esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Adaptive Priority Correction for Prioritized Experience Replay
## 4 Experiments
### Performance Comparison
#### 4.2.1 Accuracy of Priority Prediction  
First of all, we need to verify the accuracy of TD-error's sum. In our experiment, the training period of the predicted model is \(10^{5}\). During this period, the prediction model should keep accuracy. Figure 10 shows the accuracy of predicted TD-error's sum on game BeamRider and Pong. The green curve represents the predicted TD-error's sum which using the last updated model. The blue curve represents the predicted TD-error's sum which uses the current updated model. The prediction accuracy of the model is guaranteed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Hyperameter** & **Memory** & \(\alpha\) & \(\beta\) & **Batch Size** & \(\gamma\) & \(\eta\) & **Target Q** & \(\epsilon\) \\ \hline Value & \(10^{6}\) & 0.6 & 0.4\(\rightarrow\)1.0 & 32 & 0.99 & 0.0001 & 40,000 & 0.0\(\rightarrow\)1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main hyperparameters for every baseline. There are some other hyperparameters that are the same as ATDC-PER. All tasks use the same hyperparameter settings.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Hyperameter** & **Memory** & \(\alpha\) & \(\beta\) & **Batch Size** & \(\gamma\) & \(\eta\) & **Noise \(\sigma\)** & **Noise \(\theta\)** \\ \hline Value & \(10^{4}\) & 0.6 & 0.4\(\rightarrow\)1.0 & 32 & 0.99 & 0.0001 & 0.2 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main hyperparameters for every baseline. There are some other hyperparameters that are the same as DDPG + PER [12]. All tasks use the same hyperparameter settings.  
Based on this prediction model, we can directly calculate the true priority. In this work, we use the Mean Absolute Percentage Error (MAPE) [41] to estimate accuracy, which is defined as Equation (13). As we only need the priorities of the current mini-batch, the \(m\) in Equation (13) is set to the batch size.  
\[MAPE=\sum_{i=1}^{m}\left|\frac{y_{i}-\hat{y_{i}}}{y_{i}}\right|\times\frac{100} {m} \tag{13}\]  
where \(y_{i}\) is the real value and \(\hat{y_{i}}\) is the predicted value. We split the training process into three stages, and we sample 2000 data at each stage. The mean MAPE of 2000 data represents the accuracy in each stage. Table 3 shows the result of each stage. Our algorithm Imp-PER has very high accuracy compared to ATDC-PER. There are two reasons for this: One is that the sum of prioritizing is very predictable compared to the priority of each data, which is shown in Figure 4a. The other one is that we calculate the real TD-errors of data with absolute accuracy. These two reasons determine the accuracy of the final priority prediction, which is shown in Figure 4b. Meanwhile, as the training progresses, the prediction accuracy of both algorithms is gradually improving.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 4 Our Proposal: Difference DV_Distance Using Correction Coefficients of Unknown Nodes
### Calculation of Correction Coefficients for Direction Anchor Node
The exponent \(h_{p,k}/\left(h_{p,i}+h_{p,k}\right)\) shows the ratio of the hop number \(h_{p,k}\) to the sum of hop numbers \(h_{p,i}\) and \(h_{p,k}\). The smaller this ratio is, the closer the direction anchor node \(k\) is to the unknown node \(p\), and then the calculated value of projection parameter is greater according to the property of exponential function.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Secondary Cryptography on Privacy
#### 3.2.2 Homomorphic Encryption for Achieving Blockchain Privacy  
Homomorphic encryption is a cryptographic technique that allows computation to be performed on the encrypted data without accessing the secret key. The computation results obtained are same as that of the original data. Moreover, it utilizes proxy re-encryption technology to protect the selected ciphertext from being attacked [66]. It can also be seen as an extended version of either symmetric-key or public-key cryptography. In [67], homomorphic encryption was deployed to enhance blockchain security. Various privacy and security breaching attacks, such as collision attack, primage attack and wallet theft attacks were the motivation behind the study. The two homomorphic encryption techniques used for the study were Goldwasser-Micali and Paillier encryption schemes [68] for data privacy. The preliminary results presented in the study portrayed that these two schemes had a lower processing time and greater resilience against aforementioned attacks.
## 11 Beyond the Standard Cavity Design
### Two-Color Pulses by Intra-Cavity Gain Shaping  
Many applications require shaping and controlling the spectrum of a KLM Ti:sapphire lasers. Specifically, a multi-lobed (in particular, dual-lobed) spectrum is useful for Raman spectroscopy [91], Raman microscopy [92; 93] and direct frequency comb spectroscopy [94]. Spectral shaping inside the optical cavity, which steers the laser oscillation towards the desired spectrum, is by far more profitable than spectral shaping outside the optical cavity, since the latter is inherently lossy in nature, due to the unavoidable filtering of undesired frequencies.  
Previous attempts to obtain pulses with dual-lobed (two-color) spectra can be divided into two categories. The first is intra-cavity loss shaping, which can be achieved, for example, by a double slit after the prism pair [95], by a three prism configuration using a separate \"second\" prism for each lobe [96] or by dual output coupling using custom-designed wavelength-dependent reflectivity curves of two output couplers [97]. Unfortunately, loss shaping is very sensitive to mode competition in the laser gain medium, which inflicts a stringent requirement that the lobes be perfectly symmetrical around the gain peak (equal gain) in order to coexist. The second category overcomes mode competition either by actively synchronizing two independent sources [98, 99] or by passive synchronization using a shared Kerr medium in both cavities [100, 101, 102]. These methods suffer from the considerable added complexity of several independent oscillators, which require careful stabilization of timing jitter between the participating pulse trains.  
We recently demonstrated the use of gain shaping, instead of loss shaping, in a single oscillator [103]. As illustrated in Figure 11c, this can be achieved by placing another gain medium at a position in the cavity where the spectrum is spatially dispersed. In this additional gain medium, the gain profile can be tailored at will by controlling the spatial shape of the pump in this medium. Since the spectrum is spatially dispersed, mode competition is canceled in this additional gain medium (different frequencies do not share the same gain volume, rendering the gain effectively inhomogeneous). While placing a single gain medium in a spatially-dispersed position was considered in the past both theoretically [104, 105] and experimentally [106], however, such a laser is inherently pump-inefficient, since it requires pumping of a much wider volume in the gain medium well above medium. It is therefore most beneficial to combine a large homogeneous (efficient) gain with a small amount of inhomogeneous (inefficient) gain, where the small shaped inhomogeneous gain acts as a lever to steer the competition in the large homogeneous gain medium. Consequently, robust and complete control of the oscillation spectrum inside the optical cavity was achieved in a power preserving manner, as illustrated in Figure 11e.
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 5 Conclusions  
Monitoring the conditions of the process parameters such as the cavity pressure, temperature, and end-gap plays an important role in the HP-RTM process to produce good-quality CFRP and reduce the cycle time. In addition, it is used to determine the optimal molding process conditions. In the present study, pressure and temperature sensors were installed both at the mixing head and in the mold and LVDTs were attached to the edge of the mold to monitor the process conditions in real-time. The developed embedded monitoring system consisted of a signal processing board, a single board computer, amplifiers, filters and terminal boards. The acquired sensor signals were processed by the monitoring system based on FPGA at high speed and the signals detected and analyzed in the HP-RTM process were displayed through the HMI programmed with LabVIEW. The HP-RTM process was largely divided into the three steps of vacuum, injection, and curing. The pressure and temperature of the resin circulated in the supply line during the vacuum step were monitored. In the injection step, the pressure and temperature in the mixing head were detected in the resin injection. It was possible to monitor the resin flow, impregnation, and curing state with the cavity pressure sensors installed at various distances from the injection port. When the injection step was completed, the impregnated and cured states of the resin could be predicted from the pressure deviation of the cavity pressure sensors (CP1-4) inside the mold. The temperature sensors (T1-4) could also detect the reaction heat generated by the resin that was curing. In the case of the constant mixing ratio of the resin and hardener, the curing reaction state of the resin could be determined by detecting the maximum reaction heat and the reaction rate of the resin. It was, therefore, possible to detect abnormal conditions such as the resin flow, curing state, non-impregnation, and the breakage of the mold seal by monitoring the pressure and temperature. In addition, the compressed state of the press, the balance of the mold, and the thickness of the CFRP specimen could be inferred from the change in the end-gap by the LVDT. In this study, the proposed FPGA-based embedded monitoring system was capable of high-speed real-time signal processing using multiple sensors such as pressure, temperature, and LVDT sensors. In addition, the feasibility of the condition monitoring of HP-RTM molding process was confirmed, all of which affected the CFRP quality in the actual field. Although the monitoring system was designed and manufactured to be suitable for the HP-RTM process, it can be applied to processes such as conventional RTM and Compression RTM.  
Research conceptualization, results analysis, and writing--original draft preparation, R.-W.K.; writing--review and editing, S.-R.K.; data analysis and programming, C.-M.K.; experiment and signal processing; K.-H.H.  
The authors declare no conflict of interest.
## 4 Proposed Inference Latency Prediction Approaches
### Proposed Inference Latency Prediction between Object Detection Algorithms
Considering these similar characteristics of the normalized inference latency between devices, the actual inference latency of the RFCN model for the client, edge, and cloud can be predicted based on their statistical information, \(\alpha\), and \(Z_{client}^{SSD-MobileNet}\). First, the SSD-MobileNet model is executed on the client to obtain \(Z_{client}^{SSD-MobileNet}\), and \(\alpha\) is determined by considering the correlation coefficients of normalized inference latency between the RFCN and SSD-MobileNet models. Then, the distributions of predicted inference latency for the client, edge, and cloud \(\hat{X}_{client}^{RFCN}\), \(\hat{X}_{edge}^{RFCN}\) and \(\hat{X}_{cloud}^{RFCN}\) can be calculated as below:  
\[\hat{X}_{client}^{RFCN}=\sigma_{client}^{RFCN}\cdot(\alpha\cdot Z_{client}^{ SSD-MobileNet})+\mu_{client}^{RFCN}, \tag{6}\]  
\[\hat{X}_{edge}^{RFCN}=\sigma_{edge}^{RFCN}\cdot(\alpha\cdot Z_{client}^{ SSD-MobileNet})+\mu_{edge}^{RFCN}, \tag{7}\]  
Figure 11: The normalized inference latencies of the RFCN model for the edge and the SSD-MobileNet model for the client in the REST protocol: (**a**) \(\alpha=1\); (**b**) \(\alpha=-1\). Figure 10: The normalized inference latencies of the RFCN and SSD-MobileNet models for the client in the REST protocol: (**a**) \(\alpha=1\); (**b**) \(\alpha=-1\). \[\hat{\chi}_{cloud}^{RFCN}=\sigma_{cloud}^{RFCN}\cdot\left(\alpha\cdot Z_{client}^{ SSD-MobileNet}\right)+\mu_{cloud}^{RFCN}, \tag{8}\]  
where \(\mu_{device}^{RFCN}\) and \(\sigma_{device}^{RFCN}\) are the mean and standard deviation of the inference latency of RFCN model for each device. It is assumed that the \(\mu_{device}^{RFCN}\) and \(\sigma_{device}^{RFCN}\) for all devices are known in advance on the client and are updated periodically. The correlation characteristics of the inference latency for each inference model and the analysis and delivery of statistical information are not the scope of this paper, and we will further study these topics in the future. As it becomes possible to predict the inference latency of the RFCN model for the client, edge, and cloud devices through the normalized inference latency of the SSD-MobileNet model for the client device, the actual inference latency of the RFCN model with relatively long latency can be predicted as short latency.
## 1 Introduction
[16] was effective in extracting water bodies with long-distance discrete distributions, it failed to provide sufficient detail when dealing with fine tributaries and complex edges. [PERSON] et al. [27] introduced Trans-SANet, characterized by a U-shaped structure, which enhanced the segmentation accuracy of fine water areas. However, when processing noisy regions, it may yield unstable predictions. In summary, the field of water detection still faces numerous challenges, including the neglect of water body boundaries, a weak ability to extract detailed information and the multi-scale features of water bodies, as well as the poor suppression of complex background noise. To address these issues, this paper innovatively proposes the Global Context Attention Feature Fusion Network (GCAFF-Net) for conducting water extraction. The main contributions of this paper can be summarized as follows:  
1. GCAFF-Net is proposed, which integrates global and local information, and can accurately refine the edge contour of water bodies, extract significant multi-scale features and suppress noise interference. It demonstrates outstanding performance in feature extraction and segmentation, significantly enhancing the ability to identify various types of water bodies from SAR imagery. 2. The Attention Feature Fusion Module (AFFM) is constructed. It integrates high-level semantic features and low-level detail features, enriches image detail information, enhances the model's learning ability for water body details, and ensures the network's extraction ability under different water body features. 3. A feature extraction module, namely Global Context Atrous Spatial Pyramid Pooling (GCASPP), is proposed to effectively capture contextual information from images across different receptive fields, ensuring the network's ability to recognize both large and small water bodies. It simultaneously achieves the fine recognition of water body edges and a suppression effect on complex background noise. The remainder of this paper is structured as follows: Section 2 elaborates on the specific details of the SAR images employed in this paper, outlines the overall model structure, and provides a detailed breakdown of the specific configurations of each design module. In Section 3, we conduct water detection experiments using a dataset constructed based on Sentinel-1 SAR images and compare the experimental results with those of several advanced semantic segmentation networks for analysis. In Section 4, we systematically summarize the work of this study and outline the directions and plans for future research.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 4 Parametric Analysis
### Effects of the Seawater Depth
#### 4.1.2 Displacement of the \"H-Shaped\" Structures  
Figure 9 shows that the horizontal displacements of the left vertical support and the right vertical support in the \"H-shaped\" structures of the tunnel are in the shape of \"X\".  
Figure 8: Maximum principal stresses of the tunnel: (**a**) bottom plate, (**b**) top plate.  
The depth of seawater increases from \(H\) = 23 m to \(H\) = 44.78 m, and the reduction rate of left and right horizontal displacements is 13.63% and 36.84%, respectively; this is due to the fact that the seawater absorbs part of the seismic wave energy, and the suppression effect of seawater on the vertical frequency of the tunnel increases with the increase in the depth of the water, which can effectively increase the seismic performance of the structure [54,55].  
However, when the overall horizontal displacement of the \"H-shaped\" structures decreases, the difference in horizontal displacement between the left and right vertical supports tends to increase. The rate of increase in horizontal displacement in the lower part of the \"H-shaped\" structures is higher than that in the upper part (Figure 9 neutral axis rotated 7\({}^{\circ}\) counterclockwise). Seismic loading causes the tunnel to vibrate. This vibration amplifies the response of the tunnel to the loads and may result in injury to the weak areas of the structural design. The tunnel will dissipate the seismic energy through the deformation of the \"H-shaped\" structures and stress transfer in the walls to dampen itself and reduce the vibration amplitude. Secondly, this deformation and stress transfer will also change the fundamental frequency of the tunnel [56], preventing resonance injury and increasing seismic performance. This is consistent with the stress transfer mechanism of the top and bottom plates of immersed tunnels in Section 4.1.1.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 5 Conclusions and Future Works  
This paper proposes a human pose estimation network called RHPNet to tackle the challenges in running posture estimation. RHPNet leverages our proposed RFSP module, which can expand the receptive field and enhance multi-scale feature fusion capability. Across three datasets, RHPNet demonstrates advanced performance, and the results showcase its remarkable generalization ability. Additionally, we have constructed a novel dataset called Running Human that is focused exclusively on human running activities and that serves as a challenging benchmark for the single-motion pose estimation problem.  
Artificial intelligence can play a crucial role in sports medicine, for instance, by enabling more precise detection of various key points of the human body in images or videos and applying them to research in sports medicine. Future work will primarily involve integrating the predicted key points of running posture obtained from RHPNet with professional studies on running posture. Comparative analyses between professional running postures and those identified in images or videos will make it possible to provide targeted suggestions for correcting running postures. Additionally, the newly proposed RFSP module in this paper is a lightweight and easily scalable module. This module aids in expanding the model's receptive field and capturing detailed information. Therefore, future considerations involve applying this module to other networks or to intensive prediction tasks such as semantic segmentation and object detection.  
Conceptualization, Y.Z.; writing--review editing, Y.Z.; data curation, X.X.; writing--original draft, X.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Yunnan Ten-thousand Talents Program.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal reason.  
The authors declare no conflict of interest.
# A Review on Flavonoids as Anti-_Helicobacter pylori_ Agents
## 3 Results
### Flavonoids Usefulness in Treating H. pylori
Additionally, extracts from _Cleistocalyx operculatus_ floral buds, which contain high concentrations of flavonoids and saponins, have also exhibited activity against _H. pylori_. These extracts inhibited biofilm formation, likely through suppression of urease activity and enhanced membrane permeability. Such findings suggest that _Cleistocalyx operculatus_ extracts may interfere with critical processes for _H. pylori_ survival and colonisation [131]. The ethyl acetate fraction of _Maackia amurensis_ was also found to contain three flavonoids--(-)-medicarpin, terrorigenin, and wistin--that were potentially anti-_H. pylori_ active. Among these compounds, (-)-medicarpin has the most potent inhibitory activity with an MIC\({}_{50}\) and MIC\({}_{90}\) of 6.25 and 25 uM, respectively. In addition, administration of 50 uM allowed a total bactericidal activity to occur [132]. One area for further development lies in the combination and testing of antibiotics with flavonoids. Innovative approaches involving the co-crystallisation of antibiotics with flavonoids have shown promising results. This co-crystallisation of antibiotics such as levofloxacin and ciprofloxacin with flavonoids like quercetin or myricetin has been reported to enhance antibiotic efficacy. This improvement suggests that flavonoids may exhibit additive or synergistic effects when combined with conventional antibiotics, providing a potential strategy to improve antibiotic therapy against _H. pylori_[133]. These findings highlight the potential of flavonoid-rich medicinal plant extracts as promising therapeutic agents against _H. pylori_. Some insights on flavonoids' pharmacological pathways against _H. pylori_ are depicted in Figure 4. Further investigations are needed to confirm their efficacy and elucidate the precise mechanisms through which they exert their antimicrobial effects.
## 3 Methodology and Comparative Experimental Setup
### Comparative Experimental Setup  
#### 3.2.1 Experimental Setup  
First, the standard artificial intelligence models, namely, the BP neural network model and the Elman neural network model, were used for monthly runoff prediction. The prediction ability of the two AI models was initially investigated, and subsequent modeling was performed based on them. Secondly, the EMD and EEMD pre-processing techniques were combined with the two artificial intelligence models to construct the EMD-BP, the EEMD-BP, the EMD-Elman, and the EEMD-Elman models. The accuracy of these four models was evaluated, and the model with the best prediction outcomes was selected for subsequent study. Finally, the quantile mapping method was used to post-process the best prediction model from the previous step to see whether the prediction accuracy could be further improved.