esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 5 SA Applied to Aircraft Design  
The proposed SA approach can be used for various engineering design problems. Considering the nature of any engineering design problem, it is important to have a systematic method in place to control the sequence of design tasks and define the work transfer among various design stages [47]. The work transfer requirement will also be used to define the objective functions and the corresponding optimization parameters. Examples of these structured design procedures can be seen in the design of the handling mechanism in a racing car [48], with the design of curved structures for optimum vibrational properties [49]. Various engineering requirements including the consideration of manufacturing limitations can be introduced to the problem as optimization constraints [50,51]. The SAE organizes an annual Aero Design competition in which engineering students are challenged to design and manufacture a radio-controlled aircraft [52]. The main goal of this competition is to develop the lightest aircraft that lifts the largest possible load. Designs are required to follow a set of constraints such as a maximum allowable dimensional sum, specific motor and manufacturing materials, and runway length.  
The score is determined through a list of factors such as reliability (an accepted accuracy curve shows the maximum payload given the temperature and pressure statistics), total load (the sum of the aircraft weight and payload), and structural efficiency (ratio between payload and self-weight). During the conceptual design phase, participants define significant characteristics of their design. However, it is very difficult to determine a possible geometric design in the initial phase due to the large number of possible configurations. This work uses SA with the crystallization heuristic to search for the aircraft geometry that would have the highest score. Aircraft geometry is parameterized according to the the design characteristics and subsequently optimized to carry the largest load while being as light as possible and following the rules of the competition.
## 7 Applications
### Randomized Experimental Design via Geographic Clustering  
Recently, the authors of Reference [48] used balanced partitioning as a way to cluster geographic regions in order in order to set up randomized experiments over them. This is especially useful in web-based services, since it does not require tracking individual users or browser cookies. Given the fact that users, especially mobile ones, may issue queries from different geographical locations, it cannot be assumed that geographic regions are independent and interference may still be present in the experiments. These characteristics of the problem make it suitable for applying balanced partitioning where the geographic regions are modeled as a graph and the edge weights across output clusters are minimized while producing clusters of almost the same size.
# SEA and GATOR 10 Years Later
## 5 Function of the SEA and GATOR in Nutrient Sensing and Responding
### SEA/GATOR in Autophagy  
One of the major functions of mTOR1 is in the regulation of autophagy, which is induced when mTOR1 is inhibited. Thus, it is not surprising that deletions of SEACIT/GATOR1 components suppress autophagy in yeast [3; 9; 13; 103; 104; 128; 129], _Drosophila_[130], _C. elegans_[131] and mammals [129; 132]. Just as the opposite, mutations in GATOR2 may promote autophagy, which can happen even in the absence of nutrient starvation, as it is a case of _wdr24_ mutants in _Drosophila_[133]. In contrast, deletions of SEACAT members in yeast seem not to have a drastic effect on autophagy initiation and flux [3]. Interestingly, the nitrogen starvation deletion of _SEA1_ or double deletion of _NPR2_ and _NPR3_ resulted in the inhibition of vacuolar fusion [13]. As the inactivation of TORC1 during nitrogen deprivation promotes vacuole coalescence [134], deletions of any of the SEACIT members increase TORC1 activity during starvation, and, therefore, induce vacuolar fragmentation and defects in autophagy.  
Recently a bi-directional feedback loop, which regulates autophagy and involves SEACAT, has been described [50]. In order to control autophagy, TORC1 phosphorylates and inhibits the Atg1 kinase essential for autophagy initiation, but Atg1, in turn, can phosphorylate SEACAT components. Although it is currently not known whether that phosphorylation acts positively or negatively on TORC1 activity, this finding uncovers the important node of convergence between TORC1 and Atg1, with the SEACAT being both the regulator and effector of autophagy.  
The SEA complex is also important for specific types of autophagy. Thus, yeast with deletions of SEACIT complex members failed to activate selective degradation of mitochondria via mitophagy (Figure 4) [135; 136]. Given the conservation of the SEA/GATOR function, it is reasonable to assume a similar role of GATOR in mammals, although the involvement of GATOR in specific types of autophagy in mammals has not yet been described.
## 2 Materials and Methods
### Methods
#### 2.2.2 Feature Selection  
Laplacian eigenmaps [42], kernel principal component analysis [43], the Gaussian process latent variable model [44], and linear discriminant analysis [45] were used in the dimension exploration experiments of self-acquisition signals. Since these feature selection methods are perfect in the scientific community, they will not be described again.
## 2 General Requirements
### Grid Requirements  
Typically, the MW-level power-to-hydrogen converter is connected to a MV 6.6-35 kV power grid, where a step-down LFT is mandatory inbetween. From the grid's perspective, there are several requirements for power-to-hydrogen converters as consumption installations. First, the converter should maintain regular operations under background frequency and voltage deviations, in the ranges of 47-63 Hz and 90-110% of the nominal voltage, respectively. Furthermore, the regular operation of power converters should not cause severe power quality issues, e.g., voltage imbalance, rapid voltage change (i.e., 4%), and flickers [35; 36]. Moreover, harmonic distortions and interference in 2-9 kHz should be attenuated, according to IEC 61000-3-6 [34]. The current total harmonic distortion (THDi)  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Present** & **Future** \\ \hline Input voltage & Typical 6.6–35 kV, 50/60 Hz \\ \hline Output voltage & 640–1000 V \\ \hline Output power & 5 MW \(\times\) 2 (2 sets of electrolyzer stack, 5 kA each) \\ \hline Output current ripple & N/A & \(\leq\)5\% of rated current \\ \hline Efficiency & \(>\)94\% & \(>\)98\% \\ \hline Power factor & \(>\)0.90 & \(>\)0.99 \\ \hline THDi (2–40 th) & \(<\)5\% \\ \hline Standards & IEC 60076 series (transformer) \\  & IEC 60146-1-1 (semiconductor converter) [31] \\  & IEC 61000-6-2 (immunity) [32] \\  & IEC 61000-6-4 (emission) [33] \\  & IEC 61000-3-6 (distortion) [34] \\  & IEC 61000-3-7 (voltage fluctuations) [35] \\  & IEC 61000-3-13 (unbalanced installations) [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: General requirements of power-to-hydrogen power converters.  
Figure 3: Load slopes of the electrolyzer for the beginning and end of lifetime.  
and power factor at the connection point should be less than 5% and greater than 0.90, respectively. In order to fulfill the electromagnetic compatibility requirements, standards IEC 61000-6-2 [32] and IEC 61000-6-4 [33] apply for P2H power electronic converters.
## 5 Experimental Validation and Benchmarks
### Applied Solutions in Real-World Networks
#### 5.2.1 Federated Learning for Intrusion Detection in 5G Networks  
A major challenge in modern communication networks, particularly 5G, is ensuring security while maintaining user privacy. In this context, I applied a federated learning-based intrusion detection system (IDS) to detect cyberattacks while keeping user data local, in compliance with privacy regulations such as GDPR. The IDS was trained on a decentralized dataset spread across multiple network nodes, ensuring that raw data never left local devices.  
The system was deployed in a simulated network environment, where it identified potential threats such as denial-of-service (DoS) attacks, man-in-the-middle (MITM) attacks, and network scanning activities. The federated learning model achieved an impressive 98% detection accuracy, significantly outperforming traditional IDS systems based on centralized learning models, which only achieved an accuracy of 90%.  
Key results from the deployment include:  
* Detection Accuracy: 98% attack detection rate, with false positives reduced by 15%.
* Computational Efficiency: The federated learning approach reduced the computational cost by 20% compared to centralized systems, where data transfer and model aggregation processes are more resource-intensive.
* Latency: The latency of anomaly detection was maintained below 50 ms, ensuring that real-time attack detection did not disrupt network operations.  
Challenges: The main challenges encountered included data heterogeneity across devices, which sometimes led to slight discrepancies in model performance. However, this issue was mitigated by implementing adaptive aggregation techniques that ensured the global model remained robust without compromising data privacy.
## 4 Results and Analysis
### Experimental Results
Further, the variation in accuracies of author identification for datasets of disparate sizes, i.e., from 5 to 50 authors is presented in Figure 13. The x-axis represents the features and the y-axis represents the number of authors. Each cell of a heat map represents a grouping of data. It is observed that very low accuracies were obtained for some authors. It is also encouraging to observe that there was only a slight variation, or at times the accuracy was maintained, for individual authors when the number of authors was increased. This observation also shows the ability of the system to handle cases where the gradual increase of data over time is a common phenomenon.
###### Abstract  
The aim of using minimal food processing technologies is to preserve the raw material or to achieve a special technological goal with the least possible impact. When several technologies are used together, the intensity of each treatment can be reduced according to [PERSON]'s hurdle principle. Does the order of application of the treatments result in a detectable difference? This research focuses on the effect of the combination of the sous-vide technology and the high hydrostatic pressure (HHP) treatment. The effect of the pressure level (300 and 600 MPa) and the influence of the treatment order was investigated on pork (longissimus thoracals and lumbrum muscles (LTL)). Physico-chemical and microbiological measurements were carried out on day 0 and after 21-day storage at 2 \({}^{\circ}\)C and 8 \({}^{\circ}\)C. Significant differences were found for both the order of treatment and pressure level in weight loss (\(p<0.001\)), CIELab color parameters \(\mathbf{a}^{*}\) and \(\mathbf{b}^{*}\) (\(p<0.001\)), and denaturation enthalpy (\(p<0.01\)). The texture (\(p<0.001\)) and lipid oxidation TBARS values (\(p<0.05\)) were only influenced by the pressure level. In the challenge test, the initial count of \(10^{5}\) CFU/g _Listeria monocytogenes_ dropped below detection limit in all cases. Total aerobic and anaerobic viable numbers were below/near the detection limit in all combined-treated samples on day 0 and showed only slight or more notable growth after 21-day storage at 2 \({}^{\circ}\)C and 8 \({}^{\circ}\)C, respectively. An additional 300 MPa pressure treatment can increase the safety of sous-vide cooked pork samples while having only a minor effect on physicochemical properties. The 600 MPa pressure treatment results in a stable, albeit not shelf-stable product, but it also affects a considerable number of quality parameters such as color, texture, weight loss, and TBARS values.  
sous vide; high hydrostatic pressure; combined treatment; pork; _Listeria monocytogenes_; hurdle principle; minimal processing
## 4 Buffeting Analysis of Taizhou Bridge
### Time-Frequency Analysis  
Before conducting the buffeting control of the long-span triple-tower suspension bridge, a prior step is to find the dominant modes in charge of the buffeting responses. These modes are the basis for the design of the MTMD system. Hence, the wavelet transform is selected to characterize the energy distribution of vibrations in frequency domain. In addition, the energy variation along the time will also be clearly presented. With the Moret wavelet as the mother wavelet, the wavelet transform is applied to the lateral and vertical buffeting displacements. The distribution of the modules of the wavelet coefficients in time-frequency domain are shown in Figure 12. It is found that the energy distribution at each time instant is presented, and the dominant frequency can gather to form a bright frequency band along the time. In such a case, the energy variation is easily evaluated via the primary dominant frequency band. For example, some intermittence can be found in each bright frequency band as the input energy varies with the turbulence.  
As can be seen in Figure 12, the energy of the lateral buffeting displacement mainly concentrates on two frequencies (0.072 Hz and 0.095 Hz), which correspond to the AS-L-1 mode and the S-L-1 mode. It means that the lateral buffeting displacement is mainly dominated by these two modes. For the vertical buffeting displacement, 0.080 Hz is the most prominent frequency that plays a dominant role in frequency domain. Although there are some vague high-frequency bands captured in the time-frequency spectrum, they are so minor compared to the bright band of 0.080 Hz that they can be neglected. With the aid of Table 1, the vertical buffeting displacement of Taizhou Bridge is proved to be dominated by the AS-V-1 mode. Hence, AS-L-1 and S-L-1 modes are determined as the target modes for the design of vertical MTMD system while AS-V-1 for the lateral MTMD system.  
Figure 11: Buffeting displacements in the middle of the main girder in one span: (**a**) Lateral case; (**b**) Vertical case; (**c**) Torsional case.
## 4 Discussion  
### Mean WUE across Different Vegetation Types  
In this study, the low-productivity ecosystems had the highest WUE, such as open shrublands, grasslands, and closed shrublands, while the WUE was low in forests and farmlands (Figure 1). There are two possible reasons for this finding, namely: (1) The areas with high WUE are mainly distributed in Turkmenistan and Uzbekistan, where the vegetation is characterized by relatively low GPP growth in arid regions, and many studies have shown that plant leaf stomatal conductance in arid areas is more sensitive to drought than that in humid areas. Drought promotes the stomatal closure of plant leaves, resulting in a decrease in the rate of evapotranspiration before the photosynthesis rate has decreased [53]. This process led to improvements in the plant WUE. (2) Water restriction is a major feature of arid areas, and studies have shown that the global ET declined in the first decade of the 21 st century; moreover, the ET in Central Asia declined as a result of the El Nino effect. The global ET increased significantly from 1982-1997; then, the increasing trend began to slow until the end of 2008 [40]. Microwave remote sensing data also showed a decreasing trend in global soil moisture between 1998 and 2008 [54]. Therefore, the decreasing trend of ET due to soil moisture loss is also the cause of the change in WUE; high WUE with low GPP is caused by low ET in the arid region. High-productivity forests are usually located at high-altitude regions (Tianshan Mountains, Kyrgyzstan, Tajikistan, Xinjiang, and China), which generally have better water conditions; therefore, high temperature environments increase soil moisture evaporation and vegetation transpiration, thereby reducing the WUE in high vegetation areas.  
Figure 9: (**a**) Overall changes, (**b**) the rate of change, and (**c**) WUE changes in the different biomes under the transition from dry years (2000–2010) to wet years (2011–2014).  
Cropland WUE is relatively low compared with that of other vegetation, because most of the crops are usually water-consuming plants [55]. The differences in the spatial distribution of the mean WUE in Central Asia are mainly controlled by water distribution, vegetation type, altitude, and other factors, and this conclusion is consistent with that of other studies [28].
## 3 Results and Discussion
### Frost Resistance  
The analysis of the results of strength tests after assessing frost resistance indicates a varying degree of reduction in compressive strength, bending strength, and weight loss depending on the composition of the mortars (Table 3). The MS control series showed a  
Figure 8: The average flexural strength tested after 7 and 28 days of sample curing: (**a**) series where each waste material was dosed separately; (**b**) series where mixed waste materials were dosed in the adopted proportions.  
reduction in compressive strength (\(-32.66\%\)) and flexural strength (\(-47.7\%\)), while also showing the highest weight loss (0.82%). The series with MSWI-FA additions demonstrated smaller decreases in mechanical parameters than the series modified with CKD alone. For example, MSWI-FA-20 showed the lowest reductions in compressive strength (\(-6.10\%\)) and flexural strength (\(-0.7\%\)), suggesting a positive effect of higher MSWI-FA content on mechanical properties. CKD additions resulted in significant decreases in flexural strength (e.g., CKD-10: \(-77.9\%\)), but their impact on compressive strength was more varied, ranging from \(-35.21\%\) (CKD-10) to \(-19.52\%\) (CKD-20). MSWI-FA-CKD combinations showed mixed results. The MSWI-FA-CKD/20-20 series achieved the smallest reductions in both compressive strength (\(-1.72\%\)) and flexural strength (\(-9.9\%\)), along with moderate mass loss (0.24%), indicating favorable effects of this mix. However, some series, such as MSWI-FA-CKD/10-10 and MSWI-FA-CKD/17-17, recorded substantial reductions in flexural strength (\(-76.6\%\) and \(-56.6\%\)), suggesting suboptimal proportions of components. The results suggest that an appropriate combination of MSWI-FA and CKD can enhance mechanical properties while minimizing mass loss.
## 3 Establishment of the Scale Factor Model
### Simulation of Scale Factors in Magnetic Anomaly Detection  
Building upon the previous simulation, it was observed that when the magnetic target passes the CPA, the energy detection does not reach its maximum peak without the optimal scale factor \(\beta\). To address this issue, further simulations were conducted, selecting 50 different \(\beta\) values. The maximum orthogonal basis energy value \(E\) was found at a velocity of \(v=2\) m/s. Figure 13 provides a schematic representation of \(\beta\) from the simulations, offering essential insights into the scale factor.  
Figure 11: ROC\(-\)w function detection curves.  
Figure 12: Monte Carlo evaluation curve.  
Recording the current velocity along with its corresponding scale factor, we conducted simulations to determine the optimal energy solution under 50 different velocities. Subsequently, we attempted to use various curve-fitting models. Upon comparing the fitting performance, it became evident that the Gauss model exhibited the lowest root mean square error and residual sum of squares, signifying the best fitting performance. The data for the evaluation are presented in Table 1.  
The fitting function using the Gauss model is:  
\[\begin{array}{l}\beta(v)=k_{1}\cdot\exp\biggl{(}-\left(\frac{(x-k_{2})}{k_{3 }}\right)^{2}\biggr{)}+k_{4}\times\exp\biggl{(}-\left(\frac{(x-k_{5})}{k_{6}} \right)^{2}\biggr{)}\\ k_{1}=0.8174\quad w_{1}(-0.1119,1.747)\\ k_{2}=-0.1933\quad w_{2}(-0.9393,0.5528)\\ k_{3}=0.6701\quad w_{3}(0.2754,1.065)\\ k_{4}=2.153e13\quad w_{4}(-1.28e17,1.289e17)\\ k_{5}=-314.4\quad w_{5}(-5.791e4,5.728e4)\\ k_{6}=55.26\quad w_{6}(-4958,5069)\end{array} \tag{13}\]  
where \(k_{i}(i=1,2,\ldots,6)\) represents coefficients in the model, indicating the magnitudes of the exponential and constant terms, and \(w_{i}(i=1,2,\ldots,6)\) represents their confidence intervals. The Gauss curve is depicted in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fitting Category/** & **SSE** & **R-Square** & **RMSE** \\ \hline Power model & 0.01889 & 0.9734 & 0.02049 \\ \hline Sum of sine model & 0.09545 & 0.8654 & 0.02049 \\ \hline Weibull model & 0.07372 & 0.8961 & 0.04003 \\ \hline Gauss model & 0.01309 & 0.9815 & 0.01765 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gauss model evaluation data.  
Figure 13: Maximum energy detection Emax for optimal \(\beta\).
## 2 Development
### The Metric, Energy-Momentum Tensor, and Field Equations
#### 2.1.2 Field Equations  
After several algebraic manipulations, the field Equation (1), is written according to Equations (16)-(19) [15; 16].  
\[\frac{\rho\ +\ p\omega^{2}}{1\ -\ \omega^{  
By knowing the metric \(\beta(u,r)\) and the metric \(m(u,r)\), together with their derivatives, it is possible to determine \(p,\rho,\varepsilon\), and \(\omega\) from the symmetry equations. Equations (20)-(23) are the same for anisotropic matter [18; 33; 34; 35], so that it can be interpreted as anisotropy of the fluid, to the electric charge. Such an interpretation is possible because of how the mass (Equation (14)) is defined, so that the electric charge can be assumed as a constituent of the metric. Assuming this position, the electric charge would then become another physical variable that contributes to both energy density and pressure. This model helps with the understanding of what has been studied due to the approximation of the Bondian observers to the GR (comoving frame of reference). [PERSON] et al. [32; 36] showed that the observers used by [PERSON] for the treatment of matter in GR and those used by the standard ADM \(3+1\) of numerical relativity are the same, both have comoving and Minkowskian-type observers, as mentioned above. These observers are the same as those of the post-quasistatic approximation of GR for the study of collapsing spheres. The treatment is identical for an intrinsic anisotropic fluid, except for the mass function.  
At this point, there are four field equations for seven unknowns: two geometric variables \(m\) and \(\beta\) and five physical variables \(p\), rho, radiation flux, velocity, and charge.  
The solution algorithm is as follows:  
The geometric variables are obtained from the imposition of an additional homothetic (self-similar) symmetry, i.e., from the symmetry equations as a function of the surface variables.  
Previously, to find the surface variables, the surface equations must be solved by the Runge-Kutta method of order 4 [36].  
The electric charge is determined from the numerical solution of the charge conservation equation.  
Pressure, density, radiation flux, and velocity are determined from the field equations.