esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solving Method
This formulation incorporates the impacts of both resource consumption and task profit on cumulative returns. Given that the reward values undergo standardization prior to neural network training, the introduction of weighting coefficients to either task profit or storage consumption becomes unmeaningful. Section 3.1 describes the description of the [PERSON] decision process model in detail. Based on the above description, we propose a DRL-based TPHO framework. A deep neural network determines a task action, then based on the task action, a heuristic algorithm determines the resource action and the execution time. The decisions of the deep neural network and heuristic algorithms jointly drive the state transition of the environment. The sample (state, task action, reward, and next state) is generated in this process and trains the deep neural network model. Figure 1 shows the interaction between the environment, MDP model, and TPHO framework. The deep neural network in TPHO determines the task sequence. It receives task information sequences that describe the environment and outputs probability sequences for task selection. Since encoder-decoder architecture is a universal framework for sequence-to-sequence learning, an encoder-decoder is designed to determine the task action. The state vector comprises both static and dynamic elements, where static components maintain invariant values across decision steps while dynamic counterparts vary temporally. Given their distinct characteristics, these elements undergo separate feature encoding through individual fully connected layers. This architectural design serves dual purposes: (1) the fully connected structure prevents information loss through linear transformation preservation, and (2) parallel encoding significantly reduces computational overhead compared with concatenation before encoding. Figure 1: The interaction between the environment, MDP model, and TPHO framework. To provide scheduling sequences for the neural network, gated recurrent unit (GRU) networks receive historical static elements from previously scheduled tasks, effectively capturing sequential dependencies. GRU selection over long short-term memory (LSTM) networks is strategically motivated by two factors [58]: (1) the moderate sequence lengths in this decision-making context and (2) the limited dataset scale characteristic of satellite scheduling problems. The GRU's streamlined architecture with fewer gating mechanisms enables accelerated training convergence while maintaining essential memory retention capabilities. Section 3.2 details the encoder-decoder network and Section 3.2 details the training process. To train the encoder-decoder network, the soft actor-critic (SAC) algorithm is employed. According to [PERSON] et al. [59], proximal policy optimization (PPO) faces stability challenges, and deep deterministic policy gradient (DDPG) and SAC demonstrate robust performance, notably in moderate-elasticity scenarios. The real-world application of the SAC model further affirmed its practical viability, marking a significant performance improvement [59].
## 2 Materials and Methods
### Quantification of NR Effects on Stimuli
Speech and noise stimuli for NR 1 (purple line) and NR 2 conditions (red line) are extracted using the phase-inversion technique. (**B**). Magnitude-squared coherence is shown across frequencies up to 22 kHz between a pair of speech stimuli: NR 1 vs. NR off (purple line) and NR 2 vs. NR off (red line), respectively. Figure 2: Illustration of implementation details of noise reduction (NR) algorithms with relevant equations and parameters. 1 The a posteriori signal-to-noise ratio (SNR) \(R_{\text{post}}(p,\omega_{k})\) denotes the SNR in the current short-time frame and should be computed in each 20-ms frame \(p\) and each spectral component \(\omega_{k}\) by comparing the magnitude of signal \(X(p,\omega_{k})\) and noise spectrum \(v(\omega_{k})\). The a priori SNR \(R_{\text{prio}}(p,\omega_{k})\) represents the SNR estimated in the last frame \(p-1\) by incorporating the previous frame with a weight of \(\alpha\) parameter (0.98) and the current frame with a weight of \(1-\alpha\). 2 The spectral gain \(G\) for each short-time frame in NR 1 is determined by \(R_{\text{post}}\), \(R_{\text{prio}}\), and the function M, where I\({}_{0}\) and I\({}_{1}\) represent the modified Bessel functions with zero and first order, respectively. 3 The spectral gain \(G_{\text{log}}\) for each short-time frame in NR 2 is determined utilizing \(R_{\text{post}}\), \(R_{\text{prio}}\), and the exponential integral \(E_{1}(x)\).
## 1 Introduction
According to the literature [17; 18], the classical similarity functions that are widely used in MOST to determine the wind profile are only valid for \(R_{f}<0.25\) and overestimate the velocity gradient for \(R_{f}>0.25\). For a given aerodynamic roughness \(z_{0}\) and a hub-height inflow velocity, this overestimated velocity gradient results in a smaller friction wind speed \(u_{*}\), and consequently a lower turbulence intensity, and could influence wake modeling of wind turbines. To the best of our knowledge, the side effects of the breakdown of MOST on wake modeling under stable conditions have not been well investigated. The present paper aims to investigate these side effects through numerical simulations and to make a modification on MOST to eliminate these side effects. This modification proposes a new set of similarity functions based on field measurements to limit the velocity gradient in very stable conditions and introduce the new similarity functions into the turbulence model proposed by [PERSON] et al. [16]. The remainder of this paper is organized as follows. MOST is briefly described in Section 2. Models for wind turbine wakes under stable conditions are introduced in Section 3, which cover two actuator disk models based on the thrust coefficient and Blade Element Theory (BEM) calculations, and three turbulence models for stable conditions based on MOST. Section 3 also proposes a modified turbulence model that can be consistent with arbitrary similarity functions, e.g., the new similarity functions. The breakdown of MOST under stable conditions is experimentally investigated and new similarity functions are proposed and validated in Section 4. All test models are studied through numerical simulations: the simulation details are described in Section 5, and the results of the simulations are discussed in Section 6 and then concluded in Section 7.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Digital Core Analysis  
Non-destructive imaging techniques and their associated modeling and analysis techniques developed in recent decades have greatly improved the understanding of percolation processes in porous media [37; 38]. 3D imaging techniques can describe the nature and geometry of mineral compositions, are easy to interpret, and have excellent properties for evaluating pore connectivity. However, 3D X-ray imaging methods usually have low resolution [36] and often need to be complemented with higher resolution 2D imaging methods (using microscopy).
Beiwan port is positioned as an international gateway port, focusing on the development of containers, bulk cargo, cold-chain containers, international cruise ships, trade, and clean material transportation, while Hainan Yangpu port is positioned as a regional international container hub port, mainly attracting domestic and foreign sources of goods and promoting international transit business. The New Western Land-Sea Corridor has been built from scratch, which has improved the development efficiency of the western region. However, there are still some problems, such as the bottleneck of the transport channel, insufficient construction of collection and distribution facilities, and lack of overall planning for the logistics hubs [3]. Applying Complex Network Theory to the research of freight network can systematically analyze its spatial structure, toughness, node growth, and other evolution rules, which is conducive to exploring regional freight spatial relations and optimizing its spatial structure. Paying attention to the coordinated development of passenger transport and freight, making full use of the idle capacity of passenger transport and freight transport, and rationally allocating its resources can greatly save transport costs, explore the development potential of channel transport, and maximize the economic effect of the channel. This will not only help improve the efficiency of the comprehensive transport network, but also promote the integrated development of regional passenger flow, freight, and economy. Thus, it can effectively support the integration of the western region into the double circulation, lead the coordinated development of the regional economy, and provide a new path for promoting sustainable development. Therefore, based on the perspective of the New Western Land-Sea Corridor transportation, this study is carried out on the passenger and freight network. First, the complex network theory is used to construct the spatial correlation network of passenger and freight respectively and reveal their evolution rules. Second, based on the characteristics of passenger and freight network structure, the network optimization part is studied from the perspective of network resilience and network growth. Finally, the coupling coordination model is used to explore the coordinated development between passenger and freight.
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 3 Design of Composite Layered Cross-Coupling Controller
### Experimental Verification of Cross-Coupling Controller Based on Composite Layering
method reduced the average contour error, contour error, and IAE by 56.4%, 63.9%, and 68.7%, respectively. The method proposed in reference [34] faced challenges in tracking the target curve due to frequent curvature changes, leading to the algorithm's difficulty in timely tracking the desired trajectory, resulting in an overall system performance that fell behind the target curve. While the method introduced in reference [35] exhibited a smaller mean value than that of reference [34], the fluctuating tracking performance in the presence of external disturbances was evident from the max and IAE values. This algorithm occasionally failed to keep up with the target curve during the frequent curvature changes, and it occasionally exceeded the target curve. In contrast, the proposed method in this paper demonstrated excellent system performance when tracking spatial curves with multiple curvatures.
###### Abstract  
These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases.  
machine learning; AI; seakeeping; data analysis; numerical modelling; feature engineering; data augmentation Article
## 5 Simulation Result
### Proposed LQR Control with Integral Action Control (LQRIC)  
The simulation results of the \(LC\)-HAPF with proposed LQRIC under 50V DC-link voltage conditions are shown in Figure 5d and Table 2. After compensation, the \(\text{THD}_{\text{isx}}\) reduces to about 6.8%. The compensated \(i_{\text{sn}}\) is 0.38 Arms and the compensated \(i_{\text{sx}}\) is around 2.46 Arms. The three-phase PFs are equal to 1, and the \(\text{Q}_{\text{Total}}\) reduces to 2.1 var. The total SW loss of LQRC is 27.0 W.  
The simulation results of the \(LC\)-HAPF with proposed LQRIC under 40V DC-link voltage conditions are shown in Figure 6d and Table 3. After compensation, the \(\text{THD}_{\text{isx}}\) reduces to about 7.1%. The compensated \(i_{\text{sn}}\) is 0.36 Arms and the compensated \(i_{\text{sx}}\) is around 2.46 Arms. The three-phase PFs are equal to 1, and the \(\text{Q}_{\text{Total}}\) reduces to 2.9 var. Hence, the proposed LQRIC can achieve the smallest steady-state error. The total SW loss of LQRC is 15.9 W.
## 3 Simulations and Experiments
### Numerical Simulations
#### 3.1.2 Computing Speed Comparison  
To illustrate the acceleration performance of the proposed PC method to generate the SCGH, we compared the computation time of our method and the traditional PS method, as shown in Table 2 and Figure 7. The speedup ratio \(r_{s}\) is defined as shown in Equation (11) to measure the acceleration performance of our proposed method,  
\[r_{s}=t_{PS}/t_{PC}, \tag{11}\]  
where \(t_{PS}\) and \(t_{PC}\) denote the computation time of PS and PC method. All the simulations are calculated by Intel (R) Core (TM) i5-7200U CPU @ 2.50 GHz 2.71 GHz.  
For an object plane of the same resolution and corresponding flare angle of WRP, the computation speed of our PC method is further accelerated compared with that of the traditional PS method. Moreover, for the object planes with the same resolutions, the computation time is nearly consistent when calculated by the same method. When the resolution increases, the computation time of the PS method increases dramatically compared with our PC method. It turns out that our PC method has good scalability and it greatly speeds up the computation of the diffraction process between a plane and a sphere.  
The comparison of calculation time for the PS [17], convolution [17], and proposed PC methods is shown in Table 3. For the traditional PS method, the calculation is at an unacceptable low speed even though fewer sampling points can be used, and for the convolution method, a long period of time is required because of the staggering sampling rate required in the concentric spherical model in visible  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RESOLUTION** & **FLARE ANGLE** & **PC** & **PS** & **SPEEDUP** \\ \hline \multirow{3}{*}{\(64\times 64\)} & 90\({}^{\circ}\) & 0.012 s & 11.446 s & 953.83 \\ \cline{2-5}  & 30\({}^{\circ}\) & 0.012 s & 11.376 s & 948.00 \\ \cline{2-5}  & 7.5\({}^{\circ}\) & 0.012 s & 11.101 s & 925.08 \\ \hline \multirow{3}{*}{\(128\times 128\)} & 90\({}^{\circ}\) & 0.015 s & 187.492 s & 12499.47 \\ \cline{2-5}  & 30\({}^{\circ}\) & 0.015 s & 181.635 s & 12109.00 \\ \cline{2-5}  & 7.5\({}^{\circ}\) & 0.015 s & 183.380 s & 12225.33 \\ \hline \multirow{3}{*}{\(256\times 256\)} & 90\({}^{\circ}\) & 0.030 s & 3065.274 s & 102175.80 \\ \cline{2-5}  & 30\({}^{\circ}\) & 0.029 s & 2990.167 s & 103109.21 \\ \cline{2-5}  & 7.5\({}^{\circ}\) & 0.029 s & 2936.473 s & 101257.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation time comparison of PC and PS method with different resolution and flare angles of WRP.  
Figure 7: Speedup variation with the change in resolution and flare angle.  
range. In contrast, the SCGH can be perfectly synthesized by the proposed PC method at an obviously high speed with a small number of sampling points.
## 2 Design of Experiments  
Figure 1 displays the setup of the experiments. A continuous-wave fiber laser (YLR-6000-CL; IPG Photonics Co., Oxford, MA, USA) was used as the laser source. The laser beam emitted from the end of the operation fiber was collimated and focused with a wobble laser welding head (FLW-D50-W). The laser beam emitted from the end of the optical fiber was collimated by a lens with a focal length of 200 mm and afterwards was transmitted by a galvanometer scanner unit and focused on the specimen surface by an f-theta focusing unit with a focal length of 300 mm. The spot size of the focused laser beam was approximately 0.225 mm.  
Four-millimeter-thick 5056 aluminum alloy plates were used as welding materials. Table 1 summarizes the chemical composition of the substrate. Table 2 gives the processing parameters of the laser welding experiments. Figure 2 is the schematic diagram of beam oscillating pattern. The oscillating patterns applied in our experiments are transversal and circular. The beam oscillation diameter/width \(\mathrm{a_{y}}\) is 1 mm, and the value of the degree of beam overlap \(\mathrm{a_{x}}\) is 0.3 mm.  
Figure 1: Experimental setup: (**a**) equipment and (**b**) schematic of laser oscillation welding process.  
The welded plates were cut into standard tensile samples by electrodischarge machining (EDM), as illustrated in Figure 3. The cross sections of the welded joints were also cut by EDM, and then polished with abrasive paper and polishing cloth. The finished cross sections of the welded joints were etched by Keller's reagent, and then observed by an optical microscope (OM) (Leica DM4000M, Solms, Germany). The composition of the joints was tested by scanning electron microscopy (SEM) (Tescan MIRA3 LMU, Brno, Czech) equipped with an energy-dispersive X-ray spectrometer (EDS). An X-ray diffraction (XRD) instrument (Rigaku CD/max2200 VPC, Tokyo, Japan) was used to identify the strengthening phase in the fusion zone of the joint. The digital and intelligent micro-hardness tester (HVST-1000Z, Ningbo, China) was used to measure the Vickers microhardness. The tester has a load of 100 g force (gf) applied in a time of 20 s. Transverse tensile tests were carried out using a universal tensile testing machine (MTS CMT5105, Eden Prairie, MN, USA) at room temperature with a crosshead speed of 1 mm/min. The fractured samples were cut and examined by SEM to reveal the fracture patterns.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element** & **Cu** & **Cr** & **Mn** & **Si** & **Mg** & **Zn** & **Fe** & **Al** \\ \hline (wt.\%) & 0.10 & 0.05 & 0.05 & 0.30 & 4.5 & 0.10 & 0.40 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the aluminum alloy studied.  
Figure 3: Schematic illustration of transverse tensile test sample.  
Figure 2: Schematic diagrams of beam oscillating patterns: (**a**) without oscillation; (**b**) transverse oscillation; (**c**) circular oscillation.
## 3 Simulation Study
### Simulation Setup
This continuous model update and forecasting process was repeated until (120-s) time series observations were accumulated. In this simulation study, we applied the forecasting window, \(s\), from one to six. In each \(s\) value, we repeated this experiment 100 times and reported the average root mean square error (RMSE) values of \(s\)-step ahead forecasting obtained from these 100 repetitions. To use the EWMA methods, appropriate smoothing parameters should be determined. In this simulation, we changed the smoothing parameters between 0.1 and 0.9 with a step size of 0.1, and we reported the lowest average RMSE values among total smoothing parameter sets. The value of the smoothing parameter, 0.1, is sufficient to reduce the risk of involving the noises of latest time series observation, and also, the value of smoothing parameter, 0.9, is a sufficiently large value to consider the impact of the most current time series observations. For this reason, in previous works, the range of smoothing parameter values have been set between 0.1 and 0.9 [28; 37]. Since simulation data for pattern from 1 to 6 do not have seasonality, we conducted the experiments without the triple ES model in these patterns. Finally, the self-starting forecasting process uses only two initial data, and the period and seasonal factors of the triple ES method cannot  
Figure 2: Baselines of nine time series patterns. be accurately estimated using only this small number of time series data. Hence, in this study, we used the ground truth of these factors to determine them.
## 2 Principles and Methods
### Attention Block U-Net Deep Learning Network
#### 2.3.5 Networks for Comparison  
Another two CNN networks (U-Net, attention U-Net) are added for a comprehensive comparison. Here, U-Net is used to investigate the effect of with and without the attention mechanism, and attention U-Net is for analyzing the influence of attention designs. Attention U-Net was proposed in CVPR 2018 [74]. Like our proposed network, it is also based on U-Net, but the biggest difference is that the proposed AB-U-Net integrates attention blocks in the encoder part instead of that in decoder modules in the attention U-Net.
# A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability
## 2 Modular Assistive Robotic Platform
### Assistive Robotic Devices
#### 2.6.1 Exoskeleton Robotic Device  
An upper limb exoskeleton was designed with five active degrees of freedom corresponding to the following arm movements: shoulder abduction/adduction, shoulder flexion/extension, shoulder internal/external rotation, elbow flexion/extension, and wrist pronation/supination [11; 12; 21; 22]. This device allows the user's right arm to be moved to reach objects, thus facilitating the performance of ADLs (Figure 1).  
In addition to the arm exoskeleton, an active hand exoskeleton was designed to assist the opening and closing of the right/left hand [23; 24]. It consists of four independent modules anchored to a hand orthosis that actuate the movements of the thumb, index finger, and middle finger, and jointly move the ring and little finger. The configuration of the hand can be adapted according to the size of the hand.