esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The H\({}_{2}\)SO\({}_{4}\) + HBr System
The  
Figure 9: The normal-mode vectors corresponding to the imaginary frequencies for all transition states in the H2 SO4 + HBr system were calculated at the B3 LYP-D3(BJ)/AVTZ level, with given frequencies in cm\({}^{-1}\). H\(\cdots\)Br distance in CP6_HBr is 2.486 A. The imaginary frequencies of TS7_HBr and TS8_HBr are \(-\)89 cm\({}^{-1}\) and \(-\)310 cm\({}^{-1}\), respectively. As shown in Figure 9, the conformational and normal-mode vectors of the imaginary frequency of TS7_HBr and TS8_HBr are very similar to those of TS10_HCl and TS9_HCl for H\({}_{2}\)SO\({}_{4}\) + HCl in Figure 6. These correspond to the cis-trans conformational change of H\({}_{2}\)SO\({}_{4}\), resulting from a clockwise-counterclockwise rotation of O1-H1, which does not interact with HBr. Compared to CP4_HBr, the energies of TS7_HBr and TS8_HBr are higher by 2.88 and 5.03 kcal/mol, respectively. Interestingly, the transition from CP4_HBr to CP6_HBr encompasses multiple reaction pathways, either in one step or after two consecutive rearrangements. From CP6_HBr to CP8_HBr, the transition state TS9_HBr is involved. Its imaginary frequency is \(-\)100 cm\({}^{-1}\), and the corresponding vibration mode is similar to that of TS3_HCl (Figure 6). In this mode, the H3-Br swing moves the H3 atoms closer to O3 or further away from O3, facilitating ring-forming (B3) or ring-breaking (B8) (Figure 9). TS9_HBr is 3.16 kcal/mol higher than CP4_HBr. In addition, TS10_HBr connects CP8_HBr and CP9_HBr. In TS10_HBr, the O2-H2 near Br forms an approximate plane (D(O1 SO2H2) = 2.51\({}^{\circ}\)) with S-O1. TS10_HBr has an imaginary frequency of \(-\)159 cm\({}^{-1}\), and H3-Br, O1-H1, and O2-H2 all undergo swinging motions of varying amplitudes. TS10_HBr is 5.30 kcal/mol higher than CP4_HBr. It should be noted that CP7_HBr is not shown in Figure 7 as no reaction pathway involving CP7_HBr was found. As shown in Figure 8, nine isomers are reported in the H\({}_{2}\)SO\({}_{4}\) + HBr system. The configurations of CP1_HBr to CP8_HBr isomers are essentially the same as those of CP1_HCl to CP8_HCl in the H\({}_{2}\)SO\({}_{4}\) + HCl system. The hydrogen bonding distances (H\(\cdots\)O or H\(\cdots\)Br distances) formed in these isomers are all larger than those in the H\({}_{2}\)SO\({}_{4}\) + HCl system, as expected, because Br is less electronegative than Cl. The structure of CP1_HBr is similar to that of CP8_HBr, with similar H\(\cdots\)O and H\(\cdots\)Br distances of 2.051 and 2.451 A for the former and 2.013 and 2.457 A for the latter. CP1_HBr differs from CP8_HBr mainly in the configuration of H\({}_{2}\)SO\({}_{4}\). CP1_HBr is 1.42 kcal/mol higher than CP3_HBr.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 2 Related Work  
### Clustering Based Schema with Fixed Sink  
In this schema, a static sink is commonly deployed at one corner of the sensor field. Data are transmitted to the sink by multiple hops communication. One of the representative protocols of this schema is LEACH [19]. LEACH selects CHs in a random manner and sensors take turns to be the CHs for energy balancing. Sensors close to the same CH are classified into the same cluster. PEGASIS [20] adopts the chain structure to connect all the sensors for energy efficiency. The chain structure reduces the average communication distance between sensors, however, much data forwarding results in the heavy traffic burden of the network. HEED [21] firstly introduces the competition mechanism for clustering. Residual energy and the cost of intracluster communication are considered to calculate the competition range. This mechanism makes the CHs selection more reasonable. EEUC [22] also introduces the competition mechanism to divided sensors into clusters with different sizes. Clusters close to the sink have a smaller size so that the intracluster communication can be relieved and more energy can be used for data forwarding.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 3 Results and Discussion
### Denoising Characterization
#### 3.2.2 VMD Analysis  
Assuming that each mode had a finite bandwidth centered around a frequency, it was necessary to find K mode functions such that the sum of their bandwidths was minimized while the superposition of modes equals the original signal [38]. The expression for the constrained variational problem is given by Equation (6).  
\[\left\{\begin{array}{ll}\min\limits_{\{\mu_{k}\},\{\mu_{k}\}}\left\{\sum \limits_{k}\|\partial_{t}\left[(\delta(t)+\frac{j}{\pi t})\mu_{k}(t)\right] \text{e}^{-j\mu_{k}t}\|\mathop{}_{2}^{2}\right\}\\ \text{s.t.}\frac{\sum\limits_{k}\mu_{k}=x}\end{array}\right. \tag{6}\]  
In the equation, \(\delta(t)\) represents the Dirac delta function, \(k\) is the number of decomposed modal components, \(\mu_{k}\) is the modal component obtained after decomposition and \(\omega_{k}\) is the central frequency corresponding to the modal component.  
By introducing a quadratic penalty factor \(\alpha\), we can construct the Lagrange operator \(\lambda_{(t)}\). Through the introduction of the quadratic penalty factor \(\alpha\), the Lagrange operator \(\lambda_{(t)}\) achieved suppression of Gaussian noise while still maintaining strict constraints. The constructed extended Lagrangian function can transform the equation into an unconstrained variational problem. The extended Lagrangian function is given by Equation (7).  
\[\begin{array}{ll}L(\{\mu_{k}\},\{w_{k}\},\lambda)=\alpha\sum\limits_{k}\| \partial_{t}\left[(\delta(t)+\frac{j}{\pi t})\mu_{k}(t)\right]\text{e}^{-j\mu_ {k}t}\|\mathop{}_{2}^{2}+\|x(t)-\sum\limits_{k}\mu_{k}\|^{2}+\\ \left\langle\lambda(t),x(t)-\sum\limits_{k}\mu_{k}(t)\right\rangle\end{array}\] (7 Let \(n=0\), initialize \(\mu_{k}^{1}\), \(w_{k}^{1}\), \(\lambda^{1}\). Using the alternating direction method of multipliers (ADMM), iterate from step \(n\) to \(n+1\) to seek the minimum point of the variational equation \(\mu_{k}^{n+1}\), \(w_{k}^{n+1}\), \(\lambda^{n+1}\), ensuring that the sum of bandwidths of K modal components was minimized. The iteration process is described by Equations (8)-(10):  
\[\hat{\mu}_{k}^{n+1}(w)=\frac{\hat{x}(w)-\sum\
olimits_{\hat{x}\
eq k}\hat{\mu} _{i}(w)+\hat{\lambda}(w)/2}{1+2\alpha{(w-w_{k})}^{2}} \tag{8}\]  
\[w_{k}^{n+1}=\frac{\int_{0}^{\infty}w|\mu_{k}(w)|^{2}dw}{\int_{0}^{\infty}|\mu _{k}(w)|^{2}dw} \tag{9}\]  
\[\lambda^{n+1}(w)\leftarrow\lambda^{n}(w)+\tau\bigg{[}x(w)-\sum\limits_{k}\mu _{k}^{n+1}(w)\bigg{]} \tag{10}\]  
Based on a given criterion precision \(\varepsilon\), if the condition was satisfied, stop updating; otherwise, continue updating iteratively until the precision was met, thereby obtaining the K modal components after signal decomposition [39].  
\[\sum\limits_{k}\frac{\|\hat{\mu}_{k}^{n+1}-\hat{\mu}_{k}^{n}\|_{2}^{2}}{\|\hat {\mu}_{k}^{n}\|_{2}^{2}}<\text{e} \tag{11}\]
# Feasibility Analysis of Industrial High-Temperature Heat Pumps in Greece
## 1 Introduction
Other interesting ideas regarding HTHPs are associated with the exploitation of the electricity surplus from the grid to upgrade heat and store it in thermal tanks for use by industry when needed [16]. The optimization of such systems can lead to a 17% decrease in the operational cost and also to a 15% reduction in CO\({}_{2}\) emissions [16]. Last but not least, it is valuable to refer to the idea of using an HTHP based on the reverse-Brayton cycle to produce heating at high temperatures. [PERSON] et al. [17] investigated a reverse-Brayton cycle HTHP with CO\({}_{2}\) as the working fluid for process heat production up to 280 \({}^{\circ}\)C, giving a solution for operation in temperatures over 200 \({}^{\circ}\)C. Moreover, a reverse-Brayton cycle HTHP can be hybridized with solar thermal collectors, as [PERSON] et al. [18] studied for a paper mill in Greece with process heat production at 195 \({}^{\circ}\)C. This idea can also be integrated with heating and a cold accumulator for integrating storage in the system [6]. The goal of this work is the systematic evaluation of different HTHPs for industrial applications in the temperature range from 100 \({}^{\circ}\)C to 180 \({}^{\circ}\)C. This range is suitable for several industries like dairy, the metal industry, food industry, paper industry, etc. Moreover, the upper limit is reasonable considering the new trends in the design of HTHPs. Detailed thermodynamic analyses of different cases are carried out with a developed model in Engineering Equation Solver [19]. Different scenarios of waste heat upgrading are studied systematically, and a case study on the use of an ambient-source heat pump is examined. The importance of this investigation lies in the comprehensive investigation of different scenarios in energy, economic and environmental terms. The comparison of the two designs, with waste heat and ambient-source exploitation, is very important as it aims to show performance enhancement by utilizing possible waste heat streams in industry. This work provides a multicriteria analysis of the studied technology by examining different driving sources. The analysis is conducted for the city of Athens in Greece, and an hourly-based dynamic analysis is performed for the case of the ambient-source heat pump. The final conclusions of the present analysis could be used in the evaluation of HTHPs for various industrial processes, as well as for different scenarios involving waste heat upgrading and ambient-source designs.
## 7 Experimental Results
### Performance Comparison of EAR2 and EAR2M  
EAR2M exploits multi-path data dissemination through multiple flooding points on the \(EA\) to overcome a single flooding point in EAR2. By comparing EAR2 and EAR2M, we can observe the impact of the number of flooding points and therefore the number of data paths according to the sink speed and end-to-end distance.
## 2 Optimization Model
### Thermal Model
#### 2.3.2 Temperature Variation Model
\(\theta_{ref}(t)\) is given as follows:  
\[\theta_{ref}(t)=\frac{(A_{x,z}F_{x,z}(Q_{x}-Q_{z})+A_{y,z}F_{y,z}(Q_{y}-Q_{z}) )\times\tau}{m_{z}\times C_{Z}} \tag{36}\]  
where \(Q_{x}\), \(Q_{y}\), and \(Q_{z}\) are the radiative heat of the surfaces in zones X, Y, and Z, respectively. \(A_{x,z}\) is the contact area between zones X and Z (m\({}^{2}\)). \(A_{y,z}\) is the contact area between zones Y and Z (m\({}^{2}\)). \(F_{x,z}\) is the shape factor between zones X and Z. \(F_{y,z}\) is the shape factor between zones Y and Z. \(Q_{x}\) is given as follows:  
\[Q_{x}=\epsilon\sigma\theta_{x}^{4}(t) \tag{37}\]  
where \(\epsilon\) is the emissivity coefficient. \(\sigma\) is the proportional constant (kW/m\({}^{2}\)). \(\theta_{x}(t)\) is the temperature of zone X at time \(t\) (K). \(F_{x,z}\) is given as follows:  
\[F_{x,z}=\frac{1}{W_{x,z}\tau}\left( \begin{matrix}W_{x,z}tan^{-1}\frac{1}{W_{x, z}\pi}+H_{x,z}tan^{-1}\frac{1}{H_{x,z}\pi}-\sqrt{H_{x,z}^{2}+W_{x,z} ^{2}}tan^{-1}\sqrt{\frac{1}{H_{x,z}^{2}+W_{x,z}^{2}}}\\ +\frac{1}{4}\left(\begin{matrix}\left(1+W_{x,z}\right)\left(1+H_{x,z}\right) \left(1+H_{x,z}\right)^{2}\\ 1+H_{x,z}\right)\left(W_{x,z}+H_{x,z}\right)\\ \end{matrix}\right)^{H_{x,z}}\\ \cdot\left[\begin{matrix}\frac{H_{x,z}\left(1+H_{x,z}+W_{x,z}\right)}{\left(1 +H_{x,z}\right)\left(W_{x,z}+H_{x,z}\right)}\\ \end{matrix}\right]^{H_{x,z}}\\ \end{matrix} \right) \tag{38}\]  
where \(W_{x,z}\) is given as follows:  
\[W_{x,z}=\frac{w_{x,z}}{l_{x,z}} \tag{39}\]  
and \(H_{x,z}\) is given as follows:  
\[H_{x,z}=\frac{h_{x,z}}{l_{x,z}} \tag{40}\]  
where \(w_{x,z}\) is the width of the vertical plane of the contact surface between zones X and Z. \(l_{x,z}\) and \(h_{x,z}\) are the width and height of the contact surface of zone Z, respectively. The schematic diagram is presented in Figure 3.  
\(F_{y,z}\), \(W_{y,z}\), \(H_{y,z}\), \(w_{y,z}\), \(l_{y,z}\), and \(h_{y,z}\) are the same as those for the relationship between zone Y and zone Z.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Implementation Details  
The experiments were performed on a workstation with an Intel Xeon 2687, 64 GB RAM and an Nvidia P5000 GPU. For feature extraction two ConvNets have been used, as discussed in a previous section. The spatio-temporal ResNet architecture was taken from [PERSON] et al. [24]. It was pre-trained on ImageNet and fine-tuned on our datasets. This has residual connections inbetween motion and appearance streams to incorporate the registration of streams. RGB and flow features are extracted from the pooling layer after the _conv_5_ block from both streams. The ResNeXt model provided by [PERSON] et al. [25] was pre-trained on the large video dataset Kinetics. The top layers of network _conv_5_ and FC layer were fine-tuned for HMDB51 and UCF101 datasets. The model used for fine-tuning was trained on 64 frame input. The input size for ResNeXt-101 is 3 \(\times\) 64 \(\times\) 112 \(\times\) 112. The videos are first converted into frames using the frmpeg-python library. The frames are reshaped to 112 \(\times\) 112 pixels. Videos having less than 64 frames are looped to complete frames as necessary. Details regarding training parameters and network architecture can be seen in [25]. The training data used in training these networks were taken from the standard training splits provided with the datasets [30; 31]. For the HMDB51 dataset, each training split includes around 70 videos from each  
Figure 4: Sample frames from the UCF101 dataset.  
Figure 3: HMDB51 dataset from left to right Push-up, Chew, Cartwheel, Pour, Sword-Exercise.  
action class and for the UCF-101 dataset 18 groups out of 25 and used in training and the rest are used as testing data. The networks were fine-tuned for each of the three splits for both datasets.  
A discriminative codebook is learned using a three-step clustering approach, as discussed earlier. The k-means++ cluster initialization and Lloyd's optimization scheme were set with default parameters as they performed best in preliminary experimentation. First, a global codebook is learned considering features from all the classes. The number of clusters is varied to achieve the best results. Secondly, a class-specific codebook is formed using the features from individual classes. Finally, clusters from both codebooks are clustered together to form a final discriminative codebook. The final step in the pipeline is feature encoding. The features from each video are encoded using the proposed hybrid feature vector. These encoded vectors are L2 normalized. The final normalized vectors act as the training data for classifier, using a one-vs-all SVM classifier.
# Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System
## 4 Simulation and Discussion  
### Simulation Parameters  
In order to evaluate the tracking accuracy and robustness of the proposed control scheme for the electro-hydraulic system, three different working conditions are considered, i.e., no load condition, sudden load condition, and load condition with parameter variations. The simulations are conducted on an integrated simulation platform that uses MATLAB and Amesim. The simulation parameters are described as follows:  
The supply pressure of pump is set at 10 Mpa by a relief valve and the return pressure of hydraulic system is set at 0 Mpa. The dimensions of cylinder are 80 mm/45 mm/500 mm and the control input of the servo valve is in the range of \([-10,+10]\) mA. The system state variables, which include load position \(x\), velocity \(v\), and the chamber pressures \(P_{1}\) and \(P_{2}\) of two cylinder chambers are discretely measured using the sensors in Amesim software. All of the pressure signals are processed through a low-pass filter with 10 Hz cut-off frequency. The control algorithm is implemented in MATLAB and the system sample time is taken as 0.001 s.  
The nominal load mass m is 20 kg and the effective bulk modulus of oil fluid is 1.4 Gpa. The coefficient of cylinder internal leakage \(C_{\texttt{\tiny{}}}\) is \(2\times 10^{-15}\) (m\({}^{3}\)s\({}^{-1}\)/Pa) and servo valve flow gain \(g\) is \(5.82\times 10^{-8}\) m\({}^{3}\)s\({}^{-1}\)/(V\(\sqrt{P_{a}}\)). The original position of piston is in the middle of cylinder and the original control volumes of cylinder chambers are \(V_{1}=1.3\times 10^{-3}\) m\({}^{3}\) and \(V_{2}=8.89\times 10^{-4}\) m\({}^{3}\), respectively.  
To verify the performance of the designed controller, the proposed nonlinear adaptive robust controller (NARC) is compared with the PID controller and determined robust controller (DRC). The control parameters of the proposed controller are selected as \(k_{1}=150\), \(\eta_{2}=40\), \(H_{1}=2.5\), \(\delta=0.01\), \(\beta_{est}=1,\tau=10\), \(H_{2}=1,\eta_{3}=20\), \(\varepsilon_{g}=\varepsilon_{h1}=\varepsilon_{h2}=\varepsilon_{d}=\varepsilon _{e}=0.001\), \(T_{g}=diag\{[10^{-8},10^{-8},0]^{\mathrm{T}}\}\), \(T_{h1}=diag\{[10^{-8},10^{-8},10^{-8}]^{\mathrm{T}}\}\) and \(T_{h2}=diag\{[10^{-15},10^{-3},10^{-3}]^{\mathrm{T}}\}\). The control parameters of determined robust control are the same as that of the proposed controller except that \(T_{g}=diag\{[0,0,0]^{\mathrm{T}}\}\), \(T_{h1}=diag\{[0,0,0]^{\mathrm{T}}\}\) and \(T_{h2}=diag\{[0,0,0]^{\mathrm{T}}\}\). The range limits of parameters used in DRC and NARC are \(\pm 20\%\) of their nominal values. The initial value of parameter estimates are taken as their nominal value. While the control parameters of PID controller are chosen as \(K_{P}=150\), \(K_{I}=1\), and \(K_{D}=0.2\) by trial and error.
## 4 Routing Layer
### Sparse Networks  
In this sub-section, routing protocols mainly intended for sparse networks are discussed. An increasing number of applications for this type of network are emerging because deployment costs in underwater networks are higher than those in RF networks.  
Routing in the adaptive routing protocol [102] is based on the type of packet and requirements, adaptively obtaining high delivery ratio, efficiency, and reduced delay. The application scenarios are 3D sparse USNs divided into layers. USN nodes can move in the 2D horizontal plane and can be deployed at different depths. Nodes use 'hello' and data packets executing a geographic routing scheme. Packet priority is computed taking into account the message emergency level, age, node energy, and density.  
On the other hand, the hop-by-hop vector-based forwarding (HH-VBF) protocol [103] uses the concept of a routing pipe, similar to VBF. The main difference is that HH-VBF creates a pipe for each forwarder. This algorithm enhances the main problems of the VBF protocol. HH-VBF overcomes the previously stated problem found in sparse network forwarding nodes. Simulation results show that HH-VBF enhances the original VBF in terms of delivery rate and by being more energy efficient. On the other hand, the main VBF disadvantages are that node density greatly affects the efficiency of creating a pipe from source to destination. Additionally, the choice of thresholds can affect the routing performance significantly.
## 5 Conclusions  
In our experiments, we addressed the discrepancy issue between the continuous and discrete architectures in the original DARTS. The use of the simple Softmax function to convert the discrete search space [10; 13] into a continuous one and its successful application in the backpropagation process attracted significant attention from the NAS research community. However, the problem of selecting discrete architectures in a continuous search space still lacks sufficient investigation. To tackle this, we applied temperature annealing to the mixed operator in DARTS, resulting in improved performance compared to the original DARTS. Our study successfully mitigated the architectural discrepancy inherent in DARTS. However, complete elimination of the discrepancy has yet to be achieved. Fundamentally representing an architecture obtained through continuous relaxation as an entirely discrete architecture remains a challenging problem in applying gradient-based methods to NAS.  
Conceptualization, J.S. and D.-K.K.; methodology, D.-K.K.; software, J.S.; validation, D.-K.K. and K.P.; formal analysis, J.S. and D.-K.K.; investigation, J.S.; resources, K.P.; data curation, K.P.; writing--original draft preparation, J.S.; writing--review and editing, D.-K.K.; visualization, J.S.; supervision, D.-K.K.; project administration, D.-K.K. and K.P.; funding acquisition, D.-K.K. and K.P. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Technology development Program \"Development of Supply Chain Optimization Technology Based on Big Data and Machine Learning\" (S3126610), funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here (accessed on 8 September 2023): [[[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html))].  
The authors wish to thank members of the Dongseo University Machine Learning/Deep Learning Research Lab., and anonymous referees for their helpful comments on earlier drafts of this paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Model**} & **Params** & **Search Cost** & **Test** & **Search Method** \\  & **(M)** & **(GPU Days)** & **Accuracy (\%)** & **Gradient-based** \\ \hline DARTS (1 st order) + cutout [10] & 3.4 & 1.5 & 97.00 \(\pm\) 0.14 & Gradient-based \\ DARTS (2 nd order) + cutout [10] & 3.4 & 4 & 97.24 \(\pm\) 0.09 & Gradient-based \\ DARTS-EGS (M = 4) + cutout [19] & 2.6 & 1 & 96.99 & Gradient-based \\ DARTS-EGS (M = 7) + cutout [19] & 2.9 & 1 & 97.37 & Gradient-based \\ TA-DARTS (T = 3.0) + cutout & 4.1 & 4 & 97.40 & Gradient-based \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of test accuracy with similar gradient-based approaches on CIFAR-10 dataset. The test accuracies are obtained with retrained models.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 1 Introduction
### Hydrogeological Context of Peru
Despite increasing concerns over groundwater depletion in arid and semi-arid regions of South America, no comprehensive nationwide assessment has been conducted to quantify the spatiotemporal variability in GWS in Peru. Previous studies have been limited to local or regional case studies, lacking a unified framework that integrates satellite gravimetry with climatic and anthropogenic drivers over an extended temporal scale. This study fills this critical gap by presenting the first nationwide evaluation of GWS trends in Peru from 2003 to 2023, leveraging GRACE and GRACE-FO satellite-derived TWS data, combined with precipitation time series, and groundwater abstraction estimates. A robust and reproducible methodology was implemented using Google Earth Engine (GEE), Python 3.7-based geospatial analysis, and in situ hydrometeorological datasets to capture basin- and ecoregion-level dynamics. By linking observed GWS changes with climate variability (e.g., ENSO) and human pressures, this study provides a novel contribution to the understanding of groundwater sustainability under coupled natural-anthropogenic stressors. The findings serve as a scientific foundation for evidence-based groundwater governance, early warning systems, and strategic planning of water resources in Peru, particularly relevant in the context of climate change and increasing water demand. This study seeks to quantify and analyze GWS variability across Peru's coastal, Andean, and Amazonian regions from 2003 to 2023, using GRACE and GRACE-FO satellite gravimetry data validated via in situ measurements. By mapping annual GWS trends and assessing the influence of El Nino-Southern Oscillation (ENSO) events and anthropogenic extraction, it seeks to identify critical areas of depletion and inform sustainable water management strategies for Peru.