esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.  
active noise control; filtered-x affine projection-like algorithm; filtered-x LMS algorithm +
Footnote †: journal: Applied Sciences
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 4 Methodology
### Spatial-Temporal Self-Attention Layer  
In order to capture the intricate dynamic spatiotemporal dependencies in the traffic data, this research has devised a spatial-temporal self-attention layer, as illustrated in Figure 2. This layer employs residual connections and comprises \(l\) self-attention modules that are connected in parallel. Each self-attention module consists of key components,  
Figure 1: The overall framework of DSTSAN.  
including the feature augmentation module, spatial self-attention module, and temporal self-attention module.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset
### Roadscene Dataset  
The RoadScene dataset is a newly aligned infrared and visible image dataset published by [PERSON] et al. [85] The original infrared image's thermal noise is suppressed after the dataset first chooses image pairs from videos that have a lot of repeating situations. Following meticulous feature point selection, homologous and bicubic interpolation pairs are used to align the picture pairings. The precise registration area is then clipped. Bicubic interpolation is an interpolation algorithm used to forecast continuous pixel values on a discrete pixel grid. At each pixel point in the pixel grid, it is done by utilizing a weighted average of the adjacent pixels. The bicubic interpolation algorithm uses a 16-pixel window and determines the value of the target pixel by taking the weighted average of the 16 pixels around it, taking into account its location in the window. This calculation process is based on an interpolation algorithm for a cubic function, so it is called bicubic interpolation. Bicubic interpolation was selected because it offers improved smoothness and image quality, particularly when aligned across image pairs, minimizing distortion and artifacts between images. For accurate feature matching and the fusion of infrared and visible light  
Figure 10: Scene images of planes, villages, and forests. (**a1**–**c1**) infrared images; (**a2**–**c2**) visible light images.  
images, this is crucial. The image pairs from the RoadScene dataset that have been aligned offer tremendous convenience for the next studies.  
The RoadScene dataset includes 221 pairs of photos with detailed scenarios, including busy roadways, people walking, and moving vehicles. It fixes the issues of the reference dataset's small number of image pairings, low spatial resolution, and lack of detailed information in infrared images. Several pictures from the RoadScene collection are displayed in Figure 11.
## 2 Related Work
The concept has evolved from approaches with manual steering, in which only the automation in the longitudinal direction was considered [23; 24], to more sophisticated ones based on wireless communications and fully autonomous vehicles [25]. As noted above, the latter have largely focused on highway contexts, where the maneuvers for joining and leaving the platoons are quite simple due to the existence of several lanes in each direction--some authors (e.g., [26]) even proposed an additional lane for such maneuvers for simpler management. In practice, one of the most renowned projects is SARTRE (SAfe Road TRAins for Environment) [27], which promotes the use of platoons in such a way that several roads are scheduled to travel along the highways, and drivers can join/leave them whenever they want. SARTRE has been successfully tested several times in European roads and their authors claim that they are ready to investigate putting a final product in the market. Platooning seems to be the most immediate step towards a real Automated Highway System (AHS), but its multiple virtues should be feasible to materialize also in urban scenarios. The authors of [28] showed, by means of simulations, that the throughput of an urban road system can be doubled without changing the signal control, just making the automobiles cross the intersections in platoons rather than one by one with the typical reaction times of human drivers. This hinted to us that combining urban platooning with virtual traffic lights could achieve greater benefits, by introducing the existence of the convoys in the planning of the VTLs and managing join/leave maneuvers in the intersections and all along the road segments between them. Our proposal is to make the idea work with no reliance on any kind of infrastructure, just by means of V2V communications within vehicular ad hoc networks (VANETs). While some authors have considered cloud-based management of VTLs [29] and platoons [30], a solution based on VANETs can be much more responsive, since no data packets must travel to remote servers and back. By avoiding such latencies, we can intertwine computations with communications and keep more fine-grained track of the moves of both individual vehicles and platoons, and deal with any unexpected circumstances more swiftly.
## 6 Computational Complexity
#### Toy Example  
In this section, the construction of public and private key, together with the transformations required during the authentication procedure, are shown providing a small example.  
We start by showing the construction of a polynomial set. Let us consider the graph \(G=(U,D)\), where \(U=\{1,2,3,4\}\) and \(D=\{(1,2),(1,4),(2,3),(3,4)\}\). Consider the permutation  
\[\sigma=\left(\begin{array}{ccc}1&2&3&4\\ 1&3&2&4\end{array}\right).\]  
After applying \(\sigma\) to the set \(U\), we get the graph \(H=\{V,E\}\) defined by \(V=U\) and \(E=\{(1,3),(1,4),(2,3),(2,4)\}\). The complementary graphs \(\overline{G}\) and \(\overline{H}\) are determined by the edge sets \(\overline{D}=\{(1,3),(2,4)\}\) and \(\overline{E}=\{(1,2),(3,4)\}\) respectively. Graphs \(G,H\) and their complements (shown by dashed lines) are shown in Figure 4.  
We start by building the polynomial set by fulfilling condition (1), which appends 16 polynomials:  
\[X_{i,j}^{2}-1\qquad\quad\text{ for }i,j\in\{1,2,3,4\}.\]  
As already mentioned, these could replaced by considering solutions over a binary vector space, something useful when the amount of data to be exchanged faces restrictions. Subsequently, condition (2) is addressed by considering the polynomials  
\[X_{i,1}+X_{i,2}+X_{i,3}+X_{i,4}-1\quad\quad\text{for }i=1,2,3,4\]  
\[X_{1,j}+X_{2,j}+X_{3,j}+X_{4,j}-1\quad\quad\text{for }j=1,2,3,4.\]  
Finally, the polynomials obtained from condition (3) are added to the polynomial set. To understand the process, let us consider an edge in \(D\); say, (1,2). The edges not contained in \(H\) are (1,2) and (3,4), as seen in Figure 4. These edges introduce the polynomials \(X_{1,1}X_{2,2}\) and \(X_{1,3}X_{2,4}\). The set of polynomials obtain by considering \(\{u_{i}u_{j}\in D\wedge v_{k}v_{l}\
otin E\}\) is shown next  
\[X_{1,1}X_{2,2},\ X_{1,1}X_{4,2},\ X_{2,1}X_{3,2},\ X_{3,1}X_{4,2}\]  
\[X_{1,3}X_{2,4},\ X_{1,3}X_{4,4},\ X_{2,3}X_{3,4},\ X_{3,3}X_{4,4}.\]  
Finally, by considering the edges in \(\overline{G}\) and \(H\), we get another set of eight polynomials:  
\[X_{1,1}X_{3,3},\ X_{1,2}X_{3,3},\ X_{2,1}X_{4,3},\ X_{3,1}X_{4,2}\]  
\[X_{1,1}X_{3,4},\ X_{1,2}X_{3,4},\ X_{2,3}X_{3,4},\ X_{3,3}X_{4,4}.\]  
A root of these polynomials related to the isomorphism between these graphs can be computed by letting \(x_{i,\sigma(i)}=1\) for \(i=1,2,3,4\) and zero in other case. Explicitly,  
\[x_{i,j}=\begin{cases}&1\text{ if }(i,j)\in\{(1,1),(2,3),(3,2),(4,4)\}\\ &0\text{ otherwise}\end{cases} \tag{5}\]  
The polynomial system created with the polynomials here described together with the solution defined in (5) conform to the public key \(F_{0}\) and the private key \((F_{0},\mathbf{x_{0}})\).  
Proceeding with the iterative procedure between prover and verifier to perform the authentication step, a new polynomial system and its solution is computed using either a new graph isomorphism or directly a random permutation \(\sigma\) on the subindices, as shown in Section 5.2. The construction is similar to what we have done above.  
Figure 4: Isomorphic graphs \(G,H\) and complements indicated by dashed lines.
## 3 Results and Discussion
### Tensile Properties  
As shown in Figure 7, for the weld without beam oscillation, the ultimate tensile strength (UTS) and elongation (EL) were 226 MPa and 6%, respectively. For the welds with beam oscillation, both UTS and EL increased with the \(f\), and the best mechanical properties were achieved at_f_ as 350 Hz, which were 308 MPa and 18.2%, respectively, reaching 96.3% and 86.6% of BM, respectively. Compared with the weld without beam oscillation, UTS and EL were increased by 36.3% and 203.3%, respectively.  
It can be concluded that the variation of mechanical properties of lock bottom welds were very consistent with the porosity, because the \(f\) threshold of pore elimination was  
Figure 6: Schematic of the influence of beam oscillation on melt flow, (a) without oscillation, (b) \(r=0.6\) mm,\(f\) = 350 Hz.  
Figure 7: Effect of \(f\) on mechanical properties.  
350 Hz at \(r\) as 0.6 mm, and the highest UTS and EL were achieved. That is, the pore defects were the main factor affecting the mechanical properties of laser welding of Al alloys in lock butt configuration. In addition, when \(f\) = 50 Hz, the porosity of the weld increased, but the mechanical properties improved, indicating that the pore defect was not the only factor affecting the mechanical properties. The strong stirring effect of the laser beam inhibited the growth of columnar grains and reduced the width of the coarse grain zone near the fusion line, which played an important role in the prevention of crack propagation during the tensile test of lock bottom welds. This will be discussed in detail in Section 3.5.
# Dynamic Modeling and Simulation of a Robotic Lander Based on Variable Radius Drums
## 4 Simulation Results
### Case (2): Topple Simulation  
In this section, the case of topple simulation is investigated. More precisely, the aim of the numerical tests is to identify at which ground surface angle the lander topple. This angle is named landing critical angle \(\beta_{cr}\). To achieve this, the same model used from the previous analysis is considered. For every simulation, the model of the ground is rotated by increasing angles around the \(z\) axis, and rigidly translated along the \(y\) axis in order to keep the drop height constant. The simulations of Case (2) allow to investigate the range of admissible slope angles to achieve safe soft-landing without the lander toppling. It is important to say that all the simulations are conducted using the same maximum drop height of \(H=0.45\,\mathrm{m}\). By pursuing this strategy, it is found that the lander topples at a critical landing angle \(\beta_{cr}\approx 51.6^{\circ}\), as it can be seen in Figure 10. Moreover, some important results for the sample case of an inclined plane simulation with an inclination ratio 1:5 are reported.  
Figure 11a-c reports the lander MBS center of mass displacement, speed, and acceleration along the three axes, respectively. Figure 12a-c reports the rotations of each of the leg structure joints, the torque generated by each of the three VRDs, and the contact forces exerting between each pad and the soil during the soft-landing simulation, respectively. Finally, in Figure 13 the evolution of the kinetic energy of the lander is shown.  
Figure 10: (**a**) A time-series representation of the simulation for the lander MBS soft-landing conducted on an inclined surface; (**b**) the lander MBS toppling at the landing critical angle \(\beta_{cr}\).  
Figure 11: Information about the state of the lander’s MBS along the vertical axis: (**a**) the displacement of the center of mass; (**b**) the velocity; (**c**) the acceleration.  
Figure 12: (**a**) The curves representing the angular rotation of each of the three leg structure revolute joint; (**b**) the curves representing the torque response of each of the three VRD’s; (**c**) the curves representing the contact force between each of the legs with the soil.  
Figure 13: Kinetic energy evolution over time of the lander MBS.
## 4 Experimental Results
### Revisit Time  
Revisit time is a crucial figure of merit (FOM) in satellite mission planning, representing the periodicity of a satellite's return to a designated imaging target or region. In Figure 14, the mean revisit time is depicted based on the profit associated with each target under a test scenario involving a constellation of 16 satellites observing 100 targets five times over a 7-day period. It is calculated as the temporal difference between the initiation of the first observation and the conclusion of the fifth, divided by four intervals. Target profit is determined by Equation (1), categorized into nine segments. Figure 14 illustrates that, across all target profit segments, the mean revisit time for the MDP algorithm consistently outperforms that of the greedy algorithm. Furthermore, the outcomes of linear regression fitting, as expressed in Equations (11) and (12), indicate that MDP exhibits a steeper slope and a smaller y-intercept.  
Delving into more detailed analysis, the mean revisit time for MDP at the lowest profit of 0.3 is 9.02 h compared to 10.56 h for the greedy algorithm, and, at the highest profit of 0.9, MDP's mean revisit time significantly shortens to 3.2 h versus 5.1 h for greedy. This trend demonstrates that the difference between the two algorithms becomes more pronounced with increasing profit. The slope of the graph indicates that MDP is more effective at capturing as many high-profit targets as possible within the constraints of satellite mission planning.  
Following this detailed analysis, it becomes pertinent to examine how this research diverges from prior work. Previous studies [12; 46; 47] have predominantly aimed at optimizing satellite constellations and orbits to reduce the mean revisit time to tens of minutes across areas such as the Korean Peninsula, often treating it as a single, uniform target  
Figure 14: Target profit vs. mean revisit time (5-time revisit for 100 targets).  
for system-level performance evaluations. Contrary to this approach, the present study advances by accurately calculating the mean revisit times for individual targets within specific regions, thereby offering a more refined analysis. This shift from a broad, areal focus to a targeted, precise evaluation underscores the novelty and significance of our approach, setting it apart from earlier methodologies.  
\[\text{MDP Fit Line : y}=-9.70\text{x}+11.93 \tag{11}\]  
\[\text{Greedy Fit Line : y}=-9.10\text{x}+13.29 \tag{12}\]  
For a quantitative analysis of the results dataset, we present the mean revisit time using a box plot in Figure 15, along with key statistical metrics in Table 4. As illustrated in both Figure 15 and Table 4, the MDP algorithm exceeds greedy's performance in all metrics, excluding the minimum value. Notably, the standard deviation and presence of outlier values are significantly reduced in the case of the MDP algorithm. This emphasizes that the efficacy of the MDP algorithm is demonstrated in the satellite mission planning domain, where a robust algorithm is essential.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
###### Abstract  
The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.  
satellite derived bathymetry (SDB); nautical charts; IOPs; AOPs; unsupervised classification Article
# FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
###### Abstract  
Nowadays, the floating production storage and offload system (FPSO) is one of the most common platform types for offshore oil production. The traditional arrangement of the stiffened panels creates obstacles for automated cleaning and inspections by remote devices. This paper summarizes the results of an initial study for the design and construction of FPSO hulls with SPS in order to overcome this problem. The main goal is to have the walls and bottom of the cargo tanks free of stiffeners. This research is conducted by first designing the hull with a conventional structural arrangement using steel according to the ABS rules as a benchmark. Following that, the equivalent hull structure with sandwich plates is designed in accordance with the guidelines for SPS construction from DNV rules. Finally, this paper provides the results of a finite element analysis to compare the stresses and ultimate strengths of both types of structures. Briefly, the main results are that the SPS design provides a reduction of 2.8% of the total weight and a better overall structural performance by an increase of 26% for the ultimate strength of the hull.  
FPSO; offshore structures; SPS; finite element analysis; ultimate strength Article  
FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
## 3 Results
#### 3.1.1 Microstructure  
Figure 1 shows the microstructure and elements distribution of the Ti\({}_{1.56}\)V\({}_{0.18}\)(FeV)\({}_{0.18}\)Cr\({}_{1.08}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) co-melt alloy. Two distinct phases are present: a main dark phase and a bright secondary phase. The secondary phase is evenly distributed and the size of each 'island' of secondary phase is of the order of a few microns. The element mapping indicates that the secondary phase is rich in zirconium and nickel elements.  
Figure 2 shows a quantitative chemical analysis using EDX. The two lines (A and B on Figure 2) analysis clearly indicate that the amount of titanium, vanadium, and chromium slightly decrease in the secondary phase when compared to the matrix. Iron and nickel are almost exclusively found in the secondary phase. As far as zirconium is concerned, a small proportion is present in the matrix, but its abundance is much higher in the secondary phase. The quantitative analysis is reported in Table 1. One can notice in Table 1 that a relative variation of element abundance between the matrix and the secondary phase can be seen for iron, zirconium, and nickel, while the smallest variations are noticed for titanium and chromium. The matrix phase has a chemical composition that is very close to the nominal one except that zirconium and nickel are is a very low concentration. These two elements are almost exclusively found in the secondary phase, in which vanadium content is much reduced when compared to the matrix, but titanium and chromium concentrations are very similar to the ones that are found in the matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Element** & **Bulk Nominal Value** & **Matrix** & **Secondary Phase** \\ \hline Ti & 50.6 & 50.2 (4) & 47.0 (3) \\ Fe & 1.0 & 0.83 (1) & 1.6 (1) \\ V & 10.7 & 11.3 (1) & 7.4 (2) \\ Cr & 35.0 & 35.6 (5) & 34 (2) \\ Zr & 1.1 & 0.7 (4) & 5.7 (1) \\ Ni & 1.6 & 1.3 (1) & 4.5 (3) \\ \hline \end{tabular}
\end{table}
Table 1: Nominal and measured atomic percentage of the as-cast co-melt 52 Ti-6V-6 FeV-36 Cr + 4%Zr\({}_{7}\)Ni\({}_{10}\). Uncertainties on the last significant digits are quoted in parentheses.  
Figure 1: Backscattered micrograph and elements mappings of the as-cast co-melt Ti\({}_{1.56}\)V\({}_{0.18}\)(FeV)\({}_{0.18}\)Cr\({}_{1.08}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) alloy.
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 6 Simulation Setup, Evaluation Criteria, Dataset, Results and Discussion
### Evaluation Criteria
#### 6.2.2 Receiver Operating Characteristic (ROC) Curve  
ROC curve is a graph that represents the performance of a classification model at each classification threshold. It represents two metrics, namely the true-positive rate (TRP) and the false-positive rate (FPR). The TPR or sensitivity is a proxy for the recovery rate and is therefore expressed in Equation (5).  
\[TPR=\frac{TP}{TP+FN} \tag{5}\]  
FPR or specificity can be expressed with the help of Equation (6).  
\[FPR=\frac{FP}{FP+TN} \tag{6}\]  
The ROC is used to plot TPR against FPR at different classification thresholds. At a lower classification threshold, more elements are classified as positive, increasing both the FPR and TPR.