esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 5 Assessment of the Impact of Biofouling and Maintenance Optimization
### Results of Model-2  
In this approach, Model-2 was employed to monitor the advance of biofouling in the period corresponding to Dataset D. To achieve this, synthetic datasets were created by assigning specific values to the model features. Although the goal here was to evaluate the effect of the Days feature, other input variables played a crucial role in the output, namely the STW, the Draft, and the Trim. Thus, two loading conditions, one laden and one ballast, were considered, setting the Draft and Trim values equal to the respective mode values of Dataset D. Then, for each condition, values within the range of 12 to 14 km were considered for the STW, since most values of Dataset D were in this range (see [30]). Furthermore, a calm sea condition with no current effect was considered, so the wave, wind, and current variables were set equal to zero. Additionally, the RoT variable was assumed to be zero, indicating that the vessel was traveling on a steady course. Lastly, for each loading condition, three values for the Days feature, those most frequently found in Dataset D for each loading condition, were selected over the entire period of Dataset D. The feature values listed above are summarized in Table 13, which describes the synthetic datasets of laden and ballast conditions.  
Figure 14: Application of Model-1 to Dataset B. (**a**) Scatter plot of the actual (blue) and predicted (red) SHP over time, and (**b**) scatter plot of PI over time.  
Figure 15: Application of Model-1 to Dataset D2. (**a**) Scatter plot of the actual (blue) and predicted (red) SHP over time, and (**b**) scatter plot of PI over time.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Laden Condition** & **Ballast Condition** \\ \hline STW [kn] & 12.0, 12.25, 12.75, 13.0, 13.5, 14.0 & 12.0, 12.25, 12.75, 13.0, 13.5, 14.0 \\ \hline Draft [m] & 14.4 & 6.81 \\ \hline Trim [m] & \(-0.13\) & \(-2.56\) \\ \hline RoT [deg/min] & 0 & 0 \\ \hline Wind x [m/s] & 0 & 0 \\ \hline Wave x [m] & 0 & 0 \\ \hline Current [kn] & 0 & 0 \\ \hline Days [days] & \(51,120,354\) & \(31,229,297\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Overview of the synthetic datasets designed separately for laden and ballast conditions.
# 2D Ultrasonic Antenna System for Imaging in Liquid Sodium
## 1 Introduction  
Since France considered in 2008 that the sodium-cooled reactor (SFR) concept was the most mature for Generation IV nuclear reactors, an extensive R&D programme was launched. In-service inspection was identified as a difficult task to be performed (as the sodium coolant is opaque, hot, highly chemically reactive, and difficult to drain). Ultrasonic techniques have been extensively studied as they are well adapted to Non Destructive Evaluation (NDE) and telemetry measurement in this harsh environment.  
Thus, development of ultrasonic transducers to be immersed in sodium at about 200 \({}^{\circ}\)C in the reactor block (inspection is performed at shutdown conditions) led to a first phase of specification consolidation; then, a prequalification process involving increasingly more realistic experiments using acoustic techniques and simulations was performed with the patented CIVA code [1].  
Associated applications for inspection deal with telemetry, vision, and volumetric control for SFR reactor block systems, structures, and components and for the power-conversion system.  
Some techniques deal with short-distance imaging such as the EMAT (Electromagnetic-Acoustic Transducer) of CEA [2] and TUSS (Ultrasonic transducer for Non Destructive Testing--NDT--of structures immersed in liquid sodium at 200 \({}^{\circ}\)C) of FRAMATOME [3] that are being developed for NDE, whereas other, such as the orthogonal imaging concept based on an underwater imaging system [4] and developed in the 2000s [5], deals with long-distance vision and the exploration of a large area without moving the probe. The orthogonal imaging system allows for scanning a much larger areathan that scanned with the same number of elements arranged in a matrix pattern, and thanks to the large antenna's aperture in the focusing plane, remote targets are likely to be imaged. This concept has been taken up recently in order to reexplore its capabilities using actual simulation tools and imaging methods. The first simulated results and experimental measurements performed in water highlight the potential of the system in which manufacturing for sodium trials is ongoing at Toshiba Corporation while imaging algorithms are specifically adapted or developed for its applications.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 4 Effect of Key Structural Parameters of the PPWJ Device on Jet Characteristics
### Effect of Piston Diameter \(d_{p1}\) and \(d_{p2}\)  
Piston diameters \(d_{\rm p1}\) and \(d_{\rm p2}\) are important parameters that affect the jet performance, the boost ratio (square of the ratio of \(d_{\rm p2}\) to \(d_{\rm p1}\)), and the action area of the piston. The relationship between the jet characteristics and the variable boost ratio can be obtained by changing \(d_{\rm p1}\) while keeping \(d_{\rm p2}\) constant. As shown in Figure 10, with an increase in boost ratio, the pulse pressure increases from 43.9 MPa to 73.1 MPa and then falls to 10.8 MPa, and the maximum pulse pressure corresponds to a boost ratio of 6.25. When the boost ratio is lower than 6.25, the oil inlet pressure is equal to the value set by the throttle valve. Increasing the boost ratio will increase the pulse pressure, and \(v_{\rm sp}\) will be improved accordingly. \(v_{\rm rp}\) is not affected by \(d_{\rm p1}\), so the pulse frequency will increase and the pulse duration will decrease. However, \(q_{\rm sin}\) is limited by the rated flow rate of the pump. When the boost ratio exceeds 6.25, \(q_{\rm sin}\) reaches the maximum value. In this case, increasing the boost ratio will decrease the pulse pressure. Meanwhile, \(v_{\rm sp}\) will not be improved, and the pulse frequency and pulse duration tend toward a stable value. For a certain nozzle diameter, there must be a value of boost ratio to maximize the pulse pressure.  
Figure 8: Jet characteristics versus piston maximum displacement: (**a**) variation of jet pressure curves, (**b**) variation of pulse parameters.  
Figure 9: Jet characteristics versus piston diameter \(d_{\rm p3}\): (**a**) variation of jet pressure curves, (**b**) variation of pulse parameters.  
Figure 11 shows the relationship between the jet characteristics and the action area of the piston in the case where the boost ratio is a constant of 6.25 and \(d_{\rm p1}\) and \(d_{\rm p2}\) are changed simultaneously. Meanwhile, the action area of the piston in chamber 2 is set to a fixed value to eliminate the influence of \(d_{\rm p3}\) on the pulse frequency. If \(d_{\rm p2}\) is less than 40 mm, it is apparent that the pulse pressure will not reach the maximum value of 73.1 MPa. Although the mass of the piston decreases with the decrease in \(d_{\rm p2}\), a larger \(v_{\rm sp}\) is needed to make the pulse pressure reach the peak; otherwise, the piston is unable to achieve full acceleration within the limited maximum displacement. When \(d_{\rm p2}\) exceeds 40 mm, the pulse pressure reaches the peak value and continues for a period of time, which indicates that the piston completes acceleration and enters the uniform motion state. There is a minimum value of \(d_{\rm p1}\) and \(d_{\rm p2}\) to make the pulse pressure reach the peak value when the boost ratio and maximum displacement are determined.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 3 Proposed Methodology
### Overview of the Unified Threat Detection Pipeline
The design on this figure highlights how diverse threat data types can be handled independently but integrated coherently for final decision-making, within a layered and flexible design of different AI techniques that ensure scalability, adaptability, and extensibility. Figure 1: Overview of the suggested Unified Threat Detection Architecture. The pseudocode below clarifies the internal operational steps of the unified pipeline architecture depicted in Figure 1. This step-by-step representation outlines how incoming data is processed, classified, and optionally fused, providing a clear abstraction of the pipeline's dynamic execution flow. Pseudocode: Dual-Modality Threat Detection with GAN Augmentation. 1: Input:  
2: \(x\_img\leftarrow\) Malware binary image data  
3: \(x\_tab\leftarrow\) Network traffic log tabular data  
4: mode \(\leftarrow\) {_train_', _'val_} //_Indicates training or validation phase_  
5: Output:  
6: \(Y\leftarrow\) Final threat label (Malicious/Benign)  
7: \(\tau\_img\leftarrow\) RecognizeInputType(\(x\_img\))  
8: \(\tau\_tab\leftarrow\) RecognizeInputType(\(x\_tab\))  
9: #==== **Image Processing Branch ====**  
10: **if**\(\tau\_img\) == _'image'_ **then**  
11: **if**\(mode\) == _'train'_ **then**  
12: \(x\_img\)'s \(\leftarrow\) DCGAN(\(x\_img\)) //_Synthetic image generation_  
13: \(D\_img\)'s \(\leftarrow\)\(x\_img\) U \(x\_img\)'s //_Combine real and synthetic images_  
14: **else**  
15: \(D\_img\gets x\_img\) //_Use real images only_  
16: **endif**  
17: \(Z\_img\leftarrow\)\(\varphi\_img(D\_img)\) //_Feature extraction via CNN_  
18: \(Y\_img\gets h\_img(Z\_img)\) //_Image-based classification_  
19: #==== **Tabular Processing Branch ====**  
20: **if**\(\tau\_tab\) == _'tabular'_ **then**  
21: **if**\(mode\) == _'train'_ **then**  
22: \(x\_tab\)'s \(\leftarrow\) ZDGAN(\(x\_tab\)) //_Synthetic tabular data generation_  
23: \(D\_tab\)'s \(\leftarrow\)\(x\_tab\) U \(x\_tab\)'s //_Combine real and synthetic tabular data_  
24: **else**  
25: \(D\_tab\gets x\_tab\) //_Use real tabular data only_  
26: **endif**  
27: \(Z\_tab\leftarrow\varphi\_tab(D\_tab)\) //_Tabular feature extraction_  
28: \(Y\_tab\gets h\_tab(Z\_tab)\) //_Ensemble-based classification_  
29: #==== **Fusion and Evaluation ====**  
30: \(y\leftarrow\) (\(Y\_img\), \(Y\_tab\)) //_Decision fusion_  
31: \(s\_conf\leftarrow\) ComputeConfidence(\(Y\_img\),\(Y\_tab\)) //_Score estimation_  
32: **if**\(mode\) == _'val'_ **then**  
33: **EvaluatePerformance(\(y\_s\_conf\)) //_Metrics computation during validation_  
34: **endif**  
35: **return**\(y\) //_Final prediction (Malicious/Benign)_
## 2 Materials and Methods
### Methods  
#### 2.2.1 Basic Idea of Model Development  
The model development process is shown in Figure 1. We developed a hybrid LUE model based on ML and LUE models for GPP estimates. The basic idea is that we first assimilate site-based GPP using an ensemble Kalman filter (EnKF) [36; 37] to obtain the LUE. Second, we directly simulated LUE using multi-layer perceptron (MLP) driven by 15 variables. Third, we estimated the GPP within the LUE model framework using the modeled LUE and other required variables (i.e., PAR and FPAR). In the following sections, we provide a comprehensive account of the research method employed in this study.
## 4 Results
### Comparison among Different Classifiers
#### 4.2.5 Naive Bayes  
The NB classifier works within a stochastic frame [46], exploiting the [PERSON] theory with the naive assumption that features in a class are uncorrelated. NB has two hyperparameters: the kernel smoother, to be the box, Gaussian, triangular, or Epanechnikov one; the distribution type.  
Results gathered in Table 7 show that the training accuracy results in the range 82.4-92.6%, while the testing accuracy in the range 83.3-91.7%; the computing time instead varies from 13.7 s to 109.7 s. In model N2, as compared to model N1, the support type is changed from unbounded to positive, and the training accuracy increases from 90.7% to 92.6%, while the testing accuracy decreases from 91.7% to 88.9%. With model N3, the grid search is adopted in place of the Bayesian optimizer, without affecting the results. By enabling PCA, see model N4, accuracy gets lower as usual. In model N5, both PCA and feature exclusion are plugged in: this solution improves the accuracy with respect to the N4 case, since less predictors are included by the model to train it. Overall, it can be stated that the performance of NB results to be worse than that of the other classifiers.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 5 Discussion
### Limitations of Physical Noise Models  
The physical noise model used in this study simulates extreme low-light PSR images by tightly coupling the hardware parameters of the LROC NAC camera with environmental conditions. However, it still has certain limitations. This highly customized design makes the model difficult to generalize to other satellites or probes equipped with CCD sensors, as its noise parameter estimation heavily depends on the unique physical characteristics of LROC NAC.  
For instance, the photon flux calculation is directly based on LROC NAC's orbital altitude and short exposure time. In contrast, other imaging systems, such as the OHRC camera on Chandrayaan-2 or the optical payloads on the Chang'e series, have different orbital pa rameters (such as altitude and inclination) and exposure strategies. These differences cause significant deviations in photon statistical distributions and noise coupling mechanisms from the model's predefined conditions, leading to potential failures in noise estimation.  
Such limitations indicate that the model's physical assumptions are strongly tied to the hardware parameters of LROC NAC. Applying it directly to other CCD systems would require reconstructing the noise generation pipeline and revalidating the model, which incurs substantial engineering costs.
## 1 Introduction  
Knee osteoarthritis (OA) is one of the most common age-related diseases featured by cartilage loss and subchondoral bone damage [1, 2]. Even if these latter are typically used to quantify disease severity, it is known that knee OA affects almost all of the other components in the knee joint including the ligaments, the menisci, the nerves and the muscles acting on the affected joint [3, 4, 5, 6]. It has been postulated for a long time that the degeneration of knee joint proprioception increases as osteoarthritis worsens [4, 5, 6, 7, 8]. Sensory receptors located in the knee joint components are damaged resulting in the generation of abnormal proprioceptive sensory signals informing the brain on joint position and joint movement in an incorrect manner [4, 9]. The abnormalities in proprioception contribute to a decline in balance control, also defined as postural stability, and as a consequence to an increase in the risk of falls [10; 11; 12; 13; 14]. In addition to proprioception impairment, other variables affecting balance have been claimed: muscle strength, radiograph visual indications of the severity of knee OA, pain, and knee alignment [12; 15]. A clear relationship between the impairment of balance and proprioception and the degree of functional impairment as measured by means of common scoring systems has not yet been demonstrated [8].  
Literature has reported impairments in standing balance and proprioception in people with knee OA both through clinical tests [8; 15; 16; 17] and laboratory-based measurements [18; 19; 20; 17]. These studies clearly show that the lack or the inappropriate proprioceptive information arising from the knee joint has a negative effect on static and dynamic balance control. However, most of these previous researches were based on the instrumental assessment of postural stability in bipodialic stance. Considering that single limb stance is very common during basic movements of daily life such as walking (where single stance lasts about 80% of the gait cycle) or climbing a stair, testing single stance stability seems to be even more of relevance for people showing bipodial balance impairment. In particular, assessing postural stability in single stance with closed eyes, excluding the visual control, allows indirect evaluation of the effectiveness of the proprioceptive control [21].  
Based on these considerations, the aim of this study was to assess postural stability and proprioception in patients with knee osteoarthritis during a double-limb and a single-limb standing postural stability task, and the relation between postural stability and proprioception impairments with the radiological severity of knee OA, and impairments in functional performance as measured by clinical scores.
# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 1 Introduction
### Task Allocation for Human-Robot Collaboration in Assembly  
To address the challenge of task allocation, several methods were proposed, which can be classified into static or dynamic task allocation approaches [25; 26]. Static task allocation mainly considers the planning phase (\"offline\"), dealing with the creation of a fixed task schedule prior to execution. For this, both planning methods and optimization algorithms have been proposed. Planning methods are either carried out manually, e.g., [26; 27], or in conjunction with a planning assistance system, e.g., [35]. In such methods, the potentials of both resources are usually assessed first and then a process schedule is created using a task allocation logic based on the assessment. Optimization approaches automatically generate different allocation alternatives and select the one that best satisfies a cost function, e.g., [36; 37; 38]. In contrast, dynamic task allocation approaches, e.g., [39; 40], consider the assembly execution phase (\"online\") and focus on the (re-)allocation of tasks in the case of deviations from the initial assembly planning.  
Table 11 in Appendix A provides an overview of related work on task allocation, including a short description regarding the type of the proposed approach. Further, details on the evaluation scenario, the evaluation approach, and the main results of the evaluation are presented. The vast majority of task allocation approaches in the related work only validate the general functionality of the proposed approach. However, only a few articles [36; 38; 41] evaluate their approach by comparing the resulting task schedules or the metrics of the actual assembly process execution with those of manual assembly. Moreover, effects of real-time execution arising from the assembly execution by different, also untrained persons, or from delays due to unforeseen events, are rarely taken into account. Instead of carrying out a user study with users being not familiar with the system, the proposed approaches are often either solely tested in simulation or by the respective authors themselves.  
For considering these effects, only two references [42; 43] conducted user studies for evaluation of their proposed approaches (highlighted in light grey in Table 11). From those, the study in [42] evaluates the implications of different trust levels on human satisfaction and assembly performance. Reference [43] proposes a dynamic task allocation system that  
Figure 1: Overview of the levels of cooperation between humans and robots and their characteristics (according to [18; 33]; translated from [34]).  
aims at minimizing human fatigue and compares their approach with a static task plan in a user study.
## 2 Literature Survey
### Related Works
The main focus of this developed pattern was to appraise the coefficients in the channel using received SNR information and to reduce the static error by taking the average of squared differences between predicted values and observed values. A Recurrent Neural Network (RNN) was built in CNN to minimize the transmission distance for improving communication in wireless networks. In this proposed model, two types of fading were used to minimize the mean square error: the time-varying fading system and a quasi-static varying system. The signal strength was reduced due to the usage of various variables. The resultant outcomes were balanced with traditional techniques to illustrate the performances and effectiveness of the developed model to prove its efficiency. In 2021, [PERSON] et al. [7] demonstrated the channel estimation model in wireless networks for amplifying the communication reliability and reducing the computation cost of the network. The least square method was used in channel estimation to find the suitable set of data for the network and predict the performances of dependent variables. However, these methods produced high estimation errors. The error rate of the system was reduced using hybrid deep learning such as the Fully Connected Neural Network (FCNN) model. FCNN consists of a series of layers in which various neurons are connected in a single layer. The Doppler Effect was used in MIMO with multipath channels to retrieve 5G networks. It changed the frequency of the signal during the motion between the source signal and the user. The efficiency of the hybrid LSTM was high in the implemented channel estimation model. In 2018, [PERSON] et al. [8] developed a new model for channel estimation using deep learning. In this MIMO system, multiple numbers of antennas were used on the transmitter side, and the length of the signal was also high. Deep learning-based signal estimation and data estimation were the two types of estimation used in this wireless network. Two-layer Neural Networks (TNN) and DNN were used in this hybrid network to maintain the reliability of the system. Antennas in this wireless network were connected to the base station for transmission and receiving the signal. The resultant outcome was compared with various channel estimation methods to verify the estimation of the network model.
# Trajectories and Resource Management of Flying Base Stations for C-V2X
###### Abstract  
In a vehicular scenario where the penetration of cars equipped with wireless communication devices is far from 100% and application requirements tend to be challenging for a cellular network not specifically planned for it, the use of unmanned aerial vehicles (UAVs), carrying mobile base stations, becomes an interesting option. In this article, we consider a cellular-vehicle-to-anything (C-V2X) application and we propose the integration of an aerial and a terrestrial component of the network, to fill the potential unavailability of short-range connections among vehicles and address unpredictable traffic distribution in space and time. In particular, we envision a UAV with C-V2X equipment providing service for the extended sensing application, and we propose a UAV trajectory design accounting for the radio resource (RR) assignment. The system is tested considering a realistic scenario by varying the RRs availability and the number of active vehicles. Simulations show the results in terms of gain in throughput and percentage of served users, with respect to the case in which the UAV is not present.  
cellular-vehicle-to-anything; unmanned aerial vehicles; mobile radio network; connected vehicles +
Footnote †: journal: Sensors