esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Neural Network Models
#### 2.2.2 BiLSTM  
Bidirectional LSTM (BiLSTM) is demonstrated in Figure 4. This enhanced model augments the capabilities of traditional LSTM by incorporating two independent LSTM layers that process sequences in both forward and backward directions. The bidirectional structure enables BiLSTM to capture more comprehensive and intrinsic relationships within sequential data, as it considers both past and future information simultaneously. This characteristic makes BiLSTM particularly well-suited for battery state prediction, where future states can influence current estimates, thus enhancing prediction accuracy. Here, we further integrate an attention mechanism into the BiLSTM model to achieve improved handling of battery data, enabling the model to focus on critical features and enhance prediction performance.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 1 Introduction
The results showed that the power generation efficiency of tandem double cylinders was much higher than that of an isolated cylinder, and revealed the vibration enhancement conditions of tandem double cylinders. In conclusion, due to the influence of upstream cylinder wake vortices on the downstream cylinder, the vibration intensity and power generation potential of tandem double cylinders are both higher than that of a single cylinder, and research on multi-cylinder tandem power generation is bound to become an important development direction of FIM. However, power generation from freely vibrating multi-cylinders needs numerous power take-off (PTO) devices. If the tandem double cylinders do not vibrate freely in the cross-flow direction, but are free to vibrate rotationally and cooperatively, the vibration energy of the two cylinders can be collected at the same time. In the mode of double cylinder rotational oscillation, the single-unit capacity of the supporting power generation equipment could be increased and the amount of equipment could be greatly reduced, so this power generation method would be more economical,l both in terms of dispatching and costs. Nevertheless, in the case of rotational vibration of two tandem-coupled cylinders, whether the oscillation level is increased or decreased, compared with free vibration, is rarely mentioned in the existing research. Therefore, it is necessary to thoroughly investigate the flow-induced rotation (FIR) of two tandem-coupled cylinders and to summarize the oscillation characteristics. This paper systematically explores the FIR of two mechanically tandem-coupled cylinders at different spacing ratios, from both theoretical and experimental aspects. The aim was to further guide and promote the design and practical engineering application of marine power generation FIM devices. For the framework of this study, the remaining parts proceed as follows: The principle of the FIR system is introduced and the mathematical expression is deduced in Section 2, followed by a detailed description of the method and content of the experiment in Section 3. The rotational oscillation characteristics of the FIR system at various spacing ratios are presented and analyzed in Section 4. Finally, the main conclusions are summarized in Section 5.
## 2 Related Works
In [16], [PERSON] et al. propose a distributed resource allocation method where minimum rates of cellular users and D2D pairs are maintained. A Game theoretic algorithm (GTA) is proposed for minimizing the interferences among D2D pairs. However, this approach provides low spectral efficiency. With regards to machine learning for resource allocation in D2D communication, there are only few works, e.g., [17; 18]. [PERSON] et al. [17] and [PERSON] et al. [18] exploit machine learning algorithms for D2D resource allocation. [PERSON] et al. [17] propose Distributed reinforcement learning (DIRL), Q-learning algorithm for resource allocation which improves the overall system performance in comparison to the random allocator. However, the model of Reinforcement learning (RL) is not well structured. For example, the set of states and a set of actions are not adequately designed. Their reward function is composed of only Signal to interference plus noise power ratio (SINR) metric. The channel gain between the base station and the user, and also the channel gain between users are not considered. This is a drawback since channel gains are important to consider as these help the D2D communication with better SINR level and transmission power, which is reflected in increased system throughput [19]. Recently, [PERSON] et al. [18] propose Distributed reinforcement learning (DIRL), Q-learning to solve the power control problem in underlay mode. In addition, they explore the optimal power allocation which helps to maintain the overall system capacity. However, this preliminary study has limitations, for example, in their reward function, the channel gains are not considered. In addition, in their system model only the transmit power level is considered for maximizing the system throughput. To consider RB/subcarrier allocation in the optimization function is a very important issue for mitigating interference [20]. Moreover, the cooperation between devices for resource allocation is not investigated in these existing works. A summary of the features and limitations of classical and RL-based allocation methods is given in Table 1.  
We propose adaptive resource allocation using Cooperative reinforcement learning (CRL) considering the neighboring factor, improved state space, and a reward function. Our proposed resource allocation method helps to provide mitigated interference level, D2D throughput and consequently an overall improved system throughput. Table 1 shows the comparison of all the above mentioned works with our proposed cooperative reinforcement learning. Firstly, we categorize the related methods in two types: classical D2D resource allocation methods and Reinforcement learning (RL) based D2D resource allocation methods. We compare these works based on D2D throughput, system throughput, transmission alignment, online task scheduling for resource allocation, and cooperation. We can observe that almost all the methods consider the D2D and system throughput. None of the existing methods for resource allocation consider the transmission alignment, online action scheduling, and cooperation for the adaptive resource allocation.
# Effects of Viewing Displays from Different Distances on Human Visual System
## 3 Results
### EEG Signals Response Analysis
#### 3.2.2 \(\alpha/\beta\) Power Ratio  
The results showed that the viewing time has a significant effect on \(\alpha/\beta\) power ratio, \(F\)(7, 448) = 8.947, \(p\)! 0.001, partial eta squared = 0.123. In addition, the viewing time with respect to the type of display has a significant effect on the \(\alpha/\beta\) power ratio, \(F\)(7, 448) = 5.667, \(p\)! 0.001.  
Figure 5 shows the effect of the viewing time with respect to the display type on the \(\alpha/\beta\) power ratio in decibel (dB). The results show that while watching the 3D display, the \(\alpha/\beta\) ratio increases over viewing time while the \(\alpha/\beta\) power ratio remains stable when the participants watched the 2D display over time. There is a significant difference between watching the 2D and 3D displays at time intervals of 4, 5, 6, 7, and 8 on the \(\alpha/\beta\) power ratio at \(p\)! 0.05 based on the pairwise comparisons. When watching the 3D display, the differences between the time intervals 1 and 2; 2 and 3; 4 and 5; 5, 6, and 7; 6 and 7 are insignificant, \(p\)! 0.05. It can be concluded that when watching videos in 3D display, the participants were visually fatigued between ten and twenty minutes of watching the 3D display. Subsequently, the fatigue increases significantly for 10 min (at 30 min of watching). Thereafter, the fatigue increases gradually for the next 10 min (at 40 min of watching), and then, increases gradually for the next twenty minutes (at 60 min of watching).
## 2 Materials and Methods  
### Data Preparation  
Data preparation precedes ECG pre-processing, involving three datasets: MIT-BIH Noise Stress Test Database v.1.0.0 (NSTDB) [31], QT Database v.1.0.0 (QTDB) [32], and Lobachevsky University Electroccardiography Database v.1.0.1 (LUDB) [33]. The metadataof the three databases are shown in Table 1. In this study, the noise removal model utilizes the QTDB and NSTDB, while the delineation model uses both the QTDB and LUDB.  
NSTDB contains ECG signals with three types of noise, i.e., baseline wandering (BW), electrode motion (EM), and muscle artifact (MA). BW is an undesired low-frequency fluctuation and interferes with ECG signal, as it can alter wave amplitudes and cause errors in the detection of important features [34]. EM is a disturbance in the ECG signal that typically appears as sudden fluctuations and can affect the accuracy of the information produced [35]. Meanwhile, MA appears as a high-frequency signal that can interfere with the identification of important ECG wave morphologies [36]. The sample of three noises from the NSTDB dataset is seen in Figure 1.  
QTDB contains real ECG records representing a wide range of QRS and ST-T morphologies with considerable variability. It comprises 105 ECG signals, each 15 min long,  
\begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} Delineation process \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: The summary of NSTDB, QTDB, and LUDB.  
Figure 1: The sample of ECG signal from NSTDB with (**a**) BW, (**b**) EM, and (**c**) MA. Blue: ECG signal waveform, orange: noise.  
with two channels sampled at 250 Hz. To incorporate real baseline drifts into the QTDB, real BW signals from the NSTDB were used. The NSTDB includes 30 min of 12 ECG recordings and three recordings of typical noise encountered in stress tests, sampled at 360 Hz. On the other hand, the LUDB is used exclusively to evaluate the delineation model. It contains real ECG records from 200 subjects, sampled at 500 Hz across 12 leads. The QTDB beats are up-sampled from 250 Hz to 360 Hz, conversely, for the delineation model with LUDB, beats are down-sampled from 500 Hz to 360 Hz, ensuring consistency with the NSTDB frequency of 360 Hz.
## 5 Experimental Results and Discussion
### Evaluation Metrics
#### 5.2.1 Decoding-Complexity and Rate Control Performance  
First, the decoding-complexity and rate-controlling capabilities of the proposed algorithm are evaluated by measuring the percentage error in achieving the target decoding-complexity and rate. In this case, the percentage error in bit rate is calculated using  
\[R_{e}=100\times\frac{(R^{T}-R^{r})}{R^{T}}. \tag{33}\]  
where \(R^{r}\) and \(R^{T}\) are the achieved and target bit rate, respectively. Similarly, the overall decoding-complexity controlling performance of the proposed algorithm is measured using  
\[C_{e}=100\times\frac{(C^{T}-C^{r})}{C^{T}}, \tag{34}\]  
where \(C^{T}\) and \(C^{r}\) are the target and achieved decoding-complexity levels in terms of the CPU cycles, for a particular number of frames. Moreover, the frame-wise rate and decoding-complexity control performances are measured using the percentage error between the allocated and actual number of bits and decoding-complexity per frame, respectively. (The decoding-complexity controlling performance is presented only for the proposed algorithm as the state-of-the-art algorithms do not support a mechanism to achieve a specified decoding-complexity).
## 4 Discussion
### Displacement Evolution Monitoring  
The cemetery area in the Montescaglioso village is located on the top of a hill, a residual strip of marine terraces, which consist of conglomerates and sands. Downslope of the area, monoliths of inclined cemented conglomerates highlight the recent instability of the terrace edges. Local displacements identified with MTInSAR datasets over this test site concern the NW slope of the hill. The direction of displacements is consistent with a prevailing west horizontal component, which is also confirmed by local surveys (Figure 10b-d).  
The cause of these displacements is to be found downslope of the NW slope, where satellite images (Figure 10e) show a continuous erosion of the slope toe. Moreover, Figure 10e is valuable for interpreting the temporal trend of the P1 targets (Figure 4b): (i) at the end of 2013 there was an increase in displacement trends, probably linked to the same heavy rainfall that triggered the Montescaglioso landslide; (ii) towards the end of 2014, a decrease in velocity occurred, attributed to some earthworks shown in Figure 10e (2015); (iii) the erosion subsequently continued, as the trend and Figure 10e (2019) show. These phenomena, which were also confirmed by local surveys, may evolve in the future, causing a landslide.  
Figure 10: (**a**) Enlargement of the mean LOS CSK PS displacement map over the Montescaglioso cemetery area (same as Figure 4a); (**b–d**) ground pictures of cracks consistent with displacements detected during a local survey, (**b**) on the NE part of the cemetery area, (**c**) on the road that runs alongside the cemetery and goes towards the valley, (**d**) on the wall of a chapel inside the cemetery; (**e**) satellite images (from Google Earth) showing the evolution from 2010 to 2019 of the N slope.  
The NE side of Pomario village is an area affected by various instability phenomena, mostly retrogressive roto-translational phenomena due to the presence of cemented cover layers [29; 41]. The displacements identified by the CSK ascending dataset in this test site (Figure 9a) were also verified by local surveys. Along the road, which runs alongside the village, there are cracks and damaged structures (Figure 11) indicating continuous displacements. Again, in this case the \(FE\) index was able to recognize targets affected by phase unwrapping errors, that were corrected in order to derive reliable displacement time series. The analysis of these targets, characterized by a direction with a horizontal and downwards component (Figure 9a) and by similar trends of displacement time series (Figure 9e), suggests a retrogressive behavior of the instability process identified by the very-high-risk, R4 area. This hypothesis is confirmed by the cracks on the retaining walls and on the roads (Figure 11b,c) along the white dotted arrow in Figure 9a, detected during the local survey. This instability process, which is slowly expanding upslope, may cause further damage in the future and eventually evolve into a landslide.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 1 Introduction
In addition, MPC can be flexibly adjusted according to different control objectives, so as to improve the control performance and efficiency of the motor and meet different control requirements. For example, model predictive current control (MPCC) [62; 63] takes the current as the control target and uses the current and the given current as constraints at the next moment to optimize the optimal voltage vector output at this moment. The model predictive torque control (MPTC) takes torque and flux linkage as the control targets [64; 65]. In addition, it also includes model predictive flux control [66] and model predictive speed control [67]. However, there are many problems in the application of FCS-MPC, CCS-MPC, and DPC in PMSM. For example, they share a mismatch of parameters, which leads to a decrease in prediction accuracy, which leads to a decrease in system reliability. For their existing problems, the existing compensation measures and research directions are mainly shown in Figure 1.  
The main work of this paper is to introduce the existing problems and improvement schemes of PMSM predictive control from the perspective of technology development. The main contribution is a summary of the current state of research on different types of predictive control methods. In essence, the paper explores and studies predictive control. The feasibility, versatility and effectiveness of these methods are analyzed and summarized. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Whether PWM** & **Fixed** & **Algorithm** & **Whether to Include** \\  & **Modulation Is** & **Switching** & **Complexity** & **Constraint Processing** \\  & **Required** & **Rate** & **Complexity** & **Constraint Processing** \\ \hline Deadbeat & Yes & Yes & Normal & No \\ Predictive Control & & & & \\ \hline Hysteresis & No & No & Normal & No \\ predictive control & & & & \\ \hline Continuous-set & & & & \\ model predictive control & & & & \\ \hline Finite-set model & & & & \\ predictive control & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prediction Algorithm Feature Comparison Summary Table. Additionally, the future development and research direction of predictive control are further discussed.
## 2 Experimental Procedures
### Deuterium Implantation by Plasma Exposure  
All irradiation experiments were finished in an ultra-high vacuum accelerator device using deuterium ions at normal incidence to the specimens; this device is the linear plasma generator comprehensive ECR plasma for tritium (CEPT) consisting of an electron-cyclotron resonance (ECR) plasma source at the Science and Technology on Surface Physics and Chemistry Laboratory, Jiangyou, Sichuan Province. Microwaves (2.45 GHz) are coupled into the vacuum vessel through a waveguide. The magnetic field is created by four magnetic coils. CEPT can produce a steady-state plasma confined by a steady-state magnetic field of 0.25 T. A diverging plasma beam impinges perpendicularly onto the substrates. The energy of the ions impinging on the substrates is controlled by applying a dc bias to the substrate electrode. The background pressure was lower than 5 \(\times\) 10\({}^{-5}\) Pa, and below 0.6 Pa during implantation. The specimens were bombarded by a 100 eV D beam, which was below the energy threshold for creating lattice defects by atomic displacements and causing significant damage to the surface by sputtering. The D ion flux was about 1.8 \(\times\) 10\({}^{21}\) m\({}^{-2}\) s detected by a Langmuir probe (ESPION). The specimens were exposed to deuterium plasma at several different fluences: 1.30 \(\times\) 10\({}^{25}\), 2.59 \(\times\) 10\({}^{25}\), and 5.18 \(\times\) 10\({}^{25}\) D/m\({}^{2}\) with the flux controlled at 1.8 \(\times\) 10\({}^{21}\) D/m\({}^{2}\) s. The required fluences were achieved by adjusting the duration of the exposure (2, 4, and 8 h). During deuterium plasma bombardment, the specimen's temperature was kept at 500 K, controlled by an external open-circuit thermostat using cooling water. After the irradiation, the ion beam was turned off and the temperature of specimens was allowed cool to room temperature. The low fluence and high fluence were primarily controlled by the exposure time; thus, the specimens were exposed to plasma at different irradiation durations. The detailed experimental parameters are represented in Table 1. The difference between each set of experiments was that only one parameter changed, while all the other parameters remained as close as possible. By changing the exposure plasma fluences (exposure duration), we were able to investigate its effects on surface morphology changes and deuterium retention.
## 3 Numerical Simulation  
### Method  
Based on the ANSYS(2020) software Fluent module, a meso-scale CFD simulation was carried out to determine the reference wind pressure for selected sites in the coastal complex terrain of Taizhou. First, the computational domain would cover the whole Taizhou region (i.e., more than 10,000 km\({}^{2}\)), and a large terrain surface was established based on the topographic data of Taizhou. The maximum wind speed along the dominant wind direction was simulated. The power law was used to describe the mean wind speed profile as the inlet condition of CFD simulation. The maximum wind speed and the dominant wind direction of five main stations were taken as verification conditions for the CFD simulation results. The simulated wind speed data of each site of interest were collected. Finally, based on the simulated maximum wind speed data, the design wind speed with a 50-year return period for a site of interest was estimated according to the extreme type I probability distribution model [28]. The overall procedure to determine the design wind speed and in turn the reference wind pressure for a given site is shown in Figure 4.  
Figure 3: Annual maximum wind speed series of Hongjia station from 1978 to 2021.
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
###### Abstract
To address the shortcomings of current deep-learning-based crack segmentation methods for the crack segmentation task, an improved mobile attention semantic segmentation network, MA-Xnet, is proposed, based on the semantic segmentation models U-Net and DANet. The effectiveness of our algorithm is verified via a comparison with the public dataset. The proposed network is innovative, as set out below. 1. U-Net increases the segmentation accuracy by fusing the convolutional layer features with the deconvolution layer features through skip connections during upsampling learning, but these fused features depend on the features learned by downsampling. To reduce the dependence on the convolutional layer features fused by skip connections, and thus further improve the segmentation accuracy, a new skip connection method, cross-skip connection, is proposed. In this way, an X-shaped dual input and output backbone network, X-net, is constructed. X-Net uses two parallel U-Net models for feature learning, and the skip connection of the two U-type encoders to the decoder in the U-Net is changed to a cross-skip connection, so that the feature mapping of the two parts of the encoders is skip connected to the decoder part of the other, which enables the different feature information extracted from the two parts of the encoders to interact in the decoder part to improve the utilization of the crack feature information. 2. A position attention module (PA) and a channel attention module (CA) are used in the penultimate layer of the two decoder sections of the model to capture global contextual information in two different dimensions, spatial dimension, and channel dimension, respectively, focusing on the enhancement of the semantic features of the cracks to integrate the semantic information of deep and shallow layers and, thus, enhance the feature representation. 3. To address the problem of oversized network parameters, we used the 17-layer bottleneck residual depth-wise separable convolutional block of MobileNetV2 as the feature extraction part of the model to make the network lightweight.