esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Equipment  
The land survey was conducted utilizing the DJI Mini 2 UAV, a compact and agile drone weighing under 250 g, which makes it compliant with regulations in many areas. It is equipped with a high-resolution camera, 12 MP sensor, F2.8 focal length (equivalent to 24 mm), and a sensor size of 6.17 \(\times\) 4.65 mm. The frames were captured automatically, following a pre-drawn path for the UAV. The chosen flight height was 80 m with front overlap of 80% and side overlap of 70%.  
The STONEX S850+ GNSS receiver, able to receive signals from the GPS (Global Positioning System), GLONASS, and Galileo satellites constellations, was used to measure natural reference points. Since the study area includes several artificial structures, the use of natural points as GCPs rather than target placement provides greater flexibility, possibly serving as benchmarks for future surveys. The STONEX S850+ GNSS device was used in NRTK (Network Real Time Kinematic) mode, connected to Regione Liguria GNSS positioning service [83] to obtain the differential corrections. This well-established  
Figure 2: Methodology steps: (1) UAV and underwater image acquisition; (2) generation of orthophoto over the study area; (3) snorkeler position extraction; (4) UAV and underwater frames time synchronization; (5) underwater photogrammetry processing; (6) seamless 3D georeferenced model. The arrows indicate the sequence of steps and data flow between aerial (green) and underwater processes (red) and time synchronization (blue).  
positioning technique allowed us to achieve spatial accuracy within a few cm. A total of 18 GCPs were measured, displayed as shown in (Figure 3).  
For the underwater survey, the GoPro Hero 10 Black, a rugged action camera designed for underwater exploration, was used. The camera capabilities include 4K resolution at a rate of 120 frames per second (fps) video, a 24.4 mm lens equivalent to the 35 mm focal length, and a sensor size of 6.40 \(\times\) 5.60 mm, allowing for the capture of detailed imagery in aquatic environments. The camera robustness and advanced features made it an ideal and cost-effective tool for our underwater photogrammetry process. Videos were recorded during the underwater survey with HyperSmooth mode, then frames were extracted to ensure sufficient overlapping between consecutive images (which strongly depends on the snorkeler speed variation) and to obtain comprehensive coverage of the underwater environment. The data acquisition started for both UAV and the snorkeler simultaneously. The UAV, operating along a predefined flight-plan, systematically acquired radial images. A total of 128 images were collected by the UAV during the mission. The GoPro attached to the snorkeler collected two separate videos.
## 4 Results and Discussions
### Training Period  
The use of an RF model involved understanding the relative importance of the variables used for both time durations. The results reveal that rainfall was most important in both scenarios, followed by PET and vapor pressure. However, the relative importance could show different results if a different PET model was used to determine the SPEI index. Vapor pressure is an important parameter and has proved to be very significant, as compared to other variables, such as temperature [56] and soil moisture [57], especially toward vegetation health. The relative importance of the input variables shows cloud cover and vapor pressure were significant factors. Cloud cover is derived from the sunshine hours as a percentage value. It has been rarely considered as a variable, and few works have shown its importance in drought. As an example, [PERSON] et al. [58], in their study on droughs in the Amazonia region, analyzed its importance and found it to be a significant factor, owing to the long-term land use, land-cover change and forest loss in the region. Similarly, vapor pressure has shown a strong correlation with the water transport process in various vegetation types. The NSW region has low vegetation cover compared to Amazonia, and hence vapor pressure or soil moisture may have a lower impact on the drought index prediction capability. Although SPEI is categorized as hydrological or meteorological drought only, its effect on agricultural drought would be more prominent. However, the present work shows that, for the NSW region, vapor pressure can also play a key role in drought occurrence, and therefore future works should also include this variable, irrespective of the drought type. Figure 6 depicts the relative importance of the variables under SPEI 1 and SPEI 3. The results show that rainfall is highest among all the variables for both time scales. When considering the various temperature factors, the minimum and mean temperature variables are higher for SPEI 3 scenarios; however, the maximum temperature is higher for the SPEI 1 case. This suggests that there have been several instances of heatwaves or short periods of high temperature which have led to short-term troughs. This is also evident from the fact that the influences of PET and vapor pressure are higher for the SPEI 1 case.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 2 Current Technologies for Railway Track Maintenance
### Train-Borne System  
Train-borne systems are convenient for assessing the geometrical or structural properties of railways or for performing remote condition monitoring. These can be adapted to train vehicles and do not need separate power sources. They are economically viable and can be more efficient than the conventional track inspection method [38]. For example, machines with vision have been used to develop a train-borne track condition monitoring system based on computer vision and condition monitoring sensors for preventive maintenance [39]. With easy-to-fit hardware, this system is capable of millimeter-level accuracy and real-time data communications and processing software. Another example of a train-borne system is the RILA system from Furgo [40]. This is a remote train-mounted system combining laser scanners, computer vision, and GPS to perform unmanned remote track surveys. These systems provide significant advantages for inspection as they are easy to install on a running passenger or a freight train. However, the lack of actuation capability is the most significant disadvantage of these systems.
## 4 Results and Discussion
### Determination of Model Parameters  
In this study, the prediction step size was set to three, which meant that the reconstruction of the dataset was performed for the data points of the previous three cycles for each feature, and the fourth cycle value of the output feature was predicted to achieve regression prediction based on time features. Due to the small fluctuation range of the pigment model data and to distinguish the prediction accuracy of the different models, the number of input features was established as 11 (L, a, b, X, Y, Z, R, G, B, C, h), and the number of output features was 1 (consisting of hue h, lightness L, and chroma C). The number of nodes in the BPNN model's hidden layer was set to two, the max epoch was set to 100, the initial learning rate was set to 0.1, and the minimum error of the training objective was set to 0.01. The single LSTM model had 20 hidden units, a max epoch of 100, and an initial learning rate of 0.1. The determination of the specific parameters for the single LSTM model was based on empirical settings. Through experimental simulations, the number of gray wolves in the GWOAD and GWO algorithms was determined to be 15, with a max iteration of 10. The upper and lower boundaries were set as 15, 50, and 0.001 and 60, 450, and 0.1, respectively. The optimized parameters of the LSTM model are shown in Tables 1 and 2.  
Figure 9: Comparison chart of six pigment diseases.
## 3 Main Results
### FTESO-Based FXZNN Model Analysis with Noise  
Nosies are inevitable in practical implementation of neural networks. The FTESO-based FXZNN model in (24) with additional noises will be discussed in this part.  
**Remark 3**: _Noise mainly includes high-frequency noises caused by sensor measurements and low-frequency noises caused by hardware implementation offset errors, instantaneous decline in power sources, etc. Disturbance mainly includes internal and external disturbances. Internal disturbance is caused by parameter variation and model uncertainties. External disturbance is caused by the interaction with the environment._  
**Theorem 3**: _If the proposed FTESO-based FXZNN model in (24) is attacked by an additional bounded noise \(n(t)\) and \(n_{i}(t)\) satisfies \(|n_{i}|\leqslant\gamma g_{3}|e_{ci}|\), where \(n_{i}(t)\) denotes the \(i\)th element of \(n(t)\), no matter whether the dynamic system is in any initial state, the FTESO-based FXZNN model in (24) converges to the designed velocity in fixed time \(T_{n_{i}}\)._  
\[T_{n_{i}}=\frac{1}{\gamma g_{1}^{l_{2}}(l_{1}l_{2}-1)}+\frac{1}{\gamma g_{2}^ {l_{2}}(1-l_{2}(1-1/l_{1}))}. \tag{38}\]  
Proof: Similarly to Theorem 1, the error function array of the FTESO-based FXZNN model in (24) can be expressed as \(de_{c}/dt=-\gamma\phi(e_{c})+n(t)\), and its correspondent subsystem can also be obtained as  
\[\frac{de_{ci}}{dt}=-\gamma\phi(e_{ci})+n_{i},\quad i=1,2. \tag{39}\]  
The Lyapunov function \(V_{1}=\frac{1}{2}|e_{ci}|\) is utilized. The time differentiation of \(V_{1}\) is  
\[\frac{dV_{1}}{dt}=\dot{e}_{ci}\operatorname{sgn}(e_{ci})=(-\gamma\phi(e_{ci}) +n_{i})\operatorname{sgn}(e_{ci}). \tag{40}\]  
Since the novel activation function in (26) is adopted, \(|n_{i}|\leqslant\gamma g_{3}|e_{ci}|\), and the following formula is obtained:  
\[\begin{split}\frac{dV_{1}}{dt}&=\dot{e}_{ci} \operatorname{sgn}(e_{ci})=(-\gamma\phi(e_{ci})+n_{i})\operatorname{sgn}(e_{ ci})\\ &=\left[-\gamma((g_{1}sig^{l_{1}}|e_{ci}|+g_{2}sig^{1-1/l_{1}}|e _{ci}|)^{l_{2}}+g_{3}|e_{ci}|)+n_{i}\right]\operatorname{sgn}(e_{ci})\\ &=-\gamma(g_{1}sig^{l_{1}}|e_{ci}|+g_{2}sig^{1-1/l_{1}}|e_{ci}|)^ {l_{2}}+n_{i}-\gamma g_{3}|e_{ci}|\\ &\leqslant-\gamma(g_{1}sig^{l_{1}}V_{0}+g_{2}sig^{1-1/l_{1}}V_{0} )^{l_{2}}.\end{split} \tag{41}\]  
Based on Lemma 1, for all \(i\), the bounded time \(t_{i}\) of the \(i\)th subsystem can be obtained as  
\[T_{i}\leqslant\frac{1}{\gamma g_{1}^{l_{2}}(l_{1}l_{2}-1)}+\frac{1}{\gamma g_ {2}^{l_{2}}(1-l_{2}(1-1/l_{1}))}.\]  
Then, \(e_{ci}\) with noise converges to zero in upper-bound time \(T_{ni}\), with \(T_{ni}=max\{T_{i}\}\). Obviously, the proposed model in (24), in the presence of noise, converges to the velocity signal in (21) in fixed time \(T_{n_{i}}\), and \(T_{n_{i}}\) is also irrelevant with respect to the initial state of the system. The proof is thus completed.
## 2 Materials and Methods
### Cognitive Testing  
The participants completed a Subjective Memory Complaints Questionnaire (SMCQ) to assess their perceived cognitive state and the Montreal Cognitive Assessment (MoCA) in Korean to assess their objective cognitive function. SMCQ consists of 14 items that reflect both general and specific cognitive aspects [29] and a higher score indicates more subjective memory complaints. MoCA is used for quick screening of the global cognitive function [4] with scores ranging from 0 to 30 points. The MoCA in Korean (K-MoCA) is a validated alternative-form MoCA test suited for testing the Korean population and was used to quantify the global cognition of the study's participants.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **MEAN (SD)** & **R2** & _p-Value_ \\ \hline Age (years) * & 75.3 (4.1) & 0.058 & 0.001 \\ BMI (kg/m2) & 24.2 (2.4) & \textless{}0.001 & 0.921 \\ Years of education * & 13.4 (3.7) & 0.156 & \textless{}0.001 \\ K-MoCA score & 24.4 (2.8) & - & - \\ SMCQ score & 2.8 (2.8) & \textless{}0.001 & 0.947 \\ \hline \hline \end{tabular} SD, standard deviation; BMI, body mass index; K-MoCA, the Korean version of the Montreal Cognitive Assessment; SMCQ, Subjective Memory Complaints Questionnaire;* significant correlation with the K-MoCA score.  
\end{table}
Table 1: Participants’ demographic and cognitive characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{Q}\) & YES (_nt_/\%) & _p-Value_ \\ \hline Diabetes mellitus & 35/33.7 & 0.319 \\ Hypertension & 41/39.4 & 0.960 \\ Cardiovascular disease & 16/15.4 & 0.488 \\ Neurovascular disease * & 6/5.8 & 0.024 \\ \hline Smoking & Never & 42/40.4 & \\ Previous & 54/51.9 & 0.769 \\ Current & 8/7.7 & \\ \hline Drinking * & Never & 37/35.6 & \\ Drinking * & Occasionally & 61/58.7 & 0.023 \\ Daily & 6/5.8 & \\ \hline Physical activity & 101/97.1 & 0.898 \\ Regular ophthalmological exam * & 83/79.8 & 0.001 \\ Regular audiological exam & 69/66.3 & 0.732 \\ \hline \hline \end{tabular}  
* significant correlation with the K-MoCA score.  
\end{table}
Table 2: History of illness and habits assessment.
###### Abstract  
High-plasticity clay soils pose significant challenges in geotechnical engineering due to their poor mechanical properties, such as low strength and high compressibility. Time-cement stabilization offers a sustainable solution, but optimizing additive proportions requires advanced analytical approaches to decipher complex soil-stabilizer interactions. This study investigates the stabilization of high-plasticity clay soil (CH) sourced from Kano, Nigeria, using lime (0-30%) and cement (0-8%) for thirty (30) sample combinations to optimize consolidation and strength properties. Geotechnical laboratory tests (consolidation and UCS) were evaluated per ASTM standards. Multivariate analysis integrated principal component analysis (PCA) with regression modeling (PCR) for sensitivity and causality assessment. Optimal stabilization (15% lime + 6% cement) significantly improved soil properties: void ratio reduced by 58% (0.60\(\rightarrow\)0.25), porosity by 49.5% (0.38\(\rightarrow\)0.19), UCS increased by 222.5% to 2670 kPa (28 days), preconsolidation stress by 206% (355.63\(\rightarrow\)1088.92 kPa), and compressibility modulus by 16% (7048\(\rightarrow\)10,474.28 kPa). PCR sensitivity analysis attributed 46% of UCS variance to PC1 (compressibility parameters: void ratio, porosity, compression index; \(\beta\) = 0.72). PCR Causality analysis shows improvment with curing (R\({}^{2}\): 68.7% at 7 days\(\rightarrow\)83.0% at 28 days; RMSE: 11.2\(\rightarrow\)7.8 kPa). PCR establishes compressibility reduction as the dominant causal mechanism for strength gain, providing a robust framework for dosage optimization beyond empirical approaches.  
Article  
1  
High-plasticity clay soil stabilization; lime-cement admixture; consolidation properties; unconfined compressive strength; principal component analysis (PCA); principal component regression (PCR); PCR sensitivity and causality analysis
## 3 Results and Discussion
At 333.15 K, the pressure coefficient returns to the inlet value earlier than at 303.15 K. However, the pressure coefficient decreases more gradually at 363.15 K. This phenomenon arises from the logarithmic relationship between the saturated vapor pressure of hydraulic oil and temperature. The change is more pronounced in the  
Figure 8: The cavitation phenomena of P2 (**a**), P3 (**b**), and P4 (**c**) at different temperatures. temperature range of 333.15 K to 363.15 K compared to that in the range of 303.15 K to 333.15 K. This suggests that the thermodynamic effect is more prominent at 363.15 K. Consequently, cavitation is more effectively suppressed at higher temperatures, as evidenced by the gas volume fraction in Figure 9. This delay in cavitation suppression results in a later decrease in the pressure coefficient at higher temperatures. Similarly, due to the heightened cavitation suppression at higher temperatures, the pressure coefficient recovers more rapidly after reaching its lowest point with increasing temperature. Figure 5:0 Pressure coefficients at P2 (**a**), P3 (**b**), and P4 (**c**) at different temperatures. The temperature distribution characteristics of three points P2, P3, and P4 are analyzed by their temperature drop in Figure 5:1 It can be seen that the temperature drop is minimum at 363 K with an average temperature drop of 0.265 K and the minimum frequency of change in one cycle. The average temperature drop is 0.291 K at 333 K. The temperature drop is maximum at 303 K with an average value of 0.563 K and the maximum frequency of change. The temperature drop at 363 K is almost twice that of 303 K.  
The thermodynamic effect is characterized by the release of greater latent heat of vaporization by the liquid as temperature rises, resulting in a more substantial local temperature drop. However, in the results, an interesting observation is made: as temperature increases, the temperature drop actually decreases. It can be seen from Figure 8 that as the temperature of hydraulic oil increases, its density decreases. In a gear oil pump, the volume of the cavity is the same. A decrease in density will lead to a decrease in cavity volume fraction, as shown in Figure 5:2 Under a temperature of 363 K, the gas volume fraction is only 50% of 303 K. Therefore, at 363 K, the total amount of gas vaporization is small, resulting in a small total latent heat of gas vaporization and less heat absorption than 303 K. Therefore, it shows that as the temperature increases, the temperature drop decreases.
# Atom Probe Tomography for Catalysis Applications: A Review
## 1 Introduction
Furthermore, with 1 DAP, it is possible to image reaction intermediates, such as oxide species during the O\({}_{2}\) + H\({}_{2}\) reaction on Rh [53], the formation of C\({}_{x}\)H\({}_{y}\)\({}^{+}\) species during the Fischer-Tropsch reaction on cobalt catalysts [54; 55], and even the early formation of carbon nanotubes on Ni [56; 57]. More recent instruments, particularly the 3-dimensional atom probe (3 DAP), are not intended to perform in situ analysis, but rather focus on the atomic-scale chemical analysis of both the surface and bulk of the sample through Atom Probe Tomography (APT) and three-dimensional reconstruction. These permit the study of post-facto oxidation/reduction behavior of the specimen, surface segregation in alloy systems, and behavior in the vicinity of structural defects. APT tip specimens can be qualified as'model catalysts', bringing a valuable fundamental understanding of the catalytic systems, but are of limited value for more practical applications. With the new developments in the FIB-based sample preparation for APT, there is currently a growing interest in the analysis of'real catalysts' (as compared to'model catalysts') to highlight the presence of specific features such as core-shell structures, the homogeneity in the composition of nanoparticles, the distribution of elements within alloys, and aging effects. After a brief description of the principles of APT, specimen preparation, and the latest developments of reaction cells for APT, this review will focus on the analysis of different catalytic systems by highlighting (i) the catalytic behavior and relevance of the studied materials, (ii) the specific specimen preparation used for APT studies, and (iii) the results of the APT analysis and the fundamental understanding they enable.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 6 Biological Activity of _A. aculeata_ Extract
### Antimicrobial Activity  
This section discusses studies that investigated the antimicrobial activity of macauba leaf extracts. In more recent works, no antibiotic effect was identified for Gram-positive bacteria Staphylococcus aureus and Enterococcus faecalis, and Gram-negative bacteria Escherichia coli and _Pseudomonas aeruginosa_, as well as antifungal effects for _Candida albicans_ and _Candida parpysilosis_[11; 29]. On the contrary, another study that evaluated oil extracted from macauba seeds in different strains of bacteria, including _Staphylococcus aureus_ and _Escherichia coli_, showed positive results for antimicrobial activity. Additionally, the oil was effective against fungi of the _Candida genus_[34]. The authors suggest that macauba oil may be used as a natural alternative for the treatment of infections caused by microorganisms, especially those that show resistance to conventional antibiotics.  
However, it is important to note that further research is needed to determine the safety and efficacy of using macaiba oil in humans, as well as to better understand the mechanisms of antimicrobial action of this oil.
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
###### Abstract
In this work, we propose and numerically study a simple design of silica microsphere-based OFC generator in the C-band with FSR of 200 GHz and simulate its implementation to ensure 4-channel 200 GHz spaced WDM data transmission system. When developing OFC generators, it should also be taken into account that the dynamics of intracavity radiation strongly depends on resonator dispersion [24; 30; 31]. Additionally, thermo-optical effects should also be taken into account, due to the fact, they strongly affect WGM eigenfrequencies [32]. Therefore, it is necessary to provide detailed numerical modeling of OFC formation. A silica microsphere with an FSR of 200 GHz has anomalous dispersion. In the anomalous dispersion region, a stable DKS can exist [27; 33], which is investigated here. Furthermore, using the obtained numerical OFC spectrum, we chose four comb lines to provide the WDM system with optical carriers. Data transmission is realized on four frequency comb lines, with FSR equal to 200 GHz, which satisfies ITU-T recommendation G.694.1 [29] defined spectral grid for WDM systems. All four comb lines are modulated with 10 Gbit/s data stream and transmitted over 20 up to 60 km of SMF-28 fiber, therefore emulating a passive optical network (PON).