esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 1 Introduction
[23] studied the small-strain behaviour of unsaturated compacted sands by RC torsional shear tests and indicated that suction and soil configuration have significant effects on the small-strain behaviour of sand. [PERSON] and [PERSON] [24] experimentally studied the small-strain \(G_{max}\) of sand with different saturations based on BE tests and found that the peak for the \(G_{max}\) appears at a certain saturation. [PERSON] et al. [25] studied the effects of the initial matrix suction and net confining pressure on the small-strain stiffness of remoulded sand based on BE tests and the results showed that small-strain stiffness has a non-linear relationship with matrix suction under each value of net confining pressure. [PERSON] et al. [26] measured variations in the small-strain \(G_{max}\) of unsaturated sand during hydraulic hysteresis based on BE tests and indicated that during the drying process, the \(G_{max}\) varied in an up-and-down manner with respect to matrix suction, ranging from zero to the residual conditions. From this, it can be seen that although the small-strain shear modulus of unsaturated sand has been investigated experimentally, the continuous measurement of dynamic shear modulus on one and the same sand specimen in the whole small-strain range of \(10^{-6}\)\(\sim\)\(10^{-3}\) under a controlled suction condition was seldom achieved in previous studies, due to the limitations in different strain ranges of soil between RC and BE tests. Moreover, only the macroscopic peak phenomenon of the sand shear modulus has been revealed to vary with saturation, but there was almost no internal reason from the mechanisms to be illustrated clearly. In this work, the Stokoe RC instrument was improved and combined with a BE system, which could be used to conduct continuous tests regarding the shear modulus of a specimen under different saturations in the whole small-strain range of \(10^{-6}\)-\(10^{-3}\). Then, the BE-RC test device was used to investigate the small-strain shear modulus of unsaturated silty-fine sand in the dehydration path and the correlation between the evolution law of the \(G_{max}\) and the internal particle-water-air interaction mechanism varying with saturation was also studied in detail.
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.3 Interoperability  
The seamless integration of digital twin (DT) systems across transportation infrastructure remains a critical yet underdeveloped challenge. While DTs have demonstrated value in isolated applications--such as bridge health monitoring--their standalone deployment limits their potential to enable integrated networks. For example, in bridge health monitoring, DTs utilize embedded sensors (e.g., strain gauges, accelerometers, and temperature sensors) to collect real-time stress, vibration, and thermal fluctuation data. These parameters are selected for their capacity to capture dynamic structural responses and environmental interactions, which are critical for predicting fatigue, corrosion, and failure mechanisms. To enhance predictive accuracy, raw sensor data undergo preprocessing steps such as noise filtering, normalization, and feature extraction (e.g., identifying resonant frequencies or stress thresholds), followed by analysis with machine learning algorithms like convolutional neural networks (CNNs) and anomaly detection models. These algorithms are chosen for their ability to process high-dimensional time-series data and detect early-stage degradation patterns. However, despite their technical sophistication, existing bridge health monitoring DTs often operate independently of broader infrastructure systems, such as traffic management or weather forecasting platforms [28]. This fragmentation impedes the integration of contextual data (e.g., traffic load variations or extreme weather events), which could improve predictive models and decision-making processes. Additionally, reliance on localized sensor networks risks creating data silos, limiting the assessment of interdependencies between infrastructure components (e.g., correlations between bridge stress and adjacent roadway conditions). Resolving these interoperability gaps is essentialfor advancing from reactive, component-specific maintenance to proactive, system-level resilience strategies.  
Future research should focus on establishing DT systems that can share data seamlessly across all lifecycle stages. This will require the adoption of common data exchange standards and the creation of APIs to enable communication between diverse systems [8]. For instance, an interoperable DT for urban transportation could aggregate real-time data from roads, railways, public transit, and pedestrian pathways, providing a holistic view of the entire network [102].  
Moreover, creating a centralized data repository for transportation infrastructure, accessible to different DT systems throughout the lifecycle, could significantly improve data sharing and analysis. Achieving true interoperability, however, will require addressing technical challenges related to data integration, as well as regulatory issues concerning data privacy and security [126].
## 4 Results and Discussion
### Effects of Settlement Wavelength  
Variation in settlement wavelength has different effects on the deformation of the slab track. Figure 10 shows the deformation curve and deformation amplitude of the slab track when the settlement wavelength increases from 5 to 25 m and the settlement amplitude remains unchanged at 10 mm.  
Figure 9: Deformation of the slab track.  
It can be seen from Figure 10a that the deformation wavelength of the slab track gradually increases with an increase in the settlement wavelength of the subgrade. As shown in Figure 10b, the growth trend of the deformation amplitude of the slab track first accelerates and then decelerates with an increase in the settlement wavelength of the subgrade. The growth rate of the deformation amplitude of the slab track is the fastest when the settlement wavelength of the subgrade increases from 10 to 15 m. When the settlement wavelength reaches 25 m, the deformation amplitude of the slab track is 8.87 mm, which is closer to the settlement amplitude of the subgrade.  
Figure 11 shows the gravel contact forces in the subgrade for different settlement wavelengths, as well as the growth rate of the contact forces compared to the unsettled condition.  
As shown in Figure 11a, the average and maximum contact forces of the gravel grains increase when the settlement wavelength increases from 0 to 20 m. However, when the settlement wavelength is greater than 20 m, the average and maximum contact forces decrease with increasing settlement wavelength. This is because the slab track is still in a suspended state when the settlement wavelength is less than 20 m and a longer section of the track is suspended with an increase in the settlement wavelength. A longer section of suspended track causes the gravel grains near the boundaries of the settlement section to bear an additional load. When the settlement wavelength is greater than 20 m, the subgrade and slab track are in contact again due to the deformation of the slab track.  
Figure 11: Contact force characteristics of the gravel grains in the subgrade at different settlement wavelengths.  
Figure 10: Deformation of the slab track at different settlement wavelengths.  
Therefore, the contact forces of the gravel grains near the boundaries of the settlement section are reduced.  
Figure 11b indicates that an increase in the settlement wavelength has little effect on the average contact force. However, the maximum contact force of the gravel grains changes significantly with an increase in settlement wavelength. The maximum contact force can reach approximately 2.5 times that in the unsettled condition.
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 1 Introduction
The application of the standard genetic algorithm (GA) [24], particle swarm optimization (PSO) [25], differential evolution (DE) [26], firefly algorithm (FA) [27], artificial bee colony (ABC) [28], and gravitational search algorithm (GSA) [29] optimization evolutionary algorithms showed some important advantages: (1) they could provide a global solution to the problem via exploitation and exploration of the solution space, (2) they were free from complex mathematics, (3) they did not require the objective function being optimized to have specific mathematical properties, (4) they provided flexibility in combining multiple objectives, (5) they had a parallel nature and could be executed very fast by applying high-performance computing frameworks (GPU, Spark, etc.). The contributions of this work are as follows:  
1. The 3D porous media reconstruction was formulated as a generic optimization problem described using a multi-objective function that was able to incorporate any type and any combination of statistical functions, alleviating the restrictions of the past techniques that used a single statistical measure. 2. For the first time, the nature-inspired algorithms were applied in 3D porous media reconstruction. 3. A novel and efficient material data representation that enabled the reliable deployment of the evolutionary algorithms with a fast execution rate was proposed. 4. The emergence of the existing auto-correlation functions' weaknesses and the highlight of the needs for more descriptive auto-correlation functions. It is worth mentioning that although the standard versions of the abovementioned evolutionary algorithms were applied in this study, the generic nature of the derived optimization problem permitted the application of more sophisticated and advanced evolutionary algorithms [30; 31; 32; 33; 34]. The paper is organized as follows: Section 2 reviews the main principles of porous media 3D reconstruction and briefly describes the theory of some popular and efficient nature-inspired optimization algorithms. Section 3 presents the problem formulation in terms of optimization and illustrates the scheme of the proposed reconstruction methodology. Section 4 evaluates the reconstruction performance of the applied optimization algorithms. Section 5 summarizes the main conclusions derived from the previous experimental study, while future research directions are defined toward the development of a more efficient porous media 3D reconstruction technique.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 3 Overview of the Proposed All-Optical Processor
### Task 2: Chromatic Dispersion Compensation Filter  
The processor's second functionality, the chromatic dispersion compensation filter (CDCF), depicted in Figure 3, addresses chromatic-dispersion-induced phenomena, a vital requirement for preserving signal quality over long distances in PONs.  
Chromatic dispersion (CD) in optical fibers leads to two significant phenomena: inter-symbol interference (ISI) and power fading. ISI occurs when dispersion causes signal pulses to spread out and overlap with adjacent pulses, making it challenging for the receiver to distinguish between successive bits and thereby degrading the bit error rate. Power fading results from varying delays experienced by different frequency components of the signal, leading to constructive and destructive interference at the receiver, which causes fluctuations in the received signal power. Both ISI and power fading are exacerbated by higher baud rates and longer fiber lengths as increased data rates result in shorter pulses that are more susceptible to spreading, and longer distances accumulate more dispersion effects.  
The CDCF performs spectral slicing using cascaded couplers and finely tuned filters to divide the optical signal into four subbands. Each subband undergoes specific time delays and phase shifts, calculated to neutralize CD-induced delays that vary with frequency. The flexible design allows for tailored adjustments to phase and delay settings. This precision maintains the reassembled signal's structure and content, avoiding the power fading effect and enhancing detection quality.
## 2 Materials and Methods  
### Study Design  
This randomized, repeated-measures, cross-sectional study investigated the acute effects of blood flow restriction (BFR) on isometric force production and rate of force development (RFD). Each participant completed four laboratory sessions over two weeks separated by 48-h intervals to minimize residual fatigue. All sessions were conducted at the same time of day to control for circadian variation. A within-subject design was applied to compare three BFR conditions (0%, 40%, and 80% limb occlusion pressure [LOP]), enhancing statistical power and controlling inter-individual variability.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 5 Conclusions  
This paper proposes a new approach to video anomaly detection using the DGGAN (dual-generator generative adversarial network) framework. The method includes a reconstruction generator and a noise generator, each with a unique role. The noise generator generates pseudo-abnormal frames from normal training frames, which in turn trains the reconstruction generator to recognize both normal and abnormal patterns. The DGGAN architecture is further enhanced by introducing a second-order channel attention module, which strengthens the interdependence and correlation of information in the feature maps. Through extensive experiments on two publicly available datasets, CUHK Avenue and UCSD Ped1 and Ped2, the proposed DGGAN method shows a higher detection accuracy compared to other existing methods.  
Given that some events are considered abnormal in the video clip, a single frame input may be reconstructed as a normal event. Therefore, when an abnormal event that requires multiple frames to be judged occurs, a single frame and a video package are used for training to improve detection accuracy.  
Conceptualization, X.Q. and Z.H.; methodology, X.Q. and G.J.; software, X.Q.; validation, X.Q. and Z.H.; formal analysis, Z.H.; investigation, X.Q. and Z.H.; resources, G.J.; data curation, X.Q. and Z.H.; writing--original draft preparation, X.Q.; writing--review and editing, Z.H. and G.J.; visualization, X.Q.; supervision, G.J.; project administration, G.J.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China under grant no. 41971343.  
This work does not require ethics approval.  
The research does not involve humans.  
The relevant datasets are publicly available for download.  
The authors would like to thank the editor and reviewers for their constructive comments.  
The authors declare no conflict of interest.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion
### Effect of the Total Number of Frames on Multi-Frame Filters  
After determining the padding mode, we further explore the effect of the total number of frames on multi-frame filters, since the total number of frames used to calculate the filters represents the context information. If the total number of frames is too small, the available information will be limited, which will affect the enhancement performance. However, when the total number of frames increases to a certain size, the computing overhead will exceed the performance gain.  
Table 2 shows the effect of the total number of frames on MFMVDR and MFWF. The first two rows are the enhancement metrics scores of the noisy speech and the speech enhanced by DNN\({}_{1}\). Both filters are calculated using the enhanced speech from the first stage and are used to process the original noisy speech. From the results, we can observe that both filters perform better as the total number of frames grows. It should be pointed out that due to the limitation of computing resources, we only explored the case of up to 13 frames. When the total number of frames is further increased, whether the enhancement performance will be improved remains to be verified. Another observation is that MFWF performs much better than MFMVDR for the same total number of frames. The table also shows that the enhancement results of DNN 1 are significantly better than the two filters, which is reasonable because the input used to calculate the required parameters for both filters is the enhanced results of DNN1 rather than clean speech, which leads to an accumulation of errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Padding Mode**} & & & & \\ \hline
**Left Frames** & **Right Frames** & & & & \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: The effect of the padding mode on MFMVDR.
## 4 Results and Discussion
### Performance Analysis
#### 4.1.2 Authentication Time  
The authentication time plays a crucial role in authenticating and verifying mobile devices using a trusted third party to protect against network intrusions. Figure 5b shows  
Figure 5: Simulation results for the BSHNN method (**a**) Detection Rate. (**b**) Authentication time.  
the authentication time performance considering the increasing number of mobile users. When comparing the performance of 50 mobile users, the ML-IDP technique requires 21.9 s, whereas the proposed BSHNN method takes 16.2 s. It is interesting to note that the proposed algorithm provides an average 26% improvement in the rate of authenticating legitimate users. In fact, a sufficient increase in detection rate and a significant dip in authentication time make BSHNN a best candidate under the given network condition.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 1 Introduction
so it is not possible to compare the distances and angles over multiple phases. Machine learning trains on input data and expected outputs to reveal the relationships between coordinate time series and various physical variables without having explicit mathematical equations [24]. However, machine learning requires a wealth of historical data to form training and test sets, as well as additional observed values like geological and temperature data. This makes it more suitable for control networks with long-term or continuous observation. It is less applicable to triangulation and trilateration control networks with few observation periods and a small number of points. Because of this, this paper proposes a three-step analysis method of \"classical free network adjustment to obtain the initial value-coordinate similarity transformation to identify the stable points-quasi-stable adjustment to get the final result\", which is analyzed and verified by two-phase observation data of an ultra-large jacket control network, and the results show that this method can more effectively identify the stable and unstable points in the network compared with the traditional method.
## 3 Results
### Permeability
The Forchheimer coefficient for a water flow of 6.2 L min\({}^{-1}\) m\({}^{-1}\) for comparison purposes is also shown. Figure 12: Darcy-Forchheimer Equation (5) fitted to the experimental data of pressure drop (\(\Delta P\)) and inlet air velocity (\(u\)) for different water flows (Q) for the \(BC\) material. Blue: 2 L min\({}^{-1}\) m\({}^{-1}\), magenta: 6.2 L min\({}^{-1}\) m\({}^{-1}\) and red: 10 L min\({}^{-1}\) m\({}^{-1}\). Figure 13: Comparison of the \(BC\) pad experimental pressure drop (blue point) computed with the Darcy–Forchheimer Equation (5) considering the permeability and Forchheimer as a constant and the equation fitted for each water flow (black solid line) and considering the permeability constant and the Forchheimer coefficient as a function of the water flow (asterisk markers). (**a**) Water flow of 3.1 L min\({}^{-1}\) m\({}^{-1}\). (**b**) Water flow of 6.2 L min\({}^{-1}\) m\({}^{-1}\). (**c**) Water flow of 10.0 L min\({}^{-1}\) m\({}^{-1}\). The permeability of the alternative materials were ten times lower than that of the cellulose material, which is in the order of \(1\times 10^{-6}\). It was not possible to establish a correlation between the permeability and the magnitude of \(\Delta P\) across the alternative pads. However, this relation is clearly established with the Forchheimer coefficient. This was biggest for \(CS_{1}\) and \(BC\) pads, which are the ones that produce the highest \(\Delta P\), while the lowest \(\beta(6.2)\) values were obtained for the \(PS\) pads, which produces the least \(\Delta P\). Additionally, there was a big difference between the Forchheimer coefficient of the cellulose and the alternative pads. The Forchheimer coefficient can be considered an indicator of how big the \(\Delta P\) changes are when the inlet air velocity increases. The alternative pads with the biggest particle size (\(PS\)) present the lowest Forchheimer coefficients, while the \(CS_{1}\) pad, which has the smallest particle size, and the \(BC\) materials, which have the least porosity, present the maximum values; the effect of the inlet air velocity over \(\Delta P\) is stronger. When the water flow increases, an increase in the resistance to the air flow was also more noticeable on those pads.
## Data and Methods
### Clara-A2  
CLARA-A2[7] is an updated (from previous CLARA-A1[26]) dataset produced by EUMETSAT CM SAF using measurements of the AVHRR instrument onboard polar orbiting National Oceanic and Atmospheric Administration (NOAA) and EUMETSAT MetOp satellites, and extends from 1982 to 2015. The TCC product of CLARA-A2 is derived with a multi-spectral thresholding method implemented in the Satellite Application Facility on Nowcasting (NWC SAF) polar platform system (PPS) cloud processing software [27]. TCC is defined as the fraction of cloudy pixels per grid square compared to the total number of analyzed pixels in the grid square and it is expressed in percentage [6; 7]. In this study, the monthly mean CLARA-A2 TCC data have been averaged to a \(2.5^{\circ}\times 2.5^{\circ}\) latitude-longitude grid and cover the 29-year period 1984-2012, constrained by the availability of ISCCP-H data. The CLARA-A2 product that combines day and night measurements from all available satellites was selected in order to represent as well as possible the diurnally averaged monthly TCC. The number of satellites varies from one in the early part of the record to at least four in the last decade. With each satellite having two (daytime plus nighttime) or--towards higher latitudes--more overpasses over a specific location per day, CLARA-A2 reaches a variable coverage of the full diurnal cycle.  
AVHRR measures radiation in six spectral channels: 0.58-0.68 \(\upmu\)m, 0.725-1.10 \(\upmu\)m, 1.58-1.64 \(\upmu\)m, 3.55-3.93 \(\upmu\)m, 10.5-11.5 \(\upmu\)m, and 11.5-12.5 \(\upmu\)m, of which only one of the 1.58-1.64 \(\upmu\)m and 3.55-3.93 \(\upmu\)m channels is available at a given time. Radiance measurements from all these channels are used in the CLARA-A2 cloud-masking algorithm. The original horizontal field-of-view (FOV) size of the measurements at nadir is 1.1 km. The data used in CLARA-A2 are a resampled version of these measurements at a reduced resolution of about 5 km, defined as global area coverage (GAC).  
Original shortwave radiances were inter-calibrated and homogenised using MODIS (Moderate Resolution Imaging Spectroradiometer) data as a reference, before generating each component of the CLARA-A2 product portfolio. The inter-calibration was based on the method introduced by [PERSON] et al. (2010) in [28], which has now been updated (MODIS Collection 6) and extended (6 years have been added). This updated calibration is described by [PERSON] et al. (2017) in [29].