esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
## 1 Introduction
In recent years, several articles about current-mode wideband LNAs in standard CMOS technology have been reported [28; 30; 31; 32; 33; 34; 35; 36]. According to the input matching and noise characteristics of the circuit, LNA has two typical architectures, common source (CS) and common gate (CG) topology. The wideband input matching is provided by the CG topology, but it has a high noise figure (NF) [28]. In the CG LNA topology, most input and/or output matching is achieved by employing inductors and capacitors to achieve broadband matching, which greatly increases the chip area and cost. The advantage of CS topology can get higher gain, but compromises need to be made between gain and broadband input matching. In order to reduce the contradiction between the gain of CS topology and the input matching bandwidth, a resistive feedback technique is usually used to obtain considerable gain and wideband input matching. With these considerations in mind, we chose to apply our previously developed compact CNTFET model [37] to the design of a cascode LNA. The model was derived from electromagnetic and quantistic simulations that are supposed to mimic the actual technology that will be used for circuit manufacturing, and although it has not yet been validated experimentally, it should be a good starting point to estimate the performance this technology could achieve. The cascode architecture was chosen because it combines the high gain and low NF of a CS topology with the higher operating frequencies of the CG topology and is thus of interest in RF applications. This paper is organized as follows. In Section 2, the extrinsic CNTFET model comprising noise sources is presented. In Section 3, the simplified DC CNTFET model is described. In Section 4, the proposed device model was applied to the design flow of an LNA. Section 5 reports some simulation results and comparisons. Finally, Section 6 concludes this work.
## 5 Construction of NME Stabilised Granular Pavement Layers--Practical Aspects  
### Weather Conditions  
Weather conditions and limitations to construction as per normal road construction projects are applicable to the construction of road pavement layers using NME stabilising agents. The NME stabilising agents must be added to the construction water prior to the addition thereof to the granular materials that are being stabilised. The in-situ moisture of the materials will determine the total construction water required (together with the stabilising agent) in order to achieve optimum construction compaction conditions. Experience has shown that the best conditions are normally achieved at moisture contents just below Optimum Moisture Contents (OMC). Hence, it is recommended that the total of the added fluid content of the NME stabilising agent be taken into account in the calculation of the required construction water to be added.  
For example, if the OMC of the granular material to be stabilised is 8 percent and the granular material to be stabilised contains 5 percent moisture, the construction water with the NME stabilising agent to be added is (8-5) 3 percent. In the case where the required designed percentage of the material-compatible NME stabilising agent is 1 percent, it had to be mixed with an additional 2 (3-1) percent of construction water within the water-bowser prior to the stabilisation of the granular materials. Some modern recycling equipment allows for more than one feed into the recycler. Under no circumstances should the NME stabilising agent and construction water be fed into such equipment separately. Water in the carrier of the NME stabilising agent, the separate addition of water, and the stabilising agent will not allow for the even distribution of the NME stabilising agent within the granular material and hence the unsuccessful stabilisation of the pavement layer.  
Depending on local conditions (e.g., very hot days), allowance should be made for the evaporation of water during the stabilising process by increasing the percentage of construction water that will evaporate during the process of mixing and compaction as a function of the:  
* Type of construction equipment used (some construction equipment will take considerably longer to thoroughly mix the stabilising agent with the granular material, resulting in higher loss of moisture);
* Prevailing temperature and humidity during the stabilisation process; and
* Delays on site as a result of logistical supply problems.
# Neutralization Dynamics of Slow Highly Charged Ions in 2D Materials
## 2 Experimental Results on Highly Charged Ion Transmission  
The fabrication of ultrathin films, especially the production of freestanding single layer graphene, opens nowadays the possibility to study ions in the pre-equilibrium regime, and to get insights in the neutralization and deexcitation processes within the first monolayers of the solid. At projectile velocities \(v<v_{0}\), the equilibrium charge \(Q_{\rm eq}\) is typically very small (\(\leq 1\)) and can be estimated by [PERSON]'s stripping criterion [40] to be \(Q_{\rm eq}\approx\frac{v_{0}}{v_{0}}Z^{1/3}\), with the nuclear charge \(Z\) of the projectile and the  
Figure 3: (Color online) (**a**) Atomic force microscopy image of a KCl(001) surface irradiated with 70 keV Xe\({}^{35+}\) ions. One- to two-monolayer-deep pits resulting from single ion impacts can be observed [33; 39]. (**b**) AFM image of a LiF(001) surface irradiated with 99 keV Xe\({}^{20+}\) ions. Crater-like structures are observable [36]. (**c**) AFM image of a CaF\({}_{2}\)(111) surface irradiated with 16.5 keV Xe\({}^{33+}\) ions. Hillock structures can be observed [35].  
Figure 2: (Color online) Electrostatic potential of a Xe\({}^{30+}\) ion along the ion-surface connection line normal to the surface. Binding energies for occupied levels (straight lines) are given from Hartree–Fock calculations [32] and empty states (broken lines) from Rydberg scaling of levels for heavy atoms. Level shifts due to image charge are here neglected for clarity.  
Bohr velocity \(v_{0}=1\) a.u. \(=2.19\cdot 10^{6}\)m/s. The special combination of slow \((v<v_{0})\) highly charged projectile ions with charge states far from equilibrium \((Q_{\text{in}}\gg 1)\) and ultrathin membranes is therefore ideally suited to study pre-equilibrium phenomena.  
Although experimental investigations with truly two-dimensional (2D) materials are only possible for a few years, first attempts to study HCI interaction processes with thin materials go back more than twenty years and are shortly reviewed in the next section.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 3 Method
### Hypergraph Position Attention Convolution Networks
#### 3.3.2 ResNet-like Module  
In the field of deep learning, the residual network (ResNet) [38] is known for its unique residual module design, which mainly consists of multiple convolutional layers and shortcuts. It effectively solves the problems of gradient vanishing and network degradation caused by over-deep networks while reducing the number of parameters. DWConv [39] is a decomposition of the standard convolution operation into deep convolution and pointwise convolution. In the deep convolution stage, each input channel undergoes the convolution operation independently, thus effectively extracting the spatial features within each channel. Then, pointwise convolution fuses the features from different channels to generate the final output feature map. Inspired by the idea of the ResNet network, we introduced DWConv to construct a ResNet-like module, as shown in the red part of Figure 2a. We input the hyperedge information into the hyperedge position-attentive convolution module and multiply it with the processed node information to obtain the output feature information. We skillfully integrate this module into the hypergraph positional attention convolutional network, which not only reduces the computational complexity of the model but also significantly improves the operation efficiency of the network. The formula for the output characteristics is as follows:  
\[V_{out}=\alpha^{\prime}\odot(Conv1D(V_{F})W^{\prime} \tag{12}\]  
where \(V_{F}\) denotes a set of input vertex features, each feature \(V_{F,i}\in R^{F}\) is associated with a corresponding hypergraph vertex, and the dimensional information of each vertex includes the most basic 3D coordinate information and also other information \(XYZ\), such as colors and normal vectors. \(W^{\prime}\) denotes the weight library, defined as \(W^{\prime}=\{W^{\prime}_{1},W^{\prime}_{2},\cdots,W^{\prime}_{D}\}\), where each \(W^{\prime}_{i}\) is a weight matrix, and \(D\) controls the number of weight matrices in the weight library. \(\odot\) denotes the _Hadamard_ product, which is performed as an element-by-element product of two vectors. By introducing this innovative module, hypergraph neural networks with high efficiency in point cloud segmentation are obtained.
# Online Adaptive Parameter Estimation for Quadrotors
## 3 Simulation  
In this section, the proposed method is applied to a quadrotor model for simulations. In order to complete the simulation, some parameters should be chosen for the quadrotor model (1), which are displayed in Table 1.  
The purpose of this paper is to estimate the parameters of quadrotors to enhance the stability and robustness of quadrotors. According to Equation (2), one knows that the parameters to be estimated are: \(a_{1},a_{2},a_{3},a_{4},a_{5},a_{6}\), and \(a_{7}\). From Table 1, we have \(a_{1}=4\), \(a_{2}=7.57\), \(a_{3}=0.85\), \(a_{4}=7.57\), \(a_{5}=-0.85\), \(a_{6}=4.10\), and \(a_{7}=0\). The other parameters used in the simulations can be set as \(k=0.001\), \(\ell=1\), and \(
## 2 Preliminaries
### Graph Theory  
A graphs a pair \(G=(V,E)\), where \(V\) is called the non-empty vertex-set (or node-set) and \(E\) is called the edge-set, which is given by unordered pairs of a couple of nodes. It is possible to associate a weight with each edge and, in such cases, we will say that \(G\) is a weighted graph.  
An adjacent vertex of a vertex \(v\) in a graph \(G\) is a vertex that is connected to \(v\) by an edge. The neighborhood of \(v\) is the subgraph of \(G\) induced by all vertices adjacent to \(v\). The degree of a vertex is equal to the number of its adjacent vertices.  
The adjacency or weight matrix of a weighted graph \(G=(V,E)\) is given by \(A=(a_{ij})\), where \(a_{ij}\) is the weight of the edge connecting vertex \(v_{i}\) with \(v_{j}\). Obviously, in the case that both vertices are not adjacent, that weight would be zero. In the case of a graph, the matrix \(A\) will be symmetric and all the elements in the main diagonal will be zero.  
A sequence of consecutive vertices and edges that are mutually adjacent in a graph is known as a walk. A graph is connected if there is a walk between any pair of vertices. An arc is a walk where all the edges and vertices are different. The length of a walk is defined by the number of its edges. A cycle \(C\) in a graph \(G\) is a non-empty walk in which the only repeated vertices are the first and last ones. A cycle with length \(k\) will be referred to as a \(k\)-cycle.  
A Eulerian trail (or Eulerian path) is a trail in a graph that visits every edge exactly once. Similarly, a Eulerian circuit or Eulerian cycle is a Eulerian trail that starts and ends on the same vertex. They were first discussed by [PERSON] while solving the famous Seven Bridges of Konigsberg problem in 1736 [12].  
A Hamiltonian path in a graph is a trail that visits each vertex exactly once. A Hamiltonian cycle (or Hamiltonian circuit) is a Hamiltonian path that starts and ends on the same vertex. The computational problems of determining whether such paths and cycles exist in graphs are NP-complete. They were named after [PERSON], who invented the icosian game, which involves finding a Hamiltonian cycle in the edge graph of the dodecahedron [13].  
A vertex coloring is an assignment of labels or colors to each vertex of a graph such that there are no adjacent vertices with the same color. Vertex coloring seeks to minimize the number of colors for a given graph. Such a coloring is known as a minimum vertex coloring, and the minimum number of colors with which the vertices of a graph G may be colored is called the chromatic number, denoted by \(\chi(G)\).  
A cycle graph or circular graph of \(n\) vertices (\(n\geq 3\)), \(C_{n}\), is a regular graph of degree two with \(n\) vertices containing a single cycle through all of them. This graph also has \(n\) edges. A wheel graph of \(n\) vertices (\(n\geq 4\)), \(W_{n}\), is a graph that contains a cycle of length \(n-1\) and every vertex in that cycle is connected to one other vertex. They can be defined as the graph join \(K_{1}+C_{n-1}\), for \(n\geq 4\) and where \(K_{1}\) is just one isolated vertex.
## 3 Results and Discussion
### Influencing Factors of Vibration Isolation Effect for the Composite Subgrade Structure
#### 3.2.2 The Effect of Vibration Intensity on the Vibration Isolation  
In actual engineering, traffic loads with different weights always produce vibrations of different intensities. In this part, the vibration isolation performance of the composite subgrade structure subjected to different vibration intensities was investigated. The vibration of different intensities was achieved by a load hammer falling from different heights (5, 10, 15, 20, and 25 cm). The thickness of the XPS plates was 5 cm, which was determined in the previous section. The results are shown in Figure 8.  
Figure 8 shows that the isolation coefficient of the composite subgrade structure increased as the falling height increased. To master the characteristics of the isolation coefficient with the change in vibration intensity, the test data were fitted in linear forms, which are demonstrated in Figure 8. From Figure 8, it can be seen that the correlation coefficients of the X-direction and Y-direction were stronger than that of the Z-direction. The slope value represents the decay rates of the isolation coefficient with the increase in vibration intensity. The slope of the X-direction, Y-direction, and Z-direction were 0.014, 0.015, and 0.03, respectively. The decay rate of the isolation coefficient in the Z-direction was the lowest, which means that the vibration isolation effect in the most concerned Z-direction has the smallest change with the increase in vibration intensity, showing good vibration isolation stability. Specifically, the vibration isolation coefficient increased by 62.7% (from 0.102 to 0.166) while the load intensity increased by 400% (from 5 cm to 25 cm). The vibration isolation effect of the X-direction and Y-direction varied greatly with the increase in the vibration intensity. However, the vibration amplitude of the X-direction and Y-direction was far less than that of the Z-direction when the XPS plates were not set. In addition, the vibration isolation coefficients were all less than 1 in this study, which means that the vibration intensity of the XPS plate structure was less than that of the unset structure, so the vibration influence in the X-direction and Y-direction is very limited. When the falling height of the hammer was 25 cm, the isolation coefficient in the Z-direction was 0.166, which was still lower than 0.2. Thus, it can be concluded that the composite subgrade structure possessed great vibration isolation performance in the Z-direction, even under great intensity vibration.
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 3 System Implementation  
Figure 1 illustrates the architecture of our telexistence system, which includes two main sites: the local site and the remote site. At the local site, the user interacts with the system through a VR headset (HTC VIVE manufactured by HTC Corporation, Taoyuan City, Taiwan) [23]. Hand trackers and a haptic glove provide feedback. Meanwhile, the remote site comprises a head unit, robotic gripper, and robot arm, as shown in Figure 2. The following subsections explain each component of our system in detail, describing its implementation and features. In particular, we focus on the assistive telemanipulation system, as detailed in Section 4, where we discuss augmentation through motion blending. Figure 3 shows a detailed system architecture with each component to show how it works at a low level.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt}} \hline
**Aspect** & **Previous Study**[9] & **Current Study** \\ \hline Objective & Investigated the impact of assistive manipulation on task performance in teleoperation. & Examines the combined effects of assistive manipulation and haptic feedback on task performance and user embodiment. \\ \hline Methodology & Implemented motion blending techniques to assist users during teleoperation tasks. & Integrating multi-level haptic feedback with motion blending provides a more immersive teleoperation experience. \\ \hline Hardware Configuration & Utilized a five-finger robotic hand for manipulation tasks. & Replaces the five-finger hand with a parallel gripper to simplify control and enhance usability for novice users. \\ \hline Evaluation Metrics & Focused on task performance indicators such as completion time and accuracy. & Expands evaluation to include the Illusion of Virtual Body Ownership (IVBO) components, assessing user embodiment alongside task performance. \\ \hline Experimental Conditions & Assessed scenarios with varying levels of motion assistance. & Evaluates the interplay between different levels of assistive manipulation and haptic feedback, providing a comprehensive analysis of their combined effects. \\ \hline \end{tabular}
\end{table}
Table 1: A comparative table delineates the novel contributions of the current study compared to our previous work [9].
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 3 The Proposed Approach EKR
### Resampling Strategy  
After determining the \(k\)-value and clustering the negative class into k sub-clusters, \(T_{N}=\{C_{1},C_{2},\ldots,C_{k}\}\) where \(c_{i}\) is a sub-cluster, correspondingly, its cluster centroid is represented by \(c_{i}\), and the number of data samples of \(C_{i}\). is \(N_{i}\). Then we combine k sub-clusters with the positive class into k subsets separately, \(S_{i}=C_{i}\cup T_{P},\ i=1,2,\ldots,k\), where \(S_{i}\) expresses a subset. The imbalance ratio of each sub-cluster is \(IR_{i}\), where \(IR_{i}=N_{i}/N_{P}\). Comparing the number of data samples of each sub-cluster with that of the positive class, namely, comparing \(IR_{i}\) with 1, there are three cases:  
1. If \(IR_{i}>1\), we calculate the distance between \(c_{i}\). and all data samples in the same sub-cluster and sort from near to far, then select the nearest m neighbors to \(c_{i}\), where m = \(N_{p}\). This operate is equivalent to undersampling the sub-cluster. The reason for this is that these selected data samples have high similarity and represent the primary information of the sub-cluster. They are used to replace the whole sub-cluster and combined with the positive class to form a balanced subset.  
2. If \(IR_{i}<1\), the negative class of the sub-cluster correspond to the minority class, because it is comparatively small in quantity. So in order to balance the subset, oversampling the negative class.  
In this paper, we use ADASYN (adaptive synthetic sampling) as the oversampling method. ADASYN determines the number of synthetic data samples need to be generated according to the Equation (2):  
\[G=(N_{min}-N_{i})\times\beta \tag{2}\]  
where \(\beta\) denotes the desired balance after synthesizing data, \(\beta\in[0,1]\), we need to achieve a balanced subset, so \(\beta=1\). Then calculating n neighbors with Euclidean distance for each negative sple \(x_{i}\), \(p_{i}\) is the number of the positive class among n neighbors, distribution \(\Gamma_{i}\) is calculated as  
\[\Gamma_{i}=\frac{p_{i}/n}{Z} \tag{3}\]  
where \(Z\) is a normalization factor to ensure that \(\Gamma_{i}\) can form a distribution. In this way, if there are more positive samples around a negative sample \(x_{i}\), the higher \(\Gamma_{i}\) is. Finally, the number of samples need to be synthesized for each negative sample \(x_{i}\) is defined as  
\[g_{i}=\Gamma_{i}\times G \tag{4}\]  
ADASYN uses distribution \(\Gamma_{i}\) to automatically determine the number of samples to be synthesized for each negative sample, which is equivalent to assigning a weight for each negative sample. For the negative samples that are difficult to learn, more synthetic data should be generated.  
3. If \(IR_{i}=1\), we do not make any changes to the subset because it is balanced.  
After the above resampling strategy, we can obtain k balanced subsets \(\{BS_{1},BS_{2},\ldots,BS_{k}\}\), which can transform imbalanced classification into balanced classification and make the problem easier to solve.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Blockchain  
Blockchain is based on principles of cryptography, decentralization, and consensus, which ensure trust in transactions. It has unique safety features, so researchers have tried to integrate blockchain technology into CPS security.  
[PERSON] et al. [69] proposed an initial approach to coordinate the usage of CPS elements from UML statecharts with generating smart contracts. The authors expressed the state of a CPS with UML statechart simple states and composite states (e.g., history states). While the current target platform is Ethereum, their approach can easily be extended to other blockchain platforms. The smart contract can define an authenticated participant given an action in the whole security system.  
[PERSON] et al. [70] presented a blockchain-based access control framework for the IoT, which is tailored for distributed IoT data streams. They used the blockchain as an auditor to enable secure data storage and sharing. Secure and resilient access control management was enabled by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. The system also addresses the challenges of efficient key distribution and management by utilizing cloud storage resources as storage nodes.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 2 Introduction to GNSS Spread Spectrum Codes  
### Introduction to Spread Spectrum Codes  
There are three primary methods for generating spread spectrum codes in GNSS signals: LFSR-based codes, memory-based codes, and Weil codes. LFSR is the predominant method used for generating spread spectrum codes in most satellite navigation signals [7]. Memory-based codes involve storing pseudo random noise (PRN) codes in memory and retrieving them for use when the receiver receives the signal. This method is employed for generating Galileo E1B/C and E6B/C signals. Currently, Weil codes are exclusively utilized in GNSS systems for generating GPS L1C and BDS BIC signals [8; 9; 10].  
The majority of signals in GPS, GLONASS, Galileo, and BDS can be generated using LFSR structures [11; 12; 13; 14]. Table 1 provides the spread spectrum code generation methods for each signal, including BDS B1, B2I, B3I, B2a-d, B2a-p, B2b-I, GPS/QZSS L1C/A, L2 CM, L2 CL, L5L, L5Q, Glonass L1 OF, L2 OF, L1 OCd, L1 OCp, L2 OCp, L3 OCd, L3 OCp, Galileo E5a-I, E5a-Q, E5b-I, E5b-Q, IRNSS L5-SPS, S-SPS, L1-SPS, QZSS L1S, L5S, L6D, L6E, and SBAS L1, L5 [15]. These codes can be generated by configuring LSFR to produce the corresponding spread spectrum code sequences for each signal.