esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Vision-Based Algorithm for Blockage Recognition of Jittering Sieve in Corn Harvester
## 2 Material and Method
### Algorithm of sieve Dividing
The indexes of these maximums are denoted as \(\Gamma_{col}\);
12: For each element of \(\Gamma_{row}\), denoted as \(\gamma_{row}\), set the entries of the \((\gamma_{row}-\alpha_{row})^{th}\) row to the \((\gamma_{row}+\alpha_{row})^{th}\) row of \(\mathbf{A}\) to be \(1\); for each element of \(\Gamma_{col}\), denoted as \(\gamma_{col}\), set the entries of the \((\gamma_{col}-\alpha_{col})^{th}\) column to the \((\gamma_{col}+\alpha_{col})^{th}\) column of \(\mathbf{A}\) to be \(1\). 13: Dividing result \(\mathbf{A}\). ```  
**Algorithm 1**Dividing jittering sieve into sub-sieves  
Figure 1: Recognition of row edges. (**a**) Original image. (**b**) Trigonometric fitting result. (**c**) Selected minimums. Next, our task is to determine column edges. The strategy is also based on the luminance difference between the column edges and other positions. The difference exhibits an enhancement in the row edges. Specifically, in the sub-tensor \(\underline{\mathbf{X}}(\Gamma_{row},:,:)\), the luminance of the pixels in column edges, indicated by \(\underline{\mathbf{X}}(\Gamma_{row},\Gamma_{col},:)\), is obvious higher than that in other positions. Hence, it is feasible to determine the position of column edges by searching for the column bands that own higher Frobenius-norms (F-norms), and the F-norms of each column band is computed by \(e_{col}(j)=\|\underline{\mathbf{X}}_{j}\|_{F}\), where \(\underline{\mathbf{X}}_{j}=\underline{\mathbf{X}}(:,j,:)\), \(\mathbf{e}_{col}\in\mathbb{R}^{n_{2}}\). Similarly to the selection of row edges, the approximate cycle of column edges is required to know. For this purpose, a widespread property can be employed, that is, the aspect ratio of all sub-sieves are approximately same for a jittering sieve. Assuming that the aspect ratio is approximate \(\mu\), the cycle of column edges can be computed by \(c_{col}=\mu c_{row}\). In practical, the spaces between adjacent column edges are not completely same. Therefore, we empirically suggest using \(0.85c_{col}\) as the minimum interval for searching the maximums of \(\mathbf{e}_{col}\) to avoid missing of maximums. The parameter \(\mu\) depends on the used jittering sieve, and it should be obtained as the prior information by measuring the approximate aspect ratio of sub-sieves. In this study \(\mu\) is set to 5. The above strategies are summarized as steps 10 and 11 in Algorithm 1. The graphical representation for determination of column edges is provided in Figure 2a,b. Finally, we obtain the final dividing result as a binary image, of which the edges with a specified width are set to 1 and other positions are set to 0. The widths of row edges and column edges, \(\alpha_{row}\) and \(\alpha_{col}\), are set to \(0.03c_{row}\) and \(0.03c_{col}\), respectively. The dividing result is shown in Figure 2c.
## 5 Conclusions  
The propagating wave exhibits beat wave distribution along the propagation axis when a rectangular dielectric bar is excited by a plane wave with the incident angle of \(\sim\)0\({}^{\circ}\). The beat wave consists mainly of two modes--a leaky mode and a guided mode. The modes can be solved approximately by field matching theory. The longitudinal phase constants of the two modes are solved and the period lengths of the longitudinal field amplitude are obtained. The theoretical period length agrees well with the simulated results obtained by eigenmode simulation and FDTD simulation, respectively, so that the beat wave is demonstrated to mainly have the two modes.  
The absorbing arrays of the FR-4 bars are simulated for the microwave absorption problem in a wind tunnel experiment. The period length of the array in transverse directions is suggested to be 3\(\lambda\)-3.5\(\lambda\), which is large enough to avoid disturbing the wind speed in the wind tunnel. The reasonable longitudinal length depends on the period length of field amplitude along the longitudinal axis, especially when the edge length of the bar is small relative to the wavelength. When the edge length of the bar is 7.2 mm (close to half an equivalent wavelength inside the bar), a 10 m series of such absorbing arrays will be applied in the experiment in a wind tunnel with a total microwave absorption of \(\sim\)90%, together with the specifically designed testing object and the absorbing arrays on the wall that, to be introduced in another paper, attenuate the microwave power by \(-\)20 dB.  
Writing, R.B.; supervision, Y.T. and Y.Y.; Data Curation, N.X. and X.P.; project administration, J.W. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The study did not report any data.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 12: Electric field distributions of (**a**) guided mode and (**b**) leaky mode at 10 GHz in the cross-plane for \(a\) = 7.2 mm simulated by CST Eigenmode Solver.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 4 Security Proof
### Security Proofs
If the value \(v_{2}\) of any matched record fulfills that \(R=\sigma P+h_{1}(r\)\(\oplus\)\(v_{2}\), \(R\))\(Y_{i}\), \(\mathcal{B}\) responds with {\(m=r\oplus v_{2}\), \(\Omega=(R\), \(\sigma\))}; else, an error symbol is returned. **Challenge:** The adversary \(\mathcal{A}\) sends \(\mathcal{B}\) two messages, \(m_{0}\) and \(m_{1}\), of the same length. Next, the challenger \(\mathcal{B}\) flips an internal coin \(\lambda\leftarrow\{0,1\}\) to decide \(m_{\lambda}\), chooses \(v_{1}\), \(\sigma^{*}\in_{R}Z_{q}\), \(v_{2}\in_{R}\{0,1\}^{k}\) and generates an authenticated ciphertext \(\delta^{*}\) = (\(R^{*}\), \(\sigma^{*}\), \(r^{*}\)) where \(R^{*}\) = \(xP\) and \(r^{*}\) = \(m_{\lambda}\oplus v_{2}\). For consistency, two records of (\(m_{\lambda}\), \(R^{*}\), \(v_{1}\)) and (\(R^{*}\), \(\sigma^{*}\), null, \(v_{2}\)) are also separately added into the lists of \(h_{1}\) and \(h_{2}\) oracles. Finally, the ciphertext \(\delta^{*}\) is returned to \(\mathcal{A}\) as a target challeng. **Phase 2:** The adversary \(\mathcal{A}\) can issue new oracles as those stated in Phase 1. Yet, any ADecrypt oracle containing the target ciphertext \(\delta^{\star}\) is prohibited. **Analysis of the game:** According to previous simulation of this game, it can be seen that the public keys \(Y_{b}\) and \(Y_{c}\) are set as \(yP\) and \(zP\), respectively, and the ciphertext parameter \(R^{\star}\) is set as \(xP\). When the adversary \(\mathcal{A}\) queries an \(h_{3}\) oracle on (\(R^{\star}\), \(\sigma^{\star}\), \(Z^{\star}\)) where \(Z^{\star}=h_{3}(e(Y_{c}\), \(x_{b}R^{\star}))=h_{3}(e(P,\,P)^{xyz})\) in phase 2, \(\mathcal{B}\) would have a non-negligible advantage \(\frac{1}{h_{3}}\) to solve the BDHP instance. Such an event is referred to as H\({}_{3}\)O*. However, \(\mathcal{B}\) might respond with an error symbol for an ADecrypt oracle on some valid ciphertext if \(\mathcal{A}\) had never submitted the corresponding \(h_{2}\) oracle. We denote the event as ADecrypt_ERR and Pr[ADecrypt_ERR] \(\leq\frac{\#ADecrypt}{2^{k}}\) for the entire simulation game. When the game is perfectly simulated, represented as the event PS, the adversary \(\mathcal{A}\) has no overwhelming probability in outputting \(\lambda\), i.e., Pr[\(\lambda^{\prime}=\lambda\,\mid\,\)PS] = 1/2. Based on conditional probability and further derivations, we know that \(\mid\,\)Pr[\(\lambda^{\prime}=\lambda\)] \(-\)1/2 \(\mid\,\)\(\leq(1/2)\)Pr[\(-\)PS].
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 6 Conclusions  
In this paper, a moving drone has captured multiple moving vehicles. The coordinates of the frame were compensated for, and the moving objects were detected based on the frame subtraction. The targets were tracked with two-point initialization, a Kalman filter, and NN association. The track association scheme was proposed to merge redundant tracks and reduce the number of valid trajectories. The positioning accuracy was improved to show a higher accuracy of the fusion state.  
The proposed tracking method is useful for stand-alone drone surveillance as well as multiple drones. A specific vehicle can be continuously locked and tracked in the large area by the drones' hand-overing the target position. Thus, this system is suitable for vehicle chase as well as traffic control or counting vehicles. Multiple drones sharing a ground unit can improve target tracking accuracy, which remains for future study.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11094046/s1](https://www.mdpi.com/article/10.3390/app11094046/s1)]([https://www.mdpi.com/article/10.3390/app11094046/s1](https://www.mdpi.com/article/10.3390/app11094046/s1)), Video S1: Previous Target Tracking of Video 1, Video S2: Proposed Target Tracking of Video 1, Video S3: Previous Target Tracking of Video 2, Video S4: Proposed Target Tracking of Video 2.  
Conceptualization, methodology, estimation, S.Y.; and registration, detection, D.-H.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: 2020R1 II A3A04037203).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experiments and Results  
To verify the effectiveness of the method, first, the noise signal was collected in a relatively quiet environment, and then three kinds of typical events were selected, which were a standard knocker, walking, and manual mining. Through the method based on CEEMDAN-KL and AMNBP, the signal features were enhanced in strong noise conditions, and the AMNBP algorithm divided the three kinds of typical events and noise exactly.
# Optimizing Multidimensional Pooling for Variational Quantum Algorithms
## 3 Related Work  
Convolutional neural networks (CNNs) [20] represent a specialized type of neural network that consists of convolutional layers, pooling layers, and fully connected layers. The convolutional layer extracts characteristic features from an image, while the pooling layer down-scales the extracted features to a smaller data size by considering specific segments of data, often referred to as \"windows\" [1; 20]. Pooling also enhances the network's robustness to input translations and helps prevent overfitting [1]. For classical implementations on GPUs, pooling is usually limited to 3-D data [21; 22], with a time complexity of \(\mathcal{O}(N)\)[23], where \(N\) is the data size.  
Quantum convolutional neural networks (QCNNs), as proposed by [10], explored the feasibility of extending the primary features and structures of conventional CNNs to quantum computers. However, translating the entire CNN model onto the presently available noisy intermediate-scale quantum (NISQ) devices is not practical due to limited qubit count [18], low decoherence time [24], and high gate errors [25]. To attain quantum advantage amid the constraints of NISQ devices, it is essential to develop depth-optimized convolution and pooling circuits that generate high-fidelity outputs. Most implementations of quantum pooling [9; 12; 26; 27; 28] leverage parameterized quantum circuits (PQCs) and mid-circuit measurement as originally proposed in Ref. [10]. These techniques, however, do not perform the classical pooling operation as used in CNNs and thus do not gain the associated benefits from exploiting data locality. Moreover, PQC-based implementations of pooling increase the number of training parameters, which makes the classical optimization step more computationally intensive. The authors in Ref. [29] implemented quantum pooling by omitting measurement gates on a subset of qubits. However, the authors do not generalize their technique for varying window sizes, levels of decomposition, or data dimensions.  
In this work, we propose a quantum average pooling technique based on the quantum Haar transform (QHT) [25] and a quantum Euclidean pooling technique utilizing partial measurement of quantum circuits. These techniques are generalizable for arbitrary window size and arbitrary data dimension. We have also provided the generalizable circuits for both techniques. The proposed methods have been validated with respect to their classical-implementation counterparts, and the quality of their results has been demonstrated by reporting the metric of quantum fidelity.
# UWB-Based Human-Following System with Obstacle and Crevasse Avoidance for Polar-Exploration Robots
## 6 Simulation and Experiment Results  
### Simulation  
In this section, we directly observe the behavior of the robot when the proposed algorithm is executed through simulation experiments. The ROS platform is used for the simulation and the experiment in the following chapter. The ROS Melodic version is implemented in the Ubuntu 18.04 OS. And the proposed algorithm was implemented in a Python environment at a speed of 10 Hz. The simulation involves a scenario where the robot navigates through a narrow path with a right-angle turn, as shown in Figure 10. In the simulation, the robot's size is set to 3 m \(\times\) 2 m, similar to the actual KAREX, and the maximum speed is set to 2 m/s for both linear and rotational movements, with a turning speed of \(\pi/4\) rad/s.  
As shown in Figure 10, the explorer with the tag, which the robot must follow, moves through a corner where the LoS to the tag is obstructed by an obstacle from the robot's position. The actual positions of the target and the following robot, as well as their changes over time, are depicted in Figure 11.  
As can be seen in Figure 11, when the proposed collision-avoidance follower algorithm is applied, the robot successfully follows the explorer while avoiding obstacles, even in situations where the LoS to the explorer is not maintained. To compare the results with those of conventional LoS-based follower algorithms, Figure 12 compares the LoS and the actual direction of the robot in the simulation scenario depicted in Figure 11.  
Figure 9: A result of actual local path generation.  
As shown in Figure 12, robots applying the conventional LoS-maintaining human-following algorithms [1,2] tend to maintain a direction that may lead to a collision with obstacles in order to follow the LoS. However, with the proposed algorithm, the robot does not have a collision with obstacles in order to follow the LoS. However, the proposed algorithm, the robot does not have a collision with obstacles in order to follow the LoS.  
Figure 11: Route of robot and target during simulation: (**a**) Route of the robot; (**b**) Location and obstacle information calculated by the robot.  
Figure 10: Simulation scenario.  
not maintain the LoS to avoid collisions with obstacles. After avoiding the obstacles, the robot then follows the position of the tag carried by the explorer, naturally changing its direction to maintain the LoS again while following the tag.
## 2 Key Factors Contributing to Spatial Resolution in PET Imaging
### Positron Range  
Within the human body, a positron (\(e^{+}\)) travels a certain distance before interacting with an electron (\(e^{-}\)) and undergoing annihilation. This results in a displacement between the annihilation site and the original location of the radionuclide, referred to as the positron range. This effect contributes to event localization errors in PET image reconstruction. The extent of blurring depends on the energy of the emitted positron and the properties of the tissue it traverses. Figure 2 illustrates how the positron range phenomenon, denoted as \(R_{p}\), while Table 1 presents the positron ranges for various radionuclides in water-equivalent media.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Isotope** & **Half-Life** & \(\mathbf{E_{\text{mean}}}\) **(MeV)** & \(\mathbf{R_{\text{mean}}}\) **(mm)** & \(\mathbf{R_{\text{max}}}\) **(mm)** \\ \hline \({}^{18}\)F & 110 min & 0.250 & 0.6 & 2.4 \\ \({}^{15}\)O & 2 min & 0.735 & 3 & 8.4 \\ \({}^{11}\)C & 20.4 min & 0.386 & 1.2 & 4.2 \\ \({}^{13}\)N & 10.0 min & 0.492 & 1.8 & 5.5 \\ \({}^{64}\)Cu & 12.7 h & 0.278 & 0.7 & 2.5 \\ \({}^{89}\)Zr & 78.4 h & 0.396 & 1.3 & 3.8 \\ \hline \end{tabular}
\end{table}
Table 1: Positron range of various isotopes in water-equivalent tissue [18; 23; 24; 25; 26].  
Figure 1: A representative illustration of the non-collinearity effect within a PET scanner. The residual kinetic energy of the center of mass of the positron–electron pair at the point of annihilation leads to annihilation photon non-collinearity, creating an approximately Gaussian-shaped blurring on the reconstructed images represented by 0.0044\(R\) FWHM, with \(R\) representing the radius of the PET scanner’s cylindrical geometry.
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 6 VR User Experiments' Results
### Multi-User Ball-Grabbing Game Evaluation  
Figure 13 shows the questionnaire results of the multi-user VR experiences. Participants favor our pose regression model for the Q8 (mean = 4.66), Q9 (mean = 4.73), Q10 (mean = 4.53), and Q11 (mean = 4.73). Figure 13 illustrates that most participants prefer our model with Q12 (mean = 4.66) rather than IK.  
For statistical analysis, we conducted a paired \(t\)-test with a \(p\)-value borderline of 0.05. Table 10 shows the paired \(t\)-test's statistical analytics under our and IK methods. The results illustrate that the differences between our method and IK are significant in Q8 (t = 3.055, \(p\) < 0.01), Q9 (t = 3.500, \(p\) < 0.005), Q10 (t = 3.674, \(p\) < 0.005), Q11 (t = 2.304, \(p\) < 0.005), and Q12 (t = 2.449, \(p\) < 0.05). The Q10 about accuracy of actions and Q11 about manipulation, with \(p\) < 0.005, support hypothesis H1 strongly that the avatar's motions are natural. Moreover, Q8 about presence and Q9 about ownership, with \(p\) < 0.01, proves hypothesis H2 that displaying the upper body in a multi-user VR environment increases the feeling of presence. The questionnaire about overall user satisfaction reveals that the  
Figure 12: Average of total points in percentage in the archery game. \(\blacksquare\) Our proposed method and \(\blacksquare\) IK.  
Figure 13: Questionnaire results in the ball-grabbing game. \(\blacksquare\) Our proposed method and \(\blacksquare\) IK.  
users preferred our proposed model over the IK method. A significant difference exists between our proposed method and IK (\(p\) = 0.028 \(<\) 0.05) for this satisfaction factor.  
The five groups obtained different scores while playing the ball-grabbing game in two modes. The bar chart in Figure 14 illustrates the average number of balls grabbed. Participants scored an average of 19 points (95% of the highest possible points) using our pose regression method and 16.6 points (83% of the highest possible points) using an IK solution. These results confirm hypothesis H3 that our lower latency method enhances the participants' performance.
## 6 Thematic Taxonomy of UWSNs
### Underwater Acoustic Communications  
The area of acoustic research and development has grown significantly over the past half-century, primarily in ocean acoustics. An acoustical approach is implemented commercially to detect submarines and even marine mammals. The military field is also parallel to commercial acoustics communication, particularly regarding those applications that relate to ocean surveillance. Consequently, this section discusses the main principles of underwater acoustic communication including sound velocity features, sources, and receivers. Additionally, all factors that affect the speed of sound and how it influences the performance of the system or network devices operation are also addressed.
## 4 MOFs as Smart Carriers and Delivery Systems of Sensitive Volatile Compounds
Various families of MOFs are currently explored regarding their capability to encapsulate a plethora of EOs targeting their delivery to specific environments under specific triggers which are in general related to the action of different pathogens (Table 1). Herein we attempt to review some of the key examples that have been reported up until today emphasizing on the issue of food pathogens. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MOF** & **Essential Oil** & **Crop** & **Targeted Pathogens** \\ \hline \(\gamma\)-CD MOF [139] & Eugenol & Wheat & _F-graminearum_ \\ \(\gamma\)-CD MOF [140] & Cinnamaldehyde & Cantaloupes & _E. coli_ \\ IRMOF-3 [141] & Eugenol & Strawberries & _E. coli and S. aureus_ \\  & & & _Magnaporthe oryzae_, \\ ZIF-8 [142] & Citral EO & Rice & _Botryosphaeria dothidea, and_ \\ Zn-ascorbate MOF [135] & Marjoram EO & - & _E. coli and S. aureus_ \\ \(\gamma\)-CD MOF [143] & OregonEO & - & - \\ Cu-H\({}_{3}\)BTC [144] & Cinnamon EO & Meat preservation & _S. aureus, S. enterica, E. coli,_ \\ CD MOFs (\(\alpha\),\(\beta\),\(\gamma\)) [145] & Menthol & - & _and L. monocytogenes_ \\ \(\gamma\)-CD-MOFs [146] & Thymol & Cherry Tomatoes & - \\  & d-limonene (Lim), & & \\ UiO-66,67,68 [147] & \(\alpha\)-terpinene (Terp), & & - \\  & myrcene (Myr), and & & \\  & \(\alpha\)-pinene (Pine) & & \\ \(\gamma\)-CD MOF [148] & Carvacrol & Strawberries & _E. coli, S. aureus and B._ \\ Zn\({}_{2}\)(BDC)\({}_{2}\)(DABCO) & Thymol & - & _E. coli, S. aureus_ \\ [149] & Carvacrol & - & _E. coli, S. aureus_ \\ IRMOF-3 [150] & Carvacrol & Pork preservation & _E. coli, S. aureus_ \\ Ag-MOF [152] & Eugenol & Zucchini & _Staphylococcus aureus_, \\ IRMOF-3 [153] & Eugenol & Post harvest Strawberries & _Botrytis cinerea, Ralstonia,_ \\  & & & _Sphlingomonas, Erwinia,_ \\ \(\gamma\)-CD MOF@PCL [154] & Limonene & Preservation of fresh cut apples & _E. coli and S. aureus_ \\ HKUST-1@ALG [155] & Tea Tree Oil & Fresh-cut pineapple preservation & _E. coli and S. aureus_ \\ \(\beta\)-CD-MOF [156] & Clove EO & Chinese bacon & _Inhibition of lipid oxidation_ \\ ZIF-67 [157] & Citral & Bamboo & _E. coli and S. aureus_ \\ Zn-BDC-NH\({}_{2}\)[158] & Thymol & - & _E. coli_ \\ NH\({}_{2}\)-UIO-66 [159] & Clove EO & Blueberries & _E. coli and S. aureus_ \\ ZIF-8 [160] & Thymol & - & _Colletotrichum musae_ \\ UiO-66-(COOH)\({}_{2}\) [160] & Thymol & - & _Colletotrichum musae_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MOFs utilized for EOs controlled release, targeting food pathogens.