esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 3 Cryptomining, Cryptojacking, and Web-Based Mining Threats
For its part, the now-defunct Monero, was one of the pioneers in developing web-based cryptomining with ASIC resistance, allowing the incorporation of scripts compatible with common desktop processors and a wide range of operating systems. The sudden use of Monero API intermediary programs and the extensibility/reusability of the code allowed the accelerated expansion of sites with embedded mining scripts, which in most cases did not require user consent, as well as an authorization note to warn that the site contains cryptomining code. According to [34], due to a large number of visits in a short time, streaming sites, online games, pornographic sites, download shortener redirection sites, and e-commerce platforms remain the main target for attackers to compromise the dynamics of the web page by inserting arbitrary code; however, the constant search for more feasible and integrated (all-in-one) means to disseminate the Monero code led to the development of frameworks such as Coinhive, capable of capturing 30% of new block transactions on the black market, and designed with anonymity options such as no traceability and no perceptibility at a browser level. Although Monero started with a benign purpose, it gradually grew as a hazardous strain that quickly spread over vulnerable websites, being almost 81% of the adhere threats formally detected in [35]. Despite the fact that Monero closed its operations in 2019, cyberattacks related to browser-based cryptomining are still present and with regular intervals of an uprising. An example of which are recent malicious scripts such as Cryptoloot, JSECoin, Miner, XMROmine, and deepMinerAnonymous, that according to statistics presented in [36], as of 2020 were found within 66,000 domains with 1.4 million active URLs. To conclude this section, it is important to mention that the speculative price of cryptocurrencies, as it is a flourishing phenomenon, will continue to attract cybercriminals to attempt the disruption of web applications, at the expense of innumerable vulnerabilities, poor source code auditing, lax security configurations, and intrusion detection/prevention systems or antivirus with inadequate signatures.
## Abbreviations  
Acronym List  
ADC  
analog-to-digital converter  
ASE  
amplified spontaneous emission  
AWG  
arbitrary waveform generator  
BER  
bit-error ratio  
CD  
chromatic dispersion  
CMA  
constant modulus algorithm  
DBSCAN  
density-based spatial clustering of applications with  
noise  
DP  
density peaks  
DSP  
digital signal processing  
EDFA  
erbium doped fiber amplifier  
EON  
elastic optical network  
FEC  
forward error correction  
FO  
frequency-offset  
MFR
modulation format  
MFI  
modulation format identification  
OFDM  
orthogonal frequency division multiplexing  
OPTICS  
ordering points to identify the clustering structure  
OSNR  
optical signal-to-noise ratio  
PDM  
polarization division multiplexing  
PRBS  
pseudo-random binary sequence
## 2 Materials and Methods
### Measurement of ABTS Radical Scavenging Activity  
To measure the ABTS radical scavenging activity of the SF and SSF extracts in different solvents, 250 \(\upmu\)L of 7 mM ABTS solution containing 140 mM potassium persulfate and 22 mL ethanol were mixed and allowed to react in the dark for 16 h. Then, 10 uL of 10 ug/uL SF and SSF extracts and 200 uL of ABTS solution were mixed and incubated at 24-26 \({}^{\circ}\)C for 3 min. Finally, the absorbance was measured at 734 nm [31]. Ascorbic acid was used as the positive control, and the ABTS radical scavenging activity was calculated as follows:  
\[\text{ABTS radical scavenging activity (\%) = (1 - A/B) \times 100} \tag{3}\]  
A: Absorbance of the sample group  
B: Absorbance of the negative control
# Study of Basalt Fibers and Graphene Enriched Polymers on Bond Behavior of FRP Bars in Concrete
## 5 Conclusions  
In this work, FRP bars using basalt as fiber (BFRP) and bars with the addition of GNP in the matrix (GFRPg) were tested against a more traditional type of FRP bar where glass fiber and vinyl-ester matrix (GFRP) were used. The study with GNP was exploratory and only a fixed amount was added in a polyester matrix. Temperature effects up to 120 \({}^{\circ}\)C were also considered as well as numerical simulations. Different diameters and concrete classes were taken into consideration at room temperature. The main conclusions are:  
* Combination BRFP/helically-wounded presented a superior behavior in terms of strength than other cases studied in this work and other BRFP cases considered in the literature at room and higher temperatures. The former was the only case to have a bond strength above minimum levels defined in project standards such as GOST 31938-2012 [57] and CAN/CSA S087-19 [58], in all situations tested in this work.
* Cases were GNP was introduced in a polyester matrix (GFRPg) presented a similar behavior of bars were vinyl-ester matrix was used (GFRP). However, behavior of the former was far superior than similar cases taken from the literature (polyester without GNP), at room and higher temperatures. Therefore results are encouraging, but more studies are necessary regarding GNP use.
* Numerical simulations combining [PERSON] et al. model and an optimization method to search for parameters showed to be an excellent tool to fit experimental results. Stresses in the concrete remained always below concrete strength. At the adherent zone, these stresses stayed mostly uniform, except near the end of the zone where stress concentrations were observed.  
Figure 18: Normal Cauchy stress in \(y\) direction for case c30-12B (**a**) and case c30-12B120 (**b**) at the peak of the bond for both cases.  
**Author Contributions:** Conceptualization: G.M.B. and E.B. Methodology: G.M.B. and E.B. Software: G.M.B. and E.B. Validation: G.M.B. and E.B. Formal analysis: G.M.B. and E.B. Investigation: G.M.B. and E.B. Resources: E.B. Writing--original draft: G.M.B. Writing--review & editing: E.B. Supervision: E.B. Project administration: E.B. Funding acquisition: E.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) and Coordination for the Improvement of Higher Education Personnel (CAPES).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** Dataset available on request from the authors.  
**Acknowledgments:** The authors are pleased to acknowledge [PERSON] for permitting the use of the Construction Environment Materials and Technology Laboratory (LAMTAC/NORIE-UFRGS) where experiments were done.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of AC Waste
#### 3.1.1 Chemical Composition and Surface Morphology of AC Waste  
No significant differences in the chemical composition of the representative AC waste samples were recorded (Table 2). This confirmed the use of the same material in the construction of the entire building. The CaO, SiO\({}_{2}\), and MgO are the main components related to cement and the asbestos raw minerals.  
The content of MgO(5.62 wt. %,on average) in the AC samples is probably related to asbestos, where magnesium atoms are coordinated with oxygen atoms in the serpentine silicate [27]. The stoichiometric percentage of MgO and SiO\({}_{2}\) in chrysotile as the dihydrate of magnesium silicate Mg\({}_{3}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\) is almost the same (43.63 wt. % MgO and 43.36 wt. % SiO\({}_{2}\)). Therefore, it can be estimated that about 5.58 wt. % of the total SiO\({}_{2}\) content (20.04 wt. %, on average--Table 2) comes from asbestos. The determined amounts of these oxide components are very similar to the data published in [48; 49; 50; 51] and in a good agreement with the Mg/Si ratio for chrysotile.  
The Mg/Si ratio as the characteristic indicator for natural chrysotile is 0.712 [52]. In our case, the ratio of these main elements (Table 2) is lower (0.362) because the Si is also bounded in silicates present in the cement matrix.  
The CaO/SiO\({}_{2}\) ratio in AC sample is 1.987. However, the CaO/SiO\({}_{2}\) ratio converted to the SiO\({}_{2}\) content belonging to the cement is 2.75. This value found by our determination falls within the range of the CaO/SiO\({}_{2}\) ratio (3.1 to 2.68) in Portland cement (type I).  
The presence of Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\) and other oxides is related to cement and the asbestos raw material. The amount of chlorine found in the samples proves that some hydroxyl ions in the chrysotile structure had been substituted with chlorine ions.
## 4 Discussion  
In this study, the LRRA has been used for deconvolution and 3D reconstructions. In this section, we compare the performances of the LRRA with the LRA, NLR, matched filter and phase-only filter for the averaged donut shaped \(I_{PSF}\) with three lobes. A test object, the logo of ANSTO, was used for the study. The object intensity \(I_{O}\) was synthesized by convolving the test object with the experimental \(I_{PSF}\). The images of the test object, \(I_{PSF}\), \(I_{O}\) and reconstruction results from a matched filter, phase-only filter, NLR (\(\alpha\) = 0, \(\beta\) = 0.5), LRA (\(n\) = 100) and LRRA (\(\alpha\) = 0, \(\beta\) = 0.95, \(n\) = 9) are shown in Figure 6a-h, respectively. As it can be seen, the LRRA has a better performance than the LRA, NLR and other methods. Furthermore, the LRRA is also faster than the LRA by at least an order in both simulation and experiments. The value of \(\alpha\) was between zero and one, and the value of \(\beta\) was one for most cases while the number of iterations \(n\) was less than 10. However, with advanced computational reconstruction algorithms, it is possible to enhance the reconstruction results.
###### Abstract
[15] compared CFD solutions using the RNG k-\(\varepsilon\) turbulence model to experimental data, revealing interesting observations at different tips. In this study, the Lattice-Boltzmann method was applied to solve the flow field around a straight-bladed Darrieus-type 500W VAWT using Reynolds Averaged Navier-Stokes (RANS) and Large-Eddy Simulation (LES) approaches to model the turbulence in the flow field. Spalart-Allmarass one equation turbulence model was selected for the RANS, while Wall-Adapting Local Eddy (WALE) and Smagorinsky sub-grid scale models were used for LES. Predicted power and torque coefficient at three tip speed ratios (TSR) were compared to the experimental data from [15]. CFD solutions with a coarse and fine lattice structure were applied using XFLOW commercial CFD software.
## 3 The Conventional and Proposed UWB Localization Approaches
### Trilateration Approach for UWB Localization  
To train the proposed LSTM network with distance information from anchors and user position coordinates, we use trilateration. The trilateration algorithm estimates the true trajectory coordinates and many position coordinates from trajectory which covers the entire experiment area. The UWB tag distance from anchors and the user positions from trilateration approach are used in training the LSTM model. Figure 6 shows the trilateration approach for UWB localization.  
The distance \(d_{i}\) between the UWB tag \(P(x_{p},y_{p})\) and the anchors \(R_{i}(x_{i},y_{i})\) is expressed as  
\[d_{i}^{2}=(x_{i}-x_{p})^{2}+(y_{i}-y_{p})^{2}. \tag{7}\]  
The expanded form of (7) is  
\[d_{i}^{2}=x_{i}^{2}+x_{p}^{2}-2x_{i}x_{p}+y_{i}^{2}+y_{p}^{2}-2y_{i}y_{p}. \tag{8}\]  
When we consider a reference point \(K\), then (8) can be expressed as  
\[d_{k}^{2}=x_{k}^{2}+x_{p}^{2}-2x_{k}x_{p}+y_{k}^{2}+y_{p}^{2}-2y_{k}y_{p}. \tag{9}\]  
Subtracting (9) from (8), we get  
\[d_{i}^{2}-d_{k}^{2}+x_{k}^{2}+y_{k}^{2}-x_{i}^{2}-y_{i}^{2}=2(x_{k}-x_{i})x_{p} +2(y_{k}-y_{i})y_{p}. \tag{10}\]  
Considering \(i=1\) and varying index \(k=2,3\) we obtain  
\[\left[\begin{array}{cc}2(x_{2}-x_{1})&2(y_{2}-y_{1})\\ 2(x_{3}-x_{1})&2(y_{3}-y_{1})\end{array}\right]\left[\begin{array}{c}x_{p}\\ y_{p}\end{array}\right]=\left[\begin{array}{c}d_{1}^{2}-d_{2}^{2}+x_{2}^{2}+y_ {2}^{2}-x_{1}^{2}-y_{1}^{2}\\ d_{1}^{2}-d_{3}^{2}+x_{3}^{2}+y_{3}^{2}-x_{1}^{2}-y_{1}^{2}\end{array}\right]. \tag{11}\]  
The coordinates \(P(x_{p},y_{p})\) of the UWB tag can be obtained by solving the above system of equations. We can obtain a linear equation with three unknowns, \(Ax=b\). The \(A\), \(x\) and \(b\) values of liner equation is expressed  
\[A=\left[\begin{array}{cc}2\left(x_{2}-x_{1}\right)&2\left(y_{2}-y_{1}\right) \\ 2\left(x_{3}-x_{1}\right)&2\left(y_{3}-y_{1}\right)\end{array}\right] \tag{12}\]  
Figure 6: UWB localization using trilateration approach.  
\[x=\left[\begin{array}{c}x_{p}\\ y_{p}\end{array}\right] \tag{13}\]  
\[b=\left[\begin{array}{c}d_{1}^{2}-d_{2}^{2}+x_{2}^{2}+y_{2}^{2}-x_{1}^{2}-y_{ 1}^{2}\\ d_{1}^{2}-d_{3}^{2}+x_{3}^{2}+y_{3}^{2}-x_{1}^{2}-y_{1}^{2}\end{array}\right]. \tag{14}\]  
The solution of the equations can be \((x_{p},y_{p})\) that minimizes the \(\delta\) defined by the following  
\[\delta=(Ax-b)^{T}(Ax-b) \tag{15}\]  
\[x=\Big{[}\begin{array}{cc}x_{p}&y_{p}\end{array}\Big{]}^{T}. \tag{16}\]  
Applying MMSE (Minimum Mean Square Error) method, we can obtain x with the following expression  
\[x=(A^{T}A)^{-1}A^{T}b. \tag{17}\]  
For more details on trilateration algorithm implementation, refer to our previous works in References [28; 29].
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 2 Related Work
### Reinforcement Learning  
Reinforcement learning (RL) is applied to various fields because it learns through experience, so it can solve highly complex problems without an accurate mathematical modeling process [28]. RL, which learns through trial and error to find goals, aims to learn weights and biases using the concept of rewards, similar to existing neural networks learning weights and biases through labeled data. In addition, deep reinforcement learning (DRL), which combines deep learning, has attracted interest among researchers in various fields because it has the ability to solve large-scale, complex problems [29].  
RL is generally recognized as suitable for solving optimization problems related to the routing of distributed systems. Moreover, it is an efficient alternative to improve the online awareness of routing protocols in changing environments. Therefore, RL can be used to optimize resource utilization while providing a high level of QoS. [PERSON] and [PERSON] first proposed a Q-learning-based hop-by-hop routing algorithm called Q-routing [30]. Q-Learning Modified AODV (QLMAODV) uses RL to optimize AODV for routing in MANETs [31]. The RL-Based Self-Routing Protocol (RLSRP) applies reinforcement learning to address rapid topology changes in FANETs consisting of flying nodes [32].
## 2 Principle  
The piezoelectric haptic feedback actuator is shown in Figure 1, which includes the touch beam, LCD1602 liquid crystal display, IIC communication conversion module, Arduino controller, HC-06 Bluetooth, AD9850, operational amplifier, PDU100B piezoelectric drive module, BBxx12N-3W boost module, and other parts. An Android phone can communicate with the Arduino controller through Bluetooth. The Arduino outputs a digital signal to the DAC and converts it into a sinusoidal analog signal, which is then transmitted to the piezoelectric sheets through the operational amplifier and the piezoelectric driving module to excite the touch beam to resonate.  
The key parts of the tactile device are the touch beams. Array bars are arranged with the same spacing on the touch beams to create the piezoelectric tactile feedback. Here, they are called the ciliary-like body, which is the main factor that produces tactile differences. One of the touch beams is shown in Figure 2. The left side length of the touch beam with a ciliary-like body is longer than the right side. This is because the location of the area without a ciliary-like body is calculated for mounting the piezoelectric sheets for exciting the expected working mode.  
Figure 1: Structure of piezoelectric tactile feedback actuator.  
When a sinusoidal signal with a frequency close to the natural frequency of the touch beam is supplied to the piezoelectric sheets, the touch beam resonates and produces a bending vibration. The bars in different positions of the touch beam will vibrate along the normal direction of the standing wave. As shown in Figure 3, the ciliary-like body 3 and 4 under the finger is distributed on the right and left side of the vibration peak separately. At this time, the ciliary-like body distributed on the right side of the peak will give the finger upward inertial pressure and the rightward thrust. The ciliary-like body distributed on the left side of the peak will give the finger upward inertial force and leftward thrust. Therefore, when one finger moves to the right, the ciliary-like body on the right side of the peak will make the subject feel smoother than the feeling made by the ciliary-like bodies on the left side of the peak. Conversely, the result is the opposite, when the finger moves to the left. The change in tactile sensation is due to the fact that the equivalent friction coefficient between the finger and the surface is modulated by the vibrating ciliary-like body on the beam. The equivalent friction depends on the ratio of the ciliary-like body in both directions covered by the finger. The details for predicting the equivalent friction have been presented in [18]. We also can change the frequency and amplitude of the excitation signal to modulate a certain point of roughness feeling.
## 3 Study Results  
### Delay Results  
#### 3.1.1 Delay Results for Highway Speed Limit of 90 km/h  
Table 3 shows the simulated average network delay (in seconds per vehicle) along with standard deviations (between parentheses) for all vehicles from the 10 simulations. For the base scenario (i.e., TT speed limit of 60 km/h), the simulation results show that by increasing the traffic volume, the average network delay increases. The highest network delay happens at 2000 vehicle/h/lane with 15% TTs. These results are intuitive and are expected, which means that the simulation results are fine.  
As shown from Table 3, increasing the TT speed limit to 70, 80, and 90 km/h would result in a reduction in the average network delay for all traffic volumes (500 to 2000 vehicle/h/lane) and for all percentages of TTs, compared with the base condition (i.e., TT speed limit of 60 km/h). All the reduction cases were found to be statistically significant at the 5% significance level. The relative reduction, from 60 to 70 km/h, was between 27.25% and 62.04%, with respect to the base case (60 km/h). Increasing the TT speed limit from 60 to 80 km/h, the relative delay reduction was between 38.07% and around 71.75%, and from 60 to 80 km/h, the relative delay reduction was between 38.35% and 76.32%. The delay reduction increases with increasing traffic volume and the increase in TT percentage, and the highest reductions occur at 2000 vehicle/h/lane with 15% TTs. It is worth noting that increasing the speed limit for TTs from 70 to 80 km/h would result in delay reduction between 14.88% and 31.67%, while increasing the speed limit for TTs from 80 to 90 km/h would result in the lowest delay reductions, which would be between 0.44% and 16.18%.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
###### Abstract  
Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate that LSPP significantly outperforms traditional models, such as LSTM and GRUs, particularly in long-distance predictions and under conditions of stratigraphic changes. Notably, the model achieves an R\({}^{2}\) of 0.82 when predicting penetration after six rings, making it highly accurate and stable for engineering decision-making.  
Keywords:shield; long-distance prediction; tunnelling performance; Informer +
Footnote †: journal: Applied sciences
## 3 Results
### Comet Assay  
TCS-induced significant (\(p\leq 0.01\)) concentration-dependent DNA damage in the form of increased TL, TM, OTM and TDNA (Table 3, Figure 3). At the highest concentration of TCS, the increase over control after 14 days was 0.78-fold for TL, 1.98-fold for TM, 1.22-fold for OTM and 0.81-fold for TDNA, while after recovery, it was 0.41-, 0.90-, 0.50- and 0.47-fold for TL, TM, OTM and TDNA, respectively. Compared with the 14-days values, a significant (\(p\leq 0.05\)) decrease was observed in all the parameters after recovery. The decline for TL, TM and OTM was significant (\(p\leq 0.05\)) at all concentrations, but for TDNA, it was significant (\(p\leq 0.05\)) at 25.2 and 60 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L only.  
Figure 3: TCS-induced DNA damage during the exposure and recovery period. ND = no damage; MD = mild damage; SD = severe damage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & \multicolumn{2}{c}{**TL (\(\mu\)m)**} & **TM** & **OTM** & **TDNA (\%)** \\ \hline
25.2 \(\mu\)g/L & E & 35.36 \(\pm\) 0.60 \({}^{\text{c}}\) & 4.18 \(\pm\) 0.25 \({}^{\text{d}}\) & 6.22 \(\pm\) 0.25 \({}^{\text{d}}\) & 11.14 \(\pm\) 0.24 \({}^{\text{d}}\) \\  & R & 26.82 \(\pm\) 0.95 \({}^{\text{r}}\) & 2.60 \(\pm\) 0.19 \({}^{\text{q}}\) & 4.05 \(\pm\) 0.15 \({}^{\text{p}}\) & 8.77 \(\pm\) 0.25 \({}^{\text{p}}\) \\ \hline
60 \(\mu\)g/L & E & 39.56 \(\pm\) 1.71 \({}^{\text{d}}\) & 5.42 \(\pm\) 0.17 \({}^{\text{e}}\) & 7.84 \(\pm\) 0.16 \({}^{\text{e}}\) & 13.18 \(\pm\) 0.58 \({}^{\text{e}}\) \\  & R & 30.73 \(\pm\) 0.92 \({}^{\text{s}}\) & 3.61 \(\pm\) 0.44 \({}^{\text{r}}\) & 5.08 \(\pm\) 0.65 \({}^{\text{q}}\) & 11.29 \(\pm\) 0.95 \({}^{\text{q}}\) \\ \hline \hline Exposure & F & 148.99 ** & 267.43 ** & 268.9 ** & 184.08 ** \\ \hline Recovery & F & 57.30 ** & 30.34 ** & 11.21 ** & 25.70 ** \\ \hline \end{tabular} Values are mean \(\pm\) SD, n = 3, and different alphabets represent significant (\(p\leq\) 0.01) differences within the treatments after exposure (E) (a–e) and recovery (R) (p–s). ** represents 1\(\%\) significance level.  
\end{table}
Table 3: _Cont._  
Figure 2: TCS-induced nuclear and cellular abnormalities during the exposure and recovery period: normal cell (**a**), micronucleus (**b**), binucleated or dacrocyte (**c**), blebbed nucleus (**d**), nuclear notch (**e**), deformed nucleus (**f**), nuclear bud (**g**), extrusion nucleus (**h**,**i**), ghost cell and nucleoid (**j**), nuclear fragmentation (**k**), vesicle formation (**l**), vacuolation (**m**), stickiness (**n**), cytoplasmic bridge (**o**), chain of cells (**p**) and lytic or necrotic cells (**q**,**r**). cy = cytoplasm and nu-nucleus.