esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### API Categorisation
#### 4.3.2 Top 50 API Categories According to Chi2 and Gini  
The analysis of Windows API preferences and prioritisation using the Chi2 and Gini methods, as depicted in Figure 2, offers significant insights into the detection of malicious activities and contributes to the understanding of cyber threats. The Chi2 method focuses on internal system aspects, particularly security and file system interactions, with no consideration for thread management, networking, and component object model APIs, indicating their perceived importance in malware detection. This method's emphasis on these specific areas suggests a strategic concentration on internal system vulnerabilities and manipulations commonly exploited by malware. On the other hand, the Gini method presents a more balanced and comprehensive perspective, encompassing a broader range of API categories. This method's allocation across system management, registry operations, kernel management, system information, thread management, networking, process management, and error handling indicates its recognition of the multifaceted nature of malware. By considering both internal system processes and external communications, the Gini method acknowledges the diverse tactics employed by malware, from system infiltration to network-based activities. The distinct approaches of these two methods in prioritising Windows APIs underscore the complexity of malware detection. The Chi2 method's focused approach is beneficial for honing in on specific system vulnerabilities, while the Gini method's comprehensive coverage aids in recognising a wider array of malicious behaviours. Together, these results contribute significantly to cyber-security efforts by providing a nuanced understanding of how different aspects of Windows APIs are exploited by malware, thereby informing more targeted and effective defensive strategies against evolving cyber threats.
## 3 Control Design
### The Objective Function  
Figure 6 shows the FOFPID control scheme. It consists of two subsystems: one is the X-axis control loop, and the other is the Y-axis control loop, which possesses a mechanical coupling effect. The design of this control scheme is to reduce X- and Y-axis tracking errors, and also reduce the synchronous error in the Y-axis. To achieve this purpose with an optimization algorithm, a fitness function containing these error terms should be minimized. The ITAE performance criterion [30] is used to be the fitness function and is expressed as  
\[\mathrm{ITAE}=\int_{0}^{T}t|e(t)|dt \tag{19}\]  
where \(e(t)\) denotes the tracking error at instant \(t\).  
Figure 6: The proposed FOFPID synchronous control architecture.  
In the control design, compared to the IAE (integral of the absolute error) and ISE (integral square error) indices, the ITAE performance index has smaller oscillations and overshoots. There are eighteen tuning parameters in the control scheme: (1) \(K_{e}\), \(K_{de}\), \(\alpha\), \(\beta\), \(\mu\), and \(\lambda\) for the X-axis, (2) \(K_{e1}\), \(K_{de1}\), \(\alpha_{1}\), \(\beta_{1}\), \(\mu_{1}\), and \(\lambda_{1}\) for the master Y-axis, and (3) \(K_{e2}\), \(K_{de2}\), \(\alpha_{2}\), \(\beta_{2}\), \(\mu_{2}\), and \(\lambda_{2}\) for the slave Y-axis. To find appropriate parameters for the controller scheme, optimization algorithms can be used. For the gantry system described in Figure 4, the optimization problem is to minimize the system ITAE index. The ITAE of the whole system should consider the tracking errors for X-axis \(e_{X}(t)\), master Y-axis \(e_{YM}(t)\), and slave Y-axis \(e_{YS}(t)\), and the synchronous error, which is the difference of two Y-axes \(e_{YM}(t)\). Thus, the ITAE is given as  
\[\mathrm{ITAE}=\frac{1}{4}(\sum_{r=1}^{4}\mathrm{ITAE}_{r}) \tag{20}\]  
with \(\mathrm{ITAE}_{1}=\int_{0}^{T}t|e_{X}(t)|dt\), \(\mathrm{ITAE}_{2}=\int_{0}^{T}t|e_{YM}(t)|dt\), \(\mathrm{ITAE}_{3}=\int_{0}^{T}t|e_{YS}(t)|dt\), \(\mathrm{ITAE}_{4}=\int_{0}^{T}t|e_{YMS}(t)|dt\).
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 1 Introduction  
In recent years, the pervasive influence of \"big data\", propelled by the advancement of information technology, has permeated diverse fields, including but not limited to data mining [1; 2; 3], computer biology [4], environmental science [5], e-commerce [6], and social network analysis [7]. Within these domains, the analysis and extraction of concealed information from extensive datasets have become standard practices, often approached from innovative perspectives [8; 9; 10]. As a general data structure, the graph has found widespread application across the aforementioned fields. Simultaneously, the quest to design efficient graph mining algorithms and extract valuable subgraph patterns from graph data has become a focal point of extensive attention and research.  
The challenges associated with frequent subgraph mining in this paper primarily revolve around two key aspects [11; 12]. Firstly, the algorithm's complexity [13] poses a significant hurdle. The algorithm's complexity is contingent on two pivotal phases: subgraph isomorphism and candidate pruning. Subgraph isomorphism comparison stands out as the foremost challenge, whereas the effective implementation of candidate pruning holds the potential to substantially reduce the computational load in identifying frequent subgraphs in subsequent stages. Another challenge in frequent subgraph mining stemsfrom the escalating volume of data within a single graph, exceeding the memory capacity of a single machine [14]. Consequently, the adoption of a distributed graph processing framework becomes imperative for algorithm implementation. Within this implementation process, the integration of the algorithm into the distributed graph processing framework becomes a critical consideration in the realm of frequent subgraph mining.
## 2 Ephemeris Parameter for Pseudolites  
Before switching to a pseudolite-based positioning system, it makes sense to check for the possibility of expressing pseudolite positions using GPS ephemeris parameters. Table 1 gives the definitions of the orbital parameters using typical Keplerian orbital parameter terminology, and it also lists the relevant ephemeris parameter types. Legacy GPS receivers determine GPS satellites' orbits using these parameters [9]. The parameters are two's complements with the sign bit (\(+\) or \(-\)) occupying the MSB. Exceptions to this rule are \(e\), \(A^{{}^{1/2}}\), and \(t_{{}_{\infty}}\).  
The number of bits and scale factors of GPS ephemeris parameter are designed for GPS satellites with radii of approximately 26,000 km. In order to employ GPS ephemeris format for pseudolites located on Earth, \(\dot{\Omega}\) should be identical to the rotation rate of the Earth, which is 7.2921151467e\(-\)5 rad/s [9]. Because this is out of the range defined for \(\dot{\Omega}\), which has 24 bits and a scale factor of 2\(\times\)10-43, it is impossible to express accurate pseudolite positions on the Earth using GPS ephemeris data format. Therefore, the ephemeris format for GPS is not suitable for pseudolites on the ground. However, legacy GPS receiver can only process the ephemeris format of GPS. Therefore a new method is needed to utilize pseudolites with legacy GPS receivers.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 2 Image Sampling Methods
### State-of-the-Art Microscopes  
#### 2.1.1 Confocal Fluorescence Microscope (CFM)  
CFM has become a routine imaging tool in biological and biomedical sciences. The basic concept of confocal microscopes was first proposed in a previous study [42]. In brief, a pinhole aperture placed in front of a microscope photodetector can focus illuminated light on specimens, thereby rejecting most out-of-focus blurs during observation, as shown in Figure 1a. As commercially available instruments, CFM is widely adopted in imaging living cells, intracellular organelles, and components [43]. HeLa cells and porous CaCO\({}_{3}\) particles were recorded with the aid of a CFM, which revealed the number of internalized vaterite particles and their crystalline structures [44], as shown in Figure 1b. The actin cytoskeleton architecture of Leukemia cell lines, Jurkat, and K562 cells were fully observed with a CFM to probe the underlying F-actin remodeling mechanism [45]. With the ability to control the imaging depth of a field and reducing background blurs away from the focal plane, CFMs can be used to measure the 3D structure of specimens. Polystyrene beads were delivered into human hepatocellular carcinoma (HCC) cells and verified with a 3D measurement from CFM [46], as shown in Figure 1c. In Figure 1d, the CFM 3D reconstruction crosses the dashed red line after microinjection for 15 s [47]. The spatial resolution of CFMs is restricted above 200 nm because of the diffraction limit of optical microscopes [48]. Research efforts have been devoted to improving the CFM resolution beyond the diffraction limit. A pinhole displacement method has been proposed to increase the contrast up to 80% with a diffraction-limited resolution of 250 nm [49]. The light intensity profile of Bessel beams follows Bessel function of different orders. This intensity distribution gives it a tighter focal spot than that of classic Gaussian beams, and its use in CFM can lead to a resolution enhancement of up to 20%. A CFM using Bessel-Gauss beams improved lateral resolution to 219 nm [50]. Furthermore, a fluorescence emission difference method illuminating specimens with a solid beam and a donut beam has been established. The final measured image was the subtraction of the acquired solid image and the donut one, and its lateral resolution was 210 nm, which was much higher than that of a conventional CFM [51]. In a resolution-doubling method, a point detector was replaced with an imaging detector via a recording algorithm, and the resolution of CFM can be increased to 162 nm [52]. With these methods, minor intracellular components such as actin filaments, microtubes, and proteins can be optically discriminated in CFM observations. However, CFM has the disadvantages of slow scanning speed [53] and high-intensity light [54], which may lead to short fluorescence lifetime and phototoxicity in living cells [55]. Therefore, the imaging time should be strictly controlled during CFM operation.
## 3 Results
### Plugging Agent Optimization Results
#### 3.2.2 High-Temperature Stability Performance  
During the steam injection process, the temperature of steam injection is normally more than 260 \({}^{\circ}\)C, so the plugging agent needs to maintain long-term stability at high temperature. For this reason, the thermal stability tests for the high-temperature plugging agent, HTP, and quartz sand consolidated body were carried out at 260, 280, and 300 \({}^{\circ}\)C for a long period (test conditions: quartz sand proportion 70%; the constant temperature time was 60, 120, and 180 d), and the results are shown in Figure 9.  
It can be seen from Figure 9 that at 260-300 \({}^{\circ}\)C, the consolidated body formed by HTP and quartz sand still had a high compressive strength after a long time of constant temperature aging. With the increase of temperature, the compressive strength decreased to a certain extent, but the decrease was small. After being placed under 300 \({}^{\circ}\)C conditions for 180 days, the compressive strength was still higher than 12 MPa. This means that HTP has good temperature resistance, which can reach more than 300 \({}^{\circ}\)C.
## 6 Applications and Simulations  
We have implemented the face-based greedy routing algorithm and the volumetric harmonic mapping (VHM) algorithm introduced above, in order to achieve highly efficient peer-to-peer greedy routing in 3D sensor networks. Moreover, we further apply the proposed routing algorithm in in-network data-centric storage and retrieval. The simulation results are presented below sequentially.  
Figure 9: Illustration of tunnel. Nodes in green area are the intersection nodes in CCELL \(i\). \(t_{ij}\) is the center of tunnel \(T_{ij}\). The area (on the spherical surface) enclosed by the yellow circle with radius \(r_{ij}\) centered at \(t_{ij}\) is tunnel \(T_{ij}\).
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 2 Introduction to GNSS Spread Spectrum Codes
### Spread Spectrum Code Generation Methods Based on LFSR
#### 2.2.3 Kasami Code  
Both Kasami and Gold codes are generated as modulo-2 sums of two register sequences. The Kasami code is generated using a shift register of length \(N\) and a shift register of length \(N/2\), with a maximum length not exceeding \(2^{N}-1\)[19; 20]. The Kasami code is employed by signals such as GLONASS L1 OCp, GLONASS L2 OCp, GLONASS L3 OC, and QZSS L6. In the case of the GLONASS L1 OCp code generator [12], it comprises a 12-bit shift register G1, a 6-bit shift register G2, and a feedback tap. The feedback polynomial for G1 is \(f(x)=1+x^{6}+x^{8}+x^{11}+x^{12}\) specified as shown in Figure 5, while the feedback polynomial for G2 is \(f(x)=1+x+x^{6}\). The initial values of G1 and G2 are stored in the initial registers, and the initial value of G2 varies with the number of satellites. The modulo-2 sum of the outputs from Register 12 of Shift Register G1 and Register 6 of Shift Register G2 yields the GLONASS L1 OCp spread spectrum code.  
In particular, the QZSS L6D/L6E code generator [15] is introduced, where the structure of L6D is identical to that of L6E except for the initial value of the register. The signal number generator comprises a 20-bit shift register G1 and a 10-bit shift register G2, both with feedback taps. The feedback polynomial for G1 is \(f(x)=1+x^{14}+x^{16}+x^{19}+x^{20}\) specified as shown in Figure 6, while the feedback polynomial for G2 is \(f(x)=1+x^{3}+x^{4}+x^{5}+x^{6}+x^{9}+x^{10}\). The initial value of G1 is stored in the initial register, while the initial value of G2 is fixed at one. By setting and executing the predetermined feedback polynomial and initial value, the modulo-2 sum of the outputs from the 20 th bit of Shift Register G1 and the 10 th bit of Shift Register G2 yields the spread spectrum code of QZSS L6D/L6E.  
Figure 4: Schematic Diagram of the GPS L1C/A Code Generator.  
Figure 5: Schematic Diagram of the GLONASS L1 OCp Code Generator.
## 2 Literature Review
### iForest Outlier Detection  
Previous studies mostly concentrate on enhancing the precision of models, rather than the significance of preparing data. Identifying inconsistencies or outliers in data by using outlier detection techniques during the pre-processing stage can lead to the creation of better classifiers, resulting in better decision-making. Removing outliers from the training data will increase classification accuracy. Isolation Forest (iForest) is an algorithm for detecting outliers or anomalies in a dataset [33]. The algorithm uses the concept of isolation to separate outliers from normal observations. iForest is efficient and easy to use and can be applied to high-dimensional datasets.  
Many studies have been conducted and have demonstrated that iForest outlier detection can significantly improve classification accuracy. [PERSON] et al. [25] introduce PCB-iForest, a new framework for outlier detection in streaming data. Based on F1 scores and trade-off with average runtime, PCB-iForest clearly outperformed nine competitors on multi-disciplinary ODDS in about 50% of the data set and achieved comparable results in 80%. [PERSON] et al. [26] proposed three steps for training a classification model: signal segmentation, LOF-based anomaly score, and then isolation forest modeling. Signal sensors in the diffusion process of semiconductor manufacture capture both normal and abnormal data, but it is challenging to classify the anomalies since they only exist in a particular region. According to the experiment results, the proposed method outperforms other algorithms. [PERSON] et al. [27] proposed a preprocessing method to improve the accuracy of hourly water demand forecasting models. This method reduced the RMSE of the SVR, ANN, and GRU models by 57.5%, 27.8%, and 30.0%, respectively. The local outlier detection and correction method effectively identifies global outliers and corrects them. GRU-based models perform better than ANN and SVR-based models, with the IF-CEEMDAN-GRU model being the most accurate. The proposed method also improves the accuracy of conventional models like SVR with a lower computational load. Finally, [PERSON] et al. [28] compared five anomaly detection algorithms and found BS-iForest to be the best performing. Its AUCs on the BreastW dataset and campus CRS dataset were 0.9947 and 0.989, respectively, higher than the traditional forest method. The accuracy rates were also higher, at 0.9653 and 0.9896, respectively. BS-iForest screens sub-sampling sets before training and uses sets that are more likely to have outliers. However, compared to the standard iForest algorithm, iForest has a higher AUC index regardless of the proportion of outliers.
## 3 Aerodynamic Flutter Stability Robustness Evaluation
### Robustness Calibration of Aerodynamic Flutter Stability
#### 3.3.2. Flutter Robustness Analysis of Two Bridges to Be Built  
Based on the analysis of the above eight completed long-span bridges, the robustness evaluation was conducted for two other long-span suspension bridges, Shuangyumen Bridge (with a 1768 m main span, under construction, China) and Sunda Strait Bridge (with a 2016 m main span, in design, Indonesia), to re-check the evaluation method of flutter robustness. The statistic characteristics of the fundamental random variables of these two bridges are listed in Table 5.  
Based on the equivalent central point method and equivalent checking point method, the calculated failure probability, reliability index, and return period were compared and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Bridge**} & \multicolumn{3}{c}{**The Equivalent Checking Point Method**} \\ \cline{2-4}  & \(\beta\) & \(P_{F}\) & \(T_{m}\) **(Year)** \\ \hline Nansha Bridge & 3.2709 & \(5.36\times 10^{-4}\) & 1865 \\ Xhioumen Bridge & 3.5441 & \(1.97\times 10^{-4}\) & 5076 \\ Runyang Yangtze River Bridge & 2.9582 & \(1.50\times 10^{-3}\) & 646 \\ Jiangyin Yangtze River Bridge & 3.4504 & \(2.80\times 10^{-4}\) & 3572 \\ Tsing Ma Bridge & 3.0314 & \(1.20\times 10^{-3}\) & 822 \\ Huangpu Bridge & 3.7652 & \(8.32\times 10^{-5}\) & 12,018 \\ Humen Bridge & 3.3922 & \(3.47\times 10^{-4}\) & 2884 \\ Haicang Bridge & 3.7235 & \(9.82\times 10^{-5}\) & 10,181 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of flutter robustness evaluation for eight existing bridges based on the equivalent checking point method.  
Figure 1: Comparison of the reliability indices calculated using the equivalent central point method and equivalent checking point method for eight existing bridges.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Bridge** & \(\mu_{C_{f}}\) & \(\sigma_{C_{f}}\) & \(\mu_{U_{f}}\) & \(\sigma_{U_{f}}\) & \(\mu_{C_{b}}\) & \(\sigma_{C_{b}}\) & \(\mu_{U_{b}}\) & \(\sigma_{U_{b}}\) \\ \hline Shuangyumen Bridge & 1 & 0.05 & 84.1 & 6.30 & 1.22 & 0.09 & 36.10 & 7.22 \\ Sunda Strait Bridge & 1 & 0.05 & 93.0 & 7.0 & 1.22 & 0.09 & 40.05 & 8.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean values and standard deviations of fundamental random variables for two bridges to be built.  
listed in Tables 6 and 7. The robustness evaluation results showed that the flutter robustness of these two bridges was relatively low, and the return periods of the maximum wind speed they could resist were between 239 and 247 years. Furthermore, flutter safety problems may occur if these two bridges encounter a strong wind with a return period of 300 years.  
Figure 2 shows the comparison of the reliability indices calculated using the equivalent center point method and equivalent checking point method. The same conclusion as discussed for the eight existing bridges can be drawn from the figure.
All methods were used in a VR environment to minimize additional variation between methods and to focus the comparison on interaction techniques. The use of the HMD also allowed the participants to easily study the target from different directions by moving their head. In the medical marking task, the doctor observes the anatomical structures by turning and moving the 3D object and at the same time looking for the best location for the landmark. The main difference to the general marking task is the need for high accuracy in defining the medical marking location. The time spent for the manipulation is not easily separated from the time spent in the final marking. The doctor decides during the manipulation from which angle and how the marking will be performed, which will affect the marking time. This made the application of Fitts' law [13] impossible in our study, as it requires that a participant cannot influence target locations. We had 12 participants who were asked to perform simplified medical surgery marking tasks. To study the accuracy of the interaction methods, we created an experiment where in the 3D object there was a predefined target that was marked (pointed+selected). In the real medical case, the doctor would define the target, but then the accuracy could be easily measured. This study focused on subjective evaluations of interaction methods, but also included objective measurements. The main contributions of this experiment are in showing the following findings in the marking task: (1) The handheld controller enabled a natural interaction for manipulation resembling the use of bare hands and was approved by the participants. (2) The stylus allowed more consistent marking accuracy compared to the bare hands use. (3) Using a mouse, the participants were able to achieve higher accuracy than with the bare hands method. This paper is organized as follows: First, we go through the background of object manipulation and marking, interaction methods in a 3D environment, and jaw osteotomy surgery planning (Section 2). Then, we introduce the compared interaction methods and used measurements (Section 3), as well as go through the experiment (Section 4) including the apparatus, participants, and study task. In the end, the results are presented (Section 5) and discussed (Section 6).
# Investigation of the Motion Characteristics of Parts on a Platform Subjected to Planar Oscillations
###### Abstract  
Positioning applications are very important in a variety of industrial processes, including automatic assembly. This paper proposes a technique for positioning applications that involves employing a platform subjected to planar oscillations along circular, elliptical, and complex trajectories. Dynamic and mathematical models of the motion of a part on the platform were developed to investigate the motion characteristics of the part. The research showed that when the platform was excited in two perpendicular directions by sinusoidal waves, different trajectories of the part's motion could be obtained by controlling excitation parameters such as the frequencies and amplitudes of the waves and the phase shift between the waves. Furthermore, by adjusting these parameters, the average displacement velocity of the part could be controlled. The results demonstrate that the part can be moved in any direction at a given velocity and can be subjected to complex dense positioning trajectories. Therefore, such a platform can be applied in feeding, positioning, and manipulation tasks.  
ostiening; motion control; trajectory planning; oscillating platform; dynamics; modeling; frequency; sinusoidal waves; phase shift Article