esa-sceva/satcom-chunk-collection huggingface dataset:
## Acknowledgement to Reviewers of _Applied Sciences_ in 2019
Sci._**2020**, _10_, 725  
Almeida, Rita  
Almgren, Khaled  
Almoazen, Hassen  
Al-Mosawe, Alaa  
Al-Naji, Ali  
Alobaid, Mohammad  
Alobaidi, Wissam  
Alodjants, Alexander  
Aloimonos, Yiannis  
Alomari, Kasim  
Alonso, Fabiola  
Alonso, Francisco  
Alonso, Jose Juan  
Alonso, Miguel A.  
Alonso, Serafin  
Alonso-Benito, Alfonso  
Alonso-Caneiro, David  
Aloqaily, Moayad  
Aloulou, Ahmed  
Al-Sadoon, Mohammed  
Alsafrijalani, Mohmad Hammam  
Alsamhi, Saeed H.  
Al-Shakry, Badar  
Alshamaa, Daniel  
Alshammari, Majid R.  
Alsharif, Mohammed H.  
Alshazly, Hammam  
Al-Shibaany, Zeyad  
Alshrah, Mohammed  
Altammar, Hussain  
Altan, Hasim  
Altaner, Bernhard  
Al-temeemy, Ali  
Altemimi, AMMAR.B  
Altena, Bas  
Althunibat, Saud  
Alti, Adel  
Altinsoy, Ercan  
Altomare, Lina  
Alturbbeh, Hamid  
Alvanitopoulos, Petros  
Alvarado, Vladimir  
Alvarelhao, Jose  
Alvarellos Gonzalez, Alberto  
Alvarez Fernandez, Maria Antonia  
Alvarez Pardiinas, Angel  
Alvarez, Fernando Vanegas  
Alvarez, Jose Alberto  
Alvarez, Jose Domingo  
Alvarez, Jose Maria  
Alvarez, Rafael  
Alvarez-Bermejo, Jose Antonio  
Alvarez-Diaz, Marcos  
Alves de Sousa, Ricardo J.  
Alves Duraes, Dalila  
Alves-Foss, Jim  
Aly, Aly-Mousaad  
Aly, Hamed  
Aly, Mohamed  
Alzina Sureda, Francesc  
Amabili, Marco  
Amagase, Kikuko  
Aman, Zachary  
Amano, Ken-ichi  
Amaral, Tito Gerardo  
Amaro, Jose Pedro  
Amarpuri, Gaurav  
Amasyali, Kadir  
Amato, Giampiero  
Amato, Umberto  
Amatya, R.  
Ambra, Tomas  
Amedo, Amoresano  
Amer Jid Almahri, Fatima  
Amer, Hayder  
Amezcua, Javier  
Amezquita-Sanchez, Juan Pablo  
Amigo, Jose-Manuel  
Amin, Sakib-Bin  
Amineh, Reza K.  
Amini, M. Hadi  
Amirat, Yassine  
Amirhosseini, Mohammad Hossein  
Amiri, Ira Sadegh  
Amiri, Leyla  
Amiri, Mehdi* Amirian _et al._ (2020) Amirian, Ehsan Amirkhani, Mehdi Amiryar, Mustafa E. Amitornova, Liubov Amiya, Eisuke Ammari, Habib Ammer, Kurt Ammer, Kurt Amoly, Roozbeh Safaeian Amoretti, Michele Amovilli, Claudio Ampatzidis, Dimitrios Amsaad, Fathi Hassan Mohamed Amundarain, Aiert Amza, Catalin An, Jinwoo An, Sangmin Anagnostopoulos, Costas A. Anagnostopoulos, Iraklis Anagnostopoulos, Marios Anagnostopoulos, Theodoros Anajafi, Hamidreza Anand, Prashant Anand, Vijay Ananthakrishnan, Soundaram Jeevarathinam Anas, Andrea Roxanne Anastasiou, E.K. Anastasopoulos, Dimitrios Anastasovitis, Eleftherios Anastassiu, Hristos Ancillao, Andrea Ancione, Giuseppa Ancona, Maria Alessandra Andelkovic, Zoran Andersen, Jens Christian Anderson, Kevin R. Anderson, Michael Anderson, Paul Anderson, I.Ize Andersson, J.M.
## 1 Introduction
In this article, a new integrity monitoring method was proposed for the GNSS-based attitude determination and they constructed the test statistic by using the measurement residuals to detect the potential failure. Furthermore, they derived the attitude protection level based on the worst-case protection concept under the multiple faults hypothesis. Reference [26] indicated that carrier phase observations error does not subject to the standard normal distribution and a carrier phase-based RAIM algorithm using a Gaussian sum filter was proposed. Since pseudorange-based RAIM (PRAIM) cannot meet the requirement of PPP, References [27; 28; 29] applied CRAIM to GNSS PPP and RTK algorithm for airport surface movement. Similar to GNSS PPP and RTK, carrier phase observations must also be used in high-precision indoor pseudolite positioning. In references [30; 31], a specific algorithm for multiple constellations was studied and a method of integrity monitoring-based ambiguity validation for triple-carrier ambiguity resolution was proposed. They considered that ambiguity validation methods include the ratio test, the quantitative evaluation based on the success rate and the equivalent failure rate of ambiguity resolution. However, the ambiguity correctness validation cannot necessarily guarantee the reliability of indoor pseudolite positioning. Sometimes, an incorrectly fixed ambiguity will be obtained because of the severe environment and the poor satellite geometry. Therefore, it is necessary to carry out the integrity monitoring for indoor pseudolite rather than solely using the ambiguity validation method. The RAIM algorithm is mainly used in safety-critical fields such as aircraft navigation at present and is almost unmanned in indoor pseudolite. In fact, indoor pseudolite positioningis seriously influenced by many environmental factors, such as people's walking, multipath effects from wallsand so on. Sometimes it is difficult to obtain a high-precision position result. In addition, even if a specific positioning result can be obtained, the reliability may not be guaranteed. This paper focuses on the RAIM algorithm of indoor pseudolite system based on EKF, including PRAIM and CRAIM algorithm. Some experiments are operated to verify the feasibility of the indoor pseudolite RAIM algorithm and the reliability of the positioning results, which can expand the availability of pseudolite positioning. According to the actual characteristics of pseudolite, an algorithm to guarantee the accuracy and reliability of indoor positioning for pseudolite based on CRAIM is proposed, which is named PLRAIM (Pseudolite based RAIM). It is especially important to use this method in harsh indoor environments.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 3 Compact Public-Key Cryptosystem with KDM Security
### The Invertible Version of RLWE Problem  
According to [18], authors presented a variant of RLWE problem, defined as the RLWE\({}^{*}\) problem. It is similar to the RLWE problem except that \(a\) chooses from \(R_{q}^{*}\), in which \(R_{q}^{*}\) is the ser of invertible elements of \(R_{q}\). Therefore, we call RLWE\({}^{*}\) the invertible variant.  
RLWE\({}^{*}\) problem. For \(s\in R_{q}\) and error distribution \(\chi\), we define \(A_{s,\chi}^{*}\) as the distribution obtained by sampling the pair \((a,\,as+e)\in R_{q}^{*}\times R_{q}\), where \(R_{q}^{*}\) denote the set of invertible elements of \(R_{q}\). The Decision RLWE\({}^{*}\) problem is to distinguish between \(A_{s,\chi}^{*}\) and \(U\Big{(}R_{q}^{*}\times R_{q}\Big{)}\). Please note that for \(R_{q}^{*}\), [23] claim that for any \(q\geq 2\), the fraction of invertible elements in \(R_{q}\) is at least \(1/\text{poly}(n,\log q)\). Moreover, ref. [18] further shows that as long as \(q=\Omega(n)\), an element choosing from \(U(R_{q})\) is invertible with overwhelming probability. Hence, the RLWE problem remains hard even when applying the invertible technique.  
Scaling the noise. This technique was first formally proposed in [24] and generated the RLWE\({}^{*}\) samples as \((a,\,a\cdot s+t\cdot e)\); security is not affected when \(t\in\mathbb{Z}_{q}^{*}\) and \(q\) are relatively prime, and other parameters are as above.  
Definition 2: (Decision RLWE\({}^{*}\)). The average-case decision version of the RLWE\({}^{*}\) problem, denoted \(\text{RLWE}_{q,\chi}^{*}\), is to distinguish the following two distributions with non-negligible advantage: In the first distribution, one samples \((a_{i},b_{i})\) uniformly from \(R_{q}^{2}\). In the second distribution, one first draws \(s\gets R_{q}\) uniformly and then samples \((a,b=a\cdot s+t\cdot e)\in R_{q}^{2}\) by sampling \(a\gets R_{q}^{*}\) uniformly, where \(R_{q}^{*}\) denote the set of invertible elements of \(R_{q}\), \(e\leftarrow\chi\) and \(t\in\mathbb{Z}_{q}^{*}\).
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 3 PPA Prediction Method
### Prediction Model Derivation
#### 3.2.2 Multivariate Nonlinear Regression Prediction Model  
Inspired via [PERSON]'s law [19], the MNL-PM is proposed to estimate the PPA. [PERSON]'s law provides the maximum theoretical speedup achievable by a system which reflects the influence of each part in one system to the whole system at the same time. In detail, for each parameter \(p_{i}\), \(i\in(1,n)\), the proposed method assumes that there are rates \(w^{\prime}_{i}\), \(w^{\prime\prime}_{i}\), and \(w^{\prime\prime}_{i}\), and \(w^{\prime\prime}_{i}\), each for its proportion in performance, power, and area, while \(w^{\prime}_{i}\), \(w^{\prime\prime}_{i}\), and \(w^{\prime\prime}_{i}>0\) and \(\sum_{i\in(1,n)}w^{\prime}_{i}\),\(\sum_{i\in(1,n)}w^{\prime\prime}_{i}\), \(\sum_{i\in(1,n)}w^{\prime\prime}_{i}<1\). Taking performance (\(P_{1}\)) from PPA for instance, \(P_{1}\), the value of the baseline's performance, will turn to \(P_{1,m^{\prime}}\) and fellow equations holding when parameter \(p_{m}\), \(m\in(1,n)\) is modified to other value in its value range.  
\[S_{1,m}=\frac{1}{(1-w^{\prime}_{m})+w^{\prime}_{m}/(1+\beta_{m})} \tag{9}\]  
\[P^{\prime}_{1,m}=P_{1}\bullet S_{1,m} \tag{10}\]  
In the Equation (9), \(S_{1,m}\) is the change rate and \(\beta_{m}\) is the change rate of the modified \(p_{m}\). And the change rate \(S_{1,J}S\) can be represented as Equation (11), when multiple parameters \(J=\{p_{j}\dots\}\), \(J\subseteq\{p_{1}p_{2}\dots p_{n}\}\) are modified.  
\[S_{1,J}=\frac{1}{\left(1-\sum_{j,p_{j}\in J}^{n}w^{\prime}_{j}\right)+\sum_{j,p_{j}\in J}^{n}\frac{w^{\prime}_{j}}{1+\beta_{j}}} \tag{11}\]  
Although neither the \(\beta_{i}\) nor the \(w^{\prime}_{i}\) are known, the values of \(P_{1}\) and \(P^{\prime}_{1,j}\) are already known in the former preprocess. And \(S_{1,j}\) can directly expressed via \(P_{1}\) and \(P^{\prime}_{1,j}\), as the Equation (14) below.  
\[S_{1,j}=P^{\prime}_{1,j}/P_{1} \tag{12}\]  
Based on the Equations (11) and (12), the change rate S can be calculated via available information, as the Equation (13) below.  
\[S_{1,J}=\frac{1}{\sum_{j,p_{j}\in J}^{n}\frac{P_{1}}{P^{\prime}_{1,j}}-k+1} \tag{13}\]  
And \(P_{1,J^{\prime}}S\) can be calculated as Equation (14), when multiple parameters \(J=\{p_{j}\dots\}\), \(J\subseteq\{p_{1}p_{2}\dots p_{n}\}\) are modified.  
\[P^{\prime}_{1,J}=P_{1}\bullet S_{1,J}=P_{1}\bullet\frac{1}{\sum_{j\in J}\frac{ P_{1}}{P^{\prime}_{1,j}}-k+1} \tag{14}\]  
As the Equations (15) and (16) show separately, the power (\(P_{2}\)) and the area (\(A_{1}\)) can be calculated in the same way  
\[P^{\prime}_{2,J}=P_{2}\bullet\frac{1}{\sum_{j\in J}\frac{P_{2}}{P^{\prime}_{2, j}}-k+1} \tag{15}\]  
\[A^{\prime}_{1,J}=A_{1}\bullet\frac{1}{\sum_{j\in J}\frac{A_{1}}{A^{\prime}_{1, j}}-k+1} \tag{16}\]
## 9 Evaluation Methods for RRE
### Instrumental Measures
#### 9.1.2 Sammon Map  
The Sammon map was introduced for the evaluation of RRE in [230]. It is a non-linear projection method that maps multidimensional data onto fewer dimensions (e.g., two or three). The main property of the Sammon map is that it retains the geometrical distances between signals in a multidimensional space in two or three dimensions. Given the \(M\) magnitude responses \(|H_{k}(e^{i\omega})|\), \(k=1,\ldots,M\), of the measured RIRs, the Sammon map algorithm iteratively minimizes--by a gradient descent scheme--the cumulative sum of the differences between the Euclidean distances in the high and low dimensional space. The following objective function is minimized:  
Figure 11: Equalization results: the spectral deviation measures the distance between the equalized response curve and the flat one: (red) \(H(e^{i\omega})\), (blue) \(H_{inv}(e^{i\omega})\), (green) \(E(e^{i\omega})=H(e^{i\omega})\cdot H_{\rm inv}(e^{i\omega})\).  
\[J_{\text{Sammon}}=\frac{1}{\sum_{l=1}^{M}\sum_{m=l+1}^{M}d_{lm}}\sum_{l=1}^{M} \sum_{m=l+1}^{M}\frac{\left(d_{lm}-d_{lm}^{\prime}\right)^{2}}{d_{lm}}, \tag{9}\]  
with  
\[d_{lm} =\sum_{w=1}^{W}\left(|H_{l}(e^{i\frac{2l}{M}w})|-|H_{m}(e^{i\frac{2l }{M}w})|\right)^{2}, \tag{10}\] \[d_{lm}^{\prime} =\sum_{i=1}^{L}\left|r_{l}(i)-r_{m}(i)\right|^{2}, \tag{11}\]  
where \(W\) is the number of equally-spaced frequencies and \(L\) is the dimension of the Sammon map space.  
In the Sammon map, the point associated with \(H_{k}(e^{i\omega})\) is represented as \(\left(r_{k}(1),\ldots,r_{k}(L)\right)\). Considering a two-dimensional mapping (\(L=2\)), upon convergence, the points \(\left(r_{k}(1),r_{k}(2)\right)\) with \(k=1,\ldots,M\) are configured on a two-dimensional plane such that the relative distances between the different \(H_{k}(e^{i\omega})\) are visually discernible. After equalization, the resulting performance can be determined from the size and shape of the region defined by the equalized frequency responses on the map. A circular shape around zeros indicates uniform equalization at all locations [230]. Figure 12 shows the results obtained using the Sammon map: it can be observed that for IRs without equalization (Figure 12a), the points are located far from the center of the map, while for IRs with equalization (Figure 12b), the points are uniformly distributed around the center of the map.  
Figure 12: Sammon map results: (**a**) RIR without equalization; (**b**) RIR with equalization.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 5 Virtual Space Layer
### Data Aggregation  
The data aggregation sublayer involves the aggregation of the operational and environmental sensor data received via the CNL, as well as historic data of the physical object or system, its design specifications, and bill of materials (BOM) so that it may be used by the data modeling sublayer and the layers above. Data received can be of three types: Structured, semi-structured, and unstructured. Table 2 provides examples of each type of data based on the context of the digital twin. Aggregation of data can take place either on the enterprise premises or on the cloud platform. The aggregation step is essentially ingesting the data sent over by the PSL as well as pre-existing data of the physical object or system, which must then be transferred to infrastructures of databases, called data repositories.  
Two types of data repositories that can be used for the digital twin are data lakes and data warehouses. Data lakes, often called \"schema-on-read\", store structured, semi-structured, and unstructured data in its raw form, i.e., its form prior to being stored in the repository, tagged with metadata. Data lakes scale easily at a reasonable cost and, hence, are ideal for fast storage of massive amounts of data. The information in the data lakes will only be structured once they need to be used for further operations such as analysis. Data lakes are usually a combination of different technologies such as databases (SQL and NoSQL), cloud storage (Amazon S3, Microsoft's Azure Storage, Google Cloud Storage, etc.), and Hadoop Distributed File System. On the other hand, data warehouses can aggregate data over multiple sources like data lakes; however, the data must be organized and structured before storage, which means they are \"schema-on-write\". Hence, data warehouses contain data that are easily analyzed when accessed. Some popular vendor tools for data warehouses include AWS Redshift (Amazon, Washington, USA), Google Big Query (Mountain View, CA, USA), Azure SQL Data Warehouse (Redmond, WA, USA), Oracle Autonomous Data Warehouse (Redwood City, CA, and Snowflake (San Mateo, CA, USA).  
In the context of the digital twin, data lakes can be used for the rapid, real-time storage of operational and surrounding environmental sensors' data, which can then be structured according to the provided schema and stored in the data warehouses in non-real time.  
On the other hand, data warehouses can contain the previously attained historical data, design specifications, and BOM. The information stored in these two types of data repositories will be used to create dynamic 3D models and for further analysis by the layers above.
# A Review of Commercially Available 3D Surface Imaging Systems for Body Composition Estimation
## 3 Results  
Of the 52 3D surface imaging systems identified, six 3D body surface imaging system manufacturers were found to provide commercially available complete whole body 3D surface imaging systems from which body composition measures were estimated. The user and system details for each system are presented in Table 1, alongside the reported accuracy of the humanoid mesh, anthropometrics, and body composition estimates for each system in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**System Name** & **Bodyge Orbiter** & **Fi3D Proscanner** & **mPod** & **Naked** & **Shapescale** & **Styku 5100X** \\ \hline Manufacturer & Bodyge & Fit3D & mPort & Naked Labs & Shapescale & Styleku \\ Manufacturer & Switzerland & USA & Australia & USA & USA & USA \\ location & & Bodyge Orbiter & Fi3D Proscanner & mPod & Naked & Shapescale & Styleu 5100X \\ System & Rotating platform and & Rotating platform and & Instrumentmed boost. & Rotating platform and & Rotating Instrumentmed & Rotating platform \\ description & tablet & instrumentmed tower. & Instrumentmed mirror. & tower. & \\ Technology & Structured light & Structured light & Structured light & Structured light & Structured light & Structured light \\ Website & [[https://bodyge.com](https://bodyge.com)]([https://bodyge.com](https://bodyge.com)) & [[https://tfi3d.com](https://tfi3d.com)]([https://tfi3d.com](https://tfi3d.com)) & [[https://mport.com](https://mport.com)]([https://mport.com](https://mport.com)) & [[https://nakedlab.com](https://nakedlab.com)]([https://nakedlab.com](https://nakedlab.com)) & [[https://shapescale.com](https://shapescale.com)]([https://shapescale.com](https://shapescale.com)) & [[https://tfi3d.com/](https://tfi3d.com/)]([https://tfi3d.com/](https://tfi3d.com/)) \\ Cost hardware & (accessed 1 July 2022) & (accessed 1 July 2022) & (accessed 1 July 2022) & (accessed 1 July 2022) & (accessed 1 September 2022) \\ (USD: ex: shipping) & 56500 & 510,000 & Hardware not for sale & 51395 & 5799 & 64950 \\ Cost subscription & - & No subscription costs & 535 & No subscription costs & 59.99/512.99 & \(\epsilon\)579 per year (279 per month) \\ System volume & 150 \(\times\) 150 \(\times\) 200 & 91.44 \(\times\) 182.88 \(\times\) 180.34 & 180 \(\times\) 170 \(\times\) 240 & 76.5 \(\times\) 90.5 \(\times\) 158.8 & 120 \(\times\) 120 \(\times\) 145 & 131 \(\times\) 185 \(\times\) 117 \\ Capture volume & - & - & - & - & - & - \\ (vv \(\times\) d \(\times\) h (cm) & - & - & - & - & - \\ User mass & None/200 & -/272 & -/200 & -/150 & 11.3/160 & 23/250 \\ min/max. (kg) & & & & & & \\ User height & None/210 & -/213 & - & 100/198 & -/193 & 137/194 \\ Min. gap (years) & 16+ & 13+ & 14+ & 18+ & 2+ & 18+ \\ Capture duration & 60-90 & 35 & 7 & 15 & \(<\)60 & 30-40 \\ Post processing & 5-10 & - & - & 2-3 & - & 2 \\ \end{tabular}
\end{table}
Table 1: The user and system details for 3D surface imaging systems with the body composition estimation capability, in which “-” represents not reported.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
# Investigation of Laser Cutting Width of LiCoO\({}_{2}\) Coated Aluminum for Lithium-Ion Batteries
###### Abstract  
Lithium-ion batteries are widely used for many applications such as portable electronic devices and Electric Vehicles, because they have lighter weight, higher energy density, higher power density, and a higher energy-to-weight ratio than other types of batteries. Conventional contact-based cutting technology may be inefficient whenever cell design is changed since lithium-ion battery cells are not standardized. Furthermore, the conventional cutting may result in process instability and a poor cut quality due to the tool wear so that it leads to short circuits and local heat generation. These process instability and inefficiency may be solved by laser cutting due to advantages such as clean cutting edge, less deformation, applicability to almost all materials, possibility of precision processing, and easy modification of cutting path. Despite the importance of the laser cutting research, no clear definition of cutting widths has been presented, and there is lack of knowledge to understand the effect of laser parameters on cutting widths. Therefore, this research examines the surface of cathode cut by a laser and defines cutting widths such as top width, melting width, and kerf width. The relationship between the laser parameters and cutting characteristics with defined widths are studied. When the volume energy is less than \(6.0172\times 10^{10}\) J/m\({}^{3}\), no active electrode material is removed. When the laser power is greater or equal to 100 W, both the top and melting widths are clearly observed. The laser power of 50 W can selectively ablate the active electrode material with the material removal rate of 32.14-55.71 mm\({}^{3}\)/min. The threshold volume energy to fully penetrate the 50 \(\upmu\)m-thick current collector is between \(9.6275\times 10^{10}\)-\(8.0229\times 10^{10}\) J/m\({}^{3}\). All clearance width is less than 20 \(\upmu\)m, while the clearance width interestingly exceeds 20 \(\upmu\)m when the laser power is 200 W. The effect of material properties on heat transfer using the one dimensional transient semi-infinite conduction model is investigated. In addition, five types of physical characteristics are defined and discussed.  
laser cutting; lithium-ion batteries; LiCoO\({}_{2}\); aluminum; laser cutting width; physical characteristics +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.5 Viscosity  
The viscosity of solvents largely determines the efficiency of extraction; it is important in the development of industrial technologies and in the calculations of fluid flow systems. The choice of the optimal proportion of HBD and HBA and the amount of water in NADES is also carried out taking into account viscosity [55]. The high viscosity of NADESs is beneficial for the design of lubricants [56], supramolecular eutectogel in medicines [57], etc. However, the high viscosity of NADES causes several problems in extraction procedures due to delayed mass transfer during extraction. To overcome this problem, NADES is usually diluted with a certain amount of water. If the amount of \(\pm\) water is low, its molecules are adsorbed into the molecular matrix of NADES, and H-bonds are formed between the ions and HBD. With an increase in water concentrations, the components of NADES actively interact with water, which leads to a decrease in inter- and intramolecular interactions in NADES and destroys its structure [19]. The viscosity of tested NADES diluted with a certain amount of water was measured at room temperature and is presented in Figure 5.  
The composition and water content significantly affect the viscosity of NADES [58]. The viscosity of the tested NADESs was high and decreased in the following order: NADES2 > NADES3 > NADES1. The dilution with water weakened the hydrogen bonds between the NADES components and led to a significant reduction in viscosity. The viscosity decreased by half after adding approximately 10% water. The initial viscosity of NADES2 and NADES3 containing different saccharides was significantly different. However, after  
Figure 5: Dependence of viscosity on the percentage of added water (_w/w_) to NADESs at 25.0 \({}^{\circ}\)C. NADES1—LA:ChCl (3:1); NADES2—LA: SL (1:3); NADES3—LA:GL-W (3:5:1).  
adding 20% of the water, the viscosity was almost the same (Figure 5). Similar results were reported for NADES containing ChCl-diethylene glycol, ChCl-triethylene glycol, ChCl:PEG 200, and 1,2-propanediol-ChCl-water [59]. [PERSON] et al. (2022) have observed a viscosity reduction by 83% in LA-glycine mixture [60].