esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Fuzzy Cognitive Maps Overview  
A fuzzy cognitive map (FCM) is a directed graph in which nodes denote concepts important for the analyzed problem, and links represent the causal relationships between concepts [71]. It is an effective tool for modeling decision support systems. FCMs have been applied in many research domains, e.g., in business performance analysis [72], strategy planning [73], modeling virtual worlds [74], time series prediction [69], and adoption of educational software [75].  
The FCM model can be used to perform simulations by utilizing its dynamic model. The values of the concepts change in time as simulation goes on [68]. The new values of the concepts can be calculated based on the popular dynamic model described as follows [59]:  
\[X_{i}(t+1)=F\left(\begin{array}{cc}X_{i}(t)+&\sum_{j=1}^{n}&X_{j}(t)\cdot w _{j,i}\\ &j\
eq i\end{array}\right) \tag{1}\]  
where \(X_{i}(t)\) is the value of the \(i\)th concept at the \(i\)th iteration, \(w_{j,i}\) is the weight of the connection (relationship) between the \(j\)th concept and the \(i\)th concept, \(t\) is discrete-time, \(i.j=1,\ 2,\ \ldots,\ n,\ n\) is the number of concepts, \(F(x)\) is the sigmoidal transformation function [58]:\[F(x)=\frac{1}{1+e^{-cx}} \tag{2}\]  
where \(c\) is a parameter, \(c>0\). The weights of the relationships show how causal concepts affect one another. If \(w_{j,i}>0\), then an increase/decrease in the value of the \(j\)th concept will increase/decrease the value of the \(i\)th concept. If \(w_{j,i}<0\), then an increase/decrease in the value of the \(j\)th concept will decrease/increase the value of the \(i\)th concept. If \(w_{j,i}=0\), there is no causal relationship between the \(j\)th and the \(i\)th concepts [74].  
The FCM structure is often constructed based on expert knowledge or surveys [74]. We could also use machine learning algorithms and available historical data to construct the FCM model and determine the weights of the relationships between the FCM's concepts.
# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 3 User QoS-Based Optimized Handover Algorithm  
In this section, the optimized handover algorithm (OHA, also called Algorithm 1) and the optimized handover algorithm with QoS requirement (OHAQR, called Algorithm 2) are proposed. The former algorithm helps the UE to perform the minimum number of handovers and the latter prevents the AP from making the throughput and PLR lower than the user's requirements. The details of these two handover algorithms are explained in the following subsections.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 5 Results
### Hidden Transmission Effectiveness Assessment
#### 5.2.2 Steganographic Transmission in VoIP Channel  
Steganographic transmission in the VoIP channel was made using the free and open source PJSIP library [73]. The application was compiled and run under the XUbuntu GNU/Linux operating system. The research was carried out for two variants of the network LAN and WAN.  
The research was carried out using three different standards of speech signal coding:  
* PCMA, 64 kbit/s;
* Speex, 24.6 kbit/s;
* G.729, 8 kbit/s.  
The same test signals were used in the research that were used in the tests in the radio link [71].  
The mean BER value and the 95% confidence interval (T-Student distribution) were determined. The obtained results are shown in Figures 22-24.  
Figure 21: BER on the radio link for different synchronization methods, \(K_{min}=1250\).  
Figure 22: BER in VoIP channel for MPC method.  
For PCMA, the synchronization was achieved, allowing for the decoding of steganographic information for all methods. The use of Speex and G.729 codecs prevented synchronization in the PID method. As in the case of a radio link, an increase in the \(K_{min}\) coefficient value from 500 to 1250 causes a decrease in the average BER value.  
For PCMA and LAN, the BER value with the 95% confidence interval shall not exceed 1.2% for \(K_{min}\) = 500 and 0.8% for \(K_{min}\) = 1250. WAN transmission increases BER. The obtained values do not exceed 8.5% for \(K_{min}\) = 500 and 6% for \(K_{min}\) = 1250. Signal encoding using the Speex codec results in a BER level not exceeding 3% in the LAN and 12% and 10% in the WAN for \(K_{min}\) = 500 and \(K_{min}\) = 1250, respectively. Signal coding based on the G.729 codec translates into the largest number of errors during steganographic transmission in the VoIP channel. In the LAN, the BER value together with the 95% confidence interval does not exceed 9% (for MPC and DSS methods) or the number of errors is less than the BCH code correction capacity (for the MES method). Research in the WAN network allowed to obtain BER values below 16% for the MPC method and below 14% for DSS. In both cases, \(K_{min}\) = 1250. In the case of the MES method for the BCH code with parameters (\(k\) = 50, \(t\) = 13), regardless of the value of the \(K_{min}\) coefficient, problems with achieving synchronization were noticeable. Changing the coding variant to (\(k\) = 50, \(t\) = 27) significantly improved the efficiency of synchronization, and increasing the value of the \(K_{min}\) coefficient to 1250 allowed for 100% synchronization efficiency.  
Figure 23: BER in VoIP channel for DSS method.  
Figure 24: BER in VoIP channel for MES method.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Ablation Experiment
#### 4.3.2 Performance Comparison between LIVEWC and CID2013 Databases  
In order to distinguish it from traditional databases, this paper compares the performance of real distortion database and contrast distortion database: LIVEWC, CID2013. The experimental results are shown in Table 9. The results of NR IQA methods with the best evaluation performance are marked in bold. In LIVEWC database, the SROCC value of the proposed method is slightly lower than that of NFERM method, and the PLCC value is higher than that of other NR IQA methods. In CID2013 database, the values of SROCC and PLCC are 0.7786 and 0.7987 respectively, which are obviously superior to other methods. This indicates that TLLFD method has stronger competitiveness compared with other NR IQA methods in LIVEWC and CID2013 databases.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Database** & **NIQE** & **ILNIQE** & **BIQI** & **DIIVINE** & **BLINDS2** & **BRISQUE** & **GMLOG** & **NFERM** & **TLLFD** \\ \hline TID2008 & 0.795 & 0.870 & 0.796 & 0.852 & 0.775 & 0.889 & 0.865 & **0.904** & **0.893** \\ MICT & 0.811 & 0.711 & 0.560 & 0.567 & 0.654 & 0.601 & 0.778 & **0.834** & **0.836** \\ LIVE & 0.905 & 0.897 & 0.755 & 0.773 & 0.888 & 0.895 & **0.905** & 0.870 & **0.931** \\ \hline \hline \end{tabular}
\end{table}
Table 7: SROCC comparison on cross-database validation when NR IQA models are trained on CSIQ (the two best models indexes are shown in bold).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**t-Test** & **NIQE** & **ILNIQE** & **BIQI** & **DIIVINE** & **BLINDS2** & **BRISQUE** & **GMLOG** & **NFERM** \\ \hline LIVE & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ CSIQ & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 \\ TID08 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ MICT & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical significance \(t\)-test (1(\(-1\)) indicates our method is better (worse) than the method in the column; 0 indicates our method is statistically equivalent to the method in the column).  
Two groups of features, texture feature and gradient feature, are extracted from the proposed TLLFD method. In order to explore the contribution of these two groups of features to the final evaluation result, the performance of each group of features on five databases is evaluated respectively. It can be seen from Table 10 that among the five databases, the contribution of texture features is higher than that of gradient features, but the evaluation performance of using one group of features alone is worse than that of using two groups of features simultaneously. This indicates that both sets of characteristics are necessary in the TLLFD method, and they are complementary to the overall evaluation performance.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Optimization Models
#### 2.2.2 Minimize the Number of Wavelengths  
\[\min\sum_{w}\ (\sum\limits_{i,j\
eq j}\mu_{ij}^{w}+\sum\limits_{i^{ \prime},j^{\prime},i^{\prime}\
eq f^{\prime},P_{ij^{\prime}\cap}P_{ij}=\varnothing }\mu_{i^{\prime}j^{\prime}}^{w}) \tag{5}\] \[s.t.\ \mu_{ij}^{w}=\prod\limits_{m,n\in Q_{m}}R_{mn}^{ij,w}I_{mn}, \ \forall i,j\in V,w\in[1,W] \tag{6}\]  
\[\sum_{w}\ (\sum\limits_{n\in Q_{m}}R_{mn}^{ij,w}-\sum\limits_{n\in Q_{m}}R_{nm}^{ ij,w})=\left\{\begin{array}{l}L_{ij},\ m=i\\ -L_{ij},\ m=j\end{array},\ \forall i,j\in V\right. \tag{7}\]  
\[\sum_{w}\ (\sum\limits_{j}\sum\limits_{i\
eq j}Z_{ij}^{f,p,w}\sum\limits_{m\in P _{ij}}\sum\limits_{n\in P_{ij},n\
eq m}R_{nm}^{ij,w})=\xi_{f,p},\ \forall f,p \tag{8}\]  
and \(C_{1}\)\(\sim\)\(C_{5}\), \(C_{7}\).  
The objective function aims to minimize the required wavelengths, which are calculated in (5). It is obvious that a wavelength can be exploited more than once, so long as the virtual paths do not coincide. Constraint (6) ensures that there is at most one wavelength traversing virtual lightpath (\(i\), \(j\)) at any ISL, where the binary variable \(R_{mn}^{ij,w}\) is used to implement routing selection and wavelength assignment. Constraint (7) presents the flow conservation limitation in the optical layer, that is, the number of virtual lightpaths can be expressed as many segments of ISLs. Constraint (8) determines the state of sub-wavelength grooming, in other words, the matching results between the connection requests and the sub-wavelengths.
## 4 Evaluation
### Computational Efficiency Test  
One hundred real images are obtained using the camera and 6 \(\times\) 6 chessboard mentioned in Section 4.2, for testing computational efficiency of the proposed method. An optimized dynamic library of it is implemented in C++ code (available online: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) PgRl3 qC8 HDi49f8n8 Jwe3Q]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) PgRl3 qC8 HDi49f8n8 Jwe3Q)), to make a more objective analysis in terms of processing time compared with two mature functions \"findChessboardcorners\" and \"cornerSubPix\" built-in OpenCV. The test is run in VS2010 installed on a desktop computer (CPU: Intel Core i7-6700; RAM: DDR4-2133 16 GB; HDD: 1 TB). All the images are preloaded in the RAM for an undifferentiated access performance, instead of an unstable reading speed of the HDD.  
Table 4 lists the result of the processing time for three different algorithms. Although \"cornerSubPix\" runs two times as fast as the proposed method due to a low-cost computation based on image gradient, it is not essential for real-time detection, because sub-pixel corners are refined from their pixel coordinates located by expensive pretreatments. There is a common view that \"findChessboardcorners\" has high performance for rough detection. When comparing with two sub-pixel algorithms, however, it costs 41,715 ms, almost 32 and 14 times longer than that of \"cornerSubPix\" and the proposed algorithm. Therefore, the present efficiency bottleneck is the pixel detection, not the sub-pixel refinement. What can be expected is that this bottleneck is not unbreakable; some state-of-the-art techniques, such as CUDA and multithread computing, are powerful for addressing this kind of problem.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Experimental Results and Comments  
#### 4.4.1 Experimental Results  
The model's experimental results using 4 machine learning algorithms on the T1, T2, T3 training datasets and the TEST testing dataset described in Section 4.3.1 show that the kNN algorithm's overall classification accuracy is better with weighted neighbors than with non-weighted neighbors. The \(k\) value that gives the best classification performance is not stable and it depends on the training sets. The best classification performance using the kNN algorithm with \(k\) values of 13, 9 and 15 on the T1, T2 and T3 training sets, respectively.  
Similar to kNN, the number of decision trees of the random forest algorithm that produces the best classification results is also unstable and it depends on the training sets. The random forest algorithm gives the best classification performance with the numbers of decision trees of 30, 38 and 27 on the T1, T2 and T3 training datasets, respectively. Tables 3-5 provide the model's testing results using 4 machine learning algorithms on the T1, T2 and T3 training sets, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Machine Learning Algorithms**} & \multicolumn{5}{c}{**Measures (\%)**} \\ \cline{2-6}  & **PPV** & **FPR** & **TPR** & **ACC** & **F1** \\ \hline kNN (\(k=9\)) & 82.70 & 20.10 & 96.40 & 88.10 & 89.00 \\ C4.5 & 81.50 & 22.10 & 97.30 & 87.60 & 88.70 \\ RF (38 trees) & 84.20 & 18.10 & 96.60 & 89.20 & 89.96 \\ Naive Bayes & 82.80 & 18.80 & 90.50 & 85.80 & 86.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance of the detection model using T2 training set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Predicted class} & Bad & TP (True Positives) & FP (False Positives) \\ Good & FN (False Negatives) & TN (True Negatives) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Machine Learning Algorithms**} & \multicolumn{5}{c}{**Measures (\%)**} \\ \cline{2-6}  & **PPV** & **FPR** & **TPR** & **ACC** & **F1** \\ \hline kNN (\(k=13\)) & 89.50 & 10.70 & 91.00 & 90.20 & 90.30 \\ C4.5 & 89.10 & 11.10 & 91.20 & 90.10 & 90.10 \\ RF (30 trees) & 90.70 & 9.30 & 91.00 & 90.80 & 90.80 \\ Naive Bayes & 83.10 & 18.30 & 90.20 & 85.90 & 86.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performance of the detection model using T1 training set.
## 2 Laboratory Tests on Nonlinear Flow of Fractures
### Experimental Results and Analysis of the Nonlinear Flow of Fractures
This indicates that the inertial force is more and more obvious than the viscous force in the process of increasing water pressure. This is because the flow velocity increases gradually with the increase in water pressure, and the inertial losses caused by high velocity result in a significant nonlinearity of flow. The critical point of the flow regime transformation from linear to nonlinear can be obtained by the calculation of the critical Reynolds number. For the unfilled condition, the range of the critical Reynolds number is 2.77-3.84, and the corresponding water pressure is 0.7-1.4 MPa. However, for the conditions of filling with quartz sand, the flow regime was nonlinear when the water pressure was 0.1 MPa. This is because the larger hydraulic aperture formed by filling with quartz sand (compared with the unfilled condition), resulted in a higher flow velocity under the same water pressure, and the inertial force caused by a high flow velocity makes the flow nonlinear under a low water pressure. Analysis of the above-mentioned test results shows that the flow regime will change from linear to nonlinear under a high flow velocity in the fracture, and the variation in effective stress leads to a change in the hydraulic aperture, which has effects on the properties of nonlinear flow. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filling Condition** & **Sample** & **Linear Coefficient A** & **Nonlinear Coefficient B** & \(\mathbf{R^{2}}\) \\ \hline With 50 mesh & G1 & 3.02 \(\times\) 10\({}^{5}\) & 1.69 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\ particles & G2 & 3.10 \(\times\) 10\({}^{6}\) & 2.37 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\  & G3 & 3.50 \(\times\) 10\({}^{5}\) & 2.34 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\ \hline With 100 mesh & G1 & 8.80 \(\times\) 10\({}^{5}\) & 1.87 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\ particles & G2 & 4.94 \(\times\) 10\({}^{6}\) & 2.61 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\  & G3 & 3.91 \(\times\) 10\({}^{6}\) & 8.81 \(\times\) 10\({}^{12}\) & \textgreater{}0.99 \\ \hline With 200 mesh & G1 & 1.72 \(\times\) 10\({}^{6}\) & 2.13 \(\times\) 10\({}^{13}\) & \textgreater{}0.99 \\ particles & G2 & 1.01 \(\times\) 10\({}^{7}\) & 1.57 \(\times\) 10\({}^{13}\) & \textgreater{}0.99 \\  & G3 & 1.21 \(\times\) 10\({}^{7}\) & 4.03 \(\times\) 10\({}^{13}\) & \textgreater{}0.99 \\ \hline No filling & G1 & 8.60 \(\times\) 10\({}^{7}\) & 7.14 \(\times\) 10\({}^{13}\) & \textgreater{}0.99 \\  & G2 & 5.70 \(\times\) 10\({}^{7}\) & 4.22 \(\times\) 10\({}^{13}\) & \textgreater{}0.99 \\  & G3 & 1.01 \(\times\) 10\({}^{8}\) & 6.07 \(\times\)
# Advanced Bayesian Network for Task Effort Estimation in Agile Software Development
## 5 Conclusions and Future Work  
This paper develops a BN model for effort prediction in agile software development projects.  
The proposed model is relatively small and simple, and all the input data are easily elicited. This way, the impact on agility is minimal. The model predicts task effort, and it is independent of the agile methods used.  
The model is validated on real agile projects. It turns out that the structure and parameters of the model are well set, and the accuracy of the classification depends only on the number of instances available for learning. The conclusion is confirmed by the example of set A, where, by increasing the number of output classes from five to seven, the accuracy of the classification decreases by only 0.625%, i.e., from 99.375% to 98.75%. All misclassified instances are classified into adjacent classes.  
Models using the BN for effort prediction have an accuracy range of 52% to 97% [40], so the authors will be pleased with a prediction accuracy of 80% or more (>98% is significantly over all expectations).  
The model was also validated on the agile projects of another software company, resulting in set B. The applicability and success of the BN model were proven by combining the data from sets A and B. Tasks with varying contexts, including the following, were combined and had the same result:  
Figure 5: Misclassified instances in set A (160 instances).  
Figure 6: Set A + B—misclassified instance details (194 instances).  
* from different companies;
* of different types of software;
* from different areas of work,
* of different technologies;
* from different developers; and
* from different assessors/managers.  
That way, the proposed BN model is validated.  
It should be noted that models/methods for effort assessment are most often realized by using publicly available project databases. These databases have a defined structure, and researchers use mathematical methods to help them select the best solution. A different approach is applied in this research: variables for evaluation are defined from the known processes and data, a model is made, and then the success of the model is proven using mathematical measures.  
Future work aims to investigate the impact of the productivity of both teams and individuals on effort estimation in agile software development.  
Conceptualization, M.T. and S.C.; methodology, M.T., S.C., S.D. and L.V.; validation, M.T. and S.D.; data curation, M.T., S.C. and S.D.; resources, S.D.; writing--original draft preparation, M.T. and. S.D.; writing--review and editing, S.C. and L.V.; supervision, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Venio Indicium grant number 2023/1.  
Not applicable.  
The data used in this research is available in Google drive.  
Special thanks are given to [PERSON] for her language advice. Special thanks are given to the anonymous reviewers whose thoughtful comments have greatly helped us to improve the paper.  
The authors declare no conflict of interest.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 3 DRdA-CA Architecture Details  
### The Proposed Denoising Autoencoder Structure  
This paper proposes a dual residual denoising autoencoder with a channel attention mechanism, and the network model can be seen in Figure 2, which is composed of an encoding module and decoding module in series. The encoding module consists of a \(3\times 3\) convolutional layer and three encoding blocks, and each encoding block is the improved SE-ResNet module. Similarly, the decoding module is also composed of a \(3\times 3\) convolutional layer and three decoding blocks. It is worth noting that, in the encoding and decoding modules, we further introduce the residual connection between encoding (decoding) blocks at different levels to optimize the network degradation problem.  
Figure 1: Communication system model.  
This paper focuses on the AWGN channel, and the received modulation signal \(s^{\prime}(t)\) can be expressed as Equation (1). The task of the noise reduction autoencoder is to obtain the optimal estimation \(s^{\prime\prime}(t)\) of the pure modulation signal \(s(t)\) from the \(s^{\prime}(t)\).  
In the decoding modules of the proposed DRdA-CA, the Tanh function defined by Equation (2) is used as the activation function of the convolution layer, which is the output layer.  
\[f_{Tanh}(x_{i})=\frac{e^{x_{i}}-e^{-x_{i}}}{e^{x_{i}}+e^{-x_{i}}} \tag{2}\]  
where \(x_{i}\) is the input variable. The detailed network parameters of the proposed DRdA-CA are shown in Table 1.