esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In this study, in order to expand the scope of safety monitoring, which was previously limited to object detection and location recognition, a more realistic representation of workers' safety with respect to collision with equipment is proposed by integrating three components: (1) identifying the location in space, (2) imposing the accurate volume of equipment, and (3) proximity sensing considering workers' perception according to their posture. The monitoring algorithm proposed visually generates the proximity of hazardous objects to workers by applying CNN to the video. A CNN-based object detection and tracking technique, namely, YOLO, is conducted on consecutive images. The detected object is overlapped on the plane map created through homograph transformation to obtain the accurate distance between objects. For equipment, the volume is allocated by the moving location and direction estimated by YOLO. For workers, a CNN-based pose estimation technique, namely, OpenPose, is applied to determine their posture. Then, a safety ellipse is generated according to each worker's perception. Proximity visualization was verified using closed-circuit television (CCTV) installed at a construction site.
# Streamlining Visual UI Design: Mining UI Design Patterns for Top App Bars
## 2 Related Work
### UI Representation Learning  
UI representation learning transforms UI images into machine-readable vectors, enabling machines to understand and process UI designs. This research area includes two main approaches: task-specific and general task-oriented representation learning.  
Task-specific UI representation learning captures features relevant to a particular task. Deep learning models excel by learning feature extraction directly from task-specific annotations. This approach's effectiveness is evident in studies on screen classification [20, 21] and component detection [22], where models yield representations closely aligned with specific UI tasks. However, these tailored representations often overfit training data, limiting their general applicability.  
To address the need for more generalized representations, researchers have pursued two main directions. The first involves autoencoders that favor UI-structured representations. [PERSON] et al. [23] pioneered this approach by using autoencoders to convert UIs into vectors, initially representing UIs as layout images highlighting text and non-text regions. [PERSON] et al. [24] expanded this by including component categories in the layout image. This approach underwent further refinement [20, 25, 26], with [PERSON] et al. [26] introducing the Screen2 Vec model, which combines component text, categories, and application descriptions into a single representation. Similarly, [PERSON] et al. [27, 28] employed multiple visual autoencoders to generate fused vector representations, leveraging a seq2 seq autoencoder to include UI code. While these methods capture various UI features, they fail to account for the structural characteristics of TABs.  
The second direction draws inspiration from generic pretraining models popular in natural image processing. [PERSON] et al. [29] developed the ActionBert architecture, which processes the ResNet-derived visual features of UI components using a Transformer model to predict operational accessibility between UIs. Similarly, [PERSON] et al. [30] used this concept to predict UI similarity, emphasizing relationships between multiple UIs. In contrast, [PERSON] et al. [31] introduced UIBert, converting a single UI image into a sequence of objects, encapsulating various visual and textual properties. Their unique masking prediction algorithm aids in obtaining a comprehensive representation of the UI. [PERSON] et al. [32] also utilized visual and textual features of all UI elements. Although these studies use sequential modeling of UIs or UI components, their methodologies differ significantly from ours.  
Our approach diverges from these methods by focusing on the structural characteristics of the TAB. We employ autoregressive techniques to reconstruct the TAB structure, enhancing our modeling of non-visual features. This improvement facilitates downstream tasks that require a deep understanding of TAB design patterns.
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 3 Evaluation
### Online Computation Cost Comparison  
We compare the online computation cost of the proposed method with the widely used WKNN method, which has a relatively small computation cost. Since the computations of signal perturbation and transformation matrix can be carried during offline phase, online computation cost required by E-LFDA is the feature extraction and pattern matching processes. Denote the number of APs used, total number of reference points, and the related optimal feature dimension as \(m\), \(n\) and \(d\). The number of the matched nearest reference points \(K\) is set to five. For E-LFDA, LDA, and PCA, the number of multiplications and additions required are \((m+n)*d+n+2K+\ 2\) and \((m+2n-1)*d+3K-n-3\), respectively. For WKNN, the number of multiplications and additions required are \((m+1)*n+2K+2\) and \((2m-1)*n+3K-3\), respectively. As shown in Figure 8, the proposed E-LFDA method reduces the number of multiplications than those of LDA, PCA and WKNN by 20.0%, 42.8%, and 62.0%, respectively. This is because the feature dimension involved in E-LFDA method is only three, much less than those of LDA, PCA with four and six and WKNN using all eleven APs.
## 2 Matrix-Based Model Formulation for MES
### District Electricity Network  
Herein, the traditional AC model is considered for the power flow calculation of the DEN. In this model, buses are divided into three types, i.e., \(PQ\) bus, \(PV\) bus, and \(\theta V\) bus. For bus \(i\), its inflow and outflow active/reactive power should be conserved, which can be expressed as  
\[\left\{\begin{array}{l}P_{\mathrm{S},i}-P_{\mathrm{L},i}-|V_{i}|\sum\limits_{ j=1}^{\mathrm{Ne}}|V_{i}|\big{(}G_{ij}\cos\theta_{ij}+B_{ij}\sin\theta_{ij}\big{)}=0 \qquad\forall i\in\mathbb{E}^{\mathrm{PQ}}\cup\mathbb{E}^{\mathrm{PV}}\\ Q_{\mathrm{S},i}-Q_{\mathrm{L},i}-|V_{i}|\sum\limits_{j=1}^{\mathrm{Ne}}|V_{i} |\big{(}G_{ij}\sin\theta_{ij}-B_{ij}\cos\theta_{ij}\big{)}=0\qquad\forall i\in \mathbb{E}^{\mathrm{PQ}}\end{array}\right. \tag{1}\]  
where \(P_{\mathrm{S},i}/P_{\mathrm{L},i}\) is the active source/load power of bus \(i\); \(Q_{\mathrm{S},i}/Q_{\mathrm{L},i}\) is the reactive source/load power of bus \(i\); \(\mid V_{i}\mid\) is the voltage magnitude of bus \(i\); \(\theta_{\bar{\eta}}=\theta_{i}-\theta_{j}\) is the voltage angle difference between bus \(i\) and bus \(j\); \(N_{\mathrm{e}}\) is the total bus number; \(G_{\bar{\eta}}/B_{\bar{\eta}}\) is the conductance/susceptance of branch \(i\); \(\mathbb{E}^{\mathrm{PQ}}/\mathbb{E}^{\mathrm{PV}}\) is the set of \(PQ/PV\) buses. As can be seen, Equation (1) is essentially a set of nonlinear algebraic equations (AEs), which is neither convenient for writing nor for programming implementation. Hence, the matrix form of the AC model is introduced:  
\[\left\{\begin{array}{l}\mathbf{P}_{\mathrm{S}}-\mathbf{P}_{\mathrm{L}}- \mathrm{Real}\big{(}\mathbf{V}\!\cdot\!(\mathbf{Y}\!\mathbf{V})^{*}\big{)}=0\\ Q_{\mathrm{S}}-Q_{\mathrm{L}}-\mathrm{Imag}\big{(}\mathbf{V}\!\cdot\!( \mathbf{Y}\!\mathbf{V})^{*}\big{)}=0\end{array}\right. \tag{2}\]  
where \(\mathbf{P}_{\mathrm{S}}\), \(\mathbf{P}_{\mathrm{L}}\), \(\mathbf{Q}_{\mathrm{S}}\), and \(\mathbf{Q}_{\mathrm{L}}\) are the vector forms of \(P_{\mathrm{S}}\), \(P_{\mathrm{L}}\), \(Q_{\mathrm{S}}\), and \(Q_{\mathrm{L}}\), respectively; \(\mathbf{V}\) is the voltage vector; \(\mathbf{Y}\) is the nodal admittance matrix; \(\mathrm{Real}\!\cdot\!\cdot\!\!\cdot\!\!\cdot\!\!\cdot\!\!\cdot\!\!\cdot\!\!\! \cdot\!\!\!\cdot\!\!\!\cdot\!\!\!\cdot\!\!\!\cdot\!\!\!\cdot\!\!\!\cdot\!\!\! \cdot\!\!\!\cdot\!\!\!\cdot\!\!\!\!\cdot\!\!\!\!\cdot\!\!\!\cdot\!\!\!\!\cdot\!\! \!\!\cdot\!\!\!\cdot\!\!\!\!\cdot\!\!\!\cdot\!\!\!\!\cdot\!\!\
## 2 Proposed Methodology
### Previous Works
#### 2.2.2 System Identification  
While ADASYN focuses on generating synthetic data for the minority class based on the distribution of classes and neighboring data points, another approach to tackle data imbalance is through System Identification (SI). The SI process builds a mathematical dynamic model of the system by using the measured input and output signals. The SI technique is an important part of modern control theory [47] and has been used as a data augmentation to overcome data imbalance and the data scarcity problem [48]. The input and output signals of the system are used to reconstruct the discrete system matrix \(\mathrm{A_{r}}\), \(\mathrm{B_{r}}\), \(\mathrm{C_{r}}\), \(\mathrm{D_{r}}\), which is expressed as a state space model, where \(\mathrm{A_{r}}\) is the realized system matrix, \(\mathrm{B_{r}}\) is the realized input matrix, \(\mathrm{C_{r}}\) is the realized output matrix, and \(\mathrm{D_{r}}\) is the transmission matrix [49]. After these matrices are defined, unseen signals are used as input for the realized matrices. As a result, synthetic signals, which are augmented signals, could be obtained. The SI technique can restore the system dynamics by using only single input and output signals, which means that it does not need multiple pairs of signals. Because of this advantage, it could be used as a great solution for data imbalance and the scarcity problem, as mentioned above.
## 1 Introduction
### Pumped-Hydro Storage Technology  
Pumped storage is recently viewed as the most promising technology to increase renewable energy penetration levels in power systems and particularly in small autonomous island grids [3]. The characteristic of flexible generation in pumped-hydro energy storage can provide both up and down regulation in the power system while its quick start capabilities make it suitable for black starts and provision of spinning and standing reserve.  
Taking into consideration the absence of data in the literature on new pumped-hydro energy storage-plant development, [PERSON] et al. in [4] review the current trends in the technology based on publicly available information from utilities, government bodies, and electricity regulators in the European region. According to the authors, Current trends for new pumped-hydro energy storage development generally show that developers operating in liberalized markets are tending to repower, enhance projects or build \"pump-back\" pumped-hydro energy storage rather than traditional \"pure pumped storage\".  
[PERSON] et al. In [5] highlight the urgency and importance of storing energy for strengthening power grids and maintaining load levels. Multiple types of storage methods are compared, and pumped hydropower storage was found to be the most used for high-power applications. Pumped hydroelectric systems have conversion efficiency, from the point of view of a power network, of about 65-80%, depending on equipment characteristics. However, the main disadvantage of this technology is the site requirements.  
On the worldwide scale, there are more than 300 pumped-hydro energy storage installations with a total capacity of 127 GW [6]. According to the energy information administration, official energy statistics from the US government in 2008 the USA has an installed capacity of 21,886 MW of pumped-hydro energy storage plants accounting for 2.1% of its total installed generating capacity [7].  
Japan currently has 34 large-scale pumped-hydro energy storage plants with a total capacity of 24,575 MW [8], which accounts for 10.2% of its full installed generating capacity. The Japanese pumped-hydro energy storage revolution accompanied the nuclear power revolution as pumped-hydro energy storage could provide support for the nuclear generating plants during peak demand.  
Most pumped-hydro energy storage plants in the European region are found in Germany, France, Switzerland, and Austria. Germany has the largest number of pumped-hydro energy storage plants (23 operational plants ranging in capacity from 62.5 to 1060 MW) [3]. The largest plant in the region is the 1800 MW EDF owned \"Grand Maison\" facility in the French Alps [3].
# Unsupervised Image Segmentation on 2D Echocardiogram
## 3 Methods
### Post-Processing Through 3D Watershed Segmentation
* Watershed Transformation: The Watershed transformation is then applied, segmenting the regions based on the previously computed markers and gradient map. The segmentation is constrained to a maximum number of classes, and labels are assigned to each segmented region. * Label Assignment and Visualization: The segmented regions are labeled, and each class is assigned a distinct color using a persistent color map for visualization. This step ensures that the left ventricle is isolated for further analysis, and the colors remain consistent across frames. However, due to the nature of the algorithm, it is challenging to consistently assign the same color/label to each chamber across different videos. This variability introduced the need for on-the-fly selection of the left ventricle for subsequent mask isolation and volume calculations. The 3D Watershed algorithm was initialized by first using a threshold to isolate low-intensity regions, which typically represent the background. To reduce noise, morphological operations such as dilation and closing were applied. A distance transform was then computed, with a positional bias added to account for spatial orientation across the z, y, and x axes. Local maxima were detected in the distance-transformed volume to serve as markers for the watershed algorithm, which then segmented the volume. Consistent class labels were maintained across frames by calculating centroids for each label and matching them to reference centroids from the first frame. This approach ensures spatial coherence across the video sequence. The process can be observed in Figure 5.  
This approach was a necessary step over directly using the U-Net output because it provides more robust handling of complex regions of interest, such as the heart chambers, as shown in Figure 6. Additionally, the Watershed algorithm allows for precise control over the segmentation process through marker-based initialization, which is crucial for accurately isolating the left ventricle. By using the distance transform and introducing a positional bias, the algorithm effectively separates the chambers based on the difference in distances across the frames.
## 4 Experimental Results
### Comparison of Training Results Derived by Existing Models  
Table 2 displays the mean of accuracies obtained through five-fold cross-validation when CNN-based models were trained on Original DB-T. Specifically, the VGGNet, GoogLeNet, and ResNet18 [23] models were compared based on the mean accuracy of each model. First, the GoogLeNet model is a structure that introduces an inception module to address the gradient vanishing issues [25]. Since outputs are generated at an intermediate stage of the network, the mean accuracy was calculated based on three outputs. Consequently, the mean accuracies of the GoogLeNet, VGGNet, and ResNet18 models were calculated as 49.79%, 24.20%, and 56.96%, respectively. Notably, the VGGNet model exhibited the lowest mean accuracy compared to the other models, whereas the ResNet18 model exhibited the highest mean accuracy.
## 4 Conclusions  
The laser damage of the Ta\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) multilayer film on the fused silica substrate prepared by electron beam evaporation was studied by 1-on-1 and S-on-1 measurements using linearly and circularly polarized femtosecond lasers. The damage threshold of the film under linear and circular polarization was obtained, and the damage morphology of the film under linear and circular polarization was observed by microscope. It was found that the damage threshold of linear polarization was lower than that of circular polarization, and the damage morphology of under polarization was more evident than that under linear polarization. The main reason was that the multi-photon ionization cross-section of linear polarization was higher than that of circular polarization, resulting in a higher ionization rate of linear polarization. It was also found that the damage threshold of linearly polarized and circularly polarized femtosecond laser decreased with an increase in laser radiation number. The main reason was that repeated laser radiation had a cumulative effect on the film; the more repeated radiation, the more pronounced the cumulative effect. The result is of great significance for understanding the mechanism of femtosecond laser-induced optical film damage and the femtosecond laser projection of the film.  
Conceptualization, L.Z., A.C. and C.Z.; methodology, L.Z., X.J., Y.W., Y.Z., A.C., J.S. and C.Z.; software, Y.W. and A.C.; validation, X.J., Y.W. and Y.Z.; writing--original draft preparation, L.Z., A.C. and C.Z.; writing--review and editing, L.Z., A.C. and C.Z.; project administration, J.S.; funding acquisition, L.Z., J.S. and C.Z. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the Fundamental Research Project of the Chinese State Key Laboratory of Laser Interaction with Matter (No. SKLLIM1804) and the National Natural Science Foundation of China (Nos. 11674128 and 11974138).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Damage morphology near damage threshold for linearly and circularly polarized lasers under 5-on-1. Note: black circle indicates some unapparent damage.
## 6 Performance Evaluation
### Evaluation Results
#### 6.3.4 Total Energy Consumption
On the other hand, similar to the GIF, the vanilla NDN rebroadcasts the incoming Data packets to the downstream neighboring nodes. The frequent Data packet transmissions increase the overall energy consumption of the network which can be visualized in Figure 11.
## 3 Methods
### SE-DenseNet
#### 3.3.3 SE-DenseNet  
SE-DenseNet proposed in this paper is to add the SE module after the \(3\times 3\) convolution layer of each DenseNet structural block, and its model diagram is shown in Figure 5. The dotted box is the process of \(X_{1}\) to \(X_{2}\), and the rest of the processes of \(X_{0}\) to \(X_{n}\) are the same.  
Figure 4: SE module diagram.  
Through this fusion mechanism, the network can not only realize the lossless transmission of the original input information, but also automatically learn the global information to obtain the importance of each channel and then enhance the beneficial features and suppress the useless features according to the importance to realize the adaptive calibration of the feature channel. Table 1 lists the configurations of SE-DenseNet and DenseNet respectively. Where \(c\) is the number of characteristic channels of the convolution layer, and \(\beta\) is the dimension transformation rate of SE operation.  
As shown in Table 1, compared with the traditional DenseNet, SE-DenseNet has the following innovations: (1) After the convolution transformation of each structural block, the SE module is designed to carry out automatic weight calibration for the information of each feature channel; (2) After the first convolution layer, the maximum pooling layer is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network Layer**} & \multicolumn{2}{c}{**SE-DenseNet**} & \multicolumn{2}{c}{**DenseNet**} \\ \cline{2-5}  & **Matrix** & **Structure** & **Matrix** & **Structure** \\  & **Dimensions** & **Configuration** & **Dimensions** & **Configuration** \\ \hline Convolution & n \(\times\) n & \([3\times 3,2c]\) & n \(\times\) n & \([3\times 3,2c]\) \\ Pooling & — & — & n/\(2\times\) n/\(2\) & \(3\times 3\) Max pooling \\ Block of structure & n \(\times\) n & \(\left[\begin{array}{cc}1\times 1,\,4c\\ 3\times 3,\,c\\ SE(c/\beta,c)\end{array}\right]\times 6\) & n/\(2\times\) n/\(2\) & \(\left[\begin{array}{cc}1\times 1,\,4c\\ 3\times 3,\,c\end{array}\right]\times 6\) \\ Transition Layer & n \(\times\) n & \([1\times 1,0.5c]\) & n/\(2\times\) n/\(4\times n/4\) & \(2\times 2\) Average pooling \\ ⋮ & ⋮ & ⋮ & ⋮ \\ Classification Layer & \(1\times 1\) & \multicolumn{2}{c}{Global average pool, Fully-connected, softmax} \\ \hline \hline \end{tabular}
\end{table}
Table 1: SE-DenseNet network configuration information table.  
Figure 5: SE-DenseNet model diagram.  
removed, which can prevent the loss of low-level features caused by premature pooling operation; (3) The average pooling operation is removed from the transformation layer between structural blocks, only convolution is retained, and the global information is retained under the condition of greatly reducing the computational parameters, so as to enhance the robustness of the whole neural network.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 3 Network Model and Problem Formulation  
### Network Model  
The hierarchical network model of HMMN-EC is composed of three subnetworks: (1) the underwater acoustic subnetwork, (2) the sea-surface wireless subnetwork, and (3) the air wireless subnetwork, as shown in Figure 1.  
In an underwater acoustic subnetwork, multiple sensors deployed at representative Monitoring Points (MPs) are deployed to monitor the target areas. The MP then transmits the monitoring data to a SSN, typically via one or multiple URNs, when communication distance is beyond the transmission range of sensors at MPs. URN is a buoyancy-driven device which can hover and select a specified position to acquire and transmit data over acoustic communication channels [30; 31; 32]. The location of URN should be carefully decided as it is dependent on the influence of ocean flow and undercurrent. SSU is equipped with a wireless communication radio installed on the buoy, an acoustic-electric conversion device, and an underwater acoustic receiver located under the sea surface. After receiving the underwater acoustic signals from URNs, it converts them into radio signals and then transmits radio signals to an ECN or other SSN within its communication distance.  
The sea-surface wireless subnetwork is comprised of multiple ECNs, which are responsible for receiving radio signals from the underwater acoustic subnetwork, processing the data in an edge device, and sending the processed data to the air wireless subnetwork. Generally, unmanned ships with communication equipment and small edge servers are used as ECNs.  
The air wireless subnetwork further relays the received data from the the sea-surface wireless subnetwork to the BS. This subnetwork consists of multiple ARNs that transmit the received data to the BS over one or multiple hops through other ARNs. Finally, the BS transmits the data from the HMMN-EC network to the data center through the terrestrial wireless networks.  
In the HMMN-EC, the BS is the destination node; ARNs, ECNs, SSNs, and URNs are the intermediate nodes; and MPs are source nodes. All nodes are organized hierarchically within the communication radius of nodes, and an efficient tree architecture will finally be formed to achieve an effective communication.  
In summary, various nodes involved in the HMMN-EC have a certain communication radius, by using either radio or acoustic communications; and each node only communicate with other nodes of the same subnetwork or nodes of the upper subnetwork within their communication distance. In addition, the ARN and ECN nodes may have sufficient power supply with no stringent capacity limitation, but battery-powered SSN and URN nodes are typically of small sizes and thus are subject to certain battery constraints, which should be taken into consideration for network deployment.