esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Performance Analysis
#### 4.5.1 Forecasting Results
The closer the coordinate points in the Taylor diagram are to the observation point, the better the forecasting performance of the model. Figure 9 indicates that the point presenting the EWT-PSO-SVM model is closest to the observed values, which indicates that the EWT-PSO-SVM model has the highest forecasting capability by comparison with the other models. The evaluation results of the five forecasting models under different evaluation indexes are shown in Table 1. Comparing the first three models in the table, it is found that their performance on each of the five indicators have advantages and disadvantages. Comparing the evaluation indexes of EWT-PSO-SVM model and PSO-SVM model, the EWT-PSO-SVM model has significant improvement over the PSO-SVM model in terms of _NSE_, _RMSE_, _MAE_, and \(R\). Compared with the PSO-SVM model, the _RMSE_ and _MAE_ values of the EWT-PSO-SVM model are reduced by 46.14% and 40.79%, and the _NSE_ and \(R\) values of the are improved by 70.03% and 19.23%. The data in the last two rows of the table indicates that the EWT-PSO-SVM model outperforms the EMD-PSO-SVM model in all metrics. However, the _MAPE_ value of the EWT-PSO-SVM model performs worse among the five models. In summary, the overall simulation effects of the five models need to be further discussed to determine which is optimal. Figure 8: Scatter plot of forecasting by each model. To present an overall performance of the model based on multiple metrics, the composite rating index method is utilized. The ranking results and the final scores of the five models are shown in Table 2. It presents that the SVM model has the lowest comprehensive evaluation score of 0.24, followed by the GA-SVM, PSO-SVM, EMD-PSO-SVM models with 0.28, 0.40, 0.48, respectively, and the EWT-PSO-SVM model has the highest comprehensive evaluation score of 0.68. Therefore, the EWT-PSO-SVM runoff forecasting results have the most overall performance among the five models. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & _NSE_ & _MAPE(\%)_ & _RMSE_ & _MAE_ & \(R\) \\ \hline SVM & 0.49 & 79.18 & 47.79 & 29.08 & 0.78 \\ GA–SVM & 0.49 & 78.39 & 47.73 & 29.06 & 0.76 \\ PSO–SVM & 0.50 & 80.45 & 47.14 & 28.76 & 0.78 \\ EMD–PSO–SVM & 0.72 & 125.14 & 35.59 & 24.60 & 0.85 \\ EWT–PSO–SVM & 0.86 & 94.95 & 25.39 & 17.03 & 0.93 \\ \hline \end{tabular}
\end{table} 
Table 1: Forecasting effect of each model on validation set under the sequential structure. Figure 9: Taylor Diagram.
## 4 Experimental Results and Discussion
### Experimental Results  
We conducted comprehensive experiments in order to evaluate scAG using the three datasets described above. We also investigate the combination of a spatial-channel attention gate as well as to separate each attention gate. We further compare the performance of our scAG for self-attention methods as well as attention gate methods in medical segmentation tasks.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Cost-Effective Dimension
#### 3.3.3 Water Utilization
[PERSON] and [PERSON] [31] proposed to use pH sensors, ultrasonic sensors, soil temperature sensors, air temperature sensors and humidity sensors, and water pH sensors to obtain the irrigation water quality index, aiming to measure the water quality for crop irrigation. A separate purifying process can be added to the irrigation system to clean the water before it goes into the irrigation system.
# A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE
## 4 Performance Results  
This section analyses the Bit Error Rate (BER) performance obtained with m-MIMO, after the estimation of the channel parameters using superimposed pilots. SC-FDE transmission technique is considered. Monte Carlo simulations were employed to measure the performance of the proposed m-MIMO system, using QPSK modulation and with a block length of \(N=256\) symbols (similar results were observed for other values of \(N\), provided that \(N>>1\)). A Rayleigh fading channel with 16 equal power paths was assumed (invariant during the block duration). The BER is evaluated as a function of \(E_{b}/N_{0}\), where \(N_{0}\) is the one-sided power spectral density of the noise and \(E_{b}\) is the energy of the transmitted bits.  
Without loss of generality it is assumed that there is a pilot for each subcarrier of each block of the frame (and for each transmit antenna), i.e., \(\Delta N_{F}=\Delta N_{T}=1\), leading to \(N_{P}^{Frame}=NN_{T}\) and a pilot multiplicity or redundancy of \(N_{R}=N_{P}^{Frame}/N_{G}=NN_{T}/N_{G}\). The duration of the useful part of the blocks (\(N\) symbols) is \(1\upmu\)s and the cyclic prefix has a duration \(0.125\upmu\)s.  
A transmitter with linear power amplification is assumed, as well as perfect synchronization.
## 3 Results and Discussion
### Environmental and Agronomic Evaluation
Because of the high K-concentration in  
Figure 8: Summary of the results to compare refined bio-based fertilisers (ammonium sulphate (AS) and potassium concentrate solution (KC)) with less refined bio-based fertiliser (liquid fraction of digestate (LFD)) and mineral fertiliser (MF) with and without manure (man) on: (i) greenhouse gas (GHG) emission factor (EF) (blue), (ii) N release rate (yellow), (iii) nutrient fertiliser replacement value (NFRV), and (iv) potential field application (green). the soil, this product was not tested in the field trial. The use of K-concentrate is expected not to change total NH\({}_{3}\) and N\({}_{2}\)O emissions in the Netherlands, but during the production process NH\({}_{3}\) emissions take place [43]. Refined products are more costly to produce compared with less refined products. In this study, the production costs of LFD is approximately 3-euro tonne\({}^{-1}\) digestate and the production of AS is approximately 5.4-euro tonne\({}^{-1}\) digestate in this study. However, because LFD and AS are seen as waste products, they were sold for \(-\)15- and \(-\)5-euro tonne\({}^{-1}\), respectively, in this trial. This will change when LFD and/or AS are listed as the RENURE product or when the manure surplus in the Netherlands decreases. For MF, the potato farmer is paid 180 euro tonne\({}^{-1}\) and for manure the farmer receives 15 euro tonne\({}^{-1}\). The N-concentration of LFD is much lower (3.7 g kg\({}^{-1}\)) compared with AS (81.6 g kg\({}^{-1}\)) or MF (260 g kg\({}^{-1}\)), and therefore larger amounts of LFD are needed to apply equal amounts of N (Table 6). The application of LFD was labour intensive and machinery needed to be adapted because high amounts of LFD were applied on small plots. These results are case specific and should therefore not be applied to other cases. Although some practical issues need to be solved, the use of BBFs in arable farming is profitable at the moment. This was also confirmed by other studies [20]. However, policy restrictions on the use of LFD and AS hamper the adoption of BBF as a replacement for mineral fertiliser at the moment.
## 3 TRM Technique
### Methods of Channel Estimation in VTRM
#### 3.4.2 BPDN Algorithm  
BPDN algorithm is an effective algorithm to solve the convex optimization problem by linear programming. It is the improvement of the basis pursuit algorithm to adapt to the noise.  
The sparsity of the UWA channel can be described by the number of non-zero elements in CIR. This problem can be solved by minimizing \(l_{0}\) norms  
\[\min\lVert x\rVert_{0},\quad s.t.\quad y=Ax+v. \tag{23}\]  
But (23) is a problem of non-convex function optimization. In Reference [27], [PERSON], [PERSON], and [PERSON] pointed out that the minimization \(l_{1}\) optimization problem is equivalent to the minimization \(l_{0}\) optimization problem under certain conditions, so the problem is changed into  
\[\min\lVert x\rVert_{1},\quad s.t.\quad y=Ax+v. \tag{24}\]  
In the BPDN algorithm, the problem of sparse signal with noise can be optimized as follows  
\[\min_{x}\frac{1}{2}\lVert y-Ax\rVert_{2}^{2}+\lambda\lVert x\rVert_{1}, \tag{25}\]  
where \(\lambda\) is the regularization coefficient and controls the balance between permissible error and sparsity. It is important to select appropriate \(\lambda\) to achieve accurate signal reconstruction. According to Reference [28], spaRSA is suitable for solving these \(l_{2}-l_{1}\) problems, which is quicker to reconstruct sparse UWA channels than the original BPDN algorithm.
## 3 Materials and Methods  
The methodology used in the study was a three-step process. First, the data were collected from Twitter social media by establishing a connection using an application programming interface (API) and authenticating with the Twitter developer identification information. The data gathering was carried out in the year 2021 and resulted in 483,880 tweets. The tweets were collected weekly and written in a separate file with a comma-separated values (CSV) format. After the data collection process, all files were merged to produce a single file containing all tweets.  
In the second step of the methodology, the tweets were pre-processed. A tweet consists of sixteen attribute values. As we are interested in the opinions of the respondents, we considered only the textual part of the tweets for analysis. After extracting the text part of the tweets, the pre-processing steps were applied to eliminate punctuation, extra blank spaces, digits, and stop words from the tweets. As part of the pre-processing, the tweets were also tokenized and tokens were stemmed to the root of the token. At this point, the cleaned content of the tweets was ready for analysis.  
In the third step, a function was applied to analyze the emotional score of the tweets as sentiments. This function resulted in different emotions and their score for the entire Twitter content. After the sentiment analysis, the word cloud function was used to identify the set of frequent words belonging to each emotion. After the word cloud analysis, topic modelling was performed to identify the co-occurrences of words. Finally, the results of all the analyses were interpreted and inferences were drawn collectively. All the pre-processing steps and functions for analysis were implemented in an open-source R programming language.
## 4 Analysis of the Results
### Statistical Analysis of the Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Question Type** & \(\chi^{2}\) & \(p\)**-Value** & **Conclusion** & **C Coefficient** \\ \hline Multiple Choice & 10.67 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(10.67/(150+10.67))\approx 0.26\) \\ True/False & 4.18 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(14.18/(150+4.18))\approx 0.16\) \\ Matching & 13.92 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(13.92/(150+13.92))\approx 0.29\) \\ Short Answer & 1.44 & \textgreater{}0.05 & Fail to Reject H\({}_{0}\) & \(\surd(14.44/(150+1.44))\approx 0.09\) \\ Missing Word & 12.96 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(12.96/(150+12.96))\approx 0.29\) \\ Cloze & 8.41 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(8.41/(150+8.41))\approx 0.23\) \\ Essay & 5.76 & \textgreater{}0.05 & Fail to Reject H\({}_{0}\) & \(\surd(5.76/(150+5.76))\approx 0.19\) \\ Drag and Drop & 12.96 & \textless{}0.05 & Reject H\({}_{0}\) & \(\surd(12.96/(150+12.96))\approx 0.28\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: McNemar test results on the use of question types. \(p\)-value refers to the area under the density curve of the \(\chi^{2}\) distribution.
# Fire Behavior of U-shaped Steel Beams Filled with Demolished Concrete Lumps and Fresh Concrete
## 1 Introduction
The effect of interface thermal resistance on the fire behavior of concrete filled steel tubes has been investigated numerically by [PERSON] and [PERSON] [26] and by [PERSON] and [PERSON] [27], but it remains unknown whether their findings are applicable to concrete filled U-shaped steel beams because the cross section and loading pattern of such beams are quite different from those of steel tubular columns filled with concrete. The objective of this study is to reveal the influences of DCLs and longitudinal reinforcements on the fire behavior of the U-shaped steel beams filled with DCLs and FC and to clarify the effect of interface thermal resistance on temperature profiles in such beams. Three U-shaped steel beams filled with DCLs and FC and two reference beams filled with FC alone were tested in the fire. Numerical models are then developed using SAFIR to determine the thermal and structural responses of the specimens and the calculated results are compared with the test data. Lastly, parametric studies are carried out to further explore the effects of load ratio, thickness of fire insulation, and longitudinal  
Figure 1: Practical application of U-shaped steel beams filled with DCLs and FC. reinforcement ratio on the fire resistance of such beams. The findings of this paper provide a basis for the fire-resistance design of the U-shaped steel beams filled with DCLs and FC.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Threshold-Based Adjustment Function for Weighting Factor Stability
#### 4.3.2 Training for Adaptive Detection  
This section details the architecture, configuration, and evaluation of the IMIS-DBN model, a framework designed for enhanced ransomware detection. By integrating IMIS for dynamic feature selection with the deep learning capabilities of a DBN, the model facilitates the identification of both established and emergent attack patterns. IMIS acts as the initial layer, adaptively selecting and updating relevant features from ransomware data streams. The DBN component was configured with 100 training epochs, a batch size of 64, L2 regularization of 0.0002, momentum of 0.7, and a learning rate of 0.05. Initially, IMISemploys mutual information scores to select features, reducing data dimensionality while preserving crucial information. Subsequently, as new data is introduced, IMIS dynamically updates the feature set by recalculating mutual information and adjusting feature relevance. As depicted in Figure 10, the model comprises two primary modules: the IMIS feature selection module and the DBN detection classifier. IMIS processes the dataset through a series of steps, including initial relevance estimation, weight adjustment, relevance score updates, and top-n feature selection. The resulting feature subset is then used as input for training the DBN classifier.  
The DBN architecture is composed of stacked Restricted Boltzmann Machines (RBMs), each comprising visible and hidden units. This model incorporates a hierarchical structure of five hidden layers, with each successive layer reducing the unit count by 30% relative to its predecessor. The initial layer receives input processed by the IMIS module, and subsequent layers progressively extract higher-level data representations. Each RBM undergoes unsupervised pre-training, commencing with the bottom layer, wherein they learn to reconstruct inputs and model the underlying data distributions. This pre-training phase initializes network weights, facilitating subsequent supervised fine-tuning. Following pre-training, the DBN is subjected to supervised fine-tuning using labeled data, aiming to minimize classification errors and optimize detection accuracy. The efficacy of the trained IMIS-DBN model is evaluated using an independent test dataset, with performance metrics including accuracy, precision, recall, and F1-score used to assess its ability to discriminate between benign and malicious activities.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Overall Aerodynamic Characteristics
#### 4.3.3 Moment  
To the best knowledge of the authors, the formula for the aerodynamic moment acting on the rotating sphere in the free-molecular regime has not been reported in the literature. Following the procedures to derive the formulas of \(C_{D}\) and \(C_{L}\), we obtain the free-molecular solution for the moment coefficient \(C_{M}\):  
\[C_{M}=\frac{2\kappa W}{\pi}\left\{\frac{1}{\sqrt{\pi}S}\int_{0}^{\pi}\int_{0}^ {\pi}\sin^{3}\varphi\ \exp\left[-(S\sin\varphi\cos\theta)^{2}\right]\,\mathrm{d}\theta\mathrm{d} \varphi+\int_{0}^{\pi}\int_{0}^{\pi}\sin^{4}\varphi\ \cos\theta\ \mathrm{erf}(S\sin\varphi\cos\theta)\ \mathrm{d}\theta\mathrm{d}\varphi\right\} \tag{7}\]  
Figure 12: Drag coefficients of the sphere at different rotation parameters. Symbols: DSMC results; line: free-molecular solution.  
where \(\varphi\) and \(\theta\) are the angular parameters on the sphere. Note that, at this moment, \(\mathbf{M}\) is with respect to the sphere center, and it is toward the negative \(z\)-direction, which is just opposite to the direction of \(\mathbf{\omega}\). Equation (7) indicates the linear dependence of the aerodynamic moment on the rotation speed.  
Figure 14 shows the DSMC results of \(C_{M}\) at different rotation parameters, along with the theoretical solution in the free-molecular limit for reference. The DSMC results at \(Kt_{\infty}=0.51\) are qualitatively consistent with the free-molecular formula, but the medium rarefaction considered in the simulation makes the DSMC results smaller than the free-molecular predictions.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 3 Simulations and Experiments
### Virtual Simulation of Section Cutting Verification
#### 3.2.1 Simulation of Non-Full-Section Cutting Paths Under Different Body Poses  
In this paper, an improved NSGA-II algorithm is adopted for trajectory planning under three different roadheader body poses. Figure 17 presents the cutting trajectory planning results for three scenarios, including: (a) a three-dimensional comparison of the cutting head envelope space; (b) a comparison of the planned cutting trajectories; (c) a comparison of the joint variables for the left-side section cutting trajectories; and (d) a comparison of the joint variables for the right-side section cutting trajectories.  
Figure 17a shows that the cutting spaces of the planned trajectories under different body poses differ from the target section. Segmented cutting trajectory planning can achieve full-section cutting, but changes in the body pose lead to changes in the cutting space. The left-side section cutting poses are the same for Scenario 1 and Scenario 2, but their right-side section cutting spaces differ. Similarly, the right-side section cutting poses are the  
Figure 16: Virtual simulation platform for forming.  
Figure 17: Comparison of cutting path planning results for three different poses. (**a**) Operational space. (**b**) Cutting path. (**c**) Joint variables for left section cutting path. (**d**) Joint variables for right section cutting path.  
same for Scenario 2 and Scenario 3, with the same cutting space, but their left-side section spaces differ. Therefore, changes in the body pose affect the formation of the cutting space.  
Figure 17b compares the cutting trajectories formed by the cutting head end coordinates under the three poses, consistent with Figure 17a, indicating that the cutting head end coordinates do not fully contact the target section, demonstrating the need to consider the size constraint of the cutting head for cutting planning. Figure 17c,d present the joint variables for the left-side and right-side section cutting trajectory planning, respectively, avoiding frequent calculation of inverse kinematics solutions during automatic cutting control.  
In summary, the trajectory planning method proposed in this paper adapts to different roadheader body poses and ensures good cutting quality, but further verification and analysis are needed to determine the specific cutting quality.
## 4 Simulation Experiment and Mathematical Model Validation
### Finite Element Simulation of the Temporary Support Robot and the Floor
From Figure 10b, it can be seen that regarding the stress conditions of the floor, the stress on the surface of the floor is within the range of 3.26 MPa, which is below the uniaxial compressive strength limit of the floor material. The peak stress is concentrated at the front end of the base of support 1. Therefore, under the 0.1 MPa loading condition on the top beam, the stress on the structure of support 1 is less than the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline elastic modulus of Q690 Steel/GPa & 206 \\ density of Q690 Steel kg/m\({}^{3}\) & 7850 \\ Poisson’s ratio of Q690 Steel & 0.26 \\ yield strength of Q690 Steel/MPa & 690 \\ bulk modulus of sandstone/GPa & 21.3 \\ Young’s modulus of sandstone/GPa & 34 \\ Poisson’s Ratio of sandstone & 0.233 \\ shear modulus of sandstone/GPa & 13.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relevant material parameters for the finite element simulation. Figure 10: Finite element simulation results of support 1 under a load of 0.1 MPa applied to the top beam: (**a**) stress cloud of support 1; (**b**) stress cloud of the floor under support 1.  
yield strength of its material, meeting the strength requirements of the support, and the stress value on the floor is less than its uniaxial compressive strength, ensuring that the floor will not be damaged. From Figure (a)a, it can be seen that under a uniform load of 0.18 MPa applied to the top beam of support 2, the overall stress on support 2 is within the range of 41 MPa, with a peak value below the yield strength of Q690, concentrated on the four-bar mechanism, indicating that this part of the structure is more prone to damage under these conditions. From Figure (b)b, it can be noted that regarding the stress conditions of the floor, the stress on the surface of the floor is within the range of 1.56 MPa, which is below the uniaxial compressive strength limit of the floor material, with peaks mainly concentrated at the installation position of the support cylinder on the base of support 2. Therefore, under a loading condition of 0.18 MPa, the stress on the structure of support 2 is less than the yield strength of its material, satisfying the strength requirements of the support, and the stress value on the floor is less than its uniaxial compressive strength, ensuring that the floor will not be damaged.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 5 Discussion
### The Expansibility of the System Function  
Obviously, if the system is popularized and applied well, new requirements will be born with it, and it is inevitable to expand the functions of the system. Taking the technical features of the system architecture into consideration, some feasible extensions are recommended to be implemented first:  
1. Add the information, before the equipment is put into use, to the traceability system, including the information of primary material supply, product processing, transportation, and installation.
2. For elevator, amusement facilities, and other public service equipment, public supervision interface should be developed so that the public can query the maintenance information and safe condition of the equipment through QR code.
## 4 Discussion
### Comparison with Previous Study  
Several studies have compared satellite observations and CLM simulations. [PERSON] _et al._[35] compared seasonal and spatial variations of FPAR from MODIS and CLM2.0 from 2000 to 2002, and attribute FPAR mainly decided by LAI. [PERSON] _et al._ (2009) compared MODIS GPP with CLM4 GPP regarding magnitude, phase, annual cycle, and inter-annual changes. Our study recognizes the role of LAI in FPAR simulation and targets the solar radiation scheme, so we identify reasons for discrepancies first and then focus on those associated with the target. General agreements in FPAR's seasonal cycle, long-term trends, and spatial patterns are consistent with conclusions in previous GPP evaluations [53], though subtle differences exist between discrepancies in FPAR and GPP due to their naturally complicated correlations.