esa-sceva/satcom-chunk-collection huggingface dataset:
# ABACO: A New Model of Microalgae-Bacteria Consortia for Biological Treatment of Wastewaters
## 3 Results
### Model Concept  
In microalgae-based wastewater treatment, different types of microbial consortia appear as a function of environmental and operational conditions. Figure 2 shows the biological process taking place in the reactor when using wastewater (i.e., diluted pig slurry) as the culture medium. Under illumination, microalgae (X\({}_{\text{ALG}}\)) fix carbon dioxide (CO\({}_{2}\)) and release oxygen (O\({}_{2}\)) while assimilating nutrients, such as ammonium (NH\({}_{4}\)), nitrate (NO\({}_{3}\)), and phosphate (PO\({}_{4}\)). The O\({}_{2}\) produced by the photosynthesis is essential for the degradation of the biodegradable soluble organic matter (BSOM) by heterotrophic bacteria (X\({}_{\text{HET}}\)), BSOM being a fraction of total organic matter (COD) contained in wastewater. In turn, during bacterial oxidation of soluble organic matter, CO\({}_{2}\) is produced, it being available for photosynthesis and the nitrification process. During nitrification, nitrifying bacteria (X\({}_{\text{NIT}}\)) transform NH\({}_{4}\) already contained at the inlet culture medium into NO\({}_{3}\), while also consuming O\({}_{2}\) produced through photosynthesis.  
The developed model includes the mass balances of major compounds involved into the biological process, in addition to the growth rate of the different species involved (microalgae and bacteria) as a function of culture conditions and nutrients availability. Starting from known initial conditions and variables already measured in the reactor it is possible to simulate the evolution of the system over time, thus the variation of both compounds and microorganisms. Figure 3 shows the most relevant inputs and outputs of the model, and the initial conditions required. The inputs for the model are the variables commonly measured in photobioreactors such as irradiance, dissolved oxygen, pH and temperature. The model outputs are the concentrations of major microorganisms already considered such as microalgae, heterotrophic bacteria and nitrifying bacteria; in addition to the concentration of major components and nutrients involved into the biological process such as oxygen, carbon dioxide, total inorganic carbon, ammonium, nitrate, phosphate, and BSOM. For the right estimation of the evolution of the system it is necessary to establish values for the initial conditions, which correspond to the initial concentrations of the nutrients and the total biomass, the initial percentages of species in the photobioreactor and the calibration parameters.  
Figure 2: Biological process for microalgae biomass production coupled with wastewater treatment.
# Flow Characteristics and Loss Models of the Transonic Radial Turbine
## 4 Loss Models
### Combinations of Four Traditional Losses  
The change in total enthalpy at no entropy across the turbine \(\Delta H_{0~{}isentropic}\) can be calculated as Equation (5), while the total to total efficiency \(\eta_{tt}\) can be calculated as Equation (6). The ratio of loss to \(\Delta H_{0~{}isen}\) is the percentage of losses. \(k\) is the specific heat ratio of air.  
\[\Delta H_{0~{}isen}=c_{p}T_{0~{}intel}\Bigg{(}1-\bigg{(}\frac{P_{0~{}outel}}{ P_{0~{}intel}}\bigg{)}^{(k-1)/k}\Bigg{)} \tag{5}\]  
\[\eta_{tt}=\frac{1-\frac{T_{0~{}outel}}{T_{0~{}intel}}}{1-\bigg{(}\frac{P_{0~{} outel}}{P_{0~{}intel}}\bigg{)}^{(k-1)/k}} \tag{6}\]  
Figure 9 shows the distribution of the four traditional different percentages of losses, which contain the passage loss, the tip clearance loss, the trailing edge loss and the incidence loss. If the loss models can predict the performance of the radial inflow turbine accurately, the sum of the percentage of losses and the total-to-total efficiency will be nearly unity. Figure 9 shows that when the turbine works at a low expansion ratio, the sum of the percentage of losses and the total-to-total efficiency is nearly unity. However, the sum of percentage losses and the total-to-total efficiency is less than unity when the turbine works at high expansion, the largest error is about 10%. This shows that just the traditional four loss models are not suitable for the turbine working at a high expansion ratio. As described in Figures 6-8, when the turbine works at a high expansion ratio, its flow is transonic, the shock wave will occur near the outlet and interact with the other flow, and it will cause a number of losses. So, the loss caused by the shock wave cannot be ignored when the turbine works in transonic conditions, and the shock wave-induced model should be developed to make the loss models more complete and accurate.
## 3 Experiments and Discussion
### Experiments on Vertical Acceleration and EDR Estimation  
In order to verify the positive effect of the new method on vertical acceleration and turbulence severity estimation, an aircraft flying through turbulence with different intensities was simulated. The time history of vertical acceleration was obtained according to the computation flow shown in Figure 6. Different from Section 3.1, the nonplanar and unsteady VRM was adopted, in which the boundary condition caused by instantaneous turbulence changes in each time step. After the simulation, the estimated EDR value was compared with that of input turbulence.  
A 3D turbulence field based on von Karman model was generated according to ref [32]. The wind field generated by time-frequency domain transformation and symmetric expansion conform well to the von Karman turbulence model. Four turbulence cases (TC) were used for experiment. The intensity, vertical scale and input \(\varepsilon^{1/3}\) are shown in Table 4. A B737-800 aircraft with \(m=60,000 kg\) was initially trimmed at \(M=0.76\) and \(h=30,000 ft\), with \(\alpha\approx 2^{\circ}\).  
The theoretical EDR value is obtained according to ref [10] by:  
\[\varepsilon^{1/3}=\sqrt{\frac{55}{9\pi a^{5/3}A}}\cdot\frac{\sigma_{W_{z}}}{L^ {1/3}} \tag{26}\]  
where \(a=1.339\) and \(A=1.6\). \(\sigma_{W_{z}}\) and \(L\) represent the intensity and vertical scale of turbulence, respectively. As shown in Figure 6, the generated turbulence components \([W_{x},W_{y},W_{z}]^{T}\) according to von Karman model function as the algorithm input. In addition, the vertical turbulence was also used for computing \(I(\omega_{1},\omega_{2},t)\). After the vertical acceleration was obtained, the response energy \(\hat{\sigma}_{a_{n}}^{2}\) was computed. With computed \(\hat{\sigma}_{z}^{2}\) and \(I(\omega_{1},\omega_{2},t)\), the estimated \(\varepsilon^{1/3}\) was obtained by Equation (25).
## 3 Results and Discussion
### General
#### 3.1.4 Should Body Composition Be Assessed in the COPD Patient and When Should It Be Done?  
**Recommendation 4**.: _GARIN suggests measuring muscle mass and function, both at diagnosis and at follow-up._  
Grade of recommendation B, (92%) Strong consensus.  
Comment:  
COPD is characterized by altered body composition, especially increased fat mass and decreased muscle mass [52]. Its influence on disease severity and prognosis has been described in numerous studies [12; 14; 35; 53]. Because it is directly associated with lung function, its loss intensifies as disease severity increases. Morbidity, hospitalization rate, increased readmissions and hospital stay, and the need for ventilatory support are also increased in patients with significant muscle wasting [7; 14; 22; 35; 54; 55].  
Muscle mass is directly related to mortality; for some authors, FFMI is an independent predictor of mortality [7; 33; 56] and is suggested as a systemic marker of disease severity in COPD staging [35; 56].
## 4 Evaluation of Algorithms
### Adaptive Harmony Search Algorithm
#### 4.5.4 High-Level Overview of AHSA  
Algorithm 3 presents the top-level view of the AHSA approach. Considering there are \(N\)VM requests to be placed in \(M\) PMs, then at the beginning of each interval, the AHSA is activated.
```
Input: HostQ, ReQ Output: TargetVMQ For each interval do (TargetplanVMQ) \(\leftarrow\) Call Algorithm 4 with (HostQ, ReQ); TargetVMQ \(\leftarrow\) Actual allocation of VM to HostQ as per TargetplanVMQ; Return TargetVMQ
```
**Algorithm 3**High-level overview of AHSA  
Algorithm 4 presents the AHSA for optimal host identification. In Algorithm 4, Line 2 generates an initial HMV population vector. The feasible host of the VM is computed using Equation (1). After the initial population generation, exploration is done from lines 15-21, and the exploitation of existing search space is done from lines 6-14 by using Equation (15). The new vector is generated by identifyingthe next feasible host. If the new vector yields a better objective, it is updated in HMV in Line 11 and 17. The procedure is detailed in Section 4.5.
```
Input: HostQ, ReqQ Output: TargetplanVMQ
1 HMV-size = \(N\times M\) as in Table 1.
2 HMV \(\leftarrow\) Call Algorithm 5;
3 For each iteration to Max-iteration do
4 Choose HMCR based on Table 1
5 If rand( ) < HMCR then
6 Choose PAR based on Table 1
7 If rand( ) < PAR then
8 i \(\leftarrow\) randomly chosen row index of HMV;
9 New-assignVM \({}^{i^{\prime}}\)\(\leftarrow\) New-assignVM \({}^{i}\)\(\leftarrow\) BW using Equations (8) and (15);
10 New-obj\({}^{i^{\prime}}\)\(\leftarrow\) Objective function value obtained for New-assignVM\({}^{i^{\prime}}\) using Equation(8);
11 if New obj\({}^{i}\) < HMV[1].obj\({}^{i^{\prime}}\) then
12 HMV[1] \(\cdot\) obj\({}^{i^{\prime}}\)\(\leftarrow\) new-obj\({}^{i^{\prime}}\);
13 HMV[1] \({}^{1}\)\(\leftarrow\) New-assignVM\({}^{i^{\prime}}\);
14 else
15 set HMS Size to \(1\times M\)
16 New-assignVM \({}^{i^{\prime}}\)\(\leftarrow\) Call Algorithm 5;
17 If New_obj\({}^{i^{\prime}}\) < HMV[1].obj\({}^{i^{\prime}}\) then
18 HMV[1] \(\cdot\) obj\({}^{i^{\prime}}\)\(\leftarrow\) New_obj\({}^{i^{\prime}}\);
19 HMV[1] \({}^{1}\)\(\leftarrow\) New-assignVM\({}^{i^{\prime}}\);
20 Sort the HMV in non-increasing order based on HMV \(\cdot\) obj;
21 TargetplanVMQ \(\leftarrow\) HMV[Last];
22 Return TargetplanVMQ
```  
**Algorithm 4**AHSA proposed for power optimization Figure 2: AHSA based power optimization procedure.
## 3 Proposed Method
### Saliency Cuts Using Local Adaptive Thresholding
#### 3.2.2 Integral Images  
An integral image (also known as a summed-area table) is a fast and effective means for computing the sum of values (pixel intensity values) in a given image, or a rectangular subset of a grid (the given image). Mathematically, it can be expressed as  
\[I(x,y)=\sum\begin{array}{c}x^{\prime}\leq x\\ y^{\prime}\leq y\end{array}i(x^{\prime},y^{\prime}) \tag{1}\]  
where \(I\) is the integral value at point (\(x\), \(y\)) and \(i\) is the intensity at point (\(x\), \(y\)) in a grayscale image (saliency maps). We computed integral images and locally adaptive triple-thresholding as shown in Figures 12 and 13 (Appendix A section). Figure 4 shows an example of a \(4\times 3\) image that demonstrates the calculation process of the integral image. As shown in Figure 5, \(I\) (\(2\), \(3\)) is the sum of the intensity values of the saliency maps in the upper-left area. The integral image or summed-area table \(I(x,y)\) can be quickly calculated using Equation (2) [46].  
\[I(x,y)=i(x,y)+I(x-1,y)+I(x,y-1)-I(x-1,y-1) \tag{2}\]  
where \(i(x,y)\) is the original pixel value from the image, \(I(x-1,y)\) values are directly above this pixel, and \(I(x,y-1)\) values are directly left to this pixel from the Integral image. Finally, we subtracted the value directly top-left of \(i(x,y)\) from the Integral image, that is, \(I(x-1,y-1)\). We gave a value of 0 to \(I(x-1,y)\), \(I(x,y-1)\), and \(I(x-1,y-1)\) if \(x-1\) and \(y-1\) were outside of the image bounds.
## 2 Materials and Methods
### Volatile Compound Analysis  
#### 2.3.1 Headspace Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry Analysis  
To extract the diverse volatile compounds in distilled _soju_ samples, different types of SPME fibers were considered such as polydimethylsiloxane (PDMS; 100 \(\upmu\)m), carboxen/polydimethylsiloxane (CAR/PDMS; 85 \(\upmu\)m), polymethylsiloxane/divinylbenzene (PDMS/DVB; 65 \(\upmu\)m), and divinylbenzene/carboxen/PDMS (DCP, 50/30 \(\upmu\)m) from Supelco (Bellefonte, PA, USA). The DCP (50/30 \(\upmu\)m) SPME fiber was selected, which showed a superior extraction capability for the various volatiles. To minimize the interferences of ethyl alcohol during the extraction, the alcohol content of each sample was adjusted to 10% (\(w/v\)) using distilled water. The HS-SPME procedure was adapted from previous studies [13, 25]. A diluted distilled _soju_ sample (5 g) with 100 \(\upmu\)L of the internal standard, 2-methyl-1-pentanol (100 ng/mL in distilled water), and 1 g of sodium chloride was placed in a 20 mL headspace glass vial (Supelco, Bellefonte, PA, USA). A conditioned SPME fiber was exposed to the headspace of the shaking sample at 40 \({}^{\circ}\)C for 30 min in an autosampler (combi PAL G6504-CTC; CTC Analytics, Zwingen, Switzerland). The fiber was then retracted and immediately injected into the gas chromatography (GC) inlet on a Hewlett-Packard (HP) gas chromatograph model 7890A (Palo Alto, CA, USA) coupled to an Agilent 5975C mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) with a Stabilwax(r)-DA bonded fused capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Restek, Bellefonte, PA, USA). The GC-MS operating conditions were described in previous studies [13, 25].
## 4 Discussion
### Impact of the Polishing Suspension Concentration
This is visualised by the comparison of the absorption coefficients of the abovementioned involved media (or a particular reference material) in Table 1. For example, the absorption coefficient of the used lapping agent, silicon carbide, is more than four million times higher than the absorption coefficient of the investigated zinc crown glass. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Absorption Coefficient \(\alpha\) in cm\({}^{-1}\)** & **Reference** \\ \hline Zinc crown glass N-ZK7 & 0.0024 & [37] \\ Silicon carbide 1 & 10,070 & [48] \\ Glycol 2 & 0.22 & [49] \\ Immersion oil 3 & 1.013 & [50] \\ \hline \hline \end{tabular} \({}^{1}\) Lapping agent used in this work, 2 reference material for polyurethane (i.e., the polishing pad material used in this work), 3 reference material for mineral oil-based cooling lubricants.  
\end{table} 
Table 1: Absorption coefficients of the investigated zinc crown glass, silicon carbide, glycol, and immersion oil at a wavelength of \(\lambda\) = 1030 nm. It should be noted that this comparison of the linear absorption coefficients of the investigated glass and possible surface-adherent and subsurface contaminants is only one aspect of laser-induced damage. Especially in the considered pulse duration range of some picoseconds, a number of further effects contribute to the LIDT. This includes nonlinear optical phenomena such as self-focussing, a dynamic increase in absorption due to multiphoton absorption and the formation of charge carriers by laser-induced avalanche ionisation. However, it can finally be stated that the polishing suspension concentration and the polishing process in general may have a noticeable impact on the LIDT. This effect was also qualitatively reported by _[PERSON]_ and co-workers where the LIDT of optical crystals was increased via optimising the applied polishing process [51].
# Power Control Techniques for Interference Management-
### Research Objectives  
The main aim of this review paper is to evaluate the current state of power control techniques in 5G wireless networks to provide a basis for new research topics. This aim is achieved by accomplishing the following research objectives:  
* Identify the current publication trends in power control techniques to address interference in 5G wireless networks.
* Provide the state of the most influential authors and journal articles on power control techniques.
* Provide the current state of co-word occurrence in power control to address interference in 5G wireless networks.
* Point out power control areas in 5G wireless networks that need additional study and the mapping of opportunities and directions for future research.
## 5 Results and Discussion
### Weighting Analysis of Fire Risk Impact Subfactors  
From the results of Figure 4 above, the five biggest fire risks that affect the Hexinwu Building Complex are D5--fire rescue capability (8.7%), A2--initial firefighting capability (8.2%), C3--fire resistance level of building materials (7.8%), B2--firefighting facilities (6.9%), and C1--fire load (5.7%). The most important factor for reducing the damage caused by a building fire is the fire rescue capability of urban management, which can greatly reduce the risk of such a disaster if it is properly handled immediately at its onset. Reports from China's statistical data from 1997 to 2017 showed that the total number of fires and losses throughout China decreased overall; one of the contributing factors was cities developing and improving their rescue capabilities. All fires start as small sparks or hidden dangers; if the initial fire forms before help arrives and professional firefighters do not come to contain it, then the native inhabitant's abilities to contain the spread of said fire are crucial. Second, building materials with higher ratings in terms of their degree of resistance against flames have an incredibly important role in preventing these fires from spreading. Such measurements are based on a graded scale determined by combustion properties and exhaust threshold limits for all components used in construction [67]. The bearing structure in the Hexinwu Building Complex is mainly made out of wood, which is far more flammable compared to reinforced concrete structures in larger buildings, making it considerably harder to reduce its risk of catching fire; therefore, it is essential when preserving this ancient tangible cultural heritage site that not only is research regarding traditional tourism culture performed correctly but also that proper coating and wrapping is applied on all major structures using flameproof materials to keep fires from spreading further should they occur [68].
## 3 Results  
### Intrumental Operating Conditions  
In the present contribution, six SCs were selected for screening purposes in oral fluids. We started by evaluating the spectral fragmentation pattern of each SC using GC-MS(full-scan), which allowed us to choose the target (base peaks) and qualifier ions in order to achieve higher selectivity and sensitivity to operate in the SIM mode acquisition [15,25]. We chose 300 \({}^{\circ}\)C as the injection temperature to start the instrumental optimization since it would ensure rapid volatilization of the target compounds. However, since methylene presented a very poor mass spectra quality, it was discarded from further studies. During the preliminary instrumental optimization, it was noticed that the other five SCs (\(\alpha\)-PVP, \(\alpha\)-PVT, MDPPP, MDPPP, and MDPV) suffer partial thermal degradation during injection at 300 \({}^{\circ}\)C, resulting in decomposition products characterized by prominent iminium base peaks with \(m/z\) 2 Da lower than the parent drug, as was already reported in previous works [14,15]. To minimize this phenomenon, the inlet temperature was programmed to 250 \({}^{\circ}\)C, the initial hold time in the column was reduced from 1 to 0 min, and the liner was changed every day (20 injections per day) in order to minimize or eliminate the possible occurrence of active sites during chromatographic analysis.  
Under these conditions, the instrumental precision was evaluated through repeated injections (2.0 mg L\({}^{-1}\), \(n\) = 10), resulting in relative standard deviations (RSD) below 18.5% for \(\alpha\)-PVP, \(\alpha\)-PVT, and MDPV. Nevertheless, for MDPPP and MDPBP, the resulting signal was too unstable (RSD > 25%) probably due to a more pronounced thermal degradation. For this reason, MDPPP and MDPBP were also discarded from further assays. By monitoring the selected ions of the remaining SCs (Table 1), good sensitivity and symmetrical peak shape could be obtained under the established conditions in suitable analytical time (<18.0 min). Figure S1 (Supplementary Materials) depicts the mass spectra for the three target SCs obtained using GC-MS analysis under the full-scan mode. Subsequently, instrumental sensitivity was checked through the limits of detection (LOD) and quantification (LOQ), obtained through the injection of diluted standard mixtures and calculated with a S/N of 3/1 and 10/1, respectively. From the data obtained, values of 30.0 \(\upmu\)g L\({}^{-1}\) and 90.0 \(\upmu\)g L\({}^{-1}\) were achieved for all three SCs. Instrumental calibration was also performed using standard solutions with concentrations ranging from 250.0 to 20,000.0 \(\upmu\)g L\({}^{-1}\) (tenpoint calibration), in which some lack of linearity was observed (r\({}^{2}\) > 0.92, MDPV) due to partial thermal degradation. Even so, the instrumental calibration seemed suitable for qualitative analysis of the three SCs under study (\(\alpha\)-PVP, \(\alpha\)-PVT, and MDPV).
## 3 Results and Discussion
### Adsorption Kinetic Studies  
The pseudo-first-order and pseudo-second-order kinetic models can be expressed as Equations (4) and (5), respectively. Table 2 lists the kinetic constants calculated from the linear regression of the pseudo-first-order and pseudo-second-order models as shown in Figure 5a,b. The R\({}^{2}\) values determined according to the pseudo-first-order model are relatively low. Furthermore, the corresponding \(q_{e,\mathrm{cal}}\) values are not consistent with the \(q_{e,\mathrm{exp}}\) values obtained from the experimental data. However, the R\({}^{2}\) values estimated via the pseudo-second-order model are higher than 0.99 and the calculated \(q_{e,\mathrm{cal}}\) values fitted well with the experimental data. This finding indicates that the kinetic for MB adsorption on MPC can be explained by the pseudo-second-order kinetic model.  
\[\log(q_{e}-q_{t})=\log q_{e}-\frac{k_{1}t}{2.303} \tag{4}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature (K)**} & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & \(q_{\mathrm{max}}\) (mg g\({}^{-1}\)) & \(k_{L}\) (L mg\({}^{-1}\)) & \(\mathrm{R}^{2}\) & \(\mathrm{1/n}\) & \(K_{f}\) (L mg\({}^{-1}\)) & \(\mathrm{R}^{2}\) \\ \hline
293 & 207.04 & 2.68 & 0.9895 & 0.0741 & 165.6 & 0.8308 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Isotherm parameters for adsorption of MB onto MPC.  
Figure 5: (a) Pseudo-first-order and (b) pseudo-second-order kinetics for MB adsorption on MPC; (c) [PERSON] plot; (d) Adsorption capacity of MB on MPC in three successive cycles.