esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Eigenvalue Fusion and other Fusion Methods for Detection OFDMA Signals
### Decision Fusion for OFDMA Signals  
Now we describe how the decision fusion strategy was adapted to detect the occupancy of an OFDMA subchannel. A matrix with sample values at each CR and for each subchannel will be formed according to Equation (22), from where the corresponding sample covariance matrices are computed via Equation (23). From each of the resulting \(P\) sample covariance matrices, \(K^{\prime}\) eigenvalues are estimated in each CR and ordered as \(\{\lambda_{1,s}\geq\lambda_{2,s}\geq\cdots\lambda_{K^{\prime},s}\}\). The occupation of each subchannel is determined by comparing any of the test statistics Equations (31)-(34) with the decision threshold. The resulting decisions are then sent to the FC for binary arithmetic combining and final decisions upon each subchannel.
# A Low-Phase-Noise 8 GHz Linear-Band Sub-Millimeter-Wave Phase-Locked Loop in 22 nm FD-SOI CMOS
## 3 Experimental Results
In addition, the bandwidth obtained is among the highest, with relatively high output saturated power in comparison to relevant works observed from Table 1. Although the performance of the designed PLL appears to be formidable, this was made possible by the use of the 22 nm FD-SOI CMOS, which offers relatively higher device performance at higher frequencies. As a result, the main disadvantage of the produced PLL is the high technology cost.
# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 3 Materials and Methods
### Machine Learning Models
#### 3.4.3 Random Forest (RF)  
RF is another ensemble learning algorithm based on decision trees, in which multiple trees are trained independently and their outputs are aggregated for predictions. Each tree is constructed using a random subset of features and instances, ensuring diversity. RF offers robustness against overfitting and noise in the data [31].
# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 8 Accuracy Comparisons w.r.t. Traditional Machine Learning  
Tables 3 and 4 show that the parallel TCN approach proven to offer high accuracy due its ability to capture non-linear battery dynamics. On the contrary, more traditional approaches often proved to under perform in capturing such dynamics due to their simpler approach and linear approximations capabilities, as in the case of the EKF. Furthermore, as widely known, traditional ML models, such as DT and SVM, exhibit less computational complexity compared to NN. Table 5 reports the results achieved by testing these approaches on the dataset under consideration. Table 6 shows the percentage increase of the MSE for SoC estimation w.r.t. the parallel TCN, making these traditional ML methods inadequate from the accuracy point of view.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **MAPE [\%] Tests 10 and 16** & **MAPE [\%] Tests 10 and 37** \\ \hline Dense & 5.03 & 5.44 \\ LSTM Stateless & 4.85 & 4.30 \\ LSTM Stateful & 30.8 & 22.9 \\ GRU Stateless & 5.54 & 6.76 \\ GRU Stateful & 18.2 & 25.1 \\ Parallel TCNs & **2.96** & **3.26** \\ LMU & 4.90 & 5.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test MAPE (Mean Absolute Percentage Error) results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **MSE Tests 10 and 16** & **MSE Tests 10 and 37** \\ \hline Dense & 14.6 \(\times\) 10\({}^{-4}\) & 16.7 \(\times\) 10\({}^{-4}\) \\ LSTM Stateless & 11.2 \(\times\) 10\({}^{-4}\) & 10.7 \(\times\) 10\({}^{-4}\) \\ LSTM Stateful & 361.0 \(\times\) 10\({}^{-4}\) & 270.7 \(\times\) 10\({}^{-4}\) \\ GRU Stateless & 20.4 \(\times\) 10\({}^{-4}\) & 29.0 \(\times\) 10\({}^{-4}\) \\ GRU Stateful & 209.0 \(\times\) 10\({}^{-4}\) & 376.4 \(\times\) 10\({}^{-4}\) \\ Parallel TCNs & **5.4 \(\times\) 10\({}^{-4}\)** & **7.1 \(\times\) 10\({}^{-4}\)** \\ LMU & 19.7 \(\times\) 10\({}^{-4}\) & 25.0 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test MSE results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MSE Tests 10 and 16** & **MSE Tests 10 and 37** \\ \hline Decision Tree & 670.6 \(\times\) 10\({}^{-4}\) & 975.7 \(\times\) 10\({}^{-4}\) \\ Random Forest & 649.1 \(\times\) 10\({}^{-4}\) & 991.3 \(\times\) 10\({}^{-4}\) \\ Support Vector Machine & 74.1 \(\times\) 10\({}^{-4}\) & 67.4 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test MSE results of traditional ML methods.  
The lowest MSE increase on test cycles 10 and 16 was 1372% by SVM while DT was accountable for 12,418% increase, more than 4 orders of magnitude. A similar trend it has been observed on test cycles 10 and 37; the lowest MSE increase was 949% by SVM while DT was accountable for 13,742% increase.
## 3 Materials and Methods
### Chemicals and Reagents  
2,2-Diphenyl-1-picrylhydrazyl (DPPH), Folin-Ciocalteu reagent, formic acid, and sorbitol were purchased from Sigma Aldrich (St. Louis, MO). Spherical silica particles (surface area: 270-300 m\({}^{2}\)/g, particle size: 75-200 m, total pore volume: 0.7-0.9 cm\({}^{3}\)/g, as specified by the manufacturer) were also obtained from Sigma Aldrich (St. Louis, MO, USA). Sodium carbonate, sodium chloride, HPLC-grade acetonitrile and methanol, hydrochloric acid, sulfuric acid, sodium hydroxide, and hydrogen peroxide were purchased from Fischer Scientific Co. (Pittsburgh, PA, USA). Methoxy polyethylene glycol Silane-2000 and -5000 (mPEG-silane) were purchased from Laysan Bio (Arab, AL). Analytical reference materials were obtained as follows: Gallic acid, (+)-catechin, delphinidin chloride, p-coumaric acid, quercetin, and kaempfer were supplied from Millipore Sigma (St. Louis, MO, USA); procyanidin B2, isoquercetin, malvidin chloride, and myricetin were purchased from Cayman Chemical (Ann Arbor, MI, USA). Water was purified using a Barnstead E-Pure system (ThermoFisher Scientific, Waltham, MA, USA) with a resistivity of 18 M\(\Omega\)-cm unless otherwise specified.
# Optimizing Container Placement in Data Centers by Deep Reinforcement Learning
## 4 Container Placement Optimization
### Container Placement Optimization Using DRL
After executing the state value and action within the environment, the reward (\(r_{t}\)), next state (\(s_{t+1}\)), and termination (\(done\)) are obtained (line 8). The transitions, such as the state value, action, reward, next state, and termination status, are stored in memory (line 9). The state value is then updated to the next state (line 10), and this process repeats for the number of batch iterations (line 6). The Temporal Difference (TD) target is then computed by extracting stored transitions (line 14). Because TD learning is used, the TD target enables stepwise learning [10]. The TD error is obtained by subtracting the current state value (V(\(s_{t}\))) from the TD target (line 15). Using the TD error, the critic loss is calculated (line 16), and loss optimization is performed (line 18) using Huber loss. Huber loss is a hybrid of L1 loss (absolute difference) and L2 loss (square difference), making it robust to outliers and differentiable at all points. Next, the action probability distribution (\(\pi(s_{t},a_{t})\)) is updated using the policy gradient method. The log of the probability distribution (\(\pi(s_{t},a_{t})\)) for a specific action is multiplied by the TD error to compute the actor loss (line 17), which is then optimized (line 19). These steps continue until the termination condition is met (line 11). Upon termination, the number of unused servers is calculated, and the final reward is assigned. By repeating this process over multiple episodes (line 2), the algorithm gradually converges toward an optimal solution. ```
1:Initialize neural network \(\pi_{\theta},\ V_{w}\)
2:For episode = 1,..., M
3:Initialize \(s_{t}\)
4:\(done\) = false
5:while not \(done\)
6:for t = 0,..., \(N_{batch}\)
7: sample \(a_{t}\) - \(\pi(s_{t},\ a_{t})\)
8:execute action \(a_{t}\) in emulator and observe reward \(r_{t},\ s_{t+1}\) and \(done\)
9: store transitions \((s_{t},\ a_{t},\ r_{t},\ s_{t+1},\ done)\) in \(B\)
10:\(s_{t}\gets s_{t+1}\)
11: if \(done\)
12: break
13: extract a batch of transitions \((s_{t},\ a_{t},\ r_{t},\ s_{t+1},\ done)\) in \(B\)
14: TD Target = \(r_{t}+\gamma*\mathrm{V}(s_{t+1})*(1-done)\)
15: TD Error = TD Target - \(\mathrm{V}(s_{t})\)
16: Critic loss = huber_loss \(\mathrm{V}(\mathrm{V}(s_{t}),\ TD\ error)\)
17: Actor loss = \(-log\pi(s_{t},\ a_{t})*\mathrm{TD}\ error\)
18: Critic loss optimization
19: Actor loss optimization
```  
**Algorithm 1**Optimization algorithm based on DRL.
## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations  
#### 4.2.1 Data Preparation and Enhancement Strategies  
The dataset used in this paper contained a total of 1189 images. Of these, the number of original images was 312. Given the relative scarcity of images of the emergency airdrop stations and the apparent visual difference between the emergency airdrop station built on top of a tall building and the conventional ground view, this may lead to significant changes in the shape, size, and appearance of the target object. In addition, different lighting conditions may also have a profound effect on the visibility and detection performance of the target. In this paper, geometric transformations (e.g., flipping, rotating, cropping, scaling) and pixel transformations (e.g., Gaussian blurring, saturation adjustment) are used to enhance the data from the photographs. After these processes, the total number of original pictures with data-enhanced pictures reaches 1189. For these data-enhanced and original pictures, labelImg data annotation software was used in this paper for manual annotation. The images are shown in Figure 11. To ensure the rationality of the dataset, this paper divided the dataset into a training set, validation set, and test set in the ratio of 7:1:2.  
To further illustrate the reality of UAV load drops, Figure 12 depicts the path of the UAV and the load drop process in detail. The UAV is capable of accurately delivering supplies to a target location on the ground or to a designated drop zone on top of a high-rise building. This process requires careful planning and real-time adjustments to effectively respond to complex real-world environments. Accurate delivery of supplies in such scenarios is critical for military emergency relief operations.  
Figure 11: Example dataset. (**a**) Original image; (**b**) Original image labelling; (**c**) Data-enhanced image; (**d**) Data-enhanced image labelling. Note: The red boxes indicate the position of the target object to be detected.  
Figure 10: City (39,25) path planning and real space mapping.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 4 Conclusions  
A review on enabling wearable and implantable medical devices with RF energy is presented in this work. The principles in the construction of the RF energy system are discussed, along with the antenna design and characterization, matching network, and rectifier topologies. Multiple wearable medical devices enabled through RF energy were also analyzed. Notably, RF energy is a reliable and desirable energy source for wearable medical devices; however, implementing the RF harvester in wearable medical devices faces several challenges. For example, the reliability of connectivity between rigid and flexible system components, optimizing the power conversion performance to enhance transfer range, maintaining efficiency and pattern of radiation on the human body, and the performance fluctuation caused by mechanical deformations, particularly for the antenna. Therefore, advancements in the design of lightweight, compact, and flexible rectennas capable of harvesting the maximum amount of RF energy from different RF sources randomly distributed in space are still required. The RF power transfer of implantable medical devices in the near field, midfield, and far field was also presented, with an analysis of the recently published research works that addressed the targeted fields. Midfield power transfer methods for implantable medical devices, which permit focusing on the energy hotspot, seem to be the best choice since the near-field method suffers from a limited performance range while the far-field method suffers from deterioration of the link efficiency. In fact, most of the RFEH devices are not ready for clinical study since they require time, resources, and compliance with strict safety standards for completion. Future advancements in applying the RF harvester in wearable and implantable medical devices are achievable through extensive cooperation between academia and industry to produce an enhanced system that can provide reliable and constant power supplies.  
Conceptualization, H.Y.A., M.Y.A. and H.R.; writing--original draft preparation, H.Y.A.; writing--review and editing, H.Y.A., M.Y.A. and H.R; supervision, M.Y.A. and H.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Higher Education Malaysia via Fundamental Research Grant Scheme (FRGS/1/2020/TK0/UM/02/45) (FP124-2020).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Engineering Application Case
### Program Verification
#### 4.2.3 Third Step  
After the image is converted, the weighted morphology process is carried out. A two-stage morphological filtering process is performed, starting with the use of the open and close operations using Equations (20) and (21), where the weight coefficient is \(a=0.7\), \(B_{1}\) and \(B_{2}\) are the opening operations, and \(g(x,y)\) is the structural element. Then, the top-hat transform is used to polish the outer boundary of the image utilizing Equations (18) and (19). The results of these operations can be observed in Figure 10.  
Table 1 summarizes the performance of the morphological filtering algorithm.  
Based on these results, it is obvious that the ripples (sea background noise) remaining in the image after applying the weighted morphological filtering were significantly lower. The MSE and the number of corrugated bands, which were lower after filtering, confirm this. However, since the image still had interference, it was necessary to apply the median filter to remove it from the image.  
Figure 10: Results of morphological filtering: (**d**) converted image; (**e**) results after morphological filtering.  
Figure 9: Images and annotations (yellow and blue lines) of DHT. (**a**) S = 0.2; (**b**) S = 0.5; (**c**) S = 0.8; (**d**) converted image.  
The following can be observed in the images:  
* Figures 9d and 10d are the same; Figures 10e and 11e are the same; and Figures 11f and 12f are the same.
* Figure 9d has more than 20 corrugated bands; Figure 10e has fewer than 10 corrugated bands; Figure 11f fewer than 7 corrugated bands; and Figure 12g is free of corrugated bands. This means that the corrugated bands decreased as the filtering process progressed.
* Undesirable edges and protrusions in the target areas of the vessels were filtered out.
* The four-dimensional target feature vector \(V(l,h,r,c)\) was effective, as it showed the contour moment features of the vessels.
* The target vessels were distinguished from the surface noise by setting the aspect ratio and the area width of the connected area.
* The final results in Figure 13 indicate that there are seven vessels detected in this image.
* The same process was run on frames 300
## 3 Results  
Effect of JA Elicitation on Inhibition of Anticholinesterase and Pancreatic Lipase Activities by Basil Essential Oils  
The effects of AChE and pancreatic lipase inhibition by the studied essential oils are presented in Table 1. The IC\({}_{50}\) values for AChE of EOs from the control and JA-elicited basil were from 38.31 \(\pm\) 3.30 \(\upmu\)L mL\({}^{-1}\) for the control to 16.89 \(\pm\) 1.08 \(\upmu\)L mL\({}^{-1}\) in the case of the JA2 sample. It should be noted that the elicitation with all the concentrations of JA caused a statistically important increase in this activity exhibited by the essential oils from the basil plants. Elicitation with JA2 was the most effective, i.e., the IC\({}_{50}\) value was lower by 56% than in the control.  
The elicitation improved the inhibitory properties against pancreatic lipase. As shown by the data described in Table 1, the lowest IC\({}_{50}\) value (0.09 \(\upmu\)L mL\({}^{-1}\)) was determined for the JA2 sample. The control sample was characterized by IC\({}_{50}\) of 0.35 \(\upmu\)L mL\({}^{-1}\).  
Effect of JA Elicitation on the Ability of Basil EOs to Inhibit Selected Enzymes Involved in Diabetes Pathogenesis  
The potential inhibitory activity of essential oils from the control and JA-elicited basil against enzymes involved in the pathogenesis of diabetes was studied. It was evaluated based on \(\alpha\)-amylase and \(\alpha\)-glucosidase inhibitory activity and expressed as an IC\({}_{50}\) value (\(\mu\)L mL\({}^{-1}\))-Table 2. The highest potential ability to inhibit \(\alpha\)-glucosidase activity was determined for the JA2 sample (IC\({}_{50}\) = 0.66 \(\pm\) 0.02 \(\mu\)L mL\({}^{-1}\))-Table 2. However, it should be noted that, in the case of \(\alpha\)-glucosidase inhibition activity, the elicitation with jasmonic acid did not cause a statistically significant difference in the inhibition ability of the EOs. In turn, all the concentrations of the elicitor resulted in an approx. 10-fold increase in the ability of \(\alpha\)-amylase inhibition by the essential oils from the elicited basil. The lowest IC\({}_{50}\) value for this enzyme was determined for the JA1 sample (IC\({}_{50}\) = 1.32 \(\pm\) 0.28 \(\mu\)L mL\({}^{-1}\))-Table 2.
## 5 Simulation and Verification of Hybrid Reactive Power Compensation Program
### Static Voltage Stability Simulation Verification
Program 5: When the dynamic reactive power compensation device SVG is put in, the ultimate destabilized active power output of the wind farm voltage can reach 600 MW compared to the first two programs, which raises the voltage destabilization critical point. Program 6: When the combined reactive power compensation device of SVG and shunt capacitor is used, because SVG also can smoothly regulate the reactive power, when a serious fault occurs in the power system, it will further cause the bus voltage to drop significantly. For the combined reactive power compensation device of the shunt capacitor plus SVG proposed in this paper, its reactive power compensation performance is better than that of SVG, which can further maintain the voltage stability. The active power output of its wind farm group reaches 610 MW before the voltage instability finally occurs. In summary, the voltage support ability of the above six reactive power compensation configuration programs is enhanced in turn. The minimum is when the active power of program 1 is about 385 MW, and the bus voltage drops rapidly. The maximum is when the active power output of program 6 reaches 610 MW, and the voltage instability finally occurs. Program 6 adopts the combined reactive power compensation of SVG and capacitor, which has more advantages in maintaining voltage stability and improving voltage stability limit. Program 6 also takes into account the advantages of rapid adjustment ability and the small size of SVG, and has the advantages of the low cost and large capacity of shunt capacitors.
## 5 Conclusions  
The present study communicates for the first time endophytic fungi from the native conifer _A. yunnanensis_ listed on the IUCN Red List as a promising source of antioxidants. Among 16 fungal endophytes, _F. foetens_ AQF6 was found to produce 14 known compounds and FUS, among which 6 compounds showed moderate in vitro antioxidant activity. Current study demonstrates that AQF6 is a prospective source of plant-derived compounds, which can be utilized in the area of pharmacology. However, whole-genome sequencing to decipher fungus-plant interactions and biosynthetic gene clusters encoding for the bioactive compounds will give additional novel findings. Importantly, FUS was safe and protected _S. cerevisiae_ from H2O2-induced oxidative damage. Also, further in vivo studies should be carried out to investigate the antioxidant mechanisms of FUS. Thus, the present study provides more encouraging information, not only to reinforce the potential of endophytic fungi from an underexplored niche, but also to expand the search for new antioxidant metabolites.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14052048/s1](https://www.mdpi.com/article/10.3390/app14052048/s1)]([https://www.mdpi.com/article/10.3390/app14052048/s1](https://www.mdpi.com/article/10.3390/app14052048/s1)), Figure S1: HRESITOF mass spectrum of compound 1, Figure S2: \({}^{1}\)H NMR spectrum (CD\({}_{3}\)OD, 600 MHz) of compound 1, Figure S3: \({}^{13}\)C NMR spectrum (CD\({}_{3}\)OD, 150 MHz) of compound 1, Figure S4: HSQC spectrum (CD\({}_{3}\)OD, 600 MHz) of compound 1, Figure S5: HMDE spectrum (CD\({}_{3}\)OD, 600 MHz) of compound 1, Figure S6: COSY spectrum (CD\({}_{3}\)OD, 600 MHz) of compound 1, Figure S7: NOESY spectrum (CD\({}_{3}\)OD, 600 MHz) of compound 1, Figure S8: \({}^{1}\)H NMR spectrum (DMSO-\(d_{6}\), 600 MHz) of compound 1, Figure S9: \({}^{13}\)C NMR spectrum (DMSO-\(d_{6}\), 150 MHz) of compound 1, Figure S10: HSQC spectrum (DMSO-\(d_{6}\), 600 MHz) of compound 1, Figure S11: HMDE spectrum (DMSO-\(d_{6}\), 600 MHz) of compound 1; Table S1. Distribution and molecular identification of 16 endophytic strains isolated from _Amentotaxis yunnanensis_.  
Conceptualization, T.H.N.V. and N.S.P.; methodology, N.T.Q., P.C.L., Q.A.P. and C.C.N.; investigation, T.H.N.V., V.T.N., D.H.A., T.H.Q. and N.S.P.; writing--original draft preparation, T.H.N.V. and N.S.P.; writing--review and editing, H.H.C. and Q.-T.P.; T.H.N.V. and N.S.P. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Vietnam Academy of Science and Technology under Grant number TDCNSH.05/20-22.  
Not applicable.  
The data presented in this study are available in this paper.  
The authors would like to acknowledge the support of the VAST-Culture Collection of Microorganisms, Institute of Biotechnology, Vietnam Academy of Science and Technology.  
The authors declare no conflicts of interest.