esa-sceva/satcom-chunk-collection huggingface dataset:
# Positively Charged Lipid as Potential Tool to Influence the Fate of Ethosomes
## 2 Materials and Methods
### Cell Cultures  
Cytotoxic effect of ethosomal formulations were investigated on human keratinocytes, NCTC2544 cells, through the MTT (3-[4,5-dimethylthiazol-2 yl]),2,5-diphenyltetrazolzoliumbromide) dye assay, collecting data of cell viability. Cells were sealed in Petri dishes (\(\mathcal{O}\) 100 \(\times\) 20 mm) containing minimum essential medium (D-MEM) supplemented with glutamax, streptomycin (100 ug/mL)-penicillin (100 \(\mu\)L/mL) solution (1% \(v/v\)), amphotericin B (250 ug/mL), and FBS (10% \(v/v\)) and stored in a Water-Jacketed CO\({}_{2}\) incubator at a temperature of 37 \({}^{\circ}\)C. Medium was replaced every two days until a confluence of -80% was reached. Cells were then washed with phosphate buffer solution, treated with trypsin/EDTA (1X) solution to be detached from the dishes and transferred into centrifuge tubes. Tubes were centrifuged at 1200\(\times\)\(g\) rpm for 5 min using Megafuse 1.0 (Heraeus Sepatech, Osterode, Germany) obtaining a pellet that was resuspended in D-MEM [22].  
Evaluation of Cytotoxicity on NCTC2544 Cells were seeded in 96-well plastic culture dishes at a density of 5.0 \(\times\) 10\({}^{3}\) cells/0.1 mL to perform MTT dye test as previously reported [22]. Following 24 h of incubation, D-MEM was replaced with a fresh medium containing empty ethosomes at increasing concentration of lipidic components (0.01, 0.1, 1.0 and 10.0 ug/mL). Cells were incubated for different times (24 h, 48 h and 72 h), treated with 10 \(\mu\)L of MTT solution (5 mg/mL of salts dissolved in PBS buffer) and further incubated at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) for 3 h, which was the time necessary to living cells to form violet formazan crystals. At this time, culture medium was withdrawn and formazan crystals were dissolved in dimethylsulfoxide/ethanol (1:1 \(v/v\)), keeping the plate under constant stirring using IKA(r) KS 130 Control (IKA(r) Werke GmbH & Co., Staufen, Germany) for 20 min at 200 rpm. ELISA microplate reader (Labsystems mod. Multiskan MS, Midland, ON, Canada) set at \(\lambda_{\text{exc}}\) 570 nm and \(\lambda_{\text{em}}\) 670 nm, allowed to investigate cell viability according to the following equation:  
\[\%\;Cell\;viability=\frac{At}{Au}\cdot 100 \tag{1}\]  
where \(A_{t}\) and \(A_{u}\) represented the absorbance of treated and untreated cells, respectively. Three different experiments were performed to investigate the percentage of cell viability and results were reported as the average of the obtained data \(\pm\) standard deviation.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Inoculum
The original inoculum (I\({}_{0}\)) was collected from the anaerobic digester of a wastewater treatment plant. I\({}_{0}\) was diluted twice with the recycled water to achieve the diluted inoculum (I\({}_{2}\)). When I\({}_{0}\) was used as inoculum, the CH\({}_{4}\) yields were 238, 190, \(<\) 50, \(<\) 50, and \(<\) 50 L/kg VS at S/I ratios of 2, 4, 6, 8, and 10, respectively. While the yields were 193, 187, 176, 144 and \(<\) 50 L/kg VS when I\({}_{2}\) was used in the digesters. These results evidenced that I\({}_{2}\) had better dilution capacity for acidic compounds and mass transfer performance due to the availability of additional water in the inoculum. These characteristics of I\({}_{2}\) improve methanogenic activity [76]. [PERSON] et al. [70] studied the effects of co-inoculating RS with ruminal microbiota (RM) and anaerobic sludge (AS). Five co-inoculum mixtures were prepared with RM: AS as 1:1, 1:2, 2:1, 1:0, and 0:1 based on VS content, while the S/I ratio in all mixtures was approximately 0.25. The results showed that the co-inoculation had almost threefold higher CH\({}_{4}\) yield than the digestion of RS in the presence of RM alone. [PERSON] et al. [77] pointed out that heat treatment (heating the sludge at 100 \({}^{\circ}\)C for 15 min and then cooling to room temperature to enrich the spore-forming bacteria of the Clostridium species) of sewage sludge, which was used as inoculum for the AD of RS, caused less biogas yield in comparison to untreated sludge. It is demonstrated from the results of the studies that the source of inoculum affects the biogas yield. Digested dairy manure proved to be optimal inoculum for RS among the various sources of inocula. Biogas/methane yields tend to decrease with increasing S/I ratios. A high amount of substrate relative to inoculum may cause problems in AD, such as the accumulation of VFAs and lower biogas production rate. Heat treatment may cause the inoculum to be less effective. The use of co-inoculation seems to increase the biogas yield from the AD of RS.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 3 ACER Routing Algorithm
### Route Discovery Based on the Connectivity Feedback Mechanism
\begin{aligned} C\times(1-e^{-\frac{m}{N(A)}}) \times\frac{d}{R}&,m\in NUM_{N(i)}\\ 0&otherwise\end{aligned} \right. \tag{9}\]  
where \(n\) is the total number of nodes in the network, \(NUM_{N(i)}\) is the number of nodes in the node \(i\)'s neighbor node-set, \(d\) is the distance between source node \(i\) and intermediate node \(m\), and \(R\) is the node communication radius. The improved route discovery algorithm is shown in Algorithm 1. ```
Input: source node \(i\), Target Node \(j\), adjacency matrix \(A\), total number of nodes \(n\), let \(s=1\); Output: forwarding nodes, forwarding probability begin initializing the network; for\(s=1\): \(n\) update the set of neighbor nodes \(N(i)\) of source node \(i\); end for if\(m\in N(i)\) calculate the node \(m\) connectivity contribution \(C\) using Equation (8); calculate the forwarding probability of node \(m\) using Equation (9); end if output the node with the highest probability of forwarding and the probability value; End
```  
**Algorithm 1**Routing discovery algorithm based on connectivity feedback mechanism.
# Numerical Investigation of Methodologies for Cavitation Suppression Inside Globe Valves
## 1 Introduction
To suppress or prevent the occurrence of cavitation, valve structure optimization, adding outlet pressure, or adding stage/anti-cavitation trims is inevitable. [PERSON] et al. [4] numerically studied the effects of the numbers of valve trims on cavitation intensity, and their results showed that the numbers of valve trims should be below two. [PERSON] et al. [34] performed structure optimization of the valve port in a relief valve to reduce cavitation intensity. [PERSON] et al. [35] utilized a drainage device to suppress the cavitation in a throttle valve, and cavitation intensity could be reduced with appropriate inlet and outlet pressure. [PERSON] et al. [36] optimized the structure of the bottom plug in a three-way reversing \begin{table}
\begin{tabular}{c c c} \hline \hline
**Valve Type** & **Ball Valve [2]1** & **Control Valve** & **Globe Valve** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common valves applied in different industries. Figure 1: Damage of the valve plug results from cavitation erosion [21]-Copyright 2016, Elsevier. value to suppress valve cavitation. [PERSON] et al. [37] found that by adding a valve cage, cavitation could be limited to the region close to the valve cage, and the valve body could be protected from damage resulting from valve cavitation. Globe valves are widely used in nuclear and thermal power stations, or in petrochemical fields, where there is often a high-pressure drop condition resulting in cavitation inside globe valves [37]. In this paper, a globe valve with two valve cages is proposed to suppress valve cavitation, and each cage has 20 evenly distributed slender orifices. Firstly, the flow characteristics of the globe valve with and without valve cage are compared to investigate cavitation distribution and cavitation intensity. Then the effects of the working temperature and the valve outlet pressure on cavitation intensity for the globe valve with two valve cages are studied. At last, the effects of the installation angle of two valve cages are analyzed and the most efficient method to reduce cavitation intensity is found out.
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 6 Engagement Analysis
### Students' Profiling  
Students can be described according to their level associated with each of the six KEIs. For the indicators _Concentration_, _Reflection_, and _Autonomy_, we define two levels (_High_ or _Low_), whereas for _Persistence_, _Confidence_, and _Understanding_ we exploit a three-level categorization (_High_, _Medium_, or _Low_). Table 10 contains details of the sequences used to assign the students to a specific level of a given KEI. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Engagement Indicator** & **Pattern Type** & **Patterns** & **Indicator Level** & **Comments** \\ \hline \multirow{6}{*}{Persistence} & \multirow{6}{*}{Access patterns} & Student satisfies at least one sequence in set (A1–A4) but no sequence in set (A5–A8) & _High persistence_ & Only sequential access patterns \\  & & & Student satisfies at least one sequence in set (A1–A4) and at least one in set (A5–A9) & _Medium persistence_ & Mixed access patterns \\  & & & Student satisfies at least one sequence in set (A5–A9) but no sequence in set (A1–A4) & _Low persistence_ & Only out-of-order access patterns \\ \hline \multirow{6}{*}{Concentration} & \multirow{6}{*}{Successful patterns} & Student satisfies at least one sequence in set (S1––59) & _High concentration_ & Stays focused on an exercise until it is solved correctly \\  & & & Student does not satisfy any sequence in set (S1–59) & _Low concentration_ & Does not stay focuses on an exercise until it is solved correctly \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Sequences used to assign the students to a specific level of a given key engagement indicator. The graph in Figure 2 shows the distribution of the engagement characteristics of the students under the six identified dimensions. _Persistence_, _Concentration_, and _Reflection_ are high for most of the students, denoting a fairly high commitment to the task for the majority of the students, whereas _Confidence_, _Autonomy_, and _Understanding_ show rather variable distributions. This is understandable since the level of individual competence and skill can be different, and this influences individual self-confidence and results. _Understanding_, in particular, shows quite significant variations: few students were very quick to solve exercises (_High Understanding_), whereas most of them were able to solve them in a larger interval of time (_Medium Understanding_); the rest of the students were not able to solve the exercise in a predefined interval of time (_Low Understanding_).
## 4 Combined Occupancy Method for Capacity Assessment  
The above analysis and proof provide the combined method of occupancy time of one single train, two trains and multiple trains on a train timetable. On this basis, this paper formed the combined occupancy method for the capacity assessment of railway sections. As shown in Figure 9, a method flow chart is described below with an example.  
Step 1: Sorting  
According to the departure time from the departure station of the section, all trains in the operation diagram are sorted and named \(l_{1}\), \(l_{2}\)... in sequence. The occupancy time of each train is calculated according to Equation (1).  
Step 2: Grouping  
Starting from the first train \(l_{1}\), the relationship between the trains is judged according to Equation (4), and the trains are divided into continuous train groups.  
Step 3: Combine  
Combine all train groups into combined trains according to the earliest departure time and the latest arrival time of the trains in the train group.  
Step 4: Calculate occupancy time  
Calculate the occupancy time of all combined trains on the train timetable and add them to obtain the total occupancy time \(OT_{total}\).  
Step 5: Calculate available time  
Due to the vertical maintenance window, the available time of the train timetable needs to subtract the time occupied by maintenance, \(T_{maintenance}\), and the invalid time caused by the triangle area, \(T_{triangle}\), which can be calculated by Equation (12).  
\[T_{available}=1440-T_{maintenance}-T_{triangle}=1440-T_{maintenance}-60\cdot \theta/v \tag{12}\]  
Step 6: Assess carrying capacity  
Divide the total occupancy time of all trains on the train timetable by the available time to obtain the capacity utilization rate \(\mu\), which indicates the degree of capacity to be used. In other words, for the train timetable with \(N_{total}\) trains, \(\mu\) of the available time is  
Figure 8: Convert a train group into a combined train.  
used. Therefore, we can deduce that if all available time is used, the maximum number of trains in the train timetable is the carrying capacity \(N_{capacity}\).  
\[OT_{total}/T_{available}=\mu=N_{total}/N_{capacity} \tag{13}\]
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 2 Materials and Methods
### Proposal  
This study presents a monitoring technique to prevent intrusion accidents in a closed control network environment by detecting anomalous traffic. Anomalous traffic in a closed control network can broadly be defined into two patterns. The first pattern is a case where it is necessary to verify whether a normal operation is performed, as the expected traffic does not occur. The second pattern is when random traffic that should not occur is generated.  
Pattern 1 results from mechanical errors in hardware and software and is mainly due to equipment loss, errors, and malfunctions. Pattern 2 is referred to as human error and is caused by intentional actions or mistakes.  
For comparison and verification of the detection technique for anomalous traffic, supervised learning is applied to the previous patterns, and the false-positive rate is analyzed by generating random anomalous traffic to verify the learning performance and consistency of the detection model.
## 2 Dark Current Measurement and Noise Correction Method Based on Focal-Plane Temperature
### LWIR QWIP Detection System Response Model  
The response model of the detection system is analyzed in this section in order to clearly demonstrate the effect of dark currents and their noise on the system. The total detector response voltage consists of the response voltages of the target object radiation, internal background radiation of the dewar, background radiation of the optical system and dark current, and the bias voltages of the readout and information acquisition circuits [22].  
\[U_{det}=U_{tar}+U_{dew\_bg}+U_{opt}+U_{dark}+U_{bias}, \tag{1}\]  
where \(U_{det}\) is the total detector response voltage, \(U_{tar}\) is the response voltage of the target object, \(U_{dew\_bg}\) is the response voltage of the background light inside the dewar, \(U_{opt}\) is the response voltage of the optical system background, \(U_{dark}\) is the dark current response voltage, and \(U_{bias}\) is the bias voltage of the readout circuit and the information acquisition circuit. It is shown in the study that the focal-plane operates below the temperature of liquid nitrogen (the QWIP cooling target temperature range for engineering applications)  
Figure 1: Schematic diagram of dark current measurement and noise correction method.  
and the photocurrent does not change with the temperature of the focal-plane when facing the same target object. Therefore, with the detection target, acquisition environment, and operating mode unchanged, only the dark current response voltage change can cause a change in the total detector response voltage \(U_{det}\). The dark current response voltage is expressed as:  
\[U_{dark}=T_{int}\cdot I_{dark}/C_{int}, \tag{2}\]  
where \(T_{int}\) is the integration time, \(C_{int}\) is the integration capacitance, and \(I_{dark}\) is the dark current magnitude.  
Figure 2 visualizes the response mode of the LWIR QWIP hyperspectral detection system. This detection system works on an Earth-orbiting satellite and periodically detects certain locations on Earth. The light from the target object passes through the main optics, the beam splitting system, the detector's window, and the pixels in sequence. The radiation of the target in the same spatial dimension is spectroscopically divided by the hyperspectrometer. The value of radiation after spectroscopy is only 1% of that of the gaze-type detection system [23]. Therefore, the amount of target object radiation and incident photons received by the detector pixels is very small, while the effect of the spectrometer background radiation and the detector dark current is relatively greater.
# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Numerical Evaluation
These algorithms only performed better in a few of the functions, while their efficiency was worse in others. As a result, a dependable algorithm is defined by its efficacy in discovering the optimal solution in most functions rather than its inefficiency with fewer functions. Consequently, EJAYA is sufficiently steady compared to the others because it ranks first in most of the functions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Algorithm** & **Mean** & **STD** & **Worst** & **Best** \\ \hline \multirow{8}{*}{F4} & SCA & 2.07301E+01 & 5.63654E+00 & 2.88824E+01 & 1.30543E+01 \\ \cline{2-6}  & DA & 2.48837E+01 & 1.09484E+01 & 4.00000E+01 & 1.16565E+01 \\ \cline{2-6}  & GOA & 1.08496E+01 & 3.61725E+00 & 1.63163E+01 & 8.20644E+00 \\ \cline{2-6}  & MFO & 2.00007E+03 & 4.47212E+03 & 1.00000E+04 & 3.10525E\(-\)03 \\ \cline{2-6}  & MFO & 4.25763E\(-\)01 & 1.67833E\(-\)01 & 6.65839E\(-\)01 & 2.62136E\(-\)01 \\ \cline{2-6}  & CSA & 7.75578E+00 & 2.16753E+00 & 9.96141E+00 & 5.23108E+00 \\ \cline{2-6}  & JAYA & 6.84824E+00 & 1.48590E+00 & 8.79045E+00 & 4.99795E+00 \\ \cline{2-6}  & EJAYA & 3.61570E\(-\)05 & 6.60817E\(-\)05 & 1.53172E\(-\)04 & 4.33560E\(-\)08 \\ \hline \multirow{8}{*}{F5} & SCA & 3.18710E+02 & 4.81347E+02 & 1.15414E+03 & 2.83299E+01 \\ \cline{2-6}  & DA & 1.98429E+05 & 1.16600E+05 & 3.96126E+05 & 1.09284E+05 \\ \cline{2-6}  & GOA & 3.36730E+03 & 3.58249E+03 & 8.65663E+03 & 1.73102E+02 \\ \cline{2-6}  & MFO & 6.00002E+03 & 8.94426E+03 & 2.00000E+04 & 2.06509E\(-\)02 \\ \cline{2-6}  & PSO & 7.99993E+01 & 3.47184E+01 & 1.14977E+02 & 2.56017E+01 \\ \cline{2-6}  & CSA & 7.54176E+01 & 3.79232E+01 & 1.23610E+02 & 3.56602E+01 \\ \cline{2-6}  & JAYA & 7.66403E+01 & 6.16420E+01 & 1.63148E+02 & 1.36139E+01 \\ \cline{2-6}  & EJAYA & 4.01170E+01 & 3.68858E+01 & 1.06099E+02 & 2.33569E+01 \\ \hline \multirow{8}{*}{F6} & SCA & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & DA & 4.96992E\(-\)10 & 8.02601E\(-\)10 & 1.84421E\(-\)09 & 0.00000E+00 \\ \cline{2-6}  & GOA & 2.44249E\(-\)15 & 1.44755E\(-\)15 & 3.99680E\(-\)15 & 2.22045E\(-\)16 \\ \cline{2-6}  & MFO & 4.00002E+03 & 5.47722E+03 & 1.00000E+04 & 5.82894E\(-\)03 \\ \cline{2-6}  & PSO & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & CSA & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & JAYA & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & EJAYA & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 4 Build Fault Database  
According to the data provided by a power company, the cable with a voltage level of 110 kV was selected for simulation by PSCAD software. The schematic diagram of the power system is shown in Figure 9, the length of the main section of the cable is 1500 m, the length of each minor section is 500 m, and the cross-sectional area of the cable core is 800 mm\({}^{2}\).  
The specific cable structure parameters are shown in Table 3. The three-phase cable is laid horizontally, as shown in Figure 10, with a direct burial depth of 1 m and a distance of 0.35 m between the two phases.  
Figure 9: Schematic diagram of a power system with a cross-connected main section.  
Simulation of different fault types by changing the fault time, grounding resistance, cable laying spacing, and cable minor section length were carried out to construct a database of 14 feature vectors containing 18 grounding system operating states, as shown in Table 4. In Table 4, the first column represents the 18 fault categories and first row represents the 14 feature vectors which are \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), \(a_{5}\), \(a_{6}\), \(a_{7}\), \(b_{1}\), \(b_{2}\), \(b_{3}\), \(b_{4}\), \(b_{5}\), \(b_{6}\), \(b_{7}\). The database contains a total of 35,910 groups of data, among which, 3780 groups of normal operation state and 1890 groups of each of 17 types of faults.
# Total Internal Reflection Lens for Optical Wireless Receivers
## 3 Measurements  
### Optical Setup  
Figure 4 shows a sketch of the measurement setup. A LED is placed at a distance of \(Z_{\mathrm{o}}\) = 600 mm in front of the receiver. The LED features a center wavelength of 850 nm and an optical output power of 260 mW. The optical receiver is rotated around the y-axis by an angle \(\theta_{\mathrm{i}}\). To remove the residual influence of the LED profile, we measure the photocurrent \(I_{\mathrm{PD}}\) over \(\theta_{\mathrm{i}}\) with and without the TIR lens and define \(g_{\mathrm{o}}\) as the ratio of both results. For the \(I_{\mathrm{PD}}\) measurement, we measure the voltage drop over a resistor in series to the APD with the multimeter _Keysight UI272A_. We temporally deactivate the APD bias to avoid corruption by the APD's avalanche effect (M = 1). The optical power is derived from \(I_{\mathrm{PD}}\) by using the PD responsivity \(R\) = 0.5 A/W. The optical power with lens divided by center power without the lens is defined as the optical concentration ratio \(g_{\mathrm{o}}\). The small angular error resulting from the distance between the lens surface and the axis of rotation is taken into account during the measurements.