esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Pipeline systems are prone to defects due to the harsh service conditions, which may induce catastrophic failure if found not in time. Ultrasonic guided wave (UGW) testing provides a convenient option for pipeline detection, showing high-efficiency, non-contact, long-distance and large-scale capabilities. To address the problem that UGW signals suffer from poor signal resolution that is mainly related to the coherent noise caused by the dispersion, multi-mode and mode conversion, an advanced signal processing method called WWISSP, based on variational mode decomposition (VMD), wavelet transform (WT), and improved split spectrum processing (SSP) was proposed, of which SSP was improved by replacing the Gaussian filter bank with cosine filters of constant frequency-to-bandwidth and frequency-to-filter spacing ratios. Compared with ISSP, WWISSP shows better higher accuracy and resolution processing effects to noisy multi-defect UGW signals, which is manifested through the improvement of both the signal-to-noise ratio gain and the defect-to-noise gain. Only feature signals (defects and pipe end) are retained, whereas noise signals are eliminated completely.  
improved split-spectrum processing; variational mode decomposition; signals processing; ultrasonic guided wave testing Article
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Simulation Results
#### 4.3.1 Parameter Configuration of the OC and PAOC Techniques  
The forward offset \(\tau_{\mathrm{oc}}\) has a great impact on the performance of the OC and PAOC techniques. Figure 9 shows the carrier and subcarrier noise errors of four methods for the BOC(15, 2.5) signal without multipath, where the smoothing constant of the PAOC technique is 20. The OC-P and OC-OC methods have the same subcarrier noise performance because they use the same offset correlator in the SPLL. The OC-P and DBT methods have the same carrier noise performance because they use the same prompt correlator in the PLL. The thermal noise error increases with the forward offset for both the OC and PAOC techniques. Moreover, the thermal noise error of the PAOC technique is much smaller than that of the OC technique with the same forward offset.  
Thermal noise performance affects the tracking stability, which is the primary consideration for GNSS receivers. It is fair to compare the multipath performance of the OC and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DLL** & **SPLL** & **PLL** \\ \hline DBT & Narrow correlation & Prompt correlator & Prompt correlator \\ OC-P & Narrow correlation & Offset correlator & Prompt correlator \\ OC-OC & Narrow correlation & Offset correlator & Offset correlator \\ PAOC-PAOC & Narrow correlation & PAOC & PAOC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of the four tracking methods for BOC signals.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Front-end bandwidth & 40.96 MHz \\ Integration time & 10 ms \\ EML spacing & 0.1 chips \\ DLL bandwidth & 1 Hz \\ SPLL bandwidth & 1 Hz \\ PLL bandwidth & 10 Hz \\ \(C/N_{0}\) of the direct signal & 42 dB-Hz \\ Multipath relative amplitude & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common parameters for all subsequent simulations.  
Figure 9: Tracking jitters of various methods for the BOC(15, 2.5) signal without multipath: (**a**) sub-carrier noise error; (**b**) carrier noise error.  
PAOC techniques under the same thermal noise performance. Therefore, in the subsequent experiments, the forward offset of the OC technique is set as 0.5 chips, and the forward offset of the PAOC technique is set as 0.8 chips with a smoothing constant of 20. The integration time is 10 ms, so a smoothing constant of 20 means that the smoothing time is 0.2 s.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 2 Methods  
### Overview of ST_LSD  
Figure 2 shows the workflow of ST_LSD. Firstly, to protect the edge and remove the noise, the input gray image is preprocessed by adaptive bilateral filtering. Secondly, the improved Otsu algorithm is used to segment the target under complex illumination. The gradient magnitude and gradient direction are calculated, and pixels have a high probability that line segments that pass over are extracted as effective anchors. Thirdly, the anchors are actively grouped into line segments based on the line segment geometry and the alignment of gradient direction. Line segments are verified according to the anchor density and gradient magnitude distribution to control false positives. Finally, according to the line segment aggregation method, the adjacent small line segments that may come from the same line segment are detected and connected.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 5 FOPS Performance Evaluation
### Linearity  
Linearity presents the ratio between the transducing parameter and measurand, which can reflect the sensing stability of the entire system during the detection. A high linearity response of regression line indicates a good sensor [50]. For an RI-based FOSP system, the linearity can be plotted between the loss resonance wavelength and analyte RI.
## 4 Result
### kNDVI Sensitivity with Changing \(\sigma\)  
kNDVI has been developed to mitigate the saturation effect observed in the standard NDVI by capturing higher-order differences between the near-infrared and red spectral bands. According to [PERSON] et al. (2011), the efficacy of kNDVI hinges on the selection of the kernel function and its hyperparameters. In our study, we opted for the Radial Basis Function (RBF) as the kernel and employed a spatial averaging heuristic method to estimate the optimal kernel parameter \(\sigma\). Despite these measures, we noted that while kNDVI demonstrated higher spatial consistency with SIF compared to NDVI, and increased sensitivity during drought events, it presented the lowest correlation with site-specific GPP.  
This reduced correlation can be attributed to the influence of neighboring pixels in the estimation of the optimal kernel parameter \(\sigma\), even after accounting for cloud and water body pixels. To address this issue, we recalculated kNDVI employing a temporal averaging heuristic method for estimating the optimal kernel parameter \(\sigma\), focusing particularly on the 2009-2010 drought period (as illustrated in Figure 8). The outcomes of this recalibration revealed a marked improvement in kNDVI's (temporal) ability to capture GPP changes, surpassing kNDVI(spatial). At all three examined sites, the correlation between kNDVI (temporal) and GPP was consistently higher than that of kNDVI (spatial). Furthermore, kNDVI (temporal) exhibited a more immediate response in capturing GPP changes induced by drought. It showed no time lag at the QYZ and DHS sites and only a one-month lag at the XSBN site (Table 3). This response time was faster than that of both kNDVI (spatial), and comparable to that of SIF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sites**} & \multirow{2}{*}{**VIs**} & \multicolumn{3}{c}{**R\({}^{2}\) Values between VIs and GPP at Different Time Lags (Months)**} \\ \cline{3-6}  & & **0** & **1** & **2** & **3** & **4** \\ \hline \multirow{4}{*}{QYZ} & NDVI & 0.79 & 0.8 & 0.4 & 0.05 & 0.07 \\  & kNDVI & 0.72 & 0.81 & 0.53 & 0.12 & 0.02 \\  & SIF & 0.92 & 0.63 & 0.37 & 0 & 0.24 \\  & NDVI & 0.38 & 0.21 & 0.03 & 0 & 0.12 \\  & kNDVI & 0.37 & 0.19 & 0.08 & 0.1 & 0.03 \\  & SIF & 0.18 & 0 & 0.07 & 0.03 & 0 \\  & NDVI & 0.35 & 0.5 & 0.59 & 0.41 & 0.1 \\ XSBN & kNDVI & 0.03 & 0.07 & 0.37 & 0.61 & 0.48 \\  & SIF & 0.75 & 0.78 & 0.42 & 0.05 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Coefficient of determination (R\({}^{2}\)) for correlation analysis between monthly GPP and NDVI, kNDVI and SIF at different time lags. Red color indicates a significance test pass (\(p<0.05\)), with darker shades representing a larger lag coefficient of determination.  
These findings highlight the potential of kNDVI for effective monitoring of drought induced GPP changes in vegetation, especially when calculated using temporal heuristic algorithms, which show a higher correlation with GPP than and kNDVI (spatial). The results emphasize the crucial role of selecting the optimal kernel parameter \(\sigma\) to enhance kNDVI's performance in practical applications.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
###### Abstract
In any case, to completely burden the advantages of simulated intelligence in stroke care, difficulties, for example, the requirement for powerful, different datasets and guaranteeing that simulated intelligence models are interpretable and morally sent, should be addressed [2,3]. Late investigations on mind stroke expectation have progressively centered around utilizing advanced computational strategies, especially AI and profound learning methods. These investigations investigate the utilization of calculations to foresee stroke risk in light of a scope of clinical and segment factors, for example, age, pulse, and smoking history [3]. Scientists have applied these strategies to freely accessible datasets, determined to further develop expectation exactness by investigating designs and distinguishing key gamble factors. Different strategies, including brain organizations and helping calculations, have been investigated to upgrade stroke detection [3]. Dimensionality reduction techniques like head part examination (PCA) are normally used to improve on the information, guaranteeing that the most important highlights are held for stroke expectation. The exhibition of these models is evaluated utilizing measurements like exactness, accuracy, and review, and results are contrasted with existing strategies with show enhancements in prescient capabilities [4]. These examinations feature the continuous progressions in the field and highlight how computational strategies are turning out to be progressively necessary for stroke finding and hazard assessment [4]. The foundation for the suggested system is provided by this work, which expands upon previously published material (related work). A thorough description of the system's architecture emphasizes how well it can anticipate and identify strokes [5]. To guarantee precision and applicability to real-world situations, the dataset is meticulously chosen and preprocessed. In order to improve prediction performance, the suggested methodology presents a hybrid deep learning model that combines together the advantages of CNN, recurrent neural networks (RNNs) and long short-term memory (LSTM), algorithms [6]. The proposed system's features and utility highlight how useful it is in clinical settings and provide information on risk assessment and early identification. The efficacy of the system is demonstrated in the Result part by thorough examination, opening the door for more developments in the Future Scope [6]. Summarizing the study's contributions, the Conclusion confirms that it has the potential to revolutionize stroke prevention and diagnosis.
# Evaluation of Eco-Efficiency of Two Alternative Agricultural Biogas Plants
## 3 Results
### Levelized Cost of Electricity  
The structure of revenues in an agricultural biogas plant consists mainly of revenues from the sale of electricity to external customers and revenues from the disposal of property rights to certificates of origin of energy from renewable sources (Figure 3). It was Polish legal regulations, which were in force until 2015. This research was conducted based on data from the year 2014. It was a subsidiary system based on the sale of property rights to the origin of energy from renewable sources, the so-called \"green certificates\". Furthermore, producers of renewable energy were entitled to obtain \"yellow certificates\" in the case of the high-energy co-generation of energy and heat. Companies dealing with electrical power and selling it to the final consumers were obliged to purchase electrical power from renewable energy sources. On the other hand, feedstock substrates required for the appropriate performance of the fermentation process represent the main source of variable operating costs (Figure 4). Together with the costs related to the transport of substrates, these make up almost 50% of all operating costs.  
The results of the calculation of the distributed energy production cost in the LCOE life cycle are presented in Table 4. The costs of producing 1 MWh of electricity in Biogas Plant A are almost two times higher than those for Biogas Plant B.  
The structure of the LCOE ratio indicates that the use of generated heat has the strongest impact on the decrease of LCOE ratio (Figure 5). Moreover, variable operating costs have a significant impact on the value of the distributed electricity production cost, more precisely, the application of cheap waste raw materials instead of costly silage of energy crops. On the other hand, in both Biogas Plants A and B, investment costs have a comparable share in the total value of the LCOE ratio.
# Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maximly Sinus Surgery
## 2 Surgical Robot Modeling
### Surgical Robot Kinematics and Dynamics
\[F_{c}\tau=I(q)\ddot{q}+N(q,\dot{q})+G(q)+\delta, \tag{3}\]  
where \(F_{c},\tau,I(q),N(q),G(q)\), and \(\delta\) are the force coefficient matrix, inertial matrix, coefficients of the first-order generalized coordinate matrix, gravity, and faults and uncertainties, respectively. The force coefficient matrix transforms the input forces to the generalized forces and torques in the system. The inertia matrix is composed of four block matrices. The block matrices that correspond to pure linear accelerations and pure angular accelerations in the system are symmetrical. The matrix \(N(q)\) contains coefficients of the first order derivatives of the generalized co-ordinates. Since the system is nonlinear, many elements of \(N(q)\) contain first order derivatives of the generalized co-ordinates. The remaining terms in the dynamic equations resulting from gravitational potential energies and spring energies are collected in this matrix. Therefore, the joint position is represented by Equation (4). \[\iint\ddot{q}=\iint I(q)^{-1}\times(F_{c}\tau-N(q,\dot{q})-G(q))+\delta \tag{4}\]  
Figure 5: Surgical sinus robot manipulator. Figure 6: Fundamental drivers of the slave manipulator. Hence, the state-space equation is introduced as follows [14]:  
\[\left\{ \begin{array}{l}X_{q,F_{\varepsilon}\tau}(k+1)=I(q)^{-1}(X_{q,F_{ \varepsilon}\tau})U_{F_{\varepsilon}\tau}(k)+\xi(X_{q,F_{\varepsilon}\tau})+ \delta(k)\\ Y_{q}(k+1)=(\kappa)^{T}X_{q,F_{\varepsilon}\tau}\end{array} \right. \tag{5}\]  
Here, \(\xi(X_{q,F_{\varepsilon}\tau})=(N(q,\dot{q})+G(q))\). The \(X_{q,F_{\varepsilon}\tau}(k)\), \(U_{F_{\varepsilon}\tau}(k)\), \(Y_{q}(k)\), and \(\kappa\) are the state function, state input, state output, and Fourier coefficient, respectively. To design an observation-based fault diagnosis and fault-tolerant control, modeling of the surgical robot for the sinus is the first step.
## 2 Materials and Methods
### Spatial Adaptive Feature Modulation Mechanism (SAFMM)
#### 2.3.2 Feature Blending Module (FMM)  
To further combine local contextual information and perform channel blending simultaneously, a compact convolutional channel mixer (CCM) based on FMBConv [23] is introduced. The CCM interacts with features from different channels to further enhance image detail recovery. It normalizes the features through LayerNorm and subsequently performs cross-channel feature fusion.  
The CCM consists of a \(3\times 3\) convolution and a \(1\times 1\) convolution. In this case, the preceding \(3\times 3\) convolution encodes the spatial local context and doubles the number of channels of the input features of the hybrid channel; the following \(1\times 1\) convolution reduces the number of channels back to the original input dimension.  
The SAFM and CCM are formed into a unified Feature Mixing Module (FMM), where the features are extracted into the FMM, which combines different feature information to enhance the capture and recovery of image details. These features integrate the information through a series of blending operations. The FMM can be written as shown in Equation (12):  
\[\begin{split} Y&=SAFM(LN(X))+X,\\ Z&=CCM(LN(Y))+Y.\end{split} \tag{12}\]  
where \(LN(\cdot)\) is the LayerNorm layer and \(X,Y\), and \(Z\) are the intermediate features.
## 3 Dataset Construction of Landslides in Yiyang City  
### Data Sources  
Based on the breeding mechanism of landslides, the study collected data of the following 17 landslide predisposing factors [5]: topographic factors, geological factors, environmental factors, and hydrological and meteorological factors. The data collection of historical landslides and associated disaster factors was conducted based on landslide predisposing factors. Owing to the lack of relevant data of historical landslides and detailed time information, data of historical landslide points were extracted from the Global Disaster Data Platform in combination with the local geological hazard notification in Yiyang, supplemented with the geological disaster distribution data released by the Chinese Academy of Sciences (CAS). After preprocessing, the data were sorted out and used as the historical landslide case dataset for this paper. Table 1 presents the data sources, spatial resolution, and types of data collected for this study. The data types of landslide predisposing factors encompass raster data, vector data, and textual data.  
Figure 3: Map of landslide inventory of study area.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Impact of Data Heterogeneity  
We then evaluate the performance of MT-FBERT fine-tuned in Non-Independent Identically Distributed (Non-I.I.D) data distribution scenarios. For the datasets used in the data heterogeneity experiments, we unequally partition the dataset into 100 clients following the label distribution \(Y_{i}\sim Dir(\alpha p)\), where \(i\) is the client ID, \(p\) is the global label distribution, \(\alpha\) is the degree of Non-I.I.D and a smaller \(\alpha\) generates a high label distribution shift. We construct three different label-skewing scenarios by adjusting the value of \(\alpha\): 1.0, 5.0, and 10.0. Table 9 shows the results for fine-tuning MT-FBERT in different data-heterogeneous scenarios on four datasets.  
We can observe that the performance of MT-FBERT is stable and consistent as the degree of Non-I.I.D increases. Especially, all the metrics are higher than 99% on the USTC-TFC2016 dataset and IoT-22 dataset when we set the degree of Non-I.I.D as 1.0, 5.0, and 10.0, respectively. This demonstrates the MT-FBERT's strong generalization capability and robustness to data distribution variations. Such properties are crucial in real-world deployments, where data distributions are highly imbalanced. The generalization ability of the model directly affects its reliability and effectiveness in practical applications.  
Figure 6: Comparative results in terms of Avg. AC, Avg. PR, Avg. RC, and Avg. F1 across four datasets using different labeled sample sizes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Labeled Data** & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**40\%**} & \multicolumn{4}{c}{**80\%**} \\
**Size** & \multicolumn{4}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline
**Method** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** \\ \hline MT-FBERT & **0.7912 \({}^{*}\)** & **0.7910** & **0.7861** & **0.7883** & **0.7915** & **0.7906** & **0.7633** & **0.7795** & **0.8020** & **0.8137** & **0.8164** & **0.8022** \\ FlowPrint & 0.5062 & 0.4425 & 0.4847 & 0.4275 & 0.5465 & 0.5225 & 0.5063 & 0.5087 & 0.6348 & 0.4210 & 0.4133 & 0.5727 \\ TFE-GNN & 0.5443 & 0.5715 & 0.5443 & 0.5389 & 0.7215 & 0.7533 & 0.7215 & 0.7151 & 0.7025 & 0.7657 & 0.7025 & 0.7014 \\ YaTC & 0.5871 & 0.5855 & 0.5715 & 0.5701 & 0.6588 & 0.6816 & 0.6572 & 0.6536 & 0.7224 & 0.7251 & 0.7226 & 0.7191 \\ \hline \hline \end{tabular} \({}^{*}\) A number in bold means that the corresponding method performs better than its counterpart does, for the same metric and the same labeled sample size.  
\end{table}
Table 8: Comparison results on IoT-23 dataset using different labeled sample sizes.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 4 Experiments
### Experiment of Feature Extraction Model for Flow Fields
#### 4.3.2 Experiments of Flow Field Input and Voxelization  
The experiment first investigated the influence of different sizes of input flow field and voxel sizes on the experimental results, with the results shown in Table 1. For different flow field input sizes, the entire flow field and the part of the flow field centered around the propeller were selected for testing and comparison. Testing on the selected central region aimed at predicting only the velocity information within that region, and the unselected parts used the original RANS flow field data for testing. The results reveal that selecting the central region yielded significantly better results than directly predicting the entireflow field. Subsequently, experiments were conducted on specific selected flow field sizes. Initially, the image size was set based on the propeller diameter, followed by experiments within a certain range around that size. Finally, a size of 0.8 \(\times\) 0.8 \(\times\) 0.4 (in meters) was determined to be optimal. All subsequent experiments were conducted using this size for the input.  
In addition, the experiment examined the impact of different voxelization sizes on the accuracy of results and computational time. Various voxel grid sizes were selected for experimentation. From the results, it can be observed that excessively small voxel grids (larger generated arrays) significantly increased voxelization time with limited impact on reducing error rates in subsequent stages. Conversely, overly large voxel grids (smaller generated arrays) led to a significant increase in error rates. Therefore, selecting an appropriate voxel grid size requires a comprehensive consideration of the impact on error rates and time for generation. Ultimately, a voxel grid size of 0.0125 \(\times\) 0.0125 \(\times\) 0.0125 (in meters) was chosen as the optimal result. All subsequent experiments adopted this size as the default voxelization size.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model  
Figure 2 depicts the system model of SMTrust including different phases and the flow of how trust propagates throughout the RPL operation. The trust formation phase and attack detection phase has been further divided into sub-phases. Each block of SMTrust system model is discussed below along with the processes involved.