esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
This work aims to maximize the performance of SLAM systems in dynamic environments by concentrating on the identification and rejection of dynamic feature points. The approach presented in this research performs well in terms of localization errors in dynamic settings, according to experimental results. This benefit is explained by the system's capacity to recognize dynamic objects and lessen their interference. In the meantime, the system suggested in this paper shows a higher frame rate in the frame rate comparison experiments with similar algorithms. This is explained by the fact that MobileNetV3-Small replaces the Backbone of the target detection network YOLOv5s, making the entire network model lighter. Varying application scenarios may require varying levels of accuracy from SLAM algorithms. For example, robots working in indoor environments often have slightly lower accuracy requirements, with absolute trajectory errors needing to be limited within a few centimeters to ten centimeters. The system in this paper can still fulfill this requirement in dynamic scenarios, so the system in this paper has good practicality. While the system presented in this paper has significantly improved the map effect and localization accuracy in dynamic scenes, in real-world applications, tracking failure may occur when features in dynamic environments are absent or the camera moves too quickly. The localization accuracy of the system may be adversely affected in situations with low light or abrupt changes in illumination because the system developed in this paper is a visual SLAM and its localization depends on visual information. Future research can look into adding more sensors, like inertial measurement units (IMUs), and introducing a variety of feature types to improve the system. These upgrades allow the system to adjust to increasingly complicated settings.
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 2 Theoretical Models  
### Basic Theory  
#### Basic Acoustic Equation  
The thermal effect of focused ultrasound is the foundation of ultrasound hyperthermia. Ultrasound, a mechanical vibration propagated within the human body, induces displacement and vibrations of tissue particles, leading to thermal effects. As long as the heat absorption rate of biological tissue exceeds the heat dissipation rate, the temperature inside the tissue will increase. Biological tissues are generally modeled as thermal viscous fluids, which refer to substances that exhibit both viscosity and thermal conductivity during the flow process. When finite amplitude ultrasound propagates in thermal viscous fluids, the Westervelt equation can aptly describe the absorption and non-linear effects of ultrasound [15]. Accurately derived from the second-order term of the fluid motion equation, the Westervelt equation can be represented as follows:  
\[\
abla^{2}p-\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}+\frac{\delta} {c^{4}}\frac{\partial^{3}p}{\partial t^{3}}+\frac{\beta}{\rho c^{4}}\frac{ \partial^{2}p^{2}}{\partial t^{2}}=0. \tag{1}\]  
In Equation (1), \(\
abla\) is the Laplace operator, \(p\) is the total ultrasound pressure, \(c\) is the velocity of sound, \(\rho\) is the density,\(\beta=1+B/(2A)\) is the non-linear coefficient, B/A is the non-linear parameter of the medium, and \(\delta\) is the sound diffusion coefficient.  
In Equation (1), the first two terms describe the linear propagation of ultrasonic waves without loss. The third term is loss, which describes the ultrasonic loss caused by heat conduction and fluid viscosity. The fourth term is the non-linear term, which describes the non-linear distortion propagation characteristics of ultrasonic waves caused by finite amplitude effects.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 2 Data and Methodology  
### Experimental Data  
#### 2.1.1 The IRI-2016 Model  
The Fortran version of the IRI-2016 source code used in this study was obtained from the official website of the IRI model: [[http://irimodel.org](http://irimodel.org)]([http://irimodel.org](http://irimodel.org)) (accessed on 3 July 2024). The IG12 data used for updating were selected from GNSS-TEC, and were accurately extracted from observations of relatively uniformly distributed GNSS ground monitoring stations in the China region. The grid product of the IG-up values calculated with a temporal resolution of one hour and a spatial resolution of \(5^{\circ}\times 2.5^{\circ}\) (longitude \(\times\) latitude) can be temporally and spatially interpolated to obtain the IG-up values for a specific time and location, upon which the parameters driving the IRI-2016 model are based.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 2 Materials and Methods
### Trend Analysis  
To examine the temporal and spatial changes in China's EEQ from 2000 to 2017, this study employs a one-dimensional linear regression model to calculate the trend of interannual variation in the M-RSEQI [41]. The slope derived from the model represents the interannual trend of the M-RSEQI. The calculation formula is presented below:  
\[slope=\frac{n\times\sum\limits_{i=1}^{n}\left(i\times MRSEQI_{i}\right)-\sum \limits_{i=1}^{n}i\sum\limits_{i=1}^{n}MRSEQI_{i}}{n\times\sum\limits_{i=1}^{ n}i^{2}-\sum\limits_{i=1}^{n}i} \tag{7}\]  
where, \(slope\) is the slope in the one-dimensional regression equation of the linear fit of M-RSEQI with year for China 2000-2017; \(i\) is the time variable and \(n\) is the year; \(n=18\); \(MRSEQI_{i}\) represents the multivariate remotely sensed EEQ in year \(i\). When \(slope<0\), it indicates that the EEQ is decreasing. On the contrary, when \(slope>0\), it indicates that the EEQ is increasing. The absolute value of \(slope\) represents the rate of change of M-RSEQI, with a larger \(slope\) representing faster change and the opposite being slower.
# Study on the Technology Trend Screening Framework Using Unsupervised Learning
## 4 Experiments and Results
It shows a continuously increasing trend in the number of outliers from 2001 to 2016. In particular, there was a drastic increase in the number of patent applications corresponding to outliers from 2013. Despite the recent three-year decrease on the graph, the collected data may include unpublished patents, considering that patents are published 18 months after their filing. Thus, the data until 2017 are examined, and the results show that there were many outliers in 2017. In this respect, because the proposed method classifies the recently applied patents as outliers, this method is capable of screening technology trends. This study utilizes the topic model to identify the topic of the technology located in both inner and outlier parts. As described in Section 3.3, the optimal number of topics is set based on perplexity, and this study determines the optimal number of topics where perplexity is minimum. Figure 10 indicates the values of inner and outer perplexity values in the training and testing datasets, respectively. According to the results, the perplexity value is the lowest when k = 9 in both training and test sections of the inner set, while it is lowest when k = 1 in both training and test sections of the outlier set. We extract topics through the topic model based on the above results, which are presented in Table 8.  
Among the documents included in the training set, nine technical topics in the inner set can be identified, and one in the outlier set. The detailed topics included in the inner set \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Document No.** & **IQR** & **3 Sigma-Rule** & **iForest** \\ \hline US 10000285 & \(\surd\) & \(\bigtimes\) & \(\surd\) \\ US 10029790 & \(\surd\) & \(\surd\) & \(\surd\) \\... &... &... &... \\ US 9310207 & \(\bigtimes\) & \(\bigtimes\) & \(\surd\) \\ US 9989378 & \(\surd\) & \(\bigtimes\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of Ensemble Outlier Detection. Figure 9: Patent application trends by year based on outliers. of the training set indicate that the technology development is related mainly to the drone's hardware and functions. However, there is a relative lack of technology related to the drone control system. In contrast, nine technical topics can be identified from the documents included in the testing set, whose development was related mainly to network, fuel, and drone control. However, there is a relative lack of technology related to flight control in the outlier set of the testing set. This result may be attributed to the recent autonomous flight control technology, which could be considered to be a vacant technology.
## 3 Methodology
### Quantifying the Resilience of Sparse Road Networks
#### 3.3.2 Network Accessibility  
In transportation network analysis, connectivity, continuity, and density are commonly used indicators for evaluating network structure [45]. Connectivity is typically employed to assess whether a network maintains overall connectivity, and its calculation is provided in Equation (4).  
\[J=\frac{\sum_{i=1}^{n}m_{i}}{N}=\frac{2M}{N} \tag{4}\]  
where \(J\) represents the network connectivity index, \(N\) is the total number of nodes in the transportation network, \(m_{i}\) denotes the number of edges connected to node \(i\), and \(M\) is the total number of edges (road segments) in the network.  
While connectivity effectively evaluates whether a road network remains connected, it does not quantify the accessibility of individual nodes or the recovery process following a disruption. To address this limitation, we propose an improved connectivity-based metric network accessibility, which incorporates node flow weights to provide a more precise depiction of network performance degradation under disaster impacts. Unlike conventional connectivity measures that only assess whether nodes are linked, network reachability also accounts for the importance of nodes (e.g., traffic flow), allowing for a more dynamic evaluation over different time steps.  
In this study, the accessibility matrix is used to calculate the number of times a node can reach other nodes. The performance of a node at time \(t\) is measured by the ratio of reachable nodes at time \(t\) to the number of nodes reachable under normal conditions, as shown in Equation (5). Consequently, the overall performance of network nodes at time \(t\) is determined by Equation (6).  
\[g_{i}^{t}=\frac{c_{i}^{t}}{\mathcal{C}_{i}}\ \ (\ c_{i}^{t}=0,1,\cdots,\mathcal{C}_{i}) \tag{5}\]  
\[G^{t}=\sum_{i\in\forall V}\omega_{n}(i)g_{i}^{t} \tag{6}\]  
In the expression, \(g_{i}^{t}\) represents the accessibility performance of node \(i\) at time \(t\). \(c_{i}^{t}\) denotes the number of nodes reachable from node \(i\) at time \(t\), while \(\mathcal{C}_{i}\) indicates the number of nodes that are normally reachable from node \(i\). \(G^{t}\) signifies the overall performance of all nodes in the road network at time \(t\). \(\omega_{n}(i)\) represents the weight of node \(i\) in the network, which depends on the traffic flow.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
#### 3.1.3 Obtaining a Set of Measured RSSI Values  
When the mobile anchor node moves along the trajectory, the underwater sensor node receives messages and measures the RSSI value of each messages. The measured RSSI value in the underwater environment can be expressed as [40]  
\[\tilde{r}(l)=SL-TL(l)+\zeta \tag{2}\]  
where \(SL\) is source level, \(\zeta\) is a random noise which is assumed to be a zero-mean gaussian random variable with variance \(\sigma^{2}\), and \(TL(l)\) is the transmission loss for an underwater acoustic signal, which can be expressed as  
\[TL\left(l\right)=10\cdot k\cdot\log\left(l\right)+\alpha\cdot l \tag{3}\]  
where \(k\) is the spreading coefficient, \(\alpha\) is the absorption coefficient, and \(l\) is the transmission distance, which is expressed as  
\[l=\sqrt{(x-x_{N})^{2}+(y-y_{N})^{2}+(z-z_{N})^{2}} \tag{4}\]  
where \((x,y,z)\) is the location of any point on the trajectory and \((x_{N},y_{N},z_{N})\) is the location of a sensor node. Since the trajectory is on the surface of the sea and is a straight line, \(z\) is 0, and \(y\) can be linearly  
Figure 1: The network system architecture of the proposed localization scheme.  
Figure 2: The trajectory of the mobile anchor node.  
represented by \(x\). We use the \(x\)-coordinate to indicate the location of broadcast points. The \(x\)-coordinate of all broadcast points of the trajectory can form an original location vector  
\[\mathbf{X}=[x_{1},x_{2},\ldots,x_{m}] \tag{5}\]  
All measured RSSI values can also form a vector  
\[\widetilde{\mathbf{R}}=[\widetilde{r}_{1},\widetilde{r}_{2},\ldots,\widetilde {r}_{m}] \tag{6}\]  
When \(\zeta\) in Equation (2) is 0, a noiseless RSSI value vector is  
\[\mathbf{R}=[r_{1},r_{2},\ldots,r_{m}] \tag{7}\]  
The sensor node uses \(\mathbf{X}\) and \(\widetilde{\mathbf{R}}\) to determine the projection of a sensor node on the trajectory and the perpendicular distance from a node to the trajectory, and then locates itself using the projection and the perpendicular distance.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
###### Abstract  
Cable-driven serpentine manipulators (CDSMs), having strong compliance and flexibility, are suitable for flexible operation in confined workspace. Like many other manipulators, it is an important issue to sense the tip pose of CDSMs. In much previous research, the manipulator's tip pose is usually determined by the driving cable lengths. However, little literature has talked about the tip positioning accuracy. This paper focused on this issue, and investigated the effect of the cable hole location error and cable length error on the tip positioning accuracy of CDSMs. For this aim, we firstly established the kinematic model, and then analyzed the influence factors of the tip positioning accuracy. Simulation results show that the cable hole location error and cable length error have significant effects on the tip positioning accuracy, and especially in certain configurations, it can cause an approximate 30 mm deviation in position. Therefore, in order to improve the tip positioning accuracy of CDSMs, it is necessary to compensate for the cable hole location error and cable length error in the modeling.  
kinematics; accuracy analysis; cable-driven serpentine manipulators +
Footnote †: journal: Applied Sciences
## 3 Numerical Modeling
### CFD-Based Modeling  
A rectangular shape was selected as a fluid domain for simulating fluid flow over the submarine without the effect of reverse flow. Following the ITTC Recommendation [24], a rectangular domain of 7L in length, 4L in breadth, and 3L in height was created, where L stands for the whole length of the submarine. The distance from the bow of the submarine to the front surface of the fluid domain was set as 2L, and the submarine was located in the middle of the fluid domain. In addition, physical conditions should be applied to the outer surfaces of the fluid domain. The submarine speed was assigned for the front surface as the velocity inlet, while the pressure-outlet condition was applied for the back surface. Symmetric conditions were set for the side, top, and bottom surfaces. The no-slip wall condition was set on the surfaces of the submarine hull and control surfaces. Figure 4 presents the fluid domain and the boundary conditions on the submarine and outer surfaces.  
Figure 4: Fluid domain and boundary conditions.  
Figure 3: Definitions of the (**a**) Cross-rudder plane, (**b**) X-rudder plane, and (**c**) Y-rudder plane configurations, including their hydrodynamic forces and moments.  
To prepare for the simulation, the fluid domain was discretized using tetrahedral elements, as shown in Figure 5. The boundary layer of fluid flow surrounding the submarine was modeled using layers of prism elements. The first layer thickness was determined from the dimensionless wall distance (y+ value). With a submarine speed of 20 knots, the Reynolds number was 1.0 \(\times\) 10\({}^{9}\) and the regime of fluid flow over the submarine and control surfaces was full turbulence. According to [PERSON] et al. [25], the value of y+ is given about 3000-5000 for Re = 1.0 \(\times\) 10\({}^{9}\). Therefore, the value of y+ was chosen as 3000, and the height of the first layer thicknesses on the hull and the stern rudder plane configuration was then calculated as 9.0 mm.  
The incompressible RANS solver in Ansys Fluent was used to simulate fluid flow through the submarine with the three rudder plane configurations. According to the ITTC recommendation [24], the two-equation models of turbulence model, turbulence model of \(k\)-\(\varepsilon\) realizable, or turbulence model of \(k\)-\(\omega\) SST (Shear Stress Transport) are recommended for simulating turbulent flow around a submarine in motion. Thus, the \(k\)-\(\omega\) SST turbulence model was selected for modeling turbulent fluid flow around the submarine. The pressure field was obtained by solving the momentum equation using the SIMPLE (Semi-Implicit Pressure Link Equation) algorithm. The second-order upwind method was employed to estimate the face values through interpolation of cell center values and pressure values.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method
### Frequency-Domain Analysis
#### 2.2.1 Narrow-Band Method  
[PERSON] [12] proposed a classical narrow-band method. When the effective bandwidth \(\varepsilon<0.3\)-\(0.35\), the random process can be regarded as a narrow-band process. The peak and amplitude of the stress cycle response are assumed to be equal; that is, the mean value of the stress cycle is usually close to zero. The peak probability density function of the narrow-band response random process can be expressed as follows:  
\[p(s)_{NB}=\frac{S}{\sigma^{2}}exp(\frac{-S^{2}}{2\sigma^{2}}) \tag{5}\]  
where \(\sigma^{2}\) is the variance of the random process and \(s\) is the stress.  
From Equations (4) and (5), the fatigue cumulative damage formula of narrow-band method can be obtained:  
\[D_{NB}=E(p)\frac{T}{C}\Big{(}\sigma\sqrt{2}\Big{)}^{m}\Gamma\Big{(}1+\frac{m} {2}\Big{)} \tag{6}\]  
where \(\Gamma(x)\) is a gamma function that is usually defined as:  
\[\Gamma(x)=\int_{0}^{\infty}y^{x-1}exp(-y)dy \tag{7}\]  
The peak frequency is similar to the zero-crossing frequency; thus, it is assumed that \(E(p)=f_{0}\) is the center frequency of the narrow-band peak.
## 2 Finite Element Modeling
### Element Type, Interaction, Boundary Conditions, and Mesh  
In modeling DS-CFCST columns, where the wall thickness of the tube is significantly smaller than its other dimensions, we used 4-node reduced integration shell elements (S4R) for the steel tube and 8-node reduced integration solid elements (C3D8R) for the DS concrete and endplates. The interaction between the concrete and steel tube was captured using a'surface-to-surface contact' method, while the connection between the concrete and endplates was implemented using a 'Tie constraint'. The inner surface of the steel tube and the surfaces of the endplates were designated as master surfaces, with the outer concrete surface set as the slave surface. Normal contact was handled using a hard contact formulation, and tangential contact was managed with Coulomb friction, employing a coefficient of 0.5. For the interaction between the endplates and the steel tube, a 'Shell-to-solid coupling' mechanism was employed. Rigid plates, which lack mass properties, were primarily used to simulate axial displacement. Axial loading was centrally applied to the top surface of the upper plate via displacement control, replicating the axial loading process. The bottom plate of the stub columns was fixed, while the top plate was allowed to move axially, as illustrated in Figure 1, with the displacement specified as \(U_{y}=0.04\) L. Structured meshing techniques were used for model development. To balance accuracy and computational efficiency, a mesh size of \(D/10\) was chosen, as suggested by [PERSON] et al. (2018). The mesh configuration for the model is illustrated in Figure 1.  
Figure 1: Interaction, boundary conditions, and mesh.
## 3 Spectrum Auction Structures
### Simultaneous Multiple-Round Auction
#### 3.5.3. Collusion  
In an auction with simultaneous closing of all licenses, players can collude among themselves to skew prices downward.  
The simplified structure presented in Table 5 illustrates the collusion problem. In this example, each player is limited by a budget of USD 20, with symmetrical valuations per license set at \(V_{i}=10\). A tie-breaking rule is in effect, and we employ the simplest method: a coin toss determines which player is awarded item A, while the other item is assigned to the remaining participant. Bids are made in discrete increments of one for the sake of simplicity.  
It can be intuitively assumed that equilibrium occurs when both players bid USD 10 on each item, the coin toss takes place, and the game concludes with the payoff for each player being \(\pi_{i}=V_{i}-10=0\). In fact, this is just one of many possible equilibria. Assuming rationality for both players, each player understands that raising their bids would lead to the aforementioned outcome. Consequently, participants can anticipate this and agree to bid USD 1 each. The established rules would require coin tossing, resulting in \(\pi_{1}=\pi_{2}=V_{i}-1=9\). Both players, being rational, would have incentives to cooperate in order to maximize their profits.  
Individually closing license bids helps prevent collusion through a principle known as the trembling hand. Assume that both players agree to bid USD 1 for License A. A coin toss determines that Player 1 (\(P_{1}\)) is the winner, leaving them with a remaining budget of USD 19. When license B is auctioned, \(P_{1}\) has an incentive to breach the agreement by bidding higher for license B, which is valued at USD 10. Player 2 anticipates this behavior and therefore would bid higher on License A from the outset. According to this principle, the price of each license would be at least USD 9 (one increment below \(V_{i}\)) due to the implicit threat that each player faces if they attempt to cooperate.