esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 3 AN Maximizing Relay's SER Performance
### Selecting \(\theta_{z}\) for a Fixed \(|z|\)
In particular, from Equation (8), we have,  
\[\lim_{|z|\rightarrow\infty}\text{SER}\left(0,|z|\right)= c+2c(1-c)Q\left(\frac{|h|a}{\sigma}\right)\leq \frac{\sqrt{M}-1}{\sqrt{M}}+\frac{2(\sqrt{M}-1)}{M}\times\frac{1}{2}= \frac{M-1}{M} \tag{16}\]  
and,  
\[\lim_{|z|\rightarrow\infty}\text{SER}\left(\pi/4,|z|\right)=2c-c^{2}=\frac{M- 1}{M} \tag{17}\]  
where the equality in Equation (16) holds only when \(a=0\), i.e., when no signal is transmitted. This indicates that when the signal is present, given sufficiently high AN noise power, the QAM phase selection yields a higher SER. This analysis is illustrated by the plot in Figure 3. Also note that \(\frac{M-1}{M}\) is the SER when the relay does not have any prior information and randomly guesses the value of signal \(m\) for decoding, which serves as an upper bound on the SER of _any_ AN scheme. In this work, we term this as non-informative error performance. Moreover, Equations (16) and (17) reveal that when the AN power is sufficiently high, the \(M\)-QAM selection asymptotically achieves the non-informative error performance.
## 4 Materials and Methods
### DNA Extraction and Assessment of ACCase Gene Mutations  
Genomic DNA was extracted from the frozen leaf tissues of each biotype, using the DNeasy Plant Kit (QIAGEN SpA, Hilden, Germany) according to the manufacturer's instructions. DNA quality and concentration were assessed by means of electrophoresis on 2% agarose gel stained with ethidium-bromide and using a fluorometric method (Qubit fluorometer, Life Technologies, Carlsbad, CA, USA) according to the manufacturer's protocol. In samples with a low DNA quantity, extraction was repeated. On average, extracted DNA concentration was around 80 ng/\(\mu\)L. DNA was stored at \(-20\)\({}^{\circ}\)C.  
Restriction fragment length polymorphism (RFLP) analysis was used to detect mutations at the Ile-1781 codon and at the Ile-2041 codon of the _Echinochloa_ spp. ACCase gene involved in TSR. The ACCase gene (GeneBank a.n.: HQ395759.1) was PCR-amplified using universal primers (ACCase--Forward: 5'-CAGCYTGATTCCCAYAGAGCGYT-3'; ACCase--Reverse: 5'-CCATGACYTCTTTGAGYTCCTCGA-3') by [PERSON] et al., 2005 [60]. The reaction was carried out in a T100 thermal cycler (BIO-RAD, Hercules, CA, USA) in a 10 \(\mu\)L volume containing 2 \(\mu\)L of genomic DNA (20 ng), 2.4 \(\mu\)L (0.8 U) of GoTaq(r) Hot Start Green Master Mix (Promega, Madison, WI, USA), 0.6 \(\mu\)L of each primer (1 \(\mu\)M), 0.5 \(\mu\)L of MgCl\({}_{2}\) (2 mM), and 2 \(\mu\)L of nuclease-free H\({}_{2}\)O. The PCR profile set was as follows: an initial denaturation step (5 min at 95 \({}^{\circ}\)C) followed by 35 cycles (45 s at 95 \({}^{\circ}\)C, 45 s at 57 \({}^{\circ}\)C, and 45 s at 72 \({}^{\circ}\)C) and a final extension step (10 min at 72 \({}^{\circ}\)C). The amplification products were digested with XapI (Thermo Fisher Scientific, Waltham, MA, USA) endonuclease to detect mutation at the Ile-1781 codon or with EcoRI endonuclease (Promega) to detect mutation at the Ile-2041 codon. Digestion was performed at 37 \({}^{\circ}\)C for 24 h in a total volume of 15 \(\mu\)L containing 3 \(\mu\)L of the PCR product (200 ng), 1 \(\mu\)L of TAE buffer, 1 \(\mu\)L (12 U) of restriction enzyme, and 10 \(\mu\)L of nuclease-free H\({}_{2}\)O. The resulting products were resolved on 2% agarose gel (Certified Molecular Biology Agarose--BIO-RAD) stained with ethidium bromide. Amplicon size was determined using a 100 bp DNA ladder (Promega) using the GelDoc Go Gel Imaging System (BIO-RAD).  
Selective amplification and digestion of the ACCase gene allowed us to identify wild-type late-watergrass plants for the following gene and miRNA expression analysis.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 2 Related Works
Meanwhile, ML algorithms have been used in [39; 40] to generate lightweight detection models, resulting in 99.62% and 99.98% accuracy, respectively. However, there are limitations in current works. On the one hand, most of the anomaly-based IDS for IoT networks are designed at the system's high-level view that is usually deployable at the IoT gateway level. Anomaly behaviours can be detected earlier at the edge devices. On the other hand, they have focused on software-based metrics only, while hardware-based metrics, such as power consumption, performance and network throughput, are not mentioned. Hardware accelerators, including specialised AI microcontrollers and SoC FPGA, are promising platforms for securing IoT devices [41; 42]. IoT security monitoring systems [43; 44] have implemented accelerating semantic caching on FPGA. Researchers in [45] have introduced a mapping methodology to bring neuron network-based models to hardware. The proposed method has achieved a latency of 210 ns on a Xilinx Zynq UltraScale+ MPSoC FPGA running mapped LSTM models. FPGA acceleration for time series similarity prediction [46] has been proposed for IoT edge devices. The implemented system can reach a throughput of 453.5G operations per second with a 10.7x faster inference rate on a Xilinx Ultra96-V2 FPGA compared to the Raspberry Pi. Lightweight ANN models for intrusion detection systems are proposed on an SoC FPGA platform in [47]. They have achieved a speedup of 161.7 times faster than software-based methods on the NSL-KDD dataset. Overall, anomaly-based IDS for IoT devices on hardware accelerators is a promising approach. This paper introduces an ultra-low power consumption and low-latency heterogeneous hardware-based IDS framework. The prototype system is tested with the IoT-23 and UNSW-NB15 datasets on the MAX78000 microcontroller and SoC FPGA platforms.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 1 Introduction
### Our Contributions  
This work aims to improve decision-making in human-robot collaboration for warehouse picking by evaluating human trust in robots. To achieve this, an online trust model for human-robot collaboration is established, which involves the interaction between the worker and the picking robot, human trust dynamics, and the robot's evaluation of human trust. Based on this trust evaluation, the robot infers human intentions and adopts the optimal strategy. The existing works on the former do not adequately address such a complex operational process. This paper proposes a Stackelberg trust-based human-robot collaboration framework for warehouse picking, with the following specific contributions:  
* Human trust modeling and evaluation: We define HRC with trust for warehouse picking as the Partially Observable Stochastic Game (POSG) and exploit a Bayesian posterior belief space to assess human trust in real time. Additionally, we use the logistic function to model human fatigue, which is specified as the collaboration efficiency reward function in the POSG.
* Human-robot decision-making based on multi-round trust: We combine the Stackelberg strategy with the Bellman equation to design iterative Stackelberg trust strategy generation. It is solved by a model-based exhaustive strategy search and can achieve the optimal strategy for the robot's long-term benefits while considering human trust. Furthermore, human strategies are generated using the sigmoid function based on human trust.
* Decision analysis by probabilistic model checking: We formalize the generated human-robot decisions into a Partially Observable Markov Decision Process (POMDP). We specify the properties of the human-robot collaboration framework for warehouse picking as PCTL formulae, such as efficiency, accuracy, trust, and human fatigue. These are verified using the probabilistic model-checking tool PRISM. We establish a no-trust model and a single-round trust-based HRC for comparison with our framework.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Experimental
#### 5.1.3 Characterization  
The surface morphology of GO was observed by a scanning electron microscope (SEM), GeminiSEM 300, ZEISS, Germany; the functional-group structure of GO was observed by a Fourier infrared spectrometer (FTIR), TENSOR, Brueck, Germany; the thermal stability of GO was observed by a thermalogravimetric analyzer (TGA), STA449 F3, Netzsch, Germany.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Type of [GO(O)...Different Ions]** & **Binding Energy/Hartree** \\ \hline GO(CO)...Na & \(-\)0.0120 \\ GO(CO)...K & \(-\)0.0121 \\ [GO(CO)...Ca]\({}^{+}\) & \(-\)0.0684 \\ [GO(CO)...Mg]\({}^{+}\) & \(-\)0.0479 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adsorption energy of GO(O) with different ions.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
###### Abstract  
Extraction techniques are continuously developed by the scientific community. Meanwhile, avocado peel is a by-product of avocado processing and a source of bioactive compounds. The purpose of this review was to summarize the use of advanced techniques for extracting bioactive compounds from avocado peel to help understand which techniques have and have not been applied to avocado peel. Three primary databases were used to collect the information, including Google Scholar, Scopus, and Web of Science, by using the keywords \"avocado\", \"peel\", and \"extraction\". Additional keywords related to the extraction technique were also used, including \"Microwave-Assisted Extraction\", \"Ultrasound-Assisted Extraction\", \"Enzyme-Assisted Extraction\", \"Pressurized Liquid Extraction\", \"Supercritical Fluid Extraction\", \"Natural Deep Eutectic Solvents\", \"Three-phase partitioning (TPP)\", \"Pulsed-Electric Field\", \"High Voltage Electric Discharge Plasma\", \"Centrifugal Partition Extraction\", and \"Surfactant-Mediated Extraction\". The results show that microwave-assisted extraction, ultrasound-assisted extraction, enzyme-assisted extraction, pressurized liquid extraction, supercritical fluid extraction, TPP, and natural deep eutectic solvent extraction have been used to retrieve bioactive compounds from avocado peel. Other techniques have not yet been applied for the extraction of bioactive compounds from avocado peel. This article is the first review discussing the advanced extraction technique for retrieving bioactive compounds from avocado peel. This article creates a paradigm for future studies.  
avocado peel; high voltage electric discharge plasma; natural deep eutectic solvent extraction; pulsed-electric field; three phase partitioning +
Footnote †: journal: Applied Sciences
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 5 Conclusions  
This paper tackled a predictive maintenance model based on LSTM and its optimization, which is specialized for bearing components of the machinery. Specifically, we (i) established both effective and efficient HIs used to describe the state of bearing components and (ii) devised a GA to explore optimal hyperparameters which determine the internal structure and affect the performance of LSTM. Derivation of the HIs was conducted by correlation analysis to exclude redundant features bringing about multicollinearity and a stepwise comparison of feature vectors including independent features. In addition, we designed a GA to explore optimal hyperparameter configuration of LSTM, which was trained by using HI, kurtosis, skewness, and crest factor, obtained by feature selection. This work can be considered for the following applications. Most of all, useful features for describing the state of machinery can be drawn from raw measurement data and the resulting features can be refined by utilizing the feature selection framework. Additionally, hyperparameter tuning with a GA can be applied to establish general deep learning models as well as those other than LSTM models used for predictive maintenance.  
In addition, we suggest the following topics for further research work to be considered. We intend to apply our work to more datasets used for predictive maintenance other than bearing performance data. For instance, a dataset consisting of many features, such as an aerospace turbine engine dataset [44], can benefit from the feature selection scheme of this paper. Additional pre-processing, such as signal processing, which is suitable for predictive maintenance data recorded as raw sensor measurement data, might be another research field. Moreover, we plan to modify internal components of the model by replacing LSTM or the GA with other promising and state-of-the-art network structure or metaheuristic methods.  
Conceptualization, D.-G.K. and J.-Y.C.; methodology, D.-G.K. and J.-Y.C.; software, D.-G.K.; validation, D.-G.K. and J.-Y.C.; formal analysis, D.-G.K. and J.-Y.C.; investigation, D.-G.K. and J.-Y.C.; resources, D.-G.K.; data curation, D.-G.K.; writing--original draft preparation, D.-G.K.; writing--review and editing, D.-G.K. and J.-Y.C.; visualization, D.-G.K.; supervision, J.-Y.C.; project administration, J.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1F1A1073199).
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 4 Discussion  
This study examined the application of K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour using Locality Sensitive Hashing (ANN-LSH) in agricultural recommendation systems. The effectiveness of both algorithms was analysed in identifying similar farming profiles and suggesting contextually relevant practices. While KNN yielded highly accurate recommendations by exhaustively comparing data points, ANN-LSH demonstrated a scalable and faster alternative by leveraging hash functions to approximate neighbours efficiently.  
The results confirm that both methods successfully identify meaningful patterns in farmer profiles, aiding data-driven decision-making. In particular, KNN's precision and ANN-LSH's performance balance offer complementary benefits depending on use-case demands. Despite algorithmic differences, both have proven useful in enabling farmers to optimise crop selection, identify reliable seed suppliers, and improve pest management strategies, ultimately enhancing productivity and resource efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature** & **KNN** & **ANN** \\ \hline Time Complexity & Linear (O(n)) for search & Sub-linear with hashing \\ Accuracy & High (exact neighbors) & High for close neighbors, \\  & & lower for far ones \\ Scalability & Struggles with large & Scales well to large datasets \\ Real-Time Suitability & Limited & Suitable \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of KNN vs. ANN-LSH.  
While this study provides a promising proof of concept, several areas warrant further investigation to strengthen the system's accuracy, flexibility, and real-world deployment potential. Future implementations should integrate real-time environmental data, such as live weather updates, soil conditions, and climate anomalies, to refine recommendations dynamically and enhance responsiveness. Moreover, combining KNN and ANN-LSH with deep learning techniques could lead to hybrid models that preserve accuracy while improving scalability.  
Personalisation and adaptive learning also represent promising directions for improvement. Models that evolve based on individual farmer feedback and observed field conditions could improve the long-term relevance of recommendations. Additionally, the methodology could be extended to other agricultural sectors, including livestock management, greenhouse cultivation, and hydroponic systems, where similar decision-support challenges exist.  
Finally, user-friendly deployment is essential for widespread adoption. A mobile-accessible and intuitive interface, potentially incorporating voice command functionality, would significantly improve usability for smallholder farmers, many of whom may have limited technological proficiency. Addressing these development opportunities could help transform the proposed system into a robust and intelligent decision-support tool, thereby contributing further to the digitalisation and sustainability of modern agriculture.
## 5 Results and Discussion
### Lubrication and Temperature Characteristics of Bearings
#### 5.4.2 Temperature Characteristics of Bearings  
Figure 18 illustrates the temperature characteristics of the bearing rollers. The convective heat transfer coefficients differed at different positions on the rollers (Figure 18a). The temperature distribution of the small roller was more uniform, while the temperature difference of the big roller was larger (Figure 18b). As shown in Figure 18c,d, rollers with a higher convective heat transfer coefficient had a lower temperature. In splash lubrication, due to the comprehensive influence of heat convection and conduction, the temperature characteristics of the rollers at different azimuth angles were different. Compared to Figure 16b,c, there is no correlation between the roller temperature and the oil volume fraction on its surface. For example, the oil volume fraction on the wall of the small rollers at azimuth angles of 144-216\({}^{\circ}\) was high, and their temperature was also high. This is because the convective heat transfer coefficient of the roller wall is largely related to the lubrication environment of the roller, including the oil distribution inside the bearing cavity and the flow rate. Furthermore, the convective heat transfer coefficient and temperature of the rollers are not determined solely by the oil volume fraction of the roller wall. Additionally, this phenomenon appears in reference 18: there is no correlation between the temperature of the three test points of the bearing outer ring and the oil volume fraction in the bearing cavity obtained by simulation, and the lowest temperature of the outer ring measuring point is where the lubricating oil content of the bearing cavity is low.  
The raceways of the inner and outer rings of the bearing are the key locations for frictional heat generation and are prone to the occurrence of high-temperature areas. Consequently, a detailed analysis was performed. Figure 19 shows the temperature characteristics of the raceways. The axial section of the inner and outer rings of the bearing changes with the change in axial position, leading to differences in the heat conduction effect as well. The  
Figure 18: Temperature characteristics of bearing rollers: (**a**) convective heat transfer coefficient of rollers; (**b**) temperature of rollers; (**c**) temperature characteristics of small rollers; (**d**) temperature characteristics of big rollers.  
temperature of the bearing raceway under the combined influence of heat convection and conduction is shown in Figure 19b,d. The temperature of the middle portion of the inner ring raceway was high, while that of the upper portion of the outer ring was also high. To reduce the temperature of the outer ring, the contact area between the upper portion of the outer ring and the other solid should be increased, to enhance heat conduction.
# Vibroacoustic Optimization Study for the Volute Casing of a Centrifugal Fan
## 5 Vibroacoustic Optimization Strategy
### Optimization Objective Selection and Volute Panel Thickness Parametrization
Thus, after organization, Equation (25) can be changed to the following:  
\[W=\sum_{j=1}^{N_{c}}W_{j}=\frac{1}{2}Re(\sum_{j=1}^{N_{c}}\frac{1}{2}\int\limits_ {S_{j}}p_{j}v_{nj}^{*}dS) \tag{26}\]  
In the formula:  
N\({}_{\ell}\)--the number of finite elements on the structural casing;  
\(S_{j}\)--the area of \(j\)th finite element, m\({}^{2}\);  
P\({}_{j}\)--the sound pressure of \(j\)th finite element, P\({}_{a}\);  
\(v_{nj}^{*}\)--the normal velocity of \(j\)th finite element, m/s. The vibrational noise of the volume structure of the marine centrifugal fans that is studied in this paper belongs to the typical external opening noise radiation problem. Therefore, the second type of optimization target function should be adopted. The previous analysis in Section 4 shows that the structure modal shape can be changed by controlling the structural thickness distribution. Therefore, the volume thicknesses of the three panels (FT, BT, and ST in mm) were specified as the design variables. Thus, the design objectives can be achieved by adjusting the combination of different solute panel thicknesses in the optimization design. The thickness of each volume panel is parameterized using FEM. Figure 19 shows the solute parameterization structural FEM. Since the panel thickness of each solute is generally less than 10 mm, we assigned the panel thickness of each solute to be from four mm to 10 mm.
## 3 Consistent Path Congestion Model  
In this section, we propose a probabilistic model to describe the consistency of differential path congestion between two candidate route paths with the same source and destination nodes. Based on this model, the metric to evaluate the path congestion consistency is outlined.  
Almost all existing approaches perform static network latency prediction based on the history values of round-trip times (RTTs), assuming the latencies are stable or unchanged, whereas in reality latencies can vary dramatically over time due to changing network conditions. Thus, among the state-of-art adaptive routing strategies using latency prediction, only a few could provide an approach to avoid the oscillation of route selection during peak hours.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 4 The Proposed Scheme
Figure 3:3  
The prediction of the actual value of \(\hat{X}^{\prime}{}_{i}(k)\) and similarly that of \(\hat{X}^{\prime\prime}{}_{i}(k)\) can be found from Equation (15). Figure 3:4  
eq\hat{X}^{\prime\prime}{}_{i}(k)\), it means that the \(kth\) subcarrier's \(\hat{X}^{\prime}{}_{i}(k)\) is not valid and the assumption is made that \(H_{CDD,j}(k)=H_{CDD,j-1}(k)\). Additionally, if the \(\hat{X}^{\prime}{}_{i-1}(k)=\hat{X}^{\prime\prime}{}_{i}(k)\), then it can be assumed that \(H_{CDD,j}(k)=H_{i}(k)\), which is indicated as _Decision \(1\)_ in Figure 3:5 We argue that the two adjacent data symbols have high correlation in frequency as well. Therefore if \(\hat{X}^{\prime}{}_{i}(k)\
eq\hat{X}^{\prime\prime}{}_{i}(k)\), it indicates that the \(k\)th subcarrier's \(\hat{X}^{\prime}{}_{i}(k)\), which demapped after Equation (6) is incorrect and we should define that \(H_{iCDD,j}(k)=H_{iCDD,j-1}(k)\), i.e., the previous symbol's estimated CR. It is indicated as Decision \(2\) in Figure 3:6 Moreover, in contrast to the CDP scheme where otherwise, if \(\hat{X}^{\prime}{}_{i-1}(k)=\hat{X}^{\prime\prime}{}_{i}(k)\) then \(H_{CDD,j}(k)=H_{i}(k)\). We argue that the superior performance over CDP scheme can be obtained by taking into consideration \(H_{i}(k)\) obtained previously from Equations (3) and (5) through the Equation (16). Figure 3:7  
where \(H_{iCDD,i}(k)\) is the proposed channel estimate. The next section evaluates the proposed estimator over the severe channel conditions of [28] and also provides comparisons with the closely related schemes. Let us assume that \(|H_{(i,k)}|\) represents the absolute of the estimated channel.