esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Data Preprocessing  
The data preprocessing is divided into two successive stages, namely, noise removal and filtering followed by segmentation. Raw signals are usually noisy, particularly EMG signals, so they need to be filtered in order to remove noise, unwanted frequencies, and only retain frequencies of interest. For this purpose, the raw data from the accelerometer (ACC) and electromyogram (EMG) are filtered using a band-pass filter (20-450 Hz). Also, a notch filter at 50 Hz is further applied to EMG signals to  
Figure 1: (**a**) Accelerometer sensor placement, (**b**) electromyography (EMG) sensor placement at forearm, (**c**) EMG sensor placement at Biceps.t, (**b**) EMG sensor placement at forearm and (**c**) EMG sensor placement at biceps brachii (BB) muscle.  
Figure 2: Architecture of the proposed methodology.  
avoid AC interference. In the segmentation stage both EMG and ACC filtered signals are segmented into 150 ms segments with a sliding window of increment step of 25 ms. Each of these segments is considered as a single trial and will be used later in the feature extraction and classification steps. Thus, each trial will have a size of \(N\times T\), where \(N=1\) is the number of channels and \(T=150\) corresponds to 150 ms segment sampled at 1000 Hz.
## 2 Research Methodology
### One-Class Support Vector Machine (OC-SVM)  
The OC-SVM is an unsupervised classifier used for anomaly detection and outlier identification in datasets [44]. The objective of the OC-SVM is to learn a decision boundary that separates the majority of data points representing normal observations from the origin in a higher-dimensional feature space. This boundary is defined by a hyperplane that maximizes the margin between the data points and the origin, effectively creating a region where normal data points lie, while leaving anomalies or outliers outside of this region. The decision function, \(f(x)\) is expressed as follows:  
\[f(x)=w^{T}\phi(x)-\rho \tag{5}\]  
where \(\phi(\cdot)\) is a feature projection function that maps an input vector \(x\) into a higher dimensional feature space; \(w\) is the decision hyperplane normal vector; and \(\rho\) is the bias term. They can be obtained by solving the following objective function:  
\[\min_{w,\xi,\rho}\frac{1}{2}\|w\|^{2}+\frac{1}{vm}\sum_{i=1}^{m}\xi_{i}-\rho \tag{6}\]  
subject to  
\[w^{T}\phi(x_{i})\geq \rho-\xi_{i},\ \xi_{i}>0,\ \ \ \ i=1,\ 2,\ \ldots,m \tag{7}\]  
where \(x_{i}\) are the training examples belonging to one class; \(v\ \in\ (0,\ 1]\) determines the upper bound on the fraction of outliers and the lower bound on the number of training samples used as support vectors; and \(\xi_{i}\) are non-zero slack variables for penalizing the outliers.  
The feature mapping \(\phi(\cdot)\) which can become computationally inefficient, especially for high dimensional data, can be replaced by a kernel function \(K\big{(}x_{i},x_{j}\big{)}\). By implicitly computing the inner product \(\phi(x_{i})^{T}\phi(x_{j})\) in the feature space, this function eliminates the need for explicit feature mappings while ensuring computational efficiency and guaranteeing the existence of solutions [45]. Among the available kernels, the Gaussian kernel is a commonly chosen option due to its flexibility and effectiveness. It is expressed as follows:  
\[K(x_{i},x_{j}),=exp\ (-\frac{\|\ x_{i}-x_{j}\|^{2}}{2\sigma^{2}}) \tag{8}\]  
where \(\sigma\) is the width of the Gaussian kernel.
# Investigation of the Mechanical Behavior of Polypropylene Fiber-Reinforced Red Clay
## 1 Introduction
Studies have clearly shown that fiber reinforcement could ameliorate the mechanical characteristics of soil, thus significantly improving the strength, stiffness, liquefaction resistance, and stability of the foundation. Existing studies on fiber-reinforced soils have mainly focused on sands, with few studies on clays, especially the shear strength of red clay. Therefore, this study investigated the mechanical properties of polypropylene fiber-reinforced red clay and analyzed the effects of \(FC\), \(FL\), and cell pressure on the shear strength of the polypropylene fiber-reinforced red clay by triaxial tests. By fitting the test data, a hyperbolic constitutive model that considers the effects of \(FC\), \(FL\), and cell pressure was established. The model is of reference value in the design and construction of fiber-reinforced red clay foundations.
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
## 5 Simulations
### Data Recovery Algorithm  
In the algorithm simulation experiment, a string of a pseudo-random binary sequence (PRBS) is generated first and is divided into a series of frames with 1024-bit lengths. A fixed sequence 0 xf628 is inserted between every two frames as the frame synchronization code. The new sequence is then applied to DP-MZM as the raw signal for modulation.  
In fact, the searching algorithm plays an important role in our data recovery algorithm. This study applied a frame parameter recognition algorithm based on self-correlation to search for the frame synchronization code, and the recognition accuracy rate is claimed to be very high when bit error rate (BER) is less than 0.02 [31; 32]. In this algorithm, the frame length is searched first within a given range based on self-correlation and a peak-to-average ratio, and thresholds are set to obtain a credible frame length estimation value. Then, the synchronization code is recognized and thresholds are set to distinguish between 0 and 1. To simplify the simulation, we assumed that the frame length was known to the receiving end. If a clear correlation peak appears, which means a fixed frame synchronization code existed in the bitstream, the sequence is considered as the raw data. Otherwise, it is not. Figure 6 gives the frame synchronization code recognition result of one simulation. The upper and lower thresholds in Figure 6a were set to determine the frame length, and the thresholds in Figure 6b were set to recognize the synchronization code. The bitstream with a clear correlation peak appearing was highly consistent with the raw data. Given that bit errors existed during demodulation, we considered the bitstreams with a correlation peak as the raw data.
# Characteristics of Moduli Decay for the Asphalt Mixture under Different Loading Conditions
## 3 Test Result and Analysis
### The Critical Value of Moduli at Different Fatigue Stress Levels  
During the fatigue tests, the moduli decrease with the increase of the load cycles until the failure of the specimens. The critical value refers to the damage value when the specimen occurs fatigue failure at the end of the fatigue test. In this paper, the average value of the moduli in the last five loading cycles in the fatigue tests were taken as the critical moduli value. The tensile, compressive, and indirect tensile moduli of asphalt mixtures, which is based on the indirect tensile test, and the calculation formula was derived on the Hooke's law in two-dimensional stress states [7]. The average critical moduli values of the four parallel specimens under different stress states and different stress levels are summarized in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Stress Level/MPa** & **0.25** & **0.5** & **1** & **1.5** & **2** & **2.5** & **3** & **3.5** \\ \hline \multirow{2}{*}{Tensile moduli} & \(E_{0}\) (MPa) & 1384 & 1686 & 2121 & 2407 & / & / & / & / \\  & \(CV\) (\%) & 0.054 & 0.055 & 0.058 & 0.045 & / & / & / & / \\ \hline \multirow{2}{*}{Compression moduli} & \(E_{0}\) (MPa) & / & / & / & / & 8852 & 10,102 & 11,167 & 11,943 \\  & \(CV\) (\%) & / & / & / & / & 0.021 & 0.024 & 0.023 & 0.055 \\ \hline \multirow{2}{*}{Indirect tensile moduli} & \(E_{0}\) (MPa) & 3560 & 4027 & 4585 & 5057 & / & / & / & / \\  & \(CV\) (\%) & 0.011 & 0.019 & 0.014 & 0.019 & / & / & / & / \\ \hline \multirow{2}{*}{Compression moduli measured by indirect tensile test} & \(E_{0}\) (MPa) & 7926 & 8835 & 9975 & 10,760 & / & / & / & / \\  & \(CV\) (\%) & 0.015 & 0.018 & 0.018 & 0.014 & / & / & / & / \\ \hline \multirow{2}{*}{Tensile moduli measured by indirect tensile test} & \(E_{0}\) (MPa) & 2547 & 2935 & 3464 & 3858 & / & / & / & / \\  & \(CV\) (\%) & 0.024 & 0.021 & 0.027 & 0.012 & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 8: The initial moduli values in different fatigue tests.  
From Table 9, the similar patterns of variation can be observed that the critical moduli value varies in different stress levels. It also varies in the different fatigue stress conditions. The common characteristic in the different stress states is that the critical values increase with the increase of the stress levels. The critical compression moduli are larger than that of the tensile moduli.
## 4 Materials and Method
### Dataset
#### 4.1.1 Airborne Gravity Data  
Air-FTG is a multiple accelerometer moving platform technology that measures the accelerations in the inline and cross signals from the three Gravity Gradient Instruments (GGI) which are converted to directional gradients and provides tensor elements of Tyx, Tyy, Tyz, Txz, and Tzz tensor components [42], along with another four combination tensors (Figure 3A). The different tensor components in Air-FTG are commonly used to recognize and classify different lineaments associated with structural and/or stratigraphic changes, based on contrasting frequency slicing, that represent different scale and target depth of the geological structures. the combinations of the individual tensor components into a single interpretation and representation had been proven to extract the most accurate signature pattern of the underlying geology [43].  
Central Luconia Air-FTG data went through High-Rate Post Mission compensation, line correction and levelling, noise reduction and contact lineament processed. Depth (Z) domain tensors including Tz, Tzz, Txz and Tyz dataset (Table 1) were selected for geological analyses and interpretation because they represent the depth in vertical and horizontal components. The vertical depth components (Tz and Tzz) closely resemble the conventional gravity in that the anomaly is shown in the correct spatial position over center of geologic mass. Both Tz and Tzz vertical component is useful in estimating depth, predict compositional information of the target geobody [43] and detection of useful contrast within structures [44]. The horizontal depth components (Txz and Tyz) are selected because both data are relevant to identify, and map trends associated with structural and determination of center of mass bodies [45]. Apart from these two data, Contact Lineament Processing (CLP) at three different wavelengths was also utilized for the gravity interpretation. CLP is a method that applies a directional filter in processing the full-tensor gradiometer data. It is a new processing tool for potential field data developed in-house by Bell Geospace [41] that helps assessing the relationship between different representations of an invariant tensor field evaluated from the measured gravity tensor.  
Figure 3: **(A)** Five independent directions which are Tyx, Tyy, Tyz, Txz, and Tzz tensor components from Air-FTG data (edited from [42]. **(B)** Profiles showing magnetic responses for a source of the same size at different latitudes in the northern hemisphere. The effect is reversed in the southern hemisphere due to a negative inclination. **(A)** A symmetric response is observed at the pole. (Bi-Bv) The anomalous magnetic response becoming more asymmetric away from the poles. **(B)** The response is to finally flatten or reverse at equatorial regions.
# SIS Epidemic Propagation on Scale-Free Hypernetwork
## 2 Theoretical Backgrounds
### SIS Epidemic Model  
Infectious disease models are often used to describe the spread and development of infectious diseases. According to the division of individual states in the network, many infectious disease models have been developed, and one of the classic models is the SIS propagation model. In the SIS model, individuals are divided into two categories: Susceptible (S) and infected (I). When an infected person is in contact with a susceptible person, the susceptible one will be infected with probability \(\beta\) to be an infected person; while the infected person is cured with probability \(\gamma\) and recovered as a susceptible person. At the same time, the cured susceptible person may still be re-infected and be a new infected one. Similar to the common flu, the flu is highly variable and most people will almost get the flu again. As the disease continues to spread in the network, the density of infected persons will reach a relatively stable value, and fluctuate slightly around this relatively stable value, which indicates that the spread of the disease in the network has reached a stable state.  
The SIS model can be represented by the following differential equation:  
\[\left\{\begin{array}{l}\frac{ds(t)}{dt}=-\beta i(t)s(t)+\gamma i(t)\\ \frac{d(t)}{dt}=\beta i(t)s(t)-\gamma i(t)\end{array}\right. \tag{1}\]  
Among them, \(s(t)\) and \(i(t)\) represent the proportion of susceptible and infected individuals in the network at time \(t\), satisfying \(s(t)+i(t)=1\), \(\beta\) and \(\gamma\) are infection rate and recovery rate, respectively.
## 2 Materials and Methods  
### Materials Used in This Study  
Three different constituent materials were used in this work in order to study new asphalt mixtures incorporating a plastic waste. As usual, mineral aggregates and asphalt binders were used as indispensable components of asphalt mixtures, and a specific plastic waste was the third material used, applied both as an aggregate substitute and/or asphalt binder modifier.  
Crushed granite aggregates were supplied by a local quarry in the Northwest of Portugal, being divided into three fractions with the following dimensions: 6/14 gravel, 4/6 gravel and 0/4 dust. A small quantity of limestone filler was also used, as it is usual practice for the production of asphalt mixtures with those aggregates. These aggregates were selected to produce AC surf 14 mixtures for pavement surface layers.  
Two commercially available binders typically applied in Portugal were used in this study, both supplied by Cepsa Corporation, Madrid, Spain. A 50/70 pen grade bitumen (according to EN 12591) was used as the base binder for comparison purposes, before and after incorporation of plastic waste in the mixtures. A polymer modified binder PMB 45/80-60 (according to EN 14023) was also used for an additional comparison, taking into account that the use of plastic waste may change the base 50/70 bitumen into a PMB.  
The plastic waste applied in this study was a cross-linked polyethylene (PEX), mainly recovered from pipe, tubing and electrical cables, and it was supplied by Gintegral S.A., Povoa de Varzim, Portugal. This material is composed of particles with a lamellar flake shape and an equivalent diameter (\(\mathcal{O}_{\mathrm{eq}}\)) ranging between 0.5 and 10.0 mm. In order to reduce their size and lamellar shape, PEX flares were ground to obtain smaller particles with a nominal diameter between \(0.5<\mathcal{O}_{\mathrm{eq}}\) (mm) \(<4.0\).
## 3 Results and Discussion
### DSC Analysis  
Figure 8 shows the DSC measurement curves of alloy A(0.1 Zr) and alloy B(0.05 Sc) through different homogenization heat treatments in the solid solution quenching state. The DSC test results were used to explore the precipitations of alloy A(0.1 Zr) and alloy B(0.05 Sc). The first exothermic peak in the figure was the precipitation of the main strengthening phase GP-zone/\(\eta^{\prime}\) phase, and the second exothermic peak was the transition of the \(\eta^{\prime}\) phase to the \(\eta\) phase [25; 26]. In Figure 8a, it can be observed that the peaks of the GP-zone and the metastable \(\eta^{\prime}\) phases of alloy A(0.1 Zr) after the two-stage homogenization heat treatment were more significant than those after the one-stage homogenization heat treatment. That is because different homogenization heat treatments affected the microstructures in the grains. The two-stage homogenization reduced the recrystallization ratio, increased the amount of sub-grain boundaries, and obtained a larger hardness value after the aging process [19; 27].  
Figure 8: The DSC curve analysis after solution treatment of (a) alloy A(0.1 Zr); (b) alloy B(0.05 Sc).  
Figure 7: Microstructures observation by OM after 24 h solution treatment and aging: (a) 1-Hom. alloy A(0.1 Zr); (b) 1-Hom. alloy B(0.05 Sc); (c) 2-Hom. alloy A(0.1 Zr); (d) 2-Hom. alloy B(0.05 Sc); (e) cross-sectional direction. ND, normal direction; RD, rolling direction.  
After the two-stage homogenization, the peaks of the GP-zone and metastable \(\eta\)' phases of alloy B(0.05 Sc) were not as obvious as those of alloy A(0.1 Zr), as shown in Figure 8b. This was probably related to the smaller range of sizes of the Al\({}_{3}\)Sc grains. It can also be found that, regardless of the one-stage homogenization or the two-stage homogenization, the peaks of GP-zone and metastable \(\eta\)' phases of alloy B(0.05 Sc) were also higher than those of alloy A(0.1 Zr), showing that the Sc element reduced the precipitation rate of the strengthening phase \(\eta\)' phase more effectively than the Zr element. Therefore, if the precipitation amount of the metastable state \(\eta\)' phase was more, the precipitation amount of the stable state \(\eta\) phase became less. These findings conformed to the test results of the aforementioned microstructures and the subsequent mechanical properties.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Antenna Evolution to Provide New Degrees of Freedom
#### 2.2.4 The OAM Vortex Degrees of Freedom Technology  
The orbital angular momentum is envisioned to have applications in wireless communications, significantly enriching the degrees of freedom available for information mapping. Even quantum information (ref. [25]) could, in theory, be examined for applications, although the relative technological readiness level is in its infancy. Nevertheless, the production of a vortex wave is already here, and it is anticipated that it will not be long before OAM technology is integrated into existing degrees of freedom enhancement schemes (ref. [26]). To elaborate further, the use of OAM results in an increase in the multiplexing index n (see Equation (2)), thus significantly boosting the spectral efficiency and capacity.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Implementation of Key Parameters
#### 5.3.3. Caching Schemes  
Before the implementation of the framework, _caching schemes_ had not been implemented at all in ndnSIM. Thus, ndnSIM implicitly implements the CEE scheme since every packet is automatically cached. Since ndnSIM does not have a suitable architecture for caching schemes, its architecture has to be extended to support a Scheme and corresponding subclasses.  
The implemented subclasses of Scheme are GeeScheme, NoCachingScheme, and ProbScheme. Schemes are relevant when a Data packet arrives at the CS--specifically, when a Policy's method doMterInsert is called. Every subclass of Scheme has to implement the shouldCache method, which takes an entry reference and returns a boolean value indicating the caching decision. Each Policy calls the shouldCache method at the beginning of the doMterInsert method and, if necessary, evicts the added element again if false is returned. If true is returned, doMterInsert continues as usual.  
The decision of the Scheme subclass is independent of the implementation of the policy: Every Policy has a pointer to its own scheme and may call shouldCache on it. It does not matter which specific subclass of Scheme is implemented, and the subclass may even be changed at runtime if desired. In design pattern terminology, this approach is a realization of the Strategy pattern, as defined in [39]:  
The Strategy Pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable. Strategy lets the algorithm vary independently from clients that use it.  
The relationship of the Policy-Scheme architecture in this work is portrayed in Figure 3.  
The realization of the Scheme subclasses is straightforward since only shouldCache has to be implemented. GeeScheme always returns true, NoCachingScheme always returns false, and ProbScheme returns true with probability \(p\), which is a double value that can be passed into its constructor.  
Figure 3. A diagram of the Policy/Scheme class hierarchy.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 1 Introduction
### The Seismic Full Waveform Inversion  
The first introduction and numerical implementation of FWI was found to be developed in the 1980s; see [2; 3; 4; 5; 6; 7]. Since its first implementation, many authors have implemented and modified it for different purposes and methods. The time domain FWI approach is applied in frequency domain approach in [8; 9]. The performance of frequency domain approach of FWI is optimised with parallelisation by [10; 11]. FWI is widely used for inversion of: (a) acoustic waves, e.g., [12; 13; 14] (b) seismic waves, e.g., [12; 15; 16; 17]. With the advancement in modern day computers and parallel processing capabilities, FWI is gaining its popularity in exploration geophysics. A basic flow chart of Seismic FWI in shown in Figure 2.  
The Finite Difference Method (FDM) is one of the most widely used inversion methods in the numerical implementation of FWI. The conventional FDM is based on the straightforward numerical implementation of Partial Differential Equations (PDE) for the given Boundary Value Problem (BVP). The partial differential terms are substituted with algebraic differences for a small argument \(x\), such that \(x\to 0\), which makes it easy to implement and understand (refer Equations (6) and (7)). The finite difference formulation for seismic waves can be found in: (a) [18; 19; 20] for forward wave propagation problems and (b) [12; 21; 22; 23] for FWI problems. To satisfy Sommerfeld's radiation condition at infinity (i.e., the wave radiating towards infinity should not come back unless there are reflecting boundaries), absorbing boundaries or perfectly matched layers (PML) [12] is used. PML damps the outgoing wave field over a distance (number of grids in discrete form), making it more efficient and accurate than a normal absorbing boundary. Details about PML can be found in [24; 25; 26].  
Figure 1: A schematic representation of geophysical investigation using seismic waveform inversion of a subsurface geological region with multiple layers.