esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comprehensive Performance under Rotating Condition
Figure 18: The variation of \(Nu_{\text{up}}\)/\(Nu_{0}\) with \(Ro\). To compare the thermal exchange properties of channel in rotating condition, the variation of \(Nu_{\text{all}}/Nu_{0}\) of each case is displayed in Figure 19. With the increase of \(Ro\), it is demonstrated that the \(Nu_{\text{all}}/Nu_{0}\) increases first and then decreases. The maximal value is located near \(Ro\) = 0.3. For case2, when \(Ro\) = 0-0.4, the value of \(Nu_{\text{all}}/Nu_{0}\) is lower than that in case1. This is mainly due to the amelioration of the flow uniformity in pass2 and pass3 after arrangement of the turning vane. The reduction of disturbance makes the boundary layer develop rapidly, which greatly inhibits the heat exchange between the channel surface and the fluid in this area. Although the heat exchange is strengthened in the turning region and the tip wall, the \(Nu_{\text{all}}/Nu_{0}\) decreases. When \(Ro\) = 0.5, the interchange of heat in the turning region and the tip wall is obviously enhanced, which makes up for the loss of heat transmission in pass2 and pass3. Therefore, the \(Nu_{\text{all}}/Nu_{0}\) of case2 is improved compared with case1. For case3-6, in the range of \(Ro\) = 0-0.5, the \(Nu_{\text{all}}/Nu_{0}\) is improved compared with case1. The thicker of the turning vane, the more obvious the improvement. When \(Ro\) = 0.1, \(Nu_{\text{all}}/Nu_{0}\) of case6 is 4.0% higher than case1. It is related to the fact that the disturbance in the turning region is enhanced under the effect of a thicker turning vane, which greatly enhances the heat exchange in this zone.
# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 3 Proposed Method
### ConvLSTM  
As the input, cell output, and states of LSTM or, more particularly, Fully-Connected LSTM (FC-LSTM) are all 1-D vectors, they cannot preserve the spatial relationship between pixels in images and videos. Therefore, LSTM is not suitable for spatial sequence data [57,61]. The very first application of ConvLSTM was in forecast prediction using radar images [57]. However, in recent years, it has been applied to other applications which  
Figure 2: ResNet50 structure.  
are mainly focused on prediction in computer vision tasks like slip detection [62]. In the ConvLSTM approach, because of the convolutional layers, all the inputs, cell outputs, states, and the spatial dimensions in the last two dimensions (rows and columns), are 3-D tensors. Therefore, as ConvLSTM has convolutional gates in its structure, it can provide the model with spatial and temporal changes. The better performance of ConvLSTM in comparison with regular LSTM was proved by several experiments in [57]. Figure 3 shows ConvLSTM structure in details. Where \(X\) is the inputs and \(C\) is cell output. Hidden states shown by \(h\), and \(i_{t},f_{t},o_{t}\) are the gates in the ConvLSTM structure. As a result, it is clear that for this paper's goal, to find abnormal events dependent on spatial and temporal features, the best option is to use ConvLSTM to gain a more efficient result.  
From a complexity standpoint, as LSTM works with 1-d vectors and so after the Hadamard product, it makes too many parameters that increase the complexity of the model and enhance the chance of occurring overfitting. As a result, due to the advantages of ConvLSTM unique structure, it needs fewer parameters, reducing the model's complexity and preserving the spatial relationships, which are reasonably fit for images and videos.  
Each frame after passing from ResNet enters to a ConvLSTM cell including 256 hidden state (filters) with the kernel size \(=(3\times 3)\). The input for our ConvLSTM is a 4D tensor, (n, filter size = 256, row = 4, column = 4) so that the input of each time step is a \(4\times 4\) image with 256 channels.
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 2 Materials
### JEZ-1 Mars Regolith Simulant
Like other Martian soil simulants [4], the JEZ-1 represents an unconsolidated material as well, however, a more cohesive state can be obtained by compacting with various pressures.
## Materials and Methods
### In Situ Investigations  
Theoretical foundations of the electrical resistivity method, widely used for the last 50 years, are well known [2; 5; 38]. In practice, the soil electrical resistivity is measured using a four-electrode configuration. In this method, an electrical current is supplied to the soil using two electrodes, inducing an electric field, whereas the other two measure its potential. Apparent electrical resistivity of the soil may be determined based on the known difference between the electric field potential (\(\Delta\)V) and the current intensity (I) and the distance between the electrodes [38]. Apparent resistivity is expressed by the equation:  
\[\rho_{\text{b}}=\text{k}\frac{\Delta\text{V}}{\text{I}}, \tag{3}\]  
where \(\rho_{\text{b}}\) is the apparent resistivity [\(\Omega\)m], I is the current applied to the soil by electrodes C1 and C2 [mA], V is the potential difference between electrodes P1 and P2 [mV], and k is the geometrical coefficient of electrode positioning [m].  
Resistivity does not reflect properties of one particular soil type, but presents the capability for electrical conductivity of a subsoil located in the vicinity of a spatial electric field. Such resistivity is called apparent resistivity. The geoelectrical survey is based on the measurement of apparent resistivity accompanied with symmetrical rise of the distance between charged electrodes (current electrodes, C1 and C2). A wider distance between the electrodes results in deeper electric field penetration into the subsoil, which significantly influences the survey depth. For the purpose of the present study, electrical resistivity tomography (ERT) in the Wenner array was applied (Figure 3). A detailed description of this array and other complex configurations was published by [PERSON] et al. [5], [PERSON] [38], [PERSON] et al. [40], and [PERSON] et al. [41].  
Figure 2: Grain-size distribution curves for soil samples taken directly from the test sites and assessed in the laboratory.  
The RCPT (RCPTU) cone (Figure 4) used to assess soil porosity, is a combination of a static CPTU (cone penetration test) probe with soil resistivity measurements. The probe consists of two main elements: first for measuring parameters determined in a standard CPTU test, and the second being an electrical resistivity measuring module. It includes two or four annular electrodes (at a spacing of 5 cm) with insulating plastic elements and an alternating current flowing between the electrodes.
## 3 Methodology
### Data Processing and Dataset Selection  
An NDVI less than 0 and GPP (LAI) greater than 30,000 (248) are removed to eliminate the effects of ice- and snow-covered areas, water bodies, buildings, and other features. (The thresholds are based on the Product User's Guide provided by NDVI, GPP, and LAI data source website, [[http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov)]([http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov))).  
To uniformize the temporal resolution, all datasets are averaged to 16 days. Because the spatial resolution of precipitation is different from other auxiliary data, the precipitation product with a spatial resolution of 25 km is resampled to 500 m by the nearest neighbor interpolation method based on the assumption that the precipitation is the same within a certain range. For each GPS site, the auxiliary variable value corresponding to the longitude and latitude of NMRI is extracted from the image. The data pairs of NMRI, and the auxiliary variables of 329 sites for 10 years, are obtained.  
The approach of auxiliary datasets selection is based on correlation analysis. Firstly, for every vegetation type, a long time series variation between NMRI and auxiliary variables over the ten years from 2007 to 2016 are analyzed to verify the covariance between them. Then, for each site, the Pearson correlation coefficient (R) between each auxiliary variable and NMRI is calculated. Meanwhile, the R  
Figure 2: Flowchart of the point–surface fusion model used in this study.  
among the auxiliary variable for all the 329 sites is counted to eliminate the redundancy of datasets. Finally, the dataset is selected based on the following main requirements: (1) physical and chemical significance for the change of VWC. (2) a strong correlation with NMRI; (3) reduced data redundancy. The detailed discussion of the correlation analysis can be found in Section 4.1.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 5 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Loss** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** & **COVL** & **CSIG** \\ \hline \(\mathcal{L}_{clRM,Huber}\) & 2.900 & **3.620** & **0.946** & **18.811** & 3.493 & 4.078 \\ \hline \(\mathcal{L}_{TAP}\) & 2.746 & 3.478 & 0.939 & 17.338 & 3.442 & 4.127 \\ \hline \(\mathcal{L}_{TAP}\) & 2.746 & 3.478 & 0.939 & 17.338 & 3.442 & 4.127 \\ \hline \(\mathcal{L}_{cIRM,Huber}\) & 2.958 & **3.622** & **0.946** & 18.279 & **3.587** & **4.196** \\ \hline \(\mathcal{L}_{cIRM,MSE}\) & \(\lambda_{1}=0.025\) & 2.915 & 3.591 & **0.946** & 18.307 & 3.564 & 4.193 \\ \hline \(+\lambda_{1}\mathcal{L}_{TAP}\) & \(\lambda_{1}=0.03\) & 2.913 & 3.605 & **0.946** & 18.407 & 3.562 & 4.191 \\ \hline \(\lambda_{1}=0.04\) & 2.923 & 3.587 & 0.945 & 18.236 & 3.557 & 4.172 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The various SE performance scores of Inter-SubNet trained with different loss functions: \(\mathcal{L}_{clRM,Huber}\) as in Equation (6), \(\mathcal{L}_{TAP}\) as in Equation (11), and \(\mathcal{L}_{cIRM,Huber}+\lambda_{2}\mathcal{L}_{TAP}\) as in Equation (15) with several assignments of \(\lambda_{2}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Loss** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** & **COVL** & **CSIG** \\ \hline \(\mathcal{L}_{clRM,Huber}+\lambda_{2}\mathcal{L}_{TAP}\) & **2.955** & 3.609 & **0.946** & 18.287 & **3.584** & **4.197** \\ \hline \(\mathcal{L}_{cIRM,Huber}\) & 2.900 & **3.620** & **0.946** & **18.811** & 3.493 & 4.078 \\ \hline \(\mathcal{L}_{cIRM,Huber}\) & 2.900 & **3.620** & **0.946** & 18.287 & **3.584** & 4.197 \\ \hline \(+\lambda_{2}\mathcal{L}_{TAP}\) & 2.934 & 3.614 & **0.946** & 18.146 & 3.579 & **4.201** \\ \hline \(\lambda_{2}=0.03\) & 2.888 & 3.582 & **0.946** & 17.865 & 3.542 & 4.179 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ablation study, summarizing the influence of Huber loss and TAP loss on the performance of Inter-SubNet. enhancement techniques that rely on GAN- or attention-based mechanisms. Given its impressive performance and compact model size, we believe that the revised Inter-SubNet is highly suitable for real-world applications. Finally, Table 6 details the memory usage, training time, inference time, and the number of model parameters for Inter-SubNet trained with various loss functions. The experiments were conducted using the PyTorch programming language on a desktop computer featuring an AMD Ryzen 5 3550X 6-Core Processor and an NVIDIA GP102 GPU. The data indicate that modifying the loss function primarily influences the training time of Inter-SubNet and the model complexity when considering the TAP estimator in the count of model parameters. However, the choice of loss function does not affect the inference time of Inter-SubNet; thus, all versions presented here demonstrate very similar inference time.
## 1 Introduction
[15] considered the Faiveley CX-PG pantograph as the research object and simulated the turbulent flow characteristics and far-field radiated noise around it by LES and FW-H equation to explore the connection between the vortex structure near the pantograph and the acoustic performance. The authors found that the sound source intensity distribution of the pantograph is closely associated with the position of vortex shedding, and whether the pantograph is immersed in the wake flow of the vortex. [PERSON] et al. [16] applied LES and the acoustic finite element method (FEM) to analyze the acoustic spectrum characteristics and aerodynamic noise distribution in the near-field and far-field regions of pantographs with different installation bases: Flush and sunken. It was found that the sunken pantograph installation platform has better aerodynamic noise performance. [PERSON] et al. [17] studied the influence of cavity or sinking platform on pantograph aerodynamic noise, and compared the aerodynamic performance of the pantographs between the pantographs installed in recesses and on the roof alone. The flow field of the pantograph was simulated by improved delayed detached-eddy simulation (IDDES) and the far-field noise was computed by the FW-H analogy. In addition, it was confirmed that the cavity could reduce the pressure fluctuating on the surface of the pantograph, which in turn reduced its noise. [PERSON] et al. [18] predicted the near-field and far-field noise of the pantograph by LES, acoustic perturbation equation (APE), and FW-H equation. They divided the vortex structure behind the pantograph into the panhead area, the middle area, and the groove area. Moreover, they stated that only the dipole source was regarded as the source of far-field radiation noise since the intensity of the quadrupole source was substantially less. The authors of [19] studied the aerodynamic performance of two new pantograph forms by IDDES and FEM, and found that the pantograph with smoother insulators had no prominent effect in terms of noise, while the pantograph with smoother base frame had lower drag and noise. [PERSON] et al. [20] compared different cross-sectional panheads and showed that at 250 km/h, the elliptical cross-sectional panhead could reduce the sound pressure level (SPL) of the pantograph by over 20%. The above numerical studies for pantograph noise reduction can be divided into changing the installation type or shape of pantograph [21; 22; 23]. Moreover, the variation of aerodynamic drag under the new type of pantograph is rarely considered at the same time. Furthermore, changing the rod type of the pantograph may have an impact on its receiving current. Studies have shown that the pantograph cover becomes a feasible solution to achieve the drag reduction requirements at higher speeds. However, its effect on aerodynamic noise is still unclear and remains to be specified, and this is the purpose of this paper.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
###### Abstract  
Terahertz reconfigurable reflectarray antennas (RRAs) hold significant promise for next-generation wireless communication systems by enabling dynamic beam control to mitigate severe path loss at high frequencies. This work presents a Complementary Metal-Oxide-Semiconductor (CMOS)-based RRA for terahertz amplitude control using tunable split-ring resonators. First, a terahertz switch in standard 65 nm CMOS process is designed, tested, and calibrated on the chip to extract the equivalent impedance, enabling precise RRA element design. Next, a reconfigurable element architecture is presented through the co-design of a split-ring radiator, control line, and a single switch. Experimental characterization demonstrates that the fabricated RRA achieves 3 dB amplitude variation at 0.290 THz with \(<\)8.5 dB element loss under 0.8 V gate bias. The measured results validate that the proposed single-switch topology effectively balances reconfigurability and loss performance in the terahertz regime. The demonstrated CMOS-compatible RRA provides a scalable solution for real-time beamforming in terahertz communication systems.  
t +
Footnote †: journal: Applied sciences  
0000-0002-0002-389-2231][PERSON]  
0000-0002-3899-2231][PERSON]  
0000-0002-3899-2318][PERSON]  
0000-0002-3899-2318][PERSON]  
0000-0002-3899-2318][PERSON]  
0000-0002-3899-2318][PERSON]  
teahertz; reconfigurable reflectarray; CMOS
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 3 Test and Discussion
### Calibration Test of Distinction Threshold Between Kernels and Impurities  
In order to determine the distinction threshold between kernels and impurities in the lost material, a bench test at different impact speeds was designed under the actual operation conditions of a harvester. The impact speed was determined according to the speed of the material blowing onto the inductive plate at the different fan speeds of the rapeseed harvester cleaning system. The fan speed of the rapeseed harvester cleaning system ranged from 700 to 1200 r/min. Firstly, the speed of the blowing material hitting the inductive plate at different fan speeds was determined through a high-speed photography test. Due to the installation of the inductive plate at a height of 0.2 m below the outlet at the tail of the cleaning screen, high-speed camera analysis software was used to calculate the velocity of the material at a height of 0.2 m below the outlet. The test results are shown in Table 2. It can be seen that when the fan speed of the rapeseed harvester cleaning system was between 700-1200 r/min, the speed of the material impacting the inductive plate was 2.503-3.980 m/s, and the fan speed was linearly positively correlated with the speed of the material impacting the inductive plate. Four groups of bench tests with different impact speeds were designed. Each group used 25 carob shells, 50 kernels and 25 short stems to impact the inductive plate. The integral voltage values generated by different materials impacting on the inductive plate were recorded, and scatter plots corresponding to the different integral voltage values of the three types of materials were drawn to show the distribution law of the three types of signals. The test results are shown in Figure 12.  
Figure 11: Bench calibration test: 1. Conveyor belt. 2. Cleaning materials. 3. Support frame. 4. Inductive plate. 5. Signal processing module. 6. Detection and counting module. 7. Display module. 8. System power supply.  
seen from the table, the distinction accuracy of different impact velocities was generally good, above 90%, and the highest distinction accuracy was 93.9%.  
According to the test results in Tables 2 and 3, the relationship curve between the fan speed and distinction thresholds C1 and C2 was plotted, as shown in Figure 13. By fitting the main data in the relationship curve, the formula of the optimal distinction threshold of the cleaning system for a rapeseed harvester under each fan speed could be obtained. The fan speed followed a linear relationship with C1 and C2; the R\({}_{2}\) values of the models were 0.90620 and 0.99439, respectively. According to the formula derived from the fitting model, the corresponding thresholds C1 and C2 can be obtained by substituting any value of the fan speed in the cleaning system for a rapeseed harvester.  
\[y1 =0.00049x+0.52760 \tag{4}\] \[y2 =0.00163x+1.56280 \tag{5}\]
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 4 Underwater Acoustic Channel Model  
The SMTs have been applied for the UWA channel in [15; 39] considering the full channel information at the receiver side. Unfortunately, the UWA channel is hard to adequate estimate; in this section we will address the UWA features. The impulse response of the time-varying multipath UWA channel can be determined as [30; 40; 41]:  
\[\mathbf{H}(t,\tau)=\sum_{c}A_{c}(t)\delta(\tau-\tau_{c}(t)), \tag{18}\]  
where \(\tau_{c}(t)\) stands for the delay of the time-varying path and \(A_{c}(t))\) is the path amplitude. The given channel comprises several essential pathways; every pathway is scrutinized by time-varying pathway amplitude and delay information. We further assume the below-mentioned conditions for simplifying the receiver processing algorithms and developing while maintaining generality. The delays in time-varying channels are sparked by the movement of the transmitter as well as receiver along with scattering from the sea surface or refraction caused by sound-speed variability. The path amplitude varies with the delay as the attenuation is attributed to the traveled distance and the physics associated with the scattering phenomenon. Assuming the path amplitude \(A_{c}(t))\) remains consistent during the transmission of the same block:  
\[A_{c}(t)=A_{c}, \tag{19}\]  
and during the data symbol transmission all paths delays \(\tau_{c}(t)\) have a similar Doppler-scaling factor as:  
\[\tau_{c}(t)=\tau_{c}-\beta t, \tag{20}\]  
where \(\beta\) is the Doppler-scaling factor. Generally, each path has its own Doppler-scaling factor, but herein, we have considered the similar Doppler-scaling factor for each path. Moreover, we recommend the approximation of the underwater acoustic channel by \(\rho\) dominant discrete paths, so the UWA channel can be presented as:  
\[\mathbf{H}(t,\tau)=\sum_{c}^{\rho}A_{c}(t)\delta(\tau-(\tau_{c}-\beta t)). \tag{21}\]
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 2 Proton RFQ Accelerator Design
### Design Strategy of RFQ Beam Dynamics
#### 2.1.3 Output Energy  
The RFQ output energy or transition energy to the post Alvarez DTL is primarily determined by the mechanical length of a permanent magnet quadrupole (PMQ). The typical mechanical lengths of PMQs used in the 402.5 MHz SNS DTL [39] and 352.2 MHz CERN L4 DTL [40] are 35 mm and 40 mm, and the corresponding cell lengths (\(\beta\lambda\)) in the first cells are around 54 mm and 68 mm. Thus, for a 325 MHz DTL design with the first cell length of around 73 mm, the RFQ output energy should be no less than 3.0 MeV, for decreasing the difficulty of housing a PMQ. In contrast, for a 162.5 MHz DTL, the first cell length will be about 147 mm at an output energy of 3 MeV. There is no doubt that a PMQ can be easily housed inside the first drift tube. However, to choose a lower output energy of 2.5 MeV will leave open the possibility for BNCT research with a single RFQ accelerator-based neutron source via the proton-lithium reaction.