esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 3 Effectiveness of PGPR in Hydrocarbons and Heavy Metals Contaminated Soils
Although metals are often present as insoluble salt and need to be mobilized by chemicals, such as EDTA (Ethylenediaminetetraacetic acid) and EDDS (Ethylenediamine-N,N-disuccin acid), PGPR can enhance phytoextraction by reducing the toxicity of heavy metals in plants through helping control absorption, possible accumulation, or detoxification of heavy metals [85]. Those microorganisms can tolerate a high concentration of metals and can modify metal bioavailability by releasing chelants, such as organic acids and siderophores, and changing the soil pH [57]. Several studies report the isolation of microbial strains that effectively support phytoextraction or phytostabilization of various heavy metals. Among these, for example, _Bacillus pumilus_[57], _Rhodococcus erythropolis_[86], _Bradyrhizobium_ sp. [87], _Ralstonia eutropha_, and _Chryseobacterium humi_[88]. Some PGPR, such as _Sinorhizobium mellioti_ (a metal resistant rhizobium), can also, while co-inoculated with other PGPR, reduce the oxidative stress that some metals, such as Cu, cause to plants; this can greatly increase the metal intake during phytoextraction [89]. This effect may be attributed to the ability of rhizobium and PGPR to provide balanced nutrition to the host plant [90]. The inoculated plants had considerably lower ROS (reactive oxygen species) accumulation and a higher level of antioxidants enzymes, specifically peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX). Other studies seem to align with this result even when considering multiple growth parameters: plant biomass, the fluorescence of photosynthetic pigments, number of leaves, and shoot and root length [91; 92]. However, some studies suggest that by combining the effect of both PGPR and a chelating agent, it is possible to achieve the best phytoextraction result [93; 94; 95; 96]. In the first study, the Cu concentration in the shoots was up to 4.2 times higher in plants that were both inoculated with PGPR and treated with EDTA compared to the control. It is important to note that EDTA alone was unable to increase the biomass, likely due to the increased bioavailability of the contaminant. This effect is observable even at a high contaminant concentration [97].
## 3 Method and Model Preparation
### Experiment Preparation and Machine Learning Model
#### 3.3.4. SAE (Stacked Autoencoder)  
AE (Autoencoder) has only one hidden layer. More specifically, the input layer and output layer of AE are equal [10]. SAE is the superposition of multiple AEs. After the first AE is executed, subsequent AEs are performed in order until the Nth, and the output result is the SAE superimposition result. The algorithm flow chart is shown in Figure 10:  
Figure 8: Long Short-Term Memory (LSTM) structure diagram.  
Figure 9: Bi LSTM structure diagram.  
Table 5 shows the computer configuration.  
In the design process of related algorithms, the structure has been introduced in this section. ME, GT, HL1, HL2, AF, ILR, LRDP, LRDF, V, ETF, DTF, L2R, L2 WR, SP, MINON, MAXON, MINLN, and MAXLN are the abbreviation of Max Epochs, Gradient Threshold, Hidden Layer 1, Hidden Layer 2, Activation Function, Initial Learn Rate, Learn Rate Drop Period, Learn Rate Drop Factor, Verbose, Encoder Transfer Function, Decoder Transfer-Function, L2 Regularization, L2 WeightRegularization, and Sparsity Proportion, etc. [8; 11; 12; 13; 14] and need to be set. Table 6 shows the settings of the related parameters.  
So far, we have completed the model establishment, environment construction, and parameter setting in the prediction experiment. The next step is to prepare the experimental data. First, a data set is selected with a large sample size as a one-dimensional matrix input, and then the values in the matrix are normalized (matrix row minimum and maximum values to [-1; 1]). The set is divided after normalization; 80% of the data (including the data of the validation set) is the training set input, and 20% of the data is the test set input. Subsequent experiments do not require a verification set because the parameters of the test results do not need to be adjusted. In the selection process of the settings in Table 6, we refer to the research methods of long-term memory and bio-signal feature compression to identify negative emotions. In the comparison of algorithms [15], the next section analyzes related training results and experimental results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter Settings** \\ \hline BP & ME = 1000, GT = 1.0 \(\times\) 10\({}^{-7}\), HL1 = 20, HL2 = 20. AF = S \\ ELM & ME = 1000, AF = S \\ LSTM & ME = 250, GT = 1, ILR = 0.01, LRDP = 125, LRDF = 0.1, V = 0, L2R = 0.0001 \\ Bi LSTM & ME = 250, GT = 1, ILR = 0.01, LRDP = 125, LRDF = 0.1, V = 0, L2R = 0.0001 \\ SAE-LSTM & ME = 250, GT = 1, ILR = 0.01, LRDP = 125, LRDF = 0.1, V = 0, ETF = satin, \\  & DTF = purelin, L2R = 0.0001, L2 WR = 4, SP = 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter setting of the neural networks.  
Figure 10: Stacked Autoencoder (SAE) structure diagram.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Settings** \\ \hline Hardware & Desktop computer \\ CPU & Intel(R) Core (TM) 15-9600K 3.70 GHz \\ RAM & DDR4 8G 2666 MHz \\ Hard drive & 2T \\ Software\& Operating system & MATLAB R2019a, Windows 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computer configuration.
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 5 Discussion  
For the study of public space, the method of our research in the early stage is on the one hand through a large number of questionnaires and so on, which is labor intensive. Therefore, many teams researching public space have introduced the method of cloud computing for data collection, which is an effective method of data collection, but the invalid data in the collected data cannot be processed effectively, and need to be screened at a later stage of the data processing, which in turn increases the workload. The introduction of the method of edge computing fulfilled the requirements of real-time processing on the data side, but the cost of edge computing also needs to be considered. In addition, for the study of public space, there may be time to detect the environment as well as the dimensions of detection all need us to design a lightweight edge computing platform. Especially when we need multiple nodes to collect and process. Therefore, this system designs a low-cost front-end hardware acquisition circuit, through which the circuit acquires and inputs the data into the edge computing real-time processing. Meanwhile, the circuit data acquisition from each node can be sent wirelessly to the nearest edge platform for processing. In this way, both data acquisition and data processing have been well designed, but there are still some improvements in this system, such as the optimisation of the power consumption of the whole system, the design of the future network, and the wireless power supply, etc. There is still room for improvement.
## 3 Results and Discussion
### Adsorption Tests
#### 3.2.2 Phenolic Compounds Removal  
At pH about 6 all the phenols considered are in the protonated form (pKa = 9.89, 9.12, and 9.6-10.2 for Ph, CP, and BPA, respectively), thus the adsorption performance of the AC are reasonably due to a complex series of van der Waals and steric interactions. Similarly, to the behavior of organic dyes, adsorption for Ph and CP occurred in a two-stage process with all the tested PACs (Figure 2d,e). After 20 min of contact time, the equilibrium was almost established, reaching a reduction of 71.2% for Ph and 91.9% for CP with SCGs-AC, of 58.4% for Ph and 89.7% for CP with C1-AC, and of 72.2% for Ph and 95.6% for CP with C2-AC (Table 4). As regards BPA, the adsorption curves (Figure 2f) continuously increased without reaching the equilibrium in 3 h of contact time. According with [PERSON] and [PERSON] [63], adsorption processes longer than 3 h are not technically and economically convenient in real applications, so the tests were stopped after such duration. In agreement with what was observed for organic dyes and according to the literature [64], the pseudo-second-order kinetic was the best fitting model also for phenols' adsorption (Table 3). The kinetic constants were higher in the adsorption of Ph and CP for all PACs, with respect BPA confirming the trends reported in Figure 2. Freundlich isotherms could be used for describing the adsorption of Ph and CP for all the PACs as reviewed by [PERSON] et al. [29], while the Langmuir model was more suitable for BPA as confirmed by [PERSON] et al. [47]. It is to observe that considering the adsorption capacities and the PC values, the same trends were obtained only for Ph (SCGs > C1-AC > C2-AC). As for CP, the higher performance was demonstrated by SCGs-AC either considering the adsorption capacity or the PC values, while the behavior of C1-AC and C2-Ac resulted to be inverted considering the two different parameters. As for BPA, SCGs have lower performance with respect C2-AC in both cases, the latter being the best adsorbent after 3 h of contact time (Table 4). In general, the adsorption capacity of PACs was higher for CP without any noticeable difference between the three adsorbents (Figure 2e). The removal rates of both Ph and CP for SCGs-AC and C2-AC were comparable (about 140 mg/g), while those for C1-AC were lower (approximately 120 mg/g). Similar results were obtained for phenolic compounds by [PERSON] et al. [37; 38], [PERSON] et al. [32],
## 4 Tracking Control Based on EKF
### Predictive Tracking Control
```
1: Initialize at \(k=0\), set \(\bar{\mathbf{x}}(0)=\mathbf{x}(0)\), \(\hat{\mathbf{x}}(0)=\mathbf{x}(0)\);
2:while The AUV has not arrived the destination do
3:if Acoustic localization counter = \(n\)then
4: Obtain \(\hat{\mathbf{y}}(k)\) as in Section 3
5: Calculate \(\bar{\mathbf{P}}(k)\) and \(\mathbf{K}(k)\);
6: Correct \(\bar{\mathbf{x}}(k)\) with \(\hat{\mathbf{y}}(k)\) and \(\mathbf{K}(k)\) as in (26);
7: Update \(\mathbf{P}(k)\)
8:\(\textit{counter}\to 0\)
9:else
10: Update \(\bar{\mathbf{x}}(k)\) with dead-reckoning as in (28);
11:endif
12: Solve the optimization problem (32) subject to (22), (33)-(36) to obtain \(\mathbf{u}^{*}(k)\);
13: Apply \(\mathbf{u}^{*}(k)\) to the AUV system (1) and (2);
14: Set \(k=k+1\), _counter + \(1\to\textit{counter}\)_ and go to Step 3;
15:endwhile
```  
**Algorithm 1** Predictive tracking control of AUV with acoustic localization and EKF
## 7 Hybrid Communication Systems: VLC with Other Communication Technologies
### VLC with Acoustic Communication Systems
Other physical-layer security techniques like dynamic beam steering in VLC and noise-resilient encoding in acoustic communication can further increase the overall security. By treating these vulnerabilities, hybrid systems can have robust and secure communication for a wide range of applications. Various environmental factors significantly affect the performance of hybrid VLC-acoustic systems. The limitation of LED bandwidth and environmental noise, as shown in [71], is still a challenge for VLC systems. Moreover, acoustic signals are prone to noise and attenuation, which become worse in underwater and dynamic environments. Adaptive algorithms that may dynamically react according to environmental conditions should be developed in order to keep communication robust. The ability of the hybrid system to adapt to various environmental conditions underlines its potential for reliable performance in challenging scenarios.
## 4 Performance Evaluation
### Comparison with other Methods  
Most protocols in the literature use 2-hop information to allocate channels and some of them use random allocation. Others divide the network into clusters. Accordingly, we chose to compare our protocol with four different allocation methods: HMC without segmentation method, 2-hop method, random method and cluster method. These are variants of HMC-MAC but inspired by protocols from the literature.  
In the HMC without segmentation method, a node applies the same channel allocation scheme as HMC-MAC but without taking into account the even or odd depths nor the even or odd branches. This method is evaluated in order to show this importance of network segmentation. In the 2-hop method, a node chooses an available channel in its 2-hop neighborhood. In case all channels are used, it randomly chooses a channel. This method is used to evaluate the effect of doing an allocation based on 3-hop information and not only 2-hop information. In the random method, a node randomly chooses a channel from the list of authorized channels without checking the availability of this channel. This method is simply a default method used for comparison sake only. In the cluster method, the network is divided into \(3\) clusters. Each interface with its descendants represents a cluster. We allocate a different channel for each cluster. All nodes belonging to the same cluster use CSMA/CA to compete for channel access on the same channel. This method is the equivalent of 3 sub-networks using CSMA/CA without FDMA each sub-network using an orthogonal channel.
## 5 Testing and Evaluation
### Lab Testing and Objective Measurements
#### 5.2.3 Video Transmission  
A final measurement regarded the latency and frame rate of video transmission and display. End-to-end latency was measured, as above, with a high-frame-rate camera, which yielded an average measurement of 467 ms. While such latency is acceptable for inspection or searching, it is not ideal for piloting a drone in FPV, based on video feedback only. Video transmission latency can be considered in two steps: from the drone to the UAV interface app connected to the remote controller and from there to the HoloLens. The first part was measured at 386 ms, accounting for the larger part of the overall latency. However, this is significantly greater than the timings reported by DJI (170-240 ms) ([[https://www.dji.com/gr/mavic-mini/specs](https://www.dji.com/gr/mavic-mini/specs)]([https://www.dji.com/gr/mavic-mini/specs](https://www.dji.com/gr/mavic-mini/specs)), accessed on 20 December 2021). Further investigation into this could result in decreased video latency. A full frame rate of 30 frames per second (native to the drone's camera) was achieved.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Experimental Environment
#### 4.1.2 Dynamic Experimental Environment  
To further verify the positioning effect of the proposed method in a dynamic environment, a dynamic experiment was conducted on the campus of a university in Wuhan, China. The moving speed of the vehicle with the experimental equipment ranged from 20 to 40 km/h, and the equipment sampling interval was 1 s. In the experiment, the T30 GNSS receiver data of Sinan Navigation, whose horizontal accuracy is \(\pm(8+1\times 10-6\times D)\) mm and vertical accuracy is \(\pm(15+1\times 10-6\times D)\) mm, were used as the reference. D is the baseline length, and its unit is mm. The measurement roadmap and the connection of test equipment is shown as Figure 6.
###### Abstract  
The PCHE, as an efficient heat exchanger, plays a crucial role in the storage and reassification of LNG. However, among the existing studies, those that integrate this field with deep learning are scarce. Moreover, research on explainability remains insufficient. To address these gaps, this study first constructs a dataset of heat transfer coefficients (h) through numerical simulations. Pearson correlation analysis is employed to screen out the most influential features. In terms of predictive modeling, the study compares five traditional machine learning models alongside deep learning models such as long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), and Transformer. To further enhance prediction accuracy, three attention mechanisms--self-attention mechanism (SA), squeeze-and-excitation mechanism (SE), and local attention mechanism (LA)--are incorporated into the deep learning models. The experimental results demonstrate that the artificial neural network achieves the best performance among the traditional models, with a prediction accuracy for straight-path h reaching 0.891799 (R\({}^{2}\)). When comparing deep learning models augmented with attention mechanisms against the baseline models, both LSTM-SE in the linear flow channel and Transformer-LA in the hexagonal flow channel exhibit improved prediction accuracy. Notably, in predicting the heat transfer coefficient of the hexagonal channel, the determination coefficient (R\({}^{2}\)) of the Transformer-LA model reaches 0.9993, indicating excellent prediction performance. Additionally, this study introduces the SHAP interpretable analysis method to elucidate model predictions, revealing the contributions of different features to model outputs. For instance, in a straight flow channel, the hydraulic diameter (Dh) contributes most significantly to the model output, whereas in a hexagonal flow channel, wall temperature (Tinw) and heat flux (Qw) play more prominent roles. In conclusion, this study offers novel insights and methodologies for PCHE performance prediction by leveraging various machine learning and deep learning models enhanced with attention mechanisms and incorporating explainable analysis methods. These findings not only validate the efficacy of machine learning and deep learning in complex heat exchanger modeling but also provide critical theoretical support for engineering optimization.  
PCHE; liquefied natural gas; heat transfer coefficient; deep learning; attention mechanism; SHAP 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 1-10. External Links: Document Cited by: SS1.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
###### Abstract  
Noise is a concern in industries like aviation. Existing acoustic materials have limitations in terms of effective broadband sound attenuation and operating conditions. This work addresses these limitations by designing and developing a noise mitigation system using lightweight graded micro-porous material made from Cencospheres and high-char binder. However, Cencospheres are nearly spherical with rough surfaces, so determining the flow properties of sound propagation is challenging, and direct measurements are expensive. We developed a multivariable-fit inverse method to estimate these properties using an experimental absorption coefficient, validated first with smooth-surface glass beads and then applied to micro-porous material. The determined flow properties were used in a predictive acoustic analysis and validated experimentally. It was demonstrated that a microstructurally graded material is needed to optimize both sound absorption and transmission loss. A graded material system designed for turbofan engine acoustic liners (50 mm thick) met the target broadband sound absorption coefficient of \(\geq\)0.50 and transmission loss of \(\geq\)20 dB above 500 Hz. The study also highlights that larger particles in thicker layers enhance sound absorption, while a graded micro-structure improves overall acoustic performance. This research offers a novel approach for designing a lightweight acoustic material for aviation, marking a breakthrough in passive noise mitigation technology.  
p 2024 14 11008 10.3990/app142311008
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Performance Evaluation of the Proposed Model
#### 4.4.3 Evaluation on the AZH Dataset  
To assess the performance of our model on the AZH dataset, we designed two experiments with varying numbers of wound classes. The first experiment is performed on the three classes of wounds, i.e., Diabetic, Pressure, and Venous ulcers, whereas the second experiment is conducted on six classes. Among the six classes, four are the wound classes, such as Diabetic, Pressure, Venous, and Surgical, while the other two are the non-wound classes, namely, Background and Normal. The model is trained on the augmented training samples for each experiment and is evaluated on the testing set samples.  
For the 3-class classification experiment, our model attained an accuracy of 87.32%, a precision of 88%, a recall of 86%, and an F1-score of 86.98%. Whereas for the 6-class experiment, the proposed model achieved 83.19% accuracy  
For the 3-class classification, the lowest accuracy for the Diabetic wounds can be observed, because the Diabetic wound class has the highest false negative and lowest false positive rates, compared to the Venous and Pressure wounds. However, in the case of the 6-class classification, the model attained the lowest accuracy for the Pressure wounds since the false negatives of 16 could be observed. Moreover, it can also be noticed that Pressure wounds are mostly classified as Surgical wounds. This can be due to the reason that Pressure wounds have similarities to some Surgical wounds, in terms of shape, color, size, and texture. Additionally, non-wound classes are accurately identified by the model, with classification accuracies of 96% and 100%. Overall, the classification ability of the proposed SEEN-B4 for the AZH dataset is relatively reasonable. However, the results presented in the confusion matrices reveal that the Pressure and Diabetic wounds are the most difficult to classify.
## 2 Results and Discussion
### Determination of the Mineral Composition
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & & & & & & \\
**[mg/g]** & **Blend80** & **Blend90** & **Blend100** & **Origin** & **Origin** & **Origin** \\
**Al** & **ND** & **0.0119** & **ND** & **ND** & **0.0032** & **ND** \\
**As** & **ND** & **ND** & **ND** & **ND** & **ND** & **ND** \\
**Ca** & **0.6110** & **0.7025** & **0.9110** & **0.6280** & **0.5565** & **0.4523** \\
**Cd** & **ND** & **0.0003** & **ND** & **ND** & **0.0002** & **ND** \\
**Cr** & **ND** & **ND** & **ND** & **ND** & **0.0003** & **ND** \\
**Cu** & **0.0021** & **0.0113** & **0.0084** & **0.0041** & **0.0067** & **0.0010** \\
**Fe** & **ND** & **0.1090** & **0.0444** & **ND** & **0.0796** & **ND** \\
**K** & **5.4550** & **6.5098** & **8.2400** & **5.4900** & **4.6038** & **4.7450** \\
**Mg** & **2.1331** & **2.5327** & **3.3941** & **2.1576** & **1.8112** & **1.8446** \\
**Mn** & **0.0210** & **0.0215** & **0.0206** & **0.0539** & **0.0156** & **0.0168** \\
**Mo** & **ND** & **ND** & **0.0005** & **ND** & **ND** & **ND** \\
**Na** & **ND** & **ND** & **0.0013** & **0.0205** & **ND** & **ND** \\
**Ni** & **ND** & **0.0014** & **ND** & **0.0023** & **0.0030** & **ND** \\
**P** & **2.4418** & **3.1926** & **3.8668** & **2.5468** & **2.3651** & **2.4208** \\
**Pb** & **ND** & **0.0006** & **ND** & **0.0002** & **0.0003** & **0.0005** \\
**S** & **0.8282** & **1.0812** & **1.2092** & **0.8087** & **0.7442** & **0.7987** \\
**Sr** & **0.0081** & **ND** & **0.0124** & **0.0064** & **ND** & **0.0033** \\
**Zn** & **0.0074** & **ND** & **0.0214** & **0.0078** & **ND** & **0.0039** \\ \hline \hline \end{tabular} ND—not detected.  
\end{table} 
Table 3: The mineral composition of samples. specific cocoa and chocolate products. This limit was set to 0.8 mg/kg in chocolates with 50% or more total dry cocoa solids [34]. None of the tested samples exceeded this limit. Al was found in the same samples as Cd. The acceptable daily intake for Al is 1 mg/kg body weight [35], which would be hardly reached from consumption of dark chocolate in daily life. As was not detected in any evaluated sample.