esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 1 Introduction
[19] studied the relationship between the modal resonant frequency and pressure ratio of standing waves in exponential-shaped resonators. [PERSON] [20] studied the pressure and velocity waveform in several types of resonators with the finite-volume method. [PERSON] et al. [21] investigated the effect of the cross-section area changing rate and gas properties on the non-linear standing-waves in exponential ducts based on the linear theory. Although the abovementioned work obtained an improved performance, it could be admitted that the one-dimensional model is not sufficient to describe the complicated two-dimensional flow fields, especially the streaming in real thermoacoustic devices. Therefore, the two-dimension model should be explored for the compressible oscillating flow in varying cross-section resonators. [PERSON] et al. [22] employed the SIMPLEC algorithm to solve the axisymmetric two-dimension Navier-Stokes (NS) equations and investigated the secondary flow in a tapered tube. In past decades, with the development of mesoscopic methods, the gas-kinetic scheme (GKS) has been widely used to study the compressible fluid flow, heat transfer, turbulent flow, hypersonic, and non-linear acoustic problems [23, 24, 25, 26, 27, 28, 29, 30, 31], etc. [PERSON] et al. [23, 24] developed and continually improved the multidimensional gas-kinetic Bhatnagar-Gross-Krook (BGK) scheme to improve its favorable properties in capturing discontinuities. [PERSON] et al. [25] studied the sound propagation problem based on the linearized kinetic equation. Based on the time-dependent linear BGK equation, [PERSON] [26] investigated the pulsatile-pressure-driven fully developed flow of a rarefied gas through an orthogonal duct. [PERSON] et al. [27, 28] applied the GKS method to solve the two-dimensional compressible NS equations and simulated the high-amplitude non-linear standing waves, flow fields, and streaming behavior in cylindrical resonators, with satisfactory results. Then, [PERSON] et al. [29] extended the GKS method to simulate two-dimensional non-linear flow fields and streaming motionsin exponential varying cross-section resonators. They found that the exponential resonators have the ability to suppress the shock waves, acoustic streaming, and vortices and gave an optimum index of winding for exponential resonators. Stimulated by the successful applications of GKS in turbulent flow [30] and acoustic and shock-wave capturing [31], we applied the GKS method to study the problem of high-amplitude acoustic fields in shaped resonators and investigated the influence of driving strength on the flow and streaming. The purpose of this work was to obtain an optimal shape parameter and driving strength for a highly efficient acoustic resonator that has the highest pressure fluctuation amplitude, ratio of output work to input work, and the capacity of suppressing dissipation effects (including waveform distortion, streaming, secondary flow, etc. ).
## 4 Analog Front-End  
Apart from the digital receiver and the NFC resonators, the data link includes an analog front-end with a receiving amplifier and a reactive network which provides both filtering of the energy carrier and impedance matching of amplifier and resonator. Its topology and connection to the low-voltage differential signaling (LVDS) comparator stage and the output buffer of the FPGA are shown in Figure 7.
## 1 Introduction
In order to ensure safe and hygienic working conditions for ARCM users of wearables, the following are necessary: (1) reliable recognition and assessment of electromagnetic hazards related to the use of the ARCM and (2) optimization in terms of ARCM functionality and minimization of electromagnetic hazards to users. The electromagnetic hazards should be assessed, taking into account factors such as (1) the type of WIT technology, (2) the amplitude-frequency characteristics of the ARCM emission, and (3) the location of the ARCM in relation to the user's body (covering \"all intended operating conditions and the reasonably foreseeable conditions\" as required by European Directive 2014/53/EU (RED directive) [22]. Therefore, the aims of this study concern investigations focused on the following: (1) how the position of a wearable ARCM on or near the body affects the parameters of its EMR emissions (antenna parameters important in context of the device functionality); (2) the SAR values caused by the absorption of EMR in a body adjacent to the ARCM (important when evaluating the electromagnetic impact of RCM devices on a user); and (3) the applicability of anatomical or simplified (multi-layered) models of human body parts in such investigations. The object for our study was a typical wearable WIT device equipped with an ARCM designed for free-space operation in the 2.45 GHz ISM band (used, for example, for Wi-Fi 2G/Bluetooth applications). _Characteristics of the Considered WIT Systems Designed for Free-Space Operation in the 2.45 GHz ISM Band_  
The common WIT systems designed for free-space operation in the 2.45 GHz ISM band (and adjacent bands) are characterized in Table 1.  
The following parameters and the way in which WIT devices are used determine the profile of exposure to EMR and the direct electromagnetic impact on users and other people in the vicinity of these devices:  
* Maximum power (set by regulations for devices not requiring special administrative permission for use in a public environment)--from 1 mW (e.g., Bluetooth) to at least 4000 mW (e.g., SHF RFID);
* Duty cycle of the emitted signal--from 0.01 (e.g., SHF RFID) to 1.0 (e.g., Wi-Fi);
* The method in which WIT devices are used--portable devices, wearables, stationary devices mounted on infrastructure (e.g., walls, ceilings, poles, masts, or furniture), and devices mounted on or in vehicles, machines, etc. ;
* Intended application--e.g., used in monitoring; remote control; real-time identification and localization/tracking (including WBANs in healthcare, asset and livestock tracking, sensor networks, or process control in industry); communication (including Machine to Machine); logistics (including vehicle management); smart cities, buildings, and factories; dangerous event detection; and remote meter reading, etc. \begin{table}
## 4 Data and Experimental Set-Up
The microphones, speakers and cameras are located on the head and chest of the robot to perform voice communication and image acquisition functions. A touch screen is provided on the robot's chest to allow people to interact with and display necessary information. _BonBon_ can communicate with people through a voice and can perform actions such as greetings, dancing, singing and expressing emotions verbally. The proposed methodology presented in Section 3 and the techniques to which it was compared in Section 5 were implemented in _BonBon_ for comparison and real-time performance evaluation. Figure 4: The self-developed intelligent humanoid robot _BonBon_ that was used for testing the proposed framework. _BonBon_ houses two computers: an embedded computer with an Intel Core i7-8559U CPU to manage all the motion control tasks and safety functions of the robot and a central control computer (i.e., an NVIDIA Jetson AGX Xavier with a 512-core GPU and an 8-core, 64-bit ARM CPU with 16 Gb RAM) for managing the vision and speech processing systems as well as the task planning function of the robot. The robot control software was developed using the robotic operating system (ROS) [47]. Dlib (dlib.net, accessed on 8 December 2021), which is a modern C++ toolkit, is used for face detection and alignment.
## 3 Theoretical Penetration Model and Model Verification
### Validation of the Theoretical Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Resource** & \(\mathbf{v_{0}}\) **(m/s)** & \(\mathbf{r_{0}}\)**/\(R_{\rm P}\)** & \(\mathbf{P_{\rm e}}\) **(mm) *** & \(\mathbf{P_{\rm max}}\) **(mm)** & \(\mathbf{P_{\rm max2}}\) **([PERSON] et al., 2011)** & \(\mathbf{P_{\rm max3}}\) **([PERSON] et al., 2011)** \\ \hline \multirow{8}{*}{Experimental results [23]} & 1500 & 4.90 & 87.25 & 97.14 & 105.59 & 102.96 & 105.65 \\  & 1500 & 4.90 & 88.49 & 97.14 & 105.59 & 102.96 & 105.65 \\  & 1500 & 6.53 & 86.31 & 88.47 & 99.49 & 96.49 & 99.90 \\  & 1500 & 6.52 & 86.00 & 88.51 & 99.52 & 96.52 & 99.93 \\  & 1500 & 13.10 & 70.97 & 72.83 & 87.72 & 83.14 & 88.47 \\  & 1500 & 13.12 & 74.86 & 72.81 & 87.70 & 83.12 & 88.45 \\  & 1500 & 19.83 & 69.25 & 67.23 & 83.11 & 80.60 & 83.97 \\  & 1500 & 19.79 & 69.72 & 67.25 & 83.13 & 80.62 & 83.98 \\  & 1500 & 19.88 & 66.76 & 67.20 & 83.09 & 80.58 & 83.95 \\ \hline \multirow{8}{*}{Numerical results [23]} & 1500 & 3.26 & 105.94 & 112.38 & 115.69 & 113.51 & 114.64 \\  & 1500 & 4.89 & 89.66 & 97.20 & 105.64 & 103.01 & 105.69 \\ \cline{1-1}  & 1500 & 6.52 & 84.37 & 88.51 & 99.52 & 96.52 & 99.93 \\ \cline{1-1}  & 1500 & 13.04 & 69.88 & 72.91 & 87.78 & 83.22 & 88.53 \\ \cline{1-1}  & 1500 & 19.56 & 67.62 & 67.36 & 83.22 & 80.71 & 84.08 \\ \cline{1-1}  & 1500 & 29.34 & 67.15 & 64.43 & 80.94 & 78.52 & 81.57 \\ \cline{1-1}  & 1500 & 77.66 & 66.84 & 62.10 & 79.42 & 77.06 & 79.55 \\ \hline \hline \end{tabular}  
* \(P_{\rm e}\) are the experimental and numerical results of penetration depth in [23]; \(P_{\rm max}\), \(P_{\rm max2}\), \(P_{\rm max3}\), and \(P_{\rm max4}\) are penetration depths calculated by the proposed model and the models in [23; 25; 26], respectively.  
\end{table} 
Table 2: Summary of initial penetration conditions, penetration depths, and calculation results of different theoretical model [23]. Figure 3: Comparison of calculation results of penetration depths using the model in [23; 25; 26] and in this article with experimental and numerical results in [23]. The comparison of the calculation results of penetration depths with experimental results [24] for different initial impact velocities is shown in Figure 5. From Figure 5, the following can be concluded:  
* The proposed model is in good agreement with the experimental results. The calculation results of the proposed model continue to increase and tend towards the fluid dynamic limit as the initial impact velocity increases;
* The calculation results of the other three models are all significantly greater than the experimental results because the \(R_{\rm T}\) predicted by the three models is less than \(Y_{\rm P}\). When the penetration velocity is less than \(v_{\rm r}\approx 2500\) m/s, there is only a rigid body penetration phase. However, when the initial impact velocity is greater than \(v_{\rm r}\), the penetration process exhibits an eroding penetration stage. The calculation results of the three models rapidly decrease and tend towards the fluid dynamic limit.
## 2 Materials and Methods
### Methodology
#### 2.3.3 Trend Analysis  
The combination of the Theil-Sen median trend analysis and the Mann-Kendall (M-K) trend test is a very effective method for analyzing time series data [54]. Compared to other trend analysis methods, the Theil-Sen trend has the property of being insensitive to outliers, which reduces the impact of outliers to a large extent [46; 55]. The M-K test is a rank-based nonparametric statistical test [56]. It is applicable to all distributions (i.e., the sample data can fail to satisfy the normal distribution) and can eliminate the effects of outliers.
## 2 Results  
### NR4A2 and NR4A3 Are CO2 Responsive in the Presence of LPS  
To determine whether NR4A family members (1-3) are responsive to CO2, we exposed THP-1 monocytes to pre-equilibrated, buffered media in 5% or 10% CO2, for 8 h. For lipopolysaccharideaccharide (LPS)-treated cells, LPS was added 6, 4, 2, 1, or 0.5 h before the end of the experiment. Untreated cells were used as controls. All NR4A family members were temporarily induced by LPS. _NR4A1_ had the lowest fold change increase in response to LPS, while _NR4A3_ had the greatest inducibility. There were no statistically significant differences between 5% and 10% CO2 for _NR4A1_ at these time points. _NR4A2_ expression was significantly lower at 10% CO2 following 2 h and 4 h of LPS exposure. _NR4A3_ expression was significantly higher in 10% CO2 at 2 h of LPS exposure compared to 5% CO2 (Figure 1A-F). Taken together, these data indicate that elevated CO2 exposure shifts the temporal response of _NR4A2_ and _NR4A3_ to LPS stimulation. This effect is most evident at 2 h and 4 h of LPS treatment (Figure 1C,E). The CO2-dependent impact on LPS-inducedNR4A expression was also evident, with decreased protein levels of NR4A2 in THP-1 cells exposed to buffered media in 5% or 10% CO\({}_{2}\) +/ \(-\) LPS (Supplementary Figure S1).  
Figure 1: NR4A family members are sensitive to CO\({}_{2}\) in THP-1 monocytes. Relative expression of _NR4A1_ (**A,B**), _NR4A2_ (**C,D**), and _NR4A3_ (**E,F**) in THP-1 cells exposed to 5% or 10% CO\({}_{2}\) for 8 h, +/ \(-\) LPS (2.5 \(\mu\)g/mL) for up to 6 h. \(\beta\)-actin_ was used as a housekeeping gene, and samples were normalised to a 5% CO\({}_{2}\) untreated control. Data are presented as mean +/ – Standard Error of the Mean (SEM) and are representative of \(n=3\) individual experiments. (**A,C,E**) show bar graphs, including statistically significant differences using two-way Analysis of Variance (ANOVA), followed by Sidak’s multiple comparisons test (\(p<0.05\) denoted by \({}^{*}\), \(p<0.01\) denoted by \({}^{**}\). ns denotes not statistically significant difference, \(p>0.05\))). (**B,D,F**) display the same data visualised as a line graph over time.
## 3 Results
### Electro-Optical Measurements of the Optical Links
#### 3.3.1 Electro-Optical Measurements of the Visible Link  
This section presents the electro-optical characterization of the system's visible link. The measured parameters encompassed the bandwidth, illumination level of the ultra-white LED, minimum signal-to-noise ratio (SNR\({}_{\mathrm{min}}\)) to ensure UHD video transmission, bit rate, bit error rate (BER), packet error rate (PER), and length of the visible optical link.  
Bandwidth (BW) of the Visible Link  
The system's bandwidth is constrained by the white LED used as the video transmitter. This LED has been electrically and optically characterized. The optical measurements conducted with the Thorlabs PDA10A2 photodetector, which has a bandwidth of 150 MHz, revealed that the LED's bandwidth was 1.8 MHz. As this bandwidth was deemed insufficient for UHD video transmission, a pre-equalizer stage was engineered into the LED driver to increase it to 14.45 MHz. The frequency behavior of the signal emitted by the LED was assessed using a RIGOL DSA 815 spectrum analyzer, bearing a bandwidth of 1.5 GHz. Although the equalized visible LED bandwidth reached 14.45 MHz, the visible PD driver achieved 11.66 MHz of bandwidth, which set a limit on the downlink bandwidth. In essence, the bandwidth of the downlink was 11.66 MHz. This value surpasses the requirement of 10 MHz, as the system transmits information at 10 MHz. This frequency ensures an exact number of samples per symbol, as it is a submultiple of the 40 MHz sampling frequency of the A/D and D/A converters.  
The illumination level of the LUW-CN7N-KYLX-EMKM OSRAM LED, operating at a current of 170 mA in the set-up shown in Figure 34, was measured using the MAVOLUX 5032C USB luxmeter, obtaining a value of 75,000 lx.  
Signal-to-Noise Ratio (SNR) of the Visible Link  
A minimum signal amplitude of 200 mV\({}_{\text{pp}}\) at the receiver's output is necessary to ensure the correct performance of the visible downlink. This value was experimentally determined in the set-up portrayed in Figure 34.  
The noise amplitude measured at the receiver's output was 45 mV\({}_{\text{pp}}\), and this measurement was carried out without transmitting any data from the LiFi router.  
The SNR\({}_{\text{min}}\) was calculated using the aforementioned values, yielding a result of 10.74 dB. This SNR\({}_{\text{min}}\) was achieved at a 72.5 cm distance between the visible LED and the visible PD within the passenger van. Meanwhile, a similar SNR value was observed in the set-up depicted in Figure 27 at a 110 cm distance between the visible LED and the visible PD, where both were positioned in the indoor set-up.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data  
### Research Flow  
Urban centers in South Korea were selected as the target area, and a machine learning-based ground subsidence risk prediction model was constructed utilizing data on ground subsidence history, six types of underground facility attributes, the density of underground pipelines, permeability coefficient, height, and stratigraphic thickness within the target area. The ArcGIS software (version 10.4) was used to visualize the collected spatial data within the target area. The area represented by the data was subdivided into a total of 2391 grids, each measuring 500 \(\times\) 500 m. The segmented grid contained information on the attributes of six types of underground facilities, as well as the permeability coefficient, stratigraphic thickness, height, and ground subsidence data. The attribute information of six types of underground facilities was consolidated into a single underground facility type to facilitate the construction of a seamless dataset. The ground subsidence history data were used to assess the frequency of ground subsidence occurrences within the grid, and the ground subsidence risk was categorized into three levels. The attribute information of underground facilities, stratigraphic thickness, permeability coefficient, height, and density of underground pipelines were chosen as the input variables, whereas the output variable was ground subsidence risk.  
The entire dataset was partitioned into training and evaluation data at an 80:20 ratio. A total of 80% of the training data were used to train the machine learning algorithm and develop the model, and 20% of the evaluation data, which were not included in the training, were used to verify the model performance. The imbalance among the ground subsidence datasets was addressed by applying the SMOTE sampling technique to augment the minority data within the training dataset. The machine learning algorithms random forest (RF), extreme gradient boosting (XGBoost; XGB), and light gradient boosting machine (LightGBM; LGBM) were applied to the constructed training dataset, and the hyperparameters were adjusted to develop a model with optimal model performance. The performance of the model was evaluated using the evaluation data, and the F1-score and accuracy metrics were used to evaluate and compare the performance of the model. The performance metrics of the training and evaluation sets were compared to examine overfitting, and models exhibiting a difference of 0.1 or less between the two metrics were designated as optimal. Finally, the developed model was used to evaluate the importance of the influence factors and build a ground subsidence risk prediction model for the target area. Figure 1 illustrates the flowchart of this study.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 3 Proposed System
### Transformation Profile Generation Module
#### 3.1.2 CTP Generator  
If image \(A^{\prime}\) in Figure 3 has been obtained by geometric transformation, then a color transformation process is performed to get image \(B^{\prime}\) by correcting the color distortion that occurred during the projection of image \(B\) and capturing it with a camera. The color transformation model can be obtained by machine learning. It uses the coordinates and color values of each pixel of image \(B\) as the input data and then produces the outputs, which are the color values, to construct image \(B^{\prime}\). The required model can be expressed as Equation (2).  
\[f(\text{pos}\ X,\text{pos}Y,R,G,B)=R^{\prime},G^{\prime},B^{\prime} \tag{2}\]  
Equation (2) converts the colors \(R\), \(G\), and \(B\) of the coordinates (\(x\), \(y\)) of the original image (i.e., image \(B\)) to the intentionally distorted colors \(R^{\prime}\), \(G^{\prime}\) and \(B^{\prime}\) of the same coordinates of the output image (i.e., image \(B^{\prime}\)). It is not easy to get the perfect color-matching results from this transformation. However, it can reduce the considerable color differences between those two images.
## 3 Benchmark Results
### Key Generation Algorithms
The performance for ECC key generation is very good as it only involves one scalar point multiplication; that is, it multiplies a scalar \(k\) (a large integer) and a point \(P\) on an elliptic curve. All in all, the scalar multiplication is the most time consuming operation in ECC scheme. This operation can be performed on an elliptic curve group by adding point \(P\)\(k-1\) times to itself. The difference between the computational effectiveness of ECC over RSA and DSA is obvious and it gets more notorious as the required security strength increases and so the length of the keys. Finally, the key generation in pairing based schemes is also quite efficient because it is based on modular exponentiations over a prime field. The key generation for BLS only requires one exponentiation, and the one for BB signature requires two exponentiations, and one bilinear pairing. The results show that key generation in BB is about three times greater than in BLS. Several papers (_i.e._, [15, 16, 17, 18]) have made comparisons between ECC, RSA and DSA, finding ECC to be significantly faster than others, although the magnitude of the difference varies. DSA is the slowest of them. Regarding energy costs, [PERSON] _et al._[5] studied the energy consumption of symmetric and asymmetric cryptography using a StrongARM SA-1110 processor at 206 MHz. The results they obtained for AES are similar to ours in the iPAQ3970. However, the results of asymmetric key generation algorithms are quite different. They report the RSA-1024 key generation to consume 270.13 mJ, the DSA-1024 to be 293.20 mJ and ECDSA keys in a field the order of which has 163 bits to be 226.65 mJ. Our results also identify ECDSA as the most energy efficient algorithm and DSA the least; yet, the values and ratios between our results and theirs differ. [PERSON] _et al._[5] also benchmarked the signature generation and verification operations, as we will comment in Section 3.5. Using their results it can be concluded that RSA and DSA signatures generation are energetically more expensive than key generation, but this is not consistent with the fact that key generation processes are more complex. Thus, we consider in this case Potlapally results for key generation algorithms cannot be generalized, and we assert our values.