esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Properties of Asphalt Mixtures under Triaxial Compression
## 4 Conclusions and Recommendations  
Triaxial compressive tests were conducted with confining pressure levels ranges from 0 to 1 MPa at 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C. The results could be applied to investigate the shear failure behavior of asphalt mixtures. The following conclusions can be drawn:  
(1) The failure behavior of asphalt mixtures SMA-13, AC-13, and OGFC-13 are affected significantly by the confining pressure and temperature. It was observed that the increase of temperature degrades the triaxial strength of asphalt mixtures mainly due to the decrease of cohesion strength; when the confining pressure increases, the shear failure strength of asphalt mixture increases with the increase of cohesion strength, while the friction angle of asphalt mixtures decreases.  
(2) The linear Mohr-Coulomb criterion is not suitable to describe the failure behavior of asphalt mixtures since its shear failure envelope is highly affected by the testing conditions. Therefore, a nonlinear regression/prediction model of shear strength for asphalt mixtures considering the influence of temperature and confining pressure was proposed. The nonlinear evolutional laws of cohesion strength and friction angle are also proposed. It is reasonable to use the nonlinear failure criterion in the design of asphalt pavement materials and structures.  
(3) Compared with the continuous-graded AC-13 asphalt mixture and the open-graded OGFC-13 asphalt mixture, the gap-graded SMA-13 asphalt mixture exhibits better performance on the resistance to shear damage under complex stress condition. Considering the overloading phenomenon that generally exists in China, the SMA-13 asphalt mixture is recommended as the upper layer material to improve the shear performance of asphalt pavement structure.  
(4) Triaxial compressive tests should be done at different loading rates in the future. And further study is required for asphalt mixtures with varying bitumen-aggregate ratio, or varying amount of asphalt binder and so on in the lab to improve the shear resistance of asphalt mixtures at high-temperature conditions.  
Conceptualization, T.H. and S.L.; Data curation, T.H., S.Q. and H.L.; Formal analysis, T.H. and H.Y.; Funding acquisition, T.H.; Methodology, T.H. and S.L.; Project administration, T.H. and H.L.; Resources, T.H. and S.L.; Validation, S.L.; Writing--original draft, T.H., S.Q., and H.Y.; Writing--review and editing, T.H. and S.L.  
This research was funded by the National Natural Science Foundation of China, grant number [51608055, 516808058], National Key R&D Program of China, grant number [2018 YFB1600100], Natural Science Foundation of Hunan Province, grant number [2019J40297, 2018J]2433, 2018J[33550], the education department of Hunan province, grant number [18B143,18B144], Open Fund of the Key Laboratory of Highway Engineering of Ministry of Education (Changsha University of Science & Technology), grant number [kfj160201].  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Deterioration of Loess Due to Saturated Infiltration  
Figure 4 shows the variation in the saturated permeability of the soil during the test, which can be used to investigate the deterioration of loess due to infiltration. The initial measured saturated permeability of the CH soil sample with a dry density of 1.45 g/cm\({}^{3}\) is 5.63 \(\times\) 10\({}^{-6}\) cm/s, and after 8856 s, the saturated permeability is 5.37 \(\times\) 10\({}^{-6}\) cm/s, a decrease of 0.26 \(\times\) 10\({}^{-6}\) cm/s. When the dry density of the soil sample is 1.60 g/cm\({}^{3}\), \(k_{\rm s}\) decreases from 7.83 \(\times\) 10\({}^{-7}\) cm/s to 7.79 \(\times\) 10\({}^{-7}\) cm/s after 16,475.5 s, a decrease of 0.04 cm/s. The variation curves of the saturated permeability versus time of the soil samples in the other areas exhibited the same pattern. For the same soil sample with the same dry density, the soil saturated permeability decreased over time, but the changes were within the same order of magnitude. Moreover, during the same time period, the decrease in the saturated permeability of soil with lower dry density was more significant, and the decrease rate was larger. These results suggest that the deterioration of loess permeability is evident in the long-term infiltration of low-density soils, while the development of deterioration is relatively slow in high-density soils. Under saturated infiltration conditions, the long-term interaction between water and loess particles could cause irreversible changes in loess structure, which is the main cause of infiltration degradation in loess. [PERSON] et al. observed the appearance of samples before and after the saturated infiltration test and found that the structure of the compacted loess sample was uniform before the saturated infiltration test, while after the 20 h saturated infiltration test, pockmarks appeared on the lower surface of the sample due to particle transport [37]. The loss of soluble substances in the soil during long-term infiltration causes the cohesion of the particles to continuously decrease, and the upper fine particles displace under the action of the water infiltration and block the pores; therefore, the inhomogeneity of the pores along the infiltration direction gradually increases [37], and irreversible effects are induced in the pore structure, which reduces the  
Figure 3: Sample preparation for the filter paper test. (**a**) Whatman Quantitative Filter Papers, (**b**) component of specimen, (**c**) Sealed with waterproof tape, (**d**) sealed with plastic wrap, (**e**) Sealed with finfoil, (**f**) Sealed with paraffin, (**g**) Numbered sample after completely sealed.  
proportion of effective infiltration channels. According to the principle that the minimum pore size determines the permeability, the saturated permeability gradually decreases during the infiltration process, causing the deterioration of the saturated infiltration flow.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### Incremental and Online Learning  
Despite DRL, algorithms are practical for extremely high-dimensional application domains such as data center and 5G/6G networks. They usually operate with environments that remain unchanged during learning, as a result, they may present shortcomings when coping with dynamic environments where the reward function, state transition function, or state action spaces change over time. Incremental DRL [177; 178] and online DRL [179] have been proposed recently for enhancing DRL. Incremental DRL algorithms can learn continuously, adapt their models without forgetting the learned earlier, and produce faster forecasting than traditional DRL algorithms operating with minibatches. Online DRL algorithms are incremental, operate in environments with restricted resources and hard run-time constraints, and have lifelong learning with limited data. As incremental DRL and online DRL are still in their infancy in the networking domain, we consider it worth investigating their benefits to a dynamic application domain such as 5G/6G network slicing resource management in depth. Several critical questions need an answer during those investigations: 1 Which online/incremental DRL algorithms match resource management (or a particular phase)? 2 What is the performance of these algorithms when solving resource management tasks? 3 How do we optimize existing online/incremental algorithms for resource management phases?
## 1 Introduction
### Objectives and Summary  
In the literature, most robotic manipulators dedicated to craniotomy are based on classical industrial architectures (anthropomorphic or hexapod). A recent trend seems to favor more dedicated architectures based on RCM. According to the requested craniotomy Degrees of Freedom (DoF), two rotations, an SPM is proposed to handle the drilling tool. This type of architecture is known for generating RCM kinematics. This mechanism has a total of three angular DoF that allow one degree of redundancy. The contribution of this work focuses first on the experimental analysis of the craniotomy surgery task. Secondly, the redundancy of the SPM is used as an advantage by using the spinning motion as a reconfiguration variable in order to improve the parallel robot behavior. The present method simply relies on identifying the redundant configuration that will generate the minimum torque for each point of a preplanned trajectory. It is assumed that a craniotomy trajectory on the patient's skull will be planned during the preoperation phase. A simulation will then allow determining how the SPM shall generate this trajectory. A specific strategy is used to determine the reconfiguration to minimize the motor torques while generating enough reaction force to drill the patient's skull. In the current research work, an experimental campaign of motion capture and force recording is conducted for the analysis of the kinematic and force interaction characterizing Burr hole craniotomy procedures. These experimentations were carried out on a human cadaver to ensure a higher degree of realism. The collection of these data will allow the trajectory planning of robotic-assisted craniotomy using the SPM.  
The rest of the study is organized as follows: Section 2 will describe the motion capture experiments that allowed determining the trajectory of the surgical drill. Section 3 illustrates how the collaborative robot was used to determine the magnitude of the skull reaction force. The strategy used to minimize the required torque to generate this force is detailed in Section 4. Finally, Section 5 presents the conclusion of this study.
## 2 Materials and Methods
### Calibrate the Machine Learning Model  
The spectrally resampled database (which consists of resampled pixels per sensor) was standardized using standard normal variate (SNV) standardization [13], and then an analysis of variance (ANOVA) test was performed to understand which bands can be considered good separators between the two classes (clean or oil). The result of such a test is the F statistic, where a higher F statistic indicates better separation.  
Then, the spectrally resampled database was subset to include only the bands that had an F statistic value above the threshold. SNV was applied to that database so as to create a new standardized database (see DB4 in Figure 1). For each sensor in DB4, a partial least squares (PLS) [14] model was used for dimension reduction, and the number of latent variables (LV) was selected according to the overall accuracy calculated from the cross-validation process. Then, linear discriminant analysis (LDA) [15] was applied to the LV of each sensor. LDA makes predictions by estimating the probability that a new set of inputs belongs to each class. The class that gets the highest probability threshold is the output prediction class. The default threshold is 50%, but here, an analysis was performed to choose the best probability threshold for each sensor, which optimized the metric accuracies. In other words, all the possible thresholds were examined, and the one that minimized the difference between the accuracy metrics was chosen. The accuracy metrics used here were the overall accuracy, the sensitivity, and the specificity:  
\[Overall\ Accuracy=\ \frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Sensitivity=\ \frac{TP}{TP+FN} \tag{2}\]  
Figure 3: Average spectrum of clean and oil per sensor after spectral resampling. The spectra of the hyperspectral sensors are plotted as a continuous line, while the black dots in the spectra of the multispectral sensors represent the band centers. Please note that the x-axis represents the specific wavelengths for each sensor.  
\[Specificity=\ \frac{TN}{TN+FP} \tag{3}\]  
where TP (true positive) is the number of pixels correctly classified as oil, FP (false positive) is the number of pixels incorrectly classified as oil, TN (true negative) is the number of pixels correctly classified as clean, and FN (false negative) is the number of pixels incorrectly classified as clean. In addition, the Kappa coefficient (\(k\)) [16] was also calculated, because it is considered a more stringent metric, as it takes into account the possibility of agreement occurring by chance, as follows:  
\[k=\ \frac{P_{0}-P_{e}}{1-P_{e}} \tag{4}\]  
where \(P_{0}\) is the relative observed agreement among the raters, and \(P_{e}\) is the hypothetical probability of chance agreement.
## 4 Discussion and Results of Experiment
### Analysis of Experimental Results
#### 4.3.2 Model Performance with Different Samples  
Mechanical failures of HVCBs occur less frequently, and failure samples are scarce in practice. Hence, given the limited availability of samples, there is a need for further research on the fault diagnosis accuracy of HVCBs in scenarios involving small sample sizes. To assess the efficacy of the proposed approach under conditions of limited sample size, the proposed model was used with different sizes of samples and the gcForest, GWO-RF, RF, PSO-SVM, and SVM results were compared with each other. The base unit of the sample size was 40 (100%) per category.  
The results are presented in Figure 9. One can see that accuracy gradually decreases as the sample size decreases, with the SVM model dropping the most rapidly as the sample quantity decreases. The GWO-gcForest model accuracy decreases with the decreasing sample size, and the degree of decline is relatively gentle compared with that of other models. The accuracy was \(\sim\)90% or higher before the sample size dropped to 75%. When the sample size was further reduced to 25%, it is observed that only the method introduced in this study outperforms other models in terms of the accuracy of fault diagnosis. This finding suggests that the proposed model possesses certain advantages even when the number of samples for each fault type is limited. Based on the downward trend of the accuracy rate, the GWO-gcForest model has a better diagnostic effect when the sample size is small.
## 2 Materials and Methods
### Data Reconstruction  
Block coordinate descent is an efficient federated learning algorithm, but can leak information about the used data set. In [4], it is explained that the attackers may reconstruct the used data set up to a rotation. From discussions with the authors of [4], we have learned that the data is better protected than by a rotation. The original data can be approximated within a quantifiable margin of error, depending on the amount of shared intermediate results. Earlier reconstruction attacks suggest that an external attacker with supplementary information might be able to mimic this approach even without access to the intermediate results. Although the design, feasibility and success of such an attack are merely hypothetical, the fact is that at this point, we cannot say to what extent the approach in [4] protects the processed data. This is one of the reasons to study a differentially private version of BCD.  
This is an example of the broader problem of data privacy. It is hard, if not impossible, to measure. The reason for this is that typically no optimal attack exists. Since it is hard to know how much some optimized approach may uncover, evaluating the 'privacy' of data processing is hard. This is one of the reasons to work with theoretical upper bounds on the amount of information that is leaked. Differential privacy does precisely this. It bounds the certainty an attacker may obtain from the results of any study, regardless of the extra information or computational power the attacker may have.  
**Example 1**.: _Assume that an attacker has obtained a small list of possible data sets \(\{(\mathbf{X}^{(i)},\mathbf{y}^{(i)})|\)\(1\leq i\leq n\}\), of which one is used in a deterministic study, meaning that the outcome is a function of the dataset and the label. The result of this study is a vector \(\mathbf{\beta}\), solving (1), of weights belonging to a linear model. The attacker can simply test all possible data sets to see which ones generate the optimal weight vector \(\mathbf{\beta}\). In this way, the attacker may determine which data set was used. This shows that deterministic methods cannot provide sufficient privacy guarantees._
## 2 Materials and Methods
### Fetswcm-Swan Model  
The FETSWCM-SWAN model which is developed to compute wave-induced water level rise when coupled with SWAN [12] has been used to forecast typhoon surges and wave disasters by the Fujian Marine Forecast Center since 2016. The FETSWCM-SWAN is a two-way model coupling of FETSWCM and SWAN wherein the FETSWCM model runs first on the coupling interval using the atmospheric forcing and tidal forcing on ocean boundary but \(\tau_{\text{wx}}\), \(\tau_{\text{wy}}\) are set to zero. Once the time steps of coupling interval are complete, FETSWCM passes on the water levels and currents to SWAN. SWAN is driven by wind speeds, water levels, and currents computed at the vertices by FETSWCM. The water levels and ambient currents are computed in FETSWCM before being passed to SWAN, where they are used to recalculate the water depth and all related wave processes (wave propagation,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source Term** & **Scheme** \\ \hline Linear wind growth & [PERSON] (1981) [20] \\ Exponential wind growth & [PERSON] et al. (1984) [21] \\ White capping & [PERSON] et al. (1984) [21] \\ Triad interaction & [PERSON] (1996) [22] \\ Quadruplet interaction & [PERSON] et al. (1985) [23] \\ Depth induced breaking & [PERSON] (1985) [24] \\ Bottom friction & [PERSON] et al. (JONSWAP) (1973) [19] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Source Term of SWAN.  
depth-induced breaking, etc.). The FETSWCM model is driven by radiation stress gradients that are computed using information from SWAN. These gradients \(\tau_{wx}\), \(\tau_{wy}\) are computed by:  
\[\tau_{wx}=-\frac{\partial S_{xx}}{\partial x}-\frac{\partial S_{xy}}{\partial y}\]  
\[\tau_{wx}=-\frac{\partial S_{yx}}{\partial x}-\frac{\partial S_{yy}}{\partial y}\]  
Because of the sweeping method used by to update the wave information at the computational vertices SWAN takes much larger time steps than FETSWCM, which is diffusion- and also Courant time-step limited due to its semi-explicit formulation. The time step is 300 s in FETSWCM and 900 s in the SWAN model. Therefore, the time interval of 1800 s for the two models guarantees computational efficiency and ensures continuity of the transferred variables. We performed a series of simulations using different coupling time intervals. The processing time of the coupled model was found to be too long when the time interval was small (e.g., \(\leq\)600 s), while certain short-term tide-surge-wave interaction processes vanished when the time interval was too large (e.g., \(\geq\)3600 s). Thus, we selected the time interval of 1800 s as optimal for the requirements of this study. After each 1800-s interval, the real-time waves determined by SWAN are given to FETSWCM, and FETSWCM returns the real-time water level field and current field to SWAN. The two models perform the data exchange process as shown in Figure 1.
## 5 Statistical Analysis of the Protocols Based on Subsets of Responses and ECC
### Error Management in the Cryptographic Keys  
Mainstream ECC is difficult to implement in this approach since the data helpers needed to correct the key K are also affected by the noise injection. Thus, data helpers for the data helpers are called for, not an easy problem to resolve at high error rates. Therefore, we are proposing, in this section, several light error management schemes that can tolerate the residual errors created in K in the range of 1 to 3 bad bits per each 256-bit long key. With \(T=0.35\) and \(P=80\), 35% BERs in the responses generate \(E(K)\)\(\approx 3.0\times 10^{-4}\) BERs in the keys, which represents less than one bad bit per 256-bit long key.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## Experimental Study
### Study Results
#### 6.2.2 Experimental Results: Dynamic M-CVRP
The approaches proposed by existing works in the literature for the smart waste collection problem did not respect all the constraints, especially in the dynamic case; this is why our results in the dynamic case, were not compared with those found in the literature. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Day/Route**} & \multirow{2}{*}{**Dod (\%)**} & \multicolumn{4}{c}{**Our Approach**} \\ \cline{3-6}  & & **Distance** & **Visited** & **Visited** & **Bins** \\  & & **(km)** & **Stations** & **PC** & **PM** \\ \hline
1/13 & 0 & 76.34 & 35 & 35 & 30 \\  & 10 & 80.32 & 35 & 35 & 30 \\  & 30 & 97.91 & 35 & 35 & 30 \\  & 50 & 78.56 & 35 & 35 & 30 \\  & 70 & 91.47 & 34 & 34 & 30 \\  & 90 & 95.52 & 35 & 35 & 30 \\
7/11 & 0 & 78.23 & 33 & 33 & 33 \\  & 10 & 92.37 & 32 & 32 & 32 \\  & 30 & 100.67 & 33 & 33 & 33 \\  & 50 & 83.78 & 32 & 32 & 32 \\  & 70 & 81.53 & 31 & 31 & 31 \\  & 90 & 105.14 & 33 & 33 & 33 \\
8/6 & 0 & 119.52 & 51 & 51 & 36 \\
8/13 & 10 & 130.74 & 51 & 51 & 36 \\  & 30 & 145.23 & 51 & 51 & 36 \\  & 50 & 127.17 & 50 & 50 & 50 \\  & 70 & 138.31 & 51 & 51 & 51 \\  & 90 & 122.28 & 51 & 51 & 51 \\
15/13 & 0 & 87.95 & 39 & 39 & 39 \\  & 10 & 91.15 & 38 & 38 & 38 \\  & 30 & 115.02 & 39 & 39 & 39 \\  & 50 & 98.39 & 39 & 39 & 39 \\  & 70 & 94.55 & 39 & 39 & 39 \\  & 90 & 111.28 & 39 & 39 & 39 \\
19/11 & 0 & 54.05 & 26 & 26 & 26 \\  & 10 & 60.34 & 26 & 26 & 26 \\  & 30 & 77.12 & 26 & 26 & 26 \\  & 50 & 71.88 & 26 & 26 & 26 \\  & 70 & 65.17 & 26 & 26 & 26 \\  & 90 & 57.07 & 24 & 24 & 24 \\
22/6 & 0 & 116.21 & 49 & 49 & 42 \\
22/13 & 10 & 120.05 & 49 & 49 & 42 \\  & 30 & 141.76 & 49 & 49 & 42 \\  & 50 & 125.92 & 45 & 45 & 41 \\  & 70 & 118.54 & 47 & 47 & 42 \\  & 90 & 135.16 & 48 & 48 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for DM-CVRP. In addition, to show the effectiveness of our approach, we focused on comparing our result in the static case with the current situation of the Valorsul Company.
## 5 Simulations Results and Analysis  
The simulation model including the 6-DOF model, the ride comfort controller, the handling stability controller, the switch strategy, and the road input models are built in MATLAB/Simulink, as shown in Figure 10. In order to verify the quality of the proposed switch control strategy, the simulations are performed under the J-turn maneuver and fishhook maneuver to evaluate the ride comfort and handling stability of the vehicle.