esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Clustering under the Maximum Norm+
## 4 Tractability Results
### Fixed-Parameter Tractability
Conversely, if \(\mathcal{A}\) admits a \((2,\ell)\)-co-clustering satisfying \(\mathcal{U}\) with \(|I_{1}|=h_{1}\), then, by the discussion above, there exists a co-clustering where each column \(j\) is in one of the column blocks \(J_{s_{j,1}}\) or \(J_{s_{j,2}}\). In the corresponding Boolean assignment, each clause of \(\phi_{\mathcal{A}\mathcal{U}}\) is satisfied and each new column clause of \(\phi^{\prime}\) is also satisfied. Hence, \(\phi^{\prime}\) is satisfiable. Overall, for each cluster boundary \(\mathcal{U}\) and each \(h_{1}\), we construct and solve the formula \(\phi^{\prime}\) defined above. The matrix \(\mathcal{A}\) admits a \((2,\ell)\)-co-clustering of cost one if and only if \(\phi^{\prime}\) is satisfiable for some \(\mathcal{U}\) and \(h_{1}\). The running time for constructing and solving the formula \(\phi^{\prime}\), for any fixed cluster boundary \(\mathcal{U}\) and any height \(h_{1}\in[m]\), is in \(O(nm)\), which gives a running time of \(O((2|\Sigma|)^{\ell}nm^{2})\) for this last part. Overall, the running time is thus \(O(|\Sigma|^{2\ell}n^{2}m^{2}+|\Sigma|^{2\ell}n^{2}m^{2}+(2|\Sigma|)^{\ell}nm^{ 2})=O(|\Sigma|^{2\ell}n^{2}m^{2})\). Finally, we obtain the following simple corollary. Corollary 2: \((2,*)\)-Co-Clustering\({}_{\text{\tiny{oo}}}\) _with \(c=1\) is fixed-parameter tractable with respect to parameter \(|\Sigma|\) and with respect to parameter \(\ell\)._  
Proof. : Theorem 7 presents an FPT-algorithm with respect to the combined parameter \((|\Sigma|,\ell)\). For \((2,*)\)-Co-Clustering\({}_{\text{\tiny{oo}}}\) with \(c=1\), both parameters can be polynomially upper-bounded within each other. Indeed, \(\ell<|\Sigma|^{2}\) (otherwise, there are two column blocks with identical cluster boundaries, which could be merged) and \(|\Sigma|<2(c+1)\ell=4\ell\) (each column block may contain two intervals, each covering at most \(c+1\) elements).
# Forward Fall Detection Using Inertial Data and Machine Learning
The raw acceleration data is normalized and is used to train and test an artificial neural network (ANN) based on the LSTM network and a supervised learning classifier, such as the k-nearest neighbors. To test the trained machine-learning models, the raw acceleration values corresponding to forward falls, both damped and undamped, are recorded by mounting an accelerometer sensor on the pelvis area of six volunteers. The data is inserted into both developed models to evaluate the fall detection accuracy, and is also based on the type of activity such as normal gait, sitting on a bed, falling forward with the knees, and falling forward with no damping. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Head** & **Torso** & **Thigh** & **Calf** & **Upper Arm** & **Forearm** \\ \hline Weight [kg] & 3.94 & 33.99 & 6.52 & 2.69 & 1.84 & 1.11 \\ \hline Length [cm] & - & 66.45 & 45.82 & 37.53 & 30.52 & 26.3 \\ \hline Width/Diameter [cm] & 20.72 & 28.62 & 13.71 & 9.96 & 10.44 & 9.06 \\ \hline \multirow{3}{*}{Principal Moments} & I\({}_{\text{xx}}\) & 0.00001708 & 0.00161937 & 0.00011514 & 0.00003362 & 0.00001330 & 0.00000647 \\ \cline{1-1}  & I\({}_{\text{yy}}\) & 0.0000164 & 0.00108763 & 0.00012212 & 0.00000304 & 0.00001327 & 0.00000630 \\ \cline{1-1}  & I\({}_{\text{zz}}\) & 0.00002008 & 0.00037851 & 0.00002125 & 0.000000701 & 0.00000220 & 0.00000086 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean values of anthropometry data for the 6 subjects [22].
# Elastic-Plastic Threshold and Rational Unloading Level of Rocks
## 3 Mechanical Analysis of Different Rocks Under Loading
### Exploration of Rational Unloading Level for Unloading Tests  
When the loading tests and analysis results for siltstone, mudstone, and marble are combined, an elastic-plastic threshold in rocks during loading is revealed. At the corresponding stress level, the variables for rocks show an inflection of change or an initial sensitivity to axial pressure change. At this point, there is no irreversible deformation or failure inside the rocks. When the loading pressure continues, damage accumulates inside rocks. With increasing axial pressure, the scale of rock damage increases. During unloading of the confining pressure at higher stress levels, some damage has formed previously and cannot be eliminated, which has a definite impact on subsequent tests. Therefore, selecting a rational unloading level for unloading tests is vital.  
Figure 8: Evolution curve of mechanical properties of mudstone under loading.  
To demonstrate the relationship between confining pressure and unloading level, the stress-strain curves of siltstone during loading at confining pressures of 20 and 30 MPa (Figure 9) are provided below.  
Taking the deformation parameter as an example, the relationships between the axial pressure increment ratio of nominal elastic modulus and nominal Poisson's ratio versus the stress level are shown in Figures 10 and 11, respectively.  
According to in Figures 10 and 11, for siltstone, the stress level at the unloading point should be no higher than 67.47% at a confining pressure of 20 MPa, whereas at a confining pressure of 30 MPa, it should be no higher than 78%.  
When the confining pressure is at 10 MPa, the stress level \(\sigma_{\varphi}\), is expected to correspond to the threshold for an inflection in the deformation parameter, indicating whether a rock is damaged or not. The stress level \(\sigma_{\varphi}\) exhibits an approximately linear relationship with the confining pressure (Figure 12). The fitted formula is expressed as  
\[\sigma_{\varphi}=0.9\sigma_{3}+50.3 \tag{11}\]  
Figure 9: Stress–strain curves of siltstone under different confining pressures.  
Figure 11: _Cont._  
Figure 10: Evolution curve of deformation parameter–axial pressure increment ratio for siltstone (\(\alpha_{3}\)= 20 MPa).  
It should be noted that the coefficient in the formula is related only to the intrinsic characteristics of the rocks.  
The deformation of rocks in the elastic stage is reversible, and no substantial damage is detected. Therefore, for the studies of unloading tests of rocks under complex stress paths, we recommend selecting an unloading level that is equal to or slightly lower than the stress level at the elastic-plastic threshold (extreme value point of the deformation parameter) of rocks. Especially when unloading under low confining pressure, a stress level slightly below the value point of the deformation parameter can effectively prevent interior damage of rocks caused by a relatively high stress level before unloading at the conclusion of later tests.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.4 Collision Avoidance Technology
(2021) proposed an autonomous collision avoidance algorithm for ships based on the improved speed barrier method that includes a path replanning algorithm by fusing the dynamics model of unmanned ships, the motion model of the encountering ship and the international maritime collision avoidance rules [77]. The last one consists of hybrid algorithms, such as the hybrid algorithm for smart ship path planning, which improves the typical Artificial Potential Field (APF) algorithm and the Velocity Obstacle (VO) method while combining them with the international rules for collision avoidance at sea [78], and the intelligent hybrid collision avoidance algorithm based on deep reinforcement learning, which improves the original sampling mechanism of DDPG, among others [79]. A comparison of the collision avoidance decision processes for manned ships and smart ships is shown in Figure 8:  
Figure 8: A comparison of collision avoidance decision processes.
# Effects of Ethanol Concentration on Oral Aroma Release After Wine Consumption
## 2 Results and Discussion
### Effects of Ethanol and Individual Differences on the Prolonged Oral Aroma Release
Although additional studies are required in order to elucidate the mechanisms of action of ethanol content on the persistence of aroma compounds after wine intake, the higher prolonged oral aroma release observed in the presence of ethanol (from 5% _v/v_ onwards) could be due to different facts. On the one hand, ethanol might induce changes in surface tension affecting the distribution of the wine in the mouth and pharynx during consumption, allowing the sample to better spread out and favoring the formation of a larger surface in the oral cavity for volatile release [20]. On the other hand, in the presence of ethanol, esters could be more solubilized in the layer of wine that remains in the mouth after wine rinsing and, therefore, these compounds could be released more slowly over time. Finally, the mixture of wine lingering in the oral cavity could be submitted to the Marangoni and Rayleigh convection phenomenon described in vitro by [PERSON] and co-authors [15]. In this case, the presence of ethanol would help aroma compounds be released more easily from the layer of wine to the oral headspace. Additionally, other mechanisms related to the interactions of ethanol with salivary proteins or enzymes could not be discarded. In this regard, it could be possible that the presence of ethanol could have inhibited certain salivary enzymes (e.g., esterases), more prone to metabolize long chain esters [21], like ethyl octanoate and ethyl decanoate. Additionally, the presence of ethanol (in higher concentrations than those of the aroma compounds) could have occupied the binding sites of the salivary proteins modifying the interactions between aroma compounds and oral proteins as suggested previously in the presence of sugar [22]. Therefore, additional studies are required in order to corroborate these hypotheses.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
###### Abstract  
Maintaining critical data access latency requirements is an important challenge of Industry 4.0. The traditional, centralized industrial networks, which transfer the data to a central network controller prior to delivery, might be incapable of meeting such strict requirements. In this paper, we exploit distributed data management to overcome this issue. Given a set of data, the set of consumer nodes and the maximum access latency that consumers can tolerate, we consider a method for identifying and selecting a limited set of proxies in the network where data needed by the consumer nodes can be cached. The method targets at balancing two requirements; data access latency within the given constraints and low numbers of selected proxies. We implement the method and evaluate its performance using a network of WSN430 IEEE 802.15.4-enabled open nodes. Additionally, we validate a simulation model and use it for performance evaluation in larger scales and more general topologies. We demonstrate that the proposed method (i) guarantees average access latency below the given threshold and (ii) outperforms traditional centralized and even distributed approaches.  
Industry 4.0; data management; Internet of Things; performance analysis; experimental evaluation +
Footnote †: This paper is an extended version of our paper published in The 4 th IEEE International Workshop on Cooperative Wireless Networks (CWN) 2017, which was co-located with the IEEE 13 th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).
## 6 Simulation Result and Analysis
### Experiments Establishment
#### 6.1.2 DRL Training Parameters  
The adopted DQN structure is a convolutional neural network with an input layer, two convolutional layers and two fully connected layers. The conv layers consist of 16 3 \(\times\) 3 filters and 32 2 \(\times\) 2 filters. The number of neurons in the hidden layer is 128 and 48, while the ReLu is utilized as the activation function. All other parameters related to the DQN are listed in Table 2. Note that the listed parameters are selected from multiple simulation tests to balance the complexity and performance of the DRL algorithm.
## 3 Conclusions  
We investigated the novel design of a printed 5G monopole antenna with the parasitic elements and the lattice-structure reflector on the vehicle window glass. The parasitic elements were placed on either side of the monopole radiator to improve the bore-sight gain. To solve the radiation pattern distortion problem that occurs due to the thick vehicle window glass, the lattice-structure reflector was printed opposite to the monopole radiator. The proposed antenna had a measured reflection coefficient of \(-20\) dB, and a bore-sight gain of \(1.6\) dBi at \(28\) GHz. The results demonstrated that the proposed 5G monopole antenna with the parasitic elements and the lattice-structure reflector was suitable for 5G communication in vehicle applications. In future work, more design considerations such as the large window glass and delicate fabrication process might be needed for practical use of the proposed antenna.  
Figure 11: Simulated radiation patterns of the proposed antenna considering the vehicle platform effects: (**a**) 3D patterns according to the mount location; (**b**) 2D radiation patterns with and without the vehicle model.  
**Author Contributions:** Conceptualization, C.I., T.-H.L., D.J., N.-K.K. and H.C.; methodology, C.I., T.-H.L. and D.J.; software, C.I.; validation, C.I., T.-H.L., D.J. and H.C.; formal analysis, C.I., T.-H.L., D.J. and H.C.; investigation, C.I., T.-H.L. and D.J.; resources, C.I.; data curation, C.I.; writing--original draft preparation, C.I., T.-H.L., D.J. and H.C.; writing--review and editing, C.I., T.-H.L., D.J. and H.C.; visualization, C.I.; supervision, H.C.; project administration, H.C.; funding acquisition, N.-K.K. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the HYUNDAI Motors, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2017R1A5A1015596), and the NRF grant funded by the Korea government (no. 2015R1A6A1A03031833).  
The authors declare no conflict of interest.
###### Abstract
obtained 110 features, in all, from ECG, respiration, skin conductivity, and EMG signals, selecting partial features depending on the subject [30]. [PERSON] et al. proposed an emotion recognition method using multivariate extension empirical mode decomposition (MEMD) based on EEG signals [34]. [PERSON] et al. used electro-dermal activity (EDA), PPG, and EMG signals and extracted statistical features from the PSD of the amplitude versus the occurrence distribution [35]. Although manual features were effective at emotion recognition, they had a limitation in that they could not contain the overall details about the signal. As statistical features deal with statistical facts, information loss can occur. The second method of extracting features is based on deep-learning where features are extracted using various neural network models [36; 37]. [PERSON] et al. introduced a parallel model of a recurrent neural network (RNN) and a CNN with an EEG signal to acquire meaningful features [38]. [PERSON] et al. attempted to reconstruct the respiration signal with an auto-encoder model [39]. These deep-learning-based models have the advantage that many significant features can be extracted automatically. However, deep-learning features cannot include statistical meanings, for example the mean and standard deviation. Moreover, the features extracted from a deep-learning model do not contain information about the frequency domain. In this study, we took advantages of both methods by fusing statistical features and deep-learning features. For statistical features, we obtained six features in the time domain and 13 features in the frequency domain. For deep-learning features, the normal-to-normal (NN) interval and the PPG signal were used as the inputs of the CNN model. As a result, both statistical features and neural network features were used for accurate emotion recognition. Furthermore, we focused on fast-emotion recognition in which the recognition interval was short. In general, the existing research on emotion recognition has a recognition interval between 30 s and 1 min, which makes it difficult to detect fast-changing emotions. Therefore, we proposed a feature extraction method using a 10 s PPG signal. We extracted three types of PPG features for emotion recognition. The first feature was extracted using a statistical method based on both the time domain and the frequency domain. Then, we selected the meaningful statistical features by adopting [PERSON]'s correlation. Secondly, we acquired the NN interval features by using a CNN. The NN interval is used frequently to analyze a PPG signal. Lastly, we obtained PPG features through a normalized PPG signal using a CNN. We concatenated statistical features and deep-learning features and trained the deep-learning model. The rest of this paper is organized as follows. Section 2 provides a method of representing emotion and the typical parameters of emotion. Section 3 describes the proposed methodology of extracting PPG features and emotion classification. In Section 4, the description of the dataset and the experimental results are presented, and Section 5 presents the conclusion.
## 2 Materials and Methods
### Characterization  
To investigate the crystal structures of the synthesized TiO\({}_{2}\), X-ray diffraction (XRD) patterns were obtained from the TiO\({}_{2}\) NS-powder and analyzed. As shown in Figure 1, there were no XRD peaks for the as-synthesized TiO\({}_{2}\) NSs, but after heat treatment, XRD peaks were clearly shown and well-corresponded with the crystal planes of (101), (004), (200), (105), (211), (204), (116), (220), (215), and (303) of anatase TiO\({}_{2}\) (JCPDS No. 21-1272) [23]. Consequently, we confirmed that the synthesized amorphous structure of the TiO\({}_{2}\) NS was successfully crystallized to an anatase structure after heat treatment.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 3 Results and Discussion  
### Method Validation  
To account for inter-annual climate fluctuations, the long-term effects of cascaded reservoirs' operation were utilized to validate the proposed estimation method in this study. The historical hydrological data was divided into two periods based on the commencement of major reservoirs' storage. The first period represents the purely natural condition without any dams, while the second period represents the regulated situation after the completion of the main reservoirs' construction. This division provides suitable validation data for evaluating the performance of the estimation method.  
Figure 6: Framework for river runoff increment estimation.
## 4 Case Study
### Sensitivity and Comparison Analysis
#### 4.4.1 Sensitivity Analysis  
When calculating utility, it is important to convert the normalized rough values into crisp values. This conversion requires the introduction of a risk propensity indicator, \(\alpha\). If the DMs exhibit a more optimistic attitude to the results, a larger \(\alpha\) value (\(\alpha>0.5\)) should be selected; if the DMs exhibit a more pessimistic attitude to the result, a smaller \(\alpha\) (\(\alpha<0.5\)) value should be selected; if the DMs maintain a practical and moderate attitude, o.5 should be selected as the \(\alpha\) value. The CV and economic utilities associated with seven values of \(\alpha\) were calculated, as presented in Figure 5. The utilities increase with increasing \(\alpha\) and the changes in CV and economic utilities with \(\alpha\) are substantially similar. Neither of these trends exhibits any significant fluctuation. Based on these behaviors, it can be deduced that the comprehensive criteria weights obtained by combining rough BWM and EWM values can eliminate uncertainties due to design preferences while ensuring stability in the calculation of CV and economic utilities.  
The ranking of warehouse logistic system solutions for each \(\alpha\) value is presented in Table 11. By comparing the sensitivity results, when DMs exhibit a strong negative attitude to risk propensity (\(\alpha\)= 0, 0.1, 0.3), CS\({}_{4}\) is the optimal solution. In this case, the DMs can tolerate higher costs to improve PS (i.e., \(U_{P}>\varphi_{P}(v)\), \(U_{E}<\varphi_{E}(v)\)) and minimize risk-induced design uncertainties in the decision-making process. When DMs exhibit a positive risk profile or weaker negative attitude (\(\alpha\) = 0.5, 0.7, 0.9, 1), CS\({}_{1}\) has a maximum priority. Because the DMs have adopted an optimistic view toward the solution values, they will believe that it is possible to ensure adequate service quality while prioritizing economic value (\(U_{P}>\varphi_{P}(v)\), \(U_{E}<\varphi_{E}(v)\)). Therefore, the proposed decision-making framework can be flexibly used by enterprises or researchers, which enables the framework to adapt to  
Figure 4: Game utility of CV and economic objective under different solutions and Shapley value.  
different market conditions and guarantees that the selected warehouse logistics system solution will always maximize the overall design value.
###### Abstract  
Unmanned aerial vehicles (UAVs), when interconnected in a multi-hop ad-hoc fashion, or as a flying ad-hoc network (FANET), can efficiently accomplish mission-critical tasks. However, UAVs usually suffer from the issues of shorter lifespan and limited computational resources. Therefore, the existing security approaches, being fragile, are not capable of countering the attacks, whether known or unknown. Such a security lapse can result in a debilitated FANET system. In order to cope up with such attacks, various efficient signature schemes have been proposed. Unfortunately, none of the solutions work effectively because of incurred computational and communication costs. We aimed to resolve such issues by proposing a blind signature scheme in a certificateless setting. The scheme does not require public-key certificates, nor does it suffer from the key escrow problem. Moreover, the data that are aggregated from the platform that monitors the UAVs might be too huge to be processed by the same UAVs engaged in the monitoring task. Due to being latency-sensitive, it demands high computational capability. Luckily, the envisioned fifth generation (5G) mobile communication introduces multi-access edge computing (MEC) in its architecture. MEC, when incorporated in a UAV environment, in our proposed model, divides the workload between UAVs and the on-board microcomputer. Thus, our proposed model extends FANET to the 5G mobile network and enables a secure communication between UAVs and the base station (BS).  
blind signature; security; MEC; UAVs; FANET; 5G; IoT +
Footnote †: journal: Accepted: 22 December 2019; Published: 26 December 2019