esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
For this review, an electronic search was performed through the PubMed, Medline, Cochrane Library, Embase, and Scopus databases; the search words were \"Materials\" and \"Clear aligners\".  
The following parameters were considered for the selection of the studies:  
* Studies conducted in the last 10 years in English;
* Studies concerning only the dental field;
* Studies describing the currently used clear aligner materials and their characteristics;
* Articles for which the material of the aligner was not specified were excluded from the analysis.  
A total of 737 articles were identified within the aforementioned search in the databases. From this selection, all the articles found were entered into EndnoteWeb(r) to eliminate duplicates. Finally, 720 articles were considered. After screening, based on the title and abstract, 128 studies were considered eligible.  
These 128 articles were subsequently included in the Rayyan program to verify the relevance of the above inclusion and exclusion criteria established by two reviewers, who also took care of the data collection. Inclusion was confirmed by a third supervisor who intervened in the case of disagreement (Figure 2).  
Figure 1: Generation of models and clear aligners using the CAD/CAM technique.  
The following specific criteria based on study content served to determine the final inclusion of the studies.  
Inclusion Criteria:  
* Studies linking different types and materials of clear aligners;
* Studies that consider the mechanical, thermal, physical, chemical, viscoelastic, and optical properties of the materials.  
Exclusion criteria:
* Studies that consider the variation of material properties following aging;
* Studies concerning only the cytotoxicity of the materials;
* Studies focusing on device design;
* Studies comparing clear aligners and fixed appliances.  
The application of specific criteria led to the selection of 34 eligible studies, for which the full text was obtained and assessed. Finally, 23 articles were excluded because they did not match the previously established selection criteria, and 11 relevant articles were included. These studies evaluated 17 different clear aligner materials and their properties by comparing the different materials analyzed:  
* Thermal, mechanical, physicochemical, and viscoelastic properties;
* Roughness and surface energy;
* Stability against intra-oral coloring agents, optical properties, and water absorption behavior;
* Structural variations (hardness and infrared spectroscopic analysis);
* Properties of absolute stress and stress decay.
## 7 Performance Results of Proposed Algorithms
### Discussion  
The proposed algorithms can enhance both the network energy consumption and average throughput because they produce a mixed combination of the scenarios shown in Figures 12-15 with the aid of the SDN. This means that certain nodes may only use the WiFi interface, others may only use the LTE interface, and some may use both interfaces based on the channel conditions of their interfaces. This implies that the proposed algorithms indirectly distribute the LTE and WiFi network resources among the receiving nodes, helping them to achieve high average throughput with low energy consumption.  
Consequently, based on both algorithms, the SDN controller oversees and makes informed decisions about which interface(s) should be used at each node. This makes the proposed SDN-based scheme able to reduce the energy consumption by 19% compared with MPTCP BALIA or MPTCP Cubic, while achieving, on average, 8% higher throughput than them, as can be observed from Figures 12-15 and Tables 2 and 3. This is because these MPTCP variants activate both interfaces even if one suffers from a poor channel condition, leading to more energy consumption without a significant enhancement in the average throughput.
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 4 Model Testing and Evaluation  
### Network-Based Clustering Use Case: Router/Coordinator Node Capacity  
From a network performance point of view, the network scalability/sparsity scenarios were explored with the increase in sampling rate to reflect the packet loss measurements. Mainly, the testing relates to the network connectivity (router or gateway) capacity tested in the virtual platform using the Contiki Cooja simulator. Herein, the physical network test scenario objective is aligned with our road traffic analysis capacity and virtualization capacity testing scenarios [8; 9]. The physical network test was performed, and data were collected when a set of (10 and 20) TI CC2538s connected to a coordinator node reported RSSI values based on a set of sampling rates (1, 2, 3, 4, and 5 samples/s). Figure 25 depicts the total number of packets lost when 10 TI CC2538 nodes are connected to a coordinator node: seven packets at five samples/second. However, the total number of packets lost increases to 12 packets at the same sampling rate when the number of nodes connected to a coordinator node is set to 20 nodes. Although the physical network experienced slightly more packet loss due to real-world factors impacting the quality of the RSSI signal, such as the network deployment environment, obstacles, etc., the physical test indicated the importance of having the virtualization for network soft trials. Variations in sampling rates, the number of nodes, and the type of function of a node in the virtual platform can provide the best scenario for a certain use case that can be realized in the physical network.
## 3 Actuator Line Numerical Analysis
### Helicopter Main Rotor Numerical Analysis  
Following the preceding analysis, a helicopter MR is examined. The AL numerical parameters have been tested on case 851 which is a forward flight case at 12.3 m/s flight  
Figure 12: Minimum value of \(\epsilon\) investigation. Flapwise deflection in axial uniform free-stream flow at 8 m/s.  
Figure 13: Minimum value of \(\epsilon\) investigation. Flapwise deflection in axial uniform free-stream flow at 11 m/s.  
speed. The blades have been considered to be rigid in this study as well. A lot of conclusions extracted from the WT analysis have been used here without further investigation, namely:  
* As depicted in Figure 14, a hexahedral grid set-up has been utilized;
* The ratio \(\Delta w=10\Delta x\) is used again for the near wake region;
* The Gaussian kernel is defined by the relationship \(\epsilon=max(2\Delta x,0.47c)\) (no further increase of \(\epsilon_{min}\) was found to be necessary for MR aeroelastic simulations).  
In Table 6, the rotor thrust predictions are listed with respect to different values of \(\Delta x\). Thrust was preferred as a metric over power in this case, due to its higher sensitivity to changes in the numerical parameters in helicopter cases. Ratio \(\Delta x=c/8\) is considered to be the optimum compromise between accuracy and computational cost.  
In Table 7, the effect of \(\#strips\) is investigated. Due to constant airfoil along the blade span, reasonable accuracy in thrust prediction is obtained even with a lower number of control points. For MR aeroelastic simulations, a uniform distribution of control points with \(\Delta r=2\Delta x\) is used. Since \(\#strips\) does not penalize computational cost, the main criterion for choosing this parameter is the smooth radial distribution of the aerodynamic forces.  
The time-step \(\Delta t\) is investigated in Table 8. In this case, the rule of thumb \(\left(\Delta t=\Delta x/V_{tip}\right)\) seems to be satisfactory. Overall blade grid and time resolution requirements are found to be relaxed in the helicopter case as higher values for \(\Delta r\) and \(\Delta t\) provided (\(\#strips\)) and time independent loads.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & _Thrust_ \\ \hline \(\Delta x=c/4\) (\(830\)\(thousand\ cells\)) & \(-3.4\%\) \\ \(\Delta x=c/6\) (\(1.65\)\(million\ cells\)) & \(-1.7\%\) \\ \(\Delta x=c/8\) (\(4.22\)\(million\ cells\)) & \(-0.6\%\) \\ \(\Delta x=c/10\) (\(9.33\)\(million\ cells\)) & \(4.706\) kN \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variation of thrust [kN] with increasing the structured region characteristic length \(\Delta x\) [m]. Reference thrust corresponds to minimum value \(\Delta x=c/10\).
## 4 Experiment Results
### Compared Methods  
In the scenario described above, to compare with existing supply chain inventory management research, we select several methods that have performed well in recent studies by researchers as comparisons. The selected methods are as follows:  
SRL: The method that treats the problem as a single-agent reinforcement learning problem [18].  
MAMPO: There are some research about supply chain inventory management based on MARL [28], and our proposed method is modified from the MAPPO algorithm. So the MAPPO algorithm is selected for comparison.  
DualPolicy-w/oT (ours): Our method, but without the information transmission mechanism as an ablation experiment for comparison.  
DualPolicy-IT (ours): Our MARL method based on the dual policy model with the information transmission mechanism.  
The experimental results are obtained by training the agents in the environment with different algorithms. During the training, 10 environment parallel samples are used, and the length of each episode is 1000. For each algorithm or control experiment, 10 training was performed, and the solid line in the results represents the average of ten trainings, and the shadow represents the maximum and minimum values of 10 trainings.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 5 Experimental Results  
In this section, we investigate the BER and FER performance metrics to evaluate SC decoding algorithms through Monte Carlo simulations. For simplicity, the non-systematic SC decoding algorithm is only considered in this work. The polar codes are modulated using BPSK over AWGN channels. Fractional values are disregarded during quantization to enforce integer-domain calculations, except for the floating-point SC decoding algorithm, which accounts for infinite decimal precision in its computations. Additionally, fixed-point calculations represent values with a limited number of decimal places. The polar code construction follows the GA method and is optimized for a design-SNR of 2 dB for all experiments.
## 3 Results and Discussion  
In this section, the system is evaluated in terms of the BER and EVM. The BER corresponds to the proportion of incorrectly received bits compared to the total number of transmitted bits [39]. The EVM quantifies the deviation of a received signal from its ideal reference in the IQ plane, which is expressed as the root-mean-square (RMS) error normalized to the ideal signal power [40]. A more detailed theoretical background on the BER and EVM can be found in [41, 42].
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Autonomous and Cooperative Driving  
The advent of autonomous and cooperative driving technologies presents new opportunities and challenges for V2V communication. Future research should focus on developing advanced cooperative driving algorithms and communication protocols to enable safe and efficient coordination between autonomous vehicles and human-driven vehicles. Additionally, exploring the integration of V2V communication with other sensing modalities, such as LiDAR and radar, can further enhance situational awareness and collision avoidance capabilities in autonomous driving scenarios.
# Permeability and Stiffness Assessment of Paved and Unpaved Roads with Geocomposite Drainage Layers
## 3 Test Methods
### Core-Hole Permeameter (CHP) Test  
The CHP device (Figure 4a) was developed to assess the in situ drainage capacities of pavement base systems. The test method follows the procedure in ASTM D6391 [16]. In this study, the CHP test results were used to compare the relative drainage capacities of the foundation system with and without a geocomposite layer placed at the pavement/base layer interface. The condition this simulates is the ability to drain any infiltrated water through the pavement surface via joints/cracks. [PERSON] et al. [17] showed that by using the American Association of State Highway and Transportation Officials (AASHTO) 1993 design guide PCC pavement design procedure, an increase in the drainage coefficient (\(C_{d}\)) value of the base material from 1.0 to 1.2 can reduce the required PCC pavement thickness by up to 10%. The CHP test uses the falling head method to measure the in situ hydraulic conductivity of the drainable base and geocomposite drainage layers of the paved test sections after drilling cores, as shown in Figure 4b,c. The test involves drilling a 15 cm diameter core hole through the pavement surface to the underlying base or geocomposite layer, inserting the device and sealing it against the bottom foam ring and interior of the core hole using a rubber tube inflated to 135-175 kPa, and recording the rate of water head loss from the device over a period of 30 to 60 min. The hydraulic conductivity (K) of the tested  
Figure 3: Photo of the large-scale horizontal permeameter test (HPT) device used in this study.  
layer was calculated using Equation (1). The testing procedure is described in detail by [PERSON] et al. [18].  
\[K=R_{t}G_{1}\frac{\ln(H_{1}/H_{2})}{(t_{2}-t_{1})} \tag{1}\]  
\[R_{t}=\ 2.2902\Big{(}0.9842^{T}\Big{)}/T^{0.1702} \tag{2}\]  
\[G_{1}=\big{(}\pi d^{2}/11D_{1}\big{)}[1+a(D_{1}/4b_{1})] \tag{3}\]  
where \(H_{1}\) and \(H_{2}\) are the effective heads (cm) at time \(t_{1}\) and \(t_{2}\) (s), respectively; \(R_{t}\) is the ratio of the kinematic viscosity of the permeant at the temperature of the test during time increment \(t_{1}\) to \(t_{2}\) to that of water at 20 \({}^{\circ}\)C; T is the temperature of the test permeant (\({}^{\circ}\)C); \(d\) is the inside diameter of the standpipe (3.6 cm for the top standpipe and 33 cm for the middle standpipe); \(D_{1}\) is the inside diameter of bottom casing (12.7 cm); \(b_{1}\) is the thickness of tested layer (cm); and \(a\) is equal to \(+1\) for an impermeable base at \(b_{1}\), 0 for the infinite depth of the tested layer (i.e., \(b_{1}>20D_{1}\)), and \(-1\) for a permeable base at \(b_{1}\).
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 8 Results and Discussion
### The Results Measured from the Final Device
Furthermore, our design outperforms the work presented in [7] in terms of accurate ECG monitoring. We conducted tests using both a patient simulator and human signals, while they proposed a simple circuit simulated on Proteus software and built it on a breadboard without employing any microcontroller. Importantly, they did not measure a real ECG signal from the human body. The ECG signal they measured was characterized by noise, instability, and inaccurate peaks, rendering their system unsuitable for effective ECG monitoring. In addition, [PERSON] et al. [8] conducted their study using a stripboard instead of a microcontroller, and they solely tested the implementation using LabView. On the other hand, [PERSON] et al. [31] did not construct a fully functional ECG device; instead, they connected a basic circuit on a breadboard and displayed the data on an oscilloscope. The circuit design details were not provided, and no human tests were conducted. Their data collection was limited to a patient simulator, and they did not demonstrate different cases or heart rates. Moreover, the presented signal did not exhibit S and T waves. In conclusion, our group is the first to develop a comprehensive ECG monitoring device utilizing Raspberry Pi. A recent study conducted by [PERSON] et al. [32] introduced a portable system based on Raspberry Pi for diagnosing heart valve diseases. However, it is important to note that their monitoring system is specifically designed to measure the phonocardiogram (PCG) signal, not the ECG signal.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### 5G Cryptomining Attack Detection Scenario
#### 9.1.2. Cryptomining Attack Detection Using Machine Learning
[39] uses a much larger feature set, consisting of a total of 51 informative features that were extracted using the Tstat tool. In addition, more complex models, such as Random Forest and Deep Neural Networks, were applied to increase the accuracy and improve the identification of cryptocurrency mining activity. More importantly, encrypted traffic was also taken into account in this work, providing a more realistic approach to detecting cryptocurrency mining activity at the network level for real-life scenarios. Following a more traditional direction, [PERSON] et al. [42] proposed a method to detect and block unencrypted cryptocurrency mining connections originating from a web browser when the user visits web pages that have embedded third-party resources dedicated to performing cryptocurrency mining as a background activity while the user navigates (e.g., CoinHive, Crypto-Loot). Their approach is based on the deployment of a proxy server within the local network and the use of DPI to analyze real-time traffic and detect and block web pages calling cryptomining scripts. However, this approach has several disadvantages. First, it does not cover all types of mining traffic, as it only applies to traffic generated by the web browser and, therefore, considerable cryptomining activity may go unnoticed. Second, using DPI to analyze real-time traffic is a very demanding process that requires a large number of computational resources to be effective and also fast enough to enable the real-time detection of cryptomining activity. Third, the use of DPI to analyze real-time traffic can negatively affect the user experience when browsing the web if a high number of false positives are triggered by the detection mechanism. Finally, this approach is not applicable to encrypted traffic, which, as mentioned above, represents a considerable percentage of traffic on today's Internet. The method we apply in this exercise follows the same approach proposed by [39]. We train complex ML models using a large set of network features obtained from flow statistics to perform cryptomining activity detection for both non-encrypted and encrypted connections with a high level of accuracy.
## 2 Methodology
### Sobol Method  
The Sobol method is widely employed in sensitivity analyses, as it can elucidate the individual impacts of each random variable or group of random variables on the system response variance.  
The unconditional variance \(V(Y)\) can be decomposed into partial variances accordingly, where \(V_{i}\) and \(V_{ij}\) represent the variances of \(f_{i}\) and \(f_{ij}\) respectively:  
\[V(Y)=\int_{\Omega}f^{2}(X)dX-f_{0}^{2} \tag{7}\]  
\[V(Y)=\sum_{i=1}^{t}V_{i}+\sum_{i=1}^{t-1}\sum_{j=i+1}^{t}V_{ij}+\ldots+V_{1, \ldots t} \tag{8}\]  
Using these partial variances, the first-order and total Sobol sensitivity indices can be defined on the basis of the total variance:  
\[S_{i}=\frac{V_{i}}{V(Y)}=\frac{V_{x_{i}}\left[E_{X\sim i}(Y\mid x_{i})\right] }{V(Y)} \tag{9}\]  
\[ST_{i}=1-\frac{V_{\sim i}}{V(Y)}=\frac{E_{X\sim i}[V_{x_{i}}(Y\mid X\sim x_{i} )]}{V(Y)} \tag{10}\]  
The first-order index \(S_{i}\) indicates that the fraction of the output variance is reduced on average by fixing \(x_{i}\) within its range. The total sensitivity index, \(ST_{i}\), encompasses the contributions of the first-order index \(S_{i}\) and the comprehensive effects of all higher-order interaction terms. The difference between \(S_{1}\) and \(S_{T}\) can reflect the importance of the interactions of this parameter with other parameters.
## 4 Results
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{3}{c}{**Dataset: GSE10072**} \\ \hline Prob\_ID & Gene Symbol & Gene Name \\ \hline
209074\_s\_at & FAM107A & family with sequence similarity 107 member A (FAM107A) \\
200700\_s\_at & KDELR2 & KDEL endoplasmic reticulum protein retention receptor 2 (KDELR2) \\
201983\_s\_at & EGFR & epidermal growth factor receptor (EGFR) \\
210852\_s\_at & AASS & aminodipate-semialdehyde synthase (AASS) \\
202037\_s\_at & SFR1 & secreted fizzled related protein 1 (SFRP1) \\
203295\_s\_at & ATP1A2 & ATPase Na\({}^{*}\)/K\({}^{+}\) transporting subunit alpha 2 (ATP1A2) \\ \hline \multicolumn{3}{c}{**Dataset: GSE19804**} \\ \hline Prob\_ID & Gene Symbol & Gene Name \\ \hline
1555636\_at & CD300 LG & CD300 molecule like family member g (CD300 LG) \\
206938\_at & SRD5A2 & steroid 5 alpha-reductase 2 (SRD5A2) \\
44654\_at & G6 PC3 & glucose-6-phosphate catalytic subunit 3 (G6 PC3) \\
45297\_at & EHD2 & EHD domain containing 2 (ETHD2) \\
1552696\_at & NIPA1 & non-imprinted in Prader-Willi/Angelman syndrome 1 (NIPA1) \\
45687\_at & prr14 & proline-rich 14 (PRR14) \\
203373\_at & SOCS2 & suppressor of cytokine signaling 2 (SOCS2) \\
210984\_x\_at & EGFR & epidermal growth factor receptor (EGFR) \\ \hline \multicolumn{3}{c}{**Dataset: GSE4115**} \\ \hline Prob\_ID & Gene Symbol & Gene Name \\ \hline
205560\_at & PCSK5 & pro-protein convertase subtilisin/kexin type 5 (PCSK5) \\
200003\_s\_at & MIR680 & microRNA 6805 (MIR6805) \\
201983\_s\_at & EGFR & epidermal growth factor receptor (EGFR) \\
210187\_at & FKBP1A & FK506 binding protein 1A (FKBP1A) \\
205364\_at & ACC0X2 & acyl-CoA oxidase 2 (ACOX2) \\
206628\_at & SLCSA1 & solute carrier family 5 member 1 (SLC5A1) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The genes selected by \(L_{1/2}\) for different datasets. Figure 3: The box plots of selected genes by \(L_{1/2}\) for dataset GSE10072. Figure 4: The box plots of selected genes by \(L_{1/2}\) for dataset GSE19804. Figure 5: The box plots of selected genes by \(L_{1/2}\) for dataset GSE4115.