esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed System  
The flowchart of the proposed system is presented in Figure 1. The system expects a reference article as input. Then, it retrieves a set of target articles that refer to the same topic/event from a set of sources (e.g., newspaper websites) and outputs a summary of the information, which is contained in the target articles and is considered to not be very similar to the one contained in the reference article. We attempt to capture the desires of the typical news readers who read an article from their favorite newspaper websites but also wish to acquire opinions on the same event from other newspapers, possibly of different political connotations. The system consists of four main steps: target article retrieval, sentence classification, target sentence reduction, and target sentence summarization. The core of the system is the algorithm used in the 'Sentence Classification' module, which allows labeling the sentences of the target articles into three classes, namely similar sentence (SS), different sentence (DS), and very different sentence (VDS),based on the values of similarity with the reference article computed exploiting sentence embeddings extracted by a Sentence-BERT pre-trained model. We will detail this algorithm in Section 3.2.  
Before performing sentence classification, several pre-processing operations are applied to the articles. In particular, we perform standard text annotation actions, such as sentence splitting and named entity recognition. We also extract keywords using KeyBERT. Then, we use a Sentence-BERT model to obtain the representation for each sentence. Finally, we perform the target sentence classification: each sentence is associated with one of three similarity classes.  
Concerning the target sentence reduction, we filter out all the target sentences that contain redundant information with respect to the target articles (i.e., those sentences classified as different from all the reference sentences, but containing information redundant with other target articles). This aspect is described in detail in Section 3.3.  
Finally, for the target sentence summarization, we first choose the sentences to be shown to the user from each target article. Then, we exploit an abstractive text summarization algorithm to generate a summary of the text resulting from the union of these sentences in order to provide an easy-to-read output for the user. We detail the target sentence summarization in Section 3.4.
# Photocatalytic Recycled Mortars: Circular Economy as a Solution for Decontamination
## 3 Experimental Program and Methods
### Mortar Characterization  
The consistency of the mortar families in the fresh state was measured in accordance with EN 1015-3. The hardened state properties analyzed in the mortar families were compressive and flexural strength (EN 1015-11), water absorption by capillarity (EN 1015-18), water absorption capacity, bulk and skeletal density and open porosity for water (Spanish Standard UNE 8398), carbonation depth (EN 13295), and photocatalytic activity (Spanish Standard UNE 83321 EX). These properties were evaluated after 28 days of curing time.  
In addition, X-ray diffraction (XRD) analysis was carried out to identify the main crystalline mineral components. For that purpose, a piece of the central part of each mortar specimen was crushed and sieved through a 0.125 mm sieve. The machine used for this technique was a Bruker D8 Discover A25 with Cu-K\(\alpha\) radiation, and the goniometric exploration used was swept from 5\({}^{\circ}\) to 80\({}^{\circ}\) (20\({}^{\circ}\)) at a speed of 0.0142\({}^{\circ}\) min\({}^{-1}\). The Joint Committee on Powder Diffraction Standards database was used to identify the phases formed in the mortars [52]. A JEOL JMS-7800 scanning electron microscope (SEM) was used to determine the mortar's chemical composition.  
For the carbonation depth test, the mortar specimens were introduced into a carbonation chamber under conditions of relative humidity of 55-65%, temperature of \(23\pm 3\)\({}^{\circ}\)C, and CO\({}_{2}\) concentration of \(5\%\pm 0.1\%\). After 56 days of CO\({}_{2}\) exposure, a phenolphthalein pH indicator spray was used on the mortar fracture surface. The noncarbonated mortar surface showed a purple color due to its high alkaline pH. The carbonation depth was measured on the mortar fracture surface from the edge of the specimen to the purple area.  
Despite the successful application of TiO\({}_{2}\) photocatalysis to cement-based materials, an ideal method to determine the photocatalytic activity is still not available. The experimental conditions and data treatment differ in many aspects (light source, UV intensity, temperature, humidity, flow rate, characteristics of test samples, contaminant analyzed), even leading to noncomparable results [42]. The experimental method proposed by Spanish standard UNE 83321 EX was used in this research. This was aimed at evaluating the degradation of nitrogen oxide, in the gas phase, of inorganic photocatalytic materials contained in cement concretes by a continuous flow test method. For the measurements and calculations required in this test, the concentration of nitrogen oxides (NO\({}_{\rm x}\)) was defined as the stoichiometric sum of nitrogen oxide (NO) and nitrogen dioxide (NO\({}_{2}\)).  
Likewise, a sample was extracted from the center of each prismatic specimen mortar to analyze its photocatalytic power according to the standardized methodology through the reduction capacity of NOx. The test was carried out on the photocatalytic mortar family in addition to the reference mortar (M0).
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results
### Age Biased German Dataset
#### 4.2.2 Evaluation Fairness Metrics  
In general, to access the performance of the fair model which indicates no discrimination against the bias or protected attribute, we report widely-used fairness metrics for bias mitigation methods. These protected attributes include gender, age, color, race, religion, etc. In this study, we will focus on metrics to evaluate group fairness based on age and gender. The fair model will provide predictions that are not influenced by protected attributes. Four metrics have been selected to evaluate the bias mitigation ability of the proposed approach since testing datasets of age-biased German and gender-biased adult datasets are not unbiased and contain age and gender bias, respectively.  
We use the Demographic Parity or Statistical Parity Difference (SPD) fairness metric [30] to check if decisions are independent of protected attributes. Disparate Impact (DI) [31] is the same as SPD but formulated as proportion, respectively. SPD and DI are given as:  
\[\text{SPD }=\text{P}(\hat{Y}=1|B=0)-\text{P}(\hat{Y}=1|B=1) \tag{5}\]  
\[\text{DI }=\frac{\text{P}(\hat{Y}=1|B=1)}{\text{P}(\hat{Y}=1|B=0)} \tag{6}\]  
The Equality of Odds Difference (EOD) metric [32] is used for separation, i.e., to check the independence of the decision and protected attribute separately for individuals. EOD measures the difference in true positive rates for protected and unprotected groups whereas Average Odds Difference (AOD) [32] measures the difference between the true-positive rates as well as false-positive rates for each group. EOD and AOD are formulated as:  
\[\text{EOD }=\text{ TPR}_{B=0}-\text{TPR}_{B=1} \tag{7}\]  
\[\text{AOD }=0.5*[(\text{FPR}_{B=0}-\text{FPR}_{B=1})+(\text{TPR}_{B=0}-\text{ TPR}_{B=1})] \tag{8}\]  
In the above Equations (5)-(8), \(B\) is bias variable which can be age or gender and is 0 when it represents a privileged group and 1 when it represents an unprivileged group. \(P\) is the classification probability whereas \(\hat{Y}\) is model prediction. FPR and TPR represent a false positive rate and true positive rate, respectively. Lower values of SPD, EOD, and AOD indicate less bias, and higher values of DI show more fairness and less bias. Balanced accuracy is calculated as the average of sensitivity and specificity.
## 4 Experimental Scenarios
### Scenario 2: Testing Material Handling Systems  
In the second scenario, the experiments were carried out to compare centralized and heterarchical control of the material handling systems, used for the assembly process of the finished product reported in Figure 3. In this scenario, the final product is made in the EDF laboratory of the Technische Universitat at Chemnitz (Figure 4). The EDF is divided into two sections: the Experimental Center (including all the facilities and their components) and the Digital Center (including all the components related to factory planning and visualization). The material handling systems available in the EDF to move materials and components within the manufacturing plant are the electric monorail (EHB), the AGV, and the roller conveyor (RC).
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 5 Simulation Results and Analysis
### Sound Field Error Estimation over The Region  
To evaluate the reconstructed sound field over time, we calculate the instantaneous average squared spatial error over time, which is defined by  
\[e(\
u)\triangleq\frac{\sum_{\Omega}\|S_{r}(x,\
u)-S_{d}(x,\
u)\|^{2}}{\sum_{ \Omega}}. \tag{33}\]  
Figure 10 shows how the error fluctuates with time in a tolerable range (no more than \(5\times 10^{-4}\)) with a 900 Hz tone and a 1072 Hz tone. We have already observed in Figure 8 that the error of the sound pressure at a point of interest is proportional to the desired sound pressure. We observe the same trend when we evaluate the error over the region that the error increases when the sound field inside the region of interest is at peak amplitude. We also observe in Figure 10 that the error with 1072 Hz signal is higher than 900 Hz signal. This is due to that there is a Bessel zero of the zeroth order (\(j_{0}(kr)\)) at 1072 Hz in the proposed spatial ANC system. Hence, the amplitude of \(a_{00}(\
u)\) is suppressed by the proposed method, leading to a higher error in reconstructing the sound field.  
Figure 10: Instantaneous region averaged squared spatial error of the proposed method for sound field reconstruction over space at 900 Hz and 1072 Hz (Bessel zero).  
Figure 9: Comparison between (**a**) reconstructed sound field and (**b**) desired sound field at the horizontal plane with a height of z = 0.02 m.
## 1 Introduction
Different research groups have developed software tools for modeling and analyzing different \(PN\) types. For instance, GreatSPN [23] and PIPE2 [24] are well-known tools for performance analysis in generalized stochastic \(PNs\). TimeNET [25] allows the analysis of non-Markovian stochastic \(PNs\). Similarly, GPenSIM [26] can be used for performance evaluation in event graphs. CPN Tools [27] and Access/CPN [28] were proposed for the modeling, simulation, and analysis of colored \(PNs\). Continuous and hybrid \(PNs\) can be simulated in HYPENS [29] and SimHPN [30]. PNetLab [31] was developed to model and simulate timed/untimed \(PNs\) and colored \(PNs\), allowing the possibility to simulate \(PNs\) under control, where the controller is a subroutine that provides a conflict resolution policy. In [32], a combination of the graphical editor Petri Net Editor and the toolbox Petri Net Engine is used to implement a \(PN\) conflict resolution policy into a microcontroller. All of these tools allow us to graphically define a \(PN\) system, simulate the system, and perform particular types of analysis (mainly performance analysis and structural analysis). Nevertheless, despite the large amount of \(PN\) software, none of the existing tools allow us to define \(IPN\) with place and transition labels, and to synthesize regulation controllers. Moreover, to the best of our knowledge, none of the tools allow us to synchronize \(PN\) simulations with external software/hardware via standard protocols, and to automatically generate PLC code. Furthermore, the implementation of \(RCPetri\) on MATLAB(r) will allow other researchers to use it for the analysis and application of other algorithms, such as GMEC control, \(PN\) observers, and \(PN\) diagnoses, among others. This paper is organized as follows: Section 2 presents basic definitions on \(IPN\). Section 3 explains the regulation control framework. Section 4 introduces the \(RCPetri\) app with its main features, and the algorithms implemented for the main functionalities. Section 5 presents the application of \(RCPetri\) in the three examples previously mentioned. Section 6 summarizes the conclusions.
###### Abstract  
The research work hereby presented, emerges from the urge to answer the well-known question of how the uncertainty of intermittent renewable sources affects the performance of a microgrid and how could we deal with it. More specifically, we want to evaluate what could be the impact in performance of a microgrid that is intended to serve a smart-building (powered by photovoltaic panels and with battery energy storage), when the uncertainty of the photovoltaic-production forecasts is considered in the energy management process through the use of quantile forecasts. For this, several objectives (or services) are targeted based in a two-step (double-objective) energy management framework, which combines optimization-based and rule-based algorithms. The performance is evaluated based on some particular services, namely: energy cost, carbon footprint, grid peak power, and grid commitment; with the latter being a novel service proposed in the domain of microgrids. Simulations are performed while using data of a study-case microgrid (Drahi-Xnovation center, Ecole Polytechnique, France). The use of quantile forecasts (obtained with an analog-ensemble method) is tested as a mean to deal with (i.e., decrease) the uncertainty of the solar PV production. The proposed energy management framework is compared with basic reference strategies and the results show the superior performance of the former in almost all of the proposed services and forecasting scenarios. The fact of how optimizing for some services during the scheduling (i.e., grid commitment) can be highly detrimental for the performance of the non-targeted services, is an interesting finding of this work. The differences regarding the optimal forecasting eccentricity (i.e., the forecasting quantile) required when optimizing for the different services and seasons of the year is also considered an important conclusion of the study. This fact highlights the usefulness of the quantile forecasting approach in an energy management system, as a tool to deal with the intrinsic uncertainty of PV power production.  
microgrid; energy-management-system; quantile-forecasts; smart-building 2020, 135882; doi:10.3390/en13225882
# Shear Lag Effect on Box Steel Beams with Wide Curved Flanges
## 5 Influence of Flange Curvature on Shear Lag Effect  
On the following diagrams (Figures 15-17), the graphical representation of the result sets can be found. The results are shown as curves (polygon curves connecting discrete result points) on separate diagrams for each section A-A, B-B, and C-C. With variable \(\kappa\) [/] on the horizontal abscissa axis, each b/2R ratio is represented by one curve which shows the correlation of \(\kappa\) with vertical ordinate--effective width (angle) factor \(\beta\) [/]. Width b\({}_{0}\) (or b/2) to span \(\rm L_{e}\) ratio \(\kappa\) [/] is calculated separately for the field (A-A and B-B) and support area (C-C) cross-sections using Formulas (10) and (11) provided by Eurocode [2]:  
\[\rm L_{e} =0.85\times L_{1}\ and\ L_{e} =0.70\times(L_{1}+L_{2})\ -\ for\ s\rm sagging\ bending \tag{10}\] \[\rm L_{e} =0.25\times(L_{1}+L_{2})\ -\ for\ s\rm hogging\ bending \tag{11}\]  
Figure 15: Effective width in the middle of the first span for different flange curvatures.  
On each diagram blue smooth line represents the analytical correlation between \(\kappa\) and \(\beta\) using expressions (12)-(16) provided by [2]  
\[\text{For }\kappa\ \leq\ 0.02;\ \beta=1,0 \tag{12}\]  
\[\text{For }0.02\ <\ \kappa\ \leq\ 0.70;\ \beta=\beta_{1}=\frac{1}{1+6.4\kappa^{2} }\ -\ \text{for sagging bending} \tag{13}\]  
\[\beta=\beta_{2}=\frac{1}{1+6.0\Big{(}\kappa-\frac{1}{2500\kappa}\Big{)}+1.6 \kappa^{2}}\ -\ \text{for hogging bending} \tag{14}\]  
\[\text{For }\kappa\ >\ 0.70;\ \beta=\beta_{1}=\frac{1}{5.9\kappa}\ -\ \text{for sagging bending} \tag{15}\]  
Figure 16: Effective width in the middle of the second span for different flange curvatures.  
Figure 17: Effective width in the middle of the second support for different flange curvatures.  
\[\beta=\beta_{1}=\frac{1}{8.6\kappa}\ -\ \text{for hogging bending} \tag{16}\]  
In Figures 15 and 16, there is minimal deviation in the values of the effective width factor \(\beta\) from the curve given by the expressions in the Eurocode for flanges with a small b/2R ratio--practically flat flanges. It is also evident that the gradual rounding of the flange and the distancing of the fibers from the neutral axis (which results in increased stress according to the Euler-Bernoulli hypothesis) to a certain extent reduces the effect of the cross-sectional reduction caused by the shear deformability of the flange. It was observed that this effect is less pronounced in the case of the middle support where the bending moment is of negative sign with the presence of a concentrated support reaction (large shear force).
# Effective Modal Volume in Nanoscale Photonic and Plasmonic Near-Infrared Resonant Cavities
## 3 Results
### Coaxial Resonator with AZO  
A cross section of the AZO-cladded coaxial cavity is shown in Figure 4a, along with the simulation space in Figure 4b. It is evident that the mode is rather poorly confined to the active region with a substantial portion of modal energy escaping the cavity. Hence, this cavity functions more as an optical antenna with a quality factor on the order of 10, one and two orders of magnitude smaller than the coaxial cavity with Ag and the metallo-dielectric cavity, respectively.  
The evaluated effective modal volumes of the AZO-coaxial cavity are shown in Figure 5a, for various simulation conditions. These conditions include the spherical air domain of varying \(d_{\text{air}}\) values. \(V_{\textit{eff},1}\), \(V_{\textit{eff},2}\), and \(V_{\textit{eff},3}\) yield diverging and identical results, increasing nonlinearly as \(d_{\text{air}}\) increases from 0.5\(\lambda\) to 2.0\(\lambda\), thus these three \(V_{\textit{eff}}\) do not have a well-defined value. On the contrary, \(V_{\textit{eff},A}\) and \(V_{\textit{eff},5}\) show converged \(V_{\textit{eff}}\) values. The converged effective modal volume values are attributed to the surface integration term in Equation (10) and the PML integration term in Equation (14), which compensate the diverging volume integration term.  
Figure 4: (a) Closeup of the mode of the coaxial cavity with AZO (aluminum-doped zinc oxide) as cladding material (dimensions of all material regions are identical to coaxial cavity with Ag). (b) Side view of entire spherical simulation space. (Q = 6.6884 at 2.2991e14 Hz (\(\lambda_{0}\) = 1304.0 nm).)  
Figure 3: Effective modal volume of the coaxial cavity using Ag as metal for various simulation conditions, using (a) fine meshing and (b) normal meshing. \(\lambda\) = 1590 nm, which corresponds to the upper boundary of emission bandwidth of InGaAsP. Note that the traces for \(V_{\textit{eff},A}\) and \(V_{\textit{eff},5}\) are nearly identical.  
While AZO exhibits an attenuation coefficient one to two orders of magnitude smaller than that of Ag over the spectral range of emission of InGaAsP (see Figure 5b), the absolute value of the real part of the AZO permittivity is close to unity, which leads to more of the mode residing in AZO and thus leading to a spatially divergent field because of the impedance match with the surrounding air domains. Therefore, it becomes problematic to define the quasi-normal mode (QNM) norm with some integral of electromagnetic field over the entire space. In this sense, \(V_{\mathit{eff,A}}\) and \(V_{\mathit{eff,5}}\) provide a way for the normalization of QNM of low-Q cavities or plasmonic resonators, i.e., non-Hermitian systems.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Isotropic Eavesdropper
#### 5.1.1 Eavesdropping Probability in IUSNs  
Figure 7 shows the simulation results of eavesdropping probability in IUSNs with an isotropic eavesdropper. We assign the acoustic signal frequency \(f\) with \(10\) kHz, \(30\) kHz and \(90\) kHz under  
Figure 6: Eavesdropping region is a circle with radius \(d_{\max}\).  
different values of spreading \(k\) and wind speed \(w\) and we fix the reference transmission power \(P_{t}^{i}=100\,\mathrm{dB}\). In particular, the analytical results are shown as curves and the simulation results are shown as markers. We can see from Figure 7 that there is an excellent agreement of the simulation results with the analytical results. This indicates that our proposed analytical model is accurate. Besides, as shown in each curve of Figure 7, the eavesdropping probability increases with the node density \(\rho\). This phenomenon can be explained by Equation (19), _i.e._, the more nodes, the higher the chance of being eavesdropped.  
Furthermore, we compare the results with different values of frequency \(f=10\,\mathrm{kHz}\), \(f=30\,\mathrm{kHz}\) and \(f=90\,\mathrm{kHz}\), denoted by the blue, red and black curves, respectively. More specifically, we find that the eavesdropping probability decreases with the increased values of frequency \(f\). This is because the acoustic signal attenuation goes much faster with the increased signal frequency in underwater environment when \(f\) is high (_i.e._, \(f>8\,\mathrm{kHz}\)). Moreover, Figure 7 also shows that the eavesdropping probability decreases when wind speed \(w\) increases or spreading factor \(k\) increases. This trend confirms our aforementioned observation in Section 4.