esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity with Direct Simulation Method
#### 4.2.2 Sensitivity Estimation with Line Sampling  
Line sampling was first introduced in [44] and aims to correct the FORM/SORM approximations with sampling. Suppose the design point \(P^{*}\) of the system is unique. The idea is to sample on the hyperplane denoted \(\mathbf{V}_{1}\) perpendicular to the direction pointing towards the design point, in the standard normal space \(\mathbf{X}\). In order to do so, the sampling space is rotated and reduced by one dimension: \(\mathbf{V}=\mathbf{R}\mathbf{X}\), with \(\mathbf{V}=\begin{bmatrix}\mathbf{V}_{1}^{\top},V_{d}\end{bmatrix}^{\top}\) where \(\mathbf{V}_{1}\) contains the \((d-1)\)-dimensional subspace, \(V_{d}\) is a coordinate parallel to the direction of the design point \(P^{*}\), and \(\mathbf{R}\) is a rotation matrix. The rotation matrix verifies \(\mathbf{R}^{\top}\mathbf{R}=\mathbb{I}_{d}\) and its \(d\)-th row is the unit direction \(P^{*}/\|P^{*}\|\); see [42]. In the LS framework, the root \(b(\mathbf{v}_{1})\) represents the distance to the limit state surface in a direction orthogonal to the hyperplane \(\{v_{d}=0\}\) at \(\mathbf{v}_{1}\). We focus here on the case where the root of the limit state function for the vector \(\mathbf{v}_{1}\) is unique. The probability of failure with the dependency on \(\mathbf{s}\), is expressed as  
\[P_{f}(\mathbf{s})=\int_{\mathbb{R}^{d-1}}\
abla(-b(\mathbf{s},\mathbf{v}_{1}) )f_{\mathbf{v}_{1}}(\mathbf{v}_{1})\mathrm{d}\mathbf{v}_{1},\]  
where \(\mathbf{v}_{1}\mapsto b(\mathbf{s},\mathbf{v}_{1})\) is the function which assigns to every vector \(\mathbf{v}_{1}\) its root, with the dependency of \(\mathbf{s}\) underlined, \(\
abla\) denotes the cdf of an univariate standard normal variable and \(f_{\mathbf{V}_{1}}\) is the pdf of the \((d-1)\)-variate standard normal random vector on the hyperplane \(\{v_{d}=0\}\). The sensitivity of \(P_{f}\) can simply be derived with the equation  
\[\frac{\partial P_{f}(\mathbf{s})}{\partial s_{\ell}}=\int_{\mathbb{R}^{d-1}} \bigg{(}-\frac{\partial b(\mathbf{s},\mathbf{v}_{1})}{\partial s_{\ell}} \phi(b(\mathbf{s},\mathbf{v}_{1}))\bigg{)}f_{\mathbf{V}_{1}}(\mathbf{v}_{1}) \mathrm{d}\mathbf{v}_{1},\]
## 5 Conclusions  
Triply periodic minimal surfaces (TPMSs) have shown great potential as porous structures in different engineering applications. Amongst them is the cooling heat sink, which is the subject of this paper. The advantage of such a structure is the ability to design it based on the applications. For biomedical applications, the interest is in the strength of the structure to replace any bone in the body. In thermal management, it is lightweight and could provide good heat removal. The present paper used a TPMS to study its effectiveness in heat removal. Two different Newtonian fluids, water and a nanofluid, were used, with a concentration of 0.6% nanoparticles. The experiments were conducted for various flow rates, inlet temperature conditions, and two different TPMS structures, aluminum and silver, by maintaining a constant heat flux. The TPMS structure was a gyroid, having a porosity of 0.7. The experimental measurements were compared to the finite element technique's numerical results. The results achieved in this paper demonstrate that:  
1. TPMS is a suitable porous structure that exhibits excellent heat removal with a reasonable pressure drop. The pressure drop may vary depending on the structure design.
2. Since wavy and straight channels generally help with heat extraction by maintaining low pressure drops, TPMSs could be designed to contain channels in the structure.
3. Heat distribution was uniformly distributed; thus, no boundary layer formation was detected. This is an essential finding for removing the boundary layer and allowing the heat to be absorbed by the moving fluid.
4. The thermal conductivity of the TPMS plays an additional role in enhancing heat transfer in the system. However, the material used for TPMSs also plays a significant role in heat enhancement.
5. The nanofluid, having a higher thermal conductivity, can improve heat removal but at the expense of the pressure drop. The performance evaluation criteria were higher when the silver TPMS was used.
6. The Nusselt number exhibits a higher value in the nanofluid's presence due to the fluid's greater thermal conductivity.
7. The performance evaluation criteria was found to be greater for the nanofluid compared to water. This is due to the higher Nusselt number.
8. Although the nanofluid exhibits a higher Nusselt number at the expense of a higher pressure drop, special attention must be paid to the nanoparticle agglomeration inside the void in the application.  
Due to the lack of experimental measurements using TPMSs, this paper should be used as a benchmark for enhancing numerical models in general.  
Conceptualization, M.Z.S. and G.A.K. methodology, M.Z.S.; software, M.Z.S.; validation, M.Z.S.; formal analysis, M.Z.S. and G.A.K.; investigation, M.Z.S.; resources, M.Z.S.; All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of NSERC, grant number RGPIN-2020-07021.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Controllable Polarization Rotator
Figures 11d,e show the EIT-induced ellipticity changes, \(\Delta\varepsilon\), and the EIT-induced orientation angle changes, \(\Delta\varphi\), respectively, of the transmitted probe light at different coupling intensities at the exact two-photon resonant point. It can be seen that, for a linearly polarized input probe light, the transmitted probe light after the Pr:YSO crystal stays linearly polarized, but the orientation angle, \(\varphi\), of the transmitted probe light rotates with respect to the input probe light, demonstrating a controllable polarization rotator under the exact two-photon resonant condition. What is more, if a coupling beam with a structured transverse intensity distribution is used, a light field with different polarization states across the transverse section can be obtained, that is, a vector beam, which can be useful in optical tweezers and microfabrication techniques. Figure 11: **(a)** Schematic definition of the ellipticity, \(\varepsilon\), and the orientation angle, \(\varphi\), of an elliptically polarized light in the crystalline \(Oxy\) coordinate system. **(b)** The measured EIT-induced frequency-detuning spectrum of the ellipticity, \(\varepsilon\), and **(c)** the measured EIT-induced frequency-detuning spectrum of the orientation angle, \(\varphi\), respectively, around the two-photon resonant condition. **(d)** The dependence of the EIT-induced ellipticity change, \(\Delta\varepsilon=\varepsilon-\varepsilon_{0}\), and **(e)** the dependence of the EIT-induced orientation angle change, \(\Delta\varphi=\varphi-\varphi_{0}\), respectively, on the coupling intensity at the exact two-photon resonant point. The dashed curves are guided for the eyes. Here, the polarization state of the probe light and the coupling light are initially the same and are set to be (\(\varphi_{0}\), \(\varepsilon_{0}\)) = (20, 0). The coupling light is 3.7 mW for **(b,c)**, and the probe light is 150 μW for **(b–e)**, respectively. Reprinted with permission from Springer: Applied Physics B [111], Copyright 2016.
## 4 Results of Optical-System Design and Simulation
### Dichroic System  
The light from the telescope is divided into three channels by two dichroic beamsplitters in the dichroic system. The beam-splitting plane is perpendicular to the \(Y\) axis. A collimated beam in 0.5-2 \(\upmu\)m is delivered into the pointing system, focused beams over 2-4 \(\upmu\)m (CH1) and 4-8 \(\upmu\)m (CH2) are injected into two entrance slits of the spectrometer channels by parabolic focus mirrors.  
According to the above principle, ZEMAX optical software was used to plot the layout of the dichroic system, as shown in Figure 6a, in which blue, green, and red represent the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wavelength coverage & 0.5–8 \(\upmu\)m \\ FOV & Diffraction limited: 27.9\({}^{\prime\prime}\), unvignetted: 44\({}^{\prime\prime}\) \\ Collecting area & 3.46 m\({}^{2}\) \\ Aperture & 2.1 m \\ Output-beam dimension & 0.2 m \\ Angular magnification & \(-\)10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optical-design parameters of telescope.  
Figure 5: (a) Spot diagram at different FOVs of telescope output. Red spots, image spots; black circles, Airy spots. FOV and parallelism of output beams shown above and below each box, respectively. Imaging spots in the required FOV were far smaller than Airy spots. (b) RMS WFE map over full FOV at telescope output.  
channels of the pointing system, CH1, and CH2, respectively. Figure 6b shows the shaded model of the dichroic system combined with the telescope.  
Figure 7 shows the field maps of RMS WFEs at the exit of dichroic system for (a) CH1 and (b) CH2. Similar to Figure 5b, Figure 7 evaluates the imaging performance of light passing through the dichroic system and telescope. Maximal RMS WFEs at the maximal wavelength of CH1 and CH2 were 0.2544 \(\upmu\)m (0.0636 \(\lambda\)) and 0.2568 \(\upmu\)m (0.0321 \(\lambda\)), which were less than the diffraction limit of 0.072 \(\lambda\). Figure 8 shows the matrix-spot diagrams at the exit of the dichroic system (slit surface) for CH1 and CH2. The radii of the image points in each FOV were smaller than the Airy radius.  
Figure 6: (**a**) Dichroic system. (**b**) Shaded model of dichroic system combined with telescope. CH1 channel, green; CH2, red; pointing system, blue.  
Figure 7: Root-mean-square (RMS) wavefront error (WFE) maps over full FOV at exit of dichroic system for (**a**) CH1 and (**b**) CH2.
## 4 Experiments Analysis
### Dataset  
Three publicly available databases were used to evaluate the proposed method: the JAFFE [60], RaFD [61; 62], and KDEF [63] databases. Face images and challenging conditions were included in these databases.  
Japanese Female Facial Expression (JAFFE): These databases contain various face images and challenging conditions. A sequence of 2-4 samples was provided for each expression, and the images were 256 \(\times\) 256 pixels in size. In Figure 3, sample data from the JAFFE database are presented. As shown in Table 1, each prototypical expression had several images in the databases.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Dataset** & **FE** & **AN** & **DI** & **HA** & **SA** & **SU** & **NE** & **Total** & **Resolution** \\ \hline JAFFE & 30 & 30 & 28 & 29 & 30 & 30 & 30 & 207 & 256 \(\times\) 256 \\ KDEF & 140 & 140 & 140 & 140 & 140 & 140 & 140 & 980 & 562 \(\times\) 762 \\ RaFD & 67 & 67 & 67 & 67 & 67 & 67 & 469 & 681 \(\times\) 1024 \\ \hline \end{tabular}
\end{table}
Table 1: The used facial expression datasets.  
Radboud Faces Database (RaFD): 67 face models expressing basic emotions were represented in this database with 536 images. FACS experts trained all face models to express prototypical basic emotions. Additionally, all pictures were validated by FACS coders and 238 non-expert judges (N = 238) [60]. Twenty-seventy-three pictures were included in this study of 39 white adults expressing anger (AN), disgust (DI), neutrality (NE), fear (FE), sadness (SA), happiness (HA), and surprise (SU). A Caucasian face seemed to be more accurate with algorithms for facial expression analysis than a non-Caucasian face. White faces allow comparisons with previous validations of emotional facial expression categorization methods [63] and across different facial databases [64]. The sample figures of the RaFD datasets are presented in Figure 4.  
Karolinska Directed Emotional Faces (KDEF): The KDEF [63] database trained and tested our models for emotion recognition. Each individual in the corpus displayed seven different emotional expressions from five perspectives. The group comprised 70 individuals aged 20-30 (35 men and 35 women). The images were all taken in a controlled environment with fixed image coordinates for eye and mouth placements [63], as shown in Figure 5.  
Figure 4: A sample facial expression sequence of the RaFD dataset.  
Figure 3: A sample facial expression sequence of the JAFFE dataset.
## 4 Results
### Performance Evaluation of the DQN-Based Strategy
#### 4.4.1 Performance for Different Trajectories
Figure 6 presents a more specific and detailed analysis of this trajectory during a period of 25 s between the points (X\({}_{1}\) = 285, Y\({}_{1}\) = 181) and (X\({}_{2}\) = 260, Y\({}_{2}\) = 181). In particular, Figure 6a represents the evolution of the SINR experienced by the UE in the LTE and 5G NR cells during this period. Since one of the NR cells has a higher SINR value, this will be the selected cell with the SINR-based strategy during the whole analyzed period. In  
Figure 4: Average reward for different trajectories. contrast, the DQN-based strategy is able to split the traffic through the LTE and 5G NR cells in accordance with the experienced conditions in terms of signal and load. In this case, Figure 6b shows the evolution of the values of \(\beta_{m,n}\) selected by the algorithm. During the first 15 s the DQN-based strategy splits the traffic just using both LTE cells, i.e., \(\beta_{1,2}\) and \(\beta_{1,1}\) values are different from 0, even when those cells do not have the highest SINR values. For the remaining seconds, the traffic of the UE is delivered by using different MC configurations; for example, LTE-NR by adjusting the values of \(\beta_{1,2}\) and \(\beta_{2,4}\). As a result, Figure 6c shows the evolution of the reward with both strategies during the analyzed period, and it can be observed that the DQN-based strategy clearly outperforms the SINR-based one. In fact, if we consider average reward values for both strategies, the obtained with the SINR-based strategy is around 0.27, while the obtained with the DQN approach is 0.62, which is more than twice. It is worth mentioning that during the analyzed situation, the optimal strategy also provides a relatively low reward of 0.71. This is due to the traffic dynamics of the background UEs that lead to a high load in the cells during the analyzed time period. This high load does not allow ensuring the required bit rate with any of the combinations, which reduces the reward.
## 2 Basic Generator Topologies
### Pulse Forming Using Capacitor Discharge Circuits
In case the bipolar pulses are required to optimize the electroporation applications (e.g., food products treatment) [54, 55], the direct capacitor discharge pulse generator concept can be enhanced with half-bridge or full-bridge circuit [56] as presented in Figure 7.  
In both cases the generator can produce positive and/or negative high-voltage pulses. However, the half bridge topology has limitations in operation with capacitive-type of loads, hence full-bridge concept is more common. The increase of the number of the switches in a full-bridge concept brings additional operational flexibility similarly to the direct capacitor discharge topology. Moreover, to enhance the pulse control and scalability several half-bridge or full-bridge pulse forming circuits can be stacked in a modular approach [30, 32, 57]. Additionally, Marx generators can be arranged in a bridge configuration to enable the generation of bipolar pulses [30, 31, 58]. Nevertheless, independently on the topologies described above the capacitor discharge circuits are limited by switching dynamics of semiconductor devices, thus the minimal pulse durations are typically in the sub-microsecond range. In order to form shorter pulses (i.e., less than 100 ns) other electroporator topologies are required. Figure 5: The principle circuit of modular direct capacitor discharge pulse generator. Figure 6: The circuit of pulse generator based on Marx topology.
## 5 Discussion
### Limitations and Future Research
Future research should also explore how feedback is perceived, focusing on technology acceptance to enhance the adoption of real-time feedback technologies. This involves examining usability, perceived usefulness, and resistance to monitoring. By gathering user feedback and applying user experience research methodologies, feedback mechanisms can be refined to better meet team needs. Investigating different feedback content, timing, and methods, and comparing feedback modalities (e.g., visual, auditory, and textual) will be crucial for understanding their impact on virtual team dynamics. Additionally, exploring cultural differences in feedback reception and effectiveness in global virtual teams is essential to ensuring the universal applicability of these tools. Integrating biometric data could add depth to team dynamics analysis, and developing automated algorithms for early conflict detection and resolution could serve as proactive measures to maintain team cohesion.
# Microfabrication Bonding Process Optimization for a 3D Multi-Layer PDMS Suspended Microfluidics
## 2 3D Suspended Polymeric Microfluidic Resonator Fabrication (SPMF\({}^{3}\))
### Fabrication Method  
Soft lithography [27] is one of the main techniques for fabricating a PDMS microfluidic chip that starts with microchannel mold fabrication on a clean silicon wafer. The silicon wafer is coated with a photoresist material, SU8-2075, up to the desired microchannel depth by a spin-coating machine. After a pre-exposure shaking step at 95 \({}^{\circ}\)C for 10-15 min, the silicon wafer is patterned using a mask with ultra-violet light (UV). Then, post-exposure shaking is required to cure the patterned microchannel. Finally, the patterned photoresist material on the silicon wafer is submerged in a developer solution, which removes the unpatterned regions on the silicon wafer. Then, the mold is employed to fabricate the microchannel using a polymeric material such as PDMS. After having the microchannel substrate cured in an oven, the microchannel is bonded to a glass slide to form the microfluidic system and closed microchannels, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cantilever Size,** & **Microchannel Size,** & **Nozzle Size,** \\
**L, W, T (\(\upmu\)m)** & **W, T (\(\upmu\)m)** & **W, L (\(\upmu\)m)** \\ \hline \hline
6000 \(\times\) 2000 \(\times\) 600 & 200 \(\times\) 100 & 200 \(\times\) 400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for suspended microfluidics simulation [25], where L denotes length, W denotes width, and T denotes thickness.  
Figure 1: Three layers of the suspended microfluidics.  
Figure 2: Schematic view of microfluidic system fabrication process using soft lithography; (**a**) mold fabrication step using soft lithography; (**b**) microchannel fabrication step using polymeric material; (**c**) bonding step using oxygen plasma bonding.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 1 Introduction
The first part describes the current background of typhoon detection and the need for integration and lightweighting of droppable devices for typhoon detection. The second part outlines the principle of the four-armed spiral antenna used in this study and the estimation of its parameters based on the available frequencies and device dimensions. The third part describes the simulation of the preliminarily designed four-arm spiral antenna via HFSS to verify its feasibility. The fourth part summarizes the shortcomings of the antenna and describes further improvements. The fifth part provides the conclusion.
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 3 HAPS Link Budget and Channel Modeling
### HAPS Link Budget
\tag{13}\]  
where \(P_{e}\) is the bit error rate, the value of \(P_{e}\) is different based on different code mode. \(\phi\) is the elevation angle of HAPS.
## 2 Materials and Methods
### Automatic Range-Gate-Tracking Scheme in FPGA
Its main role is to suppress out-of-band noise of the ADC output. Figure 5: Concept of tracking a vessel region on a scanline. Figure 6: Timing diagram of automatic range-gate-positioning scheme; HV: high voltage; PRF: pulse repetition frequency; BPF: band-pass filter, DC: direct current. The output of _BPF1_ is selectively fed into one of the FIFO memories in a ping-pong fashion. To compute the absolute difference between two single-port FIFO memories, an adder and an absolute-value operator follow. This computation can result in the Doppler amplitude deviation between adjacent ensemble signals. The amplification of Doppler amplitude deviation is executed with a dual-port FIFO memory and an accumulator. After a pre-determined number of accumulation operations, the dual-port FIFO memory delivers its code into the filter _BPF2_. As shown in Figure 6, the baseline of accumulated code also increases due to the accumulation of uncorrelated noise. The filter _BPF2_ removes the DC component, and the alternating signal component among the filter output provides the information to estimate a focal-point number for the vessel region. Since the filter _BPF2_ removes the DC component, the alternating signal of the filter output has a zero-crossing feature. Therefore, we need to generate a short-period one-pulse signal (i.e., _Range gate_ in Figure 7) at a time duration where an alternating-signal packet with sufficient amplitude occurs. To generate a properly toggled _Range gate_ signal, we adopt a hysteresis operation on a digital comparator. A timing diagram and conceptual operation of the hysteresis comparator are shown in Figure 4:0 When an alternating-signal packet with sufficient amplitude occurs at the output of _BPF2_, it is detected by a slicing operation with a hyper-parameter threshold value \(D_{\text{TH}}\). That is, when the output of _BPF2_ surpasses the threshold value, the signal _Range gate_ is enabled. We need to maintain the high value of the signal _Range gate_ during the duration of the alternating-signal packet. Therefore, we utilize a reference counter to check whether the alternating signal diminishes or not. The reference counter starts counting up while the output of _BPF2_ is smaller than the threshold value. In addition, the up-counter is changed to a reset state whenever the output of _BPF2_ is higher than the threshold value. As shown in Figure 8, if the code of the reference counter reaches the pre-determined reference code, then the signal _Range gate_ is set to a state of logic low. Through this comparison and counting operations, the equivalent hysteresis operation can be performed.