esa-sceva/satcom-chunk-collection huggingface dataset:
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 3 Numerical Experiments and Discussion of Results
### Choice of Discretization Parameters  
To accommodate various performance capabilities on mobile devices, _paint2 sim_ offers four resolution options in terms of \(\Delta x\), representing the voxel length. In alignment with this, we conducted corresponding simulations using the _OpenLB_ framework. The timestep \(\Delta T\) scales diffusively with the resolution, meaning that it decreases or increases quadratically in correspondence with \(\Delta x\). To demonstrate that _OpenLB_ produces consistent results with those presented in [PERSON] et al. (2018), we also incorporated a higher resolution. Due to the performance restriction inherent in mobile devices, this higher resolution cannot be executed on mobile devices. _paint2 sim_ differs from the validation case in three aspects. Firstly, it employs the Smagorinsky BGK Collision Model. Secondly, it incorporates the fringe region technique to ensure stability. Thirdly, the application exclusively employs the Bounceback boundary condition instead of the Bouzidi second-order condition. The decision between the first and second orders is rooted in numerical analysis and depends on the nature of the boundary--being first order for curved boundaries and second order for axis-aligned cells. The prevalence of a first-order condition in most scenarios is attributed to the staircase approximation. This approach is preferred due to the challenging extraction of the real geometry surface required by Bouzidi from an already discretized scanned domain.  
_paint2 sim-1_: The fringe region technique as well as the Smagorinsky BGK Collision Model are used with the Smagorinsky Constant \(\text{C}_{\text{s}}=0.15\).  
_paint2 sim-2_: The fringe region technique is used while the Smagorinsky BGK Model is replaced with the BGK Collision Model.  
_paint2 sim-3_: The fringe region technique is removed, and the Smagorinsky BGK Model is replaced with the BGK Collision Model.  
_paint2 sim-4_: The fringe region technique is removed, and the Smagorinsky BGK Model is used.  
In order to perform a comparison between Bouzidi and Bounceback, we also ran the validation case from _OpenLB_ with Bouzidi _OpenLB-1_ and with Bounceback _OpenLB-2_.
## 3 Results and Discussion
### Antibacterial Activity  
The antimicrobial properties of these materials were investigated via the Kirby-Bauer Test (Figure S4) to validate their safety by the ability to inhibit the growth of pathogenic bacterial strains.  
Representative images (Figure 9A) of antibacterial tests of SiO\({}_{2}\)/PEG50% with increasing amounts of Fe(II)C in the hybrid matrix show that iron has an inhibitory effect on both bacterial strains tested. Indeed, the higher the iron concentration in the silica matrix, the higher the inhibitory effect on bacterial growth, as shown in (Figure 9B). In the literature, it is reported that despite iron being an essential nutrient for bacterial growth, thanks to its main role as an electron transporter, excessive concentrations can become toxic to most bacterial strains. [PERSON] et al. reported that alterations of the H\({}_{2}\)O\({}_{2}\) and iron concentrations can produce high amounts of free radical species that damage the DNA, proteins, and lipids of bacterial cells [35].  
Figure 8: A representative XRD of SiO\({}_{2}\)/PEG 50 wt%/Fe(II)C 20 wt% soaked in SBF solution for 21 days.  
Figure 9: (**A**) Representative inhibition halos (ID) of S. _aureus_ and _E. coli_ with SiO\({}_{2}\)/Fe(II)C 5, 10, 15, 20 wt%/PEG 50 wt%. (**B**) Diameter (mm) of inhibition halos of all materials incubated with _S. aureus_ and _E. coli_. Values are the mean \(\pm\) SD of measurements carried out on samples analysed three times. (**C**) Bacterial viability (%).  
In accordance with [PERSON] et al. [7], low concentrations of iron did not show a significant inhibition, while the bacterial growth of _S. aureus_ and _E. coli_ decreased when 20 wt% of Fe(II)C was encapsulated (Figure 9C).  
The bacterial inhibitory effects of the increasing amounts of PEG in the SiO\({}_{2}\) matrix when 20% of Fe(II)C was encapsulated were evaluated, as shown in Figure 10. The increasing amounts of PEG had an effect on _E. coli_ and _S. aureus_ growth, as can be seen from the Petri dish images in (Figure 10A). Thus, the highest concentration of PEG allows bacterial inhibition.
## 3 Materials and Methods
### Materials  
Given the potential impact of Web 3.0 on IoT security and user data autonomy, our study uses a combination of IOTA's blockchain-based solutions tailored for IoT applications. The selected hardware and software components include a Raspberry Pi for device simulation, IOTA Identity for decentralized identity management, IOTA Streams for secure communication, and IOTA Stronghold for key management. These tools were chosen for their compatibility with lightweight devices and their alignment with Web 3.0 principles of decentralization and user-centric data management.  
The implementation was carried out on a Raspberry Pi 4 Model B with 4 GB of RAM as the IoT device platform, using IOTA Identity for Decentralized Identifiers and IOTA Streams for secure communication. Key management was facilitated by IOTA Stronghold, which securely handles sensitive cryptographic material, ensuring the privacy and security of device identities.  
The software utilized includes  
* **IOTA Identity** for managing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs).
* **IOTA Streams** for secure, decentralized data transmission between devices, with access control to maintain message confidentiality.
* **IOTA Stronghold** as a secure key management tool for safely storing cryptographic material, ensuring that private keys are protected.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 2 Materials and Methods
### Methodology  
The proposed method involves three stages: preprocessing, segmentation, feature extraction, and classification. Figure 2 is the block diagram depicting various steps of the proposed method.
## 2 Methodology
### Model Parameterization and Calibration
Figure 3: Nkayi soils map converted to 2 km resolution grid is used as inputs to the SAVANNA model. The elevation, slope and aspect were all obtained from the USGS Shuttle Radar Topography Mission (SRTM), 1-arc-second (90 m), digital elevation map [35], that was resampled to 2 km grid resolution (Figure 5). As rainfall in the region has a strong effect on seasonal browse production [10] and livestock performance, two water availability maps were created to represent dry and wet season ephemeral water availability for livestock along with perennially available boreholes. Seasonal water availability (Figure 6) was derived from combining distance to river (perennial and ephemeral) and borehole point data from [36] to achieve the minimum distance (m) for the average wet season (November to April) and the average dry season (May to October). Figure 5: Digital elevation map (2 km grid resolution) for the Nkayi district. The grid was resampled from the worldwide SRTM 90 m elevation grid [35]. Figure 6: Distance to water (km) maps used as inputs to the SAVANNA model. The wet season grid was used in simulations from November to April while the dry season grid was used from May through October. SAVANNA did not simulate any crop production in the agricultural areas but treated it as available forage for the season. Given that there was significant wildfire suppression and prevention efforts active in the region due to its proximity to forest conservation areas [40], the SAVANNA fire module was switched off (via parameter settings). Thus, any anthropogenic fires were assumed to be of limited size and low intensity as well as fully contained against any accidental spread. As a result, fire dynamics were not simulated in any of the climate scenarios. Additionally, shrub and tree biomass collection for firewood was not present in this version of the model. Livestock populations in Nkayi district were set to levels described by the Department of Veterinary Services [31] with the total number of cattle at 110,000, one TLU being an animal weighing 350 kg. This level gives stocking rates of approx. 5.0 ha/TLU for cattle. Goat, sheep, and donkey populations stood at 25,000, 15,000, and 10,000, respectively [4]. SAVANNA simulations were executed with two sets of herbivore population demography options to explore the effect of herbivore population on rangeland productivity. The first option was to set all livestock population to constant values based on their initial 1980 levels. The second option allows livestock populations to rise and fall with effects from rangeland condition and water access. In both options, livestock populations were distributed according to habitat suitability with respect to forage, water, and animal densities. This meant that no specific field ownership of specific herds was assigned to control grazing locations as is the norm in most community grazing schemes, so the livestock were generally distributed according to the most satisfactory forage and water suitability. Additionally, no wildlife or other herbivores were simulated in addition to livestock.
###### Abstract  
It is essential to reduce structural damages caused by earthquakes in severe conditions, such as layered ground, especially when a soft soil layer is close to the surface. In this study, the kinematic and inertial interactions, two mechanisms of soil-foundation-structure interaction (SFSI), of different soil-foundation-structure systems (SFS) were investigated on uniform and layered grounds. Two layered soil profiles composed of a low stiffness layer laid over another were prepared in an equivalent shear beam container. Nine centrifuge experiments were carried out for three structures located on the surface of each ground and exposed to the Hachinobe earthquake while increasing the peak acceleration of the input motion. Numerical simulations were performed to simulate the centrifuge tests. It was found that roof motion (RM) of the tall structure increased in layered profile even though the free-field motion (FFM) decreased compared to homogeneous ground. The appearance of a soft layer beneath structures modifies the SFS system's stiffness that causes kinematic and inertial interactions to alter to those on uniform soil profile.  
geo-centrifuge experiments; soil-foundation-structure interaction; earthquake; layered ground; FLAC3D 2021  
site conditions on soil amplification as well as the structure's response [9; 10; 11]. Seismic waves, in general, tend to be amplified more in softer geologic grounds. As a result, more severe damage occurs to structures during earthquakes. Based on the analytical approach, numerical modeling, and centrifugal experiments, several types of research reported that it is essential to understand the effects of the local site on soil amplification and SFSI to reduce earthquake damage: (1) damage to structures was more significant as the thickness of the soft layer increased, and higher structures with more stories experienced more damage than shorter structures built on the same ground [12]; (2) an increase in structure response on a layered profile was observed because the static stiffness of the foundation decreases due to the pressure bulb of horizontal shear stresses in the soil layer beneath the structure located on layered profiles [13; 14; 15; 16; 17], and (3) structure displaced more when the thickness of the soft layer decreased [14; 15; 17]. However, most of the studies focused on the structure responses without considering the effects of stratification on kinematic and inertial interactions with incoherence parameters and rocking behavior.  
The objective of this study is to evaluate the effect of seismic behavior on various foundation-structure systems located on different layered grounds. Nine centrifuge experiments were carried out with three different structures located on three soil profiles combined into two soil layers with different thicknesses and stiffnesses. Numerical models were performed with nonlinear soil properties to simulate the centrifugal experiments. The kinematic and inertial interactions with regard to the response of three structures were investigated under various local site conditions.
## 1 Introduction
In order to assess the vulnerability of mainshock-damaged structures, the incremental dynamic analysis process was generally applied to aftershock ground motions, and the intensities (usually quantified by _PGA_ or \(S_{\mathrm{a}}\)) of aftershock records were scaled incrementally until the mainshock-damaged structure collapse [27; 28; 29; 30; 31; 33; 36; 42; 43; 44; 45; 46; 47]. However, in the aforementioned studies, large-scale factors are often required during the process of scaling aftershock records. Over-scaling aftershock records can introduce unacceptable bias in the additional damage induced by aftershocks and subsequently affect the cumulative damage caused by MSAS sequences. Moreover, the introduced bias significantly depends on the scale indicator and scale factor used. Therefore, it is crucial to carefully determine the scale indicator and scale factor applied to aftershock records to avoid unacceptable bias. Nevertheless, to our knowledge, no previous work has specifically focused on the bias introduced by aftershock ground motion scaling, and no existing method has been developed to quantitatively determine the scale indicator and scale factor for aftershock records. The innovation of this study lies in assessing the impact of scaling ground motion on additional damage by quantifying the introduced bias caused by aftershock ground motion scaling. To achieve this, the study uses a benchmark database consisting of 274 recorded MSAS ground motions obtained from crustal earthquakes worldwide. Single-degree-of-freedom (SDOF) systems have been employed for assessing structure responses to select and scale ground motions [48; 49]. Various SDOF systems with different hysteretic behaviors, such as stiffness degradation, pinching, and strength deterioration, are employed. The manuscript proposes a methodology to measure the bias introduced by aftershock ground motion scaling. Eight different intensity measures are chosen as alternative indicators for scaling, and the biases resulting from aftershock ground motion scaling are analyzed using statistical methods.
# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 2 Related Theoretical Work
### Gradient Normalization  
Cycle generative adversarial networks are volatile, prone to breakdowns during training, and slow to converge, mainly due to the wide gradient space of discriminator. Some researchers have proposed methods such as L2 normalization [32], gradient penalty and weight clipping to address this problem, which can indeed make the network model stable. Still, these methods limit the model capacity of discriminator to a certain extent. Parameter clipping and spectral normalization are similar in the sense, ensuring that the L-constant at each layer of the model is bounded by constraining the parameters so that the total L-constant is also bounded. Whereas the gradient penalization notices that a sufficient condition for \(\parallel f\parallel_{L}\leq 1\) is \(\
abla_{x}f(x)\leq 1\) and therefore imposes a soft constraint on the model by using a penalty term (\(\parallel\
abla_{x}f(x)\parallel-1\))\({}^{2}\).  
The gradient normalization introduced in this paper [33; 34; 35] also uses the gradient to transform \(f(x)\) into such that it can automatically satisfy \(\parallel\
abla_{x}\hat{f}(x)\parallel\leq 1\). Specifically, by regarding leakyReLU as the activation function in which \(f(x)\) is actually viewed as a segmented linear function, it shows that, except for the boundary, \(f(x)\) is a linear function in a locally continuous region, and the corresponding \(\
abla_{x}f(x)\) then becomes a constant. Therefore, gradient normalization contemplates \(\hat{f}(x)=f(x)/\parallel\
abla_{x}f(x)\parallel\), which gives the formula as shown in (1).  
\[\parallel\
abla_{x}\hat{f}(x)\parallel=\parallel\frac{\
abla_{x}f(x)}{ \parallel\
abla_{x}f(x)\parallel}\parallel=1 \tag{1}\]  
To avoid errors caused by dividing the model by zero, function norm \(|f(x)|\) should be introduced into the denominator, thus ensuring that the function is bounded, which gives the formula as shown in (2).  
\[\hat{f}(x)=\frac{f(x)}{\parallel\
abla_{x}f(x)\parallel+|f(x)|}\in[-1,1] \tag{2}\]  
In this paper, a gradient-normalized discriminator is introduced to solve the problem of difficult model convergence without limiting the model capacity of discriminator. By weighting the normalized gradient loss with the loss of original discriminator, the model convergence is accelerated and the training process is stabilized. The overall gradient normalization function is shown in Equation (3) below.  
\[\hat{f}(x)=|\frac{f(x)}{\parallel\
abla_{x}f(x)\parallel+\zeta(x)}|^{2} \tag{3}\]  
\(\zeta(x)\) is a universal term that can be associated with \(f(x)\) or a constant. As \(f(x)\) tends to infinity, \(\
abla_{x}f(x)\) tends to 0, and \(\zeta(x)\) approximates \(|f(x)|\). When the discriminator is saturated by overfitting, the normalized gradient norm approaches 0. This self-control mechanism prevents the generator from acquiring exploding gradients, thus stabilising the model training process of cycle generative adversarial networks.
## 4 Distributions of Path Delay, Path Attenuation, Normalized Doppler Shift of the Scattering Path
### Simulations of CIR, TF, ACF, and Normalized Doppler PSD
#### 5.3.2 The Distribution of \(\alpha_{\text{DIF}}\)  
By assuming the distribution of the scatterers is UD, the CDFs of the path attenuation of the scattering path \(\alpha_{\text{DIF}}\) relative to the minimum scattering path attenuations at the moments \(t=0\) s, \(t=100\) s and \(t=700\) s respectively, of the rising state, which are corresponding to the moments of \(t=1800\) s, \(t=1700\) s and \(t=1100\) s on the descending state, are shown in Figure 13. The CDFs of the path attenuation of the scattering path \(\alpha_{\text{DIF}}\) relative to the minimum scattering path attenuation at the moments \(t=0\) s, \(t=700\) s and \(t=1700\) s of cruising state are also shown in Figure 13.  
From Figure 13, we can observe that, in the rising state, as the distance between the aircraft and the satellite decreasing with \(t\) increasing, the path attenuation difference tends to diminish. In the cruising state, quasi-stationary feature can be observed through very close CDFs at different moments. Compared with the features obtained in the rising state, a reverse trend can be observed with the simulation results in the descending state.  
The CDFs of \(\alpha_{\text{DIF}}\) in rising state can be fitted with two terms Fourier function as shown in (65), with the coefficients listed in Table 3.  
\[F_{\alpha_{\text{DIF}}}\left(\alpha_{\text{DIF}}\right)=a_{\alpha,0}+a_{\alpha, 1}\cos\left(w\alpha_{\text{DIF}}\right)+b_{\alpha,1}\sin\left(w\alpha_{\text{ DIF}}\right)+a_{\alpha,2}\cos\left(2w\alpha_{\text{DIF}}\right)+b_{\alpha,2}\sin \left(2w\alpha_{\text{DIF}}\right) \tag{65}\]  
The CDFs of \(\alpha_{\text{DIF}}\) in cruising state can be fitted with one term Fourier function as shown in Equation (66), with the coefficients \(a_{\alpha,0}=0.6214\), \(a_{\alpha,1}=0.3768\), \(b_{\alpha,1}=0.4866\), and \(w=2.232\).  
\[F_{\alpha_{\text{DIF}}}\left(\alpha_{\text{DIF}}\right)=a_{\alpha,0}+a_{\alpha,1}\cos\left(w\alpha_{\text{DIF}}\right)+b_{\alpha,1}\sin\left(w\alpha_{\text {DIF}}\right) \tag{66}\]  
Figure 13: The CDF of relative path attenuation.  
\begin{table}
\begin{tabular}{l l l} \hline
**Cases** & **Coefficients** & **Range of Relative \(\alpha_{\text{DIF}}\)(dB)** \\ \hline Rising \(t=0\) s \& Decending \(t=1800\) s & \(a_{\alpha,0}=0.5244\), \(a_{\alpha,1}=0.4736\), \(b_{\alpha,1}=-0.1739\), \((-18.4683,0)\) \\  & \(a_{\alpha,2}=-0.005626\), \(b_{\alpha,2}=0.1003\), \(w=0.1795\) \\ \hline Rising \(t=100\) s \& Decending \(t=1700\) s & \(a_{\alpha,0}=0.5498\), \(a_{\alpha,1}=0.4946\), \(b_{\alpha,1}=0.04013\), \((-8.6324,0)\) \\  & \(a_{\alpha,2}=-0.04481\), \(b_{\alpha,2}=0.07103\), \(w=0.374\) \\ \hline Rising \(t=1100\) s \& Decending \(t=700\) s & \(a_{\alpha,0}=0.8712\), \(a_{\alpha,1}=0.2077\), \(b_{\alpha,1}=0.7888\), \((-2.1539,0)\) \\  & \(a_{\alpha,2}=-0.07236\), \(b_{\alpha,2}=-0.1293\), \(w=0.9922\) \\ \hline \end{tabular}
\end{table}
Table 3: Coefficients in Fitting Functions of CDFs of relative path attenuation \(\alpha_{\text{DIF}}\) with UD of \(D\).
# Fine-Grained Boundary Conditions in Field-Based Routing
## 5 Advanced Fine-Grained Boundary Conditions
### Revisit of Boundary Conditions in Physics  
We start by briefly examining the definitions and unique features of well-known boundary conditions in physics [15]. Table 1 presents the mathematical expressions for these boundary conditions. Dirichlet boundary conditions specify the value of a function on boundary \(\partial\Omega\) of given domain \(\Omega\) in a differential equation, while Neumann boundary conditions specify the normal derivative on a boundary in the context of a differential equation. Robin boundary conditions, on the other hand, specify both the value of the function and the value of the normal derivative on the boundary simultaneously, making them a weighted combination of Dirichlet and Neumann boundary conditions.  
By examining these well-known boundary conditions, we can see that Neumann and Robin boundary conditions permit changes in the potential value of outer boundary nodes based on the derivative concept, which is mathematically expressed by \(\frac{\partial\phi(n)}{\partial n}\). Consequently, it becomes apparent that adopting Robin boundary conditions as new boundary conditions is the most suitable choice, as they can combine the advantages of both Dirichlet and Neumann boundary conditions.
# Design of a Power Converter for Solar Energy Storage System
## 2 Circuit Structure and Operation Model
### G2B Mode: Where Energy from a DC Grid Is Stored in a Battery  
Figure 4 shows the circuit diagram for when energy from a DC grid is stored in a battery and presents the definitions of the voltage polarity and current flow direction. Figure 5 depicts the theoretical waveforms of a DC grid storing energy to a battery at a phase-shift angle of 108\({}^{\circ}\) between the left (\(S_{5}\), \(S_{6}\)) and right (\(S_{7}\), \(S_{8}\)) arms. In this mode, the energy supplied by the DC grid passes through the full-bridge power switches \(S_{5}\)-\(S_{8}\) and feeds current to the left winding of the isolated transformer. This energy is first transferred through electromagnetic induction to the right side of the transformer and then rectified by the intrinsic diodes \(D_{S1}\)-\(D_{S4}\), thus storing energy to the battery unit. Although some current passes through the inductors \(L_{1}\) and \(L_{2}\), the average current remains at 0.