esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 3 Optimization Methods
### Variable Number of Dimensions MOPSO
#### 3.1.2 Vndompos
The list of control parameters and their recommended values based on the results of different benchmark studies are summarized in Table 1.  
Figure 2: The VNDMOPSO DSVs manipulation for the velocity update formula: (**a**) the selection of new dimension, \(N_{p}\), of the \(p\)-th particle, (**b**) corresponding update of vectors global best, \(\mathbf{g}_{p}\), personal best, \(\mathbf{b}_{p}\), and previous position, \(\mathbf{u}_{p}\). ```
1:Set of user-defined controling parameters \(\mathcal{S}\), objective functions \(\mathbf{f}\), list of feasible dimensions \(\mathcal{N}\), decision space limits \(\Gamma\)
2:Non-dominated set \(\mathcal{U}\)
3:Generate random dimension for every particle in the swarm. 4:Generate random initial positions \(\mathbf{u}(i=1)\) and velocities \(\mathbf{v}(1)\). 5:Evaluate objective function values for all particles \(\mathbf{f}(\mathbf{u})\). 6:Create extarnal archive \(\mathcal{E}\). 7:Assign personal/global best positions (see Algorithm 1). 8:while Stop condition do
9:for Every particle \(\mathbf{u}_{p}\)do
10: Find current dimension \(N_{p}\) using Algorithm 2. 11: Create copies of previous \((i-1)\) DSVs \(\mathbf{g}_{p}\), \(\mathbf{b}_{p}\), and \(\mathbf{u}_{p}\) (\(\mathbf{v}_{p}\)) with a dimension \(N_{p}\). 12: Update the velocity \(\mathbf{v}_{p}(i)\), and position \(\mathbf{u}_{p}(i)\) using (6), and (7). 13:if\(\mathbf{u}_{p}(i)\
ot\in\Gamma\)then
14: Apply boundary condition on \(\mathbf{u}_{p}(i)\). 15:endif
16: Evaluate objective function values \(\mathbf{f}\big{(}\mathbf{u}_{p}(i)\big{)}\). 17: Update the personal best \(\mathbf{b}_{p}(i)\). 18:endfor
19: Update external archive \(\mathcal{E}\). 20: Update global best positions using Algorithm 1. 21:endwhile
22:Return non-dominated set \(\mathcal{U}=\mathcal{E}\). ```  
**Algorithm 3**VNDMOPSO general pseudocode
## 2 Methodologies
### Dual-Tree Discrete Wavelet Transform (DTDWT)  
DWT employs analysis filters to decompose signals into approximation components and detail components. Then 1/2 downsampling was applied to obtain the approximation coefficients and detail coefficients. The downsampling procedure may introduce severe aliasing, leading to distortion which may reduce the ability of wavelet coefficients to characterize the original signals. DTDWT is an enhancement of DWT which can overcome the above mentioned drawbacks. The flow chart of DTDWT is shown in Figure 2. As shown in Figure 2, DTDWT employs two wavelet trees for signal decomposition. For the first level decomposition, if the delay between two wavelet trees equals to the sampling interval, then the sample value discarded during the downsampling operation of the 1 st wavelet tree is exactly the reserved one of the 2 nd wavelet tree (equivalent to no downsampling operation). For decomposition in a higher level, alternate odd and even-length linear phase filters are utilized in DTDWT. However, the use of alternate odd/even filter approach is impractical in some scenarios. Therefore, a \(Q\)-shift [20] dual tree structure was proposed so that all filters beyond level 1 decomposition were of even-length. In our work, the 10-tap Q-shift filter was selected, which has been proven effective and is the common choice in many applications of DTDWT [21; 22].  
Through DTDWT (Figure 2), we can obtain 14 coefficient sets (2 sets of approximation coefficients: \(A_{T1L6}\), \(A_{T2L6}\); 12 sets of detail coefficients: \(D_{T1L1}\), \(D_{T2L1}\), \(D_{T1L2}\), \(D_{T2L2}\),..., \(D_{T1L6}\) and \(D_{T2L6}\)) for each EEG channel. Complexity and energy features were extracted directly from these wavelet coefficient sets.  
Figure 1: Framework of RR-DTDWT.
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 4 Experiments and Evaluation
### Evaluation of the XGA Model  
We compare the proposed XGA model with other widely used approaches in rumor detection, including TextCNN, RNN, LSTM, att-BiGRU (BiGRU with the attention mechanism), and BERT. We use _Accuracy_, _Precision_, _Recall_, and _F-score_ as the evaluation metrics of the detection approaches. The performance of different deep learning approaches and XGA is shown in Figure 2. The ROC curve is shown in Figure 3.  
As shown in Figure 2, we can see that the XGA model achieves the F-score of 0.9267 on CR-Dataset and shows the best result in all the four metrics. This is because XLNet uses Transformer-XL as its feature extractor, which has better performance than CNN (used by TextCNN), RNN (used by RNN, LSTM, and att-BiGRU), and Transformer [33] (used by BERT) on semantic feature extraction. In addition, the AUC score shown in Figure 3 proves that att-BiGRU performs better than TextCNN, RNN, and LSTM, which indicates that BiGRU and the attention mechanism have an advantage of extracting features, and that is why we use them in the XGA model to create semantic feature vectors for Cantonese tweets. What's more, the XGA model outperforms att-BiGRU by a margin of 2%, which proves the effectiveness of XLNet and sentiment views involved in our model. In conclusion, compared with other deep learning approaches, the proposed XGA model using XLNet, BiGRU, and the attention mechanism is more effective in Cantonese rumor detection. In the experiment, among the test set which includes 1300 tweets, non-rumors are misclassified 55 times, and rumors 41 times. In some cases, a non-rumor is posted to refute a certain rumor and often contains reasoning or turns. For example, a non-rumor may first explain an existing rumor, and use examples or reasoning to prove that the rumor is false in the subsequent text. So, the final detection result of the non-rumor will be affected by the rumor with negative emotional tendencies. That is the reason why some non-rumors cannot be detected correctly. Moreover, the sentiment polarity of some rumors is unclear, which results in the misclassification of rumors. In the future, to reduce the misclassification, we can optimize our model to make it learn the mutual negation between paragraphs in the tweet.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 3 Distributed DL Communication: Libraries and Architectures Compared
### PyTorch Environment
#### 3.2.2 Ring All-Reduce Method  
As depicted in Figure 12, in contrast to the parameter server method, the ring all-reduce approach, which is a decentralized method, does not waste GPUs in the NCCL library because all nodes are set up as workers. This means that the architecture of all communication libraries is the same in this case. The ring all-reduce architecture works by dividing the updated gradient stored in the GPU memory of each worker by the number of workers. Then, it transmits this divided gradient value to the next worker and receives the entire gradient value from the previous worker. This process repeats recursively, updating all gradient values through \(2(N-1)\) executions when there are \(N\) workers in the system.  
Figure 11: PyTorch gather architecture.  
Figure 10: PyTorch broadcast architecture.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Architecture
#### 2.2.1 The Traffic Sensing Layer  
This layer combines two types of entities in a hierarchical architecture: a large number of mobile vehicles with limited computation and communication capabilities, and a set of roadside units (RSUs). Each entity has its own local environmental view. Each RSU is equipped with a processing unit for collecting data within its transmission range. Specifically, RSUs sense passive data within their coverage including the number of vehicles and license plate numbers. On the other hand, vehicles act as a source of active data including speed, direction and location information.
## 5 Simulations
### Simulation Results
#### 5.2.4. Considering SNR and a Localized Noise Source  
Even though the proposed algorithm is designed for the RSS measurements, we show shortly that it can also handle the SNR measurements without any modifications if the wireless transceivers are capable of measuring noise power (as in [16,17]).  
We simulate the RSS as before and simulate a localized external noise source (with the noise power \(PN\)) in the environment and then derive the SNR using the Equation (3). There can be many external noise sources existing in a radio environment. We provide an example simulation using one external noise source, but the principle can be extended to multiple localized external noise sources.  
In addition to the simulated settings used before, a localized external noise source (with 10% of the power of the server/client signal) is added at \((5,5)\) in the environment, as can be seen in Figure 12(a). A sample output of simulation Case 2 (LOS and a Gaussian spatio-temporal noise of 2 dBm) using the SNR measurements is presented in Figure 12(b). The Figure 13 shows the efficiency of the algorithm in avoiding the external noise location and driving the relay node towards the global optimum position adapted to the noisy environment.  
**Figure 13.** A sample simulation using SNR measurements for the LOS scenario with 2-dBm Gaussian RSS noise and a localized external noise source. The theoretical optimum is indicated as *. (**a**) Map of SNR values. Server node at (\(-\)30,0), client node at (30,0) and a localized noise source at (5,5); (**b**) Path taken by the relay node in reaching the global optimum overlaid on the objective function value.  
Figure 12: Influence of sensor separation distance on the mean absolute error (MAE).
###### Abstract  
The aim of this study was to determine antioxidant activity (DPPH and phosphomolyb-denum method), polyphenols content (total polyphenols, flavonoids, and phenolic acids), mineral compounds composition (Cu, Zn, Mn, Fe, Cr, Ni, Co, Pb and Cd) and antimicrobial activity (with disc diffusion method) of medicinal herbs traditionally used in the Slovak republic. The tested plants belonged to the Primulaceae, Utricaceae, Grossulariaceae, Rosaceae, Lamiaceae, Asteraceae, Equisetaceae, Tropeaolaceae, and Plantaginaceae families. The highest antioxidant activities were found in samples of _Rosa canina_ L. (DPPH--29.43 \(\pm\) 0.11 mg TE/g; TE--Trolox equivalent) and _Fragaria vesca_ L. (phosphomolybdenum method--679.56 \(\pm\) 3.06 mg TE/g), both from the _Rosaceae_ family. Total polyphenols (determined using the Folin-Ciocalteu-reagent) were most abundant in a sample of _Fragaria vesca_ L.--124.51 \(\pm\) 5.05 mg GAE/g (GAE--gallic acid equivalent), total flavonoids (determined using the aluminum chloride method)--in a sample of _Primula veris_ L.--48.35 \(\pm\) 3.77 mg QE/g (QE--quercetin equivalent), and total phenolic acids (determined using Arnova reagent)--in a sample of _Thymus serpyllum_ L.--102.31 \(\pm\) 2.89 mg CAE/g (CAE--caffe acid equivalent). Regarding mineral compounds composition, samples of _Fragaria vesca_ L. and _Thymus serpyllum_ L. showed the highest levels of iron. In samples of _Calculation officinalis_ L. and _Trapuselum uris_ L., the highest amounts of zinc were determined, while copper was the most abundant in samples of _Utrica dioica_ L. and _Melissa officinalis_ L. The amounts of heavy metals were within legally acceptable limits. The extract of _Equisetum arrenese_ L. showed the strongest inhibitory activity towards _Clostridium perfingens_ CCM 4991 (6 mm), while the one from _Methla pipertia_ L.--towards _Candidia glabrata_ CCM 8270 (4.83 mm) and _Candidia tropicalis_ CCM 8223 (4.33 mm).  
plants; bioactive compounds; antibacterial activity; microelements; heavy metals 2023
## 4 Discussion
Our present modeling strategy showed a similar phenomenon that the average testing accuracy with the lowest (one) and highest (six) dimensions of the feature were 51.83% and 99.98%, respectively (Figure 3). Moreover, we found that the morphological feature \(n\) achieves the best classification performances among the six morphological features, followed by \(AER\), while the \(\frac{D_{n}^{3}}{D_{n}^{3}}\) achieves the worst (Table 6). The results also indicated that two combinations in the two-dimensional section achieved the testing accuracy as high as 90% (Figure 3). This classification accuracy was remarkably higher than that of the other combinations with two input features. Further studies suggested that these two combinations were (\(n\) & \(\frac{D_{n}^{3}}{D_{n}^{3}}\)) and (\(n\) & \(AER\)) (Table 7). These results were consistent with our recent study which predicts that the \(\left|\frac{n-3}{3}\right|\) and \(AER\) were the best two indicators for CAD prediction (data not shown). Our results further indicated that the volume of training data was another factor that can impact the classification performance (Table 5). A study by [PERSON] et al. indicated that the whole data were split into 80% (1652# of 2066#) for training and the remaining 20% for testing achieved high accuracy of 97.5% [31]. Our present strategy showed similar high performances when no less than 50% of the morphometric data were used for training. In addition, the performance did not improve significantly as the volume of training datasets further increases from 50% to 75% (Tables 4 and 5 (_Case 1_)). Specifically, 50% and 75% of morphometric data were selected for training resulting in a mean testing accuracy of 99.96% and 99.98%, respectively. These results were obtained by the polynomial-SVM model. These suggest that the polynomial-SVM model was a stable and powerful approach for the detection of coronary artery disease. Although the polynomial-SVM model performed the best in this study, this algorithm could also have its inherent pros and cons. Compared to other models used in this study, the most pros of the polynomial-SVM model is that it has the best performances when combined with measured morphological features. However, it also has its cons side. For instance, to have the best classification performances, parameters tuning is necessary for the polynomial-SVM model. In addition, when dealing with large amounts of datasets, the polynomial-SVM model will be time-consuming [24]. A survey study by [PERSON] et al. indicated that different types of features data could suit for different machine learning models [36]. Our present study demonstrated that, compared to other considered models, the polynomial-SVM model is the best for detecting the changes of the morphological features, however, whether it is also the best for detecting other types of features needs further exploration.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 3 Management of Acute Asthma Exacerbation
### Systemic Corticosteroids  
The beneficial effects of corticosteroids in the management of acute asthma have long been known [25]. A 2001 Cochrane review including 12 studies and 863 patients concluded that corticosteroids given within the first hour of presentation to the ED significantly reduced the rate of hospital admission in patients presenting with acute asthma [26]. Corticosteroids reduce mortality, asthma relapses, subsequent hospitalisation, and requirement for \(\beta\)2 agonist therapy. Since they take time to work, the earlier they are given in the acute attack, the better the outcome [27]. The BTS guidelines advise giving steroids in adequate doses to all patients with acute asthma exacerbation [10]. Regarding the route by which steroids are given, studies have shown that the efficacy between oral, intravenous, and intramuscular routes is similar. Furthermore, there appears to be little difference in efficacy between different formulations of corticosteroids--for example, prednisolone, dexamethasone, and methylprednisolone [28; 29; 30; 31]. A standard dose of 40-50 mg of prednisolone or 400 mg of hydrocortisone (100 mg four times a day) for a minimum duration of 5 days is currently recommended [10]. Although there are several acute side effects from systemic corticosteroids use in ICUs, including hyperglyaemia, hypernatremia, water retention, delirium, and ICU-acquired weakness [32], the benefits of short-term modest doses of systemic corticosteroids outweigh these adverse events.
## 2 Numerical Model and Analysis
### CFD Analysis of Concept Model and Corresponding Results  
The initial channel design for the cooling system consists of six channels with geometrically identical areas assuming that the incoming cooling water is uniformly distributed among the channels. For uniform cooling efficiency, it is important that the flow rate of cooling water is the same in each flow channel. We examine the flow characteristics of the cooling water by performing CFD analysis based on this initial design model. The density and dynamic viscosity of the water used for cooling is 1000 kg/m\({}^{3}\) and 0.001 N-s/m\({}^{3}\). The allowable inflow volume of cooling water of OBC is designed to be about 9 L/min. Checking the convergence of the results of the pretest analysis, the element size was determined to be 0.7 mm, and the number of elements is about \(1.95\times 106\), and the skewness of mesh quality is 0.78. Figure 2 shows the finite element model and boundary condition of the initial design model.  
Flow analysis was performed using the ANSYS Fluent 8.0 software. Figure 3 shows the flow analysis results for the flow velocity and pressure as representative flow characteristics. Furthermore, Table 2 lists the average flow velocity and pressure in the different channels as well as the flow volume distribution of the cooling water for each channel along with the normalized values. If the inlet flow volume were the same in each channel, then it would be one-sixth (16.67%) of the total flow. However, as indicated by the values in Table 2, the volume flow ratio of the first cooling water channel is the lowest at 13.19%, while that of the second cooling water channel is the highest at 22.22%. Thus, the maximum-minimum difference of the volume flow rates is as high as 54%; this large deviation in flow volume distribution can be attributed to a directional change in water flow at the first inlet on the cooling plane, which affects the fluid flow due to the momentum generated from the inertia of the fluid. Moreover, as the inflow volume varies by channel, a different dynamic pressure would be realized in each channel. Based on these analysis results and characteristics of [PERSON]'s theorem considering the dynamic flow pressure difference in the different channels, we propose a design improvement that involves changing the shape of the channels to reduce the rotational momentum at the first inlet. Accordingly, we selected inlet volume flow rate and length of the first channel (to control the movement characteristics of the curved part) as design variables to control the flow volume distribution; in addition, we include a rear pipeline based on the dynamic pressure inside the flow channel for pressure balance. Figure 4 highlights the selected design variables that are considered in the improved cooling channel design.  
Figure 2: Finite element model and boundary condition for computational fluid dynamics (CFD) analysis.
## 2 Dynamic Lognormal Shadowing Framework
### Random Field of Shadowing
A reasonable choice reflecting the commonly accepted correlation dependencies is given in [36] by:  
\[p(\gamma)=\frac{\xi}{\gamma\sqrt{2\pi}\sigma}e^{-\frac{(10\log\gamma-\mu)}{2 \sigma^{2}}} \tag{2}\]3  
where \(\theta_{x_{i}}^{j}\) is the direction of point \(\mathbf{x}_{i}\) from base station \(j\) relative to some reference direction and \(d_{x_{i}}^{j}\) is the distance of point \(\mathbf{x}_{i}\) from base station \(j\). The functions \(g\), \(h\) are chosen in a way to satisfy positive definiteness according to [36]. A detailed account for issues concerning the feasibility of selected correlation models can be found in [9]. In [24] another model based on an underlying spatial loss field is proposed in terms of a line integral across links, permitting the shadowing correlation calculation, even for links without a common node. In any case of a feasible spatial correlation model chosen, the proposed model induces an exponential temporal correlation as in (13). As shadowing is considered a Gaussian random field [36] an appropriate selection of a feasible correlation model gives a positive definite covariance matrix for any selection of space and/or time points. Before proceeding in the next section to the formulation of the proposed dynamic model it should be noted that the location variabilities appearing in (5)-(12) depict the fact that shadowing is being dealt with in a statistical manner as path gain models do not take into account the exact propagation environment. This kind of modeling is described as non-site specific. This is one of the main advantages of the proposed dynamic channel model framework. The location variability represents the statistical spread of shadowing over the space-time points used for averaging. In case that detailed studies for a specific environment have been carried out by use of other techniques, like ray tracing, then it is only the influence of moving obstacles that has to be studied in a statistical manner and only temporal averaging is used. We refer to these cases as site-specific. Then \(\sigma\) describes the variability that these moving objects can cause to the received power for each specific location and the value of \(\sigma\) is expected to be lower, than in the case that coarse path gain models are used and space averaging is used. Even in cases where detailed measurements have not been carried out, but some specific propagation conditions may be recognized, then this can impact the value of the location variability through the introduction of an appropriate bias, like in [37; 38]. Now based on the temporal exponential model and the implicit Markov assumption the model proposed here can be formulated in the next section in terms of a system of stochastic differential equations (SDEs). Such a formulation permits a phenomenological description of the shadowing field with an additional solid mathematical background providing analytic calculations and straight-forward simulation methods.
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Identification
The focus has primarily been on the use of semi-automatic identification approach and gradually improving the algorithms used to detect and classify landslides [16; 28]. The classification and segmentation techniques need to be improved using computational techniques such as Machine Learning (ML) and Artificial Neural Networks (ANN) [39]. In addition, the studies have largely been concentrated in a single area and need to be applied to other Himalayan regions to understand their applicability and reliability.