esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
## 3 Proposed Method  
In this study, we propose a human-height estimation method using color and depth information. It is assumed that a depth camera is fixed in a certain position. Color and depth videos are captured by the depth camera. Then, a human body and a human head are extracted from current frame in color or depth video. A head-top and a foot-bottom are found in the human head and the human body, respectively. Two head-top and foot-bottom points are converted into 3D real-world coordinates by the corresponding pixel values of the frame in depth video. Human height is estimated by calculating a distance between two real-world coordinates. The flow of the proposed methods is shown in Figure 1.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 1 Introduction
Thus, the estimated CSI is an imperfect version of the instant of packet transmission, and the zero-forcing secure MIMO algorithm should be re-designed. In this paper, two deep CNN-based detectors are proposed, and a training set with imperfect CSI as well as the original messages or ideal CSI is fed to the CNN model to produce the refined CSI. Simulation results show that the proposed deep learning-based detectors outperform the classical maximum likelihood detector (MLD), especially in small correlation factor cases. The impacts of system parameters, such as the correlation factor, the antenna number, are evaluated in different setup scenarios. The main contributions of this paper are as follows:  
* We employ the deep learning-based technique for secure MIMO communications in heterogeneous networks, which can exploit the benefits of CNN learning model to produce more accurate CSI and meanwhile reduce the bit error rate (BER) of the receiver. * We provide the detailed framework of deep learning-based detectors, where imperfect CSI as well as the original messages or ideal CSI are included in the training set, and can be used in different application scenarios. * We present simulation results for deep learning-based detectors in heterogeneous networks. With the help of the CNN technique, the proposed detectors show obvious performance gain over the MLD with acceptable computational cost. _Notations_: We use \(\mathcal{CN}(\mu,\sigma^{2})\) to represent the circularly symmetric complex Gaussian random variable with mean \(\mu\) and variance \(\sigma^{2}\), \(f_{X}(x)\) and \(F_{X}(x)\) denote the probability density function (PDF) and cumulative distribution function (CDF) of a random variance \(x\), respectively, \(\text{diag}(\mathbf{A})\) is a row vector consisting of all diagonal elements of \(\mathbf{A}\), \(\mathbf{A}^{*}\) is the conjugate transpose of the \(\mathbf{A}\), and \(\mathbf{H}_{FM}\) denotes the wireless channel fading matrix from \(M\) to \(F\).
## 4 Results and Discussion
### Construction of a Quantitative Optimization Model for Prospecting Target Areas
The cumulative area of the 93 level-II prospecting target areas is 1022.27 km\({}^{2}\), with the smallest area measuring 0.5 km\({}^{2}\) and the largest area measuring 33.77 km\({}^{2}\). The cumulative area of the preferred I- and II-level target areas is 1861.02 km\({}^{2}\), representing a mere 3.67% of the total area of the study region (49,430 km\({}^{2}\)). A total of 150 I- and II-level target areas have been identified for prospecting Au mineralization regions, with the selection process based on quantitative optimization using geophysical and geochemical prospecting information. Of these, 22 target areas are known ore-bearing target areas, representing a ratio of 33.3 times higher than proportion of target areas with minerals. The quantitatively optimized I- and II-level target areas are distributed across a mere 3.67% of the total area of the study area. It is believed that the quantitative target selection model for Au mine searching based on geophysical and geochemical exploration information is highly accurate for determining and selecting the target area for Au mine searching in the Beishan area of Gansu Province. In comparison with the traditional selection method, it is hypothesized that the precision of the target area is significantly enhanced and the area of the target area is considerably reduced.
# A Multilevel Road Alignment Model for Spatial-Query-by-Sketch
## 3 Materials and Methods
### Characteristics and Matching Approach for Composite Roads
#### 3.3.4 Relative Positions of Intersections  
The relative positions of the intersections (\(RPI\)) in two connected roads can further clarify the connection between two roads. As shown in Figure 11, \(P\) is the intersection of two connected roads (green and blue lines). \(L_{1}\) and \(L_{2}\) refer to the lengths of two parts of the road (in green) separated at \(P\).  
The similarity between two roads in terms of the \(RPI\) can be determined using the following process:  
* The lengths of two parts of a road separated by \(P\) are computed for both the sketch and OSM aspects to obtain \(L_{1 OSM}\), \(L_{2 OSM}\), \(L_{1 Sketch}\), and \(L_{2 Sketch}\);
* The absolute distances \(\Delta d_{OSM}\) and \(\Delta d_{Sketch}\) are computed using Formulas (8) and (9): \[\Delta d_{OSM} =|L_{1 OSM}-L_{2 OSM}|\] (8) \[\Delta d_{Sketch} =|L_{1 Sketch}-L_{2 Sketch}|\] (9)  
Figure 11: \(P\) (yellow circle) is the intersection of two roads (green and blue). \(L_{1}\) and \(L_{2}\) denote the lengths of two parts of one road (green) separated at \(P\).  
Figure 10: \(P\) is the intersection of two roads \(R_{Sketch}1\) and \(R_{Sketch}2\). _Buffer_ is the buffer of a certain radius at \(P\). Points \(p_{1}\), \(p_{2}\), and \(p_{3}\) indicate the intersections between _Buffer_ and the two roads. \(x_{1}\), \(x_{2}\), \(x_{2}\) are the \(x\)-coordinate values of points \(p_{1}\), \(p_{2}\), and \(p_{3}\), respectively.  
* \(\Delta d_{OSM}\) and \(\Delta d_{Sketch}\) are normalized on the basis of the length of the roads in OSM and sketches to obtain \(Nor_{OSM}\) and \(Nor_{Sketch}\) (see Formulas (10) and (11)), respectively: \[Nor_{OSM}=\Delta d_{OSM}/L_{OSM}\] (10) \[Nor_{Sketch}=\Delta d_{Sketch}/L_{Sketch}\] (11)
* The distance \(D_{Nor}\) between \(Nor_{OSM}\) and \(Nor_{Sketch}\) is compared to obtain \(SimPI_{(Sketch,OSM)}\) in terms of the \(RPI\). If \(D_{Nor}\) is smaller than or equal to \(Threshold_{PI}\), the composite roads from a sketch and OSM are considered similar, as expressed in (12) and (13): \[D_{Nor}=|Nor_{OSM}-Nor_{Sketch}|\] (12) \[SimRPI_{(Sketch,OSM)}\gets D_{Nor}\leq Threshold_{PI}\?\ True\ :\ False\] (13) where \(SimRPI_{(Sketch,OSM)}\) represents the similarity between two composite roads from the sketch and OSM, in terms of the \(RPI\), and \(Threshold_{PI}\) is the threshold defined to compare the similarity in terms of the \(RPI\).
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 5 Architecture Description and Evaluation
### Messages Size & Overhead  
Table 2 presents a summary of the authentication message sizes and their respective NB-IoT LTE PDU length. The application payload of NB-IoT LTE for the authentication process carries the IP packets outside of the 4G system. The figure in the _Message Length_ column in Table 2 does not take into account lower layer protocol headers. On the contrary, the figure of the _NB-IoT PDU Length_ column considers the  
headers of NB-IoT, IP, and UDP, with values of 10, 20, and 8 bytes, respectively. Furthermore, the LTE RAN of NB-IoT employs a stack consisting of three different protocols, from lower to higher positions in the stack: Medium Access Control (MAC) [49], Radio Link Control (RLC) [50], and Packet Data Convergence Protocol (PDCP) [51]. Each of these protocols has a variable length header, and its size depends on several circumstances related to the previous attachment of the UE to the eNodeB. In addition, during the attachment process, signaling data is exchanged between the UE and the eNodeB, which serves the purpose of readying both entities for the following user plane exchanges. Hence, an exact figure of the total overhead introduced by the NB-IoT LTE protocol stack cannot be achieved due to the variability of header sizes in each message exchange. Thus, for the purpose of the overhead analysis, an average header length value of 10 bytes was fixed, which conveys the entire NB-IoT LTE protocol stack. As a result, a summary of the total messages and header sizes is presented in Figure 6.  
\begin{table}
\begin{tabular}{l l r r} \hline \hline
**Protocol** & **Message** & **Message Length** & **NB-IoT PDU Length** \\ \hline \multirow{8}{*}{LO-CoAP-EAP} & POST & 29 & 67 \\  & POST(EAP-PSK1) & 36 & 74 \\  & ACK(EAP-PSK2) & 69 & 107 \\  & POST(EAP-PSK3) & 68 & 106 \\  & ACK(EAP-PSK4) & 48 & 86 \\  & POST(EAP-Success) & 38 & 76 \\  & ACK & 23 & 61 \\  & **Total** & **311** & **577** \\ \hline \multirow{8}{*}{PANA} & PCI & 16 & 54 \\  & PAR & 40 & 78 \\  & PAN & 40 & 78 \\  & PARReq(Id) & 48 & 86 \\  & PANRep(Id) & 60 & 98 \\  & PAR(EAP-PSK1) & 56 & 94 \\  & PAN(EAP-PSK2) & 84 & 122 \\  & PAR(EAP-PSK3) & 84 & 122 \\  & PAN(EAP-PSK4) & 68 & 106 \\  & PAR(EAP-Success) & 88 & 126 \\  & PAN & 52 & 90 \\  & **Total** & **636** & **1054** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length of messages exchanged in each protocol.  
Figure 6: Total sum of authentication process messages and header sizes grouped by protocol.
## 5 Simulation Experiment
### Performance Comparison Test  
In order to enhance the stability and reliability of the experimental results, 30 repeated experiments were conducted to mitigate the influence of random factors and improve the accuracy and reproducibility of the experiment. Meanwhile, to verify the effectiveness of the proposed mathematical model, the IBM ILOG CPLEX 12.9.0 solver was utilized to solve exactly 15 FJSP test instances. During the solving process, the maximum computation time for CPLEX was set to 3600 s, and the exact solutions found within the specified time limit were used as reference solutions. The test results of the 15 instances are shown in Table 5, in which [30] the known optimal solution, \(B_{KS}\), of each example is obtained from the literature, \(t_{B}\) is the optimal value of the completion time in the 20 solution experiment results of the example, \(t_{a}\) is the average value of the 20 solution experiment results of the example, and \(t_{av}\) is the average calculation solution time of the \(t_{B}\) value. From Table 5, it can be observed that CPLEX is able to obtain known optimal solutions for small-scale instances (Kacem01-03; MK01-02). However, for larger instances, it fails to reach the optimal solution within the given time limit. Additionally, the algorithm in this paper can obtain the best result when the scale of the example is small. The optimal scheduling Gantt charts for Kacem01 and Kacem03 solved by the algorithm in this article are shown in Figures 9 and 10, respectively. Because this paper is based on a multi-objective mathematical model, when the scale of the example is large, the optimal result obtained is inferior to the optimal solution of the known single-objective model. Figure 11 shows the Gantt chart of the optimal _makespan_ of 327 for the larger-scale instance, MK09, solved by the algorithm in this article. However, in all the examples, the average value of the optimal solution of 30 experiments can smoothly approach the optimal result, which shows the effectiveness and reliability of the IDPSO algorithm. Figure 8: Parameter factor mean effect graph. To further demonstrate the solution effectiveness of this algorithm, it is compared with the discrete particle swarm optimization (DPSO) algorithm proposed by [PERSON] et al. (2018), the efficient heuristic algorithm based on the construction process proposed by [PERSON] (2018), the improved discrete moth fire-fighting optimization (IDMFO) algorithm proposed by [PERSON] et al. (2018), and the hybrid gray wolf optimization (HGWO) algorithm proposed by [PERSON] (2018). We compare and analyze from the perspective of relative error, and calculate the relative error, \(e\), of the \(t_{B}\) value and the \(B_{KS}\) value. The calculation formula is shown in Equation (22):  
\[e=(t_{B}-B_{KS})/B_{KS}\times 100 \tag{22}\]  
Then, for each algorithm experiment, we compute the average value MRE of the relative error, \(e\), of the 15 FJSP test instances and count the number of times, \(N_{B}\), that the optimal value was obtained.
## 3 Phytohormonal Regulation in Salinity and Water Deficit Stresses
### Abscisic Acid  
ABA contributes to a variety of regulatory networks involving plant's response to drought and salinity stresses that cause osmotic and ionic stresses. Under conditions of osmotic stress, abscisic acid is generally considered as a stress signaling hormone, and the expression of stress-responsive genes in plants is primarily regulated by ABA-dependent and ABA-independent pathways based on the _cis_-acting regulatory elements present in the promoter regions of the stress-responsive genes [49; 50]. The _cis_-acting elements are regulatory sequences found at the promoter region of many genes, and they are important because they act as physical targets for many transcription factors in order to activate the downstream target genes. The transcription factors usually carry a specific DNA-binding domain that binds to particular _cis_-regulatory elements [51]. In the ABA-dependent gene expression, the _cis_-acting element is the ABA-responsive element (ABRE) and involves the function of the ABA-dependent ABRE-binding protein/ABRE-binding factor (AREB/ABF) transcription factors that target the sequence known as G-box-like cis-acting element with the sequence of ACGTG/TC [52].  
Abscisic acid phytohormone regulates the activation of calcium channels that in turn lead to the activation of the previously mentioned enzyme NADPH oxidase, which has the ability to produce de novo ROS molecules. Moreover, ROS interacts with ABA and other hormones for the main purpose of defending the plant by regulating the signaling cascades and the expression of many stress-responsive genes. Such genes' products are antioxidant enzymes with ROS termination functions such as _superoxide dismutase (SOD)_, _catalase (CAT)_, _peroxidase (POD)_ and _ascorbate peroxidase (APX)_[43].
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 5 Simulation Results
### Average Sum Rate According to SNR  
Figures 11 and 12 show the average sum rate comparing the conventional hybrid beamforming system and the proposed hybrid beamforming system. Also, the figures are different according to the bit and the number of antennas. As the results of the average sum rate in Figures 11 and 12, the proposed hybrid beamforming system for 1-bit has higher average sum rate than the conventional hybrid beamforming system for 3-bit has higher average sum rate than the conventional hybrid beamforming system for 3-bit. Therefore, the proposed hybrid beamforming system has better average sum rate than the conventional hybrid beamforming system because the conventional hybrid beamforming system divides the azimuth and elevation angles linearly and forms beam patterns through combination of the divided azimuth and elevation angles. However, the proposed hybrid beamforming system forms the beam patterns around the path with the high power.  
Figure 10: The bit error rate of the hybrid beamforming schemes, \(N_{t}=32\).  
Figure 9: The bit error rate of the hybrid beamforming schemes, \(N_{t}=16\).
## 6 Results and Discussions
### Validation of the Present Method
#### 6.2.2 Comparison with the Commercial Software  
The present method can selectively consider the effect of the added mass and the structural damping. When \([A]=[B]=[b]=0\), the generalized hydrodynamic coefficients and the generalized structural damping matrix are ignored in the dynamic equation. Regardless of whether the generalized hydrodynamic coefficients and the generalized structural damping matrix are considered or not, the numerical scheme of the present model is the same. With reference to the work of [PERSON] et al. [28], the commercial finite element software can calculate structural responses under the condition without considering the added mass and the structural damping. In order to validate the numerical scheme of the present model, results of the present model and the commercial software LS-DYNA are  
Figure 6: Comparison of the impact force in the stages before and after the flow separation. (**a**) 10\({}^{\circ}\) deadrise angle; (**b**) 20\({}^{\circ}\) deadrise angle.  
Figure 7: Comparison of the time-history deflection results at the center of the length \(L\).  
compared. Results of the commercial software refer to the impact pressure that is computed by using the semi-analytical model of rigid wedges mentioned above; the dynamic response is solved using LS-DYNA.  
Through the modal analysis in Appendix B and the modal convergence study in Appendix C, it is considered that the first 50 modes are sufficient to calculate the structural response of the present stiffened plate. Figure 8 shows the comparison between results of the present model and the commercial software LS-DYNA. The impact velocity of the wedge is assumed to be a constant velocity 6 m/s in the present study, which is the impact velocity that the designer expects the stiffened plate to withstand according to the rule for the design of ship structures [30]. It can be seen that the results of the two methods are in good agreement, which not only includes the impact stage before the flow separation, but also the vibration stage after the flow separation. Some slight discrepancies between the two methods may be caused by different algorithms for solving structural dynamics equations. The present model in this paper is solved using the normal mode method, while the software LS-DYNA is based on the explicit finite element method. In general, the numerical scheme of the present model is suitable for the prediction of the impact response of the 3D elastic wedge.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 1 Introduction
* Their capacity extends to efficiently covering extensive and precise target areas. This paper delves into the realm of fire detection, with a particular focus on the integration of drone technology and vision systems for enhanced capabilities. It explores the challenges posed by fires in complex and dynamic environments, the necessity for reliable detection, and the potential for innovative solutions to address these challenges. By combining cutting-edge technology with data-driven approaches, we aim to improve the accuracy, speed, and adaptability of fire detection systems, ultimately aiming at contributing to more effective fire prevention and response strategies in the face of an increasing fire landscape. In this work, we propose a framework that consists of two main stages for fire detection and localization. The first stage is dedicated to offline fire detection using YOLO (you look only once) models. The second stage is dedicated to online fire detection and localization using the best YOLO model selected in the first stage. The main contributions of this work are as follows. * We compiled a large dataset that contained more than 12,000 images by using state-of-the-art datasets such as BowFire [12], FiSmo [13], and Flame [14] along with newly acquired images. The compiled dataset included images with scenes documenting several fire and smoke regions. * We considered three classes, namely fire, non-fire, and smoke, which is an original feature of our work compared to recent works. The non-fire class was used to identify regions that might be mistaken for fire regions because they reflect fire, such as lakes. The dataset has been labeled based on the three mentioned classes. * The framework applied recent YOLO models to tackle the multi-class fire detection and localization problem. We defined the problem of wildfire observation using UAVs and developed a localization algorithm that uses a stereo vision system and camera calibration. * [PERSON] designed and controlled a quadcopter based on Pixhawk technology, enabling its real-time testing and validation. This paper is structured as follows. In Section 2, we survey the existing literature on fire detection using UAVs, fire localization, and vision-based detection methods. In Section 3, we provide an outline of the proposed two-stage framework for fire detection and localization. In Section 4, we describe the methods used in the first stage devoted to offline fire detection using YOLO models. In Section 5, we present the experimental study and results of the first phase. In Section 6, we describe the camera calibration and pose estimation processes for fire detection and localization in a real scenario. The results of the experiments are reported in Section 7. Finally, in Section 8, we draw conclusions and outline plans for future work.
###### Abstract  
This research introduces a novel, high-performance multiple-input-multiple-output (MIMO) antenna designed to operate in allocated millimeter-wave (mmWave) 5G wireless communications. Operating in the tri-band, 28, 35, and 38 GHz, the four-port MIMO antenna possesses a compact size--measuring just 50 \(\times\) 50 \(\times\) 0.787 mm\({}^{3}\) (4.67\(\lambda_{0}\)\(\times\) 4.67\(\lambda_{0}\)\(\times\) 0.73\(\lambda_{0}\)). The antenna delivers a remarkable performance, achieving peak gains of 9.6, 7.8, and 13.7 dBi in the tri-band, respectively. The realized bandwidths are 1.1, 2.2, and 3.7 GHz, at the tri-band frequencies. The antenna's performance was significantly improved by carefully spacing the elements and employing a decoupling technique using metamaterial cells. This minimized interference between the antenna elements, resulting in efficient MIMO operation with a low envelope correlation coefficient of 0.00015 and a high diversity gain approaching 10 dB, and high isolation of 34.5, 22, and 30 dB, in the tri-band. This proposed design is confirmed with experimental measurements and offers a promising candidate for multi-band use of mmWave communication systems.  
m _Applied Sciences Article_