esa-sceva/satcom-chunk-collection huggingface dataset:
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 3 Reconstruction of the Scene  
### Matrix Discrete Formulation  
We reformulate the previous equations in a discrete context. A target on ground in position \(\xi_{l}\) is acquired by the formation observing the scene for \(T_{obs}\) seconds. \(M\) observations are acquired by each receiver, where \(M=T_{obs}\cdot PRF\). The acquired data can be set in a matrix \(\mathbf{S}\) collecting the signals acquired by all the receivers whose size is \([NM\times P]\) with \(P\) the number of range samples within a narrow strip in range (We remember that, formally, the SAR impulse response is time variant and the transfer function is different range by range. See Section 3.3).  
Using the approximation in Equations (14) and (15), the formulation in Equation (12) can be written in a compact form:  
\[\mathbf{S}=\mathbf{H}\mathbf{D}+\mathbf{N}, \tag{16}\]  
where \(\mathbf{H}\) (of size \([NM\times R]\)) is the (bistatic) combination matrix filled by contributions in Equation (15) and having \(N\cdot M\) rows (the number of receivers times the Doppler wavenumber bins) and \(R\) columns, with \(R\) being the number of frequency sets of width PRF we want to unfold (in principle, infinite, in practice, we can disentangle up to \(N\) replicas). The matrix \(\mathbf{D}\) (of size \([RM\times P]\)) is the wavenumber domain support of the signal observed by a monostatic equivalent SAR system. It is the multiplication, element-wise, of two matrices: \(\mathbf{D}=\mathbf{H}_{\mathbf{m}}\circ\mathbf{\Gamma}\), where \(\mathbf{H}_{\mathbf{m}}\) (of size \([RM\times P]\)) is a matrix including the contributions of Equation (14), i.e., the equivalent monostatic SAR system put at the center of the formation and \(\mathbf{\Gamma}\) (of size \([RM\times P]\)) the ground backscattering, both sampled \(R\) times denser than each bistatic term, i.e., at nominal positions \(\upsilon/(R\cdot PRF)\). \(\mathbf{N}\) is finally the noise matrix.
## 6 Performance Evaluations
### Simulation Results
#### 2.2.2 Average end-to-end delay evaluation under the second scenario  
This simulation experiment shows how end-to-end latency changes with traffic rate and sinks nodes in the second scenario. The first experiment examines how end-to-end latency increases with the traffic rate in the second scenario. This simulation experiment varied the average traffic rate from three to 11 packets per second while maintaining three sink nodes. Figure 18 shows end-to-end latency vs. traffic volume. This figure demonstrates that the proposed approach reduces end-to-end latency. The proposed approach outperformed other algorithms in the first scenario, which is reasonable. The proposed approach tries to create a data path less likely to be cut off due to environmental factors than those estimated by SMRP, leading to more reliable links and less delivery delay. EERP has the greatest end-to-end latency since it ignores external environment impact on routing performance.  
The second experiment examines end-to-end delay variation with various sink nodes in the second scenario. The experiment deploys one to three sink nodes while keeping the average traffic rate at three packets per second. Figure 19 depicts end-to-end delay variation with a varied number of sink nodes. With additional sink nodes, end-to-end latency decreases because data packets travel a shorter distance, reducing delivery time. The proposed algorithm has the least end-to-end delay. In addition to the reasons described above, the routing paths in the proposed method are less likely to be cut off due to environmental factors than those computed using SMRP. Environmental factors easily cut off EERP's routing paths since it does not consider how to circumvent danger zones, causing packet loss and delivery delays. This causes EERP's excessive end-to-end delay.  
Figure 18: Influence of average traffic rate on average end-to-end delay for the second scenario.
## 2 Proposed Seismic Damage Prediction Model for a Building Structure
### Neural Network Configurations  
This study proposes four neural network configurations for seismic damage prediction, including a baseline network. The prediction performances of these configurations are comparatively evaluated in Section 3. Besides the baseline network, each neural network configuration contains an adaptive frequency feature extraction network module and a classification network module. The effects of the network parameter settings for both modules are also analysed.
## 5 Results and Analysis
### Impact of Activation Functions  
The role of activation functions in a DNN is crucial, as they introduce non-linearity, enabling the model to learn complex relationships within the data and enhance signal detection accuracy [17]. Selecting an appropriate activation function significantly influences the DNN's ability to make accurate decisions, ultimately reducing SER and improving system performance [18]. In MIMO systems, the primary objective is often symbol detection, which necessitates solving a complex optimization problem requiring non-linear modeling capabilities [19].  
An experiment was conducted using a three-layer neural network architecture with 300, 200, and 100 neurons per layer, respectively. The experiment evaluated the performance of three activation functions: the rectified linear unit (ReLU), sigmoid, and hyperbolic tangent (tanh) [20]. A dataset of one million training samples and ten thousand testing samples was employed.  
The results, depicted in Figure 10, demonstrate that the sigmoid function yielded the lowest SER across all SNR levels compared to the ReLU and tanh functions. This suggests that the sigmoid function introduced a more suitable level of non-linearity for this specific MIMO detection task.  
Figure 10: Application of activation function: SER vs. SNR.  
Figure 9: MIMO-based-DNN showing dataset of [300, 200, 100] Neurons per Layer.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion
### Handling of Difficult Examinations  
Gathering data from different sources with no pathological filtering can introduce difficult cases that, although uncommon, are susceptible to being encountered in clinical practice. Several factors contribute to the complexity of analyzing an examination. The first one is anatomical and/or pathological, especially with deformed hearts, as seen in conditions such as the tetralogy of Fallot, dilated right ventricle or aneurysm. A second factor resulting in challenging cases stems from image-quality degradation. These alterations result from various causes such as field inhomogeneities, noise and artifacts related to motion, partial volume effects or cardiac implants. These two factors affect myocardium delineation, as observed in Figure 9a,c, in the case of artifacts or severe anatomical deformation.  
A third factor is related to the choice of the slice axis plane, as it may influence the potential occurrences of visible anatomical regions to be detected. This is especially true with the aortic root in the four-chamber orientation, as its presence depends on the plane chosen during acquisition. Pertaining to these aspects, our hierarchical data augmentation plays a crucial step in reducing potential detection errors related to the first two factors mentioned (Figure 7).  
It is worth mentioning that in these examples, a region corresponding to the aortic root was falsely segmented with data augmentation, even when it was not present in the  
Figure 9: Examples of various difficult scenarios encountered in 2-chamber (**a**) and 4-chamber (**b,c**) views. In (**a**), the following color scheme is used: LA = red, LVC = blue and MYO = green. In (**b,c**) the following color scheme is used: RV = red, LVC = blue, MYO = green, LA = yellow, RA = cyan and AO = purple. It is also worth noting that the aortic root was detected in row (**b**) thanks to data augmentation. * Examination extracted from validation set.  
original ground truth in Figure 9b. This can be explained by the difficulty for the network to determine a clear boundary feature for this anatomical region, which is both induced by its nonsystematic presence across the frames as well as the significant anatomical changes in the images between subjects and between the end-diastole and end-systole phases. These challenges came into play with the computed performance, resulting in lower metrics and a higher rate of failures in this class compared to other regions. Nevertheless, in the above example, the model produced a noncorrect but visually plausible segmentation map with respect to these points.  
For the current experiments, the aortic root was segmented to reduce the gap that was sometimes present between atria and as an attempt to mitigate associated segmentation errors. Sensitivity and specificity were also computed for this segmentation class in Table 5.
## 4 Discussion
### Quantitative Assessment  
To quantitatively assess the algorithms' performance, we introduced kurtosis and root mean square (RMS) parameters. Kurtosis values provided insights into the recovered Gaussian function widths, with positive values indicating a slim, high-profile curve and negative values suggesting a flattened profile [31]. Notably:  
* FSD began to falter when \(F_{xx}\) exceeded 7;
* FSD-HSO exhibited a diminishing response for \(F_{xx}\) values greater than 6;
* FSD-PSO showcased a more favorable performance, deteriorating only slightly when \(F_{xx}\) approached higher values, as shown in Figure 6.  
Similarly, RMS values illustrated divergence from the original phase as fringe frequencies increased:  
* FSD-HSO began to diverge when \(F_{xx}\) exceeded 6;
* FSD exhibited divergence when \(F_{xx}\) surpassed 8.  
FSD-PSO showed remarkable consistency with the original phase, diverging only significantly higher values, as shown in Figure 7.  
Figure 6: Kurtosis vs. Gaussian width \(\left(F_{xx},\ F_{yy}\right)\).
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 2 Optimization of Void Shape
### Determination of the Optimal Void Shape
After that, a certain void shape, which showed the smallest deflection, was decided as the optimal void shape from among the rest of three void shapes in the former eliminating process. Figure 6a showed the results of the first eliminating process of the sequential elimination method. The voided slab with 'Rect Donuts (D = 50)', 'Rect Donuts (D = 30)' and 'Round Rect (R = 70)' showed the largest values of the residual load among the above eight types of void shape. Therefore, these void shapes were selected as the top three in terms of the load-bearing capacity, and the other void shapes were eliminated through the first sequential elimination process. Figure 6b showed the results of the second eliminating process in terms of the flexural stiffness. Among the three remaining void shapes through the first eliminating process, the voided slab with 'Rect Donuts (D = 50)' had the smallest deflection of 9.5 mm at the service load. As a result, 'Rect Donuts (D = 50)' was decided as the optimal void shape in this study. It showed good load-bearing capacity with the residual load-bearing capacity of 26.12 kN/m\({}^{2}\) and the ultimate load-bearing capacity of 33.94 kN/m\({}^{2}\), which were 108% and 100% of their counterpart with the solid slab, respectively. In addition, it also showed good flexural stiffness at the service load, which was 94% of their counterpart with the solid slab, and a good self-weight reduction ratio more than 30%.
## 7 Applicability of PBA Optimization Method  
The PBA method proposed in this work is suitable for solving network-planning tasks in packet-switched Xhaul access networks, which support soft network slicing while ensuring slice isolation. These tasks involve optimizing the placement of the DU, CU, and MEC processing resources and routing FH and MH flows for a given set of RUs according to specific slices' latency, bandwidth, and resource placement requirements. Soft slicing is realized by sharing bandwidth and carrying the packet flows belonging to different slices over common network links.  
The numerical results in Section 6 indicate that the PBA can provide good-quality solutions (optical or near-optimal) for the network instances comprising at least some tens of RUs in the mesh networks with some tens of switches, outperforming the classical MILP approach considerably. At the same time, the PBA running times increase with the problem size and, in larger network scenarios, can reach the one-hour limit imposed on the algorithm. For this reason, the method is suitable for network design problems where the solution provisioning time is not critical, for instance in the design of network connections and resource placement for a defined set of traffic demands (RUs) and network slices. Another applicable scenario of the method is the provisioning of processing resources and network connectivity for dynamic network slice requests. In this case, the only problem variables to be determined are related to the new slice request, whereas the variables corresponding to the already allocated slices are fixed. By reducing the number of problem variables in this scenario, the processing times can be expected to be lower than when optimizing the network for all slices (as in the network-planning case).  
The primary advantage for a network operator in using the proposed method is the optimization of PP resources while ensuring that the packet-switched Xhaul transport network meets the latency requirements of latency-sensitive flows, specific to particular network slices. Achieving this is challenging in a network with dynamic latencies caused by packet buffering.  
Figure 16: Optimized placement of DU/CU/MEC and routing of FH/MH flows (in uplink direction) for eMBB and URLLC slices in network WRO-17; filled squares represent active PPs, and the numbers are nodes’ indexes.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 1 Introduction
Considering the constraints of flight distance, initial heading, and terminal heading, an immune genetic algorithm was proposed to improve the target search efficiency of UAVs in uncertain environments [13]. A new bee colony algorithm was proposed in [14], which applied an improved search equation to generate candidate solutions to improve the search ability of the bee colony algorithm. In [15], an improved ant colony algorithm was proposed for multi-UAV cooperative search planning, and a new state transition rule and pheromone update rule were designed to ensure that the UAVs could return to the initial point under the constraint conditions as required after completing a task. However, the proposed methods and technologies are not suitable for the anti-submarine search problem that we are interested in. In particular, anti-submarine search remains an open and challenging problem due to the frequent use of quiet state in submarines, as well as the low detection rate and high false-alarm rate of onboard sensors. Related works are summarized in Table 1.  
Inspired by the above research, in this paper, we propose an association rule-driven cooperative anti-submarine method for multi-UAVs, which fully considers the cooperative characteristics between the target submarine and the warship and can still find the target quickly and effectively when the target submarine and the warship have attack abilities. The main contributions of this paper are as follows: First, the association rule database and the association rule function applicable to submarine targets are constructed. On this basis, an association rule-driven probability map model is established. Secondly, an improved rule-heuristic multi-ant colony algorithm is proposed. The association rules are used as heuristic information to design the state transition rules of an ant colony, and the attack threat of a submarine is considered in pheromone updating. Simulation results show that the proposed controller achieves improved search performance. The rest of this paper is organized as follows: In Section 1, we establish the environment model, the association rule-driven probabilistic graph model, the association rule-driven probability map update rule, the UAV model, and the association rule-based multi-objective efficiency function. In Section 2, we propose an improved rule-inspired multi-ant colony algorithm. In Section 3, we present simulation results, demonstrating the effectiveness and advantages of the proposed method. In Section 4, we present our conclusions.
## 5 Experiments and Results
### Evaluation Metrics
#### 5.2.5 FSIM Score (Feature Similarity Index Matrix)  
Using two primary attributes, Phase Congruency (PC) and gradient magnitude (GM), to assess image quality, the FSIM evaluates images based on these characteristics, encapsulating their assessment in the formula:  
\[SL(x)=[SPC(x)]^{\alpha}\cdot[SG(x)]^{\beta}\]  
Here, \(PC\) represents phase compatibility, crucial for identifying invariant features across different lighting conditions, while \(GM\) denotes the gradient magnitude, which highlights edge details by quantifying the rate of intensity change across the image. The parameters \(\alpha\) and \(\beta\) allow the adjustment of the relative importance of these attributes in the final similarity score.  
The gradient magnitude \(GM(x)\) is calculated as follows:  
\[GM(x)=\sqrt{G_{x}^{2}+G_{y}^{2}}\]  
where \(G_{x}\) and \(G_{y}\) are the horizontal and vertical gradients of the image, respectively.  
The Phase Congruency (SPC) is computed with:  
\[SPC=\frac{2\cdot PC1\cdot PC2+T_{1}}{PC1^{2}+PC2^{2}+T_{1}}\]  
In this formula, \(T_{1}\) is a constant added to enhance the stability of the calculation, ensuring that \(SPC\) values range between 0 and 1. This approach effectively captures both the structural integrity and the perceptual likeness of the images, making the FSIM a robust tool for evaluating image quality in various applications.
## 3 Methodology
### Extreme Gradient Boosting (XGBoost)  
XGBoost is an advanced Gradient Boosting with improvements in speed, efficiency, and scalability. It has produced high accuracy in various applications such as prediction of Type 2 diabetes risk [54], hot spots at protein-protein interfaces [55], sales forecasting [56], and has been used in solving various problems in Kaggle competitions [57]. Like any boosting algorithm, it is made up of Classification and Regression Trees (CART) as base learners. Sometimes, linear functions are also used as base learners. The base learners are fitted to minimize a loss function. The loss function determines how well the model performs, and it is minimized during training [58; 59]. Minimization of the loss function is achieved through gradient descent [60]. In XGBoost, a regularization term is added to the loss function, and Taylor Series Expansion is used in approximating the modified loss function. Optimization of the tree structure is performed using an approximate greedy algorithm. XGBoost uses column subsampling such that portions of features are collected to grow trees, thereby minimizing overfitting and improving training speed. In addition, it uses a presorting technique and storing mechanism whereby datasets are stored in blocks, and the search for the best split is performed by scanning columns in a block. It also uses techniques such as Block Compression and Block Sharding for out-of-core computations and when fetching data from multiple drives, respectively, thereby enhancing the execution time [58; 61]. The boosting system can be composed of normal trees, as in the case of Gradient Boosting, or the use of Dropouts meet Multiple Additive Regression Trees (DART). DART is a process whereby multiple numbers of regression trees are fitted at a low learning rate, and trees fitted earlier are maintained, whereas those fitted later are dropped in order to prevent overfitting [62]. In this work, the number of trees, learning rate, and type of booster were ascertained based on hyperband for each of the combinations of numeric features with one or two feature extraction methods used on the input image, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extraction Method** & **Number of Trees** & **Learning Rate** & **Booster** \\ \hline No Image & 248 & 0.2912 & gthree \\ LBP & 209 & 0.2698 & dart \\ HOG & 260 & 0.1035 & dart \\ GLCM & 251 & 0.2644 & gthree \\ DWT & 261 & 0.1375 & gthree \\ SFTA & 202 & 0.1582 & gthree \\ GLCM + LBP & 263 & 0.1649 & dart \\ GLCM + HCG & 287 & 0.1178 & dart \\ GLCM + DWT & 276 & 0.2807 & dart \\ GLCM + SFTA & 219 & 0.1994 & dart \\ CNN & 100 & 0.3000 & gthree \\ CNN + LBP & 195 & 0.1704 & dart \\ CNN + HOG & 185 & 0.0436 & dart \\ CNN + GLCM & 293 & 0.0908 & gthree \\ CNN + DWT & 209 & 0.0711 & dart \\ CNN + SFTA & 199 & 0.0997 & gthree \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters of XGBoost for combinations of numeric features and features from one or two feature extraction method(s).
## 1 Introduction
### The Ulaca Oppidum  
Ulaca is located near Villaviciosa (Solosancho, Avila, Spain) (Google Earth DMS coordinates of 40\({}^{\circ}\)31\({}^{\prime}\)48.53\({}^{\prime\prime}\) N and 4\({}^{\circ}\)53\({}^{\prime}\)01.04\({}^{\prime\prime}\) W) (Figure 1a). The site occupies the top of a hill that provides visual control over the Ambles Valley [42; 43]. Ulaca is an urban center or _oppidum_ of the end of the Iron Age (c.III-I BC) erected by people known as the _Vettones_. The _Vettones_ were one of the most remarkable _populi_ in Celtic Iberia. Texts addressing these populations from classical Greek and Roman authors described them as groups occupying the territory of the current provinces of Avila and Salamanca, as well as parts of Zamora, Caceres, and Toledo. Ulaca is one of the largest fortified sites in the Iberian Peninsula [44; 45], with more than 70 ha and over 3000 m of walls, where a ca 1500 people would have lived [42]. The site is constituted by well-preserved structures, including impressive granite quarries, a rock sanctuary (Figure 1d), and a sauna (Figure 1b) or a voluminous building known as \"El Torreon\" (the Tower) (Figure 1c) [42; 43; 46; 47; 48]. This _oppidum_ shows a complex spatial organization with different areas for distinct functions, such as an area of artisan workshops or a cemetery [49; 50; 51].  
The inhabitants of Ulaca practiced different economic activities, such as craft, cereal agriculture, and livestock practices. The proximity of some water courses (Figure 1a) allowed agricultural activities to be carried out [50; 52]. Zooarchaeological studies have shown a predominance of cattle (40% faunal remains), followed by horses, suidae, and  
Figure 1: (**a**) Location of the Ulaca _oppidum_ (Solosancho, Avila, Spain). Details of some of the most characteristic structures of the site: (**b**) The sauna; (**c**) The Tower; (**d**) The rock altar.  
ovicaprids, although the latter groups were poorly represented [41]. Thus, the economic base of the Vetton social elites resides in the control of the land and the cattle [51].  
Taphonomic evidence in the form of cut marks associated with episodes of skinning, fleshing, and disarticulation has shown that animals, including cows and horses, were also consumed at the site [41]. The observed cut marks are usually limited to fine traces, but, in some cases, deep marks, which could be associated with cuts produced by metal tools, have been identified [41] (Figure 4a). Metal tools are not frequent in Ulaca; for instance, in the 233 m\({}^{2}\) area studied by [41], a total of 150 metal objects/fragments were found, of which 84 were made of iron. Among the objects, some knives stand out, such as those included in Figure 2. Alongside the metal tools, a blade, a core, and some flakes of flint have also been found in this area. In order to define the implications of metallurgy in daily life in Late Iron Age Ulaca, the present study is focused on analyzing the cut marks included in [41] to determine the type of tool used in carcass processing.