esa-sceva/satcom-chunk-collection huggingface dataset:
# Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning
## 3 Servo System Control Strategy Based on Reinforcement Learning
### Design Scheme of Reinforcement Learning Agent  
#### 3.3.1 Network Design for Actor  
Two four-layer neural networks are established on the basis of the deterministic policy gradient algorithm. They are the actor evaluation and target networks, which have the same structures but different functions. The structure of the actor network is shown below:  
As illustrated in Figure 5, the actor evaluation network has exactly the same number of neurons and structure as those of the actor target network. The input layer of the actor evaluation network has six neurons, which correspond to six input nodes, namely, for the angular velocity \(X(t-1)\) at the last moment, the output error value \(e(t-1)\) of the previous moment, the angular velocity \(X(t)\) at the current moment, the output error value \(e(t)\) at the current time, the signal \(H(t+1)\) needed to track in the next moment, and the signal \(H(t+2)\) needed to track after two times, respectively. \([X(t-1),e(t-1),X(t),e(t),H(t+1),H(t+2)]\) is a set of reinforcement learning agent state \(s_{t}\). The action value \(\mu(s_{t}|\theta^{\mu})\) of the evaluation network is output. The inputs of the actor target network are the current angular velocity \(X(t)\), the output error value \(e(t)\) at the current time, the angular velocity \(X(t+1)\) at the next moment after the interaction between action \(\mu(s_{t}|\theta^{\mu})\) and the environment, the output error value \(e(t+1)\) at the next moment, the signal \(H(t+2)\) tracked after two times, and the signal \(H(t+3)\) tracked after three times. \([X(t),e(t),X(t+1),e(t+1),H(t+2),H(t+3)]\) is a set of states \(s_{t+1}\) at the next moment. The action value \(\mu\left(s_{t+1}|\theta^{\mu}\right)\) of the target network is output. The actor network contains three hidden layers. The first hidden layer comprises 200 neurons, and the activation function in this layer is ReLu6. The second hidden layer also consists of 200 neurons and the activation function is ReLu6. The third hidden layer is composed of 10 neural networks, and the  
Figure 4: Adaptive PID current compensation control structure diagram based on reinforcement learning agent 2.  
activation function is ReLu. Each layer of neurons uses L2 regularization to prevent overfitting of the neural network.
###### Abstract  
In the cloud datacenter, for the multi-tenant model, network resources should be fairly allocated among VDCs (virtual datacenters). Conventionally, the allocation of cloud network resources is on a best-effort basis, so the specific information of network resource allocation is unclear. Previous research has either aimed to provide minimum bandwidth guarantee, or focused on realizing work conservation according to the VM-to-VM (virtual machine to virtual machine) flow policy or per-source policy, or both policies. However, they failed to consider allocating redundant bandwidth among VDCs in a fair way. This paper presents a bandwidth that guarantees enforcement framework NXT-Freedom, and this framework allocates the network resources on the basis of per-VDC fairness, which can achieve work conservation. In order to guarantee per-VDC fair allocation, a hierarchical max-min fairness algorithm is put forward in this paper. In order to ensure that the framework can be applied to non-congestion-free network core and achieve scalability, NXT-Freedom decouples the computation of per-VDC allocation from the execution of allocation, but it brings some CPU overheads resulting from bandwidth enforcement. We observe that there is no need to enforce the non-blocking virtual network. Leveraging this observation, we distinguish the virtual network type of VDC to eliminate part of the CPU overheads. The evaluation results of a prototype prove that NXT-Freedom can achieve the isolation of per-VDC performance, which also shows fast adaption to flow variation in cloud datacenter.  
cloud datacenter; cloud networks; per-VDC fairness; bandwidth guarantees +
Footnote †: journal: Applied Sciences
## 4 Results
### Dynamic Setup
#### 4.2.1 CNo  
The findings for the signal-to-noise ratio are displayed in Figure 9, and they were collected to achieve the results with a moving vehicle.  
In four of the 11 scenarios evaluated, the circularly polarized devices performed better. On the other hand, five out of the 11 test scenarios showed better performance from the devices using linear polarization. The two remaining scenarios showed a virtually comparable performance from both devices, with the linearly polarized devices performing 0.1 dB better.  
Figure 8: TTFF results were obtained in the front dashboard location of a car, with the vehicle parked in a place with open space.  
Figure 9: Result of the signal-to-noise ratio obtained at different locations of the car on a route, including highway, city, and forest, following the path in Figure 4.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Machine Vision Systems for Bottling Quality Inspection
#### 4.3.1 Detecting Imperfections  
AI-driven machine vision systems can identify a wide range of defects during the bottling process. These defects can include cracks or chips in bottles, incorrect or misaligned labels, and inconsistencies in fill levels. By using machine learning algorithms, these systems can learn from historical data to improve their accuracy over time. This capability ensures that only products meeting stringent quality criteria proceed to the market, thereby reducing the risk of customer dissatisfaction and recalls.  
For example, a study by [PERSON] et al. demonstrates the implementation of a machine vision system for industrial quality control inspections, highlighting the significant improvements in defect detection accuracy achieved through the integration of AI technologies [95].  
Additionally, a more recent study by [PERSON] and [PERSON] discusses an in-process intelligent inspection system that uses machine vision and deep learning techniques to detect flaws in the dimensions of bottles on a production line. This system achieved a mean average precision of 99.5% for object detection, demonstrating the efficacy of AI in maintaining high-quality standards in manufacturing processes [96].  
These systems can also detect micro-defects that might be missed by human inspectors, ensuring a higher level of quality control. For instance, [PERSON] et al. highlight the role of machine vision in detecting minute defects and ensuring the quality of visual inspections, emphasizing the importance of deep learning in enhancing image analysis and defect detection [97]. Machine vision systems, powered by AI, are revolutionizing the bottling process by providing advanced quality inspection capabilities. These systems utilize high-resolution cameras and sophisticated image processing algorithms to detect imperfections in bottles, labels, and packaging materials in real-time. Recent advancements in machine vision technology have enabled the development of highly accurate and efficient inspection systems, which are crucial for maintaining the high standards expected in the winemaking industry.
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 4 Analysis of the Results of the Numerical Simulation of the Original Scheme  
### Development of Temperature Field Cloud Maps Under Seepage Conditions  
To gain a better understanding of the distribution and variation of the temperature field in the freezing process, we analyzed the temperature field cloud at a cross-section of Z = 10 m. The development and distribution of the temperature field cloud at (1 d, 5 d, 15 d, 30 d, 40 d) for the Z = 10 m cross-section are illustrated in Figure 6. Examining the results from Figure 6 reveals that the development of flow lines between the frozen curtain and the groundwater clearly demonstrates the coupling between the temperature field and the seepage field. At the initial stage of freezing (1 st day), freezing tubes have just initiated in the entire soil body, and the minimal cold generated by the freezing tubes is confined to the immediate vicinity of the tubes. The blue area is limited to the vicinity of the freezing tubes, with cold from the tubes not yet diffusing into the surrounding soil. At this time, groundwater flow moves from high to low heads through the entire freezing area. By the 5 th day of freezing, the density of freezing tubes makes it evident that the blue color in the strong freezing area is more pronounced than in the weak freezing area in the temperature field map. The deeper blue in the temperature field cloud diagram indicates a lower average temperature in the strong freezing zone compared to the weak freezing zone, showcasing a superior freezing effect. The freezing curtain is gradually forming, and the curtain in the strong freezing zone begins to impede water flow. Groundwater flow lines start to bypass the freezing curtain, though some water flow persists within the curtain. By the 15 th day of freezing, it is evident that most of the frozen area has reached the freezing temperature, and the frozen soil curtain is largely formed, obstructing groundwater flow. At this point, there is no groundwater flow within the frozen soil curtain, indicating that the curtain can effectively hinder groundwater flow. On the 30 th day of freezing, the permafrost curtain is well-developed, completely isolating groundwater flow. At this time, it is clear that groundwater flows around the entire permafrost curtain. From 30 d to 40 d of freezing, the permafrost curtain enters a period of development and stability, becoming increasingly thicker. This process highlights the role of the permafrost curtain in stopping groundwater flow and reinforcing the soil body.
## 3 Results
### Components of the GNSS System  
The onboard GNSS receiver system consists of two GNSS receiving antennas, one low-noise front-end amplifier, and two GNSS navigation receivers. The GNSS antennae collect navigation signals, which are first filtered and boosted by the front-end amplifier before being routed to the navigation receivers. These receivers perform signal processing functions like down-conversion, digital sampling, demodulation, despreading, acquisition, and tracking in order to obtain raw navigation observation data. These data allow the satellite to execute key activities such as positioning, orbit determination, and timing. The GNSS system architecture is illustrated in Figure 3.  
Figure 2: Data preprocessing workflow.  
The Gaofen-7 satellite's onboard receiver has an FT601 floating-point DSP processor with a 32-bit external bus and word-access mode. With a clock speed of 160 MHz, the CPU can achieve up to 960 MFLOPS in floating-point operations and 1280 MIPS in fixed-point performance. The device features two multichannel buffered serial ports (MCBSP), 1 Mbit of on-chip SRAM, and two 32-bit timers. The processor's EMIF interface allows for connectivity with a variety of external memory types, including SBSRAM, SDRAM, and asynchronous memory. In this investigation, the DSP operates at a frequency of 112 MHz. The BOOTMODE pin is set to allow MAPI memory mapping, and the system initializes programs using a 16-bit FLASH loading method. Several critical operations in spaceborne GNSS receivers must be done in under a second, including inter-board communication, data packaging, and clock disciplining. Among these jobs, the sub-meter-level orbit determination technique normally requires a large amount of computer power. Figure 4 provides an overview of the Gaofen-7 receiver system architecture, while Figure 5 outlines the software execution flow of the orbit determination module.  
Figure 4: Receiver Example Diagram.  
Figure 3: GNSS system architecture.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 3 Proposed Algorithm
### Power Allocation Algorithm  
In this section, we first introduce the basic particle swarm. Subsequently, we modify the particle swarm optimization algorithm. Our work is mainly reflected in the following aspects. We add random adjustment numbers that follow beta distribution to inertial weights. Then, we introduce mutation and crossover operations in differential evolution to update the position of particles. Finally, the suboptimal solution of power allocation is obtained by iterative search.
## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.2.3 Privacy Protection Evaluation  
Figure 10 shows the comparison results of the impact of different privacy budgets on model accuracy based on the MNIST dataset.  
Figure 10 illustrates the comparison of Accuracy, Precision, F1-Score, and Recall metrics for federated learning in space-air-ground integrated networks under different privacy budget values based on the MNIST dataset. The experimental results clearly demonstrate the trade-off between performance and privacy protection strength: the larger the privacy budget \(\varepsilon\), the higher the performance metrics generally are, but the corresponding privacy protection strength decreases. In terms of convergence speed, higher privacy budgets (\(\varepsilon=5.0\) and \(\varepsilon=10.0\)) exhibited significantly faster convergence rates, with rapid performance improvements in the first 3 iterations. In contrast, low privacy budget settings (\(\varepsilon=0.5\) and \(\varepsilon=1.0\)) showed slow and unstable convergence processes. The \(\varepsilon=10.0\) configuration reached an accuracy of approximately 98% by the 10 th round, while the  
Figure 8: Training time overhead comparison based on the MNIST dataset.  
Figure 9: Training time overhead comparison based on the UC Merced Land Use dataset.  
\(\epsilon=0.5\) configuration only achieved an accuracy of about 18%, indicating that the model struggles to effectively learn data distributions under extremely low privacy budgets.  
As can be observed from the figure, there are noticeable fluctuations in the performance metrics at lower privacy budget values (such as \(\epsilon=0.5\) and \(\epsilon=1.0\)). This phenomenon primarily stems from the inverse relationship between privacy budget and noise interference--smaller privacy budgets necessitate larger noise addition, resulting in more unstable gradient information during model updates. Additionally, the exploration behavior inherent in the dual-layer reinforcement learning strategy occasionally leads the system to investigate new device combinations across certain rounds, causing temporary performance variations. While these exploratory actions are essential for long-term optimization, they inevitably introduce short-term fluctuations in performance metrics.
## 2 System Model  
This paper considers a real-time transmission system, such as that depicted in Figure 2. Specifically, the system is designed to transmit packets via parallel fading channels within a hard delay constraint. Time is discretized into time slots, with each slot spanning a duration of \(T\) seconds. Meanwhile, we suppose that the transmitter has perfect knowledge of the channel state information. This assumption facilitates transmit power adaptation, which partially mitigates the detrimental impact of channel fades.
## 3 Results
### Work Stability and Movement Pattern
#### 3.2.1 Postural Stability Analysis  
Postural stability was evaluated through the center of mass (COM) displacement metrics derived from the waist-mounted IMU sensors. Figure 6 illustrates the average sway area for participants during different construction tasks with and without an exoskeleton.  
Figure 6 illustrates the comparison of average sway area between conditions with and without the exoskeleton across six different task conditions (CT-Low, CT-High, MT-Low, MT-High, ST-Low, ST-High). The blue bars represent sway area without the exoskeleton, while green bars show sway area when wearing the exoskeleton. A reduction in sway area is observed across all task conditions when using the exoskeleton, with particularly pronounced decreases in high-intensity tasks (CT-High, MT-High, ST-High). This visual representation clearly demonstrates that the exoskeleton enhances workers' postural stability, with greater benefits observed during more challenging high-intensity tasks.  
Analysis of the COM displacement data revealed significant differences in postural stability between exoskeleton and non-exoskeleton conditions. As shown in Figure 6 and Table 3, the mean sway area was reduced by 27.3%, 31.5%, and 24.8% for CT, MT, and ST tasks respectively when using the exoskeleton under low-intensity conditions. Under high  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Task Type** & **Intensity** & **\(t\)-Statistic** & **\(p\)-Value** & **Effect Size** & **Significance** \\ \hline \multirow{2}{*}{Sack Carrying (CT)} & Low & 16.28 & \textless{}0.001 & 3.92 & \(\Circle{}\) \\  & High & 19.74 & \textless{}0.001 & 4.85 & \(\Circle{}\) \\ \hline \multirow{2}{*}{Masonry Bricklaying (MT)} & Low & 14.63 & \textless{}0.001 & 3.37 & \(\Circle{}\) \\  & High & 17.51 & \textless{}0.001 & 4.12 & \(\Circle{}\) \\ \hline \multirow{2}{*}{Scaffolding Installation (ST)} & Low & 13.52 & \textless{}0.001 & 3.08 & \(\Circle{}\) \\  & High & 18.46 & \textless{}0.001 & 4.53 & \(\Circle{}\) \\ \hline \end{tabular}  
* The circle (\(\Circle\)) indicates statistically significant differences at \(p\) < 0.001.  
\end{table}
Table 2: Paired \(t\)-test results for productivity comparison between exoskeleton and non-exoskeleton conditions.  
intensity conditions, these improvements were even more pronounced, with reductions of 33.3%, 35.4%, and 31.3%, respectively. This reduction in sway area indicates improved stability and balance maintenance during task execution.  
Particularly noteworthy was the significant reduction in mean sway velocity across all task conditions when wearing the exoskeleton. The average reduction in sway velocity was 25.0%, 20.0%, and 22.9% for low-intensity CT, MT, and ST tasks, respectively, and increased to 30.9%, 31.7%, and 28.8% for the corresponding high-intensity conditions. These results demonstrate that the exoskeleton provides greater stability benefits during more physically demanding tasks, allowing workers to maintain better control over their movements despite increased workload.
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 4 Simulation Results
As shown by the results, it is worth noting that the performance of the proposed mobility scheme can be further improved either by increasing the transmit power or by employing sophisticated modulation techniques such as pulse position modulation (PPM). The proposed VLC-OBAN mobility model has advantages of reliable data transmission with no interference between nodes. It is also relatively easy to design and implement practically.
###### Abstract  
Accurate prediction of the time-dependent system dynamic responses of floating offshore wind turbines (FOWTs) under aero-hydro-coupled conditions is a challenge. This paper presents a numerical modeling tool using commercial computational fluid dynamics software, STAR-CCM+(V12.02.010), to perform a fully coupled dynamic analysis of the DeepCwind semi-submersible floating platform with the National Renewable Engineering Lab (NREL) 5-MW baseline wind turbine model under combined wind-wave excitation environment conditions. Free-decay tests for rigid-body degrees of freedom (DOF) in still water and hydrodynamic tests for a regular wave are performed to validate the numerical model by inputting gross system parameters supported in the Offshore Code Comparison, Collaboration, Continued, with Correlations (OC5) project. A full-configuration FOWT simulation, with the simultaneous motion of the rotating blade due to 6-DOF platform dynamics, was performed. A relatively heavy load on the hub and blade was observed for the FOWT compared with the onshore wind turbine, leading to a 7.8% increase in the thrust curve; a 10% decrease in the power curve was also observed for the floating-type turbines, which could be attributed to the smaller project area and relative wind speed required for the rotor to receive wind power when the platform pitches. Finally, the tower-blade interference effects, blade-tip vortices, turbulent wakes, and shedding vortices in the fluid domain with relatively complex unsteady flow conditions were observed and investigated in detail.  
computational fluid dynamics; floating offshore wind turbine; dynamic fluid body interaction; semi-submersible platform; OC5 DeepCwind +
Footnote †: journal: Applied Mechanics and Physics Letters
## 3 Methodology
### Bootstrapping DEA Technique  
Data errors may severely affect the efficacy of DEA. The DEA method uses a sample for the efficiency analysis, so the specific differences that can be found in estimations may be only motivated by sampling noise to a great extent than precise deviation in efficiency scores of the units. In this paper, we use a bootstrapping approach to address to this problem. Bootstrapping, introduced by Efron [51], is based on the idea of re-sampling from an original sample to create replica datasets which we can use to make statistical inferences. The'smoothed bootstrap' approach of [PERSON] and [PERSON] [52; 53] is used in this study. The theoretical foundations of this method are set out in [53].  
The key assumption underlying this approach is that the known bootstrap distribution will mimic the original, unknown distribution provided that the known data generating process (DGP) is a consistent estimator of the unknown DGP. The most common approach is to estimate the original densities of the performance scores non-parametrically using kernel smoothing methods, combined with a reflection method [54]. If this approach is followed, the bootstrap process will generate values that mimic the distributions that would be generated from unobserved and unknown DGP [52; 55]. This work uses the bootstrapping technique proposed by [PERSON] and [PERSON] [56] which can be summarized as follows:  
1. Calculate the DEA efficiency score \(\{\hat{\theta}_{i};i=1,2,\ldots,n\}\) with the original data \((x_{i},y_{i})\).
2. Use Kernel density estimation and the reflection method to generate a random sample \(\left\{\theta_{i}^{*};i=1,2,\ldots,n\right\}\) with replacement from the original DEA efficiency score \(\hat{\theta}_{i}\).
3. Generate \(\theta_{i}^{*}\) using: \[\overline{\theta}_{i}^{*}=\ \theta_{i}^{*}+h\varepsilon_{i}^{*},\ i=1,\ 2,\ldots,\ n,\] where \(\varepsilon_{i}^{*}\) is a random draw from a standard normal distribution and \(h\) is a control parameter.
4. Obtain \(\theta_{i}^{**}\) from \[\theta_{i}^{**}=\overline{\theta}^{*}+\frac{\overline{\theta}_{i}^{*}- \overline{\theta}^{*}}{\sqrt{1+\frac{h^{2}}{s^{2}}}},\ i=1,\ 2,\ \ldots,\ n.\]
5. Generate resampled pseudo-efficiencies \(\gamma_{i}^{*}\) using \[\gamma_{i}^{*}=\left\{\begin{array}{ll}2-\theta_{i}^{**},\ if\ \theta_{i}^{**}<1\\ \theta_{i}^{**},\ i.o.c.\end{array}\right..\]
6. Obtain a0 new data sample using \(\left(x_{i}^{*},y_{i}\right)=\left(\frac{\gamma_{i}^{*}}{\hat{\theta}_{i}}x_{ i},y_{i}\right)\).
7. Calculate the DEA efficiency score with \(\left\{\hat{\theta}_{i}^{*};i=1,2,\ldots,n\right\}\) data, \(\left(x_{i}^{*},y_{i}\right)\).
8. Repeat steps 2 to 7 \(B\) times to create a set with \(B\) efficiency estimates for each unit: \[\hat{\theta}_{i,b}^{*};i=1,\ 2,\ \ldots,\ n;\ b=1,2,\ldots,\ B.\]  
The bootstrap estimate of the DEA bias is obtained through  
\[bias_{i}=\frac{1}{B}\sum_{b=1}^{B}\hat{\theta}_{i,b}^{*}-\hat{\theta}_{i},\ i=1,2, \ldots,n.\]A bias corrected efficiency estimator is then obtained by defining  
\[\overline{\theta}_{i}=2\hat{\theta}_{i}-\frac{1}{B}\sum_{b=1}^{B}\hat{\theta}^{*} _{i,b},\ i=1,2,\ldots,\ n.\]