esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
Experimental Investigation on Compressive Properties and Carbon Emission Assessment of Concrete Hollow Block Masonry Incorporating Recycled Concrete Aggregates  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 2, [PERSON] 3 and [PERSON] 4**  
1 College of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China  
2 College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (G.B.)  
3 Xi'an Research and Design Institute of Wall and Roof Materials, Xi'an 710061, China; [EMAIL_ADDRESS]  
4 Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi'an 710123, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-180-9256-1062  
Received: 17 October 2019; Accepted: 12 November 2019; Published: 14 November 2019  
Featured Application: The compressive properties and carbon emission assessment of recycled aggregate concrete hollow block masonry were analyzed. The formula for calculating the compressive strength of hollow block masonry was presented. The global warming potential between natural and recycled aggregate concrete hollow blocks was compared. This study will contribute to the popularization and application of recycled aggregate concrete hollow block masonry.  
**Abstract:** In this paper, the compressive strength experiment for three groups of recycled aggregate concrete (RAC) specimens with different replacement ratios of recycled aggregate (0%, 50%, 100%) was carried out. The mechanism of the block and mortar properties on the compressive strength of block masonry was investigated by means of a static loading test. The formula for calculating the compressive strength of a recycled concrete block was obtained based on experimental data. Moreover, the global warming potential (GWP) of recycled aggregate concrete (RAC) block masonry was evaluated by life cycle assessment (LCA) methodology. The feasibility for application of RAC block masonry was discussed combined with environmental impact data analysis. The results show that the strength of RAC blocks is the principal element affecting the compressive strength of block masonry, and the strength of mortar also has a certain impact for the compressive of RAC block masonry; the sub-coefficient of material performance should be enhance appropriately for ensuring the construction quality of RAC block masonry; the total GWP of RAC block is lower than that of natural aggregate concrete (NAC) block. The environmental benefits of the promotion and application of RAC block masonry are inspiring.  
**Keywords:** recycled aggregate concrete; block masonry; compressive strength; carbon emission; stress-strain curves
## 3 Results
### Sensitivity Analysis  
The main core factors in predicting dissolved pollution are the rainfall intensity, rainfall duration, and garbage accumulation rate. These parameters can introduce uncertainty. For example, the parameters in Equations (A1)-(A4) were prescribed with assumed values for the runoff coefficient, standard runoff coefficient, and surface erosion coefficient. Compared with these variables, the accumulation weight of garbage and manure in a day, the duration between two rainfalls, and the remaining pollutant level on the ground after a rainfall change with time and space [27]. Data for such variables were obtained from statistical yearbooks.  
We used [PERSON]'s method for sensitivity analysis as described in Section 2.3.5. [PERSON]'s method is a global parameters sensitivity analysis based on variance, and it can effectively handle nonlinear and non-monotonic functions of models. For Equation (2), the analyzed results are shown in Figure 10, where items 1-9 are \(\epsilon\), \(\epsilon_{0}\), \(k\), r, t, Q\({}_{\text{IN}}\), L\({}_{\text{IN}}\), N\({}_{\text{i}}\), and S\({}_{\text{i}}\), respectively. From Figure 10, we can see that parameters 2, 6, 8, and 9 influenced the final prediction results the most.  
For Equation (4), parameters R, K, L, S, C, and P were analyzed using Sobol's method. We found that the top four parameters that most influenced the prediction results were R, P, S, and L which is shown in Figure 11 and their corresponding ST values were 0.5564, 0.5204, 0.5065, and 0.3731, respectively.  
From the analysis result of Figures 10 and 11, the index ranking of Sobol's sensitivity analysis for dissolved pollution and absorbed pollution are shown in Table 5.  
Figure 9: Simulation results of absorbed pollution.  
Figure 11: Sobol global sensitivity analysis for the absorbed pollution function.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{l}{**Dissolved Pollution**} & \multicolumn{2}{l}{**Absorbed Pollution**} \\ \hline Rank & Parameter & ST & Parameter & ST \\
1 & \(\varepsilon_{0}\) & 0.9456 & R & 0.5564 \\
2 & N\({}_{\text{i}}\) & 0.2095 & P & 0.5204 \\
3 & Q\({}_{\text{iN}}\) & 0.2094 & S & 0.5065 \\
4 & \(\varepsilon\) & 0.0817 & L & 0.3731 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Total effect index ranking of Sobol’s sensitivity analysis for dissolved pollution and absorbed pollution.  
Figure 10: Sobol global sensitivity analysis for the dissolved pollution function.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses
### Parameters Selection Experiments
#### 4.1.1 Number of Selected Bands  
First of all, the number of selected bands was set to \(n=1,2,3,4,5,8,10\). When \(n>10\), more than 30 spectral bands would be selected, which makes dimensionality reduction meaningless. The window size was set to \(m=11\), while the value of \(\sigma\) varied. Using the dataset of Indian Pines, the effects of the number of selected spectral bands on UE, BR, ASA are shown in Figure 7. The polylines in different  
Figure 6: Ground truth of PaviaC dataset.  
colors represent different numbers of selected spectral bands. For example, the red polyline represents the values of UE at \(n=1\) and varying values of \(\sigma\).  
Figure 7: The superpixel segmentation results at different values of n and window size \(m\) (\(\sigma=0.1\)) on Indian Pines: (**a**) UE; (**b**) BR; (**c**) ASA.  
Figure 7a shows the results of UE at different values of \(n\) and \(\sigma\). From Figure 7a, when \(n=1\) and \(\sigma\) were set to different values, the obtained UE in red polyline was mostly the smallest, indicating the better performance result at \(n=1\).  
Figure 7b shows the BR results at different values of \(n\) and \(\sigma\). The red polyline represents the BR values at \(n=1\), that is, the experimental results when only three spectral bands were selected. From the figure, when \(n=1\), the obtained polyline (red) was always above other polylines at any value of \(\sigma\). Therefore, when \(n=1\), the value of BR was the largest, and the obtained segmented image had the best performance.  
Figure 7c shows the ASA results at different values of \(n\) and \(\sigma\). When \(\sigma=0.05\), the red line is at the highest point and the value of ASA is the largest. Meanwhile, the red line is more stable than the orange line. Therefore, when \(n=1\), the best segmentation results can be obtained.  
From the results, when \(n=1\), UE was relatively low, BR and ASA were relatively high. At \(n=1\), the segmentation results were more satisfactory than the results at other values of \(n\). When \(n=1\), three least relevant spectral bands were selected, and they contained the largest amount of information and minimum redundancy. Therefore, when the value of the parameter \(n\) was 1, the performance was optimal.
## 2 Observations and Methods
### The Improvement of the RS IOD Method and the Association Method
#### 2.2.2 The Association Method  
The geometrical method is used for the correlation of the initial orbital elements, the core step of this correlation algorithm, and the specific implementation process can be referred to [14]. The association technique is founded on the fact that the error of SMA accumulates to produce an along-track bias at the midpoint between two propagated positions, which are estimated from two IOD solutions. By iteratively modifying the SMAs of these solutions until the errors in the SMAs are of equal magnitude but opposite signs, the along-track bias at the midpoint approaches zero if the two orbit-determination solutions pertain to the same object.  
The core ideas of the initial track-association method used in this paper are as follows.  
1. Determine the two sets of initial orbit elements for conducting the association;
2. The two tracks are propagated to the intermediate moments of the two by using the analytical method of orbit propagation. This allows for the correlation of the two tracks;
3. After propagation, the differences between the two initial tracks are calculated in the along-track, cross-track, and radial directions (ACR). The semimajor axis (SMA) of the two initial tracks is then adjusted based on the differences in the along-track direction. The tracks are repropagated, and the differences in the ACR directions of the two initial tracks are recalculated;
4. The final ACR difference is judged after applying multiple corrections in succession, and, if it is less than the preset threshold, the two tracks are considered to be from the same object. Otherwise, they are from different objects.
# An Ensemble Transfer Learning Model for Detecting Stego Images
## 5 Conclusions  
The study evaluated the performance of four deep learning models, namely, CNN, AlexNet, ResNet-50, and Inception, for detecting steganography at different payload bitrates using the BOSS base 1.01 dataset. The evaluation metrics used were accuracy, precision, recall, F1 score, and error detection rate. The results showed that the CNN model performed better than the other models did at lower payload bitrates while ResNet-50 and Inception were more robust at higher payload bitrates. However, the CNN model achieved the best results overall based on the evaluation metrics used. It is important to note that the results may not necessarily be generalized to other scenarios.  
In conclusion, the proposed method for detecting stego images involved collecting images and mixing them between precise and stego images in the first step, followed by pre-processing to clean the low-quality images, segmenting the images, and extracting features to create a dataset. The resulting dataset was then used as an input for the proposed categorization scheme, which employed four classification models, namely, CNN, AlexNet, Resnet50, and Inception. The authors used 22 features, including co-occurrence matrices,means, STDs, skewness, kurtosis values, color moments, and moment-invariant properties, for feature extraction. Evaluations of each model were performed, and the system voted for the best model. The proposed method has potential applications in digital forensics for identifying steganography in images. It is a useful tool that can be used by law enforcement agencies to detect steganography and prevent the misuse of sensitive information.  
Conceptualization, D.Y.M., R.S.H. and S.W.K.; methodology D.Y.M., R.S.H. and S.W.K.; software, S.W.K.; validation, D.Y.M., R.S.H. and S.W.K.; formal analysis, D.Y.M., R.S.H. and S.W.K.; investigation, D.Y.M., R.S.H. and S.W.K.; resources, S.W.K.; data curation, S.W.K.; writing--original draft preparation, S.W.K.; writing--review and editing, D.Y.M. and R.S.H.; visualization, D.Y.M., R.S.H. and S.W.K.; supervision, S.W.K.; project administration, R.S.H.; funding acquisition, D.Y.M. and R.S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Decoding Visual Motions from EEG Using Attention-Based RNN
## 4 Methods
### Model Configuration and Training  
Layer normalization [48] is added before each layer of stacked GRU and applied over the channel dimensions of the input signal to speed up convergence and improve performance. Based on our pre-experimental results, layer normalization can effectively improve model performance, and it is better to place before GRU than after GRU. Dropout [47] is added to non-recurrent connections to prevent overfitting, whereas the early stopping method is adopted to achieve optimal generalization performance. RAdam optimizer is used as the loss function optimization algorithm, with a learning rate of 0.001 [49]. The optimizer ensured a comparable or even better performance without warm-up for the learning rate. [PERSON] initialization [50] and [PERSON] initialization [51] are adopted to initialize the parameters of GRU and the fully connected layer, respectively. The model is trained by minimizing the cross-entropy loss:  
\[Loss(O,L)=-\sum\
olimits_{i=1}^{N}\log(o^{i})\cdot l^{i} \tag{5}\]  
where \(O\) is the probability vector normalized by the softmax function with a dimension of \(N\), \(N\) is the number of signal categories, and \(L\) is the one-hot vector of the true label of signals. All models used in this study were built in PyTorch [52] and trained on an NVIDIA GEFORCE RTX 2080 Ti GPU.  
### Model Configuration and Training  
EEGNet and DeepConvNet [26; 53], two prevalent CNN-based structures for decoding EEG, are selected for comparison. EEGNet is an EEG-specific network that incorporates EEG feature extraction concepts and contains comparatively less trainable parameters. The EEGNet-8,2 model, a version of EEGNet that performs well on almost all the EEG classification tasks, is used in the experiment. DeepConvNet is a generic architecture to extract a wide range of features from EEG inspired by successful approaches in computer vision. On the basis of the original framework of EEGNet-8, the last average pooling layer is replaced with the global average pooling type to reduce the parameters, and the dropout layer is removed. This minimizes the disharmony between dropout and batch normalization [54]. In fact, both batch normalization and global average pooling have regularization properties, and some successful architectures, such as ResNet and DenseNet, achieve their best  
Figure 4: The processes of combination strategies. ERA—early randomly averaging. ERC—early random concatenation. LRA—late randomly averaging.  
performance without dropouts through the application of these techniques. In our pre-experiments, we find the performance of EEGNet-8,2 to be better and more stable after the replacement.
## 2 Related Work  
### Registration Methods  
In registration research, three main approaches currently prevail: methods based on original point information, methods based on feature points, and methods based on deep learning.
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
###### Abstract  
Radio-frequency spectrum resources are finite and scarce, but their demand is increasing exponentially every year. Therefore, wireless network resources are too expensive to be wasted. To avoid waste, pricing techniques can efficiently control resource usage and manage user needs in networks. This study focuses on QoS-aware pricing for usage-based mobile Internet access charging. Specifically, I propose a heuristic algorithm for priority pricing with multiple service levels. The proposed algorithm is built on top of the existing equilibrium analysis methods. While being extensively studied for optimal price selection, the equilibrium methods make a few unrealistic assumptions, and so my methods adjust the solutions of the equilibrium methods to account for distortions that the real world creates. The evaluation results indicate that multiple equilibrium prices may exist, and the proposed scheme produces a pricing plan that is substantially more effective than existing equilibrium methods.  
QoS design; priority pricing; equilibrium analysis; mobile internet access; search pruning; heuristic search +
Footnote †: journal: Applied Sciences  
1
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 5 Evaluation
### The Efficiency of ColluDroid  
All of our experiments are tested on a PC with an Intel Core i5-2320 CPU processor (6 MB Cache, 3.0 GHz, made by Intel, CA, USA) and 16 GB of main memory (2*8 GB DDR3, 1600 MHz, made by Samsung Group, Seoul, Korean). According to our experiments, the analysis time includes feature extraction time, communication link computing time, classification training time, and malware detection time. We perform the first experiment (Section 5.1.1) five times, and Table 3 shows the average time in each step. The feature extraction process takes up most of the time because we need to extract the feature of every app and there are 10,000 apps in total in our dataset. The feature extraction time of each app is shown in Figure 7a. The \(x\)-axis is the size of test apps and the \(y\)-axis is the analysis time. We can figure out that the larger the apps are, the more extraction time is needed. This is quite straightforward, as the larger the apps are, the more codes they contain, the more time is needed to traversal all the codes and extract features. On the other hand, ColluDroid can complete the feature extraction of most apps within 60 s.  
For comparison, we analyzed the efficiency of traditional data-flow analysis methods. In our experiment, we take FlowDroid [3] as an example for comparison. FlowDroid is a pioneering work that introduces a novel on-demand data flow analysis algorithm with high efficiency and precision. Many researchers followed their work and built a number of tools and have similar analyzing performance. Figure 8 shows the experimental results. From Figure 8b, we can see that FlowDroid can finish analyzing 80% of malware apps in 60 s because malware has a smaller size than benign apps. The larger the apps are, the time required will increase exponentially. As a result, benign apps will need far more time to complete analysis. Figure 8b shows that nearly 20% of them went over the time limit. (We restrict the analysis time to 20 min when analyzing the benign apps due to the time limitation.) The average analysis time of FlowDroid comes to 15 min. If we did not set a time limit, the average analysis time would be even longer. On the contrary, ColluDroid can complete 90% of the app in one minute, which is close to 15 times faster compared with the traditional data-flow analysis methods. Especially, the feature extraction time of each app can be parallelized. Thus, we believe that it is quite reasonable for a large scale analysis.  
From the results, we can figure out that it is pretty easy to split an app to evade detection. The difficulties would be to induce end users to install our split apps. The more apps you split, the more difficulty you face to have all the apps installed. Only when all apps are installed can a collusion attack be performed successfully. In this case, social engineering approaches can provide references.  
Figure 6: Performance of different methods under Collusion and Evasion attack. (**a**) perfect knowledge; (**b**) limited knowledge.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 6 Conclusions  
In this paper, several moving people and cars were captured by an SUAV. The objects were detected based on frame subtraction. Ten targets were tracked with the Kalman and IMM filters. Experimental results show that moving objects were well detected and tracked with good accuracy. The number of filter modes and the target dynamics of each mode, such as the process noise variance, should be determined properly to cope with the maneuvering of multiple targets.  
For security and defense applications, the trajectories and the states of targets can be transferred to a control tower in real-time. Also, this system is suitable for people counting in a crowd area. Fully autonomous and stand-alone aerial video surveillance systems are very useful in commercial as well as military/government applications. In this work, the drone was fixed in the air as a stationary sensor (platform). Target tracking with a moving platform remains a subject for future study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target No.**} & \multicolumn{2}{c}{**Kalman Filter**} & \multicolumn{2}{c}{**Two-Mode IMM Filter**} & \multicolumn{2}{c}{**Three-Mode IMM Filter**} \\ \cline{2-7}  & **Position** & **Velocity** & **Position** & **Velocity** & **Position** & **Velocity** \\  & **(Meter)** & **(m/s)** & **(Meter)** & **(m/s)** & **(Meter)** & **(m/s)** \\ \hline Target 1 & 0.8833 & 0.3412 & 0.8839 & 0.3419 & 0.8827 & 0.3419 \\ Target 2 & 0.5420 & 0.9592 & 0.5428 & 0.9589 & 0.5412 & 0.9589 \\ Target 3 & 0.7956 & 0.4418 & 0.7958 & 0.4466 & 0.8033 & 0.4466 \\ Target 4 & 0.5460 & 0.4186 & 0.5429 & 0.4162 & 0.5563 & 0.4290 \\ Target 5 & 0.4543 & 0.6297 & 0.4547 & 0.6295 & 0.4540 & 0.6299 \\ Target 6 & 0.6071 & 0.6922 & 0.6105 & 0.6941 & 0.6039 & 0.6904 \\ Target 7 & 1.2829 & 0.5496 & 1.2786 & 0.5476 & 1.2871 & 0.5493 \\ Target 8 & 0.5825 & 0.6356 & 0.5832 & 0.6358 & 0.5818 & 0.6354 \\ Target 9 & 1.0639 & 0.4462 & 1.0627 & 0.4444 & 1.065 & 0.4481 \\ Target 10 & 1.2842 & 0.7522 & 1.2842 & 0.7522 & 1.284 & 0.7522 \\ Average & 0.8042 & 0.5866 & 0.8039 & 0.5860 & 0.8060 & 0.5882 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root-mean squared errors (RMSE) of position and velocity.  
**Author Contributions**: Conceptualization, methodology, software, validation, S.Y.; and visualization and experimental assistance, I.-J.C.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: 2017R1D1A3B03031668).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Development of the Hole Diameter Measuring Machine
### Measurement Process Planning  
The measurement process was planned to achieve fully automated hole attitude adjustment and diameter measurement of any cross-sections. The influence of personnel operations could be significantly reduced through automatic adjustment and measurement.  
As shown in Figure 11, there were six major steps in the measuring process. First, the upper end face of the hole to be measured was determined with the SSEP probing sensor, and used as the depth reference of the cross-section hole diameter to be measured in the subsequent steps. Then, the hole attitude was finely adjusted using the principles described in Section 3.3. The part was moved vertically in the Z direction until the probing ball center was in the desired cross-section. The probe was then moved in the Y direction to find the inflection point, thus moving the probing ball center to the measurement line of the hole diameter to be measured.  
After the adjustment process above, automatic diameter measurement was carried out to obtain \(l\), \(\delta_{1}\), and \(\delta_{2}\). Measurements were repeated ten times, and ten measurement results were averaged.