esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mathematical Models
### Models at the Macro-Scale
#### 4.2.2 Immersed Boundary Method
We note that for the curvature \(\kappa\) of the interface \(\Gamma\) and for the unit normal vector \(\mathbf{N}\) outward to \(\Gamma\), the following equations are valid:  
\[\mathbf{N} = -\frac{\
abla\varepsilon}{|\
abla\tilde{c}|} \tag{51}\] \[\kappa = -\text{div}\bigg{(}\frac{\
abla\varepsilon}{|\
abla\tilde{c}|} \bigg{)} \tag{52}\]  
Figure 5: The transition region (unshaded) separating two fluids used in the CSF formulation: \(h\) is the width of the region. Credits: image inspired by the work of Brackbill et al. [61]. The function \(\tilde{c}(\mathbf{x})\) can be defined as a convolution of the function \(c(\mathbf{x})\) with an interpolation function \(\mathcal{J}\) according to Brackbill et al. [61]:  
\[\tilde{c}(\mathbf{x})=\frac{1}{h^{3}}\int_{V}c(\mathbf{y})\mathcal{J}(y-x)dy \tag{53}\]  
where \(\mathcal{J}\) has to satisfy the following conditions \begin{tabular}{l l} (a) & \(\int_{V}\mathcal{J}(x)dx=h^{3}\), \\ (b) & \(\mathcal{J}(x)=0\) for \(|x|\geq h/2\), \\ (c) & \(\mathcal{J}(x)\) is differentiable and decreases monotonically with increasing \(|x|\). \\ \end{tabular}   
Volume force \(\mathbf{F}_{sv}(\mathbf{x})\) will result in the same total force as \(\mathbf{F}_{sa}(\mathbf{x}_{T})\), but spread over the finite interface width, i.e.,  
\[\lim_{h\to 0}\int_{\Delta V}F_{sv}(\mathbf{x})dx^{n}=\int_{\Delta A}\mathbf{F}_{sa}( \mathbf{x}_{T})dA \tag{54}\]  
Coupling the _continuum surface force (CSF)_ model with problems related to two-phase Navier-Stokes incompressible flow requires the reformulation of the Problem 1 as follows:  
**Problem 4**. : _Equations of two-phase Navier-Stokes incompressible flow defined in \(\Omega\times\Theta\) using the CSF model:_  
\[\frac{\partial}{\partial t}(\mathbf{\varphi}\mathbf{v})+(\rho\mathbf{v}) \odot\
abla\mathbf{v} = -\
abla p+\text{div}\ \mathbf{T}+\rho\mathbf{g}+\mathbf{F}_{sv}(\mathbf{x}) \tag{55}\] \[\text{div}\ \mathbf{v} = 0\] (56) \[\mathbf{v}_{|t=0} = \mathbf{v}_{0}\] (57) \[p_{|t=0} = p_{0} \tag{58}\]  
_where \(\mathbf{v}\) is the velocity field, \(p\) is the pressure field, \(\mathbf{g}\) is the gravitational acceleration, and \(\rho(x)\) and \(\mathbf{T}(x)\) are the piecewise constant fluid densities and stress tensors described by the Heaviside function. The following equations hold [61]:_  
\[\mathbf{T}(x) = \mathbf{T}_{1}H_{1}(x)+\mathbf{T}_{2}(1-H_{1}(x)) \tag{59}\] \[\rho(x) = \rho_{1}H_{1}(x)+\rho_{2}(1-H_{1}(x))\] (60) \[H_{1}(x) = \left\{ \begin{array}{ll}1&\text{for }x\in\Omega_{1}(t)\\ 0&\text{for }x\
otin\Omega_{1}(t)\end{array} \right. \tag{61}\]  
_Note that the term \(\mathbf{F}_{sv}(\mathbf{x})\) (as defined in Equation (48)) is added to Equation (55) to model the surface tension forces [61]._
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 7 Conclusions  
In this paper, we proposed a new optical CDMA system using inverted MPSCs with MUI cancellation schemes incorporated. The proposed system provides higher average light intensity and lower normalized fluctuation than the conventional systems with non-inverted MPSCs without losing the capabilities of channel multiplexing and the MUI canceling. We confirmed that the proposed system achieves our goal of an average light intensity that is more than half of the potential, which is the light intensity that non-modulated light sources inherently possess. Then, we investigated BER performances of the proposed systems in comparison with those of the conventional systems. The results show that the BERs of the systems with a non-inverted MPSC and an inverted MPSC are nearly identical for each MUI cancellation scheme.  
Conceptualization, T.K.M. and S.Y.; methodology, T.K.M.; software, K.O.; validation, T.K.M., S.Y. and K.O.; formal analysis, H.T.; investigation, T.K.M.; resources, T.K.M.; data curation, T.K.M.; writing--original draft preparation, T.K.M.; writing--review and editing, T.K.M.; visualization, K.O.; supervision, T.K.M.; project administration, T.K.M.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Japan Society for the Promotion of Science KAKENHI Grant Numbers 19K04403, 22K04113, and 22K04091.  
**Acknowledgments:** We express our gratitude to [PERSON]. We also express our appreciation to [PERSON] and [PERSON] for their help. The results of this work were partly presented at the sixth International Workshop on Signal Design and Its Applications in Communications [28] and the IEICE technical workshop on Wideband System [29].  
Figure 8: Visible-light CDMA experimental system using the proposed scheme.
## 5 Brillouin Optical Correlation Domain Reflectometry
### Dynamic Grating Generation with Gain Saturation Phenomenon in Erbium-Doped Optical Fibers  
Figure 38 shows an experimental setup for distributed strain measurement, in which a dynamic grating caused along a polarization maintaining erbium-doped fiber (PM-EDF) is localized by the SOCF principle [94].  
A continuous laser lightwave emitted from LD1 with a 1.55-\(\upmu\)m wavelength is divided into two ways by a fiber coupler. The two waves are launched into the PM-EDF in one of its polarization axes, X, at each fiber end, respectively. In the EDF, a standing wave is generated by the two counter-propagating waves. This standing wave, of course, creates a periodical lightwave intensity change that has the period of half the wavelength, which creates the periodical gain saturation, namely the periodical gain grating. When another orthogonal polarization lightwave is launched into the fiber, it shall be reflected under the Bragg condition that is satisfied. Here, the SOCF is introduced with a FM to LD1, which makes the gain grating localized at the correlation position along the fiber. This is the localization of the gain saturation-induced dynamic grating manipulated by the SOCF technology [94].  
Meanwhile, LD2 output is launched into the PM-EDF in the other polarization axis, Y. By sweeping the lightwave frequency, the reflection spectrum can be measured as the output of a photo diode (PD). It is important that the Bragg condition for the Y polarization is shifted, due to the fiber birefringence,  
Figure 38: Experimental setup for distributed strain measurement in a polarization maintaining erbium-doped fiber (PM-EDF) [94]. In the fiber, the two lightwaves in one polarization axis are propagated in opposite directions with respect to each other, which makes a standing wave that induces gain grating along the fiber. The Bragg reflection spectrum is read by the third lightwave launched in the orthogonal polarization. The difference of the Bragg reflection frequency between the two polarization lightwaves corresponds to the fiber birefringence, which tells us the strain. The SOCF scheme is applied to have the position selective measurement of the birefringence.  
compared with the X polarization. When the strain is applied to the fiber, the Bragg frequency is additionally shifted. Then, the system can provide the distributed strain measurement function. To reduce the non-desired reflections and Raleigh backscattering, a two-stage lock-in detection scheme is introduced in the system, which is similar to the \"double lock-in scheme\" for the BOCDA systems.  
Figure 39a shows the experimental results when a 20-cm section in the PM-EDF is strained by 1400 \(\upmu\varepsilon\). The spatial resolution, which corresponds to the width of the synthesized delta function-like coherence peak, is about 20 cm in this experiment. Figure 39b shows the peak frequency plots obtained from Figure 39a. The strained section is recognized through the distributed measurement data to be about 30 cm. Considering the spatial resolution, the distributed data that are shown in Figure 39 are reasonable [94].
## 6 Future Directions
### AI/ML-Enabled Networks  
Considering generation-to-generation cellular systems, sixth-generation cellular systems have become complicated in many ways. It is becoming increasingly difficult to manage this complex structure effectively with the old approaches developed for older generations. There is a need for an in-depth introduction of artificial intelligence technology and solutions, which is one of the latest developing areas, into the sixth-generation cellular systems. The biggest difference that distinguishes 6G from its previous generations is that it has the feature of being a platform for AI. One of the developments that awaits humanity in the future is that both networks and AI will feed each other, and even AI will be presented as \"AI as a Service (AlaaS)\" [37]. The vision of AI is that it acts as the engine for automation. Artificial intelligence technologies have a wide range of lower legs, such as machine learning, deep learning, deep reinforcement learning, computational intelligence [127], distributed learning [128], and federating learning [129]. Artificial intelligence algorithms are vital for the success of goals such as low latency, low path loss, and fast data transmission in the integration of 6G and space networks. In the future, AI will be a technology that is natively in everyone's pocket rather than a technology that is only in the cloud. 6G aims to make access to AI possible from anywhere. The main focus of new mobile communication technologies is \"intelligence\". It is envisaged that the part of intelligence will also be provided with technologies such as AI ML based on data rather than code. However, the fact that artificial intelligence technologies are data-based creates a limit for these technologies. 6G and space networks are dynamic and complex networks. Therefore, the provision of data collected from these networks as input in artificial intelligence technologies may adversely affect the performance of the output. For this reason, collective implementation of traditional and artificial intelligence-based solutions can provide better results. The storage and processing of huge amounts of data that will be collected from billions of devices in the future will be critical. Quantum computing is promising at the point of processing data [78]. In addition, the use of centrally distributed technologies for this data is promising for security. More research should be carried out to use deep reinforcement learning approaches that produce results by trial and error with the experiences gained from their environment rather than data on the communication side. Concepts such as federating learning also give hope to minimizing privacy violations.
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS  
It was possible to establish the most commonly employed techniques for detecting available parking slots in public or private areas that allow SPS to be fed with the necessary information to guide drivers to such spaces.  
VDT is divided into six categories: in-roadway sensors (IRS), over-roadway sensors (ORS), roadside units (RSU), crowdsensing (CS), prediction models (PM) and data from a third party (DFTP).  
Some of the documents selected did not present a specific description of a VDT for an SPS. These studies assumed that data on the availability of parking slots will be obtained by any means, or they described algorithms implemented regardless of how the information was obtained from each parking slot. These items were added to the without detection (WD) category.
## 4 Establishment of Numerical Test Model
### Material Constitutive Model  
#### 4.3.1 Concrete Constitutive Model  
The total strain crack model in MIDAS/FEA can better reflect the mechanical properties of concrete materials and steel fiber concrete materials. The Thorenfeldt compression model is used for the compression model, and the brittle model is used for the tensile model.  
(1) Compressive Constitutive Model  
As shown in Figure 8, the Thorenfeldt compression curve illustrates the behavior of concrete under compressive load. The Thorenfeldt compression model in MIDAS/FEA effectively accounts for the compressive properties of concrete [24; 25].  
(2) Tensile Constitutive Model  
Due to the inherent properties of concrete, its tensile strength is relatively low. In this study, the \"Brittle model\" from the tensile model in MIDAS/FEA is chosen as the constitutive model for concrete under tension. As shown in Figure 9, when the tensile stress in the material exceeds its design tensile strength, the tensile stress does not increase further and drops to zero.
## 4 Conclusions
Nomenclature  
ADUAnalog-to-digital unit  
CCDCharge couple device  
CCECharge conversion efficiency  
CMOSComplementary metal-oxide-semiconductor  
COTSCommercial off-the-shelf  
FOVField of view  
FWHMFull width at half maximum  
GEOGeostationary Earth orbit  
ISSInternational Space Station  
LEOLow earth orbit  
MPMegapixel  
RSOResident space objects  
SNRSignal-to-noise ratio  
STRStar tracker  
TECThermoelectric cooler  
TLETwo Line Element
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.8 Analysis of Different Hidden Layer Sizes  
In the original model, a recurrent neural network (RNN) is utilized. Apart from the input and output layers, all other layers, including the convolutional layer, activation layer, pooling layer, and fully connected layer, are considered as hidden layers. Increasing the number of hidden layers in a neural network allows for the extraction of more features, leading to improved learning outcomes. However, it is important to note that the performance of the neural network does not necessarily improve with an increasing number of hidden layers. This is because it can lead to the problem of gradient explosion, and there is a limit to the number of layers a neural network can have. As the number of hidden layers increases, the network will have more parameters, resulting in longer training times. Therefore, setting an appropriate number of hidden layers significantly impacts the performance of the proposed model. The experimental results are depicted in Figure (c)c.  
The figure clearly demonstrates that our proposed model achieves optimal performance when the hidden layer size is set to 250. Among the other baseline models, such  
Figure 11: Parameter sensitivity analysis.  
as CNN + BiLSTM, MF-CSEL, and C-GRU, consistent and stable performance is observed across different hidden layer sizes. However, when the hidden layer size is increased to 400, the LSTM model exhibits poor performance, and is outperformed by the Bi-LSTM model.
## 3 Methodology  
The proposed system was developed using a hybrid ensemble approach integrating ANN and SVM, with an RF classifier acting as a meta-learner. The methodology of the research study encompasses data preparation, model construction, ensemble integration, real-time deployment, and explainability integration, as outlined in Figure 4.  
The following subsections detail each stage, highlighted in Figure 4:
## 4 Model Training
### BCE-Dice Loss Function  
The binary cross-entropy loss (BCE) and Dice loss are combined to form the loss function of this study. The BCE loss focuses on the classification correctness of each pixel and provides strong supervision for the prediction of foreground and background, while the Dice loss pays more attention to the overlap between the predicted area and the real area and is more robust to the problem of class imbalance. For a single sample, \(\text{Loss}_{\text{BCE}}\) is defined as:  
\[\text{Loss}_{\text{BCE}}(p,t)=-[t\cdot\log(p)+(1-t)\cdot\log(1-p)]. \tag{5}\]  
where \(p\) is the predicted probability, which is converted to [0, 1] by the sigmoid function; t is the target label (0 or 1).  
The Dice coefficient is an index to measure the overlap of two sets, defined as:  
\[\text{Dice}=\frac{2|X\cap Y|}{|X|+|Y|} \tag{6}\]  
where \(X\) is the predicted area and \(Y\) is the real area. In the semantic segmentation task, the Dice coefficient can be expressed as:  
\[\text{Dice}=\frac{2\sum\limits_{i}p_{i}\cdot t_{i}+\varepsilon}{\sum\limits_{ i}p_{i}+\sum\limits_{i}t_{i}+\varepsilon}. \tag{7}\]  
where \(\varepsilon\) is added to avoid the denominator being zero, and \(\varepsilon\) is taken as \(1\times 10^{-5}\). Then the Dice loss is defined as:  
\[\text{Loss}_{\text{Dice}}=1-\text{Dice}. \tag{8}\]  
Finally, the BCE-Dice loss function can be obtained as:  
\[\text{Loss}_{\text{BCE}-\text{Dice}}(p,t)=\alpha\cdot\text{Loss}_{\text{BCE} }(p,t)+\beta\cdot\text{Loss}_{\text{Dice}}(p,t). \tag{9}\]  
where \(\alpha\) is the weight of BCE, and the value is 0.5; \(\beta\) is the weight of Dice, and the value is 1. The weight configuration (\(\alpha\) = 0.5, \(\beta\) = 1) integrates Dice loss advantages in handling class imbalance and optimizing region overlap with BCE loss's strong pixel-wise supervision. This dual approach addresses tunnel leakage identification requirements--small targets, strong interference, and precise boundary delineation--thereby enhancing model segmentation performance.
## 3 Results of Studies of AE Signals
### Results of Studies of AE Signals When Processed Using Concentrated Energy Flows
If the dimensions of the laser focal spot are kept constant but the power of the radiation generator changes, then the power density of the heat flow will be proportional to the specified power. Figure 15 compares the amplitude spectra of AE signals when a workpiece made of stainless steel AISI 410 is exposed to laser pulses with a power of 60% and 40% of the maximum laser power. Figure 13: Examples of changes in the parameters of AE signals during WEWDM processing of the HG20 alloy on the CUT30P machine from the beginning of cutting to the break of the wire electrode: (a)—1/3 octave spectra of the start of processing (1, blue) and before the break of the wire electrode (2, yellow); (b)—recordings of the AE signal in the high and low frequency ranges throughout the entire processing; (c)—change in RMS amplitudes of the AE signal in different frequency ranges and the K\({}_{\rm f}\) parameter from the beginning of processing until the electrode breakage. In Figure 15, the amplitude of the AE signal over the entire frequency range at a power of 60% is greater compared to the radiation at a power of 40%. However, it is seen that when the power is reduced by 1.5 times, the decrease in the AE amplitude at high frequencies is significantly greater than the range of lower frequencies. Figure 16 shows the change in RMS amplitudes in the 8 and 16 kHz octave bands when a steel workpiece is exposed to a series of pulses with different power densities. If, during electron beam processing, an increase in the pulse power density was accompanied by an increase in the amplitude of the AE signal at high frequencies and a barely noticeable decrease in the amplitude in the low frequency range (Figure 14b), then in Figure 15, an increase in amplitudes is visible in both frequency ranges. Since the rate of amplitude growth in the 16 kHz octave is significantly higher than the rate in the 8 kHz octave, the regularity of changes in the K\({}_{\rm f}\) parameter corresponds to the previous examples. With a decrease in process performance (quality reduction) due to a decrease in power density, the K\({}_{\rm f}\) parameter increases. Figure 14: The dependence of the AE signal parameters on the charging voltage (power density) of the electron gun during irradiation of the M20 alloy [54]: (**a**) the spectra of the AE signal in the range of 30–40 kHz at the moment of applying an electron pulse with a charging voltage of 16 kV (1) and 22 kV (2); (**b**) the change in RMS amplitudes of the AE signal in different frequency ranges and the K\({}_{\rm f}\) parameter with increasing charging voltage (power density). Figure 15: Comparison of the spectra of AE signals when the workpiece is exposed to laser pulses with a power of 60% (1) and 40% (2): (**a**) frequency range of 7–11 kHz; (**b**) frequency range of 12–22 kHz.
# Solar Extreme and Far Ultraviolet Radiation Modeling for Aeronomic Calculations
## 3 Results
### EUVT Model
The analysis of the Student's statistical criteria t, which represent a special ratio of the values of the coefficients to the estimation of their errors, showed that their values always exceed the critical values (t = 1.96 for 95% confidence interval), corresponding to the volume n of the studied samples (n \(\approx\) 5400 observations for each wavelength interval). Thus, all coefficients are statistically significant. Figure 2: Changes in R\({}_{\lambda}\) values with the level of solar activity, as measured by the N (LaC) index. **Purple**–\(\lambda\) = 30.4 nm; **orange**–\(\lambda\) = 102.6 nm; **green**–\(\lambda\) = 58.4 nm. value exceeds the critical value. In the opposite case, the coefficient B1\({}_{\lambda}\) in Equation (3) can be set equal to 0, and it is reduced to a simple linear dependence on N\({}_{\text{L\alpha}}\). The modeling accuracy for each line is displayed in Table 1 by the mean relative deviation  
\[\varepsilon=(\Sigma\,\text{|}\,\text{N}_{\lambda\text{s}}-\text{N}_{\lambda \text{c}}\,\text{|}\,/\text{N}_{\lambda\text{s}})/\text{n}\ \ 100\%, \tag{4}\]  
where the indices \(\lambda\)s and \(\lambda\)ccorrespond to the source and model-calculated values at a given wavelength \(\lambda\), n--the number of measurements. It is seen that the error for most of the wavelength intervals is small and, on average, for the entire spectrum, the value of \(\varepsilon\) is close to \(\sim\)5.5%. For example, Figure 3 shows a comparison of the observed values of the flux of quanta N\({}_{30.4\text{s}}\) in the one of the brightest EUV line He II 30.4 nm with the model values of N\({}_{30.4\text{C}}\). It can be seen that the deviations from the calculated values are small. In 75% of cases (3 rd quartile of the distribution), they do not exceed 6.5% and only in one case, for a highly variable coronal line Fe XV, the value of \(\varepsilon\) exceeds 9%.
# Recent Trends on UV filters
## 1 Introduction
In 2015, the EU removed 3-benzylidene camphor from the list of approved UV filters because of its toxicity towards marine organisms [15] and endocrine-disruptor potential [16]; in contrast, ZnO was approved in the following year (2016) [13; 14; 17]. Additionally, the effectiveness of the sunscreen formulations should be tested by standardized testing methods, namely ISO guidelines which include in vivo methods for determination of UVB SPF [18; 19], in vitro and in vivo evaluation of UVA protection [20; 21; 22], and also evaluation of the water resistance of the product [10; 23]. Considering the importance of the market characterization of sunscreens and in order to understand the usage frequency of UV filters in these photoprotective formulations, this work aims to provide readers with a market study focused on products commercialized in 2021 in pharmacies and parapharmacies. The usage frequency of UV filters was also evaluated with regard to adults' and children's photoprotective formulations. The evolution of UV filters was assessed with a comparison of sunscreens launched in 2015, unveiling the main trends and their underlying causes.
## 3 Results  
### Extent of Intertidal Area of the Bijagos Archipelago  
From the Sentinel-2 tidal time-series images, considering water heights ranging from 1.04 m to 4.69 m, we extracted the intertidal area of the Bijagos Archipelago Biosphere Reserve, which amounted to 450.2 km\({}^{2}\). When considering a water height of 1.65 m (see 2.5.5 in methods), the extent of the final study area was estimated at 426.8 km\({}^{2}\).