esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Issues Facing CVs and Solutions
### Wi-Fi  
As another type of short range communication, Wi-Fi satisfies some types of V2X communications with the advantages of low cost and the ease of deployment [42]. In [43], Wi-Fi and WiMax provide viable solutions for V2V and V2I communications. Some researchers even suggested using Wi-Fi directly to facilitate the relaying of information from one vehicle to another [44; 45; 46]. The built-in Wi-Fi module or Wi-Fi enabled mobile device in a vehicle allows the vehicle to approach the internet when it is moving into the coverage of Wi-Fi hotspots, which is referred as the Drive-Through Internet. With the increasing deployment of the urban-scale WLAN (i.e., Google Wi-Fi in the city of Mountain View), the Drive-Through Internet would rapidly increase, which could provide a complementary solution to V2X with low cost. In actual design, some technologies are applied to enhance Wi-Fi capabilities to fit the high-speed and secure requirements of vehicular communications, such as Multiple-Input Multiple-Output (MIMO). Recent advances in Passpoint/Hotspot 2.0 provide Wi-Fi with some secure connectivity [47], which makes Wi-Fi more competitive in V2X communications.  
Figure 3: The wireless communications of CVs.  
Even so, Wi-Fi was not originally designed for a high-mobility environment; the stringent V2X service requirements impose some challenges on the reliability, robustness, and security of Wi-Fi-based V2X connectivity [48; 49]. Table 3 lists the applications and potential security issues of Wi-Fi. In terms of security, high vehicle mobility yields a very short connect time to the AP, while Wi-Fi communication usually requires a long establishing procedure. Time spent in Wi-Fi association, authentication, and IP configuration before actual data transmission cannot be negligible. For instance, if cryptography (i.e., Wi-Fi Protected Access, WPA) is applied in Wi-Fi communication, it will generate a considerable delay, up to 250 ms, which is fatal to the safety-related application in VANET. In addition, Wi-Fi is typically set as the \"discoverable\" mode for users' search and connection as Bluetooth, thus, it requires a large bandwidth to guarantee the high transmission, which provides opportunities for attackers to launch attacks such as cracking, DoS, and karma attack [50]. Especially, with security-enhanced WAP2 being cracked recently [28], it must raise more attention and research efforts to enable Wi-Fi in connected vehicles' communication.
# Trust Management for Artificial Intelligence: A Standardization Perspective
## 4 AI Trust Management Framework
### Trust Target and Management Elements by a Layer of Artificial Intelligence System
#### 4.1.1 Data Layer  
Operators that provide services based on AI systems install, operate, and manage data sources such as IoT sensors and web services, collect data directly or purchase necessary data from data sellers through data exchanges.  
Figure 1: Trust managed objects in the AI hierarchy.  
It is impossible to recover data through preprocessing using a Data Pre-Producer if there are many errors or omissions in the data acquired through collection and purchase. When low-quality data is provided to an AI model, it in turn leads to erroneous reasoning by the AI model, while intentionally biased data may be supplied, and in this case, the AI model is biasedly trained, which may also lead to erroneous results.  
Therefore, quality management of collected and purchased data is a key prerequisite for the normal operation of the AI model, and analysis and management of trust targets in three aspects are required.  
First, in the case of self-collection by installing a data source such as an IoT sensor, the data source should be regarded as the object of trust and trust properties and indicators should be defined and managed. For example, errors or missing data may be supplied due to the aging or hacking of IoT sensor nodes; therefore, it is necessary to continuously monitor and manage the quality of data generated by each data source.  
Second, if third party data are purchased through data exchange, the third party data seller can be viewed by the purchaser as a major trust target. At this time, trust analysis and management of the data seller is required in terms of purchasing data since the third party's data collection and management is under the jurisdiction of the data seller.  
Third, data quality degradation due to errors occurring in the process of transmitting data from the data source or seller to the data collector should be considered. In particular, there is a high probability of errors occurring in the process of wireless data transmission in ships and factories divided by steel walls, etc., and trust in the data transmission network must be analyzed and managed.
## 3 Experiment and Results
### Results and Discussion  
By using the actual bending stress combined with the phase lag stress, the true dynamic bending moments during every full shaft revolution were taken in both horizontal and vertical direction then illustrated as in Figure 7. The displacement movement measured by the laser sensor, described in Figure 7c, confirms the accuracy of the establishing method. Accordingly, in the vertical direction, the waveforms of the bending moment and displacement are similar to each other.  
Figure 7: Vibration behavior during a full revolution during NCR straight going.  
According to the tendency of nominal values, the bending moment of the propeller shaft had moved downward counterclockwise at the beginning (Stage (1) to Stage (3)) and then returned in the same trajectory at the end (Stage (3) to Stage (4)) of the port turn, as described in Figure 11a. In the event of a starboard turn, the bending moment had moved in the opposite direction, upward clockwise at the beginning, and then retuned at the end, as in Figure 12a. This phenomenon was due to the influence of the propeller's eccentric thrust on the propeller shaft. The propeller worked like a seesaw: when this end moved up, the other end moved down [25]. The eccentric thrust in the case of the port turn is opposite to that of the static load of the propeller's weight. This results in the reduction in load on the after-stern tube bearing (ASTB) [39]. The opposite effect during the starboard turn causes the ASTB to go in and out of stable condition.  
Figure 11: Trajectory plot of bending moment during rapid port turn. (**a**) Nominal bending moment; (**b**) True dynamic bending moment (orbit plot).  
Figure 10: Displacement in vertical direction during rapid port turn.  
The shaft behavior can be evaluated in more detail during a revolution by the true dynamic bending moment, which shows the dynamic movement of the shaft. As depicted in Figures 11b and 12b, the moment orbit in Stage (3) is larger than the others for both tests. This means the propeller force fluctuation was greater than the previous stage. The phenomenon occurred more clearly in the case of the starboard turn. This results in a non-uniform oil film in the bearings. This instability can be referred to as an oil whirl or even an oil whip regarding the intensity of the turbulence [40; 41; 42; 43]. An oil whirl occurs due to the external fluctuating forces such as those generated by the propeller blades. It is transmitted to the bearings, which then creates the imbalance in the hydraulic forces on the oil film. When the oil whirl becomes intense, it becomes an oil whip, which possibly leads to bearing wear and premature failure. In the case ship in this study, the fluctuation behavior was not significant. However, the early detection of signs of bearing instability is essential for the safe operation of bearings.
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
## 2 Material and Mixtures  
A white cement, used as the primary binder by [PERSON] et al. from 2012 to 2014 [17], is to be considered for the elasticity analysis in the present study. The batches of white cement were provided by Aalborg Portland. The mineral composition of this cement, as well as the cumulative Particle Size Distribution (PSD), are reproduced in Table 1 and Figure 1. The PSD can be fairly defined as a Rosin-Rammler function, i.e., \(f(D)=1-e^{-bDn}\), where \(D\) is the diameter of the cement particle, and \(b\) and \(n\) are coefficients. By employing a curve fitting analysis, the values of 0.062 and 1.00 were calculated for \(b\) and \(n\), respectively. The fitted curve is also plotted in Figure 1.  
Elastic properties of the individual components of cement clinker (e.g., Alite, Belite, etc.) can be experimentally measured by means of a nano-indentation testing technique at the microscopic scale, as reported by [PERSON] et al. [18], or they can be calculated by force field atomistic methods, as recommended by [PERSON] et al. [19]. Young's moduli of the major phases of Portland cement (i.e., C2S, C3S, C2A, and C4 AF), reported by these authors, are provided in Table 2. An average value of these individual moduli is considered for the numerical simulations in the present study. The final elastic properties of the cement clinker are averaged by the weighting factors of the major phases as given in Table 1.  
For preparing the cement paste, typically 80 g of anhydrous powder was mixed with water at the desired \(w/c\) mass ratios (\(w/c\) = 0.32, 0.40 and 0.48). The mix was performed with the aid of plastic containers, using a paste mixer for 2 min at 1600 rpm [17]. Samples were then cured in a controlled temperature environment (at T = 20 \({}^{\circ}\)C) in containers and used for parallel experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cement Clinker Component** & **C3S** & **C2S** & **C3A** & **C4 AF** & **Others** & \\ \hline Fraction (\%) & 66.89 & 20.59 & 3.62 & 1.03 & 7.87 & \\ Density (g/cm\({}^{3}\)) & 3.21 & 3.28 & 3.03 & 2.32 & \(\sim\)3.0 & Avg. 3.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral composition of the white cement [12].  
Figure 1: Particle Size Distribution (PSD) of the white cement and the fitted analytical curve.
## 3 Results
### Microscopic and Macroscopic Droplet Measurements
#### 3.2.2 Effects of Ethanol Admixture  
The effects of ethanol admixture were studied at a constant dye concentration (100 mg/L mile red) at reference conditions (293 K, 0.1 MPa) in terms of the LIF/Mie ratio. The fitting curves for the LIF signals as a function of the droplet diameter are displayed in Figure 6.  
The microscopic LIF, Mie, and LIF/Mie ratio of E20 at 293 K can be described by the following equations:  
\[I_{\text{LIF}}\_{\text{E20}}\_{293K_{(micro)}}\ =\ f\big{(}d_{ droplet}\big{)}\ =\ 54.97\cdot d_{ droplet}^{3.34} \tag{5}\]  
\[I_{Mie}\_{\text{E20}}\_{293K_{(micro)}}\ =\ f\big{(}d_{ droplet}\big{)}\ =\ 9355\cdot d_{ droplet}^{2.02} \tag{6}\]  
\[I_{\text{LIF}}/\text{Mie\_E20}\_{293K_{(micro)}}\ =\ f\big{(}d_{ droplet}\big{)}\ =\ 0.0087\cdot d_{ droplet}^{1.14} \tag{7}\]  
The microscopic fitting curves showed a similar trend for both investigated fuels. The ethanol admixture led to an increase in the microscopic LIF signal exponent from \(\sim\)3.03 to \(\sim\)3.34 and the Mie exponent from \(\sim\)1.87 to \(\sim\)2.02 in comparison to the base fuel Toliso. The resulting microscopic LIF/Mie-ratio exponents were not dependent on the ethanol admixture and had the same value of 1.14.  
Figure 5: Fitting curve of the experimental data plotted for the LIF/Mie ratio as a function of the droplet diameter for Toliso with their corresponding standard deviations at the reference conditions (293 K, mile red 100 mg/L).  
According to [PERSON] [6], an exponent greater than three for the LIF signal and an exponent greater than two for the Mie signal are mainly attributed to MDR emissions (i.e., \"cavity mode effects\") in the droplets. This can be confirmed by Figure 7a showing the bin-wise (\(d_{\text{droplet}}\pm 0.25\)\(\upmu\)m) averaged LIF droplet signals for a droplet size ranging from 25 \(\upmu\)m to 50 \(\upmu\)m. There, E20 exhibited stronger MDR effects which were characterized by ring-shaped structures at the outer part of the droplet.  
Figure 6: Fitting curve of the experimental data plotted for the LIF/Mie ratio as a function of the droplet diameter for Toliso and E20 at the reference conditions (293 K, nile red 100 mg/L).  
Figure 7: **(a)** Bin-wise averaged LIF signals (\(d_{\text{droplet}}\pm 0.25\)\(\upmu\)m) of micro droplets in diameter in a range of 25 \(\upmu\)m to 50 \(\upmu\)m at reference conditions (293 K, nile red 100 mg/L). The intensity was normalized to the respective maximum signal. **(b)** Single -hot fluorescence images for 30 \(\upmu\)m and 45 \(\upmu\)m droplets (E20) exhibiting MDR.  
These structures are more visible in the single-shot images in Figure 7b where two droplet size classes (30 \(\upmu\)m, 45 \(\upmu\)m) are displayed. These MDRs can occur in the form of complete rings or fractions of it, sometimes only a few bright spots are apparent inside the droplets.
## 4 Simulation Results and Discussion  
The performance of the RC configuration is evaluated by analyzing various irradiance distribution patterns. The computational results are compared to the TCT, an SP based on the PV array's output power. A 6 \(\times\) 6 PV array model is developed using the MATLAB/SIMULINK (R2020a) platform. The characteristic curve, mismatch loss power, and fill factor of the PV array are analyzed to validate the RC's advanced configuration. Furthermore, simulations are conducted under real flight conditions, comparing the cumulative output energy to the TCT and SP configurations.
## 1 Introduction  
With the rapid increase in the number of multimedia-capable and internet-connected devices, there is an increasing need for further development of wireless technology in order to meet these demands. The most common means of data consumption are watching high-definition (HD) videos and accessing cloud-based services. Most of the data consumption has been observed to occur in indoor environments, where users consume data using their smart devices at their residence, in shopping malls, in aircrafts, and in other vehicles. This high demand of data usage is expected to continue to grow in the foreseeable future. With highly-congested radio frequency (RF) bands, visible light communication (VLC) is considered to be a supplementary technology that can be adopted to bridge the gap between user demand and capacity.  
VLC uses light emitting diodes (LEDs) to accomplish illumination and data communication. VLC has shown potential to be an integral part of the upcoming 5G network. While the market has continued to push the limits of the network data rate and capacity offered to users, the wireless communication industry has found it difficult to meet these demands. It is estimated that for 5G networks, there will be a thousand-fold increase in data traffic [1]. In order to meet these demands, 5G networks will have to rely on more efficient technology. It is highly likely that it will incorporate smaller cells (atto-cells), utilize additional spectrum resources, employ techniques to ensure communication is more energy efficient, and have a provision for heterogeneous network (Het-Net) integration [2].  
In comparison to traditional RF wireless communication, VLC has many favorable characteristics [3]; for example, it provides a spectrum of visible light for free, it can be securely used for indoor transmission as light cannot penetrate solid objects, it can be deployed wherever LEDs are installed [4], and it usually has a high signal-to-noise ratio (SNR) due to its high illumination requirements.  
VLC can provide a high bandwidth density, which can help in bridging the gap between the demand and supply of high bandwidth that currently affects RF-based networks. Considering these favorable characteristics, VLC is currently best suited to assist Het-Nets in 5G networks, making it a promising supplementary technology for 5G systems that can be deployed as an affordable commercial product for smart homes in the near future. However, it has various new challenges that will spark interesting new research in the future [5; 6].
## 5 Conclusions  
Central pull-out tests were carried out to investigate the bond behavior of high-strength rebar and SFRC at high temperatures in this study. The conclusions based on the experiment are as follows.  
1. A steel fiber reinforcement factor (\(\beta_{V}\)) and temperature reduction factor (\(\beta_{T}\)) were introduced to analyze the effect of the fraction of steel fiber by volume (\(V_{f}\)) and temperature on bond strength. The effect of \(V_{f}\) on the improvement in bond strength is more obvious between 400 and 600 \({}^{\circ}\)C. The bond strength between rebar and SFRC decays significantly between 400 and 600 \({}^{\circ}\)C.
2. The interaction between the steel fibers and the aggregate creates a rigid skeleton for bridging cracks. The bridging action retards the expansion of concrete cracks. This improves the bond strength between rebar and SFRC.
3. The bond-slip curve between rebar and SFRC can be divided into five stages, namely the initial micro-slide phase, slip phase, splitting failure phase, stress drop phase and residual pull-out phase. At the splitting failure phase, the rising trend of curves becomes flatter with the increase in \(V_{f}\) before the peak point and the bond-slip curve near the peak point flattens out with the increase in temperature at the same \(V_{f}\). At the residual pull-out phase, the bond-slip curves of SFRC specimens appear as longer horizontal segments. This indicates that residual bond stress is kept at high temperatures.
4. In NC specimens, splitting failure occurred below 400 \({}^{\circ}\)C and split-pullout failure occurred above 600 \({}^{\circ}\)C. In comparison, split-pullout failure occurred in all SFRC specimens at each temperature. The crack width after the failure was smaller for the SFRC specimen than for the NC specimen.
5. A model of the bond-slip relationship between rebar and SFRC considering the temperature and the fraction of steel fiber by volume was proposed by improving the existing model of the bond-slip relationship between rebar and concrete. The results show that the bond-slip relationship model established in this paper is in good agreement with the test results.  
Conceptualization, X.L.; methodology, X.L.; software, C.L.; validation, X.L., C.L. and L.Z. ([PERSON]); formal analysis, C.L.; investigation, L.Z. ([PERSON]); resources, X.L. and L.Z. ([PERSON]); data curation, L.Z. ([PERSON]) and C.L.; writing - original draft, C.L.; writing - review & editing, X.L., Y.C. and C.L.; supervision, X.L. and C.L.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China [grant number 52108223, 51922052, 51778309].  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 6 Result
### Stepwise Multivariate Linear Regression
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{3}{c}{**Subjects 1 and 2**} & \multicolumn{3}{c}{**Subject 1 Only**} & \multicolumn{3}{c}{**Subject 2 Only**} \\ \cline{2-7}  & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **DBP** \\ \hline (dppgH) & X & X & \(\surd\) & \(\surd\) & \(\surd\) & X & \(\surd\) \\ \hline (dppgW) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X & \(\surd\) \\ \hline (PATd) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X & X \\ \hline (ddppgPH) & X & X & X & X & X & X \\ \hline (ddppgFH) & \(\surd\) & \(\surd\) & X & \(\surd\) & X & X \\ \hline (ST) & \(\surd\) & \(\surd\) & X & X & X & X \\ \hline (DT) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline (PIR) & X & \(\surd\) & \(\surd\) & \(\surd\) & X & \(\surd\) \\ \hline (HR) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X & X \\ \hline (PATp) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X \\ \hline (PATf) & X & X & X & X & \(\surd\) & X & X \\ \hline (ASI) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline (AUC1) & \(\surd\) & \(\surd\) & \(\surd\) & X & \(\surd\) & X \\ \hline (AUC2) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X \\ \hline (AUC3) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X & X \\ \hline (AUC4) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X & \(\surd\) \\ \hline (IPA) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & X \\ \hline (AI) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline (ddppgW) & \(\surd\) & X & \(\surd\) & \(\surd\) & X & \(\surd\) \\ \hline (PPCk) & X & X & \(\surd\) & X & X & \(\surd\) \\ \hline (ddppgH) & X & X & \(\surd\) & X & \(\surd\) & X \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rejected or not rejected feature during the stepwise linear regression with subsets. ’X’ means rejected or discarded and ‘\(\surd\)’ means ’not rejected’. We consistently observed that feature ddppgPH was removed in all training sets, indicating a significant likelihood of its model coefficient being zero. This suggests that this feature may not exhibit as strong a correlation with both SBP and DBP as the other features. Additionally, the parameter PAT\({}_{f}\) is consistently excluded from the predictor vector across all training sets, except when estimating DBP with subject 1's dataset. This further suggests that this particular feature may not be as strongly correlated with blood pressure as the other features. Based on our sample data, the features that appear to exhibit the highest correlation with blood pressure values are those that were retained without removal when the model was applied to each training set. These features include DT, ASI, and AI, respectively.