esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### K-Means Clustering  
K-means clustering is an unsupervised machine learning algorithm which clusters data into \(k\) clusters. More specifically, the algorithm first randomly selects \(k\) samples from the data set to initialize the \(k\) cluster centroids. Each data point is then assigned to the cluster corresponding to the closest centroid as measured by the Euclidean distance. Centroid locations are then updated to correspond to the mean of all data points in the corresponding cluster. The process of assigning data points to the nearest cluster centroid and adjusting centroid locations is repeated until cluster centroids are stable. Hence, k means clustering attempts to identify natural clusters in the data [35]. We used k-means clustering as implemented in the Python library scikit-learn [36].  
We note that more advanced clustering methods exist, but k-means is a reasonable choice for our dataset. Since data are already custom scaled for modeling outdoor acoustic environments via machine learning, the Euclidean distance is likely a decent indicator of similarity between points in feature space. More sophisticated clustering methods may use different distance measures or rescale data, undermining the effect of scaling the data to describe outdoor acoustic environments.  
One challenge of k-means clustering is determining the appropriate number of \(k\) clusters such that data are classified meaningfully and descriptively. Two common methods for determining the appropriate number of clusters are silhouette analysis and elbow analysis. Silhouette analysis is performed by calculating the average silhouette score for k-means clustering models trained using various values of \(k\) and selecting the model with the highest score. The silhouette score is a measure of how similar points within the same cluster are and how dissimilar points from different clusters are [37]. The silhouette score ranges from -1 to 1, where 1 represents perfectly clustered data and -1 represents poorly clustered data.  
Similar to the silhouette analysis, elbow analysis requires training multiple k-means clustering models for different values of \(k\). Elbow analysis uses the inertia (i.e., the sum of squares distance between the data and its nearest cluster) to identify an appropriate number of clusters. The inertia is a monotonic decreasing function, and the optimal number of clusters is the point where adding another cluster to the model begins to only marginally reduce the inertia [38]. This happens at the \"elbow\" in a plot of the inertia. We used silhouette and elbow analyses to identify the appropriate number of \(k\) clusters for a set of 51 geospatial layers over the CONUS.
## 3 Results
### WorldView-2 (Synthetic)  
We applied 11 existing and two new algorithms in the form of 25 variants to synthetic 1.8-m, WorldView-2 imagery to examine the degree of portability of some of the simpler algorithms between (synthetic) satellite imaging systems (Tables S1, S2 and S4 and Figure 8). WorldView-2 has a narrow band capable of measuring the 620 nm phycocyanin absorption feature. We also applied a new subtraction algorithm (Be162 Bsub) and a new modified NDCI algorithm [32] (Be16 NDPhyI) tuned to the 620 nm phycocyanin absorption feature. In decreasing order of performance, seven algorithms, WV2 Be162 Bsub, WV2 SiO52 BDA, WV2 Am092 Bsub, WV2 Be16 NDPhyI WV2 Mi092 BDA, and WV2 MM12 NDCI, had acceptable performance ([PERSON]'s r\({}^{2}\) > 0.6; \(p\) < 0.001) with this sensor in this exercise (Figure 8). The performance of each algorithm in Table 2 and Table S1 applied to the synthetic WorldView-2 imagery was evaluated using 29 coincident surface observations (Table S2 and S4). As with CASI, simple subtraction and ratio-based algorithms that include the 620 nm phycocyanin absorption feature and/or reflectance in the near-infrared suppress illumination variation well and  
Figure 7: Results of CASIBe152 BsubPhy (715 sub615) algorithm (this paper) as raw index values as applied to original CASI imagery with brighter pixels in the reservoir indicating higher BGA/PC values (**a**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw CASIBe152 BsubPhy (715 sub615) index value with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.776 \(p\) < 0.001, \(n\) = 29 to avoid shorelines) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA values (CASIBe152 BsubPhyPHY) with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.776, \(p\) < 0.001, \(n\) = 29 to avoid shorelines) (**c**). Details of the synthetic bands and band math are available in Tables 1 and 2, respectively.  
had the best performance with regard to BGA/PC value estimation. Index imagery with raw and normalized index Type 1 (Pearson's r) linear regressions are shown for the best performing algorithm (WV2 Be162 Bsub) in Figure 8.  
Figure 8: Results of WV2 Be162 Bsub algorithm (this paper) as raw index values as applied to synthetic WorldView imagery with brighter pixels in the reservoir indicating higher BGA values (**a**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw index value (WV2 Be162 Bsub) with [PERSON]’s r\({}^{2}\) (\(r^{2}\) = 0.790, \(p<0.001\), \(n\) = 29 to avoid shorelines) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA values (WV2 Be162 BsubPHY) with [PERSON]’s r\({}^{2}\) (\(r^{2}\) = 0.790, \(p<0.001\), \(n\) = 29 to avoid shorelines) (**c**). Details of the synthetic bands and band math are available in Tables 1 and 2, respectively.
## 4 Computational Experiments
### Ablation Experiments
#### 4.4.2 Effectiveness Verification of the Local Attention Mechanism  
To demonstrate the contribution of the local attention mechanism, we replace it with the global attention mechanism to build a comparison network, train the comparison network with the same training algorithm, and compare the testing results of the two algorithms on the dataset _Testing_R_1. This deep reinforcement learning algorithm with the global attention mechanism is denoted by DRLGA.  
Table 10 lists the testing results of DRLLA and DRLGA, where the best values are bold. As for MPR, DRLLA is significantly better than DRLGA, indicating that the local attention mechanism is superior to the global attention mechanism in improving solution quality. The main reason is that the local attention mechanism, in comparison with the global attention mechanism, can guide the network model to focus on the more promising candidate task range, preventing it from generating unreasonable task permutations. However, the MCT values of DRLGA are much lower than those of DRLLA. The main reason is that the global attention mechanism tends to generate inappropriate task permutations, leading some unscheduled tasks to lose their observation opportunities and reducing the number of decoding steps. This mechanism shortens the computation time but seriously sacrifices the solution quality. In comparison, the local attention mechanism takes more computational time, but it dramatically improves the solution quality.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task Number** & **50** & **75** & **100** & **125** & **150** & **175** & **200** & **225** & **250** \\ \hline \multirow{3}{*}{MPR} & MHSA & **1.0** & **0.984** & **0.953** & **0.893** & **0.822** & **0.746** & **0.687** & **0.636** & **0.586** \\  & CNN & 0.983 & 0.947 & 0.895 & 0.826 & 0.760 & 0.683 & 0.632 & 0.578 & 0.537 \\  & LSTM & 0.981 & 0.957 & 0.926 & 0.878 & 0.818 & 0.727 & 0.656 & 0.608 & 0.567 \\ \hline \multirow{3}{*}{MCT (s)} & MHSA & 0.147 & 0.148 & 0.273 & 0.309 & 0.346 & 0.481 & 0.512 & 0.574 & 0.615 \\  & CNN & 0.087 & 0.148 & 0.221 & 0.282 & 0.353 & 0.417 & 0.585 & 0.560 & 0.626 \\  & LSTM & 0.090 & 0.156 & 0.224 & 0.303 & 0.375 & 0.473 & 0.538 & 0.620 & 0.796 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Testing results of MHSA, CNN and LSTM on _Testing_R_1.
## 2 Materials and Methods
### Research Methods and Ideas  
#### 2.3.1 Maximum Entropy Model  
The maximum entropy (MaxEnt) model is a predictive model for species distribution, developed through machine learning and statistical methods based on the principle of maximum entropy [37]. Its core principle involves predicting a probability distribution that maximizes entropy, selecting the distribution with the highest entropy that meets known constraints on the unknown distribution. The MaxEnt model has already been successfully applied to forest fires [38], mudslides [39], and landslides [40]. The MaxEnt model has demonstrated higher accuracy than traditional information models and artificial neural networks in assessing the susceptibility and spatial distribution of these disasters [41; 42]. This model is favored in susceptibility assessments due to its operational simplicity and compatibility with datasets containing as few as five data points, allowing for effective modeling even with limited sample data [43; 44]. Compared to models with strict variable requirements, the MaxEnt model imposes fewer restrictions on input data, allows flexibility in constraint settings, and achieves high predictive accuracy. For grassland fire susceptibility analysis, the MaxEnt model requires only the influencing factors for the sample points and the region to fit a probability distribution that maximizes entropy. This enables the simulation model to identify key influencing factors and predict areas with high susceptibility to grassland fires.
## 3 Results and Discussion
### Selection and Distribution Analysis of Priority Management Areas  
The priority management areas were determined by implementing a newly established LMO management scheme (Figure 2). As shown in Figure 3, 198 priority management areas were selected and surveyed in six administrative districts over eight years, from 2014 to 2021. The distribution of these sites varied annually, with 11 areas selected in 2014, 19 in 2015, 24 in 2016, 22 in 2017, 23 in 2018, 32 in 2019, 38 in 2020, and 29 in 2021. The district with the highest priority management areas was Gyeonggi-do (80), followed by Chungcheong-do (33), Gyeongsang-do (29), Jeolla-do (35), and Jeju-do (12). Nine priority management areas in Gangwon-do were only selected from 2014 to 2016.  
Figure 3: Distribution of priority management areas in South Korea using the GIS system. Survey sites were assigned as priority management areas from 2014 to 2021. Management areas in six administrative districts: Gyeonggi-do (**a**), Gangwon-do (**b**), Chungcheong-do (**c**), Gyeongsang-do (**d**), Jeolla-do (**e**), and Jeju-do (**f**). Red circle indicates the distribution of priority management areas in South Korea.  
Of the 198 priority management areas with 55 addresses, 192 (97%) sites with 49 addresses were re-selected twice or more, and 6 (3%) sites with 6 addresses were selected once. In particular, of the re-selected 49 areas, 3 areas were re-selected 8 times (5.5%), 2 areas 7 times (3.6%), 5 areas 6 times (9.1%), 6 areas 5 times (10.9%), 6 areas 4 times (10.9%), 16 areas 3 times (29.1%), and 11 areas 2 times (20%). These results suggest that many sites reassigned as priority management areas require intensive surveys and post-management. Priority management areas are distributed nationwide and are not restricted to any region or location.
# The Optimization of the Geometry of the Centrifugal Fan at Different Design Points
## 1 Introduction
Despite the guarantee of finding a solution, deterministic algorithms are often rejected due to numerical differentiation and long execution time;* Stochastic algorithms are more popular in engineering when it comes to solving optimization problems. The main drawback of stochastic algorithms is that there is always a probability of not finding the best solution within a reasonable time or number of iterations. When solving problems with these algorithms, the results are often not entirely accurate. However, in real-life scenarios, a theoretically absolute and precise optimal solution is not always necessary. Stochastic algorithms do not guarantee an optimal solution to a problem; therefore, the same problem needs to be solved multiple times. Conclusions can be drawn, or the average of the obtained solutions can be calculated from the multiple solution results. Stochastic algorithms do not guarantee an optimal solution to the problem, so the same problem needs to be solved multiple times. Conclusions can be drawn from the obtained results, or the average can be calculated. Numerous studies have focused on how to optimize the geometry of centrifugal fan impellers with the goal of increasing efficiency at a certain operating point. The fan must be studied in the broadest operating range feasible to design a device with high efficiency throughout a variety of operating modes. Due to these factors, the impeller geometry is optimized at various design points in this paper. A correlation between the design point choice and the final performance and efficiency curves of the fan is sought after an evaluation of the changes in the researched geometrical parameters. The focus here is on formulating the problem. A general model of a centrifugal fan was developed by researchers with the aim of investigating whether optimizing the fan impeller for specific operating points leads to improved performance. In this research, emphasis is placed on efficiency through optimization rather than statistical analysis. While statistical analysis may provide insight into efficiency from an empirical standpoint, optimization enables a comprehensive evaluation to ensure solution reliability. This study not only presents expected fan efficiencies but also explores the search fields of angles \(\beta_{1}\) and \(\beta_{2}\) to achieve maximum efficiency with minimum energy consumption. This process highlights the scientific approach taken in optimizing the geometric parameters of the fan. For each calculation point, specific blade parameters are individually optimized, with the values of \(\beta_{1}\) and \(\beta_{2}\) adjusted systematically. During this research phase, over 1000 airfoils with different geometric parameters are experimented with. The efficiencies of these airfoils at specific optimization points are computed. By the end of this stage, the impeller with the highest overall efficiency is identified for each operational segment. This study comprehensively evaluates the entire search space and conducts thorough analyses.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol  
### Network Model  
A typical network is assumed to have the following characteristics:  
1. All the nodes are static.
2. The nodes are capable of varying their transmission power.
3. The data sampling rate is fixed.
4. The BS is located somewhere in the sensing region.
5. All the nodes are homogenous with a limited battery power, except for the BS.
## 3 Proposed Approach
### Preprocessing: Reconstructing Images Using Fuzzy Colour Technique  
The fuzzy technique is accepted because of its high degree of accuracy. The fuzzy colour approach is widely used in image analysis applications. The colour separation is carried out using similarity and difference functions, which are evaluated using membership and non-membership functions. In order to improve the quality of the indoor scenes and eliminate the noisy information, we have preprocessed our dataset using fuzzy colour stacking [48] techniques. They help reduce the noise in the background, thus improving the foreground quality. Here, the input data are separated into a blurred window. The steps involved in the fuzzy technique can be summarised as follows:  
* In each window, there is a membership degree associated with each image pixel;
* Based on the distance between the pixel and the window, we calculate the membership degrees;  
Figure 1: Block diagram of proposed framework.  
* We sum up the weights of all the blurred windows, and we create the output image from the average value;
* Two images from a row are combined, and then they are divided into two parts, background and overlay, in order to eliminate the noise from the input image;
* The stacking technique eliminates the noise from the image by considering parameters such as contrast, brightness, opacity, and combining ratio;
* Here, we have stacked our original dataset on the reconstructed dataset using the fuzzy colour technique.  
In our work, we have used a contrast value of 1.5, an opacity of 0.6, a brightness value of 80, and a 50% combination ratio. We evaluated our dataset with different values for the above parameters and found that these were more efficient for our dataset. A normalised histogram is evaluated for the input image and the preprocessed image to measure the performance of the preprocessing method.  
\[\mathrm{P_{r}}\left(\mathrm{r_{k}}\right)=\mathrm{n_{k}}/\mathrm{n} \tag{1}\]  
where k = 0 to 255, \(\mathrm{r_{k}}\) is the kth intensity level, \(\mathrm{n_{k}}\) is the count of pixels with intensity \(\mathrm{n_{k}}\), and n is the total number of pixels in the image.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Measurement Placement  
MEC extends a cloud computing scheme and leverages servers near end-users at the network edge to provide a cloud-like environment. An appropriate measurement placement strategy plays a crucial role in the performance of such service-based applications. The features of modern smart grids, such as large-scale geographical distribution, bring challenging issues to measurement placement. In this subsection, several research topics on measurement placement are summarized and discussed.  
[PERSON] et al. (2020) focused on low-delay resource scheduling in power systems. The paper presents that as an increasing number of smart devices are used in smart grids, the massive data and calculations become heavy burdens for the power system. To improve processing efficiency, the authors proposed two heuristic algorithms combined with edge computing architecture. For the virtual machine sequence issue, sorting the virtual machines with the critical path algorithm is ideal for reducing the computing time. The second stage is to use an enhanced best-fit algorithm to improve the placement strategy, avoiding an increase in computing time. The experiments show that this two-stage algorithm ensures the optimal placement of virtual machines and effectively shortens the computing time of the power system.  
Fault detection plays a primary role in modern smart grids. In paper (2020), a framework based on the phasor measurement unit (PMU) and edge computing is proposed for fault detection and localization. In this framework, PMUs with embedded personal computers are placed at a primary substation and a secondary substation at the end of a feeder. This framework makes the fault detection and localization perform at the edge of the local network. The communication delay and the data loss are eliminated.
## 3 The PSOTSC Algorithm
### Satisfaction Judgment  
If the number of segments of the trajectory is 1, i.e., there are no segment feature points except the first and last two points of the trajectory, then the compression ratio of the original trajectory is the best. On the contrary, the segmentation accuracy can be improved by setting as many segmentation feature points as possible and retaining the profile information of the original trajectory, but a certain data compression ratio will be sacrificed. Therefore, we need to find a balance between the number of segments and the accuracy of segmentation. When the satisfaction is reached, we judge that the trajectory segmentation and compression is complete.  
Figure 4: NARJ particle update strategy.  
data. Some trajectories are shown in Figure 6. If each target is represented by only one trajectory, the number of points of the longest one is 4007.  
The maneuvering target simulation trajectory data set is based on the maneuvering target tracking simulation model in Section 4.2 of [31], which generates 50 trajectories, each with a length of 50. Figure 7 shows all the trajectories of the simulation data set and some typical trajectories. It can be seen that the trajectories generated by the simulation reflect stronger target motion changes and contain certain randomness, which are in line with the motion characteristics of maneuvering targets in the battlefield.  
The PSOTSC algorithm in this paper and the compression algorithms such as DP, TRACLUS and TCSS used for comparison were edited and implemented in MATLAB R2016b. All experiments were carried out on Windows 7 operating system with Intel(r) Core(tm) i5-7500, memory size 8 GB.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 4 Simulation Results and Discussion of the Thermal Deformation of Head  
The back radiative heat flux from disk to head, which can be regarded as the thermal source on the disk heating the slider on the head, will cause a thermal deformation of the slider. The slider is modelled to find out the maximal deformation of the head. It makes no sense to consider the whole size of the magnetic head because, in comparison with the heated area, the magnetic head is too big; therefore, a calculation diameter of 4 \(\upmu\)m for the cylinder head is taken. The heated area is taken to a 200 nm \(\times\) 200 nm square in the center of the head. The thickness of the cylinder head is much larger than the size of the heated area too, so a thickness of 10 times the size of the heated area, 2 \(\upmu\)m, is taken. After heating the square area by the radiative heat flux calculated in Section 2, the flying height of 1 nm was picked, under which distance the radiative heat flux is large enough to obviously observe the deformation, the distribution of temperature is plotted in Figure 9. The high temperature appears inside the heating square. The temperature is higher inside and lower outside the square. The heating has almost no influence on the other area in the head model.  
The deformation caused by the radiative heat flux is shown in Figure 10. As expected, the largest deformation is in the center of the head model (Figure 10b), and the deformation in the square area is larger than the other area in the head model (Figure 10a). It shows that when the flying height is 1 nm, the maximum deformation is about 0.058 nm. It is going to make a big difference on the air bearing, i.e., the 1 nm-thick air film.  
The thermal deformation and deformation rate, i.e., the rate of the deformation under different flying heights from 1 nm to 10 nm, is plotted in Figure 11. As shown in Figure 11, the deformation rate is increased when the flying height gets smaller, especially when the flying height is smaller than 5 nm, the deformation rate is too big to be ignored. It is because when the flying height is increased, the radiative heat flux is decreased, as  
Figure 10: The deformation caused by the radiative heat flux.  
Figure 9: The distribution of temperature after radiative heating.  
discussed in Section 2. Ref. [10] shows that radiation at very small gap spacing could have a more significant effect on the thermal protrusions (thermal deformation) by comparing the thermal deformation with and without back radiation considered. The simulation results in Figure 11 basically coincide with the results in Ref. [10]. Now that the flying height reaching 2 nm, the discussion of the thermal deformation is meaningful.