esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Structural Properties of Mushroom Laccases
The initial product is unstable and then propagates a second oxidation by enzymatic catalysis or a non-enzymatic reaction [136; 137; 138]. Although lactose has lower redox potential (\(\leq\)0.8 V) compared to other peroxididases for oxidation of a phenolic moiety in lignin degradation, a variety of low molecular substrates, intermediators, and products (e.g., reactive and unstable cationic radicals) oxidized by the enzymes can participate as redox mediators to enhance its oxidoreduction [137]. These lactose mediators can expand the catalytic ability toward non-aromatic and aromatic compounds, more than 100 mediators known as synthetic and natural compounds [38; 139; 140; 141; 142]. Synthetic mediators include 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (ABTS), 1-hydroxybenortiazole (HBT), \(N\)-hydroxypthalimide (HPI), and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO). Natural mediators include acetosyringone (AS), catechol, \(p\)-coumaric acid, 4-hydroxybenzoic acid (4-HBA), 4-hydroxybenzilic alcohol, syringaldehyde (SA), valillin, veratryl alcohol (VA), and violuric acid (VLA). The co-utilization of these mediators and a lactose, known as lactose-mediator systems (LMS), has been intensively studied to apply a variety of the enzymatic processes to pulp and paper bleaching, bioremediation, dye-decolorization, and the degradation of antibiotics and xenobiotics [38; 39; 40; 63; 134]. Moreover, the dual or triple mediators (ABTS and others) used with lactose for degradation of polyaromatic carbons showed synergistic effects of enhanced oxidative activities compared with a single mediator for the enzyme reaction, due to the recycling of ABTS radical formation [143]. The redox potential of mediators differs depending on their chemical structure and the biochemical activity of an individual lactose [144]. Based on many case studies for LMS, an effective mediator should be a favorable substrate because it has high oxidation potential, which means the oxidized radical form has a long half-life and can propagate more oxidation of the remaining substrate. Nevertheless, synthetic mediators have a limitation because they are unable to be used in a wide variety of industrial applications due to their cost, potential toxicity, and inhibition of lacases [145]. In contrast, natural mediators, such as oxidized lignin units, AS, \(p\)-coumaric acid, 4-HBA, SA, vanillin, VA, and VLA, derived from lignin degradation could be potential substances for use instead of synthetic compounds. These naturally-derived phenolic compounds were used as lacases mediators and worked effectively in the application of paper pulp delignification and the degradation of recalcitrant chemicals such as xenobiotic compounds, antibiotics, pesticides, dyes, and polycyclic aromatic hydrocarbons petroleum derivatives [140; 142].
## 2 Materials and Methods
### Irrigation and Cereal Cover Mask  
To study the areas of rainfed wheat and barley crops, two databases were used to create a mask that filters out all the areas other than the target areas. To exclude irrigated areas, the Digital Global Map of Irrigation Areas of the Food and Agriculture Organization (FAO) was used, which represents the global area equipped with irrigation at a spatial resolution of 5 arc minutes or 0.083 decimal degrees [49]. Additionally, to discard areas with different land cover types, the Climate Change Initiative (CCI) Land Cover (LC) map from the European Space Agency (ESA) was used. It describes Earth's land surface in 37 original LC classes based on the United Nations Land Cover Classification System (UN-LCCS) [50], with a spatial resolution of 300 m [51].  
In this study, ArcGIS v10.8 software (ESRI(r), Redlands, CA, USA) was used to create the mask of irrigated areas, together with land cover maps. First, the irrigation and the land cover maps were resampled in the grids of the soil moisture and the GPP-LAI databases. Then, one mask for each database (soil moisture and GPP-LAI) was calculated by hiding all the pixels with more than 10% irrigated area and those with a land cover other than rainfed cropland, assuming that barley and wheat are the main cereals in those areas.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Study Areas and Datasets
#### 2.1.2 Sentinel-2 Satellite Image Time Series  
Copernicus Sentinel-2 Level-1C (S2-L1C) products provide orthorectified Top-Of-Atmosphere (TOA) reflectance, including radiometric and geometric corrections. The Sentinel-2 SITS used in this study were composed of 28 Sentinel-2A and 2B images for TA1 (subset of S2-L1C T11 SPS tile) and 20 images for TA2 (subset of S2-L1C T13 SGA) (Figure 2). When analyzing time series, atmospheric correction is an important preprocessing step and, therefore, we processed the reflectance images from TOA Level 1C to Bottom-Of-Atmosphere (BOA) Level 2A using the _sen2 cor_ plugin [(43; 44)]. The images were selected manually to minimize the cloud coverage percentage. The clouds and cloud shadow contamination occurred in two images for each SITS, covering less than 20%. The temporal distribution of the images covers well one agricultural year, from 23 September 2016 to 23 September 2017 for TA1 and from 18 October 2016 to 2 November 2017 for TA2. The biggest time differences between two consecutive images are 40 days for TA1 and 50 days for TA2, both in autumn and winter months when cloud covered images were unusable. The smallest time difference is 5 days in both test areas since the advent of Sentinel-2B images in July 2017 (Figure 2).  
Figure 1: Test area locations in California (first test area (TA1)) (**a**) and Texas (second test area (TA2)) (**b**). The false color composites for TA1 (1 May 2017) and TA2 (20 July 2017) are depicted in (**c**) and (**d**), respectively.  
Figure 2: Timeline of the two satellite image time series (SITS) used, composed of Sentinel-2A and 2B images with an irregular temporal distribution.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 3 Simulations and Results
### Scass-C
However, at most, it is only 0.24 dB more directive than the SCASS-C; on the other hand, its PSLR is 8.75 dB lower than the SCASS-C. In Table 1, the performance metrics of NULA with PSO-optimized spacing and uniform weights [10] are also shown in the last row. This array has not been simulated in this research work and its HPBW and PSLR values have been taken from Table 3 in [10]. It is obvious that its PSLR and HPBW are inferior to the proposed SCASS-C even for the broadside direction. If steered away from the broadside, its performance worsens even further. Hence, only the proposed array can maintain all three performance metrics at decent values for all the given steering angles. A noteworthy fact is that the SCASS-C uses \(\Delta\) as a tool to control the HPBW; the greater \(\Delta\), the smaller the HPBW. If this tool is used in conjunction with Chebyshev weights, HPBW and PSLR are both improved. This is in contrast to the existing arrays in which if one of the parameters is improved while the other suffers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name of Array** & \(L\) & \(\theta_{0}\) & **Configurable Parameters** & **HPBW** & **PSLR (dB)** & **Dir (dBi)** & **Power Loss (dB)** \\ \hline \multirow{3}{*}{SCASS-C} & 14 & 0\({}^{\circ}\) & SLA = 22.10 dB, \(\Delta=0.2^{\circ}\) & 1.82\({}^{\circ}\) & 22.0 & 17.24 & 0.1 \\  & 14 & 30\({}^{\circ}\) & SLA = 22.15 dB, \(\Delta=0.3^{\circ}\) & 1.99\({}^{\circ}\) & 22.0 & 17.44 & 0.15 \\  & 14 & 60\({}^{\circ}\) & SLA = 22.5 dB, \(\Delta=0.9^{\circ}\) & 2.93\({}^{\circ}\) & 22.0 & 17.41 & 0.5 \\ \hline \multirow{3}{*}{SCA-U [33]} & 14 & 0\({}^{\circ}\) & & 1.89\({}^{\circ}\) & 12.9 & 15.65 & \\  & 14 & 30\({}^{\circ}\) & & 2.18\({}^{\circ}\) & 12.9 & 15.65 & \\  & 14 & 60\({}^{\circ}\) & & 3.88\({}^{\circ}\) & 12.9 & 15.65 & \\ \hline \multirow{3}{*}{SCA-C} & 14 & 0\({}^{\circ}\) & SLA = 22 dB & 2.15\({}^{\circ}\) & 22 & 16.59 & \\  & 14 & 30\({}^{\circ}\) & SLA = 22 dB & 2.50\({}^{\circ}\) & 22 & 16.59 & \\  & 14 & 60\({}^{\circ}\) & SLA = 22 dB & 4.38\({}^{\circ}\) & 22 & 16.59 & \\ \hline \multi
## 2 Cumulative Damage and Failure Intensity  
### The Cumulative Damage Interpretation of Failure Probability  
The failures of repairable systems can be described by the non-homogeneous Poisson process (NHPP), and the probability of \(n\) failures in \((s,s+t]\) can be expressed as [32]:  
\[P(N_{s,s+t}=n)=\frac{\left(W(s+t)-W(s)\right)^{n}}{n!}\cdot\exp(-(W(s+t)-W(s))), \tag{1}\]  
where \(W(t)\) is the cumulative intensity function (CIF) that is a function of time. Assuming that the system is continuously impacted during its operation, the level of each impact is related to WCs. The probability of failure is related to the cumulative damage within a certain period. The increment of cumulative damage means an increased probability of failure. The probability of \(n\) failures in \((s,s+t]\) under \(e_{i}\) can be expressed as:  
\[P_{i}(N_{s,s+t}=n)=\frac{\left(W(M(0,s+t|e_{i}))-W(M(0,s|e_{i}))\right)^{n}}{ n!}\cdot\exp(-(W(M(0,s+t|e_{i}))-W(M(0,s|e_{i})))) \tag{2}\]  
where \(M(0,t|e_{i})\) is the cumulative damage under WC \(e_{i}\) from 0 to \(t\). Thus, the cumulative damage of the system can be easily seen as related to the WC and operation time under this WC. If \(m(t|e_{i})\) is the unit time damage of the system under WC \(e_{i}\), then:  
\[M(0,t|e_{i})=\int_{0}^{t}m(u|e_{i})du, \tag{3}\]  
As the unit time damage is always greater than or equal to zero, the cumulative damage does not decrease with the passage of time. \(M(0,t|e_{i})\) can be seen as a monotone increasing function. Through the introduction of cumulative damage, the probability of the failure function with respect to time is converted into a function with cumulative damage as an independent variable. It can also be regarded as a composite function of time. This scenario effectively connects the relationship between the probability of failure, time, and WCs in series, which provides an effective channel to determine the impacts of WCs.
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Processing  
#### 4.2.1 Big Data Analytics (BDA)  
A large amount of data that usually is transmitted through advanced wireless technologies consists of Big Data (BD). Big Data is a term used to describe data gathering that is huge in volume and yet grows exponentially over time. For simplicity, BD is also data but with a huge size. Such data are so huge and complex which none of the conventional data management tools can store or process it effectively. In terms of medicine, models from BD can support the prediction of a pandemic or help for the production of the cure. As shown in Figure 4, Big Data Analytics (BDA) is the use of advanced analytic methods for inquiry of large and diverse data sets, such as structured, semi-structured, and unstructured data, from different sources, and in different sizes, in order to reveal worthy information, which can assist businesses to make informed decisions [40].  
BDA could be a good method that can be applied in medicine for analyzing the gathered data from the patients' sensors. BDA can lead to a more accurate clinical image and thus, to improved medical diagnosis and treatment. This can be achieved by utilizing advanced BDA tools to a series of medical issues and infectious diseases. It is fact that BDA plays a vital role in controlling and facing the current COVID-19 pandemic [41].  
Many BDA platforms and tools are available for use in the healthcare domain for improved analysis of e-health data. These tools include Apache Hadoop Distributed File System (HDFS), MapReduce Yet Another Resource Negotiator (YARN), mapR, IBM Big Structured Query Language (SQL) integration with Apache Spark, Amazon data analysis system, Microsoft Azure, Qubole, High-Performance Computing Cluster (HPCC) system, Konstanz Information Miner (KNIME) Analytics Platform, Datameer Spotlight and Spectrum, etc. [41].
# Free-Space QKD with Modulating Retroreflectors Based on the B92 Protocol
## Appendix A
The polarization of the beam depends on the intensity of two combined beams, satisfying \(\theta=\arctan(\sqrt{M_{2}/M_{1}})\). We can adjust the ratio of \(M_{1}\) and \(M_{2}\) to realize different linear polarizations. However, only a few polarizations can be achieved in this manner. While the phase difference is zero, only linear polarizations with axes \(\theta\in(0,\pi/2)\) are possible. Therefore, we use the B92 protocol, which requires only two states with polarization direction \(\theta\) and \(\theta+45^{\circ}\) instead of the BB84 protocol. When the polarization direction of the output beam is \(\theta\), the corresponding modulation efficiency of MQW1 and MQW2 are \(\sqrt{M_{1}}=C\cos\theta\) and \(\sqrt{M_{2}}=C\sin\theta\), respectively. While the polarization direction is \(\theta+45^{\circ}\), \(\sqrt{M_{1}}=C\cos(\theta+45^{\circ})\) and \(\sqrt{M_{2}}=C\sin(\theta+45^{\circ})\). The contrast ratio (CR) of MQW1 and MQW2 while modulating the different polarizations are  
\[CR_{1}=\frac{M_{1}}{M_{1}^{\prime}}=\frac{\cos^{2}\theta}{\cos^{2}(\theta+45^{ \circ})},\quad CR_{2}=\frac{M_{2}^{\prime}}{M_{2}}=\frac{\sin^{2}(\theta+45^{ \circ})}{\sin^{2}\theta} \tag{12}\]  
As shown in Figure 11, \(CR_{1}\) and \(CR_{2}\) exhibit opposite trends while \(\theta\) increases. When \(\theta=\pi/8\), \(CR_{1}=CR_{2}\approx 7.6\) dB, which is approximately equal to the extinction ratio of a MQW [18]. These correspond to \(22.5^{\circ}\) and \(67.5^{\circ}\) linear polarization, respectively. Therefore, we used these two polarizations. According to the superposition principle of two perpendicular polarization states, we can see that the polarization state of the retroreflected beam is dependent on the intensity and phase difference of two beams inputting the PBS. We modulated the intensity of the beam by adjusting the modulation efficiency of the MQW to obtain the expected polarization while keeping the phase difference constant over a fixed opmodulator in every path and two MQW modulators in total for polarization modulation were required. However, it is worth noting that only a few polarizations can be achieved in this manner. Therefore, the B92 protocol which uses two nonorthogonal states was adopted in our system. Additionally, MQW3 was used for the decoy state, and there were only three MQW modulators in our system, which is less than the eight required by RM2018.
## 3 Results and Discussion
### Effect of Hit Compounds on Biofilm Structure  
Scanning electron microscopy (SEM) allowed us to probe the structure of the biofilms treated with the hit compounds. Biofilms were grown on metal pins for 3 days that were transferred daily to fresh LB medium using the JEKMag technique [51]. We found that, while there was not a large reduction in biofilm mass by the compounds alone, there was a very substantial change to the structure of the extracellular matrix of the biofilms. Biofilms treated with the 'hit' compounds (40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL S3-G1A and 40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL S3-G1B) exhibited a greatly reduced levels of matrix compared to untreated WT _E. coli_ C (Figure 6). Treatment with S3-G1B also resulted in the elongation of the cells, indicating the possibility of an unknown off-target effect inducing filamentation.  
Figure 5: Synergistic effect of compounds and ampicillin on amount of biofilm and cell viability in biofilm. (**A**) Biofilm degradation utilizing hit compounds and ampicillin. A = S1-G1A (50 \(\mathrm{\SIUnitSymbolMicro m}\)), B = S1-G1B (50 \(\mathrm{\SIUnitSymbolMicro m}\)), Amp# = Ampicillin \(\mathrm{\SIUnitSymbolMicro g}\)/mL, SH = serine hydroxamate, IDR = IDR 1018; (**B**) alamarBlue viability assay following combined treatment of cells with hit compounds and ampicillin showing a reduction in bacterial viability. * Indicates statistical significance.  
Figure 6: SEM images of _E. coli_ C biofilm. (**A**) Untreated wild type _E. coli_ C biofilm; (**B**) _E. coli_ C biofilm treated with 40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL S3-G1A; C _E. coli_ C biofilm treated with 40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL S3-G1B. Scale bar = 1 \(\mathrm{\SIUnitSymbolMicro m}\).
# A Scalable Moving Boundary Treatment in the Lattice Boltzmann Method
## 1 Introduction  
The problem of complex fluid flow has always been one of the most critical and challenging research fields in large-scale scientific and engineering computing. From a mesoscopic perspective, the lattice Boltzmann method (LBM), which originated from the lattice Gas Cellular Automata (LGA) method, is characterized by its simplicity and efficiency [1]. Due to the kinematic characteristics of the Boltzmann equation itself, the pressure field and local stress tensor can be directly obtained without solving the pressure Poisson problem. The feature makes LBM significantly suitable for large-scale parallel computing of complex flow problems [2]. This method has been widely used in many fields, such as two-dimensional multiphase flow [3], microscale fluid flow, biomechanics [4], and porous media [5]. However, there are problems in actual flow calculations, such as large computational grids, slow convergence speed, and weak numerical stability [6, 7]. In calculating the single relaxation time lattice Boltzmann model (SRT-LBM), this shortcoming can be overcome with the help of some turbulence model methods. Based on the eddy viscosity model theory, [PERSON] et al. [8] introduced the Smagorinsky vortex viscosity model into SRT-LBM, which effectively improved the Reynolds number limit of SRT-LBM and improved the flexibility of the algorithm. Hence, the followup research direction of this article is vascular fluid-solid coupling. The computational fluid is two-dimensional blood in the current research, and the Reynolds number is relatively low. SRT-LBM is sufficient to meet the needs of simulation.  
One of the most concerning issues in computational fluid dynamics is the fluid-structure coupling problem. One of the main challenges in solving the fluid-structure coupling problem is the moving boundary problem. [PERSON] [9] first proposed a Bounce Back (BB) method to deal with motion boundary problems in 1993. Based on this curveboundary processing method, many researchers [10; 11; 12; 13; 14; 15; 16] have expanded and improved to reduce the frequency fluctuation caused by the movement of grid points. In 2018, [PERSON] et al. [17] used the improved BB method to calculate the solid near and far areas by setting the main grid and the subgrid and proposed a floating grid method to deal with moving boundary problems. Because the immersion boundary method (IBM) is based on the Cartesian coordinate system and is suitable for combination with LBM, the application of IBM [18; 19; 20; 21; 22; 23; 24] in LBM is also another widely used moving boundary processing method.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 2 Related Work  
Considering Gaussian wiretap channel, [PERSON] et al. [32] introduced an auto-encoder to model the noised wireless channel with a novel security loss function. The generative model of the auto-encoder was trained to encode a message such that the eavesdropper cannot decode it correctly. Results show that the proposed scheme learns a trade-off between legitimate communication rate and secrecy capacity. With the help of the channel's statistical characteristic in relay networks, the authors in [33] proposed a new deep learning-based algorithm to design secure beamforming vector. Considering visible light communication, [PERSON] et al. [34] proposed deep reinforcement learning (DRL)-based secure communications strategy. Since the optimization of the system secrecy rate is non-convex and NP-hard, a suboptimal solution on beamforming vector can be obtained by introducing zero-forcing beamforming gain to the eavesdropper.  
With perfect CSI, the deep feedforward neural network (DFNN) with three layers was adopted for time-slot wireless powered system in [35]. In the proposed scheme, the tuple system parameters, such as the time allocation factor, the power allocation factor as well as the rate of the wiretap channel were produced by the DFNN. During the training phase, the output of the DFNN was compared with the optimal system parameters obtained from exhaust search, and the mean squared error (MSE) was adopted as the performance loss function. Numerical results of the DFNN and the optimal parameters were provided to validate the proposed scheme. Using Stackelberg equilibria, the authors in [36] proposed a secure mobile crowd-sensing (MCS) scheme, where the DRL technique was adopted to derive the optimal MCS policy.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Free Vibration Testing
#### 4.1.3 Free Vibration Testing: Discussions on the Influence of the Central-Cut Width
A closer look into the impact experimental results is possible by comparing all central-cut systems to one another, as shown in Figure 10. It follows from these plots that the 0.1\({}^{\prime\prime}\) cut system consistently has the lowest frequencies at each dominant peak among all systems, followed by the 0.5\({}^{\prime\prime}\) cut and 0.25\({}^{\prime\prime}\) cut systems, in that order. This trend is correct, except for the second mode, where the 0.5\({}^{\prime\prime}\) cut system has the lowest dominant frequency. This variability may be due to the influence of the central-cut width on the coupling between the second and third modes. The amplitudes of the Y- and Z-direction responses are also different, with the Y-responses typically being higher than those in the Z-direction. Figure 10: A comparison of impact results using (**a**) the side accelerometer and (**b**) the RC accelerometer. Figure 9: Impact vibration testing comparing (**a**) X-direction, (**b**) Y-direction, and (**c**) Z-direction responses from different accelerometers for the 0.5\({}^{\prime\prime}\) cut BARC system.