esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Determination of Weights
#### 3.2.2 Determination of Weights for \(R_{\text{lat}}\)  
Figure 3 presents the sensitivity analysis of the lateral risk metric \(R_{\text{lat}}\) concerning lateral speed difference (\(\Delta v_{y}\)) and lateral spacing difference (\(\Delta y\)) under different values of the weighting parameter \(\gamma\). The six subplots show the variation from \(\gamma=0.0\) to \(\gamma=1.0\).  
The analysis logic is similar to that of Figure 2. When \(\gamma=0.0\) (Figure 2(a)), \(R_{\text{lat}}\) depends entirely on the lateral spacing difference (\(\Delta y\)). The contour lines are almost unaffected by changes in \(\Delta v_{y}\), indicating a negligible influence of speed difference. Conversely, when \(\gamma=1.0\) (Figure 2(f)), \(R_{\text{lat}}\) depends solely on lateral speed difference (\(\Delta v_{y}\)). The contour lines are parallel along the \(\Delta y\) axis, indicating little impact from the spacing difference.  
The choice of \(\gamma=0.6\) is justified by Figure 2(d), where the contours show a balanced response to both \(\Delta v_{y}\) and \(\Delta y\). The color gradient suggests that both parameters have a nearly equal influence on risk. Neither dominates nor is ignored. Thus, selecting \(\gamma=0.6\) ensures a balanced consideration of speed and spacing effects, capturing the true impact of lateral interactions. This helps achieve a more comprehensive and balanced risk assessment.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 4 Results
### Space Search  
Figure 5 shows the relative codephase estimator \(L_{\delta\phi}\) plotted for six visible satellites over a region ten degrees in width and height centred at the actual receiver location. This figure shows that the search for the global minimum is a complex nonlinear minimisation problem. However, the Doppler search is sufficiently accurate to place the receiver within the central local minimum region and the relative codephase least-squares estimator can be easily minimised once this is known.
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 3 Image Feature Extraction Method Based on Invariant Learning
### Validity Verification of Invariant Feature Extraction  
In order to confirm the suggested method's efficacy in feature extraction, the features extracted by IRM-ResNet18 and ResNet18 were, respectively, used as input variables for training SVM and random forest models. Their prediction accuracies on the test set were compared, as shown in Table 3.  
The findings show that, whether the model is a random forest or an SVM, the prediction accuracy of the two feature datasets on the training dataset is very similar. However, the invariant features extracted by IRM-ResNet18 show higher prediction accuracy compared to the traditional network, on the test dataset. Among them, the SVM's prediction performance on the test data is superior to that of the random forest model. This is because the random forest is entirely trained based on the ERM principle, while the SVM considers both empirical risk and structural risk, giving it a slight performance edge over the random forest in out-of-distribution generalization scenarios. However, due to the high dimensionality of the input features (512 dimensions), the computational complexity of the SVM is excessively high, making its training time significantly longer than that of the random forest.  
In summary, the IRM-ResNet18 algorithm can effectively learn and extract invariant features from images, transforming causal information in images into a 512-dimensional feature dataset, thereby achieving out-of-distribution generalization.
# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 5 Conclusions  
### Concluding Remarks  
The study performs a Systematic Literature Review with a meta-synthesis of prescriptive maintenance research. Here, we have extracted relevant metadata and qualitative evidence comprising applications, methods, tools, and prescriptive actions to describe the existing work of prescriptive maintenance. In addition, meta-synthesis is performed using content-based data, leveraging multiple correspondence analyses with hierarchical clustering. The results identified three major research clusters that characterise the role of PsM in operational (Cluster 1), enterprise-level (Cluster 2), and ecosystem (Cluster 3) contextual settings. The review shows that while data-driven predictive techniques are well-established, the prescriptive layer--aimed at transforming predictive insights to actionable intelligence--lacks empirical validation. In particular, the scalability and generalizability of theoretical models suffer from a lack of practical demonstrations and applicability. Although not to a full extent, we infer that the lack of end-to-end holistic frameworks requires dedicated work on horizontal (across functional areas) and vertical (across decision-making levels) integration. Furthermore, PsM efforts are increasingly aligned with product/service quality and lifecycle performance, rather than traditional maintenance metrics (e.g., availability or downtime alone).
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 3 The KDN Framework
### Evaluation of a DRL + GNN Agent for Routing Optimisation in Optical Networks
In particular, the boxplots show the performance achieved across 1000 evaluations with different src-dst traffic demand sets randomly generated. As a reference, we also show the performance achieved by a traditional Load-Balancing policy (LB). Thus, we can also observe that LB achieves \(\approx\)30% less performance than our DRL + GNN agent if we compare the median values obtained across all evaluations. Likewise, Figure 2b shows the results when the DRL agents are trained in a network (NSFNet; 14 nodes) and then tested in another network unseen during the training phase (Geant2; 24 nodes). In this case, we can observe that the state-of-the-art DRL agent (using a fully connected NN) considerably degrades its performance, falling behind a traditional LB policy. This is due to its lack of generalisation capability over different networks. In contrast, the proposed DRL + GNN agent achieves a good performance level in this new network unseen during training, which reveals the capability of this solution to flexibly adapt to new network scenarios, by exploiting the information of networks structured as graphs. To further evaluate the DRL + GNN agent in other networks, we apply it over a set with 136 real-world topologies from the well-known Internet Topology Zoo repository [31]. The plot of Figure 2c shows the improvement (%) over a LB routing policy across all these networks (x-axis), which were not seen by the DRL agent during the training phase. Overall, we can observe an average improvement of 21.39% in performance compared to the traditional LB baseline, again reflecting the outstanding capability of the DRL + GNN agent to generalise to other networks. We refer the reader to [25] for a more extensive evaluation of this work. Figure 2: Evaluation of our DRL + GNN agent for routing optimisation in optical transport networks: (**a**) Training/Evaluation in the same topology (NSFNet); (**b**) Training/Evaluation in different topologies (trained in NSFNet; evaluated in Geant2); (**c**) Evaluation of our DRL + GNN agent over 136 topologies from Internet Topology Zoo unseen during the training phase.
## 3 Results and Discussion
### Nutrient Concentrations from Preserved Samples
#### 3.2.1 Nitrate-N Results
This increase could be due to organic and particulate matter being broken into soluble forms of nitrogen from the sulfuric acid. Additional soluble nitrogen, in the form of nitrate-N, would then cause an increase in concentration, but would not be represented in the initial concentration of nitrate-N at Day 0. The concentrations for both pond and stream water were variable, increasing by as much as 9.92% on Day 1 for stream water and decreasing by as much as 6.25% on Day 7 for stream water. Overall, the findings in this study are in agreement with other studies that found sulfuric acid to be a sufficient preservative for nitrate-N for up to seven days [10]. However, this study indicates suitability of sulfuric acid to preserve samples for up to 14 days. Sodium omadine is a salt that had not been previously studied for preservation of nitrate-N in water samples. Day 0 showed decreases in nitrate-N concentrations, ranging from 3.35% for tile water to 6.72% for pond water. This immediate decrease in concentrations shows an instant reaction in the water sample that is impacting the stability of nitrate-N. Every time point for all three source waters showed a decrease in nitrate-N for the sodium omadine preservation method, with the exception of Day 14 for stream water, which showed a slight increase of 0.13%. Table 4 compares the nitrate-N concentrations over time and presents significant differences (\(p<0.05\)) between the concentration on each day and the baseline concentration. It is noteworthy that the control samples were not significantly different from the baseline concentration for any of the source waters or time points tested. Meanwhile, there were significant differences between the three preservation methods and the baseline concentration. For the sulfuric acid preservation method, there was a significant difference for the pond water on Day 7 (\(p=0.003\)) and for the stream water on Day 1 (\(p=0.014\)). The filtered samples were significantly different from the baseline concentration on Day 7 of the tile water (\(p=0.044\)) and pond water (\(p=0.046\)). The samples containing sodium omadine were significantly different from the baseline concentration on Day 7 of the tile water (\(p=0.019\)), Days 0.5 and 7 of the pond water (\(p=0.003\) and 0.002, respectively), and Day 5 of the stream water (\(p=0.032\)). The addition of sulfuric acid is the US EPA recommended preservation method for nitrate-N. There was no significant difference from the baseline concentration for tile water, and only one instance each of when it was different for the pond and stream water. Therefore, sulfuric acid proved to be a reasonably good preservation method for nitrate-N over a period of 14 days. While
# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 2 State of the Art
### ELM with Descriptors
The obtained optimal classifiers were compared against the most recent fingerprint classification methods reported in the state of the art [3; 10; 23; 35; 36] in terms of classification accuracy, robustness, and computational complexity. Results indicate that the ELM classifier, using the Hong08 [14] descriptor, achieved a high accuracy of 95.55% with a low penetration rate (0.299) for the NIST-4 database. Moreover, the training and testing times were significantly lower compared to CNN-based approaches, with 0.3811 s for training and 0.0466 s for testing.
###### Abstract  
(1) Background: Preoperative templating is mainly conducted on an anteroposterior pelvic overview X-ray. For short stem hip arthroplasty, the choice of the optimal size is especially crucial to avoid complications. Thus, the study aimed to determine if there is an increased rate of correctly planned sizes using two radiological planes. (2) Methods: 50 patients with a conventional stem and 100 with a short stem total hip arthroplasty were analyzed. Without knowing the implanted size, three independent orthopedic surgeons performed digital templating: once using the anteroposterior pelvic overview only and once using the lateral view in addition. (3) Results: The rate of correctly planned sizes (+/\(-\)1 size compared to the inserted size) of templating with one plane was 86.3% \(\pm\) 9.5% in short stem hip arthroplasty and 88.4% \(\pm\) 6.0% in conventional stem arthroplasty. By adding the lateral view, the rate of correctly planned sizes was 89.9% \(\pm\) 12.0% for the short stem hip arthroplasty group and 89.4% \(\pm\) 9.8% for the conventional group (\(p\) > 0.1). (4) A potential positive effect of preoperative templating using an additional lateral X-ray view for short stem implants may be suggested based on the results of this study, which did, however, not reach statistical significance.  
digital templating; short-stem; lateral view; femoral torsion +
Footnote †: journal: Applied Sci.
overview X-ray [13]. Some software programs allow for digital templating also use the lateral view of the proximal femur for templating the stem. Although efforts have been made to further improve the accuracy of preoperative templating by using CT-based three-dimensional computerized models [14], the simple method using only the pelvic overview X-ray is still the standard procedure in clinical routine due to its high practicability on the one hand and the high radiation exposure when using CT scans on the other. The fixation of short stem THA (STHA) with predominantly metaphyseal anchorage is different from conventional THA (CTHA). While the conventional stem has a metaphyseal-diaphyseal anchorage with an intramedullary fixation within the proximal third of the femur [11], STHA with predominantly metaphyseal anchorage adapts its orientation to the femoral neck and thus to its ante torsion (Figure 1).  
This study aimed to determine whether there is a difference in accuracy when templating THA stems using not only the a.p. view of the pelvis but also the lateral view of the proximal femur. Furthermore, we analyzed whether there is a difference in the rate of correctly planned sizes when templating CTHA stems in comparison to STHA implants with a predominantly metaphyseal anchorage.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 6 Issues and Challenges
### Deployment  
The deployment of network edges within the city environment relies on both static components (such as road layout) and dynamic factors (such as accident frequency). Edge nodes are expected to be presented by equipping fixed RSUs or unmanned aerial vehicles (UAVs) with MEC servers. Many new ITS applications could be enabled by utilizing automated UAVs to improve traffic safety and enhance travel comfort. Before that, there are some serious issues to address including limited energy, processing capabilities, and signal transmission range [34]. Taking into account the technology revolution in the past decades, there is a great potential that these limitations in UAV utilization will be resolved in the near future.
## 3 Results
In Figure 6, a comparison between predictions of dNBR, Single U-Net, and Double-Step U-Net is shown in two areas of the green fold. At a first glance, delineating the wildfire contours just looking at the RGB acquisition (pictures a1 and b1) seems feasible, but assigning different severity levels appears to be more challenging. In both the acquisitions, the Binary U-Net predictions resulted to be highly accurate (pictures a3 and b3), compared to the Copernicus EMS annotation (GT, pictures a2 and b2). The dNBR (pictures a4 and b4) show a good match with the GT, except for some noise in the vast unburned regions. The Single U-Net (pictures a5 and b5) correctly identifies the burned region and the contours of different burned areas, but it tends to underestimate the severity. In the end, the Double-Step U-Net (pictures a6 and b6) improves the prediction of the Single U-Net, resulting to be more similar to the GT. The time required by the Double-Step U-Net to perform the damage severity evaluation using one NVIDIA GTX 1080 Ti on an input tile of size 100 km \(\times\) 100 km is, considering the highest spatial resolution available (reported in Table 1), 28 s on average. The time required to predict the severity level estimation by the proposed deep learning model for an image of 480 \(\times\) 480 resolution with 12 spectral bands is inferior to 1 s.
# Superconducting Phases in Neutron Star Cores
## 2 The Ginzburg-Landau Formalism  
Our goal is to formulate a simple, phenomenological model of the neutron and proton condensates that includes (a) their mutual entrainment, and (b) the coupling to the magnetic field. The simplest such model is the Ginzburg-Landau model, in which the free energy of the condensates is expressed in terms of complex scalar order parameters for the proton and neutron condensates, \(\psi_{\rm p}\) and \(\psi_{\rm n}\), and the magnetic vector potential, \(\mathbf{A}\). We acknowledge that such a model can only be rigorously justified close to the condensation temperatures for the protons and neutrons, whereas mature neutron star cores are generally well below both of those temperatures. This limits the rigorous applicability of the Ginzburg-Landau model to the part of the core in which the condensation temperatures are similar [37], and to the narrow time window in which the condensation occurs. Furthermore, the Ginzburg-Landau model neglects spin-orbit interactions, and therefore does not include all of the microphysics of a neutron star. In particular, the neutrons are believed to form an anisotropic \({}^{3}P_{2}\) superfluid throughout much of the core, which cannot be described by a single scalar order parameter, e.g., [38]. Nevertheless, the relative simplicity of the Ginzburg-Landau formalism makes it an appealing phenomenological model, and hence it has been widely used to model the superfluid components of neutron stars, e.g., [7; 8; 39; 40; 41; 42; 43]. Moreover, phenomenological Ginzburg-Landau models have been shown to reproduce many properties of laboratory superconductors, even well below the condensation temperature [44]. We will therefore take a similar approach to model the neutron star core.  
In what follows, we normalize the order parameters such that \(|\psi_{\rm p}|^{2}\), for example, is the number density of proton Cooper pairs, and so \(\rho_{\rm p}=2m_{\rm p}|\psi_{\rm p}|^{2}\) is the mass density of the proton condensate, where \(m_{\rm p}\) is the mass of a proton. The order parameters are therefore two-particle mean-field wave functions for the condensates. As noted earlier, the core temperature in mature neutron stars lies far below the critical temperature for superconductivity, so in the absence of magnetic flux practically all of the proton matter would reside in the condensed state. In the presence of magnetic flux, however, there will be normal proton matter present in the cores of fluxtubes and in any non-superconducting regions, where the proton condensate is absent. We are concerned here only with the ground state for the condensates, wherein interactions with any \"normal\" components of the core, which include electrons, thermal excitations and normal protons and neutrons, are suppressed. Hence, we can safely disregard the normal matter in what follows.
## 1 Introduction
The predicted position of the robot's end-effector estimated by the fixed-point and the laser stripe could be misaligned. These techniques are often inconvenient, time consuming and it may not be feasible for some certain applications. To overcome the above limitations, we develop a novel and flexible indirect calibration method for the vision-based robot application that need small position errors. This is a straightforward and efficient method to reduce the absolute position error. Our method does not require the complex solution of the kinematics parameter equations and the complicated procedures of the traditional robot compensation methods. As a result, we successfully reduced the absolute position error the robot's end-effector in the workspace, the position/orientation of the robot's end-effector is compensated without modifying the parameters of the robot. Using the robot's end-effector as an input for a neural network, and the camera attached on the end-effector to observe the object and gather information for training the neural network, the absolute position error of the robot's end-effector is improved. The proposed method is well suited for easy deployment of the robot visual system in the different manufacturing environments because no external measuring equipment or no complicated setup is required in the error compensation and makes the calibration procedure more convenient to implement. The purpose of our research in this paper is to improve the degree of work in an object picking application by compensating the absolute position error of a six-axis industrial robot by applying a vision-based measurement system. First, the position/orientation of the end-effector was estimated using a vision-based approach combined with the neural network. A novel indirect approach proposed in this paper was used to collect the data in the workspace and to train the neural network, and the calibration methodology among the robot base coordinate, camera system frame and the workspace is described in detail. Then, some simulations and experiments were performed to evaluate the performance of the proposed indirect calibration, and the results were compared in detail to demonstrate the excellence of the proposed method. Finally, some experiments to pick up objects using an industrial robot were conducted to guarantee the positioning performance of the proposed algorithm.
## 2 Materials and Methods
### Characterization of \(n\)-Zvi
#### 2.5.1 Texture and Morphology  
Textural characterization was carried out using gas adsorption (N\({}_{2}\) at 77 K). Semiautomatic Quantachrome Autosorb-1 equipment was used for the determination of the N\({}_{2}\) adsorption isotherms. Briefly, 0.15 g of the sample was introduced into the glass vessel and degassed at 250 \({}^{\circ}\)C for 12 h at a pressure below 10\({}^{-3}\) Torr. From the measured adsorption isotherms, the micropore volume (V\({}_{\rm mi}\)) was calculated by merely reading the volume of N\({}_{2}\) adsorbed at p/p\({}^{\circ}\) equal to 0.10, whereas the mesopore volume (V\({}_{\rm me}\)) was also obtained from the N\({}_{2}\) adsorption isotherm as V\({}_{\rm me}\) = V\({}_{0.95}-\) V\({}_{\rm mi}\), where V\({}_{0.95}\) is the volume of nitrogen adsorbed at p/p\({}^{\circ}\) equal to 0.95. Both V\({}_{\rm mi}\) and V\({}_{\rm me}\) are expressed as liquid volumes (conversion factor, 1.4656 \(\times\) 10\({}^{-3}\)).  
The specific surface area (S\({}_{\rm BET}\)) was estimated using the Brunauer, Emmet, and Teller (BET) method. The micropore volume (W\({}_{0}\)) was obtained by applying the Dubinin-Radushkevich equation.  
The morphology of the samples was analyzed by Scanning Electron Microscopy (SEM). Images were acquired with the aid of HITACHI S-4800 equipment (MRL, Urbana, IL, USA), operating under high vacuum mode and with acceleration voltages between 0.5 and 30 kV. Secondary electrons (SE) and transmitted electrons (TE) detectors were used.