esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Image Segmentation  
Image segmentation is a computer vision task that aims to label pixel-level corresponding categories. Image segmentation involves segmenting an image into multiple objects or segments [10]. [PERSON] et al. [36] first proposed an approach that adopted full convolutional networks (FCNs) for semantic segmentation. [PERSON] et al. proposed architecture that combines semantic information on the deep layer with appearance information on the shallow layer. However, FCN can ignore useful semantic context [37]. Methods of incorporating probabilistic models into the deep learning architecture proposed to improve FCN [38; 39]. Popular deep model architectures for image segmentation are based on the encoder-decoder structure. Most deep-learning-based segmentation tasks use a kind of encoder-decoder model [37]. U-Net [26] is an encoder-decoder model that improves upon the FCN architecture using the skip connection which makes the feature map contain local information by skipping some layers. [PERSON] et al. [40] proposed U-Net++, which improved the performance of U-Net by redesigning the skip connection to reduce the semantic difference between the feature maps of encoder and decoder layers. [PERSON] et al. [41] proposed DeepLab, a segmentation model based on the atrous convolution that explicitly control the resolution at which the feature response is calculated within deep learning model. [PERSON] et al. [42] proposed DeepLabv3 based on modules which employ the atrous convolution to capture multi-scale context by adopting multiple atrous rates. [PERSON] et al. [43] proposed DeepLabv3+, which combines the advantages of each of the spatial pyramid pooling module and the encode-decoder structure. [PERSON] et al. [44] pro posed the pyramid scene parsing network (PSPNet), which exploits the capability of global context information using the pyramid pooling module. Recently, the segmentation study using high-spatial-resolution satellite images has been proposed. For an efficient land-cover semantic segmentation in high-spatial-resolution satellite images, [PERSON] et al. [45] developed a hybrid segmentation model that combined the benefits of DenseNet [46] and U-Net. DSMSA-Net [47], an encoder-decoder network integrated with attention units, was proposed to handle road segmentation tasks in high-space resolution satellite images. Furthermore, [PERSON] et al. [48] proposed a segmentation framework that extracts building footprints from satellite images.
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 4 DNN Architecture, Implementation, Performance Characterization and Optimization  
### DNN Design Considerations  
Processing of image streams is commonly performed in machine learning using deep convolutional neural networks (CNN) [21]. This powerful DNN type (for consistency we use this term instead of deep CNN) can provide efficient extraction of image spatial features approaching or even surpassing humans in this task [22].  
Visual assessment of scintillation images such as in Figures 2 and 4 suggests that they can be differentiated by spatial features of bright and dark regions (speckles) including speckle size, location, shape and contrast. Correspondingly, a desired DNN topology should be efficient in extraction of this type of spatial feature from experimentally recorded or simulated scintillation patterns.  
There are many options for configuring such DNNs [23]. A typical DNN designed for spatial feature extraction from image streams is comprised of a sequence (stack) of convolutional and pooling image processing layers. Each convolutional layer receives a 2D array of data (an image or feature map of \(N_{c}\times N_{c}\) pixels) and applies several (\(N_{filter}\)) trainable spatial filters and bias terms to obtain a set of \(N_{filter}\) 2D feature maps of the same size at its output. The kernel size \(n_{c}\times n_{c}\) of such filters may vary from layer to layer (e.g., from \(n_{c}=7\) to \(n_{c}=1\)). A 2D array of data (feature map) processed by the convolutional layer enters a corresponding pooling layer.  
Figure 4: Examples of selected scintillation images from SIM- Kolmogorov (KO) (**top row**) and ATM#1 (**bottom row**) datasets corresponding to different turbulence strengths as indicated by \(C_{n}^{2}\) values above each column.  
The major goal of the pooling layers is to reduce the dimension (subsample) of feature maps. In the so-called max-pooling layers, which are quite popular in CNN-based image processing applications, only the max value of the pooling kernel of size \(n_{p}\times n_{p}\) propagates to the next computational layer, thus reducing the dimension of the feature map by a factor of \(n_{p}^{2}\).  
In a typical deep CNN, the stack of convolutional and pooling layers succeeds with several layers of fully connected neurons receiving a 1D array of image features (feature vector) as an input. This transition from 2D feature map to feature vector (flattening) is commonly performed after the last pooling layer.  
The described general building blocks (details can be found in several prominent publications in this field [24; 25]) were utilized in the DNN topology, referred to here as Cn^2 Net, designed and optimized for \(C_{n}^{2}\) prediction based on DNN-based processing of short-exposure intensity scintillation patterns (images).
###### Abstract  
This study focuses on investigating the surface settlement characteristics induced by the construction of a super-large-diameter shield tunnel in composite strata. By utilizing a combination of field monitoring and numerical simulation analysis, the surface settlement patterns encountered during the construction process in horizontally distributed typical soil-rock composite strata were summarized based on the 16.03 m super-large-diameter shield tunnel project in the southerly extension of He'ping Avenue in Wuhan. In addition, the collected data were used to enhance the Peck empirical formula. The results of the study show the following: (1) Significant non-uniform settlement occurs along the tunneling direction when the shield machine passes through soil-rock composite strata. The range of non-uniform settlement is approximately 3.1 times the tunnel diameter (D) in soil sections and 1.9 times the tunnel diameter (D) in rock sections. (2) The impact of composite strata on the maximum settlement is greater than its effect on the settlement trough width, with a larger impact within the soil sections compared to the rock sections. (3) The Peck correction formula, which takes into account the distance between the monitoring cross-section and the composite interface, provides more accurate predictions than the original Peck empirical formula.  
soil-rock composite stratum; revised Peck formula; numerical simulation; on-site monitoring
## 2 Modelling and Methods
### Urea Dosing Uniformity  
It is a common approach that the non-uniform flow distribution is not considered in this effective single channel model when performing larger parametric simulation, but the effect of this could not be neglected in practice [21]. For the urea maldistribution measures and expectations, either experimental measurements or CFD calculation could be used to estimate the spatial distribution of urea/ammonia across the catalyst inlet. However, a much simpler way without detailed testing or modelling could be applied, that is, the uniformity of urea distribution within a cross section is described with the uniformity index (UI) as given in Equation (6), where the X can be the concentration of urea/NH\({}_{3}\)/HNCO, Ai is the area of each grid, X mean is the average of X within the cross section, and A is the area of the cross section. In accordance with the definition of the uniformity index, its scope lies between 0 and 1. A value that is closer to 1 indicates a superior uniformity distribution. The uniformity index at the SCR inlet typically ranges from 0.64 to \(-\)0.98. An aftertreatment system without a static mixer will have a UI value of \(\sim\)0.75, and a good mixer design will result in a UI value between 0.85 and 0.9, while an excellent mixer design can help to obtain a UI value of \(\sim\)0.95 or even higher. A probability distribution function, such as normal distribution or beta distribution, was used for generating urea index non-uniform input based on the uniformity index for urea dosing.  
\[UI=1-\frac{1}{2}\sum_{i=1}^{n}\frac{\left|X_{i}-X_{mean}\right|A_{i}}{AX_{ mean}} \tag{14}\]  
The DOE function within BASF CatSim modelling software (GT-suite V2016 version) can be adapted to study the effect of the UI factor on SCR performance and NH\({}_{3}\) slip. Firstly, a DOE design is prepared, where the local urea dosing/average dosed urea can be calculated with a normal distribution function to describe a probability density distribution under different maldistribution levels (UI),  
\[\rm local\ urea\ dosing/average\ dosed\ urea=f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{- \frac{(x-y)^{2}}{2\sigma^{2}}}(\sigma>0) \tag{15}\]  
In addition, parameters within the normal distribution function can be tuned to obtain the desired UI. Afterwards, DOE calculations are performed with the urea dosing flow rate factor as a DOE parameter. Finally, the calculation results can be processed based on the probability density distribution for each channel, and an average NOx emission and NH3 slip can be obtained and used for additional analysis. The calculation process is illustrated in Figure 2.
## 3 Results and Discussion
### Comparison of Results  
The prediction results with different model methods are shown in Figure 5. From the aspect of detection accuracy, no matter which method was used, the detection accuracy of soil nitrogen was the worst when soil was detected after a 25 \({}^{\circ}\)C placement (0.585 \(<R_{p}^{2}<\) 0.728). Besides this, the detection accuracy achieved was much better (0.916 \(<R_{p}^{2}<\) 0.966) when the drying temperature ranged from 40 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. Meanwhile, when the drying temperature rose to 105 \({}^{\circ}\)C, the \(R_{p}^{2}\) of soil nitrogen detection dropped to about 0.9. It was indicated that drying temperature did affect the calcium soil nitrogen detection, and that a low or extremely high drying temperature was adverse to soil nitrogen detection. From the aspects of algorithms effects, CARS-BIPLS-PLS achieved the best detection effects compared to other methods. The reason might be that the most useful characteristic variables were selected and combined by CARS and BIPLS. Moreover, the soil nitrogen detection effects of BIPLS-PLS were not stable and worse than those of CARS-BIPLS-PLS and CARS-BPLS when soil was dried at 40 \({}^{\circ}\)C and at a 25 \({}^{\circ}\)C placement. The reason was that it was difficult to find the best characteristic intervals of the soil spectrum for all drying temperatures, and some effective spectral information might be removed [31]. Also, some noise information was preserved in the original spectra in RAW-PLS, especially when soil was detected after a 25 \({}^{\circ}\)C placement, which was caused by the preserved water content in the soil.
# Methodology and Results of Detailed 3D Seismic Exploration in the Zhezkazgan Ore District
###### Abstract  
This article presents the results obtained from a high-resolution wide-azimuthal 3D seismic reflection method used for the prediction and detailed exploration of complex ore targets in the Zhezkazgan ore district of Central Kazakhstan. We demonstrate the ability of modern seismic data processing and interpretation systems to identify underground mine objects associated with stratiform copper sandstones and improve geological models. The 3D seismic imaging tools, along with the implementation of a modern seismic processing sequence, allow for the clarification of geological structures in the studied area. The target stratigraphic horizons, large faults, and microtectonic disturbances (small faults and cracks) are clearly delineated in the seismic volumes. The use of seismic attribute analyses on geological data is tested to identify ore horizons and deposits with volumetric predictions of copper mineralization. Recommendations for further exploration drilling were developed, and five new wells were drilled. Copper mineralization was confirmed in all recommended wells. We carried out a marketing review in Kazakhstan and uncovered an increased interest among subsoil use companies in 3D seismic exploration technology to investigate existing mining objects of different genetic types. These results demonstrate the expediency of 3D seismic exploration aimed at identifying ore targets.  
3D seismic reflection; data processing and interpretation; attributes; stratigraphic horizons; copper sandstones; ore body Article
# Seismic Ground Response Prediction Based on Multilayer Perceptron
## 2 Analytical Methods and Dataset
### Multilayer Perceptron (MLP) Model  
Multilayer perceptron (MLP) is one of the neural network models using the back-propagation training algorithm with the basic form including input, hidden, and output layers [20]. As researchers have recently suggested various architectures to improve MLP performance [21; 22; 23], it can deal with datasets with high nonlinearity and has been used widely in the civil engineering field.  
We modeled the MLP to evaluate the seismic motion at the surface through the bedrock motion (or the motion measured beneath the surface at 100 m depth). Figure 3 presents the schematic of MLP process for the seismic ground response. In the modeling, single hidden layer with 100 neurons was implemented through MLP. Mean squared error (MSE) was applied as the loss function, and the Adam [24] optimizer and rectified linear units (Relu) [25] activation function are employed in the analyses.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Station**} & \(V_{s,30}\) & \(T_{G}\) & \multirow{2}{*}{**Depth**} & **Depth** \\  & **(m/s)** & **(s)** & **where \(V_{s}\) is over 1000 m/s (m)** & **where \(V_{s}\) is over 2000 m/s (m)** \\ \hline FKSH17 & 544.0 & 0.22 & 6 & 30 \\ FKSH18 & 307.2 & 0.39 & 30 & 48 \\ IBRH11 & 242.5 & 0.49 & 30 & 30 \\ IBRH13 & 335.4 & 0.36 & 34 & 44 \\ IWTH21 & 521.1 & 0.23 & 20 & 40 \\ IWTH23 & 922.9 & 0.13 & 10 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average shear wave velocity and natural period for each station.
## 2 Study Area and Methods
### Methodology  
The data processing process is shown in Figure 3. According to the figure from left to right, the MODIS data were preprocessed first to extract ET and GPP data. Then, eWUE was calculated according to GPP and ET data, and the temperature and precipitation climatic factors were interpolated according to the original climate data of weather stations. Secondly, the spatial distribution and trend of eWUE were calculated by the linear regression method. Then, correlation analysis was used to analyze the relationship between rainfall, temperature and eWUE. The effects of vegetation types on the correlation between rainfall, temperature and eWUE were quantified.  
Figure 2: Spatial distribution of precipitation and temperature in different seasons. in Inner Mongolia. (**a–c**) Precipitation (Pre) for spring, summer and autumn, (**d–f**) temperature (Tmp) for spring, summer and autumn.
# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 5 Conclusions  
A behavior composition method, namely the uGA method, is proposed to solve the MSN-based CPSL problem. uGA combines the effectiveness of the reactive upwind sprint and the cooperative mechanism of the GA-based deliberative cooperation. Compared with the attraction mechanism used in the SS method, the C-behavior of uGA has two advantages. First, rather than just following the SN closest to the plume source, the new chromosome generated by the C-behavior of uGA is more likely to be closer to the plume source than all the current SN positions. Second, the C-behavior can help maintain the diversity of the SN positions, while the SN diversity is severely reduced when other SNs get close to the SN attracting them. Moreover, the D-behavior can further improve the search efficiency by increasing the diversity of SN positions.  
Simulation results show that the proposed uGA method achieved high efficiency during plume source localization in simulated marine environments. Considering CPSL processes are often started after the pollution material is previously encountered, which facilitates the setup of a downwind start position for the MSN, all SNs started from the downwind side of the simulated pollution source. The case that the SNs started at the upwind area of the pollution source may cause the calculation time to increase. Moreover, inaccurate measurements or transmission mistakes may cause the localization accuracy and efficiency to decrease. Further research lines include: (1) designing new behaviors for better maintaining the SN diversity and striking a balance between plume information exploration and exploitation; (2) conducting field experiments using autonomous underwater vehicles in real marine environments.  
Conceptualization, methodology, software, writing--original draft preparation, M.C.; software, H.B.; resources, supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 61801287).  
Not applicable.  
The simulation result data used to support the findings of this study have been deposited in the Github repository ([[https://github.com/aoxfrank/SimulationDat.git](https://github.com/aoxfrank/SimulationDat.git)]([https://github.com/aoxfrank/SimulationDat.git](https://github.com/aoxfrank/SimulationDat.git)), last accessed at 4 June 2022).  
The authors would like to thank Professor [PERSON] from the University of California, Riverside, USA, for sharing the codes of the FPSP.  
The authors declare no conflict of interest.
###### Abstract
coli_ to synthesize monoterepnes, such as \(\alpha\)- Pinene [25], limonene [26], sabinene [27], and \(\gamma\)-terpinene [28], was reported. Similar to other monoterepnes, GPPS catalyzed the conversion of IPP and DMAPP into geranyl diphosphate (GPP), which subsequently underwent transformation by TS to yield tricyclene. Plant terpene synthases may possess an N-terminal transit peptide, facilitating the transportation of synthases to specific organelles, followed by the subsequent removal of transit peptides by proteases to obtain mature enzymes [29]. However, the absence of transit peptide clearance mechanisms in E. coli may result in compromised protein folding and reduced enzyme activity, thereby truncating the transit peptide, potentially leading to significant alterations in terpene titers. Monoterpene synthases were reported to be between 600 and 650 amino acids in length, and most of them had N-terminal delivery peptides required for plastid localization [30]. Increasing the titers of monoterepnes in microorganisms such as _E. coli_ or _S. cerevisiae_ by truncating the transit peptides of monoterepne synthases has proven to be an effective strategy. Recently, [PERSON] et al. made attempts to enhance geraniol production by truncating CrGES (geraniol synthase enzymes from _Catharantlus roseus_), resulting in the yield being observed at 4.45 times that of untruncated CrGES [31]. [PERSON] et al. modified the inactive end of the pinene synthase by tailoring the appropriate length, and the yield of \(\alpha\)-pinene was increased by 2.5 times [10]. Furthermore, [PERSON] et al. successfully heterologously expressed \(\gamma\)-terpinene synthase from citrus unshiu in _E. coli_[32]. It was shown that the solubility of the truncated \(\gamma\)-terpinene synthase was increased. In this study, we initially established a biosynthetic pathway for tricyclene in _E. coli_ by introducing a heterologous MVA pathway, GPPS, and TS. The induction temperature and rate-limiting step for tricyclene production was determined. The tricyclene titer was greatly improved by truncating the N-terminal of TS XP_009791411 (1411). SDS-PAGE analysis revealed that the truncation of the N-terminal of TS 1411 resulted in a significant increase in protein solubility, thus leading to an improvement in tricyclene titers.
## 3 Results and Discussion
### Tests with SAR Converters with Four Chassis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test: 50 Hz, 230 Vpp with Sampling Fs = 50 kS/s**} \\ \cline{2-5}
**1 LEM Phase Errors (ns)** & \multicolumn{4}{c}{**16-bit SAR Converters with 1 LEM**} \\ \cline{2-5}  & **Chassis 1–2:** & **Chassis 2–3:** & **Chassis 1–2–3:** & **Chassis 1–2–3–4:** \\  & **A0–50 m-B0** & **B0–75 m-C0** & **A0 50 m 75 m C0** & **A0 50 m 75 m 10 m C0** \\ \hline Mean & 32.2900 & 20.2047 & 25.2347 & 20.0784 \\ \hline Median & 30.8584 & 17.1647 & 22.0443 & 17.0357 \\ \hline Variance (\(\sigma^{2}\)) & 358.9704 & 234.8637 & 308.7933 & 240.3062 \\ \hline Standard Deviation (\(\sigma\)) & 18.9465 & 15.3253 & 17.5725 & 15.5018 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Phase errors for SAR converters. Tests with one LV-25 LEM. Figure 22: Distribution density for SAR with an LEM. order of 200 ns can be achieved. The data obtained were very similar to those published by the research work of \"[PERSON] et al. [26]\". However, in this case, a different NI technology has been used. In this research study, we chose the cRIO as the TSN master and the cDAQs as the TSN slave because of their design compatibility with industrial applications. Moreover, these results guarantee synchronism levels below 1 us for distributed captures. It is conceivable that this architecture could be part of a cyber-physical system for high-quality data exchange with digital twins that can contribute to the optimisation of the energy produced in photovoltaic plants [7]. We are currently in a continuous process of digitisation of industrial sectors and digital twins are making a very decisive contribution to sectors such as energy [27].