esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods
### TF-CF: IF1213 PPP Model without the Full Estimation of PIFCB  
The TF-CF model, which is an IF1213 PPP model applied to a dual-system, was constructed by considering that the time-varying part of the phase bias of the first system cannot be ignored and the second one can be ignored. For inter-system bias (ISB), other systems estimate the difference between the receiver clock of that system and the GPS receiver clock used as a reference [36]. The TF-CF model can be expressed as follows:\[\left\{\begin{array}{l}p_{r,IF12}^{s,Q1}=\mu_{r}^{s,Q1}\cdot x+d\overline{t}_{r}+m ^{s,Q1}\cdot Z_{r}+\varepsilon_{r,IF12}^{s,Q1}\\ r_{r,IF12}^{s,Q1}=\mu_{r}^{s,Q1}\cdot x+d\overline{t}_{r}+m^{s,Q1}\cdot Z_{r}+ \overline{N}_{r,IF12}^{s,Q1}+\tilde{\tau}_{r,IF12}^{s,Q1}\\ r_{r,IF13}^{s,Q1}=\mu_{r}^{s,Q1}\cdot x+d\overline{t}_{r}+m^{s,Q1}\cdot Z_{r}+ IFCB_{r}^{s,Q1}+\varepsilon_{r,IF13}^{s,Q1}\\ r_{r,IF13}^{s,Q1}=\mu_{r}^{s,Q1}\cdot x+d\overline{t}_{r}+m^{s,Q1}\cdot Z_{r}- IFCB_{r}^{s,Q1}+\overline{N}_{r,IF13}^{s,Q1}+\tilde{\tau}_{r,IF13}^{s,Q1}\\ r_{r,IF12}^{s,Q2}=\mu_{r}^{s,Q2}\cdot x+d\overline{t}_{r}+m^{s,Q2}\cdot Z_{r}+ \varepsilon_{r,IF12}^{s,Q2}\\ r_{r,IF12}^{s,Q2}=\mu_{r}^{s,Q2}\cdot x+d\overline{t}_{r}+m^{s,Q2}\cdot Z_{r} +\overline{N}_{r,IF12}^{s,Q2}+\tilde{\tau}_{r,IF12}^{s,Q2}\\ r_{r,IF13}^{s,Q2}=\mu_{r}^{s,Q2}\cdot x+d\overline{t}_{r}+m^{s,Q2}\cdot Z_{r} +IFB_{r}+\varepsilon_{r,IF13}^{s,Q2}\\ r_{r,IF13}^{s,Q2}=\mu_{r}^{s,Q2}\cdot x+d\overline{t}_{r}+m^{s,Q2}\cdot Z_{r} +\overline{N}_{r,IF13}^{s,Q2}+\tilde{\tau}_{r,IF13}^{s,Q2}\end{array}\right. \tag{15}\]  
All the estimated parameters in the TF-CF PPP models include the following:  
\[X=[x\quad d\overline{t}_{r}\quad Z_{r}\quad IFCB_{r}^{s,Q1}\quad IFB_{r}\quad \overline{N}_{r,IF12}^{s,Q1}\quad\overline{N}_{r,IF13}^{s,Q1}\quad\overline{N} _{r,IF12}^{s,Q2}\quad\overline{N}_{r,IF13}^{s,Q2}] \tag{16}\]
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 2 Related Works
### Verifiable Search  
To ensure that LBS works without a hitch, verifying the outsourced data returned from the cloud is also an essential part of the process. [PERSON] et al. [9] constructed a data verification object using the Bloom filter to verify the correctness and completeness of data, realizing fine-grained and efficient verification of data. In [18], [PERSON] et al. implemented fine-grained access control based on blind signatures and key policy attribute-based encryption (KPABE), and used function hidden inner product encryption (FHIPE) to encrypt Bloom filters for data file authentication, achieving secure and efficient data validation. In [19], [PERSON] et al. devised a lightweight and secure comparison protocol LSCP without interactions between the cloud and users. By using this protocol, users can easily eliminate duplicate and useless encrypted LBS messages during the authentication process, making the verification process in VANETs much faster. [PERSON] et al. [20] proposed a privacy-preserving scheme for verifying location data transmission for the Internet of Vehicles, improving the security of privacy for LBS of vehicles. All of the above studies on data verifiability have certain flaws. There is no guarantee that dishonest clouds will not obtain useful information from them, and they cannot be directly applied to the data verification process in LBS either.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results
### Scenario 2:  
#### 4.2.1 Simulation Parameters  
This scenario presents the simulation of the second network topology. The list of the configuration parameters used in our experiments is presented in Table 3. These simulation parameters were initialized as in [19]. Moreover, the power consumed by nodes is 0.8 W and 0.01 mW in receiving and idle modes, respectively. The depth threshold of DBR is one fourth of the maximum communication ranges. In our simulation, a source node was randomly selected among all the randomly deployed nodes. In each simulation run, the destination of all data packets is one of the 16 sinks randomly deployed on the water's surface. The statistical and comparison results between EEDOR, EEDOR-VA and DBR presented in this section were obtained using 30 runs of our simulation.
# A Novel Adaptive PID Controller with Application to Vibration Control of a Semi-Active Vehicle
## 3 Application to Vibration Control  
### Seat Suspension Model  
In this work, a model of vehicle MR seat suspension system used in [17] is adopted to demonstrate control effectiveness of the proposed hybrid adaptive controller. The practical photograph and corresponding mechanical model are shown in Figure 2a,b, respectively.  
Figure 1: A closed-loop of the proposed hybrid controller.  
The governing equations of the MR seat suspension system are derived as  
\[m_{s}\ddot{x}_{s}=-k_{s}(x_{s}-x_{0})-c_{s}\big{(}\dot{x}_{s}-\dot{x}_{0}\big{)}+k _{1}(x_{1}-x_{s})+c_{1}\big{(}\dot{x}_{1}-\dot{x}_{s}\big{)}+F_{MR} \tag{38}\]  
\[m_{1}\ddot{x}_{1}=-k_{1}(x_{1}-x_{s})+c_{1}\big{(}\dot{x}_{1}-\dot{x}_{s}\big{)} \tag{39}\]  
The above equations can be rewritten using the state variables as  
\[\left\{\begin{array}{c}\dot{x}_{11}=\dot{x}_{s}=x_{22}\\ \dot{x}_{22}=f_{11}(x_{11},x_{22},x_{33},x_{44})+g_{11}(x_{11},x_{22},x_{33},x _{44})u\\ \dot{x}_{33}=\dot{x}_{1}=x_{44}\\ \dot{x}_{44}=f_{22}(x_{11},x_{22},x_{33},x_{44})\end{array}\right. \tag{40}\]  
Figure 2: Seat suspension with MR damper for a commercial bus: (**a**) practical model (photograph); (**b**) mechanical model.  
where,  
\[f_{11}(x_{11},x_{22},x_{33},x_{44})=-\frac{k_{s}}{m_{s}}(x_{11}-x_{0} )-\frac{c_{s}}{m_{s}}\left(x_{22}-\dot{x}_{0}\right)+\frac{k_{1}}{m_{s}}(x_{33}- x_{11})+\frac{c_{1}}{m_{s}}(x_{44}-x_{22}),\] \[g_{11}(x_{11},x_{22},x_{33},x_{44})=\frac{1}{m_{s}},\ u=F_{MR},\ f_{22}(x_{11},x_{22},x_{33},x_{44})=- \frac{k_{1}}{m_{1}}(x_{33}-x_{11})-\frac{c_{1}}{m_{1}}(x_{44}-x_{22}).\]  
**Remark 2**.: _The system (4) has two degrees of freedom. This model is derived from the experimental model shown in Figure 2a. It is remarked that the yaw and roll motions in the practical model are limited by the design of the seat. Hence, these motions can be neglected in the experiment. The objective of the control of vibration of the seat is vertical axis._  
Now, in order to determine control gains, the relationship between the damping force and dynamic parameters must be determined. Then, the control gains are changed to the input voltage to be applied to MR damper. The damping force of the MR damper can be determined as [32]  
\[F_{MR}=(c_{a}+c_{b}v)(x_{44}-x_{22})+k_{0}(x_{33}-x_{11})+(\alpha_{a}+\alpha_{ b}v)\varphi \tag{41}\]  
where, \(\dot{\varphi}=-\kappa|x_{44}-x_{22}|+\lambda(x_{44}-x_{22})|\varphi|+\phi(x_{4 4}-x_{22})\). Using Equation (41), the voltage to be applied to MR damper \(\
u\) is found as  
\[\
u=\frac{F_{MR}-[c_{a}(x_{44}-x_{22})+k_{0}(x_{33}-x_{11})+\alpha_{a}\varphi] }{c_{b}(x_{44}-x_{22})+\alpha_{b}\varphi} \tag{42}\]  
The voltage \(\
u\) will be changed to the applied current, and then this current use for the electric coil of the MR damper.  
**Remark 3**.: _Parameters written in Equation (42), which are shown in [32], include the hysteretic model. Hence, this equation can be applied to any MR actuators and there is no need to survey hysteresis model of the damper._
## 7 Evaluation
### Comparison of Boundary Detection  
The algorithms are compared in terms of detecting the boundary of the work area. Figures 2 and 3 shows a comparison of concave hull (that uses KNN) with convex hull (that uses Delaunay Triangulation). It could be observed that both algorithms performed well for a regular shaped work area.  
For irregular shape, the concave hull algorithms (KNN) have performed well determining the boundary points. While Delaunay Triangulation fails to identify the edges in detail as shown in Figure 4. Delaunay Triangulation has completely failed to identify the cave like shape, while concave hull using KNN performs well.  
Figure 3: Comparison of boundary detection algorithms (**a**) Actual GPS Plots, (**b**) concave hull using KNN and (**c**) convex hull using Delaunay Triangulation.  
Figure 2: Comparison of boundary detection algorithms (**a**) Actual GPS Plots, (**b**) concave hull using KNN and (**c**) convex hull using Delaunay Triangulation.  
In case of the very irregular field shown in Figure 5, Delaunay Triangulation again failed but concave hull using KNN was able to detect most of the shape. It is also important to note the shape formed by concave is also very close to the actual shape.  
The Delaunay Triangulation is good only for regular shaped fields, while concave hull using KNN performs well, but as the irregularity increases its accuracy is also affected. Thus, the irregularity of shape has a direct effect on the accuracy of boundary detection.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 3 BIM and 3D Concrete Printing Process
### Parametric Programming Languages and 3D Printing of Concrete  
DynamoTM is a platform that allows visual programming through a plugin that can easily work with RevitTM. It uses workflows that enable it to work algorithmically (a set of steps that follow a basic logic of input, processing, and output). DynamoTM is intended to be used by architecture and construction professionals who require its capabilities but without the need for advanced knowledge about programming [31]. Additionally, DynamoTM has proved effective in creating and estimating the 3D printing elements and components [32].  
GrasshopperTM is a complement to the CAD RhinocerosTM software. Like DynamoTM, it works through data workflows, allowing users to create acyclic graphics. GrasshopperTM focuses on sequences and constant visualization in real time, facilitating quick reaction to changes or errors, without having to restart the code [33]. GrasshopperTM is currently the most used program in 3D concrete printing, with the help of content add-on KUKA | prec.  
On its part, KUKA | prec allows the user to program industrial robots directly from the parametric modeling environment, including complete kinematic simulation of the robot. The generated files can be executed in the robot KUKATM robot, without the need for any additional software [34].  
Finally, designing the printing path by directly extracting information from the BIM platform in 3D printing processes can reduce data loss from a 3D BIM model to printing path generation [35].
## 5 Influence of Angular Velocity on Hydrodynamic Ram Effect
### Influence of Angular Velocity Magnitude on the Hydrodynamic Ram Effect
Numerical simulations were conducted by setting the angular velocity in the \(y\) direction as \(w_{y}=1\) rad/\(\upmu\)s, \(w_{y}=2\) rad/\(\upmu\)s, \(w_{y}=3\) rad/\(\upmu\)s, \(w_{y}=4\) rad/\(\upmu\)s, and \(w_{y}=5\) rad/\(\upmu\)s. Figure 18 shows the front and back perforations caused by the impact of the debris with different angular velocities in the \(y\) direction. Figure 19 depicts the attenuation curves of the translation velocity when the debris impacted the satellite tank with angular velocities of \(w_{y}=1\) rad/\(\upmu\)s, \(2\) rad/\(\upmu\)s, \(3\) rad/\(\upmu\)s, \(4\) rad/\(\upmu\)s, and \(5\) rad/\(\upmu\)s.  
Figure 17: Attenuation curves of the translation velocity when the debris impacted the satellite tank with angular velocities of \(w_{x}=0.01\) rad/\(\upmu\)s, \(w_{x}=0.05\) rad/\(\upmu\)s, \(w_{x}=0.1\) rad/\(\upmu\)s, \(w_{x}=0.5\) rad/\(\upmu\)s, and \(w_{x}=1\) rad/\(\upmu\)s.  
With the increase in angular velocity, the maximum diameter of the front perforation increased, and the shape of front perforation gradually evolved into the shape of water droplets. As seen in Figure 19, with the increase in angular velocity, the residual velocity of the debris increased, as did the diameter of the back perforation caused by debris perforation. This is because when the debris rotates around the \(y\) axis, the velocity direction of the vortex below the debris is the same as that of liquid. With the increase in angular velocity, the vortex velocity increases, the separation of boundary layer accelerates, and the separation point moves backwards. The area of boundary layer decreases, and the effective cross-sectional area composed of the debris area and boundary layer area also reduces, leading to a decrease in drag force and an increase in the residual velocity of the debris. Moreover, with the increase in angular velocity, the deviation of the back perforation in the negative direction of \(z\) axis increases. The reason is that the pressure difference above and below the debris increases, resulting in a larger Magnus force.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 3 Algorithms for Graph Partitioning Considered in Our Study
### Edge Cut Graph Partitioning  
We observe that we obtain \(|V(G)|+\sum_{i}h_{i}\) when leaving out the cubes in the objective function (2), thus making it necessary to minimize the sum of the halo nodes over all blocks.  
Figure 1: Example of using CH-partitioning for distributed evaluation of matrix polynomial \(P(A)=A^{2}\), with a thresholding operator that removes all edges/matrix entries with values below 0.01. **(a)** Input symmetric matrix \(A\). **(b)**\(P(A)=A^{2}\). **(c)** After the thresholding operator removes elements \((2,4)\) and \((4,2)\) (in bold) from \(P(A)\), the sparsity graph \(\mathcal{P}(G)\) of the resulting matrix is shown. We are considering a block of a CH-partitioning with core set \(U=\{1,2\}\) (encircled with a solid line) and halo the set \(H=\{3\}\) (encircled with a dashed line). **(d)** Submatrix \(A_{U}\) consisting of the first three (by \(U\cup H=\{1,2,3\}\)) rows and columns of \(A\). **(e)** Matrix \(P(A_{U})=A_{U}^{2}\). According to Lemma 1, the elements of the first two (by \(U=\{1,2\}\)) columns/rows of \(P(A_{U})\) are equal to the corresponding nonzero elements of the first two columns/rows of threshold \(P(A)\).  
Regular graph partitions and CH-partitions are related. Suppose we are given a regular partition \(P\). For any part in \(P\), we can define a core corresponding to that part, and a halo consisting of all adjacent vertices of the core vertices (excluding the core vertices themselves). We define the CH-partition \(\Pi\) to consist of precisely those blocks for any element of \(P\). It must then be true that either \(v\) or \(w\) is a halo vertex for any cut edge \((v,w)\) of \(P\). Conversely, there exists a core vertex \(w\) such that \((v,w)\) is a cut edge for any halo vertex \(v\) belonging to some part in \(\Pi\).  
This shows that the cut edges of \(P\) and the set of halo nodes in \(\Pi\) are related but not equal. We observe that another measure, the _total communication volume_, exactly corresponds to the sum of halo nodes. Certain tools such as _METIS_ allow us to optimize with respect to the total communication volume. By ignoring the cubes in (3), we aim to study how well CH-partitions can be produced by regular graph partitioning tools. Additionally, we improve the solutions obtained by standard graph partitioning tools with our own heuristic in Section 3.2. The three following algorithms will be used:
## 5 Limitations and Future Work  
The above experiments show that the mIoU accuracy of semantic segmentation and processing speed of our method have been improved to some extent, but the accuracy of small sample data is still very low. This leads to certain errors in visual understanding in driverless scenes, and has a certain impact on the judgment of driverless driving. In order to solve the forgetting problem caused by sample imbalance, some scholars have proposed the use of memory module to enhance the ability of small sample feature extraction. Our next task is to introduce this memory network into existing models to solve the problem of low accuracy for small samples.  
For the same dataset of urban scenes, Semantic3D has location and color information, while SemanticKITTI only has location information. This results in a slight gap between SemanticKITTI's accuracy and Semantic3D's accuracy. Considering that many IIDARs in reality do not have the ability to collect color information. We consider calculating the neighborhood normal vector information of each point to enhance the information representation ability of the point cloud. The position information and normal vector information of the point cloud are sent to the network to enhance the accuracy.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods  
This study aimed to estimate earthquakes' magnitudes and source parameters from GPS time series data recorded during an earthquake event. Here, the parameters were estimated using a GA to inverse the elastic dislocation equation based on [PERSON]'s model. The hyperparameters of the GA were tuned to obtain the best configuration settings that yielded the best parameter estimation. The selected configuration was then tested on real displacement time series data from the GPS stations affected by the 2011 Tohoku Oki earthquake. GPS time series data from all stations were processed to estimate the permanent displacement value in both the horizontal and vertical components. The estimated permanent displacement was then inversed using the GA based on [PERSON]'s model to obtain the source parameters.
## 4 Conclusions  
In this paper, the optical system, baffle, and vanes of the aspherical camera are designed. At the same time, the stray light analysis of the designed optomechanical structure is carried out, and the following conclusions are drawn:  
(1) The \(MTF\) value of the optical system is greater than 0.4 (63 lp/mm), the maximum diameter of the airy disk is less than 10 \(\upmu\)m, and the total field distortion is less than 0.4%, which verify the imaging performance of the optical system.  
(2) By combining the mechanical structure design of the camera and stray light analysis, the camera lens is optimized for the lens baffle and shading ring. The \(PST\) value of the point light source transmittance is calculated under different incident angles. When the light source's incident angle is greater than 0.5\({}^{\circ}\), the \(PST\) value will drop rapidly. When the incident angle is 30\({}^{\circ}\), the \(PST\) value of the system is about 10\({}^{-8}\). The aspherical aerial optical system has the ability to suppress stray light.  
(3) The results show that the designed baffle and shading ring can effectively suppress the stray light of the optical system, which can provide a reference for the structure design of other lenses. As the angle of incidence increases, the \(PST\) value of the aspheric optical system gradually decreases, and the stray light entering the optical system also decreases.  
In future research, the effect of improving the imaging quality of the camera system will be evaluated, and the stray light suppression ability also be quantitatively analyzed.  
Conceptualization, C.J. and Y.Y.; methodology, J.L.; writing--original draft preparation, C.J.; writing--review and editing, C.J. and X.Q.; visualization, Y.Y.; supervision, X.Q.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Evaluation
When the proportion of high-utilization tasks is high, the range of system utilization is bound to be limited by its definition. It is difficult to compare the performance of the three different processor allocation strategies under different system utilization values. Therefore, we choose 30% of high-utilization tasks to conduct experiments on 10-core performance asymmetric multiprocessors. In Figure 6, the performance of the three different processor allocation strategies is again degraded, but BSF-EDF still schedules more task sets than the other two processor allocation strategies. When the system utilization reaches 80-90%, the three processor allocation strategies can only schedule 73.8%, 34.2%, and 20.9% task sets. When the system utilization reaches 90-100%, the three processor allocation strategies can only schedule 6.1%, 0.9%, and 0% task sets. Figure 5: Performance comparison of BSF, FSF, and SSF under different system utilization values (\(m=10\)). Figure 6: Performance comparison of BSF, FSF, and SSF under different system utilization values (\(m=10\)). We compare the three processor allocation strategies by modifying the proportion of fast processors on a 10-core performance asymmetric multiprocessor. In Figure 7, with the increasing proportion of fast processors, the schedulable task set ratio of the three processor allocation strategies also increases, among which BSF-EDF increases the fastest. When the fast processor ratio reaches 100%, all processors are at the same speed. In this case, the scheduling process of the three different processor allocation strategies is not different, so the three algorithms can schedule 99.9% of the task set. Since we allow the preemption of tasks, it is important to consider the overhead of task migration. We measure this overhead by the number of task migrations. We conduct experiments on a 10-core performance asymmetric multiprocessor. In Figure 8, the number of migrations of SSF-EDF is the least for different system utilization cases. BSF-EDF has the most migrations when the system utilization is low. When the system utilization exceeds 70-80%, FSF-EDF has the most migrations. Effective processor utilization (EPU) is defined as \(EUI=\sum_{i\in\Lambda}V_{i}/P\)[15]. If the job is completed within its deadline, the value of \(V\) is equal to the execution time of the job; if the job misses the deadline, \(V=0\). \(\Lambda\) is the set of tasks that the processor executes. \(P\) is the total time of execution. The level of EPU reflects the level of effective utilization of the processor by the scheduling algorithm. In Figure 9, for different system utilization rates, the EPU of SSF-EDF is the highest. This means that SSF-EDF is more efficient for processor utilization. Figure 8: Comparison of task migration times of BSF, FSF, and SSF under different system utilization values (\(m=10\)). Figure 7: Performance comparison of BSF, FSF, and SSF for different percentages of fast processors (\(m=10\)).