esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 4 Discussion
### TD Tube vs. SPME
This is attributed to the narrower injection band from the TD instrument, which provides focused injection of all analytes including early-eluting ones. With SPME, early-eluting peaks are not effectively focused at the head of the GC column due to their relatively high volatility and low retention factor on the column, an issue not easily remedied with a typical GC-MS system. Generally, active sampling onto TD tubes was able to capture the highly volatile compounds with a low molecular mass and the low volatile compounds with a larger molecular mass compared to SPME. Compounds that overlapped between SPME and active sampling were the semi-volatile compounds. However, as mentioned previously, the compounds that were present in both techniques had a larger peak area when collected with active sampling onto TD tubes. Although the overloading of the chromatographic column can occur for very abundant compounds when using active sampling onto TD tubes (as observed in the chromatograms for Little Napoli tomato and Mojito mint), this can be corrected by optimizing the column loading (by means of the split ratio and/or sampling flows and sampling times). Optimizing these parameters would be sample and instrument-specific. When working with natural products, it is not uncommon for multiple injections to be performed for each sample: one low-split injection to characterize minor constituents and one high-split injection for the major constituents. With TD, this can be easily achieved by either collecting multiple samples in parallel from each plant or, if instrumentation permits, splitting injections from a single tube and recollecting the fraction of the sample that is not directed to the GC-MS system. Finally, it must also be noted that transporting multiple samples long distances from a sampling location (e.g., field site or greenhouse) to the laboratory is relatively trivial with TD tubes. This is much more difficult with analytes on SPME fibers, highlighting one final advantage of active sampling onto sorbent tubes over SPME when it comes to _in vivo_ plant metabolomics studies, as has been noted previously [24].
## 3 Methodology
### EMD for Separating the Modulation Terms
According to the linear regression relationship between the original signal and each IMF component, the IMF with high correlation was merged as the trend term of SNR, and the remaining IMF was merged as the modulation term of SNR. The method proposed is referred to as the IMF discriminant method, and the specific process is as follows. (1) The correlation coefficient (\(r_{0}\)) between each IMF component and the original sequence are calculated, and the correlation coefficient threshold (\(r_{m}\)) is set; (2) the IMF component corresponding to the first \(r_{0}\) greater than \(r_{m}\) is considered as the \(IMF_{T}\). Subsequently, the components from the \(IMF_{T}\) to the last IMF are combined as trend terms and deleted, while the components from the first IMF to the \(IMF_{T-1}\) are combined as modulation terms and retained. For different GNSS satellites, the target component determined by this method also varies across multiple IMF components with the change of SNR rather than being fixed. In other words, this method is based on the characteristics of the data themselves rather than on artificial restrictions or definitions; so, it has high flexibility.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
###### Abstract  
Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.  
W +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Selection and Isolation of Nitrate- and Nitrite-Reducing Bacteria  
The homogenized soils were serially diluted at a 1:10 ratio (2 g of soil/18 mL saline) up to a final dilution of 10\({}^{-8}\) in sterile isotonic (0.85%) saline. Twenty microliters of each dilution were used to inoculate 180 uL of the nitrate broth in a 5-fold replicate in a microtiter plate. Nitrate broth medium consisted of 2.5 g of NaNO\({}_{3}\) added to 500 mL of nutrient broth (Difco Inc., Detroit, MI, USA) before autoclaving. Nitrate broth is used to enrich bacteria that reduce nitrate into nitrite or produce N\({}_{2}\) gas [48]. The antifungal agent, cycloheximide (100 ug/mL final concentration) (Sigma-Aldrich Corporation, St. Louis, MO, USA), was added after autoclaving. Internal controls for replicability included cultures of strains positive for NR (_Stenotropomonas multipolilin_ ATCC 13637), DN (_Pseudomonas aeruginosa_ ATCC 27853), or negative for NR or DN (_Pseudomonas putida_ PG7) grown in nitrate broth were added singly to three microtiter plate wells to serve as positive or negative controls for DN/NR. Additionally, three uninoculated wells were included as controls for contamination. After two weeks of incubation at room temperature (23-25 \({}^{\circ}\)C) in containers that excluded light [49], all wells were examined for evidence of growth (turbidity), and those that were turbid were scored positive for growth of aerobic/facultative anaerobic heterotrophic bacteria. Subsequently, half of the volume of the wells was transferred to a fresh set of microtiter plates containing nitrate broth and tested for the presence of the expected products of nitrate reduction or denitrification using Griess reagents [50; 51]. Briefly, two drops of reagent 1 (Sulfanilic acid) and two drops of reagent 2 (N, N-Dimethyl-1-naphthylamine) (bioMerieux Vitek, Inc., Hazelwood, MO, USA) were added to every well. The presence of nitrite is detected by a red color after the addition of reagents 1 and 2, e.g., indicating nitrate reduction. If the medium remained colorless, e.g., no nitrite formed, it is due either to lack reduction of nitrate, e.g., no nitrate reduction, or the reduction of nitrite to other products, such as NO, N\({}_{2}\)O, or N\({}_{2}\), hence denitrification. If the medium remained colorless, zinc metal dust (Mallinckrodt Chemical Works, St. Louis, MO, USA) was used to detect if unreduced nitrate was present by reducing nitrate to nitrite and turning the medium pink or red. Therefore, pink or red medium color after zinc dust indicates nitrate was not reduced. Therefore, neither NR nor DN bacteria were present, or if present, did not reduce nitrate under the test conditions. If colorless after zinc dust, the well was assumed to contain DN bacteria. However, since only the loss of nitrate and nitrite was demonstrated, bacteria in the colorless wells could be incomplete denitrifiers rather than performing the entire denitrification pathway, as this protocol does not screen directly for gaseous nitrogen compounds that are products of later steps in the denitrification pathway nor for ammonium produced by DNRA.
## 3 Results and Discussion
### Species Generated in Gas Phase  
Optical emission spectrometry (OES) in the wavelength region from 200 to 665 nm was used to investigate the main excited species generated by AALCA. OES spectra of AALCA is presented in Figure 6.  
Figure 5: Thermographic image of DI water treated with AALCA for 1 min (**a**) and 5 (**b**) min.  
Figure 6: Optical emission spectrometry (OES) spectra of AALCA.  
It was found that the concentrations of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) were 7.4, 8.3, and 8.9 mg/L, respectively, after 30 s of irradiation by AALCA; moreover, the concentrations of H\({}_{2}\)O\({}_{2}\) and NO\({}_{3}\)\({}^{-}\) further increased to 14.5 and 53.7 mg/L, respectively, when the concentration of NO\({}_{2}\)\({}^{-}\) decreased to 2.7 mg/L with an increase of the treatment duration to 60 s. Owing to the longer treatment duration, NO\({}_{2}\)\({}^{-}\) might convert to more stable NO\({}_{3}\)\({}^{-}\), resulting in a change of concentration ratios. The observed concentrations were two orders of magnitude higher than those of 5 mL DI water treated for 1 min with commercially available NEAPPJ operated with He (0.37 mg/L of H\({}_{2}\)O\({}_{2}\), 0.18 mg/L of NO\({}_{2}\)\({}^{-}\), and 0.06 mg/L of NO\({}_{3}\)\({}^{-}\)). Additionally, considerably small concentrations of RONS in water treated using NEAPPJ (up to 890 mg/L of H\({}_{2}\)O\({}_{2}\), up to 180 mg/L of NO\({}_{2}\)\({}^{-}\), and up to 410 mg/L of NO\({}_{3}\)\({}^{-}\)) [20; 21; 22; 45; 46] and custom plasma jets operated with air or addition of air (up to 9.3 mg/L of H\({}_{2}\)O\({}_{2}\), up to 4 mg/L of NO\({}_{2}\)\({}^{-}\), and up to 5.5 mg/L of NO\({}_{3}\)\({}^{-}\)) [14; 28; 47; 48] were reported elsewhere, confirming the high efficiency of the developed AALCA as compared with commonly used nonequilibrium plasma jets.  
Considering the low power consumption of conventional NEAPPJ compared with AALCA, a more reliable comparison is to NEAPRS, which has a power consumption of about 50 W, which is comparable to AALCA. After treatment of 5 mL of DI water with NEAPRS for 1 min, the concentrations of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) were 3.1, 0.2, and 3.6 mg/L, respectively, which was one order of magnitude lower than the 5 mL of DI water treated with AALCA for 1 min.
## 2 Ultrasonic Waveguide in Grounded Flat Steel
### SH Wave Dispersion Characteristics and Selection of Operating Position  
Taking the phase velocity and group velocity as functions of the frequency thickness product _fd_ (_d_ = 2 h), the equations for the phase velocity and group velocity are as follows:  
\[C_{p}(fd)=\pm 2C_{T}\frac{fd}{\sqrt{4{(fd)}^{2}-n^{2}C_{T}^{2}}} \tag{1}\]  
\[C_{g}(fd)=C_{T}\sqrt{1-\frac{(n/2)}{(fd/C_{T})^{2}}} \tag{2}\]  
where \(C_{p}\) is the phase velocity and \(C_{g}\) is the group velocity.  
The grounded flat steel has an elastic modulus of 210 GPa, Poisson's ratio of 0.33, and a density of 7850 kg/m\({}^{3}\). The phase velocity and group velocity dispersion curves are solved using Equations (1) and (2), as shown in Figure 2.  
The SH wave is typically used to test the plate specimen, which possesses both dispersion and multimodal characteristics as well as wave structure characteristics that characterize the displacement of the guided wave in the thickness orientation of the flat steel. Figure 3 shows a schematic diagram of the guided wave structure and displacement field in the thickness direction of the flat steel is for each mode.  
The dispersive and multimodal characteristics of SH waves frequently impede the analysis and identification of detected signals; single-modal SH waves are typically employed in engineering structural defect detection to reduce the interference of mode-converted clutter signals and to simplify detection.  
Upon observing the dispersion curves and wave structures of SH waves in flat steel depicted in Figures 2 and 3, it becomes apparent that the wave structures of SH0 guided waves are more straightforward than other modes of SH waves. These waves' phase velocity and group velocity remain constant throughout the frequency range, and are equivalent to the wave velocity of transverse waves in flat steel, which implies that guided SH0 mode waves are not dispersive. To ensure the singularity of the detected guided wave mode for grounded flat steel with a thickness of 5.8 mm, the excitation frequency of the electromagnetic ultrasonic transducer should be lower than the cut-off frequency of the SH0 wave of 270 MHz. Notably, the wavelength of the SH wave increases as the excitation frequency of the PPM electromagnetic ultrasonic transducer decreases. After meticulous consideration, we selected the 0.25 MHz signal as the excitation signal for the electromagnetic ultrasonic transducer.  
Figure 2: (**a**) Dispersion curve of SH wave phase velocity in flat steel and (**b**) dispersion curve of SH wave group velocity in flat steel.  
According to the SH wave dispersion curve in Figure 2, the slope k of the line connecting the original point and the working position of the transducer is the wavelength \(\lambda\) of the guided wave, which is dependent on the width of the permanent magnet of the PPM-EMAT permanent magnet d(\(\lambda\) = 2 d). Therefore, a 6.3 mm wide permanent magnet is required to excite 0.25 MHz in a 5.8 mm thick grounded flat steel with a 14 mm SH0 wave.
# Enhancing Friction Pendulum Isolation Systems Using Passive and Semi-Active Dampers
###### Abstract  
Friction pendulum systems (FPSs) are a common solution for isolating civil engineering structures under ground movements. The result is a base-isolated structure in which the base exhibits low shear stiffness in such a way that the input energy of the earthquake is concentrated and dissipated into it, leaving the superstructure free of damage. As a consequence, large displacements of the FPS may be demanded depending on the earthquake intensity and the fundamental period of the FPS. To accommodate these displacements, large-size isolators with high friction coefficients are usually required. However, the FPS will then exhibit poor re-centering capacity and the risk of future shocks will increase due to previous residual displacements, especially for low-intensity earthquakes. An alternative solution is to include a semi-active damper to the FPS, keeping the friction coefficient low and achieving both, limited base displacement under high-intensity earthquakes and good re-centering capacity under low-intensity ones. Thus, this work presents a design methodology for base isolators formed by an FPS with a damper added. The design methodology is applied to an FPS with a passive damper and to an FPS with a semi-active damper. Two ON-OFF control strategies are studied: (i) a fairly simple phase control, and (ii), a mechanical energy-predictive based algorithm. The advantages of semi-active FPSs with low friction coefficients with respect to FPS with high friction coefficients are demonstrated. The results with the designed semi-active FPS are compared with the single FPS and the FPS with a passive damper. Finally, the use of semi-active FPS allows us to enhance the FPS performance as the isolator size can be reduced while keeping the capacity to cope with low and high-intensity earthquakes without residual displacements.  
base isolation; friction pendulum system; semi-active control; phase control; energy-predictive-based control +
Footnote †: journal: Applied Sciences
# A Period Training Method for Heterogeneous UUV Dynamic Task Allocation
## 3 MARL with the Attention Mechanism and Period Training Method
### Period Training Method  
The total parameter vector \(\mathbf{\theta}\) is optimized with the proposed PTM. To ensure the convergence of the MARL model, the number of agents usually needs to be fixed in the training process. In the dynamic task allocation problem, UUVs may suffer unpredictable events and be broken. This changing number of UUVs significantly reduces the performance of the MARL model [11; 12]. To adapt to this situation, the trained model needs to reallocate tasks when the UUVs' number changes.  
Figure 4 is an illustration of the PTM training framework. Different from the common MARL training algorithm, PTM uses various data with different numbers of UUVs to construct periodically changing training environments. In addition, the improved actor-critic framework is introduced in the PTM. Herein, we redesign the parameter exchange rules between the actor network and critic network to achieve the convergence of the MARL model. The parameter vector of the total model \(\mathbf{\theta}\) is optimized using the policy gradient algorithm [14], which can be calculated as follows:  
\[\
abla\mathcal{L}(\mathbf{\theta})=\mathbb{E}_{p_{\mathbf{\theta}|i,m}^{t}}[(f(\mathbf{ \pi})-f(\overline{\mathbf{\pi}}))\
abla\log p_{\mathbf{\theta}|i,m}^{t}(\mathbf{\pi})] \tag{10}\]  
where \(\mathcal{L}\) represents the loss of the training process, and \(\mathbf{\pi}\) and \(\overline{\mathbf{\pi}}\) denote the solutions obtained by the actor network and critic network, respectively.  
To ensure the convergence of the MARL model in periodically changing training conditions, we design a greedy update method to control the parameter replacement between the critic network and actor network. After finishing an epoch training step, PTM randomly samples the test data \(\left\{U,T\right\}_{1},\ldots,\left\{U,T\right\}_{L}\) from not only the current epoch but also the other epochs with different numbers of UUVs. The rule of parameter replacement of the PTM is as follows:  
\[\left\{\begin{array}{c}\theta_{c}\rightarrow\theta_{a},if\sum\left(f(\pi)-f (\overline{\pi})|\{U,T\}_{1},\ldots,\{U,T\}_{L}\right)>0\\ \theta_{c}\rightarrow\theta_{c},\text{ else}\end{array}\right. \tag{11}\]  
where \(\theta_{c}\) and \(\theta_{a}\) are the parameters of the critic network and actor network, respectively. According to (12), PTM replaces the parameters of the critic network by using the actor network parameters when the results of the actor network are better than those of the critic network. Due to periodically changing training conditions and the greedy parameter replacement rule, the critic network can maintain the best model parameters.
## 4 Experimental Results  
The overall measurement system is shown in Figure 1. The vision sensor is connected to a mobile device via a USB cable. The effective measurement distance with respect to the vision sensor  
Figure 11: Detecting 2D key points. (**a**) Input image; (**b**) structured edge maps; (**c**) 2D coordinates of the key points detected through the straight lines.  
is 0.1-2.5m. The normal working environmental temperature of the system is \(-\)15 \({}^{\circ}\)C-60 \({}^{\circ}\)C. Before these experiments, the calibration parameters of the vision sensor were calibrated in advance with the method described in Section 3.2. The detailed parameters are shown in Table 2.  
A few operational experiments under varying operating conditions were carried out to evaluate the performance and effectiveness of the proposed system and the validity of the corresponding algorithms derived above. Five experimental phases were conducted to evaluate system performance. (1) The relative angle of the boxes measured and the visual sensor was changed; (2) the distance between the visual sensor and boxes was changed; (3) systematic error and measurement uncertainty analyses experiments of the system were performed; (4) the measurement accuracy of the system on various boxes in different scenarios was verified; and (5) some online test experiments were performed.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Performance Evaluation for 5G Communications
#### 5.3.2 Reliability of Communications  
The BER for 5G communications is shown in Figure 8. When \(d_{ce}=d_{cr}=1\) and the SNR is 5 dB, the Gaussian noise based perturbation has a higher BER performance compared to the proposed perturbation and a similar result is also observed for other SNR values. Note that the adversarial perturbation by the CJ not only increases the success of covertness, but also has less effect on the BER performance of the receiver compared to  
Figure 6: BER at the receiver when \(d_{ce}=0.5\) and \(d_{cr}=1.5\).  
the Gaussian noise based perturbation for 5G communication signals. We further observe that the Gaussian noise based perturbation results in a higher BER than the proposed adversarial perturbation when \(d_{cc}=0.5\) and \(d_{cr}=1.5\).
# The Stability of New Single-Layer Combined Lattice Shell Based on Aluminum Alloy Honeycomb Panels
## 2 Experimental Study on Stability of NSCLS
### Results and Discussion
#### 2.3.2 Load-Displacement Curve of the Test Model  
Figure 6a,b show the load displacement curves of the lattice shell with no aluminum alloy reinforcing plate and combined lattice shell models, respectively. These figures show that the shapes of the curves for the six models are similar (Figure 6c) regardless of whether an aluminum alloy plate is incorporated into the lattice shell and whether there are geometric imperfections. However, the ultimate bearing capacity and the maximum displacement are different. Because the aluminum alloy plate reinforces the structure, the overall rigidity and the stable bearing capacity of the combined lattice shell structure are enhanced remarkably. The ultimate bearing capacities of the lattice shells with plates are significantly larger than that of the lattice shell with no plate. They increase by approximately 15% on average. The enhancement of the defect-free lattice shell model is more pronounced than that of the model with defects.  
Figure 4: Failure patterns of the lattice shell model without aluminum alloy plates: (**a**) The overall failure profile; (**b**) The amplified profile of a local destabilization area.  
Figure 5: Failure modes of the combined lattice shell model: (**a**) The overall failure profile; (**b**) The amplified profile of a local destabilization area.  
In addition, the test results show that the sensitivity of each type of structures to geometric imperfections is different. The bearing capacity of the model with defects (A\({}_{2}\)) is approximately 7% lower than the capacity of those without defect (A\({}_{1}\) and A\({}_{3}\)). In the combined lattice shell model, the bearing capacity of the model with defects (B\({}_{2}\)) is 12% less than the capacity of those without defects (B\({}_{1}\), B\({}_{3}\)).
###### Abstract  
Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.  
b 2020; 10, 5251; doi:10.3390/app10155251