esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Physical Model  
The gabion used in the buildings is placed outside all exterior walls, as shown in Figure 1a. A building in the south of the building group was selected as the research object. As with the others, it is surrounded by a gabion with the same height as the building. Figure 1b shows the building's plan. The distance between gabions and the exterior walls and the thickness of the gabion were, respectively, set to 100 mm and 200 mm, reflecting the dimensions and locations while avoiding errors such as negative volume grids and poor grid quality in the later meshing stage. The structure of the gabion building envelope is as illustrated in Figure 2. The combination of gabion envelopes and reinforced concrete load-bearing structures improves the level of safety. Based on the principle of controlled variables, we consider that the main difference between the ordinary external building envelope and the gabion building envelope is the absence or presence of the gabion structure. The exterior wall is structurally identical and has the same physical parameters. The relevant thermo-physical properties of materials used in the envelope are listed in Table 1. After preliminary field research of the Yanqing region, a physical model of this building group was established in Fluent software according to the surveyed terrain. The buildings were simplified to corresponding size blocks, neglecting the support elements within the building in this physical model. As shown in Figure 3, the exterior surfaces of the analyzed building were numbered for the convenience of analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{3}{c}{**Thermo-Physical Properties**} \\ \cline{2-4}  & **Conductivity** & **Specific Heat** & **Density** \\  & **(W/(mK))** & **(J/(kg-K))** & **(kg/m\({}^{3}\))** \\ \hline Anti-cracking mortar [35] & 0.93 & 1050 & 1800 \\ \hline Polystyrene sheet [35] & 0.042 & 1380 & 30 \\ \hline Reinforced concrete [36] & 1.74 & 920 & 2500 \\ \hline Lime gypsum mortar [35] & 0.76 & 1050 & 1500 \\ \hline Stone [35] & 1.04 & 1000 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant thermo-physical properties of materials.  
Figure 1: (**a**) Gabion used in the buildings; (**b**) the research building’s layout.
# Multidisciplinary Study of Mud Emissions Following the 2016 Norcia Earthquake
## 4 Results
### Rheological Behavior  
The measured rheological parameters were fitted using the Bingham model. The shear stress is given as a linear function of the shear rate and the constant material parameter yield stress and the Bingham viscosity:  
\[\tau=\tau_{b}+\eta\cdot\dot{\gamma} \tag{1}\]  
where \(\tau\) is the applied shear stress expressed in Pa, \(\tau_{b}\) is the constant parameter yield stress expressed in Pa, \(\eta\) is the Bingham viscosity expressed in Pa\(\cdot\)s, and \(\dot{\gamma}\) is the shear rate expressed in s\({}^{-1}\). Table 3 reports the rheological parameters determined for each suspension fitting the data with the Bingham model.  
The Bingham viscosities of the mud suspension (Figure 3a) decrease from 6.3E+7 Pa\(\cdot\)s (AM6) to 6.9E+5 Pa\(\cdot\)s (AM7) with increasing water content from 13.5 to 29.5 wt.%.  
The yield stress \(\tau_{b}\), i.e., the stress that has to be exceeded to start the flow, is between 1 and 404 Pa (Figure 3b). Sample AM1 (30.5 wt.% of water), AM2 (26.1 wt.% of water), AM3 (19.9 wt.% of water), and AM7 (29.5 wt.% of water) show a reasonably linear decreasing yield stress, from 404 to 36.8 Pa, with increasing water content. The exceptions are samples  
Figure 3: Bingham viscosity (**a**) decreases with increasing water content of the liquefied muds. Yield stress (**b**) tendency with water content.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rheometer** & **Sample** & **Water Content LOI** & **Yield Stress** & **Std. Error** & **Bingham Viscosity** & **Std. Error** \\
**Geometry** & **Sample** & **_w_ (wt.\%)_ & **\(\tau_{b}\) (Pa)** & & **_\(\eta\)_ (Pa-s)_** & \\ \hline Vane rotor & AM1 & 30.59 & 36.82 & 10.11 & 9.84 \(\times\) 10\({}^{5}\) & 9.09 \(\times\) 10\({}^{4}\) \\ Vane rotor & AM2 & 26.10 & 151.01 & 34.19 & 3.46 \(\times\) 10\({}^{7}\) & 2.35 \(\times\) 10\({}^{6}\) \\ Parallel plate & AM3 & 19.97 & 404.01 & 141.78 & 3.73 \(\times\) 10\({}^{7}\) & 4.53 \(\times\) 10\({}^{6}\) \\ Vane rotor & AM4 & 21.32 & 1.32 & 0.78 & 7.19 \(\times\) 10\({}^{6}\) & 4.57 \(\times\) 10\({}^{5}\) \\ Vane rotor & AM5 & 38.51 & 115.84 & 15.26 & 8.90 \(\times\) 10\({}^{5}\) & 1.26 \(\times\) 10\({}^{5}\) \\ Vane rotor & AM6 & 13.50 & 13.70 & 4.06 & 6.31 \(\times\) 10\({}^{7}\) & 1.29 \(\times\) 10\({}^{6}\) \\ Vane rotor & AM7 & 29.53 & 50.04 & 12.66 & 6.93 \(\times\) 10\({}^{5}\) & 8.55 \(\times\) 10\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rheological parameters determined for each suspension.  
AM5 (38.5 wt.% of water), AM3 (19.9 wt.% of water), and AM2 (26.1 wt.% of water) which have a high Bingham yield stress oscillating between 1.3 and 115.8 Pa.  
Notably, the rheological parameters of the seven experimentally tested suspensions, with few exceptions, show an expected decrease in viscosity and yield stress as the amount of water increases.
# Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
## 3 Accented Speech Recognition Based on End-to-End Domain Adversarial Training
### End-to-End Domain Adversarial Training Based on DANN for Accented Speech Recognition
#### 3.3.1 Feature Extractor  
We constructed the feature extractor using CNN, which is a useful neural network for learning two-dimensional data while maintaining information. The general features used in ASR are two-dimensional mel-spectrograms. Thus, in this study, mel-spectrograms extracted from raw speech data were used as input features, then the features were used to train four CNN layers. When the mel-spectrogram features were fed into the feature extractor, a filter skimmed through the features in one stride. The filter moved forward step-by-step and produced a feature map that had the weights of the parameters calculated by the convolution between the mel-spectrogram features and the filter.  
The simplified procedure of feature mapping is illustrated in Figure 5. If the CNN has many layers, its output size is drastically reduced because each layer receives the feature map of the previous layer and performs convolution again. To prevent drastic size reduction, zeros are padded into the feature map to maintain the size of the feature map. For each convolutional layer, the Gaussian error linear unit [15] is used as an activation function to receive the parameter weights and produce output. After passing through the four CNN layers, one fully connected layer changes the final two-dimensional CNN outputs into one-dimensional feature vectors. These converted features are fed into the domain classifier and the label predictor. Meanwhile, the feature extractor adjusts the features to be domain-invariant in a number of training steps. Thus, it finally extracts useful features characterizing the target accepted speech.  
Figure 4: Model training framework for end-to-end domain adversarial training based on DANN.  
Figure 5: Simplified procedure of CNN feature mapping.
###### Abstract  
The topsoil of a clayey slope is easily washed off by rain due to its loose structure. To protect the slope surface, in recent years, several types of non-traditional soil additives have been used by means of mixing with soil. In this work, a new organic polymer soil stabilizer, named aqua-dispersing-nano-binder (ADNB), was sprayed on the soil surface to stabilize the topsoil of a clayey slope. To understand the interaction between the polymer and soil particles during the infiltration process as well as the stabilization mechanism, infiltration tests, water stability tests and scanning electron microscopy (SEM) analyses were performed with different polymer contents. The infiltration tests showed that the infiltration rate of the polymer stabilizer in the soil was slower than that of water due to its characteristics of easy adhesion to soil particles, poor fluidity and large molecular volume. The maximum effective infiltration depth was achieved in the specimen treated with 2% ADNB, and the minimum was achieved in the specimen treated with 5% ADNB. The water stability of the soil increased with the content of the soil stabilizer in the soil aggregates with diameters of either 5-10 mm or 10-20 mm. The SEM analysis showed that the quantity of polymer decreased with infiltration depth; a polymer membrane was formed on the surface of the topsoil and chains were formed inside. The amelioration of the soil water stability may have been due to the bonding between soil particles and polymers generated after evaporation of water in the emulsion. The polymer stabilizer could be applied to improve the erosion resistance of the slope topsoil and reduce soil loss.  
new soil additives; effective infiltration depth; water stability; silty clay; stabilization mechanisms +
Footnote †: journal: Applied Physics Letters B
# Magnetic Properties of Nanocomposites
## 4 Ceramic Nanocomposites
### Ceramic Nanocomposites Containing CoFe  
The details of the thermal treatment and the choice of the transition metal ions are critical to the realization of a properly tailored NC material. Evidence of this is given by the study of NCs obtained by loading the parent zeolite with a mixture of two different magnetic metal ions, such as Fe\({}^{2+}\) and Co\({}^{2+}\). These metal-ceramic nanocomposites were obtained as follows: a sample of commercial zeolite A was contacted with a [Co\({}^{2+}\)] = 0.1 M aqueous solution for 3 h at solid/liquid ratio 1/50, under continuous stirring. The solid was separated from the liquid through filtration and these operations were iterated seven times. Then, the solid was washed with distilled water and came into contact twice with a [Fe\({}^{2+}\)] = 0.1 M aqueous solution for 40 min at solid/liquid ratio 1/50, under continuous stirring. This sample of (Co-Fe)-exchanged zeolite A contained Fe\({}^{2+}\) 7.2 and Co\({}^{2+}\) 14.8 wt. %. Finally, two different aliquots of the (Co-Fe)-exchanged zeolite A were thermally treated for 2 h (10 \({}^{\circ}\)C/min heating rate) at 750 and 800 \({}^{\circ}\)C under a reducing atmosphere created by a flow H\({}_{2}\)-Ar gaseous mixture (2 vol.% H\({}_{2}\)). The room temperature hysteresis loops of the resulting metal-ceramic nanocomposites are shown in the left panel of Figure 7. Note the enlarged scale used to enhance the low-field details; of course, the magnetization curves are fully saturated at H = 17 kOe. The saturation magnetization of the whole sample (including the diamagnetic host) is 1.14 and 0.71 emu/g (the higher value corresponding to the sample treated at the lower temperature).  
The remarkable differences between loops include a generally steeper, more regular loop and a smaller coercive field in sample CoFeA800C-2 h when compared with the other sample. In fact, sample CoFeA750C--2 h displays a notably asymmetric shape, which is better observed looking at the susceptibility curves \(\frac{\chi}{M_{\mathrm{s}}}=\frac{1}{M_{\mathrm{s}}}\frac{dM}{dH}\) (Figure 7, right panel). In the latter sample, the loop slope suddenly changes around \(H\) = 0 on both loop branches. This is usually the hallmark of a biphasic magnetic system, that is, a system containing two phases with different magnetic anisotropy; it is suggested that in these material Fe-rich and Co-rich NPs are simultaneously present, with the change in slope of the M(H) curve being related to the interplay of the different anisotropy constants of the transition metals. However, an increase of 50 K in the treatment temperature is enough to generate a magnetically more homogeneous NP system. It is suggested that in this case, most of the metallic NPs contain a Co-Fe alloy.
###### Abstract  
Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.  
laser scanner; time-of-flight camera; point cloud; 3D flash LiDAR; 3D measurement +
Footnote †: journal: Applied Sciences  
Citation: [PERSON] Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion. _Appl. Sci._**2023**, 13, 6488. [[https://doi.org/10.3308/appj3116488](https://doi.org/10.3308/appj3116488)]([https://doi.org/10.3308/appj3116488](https://doi.org/10.3308/appj3116488))
## 8 Discussion
### Motivations
#### 8.3.4 Network Traffic Management  
Accurate network traffic identification is the foundation of intelligent network management, and it is essential for effective network management. Service providers will be unable to maximise shared resource consumption and assure accurate billing and payment if they do not identify and measure network traffic flow. On the basis of past traffic data, machine learning algorithms can be used to find the most optimum network topologies. Similarly, network functions virtualization (NFV) can be used to provide the necessary resources and services to overcome exceptional or routine situations. Because of this, researchers in communication networks are increasingly relying on machine learning to optimise network architecture, control, and administration, ultimately leading to greater automation in network operations. Experts in the machine learning field, on the other hand, are collaborating with networking researchers to optimise network architecture and design. Furthermore, the rapid growth of machine learning has had an impact on many fields of wireless communication since it is capable of making judgments and extracting information from the data generated by a 5G network.
# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Robustness against Density of the Point Cloud  
One of the parameters of the point clouds that may affect the performance of any descriptor is the density of points. This is related to the number of points in a cubic unit. The density of point clouds may vary for a variety of reasons: sampling rate in a synthetic setup, resolution of the acquisition sensor, or preprocessing methods, such as keypoint extraction, for instance. Thus, in order to explore the tolerance to different densities of the VFLD, we resampled the datasets with 500, 1000, 6000 and 12,000 points. The default 3000 sampling rate was also incorporated. The rest of the parameters were 1 as the starting  
Figure 13: Accuracy (top) and precision–recall (bottom) curves for different amounts of the search radii for ModelNet (leftmost) and Simple Figures (rightmost) datasets.  
value, 0.5 for the increment, and 18 components of the radii. Regarding the box-counting algorithm parameter, 5 was chosen for \(nIters\). FPFH was also tested for each density rate.  
The results are shown in Figure 14. As can be seen, the results for VFLD vary for different densities, with accuracy ranging from 0.5 to 0.7. A lower density benefits the accuracy and precision of the proposed descriptor, while the performance is poorer as the number of points increases. In all cases, it significantly outperformed FPFH, which, overall, experienced the same trend. As mentioned, some parameters, such as the radius starting value, depend on the density of the clouds as lower densities require a larger radius starting point. In order to work with different densities, the radius starting points must be tuned accordingly. In addition, the increase in the performance with a lower number of points could be explained by the fact that greater density means more points, with the majority of these producing practically the same descriptor. Thus, they are more likely to be placed in uninteresting parts of the objects. This, for example, is the case of the points laying in a plane. The descriptor for the majority of these (excluding those placed in the boundaries) would yield similar values. Given this case, a confusion in the matching process can be expected.
## 3 Results
### Simulation Analysis Results
#### 3.1.1 Analysis of Path Smoothness and Deviation Correction Accuracy  
Figure 11 is a track of the roadheader center under the point position correction strategy and the conventional correction strategy. The conventional deviation correction strategy takes the roadway center line as the target path to track. The point correction strategy uses a polynomial curve or a Bezier curve as the tracking target path, as can be seen from Figure 11.  
When tracking the center line of the road, the pure tracking algorithm appears to \"overshoot\", and the smoothness of the tracking is poor.  
When the planning path is a Bezier curve or polynomial curve, the tracking is relatively smooth. By adjusting the shape coefficient Ks of a Bezier curve from 0.1 to 0.7, the shape of the tracking track will change accordingly. The smaller the shape coefficient, the larger the curvature of the tracking at the starting point and the endpoint, and the smaller the curvature in the middle. On the contrary, the larger the shape coefficient, the smaller the curvature of the trajectory at the start and endpoints, and the larger the curvature in the middle.  
The preview distance is an important parameter of the pure tracking algorithm. The larger the preview distance, the smoother the =tracking, but the larger the tracking error. The smaller the preview distance, the more accurate the trajectory tracking is, but the stability is poor. In order to analyze the influence of the pre-sighting distance on the stability of the tracking trajectory, the pre-sighting distances of 0.1 m, 0.3 m and 0.5 m were taken to compare the trend of the different curves. The analysis results are shown in Figure 12.  
As can be seen from Figure 12, for target paths such as a Bezier curve, cubic polynomial or road centerline, with the increase in preview distance the curvature of each point on the tracking track curve becomes smaller and the tracking becomes smoother.  
The greater the preview distance, the greater the \(X\) coordinate of the roadheader at the endpoint (i.e., the lateral offset of the roadheader). By comparing the tracking trajectories at different pre-sighting distances, it can be seen that the error at the endpoint is significantly larger for a pre-sighting distance of 0.5 m than for the other two values with the same coefficient, so the preview distance \(L_{fc}\leq 0.3\) m is more appropriate.  
Figure 11: A comparison of the tracking trajectories of different planning paths.
# Learning Better Representations for Audio-Visual Emotion Recognition with Common Information
## 4 Methodology
### Problem Formulation  
Here, we present the specific definition of audio-visual emotion recognition. Suppose that, in the training stage, we are given the samples \(\left\{(\mathbf{x}^{(i)},\mathbf{y}^{(i)},z^{(i)})\mid\mathbf{x}^{(i)}\in \mathbb{R}^{D_{\mathbf{x}}},\mathbf{y}^{(i)}\in\mathbb{R}^{D_{\mathbf{y}}},z^ {(i)}\in\mathcal{Z}=\left\{1,2,\cdots,|\mathcal{Z}|\right\},i=1,\cdots,m\right\}\), where \(\mathbf{x}^{(i)}\), \(\mathbf{y}^{(i)}\), and \(z^{(i)}\), respectively, represent the visual data, audio data, and the emotion category label, such as anger, happiness, and sadness. Their corresponding random variables are denoted as \(X\), \(Y\), and \(Z\). Subsequently, we use \(\mathbf{f}(X)=\left[f_{1}(X),f_{2}(X),\cdots,f_{k}(X)\right]^{\text{T}}\), \(\mathbf{g}(Y)=\left[g_{1}(Y),g_{2}(Y),\cdots,g_{k}(Y)\right]^{\text{T}}\), and \(\mathbf{h}(Z)=\left[h_{1}(Z),h_{2}(Z),\cdots,h_{k}(Z)\right]^{\text{T}}\) to represent the \(k\)-dimensional feature functions of \(X\), \(Y\), and \(Z\), respectively. We capture the HGR maximal correlation between \(\mathbf{f}\), \(\mathbf{g}\), and \(\mathbf{h}\), as shown in Figure 1, in order to learn the audio and visual features with common information to predict the corresponding emotions.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 6 Discussion
### Comparison with Other Works  
Table 2 compares our work with other reported related work. Our receiver is low-power compared to others. The 0.95 mW consumed by our receiver will even be much lower in more advanced CMOS technologies because of the predominantly digital circuits in our implementation compared to others. The receiver presented in [29] appears to be more energy-efficient and delivers a higher data rate, but in our application, the frequency used in [29] may coincide with the future uplink. Furthermore, our achieved data rate is sufficient for our application. It can be much more energy efficient by scaling the supply voltage and CMOS technology used since our architecture is predominantly digital. Compared to [30], our work is already more energy-efficient and has a higher data rate potential. Even higher data rate is possible than other works by merely scaling the carrier frequency, but a practical inductive link with sufficient bandwidth may be a limiting factor. In an IC implementation, the signals are expected to be less noisy as lengthy wires that cause interference will be eliminated. Then an accurate bit-error-rate (BER) measurement will be carried out. A BER better than \(10^{-3}\) is acceptable and expected. In the worst case, this may cause a few data packets to be occasionally discarded since the application is in real-time.
## 2 Requirements Specifications and Measurements Gaps
### Sea Surface Temperature  
The SST can be measured by microwave radiometer imagers [48] and infrared sounders [47]. Microwave radiometer imagers at frequencies of 6-7 and/or 11 GHz with coarse spatial resolution can also provide global SST data. Currently, a spatial resolution of 25 km can be achieved using the microwave imagers' (Tropical Rainfall Measuring Mission Microwave Imager, or TMI; Wind Microwave Radiometer, WindSAT; Global precipitation measurement Microwave Imager, GMI; AMSR-E; and AMSR-2) data fusion technique [24]. Infrared radiometers are capable of measuring this variable over cloud-free areas with high spatial resolution (1-4 km) using wavelength in the 10-12 \(\upmu\)m range. Microwave radiometers can improve the coverage in polar regions because microwave signals penetrate the clouds. The challenge is an achievement of a relatively small footprint (\(<\)10 km) at low frequency bands (6.8 GHz).