esa-sceva/satcom-chunk-collection huggingface dataset:
# Dispersion of OH Radicals in Applications Related to Fear-Free Dentistry Using Cold Plasma
## 4 Conclusions  
In the present work, a model was built for CAP discharges on teeth for dentistry applications for the first time. The model considered the CAP carrier gas and the ambient air through a two-phase flow model, and took into account the transport of OH radicals and H\({}_{2}\)O\({}_{2}\) species involving diffusion and convection. The developed model determined the distributions of OH radicals and H\({}_{2}\)O\({}_{2}\) species over six different chosen teeth. Different nozzle to tooth distances of 2 and 4 mm were examined and the obtained results were compared. The normalized concentration of OH radicals and H\({}_{2}\)O\({}_{2}\) species over a treated tooth decreased with the increasing nozzle to tooth distance. The distribution of OH radicals out of the nozzle was found to be asymmetrical. The dispersion of OH radicals out of the nozzle varied according to the type of treated tooth. The dispersion of the reactive species became more significant for shorter distances between the nozzle and the tooth, which led to the coverage of a larger surface area of the tooth during plasma treatment. As such, the treatment could be more efficient. The developed model and the obtained results could be useful for working towards a fear-free dentistry using CAPs.  
Conceptualization, M.S.B.; methodology, M.S.B.; formal analysis, M.S.B., K.N.Y.; investigation, M.S.B.; resources, M.S.B., W.H., K.N.Y.; writing--original draft preparation, M.S.B.; writing--review and editing, W.H., K.N.Y.; visualization, M.S.B.; supervision, K.N.Y.; project administration, K.N.Y.; funding acquisition, W.H., K.N.Y.  
This work was funded by National Natural Science Foundation of China (Nos. U1632145, 81573093 and 81227902), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, China Postdoctoral Science Foundation (No. 2016M592584).  
Figure 11: Variations of normalized concentrations of OH radicals at 1 mm below the nozzle at 1 mm (**a**) above, (**b**) below, (**c**) left, and (**d**) right of the center of the nozzle (from bird’s eye view), for nozzle to tooth distance of 4 mm. (Inset: red dot indicates the location where the normalized concentration of OH was measured).  
**Acknowledgments:** We acknowledge the support of the Neutron computer cluster from the Department of Physics, City University of Hong Kong, for the computational work involved in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 4 Proposed Adaptive DFSD Detection Scheme  
The conventional DFSD detection scheme is known as a suboptimal detection scheme because it has almost the same error performance and low complexity compared to the ML detection scheme. However the DFSD detection scheme has still high complexity in the hardware implementation. This paper proposes a new adaptive DFSD detection scheme to reduce the complexity.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### MADC Types
#### 4.3.2 Attack  
To improve defense systems, researchers also need to develop new attack methods. We categorize the attacks based on the targets' location. Attacks whose target consists of sensors and actuators are considered perception layer attacks, while attacks that focus on specific scenarios are application layer attacks. Attacks that focus on various protocols belong are network layer attacks.  
In the perception layer, attackers try to steal information or send malicious commands through various covert channels. An adversary could remotely receive data by changing the lightness of smart lights [32], monitoring covert acoustic channels [77; 78], and utilizing hidden voice methods to inject malicious commands [33; 34]. Researchers have also tried to mitigate information leakage problems by optimizing the design variables and machine  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Security Feature** & **Implementation** \\ \hline Access control & Unauthorized access to the device could be forbidden by a user-selected policy [136] \\  & or allocating security resources carefully [134]. \\ \hline  & Researchers provided several attestation schemes for different purposes, such as autonomous and dynamic networks of IoT devices [23], device swarms [122], \\ Authentication & the integrity of physical processes [125], and the defense against physical attacks [126]. \\  & Moreover, memory protection schemes [128], proof of aliveness [123], and offline software protection [127] are also leveraged for authenticating communication between devices. \\ \hline Secure communication & We encrypt communication from/to devices with lightweight crypto [102; 103; 104; 104]. \\  & Insecure encryption algorithms should be avoided [100]. \\ \hline Intrusion \& abnormal & System features could be leveraged to detect intrusion, such as \\ detection & cycle per instruction (CPI) [95]. \\ \hline Security management & Check a device’s security policies and replace the weak configurations [133] \\  & to mitigate against known attacks. \\ \hline Hidden fingerprinting & As an attacker can launch attacks based on the type of devices, \\  & hiding the fingerprinting information of devices can also protect them [108; 109]. \\ \hline \hline \end{tabular}
\end{table}
Table 3: The security features of embedded devices and the corresponding implementation.  
process [97]. In the network layer, attacks on communication protocols, such as HDMI [36] and ZLL (ZigBee light link) [37], are also proposed. For the application layer, researchers have presented attack models for certain industrial control systems, such as robotic vehicle (RV) systems [25] and smart grids [12; 14; 15]. Moreover, audit frameworks are designed to implement systematic safety tests in embedded devices [59; 145].
# Geo-Electrical Detection of Impermeable Membranes in the Subsurface
## 1 Introduction  
Infrastructure works in low-lying countries with high water tables occasionally require measures to prevent groundwater from flooding the structure. An example is the construction of a road below the groundwater table. In the case at hand, the road has been constructed in a large trench which is lined with an impermeable membrane in the 1980's. Due to the increased traffic load, a lane needs to be added to the road inside the trench. The road is of vital importance for the region and cannot be closed during the reconstruction and excavation works. For safety and to avoid flooding during excavation and construction, it is important to know the exact position of the membrane. The goal was to locate the membrane with an accuracy of 10 to 20 cm at a depth of 6 to 10 m. The approximate location of the membrane is available from archive design drawings. The actual location needs to be established through measurements. Geophysical methods that operate from the surface do not have sufficient accuracy. Therefore, the measurement tool needs to be inserted into the soil enabling measurements to be taken from a closer distance to the membrane. The difficulty is that the PVC membrane has a thickness of less than 1 mm. In addition, the tool needs to be kept at a \"safe\" distance of approximately 1 m from thetheoretical (design) position of the membrane. Puncturing the membrane is to be avoided at all costs, because flooding of the road would result in serious economic damage.  
A relevant physical contrast that is present between the membrane and the surrounding soil is the electrical resistivity. Electrical Resistivity Tomography (ERT) is a geophysical method commonly used in many near-surface applications (e.g., [1]) and has a large range of resistivity values that can be imaged, from very conductive to very resistive. In our case, the membrane acts as an insulator and has an electrical resistivity in the order of \(10^{10}\) Ohm\(\cdot\)m. The partially saturated sand on top of the membrane has electrical resistivity values varying from 15 to 800 Ohm\(\cdot\)m. Because of this large difference, the electrical resistivity tomography method was selected and adjusted for the present application.  
In this research, we explore the detection and looking-ahead capabilities of a custom-made tool that penetrates the soil. Because of the membrane's function as a liner against groundwater leakage, it should stay intact during reconstruction works. It is therefore important for our method to be able to \"look ahead\" and notice that the membrane is getting close. Measuring ERT from the surface results in a depth resolution that is too poor for the application. Measuring from boreholes is a definite no-go in this case, because the boreholes might penetrate the membrane. We therefore developed a tool that can penetrate the ground and look ahead to check whether it is safe to penetrate the tool some further distance and measure again, until membrane is located with the desired resolution.
## 4 Analysis of Existing Solutions
### Analysis of Effectiveness and Challenges
#### 4.2.1 Advantages and Disadvantages of Gamification  
Gamification in education offers numerous advantages, but it also comes with certain disadvantages. Key advantages include the following:  
1. Increased Engagement--Gamification encourages students to actively participate in the learning process through competition, rewards, and achievements. Research shows that students become more motivated when engaged in games [44].
2. Improvement of Memory and Knowledge Retention--The use of gamified elements can enhance students' ability to retain and apply learned material thanks to repetition and interactive learning methods [45].
3. Development of Social Skills--Through team activities and competitive games, students develop social and collaborative skills [46]. However, gamification also has its drawbacks:  
1. Overemphasis on Rewards--Students may become too focused on winning rewards and less on actual learning, leading to a superficial understanding of the material [47].
2. Adaptation Challenges--Gamification may not be suitable for all students or all subjects. Adapting games to be relevant and effective for different student groups can be complex [48].
3. Increased Complexity for Teachers--Implementing gamification may require additional time and resources from teachers, including training and curriculum adjustments [49].
## 6 Conclusions  
A general ESS-based virtual synchronous machine with energy recovery control is introduced in this paper, which provides a general scheme of incorporating VSM into the conventional power system frequency regulation. Based on comprehensive theoretical analysis and comparative studies, the following conclusions are drawn.  
(1) It is highlighted that the energy storage used for VSM application should meet the requirement of response speed and energy capacity in inertial and frequency support. A general control scheme, covering single ESS, hybrid ESS including FAESS and SAESS, as well as other ESS types and installation, is proposed for VSM implementation.  
(2) The energy dissipation of VSM-controlled ESS is found to be reciprocal to the integral gain of frequency regulation, while proportional to virtual damping, virtual governor gain or their sum, depending on the ESS configuration. Such an energy amount is found to be at the second time scale, for example 6.8 p.u.-s in this article, which provides a guideline for storage sizing or distributed storage aggregation in VSM realization.  
(3) With the proposed energy recovery control, ESS can maintain the original energy capacity for continuous VSM operation. A bandwidth separation rule for energy recovery control is proposed. Namely, the energy recovery bandwidth should fall between the bandwidth of secondary and tertial frequency control. Without energy recovery, the steady-state SoC of ESS-based VSM is larger than 20% in the test, which may soon incur degradation. The proposed energy recovery can maintain the SoC near the desired value 50%, without undermining frequency control performance.  
It is recommended that further studies include VSM control for renewable energy resources considering the operating limitations, fault-ride-through strategy design, transient stability analysis and improvement for VSM in contingencies.  
Conceptualization, C.S.; methodology, C.S.; software, H.L.; validation, H.L.; formal analysis, X.Z.; data curation, H.L., C.S. and X.Z.; writing--original draft preparation, H.L., C.S. and J.W.; writing--review and editing, all authors; visualization, X.Z.; project administration, N.W.; funding acquisition, N.W. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Beijing Institute of Precision Mechatronics and Controls. The project is supported by the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.  
We confirm that data will be made available on request.  
We appreciate the guidance by [PERSON], the former team lead of Distribution and Generation Team at OPAL-RT Technologies Inc., Montreal, Canada, and the help of [PERSON] from Harbin Institute of Technology in building the testbench.  
The authors claim no conflict of interest.
## 4 Analysis  
Before starting the simulation experiments, an investigation was conducted to determine the minimum satellite orbit altitudes for different station distances within the  
Figure 3: Flowchart for the optimization of the simulated annealing algorithm.  
common-view model. Two sets of simulations were then used to confirm the efficiency and applicability of the improved simulated annealing approach in identifying ideal satellite orbits from the common-view. The first test group replicated several satellite orbits over common-view times between two stations to establish the algorithm's ability to optimize satellite trajectories in the context of common-view. The algorithm's viability was then evaluated by optimizing orbits for 45 station pairs among 10 SLR stations in various regions of the common-view model. The goal was to determine the best satellite orbits for several stations within the common-view model.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Construction of Improved Variogram for Sparse Data  
The magnetic field information is continuous, unlike the discontinuity of geological information; although the resolution is different, the variogram graph should be continuous, especially near the origin. At the same time, the magnitude of \(C_{0}\) is \(10^{-10}\) by test, so the nugget \(C_{0}\) in the traditional spherical variogram is adjusted to 0.  
Secondly, it can be seen from the graph of the spherical variogram that the fitting curve of the spherical variogram is too steep and not gentle in the early stage, which is caused by the fact that the sample data carry less information at small scale, and the information bearing at large scale increases with the increase in range. For magnetic information with sparse data among the lines and dense data on the lines, the spherical variogram near the origin cannot fit the trend of lag distance and variation well because of the less-dense data samples. Therefore, this paper introduces the second power of lag distance \(l\) to make it smooth near the origin, solving the problem that the lag distance near the origin of the variogram graph is over-fitted with the range due to the resolution difference caused by data sparsity.  
At the same time, the introduction of the second power of the lag \(l\) influences the original spherical variogram. The geometric interpretation of the spherical variogram is derived from the formula for calculating the volume of the overlapping part of two spheres with radius \(a\) and spherical center distance \(2h\)[31]. As mentioned above, the introduction of the second power of the lag distance \(l\) must destroy the equilibrium of the volume formula. So, this paper introduces a hyperparameter \(\lambda\) to control the balance between the second and third powers of the lag distance \(l\). At the same time, the introduction of \(\lambda\) value also provides an auxiliary parameter for regional geometric anisotropy correction.  
In summary, the improved variogram model constructed in this paper is shown as follows:  
\[\gamma(l)=\begin{cases}C\bigg{\{}(1+\lambda)\Big{(}\frac{l}{a}\Big{)}^{2}- \lambda\Big{(}\frac{l}{a}\Big{)}^{3}\bigg{\}},&0\leq l\leq a\\ C,&l>a\end{cases}. \tag{10}\]  
Figure 2: The diagram of the two-point correlation in the magnetic map based on magnetic dipole.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 2 Related Works
### Feature Pyramid Network  
The Feature Pyramid Network (FPN) is an innovative network architecture introduced by [PERSON] et al. [22] in 2017 with the aim of enhancing the detection performance of current object detection models, focusing on the detection of small objects. An increase in the number of network layers has been shown to enhance detection accuracy. Nevertheless, the performance in detecting small objects remains inadequate. The primary factor is the growing number of pooling operations due to the deepening of network layers, which results in the loss of feature information from small objects. This ultimately affects the network model's ability to effectively detect small objects. Figure 3 is the architecture of FPN.  
The primary concept behind the FPN involves the construction of a feature pyramid aimed at extracting multi-scale feature information from images. This approach integrates features from various scales to enhance object detection capabilities. By integrating low-level features with high-level features, the resultant high-level feature maps obtained post downsampling preserve certain low-level feature details, consequently enhancing the network model's capacity to identify small objects. This methodology has been implemented in various traditional object detection networks, offering valuable insights for multi-scale object detection task.  
Figure 2: Dilated convolutional networks. (**a**) Standard convolution. (**b**) Dilated convolution.
## 3 Results
### Tunneling-Induced Ground Deformation Detection and Monitoring over Site C--Chungcheong  
Figure 14 shows the LOS mean velocity for a period of 1009 days estimated by using the PS-InSAR technique from Sentinel-1B data. In the map, the color scale is between \(-50\) and \(+30\) mm/yr. Positive (in blue color) and negative (in red color) values denote movement towards and away from the radar LOS, respectively. Spatial distribution of the PS points was relatively dispersed, showing the complete absence of a rebound deformation trough. Due to the presence of buildings in the project site, the map shows a dense distribution of PS points except in the NW and SW corner regions, where the regions are mostly cultivation lands. A remarkable subsidence trough was observed in the industrial zone where the tunnel construction has commenced and was almost nonexistent in the SE corner region. The subsidence trough consists of yellow, orange, and red color variants depending on the magnitude of deformation.  
Figure 14: Line-of-sight (LOS) mean velocity map.  
Figure 13: Map for (**a**) interferometric phase and (**b**) ground deformation.  
Figure 15 shows the time histories of InSAR-derived displacements of the selected PS points located along the two road segments (A and B) passing through the vertical shaft. All displacements are estimated in the LOS direction. The tunnel construction phases are highlighted with grey color and marked with letters A-E (Table A2). It is observed that ground deformations near the vertical shaft accelerated during Phase C. Prior to this, no appreciable displacements were recorded along the two roads. Moreover, subsidence increased as the PS distance from the shaft decreased. The settlement started to slow down gradually at the end of Phase D after the shield tunneling was temporarily stopped. No extreme ground deformation was then observed at the end of Phase D that continued towards the end of Phase E. PS-A01, B04, and B05 show the cumulative displacements of more than 5 cm towards the end of the monitoring period.
## 4 Case Studies
### A Numerical Case Study on a Synthetic Dataset of Cylinders  
Due to the sparsity and imbalance of the experimental dataset in Section 4.1, the active role of alternating the reference MD space may not be manifested sufficiently. Another case study based on a rich synthetic dataset of dogbone cylinders, composed of 3562 samples, is provided by Vibrant Corporation (link: www.vibrantndt.com) and further discussed in this section. They are modeled as nickel, single-crystal, cylindrical (tapered to gauge section) dogbones (see Figure 10) using the finite element method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Accuracy** & **Testing Accuracy** & **Number of Features** \\  & **(\%)** & **(\%)** & **(\(\_\))** \\ \hline \(\mathbf{IMCS}_{\mathbf{RF}}^{\mathbf{BPSO}}\) & \(99.93\pm 0.22\) & \(97.31\pm 0.81\) & SMD-R1: \(5.50\pm 0.73\) \\  & & & SMD-R2: \(5.33\pm 2.90\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance of the random forest (RF) technique over 20 independent runs.  
Figure 10: A generic model of the cylinder.  
Figure 9: (a) \(\mathbf{IMCS}_{\mathbf{RF}}^{\mathbf{BPSO}}\)-based classification on the training dataset; (b) \(\mathbf{IMCS}_{\mathbf{RF}}^{\mathbf{BPSO}}\)-based classification on the testing dataset.  
Two key determinants of the physical conditions of these cylinders are creep (inelastic deformation) and \(\theta\) (crystal orientation, relative to the long axis of the dogbone). The variations of creep and \(\theta\) are simulated in the ranges 0-9.9% and 0-37.9, respectively. Moreover, the feature database is generated by extracting the resonance frequencies in the bandwidth 3-190 kHz.  
Damaged parts are defined as creep \(>\)0.5%; \(\theta>10\), resulting in a balanced dataset of 1857 healthy samples and 1750 defective samples.  
First, the whole dataset is split into a training pool and a testing set with the proportion 3:7, where the training pool is further composed of training and validation sets with the proportion 6:4. In the **IMCS\({}_{\text{PSO}}^{\text{BPSO}}\)** procedure, the maximum iteration number T for the BPSO algorithm is set as 200 and the number of particles is set to N = 20. A 5-folder validation is used to train the classifier.
## 1 Introduction
In particular, we describe the complex waveform synthesis using a harmonic comb containing three and four harmonics. The method described allows the generation of attosecondpulses with energies as high as tens of microjoules and with pulse durations as short as tens to hundreds of attoseconds. The contents of the article are as follows: In Section 2, the experimental setup used to generate the harmonic comb is discussed. Section 3 describes the construction of the correlation plots and their evolution in a phase scan to extract the phases required for attosecond pulse reconstruction. The reconstruction of the attosecond pulses and complex wave shaping is demonstrated in Section 4, providing an analytic form of the intensity profile for an attosecond waveform using an arbitrary number of harmonics in the harmonic comb (Section 5).