esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
## 2 Microwave Photonic Frequency-Hopping Systems  
Microwave photonic frequency-hopping systems have wider hopping bandwidth and roughly \(10^{3}\)-\(10^{6}\) times faster hopping speed when compared with conventional electronic approaches. Figure 2 shows an illustration of a microwave photonic frequency-hopping system that consists of a pre-encoded input data module shown in Figure 2a, and a frequency-hopping carrier generation module for generating the uplink carrier shown in Figure 2b,c. The pre-encoded input data is used to modulate the RF carrier and then transmitted through a high-frequency antenna as a frequency-hopping signal. There are mainly two types of MWP frequency-hopping carrier generation schemes, including (i) MWP frequency synthesizer with ultrafast frequency switching ability, such as the use of nonlinear period-one dynamics of injected semiconductor laser to directly generate different frequency at different time [21; 22]; (ii) MWP switch for fast switching of pre-generated frequencies, such as the use of ultrafast microwave photonic RF filter to switch out different frequency carriers.  
There are two major challenges in MWP frequency synthesizer approach: First, it is the need to establish a stable desired frequency in a short period to enable fast frequency switching; second, the precise control of the hopping code sequence properties due to the required tight relationship between the synthesized frequency and the applied hopping code sequence. In MWP RF switch approaches, the two major challenges are: (i) achieving fast switching time within a cycle of the carrier, and (ii) the ability to completely switch off the unwanted frequencies. For both approaches, it is even more challenging to achieve a multi-level frequency-hopping system with picoseconds  
Figure 1: Principle of frequency-hopping communication systems. (**a**) Frequency channel assignment for a single user; (**b**) Time-varying spectral usage of the allocated frequency for a particular user.  
hopping speed and instantaneous reconfiguring capabilities. Table 1 shows a list of examples of the state-of-the-art frequency-hopping signal-generation schemes based on various RF electronics and microwave photonic approaches. In this section, we discuss and compare the principles, hopping speed, flexibility in the hopping frequency, and hopping-frequency bandwidth of various microwave photonic frequency-hopping signal-generation schemes.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Synthetic Biomaterials
#### 4.1.2 Polyethylene Glycol (PEG)  
Polyethylene glycol (PEG) is a versatile compound widely used in various fields of science and engineering. It originates from the chemical modification of petroleum, resulting in a polymer with a linear structure and varying molecular weights.  
Physically, PEG appears as a colorless, odorless, and water-soluble liquid or solid. Its molecular weight can range from a few hundred to several million Daltons, allowing for tailoring of its properties for specific applications [199].  
Polyethylene is known for its biocompatibility and mechanical strength, and it has proven to be valuable due to its unique effects on cells, making it an excellent choice for cardiac scaffolds. Notably, it acts as a hydrophilic and biocompatible substance, enabling it to interact with cell membranes without causing damage [200]. PEG's properties, such as its highwater retention capacity, low immunogenicity, and ability to reduce friction, have made it useful in cell preservation, cryopreservation, and drug delivery systems [201].  
In cardiac tissue engineering, researchers have been investigating PEG-based hydrogels as scaffolds for constructing engineered cardiac tissues. These hydrogels provide a three-dimensional environment that mimics the extracellular matrix and supports cell growth, proliferation, and differentiation [202]. PEG hydrogels can be customized by incorporating various bioactive molecules to enhance cell adhesion, survival, and tissue maturation, thus aiding in the development of functional cardiac tissue substitutes.
## 3 Self-Triggered Control for Islanded Microgrids under DoS Attacks
### Communication Topology  
To facilitate the MAS-based distributed secondary controller, each DG in the microgrid is considered as an agent that communicates with its neighbors via the communication network. The communication topology is described as an undirected graph \(G=(V,\Xi)\), where \(V=\{1,\ \ldots,N\}\) denotes the set of agent nodes corresponding to DGs, and \(\Xi\subset V\times V\) is the set of edges corresponding to the communication links. Node \(j\) is a neighbor of node \(i\) if there is an edge defined as \((i,j)\in\Xi\). \(W=\left[w_{ij}\right]\in R^{N\times N}\) is the adjacency matrix, where \(w_{ii}=0\) for all \(i\), and \(w_{ij}=1\) if \((i,\ j)\in\Xi\), otherwise, \(w_{ij}=0\). \(N_{i}=\{j\}(i,j)\in\Xi\) is defined as the set of node \(i\)'s neighbors, and the degree of node \(i\) can be expressed as \(d_{i}=\sum_{j\in N_{i}}w_{ij}\). A path is defined as a connected edge in a graph, and the graph \(G\) is connected if there is a path between any two nodes.  
**Remark 1:**_For the brevity of analysis, it is supposed that the transmissions of the estimated average information of voltage and reactive power use the common communication topology. However, different topologies may be used for information exchanges in a practical microgrid system. It is worth pointing out that the proposed self-triggered control law can be extended to accommodate the case of different communication topologies as well._
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 2 Neural Networks Training for SAR Imagery
### Autoencoders  
SAR imagery usually has high noise levels. Thus, it seems very practical to train a denoising autoencoder for scan filtering [32].  
For the sole purpose of feature extraction, it is essential to maintain knowledge of the pixel brightness outliers as they may contain important information. For example, a single bright spot can represent a car with a 3 m \(\times\) 3 m pixel size. Thus, a neural network must consider all kinds of high-frequency changes in the image. On the other hand, it can be omitted while recreating the image to maintain training stability.  
Thus, a novel algorithm for SAR imagery Deep Neural Network pretraining was proposed. The input to the network is a simple amplitude SAR image, while the expected output is the same image or one that is processed using simple filtering. The filtering is not about creating a denoising AI model but limiting the influence of random noise/speckle from the original imagery on the AI Loss Function. The filtering algorithm is described in Section 2.6.
# Anti-Breast Cancer Activity of Essential Oil:
## 3 Result
### Bioactivity of Essential Oils as Anti-Breast Cancer Agents
#### 3.4.4. Diterpenes  
Diterpenes are a group of hydrocarbons that are widely present in essential oils. Several studies reported that diterpenes are good anti-breast-cancer agents. [PERSON] et al. [98] reported that the essential oils of _Cyphotestema juttae_ have a dominant diterpene content, which is phytol compound (30%) tested against triple negative breast cancer cells (MDA-MB-231, SUM 149). Phytol substantially reduces the activation of NF-kB transcription factors, resulting in a significant decrease in several NF-kB target genes.  
The extract of _Tinospora cordifolia_ also contains diterpenes with breast anticancer activity against MCF7 cells. The results showed that the IC\({}_{50}\) values at 24 and 48 h were 3.2 mM and 2.4 mM respectively. The cytotoxicity of this compound was specific for MCF-7 cells and had no toxic effect on normal Vero cells and V79 cells. This compound also significantly stimulated the formation of intracellular ROS, even at lower doses of 0.6 and 1.2 mM. Thus, it can be concluded that the diterpene compounds from this extract are effective and selective against cancer cells, especially breast cancer cells [132]. The diterpene myrsinol compound J196-10-1, derived from the roots of _Euphorbia prolifera_, can reverse multidrug resistance, namely daunorubicin, vincristine, and topotecan, with the IC\({}_{50}\) value of daunorubicin from 29.65 \(\upmu\)M to 0.55 \(\upmu\)M, vincristine from 13.85 \(\upmu\)M to 0.063 \(\upmu\)M, and topotecan from 4.61 \(\upmu\)M to 0.65 \(\upmu\)M [106].  
Myrsinsane-type diterpenes, such as 3,7,10,14,15-tetraacetyl-5-propanoyl-13,17-epoxy-8,10,18-myrsinsadiene and 3,7,10,14,15-pentaacetyl-5-butanoyl-13,17-epoxy-8-myrsinsane, are present in _Euphorbia comuta_ Boiss. acetonechloroforum extracts. In MDA-MB cells, these compounds had an IC\({}_{50}\) value of 24.53 \(\pm\) 3.39 and 26.67 \(\pm\) 1.41 \(\upmu\)M, while in MCF-7 cells, the value was 37.73 \(\pm\) 3.41 and 34.57 \(\pm\) 2.12 \(\upmu\)M, respectively, indicating a moderately inhibitory effect on breast cancer. Other diterpenes, such as 5,6-epoxy-8,9,15-triacetyl-3-benzoyl-14-oxo-jatropha-11 (E)-ene (3), exhibited weak cytotoxicity in MDA-MB cells (IC\({}_{50}\) = 55.67 \(\pm\) 7.09 \(\upmu\)M) and moderate cytotoxicity in MCF7 cells (IC\({}_{50}\) = 24.33 \(\pm\) 3.21 \(\upmu\)M) [133].
# Triple-Band Uniform Circular Array Antenna for a Multi-Functional Radar System
## 4 Discussion  
This paper presents a triple-band radial array antenna to implement a low-profile shared aperture antenna for radar applications. The use of the radial array antenna is advantageous to design an efficient layout for the different sets of array antennas. The feed network based on microstrip and coaxial feed was implemented on the top and bottom layer, which reduces the complexity of the feed network. The prototype was fabricated to validate the design of the proposed antenna. The reasonable matching characteristics were obtained at 2 GHz, 5.8 GHz, and 10.75 GHz, and the mutual coupling among the frequency bands was sufficiently reduced. The radiation patterns at the frequency bands were measured inside anechoic chamber, which exhibited the maximum gain of 12.5-15.3 dB for the frequency bands. This work primarily highlighted the aspects of achieving a multi-band array antenna. In future, comprehensive antenna designs, including electronic steering mechanisms, can be presented for creating diverse beam configurations at each frequency band. In addition, size reduction techniques such as fractal-shaped designs can be applied to the patch element design to reduce the entire size of the array antenna.  
Conceptualization, J.-H.L. and I.K.; methodology, I.K.; software, I.K.; validation, S.-G.L.; investigation, I.K.; resources, I.K.; data curation, I.K.; writing--original draft preparation, I.K. and S.-G.L.; writing--review and editing, S.-G.L.; visualization, I.K.; supervision, J.-H.L.; project administration, J.-H.L.; funding acquisition, J.-H.L. All authors have read and agreed to the published version of the manuscript.  
Research of two of the authors, [PERSON] and [PERSON], was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2015R1A6A1A03031833).  
Figure 5: Comparison of the simulated and the measured radiation patterns at the frequency of (**a**) 2 GHz, (**b**) 5.8 GHz, (**c**) 10.525 GHz, and (**d**) photograph of the measurement setup inside the far-field anechoic chamber.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Propagation Models  
#### 2.4.1 Free Space Link Model  
The initial configuration involved placing the antennas at a distance of seven meters, with black absorbers strategically positioned on the floor to minimize reflections, as shown in Figure 8. Subsequent measurements were conducted by progressively bringing the antennas closer together after each iteration.  
The next step involves positioning the antennas in a face-to-face configuration. Subsequently, the received power will be measured as a function of distance, with data being recorded at each adjustment. The transmitted signal operates at a frequency of 1.2 GHz and a power of 10 dBm. During each measurement, the distance between the transmitting antenna (Tx) and the receiving antenna (Rx) will be increased in 0.5 m increments. This method allows for an understanding of how received power varies with distance and provides a clear view of antenna performance over different ranges.  
Figure 7: Radiation pattern setup, where A—Antenna A; B—Antenna B and E—Alignment laser.  
As the distance between the two antennas increases, the received power decreases. This phenomenon is explained by the Friis transmission equation, which states that power decreases rapidly with increasing distance. To compare the measured values with theoretical predictions, the Friis formula is used:  
\[P_{r}=\frac{P_{e}\cdot G_{e}\cdot G_{r}\cdot\lambda^{2}}{\left(4\pi d\right)^{2}} \tag{11}\]  
where \(Pr\) is the received power, \(Pe\) is the transmitted power, \(Ge\) and \(Gr\) are the gains of the transmitting and receiving antennas, respectively, \(\lambda\) is the wavelength, and \((4\pi d)^{2}\) is the distance between antennas.  
Due to limited precision in distance measurement, errors on the order of 0.01 m may occur. A second-order polynomial fit of the experimental data confirms that power is inversely proportional to the square of the distance from the transmitting antenna. However, the fit is not perfect, suggesting the influence of additional factors such as surface reflections or interference in the test environment.  
An additional step in the experimental campaign focused on polarization mismatch. To evaluate this effect, Antenna A was rotated 180\({}^{\circ}\) relative to Antenna B, as shown in Figure 9.  
Another step in the experimental campaign focused on the relative angle between antennas. In an attempt to measure the influence of the relative angle between the two antennas, Antenna A was rotated by 90\({}^{\circ}\) toward Antenna B.  
Figure 8: Free space link model setup, where A—Antenna A and B—Antenna B.
# Improving Particle Swarm Optimization Analysis Using Differential Models
## 4 Differential Evolution PSO
### Absolute Stability  
When using the single-step method to find the differential model of the initial value problem \(\left\{\begin{array}{l}y^{\prime}=f(x,y)\\ y(x_{0})=y_{0}\end{array}\right.\), due to the influence of the original data and the rounding error in the calculation process, its error is constantly changing. If the error does not grow with the evolutionary algebra, the one-step method is said to be stable; otherwise, it is said to be unstable. Since the stability is related to \(f_{y}^{\prime}\) in the method and initial value problem, when studying the stability of the algorithm, it is usually possible to directly study the model equation \(y^{\prime}=\lambda y(Re(\lambda)<0)\), where \(\lambda=f_{y}^{\prime}(x,\ y)\). Therefore, solving the model Equation (26) in a single step gives:  
\[y_{n+1}=E(h\lambda)y_{n} \tag{56}\]  
where \(E(h\lambda)\) depends on the chosen method. If \(|E(h\lambda)|<1\), the method is said to be stable, and the region where the complex variable \(h\lambda\) satisfies \(|E(h\lambda)|<1\) on the complex plane is called the absolutely stable region, and the intersection point with the real axis called the absolute stability interval. The absolute stability interval of the common single-step method is as follows:  
1. [PERSON] method: \(-2<h\lambda<0\);
2. Second-order Runge-Kutta method: \(-2<h\lambda<0\);
3. Third-order Runge-Kutta method: \(-2.51<h\lambda<0\);
4. Fourth-order Runge-Kutta method: \(-2.785<h\lambda<0\).
## 3 Membrane Performance Measurements
### Membrane Performance Calculation
#### 3.5.2 N2 Permeance  
The volume inside the cell is constant but the pressure changes during the measurement, hence, the mass of permeated nitrogen was calculated using the ideal gas equation, where the small volume changes due to compression of the O-ring and deformation of the membrane were ignored. Hence, the mass flux of nitrogen, \(J_{\text{N2}}\), is given by:  
\[J_{\text{N2}}=\ \frac{\Delta m_{\text{N2}}}{A_{m}\Delta t}=\frac{\Delta pV/R_{ \text{N2}}T}{A_{m}\Delta t} \tag{3}\]  
Here, \(R_{\text{N2}}\) is the ideal gas constant of nitrogen. As for the water vapor permeability calculations, the mass of nitrogen was converted into the molar mass of nitrogen. Permeance of nitrogen, \(P_{\text{N2}}\), is presented in Equation (4).  
\[P_{\text{N2}}=\ \frac{\Delta pV/R_{\text{N2}}T}{M_{\text{N2}}[(p_{atm}-p_{v})-p ]A_{m}\Delta t}\times\frac{10^{10}}{3.35} \tag{4}\]  
As the driving force of nitrogen is the absolute pressure difference excluding the water vapor pressure, the nitrogen pressure was divided by net pressure difference of nitrogen calculated by \((p_{atm}-p_{v})-p\). Here, \(p_{atm}\) and \(p\) are ambient pressure and absolute pressure inside the test cell, respectively. As for the water vapor permeance, the unit of Equation (4) is mol/m\({}^{2}\)-s-Pa.
# Influence of Polymer Types on the Mechanical Properties of Polymer-Modified Cement Mortars
## 3 Materials and Methods
### Test Procedures  
Mechanical tests were carried out on each PCM, as well as on the substrate concrete. Figure 1a-c show the compressive, flexural, and pull-off bond test set-ups, respectively. The compressive strengths of the PCMs were determined from three standard 50-mm cubes as per ASTM C 109 [25]. The flexural strengths of the PCMs were measured using three prismatic specimens of \(40\times 40\times 160\) mm as per ASTM C 348 [26]. The specimens underwent flexural tests at a central point of loading as shown in Figure 1b. The applied loading rates for the compressive and flexural strength measurements were 900 N/s and 0.02 mm/min, respectively, and the tests were conducted at seven and 28 days after the date of casting. Before conducting the bond strength measurement test, the applied PCM layer was cut  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mix Proportions of Polymer-Modified Cement Mortars (Mass Ratio)**} \\ \hline
**Water** & **Cement** & **Fine Aggregate** & **Water/Cement** & **Sand:Cement** & **Polymer Content (\%)** \\ \hline
40 & 100 & 200 & 0.4 & 5:1 & 0 \\ \hline
40 & 97 & 200 & 0.4 & 5:1 & 3 \\ \hline  & \multicolumn{4}{c}{**Concrete Mix Proportion**} & \multicolumn{4}{c}{**Compressive Strength**} \\ \hline Water & Cement & Fine Aggregate & Coarse & Water & \(f_{c}\) \\ (kg/m\({}^{3}\)) & (kg/m\({}^{3}\)) & (kg/m\({}^{3}\)) & Aggregate & Reducer & (MPa) \\ \hline
83 & 340 & 325 & 451 & 5.0 & 82.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mix proportions for polymer-modified cement mortars and substrate concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AC** & **PVA** & **SBR** & **EVA** \\ \hline Solid content (\%) & \(99\pm 1\) & \(99\pm 1\) & \(48\pm 1\) & \(99\pm 1\) \\ Physical appearance & Powder & Powder & Latex & Powder \\ pH value & N/A & N/A & 9.9–10.5 & N/A \\ Viscosity (mPa-s) & N/A & N/A & 40 & N/A \\ Density (g/cm\({}^{3}\)) & 0.46 & \(0.5\pm 0.1\) & 1.04 & 0.52 \\ Glass transition temperature (\({}^{\circ}\)C) & N/A & 14 & 14 & N/A \\ Minimum film-forming & \(<\)5 & \(<\)0 & \(<\)4 & \(<\)4 \\ temperature (\({}^{\circ}\)C) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of selected polymers (N/A—not available; AC—acrylate polymer; PVA—polyvinyl alcohol; SBR—styrene–butadiene rubber; EVA—ethylene-vinyl acetate).  
into 40-mm square specimens, and dollies were attached using epoxy glue to the targeted locations, as shown in Figure 1d.  
The pull-off bond strengths of the PCMs were determined from five samples as per ASTM C 1583 [27]. The pull-off rate adopted in this study was 0.02 MPa/s based on previous research [20].
## 2 Materials and Methods
### Biosignals and Spike Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average Deviation** & **Variance** & **Wavelength** & **RMS** \\ \hline
0 & 0.00849887 & 0.000187308 & 0.179557 & 0.0135544 \\
1 & 0.0065741 & \(7.09735\times 10^{-5}\) & 0.0791692 & 0.0084606 \\
2 & 0.00466847 & \(3.33185\times 10^{-5}\) & 0.0555136 & 0.0058420 \\
3 & 0.00888798 & \(0.000247485\) & 0.1864338 & 0.0156036 \\
4 & 0.00582276 & \(3.57568\times 10^{-5}\) & 0.0469245 & 0.0060722 \\
5 & 0.0043362 & \(2.65052\times 10^{-5}\) & 0.0528824 & 0.0051284 \\
6 & 0.0034435 & \(2.10582\times 10^{-5}\) & 0.0561784 & 0.0045793 \\
7 & 0.00493412 & \(2.81377\times 10^{-5}\) & 0.0615169 & 0.0057737 \\
8 & 0.00467569 & \(3.12088\times 10^{-5}\) & 0.0561566 & 0.0055327 \\
9 & 0.00974478 & \(0.00025559\) & 0.1881129 & 0.0158705 \\
10 & 0.00392492 & \(2.48305\times 10^{-5}\) & 0.0521927 & 0.0049249 \\
11 & 0.00998542 & \(0.00024017\) & 0.1824477 & 0.0153818 \\
12 & 0.00390623 & \(2.15412\times 10^{-5}\) & 0.0936657 & 0.0047380 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature matrix for a subset of 12 detected spikes within the monkey signal without normalization. The complete feature matrix contains all 327 detected spikes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average Deviation** & **Variance** & **Wavelength** & **RMS** \\ \hline
0 & 0.616557 & 0.507503 & 0.982358 & 0.690902 \\
1 & \(-0.0435587\) & \(-0.476921\) & \(-0.5054\) & \(-0.269358\) \\
2 & \(-0.697112\) & \(-0.795558\) & \(-0.859578\) & \(-0.762997\) \\
3 & 0.750005 & 1.0
## 5 Results
### Computational Results  
Matlab (R2017a) was the main development environment for the TLBA. Test instances were generated using pseudo-random number generation, according to the parameters in Table 4.  
An initial solution was calculated for every generated test instance, using a lot-for-lot method.  
Test instances were exported to spreadsheet files and imported to GAMS using _.gdx_ files. The exact solutions were obtained through a branch-and-bound method using the CPLEX solver in GAMS (version 24.8.2) with default settings. No tolerance settings or time limits were defined.  
An overview of the information flow is shown in Figure 5.  
The system specifications were as follows:  
* RAM: 12 Gb DDR3
* CPU: AMD A6-5200 APU 2.00 GHz
* Operating System: Windows 10 Professional (64 bits)  
The results obtained for each of the test instances are shown in Table 10.  
Figure 4: Main effects plot for total computational time in large instances. Source: Minitab®.  
_B&B GMOP_: Branch-and-Bound solution for the GMOP model.  
_TLBA_: Tabu List-Based Algorithm solution.  
_Min. Comp. Time_: Minimum Computational Time.  
_Avg. Comp. Time:_ Average Computational Time.  
Gaps were computed using (9), comparing the optimal solutions \(C_{Opt}\) with the obtained results in every test instance \(C_{Alg}\). Due to the random procedure for moves (see Section 4.4), the average and minimum gaps for TLBA were calculated using five replicas for every test instance.  
\[GAP=\frac{C_{Alg}-C_{Opt}}{C_{Opt}}. \tag{9}\]