esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 2 Femtosecond Laser-Induced Self-Organized Periodic Surface Structures (LIPSS)
### Conical Spikes or Columnar Structures  
Conical or columnar structures can also be formed by laser surface structuring at optimized fluence, ablation threshold, number of pulses, scanning speed, and type of material [43]. For example, Figure 5a-d shows columnar structures covered with nanostructures on a titanium surface with a TiSa Laser (65 FS, \(\lambda\) = 800 nm, 1 kHz, 0.35 J/cm\({}^{2}\)) at different numbers of pulses. It was observed that columnar structures began to form when the laser exposure reached 100 shots, while they became more prominent upon increasing the number of pulses [21]. This finding indicates that the geometrical features of columnar structures can be manipulated by optimizing the number of incident pulses. Moreover, the pulse energy and the type of material are additional factors that need to be considered. Indeed, the higher damage threshold for hard materials (e.g., for titanium it should be above 0.5 J/cm\({}^{2}\)[44]) and the lower one for soft materials (e.g., for aluminum it should be below 0.16 J/cm\({}^{2}\)[44]) need to be taken into account upon optimizing the columnar structures. Furthermore, the irradiation environment may influence the formation of conical structures. For instance, conical structures grown on titanium [45] and silicon [2] are taller and sharper when irradiated in the presence of SF\({}_{6}\) gas. Figure 5e,f shows an Si surface irradiated in air and a SF\({}_{6}\) gas environment with an average of 500 laser pulses (180 FS, \(\lambda\) = 800 nm, F = 2.47 J/cm\({}^{2}\)). The conical structures show remarkable superhydrophobicity compared with LISR and LISG [42]. Therefore, such structures are expected to be promising candidates for oil-water separation.  
Figure 4: Various LIPSS types formed by FS laser pulses of different polarization: (**a**) Grating-like Laser-induced surface ripples (LISR) formation fabricated with linear polarization; (**b**) rhomboidal multi-directional surface structures produced by radial polarization; and (**c**) columnar structures fabricated by circular polarization. The corresponding fast Fourier transform (FFT) diagrams are also shown, revealing the different spatial frequencies included in each structure [30].
# CPM-OFDM Performance over Underwater Acoustic Channels  
[PERSON]  
1 Department of Computer Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia 1,1,2,3 Department of Computer Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia 1,1,2,3 Department of Computer Engineering, Umm Al-Qura University, Makkah  
continuity [9; 10; 11]. Traditional OFDM systems use memoryless modulation schemes such as PSK and QAM in the mapper. Very limited work has been done incorporating CPM in the mapper of an OFDM system. In this work, we evaluate the performance of a typical CPM-OFDM system over doubly-selective underwater acoustic (UWA) channels [4; 12]. The main contributions of this work are as follows:  
* An overview of the work done on CPM-OFDM and similar schemes (presented in this section).
* Design of a CPM-OFDM transceiver for low bandwidth and doubly-selective UWA channel.
* Performance evaluation of the proposed transceiver over the UWA channel using computer simulations. Specifically, we present:
* Bit error rate vs. SNR plots for various rational values of the modulation indices and identification and analysis of good performing indices.
* Bit error rate vs. SNR plots for varying transmitter-receiver distances and a discussion of the results.
* Bit error rate vs. SNR plots for various values of channel bandwidths and a discussion of the results.
* Peak to average power ratio plots as a function of modulation indices.  
The remaining paper is organized in the following manner. Section 2 contains a survey of the work done in the domain of CPM-OFDM and related technologies. Section 3 presents the details of a typical CPM-OFDM transceiver that includes CPM modulator and demodulator. In Section 4, we give a detailed description of the underwater acoustic channel that was used for the simulations. Simulation setup and results are presented in Section 5 along with discussions on the outcome of our simulation studies. Finally, the paper is concluded in Section 6.
## 2 Materials and Methods
### Study Outcomes and Data Collection  
The study's primary outcome was the incidence of respiratory infections, mainly influenza-like illness (ILI) and common cold, during the intervention. The ILI diagnosis was based on the case definition used by the European Centre for Disease Prevention and Control [22] as follows: sudden onset of symptoms with one or more respiratory symptoms (cough, sore throat and/or nasal congestion) plus one or more systemic symptoms (fever, headache, myalgia and/or malaise). Common cold was defined as sudden onset of one or more respiratory symptoms (cough, sore throat and/or nasal congestion) with no systemic symptoms [23]. The total number of respiratory infections was the sum of common cold and ILI episodes.  
Secondary outcomes included the determination of the severity and duration of the respiratory infections and incidence of symptoms related to them. The severity of the respiratory infections was determined according to the number of symptoms (previously defined) presenting simultaneously. Common cold was considered mild severity if subjects presented 1 symptom and high severity if they presented \(\geq\)2 symptoms simultaneously, whereas ILI was considered mild severity if subjects presented \(<\)4 symptoms simultaneously and high severity if subjects presented \(\geq\)4 symptoms simultaneously. The consumption of analgesics and antibiotics during the follow-up period was also recorded. Finally, some safety parameters, such as nausea, lack of appetite, sleep disturbances, and changes in weight and blood pressure, were recorded during the intervention.  
All data about the health conditions of the volunteers and the consumption of medical treatments were evaluated and recorded by a medical doctor every 2 weeks in the case report form corresponding to each volunteer. Weight was determined using a Tanita BC-418 Body Composition Analyzer (Tanita., Tokyo, Japan) at baseline and at the end of the intervention. Systolic and diastolic blood pressures (BP) were measured with a validated digital automated blood pressure monitor (Medisana Healthcare, Barcelona, Spain). Compliance was assessed at the end of the intervention by comparing the number of sachets provided and the number returned. Adverse events, defined as any unfavorable, unintended effect, were recorded at the follow-up visits (at 2, 4, 6, 8, 10, 12 and 14 weeks).
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 4 NDR-Net Assisted Channel Estimation
### Dataset  
In this paper, all data are generated in the same communication system. Firstly, QPSK modulation is used to generate 16,000 sets of guide frequency sequences as the initial data stream. The data stream then passes through the OFDM communication system as shown in Figure 1, and the initial data set is obtained by rough LS estimation of the data at the receiver. Since the network cannot process complex numbers, the real and imaginary parts of each group of data in the data set are constructed in parallel. The reconstructed dataset is split into a training set and a validation set, from which 75% are used for training, 25% are used for validation. Correspondingly, a test set of the same size as the validation set is generated in the same way. For \(CNN_{N}\), the labels are composed of SNR, RMS time delay and Doppler frequency shift corresponding to each input dataset (SNR, channel model, and velocity do not match). For the \(CNN_{D}\), the labels are the real channel response matrix.
# A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations
## 4 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & & \\ \hline
**B.C** & **L/h** & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & \(\omega_{4}\) & \(\omega_{5}\) \\ \hline CC & 6 & 51.369 & 120.3003 & 184.3095 & 234.5509 & 271.6168 \\  & 8 & 32.9497 & 78.0707 & 122.6264 & 160.4316 & 189.2995 \\  & 12 & 18.8886 & 42.4297 & 67.8774 & 91.1052 & 110.9483 \\  & 16 & 12.6912 & 28.4750 & 44.9936 & 61.2217 & 75.2852 \\  & 20 & 10.1075 & 21.495 & 33.8316 & 45.7028 & 56.9546 \\  & 30 & 7.244 & 14.4196 & 22.0180 & 29.4070 & 36.6993 \\ \hline HH & 6 & 39.045 & 107.2164 & 174.6817 & 228.4481 & 268.5102 \\  & 8 & 25.4156 & 68.2893 & 114.1099 & 153.9207 & 185.1885 \\  & 12 & 14.8655 & 36.8893 & 62.0062 & 85.8467 & 106.6789 \\  & 16 & 10.7651 & 24.8817 & 41.0727 & 57.1300 & 71.8685 \\  & 20 & 8.6841 & 19.0506 & 30.7592 & 42.589 & 53.8329 \\  & 30 & 6.8041 & 13.008 & 20.1487 & 27.4042 & 34.5047 \\ \hline \end{tabular}
\end{table} 
Table 11: _Cont._  
Figure 2: Impact of temperature and nonlocal parameter on fundamental frequency of hinged–hinged (H-H) METE nanobeam for different materials. \(V_{0}=0\); \(P_{0}=1.5\); \(A_{0}=0.02\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 3: Impact of electric voltage and nonlocal parameter on fundamental frequency of (H-H). METE nanobeam for different materials. \(\Delta T=0\); \(P_{0}=1.5\); \(A_{0}=0.02\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 4: Impact of length-to-thickness ratio and nonlocal parameter on fundamental frequency of (H-H) METE nanobeam for different materials. \(P_{0}=2\); \(\Delta T=100\); \(V_{0}=-0.3\); \(A_{0}=0.02\); \(h=2\) nm; \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 5: Electrical potential distribution in (C-C) METE nanobeam for different materials. \(P_{0}=0\); \(\Delta T=100\); \(V_{0}=-0.3\); \(A_{0}=0.05\); \(h=10\) nm; \(L/h=6\); \(k_{1}=k_{2}=k_{3}=0\). Figure 7: Impact of magnetic potential and nonlocal parameter on fundamental frequency of (H-H) METE nanobeam for different materials. \(\Delta T=100\); \(P_{0}=1.5\); \(A_{0}=0.02\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 8: Impact of axial force and nonlocal parameter on fundamental frequency of (H-H) METE nanobeam for different materials. \(\Delta T=100\); \(V_{0}=-0.3\); \(A_{0}=0.03\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 9: Impact of axial force and magnetic potential on fundamental frequency of (H-H) METE nanobeam for different materials. \(\Delta T=100\); \(V_{0}=-0.3\); \(\mu=0.01\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\). Figure 10: Mode shapes of (H-H) METE nanobeam for different materials. \(P_{0}=0\); \(\Delta T=100\); \(V_{0}=-0.3\); \(A_{0}=0.05\); \(h=10\) nm; \(L/h=6\); \(k_{1}=k_{2}=k_{3}=0\). Figure 11: Mode shapes of (H-H) METE nanobeam for different materials. \(P_{0}=0\); \(\Delta T=100\); \(V_{0}=-0.3\); \(A_{0}=0.05\); \(h=10\) nm; \(L/h=6\); \(k_{1}=25\); \(k_{2}=0.05\); \(k_{3}=0.15\).
## 2 Results and Discussion
### Fingerprinting of Cinnamon Drugs Using UV/Vis  
In addition to the cinnamon's volatile constituents, it is rich in other non-volatile bioactives, including flavonoids (e.g., gossypin, hesperidin, hibifolin, and quercetin), catechin, epicatechin, and proanthocyanidins (e.g., procyanidin B2) [3,10] that cannot be easily detected using GC/MS considering their higher molecular weight or NMR attributed to the crude nature of the extract or presence at low levels [16]. These metabolites are characterized by several UV active chromophores and consequently can be used as a fingerprinting tool that could be used further as a simple, fast, and non-destructive tool for cinnamon authentication or QC analysis.  
The resulted raw data from Uv/Vis spectrometer are depicted in (Supplementary Figure S8). All samples showed relatively similar spectra with mostly two \(\lambda_{\text{max}}\) around 225 nm and 290 nm corresponding to the phenolic bioactive content, including cinnamate derivatives, flavonoids, catechins, and proanthocyanidins [21]. However, the commercial products CP-1 and CP-3 showed different spectra, whereas CP-1 demonstrated spectra without \(\lambda_{\text{max}}\). In addition, CP-3 demonstrated a distinct complex UV spectrum with several \(\lambda_{\text{max}}\) between 245 and 270 nm, which is typical for a pyridine nucleus. These findings were consistent with the NMR results revealing the presence of niacin (N14).
###### Abstract  
We propose and experimentally demonstrate a hybrid grating, in which an excessively tilted fiber grating (Ex-TFG) and a fiber Bragg grating (FBG) were co-inscribed in a reduced-diameter fiber (RDF). The hybrid grating showed strong resonances due to coupling among core mode and a set of polarization-dependent cladding modes. This coupling showed enhanced evanescent fields by the reduced cladding size, thus allowing stronger interaction with the surrounding medium. Moreover, the FBG's Bragg resonance confined by the thick cladding was exempt from the change of the surrounding medium's refractive index (RI), and then the FBG can work as a temperature compensator. As a result, the Ex-TFG in RDF promised a highly sensitive RI measurement, with a sensitivity up to ~1224 nm/RIU near the RI of 1.38. Through simultaneous measurement of temperature and RI, the temperature dependence of water's RI is then determined. Therefore, the proposed hybrid grating with a spectrum of multi-peaks embedded with a sharp Bragg resonance is a promising alternative for the simultaneous measurement of multi-parameters for many RI-based sensing applications.  
fiber Bragg grating; tilted fiber grating; refractive index sensing; temperature dependence of the refractive index of water
## 4 Conclusions  
The article presents a prototype, hybrid on-line PD monitoring system. Its unique feature is a combination of the functionality of three methods of PD detection (AE, HF, and UHF) and integrating them into one complementary system. Another important function of the system is the possibility of its integration using IEC 61850 protocol with remaining substation automation devices, but first of all with SCADA system. Due to that, the monitoring system can analyze in real-time the PD parameters registered using various detectors as well as the basic transformer parameters that affect the phenomenon of partial discharges, i.e., voltage and oil-temperature. Tests conducted on 31.5 MVA power transformer showed that in its case partial discharges ignited in particular times of day (from approx. 8:45 a.m. to 5:45 p.m.), and their increased activity was observed during daily \"hills\" and local peaks of the oil temperature curve. The results of the statistical analysis of the HF, UHF, and AE signals presented in the paper allow to conclude with a high probability that in these periods, the partial discharge phenomenon was the source of the majority of pulses registered by detectors. Laboratory tests of partial discharge detectors developed for the monitoring system have confirmed their high performance. The active dielectric window with built-in ultrasound transducer assures on average 5.80 times higher sensitivity than the popular contact AE sensor type PAC R15D. Such high sensitivity was obtained because the ultrasonic transducer is located inside the oil-filled transformer tank and is equipped with a specially designed matching layer that facilitates the transfer of acoustic energy from mineral oil to a piezoelectric crystal. In turn, the prototype HFCT sensor with MnZn ferrite core showed--in comparison to the commonly used RFCT-4 sensor--on average 1.48 times higher sensitivity of PD in oil detection and on average 1.50 times higher detection sensitivity of surface PD. Additionally, very good results were obtained for the prototype construction of the meandered planar inverted-F antenna. The antenna bandwidth (280-353 MHz) was optimized for the detection of PDs typical for oil-paper insulation. Tests performed in a shielded laboratory and with the use of a power transformer tank equipped in the dielectric windows showed that the prototype meandered PIFA compared to standard disk antenna is on average 7.80 times more sensitive in the detection of low-radiating PD (an initial form of surface PD with apparent charge below 1 nC) and on average 1.75 more sensitive in the detection of high energy creeping sparks with apparent charge about 6 nC. Future research will focus primarily on the development of the system's software layer, including development and implementation of an algorithm for identifying the type of partial discharge pulses based on PRPD patterns (for HF and UHF method) and implementation of the FFT (Fast Fourier
## 4 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline
**Variable** & **MRA** & **Elastic-Net** & **MRA + ESF** & **Elastic-Net + ESF** \\ \hline  & Coefficient & Coefficient & Coefficient & Coefficient \\ \hline Apartment & \(-\)0.09099 * & \(-\)0.09096 & \(-\)0.13904 ** & \(-\)0.13899 \\ Detached & 0.143561 ** & 0.143531 & 0.154712 ** & 0.154662 \\ Terrace & \(-\)0.12268 ** & \(-\)0.12265 & \(-\)0.18009 ** & \(-\)0.18004 \\ Size (m\({}^{2}\)) & 0.0052 ** & 0.0052 & 0.0050 ** & 0.005 \\ Year Built & \(-\)0.0005 ** & \(-\)0.0005 & \(-\)0.0002 & \(-\)0.0002 \\ Bed 4 & 0.0324 * & 0.0323 & 0.0225 & 0.0224 \\ Bed 5 & 0.0012 & 0.0012 & 0.0233 & 0.0232 \\ Bed 6 & 0.0944 * & 0.0944 & 0.068 & 0.0679 \\ Bath 2 & 0.0145 & 0.0144 & 0.0062 & 0.0061 \\ Bath 3 & \(-\)0.2777 * & \(-\)0.2777 & \(-\)0.2740 ** & \(-\)0.2739 \\ Garage size (m\({}^{2}\)) & 0.0025 ** & 0.0025 & 0.0027 ** & 0.0027 \\ Grade B & \(-\)0.0358 & \(-\)0.0357 & 0.0049 & 0.0049 \\ Grade C & \(-\)0.0367 ** & \(-\)0.0367 & \(-\)0.0489 *** & \(-\)0.0488 \\ Story 1 & 0.0547 ** & 0.0547 & 0.0511** & 0.0511 \\ Story 1.5 & \(-\)0.0462 & \(-\)0.0462 & \(-\)0.029 & \(-\)0.029 \\ Story 2.5 & 0.0309 & 0.0309 & 0.0238 & 0.0238 \\ Story 3 & 0.0341 & 0.0341 & \(-\)0.0318 & \(-\)0.0317 \\ Rural & \(-\)0.2003 *** & \(-\)0.2003 & & \\ Rural village & \(-\)0.3152 ** & \(-\)0.3152 & & \\ Suburban & \(-\)0.0022 & \(-\)0.0021 & & \\ MDM & 0.0001 *** & 0.0001 & 0.0001 *** & 0.0001 \\ PC\_2/SF\_2 & 0.7429 *** & 0.7429 & \(-\)4.9570 *** & \(-\)4.957 \\ PC\_3/SF\_3 & 0.1025 *** & 0.1025 & 4.1330 *** & 4.133 \\ PC\_4/SF\_4 & 0.1000 *** & 0.0999 & 0.5410 * & 0.541 \\ PC\_5/SF\_5 & 0.1206 *** & 0.1206 & \(-\)0.334 & \(-\)0.334 \\ PC\_6/SF\_6 & 0.3131 *** & 0.313 & 0.015 & 0.015 \\ PC\_7/SF\_7 & 0.0867 *** & 0.0867 & \(-\)1.9810 *** & \(-\)1.981 \\ PC\_8/SF\_8 & 0.3303 *** & 0.3303 & \(-\)1.7050 *** & \(-\)1.705 \\ PC\_9/SF\_9 & 0.1549 *** & 0.1549 & 2.3320 *** & 2.332 \\ PC\_10/SF\_10 & 0.1233 *** & 0.1233 & 0.5000 ** & 0.5 \\ PC\_11/SF\_11 & \(-\)0.0717** & \(-\)0.0716 & \(-\)0.355 & \(-\)0.355 \\ PC\_12/SF\_12 & \(-\)0.1976 *** & \(-\)0.1975 & 0.7620 *** & 0.762 \\ PC\_13/SF\_13 & \(-\)0.1889 *** & \(-\)0.1889 & 1.1500 *** & 1.15 \\ PC\_14/SF\_14 & \(-\)0.0922 *** & \(-\)0.0921 & 0.081 & 0.081 \\ PC\_15/SF\_15 & \(-\)0.0301 & \(-\)0.0301 & \(-\)0.6920 *** & \(-\)0.692 \\
2021Q & \(-\)0.0805 *** & \(-\)0.0804 & \(-\)0.0828 *** & \(-\)0.0828 \\
2022Q & \(-\)0.0466 *** & \(-\)0.0465 & \(-\)0.0446 *** & \(-\)0.0446 \\ C & 10.532 & 1 Whilst the findings reveal a notable increase in model performance, the inclusion of the ESFs has reduced the residual error and level of spatial autocorrelation across the study area geography (Figure 4a-d), with the delineated spatial models demonstrating larger clusters of heightened residuals within specific locales. In essence, there appears larger differences between the actual and estimated sales prices which the standard regression and Elastic-net models have not successfully addressed as a consequence of SA and not detecting the underpinning spatial patterns.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 4 Experiments of Task Assignment for UAV Swarm Using MPPWPA
### The Results and Analyses of Simulation Experiments
#### 4.2.1 Simulation Results in Scenario 1
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{\multirow{2}{*}{\(Num\)}} & \multicolumn{2}{c}{**Objective Function Value (m)**} & \multicolumn{2}{c}{**Convergence Time**} \\ \cline{3-6}  & & **Ave.** & **Std.** & **Ave.** & **Std.** \\ \hline PSO & - & 29.49 & 0.78 & 10.40 & 8.44 \\ GA & - & 31.10 & 1.58 & 1.64 & 3.23 \\ ABC & - & **28.71** & **0** & 1.30 & 0.24 \\ WPA & - & 29.65 & 1.23 & 2.22 & 3.76 \\  & 2 & 29.01 & 0.43 & 1.66 & 0.25 \\ MPPWPA & 4 & 28.90 & 1.01 & 1.28 & 0.23 \\  & 8 & **28.71** & **0** & **1.11** & 0.17 \\  & 16 & **28.71** & **0** & 1.27 & 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective function value and convergence time with different \(Num\) in scenario 1. Figure 16: Average objective function values with different \(Num\) in scenario 1. Figure 17: Average objective function values with different \(Num\) and \(P_{m}\) in scenario 1: (**a**) \(Num=2\); (**b**) \(Num=4\); (**c**) \(Num=8\); (**d**) \(Num=16\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(Num\) & & **2** & \multicolumn{3}{c}{**4**} & \multicolumn{3}{c}{**8**} & \multicolumn{3}{c}{**16**} \\ \hline \(Pm\) & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** & **Ave.** & **Std.** \\ \hline \(1\) & 29.01 & 0.45 & 28.90 & 1.01 & **28.71** & **0** & **28.71** & **0** \\ \(0.8\) & **28.87** & 0.78 & 28.85 & 1.00 & **28.71** & **0** & **28.71** & **0** \\ \(0.6\) & 29.01 & 0.81 & **28.83** & 1.02 & **28.71** & **0** & 28.86 & 0.35 \\ \(0.4\) & 28.97 & **1.14** & 28.98 & 0.82 & **28.71** & **0** & 28.89 & 0.58 \\ \(0.2\) & 28.95 & 0.82 & 28.98 & 1.00 & 28.77 & 0.77 & **28.71** & **0** \\ \(0\) & 29.59 & 1.5 & 29.20 & **0.8** & 28.98 & 0.33 & **28.71** & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective function value with different \(Num\) and \(P_{m}\) in scenario 1.