esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Conserved Motifs, Gene Structure, and Phylogenetic Analysis  
Conserved motifs of TPS proteins were analyzed with MEME software ([[http://meme-suite.org/](http://meme-suite.org/)]([http://meme-suite.org/](http://meme-suite.org/))) with default parameters. The exon-intron structure of TPS proteins was aligned with the Gene Structure Display Server (GSDS 2.0, [[http://gsds.cbi.pku.edu.cn/](http://gsds.cbi.pku.edu.cn/)]([http://gsds.cbi.pku.edu.cn/](http://gsds.cbi.pku.edu.cn/))). Multiple sequence alignment was performed using TPS proteins from _A. grandis_, _A. shenzhenica_, _A. thaliana_, _D. officinalale_, _O. sativa_, _P. eugestris_, _P. trichocarpa_, _S. moellendorffii_, _S. lycopeneum_, and _S. bicolor_ with ClustalX 2.1 software (www.clustal.org/). The alignments were manually adjusted and truncated with a focus on diagnostically conserved regions such as the DDxxD, NSE/DTE, and RRX8W motifs, based on a reported protocol [15]. A phylogenetic tree was constructed using the neighbor-joining (NJ) method [39] under the Jones-Taylor-Thornton (JTT) model with 1000 bootstrap replicates in MEGA version 7.0 [40]. The generated graph was redrawn and annotated by Figtree version 1.4.4 ([[http://tree.bio.ed.ac.uk/software/figtree/](http://tree.bio.ed.ac.uk/software/figtree/)]([http://tree.bio.ed.ac.uk/software/figtree/](http://tree.bio.ed.ac.uk/software/figtree/))). The sequences of TPS proteins used in this study can be found in Table S6.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 3 Preparation of Molecularly Imprinted Microspheres
### Controlled/Living' Radical Precipitation Polymerisation (CRPP)
#### 3.2.2 Iniferter-induced 'Living' Radical Precipitation Polymerisation (ILRPP)
To demonstrate the wide application of ambient temperature ILRPP, another study [132] used poly(4-VP-co-EGDMA) and poly(GMA-co-EGDMA) (GMA; glycidyl methacrylate) to synthesise MIMs. This study also demonstrated the 'livingness' of the MIMs using hydrophilic functional monomers (i.e., NIPAAm and HEMA) via surface-initiated ILRPP. The increase in diameter and weight of grafted MIMs compared with the ungrafted ones showed that functional polymer brushes had been grafted onto the MIMs. The MIM particles grafted with PNIPAAm and PHEMA brushes also showed a significant increase in dispersion stability in water and a decrease in static water contact angles. The ILRPP technique performed at an ambient temperature is a general and efficient methodology to synthesise various MIMs for diverse potential fields that are cost-effective and environmentally friendly. Other studies have performed ILRPP with different templates, including glutathione [67] and thymomentin (TP5) [68] (see Table 1). In contrast with ATRPP, which has limited applications due to the use of large amounts of acidic functional monomers or templates that could possibly deactivate  
Figure 3: The mechanism of inferter-induced ‘living’ radical polymerisation (ILRP) (**a**) and ILRPP (**b**). M, monomer; B, small radical; M*, propagating radical; B*, less reactive or nonreactive radical; PRT, primer radical termination; CT, chain transfer. the metal catalyst, the ILRPP methodology is compatible with many molecular imprinting systems, giving it tremendous potential for the future preparation of MIMs for various applications, such as drug delivery, chemical sensors and bioanalytical applications.
# Designing an Adaptive Underwater Visible Light Communication System +
## 8 Results
### Adaptive Angular Displacement  
After retrieving the results for the BER and power analysis for LOS and NLOS communication, trials on the adaptive alignment system were performed for different sets of angular displacements to study the performance of the adaptive system. The results were challenging to obtain because of the synchronization problem and the repetitive  
Figure 19: Power analysis for NLOS communication.  
Figure 18: BER analysis for NLOS communication.  
reconfiguration of the USRPs while they were switching between transmitter and receiver mode consistently.  
The power analysis comparison for the four sets of angular measurements can be seen in Figures 20 and 21 below. The results of the power analysis are presented in the form of bar charts.  
It is evident that, when the receiver is positioned at the large angular distance from the alignment position, the signal has maximum power at 90\({}^{\circ}\), i.e., at the line-of-sight position. It is easy for the receiver to choose 90\({}^{\circ}\) because of the huge power dip on either side, like 30\({}^{\circ}\), 40\({}^{\circ}\), 150\({}^{\circ}\), and 140\({}^{\circ}\) as shown in Figure 20. However, when the angular distance is  
Figure 21: Angular positions vs. power chart 2.  
Figure 20: Angular positions vs. power chart 1.  
reduced between respective positions (Figure 21), the power on either side is also highly comparable to 90\({}^{\circ}\). Several experimental trials show that a minor synchronization issue leads to loss of data since the receiver is not pointing at a particular direction. Nevertheless, the received signal has good quality for processing. A similar experiment was carried out in a dark room and then in ambient light in order to study the comparison between the power values at two different scenarios. The results are summarized in Figures 22-25.  
Figure 23: Dark room vs. ambient light power chart trial 2.  
Figure 22: Dark room vs. ambient light power chart trial 1.  
The graphical depiction of the result explains that, when the experiment is carried out in the dark room, the power calculated is higher compared to the same experiment carried out in ambient light.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 3 Experiments
### Ablation Study
#### 3.5.2 Ablation Study of Multi-Scale Cost Aggregation  
In autonomous driving, the car drives in non-repetitive scenes. There are many complex scenes, so the modeling process is often affected by the image's ill-posed area. There will be mismatching in the binocular stereo matching process.  
In order to verify the effectiveness of the multi-scale cost aggregation in reducing the image mismatching in the ill-posed area, we conducted validation experiments on the scene flow dataset with multiple scenes, and compared the three state-of-the-art network models. We used multi-scale cost aggregation to replace the 3D convolution method in the cost aggregation stage. The suffix S to represent the obtained model. The experimental results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Inference Time (s)** & **Scene Flow** & **KITTI 2015** \\  & & **EPE** & **EPE** \\ \hline PSM-Net & 0.047 & 0.97 & 0.75 \\ PSM-Net-D & 0.053 & 0.85 & 0.62 \\ \hline AANet & 0.095 & 0.87 & 0.68 \\ AANet-D & 0.051 & 0.67 & 0.44 \\ \hline GASPP & 0.049 & 0.92 & 0.73 \\ GASPP-D & 0.055 & 0.81 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study results of the DenseGASPP module.  
By comparing the EPEs in Table 6, we found that our method has a smaller EPE and higher accuracy than the state-of-the-art methods.
# A Machine Learning View on Momentum and Reversal Trading
## 2 Materials and Methods  
### Problem Description  
Momentum and reversal effects can be studied via observation and holding periods. According to customary notations in previous studies, the observation period is defined as \(J\), while the holding period is defined as \(K\). They can be in hours, days, or months. Obviously, different observation and/or holding periods will hugely impact the results. For some pairs of periods, the result might show effects. However, the previously observed patterns might disappear in other periods. Thus, the selection of \(J\) and \(K\) is very important.  
In this paper, \(J\) and \(K\) were 5, 10,... 20 days, i.e., \(J,K\in[5,10,15,20]\).  
\[OR_{i}^{T}=\prod_{t=T+1}^{T+J}(t_{i}^{t}+1)-1 \tag{1}\]  
\[HR_{i}^{T}=\prod_{t=T+J+1}^{T+J+K}(t_{i}^{t}+1)-1 \tag{2}\]  
In (1) and (2), \(OR_{i}^{T}\) is the total return of the \(i\)th stock in the observation period (\(J\)), while \(HR_{i}^{T}\) is the total return of the \(i\)th stock over the holding period (\(K\)). \(t_{i}^{t}\) is the daily return of the \(i\)th stock on the \(i\)th transaction day. \(T\) represents the starting day of the observation period.  
Stocks in a pre-defined asset pool may be ordered by their returns in the observation period. The top \(N\) candidates with the highest returns are regarded as winners, whilst the top \(N\) with the lowest returns are marked as losers. As for momentum trading, the winners in the observation period (\(J\)) will be selected to build a portfolio, and then hold them until the end of the holding period (\(K\)). On the contrary, the losers will be selected to build a portfolio for the reversal trading.  
\[R_{O}^{T}=\frac{\sum_{i=1}^{N}OR_{i}^{T}}{N}=\frac{\sum_{i=1}^{N}\left(\prod_{ t=T+1}^{T+J}(t_{i}^{t}+1)-1\right)}{N} \tag{3}\]  
\[R_{H}^{T}=\frac{\sum_{i=1}^{N}HR_{i}^{T}}{N}=\frac{\sum_{i=1}^{N}\left(\prod_ {t=T+1}^{T+J+K}(t_{i}^{t}+1)-1\right)}{N} \tag{4}\]  
Thus, we may calculate the average returns of the portfolio (winners or losers) in \(J\) and \(K\), respectively, according to (3) and (4), i.e., \(R_{O}^{T}\) and \(R_{H}^{T}\).  
In this research, we built prediction models using four proposed machine learning techniques to predict the effect (momentum, reversal, or no effect) that may happen in the next holding period. After that, corresponding strategies were generated based on these predicted signals.
# Mid-Infrared Grayscale Metasurface Holograms
## Materials and Methods
The size of the MCT detector was 25 \(\upmu\)m, indicating that the minimal spatial resolution of the image was 25 \(\upmu\)m. The phase-only hologram shown in Figure 2b was discretized to an eight-level hologram, meaning that 2\(\uppi\)-phase variation was divided into 0, \(\pi\)/4, \(\pi\)/2, 3\(\uppi\)/4, \(\pi\), 5\(\uppi\)/4, 3\(\uppi\)/2 and 7\(\uppi\)/4 modulation with pillar radii of 320, 420, 480, 560, 720, 820, 880 and 920 nm, respectively. To fabricate our structure, we started from 180-nm-thick gold film evaporated on a Si wafer using electron-beam (E-beam) evaporation. To increase adhesion of Si on both sides of the gold film, a 10 nm titanium film was evaporated. We then grew a 650-nm-thick amorphous silicon (\(\alpha\)-Si) layer using plasma-enhanced chemical-vapor deposition. The holographic pattern was transferred to the Si using E-beam lithography with ZEP520A electron beam resist, spin-coated onto the \(\alpha\)-Si layer at 600 rpm for 90 s. After baking the coated multilayered substrate at 180 \({}^{\circ}\)C for 3 min to evaporate the solvent, we obtained 300-nm-thick resist film for electron beam lithography. For patterning the resist, we adjusted the electron beam to an electron dose of 210 \(\upmu\)C/cm\({}^{2}\). The exposed resist films were developed in ZED-N50 developer for 60 s at 20 \({}^{\circ}\)C. After rinsing in isopropyl alcohol for 30 s and drying by nitrogen  
Figure 2: (**a**) Gray intensity distributions of an object \(I(x,y)\) to be displayed. (**b**) Calculated phase profile \(B(u,v)\) of the phase-only hologram of the object in (a). (**c**) Block diagram of the iterative Fourier transform algorithm used for calculating (b) from (a). flow, the developed resist film with the desired pattern was transferred onto the \(\alpha\)-Si using deep reactive-ion etching (RIE) Bosch process in an Oxford Estrelas system. Each cycle of the Bosch process comprised repetition of an etching (SF\({}_{6}\)) step of 2 s duration and a sidewall-passivation (C\({}_{4}\)F\({}_{8}\)) step of 1 s duration. In the etching step, SF\({}_{6}\) gas with the flow of 300 sccm was applied with an inductively coupled plasma (ICP) power of 3000 W in the applied pressure of 120 mTorr. For the passivation step, C\({}_{4}\)F\({}_{8}\) gas with the flow of 160 sccm was utilized with an ICP power of 1500 W in the same applied pressure of the etching step. This process cycle was then repeated 22 times until the \(\alpha\)-Si was fully etched. The excess of resist was removed by flashing the device into a plasma etcher. The scanning electron microscope (SEM) (LEO 1550 Gemini FESEM) image shown in Figure 3b gives an oblique view of the fabricated hologram. Each pixel of hologram is \(10\times 10\)\(\upmu\)m in size, containing \(5\times 5\) Si nanopillars. The total hologram contains \(64\times 64\) pixels. Figure 3c shows a top view of an enlarged area on the hologram. The unit of each scatter is isotropic, thus our present metasurface-based hologram is polarization-independent [28; 29].
## 5 Conclusions and Further Work  
In this work, we continued to study the problem of intelligence monitoring and decision-making in emergencies at complex technological objects. Such facilities are present in various urban infrastructure systems (power supply systems, gas, water, and heat supply systems) and in large production, mining, or processing enterprises.  
The system reaction speed is important for prompt decision-making and the choice of action programs to eliminate hazardous situations from the system, operational dispatch services, and maintenance personnel. The case-based reasoning method has a high potential to address this need, since it uses ready-made options for action and does not require the development of new solutions when a problem situation arises. At the same time, it is necessary to solve the problem of comparing situations and selecting the one in the knowledge base that is most similar to the current situation.  
We have proposed a solution to this problem using trained neural networks. In the course of our research, we relied on the ontological model of a complex technological object proposed earlier [11] and the representation of situations on such an object through the states of its elements and connections between them. In this work, the following main results are obtained:  
* The formalization of the representation of the situation with the help of embeddings is proposed, which represent situations in the state space.
* A neural network architecture, Comparator-Adder, is developed to assess the similarity of situations.
* Using the example of the house heat point system, a training and validation dataset was prepared to test the efficiency of the proposed solutions.
* Experiments were carried out to assess the accuracy in predicting the similarity of situations using the proposed neural network architecture. Experiments have shown the applicability of this model for problems of comparison and selection of situations by means of their representation in the state space.  
This work does not study the issues of classifying the states of elements and their connections. We consider that various methods and tools will be used to solve these problems, including machine learning methods and knowledge-based systems methods. A further research plan includes the detailing of these methods for different conditions and cases, as well as the development of a general architecture for a hybrid case-based reasoning.  
The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.  
Conceptualization, I.G. and D.G.; methodology, I.G.; formal analysis, I.G.; writing--original draft preparation, I.G. and D.G.; writing--review and editing, I.G. and D.G.;visualization, D.G.; funding acquisition, I.G. All authors have read and agreed to the published version of the manuscript.  
The research was funded by RFBR and Tyumen Region, project number 20-47-720004.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Antenna Single Element
### Bandwidth Enhancement: Dual Stacked Patch Configuration  
As a resonant structure, microstrip patch antennas typically provide a narrow bandwidth (3-4%). In order to overcome this issue and to fulfill the desired system bandwidth of 150 MHz (15%), a bandwidth enhancement technique has to be applied. It is well-known that the use of substrates with a lower dielectric permittivities, such as foam layers, provides greater antenna bandwidths. In addition, the use of electrically thick substrates along with the aperture coupled feeding allows for increasing the bandwidth. However, this may also lead to a reduction of the electromagnetic coupling between the feeding slot and the microstrip patch. Thus, further methods to attain a greater broadband performance are considered [34]. The dual stacked patch configuration involves the use of two patches with different sizes, thus resonating at slightly different frequencies. This way, and together with the use of foam layers, antenna bandwidths of up to 20-30% can be achieved [35].  
This stacked patch arrangement leads to a geometrical structure composed of four layers that becomes the radiating part of the antenna. Two microstrip patches (top and bottom) are implemented on a substrate Rogers RT/Duroid 6006 (\(\varepsilon_{r}=6.15\), \(\tan\delta=~{}0.0019\)) and thicknesses \(h_{sp1},h_{sp2}\), to reduce the patch size. The top patch is placed below the superstrate as it serves as a protective top layer or antenna radome. Two additional foam layers (Rohacell HF 51) with thicknesses \(h_{foam1}\) and \(h_{foam2}\) are used respectively to enhance the bandwidth and to assure a good coupling between the feeding slot and the lower patch.  
The multi-layer structure of the antenna is depicted in Figure 2. The geometrical and electrical parameters of each layer are displayed in Table 1. The overall height of the antenna is 32.1 mm.  
Figure 2: Antenna multilayer structure.
# Characteristics of Restrained Drying Shrinkage on Arched Steel Fiber-Reinforced Concrete
###### Abstract  
The volumetric changes of concrete, including drying shrinkage, are effectively controlled in steel fiber-reinforced concrete (SFRC) mixtures due to the action of the included steel fiber. The current code provision in ANSI/SDI C-2017 allows a minimum steel fiber content of 0.2% of the volume fraction of concrete to control drying shrinkage and to manage cracking in the slab. Limited research has addressed replacing the shear reinforcement in concrete beams with steel fiber. In this study, we used newly developed arched steel fiber to evaluate shrinkage characteristics, including free-drying shrinkage and restrained drying shrinkage, of SFRC and scaled-down deck slab elements. We compared the measured drying shrinkage test results to predicted results obtained from models found in the literature. We confirmed that, overall, the number, width, and length of cracks were reduced significantly at the surface of SFRC slabs when arched steel fiber at 0.2% volume fraction was included in the mixture.  
steel fiber-reinforced concrete; restrained drying shrinkage; arched steel fiber; desk slab +
Footnote †: journal: Applied Sciences  
The free-drying shrinkage of SFRC according to type of steel fiber and also found that hooked-end steel fiber is the most effective type [7]. Hooked-end steel fiber not only has been shown in previous studies to improve mechanical properties, but it also provides excellent control of drying shrinkage. Also, hooked-end steel fiber can easily be pulled out of concrete due to its low bond strength [8].  
In this study, we employed newly developed arched steel fiber to enhance the bond strength between concrete and steel fiber and evaluated the drying shrinkage of SFRC that contained arched steel fiber. We then compared our test results to various predictive model results for the drying shrinkage of SFRC.
## 5 Discussion
### Future Improvements  
Based on the previous discussion, we believe that the system performance can be further improved by building a more precise model to compensate for motion-induced artifacts and by developing ToF cameras with higher resolution and frame rate. The model we use in this study to compensate for the intensity change caused by motion is relatively coarse as we only consider the average depth change within a larger ROI. If a model can be developed to further utilize the point cloud generated by the ToF camera to take more subtle movements such as head rotation/tilting into consideration, it would expand the types of motion artifacts that the CVSM system can compensate. At the same time, efforts are made to develop an indirect ToF camera with a higher resolution and frame rate [39].  
Figure 20: (**a**) Time domain signal for RR extraction: Raw depth signal from neck region not containing RR motion (top), raw depth signal from chest region containing RR motion (bottom). (**b**) Depth signal after ICA separation: Signal component with RR motion (top), signal component without RR motion compensation (bottom).  
Higher camera resolution could be useful in improving the ROI tracking performance of the system while a higher frame rate would allow the system to capture the motion of higher frequency without aliasing or motion blur. In addition, to compensate for micro-motion that is not captured by the depth signal, other signal processing techniques such as sparse frequency estimation could be combined with our depth-based motion compensation method to improve the motion artifacts resilience of the system.
## 3 Proposed Dynamic Graph Processing Scheme  
### Overall Architecture  
This paper presents a system architecture that integrates the capabilities of both CPUs and GPUs to efficiently process large-scale dynamic graphs. By leveraging the parallel processing power of GPUs alongside the management and scheduling capabilities of CPUs, the proposed system accelerates dynamic graph processing. The architecture maximizes  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Subway (Baseline)** & **EGraph** & **Proposed Method** \\ \hline Preliminary Active Vertices & \(\mathcal{K}\) (N/A) & \(\mathcal{K}\) (Not considered) & \(\blacktriangledown\) (Considered) \\ Consideration & & & LPS (Loading– \\ Scheduling Strategy & Static (Fixed) & Processing–Switching) & Priority-Driven Scheduling \\ Operation Reduction & \(\mathcal{K}\) & Partial & \(\blacktriangledown\) \\ Dynamic Graph Processing & \(\mathcal{K}\) (N/A) & \(\blacktriangledown\) (Supported) & \(\blacktriangledown\) (Optimized support) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of existing and proposed methods.  
Figure 1: Snapshots for processing dynamic graphs.  
GPU parallelism to expedite graph computations while utilizing the CPU for preprocessing, management, and task scheduling. This integrated approach reduces computational complexity and workload, thereby enhancing overall processing performance.  
Figure 2 illustrates the overall structure of the proposed scheme for dynamic graph processing, comprising a host and a device. The host consists of the Graph Preprocessor, Scheduling Manager, Operation Reduction Module, and Partitions Dispatcher, while the device includes the Process Manager and a Streaming Multiprocessor (SM) Switcher. On the host side, key tasks include preprocessing the original graph, maintaining snapshots that capture the evolving structure of the dynamic graph, scheduling graph partitions, and transferring them to the GPU for processing. The device side is responsible for receiving graph data, executing computations on the GPU, and ensuring load balancing to optimize performance.