esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Probabilistic Corrosion Rate Estimation Model  
Guided by previous experience in researching probabilistic models that describe and estimate the speed of corrosion processes of ship structures [38; 39; 40], excellent results were obtained in our study of the corrosion behaviour of CuAlNi samples exposed to various marine influences [36], and ideas were presented in a review [25]. In this paper, we will present a statistical analysis of the analytical and probabilistic linear model applied to the assessment of the corrosion rate of two different NiTi alloys. The degree of corrosion of metal plates was characterised by the plate thickness wear (\(d(t)\)), which is a function of the elapsed time, so it is usually expressed by values measured in mm/years, nm/years, or nm/months [4; 25]. The degree or speed of corrosion processes is influenced by numerous factors that have a stochastic nature, such as environmental conditions, type and previous state of the metal. Despite that fact, in most scientific papers, the degree of corrosion is considered a constant, resulting in a linear decrease in plate thickness over time [25]. In the works of [PERSON] et al. [4], corrosion wear of metal plates is presented as a power function of the elapsed time. This functional dependence can be represented as follows:  
\[d(t)=c_{1}(t-T_{cl})^{c_{2}},\text{ for }t>T_{cl}, \tag{1}\]  
where \(d(\text{t})\) is the corrosion wear expressed in nanometres (nm); \(t\) is the exposure period; \(T_{cl}\) is the service life of the coating; \(c_{1}\) is a positive real coefficient representing the time corrosion rate, while \(c_{2}\) is a positive real coefficient that usually takes a value of 1 or 1/3 [4].  
Reference [36] confirmed the validity of Equation (1) applied to CuAlNi samples exposed to the influence of three seawater environments, air, tide and sea. The proposed model assumed that \(T_{cl}=0\), resulting in \(d(t)=c_{1}t\). This assumption was valid, since corrosion processes affect the degradation of sample thickness immediately after exposure to the marine environment, due to the fact that the analysed samples were not protected by anti-corrosion coating.
## 5 Results Analysis
### Abnormal Event Detection and Classification  
As mentioned earlier, crowd density level variation is also considered a crowd anomaly in a sequence. In this second approach, the network was transformed into a normal/abnormal classification problem. The same model was tested with the same parameters except for the final FCN layer, which was modified as a two-way classification. The results of testing both proposed feature extraction techniques (densenet121 and Efficientnetv2) are described in the sections below. In this approach, the abnormal activities were measured based on the UMN and PETS2009 dataset for detecting panic moments. The UMN dataset has three anomaly events (lawn, mall and plaza) with 7738 video frames with a 320 \(\times\) 240 resolution. In this approach, the training and testing ratio was kept as sixty to forty as in previous work. However, final classification network was changed to a binary classification (normal/abnormal).  
Experiments were also conducted on samples from the PETS2009 S3 motion flow walking and running video segments shown in Figure 14. The model performance was tested with video sequences 14-16 (walk and run) and 14-33 (gather and evacuate), respectively. The performance was measured by calculating the AUC (area under the curve) [40] using the ROC (receiver operating characteristic) [40] curve. The AUC measures the area under the ROC curve. The TPR (true positive rate) and FPR (false positive rate) calculations are explained in Equations (16) and (17). The ROC curve was plotted by using TPR against FPR values.  
\[TPR=\frac{TP}{TP+FN} \tag{16}\]  
\[FPR=\frac{FP}{FP+TN} \tag{17}\]  
We evaluated UMN's activities in the lawn, shopping mall and city urban event (indoor/outdoor) segments with existing approaches. The performance results were compared with our model (MCMS-BCNN-Attention), as shown in Table 7, which achieved the best AUC of 1.0. In this approach, the separation of actions is an extremely difficult task when people walk normally and suddenly start running to evacuate. However, our proposed model architecture with Efficientnetv2-based feature extraction achieved perfect results among all the other works. In the PETS2009 dataset, we monitored two activities such as walking/running and gathering/disappearing. We maintained the same sixty-to-forty ratio as when splitting training and testing into all original datasets' segments for the experiment. The effectiveness of our approach compared to existing recent works is shown in Table 7, and the MCMS-BCNN-Attention model reached an AUC of 1.0, yielding the best performance.  
Figure 14: Sample frames for UMN and PETS2009 datasets abnormal activity.
## 2 Methodology
### Data Preprocessing
#### 2.2.2 Anomalous Data Removal  
Identification and removal of anomalous data constitute a critical step in data quality control. This study employs anomaly detection methods based on spatial distribution and trajectory continuity, combined with visualization techniques for manual verification.  
First, the road network composed of trajectory points and intersections is visualized using the plotRoadnet() function. The visualization results clearly show one severely deviated anomalous trajectory point outside the road network range, as well as multiple trajectory points that deviate somewhat from the road network. These anomalous points may result from GPS signal drift or equipment malfunction. The manual anomaly identification process focused on clearly identifiable outliers that were visually obvious (e.g., trajectory points located tens of meters away from the road network boundary). To ensure consistency, the visual inspection was conducted by two researchers independently, with a consensus reached on all identified anomalies. For borderline cases showing minor deviations, data points were retained to avoid over-filtering. In Figure 2, the numbered labels (1-22) represent intersection identifiers, while the blue dots indicate vehicle trajectory points collected from GPS data. This visualization convention is consistently applied throughout Figures 2-5 to maintain clarity in road network representation.  
Figure 2: Preliminary road network visualization results.  
Second, the removeOutlier() function is designed to implement automated anomaly detection. This function not only identifies trajectory points with abnormal spatial positions but also detects vehicle records containing only a single trajectory point. Such vehicles lack trajectory information for effective path analysis and thus need to be removed.  
While statistical outlier detection techniques such as z-score analysis or DBSCAN clustering could provide more systematic approaches, the manual visual inspection method was selected for this study due to the clear spatial nature of the anomalies and the need for domain-specific judgment in trajectory data quality assessment. Future implementations could benefit from integrating automated statistical methods for enhanced scalability and objectivity. The results after removal are shown in Figure 3, where intersections numbered 4, 5, 6, 7, 13, and 14 are not physical intersections but rather signalized pedestrian crossings.  
The anomalous data processing results show that among 7317 initial vehicles, 65 vehicles had only one trajectory point. These vehicle records were completely removed, ultimately retaining 7252 valid vehicles. This processing ensures data quality for subsequent analyses.  
Figure 3: Optimized road network visualization results.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 1 Introduction
recently proposed an end-to-end trainable network called Dual Domain Network (DuDoNet) [25]. It consists of a sinogram enhancement network and an image enhancement network, which are connected by a Radon Inversion Layer (RIL). The RIL reconstructs the CT images using the filtered back projection (FBP) and allows gradient propagation during training. In this work, we present an end-to-end deep learning CT reconstruction called iCTU-Net, for the correction of metal artifacts. The network learns the mapping from the metal-affected sinograms to the artifact-free images. It consists of three parts, which are trained simultaneously: sinogram refinement, back projection, and image refinement. To our knowledge, we are the first to train a single end-to-end deep learning network for the task of reducing metal artifacts with a learnable backprojection operation. Since the whole reconstruction process, including the back projection, is learned, the network is able to freely adapt the reconstruction to the imperfections of the sinogram data. The reconstruction is trained in a supervised manner with simulated interventional training data. We focus on liver interventions; thus, we generate abdominal liver data, including metal objects. We compare our iCTU-Net to the classical NMAR algorithm and to a sinogram refinement and an image refinement deep learning network. Both of these networks employ the same U-Net architecture that is used in our network, which allows for a fair comparison. These reference networks were selected to investigate the performance of deep learning MAR approaches in three different domains: sinogram pre-processing, image post-processing, and reconstruction.
## 2 Proposed Enhanced PNCC Algorithm  
In this section, the modifications that were applied to the PNCC system will be explicated in detail. Figure 1 illustrates a comparison between the block diagram structures of the proposed PNCC method and state-of-the-art methods such as PNCC, GFCC, RASTA-PLP, and MFCC [15]. As shown in Figure 1, the block diagrams are divided into three stages. These stages are pre-processing, noise suppression, and final processing.
## 3 Ground Motion
### Ground Motion Time History  
Four suites of ground motion records that matched the four target spectra (UHRS, CS1, CS2, and CS3) were prepared. Each suite comprised 30 pairs of horizontal ground motions in orthogonal directions. For the UHRS, randomness due to peaks and valleys in the spectral shape was neglected under the premise that such randomness was already reflected in the PSHA. Thus, the waveform of the ground motion records was modified using a spectral matching procedure [31]. The ground motion suite for CS2, described below, was used as the seed motion for spectral matching with the UHRS. Spectral matching was conducted in accordance with requirement \"Option 2: Multiple Sets of Time Histories\" of SRP 3.7.1 [32]. The response spectra of the modified ground motion records matched the target UHRS well, as compared in Figure 6a.  
In general, the spectral acceleration of a CS at a frequency other than the control frequency is different from that of the reference UHRS and is assumed to be distributed lognormally, as described in Section 3.2. Virtual response spectra simulating the distribution of spectral acceleration for the CS were generated using the algorithm proposed by [PERSON] and [PERSON] (2011) [33]. Ground motion records that matched each simulated spectrum were selected from the PEER strong ground motion database [34], and only amplitude scaling was performed without modifying the frequency content. Ground motion records with magnitudes in the range 5.5-7.0 and an epicentral distance of 0 to 50 km were selected based on the result of deaggregation represented in Figure 4. For CS3, the lower bound of magnitude was reduced to 5.0 considering the contribution of magnitudes 5.0 to 5.5, as shown in Figure 4c. The distributions of magnitude and epicentral distances for the selected ground motion records are presented in Figure 7. Because the ground motion records were selected based on the degree of conformity with the shape of the target spectrum, the distribution of the magnitude and epicentral distance was somewhat different from the result of the deaggregation shown in Figure 4. Figure 8 shows the distribution of the GM_RotD50 spectra, a type of geometric mean spectra [35], for the scaled ground motion suite in comparison with the median, 2.5%, and 97.5% confidence levels of the target CS plotted in thick and broken red lines. The thick black broken line represents the median of the response spectra for the scaled ground-motion suite. The median and variance of the target CS were realized using the scaled ground motion suite.  
Figure 6: UHRS and spectra for spectrally matched ground motions (H1-component): (**a**) Initial matching; (**b**) Scaled for directional variability.  
Figure 8: _Cont_.  
Figure 7: Distribution of the magnitude and epicentral distance for ground motion suite: (a) CS1; (b) CS2; (c) CS3.
# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 5 Recommendation of Future Work  
In this paper, we have developed a spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC) by using spatial weight matrix of roads (SWMR) and the local Moran's I for cluster and outlier analysis. We believe that the proposed approach can be extended to the following fields, which can be considered as future work. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Id** & **Fclass** & **Name** & **Ref** & **Crash\_Count** & \begin{tabular}{c} **Local** \\ **Moran I** \\ \end{tabular} & \
\begin{tabular}{c} **\(z\)-_Score_ \\ \end{tabular} &
\begin{tabular}{c} **\(p\)-Value** \\ \end{tabular} & **Cotype** \\ \hline
24305 & motorway & & 1 35 & 492 & 209.13 & 94.51 & 0.00 & HH \\
17387 & motorway & & 1235 & 343 & 141.99 & 54.23 & 0.00 & HH \\
24483 & secondary & Fleur Drive & & 295 & 422.79 & 70.28 & 0.00 & HH \\
26939 & motorway & & 1 235 & 290 & 8.57 & 3.06 & 0.00 & HH \\
26629 & motorway & & 1 80; 1 35 & 267 & 10.70 & 4.41 & 0.00 & HH \\
27435 & motorway & & 1 80; 1 35 & 6 & 262 & 141.85 & 58.53 & 0.00 & HH \\
26612 & Primary & South Ankeny Boulevard & US 69 & 252 & 242.99 & 59.57 & 0.00 & HH \\
26533 & motorway & & 1 80; 1 35 & 250 & 141.82 & 82.74 & 0.00 & HH \\
26381 & primary & Douglas Avenue & US 6 & 246 & 751.06 & 90.18 & 0.00 & HH \\
24779 & secondary & University Avenue & & 241 & 1570.78 & 229.30 & 0.00 & HH \\
26953 & motorway & & 1 35 & 230 & 130.27 & 49.76 & 0.00 & HH \\
26621 & motorway & & 1 235 & 209 & 107.74 & 62.86 & 0.00 & HH \\
26230 & primary & Southeast 14 th Street & US 69 & 200 & 455.77 & 88.67 & 0.00 & HH \\
26884 & motorway & & 1 80 & 199 & 91.34 & 46.15 & 0.00 & HH \\
27436 & tertiary & Ingersoll Avenue & & 199 & 39.29 & 4.36 & 0.00 & HH \\
26954 & motorway & & 1 35 & 198 & 39.23 & 14.99 & 0.00 & HH \\
24841 & motorway & & 1 35; 1 80 & 193 & 7.77 & 3.93 & 0.00 & HH \\
24971 & motorway & & 1 80; 1 35 & 188 & 160.12 & 72.36 & 0.00 & HH \\
24800 & primary & Southeast 14 th Street & US 69 & 181 & 438.95 & 87.03 & 0.00 & HH \\
24845 & motorway & & 1 80 & 14.64 & 10.46 & 0.00 & HH \\
26781 & secondary & University Avenue & & 179 & 133.61 & 27.01 & 0.00 & HH \\
24918 & primary & Southeast 14 th Street & US 69 & 177 & 132.56 & 50.64 & 0.00 & HH \\
26558 & motorway & & 1 80; 1 35 & 172 & 7.82 & 4.56 & 0.00 & HH \\
24782 & primary & Northeast 14 th Street & US 69 & 167 & 78.74 & 18.26 & 0.00 & HH \\
25995 & secondary & Martin Luther King Jr. Parkway & & 161 & 378.83 & 102.33 & 0.00 & HH \\
24849 & motorway & & 1 80; 1 35 & 155 & 72.62 & 51.89 & 0.00 & HH \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Local Moran’s I, _z-score_, and \(p\)-value of roads in high–high cluster (RCHC).
###### Abstract  
In recent years, direct position determination (DPD) with multiple arrays for non-circular (NC) signals is a hot topic to research. Conventional DPD techniques with spectral peak search methods have high computational complexity and are sensitive to the locations of the observation stations. Besides, there will be loss when the signal propagates in the air, which leads to different received signal-to-noise ratios (SNRs) for each observation station. To attack the problems mentioned above, this paper derives direct position determination of non-circular sources for multiple arrays via weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) data fusion (NC-Euler-WESPRIT) method. Firstly, elliptic covariance information of NC signals and Euler transformation are used to extend the received signal. Secondly, ESPRIT is applied to avoid the high-dimensional spectral function search problem of each observation station. Then, we combine the information of all observation stations to construct a spectral function without complex multiplication to reduce the computational complexity. Finally, the data of each observation station is weighted to compensate for the projection error. The consequence of simulation indicates that the proposed NC-Euler-WESPRIT algorithm not only improves the estimation performance, but also greatly reduces the computational complexity compared with subspace data fusion (SDF) technology and NC-ESPRIT algorithm.  
non-circular sources; multiple arrays; direct position determination; Euler transformation; weighted
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd
### Traffic Adaptive Mesh Construction
#### 2.3.3. Reactivation and Path Creation for Isolated SC-BSs  
The first step of this algorithm does not guarantee the connectivity of SC-BSs to the mmWave GW. Since there might exist isolated SC-BSs who cannot connect with the mmWave GW through multi-hop relay due to the deactivation of the surrounding SC-BSs, it is necessary to reactivate backhaul interfaces of certain SC-BSs which had been deactivated in the first step, to work merely as the relay nodes for isolated SC-BSs. After identifying isolated SC-BSs which have no parental nodes already attaining connectivity with the GW, the algorithm searches for shortest paths between the isolated SC-BSs and the non-isolated SC-BSs. The optimal combination of such shortest paths is found exhaustively to ensure the smallest number of SC-BSs on the determined shortest paths to be reactivated. [PERSON] et al. [25] explained details of this heuristic approach.  
For summary, the first step roughly decides the activation of SC-BSs in hotspot areas while traffic at sparse area is offloaded to the LTE macro BS. The second step performs a load balancing between multiple sectors at mmWave GW and activated SC-BSs while attempting to guarantee UEs' traffic demand. One important characteristic of the loads optimized in this step is that it allows multiple route multiplexing from different sectors of the mmWave GW to supply backhaul resources to hotspot areas with densely concentrated traffic. In other words, a SC-BS can even support a volume of traffic larger than that can be provided by a single mmWave GW sector. Such functionalities will be tested in our testbed explained in the next section.  
Employing SDN technology is suitable for realizing the aforementioned 3-step algorithm. Although the functions of SDN technology vary widely, route control functionality can be utilized for dynamic construction of mesh networks.The control signaling are performed in an out-band manner e.g., over LTE macro cell. Such mechanism is explained in details below.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Advanced Water Treatment Technologies
#### 4.5.3 Membrane Filtration  
Membrane filtration is a modern technology utilized to eliminate NOM. Integrating enhanced coagulation and ultrafiltration has shown high efficiency in NOM removal, achieving up to 59% removal rates. Furthermore, the reduction in disinfection byproduct formation potential (DBPsFP) from the combined process of enhanced coagulation and ultrafiltration was found to be 16.9% and 30.8% higher compared to enhanced coagulation alone and ultrafiltration alone, respectively. It was noted that NOM removal through the combined coagulation and ultrafiltration processes was consistently effective irrespective of pH and the dosage of polyferric chloride. To address the two primary drawbacks of membrane filtration, namely membrane fouling and limited effectiveness in removing low molecular weight NOM, pretreatment methods such as enhanced coagulation and pre-oxidation are commonly employed before the membrane filtration process [14].  
Current research endeavors for eliminating DBPs through membrane filtration technology primarily concentrate on common aliphatic DBP compounds like THMs, HAAs, haloacetaldehyde (HALs), HANs, and other similar DBP species. The focus on these aliphatic DBPs stems from their widespread presence and potential adverse impacts on human health and the environment. Membrane filtration has demonstrated potential as an effective treatment method for the targeted removal of these specific DBP groups; however, the overall efficiency can be influenced by factors such as membrane characteristics, feed water composition, and operational parameters [12].  
Membrane filtration technology is known for its high efficiency, minimal chemical usage, flexible operation, and the absence of secondary pollution. It is extensively employed to eliminate trace pollutants from water. Nonetheless, the efficacy of membrane filtration in removing DBPs is impacted by various factors, including membrane and DBP characteristics, feed solution composition, and operational conditions [12].  
The most utilized membrane filtration processes for water treatment encompass pressure-driven membrane technologies, including microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Membrane filtration treatment has several desirable attributes in terms of DBP removal, such as high efficiency, versatility (ability to be applied to multiple water types), simplicity of operation, and flexibility. These characteristics make membrane-based processes attractive options for effectively removing DBPs [12].