esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Extreme Scenarios
### Hardware Impairment and Linear Energy Harvesting (HWiLEH)  
The outage performance of WCwEH in the scenario of HWiLEH (viz., \(\rho>0\) and \(\iota\rightarrow\infty\)) is analyzed in the following for prompt comparison with its nonlinearity counterpart in Section 3, eventually highlighting the individual effect of EHNL on the reliability of the communication. It is noted that the scenario of HWiLEH means \(P\to GH\) and therefore, the CDF of \(w\) becomes  
\[\begin{split}\tilde{F}_{w}(v)&=\Pr\Big{\{}g\leq \frac{v}{GH}\Big{\}}\\ &=\int\limits_{0}^{\infty}F_{g}\Big{(}\frac{v}{Gz}\Big{)}f_{H}(z) dz.\end{split} \tag{20}\]  
Plugging \(F_{g}(\cdot)\) in (2) and \(f_{H}(\cdot)\) in (14) into (20) after elaborate manipulations, one simplifies \(\tilde{F}_{w}(v)\) as\[\hat{F}_{w}(v)=1-\sum_{n=0}^{N}\sum_{i=0}^{\chi_{n}-1}\sum_{\sum\limits_{n=0}^{N} b_{n}=M}\frac{Q_{n}}{i!}\Big{(}\frac{v}{GY}\Big{)}^{i}\left(\prod_{n=0}^{N}\frac{ \left(Q_{n}\right)^{b_{n}}}{b_{n}!}\right)\frac{M!}{Y_{p}^{\theta}\Gamma(\theta )}\int\limits_{0}^{\infty}z^{\theta-i-1}e^{-\frac{v}{VGY_{p}}}dz. \tag{21}\]  
With the aid of [30] [Equation (3.471.9)], the integral in (21) can be solved, and therefore  
\[\hat{F}_{w}(v)=1-\sum_{n=0}^{N}\sum_{i=0}^{\chi_{n}-1}\sum_{\sum\limits_{n=0}^{ N}b_{n}=M}\left(\prod_{n=0}^{N}\frac{\left(Q_{n}\right)^{b_{n}}}{b_{n}!} \right)\frac{2Q_{n}M!}{i!\Gamma(\theta)}\bigg{(}\frac{v}{YGY_{p}}\bigg{)}^{ \frac{\theta+i}{2}}\text{K}_{\theta-i}\bigg{(}2\sqrt{\frac{v}{YGY_{p}}}\bigg{)}. \tag{22}\]  
Following the procedure in Section 3.1, one derives the outage performance of WCwEH in the scenario of HWiLEH as  
\[\mathcal{O}_{2}=\left\{\begin{array}{cc}\hat{F}_{w}\Big{(}\frac{\Lambda_{0} \sigma}{1-\Lambda_{0}\rho}\Big{)}&,1>\Lambda_{0}\rho\\ 1&,1\leq\Lambda_{0}\rho\end{array}\right. \tag{23}\]  
The result in (23) is convenient in demonstrating the individual influence of EHNL on the reliability of the communications by comparing it with (6).
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.6 Class C: Continuous Listening Nodes  
Class C devices implement mandatory Class A reception window mechanism and these devices keep reception windows open after seconds delay RECIEVE_DELAY2. This process is shown in Figure 18.  
\begin{table}
\begin{tabular}{c l c l} \hline \hline
**CID** & **Command** & **Transmitted By** & **Description** \\ \hline
0x10 & PingSlotInfoReq & Node & Periodic ping unicast message. \\
0x10 & PingSlotInfoAns & GW & Reply to PingSlotInfoReq. \\
0x11 & PingSlotChannelReq & GW & Sets ping channel for node. \\
0x11 & PingSlotFreqAns & Node & Response to PingSlotChannelReq. \\
0x12 & BeaconTimingReq & Node & Request to next beacon timing and channel. \\
0x12 & BeaconTimingAns & GW & Response BeaconTimingReq. \\
0x13 & BeaconFreqReq & GW & Configuration of beacon broadcast frequency. \\
0x13 & BeaconFreqAns & Node & Response BeaconFreqReq. \\ \hline \hline \end{tabular}
\end{table}
Table 6: LoRaWAN Class B MAC Commands.  
Figure 17: Class B reception window and beacon timing.  
Figure 18: Class C reception window.
# Artificial Neural Networks Applied in Civil Engineering
## 2 Contributions
### Structural Optimization and Decision Making  
Substantial computing resource requirements need to be met to address topology optimization problems and become prohibitive in cases with large-scale design domains discretized with fine finite element meshes. A deep learning-assisted topology optimization (DLTOP) methodology was previously presented [8] and employs deep learning techniques to predict the optimized system configuration, thus substantially reducing the required computational effort of the optimization algorithm and overcoming potential bottlenecks. Building upon DLTOP, the study by [PERSON] et al. [9] presents a novel deep learning-based model upgrading (DLMU) scheme. The scheme utilizes reduced order (surrogate) modeling techniques, which downscale complex models while preserving their original behavioral characteristics, thereby reducing the computational demand with limited impact on accuracy.  
The study by [PERSON] et al. [10] presents a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy, was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This is another step towards automating these practices within pavement management systems.  
The number of construction site accidents can be reduced by taking proactive preventative steps using prediction models developed based on factors that influence the safety climate. In the study by [PERSON] and [PERSON] [11], a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a set of significant safety climate predictors. The model was built with data collected from 401 active construction site personnel using a bootstrapped multiple ordinal logistic regression model. The model revealed five significant predictors: supervision, guidance, and inspection; social security and health insurance; management's commitment to safety; management's safety justice; and coworker influence.
## 2 FSO Vertical Channel Model  
In this study, the FSO channel was based on the Hufnagel-Valley (H-V) atmospheric model, and far-field diffraction uplink propagation with a Gaussian beam was assumed [21]. Because downlink is less affected by scintillation, uplink was modeled in this study, which considered the effect of scintillation. In addition, background noise, amplified spontaneous emission (ASE) beating noise, and dark current noise, which significantly affect the FSO channel, were considered [22].
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 4 Computing Orthonormal Eigenvectors of the DFT
### Structure of Eigenvectors  
The eigenvectors obtained using McClellan and Parks method are collinear to the eigenvectors from the DFT projection matrices and they differ only in magnitude. As discussed before, orthonormalizing these eigenvectors make them slightly sparse. These orthonormal eigenvectors have the following structure:  
\[V_{\pm 1}=\left[\begin{array}{cccc}a_{0}&0&0&0\\ a_{1}&b_{1}&0&0&\cdots\\ a_{1}&b_{2}&c_{2}&0\\ a_{1}&b_{3}&c_{3}&d_{4}\\ \vdots&\vdots&\vdots&\vdots\\ a_{1}&b_{3}&c_{3}&d_{4}\\ a_{1}&b_{2}&c_{2}&0\\ a_{1}&b_{1}&0&0\end{array}\right]\quad V_{\pm j}=\left[\begin{array}{cccc}0&0& 0&\cdots\\ b_{1}&0&0&\cdots\\ b_{2}&c_{2}&0\\ b_{3}&c_{3}&d_{4}\\ \vdots&\vdots&\vdots\\ -b_{3}&-c_{3}&-d_{4}\\ -b_{2}&-c_{2}&0&\cdots\\ -b_{1}&0&0\end{array}\right]\]  
where the matrix \(V_{\pm 1}\) represents the set of orthonormal eigenvectors corresponding to eigenvalues \(\pm 1\) and \(V_{\pm j}\) represent the orthonormal eigenvectors corresponding to eigenvalues \(\pm j\). The columns of these matrices represent the eigenvectors. The vectors in \(V_{\pm 1}\) is even symmetric and the vectors in \(V_{\pm j}\) is odd symmetric. Additionally, if the size of DFT \(N\) is an even value, the \(\left(\frac{N}{2}+1\right)^{\text{th}}\) row of \(V_{\pm j}\) will be 0 to retain the odd symmetry. The sparsity of the eigenvectors are shown in Figure 2. From this plot, it is clear that the orthonormal DFT eigenvectors are somewhat sparse. This sparse nature can be exploited to reduce the number of operations required while computing the DFT as discussed in Section 7.  
Figure 2: Plot showing the percentage of elements in the orthonormal eigenvectors that are zero against the length of DFT. Note the slight upward trend of the plot. Eigenvectors of larger DFTs are sparser than eigenvectors of smaller ones.  
The orthonormal eigenvectors of an 8-point DFT are given below to highlight the sparsity. The eigenvectors are the columns of the matrix \(V_{8}\). The eigenvalue multiplicities of an 8-point DFT are 3, 2, 1, 2 for eigenvalues \(1\), \(-1\), \(j\), \(-j\), respectively. Therefore, the first three columns of \(V_{8}\) are the eigenvectors corresponding to the eigenvalue 1, the next two columns are for eigenvalue \(-1\), the column after that is for eigenvalue \(j\) and the last two columns correspond to eigenvalue \(-j\).  
\[V_{8}=\left[\begin{array}{cccccccc}0.823&0&0&0.569&0&0&0&0\\ 0.215&0.573&0&-0.311&0.168&0.354&0.612&0\\ 0.215&-0.081&0.143&-0.311&-0.575&-0.500&0.289&0.408\\ 0.215&-0.299&-0.490&-0.311&0.168&-0.354&0.204&-0.577\\ 0.215&-0.389&0.692&-0.311&0.476&0&0&0\\ 0.215&-0.3&-0.490&-0.311&0.168&0.354&-0.204&0.577\\ 0.215&-0.081&0.143&-0.311&-0.575&0.500&-0.289&-0.408\\ 0.215&0.573&0&-0.311&0.168&-0.354&-0.612&0\end{array}\right]\]
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 2 Materials and Methods
### Multi-Label Classification Module  
The BigEarthNet dataset is processed and trained within TensorFlow, being transformed into tensor-type image data. During the classification of remote sensing imagery using the MMDL-Net model, fully connected layers are primarily employed to transform the output feature maps from the convolutional layers into the final classification outputs. These fully connected layers are typically situated at the terminus of the MMDL-Net architecture, accepting a flattened feature vector as input. The initial stages of the MMDL-Net model are dedicated to feature extraction, followed typically by a global average pooling operation. After this operation, the feature maps assume a shape of \(1\times 1\times\) N, where N represents the number of channels. Flattening these feature maps results in a feature vector of length N. This flattened feature vector is then multiplied by a weight matrix, yielding a vector that represents the scores for the various classes. Subsequently, the sigmoid function is adopted as the activation function for the fully connected layer in lieu of the softmax function. This is due to the fact that, in multilabel classification problems, each sample may simultaneously belong to multiple categories rather than a single category. This diverges from the premise of the softmax function, which assumes that each sample can be allocated to only one category, making it suitable for multiclass single-label classification tasks. The  
Figure 5: The two types of residual structures in the MMDL-Net model.  
softmax function is designed to model a categorical probability distribution over class labels, ensuring that the sum of probabilities of all possible labels for a given sample equals one. This is achieved through the exponentiation and normalization of the output scores from the last layer of the network. However, in the context of remote sensing imagery, where an image may concurrently contain multiple types of land cover, such as water bodies, vegetation, and wetlands, the model should be capable of reflecting the presence of multiple categories. The sigmoid function enables this capacity by being applied independently to each output node, treating the presence of each label as a separate binary classification problem. The fundamental rationale for employing the sigmoid activation function in a multilabel context is that it treats the output for each label independently, which allows the model to represent the probability of the existence of each class label independently. Additionally, as a nonlinear function, it is particularly useful for handling data that possess a high degree of complexity and variability, such as remote sensing imagery. The multiclass infrastructure of the MMDL-Net model is illustrated in Figure 6.
_Applied_  
_sciences_  
_Hert Rate Variability Assessment Using Time-Frequency Analysis in Hypotensive and Non-Hypotensive Patients in Hemodialysis_  
**[PERSON] 1,2**, [PERSON] 1,*, **[PERSON] 2**,  
**[PERSON] 1**, [PERSON] 1, [PERSON] 1, [PERSON] 3,  
**[PERSON] 3, [PERSON] 2 and [PERSON] 4**  
1 Departamento de Instrumentacion Electromecanica, Instituto Nacional de Cardiologia [PERSON], [PERSON] 1, Seccion XVI, Tlalpan, Ciudad de Mexico 14080, Mexico; [EMAIL_ADDRESS] (B.B.-L.); [EMAIL_ADDRESS] (R.M.-M.); [EMAIL_ADDRESS] (O.I.-V.); [EMAIL_ADDRESS] (C.L.)  
2 Instituto Politecnico Nacional, ESIME Culhuacan, Av. Santa Ana N. 1000, Coyoacan; Ciudad de Mexico 04260, Mexico; [EMAIL_ADDRESS] (J.C.S.-G); [EMAIL_ADDRESS] (G.S.-P)  
3 Unidad de Hemodialysisis, Departamento de Neftrologia, Instituto Nacional de Cardiologia [PERSON], [PERSON] 1, Seccion XVI, Tlalpan, Ciudad de Mexico 14080, Mexico; [EMAIL_ADDRESS] (H.P.-G.); [EMAIL_ADDRESS] (J.M.R.-C.)  
4 NefrosSalud Merida, Merida, Yucatan 97203, Mexico; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +52-(55)-5573-2911 (ext. 25001)  
Received: 1 June 2020; Accepted: 25 August 2020; Published: 2 September 2020  
**Featured Application: In this work, a spectrotemporal evaluation of heart rate variability during hemodialysis shows differences between hypotensive and non-hypotensive patients in the spectral bands related to the sympathetic and parasympathetic nervous system. This methodology could be implemented in a dedicated electronic system for the early detection of intradialytic hypotension events.**  
**Abstract: Intradialytic hypotension occurs in 10-30% of hemodialysis (HD) sessions. This phenomenon affects the cardiovascular system's functions, which are reflected in the activity of the autonomic nervous system (ANS). To indirectly assess the ANS during HD, we analyzed the mean R-R intervals and the spectral power of heart rate variability (HRV) from 20 end-stage renal disease patients divided into hypotensive and non-hypotensive groups. The spectrotemporal analysis was accomplished using short-time Fourier transform with 10 min epochs of HRV overlapping by 40%. The spectral power was divided into three segments according to high frequency, low frequency, and very low frequency bandwidths and averaged to fit quadratic regression models. The analysis of the mean R-R intervals showed significant differences between the groups (\(p=0.029\)). The power variation over time was significant in each spectral band (\(p\ll 0.05\)). The average power, maximum power, and time when the peak was reached differed for each band and between groups, showing the ability to correctly identify the decompensation of the ANS and discriminate between hypotensive and non-hypotensive patients. Additionally, the changes in the sympathovagal ratio were not significant and very scattered for the hypotensive group (\(p=0.23\)) compared to the non-hypotensive group, where the changes were significant (\(p\ll 0.05\)) and much less scattered.**  
**Keywords: short-time Fourier transform; hemodialysis; heart rate variability; intradialytic hypotension**
### Health Impact  
The destructive nature of fires may cause immediate bodily harm, but its emissions, some largely unseen, also bring danger to those outside of the surrounding area. For example, Oregon's Bootleg Fire had its smoke carried over three thousand miles from its origin, bringing health effects to people in distant cities who may not have been aware of the fire. It is a well-established fact that wildfires increase the concentration of particulate matter and harmful gases in the air [11], which are directly linked to various diseases, such as asthma, bronchitis, reduced lung volume, etc. [12; 13]. Although the effect on cardiovascular events are mixed, sufficient studies indicate that exposure to fire smoke leads to respiratory morbidity [14]. Emerging research also hints at a possible correlation between exposure to particulate matter and higher neurophysiological irregularities like ADHD, autism, decline in memory, etc., in children [15]. Prenatal exposure to polycyclic aromatic hydrocarbons, one of the constituents of fire, has been associated with childhood obesity [16], while exposure to PM2.5 in the prenatal stage could possibly lead to higher blood pressure in children [17]. [PERSON] et al., estimate that between 1997 to 2006, the  
Figure 1: Plot of acres burned from 1984–2020 in the United States shows an overall increase in the burn severity and impact over the years. Data was obtained from the Monitoring Trends in Burn Severity (MTBS) program by US Geological Survey Center for Earth Resources Observation and Science (EROS) and USDA Forest Service Geospatial Technology and Applications Center (GTAC).  
average annual mortality attributed to fire smoke was 339,000, with Southeast Asia and Africa experiencing the maximum brunt of the crisis [18]. Even short exposure to wildfire smoke increases risk of premature death through lasting damages to the respiratory and cardiovascular system.
## 4 Conclusions  
This study evaluated the seismic performance of pile-supported structures with batter piles with respect to the ground slope using dynamic centrifuge model tests. The acceleration, displacement, moment, and axial force of the system were experimentally derived and reviewed, and \(M\)-\(N\) interaction diagrams of the pile cross-sections were analyzed. The following conclusions were drawn.  
1. Batter piles reduced the maximum moment of the vertical piles by 88%. A large axial force occurred in the batter piles, which conversely reduced the moment of the vertical piles as the lateral load was transmitted to the axial force in the batter piles.
2. The reduction in system response in the inclined-ground model (acceleration: \(-\)48%, displacement: \(-\)50%, and moment: \(-\)84%) was greater than that in the horizontal-ground model (acceleration: \(-\)27%, displacement: \(+\)650%, and moment: \(-\)77%) because the lateral resistance increased greatly when the batter piles were installed in the inclined ground model. Contrastingly, while batter piles in the horizontal-ground model increased the lateral resistance, they also increased the asymmetry of the system, the response is not significantly reduced.
3. \(M\)-\(N\) interaction diagrams were derived to evaluate the cross-sectional stress where both the bending moment and axial force occurred simultaneously. When the batter piles were installed, the combined stress ratios, of up to 0.62 in the horizontal ground, and up to 1 in the inclined ground, were reduced, showing efficient performance in the inclined ground. Even when the sectional stress originally exceeded the allowable stress, the batter piles could reduce it to within the allowable limit to secure member stability.
4. This comparison of the seismic performance of the batter piles, installed in the horizontal and inclined grounds, found that they can improve the results for acceleration, residual displacement, moment, and member stability for a structure on inclined ground, benefiting these structures than those on horizontal ground.  
Conceptualization, [PERSON], and J.H.; methodology, [PERSON]; validation, [PERSON], J.H.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the \"Development of performance-based seismic design technologies for advancement in design codes for port structures\" project funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 7 Discussion
### Explainable AI  
In order to show the elements of the model's effectiveness and to visualize the elements taken into account in the classification of tennis strokes, the grad-CAM technique was used. Due to the fact that it operates only on a point cloud, the corresponding images from the VICON system are displayed as a background for the generated heat maps.  
It should be emphasized that the background images in Figures 8-11, depicting a player executing a specific type of stroke, serve solely illustrative purposes and do not convey the dynamic nature of the recorded motion. The model under discussion was constructed using motion capture data, recording the spatial coordinates of markers in three-dimensional space over time. As a result, the Grad-CAM visualizations may appear to suggest substantial errors. However, these discrepancies arose from the natural variability in the player's movement, rather than from inaccuracies in the model itself. It is important to note that Grad-CAM identifies regions of the input data that are most critical for the classification of the entire stroke, not of a single frame. In contrast, the background image corresponds to a single frame extracted from the analyzed stroke. The warmer colors (red,  
The analysis of the Grad-CAM visualization for the ST-GCN and FFCGAN models showed how both models tracked the player's movement during tennis strokes in a different way. The visualization for the FFCGAN model shows a much more concentrated, spatial activation, located mainly in the lower body and at the point of contact between the racket and the ball. It should be noted that the image visible as a background does not reflect the movement sequence, but presents a single frame in a static form. This suggests that the FFCGAN model primarily pays attention to the movements of the legs and racket that generate the stroke. On the other hand, the visualization for the ST-GCN model shows an activation that is quite broad, covering almost the entire player's silhouette, with the surroundings. It can be stated that the ST-GCN model integrates a wide movement context, taking into account the dynamics of the whole body and wider spatial relationships. It should be noted that footwork is crucial in performing tennis strokes. It can therefore be assumed that the adaptive spatio-temporal attention modules used in the FFCGAN model allow capturing more complex relationships between the individual biomechanical elements of tennis movement.
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 2 Study Site and Datasets
### Datasets  
The experimental dataset contained four multi-spectral images and two night-time images. Each image was stitched from eight inner CCD images for expansion of the imaging swath in a 10 m resolution. This satellite was designed with agile maneuvering imaging capability in order to improve its observational coverage. The fused images obtained using multiple channels can be applied in the monitoring of energy consumption, residential development patterns, and coastal port environments.