esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Bikeshare Transfer Characteristics  
Considering that the inconsistency of the operating time of different metro lines may lead to bias in the analysis, 6:30 to 23:00 was chosen as the temporal constraint for the extraction of bikeshare transfer behavior. As shown in Figure 3, the growth of bikeshare ridership shows a decreasing trend with the increase in the catchment area range of the metro station and stabilizes after 150 m. Therefore, 150 m is selected as the radius of the catchment area of the metro station, and the Geopandas package in Python is used to establish a buffer zone for the metro station, which is used as the catchment area of the metro station. The catchment area of the metro station is used as the spatial constraint for the extraction of bikeshare transfer behavior.  
After the selection of spatiotemporal constraints, the bikeshare feeder volume on workdays and non-workdays is obtained, as shown in Figure 4a,b. The bikeshare feeder  
Figure 3: Growth rate of bikeshare ridership within the catchment area.  
volume on the workday has obvious morning and evening peaks from 7:00 to 9:00 and 17:00 to 19:00, while the bikeshare feeder volume on the non-workday reaches the morning and evening peaks at the same time, but the peaks are not obvious.  
Feeder distance is an important consideration for travelers to choose metro feeder transport mode. As shown in Figure 4c,d, there are 88.81% and 89.38% of bikeshare feeder distances within 2 km on workday and non-workday, respectively, indicating that bikeshare mainly serves the short-distance feeder.
## 2 Methods and Materials  
Aircraft maintenance relies on a combination of Non-Destructive Testing (NDT) techniques, machine learning approaches, and advanced defect characterisation methods to ensure aerospace materials' structural integrity and safety. This section provides a comprehensive overview of the methodologies used for damage detection, defect classification, and size and depth estimation in metallic and composite aircraft structures.  
Non-Destructive Testing (NDT) techniques play a crucial role in identifying structural defects without compromising the integrity of the material. Various ultrasonic, radiographic, thermographic, and eddy current-based inspection methods are commonly used to detect subsurface anomalies, corrosion, and material degradation in aerospace components. These techniques allow for accurate and efficient defect characterisation, reducing the need for invasive inspections.  
In parallel, machine learning-based approaches have emerged as a powerful tool for automating defect detection and classification. Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Mask R-CNN, and ensemble models have significantly improved crack detection, corrosion assessment, and defect segmentation. These AIdriven techniques leverage large datasets, sensor fusion, and deep learning architectures to enhance aircraft maintenance precision, recall, and defect localisation.  
The materials used in aircraft construction, including aluminium alloys, titanium, carbon fibre-reinforced polymers (CFRPs), and hybrid composites, each present unique failure mechanisms such as fatigue cracks, delamination, fibre-matrix separation, and impact damage. Understanding the behaviour of these materials under operational conditions is essential for implementing effective maintenance strategies.  
Additionally, size and depth estimation techniques provide critical information for quantifying defect severity. Methods such as structured light 3D scanning, thermographic imaging, and ultrasonic depth profiling enable precise measurement of defect dimensions, allowing engineers to assess structural integrity and predict failure risks.  
This section thoroughly explores these methodologies, highlighting their applications, limitations, and advancements in aviation maintenance and health monitoring.
## 5 Modeling Interference  
In this section, we propose a theoretical approach which can justify the relative frequency histogram of the channel factor \(\mu\) found in Section 4.  
The channel factor \(\mu\) at the sampling time \(t_{k}\) is given by  
\[\mu(t_{k}=0)=\sum_{k=-N}^{k=+N}s_{sd}(t_{k})+\sum_{k=-N}^{k=+N}s_{ sq}(t_{k})\] \[\mu=i_{d}+i_{q} \tag{11}\]  
Now, since both \(i_{d}\) and \(i_{q}\) are stochastic variables given by the sum of many independent values, their probability density function--according to the central limit theorem--should approach the Gaussian distribution; therefore, the probability density function of \(\mu\) should be given by the convolution of two Gaussian models, i.e., another Gaussian probability distribution with a mean value given by the sum of the two mean values and variance given by the sum of the two variances; this hypothesis holds if all variables display the same order of magnitude [24, 25].  
Let us carry out some useful transformations. According to Figures 8-11, the channel factor \(\mu\) ranges between \(-1\) dB (i.e., \(10^{-1/20}=0.891\)) and \(+1\) dB (\(10^{+1/20}=1.122\)). Now, by recalling that \(\ln(x)\approx(x-1)\) if \(x\approx 1\), we can write  
\[\mu\approx\ln(\mu)+1 \tag{12}\]  
Therefore, the relationship between \(\mu\) in decibels and \(\mu\) in linear units is given by:  
\[\mu(\text{dB})=\frac{10}{\ln(10)}(\mu-1) \tag{13}\]  
Hence, if \(\mu\) is Gaussian, then \(\mu(\text{dB})\) is also approximately Gaussian. However, Figures 8-10 seem to suggest two different overlapping Gaussian distributions, especially for \(r=1\). In other words, \(\mu<1\) and \(\mu>1\) seem due to two similar sets of data of equal variance but with mean values set approximately at about \(\pm 0.3\) dB; therefore, the central limit theorem applies to these two separate distributions. The overall relative frequency histograms shown in Figure 11 confirm this general finding, especially at low fade (\(\sim\)10 dB). The peak at about \(\mu=0\) dB can be explained by the Dirac pulse produced by the constant \(-\frac{10}{\ln(10)}\) of Equation (13). In the experimental histograms, it is due to the very small spread of the sampler output amplitude due only to the reference pulse and not to the ISI or QI.  
Now, the question is: how \"bad\" is the ultra-wideband channel discussed above? In the next section, we attempt a quantitative analysis by using [PERSON]'s channel capacity.
## 3 Samples and Analytical Methods
### Analytical Methods
#### 3.2.3 GC-MS Analysis  
The samples of source rocks were first washed and pulverized. Using the Soxhlet extraction method, we extracted the samples with a mixed solution of dichloromethane and methanol (volume ratio 93:7) for 72 h, and then the asphaltene in chloroform asphalt was separated with petroleum ether. The maltenes were then divided into saturated and aromatic hydrocarbons using a chromatographic column made of silica gel and alumina. The separation standard of the group components was SY/T 5119-2008, and finally tested by GC-MS. GC-MS adopts the gas chromatography-mass spectrometer manufactured by Agilent, and the GC model is Agilent 9000. The determination standard of chloroform asphalt in rock is SY/T 5118-2005. Mass spectrometry, relative retention time, and literature comparisons are used to identify compounds.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**Lithology** & **Stratum** & **TOC** & **S\({}_{1}\) + S\({}_{2}\)** & **HI** & **T\({}_{\rm max}\)** & **TS** & **S/C** & **Pr/Ph** & **Pr/Cr\({
###### Abstract  
The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011-2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.  
remote sensing; artificial intelligence; DInSAR; deformation time series; DBSCAN; structural monitoring; built-up environment +
Footnote †: journal: remote sensing
## 2 Mathematical Modeling and Theoretical Foundations
### Chen Chaotic System  
In the field of chaos research, the Chen system [31; 32] is another type of classical chaos among continuous chaotic systems, which provides a theoretical basis for many researchers. Based on the the phase diagram of the system, it is similar to the Lorenz system, but its topological structure is more complex and has more research significance. The mathematical model of [PERSON]'s attractor is  
\[\left\{\begin{array}{ll}\frac{d^{x}x_{1}}{dl^{x}}=a(y-x)\\ \frac{d^{x}x_{2}}{dl^{x}}=(c-a)x-zx+cy\\ \frac{d^{x}x_{3}}{dl^{x}}=xy-bz\end{array}\right., \tag{1}\]  
where \(X=(x,y,z)^{\mathrm{T}}\in R^{3}\) is the system state and \(a\), \(b\), \(c\) are the system variables and all are greater than 0. The [PERSON] system enters a chaotic state when the parameters take values of \(a=35\), \(b=3\), \(c=28\) and the time interval is at \(t_{0}\in[0,100]\). Due to the initial value sensitivity of the chaotic system, the initial value \(x_{0}=1\), \(y_{0}=1\), \(z_{0}=1\) is selected here in order to observe the phase diagram and time sequence diagram of the [PERSON] attractor at a certain state, as shown in Figure 1.  
Chen chaotic systems have inherently better key space and system complexity. Chaotic masking refers to using the pseudo-randomness of chaotic tracks and broadband power spectrum characteristics as a modulating signal. At the transmitter side, the speech signals are hidden into the chaotic signal to form a seemingly noisy signals, and the useful information is completely masked by the chaotic signal to achieve confidential communication.
## 3 Calibration and Application
### Case 1: A Homogeneous Slope  
#### 3.1.1 Homogeneous Slope Description  
Figure 2 shows a slope with a height of 10 m, a top width of 10 m and a slope ratio of 1:1. Many scholars have calculated and analyzed this slope [5; 28; 35]; however, most studies only focus on the slope failure probability and there is no systematic analysis or research on the ultimate bearing capacity and slope failure risk. For this reason, the ultimate bearing capacity and slope failure risk are researched based on the proposed method. Using non-common-node finite elements discrete to the slope, 1360 finite elements, 4080 non-common nodes and 2099 discontinuities are obtained. The soil capacity \(\gamma\) is set to a constant value of 20.0 kN/m\({}^{3}\) and other calculation parameters are shown in Table 1. The quantity of random fields is \(n_{m}=1000\), and Equation (7) was used to obtain the 1000 random fields.
## 9 Recent Advances in GPC
### Graphene-Based GPC  
Geopolymers are not only environmental ecofriendly but also improve mechanical properties when compared to other binding materials [135; 226; 227]. GPC containing binders like FA or GGBFS exhibit better mechanical and microstructural properties due to their chemical composition. However, some of the binders such as clay minerals or other agricultural wastes have not much influenced the mechanical properties of GPC due to their porous microstructure. Moreover, this porous microstructure can cause brittleness, shrinkage, and cracking that in turn effects the structural properties. Therefore, some mineral admixtures or external agents are added to improve their microstructural, mechanical, and durability properties. One of the modifiers to improve the properties of GPC is graphene.  
Graphene, derived from graphite, is a single layer carbon atom in a two dimensional honeycomb structure [228]. It has excellent mechanical, optical, electrical, catalytical, and biological properties [229; 230; 231]. In structural applications, a graphene-based product called graphene oxide is most commonly used nanomaterial that greatly influence the microstructural properties of cement-based material. The incorporation of this nanomaterial make the microstructure dense, reduces nano-cracks, brittleness that directly impacts the durability properties of GPC [232]. [PERSON] et al. [233] found that inclusion of graphene oxide in slag-based GPC performed better in terms of mechanical properties with 20% increases in flexural strength. The optimum percentage of graphene oxide was found to be 0.01% by weight of slag. [PERSON] et al. [234] investigated the effect of inclusion of graphene nanoflake on FA-based GPC and found that with the addition of 1% graphene nanoflake, both the compressive and flexural strength increased by 2.16 and 1.44 times, respectively. However, it was found that the additional amount of graphene and NaOH content in GPC reduces the bending strength as presented in Figures 39 and 40, respectively [235]. Hence, it is important to determine the optimum amount of graphene in GPC in order to achieve desirable properties.  
Figure 39: Effect of NaOH on bending strength [235].  
The addition of graphene oxide in geopolymers for the application of 3D printing is popular amongst scientific community. However, with inclusion of graphene, there exists some negative impacts on properties of geopolymers such as reduction in fluidity and workability of GPC. [PERSON] et al. [232] stated that these properties can be improved by the addition of superplasticizers or silica fume. It was concluded that with inclusion of graphene oxide in GPC, the durability, rheological, and mechanical properties were improved making it possible for geopolymers to be used in 3D printing applications [236].
## 5 Results and Discussion
### Clustering of the SOM  
A _k-means_ algorithm to estimate the optimal number of sub-structures (k) (EFTs) classified the prototype vectors from the SOM. Davies-Bouldin index was iteratively ran 100 times to minimize the possibility to find a suboptimal solution when the initial prototypes are not properly chosen and the sensitivity originate for the selection of the initial cluster [87]. According to the results (see Figure 7) the optimum number of EFTs was 36; nevertheless, other local solutions were detected (e.g., \(k\) = 26). In fact, the existence of several solutions is in agreement with the essence of the ecosystem heterogeneity where patterns can be recognized at different levels of complexity and spatial detail [34].  
The final clustering partition is graphically displayed in the upper-right Figure 7. A detailed analysis of each cluster could be inferred from comparison with two-plane components (Figure 6). For instance, the upper-left cluster (red) was mainly characterize by medium INDVI (around 0.5) indicating medium biomass content, low RNDVI (0.4) that means a low intra annual variability, high MeanLST (around 25 \({}^{\circ}\)C) and high aridity index (around 62 \({}^{\circ}\)C ).  
Figure 7: **(Left-Upper)** Clusters obtained with the modified _k-means_, **(Right-Upper)** validity index values for Davies-Bouldin as a function of the number of clusters ranging from 2 to 36 and **(Down)** similarity among the dendrogram resulting from hierarchical agglomerative cluster analysis.  
To facilitate summarizing and reporting results the 36 clusters were further grouped into 12 clusters based on dendrogram of a hierarchical cluster analysis. From the 36 clusters considered _a priori_, only representative ecosystems in Spain, which reached a minimum surface of 3%, were taken into account. A high cophenetic correlation of 0.85 confirmed the goodness of fit of the dendrogram. Several cut-point were evaluated, resulting that a distance of 2 provides a coherent number of EFTs. Similar to [34] the EFTs were numerated according the cluster similarities of the dendrogram; for example, EFT.1 and EFT.2 were closely related, whereas EFT.12 presents the highest differences.
# Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations
## 1 Introduction
[PERSON] et al. [14], used the DPM model, combined with two-way coupling and wall roughness models, to obtain the erosion rate distribution in the elbow. The calculated data was very consistent with the experimental data. DPM is a Lagrangian parcel-based approach that models particle collisions and uncorrelated translations using the kinetic theory of granular flows. This approach has numerous advantages over the established Eulerian two-fluid model. These include better resolution of particle clusters and bubbles, more natural incorporation of particle size distributions, and better handling of crossing particle jets clusters [15]. Erosion analysis using DPM was verified regarding its effectiveness and validity by many previous studies. In many previous studies, simulations and experiments were performed according to the conditions of the slurry pump. They proposed a shape design that can minimize erosion. Several studies were conducted on the erosion of the impeller and casing. There are not many studies using statistical analysis methods to analyze the amount of erosion according to various combinations of factors. In this study, the process parameters were optimized to reduce the erosion rate density (E) of the throat bush while maintaining the operating conditions of the slurry pump. The erosion rate density (E) of the throat bush was first analyzed using a DPM that considered the operating conditions of the slurry pump and particle characteristics. Based on the first simulation, the effects of the gap between the impeller and the throat bush, rotation speed (rpm) of the impeller, and slurry particle diameter on the erosion rate density (E) were analyzed using a one-way layout. From the analysis results, we derived the erosion maps. The locations that exhibited the highest erosion rate density (E) were selected. A second analysis was performed to derive conditions for minimizing the erosion rate density at that location. A combination of process parameters was established using the response surface methodology (RSM) in the design of experiments methods. Accordingly, the parameters and dimensions were optimized to minimize the erosion rate density.
# Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration
## 4 Experiments
### Ablation Study on Graph Encoders  
This section presents the results of the ablation study conducted on the Citeseer dataset to evaluate the performance of different graph encoder architectures. Table 2 summarizes the node classification and link prediction results using the SmolLM2-360M-Instruct model with various graph encoders, including the GCN [8], GAT [43], graph transformer [9], and the proposed GCN-T.  
The results indicate that the proposed graph convolutional network with transformer (GCN-T) outperformed all other encoders across both tasks. GCN-T achieved a node classification accuracy of 69.68% and a link prediction accuracy of 64.86%, outperforming other encoders by a margin of 2-5%. This performance demonstrates GCN-T's ability to effectively integrate localized and global information, making it particularly suitable for tasks like link prediction, which require understanding complex relationships in graph data.  
In summary, this study highlights the strengths and limitations of existing graph encoders while demonstrating the advantages of GCN-T. The GCN is highly effective at aggregating localized information from neighboring nodes, making it strong in tasks which rely on local features. However, it struggles to model long-range dependencies, limiting its performance in tasks like link prediction. The GAT improves on the GCN by introducing attention mechanisms which adaptively weigh neighboring nodes, but its performance suffers in deeper graph structures due to noise accumulation. The graph transformer excels at modeling global dependencies using self-attention but lacks precision in capturing fine-grained local structures.  
GCN-T addresses these limitations by combining the GCN's ability to capture localized features with the transformer's capacity for modeling global relationships. This integrated design allows GCN-T to excel across diverse graph tasks, providing a balanced and robust framework which effectively bridges the gap between local and global information processing. By leveraging the complementary strengths of the GCN and transformer architectures, GCN-T sets a new benchmark for graph-based learning tasks and demonstrates its capability to handle both node classification and link prediction with superior performance.
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques
#### 5.3.5 Adaptive Neuro-Fuzzy Inference System-Based Algorithm  
The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT algorithm uses the combined strengths of neural networks and fuzzy logic to optimize the power output of PV systems [101]. ANFIS uses a hybrid approach, incorporating the learning capabilities of neural networks with the rule-based reasoning of fuzzy logic to model and predict the MPP. The algorithm adapts to varying environmental conditions by training a fuzzy inference system with historical and real-time data to improve its ability to forecast the optimal operating point of the PV system. Advantages of the ANFIS-based MPPT algorithm include its adaptability to complex and dynamic conditions. By learning from data, ANFIS can effectively handle non-linear and unpredictable variations in solar irradiance and temperature, making it highly effective in diverse environments. Additionally, the fuzzy logic component allows for a more intuitive handling of uncertain or imprecise inputs, enhancing the system's robustness. Disadvantages include the computational complexity and the need for extensive training data. ANFIS requires significant computational resources to train the fuzzy inference system and adjust its parameters, which can be demanding in terms of processing power and time. Moreover, the quality of the MPPT performance is heavily dependent on the quality and quantity of the training data; inadequate or unrepresentative data can lead to suboptimal performance [102]. Table 6 compares various hybrid MPPT algorithms.