esa-sceva/satcom-chunk-collection huggingface dataset:
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
###### Abstract  
The gyrotrons are powerful sources of coherent radiation that can operate in both pulsed and CW (continuous wave) regimes. Their recent advancement toward higher frequencies reached the terahertz (THz) region and opened the road to many new applications in the broad fields of high-power terahertz science and technologies. Among them are advanced spectroscopic techniques, most notably NMR-DNP (nuclear magnetic resonance with signal enhancement through dynamic nuclear polarization, ESR (electron spin resonance) spectroscopy, precise spectroscopy for measuring the HFS (hyperfine splitting) of positronium, etc. Other prominent applications include materials processing (e.g., thermal treatment as well as the sintering of advanced ceramics), remote detection of concealed radioactive materials, radars, and biological and medical research, just to name a few. Among prospective and emerging applications that utilize the gyrotrons as radiation sources are imaging and sensing for inspection and control in various technological processes (for example, food production, security, etc). In this paper, we overview the current status of the research in this field and show that the gyrotrons are promising radiation sources for THz sensing and imaging based on both the existent and anticipated novel techniques and methods.  
gyrotron; THz radiation; THz spectroscopy; sensing; imaging +
Footnote †: journal: Applied Physics Letters  
14 January 2020; Accepted: 28 January 2020; Published: 3 February 2020
## 3 Path-Tracking Control Strategy
### Control Allocation and \"Virtual Axle\" Method  
For the first, second, and fourth modules, the CG generalized forces need to be generated through the combination of the wheel driving torque and steering angle. Different from [33], the CG generalized force model of each module in the SRT is not identical due to the various actuator configurations. According to the CG generalized force model (28), in each control step, the wheel state control allocation (CA) of the \(i^{th}\) module can be sorted into the convex QP problem [37; 38; 39] under the linear constraints of the wheel steering angle and torque:  
\[\min\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
Because of the CG generalized force redistribution, the tires of the fourth axle do not need and should avoid producing lateral force. The \"virtual axle\" method is proposed. By ensuring that the wheel steering angle is consistent with the direction of the speed at the wheel fixed point, the sideslip of the tires is minimized as much as possible so that the tires only provide vertical force. Therefore, the wheel steering angle of the fourth axle can be calculated by the following equation:  
\[\delta_{4}=\frac{v_{y}^{3}+l_{1,2}^{3}\gamma^{3}}{v_{x}^{3}}, \tag{61}\]  
where \(v_{y}^{3}\) is the lateral velocity of the third module, \(\gamma^{3}\) is the yaw rate, \(v_{x}^{3}\) is the longitudinal velocity, and \(l_{1,2}^{3}\) is the longitudinal distance between the wheel fixed point and the CG.
## 3 The Proposed Scheme
### Discussion
We construct an architecture that combines the concepts of the chain-based routing and grid-based routing. In addition, we add the idea of load balancing with the cycle-based approach for distributing data transmission to reduce energy consumption. In CBDAS, the whole sensor field is partitioned into a logical grid of \(M\times N\) cells, each has a head. All cell heads are further linked together to form a cyclic chain. The complexity of this algorithm is therefore \(O(M\times N)\). As for other algorithms [8, 11, 15] using the greedy method, then the complexity is \(O(n^{2})\), where \(n\) is the number of sensor nodes and \(n>M\times N\).
# Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution
## 2 Background
### Hardware Description Languages  
Hardware Description Languages (HDLs) such as Verilog and VHDL are the de facto standard for circuit design in the industry due to several benefits [18]. Notable among these are the design of circuits at higher abstraction--Register Transfer Level (RTL), other than gate level [19]. Additionally, RTL designs are more interpretable, require less time to modify and easier to verify [20]. RTL designs employ programming constructs such as conditional statements, synthesisable for-loops, procedural blocks, case statements, asynchronous and synchronous constructs to provide a human readable description of a circuit's behaviour. As a result, RTL designs must undergo logic synthesis where these high level descriptions of circuits are converted to basic building blocks such as logic gates (AND, NOT, NAND, NOR and XOR)and flip-flops (or storage elements). GE has successfully evolved complex and accurate multiplier, adder, and parity using SystemVerilog [4].
## 5 Experiment Result and Comparative Study
### Comparison of Different Methods
Robust fault diagnosis of rotating machinery is realized at low \(SNR\) conditions [30]. Gaussian white noise with different standard deviations is added to the raw vibration signal to create signals with different SNRs. The formula for \(SNR\) is given below:  
\[SNR=10\log_{10}(P_{signal}/P_{noise}) \tag{22}\]  
Here, \(P_{signal}\) is the signal power, and \(P_{noise}\) is the noise power. The diagnostic accuracy of the three models is compared when the \(SNR\) ranges from \(-6\) dB to \(6\) dB. Each model records ten trials of diagnostic accuracy; the average diagnostic accuracy is utilized to quantify the model performance. The diagnostic accuracy of the different methods under different \(SNR\) is shown in Figure 11.  
It can be seen in Figure 11 that the diagnostic accuracy of different methods decreases as noise power increases. When \(SNR\) is 6 dB, the accuracy of the proposed method, the  
Figure 11: Accuracy of the different methods under different SNR. CWT + CNN method and the HHT + CNN are 92.9%, 89.1% and 72.9%, respectively. When \(SNR\) is the same, the accuracy of the proposed method is higher than that of other methods. The result shows that the proposed method has strong antinoise abilities and robustness in noisy environments.
# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 1 Introduction
[PERSON] et al., achieved bone suppression on pediatric chest radiographs by utilizing computed tomographic images of adults and pediatrics to train the AI model [27]. In contrast, our AI-DES, by adapting weighted image subtraction with artificially synthesized low-energy images, has a significant advantage in terms of generating not only soft-tissue- or bone-enhanced images but also selectively enhanced images of tissues with a specific linear attenuation coefficient. However, this paper mainly focuses on generating soft-tissue- and bone-enhanced images for comparison with existing DES systems as an initial report on the development of AI-DES. We employed pix2 pix [32], which is a well-established image-to-image translation network, to construct the AI network. Pix2 pix has been widely used in many image domain transformation tasks, such as image colorization and style transfer. It is also extensively used for medical imaging. [PERSON] et al., adopted pix2 pix to correct motion artifacts in magnetic resonance (MR) images [33]. [PERSON] et al., achieved denoising of low-dose single-photon emission-computed tomography (SPECT) images using pix2 pix [34]. Although pix2 pix usually requires identical positional information between the paired images, our task of generating low-energy images from high-energy input images can deal with this constraint. The main contributions of this work are as follows:  
1. We developed an AI-based DES system to provide soft-tissue- and bone-enhanced images using virtually generated low-energy images;
2. The virtual low-energy images were generated through the AI technique from only high-energy images, which can be obtained by routine chest radiography;
3. AI-DES has the potential to provide specific tissue-enhanced images while avoiding issues associated with DES systems, such as multiple exposures and noise increments;A comparison of the generated images with those produced by a clinically applied system suggests that AI-DES can achieve superior sharpness and noise characteristics. Furthermore, although this is a future-expanded perspective, the novelty of the AI-DES is that it allows for the selection of enhanced tissues by adjusting the weight in the image subtraction process, in comparison to existing works. In Section 2, we first introduce the developed AI-DES system. Then, we describe image datasets and prepossessing steps for AI training. Next, we specify the training setups and explain the methods used to evaluate similarity between the generated and ground truth images. Section 3 presents the generated images in comparison to the ground truth. Section 4 discusses the performance, limitations, and future perspectives of our AI-DES. Finally, Section 5 summarizes and concludes this work.
# Place Classification Algorithm Based on Semantic Segmented Objects
###### Abstract  
Scene or place classification is one of the important problems in image and video search and recommendation systems. Humans can understand the scene they are located, but it is difficult for machines to do it. Considering a scene image which has several objects, humans recognize the scene based on these objects, especially background objects. According to this observation, we propose an efficient scene classification algorithm for three different classes by detecting objects in the scene. We use pre-trained semantic segmentation model to extract objects from an image. After that, we construct a weight matrix to determine a scene class better. Finally, we classify an image into one of three scene classes (i.e., indoor, nature, city) by using the designed weighting matrix. The performance of our scheme outperforms several classification methods using convolutional neural networks (CNNs), such as VGG, Inception, ResNet, ResNeXt, Wide-ResNet, DenseNet, and MnasNet. The proposed model achieves 90.8% of verification accuracy and improves over 2.8% of the accuracy when comparing to the existing CNN-based methods.  
scene/place classification; semantic segmentation; deep learning; weighting matrix; convolutional neural network +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Thermogravimetric Analysis  
Thermogravimetric experiments can detect the temperature resistance of poly (MMA-BA-ST). Figure 3 shows the TG-DTG curve of the nano plugging agent poly (MMA-BA-ST). It can be seen from Figure 3 that the mass of poly (MMA-BA-ST) gradually decreases with the increase in temperature. The decrease in poly (MMA-BA-ST) before 300 \({}^{\circ}\)C is small, indicating that the main structure is not damaged by high temperature, which is mainly manifested by the evaporation of bound water and free water, and a small number of compounds are decomposed. We take the intersection of TG curves with different trends and determine the initial decomposition temperature of poly (MMA-BA-ST) to be 364 \({}^{\circ}\)C. At this time, the molecular chain of poly (MMA-BA-ST) begins to break down and decompose at this high temperature. The final decomposition temperature of poly (MMA-BA-ST) is 417 \({}^{\circ}\)C, and it can be seen from the DTG curve that the decomposition rate is the fastest at around 399 \({}^{\circ}\)C. The mass loss from 364 to 417 \({}^{\circ}\)C is 77.66%, which basically completes the thermal decomposition of poly (MMA-BA-ST) [22; 23]. The results of thermogravimetric experiments show that poly (MMA-BA-ST) can withstand high temperatures above 300 \({}^{\circ}\)C because the polymer contains benzene rings with strong temperature resistance, which makes poly (MMA-BA-ST) have good temperature resistance.
# An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers
## 1 Introduction
In retrospect, the evolution of botnet detection is clear from earlier and more straightforward uses of classification techniques such as clustering and Naive Bayes, the research focus has been expanded from the last step of classification to the important preceding step of constructing suitable metrics, which measures and distinguishes bot-based and human-based activities [6; 7]. This paper aims to examine the features and strategies to detect the botnets. The main contributions of this paper are as follows:  
* This article presents a multi-layer approach to classify network traffic (P2P botnet traffic and non-P2P traffic) and identify botnets by applying machine learning classifier on network features such as port filtering, DNS query, and flow counting. * Our work presents a P2P botnet detection framework based on a decision tree algorithm for feature selection to extract the most relevant features and ignore the irrelevant features. * At the first layer, we filter non-P2P packets to reduce the amount of network traffic by applying port filtering using well-known ports, DNS query, and flow counting. The second layer further classified the captured network traffic into two classes such as non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. * The proposed technique of this study covers the limitations of single stage botnet detection, e.g., class imbalance. * The accuracy of our model is 98.7% and the threshold of false alarm rate was reduced to 3. Furthermore, our experiments also demonstrate that the accuracy of proposed framework was improved up to 99% however at the expense of false reporting of benign files as botnets as well as false reporting of botnet as benign. We also consider the factor if benign files also send out search requests consistently so benign files may be reported as botnets. Additionally, it was observed the accuracy might be improved by increasing the epochs of deep learning algorithms at the expense of more execution cost. * To validate the performance of our proposed technique, we performed the experiments on diverse datasets and the results are compared with other machine learning algorithms implemented for botnet detection.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 4 Fault Diagnostic Algorithms for the Li-Ion Battery System
### Current Progress and Future Challenges of Li-Ion Battery Fault Diagnosis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Types** & **Definitions** & **Algorithms** & **References** \\ \hline \multirow{6}{*}{State estimation} & The system state is estimated from a model using filters or observers. A fault is detected from the residuals between estimated and measured values. & Particle filter & [46] \\ \cline{2-3}  & The system state is estimated from a model & Kalman filter & [2,36,47,48,52,54,75,76,77] \\ \cline{2-3}  & Luenberger observer & [49,55] \\ \cline{2-3}  & Lyapunov-analysis-based nonlinear observer & [56] \\ \cline{2-3}  & Partial-differential- equation-based observer & [57] \\ \cline{2-3}  & Proportional integral observer & [72] \\ \cline{2-3}  & Sliding mode observer & [73] \\ \hline \multirow{3}{*}{Parameter estimation} & The model parameter is estimated from the measurements using filter algorithms. A fault is detected from the change in the estimated model parameter. & Recursive least squares & [35,36,50,51,52,53,54] \\ \cline{2-3}  & A fault is detected through generating & \\ \cline{2-3}  & residuals from the input and output & \\ \cline{2-3}  & relationship between the model and the & Nonlinear parity equations & [70,71] \\ \cline{2-3}  & measurements. & The structural overdetermined part of the & \\ \cline{2-3}  & Structural analysis & system model is analyzed to detect and isolate a fault. & Structural analysis & [2,34,74] \\ \hline \multirow{3}{*}{Signal processing} & Measured signals are transformed into fault parameters, such as entropy or correlation coefficient. A fault is detected from abnormalities in these fault parameters. & Wavelet transform & [58] \\ \cline{2-3}  & These algorithms use the knowledge obtained from observations or data coming from the system to establish rules or train data to detect a fault. & Correlation coefficient & [59,60,83] \\ \cline{2-3}  & & Synchronization entropy & [39,61,62,78,79,80] \\ \cline{2-3}  & & Sensor topology & [81,82,83] \\ \hline \multirow{3}{*}{Knowledge-based} & These algorithms use the knowledge obtained from observations or data coming from the system to establish rules or train data to detect a fault. & Rule-based & [64,65] \\ \cline{2-3}  & & Fuzzy logic & [66] \\ \cline{2-3}  & Random forests classifier & [67] \\ \cline{2-3}  & Neural network & [68] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Lithium-ion (Li-ion) fault diagnostic algorithms.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 9 Numerical Results
### Energy Consumption  
For several sizes of the WSN and for several \(r_{\max}\), Figure 12 shows the total energy consumption, ring by ring. The six topologies, with \(r_{\max}\) is 0, 1, 2, 3, 4 and 5, have been analyzed for an equivalent circular radius \(R_{WSN}\), equals to 100, 200, 300, 400, 500 and 600 meters. Following the energy consumption model, we observe that for \(R_{WSN}=100\) m., a low coverage area, it is more convenient to group all the sensors in a single cluster, i.e., \(r_{\max}=0\). On the other hand, when the coverage area increases, the energy consumption also increases, mainly due to the distance between the members and the only CH, a role played in this case by the sink. Then, for large coverage areas it is more appropriate to use the two tier cluster hierarchical with a high number of \(r_{\max}\). It is worth mentioning that for a given ring, \(r_{p}\), the energy consumption increases when the coverage area increases, as expected. Obviously, this is because the coverage area of a cluster increases with \(R_{WSN}\). Nevertheless, the difference is less significant for high values of \(r_{p}\).  
Figure 10: End to end delay (in mini-slots), from sensor to the sink, for \(r_{\max}=4\), \(p_{act}=0.001\) and \((N_{msc},N_{intra};N_{msc},N_{inter})=(2,3;1,7)\).
# Learning and Comfort in Human-Robot Interaction: A Review
## 5 How to Improve Human Comfort in Human-Robot Interaction?
### Human Comfort Measurement
#### 5.1.2 Physiological Approach  
For the physiological approaches, many studies have shown that various physiological signals can be used to recognize the internal states of human subjects, such as emotions and feelings. In human-robot interaction contexts, it has been found that some affective states of humans, such as excitement, fatigue, engagement, and distractions (and likely comfort as well) [82; 83], are correlated to their physiological signals including those measured using electrodermal activity (EDA), photoplethysmography (PPG), skin temperature (ST), eye tracking (ET), and electroencephalography (EEG). EDA, also known as galvanic skin response, is the measurement of conductance or resistance across the surface of one's skin, which continuously varies as one responds to various stimuli [84]. PPG is a measurement of the changes in light absorption of the skin, which is measured using a pulse oximeter; it is a periodic signal that measures cardio performance and can be used to evaluate a participant's level of arousal [85]. ST is measured with a temperature sensor and has been used to measure stress [86]. ET measures eye motion relative to the participant's head in addition to pupil diameter via eye-tracking glasses. Studies have shown that pupil diameter is strongly related with the emotional arousal and autonomic activation [87]. EEG is a measure of the brain's electrical activity and can be used to evaluate stress, excitement, focus, interest, relaxation, and engagement of the human in human-robot interaction [88].
## 2 Materials and Methods
### Evaluation of Microgaps Formation  
Following thermocycling, the specimens were sectioned using mesiodistal dimensions in the middle of the occlusal surfaces with a low-speed diamond saw under water irrigation, and the sections were prepared for SEM observations. In particular, each section was mounted on aluminum stubs with the cut surface facing up, sputter-coated with carbon to a thickness of approximately 200 A in a vacuum evaporator (at low vacuum), and examined under a scanning electron microscope (JEOL Ltd., JSM-840, Tokyo, Japan) at an accelerated voltage of 20 kV. SEM photomicrographs were acquired at \(\times\)100 magnification. The maximum width of the formed microgaps at the interface between the restorative material and the dentin after thermocycling was recorded in um using the software of the microscope in the backscattered mode (Figure 1). Each photomicrograph was evaluated using energy-dispersive X-ray spectroscopy (EDS) for the elemental analysis to define the structures and the mode of bond failure (Figure 2). Moreover, additional specimens were prepared for SEM observations in \(\times\)50 and \(\times\)500 magnifications to compare the effectiveness of the tested surface modifications (Figure 3).  
Figure 1: Representative SEM image in backscattered mode for evaluation of the maximum width of the formed microgap in \(\upmu\)m using the software of the microscope.  
Figure 2: Representative SEM images of the material/dentin interface, where the structures were defined by using energy-dispersive X-ray spectroscopy: (**a**) elemental analysis of the right side of the SEM image indicating composite material; (**b**) elemental analysis of the left side of the same SEM image indicating dentin.