esa-sceva/satcom-chunk-collection huggingface dataset:
# A Probabilistic Hyperspectral Imagery Restoration Method
## 7 Conclusions  
We propose an HSI-oriented low-rank denoising method in this paper. Considering the clean/noisy-free HSI has a low-rank property and HSI is degraded by multiple kinds of noise, HSI restoration is modeled as a low-rank matrix analysis problem. Specifically, we use the mixture of Gaussian to represent multiple kinds of noise and treat clean HSI as a low-rank matrix. A Bayesian low-rank matrix recovery method is then introduced to infer the low-rank denoised HSI from multi-factor affected incomplete HSI. Considering endmember is an important clue to determine the rank number, a geometry-based endmember determination method is further used to initialize the rank number. The proposed method can address complex noise well and restore the missing data effectively. Experimental results verify the superiority of the proposed method on both simulated and real data.  
[PERSON] and [PERSON] designed the algorithm and wrote the manuscript together, [PERSON] and [PERSON] conduct the experiment.  
**Funding:** This work is supported by the National Natural Science Foundation of China (no. 61671385, no. 61571354).  
Figure 11: Visual results on band 188 of urban dataset: (**a**) Captured actual band, (**b**) Restoration results of VBM3D based method, (**c**) Restoration results of Godec based method, (**d**) Restoration results of SLrank based method, (**e**) Restoration results of the proposed method.
# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 2 Related Work
### The Development and Application of Attention Mechanism in Deep Learning  
Inspired by human perception, the attention mechanism is implemented. When humans visually perceive objects, they tend to focus on specific parts that are relevant or important to them. This selective observation allows humans to efficiently extract important information from a substantial quantity of visual data using limited cognitive resources. The attention mechanism mimics this process, enhancing the efficiency and accuracy of perceptual information processing. It serves as an effective solution to tackle the challenge of information overload. By incorporating the attention mechanism into computer vision tasks, the substantial computational workload can be effectively reduced. As a result, the attention mechanism has gained significant traction in the realm of deep learning, becoming a standard component in neural network architectures [28].  
Currently, the two most common attention mechanisms applied to machine vision are spatial attention and channel attention [6]. The emphasis on the former is on the location of the object within the deep learning information and spatially transforms this location information. The spatial transformer network (STN) [29] is an example of spatial attention. Additionally, channel attention emphasizes the content information of the object. The SE network, introduced by [PERSON] et al. [30], is a notable channel attention mechanism. The SE attention module enhances target recognition by adaptively calibrating channel weights, filtering important features, and using global average pooled features for computations.  
As deep learning neural networks continue to evolve, researchers have developed hybrid attention mechanisms that combine both spatial and channel attention to improve the precision and efficiency of feature recognition within large feature maps [31]. The CBAM is capable of feature map recognition through both spatial and channel attention. It starts by applying global pooling operations to the feature map, generating channel attention features. Subsequently, spatial attention features are generated by concatenating and downsampling the channels. Finally, the input features are combined with the final features [32].  
The CA mechanism integrates spatial coordinate information by embedding location details into channel attention, decomposing channel attention into two parallel one-dimensional feature encodings. This approach differs from CBAM as it does not forcibly compress the channels. The two one-dimensional feature encodings allow for more comprehensive extraction of spatial information and optimize feature extraction efficiency [33].  
Another efficient replacement attention mechanism is SA. SA combines channel attention and spatial attention using shuffling units. This lightweight and efficient attention mechanism has demonstrated better performance and lower complexity compared to CBAM and SE attention mechanisms on public datasets [34].
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Consecutive Modal Pushover Analysis (CMP)  
The consecutive modal pushover (CMP) procedure, introduced by [59], is an innovative pushover method designed to address the limitations of the conventional nonlinear static procedure (NSP) when analyzing higher-mode responses in tall buildings. Unlike the traditional NSP, the CMP procedure integrates both multi-stage and single-stage pushover analyses, with final structural responses determined by combining the results of these separate analyses. The research demonstrated that the CMP procedure significantly improves the accuracy of predicting seismic demands of tall buildings, especially in estimating hinge plastic rotations at mid and upper floor levels, when compared to results from nonlinear response history analysis (NL-RHA) and modal pushover analysis (MPA). Thus, the CMP procedure represents a substantial advancement in seismic demand prediction for tall buildings, effectively incorporating higher-mode effects [59]. The steps explained below illustrate the details of this procedure.  
1. Frequency and mode shape analysis: Conduct an eigen analysis on the linear elastic structure, focusing on the initial three modes. Ensure mode shapes are normalized such that the roof component, denoted as \(\Phi n\), is set to one.
2. Determine lateral force distribution: calculate \(Sn*=m\Phi n\), where \(Sn*\) represents the distribution of incremental lateral forces throughout the structure's height for the nth stage.
3. Total roof displacement calculation: ascertain the structure's total target roof displacement, represented as \(\delta t\).
4. CMP analysis steps: * For medium-rise structures, execute a single-stage pushover analysis using an inverted triangular load pattern. For taller structures, use a uniform force distribution. Continue until the roof's control node reaches the predefined \(\delta t\). * Implement a two-stage pushover analysis, where the second stage starts from the end state of the first stage. * For structures with a fundamental period of \(2.2\) s or more, conduct a three-stage pushover analysis. Each stage begins from the end state of the preceding stage.
5. Determine the peak responses: From the earlier pushover analyses, calculate the peak values for targeted responses, such as displacements, drifts, and hinge rotations. Represent these peak values from the one-, two-, and three-stage analyses as \(r2\), and \(r3\) respectively.
6. Calculate the overall peak responses: * If \(T<2.2\)\(s\), then \(r=max\{r1,r2\}\). * If \(T\geq 2.2\)\(s\), then \(r=max\{r1,r2,r3\}\).
7. Sequential modal analysis note: The structure's nonlinear behavior is intertwined with its response and is influenced by the loading path. In the CMP approach, modal pushover analyses should be sequentially executed, starting with the primary mode, and advancing to the subsequent ones, as detailed in [60].
## 4 Open Issues and Challenges
### Synchronization Issues  
The synchronization issue was a thorny subject in U-WSN, considering the limited communication time. In the past, many synchronization algorithms have been investigated to keep the synchrony of networked systems. The perfect time synchrony, Time-Diffusion Protocol (TDP) [61], was proposed by [PERSON] and [PERSON]. In the synchronization process, the TDP applies an iterative and weighted averaging mechanism built on the collected messages from the whole network. In reference [62], the authors through enhancing the TDP scheme to adapt to the particular applications (e.g., tracking and surveillance) that require global time synchronization and fault-tolerance. However, most of these research are based on traditional wireless networks. In the context of U-WSN, especially in dynamic topology, the beaconing mechanism is usually used for the network synchronization [17; 18; 19; 63; 64]. In [65], the authors provide a survey on the synchronization in U-WSNs.
###### Abstract  
Buildings constructed with non-seismic details are at risk of damage and collapse when an earthquake occurs due to a lack of strength, stiffness, and ductility. For reinforced concrete (RC) moment-resisting frames, seismic retrofitting methods that can enhance strength or ductility should be applied. However, such strategies have many disadvantages related to constructability, serviceability, securing integrity, and cost. In this paper, a welded concrete-filled steel tube (WCFST) system was examined in order to resolve the problems of the existing seismic retrofitting methods for RC moment-resisting frames. To evaluate the seismic performance of the proposed system, two specimens, one with non-seismic details and another reinforced with a WCFST seismic system, were manufactured for the cyclic loading tests. As a result of the experiments, the specimen retrofitted with the WCFST system showed maximum load, effective stiffness, and energy dissipation capacity values approximately 3, 2, and 2.5 times greater, respectively, than those obtained from the existing reinforced concrete frame specimen. The experimental results indicate that the proposed WCFST system is expected to be effective at improving the seismic performance by enhancing both the strength of the existing reinforced concrete frame structures and the dissipation of the seismic energy.  
seismic retrofitting; seismic performance; RC moment-resisting; welded concrete-filled steel tubes; cyclic loading test +
Footnote †: journal: Applied Sciences
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Subpicture Parallel Algorithms Analysis
Now we will verify if the above analysis is empirically consistent. In Tables 5 and 6, we present the encoding speed-up evolution for Traffic, ParkScene, FourPeople, and PartyScene video sequences, respectively. A speed-up of up to \(9.35x\) is obtained when 10 processes are used. Note that, for a particular number of parallel processes, different speed-ups are obtained. This is mainly due to the fact that some tile partition layouts produce an unbalanced processing workload. For example, for a video resolution of \(2560\times 1600\), a frame consists of \(40\times 25\) CTUs of \(64\times 64\) pixels. If we divide the frame using the \(10\times 1\) layout, then each of the 10 processes will have to encode the same number of CTUs (\(4\times 25=100\) CTUs). This means a perfectly balanced workload (Bal% = \(100\%\)). But if we divide the frame using the \(1\times 10\) layout, then 5 processes will have to encode \(40\times 2=80\) CTUs, and the other 5 processes will have to encode \(40\times 3=120\) CTUs, which implies a \(50\%\) increase in CTUs (Bal% = \(83\%\)). Using the \(1\times 10\) layout, a speed-up of \(7.77x\) is obtained for the Traffic video sequence, whereas for the \(10\times 1\) layout we obtain a speed-up of \(9.35x\). Generally, tile partitioning layouts based on columns of CTUs or on square tiles obtain better parallel performance. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**QP = 37**} & \multicolumn{3}{c}{**QP = 22**} \\ \hline  & **Y-PSNR** & **Bitrate** & **Bitrate** & **Y-PSNR** & **Bitrate** & **Bitrate** \\ \hline
**NP** & **(dB)** & **(kbps)** & **Increment** & **(dB)** & **(kbps)** & **Increment** \\ \hline \multicolumn{6}{c}{**PartyScene**} \\ \hline
1P & 32.6281 & 3265 & & 41.6476 & 20,711 & \\
2P & 32.6255 & 3310 & 1.36\% & 41.6471 & 20,831 & 0.58\% \\
4P & 32.6172 & 3386 & 3.71\% & 41.6417 & 21,017 & 1.48\% \\
6P & 32.6072 & 3486 & 6.75\% & 41.6407 & 21,268 & 2.69\% \\
8P & 32.6091 & 3558 & 8.97\% & 41.6365 & 21,421 & 3.43\% \\
9P & 32.6063 & 3596 & 10.13\% & 41.6363 & 21,524 & 3.93\% \\
10P & 32.6067 & 3614 & 10.68\% & 41.6361 & 21,549 & 4.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NP** & **Layout** & **Avg. CTU** & **Max. CTU** & **Bal. (\%)** & **Avg. CTU** & **Max. CTU** & **Bal. (\%)** \\ \hline \hline \multicolumn{8}{c}{**2560 \(\times\) 1600**} \\ \hline \multicolumn{8}{c}{**1920 \(\times\) 1080**} \\ \hline \multicolumn{8}{
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 2 Problem Formulation
### Deterministic Static TNEP (DSTNEP) Model without Security Constraint  
The standard DSTNEP based on the lossless DC model can be written as follows:  
\[\text{Min.}V=\sum_{i,j\in N}C_{ij}X_{ij} \tag{2}\]  
Which is subject to the following equality and inequality constraints:  
\[P_{Gi}-P_{di}=P_{ij},\ i,j=1,\ldots,N,\ i\
eq j,\ldots,N, \tag{3}\]\[P_{ij}-B_{ij}\left(X_{ij}^{0}+X_{ij}\right)\left(\theta_{i}-\theta_{j}\right)=0, \tag{4}\]  
\[\left|P_{ij}\right|\leq\left(X_{ij}^{0}+X_{ij}\right)P_{ij\_max},\ and \tag{5}\]  
\[X_{ij}^{0}\leq X_{ij}\leq X_{ij\_max} \tag{6}\]  
where \(X_{ij}\) is an integer.  
In the DSTNEP model, Equation (2) represents the objective function aimed to minimize total investment cost. Equation (3) represents the nodal balance, where the net power injection at bus \(i\) is equal to the difference between total generation and total loads connected to bus \(i\). Equations (4) and (5) calculate the active power flow for existing lines (\(P_{ij}\)) as determined by the product of line susceptance (\(B_{ij}\)) and voltage phase angle difference \(\left(\theta_{i}-\theta_{j}\right)\) and should be less than or equal to the active power flow limit of the \(i\)-\(j\) right of way.
## 5 Conclusions  
In the context of LOS OWC employing Gaussian divergent beams and narrow FOV receivers, a comparison is presented between the theoretical and experimental distributions of ROPs. The small discrepancy observed between the simulation and experimental results verifies the validity of geometric optics and uniform reception principles. Guided by these fundamental principles, the analysis focuses on the two major link losses inherent in the proposed system. Experimental results demonstrate the successful elimination of fiber coupling loss and the effective mitigation of geometrical path loss. Furthermore, a homogeneous ROP distribution, deviating from the Gaussian profile, is attained by manipulating MMFs, facilitating the mobility of end-users in indoor OWC. More importantly, the proposed scheme leads to a reduction in the complexity of tracking and steering operations.
###### Abstract
Comparatively little attention has been paid to the equality optimization of hierarchical (or multi-level) healthcare facilities. As existing studies [32; 33; 34] have demonstrated, spatial analysis of hierarchical facilities should account for more characteristics such as various service scopes, frictions of distance, and transport modes. Therefore, the existent MAE model is not suitable for analyzing accessibility to hierarchical facilities. Although hierarchical location-allocation problems have been studied for decades [35; 36; 37], few have addressed the equality issue or incorporated spatial accessibility into location-allocation analysis. There are still gaps in terms of simultaneously considering the hierarchical nature and accessibility equality optimization of healthcare facilities. This study's contributions are threefold. First, it develops a hierarchical maximal accessibility equality (HMAE) model, which is hierarchy-sensitive and can act as a useful tool in the equality-oriented spatial optimization of hierarchical healthcare facilities or other hierarchical facilities. Second, this study provides a method that simultaneously accounts for both existing fixed facility locations and newly added locations that are flexible in the location-allocation analysis. This can make the optimized solution more feasible because the fixed resources/stocks of existing facilities are considered in the optimization. Third, online map application programming interface (API) is introduced to improve the accuracy of estimated travel time in location-allocation analysis. The proposed model maximizes the equality of spatial accessibility to healthcare facilities by minimizing the variation in accessibility across all locations. This is achieved by both adding new facilities and reallocating the resources at existing facilities. The method is valuable for the implementation of equality-oriented healthcare planning and policymaking. Shenzhen, which is one of the first cities to have highlighted the policy goal to achieve equality in healthcare services in China, was selected as the study area to demonstrate the feasibility and usefulness of the proposed model.
## 5 Experiments
### Scenario Classification and Generation
#### 5.1.3 Layout Homogeneity  
The homogeneity of the layout corresponds to the spread of the distribution of travel distances between nodes in the navigation graph. In a homogeneous environment all nodes are located at similar distance to each other. Our definition of a heterogeneous environment, in contrast, implies that some nodes are situated at a much further distance to the rest of the graph. These nodes are outliers so to say.  
In order to achieve this, we used a simple custom algorithm to distribute the stations on a plane. The first station is placed at the origin of the environment. For the placement of each subsequent station we determine the centre of the environment using the median coordinates of the cluster of stations. Then a random angle \(\alpha\in[0,2\pi)\) is chosen at which the station is attempted to be placed at a distance \(r_{placement}\) according to the following formula.  
\[\vec{p}_{i+1}=\vec{p}_{median,i}+r_{placement}\binom{\cos\alpha}{\sin\alpha} \tag{2}\]  
Placement fails if this position is closer than a minimum distance to any previously placed station. Then a small increment is added to the placement radius and the iteration is repeated with another random angle. In case the placement succeeds, the radius is reset to its original value. This process is repeated until the desired number of stations were placed.  
The list of placed coordinates can be shuffled to avoid patterns in the relative proximity of special stations (i.e., parking position and input/output storage). Without this step, these stations would always be placed near each other, as they are always assigned to the first positions in the output list. In order to evaluate this effect we added this configuration under the name _Pseudorandom_ to the list of layout configurations.  
For the heterogeneous configuration, we used the same procedure to add a few more stations at a relatively large radius (\(r_{outlier}=10r_{placement}\)). Afterwards the same number of stations is dropped from the previously obtained intermediate result. We chose to let only assembly stations appear in these outlier positions. Otherwise the outcome of the results would be dominated too much from the fact of which type of station would be placed remotely. For the random configurations we chose to designate a number of two (out of ten) assembly stations as outliers. Figure 7 shows examples of the random environments we obtain this way. Notably, these environments do not contain walls, such that the vehicles can navigate on straight lines between the stations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Utilisation** & \multicolumn{2}{c}{**P/T Ratio**} \\  & **Low: 1** & **High: 5** \\ \hline high & 1 & 0.2 \\ low & 2 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deployed numbers of vehicles per assembly station to achieve different variations in vehicle utilisation in relation to P/T ratio for experiment configuration.  
Figure 7: Example random environments.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
###### Abstract  
Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described.  
Keywords:rail; guided wave ultrasound; broken rail detection; rail diagnostics; structural health monitoring; non destructive testing +
Footnote †: journal: Applied Physics Letters