esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Smart Energy Management in the Future IoT
### AI-Enabled Wireless Energy Transfer in IoT Devices  
Wireless energy transfer (WET) in IoT networks is based on the magnetic resonant coupling principle. This principle states that in magnetic resonant coils operating at the same resonant frequency, energy is transferred from one source coil to a receiver coil via a nonradioactive electromagnetic field [29]. According to [PERSON]'s free space equation, the received power \(\mathbf{P_{r}}\) of a signal, d meters away from the source power \(\mathbf{P_{0}}\) is shown in Equation (5) [30] as  
\[\mathbf{P_{r}}=\mathbf{G_{s}}\mathbf{G_{r}}\bigg{(}\frac{\boldsymbol{\lambda}} {4\pi\mathbf{d}}\bigg{)}^{2}\mathbf{P_{0}} \tag{5}\]  
where \(\mathbf{G_{s}}\) is the source antenna gain, \(\mathbf{G_{r}}\) is the receive antenna gain and \(\boldsymbol{\lambda}\) is the wavelength. Hence, an empirical model of WET for IoT nodes can be given in Equation (6) [30] as  
\[\mathbf{P_{r}}=\frac{\mathbf{G_{s}}\mathbf{G_{r}}\mathbf{n}}{\mathbf{L_{p}}} \bigg{(}\frac{\boldsymbol{\lambda}}{4\pi(\mathbf{d}+\boldsymbol{\beta})} \bigg{)}^{2}\mathbf{P_{0}} \tag{6}\]  
where \(\mathbf{L_{p}}\) is the polarization loss, \(\mathbf{n}\) is the rectifier efficiency, and \(\boldsymbol{\beta}\) is a parameter for adjusting the Friis' free space equation for short-distance transmission. In an attempt to investigate the feasibility of integrating this technology in IoT networks, authors in ref. [31] used a mobile charger (MC) to wirelessly charge the battery of sensor nodes periodically and thereby proved the feasibility of the technique. Some MC related concepts include demand timeliness, the number of charging devices, control system and charging points [32]. Demand timeliness describes the state of the MC when it receives a charging request while the number of charging devices describes the number of MC and nodes being charged simultaneously. The control system is the entity that determines the optimal travel path for the MC. Common challenges with WET, especially in an IoT network that underscores the need for AI techniques are the need for multiple chargers, optimized traveling path, visit time and charging period of the MCs. Table 2 summarizes the application areas of AI in the SEM group. In the next section, we present the second group of CS solutions.
## 3 Results  
### Isolation and Purification of LOX in Dry-Cured Mackerel  
The crude LOX solution extracted from dry-cured mackerel was purified via 20-40% ammonium sulfate fractional precipitation, dialysis, and DEAE Sepharose FF ion-exchange chromatography. The protein content and enzyme activity of LOX before and after purification were measured, and the purification fold was calculated by the LOX-specific activity. The results are shown in Table 1. The LOX-specific activity of crude LOX solution was 120.38 U/min.g, and the purity improved slightly after ammonium sulfate precipitation and dialysis. The dialyzed LOX solution was further purified using DEAE Sepharose FF ion-exchange chromatography, with its specific activity reaching 493.60 U/min.g, a multiplication of 4.10, indicating that LOX purity was highly increased.  
Afterward, SDS-PAGE was performed on both crude and purified samples to further visually observe the efficiency of LOX purification (the results are presented in Figure 1). Based on the SDS-PAGE, the crude enzyme bands were more numerous and more blurred, suggesting that the crude enzyme contained more impurities. However, after ammonium sulfate precipitation, dialysis, and DEAE Sepharose FF ion-exchange chromatography, there was only one band in passage 2 at 95 kDa. As reported in previous studies, the molecular weight of LOX was approximately 95 kDa [17; 18], which indicates that the LOX solution obtained in the final step was highly purified.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Purification** & **Protein Content (g/L)** & **Activity (U)** & **LOX Activity (U/min.g)** & **Purification Fold** \\ \hline Crude enzymes & 0.91 & 109.33 & 120.38 & 1.00 \\ Fractional precipitation with & 1.25 & 171.67 & 137.07 & 1.14 \\ ammonium sulfate & 1.62 & 231.67 & 142.92 & 1.19 \\ DEAE Sepharose FF & 0.23 & 114.67 & 493.60 & 4.10 \\ \hline \end{tabular}
\end{table}
Table 1: Purification of LOX in dry-cured mackerel.
###### Abstract  
Landslides pose significant threats to human safety and socio-economic development. In recent decades, interferometric synthetic aperture radar (InSAR) technology has emerged as a powerful tool for investigating landslides. This study systematically reviews the applications of spaceborne InSAR in landslide monitoring and susceptibility mapping over the past decade. We highlight advancements in key areas, including atmospheric delay correction, 3D landslide monitoring, failure time prediction, enhancements in spatial and temporal resolution, and integration with other technologies like the Global Navigation Satellite System (GNSS) and physical models. Additionally, we summarize various InSAR application strategies in landslide susceptibility mapping, identifying a gap between the static nature of most current studies and InSAR's dynamic potential for capturing deformation velocity. Future research should integrate InSAR-derived factors with other dynamic variables like rainfall and soil moisture for dynamic susceptibility mapping and prediction. We also emphasize that further development of dynamic InSAR will require more efficient SAR data management and processing strategies.  
InSAR; landslide; landslide monitoring; landslide susceptibility mapping; spaceborne remote sensing 202517999  
2025  
2025
## 4 Materials and Methods
### Electronic Nose Analysis  
An electronic nose device PEN 3 E-Nose was used, which was manufactured by Winnuster Airsense Analytic Inc., Schwerin, Germany. The sensor array system consisted 10 metal oxide semiconductors (MOS) of different chemical compositions and thicknesses to provide selectivity towards volatile compound classes, including W1C (aromatic compounds), W5S (broad-range compounds), W3C (ammonia, aromatic compounds), W6S (hydrogen), W5C (aromatic-aliphatic), W1S (methane, broad-range compounds), W1W (sulfur compounds), W2S (broad-alcohol compounds), W2W (sulfur-chlorine), and W3S (methane-aliphatic). The software of the electronic nose, Winnuster, was used for data storage and multivariate statistical processing [40].  
Two grams of BT and WT samples were put into 15-mL airtight vials (concentration chamber), respectively. The samples were prepared in a mild water bath (50 \(\pm\) 2 \({}^{\circ}\)C) for 50 min, and then, one Luer-lock needle (20 g) connected to a Teflon-tubing (3 mm) was used to perforate the seal (plastic) of the vial and to absorb the volatile gases inside it. Clean air to replace the sampled air was furnished through a second needle connected to a charcoal filter. During the measurement time (60 s), the sampling unit inhaled the volatile gases present in the headspace at a constant rate causing changes in the sensor's conductance, which was long enough for the sensor signals to reach a steady value. When a measurement was completed, a standby of 500 s was initiated with the circuit, and the chamber was flushed by clean air until the sensor signals returned to baseline. The E-Nose analysis was performed at least three times for each truffle sample.
# Review of Passive Flow Control Methods for Compressor Linear Cascades
## 3 Discussion
### Analysis of Passive Control Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Passive Technique** & **Key Findings** & **Performance Improvement** & **Stall Margin Improvement** & **Other Observations** & **References** \\ \hline Nonconventional & Doublet and & Reduction in & & & & \\ Nonconventional & generators show a & & skin friction, & No direct stall & & \\ vortex & 46\% and 32\% & especially for & margin data, but & & \\ generators & reduction in skin & & doublet and & improved flow & & \\  & friction at the bottom & & wishbone & characteristics. & & \\  & endwall. & & designs. & & \\ \hline Vortex generator & & Combined VG and & & & & \\ + slot jets & slot jet method & Improvement in & Improved stall & Enhanced flow & \\  & suppresses endwall & cascade & margin by & uniformity and & \\  & crossflow and deflects & performance due & reducing & reduced & \\  & passage vortices, & to reduced & vortex-induced & & \\  & improving flow & secondary flows. & flow disruptions. & development. & \\  & uniformity. & & & & \\ \hline Micro-vortex & MVGs, including & Up to 9.36\% & & & \\  & rectangular and & reduction in total & Delay stall onset & & \\  & curved trapezoidal & pressure loss, & and reduce & & \\  & secondary flow & 34.6\% reduction & secondary flow & & \\  & development and & in secondary & losses. & effective under & \\  & delay stall onset. & flow loss. & & high-load & \\ \hline Vortex & Optimally placed & & & & \\  & VG-inspired & & & & \\ generators & structures reduce & 10.3\% reduction & & & \\ inspired by & corner separation and & in flow losses. & reducing corner & & \\ dragonfly wings & shift the separation & & separation. & & \\  & point downstream. & & & \\ \hline Bionic slanting & & Riblets reduce total & & Significant delay & \\  & pressure loss, enhance & 14.53\% reduction & in separation, & effective in & \\ riblets & flow mixing, and & in total pressure & contributing to & controlling flow & \\  & delay separation. & loss. & improved stall & near the blade & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._sors. The Bowed LESB Cascade employs leading-edge stroke blades to reduce pressure loss by 59.7% while maintaining static pressure rise. AI-based optimizations and computational tools lead to more precise designs, enhancing compressor performance. Table 3 provides a comparison of various passive flow control methods used in linear cascades for compressors. Each method is evaluated based on its advantages and disadvantages, highlighting aspects such as effectiveness in flow manipulation, impact on performance, ease of implementation, and potential limitations like added complexity or efficiency reduction. The aim is to give a clear overview of each method's applicability and trade-offs for improving compressor performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
## 2 Dense-Timed Deontic Interpreted System
### An Example of a Real-Time MAS and Its DTDIS Model
#### Semantics of EMTLKD  
Let \(M=(\Sigma,S,\iota,\rightarrow,\mathcal{V})\) be a dense-timed deontic model, and \(\rho=s_{0}\stackrel{{\delta_{0},\overline{\delta}_{0}}}{{ \longrightarrow}}s_{1}\stackrel{{\delta_{1},\overline{\delta}_{1}} }{{\longrightarrow}}s_{2}\stackrel{{\delta_{2},\overline{\delta}_{2 }}}{{\longrightarrow}}\ldots\) represent a run in \(M\). Each run generates the unambiguous _path_\(\lambda_{\rho}=s_{0},s^{\prime}_{0},s_{1},s^{\prime}_{1},s_{2},s^{\prime}_{2},\ldots\)_corresponding to run_\(\rho\), where \(s_{0}\stackrel{{\delta_{0}}}{{\longrightarrow}}s^{\prime}_{0},s^{ \prime}_{0}\stackrel{{\overline{\delta}_{0}}}{{\longrightarrow}} s_{1},s_{1}\stackrel{{\delta_{1}}}{{\longrightarrow}}s^{\prime}_{1},s^{ \prime}_{1}\stackrel{{\overline{\delta}_{1}}}{{\longrightarrow}} s_{2}\),... hold. This is because we consider only the strongly monotonic runs. Further, \(\Pi(s)\)denotes the set of all the paths starting at \(s\in S\), and \(\Pi=\bigcup_{s^{0}\in_{\ell}}\Pi(s^{0})\) is the set of all the paths starting at all initial states. Finally, given a run \(\rho\) and a position \(j\geqslant 0\), a _duration function_\(\mathcal{D}_{\rho}:\mathds{N}\mapsto\mathds{R}_{+}\), as defined in [11], returns the sum of all the time transitions along the run \(\rho\) up to the position \(j\). There are two possible distinct semantics for EMTLKD: one referred to as \"continuous\" and the other as \"pointwise\". While both share rules for basic modalities, they diverge in their interpretation of the term \"position\". In the continuous semantics, a position in a run \(\rho\) encompasses any state encountered along \(\rho\). For example, if there exists a transition \((\ell,v)\xrightarrow{\delta\pi}(\ell^{\prime},v^{\prime})\) in \(\rho\), then any state \((\ell,v+t)\) with \(0\leq t\leq\delta\) is considered a position in \(\rho\). In the pointwise semantics, a position in a run \(s_{0}\xrightarrow{\delta_{0},\overline{\partial}_{0}}s_{1}\xrightarrow{ \delta_{1},\overline{\pi}_{1}},\ldots,s_{n-1}\xrightarrow{\delta_{n},\overline {\pi}_{\delta}}s_{n},\ldots\) is represented by an integer \(i\) corresponding to the state \(s_{i}\). In this semantics, formulas are evaluated immediately after an action is performed. We adopt the pointwise semantics. **Definition 2** (Semantics). : _Let \(\alpha\) and \(\beta\) be EMTLKD formulae, \(M\) the dense-timed deontic model, and \(\lambda_{\rho}\) a path. By \(\lambda_{\rho}[n]\), we denote the path \(\lambda_{\rho}\) with a designated formula evaluation position \(n\in\mathds{N}\).
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 2 Preliminaries
### Security Model  
#### 2.3.1 Indistinguishable Random Variables  
Let \(\mathbb{N}\) denote the set of natural numbers. A function \(\epsilon:\mathbb{N}\rightarrow[0,1]\) is called _negligible_, if \(\forall c>0,\ \exists k,\ \forall n\geq k,\ \epsilon(n)<1/n^{c}\). Let \(X=\{X_{k}\ |\ k\in\mathbb{N}\}\) and \(Y=\{Y_{k}\ |\ k\in\mathbb{N}\}\) be sequences of random variables such that \(X_{k}\) and \(Y_{k}\) are defined over the same sample space. We say that \(X\) and \(Y\) are _indistinguishable_, denoted by \(X\equiv_{c}Y\), if, and only if, \(\Pr[X_{n}=Y_{n}]\) is a negligible function.
# Frequency Modulation and Erosion Performance of a Self-Resonating Jet
## 2 Research Method
### Numerical Simulation Setup
The equations of mass and momentum of the mixture for the single fluid approach are given as:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{j})}{  
Figure 10: Average streamwise velocity profiles.  
The source term in Equation (5) can be further rearranged in the general form:  
\[\dot{m}=\dot{m}^{+}-\dot{m}^{-}=\frac{\rho_{l}\rho_{v}}{\rho}\frac{da_{v}}{dt} \tag{8}\]  
The source terms \(\dot{m}^{+}\) and \(\dot{m}^{-}\) represent the effects of evaporation and condensation during the phase change and are derived from the bubble dynamics equation for the generalized Rayleigh-Plesset equation as follows:  
\[\dot{m}^{+}=\frac{\rho_{l}\rho_{v}}{\rho}\alpha_{v}(1-\alpha_{v})\frac{3}{R_{b }}\sqrt{\frac{2}{3}\frac{\max(\rho_{v}-\rho,0)}{\rho_{l}}} \tag{9}\]  
\[\dot{m}^{-}=\frac{\rho_{l}\rho_{v}}{\rho}\alpha_{v}(1-\alpha_{v})\frac{3}{R_{b }}\sqrt{\frac{2}{3}\frac{\max(\rho-\rho_{v},0)}{\rho_{l}}} \tag{10}\]  
The bubble radius is related to the vapor volume fraction \(\alpha_{v}\) and the bubble number density \(N_{b}\), as:  
\[R_{b}=(\frac{\alpha_{v}}{1-\alpha_{v}}\frac{3}{4\pi}\frac{1}{N_{b}})^{1/3} \tag{11}\]  
To reduce the turbulence dissipative terms in the cavitation regions, a modified RNG turbulence model was implemented into the ANSYS Fluent code through the user-defined function (UDF). In the standard RNG \(k\)-\(\varepsilon\) model, the turbulent eddy viscosity is defined as:  
\[\mu_{t}=C_{\mu}\rho\frac{k^{2}}{\varepsilon} \tag{12}\]  
where \(C_{\mu}=0.0845\), \(k\) is the turbulent kinetic energy, and \(\varepsilon\) is the turbulent eddy dissipation. The standard RNG \(k\)-\(\varepsilon\) turbulence model tends to overestimate the turbulent eddy viscosity in the cavity region since it was first proposed for an incompressible single-phase flow. A modified model, which considers the compressibility of cavitating flow, was proposed by [PERSON] et al. [31] as follows:  
\[\mu_{t\_DCm}=C_{\mu}f(\rho)\frac{k^{2}}{\varepsilon} \tag{13}\]  
\[f(\rho)=\frac{\rho_{v}+(1-\alpha_{v})^{n}(\rho_{l}-\rho_{v})}{\rho_{v}+(1- \alpha_{v})(\rho_{l}-\rho_{v})} \tag{14}\]  
where \(n\) is the exponential coefficient. This treatment can artificially reduce the turbulent eddy viscosity in the cavitation region based on the vapor volume fraction by adjusting the value of the exponential coefficient. In the current simulation, the value of \(n\) was fixed at 10, because this value has been validated for many cases [32; 33; 34; 35].
## 2 Propagation Modeling and Key Rate Estimation
### Propagation Modeling
#### 2.1.1 Rotation of Polarization Angle  
Based on the unified theory of coherence and polarization, we assume that one of the properties of the quantum states, the polarization angle \(\theta_{,}-\pi/2\leq\theta\leq\pi/2\), can be represented as the major axis of the polarization ellipse made in the \(x\) direction. Thus, the polarization angle \(\theta\) can be expressed as [27]  
\[\theta(\boldsymbol{\rho};z)=\frac{1}{2}\mathrm{arctan}\bigg{\{}\frac{2\mathrm{ Re}[W_{xy}(\boldsymbol{\rho},\boldsymbol{\rho};z)]}{W_{xx}(\boldsymbol{\rho}, \boldsymbol{\rho};z)-W_{yy}(\boldsymbol{\rho},\boldsymbol{\rho};z)}\bigg{\}} \tag{5}\]  
Thus, without any loss of generality, we consider the characteristic of the received optical signal at the center point \(\boldsymbol{\rho}_{0}=(0,0,z)\), that is, the polarization angle of quantum state at the received detection can be represented as \(\theta(\boldsymbol{\rho}_{0};z)\). Thereby, the rotation of the polarization angle \(\Delta\theta\) can be expressed as  
\[\Delta\theta=\theta(\mathbf{r}_{0};0)-\theta(\boldsymbol{\rho}_{0};z) \tag{6}\]
## 3 Identification of Blackcurrant Anthocyanins  
Four major anthocyanins in blackcurrants were analyzed using high-performance liquid chromatography with a diode-array detector (HPLC-DAD), with mass spectrometry (HPLC-DAD-MS), or with tandem mass spectrometry (HPLC-DAD-MS/MS) (Figure 2). Over 70% of the antioxidant capacity of blackcurrants came from those anthocyanins [22; 48]. Four dominant anthocyanins--cyanidin-3-O-glucose, cyanidin-3-O-rutinoside, delphinidin-3-O-glucose, and delphinidin-3-O-rutinoside--accounted for 90% of the total anthocyanin content in various cultivars of blackcurrants [48; 49]. They were also the major anthocyanins discovered in the buds, leaves, and seeds of blackcurrants [10; 50]. Among the four anthocyanins, delphinidin-3-O-rutinoside was the most prevalent followed by cyanidin-3-O-rutinoside, delphinidin-3-O-glucose, then cyanidin-3-O-glucose [48; 49].  
The other minor anthocyanins that were identified in various blackcurrant cultivars using HPLC-DAD-MS included the 3-O-glucosidase and the 3-O-rutinosides of pelargonidin, cyanidin, peonidin, delphinidin, petunidin, malvidin, cyanidin 3-O-arabinoside, and the 3-O-(6''-p-coumaroylglucoside)s of cyanidin and delphinidin [22; 51].  
The bioavailability of anthocyanins in blackcurrants were also studied. After consuming a 20% blackcurrant juice (250 mL), the abundance of consumed anthocyanins in urine was 0.021 \(\pm\) 0.003% of delphinidin glycosides and 0.009 \(\pm\) 0.002% of cyanidin glycosides over a 24 h period [52]. In another study, the recoveries of delphinidin-3-O-rutinoside and cyanidin-3-O-rutinoside in urine samples were 0.040 \(\pm\) 0.011% and 0.048 \(\pm\) 0.016%, respectively, over a period of 48 h after blackcurrant juice consumption [53]. Although anthocyanins in blackcurrants present a low bioavailability, their metabolites may contribute to the in vivo protective effects observed.  
Figure 2: The structures of the major anthocyanins in blackcurrants including cyanidin-3-O-glucose, cyanidin-3-O-rutinoside, delphinidin-3-O-glucose, and delphinidin-3-O-rutinoside.
# A Systematic Control Design Method with Active Damping Control in Voltage Source Converters
## 3 Design of the Control Gain Matrix
### Weighting Matrixes' Selection
#### Selection of \(q_{3}\):  
Overall, \(q_{3}\) has a significant impact on the tracking error of dc-link voltage. A higher \(q_{3}\) causes the faster tracking of dc-link voltage at the price of higher overshoot in the inverter current. The inverter current (especially \(d\)-axis current) acts as a control input for DVC. Therefore, it can be expected that the faster convergence rate of the dc-link voltage may lead to more current variations and overshoots. Inverter overcurrent protection limits the current overshoot. Thus, there is also a compromise here that \(q_{3}\) can obtain.  
Figure 5 shows system response for two different values of \(q_{3}\). For a higher value, dc-link voltage tracks the reference one quickly and without oscillations at the expense of the higher current overshoot and vice versa.
## Nomenclatures  
\begin{tabular}{l l} \(F_{\rm s}\) & \(F\ factor=V_{\rm s}\sqrt{\rho_{G}}\), \(\left[{\rm m/s(kg/m^{3})^{0.5}}\right]\) \\ \(F_{a}\) & kinetic gas factor based on velocity toward active area \(Pa^{0.5}\) \\ \(V_{\rm s}\) & gas phase superficial velocity based on the bubbling area, \(\left[{\rm m/s}\right]\) \\ \(\rho_{G}\) & gas density, \(\left[{\rm kg/m^{3}}\right]\) \\ \(F_{\rm gas,in}\) & inlet gas flow rate, \(\left[{\rm Nm^{3}/h}\right]\) \\ \(F_{\rm CO_{2},in}\) & inlet CO\({}_{2}\) flow rate \(\left[{\rm Nm^{3}/h}\right]\) \\ \(F_{\rm air,in}\) & inlet air flow rate, \(\left[{\rm Nm^{3}/h}\right]\) \\ \(N_{\rm CO_{2}}\) & the absorbed rate of CO\({}_{2}\), \(\left[{\rm Nm^{3}/h}\right]\) \\ \(y_{\rm CO_{2},in}\) & inlet volumetric fraction of CO\({}_{2}\), \(\left[-\right]\) \\ \(y_{\rm CO_{2},in}\) & outlet volumetric fraction of CO\({}_{2}\), \(\left[-\right]\) \\ \(\Delta P_{total,tray}\) & total tray pressure drop, \(\left[{\rm mbar}\right]\) \\ \(\Delta P_{wet,tray}\) & wet tray pressure drop, \(\left[{\rm mbar}\right]\) \\ \(\Delta P_{dry,tray}\) & dry tray pressure drop, \(\left[{\rm mbar}\right]\) \\ \(h_{L}\) & liquid holdup, \(\left[{\rm m^{3}}\right]\) \\ \(A_{tray}\) & Tray surface area, \(\left[{\rm m^{2}}\right]\) \\ \(h_{cl}\) & clear liquid height, \(\left[{\rm m}\right]\) \\ \(h_{W}\) & weir height, \(\left[{\rm m}\right]\) \\ \(C\) & empirical constant, \(\left[-\right]\) \\ \(Q_{L}\) & liquid flow rate, \(\left[{\rm m^{3}/s}\right]\) \\ \(W\) & weir length, \(\left[{\rm m}\right]\) \\ \(u_{b}\) & bubbling velocity, \(\left[{\rm m/s}\right]\) \\ \(\rho_{G}\) & density of gas, \(\left[{\rm kg/m^{3}}\right]\) \\ \(\rho_{L}\) & density of liquid, \(\left[{\rm kg/m^{3}}\right]\) \\ \(\alpha_{e}\) & effective liquid volume fraction, \(\left[-\right]\) \\ \(Q\) & discharge in \(f^{3}/s\) neglecting velocity of approach, \(\left[f^{3}/s\right]\) \\ \(L\) & length of weir, \(\left[f\right]\) \\ \(h_{1}\) & head on the weir, \(\left[f\right]\) \\ \(A\) & the hole pitch, \(\left[{\rm m}\right]\) \\ \(\Psi\) & flow ratio group \\ Abbreviations & \\ \({\rm Nm^{3}/h}\) & a cubic meter of gas per hour at the normal temperature and pressure \\ PID controller & proportional-integral-derivative controller \\ MFC & mass flow controller \\ kW & kilowatt \\ DEA & 2,2\({}^{\prime}\)-miniodiethanol \\ FST & folding sieve tray \\ \end{tabular}