esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Residue Number System and Multiple Error Correction  
### Residue Number System  
A residue number system (RNS) [7; 32; 33] is a non-weighted number system. In an RNS, an integer \(X\) is represented by a \(k\)-dimensional vector \((x_{1},\ x_{2},\ \ldots,\ x_{k})\) of the remainders in dividing it by positive coprimes \((m_{1},\ m_{2},\ \ldots,\ m_{k})\) called the RNS moduli. The magnitude of \(X\) belongs to the range \([0,\ M_{K}-1]\), where  
\[M_{K}=\prod_{i=1}^{k}m_{i},\]  
and each remainder \(x_{i}\) is the least nonnegative modulo \(m_{i}\) residue of \(X\):  
\[x_{i}=X\text{mod}m_{i}\ \Big{(}\text{equivalently},\ |X|_{m_{i}}\Big{)}. \tag{1}\]  
Note that when obtaining the RNS representation of \(X\), the modulus remainders are calculated independently and in parallel. The paper [30] considered the most effective methods for calculating the remainder of the division and presented schematic diagrams for their hardware implementation.  
The inverse conversion, i.e., restoring the weighted representation of a number from its RNS representation, is a much more difficult task than the direct conversion. The weighted representation of a number (its weighted characteristic) can be found using the Chinese Remainder Theorem (CRT) [7], the CRT with fractions (CRTf) [20; 21; 22], or conversion to a mixed radix number system (MRNS) [34; 35].  
The modified multiple error correction method proposed below partially involves the CRT and CRTf.
ISPRS Int. J. Geo-Inf.**2014**, \(3\), 1491-1511; doi:10.3390/ijgi3041491  
**Abstract:** With the ever increasing volume of remote sensing imagery collected by satellite constellations and aerial platforms, the use of automated techniques for change detection has grown in importance, such that changes in features can be quickly identified. However, the amount of data collected surpasses the capacity of imagery analysts. In order to improve the effectiveness and efficiency of imagery analysts performing data maintenance activities, we propose a method to predict relevant changes in high resolution satellite imagery based on human annotations on selected regions of an image. We study a variety of classifiers in order to determine which is most accurate. Additionally, we experiment with a variety of ways in which a diverse set of training data can be constructed to improve the quality of predictions. The proposed method aids in the analysis of change detection results by using various classifiers to develop a relevant change model that can be used to predict the likelihood of other analyzed areas containing a relevant change or not. These predictions of relevant change are useful to analysts, because they speed the interrogation of automated change detection results by leveraging their observations of areas already analyzed. A comparison of four classifiers shows that the random forest technique slightly outperforms other approaches.  
**Keywords:** change detection; classification; remote sensing
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
###### Abstract  
An ultrasonic coupling agent, as an acoustic medium between the ultrasonic probe and the surface of the specimens, is indispensable in Nondestructive Testing (NDT). Whether it is liquid, air, or solid coupling agent, the problem of improving the efficiency of ultrasonic propagation in a coupling agent is one worth studying. Glycerol and hydrogels are two common liquid coupling agents in NDT. This study intended to investigate the effect of graphene addition on the performance of these coupling agents in NDT. Firstly, based on the theory of acoustic impedance matching, the authors established an index system to evaluate the performance of ultrasonic coupling agent by experiments. Secondly, hydrogel-graphene and glycerol-graphene composite coupling agents were prepared by adding three-dimensional graphene structure powders with mass fraction of 0.25%, 0.5%, 0.75%, and 1% to CG-98 hydrogel coupling agent and HG-99 glycerol coupling agent, respectively. Corresponding experiments were conducted on these composite coupling agents. Peak-to-peak value, attenuation coefficient, and energy value of first echo are calculated at different frequencies. The experimental results showed that graphene can significantly improve the ultrasonic propagation performance of hydrogel and glycerin coupling agents. In addition, when the mass fraction of graphene added was 0.75%, the coupling agent had the best performance. Finally, we measured the acoustic impedance values of the composite couplings with different graphene contents to demonstrate the reliability of the experimental results.  
graphene; ultrasonic coupling agent; time-domain analysis; acoustic impedance +
Footnote †: journal: Applied sciences
## 3 Results
### Explored Attributes  
We removed certain attributes at the start of our analyses using three major correlation matrices: one for each data transformation, accounting for the three backscatter coefficients and four calibrated products, accounting for the 19 variables from the first refined study [31; 32]. Accordingly, herein, we simply explore the information of 13 variables, thus reducing the dimensionality of the problem in relation to our earlier investigations (Figure 3). This set of variables are collectively referred to as the oil-slicks' signature:  
* Area (Area);
* Perimeter (Per);
* Ratio between Per and Area (PtoA [78]);
* Compact index (4.\(\pi\).Area/Per\({}^{2}\)[28]);
* Fractal index (2.In(Per/4)/In(Area) [79]);
* Average (AVG);
* Median (MED);
* Mode (MOD);
* Standard deviation (STD);
* Variance (VAR);
* Coefficient of dispersion (COD: the third interquartile minus the first, divided by their sum);
* Skewness (SKW); and
* Kurtosis (KUR).  
The first five correspond to the size information and the next eight are the SAR basic qualitative-quantitative statistics. The latter are divided in: central tendencies (AVG, MED, and MOD), measures of dispersion (STD, VAR, and COD), and pixel distribution metrics (SKW and KUR).  
An imperative information is that when \(\log_{10}\) is applied, only 10 variables are accounted, as Fractal, SKW, and KUR have negative values preventing their use.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Oil Seeps** & \multicolumn{2}{c}{**Oil Spills**} & \multicolumn{2}{c}{**Oil Slicks**} \\ \hline Correctly & Sensitivity & Correctly & Specificity & Correctly & Overall \\ Identified & Positive & Identified & Negative & Identified & Overall \\ oil seeps & predictive value & oil spills & predictive value & oil slicks & accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 3: Condensed confusion matrix form (i.e., Table 1) showing the statistical metrics (i.e., Table 2) explored herein to evaluate our linear discriminant analysis (LDA) algorithms.  
Figure 3: Sequential portrayal of our exploratory data analysis compared to those of our earlier investigations [29; 30; 31; 32]. See the text for more details.
## 6 FWA Transport Network Solutions
### Wireless Solution
Besides the inherent fiber features like RoF, the FSO system gives further advantages concerning cost-effectiveness and time-saving, because physical fiber deployment is not required. Therefore, FSO is an appropriate solution in setups where physical network connectivity by means of optical fiber is unrealistic and/or challenging. Moreover, FSO can be employed for broadband service provisioning in a rural area with an insufficient fiber infrastructure [2; 380; 3; 47; 381]. It is remarkable that, when employed as a complementary transport solution, it can be an attractive platform for mobile traffic offloading. This can significantly help in lessening the imposed stringent requirements on the networks. Furthermore, as stated in Section 2.2.4, the FSO system presents several salient advantages that portray it as a feasible broadband access technology. Besides, it can support the bandwidth requirements of various applications and services at a relatively lower cost. As a result, the radio-over FSO (RoFSO) notion has been introduced. RoFSO scheme is based on leveraging the inherent huge capacity of optical systems while simultaneously exploiting the wireless network easy of deployment [2; 3; 47]. In addition, with DWDM RoFSO solution implementation, multiple wireless signal transmissions can be concurrently supported [382]. Based on the enumerated drawbacks of FSO technology in Section 2.2.4, it is unreliable compared with the physical optical fiber technology. Consequently, it might not be a good solution for use cases like uRLLC applications. Thus, the FSO scheme cannot be considered an effective standalone solution. To make the FSO system viable, it is very imperative to alleviate the related turbulence-induced fading [162; 164; 165; 166]. Consequently, to address the challenges, many physical (PHY) layer concepts have been introduced [47; 167; 383]. In addition, several advanced schemes like hybrid RF/FSO and relay-assisted transmission technologies can be employed to improve the system performance concerning reliability, capacity, and availability [47].
## 2 Formulation of the PEC Analytical Model
In Equation (1), when the air-core coil is excited by harmonic excitation [15], the impedance change of air-core coil can be written as  
\[\Delta Z(\omega)=j2\pi\omega\mu_{0}n_{c}^{2}\sum_{i=1}^{\infty}\frac{\chi^{2} (\lambda_{0i}r_{1},\lambda_{0i}r_{2})}{\lambda_{0i}^{2}h^{2}[J_{0}(\lambda_{ 0i}h)]^{2}}(e^{-\lambda_{0i}z_{1}}-e^{-\lambda_{0i}z_{2}})^{2}R_{0i,1i}. \tag{3}\]  
On the other hand, for the case of the air-core coil in air [15], the impedance can be expressed as  
\[Z_{air}(\omega)=j4\pi\omega\mu_{0}n_{c}^{2}\sum_{i=1}^{\infty}\frac{\chi^{2}( \lambda_{0i}r_{1},\lambda_{0i}r_{2})}{\lambda_{0i}^{2}h^{2}[J_{0}(\lambda_{0i }h)]^{2}}[\lambda_{0i}(z_{2}-z_{1})+e^{-\lambda_{0i}(z_{2}-z_{1})}-1]. \tag{4}\]  
In Equation (3), the \(R_{0i,1i}\) can be expressed as  
\[R_{0i,1i}=\frac{(\lambda_{0i}\mu_{r})^{2}-\lambda_{1i}^{2}+e^{-2\lambda_{1i}d }[\lambda_{1i}^{2}-(\lambda_{0i}\mu_{r})^{2}]}{(\lambda_{1i}+\lambda_{0i}\mu_ {r})^{2}-e^{-2\lambda_{1i}d}(\lambda_{1i}-\lambda_{0i}\mu_{r})^{2}} \tag{5}\]  
where \(n_{c}=N_{c}/(r_{2}-r_{1})(z_{2}-z_{1})\), \(N_{c}\) denotes the number of the coil turns, \(r_{2}\), \(r_{1}\) are the outer and inner radius of the coil, \((z_{2}-z_{1})\) denotes the height of the coil, \(\lambda_{0i}\) is the \(i-th\) positive root of the Bessel function \(J_{1}(\lambda_{0i}h)\), \(J_{m}\) is the \(m-th\) order Bessel function of the first kind, \(R_{0i,1i}\) is a constant related to the conductivity, permeability and thickness of measured metallic plates, \(\lambda_{1i}=\sqrt{\lambda_{0i}^{2}+j\omega\sigma\mu}\). In order to obtain the difference PEC response for simulating transient induced voltage, the periodic pulse signal is applied. Combined with the superposition principle and inverse Fourier transform, the PEC transient response can be reconstructed from the harmonic eddy current response [17; 18]. For the \(T\)-periodic pulse, the formula can be expressed as  
\[y(t)=A[(1-e^{-\frac{t}{a}})step(t)+(e^{\frac{\tau-t}{a}}-1)step(t-\tau)]\tau<T \tag{6}\]  
where, \(a\) is the time constant, \(step(t)\) is the step function, \(A\) is the amplitude value of excitation current, \(\tau\) is the excitation time, \(T\) is the excitation period.
## 3 Results and Discussion  
### Analysis of Water Retention in Anionic Fluorinated Solutions  
1.1 Study on the Change Law of Coal Dust Weight after the Action of Anionic Fluorine-Containing Solution  
Figure 2 demonstrates a continuous decrease in water retention of the FS-60 solution with time at all concentrations. The minimum weight loss occurred at 24-36 h, followed by a rapid increase and reaching its peak at 48-60 h. The FS-61 solution exhibited considerably higher weight loss at 48-60 h compared to other solution types. For the FS-63 solution, there was an initial rise in weight loss, followed by a subsequent decline and reaching its maximum at 24-36 h. Nevertheless, for the mass fractions of 0.01% and 0.1% solutions, weight loss increased again at 48 h and beyond. This behavior indicates that the FS-63 solution undergoes certain changes or reactions during the initial hours, leading to weight loss. However, at a certain point, these reactions may slow down or reach a state of equilibrium, resulting in a decline in weight loss. The subsequent rise in weight loss at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Reagents** & **Short Form** & **Active Component** & **Molecular Formula** & **Type** \\ \hline Perfluoropropyl acrylate & FS-30 & 24–26\% & CH\({}_{2}\)=CHC(0)OC\({}_{3}\)F\({}_{7}\) & Non-ionic \\ Perfluorocotyl acrylate & FS-31 & 24–26\% & CH\({}_{2}\)=CHC(0)OC\({}_{8}\)F\({}_{17}\) & Non-ionic \\ Perfluorocotylsulfonyl & FS-34 & 24–26\% & C\({}_{8}\)F\({}_{17}\)SO\({}_{2}\)OC6H\({}_{4}\)OH & Non-ionic \\ phenoxyethyl methacrylate & & & \\ Perfluoropropanoic acid & FS-50 & 26–28\% & CF\({}_{3}\)CF\({}_{2}\)COOH & Non-ionic \\ Sodium perfluorocotanesulfonate & FS-60 \({}^{\text{a}}\) & 39–41\% & C\({}_{8}\)F\({}_{17}\)SO\({}_{3}\)Na & Anionic \\ Perfluoroisopropyl acrylate & FS-61 & 13–15\% & C\({}_{4}\)F\({}_{3}\)COOCH\({}_{3}\) & Anionic \\ Perfluorocotanesulfonate & & & \\ carbamate acid & FS-63 \({}^{\text{b}}\) & 34–36\% & C\({}_{9}\)H\({}_{6}\)F\({}_{17}\)NO\({}_{2}\)S & Anionic \\ \hline \hline \end{tabular} a: 15% isopropyl alcohol; b: 20% isopropyl alcohol.  
\end{table}
Table 2: Experimental fluorinated surfactants.  
Figure 1: Water retention test procedure.  
48 h and beyond for specific mass fractions may indicate the occurrence of a different mechanism or reaction in those concentrations.
## 5 Analysis of Test and Calculation Results
### Analysis of Measurement Data and Simulation Calculation Data at Different Loads  
Under the working condition of 3000 r/min, the test and calculation were performed according to Table 1. The results are shown in Figure 5.  
Figure 5 demonstrates that the calculation results approximate the measurement results, and the correlation coefficient is high. According to Figure 5, by increasing the load, the movement area of the axis orbit becomes larger, and the horizontal movement amplitude increases significantly. Thus, the lubrication conditions of the bearing worsened.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Province** & **WWTP Equivalent** & **m\({}^{3}\) of Biomethane** & \multicolumn{1}{c}{**GWh of**} \\  & **Habitants [29]** & **per Year** & **Biomethane per** \\ \hline Las Palmas & 1,502,729 & 2,597,512 & 34.81 \\ Santa Cruz de Tenerife & 1,111,264 & 1,920,853 & 25.75 \\ Araba/Alava & 467,864 & 808,717 & 10.84 \\ Albacete & 673,989 & 1,165,010 & 15.61 \\ Alacant/Alicante & 3,904,614 & 6,749,242 & 90.46 \\ Almeria & 702,817 & 1,214,840 & 16.28 \\ Avila & 505,487 & 873,749 & 11.71 \\ Badajoz & 1,015,101 & 1,754,632 & 23.52 \\ Illes Balears & 2,001,382 & 3,459,449 & 46.37 \\ Barcelona & 7,019,380 & 12,133,208 & 162.62 \\ Burgos & 1,034,129 & 1,787,523 & 23.96 \\ Caceres & 589,442 & 1,018,868 & 13.66 \\ Cadiz & 2,744,829 & 1,587,956 & 36.79 \\ Castello/Castellón & 990,326 & 1,711,808 & 22.94 \\ Ciudad Real & 1,217,763 & 2,104,940 & 28.21 \\ Cordoba & 947,162 & 1,637,198 & 21.94 \\ A Coruña & 1,431,609 & 2,474,579 & 33.17 \\ Cuenca & 308,873 & 533,896 & 7.16 \\ Girona & 750,521 & 1,297,298 & 17.39 \\ Granada & 947,394 & 1,637,599 & 21.95 \\ Guadalajara & 291,376 & 503,652 & 6.75 \\ Gipuzkoa & 1,129,153 & 1,951,775 & 26.16 \\ Huelva & 719,937 & 1,244,433 & 16.68 \\ Huesca & 355,530 & 614,544 & 8.24 \\ Jaén & 573,795 & 991,822 & 13.29 \\ Leon & 672,440 & 1,162,333 & 15.58 \\ Lleida & 311,168 & 537,863 & 7.21 \\ La Rioja & 456,492 & 789,060 & 10.58 \\ Lugo & 296,730 & 512,907 & 6.87 \\ Madrid & 6,883,568 & 11,898,453 & 159.47 \\ Malaga & 2,206,214 & 3,813,507 & 51.11 \\ Murcia & 2,435,998 & 4,210,695 & 56.44 \\ Navarra & 1,264,919 & 2,186,450 & 29.30 \\ Ourense & 494,431 & 854,639 & 11.45 \\ Asturias & 1,377,194 & 2,380,521 & 31.91 \\ Palencia & 336,645 & 581,901 & 7.80 \\ Pontevedra & 1,607,471 & 2,778,562 & 37.24 \\ Salamanca & 818,980 & 1,415,631 & 18.97 \\ Cantabria & 656,348 & 1,134,517 & 15.21 \\ Segovia & 261,660 & 452,287 & 6.06 \\ Sevilla & 2,424,413 & 4,190,670 & 56.17 \\ Soria & 167,044 & 288,741 & 3.87 \\ Tarragona & 734,179 & 1,269,050 & 17.01 \\ Teruel & 190,909 & 329,992 & 4.42 \\ Toledo & 1,251,219 & 2,162,769 & 28.99 \\ Valencia/Valencia & 5,280,824 & 9,128,062 & 122.34 \\ Valladolid & 1,009,007 & 1,744,099 & 23.38 \\ Bizkaia & 1,570,292 & 2,714,297 & 36.38 \\ Zamora & 205,368 & 354,985 & 4.76 \\ Zaragoza & 1,771,939 & 3,062,849 & 41.05 \\ Ceuta & 114,595 & 198,081 & 2.65 \\ Melilla & 100,000 & 172,853 & 2.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Potential of biomethane production from WWTP.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods  
### Hard Carbon Manufacturing Process  
This study focuses on the techno-economic performance of producing hard carbon from bamboo processing residues, a by-product of the established bamboo industry. As illustrated in Figure 1, the full value chain includes bamboo cultivation, primary processing, transportation, carbonization, and integration into battery manufacturing. However, this work only evaluates the bolded segments in the figure: transportation of bamboo residues and the manufacturing of hard carbon via thermal and chemical processing. A detailed breakdown of capital and operating expenditures (CAPEX and OPEX) within this system boundary is also provided in Figure 1 to clarify the scope of the analysis.  
Figure 1: The hard carbon production process and system boundary.  
The manufacturing process is constructed based on the optimized industrial pathway identified in previous life cycle assessments of hard carbon for sodium-ion batteries [17]. In this study, bamboo residues are first subjected to acid washing using sulfuric acid to remove mineral impurities and enhance carbon yield. After drying, the material undergoes pre-carbonization to eliminate most volatiles at a moderate temperature (\(\sim\)500 \({}^{\circ}\)C), followed by high-temperature pyrolysis at 1300 \({}^{\circ}\)C in an inert atmosphere to induce graphitization and form disordered graphite microcrystals. These are then further processed via ball milling to achieve the particle size required for sodium-ion battery anodes.
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 4 Modified Two-Zone Temperature Field Model
### Correction of the Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & \multicolumn{3}{c}{**CASE1**} & \multicolumn{3}{c}{**CASE2**} & \multicolumn{3}{c}{**CASE3**} & \multicolumn{3}{c}{**CASE4**} \\ \cline{2-13}  & **c**(\({}^{\circ}\)C) & **s**(\({}^{\circ}\)C) & **e** & **c**(\({}^{\circ}\)C) & **s**(\({}^{\circ}\)C) & **e** & **c**(\({}^{\circ}\)C) & **e** & **c**(\({}^{\circ}\)C) & **s**(\({}^{\circ}\)C) & **e** \\ \hline
13.2 & 91.57 & 95.76 & 0.04 & 84.22 & 83.04 & 0.01 & 68.42 & 73.82 & 0.07 & 65.23 & 61.49 & 0.06 \\ \hline
12.2 & 80.01 & 89.83 & 0.11 & 72.04 & 76.43 & 0.06 & 55.20 & 64.41 & 0.14 & 54.50 & 48.34 & 0.13 \\ \hline
11.2 & 70.06 & 78.74 & 0.11 & 61.98 & 64.60 & 0.04 & 45.53 & 46.39 & 0.02 & 38.68 & 39.33 & 0.02 \\ \hline
10.2 & 61.50 & 71.40 & 0.14 & 53.68 & 57.19 & 0.06 & 38.45 & 38.62 & 0.00 & 32.78 & 33.16 & 0.01 \\ \hline
9.2 & 54.13 & 56.52 & 0.04 & 46.82 & 43.19 & 0.08 & 33.28 & 30.51 & 0.09 & 27.17 & 28.93 & 0.06 \\ \hline
8.2 & 47.79 & 49.96 & 0.04 & 41.17 & 37.85 & 0.09 & 29.50 & 28.19 & 0.05 & 26.02 & 26.03 & 0.00 \\ \hline
7.2 & 42.33 & 44.85 & 0.06 & 36.50 & 34.08 & 0.07 & 26.73 & 26.72 & 0.00 & 25.26 & 24.04 & 0.05 \\ \hline
6.2 & 37.63 & 36.24 & 0.04 & 32.65 & 29.09 & 0.12 & 24.71 & 24.92 & 0.01 & 24.05 & 22.68 & 0.06 \\ \hline
5.2 & 33.58 & 32.54 & 0.03 & 29.47 & 27.71 & 0.06 & 23.23 & 24.26 & 0.04 & 23.54 & 21.75 & 0.08 \\ \hline
4.2 & 30.10 & 28.05 & 0.07 & 26.85 & 26.29 & 0.02 & 22.15 & 23.51 & 0.06 & 22.70 & 21.11 & 0.08 \\ \hline
3.2 & 27.11 & 26.85 & 0.01 & 24.68 & 25.67 & 0.04 & 21.36 & 23.16 & 0.08 & 22.37 & 20.67 & 0.08 \\ \hline
2.2 & 24.53 & 25.86 & 0.05 & 22.89 & 25.01 & 0.08 & 20.78 & 22.77 & 0.09 & 22.11 & 20.37 & 0.09 \\ \hline
1.2 & 22.31 & 23.74 & 0.06 & 21.42 & 23.27 & 0.08 & 20.36 & 22.03 & 0.08 & 21.51 & 20.16 & 0.07 \\ \hline \hline \end{tabular} Note: \(c\) represents the calculated temperature value; \(s\) represents the simulated temperature value; \(e\) is the error between the two.  
\end{table} 
Table 10: Calculated values with simulated values. Figure 32: Revised temperature distribution model. is suitable for the large-span spherical dome space structure, which is of significance for its fire protection structure design. Utilizing the aforementioned methodology, temperature calculations and fire numerical simulations were conducted for an 80 m-span spherical dome structure with a height of 15 m, air supply outlet areas of 40 m\({}^{2}\) and 50 m\({}^{2}\), and a fire source power of 20 MW. The results, depicted in Figure 35, demonstrate a high degree of correlation between the calculated curves and the numerical simulation outcomes, thereby validating the applicability of our calculation method.
# Comparison of Amsler-Krumeich and Sandali Classifications for Staging Eyes with Keratoconus
###### Abstract  
Keratoconus (KC) is the most common corneal etcasia characterized by progressive corneal thinning, protrusion, and irregular astigmatism. The Amsler-Krumeich classification based on the analysis of corneal topography, corneal thickness, refraction and biomicroscopy is the most commonly used; recently, a new classification based on anterior segment Optical Coherence Tomography was introduced by [PERSON] and colleagues. Since there is no information about the possible agreement between these two classifications, the aim of this study is to compare the stratification of consecutive KC patients using the Amsler-Krumeich and Sandali classifications, and to further ascertain KC cases in which one classification is preferred over the other. Overall, 252 eyes of 137 patients (41.45 \(\pm\) 16.93 years) were analyzed: in 156 eyes (61.9%), the Amsler and Sandali staging differed in one stage while in 75 cases (29.8%) it differed in two or more stages. In 222 eyes (88.1%), the Sandali staging was higher compared to the Amsler one. These results show that the two classifications are not fully interchangeable: the Amsler-Krumeich classification is more appropriate in identifying and longitudinally monitoring patients with early stages of KC, while the Sandali classification for the diagnosis and follow-up of patients with more advanced stages, particularly when a surgical planning has to be chosen.  
Keywords:kearatoconus; cornea; Amsler-Krumeich classification; Sandali classification; anterior segment optical coherence tomography (AS-OCT) +
Footnote †: journal: Accepted: 27 April 2021