esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment on the Properties of Biomass-Aggregate Geopolymer Concrete
## 3 Results
### Thermal Conductivity of Concrete  
Concrete structures used in residential buildings must not only ensure the safety of the structure but also take into account energy conservation. The use of concrete with low thermal conductivity during building construction is undoubtedly beneficial to building  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Curing Age** & **NA** & **CCSA** & **SBRA** \\ \hline
0 d & 2.6 & 19.8 & 17.4 \\
28 d & 0.9 & 14.8 & 17.1 \\ \hline \hline \end{tabular} Note: At 0 d age of concrete, the curing age of synthesized biomass recycled aggregate has reached 30 d, and crushed coconut shell aggregate is added directly in the saturated water absorption state.  
\end{table}
Table 7: Water content of synthesized biomass recycled aggregates.  
Figure 6: (**a**) Ultrasonic pulse velocity of Concretes (UPV) (**b**) Section of SBRAC.  
Figure 7: Self-shrinkage rate of concretes. (**a**) The specimens of concrete self-shrinkage; (**b**) Self-shrinkage rate of concretes.  
energy conservation. The thermal conductivity values of concrete are shown in Figure 8. Due to the different thermal conductivity of different materials, as shown in Figure 8a, the thermal conductivity of natural aggregate concrete is up to 1.49 W/mK. Secondly, the thermal conductivity of crushed coconut shell aggregate concrete is the lowest value of 0.56 W/mK. This is because crushed coconut shell is a porous biomass material, and it has a lower thermal conductivity value than natural aggregate. The thermal conductivity of SBRAC is 0.47 W/mK, which is because the cement slurry shell attached to the surface of the SBRA effectively converts the pores on the surface of the coconut shell into closed pores, thereby effectively reducing the thermal conductivity of SBRA.  
Thermal conductivities of commonly used energy-saving materials are shown in Figure 8b. The thermal conductivity of lightweight aggregate concrete is generally in the range of 0.5-0.65 W/mK [23; 33; 34]. The thermal conductivity of clay bricks is generally 0.65-0.75 W/mK [35; 36; 37]. The thermal conductivity of foam concrete is generally in the 0.1-0.2 W/mK range [38; 39]. The thermal conductivity of synthesized biomass recycled coarse aggregate concrete is lower than that of clay brick masonry structures and light aggregate concrete, which means that synthesized biomass recycled coarse aggregate concrete has a greater potential for building energy saving.
###### Abstract  
To assess the presence and absence of mycoplasma contamination in cell culture, Fourier transform infrared (FTIR) microspectroscopy, coupled with multivariate analysis, was deployed to determine the biomolecular changes in hepatocellular carcinoma cells, HepG2, before and after mycoplasma contamination. The contaminated HepG2 cells were treated with antibiotic BM-Cyclin to decontaminate the mycoplasma, and polymerase chain reaction (PCR) was then performed to confirm the presence or the absence of mycoplasma contamination. The contaminated and decontaminated HepG2 cells were analyzed by FTIR microspectroscopy with principal component analysis (PCA) and peak integral area analysis. The results showed that the FTIR spectra of contaminated HepG2 cells demonstrated the alteration in the IR spectra corresponding to the lipid, protein, and nucleic acid regions. PCA analysis distinguished the spectral differences between the groups of mycoplasma-contaminated and -decontaminated cells. The PCA loading plots suggest that lipid and protein are the main contributed molecules for the difference between these two cell groups. Peak integral area analysis illustrated the increase of lipid and nucleic acid and the decrease of protein contents in the contaminated HepG2 cells. FTIR microspectroscopy is, therefore, proven to be a potential tool for assessing mycoplasma removal by monitoring biomolecular alterations in cell culture.  
FTIR microspectroscopy; mycoplasma; contamination; HepG2 cells; culture cells +
Footnote †: journal: Applied Physics Letters  
_Appl. Sci._**2020**, _10_, 3766; doi:10.3390/appl10113766
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
## 2 Materials and Methods
### MRT Methods
#### 2.1.2 Numerical Model of the LMA Signal
The use of closed algorithms over which the client has no control can be dangerous and is a prerequisite for the occurrence of type I and II errors in the diagnosis from MRT examinations. The LMA discrete signal model is described by Equation (21):  
\[LMA(k)=signal_{LMA}(k)+noise_{LMA}(k) \tag{21}\]  
where  
* \(k\) is discrete time;
* \(signal_{LMA}\) is the LMA voltage component at the output of the Hall sensor generated by the technical condition of the wire rope;
* \(noise_{LMA}\) is the LMA voltage component at the output of the Hall sensor generated by other sources of magnetic field heterogeneity. The reliable detection of rope defects and meeting the defect symptom criterion according to EN 12927:2019 requires minimizing the noise level. The small number of Hall sensors used in the GP head, the averaging of the measured magnetic field information, and temperature drift make the LMA signal less suitable than the LF signal for synchronizing data from periodic MRT inspections of the rope, especially in the phase preceding the appearance of wire breakage or developed internal corrosion of compact ropes. Despite the advantages of a multi-sensor probe head, including the ability to obtain measurement results in the form of a magnetogram, the majority of MRT users worldwide use a single Hall sensor measurement path. In the GP head used in Poland, only two Hall sensors are used, located on the same radius as 180 degrees, which measure the average scattered magnetic field inside the magnetic circuit (measuring head, rope fragment, air gaps between the rope and the pole pieces of the magnetizing circuit based on permanent magnets). The LMA signal recorded in the MD121 flaw detector is a low-resolution low-band filtered signal, which excludes its use for time-series synchronization from periodic non-destructive testing.
## 2 Setup of Experiment
### Experimental Model of Risers  
The full-size field testing of risers has extreme requirements in terms of test equipment, time, cost, space and safety, and it is impossible to achieve in any laboratory. Therefore, a scale model is employed whose ratio of length to outer diameter is 62.5; more specifically, the length of the riser model in the test is 1.25 m, with an inner diameter (I.D.) and outer diameter (O.D.) of 0.016 m and 0.02 m, respectively.  
The locations of electric resistance strain gauges and the properties of the model risers are presented in Figure 2 and Table 1. Figure 2a shows the sketch map of the model riser, Figure 2b shows the risers used in the experiment, and Figure 2c shows the holistic test setup for the riser.  
The depth of the water is 0.6 m and electric resistance strain gauges are placed at z = 0.25 m, z = 0.50 m and z = 0.75 m (z = 0 is the bottom of the riser), respectively. At each location, 4 strain gauges are employed around the riser's outer surface (12 strain gauges in total for each riser model).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Materials** & **O.D. (m)** & **I.D. (m)** & **Length (m)** & **Density (kg/m\({}^{-3}\))** & **Modulus (Pa)** \\ \hline
1 & Al & 0.02 & 0.016 & 1.25 & 3043.17 & \(70\times 10^{9}\) \\
2 & PMMA & 0.02 & 0.016 & 1.25 & 1549.89 & \(3.5\times 10^{9}\) \\
3 & UPVC & 0.02 & 0.016 & 1.25 & 1153.57 & \(2.0\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometries and properties of the risers.  
Figure 1: Testing instrumentation. (**a**) Current generation system; (**b**) velocity monitoring system; (**c**) end condition system; (**d**) data acquisition system.  
The geometries and properties of the risers utilized in the experiment are presented in Table 1.  
Figure 2: Riser model used in the experiment. (**a**) Sketch map of the model riser; (**b**) risers used in the experiment; (**c**) holistic test setup for the riser.
## 3 Fundamental Concepts
### Packet Relaying and Energy Consumption
\begin{split} E_{t_{(p,h)}}=&\begin{cases}D_{(p,h)}.Pwt_{(p,h)},tx_{(p,h)}+W_{(p)}.Pws_{(p,h)}ts_{(p,h)}\\ 0&,h=(H_{(p)}+1)\end{cases}\\ E_{t_{(p,h)}}=&\begin{cases}D_{(p,(h-1))}.Pwt_{(p,h)},tx_{(p,h)}+W_{(p)}.Pws_ {(p,h)}ts_{(p,h)}\\ 0&,h=0\end{cases}\end{split}  \tag{4}\]  
As most energy consumption is expected in transmission and reception procedures instead of processing and storage [7, 8, 33], we can roughly state the total energy consumption in path \(p\) as expressed in Equation (5). The total consumed energy is the sum of the energy consumption in transmission and reception in all intermediate nodes (\(h=1\),...,\(H_{(p)}\)), plus the energy for transmissions from the source node (\(h=0\)) and reception of data packets at the sink (\(h=H_{(p)}+1\)). \[E_{(p)}=\sum_{h=0}^{H_{(p)}+1}\Bigl{(}E_{t_{(p,h)}}+E_{t_{(p,h)}}\Bigr{)} \tag{5}\]  
The basic formulation in Equation (5) could be even more extended to incorporate the energy costs for transmission and reception of 1-hop ACK messages. Moreover, the average packet error rates and retransmissions could also be accounted, as expressed in [7, 24]. A useful approach to estimate the average number of retransmissions is to incorporate a Gilbert/Elliot error model [34], as expressed in [7]. In that model, the error probability is a function of the size of the packets. Although such additional elements can improve the presented energy consumption model, the basic idea that more energy is expected to be consumed when more packets have to be relayed through more intermediate nodes will not change. Analyzing the presented formulation, we can then conclude that energy savings may be achieved when some packet relaying operations are avoided, reducing the value for \(D_{(p,h)}\) over segments of the network. Energy consumption in sensors depends mostly on the time wireless radio is turned on. Thereby, idle listening and sleeping time have a major role when estimating the energy consumption. In such way, analyses of the expected energy saving must consider a more realistic model, as provided by discrete event simulators. Section 5 presents simulation results for the energy consumption when employing the proposed relaying mechanism, considering a realistic duty-cycle MAC protocol.
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 3 Problem Statement and Solving Method
### Level 1: Electronic Circuit Controller  
To successfully perform power distribution via the DC/DC converter, a power split controller based on the fuzzy logic controller is applied. The fuzzy logic controller determines the power proportion between the SC and battery under several circumstances based on two inputs (\(P_{prad}\) and \(SOC_{SC}\)) and one output (\(C_{SC}\)). \(P_{prad}\) is the required power prediction of the load in which will be discussed in the following subsection. \(SOC_{SC}\) is the SOC percentage of SC and \(C_{SC}\) is the SC discharging coefficient, which is the output of the fuzzy logic controller. The diagram of the fuzzy logic controller is shown in Figure 5.  
The control rule of the fuzzy logic controller is given as follows:  
1. If the required predicted power is high/medium-high and the SOC of the SC is high/medium, then the SC will supply most of the power.
2. If the required predicted power is high and the SOC of the SC is low, then the SC will still supply most of the power.
3. If the required predicted power is medium-high and the SOC of the SC is low, then the power supplied by the battery and SC is almost balanced.
4. If the required predicted power is medium-low/low and the SOC of the SC is high/medium, then the power supplied by the battery and SC is almost balanced.
5. If the required predicted power is medium-low/low and the SOC of the SC is low, then the battery will supply most of the power.
6. An exception for rule number 4: if the required predicted power is medium-low and the SOC of the SC is high, then the SC will still supply most of the power.  
A Mamadani rule-based system is adopted for this fuzzy logic controller. The centroid defuzzification method is selected to obtain a crisp output value. The output value for this fuzzy logic controller is the SC discharging power coefficient (\(C_{SC}\)). As the \(C_{SC}\) is obtained, the power proportion to be supplied by SC can be determined by  
\[P_{SC}=C_{SC}\cdot P_{d} \tag{3}\]  
where \(P_{SC}\) is the power proportion of SC, \(C_{SC}\) is SC discharging coefficient, and \(P_{d}\) is power demand. Recall (1) and the new equation can be written as  
\[P_{batt}=P_{d}\cdot(1-C_{SC}). \tag{4}\]  
Since the variables \(P_{d}\) and \(C_{SC}\) in (4) are known, the power supplied by the battery (\(P_{batt}\)) can be determined and controlled.
## 3 DC-DC Converters  
DC-DC converters with step-up capability are widely utilized in a large number of industries and applications at different power levels from watts to multi-megawatts. Due to the ability of the converters to isolate the input from the output, they can be divided into two general categories: isolated and non-isolated. In power electronics, isolation means the electrical separation between the input and the output of a converter by using a transformer. The power converters in this category are mainly used in situations where more than one input is required [29]. However, these converters are not applicable in cases where a circuit with limited volume and weight is required. Therefore, the volume, weight, and transformer losses are the most important limiting factors for using these converters in embedded applications [30].  
On the other hand, the non-isolated converters do not have a transformer in their structure, which will not only reduce the overall size of the converter but also reduce dissipated heat from switching and eliminate copper losses [31]. Another positive feature of a non-isolated converter is its low manufacturing cost which allows for operating at higher temperatures with higher efficiency [32].
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 2 Preliminaries and Process Description
### Description of the Aircraft Descent and Approach Process at Airports
#### 2.2.3 Comparison between CDA and SDA  
In this subsection, we provide a comparison between CDA and SDA from an operational perspective.  
Considering aircraft approach speed, if a pair of aircrafts are approaching an airport for landing heading for the same runway, both aircraft approach speeds may not be the same when CDA is used, even with the same aircraft type. This is due to the fact that during descent, pilots make efforts to stabilize their approaches by controlling and balancing several parameters such as rate of descent, approach speed, thrust, and the aircraft's attitude. With CDA, landing is conducted with idle thrust as the aircraft approach speed decreases just before touchdown [32]. With SDA, the pilot utilizes thrust and adjusts speed more frequently along the descent path, and the aircraft approach speed increases just before touchdown. This comparison is illustrated in Figure 3 below. Table 1 highlights some of the differences between CDA and SDA operations.  
Figure 3: The vertical profile of CDA compared with SDA.  
Figure 2: The vertical profile of SDA based on the IAP and approach segments.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 4 The Joint Positioning Method of Angle and Distance
### The Joint Positioning Model of Angle and Distance  
To obtain more accurate and robust location information of personnel in chemical plant areas, it is necessary to use downlink ranging and uplink angle measurement information for three-dimensional position estimation. Among them, the terminal coordinates are \(\mathbf{u}=\left[u_{x},u_{y},u_{z}\right]\), the number of base stations is \(M\), the coordinates are \(\mathbf{s}_{i}=\left[x_{i},y_{i},z_{i}\right]_{i}=1,2,\ldots,M\). If each base station can transmit ranging signals, that is, the terminal can receive \(M-1\) TDOA values, the distance from the terminal to base station \(i\) is \(d_{i}\). The TDOA measurement value is:  
\[\begin{array}{rcl}d_{i,1}=d_{i}-d_{1}&=&\left\|\mathbf{u}-\mathbf{s}_{i}\right\|- \left\|\mathbf{u}-\mathbf{s}_{1}\right\|\\ &=&\sqrt{\left(x_{i}-u_{x}\right)^{2}+\left(y_{i}-u_{y}\right)^{2}+\left(z_{i }-u_{z}\right)^{2}}\\ &-&\sqrt{\left(x_{1}-u_{x}\right)^{2}+\left(y_{1}-u_{y}\right)^{2}+\left(z_{1 }-u_{z}\right)^{2}}\end{array} \tag{25}\]  
Among them, \(i=2,\ldots,M\). \(d_{i,1}\) represents the distance difference between the terminal and the i-th base station and the terminal and the first base station.  
But sometimes the hardware conditions of the base station are limited, and angle measurement is sensitive to non-line of sight, resulting in only N sets of effective direction angles \(\theta_{i}\) and elevation angles \(\varphi_{i}\) being obtained, \(i=1,2,\ldots,N\). Among them, \(N\leq M\). This article adopts a method based on comprehensive feature dynamic hierarchical optimization to identify non-line of sight paths, the direction angle and elevation angle are represented as:  
\[\theta_{i}=\arctan\left(\frac{y_{i}-u_{y}}{x_{i}-u_{x}}\right),i=1,2,\ldots N \tag{26}\]\[\varphi_{i}=\arctan\left(\frac{z_{i}-u_{z}}{(x_{i}-u_{x})cos\theta_{i}+\big{(}y_{i }-u_{y}\big{)}sin\theta_{i}}\right),i=1,2,\ldots N \tag{27}\]
## 3 Results
### Joint Angle and Joint Moment upon Forward Braking Motion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Maximum Moment**} & \multicolumn{6}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{**\(\boldsymbol{F(p)}\)**} & \multicolumn{1}{c}{**\(\boldsymbol{Post Hoc}\)**} & \multicolumn{1}{c}{**\(\boldsymbol{Effect Size}\)**} & \multicolumn{1}{c}{**\(\boldsymbol{Statistical Power}\)**} \\ \cline{3-10} \multicolumn{2}{c}{} & **A** & **B** & **C** & **D** & **F (p)** & **Post Hoc** & **Effect Size** & **
\begin{tabular}{c} **\(\boldsymbol{Statistical Power}\)** \\ \end{tabular} \\ \hline \multirow{5}{*}{Knee} & Flexion & \(1.79\pm 0.73\) & \(1.92\pm 0.63\) & \(1.84\pm 0.64\) & \(1.94\pm 0.88\) & 0.31 (0.82) & & 0.030 & 0.103 \\  & Moment & \(1.71\pm 0.28\) & \(1.65\pm 0.37\) & \(1.70\pm 0.37\) & \(-1.65\pm 0.34\) & 0.22 (0.76) & & 0.022 & 0.077 \\  & Adduction & \(1.59\pm 0.85\) & \(1.62\pm 0.80\) & \(1.49\pm 0.62\) & \(1.56\pm 0.82\) & 0.31 (0.71) & & 0.030 & 0.091 \\  & Adduction & \(0.37\pm 0.31\) & \(0.34\pm 0.19\) & \(0.33\pm 0.28\) & \(-0.33\pm 0.30\) & 0.55 (0.65) & & 0.052 & 0.150 \\  & Int. Rot. & \(0.40\pm 0.28\) & \(0.41\pm 0.27\) & \(0.42\pm 0.28\) & \(0.39\pm 0.28\) & 0.21 (0.89) & & 0.020 & 0.084 \\  & Moment & \(0.43\pm 0.36\) & \(0.42\pm 0.31\) & \(0.43\pm 0.25\) & \(-0.43\pm 0.37\) & 0.01 (1.00) & & 0.001 & 0.050 \\ \hline \multirow{5}{*}{Ankle} & Dorsiflexion & \(0.41\pm 0.25\) & \(0.48\pm 0.14\) & \(0.48\pm 0.13\) & 0.41 \(\pm 0.20\) & 1.58 (0.22) & & 0.136 & 0.372 \\  & Plantar flexion & \(1.60\pm 0.38\) & \(1.55\pm 0.23\) & \(1.55\pm 0.20\) & 1.58 (0.27) & 0.35 (0.66) & & 0.034 & 0.092 \\  & Imversion & \(0.18\pm 0.14\) & \(0.13\pm 0.12\) & \(0.15\pm 0.15\) & 0.17 \(\pm 0.15\) & 1.07 (0.38) & & 0.097 & 0.260 \\  & Inversion & \(0.51\pm 0.31\) & \(0.54\pm 0.30\) & \(0.54\pm 0.31\) & 0.55 \(\pm 0.29\) & 0.64 (0.60) & & 0.060 & 0.167 \\  & Int. Rot. & \(0.34\pm 0.11\) & \(0.37\pm 0.10\) & \(0.31\pm 0.09\) & 0.36 \(\pm 0.13\) & 3.04 (0.04)\({}^{*}\) & B-C & 0.233 & 0.654 \\  & Int. Rot. & \(0.11\pm 0.10\) & \(0.10\pm 0.06\) & \(0.10\pm 0.07\) & \(0.09\pm 0.06\) & 0.58 (0.52) & & 0.055 & 0.119 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum joint moments upon forward braking motion in a biomechanical analysis. Figure 4: Knee and ankle joints’ angles (mean of 11 participants) upon forward braking motion. IC: initial contact, Min. : minimum joint angle, TO: toe off. (Dotted red circles represent the significant difference among the outside tread patterns).
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 5 Discussions  
The experimental results were obtained in the laboratory at a temperature of 26 \({}^{\circ}\)C. In actual monitoring, the viscosity of the damping fluid will be monitored at different temperatures, and there will be temperature fluctuations, which will lead to a decrease in the density of the damping fluid and eventually affect the measurement results. The next work will focus on the influence of temperature on the monitoring of damping fluid. In practical cases, the installation of the sensors and the placement of the wireless impedance measurement system also need to be taken into account.  
In addition, the stability and reliability of the developed wireless impedance measurement system and the proposed method will be studied for long time monitoring. One limitation of this study is that the experiments were conducted in the same cylinder for different types of fluids. However, the length and diameter will differ for different dampers in actual engineering. We will further study the relationship between the measured impedance signal and the viscosity for the dampers with different sizes. Another limitation of this study is that the cylinder load was not applied in the experiments. In future work, we will conduct experimental research on the viscosity monitoring of dampers under different cylinder loads.
## 2 Experimental Procedures
### Phase, Structural Characterizations, and Transport Property  
The densities of all the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite samples were measured by Archimedes method. The constituent phases can be detected by X-ray diffractometry (Cu K\({}_{x}\), D8-Advance, Bruker Corporation, Karlsruhe, Germany). The microstructure was characterized by field emission scanning electron microscopy (FESEM, QUANTA 250 FEG, ThermoFisher Scientific, Hillsboro, OR, USA) on polished and fractured samples. Transmission electron microscope (TEM) observations were performed on a JEM-2010 instrument (JEOL, Tokyo, Japan) with the voltage of 200 KV. The electrical conductivity and Seebeck coefficient were measured simultaneously using commercial equipment (ZEM-3, ULVAC-RIKO, Tokyo, Japan). The Hall coefficient (\(R_{\mathrm{H}}\)) was measured using the [PERSON]'s method in vacuum with the magnetic field of 2 T. The carrier concentration (\(p_{\mathrm{H}}\)) and mobility (\(\mu_{\mathrm{H}}\)) were estimated from the relations of \(p_{\mathrm{H}}=1/(eR_{\mathrm{H}})\) and \(\mu_{\mathrm{H}}=\sigma R_{\mathrm{H}}\) based on the assumption of single band model, where \(e\) is the electronic charge. The thermal conductivity can be calculated by using the follow equation:  
\[\kappa=C_{p}\times d\times\lambda \tag{1}\]  
where \(C_{p}\), \(d\), and \(\lambda\) are specific heat, density, and thermal diffusivity, respectively. The thermal diffusivity was measured by a laser flash technique (LFA427, Netzsch, Wunsiedel, Germany) in a flowing Ar atmosphere. Specific heat was measured from room temperature to 700 K in argon using a differential scanning calorimeter (DSC1-1600 HT, STA 409, Netzsch, Wunsiedel, Germany). All the measurements were performed in a temperature range of 300-700 K. All the measurements of thermoelectric properties were carried out three times to ensure the accuracy of \(ZT\) value.