esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
Confirming the premises of this paper, a work by [PERSON] et al. [21] addressed the problem of noisy SR by augmenting a dataset with convolutional reverb and then assessing the performances of DL-based noise reduction algorithms. They argue that \"speaker models trained in anechoic and noise-free conditions do not generalize well to reverberation\", but they only used MFCCs and not RASTA features and they chose to employ noise reduction algorithms as a means of improving performances. [PERSON] [22] tackled reverberation for speaker verification, proposing the use of an alternate family of features, gammatone cepstral coefficients (GFCCs). These are variants of MFCCs, where gammatone-rectified filters are used in the frequency domain. The author also proposes the usage of \"multi-training\" by adding augmented (reverberated) samples with different reverb ratios to the training set, showing its potential. An interesting perspective is given by [PERSON] et al. [23], where they show how including phonetic information alongside acoustic features can improve the detection in noisy conditions; this, however, requires a specific, comprehensive training set that cannot always be feasibly obtained. A further development by [PERSON] et al. [24] involves the use of residual CNNs to denoise speech data. Although promising results were achieved, especially regarding high-SNR data, no perceptual features were employed and, more importantly, performances were only reported in terms of signal quality estimation/noise reduction and the word error rate for speech-to-text recognition. In terms of these premises, notable studies reaching state-of-the-art performances include the work of [PERSON] et al. [25], which proves how PyAnnote (x-vectors plus TDNN) offers the best tradeoff for speed vs. accuracy when applied to a 50-speaker dataset; the work outlining the ECAPA-TDNN model, which achieves state-of-the-art results on the VoxCeleb dataset [26]; or the very interesting perspective of [PERSON] et al. [27], who aim to make their models more robust to noise by identifying noisy and clean samples of a speaker and using predictions from different training epochs to smoothly correct the noisy ones. Although this offers an 86.5% mitigation of the noisy labels, it relies on training speakers that provide both clean and noisy data. However, their usage is still not standard, despite efforts by authors like [PERSON] and [PERSON] [28], whose results are limited by a very small (10-speaker) dataset and, above all, by a failure to evaluate the performance of their model on noisy data--in fact, the authors still assess MFCCs as comprising the most effective feature family. [PERSON] and [PERSON] [29] perhaps provided the closest results to those in this paper, combining feature families showing improvement rather than using single-domain analyses. Table 1 displays some notable attempts at using hybrid feature sets for speaker recognition or verification, comparing their findings and approaches to those presented here.
# Assessing the Effect of Drivers' Gender on Their Intention to Use Fully Automated Vehicles
###### Abstract  
Although fully automated vehicles (SAE level 5) are expected to acquire a major relevance for transportation dynamics by the next few years, the number of studies addressing their perceived benefits from the perspective of human factors remains substantially limited. This study aimed, firstly, to assess the relationships among drivers' demographic factors, their assessment of five key features of automated vehicles (i.e., increased connectivity, reduced driving demands, fuel and trip-related efficiency, and safety improvements), and their intention to use them, and secondly, to test the predictive role of the feature' valuations over usage intention, focusing on gender as a key differentiating factor. For this cross-sectional research, the data gathered from a sample of 856 licensed drivers (49.4% females, 50.6% males; \(M=40.05\) years), responding to an electronic survey, was analyzed. Demographic, driving-related data, and attitudinal factors were comparatively analyzed through robust tests and a bias-corrected Multi-Group Structural Equation Modeling (MGSEM) approach. Findings from this work suggest that drivers' assessment of these AV features keep a significant set of multivariate relationships to their usage intention in the future. Additionally, and even though there are some few structural similarities, drivers' intention to use an AV can be differentially explained according to their gender. So far, this research constitutes a first approximation to the intention of using AVs from a MGSEM gender-based approach, being these results of potential interest for researchers and practitioners from different fields, including automotive design, transport planning and road safety.  
vehicle automation; features; fully automated cars; Multi-Group Structural Equation Modeling (MGSEM); gender; intention; drivers; roadway technologies Article
## 3 Results and Discussion
### Step 4: Optimizing Quaternary Cementitious Matrices
#### 3.3.3 Thermal Analyses of Quaternary Cementitious Matrices  
As shown in Figure 19, given that the reference paste contained at the various ages approximately 8% Ca(OH)\({}_{2}\), a paste containing 55%PC would be expected to generate 7.3% Ca(OH)\({}_{2}\) and a paste containing 50%PC would be expected to generate approximately 6.6% Ca(OH)\({}_{2}\) Therefore, it can be argued that in all cases the \(\upmu\)S addition cause a further Ca(OH)\({}_{2}\) consumption. Lastly, as shown in Figure 20 no carbonation has taken place as the CaCO\({}_{3}\) content is lower than the 20% LS content present in the pastes in all combinations.  
The Ca(OH)\({}_{2}\) consumption is depicted in Figure 21. It is interesting to note that after day 28 and by day 56, the Ca(OH)\({}_{2}\) content in the reference paste reduced to 1.5% by mass. It has been reported that for high PC replacement by FA (\(>\) 60%), depletion of Ca(OH)\({}_{2}\) may be observed after one year [16; 17]. Therefore, given more time, Ca(OH)\({}_{2}\) in this ternary system is expected to be depleted, if the sample has not carbonated. However, the phenomena observed in composite c  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Formulation** & **PC** & **LS** & **FA** & **\(\upmu\)S** & **Total** & **Compression Strength** & **Strength/E.CO\({}_{2}\)** \\
**Composition** & **E.CO\({}_{2}\)** & **E.CO\({}_{2}\)** & **E.CO\({}_{2}\)** & **E.CO\({}_{2}\)** & **E.CO\({}_{2}\)** & **28 Days (MPa)** & **(MPa/kg/tonne)** \\ \hline PC60 LS20 FA20 & 558.0 & 6.4 & 0.8 & 0.0 & 565.2 & 51.3 & 0.09 \\ PC55 LS20 FA20+55\%US & 511.5 & 6.4 & 0.8 & 0.7 & 519.4 & 42.7 & 0.08 \\ PC50 LS20 FA20+10\%US & 466.5 & 6.4 & 0.8 & 1.4 & 475.1 & 19.1 & 0.04 \\ PC43 LS20 FA20 FA3 & 401.2 & 6.4 & 1.5 & 0.0 & 409.1 & 39.5 & 0.10 \\ PC43 LS1 FA37+35\%US & 401.2 & 5.4 & 1.5 & 0.4 & 408.5 & 30.0 & 0.07 \\ PC43 LS14 FA37+65\%US & 401.2 & 4.5 & 1.5 & 0.8 & 408.0 & 24.8 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Embodied CO\({}_{2}\) (kg/tonne) associated with strength of quaternary cementitious matrices.  
Figure 20: CaCO\({}_{3}\) content of \(\upmu\)S modified cementitious matrices based on PC60 LS20 FA20.  
In fact, it has been reported that combinations containing 65% OPC and 35% of a formulation of FA and LS produced similar amounts of Ca(OH)\({}_{2}\) after day 1. However, the minimum amount of Ca(OH)\({}_{2}\) was reached for the 30% FA and 5% LS formulation and not the 35% FA formulation [12]. This was attributed to the synergistic interaction of FA and LS. Although according to [PERSON] et al. [12] the presence of LS leads to the formation of carboaluminate hydrates, this was not detected by the TG analyses (Figure 22), neither by the XRD analyses shown in detail in other publication [25].
## 5 Simulation Results
### Performance Evaluation of Proposed Method 2  
We measured the predicted inference latency of the proposed method 2 using the SSD-MobileNet model and now compare it to the actual inference latency of the RFCN model for the client and edge devices. Figure 16 shows the actual and predicted inference latency of the RFCN model for the client and edge devices using the SSD-MobileNet model for the client device in the REST protocol. Although there are more prediction errors when compared to the proposed method 1, it can be observed that the predicted inference latency of the proposed method 2 is also highly similar to the actual inference latency. Simulation results confirm that the use of the characteristics of the negative correlation between the RCFN and SSD-MobileNet models can effectively predict the inference latency of the complex RFCN model. Additionally, the proposed method 2 predicts not only the inference latency of the RFCN model for the client but also the inference latency of the RFCN model for the edge device well.  
We also evaluated the RMSE performance of the proposed method 2 in the gRPC and REST protocols as shown in Tables 8 and 9, respectively. When calculating the RMSE, two cases, where \(\alpha\) is \(1\) and \(-1\), in the proposed method 2 were considered to understand the effect of \(\alpha\). According to the correlation given in Table 4, it can be seen that the proposed method 2 with an \(\alpha\) value of \(-1\) decreases the RMSE for a negative correlation, and the proposed method 2 with an \(\alpha\) value of \(1\) decreases the RMSE for a positive correlation. By setting \(\alpha\) to \(-1\), it was possible to improve the performance by more than 30% from the perspective of the RMSE for negative correlation. Therefore, the proposed inference latency prediction method considering correlation is suitable for predicting the inference latency of the complex RFCN model.  
Figure 16: The actual and predicted inference latency of method 2 with the RFCN model for each device using the SSD-MobileNet for the client in the REST protocol: (**a**) client; (**b**) edge.
## 4 Review of Microgrid Control and Design
### Conventional and Advanced Control Techniques in Microgrids
#### 4.6.2 Linear Quadratic Control  
In this method, the optimal control law is determined by minimizing or maximizing the performance criterion, which is the basic principle of linear quadratic control. Choosing the right weighted matrices Q and R as required by the Riccati equation to guarantee a suitable response is one of the primary difficulties presented by linear quadratic regulation. This kind of control has been used to address a number of microgrid research issues. For instance, [PERSON] [114] introduced an enhanced optimal LQR controller for the purpose of regulating frequency in a resilient microgrid subjected to cybersecurity intrusions. In [115], the authors have presented an output-feedback-based robust LQR Vf controller for PV-battery MG with a decentralized structure and consideration of generation uncertainties.
# Investment Incentives in Competitive Electricity Markets
## 8 Conclusions  
A novel bi-level framework was presented to study impacts of investment incentives on GEP in restructured power systems under uncertainty from the perspective of the price maker GENCO for both elastic and inelastic demands. Investment incentives are capacity payment and firm contract. In this paper, a new emerging concept of smart hybrid energy markets including both capacity payment and firm contract is introduced. In this model, supply function and different scenarios have been used for spot markets and investment of non-strategic GENCOs, respectively. Two case studies have been considered and analyzed in detail. The features of the proposed model and the simulations carried out allow for deriving the following conclusions:  
The investment incentives including the markets CP, FC and smart hybrid increase the willingness to invest, the total profit of investor and energy consumption in comparison with EO policy. In addition, investment incentives encourage investment in peak technologies. All budgets are used for constructing new units in the CP, FC and smart hybrid market. However, the composition of investment options is different in CP, FC and hybrid design. The investor tends to construction of units having larger capacities which have constrained investment budgets. The energy produced by new strategic units in FC and smart hybrid markets increases compared to CP policy. In addition, the smart hybrid policy improves system reliability as well as other incentives. The total profit in the smart hybrid market is more than the other market designs. In all designs with the inelastic demand, the willingness to invest in new units and the total profit has been influenced by the price cap and total profit of the strategic GENCO is second to elastic demands.  
All authors worked on this manuscript together and all authors have read and approved the final manuscript.  
J.P.S.C. acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015--POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, and 02/SAICT/2017 - POCI-01-0145-FEDER-029803.  
The authors declare no conflict of interest.  
Figure 11: Market-clearing price as a function of price cap.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### New Sensor System
#### 2.1.2 3D Data Generation  
Measurement data are generated from the stereo camera image stream supported by the aperiodic fringe projection GOBO unit. The common principle of triangulation of corresponding points in the camera images using the calibrated camera geometries is applied (see [41]). We use advanced pinhole modeling, which takes into account the refraction of the vision rays at the interfaces of the different media air, glass, and water.  
The corresponding point search is realized using the generation of structured temporal patterns by the GOBO-based projection unit. Typically, ten consecutive images are used to form the temporal pattern of grey values at one pixel. Correspondence is found by choosing the highest correlation value along the epipolar lines (see [40]).
## 5 System Implementation and Configuration
### Technical and Economic Criteria of the SPV/WTG System
#### 5.4.5 Inverter  
1. Economic issues: the initial installation costs of an inverter, the replacement costs, and the annual O&M costs per 1 kW are $400, $400, and $10, respectively.
2. Technical issues: the lifetime of the inverter, and the efficiency are set as 15 years, 95%, respectively.
3. Inverter size: the sizes of the simulation-values of the applied inverter are 0.1, 0.15, 0.2, 0.25, and 0.3 kW.  
Moreover, the technical specifications, costs, economic parameters, and system constraints that are used in the present study are summarized in Table 2 below.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
###### Abstract
The measured performance of the beam former was obtained by feeding it to the feed layer of the electromagnetically coupled multilayer patch antenna. Equal and unequal power dividers were designed with a proper impedance transformer by changing dimensions of the inner conductor but keeping same outer conductor dimensions. Smooth linear taper was provided at the impedance transformer junction. High power calculations were also presented for square coaxial lines to handle several kW of power. In this work, a large-power planar Butler matrix is implemented in C-band. Compared to prior works, this work advances the wide bandwidth design and square coaxial line for easy machining. In this paper, the aim of designing a 4 \(\times\) 4 square coaxial Butler matrix is to divide the overall structure, using four steps to complete it. Firstly, the key component, the orthogonal coupler, is designed. The design is mainly concerned with the isolation, i.e., the coupling between input ports, to ensure that the Butler matrix performance from deterioration. Then, the cross junction is optimized with the design of the coupler cascade, followed by the design of the phase shifter and the cross junction. With tunning of the cross junction, the output phase balance is optimized, which also ultimately determines the phase stability of the Butler matrix. Finally, according to the schematic diagram, performance indexes, such as isolation, output amplitude flatness, phase flatness, and others, are optimize with the comprehensive consideration of the inhibition of the multipactor. It finally results in a well-working Butler matrix.
## 3 Experimental Procedure
### Equipment for Verifying Radio Wave Absorption Effect
#### 3.3.2 Antenna Installation Position  
The transmitting and receiving horn antennas should be installed in the Franhofer area. The distance (R) that gives the boundary between the Fresnel region and the Franhofer region can be calculated from the directional gain (Gd) of the horn antenna, \(\pi\) is Pi, and the free space wavelength (\(\lambda\)) of the transmission/reception frequency. (Refer to JIS R1679) [30].  
\[\text{R}=\text{Gd}\lambda/2\pi \tag{1}\]  
The directivity gain (Gd) of the Horn Antenna can be calculated from the effective maximum dimension (Dm) of the aperture surface, Pi (\(\pi\)) and the free space wavelength (\(\lambda\)) of the transmission/reception frequency (Refer to JIS R1679) [26].  
\[\text{Gd}=4\pi\text{Dm}2/\lambda 2 \tag{2}\]  
The distance that gives the boundary between the Fresnel region and the Franhofer region in each frequency band calculated using Equation (1) is shown below:  
In the 28 GHz band, assuming that the test frequency is 28 GHz and the directional gain of the antenna is 14.5 dB, the distance that gives the boundary between the Fresnel region and the Franhofer region is calculated to be 243.7 mm. The position where the transmitting and receiving horn antennas were actually installed was set to 500 mm, which is about twice the distance of the boundary between the Fresnel region and the Franhofer region. In the 75 GHz band, each antenna was installed at a position of 250 mm. This is also about twice the distance of the boundary between the Fresnel region and the Franhofer region calculated from each condition. In this experiment, the nominal value was used for the antenna directivity gain (Gd), but if the gain was not disclosed, it could be calculated using Equation (2).
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 4 Numerical Results
### Outage Probability Comparison  
In Figure 2, the average outage probability of the proposed system is benchmarked against the relevant systems found in the literature, including the QoS-based group D2D NOMA communication network. In Figure 2, the proposed Q-CNOMA system shows a remarkable performance gain over all SINR regimes. However, the distance between the receiver and transmitter increases, and that results in an increase in the system's outage probability. In the paired D2D OMA system, the transmission requires many relay nodes, not only increasing the total time slots but also affecting the OMA transmission's effect on the performance of the system. The burden of calculating the power allocation coefficient at the group transmitter is ignored when using the fixed power allocation policy with the proposed system. However, it affects the outage of the relay networks, thus degrading the systems' performance.
## 2 Materials and Methods
### Data Correction and Calibration
#### 2.3.3 Amplitude Calibration  
The output voltage signal was calibrated by measuring the absolute magnitude of the VSF in m\({}^{-1}\)sr\({}^{-1}\) of a standard bead solution, for which the scattering magnitude is known. The 0.203 \(\upmu\)m beads were smaller than the wavelength of light, thus leading to the featureless shape of the VSF. By contrast, the large beads exhibited ripples in the angular scattering that made the calibration result highly sensitive to the angular acceptance of the detector. Following the suggestion of [PERSON] et al. [29], we used small-sized beads with a nominal diameter of 0.203 \(\upmu\)m for calibration and other large-sized beads for validation. Its attenuation coefficient \(c\) is equal to the scattering coefficient b and thus:  
\[\beta_{Mie}(\theta)=\overline{\beta}_{Mie}(\theta)*c_{VSFlab} \tag{6}\]  
where \(\overline{\beta}_{Mie}\) is the scattering phase function obtained from Mie theory. The impact of the uncertainties (measured as the coefficient of variation) of 0.203 \(\upmu\)m beads at 532 nm in the mean diameter varying within \(\mu_{D}\)\(\pm\)\(\delta_{D}\) on \(\overline{\beta}_{Mie}\) is 3.4%. The imaginary part of the refractive index of 0.203 \(\upmu\)m beads is small in the visible wavelengths and its effects on scattering could be neglected [21]. The calibration coefficient \(k(\theta)\) is derived as the slope between the output signal after scattering volume correction _Voltage_ (\(\theta\)) and the \(\beta_{Mie}\) (\(\theta\)) by applying the linear regression model.  
\[k(\theta)=\beta_{Mie}(\theta)/\textit{Voltage}(\theta) \tag{7}\]  
The calibration coefficient \(k\) at each scattering angle was determined from the solutions with various concentrations of 0.203 \(\upmu\)m beads. A 100 mL master solution of 0.203 \(\upmu\)m beads with \(c\) of 7.5 m\({}^{-1}\) was prepared and was agitated on a vortex mixer to homogenize the suspension and break down possible aggregation of particles. Subsequently, a certain amount (totally from 20 mL to 70 mL) of the master solution was added into the pure water (about 400 mL) in the basin, producing a series of solutions with \(c\) from 0.36 to 1.12 m\({}^{-1}\). After each addition, the stirring rod was used to homogenize the beads in the basin before taking 30 measurements of the VSFs. The VSF measurements should be implemented within the single scattering regime to ensure the negligible effects of multiple scattering over the path length used by a given instrument. The criterion for a single scattering regime is defined in terms of small optical thickness, which is generally less than 0.1 [30]. The path length of VSFlab is 0.065 m; thus, \(c\) of the experimental samples used in our calibration procedure was kept under 1.54 m\({}^{-1}\).
## 2 Material and Methods  
### Monte Carlo Simulation  
In the current study, in order to determine gamma attenuation parameters and kerma coefficients, EGS4 (Electron Gamma Showers) Nelson and Hirayama was used to perform the calculations [21]. Monte Carlo simulations with EGS4 were used to simulate the HPGe detector response. Since random number generation is of importance in MC calculations, one must use a verified random number generator. A RANLUX random number generator was used with the EGS4 code, as it was shown to produce relatively better distributions and a longer sequence [22]. For the model executed with EGS4, the efficiency was divided into 10,010 energy bins, each one having a width of 0.3 keV.  
The calculated area is divided into 241 cells, and a cylindrical geometry is achieved when this shape is rotated 360\({}^{\circ}\) on the axis given Figure 1. It will be assumed that Figure 1 shows a series of cylindrical circular projectiles, each with a radius represented by R1, R2,..., R11 and planes represented by P1, P2,..., P20. A part of the code was created for the point radioactive source in a manner where all photons were released over the z-axis, giving a beam of collimated photons [8; 10; 23; 24].  
In the calculations, two sources of uncertainty contributed to the data obtained: statistical uncertainties and cross-section uncertainties in the Monte Carlo program used. In all calculations, the program generated 10\({}^{8}\) photons, which reduces the statistical uncertainty to as low as 0.5% for all the photon energies, which are smaller than the symbols in the plots. The uncertainty in the MAC parameters used by EGS4 is generally around 2%. Mass absorption coefficients were determined by making calculations for gamma photons with energies of 59.5, 122, 383, 511, 662, 1274, and 1332 keV. The relative error of the simulated values is less than 1%. Therefore, we estimated the total uncertainty to be around 3%.