esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The 30-40 GHz receiver front-end for broadband sensing receivers is implemented. The two-stage stagger-tuned LNA and push-pull type mixer loaded with third-order LC ladder filter are employed to achieve the broadband operation with low current consumption. To ensure unconditional stability of the receiver front-end with single-ended input signal, a double-balanced mixer operation is employed with on-chip Balun. This chip achieves a maximum conversion gain of 16.5 dB, 5.9 dB integrated NF, and \(-\)11 dBm IIP3 from 30 GHz to 40 GHz. The power consumption is 96 mW from a 1.2 V supply. The active area is only 0.21 mm\({}^{2}\). The proposed receiver front-end shows the widest RF and IF operating bandwidth with good performance and moderate power consumption compared to previous works.  
Conceptualization, D.R.U. and J.K.; methodology, D.R.U.; validation, H.J. and S.S.; formal analysis, H.J. and D.R.U.; investigation, H.J.; resources, J.K.; data curation, H.J. and D.R.U.; writing-original draft preparation, H.J.; writing-review and editing, J.K., D.R.U., S.S., and J.K.; visualization, H.J.; supervision, S.K.-H. and S.-G.L.; project administration, S.-K.H.; funding acquisition, S.-G.L. This work was funded by the Civil-Military Technology Cooperation Program through the Defense Acquisition Program Administration and the Ministry of Trade, Industry, and Energy, South Korea.  
Figure 12: Measured (**a**) input reflection coefficient (S\({}_{11}\)), (**b**) conversion gain and NF over a 10 GHz IF frequency range; (**c**) IIP3 with frf1 = 35 GHz and frf2 = 35.1 GHz; and (**d**) 1 dB compression point with frf = 35 GHz.
###### Abstract  
We investigated the applicability of three task-specific ionic liquids (ILs) as heavy metal extracting agents by contrasting extraction capabilities with algal toxicity. The compounds tested were trihexyltetradecylphosphonium-, methyltrioctylphosphonium- and methyltrioctylammonium 3-hydroxy-2-naphthoates. Experiments were performed to assess if these ILs can provide environmentally safe residual concentrations of the target metals after extraction. Both pure water and natural mineral water samples were spiked with 20 ug L\({}^{-1}\) of Cu, Ag, Cd, Hg and Pb, respectively. Quantitative extraction (\(>99\%\)) of Hg and Ag was achieved. Cu and Hg were below the respective no-observed-effect-concentrations (NOECs) after extraction and Ag below 0.03 ug L\({}^{-1}\). Acute toxicity assays were conducted using two freshwater green algae _Raphidocelis subcapitata_ and _Tetradesmus obliquus_. Growth inhibition and maximum photochemical quantum yield of photosystem II after 72 h were assessed. ILs were less toxic than similar compounds, but still must be classified as acute toxicants for algae. An inhibiting effect on both growth and chlorophyll fluorescence was observed. The leaching of the ILs into the samples remains a limitation regarding their environmental-friendly applicability. Nonetheless, the extremely efficient removal of Cu, Ag and Hg under environmentally relevant conditions calls for further research, which should focus on the immobilization of the ILs.  
task-specific ionic liquids; heavy metal extraction; algal toxicity; growth inhibition; chlorophyll fluorescence +
Footnote †: journal: Applied Physics Letters  
1
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 5 Initial Idea of Virtual Belt Algorithm
### Deficiencies of The Initial Ideal  
If all CAVs are in the corresponding assigned VGs, there will no collision occur among these CAVs as shown in Figure 16. However, there are two deficiencies of the initial ideal, which make it difficult for practical application:  
1. There are physical limits, such as maximum speed and acceleration, of CAVs. Thus, it is impractical for a CAV to catch up with the assigned VG in no time. As shown in Figure 14, in fact CAV 1 has to spend some time to catch up with the assigned VG.
2. The performance of collision avoidance between the CAVs in the same lane cannot be guaranteed. As shown in Figure 15, if CAVs 1 and 4 reach the assigned VGs simultaneously, then collisions between these CAVs may occur, which is not considered in the initial ideal.  
These deficiencies can be resolved by planning a suitable trajectory considering actual physical constraints for each CAV. Following the planned trajectory, a CAV can catch up with the assigned VG and avoid collision within the physical limits. The method of trajectory planning will be discussed next section, which guarantees the feasibility of the proposed algorithm.
## 6 Circuit Model of the Projected Metamaterial Unit Cell  
The proposed metamaterial involved both inductive and capacitive components. All the metal bars and the metal strips of the rings (both SRR and hexagonal) acted as inductors, while the gaps within the metal bar or rings formed capacitors. Therefore, an LC resonance circuit was formed in this metamaterial unit cell. As such, the resonance frequency (f) can be derived using Equation (8) [17].  
\[f=\frac{1}{2\pi(\mathrm{LC})^{\frac{1}{2}}} \tag{8}\]  
where L is inductance, and C is capacitance. The capacitance of the capacitor formed by the slits can be calculated using Equation (9).  
\[C=\in_{0}\in_{r}\frac{\mathrm{A}}{\mathrm{d}}(\mathrm{F}) \tag{9}\]  
where, \(\in_{0}\) and \(\in_{r}\) represent the permittivity in free space and the relative permittivity, respectively, and A is the cross-sectional area of the conducting strip, while d is the split gap.  
\[L(nH)=2\times 10^{-4}\mathrm{I}\left[\mathrm{ln}\left(\frac{1}{\mathrm{w}+ \mathrm{t}}\right)+1.193+0.02235\left(\frac{\mathrm{w}+\mathrm{t}}{1}\right) \right]\mathrm{K_{g}} \tag{10}\]  
Here, Kg = Correction factor, w = width, l = length, and t = thickness of the strip  
Figure 11 represents an estimated equivalent circuit for the designed metamaterial unit cell. At the equivalent circuit, L1, L2, L3, L4, L5, and L6 refer to the inductors, while C1, C2, C3, C4, and C5 are capacitors. The circuit was optimised and simulated using the advanced design system (ADS). As such, inductor L1 and capacitor C1 were responsible for the first resonance (2.89 GHz), where L1 regulated the resonance frequency of S21, with C1 as the biasing factor that modifies the magnitude. Whereas C3 is the coupling capacitor, L3 and L4 refer to two series inductors, while L5 and L6 refer to two parallel inductors that contribute to the other two resonances. Figure 12 illustrates a comparative view of the simulated S21 derived from the CST and ADS describing the closest similarities between these two S21 values. However, the impedance was derived for equivalent circuits based on the Thevenin theorem.  
Figure 11: Equivalent circuit model of a unit cell.
# A New Signal Processing Method for Time-of-Flight and Center Frequency Estimation
## 3 Experimental Signal Processing by the Proposed Method
### Separation of the Fundamental Modes  
Another case of using the proposed algorithm is the digital processing of signals from experimental studies of \(d\) = 200 \(\upmu\)m-thick polyvinyl chloride (PVC) film [25]. The film with lateral dimensions 0.21 \(\times\) 0.297 m\({}^{2}\) was fixed in a PVC film-mounting bracket (Figure 6).  
During the experiments, the S\({}_{0}\) and A\({}_{0}\) Lamb wave modes were excited using contact point transducers with a resonant frequency of 180 kHz. The used transducers were developed at the Ultrasound Research Institute. A 500 V, 3-period sine signal was used to excite the transmitter, and the receiver moved relative to the transmitter over a range of 57 mm to 87 mm. The step of the movement was 0.1 mm.  
A B-scan of the distribution of the amplitudes of the received Lamb wave signals, normalized with respect to the maximum value, is shown in Figure 7a.  
Figure 6: The structural scheme of the Lamb wave signal generation and recording in thin PVC film.  
Figure 7: **(a)** The B-scan of the measured amplitudes of the Lamb wave signals in the PVC film of the thickness \(d\) = 200 \(\upmu\)m. **(b)** The signal recorded at a distance of 60 mm from the transmitter and the envelope of this signal with the determined maxima and minima. **(c)** Signal and filter frequency response. **(d)** Signals filtered by a filter package, and zero-crossings fixed in these signals.  
Group and phase velocities calculated by this method differed by no more than 0.7% from values calculated by the SAFE method.  
In addition, the phase velocity of the S\({}_{0}\) mode can be calculated using this method. This methodology was described in [27].
# Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis
## 3 Results
### EFNA4 Depletion Inhibits the Growth and Migration of Lung Tumor Cells  
In order to further verify our conclusion, we generate _EFNA4_ knockout cells using CRISPR/Cas9 system. _EFNA4_ was knocked out with two sgRNAs in A549 and PC9 cells (Figure S4A), which was confirmed by Western blotting and PCR (Figures 3A and S4B-D). In the Cancer Dependency Map project (DepMap, www.depmap.org (accessed on 5 October 2021), knockout of _EFNA4_ led to decreased growth in most cancer cell lines (Figure 3B). Consistent with DepMap results, our results show that _EFNA4_ deletion significantly affected cell viability (Figure 3C), and also suppressed cell migration, invasion and adhesion (Figures 3D,E and S2B). To access the in vivo effect of _EFNA4_ knockout on lung cancer cells, _EFNA4_ knockout A549 cells were xeno-grafted in nude mice via subcutaneous injection. Additionally, _EFNA4_ knockout significantly decreased the total tumor weights and size (Figure 3F). Two shRNA targeting _EFNA4_ were designed, and stable EFNA4 shRNA cell lines of A549 and PC9 were constructed. The expression level of _EFNA4_ in the stable cell line were tested by qPCR, and it was found that shRNA strongly inhibits _EFNA4_ expression (Figure S5A). As shown in Figure S5B, shRNA-mediated down-regulation of _EFNA4_ resulted in decreased number of colonies. At the same time, it was found that _ENFA4_ knockdown by shRNA inhibits cancer cell proliferation (Figure S5C), and attenuates cancer cell migration (Figure S5D). Similar results were obtained with two different cell lines, A549 and PC9. Taken together, these results suggest that deletion of _EFNA4_ can inhibit the growth and migration of lung tumor cells.  
Figure 3: The deletion of _EFNA4_ can inhibit the growth and migration of lung tumor cells. (**A**) Western blot confirmation of _EFNA4_ knockout in A549, PC9 cells; (**B**) box plot of the DepMap (CRISPR) gene dependency score for _EFNA4_. Numbers above the X-axis represent respective sample sizes. Lowerand upper parts of the box correspond to the 25 th and 75 th percentiles, respectively; (**C**) colony formation assay. Representative image of control and _EFNA4_-KO established colonies (\(n=3\)); (**D**) MTT cell proliferation assay. A549 and PC9 control cells and _EFNA4_ knockout cells proliferation was measured by MTT method; (**E**) cell migration analysis by wound-healing assay in _EFNA4_ knockout cells and control cells of A549 and PC9 cells; (**F**) volume and wet weight of xenograft tumors. _EFNA4_ knockout and control A549 cells were subcutaneously implanted in nude mice (\(n=3\)). Data represented as mean \(\pm\) SD of three independent experiments. CTL: control; KO: knockout. \(p\)-values are represented as ns (not significant), * \(p<0.05\), ** \(p<0.01\) and *** \(p<0.001\).
# Evaluation and Economics of Shale Gas Reserves in the Flysch-Eocene Formation of the Jaca Basin
## 4 Discussion
### Environmental Challenges
#### 4.3.1 Use of Large Volumes of Water  
Shale gas production consumes a large volume of freshwater that depends on the fracturing operations, the depth and length of the well, the properties of the fracturing fluid, and the fracture design. Typically, 2 to 4 million gallons (7571-15,142 MMM\({}^{3}\)) of water are needed per horizontal shale gas well [40,41].  
One way to offset the large water requirements of hydraulic fracturing is to recycle the flow back or produced water from the fracturing process [42] by adding additional chemicals as well as fresh water to make up a new fracturing solution. However, water reuse presents problems due to the high concentrations of compounds and TDS contained in the return flow [43].
## 4 Results
### Limitations  
The overall system experiment was conducted at short range. Thus, time lags were relatively very small. Research conducted in other studies indicated that lag between user motion and robot motion causes increasing errors [52; 53]. Ongoing work using Markov models and other forecasting methods can address this issue in future work, given the results in our proof-of-concept system.  
The MRVF system presented relatively low-cost and readily available components, where faster or more precise systems could provide greater accuracy, potentially reducing the improvements seen here. One purpose of this study was to use commercial off-the-shelf products to demonstrate the potential of a relatively low-cost system to achieve tele-welding. Hence, while performance can be improved with better components, it also raises the cost, for which economic feasibility is application-dependent.  
Figure 11: Subjective NASA-TLX ratings of task workload across all conditions in the tele-welding tasks. The special symbols (circles and asterisks (*)) are outliers in the data, representing that they are outside of a certain range indicated by the boxes in the box plot. The numbers next to the circles and asterisks indicate which observations in the dataset are outliers.  
Figure 12: Subjective scores on system functionality, usability, and user’s acceptance and perception of the three tele-welding modules. Higher scores represent higher preferences in all cases. The MRVF design demonstrated improvements in perceived usefulness and perceived ease of use. The special symbols (circles) are outliers in the data, representing that they are outside of a certain range indicated by the boxes in the box plot. The numbers next to the circles indicate which observations in the dataset are outliers.  
Subject numbers were limited in this study, and future work should replicate this effort with a larger study if feasible. However, the relatively low number of unskilled welders had consistent results. Thus, while greater numbers would more accurately quantify the gains to be obtained by an MRVF approach, the consistently large differences seen in both objective and subjective assessments indicate that the results should be replicable. This study aimed to enable inexperienced welders to perform quality remote welding tasks. Unskilled welders do not have frequent contact with the physical welding torch and are not reliant on its weight. It is feasible in future work to replace the handheld stylus on the haptic device with an actual welding torch or a 3D-printed torch model of the same weight to improve the professional welder's experience.
## 4 Performance Evaluation  
The statistical indices widely utilized to evaluate the performance of the prediction models include the root-mean-square error (RMSE), the mean absolute error (MAE), the coefficient of determination (R\({}^{2}\)) and the Willmott's index (WI). The RMSE and MAE are able to reflect the error between the predicted and the measured values. On the other hand, the R\({}^{2}\) and WI describe the fitting performance of the prediction models. In terms of values, the lowest values of RMSE and MAE and the highest values of R\({}^{2}\) and WI represent the best prediction model.  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(E_{i}-e_{i})}^{2}}} \tag{19}\]  
Figure 5: Flowchart of predicting the rock EM based on the four hybrid RF models.  
\[\mathrm{R}^{2}=1-\frac{\left[\sum\limits_{i=1}^{n}\left(E_{i}-e_{i}\right)\right]^ {2}}{\left[\sum\limits_{i=1}^{n}\left(E_{i}-\overline{E}\right)\right]^{2}} \tag{20}\]  
\[\mathrm{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}\left|E_{i}-e_{i}\right| \tag{21}\]  
\[\mathrm{WI}=1-\left[\frac{\sum\
olimits_{i=1}^{n}(E_{i}-e_{i})^{2}}{\sum \
olimits_{i=1}^{n}\left(\left|e_{i}-\overline{E}\right|+\left|E_{i}-\overline {E}\right|\right)^{2}}\right] \tag{22}\]  
where \(n\) is the number of the samples. \(E_{i}\) and \(e_{i}\) represent the measured and the predicted values of the rock EM, respectively. \(\overline{E}\) is the average value of the measured rock EM.
## 1 Introduction
### First Inertial Order Model  
We considered first the case, which, although trivial, provides a reference point for time-delayed processes. Certain processes can be described with a simple first-order model:  
\[G_{p}(s)=\frac{K}{1+Ts} \tag{5}\]  
where \(K\)--static gain, \(T\)--time constant.  
When using a PI controller with an \"ideal\" structure,  
\[G_{PI}(s)=K_{p}\cdot\left(1+\frac{1}{T_{i}s}\right) \tag{6}\]  
where \(K_{p}\)--proportional gain, \(T_{i}\)--integral time constant.  
With an integral time constant \(T_{i}=T\), the closed-loop transfer function is equal to  
\[G_{c}(s)=\frac{KK_{p}}{KK_{p}+T_{i}s} \tag{7}\]  
We shall assume that the desired transfer function of the closed-loop system is to be equal to  
\[G_{dc}(s)=\frac{1}{1+\tau_{c}s} \tag{8}\]  
which would provide a response without overshooting with inertia equal to \(\tau_{c}\).  
From (7) and (8), it can be seen that by selecting the proportional gain  
\[K_{p}=\frac{T}{K\tau_{c}} \tag{9}\]  
we can obtain the desired transfer function of the system (7).  
The overshoot in the response of such a system will be zero. The time constant of the closed-loop transfer function determines the settling time \(t_{s}\), i.e., the time after which the response will be permanently contained in the assumed error band \(\Delta\).  
\[y(t)=r\left(1-e^{-\frac{t}{\tau_{c}}}\right) \tag{10}\]  
where \(r\)--reference signal.  
The response of the system after a time equal to the settling time should reach a value of  
\[y(t=t_{s})=r(1-\Delta) \tag{11}\]If we take the error band \(\Delta\) equal to 2%, then by comparing (10) to (11), we calculate the formula for the expected settling time.  
\[t_{s}=3.91\cdot\tau_{c} \tag{12}\]  
\(\tau_{c}\)--closed-loop time constant.
## 3 Results
### Acoustic Analysis  
Figure 1 presents the F0 contours of the six distinct Cantonese tones for the two groups as _T values_, which were generated by the GAMs. Each data point represents the F0 value calculated from one of the 20 points extracted for each token and converted to a _T value_. Coloured contours were used to represent different tones. As can be seen in Figure 1A, in native speakers, there was a distinctive pattern in the native production, as the tones were separated from each other in two forms: T1, T3 and T6 were level tones with varying heights, while T2, T4 and T5 were rising or falling tones with different shapes. The GAM with 0 as the reference revealed more linearity for level tones (EDF (effective degree of freedom): T1 = 1.161, T3 = 2.717, T6 = 2.977) and more nonlinearity for rising or falling tones (EDF: T2 = 5.023, T4 = 4.937, T5 = 4.242).  
Figure 1: F0 contours of the six Cantonese tones produced by the native speakers (**A**) and the immigrants (**B**).  
As shown in Figure 1B, although the immigrants produced a high-level T1, the remaining five tones were distributed in a lower and narrower acoustic space. No clear linear or nonlinear distinction was found for the tones that the immigrants produced, except for T1 (EDF: T1 = 1.043, T2 = 6.023, T3 = 4.734, T4 = 4.881, T5 = 3.994, T6 = 4.681). Therefore, it appeared that the five tones in the lower F0 region were not easily distinguishable. Post hoc pairwise comparisons revealed that the F0 curves of T3, T5 and T6 overlapped significantly (T3-T5: \(\beta\) = 0.026, SE = 0.044, t = 0.597, \(p\) = 0.991; T3-T6: \(\beta\) = 0.046, SE = 0.045, t = 1.007, \(p\) = 0.916; T5-T6: \(\beta\) = 0.020, SE = 0.044, t = 0.451, \(p\) = 0.998).  
A further analysis using more detailed GAMs revealed that the variable _group_ showed significant linear effects but no significant nonlinear effects over time on T1, T2 and T6; by contrast, _group_ showed significant linear and nonlinear effects over time on T3, T4 and T5. These results indicated that the main acoustic differences between the native Cantonese speakers and the L2 Cantonese immigrants for T1, T2 and T6 were F0 heights, whereas the main differences for T3, T4 and T5 included both F0 heights and shapes. The acoustic analyses showed that the immigrant group failed to produce Cantonese tones in a native-like manner in general, although their T1 and T2 were pronounced relatively well.
###### Abstract  
In order to solve the problems such as the inability to automatically mix a variety of solid fertilizers and the unreasonable fertilizer amount, improve fertilizer utilization, and reduce production costs, this study designs a variable formula fertilization control system based on a prescription diagram, including pressure sensor, speed sensor, servo motor, fertilizer discharge actuator, Programmable Logic Controller (PLC controller), vehicle control terminal, etc. Based on pre-loaded soil prescription diagram and combining fertilizer pressure and ground wheel speed detection information, the system obtained a formula fertilization control strategy through calculation to realize the function of fast and automatic formula of nitrogen, phosphorus, and potassium fertilizers and precise variable fertilization. The experimental study on the performance of the variable formula fertilization control system showed the following: the measurement error range of the pressure sensor was 0.005-0.03%; the relationship between the motor speed and the amount of nitrogen, phosphorus, and potassium fertilizer discharged was calibrated. Three gears were established for the motor speed: low (10 r/min), medium (30 r/min), and high (50 r/min); the measurement accuracy of the speed sensor was above 98%. The test verified that the control accuracy of the variable formula fertilization system reached more than 95%, which met the requirements of fast automatic formula and precise variable fertilization and had good practicability and economy.  
prescription diagram; automatic formula; precision fertilization; control system; experimental study +
Footnote †: journal: Applied sciences