esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 3 Method  
### Large Eddy Simulation  
In LES, the direct resolution of large energy-containing eddies is coupled with the modeling of smaller-scale eddies using subgrid-scale (SGS) models. A spatial filtering operation is employed to separate the large-scale and small-scale flow features. This approach enables the precise prediction of turbulent phenomena, surpassing the capabilities of conventional RANS simulations [21].  
The continuity equation and momentum equation applied in the flow field computation are given in Equation (1) and Equation (2), respectively.  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho v_{i}}{\partial x_{i}}=0 \tag{1}\]  
Figure 6: Flow domain and boundary conditions in the flow field simulation.  
Figure 7: Acoustic domain in the sound field simulation.  
\[\frac{\partial\rho v_{i}}{\partial t}+\frac{\partial\rho v_{i}v_{j}}{\partial x_{j} }=-\frac{\partial p_{ij}}{\partial x_{i}} \tag{2}\]  
where \(p\) and \(v\) represent the pressure and velocity of the medium, respectively, and  
\[p_{ij}=p\delta_{ij}+\tau_{ij} \tag{3}\]  
where \(\delta_{ij}\) is the Kronecker symbol. The turbulent stress can be written as follows:  
\[\tau_{ij}=-2\mu_{i}\overline{S_{ij}}+\frac{\delta_{ij}}{3}\tau_{kk} \tag{4}\]  
where \(\overline{S_{ij}}\) is the strain rate of the flow.  
To ascertain the impact of small-scale vortices on flow, the LES turbulence model incorporates subgrid-scale stress. One commonly employed SGS model is the Wall-Adapting Local-Eddy Viscosity (WALE) model, which represents the turbulent viscosity, as follows:  
\[\mu_{t}=\rho\Delta^{2}S_{W} \tag{5}\]  
where \(\rho\) is the density, \(\Delta\) represents the turbulence length scale, and \(S_{w}\) is the parameter that characterizes the local strain rate of the flow.  
The flow field was calculated using the commercial software product ANSYS Fluent (v6.3). During our simulation, the SST k-\(\omega\) turbulence model was used to predict an initial flow field in a steady state. Then, based on this steady flow field, transient computations were performed until the flow field was stable, that is, the point at which the time average of the flow variables basically did not change. Finally, LES was utilized to obtain flow filed solution data, which were also the source data used for acoustic prediction. The flow field solution data for acoustic prediction were computed with a time step of 0.0001 s for a total of 10,000 time steps. The pressure at each step was saved, enabling frequency analysis up to 5000 Hz.
## 3 Results and Discussion
### Argon and Helium Plasma Jet Irradiation Increases the Transdermal Permeability of Porcine Skin
Argon Plasma Irradiation Facilitates the Permeability of 70 kDa and 150 kDa Molecules in Keratinocytes  
To investigate how to increase transdermal permeability by plasma, we further examined in vitro permeability in keratinocytes that were connected through cell-cell junctions in the epidermal layer. As high-molecular-weight molecules over 1 kDa have difficulty penetrating into the skin, we attempted to compare the penetration of 70 and 150 kDa molecules in keratinocytes with/without argon plasma irradiation. For this purpose, cultured HaCaT cells were first treated with our plasma device for 30 s or not. Subsequently, they were incubated in the medium containing 70 or 150 kDa dextran conjugated with fluorescein for 1 h. Thereafter, the cells were fixed with 4% paraformaldehyde, and the fluorescence intensity inside the cells was observed using a fluorescence microscope. As shown in Figure 7A, fluorescent signals of 70 and 150 kDa dextran were clearly observed in argon plasma-treated keratinocytes, whereas these fluorescent signals were not observed  
Figure 5: Transdermal absorption of aniline blue dye on porcine skin by argon and helium plasma irradiation at a distance of 8 mm (**A**) The quartered area of the rectangular area in porcine skin was treated with argon or helium plasma for 0, 10, 30, or 60 s at a distance of 8 mm between the plasma jet and the surface of the skin and thereafter stained with aniline blue dye. The transdermal permeability of aniline blue dye on porcine skin was quantified and normalized to that of the nontreated control. (**B**,**C**) Data are presented as means \(\pm\) S.D. n.s indicates a nonsignificant difference compared to the nontreated control (one-way ANOVA, [PERSON]’s post hoc test) with argon (**B**) or helium (**C**). n = 3 independent samples per group. Figure 6: Temperature on porcine skin surface by argon and helium plasma irradiation for 0, 10, 30 or 60 s at a distance of 4 mm between the plasma jet and the surface of the skin. in nontreated cells. Indeed, statistical analysis revealed that argon plasma irradiation significantly increased the fluorescence intensity of 70 (22.0 \(\pm\) 4.2) kDa (Figure 7B) and 150 (17.6 \(\pm\) 3.6) kDa (Figure 7C) dextran in the cells compared to nontreated cells. Therefore, these results suggest that argon plasma irradiation facilitates the penetration of over 1 kDa molecules that generally have difficulty permeating the skin due to their high molecular weight.
## 4 Discussion
### Drop Size Distribution  
Modeling drop size distribution has been addressed via both empirical and analytical approaches. According to the first approach, suitable curves are fitted to measured data for a wide range of nozzles and operating conditions. This has led to the proposal of various theoretical distribution functions, such as Weibull, log-normal, Nukiyama-Tanasawa, gamma, able to fit experimental data more or less well and describe, as accurately as possible, the spray drop characteristics in terms of size and velocity [45; 47; 50].  
The empirical approach was also adopted in this study: experimental data on drop diameters were analyzed and the theoretical gamma distribution was devised as the method that best described them. The scale and shape parameters were computed by means of the Maximum Likelihood Estimation method for each operating condition (nozzle type and spraying pressure). According to [PERSON] et al. [51], the atomization process involves the fragmentation of ligaments into droplets, and the size distribution that reasonably fits the data is precisely the gamma probability distribution function.  
The major drawback of this approach is the difficulty or the impossibility of covering conditions different from those observed. The analytical approach tries to predict the drop size distribution from an atomizer based on statistical/stochastic models such as the maximum entropy method [52; 53] or general laws of conservation (mass, energy, momentum) [54; 55]. In [54; 55] the authors propose a closed-form equation capable of predicting the \(D_{32}\) diameter in function of liquid properties and droplet velocities, especially useful for the study of injectors used in combustion processes. Other formulae for estimating the \(D_{32}\) diameter in combustion studies are discussed in [56], including both empirical and physical principles.  
Theoretical atomization models for agricultural spray nozzles used for pesticide applications are rare in the literature. Many studies are aimed at the measurement of drop diameters, at the tracing of the cumulative volumetric curves (Figure 6), and at the calculation of VMD, used for nozzle classification in terms of spray quality. In [57] the authors analyzed the influence of the spray parameters (spray angle, pressure, equivalent orifice diameter of nozzles) on drop size and velocity in the flow field. Suitable models were devised and successfully validated. Another work [58], using dimensional analysis, proposed a model to predict the volume median diameter of flat fan sprays in terms of common end-user parameters, namely nozzle size and operating pressure. However, in all cases, the models contain constants that must be estimated from experimental data.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Anomaly Definition
#### 3.1.1 Type 1: Solar Panel Failure  
Solar panel failure (or type 1) specifies an anomalous condition wherein the voltage sensor reads a value below an expected threshold of 1200 mV on one or more panels at any given observation time. The expected lowest values of the sensor readings for a non-illuminated solar panel must be within 1200 to 1500 mV. The numbers refer to the residual voltage generated by the panel system's Analog to Digital Converter (ADC) circuit. Any data below the threshold indicate a bad voltage condition for a panel of two solar cells connected in series. The team specified those expected values from the operational telemetry data of the BIRDS-3 satellite (Raavana). Thus, the dataset is labeled with the following condition:  
\[V_{i}(t)<1200\;\mathrm{mV}, \tag{1}\]  
Figure 5: +Y panel parameters correlation: (**a**) voltage vs. temperature, (**b**) current vs. temperature.  
with \(V_{i}(t)\) being the values of voltage sensor readings at samples of \(t\) and for \(i\) panel. Correspondingly, Figure 6 presents the voltage and current sensor readings in both normal (black) and anomalous (red) sections for type 1 anomalies by applying (1). It covers approximately 2.5 orbital periods. Type 1 anomalies in the dataset contaminate 8.59% of the total sample.
## 5 Experimental Results and Analysis  
Experiments were carried out to evaluate the performance of the proposed model. We used three different datasets from the NSL-KDD and UNSW-NB15 datasets. We compared the results of the proposed model with other well-known detection methods. The proposed system was implemented in the Tensorflow environment on a ThinkStation with 64 GB RAM, Intel E5-2620 CPU and 64-bit Windows 10 operating system.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 5 Conclusions  
In this paper, we propose a robust and accurate Wi-Fi fingerprint location recognition method based on a DNN. We use the adaptive constrained KF in the dynamic positioning and the HMM in the static positioning to overcome the gross error caused by the fluctuation in \(RSS\) and multipath interference and to improve positioning accuracy. We used a public dataset to fully verify the feasibility of DNN in Wi-Fi positioning. Experimental results show that the accuracy of DNN in floor recognition is 94.60%, and the error of position estimation is 5.29 m, which is superior to traditional methods. Further experiments in office buildings show that the positioning error of DNN is 2.13 m without interference caused using heterogeneous devices. Compared with classic methods, the DNN has higher positioning accuracy and better real-time performance. The dynamic positioning accuracy can reach 1.72 m after adaptive constrained KF smoothing. In static positioning, HMM is used to optimize the multi-observation results on the unknown points, which realizes 1.22 m positioning accuracy.  
In the future, we will attempt to combine DNN with semi-supervised classification to reduce training time and update fingerprint database quickly and effectively when the environment changes, so as to ensure the accuracy of the positioning system. In the meantime, we will integrate DNN-based Wi-Fi positioning results with other sensors to build other positioning schemes based on real-world situations.  
Conceptualization, Y.W. and J.G.; methodology, Y.W.; software, Y.W. and Z.L.; validation, Y.W., Z.L. and L.Z.; data curation, Y.W.; writing--original draft preparation, Y.W. and J.G.; writing--review and editing, Z.L. and L.Z.; funding acquisition, J.G. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 41604006, No. 41874006, No. 41674008) and supported by Natural Science Foundation of Jiangsu Province (No. BK20160247). The authors declare no conflicts of interest.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments
### Experimental Results  
The performance of our method on the Div2k, MS-COCO, and CelebA datasets is presented in Tables 3-6. Comparing our method with other widely used steganographic techniques clearly highlights its significant advantages. Across different embedding payloads (ranging from 1 bit to 3 bits), our method consistently achieves a 0% error rate, significantly outperforming the SteganoGAN and FNNS series methods, which exhibit a rapid increase in error rates under higher payloads (for instance, SteganoGAN's error rate rises to 13.74% at a 3-bit payload), a result that is unacceptable in image steganography. While the PSNR and SSIM values decline as the amount of embedded data increases, our method continues to demonstrate exceptional robustness in error rate control compared to other advanced techniques. This is especially evident under high payload conditions (e.g., 4 bits), where it still maintains excellent decoding accuracy and visual quality.  
It is worth noting that, in terms of the FSIM metric, our method has achieved optimal performance across all test datasets. On the Div2k dataset, our method attains FSIM values of 0.9949 and 0.9947 for 1-bit and 2-bit payloads, respectively, significantly outperforming LISO (0.9925 and 0.9836) and other comparison methods. On the MS-COCO dataset, our method is even more outstanding, maintaining a high FSIM value of 0.9892 at a 4-bit payload, compared to LISO's 0.8815 under the same conditions. Similarly, on the CelebA dataset, our method consistently achieves superior FSIM values across all payload  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Error Rate (\%)**} & \multicolumn{4}{c}{**PSNR**} & \multicolumn{4}{c}{**SSIM**} \\ \cline{2-13}  & **1 bit** & **2 bit** & **3 bit** & **4 bit** & **1 bit** & **2 bit** & **3 bit** & **4 bit** & **1 bit** & **2 bit** & **3 bit** & **4 bit** \\ \hline SteganoGAN [17] & 5.12 & 8.31 & 13.74 & 22.85 & 21.33 & 21.06 & 21.42 & 21.84 & 0.76 & 0.76 & 0.77 & 0.78 \\ FNNS-R [31] & 0.02 & 0.18 & 3.29 & 10.88 & 35.31 & 30.73 & 28.99 & 28.60 & 0.93 & 0.85 & 0.78 & 0.76 \\ FNNS-D [31] & 0.00 & 0.00 & 0.01 & 5.45 & 29.30 & 26.25 & 22.90 & 25.74 & 0.82 & 0.73 & 0.53 & 0.65 \\ FNNS-DE [31] & 0.00 & 0.00 & 0.01 & 1.75 & 18.54 & 18.02 & 17.16 & 17.38 & 0.60 & 0.53 & 0.39 & 0.60 \\ LISO [19] & \(4\times 10^{-5}\) & \(4\times 10^{-4}\) & \(2\times 10^{-3}\) & \(3\times 10^{-2}\) & 33.83 & 33.18 & 30.24 & 27.37 & 0.90 & 0.90 & 0.85 & 0.76 \\ Ours & 0.00 & 0.00 & 0.00 & 0.02 & 38.92 & 34.21 & 32.58 & 28.62 & 0.95 & 0.89 & 0.89 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other state-of-the-art steganography algorithms with different payloads on Div2k dataset.  
conditions. These results indicate that our method effectively preserves edge and global information during the steganography process.
## 3 Results and Discussion
### Histological Analysis of Brain Tissues and Neuronal Components  
Through H&E staining, the occurrence of damage to the brain tissue was cross-confirmed using magnetic resonance images. In addition, the loss of neuronal components (Nissl bodies) was confirmed by Cresyl violet (Nissl) staining. Slide scan images of H&E- and Cresyl violet-stained brain tissues obtained under each current condition (I = 0.0 (control), 1.0, 1.5, and 2.0 mA) of tACS are shown in Figure 4. Comparing the results of a safety study on tDCS [25] and our previous study on rabbit brain tissues in which brain damage was induced by a photothrombotic stroke [31], there was no area where the brain tissue density decreased under all conditions of tACS applications. In addition, slide scan images of Cresyl violet-stained brain tissue indicated no loss of Nissl bodies caused by brain damage from tACS.  
Figure 2: Longitudinal T1- and T2-weighted magnetic resonance images of rat brains acquired immediately (0 h) and 48 h after tACS with a frequency of 10 Hz and a treatment time of 20 min. Yellow boxes in MR images indicate regions segmented for quantitative analysis of mean intensity values. (T: Transcranial alternating current stimulation-applied region, C: Contralateral region).  
For a quantitative analysis of the neuronal components, we extracted five sub-images with 2500 \(\times\) 2500 pixels (=660 \(\times\) 660 \(\upmu\)m\({}^{2}\)) each from the area where tACS was applied and the contralateral area in slide scan images, and the number of Nissl bodies in the sub-images was calculated, as shown in Figure 5. Although the numbers of Nissl bodies differed between the region to which tACS was applied and the contralateral region, the difference was not significant. Specifically, there was a difference in the number of Nissl bodies between the two regions in all the experimental cases, including the control (no current), but the deviation was approximately 10%; thus, there did not appear to be a significant reduction in neuronal components due to brain damage.  
Figure 3: Ratios of the mean intensity values in tACS-applied and contralateral regions from (**a**) T1- and (**b**) T2-weighted magnetic resonance images. Although there was a slight difference in intensity between areas subjected to tACS and the opposite areas in both the T1- and T2-weighted magnetic resonance images, the difference was within a level of standard deviation.
## 2 Literature Review and Methodology
### Affecting Factors  
The charging and discharging process of the battery is a complex electrochemical process which is affected by various factors that prevent the accurate estimation of SOC and SOH values.  
* Discharge rate: One of these factors is the rate at which the current is being discharged from the battery. When the discharge rate is high, it affects the battery capacity by reducing it and making the battery utilisation rate lower because the active material in the battery has a limited action that it can perform, thereby affecting the SOC and SOH [7].
* Battery temperature: Temperature affects the battery's power. As the temperature increases, the electrolytes in the battery become more active, which leads to battery utilisation rate and available power being increased. Whereas, when the temperature decreases, the electrolytes become less active which reduces the utilisation rate and power of the battery, thereby affecting the SOH. The ideal temperature range for a typical Li-ion battery to function effectively is between the range of 0 \({}^{\circ}\)C and 45 \({}^{\circ}\)C [7].
* Number of cycles: The amount of charging and discharging cycles that the battery has performed during its usage affects the battery's life. As the cycles increase, the total charge capacity of the battery decreases [7].
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 1 Introduction
This study addresses these gaps by investigating the mechanical mechanisms and calculation methods of assembled corrugated steel initial support structures, leveraging insights from the Qipanshan tunnel project in Yunnan Province, China. This research aims to facilitate the broader adoption of corrugated steel as an innovative initial support structure in tunnel engineering through comprehensive approaches such as full-scale testing and numerical simulations.
## 1 Introduction
Based on the AE method, we studied the ability of two low-cost piezoelectric sensors attached to the external wall of a transformer to detect the elastic waves emitted by PDs, allowing non-invasive PD monitoring. Online continuous monitoring for defect prevention contributed decisively to an improvement in the quality of the electricity supply. Moreover, considerable material losses were prevented, improving the equipment's reliability and safety. First, the signals from acoustical measurements were digitally processed in the time-domain mode; this type of signal processing is commonly used in all commercially available equipment for PD analysis. When the acoustic signal crosses a threshold, the monitoring system reacts and measures certain typical AE parameters such as amplitude, rise time, duration, and oscillations (also called counts). A good SNR is necessary for this analysis to obtain a correct response. Otherwise, the system is less effective and could lead to false alarms. The better the SNR, the more sensitive the apparatus. Data from a PD's acoustic signal allows for performing a more in-depth analysis in the frequency domain using the fast Fourier transform (FFT), power spectral density (PSD), and a spectrogram that is a time-frequency analysis. To better understand the characteristics of the measured acoustic signals, this study also used the energy criterion, RMS, and other statistical parameters, such as standard deviation (STD), skewness, and kurtosis. These parameters were very helpful for a proper analysis of the sensors to detect and measure the PD phenomenon. This paper is organized as follows: Section 2 describes the low-cost acoustic sensors for PD detection and the experimental setup and present the time-domain, frequency-domain, and time-frequency analysis. Additional statistical parameters are presented and discussed in Section 3. Finally, Section 4 concludes the paper.
## 3 Environment Information and HD Map  
The map-matching-based position correction requires valid environment information such as lane and road boundaries (guardrail) and an HD map. In this chapter, we briefly present how we obtained point clouds of lane and guardrail signals and the characteristics of the HD map used in this approach.  
In order to detect the front lane, we obtained second-order polynomial coefficients and quality levels (0 (low)-3 (high)) of left and right lanes from the Mobileye camera (C2-170) installed at the center position of the front windshield. To prevent false detection, only signals of Quality 2 or higher were chosen for matching.  
The radar points are selected as a guardrail (\(Z_{selected}\)) if its absolute speed is lower than 10 kph and its position exists within 20 m of the ego-vehicle center. The guardrail information (\(Z_{guardrail}\)) for use in map-matching can be obtained by accumulating points using vehicle sensor information.  
\[Z_{guardrail}=T_{backward}\cdot Z_{selected} \tag{1}\]  
where  
\[T_{backward}=\left[\begin{array}{ccc}\cos(\gamma\cdot\Delta t)&-\sin(\gamma \cdot\Delta t)&\
u\cdot\gamma\cdot\Delta t\\ \sin(\gamma\cdot\Delta t)&\cos(\gamma\cdot\Delta t)&0\\ 0&0&1\end{array}\right]^{-1}\]  
To achieve a considerable degree of position estimation by applying map-aided matching, a map with high-level accuracy is required. The map is processed from 3D-scanning information to form a vector type (*.shp) structure. The HD map contains a variety of road information such as lanes, centerlines, road (traffic) signs, road numbers, barriers, road markers, and so on. In Figure 4, an example scene of the HD map is described. In this study, we used the position of lane and road boundaries (guardrail) information of the designated section. The HD map of the highway road section we used for position correction is produced by the Korean NGII.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sensor Type**} & \multicolumn{4}{c}{**Properties**} \\ \cline{2-5}  & **Range** & **Resolution** & **Noise (RMS)** & **Units** \\ \hline Wheel speed & 0–130 & 0.035 & 0.3 & m/s \\ Yaw rate & \(\pm\)120 & 0.0625 & 0.5 & deg/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Noise specifications of proprioceptive sensors.  
Figure 3: Environment sensor configuration of the test vehicle.