esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 2 Literature Review
### Main Methods for Detecting Workload
#### 2.2.3 Physiological Measurement of Workload  
Physiological measurement records the physiological signals of subjects to evaluate their workload [37]. Due to its objectivity and capacity for immediate feedback, this method effectively supports the ongoing assessment of pilot workload. However, most physiological measurement devices, such as EEG, ECG, and GSR, are often equipped with components that may interfere with participants' performance. For instance, the patch-based dry electrodes, although they do not use gel, could restrict participants' movements or distract their attention because of their tight attachment to the skin [47, 48].  
In recent years, several low-interference devices have been developed. For instance, remote eye trackers can measure a subject's eye movements from a distance [19]. The advantage of this device is its non-intrusiveness and freedom. It can achieve high accuracy and vast tracking robustness in eye movement detection while allowing users to barely feel the presence of the device [49]. E-Textile is a kind of textile-based sensor, which can measure various parameters [50, 51]. Unlike sensors based on silicon or piezoelectric materials, e-Textile is similar to conventional wearable fabrics in both cost and texture. As a result, these sensors can be seamlessly incorporated into various textiles for unobtrusive and low-interference detection [52, 53]. Among them, textile-based pressure sensors in seat cushions can provide valuable information for the recognition of sitting postures, which, in turn, can be used to identify workload through body gesture recognition [54]. moreover, [PERSON] et al. [55] introduced a kind of in-ear EEG device that makes EEG measurement portable and comfortable. [PERSON] et al. [56] use an echo state network (ESN) to discriminate attention states on the basis of in-ear EEGs. [PERSON] et al. [57] used features collected by a two-channel in-ear EEG system and achieved an accuracy of 79.30% in classifying workload during a visuomotor tracking task. This device relies on custom-made hearing aid earplug, which require a wax impression of the ears to ensure a tight fit with the ear canal [20]. However, pilots already need to wear headsets for communication purposes, such as maintaining contact with the ground. Although these in-ear devices cause low interference to pilots, they may conflict with the communication headsets the pilots wear. So, it is believed that the in-ear EEG sensor and associated signal processing methods should be incorporated into the aviation headset when in practical use [58]. Since this study does not have access to such integrated devices, these in-ear devices will not be considered.
# CPW-Fed Transparent Antenna for Vehicle Communications
Even though the ITO has a high sheet resistance, the experimental results obtained satisfy the frequency requirements for the standard IEEE 802.11 and LTE sub 6 GHz bands. Besides, the experimental results are similar in behavior to the simulated ones, but with a slight shift in frequency. Nevertheless, this good concordance is unusual in other reported transparent antennas based on conductive films with a high sheet impedance. The main contributions of the proposed design are a fully transparent antenna that operates at frequencies for vehicular communications standards, and small size.
# Methods for Underwater Gravity Measurement Error Compensations Based on Correlation Analysis
###### Abstract  
The measurement of Earth's gravitational field is important in geophysics, geodynamics, geodesy, oceanography, and space science. The ocean covers 71% of the earth's surface; therefore, measuring the ocean's gravitational field is crucial. Compared with shipborne gravimetry, underwater gravimetry near the seafloor is closer to gravity sources and can obtain short-wavelength gravity information that is useful for small-scale deposit detection and seawater intrusion monitoring. This article focuses on gravimetric errors caused by the poor dynamics of the carrier; an error compensation method for underwater gravimetry based on correlation analysis is proposed. By analyzing the error sources that affect the dynamics of the carrier, the relationship between the grav  
lower the accuracy of the gravity measurement. Carrier dynamics are mainly influenced by changes in depth. The more intense the depth changes and the greater the fluctuation, the worse the carrier dynamics. Because the acceleration of the carrier is obtained by the depth's second-order difference, the dynamics of the carrier will affect the estimated acceleration, thereby indirectly affecting the accuracy of the gravity measurement. Underwater gravity measurements using the bottom-tracking mode require maintaining the gravimeter at a constant height above the seabed; keeping the gravimeter close to the seabed allows more precise measurements of gravitational anomalies.  
However, because of the variability of the seabed's topography, this measurement method will lead to frequent changes in the depth of the gravimeter, the carrier dynamics will deteriorate, and the quality of gravity-measurement data will also decline. In order to improve the quality of underwater gravity measurements, gravity surveyors all over the world have performed extensive work on error compensation methods for gravity measurements for many years. The errors of airborne gravimetry systems were analyzed in [1], and the errors of different grades were compensated. A deep-learning method was employed by [2] to compensate for errors of the gravimeter, and some good results were obtained. The error sources of underwater gravity measurements were analyzed in [3], and a general model for compensating the errors of marine gravity measurement was proposed, which is applicable to highly dynamic environments. In the present work, we use a correlation analysis method to compensate for gravity-measurement errors caused by poor dynamics and ensure the accuracy of gravity measurements in the bottom-tracking mode. Our work is carried out in the following order: the first section briefly introduces the problem background, the second section introduces the error compensation method, the third section describes the experimental process and experimental results, and the fourth section discusses the experimental results and provides the conclusion.
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 4 Experiment and Evaluations
### Experimental Procedure  
The SCI process of using the proposed scheme is illustrated as follows:  
1. The PRNU noise is extracted using the Mihcak filter [23] with parameter \(\sigma^{2}\) equal to 9 for each of the 50 sample images.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Camera Model** & **Number of Devices** & **Resolution [Pixel]** & **Index** \\ \hline Canon Ixus55 & 1 & 2592 × 1944 & 01 \\ Canon Ixus70 & 3 & 3072 × 2304 & 02–04 \\ Casio EX Z150 & 5 & 3264 × 2448 & 05–09 \\ Fujifilm Finepiy50 & 3 & 3264 × 2448 & 10–12 \\ Nikon CoolPixel S710 & 5 & 4352 × 3264 & 13–17 \\ Olympus miu1050 SW & 5 & 3648 × 2736 & 18–22 \\ Pentax Option A40 & 4 & 4000 × 3000 & 23–26 \\ Pentax Option W60 & 1 & 3648 × 2736 & 27 \\ Praktica DCZ5.9 & 5 & 2560 × 1920 & 28–32 \\ Rollei RCP-7325 XS & 3 & 3072 × 2304 & 33–35 \\ Samsung L74 wide & 3 & 3072 × 2304 & 36–38 \\ Samsung NV15 & 3 & 3648 × 2736 & 39–41 \\ Song DSC-H50 & 2 & 3456 × 2592 & 42–43 \\ Song DSC-T77 & 4 & 3648 × 2736 & 44–47 \\ Song DSC-W170 & 2 & 3648 × 2736 & 48–49 \\ \hline \end{tabular}
\end{table}
Table 1: Information of cameras on Dresden dataset used in our experiments.  
2. The reference PRNU noise \(R\) is constructed by MLE of the PRNU noise extracted from the images. 3. The SEA and proposed scheme are used to enhance the reference PRNU noise \(R\). 4. The peak correlation energy (PCE) is used to measure the similarity between the reference PRNU noise \(R\) and the test PRNU noise \(R_{test}\) extracted from an image captured by an unknown camera.  
Using the PCE [24] to measure the similarity between the reference PRNU noise \(R\) and the test PRNU noise \(R_{test}\). First, the normalized correlation between the two noises is calculated as follows:  
\[Cor(u,v)=corr(R,R_{test}), \tag{15}\]  
where _corr()_ represents the correlation function, the dimensions of \(Cor(u,v)\) are the same as \(R\) and \(R_{test}\). Then we judge whether there is a peak on the surface of the two-dimensional function according to the following values,  
\[PCE=\frac{sign(Cor(u_{peak},v_{peak}))Cor(u_{peak},v_{peak})^{2}}{\frac{1}{mn-B _{peak}^{2}}\Sigma_{u,v\
otin N_{peak}}Cor(u,v)^{2}}, \tag{16}\]  
where \((u_{peak},v_{peak})\) is the correlation function peak coordinates, and \(N_{peak}\) represents the peak neighbourhood.  
In addition, we need to know the difference between intraclass and interclass matching. Intraclass matching means that the test and real reference PRNU noise come from the same smartphone camera, which should have relatively large PCE values. Meanwhile, interclass matching implies different smartphone cameras, but their PCE values should be relatively small.
## 3 Preliminary Design
### Comparison with Other Tunnels  
The energy ratio achieved is quite high, especially for an open-circuit wind tunnel, which implies an efficient aerodynamic design. The semi-empirical method used was tested by [PERSON] et al. [48] and a comparative table of results is given in Table 1.  
For the calculation of the energy ratio of the [PERSON] et al. IWT [71], the measurements of some sections are not explicitly indicated, so they have been obtained through measurements in the figures given in the article.  
It should be noted that the energy ratio of a tunnel is not as important as the quality of the flow in the test section or the capabilities of the measuring instruments (force  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Facility** & \begin{tabular}{c} **Test Section** \\ **Speed (m/s)** \\ \end{tabular} & \begin{tabular}{c} **Current Energy Ratio** \\ **(Best Available)** \\ \end{tabular} & \
\begin{tabular}{c} **Computed** \\ **Energy Ratio 1** \\ \end{tabular} &
\begin{tabular}{c} **Difference** \\ **(\%)** \\ \end{tabular} \\ \hline NASA Ames, 40 \(\times\) 80 ft & 107.3 & 7.88 & 7.96 & +1.0 \\ NASA Ames, 7 \(\times\) 10 ft & 133.0 & 7.85 & 8.07 & +2.8 \\ Lockheed Martin low-speed wind tunnel & 52.3 & 1.10 & 1.12 & +1.8 \\ Hawder Siddeley Aviation, 15-ft V/STOL & 45.7 & 2.38 & 3.97 & +66.8 \\ University of Washington, 8 \(\times\) 12 ft & 117.7 & 8.30 & 7.20 & \(-\)13.3 \\ NASA Langley, 30 \(\times\) 60 ft & 52.7 & 3.71 & 4.73 & +27.4 \\ Indian Institute of Science 2, 14 \(\times\) 9 ft & 96.3 & 6.85 & 6.83 & \(-\)0.3 \\ Wallisch et al. icing wind tunnel (IWT) 2 & 35.0 & — & 3.01 & — \\ INTA actual IWT 2 & 70.0 & 4.16 & 4.36 & +4.7 \\ Designed IWT with no corners 2 & 70.0 & — & 6.16 & — \\ Designed IWT with one corner 2 & 70.0 & — & 4.35 & — \\ Designed and built IVT (with two corners) 2 & 70.0 & — & 4.74 & — \\ \hline \hline \end{tabular}  
* In every IWT, a 10% reduction in the energy ratio has been applied. 2 Wind tunnel of the open-circuit type (non-return).  
\end{table}
Table 1: Comparison of energy ratios in different wind tunnels.  
Figure 7: Pressure losses with fan diameter.  
measurement system, laser anemometry systems [72], etc.). This ratio is only an indicator of aerodynamic efficiency, especially important in the case of an IWT due to cooling power limitations.  
In the absence of space constraints, a straight IWT without corners (see Figure 8) and with similar characteristics would have a total length of 8.8 m, with a diffuser length of 6 m (for a taper of 3%). The pressure drop would be 464 Pa in that case, which would be 23% less than the IWT designed with two bends, and a 30% higher energy ratio.
###### Abstract  
A novel upsetting method, called Cone End Billet Upsetting (CEBU), is proposed in this paper to control bulging during the upsetting of large height-to-diameter ratio (LHDR) billets. This new upsetting method is mainly characterized by prefabricating a conical shape at the billet end, which aims to reduce the friction effect between the billet end and the anvil. In order to validate CEBU, the metal flow characteristics during upsetting of LHDR billets with traditional upsetting (TU) and CEBU were analyzed and compared by the finite element method. Experiments were also carried out to examine the deformation characteristics and microstructure of pure copper samples. The results show that, compared with TU, CEBU has a great advantage in restraining bulging and enhancing the compaction effect of upsetting. Meanwhile, bulging can be eliminated in CEBU with a 50% reduction ratio. In addition, aided by the cone end, the metal flow is no longer sensitive to the friction effect at the billet end. From the point of view of restraining bulging, a small taper angle is necessary prior to use. Furthermore, to avoid instability deformation, the height-to-diameter ratio of the billet should be below 3.0. CEBU is effective in suppressing the generation of bulging, but it also increases the pre-forming process for the end of the billet. The study on CEBU in this article is under laboratory conditions, and exploring the industrial application of CEBU will be the focus of our future research.  
upsetting; bulging; cone end billet; large height-to-diameter ratio; finite element method Article
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Influential Parameters on the Axial Behaviour of FGRBSs
#### 3.3.3 Effects of Grout Mechanical Characteristics on the Axial Performance of FGRBSs
The author discovered that incorporating additives can enhance the mechanical properties of the grout, resulting in an improvement of 5% to 12% in the axial load-bearing capacity of the FGRBS. [PERSON], [PERSON] [92] performed a series of pull-out tests considering the settling times of cement-grout rock bolts for immediate support applications. The tests were conducted for moderate and weathered rocks for various CTs including 12, 24, and 36 h. They concluded that there was a positive correlation between the ultimate pull-out capacity and the settling time of FGRBSs. [PERSON], [PERSON] [72] indicated that an increase in the UCS of the encapsulation material enhances the axial load bearing capacity and energy absorption of FGRBS. [PERSON], [PERSON] [93] studied the effects of W/G ratios and CT on the ultimate pull-out capacity of FGRBSs. The study revealed that the ultimate bearing capacity increases with CT and decreases with lower W/G ratios. [PERSON], [PERSON] [84] showed that incorporation of a small amount of FA into the grout mixture can enhance the UCS of grout and consequently the axial bearing capacities of the system.
## 3 VLC for Underwater Communication
### Configurations of the Optical Link
#### 3.2.2 Diffused LOS  
In diffused LOS, the optical signal is transmitted from a single source, and it spreads at a wider angle. Therefore, multiple receivers are in a DLOS configuration. Also, there is no need for accurate alignment of the transmitter and the receiver. There are a few drawbacks such as high attenuation, low data rates, and limited communication range [22].
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ2: What Types of Malicious Activity and Which Scenarios Involve IoT Botnets?
#### 5.2.1 Forming an IoT Botnet  
The majority of chosen studies targeted IoT botnets by their malicious activities. As explained before, cybercriminals try in the propagation phase to rally as many compromised devices as possible to expand their botnets. As has been stated in Section 3.3.3, IoT botnets are remotely managed through command and control channels to trigger different malicious activities, for example, DDoS attacks, sensitive data theft, phishing [85] or mining of cryptocurrency. An attacker that gains access to a network and gets control, spreads his malware by exploiting the IoT devices. Different vulnerabilities in IoT environments facilitate the process of forming IoT botnets. Cyber criminals are monetizing IoT botnets by actually selling them to interested customers and offering them as a service. Therefore, in the coming years, IoT botnets will continue to be a part of the threat landscape. According to the studies selected in this SLR, most research focuses on solutions for detecting IoT botnets through detecting the malicious devices. As Figure 8 explains, 51% of the studies  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Malicious Activities** & **Number of Studies** \\ \hline IoT botnet & 19 \\ \hline IoT malware & 7 \\ \hline DoS/DDoS & 6 \\ \hline Scan & 5 \\ \hline Sum & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Number of studies found by type of malicious activity.  
Figure 7: Percentages of studies focusing on the different phases of IoT botnet development.  
concentrated recruitment activity--19 studies, as Table 10 displays. Some of those studies are [45; 71; 73].
## 5 A Hysteresis Model with Damage Accumulation of Welding Materials
### Cyclic Stress-Strain Curve Based on Ramberg-Osgood Model  
The stress-strain hysteretic curves obtained from different loading strain amplitudes are placed in the same coordinate system, and the maximum and minimum stress peaks are connected to obtain the cyclic stress-strain curves, just as depicted in Figure 12, which does not represent the true stress-strain path of the welded material under cyclic loading, but is a cyclic stress-strain curve under steady state. Researches [(33; 60; 61)] show that the cyclic stress-strain curves of steady-state are different from those under monotonic loading, the cyclic stress-strain curve can be described by the Ramberg-Osgood [(43)] model approximately, as presented in Equation (5). \(E_{0}\) and \(K_{0}\) are the initial elastic modulus and hardening coefficients, respectively, which can be fitted by the Low-cycle fatigue test of the welding materials. The parameters of Equation (5) are summarized in Table 5.  
\[\varepsilon=\left\{\begin{array}{ll}\frac{\sigma}{E_{0}}+\left(\frac{\sigma} {K_{0}}\right)^{n_{0}}&;\sigma>0\\ \frac{\sigma}{E_{0}}-\left(-\frac{\sigma}{K_{0}}\right)^{n_{0}}&;\sigma\leq 0 \end{array}\right. \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Base Metal** & **Welding Material** & **E/Gpa** & \(\sigma_{y}\)/MPa & **K/Gpa** & \(m\) & \(\varepsilon_{y1}\)/\% & \(\varepsilon_{y2}\)(\(\varepsilon_{0}\))/\% & \(\varepsilon_{c}\)/\% & \(b\) & \(D_{C}\)/\% \\ \hline \multirow{2}{*}{Q235} & Butt weld & 239.70 & 391.70 & 2.73 & 0.77 & 0.16 & 1.50 & 21.25 & 0.065 & 59.24 \\  & Fillet weld & 223.30 & 401.94 & 1.82 & 0.54 & 0.18 & 1.50 & 21.34 & 0.046 & 50.03 \\ \hline \multirow{2}{*}{Q345} & Butt weld & 247.71 & 420.07 & 2.52 & 0.65 & 0.17 & 1.70 & 17.71 & 0.052 & 57.64 \\  & Fillet weld & 241.07 & 433.92 & 2.17 & 0.60 & 0.18 & 1.70 & 16.97 & 0.044 & 53.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter values of initial loading curve.  
Figure 11: Fitting results of the initial loading curve. (**a**) The schematic diagram of the initial loading curve; (**b**) Fitting result of the WA1 specimen.  
Got the logarithm of Equation (5), just as Equation (6), then, let \(y=\ln(\varepsilon-\frac{\sigma}{E_{0}})\), \(x=\ln\sigma\), \(b=-n_{0}\ln K_{0}\), Equation (6) will transform to Equation (7), thus, linear fitting can be performed.  
\[\ln(\varepsilon-\frac{\sigma}{E_{0}}) = n_{0}(\ln\sigma-\ln K_{0}) \tag{6}\] \[y = n_{0}x+b \tag{7}\]
## 3 Case Study
### The Determination of Requirement Conflict in Tunnel Construction System  
Alpine rocks exhibit high hardness and brittleness due to gravitational forces and long-term crustal activity. Presently, explosive blasting is primarily employed in alpine tunnel construction. However, the intense vibrations generated during blasting disrupt the internal stress distribution within the mountain. Moreover, the irregular free surfaces resulting from tunnel excavation lead to stress concentration, culminating in rock burst incidents.  
To diminish the risk of rock burst, an analysis of the tunnel construction system's construction method revealed the desired outcome of segregating tunnel rocks using low-amplitude vibrations. Yet, due to enhancements in vibration performance parameters, the separation force was nearly nullified, impeding the destruction of the hard rocks. Consequently, the primary product performance constraint hindering the realization of the final design objective was the high hardness of the rocks. Ultimately, the identified requirement conflict between user expectations and product performance constraints centered on the interplay between \"small-amplitude vibration\" and the \"high hardness of rock\".
## 2 Tunnel-Soil-Pipeline Interaction
### Free-Field Movements Induced by Different Force Patterns
#### 2.2.2 Frictional Force of Shield Shell  
As shown in Figure 4b, the frictional force of the shield shell is a horizontal force during the shield tunnel excavation. The element force \(dP\), the shadow area of the element in Figure 4b, can be expressed as \(pRd\theta d\eta\). The frictional force is along the tunnel periphery. Thus, the coordinate of any point load \(P\) along the tunnel periphery will be (\(R\)cos(\(\theta\)), \(\eta\), \(H\)-\(R\)sin(\(\theta\))). The parameters \(R_{1}\) and \(R_{2}\) shown in Figure 4b will be rewritten, respectively:  
\[R_{1}=\sqrt{\left(x-R\cos(\theta)\right)^{2}+\left(y-\eta\right)^{2}+\left(z- H+R\sin(\theta)\right)^{2}} \tag{17}\]  
Figure 4: Free-field ground movements induced by different force patterns: (**a**) support pressure at tunnel face; (**b**) frictional force of shield shell; (**c**) torsional force of cutter head.  
\[R_{2}=\sqrt{\left(x-R\cos(\theta)\right)^{2}+\left(y-\eta\right)^{2}+\left(z+H-R \sin(\theta)\right)^{2}} \tag{18}\]  
Due to the shield tunnel excavation along the \(Y\)-axis, the frictional force is a horizontal load along the \(Y\)-axis. The Cartesian coordinate system will translate \(\eta\) along the \(Y\)-axis. So, the \(x\) in Equation (12) should be replaced with \(y\)-\(\eta\). Compared with the support pressure in Section 2.2.1, the displacements and stress fields are obtained by double integrals. The domain of the integral of a cylindrical surface instead of a cylinder. According to Equation (12), the vertical displacement and vertical stress are given, respectively.  
\[u_{z}=\int_{0}^{2\pi}\int_{L_{1}}^{L_{2}}\frac{p(y-\eta)Rd\theta d\eta}{16\pi G \left(1-\mu\right)}\left[\frac{z-c}{R_{1}^{3}}+\frac{(3-4\mu)(z-c)}{R_{2}^{3}} -\frac{6\pi(2+c)}{R_{2}^{3}}+\frac{4(1-\mu)(1-2\mu)}{R_{2}(R_{2}+z+c)}\right] \tag{19}\]  
\[\sigma_{z}=\int_{0}^{2\pi}\int_{L_{1}}^{L_{2}}\frac{p(y-\eta)Rd\theta d\eta}{8 \pi(1-\mu)}\left[\begin{array}{c}\frac{1-2\mu}{R_{1}}-\frac{(1-2\mu)}{R_{2} }-\frac{3(z-c)^{2}}{R_{1}}-\frac{3(3-4\mu)(z+c)^{2}}{R_{2}^{2}}\\ +\frac{6c}{R_{2}^{2}}\end{array}\right] \tag{20}\]