esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Hole Diameters in the Witness Clay Pieces after Penetration
In this context, the stress  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hanji thickness (mm)** & **STF thickness (mm)** & **Linear Velocity (m/s)** \\ \hline
20 & 35 & 351.167 \\
20 & 25 & 361.294 \\
15 & 37.07 & 353.632 \\
22.07 & 30 & 354.282 \\
15 & 30 & 360.928 \\
15 & 22.93 & 367.92 \\
7.93 & 30 & 367.304 \\
10 & 35 & 360.435 \\
10 & 25 & 370.684 \\ \hlinemomentum effect on witness clay failure is negligible when penetrating the cornstarch suspension; thus, the bullet momentum was the dominant factor in witness clay failure, and the normalized perforated area decreased with decreasing bullet momentum (Figure 2b). On the contrary, the stress momentum in Hanji was much higher than the bullet momentum (Figure 7a,c); thus, we focused on the stress momentum when analyzing the witness clay failure. On increasing the thickness of the Hanji layer, the stress momentum continuously increased and could be transferred to the witness clay (Figure 7a). The perforated area on the witness clay increased with increases in the thickness of the Hanji layers owing to increases in the stress momentum. In summary, based on the perforated clay area of in-field tests, FEA assisted in analyzing how each Hanji and cornstarch suspension influenced the bullet and performed as the bulletproof layer. Despite the lack of mechanical properties considering high strain rate, FEA results were enough to indicate the bulletproof effect of materials with the results of perforated clay area through the momentum of bullet and stress propagation. Considering the clay piece as the protecting object being protected from the bullet, the STF (cornstarch) was very effective in decreasing the bullet momentum as well as the overall stress momentum. However, for ensuring the durability and stability of the armor itself, Hanji was the suitable material to absorb the stress resulting from the initial impact of the bullet. According to the National Institute of Justice 0101.06 of the U.S. Department of Justice, this study conducted the in-field bulletproof test similar with body armor classification of Type II [38]. There were no complete bulletproof plates to satisfy this classification. However, through the comparison between in-field tests and FEA simulations, it was enough to comprehend the bulletproof behaviors of Hanji and cornstarch suspension, which represent long fiber paper and STF, respectively, in a relative sense. Figure 5: Stress distribution in 30-mm-thick Hanji at the instant of penetration as viewed from the (**a**) side and (**b**) bottom. Figure 6: Stress distribution in the 15-mm-thick STF layer after passing through the 15-mm-thick Hanji layer at the instant of penetration in the (**a**) cross-section region and (**b**) bottom region. Figure 7: _Cont._
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 6 Iris Neovascularization  
Neovascularization of the iris (NVI) and the angle can occur secondary to diabetes mellitus, but also from secondary causes such as ischemia of the anterior segment following retinal vein occlusion or even long-standing mild ocular ischemia. In severe stages of DR new vessels are not confined to the retina and can grow around the pupillary border and the root of iris and can penetrate the anterior surface of the iris in severe cases. These abnormal iris vessels or 'rubeosis' often leads to serious complications such as spontaneous hyphemia and the often sight-threatening complication of neovascular glaucoma (NVG) [77]. Thus, it is useful to detect NVI at its early stage as prompt treatment may prevent the development of NVG.  
However, NVI is often diagnosed late in the clinic, with careful slit-lamp examination and gonioscopy required to diagnose NVI (Figure 9A). At times, NVI are identified incidentally during FA and ICG. However, these invasive imaging modalities are not ideal for solely screening for iris neovascularization. Moreover, patients may require two visits if both iris and retina angiograms need to be taken as the iris is imaged with the iris undilated and the retina is imaged with the iris dilated. Therefore, FA and ICG are not commonly used to image anterior segment and iris vasculature [3; 78].
## 2 Materials and Methods
### Methods
#### 2.2.3 Deviation from Gaussianity Based on KS Test  
The KS test (Kolmogorov-Smirnov test or K-S test) is one of the most useful and general nonparametric methods. The one-sample KS test can be used to compare a sample with a reference probability distribution. In this paper, we define the Deviation from Gaussianity (DfG) based on one-sample KS test. The algorithm is as follows:  
\[F_{n}(x)=\frac{1}{n}\sum_{n=1}^{N}I_{[-\infty,x]}(X_{i}), \tag{5}\]  
where \(I_{[-\infty,x]}(X_{i})\) is the indicator function, which is equal to 1 if \(X_{i}<x\) and 0 otherwise. The Kolmogorov-Smirnov statistic for a given cumulative distribution function \(F(x)\) is  
\[D_{n}=sup_{x}\left|F_{n}(x)-F(x)\right|, \tag{6}\]  
where \(sup_{x}\) is the supremum of the set of distances. We define the divergence of DfG in the distribution of RCA index as follows:  
\[\text{DfG}=D_{n}-CV, \tag{7}\]  
where \(CV\) is the critical value of KS test. A negative DfG indicates Gaussian distribution of RCA index, while a positive DfG indicates rejection of the Gaussian distribution--the more positive DfG, the larger the deviation from Gaussianity [16].
## 2 Materials and Methods
### Cell Cultures and UVB Irradiation  
Normal human dermal fibroblasts (NHDF, code CC-2511) were purchased from Lonza (Basel, Switzerland), while human immortalized keratinocytes (HaCaT, code BS CL 168) were provided by I.Z.L.E.R (Istituto Zooprofilattico della Lombardia e Emilia Romagna,Italy). Both cell lines were grown in a cell culture medium constituted by Dulbecco's Modified Eagle's Medium (DMEM--Lonza, Basel, Switzerland) High Glucose with 10% fetal bovine serum (FBS--Thermo Fisher Scientific, Monza, Italy) and 1% of L-glutamine and antibiotics (penicillin G sodium and streptomycin sulfate--Lonza, Basel, Switzerland), in conditions of complete sterility and maintained in incubation at 37 \({}^{\circ}\)C with 5% carbon dioxide (CO\({}_{2}\)) atmosphere. For the irradiation protocol, cells were seeded in a 6-well plate for 24 h and then the treatment with the product for further 24 h was performed. The day after, cells were washed with phosphate buffer saline (PBS) and exposed to UVB lamp (CAMAG(r) UV Lamp 4, wavelength 302 nm--Muttenz, Switzerland) with a dose equal to 5 mJ/cm\({}^{2}\). After irradiation, cells were maintained in a complete medium for a recovery time of 24 h at 37 \({}^{\circ}\)C before cell supernatant recovery. Untreated cells were used as control in all experimental sets.
# Dynamic Interactions between Parallel Grid-Forming Inverters in a Microgrid
## 4 Stability Analysis
### Influencing Factor Identification  
The influencing factor for a certain oscillation mode is determined using two indices: the nodal voltage mode shape analysis and the participation factor. The analysis can be conducted by using the nodal admittance matrix. The analysis can be applied to determine each node's contribution to an oscillation mode [31].  
* _Nodal voltage mode shape_  
Assuming that the eigenvalue of the \(q\)-th oscillation mode of the system is \(s_{q}\) and letting \(s=s_{q}\), the following is true: \(\det\left[Y(s_{q})\right]=0\). Since the determinant of \(Y(s_{q})\) is equal to zero, one of its eigenvalues must equal zero (for illustration, let \(\lambda_{1}=0\)). \(Y(s_{q})\) can be factorized via eigenvalue decomposition [32]:  
\[Y(s_{q})=Q\Lambda Q^{-1} \tag{21}\]  
where \(\Lambda=\text{diag}(\lambda_{1},\lambda_{2},...,\lambda_{n})\) is a diagonal matrix containing the eigenvalues of \(Y(s_{q})\), \(Q=[Q_{1},Q_{2},...,Q_{n}]\) is the left eigenvector matrix, and \(Q^{-1}\) is the right eigenvector matrix.  
Substituting (21) into (19) leads to the following  
\[Q^{-1}V^{n}(s_{q})=\Lambda^{-1}Q^{-1}I^{n}(s_{q}) \tag{22}\]  
Figure 6: The five-node circuit model of the parallel VSCs serving a load (with the feed-forward and the virtual impedance loops).  
Defining the modal voltage as  
\[U=Q^{-1}V^{n}(s_{q}), \tag{23}\]  
and the modal current as  
\[J=Q^{-1}I^{n}(s_{q}), \tag{24}\]  
then the above equation can be rewritten as  
\[U=\Lambda^{-1}J, \tag{25}\]  
or  
\[\begin{bmatrix}U_{1}\\ U_{2}\\ \vdots\\ U_{n}\end{bmatrix}=\begin{bmatrix}\lambda_{1}^{-1}&&&\\ &\lambda_{2}^{-1}&&\\ &&\ddots&\\ &&&\lambda_{n}^{-1}\end{bmatrix}\begin{bmatrix}I_{1}\\ J_{2}\\ \vdots\\ J_{n}\end{bmatrix}. \tag{26}\]  
Since \(\lambda_{1}^{-1}=\infty\), \(U_{1}\) has a larger value than \(U_{m}\)\((m=2,\ldots,n)\). Therefore, Equation (23) can be represented as  
\[V^{n}(s_{q})=QU. \tag{27}\]  
Based on that, \(Q_{1}\) can be described as the nodal voltage mode shape of the \(q\)-th oscillation mode, which can be used to examine each node's contribution to the eigenvalue \(\lambda_{1}\).  
The influencing factors can be determined using the nodal voltage mode shape. Some elements in \(Q_{1}\) might have clearly greater magnitudes than the others, implying that the correlated nodes are significantly more relevant and can be identified as influencing factors on the \(q\)-th oscillation mode.  
_Participation Factor:_  
Similarly, the participation factor (PF) can be obtained using the nodal admittance matrix. As with our previous assumption, let \(s=s_{q}\), then, \(Y(s_{q})\) is diagonalized as in (21). The zero eigenvalue (for example, \(\lambda_{1}\)) is found, and then the participation factor can be computed as  
\[PF^{n}=\mid Q_{n1}Q_{1n}^{-1}\mid\bigg{/}\sum_{m=1}^{N}\mid Q_{m1}Q_{1m}^{-1}\mid \tag{28}\]  
where \(N\) is the number of nodes and \(Q_{m1}\) and \(Q^{-1}\)\({}_{1m}\) are elements of the \(Q\) matrix.
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 4 Discussion  
In this study, the intensity, center, direction and extent of the SUHI and CLHI in Beijing from 2003 to 2020 were compared based on the Gaussian capacity model using MODIS surface temperature products, providing an important perspective for understanding the seasonal changes, spatial distribution and development trend of the urban heat island effect.
_applied sciences_  
_Article_  
**Selective Electrochemical Regeneration of Aqueous Amine**  
**Solutions to Capture CO2 and to Convert H2S into Hydrogen**  
**and Solid Sulfur**  
**[PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 1**  
CCUS R&D Program, Liquefied Natural Gas-Acid Gas Entity, Exploration Production, TotalEnergies S.E., 92078 Paris, France; [EMAIL_ADDRESS] (C.B.); [EMAIL_ADDRESS] (B.P)  
Centre de Thermodynamique des Procedes, Mines ParisTech, PSL University, 35 rue Saint Honore, 77300 Fontainebleau, France  
* Correspondence: [EMAIL_ADDRESS]  
**Featured Application: This work focuses on selective CO2 capture from gases in the presence of H2S. Examples of fields of application are natural gas, biogas and refinery gas treatment, and coal gasification.**  
**Abstract: Removing CO2 from natural gas or biogas in the presence of H2S is technically challenging and expensive as it often requires separation of both acid gases from the gas, typically using an aqueous amine solution, followed by separation of CO2 from H2S and conversion of H2S into solid S. In this work, the proof of concept of electrochemical, instead of thermal, regeneration of an aqueous amine solution is developed. This invention might be a very promising technology and has several advantages. It has H2S versus CO2 selectivity of 100%, can directly convert H2S into S and H2, and is economically competitive with CO2 desorption energy around 100 kJmol\({}^{-1}\) and H2S conversion around 200 kJmol\({}^{-1}\). If renewable energy is used for electrochemical regeneration, CO2 emissions due to the CO2 capture process can be significantly reduced.**  
**Keywords: CO2 capture; hydrogen sulfide; electrochemistry; regeneration; amine; gas treatment**
## 5 Synaptic Coupling  
A synapse is a connection gap that can transfer data from a presynaptic neuron to a postsynaptic neuron. Synaptic coupling is a significant issue in the case of memory and learning. For describing the coupling behavior of two connected neurons, the synaptic coupling system can be evaluated [93; 94]. In this approach, a terminal can be considered that incorporates a presynaptic neuron and a postsynaptic neuron. This synapse model can be given by the following equation:  
\[\left\{\begin{array}{l}\tau_{s}\frac{dZ}{dt}=[1+\tanh(S_{s}(V_{ presynaptic}-h_{s}))](1-Z)- \frac{Z}{d_{s}}\\ \\ I_{Synapse}=k_{s}(Z-Z_{0})\end{array}\right. \tag{19}\]  
In the above equation, the parameter \(Z\) is the synapse factor. Moreover, the synaptic parameters are given by the following:  
* \(\tau_{s}:\) Time delay (s);
* \(S_{s}:\) Responsible for the activation and relaxation of \(Z\);
* \(d_{s}:\) Relaxing the parameter \(Z\);
* \(h_{s}:\) Threshold parameter for the activation of \(Z\);
* \(k_{s}:\) Conductivity parameter;
* \(Z_{0}:\) Reference level of \(Z\).  
When the presynaptic neuron (\(V_{ presynaptic}\)) reaches its critical value (threshold voltage, \(h_{s}\)), the signal transmission of connected neurons is computed. Moreover, the synapse stimulus, \(I_{synapse}\), triggers the postsynaptic neuron. Table 5 shows the synapse parameters.  
The synchronization effects of the coupled neurons are significant for the processing of biological signals and play significant roles in the elucidation of diseases, such as Parkinson's disease, essential tremor, and epilepsy [95]. Consequently, by the appropriate selection of input stimulus and synaptic conductance coefficient, the synchronization effects can be controlled. This coupled proposed DSSN model is specified as follows:  
Figure 5: Error level between the original (black line) and proposed (dotted line) functions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\tau_{s}=10\) & \(S_{s}=1\) & \(d_{s}=3\) & \(h_{s}=-70\) & \(k_{s}=10\) & \(Z_{0}=0\) \\ \hline \end{tabular}
\end{table}
Table 5: Synapse Parameters.  
\[\left\{\begin{array}{l}\frac{dV_{pre}}{dt}=\frac{\phi}{\tau}(F(V_{pre})-n_{pre}+I_ {0}+I_{Stimulus})\\ \\ \frac{dn_{pre}}{dt}=\frac{1}{\tau}(G(V_{pre})-n_{pre})\\ \\ \tau_{s}\frac{dZ}{dt}=[1+\tanh(S_{s}(V_{pre}-h_{s}))](1-Z)-\frac{Z}{d_{s}}\\ \\ I_{synapse}=k_{s}(Z-Z_{0})\\ \\ \frac{dV_{post}}{dt}=\frac{\phi}{\tau}(F(V_{post})-n_{post}+I_{0}+I_{Synapse}) \\ \\ \frac{dn_{post}}{dt}=\frac{1}{\tau}(G(V_{post})-n_{post})\end{array}\right. \tag{20}\]  
Consequently, by different stimulus currents, different states of the synchronization between presynaptic and postsynaptic neurons in the proposed and original models are evaluated. These states are depicted in Figure 6.  
Figure 6: Time domain and dynamical portrait for two coupled DSSN models: (**A**) Original DSSN-coupled models that correspond to (**a\({}_{1}\)-d\({}_{1}\)**); (**B**) Proposed DSSN-coupled models by (**a\({}_{2}\)-d\({}_{2}\)**). These patterns are evoked by different stimulus as: \(I_{Stimulus}=0.1\), \(I_{Stimulus}=0.2\), \(I_{Stimulus}=0.5\), and \(I_{Stimulus}=1\), respectively.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 7 Discussion
### Challenges and Limitations  
Despite these strengths, the approach is not without challenges and limitations.  
A primary concern is the maturity of Azure Bicep itself. While Bicep has advanced rapidly since its introduction and is now a supported, production-ready tool, it is relatively new compared to alternatives like Terraform or AWS CloudFormation. As such, the ecosystem for modules, community support, and learning resources is still growing. Certain advanced Azure features or resource types may lag in Bicep support compared to ARM JSON or may require careful workarounds. For teams adopting Bicep, this can mean investing time in staying up to date with the latest releases and practices to ensure compatibility and avoid limitations.  
Additionally, Bicep's Azure-only focus is both a strength and a limitation. While this design ensures deep integration with Azure's native capabilities, it prevents truly cloud-agnostic deployments. Organizations considering multi-cloud strategies or hybrid deployments may find Bicep insufficient on its own; they may therefore need to integrate other IaC tools to support multiple providers.  
Another challenge lies in the learning curve associated with introducing programmable infrastructure practices to teams that are new to DevOps or Azure. While Bicep is simpler than ARM JSON, it still requires an understanding of Azure resources, RBAC, networking, and best practices for secure deployment. Similarly, configuring GitHub Actions securely demands familiarity with secret management, branch protection, and role separation to avoid introducing security vulnerabilities. These skills may not be immediately available in all organizations and require investment in training and documentation [34].  
Pipeline complexity can also grow as environments and requirements expand. Managing multiple parameter files, secrets, and environment-specific configurations in GitHub Actions can become challenging, especially for organizations with strict security or compliance requirements. Without careful design, pipelines can become brittle or hard to maintain.  
Finally, while the solution improves security compared to manual deployments, secret management in GitHub Actions still requires a high level of operational discipline. Secrets must be rotated, scoped carefully, and protected with branch and environment policies to reduce the risks of exposure or misuse.
# Compact 50 W All-Solid-State Picosecond Laser System at 1 kHz
## 2 Experiment and Results
### Picosecond Oscillator  
The seed pulses were generated by a Nd:YVO\({}_{4}\) oscillator pumped with a 2 W, 808 nm continuous wave (CW) laser diode. The laser crystal was an a-cut, 0.5 at. % Nd-doped YVO\({}_{4}\) crystal with a size of \(3\times 3\times 5\) mm\({}^{3}\). The mirror of M1 was plane-concave mirror with 200 mm radius of curvature. M2 and M3 had 75 mm radius of curvature. The mirror of OC was an output coupler mirror with transmission rate of 10%. Semiconductor saturable absorption mirror (SESAM) can help the oscillator run in a mode-locking state and provided \(\sim\)300 mW average power at 80 MHz repetition rate. The oscillator used an etalon with the length of 0.5 mm to directly get \(\sim\)52 ps laser pulses at 1064 nm, as shown in Figure 2.  
Figure 1: The scheme of picosecond laser system. I, Nd:YVO\({}_{4}\) oscillator; II, regenerative amplifier; III, end-pumped amplifier; IV, side-pumped amplifier.  
Figure 2: Intensity autocorrelation trace of the Nd:YVO\({}^{4}\) oscillator.
# Analytical Calculation Model of the TV3-117 Turboshaft Working Regimes Based on Experimental Data
## 6 Discussion  
In order to make a validation of the analytical calculation model to assure if the results are correctly calculated, it is required to compare the calculated data with data from the engine manual. The comparison method is obvious through the percentage variation. In this case, a first step is to calculate the percentage variation in data from the engine manual \((\Delta prm_{m})\), by applying the Equation (1). In Table 18 are shown the values of percentage variation in the main engine parameters and performance extracted from the engine manual.  
Therefore, the comparison method is based on the percentage differences, but only for the same parameters and performance shown in the engine manual. So, the percentage differences \((dprm)\) are defined by the following mathematical relation:  
\[dprm=\Delta prm_{c}-\Delta prm_{m} \tag{44}\]  
In Table 19 are presented the actual values of percentage differences of engine parameters and performance at the same gas generator speed as is specified in the engine manual.  
Overall, from Table 19 it is observed that for regimes above 80%, the percentage difference values for the parameters and performance are under 3%. Only the thermal efficiency exhibits a value of \(-\)4.2%. In the case of regimes lower than 80%, such as the idle regime, it results  
main parameters and performances, such as shaft power, fuel flow, turbine inlet total temperature, and thermal efficiency.  
In Figures 6a,b and 7a, it is observed that the curve variation in the calculated power, fuel flow, and turbine inlet total temperature relative to the data manual are almost the same. Only on the idle regime, the values are a little bit different, and in the rest of the regimes, the values are quite similar.  
In Figure 7b, it can be seen that the curve variations of calculated thermal efficiency relative to data manual have some differences, higher compared power, fuel flow, and turbine inlet temperature. The biggest difference is also at idle regime around 15%, but at higher regime, the values begin to converge.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 5 Compressive Sensing
### Good Sensing Matrices  
The most critical issue is to determine whether \(\mathbf{\Phi}\) is good for compressive sensing and reconstruction. The reconstruction seems to be impossible at first glance since the problem is ill-conditioned (\(M<N\)). However, it is now well-known that one can reconstruct the sparse signal from a limited number of measurements if the sensing matrix satisfies the restricted isometry property (RIP) [39; 40; 41]. A matrix \(\mathbf{\Phi}\in\mathbb{R}^{M\times N}\) is said to satisfy the RIP of order \(K<M\) with isometry constant \(\delta_{K}\in(0,1)\) if  
\[(1-\delta_{K})||\mathbf{v}||_{2}^{2}\leq||\mathbf{\Phi}\mathbf{v}||_{2}^{2}\leq(1 +\delta_{K})||\mathbf{v}||_{2}^{2}\]  
holds for any \(K\)-sparse vector \(\mathbf{v}\).  
Since verifying the RIP property of a matrix requires an exhaustive search of all \((\frac{N}{K})\) possibilities, most algorithms developed so far are based on randomization. Typically, the matrix elements are produced by independent and identically distributed (i.i.d.) random variables with certain distribution. References [42] and [43] proved that if the elements of matrix \(\mathbf{\Phi}\) are i.i.d. Gaussian random variables with zero means and variance \(1/N\), and \(M\geq cK\log(N/K)\) where \(c\) is a small constant, then \(\mathbf{\Phi}\) satisfies RIP with a very high probability. Therefore, the signal can be recovered from only \(M\geq cK\log(N/K)\ll N\) variables. Other available selections for robust sensing matrices are discussed in [42; 44].