esa-sceva/satcom-chunk-collection huggingface dataset:
# The Fungicidal Effect of Essential Oils of Fennel and Hops against Fusarium Disease of Pea
## Materials and Methods
### DNA Extraction and qPCR Analysis  
DNA was isolated from pure cultures as well as from stems and roots of pea plants. Genomic DNA was extracted from pea tissue (stored at \(-\)80 \({}^{\circ}\)C prior to analysis) using Maxwell 16 kits (Promega GmbH, Madison, WI, USA) for DNA isolation from plant tissue. Pure cultures of F. culmorum (OHH2014/75) were used for the qPCR assay. The quality and quantity of DNA was determined using the Nanodrop ND 2000C (Thermo Fisher Scientific, Waltham, MA, USA) and the Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Genomic DNA was rinsed in TE buffer and stored at 4 \({}^{\circ}\)C until further analysis.  
High quality gDNA (A260/280 ratio of 1.8 to 2.0) was used for the qPCR assay. The following primers and probes were used to quantify the severity of Fusarium wilt disease in pea plants in the pot experiment: FC _Tri5 forward: TCTTAACAC-TAGCGTGGCCTTC, FC _Tri5 reverse: CATGCCAACAGATTGTTTGAGGGA, FC _Tri5 probe: fam - AACAAG-GCTGCCCCACCACTITT GCTCAGCCT--Tamra [47]. The qPCR reactions were performedusing the ABI Prism 7500 Fast System (Thermo Fisher Scientific, Waltham, MA, USA). All qPCR reactions were performed in a total volume of 20 \(\mu\)L containing 3 \(\mu\)L genomic DNA solution, 1 \(\times\) TaqMan Universal PCR Master Mix with ROX (Thermo Fisher Scientific, Waltham, MA, USA), 4 \(\mu\)L of primer mix (160 nM) and 2 \(\mu\)L of probe (100 nM). The following DNA amplification cycle was programmed: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 15 s, annealing 60 \({}^{\circ}\)C for 15 s and extension 72 \({}^{\circ}\)C for 60 s. Each sample was loaded in triplicate onto a single qPCR plate. DNA was quantified using a standard curve generated from samples with known concentrations of _F. culmorum_ gDNA obtained by serial (10-fold) dilution ranging from 10 to 0.001 ng/\(\mu\)L using sterile, deionised water.
## 5 Classification of Harmonic Emissions Profiles
### Implementation of Support Vector Machines
#### 5.3.1 Radial Basis Kernel Function  
One of the main advantages of SVMs is the use of kernel functions, which allow for the classification of non-linear separable data sets. The reason for such a mapping is that it  
Figure 7: Features of the emission profiles for the first classifier support vector machine (SVM)-1 to distinguish class D0 and classes (D1, D2, D3, D4).  
is more likely to obtain a linearly separable region in a higher dimensional space than a lower dimensional space [35]. This can be achieved with the RBF kernel function:  
\[k\big{(}x_{j},x_{i}\big{)}=\Phi\big{(}x_{j}\big{)}\cdot\Phi(x_{i})=\exp\left( \frac{-||x_{j}-x_{i}||^{2}}{\gamma}\right) \tag{3}\]  
The user-specified kernel parameter, \(\gamma\), is a second parameter that strongly affects the resulting hyperplane and the respective classification results.
# Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising
## 4 Experimental Results
The deadline noise is caused by the uneven distribution of the sensor dark current and dark voltage, which will produce some dead lines in the image and affect the image quality. Here, we list five different noise situations:  
Case 1 (i.i.d. Gaussian Noise): In this particular scenario, every band encounters independent Gaussian noise distributed uniformly with an average of zero. Case 2 (i.i.d. Gaussian Noise + Deadline Noise): In this particular instance, following case 1, the band of 60-80 also experiences interference from a certain type of noise that disrupts the signal. For our analysis, we assign a random width to each stripe, ranging from 1 to 3, and a random number of stripes, ranging from 3 to 10. Case 3 (i.i.d. Gaussian Noise + i.i.d. Impulse Noise): In this scenario, all bands encounter independent Gaussian noise that is distributed identically and exerts an average of zero. Additionally, they are subject to independent impulse noise that is identically distributed. Case 4 (i.i.d. Gaussian Noise + i.i.d. Impulse Noise + Deadline Noise): In this specific scenario, as noted in case 3, the frequency bands ranging from 60 to 80 also experience interference from the dead line noise. To address this issue, we adopt a random approach for determining the width of each stripe, varying it between 1 and 3 units. Similarly, the number of stripes is selected randomly, ranging from 3 to 10. Case 5 (non-i.i.d. Gaussian Noise + non-i.i.d. Impulse Noise + Deadline Noise): In this particular scenario, every single band experiences nonindependent identically distributed Gaussian noise that has an average of zero, as well as nonindependent identically distributed impulse noise. Additionally, the bands ranging from 60 to 80 encounter a certain level of dead line noise. The width of each stripe is determined in a random manner, falling between the range of 1 and 3. Furthermore, the number of stripes is randomly assigned between 3 and 10. We provide the findings from our quantitative assessment of various comparison methods. The comparative outcomes of five HSI techniques on the Pavia City Center dataset are displayed in Table 1. Moreover, Table 2 demonstrates the comparative results of these five methods on the Washington DC Mall dataset. Notably, we have indicated the superior results, which are highlighted in bold within the tables. In this particular study, the Gaussian noise is denoted as \(\theta\), while the impulse noise is represented as \(\rho\). To visually demonstrate the comparative methods for HSI denoising, the denoising results at band 68
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 5 Discussion  
In this paper, we have provided a detailed performance evaluation of a satellite relaying system operating in full-duplex mode. Assuming that the loop interference can not be completely suppressed, novel closed-form expressions for the outage probability and ergodic capacity are derived, which clearly reveal the effects of time multiplexing, on-broad beam angle, residual loop interference level and ES elevation angle on the considered system. We can conclude that full-duplex satellite relaying brings a significant capacity gain besides the residual loop interference, which is obtained at the cost of a certain loss in the outage probability. The findings of this paper quantitatively analyzed the impact of key system parameters on the FD satellite relaying, and also provided an intuitive guidance for the system design, performance evaluation, and implementation principles of FD technique in satellite systems.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper.  
This work is supported by the National Natural Science Foundation of China (No. 61901502) and Research project of NUDT under grant ZK18-02-11.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FD & Full-duplex \\ HD & Half-duplex \\ AF & amplify-and-forward \\ DF & decode-and-forward \\ OP & outage probability \\ SNRs & signal-to-noise ratios \\ LOS & line-of-sight \\ PDF & probability distribution function \\ CDF & cumulative distortion function \\ FHS & Frequent heavy shadowing \\ AS & Average shadowing \\ ILS & Infrequent light shadowing \\ \end{tabular}  
Figure 6: Ergodic capacity comparison between FD and HD satellite relaying systems versus \(\theta_{1}\) and \(\theta_{2}\) with \(p=5\%\).
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
###### Abstract  
Telecom operators' infrastructure is undergoing high pressure to keep the pace with the traffic demand generated by the societal need of remote communications, bandwidth-hungry applications, and the fulfilment of 5G requirements. Software-defined networking (SDN) entered in scene decoupling the data-plane forwarding actions from the control-plane decisions, hence boosting network programmability and innovation. Optical networks are also capitalizing on SDN benefits jointly with a disaggregation trend that holds the promise of overcoming traditional vendor-locked island limitations. In this work, we present our framework for disaggregated optical networks that leverages on SDN and container-based management for a realistic emulation of deployment scenarios. Our proposal relies on Kubernetes for the containers' control and management, while employing the NETCONF protocol for the interaction with the light-weight software entities, i.e., _agents_, which govern the emulated optical devices. Remarkably, our agents' structure relies on components that offer high versatility for accommodating the wide variety of components and systems in the optical domain. We showcase our proposal with the emulation of an 18-node European topology employing Cassini-compliant optical models, i.e., a state-of-the-art optical transponder proposed in the Telecom Infrastructure Project. The combination of our versatile framework based on containerized entities, the automatic creation of agents and the optical-layer characteristics represents a novel approach suitable for operationally complex carrier-grade transport infrastructure with SDN-based disaggregated optical systems.  
disaggregated optical networks; software defined networking; optical network emulation 22 February 2021
# Development of an Alternative Alloying Concept for Additive Manufacturing Using PVD Coating
## 2 Experimental
### Physical Vapour Deposition  
The thin film coating applied to the surface of the filler wires is applied using magnetron sputtering, a physical vapour deposition (PVD) technology, which is a coating process [24; 25; 26]. Such a coating process is carried out in a coating chamber of a PVD system, which is shown in Figure 2. The process chamber in the case is a vacuum chamber, which is equipped with two external heaters on the sides of the process chamber and a central heater in the middle of the charging table. The charging table in the middle of the process chamber rotates during the process. The elements to be coated are placed in the form of targets on the six cathodes in the system. These targets are plates of pure elements that are dissolved from the surface during the process by physical vapour deposition and coat the substrate. The system used for the investigations is a CC800/9 Custom from the company CemeCon.  
Figure 1: Test set-ups process and material property.  
Figure 2: PVD system.  
A process sequence for coating substrate first requires opening the vacuum chamber into which the substrate is placed on the charging table. To start the process, a component test is first run through. In this scope, a pre-vacuum is built up by a rotary vane pump, and a high vacuum is generated in the process chamber by means of two turbomolecular pumps. During the generation of the high vacuum, an argon atmosphere is simultaneously built up in the process chamber. The process chamber is heated to reach the process temperatures. The process includes a pre-treatment of the surface, which is declared as etching, which effectively represents a reverse coating. This pre-treatment improves the adhesion of the coating and removes impurities from the substrate. The actual coating process can then be started. During this process, the desired elements are applied to the surface of the substrate. During the process, it is also possible to create nitrides or oxides, which are produced by the targeted introduction of different gases by changing the atmosphere in the process chamber. After the coating process, the process chamber is cooled down and remains under vacuum. Once the aeration temperature has been reached, the vacuum pumps can be switched off, the system can be ventilated, and the substrate can be removed. Figure 3 shows the process flow in simplified form.  
The substrate, or in this case, the filler metal, is wound on the charging table, which is located in the coating chamber (Figure 2). To achieve a uniform coating of the welding wire, the charging table is continuously rotated during the coating process. By heating the process chamber, the coating adhesion is improved. A thin film coating on a welding wire is schematically shown in Figure 4 on the left. A matching wire grind is shown on the right side in Figure 4. To create the wire grind, the welding wire is embedded, ground and polished, and photographed under an optical microscope.
## 2 Research Object and Methods
### Theory of the Proposed CFD/CFA
The SST (shear stress transport) model embedded in ANSYS CFX 14.1 software (ANSYS, Inc., Commonwealth of Pennsylvania, USA) was repeatedly used at this step. Secondly, the unsteady simulation based on the DES method is processed to calculate the flow field of the model pump. Afterwards, detailed flow information, such as flow velocity, pressure, and density, are extracted and transformed as the sound source for the next acoustic simulation. Considering that the flow pattern inside the high-speed centrifugal pump is complex and the acoustic simulation domain is irregular, the FEM (finite element method) is further used in the fluid borne noise simulation, which could well transform the sound source. The infinite element method, combined with the above finite element method, is used to calculate the radiated noise field of the model pump. Meanwhile, the fluid acoustic calculation domains and radiate acoustic calculation domains are built along with the acoustic mesh generation containing the interface setting. Finally, the fluid-borne noise and radiated noise calculation are processed using Actran12.0 software (Free Field Technologies MSC Software Company, Mont-Saint-Guibert, Belgium). The one-way numerical coupling method is used when referring to structure vibro-acoustics, which means noise-induced vibrations are ignored in this simulation. The acoustic characteristics of the model pump, such as sound pressure level and frequency domain response, are analyzed after experimental verification.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Lane Detection Dataset
#### 5.1.1 Logistic Regression Classifier  
The model was run in Python with an initial focus on accuracy. The logistic regression model from the \"sci-kit library\" was run on our data. A range of values for the parameters \"solver\" and \"C\" (regularization parameter) was used to run the model. These values were run in a loop to find the logistic regression model that was closest to ideal and its corresponding parameter values for the dataset at hand. Once the preferred parameter values were obtained (C = 0.5, solver = \"lbfgs\"), all the evaluation metrics of interest, in addition to accuracy, were determined. The visual representation is shown in Figure 3.  
It can be observed from Figure 3a that there is significant lack of consistency between the precision-recall curves of each class. This seems to increase for class 0 (normal), while it drops for classes 1 and 2 (aggressive and drowsy). This is more evident in Table 1. The values also do not seem to show any pattern that can be used to reference future test values.  
The ROC curves in Figure 3b do not provide encouraging results either. The curve approaches the diagonal and therefore points to a low performance.  
Figure 3: Logistic regression simulation results: (**a**) precision–recall curve; (**b**) ROC curve.  
Furthermore, it is clear that, in the breakdown of class-wise precision, recall and F1-scores, the values of recall and F1-score for class 1 (aggressive cases) are very poor. This is a significant anomaly. One would expect a model with poor scores to have either under-fitting, in which case the class with the fewest data samples (aggressive) would have poor metrics, or to have overfitting, in which case the class with maximum samples (drowsy) would have poor metrics. This is not observed, which further highlights the poor performance of this algorithm.
## 2 Engineering Background and Numerical Simulation Scheme
### Numerical Simulation Scheme and Simulation Steps
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Middle Section/m**} & \multicolumn{3}{c}{**Principal Stress/Mpa**} & \multicolumn{3}{c}{**Inclination of Principal Stress/\({}^{\upsilon}\)**} & \multicolumn{3}{c}{**Azimuth of Principal Stress/\({}^{\upsilon}\)**} \\ \cline{2-10}  & \(\sigma\)1 & \(\sigma\)2 & \(\sigma\)3 & \(\alpha\)1 & \(\alpha\)2 & \(\alpha\)3 & \(\beta\)1 & \(\beta\)2 & \(\beta\)3 \\ \hline \(-280\) & 19.3 & 15.6 & 12.3 & 18.4 & 27.1 & 56.4 & 247.3 & 147.6 & 7.3 \\ \(-460\) & 26.08 & 9.92 & 9.72 & 6.13 & 5.22 & 81.81 & 241.2 & 150.63 & 20.47 \\ \(-460\) & 22.44 & 12.91 & 10.99 & 3.08 & 83.56 & 5.08 & 53.81 & 172.17 & 323.5 \\ \(-730\) & 32.75 & 12.23 & 8.49 & 2.25 & 25.81 & 64.08 & 48.31 & 317.22 & 142.95 \\ \(-910\) & 34.33 & 16.47 & 13.84 & 6.37 & 44.39 & 44.9 & 248.42 & 152.13 & 344.81 \\ \(-910\) & 38.1 & 33.1 & 31.1 & 22.7 & 19.9 & 59 & 249.6 & 150.9 & 23.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of original rock stress testing. This study uses the 61 line profile as a reference to establish a slope model that closely matches the actual conditions of the mining area. The dimensions of the model are X \(\times\) Y \(\times\) Z = 900 m \(\times\) 400 m \(\times\) 1000 m, with the length of the stope model set at 80 m, the width at 18 m, and the height determined based on the thickness of the ore body. Since Midas GTS employs finite element technology for mesh generation, the node and element divisions are consistent with those in FLAC3D. The conversion program MidastoFlac3D.exe facilitates the transformation of the GTS mesh model into a numerical model for FLAC3D. The final numerical model of the surrounding rock of the ore body and the ore body model for the 60 line stope are illustrated in Figure 1.  
The stability of rock masses is influenced by various factors, particularly in mines, where the stability of the rock not only depends on its inherent characteristics but is also affected by disturbances from surrounding stope extraction. Due to the significant differences in rock stability in different areas north of the Dongguashan 60 line, a comprehensive and uniform simulation analysis is challenging. Therefore, this analysis mainly focuses on the following two aspects: through numerical simulation, the patterns of stress or displacement changes during the extraction process are identified, thereby proposing solutions to the problems encountered; and, by comparing stress and displacement, potential issues faced by the stope are analyzed. For example, for adjacent slopes, if one remains stable after extraction while the other collapses, it is possible to differentiate the stress or displacement changes to determine the values that led to the collapse.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 3 Proposed Architecture
### Initial Configuration  
Initially, the mobile network knows the different types of services that the mobile network provides, for example: IMS traffic for VoIP, IMS traffic for video, connectivity with an online game server, mobile TV, etc. (all with specific QoS requirements). Each UE hires a group of these services with specific QoS characteristics to define its user profile. There is a certain number of user profiles, and each UE is associated with one of them (the associations are kept in a database). Initially, the Controller has the mobile network topology, so it is able to configure proactive flow entries in all OF-switches, OF-NBs, and OF-GWs.  
The initial proactive steps are the following:  
* To create OpenFlow flow entries for all equipment to reach the OF-GW, so they can reach the other OF-GW's and the mobile service application;
* To route the traffic without QoS requirements, the default OF-Bearer between the OF-NB and the OF-GW is created. All OF-NB must have at least one default OF-Bearer to reach one OF-GW;
* To install the appropriate default route in the OF-GW to reach the PDN network;
* To transport massive services with QoS requirements, the OF-Bearer with QoS between the OF-NB and the OF-GW connected to the services is created;
* To create the traffic detection tables at each OF-NB for all user profiles to detect traffic with QoS requirements (see Section 3.3).  
The OF-Bearers can optionally implement queue management to prioritize between different OF-Bearer types, implement bandwidth reservation, guaranteed minimum delay paths, fast recovery mechanisms to carry critical traffic, or other options. All OF-Bearers are univocally identified by an MPLS label, which can change hop-by-hop as in any MPLS network. This MPLS label is the outer MPLS label used to identify UE communication through the specific EPS-Bearer. EPS-Bearers with the same QoS requirement can use the same OF-Bearer (see Figure 3).