esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wear Resistance under Abrasive Blasting  
The tests of abrasive blasting wear resistance of the surfaced layers were carried out with the following parameters:  
* Type and granulation of the abrasive: broken steel grit WGH 40 (according to ISO 11124-3) with a hardness of 60-68 HRC, with a homogeneous martensitic and/or bainitic microstructure, with fine, well-spaced carbides, nominal fraction 0.43 mm;
* Angle of incidence of the abrasive jet in relation to the sample surface--60\({}^{\circ}\);
* Length of blasting jet: 100 mm;
* Diameter of pneumatic nozzle: 9 mm;
* Air pressure supplied to the nozzle: 4.3 bar (shown on the pressure gauge);
* Duration of the impact of the abrasive jet on the tested surface: six cycles, 10 s each.  
The cladded samples with dimensions of 50 \(\times\) 150 mm (half pipe) were prepared for the tests. These samples were taken from the welded pipes by means of electrical discharge cutting. The view of the prepared samples (on the example of a sample with a Stellite 6 coating) for the abrasive blasting test is shown in Figure 12(a).  
The research test consisted of six cycles which were performed for each material, where for each cycle, the time of the stream's impact on the sample surface was 10 s. The total time of exposure was 60 s. After each cycle, the degree of material wear was determined as a loss of mass. For this purpose, the samples were weighed with a RADWAG PS 1000.R2 (Poland) electronic balance with an accuracy of 0.001 g. Table 6 presents the test results and their graphic visualization is shown in Figure 12. The presented data show that the Stellite 6 padding weld had the best resistance to the blasting erosive jet.  
Figure 13 shows the view of the samples (Figure 12(a)) before and (Figure 12(b)) after the abrasive blasting wear test. The area of impact of the abrasive blasting stream on the tested surfaces is clearly visible. Surface wear is difficult to determine by any other method than weight loss assessment.  
The results of the tests for the individual trial series (duration time 10 s), are presented in Table 5 and in the graphical form in Figure 14. The obtained data show that for the Inconel 625 deposited coating, the weight loss recorded in the subsequent treatment cycles were smaller; for the layers made of Inconel 718 it slightly increased. This effect should be related to the inhomogeneous microstructure in the thickness of the padding welds. For  
Figure 13: View of the test specimens on the example of Stellite 6 coatings before (**a**) and after (**b**) the abrasive blasting wear test.  
the remaining layers and the native material, the weight loss recorded in subsequent series was at a comparable level. The test revealed that the wear resistance of Inconel 625 and Inconel 718 during the abrasive blasting test at room temperature is close to 16 Mo3, and the wear of Alloy 33. The wear resistance of the Stellite 6 coating is the highest among those compared.
## 3 Mathematical Modelling, Theoretical Aspects
### Rayleigh Quotient  
The strain energy is a quadratic form in displacements and the neutral equilibrium state assumes that the second-order variation of the total potential energy is zero, conditions presented through a mathematical from in Equation (57) [16].  
\[\delta^{2}U=\frac{1}{2}\{v(x)\}^{T}[K]\{v(x)\}=\{0\} \tag{57}\]Based on Equation (48) and Expression (23), Equation (57) can be expressed as in Equation (58).  
\[\{a_{e}\}^{T}\big{(}[K_{e}]-RQ\big{[}K_{ge}]\big{)}\{a_{e}\}=\{0\} \tag{58}\]  
In Equation (58) \(RQ\) represents the axial force multiplicator, which is expressed in Expression (59).  
\[RQ=\frac{\{a_{e}\}^{T}[K_{e}]\{a_{e}\}}{\{a_{e}\}^{T}[K_{ge}]\{a_{e}\}} \tag{59}\]  
Expression (59) represents the Rayleigh quotient, which approximates the axial force, value dependent on the displacement field \(v(x)\). In order to fix the value of \(RQ\), an iterative process must be launched which has as its starting value a certain displacement vector, a vector which readjusts after every iteration, with local convergence conditions. The defection shape at the moment of loss of equilibrium is not known, therefore the presented method is an approximative one [20].  
According to the Lagrange-Dirichlet condition, the equilibrium is stable if \(P<P_{cr}\) for every displacement \(v(x)\) compatible with the system's kinematic constrains. At the limit state, the moment before the loss of stability, the critical buckling force represents \(P_{cr}=min\big{(}\{RQ\}_{1\leq i\leq t}\big{)}\), where it defines the number of iterations.  
In Expression (59), the numerator represents the mechanical work of the internal efforts \(L_{ef}\) and the denominator the unitary mechanical work of the axial force, in this case, \(RQ\), \(\overline{L_{ext}(RQ)}\). Expression (59) for a beam-type element can be expressed as in Expression (60) [16].  
\[RQ(v(x))=\frac{L_{ef}}{L_{ext}(RQ)}=\frac{\frac{1}{2}\cdot\int_{0}^{L}E\cdot l (x)\cdot v(x)^{{}^{\prime\prime}2}dx}{\frac{1}{2}\cdot\int_{0}^{L}v(x)^{{}^{ \prime}2}dx} \tag{60}\]  
In Expression (60), the Rayleigh quotient is defined as an analytic expression concerning the displacement field \(v(x)\), and function \(l(x)\) is a continuous function that describes the modification of the moment of inertia of the beam-type element [20].
# Exploring Effective Bio-Cover Materials for Mitigating Methane Emission at a Tropical Landfill
## 2 Methodology
### Methane Oxidation Capacities and Methanotroph Population  
The samples were collected from three depths of 0-0.20 m, 0.20-0.40 m, and 0.40-0.60 m in the biofilters operated under stable conditions (6 th months of operation). Their methanotrophic activities and in situ microbial population were determined. To evaluate methane oxidation capacities, 10 g of wet media sample was placed in a 188 mL serum bottle capped with rubber septa and an aluminum ring. Then, 10 mL of 99.9% methane gas was injected into each bottle. All tests were incubated at room temperature (28-30 \({}^{\circ}\)C). The headspace gas composition in the bottle was determined at different times (0, 5, 24, 48, and 72 h) using the gas chromatograph. The methane oxidation rate (\(MOR\)) was then calculated using the following equation.  
\[MOR\ =\ \frac{V_{g}}{W_{m}}\left(\frac{dC}{dt}\right) \tag{4}\]  
where \(V_{g}\) = gas volume in headspace (cm\({}^{3}\)), \(W_{m}\) = dry weight of media (g), and \(dC/dt\) = linear slope of methane concentration over time (\(\mu\)mol/cm\({}^{3}\)/h).  
Moreover, the carbon dioxide production rate (CPR) was also determined from its increasing rate during the batch experiment in the same manner as \(MOR\) to confirm the stoichiometric ratio of methane consumption and carbon dioxide production.  
For the microbial study, the methanotrophic consortium including Methanotroph types I and II were examined using the fluorescence in situ hybridization (FISH) technique. The fluorescent probes My84 + My705 were used for counting the methanotroph type I population, whereas that of M\(\alpha\)450 was used for the methanotroph type II population [24]. The use of polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE) techniques followed by the determination of DNA copies in each DGGE band was performed to quantify the methanotroph populations. The detailed analytical procedures are described in [PERSON] et al. [25].
## 3 System Implementation  
The block diagram of the proposed system is shown in Figure 2. The proposed system has six implementation stages, which are: (1) sampling and recording; (2) compress and transmit; (3) receive and storage; (4) feature extraction; (5) classification; (6) monitoring platform. We describe each of these in detail in the following sections.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Material Cost** \\ \hline Acoustic sensor & \$12.8 \\ Embedded processing board & \$34.3 \\ Wireless transmission module & \$28.5 \\ Solar panel & \$10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The cost of each sensor node.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 5 Conclusions  
A new architecture for the phase detector was presented for an all-digital OPLL to demodulate a digitally modulated optical signal. The phase discriminator is a digital type that can track a signal under longer feedback loop delays and fading signals. It was based on modifying the traditional algorithm used in coherent optical communications. This phase detector made it possible to find the best operation point for the OPLL, which was then used to handle the effects of atmospheric turbulence. In addition, the OPLL using the digital discriminator showed a low standard deviation of the controlled frequency and low phase noise, making it an alternative for fully digital OPLLs.  
Moreover, the OPLL was analyzed working with two sample rates, showing that low sample rates will have a higher phase error variance, and consequently, a higher power penalty. Furthermore, due to the phase discriminator features, the OPLL could be analyzed in different scenarios of atmospheric turbulence and wind speed values. It was observed that traditional analog detectors, which have a sinusoidal characteristic, are unsuitable for a system that fluctuates the input signal amplitude. This is due to the analog detector gain related to the system parameters, such as the input signal amplitude.  
It also has been observed that the system degrades when increasing the wind speed and turbulence. However, wind speed is the principal cause of system degradation for speeds higher than 20 m/s. During the simulations, the scintillation index behavior was observed to be comparable to the obtained BER profiles. The hardware implementation of the OPLL using the proposed digital discriminator is currently being developed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & [5; 18; 21; 22] & [7; 8; 20; 32] & **This Study** \\ \hline Methods & DSP & OPLL or OIPLL & Hybrid DSP-OPLL \\  & & XOR gate, analog & VV algorithm \\ Phase estimation & VV algorithm & multiplier, or & \\  & data blocks & fourth-power & \\  & & look-up table & with SR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of previous optical coherent receivers.  
**Author Contributions:** Conceptualization, J.P.; methodology, J.P. and N.A.; software, J.P.; validation, J.P., N.A. and F.D.; formal analysis, J.P.; investigation, J.P. and N.A.; resources, F.D. and A.R.; data curation, J.P. and N.A.; writing--original draft preparation, J.P.; writing--review and editing, J.P. N.A., F.D., G.M.P. and A.R.; supervision, G.M.P. an A.R.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq/Brazil) under Grant 201259/2018-4.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset generated during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Results
### Parameter Uncertainty Analysis of Burgers' Equation
#### 3.2.1 Estimate Analysis
In the three numerical experiments, when the cost is \(10^{6}\), the MSEs estimated by the multi-fidelity models are \(1.3844\times 10^{-5}\), \(1.3648\times 10^{-5}\) and \(3.4297\times 10^{-6}\) in sequence. Therefore, the MSE of the models are further decreased when the calculation budget for output function expectation is increased. Compared with the standard MC method, under the same computational budget, the MFMC can be superior to the MC for each statistical quantity. Figure 6 shows the relative share of each model in the total number of samples. The shares of the models vary by orders of magnitude between the high-fidelity and low-fidelity models, reflecting their correlations and costs. It should be noted that the relative share of the model is not related to the calculation budget \(p\), because all components of \(m\) are linearly proportional to \(p\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & \(\mathbf{\alpha}\) & \(\mathbf{\omega}\) & \(\mathbf{m}\) \\ \hline High-fidelity (FD) \(f^{(1)}\) & \(1\) & \(385\) & \(2597\) \\ Low-fidelity (DEIM) \(f^{(2)}\) & \(0.0233\) & \(0.001\) & \(8475\) \\ Low-fidelity (OR + DEIM) \(f^{(3)}\) & \(0.0221\) & \(1\times 10^{-4}\) & \(11,784\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of samples per model, coefficients and costs for Burgers’ equation. Figure 5: The estimated MSE of the standard MC estimators and the MFMC estimators of budgets from to \(10^{6}\). (**a**) MSE estimation of \(f^{(1)}\), \(f^{(2)}\). (**b**) MSE estimation of \(f^{(1)}\), \(f^{(3)}\). (**c**) MSE estimation of \(f^{(1)}\), \(f^{(2)}\), \(f^{(3)}\). Figure 6: The total number of samples distribution in high-fidelity and low-fidelity models.
## 2 Materials and Methods
### Genetic Diversity Analysis
#### 2.4.2 ISSR Analysis and PCR Amplification  
The ISSR reaction was performed with 50 ng of genomic DNA and the PCR reaction was carried out in a 25 uL final reaction mixture volume containing Taq buffer (1\(\times\)), MgCl\({}_{2}\) (3 mM), dNTPs (0.2 mM), primer (0.5 mM), and _Taq polymerase_ (0.5 U) (all obtained from Merck Biosciences, India). PCR amplifications were carried out (SureCycler 8800, Agilent Technologies) under the following conditions: 5 min at 95 \({}^{\circ}\)C (initial denaturation) followed by 40 cycles at 94 \({}^{\circ}\)C for 1 min, 1 min at the annealing temperature (46-61 \({}^{\circ}\)C), extension at 72 \({}^{\circ}\)C for 2 min and 72 \({}^{\circ}\)C for 10 min for the final extension. Amplicons were separated on 1.8% agarose gel electrophoresis (75-80 V) for 2 h and the gels were visualized and photographed by a gel documentation system (UVC-1200, Major Science, Taiwan). All ISSR reactions were repeated at least three times to determine the reproducibility of the profiles. A set of 35 random ISSR primers were screened, out of which 12 primers were selected depending on the clarity and reproducibility of the banding patterns and the level of polymorphism detected. Using the visual method, the amplified bands were scored for presence (1) or absence (0). During analysis, only those bands which were persistent among replicates and repeated on two independent runs were included.
## 2 AMF  
### Basic Theory of MF  
There are four kinds of basic operators in MF: dilation, erosion, opening, and closing. The one-dimensional original signal is denoted by \(f(n)\), whose discrete form could be defined as \(F=(0,1,\cdots,N-1)\). \(g(m)\) is also a one-dimensional signal, whose discrete form is defined as \(G=(0,1,\cdots,M-1)(N\geq M)\). \(g(m)\) is the SE and the above four basic operations of mathematical morphology are defined as follows:  
Dilation:  
\[(f\oplus g)(n)=\max\{f(n-m)+g(m)\}\{1\leq n\leq N;1\leq m\leq M\} \tag{1}\]  
Erosion:  
\[(f\Theta_{\mathcal{G}})(n)=\min\{f(n+m)-g(m)\}\{1\leq n\leq N;1\leq m\leq M\} \tag{2}\]  
Opening:  
\[(f\circ g)(n)=(f\Theta g\oplus g)(n) \tag{3}\]  
Closing:  
\[(f\bullet g)(n)=(f\oplus g\Theta g)(n) \tag{4}\]  
where \(\oplus\), \(\Theta\), \(\circ\), and \(\bullet\) represent the dilation operator, erosion operator, opening operator, and closing operator, respectively.  
The following several commonly used morphological operators could be constructed through the cascade combination of the above four morphological operators:  
Dilation and erosion gradient operator (GDE) [31]:  
\[GDE(n)=(f\oplus g)(n)-(f\Theta g)(n) \tag{5}\]  
Closing and opening gradient operator (GCO) [35]:  
\[GCO(n)=(f\bullet g)(n)-(f\circ g)(n) \tag{6}\]  
Closing-opening and opening-closing gradient operator (GCOOC) [36]:  
\[GCOOC(n)=CO(n)-OC(n) \tag{7}\]  
Dilation and erosion average-hat operator (AHDE) [37]:  
\[AHDE(n)=f(n)-\frac{(f\oplus g)(n)+(f\Theta g)(n)}{2} \tag{8}\]Closing and opening average-hat operator (AHCO) [38]:  
\[AHCO(n)=f(n)-\frac{(f\bullet g)(n)+(f\circ g)(n)}{2} \tag{9}\]  
Closing-opening and opening-closing average-hat operator (AHCOOC) [39]:  
\[AHCOOC(n)=f(n)-\frac{CO(n)+OC(n)}{2} \tag{10}\]  
Although the feature extraction ability of the above kinds of combined morphological operators shown from Equations (5)-(10) could be enhanced greatly, their transient feature extraction performance is still unsatisfactory in the case of strong interference [40]. To solve the above problem, the morphology gradient product operator (MGPO) [40] as in the following is constructed by using the product of GCO and GCOOC:  
\[MGPO(n)=GCO(n)\bullet GCOOC(n) \tag{11}\]  
The same as the construction ideology of \(MGPO\), the \(MHPO\)[41] is introduced to extract the transient features by integrating AHCO and AHCOOC:  
\[MHPO(n)=AHCO(n)\bullet AHCOOC(n) \tag{12}\]  
The excellent performance of MGPO and MHPO in transient features extraction over the other traditional morphological operations has been proved and the other two new morphological operations named as MHPO1 and MHPO2 originating from AHDE [37] are introduced for further enhancing the extraction ability of transient features:  
\[MHPO1(n)=AHDE(n)\bullet AHCO(n) \tag{13}\]  
\[MHPO2(n)=AHDE(n)\bullet AHCOOC(n) \tag{14}\]  
In this paper, the MHPO1 shown in Equation (13) is used as a morphological operator for the reason that AHDE and AHCO had been verified much more efficiently than AHCOOC in extracting transient features [37].
## 4 Mapping Algorithm
### Feature Extraction  
To facilitate a deeper understanding of the substrate network by the agent, it is imperative to distill the essence of each substrate node and fashion it into a feature matrix that can be fed into the policy network. In this paper, we have chosen four salient attributes for every substrate node.  
1. Computing resources: In virtual network mapping, the CPU performance of the substrate nodes directly affect the ability to host virtual nodes.
2. Degree: The degree of node \(n_{i}^{S}\) refers to how many nodes are connected to it. The large degree of a node means that it has higher connectivity and importance in the network.
3. Sum of bandwidth: \(SUM^{BW}\) of node \(n_{i}^{S}\) refers to the total bandwidth available across all of the links that are next to it. The better the node can meet the virtual node's computing and data transmission requirements, the greater the total bandwidth: \[SUM^{(BW)}\left(n_{i}^{S}\right)=\sum_{l_{j}^{S}\in L\left(n_{i}^{S}\right)} BW\left(l_{j}^{S}\right)\] (15) where \(L\left(n_{i}^{s}\right)\) represents the link adjacent to node \(n_{i}^{s}\) and \(BW\left(l_{j}^{S}\right)\) represents the bandwidth resource of link \(l_{j}^{s}\).
4. Average distance to other host nodes: We take into account the mapping positions of many virtual nodes in the same request in addition to the mapping location of a single virtual node when mapping virtual nodes to substrate nodes. We can save costs by choosing to map virtual nodes to places near base nodes that have already been mapped. The shortest path is found using the Floyd-Warshall algorithm, and the number of links on the shortest path is used to calculate the distance between two substrate nodes. When mapping virtual nodes to substrate nodes, we not only consider the mapping location of a single virtual node, but also the mapping locations of other virtual nodes in the same request. By selecting to map virtual nodes to locations close to already mapped base nodes, we can reduce costs. We use the Floyd-Warshallalgorithm to calculate the shortest path, and determine the gap from the substrate node to another based on the number of the shortest path's links:  
\[AVG^{(DST)}(n_{i}^{s})=\frac{\sum_{\tilde{n}_{i}^{s}\in\tilde{N}^{S}}DST\big{(}n_ {i}^{s},\tilde{n_{i}^{s}}\big{)}}{\left|\tilde{N}^{S}\right|+1} \tag{16}\]  
where \(\tilde{N}^{S}\) denotes the complementary set of \(n_{i}^{s}\), \(\tilde{n_{i}^{s}}\) is the node in \(\tilde{N}^{S}\), and \(|\tilde{N}^{S}|\) represents the set size of \(\tilde{N}^{S}\). We normalize the feature extraction results of node \(n_{i}^{S}\) and form a feature vector:  
\[v=(CPU(n_{i}^{s}),DEG(n_{i}^{s}),SUM^{(BW)}(n_{i}^{s}),AVG^{(DST)}(n_{i}^{s}))^ {T}. \tag{17}\]  
After feature extraction for all substrate nodes, we connect their respective feature vectors into a feature matrix:  
\[M_{f}=\Big{(}v_{1},v_{2},...,v_{\left|N^{S}\right|}\Big{)}^{T}. \tag{18}\]
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 3 Data Preprocessing and Model Training
### Model Training
#### 3.2.1 Hyperparameters  
Hyperparameters are model parameters that need to be set before the training process begins, and they significantly impact the accuracy of the developed model [29].  
In this work, we predefine the range of possible values for each hyperparameter and then utilize the \"grid search\" method [30] to exhaustively explore various combinations of these parameters. Once the program traverses all the parameter combinations, the optimal set is identified and output. In the present study, the utilized hyperparameters are as follows: \(n\_estimators=120\), \(max\_features=13\), \(min\_samples\_leaf=1\), and \(min\_samples\_split=3\).
# Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops
###### Abstract  
This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.  
multi-sensor systems; robust fusion estimation; event-triggered; random packet drops; d-step state delay; deterministic control inputs +
Footnote †: journal: Applied sciences