esa-sceva/satcom-chunk-collection huggingface dataset:
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 4 Proposed Method
### Improved Dynamic Task Planning Algorithm Based on Market Auction Mechanism
#### 4.4.3 Reconnaissance Time Redistribution  
In the reconnaissance process, to maximize reconnaissance benefits, it is necessary to reallocate the reconnaissance time \(t\) for the UAVs that have changed their reconnaissance sequences. The reallocation of reconnaissance time can be regarded as a constrained single-target optimization problem. This paper comprehensively considers the area and task value coefficient of the reconnaissance target, as well as the endurance time constraint, taking Formula (5) as the objective function, under the Formula (7) condition of minimum reconnaissance benefit constraint and Formula (8) endurance time constraint. The GA (Genetic Algorithm) [55] is used to optimize the reconnaissance time in the remaining task areas of UAVs, and the reallocated reconnaissance time is obtained in a short time to obtain the maximum reconnaissance benefit.
## 3 Results and Discussion
### Change in the IES-R-K and AIS Scale before, after, and 10 Months after the Proposed Program  
Table 1 shows the scale changes in the IES-R-K and AIS before, after, and 10 months after the proposed program. The IES-R-K values were 60 points in the pre-survey, 43 points in the post-survey, and 40 points in the follow-up survey, indicating that the level was lowered from a very serious level before the program to a serious level after the program. The IES-R-K changed to 43 points in the post-survey, which represented a low level in the severe range (40-59 points), and this status was maintained until the follow-up survey (40 points). With regard to the differences by subfactors of the IES-R-K, re-experiencing decreased the most from 23 to 15 points, followed by avoidance from 24 to 17 points, and sleep and dissociation from 14 to 10 points, with hyperarousal decreasing from 21 to 20 points. The AIS change indicated that the quality of sleep improved from 10 points in the pre-survey, to 7 points in the post-survey, and finally to 6 points in the follow-up survey.  
Figure 8: Change ratio of the HRV when the subject’s negative emotion change was observed at time 0. Over the 4 sessions, negative emotion change was observed 8 times. The “_\"\"_ mark indicates that the \(p\) value is under 0.05.
## 4 Finite Element Simulations  
To analyze the plastic deformation of a circular metallic tube's nosing, we utilize an elasto-plastic FE simulation that assumes isotropic hardening and the von [PERSON] criterion. The problem was accomplished using the ANSYS version 15 package. To make the most of the tube's symmetry, we model only a quarter of the tube and die, as depicted in Figure 3. We assume a perfectly rigid surface for the die due to its high elastic modulus. This is an acceptable approximation since the die's elastic deflection is relatively insignificant compared to the tube's excessive plastic deformation. In the context of tube nosing analysis, the initial mesh of the FE model is shown in Figure 3. The 3-D FE mesh used for the tube consists of 648 solid higher-order elements, each with 20 nodes, including two elements in the thickness direction and 18 in each axial and circumferential direction. The number of elements used for discretization in the finite element mesh varies between 468 and 900, depending on the size of the tube specimen. We simulate the interface between the die and tube through 450 rigid surfaces representing the die's inner surface and flexible surfaces representing the tube's outer surface. The friction at the interface is described using Coulomb's friction law. To model the tube nosing, we move the tube while restricting the movement of the die in all directions. Therefore, all nodes attached to the surface of the tube end, indicated by Arrow C, are coupled to the motion in the Z-direction. We simulate the nosing process by displacing the coupled nodes along the Z-direction.  
The nodes connected to the side surface of the tube, as marked by arrows A and B, are constrained in the X- and Y-directions due to their symmetry. Table 2 summarises the forming process parameters utilized in the FE simulation of the tube nosing. The practical and industrial viewpoints were considered when selecting the parameter values. To forecast the possibility of buckling in the tube nosing process, the explicit dynamic FE scheme is used to solve the model. A 3-D four-node shell element is employed to model the tube in this scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Stress–Strain Relation** & **Yield Stress \(Y\)** & **Young’s Modulus \(E\)** & **Poisson’s Ratio** \\  & \(\boldsymbol{\alpha}\) **(MPa)** & **(MPa)** & **(GPa)** & \(\boldsymbol{\
u}\) \\ \hline mild steel, [1] & \(\overline{\sigma}=867(0.00538+\bar{\varepsilon})^{0.2411}\) & 332 & 205 & 0.33 \\ \hline hard aluminum, [25] & \(\overline{\sigma}=123(\bar{\varepsilon})^{0.194}\) & 127 & 70 & 0.34 \\ \hline ann. aluminum, [25] & \(\overline{\sigma}=134(\bar{\varepsilon})^{0.008}\) & 72 & 70 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties characterizing the yield strength.  
Figure 3: Finite element model showing tube and die for tube nosing.
## 4 Simulated and Measured Results
### Far-Field Radiation Patterns  
Figure 17 displays the compared 2D normalized far-field radiation patterns of the Port 5 antenna (open-loop antenna) at 5.5 GHz, both simulated and measured. Although there are slight deviations in the measured results from the simulations, which are mostly due to the presence of large feeding connectors, the deviations are within the acceptable limits. Both patterns exhibit directional characteristics similar to those of an x-directed dipole antenna.  
Figure 16: Simulated and measured results of total efficiency and peak gain of Port 1 and Port 3 antennas: (**a**) at the 5.5 GHz band, (**b**) at the 28 GHz and 38 GHz bands, and (**c**) at the 0.13 THz band.  
Figure 18 displays the results of the four-element MIMO antenna's XY pattern at frequencies of 28 GHz and 38 GHz, both simulated and measured. The results on other planes are not included for brevity. It is evident that some of the measured results deviate from the simulations, which could be due to the use of large connectors or variations in the position of the soldered connectors. Nonetheless, the co-polarized patterns at 28 GHz exhibit an omnidirectional shape in a complementary manner, indicating that the primary radiation structures at 28 GHz are the open-edge QMSIW cavity antennas. Meanwhile, the co-polarized patterns at 38 GHz are directional, confirming that the main radiators are the larger slots in each QMSIW cavity.  
In Figure 19, the results of the simulated XY radiation pattern at 0.13 THz are displayed. To guarantee the accuracy of the results at this frequency, two full-wave simulation tools, CST Microwave Studio and Ansys HFSS, were used, as the maximum frequency limit of the  
Figure 17: Simulated and measured normalized radiation patterns of Port 5 at 5.5 GHz, (**a**) _XY_ plane (**b**) _XZ_ plane. [Simulated \(E_{\theta}\) in solid black, measured \(E_{\theta}\) in dashed-dotted black, simulated \(E\varphi\) in dashed red, and the measured \(E\varphi\) in dotted red].  
Figure 18: Simulated and measured normalized XY plane patterns of the proposed four-element QMSIW mode antenna at 28 GHz and 38 GHz. [Simulated \(E_{\theta}\) in solid black, measured \(E_{\theta}\) in dashed-dotted black, simulated \(E\varphi\) in dashed red, and the measured \(E\varphi\) in dotted red].  
available VNA is 50 GHz. The figure presents the directional patterns in a complementary manner, and the cross-polarized patterns are shown to be almost less than \(-25\) dB.
# Seismic Behavior of RC Beam Column Joints with 600 MPa High Strength Steel Bars
## 3 Experimental Results and Discussion
### Energy Dissipation  
The energy dissipation capacity is a significant parameter in the understanding of the frame structures. Our study uses two parameters to investigate the energy dissipation ability of beam-column joints, including cumulative dissipated energy and equivalent viscous damping coefficient. The dissipated energy is calculated as the area underneath the hysteretic curve. As shown in Figure 9, the dissipated energy for each cycle is equal to the area enclosed by the hysteresis loop ABCDA, \(S_{\text{ABCDA}}\). Cumulative dissipated energy increases with displacements for each specimen, as shown in Figure 10a. It can be noted that dissipated energy increases with the reinforcement ratio. The dissipated energy of BD1 and BD2 are larger than those of BD3 and QD3. Moreover, the axial compression ratio has a negative influence on the cumulative dissipated energy of specimens, which can be found from the comparison between BD2 and BD1, or QD2 and QD1.  
The energy dissipation capacity of the specimen can also be expressed by an equivalent viscous damping ratio \(h_{e}\), a major index to measure the seismic performance of the specimen. It is calculated by [30]:  
\[h_{e}=\frac{1}{2\pi}\frac{S_{ABCDA}}{S_{\Delta OBF}+S_{\Delta ODE}}, \tag{3}\]  
where \(S_{ABCDA}\) is the area enclosed by the hysteresis loop ABCDA. \(S_{\Delta OBE}\) and \(S_{\Delta ODF}\) are the areas of the triangles OBF and ODE, respectively, as shown in Figure 9. The parameter can reflect the relationship between the dissipated energy and elastic energy in the loading cycle.  
Figure 10b illustrates that the equivalent viscous damping coefficients of all specimens are generally on the rise. In contrast, the development of pinching behavior leads to a slower upward trend, even a downward trend. Before the first cycle of \(2\Delta y\) level, \(h_{e}\) of BD1 is greater than that of BD2, while \(h_{e}\) of BD2 is greater than that of BD1 after the second cycle of \(2\Delta y\) level. This indicates that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Loading Direction** & \(\Delta_{y}\) **(mm)** & \(\Delta_{u}\) **(mm)** & \(\mu\) \\ \hline QD1 & up & 12 & 37.57 & 3.13 \\  & down & 15 & 46.30 & 3.09 \\ QD2 & up & 13 & 55.36 & 4.26 \\  & down & 17 & 71.40 & 4.20 \\ QD3 & up & 9 & 40.22 & 4.47 \\  & down & 11 & 34.12 & 3.10 \\ BD1 & up & 14 & 40.00 & 2.86 \\  & down & 16 & 48.09 & 3.00 \\ BD2 & up & 16 & 50.99 & 3.19 \\  & down & 18 & 53.16 & 2.95 \\ BD3 & up & 7 & 30.33 & 4.33 \\  & down & 10 & 47.05 & 4.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ductility of specimens.  
pinching phenomenon of BD1 increases with the displacement. In general, BD1 and BD2 perform better than QD1 and QD2, which indicates that the increase of reinforcement ratio in a certain range can help to improve the energy dissipation capacity.
## 3 Relaying
### Starring Hardware  
The Relay's main hardware component is the FiPy board by Pycom, an innovative choice reflecting our commitment to versatility and integration. This powerful development board supports a multitude of communication standards, including Sigfox, LoRaWAN, NB-IoT, Wi-Fi, and BLE, all within a single device. The choice of FiPy is pivotal, owing to its unique ability to navigate across various network technologies, thus facilitating seamless communication in diversified IoT ecosystems. Its dual-core 32-bit LX6 microcontroller, programmed in MicroPython, offers a flexible and efficient platform for developing IoT applications that require multi-network connectivity.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology
### Ray-Tracing  
Ray-tracing (RT) is a geometrical optics method that models propagation by following straight rays [9]. Although it is less accurate than conventional full-wave methods, it requires a much lower computational effort. Thus, it is useful for fast modeling of large scenarios at several frequencies and for several transmitter and receiver positions.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 1 Introduction
Indeed, communication protocols need to be optimized to exploit the new potentiality offered by ULP WuRx and to deal with the intermittent harvested energy, especially Medium Access Control (MAC) protocols, whose tasks are to provide mechanisms to allow several wireless nodes to share the wireless channel medium and access the network. A global energy management system, which deals with the hardware, software and network, is needed to maximize the efficiency and to achieve the goals of long lifetime and energy neutrality. In this paper, a novel Energy Management (EM) scheme, which optimizes the energy use in EH-WSN, is proposed in combination with a novel MAC protocol, which exploits the benefits of ULP WuRx. The proposed MAC protocol focuses on the star network topology, in which a central sink to which all the other nodes are connected collects the data [8]. It is therefore assumed that no communication from the sink to the nodes is required, which is the case in many monitoring applications [22]. The main contributions of this paper are as follows:  
* A novel EM for EH-WSNs is proposed. Unlike most state-of-the-art EMs, the energy management strategy proposed in this work only requires the residual energy as an input, making it practically easy to implement on real hardware platforms. * A novel MAC protocol, called SNW-MAC (Star Network WuRx-MAC) leveraging ULP WuRx for data-gathering star networks is proposed. SNW-MAC enables asynchronous communications, minimizes the cost of packet transmissions and allows error corrections. SNW-MAC significantly reduces the energy cost variability of packet transmissions, allowing accurate control of the consumed energy by the EM. Moreover, an analytical study of SNW-MAC scalability is presented. * SNW-MAC and the proposed energy management scheme were implemented and evaluated in the field, using a state-of-the-art ULP WuRx [13]. The proposed scheme was evaluated in the context of indoor light energy harvesting through exhaustive experimentation. * In order to achieve a fair evaluation of the proposed scheme, two state-of-the-art MAC protocols were also implemented on the same hardware and application scenario. Results show that ULP WuRx allow improved communication efficiency, which is exploited by the EM to achieve a higher throughput (up to more than double) compared with state-of-the-art schemes. To rigorously measure this improved energy efficiency in the context of data gathering WSNs, the Energy Utilization Coefficient (EUC) is defined and used as an evaluation metric. The remainder of this paper is organized as follows. Section 2 describes the related work. Section 3 presents the energy management scheme and the EUC metric. Section 4 details the design of SNW-MAC and presents an analytical study of its scalability. Section 5 exposes the experimental setup used to evaluate our approach, and Section 6 presents the experimental results. Finally, Section 7 concludes this paper.
# An Interleaved DC/DC Converter with Soft-switching Characteristic and high Step-up Ratio
## 5 Conclusions  
This paper proposes an interleaved dc/dc converter that is composed of two boost converters. Both boost converters share a coupled inductor that is made up of one magnetic core with two windings. It effectively reduces the product size and cost. Owing to the interleaved operation, the power capacity is doubled. Moreover, low current ripple at both the input and the output sides is achieved due to the interleaved operation. By operating the boost converters at DCM, the freewheeling diodes and the active switches can respectively fulfill ZCS operation and ZVS operation, without the need to use any active clamping or snubber circuits. It helps to reduce the component count and improve the circuit efficiency. In summary, the proposed interleaved converter has the advantages of less component count, low current ripple, high circuit efficiency, and easy control. A 200 W prototype converter was implemented and measured. The circuit efficiency is 94.3% at rated power operation.  
[PERSON] and [PERSON] conceived and designed the circuit; [PERSON] and [PERSON] performed circuit simulations and designed parameters of the circuit components; [PERSON] carried out the prototype converter, and measured as well as analyzed experimental results; [PERSON] wrote the paper and [PERSON] revised it for submission. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the Ministry of Science and Technology, R.O.C. under Grant MOST 105-2221-E-214-027.  
The authors declare no conflicts of interest.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 5 Performance Evaluation
### Parameter Analysis  
#### 5.3.1 Sensor Density Analysis  
We analyze the effects of node density on the performances of all the four schemes in the fixed \(100\times 100\,\mathrm{m}^{2}\) zone area. The numbers of nodes are 50, 100, 150 and 200, respectively. Accordingly, the sensor density ranges from 0.005 to 0.02 nodes per square meter. The results are illustrated in Figure 9.  
Figure 8: Another definition of network lifetime.  
In Figure 9, a high density leads to short network lifetimes and large transmission overheads for all the schemes, while the maximum hops to sink and the information accuracy do not change a lot. Additionally, as the sensor density increases, AggOR scheme, which always presents a high data accuracy and short transmission route in the experiments, begins to show longer lifetime and better energy efficiency than EEUC and HEER. On the whole, AggOR performs the best among the compared schemes with different sensor densities, especially in high-density sensor networks.
# Modeling of Fatigue-Strength Development
## 3 Results and Discussions
### Prediction of Fatigue-Strength Development in Emulsion Asphalt.
#### 3.2.5 Modified Fatigue Maturity Functions  
Modified fatigue maturity of the cold-emulsion reclaimed asphalt pavement mix at cure intervals of 1, 3, 5, 7, 14, 21, and 28 days was calculated using Equation (5) and the Equivalent ages tabulated in Table A2 of Appendix B and Table A3 of Appendix C. As explained in Section 1.2.2, the chronological age in the Nurse-Saul maturity function was replaced with the equivalent age in the computation of maturity. The fit parameters, k and F\({}_{\text{u}}\), of the linear hyperbolic and parabolic hyperbolic fatigue maturity functions presented as Equations (8) and (9) were obtained by fitting the modified fatigue strength values to the computed maturity values using non-linear least squares curve fitting techniques and recorded in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strain Level, \(\epsilon\)**} & \multicolumn{2}{c}{**Linear Hyperbolic Model**} & \multicolumn{2}{c}{**Parabolic Hyperbolic Model**} \\ \cline{2-5}  & **A\({}_{\text{o}}\)** & **B** & **A\({}_{\text{o}}\)** & **B** \\ \hline
125 & 0.975 & 0.0477 & 0.975 & 0.0411 \\
150 & 0.975 & 0.0532 & 0.975 & 0.0484 \\
175 & 0.975 & 0.0584 & 0.975 & 0.0559 \\
200 & 0.975 & 0.0630 & 0.975 & 0.0633 \\
225 & 0.975 & 0.0363 & 0.975 & 0.0707 \\
250 & 0.975 & 0.0384 & 0.975 & 0.0784 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Temperature sensitivity factors emulsion reclaimed asphalt pavement mixture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain Level,**} & \multicolumn{2}{c}{**5 \({}^{\circ}\)C Cure**} & \multicolumn{2}{c}{**25 \({}^{\circ}\)C Cure**} & \multicolumn{2}{c}{**40 \({}^{\circ}\)C Cure**} \\ \cline{2-7}  & **Linear** & **Parabolic** & **Linear** & **Parabolic** & **Linear** & **Parabolic** \\  & **Hyperbolic** & **Hyperbolic** & **Hyperbolic** & **Hyperbolic** & **Hyperbolic** & **Hyperbolic** \\ \hline
125 & 2.044 & 1.852 & 0.788 & 0.814 & 0.385 & 0.440 \\
150 & 2.222 & 2.068 & 0.766 & 0.785 & 0.345 & 0.380 \\
175 & 2.401 & 2.313 & 0.747 & 0.756 & 0.311 & 0.327 \\
200 & 2.574 & 2.585 & 0.730 & 0.729 & 0.283 & 0.282 \\
225 & 1.725 & 2.889 & 0.834 & 0.702 & 0.483 & 0.243 \\
250 & 1.780 & 3.242 & 0.825 & 0.676 & 0.464 & 0.208 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Age conversion factors for linear hyperbolic and parabolic hyperbolic models.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Proposed Method for Placing Multiple Markers
### Preprocessing Step
#### 2.1.1 Upscaling  
The surface of a precise mesh is composed of numerous small faces (hereafter the face is denoted as \(\mathbf{f}\), the faces are denoted as \(\mathbf{f}\)'s, and the set of faces is denoted as \(\mathbf{f}\)). Considering this, \(\mathbf{f}\) is sliced so that every edge is smaller than the threshold \(\varepsilon\) (hereafter the edge is denoted as \(\mathbf{e}\), and the set of edges is denoted as \(\mathbf{\boldsymbol{E}}\)). In this paper, \(1\%\) of the mesh scale expressed in the norm was arbitrarily used for the \(\varepsilon\) value to be sufficiently precise relative to the overall mesh scale, as expressed in Equation (1).  
\[\varepsilon=\frac{\sqrt{\left\{\max(\boldsymbol{X})-\min(\boldsymbol{X}) \right\}^{2}+\left\{\max(\boldsymbol{Y})-\min(\boldsymbol{Y})\right\}^{2}+ \left\{\max(\boldsymbol{Z})-\min(\boldsymbol{Z})\right\}^{2}}}{100} \tag{1}\]  
where \(\boldsymbol{X}\) denotes \(\{x\mid x\) values of vertices\(\}\), \(\boldsymbol{Y}\) denotes \(\{y\mid y\) values of vertices\(\}\), \(\boldsymbol{Z}\) denotes \(\{z\mid z\) values of vertices\(\}\), \(\max\) is a function that finds the maximum value in a set, and \(\min\) is a function that finds the minimum value in a set.  
If some of the \(\mathbf{e}\) constituting a specific \(i\) th \(\mathbf{f}\) (hereafter \(\mathbf{f}_{i}\)) is longer than \(\varepsilon\), the existing \(\mathbf{f}_{i}\) is sliced into four similar triangles using the triangle mid-segment theorem. Subsequently, new vertices and \(\mathbf{f}\)'s are added accordingly (hereafter the vertex is denoted as \(\mathbf{v}\), the vertices are denoted as \(\mathbf{v}\)'s, and the set of vertices is denoted as \(\boldsymbol{V}\)). This operation is repeated for each newly added \(\mathbf{f}\), and then duplicate \(\mathbf{v}\)'s are removed. Figure 2 shows an example of this process performed on a car model, which is a specific virtual large object.  
Figure 1: Prerequisite for attaching markers to the real object: (**a**) non-attachable; (**b**) attachable (=face).  
Figure 2: Example of the upscaling process: (**a**) before; (**b**) after.  
Figure 2a, where this process is not applied, is not precise enough to recognize the shapes of \(\mathbf{f}\)'s on the surface (e.g., the glass part). On the other hand, Figure 2b, where this process is applied, is shown to have a surface so precise that it is difficult to confirm whether it is composed of \(\mathbf{f}\)'s.