esa-sceva/satcom-chunk-collection huggingface dataset:
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 2 Related Work  
### Radar Through-the-Wall  
Regarding through-the-wall human detection, radar technology is currently widely used. Radar technology can utilize ultra-wideband signals through-the-wall and detect the presence of human beings.  
[PERSON] et al. [9] proposed a target localization algorithm based on an improved Hough transform frequency-fitting technique. The adaptive extended Bessel frequency fitting model is constructed by dynamically adjusting two shape parameters. The demodulation of the echo signal using the fitted curve completes the separation of multiple target components and combines it with Doppler processing methods to synthesize the target motion trajectory, realizing real-time target localization of the wall-penetrating radar targets. [PERSON] et al. [10] proposed a study of a time-frequency correlation MUSIC algorithm for the detection of human body targets in wall-penetrating radar, which is achieved by correlating the inverse fast Fourier transform (IFFT) algorithm with the MUSIC algorithm. The power enhancement of the target signal is accomplished according to the time domain distance calculation results. The signal is then converted to the frequency domain for the direction of arrival (DOA) estimation. The status and position information of the human target behind the wall can be better monitored. [PERSON] et al. [11] used radar technology to detect the presence of people behind obstacles and proposed a new signal processing method for extracting and enhancing human detection signals from radar to detect whether the human body status is well detected. However, the problem with most of the human body detection through the wall using radar technology is that the experiment has relatively high requirements for the environment, and it must be in a dry environment and within a certain linear distance or a small range to achieve better results. Otherwise, it will attenuate radar signal power; human body signals are challenging to detect, and deployment costs are high, so this technology is not suitable for all scenarios.
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### Permanent Magnet Brushless DC Motor  
Permanent magnets (PM) are the major materials of PM brushless motor drives. PM BLDC motors are PM AC machines with trapezoidal back-emf waveforms due to the concentrated windings that are used in the motor. As there are no windings in the rotor, there is no rotor copper loss, which makes it more efficient than induction motors. There is no loss of copper in the rotor due the absence of the winding, making it more efficient than available induction machine. The motor drive has light weight, smaller size, is reliable, and provides better torque and specific power with better heat dissipation. The PM BLDC motor system has less maintenance and a higher efficiency compared to the DC brushed motor system [78; 86; 87; 88; 89; 90].  
The major advantages of using PM BLDC motor are:  
* high-energy PMs, light weight, and lower volume providing higher power density offering higher efficiency due to the absence of copper loss;
* better heat dissipation and cooling;
* higher reliability due to lower heating and lower manufacturing defects.  
The Figure 8 shows the basic structure of the PM brushless DC machine  
The single-PMBLDC motor architecture system consists of a voltage-fed inverter, an electronic controller, and sensors. The position sensor ensures the synchronization of the current with the flux. The speed control is relatively simple by controlling the stator currents to align the rectangular current with trapezoidal flux.
## 4 Discussion
As a result, we could enhance the Q-factor by optimizing the PM's orientation and rod-to-rod spacing rather than by only varying the rod size. Figure 6a illustrates an optimized PM whose Q-factor was around eight times higher than the reference value (Q\({}_{\text{6N}}\) = 1751). The supermode was noted as mode S6N according to the given naming rule, and the minimum rod-to-rod spacing was 300 nm. The evanescent field of each cavity in Figure 6a was squeezed by the node planes, which suppressed photons from leaking out of the PM. Furthermore, the overall lateral dimension was less than 3 \(\upmu\)m, whose footprint was still much smaller than most of the reported microdisk WGM lasers. Figure 6b shows another single hexagon cavity whose footprint and resonance wavelength were both close to the PM in Figure 6a. However, the Q-factor was only 34% of the optimized Q\({}_{\text{6N}}\). Additionally, the PM also possessed a reduced effective mode area (A\({}_{\text{eff}}\)) rather than a single large rod. Therefore, a much-improved Purcell factor can be expected since its quantity is proportional to the Q/A\({}_{\text{eff}}\).
# Experimental Investigation on Mechanics and
## 4 Conclusions  
In this study, the triaxial compression and gas depletion experiments using both tectonic and intact coal were conducted. The mechanics and seepage characteristics of tectonic and intact coal under the coupling effect of stress and gas pressure were analyzed and compared. The key findings are summarized as follows.  
1. The triaxial compression process of coal can be divided into four stages. The damage stress and peak stress of tectonic coal is only half that of intact coal, while their compaction stress or residual stress are almost the same. The deviatoric stress of intact coal during the post-destruction stage has a sharp drop while that of tectonic coal decreases slowly which shows an apparent strain softening behavior.
2. During the triaxial compression, the primary permeability and minimum permeability of tectonic coal are 3.76 times and 3.57 times that of intact coal. However, the permeability recovery value after intact coal failure is more than twice of its primary permeability, leading to that the residual permeability of tectonic is only 75.0% that of intact coal.
3. During the gas depletion, the permeability drop ratio of tectonic coal from the maximum value to the minimum value (54.15%) is bigger than that of intact coal (7.45%), and the rebound gas pressure of tectonic coal (1.0 MPa) is smaller than that of intact coal (2.0 MPa). In addition, the permeability of tectonic coal is one order of magnitude larger than that of intact coal before the gas pressure drops to 2.0 MPa, and they become closer after that.
4. The research results provide the reference and inspiration for the applications of coalbed methane recovery: the gas extraction time at high gas pressure stage should be stabilized in the tectonic coal area, while the gas extraction time at low gas pressure stage should be increased in the intact coal area. Moreover, the tectonic coal is suitable for hydraulic flushing technology while the intact coal is suitable for hydraulic fracturing technology in order to improve coal seam permeability and coalbed methane recovery.  
This article was completed by all authors. [PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] made the experimental analysis. All authors have read and agreed to the published version of the manuscript.  
Figure 6: The evolution of coal permeability during gas depletion.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 51904293, No. 51874055), the National Science and Technology Major Project of China (No. 2016 ZX05044-002), the Natural Science Foundation of Jiangsu Province (No. BK20190627), and the China Postdoctoral Science Foundation (No. 2019M661998).  
**Acknowledgments:** The authors would like to thank the support of the laboratory and university.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Overview of the Type of Classifiers Used for MR
### Svm  
SVM is a type of supervised machine learning that can be used for classification. Although ANNs are widely used for AMC, they have certain limitations that SVMs can overcome, i.e., training may degrade the performance, resulting in overfitting and/or local minimum [33]. SVMs can solve overfittings at low SNR conditions [90]. In SVMs, a kernel function is utilized for the nonlinear mapping of features from the input to the feature domain. The common kernel functions used are radial basis function, multilayer perception, and polynomial functions [90]. SVMs are used for classification, especially on multiclass classification. The techniques used for such cases are either repetitive banded support vector machine (BSVM) or multiclass support vector machine (MSVM). In BSVM, SVMs are used to classify the first class from all other classes and then reused for classifying the second class from the rest until the last class is reached. In MSVM, a higher-dimension feature space is used [33; 45; 98]. In [99], genetic programming (GP) was adopted to select the best features from HOC features for M-QAM classification. However, this method was complex and covered small types of modulation schemes.  
In [29], particle swarm optimization (PSO) was used to configure the kernel parameter for feature selection and digital modulation type classification for improving the performance of SVMs. HOS and wavelet were used to identify different digital modulation types. This method improved the recognition accuracy for different modulation schemes at low SNR although it was computationally expensive. In [28], the researchers used spectral features based on MSVM classifier to identify six digital modulation schemes, and this method was robust to signal length under various SNR ranges. In [30], the recognition accuracy with minimum SNR was improved by using a hierarchal classifier based on a binary tree SVM classifier for discriminating digital modulation. This method required few computations but only covered few modulation schemes. In [34], the recognition accuracy for digital modulation schemes was enhanced under various SNRs. Redundant features were removed, and the remaining features were trained using an SVM classifier. However, the determination of M-ASK at low SNR remained challenging in this work.  
For the identification of optical signals, SVM was applied to estimate the OSNR and then classify the modulation format. However, these methods exploit statistical features of signals that are directly detected. Therefore, it cannot be applied to dispersion-unmanaged coherent optical-systems [100]. In [101], an SVM-based approach is applied to jointly estimate the modulation and the OSNR, which relies on the CDF of the received signal's amplitude in combination with SVM-based classification and regression for coherent optical receiver. The average modulation classification rate was 99%.
## 2 Materials and Methods
### Turbidity Compensation
#### 2.5.2 Multiple Scatter Correction (MSC)  
MSC method is a commonly used data processing method, which can reduce the influence of scattering [21]. The calculation process is as follows:  
1. Calculate the average spectrum of the sample \[\overline{A}=\frac{\sum\limits_{i=1}^{n}A_{i,j}}{n}\] (12)
2. Perform unary linear regression \[A_{i}=m_{i}\overline{A}+b_{i}\] (13)
3. Multiple scatter correction \[A_{i(\text{MSC})}=\frac{(A_{i}-b_{i})}{m_{i}}\] (14)  
Figure 6: Fitting relationship between slopes and turbidity values.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 5 Classifiers
### Classifier Selection
#### 5.1.3 Convolutional Neural Networks  
Convolutional neural networks have had much success over the years in different domains, including signal processing [39; 40], image processing [41; 42], or video processing [43]. Since the breakthrough of AlexNet in 2011, many different architectures have emerged.  
CNNs are good at processing grid-shaped data of different dimensionality. Different sequential data of one order can be formulated as a grid structure of higher order--for example, 2D + time data such as video can be processed through 3D convolution. As our data can be formulated as a rectangle [samples, features], we will utilise it. However, there might be a caveat in the fact that CNNs struggle with modelling long-term dependencies, as convolution and pooling are relatively local operations. If our data have long-term dependencies, CNNs may not perform as well as architectures created for sequential modelling.  
Our architecture is inspired by the VGG family [44]. These networks comprise blocks consisting of 2 or 3 convolutional layers followed by max pooling. Both convolutional layers have the same number of channels, and every consecutive block has twice the number of channels of the former block. Theoretically, every block should allow the model to extract relevant features for that semantic level, subsequently \"zoom out\", and focus on a higher semantic level in consecutive blocks. As our network will work with time series data, which has the shape of [data points, features], we will apply 1D Convolutions instead of 2D, as seen in the image classification problems.  
We will need to train from scratch because there are very few 1D CNNs to conduct transfer learning from. With our limited dataset, we will not be able to train the whole model of VGG16 or VGG19 as proposed in the original article. Hence, we will treat both the number of VGG blocks and the number of filters in convolutions of these blocks as hyperparameters, which we will try to optimise. An example of this CNN is visible in Figure 5.
## 5 Conclusions  
This research has provided an in-depth exploration of the security and efficiency challenges in modern Wireless Body Area Networks (WBANs), particularly focusing on a token-based communication approach for enhancing device authentication and mitigating threats. This study has looked into the integration of Human-Body Communication (HBC) as an innovative security enhancer, leveraging the physical properties of the human body for secure data transmission. By implementing unequivocal device authentication, a range of common attack vectors can be pre-emptively mitigated. Utilizing the introduced framework in conjunction with HBC and the described security protocols, overall security can be significantly enhanced. Furthermore, the role of Artificial Intelligence (AI) in WBANs is discussed, highlighting the potential of AI in optimizing system performance and addressing security concerns. The exploration of AI across various layers of the WBAN,from sensors to clinic servers, underlines the importance of robust security measures in safeguarding sensitive medical data against emerging cyber threats. Additionally, the implications of the European AI Act on WBAN systems have been considered, emphasizing the need for compliance with evolving regulatory frameworks.  
Especially in the context of the rapidly advancing healthcare technologies and further developments regarding 'beyond 5G' and 6G, adaptive, efficient, and secure WBAN systems are of great importance. The proposed token-based approach, complemented by AI integration and advanced security measures such as HBC, paves the way for future innovations in healthcare technology, ensuring patient data are both secure and seamlessly integrated into medical care. In future work, the incorporated communication framework in combination with an HBC channel will be used to integrate the proposed encryption methods alongside the ring topology. The aim is to evaluate the system, regarding overall security, energy consumption, and applicability.  
Conceptualization, J.H. and M.R.; methodology, J.H., M.R. and S.P.S.; investigation, S.P.S., J.H. and M.R.; resources, S.P.S., J.H. and M.R.; writing--original draft preparation, J.H., M.R., S.P.S. and C.L.; writing review and editing, J.H., S.P.S. and M.R.; visualization, J.H.; supervision, H.D.S.; project administration, M.R., J.H., C.L. and H.D.S.; funding acquisition, C.L., M.R., J.H. and H.D.S. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Federal Ministry of Education and Research of the Federal Republic of Germany (Forderkennzeichen 16 KISK003K, Open6 CHub and 16 KISK214, 6G-Health). The authors alone are responsible for the content of this article. Data sharing not applicable--no new data generated.  
The authors declare no conflicts of interest.
# Anthraquinone Rhein Exhibits Antibacterialativity against _Staphylococcus aureus_
## 3 Results
### Rhein Effect on the Main Virulence Factors of S. aureus  
Staphylococcal hemolysins and catalase are important virulence factors that promote bacterial invasion. The effect of Rhein on alpha-hemolysin and catalase of _S. aureus_ ATCC 6538 was evaluated through a hemolytic activity test and H2O2 sensitivity assay, respectively. The results show that the hemolytic activity of _S. aureus_ significantly decreased in response to treatment with Rhein up to the concentration of 3.13 ug/mL compared with CTR-. In detail, a 1-, 5.3-, 7.1-, 14.3- and 25-fold reduction in hemolytic activity occurred by treating the bacterial strain with 1.56, 3.13, 6.25, 12.5 and 25 ug/mL of Rhein, respectively (Figure 7). Treatment with Rhein increases the susceptibility of _S. aureus_ to H2O2. The halo of inhibition resulting from sensitivity to H2O2 in the plate with 6.25 and 3.13 ug/mL was 36 \(\pm\) 0.31 and 32 \(\pm\) 0.36 mm. An increase in H2O2 sensitivity of 0.96- and 1.16-fold occurred in response to treatment with Rhein at concentrations of 3.13 and 6.25 ug/mL, respectively (Figure 8).  
Figure 3: Inhibition zone against _S. aureus_ ATCC 6538 (**A**), multisensitive (**B**), resistant to beta-lactams (**C**), resistant to quinolones (**D**), constitutively resistant to macrolides (**E**) and resistant to methicillin (**F**) strains.
###### Abstract  
In the pine savannas of the southeastern United States, prescribed fire is commonly used to manipulate undersory structure and composition. Understory characteristics have traditionally been monitored with field sampling; however, remote sensing could provide rapid, spatially explicit monitoring of undersory dynamics. We contrasted pre- vs. post-fire understory characteristics collected with fixed area plots with estimates from high-density LiDAR point clouds collected using the unmanned aerial vehicle (UAV)-borne GatorEye system. Measuring within 1 \(\times\) 1 m field plots (\(n=20\)), we found average understory height ranged from 0.17-1.26 m and biomass from 0.26-4.86 Mg C ha\({}^{-1}\) before the fire (May 2018), and five months after the fire (November 2018), height ranged from 0.11-1.09 m and biomass from 0.04-3.03 Mg C ha\({}^{-1}\). Understory heights estimated with LiDAR were significantly correlated with plot height measurements (\(R^{2}=0.576\), \(p\leq 0.001\)). Understory biomass was correlated with in situ heights (\(R^{2}=0.579\), \(p\leq 0.001\)) and LiDAR heights (\(R^{2}=0.507\), \(p\leq 0.001\)). The biomass estimates made with either height measurement did not differ for the measurement plots (\(p=0.263\)). However, for the larger research area, the understory biomass estimated with the LiDAR indicated a smaller difference after the burn (\(\sim\)12.7% biomass reduction) than observed with in situ measurements (\(\sim\)16% biomass reduction). The two approaches likely differed because the research area's spatial variability was not captured by the in-situ measurements (0.2% of the research area measured) versus the wall-to-wall coverage provided by LiDAR. The additional benefit of having spatially explicit measurements with LiDAR, and its ease of use, make it a promising tool for land managers wanting greater spatial and temporal resolution in tracking understory biomass and its response to prescribed fire.  
field sampling; prescribed fire; UAV LiDAR; undersory height and biomass Article Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna.
## 3 Receiver Test Methodology
### Baseline Test Method  
A clean GNSS signal, in the absence of interference, is fed to the RUT to validate its performance under nominal condition. The total duration of this test is 60 min.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Geomagnetism  
The geomagnetism covers all aspects of the Earth's magnetic field and can be used in INS (see Figure 10) for providing the absolute heading estimation. The heading can be expressed as a vector and used for determining the direction of the azimuth, i.e., the magnetic North, using a compass. The local magnetic field anomalies can be used as fingerprints for distinguishing the different locations [104]. The advantage of the geomagnetism is that no additional infrastructure is required and a geomagnetic map can be created.  
In [104], a geomagnetic map was created using the accelerometer and gyroscope aiming to enhance the feature vector by estimating the different orientations. The database consisted of the raw data collection from the accelerometer, gyroscope, and magnetometer sensors. Furthermore, in [105], a combination of magnetic, acceleration, and gyroscope quaternion (MAGYQ)-based attitude angles estimation filter was used with dynamic and static motion conditions. The MAGYQ was evaluated in both indoors and outdoors, with three people walking at a normal pace. For the outdoor environment, the GNSS was used to obtain the ground truth, while for indoors, the different footpaths were overlayed on the building background map. Similarly, in [106], the ambient magnetic field was used on a smartphone for identifying different anomalies, which can be used in machine learning-based classifiers and distinguish the indoor and outdoor environment. Furthermore, magnetic maps [107] can be created by utilizing the smartphone's magnetometer for collecting data measurements. The magnetic maps can be used for improving the existing visual maps for location tracking and navigation purposes.