vibration up to a certain distance and has no effect after a certain distance in the field study. According to the field measurements, it is seen in Figure 12 the Change graph, belonging to PPV values in the southwest part and PPV values that occurred with distance from the fault zone. As seen in Figure 12, in blastings carried out behind the fault zone (Northwest direction), depending on the vibration velocity, the propagation distance differs compared to the southwest region, which does not show any significant change in its geological structure. According to the data set obtained from the field, this difference is observed up to 250 m distance. After 250 m, the effect of the fault zone on the propagation of the vibration velocity disappears.
## 5 Discussion of Experimental Results
### Second-Stage Experimental Results
#### 5.2.2 Analysis of Optimal Number of Data Items Enhanced  
On the basis of the results of the abovementioned feature number analysis, we selected the number of features with the highest accuracy for each classification according to the I.G. ranking table. Next, we analyzed the number of Probe data augmentations for the WGAN-GP data enhancement model. The number of augmented data items ranged from 1000 to 10,000. The datasets with different numbers of Probe data items augmented by WGAN-GP were fed to all of the classifiers with the best number of features. The effect of different numbers of Probe data augmentations was examined as follows.  
Figure 8a shows that when the number of augmentations was changed to 8000, the Probe recognition accuracy of the KNN classifier increased to 76% as the maximum. As for the SVM classifier in Figure 8b, we found that the accuracy of the Probe recognition increased to a maximum value of 75% when the augmentation number became 8000. For the random forest classifier in Figure 9a, when the augmentation number became 7000, the Probe recognition accuracy increased to 64% of the maximum value. For the XGBoost classifier in Figure 9b, a maximum of 55% improvement in the Probe recognition accuracy rate was found when the augmentation number became 6000. As for the MLP classifier in Figure 10a, the Probe recognition accuracy increased to 78% of the maximum value when the augmentation number became 6000. For the CNN classifier in Figure 10b, the Probe recognition accuracy increased to 72% as the maximum when the number of data augmentations became 8000.  
The results of the best recognition accuracy for different classifiers based on the optimal Probe enhancement number of WGAN-GP are shown in Table 15. We found that when using WGAN-GP, the optimal number of data augmentations to achieve the best Probe recognition accuracy was not necessarily the same for different classifiers, basically exceeding the 5000 augmentations assumed in the first stage. Table 15 shows that the accuracy of the Probe attack recognition was improved again by the two-stage fine-tuning approach when using different augmentation numbers generated by WGAN  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **KNN** & **SVM** & **RF** & **XGBoost** & **MLP** & **CNN** \\ \hline Best Feature Number & 15 & 41 & 15 & 18 & 21 & 23 \\ Probe Accuracy & 73\% & 74\% & 63\% & 53\% & 76\% & 68\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Best feature numbers for all classifiers.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 2 Relay-VLC Deployment in the Indoor Environment  
In this paper, we consider a typical office room with a dimensions of 5 m \(\times\) 5 m \(\times\) 3 m with no furniture as depicted in Figure 1a. The system consists of a transmitter (Tx), which provides both illumination and data transmission, located at the center of the ceiling at the height of 2.8 m pointing downwards with an elevation angle of -90\({}^{\circ}\), and a MP is used as either a receiver (Rx) or an RN. The [PERSON] realized an LED array with Lambertian radiation pattern. The power of LEDs is adjusted to meet the light illumination requirement of 200 to 1500 lx for an office environment as defined by International Organization for Standardization (ISO) [21].  
Furthermore, we assume that a relay-based user holding a MP at the height of 1.2 m above the floor level is randomly moving around within the room. The walls, floor, and ceiling of the room are modeled as general Lambertian reflectors as in vast majority of publications [22; 23]. We investigate an office environment including people where we consider shadowing between the Tx and the Rx, see Figure 1b. The LOS path between the Tx and the Rx will be blocked due to shadowing, and therefore the Tx will select a non-shadowed mobile user as an RN, which is located in the yellow area (see Figure 1b) to re-establish the link between the Tx and the Rx via the relay user. Note that, in a real environment the RN must be close to the the user, and such RN scheme would have very limiting application for considerably longer VLC connections. Here we consider an arbitrary orientation of the mobile-based RN.  
The coordinates of the proposed system are depicted in Figure 1c. The unit vector \(\mathbf{n}\) is specified in terms of conventions followed by room coordinates. The Tx and the Rx directions (i.e., elevation and azimuth angles) can be converted to unit vectors \(\mathbf{n_{s}}\) and \(\mathbf{n_{r}}\), respectively (see Figure 1a). An elevation of the Tx is an angle that \(\mathbf{n_{s}}\) makes with the \(xy\) plane, therefore if the Tx is directly pointing downwards, the elevation angle will be -90\({}^{\circ}\). An azimuth angle of the Tx is defined with 0\({}^{\circ}\) oriented along the negative \(y\)-axis in the projection of \(\mathbf{n_{s}}\) on the \(xy\) and it increases with the counter-clockwise orientation (i.e., the positive \(x\)-axis has an azimuth of +90\({}^{\circ}\)). All the key system parameters are summarized in Table 1[21; 24]. According to [25], the majority of mobile data usage (close to 80%) is in indoor environments, which are rather static, unlike the outdoor environments. Even though the location of RNs or users may change before it is initiated to retransmit the data, without loss of generality we can consider the device is stationary during the relaying process due to the slow movement of the users.  
Figure 1: Room model: (**a**) Tx and Rx geometry model; (**b**) users’ situation in the room; and (**c**) the coordinate system.
# Multi-Scale Universal Style-Transfer Network Based on Diffusion Model
## 3 Proposed Method
### Overall Architecture
#### 3.2.2 Refined Style-Transfer Module  
Most existing style-transfer networks can effectively accomplish style-transfer tasks, but the fusion of style and content often suffers from uneven stylization. To ensure better integration of style into the target image, the second-stage refined style-transfer module incorporates the segmentation module Segment Anything Model (SAM) [40] and ControlNet. First, the refinement focuses on the edges of the content subject. SAM can intelligently segment any part of the image and generate a mask for the selected region, which is used here to roughly separate the content subject from the background. Next, ControlNet is employed to preserve the main content area. ControlNet's primary function is to retain the original model's main parameters while maintaining and refining them through additional branches added on both sides of the original model. Thus, the use of ControlNet ensures fast processing and low cost while achieving effective optimization. The processing workflow of the second-stage refined style-transfer module is illustrated in Figure 5.  
During the style-refinement process, to ensure maximum style consistency, the large model is fine-tuned using LoRA with a Baroque style image dataset and integrated into the style-refinement module. This helps the model to stably generate images in the Baroque style. Additionally, to obtain the final stylized image, the result from the first round of style transfer is upsampled by a factor of two and then fused again with the output of the second round, maximizing the preservation of the main content details of the original content image.  
Figure 4: Channel refinement module. IP and RRB denote injective padding module and reversible residual block, respectively.
## 4 Experiments and Results
### Baseline Methods  
Multiple answer selection models are employed as baselines in a knowledge-based QA in order to conduct the effectiveness of our introduced model.  
* Bi-LSTM A Bi-LSTM neural network is employed as feature extraction for QA pairs. The model is one of the baselines of a knowledge-based dataset.
* Double Bi-LSTM This model proposes double Bi-LSTM to learn the features of given questions and candidate answers. The model is one of the baselines of a knowledge-based dataset.
* Attentive Bi-LSTM The model integrates an attention mechanism into the Bi-LSTM model for the purpose of improving the semantic understanding of questions. The cosine similarity is employed for calculation. The model is one of the baselines of a knowledge-based dataset.
* Multi-scale CNN [33] The multi-scale CNN model constructs different sizes of feature maps to extract the semantic information from the text. This model is one of the baselines of a medical QA.
* QA-CLWR [34] The method proposes a collaborative learning-based answer selection model (QA-CL), where a parallel training architecture is deployed to collaboratively learn the initial word vector matrix of the sentence by CNN and bidirectional LSTM (Bi-LSTM) at the same time.
* Attentive LSTM [35] The method combines attention and LSTM to extract the contextual representation for the question and answer separately, which is the baseline of accuracy.
* Multi-Level Composite CNNs (MCCNN) [36] This method presents stacking CNNs with different sizes to capture multi-level semantic information, which is the baseline of accuracy. The output of each CNN layer is generated by max pooling, and, finally, they are concatenated for prediction.
* BiCNN [37] This method proposed the average pooling with a bigram CNN model, which is the baseline of MAP.
* ABCNN [38] The authors propose an attention-based CNN, which is the baseline of MAP.
* Reference [39] The authors utilized LSTM-based deep learning models for non-factoid answer selection, which is the baseline of MAP.
## 2 Datasets  
The datasets adopted in this study consist of satellite remote sensing products (e.g., AOD retrievals and atmospheric profile data) derived from Himawari-8 AHI, MODIS, and CALIOP as representatives of the GEO and LEO satellite sensors. Meanwhile, the ground-based AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) are employed to validate and analyze the satellite-based AOD products. The experimental period covers the whole of 2017, during which all the satellite-based products are of ensured quantity and quality, especially CALIOP products [50]. The information on datasets is briefly described in Table 1.
## 3 Results and Discussion
### Scaling under Freezing and Thawing
#### 3.3.4 Influence of Prolonged Hydration before the Start of the Freeze/Thaw Test
The targeted air content for Mix 3 was 4.5% \(\pm\) 0.5% (with an actual average air content between the two batches of 4.5% according to SS-EN 12350-7), and 6% \(\pm\) 0.5% (with an actual average air content of 5.6%) for Mix 5. The results show, therefore, that the influence of the degree of hydration of concrete in the protection against frost scaling is more significant for concrete with lower air contents. This would be expected, since concrete with lower air contents have less voids to which the water can move in when ice starts to form. The critical degree of saturation is, therefore, reached earlier. Research carried out by [PERSON] [15] showed that drying has a relatively small, but usually positive effect on the scaling resistance of both Portland cement concrete and GGBS concrete, especially at early ages (up to 31 days of age). The author also found that this positive effect is more pronounced for GGBS concrete than for Portland cement concrete, and that it increases with an increase in slag content. These results show that, after adequate curing, GGBS concrete structures shall be allowed to dry for a long period of time before being exposed to the first freeze/thaw cycle. These results also raise a question of the applicability of the salt-frost scaling test described in SS 13 72 44 [9] for concrete with additions. This test method was developed for Portland cement concrete, and the first freeze/thaw cycle occurs at 31 days of age. The maturity of Portland cement concrete does not increase significantly after 28 days of age [4], which means that the age of testing is probably adequate for Portland cement concrete. However, for concrete with additions, the maturity of concrete continues to develop long after the 28 days of age, depending on the type, reactivity and amount of addition. Therefore, there is a possibility that this test method underestimates the salt frost resistance of concrete qualities with additions. One possibility could be to start the test at a later age. This subject should be further investigated. It should also be pointed out that carbonation has a negative effect on the frost resistance of concrete with additions of GGBS exceeding approx. 50% of the cement weight, see e.g., [16; 17; 18; 19]. In all these studies it has been shown that carbonation reduces the frost resistance of the concrete mainly due to the pore system of the concrete gets coarser from carbonation. Therefore it has been suggested to revise the pre-conditioning of the concrete samples, where concrete containing more than 25% GGBS shall be subjected to pre-carbonation before testing according to SS 13 72 44 [9]. Example of such a procedure is described in [17] and [18].
## 2 Materials and Methods
### Statistical Analyses  
Descriptive statistics are provided as mean, SD, and range. Data normality was verified using the Shapiro-Wilk test (\(p>0.05\)). To evaluate differences in the maximal number of repetitions, MV\({}_{\text{fastest}}\), MV\({}_{\text{last}}\), and %VL across the four sets, we conducted a one-factor repeated-measures ANOVA. When sphericity assumptions were not met, as indicated by [PERSON]'s test, corrections were made using the Greenhouse-Geisser adjustment. Pairwise differences were elucidated with Bonferroni post hoc analyses. A second-order polynomial regression model was utilized to establish both generalized and individualized %Rep-%VL relationships based on data from the first set conducted in session 4. The goodness-of-fit of these %Rep-%VL relationships was assessed using R\({}^{2}\) and standard error of estimate (SEE).  
The differences between the actual %Rep achieved in the 2 nd, 3 rd, and 4 th sets when a 20%VL was reached, and the %Rep predicted by the generalized and individualized %Rep-%VL equations from the first set, were measured as raw and absolute errors. These errors were analyzed using a three-way repeated-measures ANOVA (regression model [individualized vs. generalized %Rep-%VL equation] \(\times\) reference repetition [MV\({}_{\text{fastest}}\) specific to each set vs. MV\({}_{\text{fastest}}\) of the 1 st set] \(\times\) set number [2 nd vs. 3 rd vs. 4 th]). Significant interactions and main effects prompted pairwise comparisons, which were performed using paired t-tests with Bonferroni adjustments.  
The precision of the individualized and generalized %Rep-%VL relationships was classified as high (absolute error \(<5\%\)), moderate (absolute error between 5% and 10%), or low (absolute error \(>10\%\)). Statistical tests were conducted using SPSS software (Version 22.0; IBM Corp., Armonk, NY, USA). The significance threshold was set at \(p\leq 0.05\).
## 5 Results  
This section presents simulation and experimental results validating the proposed method for addressing the non-minimum phase issue in the short-horizon FCS-MPC applied to a boost converter. The simulation setup is conducted on the MATLAB/Simulink platform, where the boost converter model and FCS-MPC algorithm are implemented and tested under various operating conditions to evaluate the performance of the proposed method. Scenarios with different load changes and input voltage variations are included to assess the controller's ability to handle non-minimum phase behavior and maintain stable output voltage. Apart from the simulation verification, experimental tests are performed by implementing the proposed algorithm inside an FPGA board, specifically the Arty Z7, which is equipped with an XC7Z020-1 CLG400C FPGA (AMD, Santa Clara, CA, USA). This FPGA-based implementation enables real-time control and testing, with the controller and the boost converter's discretized model integrated into the same FPGA, ensuring synchronized operation and eliminating clock mismatch issues.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 5 Numerical Experiments, Results, and Discussion  
Simulations and experiments from measured data with an SAA configuration are described in this section. We used a spherical array with 32 elements on the surface of a rigid sphere in all cases considered. A well-detailed technical description, element positions, etc., for the SAA are given in [3]. The results are examined on the basis of the root-mean-square error (RMSE):  
\[RMSE=\sqrt{E\bigg{\{}\left(\mathbf{\Omega}-\overset{\mathsf{\Lambda}}{\mathbf{ \Omega}}\right)\left(\mathbf{\Omega}-\overset{\mathsf{\Lambda}}{\mathbf{ \Omega}}\right)^{T}\bigg{\}}}, \tag{63}\]  
and consequently compared against the square root of CRLB. Furthermore, in all of the simulations, we averaged the RMSE over 100 Monte Carlo samples.  
The signal-to-noise ratio (SNR) is given in this paper as the space-domain SNR, which implies \(SNR=1/\sigma^{2}=\sigma^{-2}\). Nevertheless, a more defined SNR value is the effective SNR, which can be computed as the relationship between the equalized noise variance and equalized signal variance, i.e., \(SNR_{e}=\left|\left|\bar{s}(k)\right|\right|^{2}/\bar{\sigma}_{a}^{2}\). In all cases considered in this paper, we simulated the noise as space-domain i.i.d. complex Gaussian random variables with variance \(\sigma^{2}\).
## 2 Related Work
The D-TSN approach proposed in [29] is able to support dynamic reconfiguration and to deal with multiple classes of ED flows by realizing online frame scheduling according to the EDF policy [56], but it does not provide support for deterministic communications. Comparing with the previous works, the approach here proposed for TSN-based networks, i.e., D-ST, is able to both handle dynamic RT flows and to encompass all the different types of RT traffic, i.e., periodic, event-driven, and time-driven, in a common framework, while meeting their diverse time requirements. Table 1 shows a qualitative comparison among some of the abovementioned related works. In particular, the second and third columns show if and how well the approaches support RT event-driven traffic and scheduled traffic, respectively. The fourth column refers to the compliance with the IEEE 802.1Q standard, while the last column gives the scheduling granularity, i.e., per-class, per-flow, or per-frame. The approaches that provide the best results for each property are those with a higher number of checkmarks. The best support for RT event-driven traffic is offered by D-TSN, Hybrid-TSN, and D-ST, thanks to the scheduling policies adopted. Hybrid-TSN and D-ST are also able to offer temporal isolation to ST traffic, with the notable difference that D-ST is fully compliant with IEEE 802.1Q-2018 switches, while Hybrid-TSN requires special handling of the transmission queues. Moreover, D-ST provides a better scheduling granularity, i.e., per-frame.