esa-sceva/satcom-chunk-collection huggingface dataset:
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 4 Findings
### Hidden Markov Model (HMM) Analysis  
To further understand the transitions between different movement states of the tracker, an HMM was applied. This statistical model was chosen for its ability to analyze sequential data where the system undergoes transitions between unobservable (hidden) states. By modeling stochastic processes, the HMM captures the probabilistic nature of these transitions, making it particularly suited for telemetry data where patterns in movement are influenced by complex and often indirect environmental factors, such as tides, currents, and physical obstructions. The analysis revealed two distinct states: \"resting\" and \"transit\". The matrix, as shown in Table 1, indicated that the tracker had a 64.4% probability of remaining in the resting state once stationary and a 35.6% chance of transitioning from resting to transit. Interestingly, once in the transit state, the probability of remaining in that state was 100%, suggesting that external forces were required to initiate movement but that continuous flow, likely driven by currents and tides, sustained the transit state.  
The HMM emission matrix provided additional insights into the tracker's behavior. While in the resting state, the tracker predominantly exhibited medium speeds, which likely reflected subtle oscillations caused by localized turbulence. In contrast, during the transit state, medium and high speeds were more commonly observed, corresponding to steady or accelerated motion driven by dynamic external forces. This process is visualized in Figure 9.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Matrix** & **State Transition/Emission** & **Probability** \\ \hline Estimated Transition Matrix & Resting \(\rightarrow\) Resting (State 1 to State 1) & 0.644 \\ \hline  & Resting \(\rightarrow\) Transit (State 1 to State 2) & 0.356 \\ \hline  & Transit \(\rightarrow\) Transit (State 2 to State 2) & 1 \\ \hline Estimated Emission Matrix & Low Speed while Resting & 0.1275 \\ \hline  & Medium Speed while Resting & 0.7934 \\ \hline  & High Speed while Resting & 0.0792 \\ \hline  & Low Speed while in Transit & 0.0165 \\ \hline  & Medium Speed while in Transit & 0.6152 \\ \hline  & High Speed while in Transit & 0.3684 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimated Transition and Emission Matrices (table).  
These results provided valuable insights into the dynamics of plastic transport, especially highlighting how transitions from resting to transit are less likely without significant external influences like tidal shifts or boat wakes. The consistent retention in the resting state also aligns with observed resting periods, suggesting that floating debris can become trapped in particular zones before external forces shift them back into motion. The prolonged retention of plastic waste in certain areas indicates that these locations may act as chronic pollution hotspots, leading to bioaccumulation of plastics in local fauna and persistent disruptions to aquatic food webs [3; 5; 10; 11].
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 3 Basic Reviews
### Why Convolved Feature Vector (CFV)?  
Most of the impulse noise removal techniques explained and reviewed in previous sections use specific computational mechanisms to estimate the central pixel value of the kernel to be noisy or noise-free. These mechanisms may produce good results under certain conditions but generality is not true in all cases. For example, median filter outperforms many filters if the input image is smooth and have low noise densities, i.e., when the number of degraded pixels are fewer than half of the size of kernel. In all other cases, median filter may replace incorrect pixel values causing blurry effects. This idea is explained in Figure 1 where a standard 'Barbara' image is artificially degraded with three types of impulses. In Figure 1a, a smooth region is artificially degraded with RVIN, whereas Figure 1b,c are degraded with high noise ratio of salt and peppers like impulses. In Figure 1b,c central pixels are identified as noise-free when a simple median filter defined in (2) is used to detect or remove the additive noise. Furthermore, absolute differences or deviations from their medians and central pixels are zero, which indicates strong relationship among the majority of neighboring pixels. In Figure 1c, two kernels are considered where the upper kernel is degraded artificially with random noise and lower kernel is noise-free. Median values of upper and the lower kernels are 171 and 169, respectively. Likewise, absolute differences are 9 and 10, respectively. These features establish a fact that both the kernels contain almost are similar features whereas one kernel is noise-free and the other one is noisy. Similar noise detection and removal problems ascend in [35; 36; 37] due to ineffective use of median filter and thresholds. In most cases, when features are alike, noise detection and removal becomes problematic. Furthermore, when noise ratio becomes high in small size kernels (as in Figure 1a,b), similar problem arises. To overcome this problem, we have proposed a new convolved feature vector based fuzzy mechanism in this paper.
## 2 Related Work
### Measures for Performance Evaluation  
Classification Accuracy, Time, Kappa Statistic, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)  
Classification accuracy measures how well a test is able to predict different categories; it shows the number of samples that are correctly classified into their respective classes and is shown as a percentage. Classification time is the total CPU time that is required to build a classification model as well as the training time required to predict the output of the test data. Kappa statistic (KS) is calculated to evaluate measurement accuracy. The closer the K value is from 0 to 1, the more reliable the classification is. When K equals 1, the correctness of classification is the safest. On the other hand, when K equals 0, the chance of classification is right and unreliable [50]. Mean absolute error (MAE) is used to evaluate the performance of the algorithm and is calculated by taking the average of all absolute errors [51]. Finally, root mean squared error (RMSE) is a popular measurement for performance evaluation. Thanks to square root, the measurement calculates the error without canceling the positive and negative errors [52].
## 5 Experimental Validation and Benchmarks
### Self-Organizing Networks (SONs)
#### 5.3.1 Autonomous Network Configuration  
AI enables self-organizing networks (SONs) to automatically configure network components, optimize parameters, and ensure that network resources are allocated based on real-time demands. This autonomous configuration capability reduces the need for manual intervention and ensures the network is always in an optimal state. Key performance metrics include throughput, latency reduction, and resource utilization, all of which significantly improve through AI-driven approaches [125].  
The performance improvements shown in Figure 9 are measured in terms of key network metrics such as throughput, latency reduction, and efficient resource utilization. These metrics reflect the ability of AI-based systems to dynamically adapt to changing demands, ensuring consistent high performance and reliability. The gradual increase in performance highlights the effectiveness of autonomous configuration in optimizing SON operations over time.
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.6 Vector 76 Attack  
In the Vector 76 attack [27; 31; 42], an adversary exploits a double-spending opportunity using two separate nodes. One node connects to the service (e.g., an E-Wallet), while the other is connected to the broader blockchain network. The attacker initiates two transactions--one with a high value and another with a low value. The high-value transaction is directed to the service node to create a false impression of a substantial deposit. Meanwhile, the attacker continues mining until a target transaction is detected and confirmed on the blockchain. At that moment, the attacker withdraws the high-value amount from the service node while broadcasting the low-value transaction to the network. As a result, the network accepts the low-value transaction, invalidating the high-value one. To mitigate this attack, blockchain networks can reduce transaction confirmation times, adopt consensus mechanisms less susceptible to this attack (e.g., Proof of Stake), and enforce stricter mempool validation rules to prevent unauthorized transactions from entering the system.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 1 Introduction
The rationale behind these choices is then discussed, considering the expected impact of loss and dual-polarization modulation, the possible role of the discrete spectrum, and the extension to a network scenario. The paper is organized as follows. Section 2 describes a standard NFDM system setup, while Section 3 discusses its suboptimality. Next, improved detection strategies and modulation techniques for NFDM are discussed in Section 4 and in Section 5, respectively. Section 6 compares the performance of different NFDM systems. Section 7 comments on some yet-to-be-investigated concepts related to NFDM systems. Finally, Section 8 draws the conclusions.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 2 Wind Farm Model  
Since the content of this paper is the active power dispatch optimization of WFs and the dynamic process of WTs is not involved in the design, the static model is enough to illustrate the effect of the proposed strategy. The WF model consists of three parts. The first part is the WT model, which is used to describe the aerodynamic and power generation characteristics of the WT. The second part is the WF layout, which is also an important attribute of the WF. The arrangement of the WTs affects the aerodynamic interaction in the WF directly. The last part is the wake model, which is used to describe the aerodynamic interaction among the WTs. Here, the Jensen wake model, which is widely used in engineering, is adopted.  
Figure 1: Normalized failure rates and downtimes of a wind turbine.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 5 Conclusions  
In this study, two optimized soft computing techniques such as PCA and TLBO were applied to improve the performance of individual ANFIS model for predicting the compressive strength of MSC. A number of 289 experimental tests of MSC were used as dataset for the simulation including inputs parameters such as mixture proportions, cement's compressive strength, water content as well as manufactured sand's characteristics. Various validation criteria such as R, RMSE and MAE were employed to validate and test the performance of the proposed model, as well as to compare with other AI models such as individual ANFIS and ANN with or without PCA treatment. Based on the results of this study, several conclusions could be deduced: (i) data pre-processing technique such as PCA is indispensable in order to improve the prediction capability of the AI models; (ii) TLBO algorithm exhibited a strong efficiency in finding the optimal parameters of individual ANFIS model; (iii) hybrid ANFIS-TLBO/P model is the best model for predicting compressive strength of MSC in compared to other models; and (iv) sand's characteristics should be integrated in the input space for the simulation of the AI models for predicting the MSC compressive strength.  
In terms of perspectives, uncertainty quantification should also be addressed for quantifying the confidence interval of MSC's compressive behavior in the presence of input variability.  
**Author Contributions:** Conceptualization, H.-B.L. and T.-T.L.; Methodology, H.-B.L., L.M.L. and T.-T.L.; Software, H.-B.L. and T.-T.L.; Validation, H.-B.L., V.M.L. and B.T.P.; Formal Analysis, L.M.L. and T.-T.L.; Investigation, H.-B.L. and D.V.D. and B.T.P.; Resources, V.M.L.; Data Curation, V.M.L. and B.T.P.; Writing--Original Draft Preparation, H.-B.L., V.M.L., T.-T.L., and B.T.P.; Writing--Review & Editing, H.-B.L., L.M.L., T.-T.L. and B.T.P.; Visualization, H.-B.L. and B.T.P.; Supervision, H.-B.L., D.V.D. and B.T.P.; Project Administration, L.M.L., T.-T.L. and B.T.P.; Funding Acquisition, B.T.P.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
**Designation**  
**Explanation**  
MSC  
Manufactured Sand Concrete  
ANFIS  
Adaptive Neuro Fuzzy Inference System  
ANN  
Artificial Neural Networks  
SVM  
Support Vector Machine  
FL  
Fuzzy Logic  
TLBO  
Teaching-Learning-Based Optimization  
PCA  
Principal Component Analysis  
PC\({}_{\text{k}}\) (k = 1:11)  
Principal components
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 3 Experimental Setup
### Venturi Tube Instrumentation  
A total of 44 pressure taps were placed along the methacrylate tube and the Venturi tube constriction, to evaluate the mass flow through the air amplifier and to provide reference data for numerical correlation. The taps were distributed in ten stations in groups of four along the tube and Venturi, allowing the averaging of the pressure values at each station. Each pressure tap was connected to a hydraulic line and finally connected to the data acquisition system. The lines all had the same lengths, which were minimized to avoid data interference. The data acquisition system consists of an electronic pressure scanning module (model ZOC33/64 PxX2, from SCANIVALVE Corporation), with a maximum range of 2.5 kPa and an accuracy of 0.15% (of the full scale). The pressure acquisition setup is shown in Figure 10. At each experiment, 4000 samples were taken at a rate of 200 Hz,  
Figure 8: Details of the pressure tap inserts inside the Venturi tube.  
Figure 6: Detail of the CNC milled Venturi tube constriction (**left**) and the air amplifier fitting (**right**).  
Figure 7: Experimental configurations based on the air amplifier location with respect to the Venturi tube constriction. Blowing mode (**upper image**) and suction mode (**lower image**). The locations of the pressure taps are marked with red dots, and the blue arrow indicates the flow direction.  
providing 20 s of testing time. The acquisition was started before the opening of the valve to capture the transient effects and to evaluate the stabilization of the flow inside the Venturi tube at each analysis. Further post-processing was required to select an appropriate window of the acquired data to perform a time-averaging and statistical analysis.  
Additional pressure transducers were installed to measure the pressure difference in the constriction section. Four Sensor Technics differential pressure transducers (see Figure 11) with a maximum range of \(\pm\)7 kPa, ran simultaneously with Scanivalve sensors to provide data on the pressure drop/jump across the Venturi tube in all tested configurations.  
Figure 11: Differential pressure transducers used on the Venturi tube constriction section.  
Figure 9: Full experimental blowing setup for the mass flow measurement.
###### Abstract  
In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators--CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers.  
autonomous driving; complex dynamics; urban environment; PGM; CARLA +
Footnote †: journal:
## 1 Introduction
### Surface Reconstruction for Freehand 3D Ultrasound Imaging
In this paper, we have chosen three well-known and widely accepted methods: the Ball Pivoting Algorithm (Ball Pivoting Algorithm, 1997), Power Crust (Crust, 1997), and Poisson reconstruction ([PERSON] _et al._, 2000). These are also representative of three types of point cloud reconstruction methods. The Ball-Pivoting Algorithm (BPA) directly forms a triangle mesh from a given point cloud. The Power Crust method uses the medial axis transform (MAT) of the object to be reconstructed and an inverse MAT to produce the surface of the object. Poisson reconstruction is an implicit-function-based method; it casts the surface reconstruction from oriented points as a spatial Poisson problem. After finding an implicit function by solving the spatial Poisson problem, the reconstructed surface is obtained by extracting an appropriate iso-surface from the implicit function. [PERSON] _et al._[PERSON] _et al._ (2016) provided a surface reconstruction method for freehand 3D US, which is an implicit-function-based method similar to Poisson reconstruction. The surface reconstruction method was based on variational interpolation, which was used by [PERSON] for shape transformation ([PERSON] _et al._, 2016); hence, it was named the variational surface reconstruction (VSR) method. The VSR method can effectively address both sparse and mutually intersected contours. Variational interpolation is commonly used in the surface reconstruction of dense point clouds from ranger scanners ([PERSON] _et al._, 2012; [PERSON] _et al._, 2013; [PERSON] _et al._, 2014; [PERSON] _et al._, 2015), and also in the segmentation of medical data ([PERSON] _et al._, 2016), but has not been used in the surface reconstruction of freehand 3D US. Although ([PERSON] _et al._, 2016) was focused on the 3D segmentation of medical images, the variational interpolation technique used was the same as that in ([PERSON] _et al._, 2016). Because this paper was not dedicated to ultrasound imaging, the specific characteristics of surface reconstruction in freehand 3D US were not addressed. However, there are three drawbacks to the previous VSR work ([PERSON] _et al._, 2016). First, the experiments were simplistic, as they only used the VSR method to reconstruct a seashell and a plastic apple. Second, the quality of the reconstructed surface was assessed by visual inspection and two simple metrics: area difference and volume difference between the reconstructed surface and the original surface. Third, the VSR method was not compared with other surface reconstruction methods. These three drawbacks make the conclusions regarding the prior VSR work ([PERSON] _et al._, 2016) less convincing.
## 5 Relations between Impact Resistance and Tensile Properties
### Relation between Blow Number \(N_{\rm f}\) and Strain \(\varepsilon_{\rm f}\)  
Besides, it can be found that \(\varepsilon_{\rm f}\) of the slab specimen increased from 0.77% to 1.16%, 1.93%, and 6.39% after more PVA fibers were added. The diameter of the crater in the center of disc specimen increased at the failure moment with the increase of PVA fiber content. It can be seen from Table 7, the crater diameter increased from 29.53 mm to 35.95 mm, 55.8 mm, and 59.28 mm when \(V_{\rm f}\) increased. Corresponding to the crater diameter, the blow number \(\rm N_{\rm f}\) of the disc specimen increased from 32  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(V_{\rm f}\) (\%)} & \multicolumn{2}{c}{**First Crack**} & \multicolumn{2}{c}{**Ultimate Failure**} \\ \cline{2-5}  & \(\sigma_{\rm c}\) **(MPa)** & \(\rm N_{\rm c}\) & \(\varepsilon_{\rm f}\) **(\%)** & \(\rm N_{\rm f}\) & **(mm)** \\ \hline
0.5 & 1.78 & 11 & 0.77 & 32 & 29.53 \\
1.0 & 2.64 & 88 & 1.16 & 211 & 35.95 \\
1.5 & 3.04 & 805 & 1.93 & 1584 & 55.80 \\
2.0 & 3.67 & 1662 & 6.39 & 3018 & 59.28 \\ \hline \end{tabular}
\end{table}
Table 7: Major test results of tensile properties and impact resistance.  
Figure 12: Elements of PVA-FRCM disc subjected to impact hammer dropping.  
Figure 13: Relation between \(N_{\rm c}\) and \(\sigma_{\rm c}\).  
to 211, 1548, and up to 3018. The N\({}_{\rm f}\) of disc specimen and \(\varepsilon_{\rm f}\) of slab specimen exhibited a parabolic relationship, as shown in Figure 14. The N\({}_{\rm f}\) of disc specimen increased obviously with an increase of \(\varepsilon_{\rm f}\). However, this is a convex parabola which is different from the concave relation between N\({}_{\rm c}\) and \(\sigma_{\rm c}\). It might be predictable that the increase of \(\varepsilon_{\rm f}\) can apparently increase N\({}_{\rm f}\) only within a certain range, and this effect would not be apparent exceeding that range. When the diameter of the impact crater approaches the diameter of the impact sphere, the range would be coming.  
According to the presented relationships between impact resistance and tensile properties of PVA-FRCM in this study, impact resistance properties of PVA-FRCM could be conventionally evaluated or predesigned by its tensile properties without conducting extra dynamic test.