esa-sceva/satcom-chunk-collection huggingface dataset:
# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
###### Abstract  
Marine pollution, which can cause damage to marine ecosystems, cut fishery production, and even harm human health, has aroused worldwide interest in recent years. Marine pollution reduction operations can stagnate in the case that the source of the pollution is unknown or hidden. In this paper, we present a novel method for marine pollution source localization using a network of mobile sensor nodes, such as autonomous underwater vehicles equipped with chemical sensors. Traditional reactive control methods can respond quickly to the shape dynamics of a chemical plume; however, they can hardly achieve intelligent cooperation unlike deliberative methods. In this study, we present a behavior composition method that attempts to combine the advantages of reactive and deliberative methods. An upwind-customized crossover operation based on the genetic algorithm was formulated as one of the elementary behaviors. The upwind sprint and movement away from the centroid of the sensor nodes were also modeled as another two elementary behaviors. Different sensor nodes are capable of different simultaneous elementary behaviors, enabling behavior composition in the mobile sensor network during plume source localization. The proposed method was evaluated using a widely used filamentous plume simulation platform, which has been used to facilitate field experiments in real marine environments. Simulation results indicate that the proposed method achieved high time-efficiency and localization accuracy during plume source localization in marine environments.  
marie pollution; pollution source localization; behavior composition Article
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction
### Automated Recipe Generation of Robot Grasping  
The robot handler needs the loading position of the target object, the unloading position to be transferred to, the trajectory of the robot manipulator, the control information of grasping, etc. for the product transfer work. The robot control information is included in the recipe and executes grasping or manipulation. In ISA-88.01 [47], the robot control information was formatted as \"Recipe.\" The user should modify the recipe information whenever the shape of the product is changed. It takes considerable time and effort for the user to teach the operator directly to modify the recipe for control by moving the robot manually. Figure 6 shows the recipe type for robot grasping in the FaaS platform. ISA-88.01 [47] deals with the entire product cycle and is divided into four recipe types. This study deals with a specific facility (robot handler), which is executed by dividing it into two steps: master recipe and control recipe. The motion recipe information for robot grasping consists of Productjson and Move.xml files, which are the master and control recipes, respectively. The master recipe includes basic feature information and process information of the target product, and the control recipe includes the trajectory information of the robot grasping. The master recipe created through object localization and measurement is delivered to the robot handler from the FaaS Manager. It includes the product ID (partID), source loading equipment (fromFacility), and destination unloading equipment (toFacility) information. The control recipe is managed within the robot handler; the two-recipe information is combined to control the robot handler [7].  
Figure 6: Recipe type for robot grasping in FaaS platform.  
The FaaS manager transmits the master recipe to the robot handler, and the IoT-based robot handler performs the grasping operation to transfer the product through the master and control recipes. (QfF) and (QtF) convert source loading equipment and destination unloading equipment in the master recipe to equipment parameters. (Qobj) of the master recipe is calculated by (Qobj) as the center point of the object. (Qgrip) of the master recipe reflects the product's horizontal and vertical size information extracted as HSV color-space-based automatic object measurement information. <header> of the control recipe indicates the total number of steps <numberOfseq>, and <task> denotes the task information for each step. <step> represents the work progress step, <position> represents the target position of the trajectory that the robot needs to move, and <unitAction> represents the robotic unit performing tasks such as movement and grasping.
## 2 Signal Modeling and CFR Method
### CFR Algorithm to Single-Band Signals with PAPR Target
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Meas**} & **LTE 5-MHz** & **LTE 5-MHz** & **LTE 10-MHz** & **LTE 10-MHz** \\  & **wo/ CFR** & **w/CFR** & **wo/ CFR** & **w/CFR** \\ \hline Min EVM (\%) Peak & 3.50\% & 19.75\% & 4.13\% & 22.48\% \\ Max EVM (\%) Peak & 9.20\% & 49.14\% & 9.59\% & 61.69\% \\ Peak-to-average EVM (\%) & 4.56\% & 30.62\% & 5.67\% & 31.03\% \\ Minimum EVM (\%) RMS & 1.59\% & 10.95\% & 2.16\% & 11.50\% \\ Maximum EVM (\%) RMS & 4.18\% & 16.89\% & 4.51\% & 17.64\% \\ Average of RMS (\%) & 1.89\% & 13.32 \% & 2.60\% & 14.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EVM performance for LTE 5-MHz and LTE 10-MHz.
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 2 Materials and Method
### PEEK Sulfonation  
The disc-shaped samples that were employed were put through the process. In order to determine the optimal times for sulfonation, five sets of different sulfonation times were tested: 5 s, 10 s, 30 s, 1 min, and 2 min. The untreated PEEK served as the control for this experiment. In order to produce sulfonated PEEK, discs made of PEEK were submerged in room-temperature solutions of concentrated H\({}_{2}\)SO\({}_{4}\) for varying amounts of time (refer to Scheme 1 and Table 1 (including Images 1A-D)). Three different post-treatment procedures were conducted in order to maximize the process of removing any residual sulfuric acid: (1) rinsing in acetone for ten minutes while ultrasonic stirring was performed; (2) immersion in a solution of sodium hydroxide (NaOH) containing six weight percent of sodium hydroxide (NaOH) for five minutes; and (3) hydrothermal treatment at a temperature of one hundred and twenty degrees Celsius for four hours.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Dckm  
DCKM, which consists of a large number of primitive reactions, provides a detailed quantitative description of the chemical behavior of all molecular components and reaction intermediates. In addition, its composition makes the prediction of reaction situation possible. Compared to empirical models, which are simple in form and can be easily coupled with flow and heat transfer equations for SCWO reactor optimization, complete DCKMs are too complex to be coupled with flow and heat transfer equations to quantitatively describe a reacting flow. Hence, in addition to the development of DCKMs for organics in the SCWO process, it is also important to develop models with simplified mechanisms that are still representative of the chemical reaction process, thus enabling the development of SCWO simulation models with chemical reactions and the further design optimization of SCWO reactors [116].  
Figure 10: Simplified reaction network of oxidative degradation of quinazoline in SCW [68].  
The high temperature and pressure reaction conditions of the SCWO technology make it difficult to apply some measurement techniques, erecting a barrier to the establishment of DCKM under SCWO conditions. However, the SCWO reaction process is similar to the gas-phase combustion process in some free radical reactions, and gas-phase combustion has developed numerous DCKMs of organics. Currently, some scholars have developed DCKMs of various compounds based on gas-phase combustion [31; 66; 72; 102; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126]. The goal of this section is not to summarize these studies, but to analyze the development and simplification treatment of DCKMs for hydrogen, methanol, acetic acid, benzene and ammonia. The main bases for the selection of model compounds in this section are as follows: Hydrogen is regarded as a possible auxiliary fuel to heat release for low heat value waste [117; 118]. Moreover, the mechanism for hydrogen oxidation will directly be used as a subset for the oxidation mechanisms of other complex substances [119]. As the most common auxiliary fuel in SCWO and hydrothermal combustion processes, methanol has an important role in regulating reaction strengthening and degradation process. Stubborn compounds, such as acetic acid, benzene, and ammonia, are often key products in the degradation of organic matters and have a significant impact on global oxidation kinetics.
# Progress in the Synthetic Holographic Stereogram Printing Technique
## 3 The Research Status of Synthetic Holographic Stereogram Printing
### Diffraction Efficiency  
Diffraction efficiency of the hologram is related to various factors, such as the properties of the recording medium, the light intensity ratio between the signal beam and the reference beam, the polarization properties of the beams, and the follow-up processing. Diffraction efficiency is highest when the light intensity ratio between the signal beam and the reference beam is about 1. A pseudorandom band-limited diffuser was used to modulate the signal beam and broaden the frequency spectrum of the Fourier-plane of the lens by [PERSON] et al., and this resulted in an improvement of diffraction efficiency [58].  
Additionally, in the optical hologram, from the perspective of the recording medium's properties, some scholars have added nanoparticles to the recording materials to improve the diffraction efficiency, and the research results could also be applied to the holographic printing. For instance, 13 nm silica nanoparticles were added to the methacrylate photopolymers by [PERSON] et al., and a noticeable reduction of scattering loss and a high-contrast refractive-index change were achieved [59]. A gold-nanoparticle-doped photopolymer was used to suppress side lobes of angle selectivity of volume holographic gratings by [PERSON] et al., achieving a higher diffraction efficiency [60; 61].  
Figure 11: Perspective images captured from different viewing points by [PERSON]’s group: (**a**) conventional method and (**b**) overlapping method. (Reprinted with permission from ref [56], (OSA)).
# Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter
## 8 Conclusions  
In this paper, a direct control approach for a quadrotor was presented. The controller was based in a Multilayer Perceptron trained with the Extended Kalman Filter. Each MLP module consists of two inputs, ten nodes in the hidden layer and one output, which is the control action. Each controllable degree of freedom required an MLP module that was trained online to adapt its control law to uncertainties, loss of rotor efficiency, time delays and changes in the dynamic model of the system. The results were first validated via simulation using the Ar.Drone 2.0 ROS driver with Gazebo simulator. The neural network was initialized with random weights for \(x\), \(y\) and \(z\) axes, and once the overshoot and oscillations were reduced, the weights were used to initialize the MLP modules in the real system. In the simulation, every degree of freedom was separately trained. For experimental tests, \(x\), \(y\), and \(z\) were initialized with the weights calculated in simulation, and all of them continued their training online. As can be seen in Figure 12, even using the simulation results, the neural networks must continue the training, since they present oscillations around the reference location. After some iteration steps, the oscillations were eliminated. For the experimental tests on the yaw angle (\(\psi\)), the weights were initialized randomly; that is, without previous training in a simulation. Figure 13 can be seen as the erratic behavior at the beginning of the experiment, and it is shown that the MLP adapt its weights to control the yaw of the quadrotor. Finally, in Figure 14, it has been shown that the controller adapts its weights when changing the dynamics during the flight.  
PTAM Parallel Tracking and Mapping ROS Robot Operating System SLAM Simultaneous Localization and Mapping UAV Unmanned Aerial Vehicle VTOL Vertical Take-Off and Landing
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 2 Algorithm Design
### K-Means  
Clustering algorithm can be used in pattern recognition, image processing, machine learning and statistics. One of the most popular clustering methods is the K-Means algorithm, where clusters are identified by minimizing the clustering error. When using the K-Means algorithm for clustering, it does not need to know the category of data in advance, but only needs to classify according to the distance or similarity between data. So, it is an unsupervised clustering algorithm [33].  
K-Means clustering algorithm is an algorithm based on partition, and the data is divided into k preset clusters, \(k<n\). Each cluster is represented by a clustering center, denoted by \(\{C_{i}\}_{i=1}^{k}\). After obtaining the clustering center of each cluster, all data are mapped to a certain clustering center, that is, the cluster where the clustering center is added. Finally, all data will be divided into corresponding clustering centers, and k clusters will be obtained, denoted as \(\{S_{i}\}_{i=1}^{k}\).  
Figure 2: Pyramid LK algorithm.  
Assuming the data-set as \(D=\left\{x_{j}\right\}_{j=1}^{n},x\in\mathbb{R}^{d}\), and the objective function is:  
\[l(S)=\operatorname*{argmin}\sum_{t=1}^{k}\sum_{x\in S_{i}}\|x-c_{i}\|_{2}^{2} \tag{9}\]  
\[c_{i}=\frac{1}{|S_{i}|}\sum_{x\in S_{i}}x \tag{10}\]  
where \(|S_{i}|\) means the quantity of the sample in category \(S_{i}\).  
Generally, K-Means clustering algorithm adopts iterative strategy to solve the local optimal solution of the objective function. The clustering process is shown in Table 1.  
The result of pyramid LK output is optical flow value, denoting the movement of pixel. The K-Means clustering algorithm is used to cluster the results into three categories; namely, smaller value, moderate value and larger value. The moderate value is selected as the correct result, which reduces the bad influence of the wrong optical flow value and reduces the error of speed.
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 3 Methodology  
### Structure of VGG-Unet  
#### 3.1.1 Overall Structure of VGG-Unet  
In general, to detect the location of the objects from images, one VGG-Unet architecture consists of two parts. One part using five down-sampling blocks is based on VGG16 structure to capture context. The other part based on U-Net [39] consists five up-sampling blocks and uses a symmetric expanding path to make precise localization enable. Figure 1 shows the architecture of VGG-Unet. This section introduces the details or backgrounds of the layers used in the VGG-Unet.  
As VGG16, the input size of the images is 224 x 224 pixels with three channels (Red, Green and Blue) in VGG-Unet. In architecture of VGG-Unet, down-sampling is combined from Convolutional (Conv) layers connected with the Rectified Linear Unit (ReLU) layer and Maxpooling layer, while up-sampling block is consisted of Upsampling layer and convolutional layer, which is followed by an auxiliary layer named Batch Normalization (BN) layer. During the process of up-sampling block, some layers are copied and then concatenated with the output of convolutional layer. After the data calculated through a down-sampling block, the width and height becomes half. The up-sampling block can double the width and height of the input. The detail of each layers is shown in Table 1.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### HVDC Applications and Optimal Control
#### 3.1.1 Frequency Stability  
An HVDC connection causes the frequency in each grid to be affected by any supply-and-demand imbalance, not only in the corresponding grid but also in other grids. Similarly, the intermittent power generation of OWFs triggers frequency deviations in all HVDC-linked grids. A strategy is required for real-time supply-and-demand balancing in HVDC-linked grids; this is essentially the coordinated control of regional power generation and AC-to-DC power transmission in either a centralized or distributed manner. For example, considering inter-grid coupling, Figure 2a shows an optimal control strategy for an LCC-HVDC system in South Korea (i.e., the Jeju-Haenam system [56]). It has a rated power of 150 MW and a rated voltage of 184 kV. The HVDC system delivers surplus power generated at the rectifier terminal of the Haenam grid to the inverter terminal of the Jeju grid via a 100 km DC cable. Each converter terminal includes a converter transformer, 12 thyristor valves, and internal controllers that regulate the firing or extinction angles. Optimal coordinated control of the DC-link voltage and current is achieved in real time to improve frequency regulation (FR) in both the rectifier- and inverter-side networks. Specifically, Figure 3 shows a small-signal model of HVDC-linked grids, wherein the input variables include the DC voltage and current references. The HVDC converters support real-time FR via primary and secondary frequency control (PFC and SFC). For PFC, the converters are integrated with feedback loops to afford frequency- and DC-voltage-power droop control, and also inertial response emulation. A linear quadratic Gaussian (LQG) controller that combines a linear quadratic regulator (LQR) with a Kalman filter is also incorporated into the feedback loops to achieve optimal SFC by minimizing the weighted sum of the instantaneous and accumulated deviations in the DC-link voltage and the frequencies of the rectifier- and inverter-side grids. Figure 4 shows the results of comparative case studies of the proposed and conventional strategies, as described in Table 3. Table 4 shows the corresponding numerical results. The proposed strategy improved real-time FR, when OWF power generation and load demand varied over time, under various conditions of the model parameters, LQG weighting factors, and the inertial emulation and droop control approaches.  
Figure 1: A schematic showing optimal control of hybrid MTDC-linked grids and OWFs.  
Figure 3: A small-signal model of HVDC-linked grids considering the dynamics of the DC link, the converters, and the inner feedback loops.  
Figure 2: Use of an LQG regulator to generate optimal reference signals for the individual local controllers of the HVDC system and the OWFs: (**a**) frequency control and (**b**) voltage control.
###### Abstract  
In modern communication, the electromagnetic spectrum serves as the carrier for information transmission, and the only medium enabling information exchange anywhere, anytime. To adapt to the changing dynamics of a complex electromagnetic environment, electromagnetic spectrum allocation algorithms must not only meet the demands for efficiency and intelligence but also possess anti-jamming capabilities to achieve the best communication effect. Focusing on intelligent wireless communication, this paper proposes a multi-agent hybrid game spectrum allocation method under incomplete information and based on the Monte Carlo counter-factual regret minimization algorithm. Specifically, the method first utilizes frequency usage and interference information from both sides to train agents through extensive simulations using the Monte Carlo Method, allowing the trial values to approach the expected values. Based on the results of each trial, the counterfactual regret minimization algorithm is employed to update the frequency selection strategies for both the user and the interferer. Subsequently, the trained agents from both sides engage in countermeasure communication. Finally, the probabilities of successful communication and successful interference for both sides are statistically analyzed. The results show that under the multi-agent hybrid game spectrum allocation method based on the Monte Carlo counter-factual regret minimization algorithm, the probability of successful interference against the user is 32.5%, while the probability of successful interference by the jammer is 37.3%. The average simulation time per round is 3.06 s. This simulation validates the feasibility and effectiveness of the multi-agent hybrid game spectrum allocation module based on the Monte Carlo counter-factual regret minimization algorithm.  
Article  
Academic Editor: [PERSON]  
Received: 18 January 2025  
Received: 7 March 2025  
Accepted: 7 March 2025  
Published: 20 March 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Intelligent Decision-Making for Electromagnetic Spectrum Allocation Method Based on the Monte Carlo Counterfactual Regret Minimization Algorithm in Complex Environments. _Atmosphere_**2025**, 16, 345. [[https://doi.org/10.3390/atmos16030345](https://doi.org/10.3390/atmos16030345)]([https://doi.org/10.3390/atmos16030345](https://doi.org/10.3390/atmos16030345))  
**Copyright:** 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
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