esa-sceva/satcom-chunk-collection huggingface dataset:
# Kriging Interpolation in Modelling Tropospheric Wet Delay
## 2 Kriging Interpolation in Modelling tropospheric Wet Delay
\begin{array}{c}\sum_{i}w_{i}\cdot C\big{(}h_{ij}\big{)}-C\big{(}h_{j0}\big{)} =0\\ \sum_{i}w_{i}=1\end{array}  \tag{3}\]  
where \(h_{ji}\) denotes the mutual distance between the all known point \(j\) and \(i\) and \(h_{j0}\) indicates the distance between \(j\) and unknown point; \(C\big{(}h_{ji}\big{)}\) is the data-to-data covariance function while \(C\big{(}h_{j0}\big{)}\) is the data-to-estimation point covariance function. A variogram function \(\gamma_{ji}\) can be deduced from the covariance function by the formula:  
\[\gamma_{ji}=C(0)-C\big{(}h_{ji}\big{)} \tag{4}\]  
It is known that the weight \(w_{i}\) of the Kriging interpolation depends not only on the distances of measured points and prediction locations, but also on the spatial variations of all measured points. Therefore, the spatial correlations must be quantified by a covariance function on the reference stations of the network. Such a function to describe the spatial relationships among the measured values and the prediction location is called the theoretical variogram model. In this study, a Gaussian model is used as the variogram model [38]:  
\[C\big{(}h_{ji}\big{)}=b\cdot exp\Bigg{(}-\frac{h_{ji}^{2}}{a^{2}}\Bigg{)} \tag{5}\]  
where the initial parameter \(b=0.001\) is given for a very short distance; initial parameter \(\mathrm{a}=1000\) km is given to govern the range of the covariance function. Note that the Newton-Raphson method is used to determine the optimum values of these parameters by carrying out a least-squares solution [39]. The distance between two locations \((\lambda_{i},\phi_{i})\) and \((\lambda_{j},\phi_{j})\) is determined by the Euclidean distance:  
\[h_{ij}=\sqrt{\big{(}\lambda_{i}-\lambda_{j}\big{)}^{2}+\big{(}\phi_{i}-\phi_{ j}\big{)}^{2}} \tag{6}\]  
where the horizontal distances are calculated because the stations are almost on a flat surface. Since this is a regional area, the spheroidal distances are not considered for the simplification of the computation. After the weights \(w_{i}\) are calculated, the unknown variable can be obtained in Equation (2), and its variance is given by  
\[var(z_{0})=C(0)-\sum_{i}w_{i}\cdot C(h_{0i}) \tag{7}\]
# Seismic Response Effect on Base-Isolated Rigid Structures by Mass Eccentricity in Nuclear Plants
###### Abstract  
The purpose of this paper is to analyze the seismic response effect caused by the mass eccentricity of individual equipment when conducting base isolation for the improvement of the seismic performance of a nuclear power plant. Recent research has interpreted and confirmed through analysis and testing that base isolation for safety-related equipment in nuclear power plants is an efficient alternative to designing for excessive seismic loads. Depending on the equipment, unavoidable mass eccentricity can occur, which necessitates verification of the response impact caused by eccentricity. In this paper, we analyze the seismic response impact of equipment with mass eccentricity using small base isolators. To do so, sensitivity analysis of the seismic response due to mass eccentricity is conducted for a base-isolated concentrated mass model. Furthermore, three efficient mass eccentricity models suitable for testing are designed and manufactured. Simulation analyses using the finite element method (FEM) models are performed, followed by three-axis shake table tests to validate the seismic response impact of mass eccentricity. In conclusion, it is confirmed that applying small base isolators to equipment with mass eccentricity can affect seismic response impact to some extent when compared for a beyond-design-basis earthquake (BDBE).  
Keywords:mass eccentricity; seismic response; base isolation; beyond-design-basis earthquake; LRB; nuclear power plant; equipment; seismic analysis; sensitivity analysis +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Model Results, Comparison and Discussion
#### 3.5.3 Discussion of Numerical Model Design
When comparing the models, a reduction of maximum stress of 3% and a reduction of the maximum total displacement of 8% was observed. Despite this large change in displacement, there was little effect on the strain profile in terms of shape and magnitude. Figure 18: Equivalent (von Mises) stresses (MPa) for (**A**) the 13 k mesh and (**B**) the 236 k mesh 2 rib model. In the stage 1 models, the longitudinal ribs were left unaltered but in the subsequent stages the longitudinal rib was grooved to better match the laboratory specimen. These grooves were made slightly deeper than the actual specimen's so that they were flush with the rest of the geometry to avoid thin geometries. To understand the effect of the inclusion or omission of the rib, model 2.1 was run both with and without the grooves. A large difference (-9%) around the front was noted but this difference converged further along with the rebar. This comparison confirmed the decision to include the grooves as it does affect the strain profile. Materials were generally taken from either experimental collected values or literature values for the given materials. The only material modification was a reduction in grout stiffness. A ratio from the curve fitting efforts was utilized [18; 71]. To understand the impact of this factor a model was run with both unmodified and adjusted values. In line with expected behaviour, strain attenuated quicker. This led to the model being even more over predictive when compared to the laboratory result. This directly leads to the largest limitation and source of error for this iteration of the model. The grout was modelled as linear elastic; however, it is clear from the laboratory results that cracking occurs and these should be included in subsequent models. This would result in greater displacement, with a plastic softening effect with increased loading. Future efforts could also include full plasticity representation, sensitivity analysis for friction values taken from literature as well as other material properties to understand their effects on results and the use of an axisymmetric model could be explored.
## 2 Materials and Methods
### Biophysical RBE Models
#### 2.6.1 Theory of Dual Radiation Action (TDRA)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Cell Lines** \\ **(\# of Data Points)** \\ \end{tabular} } & \multirow{2}{*}{**RBE\({}_{\text{LD}}\)**} & \multirow{2}{*}{**RBE\({}_{\text{HD}}\)**} & \multicolumn{4}{c}{**Parameter Values**} \\ \cline{3-8}  & & & & & \(\mathbf{p_{0}}\) & \(\mathbf{p_{1}}\) & \(\mathbf{p_{2}}\) & \(\mathbf{p_{3}}\) \\ \hline [PERSON] [57] & \begin{tabular}{c} V79 \\ (44) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & \(p_{2}+p_{3}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & 0.843 & 0.413644 & 1.09 & 0.01612 \\ \hline [PERSON] [58] & \begin{tabular}{c} V79 \\ (14) \\ \end{tabular} & \begin{tabular}{c} \(\frac{p_{0}}{\pi_{k}}+\frac{1-\text{e}^{-\gamma_{\text{D}}\text{LET}_{\text{D}} ^{2}}}{4p_{2}\text{LET}_{\text{D}}}\) \\ \end{tabular} & 1 & 0.1 & 0.0013 & 0.045 & - \\ \hline [PERSON] [59] & \begin{tabular}{c} Multiple \\ (28) \\ \end{tabular} & \(1+\frac{\text{LET}_{\text{D}}-0.22}{3028}\times\left(\frac{\overline{s}_{k}}{ \overline{s}_{k}}-1\right)\) & \(\sqrt{1+\frac{\text{LET}_{\text{D}}-0.22}{3028}\left(\frac{\overline{s}_{k}}{ \overline{s}_{k}}-1\right)}\) & \begin{tabular}{c} \(\alpha_{u}=2.696\,\left(1-\text{e}^{-3.392\overline{s}_{k}}\right)\) \\ \(\beta_{u}=0.06\,\left(1-\text{e}^{-50\overline{s}_{k}}\right)\) \\ \end{tabular} & \\ \hline [PERSON] [60] & \begin{tabular}{c} Multiple \\ (285) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & \(p_{2}+p_{3}\sqrt{\left(\frac{\overline{s}}{\overline{s}}\right)_{k}}\text{LET}_{ \text{D}}\) & 0.99064 & 0.35605 & 1.1012 & \(-\)0.0039 \\ \hline [PERSON] [54] & \begin{tabular}{c} Multiple \\ (137/48) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & 1 & 1 & 15.5 & - & - \\ \hline [PERSON] [61] & \begin{tabular}{c} Multiple \\ (11) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & 1 & 1 & 0.309 or & - & - \\ \hline [PERSON] [62] & \begin{tabular}{c} Multiple \\ (85) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & 1 & 1 & 0.645 & - & - \\ \hline [PERSON] [63] & \begin{tabular}{c} V79 \\ (19) \\ \end{tabular} & \
\begin{tabular}{c} \(\frac{p_{0}+p_{1}\text{LET}_{\text{D}}}{2\text{k}}\) \\ \end{tabular} & 1 & 0.1 & 0.02 & - & - \\ \hline [PERSON] [64] &
\begin{tabular}{c} Multiple \\ (19) \\ \end{tabular} & \(p_{0}+p_{1}\frac{\text{LET}_{\text{D}}}{\left(\frac{\overline{s}}{\overline{s}} \right)_{k}}\) & 1 & 1 & 0.434 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of empirical models examined in the present work (alphabetic order). Adapted from [14].
# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
###### Abstract  
Road traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near the road will record the noise while receiving the seismic signal. The amplitude of the traffic noise is much larger than the signal, which masks the effective information and degrades the quality of acquired data. At the same time, the traffic noise is coupled with the effective signal, which makes it difficult to separate them. Therefore, attenuating traffic noise is the key to improving the quality of the final processing results. In recent years, denoising methods based on convolution neural networks (CNN) have shown good performance in noise attenuation. These denoising methods can learn the potential characteristics of acquired data, thus establishing the mapping relationship between the original data and the effective signal or noise. Here, we introduce a method combining UNet networks with asymmetric convolution blocks (ACBs) for traffic noise attenuation, and the network is called the ACB-UNet. The ACB-UNet is a supervised deep learning method, which can obtain the distribution characteristics of noise and effective signal through learning the training data and then effectively separate the two to achieve noise removal. To validate the performance of the proposed method, we apply it to synthetic and real data. The data tests show that the ACB-UNet can obtain good results for high amplitude noise attenuation and is practical and efficient.  
road traffic noise; high amplitude; UNet; asymmetric convolution blocks +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data. _Appl. Sci._**2023**, 13, 4751. [[https://doi.org/10.3309/app1308751](https://doi.org/10.3309/app1308751)]([https://doi.org/10.3309/app1308751](https://doi.org/10.3309/app1308751))
## 4 Results and Discussion  
This section begins with an overview of the datasets [7] used in the simulation, followed by a discussion of the performance of our suggested approach under the influence of various noise distributions. However, in this study the training and testing process are performed on Python 3.9.13, using Theano/Keras based on Tensorflow 2.4.1 with GPU RTX2070, 2560 CUDA cores, and 8 GB GDDR6 VRAM on top of Intel(R) Core(TM) i5-9500 CPU @ 3.00 GHz (6 cores) processor and 16 GB of RAM. To tune the network, all models are trained in an end-to-end manner using the Adam optimizer algorithm. The learning rate in our experiment is set to \(1\times 10^{-4}\). The presented system is trained for 100 epochs, with 512 input sample batch sizes.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Square Coaxial Line
#### 2.2.3 Multipactor Simulation  
Figure 5 shows the square coaxial line's multipactor [22] simulation model. The initial particle cloud is located in the middle of the square coaxial line, the initial number of particles is 1000, the simulation time is 30 ns, and the center frequency is 4 GHz. The square coaxial line multipactor threshold simulation result is about 9500 W in Figure 6.  
When other conditions are guaranteed not to change, the frequency offset will also change the multipactor threshold power. The threshold power is related to \(f\times d\), where \(f\) is the frequency and \(d\) is the distance between two metals.  
Figure 4: Simulation results of square coaxial line S-parameter.  
Figure 5: Simulation model of square coaxial line multipactor.  
Figure 6: Simulation results of square coaxial line multipactor threshold.
## 2 Models
### Machine Learning
#### 2.2.1 Non Deep Learning  
kNN  
K-nearest-neighbors (kNN) is a training-free method that makes predictions by averaging observations with features closest to the input sample. The method of k-nearest neighbors is conceptually simple and explainable. They do not make assumptions about the data and work well with non-linear relationships, often producing accurate predictions. However, the method becomes unfeasible with large data sets or numerous features. kNNs are unable to extrapolate beyond the range of the training data and are sensitive to noisy and irrelevant features. Another limitation is sensitivity to the number of neighbors k to be compared with, and the chosen neighbor distance metric [53].  
SVM  
Support vector machines (SVMs) work by detecting a hyperplane in a higher dimensional space with minimal distance to the fitted observations [54]. SVMs can solve linear and non-linear problems due to the 'kernel trick', implicitly mapping their inputs into high-dimensional feature spaces and then using simple linear functions to create linear decision boundaries in the new space. SVMs have become a common energy price forecasting method due to a variety of strengths, such as good approximating accuracy and generalization ability to unseen data, superior performance for small-scale training data, tolerance to redundant and highly interdependent features, as well as the capacity mentioned above to solve both linear and non-linear problems. The main challenges associated with SVM models are the computational costs of training, selection of a kernel function and parameters, sensitivity to noise and missing values, overfitting, and lack of explainability [10; 42; 44; 46].  
Decision Trees, Random Forests and Gradient Boosted TreesOther popular methods are decision trees, random forests (making predictions by averaging a set of decorrelated trees built in parallel [55]), and gradient-boosted trees (which build an ensemble of trees iteratively by fitting a new tree on the residuals of the previous tree [56]). Decision trees are fast and interpretable: by retrieving the decision path for a given sample, one can see which feature values are used as criteria for the prediction. They can combine numerical and categorical features and capture non-linear relationships between features and the dependent variables. Trees are invariant under monotone transformations of individual features, robust concerning overfitting, and tolerant to outliers and missing values. Since feature selection implicitly occurs during training, decision trees are insensitive to irrelevant or interdependent features [53; 55]. A relatively low accuracy limits them. The low accuracy, however, is alleviated by ensembling methods, for instance, random forests or gradient-boosted trees, which help increase prediction accuracy while maintaining all the benefits of decision trees, except for the loss of interpretability [46].
## 5 Experimental Validation of the Designed SHM System  
The monitored structure is the main load-bearing structure of a retractable propulsion system for a sailplane, as depicted in Figure 11. It is a cylinder-like structure which is co-cured from two pieces of shell structures. The thickness of the shell structures is \(\sim\)2 mm. The structure is made from carbon fiber-reinforced plastic and glass fiber-reinforced plastic. Eight encapsulated piezoelectric transducers called PZT layers [33] were adhered to the inner surface of the structure during the manufacturing process, as illustrated in Figure 12. The PIC255 material (Physik Instrumente (PI) GmbH & Co. KG, Karlsruhe, Germany) was selected as the material of the piezoelectric sensor, and some parameters are shown in the Table 3. The diameter of the PZT ceramic is 8 mm and the thickness is 0.12 mm. The test for the proposed SHM system is illustrated in Figure 13. Several experiments, including impact amplitude and location detection test and damage detection using EMI methods, are conducted on this subject.  
Figure 11: The retractable structure of a sailplane.  
The aim of this section is to apply impact detection and damage detection using EMI method on the structure shown in Figure 11 to verify the effectiveness of the system. For the system accuracy and other system performance parameters please refer to Table 2.
## 4 Conclusions  
In this study, we proposed a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm in IRS-assisted networks, which used APEM as fingerprint data and exploited the high spatial resolution of MIMO technology. The LMaFit matrix reconstruction algorithm was used to reconstruct the fingerprint database to alleviate the large workload introduced by the full set of measurement data required to build a fingerprint database and reduce the workload of fingerprint acquisition. The similarity of fingerprints was determined using the Euclidean distance, and the coordinates of the users to be located were estimated by combining maximal ratio combining and WKNN algorithms. Finally, it was verified through simulation that the proposed method can reduce the fingerprint database construction workload by 40%, and it has an 80% probability of obtaining sub-meter localization accuracy when the sampling interval is less than 2.5 m.  
**Author Contributions:** Conceptualization, S.D. and Y.Z.; methodology, S.D. and Y.Z.; validation, S.D.; formal analysis, S.D.; investigation, S.D.; writing--original draft preparation, S.D. and Y.Z. and R.L.; supervision, Y.Z. and R.L.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported, in part, by the Sub Project of National Key Research and Development plan in 2020. NO. 2020 YFC1511704, Beijing Information Science & Technology University NO. 2020 KYNH212, NO. 2021 CGZH302, Beijing Science and Technology Project (Grant No. Z211100004421009), and, in part, by the National Natural Science Foundation of China (Grant No. 61971048).  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company ZTE Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Algorithm Improvement
### SPP Spatial Pooling Pyramid Structure  
The SPP spatial pyramid pooling diagram is shown in Figure 6. In Figure 6, three different division methods are performed with image pooling processing by the SPP structure. The input of the SPP structure is an arbitrary size feature graph obtained by upper convolutions. Firstly, the SPP structure carries out the feature map with the sizes of 1 \(\times\) 1, 2 \(\times\) 2, and 4 \(\times\) 4. Then, three different partition results are spliced. The inputs of different scales are finally normalized output to the same scale. By using different image division methods, different scales of the information can be captured. To improve the robustness, the spatial feature information of different sizes can be extracted by adding the SPP spatial pooling pyramid structure in the model [27].  
The YOLOv4-gesture model is improved based on the YOLOv4 model. In the YOLOv4-gesture model, the SPP spatial pyramid pooling module is added after the output of the last layer of CSPDarknet. After a convolution layer processing, the output of the last layer of CSPDarknet is processed with three different sizes of maximum pooling. The three kernel sizes are 5 \(\times\) 5, 9 \(\times\) 9, 13 \(\times\) 13, respectively. The above three outputs are spliced with the original output through a connection processing. The model sensibility field can be enlarged by the maximum pooling layer while the feature map is unchanged. The three maximum pooling processes with different sizes in the SPP model can not only obtain the local receptive field of the feature map, but also obtain the near global receptive field information. The expression ability of feature map can be  
Figure 5: Schematic diagram of adding the CBAM attention module to the YOLOv4 network structure.  
Figure 6: SPP spatial pyramid pooling diagram.  
scale receptive field effectively. The detection performance can be improved by effectively separate important information under the SPP model. Therefore, to enhance the expression ability of feature map to receptive fields at different scales, the SPP model is added after the other two sampling ports of the backbone network. The target features of local regions and the target feature information under the global feature map can be enriched. The detection effect of the YOLOv4 model can be improved while the accuracy of target positioning and classification is increased. The schematic diagram of adding the SPP pyramid pool structure to YOLOv4 network structure is shown in Figure 7.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### Gfdm-OQAM Transceiver  
GFDM is considered to be the most promising candidate for the air interface of 5G networks [142; 143]. One of the most distinguishing characteristics of this system is the introduction of flexible pulse shaping across each individual sub-carrier [143]. The filtering of these sub-carriers by well-designed prototype filters reduces the harmful effects of ICI while providing robustness to the system against synchronisation errors [144; 145; 146].  
GFDM introduces the CP extension properties of OFDM to combat ICI as well as well-designed pulse shaping [147; 148]. However, the modulation of each sub-carrier by non-rectangular pulse shaped filters lead to non-orthogonality of sub-carriers in GFDM based schemes [131]. Due to this non-orthogonal nature of the GFDM based system, ISI and ICI are expected to occur whose effects can be mitigated by exploiting the near orthogonality properties of the offset QAM scheme. This paper reviews the GFDM scheme based on OQAM (GFDM-OQAM). Note, to the best of our knowledge, the performance of the GFDM assuming OQAM as the underlying modulation scheme is evaluated in this paper for the first time adopting different existing CE schemes. Moreover, we explain in this paper how to combat the expected detrimental effects of ISI and ICI where each sub-carrier block is modulated against real-valued symbols, unlike the sub-carrier modulation of the complex-valued symbols of GFDM-QAM based systems.
## 4 Case Study of Engineering Application  
In order to validate the developed large-scale temperature monitoring system, a case study was investigated to apply the system on real-world LPG storage tanks as described in this section.