esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 2 Numerical Model Description
### Wave Model  
In the SWAN model, the random wave is represented by a two-dimensional dynamic spectral density, denoted as \(N(\sigma,\theta)=E(\sigma,\theta)/\sigma\), where \(N(\sigma,\theta)\) is the dynamic spectral density and \(E(\sigma,\theta)\) is the energy spectral density. In the spherical coordinate system, the energy balance equation can be expressed as:  
\[\frac{\partial}{\partial t}N+\frac{\partial}{\partial x}C_{x}N+\frac{\partial }{\partial y}C_{y}N+\frac{\partial}{\partial\sigma}C_{\sigma}N+\frac{\partial }{\partial\theta}C_{\theta}N=\frac{S}{\sigma} \tag{1}\]  
The first term on the left side of the equation represents the rate of change of the action density over time; the second and third terms represent the propagation of the action density over geometric space; and the fourth term represents the frequency shift due to flow and changing water depth. The fifth term represents the effect of refraction and shallowing due to flow and changing water depth. \(S\) on the right side of the equation represents the energy source-sink term, including the wind energy input term, white-hat dissipation, bottom friction, dissipation due to shallowing, three-wave interaction, and four-wave interaction.  
The numerical method of the SWAN model uses a fully implicit finite difference scheme to discretize the governing equations. In the action balance equation, the headwind scheme determines the state of each scheme. The SWAN model adopts a fully implicit finite difference scheme, which allows for calculations with larger time steps compared to explicit schemes used in shallow water modeling. This implicit scheme enhances the accuracy of the calculations [1].
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
###### Abstract  
In this study, at ultra-wideband (UWB) frequency band (3.1-10.6 GHz), we propose the use of compact 2:1 and 3:1 nonuniform transmission line Wilkinson power dividers (NTL WPDs) as feeding networks for simple 2 \(\times\) 1 linear UWB Vivaldi tapered and nonuniform slot antenna (VTSA and VNSA) arrays. The 2:1 and 3:1 tapered transmission line (TTL) WPDs are designed and tested in this work as benchmarks for NTL WPDs. The VTSA array provides measured S\({}_{11}\)\(<-\)10.28 dB at 2.42-11.52 GHz, with a maximum gain of 8.61 dBi, which is 24.39% higher than the single element. Using the VNSA array, we achieve 52% compactness and 6.76% bandwidth enhancement, with good measured results of S\({}_{11}\)\(<-\)10.2 dB at 3.24-13 GHz and 15.11% improved gain (8.14 dBi) compared to the VNSA single element. The findings show that the NTL and Vivaldi nonuniform slot profile antenna (VNSPA) theories are successful at reducing the size of the UWB WPD and VTSA without sacrificing performance. They also emphasize the Vivaldi antenna's compatibility with other circuits. These compact arrays are ideal for high-resolution medical applications like breast cancer detection (BCD) because of their high gain, wide bandwidth, directive stable radiation patterns, and low specific absorption rate (SAR). A simple BCD simulation scenario is addressed in this work. Detailed parametric studies are performed on the two arrays for impedance-matching enhancement. The computer simulation technology (CST) software is used for the simulation. Hardware measurement results prove the validity of the proposed arrays.  
ultra-wideband (UWB); [PERSON] tapered slot antenna (VTSA); Vivaldi nonuniform slot antenna (VNSA); specific absorption rate (SAR); linear array and high gain
## 5 Simulation Results  
This section shows the simulation results of the two proposed inference latency prediction methods in object detection. While the proposed method 1 aims to predict the inference latency between devices using a given inference model, the proposed method 2 aims to predict the inference latency between a complex two-stage and a simple single-stage inference model. For object detection, the RFCN and SSD-MobileNet are considered as two-stage and single-stage object detectors, respectively, and gRPC and REST are used as the communication protocols. We evaluate that the proposed inference latency prediction methods work well in various environments through experimental results. For prediction accuracy comparison, the root mean square error (RMSE) is calculated between the distribution of inference latency \(X\) and the distribution of predicted inference latency \(\hat{X}\).
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
###### Abstract  
This paper introduces a comprehensive and publicly accessible data set from an experimental study on an independent cart system powered by linear motors. The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. Vibration signals were collected using sensors placed along the track, alongside key system variables such as cart position, following error, speed, and set current. Experiments were conducted under a wide range of operating conditions, including different fault types, fault severities, cart speeds, and fault orientations, for both single-cart and multi-cart configurations. The data set captures the relationship between vibration signatures, system variables, and fault characteristics across diverse speed profiles. The data set includes inner race (IR) and outer race (OR) faults in both the top and bottom bearings, with fault severities of 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm in width. Eight different types of experiments were performed, classified based on the number of carts used, the section of the guide rail traversed, and the type of movement exhibited. Each experiment was conducted at two distinct nominal speeds of 1000 mm/s and 2000 mm/s, with acquisition durations ranging from 30 s to 2 min. Many experiments included multiple realizations to ensure statistical reliability. Data were recorded at a sampling frequency of 50 kHz with a resolution of 24 bits. For single-cart experiments, 5 system variables were captured, while for three-cart experiments, 15 system variables were recorded along with nine vibration channels. The total data set is approximately 400 GB, offering an extensive resource for data-driven research. Independent cart systems present unique challenges such as non-synchronous operation, speed reversals, and modularity, with each cart containing multiple bearings. In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. Unlike conventional rotary systems, where bearings are fixed around a rotating shaft, independent cart systems involve bearings that both rotate and translate along the track. This fundamental difference makes existing data sets and methodologies inadequate, emphasizing the need for specialized research. By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. The outcomes of this study lay the foundation for future research in the condition monitoring of linear motor-driven transport systems.  
**Keywords: ball bearings; data set; independent cart systems; condition monitoring; vibration analysis; fault diagnosis; clustering; machine learning**
# Mechanical and Microstructural Studies of High Performance Concrete with Condensed Silica Fume
## 3 Results and Discussion
### Flexural Strength and Fracture Properties
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Mixture** & \(\mathbf{F_{p}}\) & \(\mathbf{K_{lc}}\) & \(\mathbf{G_{lc}}\) & \(\mathbf{A}\) & \(\mathbf{J_{lc}}\) & **CTOD** & \(\mathbf{I_{ch}}\) \\
**ID** & **(kN)** & **(MN/m\({}^{1.5}\))** & **(N/mm)** & **(N \(\times\) m)** & **(N/mm)** & **(um)** & **(mm)** \\ \hline CSF0 & 5.5 & 1.18 & 0.046 & 8.71 & 0.54 & 20.50 & 43.4 \\ CSF5 & 6.4 & 1.37 & 0.060 & 9.66 & 0.60 & 23.13 & 42.2 \\ CSF10 & 6.4 & 1.37 & 0.059 & 19.58 & 1.22 & 22.70 & 36.5 \\ CSF15 & 6.1 & 1.31 & 0.055 & 29.20 & 1.83 & 21.83 & 35.7 \\ CSF20 & 5.9 & 1.26 & 0.051 & 12.63 & 0.79 & 21.25 & 37.5 \\ CSF25 & 5.4 & 1.16 & 0.045 & 6.91 & 0.43 & 20.17 & 36.8 \\ \hline \end{tabular}
\end{table} 
Table 5: Average fracture characteristics of HPC with various CSF content. The characteristic length was determined based on average values of fracture energy, elastic modulus, and tensile splitting strength. The effect of the CSF content on the characteristic length was clear. It can be seen that there is a tendency for the characteristic length to decrease with growing compressive strength and CSF content (see Table 5 and Figure 9b). The results indicate that the CSF content of 5%, 10%, 15%, 20%, and 25% decreased the characteristic length by about 3%, 16%, 18%, 13.5%, and 15% in comparison with the reference HPC. [PERSON] et al. [14] informed that the addition of 10% silica fume increased the compressive strength by approximately 15%, while reducing the characteristic length by about 35%. A 50% increase in compressive strength is reached by decreasing the W/B ratio from 0.40 to 0.25, while the average reduction in characteristic length is approximately 25%. The relationships for HPC between flexural strength and stress intensity factor, flexural strength and fracture energy, fracture energy and CTOD, compressive strength and characteristic length with and without CSF are displayed in Figure 10a-d, respectively. These correlations demonstrate linearly enhancing stress intensity factor and fracture energy with increasing flexural strength, as well as linearly improving CTOD with increasing fracture energy for HPC specimens with and without CSF. On the other hand, the last correlation demonstrates a linearly decreasing characteristic length with the improvement of compressive strength. These correlations demonstrate strong R\({}^{2}\) values for the HPC specimens. The predicted equations for stress intensity factor, fracture energy, CTOD and characteristic length for HPC specimens with and without CSF are specified by:  
\[K_{lc}=0.19f_{t,fl}+0.02 \tag{9}\]  
Figure 10: Correlation between (**a**) stress intensity factor and flexural strength; (**b**) fracture energy and flexural strength; (**c**) CTOD and fracture energy; (**d**) characteristic length and compressive strength, at 28 days. \[G_{lc}=0.01f_{t,fl}-0.03 \tag{10}\]  
\[CTOD=183.13G_{lc}+11.95 \tag{11}\]  
\[l_{ch}=-0.55f_{\epsilon}+97.31 \tag{12}\]
## 2 Methodology
### Outlier Compensation Predictor-Based Framework for Teleoperated System  
In this study, to show the possibility of integrating the proposed framework with other techniques or strategies for enhancing teleoperation performance, we used the predictor-based framework proposed by [PERSON] et al. to minimize the effect of communication delay with the teleoperator on the mobility of the teleoperated vehicle [6; 31; 32].  
First, communication signals \(y(t)\) were defined to include steering \(\delta\). The authors added the throttle pedal value and brake value through \(y(t)\); however, since this study aimed to improve teleoperation performance through the detection of communication delay, only the steering angle value was used for simplicity. In addition, the authors divided the variables into \(y_{1}(t)\) and \(y_{2}(t)\) and considered them as command signals reaching the teleoperated vehicle and, conversely, sensor signals sent from the remote vehicle to the control center. However, in this study, only \(y_{1}(t)\), the delay transmitted to the teleoperated vehicle, was considered.  
Next, using the derivative of \(\dot{y}(t)\), corresponding predicted results can be written as:  
\[\dot{\mathbf{y}}_{i}(t)=\dot{\mathbf{y}}_{i}(t-T_{c|i}(t))+\mathbf{\eta}[\mathbf{y}_{i}(t-T_{c| i}(t))-\dot{\mathbf{y}}_{i}(t-T_{c|i}(t))], \tag{25}\]  
where \(i\) is the number of communication channels, \(\dot{\mathbf{y}}_{i}(t)\) is the predictor space state, and \(\dot{\mathbf{y}}_{i}(t-T_{c|i}(t))\) is the predicted communication signals of delayed time. In addition, \(\mathbf{\eta}\) is the diagonal square matrix that has non-zero \(\eta_{j}\) (\(j=1,2,\ldots,n\)) regarding the number of communication signal elements.  
Despite numerous attempts by researchers to leverage the predictor-based framework to enhance stability and performance, as discussed in previous works [31], and endeavors to elucidate the optimal selection of the gain parameter \(\mathbf{\eta}\)[32], the issue of ensuring consistent performance remains ambiguous. This ambiguity can particularly be pronounced in scenarios where communication outliers coexist, adding an additional layer of complexity to the assessment of system behavior. Therefore, (25) is modified to have two switching sequences:  
\[\dot{\mathbf{y}}_{i}(t)=\begin{cases}\dot{\mathbf{y}}_{i}(t-T_{c|i}(t))+\mathbf{\eta}[\bm {y}_{i}(t-T_{c|i}(t))-\dot{\mathbf{y}}_{i}(t-T_{c|i}(t))]&\text{if }P(\gamma(T_{\mathrm{c}})\leq\chi_{ \alpha})\\ \dot{\mathbf{y}}_{i}(t-T_{c|i})&\text{if }P(\gamma(T_{\mathrm{c}})>\chi_{ \alpha}).\end{cases} \tag{26}\]  
The initial sequence aligns with the predictor-based framework as originally proposed by the authors. However, in the second scenario, when a communication delay is identified as an outlier, distinctive measures are employed to assess the system's stability by using the derivative at time \(t\) expected from the previous state.
## 4 Results and Discussions
### Hybrid Prediction Model for Fault Detection  
During dataset generation, the big data processing system receives the sensor data from the IoT-based sensor device and stores the data in NoSQL MongoDB. The IoT-based sensor collects data from different types of operation, including normal and abnormal events. The dataset is then labeled by expert users based on the process status (either normal or abnormal) during the period when the sensor data were collected. Next, the dataset is analyzed using the hybrid prediction model to predict the fault status. The performance comparison results for several classification models are presented in Table 9. Several conventional classification models such as Naive Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), and Random Forest (RF) were compared with the hybrid prediction model to identify and predict abnormal events. The proposed model achieved the highest accuracy (100%) compared to other classification models. There was slight improvement in model accuracy after the implementation of DBSCAN-based outlier detection. Integrating DBSCAN-based outlier detection with the Random Forest model increased the accuracy by as much as 1.462% compared to conventional Random Forest. Furthermore, the accuracy improvement has been found in other conventional classification models after applying DBSCAN for outlier detection as much as 3.173%, 0.567%, and 2.026% for Naive Bayes, Logistic Regression, and Multilayer Perceptron, respectively.  
The proposed model was implemented in Apache Storm where the streams of data from Kafka can be processed and predicted in parallel and real-time. Figure 6 shows the results of implementation where real-time prediction is performed by Apache Storm to identify whether the process is functioning normally or abnormally given the input data from the IoT-based sensor (e.g., temperature, humidity, accelerometer, and gyroscope). The results of the study are expected to help management prevent unexpected losses caused by faults at an early stage and improve decision-making during the manufacturing process.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System  
The proposed multi-UAV communication system is associated with a Penguin B aircraft fleet [20]. XBee sensors embedded in Raspberry Pi board perform U2U communication, up to the UAV hub. The FANET hub node performs UAV-to-BS (U2S) communication so that information is transmitted to the GCS. The network architecture, hardware, software and telemetry image and data transport  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Characteristics** & **Without Subdivision of the Area** & **With Subdivision of the Area** \\ \hline Formation & Close nodes as function of the field of view of the camera & Distant nodes as a function of the radio range \\ \hline Required time interval for scanning the area & Proportional to the number of the nodes & Proportional to the number of the nodes \\ \hline Size of the monitored area & Limited by the radio range of the UAV hub & It can be extended from the UAV out of range through the mesh network \\ \hline Type of communication\(\backslash\)routing & Star or Mesh, with little design modification & Mesh \\ \hline Topology of the network between the UAVs (nodes) & Start or Mesh & Mesh \\ \hline Topology of the network between UAVs and base station & Star, need of a hub node to communicate with the base station over the entire area & Mesh, wider range of communication \\ \hline Distance between nodes & Short & Long \\ \hline Required time interval for interruption in case of node failure & Short & Long \\ \hline Reorganization in case of failure node of the scanning method & Low operational cost independent of the scanning method & Medium and high cost when the back-and forth and spiral method, respectively \\ \hline Energy consumption & Low in the case of end devices & Medium/high in routers and coordinators \\ \hline Type of node & Star Topology: one or two coordinators, one router end devices required & One or two coordinators and routers required \\ \hline Application & Scanning time is the major concern, thus requiring a node with higher range and processing capacity. & Larger scanning areas. The nodes have the same transmission and processing capacity. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the Multi-UAV communication network for the proposed scanning strategies.  
protocol used are tested on small-scale four-wheel air vehicles. Figure 6 represents the testing architecture for the multi-UAV system. The next section is supposed to present a description of each subsystem.
# Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality
## 3 Results
### Changes in the Quality Indicators of Eggs under Different O\({}_{2}\) Concentrations
#### 3.2.2 Changes in the Haugh Unit of Eggs under Different O\({}_{2}\) Concentrations during Storage  
During the storage of eggs, the egg white viscosity decreases to form a thin liquid. The intuitive outcome is the decrease in the height of the egg white [29]. The most commonly used evaluation criterion for egg freshness is the reduction in the Haugh units. A Haugh unit \(>\)72 indicates AA-level, 60-72 indicates A-level, 30-60 indicates B-level, and \(<\)30 indicates C-level. Grade A and above are suitable for consumer use, Grade B is not suitable for sale, and Grade C is not edible [30]. It can be seen in Table 2 that the Haugh unit of eggs on Day 0 is about 84, indicating that they are AA Grade (\(\geq\)72). There were no significant differences among the five groups (\(p>0.05\)). During storage, the Haugh units of the five groups of eggs showed a downward trend. Among them, Groups D and E exhibited the fastest downward trend, and the eggs in Group E fell to Grade B (\(<\)60) after 30 days, which did not meet the edible standards. On the 35 th day of storage, the Haugh units of Groups D and E were significantly lower than those of the other three groups (\(p<0.05\)).
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 3 IRC Waveform Design Objective
### Pslr  
According to (8), the range sidelobes depend on the autocorrelation sidelobes property. The aperiodic autocorrelations of a waveform \(s(q),q=0,1,\ldots,MQ-1\) are defined as  
\[R(k)=\sum_{q=0}^{MQ-1-k}s(q)s^{*}(q+k) \tag{11}\]  
It can be seen from (11) that the peak of the mainlobe \(R(0)\) satisfies \(R(0)=\sum\limits_{q=0}^{MQ-1}|s(q)|^{2}\)\(=MQE[|s(q)|^{2}]\) which is the total energy of \(s(q)\).  
When low autocorrelation sidelobes are required, waveform design issues for good autocorrelation properties are usually considered. A commonly used metric to measure the autocorrelation properties of a waveform is \(\mathrm{PSLL}\), i.e.,  
\[\mathrm{PSLL}=\max_{k\
eq 0}\{|R(k)|\} \tag{12}\]  
The randomness of the communication data may cause the IRC waveforms to have different total energies. In this paper, \(\mathrm{PSLR}\) is used to measure the \(\mathrm{PSLL}\) of autocorrelation function [30]. The \(\mathrm{PSLR}\) is defined as  
\[\mathrm{PSLR}=\frac{|R(0)|}{\max\limits_{k\in\Omega}\{|R(k)|\}} \tag{13}\]  
where \(\Omega\) is the index set of peaks of all sidelobes.  
From (13), we know that increasing \(\mathrm{PSLR}\) can get a good autocorrelation property and improve radar detection performance, so in this paper, the method of improving \(\mathrm{PSLR}\) is studied.
## 4 Simulation and Analysis
#### 4.1.4 Comparison of Training Loss of Different Networks  
Figure 6 shows the loss change curve during PSLSTM model training. Equation (16) is used to calculate the training loss. The number of training epochs is represented by the lateral coordinate, and the average loss within each epoch is represented by the vertical coordinate.  
As shown in Figure 6a, the A2 recognition network has the fastest decreasing loss curve. Because after the A1 recognition network divides the longitudinal maneuver types of HGVs into two types, \(SG\) and \(QEG\), the A2 recognition network of each type has a stronger variation pattern with time and can be visually described by the altitude and the velocity of HGV. Because the intention sequence of lateral maneuvers of HGVs is more complex and random, the A3 and A4 recognition networks have a slightly slower decreasing speed than the other two networks.  
In this paper, the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) are chosen as the comparison models for the joint prediction network. M-LSTM architecture consists of a fully connected layer, two LSTM layers, and a softmax layer. The M-LSTM model employs the hierarchical principle to recognize and predict multiple parameters. DCBNN is a two-channel structure with a fully connected layer and a bidirectional gated recurrent unit in each channel (Bi-GRU). To compare the capabilities of the networks, the number of neurons in each LSTM layer and GRU layer is set to 128, and the training parameters of the network remain the same as in Section 4.1.3. For specific details of the M-LSTM and DCBNN algorithms, please refer to [18; 19].  
As shown in Figure 6b, the training loss variation curves of the joint prediction network with M-LSTM and the DCBNN. The training loss of the PSLSTM network decreases slightly slower than that of M-LSTM and DCBNN because PSLSTM has more network layers and neurons. The prediction network of the \(SG\) trajectory declines slower than that of the \(QEG\) trajectory and has a relatively higher convergence value. Because the trajectory variation of \(SG\) trajectory is more complex, the network needs more iterations to learn the complex maneuver sequence. The training loss of the PSLSTM network converges to 0.0479 for the intention prediction network of \(SG\) trajectory, and 0.0395 for the intention prediction network of \(QEG\) trajectory. Hence, the training loss convergence value of the PSLSTM network is lower than that of M-LSTM and DCBNN.