esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Systems Under Investigation and Experimental Setups
### Free Vibration Testing
#### 4.1.2 RC Accelerometer  
Figure 8 depicts the results from the impact tests using the accelerometer attached to the RC. Once again, the expectation is that the Y-direction has the highest response in the first mode among all BARC systems under consideration. This can be inferred by looking at the mode shapes from FEA in Table 2 and looking at the movement of the RC accelerometer. Looking at the test results, this is shown to be true. By referencing mode 2 in Table 2, it can be observed that the X-direction should have the highest responses, followed by the Z-direction, in almost all systems. Looking at the experimental testing results, the FEA results are seen to be true. Both the second and third modes have the highest responses in the X-direction among all systems. Table 2 shows that mode 4 has movement primarily in the X-direction when the central cut exists. The results shown here confirm that to be true, since the responses are highest for the fourth mode in the X-direction. Furthermore, mode switching can also be seen here when comparing the no-cut system to the cut systems. The peaks near 450 Hz of the cut systems (likely mode 6) are dominated primarily by the Y- and Z-responses, while the mode near 660 Hz (likely mode 4) of the no-cut system shows peaks very close to one another in all directions, with the Y- and Z-responses being more dominant.  
Figure 8: Impact testing results from the RC accelerometer: (**a**) 0.5\({}^{\prime\prime}\) cut, (**b**) 0.25\({}^{\prime\prime}\) cut, (**c**) 0.1\({}^{\prime\prime}\) cut, and (**d**) no-cut BARC systems.  
By comparing impact testing to the FEA, the results show great agreement with what was observed in the FEA simulations. For brevity, the top accelerometer is not explored since the results from those tests are relatively similar to what has already been discussed. The RC and side accelerometers will continue to be deeply investigated for the remainder of this study unless otherwise stated.
## 2 Materials and Methods
### Assessment of the Induced Power at a Pair of Wires along DBS Routings  
To evaluate the validity of the proposed numerical method in realistic clinical applications, we considered a set of two generic leads along the routings of a bilateral deep brain stimulation (DBS) implant, as were designed inside a human model (Duke) from the Virtual Population (ViP) [41]. Initially, the model was placed inside a 16-rung birdcage coil from the Birdcage Library (BCLib, IT'IS Foundation, Zurich, Switzerland) with a bore size and coil length of 60 cm and 50 cm, respectively. The resonating frequency of the coil was set to 64 MHz and clockwise polarization was assumed. The human model was positioned inside the coil, such that the center of the skull coincided with the isocenter of the coil, as illustrated in Figure 4a. The model was treated as homogenous, setting the electrical properties of the tissues as HPM.  
The generic leads were modeled as in Section 2.1 and the routings of the bilateral DBS implant were designed based on [42]. Specifically, their tips were connected to the left and right subthalamic nucleus of the head. Then, they were oriented through the brain and between the skull and the skin to the neck, as illustrated in Figure 4b. The induced power at the tips of the pair was assessed by the full-wave simulations and it was compared to the respective power calculated with the proposed method. The model was chosen  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Geometrical Properties** & **Material Properties** \\ \hline Lead Length [mm] & 300 & Conductivity of Electrodes/Wires/Ground Pin [S/m] & \(1.28\times 10^{6}\) \\ \hline Electrode Length [mm] & 3 & Dielectric Permittivity of Insulation & 3 \\ \hline  & & Dielectric Permittivity of Tissue & 78 \\ \cline{2-3} Outer/Inner Diameter of Insulation [mm] & \(1.35/0.9\) & Conductivity of Tissue [S/m] & 0.47 \\ \cline{2-3} Outer/Inner Diameter of Electrodes [mm] & \(1.35/1.15\) & \\ \hline Distance between Electrodes [mm] & 4 & \\ \hline Diameter of Air Tube [mm] & 0.45 & \\ \hline Diameter of the Wires & 0.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The properties of the construction and the materials of the realistic implant.  
Figure 3: The placement of the two realistic implants (**a**) and the cross-section at the proximal end with the layout of the wires (**b**).  
to be homogeneous, meeting the anatomical characteristics of the human body to enable the design of clinically accurate DBS paths and simultaneously to allow the comparison between the results of the modified Tier 3 methodology and the full-wave simulation. If the human model was inhomogeneous, a comparison between Tier 3 and Tier 4 procedures would be performed, which differ from each other [43]. Therefore, the estimated power deposition was overestimated compared to what would arise from the corresponding in vivo case.
# An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures
## 5 Discussion
### Influence of the Features Used in Each Model Selected
#### 5.2.3 Influence of GSR Features  
The variables obtained from GSR while the subject was in the no risk areas were relevant in the final models, while they were not relevant when the subject was in risk areas. All the GSR variables selected in the final models correspond to metrics of the phasic component of the signal, which is characterized by rapid and event-related changes, so it takes less time to show changes [99]. The GSR signal usually peaks between 2 and 10 s after stimulation and recovers at approximately the same rate [99]. [PERSON] et al. [100] found that a 3-s time window in the phasic signal is the most optimal for the prediction of valence and arousal. Since the periods of time in which subjects usually remain in risk areas are short (between 1 and 8 s) except in rooms, where they can spend more time, it is possible that changes in the phasic signal, which can be interpreted as a \"warn\" in risky situations [1], are reflected few seconds after the subject has left the risk areas.  
Another possible interpretation of these results is that changes in the phasic component are meaningful to differentiate subjects with high/low extraversion, boredom susceptibility and disinhibition based on their level of activation in the no risk zones. In this regard, decisions in no risk zones present less time pressure, and they are of the type: selection of paths or decision of whether to take spheres or not. Since time pressure has been raised as one of the influential factors in the relationship between GSR and RT [75], it is possible that, in these decisions in which there are no situational biases, decision-making is more guided by individuals' personality and temperamental factors than in risk zones.
###### Abstract  
Chlorinated organic compounds (COCs) are a serious threat to human health and the ecological environment due to their toxicity, mutagenicity and carcinogenicity. A number of corresponding treatment techniques have been developed thus far; among these techniques, adsorption is considered an efficient and low-cost method. However, it is very important to find suitable and economical adsorbent types and usage amounts. In this study, the adsorption of trichloroethylene (TCE) and perchloroethylene (PCE) onto two different adsorbents (silica gel and activated carbon) was investigated, and the obtained adsorption constants were then introduced into Langmuir and Freundlich adsorption models. The adsorption isotherm constants of these two models were used to predict the adsorbate removal efficiency and the required adsorbent mass. The results showed that the Langmuir and Freundlich models predicted the removal efficiency and adsorbent quality of the TCE and PCE adsorbed onto silica gel and activated carbon, and the errors were less than 10% compared with the measured values obtained through adsorption experiments. According to the differences in adsorption efficiency and cost between silica gel and activated carbon, it was found that, when the adsorption efficiency of the silica gel for TCE and PCE significantly decreased, adding activated carbon to continue the adsorption effectively reduced the adsorption cost of on-site remediation. This result was demonstrated by taking the Radio Corporation of America (RCA) pollution incident in Taiwan as an example, and it was estimated that 6427 \(\pm\) 172 tons and 343 \(\pm\) 21 tons of silica gel and activated carbon, respectively, would be required for the on-site remediation of TCE, and the total cost would be approximately 4,390,000 USD. The remediation of PCE would require 7553 \(\pm\) 57 tons of silica gel and 350 \(\pm\) 68 tons of activated carbon, with a total cost of approximately 5,030,000 USD. Therefore, silica gel should be used to reduce the pollutant concentration first, and then activated carbon should be added for continuing adsorption. This method of adding adsorbents is economical and can effectively reduce the adsorption cost.  
chiorinated organic compounds; silica gel; activated carbon; adsorption; [PERSON] and [PERSON] prediction
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
###### Abstract  
Thanks to their arising abilities to influence the human lifestyle, along with reducing the healthcare systems' cost, wireless body area networks (WBANs) still form a strongly growing research field. Recent advances focus on the opportunities of coexistence and communication between a group of WBANs, that will forward the sensing data, using persons as network relays, until reaching a remote analysis server or cloud servers via the Internet, forming thus a body-to-body network (BBN). Such new-style networks support a range of innovative and promising applications, including ubiquitous healthcare (U-health), interactive games, and military, to cite a few. In this paper, we first present the evolution of the single WBAN concept to the cooperative network of multiple WBANs, giving rise to the BBN concept. A synopsis of the WBAN and BBN respective standards and applications is given, and the emerging BBN challenges are highlighted. Then, we present and discuss the existing WBAN proposals, especially the candidate WBAN protocols that could be adapted and used in BBNs, focusing on four intrinsically related axes of great importance for BBN design: energy efficiency, _mobility prediction_, quality of service (QoS) and security. Further BBN open issues are also investigated, namely, the wireless propagation between humans carrying wearable devices, the interference, storage and privacy issues as well as the heterogeneity of BBN devices and traffic.  
W +
Footnote †: journal: Sensors, Actuator Networks  
18 March 2019; Accepted: 30 April 2019; Published: 10 May 2019
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
## 3 Results and Discussion
### Hourly Values
#### 3.2.6 Annual Sky Status
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Pentakomo**} \\ \hline \multicolumn{11}{c}{**G (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gold (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gpc (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gclr (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gclr (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gclr (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gclr (W/m\({}^{2}\))**} & \multicolumn{11}{c}{**Gclr (W/m\({}^{2}\))**} \\ \hline
**Month** & **Mean** & **StDev** & **Min** & **Max** & **Mean** & **StDev** & **Min** & **Max** & **Mean** & **StDev** & **Min** & **Max** & **Mean** & **StDev** & **Min** & **Max** \\ \hline
1 & 279 & 172.3 & 0 & 654 & 129 & 67.5 & 3 & 272 & 269 & 128.1 & 48 & 528 & 407 & 169.1 & 88 & 654 \\
2 & 366 & 221.1 & 0 & 816 & 118 & 69.0 & 0 & 300 & 265 & 141.4 & 69 & 600 & 513 & 169.0 & 115 & 816 \\
3 & 420 & 266.8 & 0 & 934 & 161 & 90.7 & 11 & 384 & 358 & 185.5 & 47 & 729 & 596 & 215.3 & 92 & 934 \\
4 & 495 & 292.9 & 22 & 1007 & 194 & 110.4 & 22 & 420 & 318 & 187.7 & 60 & 806 & 712 & 190.4 & 127 & 1007 \\
5 & 548 & 314.8 & 0 & 1012 & 164 & 96.1 & 56 & 364 & 291 & 180.8 & 64 & 822 & 744 & 206.9 & 86 & 1012 \\
6 & 563 & 311.8 & 30 & 991 & 250 & 123.6 & 36 & 415 & 266 & 158.0 & 44 & 822 & 777 & 171.4 & 156 & 991 \\
7 & 549 & 309.5 & 31 & 981 & 67 & * & 67 & 67 & 272 & 160.2 & 45 & 763 & 788 & 148.3 & 275 & 981 \\
8 & 506 & 301.6 & 16 & 971 & 161 & 86.1 & 62 & 331 & 310 & 164.1 & 64 & 695 & 761 & 144.2 & 392 & 971 \\
9 & 464 & 268.9 & 15 & 905 & 160 & 57.0 & 44 & 302 & 307 & 169.6 & 46 & 698 & 703 & 130.3 & 307 & 905 \\
10 & 383 & 228.7 & 2 & 808 & 139 & 63.9 & 2 & 303 & 290 & 153.1 & 50 & 659 & 563 & 185.3 & 86 & 808 \\
11 & 297 & 186.5 & 0 & 701 & 128 & 63.2 & 28 & 271 & 288 & 118.8 & 85 & 538 & 486 & 111.6 & 159 & 701 \\
12 & 248 & 163.7 & 0 & 582 & 114 & 65.9 & 0 & 257 & 253 & 116.7 & 58 & 479 & 421 & 114.0 & 106 & 582 \\ \hline
**Year** & 439 & 284.8 & 0 & 1012 & 141 & 82.8 & 0 & 420 & 291 & 159.1 & 44 & 822 & 664 & 210.0 & 86 & 1012 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Monthly statistics of global irradiance under all and different sky conditions for the four selected sites. The asterisk denotes a missing value.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**DOF**} & \multicolumn{2}{c}{**Sum of**} & \multicolumn{2}{c}{**Mean Square**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{\(R_{1}^{{}^{\prime}}\)} & B & 1 & 4.697 & 4.697 & 88.360 & 0.000 \\  & C & 1 & 0.258 & 0.258 & 4.860 & 0.042 \\  & Residuals & 17 & 0.904 & 0.053 & & \\  & \(R^{2}\) & & & 0.850 & & \\ \hline \multirow{4}{*}{\(R_{3}^{{}^{\prime}}\)} & A & 1 & 0.042 & 0.042 & 5.010 & 0.041 \\  & B & 1 & 0.270 & 0.270 & 31.990 & 0.000 \\  & C & 1 & 0.239 & 0.239 & 28.380 & 0.000 \\  & B \(\times\) B & 1 & 0.176 & 0.176 & 20.890 & 0.000 \\  & Residuals & 15 & 0.1265 & 0.008 & & \\  & \(R^{2}\) & & & 0.850 & & \\ \hline \multirow{4}{*}{\(R_{4}^{{}^{\prime}}\)} & B & 1 & 0.006 & 0.006 & 0.300 & 0.592 \\  & B \(\times\) B & 1 & 0.197 & 0.197 & 9.740 & 0.006 \\  & Residuals & 17 & 0.344 & 0.020 & & \\  & \(R^{2}\) & & & 0.370 & & \\ \hline \multirow{4}{*}{\(R_{5}^{{}^{\prime}}\)} & B & 1 & 0.137 & 0.137 & 96.240 & 0.000 \\  & C & 1 & 0.048 & 0.048 & 34.040 & 0.000 \\  & B \(\times\) B & 1 & 0.015 & 0.015 & 10.700 & 0.005 \\  & Residuals & 16 & 0.023 & 0.001 & & \\  & \(R^{2}\) & & & 0.90 & & \\ \hline \multirow{4}{*}{\(R_{5}^{{}^{\prime}}\)} & A & 1 & 0.018 & 0.018 & 50.220 & 0.000 \\  & B & 1 & 0.371 & 0.371 & 1011.950 & 0.000 \\  & C & 1 & 0.158 & 0.158 & 430.340 & 0.000 \\  & B \(\times\) B & 1 & 0.151 & 0.1519 & 411.140 & 0.000 \\  & C \(\times\) C & 1 & 0.003 & 0.003 & 8.700 & 0.011 \\  & B \(\times\) C & 1 & 0.003 & 0.003 & 9.070 & 0.010 \\  & Residuals & 13 & 0.005 & 0.000 & & \\  & \(R^{2}\) & & & 0.990 & & \\ \hline \multirow{4}{*}{\(R_{6}^{{}^{\prime}}\)} & A & 1 & 0.039 & 0.039 & 35.240 & 0.000 \\  & B & 1 & 0.558 & 0.558 & 499.670 & 0.000 \\  & C & 1 & 0.223 & 0.223 & 199.340 & 0.000 \\  & B \(\times\) B & 1 & 0.264 & 0.265 & 236.090 & 0.000 \\  & C \(\times\) C & 1 & 0.013 & 0.013 & 11.230 & 0.005 \\  & B \(\times\) C & 1 & 0.005 & 0.005 & 4.490 & 0.054 \\  & Residuals & 13 & 0.015 & 0.001 & & \\  & \(R^{2}\) & & & 0.990 & & \\ \hline \multirow{4}{*}{\(E(R)_{dist}\)} & B & 1 & 0.119 & 0.119 & 2.750 & 0.119 \\  & C & 1 & 0.096 & 0.096 & 2.220 & 0.159 \\  & B \(\times\) B & 1 & 4.128 & 4.128 & 95.410 & 0.000 \\  & C \(\times\) C & 1 & 0.168 & 0.168 & 3.880 & 0.069 \\  & B \(\times\) C & 1 & 0.449 & 0.449 & 10.380 &
## 2 Attenuation-Based Ultrasonic Scattering Measurement Method  
### Theoretical Attenuation  
The obstacle scattering theory built by [PERSON] [28] was applied for the theoretical ultrasonic scattering calculation. This theory is based on a motion equation, which describes the wave function in homogeneous solid materials. Particularly, the wave function can be obtained by solving the motion equation, and represented with the Bessel function. The total energy loss can be obtained by the integration of the scattered wave and represented as the item scattering cross section \(\gamma^{Sca}\). In this research, both the aggregates and the air voids are considered as spherical obstacles. Then, assuming the obstacles inside concrete do not interact with each other [12], the total theoretical attenuation can be depicted as Equation (1).  
\[\alpha_{f}=(1-\phi)\alpha_{a,f}+\frac{1}{2}\sum_{i=1}^{m1}n_{i1}\gamma_{i1,f}^ {Sca}+\frac{1}{2}\sum_{i=1}^{m2}n_{i2}\gamma_{i2,f}^{Sca} \tag{1}\]  
where \(\alpha_{f}\) is the total concrete sample attenuation coefficient (Nepper/m); \(\phi\) is the combined volume fraction of the air voids and aggregate (%); \(\alpha_{a,f}\) is the attenuation coefficient of the viscoelastic cement paste (Nepper/m); \(n_{i1}\) and \(n_{i2}\) are the numbers of a certain size aggregate and air void per volume respectively; \(\gamma_{i1,f}^{Sca}\) and \(\gamma_{i2,f}^{Sca}\) represent the corresponding scattering cross sections of the aggregate and air voids respectively.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 3 NDC Transmission and NRS Overview  
Due to the existence of a large number of IP infrastructures, the evolutionary deployment of ICN networks is a realistic consideration [33]. The proposed replica-selection algorithm is based on the ICN architecture compatible with IP infrastructure. In this section, we introduce the named data chunk (NDC) transmission process and name resolution system (NRS) of the model.
# Dairy Waste and Potential of Small-Scale Biogas Digester for Rural Energy in India
## 6 Future Perspective for Biogas Technology in India
The cost of biogas plant construction/production, operational cost of biogas plants, and cost/benefits of biogas plants is likely to change substantially from one country to the other and, particularly, there could be a huge difference in the cost of construction in developing countries versus developed countries due to the cost of labor and required materials. Existing reports suggest [77] that total production cost for a biogas plant (i.e., essential installations except land) is about 50-75 USD/m\({}^{3}\). Out of this, approximately 30-40% of the total cost is the digester. The running cost is equally important because operation of large-scale digester requires more human hours in supervision, disposal, gas distribution, administration, acquisition of parts, and feeding of the digesters. With the help of technology, automation can reduce the requirement of human hours, however, it will increase the production and installation costs. While calculating the capital costs, it is important to consider interest rate and lifetime of biogas plants (which is around 15-20 years) [77]. Studies available for biogas plants in California, USA suggest that the estimated total cost for dairy biogas to biomethane plants can vary from $8.12 USD-$11.82 USD/1000 ft3[78]. Life-cycle cost analysis of the operations of anaerobic digesters in Iowa, USA, suggest that the capital cost of the digester attached to a 2400 head of cattle operation is about $3.12 million USD. Internal rate of return was 4.56%, when cattle waste was co-digested with glycerin and corn husk that has 950 kW of electric generation capacity [79]. The operational cost was mainly due to the labor and maintenance, which is about 67% of the cost of the digester.  
In developing countries such as India, the high capital cost is the key barrier to adapt the biogas technology in rural areas [41]. The cost involves for the construction, labor, and equipment to complete the installation is high considering rural household incomes, which is low. Currently, the installation cost of family size biogas plants is approximately $348 USD, and government often provides $123-$200 USD subsidies (20-40% of the total installation cost) [80]. Without financial supports many households may not be able to adapt the biogas technologies in many developing counties. The large-scale adaptation of biogas technologies in both developing and developed countries is likely to be driven by subsidies and support from various government agencies under current situations.
## 2 Materials and Methods  
### Establishment of Cell Mechatronic Model  
In this study, a multiphysics model of a five-layered spherical cell was developed using COMSOL Multiphysics 5.6 software. To simplify the computational load in three dimensions, a two-dimensional axisymmetric calculation method was employed, taking advantage of the system's symmetry. The calculation domain was a rectangular region measuring 25 \(\upmu\)m \(\times\) 50 \(\upmu\)m, with the cell positioned at the center of the axis of symmetry, as depicted in Figure 1. The region outside the cell in the rectangular area represented the extracellular fluid, while the layers of the cell from the innermost to outermost were the nucleoplasm, nuclear membrane, cytoplasm, and cell membrane. A pulse source was connected to the upper boundary of the rectangle, while the lower boundary was grounded, generating nsPEFs within the calculation domain. The left and right boundaries were electrically insulated. In this study, simulation parameters commonly used in both domestic and international research [32; 33; 34] were selected, ensuring their significat  
Figure 1: Spherical single-cell multiphysics model. facilitate readers' understanding, all the parameters are provided in Table 1. In order to accurately simulate the force-electric response of cells under nsPEFs, the finite element model utilized the asymptotic electroporation model to calculate the electroporation of the cell membrane and nuclear membrane [32]. Moreover, the second-order Debye dispersion model was employed to describe the dielectric relaxation phenomenon of the biomembrane under high-frequency electric fields [35]. Additionally, considering the viscoelastic properties of the biomembrane, the Maxwell stress tensor method was utilized to calculate the stress experienced by the biomembrane in the presence of nsPEFs. The idea of the above modeling is as follows:  
When an external electric field acts on the membrane structure of a cell, the membrane structure is polarized. When the transmembrane voltage Vm on the cell membrane and nuclear membrane reaches a specific threshold (\(\sim\)1 V) [36], the screening effect of the biomembrane is \"pierced\", i.e., electroporation occurs. The occurrence of electroporation leads to an increase in the membrane's electrical conductivity, followed by a decrease in the transmembrane voltage. When electroporation occurs, the rate of pore formation can be expressed as  
\[\frac{dN}{dt}=ae^{\left(\frac{V_{0}\left(t\right)}{V_{cp}}\right)^{2}}\left(1- \frac{N}{N_{0}}e^{-q\left(\frac{V_{0}\left(t\right)}{V_{cp}}\right)^{2}}\right) \tag{1}\]  
where \(N\) is the pore density on the membrane, \(N_{0}\) is the equilibrium pore density in the absence of applied electric field, \(V_{cp}\) is the characteristic voltage of electroporation, \(\alpha\), \(q\) are constants. As the pore density increases, the membrane conductivity increases, and at this time, the transmembrane current density \(J(t)\)[34] is