esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Recommendation Generation
#### 4.2.2 Recommendation Based on Similarity  
Similarity-based recommendation is a popular method for providing personalized recommendations by analyzing the similarity between users or items. The core idea of this approach is that if a user likes an item, they may also like other similar items.  
The computation of similarity is the key step in similarity-based recommendation. The features of a user or item are represented as vectors and their similarity is calculated using methods such as cosine similarity, Pearson's correlation coefficient, or Euclidean distance. The selection of the method depends on the specific application scenario and data characteristics.  
This paper calculates cosine similarity between entities based on their attributes or features and recommends entities with high similarity to users. Cosine similarity is a common recommendation algorithm used to measure the angle between two vectors to determine their similarity. The value of cosine similarity ranges from \(-1\) to \(1\), with a higher value indicating greater similarity. The list of recommendations is sorted according to similarity.
## 4 Performance Evaluation  
This section introduces several performance metrics obtained when using the FPBR protocol. These performance metrics are compared with those obtained when using the DDOR protocol for benchmark purposes. Particularly, metrics such as packet delivery ratio, delay per hop, overhead, MAC overhead, end-to-end delay and number of hops were obtained for both protocols by means of a simulation setup programmed in the OPNET Modeler simulator. Two different propagation models were considered for the simulation setup: freespace propagation and the highway propagation model introduced in [37] for VANETs. For the simulation setup the IEEE 802.11a OPNET model was modified to resemble the IEEE 802.11p PHY/MAC layers as described below.
## 6 Conclusions  
In the present study, nonlinear FE models were established to simulate the behavior of LWAC and SFLC beams reinforced with GFRP bars. A dynamic explicit procedure together with a progressive model was employed to simulate the brittle failure of the beams. The validation of FE models was confirmed according to the experiments previously reported by the authors, and the influencing factors, including the height, reinforcement ratio, and length of span were discussed through the FE method. The following conclusions are drawn:  
1. The established FE models successfully obtained three failure modes of beams referring to the test results, namely concrete crushing, balanced failure, and FRP rupture. This confirmed that the constitutive models of LWAC and SFLC introduced in the FE model are efficient, and the inclusion of progressive damage into the properties of GFRP bars is capable of reflecting the rupture behavior. Moreover, the explicit procedure entails the uninterrupted calculation when brittle failures occurred.
2. The FE models yielded accurate ultimate capacity predictions with an average experimental-to-predicted ratio of \(1.04\pm 0.17\). At service load, the estimated deflection closely matched the measured results with an average experimental-to-predicted ratio of \(1.09\pm 0.19\).
3. The reinforcement ratio corresponding to balanced failure was higher than that given by code ACI 440.1R, which confirmed the necessity of the amplification factor of a balanced reinforcement ratio to ensure the concrete crushing of the beam.
4. For specimens that failed due to concrete crushing, the increase in the FRP reinforcement ratio did not significantly improve the ultimate capacity, but it did have an obvious effect on the reduction of deflection at service load.
5. The increase rate of the deflection at service load was higher for GFRP-reinforced beams with larger clear span lengths. Therefore, a greater reinforcement ratio is needed for beams when the span length is increased.  
Conceptualization, T.W.; Data curation, Y.S. and Y.L.; Formal analysis, Y.S. and Y.L.; Funding acquisition, T.W. and X.L.; Investigation, Y.L., X.L. and H.L.; Supervision, T.W.; Visualization, Y.S. and Y.L.; Writing--original draft, Y.S. and Y.L.; Writing--review and editing, Y.S., X.L. and T.W.  
Figure 22: Influence of clear span length: (**a**) Moment–deflection response of FE models; (**b**) \(\Delta_{\text{s}}\) of specimens with varied \(L\).  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 51708036, 51878054, and 51908041), the Natural Science Foundation of Shaanxi Province (Key Research and Development Program) (No. 2018 ZDCXL-SF-03-03-02) and Natural Science Foundation of Shaanxi Province (No. 2018 JQ5005).  
**Conflicts of Interest:** The authors declare there is no conflict of interest regarding the publication of this paper.
# Phytochemical Analysis and Amoebicidal Evaluation of Different Agave Species
## 3 Results and Discussion
### Agaxe Extracts Reduced the Cell Viability of E. histolytica Trophozoites  
The percentage of viable parasites was measured after 48 h of incubation with Agaxe extracts using the trypan blue exclusion test. Interestingly, the extracts that showed less amoebicidal activity turned out to be from the species with the highest presence of blue-stained cells (nonviable cells) (Figure 4I). With _A. maximiliana_, at 24 h, only 73%, 61%, and 52% were viable at 100, 300, and 600 ug/mL, respectively (Figure 4E). _A. maximiliana_ was the species with the highest concentration of saponins (Table 2). Saponins have properties that facilitate the formation of insoluble complexes with components of the cell membrane (steroids, proteins, and phospholipids, especially cholesterol), which contributes to the permeability of the cell membrane, leading to the formation of pores, leading to a massive release of cytoplasmic content and the formation of apoptotic bodies, which eventually induces the death of the parasite [64; 65]. In other Agaxe extracts, it has been reported that saponins from _A. lophantha_ and _A. brittoniana_ at concentrations of 500, 100, and 10 ug/mL decreased the viability of _T. vaginalis_, _G. lamblia_, and _E. histolytica_[35; 66]. The DMSO-treated cells did not exhibit any significant difference compared with the untreated cells.  
Figure 3: Comparison of growth inhibition rates of Agaxe extracts (600 ug/mL) and MTZ (1.4 ug/mL) at 24 h (**A**), 48 h (**B**), and 72 h (**C**). The boxes represent the 25–75 th percentiles, and the medians are indicated. The whiskers show the range. Error bars represent the standard deviation (n = 3 experiments performed in triplicate; * \(p\leq 0.05\), ** \(p\leq 0.005\), and *** \(p\leq 0.0001\)).  
Figure 4: Percentage inhibition of viability due to the effect of Agave ethanolic extracts in _E. histolytica. A. angustifolia_ at 24 h (**A**) and 48 h (**B**), _A. rhodacanthu_ at 24 h (**C**) and 48 h (**D**), _A. maximiliana_ at 24 h (**E**) and 48 h (**F**), and _A. Tequiliana_ at 24 h (**G**) and 48 h (**H**). Inverted optical microscope (Nikon Eclipse Ts2) pictures of trypan-blue-stained trophozoites after DMSO or Agave extract treatment (600 \(\mu\)g/mL, 48 h) (**I**). The boxes represent the 25–75 th percentiles, and the medians are indicated. The whiskers show the range. Results were obtained from three experiments independently using the triplicate SD. ******* \(p\leq\) 0.0001. Bar = 100 \(\mu\)m.
## 7 Conclusions  
In this paper, the VS control of the PMSG-based wind turbine with the auxiliary energy storage battery is researched. The system structure and control strategy are presented, where the GSC adopts the dynamics of the DC-link capacitor to achieve the PLL-less grid-synchronization, and the DC-link voltage can sense the fluctuation of grid frequency in real time. The inertial response control is added to the control loop of the MSC. The droop control of the ESC's output power with respect to the DC-link voltage can realize the primary frequency regulation function. To deal with the short-circuit fault, an LVRT control strategy of the wind turbine based on switching the virtual resistor in the GSC is proposed, which can effectively suppress the GSC's overcurrent through the coordinated control of the ESC. Based on analyzing the transient characteristics of the VS-controlled wind turbine after the short-circuit fault, a design method of the virtual resistor parameter is proposed. At last, based on the PSCAD/EMTDC simulation platform, the feasibility of the proposed virtual resistor control strategy and the correctness of the theoretical analysis are verified.  
Conceptualization, S.S. and B.P.; methodology, S.S.; software, J.H.; validation, S.S., L.Z. and X.X.; formal analysis, S.S.; investigation, L.Z.; resources, X.X.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, S.S.; supervision, S.S.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, grant number BK20200969.  
Not applicable.  
Figure 11: Simulation results of the wind turbine’s LVRT based on the virtual resistor control.
## 3 Results
### Participants
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Cycle Ergometer Exercise**} \\ \hline \multirow{2}{*}{-} & **Baseline (_n_ = 15)** & **Just after Exercise** & **20 min after Exercise** \\  & **(_n_ = 15)** & **(_n_ = 15)** & **(_n_ = 15)** \\ \hline \multicolumn{4}{c}{**Variables**} & **Mean** \(\pm\) **SD** & **Mean** \(\pm\) **SD** \\ \hline \multicolumn{4}{c}{Systolic blood pressure (mmHg)} & 121.1 \(\pm\) 14.9 & 172.5 \(\pm\) 34.4 ** & 116.1 \(\pm\) 12.7 \\ \multicolumn{4}{c}{Diastolic blood pressure (mmHg)} & 76.9 \(\pm\) 16.3 & 69.7 \(\pm\) 18.7 & 77.5 \(\pm\) 12.5 \\ \multicolumn{4}{c}{Heart rate} \\ \multicolumn{4}{c}{Heart rate} \\ \multicolumn{4}{c}{(beats per min)} & 78.7 \(\pm\) 9.5 & 169.6 \(\pm\) 13.6 ** & 92.7 \(\pm\) 12.4 ** \\ \multicolumn{4}{c}{Lactic acid (mmol/L)} & 1.8 \(\pm\) 0.5 & 10.4 \(\pm\) 2.3 ** & 6.7 \(\pm\) 2.2 ** \\ \multicolumn{4}{c}{Blood glucose level (mg/dL)} & 100.4 \(\pm\) 10.3 & 89.8 \(\pm\) 15.5 & 84.1 \(\pm\) 11.6 ** \\ \multicolumn{4}{c}{White blood cells (103/\(\mu\)L)} & 6.4 \(\pm\) 1.5 & 9.0 \(\pm\) 2.1 ** & 7.0 \(\pm\) 1.6 ** \\ \multicolumn{4}{c}{Platelets (103/\(\mu\)L)} & 234.1 \(\pm\) 36.8 & 270.9 \(\pm\) 36.6 ** & 248.9 \(\pm\) 38.2 ** \\ \multicolumn{4}{c}{ Hematocrit (\%)} & 42.2 \(\pm\) 3.7 & 46.0 \(\pm\) 4.2 ** & 43.8 \(\pm\) 4 ** \\ \multicolumn{4}{c}{Diacon-reactive oxygen metabolites (U. CARR)} & 303.6 \(\pm\) 64.2 & 321.7 \(\pm\) 64.8 & 317.1 \(\pm\) 67.2 \\ \multicolumn{4}{c}{Biological antioxidant potential (\(\mu\)mol/L)} & 2447.3 \(\pm\) 486.3 & 2592.0 \(\pm\) 320.2 & 2603.0 \(\pm\) 278.3 \\ \multicolumn{4}{c}{Total vessel density of small vessels (mm/mm\({}^{2}\))} & 11.8 \(\pm\) 2.2 & 12.3 \(\pm\) 1.5 & 12.3 \(\pm\) 2.4 \\ \multicolumn{4}{c}{Perfused vessel density (mm/mm\({}^{2}\))} & 9.2 \(\pm\) 2.6 & 9.6 \(\pm\) 1.5 & 9.5 \(\pm\) 2.7 \\ \multicolumn{4}{c}{Percentage of perfused small vessels (\%)} & 80.3 \(\pm\) 15.4 & 80.2 \(\pm\) 10.7 & 78.7 \(\pm\) 15.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Serial changes in hemodynamics, serum lactate, blood glucose levels, and oxidative stress before and after the cycle ergometer exercise.
## 2 PSA Plant Model and Simulation
The energy balance provides insight into temperature variations within the bed, which is critical to monitor since stable temperatures are essential to avoid disrupting the adsorption of molecules or atoms. The kinetic and thermodynamic model quantifies the number of adsorbed molecules and the rate at which they reach saturation. The PSA process operates under a Cyclic Steady State (CSS), and the resulting product purities are illustrated in Figure 2. \begin{table}
\begin{tabular}{l l} \hline \multicolumn{3}{l}{**Conditions for Initialization and Boundaries:**} \\ \hline \multicolumn{3}{l}{**Step I (Adsorption):**} \\ \hline \(t=0:\) & \(c_{H_{2}}(z,0)=c_{0}\), \(c_{CH_{4}}(z,0)=0\), \(c_{CO_{2}}(z,0)=0\), \(c_{CO}(z,0)=0\) \\  & \(T(z,0)=T_{0}\), \(T_{w}(z,0)=T_{0}\), \(p(z,0)=p_{0}\), \\ \(\eta_{i}(z,0)=\eta_{i}^{*}\), \(U_{z}=U_{2}\) & \\ \(z=0:\) & \(-D_{L}\frac{\partial c_{i}}{\partial z}=u[c_{i}(0,t)-c_{i}(0^{+},t)]\), \(p=p_{0}\), \(U_{z}=U_{z0}\) \\  & \(-K_{L}\frac{\partial T}{\partial z}=e_{b}C_{P}gU_{z}[T(0^{-},t)-T(0^{+},t)]\) \\ \(z=L:\) & \(-D_{L}\frac{\partial c_{i}}{\partial z}=0\), \(U_{z}=0\), \(-K_{L}\frac{\partial T}{\partial z}=0\), \(\frac{\partial p}{\partial z}=0\) \\ \hline \multicolumn{3}{l}{**Step II (Depresurization):**} \\ \hline \(t=0:\) & \(c_{H_{2}}(z,0)=c_{0}^{(I)}\), \(c_{CO}(z,0)=c_{CO^{\prime}}^{(I)}\), \(c_{CH_{4}}(z,0)=c_{CH_{4}}^{(I)}\), \(c_{CO_{2}}(z,0)=c_{CO_{2}}^{(I)}\) \\  & \(T(z,0)=T^{(I)}\), \(p(z,0)=p^{(II)}\), \(\eta_{i}(z,0)=\eta_{i}^{*(I)}\) \\  & \(z=0:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(\frac{\partial p}{\partial z}=0\) \\ \(z=L:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(U_{z}=0\), \(\frac{\partial T}{\partial z}=0\), \(F=F_{\mathrm{valve}}\) \\ \hline \multicolumn{3}{l}{**Step III (Purge):**} \\ \hline \(t=0:\) & \(c_{H_{2}}(z,0)=c_{0}^{(II)}\), \(c_{CO}(z,0)=c_{CO}^{(II)}\), \(c_{CH_{4}}(z,0)=c_{CH_{4}}^{(III)}\), \(c_{CO_{2}}(z,0)=c_{CO_{2}}^{(II)}\) \\  & \(T(z,0)=T^{(II)}\), \(p(z,0)=p^{(II)}\), \(\eta_{i}(z,0)=\eta_{i}^{*(II)}\) \\  & \(z=0:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(\frac{\partial p}{\partial z}=0\) \\ \(z=L:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(U_{z}=0\), \(\frac{\partial T}{\partial z}=0\), \(F=F_{\mathrm{valve}}\) \\ \hline \multicolumn{3}{l}{**Step IV (Representization):**} \\ \hline \(t=0:\) & \(c_{H_{2}}(z,0)=c_{0}^{(III)}\), \(c_{CO}(z,0)=c_{CO}^{(III)}\), \(c_{CH_{4}}(z,0)=c_{CH_{4}}^{(III)}\), \(c_{CO_{2}}(z,0)=c_{CO_{2}}^{(III)}\) \\  & \(T(z,0)=T^{(III)}\), \(p(z,0)=p^{(III)}\), \(\eta_{i}(z,0)=\eta_{i}^{*(III)}\) \\ \(z=0:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(\frac{\partial p}{\partial z}=0\) \\ \(z=L:\) & \(\frac{\partial c_{i}}{\partial z}=0\), \(U_{z}=0\), \(\frac{\partial T}{\partial z}=0\), \(F=F_{\mathrm{valve}}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Initial and boundary conditions used in the process.
## 3 The Role of Energy Storage in Micro-Grids
### Battery Charging Process  
Battery cell manufacturers usually provide the user with constraints about the appropriate recharging process of the battery. The suggested charging profile is the Constant-Current Constant-Voltage (CC-CV) one [70]. It consists of a first phase in which the battery is charged with a constant current. The current value depends on the cell technology, and it is in the order of the battery capacity \(C\) expressed in ampere. Fast charging usually exceeds 1 C, whereas slow charging spans from 0.1 C to 0.5 C. The faster the charging, the higher the internal cell temperature and thus the possible stress that may determine ageing. The second phase starts when the battery voltage reaches the float value. The charger keeps the voltage constant since then, to gradually fill the battery up the maximum charge level. The process ends when the decreasing current reaches a threshold, usually a rather low fraction of the battery capacity expressed in ampere. The battery inserted in the multi-source/sink micro-grid scenario, depicted in Figure 6, cannot fully adhere to the above described charging process. Instead, as the charging and discharging phases are defined according to the overall management of the energy fluxes, the BMS should verify the manufacturer constraints in terms of current, temperature, and individual cell voltages. Should it happen, the bi-directional DC/DC converter, that connects the battery to the power bus (see detailed circuit schematic in Figure 7), must be driven in such a way as to reduce the charging current and to withstand the manufacturer's limits. As the battery is not subjected to full discharge/charge cycles, the accurate knowledge of the battery SoC is mandatory to fully exploit the established energy flux policy mentioned in Section 2.3.  
Figure 6: New hybrid micro-grid with full connectivity among RES node (DC), EV node (DC) and main grid node (AC) and relevant bi-directional flows. EV: Electric Vehicle.
###### Abstract  
Magnesium is an essential nutrient for the growth and development of plants. Its deficiency is becoming a growing concern in many citrus orchards worldwide, adversely affecting numerous functions in plants and limiting their productivity and quality. Three-year-old orange (_Citrus sinensis_ cv. Newhall) plants grafted on Swingle citrumelo (_C. _pancisi_ Macf. \(\times\)_Poncirus trifilata_ L) roostock were irrigated for 63 days with nutrient solutions containing 0, 12, 24, 48, 96, and 192 mg Mg L\({}^{-1}\). Thereafter, Mg deficiency-induced changes were investigated in leaf chlorophyll concentration and fluorescence, and in proline and carbohydrate concentration in leaves and roots, as well as in the nutritional status of leaves, stems, and roots. Magnesium concentration in the nutrient solution was positively correlated with the concentration of Mg (leaves, stems, roots), Ca (rootstock's stem), K (roots), and Fe (leaves, rootstock's stem), as well as with the total Mg absorption. However, Mg concentration in the nutrient solution was negatively correlated with the concentration of Mn (rootstock's stem, roots), Ca (leaves, scion's stems, roots), and Fe (roots), as well as with the total absorption of Mn, Fe, P, K, and Ca. The lower values of the chlorophylll fluorescence parameters were observed by the effect of the highest concentration of Mg. As far as leaf chlorophylll concentration and carbohydrate and proline content of leaves and roots, they were not affected by the Mg treatments. Concluding, this research highlights the significance of Mg management in citrus farming, offering insights into increasing Mg concentrations, understanding root mechanisms in Mg absorption, and suggesting the benefits of fertilization to address Mg deficiency. It emphasizes the importance of careful Mg fertilizer dosages, considering other nutrient interactions, and provides valuable guidance for optimizing Mg nutrition and overall nutrient management in sweet orange trees.  
ch chlorophyll fluorescence; deficiency; macronutrient; photosynthesis; plant nutrition; rootstock; stress +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### IR Spectroscopy
It is noteworthy that after the grinding of CAT-H, a liquid product (catechol solution) was obtained, so the IR spectrum of CAT-H was obtained in liquid form. The fact that water acts as an interference in the IR spectrum of CAT-H means that it is not part of the crystalline lattice of the solid, discarding the hydration. This is in agreement with the absence of reports of hydrated solid forms of CAT. The IR spectrum of the CAT-TEO 1:1 mixture ground with water (CAT-TEO-H, Figure 2) showed a different pattern with respect to the anhydrous ground mixture (CAT-TEO, Figure 2). The IR spectrum of CAT-TEO-H showed two O-H bands at 3517 and 3454 cm\({}^{-1}\); meanwhile, CAT-TEO showed a single O-H band at 3151 cm\({}^{-1}\). In the same way, the IR spectrum of CAT-TEO-H showed a different pattern of C=O bands with respect to the CAT-TEO spectrum. This allows determining that the CAT-TEO-H ground solid is the cocrystal hydrate.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 3 Methods
### Deriving STA Frame  
The key to deriving the STA frame lies in how to calculate the spatial feature values of the image. The easiest method to think of is to use a mask for dilating and eroding the image, obtaining the segmented target's edge region through differencing. Next, applying Laplacian filtering or Sobel operator filtering [18] to this region produces a gradient matrix. Calculating the mean of the gradient matrix yields the spatial feature values of the image. However, the edges of segmented targets are typically irregular regions that cannot be directly subjected to filtering operations.  
To address this issue, a novel method for calculating the spatial feature values of irregular regions is proposed in this paper. Firstly, the image is converted to a grayscale image. Then, the Sobel operator's two convolution kernels are applied to the grayscale image to calculate the horizontal gradient \(G_{x}\) and the vertical gradient \(G_{y}\). Subsequently, the square root of the sum of squares of \(G_{x}\) and \(G_{y}\) is computed, yielding the overall  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Data-Medaka-Lateral-Right (N)**} & \multicolumn{2}{c}{**Data-Medaka-Ventral (R)**} \\ \hline  & **Better** & **Lower** & **Better** & **Lower** \\ \hline Train set & 9 (1870) & 7 (1455) & 6 (996) & 5 (830) \\ Val set & 1 (208) & 1 (207) & 1 (166) & 1 (166) \\ Test set & 2 (415) & 2 (514) & 2 (332) & 1 (166) \\ total & 12 (2493) & 10 (2076) & 9 (1494) & 7 (1162) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset quantity information. “better”: better quality data; “lower”: lower quality data. “12 (2493)”: 12 videos with 2493 frames of images.  
gradient magnitude \(G\) at each pixel position, thereby obtaining the gradient matrix for the entire image. The formulas are as follows:  
\[G_{x}=\sum_{i=-1}^{1}\sum_{j=-1}^{1}I(x+i,y+j)*G_{x}(i,j) \tag{1}\]  
\[G_{y}=\sum_{i=-1}^{1}\sum_{j=-1}^{1}I(x+i,y+j)*G_{y}(i,j) \tag{2}\]  
\[G=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{3}\]  
Here, \(I(x,y)\) represent the pixel value at position \((x,y)\) in the image, and \(G_{x}(i,j)\) and \(G_{y}(i,j)\) are the two convolution kernels of the Sobel operator. Following, a dilation operation is applied to the original mask image, resulting in the outward-expanding dilation mask. Subsequently, an erosion operation is performed on the original mask image, leading to the inward-shrinking erosion mask. The Region of Interest (ROI) is obtained by subtracting the erosion mask from the dilation mask. The values in the overlapping region of the entire image's gradient matrix with the ROI are accumulated and summed, resulting in the total gradient of the image's edge region. The mean of these values represents the spatial features of the image. By executing these operations on the entire video sequence, spatial feature values are computed for each frame. The frame with the highest spatial feature value in the entire video sequence is identified as the STA frame.
# Optimization of Water Pressure of a Distribution Network within the Water-Energy Nexus
###### Abstract  
Pressure control in water distribution networks (WDNs) reduces leaks and bursting. Thus, it is regarded as a valuable solution to cut costs related to the operation and maintenance of WDNs and it is recommended for use in deteriorated water distribution pipes. However, growing consumer demand for satisfactory performance from faucets, combined with reduced water pressure from water supply companies, has resulted in an increased need for domestic water pressure booster systems (WPBSs) and has led to an increase in the energy demand. This misalignment of interests between water companies and energy consumers highlights the water-energy nexus perspective. This research aims to find a solution for optimizing the pressure of any WDN through the application of WPBSs to simultaneously minimize the cost associated with water leaks, repairs of burst pipes, and energy consumption. This methodology is applied to Baharestan city, where an optimum pressure of 47.6 mH\({}_{2}\)O is calculated. According to the sensitivity analysis of the inputs, the optimized pressure and cost are most sensitive to water loss and leakage exponent, respectively. Moreover, the hourly optimization of water pressure based on changes in demand and energy prices throughout the day is estimated to cut costs by 41%.  
water-energy nexus; booster pump; water distribution network; pressure; sensitivity analysis +
Footnote †: journal: Applied Sciences