esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Delivery of Cas9-EmGFP to HEK293 Cell  
Cas9 protein contains an arginine-rich \(\alpha\)-helix domain that interacts with sgRNA [27]. Since the cationic domain was expected to electrostatically interact with the negatively charged cell surface, the behavior of the Cas9-mEmGFP fusion protein was verified with or without sgRNA. The addition of the Cas9-mEmGFP solution to HEK293 cells in a culture dish resulted in the immediate adsorption of Cas9-mEmGFP onto the cell surface (Figure 2A,B). By contrast, Cas9-mEmGFP forming an RNP complex with sgRNA at a ratio of 1:10 was not adsorbed onto the cell surface. While the adsorption of RNPs onto the cell surface was observed after 15 min, the adsorption of Cas9 was inhibited by interaction with sgRNA, which was possibly due to the neutralizing of positive charges derived from the arginine-rich domain. Because the total time taken by the NNA for the insertion of RNPs was less than 1 min, the adsorption of RNPs on cells during nanoneedle insertion ceased to be a factor. Cas9-mEmGFP proteins with or without sgRNA were incorporated into the cytosol after 22 h (Figure 2C), indicating that protein adsorption onto the cell surface progressed via endocytosis. Due to difficulties associated with escape from endosomes, most delivered molecules are degraded, indicating that direct delivery of Cas9 into the nucleus maybe preferable for genome editing purposes. Nanoneedles on the NNA are shown, indicating that at least one nanoneedle was inserted into a red fluorescent HEK293 cell (Figure 2D; green dots). Dark areas in the cells without red fluorescence are  
Figure 1: Estimation of cleavage activity of RNP released from nanoneedle array (NNA). (**A**) A schematic of Cas9 and poly-U containing single guide RNA (sgRNA) ribonucleoprotein (RNP) cleaving double stranded DNA. (**B**) Scanning electron microscope images of fabricated NNAs. (**C**) Schematic of RNP adsorption and release. The RNP solution was poured on the hydrophobized NNA and collected as the unadsorbed fraction. The RNP-adsorbed nanoneedle array was immersed in cytosolic simulation buffer. The buffer was then collected as the released fraction. (**D**) Electrophoresis of cleaved plasmid DNAs in unadsorbed and released fractions. Super-coiled plasmid DNA coding _EGFP_ (negative control, N.C.) and linearized plasmid (LP) by restriction enzyme were used.  
nuclei. The probability of the nucleus contacting nanoneedles was estimated to be about 70%, and the percentage of cells that contacted multiple needles was about 10%. Moreover, fluorescence intensities of the nanoneedles decreased after retraction from the cells, indicating that Cas9-mEmGFP had been successfully released from the surface of the nanoneedles (Figure 2E). Therefore, we concluded that our NNA system may be used for direct delivery of Cas9-sgRNA RNPs into living cells.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Circular Motion of MSs with Varied Velocity Algorithm (CMS2V2)  
We use MSs to balance the energy consumption among CHs in different coronas. The velocity of mobile agents is an important parameter in mobile sink-based WSNs. Adjusting this parameter in different locations can solve unbalanced energy consumption of CHs in different layers of the network. In CMS2V2 the MS belonging to the \(i\)th corona [Ci \(1\leq i\leq k\)] identified by its unique id \(\{i\ |\ 1\leq i\leq k\}\). MSs have circular motion in their coronas as shown in Figure 3 and their velocities vary in the different angles to achieve a balanced energy consumption ratio among CHs located in different layers. The MS belonging to the critical corona (MS1) moves with constant speed. However, the velocity of MS belonging to \(i\)th corona \(\{i\ |\ 1\leq i\leq k-1\}\) is regulated by static sink in the different angles (from (\(j\)-1)th sector to \(j\)th sector) in a way that the total energy consumption of CH belonging to \(i\)th corona and \(j\)th sector is balanced with the energy consumption of CH located in 1 st corona and \(j\)th sector. In fact, by decreasing the velocity of MSs belonging to outer coronas, a chance is given to inner MSs to increase their sojourn time in clusters and compensate the extra energy consumption of CHs, thus:  
\[\text{VelocityMS}_{1}>\text{VelocityMS}_{2}>\text{VelocityMS}_{3}> \ldots>\text{VelocityMS}_{k} \tag{23}\]  
The velocity of \(i\)th MS, between two angles is shown as Velocity (Sid, StartAngle, StopAngle, Round), where Sid denotes the id of MS and StartAngle and StopAngle denote the moving angle interval of MS in Roundth turn number of MS. Here, the term Round means a cycle of rotating MSs and returning to the starting point. Since MSs are supposed to sojourn at the bisector of the angle of sectors (\(\theta\)), Start and stop angle between (\(j\)-1)th and \(j\)th sectors are defined as follows:  
\[StartAngle=(2(j-1)-1)\frac{\theta}{2} \tag{24}\]  
\[StopAngle=(2j-1)\frac{\theta}{2} \tag{25}\]  
In the proposed strategy, whenever a MS arrived at the bisector of angle (\(\theta\)) in each sector it remained in the cluster for a limited time to serve the member nodes. In fact, the sojourn time is the time that the mobile sink spends at clusters to collect data from the sensor nodes and during this limited time CHs act as ordinary nodes in clusters. This is denoted as \(ST(i,j,\ Round)\); \(i\) and \(j\) denote the corona and sector number of cluster served by MS. CHs have to wait for MS, this waiting time parameter captures the total time spent by the MS in previous clusters during current round and in next clusters during previous round and also path length passed by MS with different velocities. This waiting time can be written as follows:  
\[WT(i,j,\ Round)=PL_{i}+\sum_{n=1}^{j-1}ST(i,n,\ Round)+\sum_{n=j+1}^{\text{ last sector}}ST(i,n,\ Round-1) \tag{26}\]  
Figure 3: Circular motion of MSs in CCWSN.
# Properties of the Iron Bacteria Biofouling on Ni-P-rGO Coating
###### Abstract  
Biofouling on heat exchange devices can decrease heat transfer efficiency, corrode materials, and even lead to safety accidents. Most heat exchange devices are made of carbon steel that efficiently produces biofouling. However, in this paper, a nickel-phosphorus-reduced graphene oxide (Ni-P-rGO) coating was prepared on carbon steel by electroless plating to investigate the properties of iron bacteria biofouling. The surface coating was analyzed via scanning electron microscopy and Raman spectroscopy. After the carbon steel and the Ni-P-rGO coating were immersed into an iron bacteria solution for 120 h, the weight of the iron bacteria biofouling on the Ni-P-rGO coating sharply decreased when compared with the carbon steel. We can conclude that the concentration of graphene can affect the weight of iron bacteria biofouling. We also found that the coating solution with 40 mg/L of graphene performed the best in inhibiting biofouling by decreasing the weight of the biofouling by 97.2% compared to carbon steel.  
biofouling; electroless; graphene; surface energy; DLVO theory; iron bacteria Article
## 2 System Model
### UAV-Mounted Access Points (UAVs)  
The proposed system comprises terrestrial and flying APs. The use of UAVs is considered to mount APs at desirable altitudes, thus increasing the probability of LoS with SN on the ground. It is assumed that 50% of the existing terrestrial APs can be replaced by UAVs. The selection of such APs can vary depending on the design objective. For this research, underperforming APs with low SNs associations are replaced by UAVs to alleviate the load on congested APs while enhancing wireless channels by increasing the number of SNs with LoS connectivity. Due to size and payload limitations, each UAV cell is equipped with a 2-by-2 transmitter, as compared to 8-by-8 in terrestrial AP. A two-tier system is considered for simplicity; terrestrial APs have a common height of 10 m, while the UAVs hover at a specified altitude. Although the selection of a terrestrial AP height is based on the 3 GPP UMi evaluation scenario, the altitude of UAVs needs further study. The UAV altitude is optimized to ensure LoS and achieve the highest spectral efficiency.
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods
### Rolling Sample Skewness (RSS)
#### 3.2.1 Calculation of Rolling Sample Mean (RSM)  
Given the rolling window size \(k\) for a time series of observation values \(x_{1},x_{2},\ldots,x_{n},x_{n+1}\), their RSM can be calculated via Equation (5).  
\[\bar{x}_{\lceil n+1\rceil_{k}}=\bar{x}_{\lceil n\rceil_{k}}+\frac{x_{n+1}-x_{o} }{k},\quad n\geqslant k>0 \tag{5}\]  
where \(\bar{x}_{\lceil n\rceil_{k}}\) stands for the sample mean of the most recent \(k\) observation values \(x_{n-k+1},x_{n-k+2},\ldots,x_{n}\), while \(\bar{x}_{\lceil n+1\rceil_{k}}\) indicates the updated sample mean after rolling the observation window to include the new value \(x_{n+1}\) while removing the oldest one \(x_{o}\) (i.e., \(x_{n-k+1}\)). The brief derivation of Equation (5) is shared in Appendix A.4.
## 3 Results and Discussion  
For convenience, the relative IAE is used to reveal the effect of the CMOS compatible thermopile with those ESCS and is defined as the IAE of that thermopile with the ESCS is relative to one without the ESCS and is written as:  
\[Relative\;IAE=\frac{\text{the IAE of that thermopile with the ESCS}}{\text{the IAE of that thermopile without the ESCS}} \tag{1}\]  
For the types of rectangular column, the variances of the relative IAEs with different \(W_{x}\) and \(W_{y}\) for the thermopiles with RC-type ESCSs at the target temperature of 75 \({}^{\circ}\)C are shown in Figure 4. It was seen that for the types of one RC, two RCs, and three RCs, the better relative IAEs were 1.128, 1.127, and 1.144 times, respectively. It was obtained that the \(W_{x}\) and \(W_{y}\) of the better case are 2.6 \(\upmu\)m and 1.8 \(\upmu\)m, respectively. For the types of ellipse column, the variances of the relative IAEs with different \(W_{x}\) and \(W_{y}\) for the thermopiles with EC-type ESCSs at the target temperature 75 \({}^{\circ}\)C are shown in Figure 5. One can see that for the types of one EC, two ECs, and three ECs, the better relative IAEs are 1.130, 1.132, and 1.152 times, respectively. It was obtained that the \(D_{x}\) and \(D_{y}\) of the better case were 3.2 \(\upmu\)m and  
Figure 3: Top-view sketch of the six ESCSs, (**a**) one rectangular column (RC), (**b**) two RCs, (**c**) three RCs, (**d**) one ellipse column (EC), (**e**) two ECs, and (**f**) three ECs.  
Figure 2: Sketch of a light-wave propagation through a CMOS compatible thermopile with SHA simulated by the finite-difference time-domain (FDTD) method.  
1.5 \(\upmu\)m, respectively. Those results of the better relative IAEs and the geometric parameters for the six ESCSs are listed in Table 1. Finally, the variances of the IAEs with the target temperature for the SHA thermopiles of (a) the best case in this study and (b) the best case in [16], and (c) the thermopile without SHA, are shown in Figure 6. One can see that the IAE of the CMOS compatible thermopiles was significantly enhanced when the subwavelength hole structure existed in the active area of the thermopiles, especially when the special structure in the rectangular hole array was added.  
Figure 4: Variances of the relative infrared absorption efficiencies (IAEs) with different \(W_{x}\) and \(W_{y}\) for the thermopiles with RC-type ESCSs at the target temperature 75\({}^{\circ}\), (**a**) one RC, (**b**) two RCs, and (**c**) three RCs.  
Figure 5: Variances of the relative IAEs with different \(W_{x}\) and \(W_{y}\) for the thermopiles with EC-type ESCSs at the target temperature 75 \({}^{\circ}\)C, (**a**) one EC, (**b**) two ECs, and (**c**) three ECs.
## 3 Results and Discussion
### Chemical Composition of Raw Materials
A typical feature of waste materials is the elevated concentrations of the elements Cl and S. The content of these elements and also heavy metals (as Cd) is higher in fruits and seeds and is related to the natural accumulation by the plant. All the test samples had high natural content of S and Cl, which is in agreement with the literature results. The aforementioned may lead to problems related to increased emissions and boiler corrosion. Biofuel producers must, therefore, respond adequately to the composition of materials in the feedstock blending process [20; 60; 61]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fruit Species and** & **C** & **O** & **H** \\
**Characteristics** & **(\%, Dry Matter)** & **(\%, Dry Matter)** & **(\%, Dry Matter)** & **O:C** \\ \hline Raspberry pomace & 51.9 \(\pm\) 2.0 \({}^{\text{\,bc}}\) & 33.46 \(\pm\) 0.15 \({}^{\text{\,d}}\) & 6.5 \(\pm\) 0.4 \({}^{\text{\,b}}\) & 0.65 \\ Sea-buckthorn pomace & 54.9 \(\pm\) 0.9 \({}^{\text{\,b}}\) & 29 \(\pm\) 1 \({}^{\text{\,e}}\) & 6.82 \(\pm\) 0.15 \({}^{\text{\,b}}\) & 0.64 \\ Grape vine pomace & 53.28 \(\pm\) 0.12 \({}^{\text{\,b}}\) & 35 \(\pm\) 1 \({}^{\text{\,ad}}\) & 5.3 \(\pm\) 0.8 \({}^{\text{\,a}}\) & 0.53 \\ Grape vine seed cake & 53.0 \(\pm\) 0.3 \({}^{\text{\,b}}\) & 40.1 \(\pm\) 0.4 \({}^{\text{\,c}}\) & 8.61 \(\pm\) 0.21 \({}^{\text{\,c}}\) & 0.80 \\ Almond shell & 46.4 \(\pm\) 0.9 \({}^{\text{\,a}}\) & 37.1 \(\pm\) 0.9 \({}^{\text{\,ab}}\) & 5.78 \(\pm\) 0.11 \({}^{\text{\,ab}}\) & 0.69 \\ Pistachio shell & 47.43 \(\pm\) 0.24 \({}^{\text{\,a}}\) & 40.28 \(\pm\) 0.21 \({}^{\text{\,c}}\) & 5.0 \(\pm\) 0.3 \({}^{\text{\,a}}\) & 0.81 \\ Almond seed coat & 45.8 \(\pm\) 0.3 \({}^{\text{\,a}}\) & 38.2 \(\pm\) 0.4 \({}^{\text{\,bc}}\) & 5.6 \(\pm\) 0.4 \({}^{\text{\,ab}}\) & 0.85 \\ Peach stone & 52.5 \(\pm\) 0.5 \({}^{\text{\,bc}}\) & 36 \(\pm\) 2 \({}^{\text{\,ab}}\) & 5.2 \(\pm\) 0.4 \({}^{\text{\,a}}\) & 0.83 \\ Plum stone & 49 \(\pm\) 2 \({}^{\text{\,ac}}\) & 37.1 \(\pm\) 0.9 \({}^{\text{\,ab}}\) & 6.1 \(\pm\) 0.4 \({}^{\text{\,ab}}\) & 0.75 \\ Date stone & 49 \(\pm\) 2 \({}^{\text{\,ac}}\) & 39.8 \(\pm\) 0.3 \({}^{\text{\,c}}\) & 5.8 \(\pm\) 0.6 \({}^{\text{\,ab}}\) & 0.76 \\ \hline \hline \end{tabular} Note: Data are expressed as an average \(\pm\) standard deviation. Averages with different letters within the column are significantly different, according to [PERSON]’s test (\(\eta\leq\) 0.05).  
\end{table} 
Table 3: Results of the elemental analysis (C, O, H).
## 1 Introduction
Instead of presenting a new robust control theory, the topic of this paper is to solve a controller design issue when applying the backstepping scheme. In the existing backstepping control algorithms, the virtual control inputs contain unmeasurable information. This issue can be dealt with in two ways: taking the time derivative on the state variable directly or designing an extra observer. On the one hand, taking the time derivative will induce serious control chattering when the state variables contain measurement noise. On the other hand, designing an extra state estimator or an observer would definitely increase realization complexities. Therefore, in the paper, an analytic way is presented. The proposed analytic flight control algorithm, which uses available state information only, can be taken as a compromised control law for practical consideration. Therefore, applying the virtual input derivative as well as designing the observer design can be avoided. Most importantly, the serious control chattering induced by measurement noise can be attenuated and thus the control saturation can be avoided as well. This advantage makes a great contribution to the position control closed-loop stability. The contributions of this study are summarized as follows: (i) proposing the integral backstepping control algorithm for the quadrotor position tracking demands; (ii) providing the corresponding stability analysis based on Lyapunov theory in the presence of the unknown disturbances; (iii) deriving the analytic form of the virtual controls so that more smooth control signals can be obtained by the proposed analytic solution instead of using the numerical differentiation directly; and (iv) highlighting the advantage of the proposed flight controller with consideration of disturbances and measurement noise for comparative studies. Simulation results reveal that the proposed integral backstepping control algorithm with the analytic virtual control law has a better trajectory tracking performance. Meanwhile, smoother realizable control signals are obtained. The organization of this study is concluded as follows: the dynamics modeling and the actuator analysis are performed in Section 2; the proposed integral backstepping control algorithm is derived in Section 3, the correlated stability analysis is provided; the comparison of the numerical simulations is presented in Section 4; finally, the conclusions are made in Section 5.
# Analysis of Underlapped Symmetrically Ported Valve-Controlled Asymmetric Cylinder Drive
## 2 Theory
### Flow Gain  
The flow gain is the gain that describes the relationship between the valve opening and the oil flow across it. In order to interpret it clearly, a physical layout of the four-way valve-controlled [16] asymmetric cylinder is presented in Figure 4. Physical layout of the four-way valve-controlled asymmetric cylinder [6].  
\(Q_{1}\) is the flow rate that is delivered into the piston side chamber, \(V_{1}\) is the volume of fluid that is trapped in the piston chamber, \(A_{1}\) is the piston side area of the cylinder, and the subscript 2 indicates corresponding parameters in the rod side chamber. \(P_{s}\) is the supply pressure, and \(P_{T}\) is the return line pressure of the oil tank, which is zero. \(A_{V}\) is the opening area of the corresponding port of the valve, and subscripts 1, 2, 3, and 4 indicate different ports of the valve which are depicted in Figure 4. Physical layout of the four-way valve-controlled asymmetric cylinder [6].  
\(x_{p}\) is the cylinder displacement. \(Q_{P1}\) is the flow passing through the \(A_{V1}\) port, \(Q_{1T}\) is the flow passing through the \(A_{V2}\) port, \(Q_{2T}\) is the flow passing through the \(A_{V3}\) port, and \(Q_{P2}\) is the flow passing though the \(A_{V4}\) port.  
The flow delivered into the chambers can be described as:  
\[Q_{1}=Q_{P1}-Q_{1T}=C_{D}\sqrt{\frac{2}{\rho}}\Big{[}A_{V1}(P_{s}-P_{1})^{ \frac{1}{2}}-A_{V2}(P_{1})^{\frac{1}{2}}\Big{]} \tag{6}\]  
\[Q_{2}=Q_{2T}-Q_{P2}=C_{D}\sqrt{\frac{2}{\rho}}\Big{[}A_{V3}(P_{2})^{\frac{1}{2 }}-A_{V4}(P_{s}-P_{2})^{\frac{1}{2}}\Big{]} \tag{7}\]  
For a symmetric ported valve, \(A_{V1}=A_{V3}\) and \(A_{V2}=A_{V4}\), so that Equations (6) and (7) can be simplified as:  
\[Q_{1}=Q_{P1}-Q_{1T}=C_{D}\sqrt{\frac{2}{\rho}}\Big{[}A_{V1}(P_{s}-P_{1})^{ \frac{1}{2}}-A_{V2}(P_{1})^{\frac{1}{2}}\Big{]} \tag{8}\]  
\[Q_{2}=Q_{2T}-Q_{P2}=C_{D}\sqrt{\frac{2}{\rho}}\Big{[}A_{V1}(P_{2})^{\frac{1}{ 2}}-A_{V2}(P_{s}-P_{2})^{\frac{1}{2}}\Big{]} \tag{9}\]  
These two equations can be applied when the valve is in the extending, retracting, and underlap region, the underlap region being the valve spool position where all port opening areas are non-zero. Even for a zero-lapped valve, due to the manufacturing tolerances [17], there will be a small underlap when the spool is placed in the null position. Figure 5 depicts the theoretical and actual situation for a nominally zero-lapped valve.  
Figure 4: Physical layout of the four-way valve-controlled asymmetric cylinder [6].  
The manufacturing tolerances can be identified as:  
1. Radial clearance between spool and sleeve.
2. Tolerance on lap.  
Such tolerance [1] will lead to variation of flow gain when the valve is in its null region. The flow gain model block diagram is depicted in Figure 6.
## 4 Photon Diagnostics Systems
### Online Photon Diagnostic Systems
#### 4.1.3 The HIREX Spectrometer  
The HIgh RESolution hard X-ray single-pulse diagnostic spectrometer (HiREX) spectrometer is an online device, based on a diamond diffraction grating used in transmission to split off a small fraction (0.1%) of the photon beam, a bent crystal as a dispersive element, and a MHz-repetition rate strip detector. The grating and crystal chambers are separated by 10 m distance. Gratings with pitches of 150 nm and 200 nm were installed. While beam transmission depends on the photon energy, typically 95% transmission is achieved. Five percent is then spread into all diffraction orders. The first order diffracted beam from the grating is sent to a bent crystal for energy dispersion under Bragg condition [54]. The 10 um thick bent silicon Si crystals have (110) or (111) orientations and are mounted with fixed bending radii of 75 mm, 100 mm or 150 mm. Two detectors are available for data acquisition: an optical camera for full transverse 2D imaging at low repetition rate, and a modified Gotthard-II 1D strip detector for fast data acquisition at 4.5 MHz.
# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 2 Results
### Preparation  
Dimension reduction methods were applied in 34 works [19; 21; 23; 25; 29; 34; 35; 37; 39; 47; 51; 54; 58; 65; 67; 70; 72; 73; 76; 77; 78; 82; 87; 94; 95; 98; 99; 100; 102; 103; 105; 106] and ranged from visual analysis to wrapper functions. In 12 works [21; 34; 39; 51; 67; 68; 77; 82; 87; 94; 102; 105], principal component analysis (PCA) was used, seven applied correlation calculations to identify redundant features [21; 23; 29; 58; 65; 72; 103] and 25 [19; 21; 23; 25; 29; 35; 37; 47; 54; 65; 69; 70; 72; 73; 76; 78; 83; 95; 98; 100; 103; 105; 106] works applied another dimension reduction method. Some authors tried more than one dimension reduction method, such as [102] who used 18 different feature selection techniques. Ref. [56] evaluated each feature separately and then performed a validation using the feature fusion of all feature sets. Ref. [34] tried two different versions of data sets, one with PCA (five features), one without. Furthermore, refs. [42; 94] tried different versions and numbers of input features and compared the performance of the classifiers.  
Table 3 gives a detailed overview of the papers where feature reduction was described and the details of both the full and the reduced feature set were given. The table states the number of features of the full data set and the number of features of the reduced data set.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Refs.** & **Number of Features before Reduction** & **Number of Features after Reduction** \\ \hline
[19] & 153 & 28 \\
[21] & 90 & 15/25 \\
[23] & approx. 2000 & 300–500, which were further reduced \\
[34] & 8 & 5 \\
[35] & 16 & 12 \\
[52] & 306 & 96 \\
[58] & 282 & 223 \\
[65] & 32 & 24 \\
[66] & 108/216 & 20 \\
[67] & 88 & 36 \\
[68] & 23 & 4 \\
[69] & 81 & 8 \\
[70] & 25 & 16 (for RF) \\
[73] & 24 & 3 \\
[78] & 840 & 52 (for RBF-SVM) \\
[87] & 120 & 1 \\
[94] & 72 & 30 \\
[95] & 84 & 11 \\
[99] & & information was compressed by a ratio of 1:109 \\
[102] & 210 & 2 classes: 7; 3 classes: 4 \\
[103] & 97 & 45, 50, 46 \\
[104] & 132 & 6 \\
[106] & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the full and reduced feature set.