esa-sceva/satcom-chunk-collection huggingface dataset:
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 2 Background
### Wake-Up Receiver-Based Sensors  
The WuRx is used for permanent monitoring of the radio channel in order to wake up the main radio for processing information during event-driven communication. The radio chip in a wireless node consumes most of the energy in comparison to the other components of the node (i.e., sensors, microcontrollers (MCU)), which leads to an unavoidable battery drain. Since the continual activity of the radio is often not necessary, switching to sleep mode can dramatically save energy. Initially, the main radio is off, and the WuRx is active in low power listening mode waiting for an incoming WuPt to wake up the whole sensor node. At this mode, the WuRx consumes only a few microamperes. This is in contrast to the power consumption of the transceiver, which is used as the main radio. Its consumption of tens of milliampere in the receiving mode represents a significant increase in power consumption compared to the WuRx. Thus, the use of the WuRx provides optimal conservation of the battery-powered sensor node energy reserves for years.  
A sensor node is typically built out of antennas, WuRx, main radio, sensors, energy source, and microcontroller, as shown in Figure 1. The WuRx, which is an additional RF reception part, has its own antenna in order to receive only WuPt. The WuRx is configured by the microcontroller. The main receiver is used for the communication of data packages when the sensor node is in active mode. During standby, the WuRx is typically the only active component in the circuit. The WuRx analyses the incoming WuPt in order to validate the address. In the case of address matching of WuPt and WuRx, the WuRx sends an interrupt signal to the microcontroller to wake up.
## 3 Theoretical Background
### Framing and Deframing  
In signal processing, framing refers to the process of splitting samples into small groups (blocks) known as \"frames\". Real-time block-based signal processing systems often use framing because it minimizes the processing and memory requirements by dividing one large chunk of samples into several small frames [53]. It also helps in extracting features from non-stationary signals (i.e., speech) by dividing them into small blocks during which they are assumed to be stationary. Figure 4 demonstrates how the frames are designed to overlap with one another to prevent data loss between successive frames [54]. In this figure \(F_{a}\), \(F_{b}\), and \(F_{c}\) represent the frames \(a\), \(b\), and \(c\), respectively, and \(OL_{ab}\) and \(OL_{bc}\) are the overlapped frames.  
To avoid spectral distortions at the frame edges and to achieve seamless continuity among consecutive frames of speech, the Hamming window (\(w(n)\)) is multiplied with each frame [55]:  
\[w(n)=0.54-0.46\cdot\text{cos}\bigg{(}\frac{2\pi n}{N-1}\bigg{)},\ 0\leq n\leq N \tag{5}\]  
Figure 4: Framing process and frame overlapping, where blue and red dashed lines represent the starting and ending of the frames, respectively.  
where \(N\) denotes the samples in the speech frame. The windowed signal \(y(n)\) can be calculated as:  
\[y(n)=x(n)\times w(n),\ 0\leq n\leq N \tag{6}\]  
Windowing acts as a low-pass filter, enhancing the signal at the center and smoothing it at the edges.
# Analysis of Influencing Characteristics of Biochars for Ammonium Adsorption
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Pyr.** & **Part.** & & & **NH\({}_{4}\)+** & & **Biochar** & & **Ad. Cap.** & **\% of Max** & & **Symbol in** & \\
**Temp.** & **Size** & **pH** & **Conc.** & **Time (h)** & **Time (m\({}^{2}\)/g)** & **Dose.** & **(g/mL)** & **(mg/g)** & **Ads Cap** & & **Figure 5** & \\ \hline
600 & 0.25 & 8.15 & 250 & 20 & 10.995 & 0.02 & 9.14 & 73 & [20] & P & Rice husks \\
600 & 0.25 & 8.15 & 500 & 20 & 10.995 & 0.02 & 17.18 & 69 & [20] & P & Rice husks \\
600 & 0.25 & 8.15 & 750 & 20 & 10.995 & 0.02 & 25.08 & 67 & [20] & P & Rice husks \\
600 & 0.25 & 8.15 & 1000 & 20 & 10.995 & 0.02 & 30.99 & 62 & [20] & P & Rice husks \\
600 & 0.25 & 8.15 & 1400 & 20 & 10.995 & 0.02 & 37.63 & 54 & [20] & P & Rice husks \\
600 & 0.25 & 7.0 & 1000 & 20 & 10.995 & 0.02 & 37.11 & 74 & [20] & q & Rice husks \\
550 & - & - & 1026 & 12 & 6.16 & 0.004 & 2.1 & 1 & [44] & q & Rice husks \\
550 & - & - & 1026 & 12 & 106.00 & 0.004 & 3.00 & 1 & [44] & q & Rice husks \\
600 & 0.059 & 7.2 & 100 & 4.3 & 285.5 & 0.0008 & 78.67 & 63 & [47] & É & Walnut \\  & & & & & & & & & & & shells \\
600 & 0.059 & 7.2 & 100 & 4.3 & 456.3 & 0.0008 & 68.78 & 55 & [47] & É & Walnut \\  & & & & & & & & & & & shells \\
600 & 0.059 & 7.2 & 100 & 4.3 & 536.5 & 0.0008 & 80.16 & 64 & [47] & É & Walnut \\  & & & & & & & & & & & shells \\
600 & 0.059 & 7.2 & 100 & 4.3 & 967.1 & 0.0008 & 108.4 & 87 & [47] & É & Walnut \\  & & & & & & & & & & & shells \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Agricultural waste experiment examples of trends from biplot. this or the methods for biochar production and experiments are responsible. To be able to accurately model the adsorption of ammonium onto biochar for implementation of nitrogen recovery in wastewater treatment for fertilizer production, standard methods for biochar production and adsorption experiments would be extremely useful. Many of the influencing factors are related to the experimental setup for either the biochar production or the adsorption testing, so standardized methods would help remove ambiguity. Pyrolysis temperature in particular is an important process condition that can affect many biochar characteristics including surface area, porosity, surface charge, etc., though feedstock choice may prove to be a more important parameter when engineering biochar for ammonium adsorption.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
### Comparison with the State-of-the-Art on Retro-reflectivity Prediction  
In this section, we will present a comparison of our work with the state-of-the-art on retro-reflectivity prediction. In Table 10, we present our best performing model and compare it with the results from other published works covered in this paper. Our neural network model with entity embedding outperforms other approaches significantly in terms of the \(R^{2}\) score. This illustrates that the neural network based system is capable of learning the degradation of retro-reflection effectively for traffic signs with different feature combinations. It should be noted that an objective comparison is not possible due to the use of different datasets by different published works including the use of different predictor variables. Accordingly, the comparison should be treated as a qualitative one in order to understand the effectiveness of different approaches.
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 4 Performance Evaluation
### Subjective Evaluation  
The MUSHRA listening test was conducted to evaluate the perceptual quality of the separated signals [36]. A separated signal with a higher perceptual quality can earn a higher score. There are two MUSHRA listening tests in this subsection. In the two listening tests, the sound source number was set as \(\{2,3,4\}\), and the source separation was \(70^{\circ}\). There were 15 listeners participating in the listening tests. The first test was conducted under the ideal acoustic case to compare the proposed separation approach with the reference methods. Condition \"Proposed\" represents the proposed approach. Conditions \"JSNCS\" and \"EM\" mean the two reference methods, respectively. The sound source signal serves as the hidden reference, which is noted by condition \"Hidden Ref\". Condition \"Anchor\" refers to the original mixed signal (i.e., W-channel signal recorded by sound-field microphone). Results of the first MUSHRA test are presented in Figure 13 with 95% confidence intervals.  
It can be observed from Figure 13 that the proposed method achieves a higher score than other reference approaches in the ideal acoustic environment. To be specific, the  
Figure 12: Average SDR and SIR in different reverberant rooms: (**a**) average SDR; (**b**) average SIR.  
Figure 13: MUSHRA test results under the ideal acoustic case with 95% confidence intervals.  
MUSHRA test scores of the proposed approach achieve over or nearly 80, indicating \"Excellent\" or \"Good\" subjective perceptual quality, which proves that the satisfying perceptual quality of separated signals can be obtained by our proposed method. Moreover, the MUSHRA test scores decrease with the increase in sound source number.  
The second test was conducted under the reverberant case where \(T_{60}\) is 300 ms. MUSHRA test results are shown in Figure 14 with 95% confidence intervals.  
It can be seen from Figure 14 that our proposed multi-source separation method greatly improves the quality of the separated signal. Compared with the other reference methods, the proposed method attains a higher MUSHRA test score in the reverberant environment. Moreover, the gaps in the test scores in the case of \(N=2\) are relatively small. Yet, the complexity of the EM method is high. In the case of \(N=4\), the advantage of the proposed algorithm is proven more obviously.  
From the above objective and subjective evaluations, it is demonstrated that the proposed approach maintains a preferable quality of separated signals and can be adapted to various acoustics environments.
# Efficient Seismic Fragility Analysis for Large-Scale Piping System Utilizing Bayesian Approach
## 1 Introduction
Specifically, to begin with, the seismic response analysis is performed on the building-piping coupled model with respect to the design earthquake loading by using the EPRI SOV method, and a prior seismic fragility curve is then derived based on this. The additional nonlinear seismic response analyses are performed based on the obtained prior seismic fragility curve, and the final seismic fragility curve is derived using the Bayesian updating technique. In order to prove the validity of the proposed method, with respect to the building-piping coupled structural model example [16], the effectiveness of the proposed method is verified by comparing the results of the proposed method with those of the existing method from the perspectives of efficiency and the accuracy. Basically, in order to increase the reliability of the numerical results of this study, the T-joint component numerical model, in which the nonlinear behavior of the piping system was concentrated, was verified using the related dynamic cyclic test results. In addition, the numerical model of the overall piping system was validated through the corresponding piping system dynamic test results. On the other hand, when dynamic testing piping systems, the experimental monitoring on the deterioration phenomena of the connections (i.e., thermal effects, prestresses, viscosity, etc.) and of the efficiency of the pipeline system (i.e., corrosion, settlements, etc.) [26; 27] can be used as important information in the numerical analysis. The experience in dynamic tests showed that the variation of eigenfrequencies was not a sensible indicator of deterioration. The visual inspections could better help monitoring procedures and have been frequently used in bridges. Especially, it had been revealed that the integration of the visual inspection with the fuzzy logic treating uncertainty expressed by linguistic judgments could lead to a robust and reliable instrument [28]. It is judged that such a technique can be applied to the identification of the real state of the piping system as well.
# A Hybrid RF/FSO Transmission System Based on a Shared Transmitter
## 2 Principle
### Channel Models
#### 2.2.2 FSO Link  
When it comes to the FSO link, considering the system as a coherent detection system, the received signal can be represented as  
\[y_{\text{F}}=\sqrt{P_{\text{F}}\eta l_{\text{I}}Is}+n_{\text{F}}, \tag{3}\]  
where \(s\) is the transmitted symbol with unit energy, \(P_{\text{F}}\) is the transmit power of the FSO link, and \(\eta\) is the coefficient of the optical-to-electrical conversion. \(l_{\text{I}}\) is the atmospheric attenuation associated with the FSO link, \(I=I_{a}I_{p}\) is the fading coefficient, \(I_{a}\) is the fading due to atmospheric turbulence, and \(I_{p}\) represents the pointing error fading coefficient. \(n_{F}\) is the AWGN with mean 0 and variance \(\sigma_{\text{F}}^{2}\). The instantaneous received SNR \(\gamma_{F}\) at the FSO receiver is given as  
\[\gamma_{F}=\frac{P_{\text{F}}\eta l_{\text{I}}I}{\sigma_{F}^{2}}. \tag{4}\]  
According to the Beer-Lambert law, atmospheric attenuation \(I_{\text{I}}=exp(-\sigma_{\text{I}}L_{F})\), where \(\sigma_{\text{I}}\) is the attenuation coefficient and \(L_{F}\) is the transmission distance of the FSO link.  
The atmospheric turbulence \(I_{a}\) follows a Gamma-Gamma distribution, and the PDF is given as [37]  
\[f_{I_{a}}(x)=\frac{2(\alpha_{\text{F}}\beta_{\text{F}})^{\frac{\alpha_{\text{ F}}+\beta_{\text{E}}}{2}}}{\Gamma(\alpha_{\text{F}})\Gamma(\beta_{\text{F}})}x^{ \frac{\alpha_{\text{F}}+\beta_{\text{F}}}{2}-1}\text{ K}_{\alpha_{\text{F}}-\beta_{ \text{F}}}\Big{(}2\sqrt{\alpha_{\text{F}}\beta_{\text{F}}x}\Big{)}, \tag{5}\]  
where \(\alpha_{F}\) and \(\beta_{F}\) is the fading coefficients of the atmospheric turbulence, and \(K_{p}(\cdot)\) represents the \(v^{th}\) order modified Bessel function of second kind.  
As for the pointing error fading coefficient \(I_{p}\), the PDF of \(I_{p}\) can be expressed as [37]  
\[f_{I_{p}}(x)=\frac{\delta_{\text{F}}^{2}}{A_{0,\text{ F}}^{2}}x^{\delta_{\text{F}}^{2}-1},0\leq x\leq A_{0,\text{F}}, \tag{6}\]where \(\xi_{F}\) is the pointing errors parameter, and \(A_{0,F}\) is the fraction of the collected power without any pointing error. Therefore, taking the above effects into account, \(\delta\) is the average received power of the FSO link, the PDF of the FSO link's SNR can be expressed as  
\[f_{\gamma_{\rm F}}(\gamma_{\rm F})=\frac{\xi_{\rm F}^{2}}{\Gamma(\alpha_{\rm F })\Gamma(\beta_{\rm F})\gamma_{\rm F}}\mathrm{G}_{1,3}^{3,0}\Bigg{(}\frac{ \alpha_{\rm F}\beta_{\rm F}\gamma_{\rm F}}{A_{0,\rm F}\delta}\Big{|}_{\xi_{\rm F }^{2}\alpha_{\rm F},\beta_{\rm F}}^{\xi_{\rm F}^{2}+1}\Bigg{)}. \tag{7}\]  
where \(G_{p,q}^{m,n}[\cdot]\) represents the Meijer-G function. Finally, the CDF of \(\gamma_{F}\) is given as  
\[F_{\gamma_{\rm F}}(\gamma_{\rm F})=\frac{\xi_{\rm F}^{2}}{\Gamma(\alpha_{\rm F })\Gamma(\beta_{\rm F})}\mathrm{G}_{2,4}^{3,1}\Bigg{(}\frac{\alpha_{\rm F} \beta_{\rm F}\gamma_{\rm F}}{A_{0,\rm F}\delta}\Big{|}_{\xi_{\rm F}^{2}, \alpha_{\rm F},\beta_{\rm F},0}^{1,\xi_{\rm F}^{2}+1}\Bigg{)}. \tag{8}\]
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### Linear Memory CTC Alignment
#### 3.4.3 \(\boldsymbol{\alpha}\) and \(\boldsymbol{\alpha_{R}}\) Computation Diagonal-Wise  
Similarly to the textbook DTW case, the Equation (15) can be computed recursively diagonal-wise, instead of row- and column-wise, and it is straightforward to observe from Figure 10 that the values of \(\mathbf{d^{k}}\) can be directly computed given the values of \(\mathbf{d^{k-1}}\), \(\mathbf{d^{k-2}}\), and \(\mathbf{d^{k-3}}\).  
The recurrence rule in Equation (15) can be rewritten as:  
\[\alpha(i,t)=\begin{cases}\max\begin{cases}\alpha(i,t-1)&p(a_{i},t)\quad\text{ if }a_{i}=\epsilon\text{ or }a_{i}=a_{i-2}\\ \alpha(i-1,t-1)&\\ \alpha(i,t-1)&\\ \alpha(i-1,t-1)&p(a_{i},t)\quad\text{otherwise}\\ \alpha(i-2,t-1)&\\ \end{cases} \tag{21}\]  
and the initialisation step Equation (16) becomes:  
\[\begin{cases}\mathbf{d^{1}}=[p(\epsilon,1)]\\ \mathbf{d^{2}}=[p(1,1),p(\epsilon,1)p(\epsilon,2)]\\ \mathbf{d^{3}}=[0,\max\{d^{1}[1],d^{2}[2]\}p(1,2),p(\epsilon,1)p(\epsilon,2) p(\epsilon,3)]\end{cases} \tag{22}\]  
Similarly to the textbook DTW case, once \(\mathbf{d^{k}}\) has been computed, only the values of \(\mathbf{d^{k}}\), \(\mathbf{d^{k-1}}\), and \(\mathbf{d^{k-2}}\) are needed to compute the next diagonal \(\mathbf{d^{k+1}}\), and \(\mathbf{d^{k-3}}\) can be dropped. Using the memory buffer circular shift already described, the requirement of this recursion scales linearly in \(\mathcal{O}(\min(|\mathbf{a}|,T)\). In the case of an audio to text alignment, there are generally much less labels than posteriorgram frames, i.e., \(|\mathbf{a}|<<T\)). The memory requirement of the algorithm therefore scales linearly in \(\mathcal{O}(|\mathbf{a}|)\), i.e., \(\mathcal{O}(|\mathbf{l}|)\).  
The same reasoning applies to compute \(\mathbf{d^{k}_{R}}\) recursively diagonal-wise.
# A Study of Emergency Aircraft Control During Landing
## 3 Proposed Solution  
### Design Principles  
The control algorithm has been prepared in accordance with following guidelines:The system starts its operation at any aircraft in-flight attitude, assuming that engines 1 and 2 are fully operational;
* The system controls the position of both engines' thrust levers;
* The system recognises the position of the aircraft's landing gear and adjusts the parameters of the control paths accordingly;
* The system does not provide any control over the flaps; it is assumed they are also subject to failure and remain in the 0\({}^{\circ}\) position;
* The control algorithm does not take into account changes in aircraft weight or control surface hardovers during failure;
* The system uses flight measurements that are typically available on board;
* The aircraft is equipped with a measuring system providing the actual flight path angle indications;
* The system provides control over the aircraft until touchdown, whereupon engine thrust is minimised and the braking system is activated.  
The aircraft's motion in a three-dimensional space is described by longitudinal and lateral channels. The cross-coupling that exists between these channels (discussed further in Section 3.5) implies that any change in the lateral channel, i.e., the aircraft rolling, results in a change in the longitudinal channel, e.g., induced oscillation in the pitch angle. In the considered controls-failure scenario, the aircraft control in both channels is performed using the same mechanism: engines No. 1 and No. 2, thereby constituting another cross-coupling in the system. This factor affects the shaping of the control algorithm and imposes the development of a method for determining the output thrust lever control commands.  
This chapter focuses on the proposed concept of the emergency thrust control system. The development process included the identification of selected aircraft mathematical model, a control system design, its implementation in a simulation environment, and Software-In-the-Loop tests. Control signals were divided into four groups (see Table 1 in Appendix A):  
* Inputs in the longitudinal channel: u\({}_{\text{EAS}}\), \(\alpha\), \(\varrho\), \(\beta\), \(\gamma\), d\({}_{\text{DME}}\), H\({}_{\text{agI}}\);
* Inputs in the lateral channel: \(\beta\), p, r, \(\phi\), \(\psi_{\text{mag}}\), \(\psi_{\text{loc}}\), \(\Delta\psi_{\text{loc}}\);
* Inputs of the thrust management module: p\({}_{\text{amb}}\), T\({}_{\text{amb}}\), lg;
* Control outputs: \(\delta\)T\({}_{\text{L}}\), \(\delta\)T\({}_{\text{R}}\).  
A detailed graphical representation of the control signals routing in the control system can be found in Section 6.4.
## Abbreviations  
Acronym  
Definition  
3S  
Sensor Stability for SST system  
AATSR  
Advanced Along-Track Scanning Radiometer  
ACSPO  
Advanced Clear Sky Processor for Ocean  
ADS  
Amplitude of Diurnal Signal  
AOML  
Atlantic Oceanographic and Meteorological Laboratory  
AVHRR  
Advanced Very High Resolution Radiometer  
BC  
Buddy Check  
CCI  
Climate Change Initiative  
CMC  
Canadian Meteorological Center  
CMEMS  
Copernicus Marine Environment Monitoring Service  
CRW  
Coral Reef Watch  
DMI  
Danish Meteorological Institute  
DTM  
Drifters and Tropical Moored buoys  
DRC  
Diurnal Reference Check  
DS  
Diurnal Signal  
ENVISAT  
Environmental Satellite  
FNMOC  
Fleet Numerical Meteorology and Oceanography Center  
FRAC  
1 km Full Resolution Area Coverage mode  
GAC  
4 km Global Area Coverage mode  
GAMSSA  
Global Australian Multi-Sensor SST Analysis  
GC  
Plausibility/Geolocation Check  
GDAC  
Global Data Assembly Centers  
GMPE  
SST Global Multi-Product Ensemble  
GPB  
Geo Polar Blended SST  
IC  
Platform ID Check  
ICOADS  
International Comprehensive Ocean-Atmosphere Data Set  
IMOS  
Integrated Marine Observing System (Australia)  
iQuam  
In Situ SST Quality Monitor  
IR  
Infrared  
LEO  
Low-Earth Orbiting  
LEXT  
Local Equator Crossing Time  
MDS  
Data Set of Matchups  
MODIS  
Moderate Resolution Imaging Spectroradiometer  
OISST  
Optimal Interpolation SST  
OSTIA  
Operational Sea Surface Temperature and Sea Ice Analysis  
OSTIA-RAN  
OSTIA Reanalysis  
PGE  
Probability of Gross Error  
RSD  
Robust Standard Deviation  
SD  
Standard Deviation  
SST  
Sea Surface Temperature  
QC  
Quality Control  
QC EXP  
Experimental QC  
QC SAT  
Satellite-based QC  
QL  
Quality Level  
RC  
Reference Check  
SC  
SST Spike Check  
SQUAM  
SST Quality Monitor  
TC  
Platform Track Check  
VIIRS  
Visible Infrared Imaging Radiometer Suite
## 2 Materials and Methods
### Investigation of Optical Parameters  
Color parameters were analyzed using a spectrophotometer (VITA Easyshade V, Bad Sackingen, Germany). The CIELab metric values were recorded and stored for color difference measurements after the discs were immersed in the simulated gastric acid solution. Calibration of the spectrophotometer was done before each specimen's measurement.  
After immersion, the disks were rinsed with water and the CIELab metric values were again determined. Mean values of \(L^{*},a^{*},b^{*}\) were compared with the data gathered prior to the immersion. The total color change (\(\Delta E\)) was calculated for each monolithic material using the following equation:  
\[\Delta E_{ab}\,=\,\sqrt{\left[\left(\Delta L^{*}\right)^{2}\,+\,\left(\Delta a ^{*}\right)^{2}\,+\,\left(\Delta b^{*}\right)^{2}\right]} \tag{1}\]  
where \(L^{*}\)--the lightness coefficient, ranging from 0 (black) to 100 (white); \(a^{*}\)--the shade of redness (positive values) and greenness (negative values); \(b^{*}\)--denotes yellowness (positive values) and blueness (negative values).  
Relative values of \(\Delta L^{*}\), \(\Delta a^{*}\), \(\Delta b^{*}\) corresponded to differences before and after immersion. Color differences were classified as follows: \(\Delta E>1\) the color change can be detected  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Classification** & **Composition** & **Manufacturer** \\ \hline \multirow{2}{*}{Triluxe Forte} & \multirow{2}{*}{feldspathic ceramics} & SiO\({}_{2}\) 56–64\%; Al\({}_{2}\)O\({}_{3}\) 20–23\%; Na\({}_{2}\)O 6–9\%; K\({}_{2}\)O & VITA Zahnfabrik (Bad Sackingen, 6-8\%; CaO 0.3–0.6\%; TiO\({}_{2}\) 0.0–0.1\% & Germany) \\ \hline \multirow{2}{*}{Cerasmart} & \multirow{2}{*}{nanoceramic resin} & 71\% Silica and barium glass nanoparticles & GC Europe (Tokyo, Japan) \\ \hline \multirow{2}{*}{Enamic} & hybrid ceramic with a & SiO\({}_{2}\) 5–63\%; Al\({}_{2}\)O\({}_{3}\) 20–23\%; Na\({}_{2}\)O 6–11\%; K\({}_{2}\)O & VITA Zahnfabrik (Bad Sackingen, 6-4\%; B\({}_{2}\)O\({}_{3}\) 0.5–2\%; CaO \(<1\%;\) TiO\({}_{2}\) \(<1\%\) & Germany) \\ \hline \multirow{2}{*}{Empress CAD} & leucite-reinforced & SiO\({}_{2}\) 60–65\%; Al\({}_{2}\)O\({}_{3}\) 16–20\%; K\({}_{2}\)O 10–14\%; & Ivoclar Vivadent (Zurich, 6-8\%; Gas-ceramic & Switzerland) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition, manufacturer, and classification of the tested monolithic materials.  
visually, but is acceptable; \(\Delta E>3.3\) the color changes are clinically unacceptable [43] and \(\Delta E<1\) are considered undetectable color changes to the human eye [44].