esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the results obtained, cactus pear residue is proposed as an interesting nutritional supplement for the market due to its good quality parameters, fiber functional properties, acceptable color for consumers, and high antioxidant activity, providing probable benefits to consumers. The GL formulation presented better performance as a possible dietary supplement.  
Further research on phytotoxicity is needed since there are no existing reports about that, as well as, in vivo model studies in order to evaluate the health effects of a dietary supplement developed from cactus pear by-products.  
Conceptualization, N.d.S.C.-C.; Methodology, S.M.-V.; Formal Analysis, O.A.J.-M.; Data Curation, E.R.-M. and J.A.-R.; Writing--Original Draft Preparation, S.M.-V.; E.R.-M.; J.A.-R., O.A.J.-M., Z.G.C.-R.; L.D.-O.; M.C.-D., D.C.-D. and N.d.S.C.-C.; Writing--Review and Editing, S.M.-V.; E.R.-M.; [PERSON]-R., O.A.J.-M., Z.G.C.-R.; L.D.-O.; M.C.-D., D.C.-D. and N.d.S.C.-C.; Supervision, N.d.S.C.-C. All authors have read and agreed to the published version of the manuscript.  
The first author would like to thank the Consejo Nacional de Ciencia y Tecnologia (CONACyT) for the fellowship assigned (CVU 708817).  
We thank the Mexican Association CoMeNTuna (Actopan, Hidalgo, Mexico) for providing the plant material.  
The authors declare no conflict of interest.  
The strengths of the present study consist of the use of an agricultural by-product, through its use in a novel product, which may have beneficial effects on human health and which also represents an economic benefit for the producers of the fruit. On the other hand, among the limitations, the cactus pear is a seasonal fruit, so the residues must be collected and stored during the harvest season. It is also necessary to subject the product to further in vitro tests in order to evaluate any positive or negative effects on human health.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GM** & **GL** & **RM** & **RL** \\ \hline TPC (mg GAE/100 g) & 718 \(\pm\) 11 b & 734 \(\pm\) 8 a & 649 \(\pm\) 12 c & 651 \(\pm\) 8 c \\ DPPH (\% Inhibition) & 20 \(\pm\) 1 b & 20 \(\pm\) 2 b & 24 \(\pm\) 2 a & 24 \(\pm\) 1 a \\ ABTS (\(\mu\)umol TE/100 g) & 4628 \(\pm\) 412 a & 4637 \(\pm\) 242 a & 4365 \(\pm\) 392 a & 4610 \(\pm\) 326 a \\ FRAP (\(\mu\)umol Fe2/100 g) & 17 \(\pm\) 0 b & 16 \(\pm\) 1 c & 19 \(\pm\) 0 a & 17 \(\pm\) 0 b \\ \hline \hline \end{tabular} Mean values \(\pm\) standard deviation (\(n=3\)). a–c Superscript letters indicate significant differences (\(p<0.05\)) between tablet formulations. GM: green cactus pear residue with microcrystalline cellulose; GL: green cactus pear residue with lactose; RM: red cactus pear residue with microcrystalline cellulose; RL: red cactus pear residue with lactose. Gallic acid (for total phenolic content (TPC)), Trolox (for DPPH and ABTS), and FeSO4 (for ferric reducing/antioxidant power (FRAP)) were used as reference standards.  
\end{table}
Table 3: The antioxidant properties of the cactus pear residue tablet formulations.
## 2 Numerical Model Development and Experimental Validation
### Experimental Setup
#### 2.1.1 Creating Weak Adhesion  
Before any experimental tests, samples were produced with multiple levels of weak adhesion. This was done by initially cutting aluminum 6061 bars into \(120\times 25\times 2\) mm parts to have the upper and lower substrates of the SLJs. Each part was then drilled with a 6 mm hole at 10 mm from one of its ends to allow it to be held at the tension testing machine, as will be described in the follow-up. The other end of each substrate, where the adhesive would go, was sand blasted to maximize adhesive interaction. Next, the parts were geometrically finalized and thoroughly cleaned with acetone to remove any particles that could alter the adhesive bonding results. After adhesive curing, piezoelectric elements were bonded on the substrates to allow LW excitation and measurement. In Figure 2, a diagram of a final part can be seen, along with the overall measurements and the Cartesian coordinates that are used during simulation. The piezoelectric elements' dimensions and their placement are also represented.  
To create multiple levels of weak adhesion, various quantities of release agent Loctite 770-NC Frekote were used. For the study at hand, four overall levels were created, comprising a total of 11 SLJs. The first level corresponded to full adhesion, with no release agent applied, and two samples were manufactured. The second and third levels, with three SLJs each, corresponded to intermediary levels of weak adhesion, where the parts were subjected to one layer of release agent and one layer of a mixture of 50% release agent plus 50% acetone, respectively. Finally, the fourth level, with three samples, was a severe weak adhesion case, where the SLJs were subjected to two layers of release agent. After the parts had the necessary quantity of release agent applied, they were placed in a dimensional jig and received the adhesive Plexus MA 560-1 [15] in order to create the SLJs.  
It is worth emphasizing that, although the SLJs were manufactured based on controlling the quantity of release agent applied, fabrication issues may affect the production and the obtention of the desired four levels of weak adhesion. Therefore, further testing is necessary to determine the real level of weak adhesion, as will be described in the follow-up.
## 2 Methods
### Fusion of Safety Performance Parameters and BP Neural Network
#### 2.1.1 Correlation of Structural Safety Performance Parameters  
In the construction process of large prestressed steel structure, due to the complex stress, high risk of aerial work, and large construction technology difficulty, construction safety is extremely important. Especially in the cable tension core link of a large span prestressed steel structure construction process, structural safety risk involves multiple factors. The complex and changeable internal and external factors during construction will inevitably bring safety uncertainty to the project implementation. This also affects the judgment of structural construction safety risks and the formulation of control decisions.  
In the process of structural construction safety prediction, the corresponding relationship between design parameters and mechanical parameters is extracted according to the simulation results. With the help of a deep learning algorithm, association rules between data are mined. According to the characteristics of prestressed steel structure, the correlation mechanism between design parameters and mechanical parameters is formed in the construction stage of the structure, which is expressed as Equation (1):  
\[f(a_{1},\ a_{2},\ \cdots,a_{m})\stackrel{{\mathcal{R}}}{{ \Leftrightarrow}}g(b_{1},\ b_{2}\ \cdots,b_{n}). \tag{1}\]  
In the Equation, \((a_{1},\ a_{2},\ \cdots,a_{m})\) denotes the set of design parameters. \(a_{1},\ a_{2},\ \cdots,a_{m}\) means the specific design parameters, such as the size of the member, the initial tension of the cable, the number of the structural pieces, etc., and \(g(b_{1},\ b_{2}\ \cdots,b_{n})\) represents the aggregate of mechanical parameters. \(b_{1},\ b_{2}\ \cdots,b_{n}\) respectively equals to specific mechanical parameters, such as cable force, stress, and vertical displacement of structure. \(\stackrel{{\mathcal{R}}}{{\Leftrightarrow}}\) denotes the corresponding relationship between different design parameters and mechanical parameters according to technical standards and data association rules of structures. Driven by association rules, the intelligent prediction of structural construction safety can be realized. The mechanical parameters of the structure can be obtained by changing the design parameters, and the safety of the structure in the construction process can be judged.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 3 Through Metal Scenarios  
This section includes the performance of the coil, in terms of harvested power and power transfer efficiency, for different types of materials, thicknesses and frequencies. Figure 8 shows a picture of the different plates used in the experiments. Two types of alloys are used: stainless steel (AISI 304) and aluminum (6061T6). Different thicknesses of each of the plates are summarized in Table 3. In these experiments, we place one coil on each side of a plate to form an inductive link as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Coil Size** & **Metal** & **Harvested** & **Coil Power** & **Frequency** & **Volume** \\
**(mm)** & & **Barrier** & **Phickness** & **(mW)** & **Efficiency (\%)** & **(Hz)** & **(mm\({}^{3}\))** \\ \hline
[9] & \(30\times\Omega 30\) &
\begin{tabular}{c} Tin \\ 0.5 mm \\ \end{tabular} & 0.03 & - & 50 & 21,205 \\ \hline
[4] & \(234\times 67\times 30\) &
\begin{tabular}{c} Stainless \\ steel \\ 1.6 mm \\ \end{tabular} & 3000 & 43 & 50 & 470,340 \\ \hline
[27] & \(80\times\Omega 240\) &
\begin{tabular}{c} Aluminum \\ 4 mm \\ \end{tabular} & 18,181 & 23 & 100 & 3,619,114 \\ \hline
[28] & \(8\times\Omega 115\) &
\begin{tabular}{c} Stainless \\ Steel \\ 20 mm \\ \end{tabular} & 7000 & 5.5 & 500 & 83,095 \\ \hline
[12] & \(50\times\Omega 98\) & \
\begin{tabular}{c} Aluminum \\ 3.1 mm \\ \end{tabular} & 5000 & 9 & 250 & 377,148 \\ \hline This & \(15\times 13\times 6\) &
\begin{tabular}{c} Aluminum \\ 1 mm \\ \end{tabular} & 440 & 2.5 & 2000 & 1170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Survey of similar works on energy and communications through aluminum and stainless steel.  
Figure 8: Plates of different materials used in the experiments.
## 2 Results and Discussion
### Cd\({}^{2+}\) Adsorption with Biochar
#### 2.3.3 Cd\({}^{2+}\) Adsorption Kinetics for Both Types of Biochar Samples
Notes: RH300, RH400, RE500, RH600, and RH700 indicate that rice husk were pyrolyzed at 300, 400, 500, 600 and 700 \({}^{\circ}\)C, respectively; CS300, CS400, CS500, CS600, and CS700 indicate that cotton straw were pyrolyzed at 300, 400, 500, 600 and 700 \({}^{\circ}\)C, respectively. groups. In recent years, cation-\(\pi\) interactions attracted great attention [42]. These interactions are mainly determined by the aromaticity of the biochar surface. The more abundant the \(\pi\)-conjugated aromatic structure, the greater the ability of biochar is to donate electrons, and the more evident this interaction is. However, the adsorption affinity in ion-exchange phenomena is stronger than cation-\(\pi\) interactions, also presenting some electrostatic effects. However, the pH does not affect such ion exchange [43]. From the SEM results, we can see that the aromaticity increased with pyrolysis temperature. In addition, the Cd\({}^{2+}\) adsorption rate on biochar produced at high temperatures was far faster than that on the materials produced at moderate and low temperatures (Figure 5). This may be due to cation-\(\pi\) interactions. Further research is required to determine whether cation-exchange effects or cation-\(\pi\) interactions are greater. In addition, some researchers [44] suggested that Cd\({}^{2+}\) removal with biochar obtained by pyrolysis at high temperature was mainly due to Cd\({}^{2+}\) precipitation caused by the increased pH (in our study, RH700 was able to remove more than 90% of Cd\({}^{2+}\) in 0.5 h, while CS700 removed 99% of Cd\({}^{2+}\) in 20 min). However, according to the solubility product constant and initial concentration at 25 \({}^{\circ}\)C, the initial pH at which Cd(OH)\({}_{2}\) precipitation occurs is 10.75 [45]. Considering the experimental conditions employed in this study and the highest pH value of biochar for sample CS700 (pH = 10.42), we can conclude that precipitation was not the main mechanism for Cd\({}^{2+}\) removal in this case.
## 2 Background  
### Industrial Internet Identification and Resolution  
The industrial internet identification and resolution system is a technical framework specifically designed to meet the demands of industrial production aimed at providing efficient, secure, and stable identification services. Its core theory is based on the precise management of object identities, enabling lifecycle management of equipment, personnel, and materials through functions such as identification distribution, registration, resolution, and routing. The essence of this system lies in achieving seamless information interchange and sharing, allowing different systems, devices, and data to collaborate effectively, thereby enhancing overall production efficiency and resource utilization. On a technical level, the current identification and resolution system primarily relies on the Handle system, a globally distributed identification service. The Handle system employs a layered service model that consists of multiple parallel Global Handle Registries (GHR) and Local Handle Services (LHS), facilitating dynamic and flexible resolution requests. Its open protocol and namespace design enable efficient identification and management of various digital objects, services, and network resources. By providing effective identity identification and resolution capabilities, the system significantly mitigates data silos, laying a foundation for real-time data analysis and decision-making.
# Compact Beam-Scanning Reflectarray Antenna with SLL Reduction Using In-Plane Panel Translations
###### Abstract  
A mechanical beam-scanning reflectarray (RA) antenna is presented for Ka band. The 1D steering of the beam is achieved through linear in-plane panel translations, which can be implemented at low cost using a rail-mounted moving RA panel. Compared to related works, a highly uniform beam level is achieved with a remarkably compact antenna profile. A new technique is also proposed to mitigate the high side lobes caused by the compact antenna optics, achieving an estimated 2.3 dB reduction in maximum SLL. The manufactured prototype has a panel size of 256.4 by 187.2 mm with 2898 elements, and an \(F/D\) of only 0.47. A measured scan loss of 1.1 dB is achieved over a 45-degree scanning range. The measured gain is 31.6 dBi and the aperture efficiency is 24.7% at the design frequency of 29.5 GHz, with SLL between \(-9.4\) and \(-17.5\) dB. In-band measurements show a 1 dB bandwidth from 28 to over 32 GHz (11.9%).  
reflectarray antennas; steerable antennas; mechanical beam steering; mm-wave 2020 Accepted: 9 April 2025 Published: 11 April 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Compact Beam-Scanning Reflectarray Antenna with SLL Reduction Using In-Plane Panel Translations. _Appl. Sci._**2025**, _15_, **4244**. [[https://doi.org/10.3390/app15084244](https://doi.org/10.3390/app15084244)]([https://doi.org/10.3390/app15084244](https://doi.org/10.3390/app15084244))  
**Copyright**: 6 2025 by the authors.  
Lousse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Methods and Data
### Data Processing  
The raw records suffer from baseline drift, background noise, and errors caused by sensor tilt. Hence, direct usage of the raw records is not appropriate. It is necessary to perform baseline correction and filtering on the selected data and then calculate the acceleration response spectrum and extract the PGA and EPA values. [PERSON] compared causal and non-causal filters and then compared the results obtained using four different filters at different orders [18]. It was found that non-causal filtering not only maintained phase integrity but also decreased the loss of useful information. Among the different orders, the Butterworth filter exhibited the best stability. Thus, we also adopted a fourth-order non-causal Butterworth high-pass filter with a cutoff frequency of 0.1 Hz (for more details refer to [PERSON] et al. [19] and [PERSON] et al. [18]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station No.** & \begin{tabular}{c} **Fault Distance** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **PGA** \\ **/gal** \\ \end{tabular} & \begin{tabular}{c} **Vs30** \\ **Sstation No.** \\ \end{tabular} & \begin{tabular}{c} **Fault Distance** \\ **(km)** \\ \end{tabular} & \
\begin{tabular}{c} **PGA** \\ **/gal** \\ \end{tabular} &
\begin{tabular}{c} **Vs30** \\ **Vs30** \\ \end{tabular} \\ \hline
2409 & 126.70 & 17.68 & 875 & 0509 & 346.85 & 6.08 & 373 \\
2101 & 135.06 & 71.37 & 519 & AMSY & 352.25 & 3.57 & 942 \\
3305 & 135.91 & 18.24 & 362 & 0506 & 354.16 & 2.87 & 284 \\
6302 & 138.19 & 51.08 & 936 & 0713 & 356.65 & 3.61 & 540 \\
0128 & 140.42 & 14.13 & 494 & 0714 & 358.44 & 2.88 & 607 \\
2305 & 141.26 & 54.39 & 907 & 5502 & 375.53 & 2.81 & 422 \\
5810 & 144.29 & 17.37 & 528 & 5506 & 388.44 & 2.43 & 1510 \\
2415 & 146.00 & 21.53 & 444 & 0507 & 391.43 & 2.82 & 368 \\
3301 & 149.23 & 45.58 & 366 & 5504 & 391.63 & 6.43 & 404 \\
3804 & 151.12 & 18.22 & 637 & 7101 & 402.68 & 3.36 & 421 \\
5809 & 155.09 & 24.68 & 340 & 0511 & 402.97 & 2.74 & 415 \\
3805 & 158.89 & 23.51 & 384 & 0510 & 403.18 & 3.48 & 363 \\
0124 & 160.84 & 8.35 & 583 & 5508 & 403.79 & 4.20 & 450 \\
99007 & 162.98 & 8.30 & 575 & 5503 & 415.92 & 4.79 & 246 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Distance (km)** & **Horizontal** & **Class I** & **Class II** & **Class III** &
\begin{tabular}{c} **Uncertain Site** \\ **Category** \\ \end{tabular} \\ \hline \(<\)3 & 28 (14) & 6 (3) & 18 (9) & 0 (0) & 4 (2) \\
3–9.9 & 18 (9) & 0 (3) & 12 (6) & 2 (1) & 4 (2) \\
10–29.9 & 30 (15) & 10 (5) & 14 (7) & 0 (0) & 6 (3) \\
30–59.9 & 16 (8) & 2 (1) & 8 (4) & 0 (0) & 6 (3) \\
60–99.9 & 46 (23) & 12 (6) & 10 (5) & 6 (3) & 18 (9) \\
100–199.9 & 106 (53) & 22 (11) & 38 (19) & 0 (0) & 46 (23) \\
200–299.9 & 127 (58) & 19 (8) & 56 (26) & 6 (3) & 46 (21) \\ \(>\)300 & 80 (14) & 5 (0) & 30 (3) & 4 (2) & 41 (9) \\ Total & 451 (194) & 76 (37) & 186 (79) & 18 (9) & 171 (72) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical summary of strong-motion records grouped by site conditions and fault distances.
## 6 Results and Discussion
### Experiment (A)
We performed the Friedman test to understand if the differences in performance produced by NLU platforms selected are statistically significant. Figure 6 presents the pairwise comparison using the Post-hoc Conover Friedman test. We can observe that with \(p<0.001\), Watson and Dialogflow perform better than Luis and Wit.ai. These performance differences are significant according to Post-hoc analysis, while the difference between Dialogflow and Watson is not statistically significant using the same \(p\)-value (\(p<0.001\)). Figure 5: Results of Experiment (A) in terms of F-Score of each of the nine intentions for all the tested platforms (i.e., (**a**) Watson, (**b**) Dialogflow, (**c**) Luis, (**d**) Wit.ai).
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 3 Results
### Pilot Scale
#### 3.2.4 Nutritional Quality  
A sketch of the improved processing steps for IF removal at pilot scale is presented on Figure 1D. It can be seen that the protein, oil and water contents of dehulled beans were equivalent before and after being subject to the whole treatment. The protein content went from 40 to 39%. The oil proportion increased from 16-20% to 18-24% (with inter-batch variation), most probably because the removal of the envelope essentially reduced the seeds' carbohydrate content [42]. Additionally, the targeted water content of 12% was successfully achieved after the drying process, as described above.  
In short, the whole process, including drenching and washing dehulled beans with water (20 \({}^{\circ}\)C, 10 min, 1:4 matter:water volume ratio and 200 RPM) and then drying them for 1 hour, from now on referred to as the improved process, succeeded in maintaining an important part of the nutritional quality of soybeans, especially the protein and lipid contents.
## 2 Materials and Methods
### Data Preparation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Country** & **Statistics** & **G\_i (W/m2)** & **A\_s (deg.)** & **T\_mb (deg. C)** & **W\_10 (m/s)** \\ \hline \multirow{4}{*}{Algeria} & Mean & 296.15 & 19.14 & 26.34 & 3.97 \\  & Std & 386.39 & 24.82 & 8.98 & 1.71 \\  & Min & 0 & \(-\)4.95 & \(-\)0.18 & 0.06 \\  & Max & 1966.4 & 85.46 & 44.94 & 12.59 \\ \hline \multirow{4}{*}{Central African Republic} & Mean & 264.24 & 20.47 & 27.75 & 3.063 \\  & Std & 358.36 & 26.17 & 4.82 & 1.443 \\  & Min & 0 & \(-\)5.53 & 14.36 & 0.010 \\  & Max & 1812.9 & 84.39 & 44.17 & 10.47 \\ \hline \multirow{4}{*}{Chad} & Mean & 305.4 & 19.68 & 26.13 & 4.751 \\  & Std & 393.25 & 25.42 & 8.128 & 1.584 \\  & Min & 0 & \(-\)5.13 & 3.03 & 0.60 \\  & Max & 1916.1 & 85.73 & 43.56 & 11.63 \\ \hline \multirow{4}{*}{Egypt} & Mean & 303.94 & 18.76 & 22.09 & 4.595 \\  & Std & 392.18 & 24.38 & 8.756 & 1.588 \\  & Min & 0 & \(-\)5.07 & \(-\)1.10 & 0.04 \\  & Max & 1753.6 & 88.95 & 43.11 & 13.31 \\ \hline \multirow{4}{*}{Ethiopia} & Mean & 278.41 & 20.42 & 29.04 & 3.516 \\  & Std & 363.85 & 26.12 & 5.29 & 1.451 \\  & Min & 0 & \(-\)80 & 13.42 & 0.04 \\  & Max & 1130.5 & 83.17 & 41.14 & 9.48 \\ \hline \multirow{4}{*}{Ghana} & Mean & 260.59 & 20.39 & 28.26 & 2.797 \\  & Std & 350.14 & 26.15 & 4.461 & 1.095 \\  & Min & 0 & \(-\)6.29 & 14.64 & 0.03 \\  & Max & 1735.3 & 89.49 & 42.8 & 8.21 \\ \hline \multirow{4}{*}{Namibia} & Mean & 276.01 & 18.95 & 21.09 & 3.477 \\  & Std & 362.98 & 24.63 & 6.297 & 1.924 \\  & Min & 0 & \(-\)5.93 & 0.75 & 0.010 \\  & Max & 2002 & 89.53 & 41.03 & 12.41 \\ \hline \multirow{4}{*}{Nigeria} & Mean & 272.79 & 20.31 & 27.31 & 2.875 \\  & Std & 362.05 & 26.03 & 5.83 & 1.047 \\  & Min & 0 & \(-\)5.28 & 9.41 & 0.040 \\  & Max & 1911 & 85.82 & 43.95 & 8.18 \\ \hline \multirow{4}{*}{South Africa} & Mean & 259.16 & 17.60 & 19.60 & 4.46 \\  & Std & 346.94 & 23.06 & 8.29 & 1.932 \\  & Min & 0 & \(-\)5.87 & \(-\)2.52 & 0.040 \\  & Max & 1911 & 82.20 & 42.86 & 17.21 \\ \hline \multirow{4}{*}{ Somalia} & Mean & 289.88 & 20.44 & 26.85 & 5.922 \\  & Std & 372.68 & 26.19 & 4.52 & 3.093 \\ \cline{1-1}  & Min & 0 & \(-\)80.81 & 13.42 & 0.010 \\ \cline{1-1}  & Max & 1157.9 & 89.02 & 39.53 & 16.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical summary of data.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 2 System Model and Problem Formulation
### Problem Formulation  
The expression for the generalized EE in downlink transmission is given as the ratio of the total delivered bits to the total consumed energy, represented mathematically as:  
\[\text{EE}=\frac{\text{R}}{\mathcal{P}_{total}}. \tag{7}\]  
To ensure quality-of-service (QoS) for each user equipment (UE), we consider the generalized EE while incorporating minimum rate requirements \(\hat{R}_{k}\) and the peak transmit power \(P_{\max}\). To simplify the problem, we assume an infinite resolution for the reflectors' phase shifting (\(2^{b}>>1\)). Furthermore, we assume perfect knowledge of all communication channels (\(H_{R,n}^{k}\) and \(H_{T,n}\)) by the BS for all \(k=1,\cdots,K\).  
Therefore, we can express the optimization problem for maximizing the EE in downlink transmission through the following formula:  
\[(P1) \max_{\mathbf{\Phi},\rho\in\mathbf{\Phi},\mathbf{\mathcal{P}}\in\mathcal{P}} \frac{\sum_{k=1}^{K}\sum_{n=1}^{N}\rho_{k,n}r_{k,n}}{\xi\sum_{k=1 }^{K}p_{k,n}+P_{BS}+KP_{UE}+MP_{m}(b)}\] (8) subject to \[\sum_{n\in\mathcal{N}}\rho_{k,n}r_{k,n}\geq\hat{R}_{k},\quad \forall k=1,2,...,K, \tag{8a}\] \[|\phi_{m}|=1,\quad\forall m=1,2,...,M, \tag{8b}\]  
where \(\hat{R}_{k}\) denotes the individual QoS constraint of the \(k_{th}\) user. Also, constraint (8b) accounts for the fact that each RIS reflecting element can only provide a phase shift, without amplifying the incoming signal.