esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Failure Mechanism of Mudstone Based on Digital Core and Digital Volume Correlation Method
## 3 Construction of the Digital Core Model  
### Composition Analysis of Mudstone  
According to the results of the XRD experiments, the main components of the samples were quartz and clay minerals, accompanied by a small amount of pyrite and plagioclase. The content of quartz was 45.1%, and the content of illite was 43.74% (Figure 3, Table 1).  
According to the SEM experimental results, when the sample was magnified to 30 times (Figure 4b), the mudstone contained a large number of quartz particles, with clay minerals mostly filling in the large particles and being attached to the edge of the large particles; the surface of the mudstone was relatively flat, the overall structure was relatively dense, and a small number of cracks were developed near the large quartz particles. When the sample was enlarged to 1300 times (Figure 4c), there were a large number of flake aggregates and microcracks in the field of view. According to the results of XRD analysis, these flake materials were clay minerals such as illite.  
According to the results of XRD and SEM analysis, the quartz content of the mudstone was high; the other materials were mainly clay minerals; and the accumulation mineral zone was quartz mineral zone, which was embedded in the clay mineral. The distribution of the two minerals in the whole mudstone sample was extremely uneven, and the quartz mineral zone in the upper right part of the sample was denser, with the distribution range being larger in the lower left part, but more discrete. To sum up, brittle minerals such as quartz are beneficial to the development of pores in rocks and the preservation of primary pores. There may be a large number of intergranular pores between brittle minerals and clay minerals. The initiation location of cracks is likely to develop from these pores and produce macroscopic cracks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Quartz** & **Illite** & **Chlorite** & **Plagioclase** & **Pyrite** & **Others** \\ \hline Content & & & & & & \\ (\%) & 45.1 & 43.74 & 4.86 & 4.6 & 0.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main chemical composition of mudstone (%).  
Figure 3: XRD test results of mudstone samples.
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation
### W-\(\Delta\)DOR DSP Core Architecture Description
#### 3.1.4 CIC Interpolation Filter  
The output of the RRC Filter needs further interpolation because the chip rate is parametric. The rate change is performed by using a Comb-Interpolation-Cascaded (CIC) filter that offers several advantages:  
* The interpolation factor is easy to reconfigure with respect to other approaches.
* The algorithm does not require multipliers.  
Figure 4: W-\(\Delta\)DOR core DSP architecture.  
* It provides a linear phase response.  
The implemented CIC filter is composed of four sections, obtaining a stop-band attenuation of 80 dB minimum. The Comb filter section registers are 12-bit wide, while the integration section registers width is 23 bit, as shown in [14]. The filter output word length is truncated to 16 bits.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach  
Five MLTs were applied: decision tree (DT), random forest regression (RFR), k-nearest neighbors (KNN) method, multilayer perceptron (MLP), and multiple linear regression (MLR). Additionally, the naive approach was chosen as a benchmark representing a minimum performance level attainable without any modeling effort.
## 5 Results and Discussion
### Accuracy of MI Responses: Results of Subject Grouping
Figure 8: Subject-wise analysis of EEG-based classification performance, highlighting the highest-performing model for each individual (denoted by an upward-pointing triangle) and the lowest-performing model (denoted by a downward-pointing triangle). Each deep learning model is color-coded to facilitate comparative interpretation. In contrast, for lower-performing individuals (rightmost subjects, such as 40, 27, and 42), accuracy values across all models decrease. However, dispersion among models becomes more apparent, and the performance gap between the best and worst models narrows. Interestingly, in several of these cases, _FBCSP_ achieves accuracy values close to or better than some deep learning models, highlighting that for challenging subjects with weak class separability, handcrafted features may still offer competitive performance. Nonetheless, in low-performing subjects (right side of the plot), _EEGNet_ often outperforms _FBCSP_, particularly where _FBCSP_ falls below the 0.7 accuracy line, indicating its resilience in handling low signal quality scenarios. Overall, the subject-wise analysis indicates that, compared to the baseline _FBCSP_ approach, DL models--particularly _TCNet Fusion_--consistently outperform it for the majority of subjects (27 out of 43), especially those exhibiting moderate to high EEG signal quality. However, for approximately 30% of the subjects (17 out of 43), using any of the evaluated DL models results in a deterioration of classification accuracy relative to the _FBCSP_ baseline. This suggests that the overall improvement across the entire cohort is achieved at the expense of high performance dispersion--where some individuals benefit substantially from DL models, while others experience significant performance degradation. Model Stability: _EEGNet_ and _TCNet_ show greater robustness across diverse subjects compared to _FBCSP_ and shallow models, highlighting their superior adaptability to heterogeneous EEG characteristics. Such variability compromises the inter-model consistency and challenges the adaptability of these models to subject-specific EEG patterns.
## 2 Material and Methods
### Machine Learning Algorithms and Statistical Methods
#### 2.5.1 Artificial Neural Network  
ANN is a machine-learning tool that can be utilized for pattern recognition. Herein, multilayer feed-forward neural network (MFFNN) structure was used as an ANN tool to predict the desired parameters. A typical MFFNN structure involves an input layer, multiple hidden layers, and an output layer. Our model architectures consisted of one-to-three hidden layers. The number of neurons ranged from 5 to 90 in each hidden layer to determine the most reliable ANN structure. Different activation functions, viz. hyperbolic tangent (_tanh_), logarithmic sigmoid (_Logsig_), linear (_Purelin_), and Gaussian, were used to update the network's weights.  
The developed model included an input layer fed by 11 features, two hidden layers, and an output layer. The first hidden layer had 10 computational units (neurons) with the sigmoid activation function. The second hidden layer had 70 neurons with the linear activation function. The ANN model had a layer configuration of 11-10-70-2. However, only the best-performing model's outcomes have been presented in the results section.  
A total of 500 epochs were used for the analysis, and the activation functions _tanh-tanh_ were applied to both pH and MC output parameters. Analyses were conducted using a total of 500 epochs. The _tanh-tanh_ activation function was applied to both output parameters to capture the complex non-linear patterns within the data: MC and pH. This activation function maps input values within a wide range to a bounded output between \(-1\) and \(+1\), offering non-linearity, continuous differentiability, and confined outputs.  
The learning rate, a crucial factor in ANN models, determines the pace at which the model assimilates information and adjusts its parameters during training. It remarkably impacts the model's convergence and ability to find an optimal solution. A trial-and-error approach was used to identify the ideal learning rate for the neural network. Different learning rates (0.001, 0.01, 0.1, 0.2, and 0.3) were explored to pinpoint the rate that led to the most reliable performance. After training iterations, it was found that a learning rate of 0.01 provided the best performance. This value expedited smooth convergence during the training process and resulted in superior prediction accuracy.
# Neighborhood Granule Classifiers
## 4 Design of a Neighborhood Granule Classifier
### The Algorithm for Neighborhood Granule Classification  
From the analysis of a structure of neighborhood granules, we propose the distance and similarity measures for neighborhood granules. Therefore, we can design a neighborhood granule classifier. The detailed neighborhood granule classification algorithm is described as follows:  
```
Input: A training set \(S=(U,A,D)\), a test sample \(t\), a neighborhood parameter \(\delta\in[0,1]\), and a similarity parameter \(\eta\in[0,1]\). Output: The label of a test sample \(t\).
```  
**Algorithm 1**Neighborhood granule classification (NGC).  
In the NGC algorithm, the neighborhood granulation process is mainly involved. In Step 3, the time complexity of neighborhood granulation is \(O(m*n)\), while the number of features is \(m\), and that of training samples is \(n\). Therefore, the time complexity of neighborhood granulation for training samples is \(O(m*n^{2})\) in Steps 2-6. Step 7 is the neighborhood granulation of the test set, and its time complexity is \(O(m*t^{2})\), while \(t\) is the number of test samples and \(t<n\). Steps 8-14 have linear complexity. Therefore, in the worst case, the time complexity of the NGC algorithm is \(O(m*n^{2})\).
## 2 Measurements of the _THz_ Spectrum of Noble Gases up to the 1990s
### 1922-1969: From Theoretical Prediction to Actual Measurements  
The hydrodynamic shape of the spectrum was theoretically predicted since the seminal work of [PERSON] [24] and the successive theoretical work of [PERSON] and [PERSON] during the 1930s [25] (see also the review by [PERSON]). However, the first experimental measurements of the Brillouin spectrum of liquids could only be performed much later [26], mainly owing to the extremely fine resolving power (\(\Delta E/E<10^{-7}\)) required for similar measurements in typical liquid samples. This performance imposes severe technical challenges that can only be addressed by complex interferometric techniques. In regards to noble gases, the first Brillouin study dates back to the measurement on Ar and Ne along the coexistence line [27] performed with a single-pass Fabry-Perot interferometer. Figure 2 provides an example of the Brillouin spectrum of dense Ar measured in this work. The hydrodynamic spectrum reconstructed using thermophysical and transport properties of the sample is also reported for comparison. It clearly appears that the latter profile essentially consists of three extremely narrow peaks, in full consistency with the simple hydrodynamic condition \(D_{\mathrm{T}}Q^{2},\Gamma Q^{2}<<c_{s}Q\). Indeed the width of triplet components measured in [27] was either coincident with the resolution function (for the Raileigh peak) or only slightly larger (for Brillouin peaks).  
From the position of the peak in the Rayleigh-Brillouin spectrum, [PERSON] and [PERSON] derived a value of sound velocity slightly lower than the one previously measured by ultrasound (US) absorption techniques at \(\approx\)1 \(MHz\). Although such an apparent frequency-decrease of sound velocity, also observed in water [29], initially raised some interest, it lacks a particular physical significance [30] (see also [22], page 263). Rather, it follows from the circumstance that the sound velocity measured by \(BLS\) (hypersonic velocity) is expectedly lower by few percent than the counterpart measured by \(US\) (ultrasonic velocity), unless competing viscoelastic effects become relevant.  
Overall, the shape of the spectrum predicted by the hydrodynamic theory was experimentally confirmed by Brillouin measurement in [27], even though a slight \(\omega\)-dependence of viscosity was inferred. At this stage the occurrence of a hydrodynamic triplet at macroscopic scales seemed well-assessed experimentally, while a remaining question concerned the persistence of a suitably generalized hydrodynamic profile down to mesoscopic scales. Most experimental studies described in the following paragraphs attempted to answer to this question.  
Figure 2: The Rayleigh Brillouin spectrum of Ar measured along the coexistence line by Brillouin light scattering. The data are redrawn with permission from [27], copyrighted by the American Physical Society. The hydrodynamic spectrum reconstructed using tabulated thermophysical properties of the sample and bulk viscosity measurements [28] are reported for comparison as a black line.
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 3 Proposed System
### DNN Layer  
The deep neural network (DNN), also known as the multi-layer perceptron neural network (MLP), is the most fundamental network. A large DNN is stacked by precursor perceptron neurons with weights and an activation function. A single perceptron cannot represent a linearly non-separable situation, even the basic logic operation \"xor\". Expanding the number of perceptrons and connecting layers can represent any mathematical function.  
The relationship between the input \(x^{(i)}\) and output \(y^{(i)}\) of the \(i\)-th layer can be calculated by Equation (3):  
\[y^{(i)}=\mathcal{F}(\mathbf{W}^{(i)}x^{(i)}+\mathbf{b}^{(i)}) \tag{3}\]  
where \(\mathcal{F}\) is the activation function, usually tanh, ReLU or the sigmoid function. \(y^{(i)}\) can be the output possibility or the input of the next layer as \(x^{(i+1)}\).  
We train a three-layer DNN as an encoder of input features \(\mathbf{X_{s}}\in\mathbb{R}^{\mathcal{N}_{s}}\) after derivation and selection into vector \(\mathcal{M}\in\mathbb{R}^{p}\), where \(p\) is the number of neurons in the last layer.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 1 Introduction
### Contributions  
This work has two main contributions. First, by presenting a multi-agent A-heuristic algorithm and piecewise cubic Bezier curve, the robot trajectory was planned. A re-path algorithm was deployed to help the robot track on the planned trajectory. It greatly reduced the processing time of the robot and maximized its speed. The second contribution is the capacity of the robot that can avoid moving obstacles by using a weighted-sum model and context analysis method. In a not-too-complicated case, this model could optimize the length of the robot trajectory very quickly, and the collision rate was quite low.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 3 Electrospun Nanofibers in Transistors: Computation and Multifunctional Electronics  
Compared with other low-dimensional materials, one-dimensional (1D) nanostructures have emerged as competing candidates for the active materials for field-effect transistors with their various advantages of large specific surface area, better uniformity, and favorable carrier mobility. In this way, the directional charge transport channel confined by the nanofibers avoids electron scattering and enhances the efficiency of charge transmission. The creative application of electrospun fibers with the above benefits to the active layers of the field-effect transistors (FETs) will bring about new changes for these conventional electronic devices. Commonly, the study of electrospun nanofibers for transistors can be separated into two categories, namely metal oxide semiconductor transistors and polymer semiconductor transistors. Representative materials are zinc oxide (ZnO), indium oxide (In\({}_{2}\)O\({}_{3}\)), nickel oxide (NiO), stannic oxide (SnO\({}_{2}\)), copper oxide (CuO), and Polyethylene oxide (PEO), polyaniline (PANI), poly(3,3''-didodecylquartethiophene) (PQT-12), and poly (3-hexylthiophene-2,5-diyl) (P3 HT), respectively.
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 2 Materials and Methods
### Improved YOLO8 Model
#### 2.3.2 C2f Module with Integrated SCConv  
The SCConv module, visually depicted in Figure 6, introduces a groundbreaking CNN compression technique that concurrently addresses spatial and channel redundancy within convolutional layers. This module, proposing two core components, particularly the Spatial Reconstruction Unit and Channel Reconstruction Unit, achieves tremendous overall-performance beneficial properties whilst extensively reducing computational load.  
Figure 5: Sampling technique comparison: Standard Convolution vs. Deformable Convolution. The green dots represent the regular sampling grid of standard convolution, while the blue dots indicate the deformed sampling positions with augmented offsets in deformable convolution.  
SRU addresses spatial-dimension challenges in feature maps by decomposing the entire feature map into multiple spatial blocks and utilizing distinct convolutional kernels for each block, thereby reducing spatial redundancy. This strategy precisely captures characteristic statistics inside every block, appreciably diminishing basic spatial redundancy, bettering function-extraction efficiency, and, at the same time, reducing computational complexity.  
On the other hand, CRU focuses on optimizing the channel dimension of characteristic maps, introducing a lightweight wholly linked layer to deal with channel records more flexibly and efficiently. This permits the mannequin to combine records from one-of-a-kind channels more effectively, decreasing channel redundancy. This approach not only strengthens characteristic discrimination but additionally similarly reduces computational requirements.  
Functioning as a versatile and compatible component, SCConv seamlessly integrates without necessitating alterations to current model structures, and it can readily substitute standard convolution. In extensive experiments, embedding SCConv into various state-of-the-art methods for image classification and object detection demonstrated superior performance and efficiency balance.  
In the context of woodland wildfire detection, SCConv takes the place of the Bottleneck in the original YOLOv8 model's C2f. This structural alteration transforms C2f into \(SCConv\_C2f\), effectively streamlining standard convolution and concurrently reducing computational demands. The network architecture for \(SCConv\_C2f\) is illustrated in Figure 7.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Effect of Diesel Contamination on Some Properties Related with Its Possible Remediation  
#### 3.11.1 Electrical Conductivity of the Soil  
The EC of soil has been used as a parameter related to the level of macronutrients and micronutrients available in the soil [46]. The EC of the natural soil decreased from 536 \(\pm\) 86 \(\mu\)S/cm to 420 \(\pm\) 38 \(\mu\)S/cm after its contamination. Due to this significant decrease (\(p<0.03\)), it is possible that the available nutrients for both microorganisms and plants may be unbalanced, and an addition of external nutrients may be needed for soil bioremediation. The observed EC reduction has been previously reported [9], and it was attributed to the low dielectric constant of the diesel which reduced the dielectric constant of the soil's pore fluid [9].
## 3 Results
We attributed the mode with the largest effective refractive  
Figure 4: (**a**) Direct plane image of the fabricated device for transverse electric- (TE-) and transverse magnetic- (TM-) polarisations of incident light. A grating-shaped spot on the right corresponds to the radiation, which was coupled to waveguide modes and subsequently radiated into the far field by decoupling grating; (**b,c**) back focal plane (BFP) and direct plane images of sample 4 with open (**b**) and closed (**c**) decoupling grating. Red circle defines numerical aperture (NA) = 1.3 of collecting objective. Figure 3: (**a**) Schematic view of the fabricated photonic device. The sizes of gratings, triangles and waveguide length were fixed. Waveguide height and width were varied; (**b**) Atomic force microscopy (AFM) image of manufactured photonic device (sample 2). (**c**) Waveguide cross-section (sample 2). index to TE\({}_{00}\) mode, and the second mode to TE\({}_{01}\) mode. The obtained effective refractive index values are presented in Figure 5.  
The same procedure was conducted for other three samples. Obtained values of the effective refractive index of every mode and geometrical parameters of the waveguides of the samples are shown in Table 1. With the increase of polymer waveguide height as well as width, the effective refractive indices of modes and the number of modes are increasing. This indicates that electromagnetic field confinement in the polymer waveguide is enhancing with the increase of waveguide size.
# Controlling the Focusing Ability of the Parabolic Reflector in Free Space at Microwave Frequencies
## 1 Introduction
In [20], it is numerically shown that the phase of microwaves can be controlled by active graphene circuit on a Salisbury screen. Then, a large-area active surface with graphene electrodes is used to control the reflection and absorption by chemical potential of graphene [21]. In that study, a switchable radar absorbing surface is produced as the pixelated hybrid system. In spite of all the aforementioned literature, most of the studied geometries are related to the planar reflectors, and the tunable graphene reflector having curved profile is studied very rarely compared to the planar ones. For example, in [16], the focusing effect of a graphene parabolic reflector is studied, and the results show that the control of field level is problematic above lower THz regions. In the present study, the graphene parabolic reflector is numerically modelled in microwaves depending on the problem parameters with the high accuracy provided by the RHP-based MAR method. The field control is performed by adjusting the chemical potential of graphene. With this modelling, it is verified that the control of the scattering pattern and field level at focus is possible in a wider range in microwaves by electrical biasing. This observation can be satisfied for all microwave ranges up to the millimeter waves, and it can be realized in practice by varying chemical potential from 0.05 eV to 1 eV. This kind of tunable reflector in microwaves may have great application as a radar absorber or signal modulation in electromagnetics. The results are shown and discussed in the numerical results part.