esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Spectral Characteristics of FEGLD  
The OES emitted from the FEGLD are illustrated in Figure 3, under the experimental conditions of 30 kV pulse peak voltage, 12 mm discharge gap, and 3.5 L/min gas flow rate. The OES of FEGLD is mainly composed of the second positive system of N\({}_{2}\) (C\({}^{3}\Pi_{\text{u}}\)\(\rightarrow\)B\({}^{3}\Pi_{\text{g}}\)), the first negative system N\({}_{2}^{+}\) (B\({}^{2}\Sigma_{\text{u}}^{+}\)\(\rightarrow\)X\({}^{2}\Sigma_{\text{g}}^{+}\)), the bands of OH (A\({}^{2}\Sigma\)\(\rightarrow\)X\({}^{2}\Pi\)), and the lines of He, H\({}_{\alpha}\) and O. The FEGLD is operated into open air filled with N\({}_{2}\), O\({}_{2}\), and H\({}_{2}\)O originating in liquid surface. In He discharge, the metastable helium is easily quenched by those molecules. These processes are shown as R(1)-R(3) in Table 1. While the pulse voltage is employed, the production of electrons via Penning ionization sustains the plasma between two discharge pulses [14]. The N\({}_{2}\) (C) state could be produced by the ground  
Figure 1: Experiment setup of FEGLD plasma with (**a**) experiment schematic and (**b**) discharge reactor diagram.  
Figure 2: Discharge images of FEGLD plasma with variations in the discharge gap.  
state of nitrogen molecules N\({}_{2}\) (X), as shown in R(4)-R(5). It should be mentioned that R(5) could release additional energy [18] contributing to temperature improvement as the gas fast heating mechanism. The first negative system N\({}_{2}^{+}\) (B\({}^{2}\Sigma_{u}^{+}\)\(\rightarrow\)X\({}^{2}\Sigma_{g}^{+}\)) in FEGLD exhibited high emission intensity, as shown in Figure 3, the physicochemical process being shown in R6. Besides, the non-elastic collisions of the energetic electrons with N\({}_{2}\), O\({}_{2}\), and H\({}_{2}\)O molecules will induce its dissociation, excitation, and ionization [33]. The process of energetic electrons created by the discharge pulse colliding with O\({}_{2}\)[34] is shown in R7. The physicochemical process of OH generation is shown in R8. Meanwhile, when the surrounding gas contains O\({}_{2}\) and H\({}_{2}\)O, the collisional quenching of OH (A) by O\({}_{2}\) is an important process, as shown in R9.
## 5 Simulation
### Comparisons of Random Access Schemes
However seemingly high number of collisions for the proposed scheme is in fact relatively small compared to the number of successful transmissions in the first frame which is shown on the right of Figure 10. We observe that the number of successful transmissions in the first frame for policy \(V\) and Q-SCHED are lower than the proposed and UNIFORM scheme. The reason is that, under policy \(V\) and Q-SCHED algorithm, there is nonzero probability that a node is 'idle', _i.e._, it does not participate in channel contention. While in the proposed and UNIFORM scheme, every node attempts transmission at every frame. This also accounts for the lower number of collisions for policy \(V\) and Q-SCHED algorithm. Figure 11 shows the mean score of the successful transmitted packet in the first frame. Since the scores of sensors in the first frame are \(n\) i.i.d. uniform RVs on [0,1] by definition, we see that the mean score for UNIFORM scheme is approximately 0.5. We see that the modified policy \(V\) and Q-SCHED has a mean score that is only slightly higher than the proposed scheme, which is possible however, by reducing the number of successful transmissions in the first frame. Figure 11 shows that the proposed scheme finds compromise on these aspects: it achieves higher mean score than UNIFORM scheme without excessively reducing the number of successful transmissions in the first frame. Although not shown here, we observed that the number of collisions subsides faster in subsequent frames for the proposed scheme, compared to other schemes. This is a synergistic effect with selective forwarding and overhearing, since successful transmissions of high-score data contribute to suppressing traffic and reducing contention by these techniques, which eventually leads to decreased latency. Here we make an important observation. When one intends to further prioritize high-score sensors, it is likely to lower the number of successful transmissions. Reducing the number of successful transmissions, especially by increasing the number of 'idling' nodes, seems to be more harmful to latency reduction as we see that UNIFORM outperforms policy \(V\) and Q-SCHED in Figure 9. Thus it is crucial that one should aggressively prioritize high score nodes, however should not excessively lower the number of successful transmissions. The proposed scheme is observed to exhibit such property. Figure 11: Mean score of successful transmissions in the first frame when \(m=20\) and \(b=3\). Figure 10: Mean number of collision and successful transmissions in the first frame when \(m=30\) and \(b=3\). Figure 9: Comparison of the mean latency for different random access schemes.
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP4
#### 3.5.2 Test Results and Analysis in Three-Dimensional Spaces  
Based on the Equations (10) and (11) and theoretical analysis, the spatial surfaces of PFG to CIV1 and CIV2 at SCP4 have similar changing rules, and the latter surfaces have one more degree of freedom. Therefore, it is just essential to choose the spatial surfaces of PFG to CIV2 for analysis. As shown in Figure 12, the spatial surfaces of PFG to CIV2 at SCP4 by Reg.1 mode still present funnel shapes in the normal operation conditions. In contrast the surfaces by Reg.1 in the severe operation condition and the surfaces by Reg.2 in all operation conditions have much larger curvatures. Moreover, the spatial surfaces of PFG to CIV2 at SCP4 by Reg.2 mode also have larger curvatures and areas than those at SCP2, revealing the OSIV of UPFC at SCP2 has enough large efficiencies to regulate the power flows at SCP4.  
Based on Equations (9) and (12), the spatial surfaces of PFG to CIV3 at SCP4 are similar to those at SCP3 as shown in Figure 13. The differences mainly lie in the distribution rules of the funnel peaks and curvatures, indicating that spatial surfaces of PFG to CIV3 at SCP4 are also beneficial and adaptive to different regulation requirements.
## 3 Results and Discussion
### Single-Factor Experimental Results and Analysis  
#### 3.3.1 Effect of pH on the Iron and Manganese Ion Removal  
PH was one of the essential factors for iron and manganese ion removal. We studied the effect of the initial pH value on the adsorption process. As shown in Figure 3, the iron and manganese ions' removal rate showed an upward trend with the increase in pH. When the pH rose from 6.0 to 7.0, the iron ion removal rate rapidly increased from 69.8% to 80.4%, and the manganese ion removal rate rapidly increased from 62.3% to 75.5%. However, when the pH was greater than 7, the iron and manganese ion removal rate increased slowly, and the change was not significant.  
The hydrated ion radius of H\({}^{+}\) in water was much smaller than Fe\({}^{2+}\) and Mn\({}^{2+}\). Under acidic conditions, a large number of H\({}^{+}\) competed with iron and manganese ions for adsorption sites [24], resulting in the low removal efficiency of iron and manganese. At the same time, when the pH was too low, an iron filter membrane was easily formed on the surface of manganese sand, and iron infiltrated the filter layer and interfered with the formation of the manganese active filter membrane, thus affecting the manganese removal effect [25; 26]. However, if the pH was too high, on the one hand, the iron ions in the solution precipitated in the form of hydroxide, which reduced the catalytic capacity, and on the other hand, it would inhibit the production of OH\({}^{-}\). The above results showed that when the pH value was close to neutral, the removal rate of iron and manganese ions was significantly improved. Based on the consideration of economic factors and manganese sand adsorption conditions [27], the optimal pH value of manganese sand adsorption was 7.0.  
Figure 2: Nitrogen adsorption–desorption curve.
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
#### 2.2.1 Hyperspectral Imaging System  
Hyperspectral image acquisition of fresh and dry flowers was conducted using the hyperspectral imaging system in our previous study [14]. Each single fresh or dry flower was placed separately in a black plate for hyperspectral image acquisition. The camera exposure time, the distance between the camera lens and the moving plate, and the plate-moving speed were adjusted to 3000 \(\upmu\)s, 14 cm, and 11.5 mm/s, respectively, to acquire clear and nondeformable images.
## 3 Results and Discussion
### Hazardous Metal Leaching and Identification Test  
Of the analyzed metals (Ba, Cu, Cr, Ni, and Zn), only the presence of Ba and Cr are considered toxic, under Colombian legislation for classifying a residue, if it exceeds the established maximum limit. In addition, according to the same legislation [53], wastes containing copper and zinc are considered to be hazardous waste, unless they do not show any of the following characteristics: corrosive, reactive, flammable, explosive, radioactive, infectious, toxic. Taking into account the origin of the materials and their characterization, it follows that the residues do not possess radioactive or infectious characteristics; additionally, according to the pH--between 6.6 and 11 in all cases--it is affirmed that they are not corrosive. Tests for reactivity, flammability, or explosivity are described in resolution 0062-2007 [52], which allow these characteristics to be determined for a material, and these tests are recommended for future studies. Table 6 presents the concentration of metals that could be considered toxic that were in the leachate, as obtained through TCLP analysis on the residues, and the maximum limits allowed under Colombian legislation. The presence of Ba, Cu, Cr, Ni, or Zn in the leachates at the concentrations reported in Table 6 do not classify any of the residues evaluated as toxic, since they do not exceed the maximum values permitted (100 and 5 mg/L for Ba and Cr, respectively), with 3.5 mg/L the highest value found for Ba. However, based on the same regulations, it should be ruled out that the residues do not present any of the other hazardous characteristics mentioned above in order to ensure that they are nonhazardous waste and, therefore, that it is not necessary to resort to special treatment before their final disposal. On the other hand, with the intention of coming close to the European regulations for hazardous waste (Decision 2003/33/EC) [47], the results obtained were compared with the maximum allowances foreseen in said legislation for inert and nonhazardous waste. Without forgetting that the conditions of the leaching test are different in the two methods, i.e., resolution 0062-2007 [52] and UNE-EN-12457-3 [46]. According to the above, with respect to the European regulations, only an approximation of the characterization of the residues analyzed in this project is presented (Table 7). In addition to the time and the liquid to solid (L/S) ratio, one of the main differences between the two methods is the extraction fluid used, one being water in the European norm and the other being the fluid used in the Colombian norm, which is a mixture with a pH of approximately 4.9 (water + acetic acid + NaOH).
###### Abstract  
High Throughput Satellite (HTS) systems aim to push data rates to the order of Terabit/s, making use of Extremely High Frequencies (EHF) or free-space optical (FSO) in the feeder links. However, one challenge that needs to be addressed is that the use of such high frequencies makes the feeder links vulnerable to atmospheric conditions, which can effectively disable channels at times or temporarily increases the bit error rates. One way to cope with the problem is to introduce site diversity and to forward the data through the gateways not affected, or at least less constrained, by adverse conditions. In this paper, a virtual network function (VNF) introduced through reinforcement learning defines a smart routing service for an HTS system. Experiments were conducted on an emulated ground-satellite system in CloudLab, testing a VNF implementation of the approach with software-defined networking virtual switches, which indicate the expected performance of the proposed method.  
reinforcement learning; learning automata; Q-Learning; SDN; VNF; routing; high throughput satellites; smart gateway diversity +
Footnote †: journal:  
_Future Internet_
## 2 Study Area and Methods
### Methods
#### 2.2.3 Soil Retention and Soil Erosion Assessment  
Two ecosystem services, soil retention and sandstorm prevention, were estimated. Soil retention, in quantitative terms, is the difference between potential soil erosion and actual soil erosion [31]. The Universal Soil Loss Equation (USLE) was used to calculate soil retention (\(SR\)) and actual soil erosion (\(SE_{a}\)), which is strongly influenced by rainfall, soil, topography, and vegetation. The model can be expressed as follows [31]:  
Potential soil erosion:  
\[SE_{p}=R\times K\times LS \tag{2}\]  
Actual soil erosion:  
\[SE_{a}=R\times K\times LS\times C \tag{3}\]  
Soil retention:  
\[SR=R\times K\times LS\times(1-C) \tag{4}\]  
where \(SE_{p}\) is the potential soil erosion; \(SE_{a}\) is the actual soil erosion; \(SR\) is soil retention capacity (expressed in tons per hectare per year); \(R\) is the rainfall erosivity factor (MJ\(\cdot\)mm\(\cdot\)ha\({}^{-1}\cdot\)h\({}^{-1}\cdot\)yr\({}^{-1}\)), which is determined in terms of the kinetic energy (megajoules) of raindrops and the intensity of rainfall (in hectare-millimeters per hour) over one year; \(K\) is the erodibility of soil, or the amount of soil lost through erosion from unit area following rainfall of a given intensity (t\(\cdot\)ha\(\cdot\)h\(\cdot\)ha\({}^{-1}\cdot\)MJ\({}^{-1}\cdot\)mm\({}^{-1}\)); \(LS\) is the topographic factor and represents the effect of the length of slope; and \(C\) is the vegetation cover factor. The model was described and validated by [PERSON] et al. [32].
# A Deep Neural Network Model for Speaker Identification
## 2 Materials and Methods  
### Background  
#### 2.1.1 Recurrent Neural Network  
In many recent studies, the RNN model has been a highly preferred method [40], especially for sequential data. A traditional RNN architecture is illustrated in Figure 1. Every node at a time step consists of an input from the previous node and it proceeds using a feedback loop. Each node produces a current hidden state and output by using the current input and previous hidden state as follows:  
\[\mathrm{h}_{t}=f(U_{h}x_{t}+W_{h}h_{t-1}+b_{h}) \tag{1}\]  
\[y_{t}=f(W_{o}h_{t}+b_{o}). \tag{2}\]  
Here, \(h_{t}\) denotes the hidden block of each time step \((t)\), \(x_{t}\) is the input at some time step, \(U\) and \(W\) are the weights for the hidden layers in a recurrent connection, \(b\) denotes the bias for hidden states and output states, and \(f\) represents an activation function applied on each node throughout the network.
## 3 VN Allocation and Enforcement Methodology
### The VN Model  
In general, the cloud providers wish to provide a standardized template to tenants, which can be used by the tenants to describe their specific requirements for VN. Recently, different VN models have been proposed for bandwidth allocation, such as the traffic matrix [13], the hose model with invariable bandwidth [14], the hose model with time-varying bandwidth [27], and the TAG model [28] based on the structure of application communication. Specific VN-to-PN mapping algorithms can be designed using these models. In this paper, SCG is presented, which is a new model that can be used by the tenants to specify their fine-grained network requirements for VDCs, including the network structure, uplink bandwidth, and downlink bandwidth. This is different from the hose abstraction, in which the models are presented based on their resemblance with typical physical networks. It is not like the TAG abstraction either, because in the TAG abstraction, the actual communication patterns of applications are modeled, while the SCG abstraction can simulate the prior network environments of tenants where these applications are actually run. By utilizing the tenant's knowledge of network structure and bandwidth demand, the SCG model is able to represent the actual networks in a concise and flexible manner. This covers the common network topologies in practice.  
As for most of applications, the multi-tier tree topology is the de-facto underlying network structure. Hence, we construct the SCG model, where each vertex stands for an entity (which could be a switch or a VM) to mimic the actual network structure of application. A simple VN modeled with SCG is presented in Figure 2, which has the oversubscribed multi-tier network architecture. In this VN, there are two bidirectional edges between the core layer and the aggregation layer, which are labeled as \(\langle B1,B2\rangle\) and \(\langle B3,B4\rangle\), respectively. The edges between the aggregation layer and the access layer are labeled in the same way. By controlling the uplink traffic and downlink traffic, the tenants can set appropriate sending rate and receiving rate for each VM in VDC, which can reduce the cost generated by excessive bandwidth.  
SCG is a generalized model that can represent almost all of applications. Our work focuses on enforcing VDC-based bandwidth guarantees using this model.  
Figure 2: An SCG modeled VN.
# Effects of Diverging Nozzle Downstream on Flow Field Parameters of Rotating Detonation Combuster
## 2 Numerical Model and Method
### Initial and Boundary Conditions  
(1) Inlet of RDC: mass flow inlet boundary. According to the performance parameters of a micro gas turbine, the injection total pressure and total temperature are set at constant values of 1.209 MPa and 665 K. The mixture is injected by shrink nozzles and the mass flow is determined by a user defined function (UDF) [10; 11; 36] of FLUENT. Considering the relationship of injection pressure between the inlet pressure and critical pressure, the specific settings are:  
(a) In the case of \(p_{w}\geq p_{in},\overline{g}=0\);  
(b) In the case of \(p_{in}>p_{w}>p_{cr}\), \(\overline{g}\,=\,\sqrt{\frac{2\gamma}{\gamma-1}\cdot\frac{P_{in}^{2}}{RT_{in}} }\cdot\sqrt{\left(\frac{p_{w}}{p_{in}}\right)^{\frac{2}{\gamma}-\left(\frac{p_ {w}}{p_{in}}\right)^{\frac{\gamma+1}{\gamma}}}}\);  
(c) In the  
where \(\overline{g}\) is the mass flow rate; \(p_{in}\) is inlet pressure of the combustor; \(p_{w}\) is the injection pressure; \(T_{in}\) is the total temperature of the injection mixture; and \(\gamma\) is the specific heat ratio.  
(2) Outlet of RDC: pressure outlet boundary. The average static pressure is 0.1 MPa.  
(3) Wall of RDC: adiabatic conditions.  
(4) Initial ignition of RDC: Chapman-Jouquet (CJ) ignition kernel. Based on the calculation by using Chemical Equilibrium with Applications (CEA) of the NASA computer program [45], the CJ pressure and temperature are 9.79 MPa and 3019 K, respectively. The initial velocity of ignition is calculated to be 1820 m/s to inject tangentially into the premixed stoichiometric methane-air mixture.
## 2 Experimental
### Characterization Techniques  
The structural investigation was carried out using Bruker D8 advanced diffractometer with Cu K\(\alpha\) radiation having \(\lambda\) = 0.1540 nm. This pattern was recorded in the 20 range of 20 to 75 degrees. The structure was further analyzed using Raman spectroscopy. The morphological analysis was done by JOEL JSM-7600F scanning electron microscopy (SEM) and then elemental analysis was done with the help of energy-dispersive X-ray spectroscopy (EDX). An accelerating voltage of 20 kV was set for EDX. The surface chemical compositions were investigated using X-ray photoelectron spectroscopy (XPS). CS350 potentiostat was used to record the CV loops at different scan rates (0.005, 0.01, 0.05, and 0.1 mVs\({}^{-1}\)) in the potential window 0-0.5 V. Using the same instrument, charge/discharge curves and impedance measurements were also recorded [20; 21].
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization  
System-level design optimization can be conducted exclusively using the FEM; however, its massive computational requirements renders it an expensive method. Therefore, several techniques have been proposed and employed to mitigate computational burden. Figure 5 illustrates various techniques that have been adopted in the literature to reduce the computational time of the optimization process by reducing the overall model complexity by adopting surrogate models such as interpolation-based methods, like the Kriging model and the response surface method, as well as machine learning-based algorithms such as neural networks. In addition, the reduction in the critical design parameters prior to optimization through the use of search space reduction techniques is commonly achieved with techniques such as sensitivity analysis, Design of Experiments (DoE), and the Taguchi method.  
Figure 5: System-level techniques and algorithms most commonly employed for a reduction in model complexity and search space.
## Results and Discussion
### PCA of Spectroscopic Data
#### 3.3.1 PCA Loadings  
The PCA loadings of first three PCs for the FT MIR, FT NIR and FT Raman methods are represented in Figure 2; the band assignment is summarized in Table 5. In the case of FT MIR (Figure 2a), PC1 and PC2 showed two similar intense negative-to-positive couplets at 1697-1619 cm\({}^{-1}\) and 1559-1511 cm\({}^{-1}\) and a negative peak at 1246-1242 cm\({}^{-1}\) corresponding to the amide I, amide II and amide III vibrations, respectively [40; 41]. By contrast, PC3 showed two negative-to-positive couplets in this region at 1633-1614 cm\({}^{-1}\) and 1564-1503 cm\({}^{-1}\). These PCs also showed several intense peaks in the region of carbohydrate vibrations of 1204-943 cm\({}^{-1}\), so they are sensitive to the amount and composition of polysaccharides [34]. Figure 2: PCA loadings of the first three PCs for the spectroscopic data: (**a**) FT MIR, (**b**) FT NIR and (**c**) FT Raman. In the case of PCA loadings for FT NIR (Figure 2b), the most intense peaks were found at 5300-5200 cm\({}^{-1}\) assigned to the combination of OH stretching and bend vibrations of polysaccharides [46, 47]. Two negative peaks at 4432 and 4378 cm\({}^{-1}\) (PC1, PC3) and a positive peak at 4405 cm\({}^{-1}\) (PC2) originated from the OH/CO stretch combination of polysaccharides. The negative-to-positive couplets (at 4894-4840 cm\({}^{-1}\) and 4675-4505 cm\({}^{-1}\) were found in PC1 and PC3 loadings. In the case of PC2, four peaks were found in these regions: two positive ones at 4900 and 4840 cm\({}^{-1}\) and two negative ones at 4675 and 4567 cm\({}^{-1}\). All these peaks originated from amide combination bands of proteins [43, 44, 45]. In addition, for all three PCs, weak peaks at 6730-6445 cm\({}^{-1}\) also arose from protein vibrations. Finally, the intense negative and positive peaks in the CH combinations and overtones at 6000-5600 cm\({}^{-1}\) and 4300-4000 cm\({}^{-1}\) had contributions from both proteins and polysaccharides. The assignment of the PC1-3 loadings for FT Raman spectra (Figure 2c) is difficult because many peaks originated from the C-H and C-C vibrations of both proteins and glucans [48, 34]. However, it is evident for PC1 and PC2 that the positive and negative peaks at 970-850 cm\({}^{-1}\) arose fromomeric sensitive vibrations of chitin, \(\alpha\)- and \(\beta\)-p-glucans [14, 26], so these PCs could be useful for distinguishing of basidiocarps based on polysaccharide composition.