esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper analyzes and simulates the optimization of the septum polarizer, improves the feeding structure of the polarizer, and proposes a wide-beam coverage dual-CP antenna based on this. Physical prototypes of two antennas operating in the K-band and Ka-band were fabricated and tested. The simulated and measured results are in good agreement. The results show that the two designed antennas operate from 19.6 GHz to 21.6 GHz and from 29.2 GHz to 31.2 GHz, respectively. The antennas have dual-CP characteristics. The beam widths of the two antennas with gains higher than 0 dBi are greater than \(\pm 55^{\circ}\), and the radiation patterns exhibit good symmetry. This indicates that the two antennas are a good choice for a lift-off relay communication system. Subsequently, the designed  
Figure 16: Simulated and measured gains and patterns: (**a**) 19.6 GHz; (**b**) 20.6 GHz; (**c**) 21.6 GHz; (**d**) 29.2 GHz; (**e**) 30.2 GHz; (**f**) 31.2 GHz.  
Figure 15: Simulated and measured antenna return loss and gain. (**a**) Return loss. (**b**) Realized gain.  
antennas will be mounted on an unmanned helicopter and communication experiments will be conducted to verify the actual performance of the antennas.  
Conceptualization, X.M.; methodology, X.M. and S.H.; validation, X.M.; formal analysis, S.H. and G.Y. investigation, T.G.; resources, S.H. and Q.F.; writing--original draft preparation, X.M. and S.H.; writing--review and editing, S.H. and G.Y.; supervision, G.Y. and S.H.; project administration, G.Y.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Research Program of National University of Defense Technology under Grant No. ZK23-58 and in part by the National Natural Science Foundation of China under Grant No. 62402510.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the first author.  
The authors declare no conflicts of interest.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 3 Simulations
### Simulation Results  
#### 3.2.1 Longitudinal Resolution  
AMI signals with different degrees of overlap can be obtained by the above simulation model, and are used to test the performance of SR and CD-SR methods in improving the longitudinal resolution. To quantitatively evaluate the performance, the amplitude error \(A_{err}\) and the position error \(P_{err}\) of the reconstructed echo are used, which are defined as:  
\[A_{err}=\frac{|A_{rec}-A_{thco}|}{A_{thco}}\times 100\% \tag{15}\]  
\[P_{err}=\frac{|P_{rec}-P_{thco}|}{P_{theo}}\times 100\% \tag{16}\]  
where \(A_{rec}\) is the peak-to-peak value of the reconstructed echo, \(A_{thco}\) is the theoretical peak-to-peak value, \(P_{rec}\) is the position of the reconstructed echo, and \(P_{theo}\) is the theoretical position. The amplitude error and position error directly affect the C-scan imaging effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Silicon Die** & \multicolumn{2}{c}{**Epoxy Molding**} & \\  & & **Compound (EMC)** & **Water** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 2330 & 1050 & 1000 \\ Longitudinal wave velocity \(c_{L}\) (m/s) & 7526 & 2400 & 1496 \\ Attenuation at 20 MHz (dB/cm) & 2.0 & 7.1 & 0.8 \\ Thickness (\(\upmu\)m) & \(d=413.9(1-\delta)\) & \(d_{1}=1000\) & \(d_{2}=2000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
When the amplitude error or position error of the reconstructed echo is greater than 30%, it is considered that the method cannot correctly reconstruct the echo before the overlap.  
In the case of an equal number of dictionary atoms, the results is shown in Figure 6, where \(A_{err(x1)}\) and \(A_{err(x2)}\) are the amplitude errors of \(x_{1}(t)\) and \(x_{2}(t)\), respectively, and \(P_{err}\) is the error in the relative positions of \(x_{1}(t)\) and \(x_{2}(t)\). The simulation results demonstrated that CD-SR has a better ability to distinguish overlapping echoes, and can distinguish and reconstruct echoes with overlap less than 63%. SR can only distinguish echoes with overlap less than 52%, and the amplitude error and position error are relatively large.  
In addition, the waveform characteristics of the reconstructed echoes are compared. When \(\delta=50\%\), the comparison result is shown in Figure 7a. It can be observed that the waveforms of \(x_{1}(t)\) and \(x_{2}(t)\) reconstructed by CD-SR are more consistent with the actual waveforms (see Figure 5a), while SR has a larger error. Figure 7b shows the reconstruction results when \(\delta=60\%\). The waveforms obtained by CD-SR still match the actual waveforms, while SR can no longer successfully separate the overlapping echoes.
# A Numerical Analysis of the Nonlinear Dynamics of Shimmed Conical Gas Foil Bearings
## 2 Theory
### Rotor System  
The equations of motion of the rotor can be derived from the single mass 3-DOF oscillator. The gravitational force \(F_{\rm G}=-m_{\rm r}g\) is acting in the y-direction, the unbalance force of the rotor in the x- and y-direction and an external axial force \(F_{\rm ax}\) is considered in the z-direction. With the rotor mass \(m_{\rm r}\), the rotor unbalance \(u\), the fluid film forces \(F_{\rm x,y,z,F}\) and the non-dimensional rotor displacements \(\varepsilon_{\rm x,y,z}=\left(e_{\rm x},e_{y},e_{\rm z}\right)/c_{0}\) the equations of motion are shown in Equation (5).  
\[\frac{\partial^{2}}{\partial\tau^{2}}\begin{bmatrix}\varepsilon_{\rm x}\\ \varepsilon_{\rm y}\\ \varepsilon_{\rm z}\end{bmatrix}=\frac{4}{m_{\rm r}c_{0}\Omega^{2}}\begin{bmatrix} F_{\rm xF}+m_{\rm r}\Omega^{2}u\cos(2\tau)\\ F_{\rm yF}+F_{\rm G}+m_{\rm r}\Omega^{2}u\sin(2\tau)\\ F_{\rm zF}-F_{\rm ax}\end{bmatrix} \tag{5}\]
## 3 Results and Analysis
### Test Results and Analysis of Improved Loess with Single Index
#### 3.1.2 Influence of Dry Density on Permeability Coefficient  
Figure 6 shows the relationship between the dry density and permeability coefficient without considering the effect of admixture. In general, the permeability coefficient of the specimen becomes smaller as the dry density increases continuously. However, affected by the soil sample's particle characteristics and grading properties, it does not show a good correlation, and the dispersion of the data is significant. Figure 7 shows the test results of improved loess without cement addition (PL14, PL15, PL16, PL17) and improved loess with 15% cement addition (PL31, PL34, PL35, and PL36) at different dry densities, with the same particle and grading characteristics of soil samples. In each data set, only the dry densities are different. The permeability coefficients of the two set samples showed a negative correlation with the dry density, i.e., the permeability coefficient decreased with the increase in the dry density. The curves in PL14, PL15, PL16, and PL17 exhibited a distinct turning point at a density of \(\rho_{\text{d}}=1.65\) g/cm\({}^{3}\). In PL31, PL34, PL35, and PL36, the turning point was \(\rho_{\text{d}}=1.82\) g/cm\({}^{3}\). After the turning point, the permeability coefficient decreases slightly when the dry density increases.  
Figure 6: Effect of dry density on permeability coefficient without admixture.  
The coefficient of permeability of the improved loess with the addition of cement is smaller than that of the specimen without admixture at a similar dry density. This is because the cement undergoes a hydration reaction among the improved soil, and the hydration products generated between soil particles on the surface area of soil particles gradually fill the large pores between soil particles. Due to the hydration reaction, the cement soil can make part of the free water form crystalline water during the hardening process, making the structure more compact. It makes the permeability coefficient decrease. It is worth noticing that, during the experiment, it was found that with the increase in cement content, the dry density of the improved loess decreases compared with that without cement. Because the optimal water content range of cement is much larger than that of the general fine-grained soil, the fine particles cannot mix well with the soil and are not easily compacted. Therefore, the contribution of these two factors to the reduction in the permeability coefficient needs to be analyzed in depth.
# Improving Sustainability of Steel Roofs: Life Cycle Assessment of a Case Study Roof
## 2 Methods and Life Cycle Assessment Processes
### Life Cycle Inventory (LCI)  
In the LCI step of this study, the data for LCA was gathered from several sources, such as case house roofing drawings, EPDs of steel roofing products, a desktop study, and a site visit to the local manufacturer. The material quantity data was calculated from the roofing plan drawing of the case study. Figure 2 illustrates the case study roof plan drawings, and Table 1 shows the material take-off of the case housing roof.  
The study quantified the steel roofs, including the ancillary items from the case building drawing. Some items such as roof trusses and brackets, fixings, and soffit boards were excluded from the calculation. The LCI information by Worldsteel on the GaBi Education database was only available for the organic coated steel coil and not for the full production processes of profile roofing. Therefore, the transport of the steel coil product, energy and resources used at the factory, and intermediate storage facility all need to be considered. Flashing and gutters were also made from steel coil and had an identical gap in available LCI data. In order to gather the unavailable data, a site visit to the local steel roofing factory was completed on 31 May 2021, to understand the full process.  
For the transportation data, an ocean container ship with a 43,000-tonne capacity was assumed to transport steel coil from Busan, South Korea, to Auckland, New Zealand. A 9350 km distance was estimated using the portworld.com website, and it had been rounded up to 10,000 km to account for the variability of the shipping routes. For road transport, a 14-tonne truck was assumed for all transport of steel coils, and a smaller 7-tonne truck was used for transport of the finished product. The distance travelled between sites was calculated using Google Maps.  
Figure 2: Roof level plan of the case study house (all measurements are in millimetres and scaled 1:100).
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 2 The Multi-Access Framework
### Nested Congestion Control  
The utilization of multipath tunnels, as introduced in Section 1, with throughput enhancement by path aggregation as the main performance objective, raises several research questions. The primary objective of employing multipath tunnels is to facilitate the simultaneous efficient utilization of multiple paths. Previous studies have predominantly focused on single-path TCP-over-TCP encapsulation [11; 12; 13] or end-to-end (E2E) transport-layer multipath protocols like MPTCP. However, employing multipath tunnels for E2E traffic introduces the complexity of parallel congestion control (CC) across different paths nested within the server CC loop.  
Figure 3 demonstrates different scenarios. The server congestion control operates end to end and in addition to this, there is a per-path congestion control necessary for the operation of multipath tunnels. Full aggregation over multipath requires that the in-flight traffic maintained by the server CC is _equal to or greater than_ the sum of the four (green) bandwidth-delay-product (BDP) components. Subtle issues arise when you consider that, e.g., the path 1 BDP also contains the buffering at the path 1 bottleneck. Thus, aggregation might depend on the quantity of data the path 1 CC maintains in the bottleneck buffer. This is illustrated in the bottom figure in Figure 3, where there is an added BDP component on path 1, but path 2 is not aggregated.  
Nested congestion control results in unforeseen side effects, and a detailed identification and analysis of these side effects is essential for the development of an optimal multipath solution. This paper offers detailed insight into this problem. The multipath scheduler, responsible for determining each packet's path and potential packet reordering, adds further complexity to the scenario.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 5 The TW Laser  
### Pulse Stretcher  
The other part of the FE laser beam is expanded, and the pulses are positively stretched in a positive chirp pulse compressor resembling an Offner-type one (Figure 5) [20]. The groove density of the blazed grating is 1200 mm\({}^{-1}\), while the spherical mirrors have a focal length of -250 mm and +500 mm. In our case, the incoming and stretched pulses are separated not in space, as is common in such cases, but by polarization with the help of a Pockels cell (Lasermetrics 5046E by FastPulseTechnology, Saddle Brook, NJ, USA) at the input of the assembly. The major advantage of this arrangement is that the alignment of the grating stretcher is more accurate, as the incoming and leaving beams follow the same optical path and are equal in size and divergence. To avoid leakage from the polarizator to the FE, a Faraday optical isolator (FR) is used before the Pockels cell. The overall transmission of the stretcher is around 10%.  
Figure 4: The spectra of the pulses from the FE and after broadening in the HCF (**a**); and the corresponding measured temporal pulse shapes and pulse durations (**b**).  
Figure 5: The layout of the pulse stretcher with a reflective diffraction grating (G), a concave and a convex spherical mirror (CV, CX), and Faraday isolator (FR) together with the elements of beam conditioning and beam stabilization.
## 3 Materials and Methods
### Data Collection
#### 3.1.2 Historical Data  
This research used a one-step-ahead prediction to testify to the prediction preciseness of the proposed model on the closing price of the stock market in Thailand. The dataset obtained from investing.com included close, open, high, low and volume. The range of closing price of the stock market is from 24 February 2018 to 24 February 2022. Only the data from trading days was used for research. The value of data in the selected period is visualized in Figure 2.  
The statistical analysis of the closing price of the stock market, including the amount of data contained in the closing index and the minimum, maximum, mean, standard deviation and \(\rho\)-value of the ADF test, is shown in Table 1. To calculate the ADF test, Python module Statsmodels is used in this research [48]. There was a significant difference between the maximum and minimum values; furthermore, the closing prices are extremely volatile due to the high standard deviation.  
In this proposed framework, the ADF test is used to check the stationary or non-stationary nature of time series data. If the \(\rho\)-value of the ADF test result (as presented in Table 1) was greater than a threshold of 0.05, which fails to reject the null hypothesis, it indicates that the dataset was highly volatile and non-stationary. Apparently, this dataset  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistics Indicators** & **Value** \\ \hline Count & 953 \\ Average & 1006.859 \\ Minimum & 1194.870 \\ Maximum & 680.070 \\ Standard deviation & 107.833 \\ ADF test & 0.469 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the closing price.  
Figure 2: The daily closing price of the stock market in Thailand.  
was suitable to use with the EMD method because it was an effective method for analyzing non-linear and non-stationary time series.  
In addition, this research selected other input features related to the closing price of the stock market, called technical indicators. A total of nine technical indicators are selected. Categories and names are shown in Table 2.
# Luminescence Materials for Dye-Sensitized Solar Cells: Advances and Directions
## 4 Luminescent Materials in DSSCs
### Energy Transfer
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Luminescent Materials** & \(\mathbf{V_{ac}}\)**(mV)** & \(\mathbf{J_{ac}}\)**(mA/cm\({}^{2}\))** & **FF (\%)** & \(\mathbf{\eta}\) (\%)** & **Reference** \\ \hline Sm\({}^{3+}\) & 690 & 16.71 & 46 & 5.31 & [70] \\ GdVO\({}_{4}\):Dy\({}^{3+}\) & 680 & 17.40 & 58 & 6.88 & [71] \\ CeVO\({}_{4}\):Dy\({}^{3+}\) & 710 & 18.18 & 56 & 7.21 & [72] \\ BaSnO\({}_{3}\):Sm\({}^{3+}\) & 810 & 0.62 & 72 & 0.36 & [73] \\ LaVO\({}_{4}\):Dy\({}^{3+}\) & 650 & 9.35 & 64 & 3.8 & [74] \\ Y\({}_{2}\)WO\({}_{6}\):Eu\({}^{3+}\) & 757 & 12.3 & 43 & 3.9 & [75] \\ Y\({}_{3}\)Al\({}_{3}\):Cl\({}_{3}\):Ce\({}^{3+}\) * & 780 & 14.5 & 74 & 8.35 & [76] \\ MgAl\({}_{2}\):Cl\({}_{3}\):Eu\({}^{3+}\) & 642 & 14.8 & 62 & 4.8 & [77] \\ SrAl\({}_{2}\)O\({}_{4}\):Eu\({}^{3+}\) & 777 & 15.68 & 58.78 & 7.16 & [78] \\ (Sr, Ba, Eu)\({}_{2}\)SiO\({}_{4}\) & 830 & 10.23 & 70.6 & 5.97 & [79] \\ Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\), Gd\({}^{3+}\) & 766 & 13.33 & 62.4 & 6.37 & [80] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Photovoltaic parameters of best DSSCs with different types of down-conversion materials.
## 3 Materials and Method
### Machine and Deep Learning Techniques
#### 3.5.2 Logistic Regression  
Logistic regression is a supervised learning algorithm commonly used for binary classification problems [45]. It determines the boundary between classes and calculates the class probabilities based on the distance from this boundary. It is considered a regression model that predicts the class membership of a data item or input using a regression equation [22]. Logistic regression offers several advantages, including simplicity of implementation, computational efficiency, effectiveness in terms of training, and ease of regularization. Unlike some other algorithms, logistic regression does not require scaling of input features [21]. One of the challenges of logistic regression is that it struggles with solving nonlinear problems. It can be overly adaptive to the training data, which may lead to overfitting. Logistic regression utilizes the sigmoid function, also known as the logistic function, for classification purposes. The sigmoid function compresses the output of the regression equation between 0 and 1, representing the probabilities of belonging to a particular class. The mathematical formula for logistic regression, as shown in Equation (3), involves the input data represented by X and the coefficients represented by \(\theta\). The sigmoid activation function used in artificial neural networks (ANNs) is also based on the sigmoid function used in logistic regression.  
\[\sigma(x)=\frac{1}{e^{-(\theta x)}} \tag{3}\]
## 3 Results & Discussions
### Soil Water Characteristic Curves (SWCC)  
The SWCC of wide-gapped topsoil in the Yindongzi landslide site describes the relations between the matric suction and moisture content, and it can be quantified by the data measured by the tensiometer, as well as moisture in exactly the same location. Figure 6 shows that the precipitation  
Figure 4: Daily and accumulative rainfall were recorded by the rain gauge at the Yindongzi landslide site from 21 May 2014 to 22 August 2017. Instruments were installed on 3 May 2014 and data collection began on 19 May 2014.  
Figure 5: Soil moisture content (SMC) recorded by soil moisture sensors (Decagon EC-5) at the Yindongzi landslide site from 21 May 2014 to 22 August 2017.  
and soil suction exhibited different trends during the wetting and drying periods. The topsoil of the natural slope frequently underwent drying and wetting processes. Although instrumentation breakdown occurred after one month of installation, resulting in the loss of matric suction data, some suction data is still available from the period of 5 May to 1 June 2016, when the soil suction underwent notable changes following rainfall events that exceeded about 39 mm in 24 h at its maximum (Figure 6). During this period, the soil moisture content fluctuated rapidly by an additional 0.01-0.08, and matric suction also underwent a homologous change by 2-40 kPa in response to rainfall. Therefore, the SWCC can be established according to the monitoring soil wetness and matric suction.  
Figure 7 shows the estimated SWCC based on the Brooks-Corey model [18]. Table 1 provides the parameters for the unsaturated topsoil in the Yindongzi landslide site for slope stability analysis, the SWCC parameters were estimated by the in situ moisture and suction data, and soil mechanic parameters were determined by triaxial shear test. As shown in this table, the value of \(\alpha\) is high, whereas the values of n are small, especially for the wetting process. The result is similar to a recent study of the SWCC of a wide gapped gravel loamy landslide in the Wenchuan earthquake region [20], which is reasonable for a post-earthquake landslide deposit, because the landslide is composed of gravelly loamy soil, features a smaller air-entry pressure due to the scarcity of fine grains and a broad grain-size distribution. The air-entry pressure (1/\(\alpha\)) was found to be 0.09 kPa.  
Figure 6: Times series of the daily rainfall and matric suction observed by real-time monitoring sensors from 5 May to 23 May 2016 in the top soil of the landslide monitoring site.  
Figure 7: Soil water characteristic curves (SWCC) of the Brooks–Corey model in the top soil of landslide monitoring site over the analyzed period.
## 2 Materials and Methods  
### CSP-Based Methods  
#### 2.1.1 Common Spatial Patterns  
In BCI systems, especially MI-based BCI systems, the CSP is widely used to extract spatial features and achieves good performance. Considering \(\mathbf{X}_{i}\in\mathbb{R}^{T\times C}\) is the EEG signal from class \(i\), \(i=1,2\), where \(T\) and \(C\), respectively, denote the number of time samples and the number of channels, the target spatial filters in the CSP algorithm can be found by maximizing the objective function  
\[J(w)=\frac{\mathbf{w
## 2 Related Work  
In traditional HMM/DNN-based statistical parametric speech synthesis, the model takes full-context features as input [6]. It is obtained from text analysis and contains various linguistic contextual information in addition to phoneme identities. Ref. [7] trains a word language model to obtain linguistic word embeddings and then uses them as part of the input features. In the end-to-end architectures, there are also several prior works trying to solve the problem by adding pretrained linguistic word embeddings in TTS. Ref. [8] obtains word embeddings from a pretrained Chinese to English word translation model and then takes both the phoneme sequence and word sequence into consideration in TTS.  
Recently, the large pretrained language model BERT [9] exhibits an impressive performance on many natural language processing (NLP) tasks, so it is also introduced to TTS [10; 11; 12]. Refs. [10; 11] extract hidden states of BERT and pass them to the TTS model. Ref. [12] tries to fine-tune the BERT parameters with a prosody prediction task but still freezes the word piece embeddings. All these works report that they have achieved some gains in naturalness.  
However, the word embeddings in all the prior works were obtained from pretrained NLP models and thus contain only linguistic information, which is not directly relevant to how the words are pronounced. Therefore, improvement in TTS is often very limited. In this paper, we train acoustic word embeddings directly with the TTS model instead of using linguistic word embeddings.