esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application of CFD-Based Digital Twins
### Application of Machine Learning Model  
Filling and emptying processes are essential operations that water utilities must perform using the CFD modelling. In the existing pipelines, these simulations can take approximately 2.5 months [72]. Given the urgency of interventions, water utilities cannot afford such lengthy simulation times.  
To address this challenge, the datasets presented in Tables 12 and 13 were used to train a predictive model capable of forecasting the behaviour of filling and emptying procedures. The MATLAB vR2020a software validated a reliable machine learning model, testing 31 algorithms. The numerical accuracy rates of various classification learner algorithms--including decision trees, support vector machines, nearest neighbour classifiers, and ensemble classifiers--are illustrated in Figure 16. Among these, for filling processes, the tree, efficient linear, and ensemble models proved the most suitable for accurately replicating the complex air-water interactions with an accuracy of 100%; while for the emptying procedures, the KNN models provided the best responses with a fit of 96.9%.  
The following variables were used to classify the scenarios for implementing these algorithms (see Appendix A): initial hydro-pneumatic pressure in the tank (\(p_{0}\)); initial air pocket size (\(x_{0}\)); internal pipe diameter (51.4 mm); maximum/minimum pressure during the transient event; and air valve diameter. Figure 17 illustrates the parallel coordinates for the predictions. Only one incorrect prediction was reached for the emptying processes.  
Figure 18 presents the confusion matrix for the best solution for the filling and emptying operations, allowing for an assessment of validation accuracy.  
The trained models effectively captured the hazardous pressure peaks generated in the Type II scenario for the filling processes (see Figure 9). In this scenario, a pressure head peak of 123 m was observed, approaching the nominal pipe pressure of 163.2 m (PN 16). This occurs when an air valve with a high expelled air capacity induces dangerous pressure surges. It is not a common situation but depending on initial conditions, it can occur. This finding is particularly significant, as it highlights the ability of machine learning techniques to identify undesirable conditions, thereby helping to prevent pipe failure in existing water pipelines. Similarly, for emptying processes, the adopted machine learning model can follow the tendency of air-water interactions with and without air valves, providing a practical tool for detecting collapse conditions depending on soil conditions and stiffness pipe class.  
Figure 16: Accuracy of classification learner algorithms: **(a)** Filling Procedures, and **(b)** Emptying Procedures.  
Figure 17: Parallel coordinates for predictions: (**a**) Filling Procedures, and (**b**) Emptying Procedures.
## 2 Dyadic Green's Function for the Electric Field
### Source and Scattered Green's Functions: Scattering at a Planar Interface  
We now want to address the problem of determining the Green's function in a system with a planar interface between media 1 and 2. To that end, we shall evaluate the electric field generated by a point dipole, characterized by an electric dipole moment \(\mathbf{d}_{0}\), located at a distance \(z_{0}>0\) from the interface. We assume that medium 1 is located in the half-space \(z>0\), whereas medium 2 is located in the complementary space, as represented in Figure 1. Note that in general \(\beta_{1}\
eq\beta_{2}\) due to the different values of the speed of light in the media. Assuming that \(z\
eq z_{0}\), the primary field emitted by the oscillating dipole in the half-space \(z>0\) reads  
\[\mathbf{E}_{0}\left(\mathbf{p}_{\parallel},z,z_{0},\omega\right)=\mu_{1}\mu_{ 0}\omega^{2}\overleftrightarrow{G_{0}}\left(\mathbf{p}_{\parallel},z,z_{0}, \omega\right)\cdot\mathbf{d}_{0}. \tag{27}\]  
We have two different values for the field, depending on whether \(z\gtrless z_{0}\). Recalling Equation (26) we obtain  
\[\mathbf{E}_{0}\left(\mathbf{p}_{\parallel},z\gtrless z_{0},\omega\right)=E_ {0,s}e^{i\hat{p}_{1}|z-z_{0}|}\hat{e}_{s}+E_{0,p}e^{i\hat{p}_{1}|z-z_{0}|} \hat{e}_{p,1}^{\pm}, \tag{28}\]  
with \(s\)- and \(p\)-polarization amplitudes being given by  
\[E_{0,s} =\mu_{1}\mu_{0}\omega^{2}\frac{i}{2\beta_{1}}\left(\hat{e}_{s} \cdot\mathbf{d}_{0}\right), \tag{29}\] \[E_{0,p} =\mu_{1}\mu_{0}\omega^{2}\frac{i}{2\beta_{1}}\left(\hat{e}_{p,n}^ {\pm}\cdot\mathbf{d}_{0}\right). \tag{30}\]  
This field corresponds to waves impinging on the interface at \(z=0\), being partially reflected and partially transmitted as depicted in Figure 2. The reflected and transmitted fields can be expressed in terms of the amplitudes of the incident field at \(z=0\) and of the reflection, \(r_{s}\) and \(r_{p}\), and transmission, \(t_{s}\) and \(t_{p}\), coefficients at the interface for the \(s\)- and \(p\)-polarizations as [33; 35]  
\[\mathbf{E}_{r}\left(\mathbf{p}_{\parallel},z>0,\omega\right) =r_{s}E_{0,s}e^{i\hat{p}_{1}(z+z_{0})}\hat{e}_{s}+r_{p}E_{0,p}e^{ i\hat{p}_{1}(z+z_{0})}\hat{e}_{p,1}^{+}, \tag{31}\] \[\mathbf{E}_{t}\left(\mathbf{p}_{\parallel},z<0,\omega\right) =t_{s}E_{0,s}e^{i\hat{p}_{1}z_{0}}e^{-i\beta_{2}z}\hat{e}_{s}+t_{s }E_{0,s}e^{i\hat{p}_{1}z_{0}}e^{-i\hat{p}_{2}z}\bar{e}_{p,2}^{-}. \tag{32}\]  
The factor \(e^{i\hat{p}_{1}z}\left(e^{-i\hat{p}_{2}z}\right)\) is acquired by the field while propagating along the positive (negative) \(z\) direction in medium 1 (2). The \(p\)-polarization vector for the reflected field is \(\hat{e}_{p,1}^{+}\) since it propagates along the positive \(z\) direction. Conversely, we have \(\hat{e}_{p,2}^{-}\) for the transmitted field, since it propagates along the negative \(z\) direction.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF
### Multi-Wavelength QKD
#### 2.1.2 Quaternary Protocol  
Unlike the ternary scheme, the quaternary scheme (Figure 1b) uses all eight states and sends quatrits instead of trits. One strength of the quaternary protocol is that because all states are used, an eavesdropper cannot infer information about the key, as mentioned in the previous section for the ternary protocol. The quaternary protocol is similar to the ternary protocol, but now [PERSON] and [PERSON] will send quatrits (e.g., \(\Psi_{1}\), \(\Psi_{2}\), \(\Psi_{3}\), or \(\Psi_{4}\)) as opposed to trits. Because of the additional states, throughput is also increased, and the quaternary protocol is a promising multi-wavelength protocol applicable to other traditional QKD protocols.
## 5 Overview of 5G-Enabling Technologies
### Integration of Non-Terrestrial Networks (NTNs) with 5G
#### 5.5.4 NTN Use Cases  
Various use case scenarios for satellite-based NTNs have been discussed by the 3 GPP systems aspects workgroup in the study item on Satellite Access in 5G [146]. The three main categories of use cases identified for 5G satellite-based NTNs are discussed below.  
Service Continuity: This use case applies primarily to applications that make use of satellites' vast coverage capabilities, such as multicasting or broadcasting. It is made to offer a network for situations when terrestrial 5G networks alone are unable to give enough 5G coverage.  
Service Ubiquity: This use case is intended for unserved or underserved areas with limited access to terrestrial networks. Examples of IoT use cases that come under this category include smart agriculture and emergency medical services.  
Service Scalability: This is for use cases where multicasting or broadcasting a specific piece of content over a limited geographic area while utilizing the wide satellite coverage area is advantageous. Applications that rely on UHD services are suitable examples for this use case.  
NTN architecture can use varying deployment options, depending on the type of NTN platform involved. Spaceborne and Airborne platforms are the two basic categories of platforms used by the NTN. Parameters such as platform altitude, orbit, and beam footprint size are the basic factors determining the classification of the platforms [150]. In Table 9, we give a summary of the classifications of the NTN platforms.
## 2 Materials and Methods
### Histological Study  
We weighed removed liver portions immediately after ELR (70% of total liver mass) and livers of lethalized rats with Adventurer Balances (OHAUS, Parsippany, NJ, USA) at room temperature. Then, liver samples of treated rats were fixed in 10% neutral buffered formalin, dehydrated in alcohols of increasing concentration, then embedded in paraffin with 5 \(\upmu\)m sections obtained, and stained with hematoxylin and eosin (H&E) (hematoxylin solution cat. No. HK-G0-F250; ErgoProduction, Russia; eosin H, cat. No. CHM117739; AO Lenreaktiv, Saint Petersburg, Russia).  
Immunohistochemical (IHC) study used anti-Ki-67 antibodies (cat. No. B68180; Beckman Coulter, Brea, CA, USA) to study proliferation, anti-caspase-3 (cat. No. AA626 Ra82; Cloud-Clone, Katy, TX, USA) to study irreversible apoptosis, and anti-caspase-9 (cat. No. ab52298; Abcam, Cambridge, MA, USA) to study reversible apoptosis. The antibodies were diluted 1:100 in phosphate saline buffer solution (PBS) with 0.1% Tween 20 (cat. No. B-60261; Eco-Service, Moscow, Russia) and 5% bovine serum albumin (BSA; cat. No. PM-T1727; Biosera, Chollet, France). Washed samples were exposed to secondary antibodies against rabbit IgG conjugated to horseradish peroxidase (cat. No. HA710043; Huabio, Hangzhou, China) in PBS solution with 5% BSA, then employing detection by 3,3'-diaminobenzzidine (DAB; cat. No. ab64238; Abcam, Cambridge, MA, USA). We counterstained nuclei with Mayer's hematoxylin (cat. No. HK-G0-DL01; ErgoProduction, Saint Petersburg, Russia).  
The calculation of mitotic index (MI) included the ratio between mitotic hepatocytes and their total number in 30 high-power fields at each time point by 24, 48, and 72 h, Days 5, 7, and 10 after ELR (%) [34].  
Morphometrics of H&E-stained sections employed light microscopy with camera by LeicaDM1000, Leica LTDCH9435, and DFC 295 (Leica Camera AG, Wetzlar, Germany), then software analysis with ImageScopeM, v12.3 (systems for microscopy and analysis, Moscow, Russia). Micrographs were obtained at \(\times\)400 and processed with ImageJ software, v.1.53h (NIH, Bethesda, MD, USA) [35; 36]. We measured overall area of cells and their nuclei and then calculated areas of cytoplasm. We also counted the number of mono- and binuclear cells. An increase in cells with more than one nucleus was considered to be a feature of cell regeneration with no mitosis.  
For transforming 2D into 3D images, we used a Leica DM 6000 B microscope (Wetzlar, Germany), Leica LTDCH9435 camera (Leica Camera AG, Wetzlar, Germany), and MATLAB software, v. R2021b, (MathWorks, Natick, MA, USA). The following types of structures are seen: oxyphilic cytoplasm of hepatocyte, basophilic nuclei of hepatocyte, connective tissue proper, intercellular substance, and empty spaces (vessels, adipocytes). Converting the images into grayscale, we constructed 3D surfaces, the models and colors specific for each image.
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 5 Image Dehazing Method Based on Full Polarization Information Inversion and Image Fusion  
The traditional polarization differential dehazing method proposed by [PERSON] is based on three assumptions, one of which is that the scene light does not have polarization characteristics, and the polarization information is only related to the skylight [8]. However, in practical applications, space targets have different polarization characteristics due to different factors, such as surface material composition. The polarization characteristics of scene light need to be used to achieve the purpose of space target recognition, so there will be a large deviation in using the traditional method to obtain the restored image. In this paper, a dehazing method based on full polarization information inversion and image fusion is proposed; considering the polarization characteristics of skylight and the polarization characteristics of scene light, the DoP image of the space target is obtained by inverting using the known parameters firstly, and then the DoP image and the light intensity image after dehazing are fused to improve the dimension of information in a single frame and the contrast of the image.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### Requester Meets an Agent  
As we explain in Section 4, adverts are propagated across the system through the use of opportunistic networking. Users download ads for their own use, but at the same time can collect ads on behalf of other users. This is highly beneficial for users that do not encounter Ad-Dealers often: such users can acquire adverts by exploiting the mobility of other users within the same social circle such as family members, friends and co-workers.  
A user that ferries requests or adverts on behalf of another will from now on be called the Agent while the user who places the request will be called the Requester. The system can support Agents who ferry ads for multiple Requesters, and it can also support users who operate as both Requester and Agent (for another Requester) at the same time. However, for simplicity, we will examine the most basic scenario where two users meet and one of them plays the role of the Requester while the other serves as his/her Agent.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 3 Methods
### The Disparity Field Initialization and Aggregation Build upon the MUD
#### 3.2.1 The UFI Module  
In the RAFT-based methods, [16; 32] use additional processes to obtain the initial disparity, while [12; 13; 14; 31] use the first iteration to obtain the initial disparity. The initial disparity of these methods is limited by the radius of the disparity window or the number of disparities in the cost volume, and they are unable to model regions with disparities exceeding the limit. In contrast, the proposed UFI module uses multi-disparity proposals \(d_{k}^{\text{init}}\in[0,d_{1}^{\text{init}},d_{2}^{\text{init}},d_{3}^{\text{ init}}]\) to initialize the disparity field, where \(d_{k}^{\text{init}}\) denotes the top three candidates with highest correlation score. They are parallel encoded by \(\text{Encoder}^{\text{init}}\) to obtain the disparity field features \(F_{k}^{\text{df}}\), which are then fed to the Initializer to initialize the hidden state:  
\[\begin{split} q_{k}&=\tau(f_{\theta\text{i}}^{ \text{q}}(F_{k}^{\text{df}},F^{\text{ctx}}))\\ z_{k}&=\sigma(f_{\theta\text{i}}^{\text{init}}(H_{k -1}^{\text{init}}))\\ H_{k}^{\text{init}}&=H_{k-1}^{\text{init}}+(q_{k}- H_{k-1}^{\text{init}})\odot z_{k}\end{split}, \tag{10}\]  
where \(\tau(\cdot)\) denotes the tanh function, \(f_{\theta\text{i}}^{\text{q}}\) and \(f_{\theta\text{i}}^{\text{q}}\) are the reset gate and update gate networks with parameters \(\theta^{\text{q}}\) and \(\theta^{\text{z}}\), respectively. The initial disparity candidates \(\hat{d}_{k}^{\text{init}}\) and uncertainty \(\hat{d}_{k}^{\text{init}}\) are decoded from the initial hidden state \(H_{1}=H_{3}^{\text{init}}\). The initial disparity is selected as \(\hat{d}_{1}=\underset{d_{k}^{\text{init}}}{\arg\min}(\hat{d}_{k}^{\text{init}})\). Notably, the entire UFI module, with the exception of the reset gate \(f_{\theta\text{i}}^{\text{z}}\), is computed in parallel between sampling windows. The preliminary experiment shown in Figure 6 indicates an apparent improvement in initial disparity accuracy with the UFI module. It validates the effectiveness of multi-disparity window scan-and-select initialization.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 4 Improved MUSIC Method against Range Dimension Deceptive Jamming
According to the orthogonality principle of subspace, when the noise subspace part and the signal subspace part are mutually orthogonal, the denominator in Equation (40) is the smallest and \(P(\theta,r)\) reaches the maximum value. At this time, the angle parameter and range parameter information is derived from the coordinates corresponding to the spectral peak position of \(P(\theta,r)\), and then the target can be located. Steps to improve MUSIC algorithm:  
1. Perform maximum likelihood estimation according to the \(N\) received signal vectors to obtain the covariance matrix estimate \(\overset{\wedge}{\mathbf{R}_{x}}\);
2. The covariance matrix \(\overset{\wedge}{\mathbf{R}_{x}}\) is decomposed into eigenvalues, and then the number of sources is determined according to the eigenvalues;
3. Sort the eigenvalues according to the size of eigenvalues, and the noise subspace part and the signal subspace part are obtained;
4. The spectral function is calculated according to the formula \[P(\theta,r)=\frac{\mathbf{\alpha}^{H}(\theta,r)\overset{\wedge}{\mathbf{U}}\mathbf{ \alpha}(\theta,r)}{\mathbf{\alpha}^{H}(\theta,r)GG^{H}\mathbf{\alpha}(\theta,r)}\]The estimated values of range and angle are obtained by traversing and searching the spectral peaks. Figure 5 is the flow chart of range dimension deceptive jamming suppression. When the FDA-MIMO radar is interfered at the range bin level and is unable to operate properly, due to the mismatch of range dimension parameter information, the spatial spectrum search can be used to locate the target at this time, determine the range information of the real target and the fake target, and then combined with the adaptive beamforming technology against the range dimension deceptive jamming.
## 3 Results and Discussion
### FTIR Results  
Figure 10 shows FTIR spectra for all the tested binders (PG 58-28, PG 70-28, 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS). The figure was used to qualify the variation of the chemical functional groups for each binder. Generally, the PG 58-28, PG70-28, and 5%GPCM-SBS samples had relatively similar peak values with no visible differences for most of the spectrum. However, for the 5%PCM, 5%GPCM, 7%GPCM, and 7%GPCM-SBS, there were visible differences in the polyaromatic group (wavenumber of 1700 cm\({}^{-1}\)) and the sulfoxide group (wavenumber of 1030 cm\({}^{-1}\)).  
At a low wavenumber area below 800 cm\({}^{-1}\), it can be noticed that 7%GPCM-SBS, 5%PCM, and 5%GPCM had higher peak values. In other words, the peak values in the carbonyl area are lower for the 5%PCM, 7%GPCM-SBS, and 5%GPCM compared to other binders (PG 58-28, PG 70-28, 7%GPCMNS, and 5%GPCM-SBS). As expected, samples that contained SBS showed higher peak values at 700 cm\({}^{-1}\) due to the presence of polystyrene in the polymer.  
Figure 11 illustrates the spectrum of the 5%PCM binder under different storage conditions: immediately after mixing and one month later. Both samples were stored in a small metal container at room temperature (23 \({}^{\circ}\)C). The figure shows that the storage conditions had almost no effect on the chemical functional groups of the modified samples with GPCM.  
\begin{table}
\begin{tabular}{c c} \hline \hline PG70 -28 & \\
5\% GPCM-SBS & \\
7\% GPCM-SBS & \\
## 4 Results and Discussion
### Mobile Application for Educational Similar
#### 4.1.3 User Interface  
In Figure 3a, a welcome screen of the proposed application is shown. The proposed application's main objective is the generation of a plot of the blood glucose concentration based on the parameters defined in the previous section. For user commodity and better distribution application's controls, a tabbed document interface (TDI) was conceived as the main container for hosting all the needed user controls required to set the simulation parameters and show the results. Specifically, for the Android SDK environment, a tab host container was utilized. This control enables the integration of plenty of components in a single form. The proposed application contains four tabs: the first three tabs allow the user to define the parameters of meal, insulin, and exercise models, and the last one is designed to generate simulation data and show the blood glucose concentration in a plot.  
Figure 3: Screens of the proposed AEDMA app. (**a**) Main screen of the proposed applications. (**b**) Components of the meal model input Table. (**c**) Components of the insulin model tab.  
In Figure 3b, the tab for the meal model parameters is shown. This tab contains interface controls where users can define each meal's time and its estimated carbohydrate contents (in grams). The estimated carbohydrate contents also can be automatically calculated based on a default estimation of calories for a day, or user can input the total calories, and the application estimates the carbohydrate content in grams based on the given calories.  
In Figure 3c, the tab for the insulin model is shown. This tab contains interface controls that define the type of insulin and the unit to be dosed in each meal, for both the rapid and prolonged effect.  
In Figure 4a, the tab for the exercise model is shown. This tab contains interface controls where the user defines the intensity, duration, start time, and exercise routine.  
In Figure 4b, the tab for simulation is shown. In this tab, the user defines the time of the simulation in weeks and days. It is also possible to set all variables to their default values, start simulation, shown insulin, carbohydrate, or glucose concentration plots.  
In Table 2, the application's default values are listed, but the user can syntonize this to get a better understanding of the model behavior.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Meal Model**} \\ \hline Breakfast time & 7:00 AM \\ Lunch time & 1:00 PM \\ Dinner time & 7:00 PM \\ Breakfast CHO (g) & 97.91 \\ Lunch CHO (g) & 138.97 \\ Dinner CHO (g) & 59.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Default parameter values for the proposed application.  
Figure 4: The last two configuration screens. (**a**) Components of the exercise model Table. (**b**) Components of the simulations tab.