esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FSMPSCC Assessment with Performance Maps  
Two requirements are needed to use performance maps. First, a performance index or figure of merit must be defined, and second, a partition of the operating space must be provided. Regarding the performance index, in the case of FSMPSCC, the stator's current tracking error and the switching rate at the VSI are usually considered. This would lead to several performance maps, one for each of the following variables: \(e_{\alpha}\), \(e_{\beta}\), \(e_{x}\), \(e_{y}\), and \(f_{sw}\). However, the use of VVV takes care of the \(x-y\) components to a satisfactory level, and the number of performance maps can be reduced to three variables: \(e_{\alpha}\), \(e_{\beta}\), and \(f_{sw}\). Furthermore, the components \(e_{\alpha}\) and \(e_{\beta}\) should be quite similar for normal drive operation, so they can be merged in a single quantity taking the mean. In this way, we will use \(e_{\alpha-\beta}\doteq(e_{\alpha}+e_{\beta})/2\), leaving the number of performance indices to two: \(e_{\alpha-\beta}\), and \(f_{sw}\). These quantities can be computed for each operating point by averaging over a time window \((k_{1},k_{2})\). The duration of this time window, \(L=k_{2}-k_{1}+1\), should include some electrical cycles to be meaningful. In mathematical notation, one gets the following:  
\[e_{\alpha-\beta} = \sqrt{\frac{1}{(k_{2}-k_{1}+1)}\sum_{k=k_{1}}^{k_{2}}\left((e_{ \alpha}(k)+e_{\beta}(k))/2\right)^{2}} \tag{7}\] \[f_{sw} = \frac{1/6}{T_{s}(k_{2}-k_{1}+1)}\sum_{k=k_{1}}^{k_{2}}SC(k). \tag{8}\]  
Regarding the partition of the operating space, for the particular case of FSMPSCC, the amplitude for the stator reference \(I^{*}\) and the electrical frequency \(f_{e}\) define the operating point. Depending on the physical characteristics of the system, these variables take values at certain intervals: \(I^{*}\in I_{a}=[0,\overline{A}]\), \(f_{e}\in I_{f}=[0,\overline{F}]\). The set of all operating points \(\Omega\) is included in the Cartesian product \(I_{a}\times I_{f}\).
# Self\(-\)Mode\(-\)Locked \(2-\)um GaSb\(-\)Based Optically Pumped Semiconductor Disk Laser
## 3 Results and Discussion
The emission spectra have multiple equally spaced peaks simultaneously, which is not conducive to realizing the mode\(-\)locking. Self\(-\)mode\(-\)locking of the GaSb\(-\)based SDLs was accomplished by inserting an aperture and an F\(-\)P etalon in the cavity. The output power of the SML GaSb SDLs is shown in Figure 5b. The additional etalon filters the spectrum into a single peak with a width of 1.1 nm. At a low output power of \(\sim\)42 mW, the laser switched immediately to self\(-\)mode\(-\)locked operation. With the increase of pump power, the emitting wavelength switched from 2031.8 nm to 2034.5 nm, as shown in the insert of Figure 5b. The maximum average output power achieved for the SML GaSb SDLs amounted to 173 mW at an incident pump power of 11.2 W.  
Figure 4: (**a**) The pulse duration of a single pulse is directly measured by the high\(-\)speed photodetector and oscilloscope. (**b**) The radio\(-\)frequency spectrum of the output pulse train. Resolution bandwidth 30 kHz. Inset, wide\(-\)scan RF spectrum.
## 2 Methods
### Pattern Period Length and Skew Angle Measurement  
The skew angles and period length can be measured based on the final center point map which reflects the center points distribution of the pattern.  
The period lengths include the weft period length \(T_{h}\) and warp period length \(T_{v}\). As shown in Figure 10b, the detected center points were denoted by the red points \(H_{i}\), \(V_{i}\), (\(i\) = 1, 2, 3,... ). One weft period length is \(T_{h}\) = \(d_{Hillj}\), where \(d_{Hillj}\) is the distance between the \(H_{i}\) and \(H_{j}\) (\(j\) = \(i\) + 1, \(i\) = 1, 2, 3... ). Similarly, one warp period length is \(T_{v}\) = \(d_{VVj}\), where \(d_{VVj}\) is the distance between the \(V_{i}\) and \(V_{j}\) (\(j\) = \(i\) + 1, \(i\) = 1, 2, 3... ).  
Figure 8: Bounding box mapping process. (**a**) Original image. (**b**) Bounding box mapping result \(F_{new}\). (**c**) Bounding box merge result \(F_{new1}\).  
Figure 10: Measurement of the skew angles and period length. (**a**) Schematic diagram of pattern parameter measurement. (**b**) The pattern center points map of (**a**).  
Figure 9: The procedure of the proposed method. (**a**) Original center point map. (**b**) Missing point filling process. (**c**) Final center point map.  
The skew angles also include the weft skew angle \(\theta_{\textit{t}veft}\) and warp skew angle \(\theta_{\textit{warp}}\). The \(\theta_{\textit{t}veft}\) and \(\theta_{\textit{warp}}\) are calculated by \(\theta_{h}\), \(\theta_{v}\) and \(\theta_{c}\) in Figure 10a, where \(\theta_{c}\) is the angle between the contour of the piece and the x-axis, measured by the measuring tool (see Figure 10a). Since this paper only studied the local pattern features of the piece, it is assumed that \(\theta_{c}\) is a known angle. The way to obtain \(\theta_{h}\) and \(\theta_{v}\) is shown in Figure 10b. First, the least squares method is used to fit the center points \(H_{i}\) (\(i=1\), 2, 3... ) in the weft direction as a weft period line \(L_{h}\). Second, calculate the slope \(K_{h}\) of the line \(L_{h}\). Finally, \(\theta_{h}\) is calculated by the equation \(\theta=\arctan(k)\). Similarly, the center points \(V_{i}\) (\(i=1\), 2, 3... ) in the weft direction are used to obtain \(\theta_{v}\). The skew angles (\(\theta_{\textit{t}veft}\) and \(\theta_{\textit{warp}}\)) can be obtained by the following equation:  
\[(\theta_{\textit{t}veft},\theta_{\textit{warp}})=(\theta_{h}-\theta_{c},\theta _{v}-\theta_{c}) \tag{3}\]
# Coseismic Stress Change and Viscoelastic Relaxation after the 2008 Great Sichuan Earthquake
## 3 Results
### Long-Term Evolution of Stress Changes
That is, the stress field in the Sichuan and adjacent areas is dominated by stress changes imparted by the 2008 Ms 8.0 Wenchuan earthquake, which was the largest event in the past 10 years. Especially in southern Sichuan, where the Xianshuihe fault and Xiaojiang fault are located, Coulomb stress has increased. The Yushu earthquake in 2010 also resulted in an increase in stress zones in the northwestern part of the study area. The epicenters of the 2013 Lushan earthquake and the two 2014 Kangding earthquakes lie in the positive lobes of stress changes caused by the Wenchuan earthquake. The 2008 Wenchuan earthquake also significantly increased the northern area near the northern end of the Wenchuan focal plane, which could have generated a large earthquake (e.g., [75]). Coincidentally, the 2017 Ms 7.0 Jijuzhaigou earthquake occurred in this area. The 2008 Wenchuan earthquake and the 2010 Yushu earthquake also led to a stress shadow (region of Coulomb stress decrease), which may explain why few events have occurred on the western side of the Longmenshan fault. As shown in Figure 8e, the state of the stress field in this area is affected by the six earthquakes. Some areas exhibit an increase in cumulative stress, and some exhibit a decrease in stress. Figure 8e also shows that the northern and southern parts of the Longmenshan fault zone are in the accumulation area of stress increases, so they might have the greatest seismic risk in the future.
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 3 Methods
### YOLOv9s-UII Network Model  
In the official version of the YOLOv9 model, the model weights are substantial, totaling 122.4 M, which poses challenges for both model portability and real-time object detection. Through a comparative analysis of scaling ratios across the YOLO series and adjusting the network depth to 0.33 and the width to 0.25, the YOLOv9s model was developed. This modified version features significantly reduced amounting to only 9.1 M, leading to an improvement in detection speed.  
This research focuses on Attention Guided Feature Extraction and Fusion. An examination of the attention computation process reveals that spatial attention involves the weighting of each locality within a feature layer, while channel attention pertains to the weighting of the entire feature layer. As regards the tasks related to object detection, the main goal is to identify important objects within an image, with levels of relevance in various regions. Spatial attention can assist the network in extracting features from crucial areas. The feature layers obtained from the backbone network contain diverse semantic information, which varies across different levels, affecting their respective roles in detecting different targets. Therefore, channel attention mechanisms can be utilized to facilitate the integration of these features.  
In the YOLOv9s network, the incorporation of the D-Mixer, Local Channel Attention, and Local Spatial Attention modules has led to the development of the YOLOv9s-UI network, as proposed in this paper. The structure of this improved network is depicted in Figure 4.  
Figure 4: Structure of a neural network model featuring the auxiliary reversible branch and attention mechanism.
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 3 Proposed Framework
### Relation to Prior Work
Finally, an important feature of our framework is the ability to choose between different sets of features for the high-dimensional space that is later projected to the visualization. Most systems use fixed sets of descriptors. Notably, Ref. [6] allows several options, includ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Segmentation** & **Audio Descriptor/s** & **Reduction Algorithm** & **Output Layout** & **Icon Style** & **Icon Feature** \\ \hline [PERSON] (2007) [10] & Onset & \begin{tabular}{c} Spectral centroid \\ Temporal centroid \\ \end{tabular} & – & – & – & – \\ \hline [PERSON] et al. (2008) [15] & – & MFCCs & SOM & – & – & – \\ \hline [PERSON] et al. (2009) [57] & Onset & MPEG-7 & Hybrid MDS & – & Color & Descriptor \\ \hline [PERSON] and [PERSON] (2012) [7] & – & Several scalar & t-SNE & – & Texture & Custom \\ \hline [PERSON] et al. (2014) [38] & – & \begin{tabular}{c} MFCCs \\ Temporal centroid \\ \end{tabular} & – & \begin{tabular}{c} Metric learning + \\ Kernelized sorting \\ \end{tabular} & Color & Isomap/Clustering \\ \hline [PERSON] et al. (2014) [39] & – & \begin{tabular}{c} MFCCs \\ Spectral flatness \\ \end{tabular} & PCA+t-SNE & Proximity grid & \begin{tabular}{c} Color \\ Shape \\ \end{tabular} & Perceptual sharpness \\ \hline [PERSON] (2015) [8] & – & \begin{tabular}{c} Beat spectrum \\ MFCCs \\ \end{tabular} & Force directed layout & – & Spectrogram & – \\ \hline Font and Bandiera (2017) [5] & – & \begin{tabular}{c} Chroma \\ MFCCs \\ \end{tabular} & t-SNE & – & Color & Tristimulus \\ \hline [PERSON] and Ruat (2020) [21] & – & \begin{tabular}{c} Amplitude envelope \\ Roughness envelope \\ Spectral envelope \\ \end{tabular} & PCA+UMAP & – & \begin{tabular}{c} Color \\ Shape \\ Texture \\ \end{tabular} & \
\begin{tabular}{c} Position \\ Amplitude envelope \\ Supervised learning \\ \end{tabular} \\ \hline [PERSON] et al. (2021) [6] & – &
\begin{tabular}{c} Chroma \\ FFT \\ MFCCs \\ RMS \\ Spectral centroid \\ \end{tabular} & PCA & – & Color & Clustering \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of research prototypes for visualization of sound collections. ing MFCCs and Chroma. It is also worth noting that AudioStellar [6] and CataRT [37] are available to musicians, whereas other systems can be mostly considered research prototypes. However, like [10], the current public version of CataRT uses scalar features as axes of the 2D visualization.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 2 Heading Estimation Based on an AR Model
### The Adaptive Kalman Filter  
KF based on the AR model can work well if the model function and noise statistic are both accurately known. However, the model function is an approximate expression. Moreover, a smartphone may be shaken due to human motion when the pedestrian is holding the smartphone for navigation, which makes the noise statistic unreliable. Therefore, it is necessary to adaptively adjust the noise for obtaining reliable filtering results.  
To reduce the influence of inaccurate state noise, we use an adaptive method based on the innovation sequence to adjust the state covariance matrix [35].  
Substituting Equation (14) into Equation (16), we can get  
\[P_{k,k}=P_{k,k-1}-K_{k}C_{k}K_{k}^{T} \tag{21}\]  
Substituting Equation (12) into Equation (21), the state noise covariance can be got as follows.  
\[Q_{k}=P_{k,k}-F_{k,k-1}P_{k-1,k-1}F_{k,k-1}^{T}+K_{k}C_{k}K_{k}^{T} \tag{22}\]  
We can get the approximate solution of the state noise covariance based on Equation (22).  
\[\hat{Q}_{k}=K_{k}\hat{C}_{k}K_{k}^{T} \tag{23}\]  
The covariance matrix of innovation can be obtained by averaging the innovation sequence over \(N\) windows.  
\[\hat{C}_{k}=\frac{1}{N}\sum_{j=0}^{N-1}v_{k-j}v_{k-j}^{T} \tag{24}\]  
where \(v_{k}=z_{k}-H_{k}x_{k,k-1}\) is the innovation vector.
# A Multi-Dimensional Matrix Product--A Natural Tool for Parameterized Graph Algorithms
## 1 Introduction
Note that when the inputs to the aforementioned operations are integers of size polynomial in the input size, then the number of required bit operations is larger than that of unit cost operations only by a polylogarithmic factor.) [PERSON] et al. showed that the maximum witness problem, i.e., the problem of reporting the maximum witness for each non-zero entry of the Boolean matrix product of two \(n\times n\) Boolean matrices, was solvable in \(O(n^{2+\lambda+o(1)})\) time [9], where \(\lambda\) satisfies the equation \(\omega(1,\lambda,1)=1+2\lambda\). We also generalize the concept of a witness for a non-zero entry of the Boolean matrix product of two Boolean matrices to include a witness for a non-zero entry of the \(k\)-dimensional Boolean product of \(k\) Boolean matrices. For a non-zero entry \(D[i_{1},\ldots,i_{k}]\) of the \(k\)-dimensional Boolean product of \(k\) Boolean matrices \(A_{1},\ldots,A_{k}\), of sizes \(n_{1}\times n,\ldots,n_{k}\times n\), respectively, a witness is any index \(\ell\) such that \(\bigwedge_{j=1}^{k}A_{j}[i_{j},\ell]=1\). The witness problem for the \(k\)-dimensional Boolean matrix product \(D\) is to report a witness for each non-zero entry of \(D\) and analogously, the maximum witness problem for \(D\) is to report the maximum witness for each non-zero entry of \(D\). We show that for \(n\times n\) matrices \(A_{1},\ldots,A_{k}\), the witness problem for their \(k\)-dimensional Boolean product can be solved in \(O(n^{\omega(\lceil k/2\rceil,\lfloor k/2\rfloor)+o(1)})\) time, while the maximum witness problem for this product admits a solution in \(O(n^{k+\lambda+o(1)})=O(n^{k+\lambda^{\prime}+o(1)})\) time, where \(\lambda\) satisfies the equation \(\omega_{k}(1,\lambda)=k-1+2\lambda\) and \(\lambda^{\prime}\) satisfies the equation \(\omega(\lceil k/2\rceil,\lambda^{\prime},\lfloor k/2\rfloor)=k-1+2\lambda^{\prime}\). By the recent results on rectangular matrix multiplication, the latter bound for \(k=2\) is \(O(n^{2.529})\). In the second part of our paper, we provide an alternative simple method of detection/counting copies of fixed size cliques based on the multi-dimensional matrix product. We show also an immediate reduction of the \(k\)-dominating set problem to the multi-dimensional matrix product implying the \(W[2]\) hardness of computing the product. Finally, we provide an efficient reduction of the problem of finding lowest common ancestors for all \(k\)-tuples of vertices in a directed acyclic graph to the problem of finding maximum witnesses of the Boolean variant of the multi-dimensional matrix product. These applications demonstrate the versatility of the multi-dimensional matrix product. Although the time complexities of the algorithms resulting from the aforementioned reductions solely match those of the known algorithms, the advantage of our algorithms is simplicity.
## 4 Results
### Sensitivity Analysis  
A crucial step in the MCDA process is the sensitivity analysis. It aids decision-makers in assessing the accuracy and robustness of results, thus providing value to research [140]. Fundamentally, sensitivity analysis evaluates the stability of results when parameter values alter. Therefore, it is evident that sensitivity analysis is a vital approach to evaluate the results. However, this fact is not significantly noticed in practical implementation. Out of all review papers, 65% make no mention of sensitivity analysis, implying that the applications studied in these papers most likely do not perform sensitivity analysis. Of the selected review papers, 20% reported limited implementation, while 11.25% reported medium level of implementation. Only three review papers (3.75%) noted that most of the applications studied conducted sensitivity analyses, underlining their significance in assessing outcome robustness and suggesting that decision-makers should always carry out this procedure [75; 103; 131].
## 3 Results
### Comparison Experiments
#### 3.2.2 Scene II  
Figure 7 illustrates that Scene II includes an extensive region of water, with multiple small areas of water and some scattered water bodies nearby. Therefore, it contains water bodies of different scales and has multi-scale characteristics. The experimental results show that GCAFF-Net and MF2 AM accurately extract small water body areas and completely segment large water body areas. U-Net has a large number of false alarms in small-area water body segmentation, and Deeplabv3+ has many missed detections. As can be seen from Table 3, the overall detection accuracy of Scene II is relatively high, reaching over 88%. Our network is improved by 3.25% compared to DeepLabV3+. While U-Net demonstrates a marginally higher detection accuracy compared to GCAFF-Net, its IoU, Recall, and F1-Score are substantially lower than those achieved by our network. In summary, the AFFM and GCAM proposed in this article have a better grasp of the intricate characteristics of water bodies. GCAFF-Net considers contextual information at different levels and effectively learns multi-scale water features.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{4}{c}{**Water**} & \multicolumn{4}{c}{**Background**} \\ \cline{2-9}  & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline U-Net & 84.60 & 81.43 & 95.61 & 89.77 & 97.21 & 87.54 & 89.79 & 93.36 \\ MF2 AM & 97.41 & 88.68 & 90.82 & 94.00 & 92.94 & 91.24 & 98.04 & 95.42 \\ Deeplabv3+ & 91.94 & 89.48 & 97.10 & 94.45 & 98.03 & 92.67 & 94.43 & 96.20 \\ Ours & 96.49 & 92.25 & 95.46 & 95.97 & 96.70 & 94.33 & 97.46 & 97.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of water detection results among various networks in the zoomed-in views of Scene I.  
Figure 7: The detection results for water bodies in Scene II using various networks. (**a**) The second SAR image for independent testing. (**b**) The ground truth. (**c**) The extracted water result for U-Net; (**d**) for MF2 AM; (**e**) for DeepLabV3+; and (**f**) for GCAFF-Net. The blue color indicates correct detections of water, the green color highlights missed detections, and the red color marks false alarms.
###### Abstract  
The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naive and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.  
machine learning; UAV-BS; macrocell; QoS; temporal and spatial flow Article
# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 2 Materials and Methods
### Data and Preprocessing  
Current remote sensing has formed a multi-level, multi-perspective, and multi-domain observation system that extends from the ground to the air and even to space. It encompasses information data collection, processing, interpretation, analysis, and application, enabling global detection and monitoring. Remote sensing has become an important means of obtaining information about Earth's resources and the environment [44]. Landsat imagery provides a way for repeated and comprehensive observation of the Earth, representing the world's longest continuously acquired space-based medium-resolution land remote sensing data. At the same time, Landsat data can play a good role in decision-making and management in many interdisciplinary fields. Generally speaking, Landsat sensors use a spatial resolution of 30 m, which is the ideal scale for observing surface changes. This article downloads image data through platforms such as Earth Explorer, Landsat Look, and Glo Vis, and in order to improve the accuracy of data processing, the enhanced Landsat instrument used has eight spectral bands, of which bands 1 to 5 and 7 have a spatial resolution of 30 m, and band 6 (thermal infrared) has a spatial resolution of 60 m. The spectral characteristics of Landsat imagery can reflect the biomass of vegetation, while DEM data can reflect the complexity and changes in terrain. Therefore, Landsat imagery and DEM data are used as independent variables of the regression model to construct an environmental model. The advantages of DEM generated based on Landsat image data for monitoring and predicting geological disasters are more obvious, mainly including that Landsat data can accelerate calculations to enhance the real-time monitoring capability of geological disasters. Landsat data have the ability to process large amounts of data and can improve the stability and fault tolerance of the system. Since Landsat data retain basic advantages such as linear computational complexity, unsupervised learning, and non-parametric methods, they can be used to predict the time and scale of possible geological disasters, as well as to evaluate the effectiveness of different disaster prevention measures.  
In this paper, 6 high-resolution Landsat image types are selected as basic data, and the band information used are shown in Table 2. Based on DEM data and related geological disaster survey results, it could be seen that the highest elevation in the region is 4288 m, and the lowest is 496 m, with a large slope difference between regions, indicating that topographic relief varies greatly in different elevation ranges. Combined with the change in cutting degree and topographic relief, it could be seen that the landform types in the study area are mainly high-altitude hills and small rolling mountains.