esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{\begin{tabular}{c} **Group Before** \\ **Smovey® Activity** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Group After** \\ **Smovey® Activity** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(p\) Value. (After** \\ **Convy® Activity** \\ \end{tabular} } \\  & & & & & **the Control Group** \\ \hline WBC [10\({}^{9}\)/L] & 5.22 \(\pm\) 1.21 & 5.41 \(\pm\) 1.20 & 0.419 & 5.76 \(\pm\) 1.87 & 0.564 \\ RBC [10\({}^{12}\)/L] & 4.17 \(\pm\) 0.24 & 4.46 \(\pm\) 0.39 & 0.010 * & 4.35 \(\pm\) 0.21 & 0.385 \\ HGB [g/dL] & 12.24 \(\pm\) 0.72 & 13.32 \(\pm\) 1.32 & 0.002 * & 13.47 \(\pm\) 0.90 & 0.720 \\ HCT [L/L] & 39.05 \(\pm\) 2.27 & 40.52 \(\pm\) 3.82 & 0.093 & 40.03 \(\pm\) 1.88 & 0.670 \\ PLT [10\({}^{9}\)/L] & 228.38 \(\pm\) 38.79 & 268.15 \(\pm\) 54.26 & 0.000 * & 241.29 \(\pm\) 48.91 & 0.188 \\ MCV [fl] & 93.77 \(\pm\) 4.36 & 90.85 \(\pm\)2.91 & 0.000 * & 92.00 \(\pm\) 3.37 & 0.418 \\ MCH [pg] & 29.38 \(\pm\) 1.60 & 29.71 \(\pm\) 2.15 & 0.275 & 30.99 \(\pm\) 2.03 & 0.124 \\ MCHC [mmol/L] & 31.42 \(\pm\) 0.52 & 32.82 \(\pm\) 0.74 & 0.000 * & 33.68 \(\pm\) 1.66 & 0.100 \\ \hline EI 0.30 [Pa] & 0.04 \(\pm\) 0.01 & 0.05 \(\pm\) 0.01 & 0.014 * & 0.05 \(\pm\) 0.01 & 0.364 \\ EI 0.58 [Pa] & 0.10 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 & 0.894 & 0.10 \(\pm\) 0.02 & 0.909 \\ EI 1.13 [Pa] & 0.19 \(\pm\) 0.02 & 0.18 \(\pm\) 0.02 & 0.509 & 0.18 \(\pm\) 0.02 & 0.816 \\ EI 2.19 [Pa] & 0.29 \(\pm\) 0.02 & 0.28 \(\pm\) 0.02 & 0.432 & 0.28 \(\pm\) 0.02 & 0.708 \\ EI 4.24 [Pa] & 0.38 \(\pm\) 0.02 & 0.38 \(\pm\) 0.01 & 0.880 & 0.37 \(\pm\) 0.02 & 0.160 \\ EI 8.23 [Pa] & 0.44 \(\pm\) 0.02 & 0.45 \(\pm\) 0.01 & 0.383 & 0.43 \(\pm\) 0.02 & 0.012 * \\ EI 15.96 [Pa] & 0.50 \(\pm\) 0.02 & 0.50 \(\pm\) 0.02 & 0.887 & 0.45 \(\pm\) 0.12 & 0.149 \\ EI 31.04 [Pa] & 0.55 \(\pm\) 0.02 & 0.55 \(\pm\) 0.03 & 0.860 & 0.53 \(\pm\) 0.03 & 0.153 \\ EI 59.97 [Pa] & 0.59 \(\pm\) 0.02 & 0.56 \(\pm\) 0.13 & 0.375 & 0.56 \(\pm\) 0.04 & 0.955 \\ AI [\%] & 62.37 \(\pm\) 4.67 & 64.65 \(\pm\) 5.70 & 0.033 * & 62.99 \(\pm\) 5.24 & 0.437 \\ AMP [au] & 25.51 \(\pm\) 2.43 & 21.11 \(\pm\) 1.99 & 0.000 * & 24.22 \(\pm\) 1.78 & 0.000 * \\ T1/2 [s] & 2.27 \(\pm\) 0.50 & 2.02 \(\pm\) 0.61 & 0.034 * & 2.19 \(\pm\) 0.56 & 0.470 \\ \hline \hline \end{tabular}  
* Significant difference (\(p<0.05\)). WBC, white blood cell count; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; PLT, platelet count; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; RDW, red blood cell distribution width; MPV, mean platelet volume; PDW, platelet distribution width; EI, elongation index; AI, aggregation index; AMP, amplitude and total extent of aggregation; T1/2, aggregation half-time.  
\end{table} 
Table 2: Intergroup comparisons of the mean values of indicators (before and after smovey® activity, and after smovey® activity vs. the control group).
# Augmented Reality Applications in Industry 4.0 Environment
## 5 Discussion
### Implementation of AR in Companies
There are two common approaches to software development: standalone software that loads onto the device and can run without an Internet connection and, second, access to AR content hosted in the cloud. The first approach is reliable and solid with a high-resolution experience but it is not easy to change. The second approach requires an internet connection but can be easily upgraded across multiple devices. The ability to publish content is now essential when large amounts of information and frequent content changes are required.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 3 Numerical Evaluation and Validation  
### Numerical Evaluation of the Solution  
Substitute Equation (19) into Equation (18), and then use the inverse Fourier transform and the inverse Laplace transform to obtain the temperature distribution \(\theta_{2}(x,\,z,\,t)\) in Equation (9). However, to analytically calculate the inverse transforms is so difficult, even impossible. Thus, a numerical evaluation method is discussed.  
Firstly, the fast Fourier transform (FFT) method could calculate the inverse Fourier transform efficiently. Noting that there is a singularity at \(p=0\). So  
\[\Theta_{\text{BC}}(p,0,s)=-\frac{1}{\text{j}p-\varepsilon}\cdot F(s) \tag{24}\]  
is used to replace Equation (17), where \(\varepsilon>0\) and \(\varepsilon\to 0\). Subtract and add the same term to the right-hand side of Equation (18) as [29]  
\[\Theta_{2}(p,z,s)=\Gamma_{2}(p,s)\cdot e^{-z\sqrt{p^{2}+s}}-\frac{\Theta_{ \text{BC}}(p,0,s)}{1+h^{*}\sqrt{s}}\cdot e^{-z\cdot(|p|+\sqrt{s})}+\frac{ \Theta_{\text{BC}}(p,0,s)}{1+h^{*}\sqrt{s}}\cdot e^{-z\cdot(|p|+\sqrt{s})}, \tag{25}\]  
making sure that the sum of the first two terms on the right side is equal to \(0\) at \(p=0\). The remained third term could be analyzed separately, as  
\[\frac{1}{2\pi}\int_{-\infty}^{\infty}\frac{\Theta_{\text{BC}}(p,0,s)}{1+h^{*} \sqrt{s}}\cdot e^{-z\cdot(|p|+\sqrt{s})+\text{j}xp}dp= \tag{26}\]  
Secondly, since the complex logarithm of the complex function \(K_{0}(\xi,s)\) is that  
\[\ln K_{0}(\xi,s)=\ln|K_{0}(\xi,s)|+\text{j}\cdot\arg K_{0}(\xi,s), \tag{27}\]the integral in Equation (22) is divided into two terms  
\[\frac{1}{\pi}\text{P.V.}\int_{-\infty}^{\infty}\frac{\ln K_{0}(\xi,s)}{(p-\xi)} d\xi=\frac{1}{\pi}\cdot\int_{-\infty}^{\infty}\frac{\ln[K_{0}(\xi,s)]}{(p-\xi)}d \xi+\frac{\text{j}}{\pi}\cdot\int_{-\infty}^{\infty}\frac{\arg K_{0}(\xi,s)}{(p -\xi)}d\xi. \tag{28}\]  
These integrals in Equation (28) are known as the Hilbert transform, which also could be approximated based on the FFT algorithm [33]. The \(\mathfrak{H}\)[.] denotes the Hilbert transform. As shown in Figure 3, the Hilbert transform of the argument function \(\arg K_{0}(\xi,s)\) has an absolute maximum of under 0.08 when the argument of \(s\) belongs to \((-\pi/2,\pi/2)\). Only half of the value of the Hilbert transform is the index of the exponential function in Equation (22). Even if it was treated as 0, the relative errors of results obtained are less than 5%. Thus, by ignoring the second term on the right-hand side of Equation (28), Equation (22) can be simplified to save computation time.  
Finally, the convolution quadrature method [34] is applied to evaluate the inverse Laplace transform.
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 5 Results and Discussion
### Comparison of Cache Hit Rate and Miss Rate
\begin{table}
\begin{tabular}{c c c c} \hline  & **Direct-Mapped** & **2-Way** & **4-Way** \\ \hline
**Cache Hit** & 279,471 & 283,428 & 289,763 \\ \hline
**Cache Miss** & 86,684 & 84,200 & 80,172 \\ \hline
**Hit Ratio** & 76.32\% & 77.09\% & 78.33\% \\ \hline
**Miss Ratio** & 23.66\% & 22.9\% & 21.67\% \\ \hline
**Average Access Delay** & 10.92 ns & 10.72 ns & 10.41 ns \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of hit and miss rates of 25 K keys. The results related to resource utilization of three cache techniques are given in Table 8. Results show that the set-associative technique utilizes more resources in terms of RAM, Look-up Tables (LUTs), and slice registers compared to the direct-mapped caching. For example, the 4-way set-associative technique requires nearly four times the RAM and twice the number of LUTs and registers compared to the direct-mapped technique. The additional cost of using higher resources provides advantages in terms of improved cache hit rates and access time.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 2 Theoretical Formulation
### Displacement Field  
By definition, the displacement of a particle with position vector \(\mathbf{X}\), in the reference configuration, to a point \(\mathbf{x}=\boldsymbol{\chi}\left(\mathbf{X},t\right)\) in the current configuration is given by the vector \(\mathbf{u}\left(\mathbf{X},t\right)\)  
\[\mathbf{u}\left(\mathbf{X},t\right)=\mathbf{x}-\mathbf{X}=\boldsymbol{\chi} \left(\mathbf{X},t\right)-\mathbf{X}. \tag{8}\]  
The displacement vector \(\mathbf{u}\left(\mathbf{X},t\right)\) can be expanded using the Taylor's series in terms of the thickness coordinate (\(\xi^{3}\)) as follows:  
\[\mathbf{u}\left(\mathbf{X},t\right)=\sum_{k=0}^{\infty}\left.\frac{\left(\xi^ {3}\right)^{k}}{k!}\mathbf{u}^{k}\left(\xi^{\alpha},t\right)\right|_{\xi^{3}=0},\]  
where \(\mathbf{u}^{k}\left(\xi^{\alpha},t\right)=\left.\frac{\partial^{k}\mathbf{u} ^{k}\left(\xi^{i},t\right)}{\partial\left(\xi^{3}\right)^{k}}\right|_{\xi^{3} =0}\). In this work, following the ones of [PERSON] & Reddy [11] and [PERSON] [14], the displacement field is expanded upto the quadratic term in thickness direction:  
\[\mathbf{u}\left(\mathbf{X},t\right)=\underline{\mathbf{u}}\left(\xi^{\alpha},t \right)+\xi^{3}\frac{h}{2}\boldsymbol{\varphi}\left(\xi^{\alpha},t\right)+ \left(\xi^{3}\right)^{2}\frac{h}{2}\boldsymbol{\Psi}\left(\xi^{\alpha},t \right), \tag{9}\]  
where the generalized displacements \(\underline{\mathbf{u}},\boldsymbol{\varphi}\) and \(\boldsymbol{\Psi}\) are defined as follows:  
\[\underline{\mathbf{u}}\left(\xi^{\alpha},t\right)=\underline{u}_{i}\left( \xi^{\alpha},t\right)\mathbf{\hat{E}}_{i},\quad\boldsymbol{\varphi}\left(\xi^ {\alpha},t\right)=\varphi_{i}\left(\xi^{\alpha},t\right)\mathbf{\hat{E}}_{i}, \quad\boldsymbol{\Psi}\left(\xi^{\alpha},t\right)=\Psi\left(\xi^{\alpha},t \right)\mathbf{n}\left(\xi^{\alpha}\right),\]  
and \(\mathbf{\hat{E}}_{i}\) denotes the unit basis vectors in the fixed orthogonal Cartesian coordinate system. The vector \(\underline{\mathbf{u}}\) is associated with the displacements at mid-surface, \(\boldsymbol{\varphi}\) is the difference vector, and it gives the change in the mid-surface director, and the vector \(\boldsymbol{\Psi}\) is defined by scaling the normal vector \(\mathbf{n}\) with the parameter \(\boldsymbol{\Psi}\). Both vectors \(\underline{\mathbf{u}}\) and \(\boldsymbol{\varphi}\) have three components (i.e., six degrees of freedom), and the term \(\boldsymbol{\Psi}\) is the seventh parameter and it denotes the thickness stretch, which is used to mitigate the phenomenon of [PERSON]'s locking that appears in six-parameter formulation [14; 39].
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide  
TiO\({}_{2}\) is the most widely investigated photocatalyst due to its remarkable photo-activity, strong chemical and thermal stability, low toxicity, and cost-effectiveness [23]. In nature, four polymorphs of TiO\({}_{2}\) are found: anatase (tetragonal), rutile (tetragonal), brookite (orthorhombic), and TiO\({}_{2}\)(B) (monoclinic). Additionally, two high-pressure forms have been synthesized starting from rutile: TiO\({}_{2}\)(II) and TiO\({}_{2}\)(H) [24]. However, rutile and anatase are indeed the two most common crystalline forms of TiO\({}_{2}\), and they find numerous applications owing to their unique properties [25]. TiO\({}_{2}\) is an n-type semiconductor, and for bulk materials, anatase has a band gap of 3.20 eV, which corresponds to an absorption threshold at a wavelength of 384 nm, while rutile has a band gap of 3.02 eV, which corresponds to an absorption threshold at the 410 nm wavelength [24]. Therefore, near-ultraviolet (UV) radiation is necessary to excite anatase, whereas the photo-response of rutile slightly extends into the visible light spectrum [26]. These varying band gap values can be attributed to differences in the lattice structures of anatase and rutile TiO\({}_{2}\), resulting in distinct densities and electronic band structures [24]. The TiO\({}_{2}\) photocatalytic activity depends on several factors, including phase structure, specific surface areas, crystallite size, and pore structure. Moreover, the photocatalytic activity of anatase is generally considerably higher than that of rutile [27]. The absorption of photons with energy equal to or greater than the band gap results in the generation of electron-hole pairs through the excitation of electrons from the valence band to the conduction band of the semiconductor. When both electrons and holes migrate to the surface of semiconductor without recombination, the photogenerated electrons can participate in reduction reactions, while the holes engage in oxidation reactions. These processes form the basis for photocatalytic water splitting and the photodegradation of organic pollutants [28].
## 4 Mobile Localization Algorithms
### Centralized Localization Algorithms
#### 4.1.4 Collaborative Localization Scheme (CLS)  
Collaborative Localization Scheme (CLS) for mobile UWSNs is proposed in [42], where nodes collaborate to determine their positions autonomously without using long range transponders on surface buoys or ships. Starting at the surface where the network is deployed, sensor nodes use buoyancy control to descend deeper into ocean. Once a maximum desired depth is reached, they travel back to the surface. While nodes are descending, although they know their depth by pressure sensors, their positions in the other two dimensions change continuously due to the motion induced by currents. In order to track the descending nodes, they are classified into two categories: profilers and followers. Initially, all nodes are at the surface, so that their positions can be obtained by GPS. A profiler travels to a depth first. Then, followers travel the trajectory of the profiler. All the nodes descend with the same speed. The profiler's location is a prediction of the followers' future locations. The followers get the profiler's location by ToA technique, as shown in Figure 8.  
CLS is an anchor-free and cost effective self-localization strategy that does not require prior node planning. The drawback of CLS is its architectural dependence; for a sparse or non-homogenous network, the performance of CLS can be affected significantly. Moreover, time synchronization is required. In order to get higher localization accuracy, the profilers have to stay closer to the followers. Otherwise, the profilers going out of communication range of the followers results in localization failures.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 2 SNP Preparation Method
### High-Speed Jet  
Recently, a physical technology has been developed based on a high-speed jet treatment which is a novel combination with high pressure and speed and a model of liquid-solid impact [69]. It is basically consisted of a piston chamber in which crude suspension is drawn in through downward stroke. The suspension passes through a small, fixed nozzle that produces a high-speed jet during the upstroke. Before colliding with the target, the jet circulates through a pipe and then it is cooled by a recirculating flow of coolant from the rupture chamber walls. However, it is suggested that the high-speed jet is an intense mechanical treatment that can affect the size and the gelatinization properties of starch [70]. The schematization of this process is shown in Figure 10 above.  
In a recent study [PERSON] et al. synthesized SNPs from tapioca starch using this physical method. Before the high-speed jet treatment, the native starch was treated with a vibrating superfine mill with different micromization periods to improve the polymer solubility and after 60 min of micromization, the optimum pretreated material was achieved. Next, the  
Figure 10: Synthesis of SNPs by high-speed jet method and TEM micrograph of tapioca SNPs. Reprinted from [71] with permission from Elsevier. Copyright (2020).  
Figure 9: Synthesis of SNPs through ultrasonic atomization method and FE-SEM micrograph of SNPs. Reprinted from [68] with permission from Elsevier. Copyright (2019).  
pretreated starch was mixed with distilled water and stirred at room temperature and then, the suspension was treated with a high-speed jet at different pressures followed by vacuum filtering and finally dried for 24 h. Particle size decreased after the high-speed jet treatment and an increase in the pressure produced a more uniform SNPs size achieving the smallest sizes (55.3 nm) with 240 MPa of pressure [71].  
Nanoparticles have been used as vehicles for the delivery of bioactive compounds for several applications in food, cosmetic and pharmaceutical formulations. Frequently, in order to obtain more effective materials with large surface area per unit of mass, hollow nanoparticles are prepared. The most common materials used for hollow nanoparticle preparation are polymers. Starch is a natural polymer that has attracted interest for its use as raw material for covering already prepared nanoparticles of several other metal materials. However, their individual use for SNPs preparation has increased during the last decade. SNPs present both advantages and disadvantages with respect to the other type of nanoparticles which are made with other materials, such as polymers, metals, or silica, as summarized in Table 1[72; 73; 74; 75].
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Generic, All-In-One Systems
#### 5.1.4 AWS Blockchain Partners Portal  
_Amazon Web Service (AWS) Blockchain Partners Portal_ provides a wide range of capabilities and the largest global infrastructure for building end-to-end blockchain platforms cost efficiently and on a large scale. _AWS Partner Network (APN)_ Technology and Consulting partners offer a rapidly growing selection of blockchain and distributed-ledger solutions with support for multiple protocols.  
The key features that AWS infrastructure offers to its users are:  
* Easy-to-deploy development environment, already familiar to the community working with Cloud infrastructure. Facilitates the design of distributed ledgers and blockchain-based applications,reducing the time to plan and implement a basic setup, in particular when a large number of partners are involved.
* Scalability and vast amount of resources (pay as you grow) that can help a lot when testing out new limits in sharing information between nodes.
* The open source code and support available through the AWS blockchain partners. The reuse of code can speed up the basic proof-of-concept building and enables more diversity for testing different customer use-cases.  
Several key players have developed their own distributed ledger solutions using the AWS platform mainly as IaaS. Most notable examples are Kaleido [65], Corda [52], and PokitDok [66].
## 1 Introduction  
Technological advances in the field and an increased clinical tolerability to treatment have heralded a significant rise in the age-specific rates of incidence and prevalence of hemodialysis (HD) treatment, regardless of the presence of a range of comorbid conditions [1]. In 2016, a mean age of dialysis patients of 62.4 years (prevalence 134 patients/per million population (pmp)) was recorded in Europe, with spikes in Israel and Portugal of 365 and approx. 332/pmp, respectively [2]. In 2017, 29% of all hemodialysis patients were aged 75 years or older (64% men, 36% women) with a five-year survival rate of 55% [3]. It is an acknowledged fact that an increase in age of HD patients produces a proportional rise in frailty, particularly related to nutritional disorders [4], seriously affecting patient survival and quality of life. Nutritional disorders are frequently manifested during the first stage of chronic kidney disease (CKD), classified according to the degree of Glomervlar Filtration Rate (GFR, mL/min/1.73 m\({}^{2}\)) [5]. As a general rule, nutritional issues become more pronounced from stage CKD3-CKD4 (GFR: 29-60 mL/min/1.73 m\({}^{2}\)) [6;7]. In patients with CKD and ESRD, metabolic and regulatory disorders including acidosis [8], systemic inflammation [9] and hormonal dysregulation may be manifested [10]. These disorders are largely attributed to the onset of hypercatabolism and risk of negative nitrogen balance. Clinicians should not underestimate aspects related to an incorrect application of dietary restrictions imposed by a hypoproteic diet in the more advanced stages of CKD, (GFR \(<\) 29 mL/min/1.73 m\({}^{2}\)) [11], including increased energy expenditure [12]. The negative influence afforded by a series of comorbidities and concomitant medications should also be considered.  
End-stage kidney disease (ESKD) patients moreover are frequently affected by loss of appetite, anorexia and gastrointestinal complaints as a result of severe dysbiosis and associated significant imbalance of uremic microbiota [13], together with increased gut mucosal permeability to protein-bound uremic toxins [14]. This situation may deteriorate further when an ESKD patient starts hemodialysis treatment. In addition to the issues listed above, patients may also experience (a) hypercatabolic status during and up to several hours after HD [15]; (b) oxidative stress and chronic inflammation [16; 17]; (c) progressive decrease in diuresis with loss of residual kidney function (RKF) caused by hemodialysis treatment applied in the lack of a tailored hemodalytic dose as recommended by precision medicine [18]; (d) insignificant depuration of protein-bound uremic toxins (PUBT) (particularly indoxyl-sulphate and p-cresol [19]), (e) loss of amino acids during hemodialysis (over 0.8 g/year) with progressive decrease of muscle mass; and (f) increased insulin resistance [20; 21; 22]. In the absence of therapeutic/nutritional countermeasures, protein-energy wasting will develop [23]. The aim of this paper is to highlight diagnosis and therapeutic procedures based on the most recent knowledge.
## 4 Results and Discussion
### Relationship between Rainfall and Groundwater Level Changes  
To investigate the relationship between rainfall and groundwater level changes, data from 2011 to 2019 were collected for analysis. The accumulated rainfall data in Figures 15 and 16a,b show the spatial distribution of accumulated rainfall and the estimated GWLF potential in the wet and dry seasons, respectively. As shown in both figures, the amount of rainfall is proportional to the estimated GWLF potential. By comparing the spatial distribution of rainfall with the GWLF data, a good correlation between the groundwater level change and seasonal rainfall for most of the wells is also found. The  
Figure 15: Estimated groundwater level fluctuation potential during 2011–2019 in the wet/dry season.  
groundwater level usually changes with the occurrence of rainfall. It can be inferred that the groundwater circulation is apparent. However, while rainfall stops, the groundwater level will return to its normal groundwater level, indicating that the aquifer cannot store groundwater easily in the mountainous area.  
In addition, the time required for the groundwater level to return to the pre-rainfall level varies for each monitoring well. The influencing factors control the response to groundwater level changes. Taking BHW-09 as an example (Figure 17), the groundwater level variation is close to rainfall changes. However, the groundwater level is obviously delayed by the actual changes. The delayed situation may be due to the process of rainwater infiltrating into the saturated aquifer, which can be affected by the geomorphological characteristics (slope, drainage density, and land use), geological characteristics (lithology and regolith thickness), and hydrogeological characteristics (hydraulic conductivity, porosity, and depth to the water table). The fast response time of this lag situation is within a few minutes to a few hours, and the slow response may take several days [16].  
Figure 16: Spatial distribution of accumulated rainfall and estimated GWLF potential during 2011-2019 in the wet/dry season.