esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation and Validation  
### Finite-Element Model of Box-Girder Segment  
A solid model of the box girder was constructed using SolidWorks and subsequently imported into HyperMesh for mesh generation. In the model, the concrete and polyurea were modeled using Solid164 solid elements, while the reinforcement was modeled using BEAM188 elements. The concrete material was defined using the \"MAT_CONCRETE_DAMAGE_REL3 keyword in LS-DYNA, the reinforcement using the \"MAT_PLAISTIC_KINEMATIC keyword, and the polyurea using the \"MAT_PIECEWISE_LINEAR_PLAISTICITY keyword. The coupling of degrees of freedom between concrete and reinforcement was achieved using the CONSTRAINED_BEAM_IN_SOLID keyword in LS-DYNA. Shared  
Figure 14: Measured rebar strain of specimen PCGR.  
Figure 15: Test results of vertical acceleration of the baseplate of chamber 2.  
nodes were applied between the concrete and polyurea without considering interface debonding. As an example, the finite-element model for specimen PCG is illustrated in Figure 16. Explosion loads were applied using the \"LOAD_BLAST_ENHANCED (LBE method) and \"LOAD_SEGMENT_SET keywords in LS-DYNA. The explosion center was positioned 0.4 m directly above the center of the second box chamber, with a TNT equivalent of 3 kg, corresponding to a scaled distance of 0.31 m/kg\(\cdot\)1/3. Based on observations from the blast test results of specimen G, no sliding or separation occurred between the support and the baseplate of the box girder. In the numerical simulation, the nodes between the baseplate of the box girder and the support were connected with shared nodes, and the bottom of the support was set as fully constrained. Considering both mesh accuracy and computational efficiency, the mesh size for the concrete and reinforcement was set to 30 mm [30].
# A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
## 4 Comparison of Selected Web Assessment Tools
### Comparative Analysis of Proprietary Tools
NetSparker detected 25, and Acunetix detected 23 vulnerabilities. AppScan detected 21, and Nessus detected 15 vulnerabilities. * _Identification and Authentication Failures:_ HP WebInspect scanner detected more identification and authentication failures vulnerabilities with a 180 score. Acunetix stood second with 165 scores. NetSparker detected 140 vulnerabilities, AppScan detected 130 vulnerabilities, and Nessus detected 100 vulnerabilities. * _Software and Data Integrity Failures:_ Acunetix scanner had detected more vulnerabilities of this type compared to other listed proprietary scanners. Acunetix detected 133 software and data integrity failure vulnerabilities. NetSparker detected 115, and AppScan detected 95 vulnerabilities. HP WebInspect detected 52, and Nessus detected 54 vulnerabilities. Therefore, the detection results of this vulnerability were good in Acunetix and NetSparker. * _Security Logging and Monitoring Failures:_ HP WebInspect and Nessus detected 15 vulnerabilities, Acunetix detected 12, NetSparker detected 11, and AppScan detected ten vulnerabilities. * _Server-Side Request Forgery:_ HP WebInspect detected 95 server-side request forgery vulnerabilities. Acunetix stood second with a 65 score. NetSparker detected 55, and Nessus detected 52 vulnerabilities. AppScan detected 45 vulnerabilities.
###### Abstract  
Microcrystalline silicon, which is widely used in the microelectronics industry, is usually fabricated by chemical vapor deposition techniques. In recent years, magnetron sputtering has been considered as an alternative because it is a simpler, cheaper and more eco-friendly technique. The big drawback of this technique, however, is the need to recrystallize the as-deposited amorphous silicon, which can be done by metal-induced crystallization. Among the different suitable metals, copper has not been extensively investigated for this purpose. Furthermore, the applicability of the microcrystalline film prepared by this method has not been evaluated for photovoltaic device fabrication. Therefore, this paper reports the fabrication of p-type microcrystalline silicon thin film by magnetron sputtering and copper-induced crystallization techniques, and evaluates its appropriateness for solar cell fabrication. In the first step, 60 nm of silicon followed by 10 nm of copper were deposited on n-type silicon wafer and glass substrates, both by the magnetron sputtering technique. Then, the as-deposited samples were annealed at temperatures from 450 \({}^{\circ}\)C to 950 \({}^{\circ}\)C. The crystal properties of the resulting films were characterized by Raman and X-ray diffraction spectroscopies and optical and secondary emission microscopies, while their electrical characteristics were determined by Hall-effect, J-V curve and external quantum efficiency measurements. These characterizations confirmed the formation of a layer of microcrystalline silicon mostly in the \(<\)111\(>\) direction with a crystallization ratio of 93% and a largest grain size of 20 nm. The hole concentration and mobility of the fabricated p-type microcrystalline silicon layer were about 10\({}^{17}\)-10\({}^{19}\) cm\({}^{-3}\) and 8 cm\({}^{2}\)/Vs, respectively. By using the fabricated film as the emitter layer of a p-n junction solar cell, a good rectification ratio of 4100 and reverse saturation current density of 85 nA.cm\({}^{-2}\) were measured under dark conditions. The highest photovoltaic conversion efficiency, i.e., 2.6%, with an open-circuit voltage of 440 mV and short-circuit current density of 16.7 mA/cm\({}^{2}\), were measured under AM1.5G irradiance. These results indicate that microcrystalline silicon created by magnetron sputtering and copper-induced crystallization has considerable potential for photovoltaic device fabrication.  
microcrystalline silicon thin film; magnetron sputtering; copper-induced crystallization; photovoltaic 2020, 10, 6320; doi:10.3390/app10186320
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 5 Conclusions  
To answer the security challenges in wireless sensor networks, the TNODE design, a sensor node microcontroller that combines a general-purpose processor with hardware support for both public-key and shared-key cryptography, was integrated on a single chip. The implemented cryptographic techniques provide a high level of security at a minimum cost of additional silicon area and increased static power dissipation. The TNODE5 chip was fabricated in IHP's 250 nm BiCMOS technology and successfully tested. The measurements confirmed the outstanding performance of the TNODE design in comparison to the existing solutions based on software implementation of cryptographic standards. Future improvements of the proposed solution should include the design of more complex clock control logic and the implementation of power gating. Also, more robust encryption standards such as AES-256 and SHA-3 are to be implemented. Furthermore, the flip chip technology can be used to minimize the physical size of the sensor node microcontroller to increase its robustness against malicious tampering. Finally, the ECC is to be enhanced with the features preventing advanced side channel attacks.  
The research leading to these results has received funding from the European Commission's Horizon 2020 under grant agreement from project myAirCoach--No. 643607.  
[PERSON] and [PERSON] defined the system architecture and testing strategy. [PERSON] contributed defining security relevant system parameters. [PERSON] designed, implemented and tested the system hardware (system-on-chip). [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] equally contributed to preparation of the paper's final version.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Results and Analysis of Surveys  
#### 4.1.1 Interaction Cues in HRI  
Most participants recruited for the first survey thought that voice was the most important interaction cue, and the other three items were face, arm action, and sound in the rankings, as shown in Figure 3(a). The data shown in Figure 3(a) were verified as random using run tests, where M = 2.36 and \(p\) = 0.752.  
Figure 4: (**a**) The data of concerned interaction cues. (**b**) The score of different interaction cues considering the level of importance.  
Figure 3: (**a**) The flow of participants in experiments. (**b**) Example scenario for HRI with interaction errors.  
The participants were also asked to rank the corresponding level of importance with a 5-point scale. The scores of unimportance and great importance were set as 0 and 4, respectively. As shown in Figure 4b and the run tests, conversation is the most important interaction cue, with an average score of 3.29 (\(p\) = 0.881), and face, arm action, and sound have average scores of 1.94 (\(p\) = 0.880), 1.95 (\(p\) = 0.508), and 1.64 (\(p\) = 0.571), respectively. This indicates that conversation and sound are the interaction cues of the most and the least concern, and face and arm action are almost the same in view of the degree of importance for users. In addition, the correlations between the importance levels of four interaction cues were analyzed by Spearman correlation coefficients. The results show that only the \(p\)-value between conversation and sound is smaller than 0.05 and statistically significant. The Spearman correlation coefficient between conversation and sound is \(-\)0.155. This reveals there are no correlations between the four interaction cues, except that conversation has a negative correlation with sound.
## 3 Results
### Evoked Activity
#### 3.2.2 The Impact of Regional Silencing on PCI  
By taking advantage of the systematic and extensive investigation of stimulation/s silencing pairs, we further explored the variability of the impact of local silencing on both spontaneous and evoked metrics and investigated the contribution of individual region activity (i.e., MFR) to PCI across all conditions. Thus, for each stimulation, we performed a linear regression of the PCI values (i.e., mean across resamples) versus the spontaneous mean firing rate of a node/region of interest (MFR\({}_{\text{ROI}}\)) across all conditions (including the control condition and all local manipulations). Figure 4a shows the linear regressions for two representative ROIs (right posterior cingulate cortex; rCCp and right parahippocampal cortex; rPHC), corresponding to the ROIs showing the third highest and third lowest average correlations across stimuli, respectively. For each of the two ROIs, we show two representative stimulations delivered to the inferior parietal cortex (PCi; R\({}^{2}\) = 0.59, \(p\) = 5 \(\times\) 10\({}^{-5}\) and R\({}^{2}\) = 0.02, \(p\) = 0.58) and the dorsolateral prefrontal cortex (PFCdl; R\({}^{2}\) = 0.23, \(p\) = 0.03 and R\({}^{2}\) = 0.01, \(p\) = 0.60). We found that PCI correlated well with MFR\({}_{\text{ROI}}\) (CCp) for most stimulations (F-statistic of R\({}^{2}\), 11 with \(p\) < 0.05, one with \(p\) = 0.051), ranging from 0.19 to 0.59 (0.41 \(\pm\) 0.14) (Figure 4b). On the other hand, MFR\({}_{\text{ROI}}\) (PHC) was always non-significantly correlated with PCI across the 12 stimulations (\(p\) > 0.05).  
Figure 4: Cont.  
The 12 regional maps of R\({}^{2}\) coefficients for each stimulation are presented topographically in Figure S5A, and the average topography across all stimulations is presented in Figure 4c. Interestingly, the three ROIs with the highest average R\({}^{2}\) across stimulations (i.e., CCr, <R\({}^{2}\)> = 0.51 \(\pm\) 0.13; PCm, <R\({}^{2}\)> = 0.45 \(\pm\) 0.14; CCp, <R\({}^{2}\)> = 0.41 \(\pm\) 0.14) were located in the posterior cortex of the manipulated hemisphere. In addition, the ROIs with the three highest R\({}^{2}\) values across all stimulations were predominantly located in the posterior regions (~89%, Figure S5B,C).  
Consistently, we repeated the same analysis for the single stimulation on Hconn and found a similar regional specificity, with the highest R\({}^{2}\) values located in posterior regions of the manipulated hemisphere (Figure S4D).
## 3 Results
In this spectral range, the human eye's luminous efficacy exceeds 0.001 of its maximum value, and therefore, this spectral range is recognized as the most significant in photometric applications. In Figure 4, SPDs are compared in pairs with only one or two differences between their components. For example, SPDs in Figure 3(a) differ only in terms of using colored LEDs at wavelengths 640 and 650 nm. This does not mean that part of the spectrum is subtracted but that the SPD was optimized for the difference between these two components. As both spectra were optimized for those components, the operation points of these two and other LEDs are different, and differences occur in the shape of the optimized SPD. In Figure 3(d)-f, an additional influence of the change in the CCT of the white-light-emitting diode is presented. Figure 3: Spectral power distributions normalized to illuminant A for 1000 lm LSTLSs, version no. 1–7 (**a**) and 9–21 (**b**), within the 430–690 nm spectral range. Figure 2: Spectral power distributions of 1000 lm LSTLSs version no. 1–7 without white-light-emitting diode (**a**) and 9–21 with white-light-emitting diode (**b**); SPDs of versions 1–7 normalized to the SPD of illuminant A for 1000 lm (**c**) and versions 9–21 (**d**); numbers according to Table 4.  
Using 43 LEDs (including broadband 640 and 650 nm) causes a slight deviation from the reference SPD within the 575-675 nm range (\(R^{2}\) = 0.99985), while utilizing narrowband 640 and 650 nm LEDs (\(R^{2}\) = 0.99994) mitigates this deviation, occurring only between 575 and 625 nm (curves 5 and 6 in Figures 2c and 3a). Employing 21 LEDs (including narrowband LED 640 nm) results in a deviation from the reference distribution within the ranges of 380-430 nm and 530-780 nm (\(R^{2}\) = 0.99342). Instead, employing broadband  
Figure 4: Spectral power distributions normalized to illuminant A for 1000 lm LSTLSs in groups differing with one component (**a**–**c**) and the additional effect of the presence of white LED (**d**–**f**). LED 640 nm (\(R^{2}\) = 0.99707) reduces the deviation within the spectral range of 530-675 nm (Figure 4c), which holds significant importance for photometric applications. The analysis of the version no. 9-20 indicates that utilizing 82 LEDs in the source allows reproducing the SPD of illuminant A with \(R^{2}\)\(\geq\) 0.99998 (Figure 2b). However, using 43 LEDs (including broadband 640 and 650 nm) causes a deviation from the reference distribution within the range of 575-675 nm (\(R^{2}\) = 0.9997), whereas employing narrowband 640 and 650 nm (\(R^{2}\)\(\geq\) 0.99997) reduces this deviation, which is in contrast to an LSTLS lacking white LEDs. Employing 21 LEDs (including broadband LED 640 nm) results in a significant deviation from the reference distribution within the ranges of 380-430 nm and 530-780 nm (\(R^{2}\) = 0.995). On the other hand, using narrowband LED 640 nm (\(R^{2}\)\(\geq\) 0.997) reduces the deviation from the target distribution within the relevant spectral range of 475-600 nm, which is contrary to an LSTLS lacking white LEDs.
## 2 Results
### Inferential Statistics
#### 2.2.5 Semantic Differential about Participation and Privacy  
Very interesting results emerged from the semantic differential analysis regarding the nationality and gender as well as their combined effect. Table 9 reports only the significant effects, that nevertheless appear to be present in both cases (i.e., semantic differential related to privacy and participation separately) for 8 dimensions out of the original 10. Some dimensions are characterized by moderate effects, with an explained variance ranging between 2.6% and 9.6%. It is worth noting some dimensions appear as strongly affected by the nationality factor. For instance the concept of participation appears to elicit impressive differences, with \"bad-good\" accounting for 61.2% of the total variance, \"dirty-clean\" for 50.2%, and \"useless-useful\" for 46.5%. Even the privacy concept appears differently represented by the two cultural samples, with \"personal-social\" dimension explaining 45.8%, \"useless-useful\" 45.2%, \"dirty-clean\" 44.1%, and \"bad-good\" 40.4%. In particular, as reported in Figures 1 and 2, the Italian sample seems to perceive the participation concept as more \"clean\", \"good\" and \"useful\" compared to the Turkish sample. The same effects are reported for the privacy concept with the only exception of the dimension \"personal-social\", in which the Turkish sample perceives \"privacy\" as more social than the Italians.  
\begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{3}{l}{**Participation Confidence**} \\
**Factor** & **F** & _Sign_ & \(\mathbf{\eta^{2}}\) \\ \hline Nationality & 20.7 & \(p\) \textless{} 0.01 & 0.105 (10.5 \%) \\ Gender & 0.1 & ns & 0.001 (0.1\%) \\ Nat\({}^{\text{C}}\)Gen & 1.8 & ns & 0.010 (1\%) \\ \hline \multicolumn{3}{l}{**Participation Concerns**} \\
**Factor** & **F** & _Sign_ & \(\mathbf{\eta^{2}}\) \\ \hline Nationality & 20.1 & \(p\) \textless{} 0.01 & 0.102 (10.2 \%) \\ Gender & 0.1 & ns & 0.001 (0.1\%) \\ Nat\({}^{\text{C}}\)Gen & 0.6 & ns & 0.009 (0.9\%) \\ \hline \end{tabular}
\end{table}
Table 7: In table the best ANOVA model for the variables connected to the participation dimension (i.e., confidence & concerns) are presented, considering the factors gender and nationality.  
\begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{3}{l}{**Privacy Confidence**} \\
**Factor** & **F** & _Sign_ & \(\mathbf{\eta^{2}}\) \\ \hline Nationality & 22.6 & \(p\) \textless{} 0.01 & 0.114 (11.4 \%) \\ Gender & 15.8 & \(p\) \textless{} 0.05 & 0.083 (8.3\%) \\ Nat\({}^{\text{C}}\)Gen & 1.3 & ns & 0.007 (0.7\%) \\ \hline \multicolumn{3}{l}{**Privacy Concerns**} \\
**Factor** & **F** & _Sign_ & \(\mathbf{\eta^{2}}\) \\ \hline Nationality & 28.9 & \(p\) \textless{} 0.01 & 0.141 (14.1 \%) \\ Gender & 3.7 & \(p\) \textless{} 0.05 & 0.021 (2.1\%) \\ Nat\({}^{\text{C}}\)Gen & 0.1 & ns & 0.001 (0.1\%) \\ \hline \end{tabular}
\end{table}
Table 8: In table the best ANOVA model for the variables connected to the privacy dimension (i.e., confidence & concerns) are presented, considering the factors gender and nationality.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 4 Results and Discussions
### Modeling the Fog Computing Network
#### 4.1.5 Workload  
The workload includes the processing tasks and data transfers needed in the network. Once the information has been gathered, it can be used to create a model of the fog computing network using the simulation tool. The model should accurately represent the network, including the devices, nodes, and communication links, and should be able to accurately simulate the processing tasks and data transfers in the network. This is critical to the simulation results' accuracy and the ability to optimize the network based on the simulation results. Figure 3 shows the keyword match percentage in the proposed keyword-based scheme for fog computing. The horizontal axis shows the keyword index, and the vertical axis shows the match percentage. Each bar represents the match percentage of a specific keyword in the search query. The bar height represents the match percentage ranging from 0 to 100%. The figure shows that some keywords have a higher match percentage than others, indicating that they are more relevant to the search query. Figure 4 shows the keyword match and trust scores in the proposed keyword-based scheme for fog computing. The horizontal axis shows the keyword index, and the vertical axis shows the match or trust score. Each bar represents the score for a specific keyword in the search query. The height of the bar represents the score, which ranges from 0 to 1. The figure shows that some keywords have a higher match or trust score than others, indicating that they are more relevant to or trustworthy for the search query. The match and trust scores can be used to rank the search results and provide users with more accurate and reliable information.  
Figure 3: Keyword match percentage.  
Figure 4 demonstrates the relationship between keywords and trust scores in the proposed keyword-based trust management system. It shows the match percentage and trust scores of different keywords used in the search query. The figure helps us understand how trust scores are determined based on matching keywords.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 8 Discussion
### Integrity  
Integrity ensures that medical data is exchanged across legitimate users in an unaltered state. To prevent tampering with the data, health records gathered through mobile platforms are often encoded, that is, IKGSR is utilized. Furthermore, mobile phone users remain unable to edit signed transactions with smart contracts, and no parties are allowed to manipulate the meaning of transaction records. Note that, in our model, smartphone users may not be able to amend or modify smart contractual agreements or access controls.
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## 3 Proposed Dimming Compatible Hybrid O-OFDM
### Pulse Width Modulation  
Due to the non-linear transfer characteristic of LED, the transmitted VLC signal has to be confined in the limited linear range. We employ the notation \(I_{l}\) and \(I_{h}\) to represent the lower and upper bound of the limited linear range, respectively. Then, the dimming level \(\eta\) can be defined as:  
\[\eta=\frac{I_{ave}-I_{l}}{I_{h}-I_{l}}, \tag{5}\]  
where we have \(I_{ave}\) to present the average amplitude of signal. Upon observing Equation (5), different dimming levels can be attained by altering the value of \(I_{ave}\). Although directly adding a bias can change the value of \(I_{ave}\), it leads to an undesired wavelength shift of the emitted lights. Therefore, the PWM signal is invoked in our proposed DCHO-OFDM scheme, which can be expressed as:  
\[d_{PWM}(t)=\left\{\begin{array}{ll}I_{h},&0<t\leq T_{ON}\ (\text{ON state})\\ I_{l},&T_{ON}<t\leq T\ (\text{OFF state})\end{array}\right. \tag{6}\]  
where \(T\) represents the period of PWM signal and \(T_{ON}\) denotes the time duration when the signal is in the ON state. The dimming level can be changed upon adjusting the duty cycle of PWM, which is referred to as \(\rho=T_{ON}/T\).
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environments with Single High Frequency Acoustic Waves
#### 5.2.6 Non-Line of Sight  
We set and test a simple case on the effect by non-line of sight (NLOS). We let a person stand between the phone and acoustic source as a blocker (\(L=10\) m, \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\)), and then we measure the standard deviation of the angular errors related to the different distances (1 m, 3 m, 5 m, 7 m, 9 m) from the blocker and the phone. In Table 2, we learn that when the person stands very close to the loudspeaker or the smartphone, the standard deviation slightly increases. We can say that the blocker between the acoustic source and the smartphone has very little impact on our experiment. In practice, it is very common to see a person passing by the line between our anchor points and the smartphone. Our system is still reliable in this circumstance. In addition, it is verified in the experiment with noisy environments.  
Another case of NLOS is that the user turns his back on the source. We already explained why VULA can not detect the acoustic source behind the users. Our method of rotating the phone around the user's body gets rid of this problem perfectly.