esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Found Bitumen Base  
As stated above, FBB is a granular base course material that has been stabilised with foamed bitumen. FBB is one of three methods for producing bitumen stabilised granular materials [6]. Bitumen emulsion stabilised base is similar, but takes advantage of the low viscosity of the emulsified bitumen to achieve a similarly workable product. Bitumen treatment base is also similar, but relies on heat to reduce the bitumen viscosity. Bitumen treated base is effectively a low bitumen content asphalt produced from unfractionated base or subbase quality granular material and is produced in a hot-mixed asphalt plant.  
Typically, FBB in Australia includes 2.5-3.5% bitumen and 1-2% hydrated lime, by mass of the granular material. The hydrated lime assists in dispersing the bitumen through the granular material, reduces moisture damage susceptibility and accelerates the stiffening of the bituminous mastic shortly after construction [7]. In other jurisdictions, different bitumen and lime (or cement) contents are commonly used and this affects the engineering properties, performance and expected failure mode of the resulting FBB. For example, increasing the bitumen content increases cost and changes the FBB stiffness, while increasing the lime content (or introducing cement) increases the stiffness and rate of strength gain with age, but introduces shrinkage cracking risk [11].  
FBB can be produced in a pugmill (ex situ) or using a pulverising and stabilising machine (in situ). It is logical that in situ stabilisation is suited to the reuse of existing pavement materials and ex situ processing is most appropriate when a new granular material is used. In situ construction of FBB reduces the cost associated with double handling of existing materials, as well as the mobilisation of separate production and paving equipment. However, ex situ processing provides the opportunity to inspect and assess the exposed subgrade, test the excavated existing material and perform secondary pulverisation of any existing asphalt surface.  
Regardless of the production process in the field, FBB mixture design and characterisation is generally performed in a laboratory mixing device. Ex situ, in situ and laboratory FBS processes all use different mixing chambers and mixing times, resulting in differently mixed materials. Aggregate breakdown, which changes the overall grading, and bitumen distribution, are both affected by mixing processes and times, meaning the three production processes can result in slightly different materials, even though the mixture design is nominally identical [11]. However, for comparison of different FBB materials, standardised laboratory production protocols are adequate and provide a consistent benchmark.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 2 Principle and Modeling  
### Principle of the DELM  
The ELM is created based on the one hidden layer and the feedforward neural network. The ELM has the characteristics of randomly selecting the weights, unlike the feedback neural network, which needs to adjust the weights between layers through the backpropagation algorithm, thus reducing the learning time and structural complexity and improving the overall training speed [5]. However, because of the network structure of the single hidden layer, ELM cannot have the ability to capture the effective features of the sample with much data and high dimensions.  
To solve the drawbacks of the ELM, the DELM (also known as the multi-layer extreme learning machine) is proposed. The weights of DELM can be initialized by the extreme learning machine autoencoder (ELM-AE). We can see the structure of ELM-AE in Figure 1. The autoencoder learns the features of samples in an unsupervised way. It maps the inputs to the feature vector of the hidden layer through the encoder and then reconstructs the original inputs from the feature vector by the decoder [6].  
The DELM is equivalent to connecting the multiple ELM from the perspective of the structure. Compared with the ELM, the DELM can capture the sample characteristics more comprehensively and improve the accuracy of processing high-dimensional inputs. The DELM carries out unsupervised training and learning through the ELM-AE layer by layer, and finally connects to the regression layer for supervised training. The system's parameters do not need to be adjusted simultaneously [6]. The structure of the DELM network is shown in Figure 2.  
Compared to ELM, the DELM is more accurate and generalizable. However, the weights of the input layer are orthogonal random matrices created randomly during the pre-training phase of ELM-AE. In the meantime, only the weight parameters of the output layer are adjusted during the ELM-AE pre-training process using the least square approach, while the weights of the input layer are left unchanged. As a result, the random input weights of each ELM-AE have an impact on the ultimate DELM effect. Therefore, the parameters should be optimized to improve the predictive performance of the DELM.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 4 Conclusions  
This paper analyzed the principle and details of driverless technology based on the dead reckoning method, including the estimation of the current vehicle position using the differential drive model, the principle of the pure pursuit algorithm, and using a GoPro for calculating the lateral error at each moment of the autopilot process.  
The experimental results show that the method can achieve better tracking results at low speeds and the average error increases as speed increases. The results also verify that the look-ahead distance significantly influences the error in the tracking process, particularly when the radius of curvature is large. The greater the look-ahead distance, the smoother the tracking and the larger the error at the turn, but if the look-ahead distance is too small, this may result in control system instability, so choosing a suitable look-ahead distance is very important. As dead reckoning is a relative positioning method, the starting position and angle have a large impact on the tracking result, particularly if the starting angle has a large deviation, which will lead to a global rotation of the entire tracking trajectory. This means that the consistency of the starting position is another key to ensuring the tracking accuracy of this method.  
Figure 17: Tracking error with rotation of different angles.  
Figure 18: Tracking error of different driving modes at a vehicle speed of 10 km/h.  
Based on the above conclusions, future research should be conducted on achieving accurate path tracking that can be preserved even when the vehicle travels at high speed and how this method can be combined with path planning as a means of achieving automatic parking in indoor parking lots.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010317/s1](https://www.mdpi.com/article/10.3390/app13010317/s1)]([https://www.mdpi.com/article/10.3390/app13010317/s1](https://www.mdpi.com/article/10.3390/app13010317/s1)), Video S1: Demonstration.  
Conceptualization, S.C. and Y.J.; methodology, S.C.; software, Y.J.; validation, Y.J.; formal analysis, Y.J.; investigation, T.T.; resources, T.T. and K.L.; data curation, Y.J.; writing--original draft preparation, S.C.; writing--review and editing, S.C. and T.T.; visualization, S.C.; supervision, T.T.; project administration, T.T.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Scholarship Council, number 202108330169.  
Not applicable.  
This work was conducted at the mechatronics laboratory \"Mechlab\" at the Hochschule fur Technik und Wirtschaft Dresden (HTW Dresden, also known as Dresden University of Applied Science) with the support and help of colleagues there, particularly [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 3 Finite-Element Setup
### Boundary Conditions  
Each twisted cable is excited at one end of the conductor segments with a total current of \(I=10\) A, distributed equally among the five segments (2 A per segment). The other end of each segment is set to ground (zero voltage). The external nodes of the surrounding air domain is enforced with a zero magnetic vector potential (\(A_{\text{Z}}=0\)), approximating a sufficient far-field condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Lay Length** & **Total** & **Total** & \(BN_{\text{ERR}}\) & \(BN_{\text{ERR}}\) \\
**(m)** & **Elements** & **Nodes** & **0 Hz (\%)** & **50 Hz (\%)** \\ \hline \(\lambda=0.1\) & 1,016,991 & 1,362,014 & 5.38 & 5.42 \\ \(\lambda=0.2\) & 506,993 & 679,553 & 3.95 & 4.63 \\ \(\lambda=0.4\) & 736,796 & 985,645 & 4.07 & 5.62 \\ \(\lambda=0.6\) & 2,972,972 & 3,963,554 & 2.87 & 5.11 \\ \(\lambda=1.4\) & 3,409,389 & 4,513,492 & 1.17 & 1.89 \\ \(\lambda=2.0\) & 4,742,205 & 6,285,546 & 1.50 & 3.63 \\ \(\lambda=2.2\) & 5,101,876 & 6,760,819 & 1.74 & 3.18 \\ \(\lambda=2.6\) & 5,978,288 & 7,921,663 & 3.42 & 3.51 \\ \(\lambda=3.0\) & 6,754,232 & 8,945,680 & 1.44 & 2.54 \\ \(\lambda=3.4\) & 7,954,225 & 10,535,941 & 1.62 & 2.06 \\ \(\lambda=3.6\) & 8,312,148 & 11,009,740 & 1.21 & 2.64 \\ \(\lambda=3.8\) & 8,713,383 & 11,539,800 & 1.52 & 2.93 \\ \(\lambda=4.0\) & 9,158,742 & 12,127,533 & 1.65 & 4.08 \\ \(\lambda=5.0\) & 11,454,819 & 15,163,705 & 2.96 & 3.02 \\ \hline \end{tabular}
\end{table}
Table 2: Three-dimensional five-segment twisted models—summary of mesh setup.
## 5 Discussion
### Effects of Local Consistency Optimization on Different Numbers of Training Samples  
Figure 9 shows the classification result comparison between MAKC and without local consistency optimization on different real SAR images, with different numbers of training samples. It can be seen that the classification performance of MAKC outperforms other single features (i.e., AP, GLCM, and WL), because MAKC utilizes the complementary information among different feature groups by local consistency optimization. The advantage of the proposed method becomes more obvious when the available training samples are limited to only TN = 10. For example, as shown in Table 6, for the SAR1 image, the proposed method has the highest classification accuracy with OA = 90.1%, whereas the OAs of other single features (i.e., AP, GLCM, and WL) are 87.2%, 85.68%, and 85.89%, with a decrease of 2.9%, 4.42%, and 4.21%, respectively. The same results can also be observed on the other two data sets (e.g., SAR2 and SAR3). This demonstrates that local consistency optimization is effective in our proposed classification framework, as it assesses information quality and confidence level to fuse different features. Table 6 shows the accuracy of various variation methods and MAKC when the number of training samples is 10 on SAR1. The best results are shown in bold.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CLASS** & **SVM** & **CKS-FCM** & **CKFF** & **SVM-CK** & **MWSG** & **MAKC** \\ \hline \hline water & 26.22 & 95.09 & 89.34 & 88.83 & 95.39 & 96.25 \\ vegetation & 0 & 42.42 & 75.60 & 78.90 & 84.26 & 76.41 \\ crop & 99.65 & 39.66 & 83.31 & 86.43 & 81.73 & 88.77 \\ bare land & 36.94 & 74.81 & 77.98 & 86.76 & 81.01 & 92.38 \\ AA & 40.70 & 62.99 & 81.55 & 85.23 & 85.59 & 88.45 \\ OA & 53.49 & 69.55 & 84.31 & 87.22 & 87.11 & 92.15 \\ Kappa & 0.36 & 0.58 & 0.77 & 0.81 & 0.81 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification precision results on SAR3.
# Effect of pH Change on the Microalgae-Based Biogas Upgrading Process
## 2 Materials and Methods
### Experimental Procedure  
Batch photobioreactors of 200 mL were used and inoculated from previous microalgae batch culture (Section 2.1). applying a light intensity of 90 umol m\({}^{-2}\) s\({}^{-1}\). The effect of pH changes on the microalgae culture was evaluated through two experiments, which considered a shift in pH caused by the addition of HCl and CO\({}_{2}\) injection. Thus, these experiments simulated the expected pH changes evidenced when real biogas is bubbled in counterflow columns in microalgae culture:  
* Change in the pH by addition of HCl 3.7%. Three conditions were evaluated: control culture at pH 7.0; pH change from 7.0 to 5.0 when the culture was exposed to light; pH change from 7.0 to 5.0 when the culture was in darkness. Moreover, for these three conditions, a biomass concentration of 0.5 and 1.3 g L\({}^{-1}\) was evaluated. The effect of the pH changes was evaluated through the photosynthetic activity and PSII quantum yield analysis.
* Change in the pH by CO\({}_{2}\) injection. Three conditions were applied: control culture at pH 7.0 (dissolved inorganic carbon concentration of 12 mM); culture exposed to CO\({}_{2}\) injection and light; culture exposed to CO\({}_{2}\) injection in darkness. CO\({}_{2}\) was bubbled into the microalgae culture lowering the pH value to pH 5.8. Then, the CO\({}_{2}\) injection was stopped and the pH, PSII quantum yield, and photosynthetic activity were determined. A biomass concentration of 1 g L\({}^{-1}\) was used. All assays were carried out in triplicate.
# A Study on the Vertical Bearing Characteristics of Screw Piles in Permafrost Regions
## 2 Model Test
### Comparison Between Numerical Simulation and Laboratory Test  
The finite element model was established according to the model parameters used in the laboratory test, as depicted in Figure 5a. The frozen soil was frozen at \(-0.5\)\({}^{\circ}\)C. The elastoplastic model of the Mohr-Coulomb yield criterion was used for the soil, while the linear elastic model was used for the pile. The 10-node quadratic tetrahedral mesh element of C3D10 was used as the soil mesh unit around the pile. To reduce the calculation time, the mesh size near the pile was small, and far away, it was large. The mesh element of the pile's body also adopted the 10-node quadratic tetrahedral mesh element of C3D10. In the model, the lateral constraints of the soil were in the \(x\) and \(y\) directions, and the \(z\) direction was free. The bottom of the soil was fixed and constrained in the \(x\), \(y\), and \(z\) directions. The loading of the pile was achieved by applying a vertical displacement of 4 mm at the top of the pile.  
Figure 6b indicates the typical shear stress-shear displacement curves from the shear test on the contact surface of the frozen concrete [11; 12]. The bond and the Coulomb friction models were combined to simulate the shear characteristics of the contact surface of frozen concrete. The ice-bonding force on the contact surface of the frozen concrete was simulated using the bonding model, in which the damage evolution was simulated to simulate the shear stress attenuation after the contact surface of the frozen concrete reached the maximum shear stress. The decay-and-stability stage of the contact surface of the frozen concrete was simulated by the Coulomb friction model. The material parameters reflect those in the relevant literature [11; 12], and the specific values are provided in Table 1.  
Figure 6: Numerical simulation and verification.  
The comparison between the model test results and the simulation results is depicted in Figure 6c. The settlement of the ordinary pile is small before failure, and its ultimate failure load is consistent with the test results. The initial stage of the screw pile's load-settlement curve fits well with the test results, and its failure load is also close to that seen in the test results. Generally, the numerical analysis results are in good agreement with the experimental results, verifying the reliability of the numerical method.
# A Security and Privacy Scoring System for Contact Tracing Apps
## 3 Results
### Privacy Review
There was no whitepaper released about the protocol that they use for Bluetooth or GPS. The information requested includes phone number, name, gender, age, profession, travel history for last 30 days, willingness to volunteer in times of need, and other information [35], which is more than what is required for contact tracing. Nothing is stated about the data governance or meta-data treatment. The data retention limit is longer [35] than the WHO infectious period. The government previously made the app mandatory for anyone employed [36]. For the sunset requirement, there was no defined time limit. It is stated to be when a health authority declares the pandemic over [37]. Due to these factors, the Aarogya Setu app was grouped as Low. **Singapore's TraceTogether** does not address all of the principles. The open-source code for a template implementation of their Bluetrace protocol was released [38] but not the code of the specific TraceTogether app. Though four independent experts were consulted to review the security of their token devices [39], there is no mention of the same assessment for the app. The app requests personal information that is not required for the operation of their contact tracing protocol, such as national ID and phone number. The data are controlled by a government authority, though the location of the server is unknown. Data are encrypted on the device. However, it is unknown whether they are transmitted securely. The data retention limit is longer [40] than the WHO infectious period of 14 days. There is no available information about meta-data treatment. For the sunset requirement, there was no defined time limit. It is stated to be when a health authority declares the pandemic over [40]. TraceTogether was grouped as Medium.
## 1 Introduction
To prove the effectiveness of the proposed scheme, we compared it with other few-shot ASC schemes in terms of ASC accuracy. We also analyzed the robustness of TAPT to hyperparameters and the training efficiency of TAPT and original MAML through the convergence speed of training accuracy. The contributions of this paper are summarized as follows:  
* We propose a novel parameter transformation scheme based on MAML to enhance the classification accuracy of DeepASC models. To the best of our knowledge, this is the first effort to use task-adaptive MAML for few-shot ASC. * TAPT utilizes gradients as task-specific knowledge and incorporates this knowledge to adaptively transform the initial parameters for each task. This corresponds to an adaptable and efficient learning process. * Task-specific initial parameters can lead to faster convergence during the fine-tuning phase. This is because DeepASC starts fine-tuning from a point in the parameter space that is closer to the optimal solution for a given task. * We show that the proposed scheme can outperform ProtoNet, a state-of-the-art (SOTA) few-shot ASC scheme, using the same backbone network through experiments on two public animal sound datasets. * We demonstrate that the proposed scheme is robust to hyperparameters, and it can significantly reduce training time compared to MAML. The rest of this paper is presented as follows: Section 2 introduces the literature review about deep learning-based ASC models and few-shot learning schemes. Section 3 describes the proposed scheme in detail, and Section 4 presents the experimental setup and results; Section 5 provides conclusions along with future work.
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 4 Two-Dimensional Materials in Perovskite Light Emitting Diodes (PeLEDs)
The graphene-based PQD device with MAPbBr\({}_{3}\) QDs EML exhibited the maximum CE of 2.12 cd/A and EQE of 0.67%, which are comparable with the values of 3.0 cd/A and 0.96% observed in the ITO device. It can be attributed to the smooth surface and the low sheet resistance of the graphene/PEDOT:PSS:Triton X-100:DMSO conductive film. These results suggest that graphene is a promising anode to substitute ITO in PeLEDs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**2D Materials** & \multicolumn{3}{c}{**Device Structure**} & \multicolumn{3}{c}{**L\({}_{\text{max}}\)(cd/m\({}^{2}\))**} & \multicolumn{1}{c}{**CE\({}_{\text{max}}\)(cd/A)**} & \multicolumn{1}{c}{**EQE\({}_{\text{max}}\)(\%)**} & \multicolumn{1}{c}{**References**} \\ \hline Graphene & Graphene/But-Hill,/AA/PbHz/TPPB/Li/Al & - & 18 & 3.8\% & [64] \\ Graphene & Graphene/PEDOT:PSS/PDPD/PD/APbHz/TPPB, Qms/TPB/Li/Al & 91 & 0.39 & 0.14 & [65] \\ Graphene & Graphene/PEDOT:PSS/Titon X-100: DMSO/PDPD/MAPbPs, Qms/TPB/Li/Al & 446 & 2.12 & 0.67 & [65] \\ Graphene & Graphene/PEDOT:PSS/poly-TPD/FA\({}_{3}\)Cs\({}_{2}\)PbBr\({}_{3}\) NCs/TPB/Li/Al & 801 & 1.35 & 0.31 & [66] \\ Graphene & Graphene/PEDOT:PSS:Triton X-100:DMSO/poly-TPD/FA\({}_{3}\)Cs\({}_{2}\)PbBr\({}_{3}\) NCs/TPB/Li/Al & 5358 & 11.37 & 2.58 & [66] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the device parameters of PeLEDs incorporating 2D materials. Figure 8: Schematic structures of perovskite light emitting diodes (PeLEDs), in which perovskite materials as EML and 2D materials as (**a**) HTL, (**b**) HIL, (**c**) dopant in HTL. Figure 7: Schematic structures of LEDs with a (**a**) p-i-n and (**b**) n-i-p configuration.
## 5 Discussion
### Modulus of Elasticity  
The relationship between modulus of elasticity and the degree of initial damage at different strain rates is shown in Figure 10. It can be seen that:  
Figure 10: Plot of the elastic modulus versus the initial damage for the axial test from \(2\times 10^{-6}\) s\({}^{-1}\) to \(100\) s\({}^{-1}\) levels of strain rate: (**a**) \(E-\); (**b**) \(E-D\).  
Figure 9: Plot of the compressive strength and yield strain versus initial damage for the axial tests from \(2\times 10^{-6}\) s\({}^{-1}\) to \(100\) s\({}^{-1}\) levels of strain rate. (**a**) \(Rc-D\); (**b**) \(\varepsilon_{c}-D\).  
The elastic modulus is between 21 and 23 Gpa, and the value of the maximum elastic modulus in \(D\) = 0.043 is 68 Gpa, which is 28.3%, 78.9%, and 195.7% higher than that in \(D\) = 0.0357, 0.625, and 0.754, respectively. Compared to the degree of damage, the strain rate has little effect on the elastic modulus. In order to quantify the extent of nonlinearity in limestone compression behavior, a nonlinear relationship is applied and expressed as:  
\[E=\ e^{(a+b*D+c*D^{2})} \tag{9}\]  
where \(E\) is the modulus of elasticity; \(D\) is the initial damage; and \(a\), \(b\), and \(c\) are the quotieties.
## 4 Simulation Results
### Simulation Tests Conducted
#### Benefits of IQV controller compared to original algorithm  
* An IQV controller is suitable for asymmetric vehicles in the longitudinal and lateral directions, so the geometric center can be different from the center of mass.
* During the realization of the tracking task of the desired trajectory, information on changes in the behavior of the vehicle due to the displacement of the center of mass is obtained simultaneously. Thus, it is a combination of a control algorithm and a scheme for analyzing vehicle dynamics.
* It is possible to estimate the effect of dynamic parameters on changes in vehicle velocity and kinetic energy reduced by each quasi-velocity (taking into account couplings). In the classical algorithm, the effect of couplings is ignored.