esa-sceva/satcom-chunk-collection huggingface dataset:
# The Strain Heterogeneity and Microstructural Shear Bands in AZ31B Magnesium Alloy
## 5 Conclusions  
The deformation behavior of the AZ31B magnesium alloy subjected to uniaxial tensile loading at room temperature was meticulously investigated through an in situ experimental approach, coupled with an analysis of the strain field distribution and microstructural evolution. The main conclusions are as follows.  
1. During the uniaxial tensile loading process, the AZ31B magnesium alloy surface exhibited a densely packed network of discrete shear bands, which effectively mitigated the strain concentration, facilitating stable plastic deformation. However, as the strength of these shear bands intensified, reaching the ultimate strength threshold of the sample, the material underwent catastrophic failure upon the intersection and surface manifestation of shear bands oriented in diverse directions.
2. In situ tensile testing and microscopic examination revealed that the deformation zone originated from twins within localized grains, emphasizing the pivotal role of com-pressive twinning. The high density of the twins within the deformation bands enhanced the dislocation slip pathways, leading to intense deformation. As the twins  
Figure 9: Schmidt factor of a representative area in the original sample. (**a**) Schmidt factor of prismatic slip; (**b**) Schmidt factor of basal slip.  
grew towards the grain boundaries, they encountered difficulties in coordinating de-formation, triggering the initiation of microcracks at the grain interfaces. Subsequently, with the progression of plastic deformation, these microcracks proliferated and coalesced, ultimately causing material failure.
3. An analysis of the Schmidt factors in the initial sample illuminated the directional nature of the prismatic slip within grains. Notably, localized enhancements in prismatic slip intensified the formation of intersecting shear bands, thereby coordinating material deformation in a harmonious manner.  
Conceptualization, Q.Z. and X.Z.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z. and X.Z.; investigation, Q.Z. and X.Z.; resources, Q.Z.; data curation, X.Y.; writing--original draft preparation, Q.Z. and X.Z.; writing--review and editing, Q.Z. and X.Z.; visualization, X.Z.; supervision, X.Y.; project administration, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [PERSON] funding number 2024 JC-YBQN-0372.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors wish to express their gratitude to Aircraft Structural Mechanics and Strength Technology Laboratory, Northwestern Polytechnical University.  
The authors declare no conflicts of interest.
## 3 Method for the Synchronous Retrieval of LAI and Cab  
Inversion is usually defined as determining the state vector by observation vector. A complex UAV observation system can be represented by the simple mathematical model as follows:  
\[\mathbf{Y}=\mathbf{M}(\mathbf{x}_{\text{a}},\mathbf{x}_{\text{b}})+\mathbf{\epsilon} \tag{4}\]  
where \(\mathbf{Y}\) denotes the UAV observation vector and \(\mathbf{M}\) denotes the forward model. \(\mathbf{\epsilon}\) is an error term  
Based on multiple iterations, the state vector at the \(\mathrm{p}+1^{\prime}\)th iteration can be described as:  
\[\mathbf{x}_{\mathrm{a}}^{\mathrm{p}+1}=\mathbf{x}_{\mathrm{a}}^{\mathrm{p}}- \alpha_{\mathrm{p}}H_{\mathrm{p}}\
abla\mathbf{O}\left(\mathbf{x}_{\mathrm{a}}^{ \mathrm{p}}\right)^{\mathrm{T}} \tag{7}\]  
where \(\mathbf{H}\) is the inverse matrix of the Hessian matrix constructed with successive gradient vectors in the quasi-Newton method, and \(\alpha_{\mathrm{p}}\) is the step factor of the iteration.  
The flow chart of the inversion framework is shown in Figure 4. The inputs of the inversion framework include a priori knowledge (prior estimates, upper and lower bounds), the cost function, and its gradient. Firstly, the observation vector \(\mathbf{Y}\) consists of the hyperspectral or multispectral radiometric measurements carried by the UAV platform, and the observation error covariance matrix \(S_{y}\) was constructed by a given observation error. At the same time, the parameters to be retrieved and a priori estimates \(x_{\mathrm{pri}}\) should be determined. The error covariance matrix \(S_{\mathrm{pri}}\) is constructed according to the uncertainties of a priori estimation. After that, the cost function can be established according to Equation (5). To find the minimum value of \(\mathbf{O}(\mathbf{x})\), the quasi-Newton method implemented by the L-BFGS-B code was introduced. The L-BFGS-B algorithm is a highly effective tool in bounded minimization problems [51; 52]. Finally, the optimal estimation of the state vector is considered to be found, if the following Equation (8) is satisfied. Otherwise, the state vector continues to be updated until the termination condition is satisfied. In other words, the crop parameters are retrieved.  
\[\left\|\mathbf{x}_{\mathrm{a}}^{\mathrm{p}+1}-\mathbf{x}_{\mathrm{a}}^{ \mathrm{p}}\right\|_{2}\leq\mathrm{Threshold} \tag{8}\]  
where \(\left\|\ \right\|_{2}\) denotes the L2 paradigm, and the superscript \(\mathrm{p}\) refers to the \(\mathrm{p}^{\prime}\)th iteration.
# Network Measurement and Performance Analysis at Server Side
## 5 Validation
### Capacity  
We used servers with Xeon 12-core, 2.4 GHz CPU, 64 GB memory and 10 Gbps Ethernet NIC. EVA captured packets from the NIC, grouped packets into flows, and stored these flows in a hash tables by a 4-tuple hash function. Note that packets were captured and analyzed in separate threads.  
Since _libpcap_[36] does not support multithreaded capture, i.e., not scalable with the number of CPU cores, we used PF_RING [6], which is an extension of libpcap with ring buffers in the kernel to accommodate captured packets. More importantly, PF_RING supports multithreaded capture if the NIC enables RSS [37]. We evaluated EVA's capturing and analysis part separately in single thread mode. The former achieved 700 Kpps without packet loss, and the latter achieved at least 600 Kpps. To evaluate EVA's scalability, we tested its processing speed with different number of CPU cores. Specifically, we ran EVA with different number of threads, one thread per core, and then used _Toreplay_[38] to replay packet traces shown in Section 4 at increasing speed until packet loss occurred. Figure 6 shows that EVA scaled sublinearly with the number of CPU cores. As a result, with six capturing threads and six analysis threads, one server can process 2.26 Mpps with 12 cores.  
While CPU limits the pps, memory limits the number of concurrent connections EVA can process. Note that they are independent metrics of EVA's capacity. Since EVA buffers in flight packet headers for each flow, we must estimate the number of in flight packet per flow. On Linux, the default maximum send buffer is 4 MB and default MSS is 1460 (Figure 5 shows that most data packets are MSS-sized), thus about 4 MB/1460 B = 2873 packet headers per flow need to be stored in memory. For each flow, we need 408 B to store analysis status and 48 B per packet header. In total, we need 408 B + 2873 \(\times\) 48 B = 135 KB per flow. Thus, each server can process around 64 GB/135 KB = 500 K concurrent connections. Table 2 shows that the number of maximum concurrent flows is no more than 1.5 K, which is far below EVA's capacity.
## 3 Analysis  
### Test Results under Static Load  
The original geometric size of the specimens and the calculation results of static uniaxial compressive strength are shown in Table 1. The failure modes of several specimens are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **Diameter** & **Height** & **Quality** & **Height-** & **Density** & **Compressive** & **Modulus of** & **Poisson’s** \\
**No.** & **/mm** & **/mm** & **/g** & **Ratio** & **/g/cm\({}^{3}\)** & **Strength/MPa** & **Elasticity/GPa** & **Ratio** \\ \hline
1-1\# & 49.90 & 102.14 & 310.6 & 2.05 & 1.56 & 35.589 & 3.058 & 0.088 \\
1-2\# & 49.89 & 101.44 & 287.9 & 2.03 & 1.45 & 33.353 & 2.666 & 0.316 \\
1-3\# & 49.66 & 99.68 & 295.2 & 2.00 & 1.51 & 31.066 & 2.827 & 0.249 \\
1-4\# & 49.67 & 99.61 & 301.0 & 2.01 & 1.56 & 25.391 & 3.55 & 0.417 \\
1-5\# & 49.99 & 100.34 & 309.1 & 2.01 & 1.57 & 28.338 & 3.316 & 0.489 \\
1-6\# & 49.64 & 102.81 & 250.7 & 2.07 & 1.26 & 13.114 & 1.969 & 0.378 \\
1-7\# & 50.01 & 100.04 & 279.2 & 2.00 & 1.42 & 25.923 & 2.365 & 0.553 \\
1-8\# & 50.25 & 98.92 & 243.0 & 1.97 & 1.24 & 20.795 & 2.344 & 0.357 \\
1-9\# & 49.97 & 102.21 & 262.8 & 2.05 & 1.31 & 16.715 & 2.257 & 0.505 \\ Average value & 49.95 & 100.31 & 289.43 & 2.01 & 1.473 & 28.636 & 2.875 & 0.353 \\ \hline \hline \end{tabular} Note: Specimens 6\# and 9\# had obvious primary cracks, resulting in their uniaxial compressive strength being much lower than that of other specimens. This was not taken into account when calculating the average value.  
\end{table}
Table 1: Test results of hard coal specimens.  
According to Table 1, the strength of six of the nine hard coal specimens in the uniaxial compression test were greater than 25 MPa. The average compressive strength was 28.636 MPa, the average elastic modulus was 2.875 GPa, the average Poisson's ratio was 0.353, and the hardness coefficient was 2.9. According to the literature [48], it can be determined that the coal used in the test is hard coal.  
Figure 4 shows three failure modes: X-shaped conjugate plane shear failure of 1-1#-1-3# test pieces, single-slope shear failure of 1-4# and 1-5# test pieces, and tensile failure of  
Figure 4: Partial coal specimen compressive strength test failure mode. (**a**) 1-1# test piece. (**b**) 1-4# test piece. (**c**) 1-9# test piece.  
1-6#-1-9# test pieces. This is mainly related to the original state of the test pieces. According to Table 1, the density and compressive strength of the 1-1#-1-5# test pieces are higher than those of the other test pieces, indicating that the integrity and compactness of the internal structure are higher. The original cracks on the surfaces of specimens 1-6# and 1-9# are clearly visible, and the generation of the original cracks will produce a weak surface. In response to the load action, the specimen will first fail along the weak surface, resulting in a form of tensile failure.
## 2 System Structure and Working Principle
### Modeling of PV Panels  
A certain capacity of PV panels can reduce the cost of electricity and lower the OCT. PV panels convert light energy into electrical energy through photochemical or photoelectric effects. The model of a PV cell can be mainly divided into a basic UI characteristic equivalent model based on the avalanche effect model and a simplified engineering model. In this study, an equivalent circuit model of a PV cell based on a single diode is selected, as shown in Figure 2.  
Figure 1: PV-HESS system architecture.  
According to the equivalent circuit, the corresponding UI characteristics are as follows:  
\[I_{\text{PV}}=I_{\text{ph}}-\frac{U_{\text{PV}}+I_{\text{PV}}R_{\text{s}}}{R_{ \text{sh}}}-I_{\text{d}}\cdot\left[\text{e}^{\frac{\delta(U_{\text{PV}}+I_{ \text{pv}}R_{\text{s}})}{\delta kT_{\text{PV}}}}-1\right] \tag{1}\]  
here, \(I_{\text{d}}\) and \(I_{\text{ph}}\) are, respectively, the reverse saturated leakage current and photo-generated current, which are determined by ambient temperature and light intensity; \(k\) is the Boltzmann constant; \(T_{\text{PV}}\) is the temperature of the PV panels; \(A\) is a fitting constant; and \(q\) is the amount of electron power. To improve the accuracy of PV panel parameters, [PERSON] et al. calibrated the parameters in the formula based on a genetic algorithm and the Newton-Raphson algorithm and proved its accuracy [23].
# A Feedrate Planning Method for the NURBS Curve in CNC
## 3 Simulation and Experimental Results
### Experiment  
The simulation results show that the CCCP method proposed in this paper can balance the computational efficiency and machining efficiency. In order to check whether the CCCP method can guarantee the final machining quality, the 2D butterfly-shaped NURBS curve and 3D apple-shaped NURBS surface are machined on the experimental platform shown in Figure 8. In the machining, a ball milling cutter W2 QX4408 (Weidiao, Shanghai, China)  
Figure 12: Planning result of the curvature threshold dividing + CCCP method.  
Figure 13: Butterfly-shaped NURBS curve and its feedrate-constraint value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Planning Method**} & **Number of** & \multirow{2}{*}{**Machining Time (s)**} & \multirow{2}{*}{**Acc/Dec Time (s)**} \\  & **Sub-Curves** & & \\ \hline \(C_{2}(s)=C_{1}(s)\) & 48 & 13.93 & 0 \\ \hline CFTM & 89 & 19.03 & 5.10 \\ Command feedrate & & & \\ threshold dividing + & 89 & 15.20 & 1.27 \\ CCCP & & & \\ CTM & 207 & 15.62 & 1.69 \\ Curvature threshold & 207 & 14.91 & 0.98 \\ dividing + CCCP & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of feedrate planning results of N-shaped NURBS curve.  
is used, and the machining material is AlMg1 SiCu aluminum and Al99.7 aluminum, respectively. The final machining results are shown in Figures 14 and 15.  
The butterfly-shaped NURBS curve is composed of two typical curved machining paths alternating between the flat and high-curvature regions, as shown in Figure 14. When machining that curve, the CCCP method can effectively decrease the feedrate in the high-curvature region to ensure the machining quality, therefore, no overcutting occurs. While in the flat region, the CCCP method can rapidly increase the feedrate to ensure machining efficiency. During the whole machining process, the machine tool runs smoothly and the machining toolpath is smooth and clean.  
The apple-shaped NURBS surface is a gradual surface, on which each point has different curvature. There is a lot of reciprocal motion during the machining process, which puts forward a higher requirement for the stability of the CCCP method. When machining this surface, the machine tool also runs smoothly, and the machining result shown in Figure 15 shows that the CCCP method can ensure fine surface machining quality.  
The above simulation and experimental results show that, the CCCP method has good real-time performance, and the machining efficiency can be improved while the machining quality being ensured.
# Recent Progress in On-Chip Erbium-Based Light Sources
###### Abstract  
In recent years, silicon photonics has achieved great success in optical communication area. More and more on-chip optoelectronic devices have been realized and commercialized on silicon photonics platform, such as silicon-based modulators, filters and detectors. However, on-chip light sources are still not achieved because that silicon is an indirect bandgap material. To solve this problem, the rare earth element erbium (Er) is considered, which emits light covering 1.5 \(\upmu\)m to 1.6 \(\upmu\)m and has been widely used in fiber amplifiers. Compared to Er-doped fiber amplifiers (EDFA), the Er ion concentration needs to be more than two orders higher for on-chip Er-based light sources due to the compact size integration requirements. Therefore, the choice of the host material is crucially important. In this paper, we review the recent progress in on-chip Er-based light sources and the advantages and disadvantages of different host materials are compared and analyzed. Finally, the existing challenges and development directions of the on-chip Er-based light sources are discussed.  
Er-based light source; on-chip waveguide amplifier; on-chip waveguide laser +
Footnote †: journal: Applied  
and gradually industrialized [9; 10; 11; 12; 13]. However, the only thing that has not been completely solved at present is the silicon-based light sources because of the indirect band properties of silicon [14]. To solve this problem, there are mainly three methods: epitaxial III-V light sources, heterogeneous III-V light sources and Er-based light sources.
# A Short-Term Forecast Model of foF2 Based on Elman Neural Network
###### Abstract  
The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.  
fontsize=, frames=single-
## 4 Application Example
### Motion-Based Design of Passive Damping Devices under Uncertainty Conditions
#### 4.3.3 Friction Damper  
Finally, the motion-based design of the friction damper under uncertainty conditions was performed. The design problem of this friction damper may be formulated as follows:  
\[\begin{split}\text{find}\ \theta=\begin{bmatrix}c_{d,e},\ k_{d,e},\ f_{f} \end{bmatrix}\text{to minimize}\ \mathbf{f}(\theta)=\begin{bmatrix}f_{1},\ f_{2},\ f_{3} \end{bmatrix}=\begin{bmatrix}c_{d,e},\ k_{d,e},\ f_{f}\end{bmatrix}\\ \text{subject to}\begin{cases}c_{\text{min}}<c_{d,f}<c_{\text{max}}\\ k_{\text{min}}<k_{d,f}<k_{\text{max}}\\ f_{f_{\text{min}}}<k_{f}<f_{f_{\text{max}}}\\ \beta_{s}(\theta)\geq\beta_{t}=1.35\end{cases}.\end{split} \tag{23}\]  
After the design process, the optimum value of the damping coefficient, stiffness coefficient, and friction force which characterize the friction damper were obtained. The optimum solution was \(c_{d,f}=1.24\times 10^{5}\) sN/m, \(k_{d,f}=6.74\times 10^{4}\) N/m and \(f_{f}=2.95\times 10^{4}\) N. The reliability index associated with this solution was \(\beta_{s}(\theta)=1.55\), which met the design requirements. Figure 8 shows the maximum displacement at the mid-span of the cable damped by the friction damper for the different elements of the sample.
## 2 Related Work  
### Related Work for Operation Sequencing Problem in the Recent Five Years  
[PERSON] et al. [9] used an AND/OR network to define the flexible machining task planning and sequencing problem, taking the shortest machining time of the overall machining chain as the optimisation goal, and using the particle swarm algorithm to solve it. [PERSON] et al. [12] introduced clustering constraints to bring the problem closer to the actual situation and proposed an ant colony optimisation to solve it. [PERSON] et al. [10] proposed a hybrid genetic algorithm combined with a simulated annealing mechanism to solve this problem. To ensure the feasibility of the solution, sequence constraints between the tasks were incorporated into the algorithm. [PERSON] et al. [13] proposed a mixed-integer programming model and integrated an improved selection crossover strategy into a genetic algorithm to ensure the feasibility of the solution obtained after the genetic operation, thus improving the efficiency of the genetic algorithm. [PERSON] et al. [6] proposed a discrete particle swarm optimisation algorithm, based on generating feasible task sequences, and used a crossover mechanism to expand the feasible solution search interval. Multiple mutation mechanisms are used to increase the diversity of feasible solutions, and adaptive mutation probability is used to increase the detection ability. [PERSON] et al. [14] introduced 'or' type network nodes, redefined the mixed-integer programming model for this problem, and solved it using a mixed evolutionary algorithm based on a genetic algorithm and simulated annealing algorithm. An adaptive evolutionary screening mechanism was used to prevent the algorithm from falling into the local optima. [PERSON] et al. [9] proposed an intelligent water drop algorithm to solve the conventional process-chain planning problem. [PERSON] and [PERSON] [7] proposed a genetic algorithm based on feasible sequence rules, which limited the search feasible region for the genetic algorithm through feasible sequence rules, thereby improving the search efficiency. The detection ability of the algorithm was improved by the hybrid crossover operator to prevent it from falling into a local optimum.  
Through the above research, over the past five years, it was found that the current task sequencing problem for machining task chains is limited to conventional task priority constraints, machining orientation constraints, machining equipment, and tool constraints. However, the inspection of semi-finished products in each clamping process, the cleaning of processing tooling, the consideration of workpiece clamping time, the deburring of semi-finished products after each machine tool process, and the routine size inspection process are not considered. These are necessary tasks that occur at all times on the daily machining production line and are indispensable auxiliary tasks for processing. Neglecting these generally necessary auxiliary tasks will limit the application of optimisation results.
Appl. Sci._**2015**, \(5\), 241-258; doi:10.3390/app5030241  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**N-Vinylcarbazole: As an Additive for Thermal Polymerization at Room Temperature with _in situ_ Formation of Ag(0) Nanoparticles**  
**[PERSON] *, [PERSON] and [PERSON]**  
Carrefour of Innovative Technology and Ecodesign (CITE), Faculty of Engineering, University of Sherbrooke, 2500 boul. de l'Universite, Sherbrooke, QC J1K 2R1, Canada;  
E-Mails: [PERSON]ESS] (S.E.); [PERSON] (M.R.)  
* Author to whom correspondence should be addressed;  
E-Mail: [PERSON]Tel.: +1-514-431-7436.  
Academic Editor: [PERSON]  
_Received: 3 July 2015 / Accepted: 28 July 2015 / Published: 5 August 2015_  
**Abstract:**\(N\)-vinylcarbazole (NVK) is proposed as an additive for acrylates thermal free radical polymerization (FRP) and epoxy thermal ring opening polymerization (ROP) at room temperature. The new initiating systems are based on a silane/silver salt/\(N\)-vinylcarbazole interaction, which ensures good to excellent polymerization. Moreover, the polymerization is much more efficient under air than under argon. The effects of the \(N\)-vinylcarbazole, silane, silver salt and monomer structures are investigated. Interestingly, silver nanoparticles Ag(0) are formed _in situ_. The as-synthesized nanocomposite materials contained spherical nanoparticles homogeneously dispersed in the polymer matrices. Polymers and nanoparticles were characterized by Differential Scanning Calorimetry (DSC), Transmission Electron Microscopy (TEM), Energy-Dispersive X-ray Spectrometry (EDS), Fourier Transform Infrared Spectroscopy (FTIR), and UV-vis spectroscopy. A coherent picture of the involved chemical mechanisms is presented.  
**Keywords:**\(N\)-vinylcarbazole; silane; silver salt; thermal free radical polymerization (FRP); thermal ring opening polymerization (ROP); silver nanoparticles