esa-sceva/satcom-chunk-collection huggingface dataset:
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 4 MAG Algorithms
### Degree
#### 4.3.1 Degree of Composite Vertices  
The degree of composite vertices of a given MAG \(H\) can be obtained directly from its composite vertices representation, \(g(H)\). Since the composite vertices representation is a traditional directed graph isomorphic to the MAG \(H\), it follows that the degree determination is done with the traditional algorithm for directed graphs with minor changes. For a given MAG \(H\) and its companion tuple \(\tau(H)\), the degrees of the composite vertices can be determined by Algorithm 8, where \(D(\pi_{o}(e),\tau(H))\) and \(D(\pi_{d}(e),\tau(H))\) stand for the numerical representation of the origin and destination composite vertices of edge \(e\in E(H)\), as defined in Section 3.2.  
```
input :\(H=(A,E)\) output :\(indegree\), \(outdegrees\)
1 Degree(H)
2\(n\leftarrow\left|\mathbb{V}(H)\right|\)
3\(T\gets CompTuple(A(H))\)// companion tuple of H, i.e., \(\tau(H)\)\(indegree\leftarrow\) vector of \(n\) integers, all 0
4\(outdegree\leftarrow\) vector of \(n\) integers, all 0
5 for each \(e\in E(H)\)do
6\(o\gets D(\pi_{o}(e),T)\)// numerical origin
7\(d\gets D(\pi_{d}(e),T)\)// numerical destination
8\(indegree[d]\gets indegree[d]+1\)
9\(outdegree[o]\gets outdegree[o]+1\)
10
11 end for
12 return\(indegree,outdegree\)
```  
**Algorithm 8**Determination of the degree of composite vertices.  
Another way for calculating the degrees of the composite vertices is computing it algebraically from the adjacency matrix of the MAG, as given by  
\[indegree=\mathbf{J}(H)^{T}\,\mathbf{1}, \tag{36}\]  
and  
\[outdegree=\mathbf{J}(H)\,\mathbf{1}. \tag{37}\]  
Further, the total degree of the composite vertices can be obtained by summing up their indegrees and outdegrees.  
To determine the time complexity of Algorithm 8, we consider that lines 2, 4, and 5 have each time complexity \(O(|\mathbb{V}(H)|)\), the determination of the companion tuple at line 3 has complexity \(O(p)\), where \(p\) is the number of aspects of the MAG, so that \(p\ll|\mathbb{V}(H)|\). Finally, since the determination of the numerical representation of vertices has complexity \(O(p)\), we have that the **for** loop initiated at line 6 has complexity \(O(p*|E(H)|)\), so that the time complexity of Algorithm 8 is \(O(|\mathbb{V}(H)|+p*|E(H)|)\). If we consider that in a given case the order of the MAG does not vary, so that \(p\) is a constant, then the algorithm's time complexity is \(O(|\mathbb{V}(H)|+|E(H)|)\).  
In the case of the example MAG \(T\) (Figure 3), whose companion tuple is \(\tau=(3,2,3)\), it can be seen that the composite vertex \((2,Bus,t1)\) has outdegree 3 and indegree 1, while the composite vertex \((1,Subway,t2)\) has outdegree 2 and indegree 2. Since \(D((2,Bus,t1),\tau)=2\) and \(D((1,Subway,t2),\tau)=10\), it follows that \(indegree[2]=1\), \(outdegree[2]=3\), \(indegree[10]=2\) and \(outdegree[10]=2\).
## 5 Discussion  
In light of the earlier discussion, we have put forth various hypotheses as displayed in Section 2, and we will further ascertain the acceptance or rejection of these hypotheses based on the findings we have disclosed in Section 4. Concerning H1, we show that investors who implement BBTS may achieve advantageous outcomes, as our results are shown in Section 4, including the results shown in Tables 2-6, with particular emphasis on the red cells in Tables 3 and 4. As a result, we may reject H1. Our findings suggest that implementing the BBTS could lead to favorable outcomes in trading cryptocurrencies [48], implying that technical analysis could be useful in trading cryptocurrencies [34; 79]. Similarly, [PERSON] et al. [80] analyze Bitcoin futures with VMA rules, offering a heatmap for effective strategies, potentially enhancing profits, and unveiling novel investment insights, suggesting that when trading cryptocurrencies, technical analysis could be considered. Regarding H2 and H3, we investigate whether implementing BBTS would or would not result in advantageous results due to the presence of herding behaviors (market overreaction). Our results in Table 4 show that, as a result of oversold trading signals given by BBTS, investors may achieve advantageous outcomes by rejecting H2 (accepting H3). Our findings indicate that investors using BBTS would be more effective if cryptocurrency markets experienced overreaction rather than herding behavior. Our revealed results indicate that investors using contrarian strategies (e.g., buying bitcoin and ETH spots as oversold signals emitted BBTS) might achieve advantageous outcomes instead of using momentum strategies (e.g., selling bitcoin and ETH spots as oversold signals emitted BBTS). That is, adopting contrarian strategies would be more effective than adopting momentum strategies in this study, which is consistent with adopting contrarian strategies being effective for trading in cryptocurrency markets [67; 81; 82] rather than adopting momentum strategies being effective for trading in cryptocurrency markets [83; 84]. Moreover, we show that investors adopting BBTS would produce an advantageous outcome after examining our hypotheses H2 and H3. In addition to BBTS, [PERSON] et al. [85] proposed a hybrid walk-forward ensemble optimization technique for predicting daily cryptocurrency prices, outperforming classical statistical and machine/deep learning
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 3 Results
### Prediction Performance Comparison  
To evaluate the performance of the proposed TDA features, we extracted the box-counting features and HRV features with 60 ECG split frames. In addition, nine TDA features, two box-counting features, and seven HRV features were combined to form 18 fusion features vector. Then, all types of the features were respectively fed into 5 ML classifiers. The classification accuracy with 10-fold cross-validation is shown in Table 5.  
It can be seen that the accuracy obtained by TDA features is significantly higher than that of the other two types of features, respectively; prediction performance was further improved by fusing the features.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 3 Experimental Setup
### Benchmark Dataset  
We used the publicly-available dataset \"Instances for the TSP with Drone\" [(43)]. This has been used in several studies [(10; 23)]. The dataset categorizes instances based upon the distribution type, instance size, and vehicle speed configuration. The categories are as follows. We used two different distribution types, based on how each instance was generated in the benchmark dataset. In a uniform distribution, the coordinates are generated independently and uniformly; in a single-center distribution, the coordinates are generated using an angle and a distance drawn from a normal distribution. The dataset contains a wide variety of instance sizes, ranging from 5 to 500 customer locations. The experiments in this study were applied using instances of sizes \(10,20,50,75\), and 100. As for the vehicle speed configuration (\(\alpha\)), we utilized two different relative speed configurations for the vehicles. With (\(\alpha=1\)), the truck and drone have the same speed; with (\(\alpha=2\)), the drone is two times faster than the truck.  
For each (type, size, \(\alpha\)) combination, the dataset contains 10 different instances. In each instance, the vehicle speed configuration (\(\alpha\)), the total number of nodes (including the depot), and for each node, the customer ID and location specified using the x and y-coordinates are defined.
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
###### Abstract  
This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R\({}^{2}\) = 0.8737) compared with that of the BPNN (R\({}^{2}\) = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.  
machine learning; backpropagation neural network; convolutional neural network; seismic damage simulation; time-history analysis; earthquake destructive power +
Footnote †: journal: Applied Sciences
###### Abstract  
This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The Asset Definition Model ensures standardized data representation based on IEC 81346-1:2022 and ISO 14224:2016, while the Asset Criticality Model prioritizes maintenance actions using risk-informed analysis. The Asset Monitoring Model enables real-time data acquisition through IoT sensors, facilitating condition-based monitoring and dynamic decision-making. Finally, the Intelligent Asset Management Models support long-term planning by simplifying data complexity and aligning with advanced maintenance strategies. A case study on bridge maintenance demonstrates the practical value of the framework, showcasing its ability to integrate real-time monitoring with predictive decision-making tools. By bridging asset monitoring and lifecycle planning, the framework enhances operational efficiency, reduces maintenance costs, and addresses the challenges posed by limited digital maturity in critical infrastructure. This approach represents a significant advancement in the digital transformation of maintenance management.  
asset digitalization; digital maintenance; IoT; AHI; civil infrastructure digitalization; bridge maintenance Article
Several studies reported positive impacts of SR models on medical interpretation and diagnosis [2; 5; 6]. Specifically, it has been reported [5] that constructing HR images via SR models allows detection and recognition of subtle lesions, and reduce the misdiagnosis rates. In addition, it has been reported [2] that enhancing image resolution can often improve the performance of several subsequent tasks including detection, segmentation, and classification. The positive impact of SR models has been reported for specific imaging modalities. For example, several studies (e.g., [6]) demonstrated that applying SR models to chest X-ray (CXR) images can significantly improve the diagnosis of pulmonary disease. Using SR models to reconstruct HR CT from LR CT has also shown a significant improvement in diagnostic accuracy as reported in [7; 8; 9]. Further, [PERSON] et al. [10] reported the positive impact of using image enhancement models (MedGA [11]) on segmentation. In particular, it has been found that using the MedGA model [11] to enhance the resolution of MRI images increased the performance of uterine fibroid and brain tumor segmentation. Therefore, we believe the use of SR models to reconstruct HR images is of great significance in medical image analysis.  
Existing methods for SISR are computationally complex, time intensive, and may require optimizing a large number of training parameters. Further, existing methods use individually trained models for SISR and visual task analysis. In this paper, we propose the _Hydra_ approach for simultaneous SISR and visual task analysis. The proposed _Hydra_ learns the mapping from LR to HR and uses several computing heads for visual task analyses. It reduces computational complexity while improving performance. The rest of the paper is organized as follows. Section 2 presents existing works for super-resolution and visual task analysis followed by a discussion of the contributions. We describe in Section 3 the CXR datasets used in this paper and present our proposed approach for SISR and visual task analysis. Section 4 presents the experimental setup followed by the results of our approach and a comparison with the state-of-the-art. Finally, we conclude the paper and discuss different directions for future research in Section 5.
## 3 Method
### Part 1: Retrieval Algorithm for Instantaneous TOA Albedo (Level-2)
#### 3.1.3 Narrowband-to-Broadband (NTB) Conversion  
Except for su nglint conditions, the albedo calculation was based on observations. Depending on the scene type, which was a combination of cloud cover and _NTB surface type_ (Table 4), the regression coefficients \(b_{i}\) (Table 3) were selected. These coefficients were then  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IGBP Class** & **NTB Surface Type** &
\begin{tabular}{c} **CERES Surface Type (ADM,** \\ **Albedo Model)** \\ \end{tabular} & **TWL Surface Type** \\ \hline Water & WATER & OCEAN & WATER \\ \hline Evergreen Needleleaf Forest & & & \\ \hline Evergreen Broadleaf Forest & & & \\ \hline Deciduous Needleleaf Forest & FOREST & & \\ \hline Deciduous Broadleaf Forest & & VEGETATION-DARK \\ \hline Mixed Forest & & & \\ \hline Woody Savannas & SAVANNA & & \\ \hline Savannas & & & \\ \hline Grasslands & & & \\ \hline Croplands & & VEGETATION-BRIGHT & LAND \\ \hline Cropland/Natural Vegetation & GRASS-CROP & & \\ \hline Closed Shrublands & & & \\ \hline Wetlands & & VEGETATION-DARK \\ \hline Urban and Built-Up & & & \\ \hline Open Shrubland & DESERT-DARK & DESERT-DARK & \\ \hline Tundra & & & or * \\ \hline Barren or Sparsely Vegetated & DESERT-BRIGHT & DESERT-BRIGHT & \\ \hline Permanent Snow and Ice & PERM-SNOW-ICE & SNOW & PERM-SNOW-ICE \\ \hline \hline \end{tabular}  
* for CERES surface types, the discrimination between dark and bright desert is done using the CERES low resolution map.  
\end{table}
Table 4: Mapping of IGBP class (water + land types) to NTB, CERES, TWL surface types.  
applied with the narrowband reflectances (\(\rho_{0.6}\), \(\rho_{0.8}\)) to obtain the broadband reflectance \(\rho_{SW}\):  
\[\rho_{SW}=b_{0}+b_{1}\cdot\rho_{0.6}+b_{2}\cdot\rho_{0.8}+b_{3}\cdot ln\left( \frac{1}{cos(\theta_{0})}\right)+b_{4}\cdot ln\left(\frac{1}{cos(\theta)}\right). \tag{3}\]  
The reflectances as well as the coefficients \(b_{i}\) are expressed as a percentage, with a range of 0-100. The regression model described by Equation (3) is partly based on existing literature, and was calibrated, validated, and documented by [PERSON] and [PERSON] [17]. Besides the actual narrowband reflectances, the model contains two more predictors which improve the regression model's accuracy: solar zenith angle and viewing zenith angle, with the inverse of the cosine being an approximation of the atmospheric optical path. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on a calibration subset: adjusted \(R^{2}\) higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions [17].
## Methods
### Comprehensive Semantic Similarity Calculation Method
#### 3.4.3 Information-Content Similarity  
In the ontology model, each node corresponding to a concept does not appear with equal probability. By statistically analyzing the occurrence probability of concepts or instances corresponding to each node in the bridge BIM model, the information content of concept nodes can be computed, as shown in the following formula:  
\[Info(c_{i})=-log(p(c_{i})) \tag{16}\]  
In the formula, \(p(c_{i})\) represents the probability of concept \(c_{i}\) occurring in the BIM model.  
In the bridge ontology model constructed in this study, the bridge architectural concepts are hierarchically organized from the whole to the parts. Concepts at the same depth in the ontology model should receive higher weights for their information similarity compared to concepts at different depths. Moreover, the greater the depth difference, the lower the weight. Following the method proposed by [PERSON] [6] and considering the features of hierarchical division in the transport infrastructure ontology model, the Information-Content similarity is defined as follows:  
\[Sim^{inf/o}(c_{i},c_{j})=\frac{2 Info(lea(c_{i},c_{j}))\times W_{dep}(c_{i},c_{ j})}{Info(c_{i})+Info(c_{j})} \tag{17}\]  
In Equation (17), the value of \(W_{dep}(c_{i},c_{j})\) depends on the depth difference between \(c_{i}\) and \(c_{j}\). Use \(D_{dep}(c_{i},c_{j})\) to indicate the depth difference between \(c_{i}\) and \(c_{j}\). The value of \(W_{dep}(c_{i},c_{j})\) can be defined in the following formula:  
\[W_{dep}(c_{i},c_{j})=\left\{\begin{array}{ll}1&D_{dep}(c_{i},c_{j})=0\\ 0.7&D_{dep}(c_{i},c_{j})=1\\ 0.5&D_{dep}(c_{i},c_{j})=2\\ 0.3&D_{dep}(c_{i},c_{j})=3\\ 0.1&\text{other cases}\end{array}\right. \tag{18}\]
## 3 Simulation and Analysis
### Sun Zenith Angle  
The sun zenith angle (\(\theta_{0}\)) is another important imaging geometric parameter, and it is the key parameter affecting SG radiance. [PERSON] et al. [20] used 170 ASTER images to verify an SSR estimation model based on multi-angle SG remote sensing. The screening rules for the selected images are: (1) the season is summer, the local time is around 10:00, (2) the spatial position is at low latitude. The purpose of these screening factors is to obtain better SG images under the appropriate sun zenith angle. Considering \(\theta_{0}\) in the range of [10\({}^{\circ}\), 40\({}^{\circ}\)] and in steps of 5\({}^{\circ}\), the trend of normalized SG radiance at different SVA is simulated as shown in Figure 12.  
According to Figure 12, the simulation results show that SG radiance is strong at small sun zenith angles. This implies that strong SG radiance will be observed in images captured during midday in the summer. Considering the SG radiance threshold of 0.004, the range of available SVA for the SSR  
Figure 11: Statistical results of \(\rho_{10}\) and \(\rho_{20}\) at different sun azimuth angles (\(\phi_{0}\)).  
Figure 12: Normalized SG radiance (\(l\)) simulated under each SVA (\(\theta\)) at different sun zenith angles.  
estimation model was found to decrease with increasing sun zenith angle. At \(\theta_{0}=10^{\circ}\), the range of available SVA is about \(-32^{\circ}\leq\theta\leq 30^{\circ}\), and at \(\theta_{0}\geq 35^{\circ}\), SG radiance is below the threshold and the range of usable SVA is unavailable.  
Figure 13 shows the distribution of \(\Delta\sigma_{0}^{2}\) at different sun zenith angles under different SVA combinations, and Figure 14 shows the statistical results of \(\rho_{10}\) and \(\rho_{20}\). According to Figure 13, the optimal SVA combination is also greatly affected by sun zenith angle. At low sun zenith angles, the SSR estimation model is more efficient. According to Figure 14, \(\rho_{10}\) is about 8.7% and \(\rho_{20}\) is about 10.0% at \(\theta_{0}=10^{\circ}\). When the sun zenith angle gradually increases, \(\rho_{10}\) and \(\rho_{20}\) gradually decrease, reaching close to 0 at \(\theta_{0}\geq 35^{\circ}\).
## 4 Results and Discussion
### Residual Strength of PG Amended Lime-Stabilized Soil  
Residual strength of PG amended lime-stabilized soil is the strength calculated from the stress-strain graph of the soil as a result of static deviatoric stress. The fracture/breaking point of the modified soil under varying strain with constant stress gives the residual strength. From the Figure 1 it is observed that the soil underwent strain softening and where the soil is compacted at the maximum dry density it has started flocculating under varying strain, causing dilation. The value of residual strength increases with an increase in lime and PG content (Figure 3). Similar to the modulus behavior, the contribution of PG to the residual strength of the lime-stabilized soil is more when the lime content is lesser than the ICL, i.e., LP1. The increase in residual strength for LP1, LP2 and LP3 are 1.7, 1.36 and 1.29 times that of L1, L2 and L3, respectively. Thus, it can be concluded that the addition of optimum PG content to the lime-stabilization of expansive soils improves the residual strength of the soil. However, similar to the modulus, the contribution of PG toward an increase in residual strength decreases with the increase in lime content.  
Figure 2: Contribution of PG to the modulus in lime modified soil.
# Trepenoid Biosynthesis Dominates among Secondary Metabolite Clusters in _Mucoromycotina_ Genomes
## 1 Introduction
Finally, SMCs responsible for terpene biosynthesis were recently identified in several _Mucor_ and _Rhizopus_ genomes [9; 16]. For instance, _Mucor irregularis_ (syn. _Rhizomucor variabilis_) produces indole-diterpenes with a fused diterpene ring called rhizovarins [17]. The best-studied compound isolated from basal fungi is a macrolide, rhizoxin, produced by an endosymbiotic bacterium (_Mycetolabitats rhizoxinica_) of _Rhizopus microsporus_ and encoded by a huge NRPS-PKS cluster [18]. Some fungi harbour the necessary genes themselves; others rely on their endohyphal bacteria (EHB). Similarly, in the _Linnemannia elongata_ (syn. _Mortierella elongata_) and _Mycavidus cysteinexigens_ symbiosis, several compounds are produced, and the genome of the bacteria encodes NRPS and other metabolite clusters [19]. Fungal secondary metabolites can also promote plant growth in harsh conditions. This process is mediated by the release of phytohormones; for example, gibberellic acid (a diterpenoid acid) produced by _Rhizopus stolonifer_[20]), _Mortierella antarctica_ and _Podila verticillata_ (syn. _Mortierella verticillata_) [21]. Moreover, the plant-fungus interaction can lead to the synthesis of novel compounds which can be produced neither by the host nor the endophyte alone and can vary depending on the host plant species, which have been shown in, for example, a _Umbelopsis dimorpha_ growing on _Kadsura angustifolia_ and wheat bran [22]. Encouraged by the relative scarcity of data on SMCs for fungi other than _Dikarya_ and the abundance of available genomic sequences, we aimed to perform a systematic screening of 157 publicly available _Mucoromycota_ assemblies for the presence of secondary metabolite biosynthetic gene clusters. We applied a scalable procedure using publicly available tools for a batch evaluation of the diversity of SMCs within this group of fungi.