esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Optimization of Vented Brake Rotors with a Fully Parameterized Model
## 2 Research Methods
### Automatic Identification of the ND and T Modes  
To make the optimization process fully automatic, ND modes and T modes must be determined automatically. Based on the analysis results of the baseline model, the following facts were observed, according to which actions were taken for the brake rotor model under investigation: All ND and T modes appeared in nearly orthogonal pairs, and for each pair, only the mode with the lower frequency was used for optimization; the largest amplitudes of the ND and T modes were located on the outermost rim of the rotor disc; the number of regions on the rim containing the positive displacements was identical to that of the negative regions, and each region of positive/negative displacements occupied a similar arc length on the rim, as shown in Figure 8. The procedure to identify the ND and T modes is described below:  
1. During modeling, 360 hardpoints evenly distributed on the rim of the top surface of the rotor disc were first defined, and the finite element nodes at these hardpoints (defined as the key nodes) were extracted and renumbered sequentially;
2. Modal analysis was performed to solve for 40 flexural modes;
3. The Cartesian coordinate system was selected;
4. The largest amplitude in the \(y\)-direction among all finite element nodes of each mode was identified, by which all nodal \(y\) displacements of the key nodes within the same mode were divided (normalization);
5. Starting from the first mode (the lowest frequency), modes with normalized \(y\) displacements at the key nodes equal or very close to \(+1/-1\) were identified, alternating behavior of positive and negative displacements at the key nodes was checked, and the numbers of alternating regions and the key nodes in each region were counted;
6. Consecutively, 2 ND through 7 ND modes were identified;
7. The cylindrical coordinate system was selected;
8. Excluding the identified ND modes, the above procedure was repeated, with \(\theta\) direction replacing the \(y\)-direction, and the 1T and 2T modes were identified, if necessary. These steps, which are efficient and easy to implement, were realized and incorporated with finite element analysis and optimization schemes in APDL codes and proved very effective in identifying the ND and T modes.
## 2 Methods  
### Basic Principle  
Figure 1 shows a schematic of multiple interferences of the monolayer as illustrated elsewhere [35; 36]. The incident light with an angle of \(\theta_{1}\) through the air media with a refractive index of \(n_{0}\) was reflected at the surface with a reflection coefficient of \(r_{01}\) and transmitted to the film media of \(n_{1}\) with a transmission coefficient of \(t_{01}\). The incident light with an angle of \(\theta_{2}\) through the film media with a thickness of \(d\) was reflected at the surface of \(n_{1}\)-\(n_{2}\) with a reflection coefficient of \(r_{12}\). The reflected light reached the surface of the air-film, was reflected at the surface with \(r_{10}\), and was transmitted to the air media with \(t_{10}\). The iteration of the procedure would produce interference from the transparent film. The spectral intensity changes periodically in the wavelength dimension. The film thickness can be resolved from the spectral interference signal.  
Without considering the absorption of light by the film, the amplitude of the electric field can be summed as  
\[\begin{array}{ll}E&=E_{1}+E_{2}+E_{3}+E_{4}+\cdots\\ &=E_{0}\big{(}r_{01}+t_{01}r_{12}t_{10}e^{i\Delta}+t_{01}r_{12}^{2}r_{10}t_{10 }e^{2i\Delta}+t_{01}r_{12}^{3}r_{10}^{2}t_{10}e^{3i\Delta}+\cdots\big{)}\\ &=E_{0}\bigg{(}r_{01}+t_{01}r_{12}t_{10}e^{i\Delta}\times\frac{1-\big{(}r_{12} r_{10}e^{i\Delta}\big{)}^{n}}{1-r_{12}r_{10}e^{i\Delta}}\bigg{)}\\ &\approx E_{0}\cdot\frac{r_{01}+r_{12}e^{i\Delta}}{1-r_{12}r_{10}e^{i\Delta}} \end{array}, \tag{1}\]  
where \(E_{0}\) is the original electric field amplitude, and \(E_{i}\) is the electric field amplitude returned from the surface of the air-film. \(\Delta\) is the phase difference passing through V-shaped optical paths, which can be expressed as  
\[\Delta=4\pi n_{1}d\cos\theta_{1}/\lambda. \tag{2}\]  
Then, the constructive interference condition can be expressed as \(\Delta=2k\pi\) (\(k\) is an integer). When the angle of incident light is close to \(0^{\circ}\), the adjacent maxima of spectra are given by the wavelengths \(\lambda_{1}<\lambda_{2}\),  
\[4\pi n_{1}d\bigg{(}\frac{1}{\lambda_{1}}-\frac{1}{\lambda_{2}}\bigg{)}=2\pi \tag{3}\]  
Figure 1: Schematic diagram of multiple interferences of the monolayer. \(n_{i}\) represents the refractive index of the \(i\)th layer; \(r_{ij}\) and \(t_{ij}\) represent the reflective and transmission coefficient, respectively, from the \(i\)th layer to the \(j\)th layer.  
According to Equation (3), the frequency and period of spectral interference signal can be expressed as \(T=\frac{1}{f}=\frac{1}{\lambda_{1}}-\frac{1}{\lambda_{2}}\), and then the thickness of \(d\) can be calculated using  
\[d=\frac{1}{2n_{1}T}=\frac{f}{2n_{1}} \tag{4}\]
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
###### Abstract  
A new underwater 3D scanning device based on structured illumination and designed for continuous capture of object data in motion for deep sea inspection applications is introduced. The sensor permanently captures 3D data of the inspected surface and generates a 3D surface model in real time. Sensor velocities up to 0.7 m/s are directly compensated while capturing camera images for the 3D reconstruction pipeline. The accuracy results of static measurements of special specimens in a water basin with clear water show the high accuracy potential of the scanner in the sub-millimeter range. Measurement examples with a moving sensor show the significance of the proposed motion compensation and the ability to generate a 3D model by merging individual scans. Future application tests in offshore environments will show the practical potential of the sensor for the desired inspection tasks.  
optical underwater 3D sensor; structured illumination; motion compensation; 3D model generation; high-accuracy 3D measurements +
Footnote †: journal: Medical Imaging
## 3 Experiments and Results
### Repeatability Testing  
Repeatability tests with different rotational velocities were carried out using the single-joint device. Speed was controlled using pulse width modulation (PWM). After receiving the start signal, the measurement and control circuit of the joint sent the PWM signal to the motor and controlled the arm to rotate until the probe touched the surface of a measuring block and triggered angle measurement. The counter value was collected and uploaded to the computer at this time. Then, the arm was controlled to rotate reversely until the probe touched the other side of the measuring block, and the angle measurement was taken once again. The process was repeated 50 times without interruption at each duty ratio of PWM. As an example, Figure 9 presents the residual error in the count value when measuring both sides of the block with a speed of 0.67 rad/s (PWM = 6%). Table 5 lists the variance values from the repeatability experiments, where \(\sigma_{\text{U}}\) and \(\sigma_{\text{D}}\) represent the standard deviations when touching the top and bottom of the standard block, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Joint**} & **Angular Acceleration (\(\textasciitif{}\)/s\({}^{2}\))** & **Peak Moment (MSC Adams)** & **Peak Moment of** & **Output Moment** \\  & **(\(\textasciitif{}\)/s\({}^{2}\))** & **(\(\textasciitif{}\)/\(\textasciitif{}\)C Adams)** & **Reducer** & **of Motor with** \\  & **(\(\textasciitif{}\)N-m)** & **(\(\textasciitif{}\)Start/Stop) (N-m)** & **Reducer (N-m)** \\ \hline
1 & 10 & 12.42 & 159 & 192 \\
2 & 10 & 118.11 & 159 & 192 \\
3 & 20 & 38.26 & 51 & 50 \\
4 & 20 & 31.77 & 51 & 50 \\
5 & 30 & 1.97 & 17 & 5 \\
6 & 30 & 0.84 & 17 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of moments between the MSC Adams simulation of six joints and idea model.  
Figure 8: Experimental setup for a single joint [31].  
According to the experimental results, the repeatability of measurement is related to the speed of triggering. A higher triggering speed results in a poor measurement repeatability. Analysis of measurement data, it is noted that the larger the duty ratio of PWM is, the higher the probability of false trigger caused by vibration is. If the duty ratio is less than 10%, the probability of the false trigger will be greatly reduced, and the maximum standard deviation is 1.1. The pulse equivalent of incremental code signal output by the grating sensor installed on the single-joint module is 0.63\", so that the self-driven joint can reach a repeatability of 1.39\" (k = 2) when the rotational velocity is less than 1.53 rad/s.
## 4 Performance Evaluations  
### Simulations  
In order to verify the performance of the proposed DAM-TEQ, a simulation analysis is carried out in this paper. The simulation will be conducted in three aspects: feasibility, reliability, and comparative performance.
# Study of Different Crystal Habits of Apreptiant: Dissolution and Material Attributes
## 3 Results and Discussion
### Recrystallization Experiments  
The slow evaporation method was used with API dissolved in organic solvents in undersaturated conditions. Crystals were observed under a compound microscope and SEM. As shown in Figure 1, APT used for recrystallization had irregular plate-shaped crystals. Acetone at 5 \(\pm\) 3 \({}^{\circ}\)C gave tabular crystal habit. Ethyl acetate at 5 \(\pm\) 3 \({}^{\circ}\)C generated a long tabular crystal habit, as crystals are elongated on axis \"a\". The habits were compared with the morphology generated using KrystalShaper (Supplementary Figure S1) which shows that the morphologically important crystal facets were (001), (00-1), (010), (010), (100), (100), and (-100). The growth pattern of crystals in a solvent depends on the solvent's polarity and molecular weight [9]. Interaction of properties of solvent with dissolved drug decides the direction in which the crystal will grow [26]. Solvent's tendency depending on its polarity to interact with the hydrophobic or hydrophilic surface of the crystals leading to the development of different morphology has been discussed in the literature [27]. In the case of APT-AC, acetone interacted with APT in such a way that tabular crystals were generated with higher growth on axis a and b and limited growth on-axis c. In the case of APT-EA, ethyl acetate interacted with APT in such a way that long tabular crystals were generated with higher growth on axis a, limited growth on axis b, and least growth on axis c. This change in facets is further discussed in Section 3.5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solvent** & **Solubility (mg/mL)** \\ \hline Methanol & 25.64 \\ Acetone & 23.25 \\ Ethyl Acetate & 14.71 \\ DCM & 4.07 \\ Chlorform & 55.55 \\ _tert_-Butyl methyl ether & Milky suspension \\ Ethanol & 10.64 \\ Acetonitrile & 45.45 \\ DMF & 35.71 \\ DMSO & 60.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solubility of APT in various organic solvents.
## 3 Results and Discussion
### Stress Relaxation Characteristics of Machine-Harvested Seed Cotton
#### 3.2.4 Effect of Section Size on Stress Relaxation Characteristics  
As shown in Figure 7a, the initial stress of seed cotton gradually decreases with the increase of cross-sectional dimensions. When the seed cotton is compressed to the same density, the initial stress gradually decreases with the increase of the cross-sectional dimensions. The stress can be gradually homogenized in the radial direction due to the larger cross-sectional dimensions, which made the compression force required decrease during the compression process [36]. Figure 7b,c show the curves of stress relaxation characteristics of parameters \(E_{\varepsilon}\), \(T_{1}\) and \(\varepsilon\) with cross-sectional dimensions of machine-harvested seed cotton under different cross-sectional dimensions. \(E_{\varepsilon}\) decreases gradually with the increase of cross-sectional dimensions, and there is a minimum value at of 200 mm. The primary reason is that the compression force decreases with the increase of cross-sectional dimensions [45], and the stress is easily homogenized along the direction of cross-sectional dimensions. Meanwhile, there is a larger compression force and a certain pressure transient drop at the small cross-sectional dimensions [26], therefore, the equilibrium elastic modulus firstly increases and then decreases with the increase of cross-sectional dimensions. However, the \(T_{1}\) decreases slightly with the cross-sectional dimensions, and the decrease is limited, because in larger cross-sectional dimensions, the stress can be fully homogenized along the compression direction and radial direction. With the increase of cross-sectional dimensions, the \(\varepsilon\) shows a fluctuating decrease, and the reason for this phenomenon is that the larger the cross-sectional dimensions, the stress rapid decay time is smaller, and the seed cotton has a longer time for stress homogenization.
## 1 Introduction
By comparing different cooling materials with a phase change material, the behavior of the materials in battery cooling systems can be better understood. Our application-oriented simulation in the use/charge cycle allows for insight into the effects of the cooling material used at different typical loads (discharging, loads, etc.). This study also provides an example of how simulation methods can be used to play through the complex interaction of the cooling system, load sequence, and safety and ageing requirements as well as how they can identify adjusting screws for the targeted optimization of the system. In most power tool battery packs used in handheld power tools, cooling is achieved by passive cooling systems. This study uses a Comsol battery and heat transfer simulation to evaluate three different materials that are in use in the passive cooling systems of battery packs. The performance of three different cooling materials--two heat-conductive polymers, one phase change material, and non-convective air as reference--are compared at typical working conditions (at 25 \({}^{\circ}\)C, with a low wind speed) in a material sweep study. To get further insights into their performance under other climatic conditions, an additional simulation study on the influence of the external temperature and wind speed was performed (a parameter sweep study). The special focus of this study was on shortening the use/charge cycle (Figure 1). This cycle resembles the typical use scenario of a power tool battery pack, e.g., used by a professional craftsman working with a power tool at a construction site over several hours. It consists of intensive use (A), cool-down phase I (B), charging (C), and cool-down phase II (D). To prevent accelerated aging during charging, the maximum charging temperature is limited to 50 \({}^{\circ}\)C (as per the recommendation of the cell's manufacturer), and therefore charging is implemented in a temperature window of 46 \({}^{\circ}\)C (the charging start temperature) and 50 \({}^{\circ}\)C (the charging stop temperature). A long us phase, which is mainly limited by the pack energy density and size, is favorable for customers. However, cool down and charging phases B and C have to be as short as possible to guarantee an interruption-free workflow with low downtimes for the pack. In particular, cool-down intervals during phases B and C limit the use of the pack. Along with guaranteeing safety by not reaching pack temperatures that are too high, it is the task of a good battery pack cooling system to minimize these downtimes. Few authors have investigated hand-held power tools with respect to their behavior in scenarios close to application and real-life conditions as carried out in this study. [PERSON] et al. [47] investigated the transient behavior of hybrid hand-held tools in nearly real load conditions in a hybrid experimental and simulative study. They developed a simulation model for the energy management of the power tool but did not look at the cooling system and strategy.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 4 SGTC: The Spectral Gap-Based Topology Control Algorithm
### The Annealing Process  
Following the simulated annealing strategy, on each iteration of SGTC, a candidate solution \(G_{i}\) is accepted as the new current solution with probability one if \(G_{i}\) has a larger value of \(\lambda_{2}\) than that of the current solution \(G_{current}\), namely if \(\Delta=\lambda_{2}^{candidate}-\lambda_{2}^{current}>0\) (Line 12). It also accepts a candidate solution following the Metropolis criterion, with probability \(p_{accept}\), if \(\Delta\leq 0\) (Line 14). The control parameter \(\alpha\), used in Line 11 of Algorithm 2 to compute the probability \(p_{accept}\), takes the role of the temperature in the annealing process and is reduced at each iteration (see Line 17) to gradually force the search towards strictly better solutions.  
SGTC stores in \(\mathcal{F}\) the feasible network topologies \(G_{current}\) visited during the annealing process. Then, it uses ParetoFront() (see Line 19 of Algorithm 2) to determine the Pareto front \(\mathcal{P}\) composed of the solutions in \(\mathcal{F}\) that minimize the network diameter and maximize the network robustness and entropy.  
Lastly, SGTC returns the network in \(\mathcal{P}\) of minimum energy cost (see Line 20 of Algorithm 2) as the output of the algorithm.
# Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis +
## 6 Conclusions  
The generation of extreme ground impact footprints map has been addressed in this paper for fixed-wing UAVs failure. In the proposed approach, the computation of impact points is based on simulation of a full dynamic model, including aerodynamics of the UAV and wind effect. Uncertainties accounted for in these simulations have been characterized, based on some real flight data. Monte Carlo simulations have been performed to generate footprints; however, it is not satisfying when we focus on extreme ground footprints. For this purpose, we have presented a rare-event simulation technique called multiple importance sampling to answer the issue of extreme drone ground impacts. We also show that at low computational cost, it is also possible to derive sensitivity indices to interpret the cause of extreme impacts.  
Future work will include these characterizations of extreme drone impacts for the risk analysis of UAV missions. Sensitivity and robustness analysis with regard to uncertainties on some parameters of the UAV (aerodynamic coefficients) will also be considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a) No Wind**} \\ \hline ROSA indices & \(t_{0.5}\) & \(t_{0.8}\) & \(t_{0.99}\) \\ \(\hat{\eta}_{1}\) & 0.07 & 0.06 & 0.06 \\ \(\hat{\delta}_{1}^{f}\) & 0.04 & 0.04 & 0.03 \\ \hline \(\hat{\eta}_{2}\) & 0.05 & 0.03 & 0.03 \\ \(\hat{\delta}_{2}^{f}\) & 0.04 & 0.03 & 0.03 \\ \hline \(\hat{\eta}_{3}\) & **0.34** & **0.31** & **0.11** \\ \(\hat{\delta}_{3}^{f}\) & 0.07 & 0.07 & **0.15** \\ \hline \(\hat{\eta}_{4}\) & **0.41** & **0.17** & 0.06 \\ \(\hat{\delta}_{4}^{f}\) & **0.16** & **0.18** & **0.16** \\ \hline \(\hat{\eta}_{5}\) & 0.06 & 0.04 & 0.04 \\ \(\hat{\delta}_{5}^{f}\) & 0.06 & 0.04 & 0.04 \\ \hline \multicolumn{4}{c}{**(b) Wind of 5 m.s \({}^{-1}\) with angle \({-90^{\circ}}\).**} \\ \hline ROSA indices & \(t_{0.5}\) & \(t_{0.8}\) & \(t_{0.99}\) \\ \(\hat{\eta}_{1}\) & 0.06 & 0.07 & 0.07 \\ \(\hat{\delta}_{1}^{f}\) & 0.05 & 0.03 & 0.03 \\ \hline \(\hat{\eta}_{2}\) & **0.13** & 0.08 & 0.06 \\ \(\hat{\delta}_{2}^{f}\) & 0.02 & 0.04 & 0.03 \\ \hline \(\hat{\eta}_{3}\) & **0.40** & **0.24** & 0.05 \\ \(\hat{\delta}_{3}^{f}\) & 0.16 & **0.22** & **0.23** \\ \hline \(\hat{\eta}_{4}\) & **0.34** & **0.13** & 0.06 \\ \(\hat{\delta}_{4}^{f}\) & **0.30** & **0.24** & **0.21** \\ \hline \(\hat{\eta}_{5}\) & **0.10** & 0.06 & 0.05 \\ \(\hat{\delta}_{5}^{f}\) & 0.04 & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ROSA indices for ground impact analysis. Bold numbers correspond to values greater than 0.1.  
**Author Contributions:** Conceptualization, J.M., B.L. and S.B.; methodology, J.M.; software, B.L.; validation, J.M., B.L. and S.B.; manuscript writing, J.M., B.L. and S.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by French DGAC in the context of the research partnership PHYDIAS with ONERA for safety improvement of UAVs.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
###### Abstract
The AFST layer is utilized to reduce the out-of-band RCS while maintaining in-band transmission, and the RTA layer is responsible for achieving 1-bit transmission phase shifting. A 16 \(\times\) 16-element prototype was designed, fabricated, and measured to verify the design concept. The main novelty of this work is in achieving in-band beam scanning and out-of-band wideband RCS reduction simultaneously, and ensuring the low-profile characteristics using multi-resonant modes. The remainder of this paper is organized as follows. Section 2 introduces the design concept of the proposed method. Section 3 shows the element structure of low-RCS 1-bit RTA. Section 4 discusses the fabrication and measurement of the 16 \(\times\) 16 prototype. Finally, the conclusion is given in Section 5, and defining abbreviations is given in Abbreviations part.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 2 Basic Settings for the Simulations
### Simulation Results and Discussions  
The acceleration responses of the building structure under historical, mid-future, and late-future wind conditions for different return periods (10-year, 50-year, and 100-year) are compared as shown in Figure 7 (along wind direction, Y-direction) and Figure 8 (across wind direction, X-direction). The acceleration responses at the 44 th floor is compared as it is the highest residential floor of the building. From Figure 7, the acceleration responses at the 44 th floor of the building in the along-wind direction are discussed as follows:  
* For a 10-Year Return Period: The maximum acceleration for historical conditions was recorded at 0.1550 m/s\({}^{2}\), which increases to 0.1674 m/s\({}^{2}\) in mid-future conditions and further to 0.1708 m/s\({}^{2}\) in late-future conditions. This trend indicates an approximate increase of 8.0% and 10.0% for mid-future and late-future conditions, respectively, compared to historical data.
* For a 50-Year Return Period: The historical maximum acceleration was 0.2319 m/s\({}^{2}\), which rises to 0.2483 m/s\({}^{2}\) in mid-future and 0.2732 m/s\({}^{2}\) in late-future conditions. This represents increases of about 7.0% and 18.0%.  
* For a 100-Year Return Period: The historical maximum acceleration of 0.2682 m/s\({}^{2}\) increases to 0.2850 m/s\({}^{2}\) and 0.3218 m/s\({}^{2}\) in mid-future and late-future conditions, respectively, reflecting increases of 6.0% and 20.0%.  
Figure 7: Comparison of acceleration response time histories at 44 th floor of the building in Y-direction for return period: (**a**) 10-year; (**b**) 50-year; (**c**) 100-year.  
Likewise, the peak acceleration values in the X-direction of the building follow the same pattern in the Y-direction. Table 6 presents the peak acceleration value at the 44 th floor of the building for different wind conditions and return periods in both directions of the building. The results indicate trends in acceleration response over time, highlighting the increasing impact of climate change on building performance.  
Figure 8: Comparison of acceleration response time histories at 44 th floor of the building in X-direction for return period: (**a**) 10-year; (**b**) 50-year; (**c**) 100-year.