esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Numerical Method: Polynomial Modal Method  
Let us consider in a Cartesian coordinates system \((\mathbf{e_{x}},\mathbf{e_{y}},\mathbf{e_{z}})\) the structure of Figure 3a which is consisted of a metallic film with arrayed nanoslits (air-gap) with a constant depth \(h\) but variant widths \((e_{k})_{k}\). Our design's objective consists of optimizing the \((e_{k})_{k}\)-sequence in order to obtain a focusing of the incident field at a desired space location.  
Figure 3: design of a one dimensional flat metalens. (**a**) Schematic of dispersive metal film perforated with a non periodic subwavelength array of 1D nanoslits. (**b**) the adjoint-based optimization method involved two different sources: an electric dipole and a polarized incident plane wave. (**a**) Sketch of the plasmonic dispersive metalens; (**b**) Assessment of the FOM variation computing.  
The Aperiodic Polynomial Modal Method (APMM) is used to perform the forward and the backward problems in two simulations. See Figure 3b. For audiences who are not familiar with this method, let us briefly outline the main steps of the polynomial Modal Method (PMM) based on Gegenbauer polynomials \(G_{n}^{\Lambda}(x)\) expansion [22; 23; 24; 25; 26]. In the PMM/APMM, the structure is divided into \(N_{p}\) intervals \(l_{x}^{(k)},k=1:N_{p}\) in the \((Ox)\) direction and \(N_{z}\) layers \(I_{z}^{(l)},l=1:N_{z}\) in \((O,z)\) direction. In the current example \(N_{z}=3\). In each layer \(I_{z}^{(l)}\), each component of the electromagnetic field is expanded on a set of eigenfunctions, i.e.; solutions \(|\psi_{q}\rangle\) of an eigenvalue equation; that is in the current case, the following TM propagation equation Equation (10):  
\[\mathcal{L}^{(l)}(\omega)|\psi_{q}^{(l)}(\omega)\rangle=(\gamma_{q}^{(l)}( \omega))^{2}|\psi_{q}^{(l)}(\omega)\rangle \tag{10}\]  
with  
\[\mathcal{L}^{(l)}(x,\omega)=\Big{(}\frac{c}{\omega}\Big{)}^{2}\epsilon^{(l) }(x,\omega)\partial_{x}\frac{1}{\epsilon^{(l)}(x,\omega)}\partial_{x}+ \epsilon^{(l)}(x,\omega). \tag{11}\]  
In each layer \(l\) the \(H_{y}^{(l)}(x,z)\) component is then written as linear combination of the eigenfunctions \(\psi_{q}\) of Equation (10):  
\[H_{y}^{(l)}(x,z,\omega)=\sum_{q}A_{q}^{(l)}(\omega)e^{-ik_{0}(\omega)\gamma_{q }^{(l)}(\omega)z}\psi_{q}^{(l)}(x,\omega). \tag{12}\]  
The relative permittivity of the upper and the lower regions are denoted by \(\epsilon^{(1)}=1\) and \(\epsilon^{(3)}=1.45\) respectively. Silver and gold are considered in our simulations and the Drude-Lorentz model [27; 28] is used to describe their dispersive relative permittivity function \(\epsilon^{(metal)}\). Our design objective involves two electromagnetic sources: an electric dipole source for the direct or forward problem and a polarized incident plane wave for the reciprocal or backward problem. The forward and backward configurations, used to compute the variation of the FOM are sketched in Figure 3b. Practical details for the numerical treatment of the dipole source implementation in modal method can be found in [29].
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Temporal Resolution  
_Ranking:_ SSN \(>\) VSN \(>\) CSN  
_Reasons:_  
One of the objectives of TNGAPMS is to increase the temporal resolution of the acquired air pollution information. And the air pollution information from all TNGAPMSs has much higher temporal resolution than that from the conventional monitoring systems. However, the temporal resolutions of the acquired pollution information in SSN, CSN and VSN are slightly different due to several reasons.  
_Explanation:_  
In terms of building a pollution map, the pollution information from SSN systems has the highest temporal resolution. Then comes the pollution information from VSN systems, followed by that from CSN systems. The ranking is based on the assumptions that the sensor nodes' sensing rates are identical in different systems and the sensors have a limited effective coverage [99]. In a single sensor node case, the pollution information's temporal resolution of a specific location (a circular area with a specific radius) in SSN systems is the sensor node's sensing rate itself. However, in VSN and CSN systems, the pollution information's temporal resolutions at a specific location depend on how frequent the location is visited and how often the pollution data are sensed at that location. Intuitively, the mobility of VSN and CSN systems lowers the temporal resolutions of the acquired pollution information. Moreover, the pollution information's temporal resolution is further reduced by redundant sampling issues like traffic jams, parked vehicles and indoor stay of users (In this case, the average temporal resolutions of SSN, CSN and VSN systems are compared. In SSN systems, only one location is monitored. In CSN and VSN systems, one sensor node typically covers several locations and this results in lower average temporal resolutions when redundant sampling issues happened).  
In terms of monitoring personal exposure, the pollution information's temporal resolution for people wearing the sensor nodes in CSN systems is the highest. For people without carrying the sensor nodes, the temporal resolution of the pollution data on personal exposure depends on the pollution map.
## 3 Genetic Algorithm and Random-Restart Hill-Climbing (GA-RRHC)
### Random-Restart Hill-Climbing (RRHC)  
A local search method is intended to exploit the information of the smart-cells to decrease the value of their makespan. The general idea is to start from each smart-cell and make small changes to improve the makespan. The metaheuristic used is the random-restart hill-climbing (RRHC) [30]. The RRHC can restart the search from a solution with a makespan worse than the original one after a given number of steps to escape local minima. The whole process ends after a fixed number of steps.  
The critical operations of the smart-cell are first detected to apply the RRHC. These operations define the value of the makespan, and these operations are linked by job or by a machine whose sum of processing times from the beginning to the end gives the makespan as a result, with no idle times between these operations [18].  
A record of the previous task of each operation is kept when calculating the makespan to know which are the critical operations of a smart-cell, where the initial operations of each job do not have a previous operation. This record allows a fast computation of the critical operations by simply taking one of the last operations with a completion time identical to the makespan. Subsequently, previous operations on the same machine and at the same job are analyzed, taking that with end time equal to the start time of the present operation. A random pick is made if the same completion time is held by both previous operations. The procedure is repeated until an operation with no preceding operation is reached. Figure 10 presents the critical path of the solution represented in the Gantt diagram of Figure 1.  
From the set of critical operations, one is taken at random, and a different feasible machine is chosen to have a new sequence \(MS^{\prime}\). Additionally, another critical operation with probability \(\alpha_{c}\) is selected, and is swapped with any other operation in \(OS\) to have a new sequence \(OS^{\prime}\) and generate a different solution. Figure 11 shows a makespan improvement changing the machine assignment of a critical operation.  
Figure 10: Critical path of the solution described in Figure 1.  
The RRHC is applied for \(H_{n}\) iterations, having a pile of \(H_{r}<H_{n}\) new solutions generated through the described process. If one of these solutions improves the makespan, this solution replaces the smart-cell, emptying the pile. If the makespan of the original smart-cell has not been improved after \(H_{r}\) iterations, a new random solution is taken from the pile to restart the climbing. Since the RRHC focuses on improving machine allocation, it works best for instances with high flexibility.
## 3 Adaptive Cruise Controller Design
### MPC Controller Design
#### 3.1.3 Design of Fuzzy Weight  
As an intelligent algorithm, fuzzy control is robust and can provide solutions for nonlinear time-varying systems and systems that are difficult to model. In addition, since the fuzzy rules can be changed at any time, it has good adaptability to the system [41]. Therefore, this study used a fuzzy control algorithm to optimize the weights. Taking the distance error (the difference between the actual distance and the expected distance) and the main vehicle speed as input, and taking the economic weight as the output, the main vehicle speed domain was set to [0, 140] m/s and the relative distance domain was set to [\(-\)5, 5] m. The main vehicle speed was divided into three types according to speed value, and the corresponding logical language was [S, M, B]. The distance error was divided into 7 types according to error value, and the corresponding logical language was [NB, NM, NS, Z, PS, PM, PB]. To fuzzy the economic weights, the logical language was [S, MS, M, MB, B], and the range of variation was [0, 20]. Both the input variables \(d_{err}\) and \(v_{f}\), and the output variable \(\omega_{3}\), used trigonometric membership functions, as shown in Figure 7. Through the analysis of traffic scenarios, it was found that when the speed is low, attention should be paid to following the driving to ensure safety, and the weight given to economic factors should be small. When the speed is high, the demand for tracking is reduced due to the long distance between vehicles, the focus is on economical driving, and the weight given to economy should be increased. The specific fuzzy rules are shown in Table 3. Since the barycenter method is intuitive, reasonable, simple, and accurate in control, the barycenter method was selected to deblur. The corresponding input and output results were obtained in accordance with the fuzzy rules, and the economic weight coefficient surface solved is shown in Figure 8.
## 4 Result and Discussion
### Prediction and Validation  
#### 4.1.1 Predictions via Support Vector Machine (SVM)  
The parameters tuned for SVM are soft margin constant (C), kernel parameter, width of Gaussian kernel (gamma), and degree of polynomial fit. Hyperparameter C is typically a regularization parameter for trade-off between the errors of training data. Kernel is iteratively selected to be radial bias function as it gives higher accuracy in this case compared to other functions. Gamma is defined as the extent of influence by a single training example and is used for non-linear hyper planes and the degree is a parameter basically to define the polynomial fit of a hyper plane. The pressure loss for laminar flow in concentric and eccentric annular annulus is predicted using SVM. The predicted values for the training and the test data are plotted in Figure 1 with observed pressure loss in the experimental setup. The RMSE of 21.92 kPa and MAE of 17.92% is calculated for train data set with R\({}^{2}\) = 0.925, while for test data RMSE is 22 kPa, MAE is 18.34% with R\({}^{2}\) = 0.83. Figure 1 illustrates that majority of the deviation from the 45\({}^{\circ}\) best fit curve lies below 50 kPa data points. The deviation is observed to be slightly increasing towards higher pressure scale.  
Figure 1: Support Vector Machine (SVM) predicted pressure plotted with observed pressure loss: (a) training dataset and (b) test dataset.
## 6 Conclusions  
GOSAT and OCO-2 are the first two satellite programs dedicated to monitoring the global CO\({}_{2}\) distribution. In this paper, we demonstrated the reliability of CO\({}_{2}\) remote sensing from space by comparing radiances and retrieved XCO\({}_{2}\) and P\({}_{\rm surf}\) values from different types of spectrometers.  
The most recent versions of GOSAT and OCO-2 spectral radiance and retrieved XCO\({}_{2}\) products from temporally coincident and spatially collocated points were compared. The measured spectral radiances from both instruments agree reasonably well; within 5% for all bands, only larger bias at shorter wavelengths in the ABO2 band. Some possible causes are imperfect GOSAT prelaunch calibration in ambient conditions and faster degradation at shorter wavelengths within a band. The high gain SCO2 radiance difference presents at shorter wavelengths in the SCO2 band, while the WCO2 systematically is below 3%. These biases are small, but may still be large enough to affect the XCO\({}_{2}\) retrieval. The XCO\({}_{2}\) retrievals over ocean agree very well, with a bias and standard deviation of \(-\)0.17 \(\pm\) 1.48 ppm. Over land, the mean biases and standard deviations are \(-\)0.57 \(\pm\) 3.33 ppm with high gain and \(-\)0.19 \(\pm\) 2.79 ppm with medium gain. The land data with high gain have larger deviation, probably due to BRDF uncertainty. Trend analysis of \(\Delta\)P\({}_{\rm surf}\) comparison also suggests the necessity of fine radiometric correction, but most of the root causes of large bias and deviation were identified. By improving individual data processing and calibration, GOSAT and OCO-2 can provide long-term uniform quality data and consistent retrieval accuracy from dark ocean to bright desert targets.  
At present, satellite observation is the only method to monitor XCO\({}_{2}\) globally and periodically. However, coverage and frequency of a single instrument are still limited. Improvement of cross-calibration on-orbit will contribute to the future satellite observation network of greenhouse gases with multiple instruments.  
**Acknowledgments:** The data used in this analysis were produced by the JAXA GOSAT team at Tsukuba Space Center and the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology. Part of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The CSU/CIRA contribution to this work was supported by JPL subcontract 1439002. Government sponsorship is acknowledged.  
**Author Contributions:** [PERSON] contributed to data analyses and preparation of the manuscript. [PERSON], [PERSON] and [PERSON] are part of the ACOS and OCO-2 retrieval algorithm team. [PERSON], [PERSON] and [PERSON] guided the GOSAT Levell algorithm and calibration. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON][PERSON] worked on the OCO-2 Level1 algorithm and calibration. All authors read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Rain Gauges Dataset  
The National Center for Space Research (INPE) is the institute responsible for receiving, storing, and organizing rain gauge rainfall data from different agencies in Brazil, such as the National Meteorological Institute (INMET) National Water Agency (ANA), Energy Company of Minas Gerais (CEMIG), Instituto Agronomico (IAC), and the Meteorological System of Parana (SIMEPAR). In this study, we used a rainfall database as the reference data. Daily rainfall data were organized in DAT format and interpolated for a regular \(1^{\circ}\times 1^{\circ}\) grid using a simple average. We used INPE's 2012 to 2016 temporal series of rain gauge data with 317 valid grid points (i.e., grid points with more than 90% of valid data).
# A Method of Two-Stage Pressure Control Based on Multistage Orifices
## 1 Introduction
This type of installation eliminates leakage, vibration, and noise caused by the oil pipe and pipe joint, but the layout of internal flow channel is more complicated, such as various right-angle bend structures, section shrinkage, or expansion [10; 11; 12; 13]. Structures force the oil to change flow rate, direction, or both, resulting in additional pressure loss. When several orifices are installed in the manifold block, the flow characteristics of fluid passing through the channel structure, solenoid valve, and orifice are coupled with each other, and the flow situation is more complex. These can have an unexpected adverse effect on low-level pilot pressure control. Experiment results of many scholars showed that, when studying the flow characteristics of the orifice, obtained results by researchers using manifold block structure were always slightly higher than those obtained by researchers using horizontal pipes [14; 15; 16]. With the development of computer technology, many scholars used computational fluid dynamics (CFD) technology to study the fluid-flow characteristics and pressure loss of the typical channel structure in the manifold block [17; 18; 19; 20; 21; 22; 23; 24; 25]; however, these studies did not analyze the impact of manifold block structure on the pressure control circuit from the perspective of overall application. So far, there is no literature on the effect of manifold block structure on the maximal and minimal control pressure of multistage series orifices. This paper proposes a two-stage pressure control method based on multistage orifices. Our objectives were to theoretically and experimentally verify the feasibility of the method, design a project implementation scheme on the basis of an integrated structure, and clarify the influence of structures of complex flow channels and solenoid valves on the expected two-stage control pressure value on the basis of this method.
## 4 Results and Analysis
### Impact with Two Degrees of Freedom  
The equations system is solved forcing \(\omega~{}=~{}0\), in order to analyse the impact with two degrees of freedom, vertical motion, and horizontal motion. Figure 11 shows the pressure distribution before the flow separation from the knuckle for a wedge with \(\beta_{1}=20^{\circ}\), \(\beta_{2}=30^{\circ}\), and velocity ratio \(v/w\) = 0.5. The results are compared with CFD simulation performed by [PERSON] [15], presenting a good agreement, the error in the peak pressure is 7.27%, with respect to [15].  
Figure 11: The pressure distribution during impact with horizontal velocity \(\beta_{1}=20^{\circ}\), \(\beta_{2}=30^{\circ}\), and \(v/w=-0.50\).  
Figure 9: C\({}_{\rm p}\) vs. \(\beta=30^{\circ}\), impact with symmetry entry.  
Figure 12 shows the results of vertical force for a wedge with \(\beta_{1}=20^{\circ}\), \(\beta_{2}=30^{\circ}\), and velocity ratio \(v/w=-1.0\). The results before and after the flow separation from the knuckle are compared with the CFD results obtained by [PERSON] (2015), presenting reasonably good agreement.  
The equations system is solved forcing \(\
u=0\), in order to analyse the impact with two degrees of freedom, vertical motion, and rotation motion. Figure 13 shows the pressure distribution before the flow separation from the knuckle for a wedge with \(\beta=45^{\circ}\), \(\mathrm{Vcg}=0.25\) m, \(\omega=-2.5\) rad/s, \(\mathrm{w}=5\) m/s, and initial heel angle \(\phi_{0}=-10\). The results are compared with [PERSON]. (2019) and the CFD simulation, presenting good agreement.  
Figure 12: Forces variation during impact with horizontal velocity, \(\beta_{1}=20^{\circ}\), \(\beta_{2}=30^{\circ}\), and \(v/w=-1.0\). (**a**) Vertical force during the impact. (**b**) Roll moment during the impact.
# BChainGuard: A New Framework for Cyberthreats Detection in Blockchain Using Machine Learning
## 4 Experimental Results
### Comparison between BChainGuard and Works on the Same Dataset  
In this section, we make a comparison between our framework result and the result of work on the same dataset. In BChainGuard, the accuracy of MLP in is 98.2% and 95% for SVM. However, in [28], the best accuracy is performed at 98.8% using Random Forest, while the smallest one is 82% for logistic regression. In the same context, the accuracy of detection abnormal transaction using SVM and KNN is 95%. Our contribution is not only improving the accuracy of detection but also securing the dataset against poisonous attack, making the detection safe by embedding the decision function as a smart contract. In addition, we chose to embed only the decision function on the blockchain to minimize the overhead of runtime and deployment, which was discussed in the previous section. In what follows, we make a comparison between BChainGuard and other frameworks.  
Table 8 shows that our framework is the first one that uses machine learning, embedded in the blockchain itself, and with the lowest deployment and runtime overhead compared to others. In future works, we plan to make a secure analysis of BChainGuard by injecting attacks on the dataset, as well as on Ethereum, to see the performance of our framework.  
Recently, many research efforts have been conducted to show the great impact of applying blockchain in Industry 4.0 [28]. This generation of industry investigates the application of the latest technology innovation in Artificial Intelligence, Big Data, the Internet of Things and Blockchain for supply chain and manufacturing improvement. On one hand, Blockchain with its great potential can raise many opportunities for Industry 4.0. On the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tools** & **Technique** & **Place** & **Overhead** \\ \hline BChainGuard & Machine learning & On-chain & Low \\ \hline TRS [17] & Graph theory & On-chain & High \\ \hline ADM [18] & Machine learning & off-chain & - \\ \hline ContractGuard [19] & Statistical analysis & On-chain & High \\ \hline SODA [20] & Statistical analysis & On-chain & High \\ \hline BAD [21] & Statistical analysis & On-chain & High \\ \hline SolGuard [22] & Statistical analysis & On-chain & High \\ \hline DefectChecker [23] & Statistical analysis & On-chain & High \\ \hline \hline \end{tabular}
\end{table}
Table 8: Frameworks comparison.  
other hand, although Blockchain is one of the most secure peer-to-peer systems, it can be the target of attacks and cyberthreats. Consequently, securing service-based blockchain has high importance. In this context, we believe that BChainGuard is a successful key to supporting the application of Blockchain in Industry 4.0. Our framework can be improved by considering more types of attacks and next integrated into Ethereum as a new protection layer against smart contract vulnerabilities. This can improve Ethereum's trustworthiness to be more attractive to the industry.
## 5 GLI-Split Implementation
### OMNeT++ Simulation  
OMNeT++ is a modular simulation environment which can be applied to all problem domains where the discrete event- based approach is suitable. Especially for communication networks, the INET Framework for OMNeT++ offers all required communication protocols to model TCP/IPv6 based networks.  
Figure 10 shows an example network in INET with single-homed GLI-domains, as well as classic IPv6 domains.  
The different domains are connected via three provider networks. A global DNS module ensures DNS-name-to-global-locator address resolution. Each domain has an administration module (admin) which configures the identifier and local locator prefixes per domain and installs required routing entries. On the global area, a similar administration module configures the global locators per domain and installs inter-domain routing entries. The GLI modules in the implemented model of our GLI-Split architecture comprise the GLI-gateways, as well as local and global mapping servers. Each GLI-domain has its own  
Figure 10: GLI-Split architecture in the INET Framework for OMNeT++ with GLI-domains and classic IPv6 domains.  
local mapping server and there is one global mapping server for the entire network which serves as interface to the mapping service. In the following, this test network is used to present a brief evaluation with respect to the round-trip-time for communications between different domains.