esa-sceva/satcom-chunk-collection huggingface dataset:
Clustering is a pattern recognition approach widely adopted which exploits the features characterizing a sample of observations to define an intrinsic distance measure and then a similarity criterion: to ensure its transparency we included several robustness analyses in the proposed framework. If clusters transparency is somehow easy to obtain, the same consideration does not hold for justification. In the XAI domain, justification consists in the elucidation of how the model provided a specific answer; in this case the problem is to provide a justification for the observed clusters. Besides, for clinical purposes we also hypothesize that a justification is not robust if it does not lead to informative conclusions. We unified these two aspects by adopting an embedding strategy. Embedding allows to represent, with a certain degree of fidelity, multidimensional data in suitable two- or three-dimensional spaces which are therefore prompted for visualization; thus, they are equivalently called dimensional reduction techniques. Visualization becomes the key for both justification and informativeness: in fact, using a two-dimensional embedding we were able to provide a clear clinical interpretation of clusters (justification) and quantify the informative content provided by the embedding (informativeness). Specifically, we adopted several embedding strategies such as the adaptive dimension reduction [13], principal component analysis [14; 15], Laplacian eigenmap [16] and distinguishing variance embedding [17; 18]. The selection of helpful prognostic factors among clinical and histological data collected in the first tumor diagnosis will characterize coherently the output of each embedding technique, verifying the data considered in current guidelines [12] by an automated procedure. Finally, we performed an extensive evaluation for the uncertainty related to our quantitative method. Quantifying how reliable a prediction is within an XAI framework is the last but probably the most important feature to ensure. In fact, all the considerations and the conclusions drawn by the model would be inconsistent if characterized by a huge uncertainty. For this purpose, we designed and performed specific robustness analyses and statistical tests to obtain uncertainty estimates and create a comprehensive XAI framework. Provided that it is possible to determine some clinically consistent similarity groups, are there any therapy differences within these communities? Answering these questions paves the way for novel computer aided therapy design systems based on specific patients' profiles and hopefully novel and more effective treatments.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 3 Related Works  
### Network Traffic Anomaly Detection Based on Machine Learning  
In recent years, machine learning methods have been widely applied in the field of network traffic detection, aiming to distinguish between malicious and benign traffic by extracting informative features from network flow data. [PERSON] and [PERSON] [10] explored the effectiveness of both supervised learning methods (e.g., SVMs and decision trees) and unsupervised learning methods (e.g., clustering) in detecting network anomalies. [PERSON] et al. [11] proposed a hybrid model, SVM-C, which integrates SVMs and clustering analysis to enhance detection performance. [PERSON] [12] introduced an intrusion detection system that combines SVMs with decision trees, aiming to improve accuracy while reducing the processing time. [PERSON] et al. [13] trained and systematically evaluated three different machine learning classifiers--SVMs, logistic regression, and k-nearest neighbors--for their effectiveness in anomaly detection tasks. Machine learning methods, with their ability to model statistical features, have demonstrated broad applicability in network traffic classification without relying on a specific payload content. However, these approaches still exhibit limitations when confronted with increasingly sophisticated modern attacks. Such attacks often adopt multi-stage, stealthy, and coordinated infiltration strategies, involving the collaboration of multiple nodes to progressively penetrate the target network. These behaviors exhibit strong structural dependencies and gradual progression. Traditional machine learning models, which typically rely on isolated flow features, struggle to accurately detect such complex attack patterns that involve intricate structural relationships.  
Recent studies have also explored feature fusion mechanisms in the context of anomaly detection. [PERSON] et al. [14] proposed MFFusion, a multi-level feature fusion model that improves malicious traffic detection by hierarchically combining deep features. Similarly, [PERSON] et al. [15] introduced a two-stage fusion framework specifically designed for cyber-physical anomaly detection, demonstrating the advantages of integrating heterogeneous data representations. Inspired by these works, our method employs a cross-gating mechanism that dynamically integrates structural and attribute features, enabling the generation of more expressive graph-level representations and enhancing detection accuracy.
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 4 Results and Discussion  
### RGB Composition Results  
The combination of bands used for the application of the method was the image before the event for the R-band and the image after the event for the G- and B-band. Different results were obtained for the two polarizations, VV and VH, as can be seen in Figure 6. There are many possible combinations, but the chosen combination favors the visualization of the flooded areas. Water surfaces that are stable on both dates, such as the Ebro river or reservoirs, are represented in black. The populations, urban and industrial areas with very high intensity values, are observed in white. Those areas, with intermediate intensity values and in which there have been hardly any variations, are consolidated in blue tones. The light pink tones are characteristic of areas with high humidity while the red ones represent the flood surfaces in which the water has completely flooded.  
Figure 6: General flood map of the entire study area using RGB composition created from the RADAR images of 6 April 2018 and 13 April 2018: (**a**) in VV and (**b**) in VH polarization with a Lee 7 \(\times\) 7 window size filter.  
It should be noted that right on the riverbank, the presence of massive riparian vegetation and vertical growth generates a double rebound dispersion or a volume dispersion (Figure 7). The vegetation structure and submerged status are conditioning factors of different scattering mechanisms that can happen, and therefore a range of backscatter values [60]. This, combined with the fact that the sensor used works in the C-band, with a penetration range in bare soil or vegetation that has a maximum of 5 cm, results in the presence of water not being shown even though the area was flooded. These areas are seen in white or slightly pink tones. For these areas, the use of an L-band sensor (23.5 cm) would provide greater penetration because the wavelength is longer than the size of the leaves within the tree mass [61]. In fully submerged locations, scattering dampening occurs as in an open flood, while scattering enhancement is observed in partially or unsubmerged locations because of the dihedral scattering dominates.  
A correct choice of polarization optimizes the discrimination of flooded areas [33]. The results obtained in our polarization configurations and the contributions of [41; 62] in the comparative studies of polarizations to monitor flood areas reaffirm that the VH polarization is more suitable for delimiting flooded areas. It generates well-defined and correctly defined surfaces, results that VV polarization cannot offer. VV polarization is clearly influenced by the roughness and heterogeneity of the terrain [33; 63]. We can see this fact in the floodplains, where the roughness of the soil of the crops fields becomes more evident in the VV polarization.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Points of Interest  
Figure 6 shows the results of coverage analysis, with the identified points of interest. A total of 1033 track waypoints were available in the Keswick area under consideration, with 72 points identified as points of interest without adequate coverage.  
The points of interest were mainly located in deep valleys surrounded by high peaks or in zones with high elevation compared to the cellular towers nearby, as highlighted in Figure 7. This suggests that the terrain profile is an important factor to be considered for the signal pathloss calculation as it causes inconsistencies in the signal propagation. The terrain effectively acts as a barrier between two points, resulting in signal loss or degradation. Drones flying at high altitudes can be effectively employed as signal relays in such situations. However, the points of interest are spread out across a large area, confirming the need for an optimal path planning algorithm.
# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 4 Methodology
### The Unified Loss Function  
For the case of class deviation, the strategy of randomly sampling negative samples is usually used to deal with it. The design loss function formula is as follows:\[L=-\frac{1}{M*N}\sum_{u\in U,v\in V}\left[y_{uv}\log(\hat{g}_{uv})+(1-y_{uv})\log(1 -\hat{g}_{uv})\right]+\lambda\sum_{u}\sum_{v}J(y_{uv},\mathbb{y}_{uv})+\lambda \|\theta\|_{2}^{2}. \tag{21}\]  
In this loss function, where the first term is the binary cross-entropy, the value of \(M*N\) represents the number of user-item pairs. The smaller the value of cross entropy is, the more accurate the model classification result. The second item is the label propagation part, which can be regarded as adding constraints to the edge weight \(w\). The last item is the \(L_{2}\) regular term used to control the model parameters so as not to become excessive or large, making the training process more stable and reducing overfitting problems.  
A recommendation for user \(u\) is actually a mapping from item features to user item labels, i.e., \(F_{u}:E_{u}\to y_{uv}\), where \(E_{u}\) is the feature vector of item \(v\). Therefore, Equation (21) exploits the knowledge graph structure information on the \(F_{u}\) feature side and the label side to capture the user's higher-order preferences.
## 3 Materials and Methods
### Underlying Software Components
#### 3.2.2 Data Management and Data Format  
All sensor-generated data were periodically saved in the database, with data management conducted on the webserver. The database module was implemented using PostgreSQL over the webserver used as Nginx.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Description** & **Min Value** & **Max\_Value** & **Min\_Distance** & **Max Distance** \\ \hline \multirow{3}{*}{DHT11} & Temperature & 0 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C & 50 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C & \multirow{3}{*}{1 m} & \multirow{3}{*}{10 m} \\ \cline{2-2} \cline{5-5}  & Humidity & 20\% \(\pm\) 5\% & 80\% \(\pm\) 5\% & & \\ \hline PIR & Motion sensor & 0 V [0] & 3 V [1] & 1 m & 7 m \\ \hline IR & Obstacle sensor & 0 V [0] & 5 V [1] & 1 m & 5 m \\ \hline \multirow{2}{*}{MQ-2} & CO\({}_{2}\) and Smoke & \multirow{2}{*}{300 ppm} & \multirow{2}{*}{10,000 ppm} & \multirow{2}{*}{2 cm} & \multirow{2}{*}{4 m} \\  & sensor & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor’s operation ranges.  
Postgres was chosen as an object-relational database management system (RDBMS). It emphasized extensibility and standards compliance, and had the ability to provide replication of the database for scalability and security.  
Nginx is an open-source reversed proxy server for HTTPS, with a load balancer. Nginx was chosen as the web server for its security, reliability, and load balancing when there was huge traffic with connected users. Its characteristics focused on high concurrency, high performance, and low memory usage.  
The data exchange from sensor nodes to the cloud was formatted to allow efficient data transmission with low bandwidth. The data were converted into JSON (JavaScript Object Notation). JSON has been adopted as a standard text-based format for representing structured data.
## 6 Real Data Applications  
In this section, we show the practical performance of the suggested method by analysing four real datasets from the biomedical field. The first biomedical dataset is guinea pigs infected, consisting of 72 observations and presenting the survival time of the guinea pigs infected with different amounts of virulent tubercle bacilli. The second dataset is head and neck cancer, having 44 observations and presenting the length of time the head and neck cancer patients stay alive. The third dataset, COVID\(-\)19 patients, contains 36 observations and denotes the death rate of COVID\(-\)19 pandemic patients belong to Canada of 36 days, from 10 April to 15 May 2020, see the link [[[https://covid19.who.int/](https://covid19.who.int/)]([https://covid19.who.int/](https://covid19.who.int/))]. Similarly, the fourth dataset represents the lifetimes of 20 patients receiving analgesics. A detailed description of all the datasets is presented in Table 5.  
The NGLog-Weib distribution was applied to all four datasets. The goodness of the fit of the newly introduced method was compared with the APTra-Weib (Alpha Power Transformed Weibull) model ([PERSON] et al. [38]), the NEXpo-Weib (New Exponential Weibull) model ([PERSON] et al. [32]), the MO-Weib (Marshall-Olkin Weibull) model ([PERSON] and [PERSON] [39]), the FRLog-Weib (Flexible Reduced Logarithmic Weibull) model ([PERSON] et al. [1]), and the Kumar-Weib (Kumaraswamy Weibull) distribution ([PERSON] et al. [40]). The CDF of the competitive probability models (or distributions) are given below:  
* The APTra-Weib distribution: \[G(x;\alpha,\Theta)=\frac{\alpha^{(1-e^{-\beta x^{\delta}})}-1}{\alpha-1},\, \alpha\
eq 1,\alpha\in\mathbb{R}^{+}\text{and }x\in\mathbb{R}^{+}\]
* The MO-Weib distribution: \[G(x;\Theta,\delta)=\frac{\left(1-e^{-\beta x^{\alpha}}\right)}{\delta+(1- \delta)\big{(}1-e^{-\beta x^{\alpha}}\big{)}},\,\delta,\alpha,\beta\in\mathbb{ R}^{+}\text{and}x\in\mathbb{R}^{+}\]
* The NEXpo-Weib distribution: \[G(x;\Theta)=1-\left(\frac{e^{-\beta x^{\alpha}}-1}{e-e^{-\beta x^{\alpha}}} \right),\,\alpha,\beta\in\mathbb{R}^{+}\text{and }x\in\mathbb{R}^{+}\]
* The Kumar-Weib distribution: \[G(x;a,b,\Theta)=1-\left[1-\left(1-e^{-\beta x^{\alpha}}\right)^{a}\right]^{b},\,a,b,\alpha,\beta\in\mathbb{R}^{+}\text{and }x\in\mathbb{R}^{+}\]
* The FRlog-Weib distribution: \[G(x;\Theta,\delta)=1-\frac{\log\Bigl{(}\delta+1-\delta\Big{(}1-e^{-\beta x^ {\alpha}}\Big{)}\Bigr{)}}{\log(1+\delta)},\,\delta,\alpha,\beta\in\mathbb{R}^ {+}\text{and }x\in\mathbb{R}^{+}\]  
Additionally, we considered various AMs (analytical measures) to verify which distribution outperformed and provided the closest fit to the considered datasets among the fitted distributions. These AMs included GFMs (goodness of fit measures) and DMs (discrimination measures) with corresponding \(p\)-values. The GFMs were computed as  
* The CM (Cramer-von Misses) test statistic:
###### Abstract  
Throughout the world, cultural heritage sites are under the direct threat of damage or destruction due to developing environmental and anthropogenic hazards, such as urban expansion, looting, and rising water levels. Exacerbating this problem is the fact that many of the most vulnerable sites' exact locations and/or full spatial extents have yet to be uncovered, making any attempts at their protection exceedingly difficult. However, the utilization of earth observation data has recently emerged as an unmatched tool in the exploration and (digital) preservation of endangered archaeological sites. The presented research employs very high-resolution WorldView-3 satellite imagery (~30 cm) for identifying and delineating previously unknown subsurface archaeological structures at the ancient Egyptian site of Hermopolis (el-Ashmunein). A particular emphasis is placed on the application of spectral indices, specifically those looking at vegetation cromparks and iron oxide levels. Through this analysis, seven promising structures were identified, including three elongated installations, which may have been utilized for storage purposes, and a potential casemate foundation structure. As 2D outlines of structures are often difficult to visualize, the newly identified archaeological features were expanded into a realistic, georeferenced 3D model using the computer programs, SketchUp Pro and Chaos V-Ray. The goal of this 3D model is to ensure that the results derived from this research are more accessible (and tangible) to a wider audience--the scientific community and the public alike. The methodological scheme presented in this article is highly adaptable and with some minor modifications can be replicated for other archaeological sites worldwide.  
remote sensing archaeology; spectral enhancement; cromparks; vegetation indices; iron oxide index; edge detection; archaeological site; cultural heritage; 3D modeling +
Footnote †: journal: Remote Sens.  
20221492[[https://doi.org/10.3390/rs14010092](https://doi.org/10.3390/rs14010092)]([https://doi.org/10.3390/rs14010092](https://doi.org/10.3390/rs14010092))
## 4 RISs Controlled by AI  
As the use of RISs for the creation of SRE is being examined, the problem of dynamically controlling the RIS occurs. This issue has been studied thoroughly, and as previously mentioned, one of the most promising ways of achieving this is through AI. There are two main approaches that most researchers have chosen to follow as described in [14]; (a) training-based and (b) training-free techniques. On the one hand, in training-based methods, the models are trained by using an existing set of data and can be reprogrammed according to different observations. These models can be developed by using either supervised or unsupervised learning approaches. Both approaches require that the dataset is large enough and the environment is not expected to change significantly. On the other hand, in training-free methods, the models are trained through feedback data collected by sensors or devices themselves and are continuously changing and improving to dynamically cover the needs of the network as they change. The most common method used is RL.  
The integration of RISs into next-generation wireless networks promises to address key challenges in high-frequency communications while enabling more precise spatial multiplexing and beamforming. Deployment scenarios include mounting these surfaces on building facades, interior walls, ceilings, or even incorporating them into smart clothing and vehicles. Research is advancing towards making these surfaces increasingly intelligent through integration with AI, allowing them to autonomously adapt to changing environments and user needs while anticipating optimal configurations based on predicted mobility patterns and traffic demands.
## 4 Optimization Strategy and Classification
### Optimization Strategy of LPLC-HDL
#### 4.1.1 Shared Dictionary Learning  
By fixing the variables \((D_{p},Z_{p})\), the objective function of our LPLC-HDL for learning the shared dictionary becomes  
\[\begin{split}\min_{D_{0},L_{0},Z_{0}}\ \sum_{i=1}^{C}&\|X_{i}^{ \prime}-D_{0}Z_{0}^{i}\|_{F}^{2}+\eta\left(\|Z_{0}\|_{F}^{2}+\mathcal{T}\!r \big{(}Z_{0}^{\intercal}L_{0}Z_{0}\big{)}\right),\\ & s.t.\ \|d_{0}^{k}\|_{2}^{2}=1,\ (k=1,2,\cdots,k_{0}),\end{split} \tag{13}\]  
where \(X_{i}^{\prime}=X_{i}-D_{p}Z_{p}^{i}\). To update the variables \((D_{0},L_{0},Z_{0})\), we turn to an iterative scheme, i.e., updating \(D_{0}\) by fixing \((L_{0},Z_{0})\), constructing \(L_{0}\) based on \(D_{0}\); updating \(Z_{0}\) by fixing \((D_{0},L_{0})\). The detailed steps are elaborated as below.  
(a) Update the shared dictionary \(D_{0}\) and matrix Without loss of generality, we first concentrate on optimizing the shared dictionary \(D_{0}\) by fixing the variables \((L_{0},Z_{0})\). The function (13) for \(D_{0}\) becomes  
\[\min_{D_{0}}\ \|X^{\prime}-D_{0}Z_{0}\|_{F}^{2},\ s.t.\ \|d_{0}^{k}\|_{2}^{2}=1,\ (k=1,2, \cdots,k_{0}), \tag{14}\]  
where \(X^{\prime}=\sum_{i=1}^{C}X^{\prime}_{i}\) and \(Z_{0}=\sum_{i=1}^{C}Z^{i}_{0}\). The quadratic constraint is introduced on each atom in \(D_{0}\) to avoid the scaling issue risk. The above minimization can be solved by adopting the Lagrange dual algorithm presented as in [28].  
Based on the obtained dictionary \(D_{0}\), the graph Laplacian matrix \(L_{0}\) can be constructed using (5) and (6).  
(b) Update the commonality representation \(Z_{0}\)  
By fixing the variables \((D_{0},L_{0})\), the optimization of the commonality representation \(Z_{0}\) in Equation (13) can be formulated as  
\[\min_{Z_{0}}\ \|X^{\prime}-D_{0}Z_{0}\|_{F}^{2}+\eta\left(\|Z_{0}\|_{F}^{2}+ Tr\big{(}Z_{0}^{\mathrm{T}}L_{0}Z_{0}\big{)}\right), \tag{15}\]  
where \(X^{\prime}=\sum_{i=1}^{C}X^{\prime}_{i}\) and \(Z_{0}=\sum_{i=1}^{C}Z^{i}_{0}\); there is a closed-form solution for \(Z_{0}\) as all the above terms are based on the \(l_{2}\) norm regularization.  
By setting the derivative of (15) to zero, the optimal representation \(Z_{0}\) can be obtained as \(Z_{0}=\big{(}D_{0}^{\mathrm{T}}D_{0}+2\eta(L_{0}+l_{0})\big{)}D_{0}^{\mathrm{T }}X^{\prime}\), where \(l_{0}\in R^{k_{0}\times k_{0}}\) is an identity matrix.
# Aerodynamic Performance Assessment of Distributed Electric Propulsion after the Wing Trailing Edge
## 4 Numerical Simulations  
### Simulation Setup  
This step was taken to figure out the aerodynamic interference between the wing and propellers located at the rear of the wing as a pusher DEP. The geometry parameters are shown in Table 2. For the simulation setup, the RANS model was applied to analyze the characteristics of the external flow field. Also, mesh refinement was performed on the propeller tip to capture the gradient of the physical field flow. In addition, the finite volume method was used to discrete the differential equations. Considering the low Reynolds number, the turbulence model \(k\)-\(w\) was selected to obtain the flow field of the DEP. Pressure correction and interpolation were performed using the semi-implicit method (SIMPLE) algorithm with the standard format. Momentum, energy equation, and turbulent viscosity are all in the second-order upwind discrete schemes. Finally, the sliding grid was used to deal with the interaction between the rotating and the stationary regions, and the multiple reference frame (MRF) method was applied as the initial condition for the slip mesh transient calculation. The mesh distribution is shown in Figure 5.  
Figure 4: Distribution of L/D: (**a**) horizontal position; (**b**) vertical position.  
It can be found that the error decreased with the increasing grids, and it is stable for the grid around \(25\times 10^{5}\), which was applied for the following simulations.