esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed GaN HEMT MMIC LNA Design
### Three-Stage GaN HEMT-Based LNA Employing a Series Inductor  
Figure 7 and Table 2 show a schematic of the designed GaN HEMT LNA MMIC, which includes the bias and matching networks applied to each stage, and values of the utilized components and transmission lines of the designed LNA, respectively. The width of each microstrip line (W\({}_{\rm TL}\)) is identical at 15 \(\upmu\)m. In the proposed design, three cascade CS amplifier stages are configured to attain targeted small-signal gains greater than 20 dB at the desired frequency. To bias utilized GaN HEMT devices, a drain voltage of 10 V is identically applied to each stage through a quarter-wavelength (\(\lambda/4\)) short stub that forms high impedance RF chokes at the operating frequency. On the other hand, the gate terminal of each device is simply biased with \(-1.7\) V through a short stub as a part of the input matching network of the device so that smaller matching and bias configuration can be realized without extra \(\lambda/4\) biasing short stubs. However, in reality, a DC current flowing through the gate of GaN HEMT devices critically influences the survivability of the LNA and mainly causes device damage by turning on the gate diode or impact ionization during breakdown. In this design, a series gate resistor (\(R_{G}\)) of 200 \(\Omega\) is chosen and applied as voltage feedback to reduce the excessive gate current by shifting the gate voltage to a lower value [16]. Parallel-connected decoupling capacitors of \(C_{B1}\) and \(C_{B2}\) (\(C_{B1}\) = 3.16 \(pF\) and \(C_{B2}\) = 0.56 \(pF\)) in combination with \(R_{B}\) (\(R_{B}\) = 10.2 \(\Omega\)) provides solid ground conditions at the wide frequency range. It also entails the improvement of the stability performance by further suppressing the RF leakage signals through bias networks. Each stage adopts source degeneration to acquire simultaneous power and noise matching as well as stability enhancement, as previously mentioned. Instead of using spiral inductors for source degeneration, microstrip lines (\(L_{S1}\), \(L_{S2}\), and \(L_{S3}\)) are used because they are suitable for realizing the required small inductance in the band of interest. In this design, noise matching is primarily considered in the first stage, whereas the second and third stages are designed to optimize the stability and available gain performance. Therefore, the series inductor-based stability enhancement technique is only applied to the drain node of the second and third stages with minimum impact on the MAG and NF performance. Spiral-type series inductors are utilized with careful consideration of the available inductance, self-resonance frequency (SRF), and quality factor (Q-factor). Series inductors of 4.3 nH with the SRF of more than 50 GHz and the Q factor of approximately 22 are utilized in the design.  
Figure 7: Schematic of the designed three-stage GaN low-noise amplifier employing inductor-based stability enhancement technique.
## 2 Preliminaries
### Model Description  
In this paper, we consider a nonlinear second-order multi-agent system with \(N\) agents:  
\[\begin{cases}\dot{x}_{i}(t)=v_{i}(t),\\ \dot{v}_{i}(t)=f(x_{i}(t),v_{i}(t))+u_{i}(t).\quad(i=1,2,\ldots,N)\end{cases} \tag{1}\]  
where \(x_{i}(t),v_{i}(t)\in R^{n}\) denote the position and velocity, respectively, of the \(i\)th agent, \(u_{i}(t)\in R^{n}\) is the designed control input, and \(f(x_{i}(t),v_{i}(t))\) is a nonlinear vector-valued continuous function describing the dynamics of agent \(i\). The virtual leader of each group for multi-agent system (1) can be described by the following model:  
\[\begin{cases}\dot{x}_{k_{i}}(t)=\bar{v}_{k_{i}}(t),\\ \dot{v}_{k_{i}}(t)=f(\bar{x}_{k_{i}}(t),\bar{v}_{k_{i}}(t)).\quad(i=1,2,\ldots,N)\end{cases} \tag{2}\]  
where \(\bar{x}_{i}(t),\bar{v}_{i}(t)\in R^{n}\) are the position and velocity, respectively, of the \(i\)th virtual leader.  
**Definition 1**.: _The second-order group consensus in multi-agent systems (1) and (2) is said to be achieved if for any initial condition, the solutions of (1) and (2) satisfy_\[\begin{cases}\lim_{t\to\infty}||x_{i}(t)-\bar{x}_{k_{i}}(t)||=0,\\ \lim_{t\to\infty}||v_{i}(t)-\bar{v}_{k_{i}}(t)||=0.\end{cases}\]  
**Assumption 1**.: _For the nonlinear function \(f(x_{i}(t),v_{i}(t))\), there exist positive constants \(l_{1},l_{2}\) such that for any \(x_{1},x_{2},y_{1},y_{2}\in R^{n}\), the following inequality holds:_  
\[||f(x_{1},v_{1})-f(x_{2},v_{2})||\leq l_{1}||x_{1}-x_{2}||+l_{2}||v_{1}-v_{2}||\]  
**Lemma 1** ([40]).: _We have the following linear matrix inequality_  
\[\begin{pmatrix}S_{11}&S_{12}\\ S_{21}&S_{22}\end{pmatrix}>0\]  
_where \(S_{11}^{T}=S_{11},S_{22}^{T}=S_{22},S_{12}^{T}=S_{21}\) is equivalent to one of the following conditions:_  
_(1)_ \(S_{11}>0,S_{22}-S_{21}S_{11}^{-1}S_{12}>0\)_,_  
_(2)_ \(S_{22}>0,S_{11}-S_{12}S_{22}^{-1}S_{21>}>0\)_._
# Cognitive Pervasive Service Composition Applied to Predatory Crime Deterrence
## 3 Materials and Methods
### Case Study
#### 3.4.2 Workflow  
Figure 4 illustrates the proposed flow of information and processing among the different agents previously described. The flow illustrates both cases: building a new plan and following up on a composed plan retrieved from memory.  
The goal is to detect and inhibit predatory crimes. We consider a situation to be a potential crime of the type we want to deter when there are two persons in proximity (1-2 m) on a street, and one of them is carrying a firearm in his hand. In our case, the solution to this problem starts concurrently in the sensor, motivation and perception agents. The motivation agent establishes the need to detect this type of crime; the sensor agent (constituted by cameras and other sensors such as microphones) provides information from the environment to the perception agent. This agent and the memory agent identify the context, firearms and persons as a potential crime situation. When a potential crime situation arises, the decision-making agent is informed. This agent sends information to the planning agent, who concurrently asks the service directory for available agents offering their services and the memory agent for a list of plans applied successfully in a similar risk situation. Two different situations may arise: that the memory knows at least one plan to apply, or that it does not know a plan. In the first case, it sends the available option to the decision-making agent who, using the information retrieved from available agents (actuators) and an evaluation function, decides which plan to execute; in the second case, the planning agent must create a plan from scratch. We have already done some initial work on this case and have some early results. However, they are beyond the scope of this article and will be published later. Briefly, a new plan is created by constructing a graph made up of the different available services, similar to generating a map from beginning to end. There are many techniques, such as forward-chaining and backward-chaining [25] that we are using. In both cases, during the execution of a successful plan, the planning agent generates an execution report (including services used, time of composition, etc.). This information goes to the memory as metadata and is available for future use. In the case the applied plan is a new one, the agent also relays the need to store the plan as an option for similar cases in the future.  
The motor agent executes the plan in our case study. A drone and other CogInfo devices represent the motor system. In our case study, once the risk situation is detected, the plan's execution makes the drone approach the area of the potential crime, inhibiting the predator from committing the crime.
## 3 Influence of Lithology on Fracture Propagation Morphology
### Analysis of Fracture Propagation Morphology of Different Minerals  
In this section, a geological model with a reservoir size of 200 m \(\times\) 200 m is established, in which only a single mineral is included. Three reservoir models containing 100% clay, 100% quartz, and 100% pyrite were established to explore the effect of a single mineral on fracture morphology. In the model, the initial maximum principal stress is 35 MPa, the horizontal stress difference is 5 MPa, and the perforation direction is the maximum principal stress direction. The mechanical properties of the minerals in the reservoir are shown in Table 2. The mechanical properties of the minerals in the reservoir are sourced from the literature on mineral mechanics in coalbed methane systems [16; 17].  
Figure 7a-c show the inversion of fracture morphology under geological conditions of 100% clay, 100% quartz, and 100% pyrite, respectively. In a single clay reservoir, the fractures form a multi-branch fracture network. The fracture length is 160 m, the fracture width is 65 m, and the overall reconstruction area is large. The fracture toughness of clay is small, and it is easy to produce open fractures. Clay has strong water absorption, resulting in the diffusion of fracturing fluid to the surrounding reservoir and increasing the scope of the fracturing fluid. In the reservoir with only quartz, the fracture develops greatly in the direction of the fracture length, and the fracture length is 195 m. However, the fracture  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Clay** & **Quartz** & **Pyrite** \\ \hline Elastic modulus/GPa & 15 & 95.94 & 305.32 \\ Poisson ratio & 0.25 & 0.08 & 0.15 \\ Permeability/mD & 0.05 & 0.05 & 0.05 \\ Fracture toughness/MPa-m\({}^{0.5}\) & 0.5 & 2.4 & 1.24 \\ Porosity & 0.2 & 0.2 & 0.2 \\ Fractal coefficient & 1 & 1.5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of different minerals.  
Figure 6: Microseismic data points and fracture morphology.  
width is only about 30 m, producing an obvious thin-long fracture. And the induced fracture density is only about 65% of the fracture in the clay reservoir. Fractures in the pyrite reservoirs are extremely undeveloped. The fracture length and fracture width are quite different from those of the clay reservoirs, and the fracture density is only about 20% of that of the clay reservoirs. From the above results, there are obvious differences in the influence of different minerals on fracture morphology. Clay easily forms dense induced fractures, quartz easily forms a single long fracture, and pyrite does not easily develop fractures.
## 2 Numerical Method
### Data Reduction  
The considered ferrofluid as the coolant is composed of water as the base fluid inserted with Fe\({}_{3}\)O\({}_{4}\) nanoparticles. Hence, the equivalent thermophysical properties of ferrofluid needs to be evaluated for employing in numerical analysis. The empirical correlations proposed in the literature are used to evaluate the dynamic viscosity, density, thermal conductivity, and specific heat of ferrofluid as presented by Equations (13)-(16) [38; 39].  
\[\mu_{nf}=\frac{\mu_{f}}{\left(1-\varphi\right)^{2.5}} \tag{13}\]  
\[\rho_{nf}=(1-\varphi)\rho_{f}+\varphi\rho_{np} \tag{14}\]  
\[\frac{k_{nf}}{k_{f}}=\frac{k_{np}+(n-1)k_{f}-(n-1)\varphi(k_{f}-k_{np})}{k_{np}+( n-1)k_{f}+\varphi(k_{f}-k_{np})}, \tag{15}\]  
\[\big{(}\rho C_{p}\big{)}_{nf}=(1-\varphi)\big{(}\rho C_{p}\big{)}_{f}+\varphi \big{(}\rho C_{p}\big{)}_{np^{\prime}} \tag{16}\]  
Here, n = 3 is a coefficient for spherical nanoparticles.  
The overall net heat transfer rate (\(Q-Q_{loss}\)) and the difference between local wall temperature and local bulk mean ferrofluid temperature are used to calculate the total heat transfer coefficient across the cooling channel length, \(h_{T}\)[40]:  
\[h_{T}=\frac{(Q-Q_{loss})}{A(T_{w}-T_{p})}, \tag{17}\]  
The bulk temperature of the ferrofluid is calculated using following Equation (18) [40]:  
\[T_{b}=T_{i}\frac{(Q-Q_{loss})A}{Amic_{p}}, \tag{18}\]  
The average Nusselt number is computed as follows [41]:  
\[Nu=h_{T}\frac{D_{hm}}{k}, \tag{19}\]  
A definition of the Reynolds number (\(Re\)) is given by [42]:  
\[Re=\frac{u_{hm}D_{hm}}{v}, \tag{20}\]
## 5 Results
### Dynamic Analyses  
#### 5.2.1 Dynamic Analysis: All Plots  
L bands show the highest \(\Delta\sigma^{\circ}{}_{\rm avg}\) values especially at cross-polarized channels. This suggests that \(\sigma^{\circ}\) at L band could be highly influenced by SVV dynamics. The higher values of \(\Delta\sigma^{\circ}{}_{\rm stdev}\) also highlight that \(\sigma^{\circ}\) is influenced by SVV depending on the cultivar; contrarily, lower \(\Delta\sigma^{\circ}{}_{\rm stdev}\) exhibited by \(X_{\rm pp}\) and \(C_{\rm pp}\) bands implies a weaker influence of the cultivar (Figure 5, left panel).  
Numerically, the highest dynamic (\(\Delta\sigma^{\circ}{}_{\rm avg}=12\) dB and \(\Delta\sigma^{\circ}{}_{\rm stdev}=3.2\) dB) is exhibited by LHV whereas the lowest is observed for CHH (\(\Delta\sigma^{\circ}{}_{\rm avg}=6.4\) dB and \(\Delta\sigma^{\circ}{}_{\rm stdev}=1.2\) dB). SVV variations are responsible of changes of dielectric and geometric properties of the soil-vegetation system (Figure 5, right panel). The variations of \(m_{\rm v}\) was on average \(\approx\)0.2 m\({}^{3}\)m\({}^{-3}\) (with standard deviation \(\Delta m_{\rm v,stdev}=0.03\) m\({}^{3}\)m\({}^{-3}\)), whereas \(\Delta F_{\rm v,avg}=0.64\) (\(\Delta F_{\rm v,stdev}=0.17\)), \(\Delta LAI_{\rm avg}=3.42\) m\({}^{3}\)m\({}^{-2}\) (\(\Delta LAI_{\rm stdev}=0.92\) m\({}^{3}\)m\({}^{-2}\)) and \(\Delta H^{\prime}{}_{\rm avg}=0.7\) cm (\(\Delta H^{\prime}{}_{\rm stdev}=0.6\) cm). To describe the differences observed  
Figure 4: Flow-chart of the inter-channel spatial/temporal correlation analysis. The flow-chart is applied to each plot and for all four SVV (\(m_{\rm v},h^{\prime}, LAI\) and \(F_{\rm v}\)). The 9 selected channel pairs are bounded in red within the \(r_{\rm s}\) domain cube.  
Figure 5: Observed \(\Delta\sigma^{\circ}{}_{\rm avg}\) and corresponding \(\Delta\sigma^{\circ}{}_{\rm stdev}\) (dashed lines) for all available channels are shown on the left panel; \(\Delta m_{\rm v,avg}\), \(\Delta F_{\rm v,avg}\) (dark-grey vertical bars, primary axis), \(\Delta LAI_{\rm avg}\) and \(\Delta h^{\prime}{}_{\rm avg}\) (bright-grey vertical bars, secondary axis), and corresponding \(\Delta\rm{SSV}_{\rm stdev}\) (dashed lines) are shown on the right panel.  
in distinct channels as resulting from SVV changes, the dynamic analysis was applied to bare plots (plots 460 and 222).  
Bare soils exhibited lower \(\Delta\sigma^{\circ}\mathrm{avg}\) (particularly at L frequency) compared with those observable during the whole investigation period (where both bare and vegetated conditions occurred).  
Over vegetation, the highest \(\Delta\sigma^{\circ}\mathrm{avg}\) at L frequency was found for sugar beet, which is characterized by a broad-leaf structure; whereas X channels exhibited highest \(\Delta\sigma^{\circ}\mathrm{avg}\) for barley and wheat; finally C channels exhibited highest \(\Delta\sigma^{\circ}\mathrm{avg}\) for rape, barley and wheat. These differences confirm that vegetation structure determines the geometric properties of the target.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 3 Constellation Observation Optimal Scheduling
### Observation Task Generation  
Considering the influence of the various factors on the observation performance of space-based monitoring equipment, we designed an efficient optimal scheduling algorithm. It included the scheduling algorithm based on the priority of space objects and the scheduling algorithm based on the priority of the object update rate. It can realize the optimal scheduling of the observation tasks for a single satellite or a satellite constellation. A large number of space object orbit data are used for the numerical simulation analysis, and the analysis results verify the feasibility and adaptability of the scheduling method.  
Figure 8: Constellation observation scheduling algorithm flow chart.  
The observation task optimization algorithm we designed can create an observation plan for a single satellite or a constellation composed of multiple satellites at the same time. The observation objects can be set to prioritize the observation of key objects. The performance parameters of the satellite detectors can be set, and the number of observation satellites can be increased or reduced as needed.  
The overall consideration for the optimization scheduling algorithm of the observation tasks is to observe as many objects as possible, and simultaneously make the rotation angle of each satellite switch smaller and observe the higher priority objects first. The priority of a single arc is higher than that of multiple arcs, considering the conditions of the sky, light, and Earth shadows, such as the Moon and the Sun.
# Determination of the Optimum Amount of Superplasticizer Additive for Self-Compacting Concrete
## Materials and Methods
### Cement Paste Flowability  
The analysis of the effect of the amount of SPA was started by determining the saturation point in the Marsh cone test, see Figure 2a,b. The Marsh cone evaluates the flowability of cement pastes or mortars. The procedure is performed according to ASTM C939-97 [33] and, in this case, consists of passing a volume of fresh cement paste through the test tool, registering the time that it takes to pass the first 200 mL (see Figure 2a). Before beginning to manufacture the mortars, the water and the SPA were mixed, after which the cement, water and SPA were mixed for 3 min.  
The test was carried out on cement pastes with SPA/C percentages of 0%, 0.5%, 1%, 1.5%, 2% and 3%. To evaluate the influence of the w/c ratio on the saturation point, or optimal SPA concentration, the tests were repeated with a paste with a w/c of 0.38 and 0.40, respectively. Additionally, as [PERSON] et al. [34] did, the flow times for 500, 750 and 1000 mL were recorded, in order to obtain more robust results. The flowability is undoubtedly influenced by the mixing time of SP addition, and testing with other time intervals for the addition of SP and/or adding different percentage amounts could result in a more accurate saturation point value. In this case, the measurement results are influenced by the time of SP addition, but this way is not necessarily more accurate. However, in order to obtain a  
Figure 2: (a) Marsh cone test assembly; (b) Marsh cone test dimensions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Mass (kg/m3)** \\ \hline Cement & 500 \\ Water & 200 \\ Silica sand (0–5) & 1000 \\ Limestone gravel (5–12): & 650 \\ Water/cement & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix proportions.  
greater precision in the testing times, the tests were recorded on video and later, from these videos, the different testing times were determined.
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 3 Results
### Tensile Test of PMMA Foils  
Tensile tests of PMMA commercial foils were carried out in normal and parallel directions. Two tests of the commercial sample were considered for each direction, as presented in Figure 7 (norm_dir 1,2 and parall_dir 3,4). Samples prepared from the bulk sample in a parallel direction display a tensile strain above 15%. However, the samples prepared from the normal direction show a noticeably lower strain in the range of 5-10%. Finally, the samples prepared by the solution cast method either from S/L ratio 18 or 24 mL show a very fragile nature in the tensile test with a strain up to 1-2% at rupture. This signifies that the commercial PMMA samples show better responses in the tensile test. The as-prepared (fabricated) sample shows a weak response in the tensile test. However, the bending deformation of the samples is evaluated in the following section. The shape recovery is examined by functioning the sample under thermal cycles.  
Figure 6: DMA behavior of (**a**) commercial foil and (**b,c**) as cast foils from 1/24 and 1/18 (S/L) ratio.
###### Abstract  
With only one piece of the power-spectrum pattern, the single-shot coherent power-spectrum imaging can provide a clear object image for real-time applications even if the object is hidden by opaque scattering media, in which the feedback coefficient \(\beta\) value for the reconstruction with least retrievals and fastest speed has to be pre-estimated through time-consuming iterative loops. Here we report a method for estimating the optimal \(\beta\) value from the captured raw power-spectrum images adaptively to optimize the single-shot coherent power-spectrum imaging for practical exposure conditions. The results demonstrate that, based on exposure level analysis of the captured raw power-spectrum images even of underexposure, moderate exposure, and overexposure cases, the \(\beta\) value could be quickly determined with a compact expression for the algorithm to achieve clear reconstruction output efficiently. The proposed method helps to push ahead of the coherent diffractive imaging devices for real-time imaging through turbid mediums in Artificial Intelligence (AI), driving assistance, and flight assistance applications.  
Keywords:scattering imaging; coherent diffraction imaging; hybrid input and output (HIO) algorithm; phase retrieval; exposure condition +
Footnote †: journal: Applied Sciences