esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Symmetric Feature Extraction
this action-specific perception, we employ the feature fusion module with attention mechanism. First, we project the features \(X_{f}\in R^{n\times 1024}\) and \(X_{c}\in R^{n\times 1024}\) extracted from the fine and coarse streams, respectively, into the lower feature space to reduce the computational burden and obtain \(X^{\prime}_{f}\) and \(X^{\prime}_{c}\). Then, the intermediate features are extracted by multi-layer perceptron (MLP). After that, the learnable weights \(W_{f}\) and \(W_{c}\) of global coarse stream and local fine stream are obtained through the activation function SoftMax, respectively. Finally, the global and local features are fused as shown in Equation (6) to obtain the motion feature \(\hat{X}\):  
\[ \begin{split}& W_{f}=\text{SoftMax}\Big{(}\text{MLP}\Big{(}X^{ \prime}_{f}\Big{)}\Big{)}\\ & W_{c}=\text{SoftMax}\big{(}\text{MLP}\big{(}X^{\prime}_{c}\big{)} \big{)}\\ &\hat{X}=\varphi\Bigg{(}\sum_{i=1}^{K}\Big{(}W^{i}_{f}X^{(i)}_{f}+ W^{i}_{c}X^{\prime(i)}_{c}\Big{)}\Bigg{)}\end{split}  \tag{6}\]  
where \(\varphi\) is the linear layer and \(K\) is the length of the features' output by MLP. Finally, the fully connected layer dimensionally restored the fused feature \(\hat{X}\), and the SoftMax classifier obtained the final prediction scores. Unlike existing methods that directly analyze the motion state of the whole point clouds, our work focuses on the critical body part when performing actions, which helps to overcome the influence of redundant data such as background on 3D action recognition. In addition, the details of the crucial parts and the global appearance of human body complement each other, which stimulates discriminative feature extraction for 3D action recognition.
## 2 CFD Simulations
### Numerical Simulations

Table 7: _Cont._lows: \(\sigma_{k,1}=1.176\), \(\sigma_{\omega,1}=2.0\), \(\sigma_{k,2}=1.168\), \(\alpha_{1}=0.31\), \(\beta_{i,1}=0.075\), \(\beta_{i,2}=0.0828\), and other model constants have the same values as for the standard k-omega model [34]: \(\alpha_{\infty}^{*}=1\), \(\alpha_{\infty}=0.52\), \(\alpha_{0}=\frac{1}{9}\), \(\beta_{\infty}^{*}=0.09\), \(R_{\beta}=8\), \(R_{k}=6\), \(R_{\omega}=2.95\), \(\xi^{*}=1.5\), \(M_{\Omega}=0.25\). For the SA turbulence model, the input boundary adopts the turbulent viscosity ratio and the value is 1. the turbulent viscosity \(\mu_{t}=\rho\widetilde{v}f_{\Omega}\), where the viscous damping function \(f_{v1}=\frac{\chi^{3}}{\chi^{3}+C_{\omega 1}^{-1}}\) and \(\chi\equiv\frac{\widetilde{v}}{\widetilde{v}}\). The production term \(G_{v}=C_{b1}\rho\widetilde{S}\widetilde{v}\), where \(\widetilde{S}\equiv S+\frac{\widetilde{v}}{\kappa^{2}d^{2}}f_{v2}\) and \(f_{v2}=1-\frac{\chi}{1+\chi f_{v1}}\). The destruction term \(Y_{v}=C_{\omega 1}\rho f_{\omega}\left(\frac{\widetilde{v}}{\widetilde{d}} \right)^{2}\), where \(f_{\omega}=g\left[\frac{1+C_{\omega 1}^{-1}}{g^{6}+C_{\omega 2}^{-1}}\right]^{1/6}\), \(g=r+C_{\omega 2}\left(r^{6}-r\right)\) and \(r\equiv\frac{\widetilde{v}}{\widetilde{S}\kappa^{2}d^{2}}\). The applied model constants are as follows, which have the default values [28].\(C_{b1}=0.1355\), \(C_{b2}=0.622\), \(\sigma_{\widetilde{v}}=\frac{2}{3}\), \(C_{v1}=7.1\), \(C_{\omega 2}=0.3\), \(C_{\omega 3}=2.0\), \(\kappa=0.4187\), \(C_{\omega 1}=\frac{C_{b1}}{\kappa^{2}}+\frac{(1+C_{b2})}{\sigma_{\widetilde{v}}}\). For the convection terms of each flow governing equation, we should confirm the appropriate discretization scheme. The discrete values of scalar \(\varphi\) are stored at the cell centers and face values \(\varphi_{f}\) are required, which are interpolated from cell center values by the upwind scheme. In a general way, we use the second-order upwind discretization. Gradients are used for constructing scalar values at the cell faces, calculating secondary diffusion terms and velocity derivatives, and there are cell-based, node-based, and least-squares cell-based three common schemes available. The least-squares cell-based averaging scheme has the advantage of being as accurate as the node-based scheme and having fewer demands for computing resources than the node-based scheme. So, we adopt the least-squares cell-based scheme for gradient reconstruction. To avoid spurious oscillations appearing near shocks or near rapid local changes in flows, the TVD slope limiter is selected as the gradient limiter [35], which uses the Minmod function to limit and clip the reconstructed solution overshoots and undershoots on the cell faces. Table 8 demonstrates the key parameters adopted in the CFD simulations. Taking the six NACA0012 models, combined with three far-field distances, SST turbulence model, and ROE flux type as examples, the numerical results and error ratios are provided in Tables 9-11.
## 4 Experiment Process
### Data Processing and Analysis  
1. Given the wavelength of mercury lamp green light as 546.1 nm, the calculation for the minimum diffraction angle and the results for the incidence angle are as follows: \[i_{\text{min}}=\frac{\varphi_{\text{min}}}{2}=\frac{18^{\circ}50^{\prime}}{2} =9^{\circ}25^{\prime}\] (10) The calculation results for the grating constant are as follows: \[d=\frac{k\lambda}{2 sin(\frac{\varphi_{\text{min}}}{2})}=\frac{546.1\times 10^ {-6}\text{mm}}{2 sin(\frac{18^{\circ}50^{\prime}}{2})}=1.669\times 10^{-3} \text{(mm)}\] (11) The formula includes the following variables. \(\varphi_{min}\): minimum diffraction angle for green light. \(i_{min}\): corresponding angle of minimum deviation. \(k\): bright fringe (spectral line) order. \(d\): grating constant. \(\lambda\): wavelength of incident light.
2. Calculate the measured and theoretical values of green light diffraction angles at different incident angles. \[\varphi_{k}=arcsin(\frac{k\lambda}{d}-sini)+i,\ \ (k=\pm 1)\] (12) The formula includes \(\varphi_{k}\), the diffraction angle of the \(k\)-th bright fringe; \(i\), the angle of incidence of the light; \(k\), the order of the bright fringe (spectral line); \(d\), the grating constant; \(\lambda\), the wavelength of the incident light.
3. Draw the graph depicting the relationship between the green light diffraction angles and incident angles, as shown in Figure 6.
## 5 Conclusions  
By the proposed hybrid feature selection approach, which combined both machine learning and traditional statistical methods, the abnormal brain regions and abnormal connections in brains of SZ were discovered. The results of SBM data showed that the abnormal brain regions of SZ were mainly distributed in supramarginal gyrus, cingulate gyrus, middle frontal gyrus, superior frontal gyrus, precuneus and caudate. These brain regions are reported to have strong association with SZ, and they are mainly involved in perception, thinking, emotion and spiritual activity. The results of FNC data showed that most of the abnormal functional connections in brains of SZ were related to FPN, DMN and SN. These three networks are closely related to cognitive deficits, especially in executive control and salience processing. All of the results suggest that the brain regions and connectivity in SZ are destroyed compared with HCs, and the abnormal activity may cause the cognitive deficits and autistic thinking in SZ. In addition, the complex network analysis further verified the significance of the selected abnormal functional connections. All findings supported the validation of the proposed hybrid feature selection method, and thus, it is promised that such a hybrid feature selection method can be further used for other kinds of medical data analysis to enhance the diagnosis ability.  
Conceptualization, C.Q.; methodology, C.Q. and L.L.; writing, original draft preparation, L.L. and L.Y.; writing, review and editing, C.Q. and P.J.K.; funding acquisition, C.Q.  
This research was funded by NSFC Nos. 11471006 and 11101327, the Fundamental Research Funds for the Central Universities (No. xij2017126), the Science and Technology Project of Xi'an (No. 201809164 CX5 JC6) and the HPC Platform of Xi'an Jiaotong University.  
The authors declare no conflict of interest.
Figure A2: Twenty eight brain regions selected for the experiment according to the AAL template.  
Figure A3: The connections between the brain regions R1 and R2 corresponding to FNC features.  
Figure 4: SVMRFE and RFFS results of SBM data, where Fea represents the feature number and Fre represents the frequency at which the feature appears in 20 experiments.  
Figure 5: SVMRFE and RFFS results of FNC data, Part 1.  
Figure 6: SVMRFE and RFFS results of FNC data, Part 2.  
Figure A7: SVMRFE and RFFS results of FNC data, Part 3.
# Selected Plants as Sources of Natural and Active Ingredients for Cosmetics of the Future
## 4 _Psoralea corylifolia_
### Bakuchiol as an Alternative to Retinol  
Although bakuchiol has been known since 1973 and exhibits many valuable biological properties, its first commercial use in cosmetic products did not occur until 2007 [70]. Clinical studies have confirmed that bakuchiol has antibacterial [71], anti-acne [70; 71], depignmenting, moisturizing, anti-inflammatory, anti-aging [70], and antioxidant effects [71; 72]. Furthermore, according to the CosIng database (Cosmetic Ingredient Database), bakuchiol is a safe component of cosmetic products that does not irritate or cause allergies. It can be used year-round and at any time of day because of its resistance to sunlight, so it does not require additional external skin protection against UV radiation [70; 73; 74]. Bakuchiol has high photochemical and hydrolytic stability and blends well with various emollients [75; 76; 77]. Due to these biological activities, the interest in the application of bakuchiol in cosmetic and pharmaceutical formulations is growing [73].  
Figure 9: The most important bioactive substances from _Psoralea corylifolia_[60].  
Bakuchiol affects multiple cellular pathways. It is involved in the modulation of retinoic acid receptor genes, as well as stimulation of genes responsible for the production of collagen. It stimulates the critical extracellular matrix component and reduces expression of IL8 and p16 in aging skin. It was shown to enhance the epidermal regeneration process and wound healing. Additionally, it inhibits the activity of tyrosinase and \(\alpha\)-melanocyte, leading to a reduction in pigmentation intensity of skin and regulates seborrhea [73; 75].  
Bakuchiol does not exhibit structural similarity to retinol, but due to its similar action and bioactive effects, it has recently been recognized as a plant-derived and functional substitute for retinol [73; 78]. Bakuchiol, being more tolerated and safer, lightens skin discolorations to the same extent as retinol [75]. It shows more favorable regenerative properties, resulting from a significant improvement in collagen stimulation compared to retinol [73; 75].  
Retinoids are used in many skin conditions such as acne or psoriasis, and they also exhibit effective anti-aging effects. However, retinoid therapy carries many negative side effects, such as irritation, redness, burning, dryness, and peeling of the skin, which limits their use in individuals with sensitive skin [73; 79].  
Additionally, retinoids have teratogenic effects, making them unsuitable for use by pregnant women (Table 2) [79].
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 3 System Implementation
### The Head Unit
The gripper is operated by mechanical links and a servomotor (XM430-W350-T (ROBOTIS Co., Ltd., Seoul, Republic of Korea), with a stall torque of 4.2 Nm). Figure 6: The robotic arm’s model in Unity3D is set up with an IK configured to follow an objective (visualized as a grey ball inside the red circle). The arm is controlled by the user’s hand using a hand-mounted VR tracker. (1) _Controlling the Gripper_  
The gripper's movement is directly connected to the user's thumb and index finger movements. The gripper closes when the user's thumb and index finger are closed. When they are far apart, the gripper opens. We used a Senso glove at the local site to control the gripper (see Figure 7). After calibrating the glove for each user, we processed the data using a Unity3D script. This script estimated the distance between the user's thumb and index finger and transformed these values into servomotor control angles. We integrated the gripper control system using the same network architecture that controlled the robotic arm. Our middleware received and executed the data to control the gripper. (2) _Providing Haptic Feedback_  
Remote site: An FSR was mounted on each side of the gripper's end-effector, as illustrated in Figure 8. These FSRs were linked to a Pololu Maestro Mini by Pololu Corporation, headquartered in Las Vegas, Nevada, USA [32] to capture their readings. The collected FSR data were transmitted to the local site via WebSocket and converted into haptic feedback signals. Local site: Upon receiving the FSR data, the system translated them into vibrotactile feedback values for the Senso glove. This process enabled the user to experience haptic feedback through the thumb and index fingers. Figure 8: The robot arm is equipped with a parallel gripper and FSR on each side of the gripper. Figure 2:0 (**Left**): a gripper end-effector equipped with FSRs. (**Right**): a sensor glove that controls the robotic hand and provides vibrotactile feedback.
## 3 Analysis of Fatigue Damage Law of Base Plate Structure
### Analysis of Force Characteristics of Base Plate Structure System
#### 3.1.2 Damage and Cracking of the Base Plate  
The maximum tension of the base, which is also the longitudinal tension stress, had a maximum value at the bottom of the base plate directly below the load application point. That is, at the mid-span position. Figure 16a shows the curve of longitudinal tensile stress of the cross-section node of the base due to the number of loads.  
Figure 16: Damage and cracking behavior of SCC. (**a**) Longitudinal tension stress of the mid-span lateral section of the bottom surface of the base plate; (**b**) cracking time at the mid-span part of the bottom surface of the base plate; (**c**) location of cracks in the bottom layer of the base plate.  
As is shown in Figure 16a, with the increasing number of load actions, the stress of the structural layer gradually increased. Before the application of 5 million times loads, the longitudinal tension stress of the base changed significantly. On average, the stress increased by 0.044 MPa for every 1 million applications of the fatigue load. However, the longitudinal stress of the base increased slowly when the load action was between 5 and 15 million times. At this stage, the stress increased by 0.025 MPa for every 1 million actions. Under fatigue force, the base concrete became gradually damaged, and the residual strength gradually decreased. When applying load 19 million times, the longitudinal tension stress of concrete at the middle span of the bottom of base reached its tensile strength (Figure 16b). Thus, cracks were observed, since the stress degraded to zero at this point. With further accumulation of loads, the cracks of the base propagated from the edge of the plate to the plate (Figure 16c).
## 4 Experiment Overview
### Sensors Characteristics and Deployment  
For all experiments discussed in this paper, we used the TelosB platform [28]. This platform is equipped with a 8 MHz microprocessor, a 10 KB RAM, a 1 MB external flash memory and a set of sensors (temperature, humidity and light). In all our experiments, the collected data were stored in the external memory.  
For communication, TelosB uses the CC2420 radio module. This module is IEEE 802.15.4 compatible and it is widely used for industry and research in the field of low power wireless sensor networks. The CC2420 radio shares with other technologies, e.g., IEEE 802.11 (WiFi) and Bluetooth, the unprotected ISM frequency band (2400-2483.5 MHz). In other words, if a WSN were deployed in an area covered by equipments using these different technologies, the communication of nodes in the network would interfere with these other devices. Following the IEEE 802.15.4 protocol [33], the spectrum is divided into 16 channels of 2 MHz bandwidth and 5 MHz spacing between consecutive channels. The CC2420 module also offers 32 different output power levels, ranging from \(-25\) dBm to 0 dBm. The transmission power and the communication channel can be programmed by changing the value of particular CC2420 registers.  
In our experiments, a set of sensors was linearly deployed with a fixed distance \(d\) between two consecutive sensors. In each experiment, the same communication protocol, avoiding in-network interferences, was executed by all sensors. To assess the impact of ground-level deployment on the quality of wireless links, we also studied a scenario where nodes follow a similar linear deployment, but placed on a support, tens of centimeters above ground. We did not control the human movement in the deployment area or the external radio interference. Figure 3 shows the sensors deployment in the two context: ground level and on a support.
## 3 Results and Discussion
### Maximum Chip Temperature  
In Figure 11, comparison of the maximum chip temperature for the new introduced design and conventional offset strip fin design is presented as a function of different volumetric flow rates. These values for different designs of heatsinks either new or conventional one decreases with the increase of volumetric flow rate. Also, this figure shows that the maximum chip temperature values of new introduced heatsink is lower than those of traditional offset strip fin heatsink for all the modeled volumetric flow rate. This decrease is in the range of 8-11% for different volumetric flow rates.
## 4 Results and Discussion
### Results
#### 4.3.1 Mathematical Model Operation  
Figures 4 and 5 are presented to show how the model proposed to operate to meet the energy demand with the available resources (Figure 4) and observe the impact of the storage devices (Figure 5) for a certain scenario without considering DSR. Thus, the _fourth period_ has been considered where, in the first hours, it is possible to meet the energy load with the DERs, due to existing high wind resources and irradiance. Furthermore, the SOC of the batteries increases during the first half of the scenario, achieving its maximum, to face the second half where the weather conditions are not the best. From the 40 th hour on, the batteries are discharged (see Figure 5) and the first nodes with low energy not supplied by the system start to appear because the discharge rate of the battery does not allow for fulfilling the gap between load and generation. Finally, at the end of the period, there is not enough energy stored in the batteries to meet the energy demand. To improve the visualization of Figure 4, the energy provided by the DERs has been overlapped to observe the share of the energy demand that is satisfied by each resource, and, for Figure 5, the curves have been normalized, taking as reference the maximum value of each curve.  
In global terms, for the 288 h considered, composed of four scenarios of 72 h, the total energy demanded by the system is 434.17 MWh/pr, of which 56.3% is provided by WT, 8.2% by PV panels, 13.4% the storage systems, and 4.6% of energy losses. The ENS when the DSR is not taken into account is 27.9%; however, when 30% of DSR is considered, i.e., every load point can reduce its energy load at a maximum of 30%; otherwise, the load is reduced to zero, the ENS drop-down 62.7%, from 27.9% to 10.4%. Therefore, for the installed capacity and for the considered scenarios, the system could provide 73.3% of the energy as maximum, and the remaining energy load would be reduced or traded through an eventual energy flexibility market.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 5 Fabrication Methods  
As already discussed, PSCs have taken many routes to achieve high performances, such as the device transition from sensitized solar cells to the recent planar thin-film solar cells, preparation of hybrid perovskite-light-active materials, the use of processing conditions, and lastly the use of different charge ETMs and HTMs. In addition, PSC fabrication methods play a role in high-quality perovskite films.
## 3 Experimental Process
### Internal Data Processing and Validation
#### 3.3.2 Data Validation  
After constructing the real-world 3D model, the accuracy of the model was verified by field measurement, mainly including model size accuracy (index \(C5\)) and model distortion area (index \(C6\)). The three indexes were evaluated according to the scale evaluation criteria shown in Table 5. The obtained data and corresponding evaluation scores are shown in Table 6.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 3 Artifacts Correction by Deep Learning  
This section presents the proposed neural network for correction of reflection artifacts in PAT imaging. We will make use of the networks to accelerate some conventional iterative algorithms in the following way. Instead of running the iterations, we train the network to learn the map between the reconstructed image by the first iteration and the ground truth. The reconstruction procedure then consists of two steps, given the measured ultrasound signals. The first step is to apply an iterative algorithm to the signals to get the output after the first iteration. The second step is to feed the output into the neural network to obtain the reconstructed image. These two steps preserve the predictability of the model-based iterative algorithm, while at the same time replace the number of iterations by the neural network to achieve improved computational efficiency. The proposed method can be referred to as the deep learning (DL) algorithm or DL reconstruction for short.