esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation into Permeable Asphalt Pavement Based on Small-Scale Accelerated Testing
## 2 Materials and Methods
### Structures and Materials  
In this study, three types of permeable asphalt pavement, including two fully permeable structures and one drainage structure, were prepared in a steel track, which was 270 cm long, 90 cm wide, and 32 cm deep. Since there were two partitions at the bottom of the steel track, the length of the three structures were respectively divided into 85 cm (4 PA-CTPB), 105 cm (7 PA-CTPB), and 85 cm (7 PA-CTB), as shown in Figure 2 and Table 2. An aquiclude using waterproof geotextile was installed between every two structures to prevent water from moving laterally. In addition, a filtration geotextile was installed between the subgrade layer and permeable base. The materials of the three structures were tested in the laboratory [32,33], and their properties are presented in Table 3. In this paper, a porous asphalt mixture (PA) was prepared with 4.6% high-viscosity modified asphalt and an aggregate with a maximum nominal size of 13 mm. The gradations of the porous asphalt mixture and base materials are presented in Table 4. The aggregate size should be small to prevent stripping. The permeability of the surface and base layer was tested in the laboratory. The field permeability was tested and compared with the laboratory test results. The results indicated that the field permeability was a little lower than that in the laboratory but still met the requirements of the infiltration function. In addition, the laboratory dynamic modulus master curve was obtained by fitting the modulus data using a sigmoid function (Equation (1)) and a temperature shift factor \(\alpha_{T}\) (Equation (2)).  
\[\log(E)=\delta+\frac{\alpha}{1+e^{\beta+\gamma\log(f\alpha_{T})}} \tag{1}\]  
\[\log\alpha_{T}=\frac{-C_{1}(T-T_{r})}{C_{2}+T-T_{r}} \tag{2}\]  
where \(E\) is the modulus (MPa);\(f\) is the frequency (Hz); \(\delta\), \(\alpha\), \(\beta\), \(\gamma\), \(C_{1}\), and \(C_{2}\) are the fitting parameters; \(T\) is the test temperature; and \(T_{r}\) is the reference temperature. The fitting parameters are presented in Table 5, and the master curve is plotted in Figure 3. The reference temperature was 20 \({}^{\circ}\)C in this study. It could be seen that as the loading frequency increased, the dynamic modulus of the mixture gradually increased. As the temperature rose, the dynamic modulus gradually decreased. Therefore, it could be foreseen that in the accelerated loading test, under the condition that the loading frequency and pressure were fixed, as the temperature rose, the modulus of the mixture might decrease and the resulting strain might increase.  
\begin{table}
\begin{tabular}{c c} \hline
**MMLS3** & **Specification** \\ \hline Tire pressure (MPa) & 0.7 \\ Load per tire (kN) & 2.7 \\ Speed (km/h) & 7.5 \\ Number of cycles per hour & 6000 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Structures of 3 types of permeable pavement.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 2 System Model of Diffusive Molecular Communications  
### System Overview  
Figure 1 shows the block diagram of diffusive molecular communication. In diffusive molecular communication, a signal is converted into a biochemical phenomenon by a modulator and a transmitting molecule in response to an external input. The modulated signal propagates through the molecular communication channel, is received by the receiving molecule, and is demodulated to transfer information. Figure 2 illustrates the molecular communication system model. The system model assumes a three-dimensional fluid environment in the infinite region, and the temperature and viscosity in the environment are constant. Additionally, it is assumed that the transmitting molecule is a point source with no volume at position \(r_{0}=(x_{0},y_{0},z_{0})\). The receiving molecule is assumed as a sphere with the center coordinate at the origin and a radius of \(a\). It is assumed that green fluorescent protein (GFP) is used as the receiving nanomachine, and the amount of light is measured via a photodetector. Multiple circular receptors with radius \(r_{s}\) exist on the surface of the receiving molecule, and the number of these receptors is \(M\). By assuming one type of molecule as a signal transduction molecule, the molecule is denoted as molecule A. Note that the receptor molecules distributed randomly on the surface, where the fluctuations of the receiver molecules should be, affected the connection of transmitter and receiver molecules. The modulation is performed by On-Off Keying (OOK). When the transmission bit is \(1\), molecule A is released instantaneously at the start time of the symbol interval,  
and when the transmission bit is 0, it does not emit. After the signal transduction molecule is released into the environment, it diffuses in all directions via Brownian motion with a diffusion coefficient \(D_{\text{A}}\). At that time, it is assumed that molecule A follows the decomposition reaction as  
\[\text{A}\overset{k_{\text{d}}}{\longrightarrow}\phi \tag{1}\]  
where \(k_{\text{d}}\) is the decomposition reaction coefficient. Furthermore, \(\phi\) indicates chemical species that do not contribute to communication. After molecule A has been released from the transmitting molecule, it reaches the surface of the receiving molecule via diffusion and reacts with molecule B, which is located on the receptor on the surface of the receiving molecule. The product at this time is referred to as molecule C. The chemical reaction at the receptor can be written by  
\[\text{A}+\text{B}\overset{k_{\text{f}}}{\underset{k_{\text{b}}}{\rightleftharpoons}}\text{C} \tag{2}\]  
where \(k_{f}\) and \(k_{b}\) denote the reaction rate constants of the forward and reverse reactions, respectively.
###### Abstract  
The twin-field quantum key distribution (TF-QKD) and its variants can overcome the fundamental rate-distance limit of QKD. However, their physical implementations with the side channels remain the subject of further research. We test the side channel of a type of external intensity modulation that applies a Mach-Zehnder-type electro-optical intensity modulator, which shows the distinguishability of the signal and decoy states in the frequency domain. Based on this security loophole, we propose a side-channel attack, named the passive frequency-shift attack, on the imperfect implementation of the sending or not-sending (SNS) TF-QKD protocol. We analyze the performance of the SNS protocol with the actively odd-parity pairing (AOPP) method under the side-channel attack by giving the formula of the upper bound of the real secret key rate and comparing it with the lower bound of the secret key rate under [PERSON] and [PERSON]'s estimation. The simulation results quantitatively show the effectiveness of the attack on the imperfect devices at a long distance. Our results emphasize the importance of practical security at the light source and might provide a valuable reference for device selection in the practical implementation of the SNS protocol.  
t +
Footnote †: journal:  
Article  
Practical security; decoy-state method
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
## 2 Related Work
A coordinated multi-point reception scheme with multiple carrier-frequency offsets (CFOs) has also been proposed in the literature to support one-tap interference free equalization [6]. All the techniques discussed above provide a better OBE compared to the 4G LTE-A CP-OFDM waveform. Spectral inefficiency, however, remains a major drawback of the UFMC standard waveform, making it difficult to be used for future 5G networks.
## Appendix A  
To better understand the parameters in this article, descriptive definitions are provided below.  
1. Indices \(i\) and \(j\): The index of a pixel or a point, where \(i\) = 0, 1, 2,..., n; \(j\) = 0, 1, 2,..., n.
2. Statistical filtering \(k\): The \(k\) nearest neighbors surrounding a given point \(p\). \(\mu\): The mean Euclidean distance of the \(k\) nearest neighbors from the given point \(p\). \(\sigma\): The standard deviation of the Euclidean distances between the \(k\) nearest neighbors and the given point \(p\). \(\alpha\): A scaling factor used to adjust the stringency of filtering.
3. Bilinear interpolation \(x_{ij}\): The \(x\)-coordinate of a given pixel \(p_{ij}\). \(y_{ij}\): The \(y\)-coordinate of a given pixel \(p_{ij}\). \(l_{ij}\): The luminance value of a given pixel \(p_{ij}\).
4. Least-squares fitting \(y\): The \(y\)-coordinate of a given point \(p\). \(z\): The \(z\)-coordinate of a given point \(p\). \(x\): The \(x\)-coordinate of a given point \(p\). \(z_{ai}\): The original z-coordinate value of the \(i\)th point.
5. Surface defect detection algorithm \(\alpha_{i}\): The angular deviation between the optimal normal vector of the \(i\)th point and the average normal vector of its neighboring points. \(\mu\): The mean curvature of all points within the adaptive neighborhood. \(\sigma\): The standard deviation of the curvature of all points within the adaptive neighborhood. \(\beta\): A scale factor used to adjust the range of curvature deviation. \(\Delta z_{i,j\pm 1}\): The height difference between two adjacent points on the same fitted contour in the \(x\)-direction. \(\Delta z\): The value of the height difference (residual) to be estimated. \(\Delta z_{i}\): The height difference (residual) of the \(i\)th observation point.
###### Abstract  
In this paper we address a decentralized neighbor-based formation tracking control of multiple quadrotors with leader-follower structure. Different from most of the existing work, the formation tracking controller is given in one loop without distinguishing the motion control and attitude control by means of the theory of flatness. In order to achieve an aggressive formation tracking, the high-order states of the neighbors motion are estimated by using a proposed extended finite-time observer for each quadrotor. Then the estimated motion states are used as feedforwards in the formation controller design. Simulation and experimental results show that the proposed formation controller improves the formation performance, i.e., the formation pattern of the quadrotors is better maintained than that using the formation controller without high-order feedforwards, when tracking an aggressive reference formation trajectory.  
Keywords:quadrotors; aggressive formation tracking control; extended finite-time observer; flatness +
Footnote †: journal: Applied sciences
# Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
###### Abstract  
Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.  
Keywords: crystallization; agglomeration; deep learning; image analysis; primary crystals in agglomerates +
Footnote †: journal: _Applied Sciences_
## 2 Transient Temperature Field Model of Wear Band on the Flank of End Mills
### Temperature Rise Model of the Wear Band Affected by Heat Source in the PSZ
\tag{8}\]  
From Equation (5) to (7), \(\lambda_{t}\) is the thermal conductivity of the tool (cemented carbide material); \(h(\theta)\) is the thickness of the undeformed chips; \(\phi_{n}\) is the normal shear angle; \(\eta_{c}\) is the chip outflow angle; \(\alpha_{w}\) is the thermal diffusivity of the Ti6 Al4V material; and \(K_{0}\) is the zero order of the second modified Bessel function. The calculation of heat source intensity in the PSZ is presented in Equation (9). \[q_{I}=\frac{F_{sh}V_{sh}}{A_{sh}}=\tau_{ABCD-\max}V\cos\lambda_{s} \tag{9}\]  
The milling process is accompanied by a large plastic deformation at high temperatures, pressures and strain rates. In the present study, a flow stress model (_Johnson-Cook_) was used to characterize the yield stress (\(\sigma_{ABCD}\)) on the shear surface (\(ABCD\)) of workpiece during milling. The shear slip phenomenon of the shear surface requires the ultimate shear stress (\(\tau_{ABCD-max}\)). The relationship between them is revealed by Equation (10). \[\tau_{ABCD-\max}=\frac{\sigma_{ABCD}}{\sqrt{3}} \tag{10}\]  
The calculation for the ultimate shear stress of the shear plane (\(ABCD\)) is shown in Equation (11). \[\tau_{ABCD-\max}=\frac{\left(A+B(\varepsilon_{ABCD-P})^{n}\right)\!\!\left(1 +C\ln\frac{\dot{\varepsilon}_{ABCD-P}}{\dot{\iota}_{0}}\right)\!\!\left(1- \left(\frac{T-T_{r}}{T_{m}-T_{r}}\right)^{m}\right)}{\sqrt{3}} \tag{11}\]Johnson-Cook constitutive model has been reported on Ti6 Al4V material by [PERSON] et al. [30]. In Equation (11), \(A\) is the initial yield stress at the reference strain rate and temperature, \(B\) is the strain hardening modulus of Ti6 Al4V, \(\varepsilon_{ABCD-P}\) is the effective plastic strain at the shear plane, \(n\) is the strain-hardening exponent of Ti6 Al4V, \(C\) is the strain rate-hardening parameter of Ti6 Al4V, \(\dot{\varepsilon}_{ABCD-P}\) is the effective plastic strain rate at the shear plane, \(\dot{\varepsilon}_{0}\) is the reference strain rate; \(T\) is the current temperature; \(T_{r}\) is the reference temperature; \(T_{m}\) is the melting temperature of Ti6 Al4V; and \(m\) is the thermal softening index of Ti6 Al4V.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 4 Results and Analysis
### Evaluation Metrics  
In this paper, we consider the following metrics which evaluate the performance of the schedulers:  
* Power efficiency: we calculated it in terms of dBm per bytes, where the power in dBm was calculated using Equation (1).
* Spectral efficiency: it is defined as the ratio between the information rate (bit/s) and the bandwidth of the channel (Hz).
* Number of packets that do not satisfy the constraint of maximum tolerable delay.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application Type** &
\begin{tabular}{c} **Number of** \\ **Devices** \\ \end{tabular} & **Model** & **Maximum delay** \\ \hline Constant Bit Rate (CBR)[37] & 10 & 128 kbps with packet size of 256 bytes & 300 ms \\ VoIP & 10 & G.795 [37] & 100 ms \\ Video & 10 & H264 with 128 kbps [38] & 150 ms \\ M2M time-driven & 70\% & Inter-arrival time of \([0.05,5]\) s with packet size of 125 bytes & Equal to inter-arrival time \\ M2M event-driven & 30\% & Poisson process with \(\lambda=50\) ms and with packet size of 125 bytes & \(50\) ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Traffic model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth & 5 MHz (25 RBs are available per TTI) \\ Simulation time & 5 s or 5000 TTIs executed 10 times \\ Number of eNode B & 1 with radius 1 km \\ Mobility & 3 km/h for H2H and fixed for M2M \\ \(P_{max}\), \(P_{0}\) & 23 dBm, \(-57\) dBm/PRB \\ \(\Delta_{TF}\), \(f\) and \(\alpha\) & Not considered (set to zero) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
* Fairness: it determines whether users are receiving fair shares of RBs. It is measured using [PERSON]'s fairness index [39], as shown in Equation (3). \[J(T)=\frac{(\sum_{n=1}^{N}T(n))^{2}}{N\sum_{n=1}^{N}T(n)^{2}},\] (3) where \(N\) is the number of users and \(T(n)\) is the normalised throughput (Kbps) of \(n\)th user.
## 3 Suboptimal Subcarrier and Power Allocation
### Suboptimal Subcarrier Allocation Algorithm
(4) After each iteration, the pheromone in the maximum sum rate route is updated. The update rules can be described as  
\[\tau_{k,n}^{(i+1)}=\left(1-\rho\right)\tau_{k,n}^{(i)}+\Delta\tau_{k,n}^{(i)} \tag{10}\]  
where \(i\) is the current iteration, \(\rho\) is the pheromone volatilization coefficients and \(\Delta\tau_{k,n}\) is the added value. \(\Delta\tau_{k,n}=1/R_{\mathrm{best}}\) and \(R_{\mathrm{best}}\) is the maximum sum rate achieved till current iteration in the ACO algorithm for the subcarrier allocation. The pheromone volatilization operation ensures the random search capability of the algorithm, and avoids falling into the local optimal solution. The prospect of the pheromone increase operation is to import positive feedback and guarantee the convergence of the algorithm. (5) In order to avoid the excessive accumulation of the pheromone, the pheromone range is limited to \(\left[\tau_{\min},\,\tau_{\max}\right]\). \[\tau_{k,n}^{(i+1)}= \begin{cases}\tau_{k,n}^{(i)},\,\,\,\tau_{\min}\leq\tau_{k,n}^{(i)}\leq\tau_{\max}\\ \tau_{\max},\,\,\tau_{k,n}^{(i)}>\tau_{\max}\\ \tau_{\min},\,\,\tau_{k,n}^{(i)}<\tau_{\min}\end{cases}  \tag{11}\]The ACO algorithm is terminated when it reaches the maximum number of iterations [31] and the subcarrier allocation scheme with the highest sum rate is the final output result. The ant colony optimization (ACO) algorithm for the subcarrier allocation is summarized in Figure 3.  
After the suboptimal subcarrier allocation by ACO algorithm and equal power distribution assumption, each user is assigned a certain number of subcarriers, and the system achieves the coarse proportional fairness. The goal of obtaining the strict proportional fairness and high sum rate is achieved by the power allocation algorithm.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 3 Results and Analysis
### Comments: Shear Strain Range  
The constitutive model in Equation (1) has been validated for many different granular material systems [25; 30; 31], while this research for the first time applies the constitutive model to characterize the soil properties of geotechnical interest. The functional form determines the shape of hysteresis, while the hysteretic nonlinearity parameter \(\alpha\) quantifies the amount of the macroscopic hysteretic nonlinear effect caused by microscopic sources of nonlinearity. In addition, this parameter is not  
Figure 4: Damping ratios of sand and clay under three different confinement stress levels (sand data from 16–18 and clay data from 1–3 in Table 1): (**a**) Sand; (**b**) clay. The symbols are experimental data and the lines are defined by Equations (7) and (10).  
Figure 5: Strain-dependent dynamic response of sand under two different confinement stress levels. (**a**) Hysteresis shear stress–strain relation; (**b**) instantaneous tangent shear modulus.  
a meaningless fitting parameter but it has its own full physical meaning related to the sources of nonlinearity [24; 32]. Thus, the ad-hoc fitting procedure is not required to characterize strain-dependent soil properties. It was revealed that the constitutive model in Equation (1) is valid in the strain range up to \(10^{-2}\)[25]. For example, the \(\alpha\) parameters for the sand and clay considered in this research are on the order of \(10^{2}\). This means \(\alpha\gamma<1\) for \(\gamma<10^{-2}\), and thus Gsec \(>0\) and Gtan \(>0\) in this model. The upper validity limit shear strain of \(10^{-2}\) is high enough in most of geotechnical applications. Figure 7 summarizes the entire procedure based on the RCT data. While the data from RCT are used as an example in this research, data from the torsional shear test can additionally be used in a similar procedure.  
Figure 6: Correlation between hysteretic nonlinear parameter and confinement stress for sand and clay (S.D. stands for standard deviation).  
Figure 7: Procedure to characterize the hysteretic soil properties and responses from resonant column test data by applying the analytical model proposed in this study.
## 4 Uncertainty Quantification
### Calculation of Unknown Coefficients Using the Collocation Method  
The collocation method is adopted [42] to determine the unknown coefficients \(y_{i}\) in Equation (31). In this method, the numerical model is considered a black box. The responses at the collocation points are evaluated using the deterministic numerical model. The coefficients of the gPC expansion are then determined from these samples using a least squares minimization technique. The selection of the collocation points is critical for the accurate evaluation of the unknown coefficients. Once the order of the gPC expansion is known, the collocation points are generated as all combinations of roots of the next-higher-order polynomial basis. The origin (i.e., \(\xi=0\)) is added if it is not already included in the roots [42]. It is assumed that a set of response samples \(\mathbf{Y}=\{Y_{1},Y_{2},\ldots,Y_{q}\}^{T}\) is generated at the collocation points \(\{\xi_{1},\xi_{2},\cdots,\xi_{q}\}\). Substituting these in the gPC expansion of \(Y\) yields the following system of linear equations for calculating the coefficient vector \(\mathbf{y}\) as  
\[[\mathbf{\Psi}][\mathbf{y}]=[\mathbf{Y}], \tag{32}\]  
where  
\[[\mathbf{\Psi}]=\begin{bmatrix}\psi_{0}(\xi_{1})&\psi_{1}(\xi_{1})&\psi_{2}(\xi_{1 })&\cdots&\psi_{N}(\xi_{1})\\ \psi_{0}(\xi_{2})&\psi_{1}(\xi_{2})&\psi_{2}(\xi_{2})&\cdots&\psi_{N}(\xi_{2}) \\ \vdots&\vdots&\vdots&\ddots&\vdots\\ \psi_{0}(\xi_{q})&\psi_{1}(\xi_{q})&\psi_{2}(\xi_{q})&\cdots&\psi_{N}(\xi_{q}) \end{bmatrix},\;[\mathbf{y}]=\begin{bmatrix}y_{0}\\ y_{1}\\ \vdots\\ y_{N}\end{bmatrix}. \tag{33}\]  
The number of collocation points, \(q\), is greater than the number of unknown coefficients \(y_{i}\) for \(n\geq 2\). The vector of coefficients, \(\mathbf{y}\), is calculated using a least squares minimization technique as  
\[[\mathbf{\Psi}]^{T}[\mathbf{\Psi}][\mathbf{y}]=[\mathbf{\Psi}]^{T}[\mathbf{Y}]. \tag{34}\]
## 3 Results
### Centrality Indices
#### 3.2.3 Comprehensive Transport Index Results  
According to the Comprehensive Transport Index (Figure 11), the _accessibility_ of the villages in Anxin County changed dramatically between 1964 and 1996, but much less between 1996 and 2008. In 1996, the advantage in the Comprehensive Transport Index shifted to the north and south border of the county, such as Dawang Town in the north and Nanqing Village and Zhenzhuang Village in the south, which had an increase in transportation accessibility. Villages along the Baoding-Anxin Provincial Highway, such as the Shaopenzhuang Village-Jizhuang Village group in Dawang Town, remained at a high Comprehensive Transport Index value from 1964 to 1996. In contrast, the values for the villages along the Simen Dike and Xushui-Anxin County Highway declined from 1964 to 1996. In 2008, most high-Comprehensive Transport Index villages still maintained their advantages. In addition, the villages in Dawang Town had an even clearer advantage, while semi-waterside villages suffered a further loss. It can be concluded that high Comprehensive Transport Index levels shifted from predominantly semi-waterside villages to areas relying on the main highways.  
Figure 10: Local transport centrality indices.