esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Variations in Hydraulic Conductivity  
The variation in \(K/K_{0}\) and \(K_{i}/K_{0}\) over time in the experiment of different turbidity is shown in Figure 5. An analysis of \(K/K_{0}\) variation under different turbidity conditions revealed that an SE with a turbidity of 1.84 NTU significantly lowered the \(K/K_{0}\) of the porous media and decreased the initial permeability by 83.6% (Figure 5a). When the influent turbidity was 1.09, 0.540, and 0.160 NTU, \(K/K_{0}\) decreased by 17.6%, 13.2%, and 1.70%, respectively. The permeability more rapidly decreased when the influent turbidity was higher. Because most of the suspended particles were retained in the first layer, the \(K_{1}/K_{0}\) of this layer decreased the fastest throughout the experiment. When the influent turbidity was 1.84, 1.09, 0.540, and 0.160 NTU, \(K_{1}/K_{0}\) decreased by 96.1%, 33.3%, 25.0%, and 6.20%, respectively (Figure 5b-e). Our results showed that the \(K/K_{0}\) of porous media decreased by less than 1.70% when the turbidity was 0.160 NTU. Thus, the suspension with this turbidity was the only one that did not cause clogging.  
Figure 5: Experimental and simulated values of \(K/K_{0}\) (**a**) and \(K_{i}/K_{0}\) (i = 1, 2, 3, 4, 5) (**b–e**) for four sets of experiments over time.
# Fast and High Precise Spatial Documentation of Traffic Accident Site Using Only Low-Cost RPAS
## 4 Discussion
10 m, overlaps 70/80%, flight time of approx. 2 min, processing time of 27 images was 14 min, and the average relative measurement error was 2% maximum. This suggests that modern methods using RPAS are in general rather comparable to each other while presenting better results than currently used conventional methods used at traffic accident sites. The limitations of RPAS usage for traffic accident site inspection are closely connected especially with weather and site location. Unfortunately, it remains impossible to use low-cost RPAS during rainy or foggy weather. Strong wind is also limiting. Flying in tunnels, under bridges, or under a dense tree canopy is not feasible either. On the other hand, this multi-rotor vehicle does not require a large space for maneuvering, including take-off and landing, since it is a point take-off and landing (PTOL) RPAS. It is also able to hover during flight. Moreover, with a combination of omnidirectional obstacle sensing and automatic flight according to waypoints, it makes flights very simple and controllable. Finally, it allows flying in spatially limited space like urban areas with wires, streetlamps, poles, trees, etc., for instance. During this proof of concept, only low-cost RPAS (less than \(\ell 1500\)) was used to obtain images of an arranged traffic accident. Subsequently, commercial software was utilized. There was no need to use other external devices like GNSS or GCPs. If a very accurate absolute position is needed, RPAS with real time kinematic (RTK) GNSS on board could be used without the need for GCPs [14]. In future studies, we will use RPAS that is a quarter the price but has similar parameters. Utilizing cloud data processing instead of desktop image processing could also make the whole process much faster and cheaper. Images of the traffic accident site acquired from RPAS could be processed into a final model before traffic officers reached their office.
## 4 Hybrid Models
### Network Traffic  
Finally, in the field of network parameter forecasting, we present the work of [PERSON] et al. [59], where the ARIMA algorithm is combined with the Multi Layer Perceptron and the Multidimensional Support Vector Regression models in a hybrid approach to the network-wide traffic state forecasting problem. An increased predictive performance is observed in relation to the statistical forecasting method in cases where the network traffic is considered  
Figure 7: Flowchart of hybrid Arima and Machine Learning for time series forecasting, based on the fluctuation interval.  
both at a local and global scale with the incorporation of the hybrid prediction model. An interesting change in the hybrid model workflow with respect to the previously mentioned studies is the fact that in this case the ARIMA algorithm is used after the neural network in order to post-process the ML model residuals. This is also, according to the authors, \"necessary and a warrant at least for the situation where the time series data are not sufficiently long\".  
The specific work aims to predict the traffic state for a small city area based on the measurement and prediction of three macroscopic traffic variables: traffic volume, speed, and occupancy. The dataset is comprised of time series collected from a network of detectors along the Highway Ayalon in Tel Aviv, Israel. The proposed approach can not only capture the network-wide co-movement pattern of traffic flows in the transportation system but also seize location-specific traffic characteristics as well as the sharp nonlinearity of macroscopic traffic variables. The case study indicates that the accuracy of prediction can be significantly improved when both network-scale traffic features and location-specific characteristics are taken into account.
## 4 Results and Discussion
### Power-Law Function FGM Beam
#### 4.2.1 Axial Stress Distribution in P-FGM Beam  
Substituting the inverse of the stiffness coefficients, E(z), and normalizing the stress for the top stress one can obtain the normalized axial normal stress distributions across P-FGM beam thickness as shown in Figure 7. It should be noted that, as opposed to E-FGM analysis, the parameter in P-FGM is the power-law index (n). Therefore, the elastic moduli E\({}_{1}\) and E\({}_{2}\) should be initially specified. For this demonstration, E\({}_{1}\) = 210 MPa and E\({}_{2}\) = 390 MPa for steel and ceramic, respectively, were used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **This Method** & **Sankar [2]** & \
\begin{tabular}{c} **[PERSON] et al.** \\ [26] \\ \end{tabular} &
\begin{tabular}{c} **Numerical Model** \\ \end{tabular} \\ \hline E\({}_{1}\)/E\({}_{2}\) = 10 & \(-\)0.209 & \(-\)0.21 & \(-\)0.209 & \(-\)0.24 \\ E\({}_{1}\)/E\({}_{2}\) = 1.0 & \(-\)1.0 & \(-\)1.0 & \(-\)1.0 & \(-\)1.0 \\ E\({}_{1}\)/E\({}_{2}\) = 0.1 & \(-\)4.77 & \(-\)4.80 & \(-\)4.75 & \(-\)4.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation of E-FGM stress distribution.  
Figure 7: Normalized Axial stress \(\alpha\alpha\) distribution in simply supported P-FGM beam.
# Hybrid Energy Routing Approach for Energy Internet
## 2 Implementation of Energy Internet with Energy Routers  
In this section, the structure of ER-based energy Internet and its graph theory representation are introduced. The concept and functions of ER are discussed.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 4 Experiment and Analysis
### Data Relationship Analysis  
The cluster analysis results indicate that cluster V has no regularity as it covers every time period of the day, while other clusters have smaller scales, suggesting that the water quality changes differently from the normal water quality during a certain time period on some dates, either good or bad. The timestamps of each point in I-IV clustering exhibit periodic changes. The clusters are arranged based on the time attribute, and it is observed that I-IV clusters have distinct time segment characteristics. The water quality in the reservoir is divided into four grades, where a higher grade indicates worse water quality (Table 5). Cluster IV represents good water quality, and the corresponding time stamps are mostly during the period from 6:00 a.m. to 8:00 a.m., after which the water quality starts to deteriorate. Cluster III represents the time period from 12:00 p.m. to 3:00 p.m., and cluster II represents the water quality from 6:00 p.m. to 8:00 p.m. Cluster I represents the water quality from 8:00 p.m. to 12:00 a.m. The water quality situation represented by cluster V is between 2 and 3, which can be considered as the average situation of the water quality in the reservoir. The analysis results suggest that the water quality in Menlou Reservoir follows a daily variation rule where the water quality is generally better in the morning and worse in the evening.  
Figure 8 illustrates the comparison between the number of elements in each cluster and the total number of elements in the corresponding time period. Based on the calculation, it is found that the points with a regular reaction time in clusters I-IV account for approximately 71% of the total.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Index** & **Totle** & **S.D.** & **Avg** & **Min** & **Max** \\ \hline pH & 167,840 & 0.261720083 & 8.4619434 & 6.093347 & 9.175494 \\ NH\({}_{4}\)N & 167,840 & 29.57231904 & 0.72258126 & 0 & 4.078737 \\ Turbidity & 167,840 & 8.499657004 & 20.709381 & 0.52096874 & 61.88089 \\ \hline \end{tabular}
\end{table}
Table 4: Data set characteristics after noise reduction.  
Figure 7: (a) Data set before noise reduction; (b) Data set after noise reduction.
## 3 Collision Avoidance Strategy
### Two-Layer Model Predictive Control (MPC) Modeling
#### 3.2.1 Polynomial Fitting  
The re-planned path is given by discrete points in the prediction time domain. If these points are directly input into the tracking layer, input interfaces will be greatly occupied. As the prediction horizon changes, the number of points will also be changed, which is not conducive to the standardized design of the controller. Besides, the control period of re-planning layer is not the same as that of tracking layer, which is difficult for the controller of the tracking layer to track discrete reference trajectory points [30].  
Figure 10: The whole process of generating local reference trajectory.  
In order to reduce the calculation of the controller, meet the continuity requirements of position change, yaw angle change and acceleration change and reduce impact, a fifth-order polynomial is adopted as the fitting curve.  
\[\begin{array}{l}Y_{ref}=a_{0}t^{5}+a_{1}t^{4}+a_{2}t^{3}+a_{3}t^{2}+a_{4}t+a_{5 }\\ \varphi_{ref}=b_{0}t^{5}+b_{1}t^{4}+b_{2}t^{3}+b_{3}t^{2}+b_{4}t+b_{5}\end{array} \tag{16}\]  
The output of the re-planning layer are coefficients of the fifth-order polynomial, \(a_{p}\ =\ [a_{0}\ a_{1}\ a_{2}\ a_{3}\ a_{4}\ a_{5}]\) and \(b_{p}=[b_{0}\ b_{1}\ b_{2}\ b_{3}\ b_{4}\ b_{5}]\), and the reference longitudinal acceleration \(a_{x,ref}\).
## 3 FRBs in the Era of SKAO/ngVLA
### Expanding the FRB Parameter Space  
Searches for FRBs and radio pulses from neutron stars are based on the assumption that the pulses are narrow (between 0.1-100 ms). Due to computational constraints, box-car widths used for match filtering never go beyond a few hundred ms. This notion has recently required a fresh new look, thanks to the discovery of the FRB20191221A [41] and its quasi-periodic burst components that span a total duration of as much as 3 seconds. This begs a question: if there is a significant population of wide FRBs, how many do current pipelines miss due to width-search limitations? The right panel of Figure 6 shows that one would miss over 40% of the burst S/N due to overly narrow boxcar widths in matched filtering [42]. On the other side of the spectrum, ref. [43] recently reported the discovery of isolated micro-second duration bursts from FRB20121102A. Such narrow FRBs would be largely averaged out in the data. Together, these recent discoveries have brought to light a selection bias in all current time-domain searches for radio transients. Searches on the short timescales have generally been hampered by the computational limitations; on the long timescales, they have been hampered by the effects of RFI and system stability. Even so, it is important to expand the FRB search parameter space to cover the narrowest and the widest timescales as shown in the left panel Figure 6. Future searches for FRBs with next-generation telescopes should focus on overcoming these limitations and thus eliminating these biases.
## 4 Case Study-Accelerated Corrosion Test of Bars in Concrete Cover with Different Thicknesses  
### Corrosion Detection of Bars in Reinforced Concrete Columns  
Four corroded steel bars in a building in northern Taiwan (on 2F, 4F, and 8F) were detected by the ground penetrating radar, and the schematic diagram is shown in Figure 21. Based on the analysis of the reflected voltage of the ground penetrating radar, the distribution of the corroded steel bars was obtained by the reflected voltage of the electromagnetic wave and compared to ASTM C876, as shown in Figure 22.  
\begin{table}
\begin{tabular}{c c c} \hline
**Corrosion Status** & **Electrochemical Corrosion Potential** & **Electromagnetic Wave Reflected Voltage** \\ \hline  & & 4 cm & 1–64 mV \\ \cline{2-3} Degree of corrosion: & & 6 cm & 23–69 mV \\ \cline{2-3} less than 10\% & \(>\)\(-\)200 mV & & 8–87 mV \\ (slight corrosion) & & & (Soaked/severe corrosion) \\ \cline{2-3}  & & 9 cm & 0–12 mV \\ \cline{2-3}  & & 4 cm & 64–107 mV \\ \cline{2-3} Degree of corrosion: between & & 6 cm & 69–118 mV \\ 10\% and 90\% (moderate corrosion) & \(-\)200\(--\)350 mV & & 87–169 mV \\ \cline{2-3}  & & 7 cm & (Soaked/severe corrosion) \\ \cline{2-3}  & & 9 cm & 12–71 mV \\ \hline  & & 4 cm & \textgreater{}107 mV \\ \cline{2-3} Degree of corrosion: greater & & 6 cm & \textgreater{}118 mV \\ \cline{2-3} than 90\% (severe corrosion & \(<\)\(-\)350 mV & & 7 cm \\ \cline{2-3}  & & & (Soaked/severe corrosion) \\ \cline{2-3}  & & 9 cm & \textgreater{}71 mV \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the Degree of Corrosion by Potential and Voltage.
## 3 Acceptability Measurement of an IVIFPR
### An Algorithm for GDM with IVIFPRs  
Summarizing the analysis described above, a novel algorithm for GDM with IVIFPRs is proposed. The concrete steps are summarized below and in the flowchart depicted in Figure 1.  
**Step 1.**: Predefined consistency threshold \(\mathrm{MCI}_{0}\) and set the value of parameter \(\varphi\).
**Step 2.**: Calculate the multiplicative consistency index \(\mathrm{MCI}\Big{(}\widetilde{R}^{t}\Big{)}\) for an individual IVIFPR \(\widetilde{R}^{t}(t=1,2,\ldots,m)\) using Equation (16).
**Step 3.**: Derive DMs' weights \(\eta_{t}(t=1,2,\ldots,m)\) using Equation (35).
**Step 4.**: Using formula \(\mathrm{MCI}\Big{(}\widetilde{R}^{t}\Big{)}\leq\mathrm{MCI}_{0}\), pick out the unacceptable multiplicative consistency of IVIFPR \(\widetilde{R}^{t}(t=1,2,\ldots,m)\). If all IVIFPRs \(\widetilde{R}^{t}(t=1,2,\ldots,m)\) are acceptable multiplicative consistent, then go to Step 6; otherwise, go to Step 5.
**Step 5.**: Derive the acceptable multiplicative consistent IVIFPR \(\widehat{R}=\left(\hat{\tau}_{ij}\right)_{n\times n}\) from unacceptable multiplicative consistent IVIFPR by Equations (18) and (19).
**Step 6.**: Integrate the individual IVIFPR \(\hat{R}^{t}(t=1,2,\ldots,m)\) into a collective IVIFPR \(\widetilde{R}^{c}\) through Equation (33).
**Step 7.**: Using Equations (27) and (29), determine interval-valued priority weight \(w_{i}^{\mu}=\Big{[}w_{i}^{\mu}-\Delta w_{i}^{\mu},w_{i}^{\mu}+\Delta w_{i}^{ \mu}\Big{]}(i=1,2,\ldots,n)\).
**Step 8.**: Using Equations (30) and (31), determine interval-valued priority weight \(w_{i}^{v}=\big{[}w_{i}^{v}-\Delta w_{i}^{v},w_{i}^{v}+\Delta w_{i}^{v}\big{]}( i=1,2,\ldots,n)\).
**Step 9.**: Generate the IVIF priority weights \(w_{i}=\Big{(}\Big{[}w_{i}^{\mu-},w_{i}^{\mu+}\Big{]},\big{[}w_{i}^{v-}
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 3 BILSTM neural network  
From the above neural networks, we know that the training process of BP neural networks, RNNs, or LSTM always predicts the output of the next moment from front to back based on the temporal information of the previous moment. Such a training method cannot maximize the hidden intrinsic information in the time series of ship trajectory data and does not make good use of the data. In contrast, the bidirectional long short-term memory (BiLSTM) neural network model used in this paper is good at mining data with long-term dependencies in long time series, and it has a more powerful representation and learning ability.  
The network structure of BILSTM is the same as the standard LSTM neural network structure, which also consists of basic cell states, input gates, forgetting gates, and output gates. The basic principle of the BILSTM neural network is composed of a forward LSTM  
Figure 3: LSTM neural network structure diagram.  
and a standard backward LSTM, and the structure of the BILSTM neural network is shown in Figure 4:  
The BILSTM neural network introduced in this paper has the output of \(\overset{\rightarrow}{\text{h}_{\text{t}}}\) from two opposite directions, i.e., the output of the forward network is \(\overset{\leftarrow}{\text{h}_{\text{t}}}\) and the output of the backward network is \(\overset{\rightarrow}{\text{h}_{\text{t}}}\). The expressions for the computation of the forward and backward outputs are  
\[\overset{\rightarrow}{\text{h}_{\text{t}}}=\overset{\rightarrow}{\text{ LSTM}}\bigg{(}\text{x}_{\text{t}},\overset{\rightarrow}{\text{h}_{\text{t}-1}} \bigg{)} \tag{18}\]  
\[\overset{\leftarrow}{\text{h}_{\text{t}}}=\overset{\leftarrow}{\text{ LSTM}}\bigg{(}\text{x}_{\text{t}},\overset{\leftarrow}{\text{h}_{\text{t}-1}} \bigg{)} \tag{19}\]  
The output is the combined output of the forward and backward results stacked into  
\[\overset{\rightarrow}{\text{H}_{\text{t}}}=\overset{\rightarrow}{\text{h}_ {\text{t}}}+\overset{\leftarrow}{\text{h}_{\text{t}}} \tag{20}\]
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 2 RFID Collision Problem
### Types of RFID Collision
#### 2.1.3 Tag-to-Tag Collision  
A tag-to-tag collision occurs when multiple signals simultaneously arrive at the reader, preventing the reliable detection of all tags within its interrogation zone. This scenario is illustrated in Figure 1, where tags (T1, T2, T3 and T4) simultaneously send signals to a single reader, preventing the reader from correctly recognizing a particular tag. Without an anti-collision protocol, the data from these tags would not only collide at the reader, hence degrading their identification, but will also lead to wasted bandwidth and energy [14].
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Attention Mechanism  
The attention mechanism overcomes several limitations of traditional neural networks. It can effectively focus on task-relevant information while ignoring irrelevant details without relying on recurrent structures, and directly establishes dependencies between inputs and outputs, significantly improving computational efficiency. In previous studies, spatial attention mechanisms were introduced into densely connected convolutional neural networks to enhance the model's feature extraction capabilities. This approach reduces the required input data and helps identify bearings with varying degrees of damage [13]. A multi-scale adversarial network with channel attention successfully achieved domain adaptation in bearing and gearbox fault diagnosis [14]. A method using multi-head self-attention enhanced features extracted by LSTM, significantly improving the accuracy of bearing life prediction [15].
## 2 Materials and Methods
### Data Analysis  
Peak linear acceleration (PLA) and peak angular acceleration (PAA) were calculated from kinematic recordings by applying a rigid body transformation to the head's center of gravity. Dependent (continuous) variables included PLA and PAA. Fixed effects included the categorical variables of header type and ball delivery method. Data were visually inspected for normality using histograms and Q-Q plots. No data followed a normal distribution, and so the peak kinematics distributions (PLA and PAA) were expressed as medians and interquartile ranges (lower [25%] to upper [75%] quartiles). To minimize the error from non-uniform data, dependent variables were transformed prior to analysis (Log-transform for PLA and Square Root Transform for PAA). Linear mixed-effects models (IBM SPSS version 28) were used to evaluate nested data in clusters of individual players [29]. PLA and PAA were compared between the ball delivery method (model 1) and header type (model 2). To account for variability, the player subject factor was included as a random intercept for both models. A Bonferroni correction was applied to the \(p\)-values of the fixed effects to account for multiple comparisons. Significance was determined by comparing the \(p\)-values with an alpha level of \(p<0.05\).
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 4 Solution Using Matching Game-Based U-S. A and U-S. P Algorithms
### One-Sided Matching Based Solution Approach Sub-Problem (26)
#### 4.2.2 uRLLC UEs Utility Function  
On the other hand, each uRLLC UE ranks attached eMBB UEs at the same BS with the shortest transmission delay and the highest rate. Thus, each uRLLC UE's preference utility is dependent on the following:  
\[\Lambda_{l}(e)=R_{be}, \tag{35}\]  
Algorithm 2 describes the suggested matching-based U - S. P algorithm using the same technique as Algorithm 1. So, as UEs and BS have fixed preference relationships, algorithms are the deferred acceptance algorithm (DAA) for two-sided matching that leads to a stable match [37; 42]. We estimate the computational complexity of the described algorithms using the significant \(O\) notation. Even under the worst-case scenario, the proposed algorithm's ongoing complexity may be recognized and compared to other algorithms on both approaches, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**U-S. A Scheme** & **Complexity** & **U-S. P Scheme** & **Complexity** \\ \hline DAA [37; 39; 55] & \(O(U\mathcal{B})\) & DAA & \(O(\mathcal{ZE})\) \\ EAA [56] & \(O(U\mathcal{B}\log(U\mathcal{B}))\) & EAA & \(O(\mathcal{ZE}\log(\mathcal{ZE}))\) \\ GA [57] & \(O(U^{2}\mathcal{B}^{2})\) & GA & \(O(\mathcal{Z}^{2}E^{2})\) \\ MAX-SINR & \(O\big{(}U^{2}\mathcal{B}^{2}\log(U\mathcal{B})\big{)}\) & MAX-SINR & \(O(\mathcal{ZE}\log(E))\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of user-slice association, user-slice pairing schemes.