esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Raw Materials, Experimental Methods and Theory Background
### Raw Materials and Specimen Preparation
#### 2.1.2 Specimen Preparation  
Traditional dense-graded asphalt mixture is a well-graded asphalt mixture and is applied extensively in asphalt pavement construction in China [42]. In this study, the median gradation of asphalt mixture (AC-13) was selected, as shown in Figure 1, and the upper and lower limits of AC-13 are also illustrated. Asphal mixtures reinforced with eco-friendly basalt fiber were made by a standard Marshall design method [43; 44] approximately 0.4% basalt fiber with a length of 6 mm was added into asphalt mixtures. In accordance to JTG E20-2011 [45], the detailed preparation procedures are presented as follows:  
* Step (1): the pre-heated aggregates and basalt fiber were mixed together in a mixing pot for 90 s in order to make basalt fiber uniformly dispersed in aggregates.
* Step (2): the pre-heated asphalt AH-90 was weighted and poured into the mixing pot and the mixture was blended for 90 s.
* Step (3): the pre-weighted limestone powder was added into the mixing pot and then blended for 90 s.
* Step (4): Marshall specimens of AC-13 with a diameter of 101.6 mm and a height of 63.5 mm were prepared by compacting 75 blows on each side.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Measurement** & **Technical Criterion** \\ \hline Penetration @ 25 \({}^{\circ}\)C, 100 g, 5 s (0.1 mm) & 88 & 80–100 \\ Softening point (\({}^{\circ}\)C) & 47 & \(\geq\)44 \\ Ductility @ 10 \({}^{\circ}\)C, 5 cm/min (cm) & 43.5 & \(\geq\)30 \\ Solubility (trichloroethylene, \%) & 99.8 & \(\geq\)99.5 \\ Density @ 15 \({}^{\circ}\)C (g/cm\({}^{3}\)) & 1.05 & – \\
**RTFOT** & & \\ Mass loss (\%) & 0.22 & \(\pm\)0.8 \\ Penetration ratio @ 25 \({}^{\circ}\)C (\%) & 66 & \(\geq\)57 \\ Ductility @ 10 \({}^{\circ}\)C, 5 cm/min (cm) & 28 & \(\geq\)8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of asphalt AH-90 in this study.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 3 Results
### The Advantage of the GAN Model  
In this paper, the novel usage of the GAN model is different from before. The Generator's output is a fake image that links to the prediction values. GAN changes from an image-to-image translation network to an image-to-number translation network. Based on this method, we only need to adjust the designed image (fake image and target image) if the number of Zernike coefficients is increased to 36, as shown in Figure 1b. Additionally, we then used the modified datasets to train and test the GAN model. Moreover, the RMSE is about 0.0451 \(\pm\) 0.0237\(\lambda\) with the ideal image, and the result is also better than that in a previous article [25]. This means that the GAN model can predict more or less Zernike coefficients but does not need to change any layers or parameters.
## 3 Methodology  
### Data and Methodology  
In order to fulfill the aim of the study, this research employed an extensive data of the 25 public research institutions in Korea during summer and fall of 2018. As for details, first, this research acquired organizational, man power-related data, and pay structure of the institutions. Second, this study acquired performance-related data that were published to have validity. Third, by processing the two data sets, this research produced a AHP survey text, which has generated new data to be analyzed in the research. Then, this study utilized three types of methods. First, a three-stage analytical hierarchy process (AHP) technique was employed to set up an in-depth analysis. Second, an organizational analysis of all the 25 public research institutions was performed by using their manpower-related data. Third, this study utilized the quality function deployment (QFD) analysis to match the potential roles of research institutions with topics under the 4 th industrial revolution. Lastly, this study linked the AHP and QFD scores to derive implications for the institutions and their roles in the context of the 4 th industrial revolution.  
The AHP analysis (Table 2) was carried out to assess the existing data. While it is possible to gather and analyze institution-level performance data, it is not easy to evaluate articles and patents in pure numbers. The method will urge experts to assign weight to individual institutions by looking at \"all possible\" performance data of the institutions. The AHP analysis was conducted with 25 experts who have been active in the research community for more than 20 years after their Ph.D. This study conducted a three-stage AHP survey to obtain relative weights of factors, which will be multiplied to organizational performance data from the 25 public institutions. AHP participants were asked to give weights at the three levels from the first stage to the third so that their averaged weighting was to be applied to the existing performance data of the 25 institutions. The first round produced a result of scientific/engineering excellence (0.669), excellence in operation/management (0.126), and efficiency in manpower and social values (0.205), which was then divided to be narrowed down to the next two stages.  
QFD is a quality management technique used to match consumer demand requirements and product specifications, and later, to check the affinity between different categories. In this study, QFD was utilized to link research institutions and their potential contributions to research topics related to the 4 th industrial revolution.  
After completing both the AHP and QFD analyses, we plotted their results to link research institutions and their potential contribution to the 4 th industrial revolution. The focus was on finding those institutions that scored high on both analyses. These organizations can serve as the mediating entity for collaborative research in the 4 th industrial revolution.
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 3 Engineering and Geological Background, Test Methods, and Results
### Laxiwa Hydropower Station  
The Laxiwa dam site is located at the Longyangxia outlet section of the Yellow River, with seismic intensity level VII. It is a double-curvature concrete arch dam with a maximum height of 250 m.  
The Laxiwa dam site is situated in a Mesozoicic Indonesian granite formation with a medium coarse-grained structure. The main mineral components are quartz, feldspar, biotite, and hornblende. Rock joints generally have high dip angles, are straight and closed, and can be divided into four groups according to their extension direction.  
More than 60 plate loading tests were carried out at the selected dam site, of which only 16 were characterized in detail to estimate BQ values (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Number** & **Test Position** & **Rock Types** & **Degree of Weathering** & **Deformation Modulus GPa** & **Loading Direction** & **BQ Value** \\ \hline E18-2 & PD18 * (263 m) & & & 29.72 & horizon & 657 \\ E18-3 & PD18 (285.7 m) & & & 24.55 & vertical & 681 \\ E21-1 & PD21 (43.7 m) & & moderately & 9.20 & horizon & 452 \\ E21-2 & PD21 (81.7 m) & & & 37.13 & vertical & 570 \\ E21-3 & PD21 (88.4 m) & & & 41.26 & 644 \\ E32-1-1 & PD32-1 (5.0 m) & & & 21.36 & horizon & 582 \\ E32-1-2 & PD32-1 (4.3 m) & & & 18.05 & vertical & 582 \\ E31-1 & PD31 (158.7 m) & & & 23.95 & 640 \\ E31-2 & PD31 (170.0 m) & & & 21.81 & horizon & 666 \\ E38-1 & PD38 (120.0 m) & & & 17.20 & vertical & 669 \\ E38-2 & PD38 (137.3 m) & & & 36.51 & 640 \\ E38-3 & PD38 (150.0 m) & & & 42.04 & horizon & 590 \\ E1-1 & PD1 (114.0 m) & metasandstone & & 25.87 & & 624 \\ E1-2 & PD1 (40.0 m) & & & 23.75 & & 620 \\ E1-3 & PD1 (30.0 m) & & & 21.54 & & 565 \\ E1-4 & PD1 (3.5 m) & & moderately & 20.77 & & 508 \\ E3-2 & PD3 (66.0 m) & & slightly & 19.70 & & 611 \\ E9-1 & PD9 (9.6 m) & & moderately & 22.50 & vertical & 476 \\ E2-1 & PD2 (4.0 m) & & intensely & 7.58 & & 382 \\ E2-2 & PD2 (36.0 m) & & moderately & 21.48 & & 506 \\ E2-3 & PD2 (68.0 m) & & & 28.32 & & 574 \\ E2-4 & PD2 (84.0 m) & & & 25.73 & & 654 \\ E4-1 & PD4 (18.0 m) & & moderately & 10.90 & & 496 \\ E4-2 & PD4 (54.8 m) & & slightly & 13.60 & horizon & 524 \\ E53-1 & PD53 (20.5 m) & & moderately & 1.62 & & 313 \\ E53-2 & PD53 (36.0 m) & & slightly & 5.39 & vertical & 449 \\ \hline \hline \end{tabular}  
* PD18 is the adit number.  
\end{table}
Table 5: Deformation modulus of rock mass at Maerdang hydropower station.  
Deformation tests were performed using the aforementioned method. Most of the test loads were 7-11 MPa, suitable values for reflecting the deformation characteristics of a rock mass under heavy load.
## 2 Theoretical Model  
### Geometrical Model  
The geometrical model of a spiral-grooved face seal and the three-phase diagram of helium are illustrated in Figure 1. The critical temperature and pressure of helium are 5.2 K and 0.228 MPa, respectively, making it easy to reach the supercritical state. The thermo-hydrodynamic behaviors of the helium face seal are analyzed in its supercritical state in this manuscript. Face seals work by the flow restriction and gas compression generated by the high-speed shear between the non-contacting seal rings. Once the stator and rotor rotate relative to each other, a micron gas film is formed by the sufficient opening force, realizing the sealing effect and also generating a certain leakage of the sealing medium along the direction of the pressure gradient. In order to enhance the dynamic pressure effect of the gas film, various grooves were generally designed on the seal rings; spiral grooves are the typical grooves in practical applications [36; 37; 38]. According to the principles of gas face seals, the deformation of seal rings affects the sealing performance significantly because the clearance gap between non-contacting rings are usually 3-5 \(\upmu\)m and the distortion is recommended to be controlled within 1 \(\upmu\)m/mm [39; 40]. Therefore, the theoretical prediction of the thermo-hydrodynamic behaviors of the micron gas film are essential to the design of gas face seals under low-temperature and large ambient temperature gradients. The relevant geometrical parameters of the spiral-grooved face seal are listed in Table 1.
## Appendix B Fixed Axes Vibrations in the Circular Motion  
In two of the validation tests following a tracer particle on circular paths, as described in Section 4, the motion was constrained to 2 dimensions. The third dimension of the locations in both cases should have been a constant with time; however, in experiments, it was observed to show additional oscillations that tended to increase the uncertainty in the sinusoidal fits to the simulated location data. These vibrations in the fixed axes of rotation of the location data in the \(Z\) dimension of the button tracer and the \(Y\) dimension of the NRW100 tracer can be seen in Figures 13 and 14, respectively. It is expected that the oscillation in Figure 13 is due to vibration of the rotating disk, which is caused by a slight misalignment of the motor and the drive shaft, and the shape of the wooden disk, which had a slightly unequal distribution of mass around the central axis of rotation. It is believed that the vibration in Figure 14 was caused by a slight misalignment in the shaft mount of the disc.  
Both additional motions were added to the corresponding simulations as sinusoidal motion in the \(Z\) dimension for the ButtonTracer and sinusoidal motion in the \(Y\) dimension for the PointTracer. The sinusoidal motion in the \(Z\) dimension for the ButtonTracer and \(Y\) dimension for the PointTracer were simulated by adjusting the phase shift, frequency, and amplitude of the oscillation in the HR++ simulation by using a sinusoidal fit to the experimentally derived location data. In the case of the additional vibration in the \(Z\) dimension of the button tracer in the experimental data, the motion was well-characterised by a sinusoidal fit to the \(Z\) dimension of the locations as shown by the agreement in the fitting parameters in Appendix C, Table 13, within one standard uncertainty. However, in the case of the additional vibration in the \(Y\) dimension, the motion of the NRW-100 tracer particle was not well-characterised by a sinusoidal motion, as indicated by the lack of agreement between the fitted parameters of the sinusoidal function in Appendix C, Table 13. Therefore, despite adding the additional vibration to the motion of the tracer particle, the parameters of the sinusoidal functions fitted to the experimental data were not in agreement with the simulated data within experimental uncertainty, suggesting there was a random part of the motion present in experiments that could not be predicted to be incorporated into the simulation. This represents the challenges in performing experiments consistently and to a high enough precision to create an appropriate ground truth to simulate the situation.
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 2 Related Works
### Adaptive Duty Cycle Concept and Scheme for Enhancing the Node's Lifetime  
[PERSON] et al. [29] have given an efficient scheme based on the adaptive duty cycle (ADC) concept. The main aim of ADC based opportunistic routing is to reduce the delay of nodes in WSN. In ADC based opportunistic routing scheme, the network delay reduces by increasing the node's duty cycle in particularly those regions where it has remaining energy. The duty cycle scheme can effectively reduce the energy consumption in WSNs. For increasing the node's lifetime and enhancing the node's usability in many areas, a rational next-door neighbor detection scheme is given by [PERSON] et al. [30], the scheme also involves the systematic investigation of various next-door neighbor detection protocols and introduces the excellent next-door neighbor detection schedule. The main aim of the next-door neighbor detection issue in WSNs is to reduce the duty cycle.  
In this article, we have compared the performance of the proposed scheme with existing schemes such as ESN based scheme, AOR and Max-SNR. The ESN-based scheme utilizes an ESN to get the energy acquisition of the next slot. In AOR, the concept of area subdivision method for dynamically selecting a relay set is utilized. Two crucial factors, such as left-over energy and range from the sink node, are considered while giving priority to the relay nodes. In Max-SNR, three essential factors, such as connection superiority, left-over energy and location are used for dynamically selecting the relay nodes and giving priority to relay nodes.
# Control-Flow Integrity: Attacks and Protections
## 4 Classification of CFI Techniques
### Fine-Grained CFI  
Fine-grained CFI is referred to as the strict type CFI [48]. Labeling is one of the most usual approaches to implement fine-grained CFI. The edges of a control-flow transfer contain labels, and they are checked for integrity on each control transfer. Prior to control flow transfer, a validation process ensures that the transfer is passed to a valid destination which contains a valid label.  
Figure 1 shows an execution flow where the fine-grained approach is initiated. The execution flow consists of both direct calls and indirect calls. The direct call is fixed and cannot be altered, whereas the target address is calculated before returning the indirect call. Indirect return 4 is valid and also returns to the valid destination. However, an attack is initiated at the same return point overwriting the return address, and the attacker manages to divert the flow elsewhere.  
Fine-grained CFI usually enforces a shadow stack which incurs intense overhead [47]. Therefore although it enhances sturdy security, the drawback is a high-performance overhead. It can be complicated to achieve the fine-grained CFI because the destination of indirect branches needs to be determined statically. Fine-grained CFI does strict checking of each call and jump instruction inside of an application; therefore, it lacks the performance and is very expensive at the same time. To cope with the performance issues, a weaker form of CFI, coarse-grained, was proposed.
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 3 Data and Methods
### Evaluation Indicators  
This research uses indicators such as average detection time, number of parameters, FLOPs (floating-point operations), and model size to evaluate the complexity of the target detection model. At the same time, in order to quantify the effectiveness of the improved ship target detection model, precision, recall rate, and mAP (mean average precision) are used as evaluation indicators. The calculation formula for each evaluation index is as follows:  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{10}\]  
\[Recall=\frac{TP}{TP+FN}\times 100\% \tag{11}\]  
\[mAP=\frac{1}{n}\frac{n}{i}AP_{i} \tag{12}\]  
where \(TP\) (True Positive) indicates the number of samples that are actually positive and correctly predicted as positive by the classifier. \(TN\) (True Negative) indicates the number of samples that are actually negative and correctly predicted as negative by the classifier. \(FN\) (False Negative) indicates the number of samples that are actually positive but predicted as negative by the model. \(FP\) (False Positive) denotes the number of samples that are actually negative cases but are predicted as positive cases by the model. mAP indicates how well the model detects on all categories. mAP0.5 represents the mean average precision when the IOU threshold is 0.5.  
Figure 6: The yellow rectangle represents the true labeling box and the blue rectangle represents the prediction box of the network.
# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 1 Introduction
We used four vegetation indices (NDVI, EVI, NIRv, and CCI) calculated on weekly, monthly, and annual bases and tested whether they were able to monitor: (i) weekly and monthly temporal variability, using raw and deseasonalised time series of GPP and RSVIs, (ii) spatial variability, calculating the average annual value for each site, (iii) site seasonality, by calculating the average monthly intra-annual variability for each site, (iv) interannual variability, as the average variability amongst years for each site, and (v) trends in annual means for each site. We focused on identifying the differences amongst the performances of the RSVIs to estimate various facets of GPP across the vegetation types. Our results will therefore help researchers to select the best RSVI to use in each case and to justify their use for a particular purpose. Our results could also be used to improve GPP models by identifying the best method to parameterise these models with RSVIs.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Q-Table Update  
Algorithm 2 delineates the process of updating the Q table for vehicles. In Algorithm 1, critical factors such as link stability, link duration, received signal strength, and effective bandwidth are computed using beacon information. Algorithm 2 employs this information to calculate \(\alpha\), \(\gamma\), and \(r_{t}\). And using the way of broadcasting, we calculate the Q value of each neighbor. Beacon messages are broadcast at fixed intervals, without the use of adaptive interval timing. This choice is made because adapting interval times in the VANET environment requires frequent adjustments, which can easily lead to a deterioration in communication conditions in certain areas and increased channel congestion. The degradation in communication quality caused by control message overhead is not reasonable. Moreover, determining the appropriate adaptive interval is a challenging problem. A fixed beacon interval cannot accommodate the varying degrees of changes in parameters such as speed, signal strength, and bandwidth. Therefore, the extent of each Q-learning parameter update must be given careful consideration.  
The computation of \(\alpha\) and \(\gamma\) is crucial for determining Q values, and each Q-value update represents an adaptation to the environment. For high-speed mobile nodes, the link duration adjusts based on relative motion, thereby periodically adapting to the movement of neighboring nodes according to the beacon's intervals. Regarding the perceived signal strength, further adjustments are made to select neighboring nodes. When the signal strength is not suitable for the next-hop selection, the future reward is reduced. Conversely, in a well-performing communication environment, the future reward is maximized. The same considerations can be applied to bandwidth. The dynamic discount factor \(\gamma\) ensures that the data packets transmitted over the path have communication quality as close as possible to the optimal. Properly adjusting these variable parameters impacts the quality of data transmission.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 2 ChG Glasses and Fibers
### Overview of ChG Fibers  
The first ChG fiber was reported in 1960s and was made of As\({}_{2}\)S\({}_{3}\) glass [27]. Considered the promising materials for fiber optics in the MIR range, numerous investigations of ChG glasses for fiber  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glass** & **Refractive Index** & **n\({}_{2}\)/n\({}_{2}\) (Silica) 1** & **Transmission Range (\(\upmu\)m)** & **ZDW (\(\upmu\)m)** \\ \hline Silica & 1.44 & 1.0 & 0.2–3.5 & 1.26 \\ ZBLAN 2 glass & 1.49 & 1.2 & 0.5–5.5 & 1.71 \\ Tellurite glass & 2.03 & 19 & 0.5–4.5 & 2.13 \\ As\({}_{2}\)S\({}_{3}\) ChG & 2.5 & 200 & 0.6–12 & 4.8 \\ As\({}_{2}\)Se\({}_{3}\) ChG & 2.9 & 600 & 1.0–16 & 7.5 \\ Te-based ChG & \(\sim\)3.2 & \(\sim\)1000 & 1.5–25 & \(>\)7.5 \\ \hline \hline \end{tabular} \({}^{1}\) n\({}_{2}\), nonlinear refractive index,2 ZBLAN, ZrF\({}_{4}-\)BaF\({}_{2}-\)LaF\({}_{3}-\)AlF\({}_{3}-\)NaF.  
\end{table}
Table 1: Typical optical parameters of silica and several infrared (IR) glasses.  
Figure 1: Typical infrared (IR) transmission spectra of millimeter-thick S-, Se-, and Te-based chalcogenide (ChG) bulk samples (reproduced with permission from [22], copyright Optical Society of America, 2015).  
fabrication have been conducted over the past few decades, especially those based on S and Se [28; 29; 30; 31; 32; 33; 34]. Typical commercially available ChG fibers are mainly based on As-S and As-Se [35; 36]. In addition, the systems that commonly used for ChG fibers in scientific research include Ge-As-Se [37; 38; 39], As-Se-Te [40; 41; 42] and Ga-La-S [43; 44; 45]. The three main kinds of fiber structures are the step-index fiber, MOF, and tapered fiber. This subsection introduces ChG fibers from these aspects.