esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigated the corrosion resistance of oil impregnated anodic aluminum oxide (AAO) surfaces of aluminum 7075 for subsea application. Although aluminum 7075 has high strength, it is scarcely used in the subsea industry because of its corrosion issue. Some treatment of aluminum 7075 is required for subsea application. In this study not only a plate shape but also a cylindrical shape were investigated because a cylindrical shape is frequently used in the subsea industry for electronic device housing. Contact angles of bare aluminum and oil impregnated AAO surfaces of aluminum 7075 were measured after a salt spray test and a pressure test. The results showed that the contact angle of bare aluminum was considerably decreased after the salt spray test, whereas the oil impregnated AAO surface presented a relatively high contact angle after the salt spray test and the pressure test. These results showed that the corrosion resistance of aluminum 7075 could be enhanced by oil impregnation on the AAO surface, and thus can be utilized in the subsea industry.  
aluminum 7075; anodizing; oil-impresation; corrosion resistance; salt spray test +
Footnote †: journal: Applied Sciences
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 4 Results
### Legislative Status
\begin{table}
\begin{tabular}{c c} \hline \hline
**State and Status** & **Special Conditions** \\ \hline Arizona (AZ) & The AV owner or a representative must report accidents. The AV must stop at accident \\ S 1333 (2022): Enacted & scenes or as close as possible. Liability determined by state or federal law. \\ Human: Optional & AVs over 10,001 pounds must have a trained human safety operator to operate and shut off \\ California (CA) & the vehicle. The operator must meet all federal and state qualifications for the vehicle type, \\ A 316 (2023): Vetoed & in both automated and non-automated modes. Manufacturers must report collisions \\ Human: Required & involving AVs to the Department of Motor Vehicles within 10 days. Owners must submit \\  & annual submission of disengagement reports. \\ \hline Colorado (CO) & Human driver laws do not apply to automated driving systems, except those regulating the physical driving of a vehicle. Prohibits testing if the system cannot comply with laws, unless \\ S 213 (2017): Enacted & approved by the Colorado State Patrol and Colorado Department of Transportation. State, \\ Human: Optional & federal, or common law determines liability. \\ \hline Hawaii (HI) & Does not provide specific conditions related to AV operations on public roads. Primarily focuses on establishing an autonomous transportation task force within the Department of Transportation. \\ S 1008 (2021): Pending & focuses on establishing an autonomous transportation task force within the Department \\ Human: Unclear & of Transportation. \\ \hline Idaho (ID) & Does not provide specific conditions related to AV operations on public roads. Primarily focuses on creating a committee to study and support the testing and deployment of \\ Human: Unclear & autonomous and connected vehicles. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Autonomous vehicle legislation in western U.S. states.
## 4 Discussion
Some studies have shown that incorporation of 5 wt % HA in GICs resulted in improved mechanical properties, including compressive strength and microhardness [28; 29]. This difference could be explained with possible non-uniformed glass--HA powder (manual mixing) or inadequate liquid amount, as the volume of HA can change the reaction. It was indeed shown that resin modified GICs are more stable in water than are conventional GICs [28], but resin from RMGICs have been shown to have negative effects on dental pulp, it also exhibits cytotoxicity [30]. It would therefore be ideal in a clinical context to obtain better chemo-mechanical properties of chemically set GICs. Addition of HA in concentrations of 2 or 5 wt % HA resulted in significantly higher FR of Fuji IX in all three time points  
It was previously shown that the light or chemical curing influences fluoride release from resin modified GICs and dual-cured resin cements. Furthermore, photoinitiated polymerization enhances cross-linking density, resulting in the reduced resin matrix permeability for fluoride ions [31], what is in accordance with our results, generally higher FR results in Fuji IX GP Extra than in Fuji II. On the other hand, modification of Fuji II powder with 2 wt % HA positively influenced the fluoride release rates compared to other samples measured after 7 days and 45 days. The modification of Fuji IX GP Extra in all concentrations of HA showed improved FR in all three time points. In our study the highest values of FR were in the first 24 h ranged from 15-138 mg/L for different GICs, what agrees with previous investigations [32; 33]. The fluoride ions in the GICs structure do not take part in the setting reaction and after the initial burst effect [34], they are released into the surrounding environment via an ion exchange process. Furthermore, the glass ionomers can also absorb salivary fluoride and act as fluoride reservoirs capable of releasing the ions, which may inhibit caries formation [35]. As HA derived from cuttlefish bone was reported to be biocompatible [16] and a biomaterial without any cytotoxic effect on dental pulp cells if incorporated into restorative material, it could be interesting to perform further investigations about its impact on GICs. The limitations of the study are that it does not implement all the factors that could affect the material itself, such as mastication forces, heat change, exposure to saliva. However, all the samples were exposed to the same experimental conditions. In addition, only representative commercial materials have been used; a wide range of GICs is available and could be tested. Finally, the clinical relevance of our findings remains unclear; these types of in vitro studies provide insight into the possible effects of HA incorporation, but the results should be observed considering the conditions of the study.
# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
###### Abstract  
Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.  
Keywords:adaptive generative adversarial network; cardiac segmentation; X-ray chest radiograph; generative adversarial network; segmentation +
Footnote †: journal: Medical Image Segmentation
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 1 Introduction
These nonlinear methods have been successfully applied to solve uncertainty problems in orbital mechanics. Furthermore, among these nonlinear methods, the STT method performs the best in terms of the balance between the accuracy and computational burden [38; 39]. This paper investigates the multi-spacecraft tracking and data association problem from the view of uncertainty propagation, and proposes a novel method. Different from the previous methods, the proposed method utilizes the dynamic information to enhance the measurement prediction accuracy, and, in turn, the data association performance is greatly improved. The cores of the proposed method are the second-order analytical solutions for predicting state and measurement. Firstly, a second-order analytical solution for orbit propagation is proposed based on the STT technique. Secondly, we extend the concept of the uncertainty propagation from the orbit propagation to the measurement prediction. A second-order analytical solution for measurement prediction is proposed, and then the predicted measurement and the associated covariance can be analytically computed. In this way, the probability of each measurement belonging to one known target is easily obtained. The optimal association problem is set up based on the proposed second-order analytical solutions and is solved by the contract network algorithm. Finally, a second-order extended Kalman filter (SEKF) is designed for orbit estimation of each target. The proposed second-order analytical solutions are repeatedly used in the data association and orbit estimation. Moreover, the decentralized calculation framework is employed. In these ways, the complexity of the proposed method is greatly reduced and can be used for real-time application. The rest of this paper is arranged as follows: The state and the measurement models of the multi-spacecraft tracking problem are given in Section 2. Section 3 details the methodology of the proposed multi-spacecraft tracking and data association method. The second-order analytical solutions, the solving of the data association and the orbit estimation are respectively presented in Sections 3.1-3.3. Numerical simulations are performed in Section 4 and conclusions are discussed in Section 5.
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.4 Box-Counting  
The box-counting fractal dimension (BCFD) [47] is a well-established method for quantifying the self-similarity and complexity of a signal. BCFD serves as a powerful feature extraction technique. It effectively characterizes the intricate fluctuations in EEG signals by estimating their fractal properties, thereby capturing nonlinear dynamics associated with motor-related cortical activity. The box-counting dimension \(D\) is determined by covering the signal's trajectory with a series of boxes of size \(\epsilon\) and counting the number of occupied boxes, \(N\) (\(\epsilon\)), as a function of \(\epsilon\). The fractal dimension is then obtained by computing the slope of the linear regression in a log-log plot of \(N\) (\(\epsilon\)) vs. \(\epsilon\), as provided in Equation (11):  
\[D=\lim_{\epsilon\to 0}\frac{\log N(\epsilon)}{\log(1/\epsilon)} \tag{11}\]  
In this context, \(\epsilon\) represents the scale of the box size. In practical computations, a range of box sizes is chosen, and the relationship is approximated using a least-squares fit, shown in Equation (12):  
\[D\approx\frac{\Delta\log N(\epsilon)}{\Delta\log(1/\epsilon)} \tag{12}\]  
This measure provides an indication of the EEG signal's complexity, making it a valuable feature for MI classification, as it captures the inherent self-affinity present in the brain's electrical activity.
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion
### Complex Falls  
Judging by feature importance scores (\(\geq\)0.1), the top features were them having had a fall in the last year and their age. In the SHAP summary plot, having had a fall in the last year and having a fear of falling were also the most important features, which were followed by sex (being female had a positive impact on model output), having grip weakness, osteoporosis, and their age (older generally having a more positive impact on model output).  
The importance of the fear of falling in this model can be interpreted in the light that the complex falls included injurious events. For example, [PERSON] et al. described the psychological influence of the fear of falling on the attention and behaviour of older adults, which can in turn contribute to further falls [33]. On the other hand, generalised muscle weakness that is due to sarcopenia can persistently impair gait and balance and contribute to recurrent falls [34].  
Fragility fractures, which are fractures that are incurred from falls from a standing height or lower are associated with osteoporosis and may contribute to a sharp decrease in mobility and quality of life [35]. In 2017, the total health burden of fragility fractures within the EU6 (France, Germany, Italy, Spain, Sweden, and UK) was calculated to be 1.02 million QALYs, with an expected increase of 25.6% by 2030 [36]. Given that injurious falls are classified as complex falls, the significant contribution of osteoporosis to the risk of a fall causing fractures is observed by its rank within the top features for complex falls.
## 5 Results and Discussion
### Analysis of Proficiency Trajectory Tracking  
As shown in Figure 5, both MHMMs track the ground truth with exceptional accuracy. They successfully capture not only the stable periods within a single proficiency state but also the precise moments of transition, such as learning progressions (Novice \(\rightarrow\) Intermediate) and temporary regressions. This ability to accurately model the temporal dynamics of skill acquisition and decay is a key advantage of the MHMM framework.  
Furthermore, Figure 6 displays the step-wise prediction accuracy of both MHMM variants (Expert System and Trained) over the duration of the procedure for a sample participant. The shaded regions around each accuracy line represent simulated confidence intervals, providing an indication of the reliability and variability of the predictions at each point in time. This offers a statistical perspective on the models' consistent performance and prediction uncertainty throughout the task.  
Figure 5: Moment-by-moment proficiency state predictions for a sample participant.  
Figure 6: Prediction accuracy over procedure steps with simulated confidence intervals.
_Applied_  
_sciences_  
_Review_  
**UAV Platforms and the SfM-MVS Approach in the 3D Surveys and Modelling: A Review in the Cultural Heritage Field**  
**[PERSON] 1\({}^{\copyright}\)**, [PERSON] 2\({}^{\copyright}\) and [PERSON] 2\({}^{,}\)*\({}^{\copyright}\)  
the quality of the photogrammetric process. In addition, the great success of UASs in the photogrammetric field is also due to the development of structure from motion (SfM) and multi-view stereo (MVS) algorithms, which, implemented mainly in commercial software, have made the construction of three-dimensional models and/or high-quality orthophotos easy, intuitive, and fast [6]. The use of UAV photogrammetry in the field of cultural heritage has become a very important approach for the 3D documentation of archaeological excavations, monuments, historical buildings, archaeological sites, and landscapes [7]. The opportunity to survey archaeological structures with UAV platforms without the need for direct access to the site or remaining parts of ancient structures allows for the best possible preservation of archaeological assets and, at the same time, the acquisition of high geometric resolution. In addition, UAVs can be used to collect images and integrated with terrestrial surveying [8].  
This manuscript aims to describe an appropriate methodological workflow in the field of UAV surveying, taking into account photogrammetric aspects and technological developments related to aerial platforms and their sensors. In fact, several aspects are discussed in this review paper: (i) highlighting the relationship between the \"UAV platform\" and computer vision approach, (ii) techniques and algorithms for reconstructing 3D models, (iii) emphasising the aspects concerning satellite navigation with particular regard to the aspects related to automatic planning and geospatial data acquisition, and (iv) the ability to rapidly produce useful models for subsequent analysis in the various fields of application.
## 2 Literature Survey
### IP Multicast
BGMP is used as the inter-domain or external protocol, while domains can run any multicast IGP internally (such as CBT or PIM Sparse Mode), and can build source-specific shortest-path distribution branches to supplant the shared tree where needed. Each approach not only discerns between various structures (centralized or distributed), but also can be designed to support dense or sparse modes. The IP Multicast solutions offer benefits relating to the conservation of network bandwidth. In the case of a high-bandwidth application, such as MPEG video, IP Multicast can benefit situations with only a few receivers because a few video streams would otherwise consume a large portion of the available network bandwidth. Even for low-bandwidth applications, IP Multicast conserves resources when transmissions involve thousands of receivers like in sensor networks.
# Wall-Climbing Mobile Robot for Inspecting DEMO Vacuum Vessel
## 3 Kinematic Analysis of WMR  
### Kinematic Modelling  
The WMR has a typical three-wheel structure, with the one front wheel being passive universal wheel and the two rear wheels being drive wheels. The left and right drive  
Figure 2: The WMR 3D model. WMR top (**a**) and bottom (**b**).  
wheels are driven by two geared motors that move forward and reverse to complete the detection task. Changing the speed of the left and right drive wheels controls the crawling speed of the WMR and angular speed of rotation. Each drive gear motor forms a closed loop speed circuit with the drive wheels. The speed of the drive wheel can be changed by adjusting the output voltage of the motor.  
To describe the robot's pose, we assumed that the WMR was moving in the vertical Y-Z plane, as shown in Figure 3. The position of the robot is represented by the coordinate system established at the midpoints of the two drive wheels. The heading angle is represented by the angle \(\alpha\) formed by the axis of the robot and the \(Y\)-axis. The robot posture information is:  
\[\left\{\begin{aligned} &\mathbf{P}=\begin{bmatrix}y&z&\alpha\\ &\mathbf{q}=\begin{bmatrix}v&w\end{bmatrix}^{T}\end{aligned}\right. \tag{1}\]  
where \(y\) and \(z\) are the position coordinates of the robot; \(\alpha\) is the heading angle of the robot; \(v\) and \(w\) represent the linear and angular velocities of the robot, respectively [26; 27].  
We assumed no relative lateral sliding of the robot's wheels against the wall as well as longitudinal sliding. The WMR's motion is modelled as follows:  
\[\left\{\begin{aligned} &\dot{y}=vcos\alpha\\ &\dot{z}=vsin\alpha\\ &\dot{\alpha}=w\end{aligned}\right. \tag{2}\]  
\[v=\frac{w_{r}r+w_{l}r}{2} \tag{3}\]  
where \(r\) is the radius of the left and right drive wheels; \(w_{l}\) and \(w_{r}\) are the angular velocities of the left and right drive wheels, respectively, and \(v\) is the robot linear velocity. The angular velocity of the robot is:  
\[w=\frac{w_{r}r-w_{l}r}{2L} \tag{4}\]  
where \(2L\) is the distance between the two driving wheels.  
The robot's positioning was achieved by monitoring the information obtained from the drive motor encoder over a certain period. The robot's position in practice can be deduced by numerical integration.  
\[\left\{\begin{aligned} & y_{n}=y_{n-1}+\frac{cos\alpha_{n-1}}{2} \int_{I_{n-1}}^{t_{n}}(w_{r}+w_{l})rdt\\ & z_{n}=z_{n-1}+\frac{sin\alpha_{n-1}}{2}\int_{I_{n-1}}^{t_{n}}(w_{r} +w_{l})rdt\\ &\alpha_{n}=\alpha_{n-1}+\frac{1}{2L}\int_{I_{n-1}}^{t_{n}}(w_{r} -w_{l})rdt\end{aligned}\right. \tag{5}\]  
Figure 3: The coordinate system for WMR.
## 3 Proposed Fault Diagnosis Method
### RFE-Based Feature Selection  
After the TfEn-based feature extraction, we obtain a feature set \(TfEns\) representing the status of centrifugal pumps. The high-dimensional feature set may contain the sufficient status-related information, while it also has a price: increasing dimensions of the feature set leads to the sparseness of the feature space, which will cause the overfitting problem when the training samples are scare. Considering that there is usually not enough failure data in the practical engineering, the feature selection is a necessary operation to ensure the accuracy of fault diagnosis.  
In this subsection, a supervised feature selection method called the recursive feature elimination (RFE) [26] is applied to solve the above-mentioned issues, which can automatically select the optimal feature subset from the high-dimensional feature set. The key steps of RFE-based feature selection are illustrated in Figure 4.  
As shown in Figure 4, the RFE-based feature selection is an iterative process, which utilize the criteria developed from the coefficients in a support vector machine (SVM) model to assess features and recursively removes the features with the small criteria.  
1. Given a set of training samples \(\{x_{i},y_{i}\},i=1,\ldots,N\), where \(x_{i}\) is the feature vector sample with \(S\) features, \(y_{i}\) is the status label of centrifugal pumps. A linear SVM model is trained using these training samples, and its decision function is: \[f(X)=\omega\cdot X+b,\] (13)
2. According to the trained SVM model, the ranking criterion of features can be calculated as follows: \[\omega=\sum_{i=1}^{N}\alpha_{i}y_{i}x_{i},\] (14) \[J(s)=\omega_{s}^{2},\] (15) where \(\alpha_{i}\) is the Lagrange multiplier, \(\omega\) is the weight vector of the trained SVM model with \(S\) elements \(\omega_{s}\), \(J(s)\) is the ranking criterion for the feature \(s(q)_{s}\).
3. Eliminate the feature \(s(q)_{k}\) with the lowest criterion from the feature set: \[s(q)_{k}=\text{argmin}(J),\] (16) \[TfEns^{1}=\Big{\{}s(q)_{1},s(q)_{2},\ldots,s(q)_{k-1},s(q)_{k+1},\ldots,s(q)_ {s}\Big{\}},\] (17)  
Figure 4: The flow chart of recursive feature elimination (RFE)-based feature selection.  
where \(TfEn^{1}\) is the feature subset with \(S-1\) features in the first iteration of feature selection.
4. Repeating the above operations until all the features are eliminated from the feature set, we will obtain the \(S\) feature subsets: \[TfEns=\big{\{}s(q)_{1},s(q)_{2},\ldots,s(q)_{s^{\prime}}\ldots,s(q)_{S}\big{\}},\] (18) using the cross-validation technique [27], the feature subset \(TfEns^{*}\) with the best accuracy will be selected as the optimal feature subset for the subsequent fault diagnosis.