esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper focuses on behavior of helical piles placed in weak (loose sandy) soil. With this purpose, the effects of the spacing ratio between helixes (S/D\({}_{1}\) = 1-8), the number of helixes (N = 0, 1, 2), and the upper helix diameter (D\({}_{2}\) = 60 mm, 80 mm, 100 mm, and 120 mm) on the load-displacement and failure mechanism were investigated using physical model tests and 2D/3D finite element models.  
Based on the outputs acquired from this effort, the following conclusions are provided.  
* The Hardening Soil model employed successfully demonstrates the behavior of the helical pile placed in weak soil.
* The 3D finite element model is quite successful in replicating the behavior of helical piles but the 2D finite element model remains slightly less successful in reflecting the physical model test results.
* The distance between helixes is quite an important parameter, and the helical pile capacity increases as the distance between helixes increases until the S/D\({}_{1}\) = 3-4.
* In the case of multiple helical piles, the buried depth of the upper helix should be selected as a value greater than 5D\({}_{2}\).
* The displacement intensity in the cylindrical failure mechanism is more pronounced at S/D\({}_{1}\) = 1. Also, at S/D\({}_{1}\) = 3, the most intense zone is still around the helix, and this intensity around the helixes is preserved at all increasing spacing ratios. The interaction between the helixes decreases and the failure mechanism reaches the soil surface along with the spacing ratio of S/D\({}_{1}\) = 4.
* The capacity of the helical pile increases with the increase in the number of helixes. At N = 2, the displacement intensity decreases significantly and drops by almost half compared to N = 1.
* With the increase in the diameter of the upper helix, the capacity of helical piles increases significantly.
* As the upper helix diameter increases, the increase in capacity is reflected in the failure mechanism as a decrease in displacement intensity. The increase in helix diameter causes a change in the failure mechanism behavior. As a result of this, the cylindrical formation changes to an hourglass appearance.  
This study was supported by the Cukurova University Scientific Research Projects Coordination Unit (Project No. FBA-2021-13948).  
Not applicable.  
**Data Availability Statement:** Dataset available on request from the author. The raw data supporting the conclusions of this article will be made available by the author on request.  
The author would like to thank the authorities of the Geotechnical Laboratory of the Department of Civil Engineering of Iskenderun Technical University where the physical model tests were performed. The author would also like to thank [PERSON], who was a researcher in the other part of this two-part project but also supported this study with his ideas.  
The author declares no conflicts of interest.
# Currency Crises Prediction Using Deep Neural Decision Trees
## 2 Methodology
### Support Vector Machines  
Support vector machines (SVMs) have been shown to achieve good generalization performance over a wide variety of classification problems, where it is seen that SVM tends to minimize generalization errors, that is, classifier errors over new instances. In geometric terms, SVM can be seen as the attempt to find a surface (\(\sigma_{i}\)) that separates positive examples from negative ones by the widest possible margin [22; 23; 24].  
The search that meets the minimum distance between it and an example of training is the maximum and is performed across all surfaces (\(\sigma_{1}\), \(\sigma_{2}\,\ldots\,\)) in the A-dimensional space that separates the positive examples from the negative in the training set (known as decision surfaces). To better understand the idea behind the SVM algorithm, we take the case in which the positive and negative examples are linearly separable; therefore, the decision surfaces are \(|\)A\(|\)-1-hyperplanes. For example, in the case of two dimensions, several lines can be taken as decision surfaces. In this circumstance, the SVM method chooses the middle element of the widest set of parallel lines, that is, from the set in which the maximum distance between two of its elements is the greatest. It should be noted that the best decision surface is determined only by a small set of training examples, called support vectors.  
An important advantage of SVM is that it allows the construction of non-linear classifiers, that is, the algorithm represents non-linear training data in a high-dimensional space (called the characteristic space) and builds the hyperplane that has the maximum margin. In addition, due to the use of a kernel function to perform the mapping, it is possible to calculate the hyperplane without explicitly representing the feature space.  
In the present work, the method of minimum sequential optimization (SMO) was used to train the SVM algorithm. In general, SMO divides a large number of quadratic programming (QP) problems that need to be solved in the SVM algorithm by a series of smaller QP problems.
# Carbon Allotropes/Epoxy Nanocomposites as Capacitive Energy Storage/Harvesting Systems
## 4 Conclusions  
In the present study, series of polymer nanocomposites employing different types of carbon allotropes were successfully developed varying the filler type and content. SEM images confirmed the quality of the fabricated systems and revealed fine nanodispersions of the fillers in the polymer matrix. The thermal and mechanical properties were investigated by means of static tensile tests, Dynamic Mechanical Analysis and Differential Scanning Calorimetry. All nanocomposites exhibit higher glass transition temperature values than the neat epoxy, implying strong adhesion of the nanofillers to the polymer matrix. Storage and elastic moduli attain higher values with the addition of the reinforcing nanoparticles. The energy storage ability of the systems under AC conditions was determined by means of Broadband Dielectric Spectroscopy in a wide frequency and temperature range. Energy density increases with temperature and attaines the highest values at low frequencies due to interfacial polarization. The stored and harvested energy of the nanocomposites was also evaluated under DC conditions by employing a charge/discharge procedure. The energy storage and harvesting ability of the nanocomposites increases with filler content due to the higher values of dielectric permittivity and/or the conductivity of the nanoinclusions. The nanocomposites with carbon black and MWCNT filler content exhibit enhanced energy storage ability that exceeds several times the stored energy of the polymer matrix. On the other hand, the addition of nanodiamonds fails to enhance the energy storage capabilities of  
Figure 9: The coefficient of energy efficiency as a function of the filler content at the same instant times of \(t=5\), \(10\) and \(30\) s for the nanocomposites with (**a**) carbon black; (**b**) MWCNT; (**c**) GnP; (**d**) ND, at \(100\) V.  
the systems. Finally, the coefficient of energy efficiency (n\({}_{\text{eff}}\)) was determined for all systems. The filler content enhances the energy efficiency of the examined systems, reaching the highest value of over 80% for the 5 phr carbon black nanocomposite.  
Conceptualization and visualization were conducted by S.G.S. and G.C.P.; measurements, methodology, validation and data analysis were performed by S.G.S. and A.S.; writing and editing were performed by S.G.S., A.S., G.C.P.; project administration and supervision was performed by G.C.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are included in the article. Additional data are available upon request.  
The present research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT) under the HFRI PhD Fellowship grant (GA. No. 2327).  
The authors declare no conflict of interest.
## 5 Performance Evaluation  
This section evaluates the navigation and communication performance of DDBDM, while comparing it with ZC-NOMA [17], PN-OTFS [37], and TFBDM (time-frequency block division multiplexing). The first two are INAC schemes designed for LEO satellites, while TFBDM uses an OFDM-based TF block structure similar to DDBDM. Moreover, to compare the performance of DDBDM more generally, we also use RP-OTFSM in the ISAC system as a benchmark [31], since it uses known random pilot, and thus may be used as the signal for LEO satellites. Finally, an INAC system prototype, based on SDR, is implemented, and it is used to verify the proposed waveform and navigation receiver architecture.
## 4 Automatic Classification of Polygonal Wheels  
### Feature Extraction  
From the first phase of the methodology, the features are extracted based on applying the CWT and PCA models to the evaluated FFT responses. The results are evaluated for a set of four sensors (two on each side) located on the rail at mid-span between the sleepers.  
Figure 8: Frequency domain responses on position Acc2 for (**a**) healthy wheel and polygonal wheel with amplitude A1; (**b**) healthy wheel and polygonal wheel with amplitude A2.  
The main idea of feature extraction is to get a dimensionality reduction that identifies important relationships in the data.  
For CWT features, the principal components are determined after extracting wavelet coefficients. Furthermore, four statistical parameters are derived from the scores, encompassing the root mean square (RMS), standard deviation, skewness, and kurtosis. For each sensor and every passage of the train, a total of 324 features are extracted. For PCA features, following the computation of the principal components, the same four statistical parameters are derived from the PCA scores. A total of 4 features are extracted for each sensor and passage.  
For each sensor, the extracted features form a matrix of \(n\times m\), where \(n\) represents the number of train passages associated with the baseline or polygonal wheel scenarios, with a total of 115 simulations (mentioned in Table 1), and \(m\) represents the number of extracted features. Thus, for each method, the dimension of the matrix is as follows:  
* Matrix \(X_{\text{PCA}}\) with 115 \(\times\) 4, PCA features;
* Matrix \(X_{\text{CWT}}\) with 115 \(\times\) 324, CWT features.  
Figure 9 shows four features (two PCA statistical parameters and two wavelet coefficients) extracted by accelerometer 2. By analyzing the Figure, all features are already visible at this first step, leading to a tendency to distinguish between damaged and undamaged passages. The PCA features seem more sensitive to operational conditions, namely speed, than the CWT features showing more dispersed results in the baseline scenarios. Moreover, at this stage, a tendency is already noticeable in the PCA features to group the damage results according to the type of profile (dominant harmonics), while the CWT features seem to be more sensitive to the defect amplitude variations in each of the simulated profiles.  
Figure 9: Feature extraction for accelerometer Acc2, (**a**) PCA–feature 1, (**b**) PCA–feature 3, (**c**) CWT–feature 173, (**d**) CWT–feature 193.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 5 Applications and Prospects in Civil SHM  
In the past decades, cement-based embedded piezoresistive sensors have been applied in concrete structures because of their high sensitivity and good linearity. Nevertheless, sensing performances of the cement-based strain sensor are influenced by temperature and humidity. Its poor plasticity also placed restrictions in large deformation monitoring [137; 138]. In contrast, conductive polymer-based flexible strain sensors are capable of overcoming the above shortcomings because of their high sensitivity being compatible with large stretchability, which is helpful in monitoring not only strain but also cracks or damage in such infrastructure as bridges, high-rise buildings and other civil structures. With the development of conductive materials and fabrication technologies, flexible piezoresistive sensors in the SHM of concrete structures, steel structures and fiber-reinforced composite structures have been proposed.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Mathematical Model Building
#### 2.3.4 Hydrodynamic Wave-Sediment Coupling Model  
SCHISM, WWM-III, and the MORSELFE model all adopt an MPI parallel communication mechanism and use the same subregions and calculation grid, and so, there is no need to perform interpolation calculations. Compared to the use of couplers to perform interpolation conversion between grids of different types and resolutions, the hard-coded coupling mode in this study avoided the problem of interpolation error. Because SCHISM, WWM-III, and the MORSELFE model used different discrete time formats, different computing time steps were adopted, and the information exchange of the models was performed at a certain frequency set by the user. As shown in Figure 7, during information exchange, water level (ZI), seabed topography (Zb), wet and dry terrain markers and velocity (U, V) of SCHISM and some meteorological driving data, including wind speed (Uwind, Vwind) and air pressure, are transferred to WWM-III. The wave direction (Dw), wave height (Hw), wave length (Lw), and wave period (Pw) of WWM-III were transferred to SCHISM. Radiation stress (Rs), energy dissipation (Es), and orbital wave velocity (So) calculated by WWM-III were unidirectionally transferred to the MORSELFE model, while the suspended sediment concentration (SS) and velocity (U, V) calculated by SCHISM were unidirectionally transferred to the MORSELFE model. The above variable exchange was completed in memory, and the efficient communication mechanism based on the MPI library realized the variable exchange on different processes.
## 4 Map Spectrum-Based Spatiotemporal Clustering
### Extraction of Spatiotemporal Patterns  
Previous studies have shown that the k-means clustering method can produce stable cluster boundaries based on a spatial sensitivity analysis test [7]. Thus, the improved k-means clustering  
Figure 5: Time Spectrum of GDP maps for given column pixels from 1992 to 2012. (**a**) Position of the column that was used to generate the spatiotemporal map spectrum. (**b**) Time Spectrum.  
method was applied to dasymetric GDP maps of the Wuhan urban agglomeration from 1992 to 2012 to obtain patterns in spatiotemporal variations.  
Two major factors must be considered to obtain a reasonable clustering result. First, a set of random initial mean centers must be allocated. To reduce the dependence of the results on the initial allocation of mean centers, the initial mean centers were uniformly selected at random from the range of all values of similarity and were iterated 10 times to generate the clustering result. Second, the number of classes must be determined. The Calinski-Harabaz index [24] can be used to evaluate this number when actual values are unknown. Given a clustering approach with a certain number of classes, the Calinski-Harabaz score is defined as the ratio of the between-cluster dispersion to the within-cluster dispersion. A high Calinski-Harabaz score indicates that clusters are well separated and defined. In Figure 6, the Calinski-Harabaz index of two-class clustering exhibited the highest score. However, two classes are not sufficient for differentiating between GDP variations because one of the classes represents areas of no night light. Therefore, we chose four-class clustering, which had the second highest score, as the number of classes sufficiently represented patterns in GDP variations.  
According to the Calinski-Harabaz scores, the spatiotemporal variations in GDP were clustered into four classes. The clustering results are shown in Figure 7. The fourth class, which represented areas with no night lighting, cannot reflect the spatiotemporal variations in GDP; thus, the first three classes were used to analyze the variation pattern.  
Figure 6: Calinski–Harabaz score for different numbers of classes.  
Figure 7: Results of spatiotemporal clustering based on four classes.
## 3 Fluorophore Coupled to a Resonant Cavity in the Strong-Coupling Regime
### Strong-Coupling with a Perfect Lossless Cavity
Similar pairs exist for the quantum superposition of states \(|n_{c},e\rangle\) and \(|n_{c}+1,g\rangle\) involving the presence of \(n_{c}\) or \(n_{c}+1\) photons in the cavity, respectively. Their energy gap at resonance is given by \(\hbar\Omega_{0}\sqrt{n_{c}+1}\). In practice, perfectly lossless cavities do not exist and the criterium to enter the strong-coupling regime is that the Rabi frequency \(\Omega_{0}\) must be significantly larger than the widths \(\gamma_{c}\) and \(\gamma\) related to the cavity mode and matter excitation lifetimes, respectively. In other words, coupling must dominate over dissipative processes. This condition is required to preserve quantum coherence, as the Rabi frequency describes the rate of coherent energy conversions between matter and the radiation field in the dressed states. In general, to increase the coupling \(g\), one needs to select quantum emitters with a strong transition dipole and to confine the electromagnetic field in a small volume. To decrease \(\gamma_{c}\), high-finesse cavities are required. The team of [PERSON] (Nobel prize 2012), which is at the same time a pioneering and a leading team in the domain of strong coupling, developed high-finesse superconducting cavities cooled down to a few millikelvins, and reached huge values for the coupling factors \(g\) by using, as two-level systems, Rydberg atoms resonant with the cavity [14]. In our approach, it is rather the Purcell effect [15] that plays a prior role in increasing the value of \(g\). This explains why we realize strong coupling even at room temperatures, as explained in Section 4.
## 3 Results and Discussion
### Flow Characteristics
Overall, the B1 and B2 channels, due to their larger cavity geometries, exhibit distinct thermal behavior at varying flow rates. At low flow rates, the coolant primarily experiences laminar flow, where the heat transfer is driven by natural convection. This results in a low heat transfer efficiency and limited cooling performance. As the flow rate increases, complex flow structures, such as recirculation zones and stagnation regions, develop within the cavity. Although these flow disturbances generate increased turbulence, the turbulence is concentrated away from the channel walls, particularly near the core of the cavity. Consequently, these flow structures provide minimal enhancement to the wall heat transfer, leading to a suboptimal cooling performance despite higher flow velocities. In contrast, A1 and B1-M demonstrate better performances. Although both also exhibit laminar flow at low speeds, they differ from B1 and B2 by maintaining relatively higher  
Figure 26: Wall temperature distribution and streamline characteristic on streamwise-spanwise plane (x-y plane) of Schemes A1, A2, B1-M, and B2-M (the red dashed box represents the recirculation zone). flow velocities near the leading-edge wall, while the flow in B1 and B2 is nearly stagnant. The superior performances of A2 and B2-M are attributed not only to convective heat transfer but also to the mixing of high-temperature and low-temperature fluids facilitated by the recirculation zones near the leading-edge wall and the turbulence generated at the bends. This enhanced fluid mixing significantly improves the thermal energy exchange and heat transfer efficiency.
## 2 Materials and Methods  
### Construction of Gradient Young's Modulus FEM  
A FEM of the Ti-Nb-Sn alloy stem, which faithfully replicates the graded elasticity achieved by specialized heat treatment, was constructed and analyzed. The gradient in Young's modulus was derived from the experimental validation data obtained using a custom-designed gradient heat treatment furnace. The localized heat treatment was performed using a custom-designed gradient furnace. The proximal region of the stem was heated at 673 K for 30 min under a vacuum atmosphere (10\({}^{-3}\) Pa), while the distal region was protected using an insulating plate to preserve a lower Young's modulus. This gradient treatment method enabled a continuous transition in mechanical properties without altering the alloy composition [16]. The heat-treated Ti-Nb-Sn stem with a graded Young's modulus is shown in Figure 1. A logarithmic approximation was used to formulate the relationship between the distance from the insulating plate and the Young's modulus, which was then assigned to each element of the meshed stem model (Figure 2).  
Figure 1: Construction of the FEM for the gradient functional Ti-Nb-Sn stem. Gradient information derived from experimental validation was applied to the meshed FEM. The relationship between the distance from the heat-insulating plate and Young’s modulus was formulated, and material properties were assigned to each mesh element accordingly.  
The Ti-Nb-Sn stem was designed with a porous surface coating applied to the proximal one-third of its length to facilitate initial press-fit fixation and promote bone ingrowth. In contrast, the distal two-thirds of the stem were polished to a smooth finish to minimize stress concentration and encourage physiological load transfer to the surrounding bone. This design configuration aims to balance mechanical stability and long-term biological fixation.  
The FEM was constructed using tetrahedral elements. Young's modulus values were mapped to each element based on the formulated gradient curve, starting from the designated gradient initiation point, which was determined in consideration of the fatigue test conditions from the approval process.
## 4 Experiments  
### Datasets  
DOTA-v1.0 [48] is a large-scale benchmark dataset specifically designed for aerial image object detection. The images in the DOTA-v1.0 dataset were collected from various sensors and platforms. The DOTA-v1.0 dataset **comprises** 15 common categories, 2806 pictures, and 188,282 occurrences. The images vary in size, ranging from \(800\times 800\) to \(20,000\times 20,000\) pixels, and contain objects of diverse scales, orientations, and shapes.  
DIOR-R [11] is a large-scale benchmark dataset for optical remote sensing image object detection. DIOR-R comprises 20 common categories, encompassing 23,463 remote sensing photos and 190,288 target instances. The images in the dataset have a size of \(800\times 800\) pixels, with spatial resolutions ranging from 0.5 m to 30 m. The dataset is divided into a training-validation set, consisting of 11,725 images, and a separate test set, consisting of 11,738 images.  
HRSC2016 [49] is a high-resolution remote sensing image dataset **utilized** for ship detection. It **comprises** 1061 images, encompassing information on 2976 ship instances across 4 major categories and 19 subcategories. The dimensions of ship images range from 300 to 1500 pixels, with the majority being larger than \(1000\times 600\) pixels.  
Figure 7: Illustration of Wavelet Domain Spatial Selection Module.