esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics and Mechanisms of the Zigzag and Spiral Movement of Rising Bubbles in Still Water
## 4 Conclusions  
Bubble flow is widely used in industrial processes, such as the release of bubbles in a fluid to enhance turbulence, unify fluid properties, or cover the bottom of a ship to reduce forward resistance. This paper explored the shape change and movement characteristics of bubbles in water through experiments, found different movement trajectories for bubbles, and coupled the trajectory and wake movement to study the movement mechanism. The detailed conclusions are as follows:  
1. Only when the aspect ratio \(\lambda\) reaches a critical value can the vorticity brought by the flow make the wake flow unstable and then cause the bubble to move along the zigzag trajectory. The value in this experiment was 2.0.
2. When the bubble moves along the zigzag trajectory, it achieves dynamic equilibrium under the combined action of buoyancy and the tractive force of the trailing vortices. The shedding of the trailing vortices and the change in tractive force at the tail of the bubble cause bubble oscillation.
3. The zigzag movement of the bubble will also extend in the third direction, forming spiral movement. At this time, the low-pressure area rotates around the trailing of the bubble.
4. When the trailing vortices cause the trajectory to deflect, its oscillation frequency will also be hindered. The oscillation frequency of zigzag movement is lower than that of the wake of flow around a stationary sphere. The spiral movement causes the shedding of another group of trailing vortices, and its oscillation frequency is lower.  
Figure 12: Movement of the low-pressure area at the trailing of the bubble.  
Figure 13: Velocity comparison between zigzag and spiral movements of bubbles.  
5. Under different movement trajectories of a bubble, the movement trajectory of the trailing low-pressure area will affect the bubble's shape. When moving along a zigzag trajectory, the low-pressure area at the trailing of the bubble swings back and forth in a plane, and the bubble is flatter. When moving along a spiral trajectory, the low-pressure area rotates around the trailing of the bubble, and the bubble is more spherical. This is the result of the bubble adapting to the flow. 6. Compared with zigzag movement, the velocity of the bubble during spiral movement is higher. Focusing on the macroscopic force, this paper coupled the bubble trajectory with the trailing vortices and reasonably explained the zigzag and spiral movements of the bubble. In essence, the zigzag and spiral rising trajectories of a bubble in water are the result of the bubble adapting to the flow field.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The authors declare no conflict of interest.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Data Correlation Analysis  
Correlation analysis shows the relationship between different variables, provides a visual understanding of the data, and can be used to assess the correlation between input variables and objectives [43]. The Pearson correlation coefficient (PCC) was used in this study to show the relationship between input variables and \(PR\)[44]. The correlation between variables is calculated as shown in Equation (9).  
\[r=\frac{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{\sqrt {\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})^{2}\sqrt{\sum\limits_{i=1}^{n}{(Y_{ i}-\overline{Y})^{2}}}}}} \tag{9}\]  
where \(r\) is the Pearson correlation coefficient; \(X_{i},Y_{i}\) denote the \(i\)-th sample of two different features; \(\overline{X},\ \overline{Y}\) denote the mean of the corresponding feature; and \(n\) represents the number of measured data samples.  
Figure 7 shows the heatmap of the Pearson correlation coefficient results between \(PR\) and each feature. It can be seen that \(SS\) is the most critical feature affecting \(PR\), and the Pearson correlation coefficient between \(SS\) and \(PR\) is 0.72. In the process of shield advancement, \(SS\) represents the speed of soil transported by the shield, and in order to maintain the stability of soil pressure inside the shield, \(SS\) usually needs to be adjusted in conjunction with the advance speed. There is a strong positive correlation between \(PR\) and \(ET\), \(CD\), which indicates that when the soil body produces changes, \(PR\) will appear with corresponding changes and the shield tunnelling performance will be affected. There is a strong negative correlation between \(PR\), \(CS\), and \(SE\), which indicates that when the energy consumed per unit distance is higher, it can reflect that the current tunnelling is difficult.  
Figure 6: Comparison of outliers before and after processing. (**a**) Raw data. (**b**) Filtered data.
## 3 Results and Discussion
### Antioxidant Activity of Biosynthesisized AgNPs  
In this study, the in vitro antioxidant activity of biosynthesized AgNPs was determined using DPPH radical scavenging assay. DPPH has been widely used to measure the antioxidant activity due to its stable compound. Figure 8 shows the effect of different concentrations of AgNPs on DPPH radical antioxidant activity. Our results revealed that the biosynthesized AgNPs exhibited a moderate antioxidant activity compared to ascorbic acid, which shows greater activity. In the case of DPPH assay, the principle of the antioxidant properties is attributable to the electron-donating behavior of the AgNPs to the odd electron in the DPPH solution, and consequently to a decrease in the intensity of \(n\rightarrow\pi^{\star}\) transition at 517 nm (color changes from deep purple to pale yellow) [60], indicating the effect of antioxidant activity. In addition, AgNPs are reported to possess intrinsic antioxidant properties associated with their surface properties. Metal NPs (gold, silver, and palladium) have been reported to exhibit catalase-mimicking behavior by decomposing H\({}_{2}\)O\({}_{2}\) to H\({}_{2}\)O and O\({}_{2}\) at neutral and basic pH values [61]. Nevertheless, the different concentrations of biosynthesized AgNPs (250, 500, and 1000 \(\mu\)g/mL) have no direct influence on the scavenging activity, which may be attributed to the protein or biological compound from _L. plantarum_ TA4 that covered or layered the surface of AgNPs and interfere with the reactivity of NPs. This phenomenon was also observed by [PERSON] et al. [62], who employed fruit extract for the biosynthesis of AgNPs. They postulated that several functional groups in the fruit act as a capping agent and covered the NPs, which resulted in moderate antioxidant activity.  
Moreover, it was found that the elevated AgNPs concentration reduced the solubility of NPs, which explained the reduction in scavenging activity at concentration of 500 and 1000 \(\mu\)g/mL. Furthermore, it might also be due to a lack of DPPH content at higher concentrations. Likewise, [PERSON] et al. [63] and [PERSON] et al. [64] have also reported that the high concentration of NPs does not correlate with their scavenging activity. On the contrary, [PERSON] et al. [65] reported that the biosynthesized AgNPs employing walnut green husk extract exhibited improved antioxidant activity with increased concentration. They suggested that the biologically active compounds from the extract, such as flavonoids, ellagic acid, and valeric acid bound on the NPs, improved the antioxidant properties of AgNPs. Similarly, _Lenzites betulina_-capped AgNPs were also reported to promote stable radical reduction [66]. Therefore, the synthesis of AgNPs using biological substances influenced the biological and chemical activity through the attachment of biologically active compounds on the surface of NPs.  
In the present study, the biological compound from _L. plantarum_ TA4 did not promote the antioxidant activity but acted as a stabilizing agent.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 2 Multi-Channel Beam-Steering SAR System  
Azimuth multi-channel technology increases the number of azimuth-receiving channels, so significantly improves the spatial sampling rate; this is equivalent to increasing the PRF by using Digital Beam Forming technology. In addition, to achieve higher resolution or wider swath, azimuth multi-channel technology and beam-steering technology can be combined to provide new imaging modes, namely the multi-channel sliding spotlight mode and multi-channel TOPS mode.  
As shown in Figure 1, the beam in multi-channel sliding spotlight SAR is steered from forward-looking to backward-looking squint during an illumination period, as in single-channel sliding spotlight SAR. A target can be observed for longer than for a SAR sensor working in strip-map mode, which leads to finer resolution. The \(N\) (\(N\) = 3 in Figure 1) receivers would acquire the reflected pulse almost simultaneously, and the SAR sensor could correspondingly store \(N\) times more pulse data than single-channel spotlight SAR. This means that by reducing the PRF by the factor \(N\), the range swath could become wider while the azimuth resolution would not degrade. Similarly, multi-channel TOPS employs beam steering from backward-looking to forward-looking squint during illumination and can achieve wider swath than single-channel TOPS with the help of \(N\) azimuth channels.
###### Abstract  
In this paper, for the first time, we propose and experimentally demonstrate a novel pairwise coding (PWC)-based multiband carrierless amplitude and phase (\(m\)CAP) modulation with chaotic dual-mode index modulation (DM) for secure bandlimited visible light communication (VLC) systems. The combination of \(m\)CAP and DM can sustain a higher spectral efficiency (SE) compared with \(m\)CAP with conventional index modulation (IM), while PWC can be employed to efficiently mitigate the signal-to-noise ratio (SNR) imbalance caused by the low-pass frequency response of light emitting diodes (LEDs). Moreover, the DM is enhanced by a two-dimensional (2D) chaotic encryption scheme to guarantee the security of the useful information in VLC systems. Simulation and experimental results successfully verify the superiority of the proposed PWC-based \(m\)CAP-DM scheme with two-level chaotic encryption over other benchmark schemes.  
visible light communication (VLC); carrierless amplitude and phase (CAP) modulation; pairwise coding (PWC); dual-mode index modulation (DM); chaotic encryption +
Footnote †: journal: _Photonics_  
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Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
### Literature Review  
#### 1.1.1 Assembly Phase Considerations in the OSC Environment  
This study undertook an extensive literature review to identify the essential elements and potential risks inherent in the assembly phase, which is critical for developing assembly sequences that ensure minimal interference and optimal workflow at building sites. This phase of prefabricated construction is largely concerned with the installation of various components, primarily concentrated on crucial equipment, such as cranes. Consequently, effective resource allocation, including equipment and labor, is vital [15].  
[PERSON] et al. (2019) [20] employed Social Network Analysis to identify the constraints within prefabricated construction assemblies, effectively establishing a model to illuminate the intricate interrelationships among these constraints. The risks identified during on-site assembly include communication errors, improper installation planning, and spatial interference among workers and spaces. In particular, the absence of an optimal installation plan significantly hampers the derivation of efficient assembly sequences. [PERSON] et al. (2018) [21] utilized a Decision-Making Trial and Evaluation Laboratory model alongside the Analytic Network Process technique to discern and prioritize delay factors in prefabricated construction. Their findings underscored the profound impact of inappropriate construction planning, transportation errors, and communication discrepancies between stakeholders during the assembly phase. Combining these influential studies highlights the crucial role of defining assembly sequences. This underscores the necessity for comprehensive planning that integrates both manpower and equipment allocation, while managing spatial interference to create efficient assembly sequences. This alignment is pivotal for optimizing the assembly phase, ultimately improving workflow efficiency and reducing potential disruptions at construction sites.
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 3 Methodology  
The proposed framework for full-view area coverage is based on transforming the problem of the area covered in the continuous domain into an area coverage of boundary points. This required the development of the following subsystems:  
1. A boundary points of interest model to generate the appropriate pattern for full-view area coverage.
2. A classification method to represent the \(k\)-view geometric area coverage conditions. This subsystem collects and classifies photos with the same \(k\)-view geometric area coverage features with the application of the BoIs model in a distributed and adaptive manner.
3. A set of procedures to eliminate redundancy among photos with identical geometric conditions for VCS. This includes the management of the sensor node storage to prevent storing redundant photos and how each sensor node stores photos with maximum added values so that photos with higher coverage overlap are skipped.
# A Two-Stage Anomaly Detection Method Based on User Preference Features and the Deep Fusion Model
## 2 Related Work  
### Fast Anomaly Traffic Detection  
Recent research on the attack detection of anomaly traffic mainly focuses on detection rate and detection accuracy. Especially under the premise of ensuring that the detection accuracy meets the actual application requirements, improving detection efficiency has become a current research hotspot. Several methods have been proposed. The first category is to design detection methods with low computational complexity that reduce detection computation by approximation and other methods to achieve the purpose of improving detection rates. Such as the attack detection method based on the covariance of a small amount of traffic characteristics [7] and the attack detection method based on a fuzzy estimator [8]. These methods mentioned above improve the detection rate and cost by designing algorithms with low detection calculations. However, the lack of extensibility causes difficulties in migration for other tasks. The second category aims at the method of hardware acceleration, which improves the detection rate through hardware auxiliary operations, such as the detection methods based on Field Programmable Gate Array (FPGA) [9; 10; 11]. This kind of method combines hardware to effectively improve the detection rate. Nevertheless, the algorithm design must be adapted to the hardware, which is costly to promote. The third category is based on distributed computing platforms, which use cluster distributed parallel computing to improve the detection rate. For example, the network attack detection method based on counting is deployed on the Hadoop distributed computing platform. In addition to the network attack detection method of deploying the K-Means algorithm on the Spark distributed computing platform This kind of method distributes detection tasks with a large amount of calculation to work clusters for parallel distributed computing, sharing the pressure of detection hosts, and has fast detection speed and strong applicability. However, the current research has only realized the deployment of a few simple algorithms.
## 2 Materials and Methods
### Real-Time Reverse Transcription PCR (qRT-PCR)  
Total RNA was extracted from cells using the Quick-RNA MiniPrep Kit and reverse transcribed into cDNA using random primers and the High-Capacity cDNA Reverse Transcription Kit. Quantitative real-time PCR (qRT-PCR) was performed on the 7500 Fast Real-Time PCR System (Applied Biosystems) with iTaq(tm) Universal SYBR Green Supermix. PCR specificity was confirmed by melting curve analysis. Relative mRNA expression levels were calculated using the 2-\({}^{-\Delta\)Ct method, with \(\upbeta\)-actin as the endogenous reference gene. Primers were designed to span exon-exon junctions to avoid amplification of genomic DNA (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gene** & **Accession Number** & **Forward Primer** & **Reverse Primer** \\ \hline \(\upbeta\)-actin & NM\_001101.3 & 5′-CCTCGCGATCCAGCAAT-3′ & 5′-GCCGATCCACACCGGATACT-3′ \\ ABCCl & NM\_000927.5 & 5′-CCTCTGGGTTTCACATTCTG-3′ & 5′-ACTGCATCTCTGTGTAGCA-3′ \\ ABCCl & NM\_00499.64 & 5′-CATATG
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 4 Results and Discussion  
### Training History  
To demonstrate whether the learning progressed well under the above conditions, we plotted four graphs that contained the history of the training and validation loss of the comparison methods. As illustrated in Figure 9, the baseline models, i.e., ED and U-Net, converged well, eventually showing lower validation loss compared to training loss. For methods with the SB-LSTM module, they also exhibited well-trained curve features where no overfitting problems occurred despite the model parameter increase. During the training process, the model with SB-LSTM attached showed a faster decrease in loss per epoch.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Number of Model Parameters** \\ \hline ED & 12,540,227 \\ U-Net & 14,890,307 \\ ED with SB-LSTM & 16,888,419 \\ U-Net with SB-LSTM & 19,238,499 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of model parameters for the four different methods.  
Figure 8: Structural comparison of four different methods. From left to right: (**a**) ED, (**b**) U-Net, (**c**) ED with SB-LSTM, (**d**) U-Net with SB-LSTM.  
While the baseline methods did not show a decrease in loss from around 125 epochs, the attached models maintained a steady decrease.
## 1 Introduction
This paper utilizes this DQN technology for offloading decision-making (i.e., processing the transmitted data at centralized computing, i.e., _cloud_ or local computing, i.e., _MEC_) for real-time seamless high quality data communications under uncertainty conditions, e.g., channel dynamics. The reason why DQN is selected among the given Q-learning, MDP, DQN, and DDPG is as follows. The Q-learning and MDP are not related to DNN based function approximation; and their solutions can be computed using dynamic programming. Therefore, the computational complexity of Q-learning and MDP solution computation can be very high when the search space becomes large. The DDPG based formulation is for continuous action domains. Figure 1: Edge computing and cloud computing architecture. In this paper, our formulation is for offloading decision-making whether the offloading should be conducted or not. Thus, our action space is discrete (0 or 1), i.e., continuous domain DDPG is not suitable; and finally DQN is the best solution among given options. In mobile edge computing research, several algorithms were proposed in order to optimize their own objectives [8; 9; 10; 11; 12]. However, none of them are considering offloading decision for determining whether it has to conduct offloading or not. The proposed algorithm in this paper is the improvement of our previous contributions in [13]. Comparing to our previous work in [13], the proposed algorithm in this paper includes more detailed mathematical formulations and descriptions. Furthermore, various data-intensive performance evaluation results are included in this paper. These kinds of DRL-based algorithms including our proposed DQN-based method can be widely used and are practical due to the function approximation nature of DNN. With DQN, the states and actions are used as input and output values in DNN training. Once the training is done, we can simply infer our approximated optimal actions by putting current states into the trained DNN. Thus, the computation itself during the inference is light-weight (even though training itself is not) and thus using a DQN-based method is realistic in real-time decision-making systems, as also discussed in [14]. The rest of this paper is constituted as follows. Section 2 introduces preliminaries, i.e., deep learning foundations and related work, i.e., reinforcement learning algorithms for mobile edge computing. Section 3 proposes our system model. Section 4 describes the details of our algorithm with explanation about DQN. In Section 5, we describe the details of simulations and results. Finally, Section 6 concludes this paper and presents future research directions.
# The Impacts of Climate and Human Activities on Grassland Productivity Variation in China
## 3 Results  
### Spatiotemporal Variation in GPP in Chinese Grassland  
The spatiotemporal trend of annual GPP from 2000 to 2018 in China's main grassland-covered provinces (autonomous regions) was detected using the Sen-MK method (Figures 3 and 4). Accordingly, the GPP in the study area clearly increased by 4.75 g C m\({}^{-2}\) yr\({}^{-1}\), and approximately 14.03% of the areas were significant, mainly distributed in northern SAX, middle NX, and eastern GS. The mean significant upward trend was 10.15 g C m\({}^{-2}\) yr\({}^{-1}\), especially in northern SAX, and the increasing trend was as high as 15.64 g C m\({}^{-2}\) yr\({}^{-1}\). Contrary to the increase, only 3.21% presented a significant downward trend, spatially distributed in XJ, TB, and IMG, and the average decreasing trend was \(-8.50\) g C m\({}^{-2}\) yr\({}^{-1}\).  
Among the 10 provinces (autonomous regions), 6 provinces, including HB, SX, SAX, GS, QH, and NX, had a significant increasing trend in GPP during 2000-2018. Among them, QH and SX had the lowest and highest increasing trends of 3.00 g C m\({}^{-2}\) yr\({}^{-1}\) and 14.00 g C m\({}^{-2}\) yr\({}^{-1}\), respectively. However, while most provinces (autonomous regions) showed an uptrend for GPP, XJ showed a decreasing trend of \(-0.22\) g C m\({}^{-2}\) yr\({}^{-1}\). Although this trend was not significant, it should not be ignored.  
Figure 3: The spatial variability of GPP across the main grassland in China during 2000–2018, and the inset figure shows the areas with significant changes (Significance level is \(p<0.05\)).