esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments, Results, and Discussion
### Performance Evaluation
#### 4.4.3 Comparison with Similar Works  
Table 6 compares the efficacy of our work to that of comparable FL-based IDS approaches. The scope of the comparison covers the deployment year, datasets, ML classifiers, number of clients, and data distribution methods. As can be seen from the table, the proposed model is the only one that has addressed both IID and Non-IID data issues, and in previous section, we have demonstrated the performance of both of these data types. Ours is also the only one that evaluated the performance on the latest dataset (Edge-IIoTset).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IoT IDS** & **Year** & **Dataset** & **Classifier** & **No. of Clients** & **IID** & **Non-IID** \\ \hline [PERSON] et al. [33] & 2019 & Private & RNN-GRU & \(K=[5, 9, 15]\) & ✗ & ✓ \\ [PERSON] et al. [34] & 2020 & SEA & RNN-LSTM & \(K=4\) & N/A & N/A \\ [PERSON] et al. [35] & 2021 & Gas Pipeline & CNN-GRU & \(K=[3, 5, 7]\) & ✓ & ✗ \\ [PERSON] et al. [36] & 2021 & Bot-IoT & Lockedge & K = 4 & ✗ & ✓ \\ Proposed Model & 2022 & Edge-IIoTset & CNN & \(K=[3, 9, 15]\) & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between Proposed Model and Relevant Works.  
Figure 5: ROC curves for both CNN and RNN methods.
## 1 Energy Efficient Strategy for Throughput Improvement in Wireless Sensor Networks  
[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 2  
1 Department of Computer Science, Bahria University Islamabad, Islamabad 44000, Pakistan; E-Mails: [EMAIL_ADDRESS] (A.A.M.); [PERSON]] (S.K.)  
2 King Saud University, P.O. Box 92144, Riyadh 11543, Saudi Arabia; E-Mails: [EMAIL_ADDRESS] (M.I.); [EMAIL_ADDRESS] (K.S.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +92-332-516-2738.  
Academic Editor: [PERSON]  
Received: 22 September 2014 / Accepted: 13 January 2015 / Published: 23 January 2015  
**Abstract:** Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.  
**Keywords:** wireless sensor networks; network lifetime; clustering; energy-aware routing; throughput maximization
## 3 Preliminaries  
### Network Model  
It is assumed that numerous isomorphic nodes, which are constrained by restricted resources such as weakened battery, limited data storage space, and inefficient data manipulation, are randomly distributed in a 3D UASN to monitor the underwater surroundings. These nodes are considered static and they can obtain their locations by existing localization algorithms [22; 23; 24]. The nodes are equipped with acoustic communication modules to communicate with other devices, but the transmission range is limited. Additionally, a rechargeable AUV with autonomous navigation capability is employed as a mobile sink to collect the sensed data from nodes. A surface control center is deployed to execute centralized computing and recharge the AUV. The AUV starts from surface control center, travels along a designed path and moves around some specific locations named tour-points for data collection. The capabilities of bulk data communications of the sensor nodes are limited to single-hop transmission to the nearby AUV, to reduce energy consumption. As the AUV approaches a predefined location, it schedules a TDMA channel access protocol for data collection. At that point, the data of nodes within the collection range is transferred to the AUV and buffered there for further processing.
## 5 Case Study
### Sensitivity Analysis
#### 5.4.2 Sensitivity Analysis under Different Wind Power Capacities  
Figure 11 illustrates the impact of different wind power capacities (950 MW, 1000 MW, and 1050 MW) on thermal power electricity and wind power curtailment rates for systems equipped with only FS-PSUs, only VS-PSUs, and only T-PSUs. The results show that an increase in wind power integration leads to a decline in thermal generation and a rise in wind power curtailment rate. Notably, when the wind power capacity changes, the ternary pumped storage demonstrates greater robustness in thermal electricity and wind power curtailment rate compared to variable-speed systems, which in turn show more robustness than fixed-speed systems. For instance, as wind power capacity changes from 950 MW to 1050 MW, the wind power curtailment rate rises from 1.70% to 5.80% with only FS-PSUs, from 1.68% to 4.52% with only VS-PSUs, and from 0.43% to 2.36% with only T-PSUs. This suggests that a higher flexibility in pumped storage regulation enhances the robustness of the system to different wind power capacities. Furthermore, regardless of wind power capacity, the system with only T-PSUs consistently exhibits the lowest thermal power electricity and wind power curtailment rate. This verifies that the models of T-PSUs established in this paper have consistent performance under different wind power capacities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Wind Power Side**} & \multicolumn{2}{c}{**Load Side**} & \multicolumn{2}{c}{**Pumped Storage Side**} \\ \cline{2-5}  & **Wind Power** & **Capacity of PS When** & **Load** & **Capacity of PS When Load** & **Capacity of PS When PS** \\  & **Capacity/MW** & **Wind Changes/MW** & **Rate** & **Changes/MW** & **Changes/MW** \\ \hline
1 & 950 & 4 \(\times\) 90 FS-PSUs & 0.5 & 4 \(\times\) 90 FS-PSUs & 4 \(\times\) 90 FS-PSUs \\
2 & 1000 & 4 \(\times\) 90 FS-PSUs & 0.6 & 4 \(\times\) 90 FS-PSUs & 4 \(\times\) 100 FS-PSUs \\
3 & 1050 & 4 \(\times\) 90 FS-PSUs & 0.7 & 4 \(\times\) 90 FS-PSUs & 4 \(\times\) 110 FS-PSUs \\
4 & 950 & 4 \(\times\) 90 VS-PSUs & 0.5 & 4 \(\times\) 90 VS-PSUs & 4 \(\times\) 90 VS-PSUs \\
5 & 1000 & 4 \(\times\) 90 VS-PSUs & 0.6 & 4 \(\times\) 90 VS-PSUs & 4 \(\times\) 100 VS-PSUs \\
6 & 1050 & 4 \(\times\) 90 VS-PSUs & 0.7 & 4 \(\times\) 90 VS-PSUs & 4 \(\times\) 110 VS-PSUs \\
7 & 950 & 4 \(\times\) 90 T-PSUs & 0.5 & 4 \(\times\) 90 T-PSUs & 4 \(\times\) 90 T-PSUs \\
8 & 1000 & 4 \(\times\) 90 T-PSUs & 0.6 & 4 \(\times\) 90 T-PSUs & 4 \(\times\) 100 T-PSUs \\
9 & 1050 & 4 \(\times\) 90 T-PSUs & 0.7 & 4 \(\times\) 90 T-PSUs & 4 \(\times\) 110 T-PSUs \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensitivity analysis scenarios.  
Figure 11: Thermal power electricity (T\(\_\)electricity) and wind power curtailment rate under nine scenarios of wind power side.
## 3 Results and Discussion
### Analysis of the Mechanism of Curing Soft Soil
#### 3.4.4 SEM/EDS Analysis  
Figure 13 shows the micromorphology of seawater-mixed GC, G4, and G7 after curing for 28 d.  
As shown in Figure 13, after curing for 28 days, GC exhibits a large number of sheet-like products and a small number of network-like products. Based on the product shapes and related research [32], it can be concluded that GC contains a significant amount of calcium hydroxide (Ca(OH)\({}_{2}\)) and a small amount of calcium silicate hydrate/calcium aluminum silicate hydrate (C-S-H/C-A-S-H), with a less intimate bond to the soil. Calcium hydroxide contributes minimally to the soil's strength and tends to crystallize into coarse grains, weakening the bond at the interface. This makes it the weakest link in the cement soil. The weak interlayer connection may also be the origin of cracks in cement soil under stress and the first component to be eroded under erosion conditions [33]. In the G4 and G7 stabilized soils mixed with SWC, the number of flocculent C-S-H/C-A-S-H gels and acicular AFt is significantly higher compared to the cement soil samples. The C-S-H/C-A-S-H gels formed from the hydration of GGBS and ACP act as spelling agents. SO\({}_{4}\)\({}^{2-}\) in the slag reacts with C-S-H to adsorb free water into crystal water, generating AFt, which acts as a filler [34]. The abundance of hydration products in the stabilized soil creates a dense spatial network structure by cementing and filling the soft soil particles, enhancing the overall density and structural integrity.  
Figure 13: SEM images of GC (**a**), G4 (**b**), and G7 (**c**) at 28 d.  
Through the tests described in Section 3.3.2, it was observed that under sea erosion conditions, cement soil samples exhibited a unique white substance on their surfaces and developed cracks, while the stabilized soil samples remained intact. Figure 14 shows the microscopic morphology of the samples after 60 days of sea erosion. From Figure 14a, it can be seen that large amounts of calcium sulfate and calcium carbonate crystals were present in the cement soil according to the analysis of Energy Dispersive Spectroscopy (EDS). These crystals not only failed to provide a cementing effect but also caused excessive swelling in the cement soil, leading to cracking. In contrast, no such issues were observed in the stabilized soil samples G4 and G7.  
By analyzing the microscopic morphology of the unique white substance on the surface of the cement soil sample (Figure 15), it was found that the surface was covered with large amounts of loose and porous calcium carbonate according to the analysis of Energy Dispersive Spectroscopy (EDS). These calcium carbonate crystals did not prevent sea erosion; rather, the seawater penetrated through the pores and continued to erode the sample from within. This is why large amounts of calcium sulfate and calcium carbonate crystals were observed in the cement soil sample.  
Figure 14: SEM image of GC (**a**), G4 (**b**), G7 (**c**) and EDS of GC (**a**) after being immersed in seawater for 60 d.
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 5 Backup Path Routing and Spectrum Allocation Algorithm
### Backup Path Construction Algorithm  
The backup path construction algorithm (BPC) contains the following steps: first, if the link (\(u\),\(v\)) belongs to the primary path of some wireless request, then the link (\(u\),\(v\)) is hidden. The purpose is not to include the primary link in the process of constructing the backup path. If the link (\(u\),\(v\)) does not belong to the primary path of some wireless request, then the link weight of all the available channels is calculated according to Equation (14). Second, the backup path is constructed using the Dijkstra algorithm. If a backup path is constructed, then \(\beta(s_{i},d_{i})\) is returned, otherwise, the null path is returned.
```
Input:\(G\) = (\(V\),\(E\)), \(\gamma_{i}\) = (\(s_{i}d_{i}\),\(b_{i}\)) Output: \(\beta(s_{i},d_{i})\) (1)while (\(\forall(u,v)\in E\)){  if((\(u\),\(v\)) is Primary link for the accepted request){  if(3) Hide (\(u\),\(v\));  else{  while (\(\forall k\in K(u,v)\))}  Calculate \(f_{2}^{k}(u,v)\) according to Equation (14);  if // end while (7) // end if (9) \(\mid//\) end while (10) \(\beta(s_{i},d_{i})\leftarrow\) Dijkstra(\(s_{i},d_{i}\)); (11) if(\(\beta(s_{i},d_{i})\) is found){  While ((\(\forall\)\(u\),\(v\)) \(\in\) \(\beta(s_{i},d_{i})\)){  Calculate \(\eta^{k}(u,v)\) according to Equation (15); } }  return \(\beta(s_{i},d_{i})\);  else{  return null; }// end if
```
**Algorithm 4**BPC
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 3 Analysis of Results
### Prediction Models of Resilient Modulus
#### 3.2.1 Uzan-Witczak Model  
[PERSON] [38] introduced deviatoric stress and bulk stress to characterize the influence of stress state on the resilient modulus, and established the nonlinear constitutive model, as shown in Equation (2).  
\[M_{R}=k_{1}\theta^{k_{2}}\sigma_{d}{}^{k_{3}} \tag{2}\]  
where \(\sigma_{d}\) is the deviatoric stress and \(k_{1},k_{2}\) and \(k_{3}\) are regression coefficients.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**(a) Improved red clay with 3\% cement** & \multicolumn{2}{c}{**\begin{tabular}{c} Variance** \\ **\end{tabular}} & \multicolumn{2}{c}{**\begin{tabular}{c} Degree of** \\ **\end{tabular}} & \multicolumn{2}{c}{**Mean Square**} & \multicolumn{2}{c}{**F Value**} & \multicolumn{2}{c}{**\
\begin{tabular}{c} Critical Value** \\ **a = 0.05** \\ \end{tabular}} & \multicolumn{2}{c}{**Significance**} \\ \hline Deviatoric stress & 2397.41 & 3 & 799.136 & 134.39 & 3.86 & Significant \\ Confining & 1832.63 & 3 & 610.877 & 102.73 & 3.86 & Significant \\ pressure & 53.52 & 9 & 5.946 & & & \\ Sum & 4283.56 & 15 & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c} **(b) Improved red clay with 3\% lime** \\ \hline
**Source of** \\
**Variation** \\ \end{tabular} & \begin{tabular}{c} **\begin{tabular}{c} Variance** \\ **Sum** \\ \end{tabular} & \begin{tabular}{c} **Degree of** \\ **Freedom** \\ \end{tabular} & \begin{tabular}{c} **Mean Square** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **F Value** \\ **F Value** \\ \end{tabular} & \
\begin{tabular}{c} **
\begin{tabular}{c} **Critical Value** \\ **a = 0.05** \\ \end{tabular} \\ \hline Deviatoric stress & 1853.83 & 3 & 617.942 & 178.36 & 3.86 & Significant \\ Confining & 1870.63 & 3 & 623.544 & 179.98 & 3.86 & Significant \\ Presure & 31.18 & 9 & 3.465 & & & \\ Sum & 3755.64 & 15 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Two-factor variance analysis of resilient modulus.  
Figure 2: Variation of resilient modulus with deviatoric stress and bulk stress. **(a)** Time 3% OMC \(M_{\mathrm{r}}-\sigma_{d}\); **(b)** Time 3% OMC \(M_{\mathrm{r}}-\sigma_{\theta}\); **(c)** Cement 3% OMC \(M_{\mathrm{r}}-\sigma_{d}\); **(d)** Cement 3% OMC \(M_{\mathrm{r}}-\sigma_{\theta}\).  
\(\theta\) is the bulk stress that is given as  
\[\theta=\sigma_{1}+\sigma_{2}+\sigma_{3}\]  
where \(\sigma_{1}\) is the major principal stress; \(\sigma_{2}\) is the intermediate principal stress, and \(\sigma_{3}\) is the minor principal stress or confining pressure (= \(\sigma_{2}\) for \(M_{R}\) test in triaxial test)  
However, this prediction model has some limitations: when \(\sigma_{d}\) equals 0, the result of Equation (2) tends to infinity. When \(\sigma_{1}\) and \(\sigma_{3}\) equal 0, the result is 0\(\cdot\infty\).
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 1 Introduction  
With the growing use of financial services in smartphones, robust security is required to protect mobile devices from unauthorized access. Nowadays, one-time or static authentication mechanisms such as PIN-, pattern-based authentication, and biometrics, e.g., TouchID, and FaceID, are widely deployed on smartphones. However, they authenticate users only once at the initial log-in and are vulnerable to shoulder-surfing or presentation attacks [1].  
Continuous authentication is a promising way to provide an additional security layer for mobile devices, which monitors user identities implicitly through their behavioral biometrics using device built-in sensor data [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].  
Unlike physical biometrics, which utilizes human physical attributes such as face and fingerprint, behavioral biometrics exploits user behavioral patterns such as screen-touch gestures and gait patterns. Physical biometrics are generally more unique and stable than behavioral ones; however, they require users' cooperation to capture their biometric traits, such as touching a sensor with a finger or turning a face to a camera. In contrast, behavioral biometrics can capture users' biometric data tacitly with built-in sensors of mobile devices such as the touch screen, accelerometer, and gyroscope. Therefore, behavioral biometrics can actualize implicit continuous authentication, further enhancing the security of mobile devices without deteriorating the user experience.  
For deployment, mobile continuous authentication systems first capture sensor data of a genuine user (and impostors if available), which is used to construct the user's profile. Then, claimants are monitored during re-authentication time and judged as \"accept\" or \"reject\".
### Modulation on the Complex Amplitude in Real Space
#### 3.1.2 Double-Path Generation Method Based on Holographic Gratings
Based on the above relations, if only concentrating on the beam center and attempting to achieve an equal-amplitude superposition during propagation, the relationship between the duty ratio and the radial wave number becomes quite simple as  
\[\frac{\left(1-\frac{a_{2}}{d}\right)^{2}\sin^{2}(\frac{a_{1}}{d}\pi)}{\left(1- \frac{a_{1}}{d}\right)^{2}\sin^{2}(\frac{a_{2}}{d}\pi)}\cdot\frac{k_{r1}^{2}}{k _{r2}^{2}}=1 \tag{22}\]  
This means the equal-amplitude superposition can be effectively guaranteed during propagation by adjusting the duty ratio \(a_{1}/d\) and \(a_{2}/d\). The experimental results are shown in Figure 7b and c. The top row of Figure 7b shows the total intensity distribution while the remainder are the intensity distributions after a linear polarizer. The second and third rows show the situation when the amplitude is modified by adjusting the duty radio of 2-D BP-HG where the field center can be completely extinguished after a polarizer. This means the desired linear polarization is generated accurately. However, if no amplitude modulation is implemented at the initial plane, the central amplitude of two superposed beams will be unequal during propagation in free space. An undesired elliptical polarization which cannot be completely extinguished after a polarizer, as shown in the bottom row of Figure 7b, will also occur. The orientation angle of the central linear polarization as a function of the propagation distance is shown in Figure 7c. When the radial wave number of two superposed beams are changed, the experimental results all show extremely high linear fitness and good agreement with the theoretical calculations, which means a stable rotation of central linear polarization with controllable rotation rate can be achieved. The highest rotation rate achieved based on the amplitude-phase joint modulation method is \(\sim\)6.7\({}^{\circ}\)/mm. Relying on this amplitude-phase joint modulation method, two coherent beams with orthogonally circular polarization, different radial wave number and adjustable amplitude ratio are collinearly superposed to achieve the accurate and controllable manipulation of polarization distribution along the propagation direction in free space.
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 3 Results
### GHG Emissions of Multiblend JET A-1  
Based on the GHG emissions of the individual sustainable aviation fuels described in Section 3.1 and assessment of the provision of fossil JET A-1 (the LCA of JET A-1 resulted in a value of 90 gCO\({}_{2}\)-eq./MJ), GHG balances were carried out for the multiblends produced, including transport from the blending site to the airport and the use of the multiblends in aircraft. The GHG reduction of the multiblends for different supply chains (Section 2.2) is described and discussed below.  
Figure 4: Specific GHG emissions of the investigated sustainable aviation fuels (SAF). For readability purposes, the \(x\)-axis displaying GHG emissions from HEFA production is enlarged by a factor of 10.  
Figure 6 shows a clear GHG reduction of up to 35% for the multiblends compared to fossil JET A-1. The largest contribution to total GHG emissions was caused by engine combustion in aircraft. The GHG effect of emissions at high altitudes was not considered here; for the sake of simplicity, it was assumed that this effect would be the same for all of the SAF and multiblends considered here. As biogenic CO\({}_{2}\) emissions were not taken into account in the GHG balance, the proportion of fossil JET A-1 was decisive. This is shown by the higher GHG emissions in case B compared to case A. The reason for this was the low blending rate of SIP and the lower HEFA blending rate in line with the ASTM specification, which correspondingly led to higher proportion of fossil JET A-1 in the mix (Section 2.2). In this composition, the GHG emissions associated with the provision of sustainable aviation fuels had no significant impact on the total emissions.  
Figure 5: Sensitivity analysis of GHG accounting for ATJ production.  
Figure 6: GHG emissions and GHG mitigation potential of the multiblends for the supply chains case A and case B compared to the JET A-1 reference.
## 3 Test Results
### Splitting Tensile Strength  
According to the Korea Expressway Corporation standard, an emergency pavement repair material must attain a splitting tensile strength of at least 4.2 MPa after 28 days. This criterion was met by all of our HFLMC mixtures with no significant differences in performance (Figure 5). However, the splitting tensile strengths of the fiber-reinforced mixtures were higher than those of plain mixtures. Again, hybrid fiber systems were more effective than single fiber systems in terms of strength enhancement.  
Figure 4: Flexural strength of HFLMC mixtures.  
Figure 5: Splitting tensile strength of HFLMC mixtures.  
In general, adding fibers to concrete is more effective for increasing tensile strength than compressive strength [30]. This is because fiber reinforcement suppresses crack formation and growth, the main factors responsible for concrete fracture [31]. In addition, crack suppression is more effective with hybrid fiber mixtures than with single-type fibers [27; 28; 29; 30]. In the current study, the hybrid fiber system satisfied the 4.2 MPa requirement after only 7 days. After 28 days, the splitting tensile strengths of mixtures containing PVA, jute, nylon, jute+ nylon, PVA+jute, and PVA+ nylon fibers increased over those of plain mixtures by 7.27%, 8.65%, 7.19%, 12.24%, 12.12%, and 13.31%, respectively. The PVA+nylon hybrid fiber system yielded the highest splitting tensile strength.
###### Abstract  
Identifying the molecular mechanisms underlying radioresistance is a priority for the treatment of RMS, a myogenic tumor accounting for approximately 50% of all pediatric soft tissue sarcomas. We found that irradiation (IR) transiently increased phosphorylation of AktI, Src, and Cav1 in human RD and RH30 lines. Synthetic inhibition of AktI and Src phosphorylation increased ROS levels in all RMS lines, promoting cellular radiosensitization. Accordingly, the elevated activation of the AktI/Src/Cav1 pathway, as detected in two RD lines characterized by overexpression of a myristolylated AktI form (myrAkt1) or Cav1 (RDCav1), was correlated with reduced levels of ROS, higher expression of catalase, and increased radioresistance. We found that treatment with cholesterol-lowering drugs such as lovastatin and simvastatin promoted cell apoptosis in all RMS lines by reducing AktI and Cav1 levels and increasing intracellular ROS levels. Combining statins with IR significantly increased DNA damage and cell apoptosis as assessed by \(\gamma\) histone 2 AX (rH2 AX) staining and FACS analysis. Furthermore, in combination with the chemotherapeutic agent actinomycin D, statins were effective in reducing cell survival through increased apoptosis. Taken together, our findings suggest that the molecularly linked signature formed by AktI, Src, Cav1, and catalase may represent a prognostic determinant for identifying subgroups of RMS patients with higher probability of recurrence after radiotherapy. Furthermore, statin-induced oxidative stress could represent a treatment option to improve the success of radiotherapy.  
Akt1; catalase; caveolin-1; radioresistance; rhabdomyosarcoma; Src