esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Five different lineaments were detected by the HGM method (Figure 7a, HGM1-HGM5), and it was observed that the lineaments are generally northwest-southeast oriented. The HGM1 and HGM2 are the longest lineaments in the study area. The HGM1 is approximately 14 km long, and the HGM2 is approximately 20 km long. Other lineaments (HGM3, HGM4, and HGM5) are approximately 2-3 km long. Euler Deconvolution offers more detailed solutions than the HGM, due to the nature of the method. Eight different lineaments were detected as a result of the ED (Figure 7b, ED1-ED8). As in the HGM map, the lineaments generally extend in the north-west-southeast direction. The ED1 and ED5 appear to be the longest lineaments in the study area. The ED1 is about 14 km long, and the ED5 is about 20 km long. Other lineaments range from 2 km to 4 km in length, with only the ED6 being longer at 8 km. The horizontal gradient magnitude (HGM) method only utilizes horizontal derivatives of Bouguer gravity anomalies. Therefore, it has difficulty in detecting low-angle normal faults. The smaller the subduction angle of the fault, the less likely the occurrence of HGM maxima on the fault plane. However, Euler Deconvolution (ED) uses vertical derivatives, as well as horizontal derivatives of Bouguer gravity anomalies in calculations. At the same time, this feature allows the ED to provide information about the depths of the faults. Because of the more complex nature of the ED, it was able to reveal ED1 and ED2 lineaments that the HGM could not detect. Although all lineaments revealed by the HGM are consistent with historical earthquake locations and known faults in the literature, only the ED1 and ED8 lineaments stand out as two newly discovered results (Figure 7), since these two lineaments are not located near either of earthquakes or faults. Although the ED1 is located close to the faults proposed by [PERSON] [4], further investigation with more precise methods is needed.
## 3 Results and Discussion
### RF Predictive Accuracy for SSS and SST
Therefore, future studies could benefit from the acquisition of a larger dataset. These limitations highlight the challenges faced in accurately predicting SSS and SST using a combination of satellite data, in situ observations, and MLAs. Recognising these limitations is crucial for contextualising the findings, understanding the potential sources of error, and guiding future research to enhance predictive capabilities and data integration methodologies.
# Bol Looseness Detection Based on Piezoelectric Impedance Frequency Shift
## 1 Introduction
[30] evaluate the feasibility of fatigue crack monitoring with the electromechanical impedance (EMI) technique with a PZT transducer and find that the EMI technique is very sensitive to fatigue crack propagation. [PERSON] et al. employ the impedance technique to monitor the bond-slip between concrete and steel for concrete-encased composite structure [31]. Recently, the piezoelectric impedance method has been extended to bolt loosening detection ([PERSON] et al. [32], [PERSON] et al. [33] and [PERSON] et al. [34]). Utilizing the technical merits of piezoelectric impedance method and the Lamb wave propagation method, [PERSON] et al. [35] integrated these two techniques to investigate the structural integrity of a bolted system. A combined approach of the piezoelectric impedance technique and the guided wave-based damage detection method was developed by [PERSON] and [PERSON] [36] to detect the bolt looseness by employing the signals of these two method simultaneously from piezoelectric transducers. In this paper, the piezoelectric impedance method is applied to estimate the bolt preload by analyzing a specified peak frequency shift with a piezoelectric patch bonded on the bolt head. In contrast with the previous studies which used a damage index to estimate bolted status, in this approach, the relationship between a specified peak frequency shift of the piezoelectric admittance signature and the bolt preload is established. The peak frequency change of the admittance is used as a feature parameter of the bolt preload. Additionally, a higher sweep frequency band up to several megahertz is chosen for the measurement of the bolt head local dynamics and the piezoelectric impedance method based bolt preload measurement. Using a type CMT5105 universal mechanical testing machine to apply the precise axial load on bolts, and the piezoelectric admittance signatures of the piezoelectric patch on the bolt head are acquired under different bolt preloads. A new bolt looseness detection method based on piezoelectric impedance frequency shift is developed in this paper.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### Marine Predators Optimization Algorithm (MPA)  
Biologically inspired metaheuristic algorithms are a type of algorithm that was developed to address complicated optimization issues by utilizing natural events and biological processes. Algorithms like the whale optimization algorithm (WOA), genetic algorithm (GA), ant colony optimization (ACO), differential evolution (DE), lion optimization algorithm (LOA), grasshopper optimization algorithm (GOA), sine cosine algorithm (SCA), bat algorithm (BA), particle swarm optimization (PSO), grey wolf optimizer (GWO), simulated annealing (SA), and marine predator algorithm (MPA) are all examples of this kind. They have solved several engineering, financial, and computer science optimization challenges. One of the biological metaheuristic algorithms developed by [25] will be shown in this work. This method is known as the Marine Predator Algorithm (MPA). Optimization strategies have seen a dramatic uptick in popularity in recent years. When dealing with the many different optimization sectors, such as machine learning and feature selection, they are more effective than other strategies currently in use [49].  
The foraging strategies of marine predators in the wild serve as the basis for the MPA mathematical model. MPA can accommodate both the Levy and Brownian statistical distributions. The Levy search technique involves traversing space with a series of prominent hops, while the Brownian method makes a systematic and consistent progression across the search space. While the Brownian technique guarantees visits to far-flung places, the Levy strategy's strength lies in its thorough and precise search. This collaboration has greatly enhanced the search capabilities of MPA.  
The movement equation is an essential equation employed in the MPA method. It governs how the predators move about the solution space and is thus very important. The equation for movement is defined as follows:  
\[\mathrm{x_{i}(t+1)=x_{i}(t)+v_{i}(t)} \tag{9}\]  
where \(\mathrm{x_{i}(t)}\) represents the location of the ith predator at time t, \(\mathrm{v_{i}(t)}\) represents the velocity of the ith predator at time t, and t represents the current iteration of the algorithm.  
The MPA algorithm's strengths lie in its rapid convergence to optimal solutions and adaptability to multimodal and massively parallel optimization problems. The method might become trapped in the local optimum and requires careful parameter tuning.
## 1 Introduction  
The scale-modelling approach provides a useful means for supplementing existing data from case histories and/or prototype investigations to enable parametric studies in seismic analysis, especially when there is a dearth of available data and results. In addition to qualitative analysis, the results of scale modelling tests are frequently applied as calibration benchmarks for analytical approaches and quantitative forecasts of the prototype response. To successfully achieve this, a set of scaling relationships that can be used in the model and accurately predict the behaviour of the prototype must be developed.  
In the engineering sector, it is common practice and increasingly regular to employ simulations and computational tools to model situations in which physical testing is impractical or prohibitively expensive. It is customary to make assumptions to precisely define the system, commonly referring to the resulting system as an idealised prototype. The assumptions made during the initial stages are usually only imposed to ensure a clear and well-defined problem.  
In geotechnical engineering, the use of scale models enables the controlled modelling of complicated systems and offers the opportunity to understand the fundamental mechanisms involved in operating these systems. In certain situations, such as a static lateral pile loading test, a scale-modelled test can provide an effective alternative to the corresponding full-scale test. In investigations of dynamic soil-pile interaction, the scale modelling test allows the simulation of cases that can never be accomplished in a full-scale prototype [1]. In this context, the main aim of this paper was to develop and validate a practical technique for the scaling of dynamic soil-structure interaction problems. A novel, scaled testing technique was developed together with a calibration method for establishing the relationship between full-scale numerical analysis and scaled laboratory tests in a 1g environment, and both are described in detail.  
First, the theories and background relating to the scale-modelling approach are described, and the development of scale modelling criteria for shaking table tests is explored. Then, the adoption of scaling criteria and the design of the soil and pile models are explained. The subscale models are examined to predict the behaviour of prototype materials using a sophisticated scaling approach. The results are then validated through comparison with the results from a series of physical shaking table experiments [2]. Once the scaled approach is validated and the relationship between the experimental data and the results of the full-scale numerical analysis for the prototype system is determined, the new practical technique for the scaling and validation of dynamic soil-structure interaction problems is presented.
## 3 Proposed Solution to Improve Cooling Efficiency
### High Flow Air Diffusers  
In order to implement well the DownFlow cooling system (described in the previous section), with sufficient cold air pressure coming from the plenum, it is necessary to use diffuser plates or grilles with high airflow on the raised floor to make it easier for the cold air below the raised floor to be sucked in by the equipment installed in the racks. It  
Figure 1: DownFlow cooling scheme, where (**A**) is the flow of cold air coming under the raised floor towards the rack row and (**B**) the flow of hot air returning to the CRAC.  
is important that the cooled air leaves from the plenum only through the diffuser plates or grilles in the direction of the rows of racks, preventing the cold air from escaping through other areas of the raised floor that are not to be used for the cooling process of the servers, since this waste of resources implies cooling inefficiency and consequently energy inefficiency.  
In our old data center, we already used perforated plates with high airflow arranged near the rack's entrance and the positioning of the rows of racks alternating cold aisles with hot aisles. The layout concept, where the rows of racks are positioned face to face so that there is a constant alternation between cold and hot aisles, is shown in Figure 2.  
According to the study by [PERSON] et al. [51], floors with diffusing grids that have adjustable drivers are more efficient than those without this feature. [PERSON] et al. [52] show that integrating this type of diffusing grid with other advanced technologies can reduce the cooling cost by 30%. Thus, with bases in these studies and in the indications from refrigeration equipment suppliers [53; 54], we implemented the replacement of the perforated plates (without drivers) by using high airflow diffuser grids with airflow direction control in the cooling project of the new data center. Unlike perforated plates, grids allow adjustment in the direction and have different angles to the outflow of cold air that passes through them, making the servers receive better ventilation for equipment installed in the lower and upper parts of the racks. Figure 3 shows the comparison of the difference in the direction of the cold airflow next to the racks implemented in the old and new data centers.
## 3 Results and Discussion
### Structural Response
#### 3.2.2 Moment-Curvature Curves  
In the current experimental program, the moment-curvature (\(M-\varnothing\)) curves were also analyzed taking into consideration the pure flexural region of the beam, while Equations (13) and (14) were used to calculate the corresponding curvature (\(\varnothing\)) values. In these equations, \(\varepsilon_{c_{1}}\) and \(\varepsilon_{c_{3}}\) are the concrete's peak compressive strain at 0.02 m from the top of the beam (obtained through the experiment from the concrete's top strain gauges, Figure 4) and \(c\) = the depth of the sectional neutral axis (in meters).  
\[c=\frac{0.020}{\left[1-\frac{\varepsilon_{c_{3}}}{\varepsilon_{c_{1}}}\right]} \tag{13}\]  
\[\varnothing=\frac{\varepsilon_{c_{1}}}{c} \tag{14}\]  
By applying the above formulas, Figure 9 shows the \(M-\varnothing\) behavior of B1-B6. The moment-curvature for B6 had a premature termination due to the crushing of the extremely stressed UHPC (at top face). It is worth noting that the calculation of the beam's strains was not attainable if cracks extended to the beam's top surface, as they disturb the measurement of the concrete strains. This issue could be handled by the use of horizontal LVDTs for curvature measurements.  
Figure 7: Load–deflection responses of (**a**) B1, (**b**) B2, (**c**) B3, (**d**) B4, (**e**) B5, and (**f**) B6.  
Figure 10a depicts a representative moment-curvature response for B1-B6. This figure demonstrated that all bar-reinforced beams, except B6, were likely under-reinforced (tension-controlled). Additionally, the slope of the pre- and post-steel yielding was almost typical for all beams. This was due to the efficient hybrid fibrous system in the UHPC that controls the post-cracking curvatures of beams. The relation between the moment capacity and bar reinforcement ratio is presented in Figure 10b. This figure shows the robust linearity of this relation (with a 99% correlation coefficient).  
Figure 8: (a) Load–deflection of beams and (b) peak load-reinforcement ratio relation.  
Figure 9: Moment–curvature responses of (a) B1, (b) B2, (c) B3, (d) B4, (e) B5, and (f) B6.
## 3 Case Study
### Synchronous Prediction with Multiple Inputs and Multiple Outputs Model
This is due to the capacity of the MIMO-PSO-LSSVM model to predict the displacements of multiple measurement points synchronously, thereby markedly enhancing the computational efficiency in comparison with the single-output PSO-LSSVM prediction model. Although the SVM and BPNN models, which serve as comparison models, consume minimal time for prediction, their prediction accuracy shows a significant gap compared with the multi-output model due to the absence of optimization algorithms. Therefore, a comparative analysis of prediction efficiency between these models and the multi-output model is not considered. In summary, the MIMO-PSO-LSSVM model has been demonstrated to enhance prediction accuracy and efficiency when compared with other single-output prediction models, and it can be concluded that it is a reliable and excellent prediction method.
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Performance of Local Interpretability  
To evaluate the performance of different methods in generating localized explanations, we analyzed an image containing the target class (bird), as shown in Figure 8. Since SegGrad-CAM, Seg-XRes-CAM, and our method all employ similar mechanisms to focus on regions of interest, comparing their respective heatmap distributions allows us to directly assess their ability to preserve spatial information and accurately locate target objects.  
From the figure, it is evident that Eigen-CAM and Seg-Grad-CAM struggle to accurately localize target regions because of their inability to fully incorporate the spatial information. As a result, their heatmaps display significant background interference, with strong activations appearing in non-target areas such as branches and leaves. In contrast, Seg-XRes-CAM and our method effectively leverage spatial information, allowing more precise identification and interpretation of the model focus areas. This advantage is particularly noticeable in multi-objective scenarios; for example, in the second example in Figure 8, both methods clearly highlight the location of the bird and generate the corresponding localized explanations.  
The experimental results highlight the importance of retaining and utilizing spatial information when generating localized explanations. By effectively integrating spatial context with feature representations, our proposed method ultimately improved the precision and reliability of model explanations.  
Figure 7: Visualization comparison of evaluation performance across different methods. The figure displays the distribution of Dice score and image retention rate for different methods across 4411 test samples. The intensity of the color scale indicates the number of samples within each range, and the red circles highlight the median performance of each method.
## Results and Discussion
### Total Phenols, Total Flavonoids, Total Anthocyanins, and Color Attributes
#### 2.3.2 Higher Alcohols  
Higher alcohols are considered the intermediate compounds during the synthesis and catabolism of amino acids [19]. In wine, they are yielded from the metabolism of aldehydes, reductive denitrification of amino acids, and/or synthesis from sugars [19]. It has been accepted that appropriate levels of higher alcohols in wine could bring to the wine a desirable complexity, whereas excessive amounts of these alcohols (above 400 mg/L) negatively affect the overall aroma of wine [52]. In the present study, these wine samples all exhibited the content of the total higher alcohols above 400 mg/L, indicating that these higher alcohols might weaken the overall aroma of the bog bilberry syrup wine (Table 3). The highest content of higher alcohols was observed in the wine supplemented with 90 mg/L DAP, which was in accordance with a previous study where 90 mg/L yeast-assimilable nitrogen supplement caused a dramatic accumulation of higher alcohols in wine [22].  
Regarding individual higher alcohols, _levo_-2,3-butanediol, _meso_-2,3-butanediol, 2-phenylethanol, isoamyl alcohol, and isobutanol were the predominant higher alcohols in these wines (Table 3). These major higher alcohols had their OAV values above 1 (Table 3). Both _levo_-2,3-butanediol and _meso_-2,3-butanediol display the fruity, sweet, and buttery notes to the wine aroma, and these higher alcohols are synthesized from acetoin reduction catalyzed by yeasts [35; 53]. Their concentration was found to be the highest in the wine supplemented with 90 mg/L DAP. In addition, 2-Phenylethanol, isoamyl alcohol, and isobutanol are formed under the Ehrlich pathway, and their content in the wines showed an obvious increase with the increase of the DAP amount supplemented in the wine (Table 3). In terms of their flavor notes, 2-phenylethanol exhibits the rose and honey aroma, whereas isoamyl alcohol and isobutanol can provide wine with the solvent, sweet, alcohol, and nail polish notes [35]. Therefore, these flavor notes could be more strengthened in the bog bilberry syrup wine with high DAP amount addition. The other higher alcohols were also more accumulated in the wines with the increase of DAP amount. However, their concentration in the wine was below their thresholds, limiting their flavor contribution to the overall aroma of the bog bilberry syrup wine.
## 3 Materials and Methods
### Dimensionality Reduction Stage
#### 3.3.1 Factor Analysis (FA)  
Factor analysis (FA) is a statistical method for examining correlations by developing a set of underlying latent variables [32]. FA presupposes that the data can be initially organized according to the relationships between the variables. Strong relationships exist between the variables within a given group, but only weak relationships exist between variables in different groups. To this end, a factor may be represented by a single group, as is shown in Equation (1) [33].  
\[x_{i}=Ay_{i}+\mu+\epsilon_{i} \tag{1}\]  
where \(\mu\in\mathbb{R}^{d}\) is the mean vector of all noticed samples \(\{x_{i}\}_{i=1}^{N}\), and \(A\in\mathbb{R}^{d\times m}\) is the factor loading matrix; \(y_{i}\in\mathbb{R}^{m}\) is the Gaussian-distributed latent variable \(N(0,1)\) with its mean 0 being a zero vector and covariance matrix \(I\) being an identity matrix; the noise variables \(\epsilon_{i}\in\mathbb{R}^{d}\sim N(0,\Psi)\) and \(\Psi\) form a diagonal matrix, with the diagonal elements being different.  
When applied to the BC dataset, factor analysis will assist in identifying the underlying components that provide an explanation for the apparent variance in the data. The statistical method known as factor analysis can be used to perform the work of discovering latent variables that are responsible for explaining correlations between observable data. This effort can be accomplished by applying factor analysis. Utilizing factor analysis is one way to minimize the dimensionality of the data, which can be helpful in evaluating which elements of the BC are the most significant [33].
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd
### Design
#### 3.1.2 Symmetric Multi-Processing (SMP)  
In order to further increase performance, librbd adopts the Symmetric Multi-Processing (SMP) paradigm. The external library chosen for adding SMP support is _pthreads_. This library implements the management of threads and is compliant with the POSIX standard OS interface [48]. This library is always available on fully and mostly POSIX-compliant OSes (e.g., Mac OS and Linux). Microsoft Windows does not offer a native support to pthreads, but it is still possible to use it through one of the following two methods:  
* Download pthreads-win32, a freely available library which implements a large subset of the POSIX standard threads related API for Windows [49]. After pthreads-win32 has been downloaded, it is possible to use the desired IDE and Compiler (e.g., Visual Studio).
* Download and install Cygwin, a freely available environment (i.e., tools and libraries) which provides a large collection of GNU and Open Source tools, including GCC, and a substantial POSIX API functionality, including pthreads-win32 [50].  
In order to fully exploit the SMP paradigm, a key point is the subdivision of the task into batches. For this particular problem, the best subdivision is to assign a subset of data, i.e., a batch, to each thread. In particular, each thread receives as input the reliability values of all components over a subset of time instants. Furthermore, librbd interrogates the OS to retrieve the total number of CPU cores and uses this number as the maximum number of threads that can be created.  
The usage of the SMP paradigm adds an overhead: each time an application requests the creation of a new thread and each time a thread terminates its computation, the OS has to perform additional operations. This overhead negates the benefits of SMP when the computational task is too small. In order to mitigate this issue, several tests have been conducted to find a minimum to the batch size. This minimum has been empirically set to \(10,000\) time instants.  
The SMP functionality can be enabled or disabled at compile time. When SMP is not needed, i.e., when librbd is built as a Single Threaded (ST) library, it is compiled providing external compiler flag CPU_SMP defined with value 0. When SMP is needed, librbd is compiled without providing external compiler flag CPU_SMP or by defining it with a value different from 0.