esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results of the survey on the beams geometries and steel reinforcement configuration are given in SS2.1. The variation of the compressive strength of concrete was investigated by numerous non-destructive tests (sonic and rebound tests) along each beam in SS2.2. These evaluations were corrected by means of the results of destructive tests on some material specimens: compression tests on concrete cores and tension tests on steel rebar parts extracted after the failure tests on the retrofitted beams. The description of the retrofitting intervention to improve the beam shear strength by EB-CFRP reinforcement (U-jacketing) and the beam flexural strength at midspan by new bottom longitudinal steel rebars, is given in SS3. Three-point load shear/bending tests were carried out on the beam before the retrofitting and after retrofitting (SS4). The preliminary elastic tests on the extracted beams were performed to evaluate the stiffness of each beam before retrofitting (SS4.2). The failure tests on the retrofitted beams (SS4.3) were carried out to evaluate the shear strength, the failure mode and deflections of the beams (SS4.3.1), the beam damage (SS4.3.2), the CFRP U-strips strains (SS4.3.3) and the contribution of the steel reinforcement, the CFRP reinforcement and the concrete to the beam shear resistance (SS4.3.4). The code design equations used to predict the shear strength for the beam before and after retrofitting are given in SS5. The results obtained for the tested beams, together with the experimental data available from the literature for many beams retrofitted by EB-CFRP reinforcement, were compared to the one predicted by codes in SS6. The comparisons between predicted and measured values were performed for: 1. the beam shear strength before retrofitting assuming different inclination angle for the shear cracks (SS6.1); 2. the beam shear strength after retrofitting considering or not the concrete contribution (SS6.2); 3. the shear strength contribution of the CFRP reinforcement (SS6.3). A discussion about the research conclusions and the possible future research development starting from the given experimental data analysis is given in SS7. The code equation results are able to estimate the shear strength of the \"real\" retrofitted beams but the uncertainties due to the brittle behavior of CFRP and to the premature debonding of strips resulting from concrete cracking should be closely considered by the designer. Both beams failed in shear because of improper design of the construction details (insufficient new longitudinal rebar anchorage) resulting in a complete debonding of the CFRP strips that reduced the beam strength. Finally, experimental measures showed a significant variation of the concrete compressive strength in adjacent beams as well as in different portion of each beam. This uncertainty about the compressive strength should be closely considered by a designer to define properly the beam capacity [60; 61].
# Monolithic 3D Inverter with Interface Charge: Parameter Extraction and Circuit Simulation
## 1 Introduction  
Three-dimensional (3D) integration technology has been studied as a solution to the physical limitations of maintaining the road map predicted by Moore's Law [1, 2]. Compared to other 3D integration technologies, monolithic 3D integration (M3 DI) enables very small inter-layer via to minimize the connection between the device and interconnection. Via in nm can significantly reduce the delay caused by interconnection [3]. M3 DI is a 3D integration technology that sequentially fabricates two or more device layers instead of combining two processed substrates using through-silicon via (TSV). Due to sequential integration, low-temperature (<650 \({}^{\circ}\)C) processes must be applied to fabricate the upper layer device to ensure the quality of previous layers. Recently, LETI's CoolCubeTM technology has succeeded in lowering the process temperature to below 650 \({}^{\circ}\)C [4, 5]. However, even when a low-temperature process is applied, the silicon and oxide film interfaces ofthe previously fabricated layers are damaged by heat [6]. The generated interface trap charges make the threshold voltage and the current level of the device and the subthreshold swing (\(SS\)) worse. Subthreshold swing is amount of change in voltage according to current below the threshold voltage. When fabricating a circuit with M3 DI, the effects of interface trap charges and electrical coupling due to inter-layer dielectric (ILD) distances should be considered. Previous studies investigated electrical coupling with the distance of the ILD when the interface trap charge was ignored [7; 8; 9; 10; 11] and extracted SPICE parameters of the monolithic 3D inverter (M3 DINV) for the circuit design simulation [12]. However, for a realistic circuit design simulation, it is necessary to extract the SPICE model parameters considering the interface trap charge.  
In this study, we introduced parameter extraction of SPICE model parameters of the top and bottom transistors in the M3 DINV considering the interface trap charge distribution [13] extracted from the experimental data of the M3 DINV [14] with inter-layer dielectric thickness (\(T_{\text{ILD}}\)) of 10 (coupling) and 100 nm (non-coupling). To verify the validity of the extracted SPICE model parameters of the M3 DINV, technology computer-aided design (TCAD) and TCAD mixed-mode simulation were performed. The components of logic circuits, such as inverters, ring oscillators, 2 \(\times\) 1 multiplexer (MUX), and D flip-flop and 6-transistor static random-access memory (6T SRAM), consisting of M3 DINVs, were simulated using the extracted model parameters, and the simulation results, both with and without interface trap charges, were compared.
## 3 Materials and Methodology  
### Materials  
The CAD model displayed in Figure 2 is utilized to determine the surface area of the side view layout. The model illustrates the presence of five holes that require inspection, and the inspection circles comprise the largest portion of the model's surface area that can measure the outer edge surface. Machining parameters for a mild Delrin workpiece that has round holes with a 30 mm diameter, a spindle speed of 2000 revolutions per minute (rpm), a feed rate of 0.1 mm per revolution (mm/rev), a cutting depth of 20 mm, and a cutting speed of 250 m per second (m/s) are given. In this study, the inspection hardware is developed with the aid of IoT support devices, including a Raspberry Pi, multiple subsystem cameras, a lighting system, and a Wi-Fi router. The hardware component specifications used in the IoT configuration are listed in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline
**Machine Characteristics** & **Parameter** & **Specification** \\ \hline \multirow{2}{*}{Machine Part} & Workpiece & Delrin \\  & Size & 120 \(\times\) 100 \(\times\) 50 mm \\ \hline \multirow{4}{*}{Machining} & Feed rate & 0.1 mm \\  & Spindle speed & 2500 rpm \\  & Depth of cut & 22 mm \\  & One Circle diameter & 30 mm \\  & Cutting speed & 250 m/s \\ \hline \multirow{4}{*}{Cutting tool} & Material type & High-speed steel \\  & Diameter & 0.6 mm \\  & Number of flutes & 2 \\  & Tool type & New tool \\ \hline \multirow{4}{*}{CMM measurement} & Number of axes & Three axes, X, Y, and Z \\ \cline{2-3}  & Machine type & MITUTOYO QM-353 \\  & Resolution & 0.0005 mm (0.00002 im.) \\ \cline{1-1}  & High accuracy & Accuracy of min 0.0017 mm \\ \cline{1-1}  & Versatility & Wide range of probe systems are available \\ \hline \multicolumn{3}{l}{Software} \\ \multicolumn{3}{l}{Open CV} \\ \multicolumn{3}{l}{Python} \\ \multicolumn{3}{l}{Pycharm editor} \\ \multicolumn{3}{l}{CAD design} \\ \multicolumn{3}{l}{Operating System} \\ \multicolumn{3}{l}{MQTT protocol} \\ \end{tabular}
\end{table}
Table 1: Characteristics of parameters, hardware, and software components.
## Abbreviations  
ADS-B Automatic Dependent Surveillance-Broadcast AEKF Adaptive Extended Kalman Filter AOA Angle of Arrival CAF Cross Ambiguity Function CAPITAL Cellular Applied to IVHS Tracking and Location CDFs Cumulative Distribution Functions CIR Channel Impulse Response CKF Cubature Kalman Filter CNN Convolutional Neural Network CRIAQ Consortium for Research and Innovation in Aerospace in Quebec CRLB Cramer-Rao lower bound CWLS Constrained Weighted Least Square DDR Differential Doppler Rate DL Deep Learning D-S Dempster-Shafer DVB Digital Video Broadcasting EKF Extended Kalman Filter EGNOS European Geostationary Navigation Overlay Service ETS Ecole de Technologie Superieure FDOA Frequency Difference of Arrival GA Genetic Algorithm GAGAN GPS Aided Geo Augmented Navigation GDOP Geometric Dilution of Precision GHKF Gaussian Hermite Kalman Filter GHQF Gauss-Hermite Quadrature Filter GLONAS GLObal'naya NAVigatsioannaya Sputnikovaya Sistema GMM Gaussian Mixture Model GMM-ITSF Gaussian mixture measurement integrated track splitting filter GMT Ground Moving Targets GNS Navigation Systems GNSS Global Navigation Satellite Systems GPS Global Positioning System GSN GNSS Supervisory Authority
## 4 Analysis of Spectral Shift Demodulation Methods for Simulation Data
The combination of these two methods seems to be optimal in terms of the robustness of the demodulation methods to noise. Replacing cross-correlation with a filter provides an equally useful demodulation method. During the numerical calculations, we also checked the possibilities of raising the spectrum to a power higher than two. However, it turned out that, for \(p=3\) and \(p=4\), the RMSE results were not as good as for \(p=2\). The method of raising to the second power and then filtering is also a less advantageous solution than filtering before raising to the second power. Figure 13 shows a comparison of the effect of the spectral resolution on the RMSE of three algorithms. The spectral resolutions analysed were \(\delta\lambda\) = 0.01, 0.02, 0.04, 0.1, and 0.2 nm. The worst results were achieved for the directly applied FPC method. Both the  
Figure 11: The effect of using Savitzky–Golay filters on the RMSE value in demodulation using fast phase correlation and cross-correlation methods. Figure 12: The effect of using geometric and arithmetic–geometric filters on the RMSE value during demodulation using the fast phase correlation and cross-correlation methods. application of geometric-arithmetic filters and the initial cross-correlation reduced the RMSE values. However, slightly better results were obtained using cross-correlation. The next simulations consisted of using the two types of grating spectra shown in Figure 14. The FWHM value of both gratings was 0.5 nm. The FBG1 had a Gaussian shape, while the FBG2 had a characteristic spectrum with a flat-top shape. Such a spectrum was obtained as a result of using the fourth-order Gaussian function. As for the previous simulations, the spectral resolution was 0.01 nm. The simulations used 1001 values of the Bragg wavelength shift in the 1 nm range. Figure 15 shows a comparison of RMSE values for two gratings from Figure 14. In order to better compare the influence of the type of spectrum, individual algorithms are presented in separate parts of the figure. The analysed algorithms were the direct FPC method, FPC preceded by an arithmetic-geometric filter, and FPC preceded by cross-correlation. For the FPC algorithm, direct analysis of the spectrum and the spectrum raised to the second power was performed. Raising to the power was noticeably beneficial for FBG1. For FBG2, the RMSE improvement was small. Similar results were obtained for the use of filtration before calculating the FPC. For both direct FPC methods and the filter with FPC, the FBG2 was characterised by lower RMSE values. In the case of the cross-correlation method, there were no differences between FBG1 and FBG2. For both gratings, it was beneficial to raise the calculated cross-correlation function to the second power. Figure 14: Comparison of two spectral shapes of FBGs used in simulations. Figure 13: Influence of spectral resolution on RMSE value for three demodulation methods analysed: FPC, arithmetic–geometric filters with FPC, and cross-correlation square with FPC.
## 4 Discussion  
### Meteorological Drought Conditions and Forest Temporal Response  
The 2001-2012 meteorological drought conditions in South Tyrol were characterized by two temporal patterns: (i) 2003-2007 mild to extreme persistent drought event depicted in the 1 scPDSI PC; and (ii) gradual scPDSI decline captured in the 3 scPDSI and 4 scPDSI scores. Both were in strong accordance with local events as well as Pan-European meteorological conditions [60; 69].  
Principal components representing dominant temporal variability of forest canopy were evaluated against recognized meteorological conditions, which proved them to have relevant physical meaning. Although identified drought conditions did not dominate forest response, its linear interrelationship with vegetation status was certain. The anticipated impact of the 2003-2007 drought conditions was visible in the 2004-2007 diminished canopy water contend depicted in the 4 nNDII7 PC. The observed lag between drought inception in 2003 and decrease of canopy water content could be explained by forest resistance governed by local environmental conditions [28; 70], trees species characteristics and adaptation strategies [71; 72], as well as local tree competition [14; 22; 27; 34].  
The regional drying tendency was rendered in the downturn of photosynthesis activity captured in the 3 nNDVI PC (class 3). This subtle trend is supported by the long-term climate transformation suggested by [8]. Moreover, the perceived decline is further enhanced by the abrupt NDVI increase in 2003. This gain can be explained by enhanced productivity triggered by the 2003 Pan-European heatwave [73]. The reversed interpretation of the 3 nNDVI PC (class 1) indicated the gradual 'greenness' upturn initiated with the massive NDVI decline, which can be explained as drought triggered wilting, or even dieback followed by a subsequent re-growth of better adjusted vegetation [14]. Such phenomenon has been already observed in South Tyrol [16]. Interestingly, some forest stands reported also NDII7 increase, which could be supported by findings of [74].  
Figure 8: Marginal means plots of repeated ANOVA showing CF, GPP, SBD and SL variability (in columns) in relation to forest type, elevation and aspect (in rows). Analyses performed for the 3 nNDVI drought impact class 1 spatial footprint. Categories plotted in transparent comprise less than 50 observations. Abbreviations are explained in Figure 3.  
The presented approach based on statistical PCA decomposition does not distinguish between the cause of changes in the remotely-sensed time series, therefore treats alike stress and disturbance related signal. To our knowledge, South Tyrol has not been affected between 2001 and 2013 by any major forest disturbance event, such as forest fires or wind throws, that might impact PCA results. Local events are rather unlike to affect the leading variability and are depicted in the PCs of the higher order, which have not been discussed here.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 4 Analysis of Link Connectivity and Probability of Coverage  
In this paper, we evaluate the link connectivity of SUs in UCANs by probability of connection, which is defined as follows:  
**Definition 1**.: _Probability of connection is the probability that two SUs can successfully establish a bidirectional link._  
It is worth mentioning that we consider a bidirectional link in this paper because it is more general than previous works, as most communications between a transmitter and a receiver require an acknowledgement (i.e., ACK) [4; 7].  
In UCANs, two SUs can successfully establish a bidirectional link if and only if the following two conditions are satisfied:  
1. Both of the SUs can connect topologically;
2. Both of the SUs have the spectrum.  
Essentially, condition (1) requires that the transmission range of each SU covers each other, and condition (2) requires each SU to obtain the spectrum. Therefore, we first analyse the topological connection condition in Section 4.1. We then derive the probability that two SUs have the spectrum in Section 4.2. We next obtain the probability of connection of two SUs in Section 4.3. Finally, we analyze the probability of coverage in Section 4.4.
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 4 Inter-Dependency between Simulations
### Cases 5 and 6  
In these cases, one simulation has a valid Move function and the second simulation has valid Demand and Assign. In this situation, the middleware needs to execute Algorithm 3 (called M-DA for Move-Demand and Assign). Instructions (1)-(3) allow for composing the scales of the involved simulations. To compose the processes, the middleware iteratively executes \(\mathcal{S}_{da}\rightarrow\mathcal{S}_{m}\rightarrow\mathcal{S}_{da}\), so that \(\mathcal{S}_{da}\) provides the right demand to \(\mathcal{S}_{m}\), which in turn provides the right valuations of the transportation graph (instructions (4), (5), and (6)). Again, instruction (6) re-executes the same time step of \(\mathcal{S}_{da}\) with a different graph (\(G_{Sm}\)).  
```
Require:\(\mathcal{S}_{m}=\langle\omega_{m},\sigma_{m},\mathcal{P}_{m}\rangle\); \(\mathcal{S}_{da}=\langle\omega_{da},\sigma_{da},\theta_{da},\mathcal{P}_{da}\rangle\), Ensure:\(\mathcal{S}_{mda}=\mathcal{S}_{m}\bigotimes\mathcal{S}_{da}\) (1) \(\omega_{mda}\leftarrow\omega_{m}\circ\omega_{da}\) (2) \(\sigma_{mda}\leftarrow\sigma_{m}\bullet\sigma_{da}\) (3) \(\theta_{mda}\leftarrow\theta_{m}\diamond\theta_{da}\) while\(\
eg End\)do (4) execute \(\mathcal{S}_{da}\) and save its demand \(M_{ad,\mathcal{S}_{da}}\) in \(\mathcal{S}_{mda}\) (5) execute \(\mathcal{S}_{m}\) with the demand \(M_{ad,\mathcal{S}_{da}}\) and save the resulting \(G_{Sm}\)\(\mathcal{S}_{mda}\) (6) execute \(\mathcal{S}_{da}\) again with \(G_{Sm}\) and save its demand \(M_{ad,\mathcal{S}_{da}}\) in \(\mathcal{S}_{mda}\) endwhile (7) Return \(\mathcal{S}_{mda}\)
```  
**Algorithm 3** M-DA algorithm.
# Simulation of Electronic Quantum Devices: Failure of Semiclassical Models
## 5 Summary and Conclusions  
The simulation of novel nanomaterials and of related electronic quantum devices is often grounded on partially phenomenological transport models based on the Wigner-function formalism. Within these simplified modeling strategies, energy dissipation versus decoherence phenomena are still described via local models borrowed from the semiclassical Boltzmann theory, namely relaxation-time treatments or Boltzmann-type scattering approaches. Such a semiclassical treatment is intrinsically incompatible with the fully quantum-mechanical coherent (i.e., dissipation-free) carrier dynamics within the device active region. Indeed, during the last decades, the intrinsic limitations of such hybrid treatments emerged in different contexts, both for semiclassical thermalization models based on the conventional boundary-condition scheme and for dissipation versus decoherence treatments based on the relaxation-time approximation, as well as on Boltzmann-like scattering models.  
In this paper, our primary goal is twofold: (i) We review selected simulated experiments, showing the above-mentioned limitations in different contexts, and providing a cohesive theoretical treatment able to clearly identify the origin of such diverse anomalies faced within the Wigner-function picture. (ii) Starting from the density-matrix formalism, we show how to derive genuine quantum mechanical dissipation models, able to overcome the intrinsic limitations just reviewed.  
The main conclusion of our analysis is as follows: within the Wigner-function picture, a correct treatment of both dissipation versus decoherence phenomena inside the device and of the device-reservoir thermalization process requires the use of intrinsically non-local scattering models. In addition, our investigation shows that the most natural theoretical framework for a fully quantum-mechanical simulation of electronic and optoelectronic nanodevices is the density-matrix picture, which comes out to also be computationally less expensive. Indeed, in the presence of non-local dissipation and/or thermalization models, a direct numerical solution of the corresponding transport equation via finite-difference schemes over the Wigner phase-space is by far computationally more demanding (compared to the local case), and a direct numerical solution of the density-matrix equation comes out to be, in general, less expensive. No matter which is the adopted computational strategy (Wigner-function versus density-matrix solution), at any time, one can simply switch between the two pictures via the corresponding Weyl-Wigner transform.  
Investigation, R.C.I.; supervision, F.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 4 Energy Management Model Mathematical Formulation
### ILP Problem Formulation: OptTsNS  
#### 4.2.1 Objective Function  
In our proposal, the creation and configuration of network slices and associated services is energy-aware. Thereby, the proposed management model seeks to improve energy efficiency through the optimal use of the available supply and specifically through the minimization of power consumption. Technically, this objective is expressed as indicated in Equation (5). Nevertheless, considering that an important requirement in an energy management system for mobile networks is the promotion and use of green energy (as mentioned in Requirement R4 in Section 3.1 and as shown in the energy provision model in Equation (2)), the objective of the proposed energy model should be focused on minimizing the consumption of energy from non-renewable sources. To this end, we have established a cost function associated with consumption in the 5G ecosystem from renewable and non-renewable sources as shown in Equation (6), where \(w_{1}\) and \(w_{2}\) can be in the range [0,1] and represent the weights associated with the individual cost functions that are proportional to renewable (\(c_{P_{R}}\)) and non-renewable (\(c_{P_{NR}}\)) energy consumption, respectively.  
To promote the primary utilization of renewable energy, the cost associated with the consumption of this kind of energy in Equation (6) can be set to a minimal value (\(w_{1}\ll w_{2}\)) and even to zero. In this model, we chose the second option (i.e., \(w_{1}=0\)) with the aim of moving toward a green mobile network ecosystem. Therefore, the objective function of minimizing power consumption in Equation (5) considering the cost function in Equation (6) translates into the minimization of the cost associated with non-renewable energy consumption as indicated in Equation (7), which in turn is equivalent to  
Figure 5: Schematic of the adaptive energy management model for 5G Network Slicing.  
the objective of minimizing the \(P_{NR}\) in the total \(P_{A}\) as shown in Equation (8). This objective function must respect the available energy resources, consider the parameters of the services, the management strategies described in Section 3.2, and the constraints presented in Section 4.2.2.  
\[\text{\emph{minimize}}\ \{P_{D}\} \tag{5}\] \[Cost_{PD}=w_{1}\times Cost_{P_{R}}+w_{2}\times Cost_{P_{NR}}\] (6) \[\text{\emph{minimize}}\ \{Cost_{P_{NR}}\}\] (7) \[\text{\emph{minimize}}\ \{P_{NR}\} \tag{8}\]