esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 2 Methodology
### Optimization Objective  
Based on the stated heat-fluid coupling governing equation, interpolation model, and constraint on the lattice structure, the formulation of the topology optimization for the cooling plate can be constructed as follows:  
\[\begin{split}&\underset{\phi R}{\text{minimize}}\,c=\frac{\int \tau\alpha}{S}\\ & s.t.\left\{\begin{array}{l}\phi(u\cdot\
abla)u=\
abla(-pI+\mu( \
abla u+(\
abla u)^{T}))-\alpha u\\ \hat{\phi}\rho_{c}\
abla_{\rho}(\phi)u\cdot\
abla T=k(\hat{\phi})\
abla^{2}T+ Q\\ -R^{2}\
abla^{2}\hat{\phi}+\hat{\phi}=\hat{\phi}\\ \mathcal{g}_{k}(\hat{\phi}_{e})=\left(\frac{1}{n}\sum\hat{\phi}_{e}^{p}\right) ^{\frac{1}{p}}\leq V_{l}\\ V(\hat{\phi})\leq V\\ R_{\text{min}}\leq R\leq R_{\text{max}}\\ 0<\phi<1\end{array}\right.\end{split} \tag{16}\]  
where \(R\) is set as an optimization variable, of which the lower and upper boundaries are \(R_{min}\) and \(R_{max}\), respectively. A constant influence radius leads to a uniform lattice structure, which is suitable for the boundary condition with an even thermal load. However, nonuniform thermal load commonly appears in practice, previous methods cannot cope with such situations well. Therefore, a changeable control variable \(R\) is introduced to create a functionally graded lattice structure to improve the radiation performance of the optimal structure.  
Figure 1 shows the proposed topology optimization pipeline for the cooling plate design. It starts to initialize the design variable to the design domain and filters the design variable using Equation (10), and it projects the smoothed density to 0-1 using Equation (11). Then, we interpolate the material parameters via Equations (6)-(9) and solve the objective function shown in Equation (16) and the constraint in Equation (15). Finally, we conduct the sensitivity analysis and update the design variables via a numerical solver.
###### Abstract  
Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and I\({}_{\rm A}\) = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and I\({}_{\rm A}\) = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and I\({}_{\rm A}\) = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and I\({}_{\rm A}\) = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI.  
rock brittleness; linear genetic programming; bagged regression tree; lazy machine learning method +
Footnote †: journal: Applied Sciences  
Article  
Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Selection Model  
[PERSON]  
1 Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University,  
Dashte-Azadegan, Susangerd 6155634899, Iran; [EMAIL_ADDRESS]  
Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq; [EMAIL_ADDRESS]  
Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology,  
Behbahan 6361663973, Iran; [EMAIL_ADDRESS]  
Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia; [EMAIL_ADDRESS]  
Water Engineering Department, Faculty of Agriculture, University of Zanjan, Zanjan 4537138791, Iran; [EMAIL_ADDRESS]  
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam 6  
Correspondence: [EMAIL_ADDRESS]
## 1 Introduction
Additionally, leaves inside a crown can be shaded by other leaves in the same crown. The amount of shading in a pixel can be quantified using the sunlit fraction (\(\alpha_{S}\)), which is defined as the fraction of sunlit leaves in the field of view of the sensor. Alternatively, the shadow fraction \(1-\alpha_{S}\) can be used [20]. The brightest backscattering direction, known as the hot spot, is where no internally shaded elements are visible to the sensor, and hence \(\alpha_{S}=1\). In other directions, \(\alpha_{S}<1\). Clearly, sunlit fraction is one of the key drivers of scattering directionality, and thus is a central variable in investigating multi-angular imaging spectroscopy data. Conventionally, optical remote sensing target reflectance measurements are measured from the vertically downward looking angle, nadir. However, measurements of 3D structures such as vegetation can provide interesting information when made from an oblique angle [22]. Multi-angular measurements have shown to be beneficial in detecting a PRI-LUE relationship with a spectrophotometer [27; 28]. A similar investigation on the variability of the PRI with shadow fractions can be performed using airborne high spatial resolution data [20; 29]. Combining these two approaches, multi-angular high spatial resolution imaging spectroscopy data will thus enable the explicit investigation of the role of sunlit fractions on the measured signal. It includes two independent sources of variation in \(\alpha_{S}\). First, at sufficiently high spatial resolution, each view angle enables studying the variation of canopy appearance with sunlit fraction (sunlit and shaded crown sides) at a fixed observation geometry (illumination and view directions). Second, pixels with similar sunlit fractions can be extracted from data recorded at different view angles, thus quantifying the effect of observation geometry on the relationships of interest. Through the novel combination of angular and high spatial resolution spectroscopy, we aim to quantify the extent to which sunlit fraction alone can describe variations in PRI. To achieve this, we first determined top of canopy (TOC) spectral reflectance variation and the canopy PRI-canopy sunlit fraction relationship. The unique dataset allowed us to identify other possible factors affecting the relationship in a boreal forest stand.
## 2 Materials and Methods
### Study Selection  
This study's inclusion and exclusion criteria mainly focused on older adults (aged 60 and above) and remote health monitoring systems. Inclusion Criteria Papers considered for inclusion in the review had to meet the following requirements.  
* Publication Type: Journal papers or conference proceedings must be indexed in databases recognized for their scientific rigor.
* Topic Relevance: They must monitor vital signs, fall detection, and emergency alerts.
* Technological Scope: They should encompass a range of topics, including physiological signal analysis, artificial intelligence techniques, intelligent computing algorithms, the Internet of Things (IoT), communication technologies, information processing, storage and deployment techniques, expert knowledge-based systems, embedded systems, sensors, telehealth, and e-health.
* Integration Level: They should cover work ranging from physiological signal analysis to integrated home systems featuring a network of intelligent sensors. Exclusion Criteria The following types of works were excluded from the review.
* Publication Type: Technical reports, clinical studies, survey articles, framework proposals, usability studies, qualitative studies, studies that do not focus on the elderly, case studies specific to particular countries, and publications unavailable in English or Spanish.
# Acute Effects of Static Stretching on Submaximal Force Control of the Ankle
## 3 Results  
As illustrated in Table 1, two-way repeated-measures ANOVA identified a significant time \(\times\) condition interaction for the maximal ankle dorsiflexion angle (F (1,16) = 19.14, \(p<0.001\), partial \(\eta^{2}\) = 0.55). Post hoc tests revealed a significant increase in the dorsiflexion angle from pre- to post-intervention in the SS condition (\(p<0.001\), \(d=1.57\)) whereas no significant change was observed in the control condition (\(p>0.999\), \(d=0.50\)). Table 1 presents the results for the stiffness. Significant main effects were found for both time (F (1,16) = 7.71, \(p=0.013\), partial \(\eta^{2}\) = 0.33) and condition (F (1,16) = 12.92, \(p=0.002\), partial \(\eta^{2}\) = 0.45), with no significant interaction. Follow-up comparisons revealed a significant reduction in stiffness only in the SS condition (\(p=0.034\), \(d=0.78\)), without notable changes in the control condition (\(p>0.999\), \(d=0.14\)).  
Table 2 summarizes the RMSE results from the force-tracking tasks. A significant reduction in RMSE was found in the high-range release phase under the SS condition (\(p=0.030\), \(d=0.79\)), whereas no significant change in RMSE was observed in the control condition during the same phase. In addition, significant main effects of time were found in the low-range generation phase (F (1,16) = 15.47, \(p=0.001\), partial \(\eta^{2}\) = 0.49) and the high-range release phase (F (1,16) = 13.55, \(p=0.002\), partial \(\eta^{2}\) = 0.46). However, no significant main effects of condition or interaction effects were detected in any phase. All other comparisons were not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Outcomes** & **Condition** & **Pre** & **Post** & **\(p\)-Value** & **Cohen’s \(d\)** \\ \hline Maximal angle of & SS & 26.4 \(\pm\) 9.7 & 28.3 \(\pm\) 10.4 & \(<\)0.001 & 1.57 \\ ankle dorsiflexion (\({}^{\circ}\)) & Control & 27.0 \(\pm\) 9.8 & 27.4 \(\pm\) 9.8 & \(>\)0.999 & 0.50 \\ Muscultotendinous & SS & 1.30 \(\pm\) 0.51 & 1.20 \(\pm\) 0.47 & 0.034 & 0.78 \\ stiffness (Nm/\({}^{\circ}\)) & Control & 1.44 \(\pm\) 0.56 & 1.42 \(\pm\) 0.58 & \(>\)0.999 & 0.14 \\ \hline \end{tabular}
\end{table}
Table 1: Changes in ankle dorsiflexion range of motion and musculotendinous stiffness before and after static stretching and control interventions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Task** & **Force** & **Condition** & **Pre** & **Post** & **Change** & **95\%CI of** & _p-Value** & **Cohen’s \(d\)** \\ \hline \multirow{4}{*}{Low range} & Generation & SS & 1.40 \(\pm\) 0.44 & 1.30 \(\pm\) 0.33 & \(-\)3.12 & \(-\)16.04 to 9.81 & \(>\)0.999 & 0.32 \\  & & Control & 1.39 \(\pm\) 0.36 & 1.19 \(\pm\) 0.20 & \(-\)11.00 & \(-\)21.55 to \(-\)0.45 & 0.101 & 0.65
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method
### User Emitter Classification  
The third step is to identify the emitter ID from the designed feature. The goal is to design a classification algorithm that can learn spectrograms for robust classification results. Owing to recent research in the field of deep learning, deep neural networks are well known for their abilities to extract spatial or temporal features with nonlinear computational  
Figure 5: Examples of the spectrograms: (**a**) RT, (**b**) SS, and (**c**) FT signals.  
capabilities [21]. Thus, we aimed to construct a deep learning-based classifier to train the spectrogram of the SFs. The classification process can be obtained using  
\[\mathbf{y}=\mathbf{f}_{Classifier}(\mathbf{s}_{Feature}) \tag{15}\]  
where \(\mathbf{f}_{Classifier}\) is the deep learning-based classification algorithm, and the output vector \(\mathbf{y}\) implies the emitter ID information \(k\).
## 1 Introduction  
The fresh-cut fruits and vegetables industry has rapidly grown due to consumers' increasing demand for convenient foods with a fresh-like quality and high nutritive value. Raw peeled potatoes, as one of the fresh-cut produces that are rich in minerals, dietary fiber and other nutrients, have attracted widespread attention of households and the catering industry [1]. However, potatoes are susceptible to enzymatic browning after peeling, resulting in considerable degradation of sensory quality, safety and market values [2].  
Different methods have been studied to prevent the surface browning of potatoes, including blanching, inert gas packaging, low temperatures, radiation treatment, acidification or reduction with antioxidants and the use of chelators or natural extracts [3; 4; 5]. However, these methods have not been widely applied in practical production due to disadvantages of inconvenient operation, high cost or being unfriendly to environment. Thus, there is an urgent need to study and develop proper techniques to inhibit browning.  
Vacuum packaging is one of the most important techniques to prevent browning of fresh-cut fruits and vegetables, such as fresh-cut peaches [6], lotus roots [7; 8] and potatoes [9], etc. It has also been reported to maintain better hardness, moisture content, microbial safety and visual quality in comparison with air packaging and other modified atmosphere packaging [9; 10; 11]. Nevertheless, vacuum packaging has an extremely low oxygen concentration that could promote the growth, fermentation and metabolism of anaerobic pathogens [12; 13] and result in sensorial spoilage, particularly an off-odor, of fresh-cut produce [14]. The microbial load and sensorial quality are two main criteria to define the shelf life of fresh-cut fruits and vegetables. Extensive studies [15; 16] have reported sensorial quality decay and microbial growth separately in fresh-cut produce, but the potential correlation between particular bacteria and quality decay have rarely been investigated. Some studies have reported the effects of vacuum packaging on soluble solids, enzyme activity and phenol content in fresh-cut potatoes [17]. However, no information has been published concerning the effects of vacuum packaging on the bacterial community, especially on the correlation between particular bacteria and the formation of flavor.  
The objectives of this study were (1) to investigate shifts of the bacterial community and quality attributes in vacuum-packaged peeled potatoes during storage; (2) to reveal the potential relationship between quality attributes, especially flavor quality, and the bacterial community. The results of this research could be of great practical importance in developing effective intervention strategies to extend the shelf life of fresh-cut produce.
# Exploring Benefits of Using Blending Splines as Transition Curves
## 3 Method
### Comparison Criteria  
To evaluate the blending spline as a transition curve it was compared against the original clothoid with the following criteria, presented in [6].  
1. Common connection point: the coordinates of the two curves must be equal in the connection points.
2. Common tangent: the tangents of the two curves must be equal in the connection points.
3. Equal radius of curvature: the radii of the two curves must be equal in the connection points (eliminates discontinuities in the form of jumps in the curvature diagram).
4. Common tangent of curvature functions: the first derivative of the curvature functions of the two curves must be equal in the connection points (eliminates discontinuities in the form of breaks in the curvature diagram).
5. Equal radius of curvature of curvature functions: the second derivative of the curvature functions of the two curves must be equal in the connection points (needed for extremely high-speed railways).  
The clothoid fulfills criterion 1 to 3, which means that it has discontinuities in the form of breaks in the curvature diagram, see Figure 11. The reason for considering only the curvature, as an evaluation criteria, is the close connection it has to the jerk of the railway wagon, for instance through Lateral change of acceleration (LCA) [5].  
Figure 10: Plots of ERB-functions with varying balance parameters. The solid function is the default ERB (\(\lambda=0.5\)), the dashed function has \(\lambda=0.2\), and the dotted function has \(\lambda=0.8\).  
Figure 9: Plots of ERB-functions with varying slope parameters. The solid function is the default ERB (\(\beta=1\)), the dashed function has \(\beta=0.2\), the dotted function has \(\beta=10\), and the dashdotted function has \(\beta=50\).
## 5 Numerical Simulation Results Analysis
### Response Characteristics of Roadway-Anchored Surrounding Rock  
In order to analyze the influence of vibration waves on the surrounding rock around the anchorage, measuring points for velocity, stress, and displacement are arranged at the anchorage of the left-side bolt, the top anchor cable, and the top bolt of the roadway. Additionally, the variation range of the surrounding rock in the anchorage section is monitored. Axial force and displacement measurement points are positioned at the free end of the bolt and anchor cable, as illustrated in Figure 15. According to the input curve of the P-wave and S-wave, it is divided into the following three stages, as shown in Figure 16. The time velocity monitoring curve of each measuring point is shown in Figures 17-19.  
Figure 16: Diagram of vibrating wave velocity.  
Figure 15: Arrangement of measuring points: 1—measuring point of the left-side anchor bolt surrounding rock; 2—roof anchor bolt surrounding rock measuring point; 3—roof anchor cable surrounding rock measuring point; 4—left-side anchor bolt axial force measurement point; 5—roof anchor bolt axial force measurement point; and 6—roof anchor cable axial force measurement point.  
Stage I (0-0.12 s): This stage is characterized by the activity of P-waves. During this interval, the surrounding anchored rock undergoes damage exclusively due to the tension and compression effects generated by the P-waves. Stage II (0.12-0.23 s): At this stage, both the P-waves and S-waves exert their influence on the surrounding anchored rock. Following 0.19 s, the energy of the P-waves begins to diminish, resulting in a progressive reduction in amplitude. Stage III (0.23-0.7 s): This stage involves the action of S-waves. Research conducted by [PERSON] et al. [19] indicates that only low-frequency waves with long wavelengths can cause damage to rock masses. Consequently, the response characteristics of low-frequency, high-energy S-waves on the anchored surrounding rock are analyzed.  
Figure 19: Velocity, stress, and displacement curves of roof anchor cable anchoring surrounding rock: (**a**) horizontal direction; (**b**) vertical direction.  
Figure 17: The velocity, stress, and displacement curves of the left-side anchor bolt anchoring surrounding rock: (**a**) horizontal direction; (**b**) vertical direction.  
Figure 18: Velocity, stress, and displacement curves of roof anchor bolt anchoring surrounding rock: (**a**) horizontal direction; (**b**) vertical direction.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Computation Complexity
#### 3.8.1 Key Generation Complexity  
The complexity of an algorithm's key generation plays a big role in the overall computation complexity of the algorithm. Some algorithms have simple key-generation processes, such as algorithms using chaotic maps for key generation, while others may involve complex mathematical computations, such as the RSA key generation, which involves computationally intensive operations involving finding two large prime numbers, computing Euler's totient function, and performing mathematical operations.
## 3 Results and Discussion
### Heterogeneities  
The curing scenario proposed to describe the gradual build-up of networks by radiation-initiated chain polymerization multi-epoxides is represented in Figure 10. The progress of DGEBA polymerization illustrates the structural variations at the origin of the increase in Tg and the broadness of the transitions due to the formation of a variety of defects and heterogeneities in cross-link distribution within the networks [34; 35; 36].  
We have already reported on the importance of these structural defects at the origin of the brittleness of networks obtained by fast chain polymerization of multifunctional monomers, in particular in aromatic diacrylates [37; 38; 39; 40].  
We recently reported on the advanced understanding of the build-up of thermo-mechanical properties of networks at nanoscale by probing with new AFM techniques and measuring the peak normal forces, which allow to quantify the local modulus, adhesion, dissipation, and deformation, while simultaneously imaging sample topography of cationically-cured DGEBA and epoxy Novolak resins [22; 41].  
Figure 8: Influence of onium salt nature, DAIS-B (**a**) or TAS-SbF\({}_{6}\) (**b**) and concentration and thermal post-curing on the glass transition temperature Tg (T max E\({}^{\prime\prime}\) in DMA spectra) for 2 mm thick plates of DGEBA resin sample cured by EB irradiation with 2 passes of 50 kGy.  
These observations raise the need for progressing in the control of nanoscale morphology of these different types of networks to improve mechanical properties.
## 3 Results and Discussion
### Bioactivity  
The HA forming-ability of the synthesized hybrid materials was performed in vitro using the Kokubo Test [37]. To this aim, the samples were immersed in SBF solution for 21 days and the nucleated HA on the surfaces of the materials was investigated using FT-IR, SEM/EDX, and XRD analysis. Figure 5 reports the obtained FT-IR spectra of SP6A20 and SP12A20 hybrid biomaterials after soaking in SBF. The spectra revealed the presence of new peaks at 635, 585, 560, and 470 cm\({}^{-1}\) that are related to P-O bending and vibration modes [42; 50; 51].  
Figure 4: FT-IR spectra of (**A**) SP12 Ay and (**B**) SPxA20 hybrid systems.  
Moreover, the SEM/EDS image (Figure 6) of SP12A20 hybrid biomaterial confirmed the formation of the HA. Indeed, the sample surface was almost covered by globular-shaped HA particles [52] whose elemental composition was composed mainly of Ca and P, with an atomic ratio equal to 1.67. This finding is also in accordance with another similar system in which SP systems were doped with indomethacin [53].  
Furthermore, the XRD spectrum, reported in Figure 7, revealed that the spheres nucleated on the hybrid surfaces possess the typical HA crystalline peaks with hkl indices of (002), (211), (300), (202), (310), (002), (222), and (213), obtained by matching the spectrum with one found in ICDD database.  
Figure 5: FT-IR spectra of washed SP6A20 and SP12A20 after soaking in SBF for 21 days.  
Figure 6: Representative SEM/EDX image of SP12A20.
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
###### Abstract  
Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG(\(p,\ell\)), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of \(p\) and \(\ell\) indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER \(=10^{-9}\) for 10.2 dB of signal-to-noise ratio in one of the SVM models.  
machine learning; LG-beams; OAM-beams; optical encoding model +
Footnote †: journal: Sensors