esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Antenna Design
### Proposed Unit-Cell Structure
Figure 1: Geometry of the proposed antenna unit-cell: (**a**) isometric view; (**b**) top view of S-band layer; (**c**) top view of X-band layer; (**d**) side view. to reduce the blockage effect while maintaining the performance. Therefore, the mesh structure with polarization-selective EM transmission characteristics is employed, which effectively increases the electromagnetic (EM) transparency of the upper E-shaped element. Figure 2 shows a conceptual diagram of driving the EM transparency according to wave polarization for a periodic E-shaped element with the mesh structure. When the mesh direction is in the same direction as the polarization of the incident wave, the traveling wave is mostly reflected, as shown in Figure 2a. The simulated EM transparency for this case at 10 GHz is \(-\)30.0 dB, whereas, when the mesh direction is perpendicular to the polarization of the incident wave, as shown in Figure 2b, the EM transparency increases up to \(-\)1.5 dB. Figure 3 shows the average gain (averaged from 9 GHz to 11 GHz) of the X-band element (solid line) and the bandwidth of the S-band element (dashed line) according to the mesh length (\(w_{3}\)) in the S-band antenna of the unit-cell. The average gain increases as the mesh length increases, due to the reduced blockage effect. The bandwidth of the S-band remains almost the same until the mesh length is approximately 13 mm, as shown in Figure 3. With the optimum length of the mesh structure, a maximum average gain of 7.43 dBi and a bandwidth of 756 MHz are achieved. Figure 4a,b show the electric field distribution of the X-band antennas with and without the extended via cavity at the resonance frequency of 10 GHz. The total electric field is observed in the same reference plane as the radiating patch when 1 W of the input power is supplied at each X-band element. The average leakage field between the radiating elements in Figure 4a,b is examined. The averaged leakage field of 52.04 dB V/m is observed with the extended via cavity, while 55.24 dB V/m is observed without the extended via cavity. The electric field of the E-shaped patch antenna with the extended cavity is more confined around the radiator than without the extended via cavity structure. Figure 2: EM transparent characteristics for a periodic mesh structure: (a) for parallel polarization incident wave; (b) for perpendicular polarization incident wave. The mutual coupling characteristics among neighboring elements can be reduced by adding the extended via cavity. Figure 4: E-field distribution according to the cavity: (**a**) with cavity; (**b**) without cavity. Figure 3: Optimization of mesh length considering the bore-sight gain (X-band) and the bandwidth (S-band).
# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 5 Conclusions  
CNN and deep learning have made great achievements in the fields of computer vision and natural language processing. However, limited progress has been made in sports analysis and prediction, which mostly rely on visual materials, such as videos of competitions or player position maps. CNN shows difficulties in exploiting its capabilities for 1D data types. Therefore, a predictive model cannot be directly constructed with CNN for sports matches, which are often considered discrete and independent.  
To take advantage of the achievements of CNN in computer vision, we proposed a two-stage approach for predicting sports match outcomes. The raw data of the odds and points scored in the betting market were processed through a series of transformations into candlestick expressions comprising time series. Afterward, we used GAF to encode the time series into 2D images in order for them to converge with the CNN of the first stage. The second stage of the judgment model based on logistic regression was then incorporated to consolidate the CNN output for prediction.  
Our experimental comparisons reveal that for predicting wins and losses, the historical data imply some associations and behavioral patterns between pre- and post-games that can be used for the prediction. The behavioral patterns implied by individual teams are more useful for the prediction than the behavioral patterns implied by all teams. In addition, predicting the match outcome directly with the CNN of the first stage is not ideal. However, the prediction performance of the CNN was improved significantly by the proposed two-stage approach with the logistic regression judgment model and achieves a prediction accuracy of 69%, which was superior to the betting market.  
Future studies are suggested to further investigate the feasibility of using the proposed two-stage model in other sports matches. The hyperparameter tuning problem, which is often encountered when using CNN, can be further tested to improve prediction accuracy. Future research could be directed towards identifying opportunities for incorporating emerging deep learning techniques from traditional sports prediction methods, such as performance metrics and ranking/rating systems, and modifying them to achieve cutting-edge prediction models that keep pace with computational intelligence technology.  
Y.-C.H.: conceptualization, methodology, software, validation, formal analysis, investigation, writing, visualization. No other person is entitled to authorship.  
This research received no external funding.  
Not applicable.  
The data used in this research are available from the corresponding author upon request.  
The author declares no conflict of interest.
## 4 Discussion
A previous report showed that the effect of fucoidan on APTT was more active than on PT in _Fucus vesicolus_[26] or the dry _Fucus vesicolus_ (DFE-1 and DFE-2) extract, which are ointments used in blood clotting measures for cutaneous application [78]. The fucoidans from brown algae (11 sps) with anticoagulant activity exhibited the highest inhibitory impact on thrombin and factor Xa in the presence or absence of thrombin inhibitor and antithrombin III [79]. Many sulfated galactans resemble agar and are a part of the agar group. These are agarose derivatives with higher sulphate groups and fewer 3, 6-anhydro-L-galactose residues. [80]. The purified fucoidan of _T. decurens_ showed a dose-dependent anticoagulation of 387.8 \(\pm\) 10.3 s [81]. Heparin, a fucoidan could inhibit thrombin formation, but its anticoagulant efficacy is not the same as that of the latter. The anticoagulant and antithrombotic effects of fucoidan have been mediated by catalyzing thrombin inhibition mainly through heparin cofactor II inhibition [82]. The suppressed gene promotes cancer by enhancing unregulated cell growth and partition while breaching the cellular process, cell cycle detain, and cell decease. The molecular weight, monosaccharide content, polymeric backbone structure, and branching degree (protein, sulfate, uronic acid, and solubility) of polysaccharides play a vital role in the development of anticancer activity [24]. Similarly, the recent in vitro studies demonstrated that fucoidan from _T. conoides_ showed a dose-dependent anti-proliferation in the HepG2 cancer cells [83]. The in vitro cytotoxicity of fucoidan-derived seaweed from _Sargassum polycystum_ showed an IC\({}_{50}\) of 50 \(\upmu\)g/mL, inhibiting the MCF-7 cell line (90.40 \(\pm\) 0.25%) [49]. Similarly, polysaccharides from the seaweed _C. caledoniae_. Kylin exerted higher anti-angiogenic effect on HeLa cell exposure by lowering VEGF appearance and emission, preventing cancer cells in the vascular tubule organization [84]. The fucoidan from various brown algae, including _L. saccharina_, _L. digitata_, _F. Serratus_, _F.distichus_, and _F. Vesiculosus_ demonstrated an antitumor property that reduced the bond of MDA-MB 231 cancer cells to platelets by approximately 80% under lab conditions [85]. Additionally, fucoidans might induce apoptosis in the MCF-7 cells. In a previous study, the isolated fucoidan from marine macroalgae stimulated chromatin condensation under in vitro conditions of fragmented DNA. Furthermore, the isolated fucoidan might trigger the death of the MCF-7 cells through a caspase-8-dependent route. In vitro experiments showed that fucoidan isolated from the sea algae _Fucus evanescens_ with a concentration of 100-1000 \(\upmu\)g/mL showed the strongest virucidal and defensive impact on cells infected with tick-borne encephalitis [86].
# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 2 Background
### MI in MS  
ML techniques are increasingly being applied to MS research to assist with diagnosis, prognosis prediction, and disease subtype classification. ML algorithms can learn patterns from multidimensional clinical, imaging, and omics data to build predictive models with minimal human intervention. Regression models like logistic regression, linear regression, and regularized least squares are commonly used to predict MS outcomes and progression [17]. For example, logistic regression has been applied to predict conversion from a clinically isolated syndrome to MS [18]. Tree-based models like decision trees [19], random forests, and gradient boosted trees (e.g., XGBoost, LightGBM) [20] have shown strong performances for MS classification and prediction tasks. SVMs are effective for high-dimensional data and have been used for tasks like distinguishing between MS subtypes and predicting disability progression [21]. Different neural network architectures including convolutional neural networks and multi-layer perceptrons have been applied to analyzing neuroimaging data for MS diagnosis and lesion segmentation [22]. Given that these studies utilize a variety of features from different datasets, including clinical data, MRI scans, and patient reports, and that they employ various ML models, it poses a significant challenge for non-technical experts to interact with the ML systems effectively, let alone to train and optimize them appropriately [23].
# Advances in Bio-Hydrogen Production: A Critical Review of Pyrolysis Gas Reforming
## 3 Hydrogen Production by Catalytic Reforming of Pyrolysis Volatiles
### Slow Pyrolysis
#### 3.1.4 Space Velocity  
Space velocity is another operating factor affecting product distribution in the two-stage pyrolysis and catalytic reforming process. Decreasing space velocity, which is equal to increasing residence time in the reforming process, has a beneficial impact on catalytic reactions in the catalyst bed, tar reduction, and enhancing gas yield [73; 77]. [PERSON] et al. [75] investigated the effects of increasing space velocity from 4000 h\({}^{-1}\) to 8000 h\({}^{-1}\) during the reforming of sewage sludge pyrolysis products at 650 \({}^{\circ}\)C. Their results showed that hydrogen production decreased considerably, taking into account the increasing space velocity trend. Also, carbon deposition was enhanced when space velocity was doubled. The gas yield results with space velocity decreasing from 7200 h\({}^{-1}\) to 3600 h\({}^{-1}\) and 2400 h\({}^{-1}\) during the catalytic reforming of corncob pyrolysis volatiles demonstrated the beneficial influences of decreasing space velocity such that hydrogen yield increased from 14.2 mmol/g to 19 mmol/g and 24.8 mmol/g, respectively. Furthermore, the carbon monoxide yield was increased when the space velocity was reduced in the range of 7200 h\({}^{-1}\) to 2400 h\({}^{-1}\)[76]. Another experimental research study investigating the reforming process of corncob pyrolysis volatiles over natural limonite as a catalyst demonstrated the effectiveness of space velocity on gas yield and carbon balance [72]. The results showed that the gas yield increased from 36.22 mmol/g to 41.61 mmol/g when the space velocity decreased from 7200 h\({}^{-1}\) to 3600 h\({}^{-1}\). However, a slight change in the gas and hydrogen yield could be seen with the space velocity decreasing to 2400 h\({}^{-1}\)[72] These results from different research studies are very well aligned with the study performed by [PERSON] et al. [78], which indicates that lower space velocity and longer residence time are favorable for tar reforming and gas production.
## 3 Access Class Barring Configuration (ACBC) Scheme
With this information, the ratio of idle to available resources is calculated immediately before the \(j\)th SIB2 transmission (i.e., at the \((jt_{\text{si}})\)th RAO) by means of the scaling filter block and the comparator shown in the bottom right corner of Figure 2 as  
\[u(j)=1-\mathbf{h}^{\mathsf{T}}\mathbf{s}(j) \tag{5}\]  
where \(\mathbf{h}=\left[h_{0},h_{1},\ldots,h_{t_{\text{si}}-1}\right]\) is the column vector of coefficients of the scaling filter  
\[h_{m}=\frac{1}{t_{\text{si}}\,c\left(r,n_{\text{ug}}\right)},\qquad\text{for }m\in\{0,1,\ldots,t_{\text{si}}\} \tag{6}\]  
and \(\mathbf{s}(j)=\left[s(jt_{\text{si}}),s(jt_{\text{si}}-1),\ldots,s(jt_{\text{si}}- t_{\text{si}})\right]\) is the vector that contains the number of uplink grants assigned at each of the \(t_{\text{si}}\) RAOs within the SIB2 period. Next, \(u(j)\) serves as the input to the filtering process, whose output \(y(j)\) is used to calculate the barring rate for the \(j\)th SIB2 broadcast interval  
\[p_{\text{acb}}(j)=\min\left\{y(j),1\right\}. \tag{7}\]  
Hence, \(p_{\text{acb}}(j)\) decreases with the ratio of idle to available resources. This increases the probability of delaying the beginning of the RAP when most of the resources have been utilized. The filtering block in our ACBC scheme (at the bottom of Figure 2) is generic, which means different filtering methods can be implemented at this point. In the following, we describe three possibilities: two adaptive filtering algorithms and FIR filters with fixed coefficients.
## 3 Results and Discussions
### Elastic Modulus  
Modulus of elasticity is a mechanical property of concrete that is used in design aspects of structural members, i.e., columns, beams, and slabs. It determines the resistance of concrete members to elastic deformation against the applied load. The static modulus of FS-GPC mixes was measured by conducting tests on 150 \(\times\) 300 mm cylindrical specimens according to ASTM C469/C469M-10 at the age of 28 days. For each mix, three identical specimens were tested, and mean values of results obtained from the tests are presented in Table 8. The effect of different mix design parameters on the measured elastic modulus is presented in Figure 12.  
Figure 16: The comparison of the measured and estimated values of flexural strength of GPC mixes specimens at the age of 28 days. It is evident from Figure 12 and the results of groups A, C, and E that the elastic modulus of FS-GPC mixes increased steadily by increasing the slag content from 10% to 50%. The elastic modulus of group B mixes, designed with 12 M and 14 M molarity of NaOH solution, was higher than the corresponding mixes of group A prepared with 10 M NaOH solution. Furthermore, decrease of AAS content from 40% (group A) to 35% (group C) also led to increase in elastic modulus of GPC mixes. The trends are similar to that of the compressive strength results. However, the influence of SS/SH ratio on static modulus was not the same as that of the compressive strength results. It can be observed from the results of groups A and D that increase of SS/SH ratio from 2.0 to 2.5 has no effect on static modulus while a decrease in static modulus was observed when it was further increased from 2.5 to 3.0. The effect of additional water on elastic modulus was also like that of the compressive strength results as discussed earlier. Generally, the modulus of elasticity of conventional concrete varies with the compressive strength. By comparing the results of compressive strength and elastic modulus of all the groups, it was observed that the elastic modulus of FS-GPC mixes increased with the increase in compressive strength. Therefore, it can be inferred that the increase of elastic modulus of FS-GPC mix is attributed to the increase in compressive strength due to the variation in mix design parameters [48]. Figure 17 shows the compressive strength variations of groups A and C mixes with alkaline activator content. Furthermore, Figure 18 indicates the influence of mix design variables on elastic modulus of FS-GPC mix specimens at the age of 28 days.
## 4 Discussion
### Advantages of Developed CET
#### 4.1.5. Applicability of CET to Different Data Types  
This study has shown that CET can be successfully applied for accurate extraction and mapping of coastline from various data sources, regardless of spatial resolution. The application of CET within the validation area has demonstrated that CET achieves good mapping results on both high-resolution WV-2-derived models and on VHR models derived from field UAV photogrammetric survey. The compatibility of CET with models with different spatial resolutions allows its flexible application for various researches, ranging from local (few m\({}^{2}\)) to regional scales (few hundred km\({}^{2}\)). Besides WV imagery, CET can be used for all other types of satellite imagery, including open-source data, such as Sentinel or LANDSAT imagery.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 3 Description of the Proposed Scheme
### Computation of Spectral Efficiency  
Supposing that FBMC symbols are transmitted in a frame that contains \(N_{data}\) data symbols and \(N_{pilot}\) pilot symbols, we denote time duration and frequency bandwidth of the frame by \(T_{frame}\) and \(f_{frame}\), respectively. Note that the information quantity of a data symbol in M-order QAM is \((\log_{2}\!M)/2\)  bits because the data symbols in FBMC are real-valued symbols extracted from real or imaginary parts of complex QAM symbols. Then, the spectral efficiency can be calculated by  
\[\eta=\frac{N_{data}(\log_{2}\!M)/2}{T_{frame}f_{frame}}. \tag{21}\]  
It is obvious that \(N_{data}\) in the proposed method is larger than that in the CAP method because of a smaller \(N_{pilot}\) in the former, whereupon the proposed method has a higher spectral efficiency compared with previous methods.
# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 3 Problem Statement and Proposed Solution
### Time Series Grouping and Extraction of Sentimental Characteristics
\[LF_{i,j}=F_{i,j}\oplus e_{i,j} \tag{12}\]  
Regarding the convolution layer of the CNN with the attention mechanism, we use the convolution kernel filter \(w\in R^{h\times k}\) to perform convolution operation on the input matrix to extract new feature vectors. Equation (13) is the operation calculation formula of the three convolution kernels selected by the first hidden layer. \[ \begin{array}{l}a_{1}^{(1)}=f(W_{1}^{(1)}F+b_{1}^{(1)})\\ a_{2}^{(1)}=f(W_{2}^{(1)}F+b_{2}^{(1)})\\ a_{3}^{(1)}=f(W_{3}^{(1)}F+b_{3}^{(1)})\end{array}  \tag{13}\]  
In which \(W_{1}^{(1)}\), \(W_{2}^{(1)}\), \(W_{3}^{(1)}\) are the input weights of each convolution kernel and \(b_{1}^{(1)}\), \(b_{2}^{(1)}\), \(b_{3}^{(1)}\) are the biases of each convolution kernel. \(F\) is input vector, composed of \(F_{i1}\),..., \(F_{in}\). \(f\) is a non-linear function; the model used in this paper is the ReLU activation function. \(a=[a_{1}^{(1)},a_{2}^{(1)},a_{3}^{(1)}]\) is the newly generated feature vector. Regarding the Attention layer of the CNN with the attention mechanism, we put the attention layer between the convolutional layer and the pooling layer (see Figure 4 for details). Finally, the output after the attention layer and the output after the convolution layer are multiplied as the input of the pooling layer. The input matrix represents the features of the text. The contribution of the regions in the matrix to classification is not equally important. The spatial attention mechanism is used to find the most important part of the input matrix for processing. The calculation formula is shown in Equation (14). \[ \begin{array}{l}M(F)=\sigma(f([AvgPool(F);MaxPool(F)]))\\ =\sigma(f([F_{avg};F_{max}]))\end{array}  \tag{14}\]  
In which \(M(F)\) represents the weight of the attention operation on the input matrix which is the activation function. This paper uses the Sigmoid function,\(f\) is the convolution kernel operation, \(F\) is the input matrix, \(F_{avg}\) and \(F_{max}\) are the global pooling, the average pooling and maximum pooling operations, respectively. _AvgPool_ is the average pooling function and _MaxPool_ is the maximum pooling function. Finally, the obtained \(M(F)\) is multiplied with the new feature vector obtained by the convolutional layer to emphasize important features. The calculation formula is shown in Equation (15). \[\textit{feature\_vector}=M(F)\times a \tag{15}\]  
\(\textit{feature\_vector}\) is the final input of the pooling layer. \(a=[a_{1}^{(1)}a_{2}^{(1)}a_{3}^{(1)}]\) is the output of convolution layer mentioned above. Regarding the pooling layer of the CNN with the attention mechanism, the pooling layer can compress the feature matrix after convolution, so it can make the feature matrix smaller. Additionally, it also can extract the main features. This experiment uses maxpooling to cut the feature matrix into several regions and takes the maximum value. It also can keep the original matrix feature to obtain the pooled feature value.