esa-sceva/satcom-chunk-collection huggingface dataset:
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 3 Aggregated Control Strategy for Multi-Type Loads in Industrial Parks
### Day-Ahead Optimal Dispatch of Aggregated Multi-Type Loads in Industrial Parks
#### 3.1.4 Day-Ahead Optimal Dispatch Strategy  
From the comprehensive analysis of the day-ahead power balance equation, optimization objective function, interconnection line constraints, and controllable load-operation constraints, it can be seen that when the load start-stop process is not considered, the optimization problem is a mixed-integer linear programming problem. However, after considering the load start-stop process, the day-ahead optimization dispatch is no longer a mixed-integer linear programming problem. Ignoring the load start-stop process, the solution method based on mixed-integer linear programming can obtain the start-stop status of each load. After determining the start-stop status of each load, the day-ahead optimal control problem of the industrial park load aggregation can be solved by transforming it into a mixed-integer linear programming problem. Therefore, a two-stage solution strategy is adopted to transform the original problem into two convex optimization problems: day-ahead controllable load start-stop status optimization and day-ahead controllable load economic control optimization.  
Solution Stage 1: Assuming that the controllable load start-stop process has no transitional process, the original problem is a mixed-integer linear programming problem, and a feasible solution set of the controllable load start-stop status can be obtained after solving.  
Solution Stage 2: Based on the feasible solution set of the controllable load start-stop status, the problem in Stage 2 is also a mixed-integer linear programming problem, and the final solution of other decision variables is obtained.  
It can be seen that the two-stage solution method, on the one hand, does not change the time complexity of the original problem even if the power sequence during the start-up process is nonlinear. On the other hand, due to the polynomial time complexity of the two-stage problem, the original complex problem can still be efficiently solved by transforming it into a mixed-integer linear programming problem using the two-stage solution method, even when facing large-scale multi-type load characteristics of industrial parks.
## 4 Core Insights and Critical Reflections
### Regional Development Imbalance and Barriers to Technology Transfer  
The global research capacity in UAV-RS is primarily concentrated in China, the United States, and several EU countries or regions. Low- and middle-income countries face significant barriers in equipment acquisition, talent development, and technological application, leading to imbalanced global collaboration and limited capacity for technology transfer and knowledge sharing. Although there are examples of international partnerships--such as collaboration between Florida State University and the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences--the overall frequency and depth of international cooperation remain insufficient, constraining the diffusion of technologies and the sharing of best practices.  
Furthermore, different countries prioritize research directions based on their domestic needs, resulting in fragmented outcomes. For instance, China emphasizes precision agriculture, the United States focuses on urban monitoring, while Europe concentrates on environmental protection [221]. This lack of a unified, coordinated strategy to address global challenges hinders technology transfer and broader application. Many of the most advanced technologies remain concentrated in a few high-income countries and are difficult to scale or implement in low- and middle-income regions [222; 223]. This fragmentation not only limits the overall enhancement of global UAV-RS capabilities, but also constrains the technology's contribution to the global sustainable development agenda. Core bottlenecks in UAV-RS platforms and data processing, coupled with asymmetrical global collaboration mechanisms, continue to restrict the widespread application and long-term innovation of UAV-RS technologies. Looking ahead, coordinated efforts are needed across several fronts--including the development of energy-efficient platforms, standardization of multi-source data, advancement of intelligent analytical algorithms, and the establishment of inclusive international cooperation frameworks--to fully unlock the potential of UAV-RS in Earth observation and environmental management.
# Microsseismic Source Location Method and Application Based on NM-PSO Algorithm
## 8 Conclusions  
Based on the Time Difference of Arrival source localization theory, this paper constructs the NM-PSO algorithm for the location accuracy of microseismic sources in the coal mining process and conducts example analysis and engineering verification. The following conclusions are obtained:  
(1) A microseismic source location method based on the NM-PSO algorithm is proposed for different sensor arrays. The algorithm not only retains the technical advantages of the NM algorithm's fast iteration and easy convergence but also avoids the initial value sensitivity problem of the NM algorithm. At the same time, the global optimization characteristics of the PSO algorithm are used so that the NM-PSO algorithm has better universality and stability.  
(2) Through the example analysis and engineering verification of the NM-PSO algorithm, it can be concluded that the NM-PSO algorithm avoids the influence of improper initial value setting on the source localization error, and the source location accuracy is significantly improved compared with the NM simplex algorithm. The engineering verification results further prove the accuracy and stability of the method, indicating that the NM-PSO algorithm can meet the engineering needs and achieve the required localization accuracy.  
(3) The microseismic source's location in the coal mining process is affected by many factors, among which the anisotropy of the rock body affects the wave propagation speed, and the complex geological formations can cause different sensor arrays, which significantly affect the positioning accuracy. Therefore, in the follow-up research, a more suitable algorithm model can effectively improve the localization accuracy and more accurately realize the microseismic source localization in the coal mining process.  
Writing--original draft preparation, Z.L.; Data curation, Z.L. and T.F.; Investigation, Z.L. and W.Y.; Formal analysis, Z.L. and D.C.; Funding acquisition, W.Y.; Methodology, Z.L. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51974117, 51804114) and the Natural Science Foundation of Hunan Province (2020)J4027.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**NM**} & \multicolumn{3}{c}{**NM-PSO**} & \multicolumn{3}{c}{**Original Coordinates**} \\ \cline{2-10}  & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline A & 146.05 & 249.06 & 755.22 & 139.03 & 244.63 & 747.43 & 134.6 & 238.3 & 740 \\ B & 248.69 & 132.21 & 764.54 & 247.97 & 125.81 & 755.66 & 240.7 & 121 & 750 \\ C & 144.49 & 122.68 & 776.78 & 139.16 & 118.79 & 768.54 & 133.9 & 114.9 & 760 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Positioning results of each algorithm.  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 1 Introduction  
Wireless Sensor Networks (WSNs) are self-organized networks consisting of sensor nodes with the ability of sensing, processing and wireless communicating [1]. Coverage control is one of the most fundamental research issues in sensor networks, and studies how well a sensor network will monitor a field of interest with the proper node deployment [2; 3]. Sensor nodes often have constrained resources and it is sometimes difficult to recharge their energy, and thus coverage sustainability in such sensor networks cannot be guaranteed. How to balance the network energy consumption in coverage control is an important issue, which can be modeled as a multi-objective optimization problem of prolonging the network lifetime and improving network coverage according to the characteristics of WSNs [4].  
Multi-objective optimization problems involve two or more conflicting objectives and have not one optimal solution but many solutions which form the Pareto front representing a tradeoff of one objective against the other. In most applications, the goal of solving the multi-objective optimization problems is to compute an approximation of the Pareto front. Computational Intelligence (CI) and evolutionary algorithms provide adaptive, flexible and robust mechanisms that exhibit intelligent behavior to solve the multi-objective optimization problems of coverage control in complex and dynamic environments like WSNs [5]. [PERSON] modeled the coverage problem with two sub-problems: floorplan and placement, and used the genetic algorithm (GA) to search the optimal coverage in WSNs [6]. [PERSON] _et al._ applied artificial bee colony algorithm to the dynamic deployment of the sensor networks and obtained better deployments for WSNs than the particle swarm optimization algorithm [7; 8]. The aforementioned articles emphasized to improve the coverage rate and did not specifically take into account network lifetime and energy balance in WSNs. Memetic algorithms are computational intelligence structures and a class of stochastic global search heuristics where evolutionary algorithms are combined with multiple and various local search heuristics in order to address such optimization problems as those in WSNs [9; 10; 11].  
This paper presents a multi-objective coverage optimization algorithm based on memetic algorithm for WSNs, namely MOCOAMA, which considers the coverage degree, node utilization, node residual energy, and solves the 1-coverage multi-objective problem for WSNs [12; 13; 14; 15], and finally gets the optimal deployment of network coverage.  
The rest of the paper is organized into five sections: Section 2 briefly introduces related work. Section 3 discusses the multi-objective optimization coverage problem of WSNs. Section 4 presents the key schemes for the proposed coverage algorithm for WSNs. Section 5 describes the multi-objective optimization coverage algorithm based on memetic algorithms. The simulation experiments and evaluation are given in Section 6. Finally, the conclusions are offered in Section 7.
# On IRS-Assisted Covert Communication with a Friendly UAV
###### Abstract  
Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, through which the closed-form detection error probability (DEP) of the covert wireless communication for monitoring is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, then design an iterative algorithm to solve the optimization problem and obtain the optimal covert rate using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units and the trajectory and transmit power of the UAV are optimized jointly.  
information security; covert communication; intelligent reflecting surfaces; UAV +
Footnote †: journal: Advances in Neural Information Processing Systems
###### Abstract
Recently, to examine and measure the topography at different length scales, multiscale evaluations such as wavelet, fractal, modal, or Fourier analyses are used to determine these scales for surfaces made by additive manufacturing [28]. In this study, areal surface texture parameters, including the arithmetic mean height (S\({}_{\text{a}}\)), root-mean-square height (S\({}_{\text{d}}\)), skewness (S\({}_{\text{sk}}\)), and kurtosis (S\({}_{\text{ku}}\)), were characterized. The wavelet-based multi-resolution analysis was applied to investigate the characteristic length scales of untreated and heat-treated Ti64 specimens. Besides, a Gaussian high-pass filter was used to compare areal surface texture parameters via the measured surface in the absence and filtering presence and assess the effect of unmelted and partially melted Ti64 metal powder particles on these surface texture parameters. On the other hand, the influence of areal surface texture parameters on cell attachment has not yet been reported. Therefore, this study investigates the effects of heat treatment of SLM-fabricated Ti64 specimens on the surface morphology, areal surface texture parameters, contact angles, and cell attachment crucial in osseointegration. The use of areal surface texture parameters can be extended to explore the relationship between areal roughness parameters and cell attachment.
## 2 Materials and Methods
### Generalized Analytic Microdosimetric Models
#### 2.2.2 The Xapsos et al. 1996 Model  
This model (hereafter denoted as \"X96\") treats only direct events but uses an explicit description of ion's energy-loss straggling. Specifically, the probability density function, \(p_{x,s}\), for energy deposition \(x\) along an ion's path length \(s\) (for a single traversal), is approximated by a log-normal distribution:  
\[p_{x,s}=\frac{1}{\sqrt{2\pi}\sigma_{s}x}Exp[-\bigg{(}\frac{(\ln(x)-\mu_{s})}{ \sqrt{2}\sigma_{s}}\bigg{)}^{2}], \tag{17}\]  
where \(\mu_{s}\) and \(\sigma_{s}\) are the mean and variance of the distribution, respectively. They can be calculated from:  
\[\mu_{s}=\ln(\overline{x}_{ion})-0.5\sigma_{s}^{2}, \tag{18}\]  
\[\sigma_{s}=\sqrt{\ln(1+V)}, \tag{19}\]  
where \(\overline{x}_{ion}\) is the mean energy deposited in the site for \(s\) equal to the mean chord length of the site, and \(V\) is obtained as a sum of various contributing terms:  
\[V=V_{str}+V_{s}+V_{L}+V_{F}+V_{L}V_{s}. \tag{20}\]  
The terms in Equation (20) are as follows: \(V_{str}=\frac{\delta_{2,ion}}{\overline{z}_{ion}}\) is the relative variance of energy-loss straggling [48, 55], where \(\delta_{2,ion}\) is taken from Equations (11) and (12). \(\overline{z}_{ion}=f_{ion}\) LET\({}_{ion}s\) is the mean energy deposited in the target volume as a function of ion's path length (\(s\)), \(V_{s}\) is the relative variance of ion's path length distribution, \(V_{L}\) is the relative variance of ion's LET distribution and \(V_{F}\) is the relative variance of Fano fluctuations. For monoenergetic ions and neglecting the Fano fluctuations, Equation (20) reduces to [48, 49]:  
\[V=V_{str}+V_{s}. \tag{21}\]  
The Fano fluctuations are needed in studies of ionization fluctuations. Here, we studied the energy deposition, so it was not necessary to include it in Equation (21) [48, 49]. Then, the total probability density function, \(f_{x}\) for energy deposition \(x\), is the convolution of the straggling distribution \(p_{x,s}\) and the probability density function of the particle's path length inside the site, \(c_{s}\).  
\[f_{x}=\int p_{x,s}c_{s}\mathrm{d}s. \tag{22}\]  
For a sphere, and assuming that ions travel in straight lines [48, 49], \(c_{s}=2s/d^{2}\).
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Results and Discussion  
#### 4.2.1 Results on Simulated Noise Datasets  
The average test accuracies of the last ten epochs for FPL and all baseline algorithms across eight different settings are presented in Table 4. Regarding MNIST, SVHN, and CIFAR10 datasets, the method proposed in this paper demonstrates a clear advantage over all other comparison experiments. Moreover, the class accuracy is close to 100% on the MNIST dataset, indicating the method's effectiveness for MNIST. Additionally, for the CIFAR10 dataset, the algorithm exhibits a significant advantage over the CTRR algorithm in the case of Instance-40%, with a nearly 2% higher performance, suggesting that the FPL algorithm performs well in scenarios involving Instance noise associated with image features. For the pair flip-20%, the accuracy of the CIFAR100 dataset is lower compared to that of the ELR algorithm. ELR, unlike FPL, does not directly correct the noisy labels but instead hinders the model from memorizing them by incorporating a specially crafted regularization term. Although both are in the field of label noise, these methods approach the problem differently, resulting in different performances for different noise types. Furthermore, the ELR approach only marginally outperforms FPL by 0.03%, leaving room for chance factors to influence results. Besides, the FPL method demonstrates a smaller difference between high and low noise rates compared to other methods in similar noise types. For instance, the disparity between the 40% noise rate and the 20% noise rate under the pair flip noise type is 1.08%, while the difference for the ELR method is 1.77%. These results demonstrate that FPL yields better classification results at higher noise rates. In summary, the method exhibits good convergence and stability across the four benchmark datasets. Additionally, the FPL algorithm demonstrates a relatively small range of standard deviation variation in the last 10 epochs, further indicating the robustness of the algorithm.
## 4 Performance Evaluation and Validation
### Evaluation Method
This comparison visually demonstrates the agreement between the predicted outputs and the manually labeled actual damage areas. As shown in Figure 6, the evaluation involved determining whether each type of damage was correctly detected by analyzing the overlapped images. The objective was to calculate the detection rate for each damage class. However, in the case of cracks, which often appear irregular, continuous, or fragmented, it is difficult to assess detection accuracy based solely on discrete object counts. To address this limitation, the overlapped images were divided into a 10 x 8 grid (horizontal x vertical) for localized evaluation. Within each subdivided region, the presence or absence of damage was compared between the predicted output and the ground truth. If the predicted damage region overlapped with the actual damage in a sub-image, it was counted as a true positive (TP). If the model failed to detect damage in a region where ground truth was present, it was counted as a false negative (FN). Conversely, if the model predicted damage in a region where none was present, it was recorded as a false positive (FP), and if it correctly identified a non-damage region, it was counted as a true negative (TN). Figure 5: (**a**) Ground-truth damage labeling, (**b**) model predictions overlaid on images, and (**c**) comparison between predicted and GT damage regions.
## 3 Results and Discussion
### Gold Adsorption from Cyanide Media
#### 3.2.3 Effect of Temperature  
Aqueous solutions containing 0.01 g L\({}^{-1}\) gold(I) at pH 13 \(\pm\) 0.1 were used to investigate the effect of this variable on gold(I) adsorption. The adsorbent dosage was 2 g L\({}^{-1}\) and the temperature was varied from 20 to 60 \({}^{\circ}\)C. From the results presented in Table 3, it can be shown that the increase in the temperature leads to a decrease in metal adsorption. From the D (distribution coefficient) value, defined as:  
\[\mathrm{D}=\frac{\left[\mathrm{Au}\right]_{\mathrm{nt,e}}}{\left[\mathrm{Au} \right]_{\mathrm{s,e}}} \tag{4}\]  
and plotting log D versus 1/T, the slope indicated that the adsorption reaction has an exothermic character (\(\Delta\mathrm{H}^{\mathrm{o}}=-13\) kJ mol\({}^{-1}\)) and the intercept that \(\Delta\mathrm{S}^{\mathrm{o}}\) is \(-50\) J mol\({}^{-1}\) K\({}^{-1}\). In the above reaction, \(\left[\mathrm{Au}\right]_{\mathrm{nt,e}}\) and \(\left[\mathrm{Au}\right]_{\mathrm{s,e}}\) are the gold equilibrium concentrations in the nanotubes and in the solution, respectively. From the values of the enthalpy and the entropy values, the value of \(\Delta\mathrm{G}^{\mathrm{o}}\) was calculated as \(-28\) kJ mol\({}^{-1}\). The negative energy value indicated a spontaneous adsorption, whereas the negative entropy change is an indication of a decreased randomness at the solid/liquid interface during the adsorption process.
## 1 Introduction
### Contributions  
Since CNNs are widely used in related tasks in the computer vision field, their internal structure is constantly updated and replaced with the performance requirements of actual missions, so their feature extraction and feature selection capabilities are gradually improved. AC Block [27] originated from the ICCV conference in 2019. This module enhances the robustness of image information through asymmetric convolution and obtains better feature extraction ability. The CBAM [28] was proposed at the ECCV conference in 2018. It combines channel and spatial attention, strengthening feature selection ability from both dimensions at the same time. Therefore, AC Block and CBAM stand out in various image-recognition-related tasks [29; 30; 31; 32]. However, both of them have relative defects. AC Block has the problem of many training parameters and a large amount of calculations during training. Additionally, CBAM lacks the multi-scale perception of information when selecting efficient information for the space. Therefore, in this article, considering these two existing problems, a novel Lightweight Asymmetric Convolutional Block combined with an Enhanced CBAM network (LACB_ECBAM Net) for WTB surface damage identification is constructed. The details are as follows.  
Firstly, the Lightweight Asymmetric Convolutional Block (LAC Block) is proposed. In particular, we use depthwise separable convolution [33] and channel shuffle [34] to make lightweight improvements to AC Block. It reduces the amount of the parameters in the network training, decreases the computation, and boosts the training speed of the model. Channel shuffle can strengthen the communication of channel information between groups and compensate for the loss of feature information caused by depthwise separable convolution.  
Secondly, the Selective Kernel Spatial Attention Module (SK_SAM) is proposed. Specifically, we use the convolution of the selective kernel [35] to improve the Spatial Attention Module (SAM) [28]. The convolution with an adaptive receptive field will extract features of different sizes more effectively. It enhances the capability of the spatial attention model to capture the position for useful information.  
Thirdly, the Enhanced CBAM (E_CBAM) is proposed. It is an improvement of CBAM. Its interior consists of DPCI_SE Block [26] and SK_SAM. By analyzing the combined channel and spatial dimension, it can better focus on the significant features of the image and suppress the unnecessary regional response.