esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Circular Trajectory Placement with a Radius of 100 m
#### 4.1.4 Comparison of RSSI, AOA, and HYBRID Results  
The localization errors categorized by different radio signal information are shown in Figure 24 and Table 3.  
Figure 23: CDF of localization errors using HYBRID localization. The curve exhibits the fastest convergence among all models.  
Figure 22: Heatmap of localization errors using the HYBRID model. Most sources are localized within 10 m, demonstrating superior robustness.  
Table 3 summarizes the localization performance for each type of radio signal information in terms of mean error and the 90% cumulative distribution function (CDF90) value. Among the three methods, the HYBRID model achieved the highest accuracy, with an average error of 5.3 m and a CDF90 value of 8.7 m. The CDF also exhibits a steep slope with minimal outliers, indicating faster convergence and greater estimation stability.  
In contrast, the RSSI model showed the poorest performance, with a mean error of 16.4 m and 90% of sources within 41.9 m. Large outliers are frequently observed, which reflects the sensitivity of RSSI to multipath fading and shadowing in NLoS environments. These results are consistent with findings by [PERSON] and [PERSON] (2018), who showed that RSSI-based fingerprinting suffers from high variability and performs poorly in urban scenarios unless enhanced with additional signal features. Similarly, [PERSON] et al. (2018) emphasized the advantage of using UAV-based sensing and feature integration in environments where signal propagation is highly complex. The AOA model performed moderately, localizing 90% of sources within 14.8 m. While its average performance is better than RSSI, it is still affected by angular ambiguity, especially in areas with significant reflection or diffraction.  
Overall, the HYBRID approach effectively compensates for the limitations of individual features--RSSI's vulnerability to shadowing and AOA's susceptibility to reflection--by combining distance- and direction-based indicators. This aligns with prior work by [PERSON] (2018), who proposed combining RSSI and AOA, and [PERSON] (2018), who improved localization performance by optimizing UAV trajectories for RSSI measurements. The present study builds upon these directions by including EOA and optimizing both feature integration and UAV path design, leading to improved robustness and accuracy.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 5 _Acrocomia aculeata_ Oil Extraction Using Supercritical Fluids  
Supercritical fluid extraction is a technique used to obtain extracts with desired components from a solid or liquid material using a solvent under supercritical conditions, which occur above the critical point of the solvent where it exhibits intermediate properties between a liquid and a gas. When compared to other methods, supercritical fluid shows several advantages. In terms of economics, supercritical extraction can be easily scaled up to industrial levels with lower costs than other techniques. In terms of environmental impact, it is a green-friendly technique, where carbon dioxide is commonly used as a solvent due to its non-toxic, non-flammable, non-polluting, and highly selective properties, capable of extracting polar and nonpolar compounds by adjusting extraction conditions and adding co-solvents [15]. Thus, supercritical fluid extraction (SFE) technology allows selective extraction of specific compounds according to the pressure and temperature conditions used. In that regard, several studies have been developed with the aim of optimizing the efficiency of the process to obtain pure, concentrated, and high-quality extracts in short periods of time, an important factor for application in the pharmaceutical, chemical, and food industries [14; 24].  
Recent studies have shown that SFE with carbon dioxide is efficient in obtaining macauba pulp and almond oils, providing greater selectivity and extraction of fatty acids, especially oleic acid, compared to low-pressure extractions such as Soxhlet [14]. On the other hand, extraction using compressed propane as a solvent presented promising results, with higher yields and higher levels of phytosterols and tocopherols in just 30 min of extraction [15].
## 5 Conclusions  
This paper addresses load-balancing-related optimization problems impacting the performance of entire systems. The research proposed a ACO-NN approach for load balancing in distributed computing systems to enhance load scheduling mechanism. ACO-NN can manage task scheduling based on node status information. To expedite the ACO process,  
Figure 14: Comparison of rank values for ACO-NN, TSP-SAGEP, SOS-SA, and MO-GA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instances** & **Nb.Node** & **Optimal** & **Alg.** & **Best** & **Worst** & **Average** & **Time (s)** \\ \hline \multirow{4}{*}{Gr120} & \multirow{4}{*}{120} & \multirow{4}{*}{6942} & ACO-NN & 1770 & 1843 & 1811 & 6.6174 \\ \cline{3-7}  & & & TSP-SAGEP [52] & 6942 & 7406 & 6995 & 19.4612 \\ \cline{3-7}  & & & SOS-SA [53] & 6942 & 11,237 & 7786 & 25.3581 \\ \cline{3-7}  & & & MO-GA [54] & 6942 & 11,008 & 7655 & 236,709 \\ \hline \multirow{4}{*}{Pcb442} & \multirow{4}{*}{442} & \multirow{4}{*}{50,778} & ACO-NN & 56,815 & 58,894 & 58,333 & 40.3890 \\ \cline{3-7}  & & & TSP-SAGEP [52] & 50,811 & 52,147 & 50,878 & 51.0964 \\ \cline{3-7}  & & & SOS-SA [53] & 51,107 & 55,723 & 51,958 & 610,876 \\ \cline{3-7}  & & & MO-GA [54] & 51,097 & 55,006 & 51,828 & 594,571 \\ \hline \multirow{4}{*}{Gr666} & \multirow{4}{*}{666} & \multirow{4}{*}{294,358} & ACO-NN & 3597 & 3655 & 3632 & 83.7198 \\ \cline{3-7}  & & & TSP-SAGEP [52] & 294,419 & 305,036 & 295,542 & 79.7853 \\ \cline{3-7}  & & & SOS-SA [53] & 311,855 & 417,702 & 331,024 & 90.0014 \\ \cline{3-7}  & & & MO-GA [54] & 311,003 & 441,298 & 329,199 & 88.0163 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of ACO-NN to various approaches for Gr120, Pcb442, and Gr666.  
we reject dissatisfied requests before scheduling. The preapproprove procedure reduces the solution dimensions of the nearest neighbor and ACO, thereby saving computing time in a high-load condition.  
To validate the performance of the proposed algorithm, nine trials were carried out on ten benchmark instances taken from the TSPLIB. The experimental results of ACO-NN were compared to fourteen heuristic algorithms. Hybrid ACO-NN proves its efficiency over diverse performance metrics, the experimental results show that the proposed method outperforms existing approaches in maintaining load balance in a dynamic environment.  
[PERSON] and [PERSON] contributed to the design of research; [PERSON] implemented and performed the experiments; [PERSON] and [PERSON] analysed the study data; [PERSON] conducted the supervision and validation of the research; and [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was particularly funded by the Intelligent Development Operational Program 2014-2020 cofinanced by the European Regional Development Fund, project POIR.04.01.04-00-0074/19.  
Not applicable.  
The submitted article contains all of the data, and models developed or used during the study.  
The authors declare no conflict of interest.
## Data Used in this Study  
### Raster Data  
#### 2.1.1 Vegetation Proxies  
Satellite SIF data used in this study were based on SIF retrievals from Global Ozone Monitoring Experiment-2 (GOME-2) satellite measurements using a linear statistical approach [42; 43] (website: [[ftp://ftp.gfz-potsdam.de/home/mefe/GlobFluo/GOME-2/gridded/](ftp://ftp.gfz-potsdam.de/home/mefe/GlobFluo/GOME-2/gridded/)]([ftp://ftp.gfz-potsdam.de/home/mefe/GlobFluo/GOME-2/gridded/](ftp://ftp.gfz-potsdam.de/home/mefe/GlobFluo/GOME-2/gridded/))). Single SIF retrievals were gridded to monthly means at a spatial resolution of 0.5\({}^{\circ}\). In this study, GOME-2 SIF data covering Victoria from 2007 to 2013 were used.  
The NDVI and EVI data were derived from the moderate-resolution imaging spectroradiometer (MODIS) vegetation index product (MOD13C2, monthly 0.05\({}^{\circ}\)) (website: [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))). The NDVI and EVI data were quality filtered by good quality flags according to the quality control (QC) layer. Original 0.05\({}^{\circ}\) data were aggregated to 0.5\({}^{\circ}\) by regional (10 \(\times\) 10 window) averaging.  
According to Equations (1) and (2), GPP anomaly could be partly driven by PAR changes, which is the same case for SIF. As VIs carry no information on radiation, in order to make VIs and SIF comparable, we used APAR calculations based on VIs. This conversion was accomplished by assuming a linear relationship between VIs and FPAR [31]. NDVI and EVI data multiplied by PAR are denoted as APARNDVI and APAREVI, respectively.
## 2 Results and Discussion
### Met-Derived VOCs  
The catabolism of Met, a sulphur-containing amino acid, is initiated by a transamination step involving ArAT or the branched-chain aminotransferase (BcAT) in the presence of \(\alpha\)-ketoglutarate, yielding 4-methylthio-2-ketobutyric acid (KMBA). KMBA can be biochemically converted via a decarboxylation reaction into methional and subsequently converted into methanethiol and \(\alpha\)-ketobutyrate via an unknown pathway. KMBA can also be directly converted into 2-hydroxy1-4-methylthiobutyric acid and methanethiol via dehydrogenation. Further, demethiolation of Met produces methanethiol, \(\alpha\)-ketobutyrate, and ammonia via two pyridoxal phosphate-dependent lyases (cystathionine \(\beta\)-lyase (CBL), cystathionine \(\gamma\)-lyase (CGL)). Lastly, KMBA can be chemically converted into methanethiol [34; 36; 58; 63]. Methanethiol produced through these pathways can be further converted into dimethyl sulphide, dimethyl disulphide, and dimethyl trisulphide by auto-oxidation [36; 63]. In addition, methanethiol can also react with carboxylic acids to produce thioesters [58]. The \(\alpha\)-ketobutyrate formed from the Met catabolism can be converted into propanoic acid and yields ATP via substrate-level phosphorylation [63].  
In the current study, the highest peak area for methanethiol was in DM at 0.1% acetate supplemented with the Aamix (Figure 4A, medium DMAa), which contained Met along with other amino acids. The highest peak area for methional was in medium supplemented with Met (Figure 4B, medium DMM), where a significantly (\(p<0.05\)) higher peak area was obtained for 1.2% acetate compared to 0.1% acetate. For dimethyl disulphide, the highest peak area was observed in the medium with Aamix added (Figure 4C, medium DMAa) at 1.2% acetate, suggesting that it was converted from methanethiol.  
According to a study by [PERSON] et al. [53], the genes _cbA_ and _cglA_, which encode CBL and CGL lyases, are present in _Lev. Brevis_ ATCC 367, and catalyse the demethiolation reaction of Met. Lyases, play an important role in the biosynthesis of sulphur-containing amino acids but not a major role in Met catabolism by LAB [36]. Therefore, aminotransferases ArAT or BcAT, which might be present in LB672 and involved in the transamination reaction, would appear to be the major source of the degradation products. As the peak area for methanethiol was high and the peak area for methional was low, the results suggest that either methional was produced through transamination (either by ArAT or by BcAT) and converted into methanethiol or that methanethiol was produced directly from Met via the activity of CBL/CGL.
## 4 Experiment and Simulation
### Lighting System Simulation  
The service execution results of the proposed lighting system were verified by a user's outdoor activity type simulation based on the actual UV data of a natural light of 4.1. At this time, it was assumed that a user started outdoor activities at 9 a.m., where UVI was near the average UVI per day, and performed outdoor activities for 30, 20, 10, and 0 min, respectively. When the user who carried out outdoor activities entered the indoor environment, the indoor UVB LED lighting was provided and the irradiation time of the UVB LED lighting was calculated considering the UV exposure amount of the user. For the simulation, the _RVUID_ for each age according to \(PBE\) was calculated using Equation (4) to define the individual characteristics of the user as shown in Table 2. At this time, the skin type was set to 2 or 3, which is the common skin type of Asians, and four cases were considered according to the classification step of the \(AF\). Considering age, skin type, and exposed area, the _RVUID_ required for the day is about 26-312 J/m\({}^{2}\) in the case of Asians as shown in Table 2. The outdoor UV dose (\(V\!UID_{out}\)), the recommended exposure time to the lighting (\(t_{r}\)), the satisfactory levels of UV dose to be provided indoors (\(V\!UID_{in}\)), and _RVUID_ are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{4}{c}{_RVUID_} & \multicolumn{2}{c}{_Kain Type III_} \\ \cline{4-11}
**Experiment Cases** & **Age** & \(AF\) & \multicolumn{2}{c}{_RVA (IU)_} & \multicolumn{2}{c}{_Skin Type II_} & \multicolumn{2}{c}{_Skin Type III_} \\ \cline{4-11}  & & & & & **10\%** & **15\%** & **30\%** & **10\%** & **15\%** & **30\%** \\ \hline CASE 1 & 0–21 & 1 & 400 & 77 & 51 & 26 & 102 & 68 & 34 \\ CASE 2 & 22–40 & 0.83 & 400 & 92 & 61 & 31 & 123 & 82 & 41 \\ CASE 3 & 41–59 & 0.66 & 400 & 116 & 77 & 39 & 155 & 103 & 52 \\ CASE 4 & 60– & 0.49 & 600 & 234 & 156 & 78 & 312 & 208 & 104 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Age-specific Recommended Vitamin D UV Dose (_RVUID_, skin types 2 or 3) according to the exposure area of the user.
# Gaussian Process Modeling of Specular Multipath Components
## 4 Results  
### Experimental Setup  
Figure 1 shows the laboratory room at Graz University of Technology that was used for the experimental validation. The room consists of two plaster board walls and two reinforced concrete walls (shown as black outer lines), three glass windows at the north wall (shown as thick gray lines), one white board and one metal door at the south wall (indicated by A\({}^{*}\) and C\({}^{*}\), respectively). We introduce the following labels to refer to the involved reflection surfaces:  
**EPB**: East plaster board.
**SW**: South wall.
**WW**: West wall.
**NGW**: North glass wall.  
To conduct the channel measurements, an Ilmsens Ultra-Wide band M-sequence device [32] was used, c.f. Figure 2a. The measurement principle is correlative channel sounding [33], that is, a binary code sequence with suitable autocorrelation properties is transmitted over the channel. At the receiver, the channel impulse response is recovered using a correlation with the known code sequence. The channel sounder has one transmitter port and two receiver ports. A 12-bit M-sequence has been employed, corresponding to a sequence length of 4095 samples. This allows for an unambiguous delay window of 589.2 ns at a clock rate of 6.95 GHz. The M-sequence is modulated onto a 6.95 GHz carrier, yielding a probing signal that covers a frequency band between approx. 3.5 and 10.5 GHz. The measurement data are available online in the \"MeasureMINT\" data base. Please refer to: [[https://www.spsc.tugraz.at/databases-and-tools/uwb-indoor-channel-experimental-data.html](https://www.spsc.tugraz.at/databases-and-tools/uwb-indoor-channel-experimental-data.html)]([https://www.spsc.tugraz.at/databases-and-tools/uwb-indoor-channel-experimental-data.html](https://www.spsc.tugraz.at/databases-and-tools/uwb-indoor-channel-experimental-data.html)).  
Each of the ports was connected to a dipole coin antenna as shown in Figure 2b. According to Reference [34], the coin antenna has a very wide bandwidth ranging from 3 to 9 GHz. It also has a nearly isotropic radiation pattern in the horizontal plane.  
We used the two receiver ports as anchors and placed their antennas at fixed positions \(\mathbf{a}_{1}^{(1)}\) and \(\mathbf{a}_{1}^{(2)}\). The transmitter port is connected to another antenna that was moved along a trajectory with 595 points \(\mathbf{p}\), as shown in the Figure 1, to obtain the same number of channel measurements. All antennas were mounted on tripods at the same height, therefore only the co-polarized, azimuth radiation pattern of the antenna has an impact on the data, c.f. Equation (2). The raw measurements at the receiver ports were filtered with an RRC pulse with center frequency 6.95 GHz, roll-off factor 0.5 and bandwidth \(1/T_{s}=2\) GHz to obtain the received signals corresponding to the model in (1).
## 2 Related Work
### Spectral Efficiency of the System
\[\mathrm{SE}_{k}^{(1)}=\left(1-\frac{\tau_{p}}{\tau_{c}}\right)\mathbb{E}\Big{\{} \log_{2}\Big{|}\mathrm{I}_{N}+\mathrm{D}_{k,(1)}^{H}\Sigma_{k,(1)}^{-1}\mathrm{ D}_{k,(1)}\Big{|}\Big{\}} \tag{15}\]  
where \(\mathrm{D}_{k,(1)}\triangleq\mathrm{V}_{k}^{H}\hat{\mathrm{H}}_{k}\mathrm{F}_ {k}\),  
\[\Sigma_{k,(1)}\triangleq\mathrm{V}_{k}^{H}\Big{(}\sum_{l=1}^{K}\ \hat{\mathrm{H}}_{l}\hat{ \mathrm{F}}_{l}\hat{\mathrm{H}}_{l}^{H}+\sum_{l=1}^{K}\ C_{l}^{\prime}+\sigma ^{2}\mathrm{I}_{ML}\Big{)}\mathrm{V}_{k}-\mathrm{D}_{k,(1)}\mathrm{D}_{k,(1) }^{H}\]  
\(\hat{\mathrm{F}}_{l}=\mathrm{F}_{l}\mathrm{F}_{l}^{H}\), and \(\mathrm{C}_{l}^{\prime}=\mathrm{diag}\big{(}\mathrm{C}_{1l}^{\prime},\ldots, \mathrm{C}_{Ml}^{\prime}\big{)}\), and the \((i,j)\)th element of \(\mathrm{C}_{ml}^{\prime}\) which is expressed as  
The MMSE combining stated in Equation (9) can be further minimized as \(\mathrm{MSE}_{k}=\mathbb{E}\Big{\{}\|\ \mathrm{x}_{k}-\mathrm{V}_{k}^{H}\mathrm{y}\ \|^{2}\ |\ \hat{ \mathrm{H}}_{k}\Big{\}}\) and resulted in the maximization of spectral efficiency defined in Equation (15). The maximized SE using the minimized MMSE combining method can be evaluated as shown in Equation (16),  
\[\mathrm{SE}_{k}^{(1)}=\left(1-\frac{\tau_{p}}{\tau_{c}}\right)\mathbb{E}\Big{\{} \log_{2}\Big{|}\mathrm{I}_{N}+\Big{(}\mathrm{D}_{k,(1)}^{\prime}\Big{)}^{H} \Big{(}\Sigma_{k,(1)}^{\prime}\Big{)}^{-1}\mathrm{D}_{k,(1)}^{\prime}\Big{|} \Big{\}} \tag{16}\]  
where \(\mathrm{D}_{k,(1)}^{\prime}\triangleq\mathrm{\hat{H}}_{k}\mathrm{F}_{k}\) and \(\Sigma_{k,(1)}^{\prime}\triangleq\sum_{l\
eq k}^{K}\ \hat{\mathrm{H}}_{l}\hat{ \mathrm{F}}_{l}\mathrm{H}_{l}^{H}+\sum_{l=1}^{K}\ C_{l}^{\prime}+\sigma^{2} \mathrm{I}_{ML}\).
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design
### Design of Reader Side Scheme
#### 3.1.3 The Third Sub-Slot  
In the last sub-slot, the selected tag communicates with reader. The \(i\)-th tag and \(j\)-th tag of the selected tags transmit data signal and artificial noise concurrently. The signal received by the reader can be expressed as:  
\[y_{r}(n)=h_{r}s(n)+s(n)B_{i}(n)d_{i}\eta_{j}h_{i}+B_{j}(n)d_{j}\eta_{j}h_{j}+ \omega_{r}(n) \tag{9}\]  
It is worth noting that in the IANT scheme proposed in this paper, as the channel quality between tags and reader improves, the security performance of the system may be reduced. If there is no effective way to eliminate the artificial noise of the reader side, the signal-to-noise ratio of the reader must be reduced. It is known from the Formula (9) that the artificial noise consists of two parts: the noise sequence and the channel parameters. The noise sequence can be generated by a pseudo-random binary sequence (PRBS) generator with the noise tag index \(j\) as the seed of the random number. PRBS can be generated by Texas Instruments' CD4015 BM96 dual four-channel static shift register and CD4030 BM96 four-channel XOR gate. It has a simple structure, low cost, and is easily integrated into tags and readers. In the ABC environment, the method proposed by [PERSON] [16] team can be used to iteratively obtain the estimated values of channel parameters based on the expectation maximization algorithm, and there is no need to send pilot signals for channel estimation. Based on the above operation, the reader side can eliminate the influence of artificial noise completely. To detect the symbols \(B(n)\) backscattered by tags,we first calculate the average power of the signal received by the reader receiver, and then we use the method proposed by the [PERSON] team [17] to detect the data sent by the tag. The average power of the received signal on the reader side can be expressed as:  
\[\Phi_{B}(q)=\frac{1}{N_{t}}\sum_{n=(q-1)*N_{t}+1}^{q*N_{t}}\left|y_{r}(n+N_{p}+N _{s})\right|^{2} \tag{10}\]  
where \(N_{t}\) represents the number of symbol s(\(n\)) transmitted by RF signal source during the duration of one tag data symbol. Next, we use the reader to compare the calculated results with the Formulas (5) and (6) and determine whether the symbol backscattered by tag is '1' or '0' according to the following rule:  
\[\hat{B}_{i,r}(q)=\left\{\begin{array}{ll}0&\text{if }|\[PERSON]_{B}(q)-\[PERSON]_{0}|<| \[PERSON]_{B}(q)-\[PERSON]_{i}|\\ 1&\text{if }|\[PERSON]_{B}(q)-\[PERSON]_{0}|>|\[PERSON]_{B}(q)-\[PERSON]_{i}|\end{array}\right. \tag{11}\]  
Formula (11) can be equivalent to a more intuitive form:  
\[\hat{B}_{i,r}(q)=\left\{\begin{array}{ll}0&\text{if }\[PERSON]_{B}(q)<(\[PERSON]_{0}+ \[PERSON]_{i})/2,\[PERSON]_{0}<\[PERSON]_{i}\\ 1&\text{if }\[PERSON]_{B}(q)>(\[PERSON]_{0}+\[PERSON]_{i})/2,\[PERSON]_{0}<\[PERSON]_{i}\\ 1&\text{if }\[PERSON]_{B}(q)<(\[PERSON]_{0}+\[PERSON]_{i})/2,\[PERSON]_{0}>\[PERSON]_{i}\\ 0&\text{if }\[PERSON]_{B}(q)>(\[PERSON]_{0}+\[PERSON]_{i})/2,\[PERSON]_{0}>\[PERSON]_{i}\end{array}\right. \tag{12}\]
## 3 Proposed Methodology
### The Proposed DRL-Aided Algorithm
```
1:Initialize number of channels \(L\), number of episodes \(Ep\), episode duration \(T\), learning rate \(a\), discount factor \(\gamma\), and weights \(\theta\) of the policy network;
2:for\(i\gets 0\)to\(Ep-1\)do
3: Set \(\
abla_{\theta}J(\theta)=0\);
4:for\(t\ \gets 0\)to\(T-1\)do
5:for\(l\ \leftarrow 0\)to\(L-1\)do
6: Fetch recorded CSI data\(\left\{h_{tJ}\right\}\) for each \(l\)th time-step and \(l\)th optical channel;
7: Sample the allocated power\(\left\{\pi(h_{tJ},\theta)\right\}\) using the current policy distribution;
8: Calculate the returns\(\left\{r_{t+1,l}\right\}\) from (12);
9: Calculate \(G_{tJ}=\sum_{t=t+1}^{T}\ \gamma^{tJ-1}r_{tJ}\) from (13);
10:\(\
abla_{\theta}J(\theta)\leftarrow\
abla_{\theta}J(\theta)+\
abla_{\theta} \log\pi(h_{tJ},\theta)G_{tJ}\) from (17);
11:end
12:end
13:end
14:\(\theta\leftarrow\theta+\alpha\
abla_{\theta}J(\theta)/T\) gradient ascent the policy parameter (DNN's weights);
15:\(v\leftarrow\left[v-\alpha\sum\limits_{t=0}^{T-1}\left\{\sum\limits_{l=0}^{L-1} \pi(h_{tJ},\theta)-P_{av}\right\}/T\right]_{+}\) update dual variable (total power constraint);
16:end
```  
**Algorithm 1** DRL-aided Proposed Power Allocation Methodology
## 7 Simulations and Results
### Scenario 1: HEMS with Grid Only
#### 7.1.3 AWT Using RTP and CPP  
In the optimal scheduling problem, the consumption cost and appliance waiting time are the two quantities among which a trade-off exists. In this scenario, the AWT for GA, GWO, and the proposed HGWGA was found to be 1.65 h, 1.543 h, and 1.72 h, respectively. It can be observed that HGWGA had the maximum AWT compared with the other implemented optimization techniques, which meant that the consumers were bound to sacrifice their comfort to achieve the cost-minimization objective. The AWT of scenario 1 for the GA, GWO, and HGWGA is presented in Figure 11.  
Figure 10: Peak-to-average ratio (PAR) without an ESS or PV.  
Figure 9: Total cost using the critical peak pricing (CPP).  
Keeping in mind the different results for scenario 1, the percentage reduction in cost and PAR for both the RTP and CPP tariffs displays the effectiveness of the proposed hybrid technique (HGWGA) as it gives improved results in comparison with the unscheduled and other scheduled cases (GA, GWO).