esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HPLC-UV Analysis  
The HPLC system was equipped with a pump, online vacuum degasser, auto-sampler, Peltier column oven, UV-Vis detector (Perkin Elmer Series 200, Waltham, MA, USA) and autosampler (Perkin Elmer series 220). The chromatographic data was analyzed using a Perkin Elmer Total Chrome workstation (version 6.3.1.). HPLC was equipped with Luna C18:2 column (5 \(\upmu\)m, 100 A, 250 mm \(\times\) 4.6 mm) (Phenomenex(r), Castel Maggiore, Bologna, Italy). Solvent A (H\({}_{2}\)O + 0.1% H\({}_{3}\)PO\({}_{4}\)) and solvent B (CH\({}_{3}\)OH + 0.1% H\({}_{3}\)PO\({}_{4}\)) were used for the mobile phase. Chromatographic conditions were set as follows: the flow rate was 1.5 mL/ min in isocratic, the column temperature was set at 30 \({}^{\circ}\)C, the injection volume was 10 \(\upmu\)L. so the analysis was performed using eluent A 5% and eluent B 95% for 15 min. Chromatograms were acquired at 314 nm. Three injections from three independent extractions were performed for each sample. For the quantification of \(\beta\) and \(\alpha\) acids, a calibration curve was obtained from dilution ofs ICE-3 standard, according to the official method (Analytica-EBC, method 7.7) [32].
# Finite Element Analysis of the Distribution Parameters of a Metal Dot Array in a SAW Gyroscope
## 3 Modeling and Simulation
### Simulation Results and Discussion
#### 3.2.1 The Influence of the Size of the Metal Dot Array on the SAW Gyroscope Effect  
When the model size of the piezoelectric substrate is fixed, the influence on the SAW gyroscopic effect is calculated by adjusting the distribution parameters of the metal dot on the upper surface of the piezoelectric substrate. Taking \(\omega\) to denote the angular frequency of the SAW, which is defined as \(\omega=2\pi f_{0}\), and taking a parameter \(a\) to represent the normalized angular velocity, that is, \(a=\Omega/\omega\), then the angular velocity of the model can be expressed as \(\Omega=2\pi f_{0}\times a\)[45]. In simulation, \(a\) is used as one of the parameters of the parametric sweep to control the angular velocity of the substrate.  
Firstly, the factor \(g\) that represents the SAW gyroscopic effect of three piezoelectric substrates is analyzed, respectively, when \(a=1\times 10^{-3}\), and the simulation results shown in Figure 6 demonstrate the variation rule of the factor \(g\) with the size of the metal dot 'width'. It is clear from Figure 6 that the value of the 'width' affects the intensity of the SAW gyroscopic effect significantly. When metal dots of different sizes are distributed on each piezoelectric substrate, the intensity of the SAW gyroscopic effect exhibited is very different from the results in the figure. It can be concluded that distributing a metal dot array of suitable size on the piezoelectric substrate can effectively improve the intensity of the SAW gyroscopic effect. The simulation results also show that, for three different piezoelectric substrates, the optimum sizes of the metal dot that can make the SAW gyroscopic effect the strongest are: \(3/16\lambda\) for ST-X quartz, \(5/16\lambda\) for \(128^{\circ}\)YX-LiNbO\({}_{3}\) and \(1/16\lambda\) for X112\({}^{\circ}\)Y-LiTaO\({}_{3}\). Besides, ST-X quartz with metal dots of \(3/16\lambda\) has the strongest SAW gyroscopic  
Figure 5: Displacement distribution of the SAW: (**a**) symmetric mode; (**b**) anti-symmetric model.  
effect, followed by 128\({}^{\circ}\)YX-LiNbO\({}_{3}\) with metal dots of 5/16\(\lambda\), and X112\({}^{\circ}\)Y-LiTaO\({}_{3}\) with metal dots of 1/16\(\lambda\) is the weakest.
## 3 Results
### Demonstration on a Case Study
#### 3.2.4 Comparison of the Calibrated Models  
It is useful to make a comparison among all the models introduced to get insights on their accuracy in predicting the response of the oscillator. For the sake of simplicity, from now _Model 1_, _Model 2_, _Model 3_ and _Model 4_ will refer to the models with the degrading law of the original BWBN model, the exponential law, the Weibull and the logistic distribution functions, respectively. It is also worth noticing that, in order to reduce the number of parameters to infer, only the initial stiffness \(k_{i}\) and the degrading term have been involved in the calibration process. This can be easily justified by the fact that, for practical earthquake engineering applications, the focus relies on the quantification of the structure's stiffness and its drop (especially for masonry structures which are prone to crack during a seismic event). Prior information on the possible value of the model parameters was obtained by visual inspection of the dissipated energy versus time curve. For both parametric and non-parametric models, the velocity response of the system obtained was almost identical to Figure 11 for a low-level of degradation. Figure 12 depicts the posterior distributions of the initial stiffness parameter \(k_{i}\) for each model considered. From this inspection, it can be seen that all models were able to predict the correct value of the initial stiffness. The choice of the MAP (or of the mean of the distributions) as a point estimate after the uncertainty propagation led to a slightly underrated initial stiffness regardless of the model adopted. However, this is neglectable from an engineering point of view. Moreover, Figure 12 depicts the distributions of the discrepancy variance term \(\sigma^{2}\) as well. All the models share the same order of magnitude for \(\sigma^{2}\) and, more or less, the same variance. This can be read as follows: the adopted Gaussian discrepancy term, with mean null and unknown variance, performs as a good function to embody all the model errors arising. Thus, at the end, it appears clear how the choice ofthe model used to infer the stiffness of the oscillator is not meaningful in the presence of a low-level of damage.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Experimental  
#### 5.1.1 Reagents and Instruments  
The chemicals required for the experiment include the following: graphite (325 mesh, QingdaoTengshengda, Qingdao, China), sodium nitrate (Aladdin, Los Angeles, CA, USA), 98% concentrated sulfuric acid (Tianjin Damao, Tianjin, Chian), potassium permanganate (Tianjin Damao), 30% hydrogen peroxide (Tianjin Damao), sodium hydroxide (Tianjin Damao) and hydrochloric acid (Tianjin Damao), all of which were commercially available analytically pure, and the experimental water was deionized water.  
The analytical Instrument required for the experiment was an atomic absorption spectrometer (ContraA700, Jena, Thuringia, Germany), which was used to determine the concentration of Co\({}^{2+}\) in the solution before and after adsorption; the equipment required for material preparation included an ultrasonic constant-temperature oscillation box, a constant-temperature oscillation box, a blast-drying box, a freeze dryer and a precision pH meter.
## 4 Results and Analysis
### Node-Based Analysis  
#### 4.1.1 Packaging for the Outdoor Deployment  
In order to resist the extreme outdoor weather, the packaging of each sensor node is necessary. A waterproof box with an Acrylonitrile-Butadiene-Styrene (ABS) material, which can reach the waterproof level of IPX65 was selected. Several hoops in the back of the box are used for erect pole installation with two metal hoops, as shown in Figure 5a. A sealing strip inside the box achieves further waterproofing, as shown in Figure 5b. The battery and power controller are packaged in the back of the solar panel, as shown in Figure 5c. The different parts were all connected and placed inside the box and then connected with the solar panel, as shown in Figure 5d. The packaging of a sensor node is shown in Figure 5e. Such packaging makes it more effective in water tightness and solarization with convenient installation in the outdoor environment.  
Figure 5: The structure of a sensor node. (**a**) External appearance of the waterproof box; (**b**) interior view of the waterproof box; (**c**) the solar panel with a battery and power controller inside; (**d**) the connection of different parts inside the box; (**e**) a whole sensor node.
## 4 Results
### Developed Potentials on the Grounding System  
Figure 10 illustrates the total resistance of each grounding grid as a function of the soil resistivity (\(\rho\) = 15-1000 \(\Omega\)m). According to the figure, we can safely conclude that there exists a linear relation between the total resistance of the grounding grids and the soil resistivity in the case of uniform soil resistivity; however, this is not the case in the horizontal two-layer soil structure. Figure 8: Study of the grounding grids flow chart. Additionally, this study evaluates the developed touch voltages across the grounding grids by comparing them with the safety voltage thresholds as shown in Figure 11. Notably, the investigation focuses on scenarios involving SC incidences at the middle of the MV line (L = 10 km). It is crucial to underscore the importance of the absence of hazardous developed potentials within the center of the grounding grids, particularly in proximity to the metallic control panel of the protection device. Figure 10: Grounding grid total resistance. Figure 9: Single-line-to-ground fault current distribution. As depicted in Figure 11, the developed touch voltage exceeds the safety threshold for all three grounding grids across the entire range of soil resistivities exceeding approximately 15 \(\Omega\)m. However, upon the introduction of a surface layer characterized by high resistivity (\(\rho=2.500\)\(\Omega\)m with at least 15 cm thickness), the grounding grids demonstrate improved protective capabilities, extending up to approximately 100 \(\Omega\)m. Furthermore, a surface layer with an exceptionally high value of soil resistivity guarantees sufficient protection against developed touch voltages across the entire spectrum of soil structures for all three grounding grids. The following equations describe the trendlines modeling the developed touch voltages across the three variations of the grounding grid. \[\text{GRID A}:\ V_{t}=700\cdot\ln\left(\rho\right)-1300 \tag{5}\]  
\[\text{GRID B}:\ V_{t}=711\cdot\ln\left(\rho\right)-1546 \tag{6}\]  
\[\text{GRID C}:\ V_{t}=694\cdot\ln\left(\rho\right)-1540 \tag{7}\]  
In Figure 12, the distribution of step and touch voltages across the surface of GRID A is depicted under several variations regarding the top layer surface. These variations are pertinent to an SC event occurring at the middle of the MV line, assuming a uniform soil structure with soil resistivity of 300 \(\Omega\)m. This value is considered as an average worst-case scenario, which is subject to seasonal variations and the depth at which the grounding grid is placed beneath the Earth's surface. It is essential to highlight that regions represented by color gradients denote areas deemed unsafe, wherein the developed voltage surpasses the safety thresholds, while colorless regions signify safety compliance. Moreover, in Figure 13, the distribution of step and touch voltages for GRID B and GRID C is depicted, incorporating an insulating surface layer composed of asphalt (\(\rho=10.000\)\(\Omega\)m). Figure 11: Developed touch voltage and safety thresholds (0.15 s fault clearing time).
## 2 Materials and Methods
### Stability and Metal Ion Release Test  
To test stability of **Cu(AZPY)-MOF**, thermogravimetric analysis (TGA) was performed on a TG 209 F3 Tarsus Instrument under a nitrogen atmosphere. 11.76 mg of the sample was used. The gas flow rate was 20 mL/min and the heating/cooling rate was \(-2^{\circ}\)/min. To confirm the stability of **Cu(AZPY)-MOF**, MOF degradation tests were performed after 1 day in H\({}_{2}\)O and PBS by PXRD. PXRD patterns were recorded on a Rigaku Miniflex diffractometer. The X-ray source is Cu K\(\alpha\) source, the speed rate was \(2^{\circ}\)/min, the step size was 0.02\({}^{\circ}\) and 2\(\theta\) range was \(5^{\circ}\leq 2\theta\leq 50^{\circ}\).  
H\({}_{2}\)O and PBS solutions of MOF were prepared after 1 day and 4 days to measure the amounts of CuII ion released from **Cu(AZPY)-MOF** by ICP-MS The solutions were in concentration of 1 mg\(\cdot\)mL\({}^{-1}\) in H\({}_{2}\)O and PBS.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 4 Discussion and Conclusions
### Galvanic Skin Response (GSR) Analysis  
The galvanic skin response (GSR) measurements with E4 revealed statistically significant increased conductance between demanding driving and baseline phases. However, there was a high standard deviation and standard error in the collected GSR signals during the driving phases, which can imply that the E4 may not have had skin contact during the whole recordings, probably due to the hand movements while steering.
# Applications of Cold Atmospheric Pressure Plasma in Density
## 6 CAPP Application in Oral Oncology  
The incidence and prevalence of oral cancer have been continuously increasing in numbers, as well as the mortality rates, especially among younger patients [106]. Oral cancer stands as an international public health problem and one of most common cancer with more than 177,000 deaths and 354,500 new cases year worldwide [107].  
Recent advances and future directions have been proposed on oral oncology [108, 109]. In addition to surgical approaches, the stereotactic body radiotherapy associated with smart drug delivery systems (SDDSs) have been proposed to oral cancer therapy [109, 110, 111]. The immunotherapy also advanced considerably in the last years [111, 113, 114, 115]. However, those new discoveries involve expensive treatments and ultra-expensive drugs [116, 117, 118]. For this reason, the search for alternative is needed.  
The potential of CAPP in oral monotherapy is based on its selectivity towards malignant cells, capacity to induce cell death, immune response, and controlled discharge of RONS that can interfere on the molecular mechanisms of the disease [51, 119, 120]. The role of oxidation/reduction potential is already understood as key factor for the progression and establishment of the disease based on the HOCI or the \(\cdot\)NO/ONOO\({}^{-}\) signalling pathway [119, 121]. In this way, NO and nitrite therapies have already been applied as anti-cancer agent based on their effects on cancer cells and on catalase-dependent apoptotic pathways, which are implied in development and regression of the disease [51, 121, 122].  
CAPP treatment as a tool to control oral cancer cells has been studied in the last years. [PERSON] et al. [123] reported that N\({}_{2}\)-CAPP induced DNA damage in SCC-25 oral cancer cells. The effect on head and neck squamous cell carcinoma (HNSCC) was also detected [124]. CAPP can also be an alternative for the treatment of oral lichen planus, a precancerous lesion [125]. Interestingly, cancer cells such as SCC-15 and HNSCC were more sensitive to CAPP when compared to non-cancer cells lines [124, 126].  
Clinical researches showed that CAPP can reduce the microbial load in the lesions and the pain, as well as partial remission on head and neck cancer patients [127, 128]. The proposal to use CAPP for oral cancer treatment is recent and the understanding of in situ effects requires more studies as a promising pro-oxidant therapy [120, 121].
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 4 Specific Absorption Rate and Link Budget Analysis
### Link Budget Analysis  
A link budget analysis [33; 34] was conducted to estimate the approximate performance of an implantable wireless communication system equipped with the proposed Vivaldi antenna. We assumed a point-to-point wireless communication system whose transmitting antenna was the proposed Vivaldi implanted in the brain and the receiving antenna was a broadband testing antenna [35] situated outside of the head. Table 4 summarizes the parameters for the link budget analysis. The link margin (LM) is a power margin at the receiver, allowing a satisfying wireless communication quality. More specifically:  
\[LM(dB)=P_{\mathrm{r}}-P_{\mathrm{r}(min)} \tag{1}\]  
where \(P_{\mathrm{r}}\) corresponds to the received power and \(P_{\mathrm{r}(min)}\) denotes the minimum required power for the receiver. \(LM\) typically spans from 3 to 20 dB. We set it to 20 dB, the most demanding requirement, to reflect the harsh wireless communication environment. The assigned transmit power (\(P_{\mathrm{t}}\)) was \(-25\) dBm, which is a typical output power of a transmitter for implantable devices [31]. The realized gain of the transmitting (Tx) antenna (i.e., implanted antenna) was given by \(-16.67\) dB based on the measurement in the data presented in Section 3. Furthermore, the receiving (Rx) antenna's realized gain was set to 6.65 dB, which corresponds to the antenna gain at 4 GHz of a broadband tapered slot antenna [35]. The required signal-to-noise ratio (SNR) per bit, or the energy per bit to noise power spectral density ratio (\(E_{\mathrm{b}}/N_{0}\)), was set to 9.6 dB by assuming an ideal phase-shift keying (PSK) performance. The bit rate was set to 256 Mbps, which is reasonably high for brain-signal monitoring [34]. Finally, the path loss (\(L_{0}\)) was calculated for the free-space attenuation. In the equation, \(\lambda_{\mathrm{4 GHz}}\) denotes the free-space wavelength of 4 GHz (i.e., 75 mm) and \(D\) denotes the distance between the Tx and Rx. In fact, \(D\) is the parameter of interest for this link budget analysis. According to the [PERSON] transmission formula [33]:  
\[P_{\mathrm{r}}(dB)=P_{\mathrm{t}}+RG_{\mathrm{t}}+RG_{\mathrm{r}}-L_{0} \tag{2}\]  
\[P_{\mathrm{r}(min)}(dB)=\frac{E_{\mathrm{b}}}{N_{0}}+KT_{0}+B \tag{3}\]  
Plugging (2) and (3) into (1) and then applying the parameters from Table 4 provided \(D\) = 108.1 mm, i.e., the transmitted brain signal can be reliably received with \(LM\) of 20 dB at the receiver located at 108.1 mm above the head. We note that this distance was reduced to 80.4 mm if the conventional Vivaldi with a realized gain of \(-19.24\) dB was used instead of using the proposed Vivaldi.  
Figure 16: Measured SAR distribution: (**a**) 2.45 GHz and (**b**) 5.8 GHz. Red boxes indicate the hot spot.
## 3 Results and Discussions  
### Effect of Time on \(Twl\)  
The \(TWL\) was studied to understand the effect of time on erosion-corrosion. The experiment was conducted at a jet velocity of 20 m/s and sand loading of 600 mg/L. It was found that the \(TWL\) increased linearly with time for an impingement period of four hours as shown in Figure 4 and Table 1. This is consistent with the results obtained by [PERSON] and [PERSON] [32] for API-X65 under CO\({}_{2}\) saturated conditions. It can be seen from Figure 4 that after a 4-h test, a large amount (71 mg) of material was removed from the surface. Therefore, the time for conducting the experiments to investigate the velocity and sand loading effects was set to 4 h.  
The SEM image in Figure 5 shows the center of the specimen with a diameter \(<\)1 mm from the center before impingement, after 1-h impingement and after 4-h impingement. The image reveals plastic deformation after impingement tests on the surface along with the formation of pits and ploughing tracks. Figure 5b shows discrete cracks and isolated pits after one-hour impingement. These cracks and pits on the surface of the specimen are evidence of material loss as a result of impingement. Figure 5c shows thick elongated  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time (hours)** & **TWL (mg)** & **Error (mg)** \\ \hline
1 & 22.2 & \(\pm\)0.46 \\
2 & 38.8 & \(\pm\)0.75 \\
3 & 55.8 & \(\pm\)0.79 \\
4 & 71 & \(\pm\)1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(TWL\) with respect to time at 20 m/s and 600 mg/L.  
Figure 4: Change of \(TWL\) with respect to time at 20 m/s and 600 mg/L.  
tracks and large holes caused after a 4-h impingement. It is believed that the formation of these large holes is due to the drilling action of impinging slurry at the surface [41].  
Figure 6 shows an SEM image of the specimen at a diameter of 2 mm away from the center after 4 h of impingement. The material loss in this region, where the impingement angle is less than 90\({}^{\circ}\), is significant. According to the observations of [PERSON] et al. [42], higher material losses occur at lower impact angles. This is due to the formation and cutting of platelets by particles at lower impact angles. The SEM image (Figure 7) of the specimen at a diameter of 3 mm away from the center shows elongated pits and discrete cutting tracks aligned in the flow direction from the center. It is evident from Figures 6 and 7 that the material damage at an area of 3 mm from the center was less than the damage at 2 mm away from the center. This is because the area 3 mm away from the center was less impacted by the solid particles compared with the area which was 2 mm away from the center, though the particle impinges at a lower (<90\({}^{\circ}\)) angle.  
Figure 5: Surface of the specimen central zone: (**a**) parent metal, after impingement test at 20 m/s and 600 mg/L; (**b**) after 1 h; and (**c**) after 4 h.
## 2 Methodology
### Experimental Setup and Measurements
#### 2.2.3 Test Procedure  
In this experiment, the rotation speed of the engine was set to 750 rpm at idle speed as the ECU setting. The engine loads were 0 Nm, which is the no-load condition, and 40 Nm to simulate using an external load device on the actual vehicle. The auxiliary electrical losses, idle losses and parasitic losses on the vehicle were about 11% [36]. An uneven air-fuel ratio formed, PM formed in the rich region, and NO\({}_{\rm x}\) is generated in the stoichiometric air-fuel ratio region because the diesel engine directly injects fuel into the combustion chamber at the end of the compression stroke. Many methods are used to reduce such PM and NO\({}_{\rm x}\). Applying a pilot injection is a good way to reduce NO\({}_{\rm x}\) without increasing PM [37]. Almost all the engines used in the current vehicle employ a pilot injection strategy. This test engine also employed pilot injection. The main injection timing was fixed at BTDC 2 \({}^{\circ}\)CA and pilot injection timings are BTDC 15 \({}^{\circ}\)CA and 20 \({}^{\circ}\)CA. The injection timings and durations by engine loads and pilot separations are shown in Figure 2. In addition, the exhaust gas recirculation (EGR) rate was set to zero to ensure stability of combustion in the idle state and to eliminate factors that could affect the combustion of palm oil biodiesel. The injection pressure was fixed at 280 bar. The experimental conditions are summarized in Table 3.  
Figure 1: Schematic diagram of the experimental setups.  
For each experimental fuel condition, combustion pressure and exhaust measurement were started when the coolant temperature was 85 \(\pm\) 5 degrees and the engine speed was 750 \(\pm\) 10 rpm with sufficient engine warm-up time and stabilization time. The combustion pressure is calculated as the average of 200 cycles. The sampling tube of the exhaust measuring instrument and the soot measuring instrument was directly inserted into the exhaust pipe. The analysis equipment for emission and PM which inhale the exhaust gas can lower the exhaust pressure from the engine can affect the engine power under low exhaust flow and pressure conditions, such as idling. Thus, combustion pressure, exhaust and soot measurements did not run at the same time. In addition, the emitted soot was directly collected from the exhaust pipe on the copper grid to measure the size and shape of the soot. The collected soot grids were evaporated in a 60-degree vacuum chamber for 12 h and images were taken at 100,000 times using TEM and at 250,000 times using SEM equipment.