esa-sceva/satcom-chunk-collection huggingface dataset:
# Development Characteristics and Mechanism of Crack in Expansive Soil under Wet-Dry Cycling
## 3 Indoor Wet-Dry Cycle Test of Expansive Soil
### Experimental Results and Analysis
The curve demonstrates that the crack width increases with increased thickness. The average crack widths of the 4 cm-thick specimens of medium and highly expansive soil are 1.77 mm and 3.7 mm, respectively. In contrast, the average crack widths of the 1 cm-thick specimens of medium and highly expansive soil are only 0.78 mm and 1.39 mm, respectively. There are significant differences in average crack width among different thicknesses. This phenomenon indicates that thickness noticeably influences the width development of cracks in expansive soil, with an increase in thickness promoting crack widening. Figure 9: \(Cont\). The influence of specimen thickness on the crack development characteristics of expansive soil samples is relatively complex. Generally, an increase in thickness leads to a decrease in the total number and length of cracks, while the crack ratio and average crack width of the specimens increase. With an increase in specimen thickness, the influences of boundary effects on volume deformation become more apparent. The water loss shrinkage process mainly occurs at the surface of the specimens, and thicker specimens face more difficulty in evaporating moisture, which inhibits crack development and expansion to some extent. Meanwhile, existing cracks provide fast flow channels for rainwater infiltration and evaporation, and the repeated dry-wet cycles further intensify the transverse expansion of cracks, resulting in an upward trend in average crack width with increased specimen thickness. Analysis indicates that the initial moisture content, compaction level, and thickness significantly affect the development of cracks in expansive soil. Higher initial moisture content, lower compaction, and reduced thickness correlate with enhanced crack development, aligning with previous studies [11; 12; 21]. This research further explores the intricate evolution of cracks under the influence of multiple factors (initial water content, thickness, compaction degree, and the number of different wet-dry cycles) during repetitive wet-dry cycles, revealing a more complex pattern of crack progression. Figure 9: Variation curve of basic morphological parameters of expansion soil cracks under different thickness conditions.
## 1 Introduction
The work presented in this paper serves as a feasibility demonstration, in which the integration of two elements (namely, FlexSim and Python) that have not been previously documented as working together in scientific literature is showcased. This novel combination posed non-trivial challenges to the case study analyzed, i.e., learning from the realistic feedback of a commercial software implementation is not guaranteed. As reported in the related work section, the use of simplified simulation environments is a common approach, and consequently, the use of a more realistic model for the RL environment can be considered as the main contribution of this study. The second contribution is the resolution of a dynamic version of the storage location problem without using any prior knowledge and with a relatively small effort, at least for the validation instance considered. This problem, for a more realistic instance, is very difficult, or even intractable, using traditional methods. The study also covers a performance discussion, where the validation instance is scaled up, and the results are reassessed. The final contribution of this article is devoted to indicating where further research effort could go. In fact, this work can be seen as the first step in order to lay the ground for more advanced studies, in particular in the use of commercial simulation software in the context of digital twin optimization. The selection of a simplified case was carried out on purpose in order to be able to demonstrate its feasibility, to validate the effectiveness of the RL approach, and to provide a benchmark on the topic. The rest of the paper is structured as follows. Section 2 reviews recent work on the combination of simulation and RL applied to warehouse operations. Afterward, Section 3 describes the algorithmic implementation for combining simulation and reinforcement learning. The case study is described in Section 4. Finally, Section 5 discusses open research lines in this area, while Section 6 highlights the most important findings and draws some conclusions.
# Robot Bionic Vision Technologies: A Review
## 2 Literature Review
### Robot Bionic Vision Development Based on Human Vision
Many physiological studies have shown that eye movement is a complex physiological process that includes the cooperation of brain nerve centers and multiple extraocular muscles to achieve the function of human eyesight shifting. To understand the eye-movement mechanism, simplify the human eye model, and make the structure and bionic kinematics of the human eye easy to realize, researchers proposed a hypothetical eye-movement center, eye-rotation axis, and Listing's law. Some bionic vision optomechanical devices with anthropomorphic vision functions, intelligent image processing algorithms, and servo motion control models have been developed. However, after millions of years of continuous evolution, the human eye hasbecome a complete visual system that integrates image acquisition, information processing, and feedback, including the cornea, iris, lens, retina, optic neuron, and cerebral cortex visual center [135,136]. At present, the image acquisition, processing speed, and imaging performance of optomechanical devices, intelligent algorithms, and servo motion control models are far from those of the complex human visual system. This limits the application and development of intelligent robots in complex dynamic environments.
## 3 Numerical Analysis Results and Discussion under Static Loading
### Vertical Displacements at the Top of the Wall Adutment
#### 3.2.2 Vertical Displacement Increments at the Top of the Wall Abutment due to Applied Loadings  
The main function of soil reinforcement is to restrain soil deformation. Hence, the effects of soil reinforcement can be observed in Figure 10. The figure shows the vertical displacement increments (\(\Delta dy\)) obtained from the current calculation phase for (a) the applied general traffic loads (Case 1B, 2B, 3B) and (b) applied railway loads (Case 1C, 2C 3C).  
In this condition, the geosynthetic reinforcements worked well in the backfill soil by resisting the \(\Delta dy\) from the vertical earth pressures induced by the applied surcharge loads. The results in Figure 10a,b show that the GRS-IB models with FHR facing and reinforced backfill exhibited the least vertical displacement increments (\(\Delta dy\)) at the top of the wall abutment. In this study, both the GRS-IB and IB models have the same loading conditions. Due to the presence of the reinforcements, the GRS-IB models have reduced the \(\Delta dy\) by 4.7 times and 1.3 times (maximum) after the applied general traffic loads and railway loads compared to the IB models, respectively. Moreover, both the GRS-IB and GRS-IBS models use geosynthetic reinforcements. Yet, the GRS-IB models have reduced the \(\Delta dy\) by eight times and three times (maximum) after the applied general traffic loads and railway loads, respectively. This is because for GRS-IBS models, the bridge loads are supported by the reinforced bridge seat in the abutment (0.75 m to 2.25 m from the wall facing), while for GRS-IB models, bridge loads are supported by the 0.90 m-thick FHR facing.
## 4 Results and Discussion
### Impact of PCV
#### 4.3.2 Validation and Discussion  
Comparing solutions without and with PCV corrections, the improvement of overlap orbit comparisons by PCV estimation is analyzed (seen Figure 11) and the RMS values of residual analysis and orbit comparisons in the R, T, N and 3D directions are listed in Table 4. For GRCC, the mean RMS of the dynamic post-fit residual can be significantly reduced from \(7.81\) mm to \(7.31\) mm by applying the PCV model. When considering PCV corrections, the accuracy improvements in the 3D RMS of overlap orbit comparison and JPL orbit comparison amount to \(0.3\) mm and \(2.3\) mm, respectively. For CS01, with larger PCV scales, the post-fit residual can be reduced from \(9.31\) mm to \(8.55\) mm, and the accuracy of the 3D RMS of overlap orbit comparison can be improved from \(1.88\) cm to \(1.72\) cm. Meanwhile, the PCV corrections also can significantly affect the consistency of clock offset (see Figure 12), where the RMS of the clock offset difference for GRCC and CS01 is \(0.043\) ns and \(0.027\) ns, respectively. The combined orbit and clock SISRE for GRCC and CS01 caused by PCV corrections is \(1.30\) cm and \(0.86\) cm, respectively. This shows that applying phase residual estimations for ionosphere-free PCV correction is necessary in high-precision LEO orbit and clock offset determination.  
Figure 10: Azimuth-elevation maps of the \(5^{\circ}\times 5^{\circ}\) PCV (mm) for the ionosphere-free linear combination of the GRCC (**left**) and CS01 (**right**).
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 3 Results
### The Workflow for Aircraft Parts Tracking Using NTF-Based Technology  
The workflow for aircraft parts tracking, as depicted in the flowchart in Figure 4, illustrates a comprehensive lifecycle management system utilizing NFT technology.  
Figure 4: Flowchart of the workflow for aircraft part tracking.  
This process begins at the manufacturing stage, where each individual aircraft part is produced. Immediately following production, a unique NFT is created for the part, serving as its digital representation and establishing the foundation for its traceable history.  
The part then undergoes initial certification, a critical step to ensure it meets all required standards and specifications. This certification information is recorded by updating the associated NFT, creating an immutable record of the part's validated quality and compliance. Once certified, the part moves into inventory, with its NFT again being updated to reflect its storage location and availability status.  
When the part is selected for use, it enters the installation phase. The NFT is updated to record details of its installation, including the specific aircraft it is fitted to and the date of installation. This marks the transition of the part into active service.  
The in-service stage is where the part spends most of its operational life. During this phase, the NFT plays a crucial role in ongoing management. It receives periodic updates to record flight hours, operating conditions, and any notable events. If the part requires maintenance, it temporarily leaves the in-service status. The maintenance process involves inspections, repairs, or overhauls as needed, with each action meticulously documented through updates to the NFT.  
Following maintenance, the part undergoes a status check. This evaluation determines whether the part is still serviceable. If deemed serviceable, it returns to in-service status, with the NFT updated to reflect its renewed condition and any maintenance performed. The NFT's historical data help inform decisions about the part's continued airworthiness and suitability for further use.  
If a part is found to be unserviceable during the status check, or if it reaches the end of its operational lifespan, it moves to the retirement phase. The NFT is updated one final time to record the retirement, including the reason and date, effectively closing the digital record of the part's lifecycle.
## 2 Materials and Methods
### Feature Extraction  
After the artifact removal, the HRV was calculated using Heart Rate Variability Analysis Software (HRVAS) [38]. It is an open-source program that calculates HRV features. Ten HRV features were used: 5 time-domain features, 1 nonlinear feature, and 4 frequency-domain features from PSD.  
Time-domain features are derived from the detection of the QRS complex in the ECG signal. Each R wave was detected and the R-R intervals are obtained along with the instantaneous heart rate. From the R-R intervals, the mean R-R interval (RRI mean), heart rate (HR), standard deviation of NN intervals (SDNN), root mean square of successive differences of NN intervals (RMSSD) and percentage of differences between adjacent RR intervals that are greater than 50 ms (pNN50) can be derived. To calculate for SDNN, normal beats were used. RMSSD and pNN50 strongly correlate to parasympathetic activity [34].  
Sample entropy (SampEn) is a nonlinear measure often used to measure complexity in any time series. In HRV analysis, SampEn is presented to measure the rate of entropy in short-term NN intervals [39]. High SampEn readings might indicate a high degree of complexity and little predictability. Lower values might imply a lack of complexity and a high degree of regularity. [34].  
Frequency-domain features are derived from the PSD. There are numerous methods in calculating the PSD. The PSD was calculated using a parametric approach called autoregressive (AR) power spectral analysis. Even using fewer samples, parametric approaches offer smoother spectral components and more accurate PSD. Low frequency (LF) and high frequency (HF) are the two major spectral components considered in this investigation (HF). The LF band, which spans 0.04 to 0.15 Hz, is more closely linked to sympathetic activity. The HF band, on the other hand, is related with parasympathetic activity and runs from 0.15 Hz to 0.4 Hz [40].
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods
### Satellite Imagery Datasets
#### 2.2.3 Planet Fusion Surface Reflectance  
We processed Planet Fusion images using an algorithm based on the CubeSat-enabled spatiotemporal enhancement method [8], which enhances, inter-calibrates, and fuses satellite imagery from multiple sensors. Planet Fusion has unique features, including (1) precise co-registration and sub-pixel fine alignment for different image sources, (2) PlanetScope scenes with near-nadir field of view, resulting in minimal bidirectional reflectance distribution function (BRDF) variation effects, and (3) pixel traceability to identify imagery sources and to assess the confidence of daily gap-filled images.  
To generate Planet Fusion surface-reflectance images, we used the same approach described for Basemap (i.e., FORCE [40]), with top-of-atmosphere (TOA) PlanetScope scenes (3 m), Sentinel-2 TOA reflectance (10-20 m), Landsat 8 TOA reflectance (30 m), and daily tile-based MODIS or VIIRS normalized to a nadir-view direction and local-solar-noon surface reflectance. The Planet Fusion algorithm uses MODIS MCDI3A4 surface-reflectance product in seven spectral bands that are corrected for reflectance anisotropy using a semi-empirical BRDF [46], which utilizes the best observations collected over a 16-day period centered on the day of interest. In addition, VIIRS products (VNP43 IA4 and VNP43 MA4) are used as a backup to ensure continuity if MODIS data is not available.  
The Planet Fusion algorithm guarantees spatially complete and temporally continuous images by gap-filling radiometric data (i.e., synthetic pixel values). The gap-filling process uses both spatial (i.e., neighboring and class-specific pixel information) and temporal interpolation techniques to estimate the pixel values. In general, uncertainty will vary based on Earth's surface characteristics (e.g., vegetation dynamic changes), and it will be higher for longer daily interval gaps. Planet Fusion images are accompanied by a quality-assurance product, which is a thematic raster layer using the same spatial grid (i.e., UTM grid system in 24 km by 24 km tiles) as the corresponding Planet Fusion spectral data [17]. We used the quality-assurance product to assess the percentage of synthetic (i.e., gap-filled) versus observation data (PlanetScope and Sentinel-2) used to generate the pixel value. The observation data can be a combination of PlanetScope and Sentinel-2. A value of 1 indicates no gap-filling, whereas a value of 100 indicates an entirely gap-filled pixel value. Specifically for our study case, when clipping Planet Fusion images using the OFR boundaries, the clips can have real pixels, synthetic pixels, or a combination of both. Additionally, there are known issues associated with the gap-filling process used by the Planet Fusion algorithm, including false cloud or cloud-shadow detection and image artifacts (e.g., strips, distortions). These issues are most common during prolonged cloudiness and in study regions with significant terrain shadowing.
# Dynamic Balance Simulation and Optimization of Electric Vehicle Scroll Compressor Rotor System
## 5 Simulation Results and Analysis of the Driveline
### Dynamic Simulation Analysis  
In the operation of scroll compressors at high speeds, the eccentric mass within the rotor system induces the creation of centrifugal inertia forces. These forces can have a substantial impact on overall machine stability and may result in increased vibration, unbalanced operation, and other issues that could affect normal functionality and performance [19; 20]. In the virtual prototype, the rotor system model includes measurement variables at the drive-bearing position to measure the magnitude of force applied to the bearing.  
When the measured variables are established, Figure 8 presents the results, highlighting the initial substantial force impacts on the drive bearings, along the horizontal and vertical directions. These impact forces are triggered by the gravity of the eccentric wheel and scroll disk, in close proximity to the drive bearings, and the rotational inertia forces accompanying them.  
Figure 6: Displacement curves in horizontal and vertical directions of the center of mass of the dynamic bearing. (**a**) Displacement curve in the horizontal direction of the center of mass of the dynamic bearing. (**b**) Displacement curve in the vertical direction of the center of mass of the dynamic bearing.  
Figure 7: Velocity profiles of dynamic\(-\)bearing center of mass in horizontal and vertical directions.  
At the same time, the data presented in Figure 8 highlight an increase in the range of force fluctuations on the drive bearing, in both horizontal and vertical directions, showing clear cyclical changes. This cyclical change in force fluctuations leads to wear and tear of the bearing, reducing its life span.  
In addition, the force transmitted to the compressor casing from the drive bearing results in severe vibration of the compressor, which negatively affects the overall performance of the machine. This indicates that there are still some unbalanced forces on the main shaft, due to gas forces, necessitating further optimization of the eccentric components of the compressor.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model
### Network Training
#### 3.1.3 Log-Sigmoid Function  
The log-sigmoid function (logistic function) represents the binary outcomes in statistics and machine learning (Figure 8). The odds ratio of success to failure is its logarithm. Log-sigmoid functions convert real-valued inputs to probabilities between 0 and 1. Gradient descent optimization strategies leverage the function's differentiability and unique derivatives in all places. Artificial neural networks employ them in the output layer of binary classification problems. The function for log-sigmoid is shown in Equation (9).  
\[\mathrm{b}=\frac{1}{1+e^{-1}} \tag{9}\]
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 6 Conclusions  
This article presents a novel eight-element antenna for the Sub-6 GHz band. With an average peak gain of 4 dBi and efficiency of 90% across the operational spectrum, the antenna has a measured impedance bandwidth of 20% (4.5-5.5 GHz). The proposed antenna offers notable isolation of 24 dB throughout the operating range. The MIMO performance is satisfactory with good MIMO diversity values of ECC < 0.07, MEG ratio - 0, TARC < \(-\)10 dB, DG \(\sim\) 10, and ME < \(-\)0.5. The calculated peak channel capacity over bandwidth value of 42.6 bps/Hz is very close to the ideal 8 \(\times\) 8 MIMO system. Further, the conformal analysis of the proposed antenna was investigated and the effect of antenna deformation on the performance characteristics of the antenna was investigated. The proposed antenna shows very good performance that extends the application possibility of the proposed antenna for wearable applications in the specific band. Through the performance evaluation and the corresponding results obtained, it can be concluded that the presented antenna is a suitable candidate for Sub-6 GHz MIMO scenarios. As a future direction, different decoupling structures and gain enhancement techniques can be investigated to further improve the performance of the proposed MIMO antenna.  
Conceptualization, D.M.J., T.A. and R.M.D.; methodology, D.M.J., S.V., S.P., A.-A.A.B., J.A. and T.A.; software, D.M.J. and T.A.; validation, A.-A.A.B., J.A., T.A. and R.M.D.; formal analysis, D.M.J. and T.A.; writing--original draft preparation, D.M.J.; writing--review and editing, S.V., S.P., T.A. and R.M.D.; supervision, T.A. 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 provided in the manuscript.  
The authors declare no conflict of interest.
# Review on Structural Health Evaluation with Acoustic Emission
## 3 Source Location
Here, a matched filter is created that maximizes the ratio of output amplitude to the square root of the input energy. For source location, a number of waveforms recorded by a single sensor containing the impulse response of the medium are used, aided by scattering, mode conversions, and boundary reflections of the medium. As it uses a virtual focusing procedure, no iterative algorithms are used and no prior knowledge of the properties or anisotropic wave speed is needed. See also [PERSON] et al. [141] and [PERSON] et al. [142]. Other special topics are of interest. [PERSON] et al. [143] examined source location errors when thick-walled vessels are tested. Traditionally, the accuracy limit of source location was empirically thought to be the thickness of a structure [5]. They defined errors in three different ways and conducted experiments using large concrete blocks with the thickness of 150 to 600 mm and sensor spacing of 4 to 5.5 m. Errors were found to be 2% to 10%, while the absolute errors were 5% to 30% of the thickness. Thus, results indicate that empirical estimates were realistic considering errors coming from the uncertainties in TDOA. A related report is on the inspection of heavy wall radioactive waste containers [144]. This does show we have to deal with thick-walled containers. [PERSON] et al. [145] presented a source location method for open structures, like trussed towers or beams. Each element is modeled in 1-D and these elements are combined for complex spaced structures. A 2-D truss was modeled and compared to a laboratory scale bridge, confirming their approach to be valid. However, the practicality of such an approach depends on the attenuation of flaw AE signals and noise generated at joints inevitably present in large truss structures. Section 3 covered various approaches to locate the origin of an AE signal, often referred to as an AE event. Basic location method developed from concepts used in seismology about 50 years ago, and was adapted to available instrumentation over the years. With advances in data processing capacity, newer approaches have sprung up in the past 20 years or so. Representative works were collected with brief description in this section.