esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fines Content on the Compression Behavior of Calcareous Sand
###### Abstract  
Due to the hydraulic sorting effect in the hydraulic filling process, a fine-grained aggregate layer dominated by silty fine sand with uneven distribution is easily formed in reclamation projects, which triggers issues with the bearing capacity and nonuniform settlement of calcareous sand foundations. In this study, a series of one-dimensional compression tests were conducted to investigate the effect of different fines contents (\(f_{\rm c}\)) on the compression behavior of calcareous sand. The results show that at the same relative density (medium-density, \(D_{\rm r}=50\%\)), the addition of fine particles leads to a reduction in the initial void ratio (for \(f_{\rm c}\leq 40\%\)). Furthermore, while the compressibility of the soil samples increases with the rising of fines content, it begins to decrease with further addition of fine particles beyond a threshold value of fines content (\(f_{\rm c+th}\)). Additionally, particle crushing contributes to the compressive deformation of calcareous sand, and the particle relative breakage of calcareous sand increases at the initial stage of adding fine particles. Moreover, a comparison of the compression test results between calcareous silty sand (\(f_{\rm c}=10\%\)) and clean sand reveals that the addition of fine particles accentuates the compressibility differences among calcareous sands with different relative densities. These findings provide valuable insights for addressing the challenges posed by fine-grained layers in calcareous sand foundations.  
Keywords:calcarous sand; fines content; relative density; initial void ratio; compressibility; particle breakage; interparticle contact +
Footnote †: journal: Applied sciences
## 1 Introduction
At high fields, the electron temperature \(T_{\rm e}\) in GaN is higher than the lattice temperature \(T_{\rm L}\), even for the DC-mode measurements [34]. It is well known that the Joule heating (self-heating)-related increase in \(T_{\rm L}\) is lower in pulsed experiments. Hot-electron temperature is also related to the strong electron-LO-phonon coupling in GaN and the associated hot-phonon effect [25, 35]. The experiments on microwave noise thermometry conducted at nearly isothermal lattice temperatures over a wide range of electric fields show that the hot-electron temperature (and electron energy relaxation time) depends on the dissipated power and on the 2 DEG density in the GaN-based heterostructures with 2 DEG channels [36]. An increase in \(T_{\rm L}\) is related also to the thermal quenching phenomena, when \(T_{\rm e}\) decreases along the voltage pulse [37]. For higher electron density, especially in the bulk, self-heating is stronger; therefore, the usage of short duration pulses is required [38]. In this paper, the self-heating of annealed Ti/Al/Ni/Au contacts to the 2 DEG in AlGaN/(AlN)/GaN heterostructures was investigated under pulsed electric fields ranging up to 12 kV/cm and reaching current density values up to \(\sim\)1 A/mm. For this reason the pulsed current-voltage (I-V) and microwave noise measurements of the TLM test structures were performed. The paper is organized as follows. In Section 2, the samples' growth and ohmic contact fabrication are described, also, the measurement techniques are explained. In Section 3, the estimation of current dependent contact resistance is explained. A significant increase in the \(R_{\rm c}\) with current was found. The change in \(R_{\rm c}\) relative to its low-field value was found to be proportional to the dissipated electric power in \(R_{\rm c}\). In Section 4, the microwave noise measurements are presented and the noise temperatures of \(R_{\rm c}\) and \(R_{\rm ch}\) are extracted, demonstrating much higher excess noise temperature in contacts than in the channel. In Section 5, the increase in \(R_{\rm c}(I)\) was analyzed in terms of self-heating. It was found that the self-heating of \(R_{\rm c}\) is more intense than that of \(R_{\rm ch}\), resulting in a rapid increase in the ratio \(R_{\rm c}(I)/R_{\rm ch}(I)\) with the current as the main result of this work. Finally, the results are summarized.
###### Abstract  
High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m\({}^{2}\), which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors.  
terrestrial laser scanning; DEM of difference; Cloth Simulation Filter; ground point classification; bulk rock density; mineral resource estimation +
Footnote †: journal: Applied Soft. 2020, 10, 7409; doi:10.3390/app10217409
# Madeira Wine Volatile Profile. A Platform to Establish Madeira Wine Aroma Descriptors
## 3 Materials and Methods
### GC-qMS Analysis for Madeira Wines Profiling  
The GC-qMS methodology was based on a previous study [50]. After the extraction/concentration step, the SPME coating fiber containing the VOCs was manually introduced into the GC injection port at 250 \({}^{\circ}\)C (equipped with a glass liner, 0.75 mm I.D.) and kept for 7 min for desorption. The desorbed VOCs were separated in an Agilent Technologies 6890N Network gas chromatography equipped with a BP-20 fused silica capillary column (30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness) supplied by SGE (Darmstadt, Germany) connected to an Agilent 5973N quadrupole mass selective detector. Helium (Air Liquid, Portugal) was used as the carrier gas at a flow rate of 1.0 mL/min (column-head pressure: 12 psi). The injections were performed in the splitless mode (7 min). The GC oven temperature was programmed as follows: 45 \({}^{\circ}\)C (1 min) then ramped at 2 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C (3 min), 5 \({}^{\circ}\)C/min to 130 \({}^{\circ}\)C (5 min), and finally 2 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C (2 min). For the MS system, the temperatures of the transfer line, quadrupole and ionization source were 250, 150 and 230 \({}^{\circ}\)C, respectively; electron impact mass spectra were recorded at 70 eV and the ionization current was about 30 \(\upmu\)A. The acquisitions were performed in full scan mode (30-300 \(m\)/z). The VOCs identification was achieved as follows: (_i_) comparison the GC retention times and mass spectra with those of the standard, when available; (_ii_) all mass spectra were also compared with the data system library (NIST, 2005 software, Mass Spectral Search Programv.2.0d; Nist 2005, Washington, DC); (_iii_) Kovat's index (KI) values were determined according to the van [PERSON] and Kratz equation [51]. For the KI determination, a C\({}_{8}\)-C\({}_{20}\) n-alkanes series was used, and the values were compared, when available, with values reported in the literature for similar columns [21; 22; 23; 24; 25; 26; 27; 28; 29; 30].  
The VOCs concentration was estimated, semi quantitatively, using the added amount of 4-methyl-2-pentanol (IS) according the following equation: VOCs concentration = (VOC GC peak area/IS GC peak area) \(\times\) IS concentration. However, our main aim is regarding the relation between the varietal, fermentative and aging aroma compounds, and their aroma descriptors with the Madeira wine sensorial properties. This semi quantification approach was already performed in previous scientific studies [52; 53].
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 2 Related Work
### Multivariate Time Series Prediction  
When reviewing related work on multivariate time series prediction, researchers have explored various methodologies to enhance forecasting accuracy and address the complexities inherent in analyzing multiple interrelated variables over time [44; 45; 46]. Additionally, existing literature has investigated diverse applications of multivariate time series prediction, ranging from financial markets to healthcare, contributing valuable insights into the challenges and advancements within this interdisciplinary field.  
[PERSON] et al. [47] introduced a novel graph neural network (GNN)-based architecture, TISER-GCN, for multivariate time series regression, in the context of sensor networks. It addresses the limitations of existing deep learning techniques that focus solely on time series data, neglecting spatial relations among geographically distributed sensors. The proposed model was evaluated using high-frequency seismic data, demonstrating its effectiveness when compared to baseline models and traditional machine learning methods, with contributions including the development of a flexible architecture for various use cases, a thorough evaluation of diverse seismic datasets, and a systematic analysis of the model's capabilities through extensive experimentation.  
[PERSON], [PERSON] et al. [48] proposed an extension of the lottery ticket hypothesis to time series Transformers, demonstrating that pruning and binarizing the weights of the model maintains accuracy similar to that of a dense Transformer. Employing the Biprop algorithm, a technique proven on complex datasets, the combination of weight binarization and pruning was applied to achieve computational advantages, reducing non-zero floating-point operations (FLOPs) and storage sizes. The approach was specifically tested on multivariate time series modeling, showcasing its effectiveness in tasks like classification, anomaly detection, and forecasting, with potential applications in resource-constrained environments such as IoT devices, engines, and spacecraft.  
[PERSON], [PERSON] et al. [49] addressed the importance of accurate predictions in various applications of multivariate time series, such as stock prices, traffic prediction, and COVID-19 spread forecasts. They introduced the challenges faced in capturing both temporal relationships and variable dependencies in existing forecasting methods, emphasizing the need for a comprehensive understanding of underlying patterns. The work proposed a spatiotemporal self-attention-based LSTNet model, integrating spatial and temporal self-attention mechanisms to capture relationships among variables and historical observations. The contributions included the effectiveness of the proposed model in capturing spatiotemporal relationships, a novel objective function to address imbalanced errors among variables, and extensive experiments demonstrating the efficiency of LSTM-based methods in multivariate time series forecasting.
## 2 Research Methods
### Gathering General Information and Selecting Documents  
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was followed to ensure the research work's transparency and reproducibility. The search strategy was implemented in internationally recognized scientific databases such as Scopus (www.scopus.com, accessed on 5 March 2025), Web of Science--WOS (www.webofscience.com, accessed on 5 March 2025), and Google Scholar (www.scholar.google.com, accessed on 5 March 2025). No publication period restrictions were applied, but languages were limited to English and Spanish. Search strings with the following keywords were used: \"expansive soils\", \"soil stabilization\", \"sustainable techniques\", \"geotechnical innovation\", \"soil improvement\", and \"Ecuador\", using the Boolean operators AND/OR, as shown in Table 1.  
In the PRISMA selection process, 510 records were identified through databases. Eleven additional records were obtained through cross-referencing, i.e., by reviewing the bibliographic lists of previously selected articles and locating relevant studies that had not been retrieved in the initial search. After removing 275 duplicates, 246 records were evaluated. From them, 78 were excluded after title/abstract review. A total of 168 articles were then evaluated, from which 20 were excluded due to not meeting the inclusion criteria and 13 due to presenting a high risk of bias. Ultimately, 135 relevant studies were included. Figure 2: The four stages applied to the methodological approach (WOS meaning Web of Science). The inclusion criteria considered the following:  
* Studies related to the stabilization of expansive soils. * Publications addressing sustainable or innovative techniques in geotechnics. * Papers referring to the geotechnical context in tropical or similar areas to Ecuador. * Studies with sufficient methodological description. The exclusion criteria considered the following:  
* Duplicates or publications without full access. * Non-scientific sources (e.g., journals without peer review). * Papers with unclear methodologies or without verifiable results. * Studies with a high risk of bias, which are determined through qualitative assessment based on criteria such as lack of experimental control, absence of peer review, or undeclared conflicts of interest. Figure 3 shows the PRISMA flow diagram for this investigation, where the process used for identification, screening, and included registers can be seen. Based on the filtered results, a bibliometric analysis was performed using VOSviewer software [38], which facilitates the construction and visualization of bibliometric networks. In this case, keyword co-occurrence analysis was chosen, a bibliometric technique that identifies and visualizes the frequency with which two terms appear together in documents.
## 3 Metasurface Antennas in Small Cavities for Telemetry Applications
### Cavity Diameter of 0.12 \(\lambda_{0}\)
#### 3.2.2 Design for Projectiles with h\({}_{\rm c}\) = 12.6 mm  
With the objective to integrate a telemetry unit onboard two types of gun-launched projectiles of caliber 24 mm, an upgrade of the metasurface antenna presented above is proposed here in order to further reduce its thickness down to \(h_{\rm c}\) = 12.6 mm. The first one is illustrated in Figure 17a, and is an innovative concept of a Long Range Projectile with large wings [18; 19]. The second one is a gyrostabilized projectile, which can be seen in Figure 17b. Its profile is described as Secant Ogival Cylinder Boat Tail (SOCBT) [20]. For these two projectiles, the antenna should be integrated in the rear part, within a reduced section, as can be seen in Figure 17. A protective layer has been added on the top of the metasurface antenna to protect it from potential powder deposits and from flames during the firing.  
The optimization of this new design was partially presented in [21]. The cavity diameter is \(a\) = 16 mm and its thickness is \(h_{c}\) = 12.6 mm. The PP block at the bottom is 7.7 mm high. Six layers of metasurface are stacked, requiring seven layers of RO3010 material, each with a thickness of 0.63 mm. Their shape is constituted of two parts, as shown in Figure 14a. On the top of this stack up, a thin layer  
Figure 16: Measured radiation patterns in the E- and H-planes of the metasurface antenna for a cavity diameter of \(a\) = 16 mm, at the frequency of \(f\) = 2370 MHz.  
Figure 17: Instrumented projectiles: (**a**) Long Range Projectile of caliber 24 mm and (**b**) Secant Ogival Cylinder Boat Tail (SOCBT) of caliber 30 mm.  
of RO4350 (of thickness 0.5 mm and of permittivity \(\varepsilon_{\tau}\) = 3.5) is glued. The optimized dimensions are given in Table 4. The matching level and resonance frequency are tuned using three main parameters, namely \(g\), \(L_{t}\), and \(W_{t}\), and their respective influences are described in [21]. In this set up, \(g\) has a strong influence on the resonance frequency, whereas \(L_{t}\) and \(W_{t}\) are mostly used to match the antenna.  
The measured radiation patterns are given in Figure 18, whereas the overall performance is summarized in Table 4. It should be noted that due to the projectile body, the radiation patterns are slightly more directive than for the antenna of diameter 16 mm alone (see Figure 16).  
These small antennas have been successfully tested at ISL, France, during measurement campaigns for both projectile types under accelerations of up to 25,000 \(g\) and a spin rate of about 300 Hz [19].
## 3 Gaussian Processes for Mobile Networks
### GP Mean Function
#### 3.1.3 3 GPP Mean  
Another promising basis for a prior mean is a formula collection based on measurement data from the 3 rd Generation Partnership Project (3 GPP) [18; 19]. Based on parameters such as the terrestrial distance to the nearest cell tower (\(d_{\text{2D}}\)) and the altitudes of both the UAV (\(h_{\text{UAV}}\)) and the base station (\(h_{\text{BS}}\)), path losses (\(PL\)) and probabilities (\(Pr\)) for LOS/non-LOS (NLOS) connections to the base station are calculated. The received signal power (\(P_{\text{3 GPP}}\)) can then be estimated as follows:  
\[\begin{split} P_{\text{3 GPP}}(x)=& Pr_{\text{LOS}}(x) \cdot PL_{\text{LOS}}(x)\\ &-(1-Pr_{\text{NLOS}}(x))\cdot PL_{\text{NLOS}}(x).\end{split} \tag{6}\]  
The 3 GPP provides tabulated coefficients to calculate \(Pr_{\text{LOS/NLOS}}(x)\) and \(PL_{\text{LOS/NLOS}}(x)\). The coefficients are dependent on the environments where both the cell tower and the receiver are located. A distinction is made between urban (\"Urban Micro, UMi\"), suburban (\"Urban Macro, UMa\"), and rural (\"Rural Macro, RMa\") environments.  
Based on the assumptions made by the 3 GPP, a local 5G campus network at an industrial site coincides most closely with the UMi environment. Using the corresponding coefficients, the resulting mean function, i.e.,  
\[m_{\text{3 GPP-UMi}}(x)=m_{0}+P_{\text{3 GPP-UMi}}(x), \tag{7}\]  
also uses \(m_{0}\) as an offset parameter that is learned as a hyperparameter based on training data.  
Alternatively, the coefficients can also be learned as additional hyperparameters. The resulting mean function (\(m_{\text{3 GPP}}\)) has a total of six free hyperparameters. Note that the mean function only covers the LOS case, while the NLOS case of (6) is disregarded for simplification. For the sake of clarity, the non-linear logarithmic expressions for the path loss (\(PL_{\text{LOS}}\)) and the probability (\(Pr_{\text{LOS}}\)) are not written out. The interested reader is referred to [18; 20] for further details.
# Numerical Solution for the Single-Impulse Flyby Co-Orbital Spacecraft Problem
## 5 Numerical Examples
### The Lambert Problem  
The algorithm was tested on a series of 100,000 randomly generated sample problems, with values of \(\Delta\theta\) between 0 and 2\(\pi\), \(r\) ranging from 7000 to 36,000, and the desired nondimensional transfer time varying between 0.01 and 10,000. The result is shown in Figure 6.  
As Table 1 shows, compared with the universal variable method, the efficiency was sharply improved by 92.09%. It also improved by 45.69% compared with the traversal search method. In terms of the number of iterations, the number of times was reduced from 58.95 to 3.26--a dramatic decline of 94.47%. Figure 7 shows that the calculation results of both algorithms were almost identical, with an average error in the order of \(10^{-7}\) as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average Computation Time (ms)** & **1000 Times** & **10,000 Times** & **50,000 Times** & **100,000 Times** & **Efficiency Improvement** \\ \hline Universal variable method [35] & 1.3581 & 1.3593 & 1.3547 & 1.3543 & 92.09\% \\ Traversal search method & 0.2120 & 0.2039 & 0.1977 & 0.1972 & 45.69\% \\ Our method & 0.1118 & 0.1114 & 0.1075 & 0.1071 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for the computation time of the simulation.  
Figure 6: Time consumption of each method.
## 2 Anodized Aluminum Oxide (AAO) Templates Geometry
As has been recently reported, branched AAO templates are the focus of intensive research due to the multi-sized and material-dependent properties they offer. For  
Figure 2: Top panels: Scheme of AAO templates (**A**); SEM images of a commercial (**B**) and a lab-made AAO template (**C**). Bottom panels: SEM images of top views of lab-made AAO templates with different pore diameters, from left to right: 25, 35, 60, 185 and 400 nm. further information, see the reviews reported by [PERSON] et al. and [PERSON] et al., among others [19; 21; 26]. In the literature, different polymer infiltration methods in AAO templates have been reported for the fabrication of \"tailored\" polymer-based nanostructures, such as nanorods and nanotubes, as well as more sophisticated hierarchically shaped nanostructures including core-shell, branched, pyramidal or nanostructured surfaces [88]. Nevertheless, in the polymer infiltration process, some drawbacks have been found. For instance, in the preparation of polymethyl methacrylate (PMMA) solid nanofibers or PMMA/Lu\({}_{2}\)O\({}_{3}\) NPs composite nanorods, long timescales and high temperatures were required for polymer infiltration. On the other hand, the infiltration of polyisoprene (PI) leads to the thermal degradation of the polymer. Therefore, the infiltration process of a polymer melt in the AAO nanocavities is not a suitable route when the polymer infiltration takes a long time (days, in some cases); the polymer degrades as a consequence of the high temperature needed for infiltration or the polymer does not melt, i.e., thermoset polymers. The in situ polymerization of monomers within AAO templates has emerged as an easy and direct approach to overcome the above limitations and thus to fabricate nanostructures of many polymers that cannot be produced by polymer infiltration methods. Nevertheless, contrary to infiltration methods where the final chemical structure of the obtained polymer is the same as that of the precursor polymer, i.e., stereoregularity, molecular weight, polydispersity, etc., in the situ polymerization method, the final chemical structure of polymerized compound is not easily predictable and needs to be studied case by case, as described in the following section.