esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a new triple phase shift (TPS) closed-loop control scheme of a dual active bridge (DAB) LCC resonant DC/DC converter to improve wireless charging power transfer efficiency. The primary side inverter phase shift angle regulates the battery charging current/voltage. The secondary side rectifier phase shift angle regulates the rectifier AC load resistance to match its optimized setting. The inverter-to-rectifier phase shift angle is set to achieve unity power factor operation of the DAB rectifier and inverter. The mathematical formulation of the TPS shift control is given for each phase shift angle. The analytical calculation, circuit simulation, and experimental test are carried out in a power scaled-down DAB LCC resonant wireless charging converter laboratory hardware setup to validate the proposed TPS close-loop control scheme. The PLECS circuit simulation shows that DAB LCC resonant SiC MOSFET operates at zero-voltage-switching (ZVS) with a unity power factor in emulated constant current (CC) mode battery charging. In constant voltage (CV) mode operation, one inverter/rectifier Leg does not operate at ZVS switching when SiC MOSFET is switched on near zero current. The experimental results show that the efficiency is greatly improved for CV mode charging with large DC load resistance connected if rectifier AC load resistance matching control is enabled. The measured efficiency matches well with the analytical calculation. The estimated efficiency improvement will be much more significant for EV applications in the kW power range with greater winding loss. The challenges and possible solutions to implement TPS PWM modulation in two separate inverter and rectifier control hardware are explained for future TPS control algorithm development in practical wireless charging products.  
wireless charging; dual active bridge (DAB); DC/DC converter; LCC resonant circuit; triple phase shift (TPS); constant current (CC) battery charging; smart voltage (CV) battery charging; zero-voltage switching (ZVS); and unity power factor operation
## 1 Introduction
Most prominently, callus and cell suspension cultures of several plants have been established for the enriched production of flavonoids. Both culture types ensure homogeneity in in vitro cell culture, a higher growth rate and productivity, as well as good reproducibility [20]. In addition to this, transgenic hairy root cell lines for the stable production of flavonoids have also been established [21]. Despite the successful in vitro production of plant secondary metabolites, there are still some bottlenecks, such as inconsistent yield, poor quality and lower productivity for large scale cultivation. To counter this, biotechnologists have devised different strategies to enhance the production of secondary metabolites in in vitro cultures, such as media modification, genetic engineering, precursor feeding, screening of high yielding cell lines, bioreactors, immobilization, biotransformation and elicitation [22]. Among different strategies, elicitation is the best known and effective yield enhancement strategy. Elicitation is the addition of exogenous elicitors, which are compounds that stimulate any type of plant defense and promote plant secondary metabolism to impart protection to the plant against stress [23]. Elicitors are perceived by specific receptors/ion channels in the plasma membrane, trigger gene expression and induce a stress response in plants, which may result in the production of higher amounts or new forms of valuable secondary metabolites [24]. To date, different types of biotic and abiotic elicitors have been used for the elicitation of plant secondary metabolism. Among different elicitors, nanoparticles (NPs) showed a great potential to act as novel elicitors of flavonoids in plants due to their unique properties. Currently, different types of NPs have been used as novel and effective elicitors of flavonoids in in vitro cultures of various plant species, as shown in Figure 2[25]. Different types of NPs, including metallic NPs, metal oxide NPs and carbon-related NPs, are widely reported as novel elicitors of flavonoids. These NPs include silver NPs (AgNPs), copper NPs (CuNPs), iron oxide NPs (Fe\({}_{3}\)O\({}_{4}\)NPs), zinc oxide NPs (ZnONPs), copper oxide NPs (CuONPs), carbon nanotubes and chitosan based NPs [26]. In this review, an in-depth literature on the role of NPs as elicitors of industrially important flavonoids has been critically reviewed, along with the mechanism of nano-elicitation. Furthermore, the review is concluded with future directions. Figure 2: Graphical representation of different types of nano-elicitors of commercially important flavonoids in various in vitro systems of plants (NPs: nanoparticles).
## 4 Experimental Results and Analysis
### Experimental Results  
Typical predictions of the proposed model are shown in Figure 9, and the performance of the aforementioned models on the testing set of A2018 and B2019 are summarized in Table 4. As shown in Figure 9, the proposed model can make satisfactory predictions of the trend of wind speed. As shown in Table 4, the proposed model achieved the highest accuracy on both the testing sets of A2018 and B2019, which indicates superiority of the  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Definition** \\ \hline PM & Persistence model \\ ARIMA & Autoregression integrated moving average model \\ ELM & Extreme learning machine \\ CNN & Convolution neural network \\ GRU & Gated recurrent unit neural network \\ SSA-BRTD-GRU & GRU with basic real-time decomposition \\ SSA-QRTD-GRU & GRU with quasi real-time decomposition \\ SSA-TRTD-GRU & GRU with truncated real-time decomposition \\ SSA-TRTD-MR-GRU & The proposed model: GRU with real-time \\  & decomposition and multi-resolution data \\ \hline \end{tabular}
\end{table}
Table 3: The summary of models.  
proposed method. To illustrate, the MAE and RMSE of the proposed model on the testing set of A2018 are significantly smaller than those of other models. Two detailed analyses are conducted to reveal the effectiveness of the proposed model, which are presented in Sections 4.3 and 4.4.
## 3 Methods
### Inundation Mapping Based on Modest AdaBoost  
#### 3.2.1 Permanent Pixels Labelling  
According to the previous section, we get the set of permanent pixels. In order to utilise the permanent pixels for training the classifier later, we need to label the permanent pixels into water and non-water categories. In keeping the whole process automatic, manual labelling should not be used. In this study, we adopt the openly accessible MODIS 250 m land-water mask, which is called MOD44W for short, to achieve this purpose. MOD44W is a constant product, which is derived from Terra MODIS data MOD44C 250 m 16-day composites. If a pixel is identified as water in more than 50% of the period May to September of years 2000-2002, this pixel is labelled as water in the MOD44W product. This method effectively smooths the short-term water surface changes caused by flood and drought. Therefore, although the MOD44W was produced years before the case study, it is widely accepted as the description of average water distribution [33]. Here, we adopt MOD44W to label the permanent pixels. It is acknowledged that there are most likely some mistakes, caused by small changes in water surface over the years. But, as the general condition changes little, and the permanent pixels have high probability of being unchanged, the labels from MOD44W are generally reliable.  
For both the first and second study area, there is only one scene of MOD44W data. We resample the MOD44W data to the same spatial resolution as the experimental image, and then label the permanent pixels into permanent water pixels and permanent non-water pixels according to the MOD44W values. As the labels of the permanent pixels are used for classifier training, the proportion of the permanent pixels of each class will influence the classifier training result. However, as most changes happen inside or around the river regions, it can be inferred that the permanent confidence is generally higher in non-water regions than in water regions. Among the pixels of the highest confidence values, we selected the water and non-water permanent pixels with the same proportion as that in the same scene in MOD44W. The sum of water and non-water permanent pixels remained \(n\%\) of the total. With the labelled permanent pixels, the Modest AdaBoost classifier is trained. Then it is applied to the testing set consisting of multiple features of image #2. The final inundation mapping result can be calculated.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 5 Fusion Algorithm and Simulation Experiment  
_The Storage Structure of Nodes Is Optimized_  
The traditional A* algorithm cannot avoid the random dynamic and static obstacles, and there are security risks. The Open_Planner algorithm tends to fall into local optima when planning local paths between the current node and the next node. To address the above problems, the following improvements were made. The key nodes on the path planned by the improved A* algorithm were used as the input nodes of the Open_Planner  
Figure 5: Components of rollouts.  
algorithm in local planning, and, finally, the path planning was completed by the fusion algorithm. The fusion algorithm flow is shown in Figure 6.  
In Figure 6, firstly, in the global static map, A* algorithm is used to plan a global path, obtain the key points in the A* algorithm, delete the redundant extended nodes, put the key points into the list, and use heap sort to rearrange the node order. The key points are used as target points in the local planning of the Open_Planner algorithm. Key sections and position information are input, and multiple trajectories are obtained by sampling the intercepted global paths. Three cost functions, priority cost, collision cost and transition cost, are used to evaluate the sampling path. Finally, an optimal trajectory is selected as the path to reach the next target point, and the above steps are repeated until the final target point is reached to realize the fusion of the two improved algorithms.
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Study Selection  
The study selection process, encompassing identification, screening, and inclusion of eligible studies, is presented in the PRISMA flow diagram (Figure 1). A total of 725 records were identified through systematic searches conducted across four major electronic databases: PubMed, Scopus, Web of Science, and IEEE Xplore. No additional records were retrieved from other sources or registers.  
During the initial identification phase, 406 records were excluded based on predefined criteria. These exclusions included 140 records published outside the specified date range, 212 excluded due to document type, 27 based on source type, and 27 for language reasons. Additionally, 44 duplicate records were removed, resulting in 275 unique records eligible for title and abstract screening.  
In the screening phase, the titles and abstracts of the remaining records were thoroughly analyzed to determine their alignment with the inclusion criteria. A total of 210 records were excluded for failing to meet criteria related to the population, methodology, or outcomes. Specifically, these records either did not focus on professional truck drivers, failed to evaluate monitoring technologies aimed at enhancing driving safety, or reported outcomes unrelated to road safety. Following this screening, 65 records were selected for full-text review.  
Of the 65 records identified for full-text retrieval, 60 articles were successfully obtained. Despite efforts to access them through institutional resources, five records could not be retrieved. The 60 full-text articles underwent an eligibility assessment, resulting in the exclusion of 20 studies. Eight studies did not focus on professional truck drivers, seven failed to evaluate monitoring technologies, and five examined outcomes unrelated to road safety.  
Ultimately, 40 studies met all inclusion criteria and were included in the qualitative synthesis, representing a diverse range of geographical contexts, methodologies, and study designs. The majority of studies identified in the literature search were published in high-impact journals, with 30 appearing in Q1 journals, followed by 8 in Q2, as detailed in Table 1.  
Figure 1: The identification, screening, and inclusion process of eligible studies using PRISMA 2020.
## 4 Work of the Benchmark Model  
To address the above research gaps and issues, our benchmark model focuses on the following aspects:  
* Enhanced Privacy Preservation: Our benchmark model integrates advanced privacy-preserving techniques, such as differential privacy and secure multi-party computation, to ensure the confidentiality of sensitive patient information during the federated learning process. This approach provides stronger privacy guarantees and aligns with regulatory requirements. [27].
* Scalability Considerations: Our benchmark model incorporates strategies for efficient data aggregation and model updating to handle large-scale healthcare systems. It addresses the challenges of diverse data sources, varying data distributions, and heterogeneous network conditions, enabling scalable federated learning across a wide range of healthcare institutions [28].
* Transparency and Auditability Mechanisms: Our benchmark model implements a transparent and traceable framework. It leverages blockchain technology to maintain an immutable record of the federated learning process, allowing stakeholders to verify the integrity of data aggregation, model updates, and reward mechanisms. This enhances transparency, reproducibility, and trust in the obtained results [29].
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 5 Design and Implementation
### Two-State Finite State Machine  
The CPU utilization threshold is an essential parameter related to when our data plane switches strategies. It is worth noting that if the CPU utilization \(\eta\) oscillates around \(H\), our data plane will switch strategies frequently, which inevitably brings unnecessary overhead, so we introduce a two-state finite state machine (FSM) to solve this problem. The two-state FSM we designed is shown in Figure 10. When the data plane runs on \(S_{0}\), it will switch to \(S_{1}\) if \(\eta<\alpha H\). When the data plane runs on the \(S_{1}\) state, it will switch to \(S_{0}\) if \(\eta>\beta H\). Note that we need to make sure that \(0<\alpha<\beta\), \(\beta H<100\%\), e.g., \(\alpha=0.9\) and \(\beta=1\). The use of the FSM can effectively cope with CPU utilization oscillating around a single threshold, thus reducing the frequency of data plane switching strategies.
## 4 Discussion
Acidic amino acid residues of Asp and Glu were reported to have great influence on the antioxidant activities of Asn-Tyr-Asp-Gly-Ser-Thr-Asp-Tyr-Gly-Ile-Leu-Gln-Ile-Asn-Ser-Arg and Leu-Asp-Glu-Pro-Asp-Pro-Leu-Ile [56; 57]. In addition, Gly residue can keep the high flexibleness of the polypeptide skeleton and its single hydrogen atom can be donated to neutralize ROS [5; 31; 58]. Therefore, polar amino acids, including the Asp residue in WMFDW, Gly residue in WMGPY, and Gly and Glu residues in EMGPA, should play a critical role in their scavenging activities of hydroxyl radicals. Heating and acid-base treatments are common processing methods in the food industry. APs with heat-resistant properties are beneficial to increase the quality guarantee period of food products when they can retain all or major activity after heating treatment, and APs can be used in more kinds of liquid products if they keep their bioactivity in a wide range of pH values [19]. Therefore, the thermal and pH stability of APs are key properties for designing their specific parameters for application in food products [13; 59; 36]. [PERSON] et al. prepared two antioxidant hexapeptides (Trp-Ala-Phe-Ala-Pro-Ala and Met-Tyr-Pro-Gly-Leu-Ala) from the hydrolysate of blue-spotted stingray and confirmed that the two hexapeptides had excellent stability because no significant difference was found when they were treated at 25-100 \({}^{\circ}\)C or at pH values of 3-11 (\(p>0.05\)) [19]. In contrast, Ala-Thr-Ser-His-His from the hydrolysate of sandfish incubated at 50-90 \({}^{\circ}\)C reduced its partial DPPH radical scavenging activity. In addition, ATSHH absorbed moderate losses of activity when it was treated at pH 10-12 or pH 2. [PERSON] et al. released homologous conclusions that Met-Asp-Leu-Phe-Thr-Glu and Trp-Pro-Pro-Asp from protein hydrolyses of _Tergillarca granosa_ were not appropriate for high-temperature processing (>80 \({}^{\circ}\)C) and alkaline conditions (pH \(>9.0\)) in food products [13]. The present results indicate that P2 (WMFDW), P4 (WMGPY), and P6 (EMGPA) have similar thermal and pH stability with ATSHH,MDLFTE and WPPD, because they can significantly decrease antioxidant activity when they are treated under high temperature (80, and 100 \({}^{\circ}\)C) and acid (pH 3) and alkali (pH 9) conditions.
## 2 Related Work  
### Super-Pixel Segmentation Algorithm  
Simple linear iterative clustering (SLIC) [32] is the most widely used super-pixel segmentation algorithm. The advantage of super-pixels is that a small number of super-pixels can be used to express image features instead of a large number of homogeneous pixels while reducing the complexity of image processing [33]. The super-pixel image blocks obtained via super-pixel segmentation generally not only compromise the boundary information of the objects in the image, but also retain the effective feature information for subsequent intelligent image processing. Therefore, the super-pixel image blocks are suitable for HSI classification and segmentation tasks.  
The main algorithmic steps are as follows [34]:  
(1) Dimensionality reduction in HSI data: PCA is used to downscale the hyperspectral data to three dimensions. The first three principal components are used to generate false color images.  
(2) Cluster center initialization: If N pixels are pre-segmented into K super-pixels of the same size, each super-pixel has N/K pixels. The distance between adjacent clustering centers is obtained via S = \(\sqrt{N/K}\). The clustering centers are uniformly set in the original image based on the number of super-pixels.  
(3) Re-optimize the clustering centers: The gradient values of all pixels in the n \(\times\) n neighborhood are calculated. The seed points are moved to the lowest gradient, thus preventing the seed points from falling on the boundaries with large gradients.  
(4) Determine the clustering center of each pixel in the domain formed by each clustering center: to accelerate the convergence of the algorithm, the search range of the algorithm is set at 2S \(\times\) 2S.  
(5) Distance metric: we calculate the color distance \(d_{c}\) and spatial distance \(d_{s}\) of each pixel to the cluster center to obtain the final distance metric \(D^{\prime}\), using the following equation.  
\[D^{\prime}=\sqrt{\left(\frac{d_{c}}{N_{c}}\right)^{2}+\left(\frac{d_{s}}{N_{s }}\right)^{2}} \tag{1}\]  
\(N_{s}\) is the maximum spatial distance within the class. \(N_{s}=\text{S}=\sqrt{N/K}\). \(N_{c}\) changes with the image. It indicates the maximum color distance, and its default is taken as 10. After that point, the seed point with the shortest distance from each pixel is taken as the cluster center of that point.  
(6) Iterative optimization: we keep iterating the above steps until the clustering center of each pixel no longer changes. It is found that after 10 iterations, most of the images can achieve more satisfactory results, meaning that the number of iterations is usually set to 10.
## 3 Results and Potential IIoT instantiation
### Results and Findings
#### 3.1.3 Discussion  
The results above present interesting model performance results as a consequence of data quality. Figure 15 below shows why there was no direct relationship between the SPC FDI performance algorithm and the quality of the datasets defined in this paper. Assuming \(T_{1},T_{2}\) are the lower and upper bounds, respectively, for the description of a process condition on a system; \(x_{1},x_{2}\) are the process parameters at time \(t_{1},t_{2}\), respectively, and \(S_{1},S_{2}\) are the lower and upper bounds, respectively, for datasets with low data quality, the SPC FDI algorithm returned a true prediction only when the conditions in cases 1, 2 and 3 were met regardless of the level of quality in the data recorded by the measurement system (S: synthetic data, A: actual data, P: performance). Case 4 is the only scenario where a true prediction was not observed by the SPC FDI algorithm. That explains why in Figures 13 and 14 the SPC model developed with a dataset with an average quality of 0.794 performed better than the data from the testbed's measurement system, with a data quality of 0.99. From the analysis of the performance of the SPC FDI model on the rest of the test dataset (see Figures 13 and 14) for the various multi-component degradation scenarios, synthetic data could be used to augment the performance of the SPC model for better performance, and it is worth investigating the requirements for an augmented SPC model with synthetic data.  
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{5}{c}{**Historical Dataset with Average Data Quality of 0.99**} \\ \hline Model & SPC & Best Classifier & Ensemble & Neural Network \\ Mean accuracy (\%) & 40.18 & 87.28 & 72.72 & 87.27 \\  & Synthetic Dataset with Average Data Quality of 0.69 & & \\ Model & SPC & Best Classifier & Ensemble & Neural Network \\ Mean accuracy (\%) & 72.72 & 87.27 & 27.27 & 78.12 \\ \hline \end{tabular}
\end{table}
Table 6: Summary of average performance of FDI models for T17.  
Figure 14: Performance of FDI models using synthetic dataset for test degradation scenarios.  
On the other hand, the data-driven methods in most cases required datasets with high data-quality levels for better model performance, as seen in Figures 11 and 12 as well as Figures 13 and 14. The neural network model showed adaptive behaviour for model prediction due to a neural network's ability to approximate system dynamics with effective architecture. Its performance did not generally decrease with a decline in data quality, unlike what was seen in the ensemble model. Implementing the best classifier approach would be impractical from an operational standpoint, as the best classifiers for a specific case of multi-component degradation as the best classification algorithms might not be known and would require simulating and testing all possible MCD combinations. For a large system, this becomes untenable because of resource constraints.
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
###### Abstract
Transforming the dual-parameter optimization problem involving transmission amplitude gain and noise allocation ratio into a single-parameter optimization task significantly simplifies the complexity of the optimization process. Figure 1: Covert communication in jammer–assist systems. (4) Through numerical simulations, we validate our proposed method and theory. The precision of the Taylor approximation and the efficacy of the single-parameter optimization are confirmed, providing robust evidence for the validity of our proposed approach. The remainder of this paper is organized as follows. In Section 2, the modeling of asymmetric noise communication scenarios is introduced, encompassing a scenario overview and problem formalization. In Section 3, the performance analysis of covert communication is provided, including transmission rates and covertness performance. In Section 4, the optimization method of transmission amplitude and noise power ratio is proposed. Section 5 presents numerical simulations to evaluate the outcomes of the optimization. Finally, Section 6 provides the conclusion and summary of the paper.