esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Measurement  
In order to verify the accuracy of the model in this paper, the temperature and air velocity of multiple measurement points and the system purification efficiency were tested during peak cooking hours (10:55-11:05) on three typical working days. The layout of the measurement points is displayed in Figure 5.  
As shown in Figure 5, the measurement points A1-A3 were 0.1 m from the edge of the cooktop and 1.5 m above the ground, and they were used to measure the temperature and air velocity of the kitchen cooking area. Points B1-B5 were 1.6-4.0 m from the cooktop and 1.0 m above the ground, and they were used to measure the temperature and air velocity of the kitchen zone far from the stoves. Points C1-C3 were located at the edge of the cooktop and 0.75 m above the ground to measure the temperature and air velocity of the control point of the exhaust system. Points D and E were located on the pot surface and the burner smoke outlet, and they were used to measure the temperature of the pot surface and exhaust gas, respectively. Points F1-F3 were located at the suction vents connecting the hood and air duct to measure the temperature, air velocity and fume concentration at the suction vents. Point G was used to measure the temperature and air velocity of the hood opening. Points H1 and H2 were located at the doors to measure the temperature of transfer air from adjacent rooms. Point I was located at the discharge outlet to measure the exhaust air rate, temperature and discharge fume concentration. Point J was located outside the building without direct sunlight, 0.5 m from the exterior wall and 1.5 m above the ground, to measure the outdoor temperature.  
The air temperature and velocity of the measurement points in the kitchen (points A and B) and outside (point I) were measured by a self-recording temperature instrument and hot-wire anemometer. The temperature of points D and E were measured through a thermocouple. The air velocity and temperature of measuring points C, F, G, H, and I were measured by a hot-wire anemometer. The cooking fume concentration at points F and I were measured by an infrared spectrophotometer (GB18483, 2001). The average of test results during peak cooking hours (10:55-11:05) was taken as the final value of each measurement point and the measurement error (\(U\)) was calculated by the following formula.  
\[U=s/\sqrt{n} \tag{21}\]  
Figure 5: Layout of measurement points.
## 4 Discussion
### Tracking Tumor Cell Migration: Existing Evidence and Clinical Implications  
In addition to studying clinical outcomes (i.e., survival) and its correlation with novel tractography-derived metrics, we attempted to explore the potential pathologic underpinnings of tumor-associated tractography with respect to migrating cells through RNAseq and PDX model analyses (Figures 4 and 5). Previous work visualizing migrating tumor cells in vivo shows some of the same migratory patterns as our results on tumor-associated tractography and microscopy in this study (including the hippocampus being a cancer cell-free zone in the PDX model), further supporting the validity of the presently observed association between tumor tractography and migrating cells [69; 70]. Our results collectively support the further study of the possibility that migratory glioblastoma cells may co-localized with observable tumor-intersecting tracts. This future work is important because tracking glioblastoma cell migration more precisely in a non-invasive manner may open the door to targeted therapeutics in the future [29]. For example, confirmation that specific white matter bundles are riddled with migrating cells, which may mediate recurrence, could enable future therapeutic strategies selectively targeting these tracts. Possible localized therapeutic modalities could include radiotherapy, convection enhanced delivery, laser interstitial thermal therapy, and others still in development which may not yet have been introduced clinically [71; 72; 73; 74]. Although this is an exciting possibility for the future, significant additional work is needed, and currently tractography is not a consideration for routine radiotherapy treatment planning [75; 76; 77; 71]. While the clinical application of diffusion tractography currently centers around identification of functionally important white matter pathways near to the tumor which may be identified preoperatively and spared during maximal safe resection [32; 33; 34; 35], targeting tumor-infiltrated tracts would generate new treatment approaches in this disease. The extent to which this technique can be used for live migration monitoring is uncertain at best, given that there have been no longitudinal studies examining multi-timepoint QSI to assess tumor-associated tractography. Yet, the finding that individual tract bundles may sometimes span multifocal ROI within a single patient (Figure 2) provides anecdotal evidence which encourages the follow-up of this hypothesis. Furthermore, emerging evidence suggests that glioma cells play an active role in remodeling the extracellular matrix to promote localized invasion through brain tissue and eventually mediate disease recurrence [78]. The extent to which diffusion-derived tractography is biologically related to existing white matter bundles and/or invasion pathways involving ECM remodeling remains unclear. The latter of these, if differentiated from the first, may lend an aspect of disease specificity to the observed tractographic features, which could aid treatment targeting, if confirmed.
## 3 Results  
### Performance Evaluation  
#### 3.1.1 Temperature, Relative Humidity, and Water Content in Air  
The results of the temperature and relative humidity collected by the sensors installed inside the ISD and on the tarpaulin for DSD are presented in Figure 2, together with the calculated absolute humidity (water content in the air) for batch 1.  
A significant higher temperature was recorded in the ISD compared to the conditions under DSD (p\({}_{\alpha\leq 0.05}\) = 0.0001). An average temperature of 47 \({}^{\circ}\)C was recorded in the ISD, 7 \({}^{\circ}\)C higher than the temperature under DSD. The maximum temperature in the ISD was reached at 13:30 on the second day of the drying experiment with a temperature of 63.7 \({}^{\circ}\)C, while, for DSD, the maximum temperature was only 54 \({}^{\circ}\)C. An average relative humidity of 28% was recorded in the ISD, and for DSD, the average relative humidity was 34%. The minimum relative humidity of 14% was recorded inside the ISD at the same time when the temperature was highest, while, for the DSD, the minimum relative humidity was 19%. The change of the relative humidity in the ISD and for DSD is a typical characteristic of a psychometric heating process where the increase in air temperature at a constant humidity results in a reduction of the relative humidity [29].  
The absolute humidity, which is the measurement of the amount of water vapour in the air; inside the ISD, it was higher than the one observed for DSD. This is explained by the uptake of vapour from the evaporation of water in the maize grains. For the same reason, the absolute humidity was higher at the outlet of the dryer compared to the outlet of the solar collector. The maximum absolute humidity of 27.37 g kg\({}^{-1}\) was recorded at 13:00 at the outlet of the ISD, the time when the temperature was highest. The saturation deficit was 10% lower than the one recorded for sun drying, an indicator of an efficient drying process. A similar pattern in absolute humidity was observed in maize drying by [PERSON] et al. [33], which was attributed to a higher drying rate, as the temperature was higher inside the ISD.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 5 Conclusions and Future Work  
In this paper, the LRC theoretical model of an eddy current sensor for tension measurement was presented based on the inverse magnetostrictive effect and impedance parameters approaching the resonant frequency were analyzed. The finite element model of the sensor was simulated and experimental tension measurement system was designed. The main conclusions from this work are as follows:  
1. A novel sensor was designed, analyzed, and experimentally verified on 45# carbon steel with diameter of 12 mm. Compared with the traditional tension measurement methods, the eddy current sensor with a single-coil structure has the advantages of smaller size, lower cost, easier installation and less mutual interference.
2. This paper proposes a novel approach to tension measurement in ferromagnetic materials based on the impedance change of an eddy current sensor at the resonant frequency. In six repetitive experiments, the results show that the impedance parameters are efficient for estimating the tension. In all cases, the correlation coefficient reaches to 0.974 and the sensitivity coefficients are 2.581 ohms/kN in impedance magnitude analysis and 0.443 kHz/kN in impedance phase analysis, respectively.
3. In order to improve sensor performance, a sleeve structure was presented. Both finite element simulations and experimental results match well with the theoretical analysis, which indicates that the utilization of a sleeve structure remarkably improves sensor sensitivity and correlation coefficient.  
In the next step, the flexible eddy current sensor will be further researched for repeatable use and easy installation and disassembly. In addition, due to the limitation of experimental conditions, the maximum loading tension is 15 kN, which is within 42% of the elastic tension. In the future, the measuring range of the sensor will be experimentally ascertained and the sensor performance will be studied over the elastic range of the test specimen. Furthermore, this paper is the preliminary study and has not considered the effects of temperature and sensor robustness. Hence, it is necessary to solve the problem of temperature compensation and improve sensor robustness in order to apply the sensor to practical applications, such as measuring tension in post-tensioning (PT) tendon, steel bar and steel cables.  
This work was supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 51421064), the National Key Basic Research and Development Program (973 Program) (No. 2015 CB060000), the Natural Science Foundation of China (No. 51327003) and the Fundamental Research Funds for the Central Universities (DUT15 YQ107). These grants are greatly appreciated.  
[PERSON] and [PERSON] proposed the LRC theoretical model; [PERSON] contributed to the experiments and data analysis.  
The authors declare no conflict of interest.
# Multiple Factor Analysis Based on NIPALS Algorithm to Solve Missing Data Problems
###### Abstract  
Missing or unavailable data (NA) in multivariate data analysis is often treated with imputation methods and, in some cases, records containing NA are eliminated, leading to the loss of information. This paper addresses the problem of NA in multiple factor analysis (MFA) without resorting to eliminating records or using imputation techniques. For this purpose, the nonlinear iterative partial least squares (NIPALS) algorithm is proposed based on the principle of available data. NIPALS presents a good alternative when data imputation is not feasible. Our proposed method is called MFA-NIPALS and, based on simulation scenarios, we recommend its use until 15% of NAs of total observations. A case of groups of quantitative variables is studied and the proposed NIPALS algorithm is compared with the regularized iterative MFA algorithm for several percentages of NA.  
a +
Footnote †: journal: _Algorithms_  
0000-0001-9991-4000-0000-0000-0000-0000-00000-00000-00000-00000-  
solve missing data problems in PCA, multiple correspondence analysis (MCA), the inter-battery method, in state-space models, and others [18; 19; 20; 21; 22; 23]. In addition, NIPALS presents the same results of a PCA when the dataset does not have NAs [24]. NIPALS is the most powerful algorithm for partial least squares (PLS) regression methods, which have been used in chemotherapy, sensometry and genetics [25], and in cases where datasets include more variables than observations (\(p>n\)). Hence, PLS methods are more suitable for these problems [26; 27]. In this way, NIPALS offers advantages while working with NAs and datasets with more variables than observations. Moreover, NIPALS is directly related with PCA, which allows NIPALS to be easily adapted to MFA because MFA performs a weighted PCA in its last stage (see Section 2.1).  
Considering NIPALS and the available data principle, this paper proposes the MFA-NIPALS algorithm to solve missing data problems. Specifically, we analyzed the missing data problem in quantitative variable groups and compared the proposed MFA-NIPALS algorithm with classic MFA and RIMFA ones. The Methods section describes these techniques, whereas the Application section presents a dataset to illustrate the performance of the proposed methods and some simulations for comparison and for several percentages of missing data.
## 2 Materials and Methods
### Data Acquisition
(**a**) The hierarchical structure of the data acquisition module, consisting of the sensor array, acquisition circuit, MCU module, data transmission module, and computer data receiving end from bottom to top. (**b**) A photograph of the serial-to-USB adapter. (**c**) A photograph of the data acquisition circuit module. (**d**) A photograph showing the MCU module connected to the data acquisition circuit via an adapter board. (**e**) A schematic diagram of the data acquisition circuit. (**f,g**) A schematic and photograph of the sensor array, showing nine sensors distributed across five fingers. Here, (\(V_{5}-V_{4}\)) represents the voltage difference across \(R_{1}\), which is the converted voltage signal. When \(R_{X0}\) senses a change in pressure, a corresponding voltage change will be reflected across \(R_{1}\). To ensure that the output of the operational amplifier is not distorted, we conservatively set \(R_{2}=R_{4}\), resulting in \(V_{3}=(V_{5}-V_{4})\). \[V_{5}-V_{4}=V_{0}R_{1}/(R_{1}+R_{X0}) \tag{5}\]  
This section explains the relationship between (\(V_{5}-V_{4}\)) and \(R_{X0}\). Here, \(V_{0}\) is directly provided by a pin of the STM32, with a value of 3.3 V. In this case, during the component selection, we ensured that the resistance of \(R_{1}\) is much smaller than that of \(R_{X0}\), allowing the relationship between the two to be simplified as follows:  
\[V_{5}-V_{4}=V_{0}R_{1}/R_{X0} \tag{6}\]  
The second operational amplifier primarily amplifies the voltage signal. Again, based on the principles of virtual short and virtual ground, we can derive the following from the second operational amplifier:  
\[V_{OUT}=V_{3}(R_{6}+R_{7})/(X+R_{7}) \tag{7}\]  
where \(X\) represents the resistance value of the adjustable resistor \(R_{6}\), which is situated near \(R_{7}\). \(X=0\) for \(R_{7}\) ensures that the inverting terminal of the operational amplifier is not directly grounded. \(R_{X0}\) represents the sensors. The relationship between the output signal and the sensor can ultimately be expressed as follows:  
Figure 2:0
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi
### Fungal Halophilic Enzymes
#### 5.1.2 Oxidoreductases  
Other enzymes from halophilic fungi which gained much attention are lignin-degrading enzymes (lignin peroxididases, manganese peroxididases, and lacases) with ability to degrade lignocellulose [58]. It was reported that marine fungi, mostly belonging to Ascomycota and Basidiomycota, carry out a 'white-rot like' role in marine environments [50; 76]. It is possible that these ligninolytic enzymes play an important role in the decolorization of dyes, treatment of colored effluents, degradation of other organic pollutants, and bioremediation because of their ability to degrade saline and alkaline pollutants under both saline and non-saline conditions [50].  
Examples of enzymes from the class of oxidoreductases produced by halophilic fungi are presented in Table 2.
###### Abstract  
The present study investigates the hydrodynamics and mass transfer of the liquid-gas ejector using three-dimensional (air-water) and two-dimensional (CO\({}_{2}\)/air-MEA (Monoethanolamine) solution) computational fluid dynamics (CFD) modeling. For 3D simulation, validation of the CFD results of this ejector with experimental data (error less than 5%) showed high simulation accuracy. The effects of motive liquid flow rate and outlet pressure parameters on the air entrainment rate and air hold-up are also investigated. It was found that by increasing the outlet pressure by about 70% (from 3587 to 6127 Pag), the rate of gas entrainment and gas hold up decreased by about 37% and 20%, respectively. On the contrary, these parameters showed increasing behavior of about 74% and 15%, respectively, when the mass flow rate of liquid increased by about 21%. In addition, three-dimensional phenomena such as mixing shock and the location of its occurrence are examined, which is the reason for recirculation and vortex in the ejector. Next, by simulating a two-dimensional simulation and changing the inlet fluids to CO\({}_{2}\)/air-methanol amine, the ejector was designed to simultaneously increase the gas pressure and absorb carbon dioxide. A user-defined function code was used to express the mass transfer from the gas to the liquid phase. The results, in this case, showed that with increasing the outlet pressure of the ejector (from 0 to 2000 Pag), and enhancing the concentration of MEA solution (from 10% to 30%), the CO\({}_{2}\) removal boosted from 83% to 95%. A similar behavior was shown when the L/G ratio increased from 3.5 to 5.5. This study serves as a showcase on how to do an exact design and analysis for liquid-gas ejectors in flare gas recovery systems.  
ejector; computational fluid dynamics; mass transfer; CO\({}_{2}\) capture; monoethanol amine; Eulerian-Eulerian approach +
Footnote †: journal: Applied Sciences
# Influence of Processing Steps on Structural, Functional, and Quality Properties of Beef Hamburgers
## 3 Results and Discussion
### Mechanical Considerations  
Figure 5 illustrates changes in the meat structure as well as the mechanical stress acting on the meat during the different processing steps.  
As previously discussed, the first and second grinding steps account for the strongest structural, functional, and chemical changes. During grinding, particle sizes are reduced by cutting and shearing the meat through end-hole plates. Muscle fibers are fragmented into smaller pieces, muscle structures are disintegrated, and more open ends are created [2; 10; 13]. This can be underlined by the mechanical forces acting on the meat in comparison to the mixing and forming steps where mainly wall friction and pressure applied to the meat; shear force, wall friction, and pressure stresses the meat during grinding, therefore, applying a higher mechanical load. This leads to stronger disintegration of the material [10].  
In the first grinding step, meat is cut from a cube into cylindrical-cone-shaped particles of 13 mm diameter, thereby reducing the surface area and volume per particle but increasing the total number of particles and the total surface area. This leads to an increased number of cut muscle fibers. During the second grinding, the particles are ground to a final particle size of 2.4 mm diameter, which further decreases the surface area (factor 18, Figure 5) and volume per particle and the increasing total number of particles and total surface area. A stronger increase during grinding is in accordance with the findings of [PERSON] [2] who reported a cumulative increase in muscle fiber disintegration if several mechanical methods are combined. Another reason for the stronger increase in _ANIC_ after the second grinding is the higher grinding screw pressure of 3.63 \(\pm\) 0.68 bar in the second grinding step (Figure 6) compared to a strongly variating and lower pressure in the first grinding step of 0.72 \(\pm\) 0.29 bar (Figure 6).  
This might be attributed to the higher cutting resistance in already ground meat, which has more viscoelastic properties compared to the intact meat in which elastoplastic properties dominate [10]. Higher pressure is, according to [PERSON] et al. [33], associated with increased friction and destruction of the meat. Fluctuations in cutting set pressure might be caused by fluctuating raw material quality, as the processability of the meat strongly depends on the meat's properties and thus, determine the cutting set pressures.  
Figure 5: Changes in material conditions and main mechanical forces in meat upon hamburger manufacturing.
## 2 Theory of the Electrokinetic Coupling  
The streaming current is controlled by the relative movement between the charged solid surfaces and pore fluid and is directly related to an electric double layer (EDL) existing at the interface between the fluid and solid surfaces, e.g., [35]. This streaming current is then balanced out by a conduction current, leading to a so-called streaming potential. The important parameter for SP is the SPCC. At the steady state condition, the SPCC is defined as, e.g., [39]:  
\[C_{S}=\frac{\Delta V}{\Delta P}, \tag{1}\]where \(\Delta V\) (V) and \(\Delta P\) (Pa) are the generated streaming potential and applied fluid pressure difference, respectively. There normally exist two expressions to determine the SPCC at fully saturated conditions in literature. The first one is the HS equation given by [62]  
\[C_{S}=\frac{\epsilon_{r}\epsilon_{0}\zeta}{\eta\sigma_{w}}, \tag{2}\]  
where \(\epsilon_{r}\) (no units) is the relative permittivity of the fluid, \(\epsilon_{0}\) (F/m) is the dielectric permittivity in vacuum, \(\zeta\) is the zeta potential, \(\eta\) (Pa s) is the dynamic viscosity, and \(\sigma_{w}\) (S/m) is the electrical conductivity of the fluid. Equation (2) is valid when the surface electrical conductivity of porous media is neglected, e.g., [38]. When the surface electrical conductivity is taken into account, one can apply the modified HS equation given by, e.g., [36; 40; 41]  
\[C_{S}=\frac{\epsilon_{r}\epsilon_{0}\zeta}{\eta(\sigma_{w}+2\frac{\Sigma_{s}} {\Lambda})}, \tag{3}\]  
where \(\Sigma_{s}\) (S) is the specific surface conductance and \(\Lambda\) (m) is a characteristic length scale [63].  
Addition to the HS or modified HS equation, one can express the SPCC via the excess charge density \(\widehat{Q}_{v}\) (C/m\({}^{3}\)) that is dragged by pore water in porous media as, e.g., [42; 43; 44]  
\[C_{S}=-\frac{k\widehat{Q}_{v}}{\eta\sigma}. \tag{4}\]  
where \(k\) (m\({}^{2}\)) and \(\sigma\) (S/m) are permeability and electrical conductivity of porous materials at fully saturated conditions, respectively.
## 5 Methods, Results and Discussion
### Holding Current Influence on Convevance Trajectory  
Next, we explored the holding current influence on multiple displacement steps for straight trajectory generation. This was important as the trajectory algorithm was designed to execute uni-dimensional (axial) displacement steps and we are in an open loop control scheme. We executed 6 consecutive displacement steps in the \(+x\)-axis with and without a 1 A holding current and measured the deviation along the \(y\)-axis. This axial trajectory was executed 10 times to obtain a position standard deviation, represented by the error bars in Figure 13, that shows the results.  
From Figure 13, the holding current improved the straightness of the axial displacement. The cumulative \(y\)-axis deviation without holding current was 37.8 \(\upmu\)m whereas with holding current was 2.55 \(\upmu\)m. The deviation was dominant towards \(+y\)-axis without holding current, whereas it had no tendency with the holding current; this deviation could be caused by a tilt error of the DAA. We observed that the holding current minimizes this tilt error on the executed trajectories, easing the DAA's set-up.