esa-sceva/satcom-chunk-collection huggingface dataset:
# A Strain Hardening and Softening Constitutive Model for Hard Brittle Rocks
## 2 Simplification of the Complete Stress-Strain Curve  
To analyse the strain hardening and softening characteristics of hard brittle rock before and after the peak, a reasonable simplification of the complete stress-strain curve for hard brittle rocks is needed. The characteristic stress at the reversal point of the volume-axial strain curve is taken as the initial yield point [31], as shown in Figure 1a, which is denoted by the initial yield stress \(\sigma_{s}\). The initial yield stress \(\sigma_{s}\), peak stress \(\sigma_{p}\), and residual stress \(\sigma_{r}\) are regarded as segmentation points, and the typical complete stress-strain curve was idealised as a four-stage simplified complete stress-strain curve, which is a prerequisite for establishing a strain hardening and softening constitutive model. The simplified complete stress-strain curve covers the linear elastic stage, pre-peak hardening stage, post-peak softening stage and residual strength stage [32]. The simplified curve is shown in Figure 1b. Note that the OA-AB-BC segment of the curve in Figure 1a is simplified by a straight line (OC).  
In addition, the following statement is made in this study:  
The Mohr-Coulomb yield criterion, which has been widely adopted to describe the shear failure mechanism of rock [33; 34; 35; 36], and based on this criterion, the strain hardening and softening constitutive model is studied. That is,  
\[\sigma_{1}=\frac{1+\sin\varphi}{1-\sin\varphi}\sigma_{3}+\frac{2c\cdot\cos \varphi}{1-\sin\varphi} \tag{1}\]  
where \(\sigma_{1}\) is the maximum principal stress; \(\sigma_{3}\) is the minimum principal stress; \(c\) is cohesion; and \(\varphi\) is the internal friction angle.
## 4 Discussion
In a recent meta-analysis, [PERSON] et al. [39], concluded that better and more consistent studies are needed to support the use of those technologies when comparing the precision of the PSI, the accelerometer, navigation, robotics and conventional cutting guides. The proportion of outliers (+/ -3\({}^{\circ}\)) in coronal femur alignment was lower for the accelerometer group when compared with the PSI (1% vs. 11%) or conventional (1% vs. 14%) groups. Regarding frontal tibial outliers, navigation demonstrated the lowest proportion when compared with standard cutting guides (5% vs. 10%) or with the PSI group (5% vs. 10%). In the present study, the outliers (+/ -3\({}^{\circ}\) referencing the aimed angulation) were 14.7% in the frontal femur (50) and 18.2% (62) in the coronal tibia. Remaining mindful of these results and considering the discrepancy observed in the present study, the accuracy of conventional cutting guides should be considered acceptable with no need for additional technology. There are three main limitations of the present study. The first is the use of full-leg standing radiographs to measure the angles, acknowledging that it is probably less accurate than a CT scan. However, both observers were trained together to identify the landmarks in the same manner, and a strict patient positioning protocol was adhered to. The protocol was overseen by the radiologist in charge of performing specific imaging studies. The radiologist was involved from the moment our general database was created. The second limitation is the lack of measurements on the sagittal and axial planes. Nevertheless, the most objective planned measures are on the coronal plane. It is difficult to define the planned sagittal (with the extramedullary guide) and rotational angulation in the tibia and the planned rotational and sagittal position in the femur due to the fact that inexact references were chosen using conventional instrumentation. Furthermore, a major additional limitation is we could not assess the intra-observer correlation because the observers automatically calculated the mean value of their two measurements. In the cases in which the measurements were more than 0.5, they assessed a third measurement.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 3 Probiotic Encapsulation Techniques
### Drying Method  
#### 3.4.1 Spray-Drying  
In food industries, spray-drying is the most used method to dry the encapsulated mixture into powdered probiotics (capsule sizes: 5-150 \(\upmu\)m). The principle in spray-drying is the simultaneous mass and heat transfer processes between hot air and droplets. There are three main processes involved in the spray-drying process (i) atomization of a solution comprising probiotics and core material into fine droplets, (ii) droplets evaporation in a heated gas stream, and finally, (iii) separation and collection of spray-dried powder [43; 44].  
The advantages of this drying technique include (i) the process is rapid and continuous, (ii) this technique does not require a high operational cost (10 times cheaper compared to freeze-drying), (iii) highly reproducible, easy for scaling up and suitable for industrial application, and (iv) the spray-dried products are typically dry, low in water activity, highly stable, and have low bulk density. Studies about the encapsulation of probiotics by spray-drying have been extensively reported. For example, [PERSON] et al. [57] showed that probiotic _Saccharomyces cerevisiae_ var. _boulardii_ microencapsulated with gum Arabic, pea protein, and gelatin by spray-drying were more resistant to simulated stomach solution. [PERSON] et al. [58] also demonstrated that probiotic _L. reuteri_ cultivated in whey slurry microencapsulated by direct spray-drying showed a 32% greater survival rate upon exposure to simulated digestive juice than those without encapsulation. Numerous studies have used spray-drying encapsulation in non-dairy probiotic food. [PERSON] et al. [20] demonstrated that spray-dried Sohiong juice fermented with _L. plantarum_ remained viable (6.12 log CFU/g) for 36 days of storage at 25 \({}^{\circ}\)C. A study by [PERSON] et al. [28] showed that the viability of spray-dried _L. rhamn_s GG in a matrix of whey protein isolate and hydrolyzed extruded huauzontle starch was stable in a ready-to-drink green tea beverage during the 5 weeks refrigerated storage.  
The drawback of this technique is the harsh processing conditions, which can cause adverse effects on the stability, viability, and survivability of the probiotics [1, 40, 43]. For instance, the high temperature and osmotic stress applied during spray-drying can kill the probiotics. Furthermore, high air velocities during spray-drying can result in microcapsules formed with poor uniformity in terms of particle size and morphology.
## 4 Conclusions

Table 14: Class Incremental Method, initial 60 DGA families without Darkshell precision, recall, and F1 score of each DGA family. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline bamital & 99.79\% & 100.00\% & 99.89\% & padcrypt & 97.73\% & 98.12\% & 97.93\% \\ banjori & 99.78\% & 99.91\% & 99.84\% & phorpiex & 89.45\% & 86.74\% & 88.08\% \\ bazarloader & 99.96\% & 100.00\% & 99.98\% & pitou & 95.68\% & 98.14\% & 96.89\% \\ chinad & 98.61\% & 98.85\% & 98.73\% & proslikefan & 33.27\% & 30.06\% & 31.59\% \\ conficker & 47.87\% & 31.26\% & 37.82\% & pseudoman & 38.81\% & 98.52\% & 55.69\% \\ corebot & 99.20\% & 98.87\% & 99.04\% & pushdo & 96.89\% & 93.85\% & 95.35\% \\ cryptolocker & 44.20\% & 60.71\% & 51.16\% & pushdotid & 88.65\% & 96.41\% & 92.37\% \\ darkwatchman & 99.74\% & 99.78\% & 99.76\% & pykspa2s & 97.54\% & 97.41\% & 97.48\% \\ dircrypt & 5.54\% & 28.67\% & 9.28\% & qadars & 96.63\% & 98.34\% & 97.48\% \\ dyre & 99.98\% & 100.00\% & 99.99\% & qakbot & 79.25\% & 63.82\% & 70.71\% \\ [PERSON] & 93.96\% & 99.66\% & 96.73\% & [PERSON] & 99.56\% & 96.07\% & 97.8\% \\ [PERSON] & 68.44\% & 100.00\% & 81.26\% & [PERSON] & 98.25\% & 99.94\% & 99.09\% \\ [PERSON] & 89.74\% & 95.51\% & 92.54\% & [PERSON] & 57.06\% & 50.55\% & 53.61\% \\ [PERSON] & 25.00\% & 40.28\% & 30.85\% & [PERSON] & 76.91\% & 91.57\% & 83.60\% \\ gameover & 99.98\% & 99.95\% & 99.96\% & vronix & 95.14\% & 98.78\% & 96.93\% \\ gameover\_p2p & 72.00\% & 57.43\% & 63.89\% & shifu & 61.24\% & 97.81\% & 75.32\% \\ gozi & 76.64\% & 76.94\% & 76.79\% & simda & 98.65\% & 97.07\% & 97.85\% \\ granderoire & 95.09\% & 99.40\% & 97.19\% & sphix & 29.48\% & 88.18\% & 44.19\% \\ locky & 70.31\% & 47.76\% & 56.88\% & suppobox & 96.77\% & 98.44\% & 97.60\% \\ m0 ytdd & 41.67\% & 98.45\% & 58.55\% & symmi & 99.39\% & 99.54\% & 99.46\% \\ metastealer & 99.99\% & 100.00\% & 99.99\% & tinba & 94.20\% & 99.19\% & 96.63\% \\ murofet & 81.04\% & 51.95\% & 63.32\% & tinynuke & 99.30\% & 100.00\% & 99.65\% \\ murofetweekly & 99.89\% & 99.66\% & 99.78\% & tufik & 10.81\% & 40.72\% & 17.08\% \\ mydoom & 77.32\% & 93.17\% & 84.51\% & urlzone & 99.51\% & 91.27\% & 95.21\% \\ ncore & 99.78\% & 97.73\% & 98.75\% & vavtrak & 70.26\% & 73.35\% & 71.77\% \\ mydaom & 99.00\% & 80.37\% & 88.72\% & virtut & 99.66\% & 99.99\% & 99.82\% \\ ngioweb & 96.15\% & 80.02\% & 87.35\% & xhex & 98.90\% & 100.00\% & 99.45\% \\ nymaim & 33.38\% & 26.06\% & 29.27\% & zloader & 89.57\% & 99.96\% & 94.48\% \\ nymaim2 & 86.38\% & 55.47\% & 67.55\% & darkshell & 49.16\% & 93.33\% & 64.40\% \\ orchard & 94.56\% & 99.12\% & 96.79\% & Average & 80.85\% & 85.15\% & 81.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 2 Background
### Digital Twin  
Similar to AR, the DT is a concept that, in different forms, has been known for quite some time. The first visions of systems, today called a Digital Twin, have been presented as early as 2002 [19]. However, only over the last five years have DTs gained an increasing interest throughout several research and industry domains. The reasons for this renewed interest in the DT are similar to those leading to the rise of AR: the technology needed to implement a DT is now, for the first time, mature enough to render DTs possible at all. However, a wide variety of existing challenges in the DT realm highlight that the concept itself can still be considered to be in its infancy [20].  
The DT presents a concept that applies in many different fields [21]. Owing to these vast application domains [22], definitions of the term itself vary widely. Most generically, a DT can be regarded as a virtual representation of a real-world counterpart along its entire lifecycle. Thereby, dominating DT features are its sole focus on one asset (i.e., the real-world counterpart, physical twin), its mere virtual embodiment and the novel insights [23] the concept can provide. One of the primary purposes of DTs is, thus, to monitor [24] and manage real-world assets.  
Besides, several related terms exist which describe similar aspects and are erroneously used as synonyms to the term \"Digital Twin\". For example, the term \"digital shadow\" does not comprise the entire lifecycle of a real-world asset but, rather, refers to a digital footprint.  
Figure 1: The Reality-Virtuality Continuum according to [PERSON] et al. [10].  
Therefore, throughout this work, we define a Digital Twin as an asset's virtual counterpart enabling an organization to monitor and analyze the asset during its entire lifecycle.  
Novel insights can be achieved with the DT, thanks to its key characteristics presented in Figure 2. Data concerning the real-world counterpart is enhanced with semantic technologies in order to represent the counterpart virtually [25], e.g., by creating an emulation or model. On this basis, simulations, analyses, etc., are conducted [26]. These, in turn, provide deep insights into the real-world counterpart's present state and potential future.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 5 Conclusions  
For the large scale wireless sensor networks with one mobile sink node, multi-source sparse cooperation schemes integrating distributed LDPC codes are proposed to jointly collect different source data to the sink node. Specifically, two cooperation models are formed considering the geographical locations of the source nodes, practical channel condition and the moving trajectories of the sink node. Then, directed graphs and cooperation matrices are derived for the one-round and two-round cooperation models and the distributed LDPC codes are designed to match different cooperation models. Finally, simulation results are given to exhibit the significant performance of the proposed one-round and two-round cooperation schemes compared with the non-cooperation, while each source node has very low processing complexity attributed to the distributed encoding and transmission in the cooperation schemes. Furthermore, the performance of the two-round data collection schemes is also evaluated considering different moving trajectories and the variant data sizes.  
This work was supported in part by Maritime Economies Promoting Program with Science and Technology in Tianjin (KJXH2011-02) and Joint Laboratory for Ocean Observation and Detection, National Laboratory for Marine Science and Technology.  
[PERSON] proposed the algorithm, performed the simulations, and wrote the paper; [PERSON] analyzed the data and revised the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Effects of the Saturation Limit of the Active Twist Rate  
In this study, the major assumption is that the twist actuation is modeled as a linear variation of the twist angle within each control segment. The torsional deflections are smaller than the amplitude of twist actuation and they are expected to be involved in the optimal active twist schedules. All of the optimal results presented thus far are obtained with a presupposed saturation limit of the active twist rate. The sensitivity of the saturation limit on the optimal segment twist control configuration is examined. A parametric study of the saturation limit is conducted on the uniform twist control with multi-harmonic inputs and the 2-seg control at condition B. The saturation limit of the active twist rate is varied, from 0.4\({}^{\circ}\)/m to 1.0\({}^{\circ}\)/m, in the steps of 0.2\({}^{\circ}\)/m. The rotor power reductions vary linearly with the saturation limits, as shown in Figure 27. With the saturation limit of 0.4\({}^{\circ}\)/m, the rotor power reductions using the uniform twist control with multi-harmonic inputs and the 2-seg control are 4.61% and 5.89%, respectively, at condition B. It shows that the multi-harmonic inputs gain more benefits than single harmonic inputs with the  
Figure 26: Dimensionless drag load increment distributions of the 3-seg control relative to the 2-seg control for condition B.  
Figure 25: Blade shaft moment increment of the 3-seg control relative to the 2-seg control rotor.  
same saturation limit. The optimal control schedules of the uniform twist control and 2-seg control with saturation limits are demonstrated in Figures 28 and 29, respectively. The optimal control schedules remain in the same patterns. Additionally, the optimal segment layouts also remain the same. Thus, the optimal segment control configurations are less sensitive to the saturation limits of the active twist rate. In other words, the established optimization framework is robust.
## 3 Dynamic Area Optimization Based on an NN Considering Intercell Relationships
### Ensemble Approach for Objective Function Maximization  
In the example depicted in Figure 6, the cells are output in an order from #1 to #3. However, the capability of the CC might fluctuate depending on the sequence of the cell output. This variation happens because the optimal prioritization for cell predictions depends on user distribution. To address this problem, we apply an ensemble approach that produces the CCs for potential sequences and chooses the solution with the highest objective function value from the obtained solutions. An example of the ensemble method for three-cell configurations is illustrated in Figure 7. When optimizing the horizontal beam direction for three cells, there could be \(3!\) (i.e., \(6\)) possible permutations of CCs. Among these, the solution scoring the highest objective function value is chosen. The definition of the objective function is consistent with that of GA, as indicated in Equation (13). The best sequence of all CC permutations (\(i_{\text{best}}\)) is expressed as follows:  
\[i_{\text{best}}=\arg\max_{i}\Bigl{\{}I\bigl{(}\boldsymbol{a}^{CC_{i}}\bigr{)} \Bigr{\}}, \tag{20}\]  
where \(\boldsymbol{a}^{CC_{i}}\) and \(I\bigl{(}\boldsymbol{a}^{CC_{i}}\bigr{)}\) are the beam directions predicted by the \(i\)-th order CC and the objective function by the beam directions \(\boldsymbol{a}^{CC_{i}}\), respectively.
_Article_  
Tunable-Q Wavelet Transform Based Multiscale  
Entropy Measure for Automated Classification of  
Epileptic EEG Signals  
[PERSON],* [PERSON], [PERSON] and [PERSON]  
1 Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India; [EMAIL_ADDRESS] (R.B.P.); [EMAIL_ADDRESS] (A.U.)  
2 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic,  
Singapore 599489, Singapore; [EMAIL_ADDRESS]  
3 Department of Biomedical Engineering, School of Science and Technology, SIM University,  
Singapore 599491, Singapore  
4 Department of Biomedical Engineering, Faculty of Engineering, University of Malaya,  
Kuala Lumpur 50603, Malaysia  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +91-732-430-6593  
Academic Editor: [PERSON]  
Received: 10 January 2017; Accepted: 5 April 2017; Published: 12 April 2017  
This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (\(Q\)) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The \(Q\) -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of \(Q\) and the redundancy parameter (\(R\)) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.  
Tunable-Q wavelet transform; K-nearest neighbor entropy; EEG signal; wrapper-based feature selection; support vector machine; epileptic EEG classification
## 3 Results and Discussion
### Accuracy of Comprehensive Segmentation Algorithm  
Table 4 shows the average percentage of background pixels removed by each segmentation method in comprehensive segmentation algorithm. This table shows that the highest percentage of background pixels removed is related to the method of threshold in Luv color space with value 36%. As this table shows, no single method can do segmentation operations alone. Therefor a combination of different method is need for segmentation with high accuracy. The combination of different segmentation techniques and their arrangement can be considered as an innovation. Table 5 shows the confusion matrix of thresholding function. As this table shows the error of this segmentation method is less than 0.8.  
Table 6 shows the confusion matrix and the percentage of the total detection of the proposed segmentation algorithm. As observed, objects in the images are divided into two classes of apple and background objects. This table shows that 324 samples out of 42,750 apple samples are mistakenly located in the background objects class by the segmentation algorithm, so the segmentation algorithm has 0.758% error in this class. This algorithm also mistakenly classified 691 samples of the objects in the background with the total members of 60125 in the apple class. This leads to a 1.15% error in segmentation algorithm for this class. Finally, the percentage of total detection of the segmentation algorithm is 99.013%. This accuracy is very good for this sample number, which proves the algorithm was configured properly.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 5 Conclusions and Future Research Directions  
Disaster situations require a flexible and reliable information dissemination mechanism to facilitate communication. A UAV-enabled IoT platform can significantly enhance the performance and reduce the cost for disaster management scenarios. In this paper, we present an architecture for a UAV-enabled IoT platform for disaster management and highlighted the key challenges in the area including wireless connectivity, data analytics, ground IoT network deployment, and experimental validation. We then present a case study for the ground IoT network in which NDN architecture was used resulting in a reduced delay for decision making, increased efficiency of content delivery, and greater data security. The improved performance of the ground IoT network is demonstrated through the results of the case study (NDN architecture for IoT platform).  
In this paper, we only address the ground IoT network. Future work is to consider communication technologies for ground and aerial connectivity, and data analytics. There are several open research areas in this area that need attention. Here, we briefly discuss four main areas.