esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 6 Simulations
Figure 12: Probability of users keeping the same clusters, as a function of the AoA error. Figure 13 plots the average number of iterations until convergence as a function of the AoA standard deviation of error and the number of clusters. It can be noted that the convergence was less dependent on the error and slightly increased for higher AoA error values. To plot the sum rate performance of a hybrid BD-THP precoding scheme, in Figure 14, we used the busy hour user distribution above and applied the BD linear precoding between the three clusters. Inside each cluster, 6, 6, and 4 users were randomly selected and precoded through a non-linear THP precoding scheme, as a total of 16 users could be co-scheduled through MU-MIMO. Compared to the state-of-the-art precoding schemes like THP and RZF, the hybrid precoding followed the trend of the THP precoding, although the performance was slightly reduced due to the inferior performance of the linear precoding. It can be shown that even in Rayleigh uncorrelated channels, the linear precoding performance is below the non-linear precoding performance. In BD-RZF, K-means clustering with a chordal distance metric was used to group the users into a two-stage precoding scheme. BD-RZF scheme had lower performance than BD-THP because, for \(K=N_{T}\), there were not enough degrees of freedom to separate the users in the spatial domain when applying linear precoding techniques. The performance was even worse than of the RZF precoder, due to reducing the effective channel matrix and using statistical CSI to reduce the complexity. This observation is also in line with the findings in [13]. Figure 14: Sum rate vs. \(E_{b}/N_{0}\) for busy hour user distribution and random user selection. Figure 13: Number of iterations until convergence, as a function of the AoA error.
## 3 Results and Discussion
### Optimization and Verification of Predictive Responses  
Based on the interaction of the three critical parameters (extraction time, temperature, and pH), the UAE process was optimized in order to obtain the highest yield of protein recovery and antioxidant activity (ABTS and ORAC values). The optimal UAE conditions obtained are presented in Table 9. Furthermore, in order to confirm the accuracy and the reliability of the optimal conditions and to validate the adequacy of the model, additional experiments were carried out under the optimal conditions. The predicted and the experimental values for the different responses are shown in Table 9. As it can be seen, the experimental values were close to the expected values, confirming the validity of the model. Thus, this model has high accuracy in predicting the experimental optimal conditions, and it can be greatly applicable and operable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Side Stream** & **Extraction Time (min)** & **Temperature (\({}^{\circ}\)C)** & **pH** & **Antioxidant Capacity (\(\mu\)M TE) (ORAC)** \\ \hline Head & 30 & 50 & 8.5 & 369 \\ Skin & 28 & 25 & 5.5 & 389 \\ Bone & 20 & 20 & 7.8 & 679 \\ Viscera & 30 & 50 & 7.0 & 5996 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal conditions for ORAC optimal values.
## 4 Performance Metrics
### Performance Metrics for Emergency Packets
#### 4.1.2 Emergency Packets' Throughput  
Emergency packets' throughput \(\theta_{1}\) is the amount of emergency packets forwarded completely in a unit time slot, expressed as follows.  
\[\theta_{1}=p_{1}-\beta_{1} \tag{20}\]
# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
###### Abstract  
Gaze estimation plays a significant role in understating human behavior and in human-computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.  
gaze tracking; convolutional neural network; human computer interaction +
Footnote †: journal: Applied Sciences
# Preamble Design and Collision Resolution in a Massive Access IoT System
## 3 Preamble Design  
In this section, the conventional contention-based RA procedure and the structure of conventional preamble sequence are first introduced respectively. To address the drawback of conventional preamble sequence, a new sub-preamble sequence design is then proposed.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### Dataset
#### 3.4.1 Fire Identification  
The suggested model may be thought of as a combination of all three existing ideas, whether it is from research or a product. As introduced in Section 1, people have made many different efforts to prevent wildfires from happening or spreading. We converged all three existing ideas into one, where we can use smell and sight by converging IoT and AI. IoT device refers to all the devices that are connected to the Raspberry Pi 4 listed in Section 3.2. Section 3.3 contains an MQ-2 flammable gas and smoke sensor that immediately activates the camera, which is operating and looking for fires whether or not the MQ-2 detects any smoke, when it detects any gas or strange odor. AI is then used to determine if a fire is present. AI is generally explained as artificial intelligence but, in this manuscript, it refers to the technique of YOLOv5. YOLOv5 is used to train an image and label it into classes to be identified through images. Moreover, the YOLOv5 correctly identifies the fire using its algorithm. Once the fire is correctly identified, it records and sends those highlighted images to the fire department. Then, the fire department can verify and validate if the recording and the images are an actual fire or just a false alarm. Once it is identified as a fire, the fire department hits confirm on their screen to send an alarm notification within a 1-mile radius of the recording. The notification sends a nearby emergency route to quickly guide anybody within that 1-mile radius away from harm. However, if it happens to be a camper trying to cook with fire, or if it is someone trying to smoke in the forest, a notification is sent within the 1-mile radius reminding the people with access to fire of the penalty of lighting a fire inside a forest and suggesting to turn off any fire hazard items.
## 2 The Proposed Pulse-OTFS Signal
### Derivation of ACF and PSD
#### 2.2.2 Derivation of PSD  
Similar to OFDM, the information symbols on each subcarrier or symbol period of the signal in (3) are statistically independent and orthogonal stochastic processes [36; 37]. Consequently, the PSD of the Pulse-OTFS modulation is:  
\[G(f)=\frac{T}{M}\sum_{k=0}^{M-1}\left|\frac{\sin[\pi(f-f_{k})T]}{\pi(f-f_{k})T} \right|^{2} \tag{14}\]  
To verify the correctness of (14), we compared the theoretical and simulation PSDs of the Pulse-OTFS(1,1023) signal, where \(f_{\mathrm{c}}\) is 1.023 Mcps, the sampling rate is 2 MHz, the delay bins \(M=1023\), the Doppler bins \(N=10\), and the signal duration is 100 ms. Figure 2 shows that the theoretical PSD is consistent with the simulation result.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 3 Analysis of Angle Measurement Error
### Angle Measurement Error Caused by Radial Error Motions  
The axis of rotation and axis average line are not at the same position when there are error motions of the rotating shaft. As shown in Figure 5, \(O\) and \(O_{\rm O}\) are not the same point in the XY plane. The geometric center of the grating disk \(O_{1}\) and axis of rotation \(O\) are the same point when there is no installation eccentricity. The trajectory of the axis of rotation \(O\) is an irregular figure. The radial error motions in the X and Y direction related to the angle \(\theta\) are \(\delta_{\rm X}(\theta)\), \(\delta_{\rm Y}(\theta)\).
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 3 Problem Formulation
### PSO Implementation  
A MATLAB code is built to emulate the proposed PSO-based approach. Based on our experience, PSO performance is mainly affected by the initial inertia weight and the maximum allowable velocity. To obtain the effective values of these parameters, several runs have been done. Using uniform distribution, the random values \(r_{1}\) and \(r_{2}\) could be generated in the traditional PSO [41]. In addition, to get the global optima effectively and quickly without falling into the local optima, large-scale random values should be selected in the PSO algorithms. However, a range of [-1, 1] is more beneficial to improve the global searching capability in a low dimensional practical optimization problem [41]. For PSO algorithm with different types of random values, the impact of random values on the particle velocity was discussed in details [41]. Actually, to achieve an efficient PSO performance, these parameters should be carefully selected [36]. In this work, the following PSO parameters are assumed as given:  
* Population size = 20;
* Decrement constant (\(\alpha\)) = 0.98;
* Inertia weight factor = 1;
* Acceleration constants: \(c_{1}=c_{2}=2\);
* Generation or iteration = 100.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 5 Future Work
### Energy-Efficient Routing  
As noticed, the primary goal of any routing protocol for LLNs is to conserve power and a reliable data delivery [74; 75; 76]. As highlighted in Table 2, the energy-aware routing, FLEA-RPL [60] is based on load, residual energy, and ETX. However, we note that in a lossy environment, lack of sink-to-sink coordination [56] and detached mobile nodes [68] lead to duplicate transmission and hence, unnecessarily high energy consumption. Hence, it is important to address issues with 'node coordination' and 'detached mobile nodes' in a comprehensive framework. We note that Opportunistic Routing (OR) [77] improves the network reliability with reduced retransmission, furthermore, the network coding [78; 79] has been proved to be efficient for data collection applications in dynamic and lossy networks [80]. Therefore, these techniques can be used for RPL to propose energy-efficient routing due to sink-to-sink coordination and detached mobile nodes.
###### Abstract  
City growth and its resulted environmental issues are promoting a citywide application of sustainable steel structures. However, designers and constructors often hold conflicting perspectives on the sustainable construction of steel structures, which has been identified as a barrier to achieve sustainability in this area. Given that the existing sustainability indicator systems of steel structures are either design-oriented or construction-driven, this study aims to develop a new one by synthesizing both designers' and contractors' opinions in the development of such indicators. Semi-structured interviews were conducted to identify potential indicators. A questionnaire survey was then used to collect the viewpoints of designers and constructors on the identified indicators. Finally, a fuzzy set algorithm and hierarchical clustering were employed to detect the indicators' relationships. The results present a three-dimensional indicator system composed of social-technical sustainability, greenness, and economic sustainability. Furthermore, it is found that designers and constructors have discrepant opinions on social-technical sustainability and greenness, while their views on economic sustainability are similar. The research findings provide managerial hints to the attainment of sustainable steel structures and enhance stakeholders' understanding of design-and-construction integration in steel structure projects.  
steel structures sustainable construction; synthesizability; construction management; discrepancy 2021 11  
building orientation, internal layout, landscape) in the design phase, and they might be unacquainted with the subsequent construction process [3]. On the contrary, constructors focus more on the construction activities, and they fulfill their construction duties in a way different from that of designers. Consequently, designers and constructors tend to have different perspectives on attaining sustainable steel structures [4].  
The importance of both design and construction has been implied in the mainstream and leading sustainability assessment systems, such as the Leadership in Energy and Environmental Design (LEED) in the U.S. and the Environmental Assessment Method (BREEAM) in the U.K. For instance, LEED places importance on construction-related indicators such as \"construction water efficiency\" and \"construction electricity consumption\". BREEAM stresses the design-related indicators, including \"sustainable building site selection.\" Due to the different focuses, the existing sustainability assessment systems cannot integrate both designers and constructors' concerns in the development of sustainable steel structure projects. No effective approaches are available to develop a set of steel structure sustainability indicators that reflect the integrated requirements of design-construction. The study aims to address this research gap by synthesizing perspectives of both designers and constructors on the sustainability of steel structures.
# Development of a Metalens for Radio Wave Ground- Penetrating Radars
## 4 Discussion and Conclusions  
The study presented in this paper showed the results of improving the systems of geosounding of roadways. The theoretical part of the article showed the results obtained using 3D numerical simulation of FDTD. It was shown that a composite medium in the form of a layer of finite dimensions, composed of a hollow cylinder with an annular cross-section, has the properties of a metamaterial in the UHF region of wavelengths. The emerging negative refractive index generates a curvature of the phase front of the transmitted wave. This, combined with the small transverse size of the layer (on the order of the Fresnel zone size), leads to the overfocusing effect. The resulting transverse size of the focus area is less than the diffraction limit set by the wavelength 0.8 \(\lambda\). The effect persists over a wide range of VHF wavelengths. An experimental study confirmed the possibility of focusing by the compositional structure of a metamaterial. The created mock-up probing systems of UWB antennas and metamaterial lenses in the course of a comparative experiment  
Figure 14: Experimental study using an antenna with a metamaterial photo booth (**a**) and the reconstructed image (**b**), scheme of the experimental setup (**c**), and one-dimensional section of the georadiogram (**d**).  
Figure 15: Experimental study using an antenna with a metamaterial photo stand with metal plates (**a**), reconstructed image (**b**).  
showed superiority over a system with a parabolic mirror. The georadiograms obtained using the developed system had more contrast, which indicates a greater signal energy. An experiment simulating the sounding of a railway track showed the viability of using the developed system, and a wooden heterogeneity in the sand was detected. In the future, it will be necessary to create an antenna array from the presented elements of a UWB antenna with a metamaterial, but this represents much work because, for such a configuration it would be difficult to decouple the elements due to significant reflection from the focusing system located near the emitter.  
Conceptualization, A.G. and A.M.; methodology, S.S.; software, A.G.; validation, I.T., S.S. and R.S.; formal analysis, S.S.; investigation, R.S.; resources, A.G.; data curation, A.M.; writing--original draft preparation, A.G.; writing--review and editing, S.S.; visualization, A.G.; supervision, R.S.; project administration, A.G.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Russian Science Foundation Grant No. 22-22-20101 \"Development of methods for identifying potentially dangerous sections on highways using georadio tomography and funded by the Administration of the Tomsk Region\".  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; nor in the decision to publish the results.
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 3 Proposed Method
### Interaction  
We determine the initial value of the prior probability \(\mu_{i}(k-1)\), the Markov transition probabilities \(p_{ij}\), the prior state \(\hat{X}_{i}(k-1|k-1)\), and covariance matrix \(P_{i}(k-1|k-1)\) of the proposed algorithm. However, the transition probabilities \(p_{ij}\) are not known in practice, so the initial of the transition probabilities \(p_{ij}\) are chosen based on the prior knowledge. Before the interaction, determination of the mixing probabilities \(\mu_{ij}(k-1|k-1)\) is required. The mixing probability \(\mu_{ij}(k-1|k-1)\) is given as:  
\[\mu_{i|j}(k-1|k-1)=\Big{(}1/\overline{c}_{j}\Big{)}p_{ij}\mu_{i}(k-1),\;k=1,\; \ldots,\;K. \tag{7}\]  
The normalization factor \(\overline{c}_{j}\) is:  
\[\overline{c}_{j}=\sum_{i}p_{ij}\mu_{i}(k-1), \tag{8}\]  
where, the mixed probabilities \(\mu_{ij}(k-1|k-1)\) obtained are applied to the interaction process. Furthermore:  
\[\hat{X}_{0j}(k-1|k-1)=\sum_{i}\hat{X}_{i}(k-1|k-1)\mu_{ij}(k-1|k-1), \tag{9}\]  
\[\overline{X}_{ij}(k-1|k-1)=\hat{X}_{i}(k-1|k-1)-\hat{X}_{0j}(k-1|k-1), \tag{10}\]  
\[P_{0j}(k-1|k-1)=\sum_{i}\mu_{ij}(k-1|k-1)\Big{\{}P_{i}(k-1|k-1)+\widetilde{X}_ {ij}(k-1|k-1)\widetilde{X}_{ij}^{T}(k-1|k-1)\Big{\}}, \tag{11}\]  
where \(\hat{X}_{0j}(k-1|k-1)\) is the mixed state estimate of the LOS/NLOS model at time step \(k-1\) and \(P_{0j}(k-1|k-1)\) is the mixed state covariance matrix estimate of the LOS/NLOS model at time step \(k-1\). Model matching of the proposed algorithm is performed after the interaction is completed.  
Figure 2: The flowchart for the proposed algorithm. EKF: extended Kalman filter.
## 7 System Prototype
### Experimental Results
The DR without retransmissions was low (61.2%) because, as Figure 15 shows, this channel overlaps with channel 11 of IEEE 802.11. The retransmission mechanism was able to recover only 43% of the lost packets, since the beacon and the retransmitted packet were also subject to the interference. The three cases where the frequency hopping mechanism was used presented similar results for the DR without retransmissions, ranging from 89.6% to 92.5%. Results are better than in the previous case since only 25% of the superframes (corresponding to four of the 16 IEEE 802.15.4 channels) were significantly affected by the 802.11 interference. The combination of the contention-free retransmissions mechanism with the frequency hopping mechanism makes the lost transmissions and the corresponding retransmissions occur in different IEEE 802.15.4 channels. When a transmission is lost due to interference from a IEEE 802.11 channel, a jump of five IEEE 802.15.4 channels is always enough to allow the retransmission to escape from that interference. Therefore, as the results show, with \(n_{j}=5\), the retransmissions where able to recover all lost data packets, allowing the DR with retransmissions to reach 100%. Retransmissions are less efficient with jumps of one (\(n_{j}=1\)) or three (\(n_{j}=3\)) channels, because such jumps are not long enough to allow the retransmissions to escape interference from an IEEE 802.11 channel all the time.
## 5 Numerical Validation with Analytical Method
For the design buckling resistance, the deviation between the finite element method and the analytical results is between 0 and 14.7%. The higher relative errors are obtained for lesser cross-section sizes in the study. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Fixed-Fixed**} & \multicolumn{4}{c}{**Fixed-Pinned**} & \multicolumn{4}{c}{**Pinned-Pinned**} & \multicolumn{4}{c}{**Fixed-Free**} \\ \cline{2-13} \cline{13-13}  & \(L\) & \(N_{\text{w},A}\) & \(\mathbf{k_{\text{N},kA}}\) & \(\mathbf{E_{\sigma}}\) & \(\mathbf{E_{\text{b},kl}}\) & \(N_{\text{w},A}\) & \(\mathbf{k_{\text{N},kA}}\) & \(\mathbf{E_{\sigma}}\) & \(\mathbf{E_{\text{b},kl}}\) & \(N_{\text{w},A}\) & \(N_{\text{b},kA}\) & \(\mathbf{E_{\sigma}}\) & \(\mathbf{E_{\text{b},kl}}\) \\  & **mm** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** & **kN** \\ \hline \multirow{3}{*}{**P**} & 500 & 3774.8 & 68.0 & 0.3 & 2.0 & 1398.4 & 66.0 & 0.3 & 0.8 & 949.7 & 67.2 & 0.3 & 0.2 & 235.3 & 63.1 & 0.6 & 1.2 \\  & 1000 & 941.7 & 65.8 & 0.5 & 2.3 & 486.1 & 58.5 & 0.6 & 10.8 & 235.9 & 56.1 & 0.3 & 10.1 & 61.7 & 48 & 4.2 & 14.5 \\  & 1500 & 423.2 & 55.5 & 0.6 & 14.7 & 211.4 & 63.0 & 1.5 & 2.0 & 98.5 & 51.8 & 6.4 & 4.4 & 28.1 & 24.4 & 6.8 & 8.6 \\ \hline \multirow{3}{*}{**P**} & 500 & 6646.0 & 82.1 & 0.1 & 2.4 & 3402.0 & 82.1 & 0.4 & 1.4 & 14.659.4 & 78.7 & 0.1 & 4.1 & 415.3 & 80.2 & 0.0 & 3.3 \\  & 1000 & 1661.3 & 74.2 & 0.0 & 9.6 & 846.3 & 77.8 & 0.1 & 3.3 & 418.1 & 70.3 & 0.7 & 9.3 & 105.9 & 67.2 & 2.0 & 10.2 \\  & 1500 & 737.4 & 70.4 & 0.1 & 12.0 & 375.2 & 82.1 & 0.4 & 6.5 & 184.2 & 78.1 & 0.2 & 9.9 & 48.0 & 40.1 & 4.0 & 8.0 \\ \hline \multirow{3}{*}{**P**} & 500 & 16025.3 & 109.4 & 0.1 & 3.6 & 8179.1 & 110.1 & 0.1 & 2.3 & 4012.3 & 106.9 & 0.0 & 4.1 & 1016.3 & 92.4 & 1.3 & 13.9 \\  & 1000 & 4015.0 & 101.1 & 0.1 & 1.3 & 2044.1 & 101.7 & 0.1 & 7.5 & 1001.6 & 103.7 & 0.1 & 3.4 & 2502.2 & 100.8 & 0.2 & 5.4 \\ \cline{1-1}  & 1500 & 1783.4 & 102.4 & 0.0 & 6.5 & 909.8 & 99.0 & 0.0 & 7.4 & 446.0 & 101.1 & 0.0 & 1.2 & 112.7 & 83.2 & 1.2 & 12.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results in all different SHS columns. Figure 7: Analytical and numerical solution of design buckling resistance loads, where for analytical solution the variable is \(N_{\text{b},\leavevmode\
obreak\ kd}\) and for numerical results it is \(N_{\text{b},\leavevmode\
obreak\ kd,A}\). In the following work, other numerical solutions to be implemented may provide closer results if the tolerance imposed on the convergence criteria is different. Parameters related to the nonlinear analysis performed on these numerical results used the large displacement method, but the arc length method can also be implemented to compare other numerical solutions. The present results increase the solution already started by the author [19] but now cover more columns under analysis and with different variables under test.
## 5 Results and Discussion
### Instantaneous Goodput
#### 5.2.3 NTRUEncrypt-Secured Network  
Figure 15 shows that goodput increases with a notable trend every 25 s. The system takes the first 25 s to generate the keys, and after that, the system finds the rerouting and shows a rapid increase due to rerouting. Hence, the value goes to 2500 kbps at 26 s. Later, the system finds the optimal path due to rerouting almost every 25 s and shows the increase in goodput, up to 19,500 kbps at 200 s. It shows that our system operates safely under a black hole attack.