esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 4 Discussion
### Total Phenolic Content, Total Flavonoid Content and Total Ortho-Diphenolic Content
#### 4.1.1 Effect of Hydrolysis on the Different Classes of Phenolic Compounds
Flavonoids consist of C-glycos bonds and exits as oligomers and dimers, and industrial processing such as boiling or even heating results in the formation of monomers through the hydrolysis of C-glycos bonds [43]. Comparing the results obtained in this study with the literature, alkaline and acid hydrolysis produced far less flavonoid content than that of methanolic extracts. In fact, the results obtained are in line with the study conducted by [PERSON], [PERSON] and [PERSON] [40], where methanolic extracts had the highest flavonoid content. Although TPC and TFC were affected by hydrolysis, the effect on TdOPC was not evident. In fact, there was no significant difference between the macerated, acid and alkaline hydrolysed extracts. Figure 12 shows the ortho-diphenols, such as oleuropein, oleuropein-aglycone, verbascoside and demethyloleuropein. These are considered the most important in relation to their antioxidant activity [44,45]. In contrast to TPC, when hydrolysed, hydroxytrosol, tyrosol and glucose are formed. The Folin-Ciocalteu reagent tends to be sensitive to reducing compounds such as polyphenols, but it also reacts with other reducing substances, such as aromatic amines, sugars, ascorbic acid, sulphur dioxide and organic acids. Therefore, the presence of sugars within the olive waste extract might lead to an overestimation of polyphenols [46].
## 2 Controlled Beam Model and Equations of Motion  
In this study, we consider a dynamic beam structure subjected to the dynamic load \(P\), which moves along the longitudinal direction of the beam with a velocity \(V\) and under active control by a servomechanism, as shown in Figure 1. The motion equation of the beam is shown as follows [9]:  
\[EIv^{\prime\prime\prime\prime}+m\dot{v}+c\dot{v}=P\delta(x-Vt)+M_{0}\delta^{ \prime}(x-a)-M_{0}\delta^{\prime}(x-L+a), \tag{1}\]  
where the prime indicates the derivative with respecting to \(x\) and the dot indicates the derivative with respect to \(t\); \(E\) is the Young's modulus of the beam; \(I\) is the moment of inertia of the cross-section; \(v(x,t)\) is the transverse displacement of the beam; \(m\) is the mass per unit length of the beam; \(c\) is the damping coefficient; \(\delta\) is the Dirac delta function and \(\delta^{\prime}\) is the derivative; \(L\) is the span length of the beam; \(a\) is the distance between the servohinge and the end of the beam; In order to balance the bending deformation of the beam under moving load, a servomechanism is installed below the middle of the beam span to generate a control torque \(M_{0}\). The working principle is to adjust the control spring to achieve the control effect. The control torque is designated as:  
\[M_{0}=lK\Delta=Kl\left[u(t)+lv^{\prime}(a,t)-lv^{\prime}(L-a,t)\right], \tag{2}\]where \(K\) is the stiffness of the spring; \(l\) is the arm length of servomechanism; \(u(t)\) is the spring displacement caused by servomechanism, and \(\Delta\) is the displacement of the spring. When \(u(t)=0\), it is a passive control system.  
Using the [PERSON] method, the equation of motion of the first mode can be written as (the details of the derivations are given in the Appendix A):  
\[\ddot{q}(t)+\omega_{0}^{2}q(t)+\mu\dot{q}(t)=f\sin\omega t+\alpha q^{3}(t)+u(t), \tag{3}\]  
where \(q(t)=(2/L)\int_{0}^{L}v(x,t)\phi_{1}(x)\mathrm{d}x\), \(\omega_{0}^{2}=\omega_{1}^{2}-2\pi Kl\cos\frac{\pi\alpha}{L}\), \(\mu=2\zeta\omega_{1}\), \(f=2P/(mL)\), \(\alpha=\pi^{4}E^{2}/(L^{4}A^{2})\), \(A\) is the cross-section area of the beam. \(u(t)\) is the active control force. In this paper, the time-delay speed feedback and time-delay acceleration feedback control strategies are adopted. Next, in Sections 3 and 4, the primary resonance response and \(1/3\) subharmonic resonance response of the beam with the two different feedback controls are studied respectively.
## 3 Communication System Analysis in the Presence of Fading
### N-Correlator Receiver  
Following the procedure explained in Section 2.2 for the fading coefficient different from one, _i.e._, \(\alpha\
eq 1\), the output of the \(n\)-th correlator can be expressed as  
\[w_{n}=\sum_{i=1}^{2\beta}z_{i}c_{in}=\ \alpha\sqrt{E_{c}}\sum_{i=1}^{2\beta}c_{ i1}c_{in}+\sqrt{E_{N}}\sum_{i=1}^{2\beta}n_{i}c_{in}, \tag{36}\]  
having zero mean and variance  
\[\sigma_{wn}^{2}=\ \alpha^{2}2\beta E_{c}E\{c_{i1}^{2}\}E\{c_{in}^{2}\}+2\beta E _{N}\sigma_{ni}^{2}E\{c_{in}^{2}\}. \tag{37}\]  
In the case of binary chip symbol transmission the variance is  
\[\sigma_{wn}^{2}=\ 2\beta(\alpha^{2}E_{c}+E_{N}). \tag{38}\]  
From the expressions for a single and \(N\)-correlator receiver, we can see that there are two variables that are approximately Gaussian with the mean value zero (for \(w_{n}\)) and the mean value that depends on \(\alpha\) (for \(w_{1}\)). Following the procedure in Section 2.2, the probability of bit error can be expressed as  
\[\begin{split} p_{e}(\alpha\ )=\frac{N/2}{N-1}\frac{1}{\sqrt{2\pi\sigma_{w1}^{2}}} \sum_{i=1}^{N-1}\binom{N-1}{i}\left(-1\right)^{i+1}\int_{-\infty}^{\infty}[ \frac{1}{2}erfc\,\frac{w_{1}}{\sqrt{2\sigma_{wn}^{2}}}]^{i}\\ \cdot e^{\left(-w_{1}^{2}/2\sigma_{w1}^{2}\right)}dw_{1}\end{split} \tag{39}\]In this expression the probability of bit error depends on the fading coefficient \(\alpha\), which is inside the expression for the variance of \(w_{n}\). Therefore the probability of error needs to be calculated as the mean value of this random function to get the probability of bit error for fading channel as  
\[p_{e}=\int_{-\infty}^{\infty}p_{e}(\alpha)f_{\alpha}(\alpha)d\alpha=\int_{- \infty}^{\infty}p_{e}(\alpha)\,\frac{\alpha}{b^{2}}\,e^{\left(-\frac{\alpha^{2 }}{2b^{2}}\right)}d\alpha, \tag{40}\]  
where variable \(\alpha\) has Rayleigh density function defined as  
\[f_{\alpha}(\alpha)=\frac{\alpha}{b^{2}}\,e^{\left(-\frac{\alpha^{2}}{2b^{2}} \right)}.\]
# Enhanced Control Strategy for Three-Level T-Type Converters in Hybrid Power-to-X Systems
## 4 Proposed Control Design
### Inverter Side Inductor Inner Control Loop
Loop-gain with practical modification is defined as follows:  
Figure 5:1  
or  
Figure 5:2  
loop-gain phase and magnitude can be expressed as  
Figure 5:3  
and  
Figure 5:4  
Let \(\omega_{BI}\) define the crossover frequency of the inner current loop, i.e., loop-gain magnitude is equal to 0 dB and the phase equal to \(-180^{\circ}+PM\), where \(PM\) is the desired phase margin to avoid system instability (typically \(45^{\circ}\)). Assuming \(\omega_{BI}\) is much higher than fundamental frequency \(\omega_{0}\) (\(\omega_{0}\) -(50-60)2\(\pi\) rad\(\cdot\)s\({}^{-1}\)), (15) can be defined as the following:  
Figure 5:5  
According to [36], the relation between crossover frequency \(\omega_{BI}\) to transient response frequency \(\omega_{CI}\) is the following:  
Figure 5:6  
Then, (17) can be rewritten as follows:  
Figure 5:7  
And transient response frequency \(\omega_{CI}\) is therefore derived as follows:  
Figure 5:8  
The value of the resonant term bandwidth \(\xi\) is selected to achieve desired steady-state performance, namely robustness to fundamental frequency variations in the following range \(0.001\omega_{0}<\omega<0.01\omega_{0}\). Then, the typical value of \(\xi\) is 0.01-0.001. The design process adheres to the following steps:  
* Specify the target phase margin \(PM\) of the inner current loop;
* Determine the delay value \(T_{d}\) according to the switching frequency (\(T_{d}\)\(\sim\)1.5 \(T_{s}\));
* Set the value of crossover frequency \(\omega_{BI}\) according to (18) and (20);
* Set the value of \(\xi\) to be between 0.01-0.001;
* Shape the controller and loop gain according to (12) and (13), respectively. A Bode diagram of the resulting open loop transfer function of the current loop with system parameters, which is summarized in Table 1, is shown in Figure 7a. The system desired \(PM\) set to \(45^{\circ}\), and the resulting calculated bandwidth according to (18) following the above design process is \(\omega_{BI}=2\pi\cdot\)3000 rad\(\cdot\)s\({}^{-1}\). It can be seen that system parameters, _PM_, and bandwidth correspond to calculated parameters.
## 2 Materials and Methods
### UPLC-MS/PDA Instrumentation and Analysis Conditions  
Analysis was performed using the Waters ACQUITY H-Class Ultra Performance LC (UPLC) system (Waters, Milford, MA, USA), coupled with both PDA and QDa mass detector.  
The PDA detector was set in 2D channel mode at 268 nm with a resolution of 4.8 nm. Additionally, it was operated in 3D mode, scanning from 200-400 nm through the entire method to ensure no interference with the signal of arachidonic acid. The QDa detector operated in ESI negative mode with a capillary voltage of 0.8 kV and a probe temperature of 600 \({}^{\circ}\)C. The sampling rate was set at 20 points/s. The following ions for AA were traced: \(m/z\) 303.3 Da (CV 15) as quantification ion \(m/z\) 304.3 Da (CV 30), and \(m/z\) 259.30 Da (CV 35) as confirmation ions. For the internal standard, \(m/z\) 311.32 Da (CV 20) was monitored. MS scan (\(m/z\) 259-315 Da, CV 15) in negative mode was tracked to determine the purity of the analytical peak.  
Chromotographic separation was performed on an AccQ-TAG Ultra C18, 1.7 \(\upmu\)m, \(2.1\times 100\) mm column (Waters, USA), with column temperature of 50 \({}^{\circ}\)C. Two mobile phases were used: mobile phase (A) 40/60% \(v/v\) acetonitrile/water with 5 mM ammonium formate and 0.1% formic acid; while mobile phase (B) was composed of 89/10/1% \(v/v\) acetonitrile/isopropanol/water with 5 mM ammonium formate and 0.1% formic acid. A gradient elution was carried out at a flow rate 0.35 mL/min. Initially, elution started with 10% B and changed to 100% B over 8 min, convex gradient (gradient curve 4). From 8-16 min, 100% B was maintained and, at 16 min, the elution returned to the initial conditions. The total run time was 20 min, with AA eluting at 4.72 min and IS at 4.69 min.  
Data analysis and processing were performed using Empower\({}^{\text{\textregistered}}\) 3.0 Chromatography Data Software (Waters, USA).
During fermentation with LAB, the most important process is the utilization of carbohydrates, as a source of carbon [27], starch degradation being crucial for LAB growth and development [28]. Furthermore, LAB are able to metabolize starch, leading to the drop of the pH in the raw matrix and enhancing the production of organic acids such as lactic and acetic one [29]. It is generally accepted that _Lactobacillus plantarum_, like all facultative hetero fermentative lactobacilli, fermentas hexoses to lactic acid via Embeden-Meyerhof-Parnas pathway (EMP) and degrades pentoses and gluconate via the pentose phosphate (PP) pathway, producing acetic acid, ethanol, and formic acid [30; 31]. _Lactobacillus plantarum_ ATCC 8014 (_Lp_) was able to grow in quinoa sourdough, having a good adaptability and being able to improve the amino acids, volatile compounds, and sensory features of the final baked muffins through starch and protein degradation [3]. As supported by a large body of literature, dough fermented by LAB confer high sensory quality to bakery products [32], and they can improve the nutritional value of gluten-free flours [3; 24; 33]. On the other hand, spontaneous fermentation could lead to variations in the quality of the final baked goods; therefore, the use of selected starters is recommended [34]. [PERSON] et al. [1] reported that the use of quinoa sourdough in bread improved the sensorial and textural quality, in terms of lower hardness, higher porosity specific volume, and protein content than the control (wheat bread). In the research published by [PERSON] et al. [35], _Lactobacillus sanfrancisensis_ LS40 and LS41, and _Lactobacillus plantarum_ CF1 were selected and used as sourdough starters for the manufacture of GF bread following a two-step fermentation process. By this approach, the improvement of the nutritional, textural and sensorial characteristics were targeted. The facultative heterofermative _Lactobacillus plantarum_ was among the dominant lactic acid biota in gluten-free sourdoughs from rice, amaranth, quinoa [36], or buckwheat and teff flour [20,37]. In the present study, quinoa flour fermented with _Lactobacillus plantarum_ ATCC 8014 (\(Lp\)) was used as ingredient in gluten-free muffins manufacturing to improve their nutritional characteristics. In order to achieve this goal, the impact of \(Lp\) on carbohydrate metabolic conversion to organic acids and rheological features of quinoa sourdough was assessed.
## 1 Introduction
Thus, the antennas must be characterized over a wide frequency range (typically, between 7 and 30 MHz) using probes with stable electrical and mechanical characteristics, in order to accurately assess the skywave incidence angle in situ. Table 1 shows a comparison between our measuring configuration and two other measuring systems for HF antennas, one of them using a drone-borne receiver with an active antenna [24], and the other one using a drone-borne transmitter with a resonant antenna [25]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{3}{c}{**Type of HF Antenna-Measuring System**} \\ \cline{2-4}  & **Drone-Borne Receiver** & **Drone-Borne Transmitter** & **Over Method: Drone-Borne Receiver, Near-Field** \\  & **with Active Antenna**[24] & **with Resonant Antenna**[25] & **Measurements with an** \\  & & & **Electrically Short Antenna** \\ \hline Drone-borne antenna type and & & 5 m long, one-end & 1 m long \\ approx. size & 6 mm E-field probe & 5 m long, one-end & 1000 km & folded dipole \\ approx. size & /300 mm & /100 mm & /100 mm \\ \hline Bandwidth & Wide & Single frequency & Wide \\ \hline Pattern diagram stability over the & & & High, but dual-mode: loop \\ HF range of the drone-borne & High & Low (narrow band) & mode at lower frequencies, \\  & antenna & & dipole mode at higher \\ \hline Mechanical stability of the & & & High \\ drone-borne antenna & High & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between drone-borne measuring configurations for HF antennas. Compared to the drone-borne transmitter type configurations using full-wave antennas [25], our approach is based on an electrically short probe providing a wide frequency band of operation, and better mechanical stability, since the antenna is firmly attached to the drone. The cost of our system is kept low since the measurements are performed in the near-field zone, and thus the receiver consists of a simple peak detector. As opposed to other measuring systems based on small-size transducers [24], the equipment to be carried by the drone is not only cheaper but also lighter and consequently a cheaper, low-payload drone is needed.
## 5 Case Study  
To assess the validity of our method, it is applied to a real case application. This application is defined by a kitting machine in which small parts are placed within a plastic film to be grouped and a label is applied to the film.  
The small parts (e.g., screws) are serialized via bowl feeders and hoppers, while the film and the label are installed directly in the kitting machine. The small parts fall through the hoppers inside the film to form a bag, and then the label is applied to the film. Different products require different numbers of feeder retooling. The higher this number, the higher the number of retooling, and thus, the higher the setup time.  
In this sense, the film and the label changes are the leading parts in terms of setup time; thus, it is important to reduce the number of film and label changeovers.  
As a result, our multi-clustering approach is applied to both the film and the label. Firstly, the products are clustered based on the film (\(F_{1}\) of Figure 2). Then, the resulting clusters are clustered based on different labels (\(F_{2}\) of Figure 2). The total number of possible clusters is 24. The matrix \(C\) contains the number of feeder retooling between two kits, considering all the retooling times to be equal.  
The company that owns the kitting machine is able to handle hundreds of different products on the same device. Since not every product has to be produced in a certain timespan, the company is interested in optimizing the retooling of the machine with a small number of products. For example, the company may be interested in managing a week of production in which 100 different products have to be produced, based on costumers needs. As a result, our method is applied to different \(N_{p}\) values.  
The results are depicted in Figures 6 and 7. The overall cost provided by the cluster TSP (blue line of Figure 7) is always higher than the cost of the standard TSP (red line), which is an expected result: the clustering applies some constraints that drift the final  
Figure 5: Maximum, median and minimum cost of all the possible combinations of products \(N_{p}\) (\(N_{c}=10\)), simulated 20 times. The TSP solutions lie on the blue curve.  
solution away from the optimal one provided by the standard TSP. However, the clustering, in the same way as shown previously in Section 4, is generally more computationally efficient. Figure 6 shows how the standard TSP generally requires higher computational time, due to the increased difficulty in reaching the optimal solution via the optimizer. Moreover, in some cases, the standard TSP even fails at providing a solution within the constrained maximum computational time.  
Figure 6: Computational time of the case study with different \(N_{p}\). On the upper left corner, a zoom of the lowest \(N_{p}\) values.  
Figure 7: Overall cost of the case study with different \(N_{p}\), where the cost identifies the total number of retooling to process all the products.
## 3 Results
### Model Performance against Satellite-Based GPP Products  
Furthermore, this study evaluated the model performance (GPP\({}_{\text{SIF}}\)) against satellite-based GPP products including MODIS GPP (GPP\({}_{\text{MOD}}\)) and BESS GPP (GPP\({}_{\text{BESS}}\)) across the two subtropical forests (Figures 6-8). Overall, among these GPP estimates, GPP\({}_{\text{SIF}}\) exhibited the optimal performance in promising the variability in site-level GPP observations. However, GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{BESS}}\) showed distinctly different performances for the evergreen needleleaf forest and evergreen broadleaf forest, respectively. As illustrated in Figures 6 and 7 at the QYZ site, GPP\({}_{\text{MOD}}\) consistently underestimated the tower-measured GPP with large discrepancies (Figure 6a; \(RMSE\) = 2.92 g C m\({}^{-2}\) d\({}^{-1}\)), whereas GPP\({}_{\text{BESS}}\) agreed well the variability in the measured GPP with \(RMSE\) of 1.10 g C m\({}^{-2}\) d\({}^{-1}\) (Figure 6c). As illustrated in Figures 6 and 8 at the DHS site, GPP\({}_{\text{BESS}}\) severely overestimated the tower-measured GPP in the evergreen broadleaf forest with \(RMSE\) of 2.69 g C m\({}^{-2}\) d\({}^{-1}\) (Figure 6d), whereas GPP\({}_{\text{MOD}}\) provided slightly better estimates (Figure 6b; \(RMSE\) = 1.44 g C m\({}^{-2}\) d\({}^{-1}\)). Nevertheless, this study also found that GPP\({}_{\text{SIF}}\) seemed unable to capture the dramatic decline in the coniferous site in summer.  
Figure 6: Leave-one-out cross-validation of the model-estimated GPP relying on time-series SIF data (GPP\({}_{\text{SIF}}\)) and the associated MODIS GPP (GPP\({}_{\text{MOD}}\)) and BESS GPP (GPP\({}_{\text{BESS}}\)) products across the two subtropical forest (QYZ, evergreen needleleaf forest; DHS, Evergreen broadleaf forest) flux sites. The dotted line is a 1:1 line.  
Figure 8: Seasonal consistency between tower-based GPP observations (\(\mathrm{GPP_{EC}}\)) at the DHS flux site (evergreen broadleaf forest) and three different satellite-based GPP products including the SIF-based GPP (\(\mathrm{GPP_{SF}}\)), MODIS GPP (\(\mathrm{GPP_{MOD}}\)), and BESS GPP (\(\mathrm{GPP_{BESS}}\)), respectively.  
Figure 7: Seasonal consistency between tower-based GPP observations (\(\mathrm{GPP_{EC}}\)) at the QYZ flux site (evergreen needleleaf forest) and three different satellite-based GPP products including the SIF-based GPP (\(\mathrm{GPP_{SF}}\)), MODIS GPP (\(\mathrm{GPP_{MOD}}\)), and BESS GPP (\(\mathrm{GPP_{BESS}}\)), respectively.
# Farmed Insect Frass as a Future Organic Fertilizer
## 3 Results and Discussion
### Chemical Changes in Acid Peat
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fertilizer**} & \multicolumn{6}{c}{**Nitrogen Rate (mg dm\({}^{-3}\))**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-6}  & **0** & **50** & **100** & **200** & **400** & \\ \hline \multicolumn{6}{c}{S-SO\({}_{4}\)} \\ \hline Frass & 48.6 & 45.8 & 49.6 & 54.9 & 75.3 & 54.8 \\ Urea & 48.7 & 47.1 & 51.2 & 50.9 & 53.1 & 50.2 \\ Significant & ns & ns & ns & ** & *** & ** \\ Mean & 48.6 D & 46.5 C & 50.4 BC & 52.9 B & 64.2 A & \\ \hline \multicolumn{6}{c}{Cl} \\ \hline Frass & 15.3 & 15.1 & 17.0 & 19.3 & 33.6 & 20.1 \\ Urea & 14.1 & 10.3 & 10.6 & 11.7 & 12.8 & 11.9 \\ Significant & ns & *** & *** & *** & *** & *** \\ Mean & 14.7 C & 12.7 E & 13.8 D & 15.5 B & 23.2 A & \\ \hline \multicolumn{6}{c}{P} \\ \hline Frass & 17.3 & 21.4 & 31.1 & 54.3 & 96.7 & 44.2 \\ Urea & 18.4 & 17.1 & 20.1 & 18.5 & 20.7 & 19.0 \\ Significant & ns & *** & *** & *** & *** & *** \\ Mean & 17.9 D & 19.3 D & 25.6 C & 36.4 B & 58.7 A & \\ \hline \multicolumn{6}{c}{Ca} \\ \hline Frass & 705 & 716 & 719 & 707 & 629 & 695 \\ Urea & 708 & 656 & 631 & 611 & 562 & 634 \\ Significant & ns & *** & *** & *** & *** & *** \\ Mean & 707 A & 686 B & 675 B & 659 C & 595 D & \\ \hline \multicolumn{6}{c}{Mg} \\ \hline Frass & 33.9 & 37.1 & 40.9 & 48.2 & 62.3 & 44.5 \\ Urea & 32.5 & 38.5 & 40.9 & 40.6 & 38.5 & 38.2 \\ Significant & ns & ns & *** & *** & *** \\ Mean & 33.2 E & 37.8 D & 40.9 C & 44.4 B & 50.4 A & \\ \hline \multicolumn{6}{c}{K} \\ \hline Frass & 37.1 & 52.2 & 72.3 & 107 & 198 & 93.3 \\ Urea & 37.4 & 54.5 & 53.8 & 58.1 & 51.8 & 51.1 \\ Significant & ns & ns & *** & *** & *** \\ Mean & 37.2 E & 53.4 D & 63.0 C & 82.5 B & 124.9 A & \\ \hline \multicolumn{6}{c}{Na} \\ \hline Frass & 15.8 & 17.5 & 21.2 & 26.1 & 36.9 & 23.5 \\ Urea & 15.6 & 13.9 & 17.8 & 15.2 & 14.5 & 15.4 \\ Significant & ns & *** & *** & *** & *** \\ Mean & 15.7 D & 15.7 D & 19.5 C & 20.6 B & 25.7 A & \\ \hline \multicolumn{6}{c}{pH} \\ \hline Frass & 5.11 & 4.94 & 5.00 & 4.88 & 4.86 & 4.96 \\ Urea & 5.13 & 5.28 & 5.00 & 4.84 & 4.96 & 5.04 \\ Significant & ns & *** & ns & *** & *** \\ Mean & 5.12 A & 5.11 A & 5.00 B & 4.86 D & 4.91 C & \\ \hline \multicolumn{6}{c}{EC} \\ \hline Frass & 0.131 & 0.132 & 0.270 & 0.566 & 0.994 & 0.419 \\ Urea & 0.132 & 0.296 & 0.436 & 0.708 & 0.830 & 0.480 \\ Significant & ns & *** & *** & *** & *** \\ Mean & 0.131 E & 0.214 D & 0.353 C & 0.637 B & 0.912 A & \\ \hline \hline \end{tabular}  
* Significant differences between fans and urea groups across nitrogen rates:
* significant at \(p<0.001\); **
* significant at \(p<0.0001\); **
* significant at \(p<0.0001\); *— not significant. Capital letters A, B,\(\ldots\), E indicate the significance of differences between nitrogen rates.  
\end{table} 
Table 4: Average content of nutrients and the pH and electrical conductivity (EC) of acid peat supplied with different fertilizers and nitrogen rates.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
### Objectives and Research Framework
#### 1.2.1 Objectives  
The main research objectives for optimizing the intelligent maintenance system in smart factories through the state space search algorithm are as follows:  
* To investigate methods for reducing unplanned downtime by designing a new intelligent maintenance system that integrates Industrial Internet of Things (IIoT) technologies and SSS algorithms.
* To examine the feasibility of utilizing the SSS algorithm to optimize the maintenance cycle by minimizing path costs, maximizing the remaining lifetime of spare parts, and evaluating risks through FMEA methods.