esa-sceva/satcom-chunk-collection huggingface dataset:
CFD Analysis of Aerodynamic Characteristics in a Square-Shaped Swarm Formation of Four Quadcopter UAVs  
[PERSON]  
and [PERSON]  
The aerodynamic behavior of a square-shaped formation of four quadcopter UAVs flying in a swarm is investigated in detail through three-dimensional computer simulations utilizing Computational Fluid Dynamics (CFD) methodology. The swarm configuration comprises four UAVs positioned with two in the upper row and two in the lower row along the same propeller axes. The flow profile generated by the UAV propellers rotating at 10,000 revolutions per minute is analyzed parametrically using the Multiple Reference Frame (MRF) technique. UAVs within the swarm are positioned at 75 cm from the motion centers of adjacent propellers. This distance, the effects of horizontally and vertically positioned UAVs on each other, and the collective behavior of the swarm are thoroughly examined. Pressure, velocity, and turbulent kinetic energy values are meticulously analyzed. This research represents a milestone in understanding the aerodynamic characteristics of UAV swarms and the optimization of swarm performance. The findings highlight effective factors in swarm flights and their consequences for UAVs. Additionally, the article describes the \"near-UAV phenomenon\". Furthermore, the methodology developed for CFD simulations provides an approach to analyzing close flight scenarios and evaluating their performance in various swarm configurations. These achievements contribute to the future development of UAV technology.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships  
This section proposes the multi-object detection framework for inland ships based on the YOLOv5s architecture. The Shuffle Attention mechanism, transfer learning training strategy, and small object detection anchor are introduced to improve the method.
# Random Access for Underwater Acoustic Cellular Systems
## 3 RAP Design
### Proposed RAP  
In order to overcome the limitations of the LTE-based RAP, we propose two different RAPs that are more suitable for underwater acoustic channels. The proposed RAPs, which are generated and detected in the time domain, have short symbol duration, compared with the LTE-based RAP. The proposed RAP is transmitted in the lowest frequency band (UL0), which is common to all the UEs in Link 2 (or UBSs in Link 1) in the initialization stage. A UE transmits an RAP at the predefined time slot obtained by decoding the downlink broadcast information. As shown in Figure 5, RAP detection is performed at the receiver side by correlating the received signal with local (reference) RAPs within the RAP window. The output of linear-shift (not cyclic-shift) correlation is compared with a threshold value to detect the RAP. The RAP window should be sufficiently long to cover RTD, MDS, and RAP length. Additional guard time (GT) might be configured if necessary.
## 4 Discussion  
### Evaluation of GPP, ET, and WUE  
Changes in WUE in ecosystems have been explored using various methods at various spatial scales, including field control experiments, eddy covariance, isotope observation  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vegetation Types** & **Precipitation** & **Temperature** & **Solar Radiation** \\ \hline Coniferous forest & \(-\)0.121 \(\pm\) 0.290 & 0.009 \(\pm\) 0.174 & \(-\)0.023 \(\pm\) 0.207 \\ Broadleaf forest & 0.046 \(\pm\) 0.370 & \(-\)0.035 \(\pm\) 0.151 & 0.029 \(\pm\) 0.232 \\ Meadow steppe & 0.197 \(\pm\) 0.379 & \(-\)0.005 \(\pm\) 0.171 & 0.034 \(\pm\) 0.231 \\ Typical steppe & 0.439 \(\pm\) 0.192 & \(-\)0.106 \(\pm\) 0.223 & 0.004 \(\pm\) 0.209 \\ Desert steppepe & 0.450 \(\pm\) 0.222 & \(-\)0.107 \(\pm\) 0.242 & \(-\)0.134 \(\pm\) 0.233 \\ Shrub & 0.398 \(\pm\) 0.262 & 0.058 \(\pm\) 0.178 & 0.114 \(\pm\) 0.212 \\ Sand land vegetation & 0.515 \(\pm\) 0.180 & 0.025 \(\pm\) 0.245 & 0.071 \(\pm\) 0.205 \\ Cropland & 0.497 \(\pm\) 0.226 & 0.075 \(\pm\) 0.192 & 0.116 \(\pm\) 0.214 \\ Entire region & 0.345 \(\pm\) 0.325 & \(-\)0.025 \(\pm\) 0.217 & 0.019 \(\pm\) 0.231 \\ \hline \end{tabular}
\end{table}
Table 2: Sensitivity of WUE to climate change for different vegetation types in Inner Mongolia.  
Figure 10: Sensitivity coefficient and their gradient change between WUE and climate factors in Inner Mongolia from 2001 to 2020. **(a,b)** Precipitation, **(c,d)** Temperature, and **(e,f)** Solar radiation.  
techniques, process-based models, and remote sensing products [13; 17; 53]. In this study, the long-term series datasets of GOSIF GPP and the GLEAM ET products were used. Many studies found that GOSIF GPP products have good performance in monitoring carbon sinks at regional and global scales [34; 54]. Meanwhile, numerous studies have confirmed that GLEAM ET is highly applicable to various ecosystems, particularly grassland ecosystems in arid and semi-arid regions [36; 55; 56; 57]. Therefore, the estimation of WUE in Inner Mongolia using GOSIF GPP and GLEAM ET products has certain reliability.
## 3 Results
### Comparisons  
Classification by the sample-encoding Kohonen network has been compared to reference classifiers used for this type of application, namely: K-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA), Hotelling statistical hypothesis testing, and traditional Kohonen network.  
Figure 7 shows the classification results of two experiments with different datasets, i.e., two classes and three classes classification.
## 5 Nanocarriers as a Strategy to Fight Biofilms
### Antimicrobial Protection  
Biofilm resistance partly arises from the problematic interaction between EPS and antimicrobial agents, which can lead to the deactivation of these substances [83]. Nanocarriers offer a solution by shielding antimicrobial agents from enzymatic inactivation and preventing their binding to DNA and polysaccharides produced by biofilms. This protective role enhances the effectiveness of antimicrobial treatments against biofilm infections.
## 3 Path Planning Problem
### Path Planning Objectives  
In this work, to obtain precise and effective solutions, we optimized three objectives: path length, path safety and path smoothness.  
The energy loss of a mobile robot is related to the length of the path--the longer the path, the greater the loss of energy. Besides the path length, the energy loss is also related to the smoothness of the path. For the same path length, the smoother the path, the smaller the energy loss. When the robot moves forward at a constant speed and only changes in velocity direction, the walking time of the robot is also related to the length. In addition, the most important factor that we need to consider is the safety of the path. The safe path distance reflects the distance from the surrounding obstacles--the greater the safety distance, the safer the path. So, this article describes the energy loss, walking time, and safety distance through the length, smoothness, and safety. We want to find the shortest, smoothest, and safest path. This is known as the multi-objective optimization problem. For the sake of convenience, we unified to minimize the three objectives. The mathematical definitions of these objectives are described in the following subsections.
# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion
### Multi-Target Mask Correction Effect
target), the better the experimental result. The smaller the values of FPR, FNR, and PWC (the effects of background subtraction and illumination), the better the experimental result. Table 1 shows the results of experiments using all the data sets used in this study. As shown in Table 1, The proposed algorithm outperforms the other algorithms for all the experimental criteria. The larger values of Pr, Re, Sp, and F1 indicate that the algorithm is better at identifying the target pixel, mainly because this paper is based on the skeleton divergence algorithm. The overall strategy of mask correction is established by using skeleton partitioning, which leads to the edge of the area to be merged. The features are more concentrated, and the target pixels are easier to identify. The algorithm is compared with other algorithms. The CUR and AIF algorithms are all for global point correction. The edge feature information is scattered and susceptible to image noise. GOP utilizes the point-surface correlation feature, and IFMcaS uses the feature matching integrity score to improve the partial mask correction accuracy. However, the error accumulation cannot be avoided in the partial detail mask correction, and the target pixel is not easily recognized. The PWC value is better than other algorithms, which shows that the proposed algorithm is robust to the interference of holes and noise in the binary mask. Because this paper uses the skeleton partition to establish the overall strategy of mask correction, the pixel features to be merged are more concentrated, and the influence of interference is reduced. At the same time, because this paper uses the hole filling technology, the cavity filling technology utilizes the morphology. Feature and structural similarity features reduce the effects of image noise.
## 3 Results and Discussion
### Performance under Alternating Temperature Condition  
Figures 12 and 13 show the ambient temperature and air temperature inside the container with water and with OP5E as PCM under alternating temperature conditions, respectively. The container without PCM was not tested under this condition since the holding time was just one hour at both of the extreme temperatures. The most measuring points in the container indicated a temperature below 2 \({}^{\circ}\)C when using water as PCM. The air temperature kept at 0 \({}^{\circ}\)C for the first 36 h and at \(-1\)\({}^{\circ}\)C for the second 36 h. It may pose a subcooling risk to drugs or food. However, the container incorporated with OP5E maintained a stable internal temperature at 4-5 \({}^{\circ}\)C for the first 72 h and dropped to 2 \({}^{\circ}\)C after 100 h.  
The experimental temperature-controlled time of the container with OP5E was compared with that theoretically calculated according to the equations in Section 2.2. From Table 2 it can be seen that the deviation was within an acceptable range of \(\pm\)10%. The experiment error mainly resulted from the inhomogeneous temperature distribution inside the container. In most cases, the temperature in the corner firstly exceeded the maximum or minimum temperature that was allowed because of the air leakage.  
Figure 11: The ambient temperature and air temperature inside the container with OP5E as PCM under extremely low temperature condition.  
Figure 12: The ambient temperature and air temperature inside the container with water as PCM under alternating temperature condition.
## 4 Numerical Results
We finally proceed with the resource utilization of the system demonstrated in Figure 5 for the same set of input parameters as in Figure 4, as a function of URLLC offered traffic load, \(\rho_{1}\), and eMBB maximum requested rate, \(b_{2}^{\max}\). By analyzing the results presented in Figure 5a, we observe that across the whole range of \(\rho_{1}\) the mixed strategy shows consistently better results by approximately 10%. Notably, in overloaded conditions with a certain level of isolation supported, the resource utilization is just 5% off from 100%. In fact, this is the best side of the considered mixed strategy. Qualitatively similar results are also observed in Figure 5b, where resource utilization of the system as a function of the eMBB maximum requested rate \(b_{2}^{\max}\) is shown. Here, we might observe that as the system starts to be overloaded, the mixed scheme allows 95% of utilization to be reached, while the \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Arrival intensity of URLLC sessions & 10,000 sess./s \\ Arrival intensity of eMBB sessions & 1 sess./s \\ Arrival intensity of mMTC sessions & 10,000 sess./s \\ Mean service time of URLLC sessions & 1 ms \\ Mean service time of eMBB sessions & 10 s \\ Mean service time of mMTC sessions & 1 ms \\ Overall amount of resources in the system & 39 PRB \\ Amount of reserved resources for URLLC & 10, 13 PRB \\ Amount of reserved resources for eMBB & 10, 13 PRB \\ Amount of reserved resources for mMTC & 10, 13 PRB \\ Resource requirement of URLLC sessions & 1 PRB \\ Minimum resource requirement of eMBB sessions & 1 PRB \\ Maximum resource requirement of eMBB sessions & 3 PRB \\ Resource requirement of mMTC sessions & 1 PRB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters utilized for numerical assessment.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 3 Function Finding Algorithm by Using Gene Expression Programming for Wireless Sensor Networks Data  
### Function Finding in Wireless Sensor Networks  
Generally, for data mining in wireless sensor networks, firstly, data are collected and preprocessed by various sensors and transmitted directly to the servers by means of wireless communication. Then, these data can be quickly analyzed by strong data processing and analysis ability of servers. Finally, the knowledge is attained. The whole framework is shown in Figure 1.  
From Figure 1, it is known that data mining for wireless sensor networks consists of five main components: acquisition layer, preprocessing layer, transmission layer, analysis layer and virtualization layer. The acquisition layer is responsible for collecting all kinds of data (e.g., weather, spectral, temperature, humidity, gas, _etc._) through various sensors (e.g., weather sensor, hyperspectral sensor, temperature sensor, humidity sensor, gas sensor, _etc._). The preprocessing layer focuses on data aggregation, normalization and cleaning to provide favorable data form for data mining in wireless sensor networks. The transmission layer mainly addresses security transmission of data between sensors and terminals. The analysis layer provides all types of data mining services for data from various sensors. Finally, the results of data mining are shown by the virtualization layer.  
Figure 1: Data mining framework in wireless sensor networks.  
Function discovery is an important part of data mining framework in wireless sensor networks. It is vital to find the function model among sensor data for the concrete application and analysis on WSNs. This paper proposes function finding algorithm using gene expression programming (FF-GEP) for sensor data. The details are shown as follows.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 2 System Model
### Targeted Problems
#### 2.2.3 Anti-Jamming Decision-Making Based on the Search of Knowledge Graph  
Using the constructed anti-jamming knowledge graph to make search decisions, it is necessary to consider how to generate search results for diverse search requirements. Above all, since the knowledge graph stores knowledge relations rather than cases, for different parameters of the same jamming pattern, knowledge graph search results are the same, but the actual decision-making results may be different. This is because the knowledge graph stores all possible decision-making results of the patterns. Therefore, when the anti-jamming knowledge graph search is used to obtain anti-jamming decision-making, it is also necessary to integrate the strategies and select the optimal strategy. The main solution of this paper is to design a search decision-making algorithm, which can select the optimal strategy under different parameters and output anti-jamming strategies and corresponding decision parameters. The search decision-making algorithm and search sentence design will be carried out in Section 4.
## 4 Results
### Comparison Between Fractal Features Combinations  
This subsection compares the performance of the fractal feature combinations presented in Table 3. The classification performance of different fractal feature combinations is evaluated across multiple subjects. The results indicate that linear support vector machine (LinearSVM) consistently achieves the highest classification accuracy across all subjects and feature sets. With a mean accuracy ranging between 78.57% and 79.16%, LinearSVM emerges as the most stable and reliable classifier for EEG-based motor imagery classification. Other classifiers, such as GSVM and SVM with a polynomial kernel (SVM Poly), present competitive performance but fall slightly behind LinearSVM in terms of mean accuracy. The SGD classifier achieves lower accuracy when compared to the other SVM-based models. However, CART decision tree classifiers yield the lowest classification results, with a mean accuracy of approximately 58-59%, which suggests poor generalization across the subjects.  
Among the various features sets tested, the combinations of Katz (KFD), box-counting (BCFD), and correlation dimension (CDFD) yields the highest mean accuracy of 79.16% when used with LinearSVM. This suggests that integrating multiple fractal dimension measures enhances the discriminative power of EEG features. Other high-performing feature sets include KFD vs. PFD vs. CDFD and KFD vs. CDFD vs. MEDFA, both yielding mean accuracies above 79%, indicating that KFD is a crucial feature for EEG-based classification. In contrast, feature sets integrating detrended fluctuation analysis (DFA) show slightly weaker performance, suggesting that DFA alone may not be as effective in capturing the complexity of motor imagery EEG signals.  
The [PERSON]'s fractal dimension (KFD), [PERSON]'s fractal dimension (PFD), and [PERSON]'s fractal dimension (HFD) combination continued to perform well among the other feature sets. However, following the correction of the Higuchi fractal dimension (HFD) function, the accuracy for this combination slightly decreased from 79.04% to 78.95%. This minor reduction in accuracy confirms that the original function's sign error did not substantially distort the results; however, its correction ensures mathematically valid feature extraction.  
The results further reveal considerable inter-subject variability in classification accuracy. Specifically, subjects 1, 3, 7, 8, and 9 consistently achieve a high classification performance (85-93%), while subjects 2, 4, and 6 present significantly lower accuracy (42-70%) across multiple classifiers and feature sets. This difference suggests potential challenges related to subject-dependent EEG signal characteristics, which may be caused by differences in brain activity or signal quality.  
Given these findings, LinearSVM is the preferred classifier due to its superior performance and stability. Furthermore, feature sets incorporating KFD, BCFD, and CDFD appear to be the most effective for motor imagery classification, highlighting the importance of combining multiple fractal dimension measures.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 1 Introduction
The results showed that the Kernel Density Estimation with Least Square Method exhibited commendable performance with the smallest cumulative evaluation index values. [PERSON] et al. [27] proposed a procedure for outlier elimination based on vector quantization and density-based spatial clustering algorithms in estimating the power curves of wind farms. The proposed methodology was demonstrated to be highly efficient in the presence of noises. Power curve modeling for wind turbines' power performance monitoring is intuitive but has limited capability in capturing the precise correspondence between wind speed input and active power output due to its insufficient nonlinear fitting abilities, since the averaging method dilutes the volatility and diversity of operational data. Power curves are unable to provide adequate quantitative information for power performance optimization or degradation monitoring because of the \(\pm\)5% inherent uncertainty according to IEC61400-12-1 standards [28]. Overall, the research on power performance monitoring technologies is not systematic and in depth enough, and implementable technical solutions are lacking. In this study, an effective power performance variation tendency monitoring method independent from the power curve is proposed. The operational data after wind turbine grid connection are concentrated. The abnormal operating conditions, such as shutdown data, fault data, and power limit data, are thoroughly cleaned through conditional filtering. After data acquisition and pre-processing, feature construction and data dimensionality reduction are performed based on the existing parameter variables. The kernel principal component analysis (KPCA) method is applied in extracting the top-nine principal components with the Cumulative Percent Variance (CPV) exceeding 96%. Then, a high-accuracy strong learner combined with the AdaBoost enhancement algorithm is employed to construct a power prediction model. The experimental results indicate that the accuracy can be improved by relying on adequate feature construction and regression reinforcement. Finally, the effectiveness of the proposed method is demonstrated through two application cases of wind turbines with performance fluctuations.
# Uncertainty of the 2D Resistivity Survey on the Subsurface Cavities
## 2 Methodology
### Data Analysis  
We analyzed the numerically measured apparent resistivity data using a statistical variance, which describes how far the data are spread out from the average value. Variance was also used to examine the association of measured resistivity data with the cavities since the resistivity method explicitly depends on the subsurface resistivity variations. A higher variance may indicate larger resistivity contrast and vice versa. Hence, the variance (\(\sigma_{\rho}\)) for the measured apparent resistivities (\(\rho_{\rm i}\)) with the mean value (\(\mu_{\rho}\)) was calculated as follows:  
\[{\sigma_{\rho}}^{2}=\frac{1}{\rm N}\ \sum_{i=1}^{\rm N}(\rho_{\rm i}-\mu_{ \rho})^{2} \tag{1}\]  
The anomaly effect (AE) was first introduced by [PERSON] [54], commonly used to evaluate arrays' measuring abilities. The anomaly effect was also used to examine arrays' applicability for different subsurface features, and it is calculated using the expression [25; 55]:  
\[{\rm AE}=\left|\max\left(\frac{\rho_{\rm a}}{\rho_{\rm av}}\right)-\min\left( \frac{\rho_{\rm a}}{\rho_{\rm av}}\right)\right| \tag{2}\]  
where \(\rho_{\rm max}\), \(\rho_{\rm min}\), and \(\rho_{\rm av}\) represent the maximum, minimum, and average apparent resistivities, respectively. We applied the anomaly effect to assess the recovering abilities of the arrays and inspect the corresponding image quality as a function of cavity depth.  
Data misfit between the measured and predicted apparent resistivity using relative root mean square (RMS) error [56] was used to assess arrays' inversion error with cavity depth.  
The depth of investigation (DOI) index [28; 57] calculated the depth below, for which the obtained model influenced by the reference model rather than the measured data, by inverting the same dataset twice with different reference models (m\({}_{01}\) and m\({}_{02}\)) using 0.1 and 10 times of the average apparent resistivity. DOI value of the bottom model (\(\rm{R_{b}}\)) was used to normalize the DOI index, and it is presented as:  
\[\rm{DOI\ (x,z)}=\ \frac{m_{1}(x,z)-\ m_{2}\ (x,z)}{R_{b}(m_{01}-m_{02})} \tag{3}\]  
where \(\rm{m_{1}}\) and \(\rm{m_{2}}\) indicate the inverted resistivity values of the same cell for the two inversions.  
We also examined the arrays' model accuracy based on the image correlation between the actual and inverted cavity models. The similarities were quantified using a statistical correlation coefficient that is expressed by:  
\[\rm{Correl\ (\rho_{i},\rho_{a})}=\frac{\sum(\rho_{i}-\mu_{i})(\rho_{a}-\mu_{a})}{ \sqrt{\sum(\rho_{i}-\mu_{i})^{2}(\rho_{a}-\mu_{a})^{2}}} \tag{4}\]  
where \(\rm{\rho_{i}}\) is the inverted resistivity data points, \(\rm{\rho_{a}}\) is the actual model resistivity data points, \(\rm{\mu_{i}}\) is the mean value for the inverted resistivity data, and \(\rm{\mu_{a}}\) is the mean value for the actual resistivity data.