esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Indoor Localization Systems
Such a radio map is valid only for a specific configuration of a propagation environment and every change, such as moving a piece of furniture, may negatively impact a system's accuracy. Radio map creation can be sped up using crowdsourcing [15] or interpolation [14], but since they still require performing some measurements, it would be hard to implement fingerprinting in a home environment. Systems using RSS ranging are much easier to deploy. In that method, the user is localized by estimating their distance from the anchors based on a propagation model. The accuracy of the method depends on how accurately the assumed model reflects complex propagation conditions of the deployment area. Typically, the systems use simple models such as path loss exponentials [6] or multi-walls [16]. Narrowband systems average localization errors typically does not go below one meter. The accuracy highly depends on a place where the system is deployed and the number of anchors comprising the infrastructure. In apartments with extensive furniture, propagation models might not properly reflect the real conditions, and the accuracy might suffer. Thus, the mean errors of the solutions found in the literature cover a broad range of 1-2 m [6] to 4-6 m [14]. A localization technique, which deserves attention is the Angle-of-Arrival (AoA). It can be used in both ultra wideband and narrowband systems and consists of estimating the directions from which the signals transmitted by localized object reach system anchor nodes. It can be done by rotating a directional antenna [17] or analyzing phase differences using antenna arrays [18]. The method does not require maintaing precise synchronization of the infrastructure [19] and allows to achieve results of accuracy comparable to RSS-based methods (average error of few meters) [20]. The localization accuracy that is possible to achieve with UWB-based systems is much higher than in case of narrowband WiFi or BLE solutions. Unfortunately, energy consumption of UWB devices is on a relatively high level. For the ToA-based system described in [9], it was estimated that the tag would work without recharging for 74 h if it were powered with 6000-mAh battery pack. In case of wearable devices, which usually use smaller batteries this time would be even shorter. BLE is more energy efficient. In case of BLE-based systems, in case of rare transmission tags, working times could reach 2 years powered by a coin battery. Moreover, due to the UWB technology being less popular and widespread, it is much more expensive and the overall cost of the localization system could be too high for the majority of older persons. That would make BLE a preferred choice for AAL solutions, but since its localization accuracy might not be enough for some services, it should be improved. It can be done by using hybrid localization methods combing measured BLE RSS with results obtained using different technologies.
## Appendix A Proof of Proposition 1  
By using Equations (2), (7) and (8), the outage probability for the case of DF relay can be expressed as  
\[\mathcal{P}_{out,D_{1}}^{DF}=1-\Pr\left(\tilde{\gamma}_{SR}>\kappa_{1},\| \mathbf{h}_{1}\|^{2}>\frac{\kappa_{1}}{\phi\tilde{\gamma}_{SR}}\right), \tag{11}\]  
where \(\kappa_{1}=\frac{\Psi_{1}}{\Xi_{1}-\Psi_{1}\Xi_{2}}\). Based on result from Equation (15) and CDF of \(\|\mathbf{h}_{i}\|^{2}\), it can be rewritten as  
\[\begin{split}\mathcal{P}_{out,D_{1}}^{DF}&=1-\int \limits_{\theta}^{\infty}f_{\gamma_{SR}}\left(x\right)\tilde{F}_{\|\mathbf{h} _{1}\|^{2}}\left(\frac{\kappa_{1}}{\phi x}\right)dx\\ &=1-\sum\limits_{i_{1}=0}^{m_{SR}-1}\cdots\sum\limits_{i_{M}=0}^{ m_{SR}-1}\sum\limits_{n_{1}=0}^{N_{1}m_{1}-1}\frac{\Theta\left(M\right)}{n_{1}! (\rho_{S})^{\Delta}}\left(\frac{\kappa_{1}}{\phi\Lambda_{1}}\right)^{n_{1}}\\ &\times\int\limits_{\theta}^{\infty}x^{\Delta-n_{1}-1}e^{-\frac{ \kappa_{1}}{\phi\Lambda_{1}^{2}}-\frac{(\beta-\delta)x}{\rho_{S}}}dx\end{split} \tag{12}\]  
Using the results of [Eq.4] in [16] and after some straightforward mathematical steps, outage probability is expressed as the generalized incomplete Gamma function  
\[\int\limits_{x}^{\infty}t^{a-1}e^{-t-\frac{1}{\delta}}dt=\Gamma\left(a,x,b \right), \tag{13}\]  
where \(\Gamma(a,x,b)\) is the generalized incomplete Gamma function [16]. Then, substituting Equation (13) into Equation (12), the expression in Equation (19) can be obtained.
## 4 Discussion
These values can still be increased a little further by adjusting the values of the separation between resonators, as it can be seen in Figure 7 in the paper. Note that using practicable values of such separations, our design would be able to compete with those with the highest values of FBW reported in Table 1.
3. **Insertion loss**. The average IL of the designs presented in Table 1 are 1.63 dB and 2.25 dB for the first and second passbands, respectively. Note that the measured IL values for our design is, once again, competitive with the state-of-the-art, providing one the lowest values of IL for the first passband. 4. **Band-to-band isolation**. The best way of obtaining good band-to-band isolation is to introduce one or more transmission zeros between the passbands. This is not the case in the presented design. Nevertheless, the IL in the frequency region between passbands reaches 36 dB, this being a very good value for many applications. Ten of the designs included in Table 1 exhibit transmission zeroes between passband. Thus, this point could be considered the main drawback of the proposed filter topology and adding elements to introduce transmission zeros would result into a significant improvement, provided the other relevant features of the filter are not significantly affected. 5. **Common-mode Rejection Ratio (CMRR)**. The average CMRR values of the filters in Table 1 for the first and second differential passbands is 37.95 dB and 32.55 dB, respectively. These values are very close to the ones achieved with the proposed design. The advantage of our proposal is that this high level of CMRR is inherent to the coupling scheme and no additional components have to be added to enhance the CM suppression (such as lumped inductors, capacitors or resistors, additional CM rejection stages, additional open-circuit stubs, etc.). The introduction of these elements usually entails disadvantages (increase in complexity and the need of reaching a trade-off to avoid the deterioration of other filter parameters). In brief, the design presented in this work is one of the smallest reported in the literature, it has low complexity and allows for easy configuration of the two passbands (both, center frequency and FBW). At the same time, leads to high CMRR values without relying on additional elements that might disrupt the differential-mode response. The main drawback of our proposal could be the lack of transmission zeroes in the frequency region between passbands. Nevertheless, the band-to-band isolation level presented in this work is satisfactory for most applications.
# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 7 Discussion  
We carried out further tests and simulations in order to validate our model. The realized high-resolution model takes into consideration many factors and parameters to tune up the infection spreading process (see [6] for better understanding how we used it). In particular:  
1. The contagion susceptibility by age
2. The protection achieved thanks to wearable protective devices
3. The quarantine mechanism  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **RMSE** & **Pearson Correlation** \\ \hline Simulated Data—Real Data & 398,042 & 0.9903 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pearson correlation and root mean squared error (RMSE) of our model with respect to the serological data projection.  
Figure 14: Positives’ daily representation—Lombardy region.  
Moreover, we analyzed how these parameters affect the spreading procedure. Hence, we created a model that does not take these parameters into consideration, but makes the infection process dependent only on transmission probability, without any additional damper. Figure 15 shows different infection trends with different TP values. Considering also the values reported in Table 2, we can assert that the parameters in our model create a sufficient contagion dampening, validating our infection process modeling.  
We continued our analysis with further studies. For validating our model, we compared our results with other SEIR models. The comparison is shown in Figure 16. We took into account three different SEIR models. The first one (SEIR 1 in Figure 16) is a simple based system dynamics SEIR [2]. The second one [66] (SEIR 2 in Figure 16) takes into account the transmission rate of asymptomatic subjects and a piecewise exponentially-decreasing \(R_{0}\). The third one [67] (SEIR 3 in Figure 16) optimizes the model's parameters using computational swarm intelligence to model the early stage of the pandemic in Italy. Moreover, we presented in Table 3 a numerical analysis of these models compared with ours. The chart and the analysis showed that our model produces a more accurate prediction of the infection spreading in terms of RMSE and the Pearson correlation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **RMSE** & **Pearson Correlation** \\ \hline Simulated Data—Serological & 105,343 & 0.9903 \\ \hline Null model with TP = 0.2–Serological & 1,301,649 & 0.8674 \\ \hline Null model with TP = 0.4–Serological & 4,662,723 & 0.9456 \\ \hline Null model with TP = 0.6–Serological & 5,840,197 & 0.9805 \\ \hline Null model with TP = 0.8–Serological & 6,422,819 & 0.9763 \\ \hline Null model with TP = 1–Serological & 6,759,554 & 0.9616 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of our model to the null models in terms of root mean squared error (RMSE) and Pearson correlation n.  
Figure 15: Simulation results without damping parameters with different transmission probability (0.2, 0.4, 0.6, 0.8, 1) (incremental representation—y-axis scale is logarithmic).
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Hybrid Graph Embedding Module
#### 3.4.1 Spatial Graph Convolution  
In this process, local spatial information is extracted by applying convolution across the graph structure. The formula is as follows:  
\[\mathrm{h}_{\mathrm{i}}^{(1+1)}=\sigma\left(\sum_{\mathrm{j}\in\mathrm{N}( \mathrm{i})}\frac{1}{\mathrm{c}_{\mathrm{ij}}}\mathrm{W}^{(\mathrm{I})} \mathrm{h}_{\mathrm{j}}^{(\mathrm{I})}\right) \tag{8a}\]  
In Equation (8a), \(\mathrm{h}_{\mathrm{i}}^{(\mathrm{I})}\) is denoted as the feature representation of node \(\mathrm{i}\) at layer \(\mathrm{I}\). \(\mathrm{W}^{(\mathrm{I})}\) is denoted as a learnable weight matrix. \(\mathrm{N}(\mathrm{i})\) is denoted as a set of neighbors for node \(\mathrm{i}\). \(\mathrm{c}_{\mathrm{ij}}\) is represented as the normalization factor for edge \(\mathrm{ij}\). \(\sigma\) is an activation function (e.g., ReLU). This operation groups information from neighboring nodes to inform the representation of each node.  
Dimension: \(\mathrm{h}_{\mathrm{i}}^{(\mathrm{I})}\in\mathbb{R}^{d}\), where \(\mathrm{d}\) is the feature dimension.  
In Equation (8a), the term neighbors refers to the set of adjacent nodes used in the spatial graph convolution operation. The connectivity pattern of the graph representation of keypoints in the sign language recognition model determines the number of neighbors.  
The eight-connected neighborhood structure improves spatial representation, contextual awareness, and recognition accuracy by capturing diagonal relationships in hand motion. It enhances feature aggregation, leading to robust model performance and a well-defined graph convolution framework, ensuring a more detailed understanding of hand motion.  
For a node located at a position \((i,j)\), the neighboring nodes are defined as follows:  
\[N(i,j)=\{(i-1,j-1),(i-1,j),(i-1,j+1),(i,j-1),(i,j+1),(i+1,j-1),(i+1,j),(i+1,j+1)\} \tag{8b}\]  
In Equation (8b), \((i-1,j-1)\), \((i-1,j+1)\), \((i+1,j-1)\), and \((i+1,j+1)\) are diagonal neighbors. \((i-1,j)\), \((i,j-1)\), \((i+1,j)\), and \((i,j+1)\) are direct horizontal and vertical neighbors.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Self-Optimization Positioning Method for Base Stations  
According to the trilateration positioning algorithm, it is evident that the distance information from three known coordinate nodes to the target node is sufficient to determine the coordinates of the target node. However, as explored in Section 2, when the mobile  
Figure 3: Area division.  
terminal is too close to or too far from the base station node, the absolute error in the measured distance information is relatively large. According to the regional division in Section 3.2, the base station self-optimization is specifically divided into three modes. When calculating the positioning coordinates, the selection of the base station self-optimization mode is based on the positioning coordinates from the previous moment.
## 3 Skin Lesion Experiments and Analysis
### Results of Skin Lesion Segmentation  
The loss function of the FuzzyTransNet proposed in this paper consists of a binary cross-entropy loss employing the sigmoid activation function. In this paper, the evaluation metrics used on the ISIC dataset include the Dice coefficient, Jaccard Index (JI), and pixel-wise accuracy (ACC). A comparison with state-of-the-art methods on the ISIC2017 dataset is presented in Table 2. As shown in Table 2, our proposed algorithm achieved superior performance in terms of the JI, Dice coefficient, and ACC, surpassing U-Net by 3.6%, 3.9%, and 2.7%, respectively. It also outperformed DCL-PSI by 2.2%, 3.1%, and 1.4%, and Ms RED by 1.4%, 0.7%, and 1.4%. On the ISIC2018 dataset, which contains more complex  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Description** \\ \hline Dice & \(\frac{2|X|Y|}{|X|+|Y|}\) \\ mIoU & \(\frac{1}{K+1}\) & \(\frac{1}{K}\) \\ \(\frac{1}{K+1}\) & \(\frac{1}{K}\) & \(\frac{TP}{FN+TP+TP}\) \\ Jaccard Index (JI) & \(\frac{TP}{TP+FN+TP}\) \\ Accuracy (ACC) & \(\frac{TP+TN}{TP+IN+TP+FN}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics.  
Figure 4: (a) Original image. (b) Image after histogram equalization and wavelet transformation.  
skin lesions, the lesion boundaries are more ambiguous compared to the ISIC2016 and ISIC2017 datasets, making it challenging to separate the lesion area from the background. Consequently, most models exhibited lower performance on the ISIC2018 dataset compared to the ISIC2016 and ISIC2017 datasets. For instance, U-Net++ achieved metrics of 76.1%, 85.1%, and 92.5% on the ISIC2018 dataset, reflecting a decrease of 1.2%, 0.4%, and 1.3% compared to the ISIC2017 dataset. In contrast, FuzzyTransNet demonstrated favorable results in terms of the JI, Dice coefficient, and ACC, reaching 80.1%, 88.9%, and 96.1%, as shown in Table 3. In Table 3, it is evident that our proposed method outperformed the previous state-of-the-art approach, CA-Net, by 0.7%, 2.5%, and 1.3% in terms of the JI, Dice coefficient, and ACC. Furthermore, compared to using ResNet34 and ViT as the backbone in TransFuse, FuzzyTransNet exhibited an improvement of 1.0%, 2.0%, and 1.9%. However, on the ISIC2017 dataset, FuzzyTransNet only slightly surpassed TransFuse by 0.4%, 1.4%, and 1.1%. These findings highlight the greater advantages of FuzzyTransNet in the face of more complex lesion boundaries in medical images. This can be attributed to the well-designed data augmentation block and fuzzy block, which effectively preprocessed the experimental data.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2 Route Setup  
The route setup scheme is also application-specific. Generally, its classification is similar to the case of a CRAHN, as presented in Section 7 above. The main issues that the route setup must tackle include spectrum sensing, control signaling and channel decisions.
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
## 5 Conclusions  
Among the different strengthening techniques used to enhance the in-plane shear capacity of existing masonry walls, steel-reinforced plaster coating is very common, even though it is difficult to obtain a satisfying characterization of the method from both a theoretical and an experimental point of view in the scientific literature. The scope of this study was to numerically investigate the effect of major parameters on the in-plane behavior of brickwork masonry retrofitted with this technique. After a validation of the numerical model, which was performed by using the preliminary results of an experimental program ongoing at Politecnico di Milano, several nonlinear analyses were performed, and the main conclusions are as follows:  
* In URM walls, when the strength of the mortar joint is similar to that of the brick, the response of the wall is more ductile and the damage is localized along the main diagonal; for lower mortar strengths, the capacity is clearly reduced, and the damage spreads over a wider area.
* The main parameters that influence the capacity of RP-retrofitted walls are the compressive strength and the thickness of the plaster; however, especially in seismic areas, it is recommended to limit the thickness in order to avoid a significant increase in the seismic mass.  
Figure 30: Tensile damage pattern at the peak load for different plaster thicknesses: (a) 20 mm; (b) 30 mm; (c) 50 mm.  
Figure 29: Load–displacement curves for different plaster thicknesses.  
* The number of connectors does not significantly affect the maximum load, but it plays a fundamental role in preventing instability in the coating layers (due to plaster-masonry debonding) by reducing the amount of out-of-plane displacements.
* The use of steel reinforcement in the plaster results in the distribution of damage over a wider area; the use of fiber-reinforced concrete with very low amounts of steel fiber may be considered as an alternative to steel nets, with analogous capacity but more limited damage.  
Conceptualization, L.B. and S.C.; methodology, L.B., S.C. and M.S.; software, N.V. and M.S.; validation, N.V. and M.S.; formal analysis, N.V. and M.S.; investigation, N.V. and M.S.; resources, S.C. and L.B.; data curation, S.C.; writing--original draft preparation, M.S.; writing--review and editing, M.S., S.C. and L.B.; visualization, N.V. and M.S.; supervision, S.C. and L.B.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix A Kinetic Modeling
### Transference Function Model  
The transference function model (TFM) was applied to fit the experimental data of methane production during the above-mentioned BMP tests (Equation (1)). This model was successfully applied by several authors for biomethanization of different organic wastes [54; 55]:  
\[B=B_{max}(1-exp[-(R_{max}(t-\lambda))/B_{max}]) \tag{2}\]  
where \(B\) (mL CH\({}_{4}\)/g VS\({}_{\text{added}}\)) is the cumulative specific methane production, \(B_{max}\) (mL CH\({}_{4}\)/g VS\({}_{\text{added}}\)) is the ultimate methane production, \(R_{max}\) is the maximum methane production rate (mL CH\({}_{4}\)/g VS\({}_{\text{added}}\)-d), \(t\) (d) is the digestion time, and \(\lambda\) (d) is the lag time. In this model, it was assumed that the kinetics of methane production were proportional to the bacterial growth rate inside the batch reactors [54].  
Determination coefficients (R\({}^{2}\)) and standard errors of estimates (S.E.E.) were calculated to evaluate the goodness-of-fit and the accuracy of the results for both models. The kinetic parameters for each experiment and mathematical adjustment were determined numerically from the experimental data obtained by non-linear regression using the software Sigma-Plot v.11.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 1 Introduction
Furthermore, we assume that we can measure or reproduce the scattered field produced by some scatterer, that is the difference between the total field recorded in the presence of the defect and the incident field which corresponds to the healthy structure. Our approach is purely computational and therefore the response recordings are being produced numerically (the so-called, pseudo-experiment). Because of reflections from the boundaries, the scattered field is complicated and might appear at multiple arrival time peaks. Following work in the field of acoustics [27], the objective of this paper is to show that the time-reversal invariance can be exploited in the context of structural dynamics to accurately control wave propagation and to improve target detection through structures. In this work, we introduce the computational time reversal approaches for engineering structural problems focusing, but not confined, to frame structures. The investigation is purely numerical, utilizing the so-called pseudo-experiments. Nevertheless, in view of future experimental verification, the important influence of the signal-to-noise ratio was investigated for various numbers and placements of sensors. The paper is structured as follows. The theory of time reversal refocusing from a reciprocity theorem's perspective is presented in the next section. Section 3 presents the computational dynamics steps used for the implementation of the method, including the consideration of stochastic Gaussian noise and signal-to-error sensitivity analysis. The numerical results are presented in section four. The next session presents some investigations relevant to optimal sensor placement. The paper closes with conclusions and proposals for further research.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 3 Connectivity Models for Vehicular Communications  
In this section, the system model and vehicle connectivity model are presented followed by the strongly connected (STC) route selection.
## 3 Results
### Seasonal Cycle
  
**Figure 4.** Comparison of 2003 to 2005 averaged seasonal cycle of FPAR anomalies between remote sensing and CLM4 FPAR at (**a**) global level and at biome level: (**b**) evergreen needleleaf forest, (**c**) Evergreen broadleaf forest is compared based on FPAR absolute value due to its special seasonality, (**d**) deciduous needleleaf forest, (**e**) deciduous broadleaf forest, (**f**) mixed forests, (**g**) open shrublands, (**h**) woody savannas, (**i**) savannas, (**j**) grassland, (**k**) croplands. In order to identify the reasons for the discrepancies in FPAR seasonality, we compared the seasonality of CLM4 LAI and GIMMS LAI3g to various estimates of FPAR (Figure 5). CLM4 LAI lacks seasonal variations in comparison with GIMMS LAI3g at the global level (Figure 5(a)), which can explain why CLM4 FPAR has fewer seasonal variations. For savannas, significant disagreements in FPAR, between model and observations, are also shown to have similar discrepancies in LAI (Figure 5(b)). As asserted earlier, the FPAR seasonality could be considered as a manifestation of plant phenology. The comparisons between LAI and FPAR, from the model and observations, verify this assertion and, hence, suggest that the discrepancies in seasonality between CLM4 FPAR and observations are due to problems in the calculation of LAI.