esa-sceva/satcom-chunk-collection huggingface dataset:
# FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
## 2 TFM in Anisotropic Material  
### TFM Algorithm in Isotropic Material  
The full matrix capture (FMC) approach uses the complete set of time-domain data (A-scans) from all combinations of transmitting and receiving elements. During an FMC inspection process, ultrasonic waves are transmitted from one array element, and all array elements capture the reflected signals, which is repeated to cover every combination of all transmitting and receiving elements. For an array system consisting of \(N\) elements, an FMC signal matrix is composed of \(N\times N\) A-scan signals. The TFM imaging algorithm uses an FMC matrix [18]. Figure 1 illustrates the concept of the TFM algorithm. The position of a single point reflector within the media is defined in terms of the \(x\)- and \(z\)-coordinates, (\(x\), \(z\)). The wave propagation distance, \(d_{ik}\), from transmitter \(i\) to the reflector and back to receiver \(k\), is calculated for each possible combination of \(i\) and \(k\) using Equation (1).  
\[d_{ik}=\sqrt{\left(x-x_{i}\right)^{2}+\left(z-z_{i}\right)^{2}}+\sqrt{\left(x -x_{j}\right)^{2}+\left(z-z_{j}\right)^{2}} \tag{1}\]  
The propagation time, \(t_{ij}\), is determined by dividing the propagation distance by the velocity of the longitudinal wave \(c\) in media. In the TFM, the ultrasonic beam is focused at any target point (\(x\), \(z\)). The TFM algorithm first discretizes the target region into a grid. The signals from all the elements in the array are then delayed and summed to focus on the target point in the grid. The intensity of the grids \(I(x,z)\) at every point in the grid is expressed as follows:  
\[I(x,\ z)=\left|\sum_{i}^{N}\sum_{j}^{N}h_{ij}\bigg{(}\frac{d_{ij}}{c}\bigg{)}\right| \tag{2}\]  
Figure 1: Schematic representing the basic concept of phased array imaging and the geometry of the phased array system.  
where \(h_{ij}\) is the analytical signal associated with the signal recorded by element \(j\) as element \(i\) transmits, and \(N\) is the element number.  
In general, the TFM is used to image point-like reflectors in isotropic media, in which the ultrasonic beam propagates in a straight line. However, in austenitic welds, the anisotropic material properties and the distribution of the grain orientation associated with the position cause skewing and splitting of the ultrasonic beam. Therefore, it is necessary to accurately calculate the ultrasonic beam propagation path in austenitic welds for TFM applications.
# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 3 New, Improved UCP--Our Approach
### The Methodology Used within the Improved UCP Model
#### Step 2:  
All input values are transformed according to the following formula: The function \(\mu_{D}(X)\): \(R\rightarrow[0,1]\) translates the real values of input signals into coded values from the interval \([0,1]\) in the following way: \(\mu_{D}(X_{i})=(X_{i}-X_{min})/(X_{max}-X_{min})\)[33; 34], where \(D\) is the set of data on which the experiment is performed, \(X_{i}\) is the input value, \(X_{min}\) is the smallest input value, and \(X_{max}\) the greatest input value on the observed dataset.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 2 Materials and Methods
### Concepts Used  
Understanding the fundamental concepts is essential to addressing the methodology proposed in this study. Among the most relevant images that have been considered in the development of the process are: These concepts are fundamental to understanding the proposed method in this study and how they are applied in detecting anomalies in critical infrastructure through machine learning and using IoT devices in real-time.  
* Critical Infrastructure: Critical infrastructure refers to essential physical and virtual assets that are fundamentally important to a society, the economy, and national security.  
This includes electrical grids, water supply systems, transportation and communications networks, and other vital assets [21].
* Cybersecurity: Cybersecurity protects digital systems, networks, and data from cyber threats, including attacks, theft, malware, and other risks [22]. Ensuring cybersecurity is critical to preventing unauthorized access and ensuring the integrity of critical systems.
* Anomaly Detection: Anomaly detection involves the identification of unusual patterns or atypical behaviors in a data set. In cybersecurity, anomaly detection is used to identify suspicious or unauthorized activities that may indicate a cyber-attack.
* Deep Neural Networks: Deep neural networks are a type of machine learning algorithm inspired by the human brain's structure. These networks have multiple hidden layers for complex data processing and pattern identification in high-dimensional data.
* Devices IoT: IoT devices are physical objects connected to the network and can collect and transmit data. These devices may include sensors, cameras, meters, and other devices that collect real-time information.
* Threat Prevention: Threat prevention involves taking proactive measures to prevent cyber threats from becoming successful attacks [23]. This may include blocking unauthorized access, strengthening security, and taking preventive measures to reduce risks.
* Real-Time Data Integration: Real-time data integration involves combining and analyzing data as it is generated and transmitted. In the context of critical infrastructure, this enables a faster and more adaptive response to events and threats.
* D: IoT dataset containing measurements and logs collected from sensors in a smart city.
* X: feature matrix of data set D, where each row represents an observation and each column corresponds to a specific feature.
* y: vector of class labels indicating whether an observation in data set D is an anomaly (y = 1) or a normal instance (y = 0).
* Isolation Forest: specialized in detecting anomalies related to energy consumption in the electrical grid of a smart city.
* Recurrent Neural Networks (RNNs): used to model temporal sequences in urban IoT data and detect anomalous patterns in time series.
* Variational Autoencoders (VAEs): They learn high-dimensional latent representations of urban data, facilitating the detection of anomalies in complex data.
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
## 4 Residual Stress Relaxation of DRWDs by External Cyclic Loading  
### Welding Residual Stress Release of DRWDs under Constant Amplitude Cyclic Loading  
To investigate the combination influence of stress ratio and stress amplitude on the multi-axial residual stress relaxation effect of DRWDs, 25 constant amplitude cyclic loading cases are applied to the multi-scale coupled stress analysis model of OSDs. The five transverse stress amplitudes from 10 MPa to 130 MPa are designed, and five stress ratios from -0.5 to 0.5 with 0.25 increments for each of five stress amplitudes are studied. For convenience, the transverse stress amplitude, longitudinal stress amplitude, and stress ratio are denoted as TSA, LSA, and R. The nominal stress is usually used to calculate the fatigue life in the specifications. As a result, the transverse stress is used in this paper to set loading cases and the location of the transverse stress is 1.5 t away from the weld toe, where t = plate thickness. That is to say, the TSA (the stress is perpendicular to the welding direction) and LSA (the stress is parallel to the welding direction) at the joints 1.5 t away from the weld toe (Figure 11) are extracted as TSA and LSA, respectively, after the external load is applied to the multi-scale coupled stress analysis model of OSDs.  
A residual stress relaxation coefficient A is introduced to represent the change of welding residual stress relaxation under different loading cycles, which can be expressed as follows:  
\[\text{A}= (\sigma_{res})_{after}/(\sigma_{res})_{ini}, \tag{7}\]  
where \((\sigma_{res})_{after}\) is the stable coupling stress that considers the welding residual stress and the mechanical stress induced by external cyclic loading; \((\sigma_{res})_{ini}\) is the initial residual stress.
## 1 Introduction
### Contribution and Organization  
To our knowledge, [PERSON] et al. [15] presented the first implementation of the spectral clustering algorithm to compensate for nonlinear phase noise in 16-QAM modulation. We extend the work mentioned above by performing a study on the response of the spectral clustering algorithm to the variation of nonlinear phase noise and signal-to-noise ratio (SNR) with a range from 5 dB to 20 dB, applied to modulation formats with 16-QAM, 32-QAM, and 64-QAM. Each constellation was generated with a frequency offset, with values of \(-90\), \(-100\), and \(-110\) dB/Hz at 1 MHz, for a transmission frequency of 10 GHz. The contributions of this work can be summarized as follows:  
* Presentation of a modified spectral clustering algorithm to work with data streams for future real-time implementations and integration with a demapping stage for m-QAM.
* The methodology involves simulating scenarios that approximate real-world conditions using widely accepted transmission values and large transmission formats.
* The comprehensive approach allows us to explore the response of the spectral clustering algorithm in three scenarios of nonlinear phase noise and SNR variation.  
The article is organized as follows: Section 2 presents the spectral clustering algorithm, an example of its operation, and a proposal for its use for data streaming. Section 3 presents the configuration used for the data simulation. The results are presented in Section 4, and the discussion and conclusions are presented in Sections 5 and 6, respectively.
## 2 Methods
### Participants
#### 2.1.1 CLIS Patient  
The patient, a 54-year-old female with limb onset ALS diagnosed 46 months prior to the initial BCI session, in February 2023, had an ALS functional rating scale-revised (ALSFRS-R) score of 1. At the beginning of the experiments, she had no eyelid and vertical eye movements and could no longer use the eye-tracking device she used previously. She had slight horizontal movement of the eyes, interpreted as a 'Yes' response, while the absence of movement indicated a 'No'. Only the patient's husband could interpret the slight eye movement.  
Although she had lost control of the eye-tracking device, it continued to serve as a means for her family to discern whether her eye was open and to detect attempts at eye movement, which were indicative of communication efforts. The patient subsequently entered CLIS some months later. Despite the loss of direct communication, the ERP waveform analysis suggested that the patient retained visual, hearing, and cognitive functions, which motivated us to continue the experiments. Table 2 summarizes the main clinical characteristics of the patient 8 months after the initial BCI session.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Participants** & \begin{tabular}{c} **Data** \\ **Acquisition** \\ \end{tabular} & \begin{tabular}{c} **Stimuli** \\ **(Nr. of Sessions)** \\ \end{tabular} \\ \hline [PERSON] et al., 2007 [10] & 17 ALS CLIS & fNIRS & \begin{tabular}{c} Auditory yes/no \\ questions (binary) \\ \end{tabular} & 79.6\% * (NA) \\ [PERSON] et al., 2019 [11] & 4 ALS CLIS & fNIRS & \begin{tabular}{c} Auditory yes/no \\ questions (binary) \\ \end{tabular} & 56.9\% (46) \\ [PERSON] et al., 2022 [7] & 1 ALS CLIS & EEG & \begin{tabular}{c} Visuo-auditory P300 \\ paradigm (7-class) \\ \end{tabular} & 30\% (1) \\ [PERSON] et al., 2018 [6] & 3 ALS 1 progressed & EEG & \begin{tabular}{c} Steady-state visual \\ evoked potentials \\ elicited by blue and \\ green LEDs (binary) \\ \end{tabular} & 80\% (191) \\ [PERSON] et al., 2017 [8] & 3 ALS CLIS & EEG & \begin{tabular}{c} Vibrotactile P300 \\ paradigm (binary) \\ \end{tabular} & 70\%, 90\% (NA) \\ [PERSON] et al., 2019 [9] & 1 ALS CLIS & EEG & \
\begin{tabular}{c} Motor imagery (binary) \\ Auditory \\ \end{tabular} & 87.5\% (4) \\ [PERSON] et al., 2022 [12] & 1 ALS CLIS & Intracortical &
\begin{tabular}{c} Neurofeedback (binary \\ with decision tree) \\ \end{tabular} & \textgreater{}80\% (135) \\ \hline \hline \end{tabular}
\end{table}
Table 1: BCIs studies with CLIS patients. NA—Not available. * Considering only 7 of the 17 CLIS patients.  
This study was approved by the Ethical Committee for Health of the University Hospital Center of S. Joao, Porto (CHUSJ), complying with the code of Ethics of the Declaration of Helsinki. Informed consent was obtained from the patient's husband. The decision to participate in this study was made by the patient's family after meetings with the research team and the medical staff of the CHUSJ palliative care service where the patient was being followed.
# Prediction Models for Mechanical Properties of Cement-Bound Aggregate with Waste Rubber
###### Abstract  
The high stiffness of cement-bound aggregate (CBA) is recognized as its main drawback. The stiffness is described by the modulus of elasticity, which is difficult to determine precisely in CBA. Incorporating rubber in these mixtures reduces their stiffness, but mathematical models of the influence of rubber on the mechanical characteristics have not previously been defined. The scope of this research was to define a prediction model for the compressive strength (f\({}_{\text{c}}\)), dynamic modulus of elasticity (E\({}_{\text{dyn}}\)) and static modulus of elasticity (E\({}_{\text{st}}\)) based on the measured ultrasonic pulse velocity as a non-destructive test method. The difference between these two modules is based on the measurement method. Within this research, the cement and waste rubber content were varied, and the mechanical properties were determined for three curing periods. The E\({}_{\text{dyn}}\) was measured using the ultrasonic pulse velocity (UPV), while the E\({}_{\text{st}}\) was determined using three-dimensional digital image correlation (3D DIC). The influence of the amount of cement and rubber and the curing period on the UPV was determined. The development of prediction models for estimating the f\({}_{\text{c}}\) and E\({}_{\text{st}}\) of CBA modified with waste rubber based on the non-destructive test results is highlighted as the most significant contribution of this work. The curing period was statistically significant for the prediction of the E\({}_{\text{st}}\), which points to the development of CBA elastic properties through different stages during the cement-hydration process. By contrast, the curing period was not statistically significant when estimating the f\({}_{\text{c}}\), resulting in a simplified, practical and usable prediction model.  
prediction models; cement-bound aggregate; waste rubber; compressive strength; modulus of elasticity; ultrasonic pulse velocity; non-destructive testing Article
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 6 Performance Analysis  
The bit error rate \(P_{b}(\varsigma)\) for the IEEE 802.15.4 radios is defined in [1] as:  
\[P_{b}(\varsigma)=0.033\ \sum_{i=2}^{16}(-1)^{i}\binom{16}{i}e^{20\varsigma( \frac{1}{I}-1)} \tag{17}\]  
where \(\varsigma\) is the instantaneous \(SINR\). A node after generating the packet will access the channel before entering the transmission state. The transmission in the shared slots will be successful if only one node accesses the channel successfully without any interference or link error. Hence, the probability of the successful transmission in the shared slot is:  
\[P_{TSSS}=\frac{N_{E}\phi(1-\phi)^{N_{E}-1}}{1-(1-\phi)^{N_{E}}}(1-P_{b}(\varsigma) )^{L_{data}} \tag{18}\]  
where \(L_{data}\) is the length of the data packet in bits and \(N_{E}\) is the expected number of nodes attached to the concerned shared slot.
## 5 Simulations
### Simulation Description  
For the sample PTP network with 20 BCs, 16 OCs, eight TCs and one GMC, the OC was regarded as terminal nodes, the BC and TC were considered to be intermediates nodes, and the GMC was considered to be the source of the clock synchronization, as shown in Figure 2. We considered multiple cases in which link failure occurred between the MC ports and SC ports in the network. First, we examined a scenario in which a link failure occurred in only one node in the network; afterwards, we increased the number of link failures up to 20, as shown in Figure 7.
## 1 Introduction
### Definition of the Common Coordinate System  
* Carrier coordinate system (\"b system\"): As shown in Figure 1, the track geometry state detector carrier coordinate system is \(\text{OX}_{\text{b}}\text{Y}_{\text{b}}\text{Z}_{\text{b}}\), where the origin of O is the center point of the car body structure; the \(\text{Y}_{\text{b}}\) axis is along the longitudinal axis of the track geometry state detector pointing in the direction of motion; the \(\text{X}_{\text{b}}\) axis is along the track geometry state detector horizontal axis to the right; and the \(\text{Z}_{\text{b}}\) axis is vertical. The \(\text{X}_{\text{b}}\), \(\text{Y}_{\text{b}}\), and \(\text{Z}_{\text{b}}\) axes constitute the right rectangular coordinate system.  
* Geographical coordinate system (\"n system\"): As shown in Figure 2, the geographic coordinate system is \(\text{OX}_{\text{n}}\text{Y}_{\text{n}}\text{Z}_{\text{n}}\), where the origin of O is the center of the mass of the carrier; the \(\text{X}_{\text{n}}\) and \(\text{Y}_{\text{n}}\) axes are in the local horizontal plane, pointing to the east and north, respectively; and the \(\text{Z}_{\text{n}}\) axis points to the sky along the local vertical line. The geographic coordinate system moves with the carrier, and the coordinate axis maintains its original definite direction.  
Figure 1: The carrier coordinate system (b system).
## 2 Materials and Methods
### Satellite-Based Datasets
#### 2.1.3 MODIS NBAR Reflectance Product  
The MODIS Collection 6 Nadir Bidirectional reflectance distribution Adjusted Reflectance (NBAR) product (MCD43C4) was employed to explain the SIF structural information. The BRDF product uses a combination of Terra and Aqua data over a period of 16 days to generate the highest possible quality data for each day. The NBAR product has a spatial resolution of \(0.05^{\circ}\times 0.05^{\circ}\) and computes the reflectance at a nadir viewing angle for each pixel at local solar noon, which should result in a more stable and consistent product ([PERSON] et al., 2018).  
As the swath width of TanSat satellite is narrow and the selected samples were acquired in nadir mode, the use of NBAR was considered reasonable. Additionally, only four bands of MCD43C4 (red, NIR, blue and green) were used as explanatory remote sensing variables in the modelling of the SIF, which is the same as in [PERSON] et al. (2018) and [PERSON] and [PERSON] (2018). These four bands contain most of the vegetation information and drive most of the variation in the SIF ([PERSON] et al., 2018). In order to reduce the uncertainty in modelling the SIF, only high-quality MCDC43C4 data (Quality = 0) were used.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Study Area
#### 3.2.5 Models MCD19A2  
MCD19A2 is a Level 2 product of aerosol optical depth (AOD) which is calculated by combining terra and aqua imagery into one single multi-angle implementation of atmospheric correction (MAIAC) model. It is produced daily at 1 (km) resolution, and this study used the blue band AOD at 0.47 \(\upmu\)m Science Dataset (SDS) layer. The \"QA\" bands contain flag information about cloud, adjacency, surface type, and surface change. In this study, we collected data that had a cloud-free QA band. This dataset's inclusion was motivated by the effects of aerosol optical depth on pollutant dispersion, tropospheric NO\({}_{2}\) retrieval, and correlation to meteorological conditions ([PERSON] et al. [23], 2019; [PERSON] et al. [24], 2019). MCD19A2 was used as input in this study.
## 1 Introduction
[14] derived critical grouting effectiveness criteria through shear testing and numerical simulations. Existing studies have extensively investigated the mechanical and physicochemical properties of commonly used polymer and geopolymer grouting materials through experimental validation. However, current research on evaluating the effectiveness of asphalt pavement grouting primarily relies on deflection values measured by falling weight deflectometers (FWDs). This method is time-consuming, labor-intensive, and susceptible to testing conditions and fails to reflect the structural stress response under vehicular loads. Although some scholars have employed finite element analysis to study mechanical behaviors before and after grouting, the lack of field-measured data undermines the reliability of numerical simulations. Fiber Bragg grating (FBG) sensing technology, one of the most rapidly advancing high-tech applications in civil engineering [15], has gained increasing attention in pavement engineering for temperature field monitoring [16; 17] and strain field measurements [18; 19]. Nevertheless, its application in holistic pavement rehabilitation assessment remains limited. Traditional evaluation methods focus on static performance improvements, neglecting dynamic stress-strain tracking and mechanical interactions between treated and adjacent areas. This study proposes a quantitative evaluation method for the grouting treatment of defects in semi-rigid base asphalt pavements, integrating FBG monitoring technology with structural mechanics analysis. The methodology provides critical guidance for the design of grouting reinforcement materials, the optimization of injection processes, and post-treatment structural maintenance strategies.