esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Lignin, Waste Tire Rubber, and Waste Glass for Soil Stabilization
## 4 Results and Discussion
### Results of the California Bearing Ratio (CBR) Tests  
The CBR value at optimum moisture content (Dry CBR) and the soaked CBR value (Wet CBR) of the test specimens are shown in Figure 7. The addition of GP and LG in equal percentages (S1, S2, S3) shows that the Dry CBR values are relatively low, but there is a significant increase in the Wet CBR values as the percentage of GP and LG increases. The samples with GP and RT show a significant increase in both Dry and Wet CBR values as the percentage of GP and RT increases. Sample S7 (20% GP + 3% RT) has the highest Dry and Wet CBR values, indicating the highest strength among the tested samples.  
The test results show that the addition of GP and LG generally decreases the Dry CBR values by up to 47% (S1) but slightly increases the Wet CBR values by up to 47% (S3), especially at higher proportions. In contrast, the addition of GP and RT significantly  
Figure 7: Results of the California Bearing Ratio (CBR) tests of the test specimens.  
increases both Dry and Wet CBR values, with Dry CBR increasing by up to 209% and Wet CBR by up to 190%. The most significant improvements are observed in samples with higher proportions of GP and RT. The reason for this is that the steel fibers in the tire rubber have a positive effect [65; 66; 67].  
The CBR value of soils is one of the most important parameters in the pavement design. The higher the CBR value, the lower the thickness of the pavement that needs to be produced on the subgrade for a given level of traffic. This not only reduces the budget required for road construction but also contributes to sustainability, as fewer natural materials need to be used for road construction.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation
### System Performance in the Gaussian Channel
#### 3.1.1 System Performance under Ideal Carrier Synchronization
Scheme II and Scheme III have the same success rate of watermark transmission, but when the signal-to-noise ratio is low, the success rate of watermark authentication in Scheme III is slightly higher than that in Scheme II, which shows that using pseudo-user-bits as the input of hash operation can further improve the success rate of watermark authentication. Figure 10: Bit error rate of the watermark bits under ideal synchronization in the Gaussian channel.
## 3 Results and Discussion
### Comparison between IFOA-LS-SVM Model and MR Model
#### 3.4.2 Performance Comparison of IFOA-LS-SVM Model and Multiple Regression Model  
In order to compare the prediction performance of these two models, a good indicator, the coefficient of correlation between predicted and experimental values, was used in this study. The relationships between predicted and experimental values obtained from the IFOA-LS-SVM model and MR model for the extraction yield of quercetin are shown in Figure 13. As can been seen, the IFOA-LS-SVM model and MR model have good correlation coefficients with \(R^{2}=0.975\) and \(R^{2}=0.895\), respectively. It indicated that the IFOA-LS-SVM model has a uniform and very small deviation from the quadrant line, which revealed its excellent predictive ability in comparison with MR model for the extraction yield of quercetin.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Independent Variables** & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\) & \(\mathbf{X_{5}}\) & \(\mathbf{X_{6}}\) & **Constant** \\ \hline Coefficient & 0.236 & \(-0.685\) & \(-0.198\) & 0.702 & 0.225 & \(-0.663\) & \(-12.146\) \\ Std. error & 0.088 & 0.133 & 0.665 & 0.054 & 0.036 & 0.147 & 0.456 \\ Sig. level & 0.280 & 0.011 & 0.002 & 0.421 & 0.126 & 0.008 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model summaries of multiple regressions for prediction of extraction yield of quercetin.  
To show the deviations from the experimental values of quercetin yield, the distances between the predicted values from IFOA-LS-SVM and MR models and the experimental values were also calculated and are plotted in Figure 14.  
The graphic showed that the deviation interval (from \(-0.01\) to \(0.0198\) mg/g) of the predicted values from IFOA-LS-SVM model was much smaller than that obtained from MR model (from \(-0.032\) to \(0.045\) mg/g). The results indicated that IFOA-LS-SVM model has higher prediction performance compared to MR model.
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
## 4 Discussion  
Experimental results obtained by using different backsheets and gap spaces between PV cells concluded that those two factors are important to improve PV module efficiency. It indicated that wide gap spaces improve both MBB and shingled PV module efficiencies. As discussed in Section 1 Introduction, however, one should note that incorporating wide gap spaces use space where more PV cells could be placed. Balancing between the appropriate gap spaces and the number of cells is, therefore, the most critical factor.  
When gap space was 2.5 mm for the MBB PV module and 6 mm for the shingled PV module, the increase in P\({}_{\rm max}\) was 1.4% and 2.1%, respectively. In general, a couple more cells were needed to improve about 1-2% of power in a certain area. This indicates that the number of PV cells required to achieve a desired power or efficiency can be reduced by incorporating gap spaces, hence allowing for a reduction in PV module cost.  
Figure 4: (**a**) The short-circuit current (I\({}_{\rm sc}\)) and (**b**) maximum power (P\({}_{\rm max}\)) of the shingled PV modules with white, black, and W-B hybrid backsheets and various cell spaces (2 and 6 mm).  
Figure 3: (**a**) The short-circuit current (I\({}_{\rm sc}\)) and (**b**) maximum power (P\({}_{\rm max}\)) of the MBB PV modules with white, black, and W-B hybrid backsheets and various cell spaces (0.5, 1.5, 2.5 mm).
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 4 Simulation and Results
### Quantity of Responsive SPs and Benefit Analysis of the SR under the Bidirectional Trust Evaluation  
Figure 8 shows the number of responsive SPs and benefits of the SR when the RPE is from 10% to 90% for a certain task. It can be seen that when the RPE is less than 0.5 and the RTS is large, there are more negative opinions referenced. Therefore, the trust evaluation result of SR from the viewpoint of SPs is generally low, and few SPs respond. Therefore, the SR cannot select a suitable SP, and the income is low. With the increase in RPE, the trustworthiness of the SR increases and the number of responding SPs gradually increases, so the SR can obtain a better delegation scheme, which improves the overall revenue. In addition, in the case of small RPE, although the expected benefit of SR is higher when the RTS is lower (the reason is that some SPs cannot correctly estimate the trustworthiness of SR, resulting in a wrong response to the service), it may lead to lower service quality of SPs and failure to guarantee the benefits of SPs. Higher RTS will lead to more accurate bidirectional evaluation results, and more SPs choose not to respond to the service request when RPE is low. On the other hand, in the case of higher RPE, higher RTS will make the bidirectional evaluation between SPs and SR more accurate, so the overall benefit of the SR will be higher.  
Figure 8: The number of responsive SPs and the SR’s utility with different RPE.  
Figure 7: Trust evaluation of the SP with different number of intermediate nodes.
## 2 Literature Review
### Scene Text Recognition
#### 2.2.2 Text Recognition  
Most text recognition methods recognize the scene text by grouping the recognized characters, making text recognition inefficient and difficult for real-time applications. In [29], and accurate scene text recognition method without character-level segmentation was proposed based on a Recurrent Neural Network (RNN). [PERSON] [30] presented a convolutional recurrent neural network (CRNN) to recognize the scene text in arbitrary lengths in scene images by stacking CNN and RNN. This method is not limited to any predefined lexicon and can achieve remarkable performances in scene text recognition tasks. In [31], recursive recurrent neural networks with attention mechanisms [32] were developed to achieve good performance for dictionary-free scene text recognition. [PERSON] [33] proposed a TextBoxes method by combining the SSD and CRNN to speed up the text recognition task to recognize text in scene images.  
Although numerous studies have been done, the limitations and challenges of scene text recognition while applying it to real-world manufacturing tasks have not been researched. Efficiency and precision are two main concerns using text recognition methods to obtain the working state of a CNC machine. Text blurring often happens when the mobile robot moves due to camera shake and de-focus, which degrades recognition accuracy [34]. In addition, efficiency is a shortcoming of deep learning-based methods, making it challenging to deploy those methods on mobile devices and lightweight systems [35]. Therefore, this study integrated some pre-processing steps that reduce the complexity of images into the developed text recognition method to improve image quality. In addition, the combination of HOG and CRNN can realize real-time working status recognition of CNC machines while ensuring accuracy.
## 1 Introduction
The generation of temperature particles [9] near each pivotal point of a solid boundary is made through a direct interpretation of Fourier's Law [11; 22]. The buoyancy forces are computed considering the temperature induced by temperature particles on each vortex blob; this approach can be interpreted with a new term of momentum generation into the fluid domain [10]. The buoyancy forces inclusion links the vorticity transport equation with the energy equation via Boussinesq approximation. The solution of the advection of vorticity and heat is exemplified through the first-order Euler time marching scheme (cylinder case) and the second-order Adams-Bashforth time marching scheme (airplane wingtip vortices case) [23]. The solution of the diffusion of vorticity and heat is exemplified through the random-walk method [7], both including LES theory. The pressure field on a cylinder surface is obtained using an integral formulation derived from an equation of Poisson for the pressure [24]. The results obtained with the development of this numerical approach will allow advances in the physical analysis of the related mechanisms, even in two-dimensional space. The flow past a circular cylinder, for instance, provokes boundary layer development, separation and alternatively shedding vortices on both sides of the body, which induces periodically fluctuating in-line and cross-flow forces [25; 26]. These mechanisms are complex, which can cause the structure to vibrate if it is elastically mounted [2; 27; 28; 29]. In practice, when the vortex shedding frequency locks with the natural frequency of the cylindrical structure, the lock-in phenomenon can occur with large vibration amplitude, causing fatigue damage to the structure. In accordance with [PERSON] et al. [30], the control of vortex shedding by thermal effects is classified as an active control method, in which external energy is necessary to affect the fluid flow. The present paper also contributes with the inclusion of buoyancy forces linked with turbulence modeling when discussing the bluff body aerodynamics. The study of the decay of aircraft vortex wakes near a ground plane is of great interest in aviation problems to determine the hazard potential and wake lifetime [31; 32; 33; 34; 35; 36]. The present paper contributes to the referred study, including turbulence modeling for both the vorticity transport equation and energy equation linked with buoyancy force and crosswind interference. Undoubtedly, the combination of crosswind with buoyancy forces will disrupt the flight of a following aircraft entering the wake of a leading aircraft, which will be a potential safety hazard. The software employed in aviation must include the combined effects of the ground plane, crosswind and buoyancy forces to simulate dissipation of the aircraft wake vortices. In general, CFD data are useful to help the development of wake models associated with systems such as \"Wake Turbulence Re-Categorization or Wake Recat\".
## 4 Methodology
### Metric Definition  
The receiver operating characteristic (ROC) curve is a graphical representation depicting a classification model's performance across various classification thresholds. The following is imperative for this metric: the calculation of true positive (TP), i.e., correct positive case classifications, e.g., a sample correctly identified as an anomaly; false positive (FP), i.e., incorrect positive case classifications, e.g., a sample incorrectly identified as normal; false negative (FN), i.e., incorrect negative case classifications, e.g., a sample incorrectly identified as normal.  
The ROC curve illustrates the relationship between the following two key parameters:  
1. True positive rate (TPR), also known as recall, is defined as follows: \[TPR=\frac{TP}{TP+FN}\]
2. False positive rate (FPR) is defined as follows:  
Figure 5: Preprocessing pipeline: (**A**) raw data at 48,828 sps, (**B**) decimated data at 2048 sps, and (**C**) MAF filtered data, 2048 sps, MAF size 15.  
\[FPR=\frac{FP}{FP+TN}\]  
The ROC curve showcases the TPR vs. FPR at different classification thresholds. Lowering the threshold increases the number of positive classification actions, thereby increasing false positives and true positives; raising it results in more negative classifications, increasing both false negatives and true negatives.  
The area under the ROC curve (AUC) quantifies the entire two-dimensional area beneath the complete ROC curve, ranging from (0,0) to (1,1). It offers a measure of performance across all potential classification thresholds. A way to interpret AUC is the probability that the model assigns a higher rank to a randomly chosen positive example than a randomly chosen negative one. A value closer to 1.0 on the AUC is optimal for a system.
## 2 Model and Method
### Operational Strategy  
#### 2.5.1 FEL Operational Strategy  
When performing the FEL, the load rate of the gas turbine is determined by the electrical load of the building [19]. When the heating load is less than the waste heat generated by the gas turbine, the flue gas generated by the gas turbine is discharged without fully utilizing the waste heat, which leads to a waste of energy. Therefore, the surplus heat is ignored in the optimization model. If the heat load demand is greater than the waste heat generated by the DES, the gas-fired boiler supplements the deficit heat.  
Figure 4 shows the operational cost calculation process of the DES when performing FEL. If the operating power of the gas turbine is lower than the minimum power limit, the efficiency of the equipment is reduced. After inputting the load and relevant parameters, if the user's electrical load does not meet the minimum power limit of the gas turbine, the cooling, heating, and power loads are provided by electric refrigerating units, gas-fired boilers, and power grids, which are equivalent to SP. If the electrical load of the user meets the minimum power limit of the gas turbine, the operating efficiency of the gas turbine is determined by the electrical load. The insufficient cooling and heating load is provided by other equipment. The cost of the DES-FEL is calculated and compared with the SP under the same load to choose the best operational strategy at time \(i\). Finally, the total annual cost of running FEL can be obtained by aggregating the operating costs at all times of the year. Based on the operating principles of FEL, the operational constraints of FEL are as follows:  
\[x_{i}=\begin{cases}0&E_{i}<minE_{gt}\\ \frac{E_{i}}{V_{gt,E}}&minE_{gt}\leq~{}E_{i}<V_{gt,E}\\ 1&V_{gt,E}\leq~{}E_{i}\end{cases} \tag{20}\]
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 3 Preliminaries  
### Threat Model  
This paper adopts the Dolev-Yao (DY) model [29; 30; 31] as the threat model to evaluate the security of the proposed protocol. Based on the DY model, the capabilities of adversary \(A\) are presented below:* Adversary \(A\) can eavesdrop on, delete, forge, inject, or modify messages exchanged over the public channel [32].
* Adversary \(A\) can steal a legitimate user's mobile device and obtain secret values stored in memory through power analysis attack [33; 34].
* Adversary \(A\) can physically capture a drone and obtain secret credentials in its memory.  
With these capabilities, \(A\) can perform security attacks, including impersonation, offline password guessing, and session key disclosure.
## 2 Materials and Methods
### Model Calibration and Validation  
Software: The model was calibrated using SWAT-CUP software 2012 (SWAT Calibration and Uncertainty Programs). SWAT-CUP is software that integrates the outputs of the SWAT model and five calibration algorithms, including SUFI2 (Sequential Uncertainty Fitting 2; [PERSON]; [PERSON]; [PERSON], 2004), which stands out for its speed and accuracy in processing and consists of three major steps: modifying the values of SWAT inputs, running SWAT and extracting the desired output values.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 7 Conclusions  
The task offloading for a vehicular platooning assisted by an MEC server was investigated in this paper. An optimized task offloading algorithm was proposed to reduce the average total energy consumption while meeting the deadline of tasks. In this paper, the targeted problem is simplified by the Lyapunov optimization method, and the simplified problem is approximately solved by a greedy algorithm. Thus, the sub-optimal task offloading decision is obtained. By setting a reasonable control parameter \(V\) for the Lyapunov optimization function, energy consumption takes a decisive proportion in the whole optimization function. In addition, the proposed algorithms can not only ensure the stability of the task computation queue, but also dynamically adjust the task offloading strategy according to the amount of the task data. It is one of the effective and feasible ways to realize the task dynamic offloading.  
Conceptualization, Y.H.; Funding acquisition, T.C. and B.S.; Project administration, T.C.; Supervision, T.C.; Validation, Y.H.; Writing--original draft, Y.H.; Writing--review and editing, Q.C. and T.C.  
This work was supported in part by the National Natural Science Foundation of China (61831002,61401053), and the Innovation Project of the Common Key Technology of Chongqing Science and Technology Industry (cstc2018 cyjAX0383), the special fund of Chongqing key laboratory (CSTC), and the Funding of QUPT (A2016-83, A2019-12).  
The authors declare no conflicts of interest.
# Multimodal Biometric Template Protection Based on a
## 1 Introduction
### Related Work  
#### Multimodal Biometrics with Deep Learning  
In this subsection, we review several works that have applied deep learning to multimodal biometric systems. [PERSON] et al. [9] proposed a multimodal face recognition system composed of global facial features, rendered a frontal face image using a 3D face model, and uniformly sampled local face image patches. A combination of multiple convolution neural networks (CNNs) and a stacked autoencoder were used for feature learning and performing feature-level fusion.  
Figure 1: Illustration of a general cancelable biometrics scheme.  
[PERSON] et al. [10] outlined a multimodal biometric system known as IrisConvNet. The IrisConvNet fuses left and right irises at the ranking-level. [PERSON] et al. [11] considered iris, face, and finger veins and processed them with separate CNNs, with the output of each network fused at the score level. [PERSON] et al. [12] also proposed a deep multimodal biometric system consisting of the iris, face, and fingerprint, called deep contourlet derivative weighted rank (DCD-WR) network. The matching is achieved with a deep learning template matching algorithm.  
The study in [13] presented a multifeature deep learning network (MDLN) architecture that fused the facial and periocular regions, with the addition of texture descriptors. MDLN was designed as a feature-level fusion approach that correlated raw biometric data with texture descriptors to produce a new representation.  
[PERSON] [14] fused the iris and periocular region using a hierarchical fusion network. Their network allowed the system to automatically explore and discover the best strategy for combining the individual biometric scores. [PERSON] et al. [15] outlined a deep neural network for fusion of the iris and periocular features. A co-attention feature fusion module was used to fuse the features adaptively, to obtain iris-periocular features for accurate recognition.  
[PERSON] et al. [16] proposed a teacher-student network for periocular representation learning. The teacher network, which is pre-trained with face images, is leveraged to regulate the student (periocular) network in order to enhance the periocular performance. [PERSON] et al. [17] suggested a generalized compact bilinear fusion algorithm composed of multiple CNNs. Three multimodal features (iris, face, and fingerprint) are fused through a fully connected layer.  
In summary, deep learning is a natural and well-suited approach for multimodal biometric systems, as deep neural networks enable feature extraction, fusion, and authentication to be performed \"under one roof\". However, this approach does not consider the issue of template protection.
###### Abstract  
The study evaluates the effects of a 4-week program of combined resistance and aerobic training and volleyball training on physical fitness in young sedentary adults. Twenty-eight males (age 20.5 \(\pm\) 1.5 years; body mass 87.2 \(\pm\) 28.5 kg; height 173 \(\pm\) 8.1 cm; BMI 28.9 \(\pm\) 8.4 kg/m2) were divided into two groups. While experimental group 1 (COM) underwent a fitness program consisting of a combination of strength and aerobic exercises, experimental group 2 (VOL) performed intermittent exercises in volleyball (four times per week for 50 min). The results showed that both training groups significantly improved in all fitness tests except the bcep test, while only the COM group achieved a significant change in the number of repetitions (\(p=0.041\)). Between-group analyzes revealed a greater change achieved by the COM group in standing long jump lengths than VOL (12.3% vs. 4.3%, \(p=0.011\)). There were no other between-group significant differences in 20m Multistage Fitness Test (8.1% and 4.4%,), sit-ups (20.9% and 21.0%), flexibility (24.5% and 23.3%) and shuttle run 5 \(\times\) 10 m (11.95 and 9.52%) or in anthropometric parameters (BMI, Fat %, Muscle Mass %, Visceral Fat %). These findings indicate that combined resistance and aerobic training are more effective in improving the explosive power of lower limbs and aerobic endurance than playing an intermittent-type sport such as volleyball. This program can be effectively applied to university students with a predominantly sedentary lifestyle.  
fitness program; physical education; motivation; concurrent training +
Footnote †: journal: Applied Sciences  
13 June 2021