esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Development of a Wideband Planar Yagi Antenna Using Tightly Coupled Directive Element
## 6 Conclusions  
In this paper, the concept of bandwidth enhancement of a conventional printed Yagi bi-layer antenna operating at the S-band was presented. Both metalized layers at the top and bottom antenna planes comprise ground plane, microstrip feedline, driven elements, and directive elements. The bottom plane directive elements are placed with some offset toward the direction of propagation to enhance the overall effective width. The aim was to simultaneously maximize gain and bandwidth. To achieve broad bandwidth matched to 50 \(\Omega\) input impedance, elliptically-shaped directive elements were introduced in close proximity of the active dipole elements. Full-wave analysis of the tightly-coupled directive elements was performed by conducting multiple electromagnetic simulations using CST Microwave Studio. The performance of proposed antenna element having wide directive element is compared with the typical design without the wide directive element. Compared to the traditional Yagi antenna, the proposed antenna outperformed by presenting simultaneously, larger bandwidth and higher gain values. The designed antenna exhibited a 49.3% fractional bandwidth operating in the frequency range 2.6-4.3 GHz. Plated via-holes were introduced near the left and right boundaries of the PCB board to concentrate the electromagnetic energy toward the main direction of the electric-field flow. The proposed antenna exhibits excellent radiation performance with an average gain of more than 7 dBi and maximum side-lobe levels of less than \(-10\) dB. Due to the high-fidelity factor value, the proposed antenna can be used in broadband RF applications.  
Main idea, design and simulation results production by [PERSON]. ([PERSON]). Original draft preparation and rivision by A.T. Advisory comments for paper improvement by K.J. Measurements and graphs generation by [PERSON]. ([PERSON]). Supervisory role for the entire project by A.R.S. All authors have read and agreed to the published version of the manuscript.  
This Project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-ELE2462-02).  
The authors declare no conflict of interest.
## 2 The Proposed DCLNN Method for Failure Time Prediction
### Convolutional LSTM Neural Network  
The significant characteristic of the failure time series in this paper is a few samples with non-uniform sampling. When directly using LSTM for data modeling, the features in time series are not obvious, which may lead to poor accuracy of the modeling and prediction. The deep neural network used in this paper is a combination of CNN and LSTM. The main target of this paper is time series prediction. With the help of the CNN's feature extraction ability, high-dimensional features can be trained to understand the failure time series more comprehensively. Then, these high-dimensional features can be used for time series prediction in LSTM. Based on the advantages of CNN and LSTM, a convolutional LSTM neural network (CLNN) is proposed to achieve the goal of failure time prediction for an escalator.
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
## 2 TNS-0 #2 Description  
The TNS-0#2 nanosatellite was developed by JSC Russian Space Systems. It was successfully launched on 17 August 2017 from the International Space Station during the spacewalk of Russian cosmonauts. TNS-0 #2 nanosatellite is a hexagonal prism with a 26.4 cm height and an 18.7 cm diameter. The sides are covered with solar panels. Globalstar antennas, a set of Sun sensors, and a radio link antenna are located on the upper side of the satellite. A handle is attached at the bottom for the cosmonaut to hold the satellite during the spacewalk. The satellite is presented in Figure 1.  
The mass of the satellite is 4.8 kg and its center of mass is located almost on the axis of geometric symmetry of the satellite body, as shown in Figure 2. The inertia tensor in the reference frame with the origin in the center of mass of the satellite is  
\[\mathbf{J}=\left[\begin{array}{ccc}0.06153&-0.00013&-0.00033\\ -0.00013&0.06669&-0.00012\\ -0.00033&-0.00012&0.01287\end{array}\right]\text{kg}\cdot\text{m}^{2}.\]  
Figure 1: TNS-0 #2 nanosatellite (Credit: JSC Russian Space Systems).  
The satellite is equipped with the passive magnetic attitude control system developed by the Keldysh Institute of Applied Mechanics of RAS. The system consists of a set of hysteresis rods for the angular velocity damping and a permanent magnet located along the axis of symmetry to stabilize this axis along the local geomagnetic field induction vector. The dipole moment magnitude of the permanent magnet is 2.2 A\(\cdot\)m\({}^{2}\). Grids of hysteresis rods were installed on the upper and lower sides of the nanosatellite body. The location of the permanent magnet, magnetometer, and Sun sensors is shown in Figure 2.  
Three-axis magnetometer and a set of Sun sensors were installed onboard. Attitude motion was reconstructed using their measurements. The measurements root-mean-square error of the magnetometer is \(\sigma=100\) nT. The non-orthogonality of the measuring axes is not worse than 1 degree. Eight optical sensors are installed on the nanosatellite THS-0 #2: six photodiode sensors, one ultraviolet Sun sensor, and one infrared horizon sensor.  
More details on the passive attitude control system can be found in [17].
## 4 Simulation Results and Analysis
### Initial Settings of Simulations  
In this section, an emergency double lane change (DLC) test (ISO3888-1:1999) is simulated with a driver preview time of 0.5 s, which is a short but appropriate value for an emergency situation. The desired longitudinal velocity is 100 km/h controlled and the tire/road friction coefficient is set as 0.8. Such a critical driving condition should be persuasive, since the lateral vehicle motion response has a relatively large variation scale. VSC guarantees that the vehicle passes the DLC test successfully.  
In the simulation process, the sensor signals are assumed to be obtained in real time. Furthermore, Gaussian noise is added to the simulated measurements to realistically represent the real application. The standard deviation of longitudinal acceleration sensor noise is 5 \(\times\) 10\({}^{-2}\) m/s\({}^{2}\); the standard deviation of lateral acceleration sensor noise is 5 \(\times\) 10\({}^{-2}\) m/s\({}^{2}\); the standard deviation of yaw rate sensor noise is 2.4\({}^{\circ}\)/s; the standard deviation of wheel rotational velocity noise is 10\({}^{-3}\) rad/s [37].  
In addition, the parameters used in the UKF and MEH design are very important for the performance of the proposed estimators. Some design principles have been well investigated [28; 38; 39]. According to the recommended options and our own tests, the parameters are given as follows: \(\alpha=10^{-3}\), \(\beta=2\), \(\kappa=0\) and the fixed time horizon \(N=10\). Furthermore, the parameters used in the UKF-based arrival cost calculation in MHE are the same as the UKF estimator's parameters.
## 5 Results and Discussion
### Nucleus-Based versus Region-Based Approaches  
This section provides a comparative analysis between the performance achieved by the proposed nucleus-based methodology and the previously reported region-based approach [6]. To allow the benchmarking of these two deep learning strategies for mobile-based cervical lesion detection, the test set used is the same for both approaches, thus allowing a fairer comparison. Nevertheless, it should be noted that the patch size and the number of annotations per class are not the same due to the annotation refactor detailed in Section 3.1. The results regarding [EMAIL_ADDRESS] IoU and AR@10 can be observed in Figure 8.  
Figure 6: Per-class [EMAIL_ADDRESS] IoU results for the best hyperparameter combination.  
Figure 7: [EMAIL_ADDRESS] IoU results for each LR-BS combination, with and without data augmentation.  
Analyzing the relative performance gain for each metric, it is possible to see that the proposed nucleus-based approach allows an [EMAIL_ADDRESS] IoU increase ranging from 53.6% to 1216.2%, except for the HSIL class, with a decrease of 20.6%. Regarding AR@10, the nucleus-based approach also brought clear performance improvements, from 95.8% to 267.2% for the different classes.
# Stability Analysis of Isolated Roof in Overlapping Goal Based on Strength Reduction
## 3 Stability Analysis of Isolated Roof Under the Staggered Support
### The Safety Factor of the Isolated Roof When the Overlap Rate Is 0  
Generally speaking, most studies believe that the overlap rate of the pillars has a direct impact on the stability of the roof. The smaller the overlap rate, the greater the possibility of the roof collapse. However, from the research that has been carried out, there is a misunderstanding about the stability of the roof when the overlap rate is \(\eta=0\). Among them, the most prominent problem is that most studies believe that when the overlap rate is \(\eta=0\), the stability state of the roof remains constant, that is, as long as the overlap rate is \(\eta=0\), the tensile stress or shear stress of the roof will be a fixed value and will not change inevitably.  
But the fact is that the relative position of the upper and lower pillars determines whether the isolated roof is stable. As long as the lower pillar moves between the adjacent upper pillars, the overlap rate of the pillar is 0. In other words, even if the overlap rate is 0, the relative positions of the upper and lower pillars may be different. Therefore, it is not accurate to evaluate the stability of the roof by simply using the overlap rate. In Figure 7, when the overlap rate \(\eta\) is reduced from 1 to 0, the safety factor continues to decline, which is basically consistent with other research results. However, it should be noted that when the overlap rate is \(\eta=0\), the safety factor of the roof of the partition is still changing, which  
Figure 7: Variation in the damage type with the dislocated width. (**a**) Mining depth of 100 m; (**b**) Mining depth of 500 m; (**c**) Mining depth of 800 m.  
is significantly different from other research results. In Figure 7a, the variation range of tensile factor is 1.61-3.96, and the variation range of shear factor is 1.29-3.98. In Figure 7b, the variation range of the tensile factor is 0.56-1.55, and that of the shear factor is 0.32-1.46. In Figure 7c, the variation range of the tensile factor is 0.30-0.95, and the variation range of the shear factor is 0.09-0.94. This series of data all show that as long as the dislocated width changes, regardless of whether the overlap rate \(\eta\) changes, the safety factor will change.  
Furthermore, the safety factor is used to evaluate the stability of the roof when the overlap rate is 0. According to the principle of the most unfavorable value, the minimum value of the tensile factor and shear factor is taken as the safety factor of the roof. Referring to relevant research, a safety factor of 1.5 is taken as the critical point. If the safety factor is less than 1.5, the roof is judged to be unstable, otherwise it is stable. According to the above methods, the stability state of the roof shown in Figure 7a,c was evaluated, and the results are shown in Table 2. It can be seen that when the overlap rate is \(\eta\) = 0; due to the change in the dislocated width, the safety factor will also change, which causes the change in the stability state of the isolated roof.
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 2 Techniques and Materials
### In Situ Impregnation  
In the in situ impregnation, the combination of dry continuous fibre and a thermosetting or thermoplastic polymer material occurs in the heated printing head during the extrusion process [35; 36; 37]. Then, the combined material extrudes from the nozzle onto the building plate and builds up the object, which cures on the spot. It is common for a UV light to be used on the printhead for curing the thermosetting material without the need for high-temperature curation. This technique eliminates the need for pre-impregnated materials, allowing for greater flexibility in material selection and cost reduction. The in situ impregnation process ensures that the fibres are thoroughly coated with resin just before deposition, enhancing the final part's interlaminar bonding and mechanical properties. This method gives an advantage in creating large-scale structures and parts with complex fibre orientations that can have load-bearing capabilities. Key companies for the robotic machinery of this technology are Continuous Composites and Orbital Composites.
## 3 Control Strategy in Fast Frequency Response  
### Fast Frequency Response  
In traditional frequency regulation, low inertia systems have a low frequency nadir owing to the large initial RoCoF, which can adversely affect system stability. As renewable energy sources expand, system inertia inevitably decreases because of the increase in IBRs. To solve these system stability problems, North American Electric Reliability Corporation (NERC) introduced the FFR concept, and Electric Reliability Council of Texas (ERCOT) applied the FFR ancillary service in 2020 [12].  
FFR is an ancillary service that allows IBRs to inject or absorb active power in a power system by detecting frequency deviations to improve the frequency nadir or initial RoCoF. FFR utilizes IBRs that can respond quickly when a frequency drop occurs, so it has the advantage of being slower than an electromagnetically connected inertial response but faster than a primary frequency response operating through a droop. FFR is a reaction that uses a power electron device rather than a mechanical reaction, so the activation time is very fast. However, because it is not physically connected to the power system, frequency and RoCoF measurements are required, a process which results in delays in measurement and communication. RoCoF calculation using differentiation is difficult and error-prone. In this study, the RoCoF calculation is given by  
\[\text{RoCoF}(t)=\frac{f_{t}-f_{t-0.05}}{0.05} \tag{8}\]  
where \(f_{t}\) and \(f_{t-0.05}\) are frequency at time \(t\) and frequency 0.05 s before \(t\).  
To activate wind turbine resources for FFR, the wind turbine provides additional output power by utilizing the rotational energy of the drivetrain or the potential energy  
Figure 4: Proposed control operation on the MPPT curve: (**a**) power–rotor speed curve; (**b**) torque–rotor speed curve.  
of the DC-link capacitor of the converter. Photovoltaic systems can be used as an FFR resource along with ESSs.  
Types of conventional FFR control include proportional frequency deviation and RoCoF, input of a certain amount of active power when reaching deadband, reduction in proportional frequency deviation and RoCoF load, and reduction in a certain amount of load when reaching the deadband. These methods do not perform coordinated control between multiple resources but, rather, cooperative control for efficient resource utilization, because they activate resources simultaneously when a frequency drop for each resource is detected.  
Table 1 shows examples of activation times for various resources in [13]. In addition, Table 2 describes the detection and communication delay for frequency and RoCoF detection in [14]. In this study, we configured a delay of 0.06 s on the frequency meter to reflect the measurement and communication delay time and a delay of 0.05 s on the converter controller to reflect the activation delay.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## Appendix A  
Tables A1 and A2 show the parameters \(a_{ij}\) used to fit the \(CINR_{b}\) (Equation (16)) for different link availabilities within a confidence interval of 99.1%.
## 4 Statistical Results and Analysis  
In order to address the fourth research question mentioned in Section 1.2, we analyze data obtained through HIL studies to draw meaningful conclusion and form reasonable HMT guidelines by performing statistical analyses on two sets of data, namely performance metrics and survey results in this section. In addition, we also present results and analysis from the HIL simulations of two cases: (i) Parametric Training of MARCHp Protocol and (ii) Medic treating patient through ABC. First, identify how different groups performed on the HIL experiments and how statistically significant it is. Then we analyze how parameters such as interface (NLP), machine error, human error affect total outcome and performance of HMT based on the statistical significance. Finally, after analysing the aforementioned parameters we propose a few rules for designing future VBSA- based HMT.  
Employing statistical methods such as [PERSON]'s \(t\)-test, two-tailed Wilcoxon sign ranked test, two-way analysis of variance (ANOVA), and item response theory (IRT) help to understand how different groups identified in these cases performed relative to overall task success. In addition, we also analyze such parameters as team situation awareness, team cognition, human/machine errors, and time of response that affect team performance and success. Understanding of these factors can help successfully draft guidelines or suggest rule to design a standardized HMT. Moreover, using statistical results obtained in this section will help us formulate and summarize parameters during the standardization of an HMT because those parameters along with appropriate values are necessary to achieve successful HMT formation.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 4 Experiment and Analysis
### Ablation Studies and Analysis  
#### 4.5.1 Effectiveness of Each Proposed Components  
To assess the impact of each component in the AuxDepthNet framework, we conducted ablation studies highlighting their contributions to overall performance. (a) The baseline utilizes only context-aware features for 3D object detection, without incorporating depth-sensitive modules. (b) Context-aware and depth-sensitive features were integrated using a convolutional concatenate operation as an alternative to the depth-sensitive transformer module. (c) Depth-sensitive features in the transformer were replaced with object queries similar to DETR, creating a baseline with a DETR-like transformer. (d) Depth-sensitive features were replaced with those extracted from depth images generated by a pretrained depth estimator (DORN), instead of being learned in an end-to-end manner.  
The results in Table 3 highlight several key observations: substituting object queries in the transformer with depth-sensitive features (c\(\rightarrow\)e) significantly enhances depth representation, resulting in notable performance improvements. Furthermore, using features derived from a pretrained depth estimator (d) underperforms compared to the end-to-end approach in our full framework (e), emphasizing the benefits of directly learning depth-sensitive features. Our depth-sensitive transformer module (e) effectively combines context- and depth-sensitive features, outperforming the simpler convolutional concatenation method (b). Additionally, the inclusion of the depth prototype enhancement module (d\(\rightarrow\)e) further refines detection accuracy, particularly under more challenging scenarios. Ultimately, the complete AuxDepthNet model (e) delivers marked advancements across all levels of difficulty relative to the baseline (a), demonstrating the combined contributions of the proposed modules to robust and precise 3D object detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Ablation**} & \multicolumn{3}{c}{**AP3D@IoU = 0.7**} \\ \cline{3-5}  & & **Easy** & **Mod.** & **Hard** \\ \hline (a) & Baseline & 19.50 & 15.51 & 12.66 \\ (b) & w/o depth prototype enhancement & 23.91 & 18.27 & 15.21 \\ (c) & Depth-sensitive feature \(\rightarrow\) object query & 20.25 & 16.15 & 13.89 \\ (d) & Depth-sensitive feature \(\rightarrow\) DORN [49] & 24.27 & 17.15 & 13.84 \\ (e) & AuxDepthNet (full model) & 24.72 & 18.63 & 15.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of AuxDepthNet components on the KITTI validation set (Car category).
# Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions
## 3 Results
### Task 3-Accuracy of the Vertical Displacement
In the case of strong winds from 2.70 m to -3.30 m (COM-1) and from 2.2 m to -2.15 m (COM-2). The value of the standard deviation of the differences, larger by 0.50 m for strong wind conditions for both computers, also clearly points to the influence of wind on the recorded differences. For the values that were obtained from the LOG files in relation to the design data, the extreme values for COM-1 in light wind  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**PROJ-MS50**} & \multicolumn{4}{c}{**PROJ-LOG**} & \multicolumn{4}{c}{**MS50-LOG**} \\  & & & **N [m]** & **E [m]** & **A [m]** & **N [m]** & **E [m]** & **A [m]** & **N [m]** & **E [m]** & **A [m]** \\ \hline \multirow{11}{*}{LIGHT WIND} & \multirow{4}{*  
conditions range from 1.60 m to \(-\)0.90 m, and in a strong wind from 3.30 m to \(-\)4.6 m. Extreme values for COM-2 in light wind conditions range from 1.40 m to \(-\)1.50 m, and in a strong wind from 1.20 m to \(-\)1.1 m. The differences between MS50 and the data from the UAV allow for determining the accuracy of the device. The standard deviation of horizontal accuracy of this task is similar for COM-1 and COM-2, and it amounts to approximately 0.7 m in light wind and 1.00 m in strong wind. The results of the altitude measurements for both devices confirm the results of task 1 and 3. Based on the analyses of the figures and tables, it can also be noticed that there is a systematic drift of entire missions if compared to the design values. It results from the inaccuracy of the solution of the coordinates performed by the GNSS receiver. In addition, the table demonstrates the differences in the altitudes for the subsequent locations of the shutter release. Table 10 lists the differences in an analogous manner, as in Table 1 from task 1. The systematic drift of the entire series and the UAV's failure to reach the predetermined altitude have both confirmed the results of tasks 1 and 2. By analysing Figure 8, it can be stated that, in strong wind conditions, the rectilinearity of the flight is strongly disturbed, which results in large discrepancies in the distances between successive flight lines. The obtained results concerning the accuracy of reaching a position are similar to the results that were published for the helicopter-like UAVs [44]. The 3D-Plot of the trajectory also demonstrates that the helicopter is influenced by the wind on the stop points. The helicopter moves within a certain limit around the defined point. As is well known, the accuracy values bring to the conclusion that the positioning resulting from low-cost GNSS sensors might be usable as an approximation for the positions of the UAV-image data in photogrammetric software. The further part of the discussion will analyse the changes in the parameters of the side overlaps in photogrammetric flights that are caused by the above-mentioned discrepancies.