esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing Dynamic Power Consumption in Mixed-Critical Real-Time Systems
## 5 The Proposed Scheme
### DVFS Scheduling  
The notation for the scheduling algorithm is shown in Table 2. The task utilization of \(\tau_{i}\) is denoted as \(U_{i}\). Each job, denoted as \(J_{k}\), in the waiting queue is defined by (\(C_{k}\), \(D_{k}\)) so that a job requires \(C_{k}\) execution time by the deadline \(D_{k}\). The values are determined at the time of job release. Figure 4: The proposed scheduling framework. The proposed scheme is defined by functions that are called at a certain event. The algorithms are given in the followings pseudo-code in Algorithms 1 and 2. * _Job-Release (\(\tau_{i}\)):_ Every \(P_{i}\) period, a task \(\tau_{i}\) releases a job. The function _Job-Release_ is called;
* _Job-Finish (\(J_{i}\)):_ The function is called when a job completes its execution or over-runs the execution time;
* _Power-aware Schedule ():_ At the time of a job release or completion, the function re-schedules jobs in the queue;
* _Frequency-Adjust ():_ The CPU frequency is adjusted at the time of job allocation to the CPU. When a job is released in low mode, the job is inserted in the ready queue. The task utilization is also updated. Since the frequency-level of a LO-criticality task is given by \(f_{LO}^{LO}\), the task utilization is determined by the equation in line 5 of Algorithm 1. In case of a high-critical job of \(C_{i}(HI)-C_{i}(LO)\) every period so that the utilization is given by the equation in line 7. If the current system mode is low, we terminate or ignore the low-criticality job. If the current mode is high, we execute the high-criticality job (line 14). The job is inserted in the ready queue, we call the scheduling algorithm in line 19. When the job \(J_{i}\) finishes its computation, if the current system mode is low, nothing is executed. We only check \(X_{i}\) = HI. We have two cases if \(J_{i}\) finishes. If \(J_{i}\) does not complete, the system mode becomes high. When the ready queue is empty and there is no high-criticality job in the ready queue, the system mode is changed from high to low (lines 29-31). The function _Power-aware Schedule ()_ dispatches jobs using EDF (line 38-43 of Algorithm 1). At each scheduling event, _Frequency-Adjust ()_ function is called so as to adjust the CPU frequency dynamically. As shown in Algorithm 2, if the system is in high-criticality mode, we minimize the frequency of high-criticality mode which is set as \(f_{HI}^{HI}\). The frequency level is set as the frequency level sufficient to schedule current jobs. Thus, the relative speed level of the frequency is greater than or equal to the current utilization. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(U_{i}\) & The utilization of the task \(\tau_{i}\) \\ \(J_{k}=(C_{k},D_{k})\) & The job of task \(\tau_{i}\) \\ \(t\) & The current time \\ \(\Omega_{ready}\) & The CPU ready queue \\ \(J_{curr}\) & current job of execution \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations.
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Session-Key Computation Attack  
The objective of network attackers in this scenario is to find a way to compute the session key that the two communicating parties generate during the process. The session key in our proposed approach is generated using the following parameters and formula.  
1. \(\text{SK= H(S}_{1}\mid\text{A}\mid\text{S}_{2}\mid\text{|S}_{3}\mid\text{|SA} \mid\text{AC}_{\text{i}})\)  
As can be seen, different factors such as random numbers, a root value of the private key of the communication parties, ECDH, and ECDPL theory are considered in the process of calculating the communication session key. As stated by the ECDLP, even with the values of K, the attacker cannot obtain the N\({}_{\text{i}}\). Indeed, the utilization of private parameters S\({}_{1}\), A, S\({}_{2}\), S\({}_{3}\), SA, and AC\({}_{\text{i}}\) in the session-key generation process provides an additional layer of security in the protocol. It is crucial to emphasize that neither of these parameters is exposed or exchanged over the communication channel during the protocol's execution, bolstering the protocol's security against potential attacks. The security of the parameters is detailed in the preceding attacks and the protocol text.
## 4 Conclusions  
The efficiency and operating parameters for the treatment of TA by the UV/persulfate and photo-Fenton were evaluated and compared. The results from this study showed that the performance of photo-Fenton process is significantly superior to that of UV/persulfate process in the degradation of TA aqueous solutions. The optimum conditions obtained for the best degradation were a pH = 9, a persulfate concentration of about 53.10 mM, a TA concentration of 0.1 mM and a temperature of 25 \({}^{\circ}\)C for UV/persulfate process and a pH 3, a ferrous iron concentration of 0.18 mM, a hydrogen peroxide concentration of 29.4 mM, a TA concentration of 0.1 mM, and a temperature of 25 \({}^{\circ}\)C for the photo-Fenton process. Under optimal conditions, removal efficiency for UV/persulfate and photo-Fenton were 96.38% and 99.32% for absorbance removal and 54.41% and 94.27% for TOC removal, respectively. The results obtained have shown that a more complete mineralization was obtained in the photo-Fenton process. It can be summarized that photo-Fenton process more effective than UV/persulfate process. The efficiency of photo-Fenton can be explained by the catalytic decomposition of H\({}_{2}\)O\({}_{2}\) by Fe\({}^{2+}\) ions, the photo reduction of hydrogen peroxide by irradiation UV and the photo-reduction of hydroxyl iron complex, which lead to the production of higher contents of hydroxyl radicals. Of those hydroxyl radicals, HO\({}^{\bullet}\) is a more powerful oxidant than sulfates radicals  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**\% ABS Removal**} & \multicolumn{2}{c}{**\% TOC Removal**} \\ \cline{2-5}  & **UV/PS** & **PF** & **UV/PS** & **PF** \\ \hline
20 \({}^{\circ}\)C & 92.4 \(\pm\) 0.4 & 94.9 \(\pm\) 0.6 & 50.4 \(\pm\) 0.1 & 88.9 \(\pm\) 0.5 \\ \hline
25 \({}^{\circ}\)C & 96.4 \(\pm\) 0.7 & 99.3 \(\pm\) 0.3 & 54.3 \(\pm\) 0.2 & 94.3 \(\pm\) 0.5 \\ \hline
35 \({}^{\circ}\)C & 95.8 \(\pm\) 0.3 & 97.3 \(\pm\) 0.4 & 53.6 \(\pm\) 0.6 & 93.8 \(\pm\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of temperature on UV/PS and photo-Fenton efficiencies during the degradation of TA for 2 h UV irradiation. (UV/PS: [K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)]/[TA] = 531, pH = 9 and T = 20–35 \({}^{\circ}\)C; UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\): [H\({}_{2}\)O\({}_{2}\)]/[Fe\({}^{2+}\)] = 164, [H\({}_{2}\)O\({}_{2}\)]/[TA] = 294, pH = 3, and T = 20–35 \({}^{\circ}\)C).  
SO4\({}^{\bullet-}\). Even though, PS is widely known to produce less powerful radicals, the fact of having an optimal pH at nine may be easily applied to real cases and the costs of adjusting the pH can be fewer.  
[PERSON] and [PERSON] designed the experimental part; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] supervised experimental work and data analysis; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript.  
This research received no external funding. The publication of this article was funded by the Qatar National Library.  
All authors of this article declare no conflicts of interest.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design
### Network Coding  
Random Linear Network Coding (RLNC) is one of the network coding strategies that combines packets with coefficients selected from a Galois field (GF) to create random linear combinations. The sender initially encodes multiple packets of \(P_{n}\) to generate \(k\) random linear combinations of \(P^{\prime}\) by:  
\[P^{\prime}_{k}=\sum_{n=1}^{k}c_{n}*P_{n} \tag{5}\]  
where \(c_{n}\) is the selected coefficient for each packet. The sender transmits this linear combination along with its selected coefficients in the header. Thus, it increases the header overhead. The sender transmits these random linear combinations until the receiver obtains linearly independent combinations for successful decoding. The receiver decodes the combination via Gauss-Jordan elimination method [36]:  
\[\begin{vmatrix}P_{1}\\ \vdots\\ P_{k}\end{vmatrix}=\begin{vmatrix}c_{11}&..&c_{1k}\\ \vdots&..&\vdots\\ c_{k1}&..&c_{kk}\end{vmatrix}^{-1}\begin{vmatrix}P^{\prime}_{1}\\ \vdots\\ P^{\prime}_{k}\end{vmatrix} \tag{6}\]
## 4 Employability Scenario: Assessing the Significance of the Enhanced Visibility Estimation Process  
[PERSON] [26], in her study on path losses of wireless sensor networks (WSNs) during sand/dust storms (SDS), measured signal attenuation under four environment conditions. These conditions were ambiguously labelled clear sky, dusty sky, sand storm, and heavy sand storm to indicate the severity of dust concentration at the experimental site.  
However, the study set wind speed as an alternative meteorological representative of the environmental conditions, which may be used to derive particle concentrations (TSP). Therefore, before identifying visibilities, a preceding step is introduced to convert the meteorological condition into its particle-concentration equivalent, through which visibility calculation becomes possible.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Set of Equations (Total Recurrences)**} & \multicolumn{4}{c}{**Statistical Description of Recurrences**} \\ \cline{2-6}  & **Mean (\(\mu\))** & **SD (\(\sigma\))** & **Min.** & **Max.** & **P (\(\mu<Z<\) Max)** \\ \hline \multirow{4}{*}{9 (91)} & \multirow{4}{*}{10.11} & \multirow{4}{*}{9.02} & Combination Equations & \multirow{4}{*}{26} & \multirow{4}{*}{46.09\%} \\  & & & Single Equation & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & \multirow{4}{*}{13} & \multirow{4}{*}{6.22} & \multirow{4}{*}{4} & \multirow{4}{*}{18} & \multirow{4}{*}{28.93\%} \\  & & & & & \\ \cline{4-5}  & Mean (\(\mu\)), \% & SD (\(\sigma\)), \% & Min., \% & Max., \% & \\ \cline{4-5}  & & & Average of Two Equations & & \\ \cline{4-5}  & \multirow{4}{*}{11.66} & \multirow{4}{*}{9.99} & 0.28 & 36.8 & 37.27\% \\ \cline{4-5}  & & & Single Equation & & \\ \cline{4-5}  & & & 0 & 119.18 & 29.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical descriptions for equation recurrences in two states: singly and in combination, along with their estimation percentage of error probabilities.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Source** & **E1E4** & **E1E5** & **E2E4** & **E1E3** & **E2E3** & **E2E5** & **E4E5** & **E3E4** & **E3E5** \\ \hline KSA [17; 18] & 19.93 & 18.66 & 13.29 & 15.91 & 9.31 & 12.04 & 3.02 & 1.42 & 2.09 \\ USA [3; 19] & 84.77 & 93.21 & 68.30 & 77.88 & 61.59 & 76.93 & 13.68 & 11.66 & 8.69 \\  & 0.53 & 0.63 & 1.09 & 0.46 & 1.10 & 0.85 & 2.10 & 2.36 & 2.12 \\ North-East Asia [14; 20] & 56.03 & 59.94 & 46.06 & 51.55 & 41.96 & 50.34 & 14.38 & 6.06 & 10.61 \\  & 30.72 & 30.81 & 22.15 & 25.97 & 17.85 & 22.48 & 6.30 & 6.04 & 7.17 \\ Australia [15] & 55.31 & 62.90 & 48.24 & 53.02 & 46.57 & 56.38 & 11.62 & 4.89 & 11.42 \\ \hline Average & 41.22 & 44.36 & 33.19 & 37.46 & 29.73 & 36.50 & 8.52 & 5.40 & 7.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance (RMSE) of the algorithm-extracted combinations of equations.
# Scaled Modeling and Measurement for Studying Radio Wave Propagation in Tunnels
In addition, a 1:10 scale model was built out of wood coated in Formica for testing frequencies of 10 and 94 GHz, which correspond to the scaled frequencies of the real experiment. We compared the field and scaled experiments to a modified simulation model based on ray tracing. The experiments used directional antennas, contrary to other known models where the number of reflections is infinite [4; 12] along the tunnel. In reality, the number of reflections is a function of distance and tunnel dimensions, its starting value of zero gradually increasing with the distance between antennas. We observed that every ray incident on the receiver had a significant impact when in close vicinity to the transmitter. Our results showed a correlation between theoretical and experimental results, which indicates that wave propagation in a tunnel environment can be realistically modeled by ray tracing. A good fit between the scale model experiments and the simulation demonstrates that scale models can be used to imitate real-world experiments in a laboratory environment.
## 6 Interactions between Segments along the Longitudinal Direction
### Outward Radial Deflection Error
Figure 19: Damage to segment in the presence of lateral deflection when the asymmetric error angle is \(2.4^{\circ}\): (**a**) first segment assembly (%); (**b**) second segment assembly (%); (**c**) third segment assembly (%). (Take the lateral segment as an example). The outer joint width of the segment gradually decreases with the increase of the outward radial assembly error, as shown in Figure 18. During the segment assembly process, the joint quality of the segment is gradually improved, which means that the assembly process reduces the deflection freedom of the segment. The action position of the jack is distributed along the center of the bearing surface, and the gasket of the segment is closer to the outer side of the segment. Although the component force caused by the outward deflection error causes the segment to deflect to the outside, the component force is too small compared to the jack thrust in the synchronous propulsion and assembly mode. Therefore, under the outward radial deflection error of the segment, the influence on the maximum joint volume of the segment is small, and the maximum reduction is only 1 mm. Obviously, in the synchronous push-and-assemble mode, when the segment is subject to assembly error, the segment will be damaged within the maximum deflection error range. In practical engineering, it is necessary to control the assembly error of the segment within the damage angle so as to ensure the smooth development of the synchronous push-and-pull mode. \begin{table}
\begin{tabular}{c c c c} \hline
**Location** & **Assembly Process** & **Damage Angle (°)** & **Destruction Location** \\ \hline \multirow{3}{*}{Top} & Assembly of the first segment & 1.40 & Sloping side of segment \\  & Assembly of the second segment & 1.81 & Sloping side of segment and extrusion corner \\  & Assembly of the third segment & 1.38 & Extrusion corner \\ \hline \multirow{3}{*}{Lateral} & Assembly of the first segment & 1.41 & Sloping side of segment \\  & Assembly of the second segment & 1.82 & Sloping side of segment and extrusion corner \\  & Assembly of the third segment & 1.37 & Extrusion corner \\ \hline \multirow{3}{*}{Bottom} & Assembly of the first segment & 1.39 & Sloping side of segment \\  & Assembly of the second segment & 1.89 & Sloping side of segment and extrusion corner \\  & Assembly of the third segment & 1.38 & Extrusion corner \\ \hline \end{tabular}
\end{table} 
Table 6: Damage to the segments. Figure 20: Maximum compression damage factor in the presence of lateral deflection.
# Mechanical and Tribological Performance of Carbon Fiber-Reinforced PETG for FFF Applications
## 1 Introduction
However, extrusion temperature is not the sole critical parameter; layer thickness also plays a crucial role in mechanical properties, significantly affecting tensile and compressive strength [16], as well as flexural strength [17; 18]. This may be due to the fact that modifying the layer thickness affects the internal voids of the parts, altering their internal behavior [17], in line with the layer theory [19]. Moreover, reinforcing PETG with carbon fiber yields specific behaviors. According to [PERSON] et al. [20], the inclusion of carbon fibers in PETG results in a moderate reduction in damping capacity in alternating fatigue tests. This study concludes that the addition of carbon fiber significantly increases both the hardness and the elastic modulus of the material. [PERSON] et al. [21] also found that the inclusion of carbon fibers leads to increased displacements when compressive stresses are applied. Furthermore, tensile strength and stiffness are enhanced, although these outcomes are highly dependent on the orientation and deposition pattern [22]. PETG's attractive environmental performance has contributed to its increased interest. Regarding sustainability in PETG usage, some authors have thoroughly studied the material's recyclability, specifically the preservation or loss of mechanical properties after multiple recycling cycles. It has been determined that recycled PETG has a lower molecular weight than virgin PETG and lower tensile strength. Nevertheless, the flexural results of both materials are quite similar. The addition of continuous carbon fiber to the composite significantly enhances the properties of both materials, making the use of recycled PETG with added carbon fibers an appealing option from a sustainability perspective [23], even when using different concentrations of reinforcements [24]. Some systematic studies comparing the mechanical properties of parts produced with PETG and CF-reinforced PETG have been conducted; however, a reduced number of studies concerning the tribology of reinforcing materials with PETG has been detected [25; 26; 27]. Therefore, this study aims to examine the mechanical performance of parts produced with PETG and 20% CF-reinforced PETG, considering critical manufacturing parameters such as printing temperature and layer thickness. In addition, the printing limitations of PETG reinforced with carbon fiber have been studied based on the main effects in the use of PETG-CF filament on printing devices and the related defectology of printed parts. Finally, the tribological behavior resulting from the printing effects on the reinforced material has been evaluated by friction and wear analysis.
# Secure Voting Website Using Ethereum and Smart Contracts
## 3 Literature Review
ZKPs allow for verifying a statement without revealing information beyond what is necessary to prove the statement's truth. This means it is possible to establish a valid vote without revealing the voter's identity or any information about their voting preferences. One example of using ZKPs in blockchain voting is the implementation of a ZKP-based authentication mechanism, where voters provide their identity information to a trusted third party, which then generates cryptographic proof that the voter is eligible to cast a vote. The evidence is then submitted to the blockchain and verified using ZKPs, allowing the voter to cast a ballot without revealing their identity. Another example is using ZKP-based anonymous voting schemes, where voters can cast their votes without revealing their preferences or identity. This is achieved using ZKPs to prove a valid vote without revealing any information. The use of ZKPs in blockchain voting provides a promising avenue for addressing the challenge of verifying the rightful owner's identity while preserving the voter's privacy and anonymity. To address the shortcomings of previous blockchain voting systems, our proposed blockchain voting system offers three key contributions:  
1. A novel blockchain architecture with a hybrid consensus mechanism and a modular design was proposed, effectively improving the system's security, scalability, and transparency capabilities for conducting secure and efficient voting. 2. The proposed blockchain voting system integrates face recognition to deal with the issue of unauthorized access and impersonation. AIML-based face recognition can significantly enhance the security of online voting systems by reducing the risk of unauthorized voting. 3. The proposed blockchain voting system has better performance and security capabilities than other classical models and can ensure the voting results' anonymity, integrity, and accuracy while maintaining the voters' privacy.
## 1 Introduction
### Contribution  
To the best of our knowledge, all previous works considering the TWDP fading model are based on the assumption of ideal reference carrier phase extraction [7; 8; 9; 10; 11; 12; 13]. The results obtained in such a way should be considered optimistic, in general. In this paper, we consider the effect of imperfect phase recovery of reference signal on system performance when signal propagates over a TWDP channel. The reference signal recovery can be performed from modulated received signal or from pilot signal. Both ways for reference signal phase recovery over TWDP channel are open problems. For illustrative purposes, we are focusing on reference signal recovery from the pilot signal, where the loop bandwidth is much lesser than the channel bandwidth [3; 4; 5]. The signal phase at the voltage-controlled oscillator (VCO) output does not perfectly follow the phase of the signal being detected. Generally, a stochastic difference exists between the incoming signal phase and the signal phase at the VCO output. The phase noise is modeled by Tikhonov distribution according to [2; 3; 4; 5; 6]. Our goal in this paper is to determine the impact of phase noise on the BER performance and mutual information [15; 16] when multilevel phase-shift keying (PSK) signal is transmitted over a TWDP channel. We estimate the error probability in three different ways. Firstly, we present the Fourier Series Method (FSM) for estimatingthe symbol-error rate (SER) in detecting multilevel PSK signals over a TWDP channel. This method is based on representing the PDF of the composite received signal phase in terms of Fourier series with coefficients depending on the channel conditions. This method is presented in [2] for the case when multipath fading is described by Nakagami-\(m\) distribution. The main challenge in application of FSM is to express the Fourier series coefficients in appropriate analytical form for given propagation environment. Here, we derive new analytical formulas for Fourier coefficients, which provide us with the possibility to determine the SER for any value of the number of phase levels (denoted by \(M\)) and for all numerical values of TWDP channel parameters encountered in practice. Secondly, we provide formulas for numerically calculating the BER when detecting BPSK and QPSK signals followed by explanations of numerical evaluations. Thirdly, numerical values are verified by independent Monte Carlo simulations. Further, by using Monte Carlo simulations, we calculate mutual information for modulation orders of two, four, and eight. We connect error rate and mutual information with the standard deviation of phase noise, channel parameters and SNR in the case when reference signal phase recovery is performed from the pilot signal. The results presented here for QPSK correspond directly to the fourth-order quadrature amplitude modulation (4 QAM) format suggested for 5G and beyond networks [17]. We show that our results can be reduced to already published results from the literature in the special case when the assumption is introduced that the reference signal phase recovery is perfect [9].