esa-sceva/satcom-chunk-collection huggingface dataset:
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 2 Eye-Opening Arbiter-PUF
### Detailed Explanation of EoA Operation  
As indicated in Figure 1c, the arbiter consists of two D-FFs, which are connected to the two ROs. The data input of D-FFs is connected with ROs, while the clock input is connected  
Figure 2: Illustration of deadzone (red shaded area) in the EoA-PUF, preventing a decision while the impact of noise (illustrated by colored shade around the RO signals in green and blue) is high compared to the extracted entropy (left in (a)). After multiple rounds, the extracted entropy accumulated to a value larger than the impact of noise, and a decision can be reached.  
with \(\text{RO}_{\text{b}}\) and vice versa for \(\text{D-FF}_{\text{b}}\). If, e.g., \(\text{RO}_{\text{a}}\) is faster than \(\text{RO}_{\text{b}}\) and the two rising edges thus appear with a large enough phase difference \(\Phi_{\text{ab}}\), \(\text{D-FF}_{\text{b}}\) is first triggered and samples a 0, while \(\text{D-FF}_{\text{a}}\) samples a 1 delayed by \(\Phi_{\text{ab}}\); see Figure 2a. The subsequent XOR-gate in Figure 1c indicates a final decision by outputting a 1. Then, the value of \(\text{D-FF}_{\text{a}}\) is chosen as PUF response. Thereby, due to the setup and hold time requirements (\(t_{\text{SH}}\)) of the D-FFs, the phase difference \(\Phi_{\text{ab}}\) of the two competing ROs has to be larger than \(t_{\text{SH}}\); otherwise, \(\text{D-FF}_{\text{a}}\) also samples a 0 and the subsequent XOR outputs a 0, which indicates that no final decision is made yet. This requirement of \(\Phi_{\text{ab}}\ >\ t_{\text{SH}}\) forms an intrinsic deadzone, which was used in the original contribution of the eye-opening comparator as in [29], and also in the circuit simulations of the EoA-PUF in [28].  
Using this deadzone is basically the core idea of the EoA-PUF as it comes with the advantage that--on the one hand--only large enough phase differences yield a PUF decision, which is good to avoid noisy and unstable decisions, and--on the other hand--the ROs only operate for just enough cycles to exceed this minimum required phase difference. Disadvantageously, the intrinsic deadzone of the arbiter varies over process, temperature, and supply. Moreover, some decisions might still be unstable due to noise if the intrinsic deadzone is too small. This raises the motivation to add additional and adjustable delays to the clock input of the D-FFs, and therewith increase the benefits of the deadzone. This is also indicated in Figure 1c: by adjusting the deadzone realized by the additional delays, the required phase difference for a decision can be changed. Increasing the deadzone results in even better stability, but at the cost of (slightly) longer evaluation times. As soon as \(\Phi_{\text{ab}}\) is larger than the intrinsic plus the adjustable deadzone, the clock input of one D-FF is triggered before its D-input is set, and both D-FFs sample different values. Then, the XOR-gate outputs 1, stops the oscillation, and indicates a ready EoA-PUF response bit.
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 3 Results  
In this section, the prediction results for \(t_{\text{d}}\), \(S_{11}\), and \(S_{21}\) will be presented with three different solutions using artificial neural networks. The prediction results will be compared with the results obtained with the Sonnet(r) commercial software package, which is based on the MoM method. The neural network methods are also compared with the well-known ensemble learning technique of least-squares boosting (LSBoost), which comprises an ensemble of boosted regression trees. The experimentation and evaluation were conducted utilizing the implementation available in MATLAB.  
The quality of the prediction was evaluated based on the mean absolute error (MAE) with a standard deviation (SD). Training and prediction with ANNs was performed using a stationary computer with an NVIDIA GeForce RTX 4090 graphics card and an Intel Core i7-7/00 CPU operating at 3.60 GHz supported by 16 GB of RAM.  
The dataset of 369 structures was collected for the investigation, where 20% of the overall dataset was left for testing, constituting 74 unique structures. For visual purposes, and to be easy to read, the results for \(t_{\text{d}}\), \(S_{11}\), and \(S_{21}\) for four unique meander structures, whose geometrical dimensions are in Table 1, are presented graphically. The average prediction results, represented as the MAE with the SD for all 74 unique meander structures, are summarized in Table 2.  
These visual representations contribute to a nuanced understanding of how changes in a meander structure impact a system's frequency-dependent response, aiding in the interpretation and optimization of the temporal characteristics in the studied phenomena.  
Figure 5: Architecture of the designed CNN.
## 3 Results and Discussion
### Correlations between Physical and Mechanical Properties
Figure 8: Relationship between ultrasound pulse velocity and bulk density. Figure 9: Relationship between bulk density and Young’s modulus. Figure 13 shows a power correlation between the bulk density and the thermal conductivity; the correlation coefficient is higher than 0.97. The lightweight mortars present the lowest density, as well as the lowest thermal conductivity coefficients. However, for a high variation in bulk density of  
Figure 11: Relationship between bulk density and compressive strength. Figure 12: Relationship between ultrasound pulse velocity and compressive strength. Figure 10: Relationship between ultrasound pulse velocity and Young’s modulus. lightweight mortars (350 to 790 kg/m\({}^{3}\)) a small variation of thermal conductivity was registered (0.06 to 0.202 W/(m.K)); this shows the difficulty to decrease the thermal conductivity of mortars to reach the performance of thermal insulating materials, which should present thermal conductivity below 0.07 W/(m.K) [68]. Regarding the relation between the mortars' thermal conductivity and UPV (Figure 14), an acceptable correlation was also found, with a correlation coefficient higher than 0.72. A linear correlation is observed between the thermal conductivity and the Young's modulus (Figure 15), with correlation coefficients of 0.95, 0.86 and 0.81 for the control temperature, 150 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. Figure 16 shows the influence of temperature on the relationship between the thermal conductivity and compressive strength. In this case, the values of the linear correlation coefficient vary between 0.89 and 0.91. Figure 14: Relationship between ultrasound pulse velocity and thermal conductivity coefficient. Figure 7:0 Relationship between bulk density and thermal conductivity coefficient. The results presented above show that in general there is a good correlation between the thermal conductivity and bulk density, UPV, Young's modulus and compressive strength, even after the mortars are exposed to elevated temperatures. Thus, these correlations can be used in the assessment of thermal mortars exposed to elevated temperature. As mentioned, as a general rule, less compact mortars with less deformation capacity are those with lower bulk density, more voids and, consequently, with lower thermal conductivity. The effects of elevated temperature on mortars depend on the constituent materials (aggregates and cement-paste); however, although exposure to elevated temperature may change (moderately) the values of the correlation coefficients, the overall relations between parameters seem to remain.
Although less commonly employed than traditional thermosetting composites, TPCs are rising in popularity, with a global market of 17.7 Mt in 2021, mainly driven by the automotive, construction and aerospace industries [23]. A big advantage of thermoplastic composites compared to traditional ones is their better processability and lower cost, coupled with their ability to be recycled through grinding, melting, and reforming operations [24; 25]. The major disadvantages of TPCs compared to thermosetting composites are their lower strength and thermal stability, which in turn lead to the need for more repair or re-consolidation operations throughout their service life. Although TPCs can be thermally repaired simply by melting/softening the matrix, this operation often results in high costs and can also cause geometrical distortions of the components. Therefore, the implementation of the intrinsic self-healing mechanism inside the thermoplastic matrix in TPCs could bypass this limitation, thus obtaining a cost-effective and environmentally friendly solution to maintain the structural integrity of these composites. Although many studies about thermosetting self-healing composites, mainly focused on epoxy-based laminates [21; 26; 27], can be found in the open literature, only a few papers have focused on the self-healing mechanism exploited in thermoplastic matrices for the development of TPCs. Moreover, these studies are mainly focused on elastomers or thermoplastic polyurethane [28; 29]. In the present study, polyamide 6 (PA6) was chosen as matrix material, as it is a high-performance thermoplastic polymer widely used in TPCsfor the automotive and aircraft industry [30]. In the present paper, the polymer selected as a healing agent is polycaprolactone (PCL), which has already proven to have healing capability in thermosetting matrices [21; 22]. Therefore, PA6/PCL blends at different relative amounts were prepared through melt compounding, and a comprehensive rheological, morphological, chemical, thermal, and mechanical characterization was carried out on the resulting materials. Considering that the evaluation of healing efficiency is usually carried out as the ratio of specific mechanical properties of the virgin and damaged materials after healing [31], it is possible to evaluate the repair capability through the comparison of the fracture toughness (\(K_{\mathrm{JC}}\)) of the virgin and healed materials, as reported in the literature [32]. Therefore, in the present work, the fracture toughness (\(K_{\mathrm{JC}}\)) of these blends was evaluated both in quasi-static and impact conditions, and the samples broken in these tests were thermally repaired by using a lab-made device. In this way, it was possible to understand the effect of the healing agent in the worst-case scenario, i.e., the complete failure of the component.
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 3 Results and Discussion  
### Fe\({}_{3}\)O\({}_{4}\) Nanofluid Characterization  
The FESEM image of the Fe\({}_{3}\)O\({}_{4}\) nanoparticle used to prepare the magnetite nanofluid for saturating the core rock sample is shown in Figure 4a. The FESEM image shows that the nanoparticles have a spherical shape and an average particle diameter of 36 nm. The spherical shape of the nanoparticles combined with its small particle size is beneficial for enhancing oil recovery [38; 39]. The nanoparticles will thus possess high mobility through pore throats and good adsorption of oil molecules and will easily detach oil molecules from the rock surface leading to an improved recovery factor. In contrast, the large particle size in the order of microns could solidify upon application of an external magnetic field and cause pore throat blockage [40]. Figure 4b displays the hysteresis curve of the magnetite nanoparticle, which has a saturation magnetization (Ms) of 42.3 emu/g and retentivity (Mr) of 3.0134 emu/g. The prepared Fe\({}_{3}\)O\({}_{4}\) nanofluid has a DC conductivity of 2.19 S/m and dielectric permittivity of 74.2 at 600 MHz.
## 4 Result and Discussion
### Evaluation Parameters
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**No. of Operations** & **EEFO** & **DEERA** & **ELBS** & **DESTS** & **DEELB** \\ \hline
10 & 850.18 & 935.86 & 1083.36 & 1195.75 & 1373.98 \\ \hline
20 & 826.95 & 976.48 & 1107.48 & 1209.56 & 1392.74 \\ \hline
30 & 850.84 & 952.32 & 1113.65 & 1200.85 & 1380.67 \\ \hline
40 & 806.72 & 1040.57 & 1178.74 & 1284.86 & 1470.73 \\ \hline
50 & 913.87 & 1070.65 & 1152.85 & 1346.29 & 1500.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Throughput analysis of the DEELB method with the existing systems. Figure 6: Bandwidth utilization analysis of DEELB method with existing systems. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**No. of Operations** & **EEFO** & **DEERA** & **ELBS** & **DESTS** & **DEELB** \\ \hline
10 & 1500.97 & 1400.65 & 1200.65 & 1000.82 & 900.32 \\ \hline
20 & 1600.12 & 1400.91 & 1200.83 & 1000.96 & 900.75 \\ \hline
30 & 1600.72 & 1500.15 & 1200.96 & 1100.12 & 910.65 \\ \hline
40 & 1600.85 & 1500.65 & 1300.13 & 1100.65 & 970.12 \\ \hline
50 & 1700.91 & 1500.82 & 1300.65 & 1200.15 & 990.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bandwidth utilization analysis of the DEELB method with the existing systems. (f) Reliability Figure 8 and Table 7 illustrate a comparative reliability examination of the DEELB approach with other existing methods. The figure shows that the IoT approach has resulted in higher performance with reliability. For example, with 10 operations, the reliability was 88.178% for DEELB, whereas the EEFO, DEERA, ELBS, and DEBTS models obtained reliability of 80.153%, 82.657%, 84.297%, and 85.612%, respectively. However, the DEELB model has shown maximum performance with a varied number of operations. Similarly, under 50 operations, the reliability value of DEELB is 91.652%, while it is 81.749%, 84.132%, 85.477%, and 87.322% for EEFO, DEERA, ELBS, and DEBTS models, respectively. Figure 8: Reliability analysis of the DEELB method with existing systems. Figure 7: Throughput analysis of the DEELB method with existing systems.
## 3 Synchronization-Integrated Feedrate Scheduling
### Feedrate Scheduling for Tool-Tip Position  
The goal of feedrate scheduling is to find a smooth interpolation under machining constraints. Usually, the interpolation chord error, the axial kinematics and the tangential kinematics are important constraints for high speed and high accuracy machining. Under the circumstances, the interpolation error, the axial velocities and accelerations are considered to determine the allowable feedrate along the tool-tip position curve \(\mathbf{C}(u)\). Then the feedrate is scheduled using the S-shape jerk-limited method taking tangential acceleration and jerk into account.  
The interpolation chord error along \(\mathbf{C}(u)\) at the parameter value \(u\) can be approximated as [25]:  
\[\delta(u)=\rho(u)-\sqrt{\rho(u)-\big{(}f(u)T/2\big{)}^{2}}, \tag{13}\]  
where \(T\) is the interpolation period, \(\rho(u)\) is the curvature radius, and \(f(u)\) is the feedrate at the parameter value \(u\). Hence the interpolation chord error limited feedrate can be obtained as:  
\[f(  
The velocity of each axis can be calculated as:  
\[v_{M}(u)=\frac{dM(u)}{dt}=\frac{dM(u)}{du}\frac{du}{dt}=M_{u}(u)p, \tag{17}\]  
where \(M(u)\) represents the drive displacement. If the maximum velocity of each axis is set as \(V_{\text{max}}^{M}\), the axial velocity constraint is written as:  
\[p(u)\leq\frac{V_{\text{max}}^{M}}{M_{u}(u)}. \tag{18}\]  
The acceleration of each axis can be calculated as:  
\[a_{M}(u)=\frac{dv_{M}}{dt}=\frac{dv_{M}}{dM}\frac{dM}{dt}=v_{M}\frac{dv_{M}}{ dM}=\frac{1}{2}\frac{d({v_{M}}^{2})}{dM}=\frac{1}{2}\frac{d({pM_{u}})}{dM}. \tag{19}\]  
If the maximum velocity of each axis is set as \(A_{\text{max}}^{M}\), the axial velocity constraints can be written as:  
\[\frac{d({pM_{u}})}{dM}\leq 2A_{\text{max}}^{M}. \tag{20}\]  
The parameter interval of the tool-tip curve \(\mathbf{C}(u)\) is divided into N equal sub-intervals at \(u_{i}=i/N(i=0,1,\dots,N)\). Using the monotonic rational quadratic spline proposed by [PERSON] and [PERSON] [34], the discrete parameter value \(v_{i}\) of the tool orientation corresponding to \(u_{i}\) is computed. When \(N\) is large enough, the length of the parameter sub-interval is so small that \(M_{u}\) in Equations (17) and (19) can be approximated using the finite differential method. We can address the interpolation chord error, the axial velocities of the axial acceleration constraints as an optimization problem with the objective of \(\max\sum_{i=0}^{N}p_{i}\) and constraints from Equations (16), (18) and (20). By solving the optimization problem, the allowable discrete feedrate values \(f_{i}=\|\mathbf{C}_{u}(u_{i})\|\)\(p_{i}\) is acquired. Then the feedrate of the tool-tip position is generated utilizing the curve splitting and bi-directional scanning techniques.
## 3 Numerical Results and Discussion
### Al Doping Dependence of the Magnetization in CFO  
The magnetization of CFO can be altered by substituting various ions at the Fe sites. The introduction of dopant ions in place of Fe leads to modifications in the microstructure, causing lattice distortions. The magnetic properties of CFO are mainly governed by the 3d orbitals of Fe, especially through Fe-Fe exchange interactions. Therefore, substituting Fesites to introduce different magnetic interactions has been extensively utilized to investigate the magnetism in CFO. In this study, we focus at first on the effect of Al\({}^{3+}\) doping on the magnetic behavior of CFO. The ionic radius of the host Fe\({}^{3+}\) ion is 0.69 \(A\), whereas that of the substituted Al\({}^{3+}\) ion is slightly smaller, namely 0.675 \(A\). This size difference generates compressive strain, resulting in a reduction in the lattice parameters. Within our theoretical framework, this implies that the exchange interaction constant in the doped state, denoted by \(J_{d}\), which is inversely related to the lattice parameters, becomes larger than that in the undoped material (\(J_{d}>J\)). Consequently, the magnetization is enhanced in Al-doped CFO. The calculated magnetization as a function of doping concentration \(x\) (ranging from 0 to 0.02) is presented in Figure 1, showing good agreement with experimental observations reported by [PERSON] et al. [3].  
Let us note that within the framework of our developed model, we operate in such concentration ranges of the dopants that the solubility limit (referring to solid solutions) is not exceeded, i.e., no clusters or secondary phases of the dopant ions are formed. Beyond this limit, synthesis processes become highly influenced, difficult to control, and unsuitable for the fabrication of components in device engineering, as the resulting experimental outcomes are non-reproducible. We assume thermodynamic stability to be present, as we are working with materials for which experimental measurements exist and the results are reproducible, which implies such thermal stability.  
The Neel temperature \(T_{N1}=14\) K remains essentially unchanged upon doping. Notably, at low Al doping levels, the system exhibits predominantly antiferromagnetic order, followed by a regime where antiferromagnetism coexists with ferromagnetic components.  
These results underscore the sensitivity of the magnetic ground state to subtle lattice distortions induced by ionic substitution. Further investigations are required to elucidate the interplay between doping concentration, strain effects, and magnetic phase transitions in CFO.
# Full Life Cycle Evaluation of Stability Pile in High Slope with Multi-Layer Weak Interlayers
## 7 Conclusions  
Based on a high slope case, the three-dimensional strength reduction elastoplastic finite element method and the full life cycle evaluation method (including the initial state, the 1.35 state, and the limit equilibrium state) were applied to explore resistance to the slide effect for position, length, diameter, and the ratio of on-center spacing to the diameter of the stability pile in a high slope with multi-layer weak interlayers.  
High slopes are vulnerable to failure, and support measures are indispensable. The piles at the lower to lower-middle part can effectively improve slope stability. In our study, \(F\) increased from 1.33 to 1.43 in Model 1 and from 1.35 to 1.42 in Model 2 as the pile lengthens. As long as the length exceeded the critical length (28 m and 30 m in Model 1 and Model 2), \(F\)s barely increased. A limit of resistance to sliding exists based on pile length. The \(F\) slightly increased from 1.38 to 1.41 in Model 1 and from 1.37 to 1.40 in Model 2, and the maximum compressive stress, the maximum shear stress of the pile, and the maximum displacement of pile head decreased from 20.84 (81.24) MPa to 16.15 (18.80) MPa, 11.19 (42.02) MPa to 7.77 (10.43) MPa, 714.10 (4585.00) mm to 396.30 (1272.00) mm in Model 1 (or in Model 2) as the pile diameter increased. With increases in the ratio of on-center spacing to diameter, the arching effect and the pile group effect weakened and \(F\) decreases. Given the maximum compressive stress, the maximum shear stress in the 1.35 state, the contributions of the piles, the arching effect, and the plastic zone distribution in the limit equilibrium state, piles of length of 30 m, diameter of \(>\)1.4 m, and with a ratio of on-center spacing to pile diameter of \(<\)3 should be optimal.  
In follow-up work, the interactions among every layer and pile, the section shape, structure, and horizontal arrangement of the pile will receive more attention.  
Conceptualization, J.Z.; methodology, G.S. and J.Z.; software, J.Z.; validation, M.L., F.L. and P.G.; formal analysis, G.S.; investigation, J.Z., M.L. and F.L.; resources, M.L.; data curation, J.Z.; writing--original draft preparation, G.S.; writing--review and editing, C.W.; visualization, G.S. and P.G.; supervision, J.Z.; project administration, M.L. and C.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China, grant number 41807258.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors thank [PERSON] for his technical advice.  
The authors declare no competing interests.