esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### RQ3: How Feasible Is the System-Level Implementation of LinearTGAN in Practical Applications?  
The system-level feasibility of LinearTGAN is validated through a structured deployment framework, as illustrated in Figure 7. The model is initially trained using Python and the PyTorch deep learning framework, generating a pre-trained._pth_ file. For deployment, this file is converted to the Open Neural Network Exchange (ONNX) format to ensure compatibility with ONNX Runtime, enabling seamless integration into a C# environment. This conversion supports efficient inference execution within the software backend. The ONNX model is encrypted before deployment to comply with security standards in industrial applications, ensuring protection against unauthorized access and supporting secure inference execution. These processes demonstrate the practical applicability of LinearTGAN for real-time deployment in industrial systems.
# Novel Insights on Plant Extracts to Prevent and Treat Recurrent Urinary Tract Infections
## 6 Plants Commonly Used in the Treatment of rUTI
### Promising Medicinal Plants to Treat rUTI
#### 6.2.1 P. granatum
In contrast, the lower concentration of the extract (5 mg/mL) showed higher bactericidal activity than tetracycline [54]. Pomegranate arid extracts (30-90 \(\mu\)g/mL) from six different varieties from Turkey (Mediterranean region) have proven antimicrobial effects on other microorganisms [55], including _E. coli_ DM. The authors demonstrated that the antimicrobial effect of extracts is dependent on the plant cultivar, not on the phenolic content. The most acidic plant variety with the second-highest phenolic content from all varieties tested had the most significant inhibitory effect on _E. coli_[55]. Ellagic acid is a bioactive tannin from the methanolic extract of pomegranate that inhibits biofilm formation by bacteria _Staphylococcus aureus_ and _E. coli_[56]. Moreover, it has been proven that pomegranate polyphenols or the interactive effect of different compounds present in the extract may indirectly inhibit quorum-sensing, the signaling mechanism used by bacteria to communicate and form a biofilm [43]. Because of the health benefits linked to its polyphenol content, pomegranate extract pills/juice are becoming increasingly popular. They have been acknowledged as an effective plant extract for urinary tract infections [57]. Still, clinical trials that use only pomegranate extracts to prove their efficiency in UTI prevention or treatment are missing. Polyphenols, ellagic acid, anthocyanins, and glycosides are the main bioactive substances that support the use of pomegranate in rUTI prophylaxis, alone or in combination with antibiotic therapy.
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 3 Results and Discussion
### Cone Calorimetry Experimental Results and Modeling  
The results of the cone calorimetry experiments performed on the Tuff-R foam core samples at 50 kW m\({}^{-2}\) of set radiant heat flux are given in Figure 6. The mean integral HRR and integral MLR, both normalized by the initial front surface area, are presented as a function of time. The data are presented in an integral form to help reveal the impact of the blowing agent. The error bars were calculated from the scatter of the data as two standard deviations of the mean. The cone calorimetry modeling results are also shown on the figure (as dashed lines). The model systematically underpredicts the integral HRR and, to a lesser degree, the integral MLR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & \(\epsilon\) & \(\alpha\) & \(\rho\) & \(k\) \\  & **(-)** & **(m\({}^{-1}\))** & **(kg m\({}^{-3}\))** & **(W m\({}^{-1}\) K\({}^{-1}\))** \\ \hline Glass fiber & 0.81 & 4160 & 2600 & 0.36 \\ PIR & 0.95 & 3200 & 31.4 & 0.05 \\ PIR\_Int\_1 & 0.95 & 3200 & 15 & 0.09 \\ PIR\_Int\_2 & 0.95 & 3200 & 30 & 0.04 \\ PIR\_Int\_3 & 0.95 & 3200 & 18 & 0.08 \\ PIR\_Int\_4 & 0.95 & 3200 & 17 & 0.06 \\ PIR\_Int\_5 & 0.95 & 3200 & 16 & 0.14 \\ PIR\_Int\_6 & 0.95 & 3200 & 14 & 0.15 \\ PIR\_Int\_7 & 0.95 & 3200 & 14 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal transport properties of condensed-phase components of the PIR foam.  
Figure 6: Mean experimental results and model predictions for the cone calorimetry tests performed on the PIR foam at 50 kW m\({}^{-2}\) of set radiant heat flux.  
To determine whether the blowing agent is responsible for the observed discrepancies, a blowing agent component was added to the PIR foam pyrolysis model. This component was designated as a gas and was added in the amount of 7 wt.%, which was the blowing agent content reported in a safety data sheet provided by the manufacturer [33]. According to this data sheet, the blowing agent was iso-pentane. Therefore, the heat capacity and heat of combustion of the blowing agent component were set to the corresponding properties of iso-pentane [42], 1667 J kg\({}^{-1}\) K\({}^{-1}\) and 4.95 \(\times\) 10\({}^{7}\) J kg\({}^{-1}\), respectively. In addition, the PIR component was reset within the model to be impenetrable to gas flow (the gas transfer coefficient of 1.0 \(\times\) 10\({}^{-30}\) m\({}^{2}\) s\({}^{-1}\)), which made sure that the blowing agent component was retained within the foam matrix until the PIR component decomposed to PIR_Int_1 through Reaction 1 (see Table 1). The results of the simulations obtained with the version of the pyrolysis model including the blowing agent component are shown in Figure 6 (as solid lines). These results capture both the experimental integral HRR and the integral MLR significantly better than the simulation results obtained with the model without the blowing agent.
# Quick Fabrication VCSELs for Characterisation of Epitaxial Material
## 3 Results and Discussion
### Threshold Current
Figure 4: Lowest measured threshold currents as a function of active area. Figure 3: Near-field images of (**a**) pumped QF VCSEL, showing light emission along the bridge due to leakage current; (**b**) pumped standard VCSEL, for comparison. We also considered the relative change in performance for QF bridge-mesa devices without an oxide aperture, resulting in a 54% reduction in fabrication time relative to the standard VCSELs. For these devices, there is no insulating oxide layer to channel the injected current and, as such, the leakage current is greater than that of oxidised QF devices. Threshold currents range from 4.3 mA for a 12 \(\upmu\)m mesa device, to 11.4 mA for a 32 \(\upmu\)m mesa. By, again, comparing the offset in intercepts of the linear plots of threshold current as a function of active area in Figure 4, we find the increase in leakage current to be a further 1.9 \(\pm\) 0.02 mA relative to an oxidised QF device, where here the active area for unoxidised QF devices is defined as the cross-sectional area of the mesa. Relative to the oxide aperture diameter, the uncertainty in the mesa diameter of the unoxidised devices is negligible, hence we omitted error bars in the plot for these devices. Finally, we also considered the impact of substrate lapping on the performance of the unoxidised bridge-mesa structures, with the processing time for these devices being reduced by 63% compared to the standard VCSELs. Applying the same treatment as above, we find that lapping does not have a significant impact on threshold current. Values are seen to range from 5.5 mA, for a 12 \(\upmu\)m mesa, up to 11.3 mA for a 32 \(\upmu\)m mesa, which very closely matches the values for the lapped structures. Additionally, in Figure 4, the dependence on active area is seen to be equivalent to the other structures and there is no offset in intercept relative to the lapped material. This is expected given that the bridge leakage current is independent of the substrate thickness. Hence, we find that the threshold current of the QF devices is degraded relative to standard structures and, as such, the applicability of this performance characteristic to the application of QF VCSELs for epi-material characterisation in a manufacturing setting is limited by the assumption of a linear offset in threshold behaviour. For greater confidence in the use of QF VCSELs to predict the performance of standard VCSELs, this observed degradation in threshold behaviour should be minimised, however, we are still able to assess device yield and material uniformity with the current form of the QF structures, which will be valuable in manufacturing.
## 3 Reliability Analysis Method Based on PSO-BPNN  
### Back Propagation Neural Network  
The BPNN is a multilayer, fully connected feedforward artificial neural network trained using the error back propagation algorithm. The error backpropagation algorithm used in the BPNN is a supervised neural network training algorithm characterized by its simple structure, strong nonlinear approximation capability, and self-learning and self-adaptive features [28, 29]. A large amount of data generated through ANSYS 2022R1 finite element analysis software can be used as sample data. The error back propagation algorithm is employed to calculate the weights of the neuron connections, thereby establishing an optimized neural network model capable of accurately representing the input-output relationships.  
The neural network structure is designed with \(n\) input nodes, \(l\) hidden layer nodes, and \(m\) output nodes, as shown in Figure 1. A BPNN with one hidden layer and a continuous activation function can theoretically approximate any nonlinear continuous function. The hidden layer employs the sigmoid activation function \"tansig\", while the output layer uses the linear activation function \"purelin\". The network's learning algorithm is based on the Levenberg-Marquardt algorithm.  
The BPNN is trained using a large number of samples \((X,Y)\), with the weights \((W_{1},W_{2})\) and biases \((B_{1},B_{2})\) continuously adjusted via the network's adaptive algorithm. The training process aims to minimize the mean squared error (MSE) of the objective function, thereby accurately capturing the input-output relationship. However, a major limitation of the BPNN is its susceptibility to becoming trapped in local extrema. This limitation arises from the fact that no additional parameters are introduced during the training process, and the algorithm solely relies on adjusting the initial weights and biases based on the training samples. As a  
Figure 1: Structure diagram of the BPNN.  
result, the performance of the model is highly sensitive to the initial values of these parameters. If the weights and biases are initialized randomly, there is a considerable risk that the MSE optimization process will converge to a local extremum, leading to suboptimal weight and bias values. Consequently, this issue may cause the trained network to produce inaccurate predictions, negatively impacting its reliability and generalization capability.
###### Abstract  
Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edible, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned \"Mediterranean diet\", and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.  
electronic nose; food industry sensors; information technology; quality control; sensory panel +
Footnote †: journal: Applied Sciences
## 4 Performance Analysis and Simulation Outcomes
### Simulation Setup
* Underlaying D2D without IRS without RL (scheme-2): A traditional cellular system underlaying the D2D network without IRS is considered scheme-2, where a two-stage approach is proposed to solve the optimization problem instead of RL [6].
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
#### 2.3.2 Optimizing the Navigation Filter  
In the field of VT, much of the innovation in filter design has focused on enhancing navigation filters within centralized VT architectures. Researchers such as [PERSON], [PERSON], and [PERSON] established the EKF as the mathematical foundation for navigation filters. They derived the principles of EKF mathematically and demonstrated its superior performance [31; 33; 105], providing a solid theoretical foundation for further advancements in navigation filters.  
To address stability issues in VT, [PERSON] introduced a robust vector tracking loop (RVTL) based on a diagonal weighting matrix. This method effectively resolved stability problems by replacing the traditional weighting matrix [40]. [PERSON] further enhanced the EKF algorithm by integrating an adaptive covariance matrix, which improved tracking performance [35].  
To mitigate the truncation errors introduced by EKF in nonlinear systems, [PERSON] proposed the Iterative EKF (IEKF). This approach reduces bias and estimation errors through iterative operations. By combining it with the Adaptive EKF (AEKF), the Adaptive Iterative EKF (AIEKF) was introduced, resulting in significant improvements in tracking performance [34]. [PERSON] achieved adaptive EKF VT by adjusting the measurement noise covariance matrix and process noise covariance matrix based on the signal's C/N0 ratio and the innovation sequence [36]. [PERSON] further refined the system by fine-tuning all adjustable parameters using a fuzzy method, achieving more accurate adaptive VT [37].  
Alternatively, [PERSON] employed the Unscented Kalman Filter (UKF) to replace the EKF, improving positioning accuracy by using pseudo-range and pseudo-range rate errors as measurement parameters [38]. [PERSON] developed a square-root UKF (SRUKF) tracking loop that integrates square-root filtering with an adaptive mechanism. This mechanism dynamically adjusts filter parameters based on signal noise and Doppler feedback errors [38].  
In addition, some researchers have explored combining Graph Optimization (GO) techniques with VT to optimize navigation solution estimation. This approach incorporates NLOS-induced distortions into the state vectors for real-time estimation. Compared to the KF-VT approach, the GO-VT framework is more flexible in handling state vector variations, using state transformations and measurement models as constraints to optimize the estimates [106; 107].  
Currently, most innovations in navigation filters are focused on the EKF algorithm. While several optimization strategies have been proposed, challenges remain in tracking in complex environments. Therefore, further research is needed to optimize navigation filters for improved performance in such environments.  
Table 5 summarizes the major optimization techniques for navigation filters in vector tracking systems, highlighting their benefits, limitations, and representative studies.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 1 Introduction
A peridynamic beam theory proposed in [18] is derived from three-dimensional PD equations and cross-section approximations for the deformation vector. The governing peridynamic equations of motion are of integro-differential type, unlike CCM and strain gradient theories, where partial differential equations are introduced. To solve PD equations numerically, various approaches such as the mesh-free method [19] and discontinuous finite element formulation [20] have been proposed. Various numerical solutions are presented to demonstrate the ability of PD to represent complex phenomena such as crack initiation [8], delamination [21], and propagation of cracks [22; 23]. However, only several closed-form analytical solutions to PD integral equations are derived. Examples include solutions for straight axially loaded rods [24; 25; 26]. For initially straight beams with specific boundary constraints, series solutions to PD equations are presented in [27]. Series solutions for rectangular plates under special boundary conditions are given in [28]. General solutions to peristatics and peridynamics for three-dimensional media are presented in [29; 30]. Analytical solutions explicitly illustrate the effect of non-locality on the deformation state. In particular, the PD horizon size enters the results for the beam deformations and can be varied to analyze the convergence to the classical solution. This gives an advantage over numerical methods, where the horizon size is related to the size of the spatial discretization and can be only varied in a small range. Furthermore, different types of constraints can be introduced and analyzed, as they affect the final results, by applying the analytical solutions. Furthermore, closed-form analytical solutions are useful as reference solutions in order to validate the numerical methods developed to solve PD equations, such as the mesh-free method, and to analyze the convergence of numerical results. The aim of this study is to derive a family of novel closed-form analytical solutions to both peristatic and peridynamic equations of shear-rigid beams. In addition to results presented in [27] for simply supported cantilever beams, various boundary conditions, including clamped-simply supported, clamped-clamped, simply supported-roller-clamped, and clamped-roller-clamped beams, will be considered. Furthermore, novel closed-form solutions for different types of loadings, including uniformly distributed load, concentrated force, and concentrated moment, will be introduced. The results will be validated by comparing the derived solutions against available solutions to the classical Bernoulli-Euler beam theory (CBT).
## 3 Results
### Modeling of Daily Net Ecosystem Exchange  
Table 8 summarizes the accuracy of the applied models for forecasting daily NEE in the testing period. It can be clearly seen from the table that the models with full input variables (\(T_{a}\), \(R_{n}\), \(R_{h}\), \(T_{s}\)) generate the best accuracy for each method and site. The four machine learning models with these variables consistently perform better than the corresponding MLR models at the three sites. At CA-Obs site, the ANN model generally gives the best precision among the five models. The SVM, GRNN and ANFIS models yield similar accuracy in terms of \(R^{2}\), NSE and RMSE. Unlike CA-Obs site, the ANFIS model at CA-Oas site is superior to the other models in terms of \(R^{2}\), NSE and RMSE. In addition, the ANN and SVM models have similar accuracy in predicting the daily NEE and they both outperform the GRNN model. At CA-Gro site, the modeling performance among the ANN, GRNN and ANFIS models is nearly consistent in the testing period. These three models are superior  
Figure 4: Eddy covariance measured and model simulated daily ecosystem respiration (\(R\)) in the whole period in the three boreal forest stands. (**a**) For CA-Obs stand using ANN model; (**b**) for CA-Oas stand using ANFIS model; and (**c**) for CA-Gro stand using GRNN model. to the SVM and MLR models. As shown in Table 5, regarding the prediction of daily NEE, the ANN, ANFIS and SVM models achieve similar mean accuracy for the three sites. However, the MLR model gives the worst accuracy. Furthermore, it can be seen from Table 6 that the models for daily NEE prediction provide similar performance for CA-Obs and CA-Oas sites according to \(R^{2}\) and NSE criteria. Generally, the applied models for these two sites perform better than those for CA-Gro site. The measured and predicted values of daily NEE using the five models for the three stands are compared in Figures 5 and 6. It is apparent from Figure 5 that the fit lines of the applied models during the testing period at CA-Obs and CA-Oas sites are closer to the ideal fit lines (\(\mathbf{y}~{}=~{}\mathbf{x}\)) than those obtained at CA-Gro site. Meanwhile, the mean values of \(R^{2}\) and NSE generated by the five models at the former two sites are substantially higher than those at the latter site (Table 6). Additionally, all the models at the CA-Gro site have more scattered estimates in comparison with the other two sites.
## 3 Materials and Methods
### Modeling Uncertainty by Correlated Random-Fuzzy Geometric Brownian Motion (GBM)
Therefore, Equation (3) can be expressed as the following forecasting model for parameter \(q_{i}\)  
\[\widetilde{q}_{i,t}=\widetilde{q}_{i,t-1}exp[(\widetilde{\mu}_{i}-\frac{ \widetilde{\sigma}_{i}}{2})\Delta t+\hat{Z}_{i,t}\widetilde{\sigma}_{i} \sqrt{\Delta t}],\;i\in I_{k}. \tag{4}\]  
Here, it is assumed that \(\widetilde{\mu}_{i}\) and \(\widetilde{\sigma}_{i}\) are possibilistic parameters, \(\hat{Z}_{i,t}\) is a correlated random normal variate and \(\widetilde{q}_{t}\) is a random-fuzzy variable. To define \(\hat{Z}_{i,t}\), the Cholesky decomposition may be utilized to decompose the correlation matrix (as described in [45]). Possibilistic distribution may be viewed as a nested set of intervals (called \(\alpha\)-_cut_)  
\[\widetilde{\mu}_{a}=\Big{\{}x\in R\Big{|}\widetilde{\mu}_{a}\geq\alpha\Big{\}},0\leq\alpha\leq 1. \tag{5}\]  
Interdependence between \(\widetilde{\mu}_{i}\) and \(\widetilde{\mu}_{j}\), \(i,j\in I_{k}\) can be modeled by interval regression [40]. Below, we present the equations that define the relationships between these variables, using the notation of \(\alpha\)-levels [38]  
\[\widetilde{\mu}_{i,a}\geq\inf\!\left(\mathrm{a}_{1}^{ij}\right)\times \widetilde{\mu}_{j,a}+\inf\!\left(\mathrm{a}_{2}^{ij}\right),\;i,j\in I_{k}, \tag{6}\]  
\[\widetilde{\mu}_{i,a}\leq\sup\!\left(\mathrm{a}_{1}^{ij}\right)\times \widetilde{\mu}_{j,a}+\sup\!\left(\mathrm{a}_{2}^{ij}\right),\;i,j\in I_{k}, \tag{7}\]  
where \(a_{1}^{ij}\), \(a_{2}^{ij}\) represent the coefficients of the interval regression equations, which are pivotal in establishing the dependencies between parameters \(\widetilde{\mu}_{i}\) and \(\widetilde{\mu}_{j}\). These coefficients \(a_{1}^{i,j}\) and \(a_{2}^{ij}\) can be ascertained using the method proposed by [PERSON] and [PERSON] (crisp input-crisp output variant) [46]. Interval regression, applied here to quantify the interdependence among imprecise drift parameters, yields coefficient bands rather than point slopes (Equations (6) and (7), and results in Tables 3 and 4). The lower edge of each band reflects the most conservative transmission of an economic shock--e.g., a sudden rise in scrap-steel prices--into the revenue stream, while the upper edge captures the most favorable response. Interpreted as interval elasticities, these bands can be propagated directly through the hybrid simulation. Moreover, because the bands are fitted simultaneously for mutually dependent parameters, the method curbs the arbitrariness of expert elicitation: permissible fuzzy drifts are no longer assigned in isolation but must satisfy data-driven dependence constraints, ensuring internal consistency across correlated inputs and sharpening subsequent valuation results.