esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_ Biosynthesis and Biomedical Applications of Gold Nanoparticles Using _Eclipta prostrata_ Leaf Extract  
**[PERSON] 1, [PERSON] 2, [PERSON] 3,*, [PERSON] 1, [PERSON] 4, [PERSON] 1, [PERSON] 1 and [PERSON] 1,* 1**  
Department of Applied Bioscience, College of Life and Environmental Science, Konkuk University, Seoul 143 701, South Korea; [EMAIL_ADDRESS] (G.R.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (T.-J.L.)  
Department of Chemistry, D.K.M. College for Women, Vellore, Tamil Nadu 632001, India; [EMAIL_ADDRESS]  
Unit of Nanotechnology and Bioactive Natural Products, Post Graduate and Research Department of Zoology, C. Abdul Hakeem College, Melvisharam-632 509, Vellore District, Tamil Nadu, India  
Department of Biochemistry, Periyar University, Salem, Tamil Nadu 636011, India; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS] (A.A.R.); [EMAIL_ADDRESS] (I.-M.C.); Tel.: +91-9442-3101-55 (A.A.R.); +82-1054-7083-01 (I.-M.C.)  
Academic Editor: [PERSON]  
Received: 17 June 2016; Accepted: 29 July 2016; Published: 9 August 2016  
This study reports the biological synthesis of gold nanoparticles (AuNPs) by the reduction of HAuCl\({}_{4}\) by using of _Eclipta prostrata_ leaf extract as the reducing and stabilizing agent. AuNPs were characterized using Ultraviolet-visible (UV-vis) spectroscopy, X-ray diffraction (XRD), Fourier Transform-Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), High Resolution-Transmission Electron Microscopy (HRTEM), and Energy Dispersive X-ray analysis (EDAX). The UV-visible spectrum of the synthesized AuNPs showed surface plasmon resonance (SPR) around 534 nm. The face-centered cubic (FCC) structure of the AuNPs was confirmed by XRD peaks at 38.10\({}^{\circ}\), 44.13\({}^{\circ}\), 64.43\({}^{\circ}\), and 77.32\({}^{\circ}\), which correspond to (111), (200), (220), and (311) miller indices, respectively, with clear circular spots in the selected area electron diffraction (SAED). FTIR measurements showed the AuNPs having a coating of phenolic compounds, indicating a possible role of biomolecules responsible for capping and efficient stabilization of the AuNPs. The HRTEM images determined the particles are spherical, hexagonal, and triangular in shape, with an average size of 31 \(\pm\) 1.6 nm. The synthesized AuNPs show good antibacterial, antioxidant, and cytotoxic activity. The outcomes of this study indicate that these nanoparticles could be effectively utilized in pharmaceutical, biotechnological, and biomedical applications.  
gold nanoparticles; _Eclipta prostrata_; characterization; antibacterial activity; cytotoxic activity
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 2 AIRs, CM, and Proposed Design
### Achievable Information Rates (AIRs)
The results are shown with markers in Figure 1. Binary LDPC codes are simulated with code rates \(R\in\{3/5,2/3,3/4,5/6,8/9\}\) from the DVB-S2 standard, with a block length of \(N=64800\) bits, and 50 decoding iterations. SCCs are simulated with higher code rates \(R\in\{0.83,0.85,0.90,0.92\}\) and block size \(w\in\{114,126,192,252\}\), where \(w\) follows the notation in ([47], Figure 1b). The component codes we chose for these SCCs are the most popular ones, i.e., binary Bose-Chaudhuri-Hocquenghem (BCH) codes but extended with 1 extra parity bit. The error-correcting capability of these BCH codes are 2. Decoding is done using iterative bounded-distance decoding (BDD) with a window length of 8 and 7 iterations per window. More details about this are given in Section 2.2._  
_Figure 1 shows that both LDPC and SCC have approximately 1 dB gap to their corresponding AIRs. It is also reported in [53; 54; 55] (SC-LDPC) that hardware-implementable state-of-the-art SD-FEC codes also have approximately  
Figure 1: Signal-to-noise ratio (SNR) vs. achievable information rates (AIRs) for soft-decision forward error correction (SD-FEC) and hard-decision forward error correction (HD-FEC) with dual-polarization 64 QAM. The results obtained with low-density parity-check (LDPC) codes and staircase codes (SCC) are shown with markers. 0.5-1.5 dB penalty from the AIR because of implementation limitations such as power consumption and memory limitations. The gap between AIR and practical FEC is due to the suboptimality of the codes and can be decreased by increasing the code length or performing a more efficient decoding algorithm. Although AIRs give a good first indication of performance, post-FEC BER simulations are necessary to evaluate the performance of a given FEC. From the example above, we can see that there exist opportunities to enhance the achieved data rate in practice by improving the CM scheme to reduce the gaps. Considering the strict latency and complexity requirements, simple but powerful CM systems (e.g., CM with HD-FEC) are desirable. Optimizing the modulation format and improving the FEC performance is therefore of great importance.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 6 Application  
The final developed system is capable of accurately distinguishing ties and ballasts in real time while mounted on moving platforms such as Hyrail vehicles and track geometry cars. It operates at speeds of up to 40 mph (65 km/h), which aligns with the operating speed of Class I railroads and performs successfully across various track conditions. This system can be integrated with other track inspection technologies, including ground-penetrating radar, infrared sensors, ultrasonic sensors, and Doppler LiDARs, to focus on their respective targets more effectively. Many of these devices, currently limited to stationary or quasi-stationary applications due to their inability to differentiate between targets, can now perform in-motion analysis over extended distances with the help of this system, facilitating railroad maintenance, reducing costs, and enhancing safety.
## 2 Experimental Procedure
### Batch Adsorption Experiments  
A certain amount of the phenol solutions was separately added to sealed conical flasks and then positioned on a shaker at 180 rpm. The solution volume was made up to 45 mL by water, and the typical pH of the solution was adjusted to 7. After that, 3 mg GO or RGO aqueous dispersion (5 mL) was added into the solution. The initial concentration of GO or RGO was 60 mg L\({}^{-1}\). The initial concentration ranges of PE, ME, IPE, BPA and PPE were 0.053-0.85, 0.046-0.55, 0.073-0.587, 0.087-0.35 and 0.059-0.47 mmol L\({}^{-1}\), respectively. Solutions were sampled at regular intervals and centrifuged to remove the solids. The phenol concentration in the supernatant was determined on a UV-Vis spectrophotometer (CARY 50 Scan). The adsorbed amount of phenols at equilibrium was calculated with Equation (1),  
\[q_{\mathrm{e}}=\frac{(c_{0}-c_{\mathrm{e}})}{m}V \tag{1}\]  
where \(q_{\mathrm{e}}\) (mmol g\({}^{-1}\)) is the equilibrium adsorption capacity of the adsorbent, \(c_{0}\) and \(c_{\mathrm{e}}\) (mmol L\({}^{-1}\)) are the initial and final concentrations of the phenols in solution, \(V\) is the solution volume, and \(m\) is the mass of the used GO and RGO. The adsorption kinetic experiment times included 1, 5, 10, 15, 30, and 60 min. The adsorption thermodynamics temperatures included 298, 313 and 333 K and the pH range for the effect on adsorption was 3-11.5. Generally, the typical experiments were performed in duplicate/triplicate.
## 3 Results  
### Pulse Current Curve Parametric Statistics  
The main parameters' peak impulse current, \(I_{p}\), and half-discharge period, \(T/2\), are shown in Table 5. \(I_{p}\) and \(T/2\) are physical quantities determined by measuring the instrument directly. The Rogowski coil, ranging from 0 to 50 kA, usually reads to two decimal places. The peak impulse current is used to measure the intensity of energy deposition. When the load wire is in three diameters and three hollow degrees of loading conditions, the peak impulse current value of electric explosion ranges from 10.89 to 22.64 kA. The load wire deposits energy and explodes electrically during the half-discharge period, \(T/2\), which ranges from 1.92 to 2.54 us. Therefore, the characteristic frequency range of the effective deposition period of the wire electrical explosion is about 200 to 260 kHz. The variation trend of the peak impulse current and half-discharge period is shown in Figure 3.  
As shown in Figure 3, The peak impulse current, \(I_{p}\), and the half-discharge period, \(T/2\), both increase with the diameter and decrease with the hollow ratio. When the hollow ratio, \(a\), is constant, the explosive inflection point of the discharge current curve appears  
Figure 3: The variation trend of peak impulse current and half-discharge period. Black, red and blue represent wires with a hollow ratio of 0, 0.5 and 0.7, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Diameter** & **Hollow Ratio** & **Peak Impulse Current** & **Half-Discharge Period** \\
**D/mm** & _a (d/D)_ & **\(I_{p}\)/kA** & **T/2/ μs** \\ \hline  & 0 & 14.59 & 2.00 \\
0.3 & 0.5 & 13.02 & 1.95 \\  & 0.7 & 10.89 & 1.92 \\ \hline  & 0 & 18.63 & 2.26 \\
0.4 & 0.5 & 17.34 & 2.23 \\  & 0.7 & 14.73 & 2.22 \\ \hline  & 0 & 22.64 & 2.50 \\
0.5 & 0.5 & 21.42 & 2.42 \\  & 0.7 & 17.91 & 2.40 \\ \hline \end{tabular}
\end{table}
Table 5: Peak impulse current and half-discharge period with different diameters and hollow ratios.  
later with the increase of the wire diameter. When the outer diameter, \(D\), is constant, the explosive inflection point of the discharge current curve appears earlier with the increase of the wire hollow ratio [30]. The optimal wire should be thick and hollow.
## 2 Methods
### Combination of the Smallest Disturbance Constraint and the Smoothest Constraint  
In order to reduce ill-posed and inversion multi-solution problems, we introduce the Tikhonov function with a priori information to solve the continuous medium inversion problem. The Tikhonov regularization function can be written as follows:  
\[\Phi(s)=\|W_{d}[t-d(s)]\|^{2}+\lambda^{2}\|W_{m}\big{[}s-s^{0}\big{]}\|^{2} \tag{30}\]  
Before inversion, three parameters need to be determined, the data weighting matrix \(W_{d}\), the model weighting matrix \(W_{m}\), and the regularization factor \(\lambda\).  
Generally, \(W_{d}\) selects the \(M\times M\)-order unit matrix. In refraction travel time tomography, the observed travel time data are uniformly weighted.  
The selection of \(W_{m}\) is different. The commonly used regularization constraints can be divided into the following four types:  
1. Smallest model constraints: \(W_{m}=\|m\|^{2}\);
2. Flattest constraints: \(W_{m}=\|m^{\prime}\|^{2}\), where \(m^{\prime}\) is the first derivative of the model space;
3. Smoothest constraints: \(W_{m}=\|m^{\prime\prime}\|^{2}\), where \(m^{\prime\prime}\) is the second derivative of the model space;
4. Smallest perturbation constraints: \(W_{m}=\|m-\widetilde{m}\|^{2}\), where \(\widetilde{m}\) is a priori reference model;  
In this paper, the results of the t0 difference method are used as an a priori slowness model for presumptive information. However, because of the limitations of the t0 difference method, we will adopt the composite constraint method, which combines the smallest perturbation constraints and the smoothest constraints. This method not only guarantees the uniqueness and rationality of the solution but also guarantees the stability and convergence of the solution.
## 6 Case Study
### Validation on a Large-Scale Case  
To validate the effectiveness of the proposed outage sequence optimization method for higher-order faults, two additional faults were introduced in this section. These faults occurred between nodes 15 and 16 and between nodes 24 and 26 with repair times of 2 h and 3 h, respectively. Figure 11 shows the topology.  
The outage sequence was optimized using the methodology of this paper, and the same comparison cases as in the previous paper were used. Case 4 represents the outage optimization strategy proposed in this paper, whose main objective is to reduce the loss of integrated customer importance by optimizing the outage sequence. Case 5 minimizes the number of lost load customers as the objective function. Case 6 uses the method of CeES software. Table 2 shows the specific performance of the three strategies at different time points (t = 1 h, t = 2 h, and t = 3 h).  
Table 2 shows that at different time points, the comprehensive user importance loss in Case 4 is significantly lower than that in Case 5 and Case 6. This finding indicates that optimizing the outage sequence can more effectively reduce the importance loss of LV users. Although the three strategies have similar numbers of load-shedding users, Case 3 has slightly more load-shedding users than Case 4 and less than Case 6. This result suggests that while optimizing the outage sequence, the impact on the number of load-shedding users should be considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indices** & **Case** & **t = 1 h** & **t = 2 h** & **t = 3 h** \\ \hline Comprehensive user importance loss \(S\) & Case 1 & 22.5548 & 18.9558 & 9.6284 \\ \hline Number of load-shedding users \(N\) & Case 2 & 25.1269 & 21.3674 & 14.2865 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of \(S\) and \(N\).  
Figure 11: Distribution network topology of the large-scale case.  
In summary, the proposed outage optimization strategy demonstrates high effectiveness in higher-order fault scenarios, which significantly reduces the comprehensive user importance loss and enhances the system reliability. Therefore, applying the optimized outage sequence strategy in the operation and maintenance of actual power systems can better ensure reliable system operation and satisfy the electricity needs of important LV users.
###### Abstract
Recently, Sivasithamparam and Castro [31] extended their anisotropic solution [25] by incorporating the effects of structure on the soil behavior by updating the employed constitutive model from the S-CLAY1 [32] to the S-CLAY1S model [33]. This solution is at the cost of complexity, though the better results can be obtained. For this reason, Li et al. [34] presented a drained solution for structured soils considering structure degradation, based on the Structured Cam Clay (SCC) model proposed by Liu and Carter [35]. Actually, the pile installation, standard pressuremeter, and cone penetration tests in clay usually involve an undrained process; the undrained cavity expansion solution, as a theoretical tool to model these engineering problems, is more urgently needed than the drained one. Therefore, an undrained solution was also proposed by Li et al. [36], in which the Modified Cam Clay model of structured loess was adopted. However, in their solution [34, 36], only the initial soil structure was considered; the structure could not be completely removed even though the ultimate critical state was reached, which resulted in an inability to capture the softening behavior of structured. In this study, an undrained elastoplastic solution for cylindrical cavity expansion in structured soils is proposed. The destructuration effects, including the structure degradation and crushing, are considered by employing the SCC model of Carter and Liu [37], in which the complete removal of the soil structure can be achieved. By introducing an auxiliary variable, the mechanical behavior of structured soils around the cavity is governed by a system of differential equations, though the flow rules in the SCC model follow different forms during the hardening, softening, and crushing processes. The significance and efficiency of the present solution was demonstrated by comparing them with the previous solutions. Subsequently, the parametric studies were conducted to investigate the effects of soil structure and destructuration on the cavity expansion process. Finally, the jacking of a casing during the sand compact pile installation and in situ self-boring pressuremeter (SBPM) tests were analyzed to illustrate the applicability of the present solution.
## 3 Methodology
### Reactive Principles  
These principles help to make a high-level abstraction and to provide reactive characteristics to the system [8]. These four principles are:  
1. Responsive: Response times must be consistent with the needs of the applications with a certain quality of service;
2. Resilient: Some features, such as replication, containment, isolation, and delegation, should be system objectives to maintain response even in the case of any failure;
3. Elastic: Requests to the system must be addressed with replicated components to prevent bottlenecks or containment points;
4. Message Driven: The use of asynchronous messaging queue systems and the design of some form of back pressure facilitate load management, elasticity, and flow control, which provides low coupling, isolation, and location transparency.
## 4 Discussion
### Acetabular Inclination Angle  
While the previous parameters showed comparable results between the two surgical methods, a key difference emerged in the analysis of the acetabular inclination angle. The deviation in the reconstructed acetabular inclination angle was the only one to present a significant difference, where the 3D-printed implant group had a more superior precision, \(p\)-value < 0.001. This is a clear indication of the advantages that 3D-printed customized implants have, because a more precisely reconstructed inclination angle reduces the risk of impingement of the femur implant neck with the acetabulum implant cup and subsequent dislocation by increasing the range of motion [53; 80; 81], which directly increases the prosthesis survivorship. Even with the presence of an experienced surgeon that has carried out a high volume of massive hip replacements, there is still room for improvement.  
With the use of 3D-printed personalized implants, patient-specific instrumentation can also be produced to better execute the planned tumor resection process intra-operatively. This is as a result of having already recreated the 3D model of the patient anatomy. Additionally, the personalized implant better mimics the native patient anatomy compared to standard off-the-shelf implants. Both factors can contribute to superior precision in reconstructing the acetabular inclination angle.  
Some reconstructed acetabular inclination angles are intentionally outside both safe zones due to the fact that not every patient will benefit from being in the average range of angles [82; 83], especially in this case, where the angle was chosen by the surgeons during pre-operative planning to match the healthy contra-lateral inclination angle.
## 2 Methodology
### Reconstruction Method vs. Mie Theory-Based Calculation  
Two methods are conventionally used to calculate the optical properties of aerosols (e.g., extinction coefficient), namely the reconstruction method (e.g., Equations (2) and (3)) and the Mie-theory based calculation (e.g., Equation (1)). Aerosol mass concentrations are frequently available from numerical models and observations and, thus, the reconstruction method is more convenient than the Mie theory-based calculation because the reconstruction method can directly convert the aerosol mass concentrations to the optical properties, such as an extinction coefficient. However, the reconstruction  
Figure 3: Schematic diagram for the calculations of mass extinction efficiency (MEE) approximation for polydisperse aerosol particles in this study. Mass scattering efficiency (MSE), mass absorption efficiency (MAE), MEE for small particle size range (MEE\({}_{S}\)), MEE for large particle size range (MEE\({}_{L}\)), geometric volume–mean diameter (\(d_{gv}\)), and geometric standard deviation (\(\sigma_{g}\)) are also specified.  
method does not consider polydispersed particle size distribution as mentioned above. Theoretically, MEE for different sizes can be obtained based on Mie theory (i.e., Equation (1)). The Mie theory-based model calculates MEE from modeled aerosol physical characteristics including the refraction index, volume concentration, and aerosol size distribution. The Mie theory-based model yields accurate MEE if all the aerosol physical properties are known and correct. However, the reconstruction method (e.g., Equations (2) and (3)) is more widely used because of its simplicity and convenience. In this study, the modified reconstruction method was developed for deriving the parameterized MEE and the optical properties of polydispersed various species of atmospheric aerosol particles, and the results were compared with the Mie theory-based calculations.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 4 Datasets and Performance Metrics
### Two Datasets  
This study employs two datasets, TVWS Dataset and NR Dataset, generated in MATLAB R2022b to train and evaluate the proposed method. Each dataset contains 200,000 samples, each sample contains 2048 I/Q sampling points, and the SNR of the signals is uniformly distributed between [\(-\)10, 10] dB.  
The TVWS signal occupies a total bandwidth of 320 MHz, consisting of 160 non-overlapping sub-channels, each with a bandwidth of 2 MHz. The signal model is expressed as  
\[x(t)=\sum_{k=1}^{K}\sqrt{E_{k}B}\;\mathrm{sinc}[B(t-t_{k})]e^{j2\pi f_{k}t}+n(t), \tag{19}\]  
where \(x(t)\in F=[0,320]\) MHz is divided into \(L=32\) channels, \(K\) denotes the number of users, \(E_{k}\in[0.3,1]\) represents the energy of each user (based on parameters in [13]), \(t_{k}\) is the time offset of each user, \(f_{k}\) indicates the carrying frequency of each user, \(n(t)\) represents additive white Gaussian noise, and \(B\) is the bandwidth of each user, set to 2 MHz. The sparsity level of the TVWS dataset is defined as  
\[\mu_{TVWS}=\frac{2K}{320}. \tag{20}\]  
The NR signal occupies a total bandwidth of 400 MHz, consisting of 132 non-overlapping sub-channels, each with a bandwidth of 2.88 MHz. This dataset is created by simulating 5G NR link transmissions in the physical uplink shared channel (PUSCH). Within this dataset, the subcarrier spacing of the signals is 120 kHz and each resource block occupies a 1.44 MHz channel. Each user occupies two resource blocks. The sparsity level \(\mu\) of the NR dataset is defined as  
\[\mu_{NR}=\frac{2.88K}{400}. \tag{21}\]
###### Abstract
Figure 1: UAV recharging a single node [7]. We performed some simulations introduced from a preliminary study [17] to investigate these issues that have shown that UAV charging can extend the network life by approximately three times. Then, we look at other issues and set up a list of guidelines dependent on the size of the network. According to the guidelines, we select the nodes (sink or low power nodes), the amount of UAVs required to enter that node, and what node to choose next. Interestingly, as the size of the network grows, it becomes more effective for the sink to remain intact, with up to 30% charged at every interval. However, when the network extends the number of nodes to 200, a change of sink is required. This paper is further structured as follows: In Section 2, we review existing techniques used in detection systems with sensor nodes, in energy optimization models, in wireless charging models, and in energy maximization. Section 3 gives our complete research statement. Section 4 describes a range of reproducible sensor network problems in energy transfer through UAVs. Section 5 describes the energy utility and re-usability model, and Section 6 describes the UAV energy consumption and recycling model used to optimize energy distribution and shows the results of our analysis. Section 7 gives our conclusion and insights into future work.